# Structural Transformation and Environmental Externalities

Teevrat Garg, Maulik Jagnani, and Hemant K. Pullabhotla111Garg: School of
Global Policy and Strategy, University of California at San Diego, La Jolla,
CA 92093 (email: teevrat@ucsd.edu); Jagnani: Department of Economics,
University of Colorado Denver, Denver, CO 80204 (email:
maulik.jagnani@ucdenver.edu); Pullabhotla: Center on Food Security and the
Environment, Stanford University, Stanford, CA 94305 (email:
hemantpk@stanford.edu). We thank Sam Asher, Chris Barrett, Patrick Behrer, Jim
Berry, Marshall Burke, C. Austin Davis, Paul J. Ferraro, Sam Heft-Neal, Paul
Novosad, Tom Vogl, and numerous seminar audiences for valuable feedback. An
earlier version of this paper was circulated as “Agricultural Labor Exits
Increase Crop Fires”.

(This draft: September 2022
First draft: May 2020)

###### Abstract

Even as policymakers seek to encourage economic development by addressing
misallocation due to frictions in labor markets, the associated production
externalities – such as air pollution – remain unexplored. Using a regression
discontinuity design, we show access to rural roads increases agricultural
fires and particulate emissions. Farm labor exits are a likely mechanism
responsible for the increase in agricultural fires: rural roads cause movement
of workers out of agriculture and induce farmers to use fire – a labor-saving
but polluting technology – to clear agricultural residue or to make harvesting
less labor-intensive. Overall, the adoption of fires due to rural roads
increases infant mortality rate by 5.5% in downwind locations.

##  1 Introduction

The persistence of the agricultural productivity gap – a stylized fact that
marginal product of labor is substantially lower in agriculture than in other
sectors, especially in low- and middle-income countries – suggests that labor
is greatly misallocated across sectors (Gollin, Lagakos and Waugh, 2014;
Lewis, 1954; Kuznets, 1955). Understanding the causes of this labor
misallocation and identifying policies that enable structural transformation
away from low productivity agriculture has been a central focus of development
economics (Restuccia and Rogerson, 2017; Banerjee and Newman, 1998; Bryan,
Chowdhury and Mobarak, 2014). However, we know little about production
externalities associated with policies that enable labor reallocation away
from agriculture. If production externalities exist, by focusing exclusively
on private productivity, these policies may over- or under-correct the extent
of labor misallocation. In this paper, we provide the first evidence that
policies that address misallocation due to frictions in labor markets can
generate environmental externalities.

Using a regression discontinuity design that leverages a sharp increase in the
likelihood of road construction at population thresholds under the Pradhan
Mantri Gram Sadak Yojana (PMGSY) in India, we show rural roads increase
agricultural fires and particulate emissions. A new road results in a 60%
increase in the annual number of fires (or 1.6 additional fires) and a 1.1%
(0.5 $\mu$g/$m^{3}$) increase in annual satellite-measured PM2.5 levels in the
village. The effect of rural roads on particulate pollution is driven almost
entirely by increase in emissions from biomass burning, and concentrated in
the winter harvest and post-harvest months. This is consistent with the
explanation that agricultural fires (as opposed to, for example, vehicular
emissions or economic activity) are the primary mechanism linking rural roads
to increased local PM2.5 levels.

To explore mechanisms we look to the literature on the impacts of rural roads
constructed under PMGSY in India. Using the same regression discontinuity
design described above, Asher and Novosad (2020) find rural roads singularly
increased labor exits from agriculture while leaving most other economic
outcomes unaffected.222Asher and Novosad (2020) find rural roads increased the
availability of transportation services, and led to a 10 percentage point
(20%) decrease in share of workers in agriculture and a quantitatively similar
increase in share of workers in the non-agricultural sector. They find no
evidence for increases in assets or income. Farmers do not own more
agricultural equipment, move out of subsistence crops, or increase
agricultural production. We replicate their results in our sample. Consistent
with farm labor exits, we find descriptive evidence that rural roads increased
village agricultural wages. Higher agricultural wages can cause farmers to
substitute labor with fires to harvest sugarcane or clear rice harvest
residue. Indeed, we show that at baseline there exists a robust positive
correlation between relative farm wage rate and agricultural fires as well as
a robust negative correlation between farm labor share and agricultural fires.

Sub-sample analyses lend further support for farm labor exits as a likely
mechanism responsible for the increase in agricultural fires. First, rural
roads facilitate movement of workers out of agriculture, and increase
agricultural fires and particulate emissions in districts where relative
agricultural wage is lower (below median) at baseline, with comparatively
modest effects in districts where relative agricultural wage is higher (above
median). Second, the effect of rural roads on agricultural fires and
particulate emissions is concentrated in districts with a higher (above
median) production of crops that benefit from use of fires in the face of
agricultural labor exits at baseline, either to clear harvest residue off
fields within a narrow time window before planting in the next season (rice)
or to make harvest less labor-intensive (sugarcane). Together, precisely as
one might expect, we find the increase in agricultural fires and particulate
emissions is driven by districts with a higher production of rice or sugarcane
and where relative agricultural wage is lower at baseline, with small and
statistically insignificant effects in other districts.

Lastly, we fail to find evidence for alternative explanations linking rural
roads to agricultural fires, like changes in planting dates, and adoption of
mechanized harvesting.

In the final part of the paper, we test whether rural roads, which have
significant effects on agricultural fires and air pollution, were associated
with adverse health impacts. Using the same regression discontinuity design
described above, we find villages located within a 50 km distance downwind
from treated villages observe a 2.3% (1 $\mu$g/m3) increase in annual PM2.5
levels. However, as one might expect, we fail to find an increase in PM2.5
levels in villages located within a 50 km distance in all other (non-downwind)
directions from treated villages. Correspondingly, we find rural roads
increase the infant mortality rate by roughly 5.5% (0.3 percentage points) in
villages located within a 50 km distance downwind from treated villages, with
small and statistically insignificant effects in villages located within a 50
km distance in non-downwind directions from treated villages. Because, unlike
air pollution, other socioeconomic effects of rural roads are not correlated
with wind direction, the increase in infant mortality in downwind locations is
likely solely driven by the increase in air pollution from crop fires.

Our results contribute to a broad literature examining trade-offs and
synergies between economic development and environmental quality (see
Jayachandran, 2022a, for a review). Within this literature, the most closely
related are a set of papers that examine the effects of anti-poverty programs
on environmental quality (e.g., Alix-Garcia et al., 2013; Ferraro and
Simorangkir, 2020; Asher, Garg and Novosad, 2020; Behrer, 2022). Alix-Garcia
et al. (2013) and Ferraro and Simorangkir (2020) examine how cash transfer
programs that raise consumption and income affect deforestation, but find
opposite results. Asher, Garg and Novosad (2020) study rural road construction
under PMGSY in India to examine the deforestation effect of transportation
infrastructure, but find rural roads do not affect deforestation.333They also
study the effects of highways and find that those lead to large increases in
deforestation through increased demand for commercial timber. In a concurrent
paper, Behrer (2022) finds that a rural workfare program in India increased
incomes and induced farmers to invest in labor-saving capital technologies
like combine harvesters that left more crop residue and increased use of
agricultural fires.444Similarly, a recent working paper, Davis (2017), shows
that increased labor demand motivated sugarcane growers to adopt mechanized
harvesting in Brazil. Because sugarcane fields are burned in preparation for
manual harvesting but not mechanical harvesting, adoption of mechanized
harvesting may have decreased crop fires and improved environmental quality.
However, that paper does not examine effects on crop fires or air pollution.
That paper, like ours, is thus an exception in that its focus is on air
pollution – one of the leading causes of mortality worldwide – and not
deforestation. Overall, as these empirical investigations suggest, the
relationship between economic development and the environment is nuanced, and
different elements of development can accelerate or slow down environmental
degradation. Therefore, in the absence of a general theoretical result, it is
crucial to examine how and in what circumstances economic development helps or
hurts different dimensions of environmental quality (Jayachandran, 2022a).

We make two key contributions to this literature. One, we provide the first
empirical evidence for the direct substitution between environmental quality
and factor inputs like labor – a link that has long been part of the concept
of sustainability in environmental economics (Solow, 1993). Indeed, while
environmental economists have had a keen interest in studying the implications
of environmental policies for sectoral labor reallocation (Walker, 2013),
environmental externalities associated with policies that facilitate sectoral
labor reallocation remain entirely unexplored. The issue is particularly
salient in many low- and middle-income countries that are undergoing rapid
structural transformation while simultaneously experiencing dangerously high
levels of air pollution (Greenstone and Jack, 2015; Barrett, Ortiz-Bobea and
Pham, 2020).

Two, we are the first to causally relate human health to the environmental
quality impacts of anti-poverty programs, or more broadly, economic
development, for that matter. In order to calculate the environmental costs or
benefits of anti-poverty programs, accurate estimates are needed that link
environmental quality effects to observable health outcomes. However, previous
work does not examine any downstream impacts, health or otherwise, likely
because the environmental quality effects of anti-poverty programs are
correlated with the socioeconomic effects of anti-poverty programs. We
overcome this issue by exploiting pollution variation from wind direction
(e.g., Pullabhotla et al., 2022; Rangel and Vogl, 2019; Schlenker, and Walker,
2016; Deryugina et al., 2019), that is unlikely to be correlated with the
socioeconomic effects of rural roads.

##  2 Background

Air pollution remains one of the leading causes of mortality, accounting for 9
million premature deaths annually or roughly 16% of all deaths worldwide and a
staggering 268 million disability-adjusted-life-years (Landrigan et al.,
2018). Nowhere is the problem more pronounced than in India, which is home to
14 of the 20 most polluted cities in the world. In fact, if the city of New
Delhi, the capital of India, were to meet World Health Organization air
quality standards, average life expectancy would increase by 10 years
(Greenstone and Fan, 2019), roughly equivalent to the gains in life expectancy
made by the country on average in the 21st century (Max Roser and Ritchie,
2013). Of course, a number of factors – both moderate but perpetual and
seasonal but acute – contribute to the poor air quality in India. In this
section, we discuss (a) use of agricultural fires – a point source that
contributes to as much as half of the particulate pollution in many parts of
the country during winter months (Cusworth et al., 2018; Bikkina et al., 2019;
Shyamsundar et al., 2019), and (b) how rural roads may affect agricultural
fires.

###  2.1 Agricultural Fires in India

##### Why do farmers use fire?

Agricultural fires serve many purposes, including (i) clearing harvest residue
off fields in preparation for planting in the next season, (ii) making
sugarcane harvesting less labor-intensive, and (iii) clearing undergrowth on
fields left fallow between cropping seasons. Figure 1 shows wide spatial
distribution of agricultural fires across districts in India.555In Figure A.1
we report state-wise annual average number of fires between 2001 and 2013.
Figure A.2 shows the increasing trend in the number of fires annually between
2001 and 2013, the period of study. In 2003, there were roughly 55,000 fires
whereas in 2012 there were over 90,000 fires.

The use of fire is particularly prevalent in a coupled rice-wheat cropping
arrangement – a system of agriculture widespread across India (Jain, Bhatia
and Pathak, 2014; Prasad, Gangaiah and Aipe, 1999). In this system farmers
grow rice during the monsoon season (_kharif_) from June to November, and
wheat immediately following rice harvest during the winter season (_rabi_)
from January to May. A narrow window of time between the harvest of rice (in
October-November) and the planting of wheat (in December-January) requires
large-scale and quick removal of crop residue, and setting fire to crop
residue is particularly helpful in this process.666Farmers have an average of
only 13 days between the harvesting of rice and the sowing of wheat. On the
other hand, in the case of the wheat harvest, farmers report a window of 46-48
days between wheat harvest and planting of rice. Consistent with time
availability being a significant factor, nearly 90% of the farmers report that
they engage in burning after the rice harvest. In contrast, with a longer
interval available after the wheat harvest, only around 11% of the farmers use
burning to clear residue after the wheat harvest (Kumar, Kumar and Joshi,
2015).

Fire also plays a role in the production process for sugarcane - an important
crop across the country (Fair Labor Association, 2012). Farmers light
sugarcane fields to remove the outer leaves around the cane stalk before
harvesting the cane to make the process easier and require less manual labor
(Jain, Bhatia and Pathak, 2014).

Finally, the use of fire is widespread alongside forest lands in central and
north-east India that follow shifting cultivation, where fields are left
fallow for more than a year, and farmers switch between alternate plots of
land (Venkataraman et al., 2006; Ramakrishnan, 1992). Fallow fields are often
overtaken by undergrowth, which needs to be cleared before subsequent season’s
planting.

##### The private costs of agricultural fires.

Although fires offer an easy and inexpensive means of clearing agricultural
residue, they impose private costs on agricultural households. First, burning
crop residue carries civil and criminal penalties under Section 188 of the
Indian Penal Code and the Air and Pollution Control Act of 1981. While
enforcement is not perfect, it is far from absent.777See for example,
https://www.downtoearth.org.in/blog/agriculture/stubble-burning-a-problem-for-
the-environment-agriculture-and-humans-64912

Second, crop residue burning decreases the productivity of agricultural land
by destroying micro-nutrients in the soil, and removing valuable fertilizer
including nitrogen and phosphorus (Smil, 1999; Stan, Fîntîneru and Mihalache,
2014; Swayer, 2019; Prasad, Gangaiah and Aipe, 1999). Prior work has
demonstrated that burning of rice and wheat residue can result in the loss of
about 80% of nitrogen, 25% of phosphorus, 21% of potassium and 4 to 60% of
sulphur from the soil (Mandal et al., 2004).

Third, source-apportionment studies suggest pollution from agricultural fires
can raise local concentrations of PM2.5 to more than 1,000% above the WHO
24-hour guideline of 25$ug/m^{3}$ (Bikkina et al., 2019; Liu et al.,
2018).888Local PM2.5 concentrations in almost all Indian villages are above
the WHO 24-hour guideline of 25 $ug/m^{3}$ (Figure A.3). Exposure to pollution
from crop fires decreases birth weight, gestational length, and in utero
survival (Rangel and Vogl, 2019), increases infant mortality (Pullabhotla et
al., 2022), decreases child height for age and weight for age scores (Singh et
al., 2019), decreases cognitive performance (Graff Zivin et al., 2020), and
increases risk of acute respiratory infections (Chakrabarti et al., 2019).
Unsurprisingly, local households incur significant expenses to mitigate the
consequences of burning-induced air pollution: medical records of local
hospitals in the rice-wheat belt showed a 10% increase in the number of
patients within 20–25 days of the burning period every season (Kumar, Kumar
and Joshi, 2015); the same study finds inhabitants spend roughly USD 1 million
every year on treatment for ailments caused by stubble burning.

Fourth, residue collected from the fields is of substantial economic value
(Berazneva et al., 2018). Crop residue generated from the coupled rice-wheat
cropping system can be used as livestock feed, which is in short supply across
India, by roughly 40% (Kumar, Kumar and Joshi, 2015). Moreover, soil treated
with crop residues can hold 5 to 10 times more aerobic bacteria and 1.5 to 11
times more fungi than soil from which residues were burnt, providing higher
yields (Beri et al., 1992; Sidhu, Beri and Gosal, 1995).

###  2.2 Agricultural Fires and Rural Roads

##### Pradhan Mantri Gram Sadak Yojana (PMGSY)

In 2000, an estimated 330,000 of India’s 825,000 rural villages lacked any
all-weather road access. The Pradhan Mantri Gram Sadak Yojana (PMGSY) – the
Prime Minister’s Village Road Program – was launched in 2000 with the goal of
providing all-weather road access to unconnected villages across India.
Importantly, the national program guidelines prioritized larger villages
according to arbitrary thresholds based on the 2001 Population Census (Asher
and Novosad, 2020). The guidelines aimed to connect all villages with
populations greater than 1,000 by 2003, all villages with population greater
than 500 by 2007, and villages with population over 250 after that.999The unit
of targeting in the PMGSY is the habitation, defined as a cluster of
population whose location does not change over time. Revenue villages, which
the Economic and Population Censuses use, are comprised of one or more
habitations (National Rural Roads Development Agency, 2005). In this paper, we
aggregate all data to the revenue village level. These rules were to be
applied on a state-by-state basis, meaning that states that had connected all
larger villages could proceed to smaller localities; for instance, states with
few unconnected villages with over 1,000 people used the 500-person threshold
immediately.101010Some states did not comply with the threshold guidelines.
Some states included several other prioritization guidelines, and it is
possible that political patronage played a role. See Asher and Novosad (2020)
for more details. In the empirical exercise that follows we limit our analysis
to states that complied with these guidelines and where we can show that there
was a clear discontinuity in the probability of receiving a rural road around
the relevant population thresholds. Rural road construction under PMGSY began
in 2000 but only really took off in 2002, continuing steadily thereafter
through to the end of the sample period in 2013 (Figure A.4).

##### How do rural roads affect agriculture?

Asher and Novosad (2020) leverage the discontinuous increase at the
aforementioned population thresholds for complying states using a fuzzy
regression discontinuity design to show rural roads – constructed under PMGSY
– cause a substantial increase in the availability of transportation services
but do not increase assets or income. We replicate the results from Asher and
Novosad (2020) in Appendix B. Farmers do not own more agricultural equipment,
move out of subsistence crops, or increase agricultural production. They do
find that rural roads lead to a large reallocation of workers out of
agriculture. They find suggestive evidence that the growth in non-agricultural
workers is due to greater access to jobs outside the village. Finally, they
decisively rule out small changes in permanent migration, implying that their
results are not the product of compositional changes to the village
population.111111They also show rural roads do not affect the share of adults
who are either not working or who are in occupations that they are unable to
classify, suggesting that changes in the workforce are not responsible for
changes in the share of workers in agriculture or non-agricultural wage work.
Overall, the main effect of rural roads is to facilitate the movement of
workers out of agriculture with no major changes in agricultural outcomes,
income, or assets.

##### How could rural roads affect agricultural fires?

Labor exits from agriculture following rural road construction may raise
village agricultural wages. Using the 1999 and 2006 Rural Economic and
Demographic Survey (REDS), we find suggestive evidence that rural-road-induced
agricultural labor exits increased agricultural wages (Appendix D). Higher
labor costs will make farm work more expensive and can cause farmers to use
fires as a labor saving technology to clear rice harvest residue off fields in
preparation for the subsequent season’s wheat planting or make sugarcane
harvesting less labor-intensive; Indian authorities have long speculated that
decreased availability of labor is one important reason for increase in
agricultural fires over the last two decades (Department of Agriculture &
Cooperation, 2014). Indeed, we show that at baseline there exists a robust
positive correlation between relative agricultural wage rate and crop fires at
baseline as well as a robust negative correlation between agriculture labor
share and crop fires (Figure A.5).

Other possibilities that would link road construction to fire use include
changes in planting dates and adoption of mechanized harvesting.

In Section 5.1.2, we examine these mechanisms. We find evidence for farm labor
exits as one mechanism responsible for the increase in agricultural fires and
fail to find evidence for alternative channels.

##  3 Data

Our primary analysis combines information on village-level rural road
construction under PMGSY with (a) satellite-based measures of agricultural
fires and PM2.5 collapsed at the village level and (b) village-level infant
mortality rates imputed from birth history data.

##### Rural roads.

We use the Socioeconomic High-resolution Rural-Urban Geographic Dataset
(SHRUG) for information on village-level rural road construction dates under
PMGSY (Asher et al., 2019). We merge data on roads with village-level
shapefiles (Meiyappan et al., 2017, 2018). We follow Asher and Novosad (2020),
who worked closely with the National Rural Roads Development Agency to
identify the state-specific thresholds that were followed, to define our
sample. That is, we restrict our analysis to villages from states that adhered
to the population priority criterion (in parentheses) set forth by the
national government: Chhattisgarh (500, 1,000), Gujarat (500), Madhya Pradesh
(500, 1,000), Maharashtra (500), Orissa (500), and Rajasthan (500). The
analysis sample is restricted to the 11,151 villages that (i) did not have a
paved road in 2001; (ii) were matched across all primary data sets in SHRUG;
(iii) had identifiable geographic coordinates required to generate satellite-
based village-level outcome measures; and (iv) had populations within the
optimal bandwidth from a treatment threshold.121212Our sample is slightly
smaller than the analysis sample in Asher and Novosad (2020) because we could
not identify geographic coordinates for 281 (roughly 2%) villages.

##### Agricultural fires.

We merge data on roads with satellite-detected fire activity data from NASA’s
Earth Observing System Data and Information System (EOSDIS) to capture annual
agricultural fires within 10 kilometers of each village. The fire activity
data are based on detections of infrared radiation that are a signature of
biomass fires (Giglio, Csiszar and Justice, 2006). The underlying MODIS
algorithm identifies a pixel (approximately one square kilometer area) with
fire activity if at least one thermal anomaly is detected within that pixel.
Unlike other remotely sensed products (for example, forest cover), fires data
are considerably more precise since temperatures of fires are orders of
magnitude higher than those of other non-fire pixels. These data on thermal
anomalies have been used extensively by atmospheric scientists to study the
effects of agricultural fires on pollution in India and elsewhere (Liu et al.,
2018). The MODIS data provides us with a daily, geocoded record of fire pixels
from 2001 - 2013. We estimate annual counts of fires within a 10-kilometer
radial buffer around the centroid of each village polygon.

##### Air pollution.

To generate pollution indicators at the village level, we rely on modeled
PM2.5 pollution estimates from Van Donkelaar et al. (2016). These gridded data
are derived from satellite measures of aerosol density, combined with chemical
transport models and calibrated to global ground-based observations of PM2.5.
The Van Donkelaar et al. (2016) data provide annual average PM2.5 values at
$0.1\degree\times 0.1\degree$ resolution for 1998-2013.

Furthermore, to examine seasonal impacts on air pollution, we complement these
annual PM2.5 data with monthly data on black carbon and organic carbon
emissions (precursors to particulate pollution) from the Modern-Era
Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)
(Gelaro et al., 2017). The MERRA-2 model apportions these emissions into those
arising from biomass burning (which are driven by agricultural fires) and
those arising from all other anthropogenic sources such as transportation,
industrial emissions, or other combustion sources.131313The MERRA-2 system
produces gridded output on aerosol diagnostics on a global scale. It uses a
chemical transport model (the Goddard Chemistry, Aerosol, Radiation, and
Transport model (GOCART) that takes in a variety of different emission
inventories as inputs to simulate aerosols arising from both natural and
anthropogenic sources (Randles et al., 2017). Emission inventories used to
model aerosols from biomass burning differ from those used to capture other
anthropogenic sources. Emissions from biomass burning rely on multiple
inventories, including the Reanalysis of the Tropospheric Chemical
Composition, the Global Fire Emissions Database, and the Quick Fire Emission
Dataset. A separate emissions inventory from AeroCom Phase II (Diehl et al.,
2012) is used to model anthropogenic emissions from residential, industry,
power generation, and transportation sectors (Gelaro et al., 2017). Validation
studies in India find that MERRA-2 simulated black carbon and organic carbon
mass concentrations show a strong correlation with ground-based measures (Soni
et al., 2021).  This allows us to separately examine the impact of rural roads
on emissions from agricultural fires and emissions from all other
anthropogenic sources. The MERRA-2 data are gridded ($0.50\degree\times
0.625\degree$) monthly estimates of emissions based on satellite and climate
reanalysis measurements. We interpolate both biomass emissions and non-biomass
emissions gridded values to each village’s geolocation in our sample.

In contrast to the fires data, our measures of air pollution suffer from two
different sources of measurement error. First, air pollution data are obtained
from calibrated chemical transport models that translate satellite imagery
into ground level exposure of air pollution. Second, air pollution, unlike
agricultural fires, can travel large distances spilling over into nearby
villages. Despite these measurement error in the air pollution data, we are
able to estimate precise effects of road construction on air pollution that
correspond with estimates of the effects on crop fires.

##### Infant mortality.

Infant mortality is a commonly used measure to document the external costs of
air pollution (Currie and Walker, 2011; Greenstone and Hanna, 2014). Infant
mortality is highly responsive to short-run variability in air pollution since
the first year of life is especially sensitive to environmental stressors.
Moreover, focusing on infant mortality obviates the need to estimate life
years lost and instead can be attributed to the full value of a statistical
life.

We generate infant mortality estimates using birth history data from the
National Family Health Survey (NFHS-IV). The NFHS-IV, conducted in 2015-16, is
the Indian wave of the Demographic and Health Surveys. The survey interviews
all women aged 15-49 within the sample households and records detailed
information on their birth histories. The birth histories provide us with data
on the year of birth and the age at which the child died (if applicable). We
use this information to assess infant mortality at the child-birth-year level
from 2001 to 2013; that is, we generate an indicator variable that takes the
value 1 if the child died within 12 months of birth, and 0 otherwise. We then
identify all NFHS clusters (villages) within a 50 km distance from each PMGSY
village to generate estimates of infant mortality effects for children in
these matched NFHS villages.141414NFHS-IV sample clusters are geocoded sample
enumeration areas roughly equivalent to a village in rural India.

##  4 Empirical Strategy

Using a fuzzy regression discontinuity design, and data from 2002 to 2013, to
estimate the effect of rural roads on agricultural fires, PM2.5, and infant
mortality rates. Due to imperfect compliance of rural road construction with
rules (population threshold) that determine award of rural roads under PMGSY,
we use a two stage least squares specification with optimal bandwidth local
linear regression (Imbens and Kalyanaraman, 2012; Gelman and Imbens, 2019):

$\displaystyle Road_{vdst}$
$\displaystyle=\gamma_{0}+\gamma_{1}\mathbf{1}(pop_{vds}\geq
T)+\gamma_{2}(pop_{vds}-T)+$ ((1))
$\displaystyle+\gamma_{3}(pop_{vds}-T)*\mathbf{1}(pop_{vds}\geq
T)+\boldsymbol{\theta}\mathbf{X_{vds}}+\mu_{d,h}+\rho_{t}+\nu_{vdst}$
$\displaystyle Y_{vdst}$
$\displaystyle=\beta_{0}+\beta_{1}Road_{vdst}+\beta_{2}(pop_{vds}-T)+$ ((2))
$\displaystyle+\beta_{3}(pop_{vds}-T)*\mathbf{1}(pop_{vds}\geq
T)+\boldsymbol{\delta}\mathbf{X_{vds}}+\eta_{d,h}+\omega_{t}+\varepsilon_{vdst}$

$Road_{vdst}$ takes the value 1 if village $v$ in district $d$ in state $s$
receives a PMGSY road by year $t$. $Y_{vdst}$ is outcome of interest (number
of agricultural fires, PM2.5, infant mortality rate) for village $v$ in
district $d$ in state $s$ in year $t$.151515As we discuss in Section 6,
because the infant mortality data is available for NHFS villages and not for
PMGSY villages, to estimate effects on infant mortality, in equation (2), the
outcome variable is at the child-birth-year level where $Y_{invdst}$ is an
indicator variable that takes the value 1 if child $i$ in NFHS village $n$,
within 50 km of PMGSY village $v$ in district $d$ in state $s$, born in year
$t$, died within the first year of birth, and 0 otherwise. The population of
the village in 2001 is $pop_{vds}$, while $T$ is the treatment threshold
(either 500 or 1000, depending on the state). Figure B.1 shows the density of
the village population distribution is continuous across the treatment
threshold; the McCrary test statistic is $-0.010$ (s.e. $0.048$) (McCrary,
2008). $\mu_{d,h}$ and $\eta_{d,h}$ are district-population threshold fixed
effects - that is, an interaction of district dummies with an indicator
variable that takes the value $1$ if village is in a state where the treatment
threshold is equal to $1,000$, and $0$ otherwise. $\rho_{t}$ and $\omega_{t}$
are year fixed effects. Thus, the RD estimates compare outcomes for villages
within the same district but on opposite sides of the PMGSY population
threshold in year $t$.

To ensure that our design follows closely with Asher and Novosad (2020), we
include a vector of baseline (2001) village characteristics,
$\mathbf{X_{vds}}$, as controls, although excluding these controls does not
alter our results appreciably. Specifically, we control for village amenities
(primary school, medical center, electrification), agricultural
characteristics including total agricultural land area, (log) share of
irrigated agricultural land, and share of workers in agriculture, and village-
level measures of socio-economic status and connectivity like literacy rate,
share of inhabitants that belong to a scheduled caste, share of households
owning agricultural land, share of households who are subsistence farmers,
share of households earning over 250 INR cash per month (approximately 4 USD),
and distance in km from the closest census town. We also control for count of
fires or PM2.5 levels or infant mortality rate in 2001 when the outcome
variable is count of fires or PM2.5 levels or infant mortality rate,
respectively. We estimate equation (1) using an optimal bandwidth (84) with a
triangular kernel that provides higher weights to observations close to the
threshold (Calonico, Cattaneo and Titiunik, 2014). Standard errors are
clustered at the village level.

##### Balance and falsification tests.

In Table 1, we present the mean values for various village baseline
characteristics, including the set of controls that we use in all regressions,
as well as mean values for our outcomes variables at baseline. Importantly, we
fail to find evidence for discontinuous changes at the threshold for (i)
various village baseline characteristics (balance tests) or (ii) baseline
values of our outcome variables (falsification tests).

##  5 Effects of Rural Roads on Crop Fires and PM2.5

Figure 2 shows the graphical representation of the reduced form effect; i.e.,
the change in likelihood of rural road construction under PMGSY (Figure 2(a))
and count of agricultural fires at the (treatment) population threshold
(Figure 2(b)). We observe a large and statistically significant increase in
likelihood of rural road construction and number of fires for villages at the
treatment threshold. Table 2 shows the corresponding point estimates. Column
(1) presents the first stage result. Villages above the PMGSY population
threshold observe a 23 percentage point increase in the likelihood of
receiving a road. Column (2) shows the instrumental variable (IV) or local
average treatment effects (LATE); we instrument access to rural roads with the
PMGSY population threshold. Villages with rural road access observe a 60%
increase (1.6 additional fires) in the annual number of fires compared to
villages that do not have access to a rural road.

Next, we examine the consequent effect of rural roads on local air quality.
Our outcome is a satellite-based estimate of the annual average ambient PM2.5
concentrations for each village. Figure 2(c) shows the graphical
representation of the reduced form effect of rural roads on PM2.5. We observe
a statistically significant increase in PM2.5 for villages at the population
threshold that determines rural road construction under PMGSY. Column (3)
shows the IV or LATE point estimates where we instrument access to rural roads
with PMGSY population threshold; villages with rural road access observe a
(0.5 $\mu$g/$m^{3}$) 1.1% increase in annual average PM2.5, compared to
villages that do not have access to a rural road.

The 1.1% increase in annual PM2.5 levels is comparable to the increase in
particulate pollution (1.6%) on days when air quality is not monitored in the
US (Zou, 2021). It is roughly 25% the size of the percentage decrease in
particulates (4%) due to subway openings worldwide (Gendron-Carrier et al.,
2022), and about 6% the magnitude of the percentage decrease in particulate
matter (19%) due to the most stringent air pollution regulations in India
(Greenstone and Hanna, 2014).

###  5.1 Mechanisms

####  5.1.1 Do agricultural labor exits increase crop fires?

In this section, we examine whether farm labor exits following rural roads
construction is a mechanism responsible for the increase in agricultural
fires. As discussed above, consistent with farm labor exits, we find
descriptive evidence that rural roads increased village agricultural wages.
Higher agricultural wages can cause farmers to substitute labor with fires to
harvest sugarcane or clear rice residue. Indeed, we show there exists a robust
positive correlation between relative farm wage rate and crop fires at
baseline.

Heterogeneity analyses lends further support for agricultural labor exits from
road construction as one mechanism responsible for the increase in crop fires.
First, rural roads facilitate movement of workers out of agriculture and
increase agricultural fires in districts where relative agricultural wage is
lower (below median) at baseline, but not in districts where relative
agricultural wage is higher (above median) at baseline.161616Wage data are
unavailable for PMGSY villages (the unit of analysis for our regression
discontinuity research design) during the period of our study. The employment
and unemployment surveys conducted by the National Sample Survey Organization
(NSSO) of India collect data on the daily activities for the past seven days
for all household members above four years of age. We use the $55^{th}$ round
of the NSSO (1999 - 2000) and compute the average earnings per day worked in
casual labor for individuals aged 18-60 residing in rural areas by dividing
the weekly earnings by the number of days worked. We then calculate the
district-level relative agricultural labor wage rate as the sample-weighted
average of the ratio of the daily wage rate for casual labor in the
agricultural sector to the non-agricultural sector. For districts in our
sample, the median ratio is $0.46$. We split the sample into higher and lower
relative agricultural wage rate districts based on this sample median. The
effects of rural roads on labor exits from agriculture and the increase in
non-agricultural manual labor share is driven by districts with lower relative
agricultural wage rates at baseline (Figure E.1; Table E.1). Unsurprisingly,
farm laborers in districts with lower relative agricultural wage are more
likely to reallocate to non-farm work outside the village due to access to
rural roads. Correspondingly, the increase in agricultural fires (Figure E.2;
Table E.2) and particulate emissions (Figure E.2; Table E.2) is concentrated
in districts with lower relative agricultural wage rates at baseline, with
comparatively smaller effects in districts with higher relative agricultural
wage rates at baseline.

Second, the effect of rural roads on agricultural fires is concentrated in
districts with a higher (above median) baseline production of crops that
benefit from use of fires in the face of agricultural labor exits, either to
clear harvest residue off fields within a narrow time window before planting
in the next season (rice) or to make harvesting less labor-intensive
(sugarcane).171717We match districts in the analysis sample to agricultural
data obtained from the ICRISAT District Level Database. The ICRISAT District
Level Data are available online from the following website:
http://data.icrisat.org/dld/src/crops.html. We use data for the year 2001 to
classify higher/lower rice or sugarcane districts based on the sample median
of acreage share for the respective crops. Figure F.1 presents the spatial
distribution of average annual fire activity and the share of rice and
sugarcane grown across districts in our analysis sample. A simple eyeball test
suggests fire counts are higher in districts where the share of rice or
sugarcane area is above the median at baseline compared to districts where the
share of rice or sugarcane area is below the median. More formally, indeed,
the effects of rural roads on agricultural fires (Figure F.2; Table F.1) and
particulate emissions (Figure F.2; Table F.1) are driven by districts with
higher share of rice or sugarcane acreage at baseline, with comparatively
modest effects in districts with lower share of rice and sugarcane acreage at
baseline.

Together, precisely as one might expect, we find the increase in agricultural
fires (Figure 3; Table 3) and particulate emissions (Figure 3; Table 3) is
concentrated in districts that observe a higher production of rice or
sugarcane and where the relative agricultural wage is lower at baseline, with
small and statistically insignificant effects in other districts. Agricultural
labor exits are higher in districts where relative agricultural wage rate is
lower at baseline; therefore, it is likely that farmers in these districts
observe a larger increase in labor costs due to rural road construction.
Consequently, farmers in districts within this sub-sample where rice or
sugarcane production is higher will drive the adoption of fire as a labor-
saving technology: either to clear rice residue within a short time window
before planting next season’s crop or to make sugarcane harvesting less labor-
intensive.

####  5.1.2 Alternative Explanations

Other explanations that would link road construction to air pollution include
increase in economic activity or vehicular emissions. Alternative explanations
that would link road construction to fire use include changes in planting
dates, increase in non-farm biomass fires, adoption of mehcanized harvesting,
and increase in school enrollment. Below we evaluate these alternative
possibilities but fail to find evidence to support these mechanisms.

##### Is the increase in local PM2.5 levels driven by increase in economic
activity or vehicular emissions?

Asher et al. (2019) fail to find evidence that rural roads increase local
economic activity, as proxied by night lights (Appendix B). However, rural
roads increased employment growth in manufacturing and retail (although the
former is not statistically significant) as well as the availability of
transportation services such as government buses. Therefore, it is plausible
that economic activity or vehicular traffic and not agricultural fires relate
rural roads to local particulate emissions.

We directly test for this possibility by comparing the effects of rural roads
on fires and particulate pollution in the winter harvest and post-harvest
months – months in which access to rural roads increases agricultural fires
(October through March) – with the effects of rural roads on particulate
pollution during the rest of the year (April through
September).181818Decreased availability of farm labor or increased labor costs
– following rural road construction – could cause farmers to use fires as a
labor-saving technology to clear rice harvest residue off fields in
preparation for the subsequent season’s wheat planting or to make sugarcane
harvesting less labor-intensive. Crop fires associated with burning of rice
residue occur between October and December (Jain, Bhatia and Pathak, 2014;
Prasad, Gangaiah and Aipe, 1999; Kumar, Kumar and Joshi, 2015). Crop burning
associated with the sugarcane harvest takes place between December and March
(Directorate of Economics & Statistics, 2019). Therefore, we examine the
effects of rural roads on agricultural fires and particulate pollution between
October and March and compare it to the effects of rural roads on agricultural
fires and particulate pollution in the rest of the year. If agricultural fires
and not economic activity or vehicular emissions are the primary mechanism
linking rural roads to increased local PM2.5 levels, one would expect to see
the effects of rural roads on particulate pollution concentrated from October
through March, with comparatively modest effects in the rest of the year.
Indeed, we find rural roads increase both agricultural fires and particulate
pollution from all sources in the winter harvest and post-harvest months of
October to March, with comparatively modest effects in the rest of the year
(Table Figures and Tables, Columns 1-3).191919The PM2.5 data is available only
at the annual level. Therefore, to examine seasonal effects, we use monthly
emissions data from the Modern-Era Retrospective analysis for Research and
Applications, Version 2 (MERRA-2) (Gelaro et al., 2017). We focus on black
carbon and organic carbon, the two major pollutants commonly associated with
biomass burning, and which form precursor elements that lead to PM2.5
particles’ formation in the atmosphere (Cusworth et al., 2018).

However, if the increase in economic activity or use of transportation
services is concentrated in harvest months, the above test would fail to rule
out economic activity or vehicular emissions as a cause for increased local
PM2.5 level. Therefore, we further show the impact on particulate pollution is
driven almost entirely by the increase in emissions from biomass burning with
no effect on emissions from other (non-biomass burning) anthropogenic sources
(Table Figures and Tables, Columns 4-7). These results suggest that
agricultural fires are the primary mechanism linking rural roads to increased
local PM2.5 levels.

##### Is the increase in crop fires driven by changes in planting dates?

Rural roads may decrease the already short turnaround time between rice
harvest and wheat planting even further in two ways, increasing fire use. One,
rural-road-induced labor exits may delay rice harvest, leaving little time to
clear the residue before subsequent season’s wheat planting. Two, road
drainage and excavation may lower the water table in surrounding areas
(Tsunokawa and Hoban, 1997). Therefore, rice planting might be delayed, and
farmers may be rushed to harvest and clear the monsoon season’s crop (rice)
and plant winter season’s crop (wheat) on time. In Appendix C, we examine the
effect of rural roads on satellite-based measures of harvest (end-)date and
planting date for the monsoon season crop (rice). We fail to find evidence
that access to rural roads affects harvest or planting dates for rice.

##### Is the increase in crop fires driven by increase in adoption of
mechanized harvesting?

Farm labor exits due to rural roads may also affect number of agricultural
fires by increasing adoption of mechanized harvesting. Behrer (2022) uses a
difference-in-differences design to show India’s Mahatma Gandhi National Rural
Employment Guarantee Act (NREGA), which guaranteed rural employment, increased
the incidence of agricultural fires. The author’s results suggest income
growth associated with NREGA induced farmers to invest in labor-saving
technologies like combine harvesters, which leave more crop residue,
increasing use of agricultural fires. Because rural roads do not increase
ownership of mechanized farm equipment (Appendix B), such an indirect
mechanism is unlikely to be operational in our study. Nevertheless, farmers
often do not own combines but rather rent a combine (Shyamsundar et al.,
2019). Therefore, we use the 1999 and 2006 REDS to examine the effect of rural
roads on village-level stock of agricultural machinery (Appendix D). We fail
to find evidence that rural roads increase the local stock of combines. We
also fail to find evidence that rural roads increase household use of hired
mechanized agricultural equipment (tractors, combine harvesters, threshers
etc.).

###### High vs. low mechanization at baseline.

As a second test for the influence of combine harvesters on our results, like
Behrer (2022), we examine whether districts where farmers could feasibly
respond to farm labor exits by mechanizing their harvests saw larger increases
in crop fires. We compare the effect of rural roads for districts with high
(above median) mechanization at baseline to districts with low (below median)
mechanization at baseline.202020To generate an agricultural mechanization
index at baseline, we use the 2001-02 Agricultural Input Census available
here: http://inputsurvey.dacnet.nic.in/districttables.aspx. The 2001-02 census
collected information on the number of mechanized equipment used in ploughing,
leveling, seeding and planting, tilling, spraying, harvesting (combines),
threshing, cane crushing, etc. Unfortunately, district-level mechanization
data are unavailable for two states in our sample – Gujarat and Maharashtra –
which are therefore excluded from the heterogeneity analysis. We fail to find
evidence for any significant differential effect of rural roads on crop fires
(Table A.1). In fact, the effect is slightly larger for low mechanization
districts; in high mechanization districts, rural roads increase crop fires by
46%, compared to 58% in low mechanization districts. We find the same pattern
of results if we restrict our sample to districts with a higher production of
rice at baseline, a crop that would benefit from combine harvesters in the
face of farm labor exits; rural roads increase crop fires by 110% in high
mechanization rice-growing districts, compared to 131% increase in low
mechanization rice-growing districts (Table A.2). We also find the same
pattern of results if we split our sample by a mechanization index that only
includes rice harvesting equipment (self propelled combine harvesters and
tractor drawn combines); rural roads increase crop fires by 96% in high
mechanization rice-growing districts, compared to 104% increase in low
mechanization rice-growing districts (Table A.3).

###### High sugarcane vs. high rice at baseline.

As a final test for the influence of mechanized harvesting on our results, we
leverage the fact that unlike mechanized rice harvesting, mechanized sugarcane
harvesting should decrease and not increase crop fires. Farmers light
sugarcane fields to remove the outer leaves around the cane stalk before
harvesting the cane manually to make the process easier and require less labor
(Jain, Bhatia and Pathak, 2014). However, mechanical harvesting does not
require burning (Davis, 2017; Rangel and Vogl, 2019). Therefore, if farm labor
exits motivate sugarcane growers to adopt mechanized harvesting (Davis, 2017),
one would expect to find a decrease in crop fires in districts with a high
(above median) baseline production of sugarcane.

We find the opposite result. We find that in high sugarcane districts, like in
high rice districts, rural roads increase crop fires. In fact, the point
estimates are almost identical: rural roads increase crop fires by 104% in
sugarcane-growing districts, compared to 103% increase in rice-growing
districts (Table F.2). We find the same pattern of results if we restrict our
sample to high sugarcane but low (below median) rice and high rice but low
sugarcane districts at baseline: rural roads increase crop fires by 89% in
high sugarcane-growing but low rice-growing districts, compared to 83%
increase in high rice-growing but low sugarcane-growing districts (Table F.3).

##### Is the increase in school enrollment responsible for the increase in
crop fires?

Adukia, Asher and Novosad (2020) show rural road construction under PMGSY
significantly increased middle school enrollment. If, prior to road
construction, children were employed to harvest sugarcane or clear rice
harvest residue, it is possible that the increase in crop fires is driven by
child farm labor exits due to increase in school enrollment.

To test this hypothesis, we examine whether crop fires increased most (least)
where enrollment increased most (least); Adukia, Asher and Novosad (2020) show
enrollment increased most where road connections are expected to raise the
returns to education the most and least where roads are expected to most raise
the opportunity cost of education. We proxy for the expected size of the
opportunity cost effect with the district-level urban-rural wage gap. To proxy
for the expected size of the returns to education effect, we identify the
difference in returns to education between individuals in rural and urban
areas.212121The assumption underlying their heterogeneity analysis is that
reductions in transportation costs will lead to factor price equalization:
when a rural village receives a new road, its wages and returns to education
will adjust toward the wages and returns in the broader geographic area. We
define binary indicator measures for the opportunity cost proxy and the
returns to education proxy, based on whether each proxy is above the sample
median.

We fail to find evidence that increase in school enrollment is responsible for
the increase in crop fires (Table A.4). First, we find the increase in crop
fires is concentrated in districts where roads are expected to most raise the
opportunity cost of education, with small and statistically insignificant
effects in other districts, the opposite of what one would expect if increase
in school enrollment were a mechanism. However, this result is exactly what
one would expect if farm labor exits were a mechanism for increase in crop
fires: after road construction, rural (farm) workers who faced the highest
opportunity cost of (farm) work in the village were most likely to exit the
local labor market in favor of (non-farm) work outside the village. Second, we
fail to find evidence that fires increased most where road connections are
expected to raise the returns to education the most: rural roads increased
crop fires by 62% in districts where road connections are expected to raise
the returns to education the most, compared to 66% in districts where road
connections are expected to raise the returns to education the least.

##### Is the increase in fire activity driven by non-farm biomass fires?

As discussed in Section 3, we use data from NASA’s EOSDIS to capture annual
agricultural fires within 10 kilometers of each village. The fire activity
data are based on detections of infrared radiation that are a signature of
biomass fires (Giglio, Csiszar and Justice, 2006). Furthermore, these data
have been used extensively by atmospheric scientists to study the effects of
agricultural fires in India and elsewhere (Liu et al., 2018). However, the
underlying algorithm does not separately identify farm fires from non-farm
biomass fires (e.g., forest fires or trash fires). Therefore, if for some
reason, the increase in non-farm biomass fires, like farm fires, is
concentrated in the winter harvest and post-harvest months, it is possible the
increase in fire activity in response to rural roads is driven by non-farm
biomass fires and not farm fires.

To evaluate this hypothesis, we examine the effect of rural roads on fire
activity in cropland areas versus non-cropland areas (Table G.1). If the
increase in fire activity is due to non-farm biomass fires, one would expect
to observe a disproportionate increase in fire activity in non-cropland areas.
However, we find the opposite result. We find the increase in fires is
concentrated in cropland areas with comparatively small and statistically
insignificant effects in non-cropland areas. Therefore, it is unlikely that
the increase in fire activity is driven by non-farm biomass fires.

##  6 Did the Increase in Crop Fires Due to Rural Roads Increase Infant
Mortality?

In this section, we explore whether rural road construction is associated with
changes in human health, as measured by changes in the infant mortality rate.
Specifically, we estimate equations (1) and (2) with two key changes.

First, because the infant mortality data is available for NFHS villages and
not for PMGSY villages, we identify all NFHS villages within 50 km of each
PMGSY village, and estimate infant mortality effects for these matched NFHS
villages.222222There exist no NFHS villages within a 50 km distance for 841 of
the 11151 PMGSY villages in our main analysis sample. In Table H.1, we show
that unmatched PMGSY villages that had to be excluded from the infant
mortality analysis are largely similar on baseline characteristics to matched
PMGSY villages included in the analysis. We also show that, for the sample of
matched PMGSY villages, there is no discontinuous change at the treatment
threshold for (i) various village baseline characteristics (balance tests) or
(ii) baseline values of our outcome variables (falsification tests). The
outcome variable in equation (2) is at the child-birth-year level where
$Y_{invdst}$ takes the value 1 if child $i$ in NFHS village $n$, within 50 km
of PMGSY village $v$ in district $d$ in state $s$, born in year $t$, died
within the first year of birth, and 0 otherwise.

Second, following a growing literature that exploits pollution variation from
wind direction (e.g., Pullabhotla et al., 2022; Rangel and Vogl, 2019;
Schlenker, and Walker, 2016; Deryugina et al., 2019), we estimate effects for
NFHS villages located within 50 km downwind from PMGSY villages separately
from NFHS villages located within 50 km in all other directions from PMGSY
villages (Figure 4).232323We use wind direction data from a climate reanalysis
dataset (ERA-5) to identify downwind locations by calculating the prevailing
wind direction at each PMGSY village location. Prevailing wind direction is
calculated by extracting monthly easterly ($u$) and northerly ($v$) wind
vectors from ERA-5 for each PMGSY village location, averaging them to the
annual-level, and calculating the direction corresponding to the annual wind
vector.

The latter change allows us to distinguish the mechanism of air pollution
associated with crop fires from other socioeconomic effects of rural roads.
That is, it is likely that NFHS villages downwind from treated PMGSY villages
experience disproportionate exposure to air pollution associated with crop
fires compared to NFHS villages located in non-downwind directions from
treated PMGSY villages. However, other socioeconomic effects of rural roads
are likely not correlated with transient changes in wind direction. Therefore,
comparing infant mortality estimates between downwind and non-downwind NFHS
villages allows us to isolate the effect of air pollution from crop fires on
infant mortality.

Figure 5 shows the graphical representation of the reduced form effects, while
Table 5 presents the corresponding instrumental variable point estimates.
Indeed, we find villages located within a 50 km distance downwind from treated
villages observe a 2.3% (1 $\mu$g/m3) increase in annual average PM2.5 levels,
with null effects for villages located within a 50 km distance in non-downwind
directions from treated villages.242424We find matched PMGSY villages at the
population threshold observe a 0.72 $\mu$g/m3 increase in PM2.5 levels (Table
H.2). The 1 $\mu$g/m3 increase in PM2.5 levels for NFHS villages located
within a 50 km distance downwind from treated PMGSY villages is consistent
with the increase in PM2.5 levels in treated PMGSY villages; that is, because
the same NFHS village may be within a 50 km distance from multiple treated
PMGSY villages, the increase in PM2.5 levels for matched downwind NFHS
villages should be at least as large as the increase in PM2.5 levels for PMGSY
villages at the treatment threshold.

Correspondingly, we observe a statistically significant 5.5% (0.3 percentage
points) increase in infant mortality rate in villages located within a 50 km
distance downwind from treated villages at the treatment threshold. However,
as one might expect, we fail to find evidence for infant mortality effects in
villages located within a 50 km distance in all other directions from treated
villages. The point estimate is small and statistically insignificant.

Overall, these estimates suggest a 1 $\mu$g/m3 increase in PM2.5 levels
increases infant mortality by 5.5%. This increase in infant mortality is
comparable to the effect of a 1 $\mu$g/m3 increase in PM2.5 levels due to dust
carried by Harmattan trade winds (5.8%) in West Africa (Adhvaryu et al.,
2022). It is smaller than the effect of a 1 $\mu$g/m3 increase in PM2.5 levels
due to automobile congestion (7.4%) in the US (Knittel, Miller and Sanders,
2016), but larger than the effect of a 1 $\mu$g/m3 increase in PM2.5 levels
due to expansion of natural gas infrastructure (2.8%) in Turkey (Cesur, Tekin
and Ulker, 2017), and due to the meteorological phenomenon of thermal
inversions (0.7%) in Mexico (Arceo, Hanna and Oliva, 2016). It is also larger
than the effect of a 1 $\mu$g/m3 decrease in PM2.5 levels due to the 1981-1982
recession (1.7%) in the US (Chay and Greenstone, 2003).252525To convert the
effect of increase in PM10 or TSP levels on infant mortality across these
studies to the effect of a 1 $\mu$g/m3 increase in PM2.5 levels, we employ the
estimated mean ratios of PM2.5/PM10 and PM2.5/TSP in the US (Lall et al.,
2004).

##  7 Conclusion

In this paper, we employ a regression discontinuity design that leverages a
sharp increase in the likelihood of rural road construction at population
thresholds in India to show rural roads increase agricultural fires by 60%
(1.6 additional fires) and particulate emissions by 1.1% (0.5 $\mu g/m^{3}$).
We find evidence that reallocation of farm labor to non-farm sectors due to
rural roads is one mechanism responsible for the increase in agricultural
fires. In effect, labor exits motivate the adoption of fire as a labor-saving
but polluting technology to clear agricultural residue or to make harvesting
less labor-intensive. Overall, the adoption of fires due to rural roads
increases infant mortality by 5.5% (0.3 percentage points) in villages located
downwind of treated villages.

The persistence of the agricultural productivity gap has generated
considerable interest amongst governments and international agencies to devise
policies that reduce frictions in labor reallocation across sectors. Our
research does not imply such efforts are misguided or should be discouraged.
Instead, our results underscore the need to complement these policies with
strategies to mitigate their potential negative environmental externalities
(Jayachandran, 2022b). In our context, future research could investigate the
design and implementation of monetary and non-monetary incentives to alter
farmers’ decisions to engage in using fire in agriculture in the face of
soaring labor costs.

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## Figures and Tables

Figure 1: Spatial distribution of fire counts across Indian districts

Notes: This figure shows the mean annual number of fire pixels detected at the
district level across India between 2001 and 2013. The states included in our
main analysis sample are highlighted via white borders. The average fire
counts at the district level range from a minimum of 0 to a maximum of 1810,
with mean of 95.7. The legend shows the values at the $25^{th}$, $50^{th}$,
$75^{th}$ and $90^{th}$ percentiles of the average annual fire counts.

Figure 2: Impact of rural roads on crop fires and PM2.5

(a) Rural roads (0/1)

(b) Annual fires (count)

(c) Annual PM2.5 ($\mu g/m^{3}$)

Notes: All panels show reduced form regression discontinuity estimates by
plotting the residualized values of the outcomes (after controlling for all
variables in the main specification other than population) as a function of
the normalized 2001 village population relative to the threshold. Each point
represents the mean of all villages in a given population bin. Figure (a)
plots the residualized probability of a village receiving a new rural road
between 2002 to 2013. Figure (b) plots the residualized annual number of fires
between 2002 to 2013. Figure (c) plots the residualized average annual PM2.5
from 2002 to 2013. All panels control for district-threshold fixed effects,
year fixed effects, and village characteristics in 2001 (baseline). Population
is centered around the state-specific threshold used for road eligibility -
either 500 or 1000 depending on the state. Standard errors are clustered at
the village level.

Figure 3: Impact of rural roads on crop fires (count) and PM2.5 ($\mu
g/m^{3}$) by relative agricultural wage rate and rice/sugar cropped area

(a) Low relative farm labor wage with high rice or sugar

(b) High relative farm labor wage or low relative farm labor wage with low
rice & low sugar

(c) Low relative farm labor wage with high rice or sugar

(d) High relative farm labor wage or low relative farm labor wage with low
rice & low sugar

Notes: All panels show reduced form regression discontinuity estimates by
plotting the residualized values of outcomes as a function of the normalized
2001 village population relative to the threshold (after controlling for fixed
effects and all baseline variables in the main specification other than
population). Each point represents the mean of all villages in a given
population bin. Panels (a) and (b) plot the regression discontinuity
relationship for annual fire counts, while panels (c) and (d) plot the
regression discontinuity relationship for annual average PM 2.5 ($\mu
g/m^{3}$). Panels (a) and (c) plots the relationship for districts which had
low (below sample median) agricultural labor wages relative to non-
agricultural labor wage rates in rural areas _and_ had high (above sample
median) share of cropped area under rice or sugar at baseline. Panels (b) and
(d) plots the relationship for districts which had either (i) high (above
median) relative agricultural wage rates or (ii) low relative agricultural
wage rates with low rice and sugar cropped areas. Rural agricultural and non-
agricultural daily labor wage rates are from the 1999 - 2000 NSSO survey data
(Round 55). Rice and sugar cropped areas are from the ICRISAT district level
data for 2001. All panels control for district-threshold fixed effects, year
fixed effects, and village characteristics in 2001 (baseline). Population is
centered around the state-specific threshold used for road eligibility -
either 500 or 1000, depending on the state. Standard errors are clustered at
the village level.

Figure 4: Schematic showing NFHS-IV villages located downwind versus NFHS-IV
villages located in all other directions from a PMGSY village

Notes: In the example depicted above, the prevailing wind direction at the
PMGSY village is from the northeast to the southwest. Therefore, downwind
locations are those NFHS-IV survey clusters within a 45-degree angle around
the wind vector southwest of the PMGSY village. NFHS-IV survey villages
outside of this cone comprise non-downwind (other directions) locations.

Figure 5: Impact of rural roads on PM 2.5 and infant mortality rates (IMR) for
NFHS villages located downwind from PMGSY villages versus NFHS villages
located in all other direction from PMGSY villages

(a) Downwind PM 2.5 ($\mu g/m^{3}$)

(b) Downwind IMR (0/1)

(c) Other directions PM 2.5 ($\mu g/m^{3}$)

(d) Other directions IMR (0/1)

Notes: All panels show reduced form regression discontinuity estimates by
plotting the residualized values of outcomes as a function of the normalized
2001 village population relative to the threshold (after controlling for fixed
effects and all baseline variables in the main specification other than
population). Panels (a) and (b) plot the regression discontinuity relationship
for annual average PM 2.5 and likelihood of infant death within 12 months of
birth, respectively, in NFHS-IV villages that are downwind from PMGSY
villages. Panels (c) and (d) plots the relationship for PM 2.5 and infant
mortality, respectively, in NFHS-IV villages in all other directions from
PMGSY villages. To define downwind, we calculate the prevailing vector-
averaged annual wind direction at each PMGSY village. Then, as described in
Figure 4, we locate NFHS-IV villages that are at a distance less than 50 km
and lie within 45-degrees on either side of the wind direction vector for each
PMGSY village. The outcomes in panels (c) and (d) are based on NFHS survey
locations that are within 50 km but more than 45-degrees away from the wind
direction vector. All panels control for district-threshold fixed effects,
year fixed effects, and village characteristics in 2001 (baseline). Population
is centered around the state-specific threshold used for road eligibility -
either 500 or 1000, depending on the state. Standard errors are clustered at
the PMGSY village level.

Table 1: Main analysis sample: summary statistics, and balance and
falsification tests

Variable | Full | Below | Over | Difference | p-value on | RD | p-value on
---|---|---|---|---|---|---|---
| sample | threshold | threshold | of means | difference | estimate | RD estimate
Primary school (0/1) | 0.959 | 0.955 | 0.964 | 0.01 | 0.02 | -0.018 | 0.59
Medical center (0/1) | 0.166 | 0.155 | 0.177 | 0.02 | 0.00 | -0.097 | 0.14
Electrified (0/1) | 0.430 | 0.414 | 0.447 | 0.03 | 0.00 | -0.014 | 0.87
Distance from nearest town (km) | 26.490 | 26.379 | 26.613 | 0.23 | 0.58 | -3.426 | 0.34
Land irrigated (share) | 0.281 | 0.276 | 0.287 | 0.01 | 0.05 | -0.025 | 0.59
Ln land area | 5.151 | 5.093 | 5.215 | 0.12 | 0.00 | -0.103 | 0.33
Literate (share) | 0.457 | 0.454 | 0.461 | 0.01 | 0.01 | -0.012 | 0.62
Scheduled caste (share) | 0.143 | 0.141 | 0.145 | 0.00 | 0.24 | -0.020 | 0.52
Land ownership (share) | 0.733 | 0.733 | 0.732 | -0.00 | 0.75 | 0.013 | 0.72
Subsistence ag (share) | 0.435 | 0.438 | 0.432 | -0.01 | 0.25 | 0.024 | 0.58
HH income $>$ INR 250 (share) | 0.754 | 0.752 | 0.757 | 0.00 | 0.37 | -0.023 | 0.63
Annual fires (count) | 0.685 | 0.699 | 0.670 | -0.03 | 0.40 | 0.194 | 0.50
Annual PM2.5 ($\mu g/m^{3}$) | 35.886 | 35.821 | 35.958 | 0.14 | 0.41 | -0.029 | 0.94
Downwind infant mortality (0/1) | 0.067 | 0.068 | 0.067 | -0.00 | 0.28 | -0.001 | 0.82
Other directions infant mortality (0/1) | 0.072 | 0.072 | 0.071 | -0.00 | 0.23 | -0.002 | 0.75
Downwind PM 2.5 ($\mu g/m^{3}$) | 36.038 | 35.879 | 36.252 | 0.37 | 0.04 | 0.431 | 0.36
Other directions PM 2.5 ($\mu g/m^{3}$) | 39.254 | 38.646 | 40.237 | 1.59 | 0.00 | 0.901 | 0.15
N | 11151 | 5859 | 5292 |  |  |  |

Notes: This table presents mean values for village characteristics, measured
at baseline. The first eight variables come from the 2001 Population Census,
the next three (below the first line) come from the 2002 BPL Census, while the
final six variables are our outcome variables measured at baseline (2001).
Columns 1-3 show the unconditional means for all villages, villages below the
treatment threshold, and villages above the treatment threshold, respectively.
Column 4 shows the difference of means across Columns 2 and 3, and Column 5
shows the p-value for the difference of means. Column 6 shows the regression
discontinuity estimate, following the main estimating equation, of the effect
of being above the treatment threshold on the baseline variable (with the
outcome variable omitted from the set of controls), and Column 7 is the
p-value for this estimate, using heteroskedasticity robust standard errors.

Table 2: First stage and IV estimates of impact of rural roads on agricultural
fires (count) and PM2.5 ($\mu g/m^{3}$)

| New road | Annual fire activity | Annual average PM 2.5
---|---|---|---
| (1) | (2) | (3)
|  $1^{st}$ stage | IV | IV
Above threshold pop. | 0.230*** |  |
| (0.017) |  |
Road built |  | 1.590** | 0.463**
|  | (0.667) | (0.199)
N | 133,788 | 133,788 | 133,788
Control group mean | 0.10 | 2.77 | 42.59

Notes: This table shows the first stage estimates (probability of receiving a
rural road) as well as the effects of rural roads on count of agricultural
fires and PM 2.5. The sample consists of the panel of villages from 2002 -
2013. “Above threshold pop.” is an indicator for the village population being
above the treatment threshold. Column (1) shows the first stage, with the
dependent variable (“Road built”) taking the value 1 if the village received a
new road during 2002-2013, 0 otherwise. Columns (2) and (3) present the IV
estimates of the treatment effects of new roads on annual fire counts and
annual average PM 2.5 ($\mu g/m^{3}$), respectively. All regressions include
district-threshold fixed effects, year fixed effects, and baseline control
variables. Standard errors in parentheses are clustered at village level.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table 3: Impact of rural roads on annual agricultural fires (count) and PM2.5
($\mu g/m^{3}$) by relative agricultural wage rate and rice or sugar
production

|  Low rel. ag. wage with high rice or high sugar |  High rel ag. wage or low rel. ag wage with low rice & low sugar
---|---|---
| (1) | (2) | (3) | (4)
| Fires | PM 2.5 | Fires | PM 2.5
Road built | 4.432** | 1.265*** | 0.694 | 0.315
| (1.984) | (0.482) | (0.628) | (0.211)
N | 55,020 | 55,020 | 72,636 | 72,636
Control group mean | 3.29 | 38.70 | 2.39 | 45.66

Notes: This table shows the effect of rural roads on count of village-level
annual fires and village-level annual average PM 2.5 ($\mu g/m^{3}$). The
sample consists of the panel of villages for the period 2002 - 2013. “Low rel.
ag. labor wage with high rice or high sugar” sample consists of districts
which had low (below sample median) agricultural labor wages relative to non-
agricultural labor wage rates in rural areas _and_ had high (above sample
median) share of cropped area under rice _or_ sugarcane at baseline (2001).
“High rel ag. wage or low rel. ag wage with low rice & low sugar” consists of
villages within districts which had either (i) high (above median) relative
agricultural wage rates or (ii) low relative agricultural wage rates with low
rice and sugar cropped areas. Rural agricultural and non-agricultural daily
labor wage rates are based on the 1999 - 2000 NSSO survey data (Round 55).
Rice and sugar cropped areas are based on ICRISAT district level data for
2001. Regressions include district-threshold fixed effects, year fixed effects
and baseline control variables. Standard errors in parentheses are clustered
at village level. Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and
∗, respectively.

Table 4: Impact of rural roads on agricultural fires (count) and particulate
emissions ($ng/m^{2}/s$) by season and point sources

Panel A: Winter harvest and post-harvest months
---
| Fires | All sources | Biomass burning | Other sources
| (1) | (2) | (3) | (4) | (5) | (6) | (7)
|  | BC | OC | BC | OC | BC | OC
Road built | 1.051*** | 0.285** | 3.146* | 0.320*** | 2.791** | -0.010 | -0.010
| (0.391) | (0.130) | (1.779) | (0.103) | (1.181) | (0.009) | (0.009)
N | 133,788 | 133,788 | 133,788 | 133,788 | 133,788 | 133,788 | 133,788
Control group mean | 1.45 | 30.47 | 120.81 | 1.17 | 13.46 | 29.30 | 107.35
Panel B: Rest of the year
| Fires | All sources | Biomass burning | Other sources
| (1) | (2) | (3) | (4) | (5) | (6) | (7)
|  | BC | OC | BC | OC | BC | OC
Road built | 0.276 | -0.219 | -0.115 | 0.192 | 0.886 | -0.010 | -0.010
| (0.438) | (0.352) | (3.123) | (0.246) | (2.683) | (0.009) | (0.009)
N | 133,788 | 133,788 | 133,788 | 133,788 | 133,788 | 133,788 | 133,788
Control group mean | 1.27 | 30.83 | 124.70 | 1.53 | 17.34 | 29.31 | 107.36

Notes: This table shows the effect of rural roads on agricultural fires, and
black carbon (BC) and organic carbon (OC) emissions in the winter harvest and
post-harvest months (Panel A) and the rest of the year (Panel B) from
2002-2013. Winter harvest and post-harvest period comprises the months from
October - March. Fire activity is measured in counts and emissions measured
are in nano-gram per square meter per second ($ng/m^{2}/s$). Columns (2) and
(3) show impact on emissions from all sources, columns (4) and (5) are
emissions from biomass sources only, and columns (6) and (7) are emissions
from other (non-biomass sources) sources. All regressions control for
district-threshold FE, year FE, and baseline controls. Standard errors in
parentheses are clustered at village level. Significance at 1%, 5% and 10% are
indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table 5: Impact of rural roads on PM 2.5 ($\mu g/m^{3}$) and infant mortality
(0/1) in NFHS villages located downwind versus all other directions from PMGSY
villages

| Downwind | Other directions
---|---|---
| PM 2.5 | Infant mortality | PM 2.5 | Infant mortality
| (1) | (2) | (3) | (4)
Road built | 1.0072∗∗∗ | 0.0031∗∗ | -0.1551 | -0.0005
| (0.1979) | (0.0015) | (0.1423) | (0.0015)
Control group mean | 43.1560 | 0.0569 | 46.3760 | 0.0588
Observations | 129,405 | 9,648,574 | 104,052 | 8,191,400

Notes: This table shows the effects of rural roads on PM 2.5 and infant
mortality in NFHS-IV clusters – within 50 km of PMGSY villages – located in
downwind (columns 1 and 2) versus non-downwind (columns 3 and 4) directions.
Road completion is instrumented using an indicator for baseline (2001 Census)
village population above the program threshold. Downwind locations are NFHS-IV
sample locations that lie within 45-degrees on either side along the wind
direction vector for each PMGSY village; locations outside of this comprise of
the other directions sample. The outcome variables for columns (1) and (3) is
the mean annual PM 2.5 ($\mu g/m^{3}$) averaged over all NFHS-IV locations in
downwind and non-downwind directions, respectively. The sample for columns (2)
and (4) consists of all births recorded during 2002 - 2013 in the NFHS-IV
locations in downwind and non-downwind directions of PMGSY villages,
respectively. The outcome variable “infant mortality” takes the value 1 if the
child born died within the first year of birth, 0 otherwise. All regressions
control for district-threshold fixed effects, year fixed effects, and baseline
controls. Standard errors in parentheses are clustered at the PMGSY village
level. Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗,
respectively.

Online Appendices

##  Appendix A Appendix Figures and Tables

Figure A.1: Annual average fire counts per state

Notes: Figure shows the annual average fire counts for 2001 - 2013 for each
state. States in the analysis sample are highlighted in darker shaded bars.
Note that the value for Punjab is truncated at 6000 for ease of visual
representation. The average fire counts per year for Punjab is $\approx$
15,000 fires.

Figure A.2: Annual number of fires and average PM 2.5 across India

(a) Fire counts

(b) PM 2.5

Notes: Panel (a) shows the number of fire pixels detected each year over India
from MODIS TERRA and AQUA satellite data from 2001 to 2013. MODIS AQUA became
operational only after July 2002. Therefore, the total number of fires
detected for 2001 and 2002 are lower compared to later years when both TERRA
and AQUA were in operation. Panel (b) shows the average annual PM 2.5 across
India using data from Van Donkelaar et al. (2016). The horizontal dashed line
shows the WHO 24-hour guideline of 25 $\mu$g/$m^{3}$ for PM 2.5.

Figure A.3: Distribution of annual average PM2.5 at the village level

Notes: Figure plots the distribution of annual average PM 2.5 ($\mu$g/$m^{3}$)
between 2001 and 2013 in PMGSY villages. The vertical line indicates the WHO
24-hour guideline of 25 $\mu$g/$m^{3}$.

Figure A.4: Roads constructed under the PMGSY between 2000 and 2013

(a) All States

(b) Sample States

Notes: Panel (a) shows the total number of roads constructed per year under
the PMGSY for all Indian states and Panel (b) shows total number of roads
constructed per year in sample states. The road construction data is drawn
from Asher et al. (2019) and Asher and Novosad (2020).

Figure A.5: Correlation between farm labor share and crop fires and relative
farm wage rate and crop fires at baseline

(a) Farm labor share and crop fires (count)

(b) Relative farm wage rate and crop fires (count)

Notes: Both panels show a binned scatter plot and linear fit line, conditional
on state fixed effects. Figure (a) shows the correlation between annual crop
fires (count) and share of wage labor in agriculture in 2001. Figure (b) shows
the correlation between annual crop fires (count) and relative agricultural
wage rate in 2001. Rural labor shares and agricultural and non-agricultural
daily labor wage rates are district-level estimates based on data from the
1999 - 2000 NSSO survey data (Round 55). Annual crop fires is the baseline
annual count of fires at the village level in 2001 within the sample of
villages used in the regression discontinuity estimates.

Table A.1: Impact of rural roads on annual agricultural fires (count) and
PM2.5 ($\mu g/m^{3}$) by agricultural mechanization at baseline

| High mechanization index | Low mechanization index
---|---|---
| (1) | (2) | (3) | (4)
| Fires | PM 2.5 | Fires | PM 2.5
Road built | 1.007* | 0.272 | 1.937 | 0.345
| (0.576) | (0.251) | (1.404) | (0.262)
N | 57,552 | 57,552 | 59,388 | 59,388
Control group mean | 2.17 | 47.52 | 3.31 | 39.21

Notes: This table shows the effects of rural roads on village-level annual
fires and PM 2.5 ($\mu g/m^{3}$). The sample consists of the panel of PMGSY
villages from 2002 - 2013. High (low) mechanization index sample consists of
districts which had above (below) sample median agricultural mechanization
index at baseline (2001). Mechanization index is measured using district-level
data on mechanized input usage from the Agricultural Input Census (2001). The
Agricultural Input Census provides district-level counts of the mechanized
equipment used for various agricultural operations. We standardize the
individual equipment counts into $z$-scores and construct the mechanization
index as the sum across all $z$-scores. All regressions include district-
threshold fixed effects, year fixed effects and baseline control variables.
Standard errors in parentheses are clustered at village level. Significance at
1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table A.2: Impact of rural roads on agricultural fires (count) and PM2.5 ($\mu
g/m^{3}$) by baseline agricultural mechanization in high rice acreage
districts

| High mechanization index | Low mechanization index
---|---|---
| (1) | (2) | (3) | (4)
| Fires | PM 2.5 | Fires | PM 2.5
Road built | 2.915* | 0.839 | 5.267 | 1.166***
| (1.622) | (0.570) | (3.373) | (0.408)
N | 29,700 | 29,700 | 28,452 | 28,452
Control group mean | 2.63 | 39.83 | 4.00 | 34.52

Notes: This table shows the effects of rural roads on village-level annual
fire activity and PM 2.5 ($\mu g/m^{3}$). The sample consists of the panel of
PMGSY villages from 2002 - 2013 within districts with high (above sample
median) share of cropped area under rice. High (low) mechanization index
sample consists of districts which had above (below) sample median level of
agricultural mechanization index at baseline (2001). Mechanization index is
measured using district-level data on mechanized input usage from the
Agricultural Input Census (2001). All regressions include district-threshold
fixed effects, year fixed effects and baseline control variables. Standard
errors in parentheses are clustered at village level. Significance at 1%, 5%
and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table A.3: Impact of rural roads on agricultural fires (count) and PM2.5 ($\mu
g/m^{3}$) by baseline mechanization specific to rice harvesting in high rice
acreage districts

| High mechanization index | Low mechanization index
---|---|---
| (1) | (2) | (3) | (4)
| Fires | PM 2.5 | Fires | PM 2.5
Road built | 2.845 | 1.730 | 3.562* | 0.753**
| (3.396) | (1.413) | (2.075) | (0.294)
N | 14,928 | 14,928 | 43,224 | 43,224
Control group mean | 2.98 | 44.00 | 3.42 | 34.91

Notes: This table shows the effects of rural roads on village-level annual
fire activity and PM 2.5 ($\mu g/m^{3}$). The sample consists of the panel of
PMGSY villages from 2002 - 2013 within districts with high (above sample
median) share of cropped area under rice. High (low) mechanization index
sample consists of districts which had above (below) sample median level of
agricultural mechanization index at baseline (2001). Mechanization index is
measured using district-level data on mechanized input usage from the
Agricultural Input Census (2001). We standardize the equipment counts for rice
harvesting equipment – self-propelled combine harvesters and tractor drawn
combines – into $z$-scores and construct the mechanization index as the sum
across all $z$-scores. All regressions include district-threshold fixed
effects, year fixed effects and baseline control variables. Standard errors in
parentheses are clustered at village level. Significance at 1%, 5% and 10% are
indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table A.4: Impact of rural roads on agricultural fires (count) activity by
size of the expected opportunity cost and returns to education effects

| Opportunity cost effect | Returns to education effect
---|---|---
| (1) | (2) | (3) | (4)
| High | Low | High | Low
Road built | 4.537*** | -0.186 | 1.773** | 1.686
| (1.629) | (0.646) | (0.871) | (1.055)
N | 65,172 | 68,616 | 67,620 | 64,596
Control group mean | 3.20 | 2.35 | 2.87 | 2.53

Notes: This table shows the effect of rural roads on village-level annual fire
activity. The sample consists of the panel of villages from 2002 - 2013.
Proxies for the opportunity cost and returns to education effects are
estimated using pre-program survey data from the 1999 - 2000 NSSO survey data
(Round 55). The size of the opportunity cost effect is proxied by the
district-level mean low-skill urban wage minus the mean low-skill rural wage.
The size of the returns to education effect is proxied by the difference
between the urban and rural Mincerian returns to one additional year of
education. The Mincerian returns are estimated by regressing log wage on years
of education, age, square of age, and the log of household land owned,
separately for individuals in urban and rural locations in each district. For
each measure, the sample is split into high/low based on whether the proxy is
above/below the sample median. Regressions include district-threshold fixed
effects, year fixed effects and baseline control variables. Standard errors in
parentheses are clustered at village level. Significance at 1%, 5% and 10% are
indicated by ∗∗∗, ∗∗ and ∗, respectively.

##  Appendix B Replication of Asher and Novosad (2020)

Figure B.1: Distribution of running variable

(a) Histograph of Village Population

(b) McCary Test

Notes: Panel (a) and (b) replicate results from Asher and Novosad (2020)
showing the distribution of village population around the population
thresholds. Panel (a) is a histogram of village population as recorded in the
2001 Population Census. The vertical lines show the program eligibility
thresholds at 500 and 1,000. Panel (b) uses the normalized village population
(reported population minus the threshold, either 500 or 1,000). It plots a
non-parametric regression to each half of the distribution following McCrary
(2008), testing for a discontinuity at zero. The point estimate for the
discontinuity is $-0.010$, with a standard error of $0.048$.

Table B.1: Impact of rural roads on indices of major outcomes

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| Transportation | Ag. occupation | Firms | Ag. production | Consumption
Road built | 0.432** | -0.370** | 0.237 | 0.050 | 0.016
| (0.190) | (0.162) | (0.159) | (0.127) | (0.138)
N | 11,151 | 11,151 | 10,405 | 11,151 | 11,151
Control group mean | -0.02 | -0.00 | 0.01 | 0.00 | -0.00

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on effect of a new road on indices of the major outcomes in each of the
five families of outcomes: transportation, occupation, firms, agriculture, and
welfare. Each index is generated following Anderson (2008). The transportation
index consists of availability of public buses, private buses, vans, taxis and
auto-rickshaws at the village level. The agricultural occupation index is a
combination of the share of workers in agriculture and the inverse of the
share of workers in manual labor. The firms index is the log of employment in
all non-farm firms in the village. The agriculture production index is a
combination of remotely-sensed Normalized Difference Vegetation Index (NDVI),
share of households owning farm equipment, share of households owing
irrigation equipment, share of households owning land, log total cultivated
acres and presence of non-cereal or pulse crops among the primary three crops
in the village. The consumption index consists of log per capita consumption,
primary component asset index, log of night light luminosity and share of
households with primary earner having a monthly earning of more than INR
5,000. All regressions include district-threshold fixed effects and baseline
control variables. Heteroskedasticity robust standard errors reported in
parentheses. Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗,
respectively.

Table B.2: Impact of rural roads on transportation

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| Govt. bus | Pvt. bus | Taxi | Van | Autorickshaw
Road built | 0.130** | 0.131* | 0.012 | -0.015 | 0.068
| (0.056) | (0.076) | (0.049) | (0.054) | (0.044)
N | 11,151 | 11,151 | 11,151 | 11,151 | 11,151
Control group mean | 0.12 | 0.20 | 0.07 | 0.14 | 0.06
$R^{2}$ | 0.30 | 0.09 | 0.09 | 0.43 | 0.26

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on availability of transportation services at the village level. Columns
(1) - (5) estimate the impact of new roads on five categories of transport
services available at the village level from the Census in 2011. Regressions
include district-threshold fixed effects and baseline control variables.
Heteroskedasticity robust standard errors reported in parentheses.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table B.3: Impact of rural roads occupation and income source

| Occupation | Income source
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
| Agriculture | Manual labor | Unemployed | Unclassifiable | Agriculture | Manual labor
Road built | -0.100** | 0.086** | 0.011 | -0.008 | -0.045 | 0.003
| (0.044) | (0.044) | (0.025) | (0.010) | (0.045) | (0.044)
N | 11,151 | 11,151 | 11,151 | 11,151 | 11,151 | 11,151
Control group mean | 0.48 | 0.45 | 0.43 | 0.02 | 0.41 | 0.51
$R^{2}$ | 0.29 | 0.26 | 0.30 | 0.17 | 0.31 | 0.28

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on occupational choice among workers and households’ primary income
source based on data from the 2011 - 2012 Socioeconomic and Caste Census
(SECC). Column (1) shows the effect on share of workers in agriculture, while
column (2) shows the effect on share of workers in non-agriculture manual
labor. These two categories make up 92% of the workers, on average, in the
sample villages. To examine any potential changes in the workforce, columns
(3) and (4) estimate the impact on the share of working age adults (18-60) who
are unemployed (including those who are studying, only involved in household
work, etc.) and the share of working age adults whose work force participation
is unclear in the SECC data, respectively. The results in columns (3) and (4)
suggest that roads do not affect the share of adults in these categories,
alleviating any concerns that the change in share of workers in agriculture or
non-agricultural wage work is biased due to changes in the workforce.
Additionally, results in Table B.8 show that rural roads had no effect on
permanent out-migration. The absence of any impact on migration suggests that
the observed sectoral reallocation of labor is the result of changes in
occupational choice rather than compositional effects due to selective
migration in the village population. Column (5) shows the impact on share of
households in the village that report their main income source as cultivation.
Column (6) shows the impact on share of households whose primary income source
is manual labor. Regressions include district-threshold fixed effects and
baseline control variables. Heteroskedasticity robust standard errors reported
in parentheses. Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗,
respectively.

Table B.4: Impact of rural roads on employment in village non-farm firms

Panel A: Log employment growth
---
| (1) | (2) | (3) | (4) | (5) | (6)
| Total | Livestock | Manufacturing | Education | Retail | Forestry
Road built | 0.241 | 0.229 | 0.243 | 0.129 | 0.318** | -0.107
| (0.162) | (0.190) | (0.196) | (0.145) | (0.157) | (0.110)
N | 10,405 | 10,405 | 10,405 | 10,405 | 10,405 | 10,405
Control group mean | 2.95 | 0.69 | 0.91 | 1.50 | 1.23 | 0.17
$R^{2}$ | 0.30 | 0.42 | 0.24 | 0.18 | 0.23 | 0.35
Panel B: Level employment growth
| (1) | (2) | (3) | (4) | (5) | (6)
| Total | Livestock | Manufacturing | Education | Retail | Forestry
Road built | 2.801 | -1.918 | 2.432 | 0.277 | 1.811 | 2.473
| (7.773) | (3.399) | (3.910) | (0.993) | (1.582) | (4.144)
N | 10,405 | 10,405 | 10,405 | 10,405 | 10,405 | 10,405
Control group mean | 32.31 | 6.85 | 5.88 | 5.11 | 4.55 | 2.88
$R^{2}$ | 0.30 | 0.46 | 0.18 | 0.13 | 0.16 | 0.36

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on employment in village level non-farm firms based on data from the
2013 Economic Census. Column (1) shows the impact on total non-farm
employment. Columns (2) - (6) show results for the five largest sectors in the
sample which constitute 79% of the non-farm firm employment (livestock,
manufacturing, education, retail, and forestry). Panel A shows the impact on
log employment in all non-farm firms and Panel B present same estimates using
levels. The sample is limited to villages where total employment reported in
the firm-level data is less than the total inhabitants in the village.
Regressions include district-threshold fixed effects and baseline control
variables. Heteroskedasticity robust standard errors reported in parentheses.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table B.5: Impact of rural roads on agricultural yields

| NDVI | EVI
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
| Max - June | Cumulative | Max | Max - June | Cumulative | Max
Road built | 0.028 | 0.002 | 0.015 | 0.043 | 0.000 | 0.024
| (0.027) | (0.013) | (0.014) | (0.034) | (0.016) | (0.019)
N | 11,054 | 11,053 | 11,054 | 11,054 | 11,053 | 11,054
Control group mean | 8.24 | 10.52 | 8.81 | 7.96 | 10.17 | 8.48
$R^{2}$ | 0.70 | 0.88 | 0.80 | 0.71 | 0.85 | 0.69

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on village-level measures of agricultural activity using three different
remote-sensing NDVI-based proxies for agricultural yields. Regressions include
district-threshold fixed effects and baseline control variables.
Heteroskedasticity robust standard errors reported in parentheses.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table B.6: Impact of rural roads on agricultural inputs

| (1) | (2) | (3) | (4) | (5)
---|---|---|---|---|---
| Mech. | Irri. | Own ag. land | Non-cereal/pulse | Cult. land (log)
Road built | -0.008 | -0.009 | 0.005 | 0.002 | -0.062
| (0.012) | (0.029) | (0.040) | (0.075) | (0.117)
N | 11,150 | 11,151 | 11,151 | 8,001 | 10,885
Control group mean | 0.04 | 0.14 | 0.57 | 0.40 | 5.04
$R^{2}$ | 0.24 | 0.40 | 0.29 | 0.45 | 0.46

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on the impact of roads on agricultural inputs based on data from the
2011-2012 Socioeconomic and Caste Census (Columns 1-3) and the 2011 Population
Census (Columns 4-5). The outcome variables in Columns (1) and (2) are the
share of households owning mechanized farm equipment and the share of
households owning irrigation equipment, respectively. Column (3) shows the
impact on the share of households owning agricultural land. The outcome in
Column (4) is a dummy variable taking the value 1 if a non-cereal and non-
pulse crop is listed as one of its three major crops at the village-level
census data. Column (5) examines the impact on log total cultivated land in
the village (villages that have no cultivable land are excluded from the
sample). Regressions include district-threshold fixed effects and baseline
control variables. Heteroskedasticity robust standard errors reported in
parentheses. Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗,
respectively.

Table B.7: Impact of rural roads on consumption and asset ownership

Panel A: Consumption indicators and asset index
---
| (1) | (2) | (3) | (4)
| Consumption per | Night lights (log) | Share of HH | Asset index
| capita (log) |  | earning $\geq 5k$ |
Road built | 0.013 | 0.037 | -0.008 | 0.120
| (0.039) | (0.169) | (0.032) | (0.135)
N | 11,151 | 10,828 | 11,151 | 11,151
Control group mean | 9.56 | 1.58 | 0.15 | -0.01
$R^{2}$ | 0.42 | 0.66 | 0.25 | 0.52
Panel B: Individual asset ownership
| (1) | (2) | (3) | (4)
| Solid house | Refrigrator | Any vehicle | Phone
Road built | 0.037 | 0.004 | -0.007 | 0.020
| (0.030) | (0.013) | (0.024) | (0.041)
N | 11,151 | 11,151 | 11,151 | 11,151
Control group mean | 0.22 | 0.04 | 0.14 | 0.44
$R^{2}$ | 0.66 | 0.26 | 0.38 | 0.48

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on indicators of consumption and asset ownership. Panel A, Column (1)
shows the impact on imputed log consumption per capita (see Asher and Novosad
(2020), Data Appendix for details). Column (2) estimates the effect on log of
mean total night light luminosity in 2011-13. Column (3) is the share of
households whose highest earning member earns more than INR 5000 per month
based on data from the 2011-12 Socioeconomic and Caste Census (SECC). Column
(4) is village-level average of the primary component of indicator variables
for all household assets included in the SECC. Panel B shows the impact on the
village-level share of households owning major assets in the SECC. Regressions
include district-threshold fixed effects and baseline control variables.
Heteroskedasticity robust standard errors reported in parentheses.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

Table B.8: Impact of rural roads on village-level population, age
distribution, and gender ratios

Panel A: Population growth (2001 - 2011)
---
| (1) | (2)
| Log | Level
Road built | -0.023 | -9.833
| (0.030) | (20.783)
N | 11,151 | 11,151
Control group mean | 6.43 | 654.59
$R^{2}$ | 0.79 | 0.84
Panel B: Age group share
---
| (1) | (2) | (3) | (4) | (5)
| 11 - 20 | 21 - 30 | 31 - 40 | 41 - 50 | 51 - 60
Road built | -0.004 | -0.004 | 0.002 | -0.002 | 0.002
| (0.005) | (0.005) | (0.004) | (0.004) | (0.003)
N | 11,151 | 11,151 | 11,151 | 11,151 | 11,151
Control group mean | 0.24 | 0.19 | 0.15 | 0.11 | 0.07
$R^{2}$ | 0.22 | 0.19 | 0.25 | 0.37 | 0.40
Panel C: Male share by age group
| (1) | (2) | (3) | (4) | (5)
| 11 - 20 | 21 - 30 | 31 - 40 | 41 - 50 | 51 - 60
Road built | -0.013 | 0.004 | 0.004 | -0.007 | 0.016
| (0.009) | (0.008) | (0.008) | (0.010) | (0.013)
N | 11,151 | 11,151 | 11,151 | 11,151 | 11,151
Control group mean | 0.52 | 0.52 | 0.51 | 0.52 | 0.51
$R^{2}$ | 0.12 | 0.19 | 0.10 | 0.07 | 0.06

Notes: This table replicates results from Asher and Novosad (2020) showing
regression discontinuity treatment estimates of the effect of new village
roads on population growth and demographic structure of the village
population. Panel A shows the effect on village population from the Population
Census 2011. Column (1) shows the outcome in log, and (2) shows the level
effect. Panel B shows the effect on share of population in 10 year age groups,
starting from 11 to 20 in Column (1) to 51 to 60 in Column (5). The outcome
variables in Panel C are the share of men in the population within each
10-year age group. Outcomes in Panels B and C are based on 2011-12
Socioeconomic and Caste Census (SECC). Panel A shows that new roads do not
affect population growth, suggesting no permanent migration impacts (short-
term migrants and daily commuters are accounted for in the Census and SECC
listings). The effect on population could be attenuated towards zero if there
had been changes in fertility or mortality that offset any net migration.
However, the null results in Panels B and C on age distribution and sex ratios
by age cohort show changes in fertility and mortality were unlikely to be a
contributing factor. Together, the results in this table suggest that roads
did not lead to any substantial out-migration. Absent any impact on migration,
the effects observed on labor reallocation (in Table B.3) can be seen as a
result of occupational choice rather than the result of selective migration.
Regressions include district-threshold fixed effects and baseline control
variables. Heteroskedasticity robust standard errors reported in parentheses.
Significance at 1%, 5% and 10% are indicated by ∗∗∗, ∗∗ and ∗, respectively.

##  Appendix C Harvest and Planting Dates

In this section, we evaluate the effect of rural roads on harvest and planting
dates. We procure satellite-based measures of harvest (end-)dates and planting
dates for the kharif season, aggregated up to the village level from 250 m
pixel data. The planting and harvest dates are estimated using Enhanced
Vegetation Index (EVI) data from MODIS and were validated using ground
data.262626We are grateful to Meha Jain, School for Environment and
Sustainability, University of Michigan, for sharing these data. Harvest
(planting) date is measured as the median pixel value of the harvest
(planting) dates within a 10 km buffer around the village. Unfortunately,
these data are not available for states that followed population thresholds to
determine rural road construction under PMGSY. Therefore, we are not able to
use a regression discontinuity design. Instead, we estimate the following
event study specification:

$\Delta
Date_{v,d,y}=\sum_{\tau,\tau\neq-1}\delta_{\tau}D_{t^{0}+\tau}+\lambda_{v}+\mu_{d,y}+\alpha_{y}X_{v}+\varepsilon_{v,d,y}$
((3))

where $\Delta Date_{v,d,y}$ is the change in harvest or sowing date for
village $v$, located in district $d$ in year $y$ from the baseline (2002).
$D_{t^{0}+\tau}$ are event time indicator variables that capture the average
treatment effect, where $\tau$ indicates the year relative to when a village
receives access to a rural road, with the year prior to treatment being the
excluded category. $\lambda_{v}$ are village fixed effects and $\mu_{d,y}$ are
district-by-year fixed effects. Village fixed effects control for time-
invariant unobservables at the village level (e.g., soil type). District-by-
year fixed effects control for time-varying district-specific confounders. For
instance, the National Rural Employment Guarantee Scheme was rolled-out in a
staggered manner across India between 2006 and 2008. Lastly, we include an
interaction of baseline village characteristics $X_{v}$ with year fixed
effects $\alpha_{y}$. Standard errors are clustered at the village level.

The identifying assumption here is that there exist no village-specific time-
varying confounders that are correlated with both access to rural roads as
well as local agriculture. E.g., if rural roads are placed in villages where
agricultural activities are changing, our estimates would be biased. While
lack of pre-trends would bolster our confidence in said assumption, if change
in local agriculture and rural road construction were to occur simultaneously,
our estimates will still be biased.

Figures C.2 and C.1 present our results. First, we don’t find evidence for any
pre-trends. Second, and more importantly, we fail to find evidence that access
to rural roads affects either harvest or planting dates. The point estimates
are small and statistically insignificant.

Figure C.1: Event study estimates: Effect of access to rural roads on monsoon
(kharif) harvest date

Notes: The graph shows event study estimates for the effect of new roads on
monsoon harvest dates. Sample is limited to the states of Punjab, Haryana,
Uttar Pradesh, and Bihar for which satellite-based sowing date measures are
available. The sample period is 2003-2013. The dependent variable is the
change in harvest completion day from the baseline (2002). The horizontal axis
shows the event year relative to the year of road completion. Each point shows
the coefficient and confidence interval on each event-time fixed effect
relative to the omitted category which is the year before road completion
($t=-1$). All regressions include village FE, district $\times$ year fixed
effects and the interactions of year fixed effects with baseline village
characteristics and harvesting date/week in 2002. Standard errors in
parentheses are clustered at village level.

Figure C.2: Event study estimates: Effect of access to rural roads on monsoon
(kharif) sowing date

Notes: The graph shows event study estimates for the effect of new roads on
monsoon sowing dates. Sample is limited to the states of Punjab, Haryana,
Uttar Pradesh, and Bihar for which satellite-based sowing date measures are
available. The sample period is 2003-2013. The dependent variable is the
change in day of sowing from the baseline (2002). The horizontal axis shows
the event year relative to the year of road completion. Each point shows the
coefficient and confidence interval on each event-time fixed effect relative
to the omitted category which is the year before road completion ($t=-1$). All
regressions include village FE, district $\times$ year fixed effects and the
interactions of year fixed effects with baseline village characteristics and
sowing date/week in 2002. Standard errors in parentheses are clustered at
village level.

##  Appendix D Rural Economic and Demographic Survey
(REDS)

We use village- and household-level surveys from the 1999 and 2006 rounds of
the Rural Economic and Demographic Survey (REDS), administered by the National
Council of Applied Economic Research (NCAER), to estimate the effect of rural
roads on agricultural wages, local stock of combine harvesters, and use of
hired mechanized agricultural equipment. REDS is a nationally representative
survey of rural households in India spanning 221 villages across 100 districts
in 17 major states. It includes a village survey that collects information on
the prevailing wage rates for agricultural labor as well as stock of
agricultural machinery at the village-level . The household questionnaire
provides detailed information on the use of agricultural inputs, including the
use and cost of hired mechanized equipment.

Because the data includes few villages in states that followed population
thresholds to determined rural road construction under PMGSY, we cannot use a
regression discontinuity design. Instead, we estimate simple difference-in-
difference specifications. ‘Treat’ is an indicator variable that takes the
value $1$ if a village receives a rural road between 1999 and 2006, $0$
otherwise. ‘Post’ is an indicator variable that takes the value $1$ if the
year is 2006, $0$ otherwise. ‘TreatXPost’ captures the effect of the
construction of a rural road between 1999 and 2006. Since political patronage
likely played a role in placement of roads as discussed in Section 2.2, the
results discussed below should only be considered suggestive.

Table D.1 estimates the effect of rural roads on agricultural wages. We find
access to rural roads increases agricultural wages for both men and women by
1.2% and 2%, respectively. This result is consistent with the key takeaway
from Asher and Novosad (2020) that rural roads induce movement of workers out
of agriculture.

Table D.2 estimates the effect of rural roads on the village-level stock of
combine harvesters. Using a village-panel regression with village and year
fixed effects, we fail to find an economically or statistically significant
impact on the village-level stock agricultural machinery across a variety of
agricultural implements either on the extensive (Panel A) or the intensive
margin (Panel B). In particular, we see no effect on the presence or number of
combine harvesters. Next, using a household-panel regression with household
and year fixed effects, we fail to detect an impact on the use of hired
mechanized agricultural equipment at the household level (Table D.3).

Table D.1: Impact of roads on agricultural wage rates - REDS village panel

| Wage rate | Log wage
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
| Male | Female | Average | Male | Female | Average
Treat X Post | 0.722* | 0.918*** | 0.856** | 0.012* | 0.020** | 0.016**
| (0.420) | (0.349) | (0.352) | (0.007) | (0.008) | (0.007)
N | 442 | 442 | 442 | 442 | 442 | 442
Sample mean | 62.99 | 47.65 | 55.25 | 62.99 | 47.65 | 55.25