The WAGE$ observational study

In June 2017, the City of Minneapolis, Minnesota, set the city's minimum wage above the state level, joining a growing number of other local jurisdictions across the United States that have passed similar ordinances since 2012 (UC Berkeley Labor Center 2020). Minneapolis is in the process of incrementally increasing its minimum wage from $9.50 to $15 by July 1, 2022, for all businesses with more than 100 employees. The minimum wage will increase from $7.75 to $13.50 during the same period for smaller businesses (City of Minneapolis 2016). The ordinance specifically states that its purpose is to “maintain workers’ health, efficacy, and general well-being.”

Estimates from 2016 of the ordinance's potential effects predict that wages will increase by an average of 22 percent for the 71,000 workers in the city making the minimum wage or just above the minimum wage. Moreover, the higher minimum wage will affect 41 percent of non-Hispanic Black workers and 54 percent of Hispanic workers, compared with 17 percent of white workers. Projections estimate a postpolicy decrease in food insecurity of 3.8 percent and an increase in food expenditures of $26 per week among affected workers (Roy Wilkins Center for Human Relations and Social Justice 2016).

Evidence from previous studies suggest that minimum wage laws may be associated with a range of health outcomes, including obesity (Bhatia and Katz Reference Bhatia and Katz2001; Meltzer and Chen Reference Meltzer and Chen2009; Kim and Leigh Reference Kim and Leigh2010; Human Impact Partners 2014; Komro et al. Reference Komro, Livingston, Markowitz and Waegnaar2016; Tsao et al. Reference Tsao, Konty, Van Wye, Hadler, Linos and Bassett2016). However, the current body of evidence is limited by design weaknesses and generally does not test causal mechanisms for the relationship between wages and health, either because the survey instruments used do not measure wages directly, or they do not measure health in sufficient detail. The minimum wage has been shown to reduce severe food insecurity, although the effects on overall food security remain less drastic (Rodgers, Reference Rodgers2016). Obesity affects over one third of Americans and is disproportionately high among non-Hispanic Blacks and Hispanics (Centers for Disease Control 2013; Krueger and Reither Reference Krueger and Reither2015).

In the current study, the city of Minneapolis serves as the subject of observational interest since it has elected to adopt a new minimum wage regime, with the minimum wage for work in the city increasing incrementally over several years. The city of Raleigh was chosen as a control primarily due to its similarity in demographic characteristics. Raleigh's resident population, median household income, firm count, share of population with college education, and distribution of racial identities were similar to Minneapolis in the time before the passage of Minneapolis' minimum wage ordinance (Shanafelt et al. Reference Shanafelt, Sadeghzadeh, Chapman, DeMarco, Harnak, Gust, Jackson and Caspi2021). In addition, North Carolina's state minimum wage preemption reduces the likelihood that Raleigh will see an increase in the minimum wage during the time of the study. This allows Raleigh to serve as a reasonable control site for the scope of the study and motivated the selection of Raleigh as a comparison site.

This article uses baseline information from Minneapolis and Raleigh, North Carolina, that reflects wages before the implementation of the Minneapolis minimum wage ordinance to examine BMI differences between two cohorts of low-wage workers. The study follows low-wage workers over five data collection time points (annually 2018–2022). We examine baseline obesity-related measures among low-wage workers (earning ≤$11.50 an hour at baseline) in Minneapolis (n = 490) with low-wage workers in a comparison city with no minimum wage increase (Raleigh, North Carolina, n = 479). We test the hypothesis that there are baseline BMI differences according to race and gender at the outset between low-wage workers in Minneapolis and Raleigh, controlling for nutrition-related and demographic differences between the cities. We interpret any residual differences between the cities to be attributable to contextual factors, such as a progressive political climate that (a) encourages a healthier lifestyle and (b) supports policy changes such as increases in the minimum wage.

Site population characteristics

Table 1 shows the city characteristics for Minneapolis and Raleigh, the control site. Raleigh has a larger Black population (29.3 percent) than Minneapolis (18.6 percent). Still, the two cities are remarkably similar on other demographics and critical characteristics relevant to this study, including population size, percent foreign-born residents, employment rate, cost of living, persons living in poverty, average BMI, and obesity rate.

Table 1. Key Characteristics of Minneapolis and Comparison City (Raleigh)

Notes:

This excludes special classes of workers with bespoke minimum wage levels, including J1 visa recipients, Commensurate Wage recipients, and training wage recipient.

^a From US Census Bureau as of 4/1/2010;

^b From CDC BRFSS as of 2011–2015;

^c Firm size based on annual sales <$500,000.

Sources: MN minimum wage: Berry (Reference Berry2021). NC minimum wage: North Carolina Department of Labor (2021). BRFSS SMART data for 2015 by Metropolitan Statistical Area accessed at https://www.cdc.gov/brfss/smart/smart_2015.html.

Body mass index

The primary outcome of interest for the WAGE$ study is body mass index (BMI). We measured BMI using standardized protocols from the University of Minnesota's Obesity Prevention Center (French, Wall, and Mitchell Reference French, Wall and Mitchell2010). BMI is the recommended method of assessing overweight and obesity among adults and was calculated as weight in kilograms/height in meters squared. Measurements in Minneapolis and Raleigh were completed by trained and certified research staff who took height and weight measures in a private room with participants dressed in light clothing, shoes removed, and pockets emptied. Weight was measured in duplicate on a portable digital scale (Seca model) and recorded to the nearest 0.1 kg. If the two measures differed by more than 0.2 kg, a third measure was obtained. The mean of two or three values was used for analysis. The scale was calibrated with a 5 kg weight at regular intervals. Height was measured in duplicate, using a portable Schorr stadiometer (Schorr Production, Olney, MD) and recorded to the nearest 1.0 cm. If the two measures differed by more than 5 mm, a third measure was obtained. The mean of two or three values was used for analysis. In adults, in-person anthropometric data collection is more reliable and valid than self-reported data (US Department of Agriculture ERS 2020). Table 2 provides these and other definitions.

Table 2. Definition of variables in regression models

Truncated version only includes BMI and base factors. See the complete table in the Appendix .

Source: Caspi et al. (Reference Caspi, DeMarco, Durfee, Oyengua, Chapman, Wolfson, Myers and Harnak2021).

Access to resources

In addition to health outcomes, the survey also includes a number of measures that capture access to resources that may influence food purchases. SNAP usage is an indicator variable based on whether the respondent has reported a nonzero amount of SNAP benefits spent. This characterizes access to government transfer programs intended to reduce food insecurity. The USDA measure of food insecurity is operationalized based on a six-question panel. Food insecurity is defined by two or more of six factors being true in the 12 months prior to the survey: the food you bought did not last and could not afford more, you could not afford a balanced meal, you cut your meal size because you were worried there was not enough money for food, you cut your meal size more frequently than once or twice within a year, you ate less because you were worried about running out of food, you were hungry because there was not enough money for food. This food insecurity indicator is also included as a measure of access to resources.

Banking access is a proxy, intended to reflect the participant's disposable wealth, and their access to private sector resources, or inversely, reliance on the public sector. Banked status is based on reported ownership of a checking or savings account with a bank or credit union.

Race, gender, and intersectionality in the sample

The field of stratification economics seeks to identify ways in which economic phenomenon may serve as expressions of power that generate the treatment of less powerful groups as subaltern. In this field, it is common to see economics problems as problems of moving targets, where no one policy instrument or market activity is singled out as a causal determinant of this subaltern status. This suggests that researchers must adopt a variety of ways of measuring classes of economic power when exploring empirical questions and compare these varieties of results to understand the way in which this moving target may be present. Furthermore, researchers cannot assume independence of the boundaries of these classes, as these distinctions are created in the first place for the purpose of this stratification of power.

In the case of the WAGE$ study, we observe a differential in BMI with respect to one group in particular, Black women. We do not see a significant differential between sites for other groups (see Table 3), but we cannot suggest that this differential is from a stratification of economic power unless we compare the results of a model that treat those factors as fixed effects to a model that treats these factors together. Race and gender may each have a bespoke relationship with economic power; however, researchers should not treat each factor as independent. Differentials in policy and economic power are often observed to be greater in interactions of race and gender than in the sum of each of those identities alone (Crenshaw Reference Crenshaw1989). For this reason, we adopt a tiered approach to explore observed BMI differentials. First, we treat these differentials as fixed effects within the sample; second, we allow these differentials to be explained as a difference in treatment of several determining factors; and third, we compare the scope of the suggested differential between these models.

Table 3. BMI differentials in the WAGE$ sample within racial group by location

Note 1: Asian, Pacific Islander, American Indian, Alaska Native, and Missing Race, and Gender Nonbinary are excluded from this table due to too few observations. If a race and gender group includes fewer than ten observations at a site, that group is not tested separately in this t-test, to avoid drawing comparisons driven by small sample size, but are included in the whole sample.

Note 2: Before each T-test, we conduct an F-test for unequal standard deviations of each group by location. If equal, a standard T-test is performed. If unequal, we use the Satterthwaite (Reference Satterthwaite1946) T-test with unequal variances.

Table 3 reports that the BMI is lower in Minneapolis than it is in Raleigh. The mean of 29.832 in Minneapolis is lower than the mean of 31.199 in Raleigh. This difference of 1.367 is statistically significant at the 1 percent level for a two-tailed test. Most of this difference is driven by the lower BMI among Black women in Minneapolis vs. Black women in Raleigh, although the difference is barely significant at the 10 percent level for a two-tailed test.

Table 4 reports similar comparisons by SNAP usage. The higher SNAP usage in Minneapolis is consistently higher across race and gender groups. These differences are only statistically significant for Black women, Black men, White women, and White men.

Table 4. SNAP usage differentials in the WAGE$ sample within racial group by location

Note 1: Asian, Pacific Islander, American Indian, Alaska Native, and Missing Race, and Gender Nonbinary are excluded from this table due to too few observations. If a race and gender group includes fewer than ten observations at a site, that group is not tested separately in this t-test, to avoid drawing comparisons driven by small sample size, but are included in the whole sample.

Note 2: Before each T-test, we conduct an F-test for unequal standard deviations of each group by location. If equal, a standard T-test is performed. If unequal, we use the Satterthwaite (Reference Satterthwaite1946) T-test with unequal variances.

A similar analysis including the probability of education in excess of High School and age is included in Tables A8 and A9, respectively. Education is not notably different according to race and gender, but the Raleigh cohort is younger than the Minneapolis sample.

The model

Although health-related outcomes are observed at the individual level, the determinants of health are based on both individual, environmental, and policy factors that are outside of the individual's control (Leigh and Du Reference Leigh and Du2018). BMI as a health outcome is partially determined by personal behaviors like diet, sleep, and physical activity, partially determined by environmental factors like workplace conditions, household conditions, and partially determined by policy factors like access to healthcare services, food security assistance, and household income. The social determinant model of health differentiates between the factors that are in the individual's control and factors that are environmental or policy related, to identify plausible positive health effects that are associated with policy changes. Many of the policy interventions designed to ensure food security are means tested programs based on household income. These policies, however, often come with limitations, like requiring interaction with the state to prove eligibility, or limiting the kinds of goods that can be purchased with transferred funds like the SNAP program.

Although household income is already an established determinant of BMI, wages may plausibly serve as a bespoke determinant. This is because monthly income is a function of both the wage rate, and the number of hours worked per month. If hours worked as employment status are fixed, increasing the minimum wage would lead to an unambiguous increase in household income. If hours are subject to change, however, an increase in the wage might be paired with a decrease in hours worked such that household income is unchanged. Hours worked may be reduced after an increase in the minimum wage through two channels. After an increase in the minimum wage, employers may cut hours and fire employees to cut costs. Economists debate the significance of this effect (Neumark et al. Reference Neumark, Salas and Wascher2014; Cengiz et al. Reference Cengiz, Dube, Lindner and Zipperer2019). Also, households may request to reduce their hours worked so their monthly income is unchanged to maintain their eligibility for government programs. On average, for each $1 increase in household income, government transfer benefits are reduced by $0.30 (Reich and West Reference Reich and West2015). This suggests that households might see more real spending power with an increase in income than reliance on these programs. Although household income would effectively replace the need for many of these programs, households that treat their income as variable and uncertain may prefer to maintain their use of these programs over a more volatile income situation from wages alone. This phenomenon is often referred to as the “Benefits Cliff.”

The minimum wage increase in Minneapolis serves as an observational study to test this model, assuming that Minneapolis and Raleigh workers face similar determinants over time. Estimating the causal relationship between the minimum wage and the BMI is left as a topic of future research (Caspi et al. Reference Caspi, DeMarco, Durfee, Oyengua, Chapman, Wolfson, Myers and Harnak2021). Although the minimum wage is an important factor that motivates the design of the WAGE$ survey, the goal of this article is to explore the extent to which existing determinants of health explain BMI differentials before the observed minimum wage policy. It is the purview of this article to explore the extent to which determinants at these sites are indeed similar.

We begin with a specification of the determinants of BMI that include measures, x _in, of race, ethnicity, gender, location, food insecurity, banked status, SNAP usage, physical activity, and primary job hourly wage. The subscript i refers to each included control, and n refers to each unique observation. This study uses the baseline time period only. Physical activity is based on the count of times in a week that the respondent reports engaging in activity for at least 15 min, separated by mild, moderate, or strenuous levels. Since all the workers in the experiment have the same range of wages, we do not directly control for income. Instead, we control for L, location. To determine whether racial and/or gender disparities cannot be accounted for by the controls or location, we also control for race, R, and gender, G, given by equation 1.

(1) $${\rm BM}{\rm I}_n = {\rm \alpha } + {\rm \Sigma }_i\;{\rm \beta }_i\;x_{{\rm in}} + {\rm \gamma }R_n + {\rm \delta }G_n + {\rm \phi }L_n + {\epsilon }_n$$

We estimate the coefficients β _i , γ, δ, and ϕ to obtain the effects of each included covariates x _i , race, gender, and location on BMI in our survey sample of workers in Minneapolis and Raleigh. New controls x _i are added with each model. We test the underlying hypothesis that whether once we control for location, race and gender effects disappear, as one would expect for matched locations. To account for the possibility that between location j and location k there may be differential impacts of race and gender on BMIs, we re-estimate equation 1 separately for the two locations. These separate estimates are obtained from equations 2 and 3.

(2) $${\rm BMI}_n^k = {\rm \alpha }^k + {\rm \Sigma }_i\;{\rm \beta }_i^k \;x_{in}^k + {\rm \gamma }^kR_n^k + {\rm \delta }^kG_n^k + {\rm \phi }^kL_n^k + {\rm \epsilon }_n^k $$

(3) $${\rm BMI}_n^j = {\rm \alpha }^j + {\rm \Sigma }_i\;{\rm \beta }_i^j \;x_{in}^{\,j} + {\rm \gamma }^{\,j}R_n^j + {\rm \delta }^{\,j}G_n^{\,j} + {\rm \phi }^jL_n^j + {\epsilon }_n^j $$

A direct test of whether any difference in BMI between the two locations is due to differences in the effects of race, gender, or other factors is to compute the counterfactual, $B\tilde{M}I^{j}$ , or the predicted value of the mean BMI in location j when the effects of each x_i covariate, gender, and race on BMI in location j are the same as they are in location k. Equation 4 shows how this counterfactual is computed: we use the jth location's means of the independent variables, but the kth location's estimated coefficients to derive what the mean BMI in the jth location would be if the jth population had the kth location's treatment. Of course, when the estimated effects are the same between locations, $B\tilde{M}I^{j} = B\bar{M}I^j$ , or the “equal treatment” BMI in location k is the same as the actual BMI in location j.

(4) $$B\tilde{M}I^j = \hat{\alpha}^{k} + \sum\nolimits_i \hat{\beta}_i^k \bar{x}_i^j + \hat{\gamma}^k\bar{R}^j + \hat{\delta}^k\bar{G}^j + \hat{\phi}^k\bar{L}^j$$

Accordingly, we can decompose the actual gap in BMI between locations j and k into a portion that is due to differences in treatment (or coefficients) and differences in characteristics (each control x_i , race and gender). Equation 5 provides the decomposition. The second row of the equation reports the portion of the BMI gap between the kth location and the jth location due to differences in treatment (coefficients). The third line in the equation represents the portion of the BMI gap between the kth location and the jth location due to differences in each control x_i , race, and gender.

(5) $$B\bar{M}I^k - B\bar{M}I^j = [B\tilde{M}I^j - B\bar{M}I^j] + [B\bar{M}I^k - B\tilde{M}I^j] = [(\hat{\alpha}^{k} - \hat{\alpha}^{\,j}) + \sum\nolimits_i (\hat{\beta}_{i}^k - \hat{\beta}_i^{\,j})\bar{x}_i^{\,j}) + (\hat{\gamma}^{-k}\hat{\gamma}^{\,j}))\bar{R}^{\,j}) + (\hat{\delta}^k - \hat{\delta}^{\,j}))\bar{G}^{\,j}) + (\hat{\phi}^k - \hat{\phi}^{\;j}))\bar{L}^{ j})] +[\sum\nolimits_i\hat{\beta}_i^k (\bar{x}_i^k - \bar{x}_i^{\,j})) + \hat{\gamma}^{\,k}(\bar{R}^k - \bar{R}^{\,j})) + \hat{\delta}^{\,k}(\bar{G}^k - \bar{G}^{\,j})) +\hat{\phi}^k (\bar{L}^k - \bar{L}^j)]$$

Equation 5 can be expanded further to account for the fact that there may be interactions between the differences in endowments vs. differences in treatment.Footnote ²

One of the common limitations of the decomposition detailed in equation 5 is that it ignores what is called in the Critical Race Theory literature “intersectionality” (Crenshaw Reference Crenshaw1989). This concept posits that the sum of race and gender's separate effects underestimates the combined impacts of being a racial minority group member and being a woman. The cross-over from legal theory to econometrics is not yet settled. One approach is to incorporate interaction terms between race and gender in equations 1–3. The alternative, which we adopt here, is to perform a separate decomposition of BMI within race and gender pairs, which amounts to assuming interactions between race and gender within locations and interactions between each control x _i and race and gender between locations.

Summary and conclusion

There are notable differences in BMI values across races and ethnicities in the current study. Because obesity and high BMI are predictors of adverse health outcomes, policymakers and health advocates have sought economic interventions that might narrow the racial gaps in BMI. The data gathered in the current project promises to shed light on whether one policy intervention—increasing the minimum wage—might promise to remedy the problem of racial disparities in obesity. The logic is that through higher incomes produced via increases in the minimum wage, Blacks and other minority group members who are disproportionately located at the lower end of the wage distribution will have access to healthier diets and, with physical activity, will reach healthier weights.

In our baseline data, however, we observe an anomaly. Black women in Minneapolis have lower BMIs than Black women in Raleigh. The two cities are nearly matched in demographic characteristics, so it is surprising that within one demographic group between the two cities, there is a nontrivial difference in baseline BMIs. The reason why this difference requires careful decomposition is that any finding in future years of the natural experiment that BMIs or other measures of health improved in Minneapolis relative to Raleigh would be contested by the fact that at the outset of the experiment, there were favorable outcomes for this demographic group in Minnesota relative to the comparison group in North Carolina.

The intersectionality concept is useful for explaining why there might be an unexplained gap in BMIs between Black women in Minneapolis and Black women in Raleigh. Although our analysis conceptually can control for differences in location and a host of other demographic, access, social, and physical activity measures, intersectionality theory suggests that the separate impacts of race and gender underestimate race and gender's joint effects for Black women.

We have modeled this intersectionality—or differential effects arising from being a Black woman in Minneapolis vs. a Black woman in Raleigh—as a combination of a residual and the interaction of residuals and endowments in an Oaxaca decomposition exercise. We interpret the residual in the decomposition as arising from differential treatment of identically situated Black women in the two locations. Although we can only speculate about why there might be differential treatment in Minneapolis vs. Raleigh, we note that Minneapolis has a history and legacy of progressive politics and higher health care access and SNAP usage than Raleigh (Myers and Ha Reference Myers and Ha2018). For example, in 2017, Minnesota SNAP usage was about 10.09 percent, while North Carolina's was about 17.14 percent, but among African Americans only, Minnesota was 40.95 percent and North Carolina was 31.87 percent (Ruggles et al. Reference Ruggles, Flood, Goeken, Grover, Meyer, Pacas and Sobek2020).

Although the OLS model of fixed effects suggests large significant differences in BMI between Black/African American respondents and female respondents between study sites, the Oaxaca decomposition of the gap in BMI between Black women in Minneapolis and Raleigh reveals that all the observed gap can be explained by age and education differences. Virtually none of the gap is attributable to the differential treatment of Black women in Raleigh vs. Minneapolis–at least as we have measured it in the model. Adding more controls for access and physical activity leaves the results unchanged. At least in this analysis, we do not find any support for the claim that the observed differences in baseline BMI are accounted by behavioral, policy, or access factors that may correlate with increasing minimum wages in Minneapolis.