Modeling Individual Earnings in CBO’s Long-Term Microsimulation Model: Working Paper 2013-04
By Jonathan A. Schwabish and Julie H. Topoleski
This paper describes the methods developed to project individual earnings in the Congressional Budget Office Long-Term (CBOLT) microsimulation model. CBOLT is used to assess the fiscal situations of the Social Security system and the federal government as a whole. Unlike many other models that project Social Security’s finances, CBOLT projects behavior at the individual level. For each individual in the model, CBOLT projects levels of educational attainment, transitions in and out of marriage, labor force participation and employment transitions, immigration and emigration, and claiming patterns for Social Security benefits. An important feature of CBOLT is that it models each worker’s annual earnings over that worker’s lifetime. Those lifetime earnings patterns are the key determinants of individual payroll taxes paid and Social Security benefits received, and thus of aggregate Social Security finances.
In CBO’s modeling, the historical pattern of rising earnings inequality continues for the next two decades, but earnings inequality generally ceases to rise by the mid-2030s. The method for projecting individuals’ earnings that was developed for CBOLT and described in this paper closely follows the method first documented by Carroll (1992), but it is also informed by the work of other researchers. In general, individual earnings are perturbed by a pair of estimated earnings “shocks.” The first shock is permanent and measures the long-run gap between a worker’s earnings and the average earnings of that worker’s group (where the group may be defined by age, sex, and education). Permanent shocks could be caused by, for example, receiving a promotion or attaining a higher level of education. The second shock is transitory and measures any additional but temporary variation in a person’s earnings. Transitory shocks could arise from, for example, receiving a bonus or missing work because of illness.