Search results

In China, a major grain producer, rice losses during harvest cannot be ignored. The purpose of this paper is to assess the extent of rice harvest losses in major grain-producing areas in China by a sampling survey, and to further analyze the main factors influencing rice harvest losses using an ordered multinomial logistic model.

In this study, data were collected using a multi-stage sampling method from ten major rice-producing provinces/regions in China. On this basis, five counties were selected from each of these provinces/regions. In the actual survey, house numbers were randomly selected, and then corresponding farmer households were visited by trained investigators.

The survey found that 56.22 percent of respondents believed that rice harvest losses were 4 percent or lower in China, though there were differences among the provinces. The proportion of family business income, planting scale, mechanization level, timely harvest, and operational meticulousness had negative effects on rice harvest losses. On the other hand, farmers’ experience of employment as migrant workers had a positive effect on rice harvest losses. In addition, bad weather and short handedness during harvest significantly increased rice harvest losses.

The meaning of rice harvest losses was defined based on previous research findings on the definition of grain harvest losses and the realities in China. The current rice harvest losses in different areas in China were analyzed based on sampling survey data from 957 farmers in ten provinces in China.

High rates of union dissolution and repartnering among parents means that today’s youth are increasingly likely to spend some time living with a stepparent. Although family structure has been linked to adolescent well-being, most work has compared those in stepfamilies with those in intact families, so it is not clear which aspects of stepfamily life are more or less consequential for adolescent behaviors among those exposed to a co-residential stepfamily.

To examine stepfamilies more closely, we focus explicitly on youth who had ever lived with a stepfather using mother and child data from the National Longitudinal Survey of Youth 1979 (n = 1,754). We specifically explore how structure and stability, timing of exposure, and sibling configuration influence risk-taking, operationalized as sexual debut and drug use at age 16.

We find that timing and sibling composition seem to be unrelated to risk-taking, but stepfamily structure and stability are highly salient. Adolescents currently in a cohabiting stepfamily and those who have experienced the dissolution of a prior stepfamily are more likely to engage in sex (and sometimes use drugs) than their counterparts living with only their stepfather in a married-parent family.

The findings highlight the importance of stability, more so than structure, timing, or sibling configuration, in understanding adolescent risk-taking. The results provide further evidence that children in stepfamilies have unique vulnerabilities and opportunities for resilience, and should be evaluated independently from samples of children from intact families to avoid a deficit approach in modeling and theorizing.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.

The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.

The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.

The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.

Better bond ratings may mitigate corporate defaults.

Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

With the growing interconnectedness of global markets brought about by globalization and technological innovation, there is a heightened worldwide risk of money laundering, posing a considerable negative impact on economies and social equality. Therefore, the primary purpose of this research is to examine factors that underpin the pervasiveness of money laundering risk.

By using a cross-section sample of 84 countries, the study uses ordered logit and multinomial logit regression to test and explain the role of main and varied determinants of money laundering risk covering countries’ economic, social, regulatory and corporate environment.

The authors conclude that, overall, the macroeconomic indicators are less relevant in influencing money laundering risk than the other factors adopted from the Basel report. Nonetheless, the volume of exports and the exchange rate were robust in both the ordered and multinomial regression analyses alongside financial secrecy, auditing standards and corporate transparency. While more financial secrecy and a higher volume of exports were found to increase this risk, the other variables showed a negative relationship. The authors further conclude that it is mostly less secrecy, more transparency and better auditing that could gradually transform a high-risk country into medium risk.

This study recommends the implementation of publicly accessible ownership registries to address the issues around secrecy, transparency and auditing misconducts. Additionally, the general strengthening of laws and policies in these three domains is also necessary alongside the application of current technologies, such as machine learning, for the detection of money laundering.

The authors believe this study uses advanced econometric techniques rarely used in the literature on money laundering. Separating the impact of economic and social/regulatory is also valuable

This paper aims to discuss the determinants of food security in Kassala state using the measurement of Household Food Insecurity Access Scale (HFIAS). We use the measurement of HFIAS and use new primary data from a food security household survey in Kassala state (2019).

This paper focuses on the determinants of food security in Kassala state using the measurement of Household Food Insecurity Access Scale (HFIAS), using new primary data from a food security household survey in Kassala state (2019) and using the multinomial logistic regression analysis and both ordered logit and ordered probit regression to examine the determinants of food security.

Our results are in support of our hypothesis that the significant determinants of household food insecurity are family-owned production (that negatively affects the probabilities of household being food insecure), household income (that negatively affects HFIAS). We observe that the effects of family-owned production on household food insecurity are particularly significant in the case of mildly and moderately food insecurity. We explain that the other factors that affect the household food insecurity include improvement in the level of agricultural services, marketing, banking services and road characteristics that reduce HFIAS. We find a gender gap related to food security in the sense that male-headed households produce more food compared to female-headed households and also families headed by males are more likely food secure. Therefore, the major policy implication from our results is the importance of increasing households income and enhancing family own production of food to eliminate food insecurity.

This paper provides a significant contribution to the Sudanese and international literature because it discusses the determinants of food security in Kassala state. Different from the two other accompanying papers that focused on the incidence of food security in Kassala state using the measurement of Household Food Insecurity Access Scale (HFIAS) and the determinants of production of food and consumption of food in Kassala state, this paper focuses on the determinants of food security in Kassala state using the measurement of HFIAS and using new primary data from a food security household survey in Kassala state (2019). We fill the gap in the Sudanese literature because we provide a more interesting analysis of the determinants of food security in Kassala state. Our analysis is useful from policy perspective since we provide useful policy recommendations to enhance food security through agricultural development in Kassala state.

The purpose of this study is to examine tax stamp evasion at Twifu Atti-Morkwa and Hemang Lower Denkyira districts in the central region of Ghana.

A cross-sectional survey design was adopted to sample 305 micro-taxpayers through the use of multi-stage sampling technique. Primary data were collected from the micro-taxpayers using structured interview. Binary and multinomial logit regression models were used to regress the tax stamp evasion on economic and non-economic factors.

The study found that the likelihood of micro taxpayers to evade tax stamp is predicted by age, application of sanctions, guilt feeling, transportation cost to tax office and rate of tax audit. Thus, the study found partial support for expected utility, planned behaviour and attributory theories in explaining tax evasion behaviour of micro-taxpayers.

There are several measures of addressing tax evasion behaviour of micro taxpayers. Evasion behaviour can be deterred by enforcement strategies such as application of sanctions and regular tax audit, establishment of more tax offices in the districts and writing normative messages on the faces of tax stamp stickers.

This study helps explains the tax evasion behaviour of micro-taxpayers of a developing economy like Ghana using a special type of tax design meant to capture such taxpayers in the tax bracket. To the best of our knowledge, the study is unique in terms of the means of measuring tax evasion and the methodologies used.

This article investigates the effectiveness of pepper spray as a means of aiding arrest. The authors aim to provide a more rigorous study than has previously been achieved. Data from the Baltimore County Police Department have been used in this analysis. The evaluation undertaken has taken into account the “effective‐ineffective” dichotomy of the incapacitating effects of pepper spray as well as whether those being arrested were drunk, under the influence of drugs or mentally disturbed. Five variables of age, weight, height, race and sex were also recorded, the latter three having little or no effect. The overall conclusion which is drawn is that using pepper spray eases arrest in the majority of instances. The conclusions drawn leave the way open for further detailed analysis of the use of the spray to ease arrest.