This article was downloaded by: [83.85.255.136] On: 22 July 2014, At: 03:33 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Review of Applied Economics Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/cira20 Does export and productivity growth linkage exist? Evidence from the Indian manufacturing industry Chandan Sharma a & Ritesh Kumar Mishra b a Department of Economics , National Institute of Financial Management (NIFM) , Faridabad‐121001, Haryana, India b Lal Bahadur Shastri Institute of Management , New Delhi, India Published online: 21 Jun 2011. To cite this article: Chandan Sharma & Ritesh Kumar Mishra (2011) Does export and productivity growth linkage exist? Evidence from the Indian manufacturing industry, International Review of Applied Economics, 25:6, 633-652, DOI: 10.1080/02692171.2011.557046 To link to this article: http://dx.doi.org/10.1080/02692171.2011.557046 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
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This article was downloaded by: [83.85.255.136]On: 22 July 2014, At: 03:33Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
International Review of AppliedEconomicsPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/cira20
Does export and productivity growthlinkage exist? Evidence from the Indianmanufacturing industryChandan Sharma a & Ritesh Kumar Mishra ba Department of Economics , National Institute of FinancialManagement (NIFM) , Faridabad‐121001, Haryana, Indiab Lal Bahadur Shastri Institute of Management , New Delhi, IndiaPublished online: 21 Jun 2011.
To cite this article: Chandan Sharma & Ritesh Kumar Mishra (2011) Does export and productivitygrowth linkage exist? Evidence from the Indian manufacturing industry, International Review ofApplied Economics, 25:6, 633-652, DOI: 10.1080/02692171.2011.557046
To link to this article: http://dx.doi.org/10.1080/02692171.2011.557046
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Does export and productivity growth linkage exist? Evidence from the Indian manufacturing industry
Chandan Sharmaa* and Ritesh Kumar Mishrab
aDepartment of Economics, National Institute of Financial Management (NIFM), Faridabad-121001, Haryana, India; bLal Bahadur Shastri Institute of Management, New Delhi, IndiaTaylor and FrancisCIRA_A_557046.sgm(Received 7 October 2009; final version received 17 November 2010)10.1080/02692171.2011.557046International Review of Applied Economics0269-2171 (print)/1465-3486 (online)Article2011Taylor & Francis00000000002011Mr [email protected]
This paper examines the interrelation between exporting and productivityperformance by using a representative sample of Indian manufacturing firms overthe period 1994–2006. Specifically, we attempt to test the empirical validity of thelearning-by-exporting and the self-selection hypotheses for our sample firms. Inorder to investigate the linkage, in the first step, we test for causality between TFPand export intensity of firms. Although overall results are rather mixed andprovide some support for both hypotheses, still the empirical results are morefavorable for the self-selection behavior of firms. In the next stage, we attempt toprovide evidence on export and productivity linkage that occurs during variousphases of transition in the export market. Our results suggest that entering in theexport market does not improve productivity performance. However, the decisionto exit from the export market does have an adverse effect on the productivity. Inaddition, our results indicate the presence of a high sunk cost of exporting coupledwith perhaps lesser information about foreign markets. Finally, our results alsolend some support to the significant role of in-house research activities andeconomies of scale in firms’ productivity performance.
Keywords: total factor productivity; learning-by-exporting; self-selection
JEL classifications: F10, F14, D21, L60
1. Introduction
The economic linkage between export and productivity growth has long been a highlydebated topic in the international economics and trade literature. But the issue hastaken on added importance since the pioneering work of Bernard and Jensen (1995,1999) that brought into focus the exceptional qualities of exporting firms by raisingthe empirically verifiable question: are exporters more productive or superior thannon-exporting firms? On the theoretical front, there is a common opinion that interna-tional trade in general, and export in particular, improves the productivity of firms,which finally leads to economic growth (see Beckerman 1962; Balassa 1988; Bhag-wati 1988). Economic policies based on trade liberalization and export-led growthstrategies have been widely supported by the argument that exposure to internationalmarkets through exporting helps to increase the productivity and efficiency of export-ers (see Krugman 1987; Rodrik 1988; Grossman and Helpman 1991). Similarly,
advocates of endogenous growth also believe that exporting plays a crucial role byimproving productivity through innovation (Grossman and Helpman 1991; Rivera-Batiz and Romer 1991) and technology transfer (Barro and Sala-i-Martin 1995;Parente and Prescott 1994). Based on these theoretical arguments, it is clear thatproductivity growth can occur due to many factors such as capital accumulation,adoption of new technologies, research and development (R&D), changes in theorganization of firms and, most importantly, through export participation.
Given the huge importance of export participation in enhancing productivity andoverall economic growth, recently an increasing number of studies have devotedconsiderable attention to investigating the causal relationship between exporting andproductivity at various levels. In this context, the influential works of Bernard andJensen (1999, 2004a, 2004b) and Bernard et al. (2003) have brought into focus theexceptional performance of exporting firms in terms of labor productivity and firmheterogeneity within sector. Melitz (2003) went a step further and made the debatemore interesting by demonstrating through a general equilibrium model that firmsself-select into export markets. To empirically establish a direct link betweenexporting and productivity growth, the center of this new debate has been the learn-ing-by-exporting and the self-selection hypotheses. Despite a huge wealth of empiri-cal literature on this issue, the overall results are rather mixed and inconclusive.While many studies have reported evidence in favor of the self-selection hypothesis,some other studies have argued that firms become more productive when they partic-ipate in export markets (see the survey by Wagner 2007). On the contrary, a growingbody of literature is of the opinion that exporting confers little or no benefit inproductivity growth (see for example, Clerides et al. 1998; Bernard and Jensen 1999,2004a; Delgado et al. 2002), rather, it predates their entry into the export market. Incontrast to the above findings, some recent studies, for example Bigsten andGebreeyesus (2009), have not only found strong evidence in favor of self-selectionof more productive firms into the export market but also for the learning-by-exporting hypothesis.
In the present study, we attempt to provide new insights into the debate over theexport–productivity linkage using firm-level data from the Indian manufacturingindustry. In other words, our main objective is to examine the validity of the self-selection and the learning-by-exporting hypotheses in the case of Indian manufactur-ing firms for the period 1994–2006. A clear knowledge about the direction of causal-ity of this relationship is relevant for understanding the firm level responses toaggregate shocks and for suitable policy responses. In India, export-promotion is oneof the key elements of the government’s foreign trade policy. For example, India’sforeign trade policy 2009–2014 aims to expand its overall share in international tradeand to generate massive employment by accelerating exports through various exportpromotional measures, such as fiscal incentives, institutional changes and proceduralrationalization (Foreign Trade policy 2009).1 As a part of the ongoing economicreform process, India has implemented several policy measures to transform the exter-nal sector of the economy by removing the existing tariffs and other barriers of inter-national trade. Further, proponents of free trade under the World Trade Organization(WTO) also believe that free trade will promote the economic growth of the countryby increasing the volume of total exports. Therefore, a clear understanding about thelink between exporting and productivity is very important to assess whether export-promotion will be an optimal policy or the productivity enhancing policies will bemore suitable for the overall economic growth in the long-run.
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The current study contributes to the growing body of related literature by addinganother national perspective of a rapidly growing economy. Specifically, the contri-bution of this study to the extant literature is manifold. First, in order to examine theexport-productivity linkage, many previous studies have used dummy variables forthe various export status of the firms in their analysis. We move a step further andconduct a separate analysis using the export intensity and productivity of firms. Useof export intensity rather than a binary variable is very relevant because it is not onlyimportant whether firms export or not, but it is also vital how much they export, as theexport intensity of firms varies from less than 1% to 100%. Therefore, in this situa-tion, awarding equal weight or number, i.e. 1, to all exporting firms may be highlyproblematic and somewhat unjustifiable, at least in some cases. In addition, the use ofexport intensity is also justified on the grounds that it is a key indicator of the degreeof firms’ participation in international trade. Second, along with empirically verifyingthe direct link between export and productivity, we also attempt to examine this rela-tion during different phases of the transition of firms into the export market, i.e. fromnon-exporter to exporter and vice-versa. Specifically, our main focus here is to studythe movements in productivity when a firm begins to export, continues to export, andexits from the export market. In addition, we also attempt to investigate whether thesunk cost of entry in the export market is important for Indian firms or not. Third, theresults of previous studies on this issue seem to be sensitive towards the choice ofeconometric methodologies, especially because of the potential presence of endoge-neity. We attempt to address this issue in our analysis by applying appropriate tech-niques, i.e. a system of Generalized Method of Moments (sys-GMM) estimator or byspecifying the model in such a way as to overcome this problem. Fourth, the estima-tion of production functions to compute total factor productivity (TFP) as a measureof firms’ performance is a highly debated issue in this area of research. Since thecomputation is sensitive towards the method of production function estimation, wetherefore utilize the innovative and most recent Levinsohn–Petrin (2003) techniquefor this purpose. It provides more consistent and unbiased estimates than the earliertechniques. Finally, although the available literature on this issue is rich, the case ofIndian manufacturing has been widely neglected so far. To the best of our knowledge,there are barely any studies available in the Indian context that provide evidence usingfirm-level data in recent times. In the current study, we employ a new database, whichprovides firm-level data and allows us to extend our data set to the most recent period,during which the Indian economy has witnessed many changes, especially in theexternal sector.
The rest of the paper is organized as follows: Section 2 presents the backgroundtheory and review of the literature. Section 3 discusses the data-related issues, TFP esti-mation methodology and their results. Section 4 examines the determinants of exportintensity and TFP for our sample firms. In Section 5, we discuss our empirical modelsof productivity performance and transition in the export market and their results. FinallySection 6 presents the main findings and conclusions of the study.
2. The theory and review of the literature
The wealth of literature on the relationship between export and firms’ productivity haswitnessed tremendous growth in the recent past. This stream of literature has mainlyexamined the export-productivity nexus by testing two major hypotheses to explainthe positive association between exporting and productivity improvements of firms.
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The first hypothesis is known as the self-selection hypothesis, which explains the self-selection of more productive firms into the export market. The much cited reasonbehind this hypothesis is the presence of sunk costs when entering and selling goodsin foreign markets. The second hypothesis is known as learning-by-exporting, whichstates that exporting to a foreign market produces many positive learning effects byexposing the domestic firms to advanced technological innovations from internationalbuyers and competitors and therefore helps to improve their productivity (Bernard andWagner 1997; Bernard and Jensen 1999).2 The findings of previous studies suggestthat exporters, in general, are found to be more productive, more skill-intensive, highwage payers, larger in size and more capital-intensive. Further, it is also argued thatexporting reallocates the available resources from less efficient plants to more effi-cient plants, resulting in the optimal use of scarce resources. In this context, Bernardand Jensen (2004a) show that the allocation effect of exporting is very large and itaccounts for up to 40% of the TFP growth in the manufacturing sector.
Most of the empirical studies have provided support to the theoretical view thatthere is a positive association between exporting and productivity of firms. Whilethese results provide some strength to the learning-by-exporting hypothesis, someother studies have argued that firms which involve themselves in exporting aretypically more productive or efficient than firms that never export or enter into theinternational market (Clerides et al. 1998). Some important studies in this categoryincludes Bernard and Jensen (1995, 1999, 2004a) for United States; Bernard andWagner (1997) and Wagner (2002) for Germany; Aw et al. (2000) for the case ofKorea and Taiwan; Clerides et al. (1998) for Colombia, Mexico and Morocco andGirma et al. (2004) for the United Kingdom. Focusing on different phases of transitionfrom an exporter to non-exporter, Bernard and Jensen (2004a) found that whileexporters have noticeably higher productivity levels, there is however no strongevidence to conclude that export participation increases plant’s productivity.Similarly, Arnold and Hussinger (2005a) reached the conclusion that firms withhigher productivity self-select into the export market and there is no strong evidenceto suggest that exporting has any significant impact on the productivity of Germanfirms. More precisely, giving empirical support to the self-selection hypothesis, thesestudies have consistently found a robust positive association between exporting andproductivity.
On the other hand, empirical evidence in favor of learning-by-exporting is ratherweak (see Wagner, 2007). Nevertheless, some important studies – for instance, byRoberts and Tybout (1997) for Colombia; Kraay (1999) for China; Baldwin and Gu,(2003) for Canada; Fernandes and Isgut, (2005) for Colombia; Kim et al. (2009) forKorea and Andersson and Loof (2009) for Swedish firms – have found that past exportperformance has a significant impact on productivity, which apparently providessupport to the learning-by-exporting hypothesis. Similarly Aw et al. (2000), VanBiesebroeck (2006), Loecker (2007) and Yasar and Rejesus (2005) have found thatfirms experience productivity improvement after entering the export markets. In ananother study, using quantile regression techniques on the plant level data of Turkishmanufacturing firms, Yasar et al. (2006) found that the exporting status of firms isstrongly associated with productivity. Contrary to this, Greenaway et al. (2005) forSwedish firms, and Damijan and Kostevc (2006) for Slovenian manufacturing havefailed to find any evidence for both the hypotheses.
In the light of the above mixed and inconclusive findings, it seems that despite volu-minous research, the issue is still in its infancy. Therefore, it is both relevant as well
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as interesting to explore the issue further to discover whether the export–productivitylinkage exists in Indian manufacturings; if so, then what is the direction of this linkage.
3. Data and TFP estimation
3.1. Data
The dataset contains yearly information on Indian manufacturing firms from 1994 to2006, obtained from Prowess database.3 Our sample covers firms from four indus-tries: cotton textile (93 firms), electrical (83 firms), pharmaceutical (87 firms) andtransport equipment (automobile & Auto-ancillary) (94 firms). We have selected theseindustries for analysis basically on two grounds: first, the significance of the industryin the domestic economy in terms of employment generation, technology improve-ment and export earnings and, second, the relative size of the industry in the database.Specifically, preference is given to those industries that have a large number of firmsin the database. Further, we pick up firms from the selected industries for analysis onthe basis of the availability of data. Firms with missing data of more than one year inthe database are excluded from the study. The primary data series extracted fromcompany accounts are sale, wage and salary expenses, gross value added, expensesincurred on raw materials, power, fuel and energy, and R&D activities. Since ourfocus in this study is on exports of firms, we also take these data along with importdata from the same database. Capital related series, namely gross fixed capital andinvestment, are also taken from the Prowess database. To obtain information on thenumber of workers working in the firms, we have also used the data provided by theAnnual Survey of Industry (ASI). This was required because the Prowess databasedoes not provide a figurative data about the workforce, but it does provide data onsalaries and wages. We obtain average wage rate (total emoluments/total man days)data of the industry from the ASI database and then divide each firm’s salary andwage by the average wage rate to obtain information about the number of workers. Forcapital series of firms, we adhere to the construction process outlined by Levinsohnand Petrin (2003) and a real capital stock series is constructed using the perpetualinventory capital adjustment method.4 All data series used in the analysis are deflatedwith appropriate deflators with base year 1994 before any econometrics treatment.Output-related series is deflated by industry specific Wholesale Price Indices (WPIs).This deflator is obtained from the Office of the Economic Adviser (OEA), the Minis-try of Commerce & Industry of India (http://eaindustry.nic.in/). The raw materialsseries is deflated by all-commodities WPI, and the energy series is deflated using theEnergy Price Index provided by the OEA. The capital data are deflated by a capitaldeflator, which is obtained from the Handbook of statistics on the Indian economypublished by Reserve Bank of India (RBI). The descriptive statistics are presented inTable A1 of the Appendix.
3.2. TFP estimation results
To accomplish the objectives of this study, our empirical analysis starts with the esti-mation of TFP. This is done separately for all four sample industries. It is noteworthythat the use of ordinary least squares (OLS) in the estimation of the production func-tion may lead to some serious problems. As pointed out by Griliches and Mareisse(1995), profit-maximizing firms immediately adjust their inputs (in particular capital)
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each time they observe a productivity shock, which ensures input levels are correlatedwith the same shocks. Since productivity shocks are unobserved, they enter in theerror term of the regression. Hence, inputs may turn out to be correlated with the errorterm of the regression, and thus OLS estimates of production functions are biased.Olley and Pakes (1996) (OP, hereafter) and Levinsohn and Petrin (2003) (LP, hereaf-ter) have developed two similar semi-parametric estimation procedures to overcomethis problem. In this study, we prefer the LP methodology, which is an extension ofthe OP technique for computation of TFP.5 This methodology explicitly recognizesand overcomes the endogeneity, which occurs because at least a part of the TFP isobserved by the profit maximizing firms early enough so as to allow the factor inputdecisions to be changed. Specifically, we follow the value added method of the LPprocedure and deflated gross value added (LY) of firms is used as a measure of output.Further, in this process intermediate inputs (raw material and energy) are used asproxy, to avoid the biasness problem. The estimated production function is reportedin Table 1, which suggests that workers (LN) and capital (LK) are significant in allindustries at conventional levels of significance. On the basis of this estimated result,the TFP of firms is calculated for the purpose of further analysis. The descriptivestatistics of TFP is presented in Table A1 of the Appendix.
4. Determining the export intensity and TFP of firms
4.1. The empirical models
After estimating the TFP of firms, we move on to estimate the determinants of TFPand export intensity (ratio of export to value of sales) in all four industries. For thispurpose, we specify following two models for the empirical estimation:
where tfp and export intensity are the TFP and export-intensity of firm i at period t inthe models. Further, we also include a set of additional control variables (X) that mayaffect the productivity and export of firms. The set of additional control variablesincludes R&D intensity,6 import intensity7 and size of the firm.8
export intensity tfp X eit it it it= + + +−α ϕ γ1 2( )
tfp tfp X eit it it it= + + +−α β δ1 1( )
Table 1. Cobb-Douglas production function estimation using Levinsohn–Petrin productivityestimator (dependent variable: gross value added (LY)).
Variables Transport Equipment Pharmaceutical Electrical Cotton
LK 0.3325826*(9.89)
0.26801*(2.50)
0.41813*(9.12)
0.67757*(11.06)
LN 0.610549*(2.24)
0.60809*(13.33)
0.46775*(3.89)
0.28172*(2.94)
Wald test (P-Value) 0.7279 0.1667 0.3087 0.5843
Notes:1. Z-test statistics are in parenthesis,2. Wald test of constant returns to scale,3. Proxy variables: Power and fuel expenses; and Raw material expenses.4. *indicates significant at 5% critical level.
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4.2. Econometrics issues
While estimating models 1 and 2, we face one major challenge: endogeneity. Thiscould lead to a biased estimation of such impact. To overcome this problem, we there-fore utilize the GMM estimator developed by Arellano and Bover (1995) and Blundelland Bond (1998). The Blundell and Bond estimator, also called the system GMM esti-mator, combines the regression expressed in first differences (lagged values of thevariables in levels are used as instruments) with the original equation expressed inlevels (this equation is instrumented with lagged differences of the variables) andallows us to include some additional instrument variables. We prefer this option to afixed-effects estimator for two reasons. First, it allows us to take into account theunobserved time-invariant bilateral specific effects. Second, it can deal with thepotential endogeneity arising from the inclusion of the lagged dependent variable andother potentially endogenous variables.
4.3. Empirical results
The estimated results of equation (1) are reported in Table 2. The results suggest thatexport-intensity has a significant effect only in the cotton manufacturing industry, andthat too only at 10% significance level. Surprisingly, in the other three industries, it isnot found to be significant. It is well documented in the standard literature that learn-ing effects from exporting take time to become visible. Keeping this phenomenon inmind, we have also included higher lags of the export variable. Now, only in thecotton textile industry, the estimated coefficient at lag two is larger (0.09) than the
Notes:1. Z-values in parentheses.2. *, ** indicate statistical significance at the 10% and 5%, respectively.3. Sargan is the Sargan (1958) test of over-identifying restrictions.
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coefficient at first lag and is statistically significant. In other industries, the second lagof this variable is not found to be statistically significant.9 Therefore, at this stage ourresults broadly do not provide any empirical support for the learning-by-exportinghypothesis.
This result may be due to the limited exposure of Indian firms to the internationalexport markets. Further, it could also be the case that the total amount of export hasprobably not reached the threshold level, where it could significantly affect theproductivity performance of firms. This argument is supported by the results from thecotton industry, where the export intensity is highest among all the sample industries(see Table A1 of the Appendix). In addition, the results for the other control variablesare also found to be mixed. The effect of R&D and import intensity is statisticallysignificant only in two industries. However, the size of firms seems to be a crucialfactor, as its coefficient is significant in all industries included in our sample.
Next, we examine the presence of the reverse effect, i.e. we test the impact ofproductivity performance on export performance of firms in our sample industries. Tothis end, we estimate equation (2) and the results are reported in Table 3.
Again, our results show that in the cotton industry, exporting is related to theproductivity performance of firms. In the transportation equipment industry, however,it is found to be significant only at 10% critical level. Since both of these industriesare export intensive, these results seem to be obvious. Therefore, this finding providessome support for the self-selection hypothesis. At this stage too, we do not find anystrong evidence for R&D and import intensity. However, both variables are found tobe significant only in two out of four industries. Finally, the result related to the size
Table 3. Results of determinants of export-intensity, 1994–2006.
Variables(period)Transport
Equipment Pharmaceutical Electrical Cotton
Export intensity (t–1) 0.791961**(25.52)
0.474492**(12.11)
0.545210**(16.79)
0.776081**(17.03)
Import intensity (t–1) 0.281907**(5.16)
0.089803*(1.69)
0.0218456(0.62)
0.053977(0.64)
Size 0.01897*(1.78)
0.08711**(5.19)
−0.02598*(−1.67)
−0.002749(−0.12)
R&D intensity (t–1) −0.42085(−1.21)
−0.181692(−0.69)
1.070623(1.59)
0.7964557(0.50)
TFP(t–1) 0.059145*(1.74)
0.057266(0.79)
0.0730778(1.36)
0.19002**(3.42)
Const −0.01259(−0.52)
−0.09131**(−2.91)
0.016391(0.91)
−0.01784(−0.35)
Sargan χ2 (P-value) 79.6572(0.3647)
79.05122(0.3828)
74.50812(0.5270)
79.05122(0.3828)
No. of Observations (Panel)
1107(94) 985 (87) 940 (83) 1011(91)
Notes:1. Z-values in parentheses.2. *, ** indicate statistical significance at the 10% and 5%, respectively.3. Sargan is the Sargan (1958) test of over-identifying restrictions.
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of the firm is very similar to the previous one and it suggests that economies of scalehave a positive effect on export performance. While, in the cotton industry, the impactof size is not found to be statistically significant, our result shows that it has a negativeimpact in the electrical industry.
5. Productivity performance and transition in the export markets
Thus far, we have analyzed the linkage between productivity and export intensity offirms. In this section, we attempt to provide evidence on export and productivity link-age that occurs during various phases of transition in the export market. This willprobably provide a better insight into this linkage by analyzing the adjustment processwithin the firm during the period of transition. For this purpose, we merge the data ofall firms included in our sample industries and construct a single panel of firms. Tocover various phases of transition in the export market, we divide a change in exportstatus into a set of indicator variables for firms entering, staying and leaving the exportmarket. We attempt to investigate the impact of these decisions on firms’ productivityperformance. The four possible situations within the sample are: stay out (firms thatdo not export in period t–1 and period t), start (firms that do not export in period t–1but do export in the period t), stop (firms that export in the period t–1 but stop export-ing in period t) and both (firms that export in both periods). Hence, we use threedummies for export status which are defined as follows:
To test the validity of both the learning-by-exporting and self-selection hypotheses,we broadly follow the empirical specifications of Bernard and Wagner (1997), Bald-win and Gu (2003), and Bernard and Jensen (2004). The mean values of thesedummies are presented in Tables A2 of the Appendix.
5.1. Testing the learning by exporting hypothesis
In the present study, our baseline empirical model to test the learning-by-exportinghypothesis is as follows:
where X is a vector of firm characteristics and ϕ1,ϕ2,ϕ3 and δ are parameters to beestimated. t and i denote year and firm respectively in the model.
For robustness check, we estimate equation (3) in a variety of ways. The estimatedresults are reported in Table 4. Column 1 of the table presents the result of the modelwith export dummies only. In column 2, 3 and 4, we include two firm specific char-acteristics, namely R&D intensity and size.10 In addition, we include productivity ofthe previous year in column 3, mainly to deal with the possible endogeneity problem
tfp start both stop X eit it it it it it= + + + + +α ϕ ϕ ϕ δ1 2 3 3( )
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(see Kim et al. 2009 for detailed discussion on this issue). We also include industrydummies in an alternative model and the related results are reported in column 4 ofthe table.
The results suggest that the ‘start’ dummy is not statistically significant in most ofthe cases (except in column 2, where it is significant at the 10% level). This impliesthat entering in the export market does not improve firms’ productivity performance.This provides some strength to our earlier findings that the learning-by-exportinghypothesis is not true for our sample firms. The second dummy in the model ‘both’captures the learning-by-using hypothesis. The coefficient of this dummy is found tobe significant, but negative in all cases. This evidence apparently provides support forArrow’s hypothesis (1962) that learning and productivity slowdown is obvious for theconstantly exporting firms since they have learned the proverbial ropes of exporting.Further, this may also be an indication of the high sunk cost of entry in the exportmarket for the Indian firms. Exporting is still a relatively new experience for firms inIndia and if the sunk cost is sizably large, the benefits of exporting may realize onlyafter operating for a long period of time in the export market. The result regarding the‘stop’ dummy is found to be significant and negative in all cases. This implies thatalthough entering in the export market does not affect firms’ performance, the exitdecision from the export market does have an adverse effect on the productivity offirms. This result is in agreement with the findings of Girma et al. (2003) for UKmanufacturing.
The results from our analysis are somewhat mixed so far and seem to be more orless in line with the standard literature. We fail to find any significant difference in
Table 4. Testing the learning by exporting hypothesis, 1994–2006.
Dependent Variable: TFPt
Variable (period) 1 2 3 4
start(t) −0.0034(−0.516)
0.0117*(1.853)
0.0061(1.135)
0.0146(1.535)
both(t) −0.0322**(−3.949)
−0.0437**(−7.987)
−0.0199**(−4.205)
−0.0474**(−8.335)
stop(t) −0.0185**(−3.363)
−0.0371**(−4.798)
−0.0149**(−2.277)
−0.0366**(−3.107)
size(t) 0.1044**(21.379)
0.0621**(12.669)
0.1310**(38.849)
R&D intensity(t) 0.5125**(3.5039)
0.4331**(3.624)
0.3775**(2.155)
tfp(t–1) 0.5421**(38.514)
Constant 0.4516**(99.019)
0.2952**(35.054)
0.1108**(11.897)
R2 0.6516 0.7813 0.8695 0.3522
Industry Dummies No No No YesEstimation Method Fixed effect Fixed effect Fixed effect OLS
Notes:1. t-values in parentheses.2. *, and ** indicate statistical significance at the 10% and 5% respectively.3. A panel of 357 firms is used for the analysis.
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productivity of firms that enter in the export market and firms that do not export.However, export participation does have some impact on the productivity perfor-mance, as leaving the export market negatively affects productivity performance.These results, to some extent, indicate the involvement of a high sunk cost for firmswhen they switch over to the export market, which possibly neutralizes the benefit ofexporting and, therefore, there is a significant adverse impact if they decide to exitfrom the export market. Considering the poor condition of infrastructure endowmentsand the presence of several regulatory hurdles in India, this argument seems to bequite plausible in the present circumstances, where some major trade reforms are yetto take place under the ongoing economic reform process.
5.2. Testing the self-selection hypothesis
Now we shift our attention to the question of ‘does productivity performance deter-mine exporting decision?’ To empirically examine this question, we first test theempirical model of Baldwin and Gu (2003). Our baseline specification to achieve thisobjective is as follows:
In this equation, TFP in period t–1 is a function of transitions in the export marketbetween periods t–1 and t, and a set of additional control variables (X) in period t–1.In the above specification, the omitted transition variable is non-exporters. The coef-ficient ϕ1 measures the productivity difference between starters and non-starters inexport markets prior to the transition period. An examination of difference betweenthe coefficients ϕ2 and ϕ3 would indicate whether more productive firms are morelikely to remain in the export market. Again, we estimate equation (4) in several alter-native ways. The estimated results are reported in Table 5. In column 1, only export-related dummies are included, while other control variables, namely size and R&Dactivity, are included in the models presented in columns 2 and 3. In addition, the thirdcolumn includes industry dummies in the model specification.
Our result suggests that there is no significant difference between the productivityof starter and non-starter firms in the previous year of entering the export market, asthe ‘start’ dummy is not found to be statistically significant. Further, the ‘both’ dummyis found to be statistically significant across the specifications, and results suggest thatfirms that continue exporting have 1% to 7% lower productivity than non-exporterfirms. Finally, we find clear evidence that the firms that have lower productivity exitfrom the export market as the ‘stop’ dummy is found to be statistically significant andthe sign of the coefficient is found to be negative across the specifications.
5.3. Testing the self-selection hypothesis: an alternative model
The above results (for the self-selection hypothesis) are somewhat surprising and notfully in agreement with the findings of the related literature. Therefore, we furtherinvestigate the self-selection hypothesis in an alternative way as suggested by Robertsand Tybout (1997). In the model, firms’ export decision (Export) depends crucially onprofits of entry/exit costs in foreign markets. A firm exports (Exportit = 1), if currentand expected revenues (Rit) are greater than current-period costs Cit in addition tosunk cost (if any), N:
tfp start both stop X eit it it it it it− −= + + + + +1 1 2 3 1 4α ϕ ϕ ϕ δ ( )
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In this framework, therefore, our baseline empirical specification is
A lag of the export dummy is included in the specification to overcome the potentialendogeneity problem. X is again a vector of firm-specific control variables. In equation(6), the participation decision does not depend on exporting history if the sunk cost isnot important in exporting. This can be examined by β2 equal to zero or not.
Now we estimate our baseline empirical model expressed in equation (6) in threealternative ways and the results are reported in Table 6. Column 1 of the table presentsthe results of the model in which only TFP and export status are included. The modelpresented in columns 2 and 3 include some other firm-specific control variables. Inaddition, the result presented in column 3 of the table is also controlled for industry-specific effects by including industry-specific dummies. These models are estimatedby using a Probit model as our dependent variables are binary. The results suggest thatproductivity is not an important determinant of exporting decision of firms, as theestimated coefficient of TFP is not found to be statistically significant in most of thecases. Only in column 1, it is found to be significant at the 10% significance level.Further, the inclusion of control variables have resulted in the insignificant coefficientof TFP, which probably suggests that, in the Indian case, size and R&D activities aremore important factors in the exporting decision than the productivity of firms. Not
ExportR C N Export
itit it it=
+ −
−1 1
051if f ( )
( )
Export tfp tfp X eit it it it it= + + + +− −α β β β1 2 1 3 1 6( )
Table 5. Testing the self selection hypothesis, 1994–2006.
Dependent Variable: TFPt–1
Variable (period) 1 2 3
start(t) 0.0004(0.061)
0.0082(1.212)
0.0156(1.5244)
both(t) −0.0175**(−2.961)
−0.0348**(−5.924)
−0.0785**(−4.312)
stop(t) −0.0284**(−3.358)
−0.0082**(−4.288)
−0.03156**(−2.5311)
size(t–1) 0.0915**(15.824)
0.1273**(36.389)
R&D intensity(t–1) 0.0271(0.222)
−0.0158(−0.1028)
Constant 0.4458**(92.809)
0.3029**(30.0101)
R2 0.6636 0.7850 0.3448
Industry Dummies No No YesEstimation Method Fixed Fixed OLS
Notes:1. t-values in parentheses.2. * and ** indicate statistical significance at the 10% and 5%, respectively.3. A panel of 357 firms is used for the analysis.
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surprisingly, the evidence apparently suggests that the previous year’s exporting statusis a key decision-making factor for exporting in the following year, as β2 is found tobe statistically significant and positive. This result also suggests that the sunk cost isa crucial factor in making export-related decisions.11
In short, our results of this section broadly provide little or no evidence in supportof the self-selection hypothesis, i.e. good firms in terms of productivity self-selectthemselves to the export market. Most importantly, this finding is broadly consistentacross the frameworks and specifications. Our result regarding the sunk cost supportsthe view that at the time of entry into the export market substantial costs are incurredin accumulating information on demand source. Subsequently, this may also indicatefor poor information available in the Indian market about foreign markets (mainlybecause of the low exposure of domestic firms in the foreign market) as well as theexisting regulatory hurdles for firms.
6. Conclusion and policy suggestions
This paper examines the relationship between export participation and productivityperformance using a sample of firms from the Indian manufacturing industries for theperiod 1994–2006. For this purpose, a variety of analyses was carried out. The resultsbased on export intensity and TFP of firms suggest that only in the cotton textile indus-try export participation has a positive and significant impact on the productivity perfor-mance of firms. In the other three sample industries, export participation is not foundto have any such impact on the productivity of firms. Based on these findings, in thecurrent study we reject the empirical validity of the learning-by-exporting hypothesisand it seems reasonable to conclude that it is not a valid proposition for the Indianfirms. Using the same approach, we also find that in the cotton textile and the transport
Table 6. Testing the self selection hypothesis: An alternative model, 1994–2006.
Dependent variable: Exportt
Variable(period) 1 2 3
tfp(t) 0.4026*(1.859)
0.0927(0.388)
−0.2755(1.0281)
export(t–1) 2.4583**(38.856)
2.3377**(35.337)
2.2962**(34.332)
size(t–1) 0.3244**(7.275)
0.4424**(7.5411)
R&D intensity(t–1) 1.9089(1.017)
1.8207(0.917)
Constant −0.9754**(−9.6756)
−1.2608**(−11.055)
Industry Dummies No No YesEstimation Method Probit Probit ProbitLog likelihood −971.0084 −923.8995 −915.4341
Notes:1. t-values in parentheses.2. * and ** indicate statistical significance at the 10% and 5%, respectively.3. A panel of 357firms is used for the analysis.
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14 C. Sharma and R.K. Mishra
equipment industries the productivity of firms has a significant impact on theirexporting, which provides some support to the self-selection hypothesis. However, nofavorable evidence is found for this hypothesis in the other two sample industries.
Further, our results about what happens to productivity during the various phasesof firms’ transition in the export market suggest that entering in the export market doesnot have any significant impact on productivity. This indicates that learning-by-exporting is not true in the Indian case. Although, we fail to find direct support for thelearning-by-exporting hypothesis, our results suggest that firms experience a declinein their productivity in subsequent years after exiting the export market. In some sense,this is the only supportive evidence we find for this hypothesis. On the other hand, forthe self-selection hypothesis, our results are also somewhat mixed. We fail to observeany significant productivity gain in the previous year for firms that enter into the exportmarket in the subsequent year. Nonetheless, it is found that leaving the export marketis closely related to the productivity performance of firms. Specifically, our results showthat firms experience a decline in their productivity in subsequent years after exitingthe export market. Firms that experience productivity loss in the previous period aremore likely to exit the export market in the subsequent year. This could be interpretedas supportive evidence for the self-selection hypothesis. Furthermore, the test resultsbased on the model of Roberts and Tybout (1997) also failed to provide any favorableevidence for the self-selecting behavior of firms. Nevertheless, this analysis advocatesthe crucial role of sunk cost of exporting in exporting decisions of firms.
To sum up, the findings of this study are broadly similar to that of Kim et al.(2009) for Korean manufacturing. They have also rejected the empirical validity ofboth hypotheses for Korean firms in the majority of industries. The weak linkagebetween productivity and exporting in the Indian case may be a result of several regu-latory hurdles, high sunk cost, less information, low exposure to foreign markets, andpoor condition of physical and social infrastructure. The results of this study raisesome serious concerns on the rationale of the export-oriented trade policy in India.The rationality behind giving a high level of subsidy and tax incentives by the govern-ment to exporting firms and export oriented units in various special economic zones(SEZs) seems to be questionable on empirical ground because there is no strongevidence to suggest that export participation leads to productivity improvement. Itappears more convincing that the trade and economic policies should focus on produc-tivity enhancement that will help firms to enter the export market after gaining realcompetitive edge. This will in turn increase the likelihood of survival of the domesticfirms in the highly competitive international export markets.
AcknowledgmentsThe authors thank two anonymous referees of this journal for their useful comments and helpfulsuggestions on the previous version of this paper. The authors would also like to thank Mark J.Roberts, and other conference participants of the 5th Annual Conference on ‘Economic Growthand Development’ held at ISI, New Delhi, India, on 16–18 December 2009, for their construc-tive criticisms and suggestions. Chandan Sharma is also thankful to Abhilaasha Misra, for herresearch assistance. Any errors or omissions are solely the authors’.
Notes1. See Foreign Trade Policy 2009 (27 August 2009 – 31 March 2014), Government of India,
Ministry of Commerce and Industry, for more details on India’s export targets and tradepolicy of export promotion (accessible at http://dgft.delhi.nic.in).
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2. See Krugman (1979) and Jovanovic and Lach (1991) for a detailed discussion on the vari-ous channels of productivity gains from exporting. In short, they argue that productivitygains from exporting are caused by: (i) learning and adoption of best production practiceand distribution methods, (ii) firms receiving valuable feedback from their internationalcustomers, suppliers and competitors, (iii) and knowledge spillovers.
3. The Prowess Database is an online database provided by the Centre for Monitoring IndianEconomy (CMIE). The database covers financial data for over 23,000 companies operatingin India. Most of the companies covered in the Database are listed on stock exchanges, andthe financial data includes all the information that operating companies are required todisclose in their annual reports. The accepted disclosure norms under the Indian CompaniesAct, 1956, makes it compulsory for companies to report all sources of income and expen-diture, which account for more than 1% of their turnover.
4. Specifically, we compute it as where K is the capital stock, I isdeflated gross investment by WPI, and δ is the rate of depreciation taken at 7%, consistentwith similar studies for India (Unel 2003 and Ghosh 2009) and t indicates year. The initialcapital stock equals the net book value of capital stock for 1994.
5. There are at least three advantages that the LP method has over the OP method in TFPcomputation. OP uses data on investment to proxy for unobserved productivity, while LPuses intermediate inputs as proxy. The use of intermediate inputs has at least three potentialadvantages. First, intermediate inputs will generally respond to the entire productivity term,while investment may respond only to the ‘news’ in the unobserved term. Second, interme-diate inputs provide a simpler link between the estimation strategy and the economictheory, primarily because intermediate inputs are not typically state variables. The thirdadvantage is mainly data-driven. OP is valid only for firms reporting non-zero investment,while using intermediate input proxies avoids the potential truncation of a large number offirms in industries with pronounced adjustment costs of capital (for detail discussion, seeLevinsohn and Petrin 2003).
6. It is well established in the related literature that R&D intensity is an important determinantof productivity and export performance of firms. The pioneering study of Griliches (1979)has shown in the ‘R&D Capital Stock Model’ that this factor has a direct effect on the perfor-mance of firms. Empirical evidence reported by Cuneo and Mairesse (1984), Lichtenbergand Siegel (1989) and Hall and Mairesse (1995) also provides strong support to Griliches’sview. To capture the R&D activities of firms, in this study, we consider the ratio of R&Dexpenditure to firm’s total sales. This variable is taken as a measure of R&D intensity offirms and it is expected to have a positive impact on firms’ productivity and export growth.
7. Import intensive firms receive technological transfer through importing material and otherinputs, which can potentially help the importing firms to enhance their productivity andexport performance (e.g. see Ben-David 1993; Sachs and Warner 1995). Therefore, on thisaccount, we include this variable in our TFP and export-intensity equations. The importintensity of firms is captured by the ratio of total import (raw material and finished goods)to value of total sales of firms.
8. It is argued that the size of the firm exerts an indirect effect on the performance of firms,as it conditions the impact of other factors on productivity (see Geroski 1998; Halkos andTzeremes 2007). Bearing this in mind, we accommodate the size of firms in the model byusing the log value of sales of firms. Theoretically, because of economies of scale, a largersize and increasing output should have a positive influence on the productivity of firms.Therefore, we expect positive sign of this variable.
9. We have also tested the impact of export intensity on productivity for the third lag, but it isnot found to be statistically significant and sizable across the industries. Keeping the spaceconstraint in mind this result is not reported here, however it is available on request. Theauthors would like to thank the anonymous referee for this suggestion.
10. It is noteworthy that the inclusion of the import-related variable may capture the effect ofinternationalization as exporters generally import their intermediate materials in order tokeep their products competitive both in terms of quality and costs. It is quite possible thatthe level of productivity is better explained by participation in the import market thanby the intensity of exports (as we have observed this fact in the previous section). There-fore, the import-related variable is not included in the model.
11. We also test two more lags of the exporting decision (Exportit–2 and Exportit–3), in an alter-native model. Similar to Roberts and Tybout (1997), we also do not find any statistically
K K It t t= − +−( )1 1δ
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significant result. This implies that after a two-year absence the re-entry cost is not signif-icantly different from that faced by a new exporter. This result is not reported in the paperbecause of space constraints, however, it is available from the authors on request.
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Table A1. Descriptive statistics on Indian manufacturing firms, 1994–2006.
