Learning-by-Exporting Effects: Are They for Real?*

Transcription

1 Learning-by-Exporting Effects: Are They for Real?* Ana M. ernandes Alberto E. Isgut The World Bank UNESCAP Abstract: This paper thoroughly examines the learning-by-exporting (LBE) hypothesis for Colombian manufacturing plants during and finds significant evidence in s favor. The results are robust to the use of different samples of the dataset, different econometric methods, and different modeling approaches. We find that export experience acquired by plants in years before the previous year has an important effect on plant productivy and that the effect of export experience on productivy is insignificant for exporters that stopped exporting in the previous year. We also find evidence of diminishing returns to export experience in that LBE effects are quantatively lower for the experienced exporters in our sample. Since these LBE results contradict those of Clerides, Lach and Tybout (998), who found no evidence of LBE for Colombia using the same dataset and sample period, we examine possible reasons for this difference. We find that the difference is due to the exclusion of raw materials from the average variable costs variable used in their regressions. Keywords: Learning by Exporting, Learning by Doing, Trade, Total actor Productivy, Exports, Export-Led Growth, Simultaney and Production unctions, ull Information Maximum Likelihood, Average Variable Costs. JEL Classification: C4, D, D4, 0, L60 April 0 * We thank Eduardo Engel, Marcela Melendez, Carmen Pages, Ariel Pakes, Dan Trefler, Jim Tybout, seminar participants at the DAIT Workshop on Export Dynamics and Productivy, Universidad de los Andes, Universy of Illinois, Universy of Sussex, Universy of Toronto, and the World Bank, two anonymous referees, and the associate edor for comments. We thank Sofronis Clerides and Johannes Van Biesebroeck for sharing their estimation routines. We are responsible for any remaining errors. Support from the governments of Norway, Sweden and the Uned Kingdom through the Multi-Donor Trust und for Trade and Development is gratefully acknowledged. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or UNESCAP. Ana M. ernandes, corresponding author, Development Research Group, The World Bank, 88 H Street, N.W., Washington, DC 0433, USA, afernandes@worldbank.org. Alberto Isgut, Macroeconomic Policy and Development Division, Uned Nations Economic and Social Commission for Asia and the Pacific, Uned Nations building, Rajadamnern Nok Avenue, Bangkok 000, Thailand, isgut@un.org.

2 I. Introduction Exporting firms have been found everywhere to be significantly more productive, larger, more capal-intensive, and to pay higher wages than non-exporting firms, but the direction of causaly between participation in export markets and firm performance indicators has been considerably more difficult to assess. In particular, the evidence on whether firms increase their productivy as a consequence of their participation in export markets has been mixed. The lack of conclusive evidence about learning-by-exporting (LBE) should not be surprising. According to Arrow (96, pp. 55), learning is the product of experience, which can only take place through the attempt to solve a problem and thus only takes place during activy. This suggests that only firms for which exporting is challenging would be able to learn from. This would certainly be the case for new exporters in developing countries, but could also apply to new and smaller firms in developed countries. By accessing a significantly larger and more competive market those firms are likely to face major technical and managerial challenges, whose resolution could require upgrading production processes, equipment and technical standards, retraining workers, and improving qualy control and inventory management techniques. As workers and managers engage in new activies to meet these challenges, they are likely to learn new skills, resulting in improvements of the firm s productivy. In contrast, we would expect to find much less evidence of LBE in countries such as the Uned States or Germany, where firms do not need to access the export market to operate in a large and highly competive market. or firms in those markets, entering the export market is likely to be just as challenging as is to enter the domestic market. Arrow (96, p. 55-6) also argues that learning associated wh repetion of essentially the same problem is subject to sharply diminishing returns and that only when stimulus suations are steadily evolving rather than merely repeating will learning-by-doing (LBD) result in a steadily increasing performance. This suggests that LBE is a temporary phenomenon, as firms get up to speed operating in a more challenging market environment. It also suggests that LBE is more likely to be observed in developing countries where a large number of domestic firms are entering the export market for the first time such as in Slovenia after s accession to the European Union. In this paper we test the LBE hypothesis using manufacturing plant-level data from Colombia for the period Because the Colombian peso depreciated significantly in the mid-980s, many plants wh no Studies that report evidence of LBE include Baldwin and Gu (003) for Canada, Bigsten et al. (004) and Van Biesebroeck (005) for African countries, Blalock and Gertler (004) for Indonesia, Castellani (00) for Italy, De Loecker (007) for Slovenia, Girma et al. (004) for the U. K., Kraay (999) and Park et al. (00) for China. Studies that find no evidence of LBE include Alvarez and Lopez (005) for Chile, Bernard and Wagner (997) and Arnold and Hussinger (005) for Germany, Bernard and Jensen (999) for the U. S., Clerides, Lach and Tybout (998) for Colombia, Mexico and Morocco, and ISGEP (008) for 4 countries across the globe. Studies that report evidence of LBE for selected types of firms include Delgado et al. (00) for young Spanish firms, and Lileeva and Trefler (00) for low-productivy Canadian firms that started to export in response to the Canada-U.S. ree Trade Agreement.

3 prior exporting experience ventured into the export market and are likely to have learned from this experience. Drawing on the empirical lerature on LBD, which measures experience as a function of past levels of production, we measure export experience as a function of past export participation, past export-output ratios, or past cumulative exports. These functions encompass as special cases the two most common export experience measures used in the LBE lerature: a lagged export participation dummy and the lagged export-output ratio. Besides allowing us to test whether further lags of exports affect productivy, the flexibily of our measures enables us to test addional aspects of the relationship between exporting and productivy, such as whether LBE effects disappear for plants that stopped exporting the previous year. Our results, which are robust to the use of different samples of the dataset, different econometric methods namely modified versions of the semi-parametric methods of Levinsohn and Petrin (003), Olley and Pakes (996), and Ackerberg, Caves, and razer (006) and different modeling approaches, provide evidence of significant LBE effects for Colombian manufacturing plants during the period We find that export experience acquired by plants in years before the previous year has an important effect on plant productivy. Interestingly, we also show that the effect of export experience on productivy is insignificant for exporters that stopped exporting in the previous year and that there are diminishing returns to export experience in that LBE effects are quantatively lower for the experienced exporters in our sample. Of particular interest is a comparison wh Clerides, Lach and Tybout (998) [henceforth CLT], who found no evidence of LBE for Colombia in that period using the same dataset. Because CLT use different samples, a different econometric method, and a different modeling approach than in the rest of our paper, we examine systematically whether any of these factors explains the differences in results but find that neher of them does. We find that the difference in results is due to the exclusion of raw materials from the average variable costs variable used in their regressions. The rest of the paper is organized as follows. Section II describes a model of LBE to motivate our empirical approach. Section III discusses our econometric strategy and describes the data. Section IV presents the main results and some robustness checks. Section V explains differences in results between this paper and CLT, and Section VI concludes. II. A Model of Learning-by-Exporting To motivate our empirical approach, this section describes a stylized model of LBE where plants

4 use labor in efficiency uns (H ), intermediates (M ), and capal (K ) to produce output wh a Cobb-Douglas technology. The first two variables are modeled as fully flexible variable inputs. ollowing Olley and Pakes (996) [henceforth OP], plant age (A ) is included as a state variable. Capal and age accumulate according to the laws of motion K I K and A A, where I - is gross investment in t- and is the depreciation rate. To account for the possibily of LBE, plant export experience (EE ) is included also as a state variable. Export experience is a function of past values of exports EE Y wh E i being the first year plant i exported: f Y, Y,..., YE () The production function is given by: i Y h m k A H M K A 0 EE exp EE () where is a productivy index known to the plant manager at the beginning of period t but unknown to the econometrician. As in OP we assume follows an exogenous first-order Markov process p,,..., ; J p i, where J - is plant i s information set in t- i, Y and Y i is the year when plant i started operations. Export experience is modeled as a predetermined variable which, like capal, shifts the mean of the production function but does not affect. The plant manager maximizes the expected discounted value of future net cash flows; her decision problem is captured by the following Bellman equation: V Z, D max, Y max, Y, I where Z K, A, EE, ) exported in t-, foreign prices, ( H H. p Y. p CY Y, Z, wt CI I D E V Z, D Z, Y, Y Y,, 0 Y H Y I D is a dummy variable equal to one if the plant Y Y are home sales, C. and. Y I H p and (3) p are, respectively, domestic and C are, respectively, the cost of production and the cost of adjustment of capal, w t. is a vector of variable input prices, and is a fixed cost of entry or re-entry into the export market. We assume that plants are price-takers in factor and goods markets at home and abroad. The existence of fixed costs of entry or re-entry implies that the export market is segmented from the domestic market. Therefore the prices in the two markets differ and if marginal costs are increasing p should be higher than H p. H p and p could This relationship between prices holds only if both the domestic and the export markets are perfectly competive. However, when both markets are segmented and the domestic market is small, is likely that in some industries plants have market power in the domestic H market but not in the export market, in which case is possible that p > p. 3

5 The timing of events is as follows. At the beginning of each period t, the manager knows the plant s age and capal stock available for production, s export experience (equation ), the value of the productivy index, and s probabily distribution for the following period. Based on this information, the manager decides whether the plant will continue in operation or ex. If the plant continues in operation, then the manager chooses how much to produce during the period Y, how much to export Y, and how much to invest I. Since labor and intermediates are fully flexible variable inputs, their choice is based on a static cost minimization problem condional on the optimal level of output chosen for the period. Investment and export choices determine the plant s capal stock and export experience available in the next production period. The cost of entry into exporting depends on whether the plant exported the period before; thus, the lagged export dummy is a state variable in the value function. 3 This setup can accommodate new plants that produce for the first time at period t. We assume that those plants need to invest in capal the year before entry, so at the beginning of period t their state variables are K >0, A =, EE =0, and D =0. or those plants, also follows a first order Markov process whose first realization is observed at the beginning of period t. Depending on that realization, in period t those plants face a choice between three possibilies: (i) not to enter the market that period, (ii) to enter only the domestic market, or (iii) to enter the export market right away. Notice that although plants operate in perfectly competive markets, the assumptions about the timing of the realization of and the fact that production depends on past decisions on investment and participation in export markets imply that during each period plants will be heterogeneous in their productivy levels. Hence, even if plants equate marginal cost to price and their markups are zero, their heterogeney in productivy levels will result in heterogeneous levels of economic profs or losses across plants. 4 In this stylized model, exports increase the plant s value in three ways: (i) by providing an addional source of revenue on top of domestic market sales, (ii) by allowing the plant to save on entry costs if exported in the previous period, and (iii) by increasing productivy through learning effects. These advantages need to be weighed against the sunk cost of entry (or re-entry), which is unaffordable for many plants. To facilate the intuion, consider a simplified version of the model where the production function depends only on labor, export experience, and productivy that is m, k, and A are equal to zero. The cost function is h h h CY, EE,, wt wt Y exp 0 EEEE h, where Y is the level of output that h solves the inter-temporal optimization problem in equation 3 and w t are wages. 5 Production costs 3 CLT assume that the cost of re-entry into export markets varies according to the number of years since the plant exported for the last time. We simplify the setup, whout loss of generaly, by assuming that this cost is the same for both new entrants and re-entrants. 4 The heterogeney of productivy levels is a common feature wh the Melz (003) model. However, that model is based on a monopolistic competion market structure where plants differ in their markups in the short run. 5 This expression is obtained from the static cost minimization to choose the optimal amount of labor. 4

6 increase in output and decrease in productivy and in export experience. Hence, isocost lines in the, EE state space are downward-sloping. igure illustrates three isocost lines of interest that define thresholds for plants entry and ex decisions. irst, at a sufficiently low level of productivy the plant will be indifferent between exing and receiving the termination payoff or continuing in operation. Second, at a high enough level of productivy, the plant is indifferent between producing only for the domestic market or producing for domestic and export markets. 6 At this second threshold the sum of the current payoff from exporting and the contribution of exporting to the plant s expected value of exporting the following period will be just enough to compensate the sunk entry cost. inally, at an intermediate level of productivy an exporter will be indifferent between stopping to export hence producing only for the domestic market or continuing to export for another period. The difference between the threshold for ex from export markets and the threshold for entry into export markets is due to the assumption of a fixed re-entry cost into exporting. Consider for example an exporter that receives a bad productivy shock that puts below the export entry threshold. This plant would need to evaluate the immediate benef of stopping to export against the need to pay the fixed re-entry cost the next period in case s productivy increases. If the plant s expected value from continuing to export exceeds the negative current payoff caused by the adverse productivy shock, then the plant will continue exporting. 7 In igure, the state space for plants that have never exported is the segment of the horizontal axis between the ex threshold and the export entry threshold marked in bold. The posion of specific plants in the, EE space is represented by N N 4 and X X 3, where N plants are currently not exporting, so they would need to pay the fixed entry cost if they decided to export in the next period, while X plants are currently exporting and face no cost if they decide to continue doing so. Plants N and N are plants that have never exported. Once plants enter the export market, they start moving up in the state space as they accumulate export experience. The curvature of the thresholds reflects the assumption that plants learn from exporting, but learning is subject to diminishing returns. Plants X X 3 are exporters. Plant X entered the export market in the current period; hence, still has not accumulated export experience [see equation ]. Plant X 3 has a negative current payoff from exporting but finds convenient to continue exporting (given the sunk re-entry cost into exporting), hoping that s productivy will increase the following period. The region between the export entry and export ex thresholds may include plants like N 3 that exported in the past but are not exporting currently. Such plants do not accumulate export experience; they move only 6 * * These thresholds correspond to the and thresholds in Melz (003). x 7 See Dix and Pindyck (994) for detailed analyses of entry and ex decisions under uncertainty wh sunk entry costs. Irarrazabal and Opromolla (006) apply these ideas to the case of entry into export markets. Our igure extends their igure 5 to the case where export experience is an addional state variable. 5

7 horizontally in the state space as do plants that never exported before but at a posive export experience level. In Section IV we will test whether export experience depreciates as a former exporter stops exporting for a few years. Specifically, we will test whether after three years whout exporting a plant like N 4 will drop to the posion of N in the figure. The stylized model just described is very close to that estimated by Van Biesebroeck (005). The main difference is that in his study the export experience measure is the plant s lagged export participation status ( D ) which is the last term of our export experience measure below (equation 5a). Whether the remaining terms of empirical question that we tackle in Section IV. EE defined EE are relevant for productivy is an III. Econometric Strategy and Data Description j Taking logs in equation, adding age squared, industry and time dummies (, t ), and an i.i.d. error ( ) we obtain our baseline estimating equation (lower case variables are in logs): y h m k a a j 0 h m k a a t EE (4) EE where lower case variables are in logs. Industry dummies capture time-invariant differences across industries in the production function intercept and in input prices, and time dummies capture variation over time in input prices and the exchange rate affecting all industries simultaneously. The problems associated wh the estimation of equation 4 are well-known. On the one hand, since plant managers decide the use of variable inputs on the basis of realizations, which are unobservable to the econometrician, OLS estimates of their coefficients are upward biased (Marschak and Andrews, 944). On the other hand, as OP point out, if the capal stock and the realization affect posively the probabily of plant survival, OLS estimates of the coefficient on capal will be downward biased. OLS estimates of our parameter of interest have two potential biases of oppose sign: a negative selection bias due to ex decisions, similar to that on the capal coefficient, and a posive bias due to self-selection into exporting. To understand the downward bias, consider plants N and N 4 in igure. Both have about the same productivy level. However, due to s posive export experience, N 4 is farther away from the ex threshold than N. If both plants suffer similar adverse productivy shocks, N is more likely to ex than N 4. Consequently, the sample may include a higher share of plants wh posive export experience at low levels of, exerting a negative bias on the estimate of EE. An upward bias in the OLS estimate of EE is possible because EE is a predetermined endogenous variable. While EE is not correlated wh contemporaneous productivy innovations, could be posively correlated wh past posive EE 6

8 productivy innovations that allowed the plant to enter into exporting, increasing the value of EE from zero to a posive value. After entry, however, exporters may receive both posive as well as adverse productivy shocks. Thus, the magnude of the endogeney bias of EE is an empirical question. In this paper we employ both semi-parametric methods described next and IML methods described in Section V to correct for this bias. Two popular semi-parametric estimation methods to control for the endogeney and selection biases of the OLS estimator are those proposed by OP and Levinsohn and Petrin (003) [henceforth LP]. They are based on the assumption that the unobservable can be proxied by a nonparametric function of observable variables, such as investment (in OP) or intermediates (in LP). While the LP method is our main estimation method, in Section IV we consider also alternative semi-parametric methods: OP and the method of Ackerberg, Caves and razer (007) [henceforth AC]. 8 A potential concern wh the LP, OP, and AC methods is that all are based on the assumption that unobserved productivy follows a first-order Markov process. To understand the implications of that assumption, let us assume that productivy follows an AR() process,, wh ρ > 0. As seen in Section II, plants self-select into exporting if an increase in productivy pushes them to the right of the export entry threshold (a move from N to X in igure ). Thus, plants that enter the market in t- will have and EE EE 0, implying a posive correlation between EE and and an upward bias in the OLS estimate of EE. The LP, OP, and AC methods control for this endogeney bias by using a function of observables to proxy for the unobservable. But if productivy follows a longer term memory process, there would be addional lags of included in the error term. To address this potential problem in Section IV we estimate a variant of the LP method suggested by Ackerberg, Benkard, Berry and Pakes (007) for the case where unobserved productivy follows a second-order Markov process. See the online appendix for details on the implementation of the semi-parametric methods used in this paper. The data used in this study come from the Annual Manufacturing Surveys (AMS) conducted by Colombia s Departamento Administrativo Nacional de Estadística. The variables used in our analysis are defined as follows. Labor in efficiency uns H is a weighted average of seven types of workers: apprentices, blue collar workers, whe collar workers, local technicians, foreign technicians, managers, and owners. The weights are given by the relative average wages of each type of worker, using the wages of blue collar workers as numeraire. 9 Capal K is the sum of the stocks of buildings and structures, machinery and equipment, transportation equipment, and office equipment in 8 A disadvantage of the OP method is that the estimation includes only observations wh posive investment, entailing a 5% sample size reduction and a bias towards plants that are larger or have more favorable productivy shocks. 9 Owners, for which AMS does not report wages, are given the same weight as managers. The average wages were based on nominal salaries and benefs per type of worker deflated the CPI and vary across Colombian regions. See the online appendix for details. 7

9 constant pesos, each of those measured at the beginning of the period and obtained through the perpetual inventory method. 0 Intermediates M are the sum of raw materials consumed, outsourcing expenses, and energy in constant pesos. Output Y and exports Y are expressed in constant pesos. We consider alternative export experience measures corresponding to different specifications for the function f. in equation using the number of years the plant exported, the plant s cumulative export-output ratio, and cumulative exports scaled by the average number of workers in the plant: EE EE EE 3 t E Di (5a) i t E ln i Y Y i i t Ei where D 0 (5b) Y i L (5c) i i Y i is a dummy variable equal to one if the plant exports in year, Y is total plant output, and L is the plant s average number of workers. These measures follow the i empirical LBD lerature, where plant experience is measured as cumulative output from the year production started. 3 If we lim the sums in EE and EE to a single term corresponding to = t- they simplify to the two most common measures used in the lerature to capture LBE effects: lagged export status and lagged export intensy. As discussed below, we also consider alternative 3 export experience measures restricting some of the terms of EE - EE to be zero. In our estimating samples we exclude plants wh less than three consecutive years of data, plants wh missing years of data, and plants wh outlier observations. 4 Our first sample includes only young plants that reported information to the AMS for the first time in 98. Since the AMS included information on exports only from 98 onwards, we observe the full export history only for those plants. In order to include also old plants, we proceed in two steps. irst, we hypothesize that the export experience of exporters that do not export for three consecutive years depreciates completely. As Section IV will show, we find strong evidence supporting this hypothesis. The 0 The depreciation rates used are taken from Pombo (999): 3.0% for buildings and structures, 7.7% for machinery and equipment,.9% for transportation equipment, and 9.9% for office equipment. Investment flows in each of the capal classes are deflated by a corresponding price index from Banco de la República. Details on the capal stock measure are provided in the online appendix. We deflate domestically sold output, exports, materials bought in the domestic market, and imported materials using industry-specific price indexes. The construction of export price deflators follows CLT (pp ). Import price deflators were constructed using the same method. See the online appendix for further details. ollowing Bahk and Gort (993), this average is taken over the last three years of data available for each plant. 3 3 The measure EE is identical to the S measure in Bahk and Gort (993) using exports instead of gross output. 4 We define an outlier observation as a plant-year in which the log difference between output and one of the main production inputs (labor, intermediates, capal) is more than.5 inter-quartile ranges away from the industry median. 8

10 following alternative export experience measures impose to our baseline measures (equations 5a-5c), the restriction that export experience resets to zero after three years of export inactivy: EER EER j 3 0 t j X i Ti 0 t Y ln T L i i if i t t3 otherwise if t t3 D otherwise i D i 0 0 j=, (6a, 6b) (6c) where i X i D i and X Y i i Y i. or young plants, for which we observe the entire export history, T is eher their first year of exports (E i ) or the year the plant re-enters the export market after three or more years whout exporting. or old plants, for which we do not observe the entire export history, T i is the first year they export after three years or more whout exporting. The EER measures allow us to treat plants that re-enter the export market after a spell of three or j more years whout exporting as new entrants. We use these export experience measures in our second sample, which includes young plants and old plants that do not export in any year during We exclude from this sample old plants that exported at least one year during , which we refer to as old continuing exporters. 5 j Using EER measures allows us to double the number of plants from about 3,000 in the young sample to almost 6,000 in the young and old whout continuing exporters sample as shown in the online appendix. These two samples still leave out the bulk of Colombia s exporters, the old continuing exporters. In order to include those plants and thus use the full sample, we impose an alternative restriction to our baseline export experience measures (equations 5a-5c): we assume that only the export experience of the last n years counts. 6 The resulting alternative measures are simply n-year moving sums where t tn X i D i and X Y i i Y i : 7 j j EEM X i j=, (7a, 7b) 5 j In this sample we exclude observations for the years for old plants since we cannot compute their EER measures during those years. 6 This restriction is imposed on all plants: young, old, and old continuing exporters. In the empirical specifications n is eher 4 or 5. 7 In this sample we exclude the first n years for each plant since they are used to construct the moving sums. The moving sum and the reset export experience measures may decrease as well as increase, in contrast to the original measures, which cannot decrease. 9

11 EEM 3 ln t tn j Y i (7c) L i i Some preliminary evidence of the importance of LBE for Colombian plants productivy can be obtained for the full sample based on relative TP Tornqvist indexes and the export experience measure EEM. Mean difference tests provided in the online appendix show that there is a statistically significant increase in average TP as plants increase their export experience. Increases in TP are observed not only for plants that enter the export market - indicative of selfselection into exporting - but also for plants that are already exporting and accumulate addional export experience. Subsequent increases in TP after plants paid the fixed costs needed to start exporting may be the result of LBE. This hypothesis is examined more thoroughly in the following section. IV. Results Table shows our LP estimation results for equation 4 using the sample of young plants and the export experience measures EE - EE. OLS estimates for the export experience coefficients are 3 also shown at the bottom of the table for comparison. The LP coefficients on export experience are all posive and statistically significant. As discussed in Section III, OLS coefficients on export experience may suffer from a downward bias due to ex decisions or an upward bias due to selfselection of the best plants into exporting. Table provides evidence of an upward bias: OLS coefficients are 8 to 37 percent higher than their LP counterparts. The LP inputs coefficients in Table are all significant and their magnudes are aligned wh those in previous studies. We find, as OP, that age has a negative coefficient in the production function. A potential rationale for this finding is that the capal stock may be under-measured when plants start producing, but s measurement becomes more accurate as more observations for the plant are available. 8 In that case, plants that are starting production may appear to be very productive given their measured capal stock, and this effect is picked up by the variable age. However, should be pointed out that in most specifications in the rest of the paper the coefficients on age and age squared are insignificant. In column 3 domestic experience, defined as cumulative domestic market sales up to the previous year scaled by the plant s average number of 8 This problem is common when the capal stock is measured using the perpetual inventory method and the inial level of capal is eher not reported or under-reported. In that case, the capal stock will be too small inially, though s measurement becomes more accurate as plants accumulate more capal over time. 0

12 workers, is included instead of age and s coefficients are also negative and often insignificant. 9 This lack of significance of domestic experience contrasted wh the posive and significant coefficient on EE suggests that learning is driven only by exporting. 3 The use of the export experience measures EE - EE in Table restricts the sample to plants for which we can observe the full export history. This restriction is unsatisfactory as young plants constute a minory of Colombia s manufacturing sector, they are smaller, pay lower wages, and are less likely to participate in export markets than more established plants. Hence, is not clear that the results in Table can be generalized to the entire manufacturing sector. In order to add to the sample some of the old plants we conjecture that the beneficial effect of export experience on productivy resets to zero if a plant ceases to export for some time. This hypothesis is consistent wh Arrow s (96) characterization of LBD. If a plant s workers and managers stop performing specific tasks required in order to export, their skills in performing those tasks will erode over time. or concreteness, we assume that export-related skills are completely forgotten after a period of three years whout exporting. 0 The hypothesis we want to test is whether the export experience of a plant that has not exported for three consecutive years resets to zero. or this purpose, we decompose the original export j j j j experience measures as EE EER EE EER for j, 3, where EER s are defined in equations 6a-6b, and we estimate a variant of equation 4 that includes both the right-hand side terms in the decomposion as separate regressors. We test whether the coefficient on j j EE EER is equal to 0 using the sample of young plants and show the results in columns - j j of Table for EER - EER. The coefficient on EE EER is shown to be insignificant. Consequently, we assume that the resetting of export experience is valid for both young and old plants. This allows us to include in the sample the old plants that do not export during , i.e., those that start exporting after 983 and those that never export. Columns 3-5 of Table show regression results using the sample of young and old plants and the EER measures. The addion of old plants more than doubles the sample size, but the estimated coefficients on export experience are still posive and significant. Two of our export experience measures, the number of years the plant exported up to the previous year EER and the cumulative export-output ratio EER are related. The former is the maximum value the latter could take, if the plant exported all s output every year, and can be decomposed as EER EER EER EER, where both right-hand side terms are non- j 9 3 This variable is defined in the same way as EE but using output sold in the domestic market instead of exports. 0 Alternatively, exporting may require specialized personnel, which is dismissed after the plant exs the export market. This decomposion cannot be used for the measure 3 EER which is not linear in past values of exports.

13 negative. This decomposion allows us to test whether that contained in EER conveys addional information to EER by estimating a variant of equation 4 where both the right-hand side terms enter as separate regressors. Using the sample of young plants, column 6 of Table shows that the coefficient on EER EER is posive and significant. This suggests that LBE effects are not proportional to the export-output ratio but rather that indivisibilies in tasks related to exporting exist. These tasks are likely to be beneficial to the plants adopting them regardless of the share of exports in their output. or instance, if exporting requires a better management of the plant s inventories, then the efficiency gains associated wh this improvement are unrelated to the share of exports in the plant s output. Recent research by Eaton et al. (007) for Colombia covering a more recent period shows that most new entrants to the export market do not last more than one year suggesting that not all of them are characterized by persistent favorable productivy shocks. We conjecture that the presence of transient exporters in the sample is likely to bias downward our estimated coefficient on export experience. igure suggests that exporters that ex the export market must have received an adverse productivy shock pushing them to the left of the export ex threshold in the, EE space. As a result, their productivy should be lower than that of active exporters and even of non-exporters which are close to the export entry threshold. The presence of exporters that are currently not exporting in the sample bring us back to Arrow (96). Since these plants are not performing specific tasks required to export, they should not be learning. Thus, pooling them wh plants active in the export market may underestimate the LBE effect. To test the hypothesis that LBE occurs when a plant is actually exporting and not when has temporarily stopped exporting, we estimate a variant of equation 4 where the export experience measures EER - EER enter separately as well as interacted wh the plant s one-year lagged 3 export status ( D ). We present the corresponding results in Table 3 in columns -3 for the young sample and in columns 4-6 for the young and old sample (excluding old continuing exporters). In all cases the coefficient on the interaction between export experience and the lagged export dummy is posive and significant, while that on the export experience measure by self is insignificant, suggesting that transient exporters do not learn from exporting. However, we do not find clear evidence that the LBE effect is underestimated if transient exporters are included in the regression. The coefficient on the interaction term is higher than the coefficient on the export experience measure in the corresponding baseline specification in Table for oppose is true for EER and 3 EER. EER, while the The interaction term is constructed using the lagged export status since the current export status is posively correlated wh, which would cause an upward bias in the estimated coefficient on the interaction term.

14 One of the contributions of our study is the use of export experience measures for the estimation of LBE effects. Hence, is important to examine whether they convey addional information relative to the two measures commonly used in the lerature: lagged export participation dummy and lagged export intensy. or that purpose, note that those two commonly used measures are the last terms of the sums that define EER and EER in equations 6a and 6b. We are interested in knowing whether, after including lagged export participation or lagged export intensy in the regression, the remaining terms of EER or EER are posive and significant. Thus, we estimate the following variants of equation 4 and test H 0 : 0 : y y x x EER D D (8a) Y Y EER Y Y (8b) where x is a vector containing all other explanatory variables in equation 4 including the intercept. The results for these tests using the sample of young and old plants (excluding old continuing exporters) are shown in columns - of Table 4. In both cases, we reject the null hypothesis at the 5% level. Therefore, our evidence suggests that export experience acquired in the years before the previous year has an important effect on plant productivy. The reset tests shown in Table allowed us to include in the analysis old plants (born before 98) that did not export during , but our analysis is still based on samples that exclude old established exporters which account for the bulk of Colombian manufacturing exports. The common use of lagged export participation or lagged export intensy as export experience measures is one way to incorporate these plants, but the tests just discussed suggest that export experience acquired in the years before the previous year has a posive effect on productivy. An alternative way to add the old continuing exporters to the sample is to redefine export experience as moving sums of functions of past exports as in equations 7a-7c. In columns 3-4 of Table 4 we repeat the tests for equations 8a-8b using the full sample and the EEM - EEM measures. The results show that the coefficients on the terms from the moving sums that represent export experience acquired in the years before the previous year are posive and significant, as are those on lagged export participation and lagged export intensy. An important question that we are able to tackle in this paper is whether there is evidence of diminishing returns to export experience. As mentioned in Section I, this element of Arrow s (96) characterization of learning may help explain the differences in the LBE effects estimated across different datasets. To answer this question, we estimate a variant of equation 4 where the export experience measures EEM - EEM enter separately as well as interacted wh a dummy 3 3

15 variable identifying the old continuing exporters, i.e., plants that export since 98, the first year when information about exports is available in the dataset ( OCE ). This group of plants includes the oldest and most established exporters in Colombian manufacturing, for which LBE would be less likely in the presence of diminishing returns to export experience. The corresponding results are shown in columns 5-7 of Table 4. The coefficient on the interaction variable is significantly negative, suggesting that the LBE effect is less important for the more established exporters. However, note that the negative coefficient on the interaction variable is no larger, in absolute value, than the posive coefficient on the export experience measure by self. This suggests that the old continuing exporters do experience LBE effects, though smaller than those experienced by new entrants into the export market. A possible explanation for this phenomenon is that the period covered by the full sample (986-99) was characterized by a very attractive real exchange rate that stimulated Colombian plants to not only enter exporting but also to increase their exports. In that context, is possible that established exporters expanded their sales abroad significantly, giving rise to opportunies for further LBE. The results presented in Tables -4 show evidence of LBE for Colombian manufacturing plants using three different samples and three alternative export experience measures. Next, we investigate the robustness of the results when using alternative estimation methods and when accounting for differences in the production functions of exporters and non-exporters. A first robustness check concerns the estimation method. Table 5 shows in columns - the results from estimating equations 8a-8b using a simpler estimation method, first differences, that control for the effects of time-invariant unobserved plant differences in productivy. Then, we present the results from estimating equations 8a-8b using the other semi-parametric methods mentioned in Section III: OP in columns 3-4, AC in columns 5-6, and LP allowing unobserved productivy to follow a second-order Markov process in columns 7-8. The estimated coefficients on export experience are always posive and significant. While there is some variation in the magnude of the estimates in Table 5, their averages are very close to the corresponding LP estimates in Table 4. 3 The robustness of our results to the assumption of a second-order Markov process in productivy is particularly important since shows that the coefficients on our export experience measures are not capturing the effects of more persistent productivy shocks. Our second robustness check allows the input coefficients to differ across exporters and nonexporters to account for the fact that the former tend to be more capal- and skill-intensive than the latter (Bernard and Jensen, 999). Not accounting for these differences could bias the estimated i 3 The average for the estimated coefficients on EER D in Table 5 is 0.06, compared to 0.08 in Table 4, while the average for the estimated coefficients on EER Y Y is compared to in Table 4. 4

16 coefficient on export experience. However, the sign of the bias is not clear a priori because export experience is posively correlated wh capal in the sample, which would bias the estimate of EE upward, but export experience is also posively correlated wh labor in the sample, which would bias the estimate of downward. In columns -4 of Table 6 we show the results from estimating EE variants of equations 8a-8b where inputs, age, and age squared enter the equation separately and interacted wh a dummy for exporters, defined to be plants that export at least one year during their presence in the sample. The estimated coefficients on export experience do not show systematic evidence of a bias and remain posive and significant at the 5% level. In an addional attempt to control for a potential bias arising from differences in the production functions of exporters and nonexporters, in columns 5-6 we show estimation results for a smaller sample that includes only exporters. The estimated coefficients on export experience for this smaller sample are also posive, significant, and are quantatively similar to those obtained using the full sample. V. Explaining differences in results wh Clerides, Lach and Tybout (998) Using the same data for Colombian plants and for the same period, CLT find no evidence of LBE effects. In principle, there could be three possible reasons for the dispary between their lack of evidence of LBE and our robust evidence of LBE in Section IV: differences in the econometric methodology, differences in the estimated model, and differences in the samples used. irst, CLT use full information maximum likelihood (IML) which models explicly the decision to export. Second, while our estimations are based on samples of plants from all manufacturing industries using an unbalanced panel, CLT estimate their model separately for a few selected manufacturing industries using balanced subpanels of plants, for which we expect LBE effects to be less important. Balanced subpanels include a larger share of old continuing exporters, the oldest and most established Colombian exporters, for which we found smaller LBE effects in Table 4. 4 inally, the model estimated by CLT has average variable costs (AVC) as dependent variable in contrast to our production function model. We examine below whether differences in methodology, model, and samples explain the dispary in LBE results. In the end we find that the key explanation for the differences is the measurement of AVC. or this exercise, we merge our dataset wh the dataset used by CLT. 5 The CLT dataset is a subset of the full Colombian dataset that we use in the rest of the paper that covers a smaller number of industries and includes only plants that reported data to Colombia s AMS every year 4 While potentially disadvantageous in terms of finding LBE effects, we should note that the use of a balanced sample of plants is a technical condion required to apply the IML estimation method. 5 The dataset and Gauss program to run the CLT IML estimation were kindly provided to us by Sofronis Clerides. 5

17 between 98 and 99. By merging both datasets we are able to estimate both production function specifications as in the rest of the paper, and average variable cost specifications, as in CLT, while using their exact industry groupings and some variables from their dataset. We first consider differences in the econometric methodology and samples. or that purpose we estimate by IML a system of equations composed of a production function and a prob model of the decision to participate in the export market. A similar specification was estimated by Van Biesebroeck (005) for firms in a sample of African countries for which he found evidence of LBE. ollowing Van Biesebroeck, we assume that productivy follows an AR() process i and replace the industry dummies. This allows us to re-wre the production function as: j in equation 4 wh a plant random effect EEM D t i y 0 aa a a x x DD EE (9a) where 0 0, o is a proxy for lagged productivy used in the estimation and defined as o y xx DD EE EEM D, x h m k, i i, and is an i.i.d. error. Notice that in this regression we are not quasi-differencing the log of age, the log of age squared, nor the time dummies. The reason is that all these variables are very highly correlated - in some cases perfectly correlated - among themselves, which causes the convergence of the maximization of the likelihood function to be extremely difficult or to fail. 6 We model the decision to participate in export markets like CLT (equation 0 and Table IIIa): D 0 if 0 RER RERt kk aa at B a B ~ ~ DD ~ D ~ D3 i 0 (9b) otherwise, D D3 where RER t is an index of Colombia s peso real effective exchange rate, B is a dummy for ~ corporations, ( j 3) are dummy variables that equal if the plant last exported in year t j D j and 0 otherwise (CLT, fn. 0, p. 93), i is a plant random effect, and is an i.i.d. error. 7 Similarly to CLT and Van Biesebroeck (005), we assume that the plant random effects of the production function and export participation equations (, ) follow a bivariate normal distribution, allow them to be correlated across equations, and use Gaussian quadrature to integrate them out of the likelihood function. To account for the inial condions problem associated wh i i 6 or instance, the correlation between the log of age and s lag is about and some of the time dummies are perfectly correlated wh the lag of other time dummies. 7 Note that our k is equivalent to lagged capal in CLT because represents capal measured at the beginning of period t, while CLT s capal is measured at the end of period t (CLT appendix and Roberts and Tybout, 996, p. 55). 6