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The purpose of this paper is to investigate self-competence—the ability to act responsibly on one's own—and likely nonlinear wage returns across different levels of self-competence as part of training curricula.

The authors identify the teaching of self-competence at the occupational level by applying machine-learning methods to the texts of occupational training curricula. Defining three levels of self-competence (high, medium, and low) and using individual labor market data, the authors examine nonlinearities in wage returns to different levels of self-competence.

The authors find nonlinear returns to teaching self-competence: a medium level of self-competence taught in an occupation has the largest wage returns compared to low or high levels. However, in occupations with a high cognitive requirement profile, a high level of self-competence generates positive wage returns.

This paper first adds to research on the importance of teaching noncognitive skills for economic outcomes, which recently—in addition to personality traits research—has primarily focused on social skills by introducing self-competence as another largely unexplored but important noncognitive skill. Second, the paper studies not only average but also nonlinear wage returns, showing that the right level of self-competence is crucial, i.e. neither teaching too little nor too much self-competence provides favorable returns because of trade-offs with other skills (e.g. technical or professional skills). Third, the paper also examines complementarities between cognitive skills and noncognitive skills, again pointing toward nonlinear returns, i.e. only in occupations with a high cognitive requirement profile, high levels of self-competence generate positive wage returns.

Whether immigrants are positively or negatively self-selected is much disputed. Whereas most previous studies have addressed this question by comparing the earnings of immigrants to those of U.S. natives, this analysis uses occupation to examine the skill level of immigrants relative to their home country population. Data on the occupational distribution of individuals granted legal permanent residence in 1995 indicate that the proportion of immigrants in skilled occupations is related to the corresponding proportion in source countries but not necessarily to the return to skill and other economic factors in the country of origin.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

The purpose of this paper is to estimate the incidence of educational mismatch in Italy and the return to investment in education, controlling for employees’ ability. Contrary to most existing studies, the heterogeneity of individual performance is measured directly through the assessment of required and provided skills.

Based on original data including over 3,600 face‐to‐face interviews, this paper appraises the incidence of self‐assessed educational mismatch in the Italian private sector and estimates wage models of the economic returns to educational mismatch, skill requirements and provided skills.

In Italy, under‐educated employees outnumber over‐educated ones and returns to required education and over‐education are lower than in other industrialised countries. Individual heterogeneous ability, as captured by individual skills, is a significant determinant of wage, although the inclusion of direct measures of required and provided skills does not substantially affect the estimated coefficients of the return to investment in education.

The omission of controls for the heterogeneous ability of employees biases the results of traditional ordinary least squares (OLS) estimates of wage models. However, the bias may be small enough to make simple OLS estimates on existing cross‐sectional data an acceptable compromise to provide policy makers with reasonably accurate and up‐to‐date information.

The paper provides a direct appreciation of individual heterogeneity that other studies can capture only through sophisticated indirect econometric techniques. In addition, the paper extends the set of available cross‐country comparisons by estimating the educational mismatch and the returns to educational and skill mismatches in the overall Italian private labour market.

A leading explanation for the growth of wage inequality is that greater use of information technology increased the demand for human capital. This paper identifies four different explanations for the relationships between computers, skills, and wages: computer-specific human capital, greater general human capital among computer users, greater general human capital for both users and nonusers due to contextual effects, and skill-biased changes in the job composition of the workforce. The paper tests the first three explanations and finds little support for them once pre-computer and other job characteristics are adequately controlled. This conclusion receives further support from a comparison of the timing of inequality growth and computer diffusion and from analyses of the contribution of computer use to overall inequality growth using DiNardo, Fortin, and Lemieux's (1996) reweighting standardization technique.

I use data from employers and longitudinal data from former/current recipients covering the period 1997 to early 2004 to analyze the relationship between job skills, job changes, and the evolution of wages. I analyze the effects of job skill requirements on starting wages, on-the-job training opportunities, wage growth prospects, and job turnover. The results show that jobs of different skill requirements differ in their prospects for earnings growth, independent of the workers who fill these jobs. Furthermore, these differences in wage growth opportunities across jobs are important determinants of workers’ quit propensities (explicitly controlling for unobserved worker heterogeneity). The determinants and consequences of job dynamics are investigated. The results using a multiplicity of methods, including the estimation of a multinomial endogenous switching model of wage growth, show that job changes, continuity of work involvement, and the use of cognitive skills are all critical components of the content of work experience that leads to upward mobility. The results underscore the sensitivity of recipients’ job transition patterns to changes in labor market demand conditions.

We use OECD-PIAAC data to estimate the earnings effects of both years of education and of numerical skills. Our identification strategy exploits differential exposure to educational reforms across birth cohorts and countries. We find that education has the strongest earnings effect. A one standard deviation increase in years of education raises earnings by almost 22 percentage points (corresponding to a return to education above 7 percentage points), which compares with a lower percentage points return to an equivalent increase in numerical skills. Our results suggest that the same set of unobservables drives the accumulation of both formal years of education and numeracy skills. OLS estimates underestimate returns to human capital, consistently with the idea that educational reforms favor the human capital acquisition of abler children from disadvantaged parental backgrounds. When we consider numerical skills alone education reforms cannot identify any significant effect of skills on wages, however, when we jointly consider schooling and skills as endogenous factors in a recursive structure we find a significant role for skills in determining wages.

This paper seeks to address two research questions: first, to what extent do highly skilled migrants intend to make personal business and financial investments in their home countries, and second, what factors influence them to invest in their home countries?

The results are based on face‐to‐face and telephone interviews which took place between September, 2008 and March, 2009 with 64 highly skilled British migrants working in Vancouver, Canada. Respondents were asked a combination of open‐ and closed‐ended questions.

The results of this study find that the vast majority of respondents are not investing in or intending to return to their home country, which indicates that they contributing to brain circulation in a limited extent.

The paper argues that governments and organisations in the home country can play an important role in facilitating brain circulation in Europe.

Much of the academic literature suggests that the brain drain has now transformed into brain gain. The findings of this study do not support this shift because most of the sample of British expatriates in Vancouver are not intending to invest in or return to Europe. This is significant because highly skilled migrants could be better utilised as resources by European governments and organisations.

Employers regularly complain of a shortage of qualified scientists and advocate that to remain competitive more scientists need to be trained. However, using a survey of graduates from British universities, I report that 3 years after graduation less than 50% of graduates from science subjects are working in a scientific occupation.

Accounting for selection into major and occupation type, I estimate the wages of graduates and report that the wage premium of science graduates only occurs when these graduates are matched to a scientific occupation – and not because science skills are in demand in all occupations. I also provide additional evidence to assess whether science graduates are pushed or pulled into non-scientific occupations. Altogether, the evidence does not support the claim that science graduates are pulled by better conditions, financial or otherwise, into non-scientific jobs.

The paper seeks to analyse the self‐selection of international migrants on observable skills.

Based on an extended version of the Roy model, which considers random migration costs, the authors analyse the self‐selection of migrants on observable skills empirically. For this purpose, the authors employ a new panel data set on the educational attainment of migrants, which covers migration from 143 sending countries into the six main receiving countries in the OECD from 1975 to 2000.

Migrants tend to be positively self‐selected on observable skills, although the inequality in earnings is larger in the sending country relative to the destination countries. The estimation results indicate that a higher inequality in the distribution of earnings in both the receiving and the sending country affects the skill bias of the migrant population favourably. Moreover, higher migration costs and selective immigration policies increase the skill level of migrants relative to those of stayers in the sending countries.

The results may be affected by measurement error, since it was necessary to approximate the returns to education by measures for the inequality of earnings.

The paper provides, inter alia, insights as to how immigration and other policies affect the self‐selection of migrants on observable skills, which may be relevant for policy makers.

To the authors' knowledge, this is the first paper to analyse the self‐selection of migrants on the basis of a panel data set.