Sudden Stops and Creative Destruction in LatinAmerica: Evidence from Labor Market Flows�

Francisco A. GallegoUniversidad Catolica de Chile

Jose A. TessadaMIT

This Version: May 18, 2007PRELIMINARY - COMMENTS WELCOME

Abstract

Sudden stops and international crises are a main feature of developing countriesin the last 25 years. While their aggregate costs are well known, the microeconomicchannels throught which they work have yet to be explored. In this paper we studytheir e¤ects on micro variables related to labor �ows using sectoral panel data for4 Latin American countries. We �nd that sudden stops are associated with lowerjob creation and increased job destruction.We also document that the impact of sudden stops on labor �ows are hetero-

geneous depending on (i) country-speci�c variables, such as the extent of labormarket regulations, and (ii) sector-speci�c variables, such as the dependence ofthe sector to external �nance. Both sets of factors seem to a¤ect di¤erent marginsof adjustment, with labor market regulation a¤ecting mostly existing �rms while�nancial frictions being more relevant for new �rms.

�Authors�email address: fgallego@alum.mit.edu, tessada@mit.edu. We would like to thank Mar-ios Angeletos, Olivier Blanchard, Ricardo Caballero, Francesco Giavazzi, Borja Larraín and seminarparticipants at CEA-U. of Chile, MIT, and PUC-Chile for very useful comments. Tessada thanks �nan-cial support from the Chilean Scholarship Program and the Finch Fellowship at the MIT EconomicsDepartment. The usual disclaimer applies.

1 Introduction

Many emerging economies have su¤ered sudden stops of capital �ows in the last threedecades. Rothenberg andWarnock (2006) document the existence of at least one suddenstop in more than 75% of the time over the period from 1990 to 2005. These sudden stopsseem to have had signi�cant impacts on most macro aggregate, including output growthand unemployment. For instance, Calvo et al. (2006) documents that output contractsby about 8% during periods of systemic sudden stops. In addition, the same authorsdocument that sudden stops are associated to big decreases in private credit, whichare actually more persistent than output contractions. In parallel, a broad literaturedocuments the e¤ects of big macro shocks �such as recessions� on job creation anddestruction in developed countries (see Caballero (2007) for a review of the theory andthe evidence). Another related literature studies the e¤ects of exchange rate movementson the process of creative destruction in open economies (e.g. Gourinchas (1998),Gourinchas (1999), and Klein et al. (2003)).1

This paper studies the e¤ects of sudden stops on job creation and destruction ina sample of Latin American countries. Actually, sudden stops are clear big shocks toemerging economies that should provide sort of a extreme experiment to study the e¤ectsof negative shocks on job �ows. As previously discussed, sudden stops are associatedwith big output and credit contractions. Moreover, there are good theoretical reasonsto think that the e¤ects of sudden stops on job �ows should be heterogeneous, depend-ing on country- and sector-speci�c variables. Sectors that depend more on external�nance should su¤er the most from a negative external shock, depressing job creationand increasing job destruction. These e¤ects should be stronger for new �rms as long asthese �rms have less access to �nancial markets. At the same time, countries with morestringent labor regulations should depress job destruction and, maybe, job creation.

The sample we analyze is particularly interesting because even tough these countriespresent at least one occurrence of sudden stop in the relevant sample, the macroeconomice¤ects of these external shocks have varied signi�cantly across countries. For instance,while the sudden stop observed in Chile in the late 1990s produced a soft decrease inoutput growth (a negative growth rate of -0.4% in 1999 that reverts to a positive 4%in 2000), a similar negative shock in Colombia in the same period produced a severecontraction in 1999 (a negative GDP growth rate of 4% in 1999 followed by a growthrate of just 2.9% in 2000). One potential explanation consistent with the previousdiscussion is that Chile has some institutional features that allowed the country to reactin a di¤erent way to a similar shock (notice that the macro policy mix was quite similarin both countries over the same period, an in�ation targeting regime accompanied witha �exible exchange rate regime).

We use data mainly for four Latin American countries (Brazil, Chile, Colombia, andMexico) that we complement with some robustness checks for two additional countriesfor which we have some limited amount of information (Argentina and Uruguay). The

1On a related area, Haltiwanger et al (2004) use the same labor �ows data that we use to study thee¤ects of changes in tari¤s on labor �ows.

1

sample includes sectoral data on job creation and destruction and covers a time periodspanning from 1978 (for Colombia) to 2002 (for Argentina). We have sector-country-time variation in job �ows that we relate to (i) country-time variation of sudden stopsand interactions of sudden stops with labor regulation and rule of law in each country,and (ii) sector-country time variation of the interaction of sudden stops with a proxy forexternal dependence of each sector.

Using this information, our main results are:

1. Sudden stops produce period during which job creation decreases and job destruc-tion increases.

2. The e¤ects of sudden stops on job creation are heterogeneous: job creation insectors with strong demand for external �nance tend to over-react to sudden stopsand job creation in sectors in countries with more stringent labor regulations tendto react more negatively to sudden stops. Similarly, the positive e¤ects of suddenstops on job destruction are stronger in sectors with a higher sensitivity to external�nance and weaker in countries with more stringent labor regulations.

3. Comparisons of the result including all and continuing �rms suggest that entrydecisions of new �rms constitute a relevant margin to explain the e¤ects of suddenstops on job reallocation: (i) the negative e¤ect of sudden stops on job creationis only relevant for continuing �rms and insigni�cant overall suggesting that new�rms do not signi�cantly react to sudden stops in countries with more stringentlabor regulations and (ii) the negative e¤ect of sudden stops on job creation arestronger for all �rms, suggesting that job creation by new �rms su¤ers stronglyfrom external shocks.

Overall, these results are consistent with the initial discussion and suggest that �nan-cial frictions and labor regulations play a signi�cant role as intermediating factors thatamplify or reduce the e¤ects of sudden stops on job reallocation. While labor regulationsseem to reduce the e¤ects of sudden stops on job destruction, �nancial frictions seemto explain the negative e¤ects of sudden stops on job creation. We present a numberof additional checks that suggest that these results are robust to including more coun-tries, using di¤erent de�nitions of sudden stops, including country-time �xed e¤ects, andadding additional controls.

The paper is organized as follows. Section 2 reviews the relevant literature andprovides the background for this paper. Section 3 discusses the data and describes theempirical strategy. Section 4 presents the main results of the paper together with somerobustness checks, and section 5 brie�y concludes.

2 Literature Review

The study of the e¤ects of big macroeconomic shocks on job and worker realloca-tion in developed economies have received a lot of attention in the last years, e.g. see

2

Caballero and Hammour (2005), Caballero (2007), Davis et al. (2006), Shimer (2004,2005, and 2006), and Hall (2005). These papers study how recessions are linked withperiods of high job destruction and increased unemployment that, from the worker �owsside, seem to be more related to decreased transitions from unemployment to employ-ment. The recent contributions by Caballero (2007) and Caballero and Hammour(2005) present the theoretical motivation for most of our exercises. Firms reaction tonegative shocks depend on (i) �nancial aspects related to the ability of entrepreneurs toraise external funds to keep the �rm running, (ii) labor regulations that determine thecosts of destructing a job and the relative bargaining power of entrepreneurs, and (iii)productivity of �rms. One of the conclusions of this class of models, which is particu-larly relevant for our paper, is that the reaction of big macro shocks should depend on�nancial and labor aspects that tend to distort the frictionless reaction to shocks.

An empirical branch of this literature has studied the e¤ects of exchange rate shocksto job reallocation in open economies. Gourinchas (1998) and Gourinchas (1999)study the e¤ects of real exchange rate movements on job reallocation within and acrosssectors in France between 1984 and 1992 using �rm level data. The main results, for thepruposes of our paper, are that (i) job creation and destruction in tradable sectors co-move positively after an exchange rate shock, ie. both creation and destruction increases(decreases) after a real exchange rate appretiation (depreciation) and (ii) job creationis much more volatile than job destruction. Klein et al. (2003) use sectoral data forthe US for manufacturing �rms over the 1973-1993 period to study the e¤ects of trendand cyclical variation of real exchange rates on job reallocation. Their �ndings con�rmGourinchas�results in that trend movements produce periods in which job creation anddestruction co-move, so net employment is not a¤ected by exchange rate movements.However, they also �nd that cyclical exchange rate movements a¤ect net employmentthrough movements in job destruction. Finally, Haltiwanger et al (2004) analyze thesame topic using the same dataset we use in this paper, and con�rm previous results inthat real exchange rate appreciations are periods of increased job reallocation. Overall,this literature documents signi�cant e¤ects of exchange rate shocks on job reallocation.Our estimates are related to this literature, but we exploit an extreme case of an externalshock, which are (i) more related to external conditions that exchange rate movementsand (ii) more exogenous.

The literature of sudden stops is wide and covers a number of dimensions, someof which are not relevant for this paper. The most relevant literature documents bigmacroeconomic e¤ects of sudden stops on most macro aggregates. For instance, Calvo etal. (2006) document that systematic sudden stops causes cyclical movements associatedwith decreases in GDP, TFP, investment, and the availability of credit. Gallego andJones (2005) and Rothenberg and Warnock (2006) document that sudden stops area fairly frequent phenomenon in emerging economies. For instance, Rothenberg andWarnock (2006) document that in more than 75% of the months from January 1989through December 2005 at least one emerging country had a sudden stop. They alsoshow some bunching of sudden stops around the big international crises of this period:in 1991 following a crisis in the stock market in the US and around the �rst Gulf War, in1995 after the Tequila crisis, and in the 1997-2000 period during the Asian and Russian

3

crises. Interestingly, these bunching periods coincide with periods in which the supplyof funds to emerging countries and �rms with similar credit ratings in the US contracts,as documented by Gallego and Jones (2005). This evidence suggests that most suddenstops seem to be driven mainly by external conditions and not by internal conditions,which is an important identi�cation assumption in our empirical strategy.

Another literature related to our paper studies how �nancial development a¤ectthe response of economies to macro shocks. Braun and Larrain (2005) show thatindustries that are more dependent on external �nance are hit harder during recessions,using a cross-country sample of manufacturing industries over forty years. This resultis particularly stronger in dependent industries located in countries with poor �nancialcontractibility. Thus, this paper shows how the growth rate of output in industriesrespond in an heterogeneous way to macro shocks depending on �nancial conditions. Ina related paper, Larrain (2006) shows that output volatility is dampened in countrieswith developed bank systems, in particular, the redution in volatility is achieved viacounter-cyclical borrowing: at the �rm level, short-term borrowing is less correlatedwith sales and inventories in countries with more developed bank systems. Raddatz(2006) present similar evidence suggesting that liquidity provision by more developed�nancial systems helps reducing the volatility of output in high-liquidity need sectorsthrough two mechanisms: a large reduction in the volatility of output of existing �rmsand some reduction in the volatility of the number of �rms. Therefore, all these threepapers suggest a role for �nancial frictions in terms of the transmission of big shocks tosectors.

Similarly, other papers document a role for labor regulations in the way economiesreact to shocks. Blanchard and Wolfers (2000) argue that the interaction between macroshocks and labor market institutions is what explains the heterogenity in the reactionof unemployment to macro shocks. Caballero et al. (2006) document that economiesjob security regulation hampers the creative-destruction process, especially in countrieswhere regulations are likely to be enforced. The annual speed of adjustment to shocksand productivity growth is decreased in countries with likely-enforced labor regulations.Therefore, these papers suggest that labor market regulations should a¤ect the wayeconomies react to shocks.

In this paper, we take the �ndings of these di¤erent literatures as a motivation tostudy the potentially heterogeneous e¤ects of sudden stops on creation and destructionat the sector level. Theoretically, we are motivated by a simpli�ed version of the modelspresented in Caballero (2007) and Caballero and Krishnamurthy (2004). We assumethat �rms use capital and labor and have some (limited) internal resources and produc-tivity. Continuing �rms face a liquidity shock in each period and have to pay a dismissalcost if they �re workers. In contrast, (potentially) new �rms also face a liquidity shockand have to pay a �xed cost to enter the market. In this context a sudden stop is ashock that decreases the availability of external funds and/or increases the size of theliquidity shock.

In this context, we expect that creation should decrease and destruction shouldincrease during periods of sudden stops. Moreover, the e¤ects should be heterogeneous:

4

� Firms in sectors depending more on external �nance should be more a¤ected.Thus, we should observe more destruction and less creation in these sectors.

� Firms in countries with less stringent labor regulations should be more a¤ected bysudden stops. In particular, we should observe more destruction and creation inthese sectors.

� If labor regulation is more stringent for continuing �rms, these �rms should createand destruct less.

� Firms with more internal funds and better access to capital markets (continuing�rms?) should be less a¤ected by sudden stops.

We study these hypotheses in the next sections of the paper.

3 Data and empirical approach

3.1 Data Description

Labor Flows. Data on sectoral gross �ows comes from Haltiwanger et al (2004).Data is at the 2-digit sector level for 6 Latin American countries from 1978 to 2001.As part of an IADB project, the database was constructed using �rm level data from:Argentina, Brazil, Chile, Colombia, Mexico and Uruguay. The original data containedemployment at the �rm level and it was aggregated by sectors.2

The original surveys record �ows in workers or jobs, not in hours, hence our studycaptures only the extensive margin on workers. Consider a given sector and country, letp index the plants and t the period, then Ep;t employment in plant (�rm) p at time t.Net employment growth is given by

Netp;t = 2

�Ep;t � Ep;t�1Ep;t + Ep;t�1

�(1)

Job creation corresponds to the sum of net employment growth over all plants withpositive net employment growth (for a given country-sector pair) between period t � 1and t,

Creationt =Xp

�p;tmax (Netp;t; 0) ; (2)

where �p;t is employment share of plant p.

Job destruction is then the sum of (the absolute value) of net employment growthover all plants with negative employment growth between period t� 1 and t,

Destructiont =Xp

�p;t jmin (Netp;t; 0)j : (3)

2See Appendix A.

5

We use data for manufacturing sectors, as it is the only available for all countries.For each series we have two types of data, continuing and all plants/�rms. Data forcontinuing plants/�rms includes information from those plants alive in both t and t-1;all plants/�rms include all plants/�rms observed in t irrespective of whether they arenew or not. For Argentina and Uruguay we only have data for continuing plants.3

Table 1 presents the summary statistics for the whole set of countries and for themain group of countries in our estimation (Brazil, Chile, Colombia and Mexico). Wecan see that there is large variation both on creation and destruction across countries;Mexico has the highest rates of creation, but Chile shows the highest destruction rates,both for continuing and all plants. Chile also presents the largest di¤erences betweenthe maximum and minimum values for creation and destruction in the sample.4

There is one dimension our dataset misses. We do not observe plant turnover data,i.e., we have no information on �ows associated to closing down plants, neither theplant/�rm �ows by sector. The latter dimension is important when studying the e¤ectsof �nancial shocks, as liquidity needs may drive �rms out of the market if they cannotborrow to maintain operation. It is also relevant to observe �rms that change prop-erty, either because of bankruptcy procedures or because of �re-sale when in suddenstops. Thus, these potential channels are not studied in this paper, but our results high-light another potential channel for the transmission and propagation of sudden stops indeveloping countries.

Sudden Stops. We follow Calvo et al. (2004) and Gallego and Jones (2005), obtainingthe sudden stop episodes directly from quarterly capital �ows data. In particular asudden stop is a period that

1. Contains at least one observation where the year-on-year fall in capital �ows liesat least two standard deviations below its sample mean;

2. Ends once the annual change in capital �ows exceeds one standard deviation belowits sample mean;

3. Begins the �rst time the annual change in capital �ows falls one standard deviationbelow the mean.

Based on this de�nition we construct two variables:

� a dummy variable that takes a value of 1 if there is a sudden stop in any quarterof the year (sudden_av),

3There are other di¤erences in the data in the case of Argentina and Uruguay; we will explain themin more detail as we outline our empirical strategy in section 3.2.

4This empirical study could greatly bene�t from the dataset of Bartelsman et al (2004) if it becomesavailable; this dataset includes sectoral data for 24 countries.

6

� a variable that measures the fraction of quarters in which a sudden stop happened(sudden_any).

We present results using the �rst of the two, but the results are qualitatively thesame if we use the latter variable instead.

Table 3 shows the years for which we identify a sudden stop together with the samplefor each of the six countries. We can see that we do not identify any sudden stop forUruguay according to this de�nition.5 On the other hand, we �nd that Argentina hasspent about half of the sample period in sudden stops. All of our sudden stop episodeshave been identi�ed before in other studies, and we believe are reasonable accordingto previous knowledge and work on the topic.6 Interestingly, with the exception ofMexico 1994-1995, all the sudden stops identi�ed in our sample correspond to periodsof bunching of sudden stops in the paper by Rothenberg and Warnock (2006), which inturn correspond to periods in which credit conditions worsen due to exogenous reasonsas documented in Gallego and Jones (2005). Chile is usually identi�ed as having donereforms earlier than the rest of the Latin America. In our sample it also is the onlycountry for which we identify a sudden stop in the 1980s, thus in the empirical sectionwe check whether this is relevant for our main results running our regression with datafrom the 1990s only.

Labor Regulation. Labor regulation measures are from La Porta et al. (2004). Outof all the measures they compute we focus on the cost of �ring workers and dismissalprocedures; in our estimations we use the sum of the two. The cost of �ring workersis a measure of how expensive is for a �rm to �re 20% of the workers, it includes allthe compensations and penalties needed to pay in this case. The dismissal proceduresvariable counts the number of measures a �rm must undertake in order to be able todismiss a worker. The highest value of our labor measure corresponds to Mexico with1:28 out of a maximum of 2; the minimum is 0:83 in Colombia.

It is worth to keep in mind that La Porta et al. (2004)�s study compares laborregulation as of 1997 for a total of 85 countries. This is another reason why we laterconsider regressions using samples restricted to the 1990s only.

Sector Characteristics. We use two types of sector characteristics. One related to�nancial conditions, and the second related to turnover and reallocation of labor.

Finance Exposure: The main sector level characteristic we use corresponds to theRajan-Zingales external �nancing dependence variable. It captures a sector�s de-pendence on external �nancing measuring the fraction of their assets that is �-nanced with external funds. A sector with a higher Rajan-Zingales measure should

5This is one of the reasons why we do not incorporate Uruguay in our main regressions later.6See Caballero and Panageas (2005) and Calvo et al. (2006). Some studies, eg. Caballero and

Panageas (2005) have identi�ed a sudden stop in early 80s in Colombia, but according to our de�nitionthis is not the case.

7

su¤er more in the event of a �nancial crunch or any other reduction in the accessto credit.

Labor Reallocation: Taken from Micco and Pages (2006), it measures industry real-location in US industries as the sum of job creation and job destruction as fractionof total employment.7 Like in the case of the Rajan-Zingales measure for �nancialexposure, the underlying assumption is that measures in the US captures techno-logical components that are valid to rank sectors in other countries.

3.2 Empirical Strategy

We estimate the following equation:

yijt = �Sjt + �xjt + �wjSjt + �ziSjt + �+ "ijt; (4)

where yijt is some measure of job �ows (creation, destruction, net employment growth)in sector i, country j, and time t, S is a measure of external shocks -sudden stops in thispaper-, xjt is a vector of institutional characteristics and controls that varies at the timeand country level, wj is a vector of country speci�c institutional variables (labor marketregulation), zi is a vector of sector speci�c characteristics (�nancial dependence), and� is a vector of dummy variables and �xed e¤ects. All our regressions include country,year and sector �xed e¤ects.8

The interaction e¤ects (ziSjt and wjSjt) are the most important part in this regres-sion. Even if the direct e¤ects are relevant, we expect that speci�c characteristics of eachsector and/or institutional di¤erences across countries would lead to di¤erent responseswhen in a sudden stop. While the sector speci�c characteristics are related to industrialcomposition of the manufacturing sector, the e¤ects related to labor market regulationcan be relevant for policy analysis. Labor market regulation is a usual suspect in manycases, with this case not being an exception; theoretical work shows that there are po-tential connections in this situation, and our work shows empirical correlations alongthese lines.

As it has been insinuated before, our main analysis restricts the sample of countries toBrazil, Chile, Colombia and Mexico. There are two di¤erent reasons to drop Argentinaand Uruguay. First, we do not identify any sudden stop in Uruguay during the years forwhich we have labor �ows data. Second, the nature of the original surveys from whichdata was collected di¤ers from the rest. For both countries there is no informationon new plants, as only continuing plants are observed in their sampling. This lack ofdata makes hard to compare continuing and all plans data, thus impeding our inferenceon the role played by new �rms in the adjustment process, which we consider to bevery relevant in our case. As a robustness check, in Appendix A we also present someregressions where we use all the sample.

7See Davis and Haltiwanger (1999).8Not all the regressions include the corresponding interactions.

8

3.2.1 Identification

The use of sector level data allows us to use two sources of identi�cation. First,cross country variation in labor market regulation allows us to compare sectors acrosscountries. While not absolutely bullet proof, this identi�cation strategy provides initialevidence on the variables that may indeed a¤ect the reallocation process.

Second, sector level data allows us to control for unobserved country characteristicsand rely on particular sector speci�c (but not country-sector speci�c) variables to identifythe e¤ects of sudden stops. Part of this e¤ect comes from interaction e¤ects betweensector characteristics and the prevalence of sudden stops, e.g. we expect sectors that relymore on external �nancing or with less access to collateral su¤er more during a suddenstop than a sector with better chances of self-�nancing its operations (or at least partof them).

The identi�cation assumption di¤ers according to the source of variation we areexploiting. In the case of country characteristics, we need that neither intensity nor tim-ing/frequency determinants of sudden stops are correlated to labor market regulationsor its determinants. Suppose a country is pegging its nominal exchange rate, if tighterlabor market regulations make the country less likely to defend against a speculativeattack because the cost of the defense is higher, we would have a case where there issome reverse causality and hence our identi�cation assumption is violated.9

In the case of sector level variation, the identi�cation assumption is milder. We needany determinant of the sudden stop (or its size) not to be systematically correlated withsector characteristics that determine the sensitivity of �rms in each sector to the suddenstop, which in our case is �nancial dependence. Notice, that it does not require thesudden stop to be independent of country characteristics, but to be uncorrelated withdeterminants of the sector speci�c sensitivity to them. We believe this condition to beweaker than then one mentioned in the paragraph above, and also likely to hold in oursample.

Other Caveats. The use of US based measures has caused some controversy in theliterature because of the assumption that we can extrapolate to di¤erent countries. Thereare two elements to consider there. First, there is some evidence that rankings basedon the Rajan-Zingales measure of �nancial dependence performs well in other countries.Second, as we are interested in intrinsic (most likely technological) characteristics thatmake sectors di¤er on their �nancial decisions, we can think of equation (4) as thereduced form of an IV estimation where the US-based measure is used as instrument forthe country speci�c variables.

9Alternatively, if labor market regulation is endogenous to the intensity and frequency of suddenstops, then the same problem arises. Particularly so, because our measures of sudden stop correct onlypartially for intensity of sudden stops (size of the drop).

9

4 Results

In this section we review the main results of the paper, we provide some economicinterpretation for them, and also present some robustness exercises.

4.1 Main results

This subsection presents the main results, showing how labor market regulation andsectorial characteristics interact with sudden stops in the determination of the e¤ects onjob �ows.

4.1.1 Sudden Stops and Labor Flows

Although not totally comparable with most of the shocks used in the related litera-ture, sudden stops are large and important shocks in emerging markets. If sudden stopscome together with a tighter credit market, we should expect a crunch in domestic de-mand, accompanied with incentives for sectorial reallocation due to changes in relativeprices. Moreover, the �nancial e¤ects are also likely to a¤ect �rm level decisions causingchanges in investment plans, entry or inducing liquidation when in �nancial distress.This should further dampen the reallocation process as both destruction and creationmargins are distorted.

The main results for the e¤ects of sudden stops can be observed in the top rows ofTables 4, 5, 6, and 7. The �rst two tables show the e¤ects on job creation, where we cansee that after controlling for labor regulation and sectorial �nancial exposure suddenstops have a (weakly signi�cant) negative e¤ect on job creation by all �rms. On theother hand there is no signi�cative evidence of an e¤ect on job creation in continuing�rms, suggesting that new �rms (as they constitute the di¤erence between the two series)are the most a¤ected. The results for job destruction are in the �rst row of tables 6,and 7; where we observe that during sudden stops destruction is between 50% and 85%larger than in an average year (in the average sector and country), implying a very largee¤ect of sudden stops on labor �ows, particularly on destruction of jobs. This resultis important, particularly because it implies that labor market �ows (and potentiallyfrictions) are relevant in any model that wants to explain the economic e¤ects on adeveloping economy.

4.1.2 Labor Market Regulation: Firing and Dismissal Procedures

In our main speci�cation (equation 4) we aim to identify the e¤ect of the interactionbetween our labor market regulation measure and the sudden stop dummies (� in ourregression). This coe¢ cient re�ects the variation in the response of labor �ows to asudden stop that arises from the di¤erent levels of labor market regulation. Althoughmany theoretical models, the empirical literature available has not been totally successfulidentifying these e¤ects. In our case, we do not identify the e¤ects during tranquil time,

10

but can say something about the reaction to a shock.

Tables 4, and 5 present the e¤ects on job creation. We can observe a signi�cantnegative e¤ect on job creation by continuing �rms, with a point estimate of roughly 0:1,implying that for an average sector job creation would be 4:4 percentage points lower ifmoved from Colombia to Brazil (the minimum and maximum of labor regulation in oursample) during a year long sudden stop.10 For the case of data for all plants, we do not�nd evidence of a signi�cant e¤ect of labor market regulation, implying that new plantsnot only are less sensitive to it, but that in general they create more jobs exactly in thecountries where regulation is sti¤er. If we add both pieces we observe that on the total,there is no evidence of an e¤ect of labor regulation on job creation. However, two thingsare worth to mention here. First, this �no e¤ect�results hides a signi�cant degree ofheterogeneity; second, the particular pattern of responses is not easy to explain, withouta clear reason to justify the strong responses by new �rms, we believe an exploration ofthis particular result is for now beyond the scope of this paper (and of the data we haveat hand).

Results for job destruction are contained in the second row of Tables 6, and 7.As expected, we �nd a signi�cant negative e¤ect of labor market regulation on jobdestruction during a sudden stop. For the case of continuing plants, our point estimateimplies that moving from Colombia�s to Brazil labor regulation�s level would decreasejob destruction by approximately 6 percentage points. The e¤ect on job destruction ofall plants is somewhat smaller and less signi�cant, but it is still robust to the inclusionof sectoral controls. Table 8 presents the result when we pool all observations togetherand we introduce a dummy indicating whether the particular observation correspondsto a point for all plants.11 If we look at the column labeled as �Destruction�we see thatthe coe¢ cient corresponding to the interaction of labor regulation with the sudden stopvariable and the �All�dummy is not signi�cantly di¤erent from 0. This implies that thedi¤erence between the e¤ect on new and existing plants is not statistically signi�cant,hence both types of �rms react in a similar fashion.

Using Sectoral Labor Reallocation. We perform one further test of the labor mar-ket regulation e¤ects. In this case we use data on labor reallocation by sectors in theUS, from Micco and Pages (2006), and interact it with the sudden stop variable and thelabor market regulation measure. Labor reallocation from the US is supposed to capturesector level variation on a sector�s need to reallocate labor, with the US being a bench-mark �exible market. Higher �ring costs and dismissal restrictions should a¤ect moresectors that have higher reallocations. Our results con�rm these hypothesis; in Table 8we can see that labor regulation does not have a signi�cant e¤ect on job creation, butwe �nd evidence that labor regulation lowers job destruction among continuing �rms,especially so in sectors with higher labor reallocation. Notice that as before we �nd no

10Remember that our main results include a variable that measures the fraction of the year a countryis in sudden stop (taking values from 0 to 1 at intervals of 0.25).11This speci�cation assumes that all other controls and �xed e¤ects are the same for continuing and

all plants.

11

evidence of an e¤ect on job destruction on all �rms, implying that the sensibility of jobcreation in new �rms is lower than for plants/�rms that were already operating.12

4.1.3 Sector Characteristics: RZ Financial Dependence

Financial fragility or exposure is likely to a¤ect hiring and �ring decisions by �rms;new projects may be delayed, some plants/�rms may reduce their scale because of �nanc-ing problems, etc. Our results show that job �ows are indeed a¤ected on this dimension,sectors with higher �nancial exposure create less jobs during sudden stops.13

Tables 4, 5, 6, and 7 contain the results of our benchmark estimations. The rowslabeled Fin1-Sudden correspond to the interaction of the Rajan-Zingales measure of�nancial dependence by sector with the sudden stop. We can observe the clear pattern,job creation is lower during sudden stops and more so in sectors with higher �nancialdependence. Moreover, there is some weak evidence that new �rms are more sensitive tothis, as the point estimates for all plants/�rms are larger in absolute value than the onesfor continuing �rms only. The rows labeled as Fin2-Sudden correspond to an alternative(more recent) measure of the Rajan-Zingales indicator, the picture is the same, althoughwith smaller coe¢ cients. In this case the di¤erence on the e¤ect on job creation betweenall and continuing plants is smaller too.

Our main results using Fin1-Sudden suggest that during a year long sudden stop, jobcreation in the sector with the highest �nancial exposure is approximately 2:7 percentagepoints higher than in the sector with the smallest �nancial exposure, and approximately1:6 percentage points higher than in the average sector of our sample.14

As we mention before, the results in table 8 correspond to the pooled estimation. Thepooled estimation presents a similar picture: no signi�cant e¤ect on job destruction anda negative e¤ect on job creation, although we �nd no evidence that new plants/�rms aremore sensitive to sudden stops on average when compared to continuing plants/�rms. Allin all, this con�rms our broad �gure, �rms in sectors that are more exposed to �nancialconditions display signi�cantly stronger responses to sudden stops in the destructionmargin, as measured by job destruction.

4.2 Robustness Checks

In order to check the robustness of our results we perform two di¤erent tests. Asa �rst check we reestimate our main equations with a restricted sample that considersdata only after 1990. Additionally we perform a second check, and estimate the mainequations using country-time �xed e¤ects in addition to the interaction of �nancialdependence and sudden stop variables.

12In this case we introduce as additional controls the interaction of sector speci�c labor reallocationand sudden stops, and the interaction of country labor regulation and sudden stops.13Another margin refers to destruction of plants and the consequent separation of workers, unfortu-

nately, as we mentioned before, we cannot study this last channel.14The same numbers are 1.8 and 1.0 for continuing plants only.

12

Restricted Sample: 1990-2000. There are two main reasons why restricting thesample in the regressions could potentially lead to changes in the results. Given theunbalanced panel data we have, we repeat our benchmark regressions using only datafrom the 1990s. Consequently we eliminate the series of data available for the 1980s fromChile and Colombia. This reduces our sample to 296 observations and we lose the debtcrisis observation for Chile. Hence, this sample change reduces the weight of Chile andColombia in our estimates and obtains a more homogeneous set of observations. Second,our labor regulation and �nancial dependence variables are both measured in the 1990s,hence restricting the sample also allows to avoid problems arising from changes in bothmeasures coming from labor or �nancial market reforms.

The results using the restricted sample with observations after 1990 are in Tables 10and 11. Qualitatively the picture remains similar. Regarding labor market regulation,we observe a negative e¤ect on job destruction, with an e¤ect on destruction on all�rms that is more than twice as large as before (and more signi�cant too), while forcontinuing plants the e¤ect is of the same magnitude. Sudden stops are also periods inwhich we �nd signi�cant increases on job destruction across the board, with relativelysimilar magnitudes too.

Evidence for �nancial dependence at the sector level is also similar to the results inour main speci�cations. Higher �nancial dependence is correlated with lower creationduring sudden stops, with point estimates of similar magnitude to the ones before, wealso observe some evidence of more sensitivity by new �rms in the samples. Althoughall coe¢ cients are positive, we do not �nd signi�cative e¤ects of �nancial dependence ondestruction, which con�rms the same qualitative results we described in section 4.1.3.

Time-Country Fixed E¤ects Regression. Given that not all sudden stops areequal, we may worry that the aggregate responses at the country level might defer. Forexample, exchange rate policy responses may vary and hence not all the resulting changesneed to be the same. If di¤erent �rms respond more to �nancial aspects (because ofcurrency or maturity mismatch), then the exact mix of out�ow and other aggregatee¤ects of the sudden stop may matter.15

To address this issue, at least in part, we reestimate the regressions with the fullset of country-time �xed e¤ects. This speci�cation should capture any time varyingvariable at the country level, but it does not control for interactions of these variablesand sector speci�c e¤ects. The results are in Tables 12 and 13, there we observe that ourprevious estimations of the e¤ect of sectoral exposure remains robust to the inclusion ofcountry-time �xed e¤ects. Furthermore, the point estimates do not change much whencompared to those in our benchmark speci�cations in tables 4, 5, 6, and 7, and hencethe interpretation of the e¤ects remains the same.

15Financial conditions are not the only ones. Real exchange rates or lack of external �nancing maygenerate di¤erent domestic demand changes for example.

13

4.3 Summary and Discussion

A clear picture emerges from section 4.1. Sudden stops are shocks associated withlower creation and higher destruction for a given country and sector. These results aretrue for continuing and new plants, and shows that the e¤ects are not restricted to justone type of plants/�rms.

On top of these main e¤ects we also �nd that labor market regulation reduces jobdestruction during sudden stops. This means that countries with higher �ring costs orwith more dismissal procedures are also the ones that on average see lower destructionby surviving �rms during sudden stops. Although theoretical models suggest that �ringcosts disincentive the use of labor, we do not �nd consistent evidence of this in ourestimations; while continuing plants in countries with higher labor regulation show lowercreation, the overall e¤ect is 0. This is somewhat puzzling, as it suggests that new �rmsare not only less sensitive to it, but that creation in them during sudden stops might bepositively correlated to labor market regulations. Except for this last detail, the pictureis consistent and robust to changes in sample size and controling for di¤erent measuresof sectoral �nancial dependence.16

Sudden stops are generally associated with an abrupt reduction in the willingnessto lend to the country. It is reasonable to believe that this external constraint willa¤ect also domestic �nancial markets, hence agents that are more exposed to �nancialmarket conditions should su¤er more on this situation. We �nd results along these lines,as our estimations indicate that sectors with higher �nancial dependence have lowerjob creation during sudden stops. Interestingly, we also �nd some evidence that new�rms/plants are more a¤ected than continuing ones, a reasonable result if start-up costshave to be �nanced with external funds. Alternatively, information related argumentscan also justify more stringent credit constraints for new �rms. Irrespective of the reason,we believe this particular aspect to be intuitive and in line with our priors.

Somewhat surprisingly, we do not �nd a signi�cant relation between �nancial de-pendence and job destruction. This result should be interpreted carefully though. Ourdata does not contain information on plant/�rm turnover, hence if the e¤ect of �nancialdependence does not re�ect on the size (measured as employment) but on the survivalof them, our data severely underestimates this dimension.

From a more general point of view, we can think of our results as exploring therelevance of three main channels of adjustment: the creation of new �rms, size of newand existing �rms, and exit decision by �rms. As we have already mentioned we donot have evidence on the latter, but we have shown how each of the �rst two channelsare present in the particular patterns we �nd. The results also show that each of thesechannels are more responsive to �nancial characteristics or labor market regulation: sizeadjustments �particularly downsizing- are more sensitive to labor market regulation,while entry decisions seems to be more sensitive to �nancial characteristics of di¤erentsectors.

16See appendix A for the results when we also include data for Argentina and Uruguay in the esti-mations.

14

5 Final Remarks

5.1 Conclusions

This paper studies the e¤ects of sudden stops on job creation and destruction in asample of Latin American countries.

We �nd consistent evidence that sudden stops are associated with decreased jobcreation and increasing job destruction. We also observe the e¤ects of the sudden stopson job creation to be heterogeneous: job creation in sectors with strong demand forexternal �nance tends to react more to sudden stops. Similarly, the positive e¤ectsof sudden stops on job destruction are stronger in sectors with a higher sensitivity toexternal �nance.

We can also draw some conclusions regarding the role played by new and existing�rms in these responses. We do so by comparing data for all plants/�rms and for contin-uing ones only. On the one hand, we observe that the negative e¤ect of sudden stops onjob creation is only relevant for continuing �rms and insigni�cant overall, particularlywhen the additional e¤ect of labor market regulations is considered. On the other hand,the negative e¤ect of sudden stops on job creation is stronger for all �rms, suggestingthat job creation by new �rms su¤ers strongly from external shocks.

Overall, our results are consistent with the mainstream theoretical arguments andprovide an interesting look at the mechanics of sudden stops within countries. By doingthis, we contribute to the sudden stop literature with some interesting empirical factsthat can help us understand the diversity of responses and recoveries we observe in thedata. In a world with frictions the di¤erences in the creation and destruction �ows cana¤ect the speed of adjustment and recovery during and after the shocks. This paperthen provides some prima facie evidence that labor market �ows are indeed a possiblereason why responses may di¤er across countries and across sudden stops.

Additionally, our results also contribute by providing yet more evidence that labormarket regulations, in particular �ring costs and dismissal procedures, can come at acost when adjustment is needed.

15

6 References

References

Aghion, P., Angeletos, G., A. Banerjee, and K. Manova (2005) �Volatility and Growth:Credit Constraints and Productivity-Enhancing Investments�. Mimeo.

Bartelsman, Eric, John Haltiwanger and Stefano Scarpetta (2004). �Microeconomic Ev-idence of Creative Destruction in Industrial and Developing Countries�. Mimeo, Uni-versity of Maryland.

Braun, M. (2003). �Financial Contractibility and Assets�Hardness,�mimeo, AndersonSchool UCLA.

Braun, Matias and Borja Larrain (2005). �Finance and the Business Cycle: International,Inter-industry Evidence,�Journal of Finance, 60 (3), pp. 1097-1128.

Caballero, R. J. (2001).Macroeconomic Volatility in Reformed Latin America: Diagnosisand Policy Proposals. Washington, D.C.: Inter-American Development Bank.

Caballero, R. J. (2007) The Macroeconomics of Speci�city and Restructuring. Yrjo Jahns-son Lecture, Forthcoming, MIT Press.

Caballero, R. J. and M. Hammour (2005). �The Cost of Recessions Revisited: A Reverse-Liquidationist View,�Review of Economic Studies 72: 313-341.

Caballero, R. J. and A. Krishnamurthy (2004) �Exchange Rate Volatility and the CreditChannel in Emerging Markets: A Vertical Perspective.�Mimeo, May.

Caballero, R. and S. Panageas (2005). �A Quantitative Model of Sudden Stops andExternal Liquidity Management�. Mimeo, MIT.

Calvo et al (2005) Phoenix Miracles

Fisman, Raymond and Virginia Sarria-Allende (2004). �Regulation of Entry and theDistortion of Industrial Organization�, NBER Working Paper No. 10929.

Gallego, F. and G. Jones (2005). �Exchange Rate Interventions and Insurance: Is �Fearof Floating�a Cause for Concern?�. Mimeo, MIT.

Gourinchas, P.-O. (1998) �Exchange rates and jobs: what do we learn from job �ows?�,NBER Macroeconomics Annual 1998, pp. 153�207. Cambridge, MA: MIT Press.

Gourinchas, P.-O. (1999). �Exchange rates do matter: French job reallocation and ex-change rate turbulence, 1984�1992,�European Economic Review, 43(7), 1279�1316.

Haltiwanger, J., Kuegler, A., Kugler, M., Micco, A. and Pagés, C. (2004). �E¤ects ofTari¤s and Real Exchange Rates on Job Reallocation: Evidence from Latin America�,Journal of Policy Reform, Special Issue Dec. 2004, v. 7, iss. 4, pp. 191-208. Datasetavailable online

16

Klein, Michael W., Scott Schuh, and Robert K. Triest (2003). �Job creation, job de-struction, and the real exchange rate,�Journal of International Economics, 59, pp.239-265.

La Porta, Rafael, Florencio Lopez-de-Silanes, Cristian Pop-Eleches, and Andrei Shleifer(2004), �The Regulation of Labor,�Quarterly Journal of Economics, 119, pp. 1339-1182.

Larrain, Borja (2006), �Do Banks A¤ect the Level and Composition of Industrial Volatil-ity?,�Journal of Finance, 61 (4), pp. 1897-1925.

Micco, Alejandro and Carmen Pages (2006). �The Economic E¤ects of EmploymentProtection: Evidence from International Industry-Level Data,�IZA Discussion PaperNo. 2433, November.

Raddatz, C. (2006). �Liquidity Needs and Vulnerability to Financial Underdevelop-ment,�Journal of Financial Economics, 80(3), pp. 677-722.

Rothenberg, Alexander D. and Francis E. Warnock (2006). �Sudden Flight and TrueSudden Stops,�NBER Working Paper 12726, December.

17

BrazilVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 72 0.088 0.024 0.044 0.147Creation All 72 0.158 0.035 0.085 0.245Destruction Cont 72 0.108 0.026 0.056 0.183Destruction All 72 0.164 0.032 0.104 0.263

ChileVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 160 0.082 0.041 0.006 0.213Creation All 160 0.119 0.055 0.010 0.267Destruction Cont 160 0.074 0.046 0.005 0.294Destruction All 160 0.119 0.070 0.005 0.370

ColombiaVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 189 0.067 0.026 0.011 0.135Creation All 189 0.095 0.034 0.025 0.197Destruction Cont 189 0.105 0.043 0.029 0.316Destruction All 189 0.103 0.042 0.029 0.310

MexicoVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 63 0.126 0.041 0.064 0.254Creation All 63 0.174 0.055 0.098 0.310Destruction Cont 63 0.078 0.029 0.035 0.171Destruction All 63 0.105 0.041 0.047 0.232

Table 1: Descriptive Statistics: Job Creation and Destruction

18

All CountriesVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 646 0.075 0.038 0.006 0.254Creation All 484 0.123 0.053 0.010 0.310Destruction Cont 646 0.091 0.041 0.005 0.316Destruction All 484 0.118 0.055 0.005 0.370

Main CountriesVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 484 0.083 0.038 0.006 0.254Destruction Cont 484 0.092 0.043 0.005 0.316

Table 2: Descriptive Statistics: Job Creation and Destruction, Pooled. Main countriesinclude: Brazil, Chile, Colombia and Mexico

Country Sample Year in SSBrazil 1992-2000 1997-9Chile 1980-1999 1981-4, 1998-9Colombia 1978-1991, 1993-1999 1998-9Mexico 1994-2000 1994-5Argentina 1991-2001 1994-5, 1998-2001Uruguay 1989-1995 None

Table 3: Sample and Sudden Stop Years

19

[1] [2] [3] [4] [5]Sudden -0.03 -0.021 -0.031 -0.021 -0.031

[0.017]* [0.006]*** [0.016]* [0.006]*** [0.016]*Labor-Sudden 0.042 0.045 0.045

[0.063] [0.064] [0.063]Fin1-Sudden -0.092 -0.093

[0.038]** [0.038]**Fin2-Sudden -0.054 -0.055

[0.020]*** [0.020]***Rule-Sudden -0.002 0 -0.003 0 -0.003

[0.005] [0.003] [0.005] [0.003] [0.005]Observations 484 484 484 484 484R-squared 0.62 0.63 0.63 0.63 0.63

Table 4: Job Creation, all �rms and main countries

[1] [2] [3] [4] [5]Sudden 0.009 -0.014 0.008 -0.014 0.008

[0.012] [0.005]*** [0.012] [0.005]*** [0.012]Labor-Sudden -0.099 -0.097 -0.097

[0.047]** [0.048]** [0.048]**Fin1-Sudden -0.058 -0.057

[0.028]** [0.028]**Fin2-Sudden -0.041 -0.04

[0.014]*** [0.015]***Rule-Sudden 0.004 -0.002 0.004 -0.002 0.004

[0.004] [0.002] [0.004] [0.002] [0.004]Observations 484 484 484 484 484R-squared 0.56 0.56 0.56 0.56 0.56

Table 5: Job Creation, continuing �rms and main countries

20

[1] [2] [3] [4] [5]Sudden 0.087 0.061 0.088 0.061 0.088

[0.020]*** [0.007]*** [0.019]*** [0.007]*** [0.019]***Labor-Sudden -0.116 -0.119 -0.118

[0.064]* [0.064]* [0.064]*Fin1-Sudden 0.08 0.082

[0.052] [0.050]Fin2-Sudden 0.026 0.026

[0.027] [0.026]Rule-Sudden 0.011 0.004 0.011 0.004 0.011

[0.006]* [0.004] [0.005]** [0.004] [0.005]**Observations 484 484 484 484 484R-squared 0.62 0.62 0.62 0.61 0.62

Table 6: Job Destruction, all �rms and main countries

[1] [2] [3] [4] [5]Sudden 0.087 0.052 0.087 0.052 0.087

[0.018]*** [0.007]*** [0.017]*** [0.007]*** [0.018]***Labor-Sudden -0.15 -0.152 -0.151

[0.054]*** [0.054]*** [0.054]***Fin1-Sudden 0.054 0.056

[0.047] [0.045]Fin2-Sudden 0.025 0.026

[0.024] [0.023]Rule-Sudden 0 -0.009 0 -0.009 0

[0.005] [0.003]** [0.005] [0.004]** [0.005]Observations 484 484 484 484 484R-squared 0.61 0.61 0.61 0.61 0.61

Table 7: Job Destruction, continuing �rms and main countries

21

Creation DestructionSS-All -0.108 -0.049

[0.042]*** [0.055]Labor-SS -0.085 -0.120

[0.045]* [0.049]**Labor-SS-All 0.118 -0.029

[0.054]** [0.059]Fin1-SS -0.074 0.054

[0.028]*** [0.045]Fin1-SS-All -0.001 -0.029

[0.045] [0.066]

Table 8: Sector E¤ects of Labor Regulation, pooled regression and main countries

Creation, all Creation, cont Dest, all Dest, contLabor-SS-LR 0.826 0.529 -1.697 -1.960

[0.712] [0.532] [1.191] [0.955]**Fin1-SS -0.098 -0.068 0.032 0.031

[0.038]** [0.024]*** [0.055] [0.044]Rule-SS -0.003 0.004 0.012 0.001

[0.004] [0.003] [0.005]** [0.004]

Table 9: Sector E¤ects of Labor Regulation, DDD approach with main countries

22

Dep Variable Pos Cont Pos Cont Pos All Pos AllSudden 0.028 0.028 0.006 0.006

[0.024] [0.024] [0.034] [0.034]Labor-Sudden -0.161 -0.161 -0.071 -0.071

[0.080]** [0.080]** [0.111] [0.110]Fin1-Sudden -0.057 -0.093

[0.034]* [0.045]**Fin2-Sudden -0.037 -0.055

[0.019]* [0.025]**Rule-Sudden 0.015 0.015 0.012 0.012

[0.006]** [0.006]** [0.009] [0.009]Observations 296 296 296 296R-squared 0.6 0.6 0.61 0.61

Table 10: Job Creation, main countries 1990-2000

Dep Variable Neg Cont Neg Cont Neg All Neg AllSudden 0.08 0.079 0.176 0.176

[0.024]*** [0.024]*** [0.033]*** [0.033]***Labor-Sudden -0.128 -0.128 -0.38 -0.379

[0.072]* [0.072]* [0.103]*** [0.103]***Fin1-Sudden 0.081 0.08

[0.048]* [0.058]Fin2-Sudden 0.036 0.033

[0.026] [0.031]Rule-Sudden -0.002 -0.002 0.033 0.033

[0.005] [0.005] [0.009]*** [0.009]***Observations 296 296 296 296R-squared 0.59 0.59 0.6 0.58

Table 11: Job Destruction, main countries 1990-2000

23

All PlantsFin1-Sudden -0.053 -0.053

[0.017]*** [0.022]**Fin2-Sudden -0.09 -0.089

[0.034]*** [0.041]**Observations 484 484 296 296R-squared 0.76 0.76 0.75 0.75Cont PlantsFin1-Sudden -0.04 -0.037

[0.014]*** [0.018]**Fin2-Sudden -0.056 -0.055

[0.026]** [0.033]*Observations 484 484 296 296R-squared 0.72 0.71 0.73 0.73Sample Full 1990- Full 1990-

Table 12: Job Creation main countries, time-country �xed e¤ects

All PlantsFin1-Sudden 0.025 0.03

[0.024] [0.028]Fin2-Sudden 0.078 0.074

[0.046]* [0.053]Observations 484 484 296 296R-squared 0.76 0.76 0.74 0.74Cont PlantsFin1-Sudden 0.026 0.035

[0.021] [0.025]Fin2-Sudden 0.056 0.08

[0.039] [0.047]*Observations 484 484 296 296R-squared 0.73 0.73 0.7 0.7Sample Full 1990- Full 1990-

Table 13: Job Destruction, main countries, time-country �xed e¤ects

24

A Additional Tables

A.1 Data Description

ArgentinaVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 99 0.053 0.023 0.014 0.136Creation All . . . . .Destruction Cont 99 0.089 0.032 0.023 0.208Destruction All . . . . .

UruguayVariable Type Obs Mean Std. Dev. Min MaxCreation Cont 63 0.050 0.026 0.006 0.150Creation All 0 . . . .Destruction Cont 63 0.088 0.043 0.033 0.234Destruction All 0 . . . .

Table 14: Descriptive Statistics: Job Creation and Destruction - Additional Countries:Argentina and Uruguay

Country Argentina Brazil Chile Colombia Mexico UruguayType data Job Job+Workers Job Job Job+Workers JobSource INDEC RAI ENIA EAM DANE IMSS INEPeriod 91-01 92-00 80-99 77-91 93-99 94-00 89-95Coverage Manuf Private (Formal) Manuf Manuf Private ManufUnit Firms Plants Plants Plants Firms Plants

Table 15: Dataset Characteristics by Country

25

A.2 Results

[1] [2] [3] [4] [5]Sudden -0.006 -0.01 -0.006 -0.01 -0.006

[0.004] [0.004]*** [0.004]* [0.004]*** [0.004]*Labor-Sudden -0.051 -0.052 -0.052

[0.013]*** [0.013]*** [0.013]***Fin1-Sudden -0.043 -0.045

[0.020]** [0.019]**Fin2-Sudden -0.027 -0.028

[0.011]** [0.010]***Rule-Sudden 0.002 0 0.002 0 0.002

[0.002] [0.002] [0.002] [0.002] [0.002]Observations 646 646 646 646 646R-squared 0.55 0.54 0.55 0.54 0.55

Table 16: Job Creation, continuing plants and all countries. Same as Table 5 butincluding observations for Argentina and Uruguay.

[1] [2] [3] [4] [5]Sudden 0.035 0.039 0.035 0.039 0.035

[0.006]*** [0.006]*** [0.006]*** [0.006]*** [0.006]***Labor-Sudden 0.05 0.051 0.051

[0.018]*** [0.018]*** [0.018]***Fin1-Sudden 0.052 0.054

[0.034] [0.034]Fin2-Sudden 0.026 0.027

[0.018] [0.018]Rule-Sudden -0.012 -0.01 -0.012 -0.01 -0.012

[0.004]*** [0.004]*** [0.004]*** [0.004]** [0.004]***Observations 646 646 646 646 646R-squared 0.5 0.49 0.5 0.49 0.5

Table 17: Job Destruction, continuing plants and all countries. Same as Table 7 butincluding observations for Argentina and Uruguay.

26