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The aim is to assess how a policy of tertiary education for all affects the shape of the unconditional earnings distribution.

The paper discusses the quantile-regression literature looking at the link between education and wage inequality, also proving new evidence based on unconditional quantile regressions.

The findings support the idea that a policy of tertiary education for all increases the overall level of wage inequality.

The research has implications for public policy and administration. Among the limitations, the paper does not deal with distributional aspects related to other outcomes (e.g. health outcomes) of the policy of interest.

The analysis highlights a series of potential government interventions aimed at reducing the wage-inequality externalities of the policy of interest.

A policy of tertiary education for all, by itself, is not useful to fight wage inequality.

This paper belongs to the small group of studies using unconditional quantile regressions to study the link between education and wage inequality. It is the first study specifically looking at the distributional effects of a policy of tertiary education for all.

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue.

The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect.

Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South.

The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

We examine the potential effect of naturalization on the U.S. immigrants’ earnings. We find the earning gap between naturalized citizens and noncitizens is positive over many years, with a tent shape across the wage distribution. We focus on a normalized metric entropy measure of the gap between distributions, and compare with conventional measures at the mean, median, and other quantiles. In addition, naturalized citizen earnings (at least) second-order stochastically dominate noncitizen earnings in many of the recent years. We construct two counterfactual distributions to further examine the potential sources of the earning gap, the “wage structure” effect and the “composition” effect. Both of these sources contribute to the gap, but the composition effect, while diminishing somewhat after 2005, accounts for about 3/4 of the gap. The unconditional quantile regression (based on the Recentered Influence Function), and conditional quantile regressions confirm that naturalized citizens have generally higher wages, although the gap varies for different income groups, and has a tent shape in many years.

The objective of this work was to analyze the income differential of the rural–urban worker in relation to the rural–rural worker and in relation to the urban–urban worker in the Brazilian labor market. Two databases were used, the 2005 and 2015 PNADs (Pesquisa Nacional Por Amostra de Domicílios).

The methodology is the decomposition approach proposed by Firpo et al. (2007, 2009). This method adopts estimates of unconditional quantile regressions, based on the concepts of influence function and recentered influence function (RIF).

Among the main results, income differentials were shown to benefit the urban–urban worker when compared to the rural–urban worker, and income differences to the benefit of the rural–urban workers, when these were compared to the rural–rural workers. The educational variable was relevant in explaining the income disparity and expressing increasing effects in the higher quantiles.

The methodology used in this work is considered recent in the literature as it is based on the RIF regression (Firpo et al., 2007, 2009). The main advantage of this method is the possibility of assigning a “composition effect” and a “wage structure effect” for each variable that determines the level of income at different points of the income distribution.

The purpose of this paper is to assess determinants of returns to male and female entrepreneurship in Ghana, Kenya and Uganda at selected quantiles along the distribution, as well as examine gender gaps in returns to entrepreneurship and factors contributing these gaps.

Employing a unique data set collected in the three countries on entrepreneurial motivations, constraints and performance, the authors apply unconditional quantile regression technique to assess the determinants of returns to entrepreneurship at various quantiles along the distribution. Additionally, the authors employ decomposition techniques to assess gender gaps in returns to entrepreneurship at various points along the distribution. The data contain extensive information on entrepreneur’s personal characteristics, including parental background and household composition and structure.

The study finds substantial differences in determinants of returns to male and female entrepreneurship along the distribution, with firm asset increasing returns to entrepreneurship. There is also the presence of gender gaps in returns to entrepreneurship at the lower-end of distribution, however, gaps disappear at the upper tail of the distribution, indicative of sticky floors in returns to entrepreneurship in Ghana, Kenya and Uganda. The authors also find gender bias against female entrepreneurship in the three countries, as unobserved characteristics largely responsible for the gender gaps in entrepreneurial returns.

This work has been undertaken by the authors and has not been carried out by any other person. The study will add to the existing literature on gender and returns to entrepreneurship.

This study investigates the relative flexibility of payouts vis-à-vis investment in the UK, motivated by concerns regarding this market's distinct payout characteristics and limited relevant research. It addresses the information gap related to the use of conditional mean estimations and examines firm behavior across the investment distribution.

The sample is an unbalanced panel of 6,173 firm-year observations, from 271 non-financial firms in the FTSE-All Share Index, during 1990–2019. Estimation methods include pooled- ordinary least squares (OLS) and firm fixed-effects regressions as well as unconditional quantile regressions with firm fixed effects.

For the “average” firm results show a negative relationship between share repurchases and investment, amplified in the presence of financial constraints and growth opportunities. Quantile regressions analysis reveals heterogeneous firm behavior as this relationship becomes stronger in successive quantiles of the investment distribution and disappears at the upper/lower extremes. Results suggest that UK firms exploit the inherent flexibility of share repurchases to facilitate investment. However, this flexibility appears irrelevant to firms with extremely high/low investment, characterized by significant differences in growth opportunities, cash flows and external financing cost. Dividends and investment are independent across the investment distribution, underlining the rigidity of dividends in the UK.

To the best of our knowledge, this is the first study to investigate the relative flexibility of payouts vis-à-vis investment in the UK, using firm-level financial data and at points other than the conditional mean. Its value lies in that it shows that share repurchases facilitate rather than impede investment and thus do not corroborate relevant concerns by economists and policymakers. Additionally, by utilizing a relatively new methodology it uncovered heterogeneous firm behavior across the investment distribution suggesting that conditional mean estimations should be applied with caution at least for highly heterogeneous samples.

Micro, 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.

The study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.

The 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.

This 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.

Overall, 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.

The 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.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

The objective of this study is to estimate the gender wage gap in Vietnam and its rural and urban areas, especially with the presence of foreign firms.

The authors use cross-sectional data from three rounds of the Vietnam Household Living Standards Survey (VHLSS 2008, 2012, and 2016) to investigate this issue. The unconditional quantile regression and Oaxaca–Blinder (OB) decomposition are used in this article.

The article finds the gender wage gap favouring men, especially in higher quantiles of the wage distribution. The gap in urban Vietnam was higher than in rural areas. The OB decomposition indicates that gender wage gap is mainly driven by gender discrimination. The differences in return to participation in foreign companies only contributed significantly and positively to such a gap in some models. It suggests that the gap in those models is affected by gender discrimination in employment opportunities in foreign companies. Regarding the endowment effect, some models provide the significantly negative impacts of foreign firms on gender wage inequality.

The study suggests that policies to reduce the gender wage gap should pay more attention to foreign firms, especially at higher wage classes.

The main aim of this study was to examine gender differences in job satisfaction in Europe.

For the empirical analysis, data from the Sixth European Working Conditions Survey were used. Oaxaca–Blinder decomposition with a principal component analysis (PCA) aggregated variable, after unconditional quantile regressions in a multiple imputation background, was implemented.

Women report higher job satisfaction than men do. Women were significantly more satisfied than men for the middle levels of the job satisfaction distribution.

This study expands the evidence on the determinants of job satisfaction in the European labour market by applying a recent form of decomposition that invests in unconditional quantile regression (UQR). To the best of this study knowledge, this is the first time that the Oaxaca–Blinder decomposition with a PCA aggregated variable after unconditional quantile regression has been employed to study gender-based differences in job satisfaction.

The purpose of this paper is to examine the wage differentials along the entire distribution between immigrants and the Australian-born.

Using the Household, Income and Labour Dynamics in Australia (HILDA) Survey, the authors apply a semi-parametric method (DiNardo et al., 1996) to decompose the distributional wage gap between immigrants and native-born Australians into composition effect and wage structure effect. The authors further apply the unconditional quantile regression (UQR) method (Firpo et al., 2007) to decompose the overall wage structure effect into contributions from individual wage covariates.

Relative to the native-born, both effects favour immigrants from English-speaking countries. For male immigrants from non-English-speaking countries (NESC) the favourable composition effect is offset by disadvantage in the wage structure effect, leaving little overall wage difference. Female immigrants from NESC are disadvantaged at the lower part of the wage distribution.

The increasingly skill-based immigration policy in Australia has increased skill levels of immigrants relative to the Australian-born. However, the playing field may yet to be equal for the recent NESC immigrants due to unfavourable rewards to their productivity factors. Also, immigrants are not homogeneous. Countries of origin and gender matter in affecting wage outcomes.

The unique wage-setting system and the increasingly skill-based immigration policy have made Australia an interesting case. The authors examine the entire wage distribution between migrants and native-born rather than focus on the mean. The authors differentiate immigrants by their country of origin and gender; and apply the UQR decomposition to identify the contributions from individual wage covariates.