The Neighborhood Impacts of Local Infrastructure Investment:
Evidence from Urban Mexico*

Craig McIntosh
Tito Alegría
Gerardo Ordóñez
René Zenteno

May 2017
Abstract
This paper reports on the results of a large infrastructure investment experiment in which $68
million in spending was randomly allocated across a set of low-income urban neighborhoods in
Mexico. We show that the program resulted in substantial improvements in access to infrastructure
and increases in private investment in housing. While a pre-committed index of social capital did not
improve, we find an apparent decrease in the incidence of personal assault and teen misbehavior inneighborhoods where investments were made. The program increased the aggregate real estatevalue in program neighborhoods by two dollars for every dollar invested.

Keywords: Infrastructure impacts, property values, spillover effects
JEL Codes: O18, C93, H73* Corresponding author:

McIntosh. 9500 Gilman Drive, La Jolla CA, 92093-0519. Tel: 858 822 1125. Fax: 858 534
3939. Email: ctmcintosh@ucsd.edu. Tito Alegría, El COLEF, talegria@colef.mx. Gerardo Ordóñez, El COLEF,
ordonez@colef.mx. René Zenteno, UT San Antonio and El COLEF, rzenteno2012@gmail.com. Thanks to Beatriz
Alfaro, Miguel Ángel Ramírez, Camilo Contreras, Mario Jurado, Silvia López, Gabriela Pinillos, Ruth Rodríguez, andWilfrido Ruiz for their invaluable work on the survey and index construction, to Thomas Dickinson and Ana Quiroz for
GIS work, and to participants in seminars at CIDE, Claremont-McKenna, CU Boulder, El Colegio de la Frontera Norte,
Georgetown, IFPRI, Stanford, UCSD, USC, UC Berkeley, and the World Bank for helpful comments. The authors
were paid as consultants to perform this evaluation by the Inter-American Development Bank and the Social
Development Secretariat (SEDESOL) of the Mexican government.

1. INTRODUCTION
When governments invest in infrastructure, what is the impact, and who ultimately realizes thebenefits from these investments? Despite global infrastructure spending of $4 trillion in 2012 (PwC
report1), these questions are difficult to answer in any straightforward way. Infrastructure
investment takes place at a highly aggregated spatial level and therefore is not generally amenable torandomized experimentation in the same way as individually-targeted programs (Newman et al.
1994, Field and Kremer 2006, Hansen et al. 2012). Even where impacts can be measured, theincidence of these benefits are complex. A long literature stretching back to the Rosen-Roback
models of compensating differentials suggest that improvements in amenities will be priced into
property and real wages. This implies that renters will not realize welfare gains from improved
amenities on average because they will be forced to pay the full compensating differential to enjoy
these amenities. In this paper we present the results of a major federal infrastructural spending
experiment implemented in low-income neighborhoods across Mexico during the years 2009-2011
by the Hábitat program of the Social Development Secretariat (SEDESOL). We exploit detailed
household and block-level data to measure impacts on specific forms of infrastructure, and then use
professional property price valuations to compare the overall amenity value of the improvements tothe cost of the program (Greenstone and Gallagher 2008). Our work joins a rapidly-growing
literature using experiments to examine the impact of improvements in infrastructure, such as roadpaving (Gonzalez-Navarro and Quintana-Domeque 2016), new home construction (Galiani et al.
2013), and improvement of water resources (Kremer et al. 2011).2
The analysis of urban housing prices has long been considered in a general equilibrium setting
where locations are bundles that comprise rents, wages, and amenities (Rosen 1979). In thepresence of costless relocation by workers and firms, differences in cost-adjusted wages acrosslocations provide a measure of the amenities present in each location (Roback 1982, Blomquist et al.
1988). When neighborhood amenities are improved via public investment, property prices and rents
will both shift upward, passing the incidence of the benefits to pre-existing owners of land. We
study a program that targeted low-income neighborhoods with no ongoing conflicts over land
tenure. This, combined with the very low mortgage lending rates in Mexico, presents anenvironment in which 80% of study households own their homes outright. There is therefore scope

1

PwC, ‘Capital Project and Infrastructure Spending’, http://www.pwc.com/cpi-outlook2025

2 For recent non-experimental evaluations of infrastructure spending see Paxson and Schady 2002, Newman et al. 2002,

Duflo and Pande 2007, Cattaneo et al. 2009, Dercon et al. 2009, Khandker et al. 2009, and Casaburi et al. 2013.

1

for a successful program to generate a substantial real wealth transfer to low-income households
through this type of neighborhood infrastructural investment. From a welfare perspective, urban
slums may prove a particularly attractive place to make new infrastructure investments precisely
because they have been historically underserved (see Turley et al. 2013 for a systematic review of theevidence on slum infrastructure upgrading).
This study tracks the impact of $68 million in infrastructure investment which was randomly
allocated to poor urban neighborhoods in 60 municipalities across 20 different Mexican states. This
represents the largest-scale experimental evidence to date on the impact of infrastructure investment
on critical outcomes such as private housing investment, social capital, and aggregate property
prices. Hábitat investment is primarily used to build heavy infrastructure such as roads, water,
sewerage, lighting, and sidewalks, but is also put towards community centers, parks, and sports
facilities. The study is accompanied by a detailed panel household survey tracking outcomes on
almost 10,000 different city blocks. The results demonstrate the social and economic benefits ofinvestment in underserved neighborhoods, and help to address the paucity of infrastructureevaluation studies that are performed ‘at scale’.
The results of the study show that Hábitat investments resulted in very large improvementsin the quality of road paving, sidewalks, medians, and public lighting. An index of infrastructure
quality, to which we committed as the core outcome indicator in a pre-analysis plan, is significantly
improved by the intervention. Private investment in the housing stock is crowded in by the
program; homeowners in treated neighborhoods are more likely to install cement floors and flush
toilets. Using survey data we show that monthly rents increase by $18 off a base of $88, but thisestimate likely overestimates the causal effect of public spending because it includes the effects ofprivate investment in the housing stock. We attempt to isolate the pure effect of public investment
by using professional assessors to provide estimates of the prices of unbuilt lots at baseline, and thenfor the same lots again at endline. The value of a square meter of land in treatment neighborhoods
increases by more than $2 for every $1 invested by the program. This estimate is substantially larger
than corresponding numbers from infrastructure investment in the US (Pereira and Flores de Frutos
1999, Cellini et al. 2010), suggesting under-investment in infrastructure in these low income
neighborhoods.
A primary purpose of the designers of Hábitat has been that, through a multi-dimensional
set of investments such as walkability or sports and cultural opportunities for youth, the program
would build a sense of community. Despite this emphasis, we find an insignificant positive impact

2

of the program on our pre-committed index of social capital. We exploit the rich household survey
data to examine social capital more closely in an exploratory analysis not disciplined by a pre-analysis
plan. Domains such as participation and trust are not improved, but the treatment group entirely
avoids a strong decay in security that is experienced by the control. Consistent with investment incommunity sports, arts, and music, the program generated an absolute improvement in an index ofyouth behavior. So, while the program does not improve most of the measures used in our social
capital index (as shown in Ordóñez and Ruiz 2015), these more tenuous results suggest that it may
have led to meaningful improvements in public security.
The paper is organized as follows: Section 2 introduces the program and the data collection
strategy, Section 3 presents the estimates of the impact of the program on primary outcomes,
Section 4 presents secondary analysis, and Section 5 concludes.
2. THE HÁBITAT PROGRAM
2.1. Program Description.
Urban Mexico has been a tumultuous place to live over the past decades.

Rapid

urbanization, combined with the dramatic increase in violent crime, has put pressure on the social
fabric of Mexican cities (Vilalta 2014, BenYishay and Pearlman 2014).

The Mexican federal

government created the Hábitat program in 2003 in order to provide infrastructure investments tomarginalized urban parts of the country, and to provide public resources to improve the quality oflife in these communities. It combines centralized targeting and spending rules with decentralized
implementation in a way that makes the program comparable to the larger set of community-driven
development (CDD) interventions (see Mansuri and Rao 2004 for an overview, and Chen et al. 2008
or Casey et al. 2012 for more recent examples). The premise of the program is that simultaneous
investment across multiple dimensions of neighborhood amenities (public infrastructure, walkability,community centers, sports fields, etc.) can substantively improve social capital and strengthen thesocial fabric binding neighborhoods together.
Hábitat represents an injection of federal spending into the types of local infrastructure more
typically provided by municipal governments. To be able to receive investment from Hábitat a
polygon must be located within a state and municipality whose respective governments are willing tocooperate with Hábitat’s cost-sharing rules (which involve local governments providing 50% of
project costs; in our projects the municipalities provided 40%, the states 8%, and the beneficiaries32%).3 Hábitat defines eligibility in a spatial manner, using GIS to define a ‘polygon’ which is an
explicit geographic perimeter outlining a neighborhood that meets the criteria for inclusion. A
Hábitat polygon is smaller than a locality and is a designation not used by other layers ofgovernment. In order to be eligible to benefit from Hábitat, a polygon must consist of settled
households in a marginalized urban areas with concentrations of asset poverty greater than 50%,
located in cities of 15,000 inhabitants or more, with a deficit of infrastructure and urban services,and with at least 80% of the lots having no active conflict over property rights (this will prove
important in interpreting the welfare implications of our results, and presents a contrastingownership environment to the property titling programs studied by Field 2005, and Galiani and
Schargrodsky 2010).
Once Hábitat begins to operate in a polygon, two sets of actors worked together todetermine the specific investments to be made. Program officials would make recommendations tothe community about the observed infrastructure deficits, and a community-driven process(including local government officials and neighborhood leaders) would identify which were mostpressing. A ‘project executant’ then submits a proposal, subject to a set of program rules ofoperation (such as the fact that drainage should be installed or repaired prior to street paving).
Spending caps imposed by the federal program governed the amount that could be spent on any
specific item (although there is some variation in the magnitude of spending per capita driven by thescale of the project undertaken in a particular location). It is made explicit at the time of thedecision that all maintenance costs would be borne by the municipal governments moving forward.
While the program invested in a diverse set of activities including community development
centers, job training, and health and nutrition training for young mothers, more than 2/3rds of
Hábitat investment went into improving localized infrastructure. This includes investment in street
paving within the polygon (48% of total spending), piped water and sewerage (11%), and theconstruction of medians and sidewalks to make the neighborhood more pedestrian-friendly (4%).
Appendix Table A1 provides a detailed breakdown of how the money was spent in the 155
treatment polygons studied in this paper, and Ordóñez et al. 2013 provides a more in-depth
description of the program as well as some preliminary infrastructure impact measures.

3 This is the reason that our study does not include a pure control consisting of municipalities with no treatment.
SEDESOL felt that in return for going through the cost-sharing negotiations municipal governments should beguaranteed that they would receive at least one neighborhood assigned to treatment.

4

The randomized evaluation of the Hábitat program was designed in 2008 following on theheels of a propensity-score matching analysis that was conducted by Mathematica on the prior waveof Hábitat investment from 2003-2004 (Campuzano et al., 2007). Despite the fact that 300 million
dollars had been spent by Hábitat in those two years, the prior study returned quite disappointing
results, finding a 3 percentage point increase in access to sewerage and no improvement in access todrinking water or electricity. Because that study used outcomes available in the Mexican census andConteo, however, it was only able to look for impacts on infrastructure such as electricity and pipedwater that is already nearly universal in the country. In this study we conduct detailed household
surveys and so are able to paint a richer picture of the impacts of local infrastructure. While we also
fail to find strong impacts on the headline infrastructure outcomes studied in the Campuzano et al.
paper, we uncover a wide range of impacts on indicators to which access at baseline was lessuniversal.
2.2. Study and Survey Design.
The original study universe consisted of all eligible polygons within the set of 65
municipalities that were recruited into the experimental wave of the study by Hábitat in 2008.4 The
polygons included in the study were required to satisfy two additional eligibility restrictions beyond
the standard conditions for the program: we excluded municipalities that had only one polygon, andcities that had fewer than four polygons (large Mexican cities may be divided into multiple
municipalities). The randomization was conducted at the level of the 370 polygons that satisfied all
eligibility criteria for inclusion, with 176 treatment and 194 control polygons. This covers 65
municipalities, with an average of 5.7 polygons per municipality. The experiment featured a twolevel randomization (first the saturation of treatment was randomly assigned at the municipality level
between .1 and .9, and then treatment was randomly assigned at the polygon level to match themunicipality saturation as closely as possible). In the end, the small eligibility coverage of the
program meant that only 1% of the surface area and 3% of the population of study municipalities
are included in study polygons. (Figure 1 shows the map of the municipality of Tijuana, illustrating
the small size of the Hábitat polygons relative to the overall surface area of the municipality). Thismeans that the cross-municipality saturation results lack statistical power, and so we do not focus on

4 Mexico contains 2,438 municipalities in all, but many of the most urban municipalities in the country are contained

within this study.

5them here, other than to note that no strong spillover effects were detected. Appendix Table A2
presents the distribution of polygons by state and treatment group.
The data collection instruments for the study consisted of panel block- and household-level
surveys, as well as assessment of property values by real estate professionals. To obtain a random
sample of blocks and houses for the study, we began by listing all blocks from the 2005 Conteo
contained within study polygons. We drew into the study all blocks in polygons with 100 or fewer
blocks, and randomly sampled 100 blocks in larger polygons (only 4.3% of the total). Study blocks
then had a visual block-level survey filled by a trained enumerator to describe the basic condition ofroads, sidewalks, and other public facilities. One household on each block was randomly sampled toreceive a household survey, which was itself conducted in a long and short form. The short survey
included questions on the construction and amenities of the house, the quality of local
infrastructure, access to neighborhood facilities, health, and satisfaction with the urban environment.
The long survey included all these questions and in addition asked about transport modalities, social
capital, crime victimization, and teen behavior. Households were randomly assigned to answer the
short versus the long version of the questionnaire resulting in 6,419 long-form and 5,065 short-form
questionnaires. These instruments were conducted between the months of March to July 2009(baseline observation) and January-March 2012 (follow-up observation) in the 370 polygons. The
analysis is weighted by the number of households per block (and by the number of blocks in larger
polygons) to make the study representative of the population of study polygons.
Accurate panel measurement of property values in an RCT context, and in a country without
digital transaction records, is a challenge. First, increases in private investment (such as installationof concrete floors or bathrooms with indoor plumbing) confound the measure of increases inproperty values because the housing stock itself improves because of private, as well as public,
investment. Price increases driven by private expenditures are a valid causal effect of the program,but they complicate an accounting of the per-dollar returns to public investment. Secondly, recent
empirical work suggests that urban Mexican households typically provide over-estimates of the salevalue of their own properties (Gonzalez-Navarro and Quintana-Domeque 2009). To overcome thefirst of these issues, we use price estimates only on empty lots that have no construction on them asof the baseline, so our estimate of price per square meter of raw land is not polluted by changes in
the nature of the private housing stock. In order to get a high-quality estimate of sales prices in anenvironment in which there is no regular recording of sales prices, we used professional property

6assessors from the Instituto de Administración Avaluos de Bienes Nacionales (INDAABIN), theMexican government’s institute of real estate valuation.
These assessors provided estimates of the value of every one of the 464 un-built lots thatwere for sale in the study polygons at baseline, and then returned to the same lots at the time offollow-up and provided new estimates of the raw land value of the lot at that time (whether or not a
structure had by then been built). In each round, the assessors assembled information fromcomparable sales and put together estimates according to established INDAABIN methodology.
Assessors were blinded to the treatment design (meaning that they did not know whether they were
providing estimates in treatment or control communities). While the total number of empty lots forsale at baseline was small, this analysis provides a precise and readily interpretable impact on land
values.
The study had a pre-analysis plan submitted to the Mexican government at the time theanalysis of the baseline survey was being completed. All of the outcomes included in that preanalysis plan are presented in this paper. They include an overall infrastructure index, availability ofsix specific infrastructure types (electricity, piped water, sewerage, paved streets, street lights, andsidewalks/medians), the impact on an index of social capital, and the impact on property prices.
The social capital index is constructed at the polygon level using principle-components techniqueson the baseline data, and comprises a weighted average of 14 survey questions on participation inneighborhood groups, trust in social institutions, neighbors, and household members, levels ofconflict between neighbors and community members, and the degree of knowledge aboutcommunity organizations and social problems.

A principal-components method was used to

identify factors loadings across these various attributes using baseline data, and the loadings were
held constant and used on the endline data to provide a panel measure of changes in social capitalfor each polygon across the two waves of the survey (a more detailed presentation of theconstruction of the index can be found in Ordóñez and Ruiz 2015). That paper also shows thatusing a panel difference-in-differences strategy there is a small and insignificant increase in the social
capital index. However, the index was heavily weighted towards group participation and social
cohesion, outcomes that proved not to be affected by the program. The only significant result
reported in that paper is on ‘confidence between neighbors’, a question which included willingness
to offer mutual protection. In this section we show that variables describing stranger-driven crime7and teen misbehavior (which were not included in the index at all) did in fact see substantial
improvements as a result of the program.
We analyze impacts using population-weighted polygon-level averages for the two study
rounds, including fixed effects at the polygon level and clustering standard errors at the municipal
level to account both for spatial covariation as well as for the component of the municipal-level
design effect arising from the randomized saturation design.
2.3. Attrition and Balance.
Panel tracking was done at the house level (not the household level) to capture changes in theattributes of structures.5 This study primarily considers infrastructural outcomes at the level of theresidence, not the resident, and so naturally attempted to conduct a survey in the same house as thebaseline. If at followup the survey teams were unable to find the same household, they were
instructed first to survey the new residents of the same house, so as to provide a panel on changes tothat specific structure, and if they were not able to find residents in the same house then they wereto randomly sample a new house and household from the same block. We always capture whether
the household answering the survey is the same at endline as baseline, and so can address migration
both as an outcome of interest and as a potential confound in measuring household-level outcomessuch as social capital.
The analysis sample begins with 10,670 baseline household observations. Appendix Table A3
provides a Consort diagram of the attrition that occurred during the course of the study. Two
distinct factors cause us to lose observations from the study. First, implementation problems arosewith 5 of the original 65 municipalities in the sample (the municipal governments did not meet thematching requirements).6 Since Hábitat could not treat the polygons in these municipalities, we haveremoved them from the study altogether, treatment and control alike. This causes us to lose 748
observations, almost 7% of the sample. Second, in forming the block-level panel we lose 220
household observations. The dataset used for analysis thus consists of 19,417 panel household
surveys that provide two periods of data at the block level. We also see two additional forms of
replacement that do not cause attrition from the study, but alter the interpretation of impacts. First,

5 The data from the evaluation survey are posted at

http://www.2006-2012.sedesol.gob.mx/es/SEDESOL/ProgramaHábitat

6 These five municipalities are Cuajimpala de Morelos, La Magdalena Contreras, Xochimilco, Almoloya de Juarez, andEcatepec de Morelos.

8we have 17.6% of houses whereby the surveyors could not locate anyone to survey in the same
house as was surveyed at baseline, and hence they replaced the baseline house with a different houseon the same block. In 25.7% of cases where the survey teams were able to locate the same house,
the household currently residing in the structure had changed since baseline and hence thehousehold (but not the house) is replaced.
Table 1 tests for whether these four distinct types of attrition prove to be correlated with thetreatment. In columns 1-2, we begin with the sample of inhabited blocks and examine the attrition
caused by the dropping of the five municipalities in which treatment was not possible. While this
attrition represents a large part of the original sample, there appears to be no systematic correlation
between original treatment status and the polygons in which Hábitat was able to administer
treatment.

Columns 3-4 then examine the attrition driven by the success of COLEF field teams in

conducting a panel block-level survey. Overall attrition at the block level was low (98.5% of thepotential panel blocks were successfully tracked) and appears similarly to be balanced by treatment.
When we look at the success of field teams at locating first the same house (Columns 5-6), and thenthe same household (Columns 7-8), we begin to see evidence of significant differences across thetreatment and control. The probability of being unable to find any resident in the baseline-surveyed
house is 3.6% lower in the treatment than the control, but remains insignificant (Columns 5-6).
Once we examine the turnover rate of the actual households answering the survey, however, we seethat the treatment neighborhoods had dramatically lower rates of residential churn. More than aquarter of the houses that were panel tracked saw the resident household change in the three yearsof the study, indicating a very high rate of residential churn in the study. More importantly, thetreatment had a strong effect on this rate of churn; in treatment neighborhoods this rate of churndropped by 7 percentage points, or a quarter of the control-group rate. While we are concerned
only with the attrition through Column 4 for the basic analysis of infrastructure impacts, we return
to a discussion of this differential turnover of households when analyzing changes in private
investment and social capital.
Table 2 examines the baseline treatment-control comparison within the final analysis sample,
using the short-form survey questions on public infrastructure in Panel A and the long-form
questions on health, transportation, and social capital in Panel B. The experiment was stratified onaccess to water and electricity, and so the balance here is necessarily excellent.

Overall theinfrastructure variables appear very well balanced, and indeed different impact specifications (posttreatment single-difference, simple difference-in-differences, or polygon fixed effects analysis) all9arrive at very similar impacts. The only evidence of imbalance is in the social capital index, which issignificantly lower in the control than the treatment. We return in Section 4 to an analysis of thispotential imbalance in the social capital variables and discuss how this affects our interpretation.
3. Primary Analysis
3.1. Impact on Public Infrastructure.
Table 3 presents these main infrastructure results.

The first column shows the impact on

the core infrastructure index (the sum of the five specific infrastructure variables), and finds an
impact of .135, significant at the 99% level. This implies an 8% increase off a baseline mean of 2.7.
Relative to the increase in the index observed in the control group, which was .115, this treatment
effects suggests a more-than doubling in the rate of improvement in the overall index over the three
years of the study. We then examine the variables used for the stratification of the randomization:
availability of piped water, sewerage, and electric lighting. These variables all feature high baseline
control means, (from 82.9% for sewerage to 98.9% for electricity). The treatment estimates on allthese outcomes are small and insignificant: access to sewerage improves by just over 2%, and theimprovements in access to piped water and electric lighting are roughly two tenths of a percent.
When we turn to examining forms of infrastructure to which baseline access was lessuniversal, strongly significant positive effects are apparent. Streetlights, sidewalks, medians, androad paving all see dramatic improvements; the fraction of houses with sidewalks in front of themwas 59% at baseline, rising to 62.5% in the control at followup, but increased to almost 70% in thetreatment. Relative to the control group the pace of road paving more than doubled, sidewalkbuilding tripled, and the rate of construction of medians increased by almost a factor of five. The
fraction of blocks with functioning street lights stayed unchanged at 55.5% in the control group, but
increased by 7 percentage points in the treatment. When we weight the analysis by the number ofblocks rather than the number of inhabitants per polygon results are very similar; completely
unweighted analysis sees impacts fall by about half. The implication is that impacts were larger inthe largest polygons.
The program therefore had a really substantial effect on neighborhood attributes such as
paving and walkability that were far from universal at baseline, and a more modest effect on the
deep utility infrastructure to which access at baseline was much higher. Hábitat spent $68 million
building the infrastructure measured in Table 3, suggesting that by making an investment averaging

10$567 per household, the program was able to generate significant improvements in the quality ofbasic infrastructure.
3.2. Impact on Private Investment.
The surge in public investment induced by the Hábitat experiment provides an interesting
environment in which to investigate potential complementarities between public and private
investment. The program places public resources in communities under-served by past
infrastructural investments, and yet in which property rights are robust (see de Janvry et al. 2015 fora discussion of the improvement of property rights in Mexico). Further, 84.4% of households in thebaseline reported owning their own homes, and 74% own their homes outright (mortgage financing
is difficult to obtain in poor Mexican neighborhoods even with clear property title). Thus, there
appears to be substantial scope for the amenity value created by Hábitat investments to pass into thehands of the residents of these neighborhoods. We investigate this interplay between private andpublic investment by examining privately-financed investments in the housing stock of Hábitat
neighborhoods.
Table 4 provides evidence of complementarities between private and public investment. All
effects are positive, and significant upgrades to flooring and plumbing are made to treatment houses
suggesting that public investment is crowding in private investment from households. The only
negative coefficient is on the use of a septic system, but because this is an inferior substitute toconnection to a sewer line this indicates increasing use of centralized infrastructure. Households are
significantly more likely to have installed concrete floors, and to have working flush toilets. The
improvement in indoor plumbing is particularly interesting given that we did not see significant
impacts on sewerage in Section 4.1; in this case private investment appears to have outstripped themeasurable improvements in public infrastructure. Home ownership rates in the treatment rise by
2%, although this difference is not significant. The coefficient on having obtained a mortgage froma private bank is very small in absolute magnitude but is almost significant. The last column ofTable 4 shows the impact on rents for the 16% of households that do not own their own homes,
and indicates a substantial $17 jump in monthly rents, a nearly 20% increase over the baseline
control-group average rent of $88.
Given that the rate of churn between the two waves of the survey fell from 39% in thecontrol to 30% in the treatment, could this lower rate of churn itself be an explanation for thegreater willingness to invest in houses, and the slight uptick in home ownership and mortgages? We

11can understand the extent to which differential churn may be mechanism for the treatment effect byrunning two sets of regressions; first, we can examine the extent to which the houses that saw new
tenants in Round 2 differed from the houses of those who stayed in the same residence throughout
the study. Second, we can partition the regressions and run treatment effects only within the groupthat stayed and within the group that moved. These sub-group comparisons are endogenous andcontain selection bias (Angrist and Pischke 2008), but if both subgroups look similar to the overall
treatment effect then the program impact cannot be arising as a result of endogenous selection fromone group to the other. Appendix Table A4 shows that while households that move during thestudy are likely to live in lower quality housing and less likely to own their homes, the treatment
effect of the program within the ‘stayer’ and the ‘mover’ groups is quite similar, indicating that the
program has an impact on housing quality independent of the effect through increasing duration ofresidence. Overall, the public spending flowing through Hábitat appears to have induced ameaningful increase in private investment in the housing stock.
3.3. Impact on Social Capital.
Column 11 of Table 4 provides an estimate of the impact of the program on the precommitted index of social capital. Recalling that there was some evidence of imbalance on thisvariable, we should proceed cautiously to interpret impacts. The polygon fixed effects estimate fromthe pre-analysis plan is presented here, and shows an effect that is insignificant and very small in
absolute terms (an improvement of roughly 2.5% relative to the baseline control mean). Additional
analysis (not shown) conducts single-difference and ANCOVA analysis of this outcome andsimilarly finds small and insignificant effects. This means that three qualitatively different ways ofhandling the baseline imbalance (differencing out, ignoring, or controlling for) all arrive at theconclusion that the pre-committed index of social capital did not respond to the treatment. In
Section 4 we dig into the social capital effects in more detail.
3.4. Impacts on Property Values.
We now proceed to an analysis of the impact of the program on property values, using
professional assessment of unbuilt lots to calculate an effect purged of improvements in housing
stock driven by private investment. Of the 342 baseline polygons used in this analysis, just over
40% had any empty lots for sale at baseline. The average baseline lot had 1.25 lots for sale, with amaximum of 23 lots per polygon. The intervention sample provides us with 437 lots located in 138

12polygons. Column 1 of Table 5 shows that these neighborhoods are not representative of theoverall study, being both larger and poorer than the average study neighborhood. However, thesample selection in the real estate analysis arising from polygons in which at least one lot was for saleis balanced across treatment and control, and the baseline means of polygon-level average prices per
square meter in this attrited sample are comparable between the treatment and the control. Hence,
our analysis of real estate prices takes place in an unrepresentative sub-group (the population of
empty lots for sale at baseline), but appears to be well balanced and internally valid.
Sharp improvements in local infrastructure, lower residential churn, and increases ininvestment in the housing stock all suggest the possibility that treatment neighborhoods may haveseen an improvement in residential amenity values. The significant increases in rents in treatmentneighborhoods provide initial evidence that these improvements are being capitalized in property
prices. Real estate prices should capitalize the net present value of a flow of amenities from
improved infrastructure, and thus provide a particularly interesting way of comparing the net costsof an intervention to the net benefits realized by residents. Because of the high rates of homeownership in Hábitat neighborhoods, increases in land values are likely to translate directly into
improvements in the household wealth of the residents.

To the extent that a public investment

yields total property price increases that are greater than the amount of the investment itself,
residents would wish to be taxed to make these investments. The presence of net positive returns
would suggest ‘money left on the table’, and points to a friction in the political economy ofinfrastructure delivery.
When we turn to the difference-in-differences impacts in Table 5, we see substantial
improvements in prices being induced by the treatment. Column (6) includes municipality fixed
effects and weights the analysis by the number of households represented by each lot at the polygon
level. Relative to a baseline control value of $86.11 per square meter and a real control group
appreciation of $3.02 between 2009 and 2012, the treatment effect of the program was an additional$5.76 per square meter, meaning that the treatment group had almost triple the real rate ofappreciation as the control. This corresponds to a standardized impact size of roughly .5 relative toa control group price change SD of 11. Figure 2 shows the CDF of the changes in real property
prices in treatment and control polygons, demonstrating that improvements in the treatment firstorder stochastically dominate the control. These impacts are large, and suggest an absolute causal
price appreciation of 6.5% relative to the counterfactual price (control group endline).

By

13

comparison, Gonzalez-Navarro and Quintana-Domeque (2016) find log impacts of street paving
ranging between 9% and 16% on the professionally appraised value of a house. This suggests that
our estimate may be in a reasonable range, given that their higher estimate provides the LATE ofactually having one’s street paved, while ours is an Intention to Treat estimate based on being in aneighborhood where investment was taking place.
Are these estimates robust? Given the explicitly non-representative nature of the valuation
sample one may reasonably ask whether these estimates are externally valid to the entire sample oflots contained in this study. As a way of probing this, we estimate a selection model on the entire
sample of polygons, using a battery of baseline covariates to predict the probability that eachpolygon appears in the real estate sample. With this score, we can then conduct inverse propensity
weighting to correct our estimates for observable determinants of selection into the real estate
sample (Wooldridge 2002). The results of this exercise are provided in columns (7)-(10), whichshow the results first using only the estimated attrition weights, and then using the product of theattrition weights and the previous population weights.

The results are not sensitive to these

corrections, meaning that price change impacts in polygons likely to have valuations are not different
from impacts in polygons unlikely to have produced a valuation. It is also worth noting that while
the rent variable is selected and suspect in different ways than the real estate analysis, it indicates anappreciation of 19% in values. These facts bolster our confidence that there were meaningful priceimpacts in the overall sample, despite the fact that both of our measures of value come fromselected samples.
Perhaps the most meaningful way to put the impact numbers in context is to consider thatthe treatment polygons contain 118,491 lots with an average of 218 square meters each, for a total of
25.9 million square meters of property. If the marginal effect of $5.76 per square foot estimatedabove is applied to all inhabited property in the treatment polygons, the resulting increase in total
value is $150 million, more than two times the $68 million invested by all three levels of government
in the program. We can use the (relatively conservative) impact estimates from Column 6 of Table 5
to make a variety of confidence statements: we are 96% confident that the benefit is positive, we are
64% confident that the benefit/cost ratio is greater than one, and can be 95% confident that thebenefit/cost ratio lies between -.35 and 4.65. The bottom row of Table 5 provides the p-values on
the F-test that the benefit-cost ratio is greater than 1 (meaning that the treatment effect is greaterthan $2.62, the amount spent per square meter by the program). These range from .36 in the14

conservative estimate in Column 6 to .08 (indicating confidence at 90% levels) in the specificationfrom column 9 that includes attrition weighting. The average residence would have enjoyed $1,157
in appreciation from Hábitat investment during 2009 to 2012, while having had $574 spent on it.
Thus, even using our more conservative estimates, every dollar of public money invested ininfrastructure improvement in a polygon yielded more than two dollars of improvement in the total
privately-held value of land there.
4. Secondary Analysis
We now unpack the pre-registered results with two forms of more detailed secondary
analysis. First, we examine the implications of our results related to migration. Given the highoverall rates of residential churn, who is it that is moving? In which types of neighborhood is thedecline in residential churn caused by the program most pronounced? Might impacts on investment
or social capital driven by differential migration rates?

Second, we unpack the social capitaldimension of the program. While the pre-registered index of social capital was not significantly
improved by the intervention, were there dimensions measured by our household survey instrumentthat were improved?
4.1. Understanding the decline in residential turnover.
Decades of theory on amenity pricing suggest that improvements in neighborhood amenities
will provide net welfare benefits to homeowners, but may have more ambiguous effects on renters.
We are therefore interested in understanding whether the overall decline in mobility is masking an
acceleration in the turnover of renters.

Further, if the departure of renters is occurring

disproportionately at the bottom end of the market this would be evidence of gentrification effects
pushing out low-income renters.
To examine these questions we split the baseline sample of structures according to whether
the residents in 2007 were renters (11% of baseline households are renters, 72% own their homes
outright, and the remainder are either still paying mortgages or other arrangements). Columns 1 and
5 of Table 6 show the treatment effect of the program on the subsequent rate of churn in these two
samples. While the rate of turnover in the control is much higher for renters (58% versus 37% for
non-renters), the impact of the treatment on decreasing churn is extremely similar, a decrease of justover nine percentage points in both cases. This is comforting initial evidence that Hábitat is not15

leading renters to be pushed out en masse; in fact the program stabilizes residential patterns forhomeowners and renters alike.
Concerns about gentrification can also be addressed by examining whether the treatment
leads to a disproportionate turnover of residents at the bottom end of the housing scale. To
examine this further, we conduct an interaction analysis to understand the types of polygons andhouseholds in which the rate of churn is most strongly decreased by the program. To capture
heterogeneity in baseline levels of wealth and social capital we use the block-level infrastructureindex and the polygon-level social capital index. We also separately construct an index of baseline
property values for homeowners and renters separately. For homeowners we use the average
professional INDAABIN assessment of value per square meter in the closest available polygon. For
renters we use the reported amount being paid to rent each property at baseline. We then run linear
probability regressions explaining a binary indicator of residential turnover between 2009 and 2012,
and use the treatment dummy, the baseline values of these covariates, and their interactions as
explanatory variables.

This interaction analysis reveals patterns that are quite divergent for

homeowners and renters. The uninteracted values of the covariates are positive for homeowners,
and the interaction terms are negative. This implies that in the absence of the program there is more
turnover of homeowners in richer neighborhoods, but that the program reverses this relationshipand is most effective at getting homeowners in richer neighborhoods to stay put. For renters all of
the patterns are reversed; in general there is more turnover of renters in poor neighborhoods, but
the program is particularly effective at getting renters of low-value properties to stay put. These
results are comforting in multiple dimensions; not only does the program improve residential
stability, it improves it even among renters, and it improves it most strongly among renters in thelowest-priced units. We find no evidence of disruptive gentrification within the three-year time spanof the study.

4.2. Digging Further into Impacts on Social Capital and Crime.
As described in the introduction, the potential to improve the social capital of disadvantaged
communities was an explicit objective in the design of the program, with the idea being that whilemunicipalities primarily build infrastructure, Hábitat’s coordinated investments across a wide rangeof activities would be successful at building community. We showed in Table 4 that the core social
capital indicator to which we pre-committed was not significantly moved by the program, but the16study featured a rich social capital survey which we now exploit to conduct a more fine-grained
analysis of social capital outcomes.

The pre-committed index was made up of components

measuring group participation, access to information, and levels of neighborhood trust. It did notinclude dimensions like crime and teen behavior, but these were nonetheless captured in the survey.
To aggregate the survey domains we follow Kling et al. (2007) by constructing four sub-indexes onsocial capital; the first two (participation and trust) comprised of dimensions included in the overallsocial capital index, and the second two (security and teen behavior) comprised of questions notincluded in the original index.7
Panel A of Table 7 shows the simple DID impacts of the program on these four indexes ofsocial capital. A first striking feature of these results is the deterioration in overall social capitalobservable across the two rounds of the survey. Any impacts must be considered in the context ofthe problems facing urban Mexico in the period between 2009 and 2012. The first two indexes,
which comprise most of the variables making up the pre-committed index, show no significant
changes. The index of participation actually decreases slightly, and while the trust index shows an
improvement of .13 standard deviations, not significant. When we move to the security and youth
indexes, however, the story changes. Against a backdrop of a .28 SD decrease in the average valueof the security index, the program achieves an impact of .247, significant at the 95% level, suggestingthat the treatment allowed these communities to hold steady while the control deteriorated. The
youth index does not show the same overall decline between 2009 and 2012, but the impacts here
are also relatively large (.164 SD) and significant. It therefore appears that, while the program did
not move the participation and trust outcomes on which the pre-committed index was based, it did
lead to a meaningful improvement in security and prospects for youth.8
Given the large impact on residential churn discussed above, it is important to investigate
whether these social capital impacts are arising because of changes in mobility, or because theoutcomes for a specific set of people were improved. To provide insight into this question, Panel B
7

The participation index is comprised of underlying questions on participation in 9 different types of community
organizations, and questions on the existence of community groups, knowledge of community-level problems, andthe degree of information exchange between neighbors. The trust index is built from two questions about the levelof confidence in neighbors and local institutions, and two questions about the degree of trust within the household
and trust of neighbors. The security index is based on having not been a victim of any crime in the past 12 months,
having not been subject to assault, and a variable counting the number of activities that have not been abandoned in
the past 12 months because of physical insecurity. The youth behavior index is based on four variables; an indicatorthat youth have somewhere other than the streets to convene, the number of ‘good’ activities teens engage in, theadditive inverse of the number of ‘bad’ activities teens engage in, and an indicator variable for the absence ofgang/crime risk for youth in the neighborhoods. All indexes constructed as described in the previous section.
8
The underlying variables t

17

of Table 7 segregates the sample into the structures for which the household changed between 2009
and 2012 (the movers) and those for which it did not (the stayers). Beginning again with the timetrends, we see that overall the changes in the social capital indexes are similar for both movers andstayers, with the exception of the youth index, which improved for movers and deteriorated forstayers. It is also clear from the summary statistics at the bottom of Panel B that the Youth andparticularly the Participation index are in general superior for stayers than for movers. Looking at
impacts, we see that the significant impact on the security and the youth dimensions are confined tothe stayers, and no impacts are found for those households that moved. This group is endogenously
selected, meaning that mean outcomes among stayers are subject to both an extensive and intensive
margin impact from the program. These results suggest that there are three channels open for the
impacts on security and youth: a selection effect improving the outcome by decreasing the fractionof movers, a causal impact within the group that did not move, and a second-order effect of fromincreasing the share of people on whom the stronger treatment effect is found. The results inPanels B should not be interpreted as straightforward sub-group causal effects, but they help toprovide some context as to the mechanisms through which the treatment effects move.
We subject these social capital results to two types of robustness checks; the first examining
the problem of multiple inference, and the second the imbalance in the social capital index found in
Table 2. As a way of looking into the multiple inference problem, Appendix Table A5 shows
results for some of the key underlying social capital variables that make up the indexes used (Trust in
Neighbors appears in the trust index, columns (2)-(4) appear in the security index, and (5)-(8) appear
in the youth index). Our marginal effect of -.152 on the number of assaults in the past year would
indicate that the 118,000 households in treatment polygons suffered a total of almost eighteen
thousand fewer physical assaults as a result of the program. While highly significant, these outcomesneed to be corrected for multiple inference because they have been selected from the much broader
potential set of social capital variables (Casey et al. 2012, Humphreys et al. 2013). At the bottom of
Table A5 we therefore illustrate several different ways of penalizing ourselves for this fishing using
Anderson’s False Discovery Rate sharpened q-values (Anderson 2008).

In the first row of thebottom panel we present the q-values (comparable to p-values, which are reported in the top panelof the table) when we correct for multiple hypothesis-testing across the 9 variables presented here.
In the second row we correct using the 9 presented here plus all of the variables used in theconstruction of the social capital index. In the third row we correct using the entirety of the 70
possible social capital variables. This result suggests that when we examine impacts on the sub-18

variables that make up the four domains of social capital presented in Table 7 and fully correct for
multiple inference, only those describing the improvement in teen participation in music and sports(an area in which Hábitat invested directly) remain significant.
Another robustness check is to ask whether the dramatic deterioration in the control groupand steady outcomes in the treatment group may arise from imbalance rather than from a treatment
effect that prevented things from getting worse. To examine this, we first calculate the Round 1Round 2 change observed for our key index metrics, and then fit a regression in the control groupexplaining these changes using a battery of baseline covariates. The coefficients from this regression
are then used to predict changes for treatment and control alike, and we can examine the balance of
these predicted changes. Appendix Table A6 shows that all of the core outcomes used in the paper
appear balanced in terms of predicted changes, with the exception of the two significant social
capital indexes: security and trust. For each of these outcomes we see that the baseline attributes oftreatment communities would anyways have predisposed us to expect improvements relative to thecontrol, even in the absence of a treatment effect. However, when we compare the magnitude ofthis predicted imbalance to the impact coefficients in Table 7 we see that the impact is substantiallylarger than the predicted imbalance; .247 versus .093 for security, and .164 versus .072 for youth.
Nonetheless, neither of our measured effects would be significant relative to the predictedimbalance, and hence both the multiple inference corrections and the imbalance adjustment
introduce some skepticism into our interpretation of the impacts on crime and teen behavior.
Where does this leave us in terms of policy interpretation? While these results are admittedly
equivocal, we do find impacts on a coherent set of public security outcomes that have a clear logicaltie to Hábitat investment in streetlights and sidewalks, as well as in community centers intended togive adolescents constructive outlets. Given the tremendous focus on the rise in violent crime in
Mexico over the past decade (Dube et al. 2013, Molzahn et al. 2013), its linkages to the economic
welfare of citizens (BenYishay and Pearlman 2014, Enamorado et al. 2014), and the lack of credible
policy options on the table to deal with it (Phillips 2015), even suggestive evidence of new
mechanisms to improve public security are welcome. We therefore present these results in the spiritof intriguing but speculative findings that merit further research. Taken at face value, this study
suggests that investing in neighborhood walkability and community infrastructure that provides
constructive outlets for youth can have substantial impacts on improvements in public security.

195. CONCLUSION
This paper presents the results of a large experiment in which the Mexican federal
government improved the quality of infrastructure in low-income urban neighborhoods.

We

examine the effects of $68 million in spending spread across 118,000 treatment households, and findevidence that infrastructure investment in these neighborhoods is sub-optimal. Treatment induces alarge improvement in the access to well-functioning public lighting, paved roads, and sidewalks,private investment in the housing stock increases, neighborhood churn in real estate decreases by aquarter, crime falls, and the total increase in the value of the property in intervention neighborhoods
is more than twice the cost of the program. This high rate of return suggests substantial money ‘left
on the table’ via underinvestment. On the other hand, a program that spent an average of $550 per
beneficiary household did not improve access to water or sewerage (despite having spent more than
10% of their budget on these items), and an index of social capital was not substantially improved.
Are the returns to the program using real estate prices of a credible magnitude? Our
estimates of two-to-one returns are larger than those available from the developed world. Cellini etal. 2010 find an increase of $1.50 in the willingness to pay of homebuyers for every $1 invested inpublic schools in California, and lay out a simple political theory that says while marginal returns on
public investment should be zero, they may be positive in equilibrium because individuals within thecommunity who don’t value those things (or already have them) will be unable to support additional
spending on the margin. Pereira and Flores de Frutos 1999 use a vector auto-regression model on
public spending in the US, finding that every dollar invested returns 65 cents in private investments.
Given that we may expect infrastructure spending in poor Mexican neighborhoods to be farther
below efficient levels than in the US, a figure of $2 may not be unreasonable.
Our results should bolster the argument that we not overlook large-scale spending on macro
programs in the face of proven micro-interventions such as conditional cash transfer (CCT)
programs (Fiszbein and Schady 2009). Given that the flagship CCT was also experimentally tested
in Mexico, the Oportunidades program provides an interesting point of comparison. Oportunidades
pays an average of $71 per month to beneficiary households. This means that the Hábitat
investment of $550 per household would represent fewer than eight months of cash transfers, andhas resulted in an increase in the asset wealth of the household of twice this sum, and the broader
set of amenity benefits flowing from lower crime and more stable neighborhoods. CCT programs
are designed to create a temporary flow of consumption benefits that leave behind an improved
stock of human capital; here we see infrastructure spending generating flow improvements in the20

quality of life and leaving behind a substantially improved stock of property value. Improving
infrastructure in underserved locations can deliver real social benefits as well as a substantial surge inhousehold wealth.

21

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24

TABLES.
Table 1. Attrition.
Attrition Between Rounds 1 and 2:
Attrition at block level (block
Attrition at House level
Attrition at municipal level
sampled at baseline and in(baseline sampled house(municipality selected to be
study municipalities, but
replaced with alternate atpart of study but removed bypanel dependent variable notfollowup)
Habitat)observed)
Baseline values of:Treatment(1)-0.014(0.048)Index of Basic Services
Satisfaction with Social Infrastructure
Observation Weight(3)-0.00412(0.005)

0.095

Average fraction attrited in control group:# of Obs:(2)-0.00996(0.049)-0.00000749(0.001)0.00166(0.008)0.0000488(0.000)10,670(4)0.000965(0.001)-0.0000254(0.000)-0.000922*(0.000)-1.22e-05***(0.000)(5)-0.0363(0.030)0.016
10,4369,922(6)-0.0297(0.028)0.000614(0.000)-0.0029(0.005)0.000260*(0.000)Attrition at Household level(baseline sampled household
replaced with alternate atfollowup)(7)-0.0702**(0.030)

0.176
9,745

9,702

(8)
-0.0612**(0.026)0.000647(0.001)0.0123**(0.005)0.000352*(0.000)

0.257
9,702

8,302

8,302

Regressions include fixed effects at the municipality level, and are weighted to be representative of all residents in the study neighborhoods. Standard Errors inparentheses are clustered at the polygon level to account for the design effect. Stars indicate significance at * 90%, ** 95%, and *** 99%.

25

Table 2. Balance in Panel Sample.
Panel A: Public Infrastructure (Short-form Survey Questions).
Index ofSewerage
Electric
Basic
Piped Water
Service
Lighting
Infrastructure

Street Lights

Medians

Sidewalks

Simple Treatment-Control Difference-0.195(0.161)-0.0264(0.020)-0.0472(0.043)-0.00444(0.003)-0.009(0.024)-0.0682(0.052)-0.0779(0.062)

Baseline Control Mean
Observations
R-squared

2.740
342
0.011

0.926
342
0.006

0.829
342
0.007

0.989
342
0.004

0.555
342
0

0.588
342
0.012

0.589
342
0.014

SocialCapital:Trust

SocialCapital:SecuritySocialCapital:Youth-0.294(0.222)-0.132(0.178)-0.0114(0.085)

Panel B: Household Outcomes (Long-form Survey Questions).
SocialLocal
Social Capital
Capital:Health Index Facilities
Index
Participation
Index
Simple Treatment-Control Difference

0.178(0.107)

0.0954(0.337)-0.0190**(0.009)-0.24(0.162)

Baseline Control Mean
-0.138-0.297
0.407
0.330
0.444
0.076
-0.129Observations
342
342
342
342
342
342
342
R-squared
0.012
0.001
0.028
0.014
0.018
0.005
0
Polygon-level analysis weighted by a population weight to be representative of all residents in the study neighborhoods. Standard errors
clustered at the municipality level in parentheses, stars indicate significance at * 90%, ** 95%, and *** 99%.

Table 3. Public Infrastructure

Index of Basic
Infrastructure

Piped Water

Sewerage
ServiceElectric
Lighting

Street Lights

Medians

Sidewalks

Paved Roads(1)0.135***(0.048)0.149***(0.049)(2)0.00115(0.016)0.0113*(0.006)(3)0.0203(0.017)0.0236**(0.012)(4)0.00239(0.005)0.00609*(0.003)(5)0.0607*(0.036)-0.00587(0.033)(6)
0.0617***(0.021)0.028(0.019)(7)0.0481**(0.022)0.0428***(0.015)(8)0.0314**(0.014)0.0669***(0.013)Baseline control mean:

2.740

0.926

0.829

0.989

0.555

0.588

0.589

0.664

Observations
R-squared
Number of polygons

684
0.161
342

684
0.013
342

684
0.053
342

684
0.029
342

682
0.021
342

684
0.092
342

684
0.11
342

684
0.209
342

Intention to Treat
Dummy for R2

Polygon-level analysis with polygon fixed effects and standard errors clustered at the municipal level. Analysis is weighted by population weights to be
representative of all residents in the study neighborhoods. Standard errors in parentheses, Stars indicate significance at * 90%, ** 95%, and *** 99%.

Table 4. Impacts on Residents.
Private Investment in Housing

Treatment * Round 2
Round 2

Baseline control mean:

FinancialSocial CapitalMonthly
Index of Social
Rent, US$
Capital(for renters
(10)
(11)
16.68*
0.0104(9.697)(0.496)-0.667-0.0380***(7.176)(0.006)

Brick Walls

Concrete
FloorsSeparate
Kitchen

Separate
Bathroom

Flush Toilet

SepticSystemPiped Water

Home
Owner

(1)
0.00337(0.008)0.00763(0.006)(2)
0.0229**(0.009)0.00743(0.005)(3)0.0112(0.016)0.0307**(0.012)(4)0.00416(0.014)0.0184**(0.008)(5)0.0707**(0.031)-0.0475*(0.025)(6)-0.0273**(0.013)0.00162(0.012)(7)0.0146(0.023)0.0628***(0.015)(8)0.0208(0.022)0.00557(0.009)

PrivateBank
Mortgage(9)0.00962(0.006)-0.0134**(0.005)

0.942

0.965

0.876

0.930

0.608

0.113

0.703

0.844

0.019

88.4

0.407

Observations
684
684
684
684
684
684
684
684
683
530
684
R-squared
0.012
0.065
0.089
0.033
0.037
0.014
0.105
0.019
0.034
0.047
0.145
Number of polygons
342
342
342
342
342
342
342
342
342
299
342
Polygon-level analysis with polygon fixed effects and standard errors clustered at the municipal level. Analysis is weighted by a population weight to be representative of all residents
in the study neighborhoods. Standard errors in parentheses, Stars indicate significance at * 90%, ** 95%, and *** 99%.

28

Table 5. Property Price Impacts.

Attrition:
Outcome Variable:

Polygon has
Observation on
PricesDependent Variable: Changes in real estate price per square meter, real 2012 US Dollars.
Impact:Baseline Balance:Baseline Price

Change in Price per square meter, 2009-2012(1)-0.0986(0.07)
0.0556(0.076)-0.0786**(0.036)6.07e-05***(0.000)0.474**(0.19)(2)
2.409(14.94)(3)
3.207(15.80)(4)
6.523**(2.59)(5)
8.196*(4.19)(6)
5.756*(3.41)(7)
6.991***(2.57)(8)
5.796*(2.94)(9)8.038**(3.09)(10)
6.477**(2.95)

86.11***(8.67)

44.27***(5.69)

3.017*(1.55)

2.108(2.89)-30.75***(1.23)

3.396**(1.31)-29.06***(5.12)

2.907(1.77)-30.40***(5.07)

Observations
R-squared

342
0.216

138
0.001

138
0.788

138
0.055

138
0.080

138
0.637

138
0.062

138
0.452

138
0.079

138
0.547

Weighting:HousesTreatment
Baseline Index of Services
Baseline Index of Infrastructure
Total # of Residences
Constant

Houses

None

HousesAttrition

Attrition * Houses

Municipality FE:
N
N
Y
N
N
Y
N
Y
N
Y
All prices are in real 2012 US Dollars per square meter. Analysis of unbuilt lots as polygon averages; dependent variable is price as assessed by professionals fromIDAABIN. Polygon-level analysis with standard errors clustered at the municipal level in parentheses; stars indicate significance at * 90%, ** 95%, and *** 99%.

29

Table 6. Indexes of Survey Domains.

Treatment * Round 2
Round 2

Baseline control mean:

Satisfaction
with
Infrastructure

Satisfaction
with
Community

Transport Times

HealthLocal Amenities(1)
0.874*(0.497)-0.351(0.433)(2)
0.377(0.337)-0.764**(0.290)(3)
0.291(0.739)0.0546(0.448)(4)0.0447(0.185)0.0774(0.163)(5)
0.191(0.328)0.34(0.278)

8.984

7.628

0.432

-0.138-0.297

684
684
684
684
684
Observations
0.025
0.109
0.002
0.008
0.052
R-squared
342
342
342
342
342
Number of polygons
Polygon-level analysis with polygon fixed effects and standard errors clustered at the municipal level. Analysis is weighted
by a population weight to be representative of all residents in the study neighborhoods. Standard Errors in parentheses,
Stars indicate significance at * 90%, ** 95%, and *** 99%.

30

Table 7. Detailed Social Capital Impacts.
Panel A. Social Capital Components.
Social Capital Index Domain:
Participation

Trust

SecurityYouth-0.0168(0.103)-0.142(0.088)0.129(0.117)-0.379***(0.102)0.247**(0.117)-0.282***(0.081)0.164*(0.085)-0.0252(0.069)Round 1 Mean in Control:

0.000

0.000

0.000

0.000

# of Obs:

11,143

11,143

11,143

11,143Treatment * Round 2
Round 2

Panel B. Stayers versus Movers.
Stayers0.253**(0.110)-0.132*(0.075)Movers0.019(0.144)0.159*(0.090)Round 1 Mean in Control:
0.10
-0.16
-0.01
0.01
0.00
-0.01
0.04
# of Obs:
7,559
3,584
7,559
3,584
7,559
3,584
7,588
Household-level analysis with polygon fixed effects and standard errors clustered at the municipal level. Analysis is weighted by apopulation weight to be representative of all residents in the study neighborhoods. Standard errors in parentheses, stars indicate
significance at * 90%, ** 95%, and *** 99%.

-0.05
3,597

Treatment * Round 2
Round 2

Stayers-0.0621(0.123)-0.144(0.121)

Movers
0.0805(0.168)-0.140*(0.081)

Stayers
0.0852(0.156)-0.271**(0.134)Movers0.174(0.185)-0.561***(0.153)

Stayers0.333***(0.100)-0.303***(0.082)Movers0.074(0.254)-0.247(0.173)

FIGURES.
Figure I. Map of Hábitat Polygons in the Municipality of Tijuana (polygons are dark-shaded).

Figure 2. Cumulative Distribution Functions of Property Price Changes.

CDFs of Property Price Changes, by Treatment

0

.2

.4

.6

.8

1

Real 2012 Dollar increases per square meter

-20

0
20
Price change 2009-2012
Treatment Polygons

40

Control Polygons

Polygon-level averages

33

ONLINE APPENDIX.
Appendix Tables.
Table A1. Hábitat Expenditures by Activity.
Total Investments in Treatment Polygons, 2009-2011 (US Dollars)
2009-2011
Name of Program (S ubprogram)

Total
Investment

FederalS tateMunicipal

Households
Benefitted

S ocial and Community Development

13,922,853

7,026,328

373,053

6,523,472

256,443

Improvement of Urban Environment:

53,729,286

26,359,409

4,978,304

20,857,825

169,60732,850,121

15,905,294

3,586,772

12,246,184

43,054

PavingSewers4,877,761

2,465,322

301,398

2,047,825

7,672

Drinking water

2,644,150

1,320,621

93,867

1,160,632

5,071

Community Development Centers

2,842,852

1,397,310

198,192

1,160,519

17,536

Sidewalks and medians

2,459,935

1,300,516

260,256

830,340

4,447

Public lighting

1,752,960

881,141

30,248824,506

5,327

Trash collection
Total spending

1,801,580

915,70263,868

767,860

72,37067,743,983

33,431,659

5,364,410

27,414,167

428,590

Source: SEDESOL

34

Table A2 Hábitat Project Polygons Relative to Study Municipalities.
# Treated # Control
Polygons Polygons

State

Municipality

BAJA CALIFORNIABAJA CALIFORNIACAMPECHECHIAPAS
CHIHUAHUACOAHUILA
DISTRITO FEDERAL
DISTRITO FEDERAL
DISTRITO FEDERAL
DISTRITO FEDERALGUANAJUATOGUANAJUATOGUANAJUATOGUANAJUATOGUERREROGUERREROJALISCOJALISCOJALISCOJALISCOJALISCOJALISCOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMEXICOMICHOACANMICHOACANMICHOACANMICHOACAN
MORELOSMORELOSMORELOSNUEVO LEONNUEVO LEONNUEVO LEONPUEBLAPUEBLAPUEBLAPUEBLAPUEBLAPUEBLAQUINTANA ROO
SINALOASINALOASONORATAMAULIPASTAMAULIPASTAMAULIPASVERACRUZ
VERACRUZ
VERACRUZYUCATAN
Totals:MEXICALITIJUANACARMENTUXTLA GUTIERREZJUAREZTORREONGUSTAVO A. MADEROIZTAPALAPA
TLAHUACTLALPANIRAPUATOLEONSILAO
URIANGATOACAPULCO DE JUAREZCOYUCA DE BENITEZ
EL SALTO
PUERTO VALLARTATLAJOMULCO DE ZU-IG
TLAQUEPAQUETONALAZAPOPANATIZAPAN DE ZARAGO
CHALCOCHIMALHUACANIXTAPALUCANAUCALPAN DE JUARENEZAHUALCOYOTLNICOLAS ROMEROTECAMACTLALNEPANTLA DE BA
TOLUCA
TULTITLANHIDALGOLAZARO CARDENAS
URUAPANZAMORAEMILIANO ZAPATAJIUTEPEC
XOCHITEPEC
GRAL. ESCOBEDOJUAREZMONTERREY
ATLIXCOCUAUTLANCINGOHUEJOTZINGOPUEBLASAN ANDRES CHOLULASAN PEDRO CHOLULA
BENITO JUAREZ
CULIACAN
MAZATLANHERMOSILLO
MATAMOROSREYNOSARIO BRAVOCOATEPECCORDOBATUXPAM
MERIDA

1
13
1
1
5
3
5
3
1
3
2
4
6
1
3
4
3
2
1
1
1
2
1
2
5
6
4
2
5
5
2
5
1
1
4
4
4
1
1
1
1
1
1
2
1
1
5
2
4
1
2
5
1
6
5
1
1
1
3
2
166

4
2
3
2
6
3
1
2
3
2
3
1
1
2
8
1
2
2
3
1
5
1
2
3
1
1
4
1
8
1
1
11
3
3
1
1
1
1
1
2
1
1
2
3
2
1
15
1
2
12
4
2
6
1
6
1
3
4
2
3
176

Area ofStudy
Polygons('000 sqmeters)15,800,000
1,294
1,252,141
6,349
8,544,727
884
335,852373
3,577,673
3,168
1,269,844
1,031
87,378
802
112,537
609
85,374
1,677
312,853
3,934
843,8622,092
1,210,431
1,190
533,745
1,843
115,477
801
1,731,309
11,166
1,813,171
3,916
81,113
1,580
675,114
1,433
708,215
1,706
117,192
527
164,881
1,945
1,146,319
1,363
92,2834,202
224,017
2,676
54,1362,278
322,192
9,986
156,997
4,538
62,900444
231,117
4,216
155,849
2,917
79,861
1,249
425,728
6,977
70,383
7,687
1,136,247
1,384
1,147,958
1,215
1,006,986
1,639
332,630
1,800
67,994
1,013
55,714
1,231
92,841
4,086
147,540
1,015
244,928419
321,680
349
291,907
1,573
37,982542
172,057674
544,715
14,23258,380677
76,5281,943
2,089,925
2,349
6,238,984
1,914
2,505,102
973
16,900,000
2,465
4,588,934
1,650
3,117,632
2,624
1,568,817
619
201,130
1,590
159,1291,111
955,789
1,065
875,531881
87,331,730
147,884
Study % area: 0.17%

Municipality
Area ('000 sq
meters)Municipality
Study
Population, Population,
2010
2008
936,8266,553
1,600,000
33,928221,094
7,827
553,3745,383
1,300,000
21,592639,629
6,865
1,200,000
15,7061,800,000
16,375360,265
10,104
650,567
9,657
529,440
9,655
1,400,000
11,328173,024
15,52459,305
4,186
789,971
13,182
73,4603,823
138,226
6,831
255,681
6,991
416,626
8,539
608,1142,482
478,689
9,791
1,200,000
9,503
489,937
14,236
310,130
9,324
614,453
18,909467,361
23,081
833,779
21,9881,100,000
13,321
366,602
24,583364,579
11,247664,225
7,514
819,561
29,699524,074
19,728
117,620
3,768
178,8175,410
315,350
8,588
186,102
9,685
83,4852,299
196,953
6,687
63,382
6,498
357,937
3,267
256,970
3,418
1,100,000
4,315
127,062
6,929
79,153
3,593
63,4574,832
1,500,000
41,121
100,439
3,216
120,459
4,618
661,176
25,787
858,638
7,725
438,434
8,211
784,342
9,872
489,193
7,216
608,891
12,457
118,259
2,720
86,696
6,623
196,541
8,542
143,362
4,685
830,732
6,332
32,002,442
647,867
Study % pop: 2.02%

Municipal
Infrastructure
Expenditure,
2008('000 USD)
10,672
29,56021,316
22,772
20,22636,06430,04859,238
8,651
22,791
17,77093,823
16,8195,486
64,135
6,014
4,041
8,462
12,570
10,544
4,538
64,26737,757
5,765
19,124
9,887
16,74819,748
8,766
4,618
23,305
20,912
13,7275,010
12,929
3,848
6,024
8,069
8,093
3,171
15,3686,219
92,465

Total Habitat
Expenditure('000 USD)

392
2,175
258
837
1,930
977
358
22
64
8
355
1,121
909
248
2,635
1,964
943
799
391
172
184
1,302
226
343
3,122
3,363
1,420
1,357
1,178
3,345
667
871
542
227
328
1,386
1,464
813
340
466
603
291
115
621
2,854
208
2,273
783
45,540
5,463
2,851
832
5,055
1,248
5,957
665
32,205
194
16,133715
16,428
3,902
21,7682,338
34,241
2,289
9,472
74
6,133
407
8,384
568
8,380
639
47,800449
1,176,831
61,905
Study % spending: 5.26%

35

Table A3. Consort Diagram of Study Attrition

Originally assessed as eligible andsurveyed for baseline:
65 municipalities
370 polygons
10,670 households

Randomized

Allocated to Control
194 polygons
6,126 households

Allocated to Intervention:
176 polygons
5,254 households
Lost to unwillingness of municipalities to sign
co-funding agreements
5 municipalities

Remain in Control
176 polygons
5,135 households

Remain in Intervention:
166 polygons
4,567 households
Surveyed at Endline

176 polygons
5145 households
of whom:
3,445 same hh & house
928 same house, different hh
772 different house & hh

166 polygons
4573 households
of whom:
3,313 same hh & house
629 same house, different hh
631 different house & hh

Table A4. Infrastructure and Investment Impacts by Mover/Stayer.

PANEL A: Public Infrastructure:
Index of Basic Infrastructure

Baseline household had
moved in R2

all-0.0308(0.046)

Treatment * R2moversall
stayers-0.00923(0.006)0.121** 0.134(0.058) (0.088)-0.900***-3.192***(0.029) (0.044)
0.182*** 0.0999(0.048) (0.081)

Treatment dummy
R2 dummy

Observations
R-squared

stayers

Piped Water

9,714
0.4

13,501
0.393

Sewerage Service
moversall
stayers0.00238(0.010)-0.00096 -0.0023(0.018) (0.019)-0.0743***-0.164***(0.009) (0.009)
0.0222** -0.0057(0.011) (0.006)

5,915
0.45

9,715
0.412

13,5020.326

moversall
stayers-0.00693(0.004)

5,915
0.429

moversElectric Lighting
allstayers-0.00072(0.002)0.0146 0.0146(0.019) (0.018)-0.569***-0.575***(0.009) (0.009)
0.0476*** -0.0128(0.010) (0.023)
9,715
0.538

13,502
0.519

5,915
0.522

moversStreet Lights
allstayers
0.0472***(0.015)-0.0003 0.00898(0.004) (0.008)-0.0419***-0.00449(0.002) (0.004)
0.00587** 0.00623(0.003) (0.006)
9,715
0.129

13,502
0.061

5,915
0.107

Medians
moversall
stayers-0.00861(0.013)0.052
0.0713(0.034) (0.066)
0.200*** -0.392***(0.017) (0.031)-0.0028 -0.025(0.027) (0.054)
8,604
0.184

12,7780.146

Sidewalksmoversall-0.0153(0.014)0.0620** 0.0513(0.024) (0.038)-0.606***-0.906***(0.012) (0.019)
0.0326* 0.0249(0.018) (0.031)

5,528
0.159

9,715
0.38

movers

0.0409 0.122**(0.033) (0.053)-0.00213 -0.0749*(0.029) (0.040)-0.0242 -0.0834*(0.025) (0.044)

13,502
0.355

stayers

Paved Roads
moversall
stayers0.00405(0.014)0.0440* 0.0475(0.025) (0.036)-0.627***-0.981***(0.012) (0.018)
0.0519*** 0.0298(0.013) (0.030)

5,915
0.398

9,715
0.393

all
stayers-0.00661(0.010)moversall
0.0153(0.014)-0.0311*(0.017)-0.0025(0.023)-0.00763(0.011)-0.0153(0.027)0.0238(0.024)0.0199(0.019)13,502
0.322

5,915
0.3

13,502
0.382

5,915
0.443

movers

0.0390** 0.0138(0.018) (0.020)-0.957***-0.953***(0.009) (0.010)
0.0650***0.0687***(0.016) (0.016)
9,714
0.37

13,501
0.382

5,915
0.414

PANEL B: Investment variables:
Brick Walls

Baseline household had
moved in R2

allstayers-0.00364(0.008)Treatment * R2

0.0078 -0.00806(0.010) (0.019)
0.0001 -0.0163(0.010) (0.014)
0.00979* 0.00365(0.006) (0.011)

Treatment dummy
R2 dummy

Observations
R-squared

Concrete Floors

9,715
0.222

13,500
0.196

5,914
0.231

moversSeparate Kitchenallstayers-0.0313***(0.010)0.0144** 0.0408*(0.007) (0.022)-0.0045 -0.0421*(0.007) (0.022)
0.00976** 0.00401(0.005) (0.007)
9,715
0.04

13,500
0.035

5,915
0.04

moversSeparate Bathroom
allstayers-0.0116*(0.007)0.0191 -0.00617(0.016) (0.025)-0.0138 -0.0141(0.016) (0.019)
0.0288** 0.0339**(0.012) (0.016)
9,715
0.054

13,502
0.047

5,915
0.073

moversFlush Toilet
all0.0187(0.023)-0.0001 0.00486(0.015) (0.021)
0.00172 -0.0239(0.016) (0.020)
0.0306*** -0.00095(0.009) (0.008)
9,715
0.117

13,502
0.102

5,915
0.114

9,715
0.118

stayers

13,502
0.1

Septic System

5,915
0.091

9,715
0.32

Piped Water
stayers

Home Owner
moversall
stayers-0.184***(0.018)0.0073 0.0407(0.028) (0.043)-0.0253 -0.0478(0.026) (0.040)
0.0582*** 0.0672**(0.018) (0.029)
9,715
0.167

13,502
0.146

5,915
0.148

movers

0.0156 0.0198(0.015) (0.036)-0.0117 -0.0162(0.012) (0.028)
0.0240*** -0.0244(0.009) (0.022)
9,715
0.09

13,502
0.034

5,915
0.086

37

Table A5. Analysis of Underlying Social Capital Variables.

Trust in
Neighbors(0-8 scale)

Treatment * R2

Number of Times
Any
any Household
HouseholdMemberMemberAssaulted in
Victim of
Crime in past Street in past 12mos
12 mos?

Number of
Activities
Abandoned
Due toInsecurity(0-8)

Gang Activity Alcohol/Drugs
is a Problem are a Problem
for Youth?
for Youth?

Youth Get
Youth Get
Together toTogether toFight Between
PlayGroups?
Sports/Music?

0.483**(0.015)-0.690***(0.000)-0.0484(0.278)0.0483(0.119)-0.152**(0.028)0.125**(0.027)-0.249(0.474)
0.536*(0.091)-0.0925**(0.018)0.00533(0.887)-0.0516(0.177)
0.0363(0.148)0.212***(0.000)-0.183***(0.002)-0.131*(0.054)0.0915**(0.038)R1 Mean in Control:

4.401

0.106

0.096

2.682

0.719

0.788

0.482

0.495

# of Obs:

684

684

684

684

684

684

684

684

R2

Sharpened q-values controlling for the false discovery rate across:
Selected variables only:
0.051*
0.189
0.058*
0.283
0.051*
0.134
0.002***
0.067
Selected + SC index vars:
0.084*
0.581
0.089*
0.836
0.084*
0.436
0.003***
0.132
All SC variables
0.249
1
0.255
1
0.249
1
0.006***
0.354
Polygon-level analysis with polygon fixed effects and standard errors clustered at the municipal level. Analysis is weighted by the product of a population weight(to be representative of all residents in the study neighborhoods) and a saturation weight. P-values in parentheses, stars indicate significance at * 90%, ** 95%,

38

Table A6. Testing the Balance of Predicted Changes in Key Indicators between Treatment and Control.
Balance Tests for Predicted Changes in:Treatment
Constant

Basic
Infrastructure
Index

Satisfaction
with
Infrastructure

Satisfaction
with
Community

Transport Time
Index

Health Index

Local Amenities
Index

SC:
Participation

SC:
Trust

SC:
Security

SC:
Youth

(1)
-0.0643(0.040)0.209***(0.043)(2)0.00545(0.040)-0.0765*(0.043)(3)-0.0205(0.038)-0.245***(0.045)(4)0.00962(0.037)0.00752(0.026)(5)
0.0258(0.031)0.0319(0.028)(3)
0.013(0.033)0.129***(0.032)(4)0.0493(0.063)-0.142**(0.059)(5)0.0512(0.061)-0.380***(0.059)(4)
0.0931*(0.053)-0.278***(0.049)(5)0.0717*(0.042)-0.02(0.033)Observations
341
341
341
341
341
341
341
341
341
341
R-squared
0.011
0
0.002
0
0.005
0.001
0.005
0.006
0.024
0.016
Regression first regresses the R1-R2 change in each index on a battery of baseline covariates in the control group. Fitted coefficients are then used to predict changes in the treatment and control
group alike, and the table shows balance tests for the difference in these predicted changes between treatment and control. Polygon-level analysis with standard errors clustered at the municipal
level. Analysis is weighted by a population weight to be representative of all residents in the study neighborhoods. Standard Errors in parentheses, Stars indicate significance at * 90%, ** 95%, and*** 99%.

39