Minimum wages, poverty, and material hardship: new evidence from the SIPP

Abstract

While a number of policymakers have argued that raising the minimum wage will reduce material hardship, empirical evidence to support or refute this claim is scant. Using data drawn from the Survey of Income and Program Participation, we examine the effect of minimum wage increases on poverty, material hardship, and government program participation. Difference-in-difference estimates provide little evidence that state and federal minimum wage increases between 1996 and 2007 reduced poverty, material hardship, or receipt of public program benefits among all individuals, workers, younger individuals without high school degrees, or younger black individuals. Our findings are robust across several measures of hardship, including poverty, financial hardship, housing stress, food insecurity, durable goods deprivation, and health insecurity. We find some evidence of modest redistribution effects of the minimum wage among low-skilled individuals.

1 Introduction

A large number of studies have found that minimum wage increases are ineffective at reducing poverty because of poor target efficiency¹ (Sabia and Burkauser 2010; Burkhauser and Sabia 2007; Leigh 2007; Burkhauser and Harrison 1999; Burkhauser et al. 1996; Burkhauser and Finegan 1989; Stigler 1946) and adverse labor demand effects (Neumark et al. 2004, 2005; Neumark and Wascher 2002; Sabia 2008; Burkhauser et al. 2000a, b). However, little is known about the effectiveness of minimum wage increases at reducing material hardship despite policymakers’ frequent claims that raising the minimum wage will reduce consumption deprivation (Kennedy 2005; Obama 2008).²

Poverty researchers have long recognized important distinctions between income- and consumption-based measures of deprivation (Iceland and Bauman 2007; Mayer and Jencks 1989). While the official U.S. poverty threshold is commonly used to measure income deprivation, this measure has been found to be ineffective at predicting consumption deprivation, such as food insecurity, housing hardship, durable goods deprivation, or health insecurity (Iceland and Bauman 2007). Thus, while raising the minimum wage has been found to be an ineffective anti-poverty tool, it could be a more effective means of fighting consumption deprivation. For example, minimum wage increases may be better targeted to those in hardship than those in poverty if the share of minimum wage workers who live in material hardship is greater than the share of minimum wage workers who live in poverty. This may be the case if a number of non-poor minimum wage workers live in hardship.³ Empirically, the effectiveness at relieving consumption-based deprivation remains largely unexplored.

Using data drawn from the Survey of Income and Program Participation (SIPP), we estimate the effect of minimum wage increases between 1996 and 2007 on numerous measures of hardship, including poverty, financial insecurity, housing insecurity, durable good deprivation, food insecurity, and health insecurity. Consistent with earlier work using the Current Population Survey (CPS) (Sabia and Burkauser 2010), we find little evidence that raising the minimum wage is an effective anti-poverty tool among individuals of working-age (16-to-64) or among workers. Moreover, we find little evidence that minimum wage increases alleviate poverty among those who are younger and less-educated (ages 16-to-29 without a high school degree) or younger (ages 16-to-24) and black.

Turning our attention to material hardship, we find little evidence that increases in the minimum wage are associated with a reduction in financial hardship, housing stress, health insurance status, durable good deprivation, food insecurity, or participation in means-tested government programs such as food stamps, housing or rental assistance, energy assistance, or public health insurance. In dynamic models that explore flows into and out of poverty and onto or off of means-tested public programs, we continue to find little consistent evidence that minimum wage increases are effective in preventing individuals from falling into poverty or participating in public programs. Instead, we find modest evidence of redistributional effects of the minimum wage among some low-skilled individuals.

2 Theoretical framework and prior empirical literature

2.1 Theoretical framework

The effect of minimum wage increases on hardship is theoretically ambiguous. Workers previously earning wages between the old and new minimum wage who retain their jobs and do not have their hours significantly reduced will see earnings gains, which may reduce material hardship. Moreover, wage effects could spillover to those whose hourly earnings are higher than the minimum wage if, for instance, contracts for these workers are tied to the minimum wage.⁴

On the other hand, the wage gains that reduce hardship among some low-skilled workers will be paid for by others, which could mitigate the anti-hardship effects of the minimum wage. First, minimum wage increases could increase output prices for goods and services produced by businesses that employ relatively larger shares of minimum wage workers (Aaronson et al. 2011). To the extent that these goods and services—such as fast food and retail goods—are consumed by low-skilled workers, hardship alleviating effects of the minimum wage may be reduced. Second, earnings gains for those members of the working poor who previously qualified for means-tested public benefits may result in a reduction in these benefits or even ineligibility, thus offsetting any hardship gains. Finally, because minimum wage increases raise the costs of low-skilled labor to employers, firms may lay off workers or cut their hours, both of which could reduce earnings and increase hardship (Neumark and Wascher 2002; Sabia and Burkauser 2010). Moreover, some disemployed low-skilled workers from the covered sector—where the minimum wage binds—may seek employment in the uncovered sector (e.g. underground economy or certain types of tipped employment). The increase in the supply of low-skilled labor in the uncovered sector would tend to decrease average wages, which could increase hardship among these workers.

Given the above theoretical predictions, it is possible that minimum wage increases could redistribute hardship among low-wage workers, with some being pulled into hardship and others pushed out of hardship (Neumark and Wascher 2002). Moreover, if few of those in hardship are affected by minimum wage increases, it may be that such hikes have very small effects on the distribution of hardship. Because the effect of minimum wage increases on net hardship is theoretically ambiguous, it is left as an empirical question.

2.2 Prior empirical literature

A number of recent studies have explored the effects of minimum wage increases on poverty (see, for example, Card and Krueger 1995; Addison and Blackburn 1999; Neumark and Wascher 2002; Neumark et al. 2004, 2005; Burkhauser and Sabia 2007; Leigh 2007; Sabia 2008; Sabia and Burkauser 2010; Gundersen and Ziliak 2004), and most have found little evidence that the minimum wage is an effective anti-poverty tool.⁵ One set of studies (Card and Krueger 1995; Burkhauser and Sabia 2007; Sabia and Burkauser 2010) has used repeated cross-sectional data to generate a panel of states and years to look at aggregate poverty effects, while another has used matched Current Population Survey data to explore family-specific flows of income and poverty following minimum wage increases (Neumark and Wascher 2002; Neumark et al. 2004, 2005). Each has reached a similar conclusion about the ineffectiveness of the minimum wage in alleviating poverty.

Three key reasons explain the minimum wage’s poor performance. First, while minimum wage increases could induce labor–labor substitution that increases the employment of some poor workers, many will remain non-workers and will fail to benefit from hikes in the minimum wage (Card and Krueger 1995). Second, even among workers minimum wages are poorly targeted to those who are poor (Burkhauser and Finegan 1989; Burkhauser et al. 1996; Burkhauser and Harrison 1999; Burkhauser and Sabia 2007; Sabia and Burkauser 2010). Finally, minimum wage increases may be accompanied by adverse labor demand effects that diminish net income gains (Neumark and Wascher 2007, 2008). Neumark and Wascher (2008) reviewed over 90 studies published since Card and Krueger (1994, 1995) and concluded that there is overwhelming evidence that the least-skilled workers experience the strongest disemployment effects from minimum wage increases with median employment elasticities ranging from −0.1 to −0.3.⁶ These adverse labor demand effects may have the effect of undermining income gains to low-skilled workers.

Neumark et al. (2004, 2005) and Neumark and Wascher (2002) found that some low-skilled workers living in poor families who remained employed after a minimum wage hike saw their incomes rise and move out of poverty while other low-skilled workers lost their jobs or had their hours substantially reduced, causing income losses and increased poverty. Neumark and Wascher (2002) concluded that minimum wage increases simply result in income redistribution among low-skilled workers around the poverty line. Their results suggest that on net low-skilled workers may be made worse off.

A few studies have explored the effect of minimum wage increases on public program participation, though most of the focus has been on Aid to Families with Dependent Children (AFDC)/Temporary Assistance to Needy Families (TANF). Brandon (1995) and Turner (1999) used SIPP data to estimate the effect of minimum wage increases on the probability of exit from AFDC and reach opposite conclusions. However, these studies focused on only a few years of data and minimum wage effects are likely to be imprecisely estimated in short panels. The Council of Economic Advisors (1999) examined a longer panel and found that minimum wage hikes were associated with a decrease in welfare caseloads. However, controlling more carefully for state-specific time trends, Page et al. (2005) found that a 10 % increase in the minimum wage was associated with a 1–2 % increase in welfare caseloads.

Sabia and Burkauser (2010) recently updated the literature on minimum wages and poverty in the mid-2000s using CPS data. These authors found no evidence that minimum wage increases between 2003 and 2007 affected poverty rates and focused on the poor target efficiency of the minimum wage as one explanation for their finding.

Only one study of which we are aware has explored the effects of minimum wage increases on material hardship indicators that are available in the SIPP. Heflin (2009) used data from the 1992-2004 SIPP panels to explore the relationship between a number of state policies and (i) food insufficiency, (ii) difficulty paying bills, and (iii) difficulty paying for home expenses among families with children. In specifications that excluded state fixed effects, she found that higher minimum wages were associated with lower levels of hardship, but the estimated effect of the minimum wage became smaller with the inclusion of state fixed effects. Notably, Heflin (2009) pooled each panel’s material hardship topical module—which occurs once each panel—whereas our work includes data from each month available across three panels. As a result, we are able to exploit dynamic monthly poverty status transitions that occur throughout each panel; include dynamic indicators of the receipt of public program assistance such as food stamps, public health insurance, and energy assistance; and assess a larger number of potential hardships that are available beyond the material hardship topical modules, including the lack of access to a clothes washer and dryer, whether the person was housing cost burdened, had missed a doctor or hospital visit, or ever lacked private health insurance. Moreover, in contrast to Heflin (2009), the current study explores the hardship effects of minimum wages across groups of heterogeneous skill levels.

More recently, McCarrier et al. (2011) drew data from the Behavioral Risk Factor Surveillance System from 1996 to 2007 and found that state minimum wage increases are associated with a lower probability of unmet medical needs, but no change in insurance status.⁷

The current study builds on the work of Sabia and Burkauser (2010), Heflin (2009), and McCarrier et al. (2011) by examining the effects of minimum wage increases on a broader set of hardship measures across a number of different populations, including less-skilled and less-experienced individuals. Exploiting substantial policy variation during the period from 1996 to 2007—when 31 states and the District of Columbia raised their minimum wages above the federal level—we examine effects of minimum wages on poverty, financial hardship, housing stress, durable good deprivation, food insecurity, health insurance status, and means-tested government program participation. Finally, we build on the work of Neumark and Wascher (2002) by exploiting longitudinal data in the SIPP to examine the effect of minimum wage increases on flows into and out of poverty and onto or off of means-tested public assistance programs.

3 Data

Our analysis uses data drawn from the 1996, 2001, and 2004 panels of the SIPP, which cover the calendar years 1996–2007.⁸ The SIPP is a nationally-representative survey of the non-institutionalized, civilian population conducted by the U.S. Census Bureau. Within each 3- to 4-year panel, households are interviewed every 4 months, a period the Census Bureau refers to as a wave. The SIPP also tracks individuals as they move (Neumark and Kawaguchi 2004). Because the recall period of 4 months is relatively short, data from the SIPP are thought to be less prone to respondent recall errors than other federal surveys that collect retrospective income, household composition, program participation, and health insurance data from as long as a full year prior to the interview. Each SIPP panel consists of core and topical survey modules. The core modules include basic demographic, employment, income, and receipt of common government transfers at the monthly level. Questions that are not asked at each interview are grouped into topical modules that address a variety of topics, including food sufficiency and security and a rich set of financial assessment questions relevant for research investigating low-income populations. The timing and frequency of the topical modules varies, as does the duration of the reference period to which the question refers. As a result, some data found in topical modules are available on an annual basis whereas some topical module data are available as infrequently as one time per panel. Across each of the three panels we exploit the monthly core data whenever they exist, and use the less frequent topical module data when dictated by the design of the SIPP.

4 Measures

4.1 Poverty

We begin by generating a number of measures of poverty to benchmark our estimates in the existing minimum wage-poverty literature. First, a standard annual family income-to-poverty ratio is calculated using annual family income and SIPP-provided poverty thresholds. Then, from this annual income-to-poverty ratio, thresholds for binary poverty indicator variables are set at 100, 125, and 150 % of poverty. For each income-to-poverty indicator variable, 1 indicates an annual family income below the given poverty threshold and 0 indicates otherwise. In addition, the SIPP offers a constructed variable that includes means-tested cash transfer income received each month when reporting each month’s family income. We use this constructed variable to calculate an alternative annual family income-to-poverty ratio that accounted for means-tested cash transfer income.⁹

We next turn to consumption-deprivation measures that capture material hardship, or what Beverly (2001; p. 24) described as “the inadequate consumption of very basic goods and services such as food, housing, clothing, and medical care.” Numerous researchers have attempted to define material hardship, with the common theme being the material deprivation of goods and/or services that some or all members of a society would deem necessary for adequate living (Ouellette et al. 2004). Our hardship outcomes measure financial insecurity, housing insecurity, durable good deprivation, health insecurity, and food insecurity.

4.2 Financial insecurity

First, we generate two indicators of financial hardship. The following questionnaire item is used to assess each respondent’s ability to meet essential financial obligations:

Next are questions about difficulties people sometimes have in meeting their essential household expenses for such things as mortgage or rent payments, utility bills, or important medical care:

During the past 12 months, has there been a time when you/your household did not meet all of your essential expenses?

During the past 12 months, has there been a time when you/your household had difficulty paying the full amount of the gas, oil, or electricity bill?

Each of these financial insecurity indicators is coded 1 in the presence of the hardship and 0 otherwise.

4.3 Housing insecurity

Several indicators of a respondent’s housing security status are included in the analyses. First, we calculate a housing cost burden indicator to identify respondents whose housing-related expenditures exceed 50 % of total household income.¹⁰ Second, we generate an indicator to identify respondents living in households that reported not paying their rent or mortgage in the preceding 12 months.¹¹ Third, we create a summary indicator of housing hardship that includes the above indicators or any of the following: any “exposed electrical wires in the finished areas of your home”; “a toilet, hot water heater, or other plumbing that doesn’t work”; “broken window glass, or windows that can’t shut”; “pests such as rats, mice, roaches, or other insects” present in the home at the time of the interview; whether there was “a leaking roof or ceiling”; or having been “evicted from your home or apartment for not paying the rent or mortgage” in the preceding 12 months. Each of the housing hardship indicators is coded 1 in the presence of the housing hardship and 0 otherwise.

4.4 Durable good deprivation

Two indicators of respondents’ access to durable consumer goods that are particularly relevant for workers are included in the analysis. In separate questions for each of the durables, respondents were asked whether they had a clothes washer/dryer in their home or building. If respondents indicated that they had a clothes washer/dryer in their home or that they had access to a clothes washer/dryer in the building in which they lived, they are coded as 1. When no access in the home or building was available, respondents are coded as 0.

4.5 Health insecurity

We generate two indicators of respondents’ health security status. First, respondents were asked whether there was a time in the preceding 12 months when anyone in the household “needed to see a doctor or go to the hospital but did not go”.¹² Respondents are coded 1 in the presence of this hardship and 0 otherwise. Second, respondents were assigned a 1 if they were without private health insurance in any month of a given calendar year and 0 if they reported private health insurance in each month of that year.

4.6 Food insecurity

Food insecurity status is determined through a set of five questions available in the SIPP that were derived from the standard USDA 18 question assessment that estimates the extent to which nutritionally adequate foods are not available to the household due to economic constraints (Nord et al. 2003).¹³ The abbreviated food insecurity measure included once each SIPP panel asks each respondent at least two questions.¹⁴ Respondents are first asked whether it was “often true, sometimes true, or never true” that in the last 4 months:

The food that (I/we) bought just didn’t last and (I/we) didn’t have money to get more.

(I/we) couldn’t afford to eat balanced meals.

Based on responses to these two questions and the composition of the household, respondents may be asked one or more of the following questions that assess both adult and child food insecurity.¹⁵ Following the Economic Research Service (Nord 2002), we use “often” or “sometimes” responses to these questions to calculate food insecurity status such that the presence of food insecurity equals 1; the absence of food insecurity equal 0.

4.7 Public program participation

We draw on four measures of means-tested public program participation: food stamp program participation, rental/housing assistance program participation, energy assistance program participation, and the receipt of public health insurance. Each program participation indicator is available in every wave of the three panels. The receipt of food stamps and public health insurance are available monthly, whereas the receipt of energy assistance or rental/housing assistance is available quarterly with the question referring to any time since the first reference month of the wave.¹⁶ Each of these indicators is coded 1 when the respondent received program benefits in the given month or quarter and 0 otherwise. As noted above, minimum wage increases may decrease public program participation if earnings gains reduce eligibility; however, they may also increase program participation if adverse employment effects cause earnings losses.

5 Methods

5.1 Baseline models

We begin by drawing individual SIPP data from calendar years 1996–2007 to estimate poverty regressions similar to those estimated using CPS data (see, for example, Burkhauser and Sabia 2007; Sabia and Burkauser 2010):

$$ Poverty_{ismt} = \, \beta MW_{smt} + {\mathbf{X}}_{\text{imt}}^{\prime } \delta + {\mathbf{E}}_{\text{st}}^{\prime } \delta \, + {\mathbf{P}}_{\text{st}}^{\prime } \kappa + \theta_{\text{s}} + \gamma_{\text{m}} + \tau_{\text{t}} + \varepsilon_{\text{ismt}} $$

(1)

where Poverty _st is an indicator of whether individual i living in state s in month m at year t lives in a family that falls below the poverty threshold and MW _st is the natural log of the higher of the state or federal minimum wage in state s at year t collected from the Bureau of Labor Statistics (BLS). If the state (or federal) minimum wage changed mid-year, MW_st is calculated using the weighted average of the annual minimum wage that prevailed during the period that poverty was measured.¹⁷ In addition, X _st is a vector of individual demographic controls, E _st is a vector of state-specific controls, P _st is vector of state policy variables, θ_s is a time-invariant state effect, γ_m is a state-invariant month effect, and τ_t is a state-invariant time effect.

The demographic controls included in the vector X _it are similar to those used by Sabia (2008) in his study of the effect of minimum wage hikes on poverty rates: age, race, gender, educational attainment, marital status, and urbanicity.¹⁸ The state-level economic controls we include in E _st mirror those used by Card and Krueger (1995), Sabia and Burkauser (2010), Burkhauser and Sabia (2007) in their CPS-based study of the effects of the minimum wage on poverty: the natural log of the prime-age (ages 25-to-54) unemployment rate and the average wage rate of prime-age males. The prime-age unemployment rate is included because it captures differential economic trends across states that could be correlated with both the passage of minimum wage increases and poverty, but not be affected by minimum wage increases because relatively few prime-age males are minimum wage workers (Burkhauser et al. 2000a; Neumark and Wascher 2002; Sabia and Burkauser 2010). The average prime-age wage rate is included, to capture differences in state trends in average wages uncorrelated with the minimum wage, following a number of minimum wage studies (Card and Krueger 1995; Burkhauser et al. 2000a, b; Sabia and Burkauser 2010) that have preferred it to a Kaitz-style index (Sabia 2009).

Moreover, we follow Neumark and Wascher (2002) and include a set of state welfare policies P _st that could be correlated with minimum wage changes and economic well-being: the state and year-specific refundable percentage of the federal EITC that is paid to state taxpayers via the state tax system (obtained from the Center on Budget and Policy Priorities), an indicator of whether the state strictly and immediately enforces a work requirement for public assistance recipients, and a continuous measure of the state lifetime welfare time limit in months.

After estimating Eq. (1), we extend previous CPS-based studies by exploiting SIPP-specific information on material hardship and public program participation and estimate:

$$ Hardship_{ist} = \beta MW_{st} + {\mathbf{X}}_{\text{it}}^{\prime } \delta + {\mathbf{E}}_{\text{st}}^{\prime } \delta + {\mathbf{P}}_{\text{st}}^{\prime } \kappa + \theta_{\text{s}} + \gamma_{\text{m}} + \tau_{\text{t}} + \varepsilon_{\text{ist}} $$

(2)

where Hardship _ist is an indicator variable for whether the respondent lives in material hardship. We also estimate models where Hardship _ist is replaced by an indicator of public benefit receipt.

5.2 Dynamic Models

Dynamic Models. An important advantage of the SIPP over many samples drawn from the CPS is that it contains repeated observations on the same individual (or family) over time. This is important because it may be that some individuals and families may gain from minimum wage increases (those who keep their jobs) whereas others may lose (those who lose their jobs or have public benefits reduced or eliminated). This advantage of the SIPP data permits us to estimate models that estimate the effect of minimum wage increases on individual-specific transitions into and out of poverty or onto or off of public program participation. We do this in two ways. First, we augment Eq. (1) with individual fixed effects (α_i):

$$ Poverty_{ist} = \beta MW_{st} + {\mathbf{X}}_{\text{it}}^{\prime } \delta + {\mathbf{E}}_{\text{st}}^{\prime } \delta + {\mathbf{P}}_{\text{st}}^{\prime } \kappa + \theta_{\text{s}} + \tau_{\text{t}} + \alpha_{\text{i}} +_{\text{ist}} $$

(3)

In alternate specifications, we replace Poverty _ist with an indicator of public benefit receipt, but note that we cannot estimate Eq. (3) for Hardship _ist because this measure is not available at multiple points in time for any individual in the SIPP sample.

Second, we follow Neumark and Wascher’s (2002) strategy of using matched CPS data to separately estimate the effects of minimum wage increases on individuals’ transitions into and out of poverty during the year.¹⁹ Specifically, respondents’ poverty, food stamp, energy assistance program, and public health insurance program participation status is noted in January of each calendar year. If any changes to that status occur over the course of that status occur at any point during the remainder of that calendar year, the person is identified as transitioning into or out of poverty or onto or off of the given public assistance program. We first condition the sample on those who were initially not in poverty (or hardship) at period t and examine whether minimum wage increases affected the likelihood of transitioning into poverty (or hardship) at period t + 1. We do the same for those initially in poverty (or hardship) and examine transitions out of poverty.

5.3 Identification and limitations

Identification of β in each of our specifications comes from within-state variation in the minimum wage, which comes mostly from state changes in minimum wage policy. During the 1996–2007 period, there was substantial variation in state minimum wages (see Table 10 in Appendix). Thirty-one (31) states and the District of Columbia increased their minimum wages to an amount higher than the prevailing federal minimum wage. Much of the state policy variation came during the 2005-2007 period. Between 1996 and 2007, there were two federal minimum wage hikes: from $4.25 to $5.15 on September 1, 1997 and from $5.15 to $5.85 on July 24, 2007.²⁰ Work by Sabia (2009) suggests that increased variation in state minimum wages during the mid-late 2000s has permitted estimation of minimum wage effects with more precision than in earlier periods.

Our models, however, will only produce unbiased estimates of β in the absence of unmeasured state-specific time trends correlated with the implementation of the minimum wage and with changes in material hardship. Policy endogeneity remains a threat to internal validity.²¹ We take a number of tacks to address concerns with unmeasured heterogeneity. First, we consider adding controls for state-specific linear time trends, a method employed in recent minimum wage studies to deal with policy endogeneity (Allegretto et al. 2011; Addison et al. 2009, 2011). An advantage of this approach is that it may eliminate sources of bias due to state-specific trends; however, it may also reduce a potentially important source of identifying variation. For instance, Sabia (2012) shows that the inclusion of state-specific time trends reduces available identifying variation by over 60 %.

Our second approach is to explore parameter heterogeneity in β and identify groups for whom the minimum wage is more or less likely to affect. We expect that minimum wage increases are more likely to affect those who are low-skilled and less-experienced. Thus, we estimate our models for sub-populations that are more likely to be affected by changes in the minimum wage: younger less-educated individuals (those ages 16-to-29 without a high school degree, the population examined by Sabia et al. 2012) and younger black individuals ages 16-to-24 (a population examined by Burkhauser et al. 2000b). We also examine populations that should not be (directly) affected by minimum wages as a “falsification test”: more highly-educated individuals (those ages 30–54 with at least a high school degree).²² This approach, employed recently by Sabia et al. (2012) is designed to test whether minimum wage increases are related to economic trends that should not be affected by it, but rather are an indication of policy endogeneity.

The means and standard deviations of each of our dependent and independent variables for all individuals, workers, younger less-educated, younger black, and more educated and experienced individuals are shown in Table 1. As expected, rates of poverty and material hardship are higher in the less-educated and less-experienced control groups relative to the more highly-educated and experienced control group. We also find that poverty and material hardship are in, fact, measuring related, but distinct constructs. For instance, 60.8 % of those in poverty do not report difficulty meeting expenses, 61.0 % do not report any housing hardship, 41.2 % do not report being without health insurance at any time during the year, and 73.2 % do not report food insecurity. Moreover, only 12.5 % of those who had difficulty meeting expenses, 8.9 % of those who reported housing hardship, 10.9 % without health insurance, and 15.6 % of those who were food insecure lived in families with incomes below the poverty threshold. And, in fact, the correlation between poverty and hardship is quite low. For instance, the correlation coefficient between poverty and respondents’ report of difficulty paying bills is 0.16, with missing a rent or mortgage payment is 0.14, with durables deprivation is 0.10–0.12, and with food insecurity is 0.16. The largest correlation coefficient we observe is between poverty and food stamp program participation (0.38).

Table 1 Means of dependent and independent variables, 1996–2007

6 Descriptive evidence on minimum wage workers and poor workers

Before turning to regression results, we first use the SIPP to replicate descriptive evidence from the CPS that shows a weak relationship between low wage work and low family income. Using the CPS, Burkhauser and colleagues (Burkhauser and Finegan 1989; Burkhauser et al. 1996; Burkhauser and Harrison 1999; Burkhauser and Sabia 2007; Sabia and Burkauser 2010) show that most minimum wage workers live in families with incomes above the federal poverty threshold and most poor workers already earn wages greater than state or federal minimum wages (Sabia and Burkauser 2010). Mirroring Burkhauser and Sabia’s (2007) work in the CPS, we use the SIPP to describe poor workers and workers who stood to gain from the federal minimum wage increase from $5.15 to $7.25 during the July 2007–July 2009 period. We use the March 2005 SIPP because this panel contains earnings information on workers prior to the 2007–2009 federal minimum wage hike, as well as information on the income-to-poverty ratios of their families and whether the respondent can be classified as being in material hardship.

In Table 2, we use 2005 SIPP data on all workers ages 16-to-64 and present cross-tabulations of the wage distribution of workers by the income-to-poverty ratios of their families in 2005, following Burkhauser and Sabia (2007). The weighted average poverty threshold for a family of four in 2005 was $19,971; thus, a worker living in a family with income of $39,942 would have an income-to-poverty ratio of 2.0. The table presents results for workers who report hourly wages to avoid measurement error (Bollinger and Chandra 2005).²³ Our results show a pattern very similar to that shown by Burkhauser and Sabia (2007) in the CPS: the majority (56.6 %) of workers from poor families earn wages greater than $7.25 and thus would not directly benefit from the federal minimum wage increase (row 1). Moreover, the vast majority (86.9 %) of workers we define as minimum wage workers (those earning between $5.00 and $7.24 per hour) are non-poor.²⁴ Fifty-six (56.0) % of minimum wage workers live in families with incomes over two times the poverty threshold and one-third live in families with incomes over three times the poverty threshold (final column).

Table 2 Wage distribution of all workers by income-to-poverty ratio of their families in 2005

In Table 3 we extend this descriptive evidence to examine the wage distribution of workers by whether they report material hardship or participate in means-tested government programs. We find that most workers in hardship earn wages greater than $7.25 and most minimum wage workers did not experience material hardship. For instance, among those who reported difficulty meeting expenses, 84.8 % earned hourly wages greater than $7.25 per hour. Moreover, only 19.0 % of those who stood to directly be affected by the minimum wage hike (those earning between $5.00 and $7.24 per hour) reported difficulty meeting expenses. Further, 85.1 % of those who reported housing hardship earned hourly wage rates greater than $7.25 per hour and only 24.7 % of minimum wage workers reported any type of housing hardship. This pattern also holds for means-tested program participation. For instance, 88.9 % of working food stamp recipients earn hourly wages greater than $7.25 and only 14.2 % of minimum wage workers receive food stamps.

Table 3 Wage distribution of all workers by their material hardship status in 2005

In sum, the descriptive evidence in Table 3 suggests that the 2005–2007 federal minimum wage increase was poorly targeted to workers in hardship. However, by itself, this finding does not necessarily imply that some poor families cannot be aided by minimum wage increases.²⁵ Thus, we next move beyond a purely descriptive exercise and turn our attention to a set of behavioral estimates of the effects of minimum wage increases on poverty and material hardship.

7 Results

Tables 4, 5, 6, 7, 8, and 9 present our key regression results. All regressions are weighted and standard errors corrected for clustering on the state (Bertrand et al. 2004) appear in parentheses. Poverty, hardship, or public program participation elasticities with respect to the minimum wage are presented in brackets.

Table 4 Estimates of the effect of minimum wage increases on poverty, 1996-2007

Table 5 Estimates of the effect of minimum wage increases on workers and heterogeneously skilled sub-groups

Table 6 Sensitivity of poverty estimates to lagged minimum wage

Table 7 Estimates of the effect of minimum wage increases on material hardship

Table 8 Estimates of the effect of minimum wage increases on public program participation

Table 9 Dynamic estimates of the effect of minimum wage increases on poverty and public program participation

7.1 Poverty estimates

Table 4 shows our baseline estimates of Eq. (1) using various measures of poverty. In column (1), we use the official Census definition of poverty that does not include receipt of non-cash program transfers. We find that most of the demographic and economic controls are correlated with the probability of living in poverty in the manner theory would suggest. For instance, being non-white is positively related to the probability of living in poverty, whereas greater educational attainment, marriage, and strict work requirements are each negatively related to the probability of living in poverty.

Turning to the minimum wage, our results suggest that minimum wage increases are generally associated with a negative, but statistically insignificant impact on poverty. In column (1), we find that a 10 % increase in minimum wages is associated with a statistically insignificant 0.31 % decline in the probability of living in poverty. The precision of our point estimate suggests that for a minimum wage increase of this magnitude ($0.54), we can rule out, with 95 % confidence, changes in poverty of less than −0.93 points and greater than 0.30 points.

In contrast to our null findings with respect to the minimum wage, we find some evidence of a negative relationship between the strictness of state work requirement policies (for AFDC/TANF receipt) and poverty. We find that work requirements are associated with a 1.2 % age point decline in the probability of living in poverty.

In the remaining columns of Table 4, we experiment with alternate definitions of poverty: measuring poverty (i) as falling below 125 % of the poverty threshold, (ii) as falling below 150 % of the poverty threshold, (iii) using the continuous income-to-poverty ratio, and (iv) falling below the official poverty income when government cash transfers are included in family income. Across each of these poverty definitions, we find no evidence that minimum wage increases are significantly associated with the probability of living in poverty, though the estimates are uniformly negative. These findings are consistent with evidence in the Current Population Survey (Burkhauser and Sabia 2007; Sabia and Burkauser 2010).

In the first five columns of Table 5, we examine whether minimum wage increases affect the probability of living in poverty for particular subgroups. Column (1) reproduces the estimates from Table 4. In the second column, we restrict the sample to workers, allowing the minimum wage its best chance to alleviate poverty by eliminating any adverse poverty effects that could result because of negative employment effects of the minimum wage.²⁶ For instance, Card and Krueger (1995) find some evidence of modest negative poverty effects in such a specification. Among workers, we continue to find no evidence of adverse poverty effects, and the magnitude of the effect is not statistically different from the effect for all individuals (which include non-workers). The 95 % confidence interval around the elasticity estimate in column (2) suggests that we can rule out poverty effects of less than 0.78 points and greater than 0.25 points.

In columns (3) and (4), we examine whether the probability of living in poverty for lower-skilled individuals—those who are less-educated and less-experienced—are affected by minimum wage increases. For those who are less-educated, there is evidence of a larger negative relationship between minimum wages and poverty (elasticity = −0.515 using the official poverty definition in Panel A), though the relationship is never significant at conventional levels. However, in column (4), we find that the minimum wage is generally positively related to poverty rates of younger black individuals. Falsification tests on more experienced and educated individuals (column 5) show negative poverty effects where we would not expect to see any.

In the final five columns of Table 5, we examine the sensitivity of our results to added controls for a state-specific linear time trend. The inclusion of a state linear time trend does not alter our conclusion that minimum wages appear to be a poor anti-poverty tool. In fact, for younger less-educated individuals, we now find that minimum wage increases are associated with a statistically significant increase in the probability of living in poverty. For instance, Panel (B) of column (8) shows that a 10 % increase in the minimum wage is associated with an 8.8 % increase in the probability that a 16-to-29 year-old without a high school diploma lives in poverty. This finding does not appear to be spurious to the extent that our falsification test on more experienced and highly educated individuals (column 10) would capture any non-causal association between the minimum wage and poverty.²⁷

Finally, in Table 6, we explore whether there may be important lagged effects of the minimum wage on poverty. A number of studies (Neumark and Wascher 2008; Baker et al. 1999; Burkhauser et al. 2000a, b; Neumark 2001; Campolieti et al. 2006; Sabia 2009) have emphasized the importance of allowing lagged minimum wages to affect economic outcomes because firms’ employment and hours responses may not be contemporaneous (see, Neumark and Wascher 2007; Baker et al. 1999; Burkhauser et al. 2000a, b; Neumark 2001; Campolieti et al. 2006; Sabia 2009). Thus, in Table 6, we re-estimate the models in Table 5 using 1- and 2-year (12 and 24 month) lagged minimum wages. Our results suggest that using lagged minimum wages on the right-hand side of Eq. (1) does not alter our conclusion that minimum wages appear to be a poor anti-poverty tool.²⁸ For younger less-educated individuals, there is some evidence of a positive effect on poverty probabilities with a 1-year lag, but this is countered by a negative effect with a 2-year lag, leaving a long-run elasticity that is statistically indistinguishable from zero. For young blacks, however, there is some evidence of a long-run lagged positive effect of minimum wage increases on the probability of living in poverty (column 4).

In summary, the findings in Tables 4, 5, and 6 suggest that minimum wage increases do not significantly affect the probability of living in poverty and may even increase the probability of poverty for some younger, less experienced, less-skilled individuals, consistent with prior studies’ findings in the CPS (Burkhauser and Sabia 2007; Sabia and Burkauser 2010). Next we turn to the effects of minimum wages on material hardship.

7.2 Material hardship estimates

Table 7 shows estimates of Eq. (1) using several measures of material hardship. The sample sizes are smaller for this set of estimates because material hardship information is only available in 1998, 2003, and 2005, when 11 states changed their minimum wages. The outcomes in the first two columns measure financial hardship, columns (3)–(5) measure housing hardship, columns (6) and (7) measure consumer durables hardship, columns (8)–(9) health-related hardship, and column (10) measures food insecurity. For the full sample of 16–64 years olds (Panel A), we find no evidence that minimum wage increases reduced material hardship, consistent with Heflin’s (2009) findings on a smaller set of hardship measures for a narrower population in models that included state fixed effects. Across our 10 measures, there are more positive coefficients than negative. And, in fact, we find a significant positive relationship between the minimum wage and food insecurity for younger individuals without a high school degree (Panel C, column 10). However, most of our point estimates are sufficiently imprecise that we are cautious in interpreting this estimate as indicative of a broader pattern.

What we can conclude from Table 7 is that across all individuals (Panel A), workers (Panel B), younger less-educated individuals (Panel C), and less-experienced black individuals (Panel D), we do not observe a pattern of results consistent with the hypothesis that minimum wage increases are an effective means of ameliorating hardship. While the absence of effects for a more highly-skilled population (Panel E) does not prove that the absence of effects for lower-skilled populations is causal, the results are consistent with this hypothesis.²⁹

7.3 Public program participation

In Table 8, we examine whether public program participation is affected by minimum wage increases, focusing on the food stamp program, rent/housing assistance, energy assistance programs, and public health insurance. As noted above, the effect of the minimum wage on the probability of means-tested public program participation is ambiguous: it could increase participation if minimum wage increases cause job losses and decrease income; however, it could decrease participation if there are income gains that diminish eligibility. While minimum wage increases are marginally negatively related to the probability of food stamp participation for the full sample (Panel A, column 1), this finding does not extend to workers or less-skilled individuals.³⁰ When we examine receipt of rental/housing assistance, energy assistance, and public health insurance, we generally find that minimum wage increases are associated with statistically insignificant positive effects on participation in these public programs. In summary, the findings in Table 8 suggest that minimum wage increases are not important predictors of means-tested program participation.

7.4 Transition estimates

Next, in the spirit of Neumark and Wascher (2002), we exploit the individual panel data in the SIPP to examine the effect of minimum wage increases on individual-specific flows into or out of poverty and public program participation. We do this by first including individual fixed effects in our specification to capture the effects of minimum wages on state-specific month-to-month transitions in individual poverty levels or public program participation. Then, we separately estimate transitions into or out of poverty or program participation. We condition the sample on the respondent’s poverty or program participation status in January and examine whether the respondent transitioned from that state at any point during the calendar year. For these specifications, we use the average annual state minimum wage as the key independent variable of interest. Note, however, that we cannot conduct this analysis for the material hardship measures because the material hardship variables are available in just one topical module each panel.

Columns (1)–(3) in Table 9 present results for individual fixed effects models (column 1) as well as separately estimated transitions into (column 2) and out of (column 3) poverty. We find that minimum wage increases generally have little effect on the probability of living in poverty after controlling for fixed individual heterogeneity. There is some evidence of a small, but statistically insignificant, positive effect of minimum wage increases on the probability of an individual not in poverty moving into poverty. While minimum wage increases may help some younger less-educated individuals to exit poverty, for younger blacks ages 16-to-24, such hikes are associated with a substantial reduction in the probability of moving out of poverty. This finding is generally consistent with Neumark and Wascher (2002), who find that minimum wages may move some low-skilled families out of poverty, but may move others into poverty.

In the remaining columns (4)–(12), we explore the effects of minimum wage increases on transitions onto and off of government programs. The results are mixed. For the food stamp program, there is some evidence that increases in the minimum wage may reduce the likelihood that some younger less-educated individuals join the program (Panel C, column 5) and increase the likelihood that some less-experienced individuals exit the program (Panel C, column 6). However, for the energy assistance program, there is some evidence that even after controlling for individual heterogeneity, minimum wage increases are associated with an increase in the probability of energy assistance receipt.

For public health insurance, we find some evidence that minimum wage increases “redistribute” public health insurance participation among low-skilled workers (Panels C and D). For instance, minimum wage increases are associated with a significant decline in the probability that younger less-educated individuals (Panel C, column 12) and younger blacks (Panel D, column 12) exit public health insurance. However, such increases are also associated with a decline in the probability that younger blacks not receiving public health insurance begin receiving it.

Taken together, our findings in Tables 4, 5, 6, 7, 8, and 9 generally show that minimum wage increases have little effect on net rates of poverty, material hardship, and public program participation. Instead, the evidence points toward a modest redistribution of poverty and government program participation among low-skilled individuals.

8 Conclusions

Increasing state and federal minimum wages is often justified on the grounds that such hikes will alleviate material hardship among the working poor (Democratic Policy Committee 2007). In this study we use data drawn from the 1996, 2001, and 2004 panels of the Survey of Income and Program Participation to explore the effect of minimum wage increases on poverty and numerous measures of material hardship. Our results provide little evidence that raising the minimum wage has been effective in reducing poverty or material hardship among all individuals, workers, less-educated individuals, or less-experienced blacks. Rather, we find some evidence that minimum wage increases have modest redistribution effects among low-skilled individuals.

We conclude that the policy objective of alleviating material hardship is unlikely to be substantially advanced by increases in state or federal minimum wages because of poor target efficiency and possible adverse labor demand effects. This finding is consistent with that of Wu et al. 2005, who find that higher minimum wages do little to improve income inequality and, for particular measures of inequality, actually harm poor families. Pro-work policies such as work requirements for public assistance receipt and expansions in state supplements to the federal EITC—which are well-targeted to poor individuals (CBO 2007) and are not accompanied by adverse labor demand effects (Neumark and Wascher 2001 ³¹)—may be more successful at increasing net income among poor individuals. Future work expanding the work of Wu et al. (2005) to explore the relative welfare effects of public policies designed to alleviate material hardship will be useful in evaluating the relative merits of these public policies.

Appendix

See Tables 10 and 11.

Table 10 State and federal variation in the minimum wage, 1996−2007

Table 11 Percent of individuals ages 16–64 in hardship who are not working (2005)