"Sources of Lifetime Inequality"

This NBER Working Paper by Mark Huggett, Gustavo Ventura, and Amir Yaron assesses the relative importance luck and initial conditions in explaining inequality, and asks which type of initial condition, human capital, learning ability, or financial wealth best explains later dispersion in individual earnings. The paper finds that 60% or more of the variation across individuals is due to initial conditions rather than shocks that hit agents during their lifetimes (i.e. good or bad luck), and that among the initial conditions, variation in human capital is the most important factor.

As noted in the conclusions, because the evaluation of initial conditions is conducted at age 20, "pushing back the age at which lifetime inequality is evaluated will raise the issue of the importance of one's family more directly than is pursued here. The importance of one's family and one's environment up to age 20 is not modeled in our work..." But however that turns out, an implication of this work is that we need to do all that we can to ensure that disadvantaged children, all children, are able to build up the human capital they will need to be competitive at age 20 and beyond:

Sources of Lifetime Inequality, by Mark Huggett, Gustavo Ventura, and Amir Yaron, NBER WP 13224, July 2007 [open link]:
1 Introduction To what degree is lifetime inequality due to differences across people established early in life as opposed to differences in luck experienced over the lifetime? Among initial conditions, individual differences established early in life, which ones are the most important? A convincing answer to these questions is of fundamental importance. First, and most simply, an answer serves to contrast the potential importance of the myriad policies directed at modifying or at providing insurance for initial conditions (e.g. public education) against those directed at shocks over the lifetime (e.g., unemployment insurance programs). Second, a discussion of lifetime inequality cannot go too far before discussing which type of initial condition is the most critical for determining how one fares in life. Third, a useful framework for answering these questions should also be central in the analysis of a wide range of policies considered in macroeconomics, public finance and labor economics.

We view lifetime inequality through the lens of a risky human capital model. Agents differ in terms of three initial conditions: initial human capital, learning ability and financial wealth. As agents age, they accumulate human capital by optimally dividing their available time between market work and human capital accumulation. Human capital and labor earnings are risky as human capital is subject to uninsured, idiosyncratic shocks each period. We ask the model to account for key features of the earnings distribution dynamics by cohorts. To this end, we document how mean earnings and measures of earnings dispersion and skewness evolve for U.S. males. We find that mean earnings are hump shaped and that earnings dispersion and skewness increase with age over most of the working lifetime.[1]

Our model produces a hump-shaped mean earnings profile by a standard human capital channel. Early in life earnings are low as agents allocate time to accumulating human capital. Earnings rise as human capital accumulates and as a greater fraction of time is devoted to market work. Earnings fall later in life because human capital depreciates and little time is put into producing new human capital.

Two forces within the model account for the increase in earnings dispersion. One force is that agents differ in learning ability. Agents with higher learning ability have steeper mean earnings profiles than low ability agents, other things equal.[2] The other force is that agents differ in idiosyncratic human capital shocks received over the lifetime.

To identify the contribution of each of these forces, we exploit the fact that the model implies that late in life little or no new human capital is produced. As a result, moments of the change in wage rates for these agents are almost entirely determined by shocks, rather than by shocks and the endogenous response of investment in human capital to shocks and initial conditions. We estimate the shock process from U.S. data...[3] We find that learning ability differences are important in that they produce much of the rise in earnings dispersion over the lifetime, given our estimates of the magnitude of human capital risk.

We use our estimates of shocks and initial conditions to quantify the importance of different proximate sources of lifetime inequality. We find that as of a real-life age of 20 differences in initial conditions are more important than are shocks received over the remaining lifetime as a source of variation in realized lifetime utility, lifetime earnings and lifetime wealth.[4] We find that between 62 to 73 percent of the variation in lifetime utility and between 60 to 71 percent of the variation in lifetime earnings is due to variation in initial conditions...

Among initial conditions, we find that, as of age 20, variation in initial human capital is substantially more important than variation in either learning ability or initial wealth for how an agent fares in life. This analysis is conducted for an agent with the median value of each initial condition. We find that a one standard deviation increase in initial wealth increases expected lifetime wealth by 3 to 4 percent. In contrast, a one standard deviation increase in learning ability or initial human capital increases expected lifetime wealth by 9 to 10 percent and 30 to 34 percent, respectively. ...

7 Conclusion ... Initial human capital and learning ability are positively correlated in the initial distribution which best matches the earnings distribution facts. This may suggest to some that the importance of learning ability differences relative to human capital differences would be greater if one were to evaluate lifetime inequality at a younger age. Some intuition for this position would be that learning ability is crystallized before age 20 and that learning ability differences are an important source of human capital differences as of age 20. We think that such a line of reasoning is valuable to pursue. However, pushing back the age at which lifetime inequality is evaluated will raise the issue of the importance of one's family more directly than is pursued here. The importance of one's family and one's environment up to age 20 is not modeled in our work but is implicitly captured through their impact on initial conditions: human capital, learning ability and initial wealth.

Our analysis of lifetime inequality is based upon a parsimonious model. Thus, it is easy to think of initial differences or shocks that are not captured by the model. For example, shocks to mortality, health and preferences or shocks leading to the formation and dissolution of households are not captured by the model. It is not obvious to us that adding more sources of shocks will necessarily imply a more important role for shocks. The reason is that initial differences as of a young age may play a role in future health and preference states as well as a role in who forms households with whom.

In our view the risky human capital framework we have analyzed is likely to be important for the analysis of a number of economic policies and for many other issues. It has the potential to replace the standard incomplete-markets model with exogenous earnings or exogenous wages for both positive and normative analysis. For example, on the policy side, the framework is ideal to study tax policy. Analyzing the replacement of progressive taxation by flat-rate taxes in this setting would be of special interest. This follows from the role of progressive taxes in distorting human capital decisions as well as in reducing labor market risk, and the corresponding unexplored implications for human capital accumulation. All these reasons suggest that future work should investigate this framework in more detail.