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Genetically modified (GM) crops, particularly GM grain crops, have been controversial since their commercialization in 1996. However, only a few studies have investigated farmers’ attitudes toward adopting GM grain crops in China. The purpose of this paper is to explore farmers’ willingness to adopt GM insect-resistant rice prior to its commercial release in China and determines the factors that affect farmers’ prospective adoption decisions.

The data are collected using a questionnaire. Descriptive statistics are used to analyze the farmers’ potential willingness to adopt GM rice and level of awareness of GM rice and socioeconomic characteristics. Ordered and binary probit models are applied to identify the key factors that affect the farmers’ decision to adopt GM insect-resistant rice.

Descriptive statistics show that most farmers have little knowledge of GM rice, approximate 35.5 percent of farmers could plant GM rice, and over half of the respondents are uncertain whether or not they will adopt the new crops. The results of econometric analyses show that increasing output and income, and simplicity in crop management, have positive effects on prospective adoption, whereas the high-seed price of GM rice has a significantly negative effect. Health implications also have a significantly positive effect on the farmers’ decision to adopt GM grain crops. A comparative analysis of ordered and binary probit models demonstrates that farmers are more deliberate in their decisions when they have fewer choices. Aside from the above-mentioned variables, the following factors are also statistically significant in the probit model: government technicians’ recommendations, neighbors’ attitudes, level of environmental risks, and the farmer’s age.

Information on the major risks and benefits of GM rice was provided to the farmers in the questionnaire. The farmers were then asked to choose from the three ordered alternative answers, namely, “accept,” “uncertain,” and “reject”. Both ordered and binary probit models were applied to comparatively analyze the collected data. This study is one of a handful of studies that employ these econometric models to identify and explain the underlying factors that affect farmers’ decisions. The relevant findings have important implications for future agricultural policy in China.

This study aims to explore patterns in e‐tail spending across different demographic groups and to predict which households are the most frequent shoppers and highest spenders. Further, it aims to investigate which households are least likely to purchase from unfamiliar online stores.

Using a random sample of Florida households, the study is the first to use probit and ordered probit models to study Internet purchasing behavior.

Younger, college educated, higher income households living in suburban, rural and small towns spend and shop the most online. Caucasians purchase online more often than African Americans and Hispanics but spend about the same amount. The study also finds that male, Hispanic, college educated and younger consumers are more willing to purchase from unfamiliar online stores.

This study provides new evidence on factors affecting household online spending and buying decisions. Previous studies have not used an ordered probit to model different levels of spending and this new specification provides information about which demographic groups are the most (or the least) frequent buyers as well as which demographic groups are the highest (or the lowest) e‐tail spenders. This study also investigates which demographic groups are most likely to shop only at stores with whom they are already familiar.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

This study aims to develop an ordered probit model to explain and predict small business relative performance in Chile, South America.

The design is survey research. The sample includes 403 small businesses classified as 158 failed firms, 101 mediocre firms and 144 successful firms within all economic sectors. The model variables are: internet, starting with adequate working capital, managing good financial and accounting records, planning, owner formal education, professional advice, having partners, parents owning a business, and marketing efforts.

The eight-variable model, tested with ordered probit, is a significant predictor of the level of performance at the 0.000 level. Also, six of the eight variables are significant predictors at the 0.05 level: internet, starting with adequate working capital, managing good financial and accounting records, owner, professional advice, having partners, parents owning a business, and marketing efforts. Two of the variables – i.e. planning and formal education – were not significant. ANOVA test of differences were run for each of the eight variables based on the level of performance were also run and results reported.

The model does in fact predict relative performance, so the model can be used to improve the probability of success. Thus, an entrepreneur can use the model to gain a better understanding of which resources are needed to increase the probability of success, and those who advise entrepreneurs can help them use the model. Investors and creditors can use the model to better assess a firm's potential for success. There is an extensive public policy implications discussion regarding how to use the model to assist entrepreneurial ventures so that society can benefit in direct and indirect ways via the allocation of limited resources toward higher potential businesses. Entrepreneurs and small business educators can use the model's variables to influence future business leaders, public policy makers, and their practices.

This study improves the Lussier 15 variable success versus failure prediction model by adding the use of the internet and taking out highly correlated variables. While Lussier and others ran logistic regression with only two levels of performance, this study uses the more robust ordered probit model with three levels of performance. It presents public policy with implications for Chilean institutions to promote entrepreneurship. Finally, it contributes to the literature because, to date, no empirical success versus failure studies have been found that were conducted in Chile or any small, open economies in Latin America

The purpose of this paper is to investigate whether there are any differences in the capitalization speed‐of‐adjustment across regulatory capitalization buckets of commercial banks in the USA, for the period 2002‐2009.

The Federal Deposit Insurance Corporation (FDIC) monitors banks' capital ratio using the bucketing approach. Thus, this discrete and ordered variable is modeled in the context of a partial adjustment specification, controlling for initial conditions and cross‐sectional heterogeneity. Parameters are estimated with the generalized dynamic random effects ordered probit technique that is flexible enough to allow for differential effects of covariates across capitalization categories.

The main result is that the speed of adjustment is monotonically increasing for banks belonging in lower capitalization buckets, after controlling for bank‐specific capitalization determinants. In addition, substantial differential impacts of capitalization drivers across regulatory buckets are uncovered.

This an important finding both for regulators and market participants since it sheds light on a very crucial aspect of banks' behaviour.

This is the first paper that adopts the FDIC bucketing in the actual modelling. In addition, it uses the generalized dynamic random effects ordered probit technique in order to explore potential differential impact of capital ratio determinants across buckets.

Branching is one of the crucial strategic non-price actions for banks. Previous studies on the impact of state ownership upon banks focus on bank lending behavior. This paper aims to offer a novel investigation of how state ownership affects bank branching behavior by examining state-controlled commercial banks (SCCBs) in the context of the largest developing and transitional country China.

The two-part model (TPM) is applied to analyze the branching decision process. In the first stage, the dependent variable is the choice of bank branching dynamics and in the second stage the dependent variable is the number of new branches or the number of closed branches. For robustness check, the ordered probit selection model allowing for interdependence of the two stage decisions is also employed.

Using a unique dataset of bank branches in China, this paper finds that the branching decisions of Chinese SCCBs are driven by both profit motivated factors including population size, population density, income level, financial development and banking competition and politically motivated factors as represented with the proportion of SOEs. As a comparison, branching decisions of joint-stock banks in China are fully determined by profit motivated factors.

First, this study is the first to explore the effect of state ownership on bank branching decisions, providing a new insight on the literature regarding to the impact of state ownership on bank decisions. Second, this study explores the potential effect of politically motivated factors on bank branching decisions, filling the gap in bank branching literature. Third, this study can contribute to bank branching literature by enriching the limited understanding of how SCCBs make branching decisions. Lastly, this study applies novel empirical strategies to analyze bank branching decisions, including the TPM and the ordered probit selection model.

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 paper is to investigate the impact of employer-provided health benefits (EPHBs) on labor supply decisions of documented and undocumented farm workers. By establishing a significant linkage between EPHB and farm work decisions, this study provides important implications for farm employment retention and the financial sustainability of farm businesses.

Using data from the National Agricultural Workers Survey, objective (actual) and subjective (expected) employment data are used as outcome variables analyzed under an ordered probit model, with the data pre-processed using the coarsened exact matching method to reduce endogeneity issues within the estimation.

Results confirm the influence of EPHB on farm workers’ decisions to remain employed on the farm as well as on the duration of their farm employment. Comparatively, EPHB significantly influences undocumented farm workers’ decisions on actual employment duration and subjective working expectations while documented workers seem to ascribe less importance to EPHB in their farm employment decisions.

This study provides important financial and business viability implications as the value of farm labor services retained through EPHB can translate to high opportunity losses, if ineffective. Alternative labor-saving strategies, such as mechanization, can only potentially lead to serious financial challenges for agribusiness firms, especially small-scale farm operations. This study emphasizes the need for more effective employment retention incentives for the sake of sustaining farm business viability.

This study presents empirical evidence on the important influence of EPHB on farm employment decisions, especially those made by undocumented farm workers, that have not been extensively explored in literature.

One in nine people between the ages of 18 and 64 in the US, and every second foreign-born person in this age bracket, speak Spanish at home. And whereas around 80 percent of adult immigrants in the US from non-English-speaking countries other than Mexico are proficient in English, only about 50 percent of adult immigrants from Mexico are proficient. The use of a language other than English at home, and proficiency in English, are both analyzed in this paper using economic models and data on adult males from the 2000 US Census. The results demonstrate the importance of immigrants’ educational attainment, their age at migration, and years spent in the US to their language skills. The immigrants’ mother tongue is also shown to affect their English proficiency; immigrants with a mother tongue more distant from English being less likely to be proficient. Finally, immigrants living in ethnic–linguistic enclaves have lesser proficiency in English than immigrants who live in predominately English-speaking areas of the US. The results for females are generally very similar to those for males. The findings from an ordered probit approach to estimation are similar to the findings from a binary probit model, and the conclusions drawn from the analyses mirror those in studies based on the 1980 and 1990 US Censuses. Thus, the model of language skills presented appears to be remarkably robust across time and estimation techniques, and between the genders.

This chapter aims to answer whether foreign multinational corporations (MNCs) operating within the Chinese context differ from indigenous firms on several essential labor standards indicators: white- and blue-collar salaries, pension insurance, and working hours. In drawing upon neo-institutional and organizational imprinting theories and applying these to the Chinese context, the study addresses competing arguments regarding the expected effects of ownership type on these indicators. We employ seemingly unrelated regressions (SURs) to empirically examine a novel national survey of 1,268 firms in 12 Chinese cities. The regression results show that foreign MNCs do not provide uniquely beneficial labor practice packages to workers when compared with various indigenous firm types, including state-owned enterprises (SOEs), affiliate businesses of Hong Kong, Macau, and Taiwan, and domestic private enterprises (DPEs). Specifically, although MNCs provide relatively higher wage rates, they underperform relative to SOEs concerning social insurance. However, DPEs consistently underperform relative to MNCs across most indicators. The mixture of the results contributes important nuances to the application of neo-institutional and organizational imprinting theories to the Chinese context.