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Statistical methods of spatial analysis are often successful at either prediction or explanation, but not necessarily both. In a recent paper, Dearmon and Smith (2016) showed that by combining Gaussian Process Regression (GPR) with Bayesian Model Averaging (BMA), a modeling framework could be developed in which both needs are addressed. In particular, the smoothness properties of GPR together with the robustness of BMA allow local spatial analyses of individual variable effects that yield remarkably stable results. However, this GPR-BMA approach is not without its limitations. In particular, the standard (isotropic) covariance kernel of GPR treats all explanatory variables in a symmetric way that limits the analysis of their individual effects. Here we extend this approach by introducing a mixture of kernels (both isotropic and anisotropic) which allow different length scales for each variable. To do so in a computationally efficient manner, we also explore a number of Bayes-factor approximations that avoid the need for costly reversible-jump Monte Carlo methods.

To demonstrate the effectiveness of this Variable Length Scale (VLS) model in terms of both predictions and local marginal analyses, we employ selected simulations to compare VLS with Geographically Weighted Regression (GWR), which is currently the most popular method for such spatial modeling. In addition, we employ the classical Boston Housing data to compare VLS not only with GWR but also with other well-known spatial regression models that have been applied to this same data. Our main results are to show that VLS not only compares favorably with spatial regression at the aggregate level but is also far more accurate than GWR at the local level.

The purpose of this paper is to explore the impacts of climate change on crop net revenue by region. Particularly, the authors focus on the impact differences between north and south regions.

The authors applied the Ricardian approach which assumes that each farmer wishes to maximize revenue subject to the exogenous conditions of their farm. The climate data are based on actual measurements in 753 national meteorological stations and the socio-economic data covers 8,405 farms across 28 provinces in China.

On average, the rise of annual temperature will hurt farms both in the north or south. The impacts of climate change on both precipitation and temperatures have different seasonal impacts on producers in the north and the south of China. As a consequence, the impact on net farm revenues varies with farms in the north and the south being adversely affected (to different degrees) by a rise in the temperature, but both benefiting from an anticipated increase in rainfall. The results also reveal that irrigation is one key adaption measure to dealing with climate change. Whether in the north or south of China, increasing temperature is beneficial to irrigated farms, while for rainfed farms, higher temperature will result in a reduction in net revenues. The results also reveal that farms in the north are more vulnerable to temperature and precipitation variation than that in the south. Irrigated farms in the south are more vulnerable to precipitation variation than that in the north; but rainfed farms in the north are more vulnerable to precipitation variation than that in the south.

Applying empirical analysis to identify the differences of climate change impacts between north and south regions will help policy makers to design reasonable adaptation policies for various regions.

The purpose of this paper is to provide empirical evidence on the presence of gender-based discrimination in formal credit markets during the global financial crisis. Specifically, the study tests for gender differences in the probability of being credit-rationed, in the likelihood of being a discouraged borrower and in the price conditions of bank financing.

This paper uses the fifth wave of the Business Environment and Enterprise Performance Survey (BEEPS), which provides detailed micro data on firms from 26 transition economies in Europe and Central Asia. The empirical analysis employs linear and non-linear sample selection regression models and extended Blinder-Oaxaca decomposition techniques to assess gender differences in access to credit.

Controlling for a large set of observable firm characteristics and for endogenous selectivity, we find that female-led firms are more likely to face financing constraints and to be discouraged from applying for credit than their male counterparts. Conditional on having obtained a loan, female-led firms also face significantly higher interest rates. Furthermore, the observed gender gaps are mainly due to unexplained factors, supporting the hypothesis that banks discriminate against women-led firms in their credit-granting decision.

This study provides new insights on gender discrimination in formal credit markets, highlighting that gender differentials in access to credit significantly vary across countries and strongly depend upon the definition of the firm's gender structure. From a policy perspective, the evidence obtained stresses the need for policies aimed at promoting the role of women in the economic environment in order to reduce discrimination and raise competition in credit markets. Moreover, public interventions should support lending to creditworthy female enterprises in order to improve their perceptions about banks' willingness to grant credit and reduce their propensity to be discouraged from applying.

Are White and Asian job applicants advantaged in access to professional jobs relative to Black and Latinx job applicants at the initial screening stage of the hiring process? And, are the mechanisms of advantage for White applicants different than the mechanisms for Asian applicants? In this chapter, the author proposes a theoretical framework of “parallel mechanisms” of White and Asian advantage during hiring screening – that White and Asian applicants are advantaged compared to Black and Latinx applicants, but that the mechanisms of advantage subtly differ. The author focuses specifically on mechanisms related to two important factors at the hiring interface: referrals and educational attainment. The author applies the concept of parallel mechanisms to a case study of software engineering hiring at a midsized high technology firm in Silicon Valley. The author finds that at this firm, White applicants are advantaged at initial screening relative to Black and Latinx applicants due to average racial differences in applicant characteristics – namely having a referral – as well as differences in treatment by recruiters. For Asian applicants, average racial differences in possession of elite educational credentials, as well as racial differences in recruiter treatment, explain the racial disparity in callbacks. The author discusses the implications of parallel mechanisms of advantage for racial inequality in a multiracial context, and for organizational policy meant to address racial disparities during organizational hiring processes.

Multiple chronic conditions (MCCs) have attracted significant public policy and clinical attention. Whether MCCs determine other important outcomes, or are themselves the outcomes of health-producing activities or interventions, metrics based thereon have potential to be useful indicators of the health of populations and of differences between and among the health of subpopulations. While the attention MCCs are attracting in various policy circles is impressive, MCCs' potential roles as indicators of population health and of how health determinants influence population–health outcomes have received less attention. The purpose of this chapter is to direct attention towards questions that involve considerations of chronic condition (CC) patterns as health outcomes; specifically, this paper hopes to advance the consideration of patterns of MCCs as indicators of individual and population health. Using data from the United States (US) Behavioural Risk Factor Surveillance System (BRFSS), the chapter explores whether both the ‘intensity’ (i.e. the number or count) of CCs as well as their ‘composition’ (i.e. the patterns of particular CCs) might be jointly of interest when considering the prevalence of MCCs in populations and how the nature of MCCs may vary across subpopulations of interest. It is seen that information about intensity tells an incomplete story about MCC health outcomes.

This study explores the role of firm age as a mediating variable in the link between performance and the risk of terrorism. Theoretically, there can be vulnerabilities, liabilities or learning effects associated with age.

The empirical strategy uses randomness in the occurrence of successful terrorist incidents to estimate the hypothesized link in a sample of 1,600 Pakistani firms.

The results suggest a significant effect of terrorism for organizations lying beyond the 50th percentile of the age distribution. In addition to relevant controls – like size, ownership and location effects – the baseline results withstand alternative empirical specifications and the use of instrumental variables.

The study helps us understand the role of firm age in its performance, taking into account the presence of risks posed by weak law and order.

The changes in women’s and men’s work lives have been considerable in recent decades. Yet much of the recent research on gender differences in employment and earnings has been of a more snapshot nature rather than taking a longer comparative look at evolving patterns. In this paper, we use 50 years (1964–2013) of US Census Annual Demographic Files (March Current Population Survey) to track the changing returns to human capital (measured as both educational attainment and potential work experience), estimating comparable earnings equations by gender at each point in time. We consider the effects of sample selection over time for both women and men and show the rising effect of selection for women in recent years. Returns to education diverge for women and men over this period in the selection-adjusted results but converge in the OLS results, while returns to potential experience converge in both sets of results. We also create annual calculations of synthetic lifetime labor force participation, hours, and earnings that indicate convergence by gender in worklife patterns, but less convergence in recent years in lifetime earnings. Thus, while some convergence has indeed occurred, the underlying mechanisms causing convergence differ for women and men, reflecting continued fundamental differences in women’s and men’s life experiences.

The purpose of this paper is to examine the impact of urbanicity on rural–urban migrants' dietary diversity and nutrition intake and whether its effect differs across various urban environments of migrants.

Using the individual- and time-invariant fixed effects (two-way FE) model and five-year panel data from the China Health and Nutrition Survey (CHNS), this paper estimates a linear and nonlinear relationship between urbanicity and nutrition. The paper also explores the spatial heterogeneity between rural–urban migrants and rural–suburban migrants. Dietary diversity, total energy intake and the shares of energy obtained from protein and fat, respectively, are used to measure rural–urban migrants' nutrition on both quality and quantity aspects.

The study shows that rural–urban migrants have experienced access to more diverse, convenient and prepared foods, and the food variety consumed is positively associated with community urbanicity. Energy intake is positively and significantly affected by community urbanicity, and it also varies with per capita household income. The obvious inverse U-shaped relationship reveals that improving community urbanicity promotes an increase in the shares of energy obtained from protein and fat at a decreasing rate, until reaching the urbanicity index threshold of 66.69 and 54.26, respectively.

This paper focuses on the nutritional status of rural–urban migrants, an important pillar for China's development, which is often neglected in the research. It examines the urbanicity and the nutrition of migrants in China, which provides a new perspective to understand the dietary and nutritional intake among migrants in the economic and social development. Moreover, the urbanicity index performs better at measuring urban feathers rather than the traditional rural/urban dichotomous classification.

This chapter examines the evolution of the number of days spent on sick leave following the 2011 reform which halved the maximum sick benefit provided by statutory health insurance in Hungary. This policy change sharply decreased benefits for a large group of high earners, while leaving the incentive to claim sickness benefits unchanged for lower earners, providing us with a “quasi-experimental” setup to identify the incentives effect of sickness benefits. We use a difference-in-differences type methodology to evaluate the short-term effect of the reform. We rely on high-quality administrative data and analyze a sample comprised of prime-age male employees with high earnings and stable employment. Our results show that the number of days spent on sick leave fell substantially for those experiencing the full halving of benefits. Estimating the response of the number of sick days with respect to the fall in potential sickness benefits, we find a significant elasticity of −0.45.

The authors investigate the implications of environmental, social and governance (ESG) practices of firms for the pricing of their credit default swaps (CDS). In doing so, the authors compare European and US firms and consider nonlinear and indirect effects. This complements the previous literature focusing on linear and direct effects using bond yields and credit ratings of US firms.

For this purpose, the authors apply fixed effects regressions on a comprehensive panel data set of US and European firms. Further, nonlinear and indirect effects are investigated utilizing quantile regressions and a path analysis.

The evidence indicates that higher ESG ratings mitigate credit risks of US and European firms from 2007 to 2019. The risk mitigation effect is U-shaped across ESG quantiles, which is consistent with opposing effects of growing stakeholder influence capacity and diminishing marginal returns on ESG investments. The authors further reveal a mediating indirect volatility channel that substantially amplifies the direct effect of ESG on credit risk. A one-standard-deviation improvement in ESG ratings is estimated to reduce CDS spreads of low, medium and high ESG firms by approximately 4%, 8% and 3%, respectively.

This is the first study to examine whether credit markets reflect regional differences between Europe and the US with regard to the ESG-CDS-relationship. In addition, this paper contributes to the existing literature by investigating differences in the response of CDS spreads across ESG quantiles and to study potential indirect channels connecting ESG and CDS spreads using structural credit risk variables.