An Assessment of the Expected Impacts of City-Level Parking Cash-Out and Commuter Benefits Ordinances

Appendix G. Additional Equity Discussion

As previously noted, the parking cash-out and related commuter benefits policies examined in this analysis have various implications for equity. This appendix continues this discussion supported by the U.S. Census Bureau data and local data, where available, to comment on the distribution of expected benefits to be realized based on the studied scenarios.

Census Data Comparisons

The research team compiled the following data from the U.S. Census Bureau’s ACS PUMS data, based on ACS 5-Year 2019 estimates:

• Employee wage income⁴¹ and vehicle ownership: Given vehicle ownership is a household measure, vehicles owned in the household were divided by the estimated number of adults in the household. This indicator, vehicles per adult in the household, was thought to be a better indicator of vehicle access than household vehicle ownership alone, given distinctions in literature highlighting travel behavior differences across “car-deficit households” (Blumenberg, Brown, & Schouten, 2020).
• Employee wage income to primary commute mode: While benefits in various scenarios may be offered to all commuters, under some scenarios, only commuters using certain modes may realize such benefits, or benefits realized might be unequal across travel modes. Further, while the goal of such policies may be to encourage commuters to mode shift, some commuters may be unable to shift to certain modes (e.g., no access to transit near home and workplace too far for walking or biking).

Here, these data are used to discuss equity implications across scenarios; note, with respect to discussions about commute mode, however, that commute distributions from PUMS data are not tailored to the population receiving free workplace parking. As such, applicability to Scenarios 1 and 2 may vary. The most granular geography at which PUMS data is published is for Public Use Microdata Areas (PUMAs), which are, according to the U.S. Census Bureau, “non-overlapping, statistical geographic areas that partition each State or equivalent entity into geographic areas containing no fewer than 100,000 people each.” For these crosstabulations, only PUMAs that intersected or overlapped with the Census Place geographies for the city of interest were included in the sample. Additionally, only responses by employed individuals were included in the sample to limit the crosstabulation to the population of interest. Estimates were weighted using person-level weights.

Employee Wage Income and Vehicles Owned Per Adult in The Household

The crosstabulation between employee wage income and vehicle ownership is displayed in Table 37 and visualized in figure 13. Examining figure 13, the general trend is as employee wages increase, a lower proportion of households appear to own fewer than one vehicle per adult in the household and a greater proportion of households appear to have one or more vehicles available per adult in the household. One exception is in New York City, where a U-shaped trend is observed in zero-car ownership. This is likely because the wealthiest households can afford to locate in more central urban locations, where parking may be more expensive and where non-auto modes may be competitive with auto travel. As such, these households may choose to be car-free.

To evaluate the relationship between employee wage income and vehicle ownership per adult category, income levels were split to divide the lowest-income group (<$25,000) from all higher ones.⁴² Vehicle ownership levels were consolidated into “Less than one vehicle per adult” and “One or more vehicle per adult” categories. Then, Chi-square tests were conducted to examine the relation between this binary income and vehicle ownership splits. The relation between these income and vehicle ownership variables was significant for all cities in the analysis,⁴³ and indicated that the lowest-income group was more likely to own less than one vehicle per adult in the household compared to the consolidated higher income groups.

These distributions demonstrate that any policies where benefits are geared toward car owners, most especially free parking, will also skew benefits toward wealthier employees. If an individual does not own a car (or has fewer cars available than commuters in a household), one could assume that that person does not drive to work in their own vehicle (or if they have fewer household vehicles available, one could assume that it is less likely that they drive their own vehicle). If policies offering additional benefits are geared toward a population more likely to use and own personal vehicles, there may be shortfalls in terms of equity offerings. Further, commuters who do not receive free parking with low access to transit may not be able to realize the offered pre-tax transit or vanpool benefits under Scenarios 3 and 4, or if offered free parking, employer-paid transit under Scenario 2. Alternatively, Scenario 5 would benefit all commuters who do not drive to work equally, regardless of car ownership status (and implications of this on commute travel). As such, this scenario would be expected to have benefits realized more proportionately across income groups than the others examined.

Table 37. Employee wage income by vehicles per adult in household (HH): % of employees in wage category in vehicles per adult category.

Employee Wage Income	Vehicles per Adult in HH	Los Angeles	San Diego	Washington D.C.	Chicago	Indianapolis	Boston	New York City	Philadelphia	Houston
$0-$25,000]	0	5%	3%	24%	10%	2%	13%	41%	14%	3%
$0-$25,000]	(0-1)	58%	47%	57%	56%	31%	52%	48%	50%	45%
$0-$25,000]	[1-1.5)	32%	43%	19%	31%	53%	30%	10%	32%	46%
$0-$25,000]	[1.5+	4%	7%	2%	4%	13%	4%	1%	4%	7%
($25,000-$50,000]	0	3%	2%	20%	6%	1%	9%	32%	8%	1%
($25,000-$50,000]	(0-1)	49%	37%	55%	49%	22%	50%	52%	43%	35%
($25,000-$50,000]	[1-1.5)	43%	52%	23%	41%	61%	37%	15%	43%	54%
($25,000-$50,000]	[1.5+	6%	10%	2%	5%	16%	3%	1%	6%	10%
($50,000-$75,000]	0	2%	1%	15%	4%	1%	6%	24%	4%	1%
($50,000-$75,000]	(0-1)	34%	24%	55%	42%	13%	44%	54%	35%	23%
($50,000-$75,000]	[1-1.5)	55%	61%	27%	48%	64%	46%	20%	54%	63%
($50,000-$75,000]	[1.5+	10%	14%	3%	7%	22%	5%	2%	7%	14%
($75,000-$100,000]	0	1%	1%	11%	3%	1%	4%	21%	3%	1%
($75,000-$100,000]	(0-1)	26%	18%	56%	36%	10%	37%	51%	28%	19%
($75,000-$100,000]	[1-1.5)	60%	63%	30%	53%	67%	53%	26%	61%	63%
($75,000-$100,000]	[1.5+	13%	18%	4%	8%	23%	7%	3%	8%	17%
($100,000-$150,000]	0	1%	1%	10%	3%	1%	4%	23%	3%	1%
($100,000-$150,000]	(0-1)	19%	16%	55%	37%	6%	33%	46%	27%	16%
($100,000-$150,000]	[1-1.5)	64%	63%	32%	51%	69%	56%	27%	60%	66%
($100,000-$150,000]	[1.5+	15%	21%	3%	9%	24%	7%	4%	11%	18%
($150,000+	0	1%	1%	6%	3%	0%	3%	31%	3%	1%
($150,000+	(0-1)	14%	12%	49%	35%	6%	27%	44%	21%	10%
($150,000+	[1-1.5)	65%	63%	41%	53%	67%	58%	22%	60%	70%
($150,000+	[1.5+	20%	24%	5%	9%	27%	13%	4%	16%	20%

“]” indicates the interval includes the bracketed number; “(“ indicates the interval is greater than (but does not include) the bracketed number.

Employee Wage Income and Commute Travel

The crosstabulation between employee wage income and vehicle ownership is displayed in Table 38 and visualized in figure 14. In cities with higher public transportation commute rates overall (e.g., Boston, Chicago, New York City, Philadelphia, and Washington, D.C.), public transportation commuting tends to follow a U-shaped trend, decreasing from low-income to high-income categories, until mode share increases again in the highest income categories. Similar to the explanation provided for vehicle ownership in New York City, this trend may be due to the wealthiest households locating themselves in the city’s urban core, where access to high quality transit allows for easy travel via public transportation. A similar, albeit less pronounced, trend can be observed for walking mode shares in Boston, Chicago, New York City, and Philadelphia. In contrast, walk mode share stays relatively constant across income groups in Washington, D.C., for employees earning $75,000 or more. Note, too, that in many cities, the auto commuting mode share levels off after the lowest-income group.

Here, analysis is focused on walking commuters, as this subset of commuters is generally more likely to fall into the lowest income categories across the cities considered for analysis, where the findings for cycling in the cities that were analyzed is less clear.⁴⁴ To evaluate the relationship between employee wage income and walking commutes, income levels were split to divide the lowest-income group (<$25,000) from all higher ones except the highest income levels, given the U-shaped trend observed for walking behaviors due to unique characteristics of this group (e.g., ability to locate centrally in walkable distances for commutes). Commutes were consolidated into “Walking” and “Other mode” categories. Then, Chi-square tests were conducted to examine the relation between this binary income split and the commute mode. The relation between these income and vehicle ownership variables was significant for all cities in the analysis except Washington, D.C.,⁴⁵ and indicated that the lowest-income group was more likely to commute via walking than the higher income groups. This result indicates that, for almost all cities in this analysis, Scenario 5 would offer enhanced benefits for a greater share of low-income commuters—if those commuters are unable to mode-switch—compared to other scenarios, given its universal non-SOV benefits offerings regardless of current workplace parking subsidies.

Table 38. Employee wage income by primary commute mode: % of employees of wage category in commute mode category.

Employee Wage Income	Primary Commute Mode	Los Angeles	San Diego	Washington D.C.	Chicago	Indianapolis	Boston	New York City	Philadelphia	Houston
$0-$25,000]	Car, truck, or van	78%	85%	42%	69%	92%	60%	29%	66%	91%
$0-$25,000]	Public transportation1	11%	5%	39%	20%	2%	26%	54%	22%	3%
$0-$25,000]	Biked/walked	5%	4%	10%	6%	2%	9%	12%	6%	2%
$0-$25,000]	Worked from home	4%	5%	7%	3%	3%	4%	3%	4%	3%
$0-$25,000]	Other2	3%	2%	2%	2%	1%	2%	2%	2%	2%
($25,000-$50,000]	Car, truck, or van	88%	91%	53%	78%	95%	67%	39%	76%	94%
($25,000-$50,000]	Public transportation1	5%	3%	34%	15%	0%	22%	50%	16%	2%
($25,000-$50,000]	Biked/walked	2%	1%	8%	3%	1%	6%	8%	4%	1%
($25,000-$50,000]	Worked from home	3%	4%	4%	3%	4%	3%	3%	3%	3%
($25,000-$50,000]	Other2	2%	1%	2%	1%	1%	1%	1%	1%	1%
($50,000-$75,000]	Car, truck, or van	90%	91%	52%	76%	94%	67%	43%	78%	94%
($50,000-$75,000]	Public transportation1	3%	2%	29%	16%	0%	21%	47%	14%	1%
($50,000-$75,000]	Biked/walked	2%	1%	13%	4%	1%	7%	6%	4%	1%
($50,000-$75,000]	Worked from home	4%	5%	5%	4%	5%	3%	3%	4%	4%
($50,000-$75,000]	Other2	1%	1%	2%	1%	1%	1%	1%	1%	1%
($75,000-$100,000]	Car, truck, or van	88%	89%	47%	72%	92%	67%	44%	78%	91%
($75,000-$100,000]	Public transportation1	3%	1%	29%	18%	0%	20%	45%	12%	2%
($75,000-$100,000]	Biked/walked	2%	1%	16%	4%	1%	7%	6%	4%	1%
($75,000-$100,000]	Worked from home	5%	7%	6%	5%	7%	5%	3%	5%	6%
($75,000-$100,000]	Other2	1%	1%	2%	1%	0%	1%	2%	1%	1%
($100,000-$150,000]	Car, truck, or van	87%	88%	43%	61%	88%	64%	41%	73%	90%
($100,000-$150,000]	Public transportation1	3%	1%	31%	24%	0%	20%	46%	14%	2%
($100,000-$150,000]	Biked/walked	2%	1%	17%	6%	1%	7%	7%	4%	1%
($100,000-$150,000]	Worked from home	7%	8%	7%	8%	10%	8%	4%	8%	6%
($100,000-$150,000]	Other2	1%	1%	2%	1%	1%	2%	2%	1%	1%
($150,000+	Car, truck, or van	87%	85%	49%	55%	88%	62%	27%	70%	90%
($150,000+	Public transportation1	2%	1%	28%	28%	0%	19%	51%	14%	2%
($150,000+	Biked/walked	3%	2%	15%	8%	1%	9%	12%	7%	1%
($150,000+	Worked from home	8%	11%	6%	8%	11%	7%	5%	8%	5%
($150,000+	Other2	1%	2%	3%	2%	1%	3%	5%	1%	2%

¹Bus; subway or elevated rail; long-distance train or commuter train; light rail, streetcar, or trolley
²Ferryboat, taxicab, motorcycle, other method
“]” indicates the interval includes the bracketed number; “(“ indicates the interval is greater than (but does not include) the bracketed number.

Household Travel Survey Data

The research team conducted a scan of available HTS data for the nine cities analyzed. Results of this scan are displayed in Table 39. Regional survey data capturing income, industry, and provision of commute-related benefits (parking and/or transit) was at least partially available for six cities, with all of these datapoints available for four of the six cities. Cities without appropriate data that are excluded from this supplementary analysis include Houston and Boston/Cambridge (lacking data on provision of commuter benefits) and San Diego (lacking city- or regionally-specific survey data). Available surveys vary in recency and definitions (e.g., of industries). Findings from these surveys relevant for the six cities for which any appropriate regional data is available are summarized in this section.

Table 39. Summary of the best available household travel survey data related to each city.

Region/Survey	Income?1	Industry?2	Free Parking?	Transit benefit?
Los Angeles, CA: 2001-2002 Southern California Regional Travel Survey (National Renewable Energy Laboratory, 2022)	Yes	Yes	Yes	Yes
San Diego, CA: 2012 CA (Statewide) HTS** (National Renewable Energy Laboratory, 2022)	Yes	Yes	No	Yes
Washington, D.C.: 2017/2018 Regional Travel Survey (Metropolitan Washington Council of Governments (MWCOG), 2018)	Yes	Yes	Yes	Yes
Chicago, IL: 2018–2019 My Daily Travel Survey (Chicago Metropolitan Agency for Planning (CMAP), 2020)	Yes	Yes	Yes	Yes
Indianapolis, IN: Madison County Council of Governments – Heartland in Motion 2014 (National Renewable Energy Laboratory, 2022)	Yes	Yes	Yes	No
Boston/Cambridge, MA: MassDOT 2011 Household Travel Survey (HTS) (MassDOT, 2012)	Yes	No	No	No
New York City, NY: 2010/2011 Regional Travel Survey (New York Metropolitan Transportation Council (NYMTC), 2014)	Yes	Yes	Yes	Yes
Philadelphia, PA: 2012–2013 HTS (DVRPC, 2016)	Yes	Yes	Yes	Yes
Houston, TX: 2007–2009 Houston-Galveston Area Council (H-GAC) Metropolitan Planning Organization (MPO) HTS (Texas A&M Transportation Institute (TTI), 2013)	Yes	Yes	No	No

¹While person-level income is available in PUMS data, HTSs typically only collected household income, which is the metric flagged here.
²Specific data related to industry varies across surveys. Some provide detailed North American Industry Classification System (NAICS) codes, while others provide general employer information (e.g., private firm/company, non-profit firm/organization, government, etc.). As such, industry-related data may not be consistent across surveys or regions.
**Although the 2012 CA HTS contains some information noted in this table, it does not support weighting to the city or region level, versus city or regional surveys that would enable specific weights. As such, use of this survey to explore San Diego data has been excluded.

Visualizations of available data from the seven surveys where information is available are displayed in figure 15 through figure 25. For each survey, data is displayed segmented by income, although significant differences in benefits received were evaluated by both industry/sector and income throughout this section. Note that each survey defined industries or sectors differently (e.g., some used North American Industry Classification System (NAICS) codes, which were consolidated for this visualization; the MWCOG survey just described basic sector (e.g., public or private)). Regional weighted data (which these surveys typically display) may show slightly different or diluted trends compared to cities. It is important to consider that, unlike the PUMS, HTSs provide household-level income, not employee-level income. As such, observed trends may be skewed to not fully reflect those that would be observed with employee-level income. Most of the surveys are regional, and data for the entire region was retained so that weights could be appropriately applied.

Data for the Los Angeles region (figure 15 and figure 16) show some variance in benefits offerings by household income groups, more prominently for transit benefits (Figure 16) versus free parking offerings (figure 15). Free parking offerings seem to follow a U-shaped pattern across income groups. Transit benefits offerings, in contrast, increase with rising income overall, with some deviation in the middle-income levels. Examining trends by income and sector, Pearson’s Chi-square test revealed that the lowest income group (<$25,000) is significantly less likely to be offered free parking compared to all higher income groups consolidated within the “Agriculture, Utilities, Construction, Manufacturing, Transportation,” “Healthcare and Education,” and “Retail, Wholesale, Real Estate, Entertainment, Food Services” sectors.⁴⁶ Any policies that expand the baseline number of employees considered for benefits beyond just employees receiving free parking (e.g., Scenarios 3–5) will have positive equity implications, particularly for employees working in these sectors.

With respect to the offering of transit benefits, Pearson’s Chi-square test revealed that the lowest income group is significantly less likely to be offered transit benefits compared to all higher income groups consolidated in the “Agriculture, Utilities, Construction, Manufacturing, Transportation,” “Finance, Information, Professional Services, Management,” and “Retail, Wholesale, Real Estate, Entertainment, Food Services” sectors.⁴⁷ Given this, any policies that provide subsidized transit benefits (Scenario 2–5, with Scenario 2 only being offered to employees also receiving free parking) would have positive implications for equity for these sectors in particular.

Data for the Washington, D.C., region (figure 17 and figure 18) show some variance in benefits offerings by household income groups. Upon visual inspection, the most drastic differences in the provision of free parking benefits are observed jumping from the lowest income group to the others, after which the trend plateaus. The provision of transit benefits shows a more continuous trend, with higher income groups experiencing higher rates of workplace transit benefits compared to lower income groups. Examining trends by income and sector, Pearson’s Chi-square test revealed that the lowest income group (<$25,000) is significantly less likely to be offered free parking compared to all higher income groups consolidated within private/for-profit and State or local government sectors.⁴⁸ As such, any policies that expand the baseline number of employees considered for benefits beyond just employees receiving free parking (e.g., Scenario 3 through Scenario 5) will have positive equity implications, especially for employees working in private/for-profit and State or local government sectors.

With respect to the offering of transit benefits, Pearson’s Chi-square testing revealed that the lowest income group is significantly less likely to be offered transit benefits compared to all higher income groups consolidated within private/for-profit and nonprofit sectors.⁴⁹ Policies that provide subsidized transit benefits (Scenario 2 through Scenario 5, with Scenario 2 only being offered to employees also receiving free parking) would have positive implications for equity for these sectors in particular. It should also be noted that, in all sectors beyond Federal government work, a vast majority of employees, regardless of income group, do not receive subsidized transit benefits. This situation likely differs for Washington, D.C., compared to the rest of that region given the city’s especially high concentration of Federal workers.

Examining data for Chicago (figure 19 and figure 20), free parking offerings appear relatively stable across income groups. In contrast, subsidized transit offerings tend to increase as incomes increase. Pearson’s Chi-square test did not reveal statistically significant findings relating income and industry across sectors. However, there were significant differences in transit benefits offerings within all sectors, except Public Administration (likely due to lower sample size in the unweighted data), that indicated the lowest income group is significantly less likely to be offered transit benefits compared to all higher income groups consolidated.⁵⁰ This indicates there are equity enhancements that could be achieved by expanding transit benefits offerings across sectors.

Data for the New York City region (figure 21 and figure 22) shows sparse parking and transit benefits offerings across income groups. With respect to free parking benefits, figure 21 shows a slight skew toward higher-income levels, although the difference seems to plateau after $50,000. Pearson’s Chi-square test revealed that the lowest income group (here, this is household incomes <$30,000) is significantly less likely to be offered free parking compared to all higher income groups combined, across all sectors.⁵¹ Similar with analysis of other cities, any policies that expand the baseline number of employees considered for benefits beyond just employees receiving free parking (e.g., Scenarios 3–5) will have positive equity implications overall here.

With respect to the offering of transit benefits, although figure 22 generally seems to indicate a positive trend between income and transit benefits overall, Pearson’s Chi-square test revealed that the lowest income group is significantly less likely to be offered transit benefits compared to all higher income groups combined within the “Agriculture, Utilities, Construction, Manufacturing, Transportation” and “Finance, Information, Professional Services, Management” sectors.⁵² Policies that provide subsidized transit benefits (Scenarios 2–5, with Scenario 2 only being offered to employees also receiving free parking) would have positive implications for equity for these sectors especially.

Data on free parking and transit benefits for Philadelphia are displayed in figure 23 and figure 24, respectively. A U-shaped pattern appears for parking benefit provision, while transit benefits seem to increase with income overall, although with some deviations. Pearson’s Chi-square test showed that the lowest income group is significantly less likely to be offered free parking compared to all higher income groups in the “Agriculture, Utilities, Construction, Manufacturing, Transportation” sector, but not in any others.⁵³ With respect to transit benefits, Pearson’s Chi-square test did not reveal any significant relationship between income and transit benefits in any sectors. As such, the various tested scenarios may not enhance equity of benefits offerings between income groups and industries, at least to the degree observed in some other cities. However, Scenario 3 through Scenario 5, which offer benefits to all commuters versus just those receiving free parking, will still be more impactful here by expanding the baseline number of employees eligible for some benefit.

Data on free parking distributions in Indianapolis are displayed in figure 25. Note that the Madison County Council of Governments – Heartland in Motion 2014 (National Renewable Energy Laboratory 2017) HTS, on which Indianapolis analysis is based, includes data weights—such that the weighted distribution presented in figure 25 is representative of the region—but does not provide expansion weights; as such, population estimates are not provided in figure 25. Due to small samples that occur when grouping employees by industry and then comparing subsidy levels in this survey, Person’s Chi-square testing was not separately conducted for each industry. Instead, testing compared free parking offerings across all industries, aggregated by income. When comparing free parking offerings by income level for all industries together, Pearson’s Chi-square test did not reveal any significant relationship between income and free parking offerings.

Additional Considerations

There are a few key measures not explored in this section, described below. These areas present key opportunities for future data collection and research efforts.

Differences in Benefits Offerings by Employer Size

No identified datasets allowed the research team to examine distributions of employees receiving subsidized parking or transit benefits by income level and the size of their employer. Some regional surveys provide limited insights on these factors. For example, MWCOG’s 2019 State of the Commute Survey Report shows that 28 percent of Washington, D.C., regional employees working for employers with 1–100 employees receive subsidized transit or vanpool subsidies, compared to 44 percent of employees working for employers with 101–250 employees, 55 percent of employees working for employers with 251–999 employees, and 67 percent of employees working for employers with 1,000+ employees. Free parking offerings seem to follow a reverse trend as related to employer size, with 62 percent of employees working for employers with 1–100 employees receiving free workplace parking, compared to 57 percent of employees working for employers with 101–250 employees, 47 percent of employees working for employers with 251–999 employees, and 47 percent of employees working for employers with 1,000+ employees.

Data from the Puget Sound Region (Commute Seattle, 2016), used to scale estimates for Scenarios 1A and 3A in this analysis, reveal that 46 percent of employees at firms with fewer than 20 employees receive free parking, compared to 44 percent of employees at firms with 20 or more employees. Additionally, in this region, 44 percent of employees at firms with less than 20 employees receive subsidized transit benefits, compared to 69 percent of employees at firms with 20 or more employees. Across the cities analyzed in this report, more than 80 percent of employees worked for employers with 20 or more employees.

While these datasets do not allow us to infer variance based on employee wage income levels and employer size (given they do not provide employee wage income⁵⁴), the differences in offerings by employer size at least indicate that policies including employers of certain sizes and excluding others may have undesirable impacts related to equity and who is eligible to receive benefits. For example, if employees working for smaller employers tend to earn lower incomes, and a policy is focused exclusively on larger employers, an opportunity to enhance equity for the former could be achieved by expanding offerings regardless of employer size. Complementary strategies could be put in place to support smaller employers in their ability to comply with such policies, such as providing training or technical assistance related to implementation, including guidance on administering cash-out payments and benefits to employees, consideration on the use of third-party benefits providers, and guidance on monitoring and enforcement.

The Disproportionate Burden of Transportation Costs

A 2015 Brookings Metropolitan Policy Program report found that proximity to jobs fell for poor and non-white residents at higher rates than for non-poor and white residents (Kneebone and Holmes, 2015). Where these groups’ homes are located farther from their workplace locations, commuting via certain modes (e.g., transit, walking, biking) may become more difficult (or impossible). As such, low-income households that do not own vehicles may rely on rides in privately owned vehicles to access employment (Tomer and Kane, 2014). Additionally, rising costs associated with transportation may have disproportionately negative impacts on lower-income households (Methipara, 2014). Higher-income households located centrally in urban areas may sometimes be better able to switch their commute mode to transit to take advantage of parking cash-out benefits than lower-income households. Further, in policy situations, like Scenario 5, some lower-income households that had free workplace parking would be negatively impacted by having to absorb the cost of paid parking if they are not able to switch commute modes. Still, all non-SOV commuters—which may reflect a lower-income group on average than those who drive alone to work—benefit from reduced driving (and subsequent benefits offerings (e.g., reductions in congestion, pollution, and improvements in safety).

Equity impacts should be considered in instituting parking cash-out policies. This does not mean that free parking is the most equitable situation. There is growing evidence that parking availability and VMT have a positive relationship (Currans, Abou-Zeid, and Iroz-Elardo, 2021). Wealthier households also contribute more to VMT than their lower-income counterparts (Howell et al., 2018), which has impacts for congestion, emissions, and crashes—the burden of which is shared across all income levels, regardless of who is responsible for contributing most to related VMT. As previously stated, Scenario 5 advances transportation equity by eliminating the false free cost of parking; free parking acts to subsidize automobile use, and can subsequently increase negative externalities related to congestion, pollution, emissions, and safety. Scenario five has the greatest VMT reduction potential, which will act to mitigate these externalities for the greatest number of people.