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Opening and linking up: firms, GVCs, and productivity in Latin America

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Abstract

This work explores the relationship between exports, global value chains (GVCs)’ participation and position, and firms’ productivity. To this aim, we combine the most recent World Bank Enterprise Survey in Latin American and Caribbean (LAC) countries with the Organisation for Economic Co-operation and Development and World Trade Organization trade in value-added data. To explore the above relationship, we adopt an extended version of the standard Cobb-Douglas output function including indicators of export performance and GVCs. We control for heterogeneity among firms (by country, region, and industry), sample selection, firms’ characteristics, and reverse causality. Our empirical outcomes confirm the presence of a positive relationship between participation in international activities and firm performance. They also show that both participation in GVCs and position within GVCs matter. These findings have strong policy implications and may help policymakers in choosing the best policy options to enhance the link between GVCs’ integration and firms’ productivity.

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Notes

  1. De Loecker (2010) states that current methods used to test for learning by exporting are biased toward rejecting the hypothesis of positive effects of exports on productivity.

  2. The value added reflects the value that is added by industries in producing goods and services. It is equivalent to the difference between the industry output and the sum of its intermediate inputs. Looking at trade from a value-added perspective better reveals how upstream domestic industries contribute to exports, as well as how much (and how) firms participate in GVCs (OECD–WTO, 2012).

  3. Such as the World Input-Output Database (WIOD), the Global Trade Analysis Project (GTAP), and the EORA-MRIO database.

  4. The TiVA version we use in this work provides 39 indicators for 57 countries (34 OECD countries plus 23 other economies, including Argentina, Brazil, China, India, Indonesia, the Russian Federation, and South Africa) with a breakdown into 18 industries. Like for the WBES, the industry classification is based on the ISIC, Rev. 3.1.

  5. We note a caveat in this decomposition at the industry level. While the value added embedded in a given imported intermediate could travel across many sectors before it is exported, the adopted decomposition traces only the direct and indirect effects.

  6. Specifically, we match the TiVA GVC indicators computed for 2009 with the characteristics of firms available in the LAC WBES survey for 2010 that refer to the last completed fiscal year (2009).

  7. The limitations of the WBES firm-level data have been acknowledged in the literature: for example, their being confined to the formal economy, their focus on manufacturing firms, and their variable levels of representativeness (Grazzi et al., 2016).

  8. Only direct exporters with direct exports above 10% of annual total sales are considered exporters, whereas only firms with 10% of ownership held by private foreign investors are considered foreign owned.

  9. Note that in this descriptive analysis, we use the firm-level data from the 2010 WBES survey for Argentina, Chile, and Mexico since the information collected in the surveys refers to characteristics of the firm to the last completed fiscal year (2009), and the 2009 WBES survey from Brazil.

  10. For the industrialized economies, we selected the USA, Japan, and Germany; for the emerging economies, we selected China, India, and South Korea; and for the developing/transitioning economies, we selected Poland, Turkey, and South Africa.

  11. We use here labor productivity as a proxy of firm productivity. We acknowledge this is not the only (and probably also not the best) measure. Unfortunately, the available LAC WBES cross-sectional dataset is not suited to calculate other measures (e.g., total factor productivity) using the standard methodologies.

  12. Although data by subnational regions are not representative of their relative economic relevance, subnational dummies are included in our analysis to capture additional unobserved heterogeneity across firms.

  13. We eliminated the observations of the last 5 percentiles of our measure of labor productivity in order to clean it of potential outliers and keep consistency with the hypothesis of normal distribution. Without truncating, the outcomes do not change significantly. The results are available upon request.

  14. Since we are fully aware of the selection mechanism, we easily identify selection in exporting by looking at the following exclusion restrictions: firms’ size and international linkages. This latter is proxied by the presence of foreign ownership (i.e., firm with at least 10% ownership held by private foreign investors). The selection equation estimates are then used to construct a proxy of the non-selection hazard, the so-called “inverse Mills ratio” (IMR) term, defined as \( {\lambda}_i=\frac{\phi \left[{x}_i^{\prime}\beta \right]}{\Phi \left[{x}_i^{\prime}\beta \right]} \) (Wooldridge, 2010). This term is then included in the main equation regression where only the truncated dependent variable is considered to derive the so-called “selection coefficient” (θ IMR). A simple t test of whether H 0: θ IMR = 0 is a workable test for sample selectivity bias. Note that because the IMR is a non-linear function of the variables included in the first-stage model, the second-stage equation is identified because of this non-linearity also when there is no “exclusion restriction” (i.e., Z = X).

  15. Different from the IV technique, the CF approach controls for the likely endogeneity bias by directly adding the estimated residual of a first-stage equation (ρ) into the main regression. This residual is, by definition, uncorrelated with the endogenous variable and provides an unbiased CF estimator that is generally more precise than the IV estimator (Wooldridge, 2010).

  16. As the main excluded instrument in our analysis, we use the following variable: “average time to clear imports from customs (in days).” With respect to other options, it is characterized by a sufficient number of observations and proves to be the most relevant instrument for the value of exports. It can be argued that better performing firms are more likely to better prepare trade documents and shipments and thereby spend less time in customs or in getting a license. However, in our case, the weak correlation between firm labor productivity and the above instrument confirms that these trade obstacles are more related to causes that are external to firms (e.g., procedures, institutional efficiency, etc.). Because of the sample selection, the IMR from the selection equation has been also added as the excluded instrument in the first-stage regression.

  17. Due to space limitations, the first-stage estimates are not reported in the table but can be provided by the authors upon request.

  18. Concerning the validity of the excluded instruments, Wooldridge’s score test for over-identifying restrictions (robust to heteroscedasticity) does not reject the null hypothesis that our instruments are valid at the 5% significance level and the Angrist-Pischke (AP) F statistics strongly rejects the null of weak instruments. Furthermore, the Stock and Yogo minimum eigenvalue statistic is high, and Shea’s adjusted partial R 2 statistic shows that our instruments add enough information in the first-stage equation.

  19. Please acknowledge that the residuals in the main equation ρ actually depend on the sampling error in the first-stage equation unless ρ is equal to zero.

  20. The Levine test is similar to the standard ANOVA test but less sensitive to the violation of normality assumption. The conventional Levine test strongly rejects the null hypothesis both when centered at the mean (with Pr > F = 0.004) and when centered at the median (with Pr > F = 0.009).

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Acknowledgements

The authors are grateful to Juan Blyde, Anna Giunta, Matteo Grazzi, Christian Volpe Martincus, Siobhan Pangerl, and Adam Szirmai for their insightful comments and suggestions. They also thank the seminar participants at the Centro Rossi-Doria Workshop “Global Value Chains for Food and Nutrition Security,” University of Roma Tre, the ETSG International Conference, Munich University, and the IDB Workshop “Determinants of Firm Performance in LAC: What Does the Micro Evidence Tell Us?,” Washington, DC. Finally, they are indebted to both the Editor and the two anonymous referees for their useful comments and suggestions for improvement. This publication was made possible thanks to the support of the Inter-American Development Bank. The opinions expressed here are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent

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Authors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Pierluigi Montalbano

  2. University of Sussex, Sussex, UK

    Pierluigi Montalbano

  3. University of Roma Tre, Rome, Italy

    Silvia Nenci & Carlo Pietrobelli

  4. UNU-MERIT, Maastricht, The Netherlands

    Carlo Pietrobelli

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  1. Pierluigi Montalbano
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  2. Silvia Nenci
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Correspondence to Carlo Pietrobelli.

Appendix

Appendix

According to the methodology proposed by Koopman et al. (2011), gross exports can be decomposed into the following value-added components (see Fig. 4):

  • Direct domestic value added embodied in exports of goods and services (DVA), which reflects the direct contribution made by an industry in producing a final or intermediate good or service for export (i.e., value added exported in final goods or in intermediates absorbed by direct importers).

  • Indirect domestic value added embodied in intermediate exports (IVA), which reflects the indirect contribution of domestic supplier industries of intermediate goods or services used in the exports of other countries (i.e., value added exported in intermediates re-exported to third countries).

  • Re-imported domestic value added embodied in gross exports (RVA), which reflects the domestic value added that was exported in goods and services used to produce the intermediate imports of goods and services used by the industry (i.e., exported intermediates that return home).

  • Foreign value-added embodied in gross exports (FVA), which reflects the foreign value-added content of intermediate imports embodied in the final and intermediate gross exports (i.e., other countries domestic value added in intermediates used in exports).

These terms are complemented by a further component that quantifies the so-called “double counting,” i.e., the value of intermediate goods that cross international borders more than once (Koopman et al., 2014). The pure double-counted (DC) component reflects the (double-counted) values in intermediate goods trade that are originated at home and abroad.

Fig. 4
figure 4

Gross export decomposition in value added

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Table 6 Variables used in the empirical analysis: definition and main descriptive statistics
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Table 7 The LAC restricted sample used in the empirical analysis
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Montalbano, P., Nenci, S. & Pietrobelli, C. Opening and linking up: firms, GVCs, and productivity in Latin America. Small Bus Econ 50, 917–935 (2018). https://doi.org/10.1007/s11187-017-9902-6

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