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Small Business Economics

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29-07-2017

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

Authors: Pierluigi Montalbano, Silvia Nenci, Carlo Pietrobelli

Published in: Small Business Economics | Issue 4/2018

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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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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.

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).

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

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.

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.

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).

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).

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.

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.

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.

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.

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.

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.

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).

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).

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.

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

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.

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.

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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Title: Opening and linking up: firms, GVCs, and productivity in Latin America
Authors: Pierluigi Montalbano
Silvia Nenci
Carlo Pietrobelli
Publication date: 29-07-2017
Publisher: Springer US
Published in: Small Business Economics / Issue 4/2018
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-017-9902-6

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