دانلود مقاله ISI انگلیسی شماره 18562
عنوان فارسی مقاله

تجاری کردن ریسک بازده در سرمایه گذاری سرمایه انسانی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
18562 2007 16 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
The risk-return trade-off in human capital investment
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Labour Economics, Volume 14, Issue 6, December 2007, Pages 971–986

کلمات کلیدی
انتخاب آموزشی - مرز کارایی - سرمایه گذاری سرمایه انسانی - تجزیه و تحلیل واریانس
پیش نمایش مقاله
پیش نمایش مقاله تجاری کردن ریسک بازده در سرمایه گذاری سرمایه انسانی

چکیده انگلیسی

In this paper, we analyze investments in human capital in a way which is standard for financial assets, but not (yet) for human capital assets. We study mean-variance plots of human capital assets. We compare the properties of human capital returns using a performance measure and by using tests for mean-variance spanning. Fields differ strongly not only in common rates of return, but also in return per unit of risk. We identify a range of educations that are efficient in terms of investment goods, and a range of educations that may be chosen for consumption purposes.

مقدمه انگلیسی

Since the human capital revolution around 1970, economists have viewed education as both investment and consumption. The standard approach assumes that the individual invests an amount of time in education, and then the return shows up in terms of enhanced future earnings, cf. Becker (1993). Hence, two individuals with identical abilities and borrowing opportunities would choose identical levels of education. We extend this standard approach by allowing the individual to choose between different educations (of varying level and field) leading to future income streams with different properties in terms of means, variances, and covariances with the stochastic discount factor. We denote these different investments human capital assets. Hence, two individuals with identical abilities and borrowing opportunities might choose different educations if they differ in terms of utility over risk and return. Relatively little is known about the properties of human capital returns despite the vast amount of evidence showing the importance of human capital to the structure and evolution of earnings, employment and economic growth, cf. Becker (1993). On top of that, human capital has a dominant position in the aggregate wealth portfolio. Becker (1993) estimates the value of human capital to be three to four times the value of financial assets. While financial assets are concentrated in the portfolios of the few (cf. Haliassos and Bertaut, 1995 and Christiansen et al., 2005), human capital assets are held by all individuals, and even large changes in the market value of financial assets are unlikely to affect the human capital investment decisions of most individuals, cf. Becker (1993). In this paper, we focus on the human capital market separately from financial markets in order to compare the risk-return properties of various human capital assets. Only few papers investigate human capital investments in a fashion similar to ours. Palacios-Huerta (2006) finds that frictions in human capital markets help explain the risk-adjusted return on human capital. Palacios-Huerta (2003) uses mean-variance spanning tests to compare the properties of returns to various human capital assets. Thus, he compares the efficient frontier in the mean-variance space spanned by a subset of assets to that spanned by all assets to evaluate whether the return per unit of risk would be more favorable had the choice set been larger. The human capital market consists of a wide range of human capital assets. Each individual chooses the asset that matches the preferred combination of risk and return of future income. Our dataset comprises 104 different human capital assets that cover a matrix of fields (e.g. economics) and levels (e.g. MA). We use two different variance measures. The mean-variance plots provide valuable information as to which educations are efficient investment goods, and which educations seem to be chosen for other reasons or are related to relatively larger market frictions. To our knowledge, such mean-variance plots are new to the field of labor economics. We corroborate our findings by testing whether the mean-variance frontiers spanned by different sets of assets coincide. We find that fields differ strongly not only in common rates of return, but also in return per unit of risk. The remaining part of the paper is organized as follows. Section 2 presents our approach to the analysis of the risk-return trade-off. Section 3 presents the empirical analysis. Section 4 concludes the paper.

نتیجه گیری انگلیسی

Earlier studies identify risk-return trade-off and risk compensation by level of education. Using ideas from financial economics analyses of equity markets, we show that both levels and fields of education differ strongly not only in common rates of return, but also in return per unit of risk. We study mean-variance plots of human capital assets and compare the properties of human capital returns, using a performance measure and tests for mean-variance spanning. The result of the empirical analysis is a classification of educations into efficient and inefficient investments among which the inefficient investments are supposedly chosen for reasons other than investment purposes (e.g. consumption purposes). All the empirical strategies lead to qualitatively similar conclusions. This paper provides useful insights into the stochastic properties of human capital returns, and how they relate to level and field of education. The estimates of how much is gained (or lost) per unit of risk from investing in one human capital asset rather than another are essential to determine whether there are any relative differences in market frictions, non-pecuniary returns, and other circumstances across human capital assets. They also emphasize that more market frictions (than risk) have to be incorporated into the economic model in order to determine what is driving these relative differences.

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