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Journal ArticleDOI

Identifying FDI spillovers

TL;DR: In this paper, the authors improved on the strategy used in the literature to identify the spillover effect of horizontal foreign direct investment (FDI) by taking advantage of the plausibly exogenous relaxation of FDI regulations on China's World Trade Organization accession at the end of 2001.
About: This article is published in Journal of International Economics.The article was published on 2017-07-01 and is currently open access. It has received 281 citations till now. The article focuses on the topics: Spillover effect & Foreign direct investment.

Summary (5 min read)

1 Introduction

  • Over the past few decades, developing countries around the world have removed restrictions on foreign direct investment (FDI) and even adopted policies to attract FDI, in the belief that domestic firms can benefit from the presence of FDI.
  • The lack of consensus in the academic literature on the effect of FDI on domestic firms (generally referred to as the FDI spillover effect) prompts us to reexamine this research question.
  • Using this shock as an instrument for the presence of FDI, the authors are able to compare firm performance in their treatment group (i.e., industries that encouraged FDI entries) with performance in their control group (i.e., industries that did not have any change in FDI regulations) before and after China’s WTO accession at the end of 2001.
  • The authors find that the presence of foreign multinationals has no significant effect on the exporting performance or R&D investment of domestic firms in the same industries, leads to significant increases in the wage rate paid by domestic firms in the same industry, and decreases the exit probability of domestic firms in the same industry.

2.1 Regulations of FDI in China

  • Before 1978, China was a closed economy under rigid central planning, and there was an almost complete absence of foreign-invested enterprises (FIEs) in the country.
  • 4 Xiaoping took a tour of Southern China in the spring of 1992 to revive a slowing economy, the FDI inflows to China grew even faster, reaching US$ 27.52 billion in 1993.
  • FIEs operating in China still faced significant obstacles.
  • In June 1995, the central government of China promulgated the “Catalogue for the Guidance of Foreign Investment Industries” (henceforth, the Catalogue), which, together with the modifications made in 1997, became the government guidelines for regulating the inflows of FDI.

2.2 Data

  • The main data used in this study are from the Annual Survey of Industrial Firms (ASIF), conducted by the National Bureau of Statistics of China for the 1998—2007 period.
  • Second, there were very few changes in the 2004 revision of the Catalogue.
  • Some of the possible Catalogue products in a 4-digit CIC industry experienced an improvement in FDI regulations, but some had worsening FDI regulations, also known as 4. Mixed Industries.

2.3 Estimation Specification

  • Xfit is a vector of time-varying firm and industry characteristics used to isolate the effect of FDI spillover effect; and εfit is the error term.
  • Following Bertrand, Duflo, and Mullainathan (2004), the authors address the potential serial correlation and heteroskedasticity issues by calculating the standard errors clustered at the industry level.
  • 17The results (available upon request) remain robust when the authors include the discouraged industries in the analysis.
  • The results obtained using the full sample but controlling for foreign equity share, as is common in the literature, are qualitatively the same and available upon request.
  • To deal with the identification problem, the authors use variations across industries in the changes in FDI regulations upon China’s WTO accession as an instrument for FDI_Sectorit and to identify the FDI spillover effect on domestic firms.

2.4 Identifying Assumption and Checks

  • The selection of which industries to open up to FDI upon the WTO accession was not random; hence, the encouraged industries and the no-change industries could have been experiencing different trends before the WTO accession and these differences might have generated differential trends in their outcomes across industries in the post-WTO period.
  • To address this estimation concern, the authors first match the ASIF data to China’s Customs data to identify processing traders, and then exclude these firms from the regression sample.
  • The input tariff is constructed as a weighted average of the output tariff, using as the weight share of the inputs in the output value from the 2002 China’s Input-Output Table.
  • Post false t should produce zero effect; otherwise, it indicates the existence of the omitted variable ωfit.21.

3.1 Graphical Results

  • The authors start with total factor productivity (TFP) as a measure of firm performance for their investigation, as it is the most widely used indicator in the literature.
  • Specifically, the authors use the control function approach developed by Ackerberg, Caves, and Frazer (2015) to estimate the production function for each of the 29 two-digit industries, and then calculate the TFP for each firm and each year.
  • The treatment and control groups were balanced in TFP in the pre-WTO period, indicating a good comparability between their treatment and control groups conditional on their selected controls.
  • In the post-WTO period, the treatment group experienced a gradual and persistent decline in TFP compared with the control group, indicating that the relaxation of FDI regulations had a negative effect on firm productivity.

3.2 Regression Results

  • The instrumental variable estimation results are reported in Table 3, with first stage estimates in panel A and second stage ones in panel B.
  • With respect to their central research focus, the authors find that, after being instrumented, FDI_Sectorit consistently casts a negative and statistically significant effect on firm productivity.
  • First, in column 4 of Table 3, the authors exclude processing traders from their sample to alleviate the concern that their findings may be driven by changes in the trading regime upon China’s accession to the WTO.
  • The authors find that the distribution of these estimates is centered around zero (mean value of 0.0004), with a standard deviation of 0.0179.

4 How to Explain the Negative FDI Spillovers

  • The previous section establishes that FDI causes a negative spillover effect on the productivity of domestic firms in the same industry.
  • The authors explore the relevance of two hypotheses that are widely used to explain the negative FDI spillover effect.
  • To save space, the authors only show the second stage results of the instrumental variable estimation; the first stage estimation results are available upon request.

4.1 Agglomeration versus Competition

  • Aitken and Harrison (1999) provide a framework for understanding the negative spillover effect of FDI on domestic firms.
  • They argue that domestic firms can benefit from nearby foreign multinationals through knowledge spillovers (such as imitation of foreign multinationals’technologies, management practices, and market orientation), labor pooling (such as recruitment of employees who have had experience at those foreign multinationals), and supply of specialized inputs (for example, obtaining inputs from suppliers of foreign multinationals).
  • Such a positive effect is usually referred to in the international and urban economics literature as the agglomeration effect.
  • Domestic firms may lose market share to the generally more productive foreign multinationals, and consequently experience a fall in firm productivity due to a lack of scale economies.
  • To further understand how the competition and agglomeration effects determine the overall FDI spillovers on domestic firms, the authors explore variations in different dimen- 15 sions of FDI, and examine scenarios under which these two underlying effects have different relative strengths, leading to possibly different overall FDI spillovers.

4.1.1 FDI from Developed Countries vs. Developing Countries

  • Foreign multinationals come from different countries with different technologies and know-how, and present different trade-offs to China’s domestic firms.
  • FIEs from developing countries with a similar or even a lower level of economic development than China may not possess any advanced technology or sophisticated know-how for China’s domestic firms to benefit from.
  • Hence, the examination of possibly differential effects of FDI from developing and developed countries can reveal the interaction between the negative competition effect and positive agglomeration effect from FDI.
  • ×Outputfit∑ f∈Ωit Outputfit , where FDI_Firm_Developedfi2001 and FDI_Firm_Developingfi2001 are the foreign equity of firm f of industry i in 2001 from developed and developing countries, respectively.
  • 16 Given that the authors have two potentially endogenous regressors of interest in the estimation, they need two instruments for their identification.

4.1.2 Horizontal vs. Vertical FDI

  • Javorcik (2004) demonstrates the importance of upstream and downstream linkages as potential channels for FDI to have positive effects on domestic firms.
  • 24 Intuitively, the authors do not expect any direct competition between domestic firms and foreign multinationals that are located in different vertical stages of the same production chain, and such foreign multinationals have more incentives to educate their domestic clients or suppliers.
  • In other words, the agglomeration effect might dominate the competition effect for FDI located in either upstream or downstream industries.
  • To test this prediction, the authors follow Javorcik (2004) in constructing a domestic firm’s backward and forward FDI_Sector.
  • The effect of horizontal FDI on firm productivity remains negative and significant.

4.1.3 Local vs. Non-Local FDI

  • The competition and agglomeration effects of FDI exhibit different degrees of attenuation with distance.
  • As the positive agglomeration effect is relatively more localized than the negative competition effect, it is expected that domestic firms benefit from foreign 25αsk is calculated by excluding the products supplied for final consumption and the imports of intermediate products.
  • 26The industries in the ASIF data are more disaggregated than the sectors in China’s InputOutput (or IO) Table.
  • 28For a recent review of this literature, see Audretsch and Feldman (2004).
  • To test this prediction, the authors divide the extent of FDI in an industry into two parts: the extent of FDI located in the same city as the concerned domestic firm and the extent of FDI located outside of the city.

4.1.4 Static vs. Dynamic Effects

  • Kosová (2010) argues that the competition effect could be a short-term effect whereas the agglomeration effect may take time to become effective; in this case, the spillover 19 effect of FDI on domestic firms could become positive in the growth rate estimations.
  • Using firm-level data from the Czech Republic for the 1994 to 2001 period, she finds that growing foreign sales increases domestic firms’growth.
  • This dynamic positive effect of FDI on domestic firms is also found by Liu (2008) and Merlevede, Schoors, and Spatareanu (2014).
  • Following this line of research, the authors investigate whether the presence of foreign multinationals has a positive effect on the growth rate of firm productivity (measured as one-year differenced firm productivity).
  • These results suggest that although the presence of foreign multinationals may hurt domestic firms in the short-run (by stealing their market share), they may benefit domestic firms in the long-run (through knowledge spillover, labor pooling, etc.).

4.2 Absorptive Capacity

  • The authors analysis in Section 4.1 focuses on how various types of FDI differentially affect the agglomeration effect and competition effect, and hence the overall spillover effect of FDI on domestic firms.
  • In this subsection the authors examine how the absorptive capacity of domestic firms may affect the FDI spillover effect.
  • Lin, Liu, and Zhang (2009) find that the negative effect of FDI on firm productivity is smaller for Chinese SOEs than for other domestic non-SOEs, presumably because state-owned enterprises in China are better endowed and more capable of absorbing technology and know-how from foreign multinationals than their privately-owned counterparts.
  • The authors use the changes in FDI regulations at the end of 2001 20 to instrument the presence of foreign multinationals.
  • R&D decisions and ownership structure, the authors measure the R&D investment ratio and ownership structure using information from 2001, one year before the changes in FDI regulations.

5 Other Measures of Firm Performance

  • The above analyses focus on productivity as the measurement of firm performance.
  • The second stage results of the instrumental variable estimation are presented in Table 6; the first stage estimation results are available upon request.
  • The authors examine whether the presence of foreign multinationals helps domestic firms in the same industry to export.
  • Next, the authors investigate the FDI effect on the wage rate of domestic firms.

6 Conclusion

  • It is notoriously hard to identify the FDI spillovers on domestic firms, as the decision by foreign multinationals to enter developing countries and their various industries is obviously an endogenous one.
  • This partially explains why there is no consensus on the effect of horizontal FDI on domestic firms.
  • These mixed findings are troubling, as the governments of developing countries have been urged by both developed countries and international organizations to open up their economies to foreign direct investment.
  • This paper contributes to the literature by utilizing the 23 arguably exogenous relaxation of FDI regulations upon China’s accession to the WTO, under which some of China’s manufacturing industries became more open to foreign direct investment (the treatment group) while others encountered no change in FDI regulations (the control group).
  • In addition, this paper investigates the two underlying effects (the agglomeration effect and the competition effect) involved in FDI spillovers on domestic firms.

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References
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TL;DR: In this article, the authors randomly generate placebo laws in state-level data on female wages from the Current Population Survey and use OLS to compute the DD estimate of its "effect" as well as the standard error of this estimate.
Abstract: Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

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Abstract: This Paper builds a dynamic industry model with heterogeneous firms that explains why international trade induces reallocations of resources among firms in an industry. The Paper shows how the exposure to trade will induce only the more productive firms to enter the export market (while some less productive firms continue to produce only for the domestic market) and will simultaneously force the least productive firms to exit. It then shows how further increases in the industry's exposure to trade lead to additional inter-firm reallocations towards more productive firms. These phenomena have been empirically documented but cannot be explained by current general equilibrium trade models, because they rely on a representative firm framework. The Paper also shows how the aggregate industry productivity growth generated by the reallocations contributes to a welfare gain, thus highlighting a benefit from trade that has not been examined theoretically before. The Paper adapts Hopenhayn's (1992a) dynamic industry model to monopolistic competition in a general equilibrium setting. In so doing, the Paper provides an extension of Krugman's (1980) trade model that incorporates firm level productivity differences. Firms with different productivity levels coexist in an industry because each firm faces initial uncertainty concerning its productivity before making an irreversible investment to enter the industry. Entry into the export market is also costly, but the firm's decision to export occurs after it gains knowledge of its productivity.

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Abstract: Technological change and deregulation have caused a major restructuring of the telecommunications equipment industry over the last two decades. We estimate the parameters of a production function for the equipment industry and then use those estimates to analyze the evolution of plant-level productivity over this period. The restructuring involved significant entry and exit and large changes in the sizes of incumbents. Since firms choices on whether to liquidate and the on the quantities of inputs demanded should they continue depend on their productivity, we develop an estimation algorithm that takes into account the relationship between productivity on the one hand, and both input demand and survival on the other. The algorithm is guided by a dynamic equilibrium model that generates the exit and input demand equations needed to correct for the simultaneity and selection problems. A fully parametric estimation algorithm based on these decision rules would be both computationally burdensome and require a host of auxiliary assumptions. So we develop a semiparametric technique which is both consistent with a quite general version of the theoretical framework and easy to use. The algorithm produces markedly different estimates of both production function parameters and of productivity movements than traditional estimation procedures. We find an increase in the rate of industry productivity growth after deregulation. This in spite of the fact that there was no increase in the average of the plants' rates of productivity growth, and there was actually a fall in our index of the efficiency of the allocation of variable factors conditional on the existing distribution of fixed factors. Deregulation was, however, followed by a reallocation of capital towards more productive establishments (by a down sizing, often shutdown, of unproductive plants and by a disproportionate growth of productive establishments) which more than offset the other factors' negative impacts on aggregate productivity.

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