Printer Friendly

Impact of Increasing Minimum Wage on Homeownership and Home Equity Loans.

Introduction

Increasing the minimum wage is meant to help those with low skills or entry-level jobs. Among the beneficiaries, many are poor, minorities, and women. However, when talking about housing, the literature focuses more on minimum wage workers' ability to pay rent rather than on their chance of getting a mortgage and their propensity to have a home equity loan (HEL). This paper investigates the impact of increasing the minimum wage in 2009 on homeownership and HELs of households with at least one member earning minimum wage income.

According to the U.S. Bureau of Labor Statistics (2009), in 2009 about 3.5 million people worked at or below the federal minimum wage, or 2.3% of the labor force, of which more than 1.2 million were people of color. 51.4% of them were 25 or older, and almost half a million lived with their spouses. Their average annual family income was about $42,000. For a quick comparison, the 2009 standard level of poverty for a household of two was $14,570 and for a household of four was $22,050 (U.S. Department of Health and Human Services, 2009). From the U.S. Census Bureau's annual price report (2009), the average sale price of new homes sold in the United States in 2009 was $270,900. Buyers often had to make a down payment of about 20% ($54,180) at the beginning and pay an average 4% interest rate for the mortgage. The annual interest payments alone are already more than 50% of a minimum wage worker's annual income. It was not easy for those with similar income levels to buy a house, especially when the financial requirements for loans and mortgages were tightened in order to reduce risks after the 2007-2008 recession.

Once a household has a mortgage, they have an opportunity to get a HEL secured by the equity that a homeowner has in their home. The equity owned is the difference between the property's appraised value and the current mortgage balance. HELs are often used to pay down outstanding consumer debts or finance new consumption expenditures such as home improvements, bill consolidation, auto purchases, education, or major family events such as weddings or funerals. The market for home equity lines of credit (HELOC) alone in 2009 was a little over 50 billion dollars and increased to more than 100 billion in 2016 (Core Logic 2019). HELs are attractive because they usually have lower interest rates than credit cards and most consumer loans. Moreover, the interest is tax deductible.

While the housing market is still recovering, multiple proposals have been put forward nationwide to raise the minimum wage. This in turn could increase worker purchasing power and stimulate consumer demand (Reich et al. 2017; Dettling and Hsu 2017). The confidence a new income level instills might prompt the taking out of large loans. Moreover, a little more money each month can help facilitate paying bills on time and improve credit scores, which increases the chance of being approved when applying for loans. One lesson learned from the Great Recession is that the decision to award a risky mortgage to individuals with low ability to pay should be taken with great care. It might have a long-term impact on the housing market, and eventually the economy. Thus, it is necessary to examine the impact of increasing minimum wage on this market.

Herein, individual and household data from the American Community Survey and the regression discontinuity (RD) model with fuzzy design were used to examine whether increasing minimum wage is associated with a higher probability of having a mortgage or a HEL in households of workers who are 25 or older and earn minimum wages. In particular, the probability of obtaining a mortgage or a HEL in households of workers who are around the cutoff for minimum wage income is examined. Minimum wage income is defined as the annual income of a worker who works full time at the minimum wage level (either at the federal or state level, depending on which one is higher). The results show that the probability of having a mortgage in a household of a worker with minimum-wage income is 8% higher than that of a worker whose income is slightly above the minimum-wage income. Moreover, workers with minimum-wage income are 11 % more likely to take out a HEL than workers with slightly higher income. This paper contributes to the literature about the socioeconomic impacts of increasing minimum wage.

Literature Review

This section focuses on the literature regarding minimum wages' impact on workers' income. Either household income or its disaggregated components are likely to be related to homeownership (Haurin et al. 1996; Bourassa and Yin 2008; Hendershott et al. 2009; Carter 2011). There are many articles about the impact of minimum wage on low-skill workers' average income. Reich and Hall (2001), Easton (2006) and Krashinsky (2008) found that higher minimum wages are unambiguously associated with higher total income. According to Belman and Wolfson (2014), the median elasticity of the average wage with respect to the minimum wage is between 0.02 and 0.05. Neumark and Wascher (2011) found that minimum wage still had a positive direct effect on income even after taking into account complex interactions between the minimum wage and other federal income assistance programs. Wolfson and Belman (2004) and Belman and Wolfson (2010) focused on the industries in which there was a wage effect and considered the degree to which any wage effects were offset by declines in employment. They found that increases in the federal minimum wage had a positive and significant effect on the average wage in 16 of 23 low-wage industries, with little evidence of an employment effect.

Increasing minimum wage has a spillover effect on people who do not receive the raise as well as the families of minimum wage workers. This helps explain the reason for a regression discontinuity model with fuzzy design. The average effect of changes in the minimum wage was $255 per quarter on families with some of their income originating from a minimum wage job. When limited to families that obtain at least 20% of their income from minimum wage employment, the weighted average effect was $209. Unsurprisingly, Aaronson et al. (2012) reported that the income of families with no income from minimum wage employment were not affected by changes in the minimum wage. On the other hand, spillover may reach as high as the third decile of the wage distribution, positively affecting the earnings of workers who earn between $10 and $12 per hour. The logic is that workers form their expectations of wages and wage increases through comparison with other workers. The minimum wage creates a target that workers use to judge the adequacy of their wage. When everyone is assured a minimum wage, the minimum is identified as the wage that even the least capable employee is paid. Those who identify themselves as better than minimum wage workers adopt a reservation wage above the minimum wage (Falk et al. 2006). When the minimum wage is raised, employers who wish to maintain morale and productivity must raise the wages of employees who use the minimum as a benchmark (Falk et al. 2006; Hirsch et al. 2015; Autor et al. 2016).

Methodology

Cho (1997)'s model of the joint choice of housing alternatives, including owning or renting a house, suggests that income elasticity of homeownership probability is positive. Specifically, given the assumption that income has a bigger impact on a homeowner's level of utility than that of a renter, when income increases, the probability of owning a house will increase. Once a household decides to purchase a house, a debt optimization model (such as the one presented in Jones 1995) can be used to estimate the determinants of its excess mortgage demand, which is the deviation of the total mortgage debt from the optimal debt position. In particular, if net wealth has a negative relationship with risk aversion to leverage, excess mortgage demand, including home equity loans, will increase when income increases.

In the current paper, RD with fuzzy design was used to investigate the impact of increasing the minimum wage on the probability of having mortgage debt in households with at least one minimum-wage-income member. RD analysis applies to situations in which candidates are selected for treatment based on whether their value for a numeric rating falls above or below a certain threshold. The cutoff points are either federal minimum-wage income or state minimum-wage income levels, depending on which is higher in each state. Because the individual's hourly wage is not observed, annual income is used instead with the assumption that if a worker's hourly wage increases, her annual income would increase too. No consistent findings in the literature show otherwise. Moreover, annual income is a better proxy for a person's ability to pay than hourly wage, especially for large amounts like mortgages or HELs.

The intuition of using the RD method is that individuals around the income cutoffs have similar characteristics (to reduce selection bias) and any changes in their probabilities of mortgage debts must come from the treatment that some of them receive (an increase in their wage) while the rest do not. Table 1 summarizes the demographic characteristics of individuals who are just above and beneath the minimum-wage-income cutoff in the sample. The two groups are not statistically different in most of their observed characteristics such as education levels, number of household members, size of the city that they live in, number of children, age, gender distribution and marriage status. Figures 1 and 2 show the discontinuity in the predicted probabilities of having a mortgage or a HEL of a household of a worker with minimum-wage-income at the current federal minimum wage of $7.25 per hour. The discontinuity almost disappears at the placebo cutoff of $8.65 per hour, just 10 cents over the nationally highest minimum wage nationwide of $8.55 per hour in 2009.

In the fuzzy design, some treatment group members do not receive the treatment and some control group members do receive the treatment in a randomized experiment. This paper uses fuzzy designs because there are numerous works that show the spillover effect of increasing minimum wage on workers who are slightly above the cutoff for minimum wage. Moreover, because the outcome of interest is whether a household has mortgage debt or not, the RD with fuzzy design for discontinuous outcomes was adopted from O'Keeffe et al. (2014) and Jacob and Zhu (2012). Let [Y.sub.0] and [Y.sub.1] denote the pair of potential outcomes for unit i. [Y.sub.0] is the outcome without exposure to the treatment and [Y.sub.1] is the outcome given exposure to the treatment. Assignment to the treatment is determined by the value of a predictor (the covariate [X.sub.i]) being on either side of a fixed threshold. The fuzzy risk ratio (FRR) is:

[mathematical expression not reproducible]

in which Y is probability of a household having a mortgage or a home equity loan. These probabilities are estimated using a logit model. Z is a cutoff dummy whose value is 1 if a person has annual income equal to or lower than the minimum-wage-income level and zero otherwise. T is a treatment dummy, which is 1 if a person presumably receives a raise because of the increasing minimum wage and zero otherwise.

X is a vector of demographic characteristics such as age, race, educational attainment, annual family income, individual income, gender, marriage status, veteran status, citizenship status, English language competence, number of members in a household, number of children, number of newborns, annual utility expenses, health insurance coverage, and state dummies to control for unobserved characteristics that vary across states but do not change over time. These factors play roles in homeownership. For example, the older people are, the more likely they are to own a house. Race, educational attainment, marriage status and number of people (including children and newborns) in a household might have an influence on attitudes toward homeownership. For instance, according to the HSBC (2017) Generation Buy study, Chinese millennials were more prone to own houses than their U.S. counterparts. People who are married and have children were more likely to purchase a house. Income levels, veteran status, citizenship status and other expenses might be associated with the probability of obtaining a mortgage from banks or private lenders.

Data

The data were from Integrated Public Use Microdata Series (IPUMS), which aggregates microdata from the American Community Survey (ACS) 2010 (Ruggles et al. 2017). It comprises individual demographic characteristics and households' mortgage debts for all the states in the United States. Only the 41 states with a minimum wage change in 2009 to at least $7.25/h were used. (1) 2010 survey data were used because they include individual and household annual incomes in 2009. The data captured the income shock from the increased federal minimum wage in 2009 and potentially, the consequential change in consumer demand. Utility expenses and important events that require major financial decisions (such as a wedding, funeral, or childbirth) in 2009 were also included.

Sherk (2013) found that a majority of minimum wage workers between 16 and 24 years old worked part-time and probably still received some financial support from their parents. Therefore, this paper focused on the group of people who are 25 and older because they are more likely to settle down with their partners and purchase a house. While their credit history scores are unknown, an attempt is made to control for other factors that might affect the households' probability of obtaining a mortgage such as individual income, family income, race, gender, citizenship, ability to speak English, education, family size, and utility expenses.

Since the data do not include hourly wage, the annual minimum-wage-income level was used to predict who might be a minimum-wage-level worker and more likely to receive a raise in 2009. I assume that a full-time worker would work 40 hours per week and 52 weeks per year because about 88% of workers in the data were full time (working more than 30 hours per week). As mentioned above, employers also increase wages for workers who work at a little higher than the minimum wage. To determine whose income might be affected by an increase in the minimum wages, the change in minimum wages from 2008 to 2009 was multiplied by 2080 working hours to calculate the wedge between a state' income threshold for minimum wage workers and the fuzzy upper cutoff for a raise. For example, Alabama increased their minimum wage from $6.55 per hour to $7.25 per hour in 2009. The wedge would be $1456. Thus, workers in Alabama whose annual incomes are between $15,080 and $16,536, would be assumedly affected.

Results

Table 2 shows the logit regression results where the dependent variables are the log odds of a household having a mortgage and the log odds of a household having a home equity loan. The fuzzy risk ratio was calculated based on predicted probabilities from these equations and the conditional probabilities of having an increase in wages. The first two columns of Table 2 show the results for the log odds of having a mortgage in households. Having one more person in the household was associated with a 0.06-point increase in the log odds of having a mortgage for people at or below the minimum-wage-income cutoffs, statistically significant at the 10% level. This was expected because families with more members often seek a bigger place to live rather than a cramped apartment. However, the odds ratio of people who were above the cutoffs was not sensitive to the total number of people in the household. For these people, having a newborn less than a year prior was correlated with a higher probability of having a mortgage, statistically significant at the 1% level. Water bills also had a positive relationship with homeownership. A $100 increase in water bills was associated with a 0.03-point increase in the log odds of having a mortgage. This might be because homeowners are likely to use more water than renters to clean, water gardens, and take care of the house.

Household income plays an important role in determining the probability of having a mortgage. A $ 1000 increase in household income was associated with 0.004 and 0.006-point increases in the log odds of having a mortgage for people on the left and right side of the wage cutoffs, respectively. This fits into the well-established findings about the tight connection between household income and homeownership. Individual income had a positive and statistically significant relationship with homeownership for people above the minimum-wage-income level. However, this relationship disappeared for people at or right below the income cutoffs. Previous works also found ambiguous results regarding the impact of individual income on homeownership. Another common finding was a positive relationship between higher education levels and homeownership. One more year of schooling was associated with a 0.07 and 0.05 point higher log odds of having a mortgage for people right below and above the minimum-wage-income level, statistically significant at the 1 and 5% levels, in that order. Ability to speak English, having health insurance and citizenship might have a role in determining homeownership, but the impacts were ambiguous and highly likely to be endogenous.

Column 3 and 4 in Table 2 show the results for the log odds of having a home equity loan. Big expenses such as weddings, funerals, colleges, births, or home improvement projects often stir up demand for HELs. As expected, one more year of education, high water bills and having a baby within the previous year were all associated with higher probability of having a HEL in households of people who were above the minimum-wage-income cutoffs, statistically significant at the 1% level. However, for these people, getting married was associated with a lower probability of having a HEL. This might be because their joint income was enough to cover such expenses. These relationships were not significant for the group at or below the minimum-wage-income levels. Perhaps this is because of a lack of information about HELs and low approval rates for HEL applications. Moreover, controlling for other factors, aging was associated with a better chance of having a HEL, either because a person accumulates more equity over time to back the HEL or improves their credit history to be approved for a HEL. Importantly, holding everything else constant, household income still played an important and positive role in obtaining a HEL for both groups. If household income increased by $1000, the log odds of having a HEL increased by at least 0.003 points, statistically significant at the 1 % level. Individual income had no significant association with this probability.

Using the predicted probabilities above, the fuzzy risk ratio for mortgage debts was estimated to be 1.08. This means that raising minimum wage increases the probability of having a mortgage by 8% for the group of minimum-wage-income individuals, statistically significant at the 1 % level. The fuzzy risk ratio for home equity loans was 1.11, which means that increasing minimum wage raises the probability of having a home equity loan by 11%, statistically significant at the 1% level. One possible explanation for these findings is that even though people around the minimum-wage-income cutoffs have similar characteristics, families of the ones who received a raise had a boost in their savings toward a down payment, ability to pay bills and higher credit scores, which made them more eager and eligible to get a mortgage or a HEL.

Conclusion

This paper looks at the impact of increasing minimum wage on mortgage debts. Specifically, the RD method was used to estimate the risk ratio of having a mortgage or having a home equity loan between two groups of people around the minimum-wage-income cutoffs. This method is based on the idea that people around the random cutoffs share similar characteristics so the discontinuity in their outcomes might derive from the treatment that they receive. The individual data were from the Integrated Public Use Microdata Series (Ruggles et al. 2017,). Using the cutoff for minimum wage per hour in each state, the annual minimum-wage income level in that state was estimated in order to identify individuals around the income cutoffs in the sample. Because employers not only raise wages for workers who are strictly under the income cutoffs, but also for the ones who are very near the cutoffs to maintain fairness among their employees, fuzzy RD estimation was used. I find that increasing minimum wage from $6.55 per hour to $7.25 per hour in 2009 increased the probability of having a mortgage in the families of people who had minimum-wage income by 8%, compared with that of people whose incomes were slightly higher. The probability of having a HEL in the first group also increased by 11 % compared with the second group. This might be because their families felt more confident about their income security, had higher ability to pay bills and better credit scores when a family member got a raise. Thus, they were more likely to obtain (and be approved for) a mortgage instead of renting or sacrificing big family projects and events. One of the lessons learned from the 2007 downturn is that the decision to award a risky mortgage to individuals who may not have the ability to pay it off should be made with great restraint. It might be risky for both them and the financial and housing markets when they start taking out big loans such as a mortgage or home equity loans. While this paper is one of the first to examine the unexpected connection between increasing minimum wage and the probabilities of having mortgage debts, it has limitations such as the assumption that the maximum amount of hours a minimum wage worker works is 40 h a week, and the lack of real wage per hour and credit history for households' mortgage applications. Nevertheless, it opens the gate for future research about mechanisms and potential solutions to help low-income people become homeowners.

References

Aaronson. D., Agarwal, S., & French, E. (2012). The spending and debt response to minimum wage hikes. American Economic Review, 102(7), 3111-3139.

Autor, D., Manning, A., & Smith, C. (2016). The contribution of the minimum wage to U.S. wage inequality over three decades: A Reassessment. American Economic Journal: Applied Economics, 8( 1), 58-99.

Belman, D., & Wolfson, P. (2010). The effect of legislated minimum wage increases on employment and hours: A Dynamic Analysis. Review of Labor Economics and Industrial Relations, 24(1), 1-25.

Belman, D. and Wolfson, P. (2014). What Does the Minimum Wage Do? 210-211. W.E. Upjohn Institute for Employment Research, Klamazoo, MI.

Bourassa, S. C. & Yin, M. (2008). Tax deductions, tax credits and the homeownership rate of young urban adults in the United States. Urban Studies, 45(5-6), 1141-1161.

Carter, S. (2011). Housing tenure choice and the dual income household. Journal of Housing Economics, 20, 159-170. September 2011.

Cho, C. (1997). Joint choice of tenure and dwelling type: A multinomial logit analysis for the City of Chongju. Urban Studies, 34(9), 1459-1473.

Core Logic (2019) Home Equity Lending Landscape White Paper. Retrieve from https://www.corclogic.com/insights/homc-cquity-lending-landscape-whitepapcr.aspx

Dettling, J. and Hsu, W. (2017). Minimum ages and Consumer Credit: Impacts on Access to Credit and Traditional and High-cost Borrowing. Board of Governors of the Federal Reserve System. Finance and Economics Discussion Series No. 2017-010. Retrieve from https://doi.org/10.17016/FEDS.2017.010.

Easton, T. (2006). Metropolitan Wage Levels of Less-Educated Workers: 1986 to 1999. Industrial Relations: A Journal of Economy and Society, 45(2), 119-146.

Falk, A., Fehr, E., & Zehnder, C. (2006). Fairness perceptions and reservation wages--The behavioral effects of minimum wage Laws. The Quarterly Journal of Economics, 121(4). 1347-1381.

Haurin, D., Hendershott, P., & Wachter, S. (1996). Wealth accumulation and housing choices of young households. Journal of Housing Research, 7(1), 33-57.

Hendershott, P. H., Ong, R., Wood, G. A., & Flatau, P. (2009). Marital history and home ownership: Evidence from Australia. Journal of Housing Economics, 18(1), 13-24.

Hirsch, B., Kaufman, B. E., & Zelenska, T. (2015). Minimum wage channels of adjustment. Industrial Relations: A Journal of Economy and Society, 54(2), 188-239.

HSBC (2017). Generation Buy Report. Retrieved from https://www.hsbc.com/mcdia/media-releases/2017/generation-buy.

Jacob, R. and Zhu, P. (2012). A practical guide to regression discontinuity. Retrieve from https://www.mdrc.org/publication/practical-guidc-regression-discontinuity.

Jones, L. D. (1995). Net wealth, marginal tax rates, and the demand for home mortgage debt. Regional Science and Urban Economics, 25(1), 297-322.

Krashinsky, H. (2008). The effect of labor market institutions on salaried and self-employed less-educated men in the 1980s. Industrial and Labor Relations Review, 62(1), 73-91.

Neumark, D., & Wascher, W (2011). Docs a higher minimum wage enhance the effectiveness of the earned income tax credit? Industrial and Labor Relations Review, 64(4), 712-746.

O'Keeffe A. G., Geneletti, S., Baio, G., Sharpies, L. D, Nazareth, I., Petersen, I. et al. (2014). Regression discontinuity designs: An approach to the evaluation of treatment efficacy in primary care using observational data. The British Medical Journal. 349: g5293. September 2014. Retrieve at https://www.bmj.com/content/349/bmj.g5293.

Reich, M. and Hall, P. (2001). A small raise for the bottom: Economic development and low wage labor in California. Institute of Industrial Relations. University of California, Berkeley. Retrieve from https://escholarship.org/uc/item/9rb8m3vt.

Reich, M., Allegretto, S., and Montialoux, C, (2017). Effects of a $15 minimum wage in California and Fresno. Policy Brief. Center on Wage and Employment Dynamics. Institute for Research on Labor and Employment. University of California, Berkeley. Retrieve from http://irle.berkeley.edu/files/2017/Effects-of-a-15-Minimum-Wage-in-California-and-Fresno.pdf.

Ruggles, S., Genadck, K., Goeken, R., Grover, J. and Sobek, M. (2017) Integrated public use microdata series: Version 7.0 [datasef]. Minneapolis: University of Minnesota. Retrieve at https://doi.org/10.18128/D010.V7.0.

Sherk, J. (2013). What is minimum wage: Its history and effects on the economy. The Heritage Foundation. Retrieve from www.heritage.org/research/testimony/2013/06/what-is-minimum-wage-its-history-and-cffccts-on-the-economy#_ftn 10.

U.S. Bureau of Labor Statistics (2009). Characteristics of minimum wage workers 2009. Retrieve from https://www.bls.gov/opub/rcports/minimum-wagc/archive/minimumwageworkers_2009.pdf.

U.S. Census Bureau (2009). Median and average sales Price of houses sold by region. Retrieve from https://www.census.gov/construction/nrs/pdf7pricerega.pdf.

U.S. Department of Health and Human Services. (2009) The HHS Poverty Guidelines. Retrieve from https://aspe.hhs.gov/2009-hhs-poverty-guidelines.

Wolfson, P., & Belman, D. (2004). The minimum wage: Consequences for prices and quantities in low-wage labor markets. Journal of Business and Economic Statistics, 22(3), 296-311.

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[??] Hoa Nguyen

hoa.nguyen@montgomerycollege.edu

Hoa Nguyen (1)[iD]

(1) Department of Economics, Montgomery College, Meadville. PA, USA

(1)The nine states that did not change their minimum wage in 2009 were California, Hawaii. Iowa, Maine, Massachusetts, Michigan, New Hampshire, Rhode Island, and West Virginia.

https://doi.org/10.1007/s11294-019-09736-5
Table 1 Data summary of two groups of workers above and below the
minimum wage cutoffs

                                          $1 Below    $1 Above
                                          the Cutoff  the Cutoff

Probability of having a mortgage               0.472       0.462
Probability of having a home equity loan       0.071       0.067
Number of household members                    3.103       3.106
City population                             3264.99     3164.64
Household income                          56,403.65   55,742.23
Female                                         0.605       0.608
Age                                           45.480      45.656
Married                                        0.795       0.796
Citizenship                                    0.375       0.397
Speaks English                                 0.976       0.977
High school                                    0.927       0.927
Number of children                             0.866       0.872

                                          Difference  Std. Errors  Pr
                                                                  (|T|>
                                                                  |t|)

Probability of having a mortgage            0.009       0.004      0.028
Probability of having a home equity loan    0.005       0.002      0.022
Number of household members                -0.003       0.014      0.831
City population                           100.36      115.49       0.385
Household income                          661.42      418.39       0.114
Female                                     -0.004       0.004      0.390
Age                                        -0.176       0.114      0.124
Married                                    -0.001       0.004      0.819
Citizenship                                -0.022       0.009      0.022
Speaks English                             -0.001       0.001      0.445
High school                                 0.000       0.002      0.931
Number of children                         -0.006       0.010      0.522

Source: Own calculations using data from Integrated Public Use Microdata
Series: Version 7.0, Ruggles et al. (2017)

Tabic 2 Logit regression results for 2 worker groups near the income
cutoffs

                                 Probability of having a mortgage
                                 At or below    Right above
                                 minimum wage   minimum wage

Number of people in a household   0.066          0.044
                                 (0.002) (*)    (0.041)
Electricity costs (in thousand)   0.04           0.015
                                 (0.05)         (0.068)
Gas costs (in thousand)           0.08          -0.004
                                 (0.06)         (0.07)
Water costs (in thousand)         0.3            0.5
                                 (0.1) (***)    (0.1) (***)
Household income (in thousand)    0.0037         0.0061
                                 (0.001) (**)   (0.002) (***)
Female                            0.128         -0.324
                                  (.179)        (0.209)
Age                               0.004          0.002
                                 (0.006)        (0.008)
Married last year                -0.121         -0.548
                                  (.353)        (0.493)
Divorced last year               -0.438         -0.708
                                  (.462)        (0.619)
Widow last year                   0.679          1.55
                                  (.525)        (0.782) (*)
Gave birth last year              0.284          0.643
                                  (.186)         (.207) (***)
Black                             0.253         -0.185
                                 (0.251)        (0.264)
White                            -0.149         -0.002
                                 (0.114)        (0.149)
Citizenship                       0.180          0.317
                                 (0.122)        (0.163) (**)
Speaks English                    0.042          0.296
                                 (0.187)        (0.23)
Health insurance coverage         0.441          0.195
                                 (0.121) (***)  (0.151)
Education                         0.068          0.048
                                 (0.02) (***)   (0.025) (**)
Number of children               -0.021          0.039
                                 (0.054)        (0.062)
Veteran status                    0.896         -0.494
                                 (0.578)        (0.653)
Individual income (in thousand)  -0.0457         0.049
                                 (0.07)         (0.1) (***)

                                 Probability of having a HEL
                                 At or below    Right above
                                 minimum wage   minimum wage

Number of people in a household  -0.007          -0.122
                                 (0.061)         (0.08)
Electricity costs (in thousand)   0.081           0.062
                                 (0.1)           (0.1)
Gas costs (in thousand)           0.070           0.083
                                 (0.08)          (0.1)
Water costs (in thousand)         0.301           0.8
                                 (0.1)           (0.2) (***)
Household income (in thousand)    0.004           0.003
                                 (0.001) (***)   (0.001) (***)
Female                            0.182          -0.223
                                 (0.289)         (0.389)
Age                               0.027           0.019
                                 (0.01) (***)    (0.012) (*)
Married last year                 0.774          -1.617
                                 (0.603)         (0.831) (**)
Divorced last year               -1.37            1.19
                                 (1.141)         (0.778)
Widow last year                   0.496           0.992
                                 (0.956)         (0.751)
Gave birth last year              0.180           0.836
                                 (0.259)         (0.341) (***)
Black                             0.084           0.290
                                 (0.368)         (0.388)
White                            -0.076          -0.103
                                 (0.197)         (0.263)
Citizenship                       0.338           0.508
                                 (0.216)         (0.283) (*)
Speaks English                    1.151          -0.545
                                 (0.573) (**)    (0.592)
Health insurance coverage         0.276          -0.098
                                 (0.225)         (0.325)
Education                         0.014           0.155
                                 (0.032)         (0.055) (***)
Number of children                0.025          -0.076
                                 (0.096)         (0.114)
Veteran status                    0.949          -0.169
                                 (0.624)         (0.885)
Individual income (in thousand)   0.047           0.251
                                 (0.1)           (0.2)

Standard Errors are in parentheses. (***) indicates a level of
significance at 1 % or less while (**) indicates 5% or less and (*) 10%
or less. Regressions take into account personal weight of each
individual in the sample, city population and state dummies. Source:
Own calculations using data from Integrated Public Use Microdata
Series: Version 7.0, Ruggles et al. (2017)
COPYRIGHT 2019 Springer
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Nguyen, Hoa
Publication:International Advances in Economic Research
Article Type:Report
Geographic Code:1USA
Date:May 1, 2019
Words:5290
Previous Article:Perceptions of Quality and Household Water Usage: A Representative Study in Jacksonville, FL.
Next Article:Tourism, Capital and Labor Inflows and Regional Development.
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters