Labor demand and trade reform in Latin America - World...

Journal of International Economics 66 (2005) 423–446

www.elsevier.com/locate/econbase

Labor demand and trade reform in Latin America

Pablo Fajnzylber*, William F. Maloney

The World Bank, 1818 H Street NW, Washington, DC 20433, United States

Received 28 October 2002; received in revised form 19 May 2004; accepted 13 August 2004

Abstract

Trade liberalization is thought to result in higher own-wage elasticities of labor demand,

particularly for unskilled labor, with adverse implications for both labor market volatility and wage

dispersion. The paper first argues that theoretically the link between liberalization and labor-demand

elasticities is less clear than has previously been asserted. It then uses dynamic panel techniques to

estimate labor-demand relations for manufacturing establishments in Chile, Colombia, and Mexico

across periods of trade policy reform. The results do not strongly support the hypothesis that trade

liberalization has a direct impact on own-wage elasticities.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Trade reforms; Trade and labor market interactions; Demand for labor; Factor demand elasticities

Latin America

JEL classification: F02; F16; J23

1. Introduction

There are increasing fears that trade reform—and globalization more generally—will

increase the uncertainty faced by workers, especially those with fewer skills. In particular,

the increased competitiveness of product markets and the greater access to foreign inputs

could lead to a more elastic demand for workers which, in turn, could have adverse

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P. Fajnzylber, W.F. Maloney / Journal of International Economics 66 (2005) 423–446424

impacts on labor market outcomes. Rodrik (1997) has highlighted how an increase in the

own-wage elasticity of labor demand can worsen income distribution, lessen the

bargaining power of unions, increase the volatility of employment, shift non-wage labor

costs toward labor and facilitate the transmission of aggregate demand shocks to workers.

In short, changes in this one parameter can account for many of the concerns surrounding

globalization.

However, to date the limited empirical literature has not offered strong support for

this hypothesis. Slaughter (2001) estimated labor-demand elasticities for U.S.

manufacturing industries from 1960 through 1990 and found evidence of increasing

labor-demand elasticities for production workers. He could not, however, conclusively

link them to measures of globalization. The evidence on developing countries is also

scarce and mostly unsupportive of Rodrik’s hypothesis. Krishna et al. (2001) tested

the effect of trade liberalization on labor-demand elasticities using micro-panel data

from Turkish manufacturing industries. They found that blabor demand elasticities

seem to be unresponsive to opennessQ (p. 403), a result that proved robust to the

subset of workers under consideration, and to the use of alternative openness

measures—including trade reform dummy variables, tariff rates and import penetration

ratios.1

This paper adds comparable evidence from three countries in Latin America—Chile,

Colombia and Mexico—all of whom have experienced large changes in trade regime

across the last 20 years. We use panel data sets at the establishment level which allow

controlling for unobserved heterogeneity across firms, lessen the concerns associated with

the possible endogeneity of factor prices and provide instruments for potentially

endogenous explanatory variables. In addition, thanks to the relatively larger temporal

dimension of our datasets compared, for instance, to the one used by Krishna et al. (2001),

we are able to model the dynamics of employment adjustment using Arellano and Bond’s

(1991) Generalized Method of Moments (GMM) estimator.

To investigate the links with trade openness, we broadly follow Slaughter (2001) in

approach. We first identify patterns of change over time in labor-demand elasticities,

treating blue- and white-collar workers separately. We then test for the impact of openness

on the elasticity of labor demand by sequentially interacting the relevant variables with

different measures of trade openness. As a final exercise, we summarize the results of

carrying out the same exercises at the sectoral level.

The rest of the paper is structured as follows. Section 2 discusses theoretical

considerations regarding the impact of trade openness on labor-demand elasticities.

Section 3 briefly describes the data and summarizes the evolution of trade regimes in the

countries and periods considered. Section 4 outlines the specification and the estimation

method adopted. Section 5 summarizes the empirical results and Section 6 offers

concluding remarks.

1 Paes de Barros et al. (1999) and Cassoni et al. (1999) test the same hypothesis for the cases of Brazil and

Uruguay, respectively. They find no evidence of a positive link between labor-demand elasticities and volumes of

international trade at the industry level.

P. Fajnzylber, W.F. Maloney / Journal of International Economics 66 (2005) 423–446 425

2. Conceptual issues

In a competitive setting, Marshall’s well-known bfundamental law of factor demandQpredicts a monotonic relation between factor and output demand elasticities.2 With the

additional assumption of linear costs and more than two inputs, the own price elasticity of

factor demand (gl) can be written as:

gl ¼ mlrll þ mlg ð1Þ

where ml is the share of factor expenditures in costs, rll is the Allen partial elasticity of

substitution, and g is the elasticity of product demand.3 As suggested by Rodrik (1997)

and further elaborated by Slaughter (2001), it is reasonable to assume that trade

liberalization can enlarge the array of substitutes for local labor as well as increase the

elasticity of product demand. These effects should respectively translate into an increase in

rll and g, which given the assumption of perfect competition should directly affect factor

demand elasticities.

Recognizing that liberalization often does not occur in a competitive context, Krishna et

al. (2001) focus on a model that assumes constant return to scale and monopolistic

competition, and show that the monotonic relation predicted by Marshall between factor

and output demand elasticities still goes through. It is not clear, however, that these

predictions remain valid under more general assumptions regarding costs and market

structure. Under monopoly, for instance, and with unspecified cost structures, Maurice and

Ferguson (1973) show that the labor-demand elasticity can be written as:

gl ¼ mlrll þmlc2l

e gmr � gmcð Þ ð2Þ

where cl is the expenditure elasticity (the proportional change in usage of the factor

relative to the proportional change in total costs at constant factor prices), e is a term

capturing economies of scale, gmr is the elasticity of marginal revenue with respect to

output, and gmc is the elasticity of marginal costs with respect to output. Although the

direct relationship between gl and rll remains unchanged, the impact of increases in the

product demand elasticity on the curvature of the demand for labor becomes theoretically

ambiguous. As Maurice and Ferguson acknowledge, the product demand elasticity is

unquestionably related to the difference between the elasticities of marginal revenue and

marginal cost in the denominator. bHowever,Q they argue bthe relation is a tenuous one,

and it cannot be stated explicitly in meaningful economic terms.Q (p. 185). The effect of

trade liberalization on product elasticities can be further complicated when one moves

beyond the case of import substitutes. As a simple example, for non-tradeables a fall in

output price resulting from lower input prices would imply lower product demand

elasticities along a linear demand curve. As a second example, it is not obvious that firms

with export potential who, as a result of trade liberalization can break out of the domestic

market and export, would face a more elastic world demand curve. Indeed, global and

2 See Hamermesh (1993) and Slaughter (2001).3 See Hamermesh (1993), pp. 24 and 35. As Hamermesh notes, the expression m lrll in Eq. (1) is equivalent to

�(1�m l)rkl in the two-factor case (see p. 35, footnote 7).


domestic tastes may differ and the expenditure share of the product in richer first-world

incomes may be smaller implying a lower demand elasticity. Finally, the larger external

market may encourage specialization in products with less elastic demand.

3. Data

We work with comparable plant level data from Chile (1979–1995), Colombia (1977–

1991), and Mexico (1984–1990). Details on the construction of the datasets are provided

in Appendix A.4 We use data on the employment and wages of blue- and white-collar

workers, as well as information on plant level output and capital stocks, to estimate

separate labor-demand relations for those two types of workers. As proxies for trade

openness we use import penetration ratios, tariff rates, rates of import license coverage (as

an indicator of nontariff barriers to trade), and the real exchange rate. Although the periods

under consideration are short compared to Slaughter’s U.S. series which cover several

decades, our data span periods of dramatic change in trade regimes. As described below,

we deal with one case of extreme liberalization (Mexico) and two of renewed

protectionism followed by re-liberalization.

3.1. Chile: 1979–1995

In the context of wide-ranging and profound structural reforms initiated in the mid

1970s, Chile gradually reduced tariffs to a uniform 10% across industries by 1979.

However, starting in March of 1983, Chile significantly increased its levels of protection,

initially doubling its tariff rate to 20%, then raising it to 35% in September 1984. At the

same time, following the collapse of a currency peg the real exchange rate depreciated by

almost 60% between July of 1982 and the end of 1985. Tariffs were gradually reduced

starting in March 1985, reaching 15% by January 1988, and 11% in June 1991. Arguably,

the period of 1983–1988 witnessed substantially higher levels of protection than the rest of

the period covered in our data. This is reflected in the evolution of import penetration

ratios, which fell from 32% to 21% between 1982 and 1986, and increased gradually

thereafter (see Table A1).

3.2. Colombia: 1977–1991

While still much more closed than was the case in Chile, Colombia’s trade regime

experienced a significant liberalization during the 1970s. Tariffs and quota restrictions

were steadily reduced reaching their low points in the early 1980s. However, a worsening

trade deficit led to a tendency to reverse trade liberalization starting in 1983. By 1985, as

much as 85% of all manufacturing products were subject to import license requirements—

4 The original datasets were compiled in the context of the World Bank funded project bIndustrial Competition,

Productivity, and Their Relation to Trade RegimesQ (Roberts and Tybout, 1996). In the case of Chile, the data set

was updated to cover the period after 1986, using information provided by the Instituto Nacional de Estadıstica

(Santiago, Chile).


compared to 56% in 1980—and average tariffs had increased to more than 62% (vs. 37%

in 1980). A new gradual process of liberalization began in 1985 and accelerated in 1989.

By 1991, the import-licensing regime covered only 10% of the manufacturing sector,

tariffs had been cut by roughly half, and the average import penetration ratio in the

manufacturing sector had soared to 32%, compared to 12% in 1985.

3.3. Mexico: 1984–1990

In 1985, the De la Madrid government undertook a restructuring of the external sector

preceded in magnitude only by Chile’s reforms of the mid 1970s.5 The share of products

with import licensing requirements was reduced from more than 93% in 1984, to about

32% in 1986 and 4% in 1988. However, trade liberalization was initially accompanied by a

sharp depreciation of the real exchange rate (reversed in 1988), which mitigated the

reduction in protection. Import penetration rates jumped from 10% to 20% between 1984

and 1986, but fell again to 14% in 1987, increasing gradually thereafter (Table A3). Thus,

although the bulk of the reforms took place at the beginning of our sample, arguably their

impact was worked out exactly over our period of study.

4. Estimation issues

Because of the difficulties of deriving our specification from first principles suggested

by Maurice and Ferguson, and because of a desire for the results to be broadly comparable

with previous work on this topic, we follow Slaughter and Krishna in adopting a standard

log linear specification for the demand for labor as a function of factor prices. Given the

panel nature of our data, we follow Arellano and Bond (1991) in embedding that standard

model in the following autoregressive specification:

lSijt ¼ alSij t�1ð Þ þ bS0w

Sijt þ bS

1wSij t�1ð Þ þ bU

0 wUijt þ bU

1 wUij t�1ð Þ þ wqjt þ lt þ li þ eijt

The log of employment of skilled labor in establishment i, industry j and period t is a

function of lagged skilled employment, current and lagged skilled (wijtS ) and unskilled

wages (wijtU), average industry output ( qjt) to capture industry-specific shocks to labor

demand, time fixed effects (lt) to proxy for aggregate shocks that affect all industries

equally, individual bfixedQ effects (li) that capture unobserved heterogeneity in production

technologies, and a random error term (eijt). The establishment’s output is omitted since

we are interested both in the substitution effects of changes in wages, conditional on

output, and in the scale effects on employment that work through changes in output. A

similar specification is estimated for unskilled employment.

Neither random nor fixed-effect estimators are consistent in this context. The former

requires lack of correlation between li and the explanatory variables, which is not

defensible as lijt and hence lij(t�1) are a function of li. Moreover, observed wages are also

likely to be correlated with the unobserved bsophisticationQ of the production technology.

5 See Lustig (1992) and Maloney and Azevedo (1995).


As for the fixed-effect transformation, in this context it induces a negative correlation

between the transformed error and the lagged dependent variables of order 1/T, which, in

short panels such as those used here, remains substantial. We thus difference the data to

eliminate li and use Arellano and Bond’s (1991) GMM estimator to overcome the

problem created by the correlation between the differenced lagged dependent variable and

the transformed error. We instrument lagged differenced employment with third and

further lags of capital stocks, and observed plant wages with average wages in the

corresponding regions and industries.6

In order to test for the impact of trade openness on labor-demand elasticities, we

introduce in the base specification presented above interactive terms between period

dummy variables and all the variables of interest: lagged employment, and contempora-

neous and lagged wages of blue- and white-collar workers.7 In this approach we broadly

follow Slaughter who estimates a different set of elasticities for each cross section and then

regresses the results on time trends. To verify whether trade reform had the expected

effects, we use the free standing and time-interacted coefficients of the relevant variables

to calculate a series of long-run labor-demand elasticities.8 In a second, complementary

approach we introduce trade openness variables directly into the base specification, both

free standing and interacted with the relevant variables. We then focus on the effect of each

openness proxy on the long-run own-wage elasticity of labor demand. As a final

exploration, we repeat the analysis at the level of individual two- and three-digit industries.

Since the industry level results do not alter the main findings from the regressions

estimated for each country’s manufacturing sector as a whole, we focus on the aggregate

analysis and report only a summary of the disaggregated results at the end of the next

section.9

5. Results

Table 1 reports the results for the base specification. Long-run elasticities derived from

introducing interactive period dummy variables are reported in Table 2, along with average

6 We thank one of the anonymous referees for suggesting this approach for instrumenting wages. Previous

papers have made the assumption that individual establishments take wages as given—see, for instance, Roberts

and Skoufias (1997) and Krishna et al. (2001). However, even if one assumes that wages for given worker

characteristics are exogenous at the plant level, observed wages may vary endogenously, as they reflect firms

choices regarding those characteristics. In the case of Mexico, where the number of available instruments is

smaller due to the shorter period, we also use third and further lags of employment as instruments. As

specifications tests, we employ the test for second-order serial correlation in the residuals and the Sargan test for

over-identifying restrictions proposed by Arellano and Bond (1991).7 We use 2- or 3-year periods instead of year dummy variables since we believe they represent better the

underlying trends in the data, and also because of computational limitations associated with the larger number of

instruments involved when yearly interactives are used.

9 The industry-level results are available from the authors on request.

8 We construct the long-run own-wage elasticity in the standard way, as the sum of the coefficients on the

contemporaneous and lagged wage of the type of worker under consideration, divided by 1 minus the

coefficient on lagged employment. We use the Delta method to calculate the corresponding standard errors

(Greene, 1997, p. 278).

Table 1

Base specification: Chile (1979–1995), Colombia (1977–1991) and Mexico (1984–1990) (standard errors in parenthesis)

Country/dependent

variable (Ln Lt)

Ln Lt�1 Ln WtU Ln Wt�1

U Ln WtS Ln Wt�1

S Sargan

test

Autocorrelation

test (second order)

No. of

observations

(plants)

Own wage

long run

elasticity

Chile/blue-collar

employment

0.258***

(0.063)

�0.199***

(0.035)

0.051

(0.041)

�0.063

(0.041)

0.034

(0.040)

0.414 0.272 21,744

(4579)

�0.199***

(0.071)

Chile/white-collar

employment

0.263***

(0.072)

0.063

(0.053)

�0.011

(0.052)

�0.357***

(0.059)

0.215***

(0.070)

0.482 0.274 21,744

(4579)

�0.192*

(0.104)

Colombia/blue-collar

employment

0.260***

(0.052)

�0.272***

(0.024)

0.051*

(0.027)

0.122***

(0.028)

�0.039

(0.025)

0.638 0.565 54,029

(10,698)

�0.299***

(0.046)

Colombia/white-collar

employment

0.385***

(0.073)

0.068**

(0.033)

�0.041

(0.035)

�0.235***

(0.036)

0.101**

(0.040)

0.173 0.057 54,029

(10,698)

�0.218***

(0.082)

Mexico/blue-collar

employment

0.510***

(0.149)

�0.204***

(0.024)

0.056

(0.046)

0.090**

(0.038)

�0.101**

(0.044)

0.314 0.092 11,689

(2830)

�0.301***

(0.111)

Mexico/white-collar

employment

0.496**

(0.225)

0.015

(0.062)

�0.01

(0.047)

�0.113**

(0.048)

�0.026

(0.046)

0.182 0.337 11,689

(2830)

�0.276*

(0.166)

GMM estimates with first-differenced data. Time dummies and the log of industry output were included but are here omitted.

* Significant at the 10% level.

** Significant at the 5% level.

*** Significant at the 1% level.

P.Fajnzylb

er,W.F.Maloney

/JournalofIntern

atio

nalEconomics

66(2005)423–446

429


values of our openness proxies in the corresponding periods. Tables 3–5 report the results

of specifications with interactive terms between our basic explanatory variables and direct

measures of trade openness.

5.1. Base specification

The base specifications are largely satisfactory both in statistical and in economic terms.

The validity of the estimation approach is generally confirmed by Sargan and

autocorrelation tests, and the point estimates for the long-run elasticities of labor demand

are within the range of previous estimates (for a review see Hamermesh, 1993). For blue-

collar workers, both Mexico and Colombia exhibit a�0.3 long-run labor-demand elasticity,

while the estimate for Chile is �0.2. For white-collar workers, the estimates are slightly

smaller in the three countries: �0.19 in Chile, �0.22 in Colombia and �0.28 in Mexico.

The estimates of the coefficient on lagged employment are positive and significant in

all cases. They suggest relatively rapid adjustment processes to long-run equilibria, with

half-lives of at most 12 months in all countries, and for both types of workers.10 Since the

coefficient on the lagged dependent variable should reflect the existence of adjustment

costs, one would expect it to be greater in countries that exhibit higher labor market

rigidities—such as larger severance costs, or other legislated costs of hiring and firing

workers. Moreover, adjustment processes should arguably be slower for more skilled

workers, due to the larger importance of industry- and firm-specific human capital. The

results are broadly consistent with these expectations: in the only country for which we

find differences in adjustment speeds between classes of workers, Colombia, the implied

half lives of the adjustment processes for blue- and white-collar workers are respectively 6

and 9 months. Looking across countries, the fastest adjustment processes are found in

Chile, where half lives are close to 6 months for both types of workers, and the slowest are

obtained in Mexico, where half lives are of around 1 year. This is generally consistent with

the impressions of other studies that find government-induced labor market distortions are

lower in Chile than in Colombia, and greater in Mexico than in the two other countries.11

5.2. Period dummies

5.2.1. Chile

During the period covered by our data, long-run labor-demand elasticities for blue-

collar workers exhibit their lowest values, of around �0.2 in 1980–1982 and 1993–1995.

As mentioned above, and illustrated in Table 2, these are exactly the periods of greater

trade openness: import penetration ratios are largest and tariffs rates are lowest. Higher

estimates for long-run elasticities of blue-collar workers are obtained for the 1983–1992

10 The half life of adjustment is calculated as the ratio of the log of one half to the log of the estimated lagged

employment coefficient. See Hamermesh (1993).11 Burki and Perry (1997) construct indexes of labor market liberalization for 26 countries of Latin America and

the Caribbean: in 1990 Chile is surpassed in terms of labor market flexibility only by the English-speaking

countries of the Caribbean, Mexico appears as having one of the most distorted labor markets in the region, and

Colombia is in an intermediate position (p. 41).

Table 2

Time varying long-run elasticities (standard errors in parenthesis) and trade openness proxies

Country Period Blue-collar long

run elasticity

White-collar

long-run elasticity

Tariff

rate

(%)

Nontariff

barriers

(%)

Real

exchange rate

(1980=100)

Import

penetration

(%)

Chile 1980–1982 �0.182 (0.134) �0.309** (0.138) 10.0 n.a. 108.1 29.1

1983–1984 �0.306** (0.128) �0.281** (0.120) 20.9 n.a. 135.8 26.6

1985–1986 �0.323*** (0.117) �0.325*** (0.107) 23.0 n.a. 154.0 22.6

1987–1988 �0.308*** (0.111) �0.122 (0.105) 17.5 n.a. 155.9 22.3

1989–1990 �0.341*** (0.108) �0.371*** (0.131) 15.0 n.a. 159.6 23.6

1991–1992 �0.345*** (0.112) �0.248** (0.118) 12.0 n.a. 143.8 25.4

1993–1995 �0.204* (0.113) �0.167 (0.127) 11.0 n.a. 138.8 31.2

Colombia 1978–1980 �0.261*** (0.056) �0.216*** (0.074) 39.1 56.2 99.9 13.5

1981–1982 �0.334*** (0.049) �0.296*** (0.070) 37.6 46.6 99 15.4

1983–1984 �0.380*** (0.049) �0.306*** (0.066) 56.3 68.9 104.4 12.8

1985–1986 �0.249*** (0.049) �0.222*** (0.064) 62.7 71.1 128.7 11.6

1987–1988 �0.252*** (0.046) �0.242*** (0.060) 62.5 53.7 137.3 12.2

1989–1991 �0.223*** (0.046) �0.220*** (0.063) 35.7 35.6 150.1 19.3

Mexico 1985–1986 �0.169* (0.100) �0.257* (0.148) 32.7 61.7 110.8 15.5

1987–1988 �0.158 (0.098) �0.133 (0.109) 22.5 11.7 109.1 14.3

1989–1990 �0.215** (0.097) �0.142 (0.103) 15.2 3.5 94.7 17.2

Standard errors for long-run elasticities are calculated using the Delta method and GMM estimates with first-

differenced data.





period, characterized by higher protection levels. In the case of white-collar workers, our

lowest estimates for long-run elasticities are �0.12 and �0.17, obtained respectively

during 1987–1988 and 1993–1995. While in the former period protection levels were still

relatively high, again, in the later period tariffs had already been reduced considerably.

Overall, the evidence from Chile does not appear to favor the hypothesis of a positive

relationship between trade openness and the elasticity of labor demand. If anything, the

opposite seems to be true in the case of blue-collar workers.

5.2.2. Colombia

Long-run elasticities for blue-collar workers increase from �0.26 during 1978–1980 to

�0.38 in 1983–1984, and decrease gradually afterwards, reaching�0.22 in 1989–1991. For

white-collar workers, our point estimates are close to�0.22 during most of the period under

analysis, except during 1981–1984, when they increase to around �0.3. Hence, for both

types of workers our estimates of long-run elasticities are largest during the period of greatest

levels of protection, and decrease subsequently, together with tariffs and nontariff barriers to

trade. Thus, as was the case for Chilean blue-collar workers, the results for Colombia suggest

a negative association between trade liberalization and labor-demand elasticities.

5.2.3. Mexico

As argued above, due to the real exchange rate depreciation that accompanied the

beginning of the Mexican trade liberalization, one could make the case that it was only

Table 3

Trade openness and labor demand in Chile (1979–1995) (standard errors in parenthesis)

Dependent variable

(Lt)

Explanatory

variables


U Ln WtS Ln Wt�1

S Ln qt Constant Wald

test:

p valuea

Sargan

test

Autocorrelation

test (second

order)

No. of

observations

(plants)

Blue-collar

employment

Free-standing 0.302***

(0.072)

�0.246***

(0.094)

0.127

(0.095)

0.001

(0.102)

�0.089

(0.101)

0.025***

(0.006)

0.023**

(0.010)

0.565 0.470 0.466 21,744

(4579)

Interacted with

Tariff Rate

0.030

(0.103)

0.258

(0.516)

�0.519

(0.482)

�0.496

(0.585)

0.767

(0.599)

Free-standing 0.154*

(0.088)

�0.185

(0.393)

�0.106

(0.313)

0.096

(0.251)

0.141

(0.269)

0.024***

(0.006)

0.015

(0.022)

0.177 0.387 0.225 21,744

(4579)

Interacted with Real

Exchange Rate

0.067

(0.045)

�0.005

(0.269)

0.132

(0.219)

�0.111

(0.175)

�0.075

(0.185)


(0.064)

�0.211***

(0.045)

0.001

(0.050)

�0.119

(0.049)

0.161***

(0.048)

0.023***

(0.007)

0.023**

(0.009)

0.294 0.285 0.235 20,534

(4479)

Interacted with

Import Penetration

0.084

(0.067)

0.033

(0.102)

0.108

(0.098)

0.213*

(0.113)

�0.544***

(0.104)

0.014***

(0.004)

White collar

employment

Free-standing 0.202**

(0.092)

0.335**

(0.141)

�0.254*

(0.141)

�0.209

(0.139)

0.062

(0.140)

0.018**

(0.008)

0.042***

(0.015)

0.593 0.480 0.174 21,744

(4579)

Interacted with

Tariff Rate

0.105

(0.194)

�1.846**

(0.893)

1.870**

(0.905)

�1.130

(0.880)

0.949

(0.901)

Free-standing 0.084

(0.157)

0.189

(0.381)

�0.612*

(0.339)

0.009

(0.321)

0.147

(0.322)

0.014*

(0.008)

0.037*

(0.022)

0.010* 0.283 0.252 21,744

(4579)

Interacted with

Real Exchange Rate

0.043

(0.107)

�0.046

(0.268)

0.456*

(0.252)

�0.270

(0.225)

�0.018

(0.219)


(0.074)

�0.005

(0.064)

�0.016

(0.068)

�0.447***

(0.073)

0.221***

(0.081)

0.020**

(0.008)

0.064***

(0.014)

0.005* 0.547 0.279 20,534

(4479)

Interacted with

Import Penetration

�0.227**

(0.102)

0.222*

(0.115)

�0.025

(0.128)

0.168

(0.138)

�0.314**

(0.134)

0.003

(0.009)

GMM estimates with first-differenced data. Time dummies were included but are here omitted.a Wald Test of Joint Significance of the interactives between the corresponding openness variable and lagged employment, own current wages and lagged own wages.




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after 1988 that the full impact of liberalization was felt. The results for blue-collar workers

show long-run own-wage elasticities of around �0.16 during the 1985–88 period, and

�0.22 during 1989–1990, an increase that is clearly consistent with Rodrik’s hypothesis.

However, the evidence for white-collar workers suggests an opposite conclusion, as own-

wage elasticities for skilled workers drop from �0.26 in 1985–1986, to around �0.14 in

the period thereafter.

5.3. Openness proxies

Directly interacting the trade liberalization proxies with the lagged employment and

wage variables yields results that are broadly consistent with the over-time movements in

elasticities just described.

5.3.1. Chile

Wald test results suggest that none of the openness proxies has a statistically significant

effect on the long-run elasticity of the demand for blue-collar workers in Chile (see Table

3). For white-collar workers, significant effects are found for the import penetration ratio

and the real exchange rate. Elasticities calculated at the minimum and maximum yearly

averages of those two openness proxies suggest that, over time, the effects associated with

changes in import penetration rates—which have a sign that is consistent with Rodrik’s

hypothesis—are quantitatively less important than those derived from changes in the real

exchange rate—which have the opposite sign. Indeed, under the ceteris paribus

assumption, the 63% depreciation in the real exchange rate observed from 1980 to

1988 is estimated to increase the long-run elasticity for white-collar workers from �0.15

to �0.37. The maximum temporal change in the average import penetration rate, from

20.9% in 1986 to 32.2% in 1994, is linked to a much smaller increase in that elasticity,

from �0.32 to �0.33.

5.3.2. Colombia

Consistent with the Rodrik hypothesis, real exchange rate depreciations are

associated in Colombia with reductions in the elasticity of the demand for both blue-

and white-collar workers (Table 4). The 57.2% real depreciation observed between

1982 and 1990, for instance, corresponds to reductions in long-run own-wage

elasticities from �0.33 to �0.23 in the case of blue-collar workers, and from �0.21

to �0.13 in the case of white-collar workers. However, for skilled workers, none of the

other openness proxies has significant effects, and for blue-collar workers, tariff

protection is found to be positively related to long-run elasticities. Quantitatively, the

average tariff reduction observed between 1986 and 1991, from 62.8% to 33.2%, is

associated—ceteris paribus—with a decrease from �0.35 to �0.28 in the long-run

elasticity of blue-collar workers.

5.3.3. Mexico

In the case of unskilled workers, the real exchange rate is, as in Colombia,

negatively related to the elasticity of labor demand and hence consistent with

Rodrik’s hypothesis, but no other openness proxy exhibits significant effects (Table

Table 4

Trade openness and labor demand in Colombia (1977–1991) (standard errors in parenthesis)

Dependent

variable (L t)

Explanatory

variables


U Ln WtS Ln Wt�1


test: p

valuea

Sargan

test

Autocorrelation

test (second order)

No. of

observations

(plants)

Blue-collar

employment


(0.054)

�0.153***

(0.045)

0.010

(0.046)

0.042

(0.046)

�0.028

(0.052)

0.016***

(0.004)

0.001

(0.007)

0.023** 0.703 0.478 54,029

(10,698)

Interacted with

Tariff Rate

0.005

(0.018)

�0.281***

(0.092)

0.109

(0.094)

0.190*

(0.099)

�0.035

(0.108)

0.068

(0.280)


(0.069)

�0.156**

(0.074)

�0.065

(0.076)

0.043

(0.083)

0.022

(0.090)

0.016***

(0.004)

0.006

(0.007)

0.403 0.661 0.824 54,029

(10,698)

Interacted with

Nontariff Barriers

�0.005

(0.011)

�0.207

(0.129)

0.186

(0.132)

0.149

(0.148)

�0.092

(0.157)


(0.057)

�0.414***

(0.119)

�0.039

(0.116)

0.123

(0.124)

0.114

(0.118)

0.017***

(0.004)

0.019

(0.015)

0.096* 0.536 0.279 54,029

(10,698)

Interacted with

Real Exchange Rate

�0.041**

(0.021)

0.127 0.029

(0.089)

�0.002

(0.098)

�0.093

(0.090)


(0.053)

�0.310***

(0.032)

0.077**

(0.032)

0.118***

(0.037)

�0.042

(0.028)

0.017***

(0.004)

�0.001

(0.006)

0.400 0.660 0.631 54,029

(10,698)

Interacted with

Import Penetration

0.016

(0.050)

0.157

(0.101)

�0.132

(0.096)

0.028

(0.109)

0.004

(0.103)

�0.510

(0.482)

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White-collar

employment


(0.075)

0.091

(0.062)

�0.061

(0.064)

�0.234***

(0.069)

0.084

(0.074)

0.007

(0.005)

�0.017

(0.010)

0.985 0.151 0.054 54,029

(10,698)

Interacted with

Tariff Rate

0.003

(0.028)

�0.049

(0.127)

0.043

(0.127)

�0.003

(0.144)

0.036

(0.144)

�0.218

(0.423)


(0.079)

0.126

(0.088)

�0.086

(0.092)

�0.205*

(0.107)

0.030

(0.119)

0.008

(0.005)

�0.010

(0.011)

0.827 0.129 0.093 54,029

(10,698)

Interacted with

Nontariff Barriers

�0.009

(0.014)

�0.104

(0.151)

0.081

(0.151)

�0.053

(0.188)

0.118

(0.198)


(0.044)

0.070

(0.167)

�0.031

(0.157)

�0.155

(0.177)

�0.049

(0.159)

0.005

(0.005)

0.004

(0.021)

0.002* 0.199 0.045 54,029

(10,698)

Interacted with

Real Exchange Rate

�0.070***

(0.020)

0.021

(0.129)

�0.020

(0.121)

�0.057

(0.136)

0.131

(0.122)


(0.075)

0.033

(0.040)

�0.005

(0.042)

�0.240***

(0.046)

0.085*

(0.047)

0.008

(0.005)

�0.017*

(0.010)

0.428 0.117 0.059 54,029

(10,698)

Interacted with

Import Penetration

0.038

(0.081)

0.153

(0.122)

�0.209*

(0.121)

�0.007

(0.151)

0.117

(0.137)

�0.552

(0.747)

GMM estimates with first-differenced data. Time dummies were included but are here omitted.a Wald Test of Joint Significance of interactives between the corresponding openness variable and lagged employment, own current wages and lagged own wages.




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Table 5

Trade openness and labor demand in Mexico (1984–1990) (standard errors in parenthesis)

Dependent

variable (Lt)

Explanatory

variables


U Ln WtS Ln Wt�1


test:

p valuea

Sargan

test

Autocorrelation

test (second order)

No. of

observations

(plants)

Blue-collar

employment


(0.174)

�0.162**

(0.067)

0.037

(0.063)

0.068

(0.072)

�0.051

(0.070)

0.049***

(0.010)

�0.061***

(0.008)

0.361 0.118 0.107 11,689

(2830)

Interacted with

Tariff Rate

0.049

(0.033)

�0.219

(0.180)

0.011

(0.188)

0.106

(0.191)

�0.160

(0.205)

1.897***

(0.736)


(0.161)

�0.222***

(0.048)

0.046

(0.056)

0.093*

(0.049)

�0.075

(0.054)

0.048***

(0.009)

�0.051***

(0.011)

0.962 0.223 0.086 11,689

(2830)

Interacted with

Nontariff Barriers

0.006

(0.024)

0.016

(0.067)

�0.002

(0.075)

0.041

(0.078)

�0.047

(0.085)

�0.072

(0.151)


(0.155)

0.575

(0.843)

�0.973

(0.773)

�0.531

(0.679)

0.825

(0.744)

0.049***

(0.010)

0.049

(0.158)

0.002* 0.181 0.127 11,689

(2830)

Interacted with

Real Exchange Rate

0.011

(0.029)

�0.722

(0.797)

0.938

(0.702)

0.572

(0.652)

�0.838

(0.679)


(0.152)

�0.198***

(0.065)

0.025

(0.050)

0.081

(0.058)

�0.066

(0.051)

0.047***

(0.009)

�0.059***

(0.008)

0.705 0.172 0.064 11,689

(2830)

Interacted with

Import Penetration

0.081

(0.088)

�0.089

(0.205)

0.091

(0.144)

0.073

(0.172)

�0.109

(0.156)

�0.141

(0.722)

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White collar

employment

Free-standing 0.135

(0.199)

0.300***

(0.107)

�0.095

(0.064)

�0.257***

(0.096)

0.076

(0.063)

0.022***

(0.008)

0.001

(0.010)

0.000*** 0.495 0.338 11,689

(2830)

Interacted with

Tariff Rate

0.179***

(0.045)

�0.745***

(0.275)

0.391*

(0.221)

0.376

(0.247)

�0.506**

(0.218)

3.315

(0.888)

Free-standing 0.131

(0.224)

0.025

(0.030)

0.023

(0.045)

�0.089**

(0.035)

�0.043

(0.040)

0.024***

(0.009)

�0.018

(0.014)

0.013** 0.745 0.260 11,689

(2830)

Interacted with

Nontariff Barriers

0.107***

(0.033)

�0.033

(0.076)

�0.018

(0.076)

�0.052

(0.074)

�0.004

(0.080)

0.533**

(0.220)

Free-standing 0.204

(0.336)

�0.606*

(0.276)

0.300

(0.291)

�0.346

(0.278)

�0.179

(0.285)

0.026***

(0.008)

�0.064

(0.118)

0.051* 0.343 0.402 11,689

(2830)

Interacted with

Real Exchange Rate

0.081*

(0.043)

0.600**

(0.261)

�0.273

(0.261)

�0.429

(0.263)

�0.118

(0.261)


(0.205)

0.119

(0.085)

�0.068

(0.061)

�0.205***

(0.074)

0.046

(0.059)

0.025**

(0.010)

�0.020*

(0.011)

0.085* 0.153 0.404 11,689

(2830)

Interacted with

Import Penetration

0.222**

(0.100)

�0.253

(0.284)

0.281

(0.199)

0.226

(0.260)

�0.239

(0.219)

�1.083

(0.829)

GMM estimates with first-differenced data. Time dummies were included but are here omitted.a Wald Test of Joint Significance of the interactives between the corresponding openness variable and lagged employment, own current wages and lagged own wages.




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5). For white-collar workers, the import penetration rate appears, as in Chile,

significantly associated with higher long-run elasticities, which supports Rodrik’s

view, but all the other proxies for trade liberalization point in the opposite direction.

Further, assuming all else constant the almost doubling of average import

penetration rates between 1984 and 1986, from 10.3% to 19.6%, is associated

with a very small increase in the white-collar long-run elasticity from �0.33 to only

�0.34. Movement in the policy-based proxies, however, appears to have had larger

effects. Indeed, the average tariff reduction from 33.5% in 1984 to 15.2% in 1990

is estimated to have caused a fall in the white-collar long-run elasticity from �0.28

to �0.24, and a quantitatively similar effect is estimated for the virtual elimination

of nontariff barriers. Moreover, the 29% real appreciation observed between 1986

and 1990 had an even larger impact, causing, ceteris paribus, a reduction from

�0.30 to �0.17 in the long-run own-wage elasticity of the demand for skilled

workers.

Because exchange rate movements may be correlated with protection measures

as well as with rates of import penetration, we have also estimated, for robustness

sake, specifications in which we include a full set of interactives with the real

exchange rate.12 For Chile and Colombia, the results remain substantially unchanged. For

Mexico, the effects of the protection variables and the import penetration rate on skilled

labor-demand long-run elasticities become insignificant. However, even when other

openness proxies are controlled for, the real exchange rate continues to be positively and

significantly related with a more elastic demand for unskilled workers, and a less elastic

demand for their skilled counterparts.

5.4. Industry level findings

For the specifications that involve interactives with trade openness proxies, we repeated

the estimation for all the three-digit ISIC industries that, in our sample, contained at least

60 plants: 20 industries in Chile, 24 in Colombia and 19 in Mexico.13 In the case of the

specification that includes interactives with period dummy variables, the larger number of

instrumental variables involved made us opt for performing the estimation at the level of

two-digit ISIC industries.14 An in-depth analysis of the industry level results is not feasible

here, if only because it involves over 500 regressions (results available on request).

Nonetheless, a brief summary of the findings at the disaggregated level offers some

additional insights into the aggregate analysis reported above. The results for the

specification with interactive time dummy variables suggest a substantial degree of

heterogeneity among sectors. Indeed, while the cross-sector simple and weighted averages

of the long-run elasticities found for each period reasonably closely track the pattern found

12 Since the free standing economy-wide real exchange rate is effectively collinear with the set of time dummies

introduced in all specifications, we only introduce it as an interactive term. The results are available from the

authors on request.

14 We also restricted ourselves to industries with at least 100 plants (in our sample), which implied dropping two

out of nine industries in Chile and Mexico.

13 The specification tests performed badly for smaller samples.


at the aggregate level, there are, in all three countries and for both types of workers,

individual industries for which a different evolution is found. When we interact wages

and lagged employment with trade openness proxies, this heterogeneity is

confirmed and perhaps underlies some of the ambiguity in the aggregate results.

Because of the massive amount of data required to fully report these results, Table

B1 presents only a summary, by country and trade openness proxy, of the sign of

the significant effects on long-run elasticities. Although no mapping can be made

from these summary results to the aggregate magnitudes reported earlier, it is clear

that statistically significant impacts of both signs appear for every country,

openness proxy and skill level, with little obvious systematic tendency. Maurice

and Ferguson’s agnosticism aside, there may be factors that explain the patterns

observed, but such an exercise lies beyond the scope of this paper.

6. Conclusions

This paper first argues that conceptually, the relationship between trade liberalization and

own-wage elasticities is more ambiguous than previously thought. It then examines the

relationship between the two using establishment level panel data from three countries that

experienced significant changes in trade regime during the periods under analysis. The

results show that labor-demand elasticities do change greatly in magnitude over time and

with levels of openness to international trade. However, in both Chile and Colombia we find

that periods of greater openness to trade do not coincide with those of higher labor-demand

elasticities: if anything, the opposite seems to be the case for blue-collar workers. Moreover,

in those two countries tests of the effect of trade openness on long-run labor-demand

elasticities yield either nonsignificant or mixed results. This would suggest that if trade

liberalization—or globalization more generally—is making the lives of workers more

insecure, it is probably working through some other mechanism than that examined here.

In Mexico, nonetheless, while we find some evidence that links policy-based measures

of trade liberalization with reductions in white-collar labor-demand elasticities and only

limited effects on blue-collar elasticities, there is a statistically significant increase in the

latter across the period of liberalization. This uneven evolution could, in fact, have

contributed to the worsening of wage inequality that was observed across the period.15

In sum, the three countries offer differing stories with Mexico providing some limited

support for the Rodrik hypothesis and Chile and Colombia generally not. The underlying

causes of these differences—whether the shorter data span of Mexico might generate less

reliable results; whether there may be asymmetric effects of liberalization and renewed

protection; or whether there may be industrial composition effects as suggested by the

industry level regressions—may be sorted out as we accumulate more analyses of

liberalization experiments.

15 Other possible causes for the rise in wage inequality are, as reviewed in Harrison and Hanson (1999), the

relatively larger reductions in protection experienced by sectors intensive in the use of unskilled labor,

increased outsourcing, skill-biased technological change, falling real minimum wages and the decline in union

strength.


Acknowledgements

Our thanks to Leonardo Villar, Cristina Fernandez, Shane Greenstein, Pravin

Krishna, Eduardo Ribeiro and Luis Serven for helpful discussions, and to Matheus

Magalhaes and Lucas Siga for research assistance. Also, our appreciation to Jim

Tybout for offering the three panel data sets and to Marcelo Tokman, the Ministerio

de Hacienda, and the Instituto Nacional de Estadistica, Santiago Chile for help

updating the data series for Chile. They of course, bear no responsibility for

erroneous analysis or conclusions. This project was financed by the Regional

Studies Program of the Office of the Chief Economist for Latin America, the World

Bank.

Appendix A. The database

Tables A1–3 contain means and standard deviations of the variables utilized in the

paper, except for those that were used only as instruments or controls (average industry

wages and output, and capital stocks). The data for this paper were taken from the annual

industrial surveys produced by the National Statistical Agencies of Chile, Colombia and

Mexico.16 The three surveys use manufacturing establishments as their units of

observation. Both the Chilean and the Colombian surveys cover all manufacturing

establishments with at least 10 employees. The Mexican survey covers the largest

establishments responsible for at least 80% of the total value of production in each

industry, defined at the four-digit level.

The average number of plants per year covered in the surveys varies from

4748 in Chile, to 3218 in Mexico and 6867 in Colombia. In Chile and

Colombia, the establishment’s activity is classified using the International Standard

Industrial Classification (ISIC) Revision 2, at the four-digit level, which amounts

to 86 industries in Chile, and 96 in Colombia. In the case of Mexico, a local

Industrial Classification is used instead, comprising 129 industries at the four-digit

level.17

The three surveys collect plant-level information on employment and labor costs

for blue- and white-collar workers.18 We constructed average plant wages by dividing,

for each of these categories of workers, the plant’s labor cost by the corresponding

employment of the worker category. We calculated the plant’s gross value of output as

the sum of sales, the production of fixed capital goods for own use, the difference

16 The surveys are as follows: the bEncuesta Industrial AnnualQ (ENIA) produced by the Chilean bInstitutoNacional de EstadısticasQ (INE); the bEncuesta Annual ManufactureraQ (EAM) produced by Colombia’s

bDepartamento Administrativo Nacional de EstadısticaQ (DANE); and the bEncuesta Industrial AnnualQ (EIA)produced by Mexico’s bInstituto Nacional de Estadıstica, Geografia e InformaticaQ (INEGI).17 The classification is the same as used in the 1975 Industrial Census.18 The Chilean and Mexican survey already distinguish between blue- and white-collar workers (respectively

bobrerosQ and bempleadosQ). In the Colombian survey we define blue-collar workers as the aggregate of what the

survey denominates apprentices and unskilled workers, and white-collar workers as the grouping of management,

skilled workers and technicians.

Table A1

Summary statistics* for Chilean plants (1979–1995)

Year Blue-collar

wages

White-collar

wages

Blue-collar

employment

White-collar

employment

Tariff

rate (%)

Real

exchange rate

(1980==100)

Import

penetration

(%)

Number of

observations

1979 259.0 (124.9) 588.4 (487.0) 46.9 (85.9) 16.2 (38.4) 10.0 (0.0) 116.8 (0.0) 26.2 (19.9) 2102

1980 259.0 (125.8) 586.6 (485.5) 47.5 (93.3) 16.5 (41.4) 10.0 (0.0) 100.0 (0.0) 27.6 (18.7) 2255

1981 299.4 (149.6) 653.7 (515.6) 45.4 (79.2) 16.4 (42.0) 10.0 (0.0) 100.0 (0.0) 27.9 (19.6) 2585

1982 282.8 (145.0) 638.2 (498.4) 39.3 (67.8) 15.7 (38.0) 10.0 (0.0) 124.3 (0.0) 31.6 (21.6) 2406

1983 217.1 (120.2) 517.5 (428.6) 41.0 (71.8) 15.6 (36.2) 17.8 (0.0) 133.8 (0.0) 27.0 (20.2) 2110

1984 192.9 (110.7) 475.9 (415.0) 44.5 (74.9) 15.5 (32.5) 24.1 (0.0) 137.8 (0.0) 26.3 (21.0) 2115

1985 163.2 (96.6) 410.0 (367.8) 49.1 (83.4) 16.7 (35.4) 25.7 (0.0) 156.2 (0.0) 24.2 (21.4) 2075

1986 164.7 (97.9) 398.5 (352.9) 53.4 (87.6) 19.0 (42.2) 20.0 (0.0) 151.9 (0.0) 20.9 (22.8) 1892

1987 158.0 (92.5) 373.5 (319.4) 60.8 (101.9) 22.5 (49.7) 20.0 (0.0) 148.7 (0.0) 22.2 (22.2) 1602

1988 180.9 (106.8) 420.9 (356.0) 64.2 (109.8) 24.5 (55.3) 15.0 (0.0) 163.0 (0.0) 22.4 (22.1) 1641

1989 190.1 (105.3) 434.2 (352.3) 68.6 (106.9) 27.2 (59.9) 15.0 (0.0) 162.0 (0.0) 23.9 (22.9) 1550

1990 204.1 (113.4) 473.2 (372.2) 72.0 (118.7) 29.0 (64.0) 15.0 (0.0) 157.3 (0.0) 23.4 (23.7) 1559

1991 220.3 (123.4) 517.1 (396.2) 75.8 (125.5) 30.3 (64.2) 15.0 (0.0) 148.6 (0.0) 24.2 (22.9) 1514

1992 251.5 (140.6) 572.7 (421.3) 79.2 (130.0) 32.1 (73.1) 13.0 (0.0) 138.9 (0.0) 26.6 (22.6) 1490

1993 282.5 (153.2) 631.8 (450.7) 80.6 (135.9) 32.9 (72.6) 11.0 (0.0) 144.7 (0.0) 30.2 (23.9) 1457

1994 309.5 (160.1) 683.1 (480.3) 80.9 (133.3) 35.3 (94.1) 11.0 (0.0) 141.5 (0.0) 32.2 (24.8) 1339

1995 328.6 (164.8) 713.9 (479.0) 82.3 (127.7) 37.6 (98.7) 11.0 (0.0) 128.7 (0.0) n.a. (n.a.) 1210

Source: authors’ calculations. Wages are monthly and are expressed in constant 1990 dollars.

* Means with standard deviation in parenthesis.

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Table A2

Summary statistics* for Colombian plants (1979–1991)

Year Blue-collar

wages

White-collar

wages

Blue-collar

employment

White-collar

employment

Tariff rate

(%)

Real

exchange rate

(1980=100)

Import

penetration

(%)

Nontariff

barriers

(%)

Number of

observations

1977 146.8 (88.8) 272.6 (202.5) 71.5 (167.2) 24.8 (64.3) 44.4 (16.4) 104.9 (0.0) 12.4 (16.4) 59.0 (0.0) 3393

1978 163.9 (94.4) 287.8 (213.4) 69.1 (160.4) 24.6 (65.0) 44.5 (16.4) 102.2 (0.0) 12.6 (16.2) 57.2 (0.0) 3781

1979 171.9 (101.0) 287.9 (223.9) 67.5 (174.7) 23.9 (69.7) 36.7 (16.1) 97.9 (0.0) 13.2 (17.2) 55.6 (0.0) 4498

1980 173.6 (104.8) 282.3 (221.8) 65.2 (169.7) 23.4 (69.4) 37.1 (16.4) 100.0 (0.0) 14.5 (18.1) 56.0 (0.0) 4840

1981 178.1 (112.8) 289.2 (224.5) 64.3 (167.9) 23.5 (68.9) 37.5 (16.7) 101.4 (0.0) 16.0 (19.0) 47.9 (0.0) 4942

1982 194.0 (120.6) 308.0 (239.7) 60.2 (148.7) 23.0 (69.1) 37.8 (16.9) 96.8 (0.0) 14.9 (19.2) 45.3 (0.0) 5075

1983 208.2 (131.4) 332.7 (250.3) 61.0 (143.5) 23.7 (66.3) 50.1 (23.5) 99.1 (0.0) 13.6 (17.9) 58.6 (0.0) 4864

1984 213.4 (129.2) 338.7 (250.9) 59.8 (137.7) 23.3 (63.7) 62.4 (29.4) 109.6 (0.0) 12.0 (17.1) 78.9 (0.0) 4983

1985 203.8 (124.7) 320.0 (239.0) 54.6 (123.7) 22.6 (59.7) 62.7 (29.6) 123.2 (0.0) 11.6 (16.9) 85.2 (0.0) 5133

1986 203.4 (124.3) 318.9 (241.4) 53.1 (117.9) 22.2 (59.0) 62.8 (29.6) 133.9 (0.0) 11.6 (17.4) 57.6 (0.0) 5373

1987 200.0 (120.8) 318.3 (237.9) 52.5 (117.2) 22.4 (58.4) 62.7 (29.4) 137.2 (0.0) 12.0 (17.5) 54.7 (0.0) 5643

1988 197.7 (114.2) 318.9 (232.4) 50.9 (115.0) 22.5 (58.3) 62.4 (29.1) 137.4 (0.0) 12.5 (17.3) 52.8 (0.0) 5875

1989 197.7 (118.2) 321.4 (237.3) 49.7 (112.7) 22.4 (60.0) 40.0 (14.4) 143.8 (0.0) 12.5 (17.5) 55.3 (0.0) 6105

1990 203.5 (129.7) 332.3 (248.9) 50.8 (114.4) 23.8 (62.0) 33.3 (9.3) 154.4 (0.0) 14.7 (18.9) 38.3 (0.0) 5734

1991 205.4 (133.8) 343.0 (261.0) 53.3 (120.8) 25.6 (65.5) 33.2 (9.3) 152.8 (0.0) 32.4 (48.9) 9.5 (0.0) 5186



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Table A3

Summary statistics* for Mexican plants (1984–1990)

Year Blue-collar

wages

White-collar

wages

Blue-collar

employment

White-collar

employment

Tariff rate

(%)

Real exchange

rate (1980=100)

Import

penetration

(%)

Nontariff

barriers (%)

Number of

observations

1984 365.0 (199.7) 687.1 (402.2) 230.3 (484.3) 99.3 (171.9) 33.5 (22.1) 100.0 (0.0) 10.3 (13.4) 93.4 (14.7) 2393

1985 388.3 (217.0) 735.8 (425.7) 232.8 (483.2) 100.1 (175.1) 33.6 (22.0) 99.2 (0.0) 11.2 (14.3) 93.4 (14.7) 2431

1986 345.1 (179.6) 663.6 (366.2) 223.3 (475.8) 98.5 (174.4) 31.9 (11.4) 121.8 (0.0) 19.6 (24.0) 31.8 (38.9) 2565

1987 315.6 (168.6) 609.3 (349.8) 221.4 (469.1) 97.2 (172.2) 30.4 (8.9) 119.5 (0.0) 14.1 (17.7) 19.0 (33.4) 2649

1988 309.8 (175.9) 624.4 (380.8) 225.6 (464.2) 98.6 (171.5) 14.7 (5.0) 98.6 (0.0) 14.6 (15.9) 4.3 (15.2) 2632

1989 368.2 (204.6) 798.0 (491.1) 238.6 (492.6) 102.8 (180.8) 15.2 (3.7) 96.6 (0.0) 16.7 (16.6) 3.6 (14.6) 2440

1990 402.9 (214.2) 946.9 (557.5) 244.9 (442.6) 106.0 (170.8) 15.2 (3.7) 92.7 (0.0) 17.8 (16.6) 3.3 (14.0) 2239



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between initial and final inventories of goods in process and finished goods, and the

sales of electricity. We also used, for the construction of instrumental variables, data on

capital stocks and average wages. For the former, we employed the net value of fixed

assets, which include machinery and equipment, land, buildings and structures,

transportation equipment and other fixed assets. In the case of Mexico, net capital

stocks were valued at replacement costs, while in Chile and Colombia we used their

book values, taking into account the yearly depreciation of each type of asset.19

Average wages were calculated for the purpose of instrumenting plant wages, using

all the active plants in the same industry and region.20 Whenever there were not at least

three plants with those characteristics in the same year, national average wages in the

same industry were used, provided that they were based on at least three observations.

We used the same procedure to calculate average industry output, which was used as a

control in all regressions. All monetary variables were deflated using industry-specific

price indexes, with values expressed in 1990 prices and then converted into U.S.

dollars.

As for the variables that were used as proxies for the countries’ openness to

international trade, we used data on international trade compiled using the two-digit

level ISIC by the United Nations Economic Commission for Latin America (ECLA),

from 1970 to 1994. We calculated import penetration rates as the ratio of industry

imports to domestic consumption, defined as output minus exports plus imports. We

also used data on tariff rates, defined as the average tax rate imposed on the products

of a given industry, when imported into the country. In the case of Chile, tariff rates

were already uniform across industries and we used the simple yearly average of the

valid tariff rate. In Colombia, tariff rates were also available as yearly averages,

although at the level of two-digit industries. In Mexico, mid-year tariff rates were

used, compiled at the level of four-digit industries. Finally, as proxies for nontariff

barriers to trade, we used data on the percentage of products in each industry whose

importation was subject to the granting of import licenses. During the period under

consideration, no products were subject to such restrictions in Chile, but license rates

(again defined as the percentage of products subject to license requirements) were

available at the level of four-digit industries in Mexico, and for the whole

manufacturing sector in Colombia. Finally, we used real exchange rate series,

calculated in the standard way, as the nominal exchange rate divided by the ratio of

domestic to foreign prices.

The original samples contained 80,720 observations in Chile, 103,011 in

Colombia and 22,526 in Mexico. However, we excluded from the database all

the observations that lacked information on employment, labor costs, output, capital

stocks or our openness proxies.21 This implied the exclusion of 26,897

19 In the case of Chile, capital stocks are available in the original dataset only for 1980 and 1981. However, the

series were extended to other years using annual investment and depreciation data and the perpetual inventory

method.20 The number of regions used for this purpose is 13 in Chile, 9 in Colombia and 5 in Mexico.21 These numbers include the observations that were excluded due to the absence of at least three plants in a

given industry-year cell, as average wages to be used as instruments were not constructed in those cases (352

observations in Mexico, 88 in Colombia and 3 in Mexico).


observations in Chile, 9516 in Colombia and 2898 in Mexico. We also excluded

all observations that implied changes of more than 75% in blue- or white-collar

wages in a given year, or reductions of more than 50% in total employment

accompanied by increases of more than 50% in output. The exclusions due to

these reasons were 6370 in Chile, 6071 in Colombia and 1208 in Mexico. The

third reason for dropping observations from the database was the absence of at

least three consecutive observations for a given plant, which led to the exclusion

of 5532 observations in Chile, 11,999 in Colombia and 1071 in Mexico. Finally,

in the case of Chile, the lack of information on industry affiliation for plants

that entered the survey after 1986 led to the exclusion of 11,019 observations.

The final database used in the case of Chile contains 30,902 observations, while those

for Colombia and Mexico have 75,425 and 17,349 observations, respectively.

However, given that we use a dynamic model, and that the estimation is performed

in differences, we lose the first two observations of each plant, which leaves 21,744

observations in the case of Chile, 54,029 in Colombia and 11,689 in Mexico.

Appendix B. Results at the industry level


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