Can the Economy Explain the Explosion in the Supplemental Nutrition Assistance Program Caseload? An Assessment of the Local‐level Approach

Abstract

The Supplemental Nutrition Assistance Program (SNAP) has grown rapidly in recent years—by about 50% in the seven years between 2000 and 2007, and by another 70% in the four years between 2007 and 2011—such that in 2011, SNAP served 14% of the U.S. population. Contributing to our understanding of the causes of this very rapid increase in the caseload, this article extends the time period of analysis through and past the official end of the Great Recession, analyzes more geographically disaggregated caseloads and the impact of substate economic conditions, and considers the impact of recent major, state‐level SNAP policy changes. In models that exploit substate‐level data, we find consistent evidence of significant impacts of both the substate level and statewide economy on local area SNAP caseloads. Surprisingly, while one might have expected more geographically disaggregated data to improve the alignment of the measurement with the concept of interest (i.e., the labor market opportunities of an individual), and therefore lead to larger estimates of the impact of the economy, in fact estimates fall—perhaps due to measurement error. We find at best mixed evidence of policy impacts. Simulations indicate that the economy can account for most of the 2007 to 2011 increase in the caseload, although relatively less of the 2000 to 2007 increase. Nonetheless, the role of the economy in driving caseloads appears to be substantial in both periods.

SNAPenrollment cycles: New insights from heterogeneous panel models withcross‐sectionaldependence

“…Turning to Column (3), we find that including state-specific linear trends in the model reduces the magnitude of the estimated coefficient of the unemployment rate from 4.3% to 3.1%. This finding is consistent with Klerman and Danielson (2016), who also find including state-specific linear trends in the model shrinks the size of their estimate of the unemployment rate coefficient from 4.9% to 3.3%. The estimated effect of the labor force nonparticipation rate is no longer statistically different from zero, 28 and the estimated impact of the policy index is reduced by about one-third from 32.7% to 21.3%.…”

“…Last, information about the demographic composition of each state's population is based on data drawn from the March Annual Social and Economic Supplement (ASEC) of the current population survey (CPS) (Flood et al, 2021). Specifically, we use the CPS-ASEC data to create variables for the share of a state's population under age 15, between 15 and 64, and older than 64 (see, e.g., Klerman & Danielson, 2016).…”

The Downs and Ups of the SNAP Caseload: What Matters?

“…Other research considers the effect of specific post‐PRWORA, state‐level policies (Currie and Grogger 2001; Kabbani and Wilde 2003; McKernan and Ratcliffe 2003; Mikelson and Lerman 2004) on Food Stamp Program policies. More recent work focuses on the factors behind the increase in the Food Stamp Program caseload since 2001 using aggregate caseload data (Bitler and Hoynes 2010, 2013, 2016; Klerman and Danielson 2011, 2016; Ganong and Liebman 2018), program administrative microdata (Ribar, Edelhoch, and Liu 2008, 2010), or household survey data (Ratcliffe, McKernan, and Finegold 2008; Mabli et al 2011, 2014; Ganong and Liebman 2018; Ziliak 2015).…”

Dynamics of macroeconomic factor effects on food assistance program participation in the United States

“…In investigating factors behind changes in SNAP participation, previous studies have relied upon aggregate caseload data [ 9 – 11 , 27 – 29 ], program administrative microdata [ 30 , 31 ], or household survey data [ 11 , 12 , 32 – 34 ]. The extant literature places emphasis primarily on the level of real income, the volatility of income, and the unemployment rate as the key macroeconomic variables in affecting participation in domestic nutrition assistance programs.…”