Monday, March 30, 2020

Michael Wiseman, longtime APPAM member, passes away

APPAM mourns the loss of distinguished member Prof. Michael Wiseman, a Research Professor of Public Policy, Public Administration, and Economics at the GW Institute of Public Policy.


Session Recap: Safety Net Policies and Family Income Dynamics

November 13, 2014 11:00 AM

By Chiho Song, University of Washington


Trends in Children’s Experiences of Household Income Volatility, 1984-2009, by Pamela Morris, New York University; Lisa Gennetian, National Bureau of Economic Research; Heather Hill, University of Chicago

Which State Safety Net Policies Promote Economic Stability? by Heather Hill, University of Washington; Callie Freitag, University of Chicago

The Effect of the District of Columbia Supplemental EITC on Poverty, Employment, and Income Growth, by Bradley Hardy, American University; Daniel Muhammad, DC Office of Revenue Analysis; Rhucha Samudra, American University Presenter

Discussant: James P. Ziliak, University of Kentucky

The panel discussed three papers that examined various “safety net” policies and how they impacted the dynamics of family income.

In Trends in Children’s Experiences of Household Income Volatility, 1984-2009, Morris and her co-authors reexamined the question of whether or not household income volatility has increased over the three decades. To characterize the types of income volatility, they set up five hypothetical scenarios: 1) stable low income, 2) permanent loss (e.g. divorce), 3) temporary loss (e.g. job less), 4) highly volatile, and 5) moderately volatile. Their research question asked “What are the time trends in intra-year income volatility over the 25 years?”

To resolve this question, they use the Survey of Income and Program Participation (SIPP) panel data that is nationally representative from 1984 to 2008. To measure the intra-year income volatility, the coefficient of variation (CV) is computed. Preliminary results show that the gap in income volatility between the poorest (90-100th percentiles) and the wealthiest (0-10th percentiles) households is increasing. While the gap in levels of income volatility between the poorest and the wealthiest families had been reducing during the period from the early 80s to the mid-90s, it had substantially increased in 2001 and this increasing trend has continued. This increased gap in income volatility indicates that poor households are more likely to have unstable income compared to the rich households. This finding, Ziliak reasoned, corresponds to the rising income inequality trend suggested by the growing body of literature. Some points to be considered for refined research is 1) CV is really a measure of inequality, not volatility and 2) this study should explain why some results are inconsistent with those in the previous research.

As Hill was absent due to illness, Ziliak presented Which State Safety Net Policies Promote Economic Stability? From the discussant’s viewpoint, the purpose of the study was to estimate of multiple policy changes at the state level on household income level, volatility, and trend. To achieve this aim, this study used SIPP data combined with multiple data resources (Urban Institute’s Welfare Rules Databooks, reports from the Kaiser Commission on Medicaid and the Uninsured, USDA Food and Nutritition Service State Options Report, and Department of Labor compilations of state UI rules) to capture state-level choices about five key safety net programs (TANF, SNAP, Medicaid/SCHIP, Unemployment Insurance (UI), and child care subsidies (CCD)).

Ziliak shared that the main outcome is the intra-year income variability measured by arc percent change (APC). He mentioned it would be better to use a much more parsimonious set of policy variables, recommending some work on welfare severity indices such as Ellwood, 2000; Grogger and Karoly, 2005; and Ziliak, 2007.

Hardy assessed the combined effect of the District of Columbia (D.C.) supplemental earned income tax credit (EITC) and the federal EITC on three main outcomes (i.e. poverty, employment, and income changes within Washington State during the period of 2001-2012. To this aim, the authors attempted to identify the marginal effect of the EITC based on restricted-access municipal tax data. OLS and MLE models of regressing dichotomized indicators of poverty, employment, and between-year income changes on related individual-level demographics and socioeconomic factors (e.g. racial/ethnic composition, average education and income with controlling for state-level variation (e.g. measures of the local economy and expenditure on poverty programs such as TANF and SNAP) are used for analysis.

The implications of this study show it will help policymakers in other states understand policy options to provide upward economic mobility for workers with stagnant wages. Further avenues of research include using full time series of changes in DC EITC policy, not just ARRA change; refining control group to either drop childless, or separate by number of qualifying children; and focusing more on anti-poverty and anti-volatility effects because a measure of unemployment (1 if he/she has zero income in tax record, otherwise 0) in this paper cannot measure employment (e.g. role of shadow economy).


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