Abstract
There exists a growing body of literature which looks at export decisions made by firms. Most studies focus on developed countries and do not explore whether different behavioral patterns prevail over the firm size distribution. This paper aims at filling this gap in the literature by analyzing the export behavior of a statistically representative sample of 192 small and medium-size enterprises (SMEs) in a developing country, Argentina, over the period 1996–1998. We find that the level of employment, sourcing from abroad, investment in product improvement, and average productivity are associated with higher probability of exporting. Training activities for employees are important to export outside of MERCOSUR.
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Notes
Bernard and Jensen (1999, 2004) examine the determinants of firms’ export performance in the USA; Bernard and Wagner (2001) and Arnold and Hussinger (2005) in Germany; Castellani (2002, 2009) in Italy; Delgado et al. (2002), Barrios et al. (2003), and Blanes-Cristobal et al. (2008) in Spain; Head and Ries (2003) in Japan; Eaton et al. (2004, 2008) in France; Girma et al. (2004), Greenaway et al. (2007), and Kneller et al. (2008) in the UK; and Lawless (2009) in Ireland.
Alvarez (2004) examines determinants of export performance of Chilean manufacturing SMEs. He finds that greater effort in international business, process innovation, and use of export promotion programs are associated with higher probability of being a permanent exporter.
Das et al. (2007) find that the option value of export market participation is quantitatively important for small-scale exporters among Colombian chemical producers, whose foreign demand is relatively limited.
Thus, for instance, real exchange rate movements can have lasting effects on trade volumes after having been reversed.
MERCOSUR is a trade agreement established in 1991 by Argentina, Brazil, Uruguay, and Paraguay.
The exposure to trade only benefits the most efficient firms. The least efficient ones are forced to exit the industry. The implied trade-induced reallocations explain why trade may result in aggregate productivity gains without improving individual firms’ productivity. For further details see Melitz (2003).
The underlying heterogeneity due to persistent differences across firms in terms of gross profit from exporting might also lead to persistence of exporter status (see Tybout 2001).
Empirical evidence is abundant. However, the direction of causality is not always well established. In many cases we do not know whether firms with certain characteristics become exporters, or firms get particular attributes by becoming exporters.
Some papers introduce an explicit measure of productivity such as those estimated in the wake of Olley and Pakes (1996) or Levinsohn and Petrin (2003). The data consistently suggest a positive correlation between productivity at the firm level and exporting (e.g., Clerides 1998; Bernard and Jensen 1999; Kraay 1999; Aw et al. 1997, 2000; Pavnick 2002; Castellani 2002; Delgado et al. 2002; Head and Ries 2003; Hwang 2003; Alessandria and Choi 2007).
If high product quality requires high workforce quality, a positive link between the level of qualification of the employees and the probability to export may be expected. Evidence on the impact of this variable, as proxied by average wages and the ratio of white-collars to total employees, is however not robust (see Roberts and Tybout 1997; Bernard and Jensen 1999, 2004; Bernard and Wagner 2001; Arnold and Hussinger 2005).
In particular, smaller companies bear higher costs for information (e.g., Gimede 2004). Importantly, evidence based on data for three manufacturing sectors in Colombia (basic chemicals, leather products, and knitted fabrics) suggest that for smaller firms the option value of being able to export in future years without incurring in entry costs is likely to substantially exceed the expected export profits in the current year (see Das et al. 2007).
In order to ensure comparability of firms’ economic and financial data coming from their balance sheets, additional exclusion criteria have been used. Thus, the data set also excludes firms that due to their legal status are difficult to survey (i.e., one-person and de facto firms), those that are not relevant for the purpose of the survey (i.e., state-owned firms and nonprofit institutions), and firms that are not obliged to submit balance sheets.
The six regions consist of the following provinces. Buenos Aires: Province of Buenos Aires; Centro: City of Buenos Aires, Santa Fe, and Córdoba; Cuyo: San Juan, San Luis, and Mendoza; NEA: Formosa, Chaco, Misiones, Entre Ríos, and Corrientes; NOA: Jujuy, Salta, Tucumán, Catamarca, La Rioja, and Santiago del Estero; Sur: La Pampa, Río Negro, Neuquén, Santa Cruz, and Tierra del Fuego.
Sánchez (2008) examine the determinants of the emergence of three new successful activities in Argentina: blueberries, chocolate confection, and biotechnology applied to human health. In particular, they analyze which were the pioneering companies, what were the ex ante uncertainties regarding the profitability of exports, how these uncertainties were overcome, and to what extent business opportunities diffused. In this analysis, they consider the role played by previously accumulated capabilities, industry-specific public goods, and public policies.
In general, employment and age are defined as in most papers in the existing empirical literature. The role of imported (as opposite to domestic) inputs has been captured by Tucci (2005) through the share of expenditure on imported intermediate inputs in the total expenditure on intermediate inputs. Product improvements and product introduction can be considered to be (at least partially) outputs of innovation activities, whose influence in shaping firms’ foreign market participation has been accounted for by, among others, Arnold and Hussinger (2005) and Blanes-Cristobal et al. (2008) using measures involving expenditure on research and development (R&D). Arnold and Hussinger (2005) also explicitly control for the impact of new products by including the share of new products in firms’ total sales, whereas Bernard and Jensen (2004) incorporate a binary variable that identifies whether firms report to different industries over time (i.e., produce goods classifiable in different sectors). Spillovers can take place at different levels: region, industry, and region-industry. In the latter case, Bernard and Jensen (2004) proxy these effects using the share of exporting plants (exports) in total plants (shipments) in the same region and the same industry (excluding the plant in question).
This is a reduced-form model. Das et al. (2007) develop a dynamic structural model featuring uncertainty, plant-level heterogeneity in export profits, and sunk entry costs for firms breaking into foreign markets that characterizes firms’ decisions about whether to export as well as quantifies the volume of foreign sales among those who do.
Using a general equilibrium model where firms face an up-front sunk cost of entering foreign markets and smaller period-by-period continuation cost, Alessandria and Choi (2007) show that business cycles affect the moments when firms start or stop exporting. In particular, during economic expansions, more firms start exporting and more exporters are likely to stay in export markets than in normal times.
We report the pairwise correlation between these variables in Table 10 in Appendix A. The correlations clearly indicate that multicollinearity does not seem to be an issue in our estimations.
Ensuring consistency in this framework requires solving the initial-conditions problem. In linear models with additive unobserved effects, this problem can be solved using a transformation such as differentiation to eliminate these effects and then applying instrumental variables to perform generalized method of moments (GMM) estimations. In the nonlinear case, solving the problem is much more difficult. Wooldridge (2005) proposes, however, a simple solution consisting of modeling the distribution of the unobserved effect conditional on the initial value and any exogenous variable. Operatively, this implies including as explanatory variables the exporter status in the initial year, the exporter status in the previous year, and a vector of all (nonredundant) explanatory variables in all time periods to allow for correlation between these regressors and the unobserved effect in all years. Given the short extension of our panel, doing this creates severe multicollinearity problems, which unfortunately prevent us from using this strategy. The impossibility of properly addressing the initial condition issue as well as the potential correlation between the regressors and the firm-specific effect should be kept in mind when considering the probit random-effect estimates reported below. Recall that the probit model does not lend itself to the fixed-effect treatment (see Greene 1997).
In additional estimations, we also incorporate leverage as an additional explanatory variable (see Greenaway et al. 2007). This variable turns out to be nonsignificant. These results are not shown but are available from the authors upon request.
These estimates are obtained assuming stratification by region. Similar results are found when stratification by industry is assumed instead. These results are not reported but are available from the authors upon request.
Estimations were performed using STATA’s “svy” command for survey estimation.
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Acknowledgments
We would like to thank our editor, Rui Baptista, and two anonymous referees for valuable comments and suggestions. We also wish to thank Vicente Donato for giving us access to the Observatorio PyME database collected by the University of Bologna at Buenos Aires. Finally, we owe gratitude to Jerónimo Carballo, Eduardo Mendoza Quintanilla, and Christian Haedo for their excellent research assistance, and to Carlos Gutierrez Jr. for his valuable editing assistance.
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When the first draft was finalized, the first author belonged to the faculty of the University of Bologna. The views and interpretation in this document are strictly those of the authors and should not be attributed to the Inter-American Development Bank, its Executive Directors or its member countries. The other usual disclaimers also apply.
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Ottaviano, G., Volpe Martincus, C. SMEs in Argentina: who are the exporters?. Small Bus Econ 37, 341–361 (2011). https://doi.org/10.1007/s11187-009-9244-0
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DOI: https://doi.org/10.1007/s11187-009-9244-0