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Empirical Economics

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01-12-2014

Export and import market-specific characteristics

How they drive the decision to trade and how much

Authors: Francesco Serti, Chiara Tomasi

Published in: Empirical Economics | Issue 4/2014

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Abstract

Using a rich firm-level dataset on the Italian manufacturing industry, this paper provides a comprehensive analysis of the role that firms and market characteristics play in shaping firms’ trade activities. We enhance the previous analyses by considering firms’ engagement in international transactions, by focusing on either exports or imports. We show that the determinants of a firm’s export participation and value across countries also drive import behavior. Our research is consistent with the presence of country-specific sunk costs and with a qualitatively similar role of gravity forces and other country attributes on both sides of trading activities. Our evidence, however, militates in favor of a framework where variations in market characteristics have a larger impact on imports than exports.

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Only recently, the relationship between imports and firm characteristics has attracted attention in the empirical literature (Bernard et al. 2007; Amiti and Konings 2007; Kasahara and Rodrigue 2008).

We refer to countries that are globally more remote as more isolated from most of the other economies or close to small countries, but far away from large nations.

Note that a remote country has high shipping costs, high import prices, and thus a high aggregate price index.

Helpman et al. (2008) estimate a probit regression explaining the self-selection of firms into export markets that include both fixed and variable costs, while in the exports value equation, they include only variable costs.

Direct evidence on the positive relationship between firm productivity and importing has been provided in the case of Belgium (Muuls and Pisu 2009), Chile (Kasahara and Rodrigue 2008), France (Bas and Strauss-Kahn 2010), Germany (Vogel and Wagner 2010), Italy (Castellani et al. 2010), Hungary (Halpern et al. 2005; Bekes and Altomonte 2009), and the USA (Bernard et al. 2007).

Similarly, Kugler and Verhoogen (2009) assume that input quality and productivity are complementary in obtaining higher-quality output. Thus, more productive firms can be expected to look for higher-quality inputs from abroad and are more likely to self-select into importing.

The databases have been made available under the mandatory condition of censorship of any individual information.

Production workers include blue-collar, assistants, trainees, and home-based workers. Non-production workers are managers and clerks.

ISTAT collects data on trade based on transactions. The European Union sets a common framework of rules, but leaves some flexibility to member states. A detailed description of requirements for data collection on trade in Italy is provided in the Appendix.

Although the 20-employee threshold does not allow us to see the total number of firms involved in international trade and prevents us from analyzing the behavior and performances of smaller units, the representativeness of Micro.1 is ensured by the fact that a large amount of aggregate Italian trade is generated by large firms. As reported by the Italian Statistical Office (www.coeweb.istat.it), in 2005, firms with fewer than 20 employees accounted for 10 % of total manufacturing exports, while nearly 90 % of the aggregate value added was generated by firms with more than 20 employees.

To properly measure the Tfp, one should ideally observe the quantities and the qualities of varieties produced by a firm (Melitz 2001). To solve this problem, at least in part, the empirical literature has used deflated sales as a proxy for firm production analysis, assuming that goods produced by firms in a given industry are homogeneous.

The ten components of the index are: business, trade, fiscal, monetary, investment, financial, labor, freedom from corruption, government size, and property rights.

Data on government quality and freedom are available from a variety of sources, such as World Bank Doing Business, World Bank Governance Indicator, and Transparency International (Bernard et al. 2010). We chose the index developed by the Heritage Foundation because it covers the largest number of countries for the period under analysis. The pairwise correlation coefficients between the Heritage Foundation Index, the World Bank Governance Index, and the World Bank Doing Business Indicators (available upon request) show that these indicators are often highly correlated.

Sectoral-level information at a 3-digit level on Italian’ export and import by country is obtained from the Italian Statistical Office. We apply fixed weights (computed for year 1993) to all the sample to reduce possible endogeneity problems.

For instance, considering GDP as a country characteristic, we obtain an indicator for the sector “Textile” and the geographical area “USA–Canada” which is given by Italy’s share of export to the USA in the textile industry multiplied by the GDP of the US plus Italy’s share of export to Canada in the textile industry multiplied by Canada’s GDP.

See Serti et al. (2010) for the same methodology.

Following the literature on the microdynamics of trade (Buono et al. 2009; Eaton et al. 2008), we define a “trade-relation” as a positive shipment (either export or import) by a firm to a geographical area in a given year.

Exit/entry rate is calculated as the number of exit/entry firms from year \(t\) to year \(t+1\) and geographical area \(g\) relative to the average number of firms trading in \(t\) and \(t+1\).

The high values in the exit and entry rates for the associated EU group are mainly explained by the fact that there are very few firms trading with this area. For instance, 31 firms imported from associated EU in 1995, but none of them kept on buying from this group in 1996. At the same time 13 new firms imported from it in 1996. This implies an exit rate of 140 %. Note, however, that this group accounts for only 0.01 % of total Italian imports. The results of the regressions hold even if we exclude it from the empirical analysis.

This latter requirement ensures a more rigorous definition of persistence and allows us to define as continuous traders those firms that consecutively trade within the same geographical area during the observed period.

Although these effects seem small, the average unconditional export probability is approximately 27 % in our sample.

Note that while theoretical models would suggest a negative relationship between the probability of importing and remoteness, our results suggest an opposite pattern.

We would like to thank the anonymous referee for this helpful comment.

Results for the OLS are available upon request.

As a robustness check, we perform a validation exercise where we explore results under two more stringent definitions of trade starters. In the first case, \(D^\mathrm{TradeStarter}_{ig,t}\) takes value 1 for firms that do not trade with area \(g\) for 3 years, from \(t-3\) to \(t-1\), and start trading with it at time \(t\). In the second case, \(D^\mathrm{TradeStarter}_{ig,t}\) takes value zero for firms that have never traded at all, i.e., exported to or imported from any market. The results, available upon request, confirm the previous findings.

Our results are also robust to the alternative specification where we measure \(\mathrm{Global}\;\mathrm{Import}\;\mathrm{Share}_{f,t-1}\) as the ratio of a firm total imports to its total expenditure on intermediate inputs.

We emphasize that we cannot and do not want to give a causal interpretation for the coefficients, which simply reflects the conditional correlations of interest.

To gain additional insights into the separate and joint determinants of firm–country level import and export flows, we estimate an additional model that isolates firms that only export and only import. Unreported regressions show that results are very similar whether we use the full sample of firms or only one-way trade firms.

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Title: Export and import market-specific characteristics
How they drive the decision to trade and how much
Authors: Francesco Serti
Chiara Tomasi
Publication date: 01-12-2014
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 4/2014
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-013-0783-5

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