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Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of covariates related to the outcome of interest or the conditional distribution of the outcome given covariates. For either of these scenarios, we derive joint functional central limit theorems and bootstrap validity results for regression-based estimators of the status quo and counterfactual outcome distributions. These results allow us to construct simultaneous confidence sets for function-valued effects of the counterfactual changes, including the effects on the entire distribution and quantile functions of the outcome as well as on related functionals. These confidence sets can be used to test functional hypotheses such as no-effect, positive effect, or stochastic dominance. Our theory applies to general counterfactual changes and covers the main regression methods including classical, quantile, duration, and distribution regressions. We illustrate the results with an empirical application to wage decompositions using data for the United States. As a part of developing the main results, we introduce distribution regression as a comprehensive and flexible tool for modeling and estimating the entire conditional distribution. We show that distribution regression encompasses the Cox duration regression and represents a useful alternative to quantile regression. We establish functional central limit theorems and bootstrap validity results for the empirical distribution regression process and various related functionals.

We propose a new regression method to evaluate the impact of changes in the distribution of the explanatory variables on quantiles of the unconditional (marginal) distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function, a widely used tool in robust estimation, is easily computed for quantiles, as well as for other distributional statistics. Our approach, thus, can be readily generalized to other distributional statistics.

We analyze gender differences in expected starting salaries along the wage expectations distribution of prospective university students in Germany, using elicited beliefs about both own salaries and salaries for average other students in the same field. Unconditional and conditional quantile regressions show 5–15% lower wage expectations for females. At all percentiles considered, the gender gap is more pronounced in the distribution of expected own salary than in the distribution of wages expected for average other students. Decomposition results show that biased beliefs about the own earnings potential relative to others and about average salaries play a major role in explaining the gender gap in wage expectations for oneself.

This study provides empirical evidence of the economic effect of contract farming for the agriculture sector dominated by smallholder farms. In light of the association between contract farming and modern food distribution channels, we categorize the adoption decisions of contract farming and modern marketing channels into four mutually exclusive choices and investigate their economic effects through the simulated maximum likelihood estimation of the multinomial treatment effects model. The results provide empirical evidence supporting higher returns from the dual partnerships as choosing modern distributors generates more revenues for the those participating in contract farming than for those with no contract farming, and contract farming is more likely to help generate more revenues for those who have taken modern distributors as their major marketing channel compared with those relying on traditional channels. Moreover, we examine whether any distributional pattern of marginal economic effects, of either contract farming or modern marketing channel, is present among farmers at various scales by using the conditional and unconditional quantile regression models. Our findings suggest that the marginal treatment effects are generally in an increasing trend as the quantile increases, implying that the economic effects of contract farming or partnership with modern distributors are more pronounced for higher returns among rice farmers in Taiwan. This finding has great policy implications for developing sustainable agriculture and food supply when facing greater uncertainties due to global warming in the future, especially in an agriculture sector with most smallholder farmers.

PurposeMicro, small and medium-sized enterprises (MSMEs) are the backbone of economic development for every economy. They contribute to local economic development through household wealth creation, employment generation and poverty reduction. Despite this pivotal role, MSMEs lack access to finance, and scholarship on the enabling role of financial inclusion on micro, small and medium-sized enterprises' performance is scant. The authors contribute to closing the knowledge gap by examining the enabling effect of financial inclusion on MSMEs using the FinScope MSME 2017 survey for the Kingdom of Eswatini. This paper aims to discuss the aforementioned objective.Design/methodology/approachThe study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.FindingsThe findings from the unconditional quantile regression revealed that small changes in access to bank accounts, saving for business, formal saving, stokvel and informal saving at the 50th and 75th percentiles have a positive and statistically significant effect on microenterprises' annual turnover profit. Conversely, small changes in formal insurance have a mixed effect on annual turnover profit. At the 10th and 25th percentiles, a small increment in insurance reduces annual turnover profit but increases microenterprise annual turnover profit at the 75th percentile. Meanwhile, the evidence from the generalized ordered logit model showed that financial inclusion reduces the likelihood of microenterprises being classified as least developed and increased the chances of microenterprises falling into emerging and developed business categories.Research limitations/implicationsThis study makes use of a cross-sectional survey dataset, as a result, it does not infer causal relationships over the long term, but rather an association between the independent and dependent variables.Practical implicationsOverall, formal and informal financial inclusion enhances the annual turnover profit for microenterprises, particularly at the 50th and 75th percentiles in the Kingdom of Eswatini. The authors recommend a specialized institution such as a micro, small and medium-sized partial credit guarantee scheme to improve the quality and affordability of credit for microenterprises, and a mix of financial and non-financial supports depending on the development stage to boost a sustainable microenterprises' sector.Originality/valueThe study uses two advanced cross-sectional techniques, the recentered influence function framework and the generalized ordered logit model to analyze the data. The paper is original and contributes to the discussion of the role of financial inclusion in enabling microenterprises' success in Africa, using the FinScope 2017 survey of microenterprises in Eswatini as a case study.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

PurposeThis study aims to investigate the extent to which overeducation imposes wage effects on university graduates, taking into account the individual heterogeneity due to skills and innate ability.Design/methodology/approachUsing Graduates Occupation and Mobility Survey (GOMS) 2019 and Korea Dictionary of Occupations (KDOT) 2019, the overeducated and adequately educated graduates are differentiated by the job analysis (JA) measure. To unveil the masked results, the unconditional quantile regression (UQR) accompanying skills and field of study mismatches is adopted to explore the wage effects of overeducation across the overall wage distribution.FindingsEmpirical evidence shows that the incidence of overeducation is high; however, overeducated graduates only suffer a 6.5% wage loss relative to their adequately matched peers. The findings indicate that regardless of being derived from either overskilled or field of study mismatch, genuine overeducation impose a higher wage penalty at all percentiles relative to the apparent overeducation. Meanwhile, high-ability men suffer lower-wage penalties than their low-ability peers, whereas the inverted “U” pattern is exhibited for women. The theoretical hypotheses differ depending on the estimated results by gender.Research limitations/implicationsEach measure of educational mismatch has been criticized for its insurmountable shortcoming. The recent graduates are likely to overstate the job requires of skills.Originality/valueThis paper contributes to the insufficient evidence on the multiple aspects of wage effects of overeducation by providing new and rigorous examinations and by focusing on the country experiencing rapid economic growth, industrial upgrading and educational expansion.

PurposeThis paper aims to measure income differences between the obese and the non-obese for Brazil and understand which components explain these differences.Design/methodology/approachA decomposition method based on recentered influence functions, proposed by Firpo et al. (2007) is used, and the procedure is applied to individuals' income distribution quantiles.FindingsThe results confirm the existence of a wage gap between obese and non-obese men and women. In the case of men, the difference was favorable to the obese in all quantiles and in the case of women, favorable to the non-obese. The biggest differences were observed at the top of the distribution. This difference is mostly explained by observable characteristics that cause the wage gap between groups. The wage structure effect, which may have elements associated with discrimination in its composition, was not relevant in most quantiles.Research limitations/implicationsUnobserved factors can impact the results, but our methodology tries to minimize such impact.Practical implicationsThe authors can only observe a point in time and with that they do not know how long the individual has been obese.Originality/valueThe methodology adopted in the work is recent; moreover, studies on the effects of obesity on the labor market are still recent in Brazilian research.

This study examined the relationship between marital status and body mass index (BMI) using data collected from the Japan Household Panel Survey from the period 2009–2018. The varying effects of marital status and changes in such status across the BMI distribution were explored using fixed effects ordinary least square and unconditional quantile regression with fixed effects. Based on the analysis, three key points were identified. First, although marital status does not significantly affect BMI for men, BMI tends to be higher among married women than unmarried women. Second, a positive and significant relationship between marriage and BMI was observed among women under the age of 40, and the effect was largest at the 90% percentile of the BMI distribution. Third, the transition from single to married status has positive yet short-lived effects on BMI for men. Contrastingly, the effects are persistent for women, and the effect is largest at the top of the BMI distribution. The results confirm previous findings that marriage confers weight gain even in Japan, where rates of obesity are significantly lower than in other developed countries.

Based on Labour Force Survey data for Canada over the period 1998 to 2018, we find occupational licensing raised average real wages from 6.1% in 1998 to 13.8% by 2018, slightly less for propensity score matching estimates, suggesting the effect is somewhat lower after adjusting for unobservable factors that can affect pay. Unconditional quantile regressions indicate the licensing effects on wages are greater at the higher ends of the wage distribution, with those inequality enhancing effects becoming more pronounced over time. In contrast, the union wage premium declined steadily from 9.2% in 1998 to 5% in 2018, with a substantial premium at the bottom of the wage distribution but a steady decline at the top, becoming negative at higher parts of the wage distribution, with those equality enhancing effects becoming less pronounced over time. Overall, the estimates for Canada indicate that occupational licensing contributes to wage inequality, and the effect is increasing over time, while unions reduce overall wage inequality, but this equalizing effect is decreasing over time.

Farmland transfer is a crucial aspect of modernizing China’s agriculture, which has a significant impact on farmers’ income distribution. With rapid urbanization, farmers’ livelihood has changed significantly, and the income effects of farmland transfer will differ among heterogeneous farmers. Based on the China Family Panel Studies (CFPS) data, this study examines the impact of farmland transfer on farmers’ income growth and income disparity, as well as the income differences in farmland transfer among farmers with different types of livelihood capital. An endogenous switching regression model and unconditional quantile treatment effects are used to correct the selection bias and farmer heterogeneity. The results show that (1) farmland transfer increases farmers’ income. Specifically, farmland transfer-in increases farmers’ income by 21.15%, while transfer-out increases it by 43.33%. (2) The impact of farmland transfer on farmers’ income has a “Matthew effect” and will widen the income gap between farmers. (3) Moreover, farmland transfer exhibits diverse income effects on heterogeneous farmers with livelihood capital. Capital-rich farmers experience the largest income-growth effect from farmland transfer-in, while capital-balanced farmers experience the largest income-growth effect from farmland transfer-out. The policy implications are to further improve the farmland transfer market and enable low-income farmers to participate by improving their qualifications and abilities.