Career Ladder Identifier and Financial Forecaster (CLIFF)
The complexity of public assistance programs means that many workers may struggle to understand the timing and magnitude of benefits loss. Coupled with economic insecurity, this uncertainty can prevent individuals from actively seeking opportunities for career advancement. Further, individuals who do advance without knowledge of when assistance will end can find themselves in situations where their standard of living doesn't improve, or even declines.
Our team at the Federal Reserve Bank of Atlanta developed a suit of interactive online tools designed to provide information about benefits loss along a career path. To learn more visit the Atlanta Fed website.
The CLIFF Planner is a career path planner and budgeting tool that shows how public assistance losses intersect with local in-demand career paths. The planner allows the user to compare career choices and provide individualized results in more detail than with the CLIFF Dashboard. It also allows the user to create a budget that will mitigate financial barriers to career advancement.
The CLIFF Dashboard is an informational dashboard that shows how public assistance losses intersect with local in-demand career paths. The dashboard shows the financial tradeoffs associated with career advancement and the net gains to the taxpayer when workers advance. It also simulates policy and programmatic solutions.
Estimating Occupation- and Location-Specific Wages over the Life Cycle
With Ellie Terry
In this paper we develop a novel method to project location specific life cycle wages for all occupations listed in the Bureau of Labor Statistics Occupational Outlook Handbook. Our method builds on the commonly used Mincer equation and improves it by providing a more nuanced relationship between years of experience and wages while also incorporating occupation and location specific factors. Our method consists of two steps. In the first step, we use individual level data from the Current Population Survey (CPS) to estimate the average number of years of experience associated with each percentile of the wage distribution. In the second step, we map this estimated average years of experience to the wage level percentiles reported in the Occupational Employment and Wage Statistics (OEWS) data for each occupation and area. Finally, we develop a model capable of projecting the trajectory of wages across all possible years of experience for each occupation in the OEWS data.