Competition and Innovation in Automobile

Markets

Vivek Ghosal and Jiayao Ni

Competition and Innovation in Automobile Markets

Vivek Ghosal1 and Jiayao Ni2

Work-in-progress: Notes for conference presentationPlease do not distribute online or non-conference

Version: March 01, 2015

Abstract

Using data on the U.S. automobile market, we empirically examine the link between competition and innovation. While the automobile industry is a traditional one and has existed for well over a 100 years, it remains dynamic with multiple firms striving to push the frontiers of innovation and technology, and vying for market share. To examine the above relationship, we use firm-level time-series data over a long horizon (1969-2012) for nine well established firms selling in the U.S. market (GM, Ford, Chrysler, Toyota, Honda, Nissan, Volkswagen, BMW and Daimler). Some of our key findings are:1. Examining the relationship between firm-specific market shares and patents, we find that in general an increase in firms’ shares leads to increase in patenting. But the estimated elasticities vary considerably across the different sub-samples;2. Considering market-wide competition, we find that greater competition (lower HHI) generates an increase in total innovation (number of patents) in the market. We also find that the relationship between the HHI and total patents is moderately nonlinear, and consistent with the predictions of some of the theoretical models;3. Our results indicate relatively strong path-dependence in firms’ innovation behavior; and4. We find other interesting results related to the Clean Air Act, Voluntary Export Restraints, the Daimler-Chrysler merger, GM’s bankruptcy, and the cyclicality of innovation paths.JEL Codes: L13, L62, O31, D21, M21.Keywords: Innovation, patents, competition, automobile.

1 Contact author. School of Economics, Georgia Institute of Technology (Atlanta, USA); CESifo (Munich, Germany); and European Business School (Wiesbaden, Germany). Email: vivekghosal@gatech.eduWe thank seminar participants at Shanghai Jiao Tong University for helpful comments.2 School of Economics, Georgia Institute of Technology (Atlanta, USA)

Summary of Estimation Results

Own Market Share, HHI and Patents

(1) ( ) = + ( ) + ( ) + ( ) + + .

In (1), is the annual total number of patents for firm i, is the firm-specific intercept, is the lagged own-market share of the firm, is one lag of HHI, X is a vector of

other controls (discussed below), and is a firm-specific error term.3

We estimate specification (1) using the Arellano-Bond Estimator for dynamic panel data

models. Due to the fact that a Generalized Method of Moments (GMM) estimator is used, which

includes a wide array of instruments, the procedure automatically accounts for potential

endogeneity of any of the included firm-specific independent variables.

As a check for potential endogentity, we conducted econometric causality tests. First we

tested for whether is endogenous. Even though the variable is one-period lagged,

intertemporal dynamics in the relationship can potentially render it to be endogenous. We

implement two tests that are common in the literature. These are by Granger (1969), and

Geweke, Meese and Dent (1983). The results are presented in Table 4. Based on the results, the

only firms where there is some evidence of endogeneity of SHR are Toyota, Honda and Nissan.

Since our GMM estimator is an Instrumental Variables procedure, this controls for the potential

endogeneity of these firms’ SHR to PAT.

We also tested for potential endogentity of HHI in (1). A case can be made that market

concentration is potentially endogenous to the extent of patenting (innovation, and particular

3 Log-linear specifications are common in estimating patent specifications. We do not use negative Binomial models as our sample contains large well-established multinational firms with continuous patenting profiles. Negative Binomial models are more appropriate when the sample contains small and startup firms with over-dispersion of patent counts (e.g., many zeros combined with large jumps in patents). Given the continuous nature of our patents data, we use a log-linear specification – which has been used in numerous earlier studies on patenting: e.g., specific models in Hall and Ziedonis (2001) and Kortum and Lerner (1999, 2000), and Jaffe (1986), Kondo (1999), Branstetter (2001), Furman et al. (2002), Hu (2010) and von Graevenitz et al. (2013).

firms’ innovating activities may alter the market structure. Results of testing for endogeneity of

HHI are presented in Table 5. The tests do not reject the null that HHI is exogenous.

The results from estimating specification (1) are presented in Table 6. Our key findings

are noted below.

1. There is considerable persistence in the intertemporal path of patenting. The estimated

elasticities are in the range of 0.58 to 0.79, with the U.S. firms’ sample showing the least

persistence. These elasticities indicate considerable path-dependence of firms’ patenting. The

coefficient of the lagged-dependent variable is positive and highly significant, indicating

persistence in the path of total patents. This persistence is not surprising as firms’ R&D

processes, and innovation and patenting strategy are expected to show some continuity at least in

the short-to-medium term.

2. Increase in firms’ own market share leads to an increase in patenting. The estimated

elasticities are in the range of 0.14 to 1.07. The lowest elasticity is for the group of Japanese

firms at 0.12, while the largest elasticity is for the group of U.S. firms at 1.07.

3. The elasticities related to HHI range from 0.059 to -0.995. The elasticity is significant only for

the U.S. group (-0.995). A reduction of HHI corresponds to greater competition. The estimated

elasticity for the U.S. group implies that greater overall competition in the market increases the

patenting profile of the U.S. firms. But, given the estimates elasticities, has no statistically

significant effect on the group of Japanese or German firms. Examining the relationship between

HHI and patents allows us to shed some light on the underlying theories that make predictions

about the linkage between market-wide competitiveness and innovation activity (Scherer, 1965;

Lee and Wilde, 1980; Delbono and Denicolo, 1991; Acs and Audretsch, 1998; Blundell et al.,

1999; Hu, 2010; Aghion et al., 2005; among others). Our findings in spirit appear to support the

results in Acs and Audretsch (1998), similar to the hypotheses by Blundell et al. (1999) and Hu

(2010) – predicting that the degree of actual (and potential) competition is positively related to

innovation outcomes. It also lends credence to the analysis of Aghion et al. (2005). The latter

model is difficult to test within the context of our study as we do not observe the full range of

market structures, highly competitive to near monopoly. The market we study essentially moves

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from a tighter oligopoly (when the U.S. firms had dominant market share) to a more competitive

oligopoly resulting from the expansion of market share primarily by the Japanese firms. Our

findings do not favor Scherer (1965) who noted that market power does not have an impact on

patents. Our findings are also in contrast to the models by Lee and Wilde (1980) and Delbono

and Denicolo (1991) which predict a negative relationship between innovation investments and

competitiveness.

4. The introduction of the Clean Air Act (CAA) had mixed effects on firms’ patenting. Overall, it

appears to have increased patenting, with an estimated elasticity of 0.127. While the estimated

effects are positive for all groups of firms, it is statistically significant only for the Japanese

firms. The estimated U.S. elasticity is much larger and closer to being statistically significant that

the German group. The findings are similar in spirit to those of Lee et al. (2011) who found that

patenting increased to generate new products and processes to meet the emissions standards. But

their results and ours are not directly comparable due to difference in time periods, and the focus

of the study on environmental patents.

5. The Voluntary Export Restraints (VER) generally had no statistically significant effect on

firms’ patenting. The only exception is that the estimated elasticity for the Japanese group is a

small positive (0.09) and significant.

6. The Bankruptcy variable is targeted to control for GM’s fortunes. As expected, Bankruptcy

markedly reduces patenting, and this effect is only valid for the U.S. group of firms where the

estimated elasticity is relatively large at -0.813.

7. The Merger variable was designed to control for any residual effects of the merger between

Daimler and Chrysler. We find that the merger increased patenting in the U.S. group, implying

an increase in Chrysler’s patents. There is no statistically significant effect for the group of

German firms.

8. GDP growth appears to be positively related to firms’ patenting. This pro-cyclicality appears

consistent with several other studies in the literature we cited earlier. The pro-cyclical pattern is

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statistically significant for the U.S. and Japanese group of firms with relatively small estimated

elasticities of 0.012. The elasticity for the German firms is not significant.

The overall picture that emerges is one of considerable heterogeneity in firms’ innovation

responses to the full range of variables we examine – firms’ market shares, market-wide

competitiveness, and other factors. No one class of theoretical models finds broad support. These

observations provide rather mixed evidence on the underlying theory noted earlier (Acs and

Audretsch, 1998; Blundell et al., 1999; Hu, 2010; Scherer, 1965; Lee and Wilde, 1980; and

Delbono and Denicolo, 1991; Aghion et al., 2005; among others). At least at face value, these

results seem more in line with the “Resource-Based View” (RBV) of the firm, which has been

widely used in the Management literature to explain the idiosyncratic firm-specific responses to

common shocks as well as firm-specific shocks (e.g., for the automobile firms, see Lieberman et

al., 1990; Lieberman and Demeester, 1999; and Lieberman and Dhawan, 2005).

Own Market Share, Rivals’ Shares, and Patents

The estimated specification is:

(4) ( ) = + ( ) + ( ) + ( ) + ( ) + + .To decode specification (4), we provide the following example. Suppose we think of a

specification for GM only. The variables included would be as follows:

(a) and are the firm’s (GM’s) own patents and market share;

(b) is the summed market share of GM’s U.S. competitors (Ford+Chrysler);

(c) is the summed market share of GM’s foreign-country competitors (Toyota

+Honda+Nissan); and

(d) is the vector of other control variables. These are the same as in specification (1).

Similarly for the other firms in the sample.

These results are presented in Table 7. Some key observations are as follows.

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1. The estimated elasticities on own market shares are lower than those in table 6. This implies

that the included rivals’ market shares by groups may be capturing the dynamics better than the

market-wide indicator, HHI.

2. The market share elasticities of the own-country rivals is negative and significant for both the

U.S. and Japanese groups. This implies that an increase in own-country rivals’ market shares

results in a decline in patenting. The puzzling result, however, is that in the full panel (column

1), the coefficient is insignificant.

3. The market share elasticities of the other-country rivals is positive and significant for the

overall panel and the U.S. group. This implies that an increase in other-country rivals’ market

shares results in an increase in patenting. For the Japanese group, the estimated elasticity is

insignificant.

4. The CAA effect is now positive and significant in the U.S. group, and very small and

insignificant for the Japanese group. These estimated effects are quite different from those in

table 6. These results imply that the including the own-country and other-country rivals’ market

shares are capturing very different types of dynamics than HHI, resulting in sharply different

estimated effects on CAA.

5. For VER, the estimated effect for the Japanese group is similar to that in table 6. But the U.S.

group now shows a negative and significant coefficient. As with the CAA effects, these results

imply that the including the own-country and other-country rivals’ market shares are capturing

very different types of dynamics than HHI, resulting in sharply different estimated effects on

VER.

6. The inferences on the estimated effects of Bankruptcy, Merger, and GDP are relatively similar

to those from table 6.

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Our overall inferences remain intact when we replace the HHI effect with firms’ own-

country and other-country rivals’ market shares. The most important differences arise with the

estimated effects for the Clean Air Act and Voluntary Export Restraints.

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