Understanding the Minimum Wage: Implications for Workers, Employers, and Communities

Transcription

1 Understanding the Minimum Wage: Implications for Workers, Employers, and Communities Dr. Lou Nadeau, Speaker Dr. Gregory Acs, Speaker Dr. David Neumark, Speaker Jessica Kendall, Moderator Thursday, August 9, :00 PM EDT

2 Self-Sufficiency Research Clearinghouse 2 Poll Question

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6 Self-Sufficiency Research Clearinghouse 6 Additional Features: Events Calendar Partners Gallery Data Sets and Data Sources

7 Speakers 7 Lou Nadeau, Vice President at Eastern Research Group, Inc. Gregory Acs, Vice President of the Income and Benefits Policy Center at Urban Institute David Neumark, Economics Professor and Director of the Economic Self-Sufficiency Policy Research Institute at UC Irvine

8 Join the Conversation 8 Submit questions through the Question and Answer feature (bottom right of screen). Questions will be answered after the presentations are complete. Join the conversation on Twitter using the #SSRCWebinar hashtag.

9 The Social and Economic Effects of Wage Violations: Estimates for California and New York Lou Nadeau, Ph.D. 9

10 Overview Conducted for the U.S. Department of Labor (DOL), Office of the Assistant Secretary for Policy, Chief Evaluation Office Exploratory effort to determine the appropriate approach and data to estimate the impacts of state and federal minimum wage and overtime pay violations Began with California and New York Developed a methodology that can be applied to other states 10

11 Overview A full-time, full-year employee earning the current federal minimum wage ($7.25/hr) earns approximately $15,000 per year Failure to comply with the Fair Labor Standards Act (FLSA) has implications beyond the dollar amount of unpaid wages Increased poverty Lost payroll taxes Increased need for social programs

12 Project Steps Estimate the extent of wage violations Based on the U.S. Census Bureau s Current Population Survey (CPS) and Survey of Income and Program Participation (SIPP); used data from FY2011 Estimate the amount of lost income stemming from those violations Estimate the economic impacts Focused on impacts associated with the poverty rate, tax revenues, and program participation 12

13 Estimate the Extent of MW Violations Focus of the project was to estimate the impacts of MW and OT violations; however, data limitations prevented the estimation of OT violations First step was to determine when violations occurred among the respondents in the data Compare wages reported by respondents the applicable minimum wage, taking into account exemptions 13

14 Estimate Exemption Status Exemptions from MW and OT provisions of FLSA: Tipped employees Executive, administrative, and professional workers Exemptions allowing payment of subminimum wages: Student learners; apprentices; some full-time students Certain workers with disabilities Excluded from the analysis any workers determined to qualify for one of these exemption 14

15 Estimate the Amount of Lost Income Calculated as the difference between the hourly wage reported and the compliant hourly wage multiplied by the hours worked by the worker for each worker who experienced a violation Based on usual hours worked and usual earnings Annualized by multiplying by the number of weeks/months worked in a year 15

16 Estimate the Economic Impacts Using the estimated lost income and other data provided by workers who experienced a violation, estimate impact on: 1. Poverty rate 2. Tax revenues 3. Program participation School breakfast, school lunch Supplemental Nutrition Assistance Program (SNAP) Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) 16

17 Data Benefits CPS and SIPP are large, nationally representative datasets Publicly available datasets Both datasets provide information about demographics, individual and family income, taxes, and program participation 17

18 Data Caveats CPS and SIPP contain self-reported values, so violations must be inferred Measurement error Datasets do not contain the exact data elements necessary to develop estimates of violations and impacts Restricting the sample to estimate impacts on workers with violations leads to small sample sizes 18

19 Results- Prevalence of Violations 19 State Number of Jobs (1,000s) [a] Number of MW Violations (1,000s) All Jobs Violation Rates (%) Non-Exempt Low-Wage and Non-Exempt CPS (Weekly Estimates) California 14, % 3.8% 11.8% (9,847-18,677) ( ) (2.2%-3.2%) (3.2%-4.3%) (10.5%-13.2%) New York 8, % 3.5% 11.1% (452-16,181) (17-358) (1.8%-2.9%) (2.9%-4.1%) (8.3%-13.8%) SIPP (Monthly Estimates) California 13, % 3.5% 10.9% (13,298-13,815) ( ) (2.0%-2.9%) (2.9%-4.1%) (9.1%-12.6%) New York 7, % 6.5% 19.5% (7,694-8,152) ( ) (3.5%-5.1%) (5.3%-7.7%) (16.2%-22.8%) Notes: (1) Point estimates and 95 percent confidence intervals (in parentheses). (2) The CPS estimates represent the number of violations occurring in a week whereas the SIPP estimates represent the number of violations occurring in a month. [a] Excludes members of the military, the self-employed, and workers under 16. Includes up to four jobs per worker in the CPS and up to two jobs in the SIPP.

20 Results- Violations by Occupation California (CPS) California (SIPP) 28% 43% 33% 30% visions. 8% 8% 13% 12% 7% 18% New York (CPS) New York (SIPP) 9% 17% 7% 15% 52% 19% 9% 12% 17% 43% 20 Legend Services Transportation and material moving Sales and related Other [a] Office and administrative support [a] Includes occupations not classified elsewhere.

21 Results- Violations by Worker Characteristics Age Gender 8.0% 8.0% 6.0% 6.0% 4.0% 4.0% 2.0% 2.0% 0.0% California (CPS) California (SIPP) New York (CPS) New York (SIPP) 0.0% California (CPS) California (SIPP) New York (CPS) New York (SIPP) Ages Ages Ages 45+ Female Male Educational Attainment Race/Hispanic Origin 8.0% 8.0% 6.0% 6.0% 4.0% 4.0% 2.0% 2.0% 0.0% California (CPS) No degree Associate's degree California (SIPP) New York (CPS) High school diploma New York (SIPP) Bachelor's degree or above 0.0% California (CPS) White only Other race Hispanic (any race) California (SIPP) New York (CPS) New York (SIPP) Black only Not Hispanic (any race) 21

22 Results- Lost Income 22 State Total ($millions) Lost Income [a] Per Week for Workers with a Violation ($) As Share of Worker's Earned Income Minimum Wage (CPS) California $22.5 $ % ($13.7-$31.2) ($56-$70) (35.6%-63.0%) New York $10.2 $ % ($0.0-$20.4) ($48-$63) (30.0%-44.4%) Minimum Wage (SIPP) California $28.7 $ % ($21.7-$35.6) ($73-$99) (54.6%-87.2%) New York $20.1 $ % ($15.5-$24.7) ($50-$69) (35.9%-59.2%) Note: Point estimates and 95 percent confidence intervals (in parentheses). [a] Estimated amount of lost wages owed for any given week in FY2011. SIPP data estimates lost wages per month; for comparability, the SIPP estimates have been divided by 4.3 to get the amount of lost wages per week.

23 Results- Indirect Impacts of Violations: Poverty State (Data Source) California (CPS) California (SIPP) New York (CPS) New York (SIPP) 23 Number of Families Families with Violations (1,000s) Percent Increase in Numbers Increase in Numbers in Poverty [a] in Poverty [a] Families Individuals Children [b] Families Individuals Children Minimum Wage Violations % 10.8% 8.5% ( ) (0-20) (0-30) (0-10) % 70.7% 55.6% ( ) (31-51) (64-113) (15-39) % 31.6% 0.0% (0-302) (0-23) (0-30) [c] % 27.8% 18.3% ( ) (17-34) (39-71) (8-21) Note: Point estimates and 95 percent confidence intervals (in parentheses). Total number of families rounded to closest ten thousand. [a] Change from full compliance [b] Due to data restrictions, children in the family whose parents do not work will not be captured. [c] No family that became impoverished included children and thus confidence Intervals cannot be assessed. [d] No families with overtime pay violations would be in poverty under full compliance. Therefore, since the denominator would be zero we cannot calculate a percent change.

24 Results- Indirect Impacts of Violations: Payroll Taxes State (Data Source) California (CPS) California (SIPP) New York (CPS) New York (SIPP) Families With Violations Number of Families Decrease in Annual Payroll Taxes [a][b] (1,000s) $ % Minimum Wage Violations 300 $560 8% ( ) ($400-$710) 307 $650 11% ( ) ($590-$700) 130 $540 7% (0-302) ($450-$640) 319 $450 8% ( ) ($410-$500) Note: Point estimates and 95 percent confidence intervals (in parentheses). Rounded to closest tens. [a] Change from full compliance [b] Includes both the employee's and the employer's shares of FICA Social Security and Medicaid taxes. In the CPS these data represent tax year In the SIPP they represent FY

25 Results- Indirect Impacts of Violations: School lunches 25 State (Data Source) Increase in Total Annual Amount $1,000s [a] School Lunches $10,091 California (SIPP) ($4,344- $15,839) $4,750 New York (SIPP) ($1,342- $8,158) School Breakfasts $5,531 California (SIPP) ($1,732- $9,329) $2,984 New York (SIPP) ($895- $5,073) Notes: (1) Point estimates and 95 percent confidence intervals (in parentheses). (2) The CPS estimates benefits for the 2010 calendar year. The SIPP estimates benefits for the school year, which roughly corresponds to the FY2011. [a] Change from full compliance

26 Results- Indirect Impacts of Violations: SNAP Families with Minimum Wage Violations and SNAP Receipt State (Data Source) Increase in Families Eligible for SNAP [a] Increase in Monthly SNAP Amount Per Family [a] Increase in Total Monthly SNAP Amount (1,000s) [a] # % $ % $ California (SIPP) 3,509 29% $54 25% $858 (0-8,440) ($40-$69) ($367-$1,349) New York (SIPP) Notes: (1) Point estimates and 95 percent confidence intervals (in parentheses). (2) Change in SNAP benefits cannot be adequately assessed in the CPS. [a] Change from full compliance % $71 23% $2,779 (0-1,415) ($47-$94) ($965-$4,592)

27 Conclusion Both in CA and NY, estimate that approximately 3.5 percent of jobs subject to MW requirements involve violations Lost income per week of about $10 million in NY and $22 million in CA Lost $113 million in federal income taxes (CA & NY) Lost $238 million in federal payroll taxes (CA & NY) Lost $14 million in state tax revenues in CA; $8 million in NY Increased school breakfast & lunch benefits of $ million/year in CA; $7.7 million/year in NY

28 August 9, 2018 Forecasting the Potential Effects of Increasing the Minimum Wage SSRC Webinar Gregory Acs, Vice-President for Income and Benefits Policy The Urban Institute

29 We conduct research on policy by looking through the rearview mirror

30 but policymakers need us to look through the windshield.

31 To forecast the potential effects of a change in the minimum wage, we draw on the lessons of the past, and through the use of data and structured analytic techniques (like micro-simulation), we anticipate how that change will ripple through the economy.

32 When you raise the minimum wage Hourly earnings for some workers will go up Some individuals will be drawn into the labor force Some individuals may experience a reduction in hours worked or even job loss Changes in income will lead to changes in eligibility for and use of public assistance and tax credits Firms may change the skill mix of their workers and invest more in capital Consumer prices may go up

33 Factors that affect minimum wages impacts: Market position of employers Size of the minimum wage increase Distribution of wages across the targeted labor force Distribution of income across families with minimum wage workers How responsive employment is to changes in wages

34 When forecasting the effects of a minimum wage increase, you have to decide What outcomes do you care about and how well can you measure them What populations to examine Two examples: CBO national study of raising the federal minimum wage Urban Institute study of raising the minimum wage in DC

35 CBO (2014) The Effects of a Minimum-Wage Increase on Employment and Family Income Raise federal minimum wage from $7.25/hour to $10.10/hour Assess the impact on employment, poverty and federal budget

36 CBO Step-by-Step Approach (1) Step 1: Distribution of hourly wages CPS data from 2013 reflecting hours worked Step 2: Age the data forward to year in which the new wage takes effect Use CBO assumptions about economic growth and inflation to age the data forward to 2016 (the year when the $10.10 minimum was to take effect).

37 CBO Step-by-Step Approach (2) Step 3: Identify the directly affected workers Workers covered by FSLA, not tipped, earning between $7.25 and $10.10 per hour. Step 4: Address ripple effects Workers earning up to the amount that would result from an increase that is 50% more than increase in the effective minimum wage. Employers/stockholders/others whose incomes may change as a result of decreased profits/economic activity.

38 CBO Step-by-Step Approach (3) Step 5: Assess direct effects on employment Based on past research, a 10% increase in the minimum wage reduced teens hours worked by 1% and adults hours worked by 0.33%. The effective increase in wages is based on the differences between a worker s current wage and the new minimum. Workers earning above the new minimum would not be subject to hours reductions even if their wages go up a bit.

39 CBO Step-by-Step Approach (4) Step 6: Compute the change in income resulting from wage increases and hours reductions Step 7: Assess tax and transfers changes and macroeconomic effects

40 CBO Results Employment will fall by 500,000 full-time equivalents (0.3 percent) 16.5 million workers would see their incomes rise 900,000 would be lifted out of poverty Real income of the poor would rise by $15 billion and by $12 billion for families with income between one and three times the poverty threshold. Real incomes of families with incomes above 6 times the poverty line would fall by $17 billion. Effect on the federal budget would be very small over a ten-year period.

41 Urban Institute Study of DC Minimum Wage (2014) Understanding the Implications of Raising the Minimum Wage in the District of Columbia Raise DC s minimum wage from $8.25/hour to $11.50/hour in Assess the effect on employment and incomes of DC residents including net income after taxes and changes in public assistance program eligibility and participation.

42 Strategy 1. Start with a representative sample of low-wage workers in the DC area and their families from the American Community Survey. 2. Use DC s population forecasts to project DC s low-wage workforce in Adjust the data for under-reported income using the TRIM model. 4. Simulate the effects of raising the minimum wage.

43 Urban DC Study (1) Three years of data from the American Community Survey ( ) Assume workers with computed wages between $5.50 and $13.50 an hour will be affected 41,000 low wage workers would be affected

44 Over half of workers likely to be affected by DC s minimum wage increase live in low income families (darker blue). Affected workers by family percent of poverty Below poverty 19% percent of poverty 18% percent of poverty 17% percent of poverty 21% 300+ percent of poverty 26%

45 Over 60 percent of affected workers are prime-age. Affected workers by age Under 20 4% % % % 55 and over 19%

46 Almost half of affected workers have no more than a high school education. Affected workers by levels of education Less than high school 23% High school degree only 24% Some college, no degree 21% Associate's degree 3% Bachelor's degree 18% Above bachelor's degree 10%

47 About two-thirds of affected workers are unmarried and do not live with children. Affected workers by marriage and parental status Unmarried, no kids 68% Married, no kids 13% Married Parent 8% Single Parent 11%

48 Affected workers are spread across industries and occupations, but tend to be in: Service and retail industries Food preparation, maintenance, and support occupations Private sector

49 Effects of the Minimum Wage Increase Given the distribution of wages among low-wage workers who live and work in DC, any potential job losses are likely to be small. Percent of affected workers with job loss, by scenario Any age affected 1.2% Even doubling our higher estimate means fewer than 1,000 jobs lost. Effect confined to age <25 0.3%

50 Effects of the Minimum Wage Near poor families would get the biggest bump in earnings, on average, but higher income families would get the biggest bump in income. Median change in earnings and income among workers affected, by poverty level Below Poverty $450 $843 Change in Earnings Change in Income 1-2x Poverty $879 $1,835 >2x Poverty $1,053 $1,604

51 Percent Change in Program Caseloads and Benefits for DC Residents Cases Benefits SNAP LIHEAP Public and Subsidized Housing Child Care WIC Family Cash Assistance SSI -3.4% -3.4% -1.9% -1.7% -1.7% -0.9% -0.5% -0.6% -0.6% -0.3% -0.2% -0.8% -0.3%

52 Percent Change in Tax Credits for DC Residents Cases Tax Credit Amounts Federal EITC -2.9% -2.5% DC EITC -2.3% -2.3% Federal Child Tax Credit 0.1% 1.1% Federal Child and Dependent Care Credit 0.5% 1.2%

53 Key Points - Targeting 2 out of 5 workers who live or work in DC, live and work in DC those workers are the focus of the analysis. Over ½ of affected workers live in low-income families. About 1 in 5 live with children whom they support.

54 Key Points - Employment Research suggests that in areas like DC, employment effects are likely to be small. Simulations indicate small effects largely due to the projected distribution of wages in 2016.

55 Key Points - Income ½ of families with affected workers receive low-income transfer/tax benefits. Annual earnings would rise by about $1,500 if the minimum wage rises to $ Annual income would rise by less than $1,000, on average, because earnings are taxed away. Bigger income gains for higher income families, because they are less likely to have received transfer/tax benefits.

56 Key Points Other effects are likely to be small. Business relocation Prices Public benefits Private benefits

57 How good are prediction models? Can t assess CBO as federal wage didn t rise. DC hard to assess because of growing economy would it have grown more if the minimum wage hadn t risen? Other research is mixed on effects of minimum wage on program participation. Predictions models are useful tools for thinking about policy outcomes in a structured way. The distribution of wages, the size of the potential increase, and the practical ability of employers to relocate are key factors influencing the effects of raising minimum wages.

58 Using Minimum Wages to Fight Inequality and Poverty David Neumark 58

59 The advent of high minimum wages in the U.S. Federal MW rose to $7.25 in 2009, no change since 29 states now have MW above federal, ranging to $11.50 in DC Cities have joined the parade 59

60 What is driving the policy change? 2.60 Individual Earnings Inequality for Full-Time, Year Round Workers - Male /10 60

61 What is driving the policy change? 16.0 Individual Poverty Rate vs. Per Capita Real GDP $45, $44, $43,000 Poverty $42,000 $41,000 $40,000 $39,000 $38,000 Per Capita Real GDP 8.0 $37, $36, $35, Poverty Rate Per Capita Real GDP 61

62 Lower real federal minimum wage $10.00 Real Minimum Wage, 2017 Dollars $9.50 $9.00 $8.50 Real Minimum Wage $8.00 $7.50 $7.00 $6.50 $6.00 $5.50 $

63 Facts point to three reasons we should raise the minimum wage, perhaps a lot MW has not kept up with inflation Coupled with other changes, lower minimum wage has led to greater wage inequality Growing economy has not reduced poverty 63

64 Does a declining real MW per se imply we should raise it? Obama Administration: Since it was first established in 1938, the minimum wage was eroded substantially over several prolonged periods because of inflation. The proposal to raise the minimum wage by $1.75 by the end of 2015 would restore the real value of the minimum wage to what it was in

65 Minimum income floor has become more generous for many $22, Labor Earnings vs. Income with the EITC for One-Earner Household with Two Children Working at Minimum Wage for 2000 Hours, 2011$ $20, $18, Income $16, $14, $12, $10, Earnings Earnings+EITC 65

66 Will a higher MW counter the rise in inequality? 2.60 Individual Earnings Inequality for Full-Time, Year Round Workers - Male /10 66

67 Will a higher MW counter the rise in inequality? 6.50 Individual Earnings Inequality for Full-Time, Year Round Workers - Male /10 67

68 Will a higher MW counter the rise in inequality? 6.50 Individual Earnings Inequality for Full-Time, Year Round Workers - Male /10 50/10 68

69 Two key empirical assertions of MW advocates A higher MW will help low-wage workers A higher MW will reduce poverty 69

70 What does economic theory tell us about the effects of the minimum wage? When something becomes more expensive, we use less of it Gasoline Cigarette taxes Subsidies for green technology (reverse) Potential unintended consequence of higher MW 70

71 A little more complicated with labor Low-skill labor becomes more expensive Firms reduce use of low-skilled labor, increase use of other inputs Higher costs of production raise prices, which reduces demand by consumers Both effects imply less employment of lowskilled workers 71

72 Is the economic theory right? Earlier consensus from national studies timeseries through the early 1980s: Minimum wage elasticity for young workers: 0.1 to 0.2 % cccccccccccc iicc cceeeeeeeeeeeeccccee % cccccccccccc iicc MMMM 10% increase in MW reduces employment of strongly affected groups by 1-2% 72

73 Not so easy to estimate effects of minimum wages Problem of counterfactual State (and city) minimum wages give researchers more leverage 73

74 The U.S. economics laboratory : state minimum wages 35 Number of States with Minimum Wage higher than the Federal Number of States

75 The U.S. economics laboratory : state minimum wages 35.0% Average Percent Difference between State and Federal Minimum Wage 30.0% Average Percent Difference 25.0% 20.0% 15.0% 10.0% 5.0% 0.0%

76 What does the evidence say? Extensive review of over 100 studies (Neumark & Wascher, 2007) > 100 studies, 2/3 find negative effects 85% of more credible studies find negative effects Larger disemployment effects when studies focus on least skilled 76

77 Recent revisionist studies dispute conclusion Dube et al., 2010 (DLR), and Allegretto et al (ADR) criticize state panel data approach Question is how to construct counterfactuals for the places where minimum wages are increased issue throughout history of minimum wage research Both studies argue that only geographically proximate controls account for association of state minimum wage variation with unobserved economic shocks Complex, technical issues, ongoing work 77

78 Papers using close controls tend to find no effect on employment Authors Employment elasticity and groups studied Data/approach Geographically-proximate designs Dube, Lester, and Reich (2010) Allegretto, Dube, and Reich (2011) Near zero for teens and restaurant workers Near zero for teens Paired counties on opposite sides of state borders States compared only to those in same Census division Gittings and Schmutte (2014) Addison et al. (2013) Near zero for teens; larger negative elasticities in markets with short non-employment durations ( 0.1 to 0.98) and smaller positive elasticities in markets with long nonemployment durations (0.2 to 0.46) Varying sign, more negative, generally insignificant for restaurant workers and teens; stronger negative at height of Great Recession ( 0.34) States compared only to those in same Census division Similar methods to Dube et al. (2010) and Allegretto et al. (2011) restricted to period Slichter (2016) (teens) Comparisons to bordering counties and other nearby counties Liu et al. (2016) 0.17 (14-18 year-olds) Comparisons within BEA Economic Areas (EA) that cross state lines, with controls for EA-specific shocks 78

79 But most alternative approaches to spatial heterogeneity problem find negative effects Authors Employment elasticity and groups studied Data/approach Other or better approaches to spatial heterogeneity Neumark et al. (2014a, 2014b) 0.14/ 0.15 for teens, 0.05/ 0.06 for restaurant workers States compared to data-driven choice of controls (synthetic control), and state panel data Powell (2015) 0.44 for teens States compared to data-driven choice of controls (synthetic controls, estimated simultaneously with employment effect) Totty (2014) 0.01 to 0.04 for restaurant workers; 0.04 to 0.7 for teens States compared to data-driven choice of controls (factor model) Dube and Zipper (2015) Baskaya and Rubinstein (2012) (mean) and (median) for teens States compared to data-driven choice of controls (synthetic control) 0.3 to 0.5 for teens States, using federally-induced variation as instrumental variable Clemens and Wither (2016) Appx. 0.97, for those directly affected by minimum wage increase Targeted/affected workers versus other low-wage workers in states affected by federal increases Thompson (2009) 0.3 (for teen employment share) Low-wage counties vs. higher-wage counties in states 79

80 Conclusion is contested, but that doesn t mean we don t know the answer Some earlier, and some recent papers find no disemployment effects Minority of studies, hinge on restrictions on the data that are untested and found unsupportable Continued flow of better studies (in my view), and studies addressing recent critiques in different ways, finding disemployment effects 80

81 So does a higher MW help low-wage workers? 81

82 Does a higher minimum wage reduce poverty? The minimum wage was one of the first and is still one of the best anti-poverty programs we have Senator Edward Kennedy (quoted in Clymer, 1999, p. 449) Nobody who works full-time should have to live in poverty. President Obama (2/12/2014) 82

83 Seems like a no-brainer Income-to-needs % low-wage workers Less than 1 (poor) or above 0 Low-wage worker: < ½ average private-sector wage 83

84 Seems like a no-brainer Income-to-needs 1939 % low-wage workers Less than 1 (poor) or above 0 Low-wage worker: < ½ average private-sector wage 84

85 Many MW workers are not in poor families Income-to-needs % lowwage workers % lowwage workers % lowwage workers % lowwage workers % lowwage workers $7.25 to $10.09 Less than 1 (poor) or above

86 Many MW workers are not in poor families Income-to-needs % lowwage workers % lowwage workers % lowwage workers % lowwage workers % lowwage workers $7.25 to $10.09 Less than 1 (poor) or above

87 Many MW workers are not in poor families Income-to-needs % lowwage workers % lowwage workers % lowwage workers % lowwage workers % lowwage workers $7.25 to $10.09 Less than 1 (poor) or above Point originally made in Burkhauser and Finegan (1989): The Minimum Wage and the Poor: The End of a Relationship 87

88 What does the research show? Most research shows no net effect of minimum wages on poverty in the United States There are winners and losers among the poor, just like there are among low-wage workers 88

89 OK, but a higher minimum wage will shift support of poor to employers Recent comprehensive study of Medicaid, FRPL, public housing, TANF, SNAP, and WIC No reduction from higher MW except for SNAP 89

90 Is it as hopeless as it seems? Changes in the economy have disadvantaged less-skilled workers Central challenge: increase incomes of lowincome families, without discouraging work 90

91 The Earned Income Tax Credit solution Pays nothing to people who don t work Subsidizes, or adds to, what people earn in the labor market, making work more attractive Theory predicts more people work in response Targets low family income, not low wages Increases employment of those who gain the most (single, low-skilled mothers) Reduces poverty 91

92 Which is better redistributional policy? Government role in redistribution? MW Does not target benefits on the poor Almost surely does not redistribute from the richest EITC Targets the poor effectively Financed by taxes UBI also has these advantages (with phaseout), but more likely to discourage work Likely worse long-term effects 92

93 Research lessons Minimum wage has some negative consequences Little or no evidence of net benefits for lowincome families Alternative policy the EITC has partly replaced minimum wage to put floor on income Encourages work and raises income for many low-income families In contrast to minimum wage, likely encourages skill formation, earnings growth, and economic self-sufficiency 93

94 It s complicated 94

95 Supplemental slides 95

96 The elasticity is so low that workers on net are helped Prevailing view: With elasticity of 0.2, 10% increase in minimum implies: 2% lose their job 98% get 10% raise Average income of low-wage workers up by (.98 x 10) (.02 x 100) = 7.8% 96

97 With moderate disempl. effect, low-wage workers could fare badly (ex: 10% increase, 0.2 elasticity) Correct calculation: Impact on affected workers 80% above minimum 20% at minimum Average Wages No change Up 10% Up 2% Employment No change Down 10% Down 2% Earnings No change No change No change Incorrect calculation, based on elasticity Elasticity of 0.2 comes from: 2% employment decline 10% wage increase 97

98 With moderate disempl. effect, low-wage workers could fare badly (ex: 10% increase, 0.2 elasticity) Correct calculation: Impact on affected workers 80% above minimum 20% at minimum Average Wages No change Up 10% Up 2% Employment No change Down 10% Down 2% Earnings No change No change No change Incorrect calculation, based on elasticity 2% employment decline Elasticity of 0.2 comes from: 10% wage increase Worse given that elasticities computed for legislated increase smaller than actual increase (can still get no gain with 0.1 elasticity) 98

99 Join the Conversation 99 Submit questions through the Question and Answer feature (bottom right of screen). Questions will be answered after the presentations are complete. Join the conversation on Twitter using the #SSRCWebinar hashtag.

100 Self-Sufficiency Research Clearinghouse 100 Thank you!