On the Risks of Belonging to Disadvantaged Groups: A Bayesian Analysis of Labour Market Outcomes

Although methods of analysis based on Bayes’ theorem have had rich applications in Law and in Medicine they have not been much used in Economics. We use Bayes’ theorem to construct two concepts of the “risk” associated with belonging to a particular group in terms of a favourable labour market outcome; this, in the Indian context, is taken as being in “regular employment”. The first concept, the Employment Risk Ratio, measures the odds of a person being in regular employment to being in non-regular employment, given that he belongs to a particular group. The second, the Group Risk Ratio, measures the odds of a person being in regular employment, given that he belongs to one group against belonging to another group. We then apply these concepts of risk to data for four subgroups in India: forward-caste Hindus; Hindus from the Other Backward Classes; Dalits (collectively the Scheduled Castes and Scheduled Tribes); and Muslims. We show that, on both measures of risk, forward caste Hindus do best in the Indian labour market. This is partly due to their superior labour market attributes and partly due to their better access to good jobs. When inter-group differences in attributes are neutralised, the favourable labour market performance of forward caste Hindus is considerably reduced. We conclude that it is the lack of attributes necessary for, rather than lack of access to, regular employment that holds back India’s deprived groups

On the Risks of Belonging to Disadvantaged Groups: A Bayesian Analysis of Labour Market Outcomes

Although methods of analysis based on Bayes’ theorem have had rich applications in Law and in Medicine they have not been much used in Economics. We use Bayes’ theorem to construct two concepts of the “risk” associated with belonging to a particular group in terms of a favourable labour market outcome; this, in the Indian context, is taken as being in “regular employment”. The first concept, the Employment Risk Ratio, measures the odds of a person being in regular employment to being in non-regular employment, given that he belongs to a particular group. The second, the Group Risk Ratio, measures the odds of a person being in regular employment, given that he belongs to one group against belonging to another group. We then apply these concepts of risk to data for four subgroups in India: forward-caste Hindus; Hindus from the Other Backward Classes; Dalits (collectively the Scheduled Castes and Scheduled Tribes); and Muslims. We show that, on both measures of risk, forward caste Hindus do best in the Indian labour market. This is partly due to their superior labour market attributes and partly due to their better access to good jobs. When inter-group differences in attributes are neutralised, the favourable labour market performance of forward caste Hindus is considerably reduced. We conclude that it is the lack of attributes necessary for, rather than lack of access to, regular employment that holds back India’s deprived groups.Labour Market; Risk Ratio; India; Caste; Religion

LONG-TERM ORIENTATION IN FAMILY AND NON-FAMILY FIRMS: A BAYESIAN ANALYSIS

A stronger long-term orientation is considered a competitive advantage of family firms relative to non-family firms. In this study, we use panel data of U.S. firms and analyze this proposition. Our findings are surprising. Only in when the family is involved in the management of the firm is the firm found to invest more in long-term projects relative to a non-family firm. We also find that investment in long-term projects in family firms is determined less by cash flow variations than for non-family firms. Managerial implications of our findings are discussed. Our hypotheses are tested using Bayesian methods.Family Firm, Long-term Orientation, Myopia, Bayesian Analysis, Agency Theory, Stewardship Theory, Investment Policy

Corporate Social Responsibility in Large Family and Founder Firms

Based on arguments about long-term orientation and corporate reputation, we argue that family and founder firms differ from other firms with regard to corporate social responsibility. Using Bayesian analysis, we then show that family and founder ownership are associated with a lower level of corporate social responsibility concerns, whereas ownership by institutional investors is associated with a higher level of corporate social responsibility concerns and a lower level of corporate social responsibility initiatives. We conclude that it makes sense to distinguish between family, founder and institutional investors and their roles as owners or managers when analyzing the effects of corporate governance on corporate social responsibility.corporate social responsibility;family firms;family management;family ownership;founder firms;long-term orientation

Bayesian regression analysis.

Regression analysis is a statistical method used to relate a variable of interest, typically y (the dependent variable), to a set of independent variables, usually, X1, X2,...,Xn . The goal is to build a model that assists statisticians in describing, controlling, and predicting the dependent variable based on the independent variable(s). There are many types of regression analysis: Simple and Multiple Linear Regression, Nonlinear Regression, and Bayesian Regression Analysis to name a few. Here we will explore simple and multiple linear regression and Bayesian linear regression. For years, the most widely used method of regression analysis has been the Frequentist methods, or simple and multiple regression. However, with the advancements of computers and computing tools such as WinBUGS, Bayesian methods have become more widely accepted. With the use of WinBUGS, we can utilize a Markov Chain Monte Carlo (MCMC) method called Gibbs Sampling to simplify the increasingly difficult calculations. Given that Bayesian regression analysis is a relatively new method, it is not without faults. Many in the statistical community find that the use of Bayesian techniques is not a satisfactory method since the choice of the prior distribution is purely a guessing game and varies from statistician to statistician. In this thesis, an example is presented using both Frequentist and Bayesian methods and a comparison is made between the two. As computers become more advanced, the use of Bayesian regression analysis may become more widely accepted as the method of choice for regression analyses as it allows for the interpretation of a probability as a measure of degree of belief concerning actual data observed

A Bayes-Based Model for HIV Prediction Extinction

Abstract The human immunodeficiency virus (HIV) is one of the most serious and deadly diseases in human history. It is an infectious agent that causes Acquired Immuno Deficiency Syndrome (AIDS), a disease that leaves a person vulnerable to life threatening infections. Though, there have been increase in the level of HIV awareness throughout the world and a lot of governmental and non-governmental organizations have invested huge funds, energy, and other resources into reducing the virus across the globe, but these alone cannot be enough for its extinction. In this paper, a bayes-based model technique is used to develop a predictive model for extinction of HIV/AIDS, our method is based on generating a dataset which is gotten by administering questionnaires as a means of eliciting responses from people or respondents, we used bayes-model to analyse this data. The result shows that in some years' time, there will be extinction (or reduce to control level) of HIV/AID if certain factors are carefully considered by all

Labour Market Risks

This chapter discusses labour market risk. Every time a job-seeker applies for a job he/she runs the risk of not getting it. However, these risks may not be uniformly distributed across job-seekers: some have a better chance of negotiating obstacles to employment; others have a higher chance of stumbling. The important question relates to the determinants of such risk. In particular, does this risk differ significantly between job-seekers from different groups: gender, religion, or caste? Chapter 2 uses a famous result in statistics, Bayes’ Theorem, to make explicit the concept of risk and to explain why, under this theorem, different groups might have different rates of success of securing employment. The theoretical results are buttressed by data from two rounds of the NSS of Employment: the 68th round (July 2011–June 2012) and the 55th round (July 1999–June 2000). These data are used, in subsequent sections, to quantify the concept of risk set out in the earlier part of the chapter

Comparison of Methods for Estimating Stochastic Volatility

Understanding the ever changing stock market has long been of interest to both academic and financial institutions. The early attempts to model the dynamics treated the volatility or sensitivity of the price change to random effects as constant. However, in matching the model to real data it was realized that the volatility was actually a random variable, and thus began efforts to determine methods for estimating the stochastic volatility from experimental data. In this thesis, we develop and compare three different computational statistical filtering methods for estimating the volatility: The Kalman Filter, the Gibbs Sampler, and the Particle Filter. These methods are applied to a discrete time version of the log-volatility dynamic model and the results are compared based on their performance on synthetic data sets, where dynamics are nonlinear. All the methods struggled to provide accurate estimates, but in comparison, the Gibbs Sampler provided the most accurate estimates, with Particle Filtering providing the least accurate results. Therefore, further investigation on the topic should take place

Corporate Social Responsibility in Large Family and Founder Firms

Based on arguments about long-term orientation and corporate reputation, we argue that family and founder firms differ from other firms with regard to corporate social responsibility. Using Bayesian analysis, we then show that family and founder ownership are associated with a lower level of corporate social responsibility concerns, whereas ownership by institutional investors is associated with a higher level of corporate social responsibility concerns and a lower level of corporate social responsibility initiatives. We conclude that it makes sense to distinguish between family, founder and institutional investors and their roles as owners or managers when analyzing the effects of corporate governance on corporate social responsibility