At What Point Do Wages Make Ends Meet? Investigating the Relationship Between Wages and Material Hardship in New York City

Abstract

Several states and local areas have increased the minimum wage in recent years, and we anticipate that many policymakers and advocates will be interested in evaluating the impacts that these increases have had on material hardships such as not having enough money for rent, food, and utilities. The relationship between wages and material hardship absent policy changes has not been thoroughly documented in the literature, however. An understanding of how material hardships relate to wages is critical when trying to determine whether minimum wage policy changes have impacted material hardship. This article asks, is there a linear relationship between wages and material hardship at the bottom of the wage distribution, or do wages have to hit a certain point before we observe significant declines in material hardship? Results come from the New York City Longitudinal Study of Wellbeing (NYC-LSW), also referred to as the “Poverty Tracker,” and are specific to New York City in 2016. The results show that among workers in New York City in 2016, rates of material hardship are relatively constant at the bottom of the wage distribution, if not increasing. However, rates of hardship begin to decline more rapidly in the wage distribution around US$15 per hour.

Keywords

material hardship minimum wage food insecurity wage policy

Introduction

Across the United States, states and local areas have been raising the regional minimum wage far above the federal wage of US$7.25. By 2025, seven states and the District of Columbia will have a minimum wage of US$15 per hour.¹ As these minimum wage increases were designed to make life somewhat easier for low-wage workers, we anticipate that many policymakers and advocates will be interested in evaluating the impacts that they have had on material hardships such as not having enough money for rent, food, or bills. Conducting such an analysis, however, might be less straightforward than expected.

Unlike other economic outcomes, like poverty and income, the relationship between material hardships and wages has not been studied in depth. One study looking at the relationship between changes in the minimum wage and material hardship finds that there is “little evidence” that the state and federal minimum wage increases between 1996 and 2007 reduced material hardship (Sabia & Nielsen, 2015). The federal minimum wage increased from US$4.25 to US$5.85 (US$7.14 and US$7.18 in US$2018²) in these years, meaning that the federal increase just kept pace with inflation, while some states had more substantial minimum wage increases in this period.³ Based on their findings, Sabia and Nielsen conclude that raising the federal minimum wage is not an effective way to address material hardship among low-wage workers. They argue that wage hikes do not have an impact on hardship because (a) those who face material hardships are less likely to benefit from the wage increase (i.e., the policy has poor “target efficiency” in terms of reducing hardship), and (b) that the minimum wage increase reduces employment in the low-wage market, such that some who are eligible for the higher wage actually face unemployment and, eventually, higher levels of hardship (i.e., there are “adverse labor demand effects”).⁴ An alternative interpretation of their finding is that the wage at the end of the period was not high enough to have a noticeable effect on material hardship. This interpretation implies that the relationship between wages and material hardship is not linear at the bottom of the income distribution,⁵ but that wages need to hit a certain level before they are likely to cause significant changes in material hardship. For example, a small raise might mean that you have more money to pay the rent but still fall short of “making” rent, whereas with a larger raise you would be able to pay in full; in both instances, you receive a raise, but only in the second does your wage hit a level where you see a change in housing hardship (i.e., not being able to afford a rent or mortgage payment).

This article answers the question, is there a linear relationship between wages and material hardship at the bottom of the wage distribution, or do wages have to hit a certain point before they are associated with significant declines in material hardship? The answer to this question should affect how states and localities that have implemented higher minimum wages evaluate the impacts of these changes. In addition, the nature of this relationship is particularly consequential for social workers and advocates pushing for more just economic policies as they set policy priorities and respond to analyses of changes to the minimum wage.

It is key to note that this is not an analysis of the impacts that changes to the minimum wage have had on material hardship, and we are not predicting what might happen to levels of material hardship under a minimum wage increase. Instead, we are taking a step back to understand the relationship between wages and material hardship in the absence of a policy change. While our results show how wages relate to material hardship, an increase in the minimum wage might also be associated with reductions in labor demand and increases in prices,⁶ in turn affecting material hardship; the relationship that we document between wages and material hardship cannot account for these other outcomes, which is one reason our findings cannot be used to predict what could happen under a minimum wage increase. Regardless, the findings are important for understanding how wages relate to levels of material hardship. There is little to no research documenting this relationship, and this knowledge will be extremely useful when determining how to assess and interpret the impacts of minimum wage increases.

Material Hardship, Wages, and What We Know Already

Material hardships are often measured in multiple domains, such as housing, food, utilities, finances, and medical care. Typical examples of material hardship include often running out of food without money to buy more, not having enough money to pay the rent or mortgage, and having utility shutoffs (see Danziger et al., 2000; Iceland & Bauman, 2007). These measures of material hardship have been referred to as “more direct” measures of deprivation compared with income-based poverty measures, but they also capture an aspect of well-being that is distinct from poverty (Iceland & Bauman, 2007).

Studies of the relationship between wages and material hardship are scarce in the literature, but studies examining the relationships between poverty and material hardship and income and material hardship are more common. These studies document a moderate association between poverty and material hardship (Beverly, 2001; Boushey et al., 2001; Bradshaw & Finch, 2003; Mayer & Jencks, 1989, 1993; Perry, 2002; Rector et al., 1999). Material hardships are experienced by individuals above the poverty line; however, they are more prevalent among those in poverty (Boushey et al., 2001; Wimer et al., 2018), and material hardship has been found to be more strongly associated with deep poverty and persistent poverty (Iceland & Bauman, 2007). Variations in material hardship by income level have also been documented. Sullivan et al. (2008) find that a 10% increase in average income is associated with a 1.1 percentage decrease in the likelihood of material hardship.

Based on the documented association between income and hardship, one might assume that changes in income or wages would likely correspond to some change in levels of hardship, but how levels of material hardship change in relation to changes in income and wages remains unclear. Sullivan et al. (2008) find a weak association between changes in income and changes in hardship when evaluating this relationship using a fixed-effects framework. However, the Danziger et al. (2000) study of changes in material hardship in Michigan among former Aid to Families with Dependent Children (AFDC) recipients finds that those who transitioned to full-year work after the elimination of AFDC faced lower levels of hardship and economic vulnerability compared with those who did not work after the elimination of AFDC. However, rates of hardship were basically the same for those who did not work and those who worked part of the year, defined as 1% to 64% of the year, even though those who worked part of the year had a household income that was 40% higher than the group that did not work. These results suggest that levels of hardship do not begin to respond to changes in income until income reaches a certain level, such as that achieved by the group that worked for the full year. It is also important to note that material hardship and vulnerability were not eliminated among the population that worked for the full year; 40% of those in this group still faced material hardship, and over half reported difficulty living on their total income.

One reason that studies of material hardship and wages are scarce is data availability. A number of population-specific surveys collect information on material hardships and wages, such as the Women’s Employment Study, a longitudinal survey of single mothers in Michigan who received cash welfare in 1997, and the Fragile Families & Child Wellbeing Study, a survey of children with unmarried parents. Due to the specific nature of these samples, it is difficult to use these data sources to understand how wages relate to material hardship for all workers. A number of other surveys collect data on material hardship but not wages, such as the Early Childhood Longitudinal Program (ECLS) and the National Survey of American Families. The only national survey that collects information on material hardship and wages is the Survey of Income and Program Participation (SIPP), and this is restricted to the 2014 Panel.⁷ In addition, the New York Longitudinal Study of Wellbeing (NYC-LSW) surveys a representative sample of New York City residents and collects data on wages and material hardship. In the past, the lack of surveys representative of the working population and collecting information on wages and material hardship made it difficult to study the relationship between them, but the NYC-LSW and the SIPP now offer the opportunity to unpack this relationship.

There is one study (Sabia & Nielsen, 2015) that examines the response of material hardship to changes in the minimum wage using the SIPP, finding “little evidence” that state and federal minimum wages increase between 1996 and 2007 reduced material hardship.⁸ As stated earlier, the federal minimum wage increased from US$4.25 to US$5.85 (US$7.14 and US$7.18 in 2018⁹) in these years, meaning that the federal increase just kept pace with inflation. States had some more substantial minimum wage increases in this period. These results suggest that the modest increase in the minimum wage in the study years did not affect levels of hardship.

Overall, the association between wages and hardship has not been documented in the literature, but we might assume that there is some relationship between them given the documented relationship between income, poverty, and hardship. This article examines this relationship using data from the New York City Longitudinal Study of Wellbeing (NYC-LSW).

Method

Sample

The New York Longitudinal Study of Wellbeing, also referred to as the “Poverty Tracker,” is a multi-panel longitudinal study local to New York City that tracks annual trends in poverty, material hardship, health, and a host of other indicators of well-being.¹⁰ The first panel of respondents, comprised of 2,286 New Yorkers representative of the city’s population, were interviewed quarterly from 2012 to 2014; the second panel of roughly 3,909 respondents entered the study in 2015 and since then have been interviewed on a quarterly basis by phone or online survey, depending of the respondent’s preference. This study is approved by the Columbia University Internal Review Board.

Measures

The Poverty Tracker’s annual measures of poverty, material hardship, and health come from the annual survey that respondents complete when they enter the study and then repeat every 12 months; the quarterly surveys that respondents complete between the annual surveys cover a variety of topics related to poverty and well-being, including but not limited to assets and debts, employment and training, consumption, and service use. In 2016, questions designed to identify respondents’ hourly wages were added to the Poverty Tracker’s annual survey. This article uses data on hourly wages and material hardship collected on the Poverty Tracker’s 2016 annual survey. At the time of the survey, the minimum wage in New York city was US$9.25 and the survey’s sample size was 3,182. When weighted, this sample is representative of the New York City population in 2016.¹¹ The specific construction of the measures used in the analysis is discussed below.

Material hardship

The Poverty Tracker measures material hardship in multiple domains; specifically, utilities, housing, food, financial, and medical hardship.¹² This article explores the relationship between wages and the material hardships defined below.¹³

Housing hardship

Not paying the full amount of rent or the mortgage because there was not enough money.

Food hardship

Often worrying whether food would run out before we got money to buy more or reporting that the food purchased often did not last and there was not enough money to get more:

Bills hardship

Having phone, gas, or electricity was cut off because there was not enough money to pay the bills.

Financial hardship

Often running out of money between paychecks or before the end of the month.

Hardship

Experiencing any of the hardships listed above (housing, food, bills, and financial).

Multiple hardships

Experiencing more than one of the hardships listed above (housing, food, bills, and financial).

Hourly wages

Survey respondents who report that they are currently working are asked if they are paid hourly or if they receive an annual salary. Those who indicate that they are paid hourly are then asked their hourly wage, and those who receive a salary are asked to report their salary and their usual hours worked. The hourly wage variable used in this analysis is equal to the reported hourly wage for those respondents who reported that they are paid hourly; the hourly wage rate of those who reported a salary is constructed by dividing their weekly salary¹⁴ by their usual hours worked. This analysis also employs a categorical wage variable. The variable groups together those with wages between US$9 per hour and US$11 per hour, US$11.01 and US$15 per hour, US$15.01 per hour and US$19 per hour, US$19.01 per hour and US$22 per hour, and US$22.01 and US$30 per hour. Table 1 presents a descriptive analysis of the sample within each of these wage groups.

Table 1.

Descriptive Analysis of Wage Groups.

Characteristic	Wage group: US$9–US$11	Wage group: US$11–US$15	Wage group: US$15–US$19	Wage group: US$19–US$22	Wage group: US$22–US$30
Sample size	241	185	78	82	113
In SPM* poverty	36.3%	26.1%	13.6%	11.8%	12.3%
In hardship	35.6%	41.5%	23.0%	14.1%	23.7%
White, Non-Hispanic	6.8%	17.9%	21.0%	19.9%	43.4%
Black, Non-Hispanic	28.2%	28.3%	23.4%	28.9%	22.6%
Asian, Multi-Racial, or Other	22.0%	28.7%	34.8%	24.5%	6.5%
Hispanic	43.0%	25.1%	20.7%	26.7%	27.5%
Children in household	52.8%	42.2%	38.9%	38.5%	35.2%
Single-person (adult) household	16.2%	20.9%	13.4%	31.3%	14.7%
Female	63.8%	46.6%	40.2%	45.6%	52.1%
Less than high school education	25.8%	14.3%	7.9%	2.5%	10.2%
High school education	33.2%	27.8%	30.2%	16.1%	24.7%
Some college	29.9%	41.0%	38.5%	39.5%	33.2%
College+	11.0%	16.9%	23.4%	41.9%	31.9%
Age = 18–35 years	53.2%	49.8%	40.8%	47.7%	26.5%
Age = 36–50 years	22.4%	28.7%	29.8%	20.2%	40.2%
Age = 50–65 years	23.4%	20.2%	26.8%	21.2%	29.2%
Age = 65+ years	1.0%	1.3%	2.6%	11.0%	4.2%
Facing a health problem	12.2%	5.9%	7.5%	5.8%	7.6%
Working part-time	39.1%	48.1%	21.4%	10.3%	17.2%
Worked 1–5 months of the 12 months prior	13.0%	8.7%	3.5%	4.1%	1.4%
Worked 6–11 months of the 12 months prior	13.9%	15.0%	17.8%	11.6%	5.1%
Worked all of 12 months prior	73.1%	76.3%	78.6%	84.3%	93.4%
Spouse worked none of the 12 months prior or no spouse	72.3%	76.2%	61.3%	73.4%	47.0%
Spouse worked 1–5 months of the 12 months prior	1.0%	6.6%	6.6%	4.5%	10.1%
Spouse worked 6–11 months of the 12 months prior	10.3%	3.5%	8.6%	4.5%	10.4%
Spouse worked all of 12 months prior	16.4%	13.8%	23.5%	17.5%	32.5%
Has a spouse or cohabiting partner	36.4%	35.7%	51.2%	37.2%	64.7%
Received SNAP** benefits or cash welfare	29.4%	13.8%	9.5%	2.0%	10.5%
Average number of people in household	3.25	3.09	3.10	2.96	3.25
Average number of adults in household	2.33	2.42	2.39	2.44	2.55
Average number of children in household	0.93	0.67	0.71	0.52	0.70

*In poverty, as measured under the Supplemental Poverty Measure (SPM).

**Benefits from the Supplemental Nutrition Assistance Program (SNAP).

Analysis

The analysis is conducted in three steps. First, we produce rates of various hardships across the wage distribution using a local non-parametric weighted regression. While in traditional parametric data analysis, the aim is

to assess the contribution of independent variables and identify their impact on the dependent variable . . . [local modeling] approaches are data-analytics in which regression functions are determined by the data instead of being limited to a certain functional form as in parametric analysis. (Fan & Gijbels, 1996)

That is, the data are allowed to “speak for themselves” without being fitted to a specific functional form. In this analysis, the nonparametric models determine the local average levels of material hardship across the wage distribution. This first analysis allows us to determine how levels of hardship vary by wages. Next, we produce models that determine rates of material hardship across the wage distribution using a linear regression predicting a binary indicator of hardship with a categorical wage variable as the main explanatory variable. The advantage of these models over our nonparametric models is that we are able to isolate the relationship between wages and hardship by controlling for factors that are associated with both, including demographics, work characteristics, and benefit receipt. By defining wages categorically, we are able to see how the relationship between wages and hardship might be different at different parts of the wage distribution. Note that the analysis is limited to workers earning between US$9.25 per hour, the statutory minimum wage in New York City in the survey period, and US$30 per hour. This omits the higher wages earners from our analysis, meaning that all results are specific to the population of lower-wage workers.

While this article’s core analysis comes from the 2016 Poverty Tracker annual survey, the study continues to collect information on wages and material hardship, and can thus track how changes in wages over time relate to change in the minimum wage. A supplemental analysis in the article employs the Poverty Tracker’s longitudinal data in a fixed-effects model that predicts the change in hardship that occurs when workers pass certain wage thresholds.

Results

Among workers in New York City in 2016, rates of material hardship are relatively constant at the bottom of the wage distribution, if not increasing. However, rates of hardship begin to decline more rapidly in the wage distribution around US$15.

Figures 1 and 3 present the results from the first analysis examining the relationship between hardships and wages using the local nonparametric weighted regression. For all forms of hardship, we see a more rapid decline in the prevalence of hardship when wages are US$15 or higher (see Figures A1 –A4 in the appendix for the results for individual hardships). We find that the rate of experiencing any material hardship relatively constant (albeit increasing slightly) among those with wages under US$15 per hour with sharp declines above this wage (Figure 1). The same trend is evident when looking at the prevalence of multiple hardships (Figure 2). The results in the Appendix show that rates of food and housing hardship do not vary much for those with wages below US$15 per hour and drop thereafter (Figures A1 and A2), while rates of financial and bills hardship (Figures A3 and A4) are slightly more common among workers with higher wages (roughly US$13–US$15 per hour) than those with the lowest wages in the distribution (US$9–US$13 per hour), but then drop among workers with higher wages; reasons for these variations at the bottom of the wage distribution warrant further examination, but one possible explanation is that workers with the lowest wages are buffered from these hardships in some way that we have not identified.

Figure 1.

Material hardship rate by hourly wage.

Figure 2.

Rate of multiple material hardships by hourly wage.

Figure 3.

Percentage point difference in the likelihood of material hardship relative to the lowest wage earners (US$9–US$11 per hour).

The local nonparametric regressions examined in Figures 1 and 2 suggest that the relationship between hardship and wages is not linear at the bottom of the wage distribution. Instead, rates of hardship do not decrease relative to wages until wages reach roughly US$15 per hour, or, in some cases, even higher. One response of skepticism to these findings could be driven by the fact that lower wage workers (i.e., those with wages below US$15 per hour, in this instance) are different in some way from those with higher wages, and that the persistent levels of hardship are more likely driven by these other factors. For example, the workers with lower wages are perhaps more likely to work part-time, and it might be the difference in work hours that are keeping levels of hardship higher. To address this type of claim, we ran a series of models predicting the likelihood of material hardship as explained by wages, which were grouped categorically.¹⁵ We also controlled for a number of demographic, work-related, and income transfer receipt variables that are associated with both material hardship and wages. The results from these models confirm that rates of hardship are relatively stable among workers earning less than US$15 per hour, but when wages are higher than US$15 per hour, we see significant reductions in the hardship among workers relative to those earning less than US$15 per hour. Table 2 presents the results from these models.

Table 2.

Models Predicting the Likelihood of Material Hardship by Wage Group.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Housing hardship	Food hardship	Financial hardship	Bills hardship	Hardship	Multiple hardships
Wage group = US$11–US$15	.0758	.0266	.0785	.111**	.136**	.0653
Wage group = US$11–US$15	(.0619)	(.0484)	(.0617)	(.0561)	(.0691)	(.0629)
Wage group = US$15–US$19	−.101*	−.0554	−.0929	.0510	−.0329	−.0946*
Wage group = US$15–US$19	(.0577)	(.0382)	(.0590)	(.0716)	(.0797)	(.0545)
Wage group = US$19–US$22	−.0995	−.0478	−.145***	.00972	−.0993	−.115**
Wage group = US$19–US$22	(.0629)	(.0330)	(.0508)	(.0676)	(.0780)	(.0528)
Wage group = US$22–US$30	−.0356	−.0749**	−.123**	.0302	−.0339	−.107**
Wage group = US$22–US$30	(.0675)	(.0371)	(.0536)	(.0647)	(.0795)	(.0529)
Worked part-time	−.0601	.0358	−.0814	.0669	−.0199	.00346
Worked part-time	(.0535)	(.0419)	(.0497)	(.0533)	(.0607)	(.0534)
Number of people in household	−.0266*	−.0221**	−.0205	−.0154	−.0160	−.0314**
Number of people in household	(.0150)	(.0107)	(.0138)	(.0140)	(.0179)	(.0136)
Children in household	.00316	−.0227	−.0685	−.0115	−.0768	−.0361
Children in household	(.0549)	(.0414)	(.0566)	(.0558)	(.0648)	(.0537)
Race/Ethnicity =. Black, Non-Hispanic	.131**	.0305	.0395	.143***	.217***	.0683
Race/Ethnicity =. Black, Non-Hispanic	(.0552)	(.0379)	(.0470)	(.0544)	(.0700)	(.0487)
Race/Ethnicity = Asian, Multi-Racial, Non-Hispanic	.0337	−.00572	.0318	.0591	.0133	.0206
Race/Ethnicity = Asian, Multi-Racial, Non-Hispanic	(.0612)	(.0459)	(.0573)	(.0494)	(.0680)	(.0569)
Race/Ethnicity = Hispanic	.0569	.0164	.0549	.146***	.142**	.0694
Race/Ethnicity = Hispanic	(.0532)	(.0343)	(.0509)	(.0454)	(.0633)	(.0460)
Female	.0642	.0753**	.101**	.0365	.143***	.0954**
Female	(.0465)	(.0373)	(.0438)	(.0442)	(.0514)	(.0449)
High school	−.109	−.0723	−.0916	.0117	−.0907	−.0816
High school	(.0733)	(.0513)	(.0734)	(.0643)	(.0838)	(.0686)
Some college	−.0358	−.0307	−.0670	.0383	−.0207	−.0186
Some college	(.0778)	(.0541)	(.0749)	(.0658)	(.0856)	(.0722)
College+	−.0968	−.105**	−.0650	.000358	−.0564	−.0828
College+	(.0811)	(.0490)	(.0770)	(.0727)	(.0941)	(.0741)
Age = 36–50 years	.0501	.0402	−.00380	.00268	.0339	.00210
Age = 36–50 years	(.0573)	(.0447)	(.0565)	(.0505)	(.0634)	(.0538)
Age = 50–65 years	−.0716	−.0701*	−.123***	−.0660	−.113*	−.133***
Age = 50–65 years	(.0500)	(.0391)	(.0451)	(.0468)	(.0594)	(.0462)
Age = 65+ years	−.236***	−.131***	−.165***	−.155***	−.309***	−.218***
Age = 65+ years	(.0590)	(.0507)	(.0532)	(.0514)	(.0654)	(.0530)
Severe health problem	.243***	.0652	.0555	.149*	.190**	.133
Severe health problem	(.0902)	(.0691)	(.0837)	(.0867)	(.0876)	(.0835)
Worked 6–11 months in past 12 months	.253***	.130***	.0837	.0389	.164*	.190***
Worked 6–11 months in past 12 months	(.0680)	(.0484)	(.0656)	(.0794)	(.0952)	(.0634)
Worked all of past 12 months	.102*	.0680*	.0420	.0206	.0411	.102*
Worked all of past 12 months	(.0555)	(.0356)	(.0580)	(.0678)	(.0855)	(.0567)
Spouse worked 1–5 months in past 12 months	−.318***	−.217***	−.234**	−.177**	−.409***	−.283***
Spouse worked 1–5 months in past 12 months	(.114)	(.0836)	(.0910)	(.0824)	(.117)	(.0955)
Spouse worked 6–11 months in past 12 months	−.188*	.0220	−.126	.0604	−.107	−.0999
Spouse worked 6–11 months in past 12 months	(.113)	(.117)	(.120)	(.112)	(.115)	(.118)
Spouse worked all of past 12 months	−.229**	−.229***	−.202**	−.0377	−.223**	−.217**
Spouse worked all of past 12 months	(.0901)	(.0783)	(.0841)	(.0700)	(.0939)	(.0862)
Has spouse/partner	.256***	.200**	.200**	.0497	.277***	.210**
Has spouse/partner	(.0870)	(.0864)	(.0855)	(.0720)	(.0867)	(.0876)
Received SNAP^ or cash welfare	.0816	.00482	.0904	.0805	.137**	.118*
Received SNAP^ or cash welfare	(.0646)	(.0430)	(.0671)	(.0568)	(.0684)	(.0657)
Constant	.116	.0780	.223**	−.0349	.139	.136
Constant	(.111)	(.0738)	(.108)	(.0947)	(.133)	(.107)
Observations	699	699	699	699	699	699
R ²	.158	.180	.150	.106	.178	.183

Note. Robust standard errors in parentheses.

p < .1. **p < .05. ***p < .01.

^Supplemental Nutrition Assistance Program (SNAP).

The coefficients associated with each wage group in Table 2 represent the difference in the rate of hardship associated with those in that wage group relative to those with wages between US$9 and US$11 per hour when holding demographic, work-related, and transfer-related characteristics constant. We have visually represented these differences in Figure 3. Looking at the results, we see that those with wages between US$11 and US$15 per hour are slightly more likely to experience each form of hardship compared with those earning between US$9 and US$11 per hour, but these differences are not statistically significant for any form of hardship except bills hardship and overall hardship.¹⁶ With the exception of bills hardship, we see statistically significant declines in the likelihood of experiencing each form of hardship for those with wages over US$15 per hour relative to those earning less than US$15 per hour. We also find that there is a statistically significant reduction in the likelihood of experiencing multiple hardships when wages are greater than US$15 per hour.

Another important consideration when examining the relationship between individual wages and material hardship is household size. The wage variable we use in our models is measured at the individual level while hardships are household-level characteristics. In households with only one adult and no children, we would assume that the relationship between individual-level wages and hardship would be stronger than it is in households with multiple people. The reasoning behind this intuition is that in a household with only one adult and no children, the adult’s individual wage is likely the main source of market income that is used to buffer against hardship; in households with multiple adults, there are potentially additional sources of income that are used to offset hardship. Alternatively, there could be additional people in the household who do not have income (both children and adults), and the individual from whom we collect wage information could be financially responsible for these other household members. Thus, in multi-person households, individual wages are likely less effective at offsetting hardships, so we hypothesize that the relationship between wages and hardship is stronger for households with one adult and no children. We have tested this hypothesis using the same basic models presented in Table 2, but with an interaction term that allows us to separate the difference in the rate of hardship associated with those in the different wage group for “single-person households” versus “multiple-person households” (see Table 3—demographic controls have been suppressed, see Table A1 in the appendix for the full model). Due to sample size considerations, we collapsed the US$15 to US$19 and US$19 to US$22 wage categories.

Table 3.

Models Predicting the Likelihood of Material Hardship by Wage Group and Household Composition.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Housing hardship	Food hardship	Financial hardship	Bills hardship	Hardship	Multiple hardships
US$9–US$11 # multi-person household	0	0	0	0	0	0
US$9–US$11 # multi-person household	(0)	(0)	(0)	(0)	(0)	(0)
US$9–US$11 # single-person household	.0890	.0885	−.101	.105	.0237	.0685
US$9–US$11 # single-person household	(.159)	(.115)	(.123)	(.128)	(.149)	(.151)
US$11–US$15 # multi-person household	.0674	.0209	.0421	.118**	.135*	.0678
US$11–US$15 # multi-person household	(.0635)	(.0470)	(.0670)	(.0591)	(.0764)	(.0659)
US$11–US$15 # single-person household	.189	.133	.125	.181	.161	.109
US$11–US$15 # single-person household	(.126)	(.110)	(.128)	(.112)	(.129)	(.119)
US$15–US$22 # multi-person household	−.0640	−.0265	−.112**	.0526	−.0548	−.0583
US$15–US$22 # multi-person household	(.0527)	(.0353)	(.0538)	(.0561)	(.0694)	(.0482)
US$15–US$22 # single-person household	−.140	−.0578	−.214***	.0602	−.0648	−.199***
US$15–US$22 # single-person household	(.0848)	(.0409)	(.0672)	(.116)	(.135)	(.0638)
US$22–US$30 # multi-person household	.000180	−.0629	−.126**	.0738	.00534	−.0770
US$22–US$30 # multi-person household	(.0741)	(.0411)	(.0589)	(.0691)	(.0892)	(.0552)
US$22–US$30 # single-person household	−.0920	−.0329	−.178***	−.0570	−.176**	−.147**
US$22–US$30 # single-person household	(.0735)	(.0437)	(.0683)	(.0531)	(.0851)	(.0631)
Demographic controls suppressed
Constant	.0884	.0367	.270**	−.0756	.138	.139
Constant	(.126)	(.0864)	(.118)	(.0972)	(.144)	(.122)
Observations	699	699	699	699	699	699
R ²	.166	.188	.157	.113	.180	.192

Note. Robust standard errors in parentheses.

p < .1. **p < .05. ***p < .01.

The results in Table 3 reveal that in both types of households—single and multiple person—higher wages are associated with lower levels of hardship, but the reduction in hardship associated with higher wages is larger for single-person households than for households with multiple people. Figures 4 and 5 depict the coefficients associated with any hardship and multiple hardships for different wage groups by household composition. These findings suggest that wage increases might be more effective at reducing hardship when (a) there are fewer people in the household, or (b) multiple people in the household receive a higher wage.

Figure 4.

Percentage point difference in the likelihood of material hardship relative to the lowest wage earners (US$9–US$11 per hour) by household size.

Figure 5.

Percentage point difference in the likelihood of experiencing multiple material hardships relative to lowest wage earners (US$9–US$11 per hour).

Finally, we make use of the longitudinal aspect of the Poverty Tracker to examine the changes in hardship associated with wage changes that we observed at the 2017 annual survey (i.e., the annual survey following the 2016 survey on which we base our main analysis). In Table 4, we present results from three models predicting the change in levels of hardship associated with changing wage groups for those earning less than US$13 per hour in 2016. These are fixed effects models with controls for the individual and time. Model 1 identifies the change in hardship associated with having one’s wage rise above US$13 per hour but remaining below US$20 per hour between 2016 and 2017. This raise is associated with a very slight decrease in the level of hardship (−1.50%), but this result is not statistically significant. In Model 2, we identify the change in hardship associated with having one’s wage rise above US$15 per hour but remaining below US$20 per hour. The results are again not statistically significant, but we notice that the size of the coefficient (−6.10%) is larger than that associated with just having wages rise over US$13 per hour. In Model 3, looking at the impact of a raise over US$17, the results are again not significant, but the coefficient is larger (−14.9%). These results are consistent with the general trend that we have discussed thus far: there is little difference in rates of hardship among workers with a wage below roughly US$15 per hour, but rates of hardship appear to decline more quickly above this threshold.

Table 4.

Changes in Rates of Hardship Between Time 1 and Time 2 Among Workers With Wages Under US$13 per Hour at Time 1.

	(1)	(2)	(3)
Variables	Change in Rate of Hardship	Change in Rate of Hardship	Change in Rate of Hardship
Wages rises above US$13	−.0150
Wages rises above US$13	(.0551)
Wage rises above US$15		−.0610
Wage rises above US$15		(.0723)
Wage rises above US$17			−.149
Wage rises above US$17			(.144)
Number of adults in the household	.0354	.0362	.0368
Number of adults in the household	(.0365)	(.0365)	(.0365)
Number of children in the household	−.00648	−.00619	−.00561
Number of children in the household	(.0474)	(.0472)	(.0472)
Health problems	.0330	.0365	.0356
Health problems	(.0920)	(.0916)	(.0915)
Part-time work	−.00589	−.0111	−.00815
Part-time work	(.0688)	(.0688)	(.0686)
Months worked	.00857	.00890	.00773
Months worked	(.00807)	(.00798)	(.00794)
Constant	.256*	.256*	.262**
Constant	(.130)	(.130)	(.130)
Observations	450	450	450
R ²	.009	.012	.014
Number of groups	225	225	225

Note. Standard errors in parentheses.

p < .10. **p < .05. ***p < .01.

Implications for Practice and Conclusion

The social work profession is committed to economic justice, and it is through this professional value that social workers became involved in the Fight for 15 and other movements that address income inequality. The Fight for 15 and related grassroots efforts that have pushed policymakers to tackle wage suppression through statutory increases in the minimum wage have been a great success, and due to these efforts, workers across the country are earning more. But the results presented in this article imply that a US$15 wage may not be enough to protect workers from material hardship in New York City, suggesting that campaigns for higher wages must continue to build off of their recent successes and push for further increases in wages. According to the MIT Living Wage calculator, a model which uses regional cost data to determine the hourly earnings from employment necessary to cover a family’s basic needs, a single adult without children in the New York City would just cover basic expenses when working full-time with a wage of US$17.46 per hour, and a single adult with one child working full-time would only cover these expenses if their wage was US$31.99 per hour.¹⁷ The results from this calculator highlight that in a high-cost area like New York City, wages need to be high for individuals to get by.

Social workers at the micro-level may observe this firsthand as those impacted by the minimum wage increases express that their raise was helpful, but that it is still a struggle to make ends meet. At the macro level, those working in research must have a grasp of the existing relationship between wages and other outcomes that will likely be assessed in the evaluation of changes to the minimum wage—including hardship. Only with this understanding can one provide thoughtful interpretations of the impact that changes to the minimum wage has had. Finally, these results have implications for the interaction between wages and other policy reforms in which social workers are invested. For example, the relationship between wages and hardship is also linked to benefit eligibility, and reductions in hardship that are associated with increases in wages could be eroded by benefit cliffs. Pushing for reforms that raise benefit eligibility thresholds while raising the minimum wage is one area where social workers involved in policy and advocacy could make great headway. As research on the interaction between the recent statutory minimum wage increases and labor demand enters the public dialogue, advocates must also consider how to buffer against any responses to wage hikes in the labor market, ensuring that these responses do not undermine their efforts.

There are acknowledged limitations to the results presented in this article. First, the results are particular to New York City and they might not hold in other, lower-cost cities. Second, they are not causal estimates demonstrating how changes in the minimum wage might impact material hardship. In addition, this is only an examination of the relationship between wages and hardship, while hardship is related to many factors like health, family size, and social networks, for example. The results do, however, document the existing relationship between wages and material hardships pre-policy changes, and an understanding of this relationship is crucial to future evaluations of minimum wage reforms.

Footnotes

Appendix

Table A1.

Models Predicting the Likelihood of Material Hardship by Wage Group and Household Composition.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Model 1: Housing Hardship - with Interaction for Single Adult	Model 2: Food Hardship - with Interaction for Single Adult	Model 3: Financial Hardship - with Interaction for Single Adult	Model 4: Bills Hardship - with Interaction for Single Adult	Model 5: Hardship - with Interaction for Single Adult	Model 6: Multiple Hardship - with Interaction for Single Adult
$9 to $11 * Multi-Person Household	0	0	0	0	0	0
	(0)	(0)	(0)	(0)	(0)	(0)
$9 to $11 * Single-Person Household	0.0890	0.0885	−0.101	0.105	0.0237	0.0685
	(0.159)	(0.115)	(0.123)	(0.128)	(0.149)	(0.151)
$11 to $15 * Multi-Person Household	0.0674	0.0209	0.0421	0.118**	0.135*	0.0678
	(0.0635)	(0.0470)	(0.0670)	(0.0591)	(0.0764)	(0.0659)
$11 to $15 * Single-Person Household	0.189	0.133	0.125	0.181	0.161	0.109
	(0.126)	(0.110)	(0.128)	(0.112)	(0.129)	(0.119)
$15 to $22 * Multi-Person Household	−0.0640	−0.0265	−0.112**	0.0526	−0.0548	−0.0583
	(0.0527)	(0.0353)	(0.0538)	(0.0561)	(0.0694)	(0.0482)
$15 to $22 * Single-Person Household	−0.140	−0.0578	−0.214***	0.0602	−0.0648	−0.199***
	(0.0848)	(0.0409)	(0.0672)	(0.116)	(0.135)	(0.0638)
$22 to $30 * Multi-Person Household	0.000180	−0.0629	−0.126**	0.0738	0.00534	−0.0770
	(0.0741)	(0.0411)	(0.0589)	(0.0691)	(0.0892)	(0.0552)
$22 to $30 * Single-Person Household	−0.0920	−0.0329	−0.178***	−0.0570	−0.176**	−0.147**
	(0.0735)	(0.0437)	(0.0683)	(0.0531)	(0.0851)	(0.0631)
Worked part time	−0.0562	0.0352	−0.0789	0.0744	−0.0105	0.00732
	(0.0525)	(0.0411)	(0.0491)	(0.0527)	(0.0604)	(0.0531)
Number of people in household	−0.0241	−0.0152	−0.0270*	−0.0115	−0.0185	−0.0346**
	(0.0162)	(0.0110)	(0.0153)	(0.0144)	(0.0195)	(0.0149)
Children in household	0.0100	−0.0215	−0.0673	−0.00489	−0.0698	−0.0305
	(0.0560)	(0.0420)	(0.0573)	(0.0552)	(0.0654)	(0.0547)
Race/Ethnicity = Black, Non-Hispanic	0.147**	0.0436	0.0406	0.153***	0.221***	0.0802
	(0.0577)	(0.0405)	(0.0466)	(0.0551)	(0.0713)	(0.0507)
Race/Ethnicity = Asian, Multi-Racial, or Other	0.0354	−0.00883	0.0384	0.0643	0.0232	0.0194
	(0.0623)	(0.0477)	(0.0573)	(0.0486)	(0.0682)	(0.0578)
Race/Ethnicity = Hispanic	0.0689	0.0237	0.0518	0.157***	0.147**	0.0765
	(0.0543)	(0.0340)	(0.0513)	(0.0472)	(0.0647)	(0.0468)
Female	0.0575	0.0733*	0.0978**	0.0328	0.139***	0.0818*
	(0.0463)	(0.0375)	(0.0434)	(0.0397)	(0.0498)	(0.0448)
Variables	Model 1: Housing Hardship - with Interaction for Single Adult	Model 2: Food Hardship - with Interaction for Single Adult	Model 3: Financial Hardship - with Interaction for Single Adult	Model 4: Bills Hardship - with Interaction for Single Adult	Model 5: Hardship - with Interaction for Single Adult	Model 6: Multiple Hardship - with Interaction for Single Adult
Educational Attainment = High School	−0.109	−0.0718	−0.105	0.0181	−0.0889	−0.0826
	(0.0746)	(0.0526)	(0.0734)	(0.0639)	(0.0844)	(0.0702)
Educational Attainment = Some College	−0.0362	−0.0290	−0.0718	0.0366	−0.0253	−0.0193
	(0.0784)	(0.0543)	(0.0734)	(0.0659)	(0.0860)	(0.0725)
Educational Attainment = College +	−0.0961	−0.106**	−0.0759	0.00138	−0.0582	−0.0862
	(0.0815)	(0.0492)	(0.0773)	(0.0734)	(0.0948)	(0.0745)
Age = 36 to 50	0.0422	0.0344	−0.0144	0.00286	0.0339	−0.00205
	(0.0539)	(0.0388)	(0.0517)	(0.0455)	(0.0611)	(0.0499)
Age = 50 to 65	−0.0740	−0.0741*	−0.131***	−0.0605	−0.107*	−0.135***
	(0.0496)	(0.0384)	(0.0446)	(0.0447)	(0.0587)	(0.0456)
Age = 65+	−0.249***	−0.139***	−0.193***	−0.164***	−0.324***	−0.238***
	(0.0587)	(0.0501)	(0.0581)	(0.0453)	(0.0625)	(0.0546)
Severe health problem	0.245***	0.0667	0.0620	0.148*	0.191**	0.135
	(0.0913)	(0.0701)	(0.0824)	(0.0885)	(0.0890)	(0.0850)
Worked 6 to 11 Months in Past 12	0.253***	0.131***	0.0884	0.0397	0.166*	0.191***
	(0.0675)	(0.0480)	(0.0654)	(0.0796)	(0.0948)	(0.0635)
Worked All of Past 12	0.0962*	0.0633*	0.0475	0.0131	0.0377	0.0989*
	(0.0544)	(0.0346)	(0.0583)	(0.0672)	(0.0855)	(0.0566)
Spouse Worked 1 to 5 Months in Past 12	−0.327***	−0.225***	−0.227**	−0.190**	−0.414***	−0.287***
	(0.115)	(0.0849)	(0.0912)	(0.0820)	(0.118)	(0.0947)
Spouse Worked 6 to 11 Months in Past 12	−0.191*	0.0210	−0.133	0.0597	−0.110	−0.100
	(0.113)	(0.118)	(0.120)	(0.112)	(0.116)	(0.119)
Spouse Worked All of Past 12	−0.235**	−0.233***	−0.205**	−0.0427	−0.227**	−0.217**
	(0.0911)	(0.0789)	(0.0848)	(0.0692)	(0.0941)	(0.0870)
Has Spouse/Partner	0.260***	0.213**	0.196**	0.0558	0.274***	0.201**
	(0.0905)	(0.0877)	(0.0884)	(0.0735)	(0.0909)	(0.0905)
Receive SNAP^ or Cash Welfare Benefits	0.0812	0.00596	0.0879	0.0803	0.135**	0.122*
	(0.0635)	(0.0423)	(0.0659)	(0.0557)	(0.0678)	(0.0646)
Constant	0.0884	0.0367	0.270**	−0.0756	0.138	0.139
	(0.126)	(0.0864)	(0.118)	(0.0972)	(0.144)	(0.122)

Observations	699	699	699	699	699	699
R ²	0.166	0.188	0.157	0.113	0.180	0.192

Note. Robust standard errors in parentheses.

p < 0.1; **p < 0.05; ***p < 0.01.

Supplemental Nutrition Assistance Program (SNAP)

Acknowledgements

The author thanks the anonymous reviewers of this article for their thoughtful comments, as well as other colleagues who reviewed drafts of this article.

Disposition co-editors: Jeffrey J. Shook, Sara Goodkind, Rafael Engel, and Sandra Wexler

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for the Poverty Tracker study upon which the results in this article are based was provided by Robin Hood and the Columbia Population Research Center. The findings and interpretations reported in this article are solely the responsibility of the author and do not necessarily represent the official views of the funders.

ORCID iD

Sophie Collyer

Notes

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