# **4. Results**

Results of the estimation of equation (1) by GMM-sys are reported in tables 7 and 8. As a check on the robustness of the results of those tables to the assumptions of the Euler equation model about adjustment costs we have also estimated equation (2) again by GMM-sys, of which the full set of results we report in tables 9, 10 and 11. The basic instruments in the estimation of the Euler equation model in tables 7 and 8 include three step and longer lags of the gross investment rate, the profit rate, the sales to capital stock ratio and the debt to fixed assets ratio as well as two step or longer lags of first differences in each of these. Corresponding lags of levels and first differences of the variables of the error correction specification constitute the basic instrument set used in tables 9, 10 and 11. In all cases city and state dummies and year dummies have been used as additional instruments. All the variables that appear in tables 7 to 11 are as defined already in this and in previous sections. The variable “constrained by power shortages’’ is dummy for whether enterprise management considered problems of electricity supply as a “moderate” to “very severe’ obstacles to the business’ growth, where the rating phrases are as described in Section II. The variable “constrained by corruption” in tables 9 is likewise a dummy for whether management considered corruption as a “moderate” to “very severe” obstacle to business growth.

Turning to specific estimation results, we focus first on table 7, where we seek to identify the investment effects of our three ‘contracting institutions’ by conditioning the investment regression on the incidence of corruption, as our proxy for the quality of property rights institutions. We read three striking results from this table. One of these is that, over the full sample, the investment rate is significantly lower where labor regulation is more stringent and where power shortages are more severe. This can be seen from columns 1 and 2. Looking deeper, we see that this actually reflects what is happening in high-growth states, that is both quadrant I states and quadrant II states. The gross investment rate actually increases with the stricter labor regulation and more with the gravity of power shortages in lagging regions or quadrant III states.<sup>6</sup> variables that vary across locations, by using the identifying assumption that the location of birth of the founding entrepreneur should influence the location decisions but not the performance of businesses past a certain age. They find that the selectivity term of the productivity equation that they estimate based on the joint model is not generally statistically significant in for the 2002 wave of the FACS dataset.

6 One possible explanation for the positive association between labor regulation and the rate of investment in lagging states is that the former encourages more capital intensive production in those states. Likewise it may be that the positive association between the rate of investment and more severe power shortages reflects that own-generation of power accounts for a significant share of fixed investment lagging regions while that is not the case in more advanced regions. In a related paper (Honorati and Mengistae, 2007a), we report that greater power shortages and stricter labor regulation both lead reduce the rate of growth of enterprise in lagging as well as advanced states. It must be, then, that both factors reduce productivity in lagging states. Honorati and Mengistae (2007b) report positive evidence that labor regulation does indeed produce decline in productivity over time.

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