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Policing the poor: The impact of vehicle emissions inspection programs across income
Transportation Research Part D 78 (2020) 102207
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Policing the poor: The impact of vehicle emissions inspection programs across income
T
Ryan J Wessel1 LDS Institute at Arizona State University, United States
A R T IC LE I N F O
ABS TRA CT
Keywords: Inspection and maintenance Smog check Vehicle emissions Distributional impact
Comparing vehicle emissions inspection results with vehicle owner income shows that the Arizona vehicle emissions inspection program constrains the vehicle repair decisions of people in the low end of the income distribution more than people in the high end. Individuals who live in areas with lower annual income are both (i) more likely to drive vehicles that fail emissions inspections at a higher average rate, and (ii) more likely to fail emissions inspections conditional on vehicle characteristics. The top income quintile fails emissions inspections 20% less often than the bottom income quintile even when controlling for observable vehicle characteristics. This implies that owner characteristics, in addition to observable vehicle characteristics, have a nonnegligible impact on vehicle emissions rates. Therefore, the impact of programs designed to reduce vehicle emissions could be greater if participation were subject to a means test.
JEL: Q52 Q58
1. Introduction Motor vehicles are the largest source of nitrogen oxide (NOx), carbon monoxide (CO), and volatile organic compounds such as hydrocarbons (HC) in the United States (Environmental Protection Agency, 2007). This pollution contributes to smog and poor air quality, and according to the U.S. Environmental Protection Agency, human exposure has negative impacts on health and welfare (Environmental Protection Agency, 2017). One policy intervention used to reduce emissions of these pollutants from in-use cars and light-duty trucks is mandated emissions rate ceilings for various vehicle classes. In the United States, the 1977 and 1990 Amendments to the Clean Air Act created an enforcement mechanism for emissions rate ceilings through inspection and maintenance (I/M) programs (Environmental Protection Agency, 2006, 2007). State-run vehicle emissions inspection programs (VEIPs) were created to facilitate mandatory I/M monitoring in areas which do not meet federal air quality standards. VEIPs identify high polluting vehicles through broad inspection of a majority of vehicles on the road. Owners of vehicles that fail inspection are required to repair the vehicles’ emission abatement systems or face a penalty, typically the inability to register the vehicle for legal use on public roads. Currently, 33 states in the United States, and most other developed countries, require some level of emissions testing in some areas (Environmental Protection Agency, 2003; Harrington et al., 2000). Recent research has called into question the efficiency and effectiveness of VEIPs in controlling air pollution. Mérel et al. (2014) shows that a significant amount of emissions abatement from repairs is lost by the time a subsequent inspection is required and therefore the effect of VEIPs may be overestimated. Sanders and Sandler (2017) calls into question the link between vehicle
E-mail address: [email protected]. Special thanks to the Arizona Department of Environmental Quality and the Arizona Motor Vehicle Division for prividing data for this study, and to the Center for the Study of Economic Liberty. 1
https://doi.org/10.1016/j.trd.2019.102207
1361-9209/ © 2019 Elsevier Ltd. All rights reserved.
Transportation Research Part D 78 (2020) 102207
R.J. Wessel
inspections and local air pollution levels. Sandler (2012) shows that the cost effectiveness of emissions inspection programs has steadily declined over time, and Harrington et al. (2000) shows that the cost of such programs is higher than ex ante EPA estimates. These results raise the question of whether some other policy intervention may more effectively reduce vehicle emissions. This paper adds to the literature by investigating whether vehicle emission inspection programs disproportionately affect individuals in the lower end of the income distribution. One open question in the environmental economics literature is whether environmental policies are regressive, meaning do various policies negatively impact low income individuals more than high income individuals. The literature is mixed on this question. Bento and Freedman (2014) shows that environmental regulators often focus clean up efforts on the dirtiest geographic areas, which may disproportionately benefit low income residents who more often live in those areas. On the other side, Jacobsen (2013) shows that CAFE standards have a regressive welfare cost. Others have also weighed in, including Benzhaf (2011) which highlights the importance of understanding the distribution of benefits and costs of regulatory actions, Fullerton (2011) which enumerates types of distributional effects, and Bento (2013) which examines the trade-offs between efficiency and equity that arise in the context of various environmental policies. In this paper, I evaluate the distributional impact of VEIPs along the income dimension by creating a novel data set that combines vehicle emissions inspection results from the Arizona VEIP with a measure of household income. The combined data set allows for investigation into the regressivity of VEIPs. If regressivity is assumed to be a negative characteristic of policy interventions, as Benzhaf (2011), Fullerton (2011), and Bento (2013) seem to imply, this paper adds to the growing body of literature that suggests that some policy intervention other than broad I/M programs may be more efficient, effective, or equitable in the quest to reduce harmful emissions (Sandler, 2012,Mérel et al., 2014, Sanders and Sandler, 2017, Harrington et al., 2000). Further, an understanding of how owner income influences vehicle emissions allows for investigation into counterfactual applications of historical and existing regulatory programs. For example, the 2009 Car Allowance Rebate System based eligibility almost entirely on vehicle characteristics, not owner characteristics. If owner characteristics, such as household income, have an impact on vehicle emissions, this could imply that programs designed to induce vehicle repair or update will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for repair or replacement, not just on the characteristics of the vehicles themselves. If lower income individuals are more likely to drive vehicles that pollute more, and are more likely to fail emissions inspections conditional on vehicle characteristics, then restricting participation in the CARS program based on a means test could have increased the environmental impact of the program, holding all else constant. The paper is organized as follows. Section two introduces the emissions inspection program and the data used in this study. Section three evaluates the distributional impact of the program. Section four discusses the policy implications of the results. Section five concludes.
2. Program background and data In the United States, the 1977 and 1990 Amendments to the Clean Air Act created an enforcement mechanism for emissions rate ceilings through inspection and maintenance (I/M) programs in large, metropolitan areas which do not meet certain federal air quality standards (Environmental Protection Agency, 2016). Arizona is required by the EPA to run an emissions inspection program in two metropolitan areas, Phoenix and Tucson, that do not meet minimum air quality standards. The Arizona Department of Environmental Quality (ADEQ) administers the program and the program is implemented by a private contractor. The Arizona emissions data is detailed, contains many data points, and contains information on multiple inspection methods. This makes it a good starting point for investigating vehicle emissions inspection programs.
2.1. Data The data used for this study was created by combining information from three government agencies: 1. Arizona Department of Environmental Quality. The ADEQ data contains the universe of emissions inspection results for the 2013–2014 emissions inspection cycle. It includes basic vehicle characteristics (vehicle identification number, make, model, model year, mileage, vehicle size); whether the vehicle passed inspection; the level of pollutants emitted during each test (where available); and, in a very small number of cases, a self-reported cost of repairs made between a failing test and a retest. 2. Arizona Motor Vehicle Division. The MVD data contains owner home address data by VIN for every vehicle registered in Arizona during 2013–2014. This data is confidential, and requests for this data can be made through the Arizona MVD. 3. American Community Survey. The ACS 2014 5-year estimates give estimates of resident characteristics by home location, including annual income of households by zip code, census block, and census block group. This study focuses on data from the 2013–2014 emissions inspection cycle. Using two years of data is appropriate for a number of reasons. First, most vehicles in the data set were inspected every other year, so the natural cycle of testing is two years. Second, there is a systematic differences between the number of vehicles tested during odd years and those tested during even years because the testing program was not implemented gradually. Averaging over two consecutive years alleviates these differences. Finally, there was concern that a longer study period would conflate cross-vehicle variation with long run advances in emission reduction technology. 2
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Table 1 Emissions test summary statistics (annual average). Number of Tests
1,905,159
Location Phoenix Tucson Other
78.6% 20.1% 1.3%
Pass on First test Retest Never
91.7% 5.9% 2.4%
Test Type OBD IM147 Loaded/Idle Opacity Other/Missing
65.6% 7.1% 9.4% 4.6% 13.3%
2.2. Emissions test types There are four broad types of vehicle emissions tests used in Arizona. They are: 1. On-Board Diagnostic (OBD). Vehicles newer than 1995 model year are inspected using this test. Data that has been continually recorded by the vehicle’s on-board sensors is used to evaluate whether the vehicle passes inspection. As long as certain on-board monitoring systems are working and monitored data did not exceed 2.5 times a federally mandated level, the vehicle passes inspection. In the 2013–2014 testing cycle, 66% of vehicles were tested with the OBD test. 2. Inspection/Maintenance 147 (IM147). Most 1981–1995 model year light duty gasoline-powered vehicles in Phoenix are inspected using the IM147 test. In this test, the vehicle is driven on rollers at varying speeds to simulate urban driving. The exhaust of three pollutants (hydrocarbons, carbon monoxide, and oxides of nitrogen) is measured. Levels of all three pollutants, measured in grams per vehicle mile, must be below a certain threshold to pass. In the 2013–2014 testing cycle, 7% of vehicles were tested with the IM147 test. 3. Loaded/Idle. Model year 1967–1980 vehicles (and some newer vehicles registered in Tucson), are inspected with the Loaded/Idle test. Exhaust is tested while the vehicle idles and again at approximately 25 miles-per-hour while the vehicle is on rollers. Exhaust of hydrocarbons and carbon monoxide must be below a certain threshold to pass. About 9% of vehicles were tested with the Loaded/Idle test. 4. Opacity Test. Diesel vehicles are tested by measuring the opacity of the exhaust. Less than 5% of vehicles were inspected with this test. Approximately two million vehicles are inspected annually in Arizona at an annual inspection cost of $36 million paid by vehicle owners. Table 1 reports summary statistics for emissions inspections in the 2013–2014 testing cycle. The majority of inspections were done in Phoenix. More than nine in 10 vehicles pass the inspection on the first try. Most vehicles are testing using On-Board Diagnostics. Under current rules, OBD will be used more frequently over time as older vehicles are retired from the fleet and are replaced by new ones.
2.3. Vehicles tested Table 2 reports summary statistics for the vehicles tested during the 2013–2014 testing cycle. Vehicle characteristics are make, model, model year, and odometer reading. Vehicle make is divided into the six most common manufacturers, plus a category for other vehicles. Vehicle models are grouped into one of four model categories by size. Examples of those categories are given in Table 2. This study will focus on model year 1981–2008 non-diesel vehicles. Fig. 1 shows the number of vehicles tested in the 2013–2014 emission cycle by model year, divided among test type. Current policy exempts the five newest model years from inspection, so vehicles manufactured after 2008 are not fully represented in the 2013–2014 data; this accounts for the pronounced dip in number of 2008 model years vehicles shown in Fig. 1. Since the number of very old vehicles is low and since a new test type was introduced for model year 1981 vehicles, all vehicles manufactured before 1981 were excluded. Fig. 2 shows the vehicle inspection pass rate for model years 1981–2008. Newer vehicles pass emissions inspections at a higher rate. Honda brand vehicles pass inspection more than average, and this is especially pronounced for newer model Hondas and for Hondas made during the 1980s. Ford vehicles made during the early 1990s pass inspection at a rate higher than trend. Across all vehicles and all model years in the study data set, about 92% of vehicles pass the emissions inspection on the first try. 3
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Table 2 Vehicle summary statistics. Vehicle Type (example) Cars (Fusion) Light Trucks (Ranger) Medium Trucks (F150) Heavy Trucks (F350)
45.0% 26.6% 16.9% 11.5%
Vehicle Make Chevrolet Ford Toyota Honda Dodge Nissan Other
15.8% 15.3% 11.1% 7.5% 6.8% 6.3% 37.3%
Model Year Earlier than 1990 1990–1995 1996–2000 2001–2005 2006–2008 2009+
4.1% 8.2% 19.4% 37.6% 26.7% 4.4%
Ave. Odometer
116,340 miles
Fig. 1. Number of vehicles inspected by model year. Test type primarily used for each set of model years is listed in bold.
2.4. Income data Household income data come from the American Community Survey 2014 5-year estimates. Income data was assigned to each address by geocoding each address and spatially joining the geocoded data with census block group polygons published on the U.S. Census website. Median household income in the last 12 month in 2014 inflation adjusted dollars for the census block group or other aggregation was then assigned to each household. An assumption was made in order to assign income data to specific vehicles: a household is defined as one vehicle. About 61% of addresses in the study data have only one vehicle registered at that address. For the 24% of addresses with two vehicles and 15% of address with three or more vehicles, I assume that duplicates of the household each own one vehicle. The existence of multi-vehicle households presents an interesting challenge. In the analysis presented in this paper, the income variable is meant to be a measure of vehicle owner characteristics that captures variation outside of that described by vehicle choice. Because of this, it was decided to assign the full income of each multi-vehicle household to each vehicle. Essentially, the assumption was made that owner behavior, even in multi-vehicle households, does not change with the number of vehicles owned by the 4
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Fig. 2. Percent of cars that pass on the first try by model year. Cars older than 1996 model year are tested with the IM147 test. Cars 1996 model year and newer are testing using On-Board Diagnostics (OBD).
household. To put it another way, an owner with two vehicles and $100,000 income is more similar to an owner with one vehicle and $100,000 income than to a owner with one vehicle and $50,000 income. If owner income in a multi-vehicle household were apportioned equally to each vehicle, the opposite would be assumed. There also may be concern that multi-vehicle households divide total driving miles between vehicles. Controlling for vehicle miles in the analysis partially allays this concern. Admittedly, further research on multiple-vehicle households could yield fruitful results. In a few instances, many vehicles are registered to the same address. These were, in most cases, businesses such as car dealerships. Eliminating these addresses from the data set does not qualitatively change the results because the number of vehicles at these addresses is very small compared to the entire sample. A further limitation of the process used in assigning income to each vehicle owner is that income is measured with error at the household level. While a census block group, with about 500 households, is small relative to a zip code, the median income of the census block group is not a perfect measure of each household’s income. More detailed income data certainly exists but is severely restricted. To take seriously results of analyses done using census block group aggregations, such as the result presented in Fig. 3, one
Fig. 3. Percent of vehicles that passed emissions testing on the first try, by annual income in each zip code in which the vehicle was registered. Note that this trend is also evident when income data is disaggregated, but is presented at this level of aggregation for convenience. 5
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must assume that the difference between actual income and median income within each census block group does not differ is a systematic way across census block groups. As the available data does not allow for this to be tested, it is taken to be true as an assumption. Vehicles associated with post office boxes or registered in states outside of the inspection region were dropped because they could not reliable be connected with income data. 3. Distributional impact One open question in environmental economics is whether environmental policies are regressive in impact on people along the wealth distribution. In this section, I ask, “Are vehicle emissions inspection programs regressive?” I find that the Arizona VEIP is regressive in that it constrains the vehicle repair decisions of the vehicle owners who live in areas with low median income more than those who live in areas with high median income. Since lower income owners fail initial emissions inspections more often than high income owners, lower income owners are more often required to make vehicle repairs that they did not choose to make without the program. Fig. 3 shows the percent of vehicles at each income level that passed the vehicle emissions inspection on the first try. There is an upward trend, meaning that owners who live in areas with a higher median income are more likely to pass the emissions inspection on the first try.2 Additionally, Fig. 3 does not include new vehicles because the five newest model years are exempt from inspection by policy. Including new vehicles under the assumption that each of them passes inspection further amplifies the upward trend in Fig. 3. The effect of owner income on inspection results is suggested by other aspects of the data. For example, Table 3 shows the average number of attempts before the vehicle passes inspection for each quintile of the income distribution. Owners with lower annual income retest more often before passing. Table 3 also shows the percent of vehicles tested that never pass the emissions inspection despite multiple tries, by quintile of the income distribution. Owners with lower annual income are more likely to never pass the emissions inspection. This correlation between owner income and emissions pass rate is explored more formally in the next two subsections. The analysis will show that program regressivity, meaning lower income individuals are constrained by the program more often than higher income individuals, works through two channels. First, and unsurprisingly, vehicle owners who live in areas with lower median income are more likely to drive vehicles that fail the inspection at higher average rates. Second, and perhaps the more interesting result, lower income owners are more likely to fail emission inspections even when controlling for observable vehicle characteristics. Each of these, vehicle characteristics and owner characteristics, are explored in turn below. 3.1. Vehicle characteristics This section will show a broad correlation between likelihood of failing emissions inspections and vehicle choice. This is perhaps an unsurprising result. Vehicle owners who live in areas with lower median income are more likely to own less expensive, older, higher mileage, American made vehicles. Older, higher mileage, American made vehicles are more likely to fail emissions tests (see Fig. 2). The correlation between income and vehicle model year choice is suggested by Fig. 4. Higher income individuals are more likely to own newer vehicles. The highest income quintile owns 15% of the oldest model year vehicles and 30% of newest model year. Contrastingly, the lowest income quintile owns 22% of the oldest model year vehicles and 10% of the newest model year. A companion result is that lower income owners also drive vehicles with higher vehicle miles, a characteristic highly correlated with vehicle model year. There is also a difference in vehicle manufacturer distribution among income quintiles. Table 4 shows that 24% of Honda vehicles in data, which is the vehicle with the highest average pass rate, are owned by the highest income quintile compared to 15% owned by the lowest income quintile. Conversely, 15% of Chevy vehicles, which is the vehicle with the lowest average pass rate, are owned by the highest income quintile while 25% of Chevy vehicles are owned by the lowest income quintile. 3.2. Owner characteristics In this section, I will show that even when controlling for observable vehicle characteristics, there still exists a statistically significant difference in pass rate along the owner income dimension. To understand what vehicles characteristics and owner characteristics contribute to passing emissions inspection, I use various regression models (linear probability, logit, ridge) to regress owner and vehicle characteristics on inspection pass rate. In the linear model, the assumed data generating process is
E = β0 + β1 (w + η) + β2 V + β3 X + ∊
(1)
where E = probability of passing the emissions test w + η = annual income of owner measured with error
2
Zip codes with fewer than 50 observations were excluded from the figure for clarity, but not excluded from the rest of the analysis. 6
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Table 3 Average number of tests before the vehicle passes and percent of vehicles that never pass emissions, by quintile of the income distribution. Income Quintile First ($13K - $49k) Second ($49K - $59k) Third ($59K - $71k) Fourth ($71K - $91k) Fifth ($91K +)
Number of Tests Before Pass
Percent Never Pass
1.31 1.25 1.22 1.18 1.15
3.6% 2.5% 1.9% 1.3% 0.9%
Fig. 4. Ownership of each model year vehicle by income quintile. Table 4 Average pass rate on a first test listed by vehicle make, and percent of each vehicle make owned by the highest and lowest income quintile. Pass Rate
Highest Quintile Owned
Lowest Quintile Owned
Honda Toyota Dodge Ford Nissan Chevy
94.0% 94.0% 92.3% 92.0% 91.4% 89.1%
24.1% 23.9% 15.3% 17.3% 17.7% 15.1%
15.1% 15.2% 22.8 % 23.1% 21.2% 25.3%
Other
91.4%
22.6%
18.2%
Make
V = observable vehicle characteristics (odometer, make, model, model year). X = controls (county, test station, test type, observation year). ∊ = error. Transaction price of each vehicle would be ideal information to include in this regression. Transaction price would provide some information about differences in vehicle quality within each bin of vehicle characteristics, and perhaps some information about the owner, such as bargaining ability. Unfortunately, this information is not available and would be very difficult to obtain. It is possible to create an estimate of the purchase price of each vehicle using a Berry et al. (1995) model (hereafter BLP). The BLP model, however, predicts prices based on vehicle characteristics such as engine type and vehicle size, market characteristics such as gasoline price, and 7
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Table 5 Dependent variable is probability of passing emissions test. Linear model standard errors clustered by census block group or zip code as indicated. Logit standard errors calculated using delta method.
Income ($10k) Odometer (10k) Model Year Make Honda Toyota Nissan Ford Dodge Other
Controls Test Station County Observation Year Vehicle Size Test Type Observations R2 {Pseudo} Clusters
(1) Naive
(2) Collinearity
(3) Income by Zip
(4) Logit
0.00205∗∗∗ (0.000) −0.00269∗∗∗ (0.000) 0.00402∗∗∗ (0.000)
0.00091∗∗∗ (0.000) – (—) 0.00458∗∗∗ (0.000)
0.00204∗∗∗ (0.000) −0.00284∗∗∗ (0.000) 0.00382∗∗∗ (0.000)
0.0443∗∗∗ (0.000) −0.0437∗∗∗ (0.000) 0.00548∗∗∗ (0.000)
0.0443∗∗∗ (0.000) 0.0437∗∗∗ (0.000) 0.0198∗∗∗ (0.000) 0.0193∗∗∗ (0.000) 0.0160∗∗∗ (0.000) 0.0154∗∗∗ (0.000)
0.0381∗∗∗ (0.000) 0.0379∗∗∗ (0.000) 0.0175∗∗∗ (0.000) 0.0188∗∗∗ (0.000) 0.0174∗∗∗ (0.000) 0.0145∗∗∗ (0.000)
0.0446∗∗∗ (0.000) 0.0440∗∗∗ (0.000) 0.0199∗∗∗ (0.000) 0.0191∗∗∗ (0.000) 0.0158∗∗∗ (0.000) 0.0153∗∗∗ (0.000)
0.7593∗∗∗ (0.000) 0.7285∗∗∗ (0.000) 0.2465∗∗∗ (0.001) 0.2534∗∗∗ (0.000) 0.1977∗∗∗ (0.001) 0.1548∗∗∗ (0.000)
x x x x x
x x x x
x x x x x
x x x x x
2,336,897 0.148 3,963
2,336,897 0.0675 3,963
2,297,601 0.146 305
2,297,601 {0.165}
p-values in parentheses. p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.
firm characteristics such as domestic or foreign. Data estimated from a BLP model cannot predict price differences between vehicles within vehicle-type bins, only of vehicle-type bins themselves. Since much of the base information used in BLP is already included in the simple linear regression in (1), a BLP based price estimate would have little unique information in this application. Luckily, the owner income variable provides useful information about owner characteristics. If statistically significant variation in pass rate remains after controlling for observable vehicle characteristics, this implies that some individual characteristic outside of vehicle choice, here encapsulated by owner income, influences the emissions pass rate. The influence of the owner income variable is further isolated by controlling for test station, county, test type, and observation year. Controlling for test station rules out the possible conclusion that lower income owners tend to patronize inspection stations that for some reason fail vehicles more frequently. Controlling for county, test type, and observation year accounts for unobserved differences across geography, testing equipment, and time. Table 5 summarizes the results of the regressions. Column (1) displays the results of a naive linear probability model which includes all available data. Column (2) displays the results of a model specification that seeks to account for any collinearity among predictors. Odometer reading and vehicle model year are correlated therefore odometer reading is removed. Also, the county variable and the emissions test station variable both encode location information therefore county is removed. Ultimately, because the coefficient of interest (owner income) is not highly correlated with any of the predictor variables, multicollinearity is perhaps not of first order concern.3 Column (3) repeats the linear regression using income data at the zip code level instead of the census block group level. This is, in perhaps a small way, a cross-check of the influence of the income variable. Column (4) shows the results of the regression using a logit model. A logit specification may be appropriate because the probability of passing the emissions inspection is always close to 1. In all specification in Table 5, the coefficient on odometer is negative and the coefficient on model year is positive. This is expected. Newer vehicles pass more often. Vehicles with higher miles pass less often. The coefficients on vehicle make bins shows that Hondas are more likely to pass an emissions inspection than any other model in the study, consistent with Fig. 2. The reference vehicle make is Chevrolet. Since all the coefficients under vehicle make are positive, this means that Chevrolets are least likely to pass
3 The results of this model specification were confirmed using a ridge regression. Approximate magnitude and sign of all pertinent coefficients were quite similar to the naive linear probability model.
8
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the emissions inspection on average. The magnitude of the coefficients is better understood in light of total pass rates. Overall, about 92% of vehicle pass the emissions inspection on the first try. Table 5 shows that driving a Honda increases the probability of passing the emissions inspection by about 5 percentage points. That means that a driver switching from a Chevrolet to a Honda increases the probability of passing the emissions inspection from about 92% to about 97%, holding all else constant. The coefficient on income is significant and positive in each specification, even when controlling for vehicle characteristics and other fixed effects. This is a main result of this analysis. This implies that a person living in an area with lower annual income is less likely to pass the emissions inspection, even if she drives the same make, size, and mileage of vehicle as a person living in an area with higher annual income, and if she tests at the same emissions inspection location using the same test. The magnitude of the effect is approximately the same as that of odometer reading: a $10k increase in the income of the owner has approximately the same effect on the probability of passing the emissions inspection as a reduction of 10,000 miles on the odometer. It seems that there is, in fact, an income effect on pass rates in vehicle emissions inspection programs. To further quantify this income effect consider a group of 100 owners who live in an area with $50,000 median annual income and a group of 100 owners who live in an area with $150,000 median annual income, all of whom drive the same 2009 Honda Accord with 80,000 miles. To a first-order approximation, eight of the owners in the low income group will fail the emissions inspection and be required to make vehicle repairs, while only five of the owners in the high income group will fail. This statistically significant positive coefficient on the income variable in these regressions could be an indicator of a number of things; a few ideas are presented here. First, this results could be capturing variation in how well different income groups service their vehicles. Perhaps individuals with a higher annual income change the oil more often, replace the tires more often, or have the vehicle tuned up more often. These maintenance activities could contribute to the likelihood a vehicle will pass the emissions inspection. Second, this variation could be an indicator of quality variation within a class of vehicle characteristics. Perhaps owners with higher income purchased on average better quality vehicles within each bin of observable vehicle characteristics. Third, this variation could be caused by differences in driving habits. Perhaps higher income owners can afford to travel long distances more often and therefore are putting less-destructive highway miles on their vehicles. Conversely, it might be that lower income individuals drive only in town and are therefore putting more destructive in-town miles on their vehicles. Fourth, this could capture some variation in sensitivity to the threat of not passing an emissions test. Wealthier owners might be systematically more concerned with passing on the first try and therefore choose more often to make repairs before an emissions inspection rather than after. Since the cost of a retest is zero, lower income owners may decide to see if the vehicle passes the inspection before committing resources to make a repair. In any case, the question at hand is whether such variation exists. If it does, it implies that owner characteristics, not just commonly observed vehicle characteristics (make, model, model year, vehicles miles), contribute to the emissions level of a vehicle. This alone is a useful and interesting result. We can check robustness of this result by replacing as the dependent variable in Eq. (1) the actual emissions level of each specified pollutant rather than probability of passing the inspection as a whole. The actual emission level is recorded for the IM147 test type. We then ask if the coefficient on income is still significant when controlling for observable vehicle characteristics and other controls. The answer is yes. Even when controlling for vehicle characteristics and location fixed effects, higher income is associated with lower levels of HC, CO, and NOx. Results of these regressions are presented in Table 6. In column (1), shows that the income coefficient on CO emissions has nearly twice the magnitude as the odometer coefficient. Columns (2) and (3) show a nearly one-to-one match in magnitude of the income coefficient on HC or NOx emissions and odometer reading. These results show that for the Arizona vehicle emissions inspection program, vehicle owners with different incomes and observably identical vehicles, the owner with the higher annual income has a lower level of CO, HC, and NOx emissions, and is more likely to pass the emissions test on average. 4. Policy implications This paper has shown that low income vehicle owners in Arizona fail emissions inspections more often than high income owners. While it is true that low income owners fail more often because they choose to drive vehicles that tend to fail more often, the difference in pass rates along the income dimension still exists when controlling for vehicle choice. This is evidence that broad vehicle emissions inspection programs impact the poor more than the rich. If regressivity is assumed to be a negative characteristic of policy interventions, this paper adds to a growing body of literature that implies that some policy intervention other than broad I/M inspection programs may be more efficient, effective, or equitable in the quest to reduce harmful emissions (Mérel et al., 2014, Sanders and Sandler, 2017, Sandler, 2012,Harrington et al., 2000). What new policy might replace the current system of emissions inspection is still an open question. Recent discussion surrounds requiring real-driving emissions (RDE) tests using portable emissions measurement systems (PEMS), or fixed remote sensing stations (Borken-Kleefeld and Dallmann, 2018). Some experiments with such technology have proved promising (Yang et al., 2016). If these proposed policy updates are implemented, they may provide insight into possible reasons why emissions inspection programs may impact the poor more than the rich. For example, real-driving emissions tests with continuous emissions monitoring may reveal a systematic difference in driving behavior, vehicle quality, or vehicle repair among various groups of owners. Studying these technologies could yield fruitful results. Further, the results presented in this paper imply that programs designed to induce vehicle repair or update will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for repair or replacement, not just on the characteristics of the vehicles themselves. For example, the 2009 Car Allowance Rebate System (CARS, or commonly known as “cash 9
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Table 6 Results of linear probability regression. Dependent variable is level of pollutant specified, measured in grams emitted during first failing test.
Income ($10k) Odometer (10k) Model Year Make Honda Toyota Nissan Ford Dodge Other
Controls County, Test Station Observation Year Allowable Emissions (Vehicle Size) Observations
R2
(1) CO (grams)
(2) HC (grams)
(3) NOx (grams)
−0.297∗∗∗ (0.000) 0.156∗∗∗ (0.000) −0.757∗∗∗ (0.000)
−0.0198∗∗∗ (0.000) 0.0190∗∗∗ (0.000) −0.0129∗∗∗ (0.000)
−0.0338∗∗∗ (0.000) 0.0397∗∗∗ (0.000) 0.0195∗∗∗ (0.000)
−1.728∗∗∗ (0.000) −4.691∗∗∗ (0.000) −1.740∗∗∗ (0.000) −2.665∗∗∗ (0.000) −0.504 (0.251) −1.251∗∗∗ (0.000)
−0.389∗∗∗ (0.000) −0.481∗∗∗ (0.000) −0.342∗∗∗ (0.000) −0.363∗∗∗ (0.000) −0.0631∗ (0.013) −0.213∗∗∗ (0.000)
−0.580∗∗∗ (0.000) −0.702∗∗∗ (0.000) −0.360∗∗∗ (0.000) −0.550∗∗∗ (0.000) 0.456∗∗∗ (0.000) −0.161∗∗∗ (0.000)
x x x
x x x
x x x
108556 0.074
108556 0.122
108556 0.087
p-values in parentheses. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.
for clunkers”) was a $3 billion U.S. federal scrappage program intended to induce U.S. residents to purchase new, more fuel-efficient vehicles. Eligibility for the program was based almost entirely on vehicle characteristics, not owner characteristics, and the results of the program were debatable at best (Hoekstra et al., 2017). If one assumes external validity of the findings that in Arizona lower income individuals are more likely to drive vehicles that pollute more, and are more likely to fail emissions inspections conditional on vehicle characteristics, then restricting participation in the CARS program to lower income individuals could have increased the environmental impact of the program, holding all else constant. Perhaps a means testing provision which restricts participation to low income owners could focus future vehicle update or repair programs on the most egregiously polluting vehicles.
5. Conclusion In this paper, I examined the how the Arizona vehicle emissions inspection program impacts vehicle owners at different points of the income distribution. I showed that vehicle owners who live in areas with lower median income are both (i) more likely to drive vehicles that fail inspections at higher average rates and (ii) more likely to fail inspections conditional on vehicle characteristics. The first results is somewhat unsurprising; a lower income individual is more likely to choose a less expensive but older, higher mileage vehicle. Older vehicles are more likely to fail emissions inspections. The second result is perhaps more interesting. If two vehicle owners with different incomes drive a vehicle with the same characteristics (a 2009 Honda Accord with 120k miles for example) the owner with the lower annual income has a higher level of CO, HC, and NOx emissions, and is more likely to fail the emissions test on average. Increasing owner income by $10k has approximately the same effect on the probability of passing an emissions test as reducing the vehicle miles by 10k miles, holding all else constant. Since a failed inspection constrains the vehicle owner to purchase vehicle repairs that they did not purchase otherwise, this result implies that VEIPs constrain the vehicle repair decisions of owners in the low end of the income distribution more than in the high end. This result is significant because it informs policy. First, if one assumes that regressivity is a negative characteristic of policy, this paper adds to a growing body of literature that calls into question the efficiency, effectiveness, and equity of broad emissions inspection programs to reduce in-use vehicle emissions. Second, this paper shows that a program designed to subsidize the update or repair of the vehicle fleet will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for the program, not just the characteristics of the vehicles themselves. Therefore, a means testing provision can focus vehicle update programs on the most egregiously polluting vehicle. While evidence for the existence of variation in pass rate among owners at different points in the income distribution is presented here, future research on the causes of that variation could yield interesting results. 10
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Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.trd.2019. 102207. References Banzhaf, H. Spencer, 2011. Regulatory impact analyses of environmental justice effects. J. Land Use Environ. Law 27 (2011), 1–30. Bento, Antonio, 2013. Equity impacts of environmental policy. Ann. Rev. Resource Econ. 5 (2013), 181–196. Bento, Antonio, Freedman, Matthew, Lang, Corey, 2014. Who benefits from environmental regulation? Evidence from the clean air act amendments. Rev. Econ. Stat. 97 (3), 610–622. Berry, Steven, Levinsohn, James, Pakes, Ariel, 1995. Automobile prices in market equilibrium. Econometrica 63 (4), 841–890. Borken-Kleefeld Jens, Dallmann Tim, 2018. Remote sensing of motor vehicles exhaust emissions. International Council on Clean Transportation White Paper, February 2018. Environmental Protection Agency, 2003. Major Elements of Operating I/M Programs, updated March 2003. Environmental Protection Agency, 2006. Vehicle Emissions Inspection and Maintenance (I/M): Regulations, https://www.epa.gov/state-and-local-transportation/ vehicle-emissions-inspection-and-maintenance-im-regulations. Environmental Protection Agency, 2007. The Plain English Guide to the Clean Air Act-Cars, Trucks, Buses, and ‘Nonroad Equipment. https://www.epa.gov/sites/ production/files/2015-08/documents/peg.pdf, updated April 2007. Environmental Protection Agency, 2007. National Ambient Air Quality Standards Table. https://www.epa.gov/criteria-air-pollutants/naaqs-table, updated December 20, 2016. Environmental Protection Agency, 2017. About Smog, Soot, and Other Air Pollution from Transportation. https://www.epa.gov/transportation-air-pollution-andclimate-change/smog-soot-and-local-air-pollution, updated January 19, 2017. Fullterton, Don, 2011. Six distributional effects of environmental policy. Risk Anal. 31 (2011), 923–929. Harrington, Winston, McConnell, Virginia, Ando, Amy, 2000. Are vehicle emission inspection programs living up to expectations? Transport. Res. Part D: Transport Environ. 5 (3), 153–172. Hoekstra, Mark, Puller, Steven L., West, Jeremy, 2017. Cash for corollas: when stimulus reduces spending. Am. Econ. J.: Appl. Econ. 9 (3), 1–35. Jacobsen, Mark R., 2013. Evaluating US fuel economy standards in a model with producer and household heterogeneity. Am. Econ. J.: Econ. Policy 5 (2), 148–287. Mérel, Pierre, Smith, Aaron, Williams, Jeffrey, Wimberger, Emily, 2014. Cars on crutches: How much abatement do smog check repairs actually provide? J. Environ. Econ. Manage. 67 (2014), 371–395. Sandler, Ryan, 2012. Clunkers or Junkers? Adverse selection in a vehicle retirement program. Am. Econ. J.: Econ. Policy 4 (4), 253–281. Sanders Nicholas J., Sandler Ryan, 2017. Technology and the Effectiveness of Regulatory Programs Over Time: Vehicle Emissions and Smog Checks with a Changing Fleet, NBER Working Paper No. 23966. Yang, Liuhanzi, Chen, Qizheng, Wu, Ye, Zhang, Shaojun, 2016. Evaluating real-world CO2 and NOx emission for public transit buses using a remote wireless on-board diagnostics (OBD) approach. Environ. Pollut. 218, 453–462.
11
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Policing the poor: The impact of vehicle emissions inspection programs across income
T
Ryan J Wessel1 LDS Institute at Arizona State University, United States
A R T IC LE I N F O
ABS TRA CT
Keywords: Inspection and maintenance Smog check Vehicle emissions Distributional impact
Comparing vehicle emissions inspection results with vehicle owner income shows that the Arizona vehicle emissions inspection program constrains the vehicle repair decisions of people in the low end of the income distribution more than people in the high end. Individuals who live in areas with lower annual income are both (i) more likely to drive vehicles that fail emissions inspections at a higher average rate, and (ii) more likely to fail emissions inspections conditional on vehicle characteristics. The top income quintile fails emissions inspections 20% less often than the bottom income quintile even when controlling for observable vehicle characteristics. This implies that owner characteristics, in addition to observable vehicle characteristics, have a nonnegligible impact on vehicle emissions rates. Therefore, the impact of programs designed to reduce vehicle emissions could be greater if participation were subject to a means test.
JEL: Q52 Q58
1. Introduction Motor vehicles are the largest source of nitrogen oxide (NOx), carbon monoxide (CO), and volatile organic compounds such as hydrocarbons (HC) in the United States (Environmental Protection Agency, 2007). This pollution contributes to smog and poor air quality, and according to the U.S. Environmental Protection Agency, human exposure has negative impacts on health and welfare (Environmental Protection Agency, 2017). One policy intervention used to reduce emissions of these pollutants from in-use cars and light-duty trucks is mandated emissions rate ceilings for various vehicle classes. In the United States, the 1977 and 1990 Amendments to the Clean Air Act created an enforcement mechanism for emissions rate ceilings through inspection and maintenance (I/M) programs (Environmental Protection Agency, 2006, 2007). State-run vehicle emissions inspection programs (VEIPs) were created to facilitate mandatory I/M monitoring in areas which do not meet federal air quality standards. VEIPs identify high polluting vehicles through broad inspection of a majority of vehicles on the road. Owners of vehicles that fail inspection are required to repair the vehicles’ emission abatement systems or face a penalty, typically the inability to register the vehicle for legal use on public roads. Currently, 33 states in the United States, and most other developed countries, require some level of emissions testing in some areas (Environmental Protection Agency, 2003; Harrington et al., 2000). Recent research has called into question the efficiency and effectiveness of VEIPs in controlling air pollution. Mérel et al. (2014) shows that a significant amount of emissions abatement from repairs is lost by the time a subsequent inspection is required and therefore the effect of VEIPs may be overestimated. Sanders and Sandler (2017) calls into question the link between vehicle
E-mail address: [email protected]. Special thanks to the Arizona Department of Environmental Quality and the Arizona Motor Vehicle Division for prividing data for this study, and to the Center for the Study of Economic Liberty. 1
https://doi.org/10.1016/j.trd.2019.102207
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inspections and local air pollution levels. Sandler (2012) shows that the cost effectiveness of emissions inspection programs has steadily declined over time, and Harrington et al. (2000) shows that the cost of such programs is higher than ex ante EPA estimates. These results raise the question of whether some other policy intervention may more effectively reduce vehicle emissions. This paper adds to the literature by investigating whether vehicle emission inspection programs disproportionately affect individuals in the lower end of the income distribution. One open question in the environmental economics literature is whether environmental policies are regressive, meaning do various policies negatively impact low income individuals more than high income individuals. The literature is mixed on this question. Bento and Freedman (2014) shows that environmental regulators often focus clean up efforts on the dirtiest geographic areas, which may disproportionately benefit low income residents who more often live in those areas. On the other side, Jacobsen (2013) shows that CAFE standards have a regressive welfare cost. Others have also weighed in, including Benzhaf (2011) which highlights the importance of understanding the distribution of benefits and costs of regulatory actions, Fullerton (2011) which enumerates types of distributional effects, and Bento (2013) which examines the trade-offs between efficiency and equity that arise in the context of various environmental policies. In this paper, I evaluate the distributional impact of VEIPs along the income dimension by creating a novel data set that combines vehicle emissions inspection results from the Arizona VEIP with a measure of household income. The combined data set allows for investigation into the regressivity of VEIPs. If regressivity is assumed to be a negative characteristic of policy interventions, as Benzhaf (2011), Fullerton (2011), and Bento (2013) seem to imply, this paper adds to the growing body of literature that suggests that some policy intervention other than broad I/M programs may be more efficient, effective, or equitable in the quest to reduce harmful emissions (Sandler, 2012,Mérel et al., 2014, Sanders and Sandler, 2017, Harrington et al., 2000). Further, an understanding of how owner income influences vehicle emissions allows for investigation into counterfactual applications of historical and existing regulatory programs. For example, the 2009 Car Allowance Rebate System based eligibility almost entirely on vehicle characteristics, not owner characteristics. If owner characteristics, such as household income, have an impact on vehicle emissions, this could imply that programs designed to induce vehicle repair or update will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for repair or replacement, not just on the characteristics of the vehicles themselves. If lower income individuals are more likely to drive vehicles that pollute more, and are more likely to fail emissions inspections conditional on vehicle characteristics, then restricting participation in the CARS program based on a means test could have increased the environmental impact of the program, holding all else constant. The paper is organized as follows. Section two introduces the emissions inspection program and the data used in this study. Section three evaluates the distributional impact of the program. Section four discusses the policy implications of the results. Section five concludes.
2. Program background and data In the United States, the 1977 and 1990 Amendments to the Clean Air Act created an enforcement mechanism for emissions rate ceilings through inspection and maintenance (I/M) programs in large, metropolitan areas which do not meet certain federal air quality standards (Environmental Protection Agency, 2016). Arizona is required by the EPA to run an emissions inspection program in two metropolitan areas, Phoenix and Tucson, that do not meet minimum air quality standards. The Arizona Department of Environmental Quality (ADEQ) administers the program and the program is implemented by a private contractor. The Arizona emissions data is detailed, contains many data points, and contains information on multiple inspection methods. This makes it a good starting point for investigating vehicle emissions inspection programs.
2.1. Data The data used for this study was created by combining information from three government agencies: 1. Arizona Department of Environmental Quality. The ADEQ data contains the universe of emissions inspection results for the 2013–2014 emissions inspection cycle. It includes basic vehicle characteristics (vehicle identification number, make, model, model year, mileage, vehicle size); whether the vehicle passed inspection; the level of pollutants emitted during each test (where available); and, in a very small number of cases, a self-reported cost of repairs made between a failing test and a retest. 2. Arizona Motor Vehicle Division. The MVD data contains owner home address data by VIN for every vehicle registered in Arizona during 2013–2014. This data is confidential, and requests for this data can be made through the Arizona MVD. 3. American Community Survey. The ACS 2014 5-year estimates give estimates of resident characteristics by home location, including annual income of households by zip code, census block, and census block group. This study focuses on data from the 2013–2014 emissions inspection cycle. Using two years of data is appropriate for a number of reasons. First, most vehicles in the data set were inspected every other year, so the natural cycle of testing is two years. Second, there is a systematic differences between the number of vehicles tested during odd years and those tested during even years because the testing program was not implemented gradually. Averaging over two consecutive years alleviates these differences. Finally, there was concern that a longer study period would conflate cross-vehicle variation with long run advances in emission reduction technology. 2
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Table 1 Emissions test summary statistics (annual average). Number of Tests
1,905,159
Location Phoenix Tucson Other
78.6% 20.1% 1.3%
Pass on First test Retest Never
91.7% 5.9% 2.4%
Test Type OBD IM147 Loaded/Idle Opacity Other/Missing
65.6% 7.1% 9.4% 4.6% 13.3%
2.2. Emissions test types There are four broad types of vehicle emissions tests used in Arizona. They are: 1. On-Board Diagnostic (OBD). Vehicles newer than 1995 model year are inspected using this test. Data that has been continually recorded by the vehicle’s on-board sensors is used to evaluate whether the vehicle passes inspection. As long as certain on-board monitoring systems are working and monitored data did not exceed 2.5 times a federally mandated level, the vehicle passes inspection. In the 2013–2014 testing cycle, 66% of vehicles were tested with the OBD test. 2. Inspection/Maintenance 147 (IM147). Most 1981–1995 model year light duty gasoline-powered vehicles in Phoenix are inspected using the IM147 test. In this test, the vehicle is driven on rollers at varying speeds to simulate urban driving. The exhaust of three pollutants (hydrocarbons, carbon monoxide, and oxides of nitrogen) is measured. Levels of all three pollutants, measured in grams per vehicle mile, must be below a certain threshold to pass. In the 2013–2014 testing cycle, 7% of vehicles were tested with the IM147 test. 3. Loaded/Idle. Model year 1967–1980 vehicles (and some newer vehicles registered in Tucson), are inspected with the Loaded/Idle test. Exhaust is tested while the vehicle idles and again at approximately 25 miles-per-hour while the vehicle is on rollers. Exhaust of hydrocarbons and carbon monoxide must be below a certain threshold to pass. About 9% of vehicles were tested with the Loaded/Idle test. 4. Opacity Test. Diesel vehicles are tested by measuring the opacity of the exhaust. Less than 5% of vehicles were inspected with this test. Approximately two million vehicles are inspected annually in Arizona at an annual inspection cost of $36 million paid by vehicle owners. Table 1 reports summary statistics for emissions inspections in the 2013–2014 testing cycle. The majority of inspections were done in Phoenix. More than nine in 10 vehicles pass the inspection on the first try. Most vehicles are testing using On-Board Diagnostics. Under current rules, OBD will be used more frequently over time as older vehicles are retired from the fleet and are replaced by new ones.
2.3. Vehicles tested Table 2 reports summary statistics for the vehicles tested during the 2013–2014 testing cycle. Vehicle characteristics are make, model, model year, and odometer reading. Vehicle make is divided into the six most common manufacturers, plus a category for other vehicles. Vehicle models are grouped into one of four model categories by size. Examples of those categories are given in Table 2. This study will focus on model year 1981–2008 non-diesel vehicles. Fig. 1 shows the number of vehicles tested in the 2013–2014 emission cycle by model year, divided among test type. Current policy exempts the five newest model years from inspection, so vehicles manufactured after 2008 are not fully represented in the 2013–2014 data; this accounts for the pronounced dip in number of 2008 model years vehicles shown in Fig. 1. Since the number of very old vehicles is low and since a new test type was introduced for model year 1981 vehicles, all vehicles manufactured before 1981 were excluded. Fig. 2 shows the vehicle inspection pass rate for model years 1981–2008. Newer vehicles pass emissions inspections at a higher rate. Honda brand vehicles pass inspection more than average, and this is especially pronounced for newer model Hondas and for Hondas made during the 1980s. Ford vehicles made during the early 1990s pass inspection at a rate higher than trend. Across all vehicles and all model years in the study data set, about 92% of vehicles pass the emissions inspection on the first try. 3
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Table 2 Vehicle summary statistics. Vehicle Type (example) Cars (Fusion) Light Trucks (Ranger) Medium Trucks (F150) Heavy Trucks (F350)
45.0% 26.6% 16.9% 11.5%
Vehicle Make Chevrolet Ford Toyota Honda Dodge Nissan Other
15.8% 15.3% 11.1% 7.5% 6.8% 6.3% 37.3%
Model Year Earlier than 1990 1990–1995 1996–2000 2001–2005 2006–2008 2009+
4.1% 8.2% 19.4% 37.6% 26.7% 4.4%
Ave. Odometer
116,340 miles
Fig. 1. Number of vehicles inspected by model year. Test type primarily used for each set of model years is listed in bold.
2.4. Income data Household income data come from the American Community Survey 2014 5-year estimates. Income data was assigned to each address by geocoding each address and spatially joining the geocoded data with census block group polygons published on the U.S. Census website. Median household income in the last 12 month in 2014 inflation adjusted dollars for the census block group or other aggregation was then assigned to each household. An assumption was made in order to assign income data to specific vehicles: a household is defined as one vehicle. About 61% of addresses in the study data have only one vehicle registered at that address. For the 24% of addresses with two vehicles and 15% of address with three or more vehicles, I assume that duplicates of the household each own one vehicle. The existence of multi-vehicle households presents an interesting challenge. In the analysis presented in this paper, the income variable is meant to be a measure of vehicle owner characteristics that captures variation outside of that described by vehicle choice. Because of this, it was decided to assign the full income of each multi-vehicle household to each vehicle. Essentially, the assumption was made that owner behavior, even in multi-vehicle households, does not change with the number of vehicles owned by the 4
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Fig. 2. Percent of cars that pass on the first try by model year. Cars older than 1996 model year are tested with the IM147 test. Cars 1996 model year and newer are testing using On-Board Diagnostics (OBD).
household. To put it another way, an owner with two vehicles and $100,000 income is more similar to an owner with one vehicle and $100,000 income than to a owner with one vehicle and $50,000 income. If owner income in a multi-vehicle household were apportioned equally to each vehicle, the opposite would be assumed. There also may be concern that multi-vehicle households divide total driving miles between vehicles. Controlling for vehicle miles in the analysis partially allays this concern. Admittedly, further research on multiple-vehicle households could yield fruitful results. In a few instances, many vehicles are registered to the same address. These were, in most cases, businesses such as car dealerships. Eliminating these addresses from the data set does not qualitatively change the results because the number of vehicles at these addresses is very small compared to the entire sample. A further limitation of the process used in assigning income to each vehicle owner is that income is measured with error at the household level. While a census block group, with about 500 households, is small relative to a zip code, the median income of the census block group is not a perfect measure of each household’s income. More detailed income data certainly exists but is severely restricted. To take seriously results of analyses done using census block group aggregations, such as the result presented in Fig. 3, one
Fig. 3. Percent of vehicles that passed emissions testing on the first try, by annual income in each zip code in which the vehicle was registered. Note that this trend is also evident when income data is disaggregated, but is presented at this level of aggregation for convenience. 5
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must assume that the difference between actual income and median income within each census block group does not differ is a systematic way across census block groups. As the available data does not allow for this to be tested, it is taken to be true as an assumption. Vehicles associated with post office boxes or registered in states outside of the inspection region were dropped because they could not reliable be connected with income data. 3. Distributional impact One open question in environmental economics is whether environmental policies are regressive in impact on people along the wealth distribution. In this section, I ask, “Are vehicle emissions inspection programs regressive?” I find that the Arizona VEIP is regressive in that it constrains the vehicle repair decisions of the vehicle owners who live in areas with low median income more than those who live in areas with high median income. Since lower income owners fail initial emissions inspections more often than high income owners, lower income owners are more often required to make vehicle repairs that they did not choose to make without the program. Fig. 3 shows the percent of vehicles at each income level that passed the vehicle emissions inspection on the first try. There is an upward trend, meaning that owners who live in areas with a higher median income are more likely to pass the emissions inspection on the first try.2 Additionally, Fig. 3 does not include new vehicles because the five newest model years are exempt from inspection by policy. Including new vehicles under the assumption that each of them passes inspection further amplifies the upward trend in Fig. 3. The effect of owner income on inspection results is suggested by other aspects of the data. For example, Table 3 shows the average number of attempts before the vehicle passes inspection for each quintile of the income distribution. Owners with lower annual income retest more often before passing. Table 3 also shows the percent of vehicles tested that never pass the emissions inspection despite multiple tries, by quintile of the income distribution. Owners with lower annual income are more likely to never pass the emissions inspection. This correlation between owner income and emissions pass rate is explored more formally in the next two subsections. The analysis will show that program regressivity, meaning lower income individuals are constrained by the program more often than higher income individuals, works through two channels. First, and unsurprisingly, vehicle owners who live in areas with lower median income are more likely to drive vehicles that fail the inspection at higher average rates. Second, and perhaps the more interesting result, lower income owners are more likely to fail emission inspections even when controlling for observable vehicle characteristics. Each of these, vehicle characteristics and owner characteristics, are explored in turn below. 3.1. Vehicle characteristics This section will show a broad correlation between likelihood of failing emissions inspections and vehicle choice. This is perhaps an unsurprising result. Vehicle owners who live in areas with lower median income are more likely to own less expensive, older, higher mileage, American made vehicles. Older, higher mileage, American made vehicles are more likely to fail emissions tests (see Fig. 2). The correlation between income and vehicle model year choice is suggested by Fig. 4. Higher income individuals are more likely to own newer vehicles. The highest income quintile owns 15% of the oldest model year vehicles and 30% of newest model year. Contrastingly, the lowest income quintile owns 22% of the oldest model year vehicles and 10% of the newest model year. A companion result is that lower income owners also drive vehicles with higher vehicle miles, a characteristic highly correlated with vehicle model year. There is also a difference in vehicle manufacturer distribution among income quintiles. Table 4 shows that 24% of Honda vehicles in data, which is the vehicle with the highest average pass rate, are owned by the highest income quintile compared to 15% owned by the lowest income quintile. Conversely, 15% of Chevy vehicles, which is the vehicle with the lowest average pass rate, are owned by the highest income quintile while 25% of Chevy vehicles are owned by the lowest income quintile. 3.2. Owner characteristics In this section, I will show that even when controlling for observable vehicle characteristics, there still exists a statistically significant difference in pass rate along the owner income dimension. To understand what vehicles characteristics and owner characteristics contribute to passing emissions inspection, I use various regression models (linear probability, logit, ridge) to regress owner and vehicle characteristics on inspection pass rate. In the linear model, the assumed data generating process is
E = β0 + β1 (w + η) + β2 V + β3 X + ∊
(1)
where E = probability of passing the emissions test w + η = annual income of owner measured with error
2
Zip codes with fewer than 50 observations were excluded from the figure for clarity, but not excluded from the rest of the analysis. 6
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Table 3 Average number of tests before the vehicle passes and percent of vehicles that never pass emissions, by quintile of the income distribution. Income Quintile First ($13K - $49k) Second ($49K - $59k) Third ($59K - $71k) Fourth ($71K - $91k) Fifth ($91K +)
Number of Tests Before Pass
Percent Never Pass
1.31 1.25 1.22 1.18 1.15
3.6% 2.5% 1.9% 1.3% 0.9%
Fig. 4. Ownership of each model year vehicle by income quintile. Table 4 Average pass rate on a first test listed by vehicle make, and percent of each vehicle make owned by the highest and lowest income quintile. Pass Rate
Highest Quintile Owned
Lowest Quintile Owned
Honda Toyota Dodge Ford Nissan Chevy
94.0% 94.0% 92.3% 92.0% 91.4% 89.1%
24.1% 23.9% 15.3% 17.3% 17.7% 15.1%
15.1% 15.2% 22.8 % 23.1% 21.2% 25.3%
Other
91.4%
22.6%
18.2%
Make
V = observable vehicle characteristics (odometer, make, model, model year). X = controls (county, test station, test type, observation year). ∊ = error. Transaction price of each vehicle would be ideal information to include in this regression. Transaction price would provide some information about differences in vehicle quality within each bin of vehicle characteristics, and perhaps some information about the owner, such as bargaining ability. Unfortunately, this information is not available and would be very difficult to obtain. It is possible to create an estimate of the purchase price of each vehicle using a Berry et al. (1995) model (hereafter BLP). The BLP model, however, predicts prices based on vehicle characteristics such as engine type and vehicle size, market characteristics such as gasoline price, and 7
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Table 5 Dependent variable is probability of passing emissions test. Linear model standard errors clustered by census block group or zip code as indicated. Logit standard errors calculated using delta method.
Income ($10k) Odometer (10k) Model Year Make Honda Toyota Nissan Ford Dodge Other
Controls Test Station County Observation Year Vehicle Size Test Type Observations R2 {Pseudo} Clusters
(1) Naive
(2) Collinearity
(3) Income by Zip
(4) Logit
0.00205∗∗∗ (0.000) −0.00269∗∗∗ (0.000) 0.00402∗∗∗ (0.000)
0.00091∗∗∗ (0.000) – (—) 0.00458∗∗∗ (0.000)
0.00204∗∗∗ (0.000) −0.00284∗∗∗ (0.000) 0.00382∗∗∗ (0.000)
0.0443∗∗∗ (0.000) −0.0437∗∗∗ (0.000) 0.00548∗∗∗ (0.000)
0.0443∗∗∗ (0.000) 0.0437∗∗∗ (0.000) 0.0198∗∗∗ (0.000) 0.0193∗∗∗ (0.000) 0.0160∗∗∗ (0.000) 0.0154∗∗∗ (0.000)
0.0381∗∗∗ (0.000) 0.0379∗∗∗ (0.000) 0.0175∗∗∗ (0.000) 0.0188∗∗∗ (0.000) 0.0174∗∗∗ (0.000) 0.0145∗∗∗ (0.000)
0.0446∗∗∗ (0.000) 0.0440∗∗∗ (0.000) 0.0199∗∗∗ (0.000) 0.0191∗∗∗ (0.000) 0.0158∗∗∗ (0.000) 0.0153∗∗∗ (0.000)
0.7593∗∗∗ (0.000) 0.7285∗∗∗ (0.000) 0.2465∗∗∗ (0.001) 0.2534∗∗∗ (0.000) 0.1977∗∗∗ (0.001) 0.1548∗∗∗ (0.000)
x x x x x
x x x x
x x x x x
x x x x x
2,336,897 0.148 3,963
2,336,897 0.0675 3,963
2,297,601 0.146 305
2,297,601 {0.165}
p-values in parentheses. p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.
firm characteristics such as domestic or foreign. Data estimated from a BLP model cannot predict price differences between vehicles within vehicle-type bins, only of vehicle-type bins themselves. Since much of the base information used in BLP is already included in the simple linear regression in (1), a BLP based price estimate would have little unique information in this application. Luckily, the owner income variable provides useful information about owner characteristics. If statistically significant variation in pass rate remains after controlling for observable vehicle characteristics, this implies that some individual characteristic outside of vehicle choice, here encapsulated by owner income, influences the emissions pass rate. The influence of the owner income variable is further isolated by controlling for test station, county, test type, and observation year. Controlling for test station rules out the possible conclusion that lower income owners tend to patronize inspection stations that for some reason fail vehicles more frequently. Controlling for county, test type, and observation year accounts for unobserved differences across geography, testing equipment, and time. Table 5 summarizes the results of the regressions. Column (1) displays the results of a naive linear probability model which includes all available data. Column (2) displays the results of a model specification that seeks to account for any collinearity among predictors. Odometer reading and vehicle model year are correlated therefore odometer reading is removed. Also, the county variable and the emissions test station variable both encode location information therefore county is removed. Ultimately, because the coefficient of interest (owner income) is not highly correlated with any of the predictor variables, multicollinearity is perhaps not of first order concern.3 Column (3) repeats the linear regression using income data at the zip code level instead of the census block group level. This is, in perhaps a small way, a cross-check of the influence of the income variable. Column (4) shows the results of the regression using a logit model. A logit specification may be appropriate because the probability of passing the emissions inspection is always close to 1. In all specification in Table 5, the coefficient on odometer is negative and the coefficient on model year is positive. This is expected. Newer vehicles pass more often. Vehicles with higher miles pass less often. The coefficients on vehicle make bins shows that Hondas are more likely to pass an emissions inspection than any other model in the study, consistent with Fig. 2. The reference vehicle make is Chevrolet. Since all the coefficients under vehicle make are positive, this means that Chevrolets are least likely to pass
3 The results of this model specification were confirmed using a ridge regression. Approximate magnitude and sign of all pertinent coefficients were quite similar to the naive linear probability model.
8
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the emissions inspection on average. The magnitude of the coefficients is better understood in light of total pass rates. Overall, about 92% of vehicle pass the emissions inspection on the first try. Table 5 shows that driving a Honda increases the probability of passing the emissions inspection by about 5 percentage points. That means that a driver switching from a Chevrolet to a Honda increases the probability of passing the emissions inspection from about 92% to about 97%, holding all else constant. The coefficient on income is significant and positive in each specification, even when controlling for vehicle characteristics and other fixed effects. This is a main result of this analysis. This implies that a person living in an area with lower annual income is less likely to pass the emissions inspection, even if she drives the same make, size, and mileage of vehicle as a person living in an area with higher annual income, and if she tests at the same emissions inspection location using the same test. The magnitude of the effect is approximately the same as that of odometer reading: a $10k increase in the income of the owner has approximately the same effect on the probability of passing the emissions inspection as a reduction of 10,000 miles on the odometer. It seems that there is, in fact, an income effect on pass rates in vehicle emissions inspection programs. To further quantify this income effect consider a group of 100 owners who live in an area with $50,000 median annual income and a group of 100 owners who live in an area with $150,000 median annual income, all of whom drive the same 2009 Honda Accord with 80,000 miles. To a first-order approximation, eight of the owners in the low income group will fail the emissions inspection and be required to make vehicle repairs, while only five of the owners in the high income group will fail. This statistically significant positive coefficient on the income variable in these regressions could be an indicator of a number of things; a few ideas are presented here. First, this results could be capturing variation in how well different income groups service their vehicles. Perhaps individuals with a higher annual income change the oil more often, replace the tires more often, or have the vehicle tuned up more often. These maintenance activities could contribute to the likelihood a vehicle will pass the emissions inspection. Second, this variation could be an indicator of quality variation within a class of vehicle characteristics. Perhaps owners with higher income purchased on average better quality vehicles within each bin of observable vehicle characteristics. Third, this variation could be caused by differences in driving habits. Perhaps higher income owners can afford to travel long distances more often and therefore are putting less-destructive highway miles on their vehicles. Conversely, it might be that lower income individuals drive only in town and are therefore putting more destructive in-town miles on their vehicles. Fourth, this could capture some variation in sensitivity to the threat of not passing an emissions test. Wealthier owners might be systematically more concerned with passing on the first try and therefore choose more often to make repairs before an emissions inspection rather than after. Since the cost of a retest is zero, lower income owners may decide to see if the vehicle passes the inspection before committing resources to make a repair. In any case, the question at hand is whether such variation exists. If it does, it implies that owner characteristics, not just commonly observed vehicle characteristics (make, model, model year, vehicles miles), contribute to the emissions level of a vehicle. This alone is a useful and interesting result. We can check robustness of this result by replacing as the dependent variable in Eq. (1) the actual emissions level of each specified pollutant rather than probability of passing the inspection as a whole. The actual emission level is recorded for the IM147 test type. We then ask if the coefficient on income is still significant when controlling for observable vehicle characteristics and other controls. The answer is yes. Even when controlling for vehicle characteristics and location fixed effects, higher income is associated with lower levels of HC, CO, and NOx. Results of these regressions are presented in Table 6. In column (1), shows that the income coefficient on CO emissions has nearly twice the magnitude as the odometer coefficient. Columns (2) and (3) show a nearly one-to-one match in magnitude of the income coefficient on HC or NOx emissions and odometer reading. These results show that for the Arizona vehicle emissions inspection program, vehicle owners with different incomes and observably identical vehicles, the owner with the higher annual income has a lower level of CO, HC, and NOx emissions, and is more likely to pass the emissions test on average. 4. Policy implications This paper has shown that low income vehicle owners in Arizona fail emissions inspections more often than high income owners. While it is true that low income owners fail more often because they choose to drive vehicles that tend to fail more often, the difference in pass rates along the income dimension still exists when controlling for vehicle choice. This is evidence that broad vehicle emissions inspection programs impact the poor more than the rich. If regressivity is assumed to be a negative characteristic of policy interventions, this paper adds to a growing body of literature that implies that some policy intervention other than broad I/M inspection programs may be more efficient, effective, or equitable in the quest to reduce harmful emissions (Mérel et al., 2014, Sanders and Sandler, 2017, Sandler, 2012,Harrington et al., 2000). What new policy might replace the current system of emissions inspection is still an open question. Recent discussion surrounds requiring real-driving emissions (RDE) tests using portable emissions measurement systems (PEMS), or fixed remote sensing stations (Borken-Kleefeld and Dallmann, 2018). Some experiments with such technology have proved promising (Yang et al., 2016). If these proposed policy updates are implemented, they may provide insight into possible reasons why emissions inspection programs may impact the poor more than the rich. For example, real-driving emissions tests with continuous emissions monitoring may reveal a systematic difference in driving behavior, vehicle quality, or vehicle repair among various groups of owners. Studying these technologies could yield fruitful results. Further, the results presented in this paper imply that programs designed to induce vehicle repair or update will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for repair or replacement, not just on the characteristics of the vehicles themselves. For example, the 2009 Car Allowance Rebate System (CARS, or commonly known as “cash 9
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Table 6 Results of linear probability regression. Dependent variable is level of pollutant specified, measured in grams emitted during first failing test.
Income ($10k) Odometer (10k) Model Year Make Honda Toyota Nissan Ford Dodge Other
Controls County, Test Station Observation Year Allowable Emissions (Vehicle Size) Observations
R2
(1) CO (grams)
(2) HC (grams)
(3) NOx (grams)
−0.297∗∗∗ (0.000) 0.156∗∗∗ (0.000) −0.757∗∗∗ (0.000)
−0.0198∗∗∗ (0.000) 0.0190∗∗∗ (0.000) −0.0129∗∗∗ (0.000)
−0.0338∗∗∗ (0.000) 0.0397∗∗∗ (0.000) 0.0195∗∗∗ (0.000)
−1.728∗∗∗ (0.000) −4.691∗∗∗ (0.000) −1.740∗∗∗ (0.000) −2.665∗∗∗ (0.000) −0.504 (0.251) −1.251∗∗∗ (0.000)
−0.389∗∗∗ (0.000) −0.481∗∗∗ (0.000) −0.342∗∗∗ (0.000) −0.363∗∗∗ (0.000) −0.0631∗ (0.013) −0.213∗∗∗ (0.000)
−0.580∗∗∗ (0.000) −0.702∗∗∗ (0.000) −0.360∗∗∗ (0.000) −0.550∗∗∗ (0.000) 0.456∗∗∗ (0.000) −0.161∗∗∗ (0.000)
x x x
x x x
x x x
108556 0.074
108556 0.122
108556 0.087
p-values in parentheses. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.
for clunkers”) was a $3 billion U.S. federal scrappage program intended to induce U.S. residents to purchase new, more fuel-efficient vehicles. Eligibility for the program was based almost entirely on vehicle characteristics, not owner characteristics, and the results of the program were debatable at best (Hoekstra et al., 2017). If one assumes external validity of the findings that in Arizona lower income individuals are more likely to drive vehicles that pollute more, and are more likely to fail emissions inspections conditional on vehicle characteristics, then restricting participation in the CARS program to lower income individuals could have increased the environmental impact of the program, holding all else constant. Perhaps a means testing provision which restricts participation to low income owners could focus future vehicle update or repair programs on the most egregiously polluting vehicles.
5. Conclusion In this paper, I examined the how the Arizona vehicle emissions inspection program impacts vehicle owners at different points of the income distribution. I showed that vehicle owners who live in areas with lower median income are both (i) more likely to drive vehicles that fail inspections at higher average rates and (ii) more likely to fail inspections conditional on vehicle characteristics. The first results is somewhat unsurprising; a lower income individual is more likely to choose a less expensive but older, higher mileage vehicle. Older vehicles are more likely to fail emissions inspections. The second result is perhaps more interesting. If two vehicle owners with different incomes drive a vehicle with the same characteristics (a 2009 Honda Accord with 120k miles for example) the owner with the lower annual income has a higher level of CO, HC, and NOx emissions, and is more likely to fail the emissions test on average. Increasing owner income by $10k has approximately the same effect on the probability of passing an emissions test as reducing the vehicle miles by 10k miles, holding all else constant. Since a failed inspection constrains the vehicle owner to purchase vehicle repairs that they did not purchase otherwise, this result implies that VEIPs constrain the vehicle repair decisions of owners in the low end of the income distribution more than in the high end. This result is significant because it informs policy. First, if one assumes that regressivity is a negative characteristic of policy, this paper adds to a growing body of literature that calls into question the efficiency, effectiveness, and equity of broad emissions inspection programs to reduce in-use vehicle emissions. Second, this paper shows that a program designed to subsidize the update or repair of the vehicle fleet will have a different impact on pollution depending on the characteristics of the owners of the vehicles targeted for the program, not just the characteristics of the vehicles themselves. Therefore, a means testing provision can focus vehicle update programs on the most egregiously polluting vehicle. While evidence for the existence of variation in pass rate among owners at different points in the income distribution is presented here, future research on the causes of that variation could yield interesting results. 10
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