How have highways changed the political geography of American metropolitan areas? Chapter 3 established
that highways were responsible for the creation of new Republican suburbs, but did not address the Interstate
Highway System’s larger role in the widely documented trend of cities becoming more Democratic relative
to their suburbs. This chapter estimates highways’ effect on urban-suburban political polarization, the gap
in the two-party vote between central and outlying counties in a metropolitan region. Adding a new measure
of highways’ spatial influence based on highways’ connectivity to local street networks, the chapter uses a
variety of robust methods to estimate causal effects on polarization in the 100 largest US cities. A range of
model results uniformly generate positive point estimates, and demonstrate that a modest increase in urban
highway density generates up to a 4 point increase in urban-suburban polarization, compared to the 10.5
point average increase in urban-suburban polarization between 1952 and 2008. These results establish that
the present-day alignment of partisanship and residential location on an urban-to-rural continuum is partially
a consequence of the federal highway policies. A public policy with the potential to bring Americans closer
together instead allowed them to live among individuals with similar political preferences, worsening the
political disconnect between major cities and their hinterlands.
Policies that change space can change politics, and one way they do so is by facilitating geographic partisan
sorting, the tendency for Americans to live in enclaves of like-minded citizens. Standard models
attribute the pattern of such changes almost entirely to the aggregate effect of individual citizens’ homophily:
the natural tendency of citizens to be drawn to and cluster with similar individuals. I present an
alternative account that suggests that government policies that influence mobility also can influence citizens’
personal calculus of residential location. Using historical geographic data to estimate geographic
outcomes consistent with this hypothesis, I examine the effects of Interstate Highway System on the
political development of suburban communities. Combining construction data from the Interstate Highway
System with county-level presidential election data for the years 1948-2008, I show that suburban
communities with Interstate highways became as much as five points more Republican than they would
have been in the absence of freeway construction–a large enough effect to change a swing district to a
landslide district. A metropolitan case study based on Wisconsin precinct-level data and a multi-year
national analysis of county-level data shows that such political effects emerge quickly after freeway construction,
especially in previously undeveloped areas. These findings demonstrate that federal policies
can change politics not only by directly influencing individual welfare, but also by influencing residential
choice and the spatial relationships among citizens.
A basic feature of many field experiments is that investigators are only able to randomize clusters of individuals - such as households, communities, firms, medical practices, schools, or classrooms - even when the individual is the unit of interest. To recoup the resulting efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, many other studies avoid pairing, in part because of claims in the literature, echoed by clinical trials standards organizations, that this matched-pair, cluster-randomization design has serious problems. We argue that all such claims are unfounded. We also prove that the estimator recommended for this design in the literature is unbiased only in situations when matching is unnecessary; and its standard error is also invalid. To overcome this problem without modeling assumptions, we develop a simple design-based estimator with much improved statistical properties. We also propose a model-based approach that includes some of the benefits of our design-based estimator as well as the estimator in the literature. Our methods also address individual-level noncompliance, which is common in applications but not allowed for in most existing methods. We show that from the perspective of bias, efficiency, power, robustness, or research costs, and in large or small samples, pairing should be used in cluster-randomized experiments whenever feasible; failing to do so is equivalent to discarding a considerable fraction of one's data. We develop these techniques in the context of a randomized evaluation we are conducting of the Mexican Universal Health Insurance Program.
Background We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans.
Methods We randomly assigned treatment within 74 matched pairs of health clusters -- ie, health facility catchment areas -- representing 118,569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006) in 50 pairs (n=32,515). The treatment consisted of encouragement to enrol in a health-insurance programme and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. We estimated intention to treat and complier average causal effects non-parametrically.
Findings Intention-to-treat estimates indicated a 23% reduction from baseline in catastrophic expenditures (1.9% points; 95% CI 0.14-3.66). The effect in poor households was 3.0% points (0.46-5.54) and in experimental compliers was 6.5% points (1.65-11.28), 30% and 59% reductions, respectively. The intention-to-treat effect on health spending in poor households was 426 pesos (39-812), and the complier average causal effect was 915 pesos (147-1684). Contrary to expectations and previous observational research, we found no effects on medication spending, health outcomes, or utilisation.
Interpretation Programme resources reached the poor. However, the programme did not show some other effects, possibly due to the short duration of treatment (10 months). Although Seguro Popular seems to be successful at this early stage, further experiments and follow-up studies, with longer assessment periods, are needed to ascertain the long-term effects of the programme.