Impact of rural employment guarantee schemes on seasonal labor markets: optimum compensation and workers’ welfare

The recent enactment of the National Rural Employment Guarantee Act in India has been widely hailed as a policy that provides a safety net for the rural poor with the potential to boost rural income, stabilize agricultural production and reduce rural-urban migration. This paper models the impact of such employment guarantee schemes in the context of an agrarian economy characterized by lean season involuntary unemployment as a consequence of tied-labor contracts. Specifically, we examine labor and output market responses to a productive rural Employment Guarantee Scheme (EGS) and determine the optimal compensation to public work employees consistent with the objectives of (i) productive efficiency in agriculture and (ii) welfare maximization of the laborers. Our framework provides a theoretical framework for the evaluation of a number of (sometimes) conflicting observations and empirical results on the impact of an EGS on agricultural wages, employment and output, and underscores the importance of the relative productivity of workers in the EGS program vis-à-vis their counterparts engaged in agricultural production in determining the success of these programs.     

On adaptive linear regression

Ordinary least squares (OLS) is omnipresent in regression modeling. Occasionally, least absolute deviations (LAD) or other methods are used as an alternative when there are outliers. Although some data adaptive estimators have been proposed, they are typically difficult to implement. In this paper, we propose an easy to compute adaptive estimator which is simply a linear combination of OLS and LAD. We demonstrate large sample normality of our estimator and show that its performance is close to best for both light-tailed (e.g. normal and uniform) and heavy-tailed (e.g. double exponential and t ₃) error distributions. We demonstrate this through three simulation studies and illustrate our method on state public expenditures and lutenizing hormone data sets. We conclude that our method is general and easy to use, which gives good efficiency across a wide range of error distributions.     

On use of symmetrical balanced incomplete block design in construction of sampling design realising pre-assigned sets of inclusion probabilities of first two orders.

Gupta, Nigam and Kumar (1982) proposed a sampling scheme using certain combinatorial properties of balanced incomplete block design (BIBD) which realises first order inclusion probabilities proportional to measure of size(IPPS). Here their results have been extended by presenting samplng schemes realising pre-assigned sets of inclusion probabilities of first two orders.     

Estimation of Mean of Sensitive Characteristics for Successive Sampling

In this article, new estimators for estimating the population mean of a sensitive variable using the concept of successive sampling over two occasions are proposed. The unbiasedness and the variance properties of the proposed estimators are investigated analytically as well as numerically.     

On comutativity of design and model expectations in randomized response surveys

Interchangability of the order of design expectation ( E_p ) and model expectation ( E_R ) under randomized response ( RR ) surveys for finding expectation and mean-square error of a linear estimator e found to be permissible for a non-informative sampling design. The non-comutative property established by Chaudhuri & Adhikary ( 1990 ) under probability proportional to size with replacement ( PPSWR ) sampling is disproved.     

Bias corrected empirical Bayes confidence intervals for the selected mean

Empirical Bayes (EB) methods are very useful for post selection inference. Following Datta et al. (2002 Datta, G. S., M. Ghosh, D. D. Smith, and P. Lahiri. 2002. On an asymptotic theory of conditional and unconditional coverage probabilities of empirical Bayes confidence intervals. Scandinavian Journal of Statistics 29:139–52.[Crossref], [Web of Science ®] , [Google Scholar]), we construct EB confidence intervals for the selected population mean. The EB intervals are adjusted to achieve the target coverage probabilities asymptotically up to the second order. Both unconditional coverage probabilities of EB intervals and corresponding probabilities conditional on ancillary statistics are found.     

Testing for the Proportionality of Hazards in Two Samples Against the Increasing Cumulative Hazard Ratio Alternative

A number of tests of the proportional hazards hypothesis have been proposed in the past. In recent years, researchers have proposed tests geared specially for the alternative hypothesis of "increasing hazard ratio", keeping in mind the case of crossing hazards. This alternative may be too restrictive in many situations. In this paper we develop a test of the proportional hazards model for the weaker "increasing cumulative hazard ratio" alternative. The work is motivated by a data analytic example given by Gill & Schumacher (1987) where their test fails to reject the null hypothesis even though the faster ageing of one group is quite apparent from a plot. The normalized test statistic proposed here has an asymptotically normal distribution under either hypothesis. We also present two graphical methods related to our analytical test.     

Severity and determinants of stunting in children under age 2 years in Odisha (India): a tribal v/s non-tribal analysis

Prevalence of stunting among India’s tribal under-five children has witnessed a 3% annual decline in the last 8 years. Cross-sectional data of 1000 children (287 tribal and 713 non-tribal) aged 0–23 months from Odisha’s Rapid Survey of Children (RSOC, 2014) was analysed to identify the predictors of stunting and severe stunting among tribal children and to suggest policy and programme implications. Results show significant determinants of childhood stunting to be birth order, maternal illiteracy and determinants for severe stunting were maternal age <18 years at marriage and <20 years at birth, and <3 antenatal clinic visits. Severe stunting in tribal children was predicted by basic causes: poverty and maternal age <20 years at first birth and age <18 years at marriage. Findings strongly suggest to accelerate efforts towards reducing childhood stunting in Odisha more specifically among tribal children for whom a special strategy is urgently warranted.     

Predictive Influence of Unavailable Values of Future Explanatory Variables in a Linear Model

We consider an approach to prediction in linear model when values of the future explanatory variables are unavailable, we predict a future response y ^f at a future sample point x ^f when some components of x ^f are unavailable. We consider both the cases where x ^f are dependent and independent but normally distributed. A Taylor expansion is used to derive an approximation to the predictive density, and the influence of missing future explanatory variables (the loss or discrepancy) is assessed using the Kullback–Leibler measure of divergence. This discrepancy is compared in different scenarios including the situation where the missing variables are dropped entirely.     

SB-robust estimator for the concentration parameter of circular normal distribution

In this paper we discuss robust estimation of the concentration parameter (κ) of the circular normal (CN) distribution. It is known that the MLE of the concentration parameter is not B-robust at the family of all circular normal distributions with fixed mean direction (μ) and varying κ > 0. In this paper we propose a new estimator for κ and show that it is B-robust and SB-robust at the family {CN(μ, κ) : m ≤ κ ≤ M} where m and M are two arbitrary constants.