In this paper we investigate the relationship between income shocks and food insufficiency for U.S. households. Using Survey of Income and Program Participation data on U.S. households, we test the importance of both stable and transitory income components in determining food insufficiency. In a logistic regression model, we find that both the level of income and negative income shocks affect the predicted probability of food insufficiency, while positive income shocks do not. Although we do not have a definitive measure of a household’s liquidity constraint status, our work suggests that negative shocks may matter more for households that face liquidity constraints. Understanding the role of income shocks in determining food insufficiency is especially important in light of recent policy changes. It is likely that welfare reform in the U.S. increased the volatility of income in the low-income population. Our findings here suggest that this increase in volatility may not be without consequence.
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The Food Stamp program was renamed the Supplemental Nutrition Assistance Program (SNAP) in October 2008.
Here the stable portion of income is fixed. However, this framework can be generalized to allow for a stable trend component which reflects the age-earnings profile of household members or rising real earnings; we empirically test such a model in our sensitivity testing below.
The 36 months of interviews are actually conducted over 39 calendar months because each SIPP panel is divided into four rotation groups; one-quarter of the interviews are conducted in each calendar month.
The household head is the SIPP ‘reference person’—the person in whose name the home is owned or rented. If the house is owned jointly by a married couple, either the husband or the wife may be listed as the reference person. The household as the unit of analysis is most consistent with the concept of food deprivation put forth by the National Research Council (2006) in their assessment of such measures: “Food insecurity is measured as a household-level concept” (National Research Council 2006, p. 4).
The food insecurity measure was created by the Economic Research Service (ERS) of the USDA for the 2001 SIPP based on five questions in the Wave 8 Topical Module instead of on the standard six- or eighteen-point food insecurity scales used by the ERS. ERS has shown measures based on the latter scales to be reliable and minimally biased (Bickel et al. 2000).
Researchers have put forth several possible reasons for the finding that food insufficiency is predictive of health or nutritional status for adults but not for children: adults may protect the health status of children in their household by skipping meals themselves before depriving their children, or children may have a more robust safety net that includes subsidized meal programs at school, or food at friends houses.
In sensitivity testing, we also generate comparable results using alternative asset measures—either the actual data for months 24 or 36 or the interpolated data for month 32.
Alternatively, these equations could be estimated as probit functions (available from the authors); as noted by Allison (1999, p. 73), estimates based on probit specifications yield comparable results.
Tests for equality of coefficients are conducted using a Wald chi-square test (SAS version 9.2, SURVEYLOGISTIC procedure).
A SIPP ‘Z-type’ imputation is the case were an entire missing interview record for a respondent is replaced with actual data from another respondent who is matched on a variety of demographic characteristics.
In both specifications we also model income shocks as the mean income shock that occurs over the four-month period with little change in the result (results available from the authors).
We thank an anonymous referee for suggesting this approach. Further details of the first-stage panel regression are outlined in an Appendix available from the authors.
Marginal effects are based on Model 3 in Table 3 and (for continuous variables) are computed as dP/dX = P(1 − P) b L, where P is the probability of food insufficiency for a particular population, X is the independent variable of interest, and b L is the logistic coefficient estimate on the independent variable of interest (see Pindyck and Rubinfeld 1981, p. 299).
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The authors thank Jessica Greene, Craig Gundersen, Kathryn Magnuson, Joe Stone, Parke Wilde, and two anonymous referees, as well as participants at the following workshops for their detailed and useful comments: April 2006 IRP-USDA Small Grants Workshop, October 2006 USDA RIDGE Conference, November 2006 National Poverty Center/ERS-USDA Conference on ‘Income Volatility and Implications for Food Assistance II’ and the Spring 2007 Oregon State University Contemporary Rural Issues Seminar. This work was partially funded by the University of Wisconsin—Institute for Research on Poverty/USDA Small Grants Program under Agreement Number: F184516. Additional funding provided by the National Poverty Center, University of Michigan. An earlier version of this work appeared as a working paper at the University of Wisconsin Institute for Research on Poverty (WP# 1325-07). All remaining errors are the responsibility of the authors, however. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the Food Assistance and Nutrition Research Program, the Economic Research Service, or the United States Department of Agriculture.
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Leete, L., Bania, N. The effect of income shocks on food insufficiency. Rev Econ Household 8, 505–526 (2010). https://doi.org/10.1007/s11150-009-9075-4
- Food insufficiency
- Income shocks
- Material well-being