Simulating the Economic Impacts of Living Wage Mandates Using New Public and Administrative Data

Data and Measurement

The ACS contains detailed information on where people live and work and can therefore be used to construct a detailed portrait of the New York City workforce and the population affected by the proposed living wage law. We also use the ACS to identify workers based on their wage levels, their industry and borough of employment, their place of residence, and the characteristics of other members of their families; the latter is used for simulating the effects on the distribution of family incomes. We use the 3-year ACS sample covering 2006-2008, which collects yearly data reweighted to yield average values over the sample period.

To identify workers directly affected by the proposed living wage, we had to identify covered work sites and obtain information about workers and earnings at these sites. Typically, tax expenditures for economic development in New York City are tied to construction or renovation of real estate. Various programs in place exempt the taxation of changes in properties’ assessed values for a number of years (New York City Department of Finance, 2011; NYCEDC, 2012). The New York City Department of Finance provided us with longitudinal data on commercial and residential properties receiving tax exemptions for fiscal years (FYs) 1984-2011. ⁸ The living wage proposal included a minimum financial assistance threshold of $100,000 for mandating living wages. However, it was not specified how to calculate the threshold (net present value, yearly assistance, etc.); the analysis was conducted based on buildings that received real property tax exemptions of $100,000 or more in at least one FY. ⁹ The proposal also required that once the threshold was met the mandates would apply for the life of the financial assistance or 30 years, whichever was longer.

The information on properties receiving tax exemptions was matched to business establishments in the QCEW data for 2006-2008—confidential data accessible to NYCEDC. ¹⁰ The QCEW has information on average quarterly earnings and number of jobs by establishment and on the address of each establishment, which was geocoded to real properties ¹¹ ; we matched to sites that received assistance through FY2011 to provide the most representative snapshot of the kinds of businesses that locate in properties receiving financial assistance. We use the data to estimate the share of workers potentially affected by the living wage law and the increases in wages that would be experienced by affected workers, by borough and by industry. We use data on all for-profit employers at covered sites since many nonprofits were exempted from the proposed law.

We want to estimate how many of the workers at sites that received real property tax assistance of at least $100,000 at some point in the period FY1984-FY2011 (“covered sites”) would be affected by the proposed living wage law and by how much. Because we have no information about the distribution of wages within a QCEW establishment, however, we have to estimate this wage distribution and hence the share of workers paid less than $10 per hour. ¹² We first estimate the percentiles of the wage distribution by industry and borough of employment using the ACS. We cannot just assume that the ACS wage distribution holds equally at all establishments in the industry and borough because wage levels may vary across employers. We therefore use the QCEW for each industry and borough to estimate the wage level for covered establishments. This in turn requires an estimate of average hours worked at establishments because the QCEW counts positions (including part-time). We use the ACS data to estimate average hours by industry and borough of employment and apply this to the QCEW data, by industry and borough, to estimate average wages. Finally, we compute the percentage difference between the average ACS hourly wage and the average QCEW wage in the industry and borough, and then adjust the percentiles of the ACS wage distribution by this amount to arrive at an estimated wage distribution for employees at covered establishments in each industry and borough. ¹³ We can then calculate the wage increases needed to bring wages up to $10, and the implied average percentage wage increase for affected workers and for all workers. This calculation approximates the change in wages that would actually occur in New York City if those earning less than $10 at covered sites were brought to that level.

Finally, we incorporated information on a wide range of income support and other assistance programs that are provided through federal, state, and city resources to New York City residents. Many of these programs have eligibility requirements or determinants of benefit levels based on household characteristics that are not reported in the ACS data, and others provide benefits that cannot be directly measured in dollar terms (e.g., Home Energy Assistance Program and New York City Housing Authority Resident Employment Services). Thus, we limited our analysis to three larger programs—Supplemental Nutrition Assistance Program (SNAP; formerly Food Stamp program), the Earned Income Tax Credit (EITC; federal, state, and New York City’s), and Medicaid—for which eligibility and benefit levels can be determined or estimated from the ACS data. SNAP and EITC benefits can be measured in dollar terms, whereas the dollar benefits of Medicaid depend on the family’s usage of medical services. We therefore measure the effect of the proposed living wage in terms of dollars for the SNAP and EITC programs and in terms of participation for the Medicaid program. Because the ACS data do not provide detailed information on assistance program participation or with which to predict participation well, we assume all families that are eligible to participate in a particular program based on simulated family earnings do participate when we simulate effects on program benefits.

Living Wage Coverage

Our calculations of the percent of employees who would be subject to the living wage law, and the simulations that follow from them, are based on sites that received real property tax assistance of $100,000 or more in at least one year. This threshold also happens to be fairly consistent with living wage laws in other locations, and hence the CPS estimates of the employment elasticity (and the other CPS estimates with which we compare some of our simulation results) are, roughly speaking, applicable to a definition of coverage based on this criterion. Based on workers employed at sites that received $100,000 in assistance, the estimated percentage of workers earning less than $10 per hour who would have been subject to the living wage laws ranges from 9.9% in Brooklyn to 31.3% in Staten Island and is 12.9% across all boroughs. There is also considerable variation across industries, from 4% in construction to 24.4% in retail trade. The potential impact of the living wage legislation depends on the percentage of low-wage workers employed at sites receiving assistance, the number of low-wage workers in the industry, and wages in the industry.

Simulation Methods

We begin with wages. The QCEW data provide estimates of the share of employment in each borough and industry at covered sites. For each borough (b; based on place of work) and industry (i) we denote the share of workers earning less than $10 per hour who are employed at sites that received assistance in that borough and industry as CE_bi, which is the number of workers earning less than $10 per hour employed at sites receiving assistance in a borough and industry divided by the total number of workers earning less than $10 per hour in the same borough and industry.

To simulate the effects of the living wage, we have to assign wage increases to some workers who earn less than $10 per hour. In the ACS data, we can identify workers employed in any borough b and industry i who earn less than $10 per hour. Using the estimates of CE_bi from the QCEW data, we apply the living wage to the borough and industry using the following method. For all workers employed in borough b and industry i, we take those who earn less than $10 per hour and give them a wage of $10 per hour with probability CE_bi, while leaving their wage unchanged with probability (1 − CE_bi). For those who are assigned the living wage rate, we assume no change in hours or weeks worked. For the purposes of calculating how the living wage would affect the wage distribution, this “random assignment” is better than just giving everyone the “expected” increase. Giving everyone the expected increase would lead to badly estimated distributional effects. This is particularly important when we examine whether a family is pushed above an income threshold, which can be very different depending on how the benefits are distributed.

Some individuals in the ACS data have wages below the minimum wage (due to measurement error, noncompliance with local minimum wage laws, or inapplicability of the minimum wage). We assume most individuals who would be subject to the law have an hourly pay rate at or above the minimum wage, and therefore in the simulations restrict the population eligible for a wage increase to those who report a wage that is at or above the 2006 minimum wage that is applicable to New York City workers—$6.75 per hour. ¹⁴ These restrictions also reduce measurement error from individuals reporting unusual hours, earnings, and weeks worked in the ACS. We also exclude self-employed earners.

The ACS data are reported at the individual level, but the individuals are representative individuals with specific weights based on the number of actual persons in New York City that they represent. To apply wage changes randomly to individuals working in New York City, we expand the ACS data by creating duplicate records for each individual based on their household weight. Using this expanded file, we randomly assign the living wage to individuals earning less than $10 per hour by borough and industry based on the above method. The household weight was used so that we could aggregate individuals back to a complete household level (each person in the household receives the same household weight).

The QCEW data capture those who work in New York City without regard to where they live; however, the ACS data capture place of residence and place of work. Since we are primarily interested in the effects on residents of New York City, we report the wage and employment effects for New York City residents. Nonetheless, some of the effects of the living wage would fall on residents of other cities and states who work in New York City. Below, these “outflows” are reported separately from the results pertaining to New York City residents and are labeled “Outside New York City.”

Given that our estimates from the CPS data indicate some probability of job loss, we also assign job loss to simulate the effects of the proposed living wage law. We tie our projected employment effects explicitly to the CPS evidence, using the elasticity of −0.055 discussed earlier, although we do not do this for wages because the CPS results on wages were less precise, and for New York City the QCEW and ACS data enable us to estimate the wage distribution at affected firms—something that could not be done with the CPS data. This reflects the trade-offs between relying on longitudinal studies of other cities versus ex ante simulations; with regard to wages, we have more specific information about the likely effects on wages in New York City, and hence use that information.

For most boroughs and industries, a $10 per hour living wage rate is above the 10th percentile of the wage distribution, implying that the estimated CPS employment effects would apply only to workers earning less than the living wage. We assume, however, that our CPS employment elasticity with respect to mandated wage increases above the minimum wage for the lower decile would approximately fall on the workers who, according to the QCEW data, would have their wages affected by the living wage, hence applying the earlier estimates for workers in each borough and industry that have wages below $10. ¹⁵

We calculate the predicted decline in employment among those workers earning below $10 per hour, given the proposed living wage increase and the estimated employment elasticity. This yields, overall for the city, a predicted probability of job loss, denoted p. We use the −0.055 employment elasticity described earlier. This elasticity comes from the longitudinal evidence from other cities and estimates the effect of increasing the living wage conditional on the other controls in the regression model. It therefore provides an estimate of what would happen in a single city where, as mimicked by the regression model, everything else (including possible underlying trends) remains the same. Applying this elasticity to the increase in the wage floor from the New York state minimum wage of $6.75 to the $10 living wage that the law would entail, a 48.1% increase implies an employment decline of 2.65% (0.055 × 0.481 × 100) among those earning less than $10 per hour.

The job loss presumably occurs among those at covered sites, although we simply apply this to workers in the ACS earning less than $10 per hour—distributed by industry and borough based on their estimated wage distributions—because we cannot identify which workers in the ACS work at covered sites. For each industry and borough, we have an estimate of the share of workers earning less than $10 per hour working at covered sites (CE_bi). We want to assign job loss with higher probability to those who are more likely to be working at a covered site, based on their industry and borough of employment. To do this, we construct the probability that a worker earning less than $10 per hour working in borough b and industry i is at a covered site relative to the probability that any worker earning less than $10 per hour working in the city is at a covered site (CE_city). We then use a probability of job loss for a worker earning less than $10 per hour in borough b and industry i of p × {CE_bi/CE_city}.

The simulations give us new wages for some workers and different employment statuses for others. Using these wage and employment changes for individuals, we simulate the effects of the living wage law on families by calculating how the distribution of family income changes, in particular relative to the poverty line and one half the poverty line (“extreme poverty”), using the poverty threshold for New York City from the New York City Center for Economic Opportunity (NYCCEO; 2011); we refer to these as “CEO thresholds.” ¹⁶ One limitation of this kind of simulation study of the effects of the living wage on family incomes is that we do not know the actual distribution of those who get wage gains and those who lose jobs across families with different levels of income (or differences in other characteristics). In contrast, we simulate effects assuming that these gains and losses from living wages are randomly distributed across potentially affected workers in proportion to their representation in the data by industry and borough. ¹⁷

The living wage proposal we studied would have applied to new recipients of financial assistance and to existing recipients in case of renewal or amendment of the original agreements. We do not know when or if current sites receiving financial assistance would, in the future, be new recipients as a result of assistance renewal or when new developments would qualify for and receive assistance in the future. As a consequence, we know of no reliable way to, for example, isolate some sites that would be recipients of new financial assistance in the next year, the next 2 years, and so on. Instead, we assume that the effects apply to all covered workers. As a result, the results should be thought of as long-run effects. Given that most other cities also apply business assistance provisions of living wage laws to new projects only and have been implemented at different times, it is likely that the estimates from the CPS data that we use for the employment response are intermediate between short-run and long-run effects.

Wage and Employment Effects

The dark bars in Figure 1 show the baseline wage distribution (up to $13.50) for those living and working in New York City. The vertical distance measures the percentage in each range relative to all workers living and working in the city. The chart includes all those with positive wages, but in our simulation only those earning between $6.75 and $10.00 could have their wages changed by the living wage. The second set of bars (labeled “Implementation of Living Wage”) shows the wage distribution after simulating the wage effects, with no employment effects. This bar is below each of the baseline bars less than $10 and then spikes at $10, with no changes above the proposed living wage because those who are assigned wage changes have their wages increased to $10. The last and lightest bars show the distribution of individuals after simulating the employment effects as well, where those who become disemployed are assigned a wage of zero. In each instance, the third bar is slightly below the second bar for wages of $10 or less, and these reductions cumulatively add to the small mass in the $0 wage column, reflecting job loss.

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Figure 1.

Distribution of wages before and after implementation of a $10 living wage law (based on sites receiving $100,000 or more of assistance in at least one year).

To provide some information on the variation in these effects across boroughs and in an industry that would likely be strongly affected, Figure 2 reports results for retail by borough. The figure is limited to those who either received a wage increase (to $10) or experienced a job loss. The largest impacts appear in Staten Island and the smallest impacts in Manhattan, and of course, the impacts are bigger than those in Figure 1. ¹⁸

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Figure 2.

Distribution of wages before and after implementation of a $10 living wage law, retail trade industry, by borough (based on sites receiving $100,000 or more of assistance in at least one year).

Table 1 provides more information on these wage and employment effects. Looking citywide, the table shows that about 13% of the workforce at covered sites are estimated to earn less than $10 per hour and hence would have their wage increased by the living wage. Our estimates indicate that roughly 33,600 workers would receive wage increases. The average increase for those who receive the living wage is substantial ($1.67). ¹⁹ Our simulations imply that just fewer than 6,000 would lose their jobs.

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Table 1.

Wage and Employment Changes Overall, by Borough, and Outside New York City (Based on Sites Receiving $100,000 or More of Assistance in at Least 1 Year).

	% Of workers earning <$10 per hour employed at covered sites (“affected”)	Number of affected workers with wage increases	Average wage increase, affected workers who get wage increase, $	Number of affected workers losing jobs	% Affected relative to entire workforce	Average % wage increase, entire workforce	% Of entire workforce losing jobs
New York City	12.9	33,561	1.67	5,896	1.2	0.1	0.2
Bronx	12.9	6,017	1.70	1,067	1.6	0.1	0.3
Brooklyn	9.9	9,749	1.64	1,734	1.1	0.1	0.2
Manhattan	12.5	4,437	1.68	778	0.7	0.0	0.1
Queens	13.5	10,815	1.66	1,845	1.4	0.1	0.2
Staten Island	31.3	2,543	1.65	472	1.5	0.1	0.3
Outside New York City		2,820	1.50	490

Source. Authors’ simulations.

Relative to the entire workforce, the proposed living wage would affect a little more than 1.2% of the entire workforce. But this percentage is more than twice as high in the Bronx (1.6%) than in Manhattan (0.7%), owing to differences in both industry composition and wage levels. The living wage would have a small impact on average wages of the entire workforce (0.1% increase) and on overall employment (a 0.2% decrease). These effects vary in a similar way by borough. Finally, we see that some (approximately 8%) of those receiving the benefit of the living wage mandate reside outside of New York City, suggesting that “leakage” to nonresidents is not very large.

Effects on Poverty and Family Income

Table 2 reports on the results of the simulation for whether families are moved over the thresholds for poverty or extreme poverty. The first column reports the baseline percentages of families in extreme poverty (top panel) or poverty (bottom panel) for the city overall and for each of the boroughs, and the second column shows the percentages after we simulate the effects of living wages, assigning the wage increases and employment losses reported in the previous table. A comparison of these columns shows that the simulated changes in the income distribution in terms of these two thresholds are small and that they are mixed in terms of direction. As the top panel of the table shows, overall the percentage of all families classified as “extreme poor” would slightly increase by 0.05 percentage points or 0.5%; this would certainly be viewed as an unintended adverse consequence of the living wage. As the bottom panel shows, however, the percentage of families below the poverty line would slightly decrease by 0.02 percentage point or 0.08%.

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Table 2.

Changes in Family Poverty Status Overall and by Borough (Based on Sites Receiving $100,000 or More of Assistance in at Least 1 Year; Center for Economic Opportunity Poverty Thresholds).

	% Of all families, baseline	% Of families, after living wage implemented	Percentage point difference	% Change	Families, baseline	Families, after living wage implemented	Change in number of families
Families in extreme poverty
New York City	9.55	9.60	0.05	0.50	245,600	246,816	1,216
Bronx	14.27	14.32	0.05	0.34	54,170	54,352	182
Brooklyn	9.28	9.34	0.05	0.59	66,858	67,252	394
Manhattan	10.03	10.07	0.03	0.32	65,182	65,388	206
Queens	7.17	7.24	0.07	0.99	48,764	49,248	484
Staten Island	7.51	7.47	−0.04	−0.47	10,626	10,576	−50
Families below poverty line
New York City	20.70	20.69	−0.02	−0.08	532,333	531,910	−423
Bronx	29.49	29.48	−0.01	−0.02	111,923	111,896	−27
Brooklyn	22.07	22.06	−0.01	−0.05	158,980	158,899	−81
Manhattan	17.78	17.76	−0.02	−0.09	115,466	115,360	−106
Queens	18.21	18.19	−0.02	−0.09	123,864	123,753	−111
Staten Island	15.62	15.55	−0.07	−0.44	22,100	22,002	−98

Source. Authors’ simulations.

The numbers of affected families are correspondingly fairly small. The simulations indicate that an additional 1,200 families would enter extreme poverty, whereas about 400 families would be lifted out of poverty. In other words, some families below the poverty line would be lifted above it, whereas others below the poverty line would sink further beneath it in what is a rather stark illustration of the fact that a higher living wage, given disemployment effects, creates both winners—those who get higher wages—and losers—those who lose their jobs. Overall, the results show that although the number of workers receiving wage increases is considerably higher than the number of workers experiencing job losses, the aggregate effect on the distribution of income is negligible. In other words, the simulations suggest that the living wage mandate would mainly redistribute income from some low-skilled workers who lose jobs to other low-skilled workers who earn higher wages.

These simulated impacts on poverty are lower than those experienced on average for other cities imposing living wage laws, as discussed above with respect to the CPS estimates; however, the findings are not inconsistent. The poverty thresholds used in the simulations are those reported by the NYCCEO (2011), which are higher than the federal poverty thresholds used in the CPS estimates. In addition, when we redid the the simulations without restricting affected workers to those earning at least the 2006 minimum wage rate, the impacts on poverty look more comparable to those estimated using the CPS data, even when we apply the NYCCEO poverty thresholds.

Income Support and Other Programs

Given that many income support programs require low family income to qualify, or tie benefits to income, we might expect the beneficial effects of living wages to be more limited than the increase in earnings because rising earnings reduce eligibility for benefits or affect the amounts for which workers are eligible from social programs such as Medicaid, State Children’s Health Insurance Program, SNAP, housing assistance, and the EITC. The implication is that families that see earnings rise because of a living wage law would also receive fewer government benefits. Of course, the effects of job loss go in the opposite direction.

These changes might be of interest to local policy makers. . If benefits decline, then to the extent that these benefits come from the federal (or state) government, there would be less money coming into a city. As a prime example, the federal EITC has grown into the largest program for providing income support to lower income families (Blank, 2002). As a consequence, when a worker’s earnings rise, the inflow of federal dollars via the EITC can decline. On the other hand, the expenses for some benefits paid by the local government would fall.

Our simulation goes into more detail on how the proposed living wage law would affect local, state, and federal expenditures on income support and other programs in New York City. Program participation and benefit levels are estimated based on the rules and award levels in place at the time of the study. The specific eligibility guidelines and charts showing benefit levels were obtained through state and federal government websites providing program details. ²⁰ Medicaid and EITC eligibility and benefits are determined based on family income and number of family members. SNAP eligibility and benefits were also determined based on family size and income and applied the standard household and shelter deductions. Estimated EITC eligibility and benefit levels are based solely on family income, family size, and family structure (age of family members) and implicitly assume that families pass the other eligibility requirements for which we have no data. We calculate family eligibility and benefit levels prior to assigning wage and employment effects and also after assigning the simulated effects that we project would result from the living wage law to determine how assistance would be affected.

For Medicaid and SNAP there is a clear predicted relationship whereby eligibility or benefits decrease as earnings increase. However, for the EITC, benefits initially rise as earnings increase over some range, then remain flat, and eventually decrease. So for families affected by the living wage, EITC benefits may decline or increase depending on family income and the effects of the living wage.

Table 3 shows the aggregate impacts on eligibility and potential benefit levels for New York City families when the effects of the proposed living wage are simulated. The simulations show declines in EITC payments and in eligibility for and benefits from SNAP but an increase in the percentage eligible for Medicaid. ²¹ The changes range from approximately a 0.5% decrease to a 0.2% increase. Not surprisingly, boroughs with a higher percentage of low-wage workers covered (e.g., Staten Island) are projected to experience greater changes in eligibility and benefit levels and boroughs with a lower percentage of low-wage workers covered (e.g., Manhattan) are expected to have smaller changes. With respect to the EITC and SNAP, these conclusions imply that where a living wage law has the potential to deliver the most benefit because wages are lower, the earnings gains are likely to be more strongly offset because of declines in income from or eligibility for government assistance.

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Table 3.

Changes in Support Programs Overall and by Borough (Based on Sites Receiving $100,000 or More of Assistance in at Least 1 Year).

		% Change in EITC amount	% Change in number Medicaid eligible	% Change in number SNAP eligible	% Change in total amount of SNAP
New York City		−0.45	0.21	−0.18	−0.11
Bronx		−0.49	0.12	−0.11	−0.07
Brooklyn		−0.44	0.20	−0.16	−0.10
Manhattan		−0.45	0.16	−0.12	−0.08
Queens		−0.47	0.39	−0.29	−0.16
Staten Island		−0.28	0.06	−0.45	−0.14
	Change in total earnings, $	Change in EITC amount, $	Change in number Medicaid eligible	Change in number SNAP eligible	Change in total amount of SNAP, $
New York City	11,614,097	−4,639,732	784	−945	−198,478
Bronx	2,525,020	−1,196,001	100	−114	−27,741
Brooklyn	2,818,007	−1,517,036	226	−249	−63,228
New York	1,567,378	−613,921	132	−140	−28,573
Queens	4,679,903	−1,204,460	316	−346	−68,554
Staten Island	23,789	−108,314	10	−96	−10,383

Note. SNAP = Supplemental Nutrition Assistance Program; EITC = Earned Income Tax Credit.

Source. Authors’ simulations.

Finally, Table 3 also reports the changes in aggregate earnings and benefit amounts that are implied by simulating the impact of the living wage. Based on the simulated effects, family earnings would increase by approximately $11.6 million. Referring back to Table 2, these increases come from the approximately 34,000 workers who experience a wage increase, whereas approximately 6,000 workers experience reduced earnings due to disemployment. SNAP benefits decline only slightly, whereas EITC benefits would decline by approximately $4.6 million, offsetting over one third of the income gains.