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While crop insurance ratemaking has been studied for many decades, it is still faced with many challenges. Crop insurance premium rates (PRs) are traditionally determined only by point estimation, and this approach may lead to uncertainty because it is sensitive to the underwriter’s assumptions regarding the trend, yield distribution, and other issues such as data scarcity and credibility. Thus, the purpose of this paper is to obtain the interval estimate for the PR, which can provide additional information about the accuracy of the point estimate.

A bootstrap method based on the loss cost ratio ratemaking approach is proposed. Using Monte Carlo experiments, the performance of this method is tested against several popular methods. To measure the efficiency of the confidence interval (CI) estimators, the actual coverage probabilities and the average widths of these intervals are calculated.

The proposed method is shown to be as efficient as the non-parametric kernel method, and has the features of flexibility and robustness, and can provide insight for underwriters regarding uncertainty based on the width of the CI.

Comprehensive comparisons are conducted to show the advantage and the efficiency of the proposed method. In addition, a significant empirical example is given to show how to use the CIs to support ratemaking.

Regression discontinuity (RD) design is a sophisticated quasi-experimental approach used for inferring causal relationships and estimating treatment effects. This paper aims to educate human resource development (HRD) researchers and practitioners on the implementation of RD design as an ethical alternative for making causal claims about training interventions.

To demonstrate the key features of RD designs, a simulated data set was generated from actual pre-test and post-test diversity training scores of 276 participants from three organizations in the USA. Parametric and non-parametric analyses were conducted, and graphical presentations were produced.

This study found that RD design can be used for evaluating training interventions. The results of the simulated data set yielded statistically significant results for the treatment effects, showing a positive causal effect of the training intervention. The analyses found support for the use of RD models with retrospective training intervention data, eliminating ethical concerns from random group assignment. The results of the non-parametric model provided evidence of the plausibility of finding the right balance between precision of estimates and generalizable results, making it an alternative to experimental designs.

This study contributes to the HRD field by explicating the implementation of a sophisticated, statistical tool to strengthen causal claims, contributing to an evidence-based HRD approach to practice and providing the R syntax for replicating the analyses contained herein.

Despite the growing number of scholarly articles being published in HRD journals, very few have used experimental or quasi-experimental design approaches. Therefore, a very limited amount of research has been devoted to uncovering causal relationships.

The purpose of this paper is to empirically analyze the determinants of capital structure and their long-run equilibrium relationships with firm-specific and macroeconomic indicators for Indian manufacturing firms.

The study is conducted using the panel semi-parametric and non-parametric regression models to identify the key determinants of capital structure. Panel cointegration models are also employed for analyzing the long-run equilibrium association of capital structure with its determinants.

The study finds that each manufacturing sector has unique determinants of capital structure. The debt level is significantly affected by asset tangibility, growth opportunity, effective tax rate, non-debt tax shield, cash flow, profitability, firm size, foreign investment, government borrowing, economic growth, and interest rate. All these firm-specific and macroeconomic variables have strong long-run equilibrium relationship with capital structure as a whole.

The study analyzes the determinants of capital structure for eight manufacturing sectors of India, which helps firm managers and policy-makers to identify appropriate factors that maximize firm value. The sector-specific features of firms may lead to a new path with regard to corporate governance and ownership structure to enhance stakeholder's satisfaction.

The use of semi-parametric and non-parametric panel regression models to analyze the determinants of capital structure, and the use of panel cointegration approach to explore the long-run equilibrium relationship between the determinants and its factors are the unique contributions of the present research.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

In this paper we investigate the joint conditional distribution of health (life expectancy) and income growth, and its evolution over time. The conditional distributions of these two variables are obtained by applying non-parametric methods to a bivariate non-parametric regression system of equations. Analyzing the distributions of the non-parametric fitted values from these models we find strong evidence of movement over time and strong evidence of first-order stochastic dominance of the earlier years over the later ones. We also find strong evidence of second-order stochastic dominance by non-OECD countries over OECD countries in each period. Our results complement the findings of Wu, Savvides and Stengos (2008) who explored the unconditional behaviour of these joint distributions over time.

The purpose of this paper is to analyse the relation between occupational segregation and the gender wage differences using data on three-digit occupational level of classification. The authors examine whether a statistically significant relation between the share of men in employment and the size of the unexplained part of the gender wage gap exists.

Traditional Oaxaca (1973) – Blinder (1973) decomposition is performed to examine the differences in the gender wage gaps among minor occupational groups. Two types of reweighted decomposition – based on the parametric estimate of the propensity score and non-parametric proposition presented by Barsky et al. (2002) – are used as the robustness check. The analysis is based on individual data available from Poland.

The results indicate no strong relation between occupational segregation and the size of unexplained differences in wages. The unexplained wage differences are the smallest in strongly female-dominated and mixed occupations; the highest are observed in male-dominated occupations. However, they are probably to a large extent the result of other, difficult to include in the econometric model, factors rather than the effects of wage discrimination: differences in the psychophysical conditions of men and women, cultural background, tradition or habits. The failure to take them into account may result in over-interpreting the unexplained parts as gender discrimination.

The highest accuracy of the estimated gender wage gap is obtained for the occupational groups with a similar proportion of men and women in employment. In other male- or female-dominated groups, the size of the estimated gender wage gaps depends on the estimation method used.

The results suggest that decreasing the degree of segregation of men and women in different occupations could reduce the wage differences between them, as the wage discrimination in gender balanced occupations is the smallest.

To the best of the authors’ knowledge, this study is one of the few conducted at such a disaggregated level of occupations, and one of few studies focused on Central and Eastern European countries and the first one for Poland.

This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy.

Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA.

Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally.

To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

Testing the efficiency in the economy has been highly pronounced since the financial crisis in 2008, as many countries have started to deregulate their economic sectors. The potential impact of testing efficiency is thus the key driver of world output and welfare. For this purpose, the main objective of the Capital Market Authority consists of more regulation of securities trading to boost economic efficiency. In particular, the purpose of this paper, is to examine the efficiency of 40 investment companies in Kuwait. In this study, the authors investigate the efficiency in the investment sector in Kuwait. Studying such a case is important for several reasons. First, the investment sector in Kuwait is affected by the World Trade Organization (WTO) conditions and regulations for more market liberalization. Second, most studies on efficiency have focussed on developed countries, such as those of Europe and the USA, with very few studies examining developing countries, such as Kuwait. Third, the study efficiency features is important in helping policy makers evaluate how the investment sector will be affected by increasing competition and then formulate policies that affect that sector and the economy as a whole.

In this study, we use non-parametric data envelopment analysis (DEA) to estimate investment companies’ efficiency in Kuwait. The authors test predictions of the model using yearly data for 2006-2010. In the analysis, the authors follow the two-stage approach suggested by Coelli et al. (1998). In the literature on DEA efficiency score measurement, this two-stage approach is the most prominent. This approach uses the efficiency score, measured by the DEA model, as the dependent variable in a regression model with explanatory variables that are supposed to capture the impact of external factors (Hahn, 2007). In the second stage, the authors used a Tobit model to investigate factors affecting the efficiency in the Kuwaiti investment sector.

The findings of the second stage suggest that 2008-2010 had a negative impact on firms’ efficiency in Kuwait. The results confirm the substantial influence of the 2008 global financial crisis on the investment sector in Kuwait. In addition, the results show that factors affecting production efficiency in the investment sector in Kuwait include the total revenues, total assets, government participation, and Islamic firm dummy. These second-stage results are confirmed using different specifications of a fixed effect model, a random effects model, and a logit model.

The results may be utilized by both monetary authorities and policy makers in establishing the general economic policy in the country. A number of policy implications may be derived from the estimates obtained in the current paper. First, the results show that the investment sector in Kuwait faced a sharp drop in its efficiency in 2008 due to the global financial crisis. This result tells us that there was a spillover effect of the global financial crisis in the Kuwaiti investment market, as companies in this market are highly vulnerable to global shocks. As a result, the investment sector needs to be regulated by, for example, encouraging more company mergers and acquisitions. Second, to meet the appropriate regulations in the investment sector in Kuwait, monetary authority in Kuwait should take into consideration the WTO conditions for more openness in the economic sector. Therefore, companies in the investment sector should be more efficient to compete with foreign investment companies that decide to enter into Kuwaiti market. Therefore, the need for regulations in the Kuwaiti investment sector is more necessary than before. Third, the study of efficiency features is important to help policy makers evaluate how the investment sector will be affected by increasing competition and then formulate policies that affect that sector and the economy as a whole. Furthermore, monetary policy can play an important role in influencing the efficiency in the investment sector. Therefore, the Central Bank of Kuwait should take a leading role in regulating abnormal financial activity in the Kuwaiti market.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.