Despite Sorghum ( Sorghum bicolor ) potential to enhance food security and livelihoods, the commercialisation of sorghum remains limited, with most production occurring under subsistence farming systems. Our study evaluates determinants of the decision to participate in the sorghum market and the intensity of participation among smallholder farmers in Western Kenya. We conducted a cross-sectional survey of 300 farming households. Using the Heckman two-step sample selection model, our study evaluated the propensity to participate in the market and the intensity of participation. Out of the 300 households sampled, 103 (34%) participated in sorghum markets. We found that education (β = 0.63, p = 0.048), hired labour (β = 1.20, p < 0.001), log land size (β = 0.62, p = 0.070), group membership (β = 1.19, p < 0.001), occupation (β = 0.86, p = 0.018), market information (β = 2.06, p < 0.001), radio ownership (β = 0.0.51, p = 0.049), mobile ownership (β = 0.65, p = 0.045), distance to market (β = -0.35, p = 0.037), and geographical location (β = 1.18, p < 0.001) significantly determined market participation level. The intensity of participation was significantly predicted by education (β = 0.135, p < 0.001), log age (β = -0.53, p = 0.043), log family size (β = -0.267, p < 0.001), hired labour (β = 0.111, p = 0.002), radio ownership (β = 0.156, p < 0.001), mobile ownership (β = 0.213, p < 0.001) and distance to market (β = -0.160, p = 0.002). Our findings suggest that enhancing access to market information and promoting group membership can increase sorghum market participation and improve commercialisation. Policies aimed at improving rural infrastructure, strengthening agricultural cooperatives, and expanding educational opportunities for farmers are crucial for enhancing market access and supporting sorghum commercialisation. • The Heckman two-step model used to assess market participation and intensity • Sorghum market participation is driven by education, labor, and land size • Access to market information and group membership enhances market participation • Larger family size negatively impacts the intensity of sorghum market participation

ABSTRACT The haor areas, a freshwater wetland ecosystem located in northeastern Bangladesh, experience some of the highest rainfall levels globally, making long-duration rice variety cultivation challenging due to flash flood during harvesting period. This study identifies the factors affecting the farmers’ choice of rice varieties and ascertains the cost thresholds that would render the switch to short-duration rice varieties economically viable. A total of 720 farmers were surveyed. The study employed descriptive statistics, Excel-based simulations, and Heckman’s probit with the sample selection model. The findings indicate that more than 50% of farmers continue to cultivate long-duration varieties, as current short-duration varieties do not provide higher profitability. Simulation analysis suggested that if production costs were decreased by approximately USD 67 per hectare (6.88% of the total cost), a shift to short-duration varieties would be logical. Moreover, the probability of adopting short-duration varieties rises by 10.4% with an increase in flash flood frequency. Given the current context of flash floods and profitability, promoting exclusive adoption of short-duration varieties may not be feasible. Instead, the development of technologies that reduce production costs is essential. Targeted extension policies, particularly for farmers operating near riverbanks, could significantly facilitate adoption and bolster climate resilience in haor agriculture.

Suicidality among young adults with diabetes: Evidence from the national longitudinal study of adolescent to adult health.

This article investigates new job creation across European Union regions in 2022 and the endogenous changes in labor market participation following the COVID-19 outbreak. We jointly model new hires in 2022 and shifts in participation between 2021 and 2022 using demand-side predictors, which are rarely applied in supply-oriented studies due to limited institutional data. The analysis combines official CEDEFOP data on job advertisements posted on online portals across the EU in 2021—disaggregated by region and occupation—with the Labor Force Survey, which provides information on new hires and regional or institutional determinants affecting both outcomes. We employ recently developed sample selection models with copulas, allowing for flexible dependence structures, and include pseudo-random effects to account for the hierarchical nature of regions nested within states. To our knowledge, this is the first study to examine post-COVID-19 employment creation for all European Union regions using such models integrating survey and online job advertisement data. The results show that, although regions with strong adult lifelong learning systems are better positioned to generate new employment opportunities, job creation has been primarily driven by rising demand for medium- and low-skilled occupations, thereby stimulating participation. New employment is concentrated in regions with lower shares of technology-skilled workers, regardless of tertiary education levels, indicating a structural shift in the post-pandemic labor market. The determinants of regional job creation differ sharply between Old and New Member States, suggesting distinct policy challenges and priorities.

ABSTRACT This paper presents a novel perspective on the identification at infinity as identification at the boundary, for the intercept of the sample selection model, via a transformation of the selection index. This perspective suggests generalisations of estimation at infinity to kernel regression estimation at the boundary and further to local linear estimation at the boundary. The proposed kernel‐type estimators with an estimated transformation are proven to be nonparametric‐rate consistent and asymptotically normal under mild regularity conditions. A fully data‐driven method of selecting the optimal bandwidths for the estimators is developed. The Monte Carlo simulation shows the desirable finite sample properties of the proposed estimators and bandwidth selection procedures.

ABSTRACT Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations. However, online job advertisements may not be representative in terms of key labor market variables. The paper presents a methodology that forecasts publicly available official employment LFS recent job starters exploiting its relationship with a bias‐corrected version of online job postings, obtained by predictions of a bivariate sample selection model, which jointly estimates the number of vacancies within job profiles and the probability of endogenous selection for nonzero vacancies. LFS new hires (obtained from LFS microdata) were used as benchmark data to measure the bias of online data and adjusted predicted counts. The proposed framework is illustrated using a dataset of Italian online job advertisements spanning from the period 2013‐Q2 to 2018‐Q2 to forecast quarterly LFS recent job starters 1 year ahead and the Cedefop's Skills‐OVATE data using Italy, France, Germany, and Spain in 2022. Results demonstrated that raw vacancies present a strong bias level with respect to benchmark data, whereas sample selection models reduced this bias by half, unlike multilevel estimates. Moreover, LFS forecasts using a VECM that leverages cointegration between LFS recent job starters and adjusted online vacancy series offer a valuable alternative to traditional univariate forecasting methods.

Female labour force participation is a key indicator of economic growth, especially in developing countries like India. This study, based on a household survey in Purulia, West Bengal, examines the determinants of informal work and wage factors among female workers. The study has used correlation matrix, one-way ANOVA and post hoc test. Sample selection models were applied to correct selection bias. The explanatory variables age, material status, numbers of child and monthly contribution of other household members were found to have a significant impact on the probability of female informal labour force participation. We found that the probability of engaging in the farm activities by the female workers were significantly influenced by their age, marital status and volume of land owned. The regressors age, year of experience in present work, nature of work, working hour, place of residence had significant impact on the wage-determination of female workers in informal sector.

Purpose The challenge of access to borrowing from formal sources persists in developing economies, disproportionately affecting vulnerable groups such as older adults, women and the poor. In this context, the paper examines the role of social networks in determining access to borrowing from formal sources among older households in India. Design/methodology/approach This study analyzes data from the Longitudinal Aging Study in India (LASI) 2017–18, a nationally representative survey of older households (at least one member aged 45 and above), to examine the relationship between social networks and borrowing decisions. The study uses a Heckman sample selection model for the analysis to account for the problem of sample selection bias in estimates. Findings We find that households with stronger social networks are more likely to borrow from formal sources than households with weaker social networks. The joint estimation of bonding (ties with friends) and bridging networks (participation in social activities) suggests that bridging networks, characterized by connections that span diverse demographics, occupational and socioeconomic boundaries, appear to be more relevant for the diffusion of financial information as compared to more homogeneous bonding networks, which are formed through close relationships. Moreover, the relationship between social networks and borrowing from formal sources varies by geographical location and caste groups. Social networks increase the likelihood of borrowing from formal sources among rural households and those belonging to historically disadvantaged caste groups, particularly the scheduled castes, who have faced discrimination in accessing social and economic opportunities. Originality/value First, it extends research on social networks and financial decisions by examining borrowing decisions in a developing country context, whereas existing studies have focused on this relationship in developed economies. Second, it utilizes a unique survey of adults aged 45 and above to explore how social networks relate to the borrowing decisions of older households. Third, it distinguishes between bonding and bridging networks to show how their distinct characteristics result in different forms of information exchange relevant to borrowing decisions.

The sample selection model is widely used in microeconometrics, especially for the case with nonrandom missing dependent variables. The linear assumption between the potential dependent variable and covariates is often mentioned in the literature. However, nonlinear structures between variables are prevalent in reality, in which case the assumption of linearity can lead to serious model misspecification. To mitigate model misspecification caused by linear assumption, the Box-Cox transformation is applied to the potential dependent variable in the sample selection model, and then the estimation of the corresponding parameters is given under the linear relationship between the transformed variable and covariates. Finite sample properties are investigated by Monte Carlo simulation. Eventually, the new model is applied to analyze the potential wage equation in the labor market in China.The result indicates that ordinary logarithmic transformation of the latent dependent variable is likely to be invalid for this dataset. Furthermore, the findings suggest the presence of a notable gender wage disparity in this particular labor market.

Purpose This study aims to investigate the socio-economic factors influencing food-away-from-home (FAFH) consumption among Indian households. It aims to assess the likelihood of engaging in FAFH and the associated expenditure across diverse socio-economic groups and occupational types. Understanding these dynamics can inform strategies to address consumption behaviours. Design/methodology/approach Using data from the Consumer Pyramids Household Survey (CPHS), the study employs the Heckman sample selection model. This method allows for an analysis of both the probability of engaging in FAFH and the amount spent, while controlling for sample selection bias across different socio-economic groups and occupational types. Findings The analysis reveals that higher educational attainment, urban living, credit card ownership and longer working hours significantly increase the likelihood of choosing FAFH and the expenditure on it. Additionally, homemakers and retirees demonstrate a higher propensity for FAFH consumption compared to regular employees, suggesting lifestyle choices and social needs drive this behaviour. Research limitations/implications While the study provides important insights, it is limited by its reliance on existing survey data, which may not capture all facets of FAFH consumption across different cultural or regional contexts. Social implications This research enhances our understanding of lifestyle choices that are essential for developing social policies aimed at promoting equitable and healthier food practices across diverse demographic and occupational groups, while addressing nutritional and social needs. Originality/value This research contributes to the literature by incorporating the latest data from CMIE’s CPHS and exploring the impact of occupational types on FAFH consumption, yielding new insights into consumption patterns in India.

Day and night performance differences in detection and deceleration by pedestrian automatic emergency braking systems.

We examine the relationship between friends and relatives (FR) network and Indian households’ access to formal (bank) credit. Using a nationally representative dataset and a Heckman sample selection model with instrumental variables, we find that households with an active FR network are less likely to apply for bank loans, but conditional on applying, they are more likely to be approved. These results support our hypotheses that informal borrowing may reduce immediate credit needs while simultaneously enhancing perceived creditworthiness. Further, the FR network is complementary to land ownership in influencing loan application. In particular, it alleviates risk rationing by encouraging applications from land-rich households. These findings indicate that the FR network does not crowd out formal credit; rather, it crowds in formal credit by mitigating borrowing constraints. This study contributes to the broader literature on the interaction between the formal credit market and non-market informal institutions.

Nighttime safety of pedestrians: The role of pedestrian automatic emergency braking systems.

In real-world visual recognition tasks, long-tailed distribution is a pervasive challenge, where the extreme class imbalance severely limits the representation learning capability of deep models. Although supervised learning has demonstrated certain potential in long-tailed visual recognition, these models’ gradient updates dominated by head classes often lead to insufficient representation of tail classes, resulting in ambiguous decision boundaries. While existing Supervised Contrastive Learning variants mitigate class bias through instance-level similarity comparison, they are still limited by biased negative sample selection and insufficient modeling of the feature space structure. To address this, we propose Rebalancing Supervised Contrastive Learning (Reb-SupCon), which constructs a balanced and discriminative feature space during model training to alleviate performance deviation. Our method consists of two key components: (1) a dynamic rebalancing factor that automatically adjusts sample contributions through differentiable weighting, thereby establishing class-balanced feature representations; (2) a prototype-aware enhancement module that further improves feature discriminability by explicitly constraining the geometric structure of the feature space through introduced feature prototypes, enabling locally discriminative feature reconstruction. This breaks through the limitations of conventional instance contrastive learning and helps the model to identify more reasonable decision boundaries. Experimental results show that this method demonstrates superior performance on mainstream long-tailed benchmark datasets, with ablation studies and feature visualizations validating the modules’ synergistic effects.

The literature has shown that correcting for self-selection into work is important for the estimation of wage profiles. In this article, we analyze to what extent intensive labor supply choices add valuable otherwise unobserved information to improve wage profile estimates. We develop a panel data sample selection model that allows for discrete choices in labor supply decisions and apply this to high-quality administrative data. Compared to labor supply decisions at the extensive margin, our new approach is able to control for additional unobserved heterogeneity from intensive labor supply choices with important consequences for the existence and direction of selection into (part-time) work. Applied to the data, we find that such information is especially important for estimating part-time wage profiles for women.

Purpose This paper aims to investigate the impact of environmental protection investment (EPI) on corporate innovation within private enterprises in China – an area that has received limited attention. This study also aims to identify the potential economic mechanisms driving this relationship. Design/methodology/approach This study uses a two-stage least squares approach with instrumental variables and a Heckman two-stage sample selection model to address concerns related to endogeneity. Findings This paper finds a positive and significant association between EPI and investment in corporate innovation. Two potential mechanisms explaining this relationship are identified: enhanced access to financing opportunities and favorable tax treatment, both of which enable firms to allocate more resources to innovative activities. In addition, the positive impact of EPI is more pronounced in firms with embedded Chinese Communist Party units and those located in more marketized regions. Research limitations/implications This paper relies on a single year of data, restricting its ability to control firm fixed effects and preventing it from conducting more robust analysis. Future research should use panel data to control firm fixed effects and examine more recent data, when such data becomes available, to enhance the robustness and relevance of the analysis. Originality/value Previous research has examined the environmental dimension of corporate social responsibility and its impact on firm performance and behavior. However, these studies often overlook the unique context of unlisted private enterprises. This paper makes significant contributions by providing empirical evidence on the value of EPI for unlisted private firms in China, addressing a notable gap in the literature.

This study explores the determinants of impact on ecology in Northern Tanzania. By examining key socio-economic, institutional, and structural factors influencing engagement the study provides insights in strengthening agribusiness networks and improving livelihoods. Data was collected from 215 farmers and 320 traders through a multistage sampling procedure. Heckman AI sample selection model was used in data analysis whereby the findings showed key factors influencing farmers’ decisions on ecology were gender and years of formal education at p &lt; 0.1, and access to finance and off-farm income at p &lt; 0.05. The degree of farmers participation in social groups was influenced by age, household size, off-farm income and business network at p &lt; 0.05, number of years in formal education and access to finance at p &lt; 0.01, and distance to the market at p &lt; 0.1. The decision of traders to impact on ecology was significantly influenced by age and trading experience at p &lt; 0.1. Meanwhile, the degree of their involvement in social groups was strongly affected by gender, formal education, and trust at p &lt; 0.01, as well as by access to finance and business networks at p &lt; 0.05. The study concluded that natural ecology is influenced by socio economic and structural factors but trust among group members determine the degree of participation. The study recommends that strategies to improve agribusiness networks should understand underlying causes of impact on ecology and strengthen available social groups to improve performance of farmers and traders.

Purpose This paper examines whether an education experience abroad promotes short-term or long-term international migration intentions. Design/methodology/approach This paper uses individual-level data from the Eurobarometer survey “Intra-EU labour mobility after the pandemic”, which focuses on intentions to migrate among EU citizens. Univariate probit and bivariate probit with sample selection models are estimated. Findings The results suggest that an international education experience makes individuals more likely to have plans to work abroad in the future, but this experience is found to be associated with short-term migration intentions rather than long-term ones. Originality/value While the positive relationship between international education and labour migration is well-known in the literature, this paper advances research in the field by investigating how studying abroad impacts the expected duration of the migration.

We consider a two-step estimation procedure to estimate the panel sample selection models with interactive effects. In the first step, we follow the Robinson (1988) procedure to remove the sample selection factors. In the second step, we control the interactive effects. When the cross-section dimension N is large, we propose to use the Pesaran (2006) common correlated effects approach, and when the time series dimension T is large and N is finite we propose to follow the Hsiao, Shi, and Zhou (2022) transformed estimation procedure to eliminate the interactive effects. We show that the resulting estimators are consistent and asymptotically normally distributed. A limited Monte Carlo study is conducted, showing our methods appear to work well in a finite sample. An empirical illustration on female wage rate determination shows that an extra year of work experience could raise the expected log wage rate by 0.1507 under our maintained hypothesis, while neglecting sample selection or interactive effects could lead to seriously biased estimates.

Wetlands provide several significant benefits not only to the local community but also to those who reside far away. They are recognized across the world for their crucial role in supporting a diverse range of biodiversity and supplying products and services, as well as key natural resource sources on which rural economies rely. This study was conducted in Silte zone; to identify the determinant of household’s participation in wetland utilization and extent of utilization in the case Lake Tinshu Abaya wetland ecosystem service. A total of 178 sample households were selected from four Kebles adjacent to Lake Tinshu Abaya using a simple random proportional sampling technique. In this study, a cross-sectional research approach was used. Both primary and secondary data were used in this study. Primary data (qualitative and quantitative) was collected using field observations, Focus Group Discussions, questionnaires, and key informant interviews. Descriptive statistical analysis techniques including mean, frequency, and percentages were used to analyze the socio-economic, institution factor, and demographic variables. Econometrics models such as Heckman&amp;apos;s two-step sample selection model were used to determine the factors that influence participation in wetland utilization and the extent of wetland utilization. The study result shows that the decision to participate in wetland utilization is significantly influenced by age, family size, education, marital status, annual income, land size, off-farm activity, distance, and livestock number. And the age, family size, education, annual income, land size, off-farm activity, distance, and livestock number significantly determined the extent of wetland utilization. Lake Tnishu Abaya wetland ecosystem provides services like provisioning services, regulating services, supporting services, and cultural services. Wetland-friendly socioeconomic activity operations should designed to safeguard the long-term survival of Lake Tinshu Abaya wetland. The concerned government body should participate in conserving to preserve the sustainability of the wetland ecosystem.