Profit inefficiency of Indian farms: A system approach

This article attempts to measure the level of profit inefficiency of Indian farms using a stochastic profit function. In the process, the degree of misallocation of inputs and nonoptimal supply of output is also estimated. Likelihood ratio tests allow division of total observations into two groups-large and small farms. Results indicate that both groups are profit-inefficient due to misallocation of inputs and undersupply of output. However, small farms are less profit inefficient than their larger counterparts. Within each group relatively larger farms are less inefficient.The editor for this paper was Lennart Hjalmarsson.     

Adjustment of inputs and measurement of technical efficiency: A dynamic panel data analysis of the Egyptian manufacturing sectors

The purpose of this article is to construct a dynamic stochastic production frontier incorporating the sluggish adjustment of inputs, to measure the speed of adjustment of output, and to compare the technical efficiency estimates from this dynamic model to those from a static model. By assuming instantaneous adjustment of all inputs, a static model may underestimate technical efficiency of a production unit in the short-run. However, in this article I show that under the assumption of similar adjustment speed for all inputs, a linear partial adjustment scheme for output characterizes the dynamic production frontier. The dynamic frontier with time-invariant technical efficiency is estimated using the system GMM (generalized method of moments) estimator. Applying the model and estimation method on a panel data set spanning 9?years of data on private manufacturing establishments in Egypt, I find that (1) the speed of adjustment of output is significantly lower than unity, (2) the static model underestimates technical efficiency by 4.5 percentage points on average, and (3) the ranking of production units based on their technical efficiency measures changes when the lagged adjustment process of inputs is taken into account.     

The role of family decision in internal migration: the case of India

This paper analyzes the effects of family decisions and individual decisions on rural-urban migration in India under 2 different rural institutions--family farm and wage labor systems. An analytical framework for explaining family migration decisions reveals that whenever a member of the extended family migrates, he gives up his share in the produce of the family farm. When this happens, the number of adult members on the farm goes down and the total product is affected. 3 case studies of Indian villages are analyzed for this study. 2 empirical relations are examined: 1) if individual migration decisions are predominant, and 2) if family decisions are important in determining the overall flow of migration. Relationships between migration decisions and other variables, such as: 1) number of males in urban areas; 2) urban wages; 3) daily wage rate; 4) average agricultural income; 5) railway distance between rural and urban areas; 6) size of the labor market in destination region; 7) probability that a migrant arriving in an urban area will find a job; and 8) comsumption expenditure, in urban areas estimated by working class consumer price index, are determined. Results show that: 1) the market determined wage variable does not play a very significant role in migration decisions; 2) distance is one of the most important variables in analyzing migration; and 3) the aggregate flow of migration is affected if migration decisions are predominantly family decisions. These findings have relevant policy implications for less developed countries (LDCs), especially because large flows of rural-urban migration in recent years have forced governments to adopt policies for controlling the flows to reduce the burden of unemployment in the urban areas. Government policies affecting rural institutions will have an impact on migration flow; those that lead to a reduction of uncertainty in agriculture will affect average per-capita consumption levels in family farms and hence influence the flow of migration.     

Conditional VaR using EVT - Towards a planned margin scheme

This paper constructs a robust Value-at-Risk (VaR) measure for the Indian stock markets by combining two well-known facts about equity return time series — dynamic volatility resulting in the well-recognized phenomenon of volatility clustering, and non-normality giving rise to fat tails of the return distribution. While the phenomenon of volatility dynamics has been extensively studied using GARCH model and its many relatives, the application of Extreme Value Theory (EVT) is relatively recent in tracking extreme losses in the study of risk measurement. There are recent applications of Extreme Value Theory to estimate the unexpected losses due to extreme events and hence modify the current methodology of VaR. Extreme value theory (EVT) has been used to analyze financial data showing clear non-normal behavior. We combine the two methodologies to come up with a robust model with much enhanced predictive abilities. A robust model would obviate the need for imposing special ad hoc margins by the regulator in times of extreme volatility. A rule based margin system would increase efficiency of the price discovery process and also the market integrity with the regulator no longer seen as managing volatility.     

MaxVaR for non-normal and heteroskedastic returns¶

In this work we propose Monte Carlo simulation models for dynamically computing MaxVaR for a financial return series. This dynamic MaxVaR takes into account the time-varying volatility as well as non-normality of returns or innovations. We apply this methodology to five stock market indices. To validate the proposed methods we compute the number of MaxVaR violations and compare them with the expected number. We also compute the MaxVaR-to-VaR ratio and find that, on average, dynamic MaxVaR exceeds dynamic VaR by 5–7% at the 1% significance level, and by 12–14% at the 5% significance level for the selected indices.     

Political origins of financial structure

There is growing policy interest in the role of financial structure in promoting development. However, very little is known about how different financial structures emerge and evolve. In this paper we empirically assess the political origins of financial structure. Using difference–in difference estimation and annual data, we study the effects of democratization on financial structure in a sample of 96 countries covering the period 1970–2005. Democratization here corresponds to the event of becoming a democracy. We find that democratization leads to a more market-based financial system. Democratic change could also be incremental rather than a one off. To identify the effect of incremental democratic change on financial structure we estimate a separate model and find that democracy matters. We also find that countries with substantial democratic capital are more likely to have a market-based financial structure. Our main results are robust to a variety of controls, Arellano–Bond GMM estimation, alternative measures of democracy and financial structure, and across different samples.     

The structure of strategy-proof random social choice functions over product domains and lexicographically separable preferences

We characterize the class of dominant-strategy incentive-compatible (or strategy-proof) random social choice functions in the standard multi-dimensional voting model where voter preferences over the various dimensions (or components) are lexicographically separable. We show that these social choice functions (which we call generalized random dictatorships) are induced by probability distributions on voter sequences of length equal to the number of components. They induce a fixed probability distribution on the product set of voter peaks. The marginal probability distribution over every component is a random dictatorship. Our results generalize the classic random dictatorship result in Gibbard (1977) and the decomposability results for strategy-proof deterministic social choice functions for multi-dimensional models with separable preferences obtained in LeBreton and Sen (1999).