Wage Equations Research Articles

Educational Mismatch and Gender: A Comparison between Industry and Services in Spain

This study analysed the presence and influence of educational mismatch in the service and industry sectors in Spain, due to the lack of studies of this phenomenon in the latter sector. We also analyse its effect on wages and its role in creating a gender wage gap in the returns to a set of professional and personal characteristics. The heterogeneity in the improvement of workers’ qualifications between sectors in Spain and the lack of studies of this phenomenon in the industrial sector motivates interest in this comparative research between industry and services, which includes a gender perspective, given the interest of this topic in wage studies. To this end, an extension of the Mincer wage equation was applied to data from the 2018 Wages Structure Survey conducted by the Spanish National Statistics Institute. The results suggest that educational mismatch has a greater impact on women’s wages in the service sector than on those in the industrial sector and on men’s wages in both sectors. We also found wage differences in the returns to a set of professional and personal characteristics that suggest that the gender wage gap is greater in the service sector than in the industrial sector.

RETURNS TO EDUCATION UNDER THE HUKOU SYSTEM: WHAT IS THE ROLE OF SCHOOL TYPE?

Purpose – Estimating the gap in returns to education between China’s urban and rural areas based solely on education attainment can be misleading, especially when there are significant differences in the allocation of educational resources between urban and rural areas. This paper explores the relationship between school types, primarily representing the educational resources available in China, and education returns in urban and rural China under the Hukou system.Methodology – This paper used data from the 2013 Chinese Household Income Project (CHIP) and applied the OLS method to estimate the traditional Mincer and extended models by adding school types.Results – Firstly, we found that school types significantly impact income, and different types of schools have different returns on education. The returns to school type increase with the geographical administrative level of the school. In addition, in most cases, the impact of key schools on the income of urban people is more significant than that of rural people. Secondly, when we consider the effect of school type on income, the gap in education returns between non-agricultural Hukou groups and agricultural Hukou groups is widened compared to the results under the traditional Mincer model. Finally, migration does not appear to have brought the desired improvement to their education, with the returns to education for migrants being significantly lower than rural in some cases.Implications – For individuals, especially those rural Hukou holders, investment in education should be strengthened to improve access to key schools. Providing equal access to quality education and reducing educational disparities between urban and rural areas is essential for the government.Originality/Value – The results highlight the role of inequality in the allocation of educational resources and the inequality of educational opportunities among the groups with different Hukou statuses and also show that migrating to urban areas may place migrants in a more adverse environment.

Does environmental regulation limit the income growth of Chinese migrant population? Empirical evidence from the CHIPS

It is a controversial question of whether environmental regulation can achieve the double dividend of improving environmental quality and increasing employment and income. This paper takes Chinese migrant population as the main research object and uses the Chinese Household Income Projects data (CHIPS) and the emission data of major pollutants at the prefecture-level city jointly. Based on the classic Mincer wage equation, we investigate the impact and mechanisms of environmental regulation on the income of Chinese migrant population. The results show that: (1) The intensity of environmental regulation significantly improves the income level of the migrant population, and for every 1% increase in the intensity of environmental regulation, the wage income of the mobile population will rise by 4.96%. (2) The documented positive effect of environmental regulation is more significant in non-public firms, the eastern regions and high-skilled workers. Conversely, for the female and elderly group, 1% increase in environmental regulations will restrain their income growth by 4.95% and 0.37%, respectively. (3) The influencing mechanisms include both the direct effect of labor demand and the indirect effects of firm performance and technological innovation. (4) we find that 1% increase in environmental regulations respectively raise the income of the low- and moderate-income migrant population by 10.834% and 3.579%, while having no significant impact on the high-income group, thus reducing the income gap of the migrant population. The findings of this paper provide theoretical support and practical evidence for achieving a coordinated development of ecological environmental protection and income improvement for socially disadvantaged groups in China.

Effects of automation on the gender pay gap: the case of Estonia

Abstract This paper investigates how investments in automation affect the gender pay gap. The evidence of the effects of automation on the labor market is growing; however, little is known about the implications of automation for the gender pay gap. The data used in this paper are from a matched employer–employee dataset incorporating detailed information on firms, their imports, and employee–level data for Estonian manufacturing and service employers for the period of 2006–2018. Through the use of the imports of automation goods as a proxy for the introduction of automation at the firm level, this paper estimates the effect of automation using simple Mincerian wage equations. The causality of the effect is further validated using propensity score matching (PSM). We find that introducing automation enlarges the gender pay gap, and PSM confirms that this also has a higher causal effect on the wages of male employees than female employees. The results imply that a higher representation of women in higher-paid positions does not guarantee a reduction in the gender pay gap in the presence of automation, and appropriate measures in education and retraining are needed to tackle the effect of automation on gender inequality.

Effect of the East African Community Common External Tariff on Household Labor Income in Kenya

This study investigates how changes in prices caused by the adoption of the East African Community Common External Tariff affected the labor incomes of households in Kenya. Households in rural, urban, and fully urban areas are analyzed. Workers are classified as skilled versus unskilled, formal versus informal, and agricultural versus non-agricultural sector workers. Data from the Kenya Integrated Household Budget Surveys 2005/2006 and 2015/2016 are used in the analysis. Data from these surveys were used to categorize workers and extract the price values for commodities. One hundred and twenty-one commodities classified as agricultural or manufactured goods are analyzed. Much of the reduction in import tariffs and hence reduction in commodity prices is on manufactured goods. A Mincerian wage equation that is corrected for survey design and heteroskedasticity is estimated. Further, a sensitivity analysis using a Poisson Pseudo Maximum Likelihood model is estimated. The price-labor income effect is observed to vary with the classification of workers in the country. Skilled, formal, and non-agricultural workers gained compared to their counterparts. Workers in counties that border the EAC countries and those in the major cities in the country gained more than other workers in other counties in the country. However, in all parts where gains are experienced, the magnitude is small. This shows that generally, the Common External Tariff did not have a very significant effect on labor incomes in Kenya.

Income Source Confusion Using the SILC.

We use a unique panel of household survey data-the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008-2011-which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure of misreporting across similar benefits and between benefits and earnings. We document that many respondents fail to report participation in one or more of the unemployment programs. Moreover, they inflate earnings for periods when they are unemployed but receiving unemployment compensation. To demonstrate the impact of income source confusion on estimators, we estimate standard Mincer wage equations. Since unemployment is associated with lower education, the reports of unemployment benefits as earnings bias downward the returns to education. Failure to report unemployment benefits also leads to substantial sample bias when selecting on these benefits, as one might in estimating the returns to job training.

Innovation as a firm-level factor of the gender wage gap

PurposeThis paper investigates the relationship of both technological (product and process) and non-technological (organizational and marketing) innovation with the gender wage gap at firms.Design/methodology/approachUsing employer–employee level data from Estonia, the authors estimate Mincerian wage equations, in order to show how innovation at the firm level is associated with the gender wage gap. Next, the authors use propensity score matching (PSM) to study the effects of the movement of men and women into innovative firms, how this shapes the gender wage gap at firms.FindingsThe authors find that both technological and non-technological innovation are associated with a larger gender wage gap at firms. The relationship between innovation and the contemporaneous gender wage gap at firms reflects to a significant extent the different selection of men and women with different time-invariant characteristics to innovative firms. Further, the authors find that movement of men and women to work at innovative firms is in longer term associated with larger gains in wages for men. The authors also observe that the relationship of innovation with gender wage gap is stronger in the case of women with children.Originality/valueMuch of the prior analysis focuses on the effects of technological innovation on gender-related labour market outcomes. The authors show here that the relationship of innovation at firms with higher gender wage gap is not only specific to technological innovation, but is more general, and is observed across different types of innovation indicators, including non-technological innovation. This study's results suggest that the effects of innovation on gender wage gap may reflect to an extent the higher demand for flexibility of employees for work purposes at innovative firms, which may increase the gender wage gap, especially between men and women with children.

표본 선택 모형을 통한 밀레니얼 세대의 인턴십 경험이 취업 및 임금에 미치는 영향 분석

The aim of this study is to analyze the influence of internship experiences on the employment and wages of millennials during their college years to understand the actual labor market outcomes of internships. For this purpose, we utilized data from the 2020 Graduate Occupational Mobility Survey (GOMS 2019) to estimate a probit model and a wage equation using Heckman two-stage model. First, the model validity analysis confirms that the Heckman's sample selection model is statistically significant for this study, exhibiting a negative correlation, with smaller wages having a higher probability of being selected into the sample. Second, the first-stage of Heckman’s probit model analyzed the effects of internship experiences on employment, revealing that internships positively impact employment prospects. This impact was statistically significant with regard to gender, academic grade, metropolitan region, and the father's level of education. Third, in Heckman's two-stage wage equation estimation, it was found that internship experience did not influence wages, wherein the study showed that regular employees earned approximately 22% more than non-regular employees.

International Trade and Wage Inequality in India: Does Direction of Trade Matter?

After following import-substituting policies for nearly three decades, India opted for liberal economic regime in the early 1990s. Since then, it has emerged as one of the fastest growing economies in the world. However, concerns have been raised about the distributional consequences of liberalization. This article attempts to quantify the impact of trade on wage inequality in the Indian manufacturing sector. Estimating a relative wage equation with a panel of 49 manufacturing industries, this article found a positive association between trade and wage disparity in Indian manufacturing, but the association is contingent on the direction of trade. Our results show that after controlling skill-biased technological change and other variables, trade, especially exports to developed and developing countries, has an opposite impact on wage disparity.

Wage Differentials in the Post Liberalized Labor Market in Paksitan

ABSTRACT: In this paper, we investigate the wage differentials that exist in the post-liberalized labor market in Pakistan. The increasing wage differential contradicts standard labor theories and models. The literature also confirms the existence of wage differentials across different labor markets both in industrialized and non-industrialized countries. The firms compensate the workers both on their observable and unobservable characteristics. The mincer equation used in its semi-logarithmic decomposed form to derive wage differentials. The weighted adjusted standard deviation technique is used for further decomposition of the level and regional variations in sectoral wage gaps, as suggested by Horrace and Oaxaca. The sectoral heterogeneity, skill characteristics and regional dispersions derived by using the micro labor force data sets spread over thirty years. The results confirm the existence of inter-industry wage differentials. However, the size varies across the regions, levels of education, and types of occupations. A substantial difference in the wage gap observed in firms both in the formal and informal sectors. The financial services firms pay higher than the average national wage followed by firms in utility services, the services sector, and the mining sector. The firms in the agricultural sector pay lower than the average national wage. The dispersion in wages has risen to the magnitude of almost 25 percent over the last three decades. Firms pay higher wages to high-skill workers, while industries requiring jobs with low skills pay lower wages. The wage differentials in various regions are attributable to the persistent and relative backwardness of provinces. The high-skilled occupations in general are likely to have more benefits than those involving low-skilled workers. Individual and household characteristics account for almost 50 percent of variation regardless of the sectoral affiliation. The increase in the minimum wage, with targeted social policies in educational attainment, increased participation of women, and provision of social services in the informal sector may reduce the gap in wage difference.

Disentangling Occupational Sorting from Within-Occupation Disparities: Earnings Differences Among 12 Gender–Race/Ethnicity Groups in the U.S.

This paper quantifies the role that occupations play in explaining wage differences among 12 gender–race/ethnicity groups without including occupations as control variables in wage equations. Our approach, based on a counterfactual analysis that involves a re-weighting scheme, has three advantages. First, it allows enlarging the small list of occupations usually employed in the literature. Second, it directly addresses the fact that occupational sorting is not a gender- and race-blind mechanism. Third, it allows for the comparison of all the groups simultaneously after controlling for characteristics. Our analysis shows that, after controlling for characteristics other than gender and race/ethnicity, occupational segregation accounts for around 40–50% of the earnings losses of White, Hispanic, Native American, and “other race” women (relative to the economy’s average wage) and it reaches 63% in the case of Black women. Black men’s occupational sorting also harms them after controlling for attributes, a finding that we do not see in any other male group. On the contrary, around half of the earnings advantages of White and Asian men arises from their occupational sorting. Additionally, this paper develops a decomposition that allows identifying the occupations that bring losses/gains to each group beyond what is expected based on its characteristics, which is also a novelty in respect to what has been done in the literature.

The Contribution of Work Experience on Earnings Inequality of Migrant Workers: Decompositions Based on the Quantile Regression Equation

This paper aims at excavating the influence factors of the earning inequality, due to the increasing contribution of earing inequality to income inequality in rural region. The authors examine the contribution of work experience on the earning inequality using a survey data. Employed the quantile regression, they estimate the Mincer equation of migrant workers' earnings, and decompose earning inequality by the regression-based decomposition. It has been found that the effects of work experience had been one of most important contributors to earnings inequality, and its contribution is close to 20%. Furthermore, the authors use the same method to examine the effects for male migrant workers, the results show that work experience had a steady contribution to earing inequality.

Estimating the Value of Gender- and Race-Specific Job Injury Risk

AbstractThe compensating wage differential (CWD) for nonfatal injury and value of statistical life or injury in occupations have rarely been analysed separately by gender or race. This paper uses individual-level data from the 2012–2015 March Current Population Survey to estimate the CWD as well as the value of statistical injury (VOI) by race and gender. We find male workers command a positive risk premium, and this is higher when they are unionised. We also find a positive risk premium for White unionised workers and a slightly lower risk premium for White males. Like other investigators, we find that nonfatal risk is heterogenous, and its compensation is difficult to estimate using a standard wage equation, even with some smaller subsamples from our dataset that are gender- or race-specific. Our estimates of the VOI show us that male workers who are unionised have the highest VOI, followed by Hispanic union workers, and Black females. This last finding follows from Black females working in jobs that have the highest risk rates compared to White and Hispanic females.

Overeducation and scarring effects on the wages of young graduates

PurposeThe authors’ goal in this paper is to study if there are long-run effects on the wages of those workers who entered the labour market overeducated but who have, over time, been able to overcome this situation by obtaining a job for which they are correctly matched.Design/methodology/approachThis study universe is constituted by workers entering the labour market with a university degree between 2004 and 2012. The age range of these individuals is between 22 and 35 years old. With the data of the 8,359 selected individuals, the authors have constructed a balanced panel covering the period 2013–2017. This methodology is developed in two steps. First, the authors estimate a wage equation with the traditional variables and, second, the authors use the estimated coefficients of these variables to predict the wage paths of a representative individual in each group.FindingsThe main result the authors obtain indicates that the wages of those who manage to overcome an initial situation of overeducation do converge but very slowly to the respective wages of those others that entered the labour market correctly matched from the beginning. The authors consider this result to point towards the existence of scarring effects in wages induced by an initial situation of overeducation. The authors also present evidence, beyond education, about the influence that the occupational characteristics that a worker has on wages.Research limitations/implicationsThe factors that can influence the catching-up of wages are multiple, and it is not feasible to test all of them empirically. Therefore, the wage convergence process the authors present may also be influenced by other variables for which the authors do not have information.Practical implicationsThis paper contributes to different branches of the labour market. First, the authors present new evidence within the literature dealing with the so-called scarring effects on wages related to the conditions entering the labour market. Secondly, this study’s results provide a new argument that complements those developed so far that explain a reduction in the wage skill premium detected among young graduates in Spain. Finally, this paper contributes to advancing research about the effects that overeducation has on wages.Originality/valueThe question the authors are attempting to answer in this paper can be formulated in the following terms: when a worker manages to overcome an initial situation of overeducation, what happens to his/her wage? Will it adjust quickly to the new working situation, or will we observe a slow convergence to the wages of workers with an employment history without overeducation situations? To the best of the authors’ knowledge, this topic has not yet been studied. Researchers have mostly focused their attention on comparing the wages of overeducated workers with the wages of those who are correctly matched. In this case, the authors compare the wages of correctly matched workers, but with the difference that some were initially overeducated and others were not.

Has Internet Usage Really Narrowed the Gender Wage Gap?: Evidence from Chinese General Social Survey Data

Based on the data from a sample of 4832 from the Chinese General Social Survey in 2018, this study examines the impact of Internet usage on the wage equation for males and females by using the robust least squares regression method, the threshold regression method, and the quantile regression method and decomposes the gender wage gap on this basis by using the RIF regression decomposition method. The findings show that, firstly, in either linear or nonlinear effects, Internet usage has a significant wage premium effect on the male wage equation, and this premium effect increases as male wage income rises, but Internet usage consistently does not have a significant effect on the female wage equation. Secondly, 26.73% of the overall gender wage gap is due to Internet usage, and the presence of gender discrimination in the cyberspace is a significant contributor to this result. Thirdly, Internet usage causes the gender wage gaps at different wage levels to be further widened to different degrees. Specifically, Internet usage has the strongest effect on the gender wage gap at lower wage levels, the second strongest effect on the gender wage gap at higher wage levels, and the weakest effect on the gender wage gap at middle wage levels. The different levels of gender discrimination experienced by females in the cyberspace are an important cause of the differential widening effect of Internet usage on gender wage gaps at different wage levels. Therefore, this study proposes policy recommendations in terms of regulating gender discrimination and introducing relevant female protection policies.

STEM salaries in Russia: the importance of human capital and institutional factors

In the age of digital transformation, there is an increasing discussion about human resource shortages. In particular, IT professionals, and specialists in science and technology (STEM – Science, Technology, Engineering and Mathematics) in general. One of the decisive factors in attracting and retaining a highly skilled workforce is the level of compensation, so it is important to understand what determinants affect its level in Russia, which is the focus of this study. The influence of human capital (special experience and availability of higher education) and institutional factors (organization size, economic sector, organization profile) were considered. The data of the National Research University Higher School of Economics Russian Monitoring of Economic Situation and Health for 2010–2020 were used for the analysis. The paper presents a detailed description of the final sample (N = 4 674). The J. Mintzer wage equation is used as the main econometric model. The results show that the level of financial compensation increases when a STEM specialist has higher education, works in a large and commercial organization. Work experience has no effect on compensation. The profile of the organization (technical or not) also did not show an unambiguous result in this study.

Effect of COVID-19 Pandemic on Employment and Earning in Urban India during the First Three Months of Pandemic Period: An Analysis with Unit-Level Data of Periodic Labour Force Survey.

Urbanisation has accelerated the pace of development throughout the world. Big cities provide employment and livelihood for workers because of which workers have always migrated from rural areas to cities. However, in India, most of the migrant workers are absorbed in the low-paid and low-skilled jobs in the widespread informal sector. With the outbreak of COVID-19, lockdown was declared suddenly without any notice in India during the last week of March 2019 and most of the urban informal sector workers suddenly lost their jobs, and since they had no protection, they werepushed into poverty. Detailed analysis of such losses is of utmost importance so that perfectly appropriate remedial measures can be taken by the government. Periodic Labour Force Survey (PLFS)report of 2019-20 has analysed the situation of labour market in India for four quarters from July 2019 to June 2020. Therefore, the last quarter of the data will give us the valuable information about the urban labour market during the first three months of the COVID-19 pandemic period. This study analyses the possible reasons behind decline in monthly earnings and labour market participation of urban people in India during the period of outbreak of COVID-19 pandemic, i.e. during the period from April 2020 to June 2020, using the data of fourth quarter from each of the PLFSs of 2017-18, 2018-19 and 2019-20 since they have identical seasonal conditions. We have used cross-tabulation method to find out employment and unemployment rates of people inurban areas according to gender and type of employment for the period, from July to June, for the years 2018, 2019 and 2020. We have also tried to find the reasons behind the decline in income of workers during the first three months of the pandemic period, i.e. during the fourth quarter of 2019-20, compared to the fourth quarter of 2017-18 and that of 2018-19 using the Mincerian wage equation. Our empirical results have shown that urban workers in India have lost jobs and suffered from significant decline in income during the first three months of the COVID-19 pandemic period in almost all types of employment.

Gender wage and productivity gaps in the Ethiopian manufacturing sector

ABSTRACT The paper uses firm-level data to examine gender wage and productivity gaps in the Ethiopian manufacturing sector for the period 1996–2010. It investigates gender wage differentials between skilled and unskilled workers after controlling for factors affecting average wages. Our findings show significant gender wage and productivity gaps and the segregation of female workers into low-paying firms. Controlling for average productivity reduces the magnitude of the wage gap but does not eliminate it. However, results using a simultaneaous estimation of wage equation and production function at a firm-level indicate no significant difference between the gender wage and productivity gaps. As such, further investigation into the sources of the dual gaps using matched employer-employee data is necessary. The Ethiopian government should focus on skill development to improve the productivity of female workers and address the segregation of female workers into low-paying firms.

Bandwidth selection for statistical matching and prediction

While there exist many bandwidth selectors for estimation, bandwidth selection for statistical matching and prediction has hardly been studied so far. We introduce a computationally attractive selector for nonparametric out-of-sample prediction problems like data matching, impact evaluation, scenario simulations or imputing missings. Even though the method is bootstrap based, we can derive closed expressions for the criterion function which avoids the need of Monte Carlo approximations. We study both, asymptotic and finite sample performance. The derived consistency, convergence rate and extensive simulation studies show the successful operation of the selector. The method is illustrated by applying it to real data for studying the gender wage gap in Spain. Specifically, the salary of Spanish women is predicted nonparametrically by the wage equation estimated for men while conditioned on their own (i.e., women’s) characteristics. An important discrepancy between observed and predicted wages is found, exhibiting a serious gender wage gap.

Labor Skill, Trade and Capital Intensity, and the Fall of Manufacturing Real Wages: Panel Evidence from Mexican Industries

Abstract The paper studies the fall in manufacturing real wages that took place in Mexico after the China shock of 2001. It shows, first, that the wage reductions reflected a significant transmission of the China shock, plus a weak link between labor-productivity and real-wage growth; and second, that the weight of these two factors was conditioned by the skill level of workers and the trade and capital intensity of industries in Mexico. Thus, while the China shock hit harder medium and high-skilled workers in capital-intensive industries, the weak link between productivity and wage growth affected more the low-skilled in trade-intensive industries. Results come from the estimation of wage equations by skill level for an annual panel of 20 manufacturing industries at NAICS three-digit level, for different periods but focusing on 1996–2017 (the NAFTA period) and 2003–2017 (the post-China shock period).