Does the Presence of Surplus Labor Hinder Production in Indian Manufacturing?

Input congestion occurs at a given input bundle when the assumption of free disposability of inputs does not hold and an increase in input leads to a decline in output. In this paper we employ the nonparametric method of Data Envelopment Analysis (DEA) to examine the question on input congestion with respect to labor, using state level data from the Annual Survey of Industries for the period 1986-87 through 1999-2000. When the standard assumption of strong disposability is relaxed for the labor inputs, the nonparametric analysis of state-level data from Indian manufacturing shows considerable measure of labor input congestion. While in selected states congestion comes from non-production workers as well, the principal source of labor congestion is production labor. There is no evidence that the problem of labor congestion has become less severe during the post-Reform years. It appears that market forces without any major institutional changes in enforcement of labor discipline cannot eliminate congestion.

A Dynamic Stochastic Frontier Production Model with Time-Varying Efficiency

In this paper we introduce technical efficiency via the intercept that evolve over time as a AR(1) process in a stochastic frontier (SF) framework in a panel data framework. Following are the distinguishing features of the model. First, the model is dynamic in nature. Second, it can separate technical inefficiency from fixed firm-specific effects which are not part of inefficiency. Third, the model allows one to estimate technical change separate from change in technical efficiency. We propose the ML method to estimate the parameters of the model. Finally, we derive expressions to calculate/predict technical inefficiency (efficiency).