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: