Productivity and Exporting Status of Manufacturing Firms: Evidence from Quantile Regressions. Mahmut Yasar * Emory University

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1 Productivity and Exporting Status of Manufacturing Firms: Evidence from Quantile Regressions Mahmut Yasar * Emory University Carl H. Nelson University of Illinois at Urbana-Champaign Roderick Rejesus Texas Tech University ABSTRACT Past literature has established the relationship between productivity and exporting. This paper provides further understanding about this issue by examining the productivity effects of export status at different points of the conditional output distribution and by investigating the productivity effects of firms with different exporting status (i.e. new exporters versus continuous exporters). Plant-level data of Turkish manufacturing firms are analyzed using quantile regression techniques. The empirical results indicate that the productivity effect of exporting is present at all points along the conditional output distribution, and this effect increases as one moves from the lower tail to the upper tail of the distribution. Exporting firms that continuously exported throughout the time-period have more pronounced productivity effects compared to firms in other categories (i.e. new exporting firms, exporting firms that exit, and exporting firms that switch exporting practices). These results have implications for firm behavior and for targeting policy prescriptions to augment manufacturing competitiveness. Keywords: Exporters; Manufacturing Firms; Productivity; Quantile Regressions JEL Classification: L11, L60 *Corresponding author: M. Yasar, Department of Economics, Emory University, 306C Rich Bldg., Atlanta, GA Phone No. (404) Fax No. (404) myasar@emory.edu. *Acknowledgements: The authors would like to thank Omer Gebizlioglu, Emine Kocberber, and Ilhami Mintemur at State Institute of Statistics in Turkey for allowing us access to the data for this study.

2 Productivity and Exporting Status of Manufacturing Firms: Evidence from Quantile Regressions Introduction Recent studies based on firm- or plant-level data have found that exporting firms have higher productivity than non-exporting firms. Bernard and Jensen (1995, 1998a, 1998b, 1999a, 1999b) have shown that this behavior holds for industrial firms in the United States (U.S.). Several authors have indicated the presence of this behavior in other countries (Bernard and Wagner (1997) and Wagner (2002) for the case of Germany; Aw et al. (1998) for the case of Taiwan and Korea; Clerides et al. (1998) for the case of Colombia, Mexico and Morocco; Girma et al. (2003, 2004) for the case of the U.K). These studies typically conclude that exporting plants have better technical productivity relative to non-exporting plants within the same industry, even after controlling for plant or firm observed characteristics. These studies, however, typically show the productivity effects of export status at the average firm level only. That is, most of these studies use least squares regression analysis which expresses the expected value of the dependent variable (production output) as a function of the independent variables of interest (export status). If there is significant firm heterogeneity, the average output effect of exports may not describe how the output of each type of firm is affected by exporting. This is especially important in a lower middle income country like Turkey, where significant inter-plant variation in capital intensity, productivity, and size is likely. For example, it is important to understand if there is an export effect for small plants as well as large plants because many small plants can make important contributions to economic growth. Further, least squares coefficient estimates are known to be strongly influenced by extreme observations. Thus, an estimated effect of exports on output could be caused by the largest firms in the sample if the data is significantly positively skewed. 1

3 A more robust estimator that can take heterogeneity of the dependent variable into account is quantile regression (Koenker and Basset, 1978). This involves the estimation of conditional quantiles, rather than estimation of coefficients at a single measure of central tendency. The approach enables the evaluation of the relative effects of export status and other important regressors, such as production inputs (i.e. labor and capital), at different points of the conditional output distribution. The main objective of this paper, therefore, is to determine the effects of export status and other regressors at different quantiles of the conditional production output distribution, and to learn more about these effects if they are found to vary across the output distribution. We are especially interested to test whether smaller firms who export exhibit a positive relationship between exporting and output. The results of this study provide further understanding about the productivity effects of exporting status because the procedures used here give a more complete picture about the effects of exporting status on productivity and about the elasticity of outputs with respect to various production inputs. Furthermore, our unique data set allows us to examine the productivity effects of different export status categories, not just the effects of exporters versus non-exporters. The remainder of the paper is organized as follows. The next section describes the theoretical basis of the relationship between exporting status and productivity. Section three discusses the plant-level data used in the study. The econometric framework is described in the fourth section, the results of the analysis are presented in the fifth section, and the last section provides some concluding comments. The Effect of Exporting Status on Productivity: Theoretical Justification The theory behind the effect of exporting status on firm/plant productivity has been described in the literature as learning-by-exporting (Lucas, 1988; Clerides et al. 1998). 2

4 Krugman (1979) and Jovanovic and Lach (1991) have modeled the productivity gains from exporting as being caused by: (1) firms learning and adopting international best practice production and distribution methods, (2) firms receiving feedback from international customer, suppliers, and competitors, and (3) other knowledge spillovers. In addition, various studies in the endogenous growth literature argue that exports enhance productivity through innovation (Grossman and Helpman, 1991 and Rivera-Batiz and Romer, 1991), technology transfer and adoption from leading nations (Barro and Sala-I-Martin, 1995; Parente and Prescott, 1994), and learning-by-doing gains (Lucas, 1988; Clerides et al., 1998). The innovation argument is where firms are forced to continually improve technology and product standards to compete in the international market. The technological and learning-by-doing gains arise because of the exposure of exporting firms to cutting-edge technology and managerial skills from their international counterparts. Economies of scale from operating in several international markets are also often cited as one other explanation for the learning-by-exporting hypothesis. The mechanism behind the type of learning-by-exporting effect described above can be further conceptualized using Rosenberg s (1982) learning-by-using hypothesis. Rosenberg (1982) defines learning-by-using as knowledge that can only be acquired after a product/process has been used. In the exporting context, this refers to learning acquired after being in the export market continuously over time. This differs from Arrow s (1962) classical learning-by-doing interpretation where the largest learning (and, for that reason, productivity increases) only takes a place as firms enter the export market. Arrow (1962) hypothesized that learning and productivity turn down afterwards since firms have learned the proverbial ropes of exporting. Hence, the general conceptual mechanism behind the learning-by-exporting studies above follow Rosenberg s (1982) intuitively appealing learning-by-using effect, where exporting firms 3

5 continually learn over time through their interaction with the international competitors and consumers (Muroyama, 2004). In this framework, knowledge is freely borrowed or exchanged and the knowledge spillover to exporting firms increases with more interaction with the international firms and consumers. The intuitive appeal of the learning-by-exporting hypothesis has resulted in several authors empirically testing this theory in several different countries. For example, studies by Kraay (1999), Castellani (2001), Bigsten et al. (2002), Girma et al (2003), Van Biesebroeck (2003) and Yasar and Rejesus (2005) found strong empirical support for the presence of learning-by-exporting. However, as we mentioned earlier, none of these studies have examined the relationship between exporting status and productivity at different percentiles of the conditional output distribution. This article contributes to the literature in this regard. Data This study uses unbalanced panel-data on manufacturing plants with more than 25 employees for the apparel 1, food, and textile industries from The data were collected by Turkey s State Institute of Statistics from the Annual Surveys of Manufacturing Industries, and the data are classified based on the International Standard Industrial Classification (ISIC). The data consists of plant-level information about: output, material inputs, energy inputs, labor inputs, capital inputs, investment levels, depreciation rates, exports, and several other plant characteristics (i.e. size). Summary statistics for these variables are reported in Table 1. The reported minimum and maximum values show that there is significant variation in all of the variables. Output (Y) is the value of aggregate output deflated by the corresponding price index. Changes in the stock of output are considered in the calculation of Y. As for the calculation of 1 This industry includes all wearing apparel, except for fur and leather. 4

6 material input (M), the expenditure on these inputs is used, considering the changes in stocks. The energy input (E) variable includes the value of electricity and fuel used by the plant. The quantity of labor input (L) is based on data about the total number of hours worked in production (i.e. the product of average hours worked times the number of employees in manufacturing). 2 We divide the nominal values of all the inputs above by the corresponding price deflators to find the constant dollar value quantities of inputs at 1987 prices. The calculation of the capital input (K) variable is based on information on gross investment levels 3 and depreciation rates. The process of calculating K is as follows. First, we compute an initial (t-1) capital benchmark by taking a three-year average of investment and dividing it by the depreciation rate (see Harper et al., 1989). Then, the aggregate K at time t is estimated by applying the perpetual inventory method (PIM) on the fixed assets: Kt = Kt 1(1 δ ) + It 1, where Kt is the capital stock in period t; δ is the depreciation rate of capital 4 ; and I t is the level of the investment during the period. We assume the following service lives for the fixed assets: 40 years for building and construction; 15 years for transportation equipment; and 15 years for machinery and equipment. Using the export variable data, we were able to group the Turkish manufacturing plants into five export status categories: 1) Non-exporters (ones that did not export at any point in the time period); 2) Exporters (those that continuously exported the entire time period); 3) Exiters (those that started the time period as exporters, then stopped exporting for the remainder of the sample period); 4) Switchers (those that altered exporting practices more than once during the 2 The labor input consists of administrative personnel, technical personnel, and unskilled workers. 3 The gross investment data used here includes domestic plus imported capital purchases (less sales), with maintenance investment added. Further, the gross investment data are deflated based on the capital price index. 4 We used Diewert and Lawrence s (1999) method of computing the depreciation rates. The depreciation rates corresponding to the assumed service lives of the fixed assets are as follows: 12.5 percent for transportation equipment; 12.5 percent for machinery and equipment; and 4.9 percent for building and construction. 5

7 time period); and 5) Entrants (non-exporters that began and continued to export during the time period). Dummy variables corresponding to the exporting categories were created, with nonexporters set as the base. Econometric Framework Traditional production function analysis is used to estimate the productivity effects of a firm s exporting status across different points of the conditional output distribution. Production function analysis allows for controlling the effects of observed plant-specific characteristics and enables inference about the productivity differences between exporters and non-exporters. Productivity differences can be inferred from the estimated production functions because the coefficient of an export status dummy variable gives the percentage difference between the productivity of exporters and non-exporters. We assume that the production function of Turkish manufacturing plants can be approximated by a Cobb-Douglas specification: 5 μ (1) ln y = α + β ln x + δ ( EX ) + ψ z + it 0 j jit im im k kit it j m k where i and t are plant and time subscripts, y is output, x j is the jth input in the production process (where j = K, L, M, E), EX is a dummy variable for export status ( EX = 1 if the plant m m falls in the specific export status category in the current year; EX = 0 otherwise), are a m z k vector of dummy variables representing plant size (small, medium, large) 6, year ( ), 5 We also tried a different specification of the production function where we interacted the export status dummies with a time trend to look at the differences in total factor productivity growth (instead of productivity level) across the five firm categories based on export status, and estimated our equation in a labor productivity form. The results we found are similar to the ones reported above (i.e. continuous exporters and entrants still have the highest productivity growth compared to the non exporters). 6 Plant size is defined as follows: (1) Small plants -- less than 50 employees, (2) medium plants -- between 50 and 100 employees, and (3) large plants employees or more. 6

8 region (i.e. 6 region variables) 7, and industry (apparel, food, textile) categories, and μ it is the residual. 8 The coefficients β j, δm and ψ k represent the parameters to be estimated. In particular, β j is the elasticity of output with respect to the respective j inputs. The coefficientsδ m, on the other hand, denote the productivity differences between plants in a particular exporting status relative to firms not in that status (i.e. exporter vs. non-exporter). For example, this coefficient can show whether continuous exporters (those who exported for the whole time-period) have higher unexplained contributions to output, than non-exporters (those who did not export for the whole time-period). Plant size is included in the specification to capture differences in the production technology across plants of different sizes. Year dummies are included in the model to capture macroeconomic shocks and changes in the institutional environment over time. Regional dummies are also included to correct for the exogenous disparities in the productivity differences across the regions. Industry dummies are included to account for production differences across the three industries in the pooled data. 9 If traditional least squares regression is used to estimate (1) and there is unobserved heterogeneity, then the estimated coefficients are not representative of the entire conditional output distribution (Mata and Machado (1996); Dimelis and Louri (2002)). To account for some of the heterogeneity in the sample, observed plant-level characteristics (i.e. regions, size, etc.) are explicitly included in the regression equation. However, plants may also have sources of 7 There are seven main geographic regions in Turkey. The regions are East Anatolia, South-East Anatolia, Central Anatolia, Black sea, Agean, Marmara, and Mediterranean. Since there were not enough observations for the East Anatolian region we combined it with the Southeast Anatolian region and used only six region dummies. 8 μ it can be interpreted as the Total Factor Productivity (TFP) based on Solow s model. This is the unexplained contribution to output. 9 One could also estimate the model by running separate regressions for each industry. However, due to the small sample sizes in the food and textile industries pooling the data and using industry dummy variables is more appropriate in this case. 7

9 heterogeneity that cannot easily be observed and accounted for. For example, in our case, unobserved characteristics like entrepreneurial ability and initial endowments are not taken into account in the data and these factors may cause unobserved heterogeneity. Furthermore, recent studies have shown that idiosyncratic shocks and uncertainty in technology affect plants differently even within the same industry (see Jovanovic (1982); Hopenhayn (1992); Ericsson and Pakes (1995); and Olley and Pakes (1996)). This unobserved heterogeneity may render the dependent variable in (1), and the error term ( μ ), to be independently, but not identically distributed across plants. When observations are not identically distributed least squares estimates will be inefficient, and if there are long tails, extreme observations will have significant influence on estimated coefficients. Quantile regression estimates are considered robust relative to least squares estimates. In contrast to the least squares estimator, the quantile regression estimates place less weight on outliers and are found to be robust to departures from normality. Quantile regression can be illustrated as follows (see Koenker and Basset (1978) and Buchinsky (1998)): 10 it ln y = x' β + u with Q (ln y / x ) = x' β (2) it it θ it θ it it it θ where ln y is the vector of log output, x is a vector of all the regressors in (1), β is the vector of parameters to be estimated, and u is a vector of residuals. Q θ (ln y / x ) denotes the θ th it it 10 For discussion and implementation of quantile regressions with longitudinal data please see Koenker (2004). In this paper, Koenker (2004) suggests that unobserved fixed effects can be controlled by including firm dummies in the regression. This can be interpreted as a firm specific location-shift effect. Since we have a large sample size with about 1332 firms, the approach of using firm-specific dummies is not practically and computationally implementable (i.e. convergence problems occur). However, we have also estimated our models using a fixed effect OLS model as a means to check the "robustness" of our results. We find similar mean results when we use the fixed effects OLS approach. In addition, we undertook another "robustness" check by using a semiparametric model that controls for simultaneity and selection bias. Again, we obtained mean results similar to the ones reported here. Since our goal in this paper is to examine the productivity effect of export status at different points of conditional output distribution, we do not explicitly report the mean results from the alternate estimation procedures. However, they are available upon request. 8

10 conditional quantile of ln y it given following problem: x it. The th θ regression quantile, 0 θ 1 < <, solves the y x β y x β ) n 1 1 Min n θ ln it ' it + (1 θ) ln it ' it = Min n ρθ (uθit β β it, :ln yit x' it β it, :ln yit < x' it β i= 1 (3) where (.) ρ θ is called the check function which can be defined as: θ uθ it if uθ it 0 ρ θ (u θit ) = ( θ 1)uθ it if uθ it < 0 (4) By changing θ continuously from zero to one, any quantile of the distribution of ln y it conditional on xit can be obtained. Changing θ from zero to one relaxes the assumption made in least squares regression where the parameter estimates are assumed to be the same at all points on the conditional distribution because of the i.i.d assumption. Linear programming methods can be used to minimize the sum of weighted absolute deviations and perform the estimation. It has also been shown that the function can be fit into a GMM form, where the consistency and asymptotic normality of β θ can be confirmed and the asymptotic covariance matrix can be found. In contrast to the least squares estimator, which provides information only about the effect of regressors at the conditional mean of the dependent variable, the results of quantile regression give parameter estimates at different quantiles. Thus, this technique provides information regarding the variation in the effect of regressors on the dependent variable at different quantiles. The coefficients can be interpreted as the partial derivative of the conditional quantile of y with respect to particular regressors, (ln y / x ) / x. The derivative is th interpreted as the marginal change in y at the θ conditional quantile due to marginal change in th a particular regressor. For example, in the case of equation (1), if a plant lies in the θ quantile Q θ it it 9

11 of output distribution, then the estimated output elasticity with respect to inputs (conditional on the set of covariates) equals to β θj, where j = K, L, M, E. To test for equality of the coefficient estimates at the various quantiles, estimation of the variance-covariance matrix is required. The test statistic is computed by using the variancecovariance matrix of the coefficients of the system of quantile regressions. The variancecovariance matrix is estimated using bootstrapping techniques. 11 The null hypothesis is that the jth coefficient at the θ quantile is statistically the same as the one in the θ th quantile th z y H : β = β ); and the alternative hypothesis is where the coefficients are not equal across ( 0 θzj θ yj quartiles ( H : β β ). This test allows us to examine whether the productivity effects of a θ zj θ yj exporting status and the estimated output elasticities vary significantly across the conditional output distribution. Results Before running our regressions we tested the normality of the output variable (y). We used the skewness and kurtosis tests of D Agostino et al. (1990) to statistically show (at the 1% level of significance) that the dependent variable is positively skewed and leptokurtic (skewness = 7.10 and kurtosis = 92.85). Thus, there are a large number of firms with relatively small output and the firms with above average output are significantly above average. Skewness and kurtosis tests for the natural logarithm of y also show statistically significant departures from normality; the p-values of the skewness and kurtosis tests are zero to three significant digits. These results suggest that the distribution of the dependent variable significantly departs from normality and justifies the use of quantile regression. 11 See Bassett and Koenker (1982) and Hendricks and Koenker (1991) for further information on the computation of the test statistic. For an excellent review of quantile regressions see Koenker and Hallock (2001) and Buchinsky (1998). 10

12 We used two alternative specifications of equation (1) to explore the productivity effects of export status ( δ m ) and the output elasticities with respect to the relevant inputs ( β j ). The first specification uses only one exporting status dummy variable (Export). The Export variable in this specification indicates whether a firm exported at some point in the time-period of the data set. Hence, in the nomenclature elucidated in the second section of this paper, all plants that are in categories (2) (4) are considered exporters and the Export dummy variable will take a value of one if the plant falls into categories (2) (4); zero, otherwise. The empirical results for this specification are seen in Table 2. The second specification of equation (1) uses dummy variables for the following four categories (as described in the data section of this paper): (1) Exporters, (2) Exiters, (3) Switchers, and (4) Entrants. For example, the dummy variable Entrants will equal one if a plant is an entrant; zero, otherwise. The omitted dummy variable in the regression is the non-exporter dummy. This specification allows for more detailed insights about the productivity effects of the different kinds of exporters. The empirical results for this specification are seen in Table 3. In Tables 2 and 3, the first column shows the parameter estimates for the ordinary least squares (OLS) regression. Before running the quantile regressions, we estimated the two specifications of equation (1) using OLS regression and applied the Jarque-Bera test to examine the normality of the conditional distribution of the residuals (Jarque and Bera, 1980). The hypothesis of normality is rejected at the 0.01 significance level in both specifications. This finding further supports the use quantile regression as a robust alternative to least squares. The OLS estimates in Table 2 suggest that all the parameters have positive signs and are statistically significant at 0.01 significance level. The parameter associated with the Export dummy provides an estimate of the conditional difference between the productivity of exporters 11

13 and non-exporters at the sample mean, since the omitted category is the non-exporters. The Export dummy coefficient is significant and indicates that manufacturing plants that exported at some point in are around 19% more productive than plants that did not export at all during the period, conditional on the region, size and year dummies. 12 In Tables 2 and 3, the third, fourth, fifth, sixth, and seventh columns presents the results of the quantile regression at the following quantiles: 0.10, 0.25, 0.50, 0.75 and In Table 2, the quantile regression estimates indicate that there are significant differences in the parameter estimates across the five quantiles. The coefficient associated with the Export dummy (δ ) varies significantly from 9% to 21% as we move from the lowest quantile to the highest quantile. This provides important evidence that the positive export productivity effect is present across the entire conditional output distribution. Smaller exporting plants at the lower tail of the distribution exhibit a positive export productivity effect. The positive shift of all the quantiles means that the exporter output distribution first order stochastic dominates the non-exporter output distribution. And the larger positive shifts at the higher quantiles means that the exporter output distribution is more positively skewed than the non-exporter output distribution. This means that the productivity enhancement of exporters is stronger and more significant for the larger firms. The estimated output elasticities with respect to capital and energy inputs also increase as one moves from the lowest quantile to the highest quantile (Table 2). A one percent increase in capital input would result in a proportionately higher output effect at the upper tails of the conditional output distribution than in the lower tails of the distribution. This indicates that the plants with higher production output level are more responsive to these variables. The output elasticity for labor, on the other hand, is relatively stable across quantiles, indicating a constant 12 Note that, in the interest of space, the coefficients for the region, year, and industry dummies are not reported in Tables 2 and 3. Results are available from the authors upon request. 12

14 elasticity of output with respect to labor at all points of the conditional output distribution. Lastly, the output elasticity with respect to material input decreases as one moves from the lowest quantile to the highest quantile. Thus, material inputs have a lower output effect at the upper tail of the conditional output distribution as compared to the lower tail of the distribution, indicating that the material inputs contribute less to the production output at the upper tail of the conditional output size distribution. We include size dummies in the production function to capture some differences in the production technology by the scale of plants. In Table 2, the coefficient associated with the plant size dummies increases as one moves from the lowest quantile to the highest quantile of the conditional output distribution. This means that firms with large and/or medium plant size have higher productivity effects (relative to firms with small plant size) at the upper tails of the conditional output distribution. The productivity effects of larger firms become more pronounced at the upper tails of the conditional output distribution. Estimating the second specification of the production function allows us to see the magnitudes of the productivity effects for more specific export status categories. The estimation results for the second specification are presented in Table 3. The OLS estimates reveal that among the four export status categories, the continuous exporters (Exporters) have the largest productivity difference over non-exporters; followed by Entrants, Exiters, and Switchers. Based on the OLS estimates firms that are continuous exporters are around 30% more productive than non-exporters, while firms that alter exporting practices are only 17% more productive than nonexporters. Note, however, that the magnitude of the productivity difference of Exiters and Switchers are very similar at 17%. The productivity of Entrants, on the other hand, is about 23%. 13

15 In most cases, the quantile regression results also follow the ranking of the productivity differences of the export status dummy variables estimated with OLS (See Table 3). The continuous exporters (Exporters) always have the largest productivity difference as compared to the other exporting categories. The continuous exporters productivity difference ranges from 16% to 30% as one moves from the lowest quantile to the highest quantile. This pattern of having higher productivity differences as one moves to the upper tail of the conditional output distributions is also evident in the other export categories. This suggests that the productivity impact of exporting is lower at the lower end of the conditional output distribution and becomes larger as you move up to the upper tail of the conditional output distribution. The largest potential productivity returns for the practice of exporting may be felt at higher output levels. But, unlike least squares evidence, these results show that even firms at the lower tail of the conditional output distribution exhibit a positive export-productivity enhancement. The behavioral patterns of the estimated output elasticities and the coefficients associated with plant size dummies in the second model are very similar to the ones estimated in the first model. The output elasticities associated with capital and energy tend to be higher at the higher quantiles, while the output elasticity associated with the material inputs tend to be lower at the higher quantiles. The output elasticity with respect to labor is relatively stable across quantiles. Larger and medium plant sizes still have higher productivity differences at higher quantiles. In the interest of space, detailed results of the hypothesis tests that evaluate the statistical significance of the difference of parameter estimates at all quantiles and between pairs of quantiles are presented in Appendix Tables 1 and 2. Based on these tables, the null hypothesis that the coefficients are equal across and between pairs of quantiles is rejected. This indicates that there are statistically significant differences among the estimated quantile regression 14

16 parameters. This holds for all of the regressors used in both model specifications. The coefficients suggest considerable variation in all of the right hand-side variables at the different points in the conditional output distribution. Results of the hypothesis tests confirm the presence of unobserved heterogeneity and validate the use of quantile regression techniques. To investigate the sensitivity of the behavioral patterns observed in Tables 2 and 3, additional quantile regression runs were undertaken at the following alternative quantiles: 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80 and The estimated coefficients at these quantiles are graphically presented in Figures 1 and 2. These plots indicate that the behavioral patterns observed in Tables 2 and 3 are robust to changes in the quantiles. For example, Figure 1 still shows that the productivity effect of the Export dummy variable increases as one moves up to higher quantiles. Conclusions There have been recent studies that establish the existence of productivity effects of exporting practices. However, none of these studies examined the productivity effects at different points of the conditional output distribution and none have carefully investigated the productivity effects of different exporting status categories (i.e. new exporters versus exiting exporters). This paper provides further insights about the productivity and export status relationship by more carefully exploring the productivity effects at different points of the conditional output distribution and for different export status categories. Turkish manufacturing firm data was analyzed using quantile regression techniques. This approach allows for unobserved heterogeneity and enables determination of the productivity effects at different points of the conditional output distribution. 15

17 The empirical results support the notion that the productivity effect of exporting increases as one move from the lower tail to the upper tail of the conditional output distribution. And it provides clear evidence that the productivity effect of exporting is present for small plants who export. We also found that exporting firms that continuously exported have more pronounced productivity effects, as compared to other exporting firms in other categories (i.e. new exporting firms, exporting firms that exit, and exporting firms that switch exporting practices). The quantile regression results also showed that, in general, there is significant variation in the output elasticities at different points in the conditional output distribution. These results further support the idea that, when data sets have significant unobserved heterogeneity, substantial informational gains can be achieved by using techniques that allow a thorough analysis at different points of the conditional distribution. This paper not only established the relationship between productivity and export status, but also provided insights about its relative variation along the conditional output distribution. The results of the study also have implications for manufacturing firm behavior. Given that the productivity effects of exporting tend to be more pronounced at the upper tail, Turkish exporting firms with lower production volume may want to take advantage of this scale effect by expanding output. On the policy side, the differential productivity effects of exporting firms with higher production volume and continuously exporting firms should be taken into consideration in any policy prescription that aims to improve the competitiveness and productivity of export manufacturing firms. Government policies to improve manufacturing productivity should probably target lower volume firms that have entered the export market. Supporting these newly established manufacturing firms may allow them to stabilize export practices, survive international competition, and continually be involved in the export market. 16

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20 Grossman, G., Helpman, E. (1991). Innovation and Growth in the Global Economy. MIT Press, Cambridge. Harper, M., E. Berndt, and D. Wood (1989). Rates of Return and Capital Aggregation Using Alternative Rental Prices, In D.W. Jorgenson and R. Landau, (eds.), Technology and Capital Formation, Cambridge, MA: The MIT Press, pp Hendricks, W. and R. Koenker (1991). Hierarchial Spline Models for Conditional Quantiles and the Demand for Electricity. Journal of American Statistical Association 87: Hopenhayn, H. (1992). Entry, Exit, and Firm Dynamics in Long-Run Equilibrium. Econometrica 60: Jarque, C.M. and A.K. Bera (1980). Efficient Tests for Normality, Heteroskedasticity, and Serial Independence of Regression Residuals. Economics Letters 6: Jovanovic, B. (1982). Selection and the Evaluation of Industry. Econometrica 50: Jovanovic, B. and S. Lach (1991). The Diffusion of Technology and Inequality among Nations, NBER Working Paper, Cambridge, MA. Koenker, R. and G. Basett (1978). Regression Quantiles. Econometrics 46: Koenker, R. and K. F. Hallock (2001). Quantile Regression. Journal of Economic Perspectives 15(4), Koenker, R. (2004). Quantile Regression for Longitidinal Data. Working Paper, University of Illinois at Urbana Champaign. Kraay, A. (1999) Exports and economic performance: Evidence from a panel of Chinese enterprises, mimeo, World Bank. Krugman, P. (1979). A Model of Innovation, Technology Transfer, and the World Distribution of Income, Journal of Political Economy 87:

21 Lucas, R.E. (1988). On the Mechanics of Economic Development Planning, Journal of Monetary Economics 22: Mata, J. and J.A.F. Machado (1996). Firm Start-Up Size: A conditional Quantile Approach European Economic Review 40: Mukoyama, T. (2004). Rosenberg s `Learning by Using and Technology Diffusion, Working Paper, Concordia University, Montreal, Canada. Olley, G.S. and A. Pakes (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. Econometrica. 64(6): Parente, S., Prescott, E. (1994). Barriers to technology adaptation and development Journal of Political Economy 102: Rivera-Batiz, L. A., Romer, P. (1991) Economic integration and endogenous growth Quarterly Journal of Economics 106: Rosenberg, N. (1982). Inside the Black Box: Technology and Economics (Cambridge U.P, Cambridge, UK) Solow, R. (1957). Technical Change and the Aggregate Production Function. Review of Economics and Statistics 39(3): Van Biesebrock, J. (2003) Exporting raises productivity in Sub-Saharan African manufacturing plants, NBER Working Paper. Wagner, J. (2002). The Causal Effects of Exports on Firm Size and Labor Productivity: First Evidence from a Matching Approach. Economics Letters 77(2): Yasar, M. and R.M. Rejesus. (2005) Exporting Status and Firm Performance: Evidence from a Matched Sample. Economics Letters 88(3),

22 Table 1. Descriptive statistics for relevant variables (Constant Value Quantities at 1987 Prices, in 000 Turkish Liras) A. Continuous variables Mean Standard deviation Minimum Maximum Output (Y) Capital (K) Energy (E) Labor (L) Material (M) B. Dummy variables Percentage of plants (%) Export Non-exporters Exporters 9.57 Entrants Exiters Switchers Small Medium Large Note: (1) Total number of observations: There are 8806 observations in the data set, but after cleaning the data we end up with 6055 observations. (2) The summary statistics for industry, year, and region dummy variables are not reported here in the interest of space. It is available from the authors upon request. 21

23 Table 2: Estimation Results of the Production Function using the First Specification Independent OLS Quantile regression estimates variables estimates Material (M) (0.005)* (0.011)* (0.008)* (0.009)* (0.012)* (0.017)* Labor (L) (0.013)* (0.016)* (0.013)* (0.016)* (0.023)* (0.039)* Capital (K) (0.005)* (0.005)* (0.004)* (0.004)* (0.005)* (0.009)* Energy (E) (0.008)* (0.008)* (0.006)* (0.006)* (0.009)* (0.018)* Export (0.016)* (0.016)* (0.012)* (0.013)* (0.017)* (0.030)* Medium (0.021)* (0.023) (0.019)** (0.019)* (0.025)* (0.040)* Large (0.031)* (0.033) (0.024)* (0.032)* (0.041)* (0.069)* Intercept (0.073)* (0.080)* (0.053)* (0.072)* (0.109)* (0.159)* Number of observations = 6055 Notes: (1) *Significant at the 1% level. **Significant at the 5 percent level. (2) The P-value for Jarque-Bera normality test is <0.01, which indicates that errors are not normally distributed. (3) The null hypothesis that the coefficients above are equal across and between pairs of quantiles is rejected (p-value 0.01; See Appendix Table 1) (4) The standard errors in parenthesis for quantile regressions are bootstrapped with 500 repetitions. (5) The regression runs in this table includes dummy variables that control for region, year, and industry characteristics. However, they are not reported here in the interest of space. They are available from the authors upon request. 22

24 Table 3: Estimation Results of the Production Function using the Second Specification Independent OLS Quantile regression estimates variables estimates Material (M) (0.005)* (0.011)* (0.009)* (0.009)* (0.012)* (0.016)* Labor (L) (0.013)* (0.018)* (0.013)* (0.016)* (0.023)* (0.053)* Capital (K) (0.005)* (0.004)* (0.004)* (0.004)* (0.005)* (0.008)* Energy (E) (0.007)* (0.008)* (0.006)* (0.007)* (0.009)* (0.016)* Exporters (0.025)* (0.023)* (0.018)* (0.022)* (0.024)* (0.040)* Entrants (0.021)* (0.020)* (0.015)* (0.020)* (0.023)* (0.041)* Exiters (0.021)* (0.022)* (0.015)* (0.017)* (0.025)* (0.036)* Switchers (0.022)* (0.023)* (0.018)** (0.019)* (0.027)* (0.048)* Medium (0.021)* (0.024) (0.017)* (0.018)* (0.030)* (0.038)* Large (0.030)* (0.037) (0.023)* (0.030)* (0.042)* (0.071)* Intercept (0.073)* (0.077)* (0.051)* (0.071)* (0.109)* (0.165)* Number of observations = 6055 Notes: (1) *Significant at the 1% level. **Significant at the 5 percent level. (2) The P-value for Jarque-Bera normality test is <0.01, which indicates that errors are not normally distributed. (3) The null hypothesis that the coefficients above are equal across and between pairs of quantiles is rejected (p-value < 0.01; See Appendix Table 2) (3) The standard errors in parenthesis for quantile regressions are bootstrapped with 500 repetitions. 23

25 (4) The regression runs in this table includes dummy variables that control for region, year, and industry characteristics. However, they are not reported here in the interest of space. They are available from the authors upon request. 24

26 0.90 Figure 1: Coefficients on Inputs and Export Dummy Coefficient Material Labor Capital Energy Export Dummy Quantile 0.35 Figure 2: Coefficients on Export History Dummies 0.30 Continuous Exporters Entrants 0.25 Exiters Switchers Coefficient Quantile 25

27 APPENDIX 26

28 Appendix Table 1: Test for Coefficient Equality between Pair-wise Quntiles and across all Quantiles for the First Model Specification Quantiles being P-values of the independent variables tested M L K E Export Medium Large A. Pair-wise: 0.10 vs <0.01 <0.01 <0.01 < < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 B. Joint test for all quantiles <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Notes: (1) The null hypothesis is that the coefficients are equal between pairwise quantiles (A.) and/or the coefficients are equal across all quantiles (B.). The test statistic is computed by using the variance-covariance matrix of the coefficients of system of quantile regressions. The p-values of F-tests estimated from the system of quantile regressions are reported in the table. If the p-value is less than the level of significance, we reject the null hypothesis of equality. (2) The regression runs in this table includes dummy variables that control for region, year, and industry characteristics. However, they are not reported here in the interest of space. They are available from the authors upon request. 27

29 Appendix Table 2: Test for Coefficient Equality between Pair-wise Quntiles and across all Quantiles for the Second Model Specification Quantiles P-values of the independent variables being tested M L K E Exporters Entrants Exiters Switchers Medium Large A. Pair-wise: 0.10 vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 < vs <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 B. Joint test for all quantiles <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 Notes: (1) The null hypothesis is that the coefficients are equal between pairwise quantiles (A.) and/or the coefficients are equal across all quantiles (B.). The test statistic is computed by using the variance-covariance matrix of the coefficients of system of quantile regressions. The p-values of F-tests estimated from the system of quantile regressions are reported in the table. If the p- value is less than the level of significance, we reject the null hypothesis of equality. (2) The regression runs in this table includes dummy variables that control for region, year, and industry characteristics. However, they are not reported here in the interest of space. They are available from the authors upon request. 28