Abstract
The extant empirical studies do not wholly support the popular belief held by policy-makers on the positive influence of geographical proximity of incumbent exporters on the export decision of a non-exporter. To reconcile the mixed evidence, this paper examines the nonlinear relationship between the agglomeration of exporters and export decisions. In brief, the results show that the former positively affects the latter. Furthermore, the squared term of agglomeration calculated only by the number of skilled workers is negatively and significantly associated with export decisions, which can be explained by congestion costs in a local labor market for the skilled worker. As a result, these findings suggest an inverted U-shaped relationship between the agglomeration of exporters and the probability of being an exporter.
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Notes
The entry costs for exporting are related to gathering information on foreign consumers’ preference, establishing a distribution system in foreign countries, improving infrastructure necessary for distributing the products abroad, paying the search costs to identify local bankers, networking, adopting the product to new standards, and so on.
The mixed evidence may be attributed to the definition of the local export spillovers (restricted to multinational firms or including all exporters), the level of data disaggregation, and the different estimators used (Koening et al. 2010).
This paper employs the Levinsohn and Petrin (2003) method, which uses intermediate input, such as electricity, as the instrument variable in order to address the endogeneity problem. In order to estimate the production function, this study implements a STATA command (i.e., levpet) introduced by Petrin et al. (2004).
However, multiple plants have the same identification number. These plants are excluded as their percentage in the sample is only around 0.5 %.
Although Angrist and Pischke (2009, p. 196) dealt with peer effects in labor economics, the way to compute peer effects is the same as that for the agglomeration effect. Therefore, their suggestion is applied: “the best shot a causal investigation of peer effects focuses on variation in ex ante peer characteristics, that is, some measure of peer quality that predates the outcome variable and is therefore unaffected by common shocks.” Koenig (2009) and Bernard and Jensen (2004) also employed this approach to avoid the endogeneity problem.
A fixed effects (FE) model is preferable for its assumption that unobserved heterogeneity is random as well as the non-specification of its distribution. For the dynamic FE logit model, Honore and Kyriazidou (2000) proposed a semi-parametric estimator, which is, however, extremely data-demanding. Additionally, as the dynamic FE logit model does not assume the distribution of unobserved heterogeneity, the marginal effects cannot be computed to quantify the research interest (Wooldridge 2005). Thus, the dynamic FE logit estimator is not used.
Stewart’s (2006) STATA code is used, which suggests a shortcut implementation of Heckman’s estimator, i.e., rebprob. Despite Stewart’s shortcut implementation, the Heckman estimator still requires considerable computing time.
According to Stewart (2006), Wooldridge assumes normality of the conditional distribution of unobserved heterogeneity, given the initial observation of the dependent variable, whereas Heckman assumes bivariate normality for the unobserved heterogeneity and the initial observation of the dependent variable.
The Wooldridge’s method needs a set of covariates in order to use Mundlak’s suggestion. The set of covariates is constructed by using the logarithms of TFP, of plant size, of real wage per worker, and of agglomeration of exporting plants. However, the logarithms of real export subsidies and of foreign ownership are excluded due to the presence of zero values.
Heckman’s methods need a set of covariates in order to approximate the conditional density of initial observations, given a vector of the unobserved heterogeneity. The set of covariates is constructed by using the log of plant-level TFP.
This can be explained by the fact that exporting plants can pay higher wages to skilled workers than non-exporting plants can because of the higher productivity of exporting plants (Bernard and Jensen 1999; Kandilov 2009). In particular, exporting plants can pay their skilled workers a higher wage to prevent them from moving to a local competitor, i.e., a non-exporting plant. This is similar to foreign affiliates behavior (Fosfuri et al. 2001; Combes and Duranton 2006).
References
Akay A (2009) The Wooldridge method for the initial values problem is simple: what about performance? IZA discussion paper no. 3943
Accetturo A, Bugamelli M, Lamorgese A (2013) Skill upgrading and exports. Econ Lett 121:417–420
Andersson M, Weiss JF (2012) External trade and internal geography: local export spillovers by industry characteristics and firm size. Spat Econ Anal 7(4):421–446
Angrist JD, Pischke J-S (2009) Mostly harmless econometrics. Princeton University Press, Princeton
Atiken B, Hanson GH, Harrison AE (1997) Spillovers, foreign investment, and export behavior. J Int Econ 43(1):103–132
Barrios S, Gorg H, Strobl E (2003) Explaining firms’ export behavior: R&D. Spillovers and the destination market. Oxf Bul Econ Stat 65(4):476–496
Balsvik R (2011) Is labor mobility a channel for spillovers from multnationals? Evidence from Norwegian manufacturing. Rev Econ Stat 93(1):285–297
Bernard AB, Jensen JB (1999) Exceptional exporter performance: cause, effect, or both? J Int Econ 47(1):1–25
Bernard AB, Bradford Jensen J (2004) Why some firms export. Rev Econ Stat 86(2):561–569
Breschi S, Lissoni F (2001) Knowledge spillovers and local innovation system: a critical survey. Ind Corp Change 10(4):975–1005
Castellani D, Zanfei A (2007) Multinational companies and productivity spillovers: is there a specification error? Appl Econ Lett 14(14):1047–1051
Choquetter E, Meinen P (2015) Export spillovers: opening the black box. World Econ 38(12):1912–1946
Clerides SK, Lach S, Tybout JR (1998) Is learning by exporting important? Micro-dynamic evidence from Colombia, Mexico, and Morocco. Q J Econ 113(3):903–947
Combes PP, Duranton G (2006) Labour pooling, labour poaching, and spatial clustering. Reg Sci Urban Econ 36(1):1–28
Farole T, Winkler D (2013) Firm location and the determinants of exporting in low- and middle income countries. J Econ Geogr 14(2):395–420
Fosfuri A, Motta M, Ronde T (2001) Foreign direct investment and spillovers through workers’ mobility. J Int Econ 53(1):205–222
Greenaway D, Kneller R (2008) Exporting. Productivity and agglomeration. Eur Econ Rev 52(5):919–939
Harasztosi P (2015) Export spillovers in Hungary. Empirical economics, pp 1–30
Heckman JJ (1981) The incidental parameters problem and the problem of initial conditions in estimating a discrete time-discrete data stochastic process. In: Manski CF, McFadden D (eds) Structural analysis of discrete data with econometric applications. MIT Press, New York, pp 114–178
Honore B, Kyriazidou E (2000) Panel data discrete choice models with lagged dependent variables. Econometrica 68(4):839–874
Karpaty P, Kneller R (2011) Demonstration or congestion? Export spillovers in Sweden. Rev World Econ 147(1):109–130
Kandilov IT (2009) Do exporters pay higher wages? Plant-level evidence from an export refund policy in Chile. World Bank Econ Rev 23(2):269–294
Koenig P (2009) Agglomeration and the export decisions of French firms. J Urban Econ 66(3):186–195
Koening P, Mayneris F, Poncet S (2010) Local export spillovers in France. Eur Econ Rev 54(4):622–641
Levinsohn J, Petrin A (2003) Estimating production functions using inputs to control for unobservables. Rev Econ Stud 70(2):317–341
Masso J, Roigas K, Vather P (2014) Foreign market experience, learning by hiring and firm export performance. CELSI discussion paper no. 26
Melitz M (2003) The impact of trade on intra-industry reallocations and aggregate industry productivity. Econometrica 71(6):1695–1725
Mion G, Opromolla L (2014) Managers’ mobility, trade performance, and wages. J Int Econ 94:85–101
Molina D, Muendler M (2013) Preparing to export. NBER working papers #18962
Mundlak Y (1978) On the pooling of time series and cross section data. Econometrica 46(1):69–85
Petrin A, Poi B, Levinsohn J (2004) Production function estimation in stata using inputs to control for unobservables. Stata J 4(2):113–123
Saito H, Gopinath M (2009) Plants’ self-selection, agglomeration economies and regional productivity in Chile. J Econ Geogr 9(4):539–558
Stewart MB (2006) -Redprob- a Stata program for the Heckman estimator of the random effects dynamic probit model. Mimeo, University of Warwick
Verhoogen EA (2008) Trade, quality upgrading, and wage inequality in the Mexican manufacturing sector. Quart J Econ 123:489–530
Wooldridge JM (2005) Simple solutions to the initial conditions problem in dynamic. Nonlinear panel data models with unobserved heterogeneity. J Appl Econ 20(1):39–54
Acknowledgments
I would like to give thanks to Robert McNown, Jin-Hyuk Kim, anonymous referee and the Editor Martin Andersson for valuable comments and suggestions. I am also grateful to Luis Castro for kindly sharing the Chilean plant-level data which comes from the Chilean National Statistics Institute (INE). This work was supported by the Soongsil University Research Fund of 2015.
