key: cord-0990302-6pp2spjw
authors: Gundersen, Craig; Hake, Monica; Dewey, Adam; Engelhard, Emily
title: Food Insecurity during COVID‐19
date: 2020-10-02
journal: Appl Econ Perspect Policy
DOI: 10.1002/aepp.13100
sha: 55d6255962e003f5aac5bf1d4de4a30361fe8fb5
doc_id: 990302
cord_uid: 6pp2spjw

For a decade, Feeding America's Map the Meal Gap (MMG) has provided sub‐state‐level estimates of food insecurity for both the full‐population and for children. Along with being extensively used by food banks, it is widely used by state‐ and local‐governments to help plan responses to food insecurity in their communities. In this paper, we describe the methods underpinning MMG, detail the approach Feeding America has used to make projections about the geography of food insecurity in 2020, and how food insecurity rates may have changed due to COVID‐19 since 2018. We project an increase of 17 million Americans who are food insecure in 2020 but this aggregate increase masks substantial geographic variation found in MMG.

This year represents the tenth anniversary of Feeding America's Map the Meal Gap (MMG). On an annual basis, MMG provides county-and congressional district-level estimates of food insecurity for both the full-population and for children and, upon request, sub-county-level results at, for example, the zip-code level. Along with being extensively used by food banks to direct scarce resources to those most-in-need, it is now widely used by state-and localgovernments to help plan responses to food insecurity in their communities.

For MMG, food insecurity rates are calculated with an imputation method that uses information from the Current Population Survey (CPS) and the American Community Survey (ACS). Since the Core Food Security Module (CSFM) is in the December CPS and the resulting data isn't publicly released until September of the following year and the ACS isn't released until December, this has meant that MMG, released in the spring, is based on data that is roughly 18 months old. Given that post-Great Recession, rates have remained relatively stable from

year-to-year as have geographical differences within and across states, this release schedule has not produced issues regarding timeliness. Of course, COVID-19 has changed all of this and, given the sharp projected increases in unemployment (and, hence, food insecurity) the levels of food insecurity across the United States are likely to be far higher in 2020 than in 2018.

In this article, after describing the methods underpinning MMG, we detail the approach Feeding America has used to make projections about the geography of food insecurity in 2020 and how this may differ from 2018. Central to these projections is that the methods used in MMG allow for us to do this when information about predictions of the underlying variables are available. We then turn to a description of multiple aspects of this changed geography of food insecurity. This article is protected by copyright. All rights reserved.

The official measure of food insecurity in the U.S. as established by the USDA uses responses to 18 questions about food hardships due to financial constraints experienced by households (10 for households without children and 18 for households with children). Examples of survey questions include: Did you or the other adults in your household ever cut the size of your meals or skip meals because there wasn't enough money for food?). Were you ever hungry but did not eat because you couldn't afford enough food? and Did a child in the household ever not eat for a full day because you couldn't afford enough food? (the most severe question). (For the complete set of questions, see Coleman-Jensen et al. 2019.) The responses for these questions are sometimes, yes or no. In other cases, respondents are asked if something happened never, sometimes, or often. A response of sometimes or often is counted as an affirmative response. Other questions ask respondents if something happened almost every month, some months but not every month, or in only 1 or 2 months. A response of almost every month or some months but not every month is counted as an affirmative response.

Based on these responses, households are delineated into three categories: A household is said to be food secure if they respond affirmatively to two or fewer questions; low food secure if they respond affirmatively to three to seven questions (three to five questions for households without children); and very low food secure if they respond affirmatively to eight or more questions (six or more questions for households without children). Food insecure households are those without access at all times to enough food for an active, healthy life for all household. The questions employed in MMG to define food insecurity are the same as those used by the USDA since 2001 to define food insecurity.

We proceed in two steps to estimate the extent of food insecurity in each county (congressional district).

Step (1) where s is a state, t is year, UN is the unemployment rate, POV is the non-undergraduate student poverty rate, MI is median income, HISP is the percent Hispanic, BLACK is the percent African-American, OWN is the percent of individuals who are homeowners, DSBL is the percent of individuals who report a disability, μ t is a year fixed effect, υ s is a state fixed effect, and ε st is an error term. This model is estimated using weights defined as the state population. In previous iterations of MMG we used data back to 2001 but the disability variable is only available since 2009 and, hence, why the model is estimated from 2009 to 2018.

Our choice of variables was first guided by the literature on the determinants of food insecurity. We included variables that have been found in prior research to influence the probability of a household being food insecure. (For an overview of that literature in this context see Gundersen and Ziliak, 2018 .) While the food insecurity measure is defined at the household level, we assume all members of a food insecure household are food insecure consistent with the This article is protected by copyright. All rights reserved. Accepted Article approach found in, e.g., Table 1 of Coleman-Jensen et al. (2019) . Next, we chose variables that are available both in the CPS and the ACS.

Step 2: We use the coefficient estimates from Step 1 plus information on the same variables defined at the county level to generate estimated food-insecurity rates for individuals defined at the county level. This can be expressed in the following equation:

where c denotes a county. This is because the true resources available to undergraduates are, on average, not reflected in the poverty rate. Consistent with this are the much lower food insecurity rates among college students in comparison to non-college students of similar ages (Gundersen, 2021) .

The above methods allow us to establish a base measure of food insecurity for all counties for the full population and for children. Using this base measure, we establish what we believe will happen to food insecurity in the U.S. in 2020 because of the COVID-19 pandemic.

To do so, we consider what will occur if two of the variables in the model above, the unemployment rate and the poverty rate, increase along the lines predicted by expert opinions.

(The other variables in our model are unlikely to change due to COVID-19.) In our most recent estimates of the upper-bound impact (as of July 13, 2020), we have assumed that the annual This article is protected by copyright. All rights reserved.

Accepted Article average unemployment rate will increase to 11.5% (up 7.6 percentage points compared to 2018) and the poverty rate will increase to 16.6% (up 4.8 percentage points compared to 2018). When we wrote the first version of this paper, there had not been expert projections of changes in poverty rates due to COVID-19 so we assumed that the proportional change in the poverty rate viz. the unemployment rate would be roughly the same as during the Great Recession. To put this into terms of equation (2), we assume the value of UN C will increase (on average) by 0.076 and the value of POV c will increase by 0.048.

This increase in the unemployment rate, though, is unlikely to be uniform across all counties in the U.S. Instead, certain industries and occupations will be disproportionately affected by COVID-19. So, we further adjust the county-level and CD-level unemployment projections for the proportion of the population that is likely to lose their jobs, combining data from the American Community Survey with estimates established in Hatzius et al. (2020) .

At the national level, we project 54 million food insecure Americans in 2020, approximately 17 million higher than in 2018. For children, the food insecurity rates are projected to increase to 18 million, up nearly 7 million from 2018.

These national estimates, though, mask substantial heterogeneity across the country in terms of the projected impacts of COVID-19. This is not unexpected given the geographic diversity in the base models for both the full population ( Figure 1 ) and children (Figure 2 ).

Looking at states, the highest five states are the same whether or not COVID-19 occurred -Mississippi, Arkansas, Alabama, Louisiana, and New Mexico. However, there are some states that will see much higher food insecurity rates. Nevada stands outpre-COVID-19 it would This article is protected by copyright. All rights reserved.

Accepted Article have been 20 th but post-COVID-19 it is projected to be 8 th . For children, Louisiana and New

Mexico are first and second with or without COVID-19 but Nevada is now third (ninth without COVID-19). The substantially higher projected rates for Nevada is primarily due to their reliance on service sector jobs which have been disproportionately affected by COVID-19.

One of the key contributions from MMG is its portrayal of the substantial heterogeneity in local food insecurity that is seen in Figures 1 and 2 . The responses to COVID-19 will also vary based on geography albeit, consistent with the state results, there is likely to be some similarities in terms of county rankings. For the full population (Table 1) , we display, first, the 15 counties with the highest rates of food insecurity in the base case and due to our projections.

In the base case, the 15 counties are all in the South or counties with Indian Reservations. The highest is Jefferson County, Mississippi (30.4%) and the 15 th is Harlan County, Kentucky (24.8%). Without the adjustments for county-level differences in unemployment rates described above, these would be the same orderings after COVID-19 but, due to these adjustments, there are some differences. The five highest remain the same but, for example, Washington County, Mississippi and Wolfe County, Kentucky are now in the top 15.

In the final two columns of Table 1 we display the projected percent increases in food insecurity for the highest 15 counties. These are all counties with base rates that are relatively low and, hence, this is the reason for the dramatic increases. The increases range from 157.3%

in the highest (Burke County, North Dakota) to 109.5% in the 15 th highest (Daggett County, Utah). These dramatic increases may be one reason for why some food banks reported being especially strained in response to COVID-19. 

The proceeding discussionand the full set of results with updates as needed at http://map.feedingamerica.org/ -provides an overview of the geographic diversity in food insecurity rates across the U.S. and what may happen due to COVID-19. We conclude with three main points. First, while these projections of increased food insecurity rates are of great concern, they would have been for worse were it not for the resiliency of the agricultural supply 

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