Our samples, which consist of individuals in employment aged between 16 and 65, drawn from the BHPS, the GSOEP and the PSID comprise 3,486, 5,548 and 1,123 heads of households respectively. We exclude the self-employed, agricultural workers and individuals with more than one job. For each country we explore how investments held in financial assets affect estimated returns to human capital by comparing the returns to education in a standard mincerian wage equation with the returns to education allowing for interactions between human capital investment and risky financial investments. To be specific, for each country we initially estimate the following semi log mincerian wage equation:
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From policy perspective, our results are interesting for the Ministry of educa- tion that is responsible for the educational policy of higher education in Greece and also for the HOU administration authorities. This is so, because our findings suggest that a distance learning University may not only be considered as a sec- ond chance to education for mature students often facing time and budget re- strictions, but, it may also be seen as a worthwhile investment enabling much higher private returns compared to the alternative route, i.e. following studies in traditional universities. Therefore, distance learning universities may be used by policy authorities as a vehicle to reduce income inequalities and increasing social mobility.
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Abstract: This paper estimates private and social returns to investment in education in Turkey, using the 2017 Household Labor Force Survey and alternative methodologies. The analysis uses the 1997 education reform of increasing compulsory education by three years as an instrument. This results in a private rate of return on the order of 16 percent for higher education and a social return of 10 percent. Using the number of children younger than age 15 in the household as an exclusion restriction, the analysis finds that returns to education for females are higher than those for males. Contrary to many findings in other countries, private returns to those working in the public sector are higher than those in the private sector, and private returns to those who followed the vocational track in secondary education are higher than those in the general academic track. The paper discusses the policy implications of the findings.
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Baumol (1990) argues that the effect of knowledge on output might depend on some specific characteristics of a country, such as political and economic stability. This motivates us to add these country specific variables to our econometric model. In Kremer’s (1993) model for productivity growth, defined as output per worker, the augmented production function with knowledge and land added is divided through by labor. The results show that knowledge always affects productivity growth positively even with diminishing returns to knowledge itself. Barro et al. (1995) examines an open economy and find that education increases output per worker only if a nation can finance its education with its own savings instead of borrowing from foreigners.
FNU completed her M.A in Education and Social Policy at New York University, and received her B.A in International Politics from Peking University in 2010. She believes education has a lifelong effect that extends beyond academic achievement. She is a dedicated researcher and advocate for the provision of quality education for all. As founder of Ed Analyzer, FNU is working together with her talented team members to build a Chinese education database as well as provide valuable insight and advise to policy makers in the field of education.
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thesis identifies a number of cognitive errors that have not been previously identified, so if appropriate education improves decision-making in general then we might have more hope that identification of these errors may help reduce their prevalence in future, to the extent that understanding of these errors percolates through to investors. Surveys have shown many borrowers to be unaware of the interests rates that are charged on their mortgages and credit cards (Lusardi 2011, Disney and Gathergood, 2012). There is, of course, likely to be a substantial amount of endogeneity in the relationship between financial literacy and other relevant factors such as IQ. Van Rooij et al (2011) seek to avoid this problem by controlling for the (exogenous) financial experiences of parents and siblings. Lusardi and Mitchell (2009) instrument using the different degrees to which financial education has been mandated in different US states. Both studies find a separate effect of financial education in increasing equity market participation. The panel-based model of Alessie et al (2011) confirms that financial literacy has a positive effect on retirement planning.
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This section is concerned with how innovation has both reduced and changed the nature of the burden of disease in most countries and the challenges that remain in understanding and treating disease. Fundamental to the progress that has been achieved is the development of new knowledge about the causes and mechanisms of disease and the resulting technological innovations that have occurred which use this knowledge. The principal technological innovations have been the discovery and development of new medicines, the improvements in medical and surgical practices, and better diagnostic and treatment technologies. At the system level, innovations have been made in ensuring safe water supply and sewage disposal, mass immunisation against infectious diseases, and education programs aimed at changing unhealthy behaviour such as smoking, consumption of high levels of fat, and unsafe driving. Investment in physical infrastructure such as hospitals and clinics has been important in being able to deliver better health care as has the investment in developing and maintaining skills among health care personnel.
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The average rate of return to an undergraduate degree stands at 15.6% for men and 14.8% for women. Clearly, there is a strong relationship between the gross graduate premium and the rate of return associated with this investment in higher education. However, given that one of the primary costs associated with obtaining a qualification relates to the foregone earnings while engaged in qualification attainment, for degree subjects associated with longer than average course durations, the rate of return can fall considerably even if there are very high net lifetime benefits to be had. Conversely, degree subjects associated with modest earnings and employment outcomes that are also of relatively short duration can be associated with higher than average rates of return. The highest rate of return for any degree level subject for men is associated with
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Most studies of the economic contribution of education do not explore the effects of different types of education on economic growth. Investment in liberal arts studies is regarded as valuable as investment in technical, vocational or professional studies. However, some economists have argued that what matters is getting talented individuals into productive occupations. Young people should be encouraged to devote their energies to problem solving, raising productivity, and economic innovation rather than the variety of rent-seeking activities (military conquest, political scheming, religious argumentation, tax farming, legal ingenuity, etc) that have attracted talented individuals throughout most of human history. Murphy, Shleifer and Vishny (1991) (MSV) suggest that the extent to which a country’s educational system achieves this could be measured by looking at the proportion of university students in legal studies (preparing for careers as rent-seekers!) relative to the proportion in engineering (preparing for productive careers!). MSV find some empirical evidence to support the view that the structure of education, measured in this fashion, contributes to our understanding of economic growth. They report that countries with high proportions of lawyers among their student populations have lower rates of economic growth, ceteris paribus.
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The literature provides two major theories to explain differences in labor earnings; human capital theory (Becker, 1962) and the signaling theory (Spence, 1973). Human capital theory explains wage differentials as a result of an individual’s productivity level enhanced by investment in formal education, health, and training, while the signaling theory assumes wage differentials are due to an individual’s innate abilities that are signaled by an individual’s characteristics which includes educational attainment (Kavuma et al., 2015).
In terms of investment priorities in education in the future decade, it is clear that under-achievers and early school leavers must be high on the government’s list. It is sobering to realise that a child who leaves the education system after primary school has had only £11,400 spent on him/her by the State. This compares with £15,850 spent on the pupil who leaves after two years of secondary school and is in sharp contrast to the £37,525 spent by the State on a student who completes a four year programme at third level 12 . Since these figures relate to 1995 and since university fees have been abolished since then the current figure for the latter category is likely to be a good deal higher. In terms of minimum equity, it is reasonable to ask that priority be given to investment in the former group of young people. Such investment also makes sense from a social point of view as stated in the Report of the National Education Convention 13 .
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The literature on subjective beliefs about child development also explores biases in those beliefs. For example, Nguyen (2008) and Jensen (2010) document downward biased beliefs (on average) about the returns to education in Madagascar and the Dominican Republic, respec- tively, further showing that these beliefs respond to newly provided information. Both studies show that actual schooling choices can be influenced by the provision of new information; however, Jensen (2010) estimates no behavioral responses among the most poor who may be financially constrained. 30 In this chapter, we are more interested in the implications of biased beliefs about the productivity/value of earlier investments in children. Cunha, Elo, and Culhane (2013) and Cunha (2014) show that a sample of mothers from Philadelphia under-estimates, on average, the value of time investments for cognitive development in young children. Cunha (2014) further demonstrates that black mothers are more pessimistic about the productivity of these investments than white mothers, arguing that this difference may explain one-fourth of black-white early investment gaps. Dizon-Ross (2015) shows that parents in Malawi hold distorted beliefs about their child’s school achievement levels with greater biases held by the least-educated parents. Furthermore, providing accurate information in a simple format for parents to understand leads to a re-allocation in the types of investments they make in their children (e.g. purchasing remedial vs. advanced workbooks) with greater responses observed among less-educated parents. Interestingly, the differential responses by parental education do not lead to corresponding differences in schooling outcomes (e.g. educational expenditures, school attendance).
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At the outset, a word about the differences in the two contexts: For private “for profit” investors putting money into educating India’s poorest children, the timeframes and the risks involved are huge, with a gap of up to fifteen years or so before the paybacks begin. Investors in America looking at paying for the college education of talented but deprived students do not run this kind of risk because they would step in only after the potential beneficiaries have revealed evidence of their talent and capabilities. Moreover, the duration of a typical college degree programme—effectively, the period of wait before the returns come through—runs to only about four years. Consequently, there seems no real reason to go anywhere near to the extent of signing away their entire future income tax or even extending the payments to their lifetime contributions.
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2.1. Investment Risks in Human Capital Formation Risk related to human capital investment is associated with uncertainty on returns on education in labour income. Becker and Chiswick [3 p. 55] described returns on education in additive form with each component as a respected risk premium to different kinds of investments. Arrow and Lind , Hamilton , Judd , Laevhari and Weiss , and Williams  implement investment risk to compare uncertainty of private and public (social) investment and evaluate public investment decisions. Idiosyncratic and systematic risks are in consideration. There are not many papers extending classic portfolio approach to study investment risk in human capital. This can be explained by basic difference between human and physical capital, namely, non-diversifiable idiosyncratic risk of human capital [25 p. 950]. In contrast to assets or physical capital, human capital “cannot be bought or sold and cannot be separated from its owner” [ibid, p. 950] and investment risk in human capital is not proportional to the size of investment [15 p. 4]. These are strong constraints to implement portfolio analysis. Idiosyncratic risk is related to individual characteristics of a worker and associated with moral hazard caused by private character of employment contract [ibid p. 3]. In particular, Williams [36 p. 65] points on “ambiguous inputs in the production of skills, a risky rate of depreciation or obsolescence of existing skills, and a stochastic future wage”. Krebs [24 p. 715] describes two kinds of risks negatively influencing human capital investment. The first risk relates to human capital depreciation when job is terminated. Another risk is related to health deterioration. Systematic risk, as a systematic difference in returns on investment, can be observed in comparing returns in different economies or periods. This is employed in the paper to compare investment risks in the northern and no-northern regions.
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the concurrence (competitive) theory in the work (of Thurow, 1975) mainly to contrast (opposition) to the basic premise of the theory of capital human which says that the education increases the productivity of the individual and considers that the productivity is not the characteristic of the individual, but the characteristic work (the technology), and education (teaching) reveals only the capacities of learner to adapt and learn ,he considers that workers do not differ from each other in the productivity, but by the costs of they afford in learning , training, experience an experience, therefore this theory consider that the employer is ready to form (train) the workers to occupy the suitable jobs (the internal market),and also ready to give higher salaries to the holders of skills to keep them for (a specialized training), but if he is oblige to the external market for work, the educational level is considered as the main fixer to differentiate between the candidates, whereas the employer chose the candidate who realized the conditions of the work’s post with the minimum of educational level required, and when the educational level does not make a difference the concurring candidate for the post the employer then chose one of them.
Investors who engage in investment activities in the company's common stock expect to obtain dividend yield in the future. This raises the question, whether the high factor or low net income earned by the company can affect the dividend yield for investors? This study predicts that net income affects dividend yield. This prediction refers to the results of several previous studies [9; 28; 33; 37] which have proved that net income has a significant effect on dividend yields proxied by dividend payout ratio. However, it does not refer to the results of a study by Noviyanto  which proves that net income has no effect on dividend yield.
In recent years, investors are increasingly concerned about whether the credit risk will affect the return on investment. This paper discusses the credit rating and momentum investment strategy relationship. The research period is from January 2005 to December 2010, and the sample is the ordinary shares of companies listed on Taiwan Stock Exchange (TSE). By calculating the cumulative returns of the investment portfolio of the holding period, and grouping the research samples by credit rating, this paper tests the relationship between credit rating and momentum investment strategy in Taiwan’s stock market. Second, in the exploration of the factors affecting credit rating and stock returns, this paper uses variables including firm size, financial leverage, turnover rate, company age and industry to analyze the impact of factors including information asymmetry and industry on the investment strategy. Moreover, this paper probes into the impact of January Effect and business cycle on credit rating. The empirical results reveal that Taiwan’s stock market does not have the momentum effect, although there is reverse investment strategy. In other words, the returns of stocks of investment portfolio of better credit rating are higher than those of poorer credit rating; and the results are reverse if the reverse investment strategy is applied. The empirical results are not affected by adding variables such as firm size, financial leverage, turnover rate, company age and industry. Hence, momentum investment strategy of Taiwan’s stock market is not affected by credit rating.
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Actuarial valuations for funding purposes, that is, with the objective of recommending a contribution rate are considered in this paper. A deterministic valuation basis is typically employed. Factors of a demographic nature about which assumptions are made include the mortality of plan participants at various ages, as well as their disability and withdrawal rates from the plan. Assumptions about economic factors such as price and wage inflation are also required when pensions are a function of final or career-average salary and when they are indexed with price inflation. An assumption about investment returns on the pension plan assets is also made.
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The fourth and fifth columns incorporate additional terms to control for unobserved heterogeneity. First only family background interacted with region dummies are introduced. The family background variables are individually and jointly significant at conventional levels, and the average return is a little above two percent. Controlling for unobserved individual ability this way, private returns decrease to 5.2% and social returns are 3.3% while the latter is now marginally significant. The last column in the table reports results of the regression that includes regional population density, unemployment and two demand shift variables one for highschool and above, the other for lower than highschool graduates as defined above. None of these regional variables turn out to be significant even though they have expected sign, that is, the density variable has a positive coefficients and unemployment has a negative coefficient. Most probably these variables change very little in the period examined here.
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Between the trough of 1.4% in 1993 and peak of 9.4% in 2004, there is a continuous rising trend of education returns for one additional year, which is also noted in the most recent literature (for example, Liu et al. (2010)). After 1997, the four groups experience different, but still increasing paths of education returns. Groups born before 1950 (8% for one additional year) and 1962-1980 (10% for one additional year) have a peak in 2004 as for the employee sample, while the other two groups experience peaks in 2006 (both around 10% for one additional year). The decline of education returns after 2004 is caused mainly by the structural break of education returns of the group born before 1950. Their dramatic fall of education returns (from the peak value to insignificance) may reflect the human capital loss of compulsory retirement (especially for women), and the rapidly depreciated human capital from education by the new skill- biased technology (Liu et al. 2010). This loss can only be partly offset by the rising employment proportions of the younger groups with still significant and high education returns.
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