Caste Discrimination and Transaction Costs in the Labor Market: Evidence from Rural North India

This paper is an empirical attempt to quantify caste-based discrimination in the labor market using household data taken from rural North India. In the regression analysis, transaction costs associated with entry into the labor market and reservation wages are estimated along with market wages. The estimation results provide evidence of the existence of transaction costs in the labor market and discrimination against backward classes with regard to access to regular employment. In line with previous studies, the results suggest that the achievements of India's reservation policy so far have at best been limited. In addition, a comparison between the estimates from the model employed in this paper and conventional (reduced-form) approaches shows that discrimination in labor market entry is likely to be underestimated in the conventional reduced-form approaches.regular employment, casual employment, labor market, India

Education and Its Distributional Impacts on Living Standards

This paper investigates the determinants of living standards (measured by per capita consumption expenditure) at the household level, addressing heterogeneity in returns to education and endogeneity of educational status. The estimation results obtained through an instrumental variables quantile regression suggest that the endogeneity of education matters in determining the causal effect of education on living standards, while no evidence of the heterogeneity in the rate of returns to education is found. However, the results also provide evidence that impacts of other determinants vary significantly over the outcome (expenditure) distribution, and consequently a simulation based on the results shows that poverty alleviation impacts of education differs substantially between the instrumental variables quantile regression and standard instrumental variables regression results. The comparison of the two indicates the possibility that the impact on poverty reduction is likely to be overestimated in the standard instrumental variable regression.poverty, heterogeneous returns to education, instrumental variables quantile regression

How to solve the cake-cutting problem in sublinear time

In this paper, we show algorithms for solving the cake-cutting problem in sublinear-time. More specifically, we preassign (simple) fair portions to o(n) players in o(n)-time, and minimize the damage to the rest of the players. All currently known algorithms require Omega(n)-time, even when assigning a portion to just one player, and it is nontrivial to revise these algorithms to run in $o(n)$-time since many of the remaining players, who have not been asked any queries, may not be satisfied with the remaining cake. To challenge this problem, we begin by providing a framework for solving the cake-cutting problem in sublinear-time. Generally speaking, solving a problem in sublinear-time requires the use of approximations. However, in our framework, we introduce the concept of "eps n-victims," which means that eps n players (victims) may not get fair portions, where 0< eps =< 1 is an arbitrary constant. In our framework, an algorithm consists of the following two parts: In the first (Preassigning) part, it distributes fair portions to r < n players in o(n)-time. In the second (Completion) part, it distributes fair portions to the remaining n-r players except for the eps n victims in poly}(n)-time. There are two variations on the r players in the first part. Specifically, whether they can or cannot be designated. We will then present algorithms in this framework. In particular, an O(r/eps)-time algorithm for r =< eps n/127 undesignated players with eps n-victims, and an O~(r^2/eps)-time algorithm for r =< eps e^{{sqrt{ln{n}}}/{7}} designated players and eps =< 1/e with eps n-victims are presented.Comment: 15 pages, no figur

Multidimensional Poverty Rankings based on Pareto Principle: A Practical Extension

This paper proposes a ranking method of multidimensional poverty and extends it aiming to enhance its practical utility. While our original ranking method that assumes non-comparability among different dimensions of poverty succeeds in eliminating some implicit arbitrariness in existing ranking, it also confronts a disadvantage that a non- negligible number of objectives (countries) are ranked at the same level. In order to improve this disadvantage, we propose an extended ranking method, where we allow the data to have a certain range of bandwidth. The introduction of bandwidth improves the usefulness of our ranking in the sense that it decreases the number of countries with the same rank. In addition, a simulation exercise shows that this extension also improves the robustness of the ranking against measurement errors.

Weather Risk and the Off-­Farm Labor Supply of Agricultural Households in India

As one of the measures to smooth income, this paper focuses on the diversification of labor allocation across activities. A key feature of this paper is that it pays particular attention to differences in the covariance between weather risk and agricultural wages and between weather risk and nonagricultural wages. We estimate a multivariate tobit model of labor allocation using household data from rural areas of Bihar and Uttar Pradesh, India. The regression results show that the share of the off­farm labor supply increases with the weather risk, and the increase is much larger in the case of nonagricultural wage work than in the case of agricultural wage work. Simulation results based on the regression estimates show that the sectoral difference is substantial, implying that empirical and theoretical studies on farmers' labor supply response to risk should distinguish between the types of off-­farm work involved.covariate risk, nonfarm employment, selfemployment, food security, India, Labor and Human Capital, Q12, O15, J22,

Weather Risk, Wages in Kind, and the Off-Farm Labor Supply of Agricultural Households in a Developing Country

This paper investigates the effects of weather risk on the off-farm labor supply of agricultural households in a developing country. Faced with the uninsurable risk of output and food price fluctuations, poor farmers in developing countries may diversify labor allocation across activities in order to smooth income in real terms.A key feature of this paper is that it distinguishes different types of off-farm labor markets: agriculture and nonagriculture on the one hand, and, wages paid in cash and wages paid in kind on the other. We develop a theoretical model of household optimization, which predicts that when farmers are faced with more production risk in their farm production, they find it more attractive to engage in nonagricultural work as a means of risk diversification, but the agricultural wage sector becomes more attractive when food security is an important issue for the farmers and agricultural wages are paid in kind. To test this prediction, we estimate a multivariate twolimit tobit model of labor allocation using household data from rural areas of Bihar and Uttar Pradesh, India. The regression results show that the share of the off-farm labor supply increases with weather risk, the increase is much larger in the case of nonagricultural work than in the case of agricultural wage work, and the increase is much larger in the case of agricultural wages paid in kind than in the cash wage case. Simulation results based on the regression estimates show that the sectoral difference is substantial, implying that empirical and theoretical studies on farmers' labor supply response to risk should distinguish between the types of off-farm work involved.covariate risk, non-farm employment, self-employment, food security, India

Hybrid Simulation between Molecular Dynamics and Binary Collision Approximation Codes for Hydrogen injection onto Carbon Materials

Molecular dynamics (MD) simulation with modified Brenner's reactive empirical bond order (REBO) potential is a powerful tool to investigate plasma wall interaction on divertor plates in a nuclear fusion device. However, MD simulation box's size is less than several nm for the performance of a computer. To extend the size of the MD simulation, we develop a hybrid simulation code between MD code using REBO potential and binary collision approximation (BCA) code. Using the BCA code instead of computing all particles with a high kinetic energy for every step in the MD simulation, considerable computation time is saved. By demonstrating a hydrogen atom injection on a graphite by the hybrid simulation code, it is found that the hybrid simulation code works efficiently in a large simulation box.Comment: 5 pages, 5 figure