Abstract
Introduction:
Housing instability and food insecurity are two social determinants of health with considerable overlap and complex dynamics among populations experiencing economic instability. This study sought to examine the magnitude and direction of the temporal associations between housing instability and food insecurity among low-wage workers in two U.S. cities.
Methods:
This study was a secondary analysis was conducted in 2024 using five years of data (2018-2022) collected from a cohort of low-wage workers in Minneapolis, MN, and Raleigh, NC. Annual measures included a 6-item food insecurity measure, a 3-item housing instability measure, and demographic characteristics. Four Dynamic Panel Models tested the effect of housing instability on food insecurity in the subsequent year and the effect of food insecurity on housing instability in the subsequent year.
Results:
At baseline, most participants experienced food insecurity (74.9%) and housing instability (70.8%), both of which declined in each subsequent year before increasing in 2022. Participants who experienced housing instability or food insecurity were more likely to experience the same hardship in the subsequent year. Overall, housing instability was negatively associated with subsequent food insecurity (b [95% CI], −0.082 [−0.136, −0.028]). Similarly, food insecurity had a negative association with subsequent housing instability (−0.125 [−0.189, −0.060]).
Conclusion:
Housing instability and food insecurity have a complex and dynamic relationship. Expansion of federal nutrition assistance programs coordinated with other safety net programs, such as eviction prevention or rental assistance, as was implemented during the COVID-19 pandemic, could provide critical protection from these hardships.
Introduction:
The United States is experiencing an unprecedented housing crisis. Low-income families are struggling to keep up with the rising rent and mortgage rates, and many are experiencing homelessness.1 Although housing instability is not routinely monitored, the U.S. Census Bureau reported that, in 2023, 31.3% of households were cost-burdened, meaning that they spent more than 30% of their income on rent, mortgage payments, or other housing costs.2 In addition, the U.S. Department of Housing and Urban Development reported that about 770,000 people experienced homelessness on a single night in January 2024.3 Low-income families are at the same time experiencing high rates of food insecurity. During 2023, about 18 million (13.5%) families were struggling to stay food secure, meaning that they had difficulty in providing enough food for all members because of a lack of resources.4 This prevalence has increased from 12.8% in 2022. Housing instability and food insecurity have considerable overlap and complex dynamics among families who live in poverty.5 It could be that housing instability, due to its cost burdens, is the impetus of food insecurity or that food insecurity is a bellwether for subsequent catastrophic housing loss. Yet relatively little is known about the dynamics between these social exposures over time.
Several cross-sectional studies have found that housing instability and food insecurity are highly prevalent among low-income families, but their association has not been fully explained.6-9 A recent meta-analysis that studied the prevalence of food insecurity among people experiencing housing insecurity and homelessness, as defined by the U.S. Department of Health and Human Services,10 found that the prevalence of food insecurity was four times higher than the average for the general U.S. population.11 However, experiences of food insecurity are often different in terms of duration and severity among those who experience housing instability. While some studies have found that homelessness and food insecurity are positively related,6 others found that among those experiencing homelessness, food insecurity varies depending on the resources available and the hardships they experience.12,13 . This study examines temporal associations between housing instability and food insecurity among low-wage workers in two U.S. cities.
Methods
This study is a secondary analysis using data collected yearly (2018-2022) in the WAGE$ study,14 which followed a cohort of low-wage workers to evaluate the health effects of increased minimum wage in Minneapolis, MN and a comparison community with no minimum wage increase, Raleigh, NC. This secondary analysis used pooled data from both study sites.
Low-wage workers were eligible to participate if they: (1) were 18 years or older, (2) worked at least 10 hours per week at a wage less than or equal to $11.50/hour in Minneapolis/Raleigh or were employed at that wage within the last six months and were currently seeking work in Minneapolis/Raleigh, (3) planned to serve in the workforce for at least five years, (4) agreed to be contacted for follow-up, and (5) spoke English or Spanish. Participants were excluded if they were federal or state workers, full-time students, or planned to retire or move more than 100 miles away. Participants were recruited in person at community organizations located in the two study cities. Recruitment methods were described in detail elsewhere.15
Data collection occurred in person at community locations in 2018 and 2019. Due to the COVID-19 pandemic, data collection in 2020 occurred remotely. In 2021 and 2022, participants completed data in person or remotely. Data collection included a survey, pay stub submission, height and weight measure, and food receipts (2018 and 2022 only) for which participants received up to a $100 incentive per year. However, only the survey variables described below were relevant for the current secondary analysis. The analytic sample for this secondary analysis included the complete primary study sample (n=969). Participants who responded to the survey in all years (n=449) were more likely to identify as female and less likely to identify as non-Hispanic Black (Appendix Table 1). Participant retention for the primary study is shown in Appendix Table 2. This study was approved by the Institutional Review Board at the University of Minnesota, University of North Carolina-Chapel Hill, and University of Connecticut. Free informed consent from participants subjects was obtained.
Food insecurity was measured annually using the 6-item Household Food Security Survey Module,16 with a 12-month reference period. Participants responded with “often true”, “sometimes true”, or “never true for your household in the last 12 months” to items like “The food that I bought just didn’t last, and I didn’t have money to get more.” Items were summed and classified into a binary variable with food secure (0-1 score) or food insecure (2-6 score) categories.
Housing instability was measured annually using three items adapted from the Children’s Health Watch Housing Instability Screening:17,18 (1) In the last 12 months, was there a time when you were not able to pay the mortgage or rent on time? (yes/no); (2) In the last 12 months, how many places have you lived? (0, 1, 2, 3, or > 3); and (3) In the last 12 months, was there a time when you did not have steady place to sleep or slept in a shelter (including now)? (yes/no). An affirmative response to any yes/no question or living in 0 or 3 or more places was considered positive for housing instability. A binary variable was created, with housing stability (1) or housing instability (0) categories.
Baseline demographic covariates were included in the analyses: city, age, gender, race/ethnicity, education, household composition (i.e., single or multiple adults), and household size (i.e., number of people living in the household). In addition, the time-variant variables employment status and income, assessed in all the study years, were included as covariates. Employment status (i.e., employed or not employed) was designated based on whether participants were working for pay and self-reported average monthly income categories at the time of their annual assessment. Participation in the Supplemental Nutrition Assistance Program (SNAP) in the last 30 days (yes or no) was also assessed in all study years.
Statistical Analysis:
Descriptive statistics were used to summarize participants’ demographic characteristics across categories of food insecurity and housing instability and the prevalence of food insecurity, housing instability and measures of economic instability across the study years. A Structural Equation Model, proposed by Allison (2017)19-21, was used to conduct four Dynamic Panel Models (i.e., two unadjusted and two adjusted) to test (1) the effect of housing instability on food insecurity in the subsequent year and (2) the effect of food insecurity on housing instability in the subsequent year. Stata SE version 18 user-generated command, xtdpdml19-21 was used to estimate year-by-year effects and the overall effect, which is the effect of each variable across years. This model implemented maximum likelihood estimation to find the parameter values that best fit the data. Since the outcomes are binary, these were interpreted as linear probability models. Compared with the traditional cross-lagged models, this model effectively accounts for time fixed-effects and person fixed-effects, so that the estimated dynamic effects can be interpreted as within-person. Additionally, this model is robust to violations of the normality assumption.19,22 To account for possible clustering and heterogeneities in the error variance, the clustered and robust standard errors were reported. This model controlled for: 1) common time trends across study sites that could affect social and economic conditions of participants related to housing and food and 2) any time-invariant participant-specific traits could confound the independent variables. To assess the robustness of the findings, the models were compared with those including complete cases (n=449). The direction and significance of all key coefficients remained consistent across both analyses suggesting that the findings were relatively robust to the sample choice (n=969).
Results:
Baseline demographic information (n=969) is presented in Table 1, overall and stratified by food security status and housing stability status. Participants had an average age of 41.4 years and most identified as female (55.3%), non-Hispanic Black (71.7%), and about half (54.7%) had complete or incomplete high school education. About one-third of the participants with food insecurity (38.5%) or housing instability (39.9%) lived in a household with children. Most (70.6%) had been SNAP participants in the past 12 months, and this was even higher among those who had experienced food insecurity (72.3%) or housing instability (73.0%).
Table 1. Baseline sociodemographic characteristics by food insecurity and housing instability among U.S. low-wage workers.
| Total (n=969) |
Food security (n=242) |
Food insecurity (n=721) |
Housing stability (n=281) |
Housing instability (n=682) |
|
|---|---|---|---|---|---|
| Study site, n (%) | |||||
| Minneapolis | 487 (50.6) | 123 (50.8) | 364 (50.5) | 154 (54.8) | 333 (48.8) |
| Raleigh | 476 (49.4) | 119 (49.2) | 357 (49.5) | 127 (45.2) | 349 (51.2) |
| Age (mean, SD) | 41.4 ± 13.7 | 40.9 ± 14.6 | 41.5 ± 13.4 | 42.3 ± 14.9 | 40.9 ± 13.2 |
| Female, n (%) | 536 (55.3) | 127 (52.5) | 407 (56.5) | 155 (55.2) | 379 (55.7) |
| Race, n (%) | |||||
| Non-Hispanic White | 160 (16.5) | 43 (17.8) | 116 (16.1) | 61 (21.7) | 98 (14.4) |
| Non-Hispanic Black | 695 (71.7) | 170 (70.3) | 524 (72.7) | 178 (63.4) | 516 (75.7) |
| Other | 106 (10.9) | 27 (11.2) | 77 (10.7) | 39 (13.9) | 65 (9.5) |
| Household size (mean, SD) | 2.6 ± 1.7 | 2.6 ± 1.6 | 2.6 ± 1.7 | 2.5 ± 1.6 | 2.6 ± 1.7 |
| Household composition, n (%) | |||||
| No children | 566 (58.4) | 138 (57.0) | 428 (59.4) | 170 (60.5) | 396 (58.1) |
| Single adult with child/ren | 129 (13.3) | 30 (12.4) | 99 (13.7) | 40 (14.2) | 89 (13.1) |
| Multi-adult with child/ren | 248 (25.6) | 69 (28.5) | 179 (24.8) | 65 (23.1) | 183 (26.8) |
| Education, n (%) | |||||
| Less than high school | 163 (16.8) | 36 (14.9) | 127 (17.6) | 46 (16.4) | 117 (17.2) |
| High school / GED | 367 (37.9) | 101 (41.7) | 266 (36.9) | 111 (39.5) | 256 (37.5) |
| Some college | 102 (10.5) | 24 (9.9) | 78 (10.8) | 24 (8.5) | 78 (11.4) |
| Associate’s/Technical degree | 239 (24.7) | 52 (21.5) | 187 (25.9) | 66 (23.5) | 173 (25.4) |
| Bachelor’s degree or more | 90 (9.3) | 28 (11.6) | 62 (8.6) | 33 (11.7) | 57 (8.4) |
| SNAP participation | 684 (70.6) | 162 (66.9) | 521 (72.3) | 185 (65.8) | 498 (73.0) |
GED= General Education Development
SNAP= Supplemental Nutrition Assistance Program
At baseline, most participants experienced food insecurity (74.9%) and housing instability (70.8%), both of which declined in 2019 and declined again in 2020 (Table 2). Housing instability rose in 2021 and 2022, whereas food insecurity reached its lowest point in 2021 before increasing in 2022. Unemployment increased from 14.9% in 2018 to 22.3% in 2020 but decreased in the subsequent years.
Table 2. Housing instability, food insecurity, income, and unemployment among U.S. low-wage workers (2018 – 2022), n (%).
| 2018 | 2019 | 2020 | 2021 | 2022 | |
|---|---|---|---|---|---|
| Food insecurity | 721 (74.9) | 421 (66.8) | 533 (55.7) | 298 (48.5) | 300 (53.5) |
| Housing instability | 682 (70.8) | 367 (58.3) | 356 (55.9) | 347 (56.2) | 318 (56.8) |
| Unemployed | 144 (14.9) | 92 (9.5) | 216 (22.3) | 189 (19.5) | 106 (10.9) |
| Average monthly income | |||||
| <$500 | 236 (24.4) | 123 (12.7) | 164 (16.9) | 158 (16.3) | 141 (14.6) |
| $501 - $1000 | 381 (39.3) | 194 (20.0) | 148 (15.3) | 134 (13.8) | 108 (11.2) |
| $1001 – 1500 | 234 (24.2) | 147 (15.2) | 140 (14.5) | 101 (10.4) | 97 (10.0) |
| $1501 - $2000 | 83 (8.6) | 86 (8.9) | 95 (9.8) | 101 (10.4) | 66 (6.8) |
| $2001 - $2500 | 12 (1.2) | 56 (5.8) | 49 (5.1) | 70 (7.2) | 77 (7.9) |
| >$2500 | 6 (0.6) | 22 (2.3) | 37 (3.8) | 39 (4.0) | 70 (7.2) |
Table 3 presents regression coefficients from the fixed effects dynamic panel models that examined the overall (across all study years) relationships between housing instability and food insecurity. The models showed that, overall, participants who experienced housing instability had, on average, 19.1% percentage points higher housing instability in the subsequent year. Similarly, those who experienced food insecurity had, on average, 10.4% percentage points higher food insecurity in the subsequent year. This result was driven by the positive effects in periods 2020-2021 and 2021-2022. Figure 1 shows these year-by-year effects.
Table 3. Fixed effects dynamic panel models testing the relationship between housing instability and food insecurity (2018-2022).
| Unadjusted | Adjusteda | |||||
|---|---|---|---|---|---|---|
| Food insecurity | SE | 95% CI | SE | 95% CI | ||
| Housing instability | −0.086 | 0.028 | −0.140, −0.032 | −0.082 | 0.028 | −0.136, −0.028 |
| Food insecurity | 0.107 | 0.034 | 0.039, 0.174 | 0.104 | 0.034 | 0.036, 0.171 |
| AICb | 7369.96 | 39332.52 | ||||
| BICc | 7545.36 | 40644.24 | ||||
| Housing instability | SE | 95% CI | SE | 95% CI | ||
| Food insecurity | −0.096 | 0.029 | −0.154, −0.039 | −0.125 | 0.033 | −0.189, −0.060 |
| Housing instability | 0.168 | 0.036 | 0.097, 0.239 | 0.191 | 0.041 | 0.111, 0.271 |
| AICb | 7384.83 | 13772.12 | ||||
| BICc | 7560.22 | 14232.11 | ||||
Adjusted for time invariant (study site, age, gender, race/ethnicity, education, household composition, household size) and time variant (employment status and income) variables. Boldface indicates significant coefficients (p<0.05)
Akaike’s information criterion
Bayesian information criterion
Figure 1.
Year-by-year fixed effects dynamic panel models testing the year-by-year relationships between housing instabiliy and food insecurity among low-wage workers in the U.S. (n=969)a
Adjusted for study site, age, gender, race/ethnicity, education, household composition, household size, employment status, and income. Bolded arrows represent significant coefficients (p<0.05)
The models also showed cross-lagged effects between housing instability and food insecurity. Overall, housing instability was negatively associated with subsequent food insecurity. In other words, participants who experienced housing instability had, on average, 8.2% percentage points lower food insecurity in the subsequent year. This was driven by the negative association between housing instability in 2020 and food insecurity in 2021 and between housing instability in 2021 and food insecurity in 2022, when this negative association became more pronounced (Figure 1).
The models showed cross-lagged effects of food insecurity on housing instability. Overall, food insecurity had a negative association with housing instability. In other words, participants who experienced food insecurity had, on average, 12.5% percentage points lower housing instability in the subsequent year. This result was driven by the negative association between food insecurity in 2021 on housing instability in 2022 (Figure 1).
Discussion:
This study found that housing instability and food insecurity have a complex and dynamic relationship. The results from this study suggest that experiencing housing instability or food insecurity predicts that hardship in the future. A bidirectional and negative association was also found between these hardships. In other words, housing instability was negatively associated with subsequent food insecurity, and food insecurity was negatively associated with subsequent housing instability. While the negative effect of housing instability on food insecurity was driven by the year-by-year dynamics, significantly during 2020-2022, the negative effect of food insecurity on housing instability was driven only by the dynamics of 2021 to 2022. These results suggest that participants faced trade-offs between housing and food costs that were more pronounced during the COVID-19 pandemic, and policies to address food and housing instability during this time produced an unusual patterning of these hardships.
Economic constraints force individuals into a cycle of hardships that include housing instability and food insecurity. This study found that those who experienced these hardships were more likely to continue to experience them in the future. This is consistent with recent nationally representative studies in the U.S. One study using several cross-sectional waves (1999 – 2019) of the USDA Household Food Security Survey Module (HFSSM) found that half of the families that experienced food insecurity from 1999 to 2003 also experienced that same hardship at least once in future waves.23 Another study that included four waves (1999 −2007) of the HFSSM found that one-third of households that experienced food insecurity also had persistent food insecurity over at least two consecutive waves.24 Research has also shown that housing instability leads to episodes of housing instability in the future. A study that collected retrospective residential history among renters found a positive relationship between involuntary housing loss and subsequent unforced moves.25 It also found that renters that had experienced an involuntary housing loss were more likely to experience another involuntary move in the following year. This suggests that, after experiencing housing loss, individuals might fall into lower quality housing conditions and persistently attempt to find housing with better conditions in the subsequent years.
The findings from this study suggest that housing instability had an overall negative association with food insecurity in the subsequent year. There are some potential reasons that might explain this relationship. First, low-income families spend one-third of their income on housing26 and, usually, this expenditure takes precedence over other essential household needs due to the potential consequences of not paying on time.27-29 Second, families experiencing housing instability might seek out and qualify for housing assistance programs, which could free up income for other necessities, including food30. Third, the overall negative association between housing instability and food insecurity in this study was driven by the dynamics between these variables from 2020 through 2022, during the COVID-19 pandemic, when exceptional federal housing and nutrition policy responses had the potential to help families to mitigate these hardships.31 For instance, the eviction moratorium protected families with housing instability because they couldn’t pay rent in 2020, which may have provided more resources for food– i.e., by disrupting the phenomenon whereby “the rent eats first.”32 Once this eviction moratorium expired, emergency rental assistance programs33 were put into place. Finally, food insecurity and housing and instability are both manifestations of financial instability. Experiencing either one suggests that households require more economic stability either through assistance or through higher wages. However, these findings are not consistent with other longitudinal studies looking at the relationship between housing instability and food insecurity34,35. This study shows that the relationship between food insecurity and housing instability is complex and requires comprehensive policy to help stabilize resources, especially during times of increased need.
This study also found that those who experienced food insecurity were less likely to experience housing instability in the subsequent year, another finding that is not consistent with other studies.34,35. There are several potential explanations for this finding in the context of this study. Low-income households are more likely to limit food purchases to ensure payment for household bills and rent.36,37 The experience of food insecurity may make people rely on support mechanisms that include federal nutrition assistance programs, like SNAP, which eases their food insecurity38,39 and their overall financial burden that will lead to less housing instability. Families who experience food insecurity may also rely on family, friends, or community organizations that help alleviate household food insecurity by providing food, credit, information, transportation, or other resources that expand their access to food, leaving more room in their budget to ensure stable housing.40,41 In addition, the federal response to the COVID-19 pandemic led to the expansion and flexibility of existing housing and nutrition programs and the development of strategies to support struggling families42. Notably, SNAP enrollment and reauthorization requirements were more flexible, eligibility was expanded for Able Bodied Adults Without Dependents, and benefit amounts were increased though Emergency Allotments, a 15% increase, and a subsequent re-evaluation of the Thrifty Food Plan. New benefits were also provided to families with children through the Pandemic Electronic Benefit Transfer program. Collectively, these initiatives contributed to a significant increase in participation43 and a decrease in food insecurity risk44,45 The findings from this study, especially those during 2021 and 2022, could reflect the protective effect of nutrition assistance programs, even in households that were experiencing housing instability. The government response also included other types of relief, like the Child Tax Credit, which were associated with a reduction in food insecurity.32,46 Given the evidence of the protective effect of the pandemic-era supports, program flexibilities and expansions such as these should be considered in times of economic or other crises; some program changes such as increased SNAP benefits and less stringent work requirements could even be made permanent.
This study has several strengths and limitations. The Dynamic Panel Models allowed for the estimation of the effect of one hardship on another hardship in the subsequent year, while accounting for autoregressive effects and covariates. Furthermore, these models were controlled by fixed effects, time-variant, and time-invariant variables. However, it is important to note that limitations in the housing instability and food insecurity measures used in this study might have produced unexpected results. The housing instability measure focuses on missing mortgage or rent payments, frequent relocations, and not having a steady place to sleep. This might have underestimated other housing hardships. Using 12-month measures reflect a more static condition, which could mask the dynamic reality of these hardships and the coping strategies that individuals resort to. Measuring these hardships at different time points during the year would provide information about the transitions into and out of housing instability and the opportunities for intervention. The binary nature of the housing instability measure also oversimplifies these complex experiences. Using ordinal or continuous measures could provide a better understanding of the issue, greater sensitivity, and precision. In addition, in dichotomizing the food security measure, we did not model changes in the most severe forms of food insecurity which may have demonstrated different patterns. Another limitation is that the cohort data from the two study sites used for this secondary analysis is from low-wage workers in a community-based sample in 2018; results therefore may not be generalizable to other populations who are also at risk for food insecurity but not represented in the sample – for instance, those with disabilities that prevented them from working.
Conclusion:
In conclusion, given the complexity of the relationship between housing instability and food insecurity, there is a need for better measures to inform individuals about the dynamics of these hardships. This is particularly the case for housing instability. While it is not routinely monitored in national samples, it has a high prevalence among low-income populations and is strongly related to food insecurity. The predictive relationship between housing instability and food insecurity was disrupted during the COVID-19 pandemic, when the federal government strengthened policies addressing food insecurity and housing instability. These results suggest that expanding federal nutrition assistance programs coordinated with other safety net programs, such as eviction moratoria or rental assistance implemented during the COVID-19 pandemic, could provide critical protection from these hardships.
Supplementary Material
Funding:
This research project was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01DK118664, PI C.C.), and the National Center for Advancing Translation Sciences (UL1TR002494) supported data management. Research personnel were supported by the National Institute of Food and Agriculture (7002638, PI C.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Institute of Food and Agriculture.
Footnotes
Conflict of Interest Statement: Authors have no conflict of interest to declare. Study sponsor had no role in the study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.
Financial disclosure: No financial disclosures were reported by the authors of this paper.
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