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Vertical Integration and Firm Performance Yi Lu and Zhigang Tao The University of Hong Kong This version: July 2008

Abstract Theoretical and empirical research in the past decades has advanced our understanding of what determines the vertical boundary of a …rm. An equally important but much less understood issue is the impacts of vertical integration on …rm performance, mainly because of the endogeneity issue. Using a survey of China’s manufacturing …rms, we establish the causal impacts of vertical integration on …rm performance by adopting the instrumental variable approach to deal with the endogeneity issue. We …nd that the degree of vertical integration causes a negative impact on …rm sales, market share and productivity, but a positive impact on product prices.

Keywords: Vertical Integration, Firm Performance, Instrumental Variable JEL Codes: L22, D23, L25

1

Introduction

Since the seminal work of Ronald Coase (1937), much attention has been drawn toward understanding what determines the vertical boundary of a …rm. Leading theories on the vertical scope of a …rm include the transactioncost theory developed by Williamson (1975, 1985) and Klein, Crawford, and Corresponding author: Zhigang Tao, Faculty of Business and Economics, The University of Hong Kong, Pokfulam Road, Hong Kong. Tel: 852-2857-8223; Fax: 852-2858-5614; Email: [email protected]. Financial supports from CERG and University of Hong Kong are also greatly acknowledged.

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Alchian (1978), and the incomplete contract theory of the …rm developed by Grossman and Hart (1986) and Hart and Moore (1990). There is also a large body of empirical studies on the relevance of these theories for …rm vertical scope.1 An equally important, but much less studied issue is the consequence of vertical boundary decision. Existing studies tend to focus on the anticompetitive implications of and policy recommendations regarding …rm vertical integration decisions.2 However, for …rms making decisions on their vertical boundary, they are equally, if not more, concerned about the direct impacts of vertical integration on their own …rm performance. Empirical studies along this line are quite limited due to the problem of data availability, with most of them being industry-speci…c studies (Levin, 1981; Mullainathan and Scharfstein, 2001; Forbes and Lederman, 2007; Hortaçsu and Syverson, 2007a; Novak and Stern, 2008). An even more serious di¢ culty is to establish the causal relation from …rm vertical integration to …rm performance, i.e., the endogeneity problem. In this paper, we present an empirical study of the impacts of vertical integration on …rm performance using a survey of China’s manufacturing …rms conducted by the World Bank in early 2003. We establish the causal e¤ects of vertical integration on …rm performance by using the instrumental variable approach. Speci…cally, we use the degree of local purchase (a proxy for the extent of site speci…city) as an instrument for vertical integration, as Williamson (1975, 1985) argues that the former is a determinant of the latter. We …nd that the degree of …rm vertical integration causes a …rm to have fewer sales, lower market share, and lower labor productivity, but a higher price of its main product. The validity of our instrumental variable estimation hinges upon the satisfaction of two conditions: the relevance condition and the exclusion restriction. The relevance condition is con…rmed by the highly signi…cant correlation between the instrumental variable and the vertical integration index, and by the results of two relevance tests (i.e., Anderson canonical correlation LR statistic and Cragg-Donald Chi-sq statistic). Meanwhile, the Shea test for excluded instrument and the Cragg-Donald F-statistic rule out the concern for weak instrument. With regard to the exclusion restriction, we conduct several empirical tests. First, we identify possible channels through which the instrument may a¤ect …rm performance other than vertical integration. 1 For a review of theoretical studies, see Holmstrom and Roberts, 1998; Whinston, 2003; Gibbons, 2005; for a review of empirical studies, see Klein, 2005; Lafontaine and Slade, 2007. 2 For a review of theoretical and empirical studies on anticompetitive implications of vertical integration, see Lafontaine and Slade, 2007.

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Second, we control for variables that may determine both the instrumental variable and …rm performance. Third, we include other variables that may correlate with both the instrumental variable and …rm performance. The results are robust to these tests, suggesting the satisfaction of the exclusion restriction. For further robustness check, we use an alternative instrumental variable, the percentage of a …rm’s suppliers located in the same province where the …rm is, and …nd that our results remain robust. We also repeat the analysis for the sub-sample excluding some outliers, the sub-sample of …rms with focused businesses, and the sub-sample of private …rms, and again our results are robust. Our empirical …ndings suggest that vertical specialization (the opposite of vertical integration) leads to an increase of sales and market share, through which a …rm could improve production e¢ ciency and a¤ord to charge a lower price for its main product. These …ndings are consistent with the extent of market argument by Stigler (1951), Holmes (1999), McLaren (2000), and Chen (2005), and with the stated reasons for the divesture decisions of many business corporations such as GM-Delphi (Tait, 1999), and AT&T and its equipment division (Kirkpatrick, 1995). Our paper is one of the …rst few studies investigating the consequences of …rm vertical boundary decision across a range of industries and furthermore establishing its causal impacts on …rm performance. One recent paper closest to our study is Hortaçsu and Syverson (2007b). Using two unique data sets, they systematically document the di¤erences between vertically integrated plants and non-integrated ones in the U.S. manufacturing industries from 1977 to 1997. However, their study does not address the potential endogeneity problems and could not establish the causal e¤ects of vertical integration, which is the focus of this paper.3 The structure of the paper is as follows. Data and variables are described in Section 2, and our main …ndings are presented in Section 3. The paper concludes with Section 4.

2

Data and Variables

The data set used in this study comes from a survey of …rms in China, conducted by the World Bank in cooperation with the Enterprise Survey Or3

Hortaçsu and Syverson (2007b) acknowledge that “di¤erences [between vertically integrated plants and non-integrated ones] primarily embody persistent di¤erences in the plants that are started by or brought into …rms with vertical structures, but to some extent they also re‡ect changes that formerly unintegrated plants experience upon integration.”

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ganization of China in early 2003.4 A total of 18 cities were chosen from …ve supra-regions of China for balance of representation: 1) Benxi, Changchun, Dalian, and Haerbin in the Northeast; 2) Hangzhou, Jiangmen, Shenzhen, and Wenzhou in the Coastal area; 3) Changsha, Nanchang, Wuhan, and Zhengzhou in Central China; 4) Chongqing, Guiyang, Kunming, and Nanning in the Southwest; 5) Lanzhou and Xi’an in the Northwest. In each city, 100 or 150 …rms were randomly sampled from the following nine manufacturing industries and …ve service industries: garment and leather products, electronic equipment, electronic parts making, household electronics, auto and auto parts, food processing, chemical products and medicine, biotech products and Chinese medicine, metallurgical products, transportation services, information technology, accounting and non-banking …nancial service, advertisement and marketing, and business services.5 The total number of surveyed …rms is 2,400. In this study, we focus on the subsample of 1,566 manufacturing …rms, for which some of the key variables (i.e., our instrumental variables) are available. The dependent variables in our study are indicators of …rm performance, including Labor Productivity (measured by the logarithm of value added per worker), Sales (measured by the logarithm of total sales), Market Share (a dummy variable taking value one if the …rm has a share of more than 1% in the market of its main product and zero otherwise), and Price (a categorical variable taking value one if the …rm decreased the price of its main product in the previous year, value two if the …rm maintained the price in the previous year, and value three if the …rm increased the price in the previous year). In the survey, there is a question regarding the percentage of a …rm’s parts in value that are produced within the …rm. The reply to this question is then used to construct the key explanatory variable of this study, i.e., the degree of Vertical Integration.6 There are substantial variations in the degree of vertical integration across …rms, with a mean value of 0.339 and a standard deviation of 0.401. To deal with the issue that the degree of Vertical Integration could be endogenously determined, we use the percentage of Local Purchase (measured by the percentage of a …rm’s inputs that are purchased from the same 4

The data set has been used by Cull and Xu (2005), Ayyagari, Demirgüç-Kunt, and Maksimovic (2007), and Lu, Png and Tao (2008). 5 This classi…cation is between the two-digit and three-digit SIC codes. 6 The measurement for the degree of vertical integration at the …rm or plant level has always been a challenging problem because of data availability. As a result, indirect measures have been used in the literature (for example, Fan, Huang, Morck, and Yeung, 2007; Hortaçsu and Syverson, 2007b; Acemoglu, Johnson, and Mitton, 2008). Note that our measure is a direct measure for the degree of vertical integration.

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province where the …rm is located) to instrument the degree of Vertical Integration. As a robustness check, we use an alternative instrumental variable, Local Suppliers, measured by the percentage of a …rm’s suppliers located in the same province where the …rm is. The rationale for using these instrument variables comes from Williamson (1983, 1985)’s argument for the e¤ect of site-speci…city on vertical integration, which will be discussed in details in Section 3. To address the concern that the percentage of Local Purchase may a¤ect …rm performance other than Vertical Integration, we control for Transportation Cost (measured by transportation costs divided by total sales), Inventory (measured by the inventory stocks of …nal goods over total sales), Input Speci…city (measured by the percentage of a …rm’s inputs that were made to the …rm’s unique speci…cations), and Delivery Loss (measured by the percentage of sales that were lost due to the delivery delays in the previous year). In the empirical analysis, we also control for other factors that may a¤ect …rm performance. Variables related to …rm characteristics include: Firm Size (measured by the logarithm of …rm employment), Firm Age (measured by the logarithm of years of establishment), Property Rights Protection (measured by a …rm’s perception of the likelihood of local government o¢ cials acting as a helping hand for the …rm rather than a hindering hand), Contract Enforcement (measured by a …rm’s perception of the likelihood of legal system to uphold private contracts), and Lagged Labor Productivity (measured by the logarithm of value added per worker in the previous year). Variables concerning CEO characteristics (similar to those used by Cull and Xu, 2005) are: Education (years of schooling), Tenure (years of being CEO), Deputy CEO Before (a dummy variable indicating whether the CEO was the …rm’s deputy CEO before he became CEO), and Government Cadre (a dummy variable indicating whether the CEO was a government o¢ cial before he became CEO). Variables related to city characteristics include: Logarithm of GDP per capita, and Logarithm of Population. Finally, we include industry dummies to account for the industry e¤ects. Summary statistics of all key variables are listed in Table 1, and their correlations are reported in Table 2.

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3 3.1

Empirical Analysis Main Results

To investigate the impacts of vertical integration on …rm performance, we estimate the following equation: yeic =

+

0 + "eic Vertical Integration eic + Xeic

(1)

where yeic are the measures of …rm e’s performance in industry i and city c (including Labor Productivity, Market Share, Sales, and Price), Vertical In0 is a vector of control tegration eic is …rm e’s degree of vertical integration, Xeic variables including …rm characteristics, CEO characteristics, city characteristics, and industry dummies, and "eic is the error term. Standard errors are clustered at the city level, allowing for arbitrary correlation within the city. Ordinary least square estimation (OLS) results are reported in Table 3. The degree of vertical integration is negatively correlated with labor productivity, sales and price, but positively correlated with market share. All estimates except that for price are not precisely estimated. Presumably, the inconclusive OLS results are due to the endogeneity problem associated with the degree of vertical integration. For example, there could be some unobservable variables a¤ecting both …rm performance and its degree of vertical integration, as a result of which the OLS estimation results are biased or even misleading. To address the endogeneity problem, we use the instrumental variable approach and re-estimate equation (1) by two-stage-least-square (TSLS) regression. Speci…cally, we use the percentage of Local Purchase as the instrumental variable for the degree of Vertical Integration. As argued by Williamson (1983, 1985), …rms with higher site-speci…city vis-a-vis their suppliers have greater potential of being held up, and they are thus more likely to vertically integrate the production of parts and components. This theoretical prediction has been supported by many empirical studies, such as Masten (1984), Joskow (1985), Spiller (1985), and González-Díaz, Arru½nada, and Fernández (2000). Panel B of Table 4 reports the …rst-stage estimation results of the TSLS regression, and con…rms the signi…cant and positive correlation between the degree of vertical integration and the percentage of local purchase. Panel A of Table 4 presents the second-stage estimation results of the TSLS regressions. It is found that the degree of vertical integration casts negative impacts on labor productivity, market share, and sales, but positive impacts on product prices, all of which are statistically signi…cant. The results imply that vertical specialization (the opposite of vertical integration) leads to an increase in …rm 6

sales and market share, under which …rms achieve better production e¢ ciency and thus a¤ord to charge lower prices for their main products. Speci…cally, one-standard-deviation increase in the degree of vertical integration leads to a 2.4-standard-deviation decrease in labor productivity, a 2.2-standarddeviation decrease in market share, a 3.1-standard-deviation decrease in sales, and a 2.0-standard-deviation increase in price. Two conditions are required for the validity of our instrumental variable estimation: the relevance condition and the exclusion restriction. The relevance condition is con…rmed by the highly signi…cant correlation between the instrumental variable and the vertical integration index (Panel B of Table 4), and the results of two relevance tests (i.e., the Anderson canonical correlation LR statistic and the Cragg-Donald Chi-sq statistic shown in Panel C of Table 4). Meanwhile, the concern for weak instrument is ruled out by the results of the Shea test for excluded instrument and the Cragg-Donald F-statistic (shown in Panel C of Table 4).7 As for the exclusion restriction, we conduct several empirical tests. First, we identify four possible channels other than vertical integration through which the instrument may a¤ect …rm performance. For example, a …rm may incur lower transportation costs when it sources its parts and components more locally, and thus the …rm with a higher percentage of local purchases could have better performance. Similarly, there is less need for inventory when a …rm is closer to its suppliers, leading to better performance. It is also possible that locally purchased inputs are made to the …rm’s unique speci…cations and they add values to the …rm’s products. Finally, local purchases could minimize delays in delivery and consequently losses in sales. To control for these possibilities, we re-run the TSLS regressions by incorporating the control variables regarding transportation costs, inventory, input speci…city, and delivery loss one by one, and …nd that our results remain robust (see Table 5). Secondly, one may argue that the instrumental variable used above may not be totally exogenous. For example, both the instrumental variable and the dependent variables could be in‡uenced by some underlying economic factors, such as lagged labor productivity, degree of contract enforcement, and quality of property rights protection. To address this concern, we include these economic factors in the regressions, and …nd that our results remain robust (see Table 6). Finally, we include other control variables that may correlate with both the instrumental variable and …rm performance, such as …rm and CEO char7

The Cragg-Donald F-statistic values for our regressions are signi…cantly above the value of 10, which is considered as the critical value by Staiger and Stock (1997).

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acteristics, city characteristics, and industry dummies in the TSLS estimations. The results are reported in Table 78 , and they are similar to those reported in Table 4 that vertically integrated …rms are smaller in terms of sales and market share, less productive, and more likely to increase prices of their main products.

3.2

Robustness Checks

First, we use an alternative instrumental variable, Local Suppliers, the percentage of a …rm’s suppliers located in the same province where the …rm is, and repeat the analysis. The results are reported in Table 8, with Panel A for the second stage of TSLS regressions, Panel B for the …rst stage of TSLS regressions, and Panel C for the various tests for the instrumental variable, respectively. The instrumental variable is positively and statistically significantly correlated with the endogenous variable, Vertical Integration, which is consistent with Williamson (1975, 1985)’s site-speci…city argument and …ndings of many empirical studies. Meanwhile, the Anderson canonical correlation LR statistic and the Cragg-Donald Chi-sq statistic show that the instrument is relevant, and the Shea test for excluded instrument and the Cragg-Donald F-statistic rule out the concern for weak instrument. Panel A of Table 8 shows that our main results regarding the causal impacts of vertical integration on …rm performance remain robust with this alternative instrumental variable estimation. One possible concern is that our results could be driven by a few outliers. To address this possibility, we exclude the bottom and top 1% observations in either labor productivity or sales. The results are shown in columns 1 and 2 of Table 9, respectively. Clearly our results remain robust to these two exercises, implying that the concern of outliers is not valid in our case. Finally, we carry out the analysis in two sub-samples. For …rms with many businesses, the degree of vertical integration could vary from one business to another. Thus our measure of vertical integration may re‡ect the average degree of vertical integration across various businesses, which may bias our estimations of the impacts of vertical integration on …rm performance. To alleviate this concern, we focus on the sub-sample of …rms with focused business (de…ned as …rms whose main business contribute more than 50% to their total sales). Our results shown in the odd columns of Table 9 suggest that our main …ndings remain robust to this sub-sample. Meanwhile, China’s state-owned enterprises are legacies of the central planning system, 8 In the analysis, we add the control variables stepwisely. As the results are quite similar, we only report the results with all control variables included in order to save space.

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and have had heavy social responsibilities. As a result, they tend to be vertically integrated and ine¢ cient. To make sure that our results are not driven by these state-owned enterprises, we focus on the sub-sample of private enterprises. As shown in the even columns of Table 9, our main …ndings remain robust to this sub-sample.

3.3

Discussion

Before concluding our analysis, we discuss how our empirical …ndings could be explained by existing theories. Most of the theories of …rm focus on the rationales for vertical integration, rather than on the implications of vertical boundary decision on …rm performance. For example, the incomplete contract theory argues that when the speci…c investment by downstream …rm D is more important than that of upstream …rm U, then …rm D should vertically integrate its upstream supplier U. What is not clear is how this vertical integration decision is going to a¤ect the performance of …rm D. This is because although …rm D will make a greater speci…c investment, its scale of operation may decrease as other downstream …rms will strategically purchase less from the upstream division of …rm D. Our empirical …ndings suggest that by being vertically specialized (the opposite of vertical integration), a …rm can have a larger scale of operation, for it can deal with many upstream and downstream …rms, and the economy of scale subsequently implies higher production e¢ ciency. These results lend support to the extent of market argument by Stigler (1951), Holmes (1999), McLaren (2000), and Chen (2005), and they are consistent with the stated reasons for the divesture decisions of many business corporations such as GM-Delphi (Tait, 1999), and AT&T and its equipment division (Kirkpatrick, 1995).

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Conclusion

After many decades of theoretical and empirical research, we now have a better understanding of what determines a …rm’s vertical boundary. The next agenda is to look at the impacts of vertical boundary decision on …rm performance. There are several papers in the literature focusing mostly on the anti-competitive e¤ects of vertical integration decision. However, more e¤ort is needed to investigate the direct impacts of vertical integration on …rm performance. The main di¢ culties encountered in this line of research is the endogeneity issue, i.e., a …rm’s decision on vertical integration is endogenously determined. 9

This paper makes a contribution to the literature by adopting the instrumental variable approach to deal with the endogeneity issue. Using a survey of China’s manufacturing …rms conducted by the World Bank in early 2003, we …nd that the degree of vertical integration causes a negative impact on …rm sales, market share and labor productivity, but positive impact on …rm product prices. Our results suggest that there are bene…ts from vertical specialization, lending support to the extent of market argument in the literature.

Reference Acemoglu, Daron, Simon Johnson, and Todd Mitton. 2008. "Determinants of Vertical Integration: Financial Development and Contracting Costs." Journal of Finance, forthcoming. Ayyagari Meghana, Alis Demirgüç-Kunt, and Vojislav Maksimovic. 2007. "Formal versus Informal Finance: Evidence from China." working paper, World Bank. Chen, Yongmin. 2005. "Vertical Disintegration." Journal of Economics and Management Strategy 14, 209-229. Coase, Ronald. 1937. "The Nature of the Firm." Economica 4(16): 386-405. Cull, Robert, and Lixin Colin Xu. 2005. "Institutions, Ownership, and Finance: the Determinant of Pro…t Reinvestment among Chinese Firms." Journal of Financial Economics 77(1), 117-146. Fan, Joseph, Jun Huang, Randall Morck, and Bernard Y. Yeung. 2007. "Institutional Determinants of Vertical Integration: Evidence from China." working paper. Forbes, Silke Januszewski, and Mara Lederman. 2007. "Does Vertical Integration A¤ect Firm Performance? Evidence from the Airline Industry." working paper, University of California, San Diego. Gibbons, Robert. 2005. "Four Formal(izable) Theories of the Firm?" Journal of Economic Behavior and Organization 58(2), 200-245. González-Díaz, Manuel, Benito Arru½nada, and Alberto Fernández. 2000. "Causes of Subcontracting: Evidence from Panel Data on Construction Firms." Journal of Economic Behavior and Organization 42(2), 167-187. Grossman, Sanford J., and Oliver Hart. 1986. "The Costs and Bene…ts of Ownership: A Theory of Vertical and Lateral Integration." Journal of Political Economy 94(4), 691-719. Hart, Oliver, and John Moore. 1990. "Property rights and the Nature of the Firm." Journal of Political Economy 98(6), 1119-1158.

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Holmes, Thomas J. 1999. "Localization of Industry and Vertical Disintegration." Review of Economics and Statistics 81, 314-325. Holmstrom, Bengt, and John Roberts. 1998. "The Boundaries of the Firm Revisited." Journal of Economic Perspectives 12(4), 73-94. Hortaçsu, Ali and Chad Syverson. 2007a. "Cementing Relationships: Vertical Integration, Foreclosure, Productivity, and Prices." Journal of Political Economy 115, 250-301. Hortaçsu, Ali and Chad Syverson. 2007b. "Vertical Integration and Production: Some Plant Level Evidence." working paper, University of Chicago Joskow, Paul L. 1985. "Vertical Integration and Long-Term Contracts: The Case of Coal-Burning Electric Generating Plants." Journal of Law, Economics, and Organization 1(1), 33-80. Kirkpatrick, D. 1995. "AT&T Has the Plan." Fortune October 16, 84. Klein, Benjamin, Robert G. Crawford, and Armen A. Alchian. 1978. "Vertical Integration, Appropriable Rents, and the Competitive Contracting Process." Journal of Law and Economics 21(2), 297-326. Klein, Peter G. 2005. "The Make-or-Buy Decision: Lessons from Empirical Studies." in Handbook of New Institutional Economics, ed. C. Menard and M. M. Shirley. Dordrecht and New York: Springer, 435-464. Lafontaine Francine, and Margaret Slade. 2007. "Vertical Integration and Firm Boundaries: the Evidence." Journal of Economic Literature 45(3), 629685. Levin, Richard C. 1981. "Vertical Integration and Pro…tability in the Oil Industry." Journal of Economic Behavior and Organization 2(3), 215-235. Lu, Yi, Ivan P.L. Png, and Zhigang Tao. 2008. "Do Institutions not Matter in China? Evidence from Enterprise-level Productivity Growth." working paper. Masten, Scott E. 1984. "The Organization of Production: Evidence from the Aerospace Industry." Journal of Law and Economics 27(2), 403-417. McLaren, John. 2000. "’Globalization’ and Vertical Structure." American Economic Review 90, 1239-1254. Mullainathan, Sendhil, and David Scharfstein. 2001. "Do Firm Boundaries Matter?" American Economic Review 91(2), 195-199. Spiller, Pablo T. 1985. "On Vertical Mergers." Journal of Law, Economics and Organization 1(2), 285-312. Staiger, Douglas, and James Stock. 1997. "Instrumental Variables Regression with Weak Instruments." Econometrica 65(3), 557-586. Stigler, George J. 1951. "The Division of Labor is Limited by the Extent of the Market." Journal of Political Economy 59(3), 185-193. Tait, N. 1999. "Spin-o¤ May Provide More Opportunities in Suppliers." Financial Times March 1, 2. 11

Whinston, Michael D. 2003. "On the Transaction Cost Determinants of Vertical Integration." Journal of Law, Economics, and Organization 19(1), 1-23. Williamson, Oliver E. 1975. Markets and Hierarchies: Analysis and Antitrust Implications. New York: Free Press. Williamson, Oliver E. 1983. "Credible Commitments: Using Hostages to Support Exchange." American Economic Review 73(4), 519-540. Williamson, Oliver E. 1985. The Economic Institutions of Capitalism. New York: Free Press.

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Table 1: Summary Statistics Variable Labor Productivity Market Share Sales Price Vertical Integration Local Purchase Local Suppliers Transportation Cost Inventory Input Specificity Delivery Loss Lagged Labor Productivity Contract Enforcement Property Rights Protection Firm Size Firm Age Education Tenure Deputy CEO Before Government Cadre

Obs 1351 1527 1558 1363 1459 1545 1541 1552 1556 1444 1524 1332 1361 1462 1565 1566 1553 1548 1566 1566

Mean 3.079 0.349 9.417 1.501 0.339 0.486 0.551 0.014 0.893 0.068 0.021 3.036 0.634 0.355 5.091 2.494 14.361 6.240 0.277 0.035

Std. Dev. 1.442 0.477 2.301 0.659 0.401 0.360 0.355 0.032 11.254 0.221 0.050 1.493 0.389 0.320 1.373 0.777 2.503 4.580 0.448 0.184

Min -3.462 0 0.405 1 0 0 0 0 0 0 0 -3.761 0 0 0 1.099 0 1 0 0

Max 11.892 1 17.226 3 1 1 1 0.821 352.274 1 0.5 9.252 1 1 9.649 3.970 18 33 1 1

Table 2: Correlation

Labor Productivity Market Share Sales Price Vertical Integration Local Purchase Number of observations: 1,107

Labor Productivity 1 0.3744 0.6935 -0.0621 -0.0302 -0.2464

Market Share 1 0.4025 -0.094 0.0254 -0.2025

Sales

1 -0.0452 -0.0328 -0.2735

Price

1 -0.0472 0.0854

Vertical Integration

1 0.0922

Local Purchase

1

Table 3: OLS Estimates 1 2 3 Labor Productivity Market Share Sales Vertical Integration -0.109 0.030 -0.188 (0.124) (0.043) (0.221) No. of Observation 1,270 1,430 1,452 R-square/Pseudo R2 0.0009 0.0006 0.0011 F-statistic 0.77 0.49 0.72 p-value for F-statistic 0.3922 0.4938 0.4083 Robust standard errors, clustered at city level, are reported in the parenthesis. *, **, *** represent and 1% level, respectively.

4 Price -0.077* (0.039) 1,284 0.0022 3.84 0.0666 significance at 10%, 5%,

Table 4: Main Results 1

2 3 4 Panel A, Second Stage for TSLS Labor Productivity Market Share Sales Price Vertical Integration -8.783*** -2.641*** -18.026*** 1.338** (2.788) (0.810) (5.593) (0.558) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration Local Purchase 0.116*** 0.106*** 0.102*** 0.109*** (0.035) (0.032) (0.030) (0.030) Panel C, Various Tests for Instrument Variable Anderson Canonical Correlations LR Statistic: Chi-sq 13.35 12.84 11.91 12.21 Anderson Canonical Correlations LR Statistic: p-value 0.0003 0.0003 0.0006 0.0005 Cragg-Donald Statistic: Chi-sq 13.42 12.90 11.96 12.27 Cragg-Donald Statistic: p-value 0.0002 0.0003 0.0005 0.0005 Shea Partial R2 of Excluded Instrument 0.0105 0.0090 0.0082 0.0095 Shea Test of Excluded Instrument: F-statistic 10.86 11.21 11.16 13.32 Shea Test of Excluded Instrument: p-value 0.0043 0.0038 0.0039 0.0020 Cragg-Donald Statistic: F-statistic 13.40 12.88 11.94 12.25 No. of Observation 1,265 1,425 1,446 1,280 Robust standard errors, clustered at city level, are reported in the parenthesis. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 5a: Exclusion Test I, Potential Channels 1

Vertical Integration Transportation Cost

-8.872*** (2.847) 4.169 (5.285)

Inventory Input Specificity Delivery Loss

Local Purchase Transportation Cost

0.114*** (0.035) 0.719 (0.468)

Inventory Input Specificity Delivery Loss

Anderson Canonical Correlations LR Statistic: Chi-sq Anderson Canonical Correlations LR Statistic: p-value Cragg-Donald Statistic: Chi-sq Cragg-Donald Statistic: p-value Shea Partial R2 of Excluded Instrument

13.08 0.0003 13.15 0.0003 0.0103

2

3

4 5 6 7 Panel A, Second Stage for TSLS Labor Productivity Market Share -8.583*** -9.575*** -8.552*** -2.705*** -2.655*** -3.015*** (2.666) (3.205) (2.726) (0.848) (0.824) (0.993) 2.603*** (1.011) -0.194** 0.001 (0.077) (0.001) 0.586 0.186 (0.402) (0.126) -1.695 (2.152) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration 0.116*** 0.114*** 0.118*** 0.104*** 0.105*** 0.101*** (0.035) (0.036) (0.036) (0.032) (0.032) (0.033) 0.801*** (0.255) -0.001 0.001*** (0.003) (0.000) 0.001 0.004 (0.034) (0.039) -0.165 (0.254) Panel C, Various Tests for Instrument Variable 13.38 12.23 13.87 12.33 12.36 10.86 0.0003 0.0005 0.0002 0.0004 0.0004 0.0010 13.45 12.30 13.95 12.38 12.42 10.91 0.0002 0.0005 0.0002 0.0004 0.0004 0.0010 0.0105 0.0103 0.0110 0.0087 0.0087 0.0082

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-2.545*** (0.768)

-0.484 (0.539) 0.110*** (0.031)

-0.192 (0.206) 13.65 0.0002 13.72 0.0002 0.0096

Shea Test of Excluded Instrument: F-statistic 10.64 10.90 10.06 11.02 10.41 10.91 9.25 12.13 Shea Test of Excluded Instrument: p-value 0.0046 0.0042 0.0056 0.0040 0.0050 0.0042 0.0074 0.0028 Cragg-Donald Statistic: F-statistic 13.12 13.42 12.27 13.92 12.36 12.39 10.88 13.69 No. of Observation 1,262 1,265 1,187 1,253 1,414 1,252 1,325 1,413 Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 5b: Exclusion Test I, Potential Channels (Conti.) 1

Vertical Integration Transportation Cost

-18.109*** (5.663) 8.409 (6.528)

Inventory

0.101*** (0.031) 0.793*** (0.258)

Inventory Input Specificity Delivery Loss

Anderson Canonical Correlations LR Statistic: Chi-sq Anderson Canonical Correlations LR Statistic: p-value Cragg-Donald Statistic: Chi-sq Cragg-Donald Statistic: p-value Shea Partial R2 of Excluded Instrument

4 5 Panel A, Second Stage for TSLS

Sales -17.646*** -7.732*** (5.505) (3.245)

11.89 0.0006 11.94 0.0006 0.0082

-17.179*** (5.204)

1.241** (0.540) -1.667*** (0.630)

6

7

Price 1.230** 1.335** (0.550) (0.557)

8

1.254** (0.544)

-0.013* (0.007) 0.241 (0.379)

Delivery Loss

Transportation Cost

3

-0.031* (0.016)

Input Specificity

Local Purchase

2

-0.071 (0.105)

-4.495 (3.759) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration 0.102*** 0.100*** 0.106*** 0.110*** 0.110*** 0.116*** (0.030) (0.032) (0.031) (0.030) (0.030) (0.032) 0.696** (0.256) 0.001*** 0.004 (0.000) (0.003) 0.004 -0.012 (0.041) (0.038) -0.213 (0.206) Panel C, Various Tests for Instrument Variable 11.92 10.65 12.84 12.40 12.35 12.83 0.0006 0.0011 0.0003 0.0004 0.0004 0.0003 11.97 10.69 12.90 12.46 12.41 12.90 0.0005 0.0011 0.0003 0.0004 0.0004 0.0003 0.0082 0.0079 0.0089 0.0097 0.0096 0.0107

0.528 (0.476) 0.112*** (0.030)

-0.230 (0.199) 12.99 0.0003 13.06 0.0003 0.0102

Shea Test of Excluded Instrument: F-statistic 10.77 11.23 9.71 11.95 13.14 13.16 Shea Test of Excluded Instrument: p-value 0.0044 0.0038 0.0063 0.0030 0.0021 0.0021 Cragg-Donald Statistic: F-statistic 11.91 11.94 10.67 12.87 12.43 12.38 No. of Observation 1,442 1,445 1,347 1,433 1,273 1,274 Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, represent significance at 10%, 5%, and 1% level, respectively.

13.19 0.0021 12.86 1,190 **, ***

13.94 0.0017 13.03 1,273

Table 6a: Exclusion Test II, Underlying Environment 1

Vertical Integration Lagged Productivity Contract Enforcement Property Rights Protection

Local Purchase Lagged Productivity Contract Enforcement Property Rights Protection

Anderson Canonical Correlations LR Statistic: Chi-sq Anderson Canonical Correlations LR Statistic: p-value Cragg-Donald Statistic: Chi-sq Cragg-Donald Statistic: p-value Shea Partial R2 of Excluded Instrument Shea Test of Excluded Instrument: F-statistic Shea Test of Excluded Instrument: p-value Cragg-Donald Statistic: F-statistic No. of Observation

2

3 4 5 6 Panel A, Second Stage for TSLS Labor Productivity Market Share -1.664*** -10.010** -8.728*** -1.797*** -3.150*** -2.859*** (0.570) (4.098) (2.892) (0.601) (1.188) (0.894) 0.796*** 0.094*** (0.039) (0.019) -0.107 -0.031 (0.486) (0.124) -0.061 -0.042 (0.312) (0.107) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration 0.111*** 0.099** 0.116*** 0.109*** 0.092** 0.100*** (0.035) (0.037) (0.035) (0.037) (0.033) (0.030) -0.010 -0.008 (0.010) (0.010) -0.038 -0.020 (0.039) (0.033) -0.034 -0.047 (0.033) (0.033) Panel C, Various Tests for Instrument Variable 11.24 8.80 12.90 10.83 8.65 10.95 0.0008 0.0030 0.0003 0.0010 0.0033 0.0009 11.29 8.83 12.96 10.88 8.68 11.00 0.0008 0.0030 0.0003 0.0010 0.0032 0.0009 0.0091 0.0078 0.0107 0.0088 0.0068 0.0081 9.81 7.03 10.82 8.59 7.97 10.79 0.0061 0.0168 0.0043 0.0093 0.0117 0.0044 11.26 8.81 12.93 10.85 8.66 10.97 1,225 1,126 1,204 1,231 1,265 1,354

Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 6b: Exclusion Test II, Underlying Environment (Cont.) 1

Vertical Integration Lagged Productivity Contract Enforcement Property Rights Protection

Local Purchase Lagged Productivity Contract Enforcement Property Rights Protection

Anderson Canonical Correlations LR Statistic: Chi-sq Anderson Canonical Correlations LR Statistic: p-value Cragg-Donald Statistic: Chi-sq Cragg-Donald Statistic: p-value Shea Partial R2 of Excluded Instrument Shea Test of Excluded Instrument: F-statistic Shea Test of Excluded Instrument: p-value Cragg-Donald Statistic: F-statistic No. of Observation

2

3 4 5 6 Panel A, Second Stage for TSLS Sales Price -9.022** -20.344*** -19.937*** 1.119* 1.996** 1.491** (3.892) (7.927) (6.568) (0.587) (0.932) (0.673) 0.796*** -0.023 (0.116) (0.020) 0.284 0.007 (0.793) (0.094) -0.159 0.066 (0.715) (0.088) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration 0.103*** 0.087** 0.094*** 0.104*** 0.081** 0.100*** (0.036) (0.032) (0.029) (0.037) (0.029) (0.028) -0.010 -0.009 (0.010) (0.011) -0.026 -0.026 (0.033) (0.035) -0.049 -0.056 (0.034) (0.039) Panel C, Various Tests for Instrument Variable 9.95 7.80 9.75 9.11 6.00 10.01 0.0016 0.0052 0.0018 0.0025 0.0143 0.0016 9.99 7.82 9.79 9.14 6.02 10.05 0.0016 0.0052 0.0018 0.0025 0.0141 0.0015 0.0079 0.0061 0.0071 0.0082 0.0052 0.0082 8.03 7.53 10.30 7.79 7.53 12.68 0.0114 0.0138 0.0051 0.0125 0.0139 0.0024 9.97 7.80 9.77 9.12 6.00 10.02 1,251 1,283 1,371 1,231 1,142 1,222

Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 7: Exclusion Test III, With more Controls 1

2 3 4 Panel A, Second Stage for TSLS Labor Productivity Market Share Sales Price Vertical Integration -7.172** -2.090** -7.811** 0.454 (3.437) (1.026) (3.971) (0.664) Chi2 for Firm Characteristics [10.14]*** [21.88]*** [256.84]*** [3.16] Chi2 for CEO Characteristics [8.72]* [7.11] [6.34] [16.90]*** Chi2 for City Characteristics [0.52] [4.22] [2.81] [2.83] Chi2 for Industry Dummies [17.71]** [22.06]*** [17.43]** [68.87]*** Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration Local Purchase 0.079** 0.072** 0.066** 0.082** (0.034) (0.031) (0.031) (0.030) Panel C, Various Tests for Instrument Variable Anderson Canonical Correlations LR Statistic: Chi-sq 5.56 5.37 4.51 6.23 Anderson Canonical Correlations LR Statistic: p-value 0.0184 0.0205 0.0337 0.0126 Cragg-Donald Statistic: Chi-sq 5.57 5.38 4.52 6.24 Cragg-Donald Statistic: p-value 0.0183 0.0204 0.0335 0.0125 Shea Partial R2 of Excluded Instrument 0.0044 0.0038 0.0032 0.0049 Shea Test of Excluded Instrument: F-statistic 5.37 5.23 4.40 6.13 Shea Test of Excluded Instrument: p-value 0.0207 0.0224 0.0362 0.0134 Cragg-Donald Statistic: F-statistic 5.49 5.31 4.46 6.15 No. of Observation 1,249 1,405 1,424 1,265 Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, **, *** represent significance at 10%, 5%, and 1% level, respectively. All the control variables are included in the first stage but not reported as to save the space (available upon request).

Table 8: Robustness Check I, Alternative Instruments 1

2 3 4 Panel A, Second Stage for TSLS Labor Productivity Market Share Sales Price Vertical Integration -7.208*** -3.075*** -17.315** 2.012** (2.629) (1.090) (7.293) (0.950) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration Local Suppliers 0.120** 0.097** 0.092** 0.097** (0.042) (0.038) (0.037) (0.042) Panel C, Various Tests for Instrument Variable Anderson Canonical Correlations LR Statistic: Chi-sq 14.02 10.31 9.37 9.25 Anderson Canonical Correlations LR Statistic: p-value 0.0002 0.0013 0.0022 0.0024 Cragg-Donald Statistic: Chi-sq 14.10 10.34 9.40 9.28 Cragg-Donald Statistic: p-value 0.0002 0.0013 0.0022 0.0023 Shea Partial R2 of Excluded Instrument 0.0111 0.0073 0.0065 0.0072 Shea Test of Excluded Instrument: F-statistic 8.19 6.48 6.05 5.26 Shea Test of Excluded Instrument: p-value 0.0108 0.0209 0.0250 0.0348 Cragg-Donald Statistic: F-statistic 14.08 10.33 9.39 9.27 No. of Observation 1,260 1,413 1,433 1,273 Robust standard errors, clustered at city level, are reported in the parenthesis after the estimated coefficients. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 9: Robustness Check II, Outliers 1

2 Panel A, Second Stage for TSLS Labor Productivity Sales Vertical Integration --8.029*** -17.313*** (2.266) (5.474) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration Local Purchase 0.119*** 0.097*** (0.035) (0.030) Panel C, Various Tests for Instrument Variable Anderson Canonical Correlations LR Statistics: Chi-sq 13.87 10.56 Anderson Canonical Correlations LR Statistics: p-value 0.0002 0.0012 Cragg-Donald Statistics: Chi-sq 13.95 10.60 Cragg-Donald Statistics: p-value 0.0002 0.0011 Shea Partial R2 of Excluded Instrument 0.0111 0.0074 Shea Test of Excluded Instrument: F 11.94 10.61 Shea Test of Excluded Instrument: p-value 0.0030 0.0046 Cragg-Donald Statistics: F-statistics 13.93 10.59 No. of Observation 1,239 1,417 Robust standard errors, clustered at city level, are reported in the parenthesis. *, **, *** represent significance at 10%, 5%, and 1% level, respectively.

Table 10: Robustness Check III, Subsamples 1

Vertical Integration

Local Purchase

Anderson Canonical Correlations LR Statistic: Chi-sq Anderson Canonical Correlations LR Statistic: p-value Cragg-Donald Statistic: Chi-sq Cragg-Donald Statistic: p-value Shea Partial R2 of Excluded Instrument Shea Test of Excluded Instrument: F-statistic Shea Test of Excluded Instrument: p-value Cragg-Donald Statistic: F-statistic No. of Observation Robust standard errors, clustered at city represent significance at 10%, 5%, and 1%

2

3

4 5 6 7 8 Panel A, Second Stage for TSLS Labor Productivity Market Share Sales Price Focused Private Focused Private Focused Private Focused Private Business Firm Business Firm Business Firm Business Firm -8.693*** -9.646*** -2.656*** -2.745*** -18.186*** -19.828*** 1.166** 1.702** (2.902) (3.331) (0.886) (0.979) (5.907) (7.342) (0.585) (0.839) Panel B, First Stage for TSLS: Dependent Variable: Vertical Integration 0.110*** 0.111*** 0.106*** 0.104*** 0.102*** 0.094*** 0.114*** 0.098*** (0.034) (0.036) (0.032) (0.034) (0.032) (0.034) (0.034) (0.036) Panel C, Various Tests for Instrument Variable 11.05 10.02 11.47 9.99 10.83 8.23 12.07 7.89 0.0009 0.0015 0.0007 0.0016 0.0010 0.0041 0.0005 0.0050 11.11 10.07 11.52 10.03 10.88 8.26 12.13 7.92 0.0009 0.0015 0.0007 0.0015 0.0010 0.0040 0.0005 0.0049 0.0095 0.0097 0.0088 0.0086 0.0082 0.0070 0.0103 0.0076 10.41 9.52 10.76 9.45 10.17 7.78 11.49 7.58 0.0013 0.0021 0.0011 0.0022 0.0015 0.0054 0.0007 0.0060 11.09 10.05 11.50 10.02 10.86 8.25 12.11 7.90 1,159 1,027 1,296 1,157 1,315 1,172 1,166 1,028 level, are reported in the parenthesis after the estimated coefficients. *, **, *** level, respectively.