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American Economic Association Traffic Congestion and Infant Health: Evidence from E-ZPass Author(s): Janet Currie and Reed Walker Source: American Economic Journal: Applied Economics, Vol. 3, No. 1 (January 2011), pp. 65-90 Published by: American Economic Association Stable URL: https://www.jstor.org/stable/25760246 Accessed: 05-11-2018 02:24 UTC JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at https://about.jstor.org/terms American Economic Association is collaborating with JSTOR to digitize, preserve and extend access to American Economic Journal: Applied Economics This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms American Economic Journal: Applied Economics 3 (January 2011): 65-90 http://www.aeaweb.org/articles.php7doi?10 J257/app.3.1.65 Traffic Congestion and Infant Health: Evidence from E-ZPass1 By Janet Currie and Reed Walker* We exploit the introduction of electronic toll collection, (E-ZPass), which greatly reduced both traffic congestion and vehicle emissions near highway toll plazas. We show that the introduction ofE-ZPass reduced prematurity and low birth weight among mothers within 2 kilometers (km) of a toll plaza by 10.8 percent and 11.8 percent, respectively, relative to mothers 2-10 km from a toll plaza. There were no immediate changes in the characteristics of mothers or in housing prices near toll plazas that could explain these changes. The results are robust to many changes in specification and suggest that traffic congestion contributes significantly to poor health among infants. (JEL112, J13, Q51, Q53, R41) Motor vehicles are50 apercent major source of(CO), air34pollution. Nationally they are respon sible for over of carbon monoxide percent of nitrogen dioxide (N02), and over 29 percent of hydrocarbon emissions, in addition to as much as 10 percent of fine particulate matter emissions (Michelle Ernst, James Corless, and Ryan Greene-Roesel 2003). In urban areas, vehicles are the dominant source of these emissions. Furthermore, between 1980 and 2003 total vehicle miles trav eled (VMT) in urban areas in the United States increased by 111 percent against an increase in urban lane-miles of only 51 percent (US Department of Transportation 2005). As a result, traffic congestion has steadily increased across the United States, causing 3.7 billion hours of delay by 2003 and wasting 2.3 billion gallons of motor fuel (David Schrank and Tim Lomax 2005). Traditional estimates of the cost of congestion typically include delay costs (William S. Vickrey 1969), but they rarely address other congestion externalities such as the health effects of congestion. This paper seeks to provide estimates of the health effects of traffic congestion by examining the effect of a policy change that caused a sharp drop in congestion (and therefore in the level of local motor vehicle emissions) within a relatively short time frame at different sites across the northeastern United States. Engineering studies * Currie: Department of Economics, Columbia University, 420 West 118th Street, New York, NY 10027 (e-mail: janet.currie@columbia.edu); Walker: Department of Economics, Columbia University, 420 West 118th Street, New York, NY 10027 (e-mail: rw2157@columbia.edu). We are grateful to the MacArthur Foundation for financial support. We thank Katherine Hempstead and Matthew Weinberg of the New Jersey Department of Health, and Craig Edelman of the Pennsylvania Department of Health for facilitating our access to the data. We are grate ful to James MacKinnon and seminar participants at Harvard University, Princeton University, Queens University, Tulane University, the University of Maryland, the University of Massachusetts-Amherst, the University of Rome, Uppsala University, Yale University, the City University of New York, the NBER Summer Institute, and the SOLE/ EALE 2010 meetings for helpful comments. All opinions and any errors are our own. t To comment on this article in the online discussion forum, or to view additional materials, visit the article page at http://www.aeaweb.org/articles.php?doi= 10.1257/app.3.1.65. 65 This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 66 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS JANUARY 20 suggest that the introduction of electronic toll collection (ETC) technology, c E-ZPass in the Northeast, sharply reduced delays at toll plazas and pollution ca by idling, decelerating, and accelerating. We study the effect of E-ZPass, and the sharp reductions in local traffic congestion, on the health of infants born to mo ers living near toll plazas. This question is of interest for three reasons. First, there is increasing evide the long-term effects of poor health at birth on future outcomes. For example birth weight has been linked to future health problems and lower educational a ment (see Currie 2009 for a summary of this research). The debate over the cos benefits of emission controls and traffic congestion policies could be signific impacted by evidence that traffic congestion has a deleterious effect on fetal h Second, the study of newborns overcomes several difficulties in making the c nection between pollution and health because, unlike adult diseases that may r pollution exposure that occurred many years ago, the link between cause and ef is immediate. Third, E-ZPass is an interesting policy experiment because, whi lution control was an important consideration for policy makers, the main m for consumers to sign up for E-ZPass is to reduce travel time. Hence, E-ZPass o an example of achieving reductions in pollution by bundling emissions reduct with something consumers perhaps value more highly, such as reduced travel t Our analysis improves upon much of the previous research linking air pollu to fetal health as well as on the somewhat smaller literature focusing specifica the relationship between residential proximity to busy roadways and poor preg outcomes. Since air pollution is not randomly assigned, studies that attempt t pare health outcomes for populations exposed to differing pollution levels ma be adequately controlling for confounding determinants of health. Since air qua capitalized into housing prices (see Kenneth Y. Chay and Michael Greenstone 2 families with higher incomes or preferences for cleaner air are likely to sort in tions with better air quality, and failure to account for this sorting will lead to ove timates of the effects of pollution. Alternatively, pollution levels are higher in areas where there are often more educated individuals with better access to h care, which can cause underestimates of the true effects of pollution on health. In the absence of a randomized trial, we exploit a policy change that created local and persistent reductions in traffic congestion and traffic related air em for certain segments along a highway. We compare the infant health outcom those living near an electronic toll plaza before and after implementation of Eto those living near a major highway but further away from a toll plaza. Specif we compare mothers within 2 km of a toll plaza to mothers who are between and 10 km from a toll plaza, but still within 3 km, of a major highway befor after the adoption of E-ZPass in New Jersey and Pennsylvania. New Jersey and Pennsylvania provide a compelling setting for our part research design. First, both New Jersey and Pennsylvania are heavily populat with New Jersey being the most densely populated state in the United States Pennsylvania being the sixth most populous state in the country. As a result, th states have some of the busiest Interstate systems in the country, systems that also pen to be densely surrounded by residential housing. Furthermore, we know th addresses of mothers, in contrast to many observational studies which approx This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIE AND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 67 the individual's location as the centroid of a geographic area or by computing average pollution levels within the geographic area. This information enables us to improve on the assignment of pollution exposure. Lastly, E-ZPass adoption and take up was extremely quick, and the reductions in congestion spillover to all automobiles, not just those registered with E-ZPass (New Jersey Turnpike Authority 2001). Our difference-in-differences research design relies on the assumption that the characteristics of mothers near a toll plaza change over time in a way that is com parable to those of other mothers who live further away from a plaza, but still close to a major highway. We test this assumption by examining the way that observable characteristics of the two groups of mothers and housing prices change before and after E-ZPass adoption. We also estimate a range of alternative specifications in an effort to control for unobserved characteristics of mothers and neighborhoods that could confound our estimates. We find significant effects on infant health. The difference-in-difference models suggest that prematurity fell by 6.7-9.16 percent among mothers within 2 km of a toll plaza, while the incidence of low birth weight fell by 8.5-11.3 percent. We argue that these are large but not implausible effects given previous studies. In contrast, we find that there are no significant effects of E-ZPass adoption on the demographic characteristics of mothers in the vicinity of a toll plaza. We also find no immediate effect on housing prices, suggesting that the composition of women giving birth near toll plazas shows little change in the immediate aftermath of E-ZPass adoption (though of course it might change more over time). The rest of the paper is laid out as follows. Section I provides necessary back ground. Section II describes our methods, while data are described in Section III. Section IV presents our results. Section VI discusses the magnitude of the effects we find, and Section V details our conclusions. I. Background Many studies suggest an association between air pollution and fetal health.1 Donald R. Mattison et al. (2003) and Svetlana V. Glinianaia et al. (2004a) sum marize much of the literature. For more recent papers see, for example, Currie, Matthew Neidell, and Johannes F. Schmeider (2009); Rose Dugandzic et al. (2006); Mary Huynh et al. (2006); Catherine J. Karr et al. (2009); Sue J. Lee et al. (2008); Jong-Han Leem et al. (2006); Shiliang Liu et al. (2007); Jennifer D. Parker, Pauline Mendola, and Tracey Woodruff (2008); Muhammad T. Salam et al. (2005); Beate Ritz, Michelle Wilhelm, and Yingxu Zhao (2006); Wilhelm and Ritz (2005); Tracey J. Woodruff, Lyndsey A. Darrow, and Parker (2008). Since traffic is a major con tributor to air pollution, several studies have focused specifically on the effects of exposure to motor vehicle exhaust (see Wilhelm and Ritz 2003; Ninez A. Ponce et al. 2005; Michael Brauer et al. 2003; Remy Slama et al. 2007; Timothy K. M. Beatty and Jay P. Shimshack 2009; Christopher R. Knittel, Douglas Miller, and Nicholas J. Sanders 2009). 1 There is also a large literature linking air pollution and child health, some of it focusing on the effects of traffic on child health. See Joel Schwartz (2004) and Glinianaia et al. (2004b) for reviews. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 68 AMERICAN ECONOMIC JOURNAL- APPLIED ECONOMICS JANUARY 2011 At the same time, researchers have documented many differences between peo ple who are exposed to high volumes of traffic and those who are not (Robert B. Gunier et al. 2003). A correlational study cannot demonstrate that the effect of pol lution is causal. Women living close to busy roadways are more likely to have other characteristics that are linked to poor pregnancy outcomes, such as lower income, education, probabilities of being married, and a higher probability of being a teen mother. This is partly because wealthier people are more likely to move away from pollution. Brooks Depro and Chris Timmins (2008) show that gains in wealth from appreciating housing values during the 1990s allowed households in San Francisco to move to cleaner areas. H. Spencer Banzhaf and Randall R Walsh (2008) show that neighborhoods experiencing improvements in environmental quality tend to gain population while the converse is also true. Most previous studies include a minimal set of controls for potential confound ers. Families with higher incomes or greater preferences for cleaner air may be more likely to sort into neighborhoods with better air quality. These families are also likely to provide other investments in their children, so that fetuses exposed to lower levels of pollution also receive more family inputs, such as better quality prenatal care or less maternal stress. If these factors are unaccounted for, then the estimated effects of pollution may be biased upward. Alternatively, emission sources tend to be located in urban areas, and individuals in urban areas may be more educated and have better access to health care, factors that may improve health. Omitting these factors would lead to a downward bias in the estimated effects of pollution, suggest ing that the overall direction of bias from confounding is unclear. Several previous studies are especially relevant to our work because they address the problem of omitted confounders by focusing on "natural experiments." Chay and Greenstone (2003a, 2003b) examine the implementation of the Clean Air Act of 1970 and the recession of the early 1980s. Both events induced sharper reductions in par ticulates in some counties than they do in others, and they use this exogenous variation in pollution at the county-year level to identify its effects. They estimate that a one unit decline in particulates caused by the implementation of the Clean Air Act (or by recession) led to between 5 and 8 (4 and 7) fewer infant deaths per 100,000 live births. They also find some evidence that declines in total suspended particles (TSPs) led to reductions in the incidence of low birth weight. However, the levels of particulates studied by Chay and Greenstone (2003a, 2003b) are much higher than those prevalent today. For example, PM10 levels have fallen by nearly 50 percent from 1980 to 2000. Furthermore, only TSPs were measured during the time period they examine, which precludes the examination of other pollutants that are found in motor vehicle exhaust. Other studies that are similar in spirit include a sequence of papers by C. Arden Pope and his collaborators, who investigated the health effects of the temporary closing of a Utah steel mill (Pope 1989; Michael R. Ransom and Pope 1992; Pope, Schwartz, and Ransom 1992) and Michael S. Friedman et al. (2001) who examine the effect of changes in traffic patterns in Atlanta due to the 1996 Olympic Games. However, these studies did not look at fetal health. Parker, Mendola, and Woodruff (2008) examine the effect of the Utah steel mill closure on preterm births and find that exposure to pollu tion from the mill increased the probability of preterm birth. This study, however, does not speak to the issue of effects of traffic congestion on infant health. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 69 Currie, Neidell, and Schmeider (2009) examine the effects of several pollutants on fetal health in New Jersey using models that include maternal fixed effects to control for potential confounders. They find that CO is particularly implicated in negative birth outcomes. In pregnant women, exposure to CO reduces the avail ability of oxygen to be transported to the fetus. Carbon monoxide readily crosses the placenta and binds to fetal haemoglobin more readily than to maternal haemo globin. It is cleared from fetal blood more slowly than from maternal blood, leading to concentrations that may be 10-15 percent higher in the fetus's blood than in the mother's. Indeed, much of the negative effect of smoking on infant health is believed to be due to the CO contained in cigarette smoke (World Health Organization 2000). Hence, a significant effect of E-ZPass on CO alone would be expected to have a significant positive effect on fetal health. E-ZPass is an electronic toll collection system that allows vehicles equipped with a special windshield-mounted tag to drive through designated toll lanes without stopping to manually pay a toll. The benefits include time saved, reduced fuel con sumption, and reductions in harmful emissions caused by idling and acceleration at toll plazas. In addition, the air quality benefits are thought to be large enough that some counties have introduced ETC explicitly in order to meet pollution migitation requirements under the Clean Air Act (Anthony A. Saka et al. 2000). Engineering estimates of the reduction in pollution with E-ZPass adoption vary. They are typically based on a combination of traffic count data, and measures of the extent to which reducing the idling, deceleration, and acceleration around toll plazas would reduce emissions for a given vehicle mix. For example, Saka et al. (2000) compared data on traffic flows through manned toll lanes and electronic toll collection lanes at one toll plaza at a single point in time and estimated that reductions in queuing, decelerations, and accelerations in the ETC lanes resulted in reductions of 11 percent for N02 and a decrease of more than 40 percent for hydrocarbons and CO relative to emissions in the manned lanes. A similar study of the George Washington Bridge toll plaza, one of those included in this study, by Mohan Venigalla and Michael Krimmer (2006), estimated that VOC, CO, and N02 emissions from trucks were reduced in the E-ZPass lanes by 30.8 percent, 23.5 percent, and 5.8 percent. Although these studies suggest that E-ZPass could lead to substantial reductions in ambient pollution, these studies may overestimate or underestimate the extent of that reduction. For example, if reducing toll plaza delays encourages more people to drive rather than take public transit, then this may offset the reduction in pollution per vehicle to some extent. Conversely, to the extent that drivers in non-E-ZPass lanes also benefit from reduced congestion, comparing delays at E-ZPass and manual lanes will understate the benefits of E-ZPass. We were unable to find a study that measured pol lution in the radius of a toll plaza before and after the introduction of ETC. However, the New Jersey Turnpike Authority commissioned a study of the extent to which E-ZPass reduced total delays at toll plazas (New Jersey Turnpike Authority 2001). This study used before and after data on traffic counts at each toll plaza, and measured the delays at toll plazas using video cameras. Evidently, the total delay is given by (number of vehicles) x (delay per vehicle). This study concluded that total delay at toll plazas dropped by 85 percent after the implementation of E-ZPass, sav ing 1.8 million hours of delay for cars and 231,000 hours of delay for trucks in the This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 70 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS JANUARY 2011 year after adoption. If pollution around the toll plaza is proportional to these delay then it is reasonable to conclude that it was also reduced considerably. The report estimated that E-ZPass reduced emissions of N02 by 0.056 tons per day, or 20 tons per year. In 2002, mobile on-road sources emitted approximately 300 tons of N02 per year (New Jersey Department of Environmental Protection 2005). Hence, a crude estimate is that E-ZPass reduced N02 emissions from traffic by about 6.8 percent. Unfortunately, the EPA's air quality monitors are placed throughout the state such that there is only one monitor located near a toll plaza in our study area Furthermore, this particular monitor only measures N02 and sulfer dioxide (S02). Nevertheless, we show evidence that suggests a sharp decline in N02 levels fo lowing E-ZPass adoption. This is in contrast to S02 levels at the same monitor, fo which we see no noticeable decline. This is consistent with the fact that cars produc a large percentage of local N02 emissions, while they are responsible for a ve small fraction of S02 emissions. An important unresolved question is how far elevated pollution levels extend from highways or toll plazas? Most studies have focused on areas 100-500 meters from roadway. However, Shishan Hu et al. (2009) find evidence that pollution from the 4 Freeway in Los Angeles is found up to 2,600 meters from the roadway. Moreover, their study was conducted in the hours before sunrise, when traffic volumes are re tively light and most people are in their homes. We investigate this issue below. We focus on the implementation of E-ZPass on three major state tollways i New Jersey and Pennsylvania: the Pennsylvania Turnpike, the New Jersey Turnpike and the Garden State Parkway. Portions of all three of these state highways rank nationally as some of the busiest in the country. In addition to these state tollways we also use the major bridge and tunnel tolls connecting New Jersey to New York (George Washington Bridge, Lincoln Tunnel, and Holland Tunnel). Each of the bridges and tunnels are extremely well traveled, transporting around 105 million 42 million, and 35 million vehicles, respectively. New Jersey has 38 toll plazas, 3 bridge/tunnel entrances to New York City, 11 along the Garden State Parkway, 2 along the New Jersey Turnpike, and 2 along the Atlantic City Expressway. There a 60 toll plazas in Pennsylvania. Figure 1 shows the toll plazas and major highwa that we use. Our research design exploits the fact that E-ZPass was installed at different time and in different locations across the two states. The Port Authority of New York and New Jersey implemented E-ZPass at the bridge and tunnels entering New Yor City in 1997. Soon after, New Jersey installed its first E-ZPass toll plazas on t Atlantic City Expressway. Starting in December 1999, New Jersey began installing E-ZPass on the Garden State Parkway. Throughout the course of the following year toll plazas were added at the rate of one per month (working from North to Sou on the Garden State Parkway), with the final plaza installed in August of 2000. In September 2000, the New Jersey Turnpike installed E-ZPass at all their toll collec tion terminals throughout the system. Similarly, the PA Turnpike installed most o their toll plazas with E-ZPass in December 2000, with a major addition occurring in December of 2001. E-ZPass adoption and take up was extremely rapid. By early 2001 (1 year after implementation of the Garden State Parkway and NJ Turnpike 1.3 million cars had been registered with E-ZPass in New Jersey. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 71 Figure 1. Locations of Toll Plazas and Major Roadways in New Jersey and Pennsylvania II. Data Our main source of data for this study are Vital Statistics Natality records from Pennsylvania for 1997 to 2002 and for New Jersey for the years 1994 to 2003. Vital Statistics records are a very rich source of data that cover all births in the two states. They have both detailed information about health at birth and background informa tion about the mother, including race, education, and marital status. We were able to make use of a confidential version of the data with the mother's address, and we were also able to match births to the same mother over time using information about the mother's name, race, and birth date. Like most previous studies of infant health, we focus on two birth outcomes: prematurity (defined as gestation less than 38 weeks) and low birth weight (defined as birth weight less than 2,500 grams).2 Using this information, we first divided mothers into three groups: those liv ing within 2 km of a toll plaza; those living within 3 km of a major highway, but between 2 km and 10 kilometers of a toll plaza; and those who lived 10 km or more away from a toll plaza. Our treatment group in the difference-in-difference design is the mothers living within 2 km of a toll plaza, while the control group is those who live close to a highway, but between 2 km and 10 km of a toll plaza. We drop mothers who live more than 10 km away from a toll plaza. In total, we have 98 toll plazas that adopted electronic tolling in our sample, and thus we have 98 separate sample regions. We also drop births that occurred more than three years before or after the E-ZPass conversion of the nearest plaza, in an effort to focus on births that occurred around the changes. All of the mothers in the sample are assigned to their nearest toll plaza. 2 Outcomes such as infant deaths and congenital anomalies are much rarer, and when we restrict the dataset to those who are within 2 km of a toll plaza, there are insufficient cases in our data for us to be able to expect to see an effect. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 72 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS JANUARY 2011 Figure 2. Research Design Showing 1.5 km and 2 km Treatment Radii and 3 km from Highway Control Group Figure 2 illustrates the way that we created the treatment and control groups for each of our toll plaza sample regions. As one can see from the figure, there are many homes within the relevant radius of the toll plaza. Moreover, housing tends to follow the highway. The areas more than 2 km away from either a toll plaza or the highway are somewhat less dense. We also repeat this procedure using mothers less than 1.5 km from a toll plaza as the treatment group, comparing them to mothers who live within 3 km of a highway but between 1.5 and 10 km of a toll plaza. In the analysis including mother fixed effects, we select the sample differently. Specifically, we keep only mothers with more than one birth in our data. We then restrict the sample to only mothers who have had at least one child born within 2 km of a toll plaza, since only these mothers can help to identify the effects of E-ZPass. (The other mothers could in principal identify some of the other coefficients in the model, but as we show below, they have quite different average characteristics so we prefer to exclude them). We use all available years of sample data, in order to maximize the number of women we observe with two or more children. We obtained data on housing prices in New Jersey from 1989 to 2009 by submit ting an open access records request. In addition to the sales date and price, these data include information about address, square footage, age of structures, whether the unit is a condominium, assessed value of the land, and assessed value of the structures. We will use these data to see if housing prices changed in the neighbor hood of toll plazas in response to amenity benefits generated from reduced traffic congestion and increased air quality surrounding E-ZPass implementation. Means of the outcomes we examine (prematurity and low birth weight) and of the independent variables are shown in Table 1 for all of these groups. Panel A shows means for the treatment and control group used in the difference-in-differences This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 73 analysis. For the control group, "before" and "after" are assigned on the basis of when the closest toll plaza converted to E-ZPass. The last column of panel A shows means for mothers who live more than 10 km from a toll plaza. They are less likely to have a premature birth, and their babies are less likely to be low birth weight. They are also less likely to be black or Hispanic. These mothers are omitted from our difference-in-difference analysis. The treatment and control groups are similar to each other before the adoption of E-ZPass except in terms of racial composition. Mothers close to toll plazas are much more likely to be Hispanic and somewhat less likely to be African American than other mothers. Mothers close to toll plazas are also less likely to have smoked during the pregnancy. These differences have potentially important implications for our analysis, since other things being equal, African Americans and smokers tend to have worse birth outcomes than others. Hence, it is important to control for these differences, and we will also examine these subgroups separately. In terms of before and after trends, both areas show increases in the fraction of births to Hispanic and African American mothers, and decreases in the fraction of births to smokers and teen mothers over time. The fraction of births that were pre mature rose over time, especially in the control areas. The fraction of births that were low birth weight showed a slight decrease in the treatment area near toll plazas, but an increase in the control areas. These patterns reflect national time trends in the demo graphic characteristics of new mothers and in birth outcomes. We can use these means tables to do a crude difference-in-difference comparison. Such a comparison suggests that prematurity and low birth weight fell by about 7 percent in areas less than 2 km from a toll plaza after E-ZPass. Appendix Table 1 shows changes in mean outcomes when the treatment group is restricted to those who were within 1.5 km of a toll plaza. Panel B of Table 1 shows means for the sample that we use in the mother fixed effects analysis. Panel B shows that, in general, the mothers with more than one birth in the sample have somewhat better birth outcomes?their children are less likely to be premature or low birth weight than in the full sample of children (panel A). The sample of women who have more than one birth and who ever had a child within 2 km of a toll plaza changes over time. Comparing columns 1 and 2 shows that over time this population has become more Hispanic, less educated, and some what more likely to be having a higher order birth. Columns 3 and 4 of panel B show that the population of women who never had a birth within 2 km of a toll plaza are quite different?they are less likely to be Hispanic, the sample tends to gain educa tion over time, and (not surprisingly) live further from a highway. Panel C shows means from the housing sales data. All prices were deflated by the consumer price index (CPI) into 1993 dollars. Comparing columns 1 and 3 suggests that sales prices were similar in areas close to toll plazas and a little fur ther away from toll plazas before E-ZPass, but that prices increased faster near toll plazas after adoption. The same comparison is shown for the area within 1.5 km of a toll plaza and areas 1.5-10 km away from toll plazas in Appendix Table 1. We show below that controlling for a fairly minimal set of covariates (month and year of sale, square footage, age of structure, municipality, and whether it is a condominium) reduces this estimate to statistical insignificance. Still, the idea that prices may have increased, thereby changing the composition of mothers in the This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 74 AMERICAN ECONOMIC JOURNAL: APPUED ECONOMICS JANUARY 2011 Table 1?Summary Statistics >2 km and >2 km and <2kmE-ZPass <2kmE-ZPass <10kmE-ZPass <10kmE-ZPass >10km before after before _after_Toll plaza Panel A. Difference-in-difference sample Outcomes Premature Low birth weight Controls Mother Hispanic Mother black Mother education Mother HS dropout Mother smoked Teen mother Birth order Multiple birth Child male Distance to roadway Observations NJ observations PA observations 0.095 0.095 0.078 0.102 0.089 0.109 0.092 0.085 0.078 0.291 0.16 13.12 0.169 0.089 0.073 1.3 0.028 0.51 1.099 0.332 0.173 13.2 0.164 0.075 0.061 1.37 0.033 0.165 0.233 0.229 0.264 13.24 0.163 0.086 0.069 0.054 0.047 12.92 0.173 0.152 0.079 1.68 0.033 0.512 21 33,758 26,415 7,343 29,677 26,563 3,114 0.082 0.512 1.074 Ever birth <2km 13.276 0.154 0.109 0.082 1.39 0.032 0.514 1.507 1.46 0.037 0.512 1.482 190,904 128,547 161,145 133,560 Ever birth Never birth 62,357 <2km E-ZPass plaza E-ZPass plaza before after 27,585 <2 km E-ZPass plaza before 185,795 70,484 115,311 Never birth<2km E-ZPass plaza after Panel B. Mothers with more than one birth in sample Outcomes Premature 0.088 0.099 Low birth weight 0.081 0.077 Controls Mother Hispanic 0.167 0.29 Mother black 0.145 0.157 0.092 0.086 0.103 0.086 0.088 0.169 0.161 0.171 Mother education 12.78 12.6 12.75 Mother smoked 0.113 0.076 Teen mother 0.041 0.044 Birth order 1.575 1.708 0.072 Mother HS dropout 0.168 0.201 Multiple birth 0.03 0.037 Child male 0.513 0.512 Distance to highway 3.702 Observations 179,537 58,180 NJ observations 85,565 47,012 <2km E-ZPass before Panel C Summary statistics for housing sales data (New Jersey only) 1.598 0.033 0.512 5.598 2.561 <2km E-ZPass after Sales price 94,883 126,006 >2 km and before after <10km E-ZPass 95,518 115,129 1951 1,646 105,341 Square footage 1,573 1,569 5.3 >2 km and Total assessed value 119,166 123,640 Observations 22,350 22,604 0.512 678,025 46,551 Year built 1952 1954 1.735 0.046 962,093 Assessed land value 42,146 43,219 Assessed building value 78,234 81,437 0.095 0.047 485,351 352,751 132,600 1,640,118 PA observations 93,972 11,168 13.13 0.162 0.178 0.135 70,093 <10km E-ZPass 116,691 46,126 69,752 114,403 1950 1,675 102,048 Notes: All observations in panels A and C are selected to be within 3 km of a busy roadway. Housing price data is only for New Jersey and pertains to housing units, not mothers, as described in the text. The housing price data has been deflated by the CPI (base year = 1993). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. I CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 75 neighborhood provides a motivation for the models we estimate below, including mother fixed effects. Figures 3-6 provide more nuanced pictures of the relationship between E-ZPass adoption, birth weight, and prematurity. Figures 3 and 4 focus on mothers within 2 km of a toll plaza and take the average values over 0.1 km bins before and after E-ZPass. Figure 3 shows that there is a dramatic reduction in low birth weight after E-ZPass in the area closest to the toll plaza. The reduction tapers off and the lines cross a little after 1 km. Figure 4 shows a similar pattern for prematurity, although here the lines cross at about 1.5 km from the toll plaza. Figures 5 and 6 compare low birth weight and prematurity in households more than 1.5 km from a toll plaza and households less than 1.5 km from a toll plaza in the days before and after E-ZPass. These figures indicate a higher incidence of low birth weight in the 500 days prior to E-ZPass adoption in the area near the toll plaza. Around the time of E-ZPass adoption, the incidence of low birth weight near toll plazas begins to decline dramatically, and falls below the control rate soon after adoption. Figure 6 shows increasing rates of prematurity in both mothers near toll plazas and mothers further away from toll plazas. Around the time of E-ZPass adop tion, the rate of prematurity begins to fall for the near toll plaza group. It is noticeable that in both figures, the incidence of poor outcomes begins to decline slightly before the official date of E-ZPass adoption. We believe that this slight dis crepancy in the timing may be explained by E-ZPass construction. Prior to the official opening date, each plaza had to be adapted for E-ZPass. The New Jersey E-ZPass contract included the installation of fiber optic communications networks, patron fare displays, E-ZPass toll plaza signs, and road stripping at a cost of $500 million (New Jersey Department of Transportation 1998). In one recent example, the toll plaza for the 1-78 Toll Bridge is being upgraded to E-ZPass. Construction took place between early January 2010 and Memorial Day, approximately 5 months.3 During that time, commuters were advised to use an alternative route so that traffic would be lighter than usual near this plaza (Warren Reporter 2010). III. Methods To implement our difference-in-difference estimator, we begin by testing the assumptions for the estimator to be valid, namely that any trends in the observable characteristics of mothers are the same across both treatment and control groups. The models for these specification checks take the following form: (1) Mom_Charit = a + bxE-ZPasslt + b2Closeit + b3Plazait + b4 E-ZPass x Closeit + b5Year + b6Month + b7 Distanceit + eit, 3 The construction included: partial demolition and removal of the canopy over a portion of the toll plaza; new overhead sign structures, construction of a canopy over the new open road tolling lanes to house the ETC array; the construction of a concrete barrier to separate the ETC lanes from the others; restriping; and the construction of electrical systems to support the ETC equipment (Delaware River Joint Toll Bridge Commission 2009). This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 76 AMERICAN ECONOMIC JOURNAL: APPUED ECONOMICS JANUARY 2011 Low birth weight by distance before and after E-ZPass Figure 3 Notes: Smoothed plots of treatment and control groups using locally weighted regression. To facilitate computation, observations are first grouped into 0.1-mile bins by treatment and control and averaged. The weights are applied using a tricube weighting function (William S. Cleveland 1979) with a bandwidth of 1. Figure 4 Notes: Smoothed plots of treatment and control groups using locally weighted regression. To facilitate computation, observations are first grouped into 0.1-mile bins by treatment and control and averaged. The weights are applied using a tricube weighting function (Cleveland 1979) with a bandwidth of 1. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH Figure 5 Notes: Smoothed plots of treatment and control groups using locally weighted regression. The weights are applied using a tricube weighting function (Cleveland 1979) with a bandwidth of 1. Figure 6 Notes: Smoothed plots of treatment and control groups using locally weighted regression. The weights are applied using a tricube weighting function (Cleveland 1979) with a bandwidth of 1. where Mom_Charit are indicators for mother f s race or ethnicity, her education, teen motherhood, and whether she smoked during pregnancy t. E-ZPass is an indicator equal to one if the closest toll plaza has implemented E-ZPass; Closeit is an indicator equal to one if the mother lived within 2 km (or 1.5 km) of a toll plaza; and Plazait is a series of indicators for the closest toll plaza. This indicator is designed to capture any unobserved, time-invariant characteristics of each toll plaza sample region. The coefficient of interest is on the interaction between E-ZPassit and Closeit. We also This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 77 78 AMERICAN ECONOMIC JO URNAL: APPLIED ECONOMICS JANUARY 2011 include indicators for the year and month to allow for systematic trends, such as the increase in minority mothers. Finally, we control for linear distance from a busy roadway. Standard errors are clustered at the level of the toll plaza, to allow for cor relations in the errors of mothers around each plaza. If we saw that maternal char acteristics changed in some systematic way following the introduction of E-ZPass, then we would need to take account of this selection when assessing the effects of E-ZPass on health outcomes. We also estimate models of the effects of E-ZPass on housing prices. These mod els are similar to equation (1) except that they control for whether it is a condomin ium, age (in categories, including missing), square footage (in categories, including missing), fixed effects for the municipality, and year and month of sale. We have also estimated models that control for the ratio of assessed structure to land values, with similar results. Our baseline models examining the effects of E-ZPass on the probabilities of low birth weight and prematurity are similar to the models from equation (1). The estimated equation takes the following form: (2) Outcome it = a + bxE-ZPassit + b2Closeit + b3Plazait + b4 E-ZPassit x Closeit + b5Year + b6Month + b7Xit + bsDistanceit + eit, where Outcome is either prematurity or low birth weight; and the vector Xit of mother and child characteristics includes indicators for whether the mother is black or Hispanic; four mother education categories (< 12, high school, some college, and college or more; missing is the left out category); mother age categories (19-24, 25-24, 35 +); an indicator for smoking during pregnancy; indicators for birth order (second, third, or fourth or higher order); an indicator for multiple birth; and an indi cator for male child. Indicators for missing data on each of these variables were also included. Again, the main coefficient of interest is b4 which can be interpreted as the difference-in-differences coefficient comparing births that are closer or further from a toll plaza, before and after adoption of E-ZPass. We perform a series of robustness checks. First, we estimate models that restrict the sample to mothers within 5 km of a toll plaza. Second, we include interactions of Closeit and a linear time trend. It is possible that areas close to toll plazas are gener ally evolving in some way that is different from other areas (e.g., racial composition), but, as we shall see, this does not seem to affect our estimates. Third, we estimated models of the propensity to live close to a toll plaza to see whether mothers were more or less likely to live near a toll plaza before or after E-ZPass adoption. The propensity models are estimated using all of the maternal and child characteristics listed above, the interactions of these variables, as well as zip code fixed effects.4 We then excluded 4 We obtained similar results using models that controlled for county fixed effects instead of zip code fixed effects. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 79 all observations with a propensity less than 0.1 or greater than 0.9 as suggested by Richard K. Crump et al. (2009). We estimated separate models for African Americans and non-African Americans since these groups tend to have very different average birth outcomes. We also looked separately at estimates for nonsmokers. As we show below, our difference-in-difference results are robust to these changes, though we do find larger effects for African Americans and for smokers. The estimates from (2) reflect an average effect of E-ZPass on people anywhere within the 2 km (or 1.5 km) window. We have also experimented with allowing the effect to vary with distance from the toll plaza. To do this requires that some assumption be made about the rate at which the effects decay with distance from the toll plaza. The engineering literature is not particularly helpful in this respect, since most studies focus on areas very close to roadways. As we show below, the esti mates are somewhat sensitive to these assumptions, but are qualitatively consistent with the results from the simple difference-in-difference models. One possible threat to identification is that new mothers with better predicted birth outcomes could select into areas around toll plazas after E-ZPass is adopted. Although we do not find evidence of changes in the average demographic charac teristics of those living near toll plazas after E-ZPass, an arguably better way to control for possible changes in the composition of mothers is to estimate models with mother fixed effects. These models take the following form: (3) Outcomeit ? at + bxE-ZPassit + b2Closeit + b3Plazait + b4 E-ZPassit x Closeit + b5Year + b6Month + b7Zit + bs Distanceit + eit, where at is a fixed effect for each mother /, and Z is a vector including child gen der and birth order, and potentially time varying maternal characteristics including mother's age, education, and an indicator for smoking. Although all the mothers are selected to have had at least one child while residing within 2 km of a toll plaza, we alternatively define the indicator for Close either as less than 2 km from a toll plaza or less than 1.5 km from a toll plaza.5 5 One difficulty with the interpretation of these models is that they are identified primarily from movers (there are few mothers with two or more births, both within 2 km of a toll plaza). This would be a problem if we thought that women systematically moved closer to toll plazas when their circumstances improved, and that improved circumstances led to better birth outcomes. The birth certificates do not record income, but marital status is likely to be correlated with maternal well-being and does change over time. We have estimated placebo models similar to (3) using an indicator for married as the dependent variable, and find a negative coefficient on the interaction of close x E-ZPass which is not statistically significant. This suggests that if anything, women are less likely rather than more likely to be married when they live near toll plazas post E-ZPass so that any bias due to movers probably causes an underestimate of the effects of E-ZPass in the mother fixed effects models. This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 80 AMERICAN ECONOMIC JOURNAL: APPUED ECONOMICS JANUARY 2011 Table 2?Testing the Validity of the Research Design: Regressions of Maternal Characteristic on E-ZPass Adoption (Difference-in-Difference Specification) Mother Teen Mother Housing Black Hispanic yrs. ed Dropout mother smoked sale price Panel 1 _(1) (2) (3) (4) (5) (6) (7) <2 km toll x after E-ZPass -0.011 -0.01 0.037 -0.007 -0.001 0.005* 0.149 [0.011] [0.010] [0.040] [0.005] [0.005] [0.003] [0.103] Observations 397,201 406,641 406,198 397,201 412,884 402,590 252,343 Panel 2 <1.5 km toll x after E-ZPass -0.014 -0.01 0.013 -0.003 0.001 0.007** 0.031 [0.055] [0.011] [0.010] [0.006] [0.003] [0.003] [0.106] Observations 397,201 406,641 406,198 397,201 412,884 402,590 252,343 Notes: Each coefficient is from a separate regression. Each coefficient in columns 1-6 is from a regression that also included controls for being within 2 km (or 1.5 km) of a toll plaza, year of birth, month of birth, indicators for each toll plaza, an indicator for post E-ZPass at nearest toll plaza, and distance to highway. Housing sale price regressions in column 7 include year and month of sale, indicators for nearest toll plaza, an indicator for condo units, distance to highway, municipality fixed effects, square footage (in categories including dummies for missing), and age (in categories, including dummies for missing). Standard errors are in brackets. ** Indicates that the estimate is statistically significant at the 95 percent level of confidence. * Indicates significance at the 90 percent level of confidence. IV. Results Table 2 shows the results of estimating equation (1), the effects of E-ZPass on the characteristics of mothers who live near toll plazas and on housing prices. Each coefficient represents an estimate of &4from a separate regression. The only maternal characteristic to show any significant changes with E-ZPass adoption is smoking, where it is estimated that E-ZPass has a positive effect. Note that if more smokers move to areas after E-ZPass adoption (or if mothers smoke more) this will tend to work against finding any net benefit of E-ZPass on birth outcomes. The last col umn shows that there is no immediate significant effect on housing prices (although the coefficient is positive), suggesting that it takes time for any effects through the housing market to be felt. These results suggest that the estimated health effects of E-ZPass are not due to changes in the composition of mothers who live close to toll plazas. Table 3 shows our estimates of (2). Again, each coefficient is an estimate of b4 from a separate regression. The first and third columns show a model that controls only for month and year of birth, toll plaza fixed effects, and distance to highway. These estimates are somewhat higher than the raw difference-in-difference esti mates implied by Table 1, suggesting that it is important to control for time trends and regional differences. The second and fourth columns add maternal characteris tics as in equation (2). Assuming our research design is valid, adding controls for mother's characteristics should only reduce the sampling variance while leaving the coefficient estimates unchanged. The results in columns 2 and 4, are consistent with the validity of the research design, since adding maternal characteristics has little This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms VOL. 3 NO. 1 CURRIEAND WALKER: TRAFFIC CONGESTION AND INFANT HEALTH 81 Table 3?Regressions of Birth Outcomes on E-ZPass Adoption (Difference-in-Difference Specification) Prematurity Prematurity LBW LBW Panel 1 _(1)_(2) (3) (4) <2km toll x after E-ZPass -0.0085 -0.0086 -0.0094 -0.0093 [0.0039]** [0.0034]** [0.0032]** [0.0028]** R2 0.0044 0.0034 0.0032 0.0028 Panel 2 < 1.5km toll x after E-ZPass -0.0088 -0.0098 -0.0077 -0.0084 [0.0051]* [0.0048]** [0.0035]** [0.0032]* R2 0.0042 0.0048 0.0035 0.0032 Maternal characteristics No Yes No Yes Observations 405,802 405,802 409,673 409,673 Notes: Each coefficient is from a different regression. All regressions also included controls f being within 2 km (or 1.5 km) of a toll plaza, year of birth, month of birth, toll plaza indic tors, an indicator for post E-ZPass, and distance to highway. Maternal characteristics includ mother black, mother Hispanic, mother education ( 1.5 km and before after < 10 km before < 10 km after >10km Toll plaza Panel A. Difference-in-Difference sample Outcomes Premature 0.096 0.096 0.102 0.108 0.085 Low birth weight 0.082 0.08 0.089 0.091 0.078 Controls Mother Hispanic 0.272 0.309 0.176 0.239 0.054 Mother black 0.159 0.174 0.227 0.256 0.047 Mother education 13.25 13.31 13.25 13.23 12.92 Mother HS dropout 0.152 0.152 0.156 0.164 0.173 Mother smoked 0.088 0.078 0.107 0.085 0.152 Teen mother 0.067 0.058 0.082 0.069 0.079 Birth order 1.3 1.37 1.38 1.45 1.68 Multiple birth 0.029 0.034 0.031 0.036 0.033 Child male 0.511 0.518 0.513 0.512 0.512 Distance to roadway 0.976 0.939 1.484 1.459 21 Observations 16,934 14,856 207,728 175,966 185,795 NJ observations 12,980 13,175 141,982 146,948 70,484 PA observations 3,954 1,681 65,746 29,018 115,311 Ever birth Ever birth Never birth Never birth < 1.5 km <1.5 km <1.5km <1.5km E-ZPass plaza E-ZPass plaza E-ZPass plaza E-ZPass plaza before before after after Panel B. Mothers with more than one birth in sample Outcomes Premature 0.0883 0.0988 0.0914 0.103 Low birth weight 0.0803 0.0755 0.0862 0.0857 Controls Mother Hispanic 0.164 0.286 0.0916 0.168 Mother black 0.144 0.156 0.168 0.17 Mother education 12.81 12.54 12.75 13.11 Mother HS dropout 0.163 0.202 0.178 0.164 Mother smoked 0.113 0.0756 0.134 0.0939 Teen mother 0.0414 0.0417 0.07 0.0464 Birth order 1.581 1.723 1.596 1.733 Multiple birth 0.0306 0.0382 0.0331 0.0451 Child male 0.512 0.512 0.512 0.512 Distance to highway 3.612 2.502 5.509 5.159 Total observations 94,473 31,188 1,725,182 512,343 NJ observations 45,215 25,376 718,375 PA observations 49,258 5,812 1,006,807 <1.5 km <1.5 km E-ZPass before E-ZPass after > 1.5 km and <10km E-ZPass before >1.5 km and <10 km E-ZPass after Panel C. Summary statistics for housing sales data (New Jersey only) Sales price 95,033 125,567 95,444 117,600 Assessed land value 45,270 45,462 45,825 45,608 Assessed building value 84,445 87,394 70,219 70,186 Total assessed value 128,899 131,867 114,531 114,363 Year built 1953 1955 1951 1950 Square footage 1,593 1,551 1,639 1,670 Observations 11,586 12,214 116,1 This content downloaded from 149.125.250.159 on Mon, 05 Nov 2018 02:24:40 UTC All use subject to https://about.jstor.org/terms 88 AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS JANUARY 2011 REFERENCES American Housing Survey. 2003. United States Census Bureau, http://www.census.gov/hhes/www/ housing/ahs/ahs03/tab28.htm. (accessed September 12, 2009). Banzhaf, H. Spencer, and Randall P. Walsh. 2008. "Do People Vote with Their Feet? An Empirical Test of Tiebout's Mechanism." American Economic Review, 98(3): 843-63. Beatty, Timothy K. M., and Jay P. Shimshack. 2009. 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