Judicial quality and regional firm performance

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As I mentioned before, two kinds of endogeneity problem may affect the results –reverse causality and omitted variable bias. One would expect the use of firm-level input to be a function of the judicial speed. And, if true, this will induce simultaneity bias. In order to control for this, I use a firm’s “input complexity”in the first year of the sample. I also use one-period lagged values of judicial quality to further check for the robustness of results. The results remain the same (not reported) in both the cases as compared to my benchmark results.
Another important concern with the estimation strategy is the omitted variable bias. I address this issue by sequentially adding various state characteristics and its interaction with “input complexity”index to my baseline specification. In other words, it can be argued that the differential effect of “input complexity”index on firms in different states may be due to other state factors that are unrelated to judicial quality. Therefore, by adding proxies of alternate state characteristics and its interaction with “input complexity”index, I am able to test whether the performance premium due to more efficient judiciary is robust to controlling for these additional channels. In addition to these control variables, the inclusion of state fixed effects in the baseline specification will also control for time-invariant state characteristics but not for time-varying unobservable characteristics. For example, it may be the case that a firm’s exports or total sales and a state’s judicial quality are correlated with the economic and political condition. I also add state-year interaction fixed effects to my baseline spec- ification to examine whether the main results are robust to controlling for time-varying, unobservable state characteristics. The primary result stays the same.

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关键词：performance Performan Judicial regional quality complexity function control further problem

A related and crucial issue is the self-selection problem of firms. The firms may self-select themselves in states which have efficient judiciaries, i.e., if a bigger exporter is located in a state with more efficient judiciary and use a higher pro- portion of intermediate inputs, then the results of this paper will reflect nothing, but a simple spurious correlation. This could potentially bias my results. Following Ahsan (2013) , I compare the exports of firms’ in high judicial quality states (ju- dicial quality above the sample median) with the exports of firms’ in low judicial quality states (judicial quality below the sample median). I find no evidence to suggest that high performance firms locate in high judicial quality states. 6 I also do not observe any evidence of systematic agglomeration in the data, which could also raise some serious concerns about the identification strategy used in this paper. The industries included in the sample are fairly well spread across various states. Thus, the potential selection of high performing firms in high judicial quality states that also experience higher intermediate input usage, as a likely explanation for the results documented in this paper is bleak. Nonetheless, in order to be thoroughly convinced that self-selection of firms doesn’t play any role in achieving the de- sired results for this paper; I carry out the following exercise: I estimate the effect of judicial quality on the firm perfor- mance using a two-stage Average Treatment Effect (ATE), utilizing the matching estimator technique. The matching esti- mator technique has been widely used in understanding the effect of institutional or judicial quality on international trade ( Nunn, 2007; Ahsan, 2013; Ma et al., 2010 ). I follow the literature and use the propensity score matching ( Rosenbaum and Rubin, 1983 ) method to generate propensity scores in the first stage and then estimate the ATE of the judicial quality–by weighing with the inverse of a nonparametric estimate of the propensity score rather than the true propensity score–on the performance indicator of a firm. This leads to an efficient estimate of the ATE ( Hirano et al., 2003 ). 7 To generate propensity scores, I do the following: I first calculate the median (50th percentile) of the pendency ratio over all the states and years. I then use each state’s average value of the pendency ratio (averaged over 20 0 0–10) to classify it as having high or low judicial quality. In particular, if a state’s average pendency ratio is equal to or greater than the median of the sample, it is classified as a low judicial quality state. 8 If a state’s average pendency ratio is below the median, it is classified as having high judicial quality. Next, using this information I construct an indicator variable judqua i , which is one if firm i is in a state at or below the median of judicial quality and zero otherwise. This indicator variable is then used to construct propensity scores by estimating the following probit model: