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While there has been increasing interest in the distribution of net worth and disposable income, these variables include negative values, making it impossible to use the distribution functions previously employed in estimating gross income distributions, as these distribution functions cannot accommodate negative values. In this study, we propose the Johnson’s $S_U$ distribution to estimate the distribution of net worth and disposable income. The $S_U$ distribution function is defined over the entire real number space and is one of the most flexible parametric distribution functions for capturing a wide range of skewness and kurtosis. Therefore, it is highly suitable for estimating the various shapes of distributions for net worth and disposable income, which can include negative values. We derive the Lorenz curve for the $S_U$ distribution in a closed-form expression. As an illustrative example, we apply both the univariate and bivariate $S_U$ distributions to estimate the distributions and Lorenz curves of net worth and disposable income using the survey data of Korea.

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This paper offers a comprehensive snapshot of the valuation of workers’ socio-demographic characteristics within the Chilean and Mexican labor markets, focusing on variations across different income quantiles over time. Utilizing nationally and regionally representative household survey data, we employ quantile regression techniques to estimate Mincerian wage equations. Our analysis reveals that in both countries, male workers, individuals possessing at least some college education, wage earners, and residents of more populous areas consistently earn more than their counterparts who are female, less educated, self-employed, and living in less populated areas. Notably, the degree of these earnings disparities significantly varies across income quantiles and evolves over time. Considering the historical emphasis on market-driven rather than redistributive policies in these countries, our findings suggest the need for policy measures specifically designed to address each critical determinant of earnings across diverse income quantiles.

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This study investigates the impact of foreign aid and human capital development on economic growth in the West African Monetary Zone (WAMZ) region, from 1999 – 2023. The study employs the autoregressive distributed lag (ARDL) model for the analysis. The results of the findings reveal that the impact of foreign aid in WAMZ, both in the short-run and long-run is negative but significant only in the long-run, however, human capital development is positive, only in the long-run. Furthermore, the findings indicate that economic growth and human capital development are causally related while foreign aid and economic growth are not. Therefore, the study recommends that aid donors should collaborate with multilateral organizations like the International Monetary Fund (IMF), as they are better suited to manage, oversee and promote economic growth projects. Meanwhile, the governments of WAMZ should strategically invest in vital sectors to increase resilience and self-sufficiency and establish macroeconomic environment that reduces reliance on foreign aid. Also, the policymakers in the WAMZ should ensure universal access to high-quality education that meets the demand of the labour market, and make investments in health-care delivery systems. Ultimately, the study contributes to a deeper understanding of the dynamics between aid-inflows, human capital, and economic growth in the region.

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This article examines the extent to which survey respondents’ stated intentions to visit regional attractions predict their actual visit behaviors. To evaluate this relationship empirically, the article constructs a unique dataset that tracks the same respondents for three consecutive years and records the change of intentions over time and their actual visit behaviors. The empirical results show that intentions strongly predict visiting behaviors even though intentions change significantly over time. Moreover, there are substantial differences in the factors that influence intentions and behaviors, suggesting that analyzing intentions data alone without behavior data may be misleading. The article presents further regression results that verify the robustness of our findings with various regional attractions such as amusement parks, beaches, and forests. The implications of these results are consistent with the article’s main findings.