Which Firms Create the Most Jobs in Developing Countries

8202019 Which Firms Create the Most Jobs in Developing Countries

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information about the constraints to job creation in emerging econo-

mies One possibility is that 1047297rm dynamics are similar to those observed

in more vibrant environments but that entry rates are lower Alterna-

tively weak job creation could be predominantly due to stagnation

amongst incumbent 1047297rms Another possibility is that job creation is ad-

equate but job destruction is excessive Of course the importance of

these mechanisms may be heterogeneous across different types of

1047297rms varying inter alia with 1047297rms size and age (Haltiwanger et al

2013) Examining which 1047297

rms create the most jobs also sheds light onthe ef 1047297cacy of the re-allocative process limited job creation could re-

1047298ect distortions and frictions inhibiting the growth of productive

1047297rms or attest to demand constraints with productivity a potentially

even more important determinant of 1047297rm growth and survival While

the data do not enable us to directly discriminate between these com-

peting explanations we can examine whether their implications are

consistentwith the patterns of employment growth we observe for ex-

ample in the former case the relationship between productivity and

employment growth would be weak whereas in the latter case it

would be strong

Tunisia a small open NorthernAfricancountry whichwas at thefore-

front of the Arab Spring provides a very relevant context to examine

these issuesLike many other countries in theregionTunisia hadhighun-

employment despite stable and relatively strong growth The economy

grew approximately 48 per annum over the period considered yet un-

employment hovered between 16 and 14 in part because the labor

force expanded by 19 per annum1 As is typical of developing countries

( Juumltting et al 2008) informal employment and small-scale non-

agricultural employment are important (Angel-Urdinola et al 2014)

with self-employment accounting for just under a third of all jobs The

highly skewed distribution of 1047297rms by size in Tunisia is also typical of de-

veloping countries For example new evidence from India and Indonesia

shows that 98 of 1047297rms have fewer than 10 workers and less than one

percent of 1047297rms have more than 50 workers (Hsieh and Olken 2014)

the same pattern as we1047297nd in Tunisia Tunisia is furthermore interesting

because its government has pursued a very active industrial policy of

which exports and small business promotion were important pillars At

the same time it is alsoknown for having relativelyburdensome business

regulation whichis often applied arbitrarily and for high levels of corrup-tion (Rijkers et al2014) Last but notleast Tunisia is oneof the few coun-

tries in theregion with a high-quality 1047297rm-census and authorities willing

to share those data with researchers

Our resultsattest to limiteddynamism Althoughthe private sector gen-

erated more than half a million net new non-agriculturalprivatesector jobs

overthe period under considerationlabor supply alsoincreased andtheag-

ricultural sector shrank in relative terms such that unemployment did not

decline drastically Informality measured as the share of employment that

is not registered with the tax authorities decreased Self-employment

rates were nonethelessverystable The1047297rm-size distribution has remained

skewed towards small 1047297rms Jump start self-employment was the domi-

nant driver of job creationover theperiod consideredeven after accounting

for upward bias in recorded entry rates of small 1047297rms due to increases in

registration rates Post-entry howeverone-person 1047297rms arethe worst per-formers in terms of net job creation such that the aggregate net contribu-

tion to job creation of self-employment is much more modest than the

gross entry numbers might suggest

While we 1047297nd a positive correlation between 1047297rm-size and net job

creation similar to that documented by Neumark et al (2011) and

Haltiwanger et al (2013) in the US this relationship is very sensitive to

regression to the mean effects and moreover entirely driven by 1047297rm

entry incumbent 1047297rms on average shed labor and small1047297rms do so rela-

tively rapidly In other words post-entry large 1047297rms consistently outper-

form small 1047297rms in terms of job creation even if we con1047297ne attention to

surviving 1047297rms Instead of aggressive market selection our results

indicate inertia churning is limited especially for larger 1047297rms and very

few 1047297rms manage to grow In conjunction with most entrants starting

very small this lack of upward mobility helps explain why the 1047297rm size

distribution has remained skewed towards small-scale production

Our results nonetheless underscore the pivotal role of 1047297rm age that

was 1047297rst pointed out by Haltiwanger et al (2013) we consistently doc-

ument a strongly negative correlation between 1047297rm age and growth

young 1047297rms tend to grow the fastest and contribute the most to net

job creation in spite of their higher exit ratesThelack of dynamism is also manifested in allocative inef 1047297ciency 1047297rm

size and age are not very strongly correlated with productivity and prof-

itability The process of creative destruction whereby resources are

reallocated towards productive resources appears anemic Productive

1047297rms and pro1047297table 1047297rms employment grows signi1047297cantly faster but

the relationship between productivity pro1047297tability and employment cre-

ationis weak Although our proxies forproductivity and pro1047297tability may

be endogenous and suffer from substantial measurement error taken at

face value our estimates suggest that ceteris paribus doubling output

per worker is associatedwith 1ndash5 higher employmentgrowthSimilar-

ly movingup a decile inthe pro1047297tability distribution (by sector and year)

is associatedwithan acceleration of employmentgrowth of approximate-

ly 1ndash2 ceteris paribus Controlling for productivity and pro1047297tability does

not affect the qualitative pattern of size and age coef 1047297cients very much

and has only a very modest impact on the estimated coef 1047297cient estimates

Overall the results highlight the relationship between weak1047297rmdy-

namics and insuf 1047297cient net job creation in Tunisia The highly skewed

1047297rm distribution with the vast majority of 1047297rms being very small and

only a small number of large 1047297rms is indicative of a failure of 1047297rms to

grow and move up the size distribution The importance of self-

employment for job creation with little evidence of growth even

amongst the more productive or more pro1047297table 1047297rms also speaks to

the static 1047297rm environment Similar aggregate statistics on labor force

participation unemployment income growth and 1047297rm characteristics

across many of the countries in the Middle East and North Africa

imply that weak 1047297rm dynamics are likely to play an important role in

explaining the poor job performance in much of the region

The remainder of the paper is organized as follows The next section re-

views related literature including a recent yet in1047298uential paper byHaltiwanger et al (2013) on patterns of job creation by 1047297rm age and size

Section three presents an overview of broad labor market trends and as-

sesses the evolution of informality and coverage of the data by comparing

the evolution of employment recorded in 1047297rm census data which covers

all employment registered with the tax authorities (ie formal employ-

ment) with employment aggregates derived from Labor Force Surveys

which cover both registered (formal) and non-registered (informal) jobs

Section four describes the data in more detail and documents salient styl-

ized facts regarding 1047297rm dynamics in Tunisia Our econometric strategy is

presented in Section 5 whileSection6 presentsour principal resultsregard-

ing the role of age and size The role of productivity and pro1047297tability is ex-

plored in Section 7 which also examines to what extent our 1047297ndings

regarding the relationship between size age and job creation re1047298ect pro-

ductivity and pro1047297tability differences A 1047297nal section concludes

2 Related literature and conceptual considerations

The ability of productive 1047297rms to expand is increasingly recognized

as critical to a countrys economic success Allocative ef 1047297ciency is typi-

cally higher in developed countries than in developing countries (see

eg Bartelsman et al 2013 and Hsieh and Klenow 2009) and this is

plausibly due to distortions or frictions preventing inputs being allocat-

ed to their optimal uses Such frictions not only may induce misalloca-

tion but also may undermine incentives to invest and grow (Freund

and Rijkers forthcoming) differences in the lifecycle of 1047297rms are anim-

portant mechanism by which differences in aggregate productivity ma-

terialize Hsieh and Klenow (2014) for instance estimate that if US

1047297rms exhibited the same dynamics as Indian or Mexican 1047297rms

1 Labor force participation rates were relatively stagnant and if anything declined due

to increasing educational attainment

85B Rijkers et al Labour Economics 31 (2014) 84ndash102



aggregate manufacturing TFP would be roughly 25 lower An impor-

tant question is thereforewhether or not productive1047297rmsin developing

countries are able to grow as quickly as those in developed countries

A parallel literature has focused on whether small 1047297rms create the

most jobs and whether or not they have specialbene1047297ts interms ofem-

ployment and productivity This debate about the role of small busi-

nesses in job creation started with the work of Birch (1979 1981)

who claimed that small 1047297rms were the most important source of job

creation in the US economy Birchs work and in particular his thesisthat small1047297rmsgrow faster than large1047297rms attracted considerable crit-

icism including Davis et al (1996) who pointed out several statistical

pitfalls underpinning his analysis such as attrition bias and a failure

to distinguish between gross and net job 1047298ows They also pointed out

that regression to the mean effects may yield a spurious inverse correla-

tion between 1047297rm size and growth since 1047297rms that experience a nega-

tive transitory shock (or whose size is measured with negative error)

are more likely to (be observed to) grow while 1047297rms that experienced

a positive shock are more likely to shrink As a consequence estimates

of the relationship between 1047297rm size and growth reliant on size-

classi1047297cations based on the start year of the growth spell ndash often re-

ferred to as base-year size classi1047297cations ndash are likely to be biased up-

wards Conversely those using size classi1047297cations based upon the end

year are likely to be biased downwards

To avoid the attendant biasesDavis et al(1996)propose to use theav-

erage of the 1047297rm size between the start and the end year of the growth

spell as the basis of the size classi1047297cation While this reduces bias consid-

erably this methodology is not without limitations In particular since

1047297rms that traverse size classes are counted as having originated in a size

class that is an average of thestarting and theending size class the contri-

bution of 1047297rms in size classes on either extreme of the size distribution is

likely to be underestimated Differences in results obtained using average

and base size classi1047297cations thus cannot be attributed to measurement

error alone mdash for they would arise even in the absence of any such error

Recently Neumark et al (2011) used both methods to study pat-

terns of job creation in the US based on the National Establishment

Time Series and found that small establishments create more jobs

Haltiwanger et al (2013) not only replicate this 1047297nding using the Longi-

tudinal Database of Firms but also show the importance of 1047297rm age inaccounting for the relationship between 1047297rm-size and job creation

once 1047297rm age is conditioned on there is no longer evidence of a system-

atic relationship between 1047297rm size and 1047297rm growth The key role for

1047297rm age is associated with 1047297rm births new 1047297rms tend to be small and

the inverse relationship between size and 1047297rm growth is due to most

new 1047297rms being classi1047297ed as small They also document an ldquoup or outrdquo

dynamic of young 1047297rms in the US such young 1047297rms grow much faster

conditional on survival but are also much more likely to exit

To what extent theldquoupor outrdquo dynamic re1047298ects a process of compet-

itive selection and whether the dynamic generalizes to developing coun-

tries are important open-ended questions While a large number of

studies have focused on the determinants of 1047297rm growth in developing

countries most of the literature has by necessity been based on datasets

that are at best partially representative (a notable exception is Klapperand Richmond 2011) In particular microenterprises are typically not

covered which is unfortunate since such 1047297rms account for a large and

often growing share of employment in developing countries Moreover

most panels tend to be relatively short and often only cover particular

sectors most notably manufacturing Nonetheless existing studies

point towards size age and productivity (eg see Sleuwaegen and

Goedhuys 2002 Bigsten and Gebreeyesus 2009 van Biesebroeck

2005 Ayyagari et al 2013) as important determinants of 1047297rm growth

but the conclusions derived from this literature are not unequivocal

For example using a panel of manufacturing 1047297rms from 9 African coun-

tries Van Biesebroeck 1047297nds that larger 1047297rms grow faster whereas

Sleuwaegen and Goedhuys (2002) conclude that small 1047297rms have the

highest growth rates using a panel of 1047297rms from Cocircte dIvoire While

the jury is out on which 1047297rms create the most jobs it is of interest to

note that across the developing world non-agricultural employment in

small 1047297rms and informality are on the rise ( Juumltting et al 2008) This

trend appears indicative of high entry into small scale activities yet it is

not clear whether this tendency towards increased skewness is offset

or catalyzed by the post-entry performance of small 1047297rms

3 Broad labor market trends and the evolution of informality

To contextualize the analysis that follows we 1047297rst describe broadlabor market trends and then assess the evolution of informality and

in the process data coverage Since our analysis relies on administrative

data on 1047297rms and entrepreneurs registered with the tax authorities the

Reacutepertoire National des Enterprises (RNE) discussed in more detail in the

next section accurate interpretation of the documented trends requires

an understanding of how representative these data are and how data

coverage may have changed over time To this end we compare aggre-

gate employment trends documented using the RNE with those derived

from Labor Force Surveys (LFS) which cover all employment both reg-

istered (formal) and non-registered (informal) The comparison helps

assess what share of employment is informal ie not registered and

how this has evolved over time

To set the stage Table 1 below 1047297rst presents information on broad

labor market trends and GDP growth in Tunisia over the period 1996ndash2010 Growth was consistently positive hovering around 5 per year

with a deceleration in growth at the end of the sample period perhaps

in part re1047298ecting the global trade collapse Unemployment was consis-

tently high but came down somewhat from approximately 16 in the

late nineties to about 13 in 2010 The employment to population

ratio remained roughly constant 1047298uctuating between 40 and 41 The

share of self-employment was high yet relatively stable over the sample

period notably typically a little above 30 Agricultural employment

which is excluded from our analysis declined somewhat in relative

terms from an estimated 218 of all employment in 1997 to 176 in

2010 In spite of minor 1047298uctuations public sector employment

remained roughly stable over time (in relative terms) accounting for

approximately just over one 1047297fth of all jobs These statistics attest to a

relatively stagnant labor market characterized by excess labor supplyand limited job creation in relative terms in spite of substantial output

growth

Nonethelessthere was substantial job creation in absoluteterms as is

documented in Table 2 which records the evolution of non-agricultural

private sector employment aggregates derived from the Labor Force Sur-

veys (LFS) with thosederivedfrom the Reacutepertoire National des Entreprises

(RNE) for 1997 2001 2005 and 2010 years for which we obtained ac-

cess to the raw LFS microdata2 Both surveys document an expansion

of employment in excess of half a million jobs between 1997 and 2010

Comparing the aggregate employment numbers derived from both in-

struments helps assess what share of aggregate employment is formal

de1047297ned here as being registered with the tax authorities and assess to

what extent employment trends documented using the RNE might be

driven by differences in registration rates over time

The comparison unveils substantial informality which has been de-

clining over time3 In 1997 31 of all employment was not-registered

whereas by 2010 informality de1047297ned as non-registration had reduced

to 24 While the reduction in informality is welcome it implies that

(relative) job creation rates derived from the RNE are likely overly opti-

mistic as they in part re1047298ect improvements in data coverage over time

According to the LFS employment grew by 44 between 1997 and

2010 while registered employment recorded in the RNE grew by 60

2 We obtained the LFS data from the Institut National de la Statistique and the World

Bank (2014)3 This is also manifested in a reduction in the Schneider index which estimates the size

ofthe shadoweconomy as a percentage of GDPfrom387 in1999the 1047297rstyearfor which

data available to 355 in 2007 the last year for which data were available

86 B Rijkers et al Labour Economics 31 (2014) 84ndash102



over that same period Nonetheless due to its imperfect coverage

the RNE still underestimates aggregate job creation in absolute

terms

Given our focus on 1047297rm size of particular concern are differences in

data coverage (trends) across different types of 1047297rms While the Labor

Force Surveys do not contain information on 1047297rm-size they do allow us

Table 1

Labor market trends and output growth in Tunisia


96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Growth ()

GDP growth 71 54 48 61 47 49 18 56 61 40 53 63 46 30 30

Labor force participation population growth and unemployment ()

Unemployment rate 159 160 157 151 153 145 139 142 125 124 124 133 130

Labor force participation rate 512 512 510 508 505 503 501 498 496 493 495 498 500 503 506Employment to population ratio 15 + 409 409 400 403 402 402 398 399 400 402 405 407 409 407 410

Population growth 15 14 13 13 11 11 11 06 09 10 10 10 10 11 10

Agricultural and public employment ( of total)

Share of employment in agriculture 218a 220a 187 193 183 177 181 176

Share of employment in the public sector 238a 218a 238a 200a

Structure of employment ( of total)

Self-employed 312 310 323 323 356 237 310 303 296 315

Wage and salaried workers 684 681 676 677 643 755 687 695 695 685

Informality ()

Schneider indexb 387 384 378 378 374 369 367 359 354

Source World Bank Development Indicators (WDI)a Authors own calculations using Labor Force Survey datab The Schneider index is a proxy for informality an estimate of the share of output that is produced informally (Schneider et al 2010)

Table 2

The evolution of employment and informality

The evolution of non-agricultural private sector employment

Labor Force Surveys (cover both registered and non-registered employment) vs 1047297rm census data (RNE) (covers registered employment only)

Total number of workers

1997 2001 2005 2010

Labor Force Surveys (LFS) (both registered and non-registered employment)

Self employmenta 371516 408934 446843 541467

Wage employmentb

1007422 1121717 1231902 1444415Total employment 1378938 1530651 1678745 1985881

Firm census data (RNE) (registered employment only)

Self-employment c 312041 350775 422625 513032

Wage employmentd 634384 804130 826230 1003911

Total employment 946425 1154905 1248855 1516943

The evolution of informality

Measured as the share of employment that is not-registered

of employment

1997 2001 2005 2010

Self-employment share (LFS self-employment LFS total employment)

Self employment share 2694 2672 2662 2727

non-registered employment (= RNE employment aggregate LFS employment aggregate)

Self employment 1600 1422 542 525Wage employment 3703 2831 3293 3050


non-registered employment mdash corrected for zombie 1047297rmse

Self employment 2273 2108 1299 1283

Wage employment 3766 2903 3360 3119


Notes LFS = Labor Force Surveys RNE = Reacutepertoire National des Entreprises Tunisias 1047297rm censusa Self-employment is calculated as the sum of individuals declaring themselves to be sole proprietors or employers excluding people working for the government or state owned

enterprises and people engaged in agricultural activities (eg those working in lsquoExploitation agricolersquo or lsquoChantier agricolersquo)b Wage employment is a residual category including apprentices unpaid family helpers (which account for approximately 2 of all non-agricultural employment) apprentices and

others(which jointly account forless than 1 ofall non-agricultural employment) again excludingthose working forthe government or state ownedenterprisesor andpeople engaged in

agricultural activities (eg those working in lsquoExploitation agricolersquo or lsquoChantier agricolersquo)c Self employment in the 1047297rm census (RNE) is calculated as the number of 1047297rms in the regime ldquoPersonne Physiquerdquo and ldquoSocieacuteteacute Unipersonnel a Responsabiliteacute Limiteacuteerdquod Wage employment in the 1047297rm census (RNE) is the sum of all salaried employment (which comes from the Social Security Database)e The correction for zombie1047297rms which are 1047297rms thatare recorded in theRNE but areno longer economically active is to assumethat 8 of registered self-employmentis in inactive

1047297rms and that 1 of all registered wage employment is in inactive 1047297rms The adjustment is based on research conducted by the INS




to distinguish albeit crudelybetween wageand self-employment Some-

what paradoxically informality rates measured by non-registration with

the tax authorities have consistently been higher for wage employment

than for self-employment and the gap has widened slightly over time

In 1997 37 of all wage jobs were not registered and by 2010 this per-

centage had declined to 31 Non-coverage of self-employment de-

creased from 16 in 1997 to only 5 in 2010 This implies that in the

RNE database small-scale (self-)employment is relatively overrepresent-

edAnalysis of jobcreation trends based on theRNE database is thus like-ly to overestimate the relative importance of entry and small 1047297rm entry

in particular In sum small 1047297rms are not only better represented in the

RNE to start with RNE coverage of them has also expanded more rapidly

than RNE coverage of wage employment

The high registration rates of especially small 1047297rms re1047298ect both low

costs of registration and high penalties for non-compliance Moreover

the tax burden on micro-1047297rms is limited since 1047297rms can opt to operate

in the so-called reacutegime forfaitaire in which they pay a 1047297xed fee of about

$38 USD (60 dinars) per yearbut do not have to pay any additional pro1047297t

or output taxes provided that output does not exceed a certain sector-

speci1047297c threshold Registration provides access to public health insurance

(including for family members) and is necessary to compete for publicly

tendered contracts and to apply for loans Improvements in tax adminis-

tration and expansion of public health insurance for registered workers

likely have contributed to improvements in registration over time

Registration rates recorded in the RNE are somewhat exaggerated since

theReacutepertoire also contains1047297rmsthatareno longer economically activede-

spite still being registered (so-called ldquoFauxActivesrdquo) These1047297rmsare typical-

ly one-person 1047297rms and account for roughly 8 of all one-person 1047297rms in

any given year and less than 1 of 1047297rms employing wage workers The

prevalence of such falsely active 1047297rms also called zombie 1047297rms has not

changed much over time As a robustness check we present informality

rates in which we attempt to correct forthe existenceof zombie1047297rmby re-

ducing thenumber of registered formal wage jobs by 1 andthe numberof

self-employment jobs by 8 While this results in slightly higher overall in-

formality rates driven by higher informality amongst the self-employed

we obtain the same qualitative pattern of results

Although coverage of theRNEis imperfect a key takeawayfrom thecom-

parison is that coverage of theRNE is decentoverallespeciallywhen onecon-siders that discrepancies in employment aggregates are predominantly

accounted for by informality in the construction sector as is documented in

Table 3 which provides a breakdown of informality rates (and consequently

RNE coverage) de1047297ned as non-registration by sector for the years 1997 and

2010 In constructionunder-reportingis rifeand informaljobs account forap-

proximately three-quarters of all employment Excluding the construction

sector only 9 of all employment was informal in 2010

4 Data and descriptive statistics

41 Data

The main dataset used for this paper is the Tunisian registry of 1047297rms

the Reacutepertoire National des Entreprises (RNE ) for the period 1996ndash2010collected by the Tunisian Institut National de la Statistique (INS ) The

RNE draws on information from a host of constituent administrative da-

tabases including from the social security fund (Caisse Nationale de la

Seacutecuriteacute Sociale mdash CNSS) which is the source for the employment data

as well as from Tunisian Customs the Tunisian Ministry of Finance

and the Tunisian Investment Promotion Agency (lAgence de Promotion

de lIndustrie et de lInnovation mdash APII) containing data on all 1047297rms reg-

istered with the tax authorities (see INS (2012) for detailed information

on its construction) It has information on inter alia the employment

age and main activity of all registered private4 non-agricultural 1047297rms

except cooperatives A major and unique advantage of the Reacutepertoire is

that it has no 1047298oor in terms of size and records information on 1047297rms

without paid employees ie the registered self-employed which ac-

count for the bulk of all enterprises This renders it feasible to examine

the dynamics of these 1047297rms which are often not covered by 1047297rm cen-

suses and to assess their contribution to aggregate net job creation

which we will demonstrate to be very important

Another key strength of the Reacutepertoire is that it is comprehensive It

covers all non-agricultural sectors and spans a relatively long time peri-od The database also allows us to track and entry and exit over time

and thus to avoid survival bias

To assess the role of productivity and pro1047297tability which are widely

recognized to be critically important but not routinely available in 1047297rm

census data the RNE was merged with pro1047297t and turnover data from

the Tunisian Ministry of Finance spanning the universe of private

1047297rms tax records for the period 2006 through 2010 Combining these

different data-sources enables us to assess to what extent the striking

relationships between 1047297rm size age and growth documented by

Haltiwanger et al (2013) re1047298ect performance differences associated

with scale and across the lifecycle

Some features of the data have to be borne in mind when interpreting

the results As already alluded to the Reacutepertoire only provides information

on registered employment Consequently it does not document informal

employment which is substantial in Tunisia as was shown in the previous

section The employment numbers (and1047298ows) in our data are likely to be

biased downwards both due to under-reporting of labor by registered

1047297rms and because some 1047297rms may not register at all In addition the supe-

rior coverage of self-employment in our data compared to wage employ-

ment suggests that estimates of the skewness of the size distribution are

likely somewhat exaggerated Underreporting may also impact estimates

of the relationship between 1047297rm size and net job creation if the extent of

underreporting conditional on being formal increases with1047297rm size results

regarding the relationship between 1047297rm size and growth might be biased

downwards On the other hand microenterprises that register may be

more successful then ones that choose to remain informal which may

bias recorded employment growth of small 1047297rms upwards

Second our database is a database of 1047297rms not establishments we

thus do not observe job-reallocation due to plant openings or closingsIn addition the INS data contain information on the number of salaried

employees but not on the number of unpaid employees or the number

of 1047297rm owners In fact the vast majority of 1047297rms do not report employing

any salaried employees because they are one-person 1047297rms in which the

proprietor also supplies all the labor To arrive at a measure of employ-

ment we assume that all 1047297rms employ at least one unpaid worker (in

the case of self-employment this implies that we count the proprietor

as employee) This assumption is not accurate since some 1047297rms do not

employ any unpaid workers which would result in upward bias in the

employment numbers whereas others may employ multiple such

workers which would imply downward bias in our employment esti-

mates Yetthis assumptionenables us to estimate the contribution of reg-

istered self-employment which we will show to be very large Moreover

it ensures that absolute size differentials in terms of the number of sala-ried workers are preserved and that we do not have to divide by zero

Data on turnover and pro1047297ts are not available for all 1047297rms even

though the database we obtained access to is the most comprehensive

database of turnover and taxes available in Tunisia The reason that

such data are missing for a number of 1047297rms is that the tax obligations

for these 1047297rms do not depend on their output and turnover and tax in-

spectors consequently do not have strong incentives to verify the tax

declarations of such 1047297rms which provide the basis for the output and

pro1047297t data from theMinistryof Finance5 In additionthe reportingqual-

ity is low for those 1047297rms in this category that do report In the analysis

4 While theRNE alsocollectsinformationon publicly owned enterprises it does notre-

liably record their employment which according to INS estimates accounts for 21 of

overall employment We drop such 1047297rms from the analysis

5 These are1047297rms in theregime ldquototalement exportatricerdquo commonlyreferred to as ldquooff-

shorerdquo 1047297rms and 1047297rms in the

ldquoregime forfaitaire

rdquo




that uses pro1047297tability and productivity measures we therefore exclude

this group of 1047297rms We also discard 1047297rms that do not report hiring any

paid laborers 1047297rms which exhibit extreme volatility in gross output

per worker as well as extreme values relative to the sector-year-

average when using information on turnover and pro1047297ts6 In

interpreting results it is therefore important to bear in mind thatthese are only representative for the subgroup of 1047297rms for which

these data are available and reliable this group is not representative

of the entire universe of all private Tunisian 1047297rms

Finally because the RNE is based on administrative data the timing of

1047297rmexit isa concern the legal date of 1047297rm closure maylag thetermination

of economic activity7 The INS has a deterministic model to identify zombie

1047297rms whichwe employ to exclude such1047297rms from theanalysis That iswe

assume that they exitin theyeartheyare1047297rst observedto beldquofalselyactiverdquo

rather than theyear that they in fact disappear from thedataFirmsthat are

always ldquofalsely activerdquo are excluded from the analysis altogether

We also adjust the year of exit of 1047297rms that have ever employed

salaried workers to the year after they stop doing so rather than the

year they legally cease to exist provided they do not record producing

output in that or any subsequent year The reason for making this adjustment is that our employment imputation procedure exacerbates

the potential problem of misclassifying 1047297rms as being active when in

fact they are inactive (remember that we assume that each 1047297rm in the

RNE employs at least one unpaid worker) Unfortunately we cannot

make this adjustment for the registered self-employed that has never

used any paid labor and we are consequently likely to overestimate

the longevity of such 1047297rms somewhat at least in the short-run These

data-cleaning procedures thus inevitably introduce a degree of asym-

metry between 1047297rms that have never employed wage workers and

theones that have but we obtain thesame qualitative pattern of results

when we focus the analysis strictly on wage employment as is shown in

Appendix B The main text focuses on the analysis of data that includestheself-employedwhich we believe to allow for a more comprehensive

analysis of the Tunisian labor market

42 Descriptive statistics

A 1047297rst look at the 1047297rm data yields a number of surprising stylized

facts To start with the Tunisian 1047297rm-size distribution presented in

Table 3

The evolution of informality ndash measured as non-registration ndash by sector

Year 1997 2010

Firm census LFS Informal Firm census LFS Informal

(RNE) (RNE)

(a) (b) (a minus b) a (a) (b) (a minus b) a

registered jobs all jobs of jobs of jobs

(000s) (000s) (000s) (000s)

Agriculture

Agriculture 23 20 minus17 28 21 minus29

Manufacturing

Agro-industries 45 43 minus3 59 68 13

Manufacturing mdash textiles 153 255 40 210 248 15

Manufacturing mdash other 140 179 22 245 271 9

Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services

Transport amp telecom 76 68 minus12 129 117 minus10

Hotels and restaurants 71 81 13 92 122 25

Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24

Total excluding construction 858 1094 22 1411 1553 9

Notes LFS = Labor ForceSurveys RNE = Reacutepertoire National des Entreprises Tunisias 1047297rm census LFS aggregates are computed excluding employment in the public sector and agricul-

tural establishments ldquoInformalrdquo is a proxy forthe share of employment that is notregisteredmeasuredas thedifferential between employment aggregates obtainedfrom theLFS andthe

RNE as a share of total employment in the LFS Note that this differential is sometimes negative because the LFS is a survey not a census such that there is some estimation error In ad-

dition the LFS classi1047297cation is based on self-reporting whereas the RNE classi1047297cation is based on the 1047297rms reporting Potential mismatch between self-reported sector classi1047297cations of

workers and1047297rmsis especiallylikely forworkersemployed bylaborintermediationagencies(suchas Manpowerand Adecco) whowillmostlikelyclassifythemselves based onthe sector

they are dispatched to rather than the sector in which the 1047297rm they have an of 1047297cial contract with is operating

6 We exclude 1047297rms which had a jumpin gross output per workerin excessof 100that

didnot persist thesubsequentperiod Wealso exclude 1047297rms who on average experiencing

swings in gross output per worker in excess of 150 Moreover we exclude the top and

bottom 1 of 1047297rms in terms of gross output per worker and pro1047297ts by sector-year7 As discussed in Section 3 surveys conducted by the INS suggest that at most 1 of

1047297rms which report employing at least one wage workers are in fact inactive For the reg-

istered self-employed that do not use any wage labor the number of such ldquofalsely activerdquo

1047297rms is 8

Table 4

Firm size and employment distributions 1996ndash2010 (annual averages)

Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106

Notethe statisticspresentedin this table areannual averagesoverthe period1996ndash2010

Entry rates are measured as the share of new 1047297rms in year t of the total number of 1047297rms

that are economically active in year t For each 1047297rm jobs are measured as the sum of all

paid employment +1 on the assumption that each 1047297rm employs at least one worker

who does not receive a salary The number in the fourth column presents the aggregate

total by 1047297rm size category Age is measured as the difference between the calendar year

and the year of startup




Table 4 is severely skewed towards employment in small 1047297rms Over

the period 1996ndash2010 one-person 1047297rms (ie the registered self-

employed) account for approximately 83 of all 1047297rms and 28 of em-

ployment Of course the recorded skewness partially re1047298ects the supe-

rior coverage of small 1047297rms in the Reacutepertoire documented in Section 3

Nonetheless substantial skewness is also observed in the upper parts

of the 1047297rm-size distribution manifested in the very limited number of

large 1047297rms on average in each year there were approximately only

51 1047297rms that employed at least a thousand workers These relatively

large 1047297rms whichtend to be older on average account for an important

share of registered employment for example even though fewer than

02 of all 1047297rms employ more than 200 workers such 1047297rms account

for more than a quarter of all employment Overall however employ-

ment is concentrated in small 1047297rms

Second the 1047297rm size distribution has remained skewed towards

small-scale production as is demonstrated in Fig 1 which depicts the

evolution of the1047297rm size distribution graphically While the 1047297gure sug-

gests that the distribution has become more right-skewed as the share

of 1047297rms that are one-person enterprises has increased this might be

due to the more rapid expansion of coverage of self-employment com-

pared to wage employment documented in Section 3 recall that ac-

cording to the Labor Force Surveys the share of the population that is

self-employed has remained roughly constant (see Table 2)

A third stylized fact is that employment is disproportionately con-

centrated in young1047297rms Table 5 documents the distribution of employ-

ment by 1047297rm size and age over the period 1996ndash2010 demonstrating

that most jobs were concentrated in old large 1047297rms and relatively

young one-person 1047297rms (ie self-employment) New 1047297rms account for

37 of all jobs on average while 1047297rms that are younger than 10 years

old account for approximately half of all jobs in total This 1047297nding in

part re1047298ects improvements in registration over time since some of the

newly registered 1047297rms might have existed for a while prior to being

Fig 1 Evolution of the 1047297rm size distribution Note The graph depicts the evolution of the 1047297rm size distribution between 1996 and 2010

Table 5

Employment by 1047297rm size and age mdash annual averages 1996ndash2010

Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717

Note Firmsize is measured as the sumof all paid employment +1 on theassumption that each 1047297rm employs at least one worker who does not receive a salary Age is measured as the

difference between the calendar year and the year of startup The statistics presented in this table are annual averages over the period 1996ndash2010 For example the interpretation of the

number35022 inthetop leftcellin the Table(0 years ofage1 worker) is that onaveragenew1047297rmsemploying oneworker only employed 35022 workers annually between 1996 and

2010




recorded in the1047297rm census in which case their registered age will be an

underestimate of their real age

Fourth prima facie the correlation between size age and 1047297rm perfor-

mance in terms of productivity and pro1047297tability appears relatively weak

which may re1047298ect measurement error Table 6 provides descriptive statistics

on real gross output per worker and real pro1047297ts per worker8 reported forthe

sub-sample of 1047297rms for which such declarations are likely to be reliable

which is not representative of the entire Tunisian private sector To start

with the largest 1047297rms are neither necessarily the most productive nor the

most pro1047297table The relationship between mean output per worker and

1047297rm size is not monotonic Once we demean output per worker by the rele-

vant sector average and focus on medians we observe a mildly positive rela-

tionship between1047297rm size and output per worker although the very largest

1047297rms record the lowest levels of output per worker This points to the pres-

ence of measurement error which is also suggested by the fact that large

1047297rms consistently report lower average pro1047297ts per worker than small 1047297rmsAnother manifestationof limited dynamism is that output per work-

er does not appear to rise very much with 1047297rm age though age is

underestimated for 1047297rms that operated informally prior to registering

with the tax authorities Pro1047297ts per worker do seem to increase with

age at least for the youngest1047297rms This might re1047298ect higher investment

activity amongst younger 1047297rms (note that1047297rms can deduct the costs of

investment spending from the pro1047297ts they report to the tax authorities

such that low reported pro1047297ts may be due to high investment spend-

ing) Consistent with the evolution of pro1047297ts per worker older 1047297rms

are also on average less likely to report losses than smaller 1047297rms save

again for the very oldest 1047297rms

While we should be cautious in interpretingthese1047297ndings regarding pro-

ductivity and pro1047297tability given the nature of the data they do not appear to

be driven by measurement error alone Mouelhi (2012) documentsverysim-

ilar patterns of output per worker and pro1047297ts by 1047297rm-size and age using the

Tunisian Annual Enterprise Survey which is an extensive survey containing

detailed information on output labor usage and pro1047297tability conducted

amongst a sub-sample of approximately 1047297ve thousand1047297rms

A 1047297fth stylized fact is that aggregate job creation has been disappointing

and driven mostly by entry as is shown in Fig 2 which decomposes net job

creation into the contributions of entering1047297rmsexiting1047297rms and continuing

1047297rms With the exception of 2001 almost all of the net new jobs were in en-

tering 1047297rms The important role of entry which accounts for 991 of all net

job creation remains even if we account for the fact that some of these

1047297rms might already been operating informally prior to registering by

subtracting from the recorded entry rates the share that is plausibly due to

improved coverage for example if we subtract from the net job creationnumbers the approximately one-hundred and 1047297fty four thousand jobs that

are due to improvements in registration over time (registration rates im-

proved by 775 over the period) and assume that these are all accounted

for byentrants the dominant role of job creation due to entry as jobcreation

by entrants would still account for 986 of all net job creation

Sixth the bulk of net job creation is driven by entry of one-person

1047297rms (self-employment)9 as is demonstrated in Table 7 which docu-

ments total net job creation by 1047297rm-size and age over the period 1996ndash

2010 using size classi1047297cations based on last years (base) size andaverage

8 Sincewe do notobservecapital and material inputs estimatingTotal Factor Productiv-

ity which would be our preferred productivity proxy is not feasible

9 Note that the contributions of one-person 1047297rms to job creation are estimated to be

even higher when using the average size classi1047297cation because new 1047297rms are classi1047297ed

at the average of their size For example 1047297rms that enter as a two-person 1047297rm will be

counted as contributing to job creation by one-person 1047297rms

Table 6

Productivity and pro1047297tability by size and age 2006ndash2010

2006ndash2010 Productivity Pro1047297tability

Ln(YL) ln(YL)

demeaned by sector

average

Pro1047297ts per worker

N = 142823 Mean Median Mean Median Median Rank Incurring a loss

(Millimes of TND) (Millimes of TND) (TND) (1 = lowest

100 = highest)

(Proportion of 1047297rms)

By size (wage workers)

1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023

Note The sample is con1047297ned to 1047297rms which employ at least one wage workers and whose tax obligations vary with their gross output andor pro1047297ts Y is measured as gross output de-

clared to thetax authoritiesPro1047297tsaremeasuredas the pro1047297ts declaredto thetax authorities Onemillime ofTND is equal to 11000Dinar Incurringa loss is a dummyvariable that takes

the value 0 if the 1047297rm reports non-negative pro1047297ts and 1 if it reports negative pro1047297ts TND = Tunisian Dinar TNDUSD exchange rate on December 17 2010 1 TND0692 USD




size Fig 3 shows these results graphically for the base-size classi1047297cation10

The table and graph show that subsequent to entry such 1047297rms exhibit farless growth such that the net contribution to job creation of one-person

1047297rmsis much more modest especially when usingtheaverage size classi1047297ca-

tion Acrosssize classes netjob creation is typicallyconcentratedamongst the

youngest1047297rms In additionit appears as thoughthecontributionof relatively

old small1047297rms to net job creation is limited in absolute terms as most of the

net new job creation by older 1047297rms is concentrated in relatively larger1047297rms

While the improvements in the registration rate documented in

Section 3 lead to in1047298ated entry numbers they do not drive the qualita-

tivepatterns we observe for instance if we subtract from the aggregate

net job creation due to entry the amount that is plausibly due to im-

provements in coverage notably approximately 1047297fty-eight thousand

new one-person 1047297rms and ninety-four thousands of jobs in 1047297rms with

more than worker11 we arrive at the same qualitative conclusions12

Seventh mobility is limited Table 8 presents transitions of 1047297rms be-tween broad size-classes both annually (the top panel) and between

1996 and 2010 (the bottom panel) the longest period available in our

database Most 1047297rms do not grow even in the long-run Only a few

1047297rms change size class even during a fourteen-year period the self-

employed is least likely to expand into a larger size class perhaps in

part re1047298ecting that traversing size classes would effectively amount to

a doubling of 1047297rm-size for them Micro and small 1047297rms hardly ever

grow large For example only 2 of all 1047297rms employing between 10

and 50 people in 1996 employed more than 100 workers by 2010 The

lack of mobility may in part be driven by very restrictive labor regula-

tions that make 1047297ring both costly and dif 1047297cult The transition matrices

also show that smaller 1047297rms are more likely to die13 but overall exit

rates seem quite low14 perhaps in part due to complex bankruptcy

procedures and a lack of competition Prima facie these statistics are

at odds with the existence of a very strong up-or-out dynamicThus at 1047297rst sight the meager net job creation that underpins

Tunisias disappointing aggregate unemployment reduction record

does not appear due to excessive job destruction but rather re1047298ects a

lack of mobility and limited entry especially of large 1047297rms

5 Econometric strategy

Our goal is to examine the drivers of job creation assessing the role

of size age productivity and pro1047297tability To this end we estimate

employment-weighted 1047297rm-level regressions of net employment

growth using as our measure of 1047297rm-level employment growth g ist the change in employment from year t minus 1 toyear t divided by average

size g ist frac14 2

E ist minusE ist minus1

E ist thornE ist minus1eth THORN where E ist denotes employment in1047297

rm i of type sat year t (following Davis et al 1996 and Haltiwanger et al 2013)15

This measure is symmetric bounded by minus2 and 2 and accommodates

both entry and exit16 By virtue of employment weighting the mean of

the dependent variable is equal to the appropriate employment weight-

ed mean and coef 1047297cient estimates can consequently be interpreted as

employment weighted conditional means17

To assess to what extentthe observed relationship between 1047297rm size

and1047297rmgrowth isdue to1047297rmsizeperseor toother1047297rm characteristics

we consider progressively elaborate sets of explanatory variables Fol-

lowing Haltiwanger et al (2013) we 1047297rst include size and age dummies

separately and subsequently jointly We use both size dummies based

on average 1047297rm size that is the average of 1047297rm size between year t and year t minus 1 and based on last years size to examine the impact of

measurement error and regression to the mean effects These variablesare available for the period 1997ndash2010 Subsequently we examine the

impact of productivity and pro1047297tability proxied by gross output per

worker and pro1047297ts per worker respectively variables which are

Fig 2 Aggregate job creation patterns

10 The advantage of the base size classi1047297cation relative to the average size classi1047297cation

in thiscontextis that it does notexaggerate thecontribution of start-up self-employment11 Recallthatthe improvement inRNE coveragebetween1997and 2010 is 108 forself-

employment and 65 for wage employment which accounted for respectively 541466

and 1444415 jobs in 2010 according to the Labor Force Surveys12 In thecase of thecontribution of jump start self-employment to netjob creation they

become quantitatively even more dramatic if we do not correct for improved coverage

jump-start self-employment accounts for 73 of all net job creation using the base-size

classi1047297cation and 80 using the average-size classi1047297cation Once we correct for improve-

ments in registration and assume that these are all accounted for by entrants (which is

likely an overcorrection) the percentages increase to 84 for the base size classi1047297cation

and 92 for the average size classi1047297cation respectively13 Note that therelationship between 1047297rm size and 1047297rm exitis not strictlymonotonic in

theshort-run which is dueto ourcorrections forthe timingof exit(see also Appendix B)14 Note that theexitrates reportedhere arenot outof linewiththosedocumented forother

countries in the Middle East and Northern Africa region (see eg World Bank 2012

Hallward-Driemeier and Thompson 2009) which are low by international comparison

15 The desirable features of this growth rate measure which is a second order approxi-

mation of thelog difference for growth rates around zero are discussed in detail in Davis

Haltiwanger et al (2013) The underlying statistical properties are discussed in detail in

Tornqvist et al (1985)16 To seethis note thatfor 1047297rms that enter at year t E it minus 1 =0 whilefor1047297rms that exit

E it = 0 such that for entering 1047297rms g it = 2 while for exiting 1047297rms g it =minus217 As explainedby Davis etal (1996)usingthis measure it is straightforwardto generate

aggregatemeasuresof job creationand destructionat anylevel of aggregation by usingap-

propriately employment weighted summations of this measure Forexamplethe jobcre-

ation rate of 1047297rms oftype s at time t canbe computed as JC ist frac14 sumi

X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist

represents the relative employment share of 1047297rm i of type s at time t






We 1047297rst include these measures separately and then jointly Our

most general speci1047297cation thus takes the form

g ist frac14 β S Size thorn β A Age thorn β P Productivity thorn β π Profitability thorn β τ τ thorn β I I thorn eit

where Size is a vector of size dummies Age is a vector of age dummiesτ

is a vector of time dummies I a vector of industry dummies and Produc-

tivity and Pro 1047297tability are the proxies for these concepts How these

proxies are de1047297ned depends on which size classi1047297cation is used forthe base-year classi1047297cation we use last periods log output per worker

and rank in the pro1047297ts per worker distribution respectively except for

entrants for whom we use contemporaneous values since lagged values

are not available The use of the pro1047297tability rank as opposed to levels

helps reduce the impact of extreme observations and thus measure-

ment error while allowing for both negative and positive values

When using the average size classi1047297cation we opt instead to use theav-

erage of logoutput per workerand the pro1047297tability rank over the period

over which the growth spell is de1047297ned This serves to minimize the im-

pact of potential measurement error For entrantswe again usethe con-

temporaneous values of these variables whereas for exiting 1047297rms we

use their last observed values

This speci1047297cation and the models it nests enable us to test a range of

hypotheses for example based on the existing literature one might ex-pect the coef 1047297cient estimate on small 1047297rms to be larger than that of

large 1047297rms β Ssmall N β Slarge when we only control for 1047297rm size Including

controls for age is likely to reduce the magnitude of 1047297rm size effects

(the β S estimates) and may well reverse their ordering (that is

β Ssmall b β Slarge) If the most productive 1047297rms expand quickly after entry

or if the most successful entrants increase both in terms of size and output

per worker one might expect that including controls for productivity

would suppress the magnitude of the impact of both size and age

dummies

Notethat the resulting coef 1047297cient estimatesshould be interpreted as

conditional correlations rather than as causal relationships The pro-

ductivity and pro1047297tability variables are possibly endogenous and there

may be omitted variables such as demand and entrepreneurial talent

that we are not able to control for

6 Regression analysis

61 Size vs age

Fig 4 presents the results of regressions of net job creation on 1047297rm-

size and age dummies The underlying regressions are presented in

Table 9 Given the large number of observations the estimated coef 1047297-

cients are always statistically signi1047297cant at the 1 level Note that the

omitted category for 1047297rm size is that of 1047297rms with more than 1000 em-ployeeswhich have been operating forat least 30 yearsThe coef 1047297cients

are thus relative to this group of 1047297rms When displaying these regres-

sion estimates graphically we follow Haltiwanger et al (2013) and do

not report the omitted category at zero but rather at its unconditional

average which we also add to all other size coef 1047297cients This does not

affect the relative pattern of coef 1047297cient estimates yet enables one to

better gauge the relative magnitude of the effects Of course we have

to bear in mind that the results may in (small) part re1047298ect differences

in coverage of the RNE over time with small 1047297rms being more likely

to be included in the survey to start with and their coverage improving

disproportionately over time which implies that they are morelikely to

be recorded to contribute to job creation

The graph shows a number of interesting1047297ndings To start with the

contribution of self-employment to net job creation stands out as is ev-idenced by the fact that job creation rates are highest for one-person

1047297rms the coef 1047297cient estimates suggest that job creation by one-

person 1047297rms is 203 higher than that of 1047297rms which employ more

than 1000 employees when using the base size classi1047297cation but only

45 when using theaverage size classi1047297cationThe vast differencein es-

timated employment growth premia between the different classi1047297ca-

tion methods is suggestive of substantial measurement error While

both graphs are crudely consistent with an inverse relationship be-

tween 1047297rm-size and net job creation the association is weak when the

average size classi1047297cation is applied According to estimates reliant on

the latter classi1047297cation the net job creation rate of 1047297rms employing be-

tween 10 and 19 workers is approximately only 17 higherthanthat of

the very largest 1047297rms while the corresponding employment creation

premium for 1047297rms with between 200 and a thousand workers is 02

Table 8

Employment transitions


Short-run annual transitions

Cells indicate what of 1047297rms in row category i in year t end up in column category j in year t + 1

Size in year t Size in year t + 1 (column category j)

(Row category i) Exit 1 [2ndash5] [59] [1049] [5099] [100999] N = 1000

1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627

Long-run 1996ndash2010

Cells indicate what of 1047297rms in row size class i in 1996 end up in column category j in 2010

Size in 1996 Size in 2010 (c olumn ca tegor y j)


1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063

Note Firm size is measured as the sum of all paid employment +1 on the assumption that each 1047297rm employs at least one worker who does not receive a salary




Controllingfor1047297rmage resultsin a signi1047297cantlypositive relationship

between 1047297rm age and size regardless of which 1047297rm size methodology is

used Using the base size classi1047297cation the contribution of net job crea-

tion by the self-employed is now 33 lower than that of the largest

1047297rms whereas it is 172 lower using the average-size classi1047297cation

Note however that once 1047297rm age is conditioned on the relationship

between 1047297rm size and age fully reverses (albeit that the relationship

between size and age is not monotonic when using the base size

classi1047297cation)

That young 1047297rms contribute the most to job creation is shown in

Fig 5 which depictsthe associationbetween1047297rm ageand growth dem-onstrating that it is strongly downward sloping Controlling for 1047297rm-

size strengthens the association between age and growth The reason

is that smaller 1047297rms which tend to be younger grow less quickly than

large 1047297rms post-entry as we shall demonstrate in the next section

62 Different margins of adjustment exit and the contribution of continuing 1047297rms

The importance of controlling for age and the importance of 1047297rm

entry suggested by the descriptive statistics presented in Section 3 beg

the question to what extent the dynamics re1047298ect entry and exit In

this section we explore this question by separately documenting the

contributions to net job creation by continuing and exiting 1047297rms

Fig 6 depicts the relationships between net job creation by 1047297rm sizeseparately for continuing 1047297rms and 1047297rms that exit The underlying re-

gressions are presented in Appendix (See Tables A1 and A2) Remark-

ably the relationship between 1047297rm-size and net job creation is now

generally positive for both continuing and exiting 1047297rms as is evidenced

by themildly upwardsloping graph forcontinuing1047297rmsand the strong-

ly upward slopinggraph for1047297rms that exit The former result is surpris-

ing for it shows that even amongst 1047297rms that survive large 1047297rms

outperform small 1047297rms in terms of job creation The latter result is of

course consistent with the pattern of exit rates documented in Table 8

since net job creation due to 1047297rm exit can be interpreted as an employ-

ment weighted exit rate In sum amongst incumbents large 1047297rms con-

sistently create more jobs than small 1047297rms

Controlling for 1047297rm age reduces the strength of the correlation be-

tween 1047297rm size and exit because younger 1047297rms are more likely to die

Table 9

Net job creation mdash all 1047297rms 1997ndash2010

Net job creation

All 1047297rms 1997ndash2010

Average size classi1047297cation Base year size classi1047297cation

1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331

2 minus00028 minus00942 00523 minus00631

[3 4] 00135 minus00666 00299 minus00467

[5 9] 00175 minus00557 00136 minus00455[10 19] 00166 minus00515 00071 minus00426

[20 49] 00171 minus00420 minus00001 minus00417




Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116

9 00124 00476 minus00192 minus00109[10ndash14] 00097 00417 minus00147 minus00064

[15ndash19] 00056 00316 minus00142 minus00064

[20ndash29] 00050 00217 00040 00077

Sector

dummies

Yes Yes Yes Yes Yes Yes

Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714

Note The dependent variable is the DavisndashHaltiwangerndashSchuh growth rate Regressions

in columns 1 2 and 3 are weighted by the average size of the 1047297rm over the period over

whichthe growth spell is measured (ie the current yearand last year) while the regres-

sions presentedin columns4 5 and6 areweightedby thebase size employment(eglast

years employment save for entrants for which we use contemporaneous employment

sincelagged employment is notavailable) The resulting coef 1047297cients are thusinterpretable

as conditional average net jobs 1047298ows Standard errors are not presented since all coef 1047297-

cient estimates are signi1047297cant at the 1 level due to the large number of observations

Fig 4 Net job creation by 1047297rm size Notes The1047297gure plots weighted regression coef 1047297cients of net job creation measured by the DavisndashHaltiwangerndashSchuh growth rate on 1047297rmsizeandage dummies controlling for sector and year The underlying regression coef 1047297cients are presented in Table 9 The unconditional mean of the omitted category (1047297rms with at least 1000

workers) is added to all coef 1047297cients to facilitate interpretation




Fig 5 Net job creation by 1047297rm age Notes The1047297gure plots weighted regression coef 1047297cients of net job creation measured by the DavisndashHaltiwangerndashSchuh growth rate on 1047297rm size and

age dummies controlling for sector and year The underlying regression coef 1047297cients are presented in Table 9 The unconditional mean of the omitted category (1047297rms which are at least

30 years old) is added to all coef 1047297cients to facilitate interpretation

Fig 6 Netjob creationby 1047297rm sizemdash continuing1047297rmsand exiting 1047297rms NotesThe 1047297gureplots weighted regressioncoef 1047297cientsof netjob creationmeasuredby theDavisndashHaltiwangerndash

Schuhgrowth rate on1047297rm size andage dummies controlling forsector andyearThe underlyingregressioncoef 1047297cientsare presentedin Table A1 Theunconditionalmeanof theomitted

category (1047297rms with atleast 1000workers) isadded toall coef 1047297cients to facilitate interpretation Notes The1047297gure plots weighted regressioncoef 1047297cients of netjob creationmeasuredby

theDavisndashHaltiwangerndashSchuh growth rateon 1047297rm size andage dummies controllingfor sectorand yearThe underlyingregression coef 1047297cients arepresentedin Table A2 Job creationdue

to 1047297rm exit is consistently negative because by de1047297nition exiting 1047297rms destroy jobs The unconditional mean of the omitted category (1047297rms with at least 1000 workers) is added to all

coef 1047297cients to facilitate interpretation




asis shown in Fig 7 and because small1047297rm tend to be younger than old

1047297rms Interestingly controlling for 1047297rm age appears to strengthen the

correlation between 1047297rm size and growth amongst continuing 1047297rms

The explanation for this 1047297nding is that young1047297rms tend to grow faster

and that small 1047297rms are on average younger Conversely controlling for

1047297rm size mutes the correlation between 1047297rm age and net job creation

due to 1047297rm exit

To summarize unconditionally we document an inverse relation-

ship between1047297

rm size and growth when using the base year size cate-gorization which diminishes dramatically when one instead uses an

average size classi1047297cation although the important contribution of self-

employment to job creation is salient in both cases Controlling for

age we 1047297nd a negative relationship between 1047297rm size and growth irre-

spective of which size-class methodology we use This re1047298ects the fact

that post-entry 1047297rms stagnate and that small 1047297rms are more likely to

exit and less likely to grow they destroy more jobs than large 1047297rms

ceteris paribus The overall picture of job creation is thus bleak incum-

bent 1047297rms do not grow on average and ultimately disappear

7 Productivity and pro1047297tability

To assess to what extent the results presented in the previous sec-

tion re1047298ect a process of creative destruction whereby the most produc-

tive 1047297rms expand and the least ef 1047297cient producers are weeded out we

explore the role of productivity and pro1047297tability in this section To min-

imize the impact of measurement error and misreporting we con1047297ne

the analysis to 1047297rms which employed at least one salaried employee

and whose tax obligations vary with their level of output and pro1047297ts

We also exclude from the analysis 1047297rms which reported implausibly

large changes in gross output per worker as well as extreme observa-

tions The resulting sample of 1047297rms accounts for roughly two-1047297fths of

all output and roughly a third of all employment

The regressions are presented in Table 10 we 1047297rst estimate regres-

sions which separately control for productivity and pro1047297tability and

include year as well as sector dummies These regressions can beinterpreted as providing insight into whether within sectors job are

being created in 1047297rms that are more productive and pro1047297table When

doing soone hasto bear in mind thepotentialendogeneityof these per-

formance measures the coef 1047297cient estimates should be interpreted as

conditional correlations rather than causal relationships Subsequently

we add controls for age and size To assess to what extent changes in

the sample drive our results we also present models which control for

1047297rm age size sector and year but not for productivity and pro1047297tability

Thespeci1047297cations presentedin columns 1 2 and 3 demonstrate that

1047297rms that are more productive and more pro1047297table generate more jobs

Note however that the explanatory power of these variables is low as

is evidenced by the low R2s Although strongly statistically signi1047297cant

the relationship between employment creation productivityand pro1047297t-

ability is weak For example a doubling of the amount of output per

worker is associated with a 39 increase in employment growth ceteris

paribus Similarly moving a decile upwards in the pro1047297tability distribu-

tion is associated with a 12 increase in job creation While these weak

Fig 7 Netjob creationby 1047297rmageminus continuing1047297rmsand exiting1047297rms NotesThe 1047297gure plotsweightedregression coef 1047297cientsof netjob creationmeasuredby theDavisndashHaltiwangerndash

Schuhgrowth rate on1047297rmsize andage dummies controlling forsector andyear Theunderlyingregressioncoef 1047297cients arepresented in Table A1 Theunconditional mean of theomitted

category (1047297rms which are atleast 30 years old)is added toall coef 1047297cients to facilitate interpretation Notes The1047297gureplots weighted regressioncoef 1047297cients of netjob creationmeasured

bythe DavisndashHaltiwangerndashSchuhgrowth rate on1047297rm sizeand agedummiescontrollingfor sector andyear Theunderlyingregressioncoef 1047297cients are presented in Table A2 Jobcreation

dueto 1047297rm exit is consistentlynegativebecauseby de1047297nition exiting 1047297rmsdestroyjobsThe unconditionalmeanof theomittedcategory(1047297rmswhichare atleast30 yearsold) is addedto

all coef 1047297cients to facilitate interpretation




relationships may in part re1047298ect measurement error (perhaps due to

misreporting) in the productivity and pro1047297tability variables resulting

in attenuation bias taken at facevalue they suggest the reallocative pro-

cess is not ef 1047297cient in (re-)allocating labor to its most productive and

pro1047297table uses This is consistent with the weak 1047297rm dynamics docu-

mented above

Controlling for 1047297rm age and size as is done in columns 5 6 and

7 results in marginally higher coef 1047297cients on both productivity and

pro1047297

tability Note that the coef 1047297

cients on 1047297

rm-size and age coef 1047297

-cients are not very different from those obtained from a speci1047297ca-

tion which does not control for productivity and pro1047297tability

(presented in column 4) most likely because productivity and prof-

itability are not very strongly correlated with size The growth pre-

mium associated with young 1047297rms increases somewhat re1047298ecting

the fact that while they tend to grow faster such 1047297rms also tend to

belesspro1047297table and productiveon average Nonethelessthese im-

pacts are certainly not large

Using a base-year size classi1047297cation as is done in Table 11 yields

stronger correlations between productivity pro1047297tability and job crea-

tion This is to be expected if there is measurement error in our

employment measure resulting in a spurious correlation between out-

put and pro1047297ts per worker and subsequent growth Nonetheless the

resulting correlations remain rather weak

In sum these results are suggestive of a severely attenuated process

of creative destruction and an extremely rigid reallocative process

which is consistent with the lackluster 1047297

rm dynamics documented inpreceding sections

8 Conclusion

Using a unique database containing information on all registered pri-

vatesector employment in Tunisiawe analyzewhich patterns of 1047297rmdy-

namics and job creation underpin weak aggregate employment growth

Table 10

Net job creation productivity and pro1047297tability ndash 2007ndash2010 ndash onshore 1047297rms employing

wage workers

Net job creation

Onshore 1047297rms employing wage workers

(eg excluding the self-employed) mdash 2007ndash2010

Average size classi1047297cation

1 2 3 4 5 6

Productivity and pro 1047297tability

Productivity

(ln(YL))

00057 00288 00154

Pro1047297tability rank 00009 00015 00013

Size

1 minus02693 minus02799 minus02920 minus02945


[3 4] minus00568 minus00643 minus00740 minus00756


[10 19] minus00064 minus00169 minus00174 minus00215[20 49] minus00010 minus00126 minus00108 minus00156

[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593

Sector dummies Yes Yes Yes Yes Yes Yes

Year Dummies Yes Yes Yes Yes Yes Yes

N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440


are weighted by the average size of the 1047297rm over the period over which the growth

spell is measured (ie the current year and last year) Standard errors are not presented

since all coef 1047297cientestimates are signi1047297cantat the 1level due tothelarge numberof ob-

servations The average size classi1047297cation categorizes 1047297rms into different bins depending

ontheaverage oftheir size inyear t andyear t minus 1 For entrants average1047297rmsizeis sim-

plytheirsize at t dividedby 2 while for1047297rmsthatexit in periodt their average sizeis their

size at t minus 1 divided by 2 ldquoOnshorerdquo 1047297rms are all 1047297rms that are not participating in the

tax-exemptedldquooffshorerdquo regimewhichrequires1047297rmsto exportat least70 oftheiroutput

or sell it to other ldquooffshorerdquo 1047297rms Productivity is measured as log output per worker The

pro1047297tability rank runs from 1 (lowest) to 100 (highest)

Table 11


wage workers

Net job creation



Base size classi1047297cation

1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085

9 00002 00141 00033 00133[10ndash14] minus00293 minus00145 minus00270 minus00159

[15ndash19] minus00127 minus00044 minus00125 minus00060



Year dummies Yes Yes Yes Yes Yes Yes

N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287


are weighted by base year 1047297rm size (ie last years size for continuing and exiting 1047297rms

and contemporaneous 1047297rm size for entrants) Standard errors are not presented since all

coef 1047297cient estimates are signi1047297cant at the 1 level due to the large number of observa-

tions The base size classi1047297cation categorizes 1047297rms into different bins depending on their

size in the previous year For entrants base year 1047297rm size is set equal to their contempo-

raneous (year t ) size ldquoOnshorerdquo 1047297rms are all 1047297rms that are not participating in the tax-

exempted ldquooffshorerdquo regime which requires 1047297rms to export at least 70 of their output

or sell it to other ldquooffshorerdquo 1047297rms Productivity is measured as log output per worker

The pro1047297tability rank runs from 1 (lowest) to 100 (highest)




Instead of private sector vibrancy we observe inertia and the 1047297rm size

distribution remaining skewed towards small 1047297rms In spite of sub-

stantial GDP growth and a reduction in informality in terms of non-

registration with the tax authorities job creation did not substan-

tially outpace the growth of the labor force and 1047297rm dynamics are

sluggish

Although our results are consistent with the notion that small

1047297rms generate the most jobs albeit that this relationship is sensi-

tive to measurement error this relationship is driven by 1047297

rmentry and the fact that most entrants start small Post-entry small

1047297rms are the worst performers in terms of net job creation even if

they survive in spite of being much more likely to exit than large

1047297rms Moreover exit rates in Tunisia are modest and mobility is

limited with few 1047297 rms managing to grow even if we consider a

very long time horizon

Our results are nonetheless consistent with Haltiwanger et al

(2013) 1047297nding that 1047297rm age is a far better predictor of 1047297rm growth

than 1047297rm size as young 1047297rms consistently create the most new jobs

Once 1047297rm age is conditioned on the relationship between 1047297rm size

and age fully reverses

Our results furthermore suggest thatthe processof creative destruc-

tion is weak in Tunisia Although the data we haveon output and pro1047297ts

are noisy only available for a sub-sample of all 1047297rms and cannot be

interpreted as demonstrating causality allocative ef 1047297ciency appears

low in the sense that the relationship between size and 1047297rm perfor-

mance in terms of productivity and pro1047297tability is not very pronounced

While both pro1047297tability and productivity are positively associated with

net job creation these correlations are weak Consistent with the idea

that the best 1047297rms have dif 1047297culties expanding and gaining market

share we observe that average productivity does not rise rapidly with

1047297rm age and if anything reduces for 1047297rms that have been in existence

for more than four years even though average pro1047297tability appears to

rise with 1047297rm age

In sum weak aggregate job creation is not due to excessive job

destruction but rather seems the result of insuf 1047297cient dynamism man-

ifested in a lack of upward mobility and stagnation amongst incum-

bents Uncovering what obstructs the process of market selection and

what explains the weak 1047297rm dynamics documented in this paper is animportant area for future research

Table A1

Net job creation continuing 1047297rms mdash all 1047297rms 1997ndash2010

Net job creation continuing 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 minus00263 minus00838 minus00499 minus005782 minus00064 minus00569 minus00394 minus00448







[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074

Table A1 (continued)



1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127

Note The dependent variable is the DavisndashHaltiwangerndashSchuh growth rate Regressions in

columns 1 2 and3 areweighted bythe average size of the1047297rm over theperiodoverwhich

thegrowthspell is measured (ie thecurrent year andlastyear) while theregressions pre-

sented incolumns4 5 and 6 areweightedby thebasesizeemployment (eglast years em-

ployment savefor entrants for which we use contemporaneous employment sincelagged

employment is notavailable) Theresulting coef 1047297cients arethus interpretableas condition-

al average net jobs 1047298ows Standard errors are not presented since all coef 1047297cient estimates

are signi1047297cant at the 1 level due to the large number of observations

Table A2

Net job creation due to 1047297rm exit mdash all 1047297rms 1997ndash2010

Net job creation due to 1047297rm exit all 1047297rms except entrants 1997ndash2010


1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age




4 minus00143 00010 minus00288 minus00067



7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001

[10ndash14] minus00082 00032 minus00163 minus00004

[15ndash19] minus00053 00037 minus00105 00022

[20ndash

29] minus00065 minus00006 minus00126 minus00039Sector

dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247

Note The dependent variable is the Davis Haltiwanger Schuh growth rate which takes the

valueminus2 if 1047297rms exit and 0 otherwise Regressions in columns 1 2 and 3 are weighted by

theaveragesizeof the1047297rm over theperiodover which thegrowthspell is measured(iethe

current year and last year) while the regressions presented in columns 4 5 and 6 are

weighted by the base size employment (eg last years employment save for entrants for

which we use contemporaneous employment since lagged employment is not available)

The resultingcoef 1047297cients are thusinterpretable as conditional average net jobs1047298owsStan-

dard errors are not presented since all coef 1047297cient estimates are signi1047297cant at the 1 level

due to the large number of observations

Appendix A Tables underpinning Figs 4 and 5




Appendix B Wage employees only

As a robustness check we document the main results when we exclude self-employment from the analysis and focus on 1047297rms with salaried

workers only The overall pattern of results we obtain is qualitatively similar to the results that include the self-employed

The mobility matrices presented in Table B1 using wage employment as a measure of employment suggest a somewhat more vibrant private sector

than Table 7 suggests with higher shares of 1047297rms transiting to inactivityexit and more dynamism amongst the smallest 1047297rm-size categories Yet job

creation regressions presented in Table B2 and Figs B1 and B2 yield qualitatively similar patterns of results as those obtained when including the self-

employed small 1047297rms create the most jobs but only if we use the base-size classi1047297cation The estimated relationship between 1047297rm-size and net job

creation is fairly 1047298

at when we use the average-size classi1047297

cation Once we condition on 1047297

rm age the relationship between 1047297

rm-size and net job cre-ation becomes positive irrespective of the size classi1047297cation used The regressions also show that young1047297rms create the most jobs a result which ob-

tains both with and without size controls

Table B1

Alternative transition matrices


Size measures based on wage employment only (eg excluding self-employment)




(Row category i) Exitno paid workers 1 [2ndash5] [59] [1049] [4999] [100999] ge1000

1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627

Long-Run 1996ndash2010


Size in 1996 Size in 2010 ( colum n c ateg or y j)


1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063

Note Firm size is measured as the sum of all paid employment

Table B2

Net job creation mdash wage employment only

Net job creation mdash wage employment

All 1047297rms hiring wage workers (eg excluding self-employment) 1997ndash2010


1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340

2 00555 minus01776 00728 minus01444[3 4] 00519 minus01380 00543 minus01121

[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References

Angel-Urdinola D Nucifora A Robalino D 2014 Labor Policy to Promote Good Jobs inTunisia Revisiting Labor Regulation Social Security and Active Labor Market Pro-grams Directions in Development World Bank Publications

Ayyagari M Demirguc-Kunt A MaksimovicV 2013Size andage ofestablishmentsev-idence from developing countries World Bank Policy Research Working Paper 6718

Bartelsman E Haltiwanger J Scarpetta S 2013 Cross-country differences inproductivity the role of allocation and selection Am Econ Rev 103 (1)305ndash334

Beck T Demirguc-Kunt A Levine R 2005 SMEs growth and poverty J Econ Growth10 (3) 199ndash229

Bigsten A Gebreeyesus M 2009 The small the young and the productive determi-nants of manufacturing 1047297rm growth in Ethiopia Econ Dev Cult Chang 55 813ndash840

Table B2 (continued)




1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

Note Thedependent variable is theDavisHaltiwanger Schuhgrowth rate computed over thenumber of wage employeeswhichtakesthe valueminus2 if 1047297rmsexit and0 otherwiseRegres-

sionsin columns 12 and 3 are weightedby the average size ofthe1047297rm over theperiod over which thegrowth spell is measured (ie thecurrentyear andlast year)whilethe regressions

presentedin columns4 5 and6 areweightedby thebase size employment (eg lastyears employment savefor entrants forwhichwe use contemporaneous employment since lagged

employment is not available) The resulting coef 1047297cients are thus interpretable as conditional average net jobs 1047298ows Standard errors are not presented since all coef 1047297cient estimates are

signi1047297cant at the 1 level due to the large number of observations

Fig B2 Net job creation by 1047297rmage mdash wage employment (eg excluding self-employment) Notes The1047297gure plots weighted regression coef 1047297cients of net job creation measured by the

DavisndashHaltiwangerndashSchuh growthrateon 1047297rm size andage dummies controlling forsector andyearThe underlying regression coef 1047297cientsare presentedin Table B2 The unconditional

mean of the omitted category (1047297rms which are at least 30 years old) is added to all coef 1047297cients to facilitate interpretation

Fig B1 Netjob creation by 1047297rm sizemdash wage employment (eg excluding self-employment) Notes The1047297gure plots weighted regressioncoef 1047297cients of net jobcreation measured by the

DavisndashHaltiwanger Schuhgrowth rate on 1047297rm sizeand agedummiescontrolling forsector andyearThe underlying regression coef 1047297cients are presented in Table B2 The unconditional

mean of the omitted category (1047297rms with at least 1000 employees) is added to all coef 1047297cients to facilitate interpretation




Birch DL 1979 The Job Generation Process Unpublished Report Prepared by the MITProgram on Neighborhood and Regional Change for the Economic DevelopmentAdministration US Department of Commerce Washington DC

Birch DL 1981 Who creates jobs Public Interest 65 3ndash14Davis S Haltiwanger J Schuh S 1996 Job Creation and Destruction MIT Press Cam-

bridge USAFreund C Rijkers B Episodes of unemployment reduction in rich middle-income and

transition economiesJ Comp Econ (forthcoming)Hallward-Driemeier M Thompson F 2009 Creative destruction and policy reforms

changing productivity effects of 1047297rm turnover in Moroccan manufacturing WorldBank Policy Research Working Paper 5085

Haltiwanger JC Jarmin RS Miranda J 2013 Who creates jobs Rev Econ Stat 95 (2)347ndash361Hsieh C Klenow P 2009 Misallocation and manufacturing TFP in China and India Q J

Econ 124 (4) 1403ndash1448Hsieh C Klenow P 2014 The lifecycle of plants in India and Mexico Q J Econ 129 (3)

1035ndash1084Hsieh C Olken B 2014 The Missing Missing Middle J Econ Perspect 28 (3) 89ndash108INS 2012 Institut National de la Statistique Tunisie Annual Report on Enterprises 2012

(Tunis) Juumltting J Parlevliet J Xenogiani T 2008 Informal employment reloaded OECD

Development Centre Working Paper No 266

Klapper L Richmond C 2011 Patterns of business creation survival and growthevidence from Africa Labour Econ 18 (S1) S32ndashS44

Mouelhi R 2012 Productivity in Tunisian manufacturing World Bank MimeoNeumark D Wall B Zhang J 2011 Do small businesses create more jobs New evi-

dence for the United States from the National Establishment Time Series Rev EconStat 93 (1) 16ndash29

Rijkers B Freund C Nucifora A 2014 All in the family state capture in Tunisia WorldBank Policy Research Working Paper 6810

Schneider F Buehn A Montenegro CE 2010 Shadow economies all over the worldnew estimates for 162 countries from 1999 to 2007 World Bank Policy ResearchWorking Paper 5356

Sleuwaegen L Goedhuys M 2002 Growth of 1047297rms in developing countries evidencefrom Cocircte DIvoire J Dev Econ 68 117ndash135Tornqvist L Vartia P Vartia Y 1985 How should relative change be measured Am

Stat 39 (1) 43ndash46Van Biesebroeck J 2005 Firm size matters growth and productivity growth in African

manufacturing Econ Dev Cult Chang 53 (3) 545ndash583World Bank 2012 Bread Dignity and Freedom Jobs in theMiddleEast and Northern AfricaWorld Bank 2014 International Income Distribution Database (I2D2) World Bank

Washington DC




information about the constraints to job creation in emerging econo-

mies One possibility is that 1047297rm dynamics are similar to those observed

in more vibrant environments but that entry rates are lower Alterna-

tively weak job creation could be predominantly due to stagnation

amongst incumbent 1047297rms Another possibility is that job creation is ad-

equate but job destruction is excessive Of course the importance of

these mechanisms may be heterogeneous across different types of

1047297rms varying inter alia with 1047297rms size and age (Haltiwanger et al

2013) Examining which 1047297

rms create the most jobs also sheds light onthe ef 1047297cacy of the re-allocative process limited job creation could re-

1047298ect distortions and frictions inhibiting the growth of productive

1047297rms or attest to demand constraints with productivity a potentially

even more important determinant of 1047297rm growth and survival While

the data do not enable us to directly discriminate between these com-

peting explanations we can examine whether their implications are

consistentwith the patterns of employment growth we observe for ex-

ample in the former case the relationship between productivity and

employment growth would be weak whereas in the latter case it

would be strong

Tunisia a small open NorthernAfricancountry whichwas at thefore-

front of the Arab Spring provides a very relevant context to examine

these issuesLike many other countries in theregionTunisia hadhighun-

employment despite stable and relatively strong growth The economy

grew approximately 48 per annum over the period considered yet un-

employment hovered between 16 and 14 in part because the labor

force expanded by 19 per annum1 As is typical of developing countries

( Juumltting et al 2008) informal employment and small-scale non-

agricultural employment are important (Angel-Urdinola et al 2014)

with self-employment accounting for just under a third of all jobs The

highly skewed distribution of 1047297rms by size in Tunisia is also typical of de-

veloping countries For example new evidence from India and Indonesia

shows that 98 of 1047297rms have fewer than 10 workers and less than one

percent of 1047297rms have more than 50 workers (Hsieh and Olken 2014)

the same pattern as we1047297nd in Tunisia Tunisia is furthermore interesting

because its government has pursued a very active industrial policy of

which exports and small business promotion were important pillars At

the same time it is alsoknown for having relativelyburdensome business

regulation whichis often applied arbitrarily and for high levels of corrup-tion (Rijkers et al2014) Last but notleast Tunisia is oneof the few coun-

tries in theregion with a high-quality 1047297rm-census and authorities willing

to share those data with researchers

Our resultsattest to limiteddynamism Althoughthe private sector gen-

erated more than half a million net new non-agriculturalprivatesector jobs

overthe period under considerationlabor supply alsoincreased andtheag-

ricultural sector shrank in relative terms such that unemployment did not

decline drastically Informality measured as the share of employment that

is not registered with the tax authorities decreased Self-employment

rates were nonethelessverystable The1047297rm-size distribution has remained

skewed towards small 1047297rms Jump start self-employment was the domi-

nant driver of job creationover theperiod consideredeven after accounting

for upward bias in recorded entry rates of small 1047297rms due to increases in

registration rates Post-entry howeverone-person 1047297rms arethe worst per-formers in terms of net job creation such that the aggregate net contribu-

tion to job creation of self-employment is much more modest than the

gross entry numbers might suggest

While we 1047297nd a positive correlation between 1047297rm-size and net job

creation similar to that documented by Neumark et al (2011) and

Haltiwanger et al (2013) in the US this relationship is very sensitive to

regression to the mean effects and moreover entirely driven by 1047297rm

entry incumbent 1047297rms on average shed labor and small1047297rms do so rela-

tively rapidly In other words post-entry large 1047297rms consistently outper-

form small 1047297rms in terms of job creation even if we con1047297ne attention to

surviving 1047297rms Instead of aggressive market selection our results

indicate inertia churning is limited especially for larger 1047297rms and very

few 1047297rms manage to grow In conjunction with most entrants starting

very small this lack of upward mobility helps explain why the 1047297rm size

distribution has remained skewed towards small-scale production

Our results nonetheless underscore the pivotal role of 1047297rm age that

was 1047297rst pointed out by Haltiwanger et al (2013) we consistently doc-

ument a strongly negative correlation between 1047297rm age and growth

young 1047297rms tend to grow the fastest and contribute the most to net

job creation in spite of their higher exit ratesThelack of dynamism is also manifested in allocative inef 1047297ciency 1047297rm

size and age are not very strongly correlated with productivity and prof-

itability The process of creative destruction whereby resources are

reallocated towards productive resources appears anemic Productive

1047297rms and pro1047297table 1047297rms employment grows signi1047297cantly faster but

the relationship between productivity pro1047297tability and employment cre-

ationis weak Although our proxies forproductivity and pro1047297tability may

be endogenous and suffer from substantial measurement error taken at

face value our estimates suggest that ceteris paribus doubling output

per worker is associatedwith 1ndash5 higher employmentgrowthSimilar-

ly movingup a decile inthe pro1047297tability distribution (by sector and year)

is associatedwithan acceleration of employmentgrowth of approximate-

ly 1ndash2 ceteris paribus Controlling for productivity and pro1047297tability does

not affect the qualitative pattern of size and age coef 1047297cients very much

and has only a very modest impact on the estimated coef 1047297cient estimates

Overall the results highlight the relationship between weak1047297rmdy-

namics and insuf 1047297cient net job creation in Tunisia The highly skewed

1047297rm distribution with the vast majority of 1047297rms being very small and

only a small number of large 1047297rms is indicative of a failure of 1047297rms to

grow and move up the size distribution The importance of self-

employment for job creation with little evidence of growth even

amongst the more productive or more pro1047297table 1047297rms also speaks to

the static 1047297rm environment Similar aggregate statistics on labor force

participation unemployment income growth and 1047297rm characteristics

across many of the countries in the Middle East and North Africa

imply that weak 1047297rm dynamics are likely to play an important role in

explaining the poor job performance in much of the region

The remainder of the paper is organized as follows The next section re-

views related literature including a recent yet in1047298uential paper byHaltiwanger et al (2013) on patterns of job creation by 1047297rm age and size

Section three presents an overview of broad labor market trends and as-

sesses the evolution of informality and coverage of the data by comparing

the evolution of employment recorded in 1047297rm census data which covers

all employment registered with the tax authorities (ie formal employ-

ment) with employment aggregates derived from Labor Force Surveys

which cover both registered (formal) and non-registered (informal) jobs

Section four describes the data in more detail and documents salient styl-

ized facts regarding 1047297rm dynamics in Tunisia Our econometric strategy is

presented in Section 5 whileSection6 presentsour principal resultsregard-

ing the role of age and size The role of productivity and pro1047297tability is ex-

plored in Section 7 which also examines to what extent our 1047297ndings

regarding the relationship between size age and job creation re1047298ect pro-

ductivity and pro1047297tability differences A 1047297nal section concludes

2 Related literature and conceptual considerations

The ability of productive 1047297rms to expand is increasingly recognized

as critical to a countrys economic success Allocative ef 1047297ciency is typi-

cally higher in developed countries than in developing countries (see

eg Bartelsman et al 2013 and Hsieh and Klenow 2009) and this is

plausibly due to distortions or frictions preventing inputs being allocat-

ed to their optimal uses Such frictions not only may induce misalloca-

tion but also may undermine incentives to invest and grow (Freund

and Rijkers forthcoming) differences in the lifecycle of 1047297rms are anim-

portant mechanism by which differences in aggregate productivity ma-

terialize Hsieh and Klenow (2014) for instance estimate that if US

1047297rms exhibited the same dynamics as Indian or Mexican 1047297rms

1 Labor force participation rates were relatively stagnant and if anything declined due

to increasing educational attainment




































































































growth






























terms




Table 1



96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Growth ()

GDP growth 71 54 48 61 47 49 18 56 61 40 53 63 46 30 30









Self-employed 312 310 323 323 356 237 310 303 296 315


Informality ()



Table 2





1997 2001 2005 2010



Wage employmentb








of employment

1997 2001 2005 2010






























































41 Data













































































rdquo






































Table 3


Year 1997 2010


(RNE) (RNE)



(000s) (000s) (000s) (000s)

Agriculture


Manufacturing




Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services



Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24














1047297rms is 8

Table 4


Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106












































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC




































































































growth






























terms




Table 1



96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Growth ()

GDP growth 71 54 48 61 47 49 18 56 61 40 53 63 46 30 30









Self-employed 312 310 323 323 356 237 310 303 296 315


Informality ()



Table 2





1997 2001 2005 2010



Wage employmentb








of employment

1997 2001 2005 2010






























































41 Data













































































rdquo






































Table 3


Year 1997 2010


(RNE) (RNE)



(000s) (000s) (000s) (000s)

Agriculture


Manufacturing




Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services



Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24














1047297rms is 8

Table 4


Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106












































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC






terms




Table 1



96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Growth ()

GDP growth 71 54 48 61 47 49 18 56 61 40 53 63 46 30 30









Self-employed 312 310 323 323 356 237 310 303 296 315


Informality ()



Table 2





1997 2001 2005 2010



Wage employmentb








of employment

1997 2001 2005 2010






























































41 Data













































































rdquo






































Table 3


Year 1997 2010


(RNE) (RNE)



(000s) (000s) (000s) (000s)

Agriculture


Manufacturing




Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services



Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24














1047297rms is 8

Table 4


Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106












































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC
















































41 Data













































































rdquo






































Table 3


Year 1997 2010


(RNE) (RNE)



(000s) (000s) (000s) (000s)

Agriculture


Manufacturing




Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services



Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24














1047297rms is 8

Table 4


Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106












































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC






































Table 3


Year 1997 2010


(RNE) (RNE)



(000s) (000s) (000s) (000s)

Agriculture


Manufacturing




Construction

Construction 88 284 69 106 433 76

Trade

Trade 239 245 2 349 373 6

Services



Other services 111 204 45 300 332 10

Total 946 1379 31 1516 1986 24














1047297rms is 8

Table 4


Size category

of workers

of

1047297rms

of

1047297rms

of jobs of

employment

Age

(years)

Entry

rates

1 344684 8330 345753 2802 804 12112 29318 746 56290 476 1259 534

[3 4] 16505 407 53696 444 1064 592

[5 9] 10223 252 64010 529 114 392

[10 19] 4657 115 61661 512 1208 293

[20 49] 3077 077 94056 782 133 236

[50 99] 1362 034 95241 792 1363 203

[100 199] 898 023 126078 1055 1585 163

[200 999] 636 016 228812 1893 1588 101

ge1000 51 001 86874 698 1895 083

Total 411412 1212472 846 1106












































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC





































Table 5


Size ( of workers)

Age

(years)

1 2 [3 4] [5 9] [10 19] [29 49] [50 99] [100 199] [200 999] ge1000 Total of

workers

Share

0 35022 2566 1568 1429 1170 1552 1256 944 1666 69 47242 390

1 30602 3508 3182 3548 3181 4670 4055 3902 6723 2177 65548 541

2 27485 3485 3235 3822 3401 5356 4820 5577 8449 3482 69113 570

3 24990 3323 3095 3741 3457 5372 5206 6093 10013 4526 69816 5764 22857 3138 2880 3641 3236 5071 4715 5805 9129 4390 64863 535

5 21006 2982 2734 3449 3264 4841 4674 5948 8139 2840 59877 494

6 19243 2819 2648 3299 3174 4610 4638 5615 8403 2788 57238 472

7 17665 2711 2484 3146 3053 4472 4595 5998 7843 2361 54328 448

8 16022 2539 2367 2984 2908 4272 4407 5738 8173 2527 51935 428

9 14432 2333 2252 2819 2749 4022 4075 5693 7854 1983 48213 398

[10ndash14] 53337 10202 9583 11652 11477 16475 16270 22315 37119 6132 194564 1605

[15ndash19] 29998 7315 7317 8172 8008 12334 12357 16273 30577 6417 138768 1145

[20ndash29] 25528 6965 7673 8667 8653 14182 14847 21126 47069 25913 180624 1490

ge30 7566 2405 2677 3641 3929 6827 9325 15050 37655 21269 110343 910

Total of

workers

345753 56290 53696 64010 61661 94056 95241 126078 228812 86874 1212472

Share 2852 464 443 528 509 776 786 1040 1887 717




2010




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC




























































Table 6



Ln(YL) ln(YL)

demeaned by sector

average




100 = highest)



1 1827 1827 010 006 43271 68 022

2 1812 1811 000 006 30175 60 021

[3 4] 1811 1812 005 010 24650 56 021

[5 9] 1809 1797 010 009 17441 50 022

[1 19] 1814 1803 018 020 15521 48 024

[20 49] 1804 1798 018 021 11807 44 028

[50 99] 1794 1791 020 030 9635 42 029

[100 199] 1782 1779 017 032 5475 37 032

[200 999] 1762 1765 011 039 2863 35 032

ge1000 1728 1748 minus038 minus017 1140 33 033

By age (years)

0 1814 1815 011 013 17309 49 035

1 1811 1809 007 009 20697 51 028

2 1814 1810 010 011 23506 53 025

3 1816 1813 012 014 25291 54 0234 1814 1812 010 012 26404 54 021

5 1814 1812 009 010 26505 55 021

6 1813 1810 010 009 26626 55 021

7 1806 1802 002 003 27422 55 019

8 1806 1804 002 004 27467 56 019

9 1807 1803 002 005 26703 56 018

[10ndash14] 1816 1812 009 011 27923 56 018

[15ndash19] 1816 1810 012 016 27097 55 020

[20ndash29] 1816 1815 009 015 28722 57 019

ge30 1818 1813 011 014 21357 53 023

Total 1814 1811 25200 023












































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC









































size g ist frac14 2








































X ist

sumi X ist

max 0 g ist f g

where X ist

sumi X ist


































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC

































dummies







61 Size vs age
































Table 8







1 651 9198 134 010 006 001 001 000[2ndash5] 816 782 7961 393 044 002 001 000

[5 9] 691 130 1418 6875 871 010 004 000

[10 49] 379 090 180 876 8051 373 049 000

[50 99] 272 061 043 050 1604 6784 1184 001

[100 999] 183 037 021 026 191 831 8656 056

ge1000 159 000 014 014 014 014 1156 8627





1 5925 3781 245 031 015 001 002 000

[2ndash5] 5336 1559 2544 429 121 005 007 000

[5 9] 5369 259 1464 1807 1021 053 027 001

[10 49] 4654 218 571 969 2893 492 202 002[50 99] 4342 177 265 187 1896 1916 1218 000

[100 999] 3811 117 193 117 737 1030 3844 151

ge1000 1875 000 000 000 313 000 3750 4063







































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC






































Table 9


Net job creation

All 1047297rms 1997ndash2010


1 2 3 4 5 6

Size

1 00452 minus01721 02033 minus00331


[3 4] 00135 minus00666 00299 minus00467






Age

0 20542 21400 20799 20884

1 04023 04664 01225 01310

2 01298 01797 00113 00201

3 00825 01268 00046 00130

4 00402 00819 minus00233 minus00154

5 00120 00531 minus00486 minus00406

6 00230 00628 minus00247 minus00162

7 00144 00529 minus00235 minus00152

8 00157 00528 minus00199 minus00116



[20ndash29] 00050 00217 00040 00077

Sector

dummies


Year

dummies


N 6211700 6211700 6211700 6211700 6211700 6211700

R2 00048 02964 03039 00196 03711 03714


































ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC
























ship between1047297























































mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC









mented above



pro1047297


cients on 1047297




















8 Conclusion




Table 10


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00057 00288 00154


Size






[50 99] 00198 00086 00096 00051

[100 199] 00127 00011 00036 minus00013

[200 999] 00423 00343 00327 00298

Age

0 21667 21831 21837 21901

1 07181 07371 07324 07405

2 02338 02503 02428 02504

3 01448 01592 01526 01592

4 00672 00794 00727 00785

5 00494 00593 00537 00584

6 00673 00754 00730 00765

7 00663 00725 00698 00727

8 01065 01128 01088 01119

9 01073 01148 01103 01139

[10ndash

14] 00636 00708 00651 00688[15ndash19] 00769 00809 00770 00791

[20ndash29] 00561 00608 00569 00593



N 129516 129516 129516 129516 129516 129516

R2 00068 00092 03360 03395 03432 03440












Table 11


wage workers

Net job creation




1 2 3 4 5 6


Productivity

(ln(YL))

00392 00555 00444


Size





[20 49] 00132 minus00105 00022 minus00129



[200 999] 00155 minus00022 00040 minus00062

Age

0 20602 20896 20768 20945

1 02434 0282 02598 02850

2 00305 00626 00412 00631

3 00053 00343 00142 00343





8 minus00033 00099 minus00014 00085






N 129516 129516 129516 129516 129516 129516

R2 00100 00081 04159 04261 04231 04287



















sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC









sluggish

































Table A1




1 2 3 4 5 6

Size








[200 999] 00000 00000 00000 00000

Age

1 04194 04736 01240 01168

2 01524 01950 00516 00508

3 00962 01339 00291 00293

4 00530 00885 minus00003 00001

5 00230 00581 minus00293 minus00285

6 00333 00673 minus00060 minus00044

7 00223 00553 minus00092 minus00074




1 2 3 4 5 6

8 00231 00548 minus00066 minus00044

9 00212 00513 minus00030 minus00006

[10ndash14] 00162 00435 minus00030 00003


[20ndash29] 00055 00197 00047 00054Sector

dummies


Year

dummies


N 5265688 5265688 5265688 5265688 5265688 5265688

R2 00093 00592 00699 00085 00108 00127

Adjusted

R2

00093 00592 00699 00085 00108 00127









Table A2




1 2 3 4 5 6

Size








[200 999] minus00031 minus00034 00037 00037

Age







7 minus00083 00058 minus00165 00033

8 minus00072 00062 minus00144 00046

9 minus00089 00038 minus00179 00001



[20ndash


dummies


Year

dummies


N 5542320 5542320 5542320 5542320 5542320 5542320

R2 00152 00086 00158 00231 00175 00248

Adjusted

R2

00152 00086 00158 00231 00175 00247



























Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC

















Table B1








1 2168 7104 662 046 017 002 001 000[2ndash5] 1177 983 7099 650 086 003 002 000

[5 9] 921 144 1398 6476 1043 014 005 000

[10 49] 507 055 181 846 7949 408 054 000

[50 99] 355 031 031 047 1586 6763 1186 001

[100 999] 231 020 013 027 186 82 8647 056

ge1000 159 014 014 000 014 014 1156 8627





1 7567 1591 669 112 051 004 006 000

[2ndash5] 5801 1059 219 676 250 009 014 001

[5 9] 5690 375 1174 1562 1092 074 033 000

[10 49] 4836 240 469 898 2804 533 219 002

[50 99] 4570 121 142 162 183 1931 1244 000

[100 999] 3875 127 144 110 736 1015 3841 152

ge1000 1875 000 000 000 313 000 375 4063


Table B2





1 2 3 4 5 6

Size

1 00528 minus03422 00969 minus02340


[5 9] 00521 minus01170 00439 minus00897

[1049] 00506 minus01032 00428 minus00730

[49 50] 00476 minus00851 00315 minus00638

[50 99] 00352 minus00667 00130 minus00537



Age

0 20196 21366 20512 21099

1 06172 06894 01818 02406

2 01369 01899 minus00436 minus00063

3 00895 01350 minus00435 minus00119

4 00335 00754 minus00884 minus00603

5 00241 00649 minus00819 minus00546

6 00327 00715 minus00540 minus00275

Age




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC




References










1 2 3 4 5 6

7 00238 00605 minus00576 minus00327

8 00241 00591 minus00546 minus00310

9 00299 00626 minus00382 minus00157



[20ndash29] 00266 00415 minus00084 00014



N 1148020 1148020 1148020 1148020 1148020 1148020

R2 00067 02989 03126 00067 03828 03873

































Washington DC






















Washington DC


Which Firms Create the Most Jobs in Developing Countries

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