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In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

This paper uses a sample of school age children from the Nepal Demographic Health Survey (NDHS) to examine the relationship between maternal education and child schooling in Nepal. Taking advantage of the two-stage stratified sample design, we estimate a sample selection model controlling for cluster fixed effects. These results are then compared to OLS and Tobit models. Our analysis shows that being male significantly increases the likelihood of attending school and for those children attending school, it also affects the years of schooling. Parental education has a similarly positive effect on child school, but interestingly we find maternal education having a relatively greater effect on the schooling of girls. Our results also point to household wealth as having a positive effect on both the probability of schooling and the years of schooling in all our models, with the magnitude of these effects being similar for male and female children. Finally, a comparison of our results with a model ignoring cluster fixed effects produces results that are statistically different both in signs and in the levels of significance.

We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. Monte Carlo experiments are provided to compare the performance of spatial entropy estimators relative to classical estimators. Finally, the estimators are applied to an illustrative model allocating agricultural disaster payments.

While subordinated debt can be used to increase market discipline on banks by playing a corporate governance role in the presence of a federal safety net that encourages risk taking, it also has implications for banks’ loan sales. Using two measures of banks’ loan sales activity, we find greater proportions of subordinated debt increase the likelihood that banks engage in loan sales activity, and are associated with greater proportions of loan sales. Our results have implications about banks’ lending efficiency as well as their transparency and disclosure policies that could play a role in the transmission mechanism of monetary policy.

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To address the endogeneity of the right-hand-side count variable, I use instrumental variables and a two-step procedure estimation approach. Two methods of estimation are employed: quasi-maximum likelihood (QML) and nonlinear least squares (NLS). Using these methods, I estimate the average partial effects, which are shown to be comparable across linear and nonlinear models. Monte Carlo simulations verify that the QML and NLS estimators perform better than other standard estimators. For illustration, these estimators are used in a model of female labor supply with an endogenous number of children. The results show that the marginal reduction in women's working hours per week is less as women have one additional kid. In addition, the effect of the number of children on the fraction of hours that a woman spends working per week is statistically significant and more significant than the estimates in all other linear and nonlinear models considered in the chapter.

I propose a general framework for instrumental variables estimation of the average treatment effect in the correlated random coefficient model, focusing on the case where the treatment variable has some discreteness. The approach involves adding a particular function of the exogenous variables to a linear model containing interactions in observables, and then using instrumental variables for the endogenous explanatory variable. I show how the general approach applies to binary and Tobit treatment variables, including the case of multiple treatments.

The positive relationship between household poverty and child labor decisions need not to be generalised across different types of works and geographical regions. This chapter studies this relationship using the 2004 Malawi Integrated Household Survey data. The study attempts to identify the influence of exogenous change in household consumption on child labor decisions by using consumer durable goods as an instrument. These findings show that child labor was most prevalent and intensive in domestic work, but significant negative relationships between household consumption and child labor supply are only found in unpaid market work. These findings support both poverty reduction and awareness campaigns as child labor eradication strategies. Promotion of non-labor intensive income sources also seems to be an attractive policy option.

This study examines the research behavior of Australian and New Zealand accounting faculty to determine the characteristics that influence research productivity. University reputations are integrally linked with research performance and determining the qualities that predict research behavior may be of particular value in the selection and recruitment process. The study finds that two key factors significantly impact performance: holding a Ph.D. and having an academe-oriented rather than profession-oriented background. These results may be interpreted as affirming the U.S. model of developing specialist academic researchers through doctoral education programs rather than employing faculty with strong professional experience.

The Indian Companies Act (2013) mandates the appointment of at least one woman director for large publicly listed companies in India in order to increase gender diversity on corporate boards. The study analyzes the relationship between corporate governance mechanisms, board gender diversity, and ownership structure on dividend payout decisions in an emerging economy like India. The study uses data collected for nonfinancial firms listed on NSE (National Stock Exchange) 500 in India from the period 2008 to 2020. Contrary to the evidence from developed economies, the study finds that increased female representation and greater proportion of female independent directors on the board are associated with lower dividend payout decisions in the Indian context. As it stands, the female representation on corporate boards in India is woefully low and appears to be mere tokenism. The study explores the role of regulation in increasing gender diversity on corporate boards and offers insights from an emerging economy where such a regulation is in place.

‘Dalits’ are considered to be the marginalized section in India. In the social milieu of this country, they are excluded from the variants of life. This chapter is a modest attempt to construct an aggregative picture of social exclusion of Dalit Groups at district level in India. It is generally claimed that social exclusion is captured in brackets of non-attainment, particularly in the spheres of livelihood, education and health. However, a true index of exclusion should include of not only non-attainment but the pangs of injustice meted out to them. Such injustice may take various forms (such as crime against Dalits, non-caring of the girl child and so on). Such injustice eats into the gains of social attainment. The speciality of our index is that it includes these variables of social injustice together with the traditional indicator of non-attainment. For the construction of this index, the study has used the standard techniques of principal component analysis (PCA) with negative weights for the attributes of injustice. In order to arrive at a comparable figure, standardization of the variables is encapsulated. In explaining the differences of index value, it has taken recourse to a large fallacy of robustness that is to arrive at the conclusion. Fiducial methods have been used to arrive at a more commendable figure. The analysis shows a close relationship between Dalit Exclusion Index (DEI) and a number of variables. General impression is that as the general wellbeing of people rises, there is a fall in the Dalit exclusion. Also, it is shown that public policy have strong effect on redresser of Dalit exclusion.