Count Data Models in the Analysis of Firm Export Diversification

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1 Count Data Models in the Analysis of Firm Export Diversification Yevgeniya Shevtsova February 2017 Abstract The paper explores the impact of firm specific characteristics on its choice of a number of export markets. The main contribution of the current study is the use of the count data models to address the discrete nature of the dependent variable. The results confirm that traditionally employed linear models significantly underestimate the impact of productivity on the number of potential export destinations chosen by the firm and overall produce biased results. JEL code: C2, F13, F23, L2 Keywords: heteregeneous firms, exports, counts data models, hurdle model 1 Introduction Many empirical studies have documented a superior performance in exporting firms. Papers by Clerides et al. (1998), Melitz (2003) and Bernard et al. (2003) provided formal theoretical frameworks to show that more productive firms self-select into export markets, giving rise to the so-called self-selection hypothesis. The reasons for the better performance of exporting firms can be clearly identified. Entrance and successful operation in export markets depend upon the ability of the firm to bear sunk-entry export costs, including the costs of marketing, distribution, establishing foreign networks, adapting domestic products to the tastes of foreign customers and on the firm s ability to sustain competition with foreign rivals. This implies that only firms whose productivity is above a certain threshold will manage to enter and successfully operate in international markets. The self-selection hypothesis is supported by the substantial factual evidence of 1

2 the differences in characteristics between exporting and non-exporting firms documented in a large number of empirical studies At the same time, most of the empirical work on self-selection effect focuses on the choice of a firm to enter export markets, while other aspects of firm internalization as the choice of the number of export destinations per se has been explored only in a limited number of studies (Muuls and Pisu, 2009; Cestellani et al., 2010; Andersson et al., 2008 and Eliasson et al., 2009). The issue has emerged only recently and, while theoretical studies (Lawless, 2009) suggest that the number of export destinations (regions/countries) is positively related to firm s productivity, the amount of empirical evidence is still relatively scarce. The available empirical findings, based mostly on aggregate descriptive statistics and linear regression analysis, suggest that only a small share of firms serve a large number of markets, while the majority of exporters concentrate on a small number of export destinations. More recently, Barba-Navaretti et al. (2010), using a linear regression framework, found that superior firm characteristics positively affect the number of export markets served by the firm. Moreover, Curzi and Olper (2010) showed that the number of export destinations is positively related to the quality of the goods produced by the firm. However, most of the empirical literature that addresses diversification aspect of self-selection, employs linear regression models that might lead to biased results, given the nature of the dependent variable. At the same time, the link between firms heterogeneity and the choice of the number of export destinations can be better addressed using count data models that account for the discrete non-negative nature of the data (Ferrante and Novelli, 2012). This paper fills in the gap and provides new empirical evidence on the impact of firm characteristics on the choice of the number of export markets using count data regression models to account for the discrete nature of the dependent variable. 2 Methodology The main purpose of this section is to estimate the causal effect of firm-specific characteristics on its choice of the number of export destinations by addressing the problem of self-selection along the country-extensive margin. The abundant empirical evidence of firms self-selection into exporting points out that inter-firm variations in export participation crucially depend on the underlying firms characteristics, including their productivity, as well as sunk costs of entry into 2

3 international markets (Greenaway and Kneller, 2007). Theoretical trade models with asymmetric countries and sunk costs advocate that self-selection is market specific, i.e. firms with low productivity will serve only limited number of markets with low productivity thresholds, while highly productive firms will be able to serve larger number of markets (Andersson et. al., 2008). Differences in the market entry thresholds arise due to many reasons, including differences in market size, intensity of competition and transport costs. Furthermore, sunk costs related to the search and negotiation with potential customers; legal and marketing expenses, contract translations and alike tend to be market-specific and depend on the firm familiarity with a specific foreign market (Johansson and Westin, 1994; Andersson, 2007; Andersson et al., 2008). Such variations in sunk entry costs imply that productivity differences between non-exporting and exporting firms may be higher when the latter target a wider range of export destinations and export products. Indeed, studies by Muuls and Pisu (2009), Castellani et al. (2010) and Andersson et al. (2008) found a positive link between firm productivity and other characteristics and the geographic and product diversification of its exporting activity. At the same time, relatively little research so far has explored the impact of the ex-ante firm characteristics on its choice of the number of export markets 1. This paper explores this aspect of the productivity-exporting nexus by using an alternative micro-econometric framework. [Table 1 about here] Overall, Ukrainian exporters exhibit significant heterogeneity in the number of export destinations. On average, around 50% of Ukrainian exporters export to only one destination, while only around 15% of exporters target more than 5 export markets (Table 1). The number of exporting firms declines with the number of export markets (Figure 1). These patterns in Ukrainian export data are in line with the empirical evidence on exports concentration along the country-extensive margin 2 reported by Eaton at el. (2004) for France, Muuls and Pisu (2009) for Belgium, and Bernard et al. (2007) for the US and Castellani et al. (2010) for Italy. [Figure 1 about here] 1 Ferrante and Novelli (2012), a notable exception, address the question using cross-sectional data on Italian manufacturing firms. 2 Mayer and Ottaviano (2007) refer to the number of countries with which firm trades as a countryextensive margin that can also be considered as the measure of geographical firm diversification. 3

4 To explore the impact of firm characteristics on its ability to penetrate larger number of export markets I estimate the model of the following form: Dest it = ϕ(ln T F P it 1, ln Age it 1, ln Emp it 1, ln Emp 2 it 1, Industry i, D i, Y ear t ) where Dest it is a discrete non-negative variable indicating the number of firm export destinations; T F P it is the estimate of the firm Total Factor Productivity calculated using the De Loecker (2011) version of the Olley Packes (1996) 3 estimator that accounts for differences in the firm export status; Age it is the age of the firm; Intang it is coded 1 if the firm has nonzero intangible assets (the average annual percentage of firms possessing positive intangible assets equals 14.8%); Emp it represents firm size measured by the average annual number of enlisted employees; Industry i, D i and Y ear t are dummy variables indicating each of the twelve NACE Rev.1 industries, region and year respectively. The outcome variable - the number of export destinations is a non-negative integer valued count variable, characterised by a skewed distribution and a high proportion of zeros. In fact, around 80% of the firms do not export in any given year. The conditional mean and variance are equal to 0.73 and 2.19 respectively, signalling the presence of significant over-dispersion and excess zeros in the data. A possible explanation of over-dispersion and excess zeros is unobserved firm heterogeneity not caught by the explanatory variables (Mullahy, 1997). An alternative explanation suggests that excess zeros emerge because export participation and export diversification decisions (i.e. the choice of the number of export destinations) are generated by the two separate probability functions (Cameron and Trivedi, 2013). In order to account for the unobserved heterogeneity and panel structure of the data, equation (1) is estimated using the random effects negative binomial model (henceforth, RE Negbin) that allows for over-dispersion by assuming particular probability distribution (the gamma distribution) of the individual error terms (Jones, 2007). Indeed, a simple plot of any firm characteristic (Figure 2 uses total factor productivity) against the number of export destinations reveals that the relation between them is not linear in nature. Hence, the results of analysis could be improved by applying a count data model as an alternative estimator. [Figure 2 about here] 3 Please see Appendix for details of the TFP estimating algorithm. (1) 4

5 I also provide OLS estimates of the equation (1) for comparison. Finally, in order to account for the potential differences in the export participation and export diversification decision equation (1) is also estimated using a hurdle model that relaxes the assumption of the same stochastic process for the participation and diversification decisions. In particular, I use a hurdle model with logit binary choice model in the first stage and zero-truncated negative binomial model in the second stage (Cameron and Trivedi, 2013). 3 Data This paper uses the data submitted to the Ukrainian Offi ce of National Statistics (Derzhkomstat) that groups consolidated annual accounts data on the census of Ukrainian manufacturing and service firms operating in Ukraine between 2000 and All firms are uniquely defined by their VAT (EDRPOU) number and divided into sectors according to the Ukrainian Offi ce of National Statistics nomenclature, which is comparable to the NACE Rev.1 classification. The data contain information on firm-specific characteristics, such as employment (measured as the annual average number of registered employees), output, sales, tangible and intangible assets, material costs and other types of intermediate expenditures (including R&D and innovation expenditure), and gross capital investment. The dataset is merged with the Ukrainian Customs offi ce data that contains information on the monetary value of firm-level exports by country and year. All variables were deflated using two-digit subsector price deflators available from the Ukrainian Offi ce of National Statistics. 5 I limit the study to cover the firms in the manufacturing sectors (NACE Rev ) with at least one employee. The final dataset used for statistical analysis comprises an unbalanced panel with an average of 35,816 firms per year and 237,577 observations covering the period , with information showing entry and exit from export markets. Table 2 shows that the average annual percentage of exporting firms in the sample is around 12%. [Table 2 about here] 4 The data is restricted and not available for public use. The unit of observation is referred to as firm in the text. 5 Ukrainian State Statistic Committee website: 5

6 Table 3 contains summary statistics for the basic variables - output, capital, employment, and material costs - for selected years. The statistics show increasing output and material expenditure and declining average size and capital, caused primarily by productivity growth and increasing number of small and medium market entrants during [Table 3 about here] The employment figures in Table 3 might cause a concern that large firms might be over-represented in the sample. However, according to the Enterprise Survey data collected by the World Bank Group 6 Ukrainian firms are among the largest in the Eastern European and Central Asian (ECA) region in terms of permanent and temporary workforce. The survey reports that Ukrainian firms have the sixth largest permanent workforce in the ECA region. The average firm in Ukraine employs 56.8 permanent workers, while average ECA firm employs only 44.0 workers, and an average EU-10 firm only 37.3 workers. Moreover, firms in manufacturing are more than twice as large as those in retail and other services. 4 Econometric Results The results of the estimation of the OLS and RE Negbin, presented in Table 4, are mostly in line with previous studies on self-selection. [Table 4 about here] The results confirm that such firm characteristics as productvity, age, size and possession of intangible assets increase firm s propensity to enter a larger number of export markets. At the same time the results clearly indicate that OLS significantly underestimates the impact of the TFP on the choice of the number of export markets by the firm. According to the results of the OLS regression a 1% increase in the firm level TFP growth increases the number of export markets served by the firm by approximately 6%, which yields a confidence interval of [0.04; 0.06]. At the same time, the results of the RE Negbin reveal that one unit increase in the TFP growth raises 6 6

7 the number of export markets served by more than 10% with a confidence interval of [0.38; 0.43]. The confdence intervals of this estimates do not overlap, indicating statistically significant difference between them. The possession of intangibles increases firm export diversification by approximately 39% according to the results of the linear regression model and only by about 5% according to the results of the marginal effects of the RE Negbin regression. Finally, a one unit increase in the size of the firm increases export divesification by 26% according to the results of the OLS estimator and only by 20% - according to the results of the count data model. The age of the firm has no statistically significant effect on the firm propensity to serve a lager number of export markets in the OLS framework. At the same time, when the discrete nature of the dependent variable is taken into account, a one unit increase in the age of the firm raises export diversification by 1%. Finally, in order to address potential differences in the export participation and export diversification decision I employ the hurdle model estimator with the logit binary choice model in the first stage and zero-truncated negative binomial model in the second stage. [Table 5 about here] The results of the hurdle model specification presented in Table 5 show that most of the explanatory variables are important determinants of both export participation and export diversification decisions. However, firm heterogeneity seems to play more important role in the second stage of the model. The age of the firm is a significant determinant of export participation and diversification, with a moderate effect. TFP seems to have a significantly higher impact on the firm s ability to diversify its exports than on its propensity to export per se. On average, a 1% increase in the TFP growth raises the firm s probability of exporting by 2%. However, in the case of incumbent exporters, a 1% increase in the TFP growth increases the average number of export markets served by the firm by 14%. The possession of intangible assets similarly has a higher impact on the probability of export divercification. At the first stage, the possession of intangible assets increases the probability of exporting by 3%, while at the second stage, it raises firm s propensity to diversify accross markets by 20%. Finally, the size of the firm has a significant impact on export diversification as a 1% increase in size brings about a 47% rise in the number of target export markets. 7

8 The results indicate that linear regression models may result in biased estimates and significanlty underestimate the effect of firm-level productivity on export diversification. Hence, overreliance on the linear gergession models, prevalent in the modern empirical trade literature should be taken with caution and alternative estimation methods should be considered to ensure the robustness of the results. 5 Concluding Remarks This paper explores the impact of firm characteristics on the number of potential export destinations using micro-level data on Ukrainian manufacturing firms over the period In order to account for the discrete non-negative nature of the outcome variable, the issue is addressed with the use of the count data models. The results of the estimation are compared with the ones produced by the standard linear regression estimator. The results of the analysis reveal that linear regression model produces biased results, significantly underestimating the effect of firm-level productivity its ability to penetrate a larger number of export markets. At the same time, the impact of such firm characteristics as size and possession of intangible assets is significantly overestimated. Overall, the results of the current study reveal that overreliance on the linear gergession models, prevalent in the modern empirical trade literature should be taken with caution and alternative estimation methods should be considered to ensure the robustness of the empirical findings. References [1] Bernard, A. B., J. B. Jensen (1995). Exporters, Jobs and Wages in US Manufacturing Brookings Paper on Economic Activity. Microeconomics (1): [2] Bernard, A. B., J. B. Jensen (1999) Exceptional exporter performance: cause, effect or both? Journal of International Economics, 47(1), [3] Bernard, A. B., Wagner, J. (1997) Exports and Success in German Manufacturing. Review of World Economics, 133(1), [4] Blundell, R.W., Bond, S.R., Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87,

9 [5] Blundell, R., Bond, S. (2000) GMM estimation with persistent panel data: An application to production functions. Econometric Reviews, 19(3), [6] Clerides, S. K., S. Lach, J. R. Tybout (1998) Is learning-by-exporting important? Micro-dynamic evidence for Colombia, Mexico, and Morocco. Quarterly journal of Economics 113, [7] De Loecker, J. (2011). Product Differentiation, Multiproduct Firms and Trade Liberalization on Productivity. Econometrica, 79(5), [8] De Loecker, J. (2013). Detecting Learning by Exporting. American Economic Journal: Microeconomics, 5(3), [9] Eaton, J., Kortum, S. Kramarz, F. (2011). An anatomy of international trade: evidence from French firms, Econometrica, vol. 79(5), pp [10] Eliasson, K., Hansson, P., Lindvert, M. (2009). Do firms learn by exporting or learn to export? Evidence from small and medium-sized enterprises (SMEs) in Swedish manufacturing (Working Paper No. 15). Swedish Business School, Orebro University. [11] Estrin, S., Rosevear, A. (1999) Enterprise Performance and Corporate Governance in Ukraine. Journal of Comparative Economics. 27(3), pp [12] Girma, S., Greenaway, D., Kneller, R. (2004) Does Exporting Increase Productivity? A Microeconomic Analysis of Matched Firms. Review of International Economics, 12(5), [13] Greenaway, D., Kneller, R. (2007) Firm Heterogeneity, Exporting and Foreign Direct Investment. Economic Journal, 117(517), [14] Heckman, J., Ichimura, H., Todd, P. (1997). Matching as an econometric evaluation estimator. Review of Economic Studies, 65, [15] Isgut, A. (2001). What s different about exporters? Evidence from Colombian manufacturing. Journal of Development Studies, 37(5), [16] Isgut, A., Fernandes, A. (2007) Learning-by-Exporting Effects: Are They for Real?, MPRA Paper 3121, University Library of Munich, Germany. 9

10 [17] Klette, T., Griliches, Z. (1996). The Inconsistency of Common Scale Estimators When Output Prices are Unobserved and Endogenous. Journal of Applied Econometrics, 114, [18] Melitz, M. J. (2003) The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity. Econometrica, 71(6), [19] Olley, S., Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6), [20] Pisu, M. (2008) Export Destinations and Learning-by-exporting: Evidence from Belgium. National Bank of Belgium Working Paper, (140). [21] Pisu, M., The International Study Group on Exports and Productivity (2008). Exports and productivity comparable evidence for 14 countries, Working Paper Research 128, National Bank of Belgium. 10

11 6 Appendix A. Productivity estimation algorithm The estimation algorithm is based on the Olley Pakes (1996) model, adapted following Van Biesebroeck (2005) and De Loecker (2007, 2011). As in the original model I assume that productivity follows an exogenous first-order Markov process ω it = E (ω it ω it 1 ) + ξ it,where productivity ω it at time t represents expected productivity, given a firm s information set I it that includes past productivity ω it 1 and a productivity shock ξ it. Every period a firm has to make a decision to stay or leave the market and, conditional on staying, it has to decide on the allocation of labour (l), materials (m) and investment (i). The choice of investment determines the stock of capital in the beginning of each period and features in the law of capital accumulation given by k it = (1 δ) k it 1 + i it 1 with i it 1 being the log of investment at time t 1. The information set I it defines a firm s perception of the distribution of the future market structure and impacts its exit and investment decision that will, in turn, generate a distribution for the future market structure. To take into account the fact that exporting firms face different market structures and factor prices when taking their exit and investment decisions, I modify the investment function to include export status: the coeffi cients of the polynomial h (.) in (2) now differ for the exporting firms by the subscript ex. The equilibrium investment function can now be presented as follows: i it = i ex,t (ω it, k it ) ω it = h ex,t (i it, k it ), (2) where ex is a dummy indicating firm export status. 7 Now, using the revenue production function : r it = β l l it + β k k it + β m m it + β y y gt + δ t D t + δ g D g + ω it + ξ it + u it (3) we can plug (2) into (3) to obtain: r it = β l l it + β m m it + β y y gt + δ t D t + δ g D g + g ex,t (k it, i it ) + u it, (4) 7 The possibility of accomodating various types of exporters characteristics, such as export experience or the share of exports in total sales in the investment function is discussed in De Loecker (2013). 11

12 ( ) where g ex,t (k it, i it ) = ηs +1 η (β s k k it + h ex,t (ω it, k it )). The probability of survival estimated in stage two of the OP procedure now also takes into account firm export status via the previous period productivity shock and via investment in the capital accumulation process. Indeed, the higher capitalintensity of the exporting firms allows them to stay active with lower productivity shocks relatively to their non-exporting rivals: Pr{χ it = 1 I t } = Pr{χ it = 1 ω t 1, ω i (k it )} = p ex,t (i it 1, k it 1 ) = Π it (5) The last stage to recover the capital coeffi cient along with the export status dummy can now be implemented by applying a nonlinear least square or GMM estimator on the following equation: E[r it I it, χ it = 1] = β l l it + β m m it + β k k it + β y y gt + δ t D t + δ g D g + (6) +ϕ((g ex,t 1 β k k it 1 ), Π it ), where ϕ((g ex,t β k k it ), Π it ) is approximated by the predicted probability of survival from (5) and a second degree linear approximation of ω it 1 = g ex,t 1 β k k it 1 = h ex,t 1 (ω it 1, k it 1 ). 8 As discussed in De Loecker (2007), controlling for the export status would solve the problem of the overestimated labour coeffi cient in the production function and control for the bias in the capital coeffi cient that may arise due to the higher capitalintensity of exporters. The obtained measures of TFP estimates may still be biased due to measurement errors 9 and imperfect competition in factor markets. However, if the bias due to the imperfectly competitive factor markets is the same within an industry, it is differenced out when applying the DID method to estimate the learningby-exporting effect. The final caveat of the OP estimation procedure is the requirement for positive investment in every period. However, following Pavcnik (2002) and De Loecker (2007) I tried using restricted (firms with only positive investment each period) and unrestricted sample (all firms) with no significant change in the results. Hence, I have implemented the analysis presented in this paper on the unrestricted sample of firms. 8 I refer to Yasar, Raciborski and Poi (2008), De Loecker (2007, 2011), Ornaghi and Van Beveren (2012) and Shepotylo and Vakhitov (2015) for further discussion of the OP estimation algorithm. 9 At the same time, Van Biesebroeck (2004) shows that semiparametric production function estimators are the least sensitive to measurement errors. 12

13 7 Tables and Figures Table 1. Distribution of Ukrainian Exporting Firms by the Number of Export Destinations, average Number of export destinations % of firms 1 50% 2 19% 3 10% 4 5% 5+ 16% Table 2. Number of firms and share of exporters (%) by year, Year Average Number of firms 31, ,963 37,786 35,816 Number of exporters 3, ,651 4,853 4,332 Share of Exporters 11.9% 11.7% 12.6% 12.8% 12.0% Number of entrants ,123 1,002 1,005 Number of quitters Entry rate % 3.0% 2.7% 2.7% Exit rate % 2.4% 2.6% 2.5% Table 3. Means (standard deviation) of production function variables (2000, 2003, 2005) Output ( ) ( ) ( ) Employment (645.31) (640.17) (334.07) Materials ( ) ( ) (101161,79) Capital ( ) ( ) ( ) Note: Capital, materials and output are expressed in constant 2000 prices, thousands UAH. 13

14 Table 4. Firm heterogeneity and Export Choices, OLS and RE Negative Binomial OLS Model. ln T F P it *** 0.101*** ln Age it *** ln Emp it *** 0.207*** ln Emp 2 it *** *** in tan g it *** 0.046*** Industry i Yes Yes D i Yes Yes Y ear t Yes Yes No. of obs. 160, ,914 No. of firms 48,603 R Log Likelihood BIC Poisson, RE: Marginal Effects LR Test Note: Dependent variable: number of export destinations ranging from zero to 49. Cluster robust standard errors in parentheses; ***- significant at 1% level; **- significant at 5% level; *- significant at 10% level. All regressions include Industry, Region and Year dummies as controls. Marginal effects were calculated using margins command in STATA

15 Table 5. Hurdle Model, Marginal Effects. First Stage Second Stage ln T F P it *** 0.140*** ln Age it *** 0.051* ln Emp it *** 0.470*** ln Emp 2 it * * lnintang it *** 0.204*** Industry i Yes Yes D i Yes Yes Y ear t Yes Yes No. of obs. 160,914 29,686 Log Likelihood Pseudo R Note: Dependent variable: number of export destination ranging from zero to 49. Cluster robust standard errors in parentheses; ***- significant at 1% level; **- significant at 5% level; *- significant at 10% level. Marginal effects were calculated using margins command in STATA

16 Figure 1. Distribution of Ukrainian Exporting Firms by Number of Export Destinations, average. Figure 2. Total Factor Productivity and the Number of Export Markets,