ITC WORKING PAPER SERIES...ITC, UN or WTO. The designations employed and the presentation of material in this paper do not imply the expression of any opinion whatsoever on the part

TRADE IMPACTFOR GOOD

ITC WORKING PAPER SERIES

WHAT BANG FOR THE BUCK? EXPORT PROMOTION AND TRADE EXTENSIVE MARGIN

WP-02-2018.E

September 2018

Eleonora De FalcisInternational Trade Centre, Geneva

Valentina RolloInternational Trade Centre, Geneva

Olga SollederInternational Trade Centre, Geneva

Rohit TickuInternational Trade Centre, Geneva

Disclaimer

Views expressed in this paper are those of the authors and do not necessarily coincide with those of ITC, UN or WTO. The designations employed and the presentation of material in this paper do not imply the expression of any opinion whatsoever on the part of the International Trade Centre or the World Trade Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Mention of firms, products and product brands does not imply the endorsement of ITC or the WTO. This is a working paper, and hence it represents research in progress and is published to elicit comments and keep further debate.


© International Trade Centre WP-02-2018.E

ITC Working Paper Series

WHAT BANG FOR THE BUCK? EXPORT PROMOTION AND

TRADE EXTENSIVE MARGIN

September 2018

Eleonora De Falcis, Valentina Rollo, Olga Solleder, Rohit Ticku, International Trade Centre, Switzerland

Disclaimer

Views expressed in this paper are those of the authors and do not necessarily coincide with those of ITC, UN or WTO. The designations employed and the presentation of material in this paper do not imply the expression of any opinion whatsoever on the part of the International Trade Centre or the World Trade Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Mention of firms, products and product brands does not imply the endorsement of ITC or the WTO. This is a working paper, and hence it represents research in progress and is published to elicit comments and keep further debate.

WHAT BANG FOR THE BUCK? EXPORT PROMOTION AND THE EXTENSIVE MARGIN OF TRADE

ii WP-02-2018.E

WHAT BANG FOR THE BUCK? EXPORT PROMOTION AND

THE EXTENSIVE MARGIN OF TRADE

Eleonora De Falcis1

International Trade Centre

Olga Solleder2


Valentina Rollo3


Rohit Ticku4

The Graduate Institute

September 20185

Abstract

Export promotion agencies (EPA) attempt to provide support to firms willing to expand their operations across borders. EPAs can assist firms in overcoming information asymmetry and find markets for their products (Olarreaga et al., 2017). The objective of this paper is to assess whether allocating more EPA’s budget to new exporters effectively increases the number of exporters. We test this on a cross-country dataset merging indicators from: the EPA surveys and the Exporter Dynamics Database. Our results confirm that allocating a higher EPA’s budget on new exporters raises the number of exporters. Interestingly, this result is led by budget allocated to small firms, while the number of exporters declines when more budget is allocated to large firms. Our findings reinforce the heterogeneous firms’ theory: trade costs affect firms differently, and smaller firms are the ones which could potentially benefit more from a higher budget allocation. Institutions can use these results to make better informed budget allocation.

JEL Classification: F13, C14

Keywords: Export promotion agency, Impact evaluation, heterogeneous trade theory, trade, SMEs.

1 Eleonora De Falcis, Trade analyst, Office of the Chief Economist, International Trade Centre, Palais des Nations, 1211 Geneva 10, Switzerland; and; Ph.D. Candidate at Department of Agro- Food Economics, Catholic University of the Sacred Heart, Via Emilia Parmense 84, 29100, Piacenza, Italy: e-mail: [email protected] 2 Valentina Rollo, Economist, Office of the Chief Economist, International Trade Centre, Palais des Nations, 1211 Geneva 10, Switzerland; e-mail: [email protected] 3 Olga Solleder, Economist, Office of the Chief Economist, International Trade Centre, Palais des Nations, 1211 Geneva 10, Switzerland; e-mail: solleder@ intracen.org 4 Rohit Ticku, Teaching Assistant, Interdisciplinary Programmes, Ph.D. Candidate in Development Economics, The Graduate Institute, Chemin Eugène-Rigot 2, 1202 Genève; e-mail: [email protected] 5 The authors thank Marion Jansen, Adam Jakubik, Jasmeer Virdee, and Mauro Boffa for useful comments and discussions. We

thank Marcelo Olarreaga for sharing the cleaned version of the Export promotion agencies (EPAs) Surveys.

mailto:[email protected]

mailto:[email protected]


Introduction

Since their rapid growth in the 1980s export promotion agencies (EPAs) have attempted to provide support to companies willing to expand their operations across borders and to integrate into global value chains (UNCTAD, 2001; Lederman, Olarreaga, & Payton, 2010). EPAs address a market failure, typically information asymmetry and related performance uncertainty, which generate sunk costs (Allen, 2014). When firms decide to start exporting or to internationalize, they need to acquire relevant information. This effort generates costs that are not recoverable if the attempt to establish a foreign market presence is unsuccessful. These costs may deter some firms from becoming exporters (Copeland, 2007).

Raising the number of exporters is often an explicit objective of the export promotion agency, as participation in export markets is considered by many as necessary for economic development. The reason for such optimism is twofold. On one side, participation in export markets can further enhance the productivity of firms (Yang & Mallick, 2010). This can occur through foreign buyers providing technical know-how to improve production (Grossman & Helpman, 1991), firms harnessing information about advanced technologies by participation in the international markets, and higher-quality standards in the international markets compelling firms to upgrade their production methods (Verhoogen, 2008).

On the other side, recent literature on structural transformation has put export diversification at the center of the economic development process (Hidalgo et al., 2007; Hausmann et al., 2007). According to this literature a country is more likely to produce goods if the associated knowledge or capabilities are already embedded in its export structure. Thus, encouraging firms to diversify their knowledge base through participation in international markets can increase the economic complexity, and overall development of a country.

Small and medium-sized enterprises (SMEs) find it more difficult to identify economic opportunities, absorb sunk costs as well as take the risk of failure. Melitz and Redding (2014) confirm that in fact only the most competitive firms reach the exporting stage during their lifecycle, assuming no market interventions. As such, EPAs could play an invaluable role for SMEs looking to expand their operations across borders. However, is there evidence of a positive impact of export support on helping firms, especially SMEs, to export? If so, what are the channels?

This paper attempts to answer these questions by assessing whether EPA’s budget allocation on new exporters affects the extensive margins of trade. We rely on a cross-country dataset combining EPAs’ surveys collected by the World Bank in 2005 and 2010 with indicators of exporters’ performance at the country, year and destination level from the Exporters Dynamics Database. Our results confirm that allocating a higher EPA budget to new exporters raises the number of exporters per destination. More interestingly, we show that this effect is led by an increase in incumbent exporters and surviving entrants, indicating that export support to new exporters has a stabilizing effect on the export base. Secondly, we assess whether budget allocation to firms of different sizes affects differently the number of exporters. We find that our baseline results are led by budget allocated to small firms, a confirmation that trade costs are particularly burdensome for small firms (Melitz, 2003) and that institutional support can help alleviate them.

Previous research has gone into the direction of assessing the spending of EPAs on both macro and micro-economic indicators. Recent findings suggest that resources devoted to export promotion can lead to country level export and economic growth (Olarreaga et al., 2017).

Firm level studies show the positive impact of export promotion programmes on firms export values (Alvarez & Crespi, 2000; Gorg, Henry, & Strobl, 2008; Freixanet, 2012; Geldres-Weiss & Carrasco-Roa, 2016), as well as on firm’s ability to enter and survive in exports markets (Cruz, 2014; Lederman et al., 2016; Broocks & Van Biesebroeck, 2017; Munch & Schaur, 2018), and geographic as well as product diversification (Alvarez & Crespi, 2000; Volpe Martincus & Carballo, 2010b; Cadot et al., 2015; Volpe Martincus et al., 2010c). Finally, a strand of the literature has shown how export promotion has a positive effect on the capabilities of small exporters (Volpe Martinicus & Carballo, 2010a; Leonidou et al., 2014; Munch & Schaur, 2018).

However, while existing literature evaluates and quantifies the effectiveness of country specific EPA support on several margins of trade, available evidence at cross-country level of the effect of budget allocated to new exporters on the export extensive margin is rather scarce. Recently, Lederman et al. (2016) have used a firm level panel of seven Latin American countries to study the effects of using EPA services on entries


4 WP-02-2018.E

and exits from international markets. They show that the impact of EPA budget allocation is significant not only on raising export value but also on the number of exporters, the latter effect being more important.

Our paper builds on Lederman et al. (2016) analysis on the effect of EPA’s budget allocation on the number of exporters. We use a panel of 27 countries across developing and developed economies. However, differently from Lederman et al. (2016), we use firm level export data aggregated at the country level. This loss of granularity is compensated by the inclusion of an additional dimension: the destination market, which allows us to reduce endogeneity issues (Hayakawa et al., 2014, and Lederman et al., 2010). This is an important aspect when evaluating EPA’s contribution to export. In particular, export destination performance would encourage EPAs to increase their support for that particular destination, thus creating a problem of reverse causality. In our paper, we address this concern using an instrumental variable approach exploiting, for the first time, information on trade strategies from ITC data. Trade strategies are correlated with EPA support strategies, but have the advantage that, being designed in advance by institutions, are not correlated with the number of exporters per destination – making them a suitable instrumental variable (IV).

To our knowledge, no paper has focused on the distributional effect of EPA budget allocation by firm size and the extensive margin of trade in a cross country framework. We aim to shed some light on this topic to better understand the role of trade institutions in improving firms’ participation in international markets.

The rest of the paper is structured as follows. We describe the dataset in Section 1. The empirical strategy is described in Section 2. Section 3 summarizes the results, Section 4 provides results of IV estimation and robustness check. Section 5 concludes.

1. Data

To establish the impact of budget allocation on firm’s participation in international markets, we rely on combining data from two main sources.

First, we use the Exporters Dynamics Database6 - from the World Bank - containing cross-country comparable measures of exporting firms’ performance at different levels of aggregation. From this dataset, we use a few indicators disaggregated at the exporting country-year-destination level.

Second, we draw information on actual expenditure and allocation from the two rounds of the Export Promotion Agencies (EPA) surveys, conducted in the fall of 2005 and 2010 by the World Bank. In particular, we rely on the clean and assembled version of this data, employed and detailed in Olarreaga et al. (2017). The dataset we use is an unbalanced panel across developed and developing countries with information on agencies budget, sources of founding and activities. From this dataset, we use information on EPAs’ budget in USD and types of expenditures, namely budget allocated to new exporters and budget allocated to firms of different size.

The budget allocation is not provided in USD, but as a categorical variable. Budget expenditure takes values from 1 to 6, corresponding to the following 6 steps: 0%, <10%, 10-25%, 25-50%, 50-75%, and 75-100% of the total budget. From this information, we build a continuous variable by multiplying EPA’s total budget in USD to the upper bound of each step (i.e. 25% in case the expenditure ranges from 10% to 25%). The budget allocations used in this analysis are: budget allocated to new exporters, budget allocated to large firms and budget allocated to SMEs.

We also use control variables from other datasets. For information on geographical distance and regional trade agreements (RTA) we resort to CEPII dataset. The data on GDP per capita are drawn from the IMF World Economic Outlook.

The baseline specification is complemented with IV estimation, based on data from the Trade Strategies Database7 of the International Trade Centre (ITC). It contains export promotion and poverty alleviation plans

6 Downloaded from: https://datacatalog.worldbank.org/dataset/exporter-dynamics-database 7 Downloaded from: http://www.intracen.org/export-strategy-map/

http://www.intracen.org/export-strategy-map/


for more than 150 countries (more information is available in ITC (2017)). From this dataset we use information on the availability and duration of trade strategies.

We provide a list of all the variables used in this paper (Table A1), as well as summary statistics (Table A2) in the Appendix. After merging the data sources, we can rely on a dataset containing information on 27 countries for two years, 2005 and 2010. Countries included belong to different economic groups and income level, specifically this analysis includes as exporting countries: Brazil, Albania, Bangladesh, Botswana, Burkina Faso, Chile, Costa Rica, Denmark, Dominican Republic, Ecuador, Estonia, Guatemala, Jordan, Macedonia, FYR Mauritius, Mexico, Nicaragua, Norway, Peru, Portugal, Senegal, Spain, Tanzania, Turkey, Uganda, Uruguay and Zambia.

2. Econometric specification

We exploit the panel framework in our data to quantify the impact of EPA budget allocation on the number of exporters. To test this relation we resort to a formal econometric exercise. As previously mentioned, our policy variable, EPA budget allocation, is disaggregated at the country-year level, while our output variable – the number of exporters – is available at different levels of aggregation. The use of the country-year-destination aggregation in our paper presents an important advantage: it reduces the reverse causality concern. While we are testing the effect of EPA budget allocation on the number of exporters, we acknowledge that the budget allocation could itself be affected by the number of exports. For example, a country with few exporters could decide to allocate more budget to them. By adding the destination dimension, we break the reverse causality issue (as budget allocations are not country specific).

Our baseline model is specified as follows:

ln(𝑌)𝑖𝑗𝑡 = 𝛼 + 𝛽1ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑡𝑎𝑟𝑔𝑒𝑡𝑒𝑑)𝑖𝑡 + 𝛽2ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑡𝑜𝑡𝑎𝑙)𝑖𝑡 + 𝛽3𝑙𝑛(𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑖𝑗 + 𝛽4(𝑅𝑡𝑎)𝑖𝑗𝑡 +𝛽5ln(𝐺𝐷𝑃𝑝𝑐)𝑖𝑡 +

𝛽6ln(𝐷𝑒𝑠𝑡𝐺𝐷𝑃𝑝𝑐)𝑗𝑡 + 𝛾7𝑌𝑒𝑎𝑟 +𝛾8𝐷𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 + 𝜀𝑖𝑗𝑡 (1)

The dependent variable 𝑌𝑖𝑗𝑡 measures the number of exporters in year t (2005 or 2010), exporting from

country i to destination j.8 Our covariant of interest is the institutional variable 𝐵𝑢𝑑𝑔𝑒𝑡𝑡𝑎𝑟𝑔𝑒𝑡𝑒𝑑 indicating the budget spent by EPA to target a specific segment of firms. In our specification, we also control for the total budget to avoid any bias due to higher level of resources available to EPA.

We specify our strategy following the gravity model literature controlling for trade costs associated to destination markets. In particular we control for the logarithm of geographical distance (𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒𝑖𝑗) between

markets, and regional trade agreements (𝑅𝑡𝑎𝑖𝑗𝑡). Also, in line with gravity settings, we account for the

economic indicators usually correlated with trade, in particular we control for 𝐺𝐷𝑃, expressed in per capita terms, of both the exporting and importing country. Our set of variables allow to control not only for time invariant characteristics that affect the number of exporters to specific countries, but also for country-pair effects. Finally we use year fixed effects to control for year specific economic shocks that might have affected all countries equally, as well as destinations fixed-effects, to control for time-invariant destination specific characteristics that affect exporting countries equally. Finally, the notation 𝜀𝑖𝑗𝑡 capture the normally

distributed error term, clustered at the year. We cluster standard errors at the country pair level to control for the possibility that disturbances are correlated at the bilateral level.

We estimate equation (1) using different input variables. First we use the EPA’s budget allocated to new exporters. This is our baseline specification to test the relationship between the budget spent on new exporters and the number of exporters per destination.

Secondly, we identify the channel through which EPA spending is more effective on increasing the number of exporters. Our dataset allow us to test if the effect is heterogeneous across firms of different size. As such we substitute the budget allocated to new exporters with the budget allocated to SMEs as well as with the budget allocated to large firms.

8 Note that in the sample we have only one EPA per country per year.


6 WP-02-2018.E

We estimate Equation 1 as an OLS linear probability model (LPM). As a robustness check we also estimate the Equation (1) as a Poisson pseudo-maximum likelihood model9 (PPML), Tobit left censored and adding +1 to our logged dependent variable in order to avoid omitting the zero observations.

3. Results

According to the strategy presented above, our results are of two main categories and results are presented accordingly. First, we show the result of the baseline regression where we analyze the effectiveness of budget spent to support new exporters on the number of exporters. Second, we show the results of the same specification where we substitute the budget allocated to new exporters with the EPAs budget allocated to small, medium-sized and large firms.

3.1. Budget allocated to new exporters

The results of our baseline specification are presented in Table 1. We find a positive relation between budget spent on new exporters and the number of exporters (Column 1). Controlling for distance, trade agreements, GDP per capita, destination and year fixed effects does not change results, it simply improves the goodness of fit. Our complete baseline model (Column 6) predicts that 1% increase in the budget allocated to new exporters increases the number of exporters by 0.46%. In other words, the budget allocated to new exporters affects the extensive margin of trade, a finding in line with related literature (Lederman et al., 2016; Munch et al., 2016; Volpe Martincus and Carballo 2008).

Since EPAs provide services to exporters, our findings suggest that these services would lower the trade costs, increasing participation to trade. In fact, being costs lower for all firms, exporting activity would be relatively more affordable for small firms, which would probably be the one participating to trade.

Table 1. Baseline results: spending on new exporters

*** p<0.01, ** p<0.05, * p<0.1. Regressions include year fixed effect and destination fixed effect. Standard errors in parentheses, clustered at country-pair level.

Since the baseline result focuses on the number of exporters per destination, it is natural to ask which channel is leading this result. Table 2 shows coefficients of equation (1) but replacing the number of exporters

9 Fernández-Val and Weidner (2016) show that a Poisson model with two fixed effects does not suffer from the incidental parameter problem as long as the regressors are strictly exogenous. Therefore, under very general conditions, inference based on the estimation by ppml including both partner and year fixed effects will not be affected by an incidental parameter problem. We test our specification under strategies that confirm the analysis to be consistent.

Dep var: ln (number of exporters per destinations) (1) (2) (3) (4) (5) (6)

ln (budget allocated to new exporters) 0.587*** 0.513*** 0.457*** 0.463*** 0.478*** 0.459***

(0.011) (0.035) (0.028) (0.027) (0.029) (0.029)

ln (total budget) 0.089** 0.179*** 0.156*** 0.153*** 0.111***

(0.041) (0.032) (0.032) (0.034) (0.035)

ln (distance) -1.210*** -1.115*** -1.138*** -1.137***

(0.029) (0.032) (0.034) (0.034)

RTA 0.407*** 0.372*** 0.329***

(0.063) (0.067) (0.068)

ln (Dest. GDP pc) 0.314*** 0.311***

(0.085) (0.084)

ln (GDP pc) 0.116***

(0.021)

FE YES YES YES YES YES YES

Observations 6,793 6,793 6,556 6,556 5,979 5,979

R-squared 0.559 0.560 0.709 0.711 0.712 0.714


per destination with the number of entrants, exiters, incumbents, and surviving entrants (Colum 1-4). Results shows that allocating more budget to new exporters increases churning: both entrants and exiters per destination increase as a result of an increase in budget allocated to new exporters. The coefficients are similar, indicating that the two effects offset each other. This is not surprising, as new exporters are often small firms, which struggle to survive. The effect of allocating budget on new exporters is positive also on incumbents. More interestingly, the effect is positive and bigger in magnitude for surviving entrants: this is an indication that focusing on new entrants helps them survive. This results are in line with Lederman et al. (2016), and emphasize that services given to entrants are of help not only for the entry of firms into a market but also for their survival.

Finally, and in line with the increase in the number of exporters, we see that allocating more budget to new exporters decreases market concentration. Although this makes intuitive sense, this result emphasizes how ultimately allocating more resource to new exporters contribute to increasing competition in each destination. This effect stabilizes incumbents and reinforces the strongest among the entrants by allowing them to survive.

Table 2. Baseline results: channels

*** p<0.01, ** p<0.05, * p<0.1. Regressions include year fixed effect and destination fixed effect. Standard errors in

parentheses, clustered at country-pair level.

3.2. Budget allocation by firm size

The baseline results indicate that the results could be led by smaller firms engaging in export activity. Therefore, we test if budget allocated to firms of different sizes has indeed different effects on the number of exporters per destination. Results are presented in Table 3. Ceteris paribus, we find that only allocating EPA’s budget to small firms increases the number of exporters per destination (Table 3, Panel A). The effect is weak for medium-sized firms (Table 3, Panel B), and even negative for large firms (Table 3, Panel C).

More specifically, our results indicate a declining return to budget allocation the large the size of the firm. Specifically, a 1% increase of budget allocated to small firms increases the number of exporters per destination by around 0.14%. Instead, 1% increase of budget allocated to medium sized firms increases the same number by 0.07%. Finally, where the budget allocated to large firms increases by 1%, the number of exporters per destinations declines by 0.27%.

This is in line with the theory that, without market interventions, only a few firms self-select in international markets: entry costs act as a barrier so that only the most productive firms can operate in international markets (Melitz & Redding, 2014). EPAs provide the services that favor market penetration, by doing so reduce the costs of entry into exporting and reduce the productivity threshold described by Melitz (2003). Large firms are probably already exporting. As such, allocating EPA budget to large firms would only

Dep. var.:

ln (number of

entrants per

destination)

ln (number of

exiters per

destination)

ln (number of

incumbents per

destination)

ln (number of

surviving entrants per

destination)

ln (market

concentration per

destination)

ln (budget allocated to new exporters) 0.477*** 0.491*** 0.504*** 0.511*** -0.260***

(0.030) (0.029) (0.037) (0.036) (0.021)

ln (total budget) -0.000 0.002 0.028 -0.054 -0.022

(0.037) (0.036) (0.043) (0.043) (0.026)

ln (distance) -1.030*** -1.020*** -1.088*** -0.946*** 0.589***

(0.033) (0.033) (0.038) (0.036) (0.025)

RTA 0.218*** 0.127* 0.350*** 0.252*** -0.101**

(0.064) (0.065) (0.076) (0.072) (0.051)

ln (Dest GDP pc) 0.271*** 0.350*** 0.562*** 0.549*** -0.062

(0.091) (0.094) (0.104) (0.109) (0.074)

ln (GDP pc) 0.073*** 0.057*** 0.077*** -0.025 -0.003

(0.019) (0.019) (0.024) (0.022) (0.016)

FE YES YES YES YES YES

Observations 5,320 5,044 4,662 3,978 5,164

R-squared 0.675 0.671 0.657 0.602 0.479


8 WP-02-2018.E

contribute to stabilizing them, rather than starting to export, and would therefore reduce the number of exporters.

These findings are in line with Munch et al. (2018). However, while they find that export promotion facilitates entry into export markets and survival across all types of firms - with the largest effect occurring for the smaller firms – we find that the number of exporters increases through supporting small firms only. The large positive effect of EPA’s budget allocation is indeed experienced by small firms also on other performance measures. Increasing budget allocation to small firms boosts the number of entrants and exiters, or, in other words, churning. More interestingly, the effect on surviving entrants is the highest in magnitude, thus confirming the findings in Lederman et al. (2016).


Table 3. Budget allocation by firm size.

Panel A: Small firms

Panel B: Medium-sized firms

Panel C: Large firms

*** p<0.01, ** p<0.05, * p<0.1. Regressions include year fixed effect and destination fixed effect. Standard errors in

parentheses, clustered at country-pair level.

Dep. var.:

ln (number of new

exporters per destination)

ln (number of

entrants)

ln (number of

exiters)

ln (number of

incumbents)

ln (number of

surviving entrants)

ln (market

concentration)

ln (budget allocated to small firms) 0.142*** 0.238*** 0.271*** 0.266*** 0.378*** -0.055*

(0.043) (0.041) (0.042) (0.050) (0.049) (0.031)

ln (total budget) 0.453*** 0.275*** 0.252*** 0.292*** 0.093* -0.237***

(0.047) (0.045) (0.046) (0.055) (0.054) (0.035)

ln (distance) -1.164*** -1.023*** -1.002*** -1.067*** -0.921*** 0.601***

(0.036) (0.035) (0.035) (0.040) (0.039) (0.026)

RTA 0.290*** 0.229*** 0.150** 0.376*** 0.282*** -0.078

(0.071) (0.069) (0.070) (0.081) (0.078) (0.052)

ln (Dest GDP pc) 0.263*** 0.225** 0.287*** 0.468*** 0.448*** -0.026

(0.085) (0.092) (0.092) (0.105) (0.107) (0.075)

ln (GDP pc) 0.154*** 0.102*** 0.091*** 0.114*** 0.007 -0.027*

(0.021) (0.019) (0.019) (0.024) (0.021) (0.016)


Observations 5,979 5,320 5,044 4,662 3,978 5,164

R-squared 0.698 0.655 0.650 0.640 0.581 0.460

Dep. var.:

ln (number of new


ln (number of

entrants)

ln (number of

exiters)

ln (number of

incumbents)

ln (number of

surviving entrants)

ln (market

concentration)

ln (budget allocated to medium-sized firms) 0.077* 0.025 0.003 0.012 0.094** -0.072***

(0.040) (0.036) (0.036) (0.046) (0.043) (0.027)

ln (total budget) 0.517*** 0.486*** 0.519*** 0.545*** 0.382*** -0.220***

(0.041) (0.038) (0.038) (0.049) (0.046) (0.028)

ln (distance) -1.173*** -1.042*** -1.022*** -1.086*** -0.943*** 0.604***

(0.036) (0.035) (0.035) (0.040) (0.039) (0.026)

RTA 0.271*** 0.195*** 0.113 0.340*** 0.239*** -0.071

(0.071) (0.069) (0.070) (0.081) (0.077) (0.052)

ln (Dest GDP pc) 0.263*** 0.218** 0.283*** 0.468*** 0.438*** -0.029

(0.085) (0.092) (0.092) (0.105) (0.109) (0.075)

ln (GDP pc) 0.146*** 0.099*** 0.091*** 0.115*** 0.004 -0.021

(0.022) (0.020) (0.020) (0.024) (0.022) (0.017)


Observations 5,979 5,320 5,044 4,662 3,978 5,164

R-squared 0.698 0.652 0.646 0.637 0.574 0.461

Dep. var.:

ln (number of new


ln (number of

entrants)

ln (number of

exiters)

ln (number of

incumbents)

ln (number of

surviving entrants)

ln (market

concentration)

ln (budget allocated to large firms) -0.270*** -0.358*** -0.337*** -0.252*** -0.277*** 0.129***

(0.034) (0.033) (0.032) (0.037) (0.034) (0.025)

ln (total budget) 0.896*** 0.898*** 0.876*** 0.841*** 0.781*** -0.437***

(0.036) (0.036) (0.035) (0.041) (0.037) (0.027)

ln (distance) -1.203*** -1.050*** -1.032*** -1.095*** -0.983*** 0.635***

(0.039) (0.038) (0.037) (0.044) (0.044) (0.028)

RTA 0.323*** 0.282*** 0.188*** 0.398*** 0.339*** -0.097*

(0.075) (0.072) (0.072) (0.087) (0.083) (0.054)

ln (Dest GDP pc) 0.346*** 0.338*** 0.377*** 0.576*** 0.568*** -0.113

(0.084) (0.091) (0.092) (0.106) (0.105) (0.076)

ln (GDP pc) 0.204*** 0.161*** 0.153*** 0.164*** 0.052** -0.066***

(0.024) (0.022) (0.022) (0.027) (0.025) (0.018)


Observations 5,334 4,723 4,507 4,145 3,494 4,630

R-squared 0.709 0.668 0.664 0.643 0.598 0.491


10 WP-02-2018.E

4. Robustness checks

4.1. Addressing endogeneity

This section explores some extensions of the above analysis and tests whether our results are robust to alternative specifications. Although the positive correlation between internationalization and policy support is stable across various specifications and econometric techniques, our results might suffer from a reverse causality problem. In particular, EPA’s decision to support may be driven by the number of exporters. EPA seeing low the number of exporters would be encouraged to increase the number of exporting firms. This would make our proxy for institutional support endogenous to the system and our results biased.

To address these concerns, we use an IV variable approach and an alternative specification based on a subsample of our data. Moreover, we test our baseline model with a Poisson model specification, PPML, Tobit left censored and adding a small constant to our dependent variable. For ease of exposition, we present one robustness test at a time.

Instrumental variable

The alternative specification for our baseline results is based on an IV approach built upon information from the Trade Strategy Map (TSM henceforth) compiled by ITC. The TSM database compiles an exhaustive inventory of documents on national plans that have a significant development and trade strategy component. In particular, the database includes strategies directly initiated and implemented by local authorities covering issues related to environment, trade, economic growth, and education. To model our IV variable, we focus on the availability and duration of strategies having a specific trade component10 and implemented in the last five years. The intuition behind this approach is that the higher the number of years a trade strategy was designed to be in place in recent years, the higher would be the budget spent on the new exporters in year t. As the trade strategy is put in place in advance, it is unlikely to be affected by omitted contemporaneous factors.

The first stage of IV model takes the following specification:

ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑛𝑒𝑤𝑒𝑥𝑝𝑜𝑟𝑡𝑒𝑟𝑠)𝑖𝑡 = 𝜋 + 𝜋1(𝑒𝑥𝑝𝑜𝑟𝑡𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦)𝑖,𝑡−5 + 𝜋2 ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑡𝑜𝑡𝑎𝑙) + 𝜋3ln(𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑐𝑑 + 𝜋4 ln(𝑅𝑇𝐴) +

𝜋5ln(𝐷𝑒𝑠𝑡𝐺𝐷𝑃𝑝𝑐)𝑖𝑡 + 𝜋6ln(𝐺𝐷𝑃𝑝𝑐)𝑗𝑡 + 𝛾7𝑌𝑒𝑎𝑟 +𝛾8𝐷𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 + 𝜀𝑖𝑗𝑡 (2)

Where 𝑒𝑥𝑝𝑜𝑟𝑡𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦𝑖,𝑡−5 is our instrument of the budget allocated to new exporters. This is zero if no

strategy is implemented in the last five years (excluding t) while, if implemented, corresponds to the average number of years a trade strategy was designed to be in place.

In the second stage regression we run the following specification:

ln(𝑌)𝑖𝑗𝑡 = 𝛼 + 𝛽1ℎ𝑎𝑡ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑛𝑒𝑤𝑒𝑥𝑝)𝑖𝑡 + 𝛽2 ln(𝐵𝑢𝑑𝑔𝑒𝑡𝑡𝑜𝑡𝑎𝑙) +𝛽3𝑙𝑛(𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒)𝑖𝑗 + 𝛽4(𝑅𝑇𝐴)𝑖𝑗𝑡 +

𝛽5ln(𝐷𝑒𝑠𝑡𝐺𝐷𝑃𝑝𝑐)𝑖𝑡 + 𝛽6ln(𝐺𝐷𝑃𝑝𝑐)𝑗𝑡 + 𝛾7𝑌𝑒𝑎𝑟 + 𝛾

8𝐷𝑒𝑠𝑡𝑖𝑛𝑎𝑡𝑖𝑜𝑛 + 𝜀𝑖𝑗𝑡 (3)

The results of the IV regression strategy are reported in Table 4 and mostly confirm what we have previously discussed. When we instrument EPA’s budget with the duration of export strategy, the effect on the number of exporters remains positive and significant. Moreover, the elasticity of the number of exporters with respect to the other control variables is also preserved in terms of sign and significance.

The bottom lines of the table show the results of the statistical tests which confirm that our instrument is relevant. Keinberg-Paap (K-P) LM first stage statistics reject the null of identification, thus we are confident that our instrument satisfied the rank condition. Keinberg-Paap Wald test (F test) is higher than any value of Stock & Yogo (2005), with the 10% threshold having a value of 16.38. Therefore the maximum bias associated with coefficients tends to be smaller than 10%. Finally, the IV model shown a goodness of fit of 60%, thus the variables included proved to be good predictors for the dependent variable.

10 These include the ones specifically focused on trade and trade promotion, trade facilitation, trade financing and strategies to provide more trade information.


Table 4. IV regression

*** p<0.01, ** p<0.05, * p<0.1. Regressions include year fixed effect, destination fixed effect. Standard errors in parentheses, clustered at country-pair level.

We also apply our regression on different samples (Panizza & Presbitero, 2014). We exclude Turkey for which we observed a four-times higher number of exporters compared to countries similar in total average budget allocated to new exporters. Indeed we find that regardless this outlier, there is a positive and significant relation between the number of exporters per destinations and the budget allocated to new exporters and the tests also confirmed our previous findings. Finally, we also reapply our IV strategy on a sample of developing countries and excluding Turkey on the export side. Again, we find confirmation that a higher EPA’s budget on new exporters increase the number of exporters. Of note is that results are close to the one based on the complete sample, suggesting that estimates are not biased upwards by an effect of developed countries, thus further confirming the importance and effectiveness of institutional support in both developed and developing countries.

In terms of the goodness of our instrument, coefficient remains positive and significant in all first stage regressions. The point estimates range between 0.09 to 0.12, thus the correlation with the variable of interest (budget allocated to new exporters) is not affected by different samples.

Testing hypothesis on a subsample

An alternative strategy to tackle endogeneity is to repeat our analysis in our baseline regression on different samples according to the export value. The intuition is that, given that the budget allocated to new exporters may be driven by poor representation in export market excluding top destination markets may change our

First stage IV First stage IV First stage IV

ln(budget allocated to new exporters) 1.422*** 0.611*** 1.123***

(0.096) (0.111) (0.216)

ln (total budget) 1.060*** -0.899*** 1.073*** 0.004 1.127*** -0.621**

(0.006) (0.101) (0.006) (0.120) (0.006) (0.244)

ln (distance) -0.055*** -1.064*** -0.040** -1.013*** 0.021 -1.043***

(0.015) (0.036) (0.016) (0.027) (0.019) (0.038)

RTA -0.095*** 0.445*** -0.010*** 0.426*** 0.006 0.665***

(0.033) (0.074) (0.034) (0.059) (0.048) (0.081)

ln (Dest GDP pc) -0.085 0.416*** -0.070 0.378*** -0.104*** 0.463***

(0.062) (0.111) (0.062) (0.132) (0.009) (0.174)

ln (GDP pc) 0.173*** 0.037 0.128*** 0.032* -0.036 0.246***

(0.009) (0.026) (0.009) (0.018) (0.080) (0.043)

Duration of export strategy 0.128*** 0.092*** 0.054***

(0.005) (0.004) (0.004)

FE Yes Yes YES YES YES YES

Observations 5,979 5,979 5,642 5,642 4,115 4,115

R-squared 0.921 0.642 0.925 0.732 0.895 0.607

KP-LM stat 406.18 218.28 113.81

p-value 0.000 0.000 0.000

KP F test 767.44 360.9 162.14

Baseline (complete

sample)Excluding Turkey

Only developing

countries, excluding

Turkey


12 WP-02-2018.E

results. In particular the coefficient on the number of exporters should increase. In that case, the budget expenditure on new exporters would be endogenous and, as a consequence, our estimate of 𝛽1 would be biased towards zero.

In order to tackle this issue and further test our assumptions, we repeat our baseline regressions progressively dropping the top 1%, 5%, 10% and 25% destinations, ordered by the size of exports (Table 5). This is based on the intuition that export performance in the key destinations are most likely to affect the export promotion policies. The results in our restricted samples are akin to the baseline results which gives us confidence that these are not being driven by reverse causality but indeed budget allocation increase the number of exporters by destinations.

Table 5. Excluding top destinations by export size

*** p<0.01, ** p<0.05, * p<0.1. Regressions include year fixed effect, destination fixed effect. Standard errors in parentheses, clustered at country-pair level.

Dep var.: ln (Number of Exporters per destinations)

Excluding 1%

destinations

Excluding 5%

destinations

Excluding 10%

destinations

Excluding 25%

destinations

ln (budget allocated to new exporters) 0.424*** 0.422*** 0.431*** 0.428***

-0.03 -0.031 -0.03 -0.032

ln (total budget) 0.132*** 0.110*** 0.126*** 0.141***

-0.036 -0.038 -0.036 -0.038

ln (distance) -1.039*** -0.000*** -1.009*** -0.959***

-0.033 0 -0.034 -0.035

RTA 0.345*** 0.879*** 0.361*** 0.334***

-0.066 -0.067 -0.067 -0.07

ln (Dest. GDP pc) 0.278*** 0.316*** 0.258*** 0.394***

-0.084 -0.086 -0.086 -0.095

ln (GDP pc) 0.067*** 0.060*** 0.071*** 0.063***

-0.022 -0.023 -0.022 -0.023

FE Yes Yes Yes Yes

Observations 5,159 5,094 4,930 4,229

R-squared 0.689 0.659 0.687 0.68


4.2. Modelling count data

A final test is to repeat our baseline regression with alternative techniques that account for missing values on our left hand side. To this end we repeat our exercise using PPML, Tobit and adding +1 to our logged dependent variable. This strategy enable us to compare countries where the number of exporters is zero for specific destinations. Since we are operating with a log-log form, the following results enable us to include the observations for which is not possible to compute the logarithm and therefore are dropped.

Results are presented in Table 6 and again confirm our expectations. A positive relation between the number of exporters per destinations an budget targeted to new exporters is significant. Poisson and Tobit regression also suggest a higher effect on the number of exporters. These strategies reinforce our hypothesis that extensive margin could benefit from higher support.

Table 6. Testing on different models.

*** p<0.01, ** p<0.05, * p<0.1. Regressions with fixed effects include year fixed effect and destination fixed effect. Standard errors in parentheses, clustered at country-pair level.

Dep var: Number of exporters per destination

ln (budget allocated to new exporters) 0.646*** 0.675*** 0.462*** 0.511*** 0.360*** 0.414***

(0.089) (0.072) (0.037) (0.028) (0.035) (0.027)

ln (total budget) 0.052 0.055 0.092** 0.109*** 0.122*** 0.123***

(0.104) (0.087) (0.044) (0.033) (0.043) (0.033)

ln (distance) -0.737*** -0.899*** -0.976*** -1.219*** -0.870*** -1.084***

(0.065) (0.055) (0.036) (0.030) (0.039) (0.032)

RTA 0.399*** 0.338*** 0.626*** 0.266*** 0.665*** 0.344***

(0.143) (0.125) (0.080) (0.065) (0.085) (0.065)

ln (Dest GDP pc) 0.386*** 0.174* 0.502*** 0.340** 0.427*** 0.300***

(0.034) (0.100) (0.016) (0.148) (0.016) (0.078)

ln (GDP pc) -0.323*** -0.311*** 0.101*** 0.126*** 0.080*** 0.106***

(0.038) (0.030) (0.027) (0.020) (0.027) (0.020)

FE No Yes No Yes No Yes

Observations 6,205 6,205 5,979 5,979 6,205 6,205

PPML Tobit left censored log + 1


14 WP-02-2018.E

5. Conclusion

Firms have to face a number of fixed and variable costs when they decide to start exporting or to internationalize. By simplifying the export process and by facilitating the information flow, export promotion agencies can reduce the costs of exporting and better connect companies to markets. The positive externality produced by the export promotion institutions are even more important for SMEs. These are more likely to lack resources and knowledge needed to enter a foreign market. The empirical literature has, in fact, shown that only the largest and most productive firms tend to export.11 The interventions provided by EPAs can lower the productivity threshold to allow smaller and less productive firms to export and enhance the productivity of firms.12

In this paper we combine information on EPA’s budget allocation with country level indicators of exporters’ performance, in 27 countries. We test whether export support to new exporters is effective in expanding participation in international markets. Our results indicate that an increase in the budgeted allocated to new exporters is positively related to the number of exporters per destination. Our baseline model, a fixed effect model, predicts that a 10% increase in the budget allocated to new exporters increases the number of exporters per destination by 4.6%. Interestingly, we observe that these results are led by the number of incumbent firms and by the number of surviving entrants, emphasizing how allocating more resource to new exporters contribute to stabilizing incumbents and to reinforce the strongest among the new entrants, by allowing them to survive.

The last result indicates that the effect of supporting new exporters could be stronger on smaller firms (new entrants). Therefore, we test if budget allocated to firms of different sizes has indeed different effects on the number of exporters per destination. Results show that only allocating EPA’s budget to small firms increases the number of exporters per destination, and that the effect is weak when the budget is allocated to medium-sized firms, and even negative when the budget is allocated to large firms. Our results are supported by a number of robustness checks - an IV variable approach and an alternative specification based on a subsample of our data - aimed at reducing concerns for endogeneity and reverse causality.

Our results confirm that trade support has the potential to help SMEs integrating in international trade and that trade promotion has a role to play in facilitating inclusiveness. The findings from this paper also suggest that if the policy objective of an export promotion agency is to increase export participation - the number of exporters - EPA support should focus on small firms. Export promotion institutions can use these results to make better informed decisions about their budget allocation.

11 Marc Melitz and Stephen Redding, Handbook of International Economics, 4th Ed, vol. 4 (Elsevier, 2014). 12 J. De Loecker, “Detecting Learning by Exporting,” American Economic Journal: Microeconomics 5, no. 3 (2013): 1–21; Alla Lileeva and Daniel Trefler, “Improved Access to Foreign Markets Raises Plant-Level Productivity ... for Some Plants,” Working Paper (National Bureau of Economic Research, August 2007), https://doi.org/10.3386/w13297.


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Appendix A

Table A1. Summary statistics13

Variable Obs Mean Std. Dev. Min Max

Number of exporters per destination per country 8,335 258 1025 0 26,890 Number of entrants per destination per country 7,552 88 317 0 9,301 Number of exiters per destination per country 6,769 87 312 0 9,512 Number of incumbents per destination per country 7,552 138 591 0 17,589

Herfindal Index per destination per country 6,619 0.31 0.27 0 1

Budget allocated to new exporters (mln USD) 7,250 20,4 30,1 0 127

Budget allocated to SMEs (mln USD) 7,250 48,2 70,8 0 270

Budget allocated to large firms (mln USD) 7,250 10,8 16,2 0 63,6

Total budget (mln USD) 7,250 50,3 71,2 0.257 270

Distance 8,017 7,674 4,521 80 19,812

Regional Trade Agreement (Dummy) 8,017

Destination GDP pc 7,296 13,475 17,758 149 106,185

GDP pc 8,335 13,252 19,555 372 87,309

Duration of export strategy 8,335 1.41 1.94 0 8.8

Table A2. Variable Definitions

Variable Definition Source

Ln (N. Exporters) Number of exporters per country per destination in logs. World Bank Exporters Dynamics Database

Ln (N Entrants) Number of entrants per country per destination in logs.

"-"

Ln (N. of exiters) Number of entrants per country per destination in logs.

"-"

Ln (N. of incumbents) Number of incumbents per country per destination in logs.

"-"

Ln (N. of surviving entrants) Number of surviving entrants per country per destination in logs.

"-"

Ln (Market Concentration) Herfindahl-Hirschman Index in logs. "-"

Ln (Budget allocated to new exporters)

Budget devoted to new exporters (USD) in logs Export promotion agencies (EPA) survey

Ln (Budget allocated to small firms) Budget allocated to small firms (USD) in logs "-"

Ln (Budget allocated to medium-sized firms)

Budget allocated to medium-sized firms (USD) in logs. "-"

Ln (Budget allocated to large firms) Budget allocated to large firms (USD) in logs "-"

Ln (Total budget) Log of the total EPA's budget (USD) in logs "-"

13 In order to retain only relevant observations for the analysis, we drop destinations where a country did not record any exporters in both years under analysis. Also, we drop destinations where a country had no exporters, no new entrants nor exiters and the mean export value was zero. These conditions reduced the sample from 13,284 to a total of 8,335.


20 WP-02-2018.E

Ln (Distance) Simple distance between the country pair (most populated cities, km).

CEPII

Regional trade agreement Dummy equals 1 if the country pair belong to the same regional trade agreement.

"-"

Ln (Destination GDP pc) Importer GDP per capita (USD) in logs. IMF WEO

Ln (GDP pc) Exporter GDP per capita (USD) in logs. "-"

Duration of Export strategy Average duration of an export strategy in years, if the strategy was implemented within t-5 years.

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