IS LEARNING BY EXPORTING IMPORTANT? MICRO-DYNAMIC...

IS LEARNING BY EXPORTING IMPORTANT?MICRO-DYNAMIC EVIDENCE FROM COLOMBIA,

MEXICO, AND MOROCCO*

SOFRONIS K. CLERIDES

SAUL LACH

JAMES R. TYBOUT

Do firms become more efficient after becoming exporters? Do exportersgenerate positive externalities for domestically oriented producers? In this paperwe tackle these questions by analyzing the causal links between exporting andproductivity using plant-level data. We look for evidence that firms’ cost processeschange after they break into foreign markets. We find that relatively efficient firmsbecome exporters; however, in most industries, firms’ costs are not affected byprevious exporting activities. So the well-documented positive association betweenexporting and efficiency is explained by the self-selection of the more efficient firmsinto the export market. We also find some evidence of positive regional externalities.

I. INTRODUCTION

Many analysts believe that export-led development strate-gies improve technical efficiency. And one oft-cited reason is thatexporters may benefit from the technical expertise of their buyers:1

Participating in export markets brings firms into contact with interna-tional best practice and fosters learning and productivity growth [WorldBank 1997].

. . . a good deal of the information needed to augment basic capabilitieshas come from the buyers of exports who freely provided product designs andoffered technical assistance to improve process technology in the context of

* We thank Eli Berman, Andrew Bernard, Donald Keesing, Lawrence Katz,Wolfgang Keller, Yair Mundlak, Dani Rodrik, and an anonymous referee for theircomments, as well as seminar participants at Johns Hopkins University, theUniversity of California at Los Angeles, the University of Maryland, the WorldBank, Michigan State University, and the NBER Summer Institute (InternationalTrade and Investment session). We are also grateful to Manuel Arellano andSteven Bond for the use of their Dynamic Panel Data program. This paper wasfunded by the World Bank research project ‘‘Micro-Foundations of SuccessfulExport Promotion,’’ RPO 679-20 and the World Bank’s International TradeDivision, International Economics Department. This paper was written whileClerides was a Summer Intern at the World Bank, Lach was visiting the IndustrialOutput Section of the Federal Reserve Board, and Tybout was visiting theInternational Trade Division in the International Economics Department of theWorld Bank. The views expressed herein are those of the authors, and do notnecessarily reflect those of the World Bank or the Federal Reserve System.

1. For a recent catalog of additional reasons, see World Bank [1993, pp.316–324].

r 1998 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnology.The Quarterly Journal of Economics, August 1998

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their sourcing activities. Some part of the efficiency of export-led develop-ment must therefore be attributed to externalities derived from exporting[Evenson and Westphal 1995].

Buyers want low-cost, better-quality products from major suppliers. Toobtain this, they transmit tacit and occasionally proprietary knowledge fromtheir other, often OECD-economy, suppliers [World Bank 1993, p. 320].

The important thing about foreign buyers, many of which have offices inSeoul, is that they do much more than buy and specify. . . . They come in, too,with models and patterns for Korean engineers to follow, and they even goout to the production line to teach workers how to do things [Rhee,Ross-Larson, and Pursell 1984, p. 41].

When local goods are exported the foreign purchasing agents maysuggest ways to improve the manufacturing process [Grossman and Help-man 1991, p. 166].

In support of this view, empirical studies often find thatexporting plants are more efficient than their domestically ori-ented counterparts [Aw and Hwang 1995; Bernard and Jensen1995; Chen and Tang 1987; Haddad 1993; Handoussa, Nishimizu,and Page 1986; Tybout and Westbrook 1995; Roberts, Sullivan,and Tybout 1995]. But, with the exception of Bernard and Jensen,none of these studies has asked the question of whether exportingcauses efficiency gains. Plausible arguments can be made forcausality to flow in the opposite direction: relatively more efficientplants self-select into export markets because the returns to doingso are relatively high for them.

In this paper we attempt to sort out the direction of causality,and in so doing, determine whether there is any evidence thatfirms learn to be more efficient by becoming exporters. Further, toassess the case for active export promotion, we test whetherexporters generate external benefits to other firms, either byacting as a conduit for knowledge that they acquire through trade,or by causing improvements in international transport and exportsupport services.2

Our methodology for detecting learning effects is based on asimple idea. If exporting indeed generates efficiency gains, thenfirms that begin to export should thereafter exhibit a change in

2. Many believe that these spillover effects are significant in developingcountries. For example, Aitken, Hanson, and Harrison [1997] write that ‘‘. . . thedevelopment of garment exporters in Bangladesh, suggests that informationalexternalities are likely to be extremely important. The entry of one Koreangarment exporter in Bangladesh led to the establishment of hundreds of exportingenterprises, all owned by local entrepreneurs. . . . Spillovers may take a variety offorms. The geographic concentration of exporters may make it feasible to constructspecialized transportation infrastructure, such as storage facilities or rail lines, ormay improve access to information about which goods are popular among foreignconsumers.’’

QUARTERLY JOURNAL OF ECONOMICS904

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the stochastic process that governs their productivity growth.Hence their productivity trajectories must improve in some senseafter they enter foreign markets. Similarly, if the presence ofexporters generates positive externalities, nonexporters in theaffected industry or region should exhibit changes in their costprocess when the number of exporters changes. Increases in thenumber of exporters may also make it easier for others to breakinto foreign markets.

To keep track of causal linkages, we begin by specifying thegeneral optimization problem that we envision firms as solving(Section II). Each manager faces stochastic cost and foreigndemand processes, and chooses which periods to participate inforeign markets. Their decisions are complicated by the presenceof sunk start-up costs when they first sell abroad, since managersmust research foreign demand and competition, establish market-ing channels, and adjust their product characteristics and packag-ing to meet foreign tastes. The basic features of this model aretaken directly from the hysteresis literature developed by Bald-win [1989], Dixit [1989], and Krugman [1989]. Our twist is to addthe possibility of learning-by-doing or, more precisely, learning-by-exporting, and examine how this affects the productivity trajecto-ries of exporters and plants that switch markets, relative to thoseof nonexporters.

Because the framework we develop does not lend itself toclosed-form solutions, we discuss its implications heuristicallyand using simulations. Under certain assumptions on the exoge-nous shocks to productivity and demand, the simulations suggestthat (a) nonexporters experiencing positive productivity shocksself-select into foreign markets, (b) exporters experiencing nega-tive productivity shocks quit foreign markets, and (c) the presenceof learning-by-exporting effects implies that firms improve theirrelative productivity after they begin exporting.

With these results in hand, we examine the actual perfor-mance of Colombian, Mexican, and Moroccan producers (SectionIII). To familiarize ourselves with the data, we begin by comparingthe productivity trajectories of producers that enter export mar-kets with those of nonexporters, ongoing exporters, and firms thatexit foreign markets (productivity is proxied by average variablecost and by labor productivity). This exercise reveals patterns thatour simulations suggest we should find in the absence of learning-by-exporting effects. That is, the plants that become exporterstypically have high productivity before they enter foreign mar-

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kets, and their relative efficiency does not systematically increaseafter foreign sales are initiated. In some instances the relativelystrong performance of exporters traces to high labor productivity;in other instances it is due to relatively heavier reliance on skilledlabor.

This first look at the actual trajectories casts doubt on theimportance of learning-by-exporting. But it does not constitute aformal test of whether becoming an exporter changes a firm’sproductivity trajectory. Accordingly, in Section IV we estimateeconometrically a reduced-form version of the theoretical modelthat takes explicit account of the two alternative, but not incom-patible, explanations for the positive association between export-participation status and productivity: self-selection of the rela-tively more efficient plants, and learning-by-exporting. For thecountries with sufficient data to support inference—Colombia andMorocco—we find that export market participation generallydepends upon past participation and (weakly) upon past averagevariable cost (AVC), as implied by the model. However, condition-ing on capital stock and past AVC realizations, current AVC doesnot typically depend negatively upon previous export marketparticipation, as implied by the learning-by-exporting hypothesis.Thus, with a few possible exceptions, the conclusion suggested byour descriptive analysis is borne out by formal Granger causalitytests.3 Finally, extending our model in order to look for externali-ties, we find some support for the hypothesis that a firm is morelikely to export if it belongs to an export-intensive industry orregion, and that Colombian firms in export-oriented regions enjoyrelatively lower production costs, regardless of their own marketorientation.

3. These conclusions are also consistent with Bernard and Jensen’s [1995]findings using data on U. S. manufacturing plants and firms during 1984–1992.Both papers address the same basic question: does exporting cause increases inproductivity, or do increases in productivity increase the probability of exportparticipation? Besides differences in the data, and in that Bernard and Jensen’spaper also looks at other measures of performance, the two papers differ in themethodology of the analysis. In our paper we build an econometric model that istightly linked to a theoretical model. The econometric model both is dynamic andaccounts for unobserved (and observed) sources of heterogeneity across firms. Thisapproach has the advantage that regression results can be interpreted within thecontext of the model, and that it enables us to deal with the endogeneity of the keyvariables in the analysis, i.e., export participation and the measure of productivity,in order to obtain consistent estimates of their effects. The disadvantage is thecomputational complexity of the estimation procedure. Despite these differences, itis comforting that the empirical findings in both papers are virtually the same.



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II. A MODEL OF EXPORT PARTICIPATION WITH LEARNING EFFECTS

Our first task is to present a model that specifies endogenousand exogenous sources of variation in the two producer character-istics we are interested in: exporting status and production costs.This model, which will guide us in our empirical analysis, is asimple modification of existing models to accommodate potentiallearning effects from export participation [Baldwin 1989; Dixit1989; Krugman 1989].

We begin by assuming monopolistic competition, so that eachfirm faces a downward sloping demand curve in the foreignmarket, yet views itself as too small to strategically influence thebehavior of other producers. Specifically, we write foreign demandq f for the firm’s product at price p8 as q f 5 z f · ( p f )2s f, where therandom variable z f captures the usual demand shifters (foreignincome level, exchange rates, and other goods’ prices) and s f . 1.4Firms face similar demand conditions in the domestic market,qh 5 zh · ( ph)2sh, and can price discriminate between foreign anddomestic buyers.

Assuming that marginal costs (c) do not depend upon output,the current period gross operating profits can be expressed as afunction of marginal costs and demand conditions in both mar-kets:

(1) p(c,z) 5 c12s fz f(s f 2 1)s f21(s f )2s f

1 c12shzh(sh 2 1)ah21(sh )2ah

5 p f (c,zf ) 1 ph(c,zh ),

where z 5 (z f,zh). The profits from exporting are the shaded areadepicted in Figure I, where (1 2 (1/sf )) · p f and (1 2 (1/sh)) · ph

are foreign and home market marginal revenue, respectively.5 Werepresent the home demand curve as approaching the verticalaxis above the foreign demand curve because transport costs andtrade barriers eat up a fraction of each unit of revenue generatedin foreign markets.

Let the per-period, fixed costs of being an exporter (e.g.,dealing with customs and intermediaries) be M. Then, the plantwill earn positive net operating profits from exporting whenever

4. This particular functional form for the demand function is generated by theDixit-Stiglitz utility function over varieties.

5. We have linearized the demand and marginal revenue curves to simplifythis figure.



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p f(c,z f ) . M. Accordingly, if there were no start-up costs associ-ated with becoming an exporter and no learning effects, producerswould simply participate in foreign markets—choosing the profit-maximizing level of exports—whenever this condition was satis-fied. As Figure I demonstrates, given demand conditions, all firmswith marginal costs below some threshold would self-select intoexport activities.

But as Baldwin [1989], Dixit [1989], and Krugman [1989]have stressed, sunk start-up costs modify the problem in anontrivial way. Suppose that an entry cost of F dollars is incurredevery time the plant decides to enter or reenter foreign markets.Then, once exporting, it may be optimal to keep exporting even ifp f(c,z f ) is currently less than M since, by remaining in the exportmarket, the plant avoids future reentry costs. So, if sunk costs areimportant—and microevidence suggests that they are (e.g., Rob-erts and Tybout [1997])—producers face a dynamic optimizationproblem where, in each period, they must choose whether or not toexport on the basis of currently available information. This makesdecision-making forward-looking and opens the possibility thatfirms export today in anticipation of cost reductions, or foreigndemand increases, later on.

Because expectations are important in this context, we mustbe specific about the processes that generate the state variables cand z. On the demand side, we assume that z is exogenous to the

FIGURE IGross Operating Profits from Exporting



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plant and follows some serially correlated, plant-specific process(for simplicity, the plant-subscript is omitted until Section IV):

(2) ztf 5 f (xt,zt

f21).

Here xt is a vector of exogenous variables that shift the demandprocesses, e.g., the exchange rate and plant-specific noise, andzt21

f 5 (zt21f ,zt22

f ,zt23f , . . .) denotes the vector of previous realiza-

tions of z up to and including period t 2 1.Marginal cost also follows a plant-specific serially correlated

process. In addition, it is potentially affected by the firm’sexporting decisions if there are learning effects:

(3) ct 5 g (wt,ct21,yt21),

where wt is a vector of exogenous cost shifters, e.g., factor pricesand plant-specific noise, ct21 denotes the vector of previousrealizations of c up to and including period t 2 1, and yt21 denotesthe history of the binary variable yt which, in turn, indicateswhether the plant was exporting j periods ago ( yt2j 5 1) or wasnot ( yt2j 5 0).

Note that equation (3) implies learning-by-exporting dependsonly on a firm’s previous participation in foreign markets, and noton the cumulative volume of its exports. This keeps the simula-tions in this section tractable. However, in Section IV we willeconometrically test alternative specifications in which the his-tory of exporting volumes enters the cost function. In addition, wewill add variables that measure the amount of exporting activityin firms’ industry or geographic region in order to test for spillovereffects.

A test for learning-by-exporting effects using equation (3)must recognize that yt21 is a vector of endogenous variablesdepending upon the same observed and unobserved factors thataffect the cost and the demand processes. Thus, we need to modelthe process by which firms self-select into export markets if we areto sort out the relationship between c and y. Following thehysteresis literature, we assume that managers take equations(1) through (3) into consideration, and plan their export marketparticipation to satisfy

(5) Vt 5 max5 yt1r6r50

`Et o

t50

`

dt[ yt1t(p f(ct1t ,zt1tf )

2 M 2 (1 2 yt1t21)F ) 1 ph(ct1t ,zt1th )],



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where Et is an expectations operator conditioned on the set ofinformation available at time t, and d is the one-period discountrate.6 Domestic profits enter this expression only because, withlearning, participation affects the future cost trajectory. Weassume that firms never wish to liquidate.

Equivalently, managers can be viewed as choosing the cur-rent yt value that satisfies Bellman’s equation:

Vt 5 maxyt

[ yt(p f(ct,ztf ) 2 M 2 (1 2 yt21)F )

1 ph(ct,zth ) 1 dEt(Vt11 0 yt )].

This characterization of behavior implies that producers partici-pate in export markets whenever

(6) p f(ct,ztf ) 2 M 1 d[Et (Vt11 0yt 5 1) 2 Et (Vt11 0 yt 5 0)]

$ F (1 2 yt21).

That is, incumbent exporters continue exporting whenevercurrent net operating profits from exports plus the expecteddiscounted future payoff from remaining an exporter is positive,and nonexporters begin exporting whenever this sum, net ofstart-up costs, is positive. Expected future payoffs include thevalue of avoiding start-up costs next period and any positivelearning effects that accrue from foreign market experience.Without learning effects, expression (6) has appeared in variousforms in the hysteresis literature; it will prove useful in Section IV.

If we allow for much generality in the cost process (3), or weallow start-up costs to depend upon exporting experience morethan one period ago, it is very difficult to characterize optimalbehavior in this framework. However, some insights can be gainedby assuming that c follows a discrete, first-order Markov processthat depends only on yt21.7 Learning-by-exporting can then becaptured by changes in the transition matrix governing theevolution of c over time. Specifically, let P0 be the transitionmatrix for c when the firm is not exporting, and let somestochastically better matrix, say P1, be the transition matrix when

6. This formulation implies that producers who exit the export market andreenter face the same start-up costs as producers who never exported. In oureconometric rendering of the decision to export in Section IV, we will allow start-upcosts to depend upon previous exporting experience.

7. Demand shifters can be held constant since their effect on profits isqualitatively the same as that of c.



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the firm is exporting. That is, when P1 governs c realizations, theprobability of a decrease in c is greater than when P0 governs c. Inthe no-learning case P0 is assigned to all firms.

In this type of problem, the optimal participation policyusually involves two threshold levels defined, for our purposes, interms of marginal cost levels cL , cU such that a nonexporting firmwill start exporting when its costs fall to cL and an exporting firmwill cease exporting when its costs rise to cU. We will assume thatthis is indeed the type of policy followed by the firm. Thus, iflearning effects are present, the transition matrix governing theMarkov process c changes at cL from P0 to P1, and returns to P0

when c reaches cU.Simulations of cost trajectories based on this relatively

restrictive framework and arbitrary parameter values are pre-sented in Figure II (details are provided in Appendix 1 of theworking paper version; see Clerides, Lach, and Tybout [1996]).The trajectories are averages over repeated simulations for foursubgroups of firms, labeled ‘‘nonexporters’’ (those that neverexport), ‘‘exporters’’ (those that always export), ‘‘entrants’’ (thosethat begin exporting), and ‘‘quitters’’ (those that cease exporting).For all firms that begin or cease exporting, we measure timerelative to the transition year (period 0). For example, period 22is two years prior to entry for the entrant group, and two yearsprior to exit for the quitter group. Firms that export may or maynot be subject to learning effects.

Several patterns merit note. First, and most obviously, regard-less of whether learning effects are present, costs among theexporter firms are lower than costs among the nonexporters. Thisreflects the self-selection of efficient firms into export markets, aswe discussed earlier in connection with Figure I.

Second, firms that become exporters exhibit cost declinesbefore they enter the market. This occurs with and withoutlearning effects. These firms self-select into exporting only whentheir unit costs fall below the entry threshold, cL, so they mustexperience a period of falling costs prior to entry. Selection effectsmay thus create the illusion that becoming an exporter actuallyretards productivity growth. More generally, self-selection meansthat cross-sectional differences in the productivity growth rates ofexporters versus nonexporters should not be used to quantifylearning effects.


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Third, regardless of whether learning effects are present,firms that eventually cease exporting exhibit cost increases beforethey quit the export market. This is also a reflection of thetwo-threshold policy followed by the firm in the presence of sunkstart-up costs.

Fourth, one distinguishing feature of the learning trajectoriesis that exporting firms exhibit ongoing cost reductions afterinitiating foreign sales as a result of the switch to a bettertransition matrix. Only when learning effects are present, dofirms continue to pull away from nonexporters after entering theforeign market.

Finally, relative to the no-learning case, learning allows firmsto enter (and stay in) export markets with higher production costs.This occurs because the incentives to export are larger whenlearning occurs. Productivity dispersion may thus be higheramong exporters when learning effects are present, and the

FIGURE IIaSimulated Average Cost Trajectories without Learning



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productivity gap between exporters and nonexporters may besmaller. More generally, this result suggests caution interpretingstudies that use productivity dispersion as a performance mea-sure (as in the ‘‘efficiency frontier’’ literature), and that relatethese measures to trade orientation.8

Of course, these results are only suggestive, and more compli-cated cost processes might reverse some of the patterns. Forexample, if costs were to follow a second- or higher-order autore-gressive process, they might continue to trend downward afterexport market entry even without learning effects. Or, if for c , cL

expected cost increases were relatively large, then learning effects

8. For example, Caves and Barton [1991] regress dispersion-based productiv-ity measures on measures of export intensity and find that ‘‘[p]articipation inexporting activities . . . is negatively related to technical efficiency’’ [p. 93]. They goon to hypothesize that ‘‘any procompetitive effects of exporting activity may beswamped by the uneven distribution of quasi-rents and apparent technicalinefficiency that exporting is likely to produce’’ [p. 94].

FIGURE IIbSimulated Average Cost Trajectories with Learning



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would not necessarily imply cost reductions after entry. Learningeffects imply only a change for the better in the stochastic processfollowed by the cost variable after the firm enters the exportmarket. Accordingly, in order to look for learning effects andexternalities, we rely on econometric estimates of a general formof the cost function (3), recognizing that export market participa-tion is governed by the behavioral rule (6).

III. LEARNING EFFECTS: A LOOK AT ACTUAL DATA

Before we report the results of this exercise, however, it isinstructive to visually examine cost data on actual producers fromthree semi-industrialized countries. Though merely suggestive,this will familiarize the reader with the basic patterns we aretrying to explain, and provide an informal check for the distinguish-ing patterns that our simulations suggest we should find whenlearning effects are present.

A. The Data

Our data allow us to follow individual producers through timein Colombia, Mexico, and Morocco. In the case of Colombia theydescribe virtually all plants with at least ten workers over theperiod 1981–1991; in Mexico they describe 2800 of the larger firmsover the period 1986–1990, and in Morocco they cover nearly allfirms with at least ten workers over the period 1984–1991.9Standard information on inputs, outputs, and costs is provided ineach of these databases, as well as information on export levels. Tosimplify estimation, we eliminate firms that do not report informa-tion for the entire sample period, creating a balanced panel.10 TheAppendix provides details on our data cleaning and deflationprocedures.

9. Although some countries report plant-level data and some report firm-leveldata, we will hereafter use the term ‘‘plant’’ to describe the unit of observation. Insemi-industrialized countries where the calculation is possible, we have found that95 percent of the plants are owned by single-plant firms.

10. Excepting the Moroccan apparel and textiles industries, firms that do notreport information for the entire sample period are mostly very small nonexport-ers. Because these firms are either new or failing, they probably have below-average performance, so their elimination from the sample probably improves theperformance of nonexporters (again, Moroccan apparel and textiles are anexception). This may have biased the estimated effect of past participation on thelevel of costs against the finding of learning effects, but given their small marketshare we do not think this bias is likely to be quantitatively large.



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Finally, to sharpen the analysis, we focus only on the export-oriented industries in each country. These industries exported atleast 10 percent of their output, and had at least twenty exportingplants.11 Table I provides descriptive information on each country.Note that there are substantial transitions in the data eventhough most plants tend to stay in or out of the export market.

B. Comparing Productivity Trajectories

We wish to familiarize ourselves with the marginal costtrajectories of plants with different export market participationpatterns, controlling for industrywide time effects, and observableplant-specific productivity determinants like capital stocks andage. We use two marginal cost proxies: average variable cost(AVC) and labor productivity (LAB). The former is defined as thesum of real labor and real intermediate input costs divided by realoutput, and the latter is real output divided by number of workers.Real labor and intermediate costs are reported nominal values ofthese variables deflated by the manufacturing-wide wholesaleprice index. Real output is the sum of nominal output for thedomestic market and nominal output for export, each deflated byits own product-specific deflator.

To purge these productivity measures of industrywide timeeffects and observable plant-specific characteristics, each is ex-pressed in logarithms and regressed on time dummies, Djt (spe-cific to year t and the jth three-digit ISIC industry), age of theplant (A), age of the plant squared, capital stock of the plant (K ),

11. In a few cases, industries that exported less than 10 percent of theiroutput were included because they had many exporting plants or accounted for asubstantial share of total manufactured exports.

TABLE IENTRY, EXIT, NUMBER OF PLANTS, AND EXPORT INTENSITY BY COUNTRY

(EXPORT-ORIENTED INDUSTRIES)

Averageannual

entry rate

Averageannual

exit rate

Averagenumberof plants

Averageexport

intensity

Colombia 1981–1991 .027 .017 1354 .095Morocco 1984–1991 .049 .037 938 .360Mexico 1986–1990 .048 .015 1327 .230



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and capital stock of the plant squared. Both age and capital stocksare measured in logarithms:

ln (AVCit ) 5 oj51

J

ot51

T

gjtDjt 1 b1 ln (Ait ) 1 b2 ln (Ait )2

1 b3 ln (Kit )1 b4(ln Kit )2 1 eitc

ln (LABit ) 5 oj51

J

ot51

T

gjtLDjt 1 b1

L ln (Ait ) 1 b2L ln (Ait )2

1 b3L ln (Kit )1 b4

L (ln Kit )2 1 eitL.

The residuals from these two regressions—denoted ec and eL,respectively—are then used as our indices of deviation from time-and industry-specific productivity norms. Note that industry-specific time dummies control for industrywide variation inrelative prices (inter alia). Also, because logarithmic variablecosts are purged of correlation with capital, the residuals can beviewed as the measure of variable factor productivity that obtainsfrom a short-run total cost function of the form, TCit 5 l0(Kit,Wit) 1l1(Kit,Wit)Qit, where parentheses denote functions, Kit is capital,and Wit is the vector of factor prices.

To isolate the relation between export market participationand productivity performance, we distinguish the five varieties ofplants defined in Table II, and for each plant we redefine period 0to be the year in which a change in export status takes place. Thenwe isolate five-year blocks of time, running from two years prior tothe status change (t 5 22) to two years after (t 5 2).12 For

12. The Colombian panel is long enough to allow us to go from three yearsprior to three years after the switch.

TABLE IITYPES OF FIRMS

Nonexporters: firms that never exported during the sample period.Exporters: firms that always exported during the sample period.Entrants: firms that began the period as nonexporters, but began exporting

during the sample period and never stopped.Quitters: firms that began the period as exporters, but ceased during the

sample period and never resumed exporting.Switchers: firms that switched exporting status more than once during the

sample period.



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nonexporters and exporters there is no change in status, so wetake five years in the middle of the sample period (for Colombia wetake seven years). Finally, after reindexing time in this way, weaggregate our productivity indices by plant type and comparethem. Because switching firms strongly resemble exporters, weomit them from our graphs to reduce clutter.13

C. Basic Trajectory Patterns

Average Variable Costs: Average trajectories for averagevariable costs are presented by plant type in Figures IIIa throughIIIc. We begin by considering Colombia and Mexico, since thesecountries show similar patterns. Most strikingly, plants thatcease exporting get steadily worse before they drop out of foreignmarkets, and are substantially less efficient than the other planttypes. Also, entering plants and exporting plants have the lowestvariable costs, and nonexporters consistently exhibit costs slightlyabove average, but less than quitters. These patterns are similarto the simulations in Figure II for both the learning and nonlearn-ing models, except in that the performance of quitting plants issomewhat worse in the actual data.

One might argue about whether the entrants show evidenceof reducing their costs after beginning to export, but the effect iscertainly not dramatic. The F-statistic for the null hypothesis thatthe mean average cost level among entrants shows no variationrelative to the industry norms and has a p-value of 0.08 inColombia and a p-value of 0.25 in Mexico.14 Further, in Mexico,entrants’ average costs after two years of exporting are slightlyhigher relative to industry norms than they were two yearsbefore.

The Moroccan graph is more complex. Most obviously, there ismuch less variation in trajectories here than in Colombia andMexico. Further, what little variation there is does not conform tothe patterns described above. Two years after exiting, the plantsthat abandon foreign markets emerge as the worst plants, buttheir average variable costs are highly volatile, and no higher

13. A possible explanation for the resemblance is that switching plants arealways relatively efficient, but they experience relatively dramatic foreign demandshocks that drive them in and out of the export market.

14. These tests are based on generalizations of the regression model toinclude annual dummies by plant type. We also allow the coefficients on age, agesquared, capital, and capital squared to vary across industry, and we treat thedisturbance as composed of a fixed plant effect plus random noise.



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than the industry norm one year after exiting. Exporters usuallydo better than nonexporters, but even this is not guaranteed.

These patterns may well reflect the fact that, unlike in Mexicoand Colombia, most of the impetus to become an exporter inMorocco came from firm-specific demand side shocks. Many

FIGURE IIIaCOLOMBIA—Path of Average Variable Cost (purged of time, age, and size

effects)

FIGURE IIIbMEXICO—Path of Average Variable Cost (purged of time and size effects)



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Moroccan exporters are young plants that were founded with theexclusive purpose of selling particular apparel and textile prod-ucts abroad [Sullivan 1995; World Bank 1994; Roberts, Sullivanand Tybout 1995]. Moroccan policies during the sample periodalso provided various subsidies to exporters, and these may haveallowed less efficient plants to compete. Once again, there is littlestatistical evidence that average costs among plants that beginexporting are anything but flat relative to industry norms (thep-value is 0.24).

Labor productivity: For several reasons (discussed in SectionIV below), average variable costs may pick up spurious correlationbetween exporting status and efficiency. Hence we also examineoutput per worker. Like average variable costs, labor productivityis not a true measure of total factor productivity, but after it hasbeen purged of correlation with capital stocks, it is conceptuallycloser.

Figure IVa presents average values of this productivitymeasure by plant type for Colombia. The entrants once againperform the best, and in contrast to their average cost patterns,they do seem to improve when they initiate foreign sales.15

Ongoing exporters continue to show the next best performance,

15. The F-statistic for the null hypothesis that average labor productivityamong these plants does not change relative to industry norms has a p-value of0.01. Also see footnote 13.

FIGURE IIIcMOROCCO—Path of Average Variable Cost (purged of time, size, and age effects)



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and quitters continue to show the worst performance, particularlyaround the time of their exit. So, in Colombia it appears thatoutput per worker is one important source of variation in averagevariable costs, and we have the first bit of evidence that exportingmight improve performance.

Quitters and ongoing exporters in Mexico follow the Colom-bian pattern. But Mexican entrants do not. Their average labor

FIGURE IVaCOLOMBIA—Path of Average Labor Productivity (purged of time, age, and size

effects)

FIGURE IVbMEXICO—Path of Average Labor Productivity (purged of time and size effects)



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productivity is very stable relative to industry norms ( p-value 5 0.61), and a bit below average (Figure IVb). So there is nosuggestion of a learning effect from exporting in this country, nor,for that matter, does it appear that cross-plant exporting patternscan be traced to differences in labor productivity.

Finally, in terms of labor productivity, Moroccan patternsappear to resemble Colombia’s (Figure IVc). Entrants’ productiv-ity jumps in the year that they begin exporting (although thep-value is 0.41), and ongoing exporters exhibit higher laborproductivity than either nonexporters or quitters.16 Interestingly,there is much more variation across plant types in our Moroccanlabor productivity series than we found in our average cost series,suggesting that labor costs covary negatively with intermediategoods costs in this country.

One fundamental source of variation in labor productivity isthe associated variation in the skill mix of employees. To seewhether differences in skill mixes are behind the labor productiv-ity differences, we analyzed the ratio of skilled to total labor inColombia and Mexico.17 The results for Colombia indicate that

16. The one exception is that labor productivity is high for exiting plantsduring their last year in foreign markets.

17. Graphs of this variable are not reported to conserve space. See Clerides,Lach, and Tybout [1996] for the figures tracing the path of labor quality for thedifferent varieties of plants. No results are presented for Morocco because of lack ofappropriate data.

FIGURE IVcMOROCCO—Path of Average Labor Productivity (purged of time, size, and age

effects)



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entrants have high labor productivity partly because they useskilled labor relatively intensively, and quitters have low produc-tivity because they use little skilled labor. There is also someevidence that relative skill-intensity increases over time for theColombian entrants (the p-value is 0.09). The high labor productiv-ity of ongoing exporters does not, however, appear to come fromunusually high skill intensity. One interpretation is that breakinginto foreign markets requires new product design and other formsof technical assistance, but established export production can beroutinized.

In Mexico, skill intensity trajectories match labor productiv-ity trajectories for ongoing exporting plants (which are skill-intensive) and exiting plants (which are not). However, althoughMexican entrants resemble Colombian entrants in terms of skillintensity, this is not sufficient to get them high labor productivity.

In sum, our performance measures indicate that entrantsgenerally do better than nonexporters and exiting plants. Theyhave higher labor productivity, and this appears to be partly dueto their heavier reliance on skilled labor. Also, despite their highquality workers, new exporters have relatively low averagevariable costs. On the other hand, we find little to suggest thatproductivity gains follow entry into foreign markets. Labor produc-tivity and skill intensity appear to improve for Colombian plantsthat begin exporting, but this phenomenon did not show up in theother countries.18

IV. AN ECONOMETRIC TEST OF LEARNING EFFECTS

The figures we report in the previous section are revealing,but they do not constitute a direct test for whether past exportmarket participation influences current costs; the F-statistics wecalculated merely indicate whether cost trajectories for entering

18. The results discussed thus far are based on unweighted averages of ourvariable cost measures. But small plants are much more common than large plantsso the figures do not necessarily describe sectorwide performance. To determinewhether aggregate performances looked similar, we redid the calculations, weight-ing each plant’s average cost by its share in the total output among those of itstype. We also redid the plant-specific average cost measures themselves, leavingcapital stocks and age out of the regression, because we did not want them to bepurged of correlation with size for this exercise. Overall, the pattern looks verysimilar to the unweighted ones, although with some exceptions. It remains true,however, that entrants exhibit relatively low average costs, both before and afterthe transition year, and that there is no obvious tendency for costs to fall afterexporting operations are initiated. Complete results, including figures, are re-ported in the working paper version [Clerides, Lach, and Tybout, 1996].



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firms deviate significantly from industry norms. Hence in thissection we develop an alternative approach to test for the presenceof learning-by-exporting effects in the data. Specifically, we esti-mate equation (3) and test whether exporting history, yit21, enterssignificantly in the cost equation. In other words, we perform atype of Granger causality test.

We obtain estimates of (3) in two alternative ways. First, wefit (3) jointly with a reduced-form version of (6) using fullinformation maximum likelihood (FIML). This approach allowsus to characterize the self-selection process, and to explore therelation between the error terms in the two equations. However,the participation equation (6) is nonlinear, dynamic, and hasserially correlated errors, so FIML estimation is relatively com-plex, and the cost function (3) must be kept fairly simple. Thus, asa robustness check, we also fit generalized versions of equation (3)using a generalized method of moments (GMM ) estimator to dealwith endogeneity and serial correlation.19

A. The Econometric Model

For FIML estimation we must develop an estimable version ofthe participation equation (6). Our approach follows Roberts andTybout [1997]. First, we generalize (6) so that firms that exit andreenter the export market pay different start-up costs than firmsthat never exported. Specifically, define F0 as the start-up cost fora nonexporter with no previous experience, and F j as the start-upcost faced by a firm that last exported j 2 1 years ago (note thatF1 5 0). Then this generalization of equation (6) implies that theith firm will export in year t (i.e., will choose yit 5 1) whenever

(7) p f(cit,zitf ) 2 M 1 d[Et (Vit11 0 yit 5 1) 2 Et (Vit11 0 yit 5 0)]

$ F0 2 oj51

J

(F0 2 Fj ) yit2j ,

where yit2j is one if the firm last exported in year t 2 j and zerootherwise.20 Next, we define the latent variable y*it as current net

19. If we allow for much generality in the average variable cost process, itbecomes very difficult to estimate all of the structural parameters of the model. Yetimposing restrictions on these processes may lead us to incorrectly conclude thatpast participation influences current costs. For example, misspecifying the costfunction to be a first-order process may force any dependence of current costs onadditional lags (e.g., ct22 or ct23) to induce correlation with yt21, given that exportmarket participation decisions are based partly on lagged costs.

20. Note that yit21 5 yit21, and for j . 1, yit2j 5 yit2j P k51j21 (1 2 yit2k).



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operating profits plus the expected future return from being anexporter in year t:

y*it 5 p f (cit,zitf ) 2 M 1 d[Et (Vit11 0 yit 5 1) 2 Et (Vit11 0 yit 5 0)].

Equation (7) then implies that yit obeys the following dynamicdiscrete process:

(8) yit 5 51 if y*it $ F0 2 oj51

J

(F0 2 Fj ) yit2j

0 otherwise.

Finally, we express the latent variable y*it 2 F0 as a reducedform in demand shifters, marginal costs, and the variables thathelp predict future marginal costs and demand in each market.21

Operationally, this means including exogenous plant characteris-tics (Xit), the exchange rate (et), a distributed lag in our marginalcost proxy, AVCit, and a serially correlated disturbance:

(9) y*it 2 F0 5 bxXit 1 beet 1 oj51

J

b jc ln (AVCit2j ) 1 hit.

Then substituting (9) into (8), we obtain a representation of exportmarket participation decisions that can be estimated:

(10) yit 5 51 if 0 # b xXit 1 beet 1 o

j51

J

b jc ln (AVCit2j )

1 oj51

J

(F0 2 Fj ) yit2j 1 hit

0 otherwise.

Notice that the estimated coefficients on our lagged participa-tion variables measure the discount on entry costs that plantswith exporting experience in previous years enjoy, relative toplants with no exporting experience. Further discussion may befound in Roberts and Tybout [1997].

As noted in Section II, if firms learn by exporting, thestochastic process that generates costs also depends upon thehistory of yit values. Accordingly, a general log-linearized specifica-tion of the marginal cost function (3) includes not only capital

21. This reduced-form approach is also used in Sullivan [1995] and Roberts,Sullivan, and Tybout [1995].



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stocks and lagged average variable costs, but lagged yit values aswell:

(11) ln (AVCit ) 5 g0 1 oj51

Jk

gjk ln (Kit2j ) 1 ge ln (et )

1 oj51

JC

gjc ln (AVCit2j ) 1 o

j51

JY

g jyyit2j 1 vit.

Here the real exchange rate (e) controls for changes in relativeprices that are common to all plants, the distributed lags in K,AVC and yit capture systematic plant-specific deviations fromthese industrywide patterns, and vit represents unobserved idio-syncratic shocks.

Together, equations (10) and (11) describe export marketparticipation patterns and marginal cost realizations (learningexternalities will be added to these equations later). Estimated asa system, industry by industry, they should reveal whethermarginal costs influence the export participation decision as themodel implies, and whether firms typically experience cost reduc-tions once they have begun to service foreign markets, as positedby the learning-by-exporting hypothesis. Tests on the cost coeffi-cients in the participation equation indicate whether firms re-spond to cost reductions by becoming more likely to export, andtests on the yit2j coefficients in the cost function indicate whetherexporting experience leads to lower costs. It is, of course, the latterdirection of casuality that is of primary interest to us.

Serial correlation is likely in both equations because persis-tent unobserved plant characteristics and demand conditionsmake some firms consistently low cost or consistently prone toexporting, conditioning on observable variables. Hence, we modelthe disturbances as composed of unobserved (random) planteffects, a1 and a2, plus transitory noise: hit 5 a1i 1 e1it and vit 5a2i 1 e2it. We allow the plant effects and the transitory noise to becorrelated across equations. Also, without loss of generality, weimpose the normalization var (hit) 5 1 so that all coefficients inequation (10) are measured relative to total unexplained variation.

Unfortunately, the combination of lagged dependent vari-ables with serial correlation creates special problems in paneldata. Equation (10) and equation (11) represent the processes thatgenerates yit and ln (AVC)it only for the years J 1 1 through T. Butrealizations on yi1 through yiJ and on ln (AVC)i1 through ln (AVC)iJ



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are themselves endogenous, since they are functions of theunobserved plant effects, a1 and a2. Treating them as orthogonalto the disturbances hit and vit for t . J would yield inconsistentestimates.

We adopt Heckman’s [1981a, 1981b] solution to this ‘‘initialconditions problem.’’ This amounts to adding J equations to thesystem that represent the probability of becoming an exporter inyears 1 through J as functions of a1i and the other variables inequation (10) except the lagged participation variables. Similarly,we also add J equations representing AVCi1 through AVCiJ asfunctions of a2i and the other variables in equation (11) except thelagged cost and participation variables. The result is a variant ofKeane, Moffitt, and Runkle’s [1988] and Sullivan’s [1997] estima-tor.22 The system is estimated using full information maximumlikelihood (FIML), integrating out the two random effects withGaussian quadrature. Details of the likelihood function may befound in Appendix II of Clerides, Lach, and Tybout [1996].

The FIML approach yields estimates of the cost function (11)jointly with the participation equation (10). But the cost function—unlike the participation equation—is linear, so it can also beestimated by itself using the GMM estimator described by Holtz-Eakin, Newey, and Rosen [1988] and Arellano and Bond [1991].This single-equation approach provides a robustness check inseveral senses. First, once we abandon our FIML estimator, it isfeasible to let average variable costs depend upon output levels.23

Second, we can test whether learning is related to firms’ cumula-tive export volumes rather than simply to export participation inthe recent past. Finally, if we are not trying to simultaneouslyidentify the parameters of the participation equation (10), our costfunction estimator demands less of the data in terms of length oftime period and number of transitions in exporting status. So wecan fit the cost equation by itself to more industries than we areable to treat with our system approach, and we can use moregeneral specifications without overparameterizing the model.

22. Sullivan’s [1995] estimator does not deal with initial conditions problemsin the equation that has a continuous dependent variable; and Keane, Moffit, andRunkle’s [1988] estimator does not deal with dynamics at all. Otherwise, thestructure of our estimator is the same.

23. To do so in the context of maximum likelihood, we would have needed toadd an additional equation representing output choices, with a disturbance thatwas potentially correlated with those of the cost equation and the participationequation. The resulting system would have involved trivariate integration, and itwould have been highly parameterized. For these reasons, we considered it anunattractive option.



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Our generalized cost function is

(118) ln (AVCit ) 5 g0 1 oj51

J 8K

gjk ln (Kit2j ) 1 o

t51

T

gtdDt

t

1 ot51

JQ

gtq ln (qt2t11

h 1 qt2t11f ) 1 o

m51

M

gmBBi

m

1 g1A ln (Ait ) 1 g2

A ln (Ait )2 1 oj51

J 8C

gjc ln (AVCit2j )

1 oj51

JY

g jyyit2j 1 v8it .

This equation is distinguished from (11) in that (a) the distributedlags on both capital stocks (K ) and average variable costs arelonger (J 8K . JK,J 8C . JC ); (b) it controls for relative prices usingannual time dummies rather than the real exchange rate; and (c)it allows average variable costs to depend upon output levels,business type (B ) and age (A ). We will also estimate a variant ofequation (118) in which the distributed lag in foreign marketparticipation dummies, yit, is replaced with a distributed lag inlogs of export volumes, ln (qit

f 1 1).

B. The Evidence on Learning

Because we expect the cost function, the profit function, entrycosts, and the potential for active learning to differ acrossindustries, we fit our system of equations separately to eachindustry in which we have sufficient observations to supportinference. We include industries that are relatively intensive inhuman capital (e.g., Chemicals) as well as those that are not (e.g.,Apparel) in order to look for corresponding variation in activelearning effects. Although the model can be fit to our Mexicanpanel, the time period spanned by that data set proved too short toisolate random effects from the lagged endogenous variables.24 We

24. In Mexico we observe only five years of data, and three of these years arelost to lags on participation and average costs, leaving two in-sample years.Estimates of the model (available upon request) attribute too much explanatorypower to lagged cost and export participation, and imply that var (a1) 5 var (a2) 5 0.Nonetheless, the results concerning the effect of lagged participation on costrealizations are consistent with those obtained in the other countries. Theconvergence of random effect Probit estimators to simple Probit estimators when Tis small is discussed in Guilkey and Murphy [1993].



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therefore focus our attention on the Colombian and Moroccanresults reported in Tables IIIa and IIIb.

Our FIML estimates of the participation equation yieldsresults similar to those reported elsewhere [Roberts and Tybout1997; Roberts, Sullivan, and Tybout 1995]. In all countries, and inall industries, plants with large capital stocks are more likely to

TABLE IIIaFIML ESTIMATES OF EQUATIONS (10) AND (11)

COLOMBIA 1983–1991

Chemicals Textiles Apparel

Participation equation

Intercept 215.06 (5.87)* 24.34 (1.33)* 25.20 (1.58)*ln (et) .462 (2.27) 3.93 (1.14)* 3.60 (0.94)*ln (Kit21) 3.73 (0.72)* 1.71 (0.34)* 2.03 (0.37)*ln (Ait) 5.94 (3.87) 20.12 (0.85) 0.64 (1.07)ln (Ait)2 20.88 (0.58) 0.09 (0.14) 20.01 (0.18)Bit 20.00 (0.28) 0.23 (0.15) 0.28 (0.14)*ln (AVCit21) 20.14 (0.28) 20.18 (0.18) 20.05 (0.11)ln (AVCit22) 0.06 (0.29) 20.16 (0.18) 20.10 (0.13)yit21 1.86 (0.41)* 2.04 (0.27)* 1.03 (0.25)*yit22 0.18 (0.40) 0.25 (0.29) 20.10 (0.25)yit23 0.26 (0.40) 20.57 (0.46) 20.20 (0.25)

Cost function

Intercept 20.16 (0.13) 20.33 (0.06)* 20.36 (0.06)*ln (Kit) 20.17 (0.11) 0.05 (0.04) 0.09 (0.06)ln (AVCit21) 0.36 (0.05)* 0.65 (0.02)* 0.74 (0.02)*ln (AVCit21) 0.45 (0.05)* 0.16 (0.03)* 0.12 (0.03)*yit21 0.09 (0.05) 0.01 (0.03) 0.08 (0.03)*yit22 0.22 (0.09)* 0.04 (0.06) 0.01 (0.08)yit23 0.11 (0.10) 0.06 (0.07) 0.01 (0.08)

Variance-covariance matrix for disturbances

var (a1) 0.298 0.167 0.574var (a2) 0.005 0.0001 0.001corr (a1,a2) 20.106 20.147 20.082var (e1) 0.702 0.823 0.426var (e2) 0.995 0.106 0.163corr (e1,e2) 0.180 20.110 20.026No. observations 567 1,854 2,547Log-likelihood 2309.61 2938.45 21,679.24

Standard errors are in parentheses. An asterisk indicates that the estimate is significant at a 95 percentsignificance level.



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be exporters. One likely interpretation is that there are fixed costsassociated with export shipments, and producers who can producelarge batches are better able to spread these costs. Consistentwith our conceptual framework, plants that have lower marginalcosts are more likely to be exporters, other things being equal.Although some of the distributed lag coefficients are positive,their sum is negative for all industries and countries. Nonethe-less, individual lags of this variable are never statistically signifi-

TABLE IIIbFIML ESTIMATES OF EQUATIONS (10) AND (11)

MOROCCO 1984–1990

Chemicals Food Textiles

Participation equation

Intercept 17.66 (18.18) 16.35 (15.04) 16.49 (10.05)ln (et) 5.69 (3.90) 3.58 (3.22) 3.50 (2.13)ln (Kit21) 2.64 (0.67)* 1.03 (0.46)* 2.45 (0.43)*ln (Ait) 2.45 (3.23) 21.56 (1.73) 22.09 (1.47)ln (Ait)2 20.39 (0.48) 0.28 (0.26) 0.35 (0.24)Bit 0.81 (0.70) 0.26 (0.15) 0.07 (0.16)ln (AVCit21) 21.16 (0.60) 20.18 (0.42) 0.06 (0.18)ln (AVCit22) 21.05 (0.89) 0.92 (0.47) 20.24 (0.30)yit21 1.14 (0.45)* 1.25 (0.54)* 0.91 (0.36)*yit22 0.09 (0.05) 1.25 (0.51)* 0.50 (0.28)yit23 0.28 (0.35) 0.67 (0.47) 0.02 (0.28)

Cost function

Intercept 0.05 (0.03) 20.07 (0.03)* 20.12 (0.03)*ln (Kit) 20.06 (0.04) 20.16 (0.04)* 20.07 (0.04)ln (AVCit21) 0.39 (0.05)* 0.20 (0.05)* 0.15 (0.04)*ln (AVCit21) 0.40 (0.07)* 0.25 (0.05)* 0.07 (0.05)yit21 20.02 (0.03) 0.12 (0.02)* 20.02 (0.02)yit22 0.03 (0.05) 0.02 (0.04) 0.00 (0.05)yit23 0.02 (0.04) 0.01 (0.10) 20.12 (0.06)

var (a1) 0.546 0.724 0.677var (a2) 0.0001 0.003 0.001corr (a1,a2) 0.046 20.559 20.590var (e1) 0.454 0.276 0.323var (e2) 0.024 0.022 0.057corr (e1,e2) 20.019 20.040 20.057No. observations 637 1,169 1,722Log-likelihood 69.13 117.79 2517.87

Standard errors are in parentheses. An asterisk indicates that the estimate is significant at a 95 percentsignificance level.



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cant, partly because of the high collinearity between them.25 Thefact that standard errors for cost coefficients are relatively largefor Morocco is consistent with the lack of cost variation acrosstypes of firms observed in the graphs discussed earlier in Sec-ion III.

The effect of previous export experience is substantial in allindustries, and most dramatic for plants that exported theprevious year. In Morocco the effect is smaller in textiles than inchemicals. This result jibes with our priors that breaking into theforeign textiles market involves less sunk cost than breaking intothe foreign chemicals markets. Although exporting experienceacquired more than one year ago proved marginally significant inearlier work on participation [Roberts and Tybout 1997], itappears to be unimportant for most industries in the presentapplication. This finding is probably due to the relatively smallsamples we use here, since the coefficients on lagged participationvariables themselves are not systematically smaller.

In theory, devaluations should increase the probability ofbecoming an exporter, but we only find significant effects of theexchange rate in Colombian textiles and apparel industries.These are the goods that Colombia sends north, so it is notsurprising that the real exchange rate vis-a-vis the dollar is astrong predictor for both industries. Chemicals, on the other hand,are mainly sold in Latin America, and producers in that industrydo not show as much responsiveness to real exchange rates. Pointestimates of the response to devaluation in Morocco resemblethose for Colombia, but have larger standard errors.

Now consider our FIML estimates of the cost equation. Asexpected, plants with larger capital stocks tend to have lowermarginal costs, although there are some insignificant coefficients.Also, conditioning on capital stocks and unobserved plant effects,marginal costs appear to follow a second-order, or higher, autore-gressive process.26 But, critically, exporting history contributeslittle to the explanation of marginal costs once we have condi-tioned on these variables. Indeed, in the few instances where

25. One reason these results are less dramatic than our graphs is that herewe condition on lagged participation, which is collinear with AVCAVC. Putdifferently, firms with low unit costs tended to be exporters in the past, so recentinnovations in AVC have limited explanatory power.

26. Because of the increased complexity of the estimation, we used the moreparsimonious specification for our FIML estimates. The same reasoning led us touse only one lag on the capital stock, and to use the exchange rate instead of timedummies. All of these restrictions were relaxed in our GMM estimates (to bediscussed below).



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lagged experience is statistically significant, the coefficient sug-gests that exporting increases costs. So these results provide evenless support for the learning-by-exporting hypothesis than ourdescriptive analysis in Section III.

Finally, note that positive unexplained cost shocks (comingthrough a2i and e2i) are associated with negative unexplainedshocks to exporting probabilities shocks (coming through a1i ande1i), as one would expect. But the variance of a2 is quite small,presumably because most of the serial correlation in costs iscontrolled for by our distributed lags.27 Hence the bias associatedwith cost function estimators that treat lagged participation asexogenous may be negligible.

Are the cost function results robust? To address this issue, wefit the more general average variable cost function (118) usingGMM estimators.28 Coefficients on our export market participa-tion variables are reported along with several specification testsin Tables IVa (Colombia) and IVb (Morocco). Coefficients onbusiness type, age, time dummies, and lagged values of ln (qit

h 1qit

f ) and ln (Kit) were estimated but are too voluminous to report.Note first that the model appears to be well specified.

Arellano and Bond’s [1991] tests reveal no evidence of serialcorrelation in v8it, even though this disturbance term should becontaminated by a2. This is consistent with our earlier FIMLfinding that var (a2i) is nearly zero (Table III), and it implies thatthere is no reason to sweep out plant effects by differencing thedata.29 Further, the instrument set easily passes Sargan’s test ofoveridentifying restrictions in all cases. Finally, our (unreported)distributed lag coefficients on ln (qit

h 1 qitf ) indicate that output

levels matter in some cases, so leaving these variables out ofequation (11) may have led to some misleading results in TablesIIIa and IIIb.30

Nonetheless, Tables IVa and IVb are consistent with theseearlier tables in terms of their basic message on learning: whether

27. Estimation of the cost function as a simple random effects model confirmsthat the variance in the plant effect is very small.

28. These estimates were obtained using Arellano and Bond’s Dynamic PanelData program.

29. Estimates of equation (118) based on first differences or orthogonaldeviations frequently failed tests for serial correlation, so we do not report them toconserve space. It is nonetheless worth mentioning that they are consistent withthe level-form results reported here in terms of their implications for learning-by-exporting.

30. In Colombia coefficients on current output are always significantlynegative, and the sum of the current and lagged coefficients is always close to zero.In Morocco output levels did not typically play a significant role in the regression.



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measured by the history of yit or by the history of ln (qitf 1 1),

participating in export markets does not typically improve firms’average variable cost trajectories. In Colombia (Table IVa), export-ing history is rarely significant. When it is, the coefficients tend tobe positive rather than negative. In Morocco (Table IVb), export-ing experience does not tend to significantly reduce costs in thefood, textiles, or chemicals industry, and prior exporting experi-ence is associated with higher variable costs among food producers.

The only qualification pertains to the additional Moroccanindustries treated in Table IVb but not in Table IIIb: apparel,leather, and metalworking. In these sectors the coefficients on ourexport experience variables tend to be negative, and in appareland leather they are jointly significant in at least one of thespecifications. One interpretation is that learning-by-exportinghas indeed occurred in these sectors. But export-oriented multina-tionals are unusually common in these sectors, so their relativeefficiency may also reflect technology transfers from parent compa-nies [Haddad and Harrison 1993; Haddad de Melo, and Horton,1996]. Further, given that apparel exporters relied especiallyheavily on imported fabrics, we may be picking up the effects ofMorocco’s duty drawback scheme, which exempted firms frompaying tariffs on imported inputs that they used to produceexports [World Bank 1994].31

In short, the data are consistent with learning by exportingonly in the Moroccan apparel and leather products industries;otherwise, we find that exporting experience either has no effecton costs or tends to increase cost. Could this lack of support for thelearning-by-exporting hypothesis be a statistical artifact? Onesource of bias derives from the fact that we are not measuringtotal costs. If the production technology for exports is morelabor-intensive (or skilled-labor intensive) than the technology fordomestic goods produced by the same firm, we might missoffsetting reductions in capital costs by focusing exclusively onlabor and materials. We might also miss learning effects ifworkers capture efficiency gains as higher wages, leaving averagevariable costs unaffected. Finally, we may well be picking up some

31. Our measure of input costs is pretariff, but duty drawbacks should stillaffect our tests relating exporting status to unit costs if (1) decisions on inputsourcing are related to the decision to export, and (2) the price of domesticallyproduced inputs is higher than the pretariff price of imported substitutes. The firstcondition is likely to be met by Moroccan apparel firms that entered intooutsourcing agreements with European firms, and the second condition is likely tobe met whenever tariffs protect domestic producers of intermediate inputs.



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of the sunk entry costs associated with becoming an exporter inour variable cost measure. But in Colombia, the (FIML and mostGMM ) estimates of the coefficients on all three lags of exportmarket participation are positive, so that for at least three years,these entry costs are not offset by productivity gains due tolearning-by-exporting effects. Overall, then, although we cannotrule out alternative interpretations, none of them seemscompelling.

C. The Evidence on Externalities

In order to test for regional and industry spillover effects, weexpanded equations (10) and (11) to include regional and industryexport intensity variables. For the ith firm, belonging to region Ri

and industry Ii, these were Sj[Riyjt21/nRi

and Sj[Iiyjt21/nIi

, respec-tively, where nRi

and nIiare the total number of firms in the region

and industry. Coefficients on these variables in the participationequation indicate whether sunk entry costs (F0) are correlatedwith previous exporting activity by other firms. In the costfunction they indicate whether all firms have lower or higher costswhen some firms have been exporting. To control for permanent,unobserved regional effects like access to ports, regional dummieswere also included.

Adding our measure of industry-specific export activity to themodel increased the collinearity of the explanatory variable setbecause, like the exchange rate, it is common to all firms in theindustry. The (absolute) correlation between these two variableslies between 0.66 and 0.76 in Colombia, and between 0.62 and0.91 in Morocco, depending upon the industry. These correlations,while high, do not prohibit estimation. However, the conditionnumber of the moment matrix of the regressors—the square rootof the ratio of the largest to the smallest characteristic root—liesbetween 648 and 1018 in Morocco, but only between 47 and 56 inColombia.32 In addition, the estimated coefficients of the exchangerate and the spillover variables in Morocco were absurdly highand imprecisely estimated. For these reasons, we decided to focusour FIML analysis of spillovers on Colombia only.

Suppressing the nonexternality coefficients, which are simi-

32. For the purposes of these calculations the regressors include only aconstant, the exchange rate, the proportion of exporters in the industry, theproportion of exporters in the region, and the regional dummies. A conditionnumber above 30 suggests potential multicollinearity problems [Belsley, Kuh, andWelsch 1980].



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lar to those in Tables III and IV, we report FIML coefficients forthe Colombian externality variables in Table V. All but one of theexternality coefficients in the participation equation are positive;however, only one is significant. Nonetheless, this seems toprovide some evidence that the presence of many exportersincreases a firm’s chances of being an exporter itself. These resultsare consistent with Aitken, Hanson, and Harrison’s [1997] conclu-sions based on cross-sectional analysis of Mexican data, and inprinciple, they suggest that export promotion may be welfareimproving. But caution is warranted, as such policies have oftenmissed their mark [Keesing and Lall 1992].

The case for AVC-reducing spillovers is weaker. Four out ofsix FIML coefficients are positive, suggesting that high exportintensity actually raises costs. The chemical industry is particu-larly noteworthy because the two coefficients are both significantand they have opposing effects. Unit production costs are reducedby export intensity in the region, perhaps because of demonstra-tion effects, and the development of better transport services forexporters. On the other hand, the presence of other chemicalexporters appears to increase unit costs, perhaps because they bidup the prices of specialized inputs in response to rising exportdemand. An alternative interpretation is that correlation betweenour industrywide externality measures and average variable costssimply reflects trends in both variables (recall that we could notinclude annual time dummies in this model).

TABLE VFIML ESTIMATES OF SPILLOVER VARIABLES

COLOMBIA 1983–1991

Chemicals Textiles Apparel

Participation equation (10)

% Firms exporting, industry 22.03 (3.07) 0.04 (6.07) 1.63 (1.30)% Firms exporting, region 2.74 (5.00) 6.14 (10.37) 5.01 (0.82)*

Cost function (11)

% Firms exporting, industry 2.72 (0.65)* 0.24 (0.85) 20.43 (0.62)% Firms exporting, region 23.31 (0.94)* 1.02 (1.30) 1.76 (1.30)

No. observations 567 1854 2547Log-likelihood 2289.28 2924.65 21,603.87

Standard errors are in parentheses; an asterisk indicates that the estimate is significant at a 95 percentsignificance level.



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As in subsection C above, we can adopt a more generalspecification of the cost function and test for robustness usingGMM estimators. Table VI summarizes the results of this exercisefor eight different versions of equation (118) augmented by spill-over variables. The first four specifications, denoted (i) through(iv), control for own exporting experience with our foreign marketparticipation dummies, yit21, yit22, and yit23, while the other fourspecifications, denoted (v) through (viii), control for own exportingexperience using the logs of export volumes: ln (qit21

f 1 1),ln (qit22

f 1 1), and ln (qit23f 1 1).

Specifications (i) and (v) use the same spillover variables thatappear in our FIML specification (Table V), while (ii) and (vi) usethe share of output exported last period by firms in the region,Sj[Ri

q jt21f /Sj[Ri

(qjt21f 1 qjt21

h ) or industry, Sj[Iiq jt21

f /Sj[Ii(qjt21

f 1

qit21h ). One would expect the number of exporters to affect the

prevalence of knowledge about foreign technologies and markets,while the volume sold abroad might affect the size and efficiency ofupstream industries that supply transport and other services toexporters.

Versions that include industry spillover variables—specifica-tions (i), (ii), (v), and (vi)—control for relative price variationusing the real exchange rate, since time dummies would beperfectly collinear with our industrywide spillover variables. Butin the remaining specifications we replace the industrywidespillover variables with time dummies and focus only on regionalspillovers. This approach better controls for relative prices, al-though it does not yield estimates of industrywide spillovereffects. As indicated in the tables, specifications (iii), (iv), (vii),and (viii) are distinguished only by the regional spillover variableand by the set of variables that control for exporting experience.

The Colombian results are presented in Table VIa. Specifica-tion (i) is closest to the cost function used for our FIML estimatesin Table V. It yields results consistent with Table V in that thetextiles and apparel industries show no significant spillovers,while the spillover effects for industrial chemicals producers aresignificantly cost decreasing for regionwide exporting and signifi-cantly cost increasing for industrywide exporting. Like textilesand apparel, most of the other industries show little evidence ofspillover effects. But in the paper industry, average variable costsare significantly lower when the number of paper exportersincreases, while average variable costs are significantly higher



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when the number of manufacturing exporters in the regionincreases.

Controlling for own exporting experience with export vol-umes rather than export market participation (specification (v))has no effect on these patterns. However, it does matter howspillovers are measured. If we focus on export volumes ratherthan on the number of exporters (specifications (ii) and (vi) ratherthan (i) and (v)), we find that significantly negative (cost-reducing) effects of regionwide exporting activity are more com-mon, as are significantly positive (cost-increasing) effects ofindustrywide exporting activity. In fact, all of the significantcoefficients fit this pattern, which we first mentioned in connec-tion with the industrial chemicals sector.

Finally, by including time dummies, we can do a better job ofcontrolling for industrywide shocks, but we must drop our indus-trywide spillover variables and focus only on regional effects. (Weare able to retain regional dummies in all eight specificationsbecause no industry produces exclusively in one region.) Thisexercise is pursued in specifications (iii), (iv), (vii), and (viii). Onceagain, the regional spillover variables tend to be associated withcost reductions, particularly when we measure exporting activitywith volumes rather than number of exporters. The only signifi-cant exception to this pattern is found in the beverage industrywhen spillovers are measured with the number of firms exporting.

One interpretation of this fairly robust pattern is thatColombian firms do become more efficient by exporting, but theycannot exclude domestically oriented producers from sharing intheir cost reductions. This would occur, for example, if exporterstended to improve upstream input suppliers through scale effectsor technical advice, making them cheaper or better. On the otherhand, we cannot rule out the possibility that regions experiencingfactor cost reductions—say falling real wages—do an increasingamount of exporting because of selection effects, and even thenonexporters enjoy lower average variable costs.

We report GMM cost function estimates for Morocco in TableVIb, which follows the format of Table VIa. However, models (iii),(iv), (vii), and (viii) are not reported because they yield identicalcoefficients for the regional spillover variables as models (i), (ii),(v), and (vi), respectively.33 Compared with Colombia, the results

33. This is because of the shorter time period available in Morocco. The twotime dummies span the same space as the exchange rate and the industrywidespillover variables, so replacing the former with the latter has no effect on theremaining coefficients (except for the intercept).



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are rather mixed. Except for metalworking and apparel, eachindustry shows significant industrywide spillovers. Yet the effectof industrywide exporting activity is cost-increasing for somesectors and cost-reducing for others. This mixed pattern holds forregional spillovers as well, although most coefficients are statisti-cally insignificant. The exceptions are apparel producers, whichappear to have lower variable costs when others are exporting intheir region, and food producers, which have higher variable costswhen large volumes of exports are coming from their region. Thelack of a clear message for Morocco probably reflects the relativelyshort time span of the Moroccan panel. Our spillover coefficientsare identified with industrywide or regionwide temporal variationin the data, but given our lag specifications, there are only threein-sample years.

V. SUMMARY AND CONCLUSIONS

Micro data in developing countries often show that exportingfirms are more efficient than nonexporting firms. This studyconfirms that pattern, and adds the finding that plants that ceaseexporting are typically less efficient, sometimes dramatically so.But more importantly, this study addresses the question ofwhether the association between exporting and efficiency reflectscausation flowing from exporting experience to improvements inperformance. Despite many anecdotes in the literature to thecontrary, we find that data from most export-oriented industriesin Colombia and Morocco are inconsistent with this causalitypattern.

If learning by exporting is important, then the stochasticprocesses that generate cost and productivity trajectories shouldimprove with changes in exporting status. To get some sense of thenature of the response, we began by plotting cost and exportingtrajectories from actual plant-level panel data from Colombia,Morocco, and Mexico. We found that plants that begin exportingtend to have relatively low average variable cost, and plants thatcease exporting are becoming increasingly high cost, as implied bythe model. Similar patterns emerged when we used labor produc-tivity as our performance measure. However, cost and productiv-ity trajectories generally did not continue to change after enteringforeign markets. That is, the patterns we found in the actual dataresembled our no-learning-by-exporting scenario, under whichthe positive association between export status and productivity is



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due solely to the self-selection of relatively more efficient plantsinto foreign markets.

To formally test whether the association between exportingand efficiency reflects more than self-selection, we simultaneouslyestimated an autoregressive cost function and a dynamic discretechoice equation that characterized export market participationdecisions. Exporting history did not significantly shift the costfunction, and to the extent that it did, the shift was usually in the‘‘wrong’’ direction. Robustness tests confirmed our cost functionresults under a variety of specifications and an expanded set ofindustries, although they also suggested that learning by export-ing may have occurred among Moroccan apparel and leatherproducers. Thus, with some possible exceptions, the associationbetween exporting and efficiency is most plausibly explained aslow-cost producers choosing to become exporters.

Finally, looking for evidence of externalities, we found thatthe presence of other exporters might make it easier for domesti-cally oriented firms to break into foreign markets. We also foundevidence that, other things being equal, production costs becomelower in those regions of Colombia where export activity in-creases, even though the exporters themselves do not enjoy aspecial advantage. One interpretation is that exporters becomemore efficient by participating in foreign markets, but domesti-cally oriented producers are able to share in the cost reductions.Another possibility is that regions that experience factor costdeclines become relatively export-oriented (because of selectioneffects), but the factor cost reductions benefit nonexporters too.

APPENDIX: DATA PREPARATION

A. The Panel Data Sets

The Colombian data were obtained from the DepartamentoAdministrativo Nacional de Estadistica (DANE) for the period1981–1991. They provide annual information on the inputs,outputs, exports, and characteristics of all plants with at least tenworkers. Similar annual survey data were obtained from Moroc-co’s Ministry of Commerce and Industry for the period 1984–1990and from Mexico’s Instituto Nacional de Estadistica Geografia eInformacion (INEGI) for the period 1984–1990, although theMexican data cover only 3200 of the larger firms and do notinclude exporting information for the first two years. The datawere cleaned and deflated in connection with a previous WorldBank project, which is summarized in Roberts and Tybout [1996].



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The analysis in this paper is based solely on firms inexport-oriented industries that reported all the variables ofinterest over the entire sample period. These industries exportedat least 10 percent of their output, and had at least twentyexporting plants. In a few cases, industries that exported lessthan 10 percent of their output were included because they hadmany exporting plants or they accounted for a substantial share oftotal manufactured exports. Table VII lists the export-orientedindustries and average number of plants by country.

Comparing the average numbers of plants in each industrywith the numbers of plants present during every sample year(reported in Tables IV and VI) reveals that many producers arenot included in the analysis. The discrepancy is particularly largein high turnover industries like food processing, textiles, leather,and apparel, where more than half of the firms turned over atsome point during the sample periods. However, the selectivityproblem is mitigated by two factors. First, plants that turn overtend to be much smaller than those that continue. For example, ina typical year during the period 1984–1989, 16 percent of theplants in Morocco’s export-oriented industries were new entrantsand 6 percent were in their final year, yet these plants accountedfor only 3.3 percent of output and 0.9 percent of output, respec-tively [Haddad, de Melo, and Horton 1996]. In Colombia theaverage entry rate for all manufacturing industries was around12.5 percent during the mid-1980s, and the average exit rate was11.4 percent; yet these two groups accounted for only 3.5 percentof output and 3.1 percent of output, respectively [Roberts andTybout 1996]. Second, with the exception of Morocco’s apparel

TABLE VIIINDUSTRIES AND NUMBERS OF PLANTS IN THE DATABASE, BY COUNTRY

Colombia Morocco Mexico

Coffee processing (127)Other food processing

(965)Textiles (457)Apparel (962)Leather products (94)Paper (353)Industrial chemicals (113)Other chemicals (284)

Food processing (937)Textiles (564)Apparel (671)Leather products (292)Transport equipment

(103)Metalworking (354)Chemicals, nonphosphate

(285)Phosphate products (324)

Food processing (376)Textiles (185)Chemicals (395)Glass (21)Metal products (166)Nonelectrical machinery

(151)Electric machinery (142)Transport equipment

(149)Electronic components



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and leather/footwear sectors, most new and dying plants werenonexporters.

B. Variables Costs

Total variable costs (TVC) were constructed as the sum oflabor costs, intermediate input costs, and subcontracted produc-tion costs. The variables in the original data sets differed some-what across countries, so it was not possible to use identical TVCconcepts in each case. Table VIII summarizes the components ofTVC by country. The Mexican and Colombian surveys weresimilar, but the Moroccan survey provided considerably lessdetail. Total personnel costs were not broken down there, andintermediate costs were not directly reported, so it was necessaryto infer these as the value of output less value-added. Haddad, deMelo, and Horton [1996] provide further details. Average variable

TABLE VIIICOMPONENTS OF TOTAL VARIABLE COST, BY COUNTRY

Colombia Morocco Mexico

Employee compensation:● salaries● benefits

Cost of personnel Total labor remuneration:● salaries● fringe benefits● social security contri-

butionsCost of intermediate

inputs:● value of raw materials

consumed● goods purchased for

sale without transfor-mation

● purchases of replace-ment parts of less thanone year duration

● energy consumed● other fuels and lubri-

cants

Materials and other non-capital expenditures

Cost of materials:● primary/raw materials

consumed● packaging materials● fuels● electricity● spare parts

Cost of services andtransport:

● payments for industrialwork by other estab-lishments

● payments to third par-ties for repairs andmaintenance

Cost of services andtransport:

● cost of subcontractedassembly work

● cost of shipping● sales commissions



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costs (AVC) were constructed as TVC divided by nominal output,so they really measure costs per unit revenue.

C. Capital Stocks, Domestic Sales, and Exports

Several other variables appear in the estimated equations.Capital stocks were constructed in each country using the per-petual inventory method and capital stock price indices. Detailson variable construction are provided in the country study chap-ters of Roberts and Tybout [1996]. Real output (qd 1 q f ) wasconstructed as domestic and foreign sales plus the change ininventories after each component of this expression was convertedto constant prices using the price deflators described below. (Finalproduct inventories were considered to be destined for the domes-tic market.)

D. Price Indices34

To deflate the domestic component of output (defined asdomestic sales plus change in final product inventory), industry-specific domestic output price indices were obtained from eachcountry at roughly the three-digit ISIC level. For Colombia thesedeflators were implied by real and nominal output figures in theoriginal data set obtained from DANE. For Morocco the industry-specific domestic output price indices were taken from variousyears of the Moroccan Statistical Bulletin. For Mexico INEGIsupplied the output price deflators.

No export price deflators were readily available, so convertingexports to constant prices proved to be an involved process. First,product-specific data on the values and quantities of manufac-tured imports and exports were obtained from the United NationsTrade Database for each of the three countries. These dataimplied unit values of trade flows at a very detailed level.However, they were expressed in nominal U. S. dollars and weremuch more disaggregated than the product classifications re-ported in our plant-level panel data, so a number of steps werenecessary before they could be used as export price deflators.

First, each country’s industrial classification system had to beconcatenated with the U. N.’s Standard International Trade Clas-sification (SITC) system. Inspection of the product descriptions foreach system yielded, for each three-digit ISIC industry, a list of

34. This section is adapted from Roberts, Sullivan, and Tybout [1995], whichis based on the same data.



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SITC products that were potentially exported and their shares intotal exports of the industry.

Second, for each product included in these lists, it wasnecessary to purge the associated SITC unit value series ofspurious variation due to changes in the mix of individual itemsbeing traded. For example, if T-shirts cease to be exported anddress shirts begin to be exported, unit export values for shirts willjump. This problem was dealt with by instrumenting each SITCunit export value series with import unit values in the sameproduct category and a time trend. (The imports of all threecountries were included in the instrument set.)

Third, the unit values were aggregated up to roughly thethree-digit ISIC level (depending upon the country) weightingeach by its average export shares of the products within eachfour-digit industry over the sample period.

Fourth, using the annual average nominal exchange ratebetween each country’s domestic currency and the U. S. dollar(taken from various issues of International Financial Statistics),the export unit values were converted to domestic currency units.

Finally, the nominal export price series for each industry ineach country were normalized to the same base year as thedomestic output price indices, so that both exports and domesticsales could be expressed in terms of the same real domesticcurrency units.

YALE UNIVERSITY

THE HEBREW UNIVERSITY AND NATIONAL BUREAU OF ECONOMIC RESEARCH

GEORGETOWN UNIVERSITY

REFERENCES

Aitken, Brian, Gordon H. Hanson, and Ann Harrison, ‘‘Spillovers, ForeignInvestment, and Export Behavior,’’ Journal of International Economics, XLIII(1997), 103–132.

Arellano, Manuel, and Steven Bond, ‘‘Some Tests of Specification for Panel Data:Monte Carlo Evidence and an Application to Employment Equations,’’ Reviewof Economic Studies, LVIII (1991), 277–297.

Aw, Bee-Yan, and Amy R. Hwang, ‘‘Productivity and the Export Market: AFirm-Level Analysis,’’ Journal of Development Economics, XLVII (1995),313–332.

Baldwin, Richard, ‘‘Sunk Cost Hysteresis,’’ NBER Working Paper No. 2911, 1989.Belsley, David A., Edwin Kuh, and Roy E. Welsch, Regression Diagnostics:

Identifying Data and Sources of Collinearity (New York: Wiley, 1980).Bernard, Andrew B., and J. Bradford Jensen, ‘‘Exceptional Exporter Performance:

Cause, Effects, or Both?’’ mimeo, 1995.Caves, Richard, and David Barton, Efficiency in U. S. Manufacturing Industries

(Cambridge, MA: MIT Press, 1990).Chen, Tain-jy, and De-piao Tang, ‘‘Comparing Technical Efficiency between

Import-Substitution and Export Oriented Firms in a Developing Country,’’Journal of Development Economics, XXVI (1987), 277–289.



at Hebrew


on March 3, 2014


ownloaded from



Clerides, Sofronis, Saul Lach, and James Tybout, ‘‘Is ‘Learning-By-Exporting’Important? Micro-Dynamic Evidence from Colombia, Mexico and Morocco,’’NBER Working Paper No. 5715, 1996.

Dixit, Avinash, ‘‘Exit and Entry Decisions under Uncertainty,’’ Journal of PoliticalEconomy, XCVII (1989), 620–638.

Evenson, Robert, and Larry Westphal, ‘‘Technological Change and TechnologyStrategy,’’ in T. N. Srinivasan and Jere Behrman, eds., Handbook of Develop-ment Economics, Volume 3 (Amsterdam: North-Holland, 1995).

Grossman, Gene, and Elhanan Helpman, Innovation and Growth in the WorldEconomy (Cambridge, MA: MIT Press, 1991).

Guilkey, David, and James L. Murphy, ‘‘Estimation and Testing in the RandomEffects Probit Model,’’ Journal of Econometrics, LIX (1993), 301–317.

Haddad, Mona, ‘‘How Trade Liberalization Affected Productivity in Morocco,’’World Bank, Policy Research Working Paper No. 1096, 1993.

Haddad, Mona, Jaime de Melo, and Brendan Horton, ‘‘Morocco, 1984–89: TradeLiberalization, Exports and Industrial Performance,’’ in M. Roberts and J.Tybout, eds., Industrial Evolution in Developing Countries (New York, OxfordUniversity Press, 1996).

Haddad, Mona, and Ann Harrison, ‘‘Are There Positive Spillovers from ForeignDirect Investment? Evidence from Panel Data for Morocco,’’ Journal ofDevelopment Economics, XLII (1993), 51–74.

Handoussa, Heba, Mieko Nishimizu, and John Page, ‘‘Productivity Change inEgyptian Public Sector Industries after the ‘Opening,’ ’’ Journal of Develop-ment Economics, XX (1986), 53–74.

Heckman, James, ‘‘Statistical Models for Discrete Panel Data,’’ in C. Manski andD. McFadden, eds., Structural Analysis of Discrete Data with EconometricApplications (Cambridge, MA: MIT Press, 1981a)., ‘‘The Incidental Parameters Problem and the Problem of Initial Conditionsin Estimating a Discrete Time-Discrete Data Stochastic Process,’’ in C.Manski and D. McFadden, eds., Structural Analysis of Discrete Data withEconometric Applications (Cambridge, MA: MIT Press, 1981b).

Holtz-Eakin, Douglas, Whitney Newey, and Harvey Rosen, ‘‘Estimating VectorAutoregressions with Panel Data,’’ Econometrica, XLVI (1988), 1251–1272.

Keane, Michael, Robert Moffit, and David Runkle, ‘‘Real Wages over the BusinessCycle: Estimating the Impact of Heterogeneity with Micro Data,’’ Journal ofPolitical Economy, XCVI (1988), 1232–66.

Keesing, Donald, and Sanjay Lall, ‘‘Marketing Manufactured Exports fromDeveloping Countries: Learning Sequences and Public Support.’’ in GeraldHelleiner, ed., Trade Policy, Industrialization and Development (Oxford:Oxford University Press, 1992).

Krugman, Paul, Exchange Rate Instability (Cambridge, MA: MIT Press, 1989).Rhee Yung, Bruce Ross-Larson, and Garry Pursell, Korea’s Competitive Edge:

Managing the Entry into World Markets (Baltimore: Johns Hopkins Univer-sity Press, 1984).

Roberts, Mark, Theresa Sullivan, and James Tybout, ‘‘Micro-Foundations ofExport Booms,’’ World Bank, mimeo, 1995.

Roberts, Mark, and James Tybout, eds. Industrial Evolution in DevelopingCountries: Micro Patterns of Turnover, Productivity and Market Structure(New York: Oxford University Press, 1996).

Roberts, Mark, and James Tybout, ‘‘The Decision to Export in Colombia: AnEmpirical Model of Entry with Sunk Costs,’’ American Economic Review,LXXXVII (September 1997) 545–564.

Sullivan, Theresa, ‘‘Micro-Foundations of Export Supply in Morocco,’’ Ph.D. thesis,Georgetown University, 1997.

Tybout, James, and M. Daniel Westbrook, ‘‘Trade Liberalization and Dimensionsof Efficiency Change in Mexican Manufacturing Industries,’’ Journal ofInternational Economics, XXXI (1995), 53–78.

World Bank, The East Asian Miracle (New York: Oxford University Press, 1993)., Kingdom of Morocco-Republic of Tunisia, Export Growth: Determinants andProspects, Report No. 12947-MNA, 1994., World Development Report 1997: The State in a Changing World (New York,Oxford University Press, 1997).



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