Local Infrastructure and the Development of thePrivate Sector: Evidence from a Randomized Trial∗rDaniel Rogger (World Bank) ○r
Leonardo Iacovone (World Bank) ○rLuis F. Sánchez-Bayardo (World Bank) ○rCraig McIntosh (UCSD) ○December 13, 2023

Abstract
We study how local public infrastructure investment affects neighborhood economies.
By tracking the impacts of US$68 million of randomized investments in Mexican
municipalities, we document how government investment leads to sustained increases in the size and profitability of treated private-sector companies. Initially,
wages rise to compensate for higher costs of living, inefficient firms die, and more
efficient firms grow faster. Over the subsequent decade treated firms increase theircapital stocks and revenues, suggesting durable improvements in the structure of
the local economy. Our results provide novel evidence of linkages between government investment, business growth, and the dynamics of local economies.

Keywords: Infrastructure, Firm Productivity, Randomized Trials
JEL Codes: R11, O18, D22, C93∗
Ordering of authors is certified random (AEA randomization code: r4-FYDEDt8MZ). We gratefully acknowledge financial support from the World Bank’s Development Impact Evaluation i2i andKnowledge for Change Program trust funds, Bureaucracy Lab and Governance Global Practice. We
are grateful to John Loeser, Niclas Moneke, Caio Piza, Marta Santamaria, and Hannah Uckat; and,seminar participants at the 11th European Meeting of the Urban Economics Association at the London School of Economics and Political Science, the University of Oxford, and the World Bank for
helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the views of the World Bank and its affiliatedorganizations, or those of the Executive Directors of the World Bank or the governments they represent.

1

Introduction

Infrastructure investment is a central driver in the development of an urban economy[Bryan, Glaeser and Tsivanidis, 2020; Jones and Romer, 2010; Lucas, 1988]. Investmentin the built environment alters the incentives for firms to start up, select locations,hire, invest, and shut down [Glaeser and Gottlieb, 2009; Glaeser, Luca and Moszkowski,
2020]. While canonical models of urban geography have long recognized productivity as
an important channel to spatial differences in property prices [Diamond, 2016; Roback,
1982; Rosen, 1979], the empirical literature examining this channel directly has been
limited by the challenges of matching exogenous shocks in infrastructure to firm-level
data. In this paper we study this relationship using experimental variation in infrastructure spending and a granular multi-wave census of firms in urban Mexico to track
the impacts of that spending.
The recent literature has highlighted two important features of any investigationof the impacts of local infrastructure development. Firstly, spatial general equilibrium
models imply that changes in infrastructure can rearrange the patterns of production,commuting, and land values within a city [Almagro and Domınguez-Iino, 2019; Balboni
et al., 2021; Franklin et al., 2023; Tsivanidis, 2019] or across cities [Allen and Arkolakis,
2014; Donaldson and Hornbeck, 2016]. A key question is the extent to which local investments generate net positive benefits to the city, versus delivering zero-sum gains that
simply relocate business activity across space. Identifying the geographic distribution of
impacts requires granular data as well as localized exogeneity in infrastructure investments.1 To understand these shifts it is particularly important to be able to distinguishintensive-margin impacts on pre-existing firms from extensive margin changes in composition. Secondly, given the potential for agglomeration benefits to firms from shifts
in infrastructure [Greenstone, Hornbeck and Moretti, 2010], it is critical to have wellidentified evidence on the extent to which infrastructure impacts become self-reinforcing
over time.
Our paper contributes to these debates by bringing together three unique features.
First, we follow the large-scale randomized implementation of Mexico’s Programa Hábitat,in which US$68m of spending on infrastructure was experimentally allocated across 370
neighborhoods in 68 Mexican municipalities over the course of three years. The empirical
study of infrastructure has employed randomized experiments at the household- [Cattaneo et al., 2009; Galiani et al., 2017; Harari, Wong et al., 2018] or block-level [Gonzalez1

Impacts across space are likely to be obscured in aggregated output data [Haughwout, 2002].

2

Navarro and Quintana-Domeque, 2016], or undertaken more macro non-experimental
studies that estimate the aggregate multiplier on national infrastructure investment[Ramey et al., 2020]. Hábitat neighborhoods are small economic spheres of their own(on average 40 blocks), spread across Mexico, offering distributed and independently
functioning economic environments in which to test the impacts of the program. Our
study uses the experimental rollout to provide a detailed causal picture of the impactsof localized infrastructure investments on the local economy.2 The experimental design
randomized the ‘saturation’ levels of investment across neighborhoods. We use this feature to make precise causal statements about the impacts of the scheme on firms inneighboring areas as investment levels vary.
Second, we study these neighborhood changes using a census of Mexican firms conducted in 2008 (pre-treatment), 2013 (shortly post-treatment), and 2018 (six years after
the end of the program). The fact that this data has linking identifiers across waves
means that we are able to document impacts on the birth and death of firms over thecourse of a decade, as well as studying the dynamics of ongoing firms in both the shortand medium term. Finally, both the intervention and the firm dataset are finely geolocated, allowing us to analyze the spatial pattern of firm responses in an unusually
granular way. Put together, these features permit a unique window into the decade-longevolution of the economic geography of this important infrastructure program.
Our results prove surprising in a number of dimensions. Despite the fact that the
program targeted its infrastructure investments towards residential amenities, it had asubstantial effect on commercial activity. In the short-term, changes from the program
are consistent with a wage shock coming from increased property values. One year
after the end of investments, wage expenditures have jumped by 18%, with impacts ofa similar magnitude in both manufacturing and trade and services. In parallel however,
we also see an increase in the total number of workers, and a modest increase in revenues
and capital investment. On the extensive margin, the treatment leads to an immediateshake-out with roughly 3% of the stock of firms being pushed out by the intervention on
top of the natural level of turnover, with these firms being replaced by newer, smaller,
faster-growing firms. Firms with high initial value added per worker are less like to‘die’ in treatment neighborhoods, and increase their capital investments the most under
the program. Hence a set of investments that focused on residential amenities led to
substantial private-sector benefits in the short term.
2

Previous work has shown that the program realized substantial improvements in residential amenities
and property price increases of 10%, as well as meaningful improvements in safety and decreases inresidential churn [McIntosh et al., 2018].

3

If anything, these impacts appear to accelerate over time. Looking six years after theend of investment, wages remain elevated, the number of workers has converged to thecontrol group average, but the increases in capital stocks and revenue have accelerated.
During this six year period, the impacts diverge by sector. By 2018 service sector firms
in treatment neighborhoods have revenues 9% higher and capital stocks 22% higher
than the control neighborhoods. Firms have re-optimized the capital-intensity of theirproduction, reflecting a more ‘mature’ operating model. In manufacturing, the story isdifferent. Revenues are only 3% higher in treatment neighborhoods, with no significant
change in capital stocks. These longer-term effects are found almost entirely on theintensive margin and suggest that a self-reinforcing dynamic of growth has taken holdin these communities.
In terms of spatial agglomeration and the geography of impacts beyond the bounds
of intervention neighborhoods, we recover a tightly estimated zero on any spillovers. We
have an unusually clear window on spatial spillovers based on our ability to narrowly geolocate infrastructure investment and measure firm activity over continuous space. Our
experimental structure lets us define buffers around treatment and control neighborhoods
in identical ways. Examining distance buffers as small as 100 meters and looking all theway up to a kilometer distant, we find small increases in firm churn in immediately
adjacent blocks but no shifts in firm numbers or business outcomes at any distance,over either the short or the medium term. Proximity to highways has some effect ofamplifying impacts, but the firm responses to this localized investment are quite invariant
to measures of market access, either within study neighborhoods or surrounding them.
Similarly, we uncover no larger-scale spillovers by exploiting the two-level design of
the wtudy in which the fraction of neighborhoods treated within a municipality was
also randomized. In contrast to the complex spatial tradeoffs demonstrated by theliterature on transport infrastructure, then, the small-scale gentrification driven by thistype of localized government infrastructure investment does not appear to have driven
agglomeration or dislocation in surrounding neighborhoods.
These results contribute to our understanding of the mechanisms through whichurban investment generates productivity and contributes to economic growth.3 On the
one hand, improvements in urban amenity values operate like a cost shock to the firm,
pushing out a set of unproductive firms. At the same time, increases in property values
improve consumer spending power and lead to a dynamic improvement in the growth3

Given the important role played by local actors and residents in deciding the specific investments to
be made in Hábitat, our study also speaks to the large literature on Community-Driven Development(CDD) programs [Labonne and Chase, 2009; Mansuri and Rao, 2004; Paxson and Schady, 2002].

4

prospects of more productive firms. In many ways, these impacts mimic the effects
of exposure to international trade which acts both to cull less productive firms and avehicle for expansion, generating impacts on both the extensive and intensive margins
of firm productivity [Mayer, Melitz and Ottaviano, 2021; Melitz and Redding, 2014].
What is different here is that these benefits are seen almost entirely among service
firms that provide the non-tradeable products that can benefit from localized shifts indemand. The implication is that governments have a tool to drive sustainable increases
in private-sector efficiency through investments in neighborhood infrastructure.4
The paper also contributes to the burgeoning empirical literature on the mechanismsof growth and gentrification in urban economics. Work on gentrification has emphasized
increases in the skill of workers [Su, 2022], decreases in retail prices [Borraz et al., 2021],
employment shifting from manufacturing to services [Hartley, Lester et al., 2013], andan increase in firm churn induced by exit of low-price firms the and growth of larger,
higher-priced firms [Glaeser, Luca and Moszkowski, 2020]. A voluminous literature has
tackled the impact of transport infrastructure on cities [Duranton and Turner, 2012], on
market integration [Brooks and Donovan, 2020; Casaburi, Glennerster and Suri, 2013;
Donaldson, 2018], and how such changes may be capitalized into land prices [Donaldson
and Hornbeck, 2016; Tsivanidis, 2019]. By exploiting randomized variation in a programthat generates infrastructural gentrification, we are able to nail down linkages between
the constructed environment and the endogenous location and growth decisions of private firms. As such, this paper provides one of the most granular and well-identified
assessments available of the ability of public infrastructure investment to stimulate private sector development.5
The rest of the paper is structured as follows: Section 2 provides the context for the
study and describes the experimental design; Section 3 describes the data used, Section 4
provides the simple experimental results, and Section 5 explores the mechanisms throughwhich the impacts are realized. Section 6 examines spillovers and conducts aggregate
cost-benefit analysis at the neighborhood level, and Section 7 concludes.
4

To assess the overall impact on government finances of such investments, we conduct an accounting
exercise using changes in value added taxes, social security contributions, and revenue taxes to calculatethat the tax take via the private sector rises by almost US$5 million per year in study neighborhoods.
This implies that the program would pay for itself in 14 years simply through firm taxation. Thissuggests that cost-benefit evaluations of infrastructure programs using property prices alone to value
benefits may miss an important vehicle for cost recovery via the private sector.
5
By following a large-scale public investment program we also help to build the relatively sparse experimental literature that studies programs implemented at scale by national governments [Muralidharanand Niehaus, 2017].

5

2

Context and Experimental Design

2.1

The Private Sector in Urban Mexico

The Mexican economy is dominated by small businesses, with the median firm being aself-employed entrepreneur who is the only employee. Average revenue is only $15,600,
and assets average just under $2,500. Micro-enterprises (defined by Mexico’s Statistical
Agency as having less than 10 employees) represent 95 percent of the total existing
businesses. Only 2% of Mexican firms are fully formalized, 11% report having accessto finance, and 2% report any online sales. This small average size and high level of
informality is widely believed to hamper the overall productivity of the Mexican economy[Levy, 2010].
The vast majority of businesses belong to commerce and non-tradable services (over
90% of total). Within this group, grocery stores make up around 25% of the total, while
stationery shops and beauty salons make up approximately 4% each. Manufacturingfirms tend to be concentrated in activities that serve the local market, focusing on
production of food and beverages, or activities related to construction and housing.
Around 3% of firms produce corn tortillas and 1% are bakeries. Ironworks, furnishingand milling activities businesses comprise close to 1% of the total each.6 Consequently,
efforts to raise value-added and increase formality are of central concern.
A second pertinent feature of the Mexican business environment is high turnover offirms. To illustrate this, Figure 1 shows the birth and death rates of all firms observed
across the three waves of the firm censuses we use (2008, 2013 and 2018). Green lines
indicate firms that we see created, the red lines firms that we see closing down, and theblue line indicates firms that exist in all three rounds. Rates of churn are very high:
29% of firms in both subsequent rounds are newly born in that five-year period, and18-20% of firms previously observed close down in each subsequent round. Only 17%of all firms observed survive through all three rounds of the data. In general the stockof firms grows over time as the rate of new business formation is 10% higher than thefirm death rate in both subsequent rounds. Given this environment of rapid creationand destruction of firms, it will be critical to isolate and examine the extensive margin
impacts of infrastructure as well as considering how investments may effect continuing firms. Fortunately, the unique firm-level identifier in the census data makes thisstraightforward in our case.
6

Statistics based on the 2019 Economic Census.

6

2.2

The Hábitat Program

The Hábitat program was created under Mexico’s Ministry of Social Development (SEDESOL)in 2003 to provide federal support for improvements in the infrastructure of marginalized neighborhoods in cities across the country (see Figure A1 for where the cities werelocated). The core purpose of the program is to make a suite of coordinated investments
in residential amenities for previously under-served neighborhoods, thereby increasing
livability and social cohesion [Campuzano et al., 2007]. The targeting and funding rules
for the program are formulaic and centralized, and the program has tightly defined eligibility rules which require matching investments from state and municipal governments.7
In terms of project selection, Hábitat pursues decentralized community-driven mechanisms to allocate funding across potential investments. The actual investments madein a neighborhood are determined by the interplay of a set of technical experts from theprogram who make recommendations based on observed infrastructure deficits, and alocally driven project selection component.8
Typical Hábitat investment includes a mixture of physical infrastructure (street
paving, sidewalk and median construction, electrification and sewerage connections,etc.) with spending on community centers, sports fields, and trainings. Figures A2and A3 show ‘before and after’ photographs from Google Streetview in two intervention
neighborhoods in Guadalajara, and illustrate the nature of typical changes in the neighborhood: street paving improved, and sidewalks, crosswalks, and bollards were installed.
Importantly, nearly all of the spending under the program is for residential amenities.
While a previous study analyzing the same experiment as this paper has shown thatthe program results in dramatic improvements in the walkability of and crime levels
in treatment communities [McIntosh et al., 2018], Hábitat funds are typically spent on
inputs that are not directly productive for the private sector.
Table A1 provides a breakdown of the money spent through the program, showingthat roughly half of the spending went to street paving and almost a quarter to a setof social and community development activities (such as after-school youth activities incommunity centers and domestic violence prevention training). Even the money spent on
7

These cost-sharing rules require local governments to providing 50% of project costs: municipalities
provide 40%, the states 8%, and the beneficiaries 2%. So the study universe consists of municipalities
that were willing and able to meet these matching requirements.
8
The carefully orchestrated role played by local residents in proposing and vetting the use of funds makes
this program similar in spirit to the large set of Community Driven Development (CDD) programs
implemented across the developing world [Mansuri and Rao, 2004]. Explicit in the decision-making
process was that municipal government would assume all maintenance costs of Hábitat infrastructure
once the construction phase was completed.

7

roads and paving is primarily used to improve residential neighborhood roads and is not,for example, building trunk roads to better connect these peripheral neighborhoods withcentral parts of the city. Hence this study examines how the residential livability of aneighborhood, which we might more typically think of under the rubric of ‘gentrification’,drives outcomes for the private sector.
The program has clearly specified poverty targeting criteria. In order to be eligible tobenefit from Hábitat, a neighborhood must consist of settled households in a marginalized urban areas with concentrations of asset poverty greater than 50%, located in citiesof 15,000 inhabitants or more, with a deficit of infrastructure and urban services, andwith at least 80% of the lots having no active conflict over property rights. This meansthat our study areas are typically poor outlying neighborhoods of major cities with highpoverty and poor infrastructure, but relatively high levels of home ownership.
Eligibility was established in a very concrete spatial manner, whereby Hábitat defined‘polygons’ that were clearly demarcated contiguous blocks that met the requirements
for the program and in which the local layers of government were willing to invest. A
Hábitat polygon is smaller than a locality and is a designation not used by other layers ofgovernment. Figures A4 to A7 illustrate the sizes of the treatment and control polygons
relative to the overall sizes of Mexico City, Mérida, Tijuana and León, respectively. The
average polygon in our study contains 40 blocks, 98 firms, 3800 inhabitants, and covers
an area of 0.4 square kilometers. Because of the presence of simultaneous investment
across multiple dimensions of urban infrastructure, undertaken in a highly targeted
way, the program provides a unique opportunity to observe the impacts of dramatic
improvements in residential amenities.

2.3

The Design of the Hábitat Experiment

We follow an experimental phase of the implementation of Hábitat in 2009-2012, in which
a set of 370 ‘polygons’ (or neighborhoods) in 68 municipalities across urban Mexico wererandomly assigned to treatment.9 The study featured a randomized saturation design,
which first selected the fraction of study polygons that would be treated within eachmunicipality using a uniform probability between .1 and .9 (so that all municipalities
have some treated and some control polygons), and then assigned treatment to polygons
according to this fraction. These sites contain 14,276 distinct blocks located in 38
cities, representing most of the large urban areas of Mexico (as exhibited in Figure
9

Full details of the experimental design are provided in Ordóñez-Barba et al. [2013] and McIntosh et al.
[2018]).

8

A1). Study polygons contained 3% of the population and 1% of the surface area ofstudy municipalities. The randomization was conducted in 2009, the project selection
process began immediately thereafter in treatment neighborhoods, and investments inthe experimental locations ran from 2010-2012. 176 polygons were assigned to thetreatment, and 194 to the control. US$68 million in federal, state, and municipal funding
was invested in treatment polygons during the period of the study.
There was a subsequent, non-randomized expansion of the Hábitat program nationally in the years after the experiment ended, and a large share of the polygons involvedin the study received some funding from that subsequent roll-out. However, not only
was spending per surface area roughly one tenth of what was experienced during theexperiment (roughly a million USD per square km in our experiment versus 100,000
USD per km in subsequent years), but the later program is perfectly balanced on theexperiment (see Table A2 demonstrating that later implementation effectively ignored
the experiment in targeting). Hence spending under Hábitat after the experiment effectively becomes a part of the regular background flow of infrastructure investment madeby multiple levels of government.

3

Data

3.1

Hábitat database

The Hábitat database contains detailed geospatial information of the blocks, called manzanas, included in the study. The Hábitat study relies on Mexico’s Statistical Agency(INEGI)’s identification system of blocks, which in most part is standardized acrossthe agency’s different projects. This makes it relatively simple to intersect data fromthe program with broader data sources compiled by the Mexican government, such as
the population and firm censuses. Each block is located within a polygon and has acorresponding treatment/control status.
The Hábitat data contains substantial richness; it is possible to observe the exact
type, amount, and location of each infrastructure upgrade a polygon received and on
which year it occurred (2009, 2010 or 2011). Because the actual investments made in agiven location were endogenous (both to the decisions of the Hábitat engineering teamand to the community-driven selection process) we abstract away from this and analyze
the treatment with a simple binary indicator.

9

3.2

Economic census database

The second data source is the Economic Censuses implemented by INEGI every five
years. For this project, we use information of the firm censuses conducted in 2008, 2013
and 2018. The timing of these censuses is remarkably fortuitous for a study of Hábitat,
given that the first interval allows us to conduct a before-after analysis of the short-term
impacts of the program on the private sector, and the 2018 wave allows us to examine
impacts 7 years after the cessation of investment.
The objective of these censuses is to capture information on firms which have a fixedlocation (i.e. not stands, stalls or other temporary buildings), irrespective of their formality status. Data is collected on income and expenses, labor, capital stock and a rangeof other variables. Businesses covered by the census are classified into manufacturing,
services, and construction sectors. The census has a very high response rate, above 98%of all firms surveyed.
INEGI uses unique identifiers for each business surveyed. Thus, it is possible to follow
firms through the censuses and hence to measure firm creation and destruction. The
core variables used as outcomes for analysis are: firm revenue, number of paid workers,
wage bill and capital stock. All financial variables are adjusted for inflation so as torepresent constant 2008 US dollar values.
The INEGI survey also contains detailed information regarding firms’ geographicallocation. Thus, firms can be placed on the block on which they are located within acity. This is crucial, as this geospatial information makes possible to cross this databasewith the Hábitat database and identify those firms contained within Hábitat polygons.
We are able to locate 84,119 firms within Hábitat polygons.10

3.3

Summary Statistics and Balance

The universal nature of INEGI’s firm census allows us to contextualize the study universe
in a very simple way, by comparing the Hábitat control polygons to the broader universeof the cities in which these firms are located. Table 1 provides summary statistics forboth firms in Hábitat polygons, and separately for those in the same city but outside ofour study polygons. As can be seen, on average Hábitat firms are poorer, smaller andless productive.
However, the differences across the distribution of firms we study are not as sub10

Given that there are slightly more control polygons, the majority of businesses are located in suchpolygons (roughly 60% of firms).

10

stantial as one might expect, given the poverty-targeting approach of the Hábitat rules.
Figure 2 provides a visual representation of this comparison, showing the densities for
our four major study outcomes: log revenue, number of paid workers, log wage bill,and log capital stock (we do not represent paid workers in logs because the majority offirms in the census have one paid employee, meaning that the firm owner is the only
employee). The firms in control neighborhoods prove to be surprisingly representativeof their cities as a whole. They are indeed slightly smaller in terms of revenues acrossthe distribution, and they are somewhat more likely to have only one paid worker. In
terms of wage bill and capital stocks they track the broader distributions quite closely.
Overall, the data suggests that our study neighborhoods contain firms that are similar to
broader urban Mexico as a whole, albeit using slightly less labor and generating slightly
lower revenue.
Table A3 focuses on the firms located within study polygons to examine the balance of the experiment. It uses the pre-treatment data (2008) to present comparative
summary statistics for the treatment and control polygons. The penultimate column
presents simple comparisons, and the final column regression-based comparisons including municipality fixed effects and clustering standard errors at the polygon level, as in themain impact analysis. The adjusted comparisons are balanced across all sectors for all
main outcomes (top panel), and when we disaggregate by manufacturing (middle panel)and services (bottom panel) we see one imbalanced outcome for each, in line with what
we would expect by random chance. Overall these results suggest a well-balanced experiment. In our impact analysis we include the baseline polygon-level average level of theoutcome variable as an ANCOVA control, which should remove any residual imbalancesthat do exist.

4

Results

4.1

Firm-Level Impacts

Our analysis uses a post-treatment cross-sectional ANCOVA specification:
Yijm1 = β0 + δτjm1 + ρȲjm0 + γjm + ϵijm1(1)where Yijm1 is the post-treatment outcome for firm i in polygon j and municipalitym, Ȳjm0 is the ANCOVA control (baseline mean outcome in that polygon), γjm is a setof fixed effects for municipality and for baseline polygon size, and ϵijm1 is a random error

11which we cluster at the polygon level to account for the design effect. In this specification,
the estimand δ̂ on the post-treatment polygon-level dummy τjm1 gives the intention-totreat effect (ITT) of Hábitat on firms in treatment polygons. We use both 2013 and 2018as outcome data, but always use 2008 as the year for the ANCOVA control. The variableȲjm0 is calculated at the polygon-level to solve the problem that would otherwise arise
in using a firm-level baseline outcome (whose existence is endogenous if the treatment
leads to extensive margin impacts).
Table 2 provides our main analysis of the impact of the program, using all extant
firms in each round of the data and so providing an omnibus test that combines theintensive and extensive margin impacts of the program. The first two columns pool
all types of firms together, and present impacts in 2013 (three years after the end oftreatment) and 2018 (eight years later) in separate columns. Columns 3-4 analyze only
manufacturing firms, and Columns 5-6 only trade and services firms.
Looking first at the short-term results that pool sectors, we see that a program has
been shown elsewhere to have led to an 18% increase in residential rents and a 10%
increase in property prices [McIntosh et al., 2018] has an impact that is consistent witha response to a cost shock: a substantial increase in the wage bill. However, far fromcutting back on this now more-expensive labor, we also see an increase in the number
paid workers in treatment areas relative to control. The wage bill increases by 18%(US$.196 thousand over a base of US$1.06 thousand), and the number of paid workers
increases by 3% (.05 workers over a base of 1.35) indicating that both the number ofworkers and the wage per worker have increased in treatment areas. Both capital stockand revenue rise in the short term to an extent that is quantitatively meaningful (∼6%)
but not significant (although both t-statistics above 1). Hence within a year or two of
the cessation of the Hábitat investment, costs and employment have risen substantiallyand revenues have not kept pace.
Over the longer term however, the 2018 data paints a substantially rosier picture.
Now 6-7 years after investments ended, revenue has risen by 8% (US$1.98 thousand overa base of US$23.4 thousand), capital stock by 17% (US$1.07 thousand over a base of
US$6.2 thousand), and while the impacts on the number of employees have largely faded
the impacts on the wage bill remain largely intact. Taken as a whole, this time pathof impacts is suggestive of Hábitat investments acting in the short term as a cost shock
to firms without compensation on the revenue side. However, over the longer term as
the dynamics of greater residential wealth lead to superior demand, firms grow more
quickly while remaining able to cover the higher wage bills necessitated by higher local

12residential costs. The positive longer-term impacts on revenues indicate that Hábitat
induces meaningful medium-term changes to the demand faced by local firms.
The subsequent columns of Table 2 disaggregate these impacts by firm sector. In
line with the literature on gentrification [Glaeser and Gottlieb, 2009; Glaeser, Luca andMoszkowski, 2020; Lester and Hartley, 2014] we find the impact of these residential
amenity improvements to be entirely confined to service-sector firms. Service sector
firms are poised to benefit from localized changes in the demand for (and possibly priceof) local non-tradeable goods. In this sector we see revenues jump even in the short term
by 4%, and over the longer term service firm revenues in treatment areas are higher by
9%, with capital stocks soaring by 22%. By 2018, manufacturing firms capital and laborstocks have converged with the control group, and revenue is only slightly higher, byapproximately 3%. Hence this highly localized program has a very substantial benefit
for, and only for, firms operating in the non-tradeable sector.
As discussed previously, in a business environment with such a high degree of churn,impacts on the stock of surviving firms could arise either through intensive marginchanges for surviving firms, or through the selective margin by driving firm birth anddeath. We investigate these two dimensions in turn, beginning by considering firm
creation and destruction as outcomes in a standard experimental context. To do this,Table 3 examines firm birth and death as outcomes of the treatment, so as to understandthe extent to which the overall treatment impacts of the program on the compositionof firms may be arising from entry and exit. The top panel of this figure defines theuniverse as all firms that existed in 2008, and examines an outcome variable which is adummy for that firm having exited the market by the time of the post-treatment survey(2013 or 2018). The probit regression results show that the program leads to short-term
excess firm death of 1.5 percentage points, or an increase of 3.5% over the control groupdeath rate of 43 percent.11 This differential actually decreases slightly when we lookat 2018, providing preliminary evidence that most of the firm exit generated by the
program is experienced immediately. These effects are confined entirely to service sector
firms and the program had no impact on exit in the manufacturing sector.
The lower panel of this table looks at firm entry, now taking the universe as theendline sample of firms and defining a dummy variable for whether that firm is newlyborn since the baseline. Here the story is a mirror image (although less significant);increases in the rate of firm birth of around 1.3 percentage points, entirely concentrated
11

Note that the percentages in this table are the fraction of baseline firms, while the percentages
presented in Figure 1 are the percentage of all firms ever observed.

13

in the service sector, and mostly experienced in the short term. Taken as a whole then,we can summarize the extensive margin results quite simply by saying that as of 2018
the treatment had resulted in about 1.5 percent of the total distribution of firms being
different than the ones that would have existed in the absence of the program, with noeffect on the total number of firms.
We can dig deeper into the dynamics of entry and exit by looking at the firm birthand death that occurs between 2013 and 2018; this helps us to understand whether the
program continues to exert a dynamic selection effect on the composition of firms. In
Table A4 we therefore use the post-treatment 2013 survey as our baseline and examine
entry and exit between that year and 2018. Using this (admittedly endogenous) posttreatment yardstick for subsequent growth we see no extensive margin impacts, meaningthat the compositional effects of the program were relatively immediate. Hence theprogram leads to a short-term shake-out on the extensive margin but does not exert
subsequent composition effects.
Given these meaningful but not qualitatively massive extensive margin effects, we
suspect that the program has led to growth of firms on the intensive margin. While
this story is difficult to tell with perfect experimental clarity, a simple way of posing
the question is to restrict the sample to the (endogenous) group of firms that survive
from baseline, and looking at impacts on these continuing market participants. Table 4
conducts this exercise and finds impacts that are roughly twice as large as the overall
impacts found in Table 2. Here we see really large effects; for example service firms
in the treatment area that survive from 2008 to 2018 see revenues that are 13% higher
and capital stocks that are fully one third higher than comparable firms in the control.
Hence the overall treatment effect is a composite of a large increase in the size of surviving
firms with a relatively small increase in the turnover of firms on the extensive margin.
Because newly entering firms are on average smaller than incumbents, this increase inchurn actually dampens the total ITT effect of the treatment on firms size relative tothe impact on ongoing firms. We now turn to a more detailed analysis of the ways inwhich the treatment altered the composition of market participants.

4.2

Heterogeneity in Impacts

We can perform a straightforward test of heterogeneity for firms that were observed atbaseline; hence we begin our analysis by looking at the intensive margin heterogeneityof treatment effects for firms present both at baseline and endline. This analysis ispresented in Table 5. This table defines dummies using the baseline distribution of14

Revenue and Value Added, identifying firms that were in the top 25, 50, or 75 percentof the relevant distribution before the program began. It then interacts treatment withthese classifications to ask whether the impact of Hábitat is larger for firms that weremore productive to begin with. While the short-term results are more equivocal, it
is clear from this table that by 2018 firms that were in the top half of the original
distribution of value added have grown more in the treatment neighborhoods. Revenue,
capital stock, and wage bill have all grown significantly more for firms that began withhigher productivity, and indeed insignificant uninteracted treatment terms meaning that
all of the impact of the program arises in the top half of the original productivity
distribution. So the new opportunity provided in these neighborhoods is exclusively
seized by productive firms.
Similarly, for firms present at baseline it is straightforward to ask whether firms thatwere initially less productive are those most likely to exit as a result of the program.
Recalling that the treatment effect on death of firms appears in 2013 and not 2018,
we again find evidence of the strongest firms surviving best. In Table A5, we see theuninteracted Hábitat treatment dummy suggesting an elevation of 3.2 percentage points
in the probability of firm death, and the interaction effect on being in the top quartileof baseline productivity is -2.5 percentage points, meaning that these most efficient 25%
of firms see little elevation in exit. Therefore, virtually all of the short-term firm deathcaused by the program is occurring in the unproductive firms.
The analysis of firm entry is less straightforward in that by definition we do not
observe pre-treatment heterogeneity. What we can do is to examine whether there aredifferences between the attributes of newly created firms between the treatment andcontrol; these differences would be a composite of true extensive margin selection effects
on entry as well as the intensive impacts of the treatment on firm growth between creationand the time of the survey. This analysis, in Table A6, also lines up with the idea thatthe treatment is having meaningful impacts on the distribution of firm productivity, withentering service-sector firms being superior on most core outcomes in 2018. So, while
we cannot cleanly say that these firms entered being more productive, it does appear
to be the case that firm growth was fastest and firm death lowest among firms thatwere originally productive in the treatment, and new treated firms grew faster. Thus
heterogeneity in the response to treatment by more productive firms plays an importantrole in explaining the total effects we observe.

15

5

Mechanisms for Firm-Level Impacts

The question of what is driving these results is important not just to better understandthe nature of change in Hábitat neighborhoods, but also to appreciate whether this wasa broader structural change in the local economy. We turn now to each of three coreelements of structural transformation - access to credit, firm formality, and the responseof residents and consumers.

5.1

Access to Credit and Financial Services

First, we look at the ways in which financial and technological access may have been adriver of business expansion. We find significant evidence of a financial channel behind
the transformation and expansion of firms in treated polygons. As shown in Table 6,these businesses have a 20 percent higher probability of having secured a loan in 2013,
an effect driven entirely by firms in the service sector. Point estimates, while small in
absolute terms, are sizeable when we compare them to the control mean. While thisvariable includes all sources of credit, answers are very similar if restricted to formal
lenders only, implying that new loans are obtained through the formal financial system(i.e. banks or savings cooperatives). We then see a higher probability of surviving firms
having a bank account in 2018, which we interpret as the loan directly helping businesses
to obtain formal access to the financial system.
The nature of the businesses in these areas is such that their key margin along which
they can expand their economic complexity is through purchasing new and improved
machinery and fixed assets rather than the adoption of more sophisticated types oftechnologies such as IT equipment. As shown previously, we find the businesses in thetreated polygons expand their capital stock and purchases of machinery and equipment,
supported by their expanded access to loans. The right-hand columns of Table 6 illustrate that use of computers is not changed, though service firms see an increase in accessto the internet in 2013 which while strongly significant is only a half of a percentage
point change in absolute value.
Importantly, this expanded access to credit does not appear to be mechanically driven
by an expansion in the value of businesses’ collateral as the value of the owned property
increases. This is shown in Table A7, which analyzes the uptake and sources of credit, as
well as the uses to which it is put, splitting the sample according to whether businesses
own the property on which they operate or not. While businesses with land collateral
have somewhat better baseline access to credit (13% versus 9% for those without),

16

the treatment effects of Hábitat are virtually identical: a short-term expansion of 2.5
percentage points in 2013 and no longer-term effect. Unsurprisingly firms with landcollateral are more likely to be served by formal banks and less likely to rely on savingsbanks, and non-landed firms put more of their money into land acquisition and inputs.
But the reduced-form change in credit access is not being driven by those businesses thatown land, removing as a potential explanation for mechanisms the fact that the private
sector expands under residential investment strictly through the collateral value channel.
Since collateral value does not seem to drive changes on the supply side, it appears thatdemand-side shifts arising from improved sales opportunities and profitability are themost reasonable explanation for the credit expansion.

5.2

Firm Formalization

The second mechanism at play which we think explains the changes occurring among
businesses in the treated polygon is formalization. Our measure of formalization relieson the definition of Busso, Fazio and Levy [2012] and focuses on contributing to social
security (i.e. having formal workers).12 The advantage of using the level of social
security contribution as a measure of formality is that this better captures the fact thatthe business is not only legal from a tax perspective but it is substantially contributing
to generate higher quality formal jobs as its employees are covered by social security
benefits (i.e. pension, health insurance, etc.). As suggested by Busso, Fazio and Levy
[2012] we estimate that businesses should be on average paying the equivalent of 18
percent of total wages in social security contributions to be fully complying with theirsocial security regulations (“strict formality definition”). However, this is an upperbound of their contribution and firms paying social security contributions that are below
18 percent of the wage total could still be fully compliant with social security regulations.
We therefore also assess a second measure of formality as those firms that pay any social
security contributions (“relaxed formality definition”). Accordingly, the latter is ourpreferred measure of business formality.
As shown in Table 7 we find that Habitat increases the likelihood of being formalthat is driven primarily by businesses in the services sector (although this is a rarecase where we see positive impacts of Hábitat on manufacturing firms as well). While
point estimates are small in absolute terms we should observe that the prevalence offormalization among these types of businesses is very low (0.3 percent using our stricter
12

We do not focus on the formal aspect of having a tax ID as it is much more common among businesses
in Mexico and less stringent measure of the degree of formalization of a business in Mexico.

17

definition of formality and 2.4 percent using our more liberal definition) and our result
imply an increase in the likelihood of being formal compared to the control mean equal
to 17 percent in 2013 and 4 percent in 2018. While the results are mainly driven by
the transformation of incumbent businesses that change their status from informal toformal, there are also some effects in the service sector along the extensive margin, with
newly entered firms being more likely to be formalized as well.

5.3

Neighborhood Population

Together, our results imply broad changes in the characteristics of firms serving residents
in treatment neighborhoods. How do residents respond in turn? This matters for theinterpretation of our findings, with our firm level results implying changes in the natureof local consumption. With greater revenues for dominantly non-tradeable items, it islikely that much of the implied consumption is local. To what extent are our results
driven by new and different populations moving into treatment neighborhoods, or byexisting populations changing their consumption patterns?
McIntosh et al. [2018] document an increase in private investment in housing, withhouseholders incorporating the higher amenity value of their surroundings into home
upgrading. In particular, they observe significant upgrades to flooring and plumbing,with a 12 percent increase in the likelihood of a home containing a flush toilet. They
also document the fact that though home ownership rates do not change significantly
in treatment areas, property values rise substantially and rental costs rise by almost 20
percent. This finding is consistent with the increase in wage bills that we observe forfirms in Table 2.
We assess the issue more broadly by analyzing the Mexican Census of Population andHousing 2020, also provided by INEGI and integrated with the same set of blocks andpolygons as our core analysis. Table 8 presents our results. We regress, at the polygon
level, measures from the 2020 Population Census on a treatment dummy and valuesof the variable from the 2010 Population Census. As such, the analysis is in the formof an ANCOVA specification, allowing us to present the most precise assessments ourdata allow. We split the analysis into variables related to the levels (or corresponding
percentage) of the variable (Panel A) and log transformations of those same variables(Panel B) to check for robustness from outliers.
Overall, we do not find significant effects of the Hábitat program on the structure or
characteristics of the population. Columns 1-3 assess the size of the population within
our study polygons in terms of total, female and male populations. In each case the co-18

efficient is small and insignificant at the usual levels. Similarly, we find no evidence that
there is a higher proportion of adults or children in treatment neighborhoods (Column
4). Columns 5-7 indicate that the population of treatment neighborhoods are similar
to control along a number of important margins. They are no more educated, no more
likely to be employed, nor married.
It does not, therefore, seem that the changes in the private sector we observe are
driven by significant changes in the demographic characteristics of populations in Hábitat
neighborhoods. This is consistent with the limited change in home ownership rates observed previously. Rather, the results are consistent with the upgrading of neighborhoods
changing the consumption patterns of local residents.
A remaining question is whether these changes were driven by wealth effects fromthe injection of investment capital from Programa Hábitat or other indirect effects ofthe program. McIntosh et al. [2018] provides detailed measurements of house pricechanges based on the assessments of professional property assessors from the Instituto de
Administración Avaluos de Bienes Nacionales (INDAABIN), the Mexican government’s
institute of real estate valuation. They find that “the treatment group had almost triple
the real rate of appreciation as the control”, implying significant increases in the wealthof many of the treatment polygon’s residents.
Bringing together the insights from the economic and population censuses, and theresults from McIntosh et al. [2018], we see that Programa Hábitat had impacts on
the nature of the private sector that do not seem to have been driven by changes in
the underlying population being served but rather their core spending power. The
program seems to have shifted the structure of the local economy - by which we mean the
consumption choices of neighborhood residents and production choices by neighborhood
firms - to a different equilibrium. That equilibrium had many characteristics of a moremature service economy - bigger, more capital-intensive firms with a greater likelihoodof indicators of formality for example. This interpretation implies that government
infrastructure investments can induce structural economic change at a very localized
level.

19

6

Understanding the Urban Geography of Hábitat

6.1

Spillovers to Adjacent Neighborhoods

Spatial spillover effects are critical for interpreting the underlying model of economic geography revealed by the program. If these investments are driving agglomeration effects,
we should see increases in investment and TFP in surrounding areas as in Greenstone,
Hornbeck and Moretti [2010], and the narrow consideration of the Hábitat polygons
would represent an under-estimate of total benefits. Alternatively, if the program has
simply driven improvement in neighborhood amenities ala Rosen-Roback, economic activity might be spurred by an increase in demand from greater local housing wealth, but
no underlying change in factor productivity would have occurred. Even in this case, if
business growth came at the expense of adjacent neighborhoods, it would help to inform
our understanding of the urban geography of demand. For these reasons, an understanding of spatial externalities is key both for welfare interpretations as well as for testingthe ‘productivities’ interpretation against the ‘amenities’ story.
Our study provides an unusually clear window on the existence of spillovers, usinga granular analysis using geographic buffers around the study polygons. Because weknow the exact physical boundaries of the infrastructure and we have an experimental counterfactual in which no investment took place, we have a straightforward andstatistically well-powered way of examining whether Hábitat drove changes in adjacentneighborhoods. Further, the neighborhoods actually treated are relatively small andnon-contiguous, meaning that both treatment and control polygons are small islands
surrounded by non-study neighborhoods. Therefore, we can look at buffers of up to
1 km without running into problems of overlapping treatment statuses, as can be a
problem in more intensive spatial treatments such as the one studied in Franklin et al.
[2023].
We begin from the outlines of the study polygons and define buffers as small as 100m
and as large as 1 km in the way for the treatment and control alike (the distance buffers
are non-inclusive, meaning that the 100-250m buffer does not include the 100m buffer).
We then locate INEGI firms within each buffer and examine the differential outcomes forfirms at different distances treatment and control polygons. This approach is simple andexperimental in an attractive way, and has very similar statistical power to the overall
study (especially as we look at larger buffers that contain more firms).13
13

As in the main analysis we cluster at the polygon level and include municipality fixed effects.

20This approach reveals some increase in the churn of firms in immediately adjacentneighborhoods, but surprisingly weak spillovers overall. Appendix Table A8 examines
the likelihood that a firm dies or is born during the ten years from 2008-2018, finding theprobability of both entry and exit rising by 3-4 percentage points with the overall numberof firms staying relatively stable. Table 9 finds no consistent evidence of spillovers on
firm outcomes at any distance; there are no results significant at the 5% level for all firms
at any distance.14 The simple takeaway is therefore that the impacts of the program arehighly localized, generating no consistent spillovers even at 100 meters, and so we haveneither any measurable agglomeration effects, nor any observable crowd-out of businessactivity from adjacent firms.

6.2

Heterogeneity by Market Access and Road Networks

To connect the effects from Hábitat more deeply into the geography of the surrounding
cities function, we can examine heterogeneity of treatment and spillover effects in several key dimensions of market access. Neighborhoods with strong market access maythemselves benefit more from infrastructure investment if they have more opportunities to exploit dense linkages, and certainly we may expect that the spillover effects ofthe program to surrounding neighborhoods will be larger when market access is better.
We explore four metrics of market access: local population, local non-poor population,
polygon size, and distance to highways.15
We first look for evidence of impact heterogeneity within study polygons, on theextensive margin in Table A9 and on firm-level outcomes in Table A10. Neither metricof market access generates meaningful heterogeneity. Polygon size does matter, in thatlarger polygons see lower churn on the extensive margin but larger increases in revenue
and wage bills, at least in the short term. Proximity to roads drives a short-term
14

Given that spillovers may both shift the composition of firms as well as altering outcomes on theintensive margin for pre-existing firms, for each buffer we show the total effect (all endline firms), theimpact on ‘survivors’ (firms that existed at both baseline and the relevant endline), and ‘entrants’(new firms since baseline). Here we do see limited evidence of positive spillovers in revenue for existing
firms at the nearest distances, but these fade quickly with distance and are not present in any of theother outcomes or for the overall group of firms.
15
The first calculates an inverse-distance weighted average population density around the centroid ofeach Hábitat polygon (treatment and control), as a measure of the local total market size. The second
measure emphasizes the spending power of the adjacent population by calculating the same metric
but using only the population above the poverty line. Our third metric explores the idea that ourpolygons may simply be too small to drive spillover effects. To investigate this we calculate the sizeof each polygon in square meters and use geographic size as a dimension of heterogeneity. Finally, we
try to tie the study to the commuting map of the city by measuring the minimum distance from eachpolygon centroid to the nearest major road.

21

improvement in revenues and capital stock, and a longer-term improvement in paid
workers. We can then apply this same approach to our spillover analysis, using buffers
and firms defined as before but now studying how the magnitude of spillovers varies
with attributes of the market access of the polygon. Table A11 looks at the extensive
margin, and Table A12 the intensive margin. All four measures of market access havethe effect of increasing churn among immediately adjacent firms, but as above new births
are roughly balanced by new deaths, leaving the total number of firms relatively stable.
In no case do we see effects that are clearly monotonic in distance, as we would expect
in any continuous spatial model of market access over such small areas.16

6.3

Saturation-driven Spillovers at the Municipality Level

Spillover effects need not be spatial in the manner analyzed in the previous section;
general equilibrium or market-level impacts of the program may alter commercial patterns more broadly. The two-level randomization embedded in Hábitat’s rollout offersa unique experimental lens on these city-wide effects, and we have the universe of firmsin every city and so can capture them very comprehensively. By exploiting the varying
saturation levels of Hábitat investment implemented as part of the experimental design,
our analysis has a rather unique lens on how higher levels of investment might lead togreater spillovers on neighboring firms.
This approach suffers from low power in that Hábitat neighborhoods represent asmall fraction of the overall city (2̃% of surface area), but nonetheless represents an
attractive and design-based way to understand the impact of localized treatment on thecity as a whole.17 Table 10 analyzes outcomes in non-study neighborhoods as a functionof the treatment saturation in study neighborhoods in that municipality, clustering at
the municipality-level to reflect the design effect from this component of the experiment.
Consistent with the overall findings from our analysis of geographic spillovers, the results
again suggest a lack of significant spillovers.
16

Given the large number of coefficients presented in Table A12 it is important to view them throughthe lens of multiple comparisons; we present 192 interaction effects in this table and find 8 significant
at the 1% level, 15 significant at the 5% level, and 14 significant at the 10% level, not far above what
would be expected by random chance.
17
We construct the sample for this analysis by first eliminating the study polygons from the data, thencollapsing the remaining blocks at the AGEB level (a geographical unit between a block and locality,
typically comprising about 100 firms). We then regress outcomes for all remaining AGEBs in thedata on the municipal-level treatment saturation, now clustering standard errors at the municipal
level to reflect the design effect from this component of the experiment. Table A13 illustrates thatthe saturation experiment is balanced on baseline outcomes.

22Given these results, what can we conclude about the economic geography of thistype of program? Whether one expects to see spillover effects that are positive (agglomeration) or negative (diversion), our design allows us to say is that improvements
in local private sector opportunity are not achieved by helping or hurting surrounding
areas. This says that the pixel size of our neighborhoods (typically 40 blocks) is notlarge enough to trigger larger neighborhood dynamics that either contribute to or detract from adjacent parts of the city. Rather, it appears to have generated a relatively
homogeneous and remarkably localized set of impacts that are narrowly concentrated
in the immediate location of the investments. The conclusion is that at the scale ofinvestment we study, the total welfare effect of the program is likely localized withinstudy neighborhoods.

7

Conclusion

We bring together a large-scale experiment in the construction of infrastructure with athree-round census on firm activity in urban Mexico. Studying a program that spent
US$68 million over three years and across 65 municipalities, we find a powerful anddurable response of private-sector firms to improvements in neighborhood amenities.
Wages, employment, and capital investment all rise. Infrastructure improvements accelerate churn in firms, and appears to lure ‘better’ entrants, particularly in the service
sector. Firms use credit to expand whether or not they have land as collateral, andformalization rates rise. If anything, these impacts accelerate over time. Firms appear
to shift onto a higher path of revenue growth that is continuing to expand relative tothe control six years after the end of the program. This pattern is consistent withwealth-driven increases from local consumers boosting demand, and suggests that ourstudy captures a kind of small-scale randomized gentrification. Improvements in urban
amenity value directly enhance opportunities for local firms in the non-tradeable sector.
In contrast to much of the recent urban literature we find minimal spillover effects,
whether using a simple buffer neighborhood approach, looking at how measures of market
access mediate treatment and spillover effects, or assessing the impact of the scale ofinfrastructure investment on its consequences for neighboring firms. Given the type of
infrastructure built here this may not be surprising. In contrast to the large recent
literature on transport infrastructure that shifts commuting patterns, Hábitat did very
little to shift linkages between neighborhoods and instead improved community amenities
within them. Unlike programs that achieve infrastructure improvement through large-23

scale public employment such as Ethiopia’s Urban PSNP [Franklin et al., 2023], Hábitat
created few direct local jobs and its impact on urban labor markets appear to arise
mostly through stimulating local demand.18
The limited spillovers imply that the incidence of the program is confined to study
areas, allowing a simple assessment of the ways in which tax receipts could recoup thecosts of the program. We undertake a back-of-the envelope calculation that calculates
the effects of the intervention on the total value of firm outcomes that are taxed by
the government (Table A14), and then applies the appropriate marginal tax rates tothese totals to back out the fiscal implications of the intervention (Table A15). These
rates are 16% for Value Added, 2% for firm revenue, and then contributions to thepension system IMSS are directly asked in the INEGI survey. These calculations imply
that the Hábitat investments we study increased annual government receipts of US$4.7
million; which put up against the US$67 million cost of the program, suggests that the
program will pay for itself in a little over 14 years just through tax intake from privatesector firms. This estimate is conservative in that it ignores impacts on property taxes(either for firms or households), but is strongly suggestive that private sector taxation
represents a meaningful channel through which the costs of infrastructure programs canbe recouped by governments even when the private sector was not the target of thoseinvestments, and even when the impacted businesses are small.
Our results imply that while property values may provide a sufficient summary statistic in terms of static welfare, the existence of a causal relationship between property
values and private sector growth adds a dynamic multiplier on to the benefits of thistype of spending. The fact that the private sector responds to shifts in property values,that these changes are dynamic and durable, and do not appear to come at the cost of
growth in neighboring areas, all suggest that private sector taxation is an underappreciated channel for recovering the costs of infrastructure spending.
Overall, the distributed, experimental, and independent nature of the Hábitat investments allow us to isolate how infrastructure investments kick-start localized agglomeration effects. The granularity and precision of our estimates showcase the dynamics ofthis process and its limitations. Local infrastructure investment can be a spur to thedevelopment and reshaping of the private sector, even in the most local of economies.
18

The 40-block size of the neighborhood improvements generated in this experiment may be unnaturally
small relative either to standard large infrastructure projects, or to the size of neighborhood that wemight typically see ‘gentrify’. Seen in this light, our results shed light on the minimum scale ofinvestment required to kick-start localized agglomeration effects. Indeed, the subsequent rollout ofHábitat that took place in the years subsequent to the study did typically operate over substantiallylarger continguous geographic areas.

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28

Tables

29

30

2.48
8.55
1.56
0.00
2.11
1.00
0.00
2.71
4.82
13.64
2.14
0.00
3.34
1.00
0.00
3.27

7.22
20.28
7.51
0.14
4.94
1.46
1.41
2.90
30.01
80.11
27.32
0.78
11.38
2.95
8.37
3.68

2008
Mean Median

87.38
261.05104.99
4.54
44.83
6.28
31.07
2.77

13.18
33.17
20.15
0.68
8.94
1.41
4.94
1.69

Std. Dev.

1,176,785
1,176,785
1,176,785
1,176,785
1,176,785
1,176,785
1,176,785
357,03144,15644,15644,15644,15644,15644,15644,156
6,461

Obs.

29.32
73.01
25.02
0.81
12.11
2.80
7.14
3.57

6.76
16.70
6.66
0.15
4.91
1.40
1.20
2.91
5.01
11.27
1.88
0.00
3.71
1.00
0.00
3.18

2.74
7.52
1.57
0.00
2.51
1.00
0.00
2.82
85.90
240.88
97.26
4.22
46.48
5.83
26.11
2.41

11.40
27.25
17.99
0.62
7.95
1.33
4.49
1.59

2013
Mean Median Std. Dev.
9.88
24.10
6.78
0.25
6.11
1.58
1.92
3.04

7.37
17.29
1.92
0.00
4.94
1.00
0.00
3.27

4.19
11.38
1.46
0.00
3.44
1.00
0.00
2.91

144.98354.33106.32
4.37
82.88
6.61
31.60
2.07

24.91
60.97
23.92
1.61
14.19
1.76
7.34
1.52

2018
Mean Median Std. Dev.

1,446,324 46.13
1,446,324 105.74
1,446,324 26.95
1,446,324 0.79
1,446,324 17.60
1,446,324 3.21
1,446,324 9.22
562,419
3.67

50,82250,82250,82250,82250,82250,82250,822
11,233

Obs.

Table provides summary statistics at the firm level for all firms included in INEGI’s census within any of the cities in which the Hábitat program
was implemented, by location within city. The 2008 wave forms the baseline for this study, and the the 2013 and 2018 waves are post-treatment.
For variables other than paid workers, values are in thousands of 2013 US dollars.

Firms in Habitat polygons
Value added
36,063
Revenue
36,063
Capital stock
36,063
Investment
36,063
Value added per paid worker
36,063
Paid workers
36,063
Wage bill
36,063
Wage
6,623
Firms in rest of the city
Value added
1,049,650
Revenue
1,049,650
Capital stock
1,049,650
Investment
1,049,650
Value added per paid worker 1,049,650
Paid workers
1,049,650
Wage bill
1,049,650
Wage
368,070

Obs.

Table 1: Descriptive statistics: by location

Table 2: Main regression results, all firms

Sample: Existing firms in endline, including those missing in baseline

All sectors

Manufacturing

Trade and Services

Dependent variable
Revenue

Capital stock

Paid workers

Wage bill

Observations

2013
2018
0.732
1.981**(0.479)(0.915)
16.43
23.43
0.529
1.071***(0.371)(0.399)6.340
6.177
0.050**
0.022(0.020)(0.027)1.352
1.540
0.196*** 0.198*(0.061)(0.102)
1.061
1.771[44,156] [50,822]

2013
0.687
(0.884)20.36-0.062(0.783)10.91
0.152***(0.057)1.870
0.469**(0.229)2.958[5,339]2018
0.758(1.283)25.86-0.022(0.711)8.778
0.009(0.073)2.184-0.028(0.276)
4.124[6,034]

2013
2018
0.684
1.984*(0.508)(1.044)15.90
23.10
0.648* 1.278***(0.358)(0.412)5.722
5.832
0.040**
0.025(0.019)(0.028)1.282
1.455
0.156*** 0.223**(0.058)(0.109)0.805
1.458[38,791] [44,750]

The table shows the impact of Hábitat on firms located within the areas treated by the program.
Specifically, the value of coefficient δ in the following specification:
YijmT = β0 + δτjmT + ρȲjm2008 + F EM unicipality + F EP olygonsize + ϵijmTEach coefficient denotes a different regression. Standard errors clustered by Hábitat polygon shown inparenthesis. Mean values of control groups in italics. Number of observations in square brackets.

31

Table 3: Effect of Habitat on probability of exit and entry of firms

Probability of exit
HabitatObservations
Probability of entry
HabitatObservations

All sectors
2013
2018

Manufacturing
2013
2018

Trade and Services
2013
2018

0.015*(0.009)0.433[36,063]0.013(0.010)0.596[36,063]0.006(0.019)0.461[4,777]0.008(0.021)
0.624[4,777]0.017*(0.009)0.429[31,286]

0.013(0.010)0.592[31,286]

0.013(0.014)0.543[44,156]0.007(0.010)0.718[50,822]0.005(0.021)
0.534[5,365]-0.000(0.016)0.713[6,072]0.015(0.014)0.544[38,791]

0.008(0.010)0.718[44,750]

Table presents coefficients from a linear probability model. Specifically, the coefficient δ in thefollowing specification:
P r(YijmT = 1|τjmT ) = β0 + δτjmT + F EM unicipality + F EP olygonsize + ϵijmTThe probability of exit (top panel) is estimated among all firms present at baseline explaining whether
they have exited by the indicated round. The probability of entry (bottom panel) is estimated among
all firms present in the post-treatment waves explaining whether the firm is a new entrant in thatround. Standard errors clustered by Hábitat polygon shown in parenthesis. Mean values of control
groups in italics. Number of observations in square brackets.

32

Table 4: Main regression results, surviving firms

Sample: Existing firms in both baseline and endline

All sectors

Manufacturing

Trade and Services

Dependent variable
Revenue

Capital stock

Paid workers

Wage bill

Observations

2013
2018
0.611
3.830*(0.602) (2.038)
19.42
29.59
0.552
2.156**(0.416) (0.840)
7.466
8.531
0.047**
0.057(0.023) (0.054)
1.368
1.600
0.230*** 0.398*(0.083) (0.238)
1.196
2.142
[20,147] [14,271]

2013
1.185
(1.111)24.45-0.837(0.989)13.87
0.215***(0.079)1.951
0.778**(0.301)
3.454[2,565]2018
1.902(2.380)33.82
0.127(1.673)14.21
0.178(0.141)2.431
0.640(0.595)5.386[1,784]2013
0.346(0.664)18.72
0.740*(0.420)6.579
0.019(0.023)1.287
0.124(0.077)0.884[17,582]

2018
3.814
(2.348)29.02
2.505***(0.895)7.762
0.035(0.055)1.487
0.349(0.251)
1.703[12,487]

The table shows the impact of Hábitat on surviving firms located within the areas treated by the program. Specifically,
the value of coefficient δ in the following specification:
YijmT = β0 + δτjmT + ρȲjm2008 + F EM unicipality + F EP olygonsize + ϵijmTEach coefficient denotes a different regression. Standard errors clustered by Hábitat polygon shown in parenthesis.
Mean values of control groups in italics. Number of observations in square brackets.

33

34

Percentile

HabitatHabitat*Percentile

Percentile

HabitatHabitat*Percentile

0.026(0.044)0.030(0.039)0.108***(0.021)
1.368[20,147]

0.106(0.138)0.160(0.124)0.367***(0.069)
1.196

0.451
(0.581)0.250(0.557)1.460***(0.353)7.466-0.185(0.817)0.883(0.796)
7.051***(0.486)
19.42

0.068
(0.049)0.018(0.030)0.133***(0.023)
1.368[20,147]0.228(0.158)0.132(0.102)
0.491***(0.077)
1.196

0.840
(0.584)0.193(0.465)2.005***(0.327)
7.466

1.230
(0.881)
0.186(0.697)7.530***(0.561)19.42-0.031(0.059)0.056**(0.026)
0.097***(0.033)
1.368[20,147]-0.106(0.205)0.261***(0.095)0.390***(0.116)
1.196

0.472
(0.808)0.476(0.414)
1.923***(0.448)
7.466

0.601
(1.165)0.630(0.617)9.030***(0.675)
19.42

0.051
(0.046)0.011(0.038)0.168***(0.025)1.368[20,147]0.230(0.151)
0.064(0.127)0.554***(0.085)1.196-0.027(0.641)0.583(0.579)2.157***(0.343)
7.466

1.169
(0.827)-0.195(0.742)9.726***(0.498)19.42

0.088*(0.047)0.008(0.028)0.292***(0.026)1.368[20,147]

0.335**(0.156)0.076(0.096)1.009***(0.090)1.196

1.163*(0.641)0.008(0.461)3.255***(0.324)7.466

1.899**(0.944)-0.229(0.657)
12.276***(0.579)
19.42

0.097
(0.068)0.029(0.025)0.456***(0.040)1.368[20,147]

0.332(0.232)0.160*(0.087)1.599***(0.140)
1.196

1.227
(0.895)0.277(0.433)4.982***(0.500)7.466

2.283*(1.377)0.113(0.618)16.495***(0.898)19.42

0.201*(0.104)-0.088(0.097)0.037(0.070)1.600[14,271]

0.665(0.514)-0.086(0.474)0.013(0.332)2.142

2.938**(1.470)0.009(1.366)-0.262(1.060)
8.531

3.754
(3.008)1.188(2.451)6.980***(2.014)29.59

0.176**(0.081)-0.023(0.067)0.087(0.057)1.600[14,271]

0.615(0.383)0.113(0.307)0.223(0.276)
2.142

3.664***(1.304)0.420(1.003)0.096(0.896)8.531

6.445**(2.749)0.947(2.261)7.305***(1.933)
29.59

0.142
(0.096)0.026(0.060)0.009(0.058)1.600[14,271]

0.482(0.419)0.289(0.272)-0.069(0.260)2.142

3.873**(1.508)1.295(0.878)0.292(0.840)
8.531

5.094
(4.563)2.875(2.360)9.083***(2.506)
29.59

0.138
(0.094)-0.042(0.094)0.137**(0.067)1.600[14,271]0.446(0.479)0.076(0.461)
0.343(0.309)2.142

2.523*(1.488)0.318(1.417)0.013(1.010)
8.531

3.833
(2.867)1.082(2.464)10.684***(1.849)29.59

0.155*(0.079)-0.013(0.061)
0.326***(0.043)1.600[14,271]

0.591(0.366)0.124(0.279)1.128***(0.178)
2.142

1.798
(1.201)1.312(0.891)
3.023***(0.678)
8.531

3.362
(3.241)
2.272(1.716)17.160***(1.722)
29.59

0.099
(0.112)0.043(0.059)0.545***(0.060)1.600[14,271]0.596(0.510)0.282(0.267)
1.945***(0.275)2.142

3.090*(1.679)1.485*(0.855)4.039***(1.037)8.531-0.591(4.462)4.070*(2.383)23.047***(3.089)29.59

2018
Initial value added per worker
Initial revenue
P-th>=25th P-th>=50th P-th>=75th P-th>=25th P-th>=50th P-th>=75th

Table presents interaction effects between Hábitat and initial value added per worker, or initial revenue. Columns interact treatment with
dummies for being in or above the 25th, 50th, and 75th percentile of these covariates, with each triad of interaction, Hábitat and Percentile
coefficients representing a different regression. The Table shows coefficients ϕ, τ and γ of the following specification:
YijmT = β0 + ϕτjmT ∗ P ercentileijm2008 + δτjmT + γP ercentileijm2008 + ρȲjm2008 + F EM unicipality + F EP olygonsize + ϵijmTStandard errors clustered by Hábitat polygon shown in parenthesis. Mean values of control groups in italics. Number of observations in square
brackets.

Observations

Wage bill

Percentile

HabitatPaid workers
Habitat*Percentile

Percentile

HabitatCapital stock
Habitat*Percentile

Revenue

2013
Initial value added per worker
Initial revenue
P-th>=25th P-th>=50th P-th>=75th P-th>=25th P-th>=50th P-th>=75th

Table 5: Heterogeneity in main results

35

HabitatObservations

Habitat

0.001(0.007)0.105[44,750]0.027***(0.010)0.112[17,582]0.004(0.008)0.0911[12,487]
0.028***(0.009)0.112[21,209]-0.001(0.007)0.111[32,263]-0.011(0.015)0.104[2,565]-0.014(0.015)0.0898[1,784]-0.013(0.015)0.110[2,800]0.004(0.011)0.0893[4,288]0.022**(0.010)0.111[20,147]0.001(0.008)0.0909[14,271]
0.022**(0.009)0.112[24,009]
0.000(0.007)0.108[36,551]

0.027***(0.009)0.112[38,791]-0.003(0.010)0.0894[6,034]-0.013(0.012)
0.107[5,339]0.001(0.006)0.103[50,822]0.022***(0.008)0.111[44,156]

0.006(0.006)
0.0527[36,551]

0.002(0.011)
0.0610[24,009]0.017**(0.007)0.0596[14,271]

0.003(0.008)0.0611[20,147]

0.011**(0.005)0.0547[50,822]0.003(0.010)0.0610[44,156]

0.002(0.011)0.0734[4,288]-0.008(0.014)0.0808[2,800]0.013(0.021)0.116[1,784]-0.008(0.015)0.102[2,565]0.006(0.011)0.0856[6,034]-0.003(0.012)0.0906[5,339]0.006(0.006)0.0500[32,263]0.005(0.012)0.0584[21,209]0.017**(0.007)0.0520[12,487]

0.004(0.008)0.0554[17,582]

0.011**(0.005)0.0506[44,750]0.004(0.010)0.0570[38,791]

Firm had a bank account(=0 no, =1 yes)
All sectors Manufacturing Trade and Services-0.005(0.006)0.0889[36,551]

0.006(0.006)
0.0939[24,009]

0.003(0.008)0.0824[14,271]-0.000(0.005)0.0847[20,147]-0.006(0.006)0.0871[50,822]0.002(0.005)0.0897[44,156]

All sectors-0.023**(0.011)0.0812[4,288]0.004(0.013)0.0778[2,800]-0.011(0.013)0.0956[1,784]-0.000(0.012)
0.0917[2,565]-0.017*(0.009)0.0853[6,034]0.005(0.010)0.0843[5,339]-0.002(0.006)0.0900[32,263]0.008(0.007)
0.0960[21,209]

0.004(0.009)0.0806[12,487]-0.001(0.005)0.0838[17,582]-0.004(0.006)0.0873[44,750]

0.002(0.005)0.0904[38,791]

Firm used computers(=0 no, =1 yes)
Manufacturing Trade and Services-0.006(0.005)0.0764[36,551]

0.004***(0.001)0.00635[24,009]

0.004(0.009)0.0682[14,271]

0.003**(0.001)0.00861[20,147]-0.003(0.006)0.0741[50,822]0.004***(0.001)0.00738[44,156]-0.024**(0.010)0.0718[4,288]0.001(0.003)0.00413[2,800]-0.020(0.014)0.0849[1,784]-0.008*(0.004)0.0189[2,565]-0.022**(0.009)0.0756[6,034]-0.002(0.003)0.0110[5,339]-0.004(0.006)0.0771[32,263]0.005***(0.002)0.00664[21,209]

0.007(0.009)0.0660[12,487]

0.005***(0.002)0.00718[17,582]-0.001(0.006)
0.0739[44,750]

0.004***(0.001)0.00689[38,791]

Firm had access to internet(=0 no, =1 yes)
All sectors Manufacturing Trade and Services

Table presents coefficients from a linear probability model. Specifically, the coefficient δ in the following specification:
P r(YijmT = 1|τjmT ) = β0 + δτjmT + ρȲjm2008 + F EM unicipality + F EP olygonsize + ϵijmT
Dependent variables are the post-treatment status of having access to a loan, having a bank account, using computers and having access tointernet. ’All existing firms’ panels include all firms existing at the respective endline. ’Surviving firms’ panels only include firms that existed inboth baseline and endline. ’Entrants’ panels groups firms that did not exist at baseline. Standard errors clustered by Hábitat polygon shown inparenthesis. Mean values of control groups in italics. Number of observations in square brackets. Every coefficient denotes a different regression.

Observations

Observations
Entrants from 2008 to 2018
HabitatObservations
Entrants from 2008 to 2013
HabitatObservations
Surviving firms from 2008 to 2018
HabitatObservations
Surviving firms from 2008 to 2013
HabitatAll existing firms in 2018

Dependent variables
All existing firms in 2013

Firm had access to a loan(=0 no, =1 yes)
All sectors Manufacturing Trade and Services

Table 6: Regressions on mechanisms

36

0.001**(0.001)0.00262[44,156]
0.003***(0.001)0.00229[20,147]0.000(0.001)0.00290[24,009]0.004**(0.002)0.0237[44,156]

0.005*(0.003)0.0268[20,147]0.005(0.003)0.0211[24,009]-0.000(0.003)0.0221[36,551]

0.007*(0.004)0.0283[14,271]0.001(0.002)0.0238[50,822]0.001*(0.001)0.00294[36,551]

0.003*(0.001)0.00288[14,271]0.001*(0.001)0.00292[50,822]-0.004(0.008)0.0389[2,800]0.012(0.012)0.0654[2,565]0.001(0.008)0.0513[5,339]0.001(0.001)0.00295[2,800]0.008*(0.005)0.00202[2,565]0.004*(0.002)0.00252[5,339]-0.011(0.007)0.0419[4,288]-0.026**(0.013)0.0792[1,784]-0.015**(0.007)0.0526[6,034]-0.002(0.002)0.00388[4,288]0.002(0.007)0.00772[1,784]-0.001(0.003)0.00498[6,034]

Manufacturing
2013
2018
Relaxed
StrictRelaxed
Strict0.006*(0.003)0.0188[21,209]

0.003(0.002)0.0215[17,582]

0.005**(0.002)0.0200[38,791]0.000(0.001)0.00289[21,209]

0.002**(0.001)0.00233[17,582]0.001(0.001)0.00264[38,791]0.001(0.003)0.0195[32,263]0.010***(0.004)0.0214[12,487]

0.003(0.002)0.0200[44,750]

0.002**(0.001)0.00282[32,263]

0.003*(0.001)0.00222[12,487]

0.002**(0.001)0.00265[44,750]

Trade and Services
2013
2018
Relaxed
StrictRelaxed
StrictTable presents coefficients from linear probability models explaining whether a firm is ‘formal’ according to a Relaxed or Strict definition.
Namely, the table shows coefficient δ in the following specification:
P r(YijmT = 1|τjmT ) = β0 + δτjmT + ρȲjm2008 + F EM unicipality + F EP olygonsize + ϵijmT’All firms’ panel includes all firms existing at endline. ’Survivors’ panel only includes firms that existed in both baseline and endline. ’Entrants’panels groups firms that did not exist at baseline.Every coefficient denotes a different regression. Standard errors clustered by Hábitat polygon
shown in parenthesis. Mean values of control groups in italics. Number of observations in square brackets.

Observations

Observations
Entrants
HabitatObservations
Survivors
Habitat

All firms
HabitatAll sectors
2013
2018
Relaxed
StrictRelaxed
StrictTable 7: Effects on Formality37-0.029(0.030)
8.034
[367]

-196.396(175.372)
18,268-0.027(0.029)
7.357
[367]

-102.319(91.515)
9,354-0.031(0.030)
7.321
[367]

-104.807(87.927)
8,904-0.005(0.005)-0.369[367]-0.004(0.003)0.695
0.001(0.007)
2.207
[367]

-0.064(0.039)9.245
0.001(0.001)-0.0197
[367]

0.000
(0.001)0.980-0.005(0.005)-0.630[367]-0.002(0.003)0.530

Characteristics of population
Population
Population
Avg. years
12+ yrs old employed 12+ yrs marriedof schooling(% of workforce)(% of pop. 12+)

The table presents the impact of Hábitat on population census variables. Population censuses were held in 2010 and 2020, the former is used asbaseline year. The table shows coefficient δ in the following specification:
Yjm2020 = β0 + δτjmT + ρYjm2010 + F EM unicipality + F EP olygonsize + ϵjm2020
Panel A presents variables either in the units they were originally in the censuses (Columns 1, 2, 3 and 5) or were transformed relative to total
population or a subgroup of that variable where relevant. Panel B shows those same variables transformed to logarithms. Throughout, allregressions are weighted by the polygon total population in 2010 and conditioned on the value of the variable in 2010. Standard errors clusteredby Hábitat polygon shown in parenthesis. Mean values of control groups in italics. Number of observations in square brackets.

Observations

Panel B: Logs
HabitatPanel A: Levels
HabitatTotal
population

Population
Total population: Total population:
18+ yrs old
femalemale(% of tot. pop.)

Structure of population

Table 8: Impacts of Habitat on Residential Outcomes

38

0.455
(6.543)100.3[40,167]-14.491(15.390)
89.49[86,470]

5.954(4.729)82.92[25,993]

1.731(4.871)
75.85[15,731]

16.197* -0.910(9.618) (12.642)
59.06
70.47
[38,720] [58,772]-4.282(5.538)92.85[125,766]-2.387(4.753)74.30[84,063]3.711(3.225)64[53,882]-1.254(8.221)
82.11[146,872]

2.649(3.175)104.8[74,109]

2.047(4.196)89.72[219,752]

500m1km0.861-1.662(1.541) (1.825)
14.51
16.74
[38,720] [58,772]

1.428
1.262
(2.336) (1.924)
27.02
26.97
[15,731] [25,993]-0.058-0.380(1.248) (1.197)
18.19
19.88
[53,882] [84,063]

0m100m

-4.248(2.756)20.32[86,470]

0.675(2.720)30.96[40,167]-0.706(1.835)23.63[125,766]

Capital stock
100m250m250m
500m

1.025
(2.546)
18.16[146,872]

0.952(1.459)31.18[74,109]1.569(1.762)22.53[219,752]

500m1km

Wage bill
100m250m250m
500m
500m1km

0.604
0.013
(0.565) (0.598)
4.631
5.244
[38,720] [58,772]

0.476
0.089
(0.651) (0.501)
7.016
7.538
[15,731] [25,993]-0.116(0.606)
5.889[86,470]0.010(0.571)
8.270[40,167]-0.307(0.609)6.279[146,872]-0.214(0.295)
9.143[74,109]

0.226
0.083
0.100
-0.062(0.371) (0.346)(0.328)(0.212)
5.333
5.948
6.630
7.241
[53,882] [84,063] [125,766] [219,752]0m100mPeriod: 2008-2018
Paid workers
100m250m250m
500m

500m1km0.128-0.007(0.122) (0.133)
2.203
2.347
[38,720] [58,772]

0.099
0.060
(0.115) (0.088)
2.652
2.768
[15,731] [25,993]-0.070(0.137)2.509[86,470]

0.007(0.102)
2.942[40,167]-0.036(0.133)2.586[146,872]-0.044(0.053)
3.123[74,109]

0.043
0.022
0.001
0.004
(0.072) (0.071)(0.068)(0.040)
2.335
2.477
2.644
2.766
[53,882] [84,063] [125,766] [219,752]0m100mTable estimates the impact of Hábitat only using untreated firms just outside of study polygons. As described in each column, distance frompolygon perimeter increases across columns, and the outer bands are non-inclusive of nearer bands. The table shows the esimatation ofcoefficient δ in the following specification:
Yikm2018 = β0 + δτmT + ρȲikm2008 + F EM unicipality + ϵikm2018
where τmT now denotes exposure to a buffer area belonging to a treatment Hábitat polygon. Note that the ANCOVA control is at AGEB level,
denoted by subsscript k.
‘All firms’ row includes every endline firm in that band, ‘Survivors’ row includes firms present at both baseline and endline. ‘Entrants’ row shows
firms existing only at the endline. Standard errors are clustered by the closest Hábitat polygon associated to each firm, and are shown inparenthesis. Control group means are in italics, number of observations in hard brackets. Every coefficient denotes a different regression.

Observations

Observations
Entrants
Treatment buffer

Observations
Survivors
Treatment buffer

All firms
Treatment buffer

0m100mRevenue
100m250m250m
500m

Table 9: Spillover effects

Table 10: Saturation effect in areas outside Habitat

Revenue

Capital stock

Paid workers

Wage bill

Value added

Payments to social security

All sectors
2013
2018
-14.947** -23.750*(7.403)(13.092)
79.75
110.4
[9,653][9,650]-2.227-5.588*(2.579)(3.228)31.36
31.90[9,653][9,650]-0.240-0.247(0.184)(0.300)3.007
3.381[9,653][9,650]-1.128-0.939(0.985)(1.448)8.181
10.30[9,653][9,650]-5.173** -9.410*(2.526)(5.021)
31.13
47.20[9,653][9,650]-0.142-0.151(0.169)(0.200)1.219
1.176[9,653][9,664]

Manufacturing
2013
2018
-0.439 -4.299(4.287) (8.280)
48.11
64.81
[7,988] [7,963]
0.169
1.789
(3.866) (3.836)
52.37
40.96
[7,988] [7,963]
0.406
0.602
(0.296) (0.539)
4.619
5.116
[7,988] [7,963]
2.147
3.725
(1.498) (2.927)
16.13
18.74
[7,988] [7,963]-0.871 -4.147(1.717) (3.501)
20.25
29.48
[7,988] [7,963]
0.282
0.455
(0.313) (0.433)
2.325
2.182
[7,989] [7,975]

Trade and Services
2013
2018
-16.543** -23.875*(7.451)(12.480)83.15
116[9,603][9,600]-4.070-6.660*(2.756)(3.581)26.90
29.17[9,603][9,600]-0.316*-0.322(0.170)(0.277)2.710
3.076[9,603][9,600]-1.615*-1.532(0.872)(1.308)6.731
8.761[9,603][9,600]-5.366** -8.491*(2.471)(4.953)32.25
49.35[9,603][9,600]-0.250-0.264(0.176)(0.183)0.998
0.975[9,603][9,614]

Table estimates the effect of treatment saturation on areas outside Hábitat polygons within cities
treated by the program. Namely, it shows the coefficients δ in the following specification:
YkmT = β0 + δτkmT + ρȲkm2008 + ϵkmT where τkmT now represents intensity of treatment (i.e.
saturation) in the k AGEB. Firm data is aggregated into AGEB level. Standard errors clustered bymunicipality shown in parenthesis. Control group means are in italics, number of observations in hardbrackets. Each coefficient denotes a different regression.

39

Figures

Figure 1: Dynamics of Firm Birth and Death in the Control. Firms across all three census rounds are divided into six
strata based on the rounds in which they existed. Green firms are observed to be born, red firms are observed to die,and blue firms persist through all three rounds.

40Figure 2: Descriptive densities of outcomes for firms in Hábitat study areas compared to City-wide averages.

41

For Online Publication: Appendix
FiguresFigure A1: Hábitat cities across the country42Figure A2: Before(above)-and-After(below) Pictures of Hábitat Treatment Neighborhood 1 in Guadalajara

43

Figure A3: Before(above)-and-After(below) Pictures of Hábitat Treatment Neighborhood 2 in Guadalajara

44

Figure A4: Hábitat program in Mexico City.Top: Hábitat polygons. Center: Polygons with buffer areas up to 1km
radius. Bottom: Polygons with buffer areas up to 5km. Green=treatment, Red=control

45

Figure A5: Hábitat program in the City of Mérida. Top: Hábitat polygons. Center: Polygons with buffer areas up to
1km radius. Bottom: Polygons with buffer areas up to 5km. Green=treatment, Red=control

46

Figure A6: Hábitat program in the City of Tijuana. Top: Hábitat polygons. Center: Polygons with buffer areas up to
1km radius. Bottom: Polygons with buffer areas up to 5km. Green=treatment, Red=control

47

Figure A7: Hábitat program in the City of León. Top: Hábitat polygons. Center: Polygons with buffer areas up to
1km radius. Bottom: Polygons with buffer areas up to 5km. Green=treatment, Red=control

48

49

Table A1: Habitat Spending

15,905,294

32,850,121

881,141915,70233,431,659

1,752,960
1,801,580
67,743,983

Public lighting

Trash collection

5,364,410

63,86830,248260,256

198,192

93,867

301,398

3,586,772

4,978,304

373,053State

2009-201127,414,167

767,860

824,506

830,340

1,160,519

1,160,632

2,047,825

12,246,184

20,857,825

6,523,472

Municipal

428,590

72,370

5,327

4,447

17,536

5,071

7,672

43,054

169,607

Households
Benefitted
256,443

Summary of the Hábitat program spending by category and level of government. Source: McIntosh et al. [2018]

Source: SEDESOL

Total spending

1,397,310
1,300,516

2,842,852
2,459,935

Community Development Centers

Sidewalks and medians

2,465,322
1,320,621

4,877,761
2,644,150

SewersDrinking water

Paving7,026,328
26,359,409

13,922,853
53,729,286

FederalSocial and Community Development

Total Investment

Improvement of Urban Environment:

Name of Program (Subprogram)Total Investments in Treatment Polygons, 2009-2011 (US Dollars)

Additional Tables

Table A2: Balance of Subsequent Habitat Treatment in Original Polygons

Status in subsequent rollout(=0 not treated, =1 treated)
2013
2014
2015
2016
2017

Treatment

Control
Mean

Std. Error

Obs.

Mean

Std. Error

Obs.

0.985
0.418
0.381
0.33
0.351

0.009
0.035
0.035
0.034
0.034

194
194
194
194
194

0.983
0.398
0.375
0.358
0.369

0.01
0.037
0.037
0.036
0.036

176
176
176
176
176

t-test(xControl-xTreatment)
Unconditional
Conditional
Difference p-value
Difference p-value
0.002
0.905
0.000
0.980
0.020
0.700
0.052
0.246
0.006
0.899
0.053
0.221
-0.028
0.572
0.025
0.560
-0.019
0.708
0.027
0.536

Table presents tests of balance between original treatment and control polygons of subsequent Habitat
waves from 2013 to 2017. With the exception of 2013, about 35% to 40% of both original control andtreatment polygons were treated yearly in subsequent waves of the Hábitat program.

50

Table A3: Balance of the Experiment at the Firm levels

Control
All sectors
Value added
Revenue
Capital stock
Investment
Value added per paid worker
Workers: paid
Wage bill
Wage
Manufacturing
Value added
Revenue
Capital stock
Investment
Value added per paid worker
Workers: paid
Wage bill
Wage
Trade and Services
Value added
Revenue
Capital stock
Investment
Value added per paid worker
Workers: paid
Wage bill
Wage

Treatmentt-test(xControl-xTreatment)
Unconditional Conditional

Mean

Std. Error

Obs.

Mean

Std. Error

Obs.

7.122
19.983
6.964
0.136
4.937
1.447
1.407
2.950

0.255
0.656
0.470
0.019
0.136
0.028
0.104
0.043

21,521
21,521
21,521
21,521
21,521
21,521
21,521
3,827

7.376
20.724
8.313
0.140
4.951
1.483
1.420
2.840

0.316
0.719
0.431
0.022
0.188
0.028
0.089
0.059

14,542
14,542
14,542
14,542
14,542
14,542
14,5422,796-0.255-0.742-1.350**-0.004-0.014-0.036-0.0140.110-0.146-0.061-0.047-0.021
0.197-0.054-0.0690.094

11.13624.831
13.484
0.231
4.736
2.213
4.008
3.192

0.71
1.407
1.234
0.03
0.224
0.093
0.345
0.062

2,752
2,752
2,752
2,752
2,752
2,752
2,752
1,058

9.960
23.32113.222
0.203
4.421
2.180
3.634
3.058

0.84
1.67
1.077
0.029
0.3
0.112
0.375
0.083

2,025
2,025
2,025
2,025
2,025
2,025
2,025
805

1.176
1.510
0.262
0.028
0.315
0.033
0.375
0.135

1.324*
2.441
1.708
0.001
0.422
0.064
0.428
0.105

6.533
19.272
6.008
0.122
4.967
1.335
1.025
2.857

0.217
0.621
0.395
0.019
0.136
0.02
0.074
0.051

18,76918,76918,76918,76918,76918,76918,7692,769

6.959
20.304
7.519
0.13
5.037
1.371
1.062
2.752

0.324
0.721
0.426
0.023
0.191
0.028
0.081
0.07

12,517
12,517
12,517
12,517
12,517
12,517
12,5171,991-0.380-0.464-0.309-0.0230.147-0.068**-0.1340.068-0.425-1.032-1.511***-0.007-0.070-0.036-0.3700.105

Table presents summary statistic by treatment arm, and tests of balance between the treatment andcontrol. The first column of balance tests is the simple clustered comparison of means, and the second
column uses the municipal FE that are implied by the research design.

51

Table A4: Entry and exit of firms after 2013

HabitatObservations

All sectors
exit
entry
-0.004-0.005(0.013) (0.010)
0.391
0.472
[44,156] [50,822]

Manufacturing
exit
entry
-0.008 -0.019(0.021) (0.018)
0.396
0.468
[5,365] [6,072]

Trade and Services
exit
entry
-0.003-0.003(0.013)(0.010)0.390
0.472[38,791] [44,750]

Table presents LPM estimates on whether firms exit (odd columns) or enter (even columns) between
2013 and 2018. Because the 2013 outcome is endogenous to treatment this analysis is used simply todescribe the time path of relative treatment effects. Standard errors clustered by Hábitat polygon
shown in parenthesis. Mean values of control groups in italics. Number of observations in square
brackets.

52

Table A5: Heterogeneity of probability of exit of firms

All firms
Habitat*Percentile
HabitatPercentile

Observations
Manufacturing
Habitat*Percentile
HabitatPercentile

Observations
Trade and Services
Habitat*Percentile
HabitatPercentile

Observations

2013
Initial value added per worker
Initial revenue
P-th>=
P-th>=
P-th>=
P-th>=
P-th>=
P-th>=
25th
50th
75th
25th
50th
75th

2018
Initial value added per worker
Initial revenue
P-th>=
P-th>=
P-th>=
P-th>=
P-th>=
P-th>=
25th
50th
75th
25th
50th
75th

-0.025*-0.010(0.014)(0.011)0.032**
0.017*(0.014)(0.010)-0.094*** -0.101***(0.009)(0.008)0.433
0.433[36,063][36,063]-0.023*(0.014)0.019**(0.009)-0.092***(0.010)0.433[36,063]-0.007-0.016(0.015)(0.012)0.019
0.021**(0.014)(0.011)-0.148*** -0.128***(0.010)(0.008)0.433
0.433[36,063][36,063]-0.012(0.014)0.017*(0.009)-0.127***(0.011)0.433[36,063]-0.0090.005(0.012)(0.011)0.018
0.007(0.013)(0.011)-0.098*** -0.104***(0.009)(0.007)0.596
0.596[36,063][36,063]-0.006(0.012)0.012(0.011)-0.098***(0.007)0.596[36,063]

0.003-0.001(0.012)(0.011)
0.009
0.011(0.012)(0.011)-0.151*** -0.134***(0.008)(0.007)0.596
0.596[36,063][36,063]0.008(0.013)0.010(0.011)-0.141***(0.009)0.596[36,063]-0.100***(0.038)0.078**(0.033)-0.033(0.024)0.461[4,777]-0.064**(0.032)0.036(0.024)-0.049**(0.022)0.461[4,777]-0.054(0.039)0.017(0.021)-0.040*(0.024)0.461[4,777]-0.002-0.017(0.040)(0.034)0.007
0.015(0.034)(0.025)-0.130*** -0.113***(0.022)(0.017)0.461
0.461[4,777][4,777]-0.003(0.039)0.003(0.021)-0.138***(0.022)0.461[4,777]-0.075**(0.034)0.062*(0.032)-0.040*(0.023)0.624[4,777]-0.031(0.030)0.022(0.026)-0.051**(0.023)0.624[4,777]-0.039(0.035)0.015(0.023)-0.056**(0.023)0.624[4,777]0.013-0.014(0.034)(0.031)-0.0020.015(0.031)(0.025)-0.129*** -0.102***(0.022)(0.019)0.624
0.624[4,777][4,777]-0.002(0.037)0.006(0.022)-0.120***(0.024)0.624[4,777]-0.012-0.001(0.015)(0.012)0.024*
0.014(0.014)(0.011)-0.102*** -0.108***(0.011)(0.009)0.429
0.429[31,286][31,286]-0.018(0.015)0.019**(0.010)-0.101***(0.011)
0.429[31,286]-0.006-0.016(0.015)(0.013)0.020
0.022**(0.014)(0.011)-0.151*** -0.131***(0.011)(0.010)0.429
0.429[31,286][31,286]-0.013(0.015)0.019**(0.010)-0.126***(0.012)
0.429[31,286]

0.002
0.011
(0.013)(0.012)0.010
0.005(0.013)(0.011)-0.107*** -0.112***(0.010)(0.008)0.592
0.592[31,286][31,286]-0.001(0.013)0.012(0.010)-0.105***(0.008)0.592[31,286]

0.003
0.001
(0.012)(0.011)0.010
0.010(0.012)(0.011)-0.155*** -0.140***(0.008)(0.007)0.592
0.592[31,286][31,286]

0.010(0.014)0.010(0.010)-0.144***(0.009)0.592[31,286]

Table presents LPM estimates on whether firms present in the baseline round had exited by eachendline round. Heterogeneity is tested through the interaction of treatment and a dummy for being in
the Nth percentile of baseline value added and revenue, percentile varied across columns. Standard
errors clustered at the polygon level are in parentheses. Mean values of control groups in italics.
Number of observations in square brackets.

53

Table A6: Characteristics of entrants in Habitat polygons

Revenue

Capital stock

Paid workers

Wage bill

Observations

All sectors
2013
2018
0.975*
1.637(0.580) (1.013)
13.92
21
0.530
0.785**(0.417) (0.372)
5.392
5.252
0.085***
0.048(0.030) (0.031)
1.338
1.517
0.217**
0.203*(0.097) (0.117)
0.947
1.625
[24,009] [36,551]

Manufacturing
2013
2018
-0.164 -0.135(1.138) (1.238)
16.78
22.65
0.874
0.312
(0.917) (0.547)
8.322
6.592
0.135* -0.026(0.082) (0.082)
1.798
2.085
0.328
-0.131(0.293) (0.310)
2.525
3.617
[2,800] [4,288]

Trade and Services
2013
2018
1.165*
1.834(0.593)(1.139)
13.54
20.79
0.544
0.885**(0.407)(0.393)5.004
5.075
0.080*** 0.057*(0.029)(0.032)1.277
1.442
0.209** 0.250**(0.092)(0.118)0.738
1.363[21,209] [32,263]

Table is estimated only among firms that newly entered in each of the endline rounds, using atreatment dummy to examine differences between attributes of entrants. Every coefficient is from adifferent regression. Standard errors clustered by Hábitat polygon shown in parenthesis. Mean valuesof control groups in italics. Number of observations in square brackets.

54

Table A7: Credit Impacts for Businesses that Do and Do Not Own LandAll firms in 2013
All sectors
HabitatObservations
Manufacturing
HabitatObservations
Trade and services
HabitatObservations
All firms in 2018
All sectors
HabitatObservations
Manufacturing
HabitatObservations
Trade and services
HabitatObservations

Did you get credit,
loan or financing
for the firm’s
operation?
Not owner Owner

What is the source of the credit,
loan or financing?

Owner

Equipment or
expansion
Not owner Owner

Uses of credit, loan
or financing received
Acquisition ofbuilding or vehicle
Not owner Owner

Input acquisition(raw materials)
Not owner Owner

Not owner

Owner

Not owner

0.020**(0.009)0.0983[26,037]0.017(0.011)
0.132[18,254]

0.007*(0.004)0.0333[26,037]0.008(0.006)
0.0464[18,254]0.012***(0.004)0.0203[26,037]0.003(0.006)0.0308[18,254]0.014**(0.006)
0.0319[26,037]0.017**(0.007)0.0439[18,254]0.001(0.001)0.00124[26,037]-0.002**(0.001)0.00282[18,254]0.008(0.005)0.0319[26,037]0.003(0.007)0.0515[18,254]-0.008(0.013)0.0915[3,460]-0.026(0.019)0.142[1,918]0.001(0.007)0.0342[3,460]-0.031***(0.012)0.0604[1,918]0.001(0.006)0.0188[3,460]0.002(0.008)0.0236[1,918]0.011(0.007)0.0277[3,460]-0.017(0.013)0.0491[1,918]-0.002(0.003)0.00328[3,460]-0.003(0.004)0.00472[1,918]-0.007(0.008)0.0324[3,460]-0.006(0.012)0.0500[1,918]0.025***(0.009)0.0993[22,577]

0.023**(0.011)
0.131[16,336]0.009*(0.005)0.0332[22,577]

0.013**(0.006)0.0449[16,336]0.013***(0.004)0.0205[22,577]

0.003(0.006)
0.0316[16,336]0.014**(0.006)0.0326[22,577]

0.022***(0.008)0.0433[16,336]0.002**(0.001)0.000928[22,577]-0.001**(0.001)0.00261[16,336]

0.011**(0.005)0.0318[22,577]0.005(0.007)0.0517[16,336]-0.001(0.007)
0.0955[32,507]

0.003(0.009)0.119[18,496]0.002(0.004)0.0348[32,507]

0.003(0.005)0.0487[18,496]-0.002(0.002)0.0199[32,507]-0.005(0.005)0.0319[18,496]-0.005(0.003)0.0260[32,507]-0.010**(0.004)0.0351[18,496]-0.000(0.001)0.00154[32,507]-0.000(0.001)0.00251[18,496]-0.002(0.004)0.0500[32,507]

0.002(0.007)0.0641[18,496]0.003(0.011)0.0771[4,043]0.005(0.018)0.117[2,057]0.004(0.007)0.0342[4,043]0.007(0.012)0.0589[2,057]-0.004(0.004)0.0147[4,043]-0.010(0.009)0.0263[2,057]-0.007(0.006)0.0274[4,043]0.003(0.011)0.0371[2,057]-0.002(0.001)0.00159[4,043]-0.004*(0.002)0.00634[2,057]0.004(0.008)0.0362[4,043]0.012(0.013)0.0571[2,057]-0.002(0.007)
0.0981[28,464]0.004(0.009)0.119[16,439]0.001(0.004)0.0349[28,464]0.002(0.005)0.0476[16,439]-0.001(0.003)0.0206[28,464]-0.005(0.005)0.0325[16,439]-0.004(0.003)0.0258[28,464]-0.011**(0.005)0.0348[16,439]-0.000(0.001)0.00153[28,464]0.000(0.001)0.00207[16,439]-0.004(0.004)0.0520[28,464]0.001(0.008)0.0649[16,439]

Bank

Savings bank

Table analyzes experimental impacts on financial impacts at the firm level, splitting the sample
according to whether firms did or did not own the land on which the business operated at baseline.
Every coefficient is from a different regression. Standard errors clustered by Hábitat polygon shown inparenthesis. Mean values of control groups in italics. Number of observations in square brackets.

55

Table A8: Spillover effects on probability of entry and exit of firms

Period: 2008-2018
100m250m250m
500m

0m100mExit
Treatment buffer

Observations
Entry
Treatment buffer

Observations

500m1km

0.040*** 0.020*(0.010) (0.011)
0.600
0.593
[40,452] [64,231]-0.008(0.015)0.599[98,063]-0.004(0.010)0.591[177,610]

0.032***
0.018(0.010) (0.012)
0.706
0.693
[54,451] [84,765]

0.001(0.015)0.689[126,637]

0.019**(0.010)0.664[220,981]

Table presents coefficients from a linear probability model. The probability of exit (top panel) is
estimated among all firms present at baseline explaining whether they have exited by the indicatedround. The probability of entry (bottom panel) is estimated among all firms present in the
post-treatment waves explaining whether the firm is a new entrant in that round. Standard errors are
clustered by the closest Hábitat polygon associated to each firm, and are shown in parenthesis. Meanvalues of control groups in italics. Number of observations in square brackets.

56

Table A9: Heterogeneity in Probability of Exit and Entry of Firms by Market AccessPopulation density
Exit
Habitat*Mkt access
HabitatMarket accessObservations
Entry
Habitat*Mkt access
HabitatMarket accessObservations

Period: 2008-2018
Wealth index Polygon size Driving distance to main road

0.012
(0.009)0.011(0.010)-0.004(0.020)0.596[36,063]0.014(0.009)0.012(0.010)-0.004(0.019)0.596[36,063]-0.018**(0.009)0.250**(0.120)0.024**(0.011)0.596[36,063]0.010(0.008)0.014(0.010)-0.005(0.008)0.596[36,063]

0.003(0.008)0.006(0.010)-0.048**(0.022)0.718[50,822]0.003(0.009)0.008(0.010)-0.035(0.023)0.718[50,822]-0.031***(0.010)0.399***(0.123)0.069***(0.012)0.718[50,822]0.009(0.010)0.008(0.010)-0.015*(0.008)0.718[50,822]

Table examines heterogeneity in entry and exit of firms by four different measures of market access: 1)
Inverse distance-weighted population at the centroid of each neighborhood, 2) Population above thepoverty line instead of total population, 3) Size in square meters of polygon, 4) Distance from polygon
to a main road. Standard errors clustered by Hábitat polygon shown in parenthesis. Mean values ofcontrol groups in italics. Number of observations in square brackets.

57

Table A10: Heterogeneity by Market Access
Revenue
2013
2018

All firms
Capital stock
Wage bill
2018
2018
2013
2013

0.061
(0.365)0.726(0.478)-0.074(0.714)16.43[44,156]

0.634(0.774)
1.954**(0.892)0.422(1.340)23.43[50,822]-0.280(0.411)0.558(0.351)
0.382(0.965)6.340[44,156]

0.219(0.342)
1.055***(0.398)-0.452(0.693)
6.177[50,822]0.005(0.059)0.199***(0.061)0.186*(0.109)1.061[44,156]

0.046(0.436)0.726(0.479)0.324(0.663)16.43[44,156]-0.023(0.768)1.956**(0.943)0.999(1.389)23.43[50,822]0.213(0.397)0.528(0.376)0.192(0.857)6.340[44,156]0.567(0.361)1.111***(0.401)-0.403(0.778)
6.177[50,822]0.057(0.064)0.195***(0.061)
0.112(0.103)1.061[44,156]

0.475**(0.217)0.518(0.523)-0.530*(0.314)16.43[44,156]

0.193(0.387)2.021*(1.039)-0.476(0.472)23.43[50,822]0.006(0.242)0.482(0.423)0.086(0.279)6.340[44,156]

0.337(0.235)0.752*(0.447)-0.072(0.316)
6.177[50,822]0.067**(0.034)0.173**(0.067)-0.090**(0.045)1.061[44,156]-1.165***(0.443)0.557(0.466)
Distance to road
0.511
(0.386)16.43[44,156]
Observations-0.725(1.014)1.790**(0.889)-1.223(0.939)23.43[50,822]-0.701*(0.422)0.436(0.379)0.591(0.410)6.340[44,156]

0.542(0.565)1.129***(0.423)-0.860*(0.475)
6.177[50,822]-0.066(0.078)0.187***(0.063)0.042(0.072)
1.061[44,156]

Habitat*PopDens
Habitat
PopDens

Observations
Habitat*Wealth
Habitat
WealthObservations
Habitat*Size
Habitat
Polygon size

Observations
Habitat*Distance
HabitatIntensive margin
Revenue
Workers: paid
Capital stock
Wage bill
2018
2013
2018
2018
2018
2013
2013
2013
Market access proxy: population density
0.187*
0.027-0.081
1.307
0.762
0.272
-0.006-0.1860.040(0.102)(0.027)(0.480) (2.364) (0.421) (0.710)(0.022)(0.070) (0.242)
0.192* 0.053***
0.022
0.610
3.641**
2.054** 0.229*** 0.371*
0.577(0.099)(0.027)(0.598) (1.774) (0.403) (0.801)(0.020)(0.084) (0.222)
0.261
0.102**-0.677-4.087-1.769-0.0970.088**
0.761
0.198(0.178)(0.045)(1.022) (4.000) (1.112) (1.324)(0.036)(0.129) (0.452)
1.771
1.540
19.42
29.59
8.531
2.142
1.352
7.466
1.196
[50,822] [44,156][50,822][20,147] [14,271] [20,147] [14,271] [20,147] [14,271]
Market access proxy: wealth index
0.244**
0.027-0.173-0.762
0.982
0.243
0.003
0.473
0.084
(0.105)(0.029)(0.556) (1.954) (0.432) (0.764)(0.023)(0.076) (0.246)
0.206** 0.049**
0.022
0.621
3.854*
2.203*** 0.228*** 0.403*
0.540(0.103)(0.028)(0.601) (2.092) (0.416) (0.834)(0.020)(0.085) (0.240)
0.287
0.115**-0.371-1.973-1.2810.069
0.079**
0.530
0.094(0.178)(0.046)(1.025) (4.193) (1.010) (1.397)(0.033)(0.139) (0.472)
1.771
1.540
19.42
29.59
8.531
2.142
1.352
7.466
1.196
[50,822] [44,156][50,822][20,147] [14,271] [20,147] [14,271] [20,147] [14,271]
Market access proxy: Polygon size (sq. m)-0.064-0.0180.466-0.5160.471-0.2340.017-0.066-0.051(0.049)(0.013)(0.295) (1.035) (0.299) (0.494)(0.011)(0.058) (0.189)
0.208*
0.023
0.430
3.482
1.582*
0.362
0.045**
0.501
0.290***(0.113)(0.030)(0.686) (2.224) (0.494) (0.899)(0.022)(0.085) (0.249)
0.113*
0.035**-0.532
1.617
0.221
0.499
-0.0220.213-0.015(0.068)(0.016)(0.442) (1.503) (0.357) (0.688)(0.015)(0.092) (0.309)
1.771
1.540
19.42
29.59
8.531
2.142
1.352
7.466
1.196
[50,822] [44,156][50,822][20,147] [14,271] [20,147] [14,271] [20,147] [14,271]
Market access proxy: Driving distance to main roads (meters)-0.142-0.071***-0.471
1.250
1.753
0.155
-0.028-0.4560.057(0.116)(0.027)(0.578) (2.417) (0.461) (1.156)(0.025)(0.076) (0.305)
0.175*
0.011
0.525
3.714*
2.394*** 0.241*** 0.422*
0.046**
0.497(0.099)(0.026)(0.593) (1.937) (0.418) (0.870)(0.020)(0.083) (0.235)
0.041
0.039
0.119
-5.569**-1.799*-0.1150.015
0.452-0.000(0.108)(0.025)(0.543) (2.659) (0.457) (1.056)(0.023)(0.066) (0.279)
1.771
1.540
19.42
29.59
8.531
2.142
1.352
7.466
1.196
[50,822] [44,156][50,822][20,147] [14,271] [20,147] [14,271] [20,147] [14,271]

Workers: paid
2018
2013
-0.006(0.023)0.049**(0.023)0.087**(0.037)1.368[20,147]

0.035(0.052)0.054(0.052)0.019(0.096)1.600[14,271]-0.008(0.024)0.045*(0.024)0.073*(0.039)1.368[20,147]

0.035(0.056)0.057(0.055)0.066(0.096)1.600[14,271]-0.018(0.016)
0.065**(0.025)-0.000(0.025)1.368[20,147]-0.046(0.036)0.042(0.055)0.112**(0.056)1.600[14,271]0.013(0.023)0.050**(0.024)-0.000(0.019)1.368[20,147]0.010(0.065)0.060(0.053)0.011(0.057)1.600[14,271]

Table examines heterogeneity in overall program impacts by four different measures of market access:1) Inverse distance-weighted population at the centroid of each neighborhood, 2) Population above thepoverty line instead of total population, 3) Size in square meters of polygon, 4) Distance from polygon
to a main road. Standard errors clustered by Hábitat polygon shown in parenthesis. Mean values ofcontrol groups in italics. Number of observations in square brackets.

58

Table A11: Heterogeneity in Spillover Probability of Exit and Entry of Firms by Market Access

Period: 2008-2018
Exit

Entry

100m

250m

500m

1km

100m

250m

500m

1km

0.018***(0.007)0.033***(0.010)-0.033**(0.014)0.011(0.008)0.013(0.010)-0.045***(0.014)-0.026*(0.015)0.005(0.010)-0.018(0.015)-0.010(0.011)0.006(0.009)0.026(0.023)0.018*(0.009)0.025**(0.010)-0.060***(0.013)0.016*(0.009)0.009(0.011)-0.066***(0.015)-0.017(0.015)0.005(0.011)-0.040**(0.017)0.013(0.008)0.009(0.010)-0.030***(0.009)0.017*(0.009)0.038***(0.010)-0.026(0.019)0.015(0.010)0.017(0.010)-0.043***(0.016)-0.029(0.018)-0.001(0.012)0.004(0.022)-0.015(0.011)
0.002(0.009)0.040(0.026)0.012(0.011)0.031***(0.010)-0.061***(0.016)0.016(0.012)0.015(0.011)-0.064***(0.019)-0.018(0.018)0.003(0.013)-0.021(0.029)0.010(0.010)0.016(0.010)-0.026(0.016)0.008(0.005)Treatment buffer
0.038***(0.011)
Polygon size
-0.003(0.004)
Driving distance from main roads
Treatment*Distance
0.007(0.009)Treatment buffer
0.039***(0.010)Distance to road-0.012*(0.007)
Control mean
0.600
Observations[40,452]

0.009*(0.005)0.019*(0.011)-0.001(0.003)0.010(0.008)-0.009(0.015)-0.006(0.005)0.007(0.005)-0.004(0.010)0.001(0.005)0.010**(0.004)0.031***(0.010)0.002(0.003)0.007(0.005)0.018(0.012)0.004(0.004)0.012*(0.007)-0.000(0.015)-0.001(0.005)0.003(0.006)0.020**(0.010)0.009*(0.005)-0.001(0.009)0.019*(0.010)-0.010(0.007)0.593[64,231]0.019*(0.010)-0.009(0.015)-0.017*(0.009)0.599[98,063]0.004(0.009)-0.005(0.011)-0.009(0.009)0.591[177,610]

0.019*(0.010)0.032***(0.010)-0.022**(0.010)0.706[54,451]0.019**(0.009)0.017(0.012)-0.020**(0.009)0.693[84,765]0.037***(0.010)-0.001(0.014)-0.027***(0.010)0.689[126,637]

0.028***(0.010)0.018*(0.010)-0.021**(0.008)0.664[220,981]

Population density
Treatment*Pop. Density
Treatment buffer
Population density
Wealth index
Treatment*Wealth
Treatment buffer
Wealth index
Polygon size
Treatment*Size

Table examines heterogeneity in spillover probability of exit and entry of firms by four different
measures of market access: 1) Inverse distance-weighted population at the centroid of eachneighborhood, 2) Population above the poverty line instead of total population, 3) Size in square
meters of polygon, 4) Distance from polygon to a main road. Standard errors are clustered by theclosest Hábitat polygon associated to each firm, and are shown in parenthesis. Mean values of control
groups in italics. Number of observations in square brackets.

59

Table A12: Heterogeneity in Spillover Effects by Market Access
Period: 2008-2018

All firms
Population density
Treatment*Pop. Density
Treatment buffer
Population density
Wealth index
Treatment*Wealth
Treatment buffer
Wealth index
Polygon size
Treatment*Size
Treatment buffer
Polygon size
Driving distance from main roads
Treatment*Distance
Treatment buffer
Distance to road
Control mean
Observations

0m100mRevenue
100m250m250m
500m

500m1km

0m100mCapital stock
100m250m250m
500m

500m1km

0m100mWage bill
100m250m250m
500m

500m1km

0m100mPaid workers
100m250m250m
500m

500m1km

-1.582(3.148)4.201(3.133)
1.082(4.094)-9.052(6.746)
1.270(3.280)7.053(6.724)-10.882*(5.866)
1.197(3.608)0.236(6.211)2.024(4.808)0.987(3.522)-0.337(4.227)0.394(1.265)-0.145(1.102)0.630(1.759)-0.583(1.432)-0.193(1.049)-0.624(1.975)-3.686*(2.111)1.324(1.004)1.996(1.863)1.417(2.502)0.879(0.953)0.151(1.298)0.317(0.407)0.166(0.327)0.723(0.591)-0.244(0.465)0.199(0.270)0.584(0.605)-0.211(0.330)0.244(0.266)0.416(0.480)-0.224(0.153)0.094(0.221)0.317(0.251)0.007(0.080)0.051(0.068)0.217*(0.121)-0.084(0.095)0.061(0.057)0.180(0.127)-0.065(0.068)0.047(0.055)0.150(0.092)-0.018(0.032)0.024(0.042)0.083(0.051)-0.425(3.674)3.767(3.210)2.596(5.011)-6.270(7.125)-1.684(4.201)5.880(8.200)-8.193(7.096)-2.666(4.229)3.213(7.276)

4.223(4.863)1.356(3.606)1.029(5.212)

1.086(1.218)-0.065(1.212)0.998(2.035)0.238(1.529)-0.424(1.125)-0.911(2.312)-3.626(2.462)0.112(1.260)3.299(2.307)1.091(2.603)1.431(1.281)0.858(1.663)0.515(0.370)0.232(0.367)0.950(0.609)0.008(0.484)0.094(0.317)0.435(0.699)-0.052(0.385)0.134(0.283)0.477(0.523)-0.079(0.204)-0.022(0.205)0.369(0.346)0.037(0.075)0.048(0.073)0.303**(0.128)-0.036(0.104)0.030(0.065)0.183(0.145)-0.047(0.081)0.019(0.057)0.187*(0.100)0.007(0.039)0.010(0.039)0.107(0.071)-0.546(3.345)4.130(3.344)1.339(1.360)0.381(3.019)-2.064(4.940)1.963(2.174)-1.385(2.919)-3.996(5.768)1.484(2.395)-1.873(2.495)
1.924(4.173)-0.741(2.672)0.367(0.715)-0.192(1.330)-0.250(0.440)-0.242(0.595)-0.235(1.226)0.578(0.590)1.295(0.863)-0.792(1.863)-0.258(0.654)1.474(0.972)
1.587(1.798)-0.493(0.697)0.387***(0.145)0.129(0.388)-0.065(0.125)0.286*(0.154)0.079(0.353)0.182(0.148)0.109(0.162)0.135(0.331)0.243*(0.141)0.388**(0.171)-0.052(0.213)-0.061(0.149)0.085***(0.031)
0.023(0.075)-0.007(0.026)

0.069**(0.032)0.019(0.073)0.032(0.031)

0.026(0.033)0.006(0.068)0.037(0.028)0.078**(0.032)0.005(0.041)-0.018(0.030)-7.761** -2.267(3.399) (3.749)
3.593-2.465(3.154) (4.761)
4.304-1.894(2.924) (4.171)
64
74.30[53,882] [84,063]-1.066(4.624)-4.285(5.644)-0.555(5.083)92.85[125,766]-4.481(3.892)
2.367(4.071)5.444*(3.170)89.72[219,752]-1.298-1.422(1.313) (1.268)-0.093-0.402(1.217) (1.162)
0.460-0.436(1.226) (1.150)
18.19
19.88
[53,882] [84,063]-2.085-1.990(1.513)(1.568)-0.6681.622(1.860)(1.746)0.382
1.048(1.250)(0.946)23.63
22.53[125,766] [219,752]-0.040(0.408)0.235(0.371)
0.200(0.376)
5.333[53,882]

0.107(0.355)0.070(0.339)-0.383(0.363)
5.948[84,063]-0.170(0.395)0.097(0.324)-0.241(0.356)6.630[125,766]-0.467(0.306)-0.054(0.209)0.177(0.246)
7.241[219,752]-0.005(0.089)0.045(0.072)0.031(0.080)2.335[53,882]

0.021(0.079)0.020(0.070)-0.073(0.082)2.477[84,063]-0.022(0.085)-0.000(0.067)-0.064(0.080)2.644[125,766]-0.116*(0.065)0.005(0.039)0.040(0.051)
2.766[219,752]

Table examines heterogeneity in spillover effects by four different measures of market access: 1)
Inverse distance-weighted population at the centroid of each neighborhood, 2) Population above thepoverty line instead of total population, 3) Size in square meters of polygon, 4) Distance from polygon
to a main road. Standard errors are clustered by the closest Hábitat polygon associated to each firm,
and are shown in parenthesis. Mean values of control groups in italics. Number of observations insquare brackets.

60

Table A13: Saturation and variables at baseline

All sectors-24.265
Revenue(24.012)89.20
Capital stock-5.629(9.497)
35.84
Paid workers-0.077(0.592)
3.258
Wage bill-0.435(3.123)
10.27
Value added-10.874(9.741)
33.23
Payments to social security-0.115(0.455)1.319[9,758]Observations

Manufacturing-2.011(12.503)
64.42
11.289(14.042)
53.77
1.510
(1.248)5.103
6.913(6.802)18.84-0.497(6.098)27.95
0.916(1.027)2.498[8,278]Trade and Services-22.994(26.098)
91.03-10.542(8.744)30.78-0.513(0.517)2.860-2.549(2.686)8.306-10.856(10.241)33.31-0.429(0.384)1.026[9,707]

Table examines the relation between the main variables at baseline and the saturation of the programat municipality level. Standard errors clustered by municipality shown in parenthesis. Mean values ofcontrol groups in italics. Number of observations in square brackets.

61

62

19.596(86.901)
2,266.0
84.571*(46.060)
877.2
3.760
(5.365)186.526.857***(9.430)147.3-24.583(42.894)931.5
2.779***(0.936)5.1[370]-58.829(227.991)
8,648.0
113.738(132.202)
2,963.0-8.769(15.074)722.2
26.910(24.693)487.1-104.644(99.763)3,624.0
7.984***(2.812)14.6[370]

175.739(145.224)
3,728.0
135.457**(67.358)
989.4-3.151(8.479)245.5
26.827(18.530)283.3
26.425(68.878)
1,541.0
6.398***(1.681)8.2[370]

254.076(362.478)
13,858.0
384.730***(142.038)
3,140.0-17.150(24.565)958.1
33.028(51.278)996.3
42.614(153.804)
5,813.0
14.998***(4.673)21.2[370]

All sectors
2018
2013
unweighted weighted unweighted weighted
25.490(18.416)357.1
15.104(14.419)192.0
3.245**(1.579)32.8
10.784**(4.397)52.1
7.635(8.863)168.6
0.388(0.235)
1.6[354]

18.630(40.758)
1,121.0-50.751(32.355)
552.8
2.322
(3.724)101.9
11.090(8.670)156.2
9.074(20.256)
530.9
0.357
(0.362)4.7[354]

28.572(29.645)
514.0
6.980
(14.158)
174.6
0.387
(2.339)43.4
3.302(6.765)81.9
19.910(15.513)258.7
1.048**(0.459)2.3[354]

78.715(80.825)
1,657.0-2.279(29.168)514.3-2.225(6.273)
137.3-1.072(13.946)260.2
57.989(40.070)
841.3
0.634
(0.720)5.9[354]Manufacturing
2018
2013
unweighted weighted unweighted weighted-1.426(76.825)
1,942.0
68.399*(39.029)
709.2
0.957
(4.359)156.7
15.603**(6.980)100.2-26.121(38.473)
781.5
2.296***(0.880)3.7[370]-69.785(212.425)
7,555.0
114.975(104.949)
2,441.0-9.955(12.889)
625.7
7.418
(18.599)331.2-99.048(92.615)
3,105.0
7.801***(2.706)10.1[370]

158.056(137.994)
3,260.0
141.090**(64.558)836.0-2.957(6.844)205.6
21.258(14.898)
208.1
20.494(65.207)
1,307.0
5.090***(1.567)6.0[370]

223.040(352.752)
12,265.0
390.414***(136.503)
2,644.0-13.268(20.847)825.0
15.151(41.019)733.7
22.432(145.931)
4,987.0
14.464***(4.947)15.6[370]

Trade and Services
2018
2013
unweighted weighted unweighted weighted

Outcomes in this table are the polygon-level sum of firm-level variables, and so provide impacts on total value of outcomes at the polygon level.
Every coefficient is from a different regression. Robust standard errors shown in parenthesis. Mean values of control groups in italics. Number ofobservations in square brackets.

Observations

Payments to social security

Value added

Wage bill

Paid workers

Capital stock

Revenue

Dependent variable

Table A14: Aggregate effect of Habitat in Polygons

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Table presents the results of a simple accounting exercise that takes the polygon-level impacts from the prior table, scales them to represent the
total impact in all treatment polygons, and then uses marginal rates on the three core taxable business outcomes to calculate fiscal recovery by
the government. This number is then compared against the total cost of all treatment to provide a number of years required to pay off those costs.

Table A15: Fiscal Impacts of ProgramTechnical Appendix
This section describes the steps taken in the gathering, cleaning and matching of theHábitat and Economic Census data sets. The process of matching required several
adjustments to make data compatible across the three censuses as well as with thegeospatial data used in both sources.

Hábitat databaseAs broadly described in the data section, the Hábitat database contains detailed geospatial information of the blocks, called manzanas, included in the study. The Hábitat
study relies on INEGI’s identification system of blocks, which in most part is standardized across the Agency’s different projects. This makes fairly simple to cross data ofdifferent projects. At the same time, each block is identified to the polygon it belongs
within the project and its corresponding treatment/control status.
The Habitat data contains substantial richness; it is possible to observe the exact
type, amount, and location of each infrastructure upgrade a polygon received and on
which year it occurred (2009, 2010 or 2011). Because the actual investments made in agiven location were endogenous (both to the decisions of the Hábitat engineering teamand to the community-driven selection process) we largely abstract away from this andanalyze the treatment with a simple binary indicator.

Economic census database
The economic census microdata provided by INEGI comprises the events held in 2008,2013 and 2018. The census has a very high response rate, above 98% of all firms surveyed.
The timing of these censuses is remarkably fortuitous for a study of Hábitat, given thatthe first interval allows us to conduct a before-after analysis of the short-term impactsof the program on the private sector, and the 2018 wave allows us to examine impacts
7 years after the cessation of investment.
The census covers all businesses in Mexico that have a fixed location (including
informal businesses) and belong to the manufacturing, services or construction sectors.
The censuses have a very high response rate (more than 98%), given that firms are
required by law to respond, and INEGI has the mandate to make individual firm data
confidential.
INEGI uses unique identifiers for each business surveyed. Thus, if a firm appears in64

two or more censuses, it is possible to link the data collected and create a panel. Thatis, it is possible to follow firms through the censuses and also identify firm created anddestruction.
Another characteristic of the census database is that firms also contain detailed
information regarding their geographical location. Thus, firms can be tracked to theblock they are located within a city. This is crucial, as this geospatial information
makes possible to cross this database with the Hábitat database and identify thosefirms contained within Hábitat polygons. The variables used for analysis are: firm
revenue, capital stock, paid workers and wage bill. Additional variables are used to try
to understand mechanisms under the Hábitat program relates to firms’ performance.
We are able to locate 84,119 firms within Hábitat polygons. Given that there areslightly more control polygons, the majority of businesses are located in such polygons(roughly 60% of firms). In terms of sectors, the vast majority of businesses belong totrade and services (over 90% of total). This is consistent with the sectoral compositionof firms across the country. Within this group, most are grocery stores (around 25% of
total), and stationer’s shops and beauty salons (approx. 4% each). Manufacturing firms
tend to be concentrated in activities related to the production of food and beverages andvaried activities related to construction and housing. Around 3% of firms produce corn
tortillas and 1% are bakeries. Ironworks, furnishing and milling activities businesses
comprise close to 1% of the total each.
In terms of size, most of firms located in Hábitat polygons are microbusinesses. The
median of paid workers is 1, which means that the typical firm only ”employs” the ownerof the firm. However, there are some firms that employ up to 50 workers. In line with
the nature of microbusinesses, most firms have rather small yearly revenues (a typicalfirm makes US$ 15,600) and limited assets (less than US$ 2,500 for the typical firm).

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