Institutional Determinant of Market Concentration and Trade in a General Equilibrium Setup

Dr. Joy Das, Decision Innovation Solutions, [email protected] Dr. Shaun M. Tanger, Mississippi State University, [email protected] Dr. J. Matthew Fannin, Louisiana State University, [email protected] Dr. P. Lynn Kennedy, Louisiana State University, [email protected]

Selected Paper prepared for presentation at the 2020 Agricultural & Applied Economics Association Annual Meeting, Kansas City, MO July 26-28, 2020

Copyright 2020 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. 1

ABSTRACT The study proposes a trade general equilibrium model and empirically tests the optimizing integration strategies by firms given the institution of a country. The study delves into the scope to which the institutional quality of a country affects the bilateral industry-level trade flows of manufacturing goods and services. Based on the interactive general equilibrium trade model of country-specific and industry-specific institutionally intensive variables, including the traditional control variables in bilateral trade, we analyze the six-digit NAICS classified industry-level bilateral trade flows from 220 countries and 389 industries for the year 1997. The study confirms that bilateral trade is affected by the institutions through channels of market concentrations.

1. Trade, Market Concentrations and Institutions

The underlying factors behind the 19th century comparative advantage in English manufacturing goods trade over many other potential countries such as Portugal, France and Denmark can be attributed to the institutions (Zingales, 2017). Institutions that have promoted the technology/innovations, capital accumulation/growth and commercial enterprises in England were far more advanced than the other European counterparts (Nunn and Trefler, 2014). The fact that the economic growth paths are shaped by the local institutional conditions reveal the role of institutions on the long-run economic growth and economic development (Acemoglu et al. 2005, Rodríguez-Pose and Storper, 2006, Alvarez et al. 2018). On the other hand, the international trade literature widely claims that institutions facilitate the volume of bilateral trade flows (Iwanow and Kirkpatrick, 2007; Das et al. 2018). Yet our knowledge is limited about the path through which the domestic institutional quality indices affect the bilateral trade patterns. High institutional quality ensures a more inclusive and pluralistic environment where economic agents are less able to take advantage of the gaps in governance to monopolize trade in their favor by abusing the market power (De Groot et al. 2004, Jansen and Nordås, 2004, Linders et al. 2005). Yu et al. (2015), also restates the literature by showing that efficient rule of law positively affects bilateral trade. As designed by Kauffman et al. (2005), rule of law is one of the six aspects of institutional quality and is defined by the World Bank as an index that “captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.” To understand the theoretical and empirical underpinning of institutions facilitating bilateral trade, Nunn and Trefler (2014) have reviewed the literature to provide evidence on the impacts of domestic institutions on bilateral trade like many others showing evidence on the channels through which the mechanism works as discussed later in the section. One of the multiple ways through which institution related factors impact trade is through the fair enforcement of contracts in the relationship specific investments when the intermediate input markets are very complex (Levchenko, 2007). From a theoretical perspective, Romalis (2004) has introduced the transport costs in trade due to institutional barriers, and monopolistic competition to modify the traditional Heckscher- ohlin model. He has empirically investigated the quasi Heckscher-Ohlin and quasi Rybczynski theorem to conclude that factor proportions determine the structure of production and hence act as an important source of comparative advantage in trade. Levchenko (2007) and Nunn (2007) 2

introduced the effect of institutions in the Ricardian model of comparative advantage. Using sectoral data on US imports in 1998 and the Rule of Law indices from the World Bank dataset, Levchenko (2007) show that institutions have positive effect on comparative advantage in more institutionally dependent complex industries. On the other hand, Nunn (2007) shows that the industries in which the relation specific investments are more important, those industries get specialized in countries with higher institutions i.e. with better contract enforcements. The interdependence of the complexity of production or the overall structure of market and institutions are important factors impacting bilateral trade (Levchenko, 2007). Inefficiencies in the market exist due to incomplete contracts. Sector wise differences in factor rewards exist due to segmented factor markets (Caballero and Hammour, 1998). Since factor rewards differ across sectors, the sectors expand and contract according to higher factor rewards hence determining the gains and losses from trade (Bhagwati and Ramaswami, 1963; Haberler, 1950; Hagen, 1958). Alternative trade policies can promote such domestic monopolies to capture a larger share of monopoly rents globally (Brander and Spencer, 1981). The inefficiencies existing due to contract incompleteness affects the relationship-specificity in the production of goods and services creating hold-up problems (Williamson, 1985). The investments that are made for producing a customized input is relation specific for a producer of final goods because it holds less value outside than within the relationship. Once the relation-specific investment is made, then the purchasers can take advantage of the situation and try to renegotiate the contracts, giving rise to the hold-up problem when there is no timely enforcement of the contracts that were written. In short, in the presence of relationship specificity in production, good contracting institutions are sources of comparative advantage (Nunn and Trefler, 2014). Coase (1937) has suggested that the producer chooses an optimal organizational structure in response to the hold-up problem to avoid or reduce the transaction cost. There are other ways to reduce holdup problems and inefficiencies such as assigning property rights and writing binding long term contracts. Our study derives its theoretical foundation from the fast growing literature on the interaction of institutions and trade. Antras (2003, 2005) and Grossman and Helpman (2002, 2003, 2005) were the first to introduce the incomplete contracts framework in international trade. Although they incorporate the institutional factors in the framework of incomplete contracts, they merely view it from technology and factor endowments point of view and fail to show institution as a source of comparative advantage. Further researches show the interaction of trade and institutions in different contexts. Acemoglu et al. (2007) and Costinot (2005) theoretically show that in the scenario of imperfectly enforced contracts differences in the level of institutions act as source of comparative advantages. Nunn (2007) uses institutional intensity measure at the industry level to test and conclude the institutional sources of comparative advantage. Anderson and Marcouiller (2002) also reported that better institutions leads to larger volumes of trade. They used a gravity model testing the effect of institutions on bilateral trade. Ranjan and Lee (2007) reported that the net exports from the institutionally dependent sectors are more sensitive to the institutional quality of the country. In line with the similar literature on institutional comparative advantage, Schuler (2003) gave a new insight by examining the trade composition changes. Investigating the command economy institutions in the soviet bloc countries (formerly known), reported the sectors that are more institutionally dependent, the exports from those sectors have decreased proportionally more than the sectors that are less institutionally dependent.

Ferguson and Formai (2013) reports that the sectors that are naturally inclined to integrate vertically, judicial quality has lesser significance in the exports of the contact intense goods from those sectors in the country. This study argues that there is no natural inclination but because the contract intense sectors are more sensitive to judicial quality, they either horizontally or vertically integrate to circumvent the holdup problem and stay competitive in the export market. Earlier works show the incidence of market integration with respect to judicial quality. One of the recent studies by Alvarez et al. (2018) have given a closer look into the effects of the structural changes in the country specific factors such as institutional quality on the structural changes in industry characteristics such as propensity to vertically integrate with time and its further effects on comparative advantages in trade. They gave intuitive insights on whether the role of institutions is getting more prominent or diminishing with time. Over the last two decades about three fourth of the US industries have moved to a more concentrated market structure enjoying immense market power by vertically or horizontally integrating. There is a shift to a weakened competition in the product markets in the US and the most concentrated firms started enjoying higher profit margins and better Mergers and Acquisition (M&A) deals. Two plausible reasons behind product market concentration in the US over the last two decades is technological innovations and lax enforcement of antitrust laws (Grullon et al. 2019). The latter is closely associated with the institution of the country. Institutions such as the rule of law (defined earlier in Chapter 2) associated with judicial quality have important roles to play in this scenario of market integrations. In the empirical model of the study, trade is explained by interactions of country characteristics and industry characteristics. Rajan and Zingales (2001) first introduced the explanation of the interactions of country characteristics and industry characteristics. They empirically examined the dependence of industries on external financing and whether those industries grow faster which are more dependent on finances from the external sources. It is to be noted that it is not the total volume of trade that is measured, but the effect of country specific factors and the industry specific factors on trade compositions are measured with our model specifications. We have designed the share of trade index as Levchenko (2007) in such a way that it captures the composition of trade, among industries and countries importing goods into the United States. Unlike Anderson and Mercouiller (2002) the effect of country characteristics such as institutional quality on volume of trade is measured using the gravity model, in our model industry fixed effects capture the effect of industry characteristics on trade and the country fixed effects capture the country characteristics on trade. Further, the construction of the dependent variable makes the more typical variables included in the gravity model redundant as it accounts for size and overall trade into the United States. Nunn (2007) has hypothesized that countries with better contract enforcements will specialize in industries that are contract-intensiv. That means with better contract enforcements, the final goods producers will use the inputs that require relationship-specific investments more intensively and hence have comparative advantage in such goods. Ferguson and Formai (2013) have used a triple interaction term of industry-specific measure of contract intensity, institutional quality and vertical integration propensity to identify the sensitivity of the exports from the contract-intense industries on institutional quality. When the industries are more vertically integrated it is less likely that contract enforcement will affect the contract-intensive industries in the scenario of comparative advantage in trade.

2. Model The multi country Heckscher-Ohlin model developed in Chapter 4 illustrates the institutional dependence of a firm's optimal level of concentration. When countries open to trade, firms optimally concentrate responding to the given level of the institution in the country in order to stay in the market. This section attempts to test this prediction empirically. The empirical model exploits the firm's dependence on the institutions of the country in which it is located and the variations in firm integrations.

2.1. Model Specification

The model is motivated to exploit all firms within an industry and their institutional dependence and corresponding optimization strategies. In our empirical model, we proxy for cross-firm institutional dependence of markets within the country with measures of horizontal integration and vertical integration of firms based on the number of companies producing similar goods and intermediate good use respectively. Second, firms in each country behave differently according to the level of the institution in the countries. We hypothesize that countries with a lower level of institutions, will have a lower level of export shares from the sectors that have lower vertical or horizontal integrations. Our empirical analysis intends to test the hypothesis using export data for countries as the dependent variable. The main independent variables are the interaction between institutions and horizontal integrating factor and the interaction between institutions and vertical integrating factor. The rationale of this interaction lies in the fact that the resulting equation controls directly for the industry fixed effects and country fixed effects. Export patterns get determined with the level of capital-skill and raw material endowments in a country. Capital and skill abundance are important in the production and hence trade as the relative factor prices get compressed by trade (Lancaster, 1957; Heckscher and Ohlin, 1991). So, the capital abundance, skill abundance and raw material abundance of countries are included in the model as Romalis (2004) have also reported the importance of factor abundance in explaining trade patterns across industries and countries. Besides the country level variable, Levchenko (2007) has argued that the intensities of production of the above-mentioned factors also explain the patterns of trade across industries and countries. Romalis (2004) has reported, that the production of skill-intensive good is concentrated in the North. So, we include the factor intensities such as capital intensity, raw material intensity and skill intensity as industry level variables to control for the industry-specific factors that determine the patterns of trade. The country-level variables interact with the industry level variables in the model for similar reasons mentioned earlier. Since our primary concern in the study is to look at the market integrations and its response to international trade with respect to the country's institutions, the factor intensities are of utmost importance. Factor intensity helps us understand the relative factor requirements of the industries and the market behaves accordingly considering the factor endowments of the country in terms of contract costs and hold up problems. For example, lesser factor endowment in a country and relatively more of that factor intensive industries in the country, the larger are the holdup problems and relatively more contracting costs. Our empirical model takes the following form,

RelShareExpie = β0 + β1 IQe∗ HHIi + β2 IQe∗ VIIi+ β3 CapIi ∗ CapAe + 5

β4 SkillIi ∗ SkillAe + β5 RawMatIi ∗ RawMatAe + φi + δe+ εie (23) Where e indexes the exporting country, importing country here is the US and i indexes industry. The data is collected for the year 1997. 푅푒푙푆ℎ푎푟푒퐸푥푝ie, is country e’s export share to the US in industry i, divided by the average share of country e in all the country’s exports to the US. 퐼푄e is the institutional quality index for country e. 퐻퐻퐼i is the Herfindahl – Hirschman index of industry i. VIIi is the vertical integration index for intermediate-goods-industry in industry “i”. 퐶푎푝퐼푖 is capital intensity of industry i. 푆푘𝑖푙푙퐼푖 is skill intensity of industry i. 푅푎푤푀푎푡퐼푖 is raw material intensity of industry i. 퐶푎푝퐴푒 is capital abundance country e. 푆푘𝑖푙푙퐴푒 is skill abundance of country e and 푅푎푤푀푎푡퐴푒 is raw material abundance of country e.

2.2. Data Descriptions and Variable Definitions

We use data on 1997 manufacturing industry exports to the US from all the partner countries classified by six-digit NAICS industry classification from the US Census Bureau and National Bureau of Economic Research database. The dependent variable in the empirical model is 푅푒푙푆ℎ푎푟푒퐸푥푝ie, is country e’s export share to the US in industry i, divided by the average share of country e’s exports to the total exports to the US. This normalized relative share measure accounts for the size of partner countries and the closeness of trade relationships between country e and the US for the ease of intercountry coefficient comparisons. The calculation of the normalized relative share of our dependent variable is shown in appendix A. We use Pseudo Poisson Maximum Likelihood (PPML) method of estimation because it includes the missing values for the countries and years in which no trade has taken place in a sector1. To answer the research question, our empirical study requires a variable that measures the industry level and market level of institutional dependence. It is indeed very difficult to obtain a good proxy for institutional dependence of market and industry. So, in this study we proxy the institutional dependence in two folds, using plausible measures of indexes of horizontal integrations and vertical integrations. Cowan and Neut (2002) have reported that Herfindahl - Hirschman index2 is a good proxy for horizontal integration. In our study we use the Herfindahl – Hirschman index of 50 largest companies. 퐻퐻퐼i is the Herfindahl – Hirschman index of industry i. The rationale for using HHI and not merely the number of firms entering or exiting the market is the following. The number of firms holding the lion’s share of market power is of interest in the

1 See Silva and Tenreyro, 2006, 2010 and Silva et al. 2015. 2 “The Herfindahl - Hirschman index HHI ranges from 1 (least concentrated) to 10,000 (most concentrated). The 10,000 figure comes from a theoretical scenario where there is only one company operating in the industry with 100% of the market share, which would lead to an HHI of 10,000. According to the U.S. Department of Justice, an HHI of less than 1,500 represents an industry with low market concentration, an HHI ranging between 1,500 and 2,500 represents moderate concentration, an HHI of more than 2,500 represents a highly concentrated industry” (Corporate Finance Institute (CFI) report). Herfindahl - Hirschman index is calculated using the following formula,

푁 2 퐻퐻퐼𝑖 = ∑ 푠푖 푖=1 Where si is the market share of firm i in the market, and N is the number of firms. 6

study not merely the total number of firms existing in the market3. This gives an idea of the company’s response to institutions in horizontally integrating with other companies producing similar products in the market over the years. Intuitively given an imperfect institution in a given country the firm and thus industry will try to compensate for the lack of institutional quality to mitigate the transaction costs involved in market participation and optimize its profit. HHI index gives us a plausible measure of whether this compensation mechanism is via horizontal integration. We use HHI index data of 1997 of 6- digit NAICS classified industries from the US Census Bureau database. There are data on 389 industries overall. Simply taking the number of companies in the market would have misguided us on the company’s response to the level of institutions. Since HHI increases with concentration the dispersion of the market can be visualized with clarity and lead us to robust estimations. Besides horizontal integration, the vertical integration part of the market is captured in the model by using the vertical integration coefficient with the help of domestic requirements table. Vertical integration index acts as a proxy for contracting costs. Higher contracting costs of the intermediate goods market create a demand for vertical integration. The study uses the vertical integration index from Acemoglu et al. (2009) which represents the opportunity of vertical integration between final good industry and intermediate good industries “i”. The degree of vertical integration for each firm is calculated following Fan and Lang (2000).

3 See Blanchard and Kremer, 1997 7

Table 1. Definitions of the Variables with Respective Hypothesized Directions

Hypothesized Variable Description Direction

Yie Country e’s export share to country j in sector g, normalized

퐻퐻퐼i Herfindahl-Hirschman index of industry i Positive Vertical Integration Index of final good industry in intermediate VIIi Negative industry i ROL Rule of Law Positive Dist bilateral log distance between the U.S. and each trading partner Negative

Capital intensity of industry g in country i Positive CapIi

Negative SkillIi skill intensity of industry g in country i

Raw material intensity of industry g in country i Positive RawMatIi capital endowment of country i Positive CapAe

Negative SkillAe skill endowment of country i Positive RawMatAe raw material abundance of country i

Skill endowment is LOGHL (human capital per worker), and capital endowment is LOGKL (physical capital per worker). Raw materials endowment (RAW) is proxyed by land area divided by population, both of which are sourced from the World Bank’s World Development Indicators database. The calculation of the factor intensities and factor endowment variables are shown in Appendix E. The idea of the above three variables are obtained from Levchenko (2007) and the data is obtained from Hall and Jones dataset. Capital and skill abundance data at country level are obtained from Hall and Jones (1999) dataset. Raw materials endowment (RAW) is proxied as land area divided by population, taken from Levchenko (2007) and data obtained from National Bureau of Economic Research (NBER) dataset.

Table 2. Summary Statistics

Variable Obs Mean Std. Dev. Min Max

Relative share of Imports 23,419 1.07 6.57 0 234.69

Vertical Integration Index 14,726 0.09 0.04 0 0.27

Rule of Law 19,743 0.51 0.98 -2.14 1.93

Herﬁndahl Hirschman Index 22,676 410.02 279.92 1.3 997.1

Skill Intensity 23,708 0.17 0.07 0.02 0.45

Skill Endowment 20,273 0.73 0.26 0.07 1.21

Capital Intensity 23,708 0.33 0.09 0.1 0.76

Capital Endowment 20,273 10.04 1.23 5.76 11.59

Raw Material Intensity 18,565 42,764.88 87,916.92 183.61 665,138.20

0.000074 Raw Material Endowment 22,046 0.02 0.03 0.25

Distance 25,125 7,513.64 3,485.39 548.39 16,764.67

Per Capita GDP 20,050 18,079.69 27,422.02 425.3 510,320.30

For the country level qualitative measures of institutions, the study uses the rule of law index because it is associated with the costs of contracts which further determines the level of integration. The index is adopted from Kaufmann et al. (2005) study. We use country level rule of law index data of 1997 from World governance indicator (WGI) dataset. The rule of law index ranges from -2.5 to +2.5. Rule of law is used as a proxy for institutional quality “γ” in the theoretical model. Although “0 < γ < 1” is assumed in the theoretical model, the World Bank uses the Unobserved Composite Model (UCM) in units of Standard Normal Distribution to calculate the value of rule of law index, hence it ranges from -2.5 to +2.5. For empirical viability in our analysis we use the rule of law index ranging from -2.5 to +2.5 as it is originally published by the World Bank. Table 1 introduces all the variables used in the study along with their respective hypothesis directions. Table 2. shows the review of the summary statistic of all variables used in the regression model. The first column shows the mean of all the variables used in the empirical model. The second column gives the standard deviation from the average value of each variable. The third and the fourth column gives the minimum and maximum values respectively, of the variables considered in the dataset.

2.3. Methodology

The study relies on the Ordinary Least Squares (OLS) method and more efficient Poisson-Pseudo Maximum Likelihood (PPML) method for the estimation. In the presence of heteroscedasticity in the continuous dependent variable, the Poisson estimator clustered on the partner countries and industries is unbiased and consistent as it includes the missing values for the industries in which the trading partners do not export manufacturing goods to the US i.e. in the presence of a number of zero trade values (Eicher et al. 2012). While the OLS give robust results the PPML is more appropriate in such models as less likely to suffer from missing observation bias (Santos Silva and Tenreyro, 2006; Santos Silva and Tenreyro, 2010; Santos Silva and Tenreyro ,2015; Francois and Manchin, 2013).

3. Results and Data Analysis The study examines the effect of institutions on market concentration and volume of trade. As discussed in Chapter 6 the relative share of imports is the dependent variable and the interaction of Rule of Law and Vertical integration index and, the interaction of Rule of Law and Horizontal integration (Herfindahl-Hirschman) index are the main independent variables. To study the effect of institution induced market structure on manufacturing sector exports, we have performed a cross-sectional regression analysis. Our data has observations of 220 partner countries that export manufacturing goods (at the 6-digit NAICS classified industry level) from about 389 manufacturing industries to the US.

3.1. Explanation of the Regression Results

The results are presented in Tables 3 and 4. We estimate the equation using data on 389 industries from about 220 partner countries for the year 1997. The baseline Ordinary Least Squares result is reported in Table 3. In Table 3, we observe that the coefficient of our interaction terms of our interest, (VII*ROL) is negative and (HHI*ROL) is positive, highly significant and are of the respective expected sign. To ensure that we are systematically obtaining the underlying effects of institution on trade through a change in market structure we perform several robustness checks. For that we have divided our analysis into three different parts. First we conform that the new model specifications and the more disaggregated 6-digit NAICS classified industry level dataset either ascertains on improvises the results reported by Romalis and Levchenko who had worked on the same line of research. Rule of law index has a very subtle range (- 2.5 to + 2.5), so a small change in the rule of law index implies a drastic change in the institutional framework of a country and have a large impact. A huge change in contract enforcement rules and property right regulations can only bring that. Hence the volume of trade is largely affected by a small change in the value of the index. Given the level of rule of law index a unit higher vertical integration leads to lowering relative trade share by about 3.08 units as shown in table 4. So countries with lower rule of law shifts toward production in industries that are lower vertically integrated to those industries that are more highly vertically integrated. Moreover, the inclusion of GDP per capita interacting with industry dummy as a control in the model, brings out the significance of production technologies. Given the level of capital endowments a more capital intensive industry will tend to significantly increase 10

the relative share of trade compared to the skill intensive or raw material intensive industries as reported in table 4. The country specific and the industry specific interactive factor that significantly and positively affects the relative share of trade in the manufacturing sector is the raw material. However, there is an increase in the relative share of trade of manufacturing goods and services with the institutional effect on all the three technological parameters considered in the model. The Poisson Pseudo Maximum Likelihood Model (PPML) provides an efficient estimation when we are clustering the variables (here with exporting countries) as it considers the unobserved characteristics within the variables that, in this case affects the exports of manufacturing goods to the US. The rationale here is that there are exporting countries that have zero export (no trade) to the US for a particular industry’s output that are considered in the PPML model but merely gets dropped with the other models except Tobit (as discussed earlier) biasing the results.

Table 3. Ordinary Least Squares Regression of Export Share on Institutional Intensity and Market Concentration (Dependent Variable = Normalized share of a country’s exports in total exports)

Variable (1) (2) (3) (4) (5) (6)

(Vertical index)*inst -3.07*** -6.07*** -3.51*** -5.60*** -3.19*** -2.71 (1.09) (2.12) (1.10) (2.06) (1.11) (1.96)

(Herﬁndahl index)*inst 0.0003* 0.0003* 0.0003* 0.0003* 0.0004** 0.0007** (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0003) (Skill intensity)*(skill endow) -2.22 -1.94 -3.80 -3.83 -1.97 -2.84 (3.00) (3.25) (3.25) (5.06) (3.01) (3.14)

(Capital intensity)*(cap.endow) 0.11 0.01 -0.11 -0.67 0.12 0.78 (0.53) (0.58) (0.45) (0.65) (0.53) (1.01)

(Raw Mat. intensity) *(Raw 35.92** 39.41** Mat.endow) (15.89) (19.50)

75.63** 146.74** (Capital intensity) *inst (32.73) (63.81)

76.72** 148.20** (Skill intensity) *inst (33.43) (65.11)

146.10** (Raw Mat. intensity) *inst (64.18)

6.42 5.05 (Vertical index) * (skill endow) (8.21) (8.62)

2.21 1.86 (Vertical index) * (cap.endow) (1.57) (1.58)

-28.38 (Vertical index)*(Raw Mat.endow) (47.56)

(Vertical index)*dist 0.89 (1.56) Country dummies Yes Yes Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes Yes Yes Industry dummies*GDP per capita No No No No No Yes Observations 9,285 9,285 9,285 9,285 9,107 9,194

Industries 389 389 389 389 389 389 Countries 220 220 220 220 220 220 Note: Herﬁndahl index of market structure measures horizontal integration; Vertical index of intermediate good use measures vertical integration. Both Herfindahl index and Vertical index are measures of market concentration via institutional intensity; inst is an index of institutional quality (rule of law) from Kaufmann et al. (2005); skill intensity = [(nonproduction workers)/(total employment)]*(1−capital intensity); skill endow is natural log of human capital per worker; capital intensity = 1−(total compensation)/(value added); and cap. endow is natural log of physical capital per worker. Endowments are obtained from Hall and Jones (1999). Sources and the variable deﬁnitions are described in detailed in the text. *p < 0.1, **p < 0.05, ***p < 0.01 (Corresponding robust S.E. are reported in the parenthesis below each coefficient). 12

Table 4. Poisson Pseudo Maximum Likelihood Regression of Export Share on Institutional Intensity and Market Concentration (Dependent Variable = Normalized share of a country’s exports in total exports)

Variable (1) (2) (3) (4) (5) (6)

(Vertical index)*inst -3.08*** -5.56*** -3.59*** -5.11** -3.13*** -3.23*** (1.11) (2.01) (1.13) (1.78) (1.11) (1.17) (Herﬁndahl index)*inst 0.0003* 0.0003* 0.0003* 0.0003* 0.0003* 0.0003* (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (Skill intensity)*(skill endow) -1.93 -1.50 -3.76 -3.58 -1.74 -2.14 (1.83) (1.93) (2.92) (3.48) (1.85) (1.80) (Capital intensity)*(cap.endow) 0.15 0.03 -0.14 -0.53 0.14 0.53* (0.40) (0.47) (0.35) (0.46) (0.41) (0.72) (Raw Mat. intensity) 27.41*** 32.40** *(Raw Mat.endow) (10.07) (14.17)

87.34** 169.53** (Capital intensity) *inst (43.24) (86.37)

88.51** 171.38** (Skill intensity) *inst (44.11) (87.90)

169.09** (Raw Mat. intensity) *inst (86.61)

5.73 4.01 (Vertical index) * (skill endow) (7.43) (7.61)

1.87 1.61 (Vertical index) * (cap.endow) (1.43) (1.34)

-33.01 (Vertical index) * (Raw Mat.endow) (37.20) 0.79 (Vertical index)*dist (2.15) Country dummies Yes Yes Yes Yes Yes Yes Industry dummies Yes Yes Yes Yes Yes Yes Industry dummies*GDP per capita No No No No No Yes Observations 9,285 9,285 9,285 9,285 9,079 9,194

We include the traditional sources of comparative advantages variables as control. The control variables are included to assure that the main independent variables of interest in our model is not merely proxying for the country or the industry characteristics. The inclusion of the control variables also ensures that the interaction terms of vertical integration index and Rule of law and, Horizontal integration index (Herfindahl-Herschman index) and Rule of law give robust estimates.

4. Conclusion and Discussion The purpose of this study was to identify and show the two channels through which institutions affect trade shares, more specifically it was to determine if industries attempt to offset poor institutional quality by internalizing institutions into the firm. It appears that, specifically for vertical integration, firms do indeed integrate in the absence of institutions, but as institutions improve and the costs of contracting are borne in the public sphere (i.e. good courts and well enforced contracts), industries no longer are incentivized to integrate in a small open economy. We argue that countries with higher Rule of Law shifts industries from highly vertically integrated industries with lower trade share to higher trade shares in industries with lower vertical integration. The main novel contribution of our study from the earlier ones is that we analyze the model using 6-digit NAICS classified data unlike 4-digit classified data. Although the more disaggregated 6- digit industry level dataset do not show as much significance as in the earlier studies with more aggregate (4-digit) level data, there are several advantages in terms of analyzing and understanding the market structure more precisely. The advantages are as follows: Herfindahl Herschman index, the proxy that we have used for the horizontal integration in the model also has a significant effect on trade shares. The HHI index positively affect trade shares for the following reasons. Firstly, as we have multiplied the variable with -1 to get an index that will increase along with institutional intensity we obtain a result that moves trade upward when in a market the number of industries increases. Secondly, horizontal integration in our model is interpreted in terms of scale economies that can only be explained with respect to the number of production units. So with higher institutions, industries will shift from higher horizontally integrated (i.e. less firms enjoying higher market power) lower trade shares to lower horizontally integrated (i.e. more firms in the market enjoying similar market power) higher trade shares. Our data considers the US economy and treats it as a benchmark for market integrations for the trading partners considering the production technologies are similar across all the trading partners for same manufacturing industry production. Thirdly, the model considers only 6-digit NAICS classified manufacturing sector dataset where most of the industries operate independently so chances of horizontal integration is very low. The study is viewed as a first step to understand industry organizational patterns across countries and building up a general equilibrium theoretical set-up on the interactions of institutions, market structures and trade. Although there are many theories on the sources of comparative advantage and the channels through which trade occurs, we know very little about the underlying mechanism. The dataset and the model along with the methodologies in this study can be used to empirically investigate the other aspects of industry organizations across different countries.

4.1. Limitations of the Study

The study accounts for only the manufacturing sector industry level data. Due to lack of data availability the study benchmarks the make table and the use table of the US and hence the vertical integration index. Similar assumption on benchmarking is done for the horizontal integration index as well. Hence the trade data has to be taken only for the US imports and not all country bilateral trade data. REFERENCES

Acemoglu, D., Johnson, S. and Robinson, J.A., 2001. The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5), pp.1369-1401. Acemoglu, D., Johnson, S. and Robinson, J.A., 2005. Institutions as a fundamental cause of long- run growth. Handbook of economic growth, 1, pp.385-472. Acemoglu, D., Antràs, P. and Helpman, E., 2007. Contracts and technology adoption. American Economic Review, 97(3), pp.916-943. Acemoglu, D., Johnson, S. and Mitton, T., 2009. Determinants of vertical integration: financial development and contracting costs. The Journal of Finance, 64(3), pp.1251-1290. Álvarez, I.C., Barbero, J., Rodríguez-Pose, A. and Zofío, J.L., 2018. Does institutional quality matter for trade? Institutional conditions in a sectoral trade framework. World Development, 103, pp.72-87. Anderson, J.E. and Marcouiller, D., 2002. Insecurity and the pattern of trade: An empirical investigation. Review of Economics and statistics, 84(2), pp.342-352. Antràs, P., 2003. Firms, contracts, and trade structure. The Quarterly Journal of Economics, 118(4), pp.1375-1418. Antràs, P., 2005. Incomplete contracts and the product cycle. American Economic Review, 95(4), pp.1054-1073. Antràs, P., Chor, D., Fally, T. and Hillberry, R., 2012. Measuring the upstreamness of production and trade flows. American Economic Review, 102(3), pp.412-16. Antweiler, W. and Trefler, D., 2002. Increasing returns and all that: a view from trade. American Economic Review, 92(1), pp.93-119. Baltagi, B.H., Egger, P. and Pfaffermayr, M., 2003. A generalized design for bilateral trade flow models. Economics letters, 80(3), pp.391-397. Beck, T., Demirgüç-Kunt, A. and Maksimovic, V., 2006. The influence of financial and legal institutions on firm size. Journal of Banking & Finance, 30(11), pp.2995-3015.

Bhagwati, J. and Ramaswami, V.K., 1963. Domestic distortions, tariffs and the theory of optimum subsidy. Journal of Political economy, 71(1), pp.44-50. Blanchard, O. and Kremer, M., 1997. Disorganization. The Quarterly Journal of Economics, 112(4), pp.1091-1126. Brander, James A., and Barbara J. Spencer. "Tariffs and the extraction of foreign monopoly rents under potential entry." Canadian journal of Economics (1981): 371-389. Breuss, F. and Egger, P., 1999. How reliable are estimations of East-West trade potentials based on cross-section gravity analyses? Empirica, 26(2), pp.81-94. Caballero, R.J. and Hammour, M.L., 1998, June. Jobless growth: appropriability, factor substitution, and unemployment. In Carnegie-Rochester conference series on public policy (Vol. 48, pp. 51-94). North-Holland. Chor, D., 2010. Unpacking sources of comparative advantage: A quantitative approach. Journal of International Economics, 82(2), pp.152-167. Coase, R.H., 1937. The nature of the firm. Economica, 4(16), pp.386-405. Costinot, A., 2005. Contract enforcement, division of labor, and the pattern of trade. Mimeograph, Princeton University. Costinot, A., 2009. An elementary theory of comparative advantage. Econometrica, 77(4), pp.1165-1192. Cowan, K. and Neut, A., 2002. Intermediate goods. Institutions and Output per Worker, Department of Economics, Massachusetts Institute of Technology. Das, J., Tanger, S.M., Kennedy, P.L. and Vlosky, R.P., 2018. Examining the Relationship between Regulatory Quality and Forest Product Exports to India: A Gravity Model Approach. Forest Products Journal, 68(2), pp.172-181. Davis, D.R. and Weinstein, D.E., 2003. Market access, economic geography and comparative advantage: an empirical test. Journal of International Economics, 59(1), pp.1-23. De Groot, H.L., Linders, G.J., Rietveld, P. and Subramanian, U., 2004. The institutional determinants of bilateral trade patterns. Kyklos, 57(1), pp.103-123. D., Kraay, A., and Mastruzzi, M., 2007. “Governance matters VI: indicators for 1996- 2006”. World Bank. WB Policy Research Working Paper No. 4280. Eicher, T.S., Henn, C. and Papageorgiou, C., 2012. Trade creation and diversion revisited: Accounting for model uncertainty and natural trading partner effects. Journal of Applied Econometrics, 27(2), pp.296-321.

Fan, J.P. and Lang, L.H., 2000. The measurement of relatedness: An application to corporate diversification. The Journal of Business, 73(4), pp.629-660. Farooq, U., Hardy, J.L., Gao, L. and Siddiqui, M.A., 2008. Economic impact/forecast model of intelligent transportation systems in Michigan: An input output analysis. Journal of Intelligent Transportation Systems, 12(2), pp.86-95. Ferguson, S. and Formai, S., 2013. Institution-driven comparative advantage and organizational choice. Journal of International Economics, 90(1), pp.193-200. Francois, J. and Manchin, M., 2013. Institutions, infrastructure, and trade. World Development, 46, pp.165-175. Fujita, M., Krugman, P.R. and Venables, A.J., 2001. The spatial economy: Cities, regions, and international trade. MIT press. Grossman, S.J. and Hart, O.D., 1986. The costs and benefits of ownership: A theory of vertical and lateral integration. Journal of Political Economy, 94(4), pp.691-719. Grossman, G.M. and Helpman, E., 2002. Integration versus outsourcing in industry equilibrium. The quarterly journal of economics, 117(1), pp.85-120. Grossman, G.M. and Helpman, E., 2003. Outsourcing versus FDI in industry equilibrium. Journal of the European Economic Association, 1(2-3), pp.317-327. Grossman, G.M. and Helpman, E., 2005. Outsourcing in a global economy. The Review of Economic Studies, 72(1), pp.135-159. Grullon, G., Larkin, Y. and Michaely, R., 2019. Are US industries becoming more concentrated? Review of Finance, 23(4), pp.697-743. Guo, J., Lawson, A.M. and Planting, M.A., 2002. From make-use to symmetric IO tables: an assessment of alternative technology assumptions. BEA. Haberler, G., 1950. Some problems in the pure theory of international trade. The Economic Journal, 60(238), pp.223-240. Hagen, E.E., 1958. An economic justification of protectionism. The Quarterly Journal of Economics, 72(4), pp.496-514. Hall, R.E. and Jones, C.I., 1999. Why do some countries produce so much more output per worker than others? The Quarterly Journal of Economics, 114(1), pp.83-116. Heckscher, E.F. and Ohlin, B.G., 1991. Heckscher-Ohlin trade theory. The MIT Press.Kaufmann, Iwanow, T. and Kirkpatrick, C., 2007. Trade facilitation, regulatory quality and export performance. Journal of International Development: The Journal of the Development Studies Association, 19(6), pp.735-753.

Jansen, M. and Nordås, H.K., 2004. Institutions, trade policy and trade flows. Karemera, D., Smith, W.I., Ojah, K. and Cole, J.A., 1999. A gravity model analysis of the benefits of economic integration in the Pacific Rim. Journal of Economic Integration, pp.347-367. Kaufmann, D., Kraay, A. and Mastruzzi, M., 2005. Governance matters IV: governance indicators for 1996-2004. The World Bank. Klemperer, P., 1995. Competition when consumers have switching costs: An overview with applications to industrial organization, macroeconomics, and international trade. The review of economic studies, 62(4), pp.515-539. Krugman, P.R. ed., 1986. Strategic trade policy and the new international economics. mit Press. Lancaster, K., 1957. The Heckscher-Ohlin trade model: A geometric treatment. Economica, 24(93), pp.19-39. Leamer, E.E., 1995. The Heckscher-Ohlin model in theory and practice. Lee, S., 2015. Three Essays on Intrafirm Trade. Levchenko, A.A., 2007. Institutional quality and international trade. The Review of Economic Studies, 74(3), pp.791-819. Linders, G.J., HL Slangen, A., De Groot, H.L. and Beugelsdijk, S., 2005. Cultural and institutional determinants of bilateral trade flows. Available at SSRN 775504. Mahoney, J. and Thelen, K., 2010. A theory of gradual institutional change. Explaining institutional change: Ambiguity, agency, and power, 1, pp.1-37. North, D.C., 1986. The new institutional economics. Journal of Institutional and Theoretical Economics,142(1), pp.230-237. North, D.C., 1991. Institutions. Journal of economic perspectives, 5(1), pp.97-112. North, D.C., 1993. Toward a theory of institutional change. Political economy: Institutions, competition, and representation, 31(4), pp.61-69. Novy, D., 2013. Gravity redux: measuring international trade costs with panel data. Economic inquiry, 51(1), pp.101-121. Nunn, N., 2007. Relationship-specificity, incomplete contracts, and the pattern of trade. Quarterly Journal of Economics 122 (2), 569–600. Nunn, N. and Trefler, D., 2014. Domestic institutions as a source of comparative advantage. In Handbook of international economics (Vol. 4, pp. 263-315). Elsevier. Ohlin, B., 1957. Interregional and international trade. Reprinted as Harvard Economic Studies, Cambridge, MA: Harvard University Press, (39). 18

Oliver, C., 1991. Strategic responses to institutional processes. Academy of management review, 16(1), pp.145-179. Oxley, J.E., 1999. Institutional environment and the mechanisms of governance: the impact of intellectual property protection on the structure of inter-firm alliances. Journal of Economic Behavior & Organization, 38(3), pp.283-309.

Porojan, A., 2001. Trade flows and spatial effects: the gravity model revisited. Open economies review, 12(3), pp.265-280.

Rajan, R.G. and Zingales, L., 2001. The influence of the financial revolution on the nature of firms. American Economic Review, 91(2), pp.206-211. Ranjan, P. and Lee, J.Y., 2007. Contract enforcement and international trade. Economics & Politics, 19(2), pp.191-218. Rodríguez‐Pose, A. and Storper, M., 2006. Better rules or stronger communities? On the social foundations of institutional change and its economic effects. Economic geography, 82(1), pp.1-25. Romalis, J., 2004. Factor proportions and the structure of commodity trade. American Economic Review, 94(1), pp.67-97. Silva, J.S. and Tenreyro, S., 2006. The log of gravity. The Review of Economics and statistics, 88(4), pp.641-658. Silva, J.S. and Tenreyro, S., 2010. On the existence of the maximum likelihood estimates in Poisson regression. Economics Letters, 107(2), pp.310-312. Silva, J.M.S., Tenreyro, S. and Windmeijer, F., 2015. Testing competing models for non-negative data with many zeros. Journal of Econometric Methods, 4(1), pp.29-46. Schuler, P., 2003, September. Institutions and the changing composition of international trade in the post-socialist transition. In annual conference of the international society of new institutional economics, Budapest. Talamo, G., 2007, January. Institutions, FDI, and the gravity model. In Workshop PRIN 2005 SU, Economic Growth; Institutional and Social Dynamics (pp. 25-27). Thelen, K., 2004. How institutions evolve: The political economy of skills in Germany, Britain, the United States, and Japan. Cambridge University Press. Venables, A.J. and Limao, N., 2002. Geographical disadvantage: a Heckscher–Ohlin–von Thünen model of international specialisation. Journal of international Economics, 58(2), pp.239-263. Vogel, J., 2007. Institutions and moral hazard in open economies. Journal of International Economics, 71(2), pp.495-514.

Yu, S., Beugelsdijk, S. and de Haan, J., 2015. Trade, trust and the rule of law. European Journal of Political Economy, 37, pp.102-115. Williamson, O.E., 1988. Technology and transaction cost economics: a reply. Journal of Economic Behavior & Organization, 10(3), pp.355-363. Zingales, L., 2017. Towards a political theory of the firm. Journal of Economic Perspectives, 31(3), pp.113-30.