Abstract
Entering new export markets is primarily a discrete choice. Even though several empirical papers have used modeling strategies consistent with this fact, no study has examined the effects of public policies aimed at affecting this decision within this setting. In this paper we assess the impact of trade promotion activities on export outcomes using trade support and highly disaggregated export data for the entire population of exporters of Uruguay, a small developing country, over the period 2000–2007 to estimate a binary outcome model that allows for unobserved heterogeneity. We find that trade supporting activities have helped firms reach new destination countries and introduce new differentiated products.
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Notes
Supporting empirical evidence for this argument has been reported in recent papers. Thus, Eaton et al. (2007) estimate transition matrices for Colombian exporters and find that firms exporting to more than three countries are more likely to keep selling abroad the next year. In the case of Uruguay, Cox estimations based on a model including (the natural logarithm of) total exports, unobserved firm heterogeneity, and year fixed effects suggest that firms that export to two countries are approximately 50% less likely to exit international markets than firms that only export to one country, whereas firms that trade with three countries have roughly a 70% lower probability of doing so. Similar results are obtained when controlling for the number of products exported. These estimates are available from the authors upon request.
For an overview of the literature on the impact of export promotion policies at different levels of aggregation (i.e., country, regions, and firms) see Volpe Martincus and Carballo (2008).
Thus, in this paper, we will focus on two specific measures of export performance: entry into new country markets and entry into new product markets (see Sect. 2). As noticed below, whether firms actually enter or not these markets is likely to affect other, more general, performance measures commonly used in the empirical literature, such as total exports and their growth rate.
More specifically, differentiated products have no reference price (see Sect. 3).
Companies may even incorporate a new market while experiencing a reduction in the total number of markets in which they are present.
This pattern is consistent with the model developed by Eaton et al. (2008) augmented to allow for serial correlated productivity shocks. According to this model, variations across firms in market entry are primarily explained by differences in efficiency.
Further, even though there are firms that enter more than one market simultaneously, correlation in the decisions to penetrate separate markets can be expected to be weak after conditioning by firms’ size, their previous export market coverage, and general macroeconomic conditions (see Eaton et al. 2007; and Lawless 2009).
An assessment of these activities from the point of view of social welfare requires contrasting the costs they incur with the benefits they generate. This is beyond the scope of this paper, which focuses only on the benefits of these actions in terms of export performance.
There are few antecedents in the use of binary outcome models to evaluate export promotion activities. Thus, Spence (2003) examines the effect of U.K. overseas trade missions estimating a standard logit model with data on 190 companies. She shows that firms whose sales are spread over a larger number of countries and accordingly have been exposed to the entry process in various markets are more likely to establish contacts and obtain leads during trade missions. Álvarez (2004) estimates a standard probit model to assess the impact of the trade promotion instruments used by PROCHILE on the probability of becoming a permanent exporter using a sample of 295 Chilean manufacturing small and medium sized enterprises. He finds that trade shows and trade mission do not affect this probability, but exporter committees do.
In particular, assisted companies are those that have used the export support services provided by URUGUAY XXI (Uruguay’s Institute for Promotion of Investments and Exports of Goods and Services). More generally, we will use interchangeably assistance, (export) support, treatment, and participation (in export promotion programs) throughout the paper.
We do not have the required data to examine selection of firms into export markets and how assistance by URUGUAY XXI shapes this selection process (e.g., sales for both exporters and non-exporters and a list of non-exporting firms assisted by URUGUAY XXI).
This would be specifically the case if consumer attach informational value to quantity and accordingly interpret low market shares as a signal of low quality (see Caminal and Vives 1996).
Formally, matching is based on two assumptions. First, conditional on a set of observables X, the non-treated exports are independent of the participation status (conditional independence assumption). Second, all firms have a counterpart in the non-treated population and anyone is a possible participant (common support). Both assumptions together are called “strong ignorability”. For additional details see, e.g., Rosenbaum and Rubin (1983); Heckman et al. (1997); Angrist and Krueger (1999); Blundell and Costa Dias (2002) and Caliendo and Kopeinig (2008).
We should mention herein that, even though the presentation hereafter focuses on the probability of incorporating a new destination, mutatis mutandis it also applies to other measures of export performance along the extensive margin (e.g., the probability of adding a new export product).
An econometric model of the form: Y * i = X i δ + λ i D i + ɛ i with λ i = X i ρ + ϑ i can be written as Y *1i = X i β 1 + U i1 for D i = 1and Y *0i = X i β 0 + U 0i for D i = 0 (see Auld 2005).
This extension is left for future work.
In this exercise, we ignore general equilibrium effects so that outcomes for each firm do not depend on the overall level of participation in the activities performed by the agency (see Heckman et al. 1997, 1998). In particular, we do not consider information spillovers. It is well known that firms may learn about export opportunities from other firms through employee circulation, customs documents, customer lists, and other referrals (see Rauch 1996). Evidence on spillovers has been presented in several papers, e.g., Aitken et al. (1997); Greenaway et al. (2004); Álvarez et al. (2007), and, to some extent, Barrios et al. (2003). If these spillovers were to be associated with participation in export promotion activities, i.e., unassisted firms obtain business information from assisted firms, then the treatment effects, as estimated here, would be underestimated. Given the number of companies actively participating in these activities (see Table 1) this risks can be expected to be low.
The linear index assumptions are imposed to reduce the dimensionality of the estimation problem. These assumptions are not critical to the empirical approach (see Aakvik et al. 2005).
If U 0i = U 1i , then the effects of the unobservables are the same in both states. In this case, firms with the same observed X will have the same treatment effect. This is the so-called common coefficient model (see Aakvik et al. 2003).
Notice that this estimation strategy is designed to correct for the correlation between the unobservables in the outcome and selection equations. Hence, if measurement errors in the export outcome and/or the assistance variables only introduce additional sources of correlation between the unobservables in the respective equations, it can be shown that under certain circumstances, estimates obtained with these kinds of econometric approaches are consistent in the presence of such errors (see, e.g., Kenkel and Terza 2001).
In this case, the correlations among the unobservables in the model are given by:
$$ Corr\,\left( {U_{D} ,U_{1} } \right) = \sigma_{D1} = {\frac{{Cov\,\left( {U_{D} ,U_{1} } \right)}}{{\sqrt {Var\,\left( {U_{D} } \right)} \sqrt {Var\,\left( {U_{1} } \right)} }}} = {\frac{{\alpha_{1} }}{{\sqrt 2 \sqrt {1 + \alpha_{1}^{2} } }}} $$$$ Corr\,\left( {U_{D} ,U_{0} } \right) = \sigma_{D0} = {\frac{{Cov\left( {U_{D} ,U_{0} } \right)}}{{\sqrt {Var\,\left( {U_{D} } \right)} \sqrt {Var\,\left( {U_{0} } \right)} }}} = {\frac{{\alpha_{0} }}{{\sqrt 2 \sqrt {1 + \alpha_{0}^{2} } }}} $$$$ Corr\,\left( {U_{0} ,U_{1} } \right) = \sigma_{01} = {\frac{{Cov\,\left( {U_{0} ,U_{1} } \right)}}{{\sqrt {Var\,\left( {U_{0} } \right)} \sqrt {Var\,\left( {U_{1} } \right)} }}} = {\frac{{\alpha_{0} \alpha_{1} }}{{\sqrt {1 + \alpha_{0}^{2} } \sqrt {1 + \alpha_{1}^{2} } }}} $$and
$$ Cov\,\left( {U_{D} ,\theta } \right) = 1, Cov\,\left( {U_{1} ,\theta } \right) = \alpha_{1}, and\;Cov\,\left( {U_{0} ,\theta } \right) = \alpha_{0}. $$Identification of \( \alpha_{0} \) (from \( Cov\,\left( {U_{D} ,U_{0} } \right) \)) and α 1 (from Cov(U D , U 1)) immediately imply identification of α 0 α 1 = Cov(U 0, U 1). This latter covariance needs neither be estimated nor normalized because it does not enter the likelihood and thus has no effect on the parameter estimates. This follows because only the bivariate distribution (D, Y 0) and (D, Y 1) is required to form the likelihood and to calculate conditional means (Y 1 − Y 0) (see Aakvik et al. 2005). The joint distribution of (Y 1, Y 0) is needed to compute the distributional treatment parameters (see Auld 2005).
This random effects factor model and the matching model of Rosenbaum and Rubin (1983) are affine. If the econometrician knew θ, then the matching conditions of the latter would be satisfied and propensity score matching could be used to estimate the treatment effect on the treated (see Aakvik et al. 2005).
These services are provided in a relatively customized way (see Jordana et al. 2010).
Unfortunately, data on these assistances are not consistently available over the sample period.
To put this low coverage into perspective, the annual budget of the agency needs to be considered. This budget is relatively small. It amounted to USD 600,000. From this amount approximately USD 480,000 (80%) are devoted to trade promotion. More specifically, as indicated above, these funds are primarily allocated to activities such as participation in international fairs and complementary training and specific information services, which are more likely to result in increased firms’ exports in the short run, thereby making possible for export promotion to generate significant positive effects despite the limited resources available with this purpose (see Sect. 4). As a reference, PROCOMER, Costa Rica’s main export promotion organization, has an annual budget of about USD 12 million and assists more than 250 companies each year, whereas PROCHILE, the Chilean counterpart is annually endowed with USD 33 million and serve more than 2,000 firms within a year (see Jordana et al. 2010). In addition, notice that, while the sub-sample of treated firms is relatively small, the total sample is large. This implies that the pool of control observations is large, which makes our particular data set suitable to estimate the treatment effect on the treated as done here (see Frölich 2004). Further, there are no difficulties in finding firms comparable to the treated ones within the non-treated group. The classical problem of sensitivity of results associated with small sample sizes are not likely to be pronounced here (see Smith and Todd 2005b). Nevertheless, given that the estimated effect will be identified based on the potentially different export outcomes of these relatively reduced number of assisted companies and that the aforementioned problems cannot be fully ruled out, caution should be exercised when drawing conclusions from the point estimates presented in the next section.
Due to some ambiguities, Rauch (1999) proposes two alternative classifications, conservative and liberal. The former minimizes the number of commodities that are classified as either organized exchange or reference-priced and the latter maximizes this number. Combining this latter good typology with a sectoral classification that identifies as manufacturing (those HS codes that correspond to) categories 5–8 of the Standard Industrial Trade Classification (SITC) (see Hummels and Klenow 2005), we can see that, in the case of Uruguay, differentiated goods are primarily manufactured products (approximately 83.4%).
MERCOSUR is a trade agreement established in 1991 whose member countries are Argentina, Brazil, Paraguay, and Uruguay. Notice that these countries, but Brazil, also share Spanish as a common language, which is an additional source of familiarity (see Rauch 1999). While Spanish is also official language in several other Latin American countries, knowledge of these markets as proxied by the relative intensity of bilateral trade exchanges is variable and on average significantly more limited. We have therefore decided to prioritize the distance criterion.
The correlation between the two variables is −0.01. These results are not shown here, but are available from the authors upon request.
Sianesi (2004) uses local participation rates to account for unobserved local factors that are relevant for both program-joining decisions and individuals’ potential labor market performance.
Volpe Martincus and Carballo (2010) examine the heterogeneous effects of export promotion programs across groups of firms with different levels of international experience. Exploring these heterogeneous effects in our setting is beyond the scope of this paper and is left for future research.
Bernard et al. (2006) find evidence suggesting that firms’ productivity is correlated positively across products, i.e., single-product firms with relatively high productivity in their product are more likely to add a new product to their mix of goods than relatively low-productivity firms producing the same initial product.
As referred to in Footnote 19, differentiated products are primarily manufactures. Hence, the share of differentiated products implicitly allows discriminating between manufacturing and agricultural and mining exporters. Further, this share differs markedly across the 2-digit sectors. Detailed tables are available from the authors upon request.
The empirical literature suggests that other firm-level time-varying factors (e.g., employment, age, innovation activities) may also contribute to explain firms’ export performance (see, e.g., Roberts and Tybout 1997; Bernard and Jensen 2004). Unfortunately, we do not have data on these additional factors in our data set.
As noticed above, the number of assisted companies is small relative to the population of exporters. Thus, one might argue that the untreated sample potentially include many firms that are not looking for adding new markets. More formally, there might be an unobserved firm-specific factor shaping the dynamics of the export extensive margin. This unobserved heterogeneity should be controlled for by our estimation procedure. Moreover, as seen before, the probability of incorporating new markets appear to be highly correlated with previous market coverage and this is explicitly accounted for in the econometric model being estimated. Further, as an additional informal check exercise in this direction, we have first constructed matched samples including only the 5 or 10 most similar non-supported firms for each supported one as identified based on their propensity scores. Second, we have estimated a non-parametric test of differences in proportions of companies in both groups that enter new markets as well as the Mantel–Hanszel test (see Aakvik 2001). Consistent with the evidence presented below, these tests clearly indicate that the proportions are significantly larger for the assisted group in all export outcome dimensions considered in this study. These results are not reported here but are available from the authors upon request.
The mean marginal effects of a continuous regressor z k in the selection equation is defined as \( E_{Z} \left[ {{{\partial P\left( {D = 1|Z} \right)} \mathord{\left/ {\vphantom {{\partial P\left( {D = 1|Z} \right)} {\partial z_{k} }}} \right. \kern-\nulldelimiterspace} {\partial z_{k} }}} \right] \), where E z denotes the expectation operation taken with respect to the distribution of Z, i.e., the mean marginal effect is the analytical derivative averaged over the unconditional distribution of Z. Further, the marginal effect of a binary explanatory variable is computed as \( E_{Z} \left\{ {\left[ {P\left( {D = 1} \right)|Z_{ - j} ,z_{j} = 1} \right] - \left[ {P\left( {D = 1} \right)|Z_{ - j} ,z_{j} = 0} \right]} \right\} \), where Z -j stands for the elements of Z excluding the binary variable z j , i.e., the marginal effect is the impact of a change from zero to one in the variable in question. Notice, finally, that the expressions for the marginal effects corresponding to the outcome equations Y 0 and Y 1 (with respect to X instead of Z) are defined analogously (see Aakvik et al. 2005; and Auld 2005).
We also estimate the correlations between unobservables in the selection and outcome equations: (−0.252; 0.445), (−0.088; 0.204), (0.263; 0.162) (0.141; 0.715) where the first component of the pairs is the estimated correlation between the unobservable of the selection equation and that of the export outcome equation for assisted firms and the second component is the estimated correlation between the unobservable of the selection equation and that of the export outcome equation for non-assisted firms.
Note that specific estimated coefficients exhibit slight differences. This is because selection and outcome equations are jointly estimated, and so, even though all selection equations aim at explaining participation in export promotion programs of the same group of firms with the same set of covariates, this selection interacts with different outcomes.
In an alternative specification of the selection equation, we have used the share of assisted firms in the main (2-digit) export sector instead of the average over all sectors in which the firm is present in international markets. Estimation results are almost identical to those reported here and are available from the authors upon request.
These estimates are not shown here but are available from the authors upon request.
These estimation results are not reported but are available from the authors upon request.
In the case of assisted firms, the effect of lagged assistance may be difficult to disentangle due to persisting status.
Detailed results from this estimation are not reported here, but are available from the authors upon request.
As mentioned above, regional markets are not explicitly targeted by URUGUAY XXI.
We have used two alternative definitions of Latin America and the Caribbean, including and excluding the MERCOSUR trading partners. Estimation results obtained with these alternative definitions are very similar. These results are not reported, but are available from the authors upon request.
This is probably related to the limited amount of resources available to the organization to perform such activities.
As a robustness check, we have performed all estimations substituting manufacturing for differentiated products among both outcome and explanatory variables. Findings are similar to those presented here and are available from the authors upon request.
Álvarez et al. (2007) show that exporting firms seem to learn from other exporters. As a robustness check, we have re-estimated our models alternatively including binary explanatory variables accounting for previous export experience by other Uruguayan exporters in country, product, and country-product dimensions. Specifically, these variables take the value of one if at least another Uruguayan firm has previously exported to the same destination country, the same product, or the same product to the same destination country, respectively. Estimation results after including these additional control variables do not differ from those shown here and are available from the authors upon request.
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Acknowledgments
We would like to thank URUGUAY XXI which kindly provided us with export and trade promotion assistance data for Uruguayan firms. We also wish to thank the editor, an anonymous referee, Juan Blyde, Mauricio Mesquita Moreira, and Matthew Shearer for valuable comments and suggestions. We owe gratitude to Mariana Sobral de Elia and Georgeta Dragoiu for competent editing assistance. 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, its member countries, or URUGUAY XXI. Other usual disclaimers also apply.
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Volpe Martincus, C., Carballo, J. Entering new country and product markets: does export promotion help?. Rev World Econ 146, 437–467 (2010). https://doi.org/10.1007/s10290-010-0062-x
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DOI: https://doi.org/10.1007/s10290-010-0062-x