«Labor Income Risk Luca Benzoni and Olena Chyruk November 2013 WP 2013-16 Human Capital and Long-Run Labor Income Risk∗ Luca Benzoni† and Olena ...»
On the other side, there is a growing ﬁnance literature that applies similar insights to the life-cycle portfolio choice problem of an agent who faces endogenous leisure/labor tradeoﬀ and retirement decisions, e.g., Bodie, Merton & Samuelson (1992), Bodie, Detemple, Otruba, & Walter (2004), Farhi, Emmanuel & Panageas (2007). A general conclusion is that the ability to vary labor supply ex post induces the agent to assume greater risks in his portfolio ex ante. This ﬂexibility beneﬁts especially the young, who can smooth consumption by buﬀering negative shocks in asset prices over a longer working life than the old. Eﬀectively this means that by adjusting his labor supply the agent can hedge some of the stock market risk implicit in his human capital position.
These two branches of the literature present common elements, but also some diﬀerences.
from the portfolio choice problem. On the other hand, ﬁnance studies of life-cycle portfolio choice with endogenous labor supply typically consider a narrower set of microeconomic shocks, and favor model calibrations over rigorous estimation of a structural model on individual level data. Blending these ideas together is an interesting area of future research. Moreover, most of this work focuses on the decisions of an individual agent, leaving open the question as to whether the results would change in a general equilibrium framework.
3.4 Health Shocks Individual health has a broad eﬀect on most life-time labor market outcomes including wages, earnings, labor force participation, hours worked, and retirement. As such, the literature views health as one of the human capital components (e.g., Becker 1964). While it is intuitive that a large negative health shock can lead to a decline in life-time earnings, numerous empirical studies have struggled to gauge the magnitude of these changes.
In their survey, Currie & Madrian (1999) discuss three main issues that are important to disentangle the eﬀect of health status on labor market outcomes. First, it is diﬃcult to measure health shocks. Thus, the estimates diﬀer based on measures of health being used (e.g., mental health, heart diseases, external accidents). Second, there is a vast crossdisciplinary literature that argues that individual socioeconomic status (e.g., education and wealth) determines the investment in one’s health, and thus, one’s health capital (e.g., Smith 1999). Third, since health and labor market outcomes are endogenous variables, estimates of the eﬀect of a health shock on wages, and vice versa, are sensitive to the identiﬁcation assumptions (e.g., Lee 1982, Haveman et al. 1994, Riphahn 1999, Au et al. 2005, Disney et al. 2006) In general, these studies focus on the adult population to determine the relationship between health and labor market activity. But there is also growing evidence that the early childhood environment signiﬁcantly inﬂuences later life outcomes. Almond & Currie (2011) provide an extensive summary of recent work. The main ﬁnding of this literature is that shocks before the age of ﬁve years lead to signiﬁcant long-run consequences. In particular, poor health in childhood aﬀects both adult health status and investments in other forms of human capital (such as education). And even a compromised prenatal environment can have long-term negative eﬀects on future health outcomes (e.g., Almond & Mazumder 2011, Barker & Osmond 1986, Kraemer 2000). Furthermore, there is evidence that poor health in childhood is associated with reduced educational attainment as well as lower wages and labor force participation (e.g., Grossman 1975, Perri 1984, Wolfe 1985, Wadsworth 1986, and Smith 2009).
The ﬁndings of the health economics literature have important implications for life-cycle consumption and investment decisions. For instance, Yogo (2007) builds on these ideas with a life-cycle model in which a retiree faces stochastic health depreciation, which aﬀects his marginal utility of consumption and his life expectancy. The retiree receives income (including Social Security) and chooses consumption, health expenditure, and the allocation of wealth between bonds, stocks, and housing to maximize lifetime utility. In this context, he examines the cross-sectional variation in health expenditure and wealth. The model predictions are consistent with stylized empirical facts, e.g., out-of-pocket health expenditure, as a share of income, falls in health and rises in age; ﬁnancial and housing wealth, as a share of total wealth, rises in both health and age; the portfolio share in stocks rises in health; and, ﬁnally, the portfolio share in housing falls in health for younger respondents and also falls in age. Key inputs to this analysis are the dynamics of health and health insurance coverage,8 which aﬀect the price of health care relative to non-health consumption. Related, Koijen et al. (2013) develop a pair of risk measures for the universe of life and health insurance products, which they use to assess whether the observed demand for insurance is close to the optimal demand, given the provision of public insurance through Social Security and Medicare. Finally, Cocco & Gomes (2012) examine the eﬀect of secular trends in longevity on optimal portfolio choice and retirement decisions.
3.5 Human Capital Investment The idea of human capital investment, in the form of education, training, and medical care, goes back to the seminal work of Becker (1964). Many studies have shown that people with more education have higher life-time income (e.g., Attanasio 1995, Hubbard et al. 1995, and Gourinchas & Parker 2002). While the returns of school and college education investment has been thoroughly studied (e.g., Lochner & Monge-Naranjo 2012), only recently the human capital literature has started to focus on the long-term eﬀects of early childhood education (e.g., Almond & Currie 2011 and Cunha et al. 2006). This research ﬁnds that early childhood intervention among children with disadvantaged background leads to higher test scores, decreased grade retention, decreased time in special education, decreased crime and delinquency, and increased high school graduation. This new wave of work stresses the need for a better understanding of the life cycle skill formation process. It diﬀerentiates between See, e.g., De Nardi et al. (2013).
early and late investments in human capital. Moreover, it recognizes the role of both cognitive and noncognitive (e.g., perseverance, self-control, reliability, consistency, motivation, and optimism) abilities in determining the returns to human capital (Cunha & Heckman 2008, Cunha & Heckman 2010, and Cunha et al. 2010). In this setup, the skill production technology exhibits dynamic complementarity (early investments increase the productivity of later investments) and self-productivity (the skills acquired in the early stage augment the skills acquired in later periods). These two features produce multiplier eﬀects as skills acquired today beget more skills in the future. According to this literature, eﬀective public policies should focus on young children’s human capital investments as they have the highest return compared to investments in later years. Moreover, such policies may potentially reduce lifetime inequality as diﬀerences in early life conditions have been found to explain a signiﬁcant portion of the variation in lifetime earnings and wealth (Huggett et al. 2006, 2011).
Yet, some low-income families do not make the same investment in early childhood programs as higher-income households do. One possible explanation is the presence of borrowing constraints. Indeed, an increase in family income at early childhood ages has a greater eﬀect on educational achievement than income received at later ages (e.g., Dahl & Lochner 2012).
This ﬁnding is consistent with the dynamic complementarities discussed above: Higher early investment leads to higher returns for later investments in education, while it is diﬃcult to amend inadequate levels of early investments with higher investments later in life (Keane & Wolpin 2001, Cameron & Heckman 1998). In an overlapping generation model of human capital production, Caucutt & Lochner (2012) show that relaxing credit constraints on young parents would increase both early investments in young children and late investments in older children. In contrast, a policy that focuses on subsidizing college eduction alone might not be as eﬀective in increasing human capital investment, as the subsidy would come too late for a credit-constrained household. The eﬀect on future generations, however, is more ambiguous. Increased borrowing causes higher debt levels that result in parents transferring less resources to their children in the long run. This in turn could limit the ability of future generations to sustain the same increased level of human capital investment.
This discussion underscores that the timing of the human capital investment over the life cycle is important. Moreover, human capital investment has signiﬁcant implications for the agent’s portfolio choice problem. Early childhood investment is critical, but it is highly illiquid and requires signiﬁcant time and ﬁnancial costs. Parents of young children often experience credit constraints. They anticipate higher income in the future (earnings are hump-shaped over the life-cycle), but they cannot borrow against it. A young college student could ﬁnd herself in a similar predicament. He trades oﬀ uncertain returns to human capital investment against upfront tuition costs and opportunity costs in terms of forgone labor earnings. To further complicate the problem, the opportunity costs vary over the business cycle, as they are often higher during an economic expansion. In either example, absent appropriate public policies the outcome could be underinvestment in human capital.
To attenuate this problem, the young agent might ﬁnd it optimal to reduce his risky asset holdings in favor of a liquid position in the risk-free asset (Palacios-Huerta 2003, Saks & Shore 2005, Roussanov 2010, Athreya et al. 2013). This shift in his asset shares helps to explain why young people hold little or no ﬁnancial wealth in stocks (Ameriks & Zeldes 2001, Campbell 2006).
4 Conclusions Quantitative analysis of human capital relies on the valuation of the ﬂows of earnings and wages that an individual generates by oﬀering his services on the labor market. Hence, a natural deﬁnition of a worker’s human capital is the present value of his future labor income stream. To make this measure operational, one needs a model for the agent’s labor income as well as an appropriate rate to discount uncertain future earnings.
Much of the ﬁnance literature has treated labor income as an exogenous process subject to aggregate and idiosyncratic shocks and discounted future earning at the intertemporal marginal rate of substitution of an agent who solves his life-cycle consumption and investment problem. While intuitive, this approach is very sensitive to the speciﬁcation of the labor income dynamics in the model.
Most previous studies of life-cycle portfolio choice assumed that the only source of correlation between aggregate labor income and the stock market are the contemporaneous shocks. This channel ﬁnds limited support in the data, where these correlations are small or even zero. Thus, the articles that evaluated human capital in this setting found it to be ‘bond like,’ i.e., the component of human capital implicitly tied up in the stock market is negligible. This result has counterfactual implications for portfolio choice, as the agent ﬁnds it optimal to hedge human capital risk with a signiﬁcant position in the stock market (uncorrelated shocks trigger a desire for diversiﬁcation). This eﬀect is especially strong for the young, whose wealth consists mainly of human capital. In practice, however, people invest little or nothing in the stock market when they are young, and the risky asset share is humped shaped over the life cycle.
A more general approach accommodates long run correlations between aggregate labor income and the stock market. This assumption ﬁnds support in the data (e.g., Benzoni et al.
2007) and is economically intuitive. For instance, a model with a Cobb-Douglas production function predicts that returns to physical and human capital are perfectly correlated even in the short run (e.g., Baxter & Jermann 1997). This more general model has very diﬀerent implications. In the presence of co-integration between the stock and labor markets, a big fraction of the agent’s human capital is implicitly tied up in the stock market, especially for the young. Consistent with empirical evidence, this property generates hump-shaped risky asset holdings.
In this article, we provide an interpretive review of these developments in the context of the life-cycle portfolio choice literature. We then extend the discussion to recent work that applies similar ideas to assess the value and risk of pension fund obligations, their funding, and the allocation of pension assets across diﬀerent investment classes. Moreover, we suggest how to enrich the environment to incorporate various important ingredients. We touch upon heterogeneity in preferences; diﬀerences in the exposure to stock market risk across agents; endogenous labor supply and retirement decisions; health shocks; and human capital investment. Along the way, we expand the review with ideas for future work.
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