Local Political Business Cycles Evidence from Philippine...

Local Political Business Cycles

Evidence from Philippine Municipalities∗

Julien Labonne†

Oxford University

November 2013

Draft

Abstract

In this paper, I test for the presence of political business cycles in Philippine municipalitiesover the period 2003-2009, a context where according to the literature such cycles are likelyto be observed. I find robust evidence for the presence of political business. This effect is onlypresent when I use quarterly data and vanishes when I aggregate the data at the yearly-level.The difference is not merely driven by a decline in statistical power due to aggregation: pointestimates for the overall effects are 7 times larger when I use quarterly data than when Iuse yearly data. This discrepancy can be explained by a drop in employment post-electionthat dilutes the yearly effects. Specifically, using data from 26 nationally representativequarterly labor force surveys, I construct a balanced panel of more than 1,100 municipalitiesand show that the share of the working-age population that is employed increases by 0.87percentage-points in the two quarters before elections. In the two post-election quarters, it is0.48 percentage-point lower than what it would have been without the elections. Results arerobust to the inclusion of a number of control variables, time trends and to two-way clusteringof the residuals along both time and geographic dimensions.

∗I am grateful to Marcel Fafchamps, Simon Franklin, Clement Imbert and Simon Quinn for useful discussionswhile working on this paper. APPC kindly shared their electoral data. Financial support from the CSAE andOxford Economic Papers Fund is gratefully acknowledged. I thank Jacobus Cilliers, Paul Niehaus, YasuhikoMatsuda and participants in the CSAE Research Workshop, CSAE Conference 2013 and UPSE Friday Seminarfor comments. All remaining errors are mine.†email: [email protected]

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1 Introduction

In this paper I examine whether employment levels are affected by election timing. Economists

and political scientists have been interested in analyzing so-called political business cycles,

the fluctuations of employment around elections, and political budget cycles, the expected

increase in government expenditures before elections, as they provide insights into voter and

politician behavior.1 To-date, the empirical evidence is not as strong as theory and anecdotal

evidence would suggest.2

A number of explanations have been proposed for the challenges in identifying political

budget and business cycles in some contexts and for the weakness of the estimated effects when

cycles are identified. Researchers have argued that cycles are a function of, among other, the

structure of fiscal federalism (Jones, Meloni, and Tommasi 2012), institutional constraints

on politicians (Shi and Svensson 2006) and, the degree of control politicians have over the

economy (Duch and Stevenson 2008). To take one example, Duch and Stevenson (2008)

argue that in an open economy politicians have less control over the state of the economy

and as such it provides a weaker signal of incumbent quality. Voters are more reluctant to

use the signal when deciding whether to re-elect the incumbent and, in turn, she has less

incentives to attempt to improve the economy before elections. Similarly, Jones, Meloni, and

Tommasi (2012) show that in a decentralized setting increased spending act as signal for the

1Recognizing that voters use retrospective information about the economy to decide whether to re-elect theincumbent, Nordhaus (1975) developed a model where politicians have incentives to decrease unemploymentand increase inflation ahead of elections. While the model assumed myopic voters, it can be modified toincorporate rational agents. For example, shifting the focus from employment to budget spending, Rogoff(1990) proposed a model where it is optimal for voters to use information about public spending when decidingwhether to re-elect the incumbent or not as it provides information about private incumbent’s type. If votersobserve an increase in public spending they rationally attribute some of the improvements to incumbent’sactions.

2Political business and budget cycles have been identified in some countries but not in others (Peltzman(1992); Franzese (2002); Besley (2006); Shi and Svensson (2006)). More recently, the empirical literature hasmoved from cross-country to subnational analyses and has tested whether local governments spending followa different pattern in election years. Evidence from countries as diverse as Argentina, Brazil, India, Indonesia,Israel, Portugal and Russia suggests that local governments increase spending and/or reduce taxes beforeelections (Akhmedov and Zhuravskaya (2004); Khemani (2004); Brender (2003); Jones, Meloni, and Tommasi(2012); Sakurai and Menezes-Filho (2010); Aidt, Veiga, and Veiga (2011), Sjahrir, Kis-Katos, and Schulze(2013)). In addition, elections appear to affect spending composition, with local governments allocating moreresources to visible investments (Drazen and Eslava 2010). This is consistent with Rogoff (1990)’s predictionthat, ahead of elections, there is a re-allocation of government spending towards easily observable expenditures.More recently electoral cycles have also been identified in foreign aid (Faye and Niehaus 2012), in cementconsumption in India (Kapur and Vaishnav 2011) and in prices paid to farmers for cane (Sukhtankar 2012).

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incumbent’s ability to extract resources from the center.

In this paper, I test for political business cycles in Philippine municipalities over the

period 2003-2009, a context where according to the literature strong cycles are likely to be

observed. First, local politicians often campaign on their ability to secure resources from

higher levels of government. Second, Philippine municipalities are often headed by strong

mayors with significant discretionary powers over budget spending. Third, mayors sometimes

act as employment brokers in both the public and private sectors.

I find robust evidence for the presence of political business. This effect is only present

when I use quarterly data and vanishes when I aggregate the data at the yearly-level. The

difference is not merely driven by a decline in statistical power due to aggregation: point

estimates for the overall effects are 7 times larger when I use quarterly data than when I use

yearly data. This discrepancy can be explained by a drop in employment post-election that

dilutes the yearly effects. A potential explanation for this decline is that, in situations where

local governments are unable to borrow, local incumbents can increase spending ahead of

elections by shifting some of their planned post-election spending in the pre-election period.3

This within-year effect is not captured by analyses using aggregated data.

The main findings can be summarized as follows. Using a unique balanced panel dataset

of about 1,140 cities and municipalities, I show that the share of working-age population that

is employed increases by 0.87 percentage-points in the two quarters before elections. This

is equivalent to an additional 470,000 jobs in April 2004 compared with what employment

would have been without the May 2004 elections. Employment as a share of the working-age

population in the two post-election quarters is 0.48 percentage-point lower than it would have

been without the elections. Results are robust to the inclusion of a number of control vari-

ables, time trends and to two-way clustering of the residuals along both time and geographic

dimensions. In addition, results are similar when I control for lagged values of the dependent

variables and estimate the model using the generalized methods of moment.

The paper provides a simple explanation for the challenges in identifying political business

cycles when using yearly data and for the weakness of the estimated effects when cycles are

identified. In addition, the paper expands the literature along three main dimensions. First, I

3Importantly, in the Philippines, the calendar and fiscal years coincide.

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return to the literature’s origin and carry out one of the first political business cycle analyses at

the subnational level.4 Indeed, to-date subnational analyses have focused on budget cycles,

capturing only part of the potential distortions to labor markets. In contexts where local

politicians have either significant business interests and/or have strong connections with local

businessmen, incumbents might be able to influence employment levels above and beyond what

one would expect from budget spending alone. For example, Bertrand, Kramarz, Schoar, and

Thesmar (2007) provide convincing evidence that, in France, firms with political connections

tend to delay firing employees until after the elections.

Second, I focus on a setting where election timing is exogenous to the outcome of interest.

Strategic election timing is a common concern in the literature but, in the Philippines, it was

decided as part of the 1987 Constitution and since then all elections have been organized as

planned. Put differently, politicians are de facto unable to organize elections in good years.

Third, results discussed in this paper contribute to the literature on clientelism. The

increase in employment in the public sector before the elections is concentrated on long-term

contracts (i.e. non-casual). This is in agreement with the model of clientelism developed

by Robinson and Verdier (2013) as it allows politicians to align bureaucrats’ incentives with

their own electoral objectives as their job tenure is implicitly tied to the incumbent’s electoral

success. In addition, the size of the effect suggests that incumbents are not merely providing

jobs to secure additional votes. The observed effects do not appear large enough to affect

election results. Rather, results are consistent with the argument that incumbents attempt to

provide their constituents with benefits that will last until after the elections. The increase in

employment in the construction sector is expected to lead to an increase in either maintenance

of existing public goods or the construction of new infrastructure.

Results presented in this paper suggest that, to the extent possible, future analyses of

political business cycles should use monthly or quarterly data. A similar point is implicit in

results presented by Akhmedov and Zhuravskaya (2004) for the analysis of political budget

cycles, but it appears that data constraints have prevented researchers from doing so.

The remainder of the paper is organized as follows. Section 2 provides some background

4To the best of my knowledge, the only exceptions are Coelho, Veiga, and Veiga (2006) who test for politicalbusiness cycles in Portuguese municipalities, using yearly data, over the period 1985-2000 and Dahlberg andMork (2011) who test for electoral cycles in public employment in Finnish and Swedish municipalities, usingyearly data, over the period 1985-2002.

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on local elections in the Philippines. Section 3 introduces the data. The estimation strategy

is presented in Section 4 and results are discussed in Section 5. Section 6 concludes.

2 Setting

In this section, I briefly present the local political context, highlighting factors that make

Philippine municipalities particularly well-suited to study political business cycles, especially

over the period 2003-2009. I start with a brief description of the institutional context, followed

by a discussion of local elected officials’ behavior and ability to affect local labor markets.

First, carrying subnational analyses allows me to control for the specificities of the insti-

tutional context. As pointed out by Drazen and Eslava (2010) in their study of the effects

of elections on local government spending in Colombia, variations in institutions make the

interpretation of cross-country regressions challenging. This is especially relevant over the

period 2003-2009 in the Philippines as the same president, Gloria Macapagal-Arroyo, was in

office throughout. Unobserved links with central government officials which could affect local

politicians’ ability to access state resources will be stable throughout the period and can thus

be controlled for by using municipal fixed-effects.

Second, since the fall of Marcos elections have followed a pre-established calendar set out

in the 1987 Constitution. This rules out concerns that election timing was endogenous to

the outcome of interest as local governments were unable to control election timing. The

literature has recognized that most governments have some control over election timing and

that observed correlations between elections and the outcome of interest could be driven by

incumbents ability to time elections as they please (Khemani 2004 Shi and Svensson 2006).

To deal with those concerns, researchers often provide evidence that results are robust to

restricting the sample to elections that were implemented according to the constitutionally

mandated schedule. The idea being that in such cases the timing of elections is exogenous

to the outcome of interest. However, if incumbents have the power to either call for elections

early or postpone them and have used it in the past, then the decision not to do so might

be correlated with the outcome of interest as well. As a result, the assumption that election

timing is exogenous is more likely to be valid when local politicians are de facto unable to

affect when elections take place; as is the case in the Philippines.

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Third, Capuno (2012) and Sidel (1999) provide credible evidence that municipalities are

headed by strong mayors, often referred to as local bosses and perceived as such by the

population. One would expect economic voting to be especially strong in such a setting.

Duch and Stevenson (2008) argue that economic voting is stronger when voters perceive

incumbents to be in control of the local economy. When voters expect elected officials to

control the local economy, they are more likely to use the state of the economy as a signal of

incumbent’s quality. Incumbents therefore have more incentives to distort the economy ahead

of the elections.

As shown by Hutchcroft (2012), most decisions regarding municipal budgets are made

by mayors who use available funds with very little oversight. This is despite the fact that

the 1991 Local Government Code established municipal councils and gave them decision-

making powers. Mayors control both how the budgets are spent and public sector employment

(Hodder 2009 Hutchcroft 2012). In addition, there is evidence consistent with the argument

that they are able to staff the bureaucracy with their relatives (Fafchamps and Labonne 2013);

thus ensuring that bureaucrats have incentives closely aligned with their electoral objectives

(Robinson and Verdier 2013). In clientelistic systems where local elected officials are often

assessed on their ability to respond to citizens’ requests, one would expect incumbents to use

their discretionary powers more frequently ahead of elections. For example, in a case study of

Naga City in the Philippines, Kawanaka (2002) notes that resident requests greatly increase

as elections near. The city government intensifies its services to reflect favorably on Robredo’s

[the mayor] leadership.

Fourth, a number of municipalities are characterized by the presence of family dynasties

which have been in power for decades. It is common for one of the incumbent’s family member

to run for the position when the incumbent has reached the 3-term limit introduced in the 1987

Constitution (Coronel, Chua, Rimban, and Cruz (2004) and Querubin (2011)). The relevant

unit of analysis in Philippine politics is the family rather than the individual politician or the

political party (McCoy 2009). As described by Fegan (2009): A family is a more effective

political unit than an individual because it has a permanent identity as a named unit, making

its reputation, loyalties, and alliances transferable from members who die or retire to its new

standard bearer. Assuming that citizens learn about politician’s quality during their time in

office, the set-up allows me to test whether political business cycles are more pronounced in

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areas where incumbent’s quality is more uncertain.

Finally, available qualitative evidence suggests that strong political business cycles are

likely to be identified around municipal elections in the Philippines. Incumbents attempt to

increase public spending, especially on visible projects, in the pre-election period. This is

similar to findings from Colombia where incumbents have been shown to allocate a greater

share of their budget to construction projects in election years (Drazen and Eslava 2010).

Thus one would expect an increase in employment in the construction sector ahead of the

elections. Given that most municipalities face strict budget constraints due to their reliance

on fixed fiscal transfers from the central government and their de facto inability to borrow,

the pre-election increase in spending might be followed by a post-election decline as fiscal

resources for the fiscal year have been depleted.

In addition, local incumbents’ electoral strategies might affect private sector employment

directly. Local politicians often use the power of their office to increase their business holdings

(Sidel 1999). They are then able to provide their constituents with jobs. Further, in a number

of Philippine municipalities, mayors act as employment brokers, helping their constituents find

jobs. For example, in a province surrounding Manila, job applicants in local factories were

required to provide letters of recommendation from local officials (Sidel 1999, pp 76-77). There

is qualitative evidence that this role intensifies before elections as voters have more bargaining

power (Kawanaka 2002).

3 Data

3.1 Employment

I use data from Labor Force Surveys (LFS) collected by the National Statistics Office of

the Philippines. The surveys are implemented four times a year (January, April, July and

October) and I have access to all 26 surveys in the period July 2003 - October 2009. Each

survey has a sample size of approximately 200,000 individuals in 50,000 households.5 Data

from the surveys are used to compute official employment statistics. A person is considered

employed if he reported at work for at least an hour during the week prior to the survey. In

5More information on the survey design is available at: http://www.census.gov.ph/data/technotes/notelfs new.htmlvisited on March 26, 2012.

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addition, information is collected on the total number of hours worked during the past week,

the sector of employment and the daily wage. I use the available data to build a balanced

panel of about 1,140 cities and municipalities, out of 1,634 in the country.

For each municipality/survey wave, I compute the share of the working age population

(above 15 year old) that is employed. It is not possible to compute employment rate as a

share of the economically active population consistently across survey waves as the definition

of the economically active population changed in April 2005. The information required to

adjust past series is not available. However, the definition of employment has not changed

and I compute the employment ratio as a share of the working age population rather as a

share of the economically active population. As a result, estimates presented in this paper

combine the effects of elections on the decision to enter/exit the labor force and of getting a

job for those in the labor force.

3.2 Political environment

In accordance with the 1987 Constitution, elections have been organized every three years.

The two elections of interest in our sample period are the May 2004 and May 2007 elections. I

distinguish between pre-election months (January and April waves) and post-elections months

(July and October waves).

A number of Philippine municipalities are controlled by so-called political dynasties which

have often been in power for decades and I expect political business cycles to be different

in those municipalities. To control for that, I use lists of elected officials at the municipal

and provincial levels for the period 1987-2007 and compute for the 2004 and 2007 elections,

the number of terms the incumbent’s family has been in office in the same municipality since

1988.6 I consider that an incumbent is related to an earlier official if they share the same

last name.7 In addition, for each municipality, I compute the number of family links between

the mayor and either the provincial governor, vice-governor or congressmen using the same

6Municipal elections elections were organized in 1988. In accordance with transitory provisions of the1987 Constitution (Article XVIII) the next municipal elections were organized in 1992. Elections have beenorganized every 3 years ever since.

7It is possible to do so due to naming conventions imposed by Spanish colonial officials in the 19th century.Cruz and Schneider (2013), Fafchamps and Labonne (2013) and Querubin (2013) use a similar strategy withdata from the Philippines. I am only able to match on last names and not on middle names and, as such, it islikely that I underestimate links.

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matching procedure

I also use yearly data on municipal budgets from the Department of Budget and Manage-

ment.8 The data are all expressed in 2000 Pesos using regional Consumer Price Indices.

3.3 Descriptive statistics

The average municipal employment rate in January and April in election years is 59.6 percent

while it is only 58.7 percent in non-elections years (Figure 1). Simple tests of equality of means

suggest that the observed differences are statistically significant (t=3.5, p-value= 0.0004). In

addition, it appears that not only are the means of the two distributions different but the two

distributions are also different. This is confirmed by results from a Kolmogorov-Smirnov test

of equality of distributions (p-value =0.003).

Overall patterns of employment rates in July and October in election and non-election

years are also consistent with the argument that the pre-election increase in employment

levels is followed by a post-electoral decline (Figure 2). The average employment rate in

the July and October waves is 59.3 percent in non-elections years while it is 58.9 percent in

elections years. As above, the differences are statistically significant (t=2.93, p-value=0.0034)

and this is confirmed by results from a Kolmogorov-Smirnov test of equality of distributions

(p-value= 0.013).

Despite the prohibition on political dynasties and the three term limit that were introduced

in the 1987 Constitution, a number of mayors come from families that have been in power

more than three times since 1987.9 About 9.1 percent of mayors elected during the 1998

elections were from families that had been elected at least 3 times since 1987. The numbers

then increased to 22.9 percent in 2001, 30.0 percent in 2004 and to 36.2 percent 2007. Such

families are better able to stay in power. For example, in the 2007 elections, a new family came

to power in 44.2 percent of municipalities where the incumbent had been in power three times

or less. The rate of turnover was only 26.7 percent in municipalities where the incumbent

had been in power four times or more. The difference between the two groups is statistically

different from zero at the usual levels of significance (χ2 = 30.4). While such descriptive

statistics are interesting, they do not provide credible estimate of incumbency advantage as

8The data are available from: http://www.blgf.gov.ph/# visited on March 26, 2012.9This is consistent with findings at the provincial and congressional levels reported by Querubin (2011).

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they could merely reflect a selection effect with more competent political families more likely

to be elected to begin with.

As expected, incumbent political families that have links with provincial politicians are

more likely to be re-elected to municipal office. For example during the 2004 elections, a

new family came to power in only 17.3 percent of the municipalities where the incumbent

family had links with provincial politicians. In municipalities with incumbent without such

links, the proportion was 28.0 percent. The difference was even stronger in 2007, with a new

family coming to power in 24.2 percent of the municipalities with provincial links and in 39.8

percent of municipalities with politicians without family links. In both cases, the differences

are significantly statistically different from zero at the usual levels of significance (for 2007:

χ2 = 6.4). Again, the statistics discussed above do not provide any evidence on the value of

connections as competent political families might be better able to be elected to provincial

office.

4 Estimation strategy

In this section, I present the empirical strategy. First, I describe how I test for the presence of

political business cycles, the fluctuations of employment around elections, including various

robustness checks. Second, I discuss channels that could explain the strength of political

business cycles. Finally, I test for the presence of political budget cycles, the expected increase

in government expenditures before elections.

4.1 Local political business cycles

I start by estimating equations of the form:

Yijt = αEt + βXijt + uij + wijt (1)

Where Yijt is the outcome of interest in municipality i in province j at time t, Et is a vector

of electoral variables, Xit is a vector of municipal characteristics that vary across time, uij is a

municipality-specific unobservable and, wijt is the usual idiosyncratic term. Each observation

is weighted using the sum of individual survey weights in the municipality at that time period.

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I also present results from unweighted regressions (Solon, Haider, and Wooldridge 2013).

I estimate equation (1) where Yijt is either the share of the working age population that

is employed, the average number of hours worked over the past week (for those with a job) or

the average log daily wage (for those with a job).10 I estimate equation (1) separately for the

public and private sector. The differences, if any, will provide information on the channels

through which the effects operate.

As discussed in Section 2, I argue that election timing in this context is exogenous. Indeed,

election timing was decided when the constitution was drafted in 1987 and all elections since

then, including the two elections of interest (May 2004 and May 2007), were implemented as

planned. To check that the results are not driven by one particular election, I also estimate

the model with separate dummies for each election and test whether the coefficients are equal.

In line with the literature, I start by estimating equation (1) with annual data. In addition,

to test my argument that granularity in data explains variations in electoral cycles, I also

estimate equation (1) with quarterly data and introduce two dummy variables, one capturing

the two pre-election quarters and one capturing the two post-election quarters.

Given the data structure, error terms are not independent and are likely to be correlated

both within municipalities, provinces and time periods. Standard errors need to be corrected

to account for the specific structure of the error term. Failure to do so would lead to downward

biased standard errors and to over-rejection of the null hypothesis of no effect. For example,

in Monte Carlo simulations reported by Cameron, Gelbach, and Miller (2011), the null hy-

pothesis of no effect is rejected twice as often when clustering is done along one dimension as

when it is done along two dimensions. When clustering levels are nested, as is the case here

with municipalities and provinces, clustering needs to be done at the most aggregated level

(Cameron, Gelbach, and Miller 2008). As a result, I use a method developed by Cameron,

Gelbach, and Miller (2011) and cluster standard errors across both time and provinces.

The vector Xijt includes controls for average age (and its square) in the municipality (for

those older than 15), education levels (for those older than 15), the share of women in the

sample, all computed using the LFS data. I also control for population levels and per capita

10As the surveys were designed to provide representative estimates at the regional level, the municipal-levelestimates used in the paper are likely measured with error. As a result, the estimates have larger variancesand the null hypothesis of no effect will tend to be under-rejected.

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fiscal transfers. I include either region-specific or province-specific quadratic time trends. Due

to the seasonal nature of employment in the Philippines, I include quarter-specific dummies

in all regressions. I also estimate equation (1) with quarter/municipality fixed effects. This

allows me to control for potential variations in the degree of seasonality across municipalities.

I check wether results are robust to alternative specifications and estimation strategies. I

assume a dynamic model of the form:

Yijt =P∑

p=1

αpYijt−p + βXijt + uij + wijt (2)

I estimate equation (2) for various values of P using the fixed effects estimator. As an

additional robustness check, I eliminate uij by taking the fist difference and use lagged values

of Yijt as instruments for ∆Yit−p. This leads to:

∆Yijt =P∑

p=1

αp∆Yijt−p + γ∆Xijt + ∆wijt (3)

When using yearly data, I estimate equation (2) using the Generalized Methods of Moment

with all the available moment conditions (Arellano and Bond (1991) and Blundell and Bond

(1998)). However, when using quarterly data, given the length of the panel, using the full set

of moment conditions might lead to over-fitting and to possible small sample bias (Roodman

2009). As a result, I only use Yijt−2 and Yijt−3 as instruments for ∆Yijt−1 when estimating

equation (3) with P = 1 and Yijt−3 and Yijt−4 as instruments for ∆Yijt−1 and ∆Yijt−2 when

estimating equation (3) with P = 2.

I then provide evidence on which politicians attempt to increase employment ahead of the

elections and which sectors of the economy are the most affected.

First, I expect political business cycles to be stronger in municipalities with relatively

inexperienced incumbents as they represent a way of signaling quality to voters. Since voters

face difficulties when trying to distinguish between incumbent’s ability and the overall eco-

nomic environment, incumbents try to improve economic conditions ahead of the elections

which voters will then partly attribute to the incumbent. Assuming that voters learn from

politician’s ability during their times in office, one would expect uncertainty about ability to

decrease with the number of terms the incumbent, or one of his family member, has been

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in office. To test whether uncertainty about the incumbent’s ability explain the presence of

political business cycles, I estimate:

Yijt = αEt + βXijt + γZijt + δEt ∗ (Zijt − Zijt) + uij + wijt (4)

where Zijt is a variable capturing the number of times a member of the incumbent’s family

has been elected mayor since 1987. Since there is no particular reason to expect that the

relationship between length in office and the strength of the political cycle is linear, and given

the wealth of data available, I also estimate the effect separately for each level of political

experience. I am unable to interpret the estimates of δ causally as municipalities that are

controlled by dynasties might be different from municipalities with inexperienced incumbents

along dimensions that could affect labor markets. However, they provide useful information

on the relationship between political experience and political business cycles and they might

allow me to rule out alternative explanations as well.

4.2 Local political budget cycles

In addition, I test for political budget cycles using annual data.11 Specifically, I estimate

equations of the form:

Bijt =P∑

p=1

αpBijt−p + βEt + γXijt + uij + wijt (5)

Where Bijt is the outcome of interest in municipality i, in province j in year t, Et is

an indicator equal to one if elections took place in year t, Xijt is a vector of municipal

characteristics that vary across time, uij is a municipality-specific unobservable and, wijt is

the usual idiosyncratic term.

The budget data are available for all municipalities and cities in the country over the period

2001-2009. As a result the fixed effects estimator might be biased and I also report results of

GMM estimation for P = 1, 2. The specification is consistent with previous contributions to

the political budget cycle literature (Drazen and Eslava 2010).

11As such, I am unable to test whether elections affect within-year allocations of resources.

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5 Results

In this section, I first present results on the presence of local political business cycles in

Philippine municipalities. I then highlight potential channels which might affect the strength

of the cycles. Finally, I discuss results on whether municipal budgets are affected by the

timing of elections.

5.1 Local political business cycles

5.1.1 Main results

Analyses carried out with yearly data fail to identify political business cycles in Philippine

municipalities over the sample period. The proportion of the working-age population that is

employed is neither higher nor lower in election years (Panel A of Table 2).12 The conclusions

are unchanged when employment in the public and private sectors are analyzed separately

(Panels B and C of Table 2). To account for the fact that I only have data for the July and

October waves in 2003, I re-estimate the model on the 2004-2009 sample. This does not affect

the results (Table A.1)

Once I use quarterly data, I find robust evidence of the presence of political business cycles.

The differences with the yearly results are not merely driven by an increase in statistical power

but rather by a post-election decline in employment which dilutes the overall effect when using

yearly data. Point estimates for the overall effects are 7 times larger when I use quarterly

data than when I use yearly data.

First, results, available in Panel A of Table 3, indicate that there is an increase in em-

ployment in the two quarters preceding the elections.13 Results are robust to the inclusion of

a number of control variables and region-specific or province-specific quadratic time trends.

The point estimates suggest that the share of the working-age population that is employed

12When I estimate the model with a separate dummy for each election, I am unable to reject the null thatthe coefficients are equal (χ2 = 0.56, p-value=0.454).

13In some cases, standard errors are 200 percent larger than standard errors obtained without clustering and100 percent larger than standard errors obtained with one way clustering. This confirms the importance ofcomputing standard errors with two-way clustering. An added complication arises from the fact that I onlyhave 26 time periods and, as a result, standard errors are downward biased and lead to over-rejection of thenull hypothesis of no effect (Cameron, Gelbach, and Miller 2008). As I have 6 time invariant regressors, asolution is to use critical values from a T distribution with 20 degrees of freedom. Results are robust to usingsuch critical values.

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increases by 0.87 percentage-points in the two quarters before elections. This is a large effect

as it translates into an additional 470,000 individuals employed in April 2004 than would have

been the case without the elections. The effects do not appear to be driven by one specific

election. When I estimate the model with separate dummies for each election, the 2004 coef-

ficient is 0.77 and the 2007 coefficient is 0.96. The differences are not statistically significant

at the usual levels of confidence (p-value=0.75).

Second, results indicate that employment in the two post-elections quarters is 0.48 percent-

age-points lower than it would have been without the elections. Again, the effects do not

appear to be driven by one specific election. When I estimate the model with separate

dummies for each election, the 2004 coefficient is -.41 and the 2007 coefficient is -.55. The

differences are not statistically significant at the usual levels of confidence (p-value=0.73).

The observed effects are consistent with the argument that they are driven by within-

year reallocation of resources. I am unable to reject the null hypothesis that the sum of the

coefficients on the pre-election and the post-election dummies is equal to zero (χ2 = 0.97, p-

value=0.325). More specifically, while data constraints prevent me from testing this directly,

results are consistent with the view that incumbents shift some of their planned end-of-year

budget spending before the elections which would explain the drop in employment in the two

post-elections quarters. This suggests that the failure to identify political cycles in a number

of countries could be partly driven by aggregation issues as the yearly effect captures both

the pre-election increase and the post-election decline.

If the argument is correct, the strength of political business cycles detected with yearly

data will depend on the timing of elections and the overlap between the fiscal and calendar

years. If elections are early in the fiscal year, incumbents might not have enough time to

increase spending before elections. Similarly if elections are late in the fiscal year, incumbents

do not have enough resources to bring forward before elections. As a result, we would expect

the effects to be stronger for elections organized in the middle of the fiscal year. In addition,

the larger the overlap between the fiscal and calendar years the more likely is the pre-election

increase attenuated with the post-election decrease. Findings from the literature tend to

support this argument. For example, Coelho, Veiga, and Veiga (2006) who test for political

business cycles in Portuguese municipalities, using yearly data, over the period 1985-2000. In

Portugal, elections take place at the end of the fiscal year which coincides with the calendar

15

year and, as such, effect sizes are expected to be small. The estimated effects indicate that total

municipal employment increases by 2.5 jobs in election years (Coelho, Veiga, and Veiga 2006).

In a similar context, Dahlberg and Mork (2011) find that, in Sweden and Finland there are 0.6

more full-time public employees per 1000 capita in election years. For the average municipality

in Sweden, it translates into 15.6 additional public-sector jobs.

I further test whether the cycles are present in both public sector and private sector

employment. Results, available in Panels B and C of Table 3, indicate that most of the

variations are concentrated in the private sector. Elections lead to a 0.15 percentage-point

increase in public sector employment and to a 0.73 percentage-point increase in private sector

employment in the two pre-election quarters. That is, 83 percent of the increase in employment

levels occurs in the private sector. Given that only 7.8 percent of those employed work in

the public sector, the relative increase is larger in the public sector than it is in the private

sector. Specifically, the public sector effect represents about 3.3 percent of the mean value

of public sector employment while the private sector effect represents only about 1.3 percent

of the mean value of private sector employment. There is no evidence of a decline in public

sector employment in the two post-elections quarters (point estimate: -0.002) but private

sector employment is 0.48 percentage-points lower than it would be if elections did not take

place.

Interestingly, there is no evidence that elections affect either the number of working hours

or wage for employed individuals (Tables A.2 and A.3). This suggests that, along those two

dimensions, jobs created during the political business cycles are neither superior nor inferior

to average jobs in the municipalities. They provide similar working hours and similar pay.

5.1.2 Channels and robustness checks

A potential concern with the above results is that they might simply be capturing the fact

that elections require labor which would automatically lead to a pre-election increase; not

related to political business cycles. Evidence discussed above seems to suggest that this is not

case. First, the increase is larger for private sector employment than it is for public sector

employment.

Second, as a further check, I use data on the type of employment contracts to compare the

effects of elections on public sector employment in long-term (i.e. non-casual) and short-term

16

(i.e. casual) contracts. If the results were driven by hiring of election workers, we would

expect an increase in employment on short-term contracts but no increase in employment

on long-term contracts. Available results suggest that there is an increase in public sector

employment on long-term contracts but no effect on employment on short-term contracts in

the two pre-elections quarters (Columns 1 and 2 of Table 4). Again, these results are not in

agreement with the view that effects are driven by hiring of election workers. In addition, this

increases the likelihood that local bureaucrats’ incentives are aligned with the incumbent’s

electoral objectives as their job tenure is implicitly linked with the incumbent’s electoral

success (Robinson and Verdier 2013 Iyer and Mani 2012).14

Further, the scale of the increase is difficult to reconcile with the number of jobs that are

actually needed to organize the elections. Taking 2004 as an example, the point estimates

suggest that pre-election increase in employment represents 1.1 percent of the population that

was registered to vote. It seems unlikely that so many individuals would have to be hired

given that a number of the required activities are carried out by existing civil servants, usually

public school teachers, and do not require additional hiring. In addition, the last pre-election

surveys are carried out about a month prior to the elections, that is before the final few weeks

of campaigning and election day which are likely to be the most labor-intensive.

In light of evidence from other countries (Drazen and Eslava 2010), incumbents might

attempt to increase both the levels of government spending and to target spending on visible

projects ahead of elections. Given that in the Philippines most government-financed con-

struction work is done through private contractors, one would expect the increase in private

sector employment to be concentrated on short-term contracts rather on long-term contracts.

Firms might be reluctant to hire employees on long-term contracts in response to short-term

increase in government spending. To test whether those channels explain the strength of the

political business cycles, I estimate equation (1) where Yijt is either the share of the working

age population that is employed on short-term contracts in the private sector, on long-term

contracts in the private sector. Results available in Columns 3 and 4 of Table 4 suggest

14As cited by Fafchamps and Labonne (2013), Hodder (2009) quotes a lawyer for the Civil Service Commis-sion: We can even go so far as saying that you cannot be appointed in local government if you do not know theappointing authority or, at least, if you do not have any [political] recommendation....And even once in place,the civil servant’s position is not secure: when the new mayor [comes], he just tells them ‘resign or I’ll file acase against you.’

17

that elections affect employment in the private sector on short-term contracts but not on

long-term contracts. In addition, consistent with findings from the literature on elections and

firm investment decisions, while the coefficient on the pre-elections quarters dummy is not

significant in the long term contract regression, the point estimate is negative (-0.855). Firms

are reluctant to invest before the uncertainty surrounding the elections is resolved (Julio and

Yook 2012).

In addition, one would expect private sector employment to be more responsive to elections

in sectors where local governments can invest in visible projects. To this end, I test whether

short-term employment in the construction sector is affected by elections. There is evidence

that employment in the construction sector increases by 0.20 percentage-points in the two

pre-election quarters (Column 5 of Table 4). This represents about 4 percent of the mean

value of employment in the construction sector. It is expected to lead to an increase in

either maintenance of existing public goods or the construction of new infrastructure. As

above, this seems to suggest that incumbents attempt to provide lasting benefits to their

constituents ahead of the elections.

I now present results from a number of robustness checks. First, further results suggest that

the main conclusions are robust to the inclusion of lagged values of the dependent variables

(Tables A.4-A.6). As expected point estimates differ slightly as one needs to account for

the additional effect through the lagged values but the results are of the same sign and still

statistically significant. This reinforces confidence that results discussed above are capturing

the presence of political business cycles in Philippine municipalities.

Second, I assess whether the results are robust to the exclusion of outliers. I estimate

equation (1) on a number of sub-samples where I exclude the top and bottom one, two, three

and four percent in the distribution of employment levels. Results, available in Table A.7, are

consistent with the ones obtained previously.

Third, as indicated in Section 4, I also estimate equation (1) with quarter/municipality

fixed effects. Results are available in Panel A of Table A.8. Overall, results are consistent with

the ones obtained above. The point estimates are similar but with slightly larger standard

errors and I can still reject the null hypothesis of no effect for overall employment, employment

in the private sector and long-term employment in the public sector at, at least, the 5%

significance level. The only exceptions are for employment levels in the public sector and

18

in the construction sector. The point estimate for the public sector is as before (.15) but

the standard error increases from .086 to .109, with the results going from being marginally

significant at the 10% level to being marginally insignificant at the 10% level. When it comes

to the construction sector, the point estimates are stable but the standard errors increase by

about 20 percent and I am no longer able to reject the null of no effect.

Fourth, I also present results from unweighted regressions (Solon, Haider, and Wooldridge

2013). Results, available in Panel B of Table A.8, are consistent with those obtained previ-

ously. There are two exceptions. Employment on long-term contracts in the private sector is

now 1.1 percentage-point lower in the two pre-election quarters. This is in line with previous

findings on firm investment ahead of elections (Julio and Yook 2012)., In addition, while the

coefficient is still positive, I am unable to reject the null hypothesis that employment in short-

term contracts in the construction sector is not affected by elections. This seems to indicate

that the increase in the construction sector is concentrated in more populous municipalities.

5.1.3 Heterogeneity

As explained in Section 4, I test whether political business cycles are dependent upon the

municipality’s political environment. More specifically, I compare electoral cycles in munici-

palities with established incumbents and in municipalities with relatively new incumbents. If

electoral cycles serve to signal incumbent’s quality to voters, they are likely to be concentrated

in municipalities where incumbents are from politically inexperienced families.

Results available in Panel A of Table 5 provide mixed support for the argument that

political business cycles arise as incumbents attempt to signal their types to voters. Once

I interact the electoral dummies with the number of times the incumbent’s family has been

elected mayor in the same municipality between 1988 and the current election, the increase in

public sector employment ahead of elections is lower in municipalities where the incumbent

or one of his family member has been elected more often.15

An alternative explanation for the results that employment rates in the private sector

are lower in the post-election months is that incoming officials are learning how to use the

15This set of results provides further evidence that results are not merely capturing the fact that organizingelections require labor as there would be no reason to expect different effects by number of terms. Estimatingpolitical business cycles separately for each level of political experience does not yield additional insights (TableA.9).

19

bureaucracy which might delay government spending. To test if that is the case, I compare

employment rates in municipalities where the incumbent was re-elected and in municipalities

where she lost. If learning about bureaucratic procedures explains the negative impact on

employment in the post-election period it should be concentrated in municipalities where a

challenger won. Results, available Panel B of Table 5, indicate that the effect of the post-

electoral period on employment in the public and private sectors are similar regardless of

whether the incumbent won or lost. The only exception is on employment on long-term

contracts in the public sector. It is 0.19 percentage-points lower in the two post-elections

quarters in municipalities where a challenger won than in municipalities where the incumbent

won. A potential explanation is that, consistent with Fafchamps and Labonne (2013), some

employees affiliated with the previous incumbent leave their positions just after the elections

and, through time, are replaced by employees affiliated with the new mayor. In addition, this

finding is in line with the model of clientelism developed by Robinson and Verdier (2013) who

argue that jobs are a credible way of redistributing resources from politicians to bureaucrats

as they have incentives to ensure that the incumbent is re-elected.

5.2 Local political budget cycles

I now test for the presence of budget cycles in Philippine municipalities. Results are available

in Table 6. Overall, spending does not seem to increase but tax collection effort is lower in

election years.

Depending on the number of lags included in the regression and the estimation method,

per capita revenues is between 2 and 5 percent lower in election years than in non-election

years. Local governments appear to collect less taxes during election years.16 In addition,

fiscal transfers from the central government are lower in election years. Given that fiscal

transfers from the central government are computed using tax collection three years before,

this suggests that overall tax collection effort by the central government are lower in election

years as well.

However, incumbents do not increase spending in election years, which is consistent with

the argument that local governments are de facto unable to borrow and, as such, are unable

16This is consistent with findings from India (Khemani 2004).

20

to increase spending in election years. It is possible that incumbents alter their within-year

spending allocation but I am unable to directly test for it as the data are aggregated yearly.

As above, a potential explanation for the drop in local tax collection is that, in munici-

palities where the incumbent lost, the new administration might need time to adjust to its

new role which could decrease tax collection effort. To test for that, I estimate equation (5)

and interact the election year dummy with a dummy equal to one if the incumbent lost the

election. There is no evidence of an additional drop in tax collection in municipalities where

a challenger won the election. The coefficients on the election year dummy are of similar

order of magnitude as above. However, there is limited evidence that spending is lower in

election years in municipalities where the incumbent lost. As quarterly or monthly data are

unavailable, this could either be due to the fact the incumbents who did not spend enough

money in the pre-election months tend to lose elections or to the fact that spending slows

down after the recently elected mayor takes office. Previous results suggest that the latter is

unlikely (Panel B of Table 5).

6 Conclusion

In this paper, using a balanced panel of about 1,140 municipalities over 26 quarters, I have

identified political business cycles in the Philippines. In election years, the share of the

working-age population that is employed is higher in the two pre-election quarters and lower

in the two post-election quarters than what it would have been without elections.

The results have methodological implications for the literature on political business cycles.

Once analyses are carried out with yearly data, I am unable to identify political business cycles.

The difference is not merely driven by a decline in statistical power due to aggregation: point

estimates for the overall effects are 7 times larger when I use quarterly data than when I use

yearly data. This discrepancy can be explained by a drop in employment post-election that

dilutes the yearly effects. Researchers interested in estimating political business cycles should

use either monthly or quarterly data.

21

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24

0.0

2.0

4.0

6D

ensi

ty

20 40 60 80 100Employment Rate in January/April

Election Years Non-Election Years

Figure 1: Municipal-level employment rate in elections and non-elections years

0.0

2.0

4.0

6D

ensi

ty

20 40 60 80 100Employment Rate in July/October

Election Years Non-Election Years

Figure 2: Municipal-level employment rate in elections and non-elections years

25

Table 1: Descriptive statistics(1) (2)

Mean Std. Dev.Share working-age population with a job in:Overall 59.14 (9.57)Public Sector 4.61 (3.15)Private Sector 54.53 (10.14)Public Sector (short-term) 0.55 (0.91)Public Sector (long-term) 4.06 (2.88)Private Sector (short-term) 44.34 (11.25)Private Sector (long-term) 10.19 (7.96)Construction (short-term) 5.05 (3.32)Other variables:No Education 2.16 (4.60)Some Primary 13.94 (10.86)Primary Graduate 14.50 (7.73)Some Secondary 16.93 (5.53)Secondary Graduate 24.27 (9.34)Some College + 28.19 (13.68)Population 82,347 (207,659)Share female 0.50 (0.04)Age 35.82 (2.23)

Observations 29,715

26

Table 2: Yearly political business cycles: Employment levels(1) (2) (3) (4)

Panel A - All sectorsElection Year 0.1608 0.1241 0.1209 0.1209

(0.128) (0.135) (0.160) (0.140)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 8,004 7,896 7,896 7,896R-squared 0.821 0.827 0.842 0.842Panel B - Public sectorElection Year -0.0338 0.0561 0.0591 0.0591

(0.139) (0.052) (0.061) (0.060)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 8,004 7,896 7,896 7,896R-squared 0.807 0.839 0.847 0.847Panel C - Private sectorElection Year 0.1947 0.0680 0.0618 0.0618

(0.179) (0.170) (0.186) (0.190)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 8,004 7,896 7,896 7,896R-squared 0.831 0.843 0.857 0.857

Notes: Results from fixed-effects regressions. The dependent variable is the yearly average of the shareof the working age population with a job in the week before the survey (Panel A), with a job in thepublic sector in the week before the survey (Panel B) and, with a job in the private sector in theweek before the survey (Panel C). Regressions in Columns 2-4 include controls for average age (andits square) in the municipality (for those older than 15), education levels (for those older than 15),the share of women, population and, per capita fiscal transfers. The standard errors (in parentheses)account for potential correlation within time period and province. * denotes significance at the 10%,** at the 5% and, *** at the 1% level.

27

Table 3: Quarterly political business cycles: Employment levels(1) (2) (3) (4)

Panel A - All sectorsPre-election quarters 0.8949*** 0.8725*** 0.8740*** 0.8760***

(0.268) (0.281) (0.276) (0.300)Post-election quarters -0.4619** -0.4843** -0.4859** -0.4864**

(0.199) (0.191) (0.231) (0.201)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 29,715 29,283 29,283 29,283R-squared 0.626 0.636 0.641 0.649Panel B - Public sectorPre-election quarters -0.0068 0.1483* 0.1488* 0.1487*

(0.077) (0.085) (0.087) (0.086)Post-election quarters -0.0909 -0.0011 -0.0017 -0.0018

(0.099) (0.034) (0.043) (0.036)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 29,715 29,283 29,283 29,283R-squared 0.548 0.610 0.611 0.618Panel C - Private sectorPre-election quarters 0.9017*** 0.7242*** 0.7252*** 0.7273***

(0.270) (0.275) (0.277) (0.281)Post-election quarters -0.3711 -0.4832** -0.4843** -0.4846**

(0.243) (0.201) (0.224) (0.205)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 29,715 29,283 29,283 29,283R-squared 0.641 0.661 0.666 0.673

Notes: Results from fixed-effects regressions. The dependent variable is the yearly average of the shareof the working age population with a job in the week before the survey (Panel A), with a job in thepublic sector in the week before the survey (Panel B) and, with a job in the private sector in theweek before the survey (Panel C). All regressions include controls for survey quarter. Regressions inColumns 2-4 include controls for average age (and its square) in the municipality (for those older than15), education levels (for those older than 15), the share of women, population and, per capita fiscaltransfers. The standard errors (in parentheses) account for potential correlation within time periodand province. * denotes significance at the 10%, ** at the 5% and, *** at the 1% level.

28

Table 4: Quarterly political business cycles: Channels(1) (2) (3) (4) (5)

Public Private ConstructionST LT ST LT ST

Pre-election quarters 0.0276 0.1211** 1.5826* -0.8553 0.2035*(0.048) (0.053) (0.847) (0.803) (0.119)

Post-election quarters 0.0334 -0.0352 -0.4466 -0.0380 -0.1039(0.048) (0.044) (0.651) (0.767) (0.092)

Observations 29,283 29,283 29,283 29,283 29,283R-squared 0.270 0.595 0.412 0.550 0.337

Notes: Results from fixed-effects regressions. The dependent variable is the share of the working agepopulation with a short-term job in the public sector in the week before the survey (Column 1) witha long-term job in the public sector in the week before the survey (Column 2), with a short-term jobin the private sector in the week before the survey (Column 3), with a long-term job in the privatesector in the week before the survey (Column 4) and with a short-term job in the construction sectorin the week before the survey (Column 5). All regressions include controls for survey quarters, averageage (and its square) in the municipality (for those older than 15), education levels (for those olderthan 15), the share of women, population per capita fiscal transfers, a dummy for whether or not theprevious municipal election led to a change in local leadership and province-specific quadratic timetrends. The standard errors (in parentheses) account for potential correlation within time period andprovince. * denotes significance at the 10%, ** at the 5% and, *** at the 1% level.

29

Tab

le5:

Qua

rter

lypo

litic

albu

sine

sscy

cles

:H

eter

ogen

eity

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Full

Pub

licP

riva

teP

ublic

Pri

vate

Con

stru

ctio

nST

LTST

LTST

Pan

elA

-In

cum

bent

’sex

peri

ence

Pre

-ele

ctio

nqu

arte

rs0.

9058

***

0.14

37*

0.76

21**

*0.

0255

0.11

82**

1.56

85*

-0.8

064

0.19

98*

(0.3

03)

(0.0

87)

(0.2

85)

(0.0

49)

(0.0

53)

(0.8

49)

(0.7

95)

(0.1

19)

Pos

t-el

ecti

onqu

arte

rs-0

.505

9**

-0.0

021

-0.5

038*

*0.

0350

-0.0

371

-0.4

319

-0.0

718

-0.1

017

(0.2

01)

(0.0

37)

(0.2

04)

(0.0

47)

(0.0

44)

(0.6

47)

(0.7

55)

(0.0

93)

Pre

-ele

ctio

nqu

arte

rsX

0.00

88-0

.070

7**

0.07

950.

0000

-0.0

708*

*0.

1440

-0.0

645

-0.0

008

Nb

term

s(0

.098

)(0

.035

)(0

.087

)(0

.012

)(0

.033

)(0

.162

)(0

.177

)(0

.034

)P

ost-

elec

tion

quar

ters

X0.

0447

-0.0

097

0.05

44-0

.001

2-0

.008

5-0

.047

80.

1022

-0.0

100

Nb

term

s(0

.094

)(0

.024

)(0

.094

)(0

.015

)(0

.031

)(0

.093

)(0

.088

)(0

.025

)R

-squ

ared

0.64

90.

618

0.67

30.

270

0.59

50.

412

0.55

00.

337

Pan

elB

-In

cum

bent

Los

tP

re-e

lect

ion

quar

ters

0.86

36**

*0.

1602

*0.

7033

**0.

0323

0.12

79**

1.59

04*

-0.8

870

0.20

94*

(0.3

03)

(0.0

87)

(0.2

84)

(0.0

48)

(0.0

53)

(0.8

50)

(0.8

03)

(0.0

99)

Pos

t-el

ecti

onqu

arte

rs-0

.499

6**

0.05

13-0

.550

9***

0.02

870.

0226

-0.5

108

-0.0

401

0.02

07(0

.198

)(0

.052

)(0

.198

)(0

.052

)(0

.058

)(0

.701

)(0

.776

)(0

.087

)P

ost-

elec

tion

quar

ters

X0.

0703

-0.1

939

0.26

410.

0039

-0.1

978*

0.18

410.

0800

0.06

30C

hang

e(0

.277

)(0

.127

)(0

.335

)(0

.050

)(0

.103

)(0

.475

)(0

.569

)(0

.063

))R

-squ

ared

0.64

90.

618

0.67

30.

270

0.59

50.

412

0.55

00.

337

Not

es:

Res

ults

from

fixed

-effe

cts

regr

essi

ons.

The

depe

nden

tva

riab

leis

the

shar

eof

the

wor

king

age

popu

lati

onw

ith

ajo

bin

the

wee

kbe

fore

the

surv

ey(C

olum

n1)

,w

ith

ajo

bin

the

publ

icse

ctor

inth

ew

eek

befo

reth

esu

rvey

(Col

umn

2),w

ith

ajo

bin

the

priv

ate

sect

orin

the

wee

kbe

fore

the

surv

ey(C

olum

n3)

,wit

ha

shor

t-te

rmjo

bin

the

publ

icse

ctor

inth

ew

eek

befo

reth

esu

rvey

(Col

umn

4)w

ith

alo

ng-t

erm

job

inth

epu

blic

sect

orin

the

wee

kbe

fore

the

surv

ey(C

olum

n5)

,wit

ha

shor

t-te

rmjo

bin

the

priv

ate

sect

orin

the

wee

kbe

fore

the

surv

ey(C

olum

n6)

,w

ith

alo

ng-t

erm

job

inth

epr

ivat

ese

ctor

inth

ew

eek

befo

reth

esu

rvey

(Col

umn

7)an

dw

ith

ash

ort-

term

job

inth

eco

nstr

ucti

onse

ctor

inth

ew

eek

befo

reth

esu

rvey

(Col

umn

8).

All

regr

essi

ons

incl

ude

cont

rols

for

surv

eyqu

arte

rs,

aver

age

age

(and

its

squa

re)

inth

em

unic

ipal

ity

(for

thos

eol

der

than

15),

educ

atio

nle

vels

(for

thos

eol

der

than

15),

the

shar

eof

wom

en,

popu

lati

onpe

rca

pita

fisca

ltr

ansf

ers,

adu

mm

yfo

rw

heth

eror

not

the

prev

ious

mun

icip

alel

ecti

onle

dto

ach

ange

inlo

cal

lead

ersh

ipan

dpr

ovin

ce-s

peci

ficqu

adra

tic

tim

etr

ends

.T

hest

anda

rder

rors

(in

pare

nthe

ses)

acco

unt

for

pote

ntia

lco

rrel

atio

nw

ithi

nti

me

peri

odan

dpr

ovin

ce.

*de

note

ssi

gnifi

canc

eat

the

10%

,**

atth

e5%

and,

***

atth

e1%

leve

l.O

bser

vati

ons:

29,2

83.

Table 6: Political budget cycles

(1) (2) (3) (4) (5)Number of lags 0 1 1 2 2

FE FE GMM FE GMMPanel A - log per capita revenuesElection Year -0.0593* -0.0546** -0.0313*** -0.0462* -0.0207***

(0.031) (0.025) (0.003) (0.024) (0.003)

Observations 14,107 12,578 12,578 10,981 10,981R-squared 0.911 0.923 0.928Panel B - log per capita local tax collectionElection Year -0.0266** -0.0235** -0.0170*** -0.0236** -0.0166***

(0.011) (0.011) (0.004) (0.011) (0.004)

Observations 14,092 12,554 12,554 10,953 10,953R-squared 0.938 0.948 0.952Panel C - log per capita transfers from National gvtElection Year -0.0666 -0.0601* -0.0385*** -0.0439* -0.0120***

(0.043) (0.032) (0.002) (0.025) (0.003)

Observations 14,103 12,574 12,574 10,977 10,977R-squared 0.939 0.953 0.962Panel D - log per capita spendingElection Year -0.0131 -0.0156 -0.0075** -0.0181 -0.0021

(0.021) (0.024) (0.004) (0.023) (0.004)

Observations 14,105 12,575 12,575 10,977 10,977R-squared 0.873 0.890 0.897Panel E - Share municipal budget spentElection Year 4.0713** 2.8342*** 2.4552*** 2.1065** 2.6537***

(1.713) (0.966) (0.259) (0.973) (0.266)

Observations 14,107 12,578 12,578 10,981 10,981R-squared 0.169 0.178 0.196

Notes: Results from fixed-effects and GMM regressions. All regressions include inColumns 2-5 controls for population, the number of times the incumbent’s familyhas been elected in the municipality since 1987, a dummy capturing family linksbetween the mayor and provincial officials, a dummy capturing change in mayorand, a simple time trend (Columns 1 and 2), region-specific time trends (Column3), province-specific time trends (Columns 4) and, municipality-specific time trends(Column 5). The standard errors (in parentheses) account for potential correlationwithin time period and province. * denotes significance at the 10%, ** at the 5%and, *** at the 1% level.

31

Table 7: Political budget cycles: heterogeneity(1) (2) (3) (4) (5)

Nb Lags 0 1 1 2 2FE FE GMM FE GMM

Panel A - log per capita revenues from local sourcesElection Year -0.0289** -0.0188 -0.0127** -0.0201 -0.0123**

(0.012) (0.012) (0.006) (0.013) (0.006)Election Year X 0.0082 -0.0140 -0.0134 -0.0106 -0.0132Change (0.024) (0.017) (0.012) (0.022) (0.012)

Observations 14,092 12,554 12,554 10,953 10,953R-squared 0.938 0.948 0.952Panel B - log per capita spendingElection Year -0.0111 -0.0102 0.0001 -0.0124 0.0027

(0.021) (0.023) (0.005) (0.021) (0.005)Election Year X -0.0075 -0.0164 -0.0231*** -0.0173 -0.0145*Change (0.013) (0.013) (0.009) (0.017) (0.009)

Observations 14,105 12,575 12,575 10,977 10,977R-squared 0.873 0.890 0.897

Notes: Results from fixed-effects and GMM regressions. All regressions include in Columns 2-5 controlsfor population, the number of times the incumbent’s family has been elected in the municipality since1987, a dummy capturing family links between the mayor and provincial officials, a dummy capturingchange in mayor and, a simple time trend (Columns 1 and 2), region-specific time trends (Column3), province-specific time trends (Columns 4) and, municipality-specific time trends (Column 5). Thestandard errors (in parentheses) account for potential correlation within time period and province. *denotes significance at the 10%, ** at the 5% and, *** at the 1% level.

32

Table A.1: Yearly political business cycles: Employment Levels (excluding 2003)(1) (2) (3) (4)

Panel A - All sectorsElection Year 0.1957 0.1417 0.1411 0.1393

(0.154) (0.151) (0.166) (0.159)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 6,860 6,752 6,752 6,752R-squared 0.857 0.863 0.868 0.874Panel B - Public sectorElection Year -0.0698 0.0656 0.0657 0.0655

(0.108) (0.056) (0.065) (0.082)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 6,860 6,752 6,752 6,752R-squared 0.837 0.867 0.868 0.872Panel C - Private sectorElection Year 0.2655 0.0761 0.0754 0.0738

(0.197) (0.196) (0.227) (0.208)

Additional Controls No Yes Yes YesTime Trend No Yes Region ProvinceObservations 6,860 6,752 6,752 6,752R-squared 0.864 0.875 0.879 0.885

Notes: Results from fixed-effects regressions. The dependent variable is the yearly average of the shareof the working age population with a job in the week before the survey (Panel A), with a job in thepublic sector in the week before the survey (Panel B) and, with a job in the private sector in theweek before the survey (Panel C). Regressions in Columns 2-4 include controls for average age (andits square) in the municipality (for those older than 15), education levels (for those older than 15),the share of women, population and, per capita fiscal transfers. The standard errors (in parentheses)account for potential correlation within time period and province. * denotes significance at the 10%,** at the 5% and, *** at the 1% level.

33

Table A.2: Quarterly political business cycles: Average hours worked(1) (2) (3) (4)

Panel A - EmploymentPre-election quarters -0.5615 -0.5702 -0.5711 -0.5755

(0.523) (0.501) (0.510) (0.585)Post-election quarters 0.1341 0.1526 0.1551 0.1544

(0.281) (0.325) (0.325) (0.308)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 29,715 29,283 29,283 29,283R-squared 0.706 0.709 0.712 0.718Panel B - Public sector employmentPre-election quarters -0.8382 -1.1972 -1.1923 -1.1968*

(0.691) (0.771) (0.772) (0.695)Post-election quarters 0.2837 0.0387 0.0408 0.0466

(0.280) (0.378) (0.390) (0.369)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 25,442 25,083 25,083 25,083R-squared 0.305 0.309 0.311 0.318Panel C - Private sector employmentPre-election quarters -0.5433 -0.5132 -0.5142 -0.5188

(0.521) (0.493) (0.502) (0.569)Pot-election quarters 0.1152 0.1651 0.1676 0.1666

(0.289) (0.324) (0.325) (0.307)


Notes: Results from fixed-effects regressions. The dependent variable is the average number hoursworked in the week before the survey for those who have a job in the week before the survey (Panel A),with a job in the public sector in the week before the survey (Panel B) and with a job in the privatesector in the week before the survey (Panel C). All regressions include controls for survey quarter.Regressions in Columns 2-4 include controls for average age (and its square) in the municipality (forthose older than 15), education levels (for those older than 15), the share of women, population and,per capita fiscal transfers. The standard errors (in parentheses) account for potential correlation withintime period and province. * denotes significance at the 10%, ** at the 5% and, *** at the 1% level.

34

Table A.3: Quarterly political business cycles: Log wage(1) (2) (3) (4)

Panel A - EmploymentPre-election quarters -0.0746** -0.0147* -0.0147 -0.0147

(0.035) (0.009) (0.009) (0.009)Post-election quarters -0.0307 0.0047 0.0047 0.0048

(0.040) (0.008) (0.008) (0.009)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 28,818 28,403 28,403 28,403R-squared 0.700 0.758 0.761 0.765Panel B - Public sector employmentPre-election quarters -0.0705** -0.0223 -0.0224 -0.0226

(0.035) (0.014) (0.015) (0.015)Post-election quarters -0.0400 -0.0167 -0.0167 -0.0167

(0.031) (0.020) (0.021) (0.020)

Additional Controls No Yes Yes YesQuadratic Time Trend No Yes Region ProvinceObservations 23,324 23,002 23,002 23,002R-squared 0.260 0.289 0.292 0.302Panel C - Private sector employmentPre-election quarters -0.0684* -0.0098 -0.0100 -0.0101

(0.035) (0.010) (0.010) (0.011)Post-election quarters -0.0263 0.0120 0.0122 0.0121

(0.041) (0.008) (0.008) (0.008)


Notes: Results from fixed-effects regressions. The dependent variable is the average log wage for thosewho have a job in the week before the survey (Panel A), with a job in the public sector in the weekbefore the survey (Panel B) and with a job in the private sector in the week before the survey (PanelC). All regressions include controls for survey quarter. Regressions in Columns 2-4 include controlsfor average age (and its square) in the municipality (for those older than 15), education levels (forthose older than 15), the share of women, population and, per capita fiscal transfers. The standarderrors (in parentheses) account for potential correlation within time period and province. * denotessignificance at the 10%, ** at the 5% and, *** at the 1% level.

35

Table A.4: Yearly political business cycles: Alternative specification(1) (2) (3) (4) (5) (6)

Full Public PrivateNumber of lags 1 2 1 2 1 2Panel A: Fixed effectsElection Year 0.1606 0.1990 0.0684 0.0190 0.0909 0.1758

(0.188) (0.206) (0.058) (0.054) (0.209) (0.253)

Observations 6,751 5,607 6,751 5,607 6,751 5,607R-squared 0.873 0.887 0.872 0.880 0.884 0.896Panel B: GMMElection Year 0.1745 0.2684* 0.0550* 0.0450 0.0925 0.2179

(0.128) (0.153) (0.031) (0.039) (0.127) (0.161)

Observations 6,751 5,607 6,751 5,607 6,751 5,607

Notes: Results from Fixed-Effects and GMM regressions. The dependent variable is the yearly averageof the share of the working age population with a job in the week before the survey (Columns 1-2),with a job in the public sector in the week before the survey (Columns 3-4) and, with a job in theprivate sector in the week before the survey (Columns 5-6). All regressions include controls for surveyquarters, average age (and its square) in the municipality (for those older than 15), education levels(for those older than 15), the share of women, population, per capita fiscal transfers and region-specifictime trend. The dependent variable is lagged once in Columns 1, 3 and 5 and twice in Columns 2, 4and 6. The standard errors (in parentheses) account for potential correlation within time period andprovince (Panel A), and within province (Panel B). * denotes significance at the 10%, ** at the 5%and, *** at the 1% level.

36

Table A.5: Yearly political business cycles: Alternative specification (Excluding 2003)(1) (2) (3) (4) (5) (6)

Full Public PrivateNumber of lags 1 2 1 2 1 2Panel A: Fixed effectsElection Year 0.1957 0.2512 0.0276 0.0037 0.1687 0.2420

(0.205) (0.172) (0.057) (0.045) (0.256) (0.185)

Observations 5,607 4,464 5,607 4,464 5,607 4,464R-squared 0.886 0.901 0.875 0.894 0.895 0.908Panel B: GMMElection Year 0.1406 0.2711* 0.0529 0.0314 0.1758 0.2238

(0.129) (0.152) (0.036) (0.038) (0.132) (0.159)

Observations 5,607 4,464 5,607 4,464 5,607 4,464

Notes: Results from Fixed-Effects and GMM regressions. The dependent variable is the yearly averageof the share of the working age population with a job in the week before the survey (Columns 1-2),with a job in the public sector in the week before the survey (Columns 3-4) and, with a job in theprivate sector in the week before the survey (Columns 5-6). All regressions include controls for surveyquarters, average age (and its square) in the municipality (for those older than 15), education levels(for those older than 15), the share of women, population, per capita fiscal transfers and region-specifictime trend. The dependent variable is lagged once in Columns 1, 3 and 5 and twice in Columns 2, 4and 6. The standard errors (in parentheses) account for potential correlation within time period andprovince (Panel A), and within province (Panel B). * denotes significance at the 10%, ** at the 5%and, *** at the 1% level.

37

Table A.6: Quarterly political business cycles: Alternative specification

(1) (2) (3) (4) (5) (6)Full Public Private

Number of lags 1 2 1 2 1 2Panel A: Fixed effectsPre-election quarters 0.7388** 0.7991*** 0.1452 0.1386 0.6013 0.6648*

(0.294) (0.271) (0.098) (0.093) (0.446) (0.361)Post-election quarters -0.5921*** -0.5750*** -0.0071 -0.0210 -0.5838*** -0.5506**

(0.192) (0.217) (0.042) (0.051) (0.218) (0.264)

Observations 28,132 26,984 28,132 26,984 28,132 26,984R-squared 0.655 0.657 0.615 0.613 0.678 0.679Panel B: IVPre-election quarters 1.2551*** 1.3011*** 0.1939*** 0.1641** 1.0612*** 1.1176***

(0.230) (0.201) (0.065) (0.079) (0.240) (0.166)Post-election quarters -0.1882 -1.2071** 0.0581 -0.1130 -0.2526 -1.1795**

(0.206) (0.480) (0.041) (0.142) (0.219) (0.509)

Observations 25,837 24,690 25,837 24,690 25,837 24,690

Notes: Results from Fixed-Effects and IV regressions. The dependent variable is the share of theworking age population with a job in the week before the survey (Columns 1-2), with a job in thepublic sector in the week before the survey (Columns 3-4) and, with a job in the private sector in theweek before the survey (Columns 5-6). All regressions include controls for survey quarters, averageage (and its square) in the municipality (for those older than 15), education levels (for those olderthan 15), the share of women, population, per capita fiscal transfers and province-specific quadratictime trend. The dependent variable is lagged once in Columns 1, 3 and 5 and twice in Columns 2, 4and 6. The standard errors (in parentheses) account for potential correlation within time period andprovince. * denotes significance at the 10%, ** at the 5% and, *** at the 1% level.

38

Tab

leA

.7:

Qua

rter

lypo

litic

albu

sine

sscy

cles

:E

xclu

ding

outl

iers

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Full

Pub

licP

riva

teP

ublic

(ST

)P

ublic

(LT

)P

riva

te(S

T)

Pri

vate

(LT

)C

onst

ruct

ion

(ST

)P

anel

A-

Exc

lude

top

and

bott

om1%

(n=

28,7

10)

Pre

-ele

ctio

nqu

arte

rs0.

8661

***

0.14

52*

0.72

08**

*0.

0285

0.11

68**

1.56

23**

-0.8

415

0.20

98(0

.269

)(0

.087

)(0

.257

)(0

.048

)(0

.055

)(0

.769

)(0

.680

)(0

.160

)P

ost-

elec

tion

quar

ters

-0.4

489*

*-0

.002

0-0

.446

9**

0.03

42-0

.036

2-0

.455

60.

0086

-0.1

042

(0.1

98)

(0.0

37)

(0.2

05)

(0.0

50)

(0.0

45)

(0.6

49)

(0.7

61)

(0.0

91)

Pan

elB

-E

xclu

deto

pan

dbo

ttom

2%(n

=28

,123

)P

re-e

lect

ion

quar

ters

0.80

41**

*0.

1386

0.66

56**

*0.

0268

0.11

17**

1.56

56*

-0.9

001

0.21

42(0

.266

)(0

.085

)(0

.251

)(0

.047

)(0

.057

)(0

.808

)(0

.751

)(0

.158

)P

ost-

elec

tion

quar

ters

-0.4

228*

*0.

0020

-0.4

248*

*0.

0334

-0.0

315

-0.4

653

0.04

05-0

.102

5(0

.197

)(0

.038

)(0

.204

)(0

.051

)(0

.044

)(0

.652

)(0

.754

)(0

.091

)P

anel

C-

Exc

lude

top

and

bott

om3%

(n=

27,5

39)

Pre

-ele

ctio

nqu

arte

rs0.

8100

***

0.13

590.

6741

***

0.02

510.

1108

**1.

5703

*-0

.896

20.

2157

*(0

.264

)(0

.083

)(0

.243

)(0

.045

)(0

.053

)(0

.834

)(0

.642

)(0

.125

)P

ost-

elec

tion

quar

ters

-0.3

909*

*0.

0043

-0.3

952

0.03

21-0

.027

8-0

.462

90.

0678

-0.1

005

(0.1

99)

(0.0

38)

(0.2

92)

(0.0

49)

(0.0

70)

(0.6

80)

(0.7

81)

(0.0

88)

Pan

elD

-E

xclu

deto

pan

dbo

ttom

4%(n

=26

,952

)P

re-e

lect

ion

quar

ters

0.81

54**

*0.

1358

0.67

97**

*0.

0249

0.11

09**

1.54

62**

-0.8

665

0.21

77*

(0.2

59)

(0.0

83)

(0.2

46)

(0.0

46)

(0.0

53)

(0.7

61)

(0.6

34)

(0.1

20)

Pos

t-el

ecti

onqu

arte

rs-0

.347

70.

0000

-0.3

477

0.02

89-0

.028

9-0

.443

50.

0958

-0.1

006

(0.2

14)

(0.0

39)

(0.2

17)

(0.0

50)

(0.0

49)

(0.6

49)

(0.7

59)

(0.0

89)

Not

es:

Res

ults

from

fixed

-effe

cts

regr

essi

ons.

The

depe

nden

tva

riab

leis

the

shar

eof

the

wor

king

age

popu

lati

onw

ith

ajo

bin

the

wee

kbe

fore

the

surv

ey(C

olum

n1)

,w

ith

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umn

2),w

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ity

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0.14

690.

7254

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0276

0.11

93**

1.57

46-0

.849

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2028

(0.3

14)

(0.1

09)

(0.2

90)

(0.0

60)

(0.0

60)

(0.9

77)

(0.7

31)

(0.1

46)

Pos

t-el

ecti

onqu

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rs-0

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011

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0338

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(0.2

29)

(0.0

52)

(0.0

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(0.6

83)

(0.8

05)

(0.1

02)

Obs

erva

tion

s29

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29,2

8329

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8329

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83R

-squ

ared

0.70

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0.72

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0.66

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00.

438

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elB

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ow

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lect

ion

quar

ters

0.79

65**

*0.

1842

*0.

6122

*0.

0490

0.13

53**

1.71

07**

-1.0

985*

0.15

68(0

.307

)(0

.106

)(0

.327

)(0

.060

)(0

.058

)(0

.738

)(0

.644

)(0

.100

)P

ost-

elec

tion

quar

ters

-0.7

115*

**0.

0187

-0.7

302*

**0.

0151

0.00

36-0

.514

2-0

.216

0-0

.089

1(0

.268

)(0

.055

)(0

.273

)(0

.042

)(0

.045

)(0

.678

)(0

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)(0

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)

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tion

s29

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8329

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0.60

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585

0.64

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0.56

10.

366

0.52

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299

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es:

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fixed

-effe

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The

depe

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the

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40

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eter

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term

0.95

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1866

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1.03

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ost-

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hird

term

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urth

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ifth

term

-0.4

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0.64

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338

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es:

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fixed

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cts

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essi

ons.

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depe

nden

tva

riab

leis

the

shar

eof

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wor

king

age

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lati

onw

ith

ajo

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fore

the

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olum

n1)

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publ

icse

ctor

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ew

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reth

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(Col

umn

2),w

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bin

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priv

ate

sect

orin

the

wee

kbe

fore

the

surv

ey(C

olum

n3)

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ha

shor

t-te

rmjo

bin

the

publ

icse

ctor

inth

ew

eek

befo

reth

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rvey

(Col

umn

4)w

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ng-t

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job

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sect

orin

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,wit

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job

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term

job

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nstr

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inth

ew

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befo

reth

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rvey

(Col

umn

8).

All

regr

essi

ons

incl

ude

cont

rols

for

surv

eyqu

arte

rs,

aver

age

age

(and

its

squa

re)

inth

em

unic

ipal

ity

(for

thos

eol

der

than

15),

educ

atio

nle

vels

(for

thos

eol

der

than

15),

the

shar

eof

wom

en,

popu

lati

onpe

rca

pita

fisca

ltr

ansf

ers,

adu

mm

yfo

rw

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eror

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the

prev

ious

mun

icip

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ecti

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nw

ithi

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me

peri

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gnifi

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atth

e5%

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***

atth

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leve

l.O

bser

vati

ons:

29,2

83.

41

Teacher Absenteeism and the Salience of Local EthnicDiversity: Evidence from African Districts

Eoin F. McGuirk∗

CEGA, University of California, Berkeley

For an up-to-date version, please click here.

Abstract

The rate of teacher absenteeism is over five times higher in Uganda than it is in New York.In India, it is two and a half times higher than the rate of absenteeism for private sectorfactory workers. One potential explanation for these observations is that, in the presence ofweak formal institutions—such as those found in many less developed countries—the likelihoodof punishment for absent teachers may be lower. In these settings, other forms of local collectiveaction are often required to produce public goods and prevent free-riding. However, a growingliterature has shown that local collective action outcomes are often adversely affected by ethnicdivisions. In this paper, I identify the impact of a new measure of ethnic divisions on teacherabsenteeism using two datasets: one collected from random, unannounced school visits inUganda, and another collected from over 20,000 survey respondents in 16 sub-Saharan Africancountries. In light of growing empirical support for constructivist theories of ethnicity, I allowthe effect of diversity to vary by the salience of ethnic identification in each district. I findthat, at high levels of ethnic salience, a one standard deviation increase in ethnic diversityincreases the observed absenteeism rate in Uganda by between 3.8 and 9.3 percentage points,or 0.08 and 0.21 standard deviations. In the multi-country survey data, the same changeincreases perceived absenteeism by 0.08 standard deviations. At low levels of ethnic salience,diversity has no positive effect on absenteeism in either dataset. Consistent with the recentliterature on the limitations of participatory programs on public service delivery, I providesuggestive evidence that social capital in the form of within-school teacher networks, ratherthan community-level monitoring, may explain the findings. The results offer one explanationfor why substantial recent investment in education does not seem to be leading to improvedtest-score outcomes for children in many poor and ethnically diverse countries. The analysisalso has implications for the measurement of ethnic divisions.

∗Email: [email protected] or [email protected]. I am grateful in particular to Pedro Vicente and TedMiguel for their invaluable guidance, support and suggestions. I thank James Habyarimana and Halsey Rogers forproviding me with access to data, and also Gani Aldashev, Pierre Bachas, Joachim von Braun, David Berger, DanielGilligan, Guy Grossman, John Hoddinott, Philip Lane, Janet Lewis, Jeremy Magruder, Lucy Martin, Julia AnnaMatz, Fergal McCann, Mark McGovern, Ben Morse, Carol Newman, Laura Ralston, Gerard Roland, Bilal Siddiqi andparticipants at the Development Economics Lunch Seminar at University of California, Berkeley, and the WorkingGroup in African Political Economy (WGAPE) for their helpful input. I gratefully acknowledge funding from theIrish Research Council and the Fulbright Commission. All errors are my own.

1

http://dl.dropbox.com/u/9353070/McGuirk_TA_Ethnicity.pdf

1 Introduction

Despite the unprecedented expansion of primary school access over the past decade, standardized

test results in many less developed countries reveal that basic numeracy and literacy skills are not

improving.1 Evidence from Chaudhury et al. (2006) and Duflo et al. (2012) strongly suggests that

teacher absenteeism may be significantly contributing to this observation. The former study found

that 19% of teachers were absent during unannounced visits to nationally representative samples

of schools in Bangladesh, Ecuador, India, Indonesia, Peru and Uganda in 2002 and 2003,2 while

Duflo et al. (2012) show that a reduction of the absenteeism rate in Udaiper, India, from 36% to

18% led to an improvement in test scores of 0.17 standard deviations.

In this analysis, I show that ethnic divisions at the district and school levels are associated with

significantly higher rates of teacher absenteeism. I design a new measure of ethnic divisions to

capture what are sometimes called the ‘evolutionary’ and the ‘constructivist’ components of ethnic

identity.3 Traditional measures of ethnic diversity, or ‘ethnolinguistic fractionalization’, are based

on the composition of ethnic groups in a given area. This is to a large extent the product of long-term

cultural drift, itself caused by historical settlement duration (Ashraf and Galor, 2013; Ahlerup and

Olsson, 2012) and geographic variability (Michalopoulos, 2012). However, the extent to which this

diversity is manifested in collective action problems or political cleavages depends on the salience of

ethnic identification, which can vary across countries and over time due to nation-building policies

(Miguel, 2004), political competition (Posner, 2004a; Eifert et al., 2010) or other historical and

contextual factors (Dunning and Harrison, 2010; Glennerster et al., 2012). In order to capture

these ‘constructivist’ conditions, I create a district-level term that represents ethnic divisions by

interacting ethnic diversity with the salience of ethnic identification. The diversity component is

based on a Herfindahl concentration index of Afrobarometer survey respondents, while the salience

component is based on respondents’ propensity to identify themselves along ethnic lines rather than

1See Uwezo (2011, 2012) for East African cases, and Pratham (2006) for Indian cases.2This is a conservative estimate. The inclusion of ‘tea-drinkers’—teachers who were present but were not teaching

as scheduled—increases the figure from 25% to almost 50% for India alone.3These are often described as ‘primordial’ and ‘instrumentalist’ respectively.

2

national lines.

I combine this new interpretation of ethnic divisions with data on (i) observed teacher absences

collected from random visits to almost 100 schools in 10 Ugandan districts;4 and (ii) perceived

teacher absenteeism amongst over 20,000 Afrobarometer survey respondents in 16 African countries.

At high levels of ethnic salience,5 I find that an increase of one standard deviation in local ethnic

diversity increases the rate of observed teacher absenteeism in the Ugandan dataset by between 3.8

and 9.3 percentage points, or 0.08 and 0.21 standard deviations. In the multi-country survey data,

the comparable increase in perceived teacher absenteeism amongst respondents is 0.08 standard

deviations.6 At low levels of ethnic salience, however, I find no positive effect of ethnic diversity

on absenteeism in either dataset: in the multi-country data there is no significant effect; while

in the Ugandan data there is a significantly negative effect, which I suggest may be explained

by residential sorting. In addition, I find that including the diversity component alone, and by

implication ignoring the role of salience, would lead to the erroneous conclusion that ethnicity does

not have any significant effect on absenteeism.

I also replicate the Ugandan analysis using several school-level alternatives to the district-level

measure of ethnic divisions. In each of the 94 schools, head teachers are asked to estimate the

shares of the three most commonly spoken mother tongues amongst pupils in that school. I use this

information to construct a measure of ethnic diversity, and interact it with the salience measure

from the Afrobarometer. In addition, I create a range of teacher-specific proxies based on their

linguistic, ancestral and regional origins. Across a broad range of specifications, the results are

consistent with those reported above.

Having robustly established the reduced-form link between ethnic divisions and teacher absen-

teeism, I take a step towards identifying the channels of causation that may explain the relationship.

This is facilitated by the richness of the Ugandan data, which includes information on teacher-level

characteristics as well as school-level infrastructural and management characteristics. In order to

4These data were collected for a World Bank project led by Habyarimana (2010) and Chaudhury et al (2006).5High (low) ethnic salience is defined as the mean plus (minus) one standard deviation.6This is a subjective measure based on a four-point scale, hence there is no meaningful percentage point change

to report for comparison.

3

establish a conceptual framework for this exercise, I draw on the literature on both ethnic diversity

and teacher incentives. In simple terms, ethnic diversity is most commonly purported to affect

social capital and cooperation in three ways. First: through a ‘taste’ for discrimination between

coethnics and non-coethnics (Becker 1957, 1974; Hjort 2012). This may increase absenteeism if

‘outsider’ teachers simply care less about non coethnic students, parents or other teachers. Second:

through its impact on the effectiveness of social sanctions (Miguel and Gugerty, 2005; Habyarimana

et al., 2007, 2009). In this case, ethnic divisions may lead to higher absenteeism for two reasons:

(i) it may reduce the capacity for collective action necessary to form local monitoring institutions,

such as parent teacher associations; and (ii) outsiders may not face the same credible threats of

informal sanctioning by parents, head teachers or other teachers that apply to coethnics. Third:

ethnic divisions may have a negative impact on group formation and social participation (Alesina

and La Ferrara, 2000). This could affect the cooperation of teachers within schools, which may

ultimately impact attendance decisions.

Against this background, I conduct three groups of tests to explain the association between

ethnic divisions and teacher absenteeism. First, using a range of measures, I reject the altruism

channel by identifying no statistical difference between ‘native’ and ‘outsider’ teachers’ attendance

decisions. Second, I find that neither parent teacher associations, school inspections nor the sanc-

tioning history of head teachers can explain the main result. This is consistent with a growing

literature on teacher incentives in developing countries.7 Evidence from Banerjee et al. (2010),

de Laat et al. (2008), and Duflo et al. (2012) indicates that participatory programs designed to

empower the beneficiaries of public services—in this case parents—by providing them with infor-

mation and access to educational authorities are unlikely to substantially affect teacher attendance.

This is largely due to a combination of weak demand amongst parents and their relative lack of

power to enforce accountability mechanisms. Moreover, evidence from Kremer and Chen (2001)

suggests that head teachers are also unlikely to incentivize attendance.8

7In addition to Banerjee and Duflo (2006), Kremer and Holla (2009) present a particularly comprehensiveoverview.

8The successful intervention evaluated by Duflo et al. (2012) was based on an objective and external monitoringand reward system that was facilitated by tamper-proof cameras.

4

Instead, I find that the strength of social networks between teachers within schools, charac-

terized by their social activities outside of official hours, can explain most of the reduced form

relationship. The finding is consistent with Alesina and La Ferrara (2000), who show theoretically

and empirically how social participation is lower in heterogenous communities. The result suggests

that non-pecuniary incentives to attend work may partially derive from a teacher’s colleagues rather

than the wider local community.

The analysis contributes to a growing literature on the negative association between ethnic

diversity and the local provision of public goods in sub-Saharan Africa (Miguel and Gugarty, 2005;

Habyarimana et al., 2007, 2009) and elsewhere (Alesina et al., 1999; Vigdor, 2004). This association

has particularly acute consequences for countries that lack the strong formal institutions required

to implement many government policies—like India, Uganda and most of the 16 countries under

analysis—where informal collective action methods at the local level are required instead to provide

public goods. To illustrate, Chaudhury et al. (2006), show that teachers in their sample of less

developed countries are rarely sanctioned formally for not attending school. In India, for example,

only one head teacher in a sample of nearly 3,000 public schools reported a case in which a teacher

was dismissed for absenteeism, despite an absenteeism rate of 25%; in the Ugandan data, the

comparable figure is just under 1.5% of head teachers, despite an absenteeism rate of 28%.9 Indeed,

as the authors venture (pp. 93):

[...] the mystery for economists may not be why absence from work is so high, but

why anyone shows up at all. For many providers, the answer must be that important

intrinsic and non-pecuniary motivations - such as professional pride or concern for the

regard of peers - affect attendance decisions.

9Accordingly, absenteeism is rarely as grave an issue either in countries with strong formal institutions or inprivate sector industries with high monitoring: administrative data from New York school districts in the mid-1980srevealed a teacher absenteeism rate of around 5% (Ehrenberg et al., 1991); while the Indian Ministry of LabourIndustrial Survey 2001-2002 shows that absenteeism amongst factory workers is 10.5%, despite the existence of rigidlabour laws. As I note above, the rate of teacher absenteeism in India is estimated (conservatively) by Chaudhuryet al. (2006) to be 25%.

5

This sentiment is reflected by Duflo et al. (2012), who find a large role for the non-pecuniary costs

of absenteeism in the decision-making process of teachers using a structural model. Identifying the

nature and source of these costs is an open area of research;10 the literature suggests that, in this

clear absence of formal monitoring and enforcement, ethnic diversity may well provide a partial

explanation.11

Of course, the characterization of ethnic divisions that I present brings with it considerable

challenges for the empirical estimation strategy. Ethnic diversity is not a random accident, nor,

especially, is the salience of ethnic identity. To account for the potential effect of omitted variables

on teacher absenteeism, I construct an extensive set of controls for inclusion in the econometric

models. For the multi-country sample, I include controls for a wide set of respondent- and district-

level characteristics, as well as fixed effects for regions, ethnicity, and pre-colonial ethnic boundaries.

I also control for the temporal and spatial proximity to recent armed conflict events and fatalities

by combining information on the geographic coordinates of each district in the Afrobarometer

with those in the Armed Conflict Location Event Database (ACLED). In addition, I show that

the inclusion of controls for endogenous sorting (based on pre-colonial ethnic boundaries) and

historical settlement patterns have no effect on the model in the presence of such an extensive set

of fixed effects. For the Ugandan teacher-level dataset, I can control comprehensively for teacher

characteristics and school-level covariates based on the specification of Kremer et al., (2005), who

use an almost identical dataset in their study of the determinants of teacher absence in India.12

An additional methodological challenge inherent in the multi-country section of the analysis is

the reliability of subjective assessments of teacher absenteeism. While it is likely to be measured

with some stochastic error, Olken (2009) suggests also that an error component may be specifi-

10In their review article on teacher absenteeism, Banerjee and Duflo (2006) reach the conclusion that “mostattempts to boost the the presence of teachers [...] have not been particularly successful.”

11Revisiting the Chaudhury et al. (2006) data with this in mind, it is interesting to note that Bangladesh hasonly the fourth highest absenteeism rate, despite being the second poorest of the six countries. This is likely to beat least partially explained by the fact that it has by far the most homogeneous population, as measured by bothethnolinguistic and cultural diversity from Fearon (2003). Absenteeism in Bangladesh is lower than in Indonesia,which is over twice as wealthy but three times more diverse.

12Indeed both are component datsets for the six-country Chaudhury et al (2006) study. As such, they are basedon very similar methodologies. The Ugandan dataset is analyzed separately by Habyarimana (2010).

6

cally correlated with ethnic diversity. He finds that people in ethnically diverse villages tended to

overestimate significantly the level of corruption in a road-building project in Indonesia. The im-

plication for this study is that ethnic diversity may be associated with disproportionately negative

perceptions of public goods delivery in general, as a result in part of feedback mechanisms over

time. Although the obvious mitigation of this concern lies in the replication with objective data

from Uganda, I also run a large set of falsification tests to show that the results are highly unlikely

to be driven by this systematic bias. These include testing for the effects of ethnic divisions on per-

ceptions of other aspects of school quality and of national-level governance issues, as well as holding

school-level characteristics constant in order to analyse variation in the error component alone. I

also offer an explanation for why the mechanism at play in the Indonesian setting is unlikely to be

applicable in this context.

This analysis contributes to the literature in three ways. First, it provides new evidence of a

significant determinant of teacher absenteeism that can partially explain such high rates in poor and

ethnically divided areas. Second, it introduces a new measure of ethnic divisions that is consistent

with the heterogenous effects of ethnic diversity on a variety of outcomes found in the literature.

Moreover, the ‘constructivist’ component of the measure leaves room for a policy response. Finally,

it presents evidence that ethnic divisions do not affect absenteeism through community sanctioning

institutions or discriminatory altruism towards beneficiaries; instead, it is the erosion of social

capital between teachers within a school that appears most likely to undermine the provision of

public education. This casts a new light on the study of teacher incentives in developing countries.

The paper is organised as follows. In the next section I discuss briefly the analysis of ethnic

diversity in the literature, including methodological challenges. I then introduce the data and

discuss measurement issues, before presenting the reduced form estimation results for both the

multi-country and Ugandan analyses. I subsequently offer an explanation for the reduced form

results by testing for competing mechanisms. I finally conclude.

7

2 Analysing Ethnic Diversity

Scholars have long highlighted the deleterious impact of ethnic diversity on economic and political

development, particularly in poor and institutionally weak countries (Easterly and Levine, 1997;

Alesina et al., 2003; Alesina and La Ferrara, 2005). The cross-country evidence that characterized

the early stages of the literature have been complemented since by a series of micro-level studies

that have made progress in uncovering the channels through which ethnic diversity affects particular

outcomes.

Of particular importance for this study are analyses that increase our understanding of how

divisions lead to collective action problems in sub-Saharan Africa. Miguel and Gugerty (2005)

provide evidence that parents in Kenya contribute to school funding more in homogeneous areas

due to the credible threat of social sanctions for non-cooperation. In a seminal study, Habyari-

mana et al. (2007, 2009) provide laboratory evidence for this social sanctioning channel amongst

residents of Kampala, Uganda. It is an especially compelling explanation in these settings, where

people are more reliant on within-group networks to organise the provision of public goods that

effective governments would otherwise provide.13 However, Hjort (2012) also points to a ‘taste’

based discriminatory mechanism, showing that floriculture plant workers in Kenya weight the util-

ity of coethnics ahead of non-coethnics. He finds that non-coethnics were even willing to incur a

cost to display this discrimination amid the heightened ethnic tensions associated with the 2007

presidential election.

This wave of micro-level studies has also shed light on the conditions under which ethnic diver-

sity may not have the expected effect on certain political and economic outcomes. Miguel (2004),

for example, shows that ethnic diversity has heterogenous effects on school funding and water well

maintenance in districts that straddle either side of the Kenya-Tanzania border. In Kenya, moving

from a homogeneous area to one with a mean level of diversity lowers local school fundraising by

25%; whereas in the neighbouring Tanzanian district, the same change has no significant effect.

These contrasting outcomes are put down to the well-known nation-building efforts made in post-

13La Ferrara (2003) also analyzes the role of kin groups in the functioning of informal credit markets in Ghana.

8

independence Tanzania, characterized, amongst other policies, by the promotion of one common

language (Kiswahili) and a strong emphasis on national unity throughout public school curricula.

In Kenya, if anything, the opposite course was followed by a succession of politicians who were

demonstrably willing to use ethnic diversity as a vehicle for their own political ends.14 These diver-

gent policies are set against a background of broadly similar colonial and historical characteristics,

providing an empirical basis, also found in Hjort (2012), for the suggestion that the salience of

ethnic diversity is politically malleable.

Direct evidence of this is presented by Eifert et al., (2010), who show using Afrobarometer

data that the salience of ethnic identity, measured as the likelihood that respondents identify

themselves along ethnic lines when faced with an open question on self identification, increases

significantly with the proximity of competitive elections. Posner (2004a) also finds variation in the

salience of ethnic cleavages across political contexts. Like Miguel (2004), he exploits the arbitrary

determination of a national border—this time between Zambia and Malawi—and observes that

the Chewa and Tumbuka groups are more likely to perceive each other as allies in Zambia than

they are in neighbouring villages on the Malawian side of border, where they view each other

with considerable antagonism and are less likely to inter-marry. This is explained by the political

landscape in each country: in Zambia, neither group is large enough to form the basis of a viable

political coalition; whereas in Malawi, by contrast, they each form large political blocs that vie for

power.

Another example of the importance of context when analyzing the effects of ethnic diversity

comes from Glennerster et al., (2012), who find that variation in ethnic diversity within Sierra

Leone does not affect the provision of local public goods. This is despite being a poor and highly

diverse country that has recently experienced major civil conflict. The authors explain the result by

documenting Sierra Leone’s unique combination of colonial history, tribal organization and language

composition which together prevent the collective action failures one may expect to find in such

settings. Similarly, Dunning and Harrison (2010) use an experimental approach to show that cross-

cutting cleavages in the form of ’cousinge’ dominate the role of ethnicity in the formation of political

14This culminated in a wave of violence following the disputed 2007 presidential elections.

9

preferences in Mali.

2.1 Methodological Concerns: Measurement

Taken together, these studies highlight the incontrovertible role for constructivist explanations of

ethnic identity, which stress the importance of context and time in shaping both the formation

and salience of ethnic identification.15 So what, if any, are the implications of these accounts for

the measurement of ethnic diversity? Early cross-country studies used a measure of ethnolinguistic

fractionalization that was calculated using the Herfindahl concentration formula on a dataset com-

piled by Soviet anthropologists in the Atlas Narodov Mira (1964). In a pointed critique, Laitin and

Posner (2001) bemoan its once ubiquitous use in the cross-country economic literature, noting, for

example, that it is akin to using the rate of inflation in 1945 as a measure for a country’s prosperity

today. This is because, firstly, it is a static measure of a changing phenomenon. Identities and

cultures change over time in response to economic and political climates. They cite, for example,

the reorganization of identities in Somalia since independence, where Isaaqs and Hawiyes would

once have considered themselves part of a shared linguistic group. Today, Isaaqs conspicuously

differentiate their speech in an attempt to justify attempts at secession. Second, the group cate-

gories on which the measure is based may have no discernible meaning in the context of political

or economic cooperation. Ethnic identities have multiple dimensions in every country, and there is

no way for a researcher to know ex-ante which ones are salient in which contexts. A third point

rests with the concept of salience itself. As the literature above shows, ethnic identities in general

may have very few implications for cooperation in some countries, while having a significant effect

in others, be it at the dyadic level (Posner, 2004a) or in general (Miguel, 2004).

In response to these and similar critiques, several researchers have compiled new measures that

incorporate better the multi-faceted nature of ethnic diversity. Alesina et al. (2003) create a

new index that includes linguistic and religious fractionalization; Laitin (2000) and Fearon (2003)

15Chandra (2012) provides a contemporary discussion of constructivist theories of ethnicity.

10

create measures that incorporate the concept of linguistic ‘distance’ between groups; Posner (2004b)

develops an index based on groups who engage in political competition, called the PREG index

(for Politically Relevant Ethnic Groups); and Baldwin and Huber (2010) highlight the importance

of accounting for economic inequality between groups.

In this paper, I use a new measure of ethnic divisions that overcomes the main pitfalls listed

above. It consists of an interaction term between district level ethnic diversity, measured by apply-

ing the familiar variant of the Herfindahl concentration formula on the self-reported ethnicities of

Afrobarometer respondents, and a district-level average of the respondents’ answers to a question

on the salience of their ethnic identity compared to their national identity. It is based on data that

is concurrent with the outcome variable; it allows the subjects to choose their own ethnic identity;

and it explicitly accounts for salience. A crucial added advantage is that it is measured at the

district level, which allows me to control for regional (and, by implication, country) fixed effects

throughout the analysis. The main implication, in light of the literature outlined above, is that it

measures relevant diversity, and is thus a more accurate tool for identifying the types of problems

that are synonymous with local heterogeneity in Africa and elsewhere.

2.2 Methodological Concerns: Estimation

While these constructivist findings have clear implications for the appropriate measurement of ethnic

divisions, they also point to the need for a more careful approach to identifying valid estimation

strategies. The ethnic divisions interaction term I use in this analysis is likely to be endogenous to

a multitude of political and economic outcomes through factors as diverse as colonial history and

current-day political competition. This calls for more comprehensive econometric specifications than

those typically found in much of the early literature, which generally treats ethnic diversity as an

exogenous phenomenon. Moreover, recent contributions from Ashraf and Galor (2013), Aherlup and

Olssen (2012) and Michalopoulos (2012) have provided empirical bases to ‘evolutionary’ theories,

which describe diversity as a function of long term settlement patterns. Specifically, Ashraf and

Galor (2013) and Aherlup and Olssen (2012) show that the duration of human settlement is a

11

significant determinant of modern day diversity across countries. This is owing to cultural drift

that happens over time in response to the need for peripheral groups to provide their own public

goods. Michalopoulos (2012) provides more evidence of cultural drift, this time due to geographic

variability—such as the variation in soil quality—which led to the development of non transferable

human capital and, eventually, the formation of new linguistic groups.

Taken together, these strands of literature necessitate the inclusion of an extensive set of polit-

ical, economic, geographic and historic controls in the analysis. I describe my data and estimation

strategy in Sections 3 and 4 respectively.

3 Data

I use two main sources of data in the analysis. The first dataset comes from the 2005 round of

Afrobarometer, a series of nationally representative surveys based on standardised interviews of a

random sample of either 1,200 or 2,400 individuals in 16 sub-Saharan African countries: Benin,

Botswana, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mali, Mozambique, Namibia, Nigeria,

Senegal, South Africa, Tanzania, Uganda, and Zambia. Figure 1 presents the location of every

district in which interviews were conducted on a map of Africa. 16 The second dataset comes

from Habyarimana (2010), and is a constituent dataset of the Chaudhury et al. (2006) survey of

teacher absenteeism. It consists of data from two visits to each of almost 100 schools in 10 Ugandan

districts, which are shown on a map in Figure 2. In each school, up to 20 teachers are selected at

random from the roster, and their attendance is recorded at each visit. In addition, a rich set of

characteristics for each teacher—present or otherwise—is recorded, as well as information on the

head teacher, the school’s facilities, its pupils and its structures of governance and management.

16I omit Cape Verde and Zimbabwe, as those respective samples do not have information on ethnicity and certainindividual characteristics necessary for the analysis.

12

3.1 Data: Ethnic Divisions

Afrobarometer multi-country sample

For the Afrobarometer sample, I measure ethnic divisions as the product of ethnic salience and

ethnic diversity. The measure for ethnic salience is recorded as the district-level mean of the

following survey question:

Let us suppose that you had to choose between being a [Ghanaian/Kenyan/etc.] and

being a ________ [respondent’s ethnic identity group]. Which of these two groups

do you feel most strongly attached to?

I ascribe a value of 1 to respondents if the answer is “only [group]” or “more [group],” and 0 if

the answer is “equal,” “more [country]” or “only [country]”.17 In Table 1, I present some external

validation that the question is in fact measuring the concept of salience that I discuss in the

previous section. Recall that Posner (2004a) found Chewas and Tumbukas to be salient adversaries

in Malawi, but not in Zambia. This was due to the political landscape in each country, as Chewas

and Tumbukas were each large political groups vying for power in Malawi, whereas in Zambia they

were too small to form the basis of any competitive coalition. In the top panel of Table 1, we can

see that Chewas and Tumbukas are significantly more likely to identify themselves along ethnic

lines in Malawi than they are in Zambia. While this is an imperfect test for their animosity towards

each other, it is nonetheless illustrative of the fact that political competition can lead to salient

sub-national identification.

In the second panel, we also see consistency with the findings from Miguel (2004). Recall again

that ethnic diversity had adverse effects on local collective action in Kenya, but not in Tanzania.

This was due to serious nation-building efforts in Tanzania that were designed to inculcate a sense

17Using the full five-point scale instead of this dichotomous interpretation does not qualitatively change the results.This is also the case when “equal” takes on a value of 1.

13

of common national identity ahead of sub-national ethnic attachments. Accordingly, ethnic salience

is two and half times higher in Kenya than it is in Tanzania, despite similar levels of ethnic diversity

and comparable colonial and precolonial backgrounds in both countries.

I measure ethnic diversity using the following Herfindahl concentration formula:

ELFd = 1−�n

g=1 s2g,

where si is the share of self-reported ethnic group g ∈ (g. . . n) in each of the 1207 sample

districts d. It reflects simply the likelihood that two randomly drawn individuals in a district d

report different ethnicities. In addition to the 2005 Afrobarometer sample, I include respondents

to the 2008 round in order to increase the power of the Herfindahl statistic. The median district

sample size for the variable is 47.18

I present non-parametric density functions of both interaction components in Figure 3, and a

scatter plot of their country mean values in Figure 4. A cursory look at the scatter plot reveals very

clearly the importance of accounting for both components in the analysis. By ignoring the salience

of ethnic identities, researchers would erroneously conclude that highly heterogeneous Lesotho is

more likely to suffer the consequences of ethnic divisions than relatively homogeneous (at the district

level) Nigeria. This does not take into account, however, the cultural closeness of groups in Lesotho

nor the history of ethnic violence in Nigeria, which may have contrasting effects on collective action.

I argue that these are captured by the measure of ethnic salience.19

While I have thus far pointed to the clear need for including a ‘salience’ component of ethnicity

in my measure of ethnic divisions, it is worth remembering that, in the context of local collective

action, it is obviously necessary to include a measure of diversity as an interaction component, for

it is unlikely that highly salient ethnic identification will lead to adverse public goods outcomes

18There is no measure of teacher absenteeism in the 2008 sample.19This may be also reflected in the degree of residential sorting in each country. In Figure A1 in the appendix, I

plot ethnic salience against diversity at the country level rather than the aggregated district level measure in Figure 4.The large difference between ELF at the district and country levels for Nigeria—and the relatively minute differencebetween them for Lesotho—suggests that coethnics may sort into districts in countries with high ethnic salience.This again highlights the importance of accounting for both diversity and salience, where otherwise highly diversedistricts could be misinterpreted as highly divided districts.

14

within a homogeneous community.

Ugandan school visits

In the Ugandan sample, I use two measures of ethnic diversity. First, I match the district-level

Afrobarometer measure to all ten districts covered by the survey, namely: Arua, Bugiri, Bushenyi,

Jinja, Kamuli, Kisoro, Luwero, Mpigi, Tororo and Yumbe. As this gives only ten data points, I

also construct a value of ethnic divisions for each of the 94 schools in the sample. During the first

random visit to each school, head teachers were asked to list the three most commonly spoken

mother languages amongst the pupils, and to estimate the corresponding share of pupils for whom

this is the case. Using these shares, I create another Herfindahl-based concentration index for

each school and interact it with the district-level mean ethnic salience from Afrobarometer. The

kernel density function for school-level diversity is estimated in Figure 5. All equations described

in Section 5 are estimated using both measures, which have a correlation coefficient of 0.48.

In Section 6, I introduce a number of additional proxy measures based on the diversity of the

teaching staff within each school. This is presented in support of the hypothesis that ethnicity

affects teacher absenteeism through its impact on social networks between teachers within schools.

3.2 Data: Teacher Absenteeism

Afrobarometer multi-country sample

In the multi-country Afrobarometer dataset, I base the measure of teacher absenteeism on responses

to the following survey question:

Have you encountered any of these problems with your local public schools during the

past 12 months: Absent teachers?

0=Never, 1=Once or twice, 2=A few times, 3=Often, 7=No experience with public

schools in the past twelve months, 9=Don’t Know,

15

I code the responses on a four point scale from 0 to 3, omitting respondents who choose the remaining

categories. This lowers the potential sample size from 21,598 to 14,100. Descriptive statistics are

presented in Table 2, showing mean values for ELFd, district-level ethnic salience, and a selection

of individual and village level variables for each response category of the question.

As is frequently the case when variables are based on subjective opinions, the major concern in

this part of the analysis is the potential for systematic bias caused by non-random measurement

error. While it is likely that the survey question picks up at least some noise, so that TAid =

TA∗sd + ui, where TAid is subjective teacher absenteeism reported by individual i in district d,

TA∗sd is actual teacher absenteeism at school s, and ui is stochastic measurement error, the danger

is that TAid = TA∗sd + ui + vi, where the error component vi is correlated with ethnic diversity. If

this is the case, the observed coefficient that describes the relationship between the outcome variable

and ethnic divisions may be driven by the error component, rather than a fundamental association

between ethnic divisions and true absenteeism. Olken (2009) suggests that this should be treated as

a genuine concern. He finds that people in ethnically heterogeneous villages in Indonesia are more

likely to overestimate the level of corruption associated with a road building project than villagers

in homogenous areas.

He explains the findings by suggesting that feedback mechanisms over time have caused people

in diverse areas to be wary of corruption in public projects, which in turn increases their scrutiny

of public funds. However, the means through which scrutiny led to lower corruption in ethnically

diverse villages for that particular project is linked to the disproportionately high rate of attendance

at ‘monitoring meetings’ that were provided by the central government. In the absence of this

exogenous facilitation of scrutiny, it is unclear whether or not diverse communities would cooperate

better than homogeneous villages to minimize corruption. In any case, I include a wide set of

falsification tests below that directly address this concern, and find it to be an unlikely driver of

results.

16

Ugandan school visits

The measure of teacher absence in the Ugandan dataset is more straightforward. Enumerators

recorded a teacher as absent if she was not present to teach a class that she was scheduled to

teach. Over two visits, they collected the information up to 20 randomly selected teachers from the

school’s roster. In the cases where schools have less than 20 names on its roster, the enumerators

collected information on all teachers.

In Figure 6, I plot the district mean values of each absenteeism variable. On the y-axis is the

mean value for the four-point Afrobarometer scale; on the x-axis is the district mean for a teacher-

level dummy variable indicating that a teacher was absent during a random school visit. I also

include a linear fit, which confirms that the Afrobarometer measure contains significant information

on actual teacher absenteeism. This provides some evidence for the validity of the multi-country

analysis, which itself allows for a general interpretation of the results across 16 sub-Saharan African

countries.

4 Estimation: Afrobarometer Multi-Country Sample

I begin the estimation section with a focus on the multi-country Afrobarometer survey data. As

I note above, the two main challenges in this section relate to the potential endogeneity of salient

ethnic divisions to actual teacher absence rates due to omitted variable bias, and also the possibility

of a correlation between ethnic divisions and the error component vi of the subjectively measured

dependent variable. The basic equation I estimate takes the following form:

TAidr = a+Ψn�

i=1

ESid

nd+ λELFd + β(

n�

i=1

ESid

nd∗ ELFd) + γXid + δVid + ηRr + eid, (1)

where TAidr is perceived teacher absenteeism reported on a four-point scale by individual i in

district d and region r; ES is ethnic salience; X is a vector of individual controls; V is a vector of

village-level controls; and R represents regional fixed effects for 184 regions in 16 countries. The

17

individual controls include measures for age, age squared, level of education, gender, employment

status, physical health, mental health, religion, individual ethnic salience,20 access to food, water,

medicine, fuel and income, as well as indicators for ownership of three assets: radio, television and

a vehicle. The village level vector controls for whether or not a village—which is a sub-district level

unit with a modal sample size of 8 respondents—contains each of the following services or facilities:

a school, piped water, a sewage system, a health clinic, electricity, a police station, a post office,

recreational facilities, a place of worship, community buildings and a tarred or paved road.

Given the incidental parameters problem, the equation is estimated initially using least squares

with standard errors adjusted for two-way clustering within ethnicity groups and within districts

(Cameron et al., 2011).21 This method produces standard errors that are higher than those pro-

duced by either ethnicity- or district-level clustering alone. In addition, I run the equivalent ordered

probit model, and report the relevant marginal effects in the robustness section below.

There is good reason to believe that this set of covariates controls for the potential sources of

endogeneity that I explore in Section 2. In particular, the control for regional-level fixed effects

at a stroke controls for country-level colonial and pre-colonial historical factors (Glennerster et al.,

2012), post-colonial national policies (Miguel, 2004) and current day political competition (Eifert

et al., 2010, Posner, 2004a), as well as the country-wide effects of macroeconomic policies that

are associated with ethnic diversity (Easterly and Levine, 1997; Alesina et al., 2003). Moreover,

the rich set of individual- and village-level controls accounts for variation in individual and local

wealth, health and economic factors that could otherwise plausibly affect our interpretation of the

coefficient of interest, β.

20This can be interpreted as controlling for the effects of an individual’s deviation from the district mean value ofethnic salience. Omitting it does not have a significant effect on the results.

21The Stata command for this estimator is ‘cgmreg’.

18

4.1 Results

In Figure 7, I present an illustration of the output from the most comprehensive specification

estimated in this section.22 I use a re-centering method to hold ethnic salience constant at a low

level, which I define as the mean value minus one standard deviation (or 0.004), and at a high level,

defined as the mean plus one standard deviation (or 0.33). The slope of ethnic diversity (ELFd) at

the low level (red) is -0.14, and is statistically indistinguishable from zero; whereas the slope at the

high level (blue) is 0.29, or 0.26 standard deviations of perceived teacher absenteeism. The p-value

for the interaction effect, which describes the statistical significance of the difference between the

two slopes, is 0.005.

The results show clearly the importance of conditioning on salience when trying to ascertain

the effects of ethnic diversity at the local level. In the absence of salience, ethnic diversity has no

significant effect on teacher absenteeism.

In Table 3, I present the regression output for the most basic specification. In column (1), I

omit the village-level controls, which could be affected themselves by ethnic divisions. While the

independent effects of ethnic diversity (ELFd) and ethnic salience do not affect perceived teacher

absenteeism at traditional levels of statistical significance, their interaction has an impact that is

both economically and statistically significant.

In column (2), I add the village-level controls described above. Although this inclusion increases

the precision of the main estimate, while simultaneously reducing bias by accounting for some

potentially endogenous factors, it also lowers the sample size from 13,468 to 12,240. In column

(3), I highlight the importance of including the interaction term in the model. Its omission would

lead us to erroneously conclude that it is only ethnic salience that reduces teacher absenteeism

within a district, rather than a combination of salience and ethnic diversity. In column (4), I

22Specifically, I use the output from a specification based on Table 5, column (6), but without the pre-colonialfixed effects, which I describe in the next sub-section. The effect of diversity for any level of salience can be foundsimply by plugging in the desired level of salience to the relevant regression output function. Figure 7 is a graphicalrepresentation of the function TAidr = 1.71 − 0.151(ELFd) + 1.33(

�ni=1

ESidnd

∗ ELFd) + Ψ�n

i=1ESidnd

+ γXid +

δVid + ηRr + eid, where�n

i=1ESidnd

= 0.33, 0.004. The black dots represent observations plotted by ELFd on thex-axis and TAidr on the y-axis.

19

present the most naïve representation of ethnic divisions, that is, ethnic diversity with no account

for salience whatsoever. Again, though common in the literature, this measure fails to capture at

all the significant effect of ethnic divisions on the outcome variable.

For the remainder of this section, I test the robustness of the association by (i) controlling

for an additional range of factors that are plausibly correlated with ethnic divisions and teacher

absenteeism; (ii) conducting a set of falsification tests to ensure that the results are not being

driven by an error component in the measurement of the dependent variable; and (iii) presenting

alternative specifications to ensure that the results are not driven by particularities in the survey

sample.

4.1.1 Controlling for Observables

Cultural and institutional persistence Nunn and Wantchekon (2012) use Afrobarometer data

to show how historical events can affect current behavior through culture, or, more specifically, the

intergenerational transmission of norms within ethnic groups. They show that members of ethnic

groups that were historically targeted by the slave trades have lower levels of trust in institutions

and other people today. This is due to the nature of the slave trade, which often rewarded trickery

and dishonesty by sparing from slavery those who provided other people for export. This led to a

profusion of distrust amongst the kin of those who were sold for export, which in turn developed

into a cost-saving heuristic that evidently survived over time. The implication for this analysis

is simple: certain ethnic groups may display common traits that could affect the salience of their

ethnicity and the development of local institutions.

In Table 4, I show how the omission of ethnicity-level covariates can lead to a biased estimate of

β. The inclusion of three such variables in column (1) changes the size and statistical significance

of the coefficient. The first variable is the natural log of the number of slave exports taken from the

respondent’s ethnicity group divided by the size of the area which it historically inhabited. It is taken

directly from Nunn and Wantchekon (2012), who use Murdock (1959) to link current day ethnic

group names to their pre-colonial ancestral groups. Historical slavery exports may affect current

20

day social capital, which could be manifested in more salient sub-national identities and lower levels

of local cooperation. The second variable is taken from the Ethnographic Atlas (Murdock 1967) and

coded by Nunn and Wantchekon (2012). It captures the political sophistication of each ethnicity’s

corresponding pre-colonial ancestral group by measuring the number of hierarchical layers in its

power structure. This organization of power could itself persist over time within ethnic groups to

reflect better local coordination today. The third variable is a proxy for each ethnicity’s historical

wealth by indicating whether or not there was a city within its pre-colonial boundaries in 1400. It

is taken from Chandler (1987) and again coded by Nunn and Wantchekon (2012), and is intended

to reflect the probability of colonial plunder, which may negatively affect the persistent quality of

institutions within groups over time.23

Slavery enters the model with no statistical significance, while the sophistication of pre-colonial

institutions and the indicator for pre-colonial wealth enter significantly with the expected signs.

Taken together, these results suggest that controlling for ethnicity-level variation in the response is

a necessary step in establishing the robustness of the main finding. Accordingly, I include ethnicity

fixed effects in column (2) and for the remainder of the analysis. I code ethnicity by country, in

order to account for the variation in ethnic salience within ethnic groups that spill across country

borders, as in Posner (2004a).

Although clearly a necessary inclusion, ethnicity fixed effects are not a sufficient means of control-

ling for variation in unobserved historical factors. Michalopoulos and Papaioannou (forthcoming)

show that pre-colonial factors—in this case the jurisdictional hierarchy measure from column (1)—

can also have persistent effects through local institutions. Indeed Nunn and Wantchekon (2012)

present evidence of this channel that is independent of the cultural persistence discussed above.

Both studies use information on the pre-colonial boundaries of ethnic groups from Murdock (1959)

to link historical factors to current outcomes. In the case of Nunn and Wantchekon (2012), this

is facilitated by recording the geographic coordinates of each Afrobarometer district in order to

determine the ethnic group that inhabited the corresponding area in pre-colonial era. I use this

information to include a spatial vector in column (3) that controls for historical fixed effects that

23Acemoglu et al (2001) provide some evidence on the colonial origins of institutional quality.

21

vary at this level of pre-colonial settlement.24

Conflict, sorting and settlement duration I now consider three more factors that could

plausibly affect the main result. First, I address the possibility that armed conflict is associated

with salient ethnic divisions and with perceived teacher absence. Montalvo and Reynal-Querol

(2005) provide empirical evidence for the link between ethnic polarization and conflict, building

on a seminal contribution from Horowitz (1985). It is certainly not unreasonable to consider that

ethnic conflict and ethnic salience may be related; nor is it implausible to suggest that teacher

attendance—or indeed perceptions of teacher attendance—could be affected by local conflict.

To account for this, I turn to the Armed Conflict Location and Event Dataset (ACLED), which

contains data on over 60,000 fatal and non-fatal incidents of conflict throughout Africa, parts of

Asia, and Haiti from 1997 to 2012.25 The dataset also includes the geographic coordinates of each

incident, which I use to measure its geodesic distance from the centroid of each Afrobarometer

district.26 The location of every recorded event is presented on a map in Figure 8. I combine

various levels of spatial and temporal proximity to the number of armed conflict events and to the

number of fatalities associated with each event. Specifically, I included all possible combinations

between 20km, 10km, 5km and 1km, and 10 years, 5 years, 2 years, 1 year and six months. I find

that only the number of conflict fatalities within 1 year and 1 kilometer from the centroid of each

district has a significant impact on perceived teacher absenteeism. In columns (1) and (2) of Table

5, I show that the inclusion of both this measure and the corresponding measure of conflict events

(fatal and non-fatal) has no qualitative effect on the main result.

Although potentially captured by the covariates already presented in the model, I include a

measure of historical sorting in columns (3) and (4). Residential sorting amongst ethnic groups,

as I discuss above, may reflect traits that could impact collective action outcomes. For example,

24Not every area could be linked to a precolonial group. I thus present results with and without these fixed effectsfor the remainder of these estimations.

25More information on the Acled dataset is available online at www.acleddata.com26Sangnier and Zylberberg (2012) also combine Afrobarometer and Acled data using location coordinates.

22

it is possible that individuals from different ethnic groups who have no animosity toward outsiders

may coexist in districts, which itself may perpetuate long-term cooperation and better local insti-

tutions. To measure sorting, I go back to the information on pre-colonial boundaries in Murdock

(1959). I define historical assimilation as the percentage of current district residents whose ethnic

ancestors were based elsewhere during the pre-colonial era. Adding this variable to the model has

no discernible effect on the main results.

Like sorting, it is likely that correlates of the ‘evolutionary’ sources of ethnic diversity are already

controlled for by the regional and pre-colonial fixed effects in the model. To see if this is the case,

I include three proxies for human settlement that are discussed by Alehrup and Olsson (2012) and

Michalopolous (2012) in columns (5) and (6). I first use the geodesic distance from each district

to Addis Ababa, which is also shown by Ashraf and Galor (2013), amongst others, to correlate

highly with the duration of human settlement and, in turn, genetic diversity. In addition, I include

distance to the equator (measured in degrees of latitude) and distance to the sea, which reflect two

theories of early human migration patterns from East Africa between 150,000 and 200,000. As I

mention above, I also include a binary variable for whether or not each village contains a tarred or

paved road, which could be interpreted as a proxy for the ruggedness of land. None of the variables

have a significant impact on the model.

4.1.2 Falsification Tests

In this section, I address the potential effects of non-classical measurement error in the dependent

variable. To recount, Olken (2009) shows that survey respondents in heterogenous Indonesian

villages overestimated corruption in a road project more than those in homogeneous areas. The

author suggests that this was caused by a higher level of skepticism in diverse villages that may have

been triggered through a feedback mechanism from corruption in previous projects. As a result,

community meetings designed to facilitate local monitoring and oversight of the road project were

22% more highly attended than meetings in homogeneous areas, which in turn led to a lower level

of actual corruption. The implication for this study is that respondents from ethnically divided

23

areas may simply write-off the quality of all public services and collective action outcomes without

regard to the true measure. This could lead to an upwardly biased β coefficient.

For the first set of falsification tests, I examine the impact of ethnic divisions on responses

to alternative survey questions. In addition to absent teachers, respondents are also asked in the

Afrobarometer survey whether they have encountered six other problems related to their local

public schools in the previous 12 months. They are: “services are too expensive,” “poor conditions

of facilities,” “overcrowded classrooms,” “poor teaching,” “lack of textbooks and other supplies,” and

“demands for illegal payments.” The questions are framed in exactly the same manner as the way

in which the dependent variable is framed, and all are sequenced together. If it is the case that

residents of ethnically divided areas have a common proclivity to overstate problems with public

goods provision, and if that is driving the main result of this section, then we should observe a

similar effect of the interaction term on all—or at least some—of the other six variables.

In Table 6, I present the effects of ethnic divisions on all seven variables, each normalized to have

a mean value of 0 and a standard deviation of 1 for comparison.27 Using the most comprehensive

specification presented thus far (from Table 5, column (6)), I show that district-level ethnic divisions

only have a significantly positive effect on teacher absenteeism. This comprehensively rules out the

possibility that a common error component of all seven measures is significantly correlated to ethnic

divisions. The tests also suggest that the channel through which ethnic divisions ultimately affect

teacher absenteeism does not apply to the other outcomes (a proposition corroborated in Section 6).

The possible explanation for this may be reflected in the final row of the table, which shows that five

of the six additional outcomes have higher within-country correlations than teacher absenteeism,

albeit by small margins. This indicates that the organisation of these outcomes may take place at

a more centralized level than teacher attendance.28 As I discuss in the introduction, absenteeism

is rarely sanctioned by official sources, and is thus likely to be affected by more local factors.

The next set of falsification tests follows a similar line of reasoning to a set presented by Olken

(2009). It is based on a simple premise: if people in ethnically divided areas systematically overstate

27This does not affect the interpretation of the test results.28The only outcome with a lower intra-country correlation, the demand for illegal payments, is positively affected

by ethnic divisions in a specification with no pre-colonial fixed effects (not reported).

24

corruption, or any other metric representing the quality of public goods or collective action, then

perceptions of common, national-level indicators should significantly differ between divided and

undivided areas. If they do not overstate these measures, their responses should not significantly

differ from those in undivided areas, as both groups are assessing the same phenomenon.

I run this test using six variables that measure responses to questions about national-level

governance. The results are presented in Table 7. In columns (1) and (2), the dependent variables

measure perceived corruption in the offices of government and the president respectively; in columns

(2) and (3) the dependent variables measure the trust held by respondents in the ruling party and

in the main opposition party respectively; and in columns (5) and (6) the dependent variables

measure respondents’ assessment of the manner in which the government is handling corruption

and education respectively. All six dependent variables are measured on four point scales.

In every case, the responses of individuals in ethnically divided (or indeed ethnically diverse or

ethnically salient) districts do not differ from the responses of those in undivided areas. It provides

further evidence that the effect of ethnic divisions on perceived teacher absenteeism cannot be

explained by this source of measurement error.29

In Table 8, I present the final set of tests for the robustness of the results to non-classical

measurement error in the dependent variable. I first test the hypothesis that only minority group

members in each district have higher perceptions of teacher absenteeism. I do this by including

a binary variable that indicates whether or not an individual is a member of a non-modal group

within their district. The result, shown in column (1), indicates that minority group members are

not driving the main findings.

In columns (2) and (3) I present the results of a test which attempts to hold the true level

29Readers familiar with the Afrobarometer surveys will be aware of several questions that probe respondents fortheir opinion on a multitude of political and social issues. I chose questions that were most likely to elicit viewson strictly national-level characteristics that plausibly affect individuals in diverse and homogeneous areas equally.Nevertheless, I could have presented alternative variables in each of the three categories shown in the Table 7 thatarguably fit the criteria. Under corruption, respondents were also asked about judges and magistrates; under trust,respondents were also asked about parliament, the president, the national elections commission and courts of law;and under government performance, respondents were also asked about crime, health, water and food. I rerun thetest with all of these alternatives, and find that the only question for which respondents in divided areas answereddifferently to others was on the government’s handling of crime, which itself is likely to be influenced at least partiallyby local factors.

25

of teacher absenteeism constant, and therefore analysing only the variation in the individual error

component. Recall that TAid = TA∗sd + ui. If I hold TA∗

sd constant by controlling for school fixed

effects, I can then observe the relationship between ethnic salience and the error component only.

As there are no school-level fixed effects in the dataset, I instead use village-level fixed effects.

The village (or “primary sample unit”) is the most granular level above the individual in the Afro-

barometer. It has a modal sample size of 8 respondents. To the extent that all 8 refer to the same

school in these surveys, controlling for village fixed effects will allow me to uncover the statistical

relationship between the error component of the dependent variable and an assortment of individual

characteristics.

In column (2) I provide strong evidence that individual ethnic salience is not related to the

individual error component of the dependent variable. In column (3) I include an interaction term

between individual salience and district level ELF . Although the inclusion of village fixed effects

implies that district-level ethnic diversity is held constant, this is still a strictly better test than

the one presented in column (2), as the interaction term gives a closer approximation to the true

district-level interaction effect in a test without fixed effects. Again, the results suggest that there

is no significant relationship between ethnic divisions and the error component of the dependent

variable.

Finally, I show that the explanation put forward by Olken (2009) for the link between ethnic

diversity and the overestimation of corruption is not applicable in this context. Recall that, in

Indonesia, attendance rates at community oversight meetings were significantly higher in hetero-

geneous communities, which led to lower levels of actual corruption. A key part of that story lies

in the fact that these meetings were facilitated as part of the nationwide road building project. It

is unclear that heterogeneous communities would have monitored the project as effectively in the

absence of this exogenous provision of community fora. I show in column (4) that, in this sample,

members of ethnically divided communities do not attend community meetings more frequently

than those in relatively undivided communities. As in Table 6, this test has implications for our

understanding of the channel or channels that explain the reduced-form relationship. 30

30Respondents are asked if they attend community meetings. The dependent variable ranges on a four point scale

26

4.1.3 Other Robustness Checks: Sample Issues and Functional Form

In this sub-section, I present a final set of robustness tests for the multi-country analysis. In column

(1) of Table 9, I use district-level averages for all variables to show that missing values for some

covariates are not affecting the estimation of β.

In column (2), I run the analysis on a sub-sample of respondents who might be expected to

have a better grasp of true teacher absenteeism TA∗sd: mothers. Case and Ardington (2006) show

using panel data that maternal deaths had a substantially more negative effect on a wide range of

schooling outcomes than paternal deaths amongst a sample of Zulu children in South Africa, while

paternal deaths had a larger impact on other socio-economic factors. This could be reflective of

a higher maternal involvement in children’s schooling. In such a case, the absence of a significant

interaction effect in a sub-sample of women between the ages of 25 and 50 would cast some doubt

on the validity of the results, as one would expect mothers to have a more accurate response to the

question on perceived teacher absenteeism. The results show that the interaction effect is larger

and is estimated with more precision.

In columns (3) and (4), I provide evidence that the results are not driven by ELF measures that

are calculated from small sample sizes, and thus have little statistical power. Indeed, as the table

shows, the results are more robust for districts that have above-median sample sizes than districts

with below-median sample sizes.

In column (5), I present the results of an ordered probit model, given that (i) the steps between

each response category in the dependent variable may not be constant; and (ii) the variable has

a limited range. In order to facilitate the interpretation of the interaction term, I show in Table

10 the marginal effects of ethnic diversity on the probability of each response at a high level of

ethnic salience (again, mean plus one standard deviation) and at a low level of ethnic salience

(mean minus one standard deviation). The results show clearly that, at high ethnic salience, ethnic

diversity significantly decreases the probability of a “Never” response, and significantly increases

the probability of individuals reporting “Once or twice”, “A few times” and “Often”. At low ethnic

from “no, would never do this” to “yes, often”

27

salience, ethnic diversity has no impact on the probability of any response. The results are consistent

with the linear results presented throughout the section.

In summary, this section presents robust evidence that the statistical association between ethnic

divisions and perceived teacher absenteeism in the Afrobarometer multi-country dataset is neither

caused by omitted variables, an error component in the dependent variable nor the imposition of

a linear functional form on the relationship. In the next section, I extend the results to a sample

using objective data on recorded absenteeism in Uganda.

5 Estimation: Ugandan Dataset

Kremer et al. (2005) and Habyarimana (2010) analyse the determinants of teacher absenteeism

using sub-samples of the Chaudhury et al. (2006) data collected during random, unannounced

school visits in 2002 and 2003. Here, I use an almost identical empirical specification in order to

establish the relationship between the probability of a teacher’s absence and ethnic divisions in

Uganda. The basic estimation equations take the following form:

Pr(TAjsd = 1) = a+ΨESd + λELF + β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd (2)

for the linear probability estimation, and:

Pr(TAjsd = 1) = Φ[a+ΨESd + λELF + β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd] (3)

for the probit estimation, where ESd is�n

i=1ESidnd

from the Afrobarometer sample, ELF is the

either district-level ethnic diversity ELFd, or school-level ethnic diversity ELFs, as described in

Section 3.1. Teacher-level characteristics, represented by Xjsd, include gender, age, marital status,

education, place of birth, employment rank, experience, contract status, union status, and career

training; Ssd is a set of school level characteristics, comprised of controls for institutional features,

28

such as the existence of parent teacher associations (PTAs), access and the quality of its facilities;

Ttdm is three sets of fixed effects for the time, day and month of the visit. All covariates are

described in more detail in the notes beneath Table 11.

5.1 Results

I present in Table 11 the estimation results for equations (2) and (3). In columns (1) and (2), I

omit school-level characteristics, which are added in columns (3) and (4). All four specifications

are estimated with a linear probability estimator. In each model, the interaction effect is large

and significant. To illustrate, a one standard deviation increase in district-level ethnic diversity at

a high level of ethnic salience (again defined as the mean plus one standard deviation) raises the

probability of a teacher not turning up to a scheduled class by 9.3 percentage points. At a low

level, the same increase of diversity actually decreases the probability of absence by 6.7 percentage

points. An even larger interaction effect is present in the school-level diversity data: a one standard

deviation increase in ELFs at a high level of ethnic salience increases the probability of absence by

3.8 percentage points; while at a low level of salience the same change in diversity decreases the

probability of absence by almost 17.7 percentage points.

In columns (4) and (5), I present results from the probit model. Ai and Norton (2003) highlight

the dangers of misinterpreting the effects of an interaction term in non-linear models. They show

how the marginal effect of an interaction term can have a different magnitude, sign and level of

statistical significance than the true cross-partial derivative.31 The results of their corrected method,

interpreted as the average interaction effect across all observations, support the linear results.32

It is possible that these symmetrical effects, i.e., the negative effects of district- and school-level

diversity on absenteeism where salience is low, reflect two forms of sorting mechanisms that are

described respectively by Glennerster et al. (2012) and Miguel and Gugerty (2005). The first case

31I explain this issue in more detail in Appendix B.32The Ai and Norton (2003) method requires that I drop the fixed effects for the time of day from the model. An

equivalent linear model produces quantitively similar results, supporting the interpretation presented above. Theremaining marginal effects are taken from the full model.

29

concerns residential sorting, whereby certain coethnics choose to live in clusters. By implication,

the remaining individuals—those who do not cluster by ethnicity—form more diverse communities

that are likely to have low levels of ethnic salience. Glennerster et al. (2012) find that ‘non-sorters’

in diverse areas od Sierra Leone tend to have higher levels of educational attainment, which I find

in the analysis to be strongly linked with lower absenteeism. Moreover, we can see from Figures 4

and A1 that Uganda has one of the highest rates of sorting amongst the entire 16-country sample:

the mean level of ethnic diversity by district is less than half the mean level of ethnic diversity for

the country as a whole.

The symmetrical effect at the school-level may be further compounded by a more straightforward

sorting mechanism described in Miguel and Gugerty (2005), whereby good schools (which are likely

to have teachers who attend class) attract pupils from a wider radius, leading to a positive link

between diversity and, in this case, teacher attendance. It is interesting to note also that teacher

absenteeism is lower in homogeneous areas where people express a high sense of common ethnic

identity, as implied by the significantly negative independent effect of ethnic salience. Again,

sorting may explain the finding, as homogeneous communities formed from deliberate sorting may

be better equipped for the type of collective action needed to provide local public goods than other

homogeneous communities.

In any case, the impact of ethnic diversity on teacher absenteeism at a high level of ethnic

salience is positive and significant in all specifications. Whether measured at the district level or

at the school level, the effects of ethnic diversity on the probability of a teacher being absent from

class is significantly higher in districts where individuals are more likely to identify themselves along

ethnic lines.

6 Testing for Channels of Causality

In this section, I attempt to identify the channel or channels through which ethnic divisions affect

teacher absenteeism using the Ugandan teacher-level data. As I discuss in the introduction, ethnic

diversity can affect local cooperation and teacher behavior in different ways. Below, I consider the

30

three prominent theories given in the literature to explain why ethnic diversity undermines the

provision of public goods in this context.

First, coethnics may have more concern for the utility of each other than for the utility of non-

coethnics (Becker 1957, 1974; Hjort 2012). This could result in native teachers having a higher

preference for increasing the wellbeing of pupils and parents than ‘outsider’ teachers, which may

explain higher absenteeism rates in ethnically divided schools or districts.

A second, more broadly-supported theory concerns the credibility of the threat of social sanctions

in ethnically diverse communities (Miguel and Gugerty, 2005; Habyarimana et al., 2007, 2009). In

the absence of well functioning formal institutions, coethnics often rely on each other for public

goods, credit or other services. Because of this, the costs of uncooperative behavior towards a

coethnic are likely to be higher than the costs of uncooperative behavior towards a non-coethnic.

In the case of teacher absenteeism, this could have two consequences: (i) diverse communities may

lack the capacity for coordination needed to develop institutions of oversight for all teachers, such as

effective parent teacher associations or other means of sanctioning; and (ii) ‘outsider’ (or non-native)

teachers may not view the threat of sanctions from native (i.e. non-coethnic) parents, communities

or head teachers as credible. Although any of these mechanisms could potentially explain the

association between ethnic divisions and teacher absenteeism, recent evidence on teacher incentives

in developing countries suggests strongly that official sanctions pose little threat to teachers, and,

moreover, parents are unlikely to exert pressure or enforce attendance.

A third mechanism follows from Alesina and La Ferrara (2000), who find that participation rates

in various social, professional and religious organizations in the USA can be explained partially by

ethnic heterogeneity. They show that, conditional on individuals exhibiting a strictly positive

preference toward socializing with coethnics, an increase in the heterogeneity of a population will

decease the formation of and participation in social groups. The implication in this case is that

teachers in divided areas may be less inclined to socialize together. This could affect attendance

decisions in at least three ways. First, teachers who socialize together may develop an in-group

altruism that directly increases the utility of attending school together. Second, the altruism may

manifest itself in a concern for colleagues’ potential obligations to mind unsupervised children in the

31

case of a teacher’s absence. Third, group members are likely to pose a credible threat of sanctioning

in the form of social ostracism that is not available to non-group members.

To investigate these possibilities, I use data on sanctioning institutions and the origins and social

activities of teachers to test the following three channels:

• Channel 1 Teacher coethnicity : teacher absenteeism is less probable if the teacher is ‘native’.

If this is rejected, i.e., if φ is not negative, we can say with some confidence that the ‘taste’

mechanism is unlikely to be driving the main result.

TAjsd = a+ φNativejsd +ΨESd + λELF + β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd (4)

• Channel 2a Sanctioning (homogeneous effect): teacher absenteeism is negatively determined

by effective sanctioning institutions, i.e., ϕ is negative.

TAjsd = a+ ϕSanctionsd +ΨESd + λELF + β(ESd ∗ELF ) + γXjsd + δSsd + ηTtdm + ejsd. (5)

• Channel 2b Sanctioning (heterogeneous effect): teacher absenteeism is negatively deter-

mined by effective sanctioning institutions, conditional on the teacher being ‘native’, i.e., θ is

negative.

TAjsd = a+ φNativejsd + ϕSanctionsd + θ(Nativejsd ∗ Sanctionsd) +ΨESd + λELF

+ β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd (6)

• Channel 3 Social networks between teachers: teacher absenteeism is negatively determined

by the extent to which teachers socialize together, i.e., ς is negative.

32

TAjsd = a+ ςSocial +ΨESd + λELF + β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd. (7)

In each case, the extent to which the channel under investigation explains the main result depends

on β, the interaction effect that describes the relationship between ethnic divisions and teacher

absenteeism. If this interaction effect loses all statistical significance, it is likely that the relevant

channel explains all of the relationship.

In all tests, I present linear probability models. Marginal effects derived from probit models

produce qualitatively similar results.

6.1 Teacher Coethnicity

In this sub-section, I test the hypothesis described in Channel 1: that native teachers are less

likely than ‘outsider’ teachers (or non-coethnics) to be absent from a class that they are scheduled

to teach. If this is the case, it may be reflective of either of the first two theories: natives may have a

higher degree of altruism (or ‘other-regarding preferences’) toward children and parents; or natives

may view the threat of sanctioning more credibly. If I find no evidence of a statistically significant

relationship, it implies that the ‘taste’ mechanism can be rejected as a unique explanation for the

link between ethnic divisions and teacher absenteeism.

I use three variables to measure whether or not a teacher is native. The first is a dummy variable

indicating whether or not a teacher speaks the local language natively. The second is a dummy

variable which indicates that the teacher’s ancestral home is in the same parish/city as the school.

The third is a dummy variable which indicates that the teacher was born in the local county. In

all specifications, I include as a control a categorical variable for where the teacher currently lives:

the included categories are the local county and the local district; the omitted category is the local

village.

In Table 12, I present the results using the district level and school level measures of ethnic

diversity respectively. Firstly, it is important to note that the interaction effect representing ethnic

divisions is large and statistically significant in all specifications, ruling out the possibility that the

33

full effect is explained by teacher-level ethnicity. In all specifications, native teachers are no more

likely to attend school than non-natives. This indicates that teachers do not make decisions about

attendance based on discriminatory altruism toward coethnics. Teachers who are natively fluent

in the local language are significantly more likely to be absent from school, whereas those who

are native by birth or by ancestry do not exhibit behavior that is statistically different from the

rest of the sample. In an auxiliary specification (unreported), I test for the heterogeneous effects

of coethnicity between ethnically divided and undivided districts/schools by including a triple in-

teraction term. This is to ensure that the average effects presented in Table 12 are not masking

significantly contrasting effects across districts or schools. The intuition is that non-native teachers

in otherwise homogeneous areas may have selected into the district or school because they are not

discriminatory. I also include triple interactions between the teacher’s current home and the ethnic

division components as controls. I find that ‘outsider’ teachers are no more likely to be absent than

local teachers either in divided or undivided districts and schools. This strongly suggests that the

‘taste’ explanation does not account for the main result of the paper.

6.2 Sanctioning Institutions

The possibility remains that ethnic diversity may affect teacher absenteeism through its impact

on either the effectiveness of sanctioning institutions (Channel 2a) or on the credibility of those

institutions’ threats in the eyes of ‘outsiders’ (Channel 2b). In Table 13, I show the effects of

parent teacher associations (PTAs), school inspections and the previous sanctioning behavior of

head teachers on teacher absenteeism. In columns (1) and (3), I include a dummy variable for the

existence of a parent teacher association, a categorical variable to indicate its last meeting (omitted

variable is “this month”), and a dummy variable to indicate that an inspection by the education

ministry had occurred within the previous six months. In columns (2) and (4), I include a dummy

variable to indicate that the head teacher has previously sanctioned a teacher for absence by either

dismissing, suspending or transferring her. I present this in a separate set of specifications due to

the obvious potential for reverse causality, as head teachers in schools with no absenteeism are not

34

required to sanction teachers.

The first point to note from the table is that the average effects of these institutions do not

represent likely candidates for the channel of causation from ethnic divisions to teacher absenteeism.

Across all four columns, the main interaction effect is positive, significant and stable, providing

indirect evidence that the sanctioning variables are not associated with ethnic divisions.

The other point to note is that teachers in schools which have had very recent PTA meetings (the

omitted group) are more likely to be absent than those who’s annual meeting is perhaps approaching

soon. There is evidence also that the sanctioning history of head teachers is significantly endogenous.

In Table 14, I present the effects of these sanctioning institutions conditional on whether or not

a teacher is native by language (columns (1) and (4)), by ancestry (columns (2) and (5)) or by

birth (columns (3) and (6)). To recount: the threat of sanctions by PTAs, local ministry officials

or head teachers may be credible only to coethnic teachers, as non-coethnic teachers may be reliant

on other groups for public goods. If this is the case, the lack of significance in the average effects

presented in Table 13 may be masking significant heterogeneous effects.

Again, in all six specifications, the effect of ethnic divisions is positive, significant and stable,

while no new patterns emerge. Taken together, these results indicate that ethnic divisions do not

affect teacher absenteeism through the credibility of social sanctions.

6.3 Social Networks Between Teachers

To explore the possibility that social group participation may help to explain the main relationship

in the analysis, I use a measure of social capital amongst teachers in each school that is based on

answers given by the head teacher to the following survey question:

When was the last time that the teachers in your school socialized with each other

outside of school hours, gathering for a meal or party for example?

35

I let “never” equal 0, and all other responses equal 1. The responses almost split the sample evenly:

54% percent of head teachers report that teachers in their school never socialize together, while

46% report that they do. I present simple OLS covariates in columns (1) and (2) of Table A1 in

Appendix A, which reveal that only ethnic divisions exhibit a statistically significant relationship

with the measure across both specifications.33 This can be interpreted as a necessary first-stage

condition for the validity of this explanation.

In Table 15, I present the results for specification (6). Moving from a school where teachers

never socialize to one in which they have socialized at least once in the memory of the head teacher

decreases the probability of a teacher’s absence by between 11.2 and 12.5 percentage points (or 19.2

and 21.7 percentage points based on marginal effects from a probit estimation). The inclusion of this

variable in the model eliminates entirely the significance of ethnic divisions using the district-level

measure, while also reducing substantially the magnitude and the significance of the effect of ethnic

divisions as measured at the school level. This provides support for the explanation that ethnic

divisions increase teacher absenteeism through the strength of social networks between teachers

within schools.

In Table 16, I examine whether or not these effects depend on the ethnicity of the teacher. The

average effect presented in Table 15 may be masking significant differences in the effects of social

networks on attendance between native and non-native teachers. I find no significant heterogeneous

effect when I proxy for coethnicity using variables based on birth or language, although when I use

the ancestral definition I find a positive effect. In all cases, the main results are similar to those

presented in Table 15.

Our ability to interpret these findings with certainty is limited by the data. Without knowing

the extent to which social capital is endogenous in the model,34 we cannot say with full confidence

33 For this, I run a school-level regression of Social on the of the right-hand side variables included in empirical

specification (2). Mean values are taken where necessary.

34 For example, it could be that high absenteeism creates animosity amongst colleagues, which may in turn reduce

the likelihood of social participation.

36

how exactly these social networks are related to absenteeism. However, it is possible to conduct

instructive falsifiability tests. If the causal channel is as mentioned, then we must expect to see a

significant role for ethnic divisions between teachers within schools. Although the main analysis was

conducted using measures based on the diversity of survey respondents within districts and of pupils

within schools, there remains a number of useful proxies for the diversity of teachers within schools.

If ethnic divisions amongst teachers are not significantly associated with higher absenteeism, we

can immediately reject our interpretation of this channel.

In Table 17, I present the effects of fives measures of teacher divisions. In column (1), I use a

Herfindahl concentration index based on the regional origins of each respondent;35 in column (2),

I use a Herfindahl concentration index based on the fluency of respondents in the local language36;

in column (3) I use the share of non natively-fluent teachers within the school; in column (4) I

use the share of non-native teachers by ancestry; and in column (5) I use the share of non-native

teachers by birth. In each case, we see a significant and substantially positive effect of school-level

divisions amongst teachers on absenteeism. In Table 18, I repeat the exercise with the inclusion of

the measure for social networks, and find results analogous to those in Table 15. In further support

of this interpretation, I run five additional school-level regressions of Social on each of the measures

for teacher divisions in columns (3) to (7) of Table A1, and find that the effects of ethnic divisions is

large, negative and, in all but two cases, statistically significant.37 Across, a range of comprehensive

specifications, higher ethnic divisions between teachers are associated with less social interactions;

and less social interactions are associated with a higher probability of absenteeism.

It is worth recalling that this interpretation of the results is also supported by the multi-country

analysis. In Table 6, we saw that no school characteristic other than teacher absenteeism was

35The categories are North (19.86%), South (0.2%), West (19.75%), East (39.71%), Central (17.7%) and Sudan(2.77%). The share of respondents teaching in their native region in 67.16%. This is the preferred measure, as manyof Uganda’s strongest social cleavages are regional. The measures that follow do not necessarily distinguish betweengroups.

36Native fluency (65.93%), Fluent (14.05%), Very Good (5.97%), Good (5.92%), Functional (2.05%), Minimal(3.65%), Not able to speak well (2.43%).

37The two specifications in which the effect is not significant are based on arguably the two least precise measuresof teacher diversity: share of non-native teachers by ancestry and birth.

37

associated with ethnic divisions, including the condition of facilities and the supply of materials.

In Table 8, we saw that ethnic divisions have no statistical association with respondents’ likelihood

of attending community meetings. Both of these findings are consistent with the assertion that

ethnicity does not affect absenteeism through a more general effect on community-level collective

action.

Taken together, the evidence suggests that the source of variation in teacher absenteeism is not

only ethnic divisions per se; rather, it is the effect of ethnic divisions on the formation of social

groups amongst teachers within schools. It is possible that the density of these social networks

may in turn increase the social cost of absenteeism; or perhaps they may foster altruism between

colleagues. We can say with confidence that the role of ethnic divisions in either the formation

or the effectiveness of community monitoring institutions, such as parent teacher associations, or

direct monitoring institutions, such as ministry inspections or the sanctioning behavior of head

teachers, is inconsequential for teacher absenteeism; as is the direct effect of a teacher’s ethnicity.

These findings serve as a clear invitation for experimental research in the field, where randomly

generated variation in social activities for teachers (or in the composition of teachers within schools)

could allow us to delve further into the relationship between ethnic divisions, social capital and

absenteeism amongst teachers.

7 Conclusion

In this paper I present robust evidence of a link between ethnic divisions and teacher absenteeism

using sub-national data from two sources: a nationally representative series of random, unannounced

school visits in Uganda; and a large opinion survey of citizens in 16 sub-Saharan African countries.

The results are robust to a comprehensive set of individual, geographic, institutional and historical

controls. I introduce a new measure of ethnic divisions which captures both ethnic diversity and the

salience of ethnic identification. Using ethnic diversity alone, a practice common in the literature,

would lead to a significant underestimation of the true effect. Across a range of specifications, and

using various combinations of data, I find that ethnic diversity increases teacher absenteeism at

high levels of ethnic salience; while, at low levels of salience, it either decreases teacher absenteeism

38

or has no effect at all. I present evidence that the effect is unrelated to community monitoring

institutions such as parent teacher associations, it is explained better instead by the effect of ethnic

divisions on within-school teacher networks. The analysis provides a partial explanation for the

apparent existence of a large, non-pecuniary cost of teacher absence.

The results invite further experimental research in the field that could determine how social

capital amongst teachers ultimately affects attendance decisions. Moreover, the demonstrable mal-

leability of ethnic salience leaves room for direct policy responses to collective action failures in

ethnically divided areas, which could involve either fostering a common national identity or sup-

pressing the attraction of ethnic electioneering. In all, this paper increases our understanding of

high teacher absenteeism in poor, ethnically divided areas. In so doing, it points to new suggestions

which could strengthen the link between educational investment and educational attainment.

39

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42

Figures

Figure 1: Location of Afrobarometer Districts

43

Figure 2: Location of Ugandan School Visits

44

01

23

4De

nsity

0 .2 .4 .6 .8 1

Ethnic SalienceELF

kernel = epanechnikov, bandwidth = 0.0157

Figure 3: Kernel Density Functions: Ethnic Salience and ELF, Multi-Country

BEN

BWA

GHA

KEN

LSOMDG

MLI

MOZ

MWI

NAM

NGA

SEN

TZA

UGA

ZAFZMB

0.1

.2.3

.4Et

hnic

Sal

ienc

e by

dis

trict

.2 .4 .6 .8 1ELF by district

Figure 4: ELF and Ethnic Salience by District, Country Means

45

01

23

Dens

ity

0 .2 .4 .6 .8ELF (School Visits)

kernel = epanechnikov, bandwidth = 0.0459

Figure 5: Kernel Density Function: ELF in Uganda (School Visits)

46

Arua

Bugiri

Bushenyi

Jinja

Kamuli

Kisoro

Luwero Mpigi

Tororo

Yumbe

0.5

11.

52

Teac

her A

bsen

ce (A

froba

rom

eter

)

.1 .2 .3 .4 .5Teacher Absence (school visits)

Figure 6: Absenteeism in Uganda: Afrobarometer vs. School Visits

47

01

23

Teac

her a

bsen

ce

0 .2 .4 .6 .8 1Ethnic diversity

Ethnic salience at m+1sd Ethnic salience at m-1sd

Figure 7: Teacher Absence and Ethnic Divisions

48

Figure 8: Location of Armed Conflict Events (Source: Acled)

49

TablesTable 1: Ethnic Salience in the Literature

Mean Ethnic Salience

Posner (2004) Test

Malawi Zambia Difference

Chewa 0.31 0.05 0.26***Tumbuka 0.16 0.07 0.09*

Miguel (2004) Test

Tanzania Kenya Difference

0.06 0.16 0.10***

50

Table 2: Descriptive StatisticsTeacher absence: Never Once/twice A few times Often Full Sample

ELFd 0.48 0.44 0.43 0.44 0.46(0.30) (0.29) (0.27) (0.26) (0.29)

Ethnic Salience 0.15 0.17 0.18 0.18 0.17(0.15) (0.17) (0.17) (0.16) (0.16)

Urban 0.35 0.39 0.38 0.32 0.38(0.48) (0.49) (0.49) (0.47) (0.47)

Village facilities: school 0.78 0.80 0.81 0.82 0.80(0.41) (0.40) (0.39) (0.39) (0.40)

Village facilities: water 0.53 0.52 0.47 0.43 0.51(0.50) (0.50) (0.50) (0.49) (0.50)

Village facilities: electricity 0.52 0.58 0.52 0.47 0.54(0.50) (0.49) (0.50) (0.50) (0.50)

Village facilities: health 0.46 0.50 0.52 0.47 0.49(0.50) (0.50) (0.50) (0.50) (0.50)

Village facilities: sewage 0.23 0.28 0.24 0.20 0.24(0.42) (0.45) (0.42) (0.40) (0.43)

Respondent characteristics

Hardship 7.51 8.27 9.20 10.45 8.69(5.66) (5.41) (5.52) (5.79) (5.90)

Age 38.25 36.53 35.18 35.98 36.53(14.72) (14.27) (13.14) (13.58) (14.76)

Male 0.48 0.51 0.52 0.54 0.50(0.50) (0.50) (0.50) (0.50) (0.50)

Post-primary education 0.49 0.50 0.48 0.44 0.63(0.49) (0.50) (0.50) (0.50) (0.48)

Observations 6,755 2,521 2,791 2,033 21,598

Urban indicates the percentage of respondents surveyed in urban areas; Village facilities indicates thepercentage of respondents surveyed in villages containing a school, piped water, electricity, a healthclinic and a sewage system, respectively; Hardship is a composite variable ranging from 0-30, where 0indicates that respondents never go without food, water, medical care, cooking fuel, a cash income, andschool supplies like fees, uniforms or books, and 30 indicates that they always do. Post-primary education isthe average of a dummy variable indicating that respondents have recieved any form of post-primary education.

51

Table 3: Teacher Absenteeism and Ethnic Divisions - Afrobarometer(1) (2) (3) (4)

Teacher absence: Afrobarometer

ELF * Ethnic salience 1.008** 1.193***(0.403) (0.425)

ELF -0.002 -0.066 0.140 0.125(0.107) (0.115) (0.085) (0.084)

Ethnic salience -0.082 -0.073 0.319*(0.166) (0.178) (0.187)

Village controls No Yes Yes YesIndividual controls Yes Yes Yes YesRegion FE Yes Yes Yes Yes

Observations 13,468 12,240 12,240 12,330Number of clusters 318/1234 318/1234 318/1234 318/1234R-squared 0.177 0.176 0.175 0.174

Standard errors are adjusted for two-way clustering within ethnic groupsand within districts. ***Significant at the 1% level; **Significant at the5% level; *Significant at the 10% level. Regression equation: TAidr =

a+Ψ�n

i=1ESidnd

+ λELFd + β(�n

i=1ESidnd

∗ ELFd) + γXid + δVid + ηRr + eid

52

Table 4: Cultural and Institutional Persistence(1) (2) (3)


ELF * Ethnic salience 1.027** 1.306*** 1.193**(0.474) (0.465) (0.533)

ELF -0.018 -0.165 -0.153(0.129) (0.116) (0.134)

Ethnic salience -0.049 -0.078 -0.075(0.184) (0.202) (0.229)

Slave exports -0.025(0.033)

Pre-colonial juris. hierarchy -0.061**(0.026)

Existence of city in 1400 0.091*(0.047)

Pre-colonial FE No No YesEthnicity FE No Yes YesVillage controls Yes Yes YesIndividual controls Yes Yes YesRegion FE Yes Yes Yes

Observations 10,772 12,240 11,593Number of clusters 318/1234 318/1234 318/1234R-squared 0.185 0.204 0.228

Standard errors are adjusted for two-way clustering within ethnic groups andwithin districts. ***Significant at the 1% level; **Significant at the 5% level;*Significant at the 10% level.

53

Table 5: Conflict, Sorting and Settlement History(1) (2) (3) (4) (5) (6)


ELF * Ethnic salience 1.299*** 1.192** 1.307*** 1.184** 1.344*** 1.201**(0.462) (0.532) (0.463) (0.527) (0.481) (0.529)

ELF -0.157 -0.149 -0.154 -0.128 -0.176 -0.138(0.116) (0.134) (0.111) (0.131) (0.117) (0.134)

Ethnic salience -0.076 -0.077 -0.079 -0.077 -0.092 -0.067(0.201) (0.228) (0.200) (0.227) (0.205) (0.226)

Armed conflicts within 1km & 1 year -0.010 -0.008 -0.008 -0.008(0.010) (0.007) (0.007) (0.007)

Conflict fatalities within 1km & 1 year 0.005** 0.004*** 0.004*** 0.004***(0.002) (0.001) (0.001) (0.001)

Share of historical migrants 0.025 0.049 0.052(0.057) (0.066) (0.068)

Distance to Addis Ababa (km) -0.000 -0.000(0.000) (0.000)

Latitude 0.010 0.013(0.023) (0.026)

Distance to sea (km) -0.000 -0.000(0.000) (0.000)

Pre-colonial FE No Yes No Yes No YesEthnicity FE Yes Yes Yes Yes Yes YesVillage controls Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes Yes

Observations 12,240 11,593 12,240 11,593 12,044 11,593Number of clusters 318/1234 318/1234 318/1234 318/1234 318/1234 318/1234R-squared 0.204 0.228 0.204 0.228 0.204 0.228

Standard errors are adjusted for two-way clustering within ethnic groups and within districts. ***Significant at the 1% level;**Significant at the 5% level; *Significant at the 10% level.

54

Table 6: Falsification Tests - Other School Variables(1) (2) (3) (4) (5) (6) (7)

Perceived school problems (normalized)Teacher abs. Expensive Facilities Crowding Teaching Materials Bribes

ELF * Ethnic salience 1.073** -0.105 -0.575 -0.131 0.127 -0.725* 0.383(0.473) (0.382) (0.374) (0.389) (0.375) (0.387) (0.395)

ELF -0.123 0.012 0.235** 0.064 0.070 0.098 -0.138(0.119) (0.098) (0.096) (0.101) (0.110) (0.101) (0.108)

Ethnic salience -0.060 0.206 0.223 0.051 0.044 0.390* 0.096(0.202) (0.168) (0.202) (0.207) (0.161) (0.211) (0.206)

Spatial controls Yes Yes Yes Yes Yes Yes YesPre-colonial FE Yes Yes Yes Yes Yes Yes YesEthnicity FE Yes Yes Yes Yes Yes Yes YesVillage controls Yes Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes Yes Yes

Observations 11,593 12,071 11,589 11,634 11,479 11,754 11,568Number of clusters 318/1234 318/1234 318/1234 318/1234 318/1234 318/1234 318/1234R-squared 0.228 0.266 0.294 0.265 0.255 0.259 0.230

Intra-country correlation 0.077 0.092 0.122 0.118 0.103 0.088 0.074


55

Table 7: Falsification Tests - Governance Variables(1) (2) (3) (4) (5) (6)

Perceptions of country-level institutionsCorruption Trust Gov. performance

Government President Ruling party Opposition Corruption Education

ELF * Ethnic salience -0.093 -0.452 0.511 0.137 -0.452 0.081(0.386) (0.343) (0.403) (0.358) (0.391) (0.273)

ELF 0.053 0.056 -0.102 0.023 0.141 -0.068(0.094) (0.084) (0.105) (0.083) (0.103) (0.087)

Ethnic salience -0.147 0.010 0.032 0.004 -0.247 -0.121(0.165) (0.130) (0.180) (0.186) (0.158) (0.159)

Spatial controls Yes Yes Yes Yes Yes YesPre-colonial FE Yes Yes Yes Yes Yes YesEthnicity FE Yes Yes Yes Yes Yes YesVillage controls Yes Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes Yes

Observations 14,324 13,943 16,595 16,126 15,857 17,051Number of clusters 318/1234 318/1234 318/1234 318/1234 318/1234 318/1234R-squared 0.221 0.266 0.294 0.167 0.226 0.257


56

Table 8: Other Falsification Tests(1) (2) (3) (4)

Teacher Teacher Teacher Communityabsence absence absence meetings

ELF * Ethnic salience (district) 1.348** -0.102(0.549) (0.365)

ELF -0.175 0.075(0.139) (0.112)

Ethnic salience -0.110 0.139(0.636) (0.232)

Minority 0.023(0.597)

ELF * Ethnic salience (individual) 0.830(0.734)

Ethnic salience (individual) 0.045 0.064 0.728 -0.098**(0.053) (0.047) (0.719) (0.039)

Village FE No Yes Yes NoSpatial controls Yes N/a N/a YesPre-colonial FE Yes N/a N/a YesEthnicity FE Yes Yes Yes YesDistrict controls Yes N/a N/a YesIndividual controls Yes Yes Yes YesRegion FE Yes N/a N/a Yes

Observations 11,000 12,974 12,974 17,359Number of clusters 318/1234 289 289 318/1234R-squared 0.225 0.396 0.396 0.255

Standard errors in column (1) and column (4) are adjusted for two-way clustering within ethnicgroups and within districts. Standard errors in column (2) and column (3) are adjusted for clusteringat the ethnicity level. ***Significant at the 1% level; **Significant at the 5% level; *Significant atthe 10% level.

57

Table 9: Robustness - Data and Specification(1) (2) (3) (4) (5)

Teacher absence: AfrobarometerDistrict Women District sample size Orderedaverage 25–50 < Med. > Med. Probit

ELF * Ethnic salience 1.218** 2.549*** 0.657 3.668* 1.323**(0.564) (0.816) (0.610) (2.101) (0.429)

ELF -0.126 -0.263 -0.142 -0.121 -0.172(0.194) (0.220) (0.157) (0.472) (0.116)

Ethnic salience 0.089 -0.807** 0.025 -0.933 -0.096(0.219) (0.356) (0.220) (1.157) (0.178)

Spatial controls Yes Yes Yes Yes YesPre-colonial FE Yes Yes Yes Yes YesEthnicity FE Yes Yes Yes Yes YesVillage controls Yes Yes Yes Yes YesIndividual controls Yes Yes Yes Yes YesRegion FE Yes Yes Yes Yes Yes

Observations 20,600 3,611 5,499 6,094 11,593Number of clusters 318/1234 274/1149 254/1036 264/200 1055R-squared 0.868 0.335 0.266 0.260Pseudo R-squared 0.105

Pre-colonial fixed effects, ethnicity fixed effects, and individual controls are included as district-level means in column (1). Standard errors are adjusted for two-way clustering within ethnicgroups and within districts in columns (1) to (4), and for clustering at the district level in col-umn (5). ***Significant at the 1% level; **Significant at the 5% level; *Significant at the 10% level.

58

Table 10: Ordered Probit Marginal Effects(1) (2)

Teacher Absence Ethnic Salience

Mean + 1 SD Mean - 1 SD

∂(Pr(outcome))

∂(ELF )

Never -0.089** 0.056(0.039) (0.039)

Once or twice 0.003** -0.005(0.001) (0.004)

A few times 0.03** -0.020(0.012) (0.014)

Often 0.057** -0.031(0.026) (0.021)

Marginal effects are calculated from the ordered probit regression presented inTable 9, column (5). ***Significant at the 1% level; **Significant at the 5% level;*Significant at the 10% level.

59

Table 11: Teacher Absenteeism and Ethnic Divisions - Uganda School Visits(1) (2) (3) (4) (5) (6)

Teacher absence: school visitsProbit

Ai & Norton (2003)Interaction effect

ELF (District) * Ethnic salience 4.410*** 4.115*** 2.36**(1.477) (1.429) (1.019)

ELF (Pupils) * Ethnic salience 4.901*** 5.253*** 2.43**(1.288) (1.343) (0.979)

Marginal effects

ELF (District) -0.818*** -0.766*** -0.744(0.272) (0.287) (0.463)

ELF (Pupils) -1.327*** -1.393*** -1.542**(0.338) (0.349) (0.722)

Ethnic salience -2.229*** -2.145*** -1.966*** -2.127*** -1.392** -1.644**(0.454) (0.446) (0.462) (0.529) (0.596) (0.701)

Teacher demographic controls Yes Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes Yes YesInstitutional controls No No Yes Yes Yes YesSchool and location controls No No Yes Yes Yes Yes

Time of day FE Yes Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes Yes

Observations 1,686 1,686 1,594 1,594 1,400 1,400Number of clusters 94/10 94/10 94/10 94/10 83 83R-squared 0.248 0.253 0.252 0.256 0.223 0.230

Teacher demographic controls include: gender, age, marital status, a dummy variable indicating completion ofA-levels (high school final exams), and a dummy variable indicating place of birth (this district or another district);Teacher’s rank is a categorical variable indicating the following ranks: deputy head, head of department, permanentteacher, private teacher, temporary teacher, volunteer teacher, and ’other’. The omitted category is head teacher.Teacher’s employment characteristics include: duration of teaching career; duration of tenure at current school, anddummy variables indicating full-time status, membership of a union, and attendance of a teacher training programin the previous year. Institutional controls include dummy variables indicating the existence of a Parent TeacherAssociation (PTA), a categorical variable indicating the time lapsed since the last meeting, and dummy variablesindicating an official inspection in the previous six months and the existence of a local means of recognition for goodteachers. School and location controls include a set of dummy variables to indicate the existence of the followingfacilities: covered classrooms, non-dirt classroom floors, a toilet/latrine, drinking water and electricity; as well as thepupil-teacher ratio, average education levels of parents, and dummy variables indicating that the school is public, thatit practices multi-grade teaching, whether it is within five kilometres of a paved road and whether it is in a rurallocation. Column (5) and (6) present marginal effects from a Probit regression. Standard errors in columns (1) to(4) are adjusted for two-way clustering within schools and within districts. Standard errors in column (5) and column(6) are adjusted for clustering at the school level. ***Significant at the 1% level; **Significant at the 5% level; *Sig-nificant at the 10% level. Regression equation: TAjsd = a+ΨESd+λELF+β(ESd∗ELF )+γXjsd+δSsd+ηTtdm+ejsd

60

Table 12: Channel 1 - Teacher Coethnicity(1) (2) (3) (4) (5) (6)

Teacher absence: school visits

ELF (District) * Ethnic salience 4.330*** 4.377*** 4.378***(1.460) (1.461) (1.460)

ELF (Pupils) * Ethnic salience 5.552*** 5.647*** 5.647***(1.333) (1.379) (1.356)

ELF (District) -0.798*** -0.814*** -0.815***(0.281) (0.293) (0.290)

ELF (Pupils) -1.436*** -1.478*** -1.478***(0.342) (0.351) (0.344)

Ethnic salience -1.974*** -1.995*** -1.995*** -2.143*** -2.177*** -2.177***(0.469) (0.479) (0.478) (0.521) (0.535) (0.528)

Native: language 0.051** 0.046**(0.021) (0.022)

Native: ancestry -0.005 -0.000(0.045) (0.049)

Native: birth 0.042 0.042(0.035) (0.037)

Teacher demographic controls Yes Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes Yes YesInstitutional controls Yes Yes Yes Yes Yes YesSchool and location controls Yes Yes Yes Yes Yes YesTime of day FE Yes Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes YesObservations 1,588 1,588 1,588 1,588 1,588 1,588Number of clusters 94/10 94/10 94/10 94/10 94/10 94/10R-squared 0.265 0.263 0.263 0.270 0.268 0.268

Standard errors are adjusted for two-way clustering within schools and within districts. ***Significant atthe 1% level; **Significant at the 5% level; *Significant at the 10% level. Regression equation: TAjsd =

a+ φNativejsd +ΨESd + λELF + β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd

61

Table 13: Channel 2a - Sanctioning Institutions(1) (2) (3) (4)


ELF (District) * Ethnic salience 4.098*** 4.061***(1.430) (1.376)

ELF (Pupils) * Ethnic salience 5.285*** 5.417***(1.333) (1.151)

ELF (District) -0.766*** -0.751***(0.287) (0.274)

ELF (Pupils) -1.411*** -1.460***(0.342) (0.297)

Ethnic salience -1.966*** -1.956*** -2.141*** -2.163***(0.465) (0.434) (0.529) (0.458)

PTA 0.032 0.047 0.030 0.054(0.104) (0.101) (0.102) (0.096)

Last PTA meet: last month 0.022 0.018 -0.015 -0.024(0.115) (0.115) (0.102) (0.102)

Last PTA meet: < six months -0.118 -0.121 -0.139* -0.145*(0.085) (0.084) (0.078) (0.078)

Last PTA meet: < one year -0.164** -0.169** -0.165** -0.173**(0.065) (0.066) (0.068) (0.069)

Last PTA meet: > one year -0.053 -0.061 -0.046 -0.058(0.081) (0.084) (0.082) (0.083)

Recent inspection 0.029 0.031 0.053 0.059(0.048) (0.047) (0.045) (0.043)

Head teacher has sanctioned 0.078 0.121**(0.067) (0.053)

Teacher demographic controls Yes Yes Yes YesTeacher rank FE Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes YesInstitutional controls Yes Yes Yes YesSchool and location controls Yes Yes Yes YesTime of day FE Yes Yes Yes YesDay FE Yes Yes Yes YesMonth FE Yes Yes Yes Yes

Observations 1,588 1,588 1,588 1,588Number of clusters 94/10 94/10 94/10 94/10R-squared 0.260 0.261 0.266 0.267

Standard errors are adjusted for two-way clustering within schools and within districts. ***Sig-nificant at the 1% level; **Significant at the 5% level; *Significant at the 10% level Regressionequation: TAjsd = a+φSanctionsd+ΨESd+λELF+β(ESd∗ELF )+γXjsd+δSsd+ηTtdm+ejsd

62

Table 14: Channel 2b - Sanctioning Institutions by Coethnicity(1) (2) (3) (4) (5) (6)


ELF (District) * Ethnic salience 3.904*** 3.570*** 3.971***(1.268) (1.364) (1.431)

ELF (Pupils) * Ethnic salience 5.363*** 5.130*** 5.462***(0.949) (1.202) (1.345)

ELF (District) -0.735*** -0.650** -0.728**(0.230) (0.276) (0.290)

ELF (Pupils) -1.427*** -1.422*** -1.477***(0.263) (0.307) (0.344)

Ethnic salience -1.931*** -1.797*** -1.898*** -2.185*** -2.054*** -2.137***(0.386) (0.449) (0.464) (0.416) (0.471) (0.504)

PTA 0.144** 0.035 0.036 0.150*** 0.044 0.045(0.062) (0.081) (0.088) (0.058) (0.078) (0.083)

Last PTA meet: last month -0.054 -0.006 -0.020 -0.080 -0.048 -0.059(0.112) (0.112) (0.104) (0.104) (0.101) (0.090)

Last PTA meet: < six months -0.151 -0.130 -0.157* -0.173* -0.158* -0.182**(0.107) (0.091) (0.091) (0.090) (0.085) (0.082)

Last PTA meet: < one year -0.249*** -0.161*** -0.182*** -0.257*** -0.164** -0.186***(0.064) (0.060) (0.067) (0.064) (0.064) (0.068)

Last PTA meet: > one year -0.118 -0.040 -0.081 -0.116 -0.037 -0.077(0.093) (0.095) (0.086) (0.089) (0.094) (0.084)

Recent inspection 0.014 0.012 0.015 0.024 0.039 0.042(0.048) (0.053) (0.051) (0.043) (0.047) (0.046)

Head teacher has sanctioned 0.156** 0.039 0.056 0.199*** 0.083 0.101**(0.068) (0.065) (0.060) (0.055) (0.054) (0.047)

Native by: Language Ancestry Birth Language Ancestry Birth

Native 0.204* -0.245*** -0.154 0.176* -0.252*** -0.148(0.106) (0.091) (0.145) (0.103) (0.091) (0.142)

Native*PTA -0.171 0.078 0.131 -0.166* 0.074 0.110(0.109) (0.135) (0.149 (0.100) (0.136) (0.142)

Native*Recent inspection 0.040 0.094 0.096 0.064 0.094 0.116(0.037) (0.092) (0.108) (0.040) (0.095) (0.107)

Native*Head teacher has sanctioned -0.142* 0.209** 0.146 -0.142* 0.197** 0.146*(0.076) (0.101) (0.102) (0.077) (0.090) (0.086)

Native*Last PTA meet: last month -0.090* -0.039 -0.196 -0.086* -0.042 -0.186(0.048) (0.284) (0.178) (0.046) (0.289) (0.178)

Native*Last PTA meet: < six months 0.011 0.170 0.029 -0.004 0.150 0.021(0.077) (0.115) (0.157) (0.082) (0.116) (0.148)

Native*Last PTA meet: < one year -0.065 0.156* 0.071 -0.066 0.160* 0.084(0.056) (0.089) (0.085) (0.049) (0.086) (0.080)

Native*Last PTA meet: > one year 0.076 -0.030 -0.176 0.083 -0.045 -0.169(0.056) (0.136) (0.117) (0.055) (0.130) (0.108)

Teacher demographic controls Yes Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes Yes YesInstitutional controls Yes Yes Yes Yes Yes YesSchool and location controls Yes Yes Yes Yes Yes YesTime of Day FE Yes Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes YesObservations 1,588 1,588 1,588 1,588 1,588 1,588Number of clusters 94/10 94/10 94/10 94/10 94/10 94/10R-squared 0.267 0.270 0.267 0.273 0.277 0.274

Standard errors are adjusted for two-way clustering within schools and within districts. ***Signifi-cant at the 1% level; **Significant at the 5% level; *Significant at the 10% level. Regression equation:TAjsd = a+ φNativejsd + ϕSanctionsd + θ(Nativejsd ∗ Sanctionsd) +ΨESd + λELF+ β(ESd ∗ ELF ) + γXjsd + δSsd + ηTtdm + ejsd

63

Table 15: Channel 3 - Social Networks Between Teachers(1) (2)


ELF (District) * Ethnic salience 2.434(1.823)

ELF (Pupils) * Ethnic salience 3.420**(1.726)

ELF (District) -0.316(0.364)

ELF (Pupils) -0.917**(0.406)

Ethnic salience -1.427*** -1.585***(0.493) (0.498)

Social -0.125** -0.112**(0.050) (0.053)

Teacher demographic controls Yes YesTeacher rank FE Yes YesTeacher employment characteristics Yes YesInstitutional controls Yes YesSchool and location controls Yes YesTime of day FE Yes YesDay FE Yes YesMonth FE Yes Yes

Observations 1,476 1,476Number of clusters 94/10 94/10R-squared 0.264 0.263

Standard errors are adjusted for two-way clustering within schools andwithin districts. ***Significant at the 1% level; **Significant at the5% level; *Significant at the 10% level. Regression equation: TAjsd =

a+φSocialsd+ΨESd+λELF +β(ESd ∗ELF )+γXjsd+δSsd+ηTtdm+ejsd

64

Table 16: Test for Heterogeneous Effects of Social Networks(1) (2) (3) (4) (5) (6)


ELF (District) * Ethnic salience 2.094 2.133 2.138(1.750) (1.772) (1.773)

ELF (Pupils) * Ethnic salience 3.230* 3.266* 3.390**(1.655) (1.718) (1.719)

ELF (District) -0.272 -0.258 -0.276(0.352) (0.361) (0.355)

ELF (Pupils) -0.860** -0.868** -0.909**(0.390) (0.402) (0.404)

Ethnic salience -1.410*** -1.399*** -1.403*** -1.564*** -1.549*** -1.582***(0.486) (0.488) (0.483) (0.491) (0.505) (0.494)

Social -0.156*** -0.139*** -0.128** -0.144*** -0.129** -0.118**(0.043) (0.050) (0.050) (0.050) (0.053) (0.052)

Social * Native: language 0.050 0.045(0.046) (0.044)

Social * Native: ancestry 0.123*** 0.110**(0.044) (0.045)

Social * Native: birth 0.044 0.040(0.054) (0.054)

Native: language 0.031 0.030(0.031) (0.032)

Native: ancestry -0.103** -0.087*(0.045) (0.049)

Native: birth -0.005 -0.003(0.037) (0.036)

Teacher demographic controls Yes Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes Yes YesInstitutional controls Yes Yes Yes Yes Yes YesSchool and location controls Yes Yes Yes Yes Yes YesTime of Day FE Yes Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes Yes YesObservations 1,476 1,476 1,476 1,476 1,476 1,476Number of clusters 94/10 94/10 94/10 94/10 94/10 94/10R-squared 0.263 0.263 0.261 0.266 0.265 0.264

Standard errors are adjusted for two-way clustering within schools and within districts. ***Significant at the 1% level;**Significant at the 5% level; *Significant at the 10% level.

65

Table 17: Teacher Absenteeism and School-level Ethnic Divisions Among Teachers(1) (2) (3) (4) (5)


ELF (Teachers: regional origin) * Ethnic salience 5.043***(1.510)

ELF (Teachers: native fluency) * Ethnic salience 3.846***(0.905)

(Share of non-native teachers: language) * Ethnic Salience 2.768***(0.663)

(Share of non-native teachers: ancestry) * Ethnic Salience 8.386***(1.989)

(Share of non-native teachers: birth) * Ethnic Salience 9.336***(1.995)

ELF (Teachers: regional origin) -1.427***(0.338)

ELF (Teachers: native fluency) -1.218***(0.222)

Share of non-native teachers: language -0.749***(0.178)

Share of non-native teachers: ancestry -1.909***(0.489)

Share of non-native teachers: birth -2.331***(0.475)

Ethnic salience -2.226*** -2.184*** -1.989*** -8.127*** -8.836***(0.562) (0.402) (0.385) (1.794) (1.663)

Teacher demographic controls Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes YesInstitutional controls Yes Yes Yes Yes YesSchool and location controls Yes Yes Yes Yes YesTime of day FE Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes YesObservations 1,588 1,588 1,588 1,588 1,588Number of clusters 94/10 94/10 94/10 94/10 94/10R-squared 0.267 0.271 0.266 0.261 0.267

Standard errors are adjusted for two-way clustering within schools and within districts. ***Significant at the 1% level; **Significantat the 5% level; *Significant at the 10% level.

66

Table 18: Teacher Absenteeism, School-level Ethnic Divisions and Social Networks Between Teachers(1) (2) (3) (4) (5)


ELF (Teachers: regional origin) * Ethnic salience 2.722*(1.511)

ELF (Teachers: native fluency) * Ethnic salience 2.320**(1.050)

(Share of non-native teachers: language) * Ethnic Salience 1.415(0.882)

(Share of non-native teachers: ancestry) * Ethnic Salience 6.105***(2.146)

(Share of non-native teachers: birth) * Ethnic Salience 6.519***(1.952)

Social -0.086* -0.089* -0.114** -0.096** -0.092*(0.046) (0.053) (0.053) (0.048) (0.047)

ELF (Teachers: regional origin) -0.912***(0.351)

ELF (Teachers: native fluency) -0.857***(0.257)

Share of non-native teachers: language -0.401*(0.215)

Share of non-native teachers: ancestry -1.083**(0.480)

Share of non-native teachers: birth -1.527***(0.427)

Ethnic salience -1.435*** -1.540*** -1.388*** -6.147*** -6.423***(0.530) (0.448) (0.416) (1.798) (1.658)

Teacher demographic controls Yes Yes Yes Yes YesTeacher rank FE Yes Yes Yes Yes YesTeacher employment characteristics Yes Yes Yes Yes YesInstitutional controls Yes Yes Yes Yes YesSchool and location controls Yes Yes Yes Yes YesTime of day FE Yes Yes Yes Yes YesDay FE Yes Yes Yes Yes YesMonth FE Yes Yes Yes Yes YesObservations 1,476 1,476 1,476 1,476 1,476Number of clusters 94/10 94/10 94/10 94/10 94/10R-squared 0.264 0.267 0.262 0.266 0.264

Standard errors are adjusted for two-way clustering within schools and within districts. ***Significant at the 1% level; **Significantat the 5% level; *Significant at the 10% level.

67

Appendix A

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Figure A 1: ELF and Ethnic salience by country

68

Table A1: Social Networks Between Teachers - OLS Covariates(1) (2) (3) (4) (5) (6) (7)

Dependent Variable: SocialELF measured by: District Pupils Teachers: Share of non-native teachers by:

Region Language Language Ancestry Birth

ELF * Ethnic salience -6.997* -5.527* -5.574* -6.692** -4.110** -2.752 -1.632(3.732) (2.833) (2.801) (2.682) (1.606) (3.400) (3.349)

ELF 1.160* 0.723 1.909** 1.755*** 0.604* 0.416 0.607(0.608) (0.683) (0.719) (0.610) (0.356) (0.691) (0.753)

Ethnic salience 1.058 0.730 0.379 1.590 1.252 1.634 0.513(1.256) (0.969) (1.027) (1.258) (0.942) (3.164) (3.097)

PTA -0.100 -0.127 -0.090 -0.040 -0.068 -0.118 -0.065(0.270) (0.274) (0.246) (0.241) (0.236) (0.273) (0.289)

Last PTA meet: last month 0.274 0.135 0.497* 0.379 0.165 0.194 0.299(0.280) (0.303) (0.274) (0.275) (0.273) (0.305) (0.314)

Last PTA meet: < six months -0.029 -0.154 0.034 -0.017 -0.205 -0.165 -0.116(0.267) (0.267) (0.268) (0.274) (0.290) (0.278) (0.279)

Last PTA meet: < one year 0.465 0.528* 0.509* 0.485 0.484* 0.404 0.418(0.297) (0.298) (0.293) (0.296) (0.285) (0.305) (0.313)

Last PTA meet: > one year 0.010 -0.135 0.019 -0.027 -0.069 -0.142 -0.079(0.224) (0.245) (0.218) (0.218) (0.228) (0.214) (0.212)

Recent inspection -0.112 -0.076 -0.142 -0.147 -0.146 -0.095 -0.125(0.178) (0.170) (0.178) (0.178) (0.184) (0.189) (0.182)

Female 0.433 0.316 0.596 0.425 0.542 0.618 0.531(0.464) (0.447) (0.455) (0.443) (0.441) (0.469) (0.480)

Age -0.015 -0.006 -0.020 -0.016 -0.005 -0.017 -0.011(0.026) (0.026) (0.025) (0.025) (0.026) (0.026) (0.027)

Education 0.201 0.053 0.249 0.351 0.133 0.107 0.091(0.343) (0.346) (0.344) (0.355) (0.317) (0.334) (0.343)

Teacher training -0.158 -0.086 -0.125 -0.156 -0.179 -0.126 -0.163(0.175) (0.161) (0.159) (0.158) (0.153) (0.176) (0.169)

Experience 0.002 0.003 0.000 0.002 0.002 -0.007 0.000(0.027) (0.027) (0.027) (0.026) (0.026) (0.029) (0.032)

Experience at this school -0.018 -0.025 -0.037 -0.029 -0.021 -0.036 -0.041(0.028) (0.025) (0.023) (0.026) (0.023) (0.025) (0.025)

Fulltime -0.881 -0.829 -0.735 -0.851 -0.616 -0.397 -0.346(1.153) (1.136) (1.111) (1.185) (1.084) (1.135) (1.114)

Union -0.171 -0.315 -0.161 -0.191 -0.281 -0.265 -0.249(0.228) (0.214) (0.223) (0.222) (0.211) (0.231) (0.238)

Married 0.404 0.453 0.265 0.338 0.447 0.385 0.405(0.294) (0.276) (0.290) (0.290) (0.281) (0.310) (0.307)

Recognition program 0.044 0.064 0.038 0.033 0.100 0.080 0.087(0.130) (0.121) (0.125) (0.123) (0.114) (0.128) (0.130)

Facilities: classroom -0.283 -0.392 -0.171 -0.117 -0.135 -0.155 -0.241(0.601) (0.653) (0.544) (0.544) (0.476) (0.586) (0.585)

Facilities: floor 0.153 0.126 0.111 0.151 0.108 0.118 0.145(0.188) (0.192) (0.177) (0.184) (0.152) (0.193) (0.200)

Facilities: toilet 0.276 -0.110 0.163 0.330 0.318 -0.137 -0.117(0.668) (0.699) (0.733) (0.678) (0.725) (0.681) (0.630)

Facilities: water -0.088 0.003 -0.182 -0.129 -0.082 -0.130 -0.167(0.139) (0.137) (0.128) (0.128) (0.126) (0.138) (0.142)

Facilities: electricity 0.110 0.093 0.073 0.108 0.166 0.126 0.093(0.200) (0.201) (0.187) (0.188) (0.183) (0.205) (0.205)

Rural -0.064 -0.022 -0.105 -0.090 -0.058 -0.061 -0.045(0.126) (0.133) (0.124) (0.125) (0.126) (0.130) (0.127)

Access 0.009 0.089 0.001 0.007 -0.003 0.035 0.058(0.118) (0.124) (0.120) (0.112) (0.115) (0.118) (0.123)

Multi-grade -0.002 0.048 -0.070 -0.049 0.252 -0.008 -0.013(0.234) (0.257) (0.217) (0.238) (0.249) (0.305) (0.320)

Pupil-teacher ratio 0.001 0.000 0.001 0.001 -0.001 0.002 0.002(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

Parental education 0.018 0.024 0.015 0.015 0.009 0.019 0.019(0.018) (0.016) (0.018) (0.017) (0.016) (0.017) (0.018)

Public School 0.204 0.180 0.239 0.231 0.315 0.168 0.182(0.199) (0.206) (0.190) (0.196) (0.208) (0.185) (0.187)

Observations 86 86 86 86 86 86 86R-squared 0.379 0.408 0.419 0.420 0.420 0.335 0.339

Robust standard errors in parentheses. ***Significant at the 1% level; **Significant at the 5% level; *Significantat the 10% level.

69

Appendix B

In order to estimate the interaction effects in the probit models presented in Table 11, I turn to

Ai and Tobin’s (2003) well-known method of calculating the cross-partial derivative. They show

that the marginal effect of an interaction term in non-linear models (i.e.∂Φ(u)∂(x1x2)

) can have a

different magnitude, sign and level of statistical significance than the true cross-partial derivative

(i.e.,∂2Φ(u)∂x1∂x2

).

Below, I show the interaction effects and z-statistics for the estimates presented in columns (5)

and (6) respectively. The corresponding marginal effects are also presented for comparison. The

plot of the z-statistics shows that every observation significantly different from zero is positive.

0

2

4

6

Inte

ract

ion

Effe

ct (p

erce

ntag

e po

ints

)

0 .2 .4 .6 .8 1Predicted Probability that y = 1

Correct interaction effect Incorrect marginal effect

Interaction Effects after Probit

Figure A 2: Interaction effect with ELF at district level, probit

70

-5

0

5

10

z-st

atis

tic


z-statistics of Interaction Effects after Probit

Figure A 3: z-statistics for interaction effect with ELF at district level, probit

71

-2

0

2

4

6

Inte

ract

ion

Effe

ct (p

erce

ntag

e po

ints

)


Correct interaction effect Incorrect marginal effect

Interaction Effects after Probit

Figure A 4: Interaction effect with ELF at school level, probit

-5

0

5

10

z-st

atis

tic


z-statistics of Interaction Effects after Probit

Figure A 5: z-statistics for interaction effect with ELF at school level, probit

72

The Long-Term Consequences of Apartheid:

Neighborhoods and Inequality among Black South Africans

Jonathan Page∗

February 27, 2014

Abstract

In this study I compare overall neighborhood effects on income for black Africans

across two geographically interlaced but characteristically different regions in post-

apartheid South Africa. Specifically, I measure the proportion of total inequality

explained by neighborhood background in a former bantustan, KwaZulu, with that in

the ‘white’ South African province, Natal, that surrounded it. This paper is the first to

decompose post-apartheid inequality into (1) inequality within neighborhoods and (2)

inequality between neighborhoods. I use a panel household survey (the KwaZulu-Natal

Income Dynamics Study) and find this proportion is 48% in KwaZulu and 89% in Natal.

This suggests reducing inequality across communities (e.g., by reducing inequalities in

school quality, distance to medical service providers, etc.) will have a larger relative

impact on reducing overall income inequality for Natal (i.e., ‘white’ South Africa) than

for KwaZulu (i.e., the bantustan). Understanding this proportion can help determine

whether neighborhoods or households should be the target of inequality-reduction

interventions.

∗Department of Economics, University of Hawaii at Manoa, Honolulu, HI (e-mail: [email protected]).I thank Anna Lou Abatayo, Chasuta Anukoolthamchote, Timothy Halliday, Karl Jandoc, Chaning Jang,Sumner La Croix, Sang-Hyop Lee, Wayne Liou, Inessa Love, Stephanie Page, and Jeffrey Traczynski forcomments.

1

1 Introduction

There is no place for [the Bantu] in the European community above the level of

certain forms of labour . . . What is the use of teaching the Bantu child mathematics

when it cannot use it in practice? That is quite absurd. Education must train

people in accordance with their opportunities in life, according to the sphere in

which they live.

Dr. Hendrik Verwoerd, Minister for Native Affairs, South Africa, 1953 (Lapping, 1986)

The above quote from Dr. Verwoerd, later the Prime Minister of South Africa from

1958-66, is representative of the efforts of this architect of apartheid to create separate

development paths for whites and the native speakers of Bantu languages1 (i.e. black South

Africans). Apartheid maintained an institutionalized system of segregation and discrimination

against non-whites in South Africa, particularly the black African majority. This included

creating nominally independent states within South Africa, called bantustans2, where black

South Africans were to be relocated. Blacks were made citizens of these bantustans according

to their tribal ancestry. This process allowed the dominant white minority to strip blacks of

their citizenship in ‘white’ South Africa. In the late 1980s the apartheid government began to

dismantle the many restrictions on blacks in response to mounting international pressure and

internal resistance. This dismantling took an important step in 1994 with the first general

election allowing blacks to vote. The election of Nelson Mandela, the black South African

resistance leader who had spent 27 years in prison, signaled to the world the end of apartheid.

This new birth of economic and political freedom held great promise in ending decades of

extreme poverty and stark inequalities. Unfortunately, since 1994 inequality and headcount

1Bantu refers both to a group of languages and to the black Africans who speak them.2The apartheid government officially referred to these as homelands. I use the label bantustan in this

paper specifically to avoid the preferred term of the oppressive apartheid government.

2

measures of poverty increased (cf. , Ozler 2007). More households were below 200% of

the household subsistence line (HSL) in 1998 than in 1993 when comparing the income

distribution in KwaZulu-Natal across these two periods. A review of the joint distribution of

income in 1993 and 1998 show the poor falling ever more behind (Carter and May, 2001).

Transition matrices, whether using endogenous income quintiles (Woolard and Klasen, 2005)

or exogenous income groups based on percent of HSL (Carter and May, 2001) reveal a society

with significant mobility, though much of that mobility entails poor households becoming

poorer.

Through 2007 the former bantustans were the most deprived in terms of income, employ-

ment, education, and living environment according to the South African Index of Multiple

Deprivation (Noble and Wright, 2012). This separate development can be seen in figure 1

which shows the lingering spatial effect of apartheid in the KwaZulu-Natal province as of

20013. The development literature offers initial household size, education, asset endowment,

employment access (Woolard and Klasen, 2005), and a highly segmented labor market (Ozler,

2007) as reasons for the increase in socioeconomic inequality. This study presents a first look

at the degree to which black African households are tied to their apartheid-neighborhood

background in post-apartheid South Africa.

In this study I compare overall community effects on income4 for black Africans across two

geographically interlaced but characteristically different regions in South Africa. Specifically,

I measure the community effect as the proportion of the total inequality explained by

cross-community inequality in a former bantustan, KwaZulu, with the ‘white South African

province, Natal, that surrounded it. Measured in this way, community effects are those factors

of income common to households in a community such as role models, social connections,

exposure to violence, and discrimination. When cross-community inequality is low relative

3Noble et al. (2009) discuss the index and the data behind it.4I use income and expenditure interchangeably here, following the custom in the development literature.

3

to overall inequality, reducing inequality across communities does little to address overall

inequality. For example, if the relevant proportion of variances, the intra-cluster correlation

coefficient (ICC), is 10%, completely eliminating inequality across communities would only

lower the overall inequality by 10%. I find ICC is relatively low for the bantustan and

relatively high for white South Africa. This suggests community-level interventions targeted

at reducing inequality will have a greater effect on lowering overall inequality for white South

Africa than for the bantustan. Likewise, inequality reduction policy in the former bantustan

should target household-level inequality within neighborhoods.

I closely follow estimation procedures used in the sibling correlation5 literature, where

ICC acts as an omnibus measure of intergenerational socioeconomic mobility. I use these

procedures to calculate ICC for a panel of black African households in the new KwaZulu-

Natal province6. In the sibling correlation literature, beginning with Solon et al. (1991), ICC

measures the importance of the factors common to all siblings within a family. In general,

the ICC measures the proportion of the total variance comprised by the variances of a family

or community-level effect7.

In the sibling context these common factors are defined by the outcomes of siblings’

parents and ICC leads naturally to a notion of intergenerational mobility8. This reasoning

does not apply directly to the household survey literature in developing countries when

the unit of analysis is a household and the grouping level is a community as opposed to

a family. However, the ICC provides insight into the sensitivity of target populations to

5Sibling correlations are equivalent to an ICC where clusters are families (i.e., groups of siblings). Myclusters are survey clusters with population sizes ranging from 331 to 317, 635. For ease of exposition I referto these as communities. When I control for population sizes of the clusters the results are unchanged. Thestandard errors are marginally inflated, but this does not affect the significance of my results.

6The new province combined the old bantustan Kwazulu with the old ‘white’ South African province ofNatal.

7Within the sibling correlation literature, a community effect is generally referred to as a neighborhoodeffect.

8For example, a sibling correlation in income of 0.4 implies that 40% of variation in incomes is due tocommon factors shared by siblings. These common factors include family and neighborhood background.

4

community-level interventions, as opposed to individual-level interventions.9

After calculating initial estimates of the ICC for KwaZulu and Natal, I calculate the

contribution of key factors to ICC10. The factors I test are education levels, urban status, and

investment in infrastructure. I proxy infrastructure investment with road quality and find it

explains much of the cross-community variation in Natal, but only a small portion of the cross-

community variation in KwaZulu. Road quality also explains more of the cross-community

variation than mean education levels or urban status for both provinces.

In my initial estimation of the ICC, I use restricted maximum likelihood (REML). Turning

again to the sibling correlation literature I show the robustness of my results to a recent

competing method (Bjorklund et al., 2009) and to various equivalence scales.

The ICC in 2004 is 0.23 in the bantustan and 0.69 in ‘white’ South Africa. This suggests

reducing inequality across communities (e.g., by reducing inequalities in school quality,

distance to medical service providers, etc.) will have a larger relative impact on reducing

overall income inequality for Natal (i.e., ‘white’ South Africa) than for KwaZulu (i.e., the

bantustan).

I describe my statistical model in the following section. In Section 3, I describe the

estimation procedure, REML, and how I intend to explain contributing factors to between-

neighborhood component of inequality. Section 4 presents an overview of the KwaZulu-Natal

Income Dynamics Study (KIDS) data and summary statistics of adult equivalent expenditure.

Section 5 presents my results, section 6 presents the analysis of factors to the correlations,

and section 7 presents robustness checks. Finally, I conclude.

9 The majority of studies in the developing world, which mention ICC, use it to adjust their standarderrors, consider it a nuisance parameter , or mention its value in passing (cf. , Tarozzi and Deaton 2009),while one paper employs ICC as a measure of spatial correlation for Burkina Faso (Grab and Grimm, 2009).Many other fields use ICC including epidemiology (Roux et al., 2001) and demography (South et al., 2011) tostudy spatial correlation.

10Here I follow the procedure in Mazumder (2008).

5

Figure 1: KwaZulu-Natal Province: Index of deprivation map. Source: Author’s calculationsusing the dataset discussed in Noble et al. (2009). Datazones are small statistical areas eachcontaining approximately the same number of individuals.

6

2 Statistical Model

The model of household income employed here has been alternately referred to as a nested-

error component model, a random effects model, a multilevel model, a mixed model, a

variance components model, or a hierarchical model (see Snedecor and Cochran, 1980; Deaton,

1997)11. The notation here mirrors that found in studies of sibling correlations (Solon et al.,

1991). The natural logarithm of adult-equivalent12 monthly expenditure, ych, for cluster c

and household h is modeled as

ych = x′chβ + εch. (1)

x′chβ includes an intercept, the number of children and the number of pensioners in order

to control for key household life-cycle effects13. The residual, εch, represents the effects of

household-specific factors unrelated to neighborhood factors. I decompose εch as follows:

εch = ac + vch, (2)

where ac is the component common to all households in community c, vch is the idiosyncratic

component for household h.

By construction, the variance, σ2ε , equals

σ2ε = σ2

a + σ2v . (3)

11Other examples include, Montmarquette and Mahseredjian (1989) who use a two-way nested-errorcomponent model to study the impact of a student’s class and school on educational achievement. Antweiler(2001) provides a succinct history of nested error models and discusses an application estimating the varianceswith maximum likelihood (ML).

12Here I use the adult equivalent scale, φ, used by Carter and May (1999) and common throughout theliterature on South African household income. φ = (A+ 0.5K)0.9, where A is the number of adults and K isthe number of children. This structure reflects children’s lower consumption relative to adults and assumeseconomies of scale.

13This follows the covariate setup in Solon et al. (1991) and Mazumder (2008) with relevant changes forthis paper’s household setting.

7

Thus, the share of variance in income due to community background, and also the income

correlation of randomly drawn pairs of households in a given community is

ρ =σ2a

σ2a + σ2

v

(4)

3 Estimation Procedure

I follow Mazumder (2008) and estimate the variance components using restricted maximum

likelihood (REML)14. While the ANOVA approach15 to calculate ρ is straightforward and

provides minimum variance estimator for balanced clusters, the same is not true for unbalanced

clusters (Corbeil and Searle, 1976). Solon et al. (1991) introduce four weighting schemes to

test robustness of results to various corrections for this imbalance. REML has the advantage,

even in the unbalanced case, of consistency, asymptotic normality, and a known asymptotic

sampling dispersion matrix. Simulations by Browne and Draper (2006) indicate that bias is

likely to be low when using REML for the number of clusters and households used in this study

given the assumption that the log of adult equivalent expenditure is normally distributed.16

Since the household survey data is unbalanced (i.e., each cluster is not restricted to the same

number of households), I select REML as the preferred method in this case to estimate ρ

(Mazumder, 2008).

14This method is alternately referred to as residual maximum likelihood. I estimate REML through thextmixed command in Stata.

15In the analysis of variance (ANOVA) approach ICC is simply the ratio of the between subjects (hereclusters) variance to the total variance.

16Following the relevant literature, I make this assumption. I produce quantile-quantile (Q-Q) plotsfor KwaZulu and Natal as a visual check of normality in figures 2a and 2b. Based on the Q-Q plots thedistributions appear normal. The Shapiro-Wilk test for normality does not reject the null hypothesis thatlogged expenditure is normally distributed for the case of KwaZulu, though it does reject normality for Natal.

8

24

68

10Lo

g Ex

pend

iture

3 4 5 6 7 8Inverse Normal

KwaZulu Q-Q Plot

(a) KwaZulu

24

68

10Lo

g Ex

pend

iture

2 4 6 8 10Inverse Normal

(b) Natal

Figure 2: Q-Q plots of logged monthly expenditure: KwaZulu and Natal

3.1 Attributing ICC to observables

In order to explore possible components of the community factor, I employ a measure proposed

by Mazumder (2008) to calculate an upper-bound estimate for the contribution of various

observables to ρ. To do this, I recalculate equation (1), adding the observed variable to X.

Define the community-level variation from this new calculation σ2∗a . I define this measure of

contribution, η, as

η =σ2a − σ2∗

a

σ2a + σ2

v

. (5)

Mazumder (2008) refers to η as an upper-bound estimate of the contribution of the factor

of interest, because it includes omitted factors which are correlated with the newly added

covariates. While it would be convenient to measure the σ2∗a using REML, Corbeil and

Searle (1976) and Robinson (1987) note that REML, in contrast to maximum likelihood

(ML), includes degrees of freedom in the estimation of the variance components17. As a

result, it is possible with REML to have σ2∗a > σ2

a when adding additional fixed effects to

17The REML estimator isσ2 = y′T ′(THT ′)−1Ty/(N − k)

where k is the number of fixed effects in the model (Corbeil and Searle, 1976), while the ML estimator is

σ2i = m−1i {d

′idi + σ2tr(Σ−122.1i + γ−1i Imi

)−1} (i = 1, 2, . . . , c)

9

the model18. Because of this issue, I diverge from the procedure in Mazumder (2008) by

replacing the variances in equation 5 with their counterparts from the standard maximum

likelihood (ML) procedure. I will continue to use REML to calculate ρ; however, since the

variance components from REML are not easily compared across model specifications, I use

ML to calculate the contributions to ρ. That is, I estimate a measure of contribution, ηML,

which I define as

ηML =σ2a,ML − σ2∗

a,ML

σ2a,ML + σ2

v,ML

. (6)

4 Data

The panel data is from the three wave KwaZulu-Natal Income Dynamics Study (KIDS)19

which was conducted in 1993, 1998, and 200420,21. All households in the survey are from the

KwaZulu-Natal province of South Africa. In 1996 it had the largest population of any South

African province with 8.4 million inhabitants, roughly 20.7% of the country’s population

(Statistics South Africa, 1996). It is approximately the size of Portugal, has two major ports

(Durban and Richards Bay) that account for the majority of the country’s cargo tonnage, and

has adequate soil and rainfall to support a wide variety of agricultural products (including

(Hartley and Rao, 1967). The incorporation of k allows σ2REML to increase when adding a fixed effect to the

model.18In fact, this is the case when estimating the contributions presented later in this paper when using REML.19This is the same data used by other studies of household income mobility covering this period in South

Africa (e.g, Klasen, 2000; Leibbrandt and Woolard, 2001; Woolard and Klasen, 2005; May et al., 2007).20The roughly 5 year gaps between observations satisfy the prescription from Naschold and Barrett (2011)

that long periods of examination are needed to accurately measure structural mobility (as opposed toshort-term fluctuations). This feature will be exploited in a robustness test of the REML procedure below.

21The KwaZulu-Natal Income Dynamics Study (KIDS) was a collaborative project between researchersat the University of KwaZulu-Natal, the University of Wisconsin, London School of Hygiene & TropicalMedicine, International Food Policy Research Institute (IFPRI), the Norwegian Institute of Urban andRegional Studies and the South African Department of Social Development. In addition to support fromthese institutions, the following organizations provided financial support: UK Department for InternationalDevelopment; the United States Agency for International Development (USAID); the Mellon Foundation;and National Research Foundation/Norwegian Research Council grant to the University of KwaZulu-Natal.

10

sugar cane, subtropical fruit, vegetables, dairy, and timber).

Though apartheid had been officially repealed in the early 1990s and the elections in

April 1994 brought peace to most of South Africa, the KwaZulu-Natal province continued a

monthly toll of 50-80 lives lost to political violence into 1995 and 1996. Due in part to the

continued violence, local elections were not held in this province until June 1996 (Johnston

and Johnson, 1997). By including in the analysis the first year of the survey (1993), I get

a sense of the household-level income mobility for a region just beginning to emerge from

the institutionalized discrimination which previously determined individual opportunity and

household-level outcomes.

I use total expenditure as constructed in the survey data as my measure of household

income. To analyze potential contributing factors to ρ, I calculate measures for community-

level education and community-level road quality. I use the mean of the years of education as

a community-level measure of education (see table 1). For 1993 and 1998 I have a measure of

the road quality for the community as a proxy for infrastructure (see tables 2 and 3). I use

dummy variables for each state of the world over the two time periods. That is, I have nine

indicator variables to fully represent the available data on road quality and investment in the

survey.

4.1 Descriptive Statistics

For each community, a summary table from the survey identifies its province, as of 1993. This

identifies whether a community is located in KwaZulu, the former bantustan, or in Natal,

the portion of the province in “white” South Africa. Table 4 lists the number of households

and communities for each year in the balanced panel as well as the average and monthly

adult-equivalent expenditure for each division.

Figures 3a and 3b show the kernel density plots for logged adult-equivalent expenditures

broken down first by whether or not the household lives in a bantustan (KwaZulu) or not

11

Table 1: Mean Years of Education Sum-mary Statistics

1993 1998 2004

Kwazulu

Rural 3.4 4.1 4.6

Urban 4.5 5.2 5.4

Natal

Rural 1.7 2.2 2.6

Urban 5.8 6.6 7.4

Values are summarize the mean years ofeducation for each cluster in the panel.

(Natal), then by year. I include rug plots22 to highlight the spread of the data. The dispersion

of incomes in KwaZulu increases over time and absolute poverty is increasing. For Natal there

is also a reduction in the concentration around the mean, but with a skewed distribution

highlighting a dispersion among the poor.

Figure 4 shows community-level monthly averages for adult-equivalent expenditures. Cross

sizes represent the community-level variance of income. The darkness of crosses represent the

number of households observed in each community. Circles represent the averages over all

communities. The bantustan communities (KwaZulu) are have higher variances and are more

tightly bundled than Natal. This suggests the community effect will be more pronounced for

Natal. In fact, the following analysis of ICC will confirm the general story presented in these

figures.

22Rug plots present vertical lines for each observation below the density plots.

12

Table 2: Road Quality Transition Matrices: KwaZulu

KwaZulu - Rural 1998 Total

(N=117) Dirt/Gravel Both Tarred 1993

Dirt 0 .71 .29 .72

Both .11 .11 .78 .23

Tarred .50 .50 0 .05

Total 1998 .05 .56 .38

KwaZulu - Urban 1998 Total


Dirt 0 0 1 .09

Both 0 0 1 .64

Tarred .67 0 .33 .27

Total 1998 .18 0 .82

Values indicate the proportion of neighborhoods with the roadquality indicated by the row in 1993 that have the road qualityindicated by the respective column in 1998. Terminal columnsand rows indicate the proportion of all neighborhoods with theindicated road quality in 1993 and 1998 respectively. The numberof neighborhoods in the sample is indicated by N.

13

Table 3: Road Quality Transition Matrices: Natal

Natal - Rural 1998 Total


Dirt 0 .25 .75 .80

Both 0 0 1 .20

Tarred 0 0 0 0

Total 1998 0 .20 .80

Natal - Urban 1998 Total


Dirt 0 0 0 0

Both 0 0 1 .08

Tarred .18 0 .82 .92

Total 1998 .17 0 .83

Values indicate the proportion of neighborhoods with theroad quality indicated by the row in 1993 that have the roadquality indicated by the respective column in 1998. Terminalcolumns and rows indicate the proportion of allneighborhoods with the indicated road quality in 1993 and1998 respectively. The number of neighborhoods in thesample is indicated by N.

14

Table 4: Monthly Mean (Median) Expenditure (2008USD)

KwaZulu Natal

Rural Urban Rural Urban

1993 56.54 94.64 35.00 189.33

(47.40) (80.35) (25.46) (128.62)

1998 35.24 66.77 25.24 183.21

(28.82) (63.09) (23.20) (130.80)

2004 42.92 67.11 29.71 274.50

(27.38) (54.94) (21.74) (196.17)

Communities 39 11 5 12

Households 460 115 31 143

Expenditures are adult-equivalent expenditures calculatedusing the approach by Carter and May (1999). This tablerepresents the cleaned, balanced, panel, not the raw data.

15

2008 USD per Day per Adult Equivalent

Den

sity

0.0

0.2

0.4

0.6

1 2 10 50

1993 1998

1 2 10 50

2004

(a) KwaZulu

2008 USD per Day per Adult Equivalent

Den

sity

0.0

0.2

0.4

0.6

1 2 10 50

1993 1998

1 2 10 50

2004

(b) Natal

Figure 3: Kernel density plots of monthly expenditure: KwaZulu and Natal. Beneath eachdensity, a rug plot indicates the frequency of the data. Within the rug plot, vertical linesmark out the location of each observation. This shows, for example, the data for Natal ismore sparse than the data for KwaZulu.

16

2008

US

D

30

60

120

240

480

960

1993 1998KwaZulu

2004 1993 1998Natal

2004

Figure 4: Community-level means of monthly expenditure.

Cross size represents the variance of income within a given community. Darker crossesrepresent communities where more households are observed. Circles represent the means over

all communities.

17

5 Main Results

To aid comparison of ρ across KwaZulu (i.e., the bantustan) and Natal, I present the variance

components of ρ. Table 5 shows the estimates of ρ stratifying the sample by location in either

KwaZulu or Natal. Overall, ρ is much higher in Natal than in KwaZulu. For example, in

2004 ρ is 23% in KwaZulu and 69% in Natal. The household components, σ2v , are similar in

both regions while ρ is consistently lower in KwaZulu. The source of this difference is the

community-level component, σ2a, as suggested by figure 4.

6 Contributing Factors

Looking now to contributing factors for ρ, table 6 presents the contribution estimates. In

KwaZulu, education and road quality are persistent factors while urban status is only a

dominant factor for 1993 and 1998. These values are lower than in Natal, suggesting even

less scope for community-level inequality-reduction policy for the former bantustan compared

to former ‘white’ South Africa.

Road quality consistently dominates the contributing factors for Natal. This suggests

infrastructure explains much of the differences across communities in Natal. Since much of the

inequality in Natal is across communities, community-level investment in infrastructure has

the potential to significantly reduce overall inequality for blacks in former ‘white’ South Africa.

As in Page and Solon (2003), urban status is an important contributor to the community

effect.

18

Table 5: Household correlations in adult equivalentexpenditure: KwaZulu and Natal

1993 1998 2004

KwaZulu

Correlation 0.217 0.261 0.229

(0.050) (0.053) (0.052)

Community component 0.055 0.111 0.131

(0.015) (0.029) (0.037)

Household component 0.197 0.316 0.440

(0.012) (0.020) (0.027)

Households 575 575 575

Communities 50 50 50

Natal

Correlation 0.760 0.693 0.693

(0.073) (0.088) (0.087)

Community component 0.767 0.731 0.794

(0.291) (0.287) (0.310)

Household component 0.242 0.324 0.352

(0.028) (0.037) (0.040)

Households 174 174 174

Communities 17 17 17

Standard errors are in parenthesis.

19

Table 6: Upper-bound estimates of the percentcontribution to the correlation: KwaZulu and Natal

1993 1998 2004

Kwazulu

Education 6.6 8.8 6.3

Roads 9.5 9.0 8.2

Urban 10.1 11.3 3.9

Education and Urban 12.6 15.3 8.1

Roads and Urban 13.6 13.8 8.7

Roads and Education 11.8 14.6 11.2

All factors 14.9 17.8 11.5

Natal

Education 21.7 42.8 35.5

Roads 54.2 50.3 51.1

Urban 46.9 44.2 41.4

Education and Urban 50.9 53.3 53.4

Roads and Urban 55.1 50.8 51.1

Roads and Education 57.3 56.6 57.1

All factors 59.7 57.0 57.3

Values are percentage contribution to ρ.

20

7 Robustness Checks

7.1 Alternate Adult-Equivalence Scales

Not everyone in a household has the same needs and not all households are the same size.

The choice of calculating household-level expenditure method may affect the validity of

cross-household comparisons (cf., Deaton (1997)). I compare my results across a variety of

equivalence scales23 common in studies of developing countries. Klasen (2000) and Woolard

and Leibbrandt (1999) provide an extensive search for meaningful equivalence scales for South

African households. Here I employ the following equivalence scales, φ,

φ = (A+ αK)θ

with A adults and K children, α is the proportion of an adult equivalent to a child, and θ

permits economies of scale. Each φ represents the number of adult equivalents in a given

household. I divide household expenditure by φ to calculate adult-equivalent expenditure. I

use the list of scales in table 7 and use two definitions for children (under 18 and under 16)

to test the robustness of the results presented earlier.

Figures 5a and 5b plot ρ using each of these scales. The diamonds represent ρ using CM

with children defined as under 16 (i.e., the scale used throughout this study). The horizontal

bars indicate 95% confidence intervals calculated using the delta method via Stata’s nlcom

command. It is clear from these figures that choice of φ has little impact on ρ.

23Adult equivalence scales adjust the number of “adults” in a household to adjust for the lower consumptionof children and the existence of economies of scale. I recognize there are general issues with using adultequivalence scales in the measurement of welfare (cf. Gronau, 1988). Instead of addressing these issues, I usethis section to demonstrate the invariance of ρ to alternate scales.

21

Table 7: Adult equivalence scales

Definition Source

1 + 0.7(A− 1) + 0.5K OECD (2013)

1 + 0.5(A− 1) + 0.3K OECD (2013)

α = 1 and θ = 0.25 OECD (2013)

α = 1 and θ = 1 (i.e., per capita) OECD (2013)

α = 0.997 and θ = 0.68 Woolard and Leibbrandt (1999)

α = 0.68 and θ = 0.72 Woolard and Leibbrandt (1999)

α = 0.5 and θ = 0.9 Carter and May (1999)

1 for the entire household

ρ

0.2

0.4

0.6

0.8

1.0

1993 1998 2004

(a) KwaZulu

ρ

0.2

0.4

0.6

0.8

1.0

1993 1998 2004

(b) Natal

Figure 5: Robustness test of adult-equivalence scale specification. Each line connects valuesfor ρ for a given equivalence scale. The diamonds represent the values of ρ for the equivalencescale used in the analysis presented in this paper. The tick marks about the diamonds markoff two standard errors above and below the estimates for ρ.

22

7.2 The model with transitory shocks

Single-period expenditure may not be representative of expenditure over all waves. To test

the robustness of the results of the REML approach (Mazumder, 2008; Lindahl, 2011), I run

the same analysis using the algorithm in Bjorklund et al. (2009). As their method exploits

the use of multiple observations, I first specify a statistical model with transitory shocks. I

model the natural logarithm of per adult-equivalent monthly expenditure in wave t, ycht for

communities c and household h as

ycht = x′chtβ + εcht. (7)

x′chtβ includes an intercept, the number of children, the number of pensioners, a wave dummy,

and their interactions to control for key household life-cycle effects24. I decompose εcht as

follows:

εcht = ac + bch + vcht, (8)

where ac is a permanent component common to all households in communities c, bch is a

permanent component unique to household h, and vcht measures wave specific deviations from

long-run income. I view bch as the household’s demeaned position in the long-run income

distribution.

By construction,

σ2ε = σ2

a + σ2b + σ2

v . (9)

Thus, the share of variance in income due to community background, and also the income

correlation of randomly drawn pairs of households in a given community (in the sibling

24This follows the covariate setup in Bjorklund et al. (2009) with relevant changes for the household setting.

23

correlation literature these would be pairs of brothers), this time considering only the

permanent components, is

ρ =σ2a

σ2a + σ2

b

. (10)

Again, ρ measures the importance of community effects on the outcomes of households,

this time controlling for transitory shocks. Household-level factors, such as level of education,

access to land, and within-community marginalization, are captured by the household-level

component, bch.

Due to the approximately 5-year gap between waves, I assume persistence in transitory

shocks to be negligible25 and specifically that vcht is a random shock with mean equal to zero,

and constant variance, σ2v .

7.2.1 Estimation Procedure

I perform OLS to calculate εcht. The decomposition of the error term εcht in equation 8

implies the following structural covariances:

E[εijtεkls] =

σ2a + σ2

b + σ2v , i = k; j = l; t = s

σ2a + σ2

b , i = k; j = l; t 6= s

σ2a, i = k; j 6= l

0. i 6= k

(11)

As in Bjorklund et al. (2009), I use the four weighting options from Solon et al. (2000) to

control for the unbalanced nature of the pairs of households drawn from the survey clusters26.

25Bjorklund et al. (2009), as an example, model v as an AR(1) process to reflect the relative importance ofpersistence in their annual context. When I include an AR(1) specification, the parameter λ is not statisticallysignificant and the general analysis remains unchanged.

26Elsewhere, survey clusters are referred to as communities.

24

That is, I need to control for unbalanced clusters. These weighting options are:

w1c =

(nc(nc − 1)

2

)−1w2c =

(nc − 1

2

)−1w3c =

(√nc(nc − 1)

2

)−1w4c = 1

where nc is the number of households in cluster c. Approach (1) weights each cluster equally

by weighting each household pair inversely to the total number of pairs contributed by its

cluster. Approach (4) weights each household pair equally, while approaches (2) and (3) are

somewhere between the extremes of (1) and (4).

Taking the chosen weights, I then compute the empirical household-pair autocovariance

matrix. Once complete, I apply GMM to the implied moment restrictions in order to estimate

σa, σb, and σv. I then construct ρ as defined above27.

For the sake of simplicity, I bootstrap the standard errors using 50 replications. Checks

with various random seeds indicate no substantive changes in the standard error estimates

implying, for the current situation, 50 replications is sufficient.

Again, when estimating using the method in Mazumder (2008) I use the xtmixed command

in STATA, though with multiple time periods it is necessary to employ nlcom to calculate

the standard errors of the correlations using the delta method.

27I implement this procedure in Python and R.

25

7.2.2 Results

The results of this robustness test are presented in table 8. The two moderate weighting

schemes (w2c and w3c) produce results strikingly similar to those from REML. The sensitivity

of ρ to alternate weightings, under the OLS-GMM method, indicates this procedure may

be less suitable for situations with very unbalanced clusters when compared with REML.

The estimated correlations are larger than before, but this is due to removing the transitory

component from the denominator28.

8 Conclusion

In order to compare the community effects on incomes for black Africans in a bantustan

with those for black Africans in ‘white’ South Africa, I presented an in-depth analysis of the

intracluster correlation (ICC) coefficient. I used ICC to analyze household income variation

due to community-level factors. Many papers have controlled for ICC (Owens et al. 2003,

de Brauw and Harigaya 2007, and de Brauw and Hoddinott 2011), while others used it

to measure the spatial clustering of their outcome of interest (e.g., Morris 2001). I used

ICC to show the lingering effect of the bantustan system on the relationship of community

inequality to overall inequality. Understanding ICC can help determine whether communities

or households should be the target of inequality-reduction interventions. In particular, where

ICC is low, reducing the inequality across communities will do little to address overall

inequality.

I found ICC is relatively low for the bantustan and relatively high for ‘white’ South Africa.

That is, I observed that outcomes for households in KwaZulu, the bantustan, are explained

28I remove the transitory component for consistency with Mazumder (2008) and Bjorklund et al. (2009).When I compute the correlation including the transitory component in the denominator, my results areconsistent with my estimates in table 5.

26

Table 8: Robustness comparison with Bjorklund et al. (2009): KwaZulu and Natal

OLS-GMMREML w1c w2c w3c w4c

KwaZuluCorrelation 0.384 0.397 0.405 0.406 0.420

[0.25, 0.53] (0.065) (0.055) (0.057) (0.067)Community Component 0.054 0.071 0.073 0.073 0.075

[0.02, 0.08] (0.015) (0.014) (0.015) (0.018)Household Component 0.087 0.108 0.107 0.107 0.104

[0.06, 0.11] (0.017) (0.017) (0.017) (0.016)Transitory Component 0.256 0.284 0.284 0.284 0.284

[0.23, 0.28] (0.020) (0.018) (0.015) (0.017)Households 578

Communities 50Natal

Correlation 0.893 1 0.914 0.914 0.823[0.84, 1] (0.078) (0.035) (0.034) (0.109)

Community Component 0.806 0.876 0.774 0.774 0.697[0.11, 1.28] (0.245) (0.143) (0.179) (0.209)

Household Component 0.097 0 0.073 0.073 0.15[0.06, 0.13] (0.058) (0.026) (0.028) (0.081)

Transitory Component 0.218 0.294 0.294 0.294 0.294[0.23, 0.28] (0.053) (0.042) (0.048) (0.052)

Households 175Communities 17

Standard errors are in parenthesis. For REML, basic parametric confidence intervals are presentedbased on 10,000 bootstrap samples. For the method discussed in Bjorklund et al. (2009), standarderrors are bootstrapped with 50 replications.

27

more by household-level effects than community-level effects, and that the opposite is true

for Natal, i.e., ‘white’ South Africa. This indicates community-level policy interventions will

be more effective in lowering overall inequality when applied to ‘white’ South Africa than in

the bantustan.

The community effect and the importance of various contributing factors to the community

effect differ across regions. I found that investments in infrastructure (proxied by road quality),

urban status and to a lesser extent education explain much of the cross-community inequality

in Natal (combined they explain 57% of the cross-community inequality). On the other hand,

these factors explain little (less than 18%) of the cross-community inequality in Kwazulu.

I have demonstrated that the measurement of ICC is insensitive to the choice of adult-

equivalence scale and that, for household surveys, REML outperforms the approach in

Bjorklund et al. (2009) as well as other ANOVA methods similarly dependent on appropriate

weighting schemes.

In this study of community effects on income, I have presented an in-depth analysis of

the intracluster correlation coefficient. This is, to my knowledge, a novel approach in the

development literature. In order to derive expectations on the impact of various public

projects intended to serve poorer communities29, it will be useful to understand how the

fate of households within a community are tied together. By taking innovations from

various literatures, such as those used in this study, we can take important steps towards

understanding these complex community bonds.

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33

Globalization and Wage Convergence: Mexico and the United States*

Davide Gandolfi

Macalester College

Timothy Halliday+ University of Hawaii at Manoa

Raymond Robertson Macalester College

Version 32.0

March 8, 2014 JEL Codes: F15, F16, J31, F22 Keywords: Migration, Labor-market Integration, Factor Price Equalization Abstract: Neoclassical trade theory suggests that factory price convergence should follow increased commercial integration. Rising commercial integration and foreign direct investment followed the 1994 North American Free Trade Agreement between the United States and Mexico. This paper evaluates the degree of wage convergence between Mexico and the United States between 1988 and 2011. We apply a synthetic panel approach to employment survey data and a more descriptive approach to Census data from Mexico and the US. First, we find no evidence of long-run wage convergence among cohorts characterized by low migration propensities although this was, in part, due to large macroeconomic shocks. On the other hand, we do find some evidence of convergence for workers with high migration propensities. Finally, we find evidence of convergence in the border of Mexico vis-à-vis its interior in the 1990s but this was reversed in the 2000s.

* We thank participants at the University of Hawaii Applied Micro Workshop for useful feedback. + Corresponding author. Address: 2424 Maile Way; 533 Saunders Hall; Honolulu, HI 96822. Phone: (808) 956-8615. E-mail: [email protected].

1

The North American Free Trade Agreement (NAFTA) significantly increased

commercial integration between the United States, Canada, and Mexico. Between 1994 and

2011, trade in goods between the two countries quadrupled in value, increasing from $108.39

billion to $461.24 billion (USCensus Bureau). The value of US goods exported to Mexico

increased from $50.84 to $198.39 billion, while the value of Mexican goods exported to the

United States increased from $49.49 billion to $262.86 billion. In 2011, total exports to Mexico

accounted for 13.4 percent of overall US exports and total imports from Mexico accounted for

11.9 percent of overall US imports (Office of the United States Trade Representative). In 2012,

the total value of trade between Mexico and the US closely approached half a trillion dollars. By

2013, total trade between all three NAFTA countries reached 1 trillion dollars.

GDP per capita has also increased in both countries. In constant 2005 US dollars, US

GDP per capita increased from $32,015 to $43,063 between 1992 and 2012. While Mexico has

had some macroeconomic setbacks, such as the December 1994 peso crisis, recovery has

generally been rapid. In constant 2005 US dollars, Mexican GDP per capita increased from

$6,628 to $8,215 over the same time period.1

Rather than converge, however, Mexican GDP per capita and US GDP per capita grew

apart. The ratio of Mexican to US GDP per capita fell from 20.7% of US GDP per capita in

1992 to 19.2% in 2011.

The persistent and seemingly growing gap between GDP per capita is at odds with

neoclassical trade theory, migration theory, and early applied general equilibrium predictions of

the effects of NAFTA. The neoclassical Heckscher-Ohlin-Samuelson (HOS) framework, one of

the canonical trade models, predicts that trade liberalization would lead to convergence in the 1 These data were taken from World Bank Development Indicators. See http://data.worldbank.org/data-catalog/world-development-indicators.

2

prices of traded goods, which in turn would induce factor price convergence. In addition to the

significant increase in trade noted above, Robertson, Kumar, and Dutkowsky (2009) find strong

support for convergence in goods-level prices between Mexico and the United States, making the

lack of convergence in income inconsistent with the prediction of trade models.2

The lack of convergence is also at odds with labor-based migration models. At the most

basic level, an increase in labor supply from migration should reduce wages if the aggregate

labor demand curve is downward sloping. Borjas (2003) provides empirical evidence for the

downward-sloping labor demand curve. Mishra (2007) provides evidence that Mexican

emigration bids up Mexican wages.3 Most migration models, therefore, predict wage

convergence. Because most Mexican migrants come from the middle to lower end of the age,

education, and wage distribution (Chiquiar and Hanson 2005), convergence should be the most

prominent for these demographic groups. Such movements would tend to raise Mexican wages

and depress US wages, thereby reinforcing the effects of free trade on wage convergence.

Early applied general equilibrium models generated predictions of NAFTA’s effects that

implied significant income convergence. Brown (1992) in particular surveys several of the pre-

NAFTA applied general equilibrium models and demonstrates that the models that included both

Mexican and US income gains all predicted that Mexican gains would be at least double (if not

an order of magnitude greater than) the US gains.

2 The lack of evidence of factor price equalization generally has prompted many to question the validity of neoclassical HOS-type models. Schott (2003) finds that we live in a “multi-cone” world that precludes factor price equalization. Davis and Mishra (2007) suggest that ignoring important variation between the mix of factors employed in the production of domestic and imported goods obfuscates the possible effect that free trade may depress the wages of workers in relatively labor-intensive domestic industries. Goldberg and Pavcnik (2007) discusses evidence of rising inequality in poorer countries in the wake of many trade liberalizations in the eighties and nineties which is very much at odds with a standard HOS story of how globalization should unfold. The authors provide numerous reasons why the predictions of the standard HOS theory may not hold in the data such as technology, the pattern of tariff reductions, and within-industry shifts. 3 For example, Card (1990, 2001) argues that the evidence for migration’s effect on wages is weak.

3

Although the above studies suggest that there should be some degree of wage

convergence between Mexico and the United States, there has yet to be a study that investigates

this directly. The closest papers to ours focus on within-country convergence or short-run

convergence. Within-country changes may help explain changes in international comparisons,

and early studies of the Mexican labor market did detect evidence of regional wage convergence

within countries (Hanson 1996, 1997 and Chiquiar 2001). Robertson (2000) finds a strong,

positive correlation between wage growth in the United States and wage growth for Mexican

workers who reside on the border with the United States. Hanson (2003) also finds a similar

result. Robertson (2005), however, finds no evidence that NAFTA increased the estimated

degree of labor market integration between the United States and Mexico.

In this paper, we measure long-run international convergence using two complementary

methodologies and four data sources. The first regression-based approach employs synthetic

cohorts and matches quarterly data from the Current Population Survey in the United States and

the Encuesta Nacional de Ocupacion y Empleo (ENOE) in Mexico. The second approach is

more descriptive and employs census data from Mexico and the United States.

Following Robertson (2000), Borjas (2003), and Mishra (2007), we first divide Mexican

and US working-age people into forty-five age-education cohorts. Comparing exclusively

Mexican and US workers in the same education-age cohort effectively controls for variation in

returns to skill and allows us to use high-frequency CPS quarterly data to identify time-series

patterns. The disadvantage is that it focuses only on workers residing in urban areas in Mexico.

The second approach overcomes this disadvantage by using data that include rural

workers, but it has the disadvantage that the data are observed only once every ten years.

However, these data have the added advantage that, in a given year, the sample sizes are larger

4

than the survey data which enables us to have a more detailed look at the data. First, we

compare mean wage differentials by education and age cohort and look at how these have

evolved over time. Next, we look deeper into the data and investigate how the relative wage

distributions have evolved over time by comparing changes in a given percentile for a given age

and education level. Finally, we conduct an exercise in which we treat the United States and

Mexico as one “integrated economy” and decompose wage inequality in this integrated economy

into between and within components and investigate how these has changed over time.

At first glance, the results demonstrate that there has been very little, if any, convergence

between US and Mexican wages over time for everyone but the least educated. While there is

evidence of some convergence in the high-migration cohorts (i.e. younger people with less than

twelve years of education), this seems to be primarily due to falling US wages at the bottom of

the US income distribution, as opposed to rising Mexican wages. However, the overall

divergence from 1990-2000 has much to do with the effect of the peso crisis of 1994. We do see

some convergence in the high frequency data post-1994 but this abates in 2001. A more detailed

look at the census data reveals that there was convergence in the border region of Mexico

relative to the interior in the 1990’s but subsequently, there was divergence in the 2000’s. Since

a lot of foreign direct investment in Mexico targets the border, this is suggestive evidence that

NAFTA may have indeed led to some wage convergence which was then reversed during the

2000’s.

Finally, we provide evidence of rising wage inequality in the United States and falling

inequality in Mexico and we show that this is driven by changes to the variation in wages within

educational/age cohorts not across them, which is not consistent with a standard HOS

explanation of how trade liberalization should impact inequality. Similarly, we also show that in

5

the US-Mexico integrated economy the variance of log wages has declined and that this is due to

reductions in variation in wages across education/age cohorts not within them which, once again,

is not consistent a standard explanation of trade liberalization and inequality since it implies that

trade liberalization should reduce the demand for a given factor in one country and raise the

demand for the same factor in the other. While these results are not consistent with the HOS

model of trade with two countries, richer models may be able to account for what convergence

we do see.

We begin presenting these results with a simple theoretic model that motivates our focus

on the equilibrium wage differential between Mexico and the United States in Section I. After

describing the data in Section II, we present empirical results in Section III and IV. We then

evaluate mechanisms that may be behind these findings and offer conclusions in Section V.

I. Theoretical Foundation

Our empirical work focuses on the long-run wage differential between Mexico and the

United States. We posit that the differential is a function of labor-market integration following

Robertson (2000). Consider an economy composed of two regions (“Mexico” and “United

States”). We assume that Mexican and US workers are price substitutes, such that an increase in

the wages of American workers increases the demand for Mexican labor. We also assume that

capital flows between the two regions are not instantaneous, such that the lagged US wage

affects the demand for Mexican labor. A general form that captures the previous assumptions is:

(1) L δ δ w δ w γw

6

where L is labor demand, w is the natural log of the US wage, and w is the natural log of

the Mexican wage. The subscript j represents an education-experience group and subscript t

represents the time period. The parameter γ captures the responsiveness of demand to lagged

wages, and δ is a group-specific effect on labor demand.

If US wages rise, Mexican workers choose to emigrate to the United States. We assume

that workers may migrate instantaneously from one region to another, because labor is more

mobile than factors that shift demand, such as capital. Therefore, the supply of Mexican labor is

responsive to wage levels in both regions. A general form that captures these assumptions is:

(2) L σ σ w σ w φw

The variable L represents labor supply. The subscript j represents an education-experience group

and subscript t represents the time period. The parameter φ captures the responsiveness of

supply to lagged wages, and σ is a group-specific effect on labor supply.

The coefficients δ and σ represent the frictions in our model. The wage differential

will be increasing as these two parameters move away from each other. We will show that when

they are the same, there is no differential. One can interpret these as the cost of migration to

demanders and suppliers of labor, respectively.4

In the presence of exogenous costs, an equilibrium differential separates regional wages.

Wage shocks may temporarily move US or Mexican wages away from equilibrium, but they will

eventually return to it. We represent the equilibrium as:

4 As an example of these migration costs, Roberts et al. (2010) estimate smuggling costs.

7

(3) δ δ w δ w γw σ σ w σ w φw

By solving (3) for the current Mexican wage, we obtain an expression in terms of the lagged

Mexican wage, the current US wage and the lagged US wage:

(4) w w w w

For the sake of simplicity, we may rewrite (4) as:

(5) w α w α w α w

As specified in Robertson (2000), Hendry and Ericsson (1991) show that long-run homogeneity

between w and w implies that the sum of α , α and α equals 1. Thus, we may take a

differenced form of (5) to obtain:

(6) ∆w α α ∆w 1 α w w

Because 1 α is positive, increases in the US wage relative to the Mexican wage will result

in higher Mexican wages tomorrow.

The long-run equilibrium implies that wages in both regions are such that labor markets

clear; as long as labor markets remain in equilibrium, wage levels do not change over time. As a

result, ∆w 0 , ∆w 0 and w w w w . We impose this

restriction and solve for w w :

8

(7) w w

This difference is analogous to the migration cost in most theoretic migration models. Although

ubiquitous, few papers analyze the long-run behavior of the equilibrium migration cost.

Deepening economic integration, changes in policy, and a host of other factors may affect the

long-run differential. For example, an increase in Mexican labor supply increases the wage gap,

while an increase in Mexican labor demand reduces the gap. Increased responsiveness to wages

(such as through a reduction in long-run migration costs that reduce the 2 and 2 parameters in

the denominator) cause the gap to fall (as long as current wages are weighted more than past

wages). Finally, if is zero, then US and Mexican wages are the same in equilibrium.

II. Data

We use four datasets that represent two separate types of data. The first type is quarterly

household survey data in which urban residents have been consistently surveyed over the period

1988-2011. As a result, urban residents are typically over-represented in Mexican household

earning data. To avoid composition bias, we restrict our analysis to Mexican urban households.

US household survey data are a representative sample of both urban and rural US households.

Second, we use census data that have two advantages over the survey data. The first is that the

Mexican census data contain much more accurate information about rural households. The

second is that the sample sizes are much larger so we can obtain a more detailed understanding

of what is happening to the relative wage distributions. That said, they have the disadvantage of

only being available in ten years intervals.

9

Household Survey Data

We extract all data on Mexican households from the Encuesta Nacional de Empleo

(ENE) over the period 1988-2004 and from the Encuesta Nacional de Ocupacion y Empleo

(ENOE) over the period 2005-2011. Data on US households are from the Merged Outgoing

Rotation Groups (MORG) data of the CPS over the entire period 1988-2011. We exclude from

the sample working-age adults who have zero or unreported earnings. The sample is further

restricted to adult males between 19 and 63 years of age. Focusing on male workers allows us to

ignore the issue of self-selection on the participation of women in the labor force, as well as the

effect of changes to self-selection patterns over time and between the United States and Mexico.

The Mexican data are reported as monthly earnings until 2005. The US data report

weekly earnings. To explore the robustness from using potentially poor measures of hours

worked, we consider both monthly and hourly earnings. We multiplied reported US weekly

wages by 4.33 to transform them into monthly wages. US hourly wages have been computed by

dividing weekly earnings by the number of hours usually worked each week. Mexican hourly

wages have been computed by dividing monthly earnings by the number of hours worked each

week times 4.33 until 2005, when the hourly wages of Mexican workers are directly available

from ENOE data.

Following Chiquiar and Hanson (2005), all earnings measures are converted into 1990

US dollar units. Mexican earnings are converted into dollars by using simple quarterly averages

of the daily official exchange rates published by the Mexican Central Bank (Banco de Mexico

2013). We then deflated the wages to 1990 dollars using the quarterly average of the US

Consumer Price Index (CPI) (Bureau of Labor Statistics). Also as in Chiquiar and Hanson

10

(2005), we only use Mexican wages that are between $0.05 and $20.00 and US wages that are

between $1.00 and $100.00.

ENE/ENOE surveys have been extended to significantly more rural areas over the last

two decades. In order to reduce the bias generated by greater participation of the rural Mexican

population, we restrict the sample to workers from major metropolitan areas and state capitals

that have consistently been part of the surveys. Such areas include Mexico City, the State of

Mexico, San Luis Potosí, Leon, Guadalajara, Chihuahua, Monterrey, Tampico, Torreon,

Durango, Puebla, Tlaxcala, Veracruz, Merida, Orizaba, Guanajuato, Tijuana, Ciudad Juarez,

Matamoros, and Nuevo Laredo. No geographical restrictions have been imposed on MORG data.

Descriptive statistics for the raw survey data are displayed in Table 1. Each column gives

an average of quarterly observations collected over a four- or five-year period. The average US

monthly wage ranges from $1466 to $1515, and it has remained roughly constant from 1988 to

2011. The average Mexican monthly wage ranges from $226 to $310. It has declined fairly

steadily over time. The average age of the US workforce has increased steadily between 1988

and 2011, from 37 to 40 years. The average age of the Mexican workforce has also risen steadily,

from 35 years in 1988-1994 to 37 in 2008-2011. The US workforce is significantly more

educated than the Mexican workforce, with about 90% of all workers in each time period having

at least completed high school education. By contrast, the number of Mexican workers who

completed high school education or attended college ranges from 30% in 1988-1994 to 32.3% in

2008-2011. Mexico has improved the education of its workforce. The steady rise in the number

of high school graduates and college attendees has been accompanied by a steady decline in the

number of workers with 0-5 years of education, which dropped from 18% in 1988-1994 to 12%

11

in 2008-2011. The largest gains emerge in the 9-11 category, when Mexico raised the

compulsory education requirement from 6 to 9 years in 1992.5

Ideally, survey data would collect information from surveyed individuals at regular

intervals, and neatly organize it as panel data. In the absence of such data, it is possible to use a

time series of cross-sectional surveys to create a version of synthetic panels (Deaton, 1985). In

our paper, we create 45 age-education cohorts when using the survey data. In the absence of

significant changes to the composition of the cohorts, the average behavior of each cohort over

time should approximate the estimates obtained from genuine panel data (Deaton, 1997). Since

our focus is not on wage growth of individuals over time, we do not “age” the cohort cells.

Working-age adults in each sample are subdivided into five education categories and nine

age categories. The first age group includes workers aged 19-23 years old; the second includes

workers aged 24-28, the third those aged 29-33, and so forth. The first education group includes

adults with 0-5 years of education; the second includes adults with 6-8 years of education; the

next comprise those with 9-11, 12-15 and finally 16 or more years of education. These categories

are roughly comparable to those employed by Robertson (2000), Borjas (2003) and Mishra

(2007). Unlike Borjas (2003), we are able to identify greater variation in the group of working

adults who have not completed high school. We are unable to distinguish between high school

graduates and workers with some college experience; we classify both groups as having 12-15

years of schooling. We exclude from the sample workers with zero or unreported amounts of

education. Once workers are assigned to the 45 categories, we take the average wage of each cell

with and without the sample (population) weights. Sample (population) weights are not

available for Mexican household surveys during the 1994-2003 period.

5 See http://wenr.wes.org/2013/05/wenr-may-2013-an-overview-of-education-in-mexico.

12

Different demographic groups have different propensities to migrate, and since migration

may drive equalization, Figure 1 shows the percentage of Mexican-born workers in the US by

age and education for each of the 45 cohorts. Most Mexican-born workers in the US are younger.

In addition, Mexican-born workers in the United States comprise a progressively declining share

of the workforce among older groups. We also see that the bulk of Mexicans residing in the

United States tend to be less educated.

Figure 2a plots the log of the real average monthly earnings of Mexican workers over

time by education-age cohorts6. Several significant macroeconomic events are immediately

apparent. The December 1994 peso crisis led to the rapid devaluation of the peso against the US

dollar, as nominal exchange rates doubled from 4 pesos/US dollar to 8 pesos/US dollar in the

space of a few months. The drastic change in exchange rates and the subsequent erosion of

purchasing power represented a significant shock to Mexican wages. The peso/US dollar

exchange rate has been floating ever since. At least some of the increase in Mexican real wages

between 1994 and 2001 may be attributed to a rebound in purchasing power experienced by

Mexican workers as the effects of the crisis waned over time. The increase in wages reverses

around 2001, which coincides with both the US recession (March 2001) and China entering the

WTO (December 11, 2001). Recovery resumes around 2005 and continues until the Financial

Crisis and Great Trade Collapse in October 2008.

Figure 2b plots the log of the real average monthly earnings of US workers over time by

age-education cohorts. Compared to Mexican wages, US wages are relatively stable. Real

wages have experienced no significant expansion or contraction over the sample period, but may

appear to decline slightly after 2001.

6 The wages of 59-63 year-old male workers with 12-15 years of education are not shown. Since this particular demographic cohort of Mexican workers is very small, it displays a wildly erratic wage pattern that obfuscates the general picture; therefore, we chose to omit it.

13

Figure 3 plots the difference between real US wages and real Mexican wages over time.

Once again, the differential experienced by workers aged 59-63 with 12-15 years of education

has been omitted for the sake of overall clarity. Figure 3 shows less dispersion across cohorts

than the individual country graphs. The differentials of different cohorts largely move together

and changes in the differential coincide with significant macroeconomic events. To see these

events more clearly, Figure 4a graphs the mean wage differential7 and identifies some of the

significant events affecting Mexico since NAFTA. The peso crisis is immediately apparent, as is

the relatively rapid recovery. The reduction in the differential accelerates until 2001, when

China enters the WTO. Dussel, Peters and Gallagher (2013) argue that China had a significantly

negative influence on NAFTA trade. The differential grows until the middle of the 2000s and

then falls until the financial crisis.

To formally identify structural breaks in the average differential, we apply tests for

unknown breaks described by Vogelsang and Perron (1998). Figure 4a plots the relevant

additive outlier test statistic. The local extrema of the test statistic indicates a trend break. The

peso crisis is the most significant break, but a smaller local maximum appears around 2000.

Therefore, in the empirical work that follows, we include structural breaks in both 1994 and

2001.

Figure 4b graphs the standard deviation of the wage differentials across cohorts. The

standard deviation of wage differential across cohorts is falling until approximately the time of

the break identified by the Vogelsang and Perron test statistic. The standard deviation rises

steadily until the end of the sample, again supporting the use of multiple structural breaks.

Figure 4b also motivates a more detailed look at changes in other measures of the wage

distribution, which we carry out using census data. 7 The mean is calculated taking the unweighted arithmetic average across cohorts.

14

While the differentials of different cohorts generally move together, there are some

differences across cohorts. Figures 5a, 5b, and 5c present the trends for three different cohorts.

Figure 5a shows that the differential for Cohort 4 (workers with 0-6 years of education and 34-38

years old) exhibits significant peso crisis effects. Around 2001, however, the recovery seems to

stop and the differential grows through the 2000s. The pattern for Cohort 38 (workers with 12-

16 years of education and 54-58 years old), shown in Figure 5b, reveals a smaller peso crisis

effect, but a rising wage gap during the 2000s. On the other hand, Figure 5c shows that the wage

gap for the “high migration” cohort (19 to 23-year-old workers with 6-9 years of education)

either remains flat or falls slightly throughout the 2000s. These differences across cohorts are

consistent with the idea that migration helps to integrate markets by closing the wage differential

across countries.

Census Data

We employ three years of census data from Mexico and the US: 1990, 2000 and 2010.

We use a 10 percent sample from the Mexican census. For the years 1990 and 2000, we use a 5

percent sample from the US census. For 2010, we employ the American Community Survey,

which is a 1 percent sample of the population.

The sample selection criteria that we use for the census data mimic that of the survey

data. Specifically, we include men between ages 19 and 63 who report positive income in the

previous year. In Mexico, hourly wages are constructed by taking monthly earnings and then

dividing by reported hours worked during a typical week times 4.33. In the United States, hourly

wages were computed by taking reported yearly earnings and then dividing by reported usual

15

hours worked per year.8 As with the survey data, all wages are in 1990 US dollars. Mexican

wages were, once again, converted to 1990 dollars by, first, converting wages in pesos to US

Dollars using the exchange rate for that year and then deflating the wages to 1990 dollars using

the US CPI.9

We employ two samples from the Mexican census. The first is a sample of all workers

meeting the criteria defined above, which we call “Sample 1.” The second is a sample of

primarily urban dwellers that includes the metropolitan areas employed in the survey data. We

call this “Sample 2.”

Table 2 displays descriptive statistics from the census data. We see that the average US

wage was between $14.21 and $15.07 for the three census years. In Mexico for Sample 1,

average wages were between $1.43 and $1.59 and increased steadily over the 20 year period.

The mean wages were slightly higher in Sample 2 when we only employed urban dwellers. The

average age in the US sample ranged between 36.83 and 39.66 and increased over time. The

average age in Mexico also increased over the 20 year period but ranged from 34.79 and 37.10 in

Sample 1 and 34.59 and 37.46 in Sample 2. Finally, as in the survey data, the statistics on years

of schooling in Mexico indicate massive gains in human capital over this period. In Sample 1,

the percentage of Mexicans with 0-4 years of schooling in 1990 was 29.56 percent but was only

11.89 percent in 2010. Similarly, the percentage of Mexicans with 9-12 years of schooling was

8 Hours worked per year were obtained by taking usual hours worked per week times the number of weeks that the respondent reported to have worked during the year. 9 We also converted Mexican wages to 1990 US dollars by first deflating the wages to 1990 pesos using the Mexican CPI and then converting them to US dollars using the 1990 exchange rate. Overall, this alternative method did not make too much of a difference.

16

27.41 percent in 1990 but was 45.53 percent in 2010.10 The numbers are similar in the other

sample.

Figure 6 shows the percentages of Mexicans residing in the United States by 45 age and

education categories. Note that for reasons discussed above the education groups in the Census

data differ slightly from the survey data. The patterns in this figure are broadly consistent with

Figure 1. One key difference, however, is that we see substantially more people in the second

education category that we label as “ed1.” The reason for this is that many Mexicans leave

school between grades 5 and 6. The category “ed1” includes grade 5 in Figure 5 but excludes it

in Figure 1.

III. Results: Household Survey Data

Our main variable of interest is the long-run US-Mexican wage differential as derived in

Section I across age-education cohorts. The trend in the long-run differentials may be affected

by exogenous shocks and differences in migration costs across cohorts. To describe the changes

in the long-run differential, we use a simple trend analysis that accounts for both the peso crisis

and the 2001 trend break. Since we expect changes in wage differentials to differ between the

migrants and non-migrant groups, we also include a dummy variable for the high migration

cohort (HMC). The following regression captures all these observations:

(8) ∗ 94

∗ 94 01 ∗ 01

10 Note that the education categories in the census data are slightly different than what we use in the survey data due to the way that years of schooling were categorized in the US census years 1990 and 2000.

17

where w is equal to the difference between the natural log of the US wage and natural log of the

Mexican wage in education-age group j. Negative values indicate wage convergence. The

variable time is a time trend; is a dummy variable that indicates whether j is the high

migration cohort (workers of age 19-23 with 6 to 9 years of schooling); is a dummy variable

indicating whether the year is 1994 or later; is a dummy variable indicating whether the year

is 2001 or later and are group-specific fixed effects for an education-age group j.

The trend analysis based on equation (8) and variations of equation (8) are reported in

Tables 3. The following results do not use weights, but in separately available results, we find

that the same qualitative results emerge when we use US sample weights, Mexican sample

weights, US cell sizes, and Mexican cell sizes as weights. All equations include fixed cohort

effects and all estimated coefficients are statistically significant at the1% level.

Table 3 displays four variations of equation (8). The first column just includes the time

trend. The positive sign indicates overall divergence, but the coefficient is quite small. Figure 3,

however, shows the importance of controlling for macroeconomic events. Column 2, therefore,

includes controls for the 1988-1994 and the 1994-2001 periods both in levels and interacted with

the time trend. The overall trend (which represents 2001-2011) more than triples, representing

overall divergence in wage differentials. Note that the controls for the two periods show the

response to shocks with high intercept terms and large and negative convergence estimates.

We are also interested in the possibility that the rates of convergence differ across cohort

characteristics. In particular, we are interested in whether or not the high-migration cohort

exhibits different trends than the rest of the sample. Columns (3) and (4) show that the high

migration cohort exhibits more convergence than the rest of the sample both with and without

18

controls for the different macroeconomic shocks. Overall, therefore, these results are consistent

with the hypothesis that migration helps close the wage gap between the United States and

Mexico but overall, the gap has not been getting smaller.

IV. Results: Census Data

Mean Wage Differentials

We begin by plotting which is the mean wage differential for education cohort i and

age k at time t in Figure 7 to provide a visual understanding of the wage differentials in the

census data. We do so using both samples from the Mexican census described in Section II. We

see that for people with less education (i.e. 0 to 8 years of education) there was little change in

the differential between 1990 and 2000 but there was a substantial decline between 2000 and

2010. This is the case in both Mexican samples. Also, noteworthy is that the mean differentials

are smaller when we use Sample 2 which is the more urban sample; this is a consequence of

urban areas being richer. Once we move on to people with slightly more years of schooling, we

see a more attenuated decline between 2000 and 2010 while there still is little difference between

1990 and 2000. Finally, for the most educated cohort (more than 16 years of schooling), there is

little difference from 1990 to 2010. Overall, this figure reflects the key finding from the survey

data which is that there is some evidence of wage convergence for less educated people, although

in the census, these results are concentrated during the 2000’s.

In an attempt to quantify some of the results in Figure 7, we estimate the following

regression model:

19

in which we regress the wage differential for each education/age cohort on a set of education

(indexed i) and time dummies together with their interactions. The results are reported in Table

4.11 In the first two columns, we employ Sample 1 from the Mexican census and in the last two

columns, we employ Sample 2. In the first and third columns, we weight age education/age/year

cells using weights from the US census and in the second and fourth columns, we use weights

from the Mexican census. These adjust each education/age/time cell for the share of the

population that they represent in either Mexico or the US for that year.12

The table essentially reinforces the results shown in Figure 7 but does provide some

additional quantitative content. First, the constants in each column range from 2.25-2.39

suggesting that in 1990, people with zero to four years of schooling earned about ten times as

much in the US than in Mexico. This is broadly consistent with the average wage differentials

shown in Table 2 for the census data as well as with figures shown in Table 2 of Hanson and

Chicquiar (2005). Note that these differentials, which are on the order of about ten, are larger the

differentials obtained from the Survey data which are on the order of five; this is not a

consequence of differences in the Mexican survey and census data but instead in differences in

the US data since US wages in the CPS are lower than in the census.

Next, the first column suggests that there was a substantial widening of the wage

differential in 2000 but this is not borne out in the next three columns. Moreover, the last two

columns, in which we employ Sample 2 from the Mexican census, show a statistically significant

narrowing of the differential from 1990 to 2000. One reason for this discrepancy could be that

11 Note that we use people ages 19-63 for the first four education groups but only people ages 22-63 for the last education group which yields 222 groups per year. 12 Once again, bear in mind that we have two layers of weighting. In the first, we use the weights from the US and Mexican Censuses to construct averages for each age/education/time cell; these weights come from their respective Census. In the second, we weight each cell average with either the US or the Mexican weights for that cell.

20

weights based on the US census place more emphasis on better educated people for whom we

see substantial wage divergence in 2000 as shown in the fifth panel of Figure 8 in the first

column. However, it is not quite appropriate to attribute the negative estimates for the year 2000

dummy to a narrowing of the wage differential during the nineties. The reason for this is that

interaction between the 2000 dummy and the education variables, in columns three and four, by-

and-large are positive and at least marginally significant for up to 12 years of schooling.

Moreover, they tend to be larger in magnitude than the 2000 dummy which is indicative of a

widening of the US-Mexico wage gap during the nineties which is consistent with the results

from the survey data.

Finally, looking at the interactions between years of schooling and the 2010 dummy, we

see evidence of convergence for less educated cohorts during the 2000’s. This is true regardless

of how we weight the regressions or what sample we use. In the first column, we see that the

interactions with 0-4 and 5-8 are -0.163 and -0.137 and in the second column, they are -0.162

and -0.096. This indicates that, for these less-educated cohorts, the wage differential in 2010

was between 85.0 percent and 90.9 percent of what it was in 2000. The corresponding

interactions are -0.110 and -0.139 in column three and -0.109 and -0.089 in column four.

Changes in the Relative Wage Distribution over Time

Next, we investigate how the US and Mexican wage distributions evolved from 1990 to

2010. To do this, we compute differences in percentiles of the US and Mexican wage

distribution by education and year for 2000-1990 and 2010-2000. To fix ideas, we let

denote the th percentile for education cohort k at year t in country l. We then plot

, , , ,

21

and

, , , ,

as a function of . The first term in parentheses in each of these expressions is the wage

differential at the th percentile between the US and Mexico in either 2010 or 2000. The second

term is the same quantity but from the previous census year. The difference in the two

expressions in parentheses is then the change in the cross-border differential at a particular

percentile over a ten year period. At this point, we only consider three educational cohorts since

computing percentiles is more demanding of the data than computing means; the three cohorts

that we consider are 0-11 (no high school), 12-15 (high school) and more than 15 years of

schooling (college).

In Figure 8, we plot the changes in the relative wage distributions for 2000-2010 and

2000-1990 using both samples from the Mexican census. The most striking results are in the first

row which displays 2010-2000. First, we see that at, all points in the wage distribution, there

was a narrowing of the cross-border differential for people with less than twelve years of

schooling. The estimates indicate that the wage differential in 2010 was roughly 85 percent of

what it was in 2000 in Sample 1 and 80% of what it was in Sample 2. For high school and

college graduates, we see convergence at the lower end of the distribution. The estimated change

in the differential is negative through the 20th percentile for the college-educated and the 40th

percentile for the high school-educated in Sample 1. In Sample 2, we do not see convergence for

college graduates and but we do until the 40th percentile for high school graduates. This indicates

that the wages of US workers in the bottom half of the distribution became closer to their

counterparts across the border in the 2000s.

22

The bottom panel displays the difference from 1990 to 2000. In Sample 1, the figure

shows no stark patterns and, overall, is not indicative of any converge in the two wage

distributions over this period. However, in Sample 2, we see some evidence of convergence

among the college-educated; in particular, their wages in Mexico in 2000 were roughly 85% of

what they were in 1990. However, the survey data results indicate that the peso crisis led to a

large divergence during the mid-90’s and that this may account for the lack of evidence of

convergence which we see in Figure 8 for the period 1990-2000.

An important question to ask at this point is whether these changes are driven by Mexico

catching up or the US falling behind. To do this, we plot the change in the wage distributions in

the US and Mexico from 1990-2000 and 2000-2010. For each Mexican sample, we display these

four profiles in three graphs corresponding to the three educational cohorts. The panel for people

with less than twelve years of schooling indicates that a large part of the convergence that we see

for the less educated is a consequence of US workers falling behind. Indeed, real wages in the

US fell about 0.12 log points at all points in the distribution over this period. In contrast, there

were modest gains in Mexican wages over this period. Turning to high school graduates in the

middle panel, we see that from 2000-2010, US wages fell behind quite a bit, particularly, at the

bottom of the distribution. Mexican wages also declined over this period but, typically, by a

smaller magnitude.

However, there is one very important difference in the behavior of the wage structure of

high school graduates from 2000-2010 between the United States and Mexico. We see that the

plot for the United States is increasing and that the plot for Mexico is decreasing. What this

means is that the losses in the United States disproportionately hit the poor, whereas in Mexico,

they disproportionately hit people towards the top of the distribution. This suggests that

23

although mean wages of high school graduates may have fallen during the 2000’s in both

countries, inequality for this group declined in Mexico but increased in the US

We now turn to the college-educated in the third row. In Sample 1, we do not see terribly

strong evidence of either Americans falling behind or Mexicans catching up during either the

1990’s or the 2000’s. However, the results are starker in Sample 2. The wages of the college-

educated in Mexico declined between 2000 and 2010 by roughly 10%. However, we also see

that between 1990 and 2000, Mexican wage growth was over 10% larger than in the US at most

points in the wage distribution. This suggests that the evidence for convergence that we saw in

Figure 8 for the college-educated between 1990 and 2000 was due to gains in Mexico.

Triple Diffs: Comparisons between the Border and the Interior

One way in which we can attempt to tease out the extent to which trade or migration is

responsible for the observed narrowing of the US-Mexico wage gap during the period 2000-2010

in the census data is to conduct a similar analysis as in the previous section but to compare these

changes between Mexico’s border and interior states. The rationale behind this exercise that, as

pointed by many including Robertson (2000), Mexico’s border is more tightly linked with the

United States than its interior. The two reasons for this are the presence of the maquiladora

industry which is concentrated primarily along the US-Mexico border and the fact that many

border cities are conduits for migrants, notably, Tijuana. In addition and perhaps more

important, Figure 3 showed that the peso crisis of 1994 most likely confounds our ability to

detect any convergence during the 1990’s that may have occurred due to trade or migration.

Because the crisis impacted the entirety of Mexico, this third difference mitigates the bias from

this confounding factor.

24

To investigate this, we consider a triple-difference version of the exercise from the

previous section. Specifically, we compute

, ,,

, ,,

, ,,

, ,,

where the superscript B denotes Mexico’s border region and I denotes Mexico’s interior.13 So,

we look at how the change in the US-Mexico wage gap between 2010 and 2000 changes as we

move from Mexico’s border to its interior.

We report the results in Figure 10. During the period 2000-2010, we do not see any

evidence that convergence was any faster along the border than in the interior. In fact, using

Sample 2 from the Mexican sample, we actually see that, relative to the interior, the wage

differential along the border expanded from 2000 to 2010. What this may then indicate is that

during the period 2000-2010 light industries may have exited Mexico’s border region thereby

reducing wages there vis-à-vis the interior. Next, we see that during the period 1990-200 that

wages in Mexico’s border region increased at a more rapid rate than in the interior. This is

particularly the case in Sample 2.

It is important to emphasize that we see large movements in wage differentials in the

border area relative to the interior at least once we restrict the sample to more urban areas.

During the 1990’s, wages in these cities close to the border saw large gains relative to the rest of

Mexico and this was subsequently reversed in the 2000’s. This is suggestive that trade has the

potential to narrow US-Mexico wage differentials but, at the same time, it also suggests that US-

Mexico trade is not responsible for the convergence that we saw in the survey and the census

13 We define “border” to be all of Mexico’s states that border with the United States which includes Baja California, Sonora, Chihuahua, Tamaulipas and Coahuila. When we employ Sample 1, we use all wages from these states which include those from rural areas. When we employ Sample 2, we only use selected cities which include large border towns such as Tijuana and Juarez.

25

during the 2000’s since wages in Mexico’s maquiladora sector took a substantial hit during this

period. Rather, it may indicate that a third factor such as Chinese competition both adversely

impacted Mexican and US wages.

Variance Decompositions

We conclude the analysis of the census data with a variance decomposition exercise. It is

common in the inequality literature (e.g. Lemieux 2008) to decompose the variance of location l

at time t into its “within” and “between” components as follows:

where

,

and

,

where is the population weight for cell i,k,t in country l, is the variance in cell i,k,t in

county l, is the average log wage in cell i,k,t in country l and is the average of the log

wage at time time t in country l. The within component measures variation in wages within

education/age cohorts, whereas the between component measures variation across education/age

cohorts. We conduct this wage decomposition for the US and Mexico. We also combine data

from the two countries and conduct the exercise for the integrated economy with appropriate

modifications to the weights for relative country sizes and using the grand mean of the wage in

the US and Mexico in the formula for the between component.

26

Before we discuss our results, it is useful to consider how a simple HOS story with two

countries would play out. In the aftermath of trade liberalization, demand for low-skilled labor

in the United States should decline but increase in Mexico and, more generally, within a given

skill set, wages should converge. What this suggests then is that in the US-Mexico integrated

economy, the within group component of inequality should decline over time. Next, given the

conventional wisdom that trade should hurt lower skilled workers in the United States but help

them in Mexico, we should also expect to see that the between component of the variance should

increase in the United States but decrease in Mexico.

The results are reported in Table 5. First, the table indicates that the total variance of log

wages in the integrated economy has steadily declined since 1990 in when we use all Mexicans

but not when we restrict the sample to urban Mexicans. We do see that the within component of

variance declined in the integrated between 1990 and 2000 but increased in 2010. Next, we see

that the variance of wages has declined steadily in Mexico since 1990, but this decline is due to

changes in the within component not the between component. Finally, inequality in the United

States has steadily increased from 1990-2010, but similar to Mexico, this increase is due to

increases in the within component of inequality. In summary, the data seem to suggest that

Mexican wage dispersion has decreased and that American inequality has done the opposite but

that this is not consistent with a textbook two-country HOS story.

V. Conclusion In this paper, we presented descriptive evidence on the evolution of wage differentials

between the United States and Mexico over the period 1988-2011. On net, we showed that

wages between the two countries diverged over this period. However, this had much to do with

the peso crisis of 1994. Subsequently, there was a large convergence until 2001, the year in

27

which China entered the WTO, after which we saw steady divergence. These findings strongly

indicate that the divergence from 1988-2011 had much to do with large macroeconomic events

which may have counteracted the effects of US-Mexico trade and migration.

A more detailed look at our data reveals that trade and migration may indeed bring more

wage convergence, despite the overall divergence in the raw data. First, in the survey data, we

show that, the peso crisis notwithstanding, there is steady convergence for young people with

intermediate levels of schooling who are precisely the people who are most likely to emigrate

from Mexico. One important topic for future work is to investigate more rigorously the effects

of migration on US-Mexico long-run wage differentials. Second, in the census data, we show

that over the period 1990-2000 that the border of Mexico caught up to the US relative to the

interior. This exercise has the added benefit that it mitigates greatly the confounding effects of

the peso crisis which allows us to better see the effects of NAFTA which should have been more

prevalent in the border. On the other hand, this same exercise reveals that during the period

2000-2010 that there was divergence in the border relative to the interior. Given that we also

saw that low-skilled US wages declined by around 10% over this period, this suggests that a

third factor may have had adverse effects on the Mexico border and low-skilled US wages.

Autor, Dorn, and Hanson (2012) show that much of the latter can be attributed to Chinese trade.

Another important topic for future work is to conduct a similar analysis in Mexico.

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30

Table 1: Summary Statistics of Survey Data

United States

1988‐1994 1995‐2002 2003‐2007 2008‐2011

Monthly Wage $1,492.69 $1,504.65 $1,515.75 $1,466.30

(679.02) (703.75) (677.00) (681.38)

Hourly Wage $8.26 $8.27 $8.41 $8.28

(3.42) (3.52) (3.41) (3.45)

Age 37.45 38.74 39.85 40.54

(0.29) (0.45) (0.19) (0.18)

Education

0‐5 1.60% 2.30% 2.40% 2.10%

6‐8 2.70% 1.60% 1.40% 1.20%

9‐11 7.50% 7.80% 7.90% 6.50%

12‐15 61.50% 59.40% 57.00% 56.60%

>16 26.70% 28.90% 31.30% 33.60%

Mean N per quarter 21,155.89 19,393.91 20,960.35 19,667.75

Mexico

1988‐1994 1995‐2002 2003‐2007 2008‐2011

Monthly Wage $310.57 $260.24 $272.11 $226.50

(175.59) (149.47) (135.21) (112.70)

Hourly Wage $2.09 $1.36 $1.41 $1.24

(1.33) (0.81) (0.74) (0.64)

Age 35.05 35.56 36.88 37.32

(0.11) (0.41) (0.35) (0.09)

Education

0‐5 18.40% 14.30% 12.90% 12.40%

6‐8 27.70% 26.80% 23.60% 22.10%

9‐11 24.10% 30.60% 31.60% 33.20%

12‐15 13.40% 13.10% 16.90% 18.90%

>16 16.40% 15.20% 15.00% 13.40%

Mean N per quarter 33,445.89 42,934.50 31,427.05 27,756.00

Notes: All wages are in 1990 US dollars. In Mexico, the monthly wage was computed by converting wages to US dollars using the exchange rate for that year and then deflating the wages using the US CPI. Standard deviations are in parentheses. Mean N per quarter represents the average number of observed individuals per quarter per period (without population weight expansion).

31

Table 2: Descriptive Statistics from Census Data

1990 2000 2010 US

Hourly Wage 14.21 (11.38)

15.07 (12.49)

14.98 (13.09)

Age 36.83 (11.59)

38.33 (11.50)

39.61 (12.27)

Education 0-4 1.56% 1.56% 1.50% 5-8 3.26% 3.20% 3.01% 9-12 37.72% 35.42% 32.36% 13-16 47.99% 49.66% 52.07% >16 9.47% 10.15% 11.06% N 1,982,151 2,361,079 496,042 MX – Sample 1 Hourly Wage 1.43

(1.82) 1.55

(1.92) 1.59

(1.81) Age 34.79

(11.20) 35.39

(11.04) 37.10

(11.38) Education 0-4 29.56% 18.10% 11.89% 5-8 30.01% 26.49% 21.60% 9-12 27.41% 37.42% 45.53% 13-16 5.62% 9.54% 12.22% >16 7.42% 8.45% 8.77% N 1,264,613 1,597,037 1,754,953 MX – Sample 2 Hourly Wage 1.61 1.77 1.74 (1.98) (2.15) (1.97) Age 34.59 35.42 37.46 (10.97) (10.91) (11.35) Education 0-4 18.38% 10.95% 7.30% 5-8 31.00% 24.65% 18.85% 9-12 33.04% 43.12% 49.24% 13-16 7.81% 11.80% 14.62% > 16 9.76% 9.47% 9.99% N 507,068 538,663 360,515 All wages are in 1990 US dollars. In Mexico, the hourly wage was computed by converting wages to US dollars using the exchange rate for that year and then deflating the wages using the US CPI. US census data were 5% samples except for the American Community Survey sample in 2010 which was a 1% sample. The Mexican census was a 10% sample for all three years. MX – Sample 1 uses all people who meet the sample criteria described above. MX – Sample 2 uses these criteria and further restricts the sample to the metropolitan areas that are employed in the Mexican survey data.

32

Table 3: Trends in US-Mexico Wage Gap

(1) (2) (3) (4) VARIABLES Trend Breaks Migrants Migrants and Breaks Time 0.002*** 0.007*** 0.002*** 0.007*** (0.000) (0.000) (0.000) (0.000)Migrant_x_time ‐0.003*** ‐0.003*** (0.001) (0.001)1988-1994 4.139*** 4.139*** (0.077) (0.076)1994-2001 3.187*** 3.187*** (0.104) (0.104)Trend in 88-94 ‐0.031*** ‐0.031*** (0.001) (0.001)Trend in 94-2001 ‐0.019*** ‐0.019*** (0.001) (0.001)Constant 1.448*** 0.393*** 1.437*** 0.381*** (0.028) (0.048) (0.028) (0.048) Observations 4,320 4,320 4,320 4,320Number of cohorts 45 45 45 45Notes: Standard errors in parentheses. *** p<0.01.

33

Table 4: Mean Wage Difference Regressions, Census Data

(1) (2) (3) (4) Constant 2.393***

(0.052) 2.402***

(0.012) 2.238***

(0.056) 2.250***

(0.016) Years of Education 0-4 - - - - 5-8 -0.207***

(0.064) -0.272***

(0.017) -0.093

(0.069) -0.154***

(0.020) 9-12 -0.314***

(0.054) -0.340***

(0.017) -0.170***

(0.058) -0.196***

(0.019) 13-16 -0.569***

(0.053) -0.586***

(0.030) -0.464***

(0.057) -0.480***

(0.028) >16 -0.332***

(0.057) -0.358***

(0.027) -0.249***

(0.061) -0.270***

(0.027) Year 2000 0.084**

(0.030) 0.033

(0.033) -0.087**

(0.032) -0.113***

(0.031) 2010 -0.009

(0.029) -0.008(0.032)

-0.003 (0.031)

-0.013(0.030)

Education*Year 0-4*2000 -0.058

(0.080) -0.005 (0.038)

0.129 (0.085)

0.157***

(0.040) 5-8*2000 -0.061

(0.059) 0.033

(0.037) 0.100

(0.064) 0.167***

(0.036) 9-12*2000 -0.033

(0.033) 0.020

(0.037) 0.120***

(0.036) 0.145***

(0.034) 13-16*2000 -0.204***

(0.032) -0.136 (0.048)

-0.025

(0.035) 0.012

(0.043) 0-4*2010 -0.163**

(0.080) -0.162***

(0.040) -0.110

(0.087) -0.104***

(0.042) 5-8*2010 -0.137***

(0.060) -0.096***

(0.037) -0.139**

(0.064) -0.084***

(0.036) 9-12*2010 0.008

(0.033) -0.007 (0.036)

-0.006 (0.036)

-0.010 (0.034)

13-16*2000 0.014 (0.032)

0.022 (0.047)

0.038 (0.034)

0.057 (0.042)

MX Sample 1 1 2 2 Weights US MX US MX R2 0.7548 0.7472 0.7213 0.6508 Number of Cohorts 666 666 666 666

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: In the first and third column, we weight the regression using weights from the US census; in the second and fourth column, we weight the regression using weights from the Mexican census.

34

Table 5: Variance Decompositions, Census Data

MX and US

MX and US MX

MX

US 1990

Within 0.420 0.404 0.649 0.588 0.345 Between 1.293 1.134 0.147 0.145 0.135

Total 1.713 1.538 0.796 0.733 0.480

2000

Within 0.405 0.400 0.519 0.482 0.366 Between 1.275 1.126 0.192 0.203 0.125

Total 1.680 1.526 0.711 0.685 0.491

2010

Within 0.426 0.427 0.461 0.462 0.414 Between 1.199 1.124 0.145 0.149 0.171

Total 1.625 1.551 0.606 0.611 0.585 MX

Sample 1 2 1

2 -

35

Figure 1: Percentage of Mexican-born Workers in the US by Age and Education, Household Surveys

Notes: The first age group includes workers aged 19-23 years old; the second includes workers aged 24-28, the third those aged 29-33, and so forth. The first education group includes adults with 0-5 years of education; the second includes adults with 6-8 years of education; the next comprise those with 9-11, 12-15, and finally 16 or more years of education.

Ed0

Ed1

Ed2

Ed3

Ed4

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

123

45

67

89

Ed0

Ed1

Ed2

Ed3

Ed4

Age Group

36

Figure 2a: Time Series Behavior of Mexican Monthly Wages

Notes: Cohort 39 is excluded.

37

Figure 2b: Time Series Behavior of US Monthly Wages

38

Figure 3: Time Series Behavior of Mean Differentials by Cohorts

.51

1.5

22

.5lo

g(w

age

)

1988

q1

1990

q1

1992

q1

1994

q1

1996

q1

1998

q1

2000

q1

2002

q1

2004

q1

2006

q1

2008

q1

2010

q1

2012

q1

Time

US-MX Difference in Monthly Earnings by Cohort

39

Figure 4a: Time Series Behavior of Mean Differentials across Cohorts

Notes: The trend break test statistic is test 2a from Volgelsang and Perron (1998), which is an additive outlier test for an unknown break. Note that peaks occur at the peso crisis (December 1994), the US recession that started in March 2001, and the Financial Crisis (October 2008).

02

04

06

0T

ren

d B

rea

k T

est S

tat

1.4

1.6

1.8

22

.2M

ean

Wag

e D

iffer

ent

ial

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Time

Mean Wage Differential Trend Break Test Stat

Mean Differential and Trend Break Test Statistic

40

Figure 4b: Time Series Behavior of Standard Deviation of Diffentials across Cohorts

Notes: The peso crisis occurs in December 1994 and China enters the WTO on December 11, 2001.

.05

.1.1

5.2

.25

.3S

td. D

ev. A

cro

ss C

ohor

ts

1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012Time

Standard Deviation Across Cohorts

41

Figure 5a: Wage Differentials, 0-6 Years of Education and 34-38 Years Old

42

Figure 5b: Wage Differentials, 12-16 Years of Education and Age 54-58

43

Figure 5c: Wage Differentials, 6-9 Years of Education and 19-23 Years Old

44

Figure 6: Percentage of Mexican-born Workers in the US by Age and Education, Census Data

Notes: The first age group includes workers aged 19-23 years old; the second includes workers aged 24-28, the third those aged 29-33, and so forth. The first education group includes adults with 0-4 years of education; the second includes adults with 5-8 years of education; the next comprise those with 9-12, 13-16, and finally 17 or more years of education.

Ed0Ed1

Ed2Ed3

Ed4

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Ed0

Ed1

Ed2

Ed3

Ed4

45

Figure 7: Mean Wage Differentials by Age, Census Data MX – Sample 1 MX – Sample 2

1.6

1.8

22.

22.

42.

62.

8U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ < 5

1.6

1.8

22.

22.

42.

62.

8U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ < 5

1.6

1.8

22.

22.

4U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 5 and <= 8

1.6

1.8

22.

22.

4U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 5 and <= 8

1.4

1.6

1.8

22.

22.

4U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 9 and <= 12

1.4

1.6

1.8

22.

22.

4U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 9 and <= 12

46

1.2

1.4

1.6

1.8

2U

S-M

X W

ag

e D

iffe

ren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 13 and <= 16

1.2

1.4

1.6

1.8

2U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ >= 13 and <= 161.

41.

61.

82

2.2

2.4

2.6

2.8

US

-MX

Wag

e D

iffer

entia

l

20 30 40 50 60Age

1990 20002010

Educ > 16

1.4

1.6

1.8

22.

22.

42.

62.

8U

S-M

X W

age

Diff

eren

tial

20 30 40 50 60Age

1990 20002010

Educ > 16

47

Figure 8: Changes in Wage Percentiles by Education

MX – Sample 1 MX – Sample 2

-.4

-.3

-.2

-.1

0.1

.2U

S-M

X W

age

Diff

: 201

0-20

00

0 .2 .4 .6 .8 1Quantile

No High School High SchoolCollege

-.4

-.3

-.2

-.1

0.1

.2U

S-M

X W

age

Diff

: 201

0-20

00

0 .2 .4 .6 .8 1Quantile


-.25

-.15

-.05

.05

.15

.25

US

-MX

Wag

e D

iff: 2

000-

1990

0 .2 .4 .6 .8 1Quantile


-.25

-.15

-.05

.05

.15

.25

US

-MX

Wag

e D

iff: 2

000-

1990

0 .2 .4 .6 .8 1Quantile


48

Figure 9: Decompositions of Wage Distribution Changes by Years

MX – Sample 1 MX – Sample 2

-.3

-.2

-.1

0.1

Wag

e G

row

th

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

No High School

-.3

-.2

-.1

0.1

Wag

e G

row

th

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

No High School

-.35

-.25

-.15

-.05

.05

.15

Wa

ge

Gro

wth

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

High School

-.35

-.25

-.15

-.05

.05

.15

Wa

ge

Gro

wth

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

High School

-.2

-.1

0.1

.2.3

Wa

ge

Gro

wth

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

College

-.2

-.1

0.1

.2.3

Wa

ge

Gro

wth

0 .2 .4 .6 .8 1Quantile

US 2000-1990 MX 2000-1990US 2010-2000 MX 2010-2000

College

49

Figure 10: DDD Results – Differences in Changes in Wage Percentiles by Education across Mexico’s Border and Interior

MX - Sample 1 MX – Sample 2

-.1

-.05

0.0

5.1

US

-MX

Wag

e D

iff -

Trip

le D

if: 2

010-

2000

0 .2 .4 .6 .8 1Quantile


-.1

-.05

0.0

5.1

.15

.2.2

5.3

.35

.4U

S-M

X W

age

Diff

- T

riple

Dif:

201

0-20

00

0 .2 .4 .6 .8 1Quantile


-.1

-.05

0.0

5.1

US

-MX

Wag

e D

iff -

Trip

le D

if: 2

000

- 19

90

0 .2 .4 .6 .8 1Quantile


-.3

-.25

-.2

-.15

-.1

-.05

0.0

5.1

US

-MX

Wag

e D

iff -

Trip

le D

if: 2

000

- 19

90

0 .2 .4 .6 .8 1Quantile


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