The labor-market effects of an anti-poverty program: results from hierarchical linear modeling

Using Hierarchical Linear Modeling (HLM), this paper estimates the impact of random assignment into an anti-poverty program, New Hope, on earnings. The use of HLM allows for an examination of program impacts on levels, as well as trends, in quarterly data spanning more than two years of earnings. Results show that New Hope is associated with higher final levels of earnings. Additionally, results indicate that experimental group members experienced rapid earnings growth immediately after the quarter of random assignment, but that their earnings growth did not increase at a significant rate over the subsequent two-year period. Like many welfare recipients today, New Hope participants were required to work in order to receive the program's benefits. The results from this paper indicate that such a policy can lead to a rapid increase in participants' earnings, but that earnings may not continue to increase over time and may not increase enough to lift families out of poverty.