metadata: authors: 0: author: Alder,Simon
1: author: Croke,Kevin
2: author: Duhaut,Alice
3: author: Marty,Robert Andrew
4: author: Vaisey,Ariana Brynn
count: Ethiopia
docna: 0: docna: The Impact of Ethiopia’s Road Investment
            Program on Economic Development and Land Use : Evidence from
            Satellite Data
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies the impacts of the
            large-scale Road Sector Development Program in Ethiopia
            between 1997 and 2016 on local economic activity and land
            cover (urbanization and cropland). It exploits spatial and
            temporal variation in road upgrades across Ethiopia,
            together with high-resolution panel data derived from
            satellite imagery. The findings show that road upgrades
            contributed to increases in local economic activity, as
            proxied by nighttime lights and urban land area. However,
            there is significant heterogeneity in the results across
            baseline levels of economic activity. Specifically, gains
            from road upgrades are concentrated in areas with
            moderate-to-high initial levels of economic activity. By
            contrast, there was little, or even negative, growth in
            areas with low levels of initial economic activity. Finally,
            the findings show that road upgrades contributed to a
            reduction in cropland in areas with medium-to-high baseline
            nighttime lights. The results suggest that Ethiopia's
            ambitious road infrastructure development program overall
            increased local economic activity and urbanization, but that
            it also had important distributional implications that need
            to be taken into account when planning such infrastructure programs.
display_title: The Impact of Ethiopia’s Road
            Investment Program on Economic Development and Land Use :
            Evidence from Satellite Data
listing_relative_url: /research/2022/04/33780545/impact-ethiopia’s-road-investment-program-economic-development-land-use-evidence-satellite-data
topicv3: Land Use Laws m1326404 2287,Transport m1326661 135
publishtoextweb_dt: 2022-04-06T00:00:00Z
dois: 10.1596/1813-9450-10000
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 1161784
2: author_key: 1436493
3: author_key: 1578937
4: author_key: 1599472
model_extractions: Policy Research Working Paper 10000 The Impact of Ethiopia’s Road Investment Program on Economic Development and Land Use

# Evidence from Satellite Data

_Simon Alder Kevin Croke Alice Duhaut Robert Marty Ariana Vaisey_ Development Economics Development Impact Evaluation Group July 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 10000

# **Abstract**

This paper studies the impacts of the large-scale Road Sector Development Program in Ethiopia between 1997 and 2016 on local economic activity and land cover (urbanization and cropland). It exploits spatial and temporal variation in road upgrades across Ethiopia, together with high-resolution panel data derived from satellite imagery. The findings show that road upgrades contributed to increases in local economic activity, as proxied by nighttime lights and urban land area. However, there is significant heterogeneity in the results across baseline levels of economic activity. Specifically, gains from road upgrades are concentrated in areas with moderate-to-high initial levels of economic activity. By contrast, there was little, or even negative, growth in areas with low levels of initial economic activity. Finally, the findings show that road upgrades contributed to a reduction in cropland in areas with medium-to-high baseline nighttime lights. The results suggest that Ethiopia’s ambitious road infrastructure development program overall increased local economic activity and urbanization, but that it also had important distributional implications that need to be taken into account when planning such infrastructure programs.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aduhaut@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Impact of Ethiopia’s Road Investment Program on Economic Development and Land Use: Evidence from Satellite Data<sup>_∗_</sup> Simon Alder<sup>1</sup> , Kevin Croke<sup>2</sup> , Alice Duhaut<sup>3</sup> , Robert Marty<sup>3</sup> and Ariana Vaisey<sup>4</sup>

> 1Swiss National Bank

> 4University of Chicago Law School

**JEL codes: R4, R12, R14**

> _∗_ The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. The views expressed in this paper are those of the authors and do not necessarily reflect those of the SNB. In addition, they do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This project benefited from UK aid from the UK government.We thank Michael Andrews for his research assistance. Computational reproducibility verified by DIME Analytics.
# **1 Motivation**

Governments invest in roads to foster national integration, promote economic development, and facilitate trade. Between 1995 and 2015, for example, World Bank-supported transport projects amounted to approximately 161 billion USD.<sup>1</sup> Yet, although road investments constitute a major portion of both donor and government capital expenditure, the economic impact of investments in large-scale road infrastructure is debated: while the effects of large economic corridors and rural connectivity programs have mostly been studied in dense, rapidly growing countries in Asia, the impacts of transport investments in lower-density contexts are less studied. In particular, the potentially heterogeneous benefits of such programs to different types of areas—and therefore the impact of such programs on growth as well as inequality—are less well understood.

This paper provides evidence on the links between large-scale investments in roads of different types and changes in local economic activity and land use (urbanization and cropland). To shed light on this issue, we study a large program of economic corridors and rural road upgrades that has been ongoing in Ethiopia since 1997: the Road Sector Development Program (RSDP). The scale of the program and the context of Ethiopia allow us to contribute to the literature in two ways. First, while most of the evidence about the development impact of rural roads and economic corridors comes from rapidly developing or densely populated countries, we study the impacts of these investments in a setting where both urbanization and industrialization are low. Ethiopia’s urbanization rate of 17% is less than half of the Sub-Saharan Africa regional average of 37% (World Bank, 2015), and at the program’s start, Ethiopia had one of the least-developed road networks and lowest motor vehicle utilization rates in the world (Adamopoulos, 2018). Second, the transport program we study includes both large economic corridor upgrades aimed at connecting cities, as well as smaller-scale rural road improvements. This allows us to examine differential patterns of effects according to the type of transport investment, as reflected in the second part of our analysis.

The large number of roads upgraded and localities connected over the program period allow us to study the impact of the program as a whole, and of the different types of investments separately. The overall objectives of the program were to support the rehabilitation, improvement, and construction of roads with access to ports (particularly important given that Ethiopia is landlocked), roads that link to major economic centers and resources, and roads connecting areas with potential for commercial agriculture (Ethiopian Roads Authority, 2015a).

> In this paper, we ask whether road upgrades promote growth in local economic activity and produce changes in land use, particularly urban land and cropland. We use three empirical approaches: (1) a

> 1Number extracted from the World Bank projects database, accessible here `https://projects.worldbank.org/en/ projects-operations/projects-list?sectorcode_exact=TI`

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differences-in-differences approach that dynamically uses areas not yet treated as a control group, (2) a longdifference approach that focuses on understanding the impact of road upgrades on incidentally connected localities, and (3) a market access approach.

First, we leverage temporal and spatial variation of road upgrades to estimate a differences-in-differences model, restricting our sample to units that received an upgrade at some point over the study period. In doing so, we control for unobserved time-invariant heterogeneity in locations, as well as common temporal trends throughout Ethiopia. Moreover, we recover average treatment effects by length of exposure to improved roads. Treatment effects in post-treatment periods allow an understanding of how long impacts took to materialize after improvements, whereas treatment effects in pre-treatment periods allow us to test whether treatment and control units had parallel trends in outcome variables before treatment.

Our second empirical approach focuses on understanding the impact of RSDP on areas that incidentally benefited from the program; that is, on localities that were not targeted by RSDP but were near an upgraded road that connected two targeted localities. Localities that were incidentally connected (because they happen to lie between two targeted localities) provide a possible source of exogenous variation in network connections. We rely on a long-difference framework, comparing non-targeted localities near an upgraded road to those far from an upgraded road. To further address endogenous road placements, we instrument road upgrades with a hypothetical road network that represents what would have been constructed if connecting all targeted locations in a least-cost manner was the chief concern.

For our third empirical approach, we construct a standard measure of market access that computes, for each unit, the size of the total market that can be reached, weighted by the inverse transportation cost of traveling to different markets. While the previous two approaches consider the treatment as an improvement of any type, the market access measure allows us to use the changes in speed limits associated with road upgrades. We use a long-difference strategy to understand how changes in market access are associated with changes in outcome variables. To uncover the causal impact of market access on outcome variables, we control for baseline levels of market access, local economic activity, and pre-trends in selected variables. In addition, to further account for non-random road placement, we instrument market access with a version of market access that excludes immediately surrounding localities when constructing the market access measure. As a robustness check on these results, we also implement a panel data model that controls for time-invariant characteristics.

These different empirical approaches each come with their own set of advantages and limitations. For example, the difference-in-difference approach enables us to leverage the granular nature of both the satellite imagery and the road data and to test whether locations were endogenously targeted based on pre-trends. However, to temporally fix the time of “treatment,” this approach considers treatment to begin the first year 3
an area benefited from RSDP. Yet, many areas benefited from RSDP road improvements in multiple years. Long-difference approaches alleviate this concern by examining aggregate changes in the road network, but they require stronger assumptions for causal interpretations to be valid. Consequently, we emphasize findings that appear consistent across the multiple approaches.

Ethiopia’s RSDP is now in its fifth phase, with each phase focusing on different priorities. The first three phases (1997–2010) focused on improving trunk roads and regional roads, while the fourth and fifth phases (2010–2020) focused on rural roads to support the development of commercial agriculture. The vast majority of upgrades took place in the fourth and fifth phases (Figure 1). Using data on road upgrades from 1997 to 2016, our analysis focuses on RSDP phases I-IV. Aggregating across phases, we find that road upgrades contributed to increases in local economic activity (proxied by nighttime lights) and urbanization. However, the aggregate results mask heterogeneity in results across baseline levels of economic activity. Specifically, while areas with moderate to high initial economic activity benefited the most from the upgrades, we have mixed evidence for areas with low initial economic activity. The differences-in-differences and long-difference models show that these areas either did not benefit or only saw small gains from upgrades, and market access results show these areas either did not benefit or even saw a reduction in outcomes due to upgrades. Lastly, we find that road upgrades contributed to a reduction in cropland in areas with moderate to high initial economic activity; these cropland areas largely transitioned to urban land.

We contribute to multiple strands of the literature. First, we contribute to a young literature on the effects of transportation infrastructure on economic development in Ethiopia. Several recent papers have examined the effect of Ethiopia’s RSDP on firm-level outcomes. For example, Fiorini et al. (2021) focus on the role of road infrastructure on the effect of trade. Using the Ethiopia manufacturing census from 1998 to 2009 to construct measures of firm productivity, they find that better local road infrastructure increases the benefits from lowering tariffs on imports and exports. In a related paper, Fiorini and Sanfilippo (2019) find that road access via RSDP increases overall employment, reduces agricultural employment, and increases service sector (although not manufacturing) employment. Using the same manufacturing census data, Shiferaw et al. (2015) estimate the relationship between RSDP road investments and firm performance, finding that road quality is linked to firm entry. Moneke (2019) studies the effects of roads and electricity and finds that roads alone had smaller welfare effects compared to roads combined with electrification. He uses an “inconsequential units” identification strategy based on colonial-era straight-line road plans, using units that were not directly of interest for the colonizer to identify the impact of the program. Relying on land use and economic activity as outcome variables, we similarly find notable spatial heterogeneity in outcomes. Adamopoulos (2018) studies the impact of RSDP on agricultural productivity in Ethiopia, using a panel from 1996-2014 at the medium administrative level (Woreda) and estimated travel times to agricultural markets.

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He calculates that RSDP reduced transport costs and increased Woreda-level average yields. Kebede (2021) uses village-level data and finds that rural roads on average increased real agricultural income. Dercon et al. (2009) study a panel of 15 agricultural villages over a decade (1994–2004) and find that access to all-weather roads reduces poverty by 6.9 percentage points and also increases consumption growth markedly (by 16.3 percentage points). Atkin and Donaldson (2015) study trade costs in Ethiopia and Nigeria and find that the effect of distance on trade costs is about four times larger than in the United States. We add to this literature a focus on land use: beyond nighttime luminosity, we also use a recently released landcover panel dataset that allows us to track landcover at an annual frequency.<sup>2</sup> This allows us to go beyond the traditional use of nighttime lights as proxy for economic activity and also focus on the quantity of land allocated to agriculture or farm production (cropland area) versus urban uses throughout the country.

Second, we contribute to the broad literature on the effects of transportation infrastructure.<sup>3</sup> Our analysis is related to that of Mitnik et al. (2018), who use nighttime lights as an outcome with a two-way fixed effects approach and find that road rehabilitation in Haiti from 2004 to 2013 leads to increases of 6—26% in luminosity (corresponding to 0.5–2.1% increases in GDP). Similarly, BenYishay et al. (2018) evaluate road upgrades in the West Bank using panel data and find a 0.341 increase in absolute luminosity; given that baseline nighttime lights was 2.6, a 0.341 increase represents a significant increase. Storeygard (2016) constructs a panel of Sub-Saharan African cities using nighttime lights data and studies the effect of trade costs on economic activity. Asher and Novosad (2020) find effects of rural roads on non-farm employment but not on agricultural outcomes, income, or assets. Asher et al. (2020) estimate the effect of road construction on deforestation using satellite-based measures of forest cover. Faber (2014) finds that a national highway program in China aimed at connecting provincial capitals caused a reduction in GDP growth in small peripheral counties that the highways passed through. We add to this literature on transportation infrastructure investments by using satellite imagery to study development in a context where urbanization rates are low and where we observe a large-scale national investment program that was gradually rolled out over regions and time.

Third, we also contribute to the urban and regional economics literature on land use more generally.<sup>4</sup> For example, Deng et al. (2008) use high-resolution satellite imagery data to study urban expansion in China. This literature often builds on the monocentric city model, where the land use in and around a central business district depends on transportation costs. Michaels et al. (2012) consider a model of urbanization and structural transformation where land is allocated to residential, agricultural, or non-agricultural land use. Motamed et al. (2014) show that the transition from rural to urban activity depends on trade costs as

> 2The data is publicly available at `https://www.esa-landcover-cci.org/?q=node/175` .

> 3See Redding and Turner (2014) and Donaldson (2015) for surveys of the literature.

> 4See Duranton and Puga (2015) for an overview of theoretical and empirical literature on urban land use.

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measured by access to water transportation. Christensen and McCord (2016) study geographic determinants of urbanization in China and find heterogeneous effects. Our contribution to this literature is to directly document the effect of transportation infrastructure investments on agricultural and urban land use based on high-resolution satellite imagery and detailed data on a national road expansion program.

The paper is structured as follows. Section 2 provides background information on RSDP and growth in Ethiopia. Section 3 presents the data. Section 4 discusses the empirical strategy. Section 5 presents the results, and Section 6 concludes.

# **2 Background**

## **2.1 The Road Sector Development Program**

As of the late 1990s, the state of Ethiopia’s road network was poor. Most areas in the country were not connected to economic centers, which isolated areas from markets and social services, and roads that existed were deteriorating. The Government of Ethiopia recognized that the poor state of the network impeded economic growth; consequently, it launched the RSDP in 1997.

This program has gone through four phases and is currently in its fifth phase. The different phases had different objectives: RSDP I (1997–2002) focused mainly on rehabilitating and upgrading federal-level trunk roads; RSDP II (2002–2007) and III (2007-2010) expanded the program’s focus to upgrading and building new link and regional roads; RSDP IV (2010–2015) introduced the Universal Rural Road Access Plan (URRAP), shifting the program’s focus to rural and community roads while maintaining investment at other levels; and RSDP V (2015–present) shifted focus back to the federal level, prioritizing construction, upgrading, and heavy maintenance of trunk and link roads (Ethiopian Roads Authority, 2015a) Phases I–III saw investments in 32,693km of roads combined, Phase IV saw investments in 85,860km of roads, and the first year of Phase V saw 9,917km of roads improved. Figure 1a shows improvements in the road network over time; the bulk of road improvements were on 20km/h URRAP roads during Phase IV.

Overall, in the first 19 years of RSDP (1997–2016), 17.4 billion USD was invested across 128,470km of roads (Ethiopian Roads Authority, 2015a). The road development was categorized across federal roads (74.7% of total expenditure), regional roads (12.1%), Woreda/community roads (12.9%), and urban roads (0.3%). Construction of new federal and regional roads comprised 40.8% of total expenditure. During this time, the road network grew 326%—increasing from 26,550km in 1997 to 113,066km in 2016. Of the roads constructed, 41.4% were built during RSDP IV, between 2010 and 2015 (Ethiopian Roads Authority, 2015a). Overall, the program has led to notable improvements in road access. The Ethiopian Roads Authority (ERA) 6
<!-- Start of picture text --> 120<br>70<br>50 50<br>45 F- 45<br>vos 2010s<br>106 —— 1015<br>No ExistingRoad<br>1996 2002 2007 2010 2016<br>Phase | Phase II Phase III Phase IV<br>Note: Width of lines corresponds to road segment length.<br><!-- End of picture text -->

<!-- Start of picture text --> Kilometers of Proportion of Road<br>Roads Improved Network Improved<br>Addis Ababa | |<br>atar |} | |<br>Beneshangul Gumu | |<br>oma I Ds<br>Somali [fj | Dy<br>Tigray | ine<br>0 10,000 20,000 30,000 0.00 0.25 0.50 0.75<br>|_| Phasease | |_| Phasease II |_| Phasease III |_| Phasease IV<br><!-- End of picture text -->
## **2.2 Economic and Urban Growth**

Along with the continued development of the Ethiopian road network, the various phases of RSDP coincided with rapid urban population and economic growth. Ethiopia’s economy has been one of the fastest-growing in the region, experiencing an average of 8.1% annual growth between 1996 and 2015, compared to a 4.6% annual growth rate for Sub-Saharan Africa. However, it has been challenged by high inflation rates between 2008 and 2010 that led to local currency depreciation, which in turn increased fuel and transportation costs (World Bank Group, 2012). Spurred by an average of 2.2 billion USD in official development assistance per year between 1996 and 2015, Ethiopia has focused its economic development strategy on education, energy, and roads projects—such as RSDP (World Bank Group, 2012). Ozlu et al. (2015) note that despite only 17.3% of Ethiopia’s population residing in urban areas in 2012, the urban population is one of the fastest-growing in the world. This growth rate was estimated to be around 3.8% per year during the 2010s. Schmidt et al. (2018) identified the drivers of this rapid urbanization as road infrastructure, secondary city development, and rural-to-urban migration.

Addis Ababa has been the main destination of this rural-to-urban migration, attracting 40% of the total rural-to-urban migration between 2008 and 2013 (Bundervoet, 2018). However, despite the high percentage of migration flowing to Addis Ababa, Bundervoet (2018) notes that smaller cities and towns were also the focus of rural migration. This demonstrates not only that major urban centers have rapidly urbanized during the RSDP period, but that population is also growing in smaller cities and rural townships.

# **3 Data**

This paper relies on three types of geospatial data for our treatment variables and the three outcomes of interest (local economic activity, urban land, and cropland). First, we rely on digitized road improvement data shared by the ERA to capture the evolution of RSDP. Second, we rely on nighttime lights to capture the evening use of light and electricity as a proxy for local economic activity. Third, we rely on annual landcover data from the European Space Agency (ESA) GlobCover program to capture the evolution of the surface area of urban land and cropland. The following four sections describe the data sources (Section 3.1); how data are aggregated to different units of analysis (Section 3.2); how units are separated into different groups of economic activity for exploring heterogeneity of results (Section 3.3); and, lastly, we present descriptive statistics for the area and period of interest (Section 3.4).

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# **3.1 Data Sources**

The road data covers the stock of existing roads and their improvement between 1996 and 2016 annually, capturing RSDP phases I-IV. Three sources of data went into constructing the dataset (created and provided by the ERA): (1) federal road data from ERA, which includes dates of budget disbursements towards RSDP; (2) regional road network studies implemented at various times within selected regions that captured the inventory and conditions of roads; and (3) a nationwide road network inventory and condition survey implemented in 2014 and 2015. Speed limits were assigned to each road segment based on the pavement type and condition of the road, and road improvements are indicated by an increase in the speed limit (see Appendix S2 for a table showing ERA’s classification for road types, conditions, and speeds).

Our first outcome variable is nighttime lights, which has been shown to be a strong predictor of local economic activity (Donaldson and Storeygard, 2016; Sutton and Costanza, 2002; Doll et al., 2006; Ghosh et al., 2013), local GDP (Henderson et al., 2012), and measures of welfare such as local levels of asset wealth (Weidmann and Schutte, 2017). Nighttime lights data come from two data sources: the Defense Meteorological Program, Operational Line Scan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). Both sources are made available by the National Oceanic and Atmospheric Administration (NOAA). We rely on both sources to capture the full study period from 1996 to 2016; DMSP-OLS is available from 1992 to 2013 at a roughly 1km resolution, and VIIRS is available from 2012 to the present at a roughly 750m resolution. Two challenges must be addressed to construct a consistent time series based on the two nighttime lights data sources. First, DMSP-OLS data is captured over time using six different satellites; differences in the satellites, including the sensors and time of orbit, have resulted in significant differences in light values captured from different satellites across years (Wu et al., 2013; Zhang et al., 2016). Second, there are notable differences between DMSP-OLS and VIIRS data: VIIRS has a finer resolution, can capture nighttime lights at both lower and higher magnitudes, and suffers less from “blooming,” whereby lights in one pixel impact surrounding pixels (Elvidge et al., 2013). We therefore rely on a dataset from Li et al. (2020), who address both issues to develop a harmonized global nighttime lights dataset from 1992 to 2018; Li et al. (2020) use a stepwise calibration approach to create a consistent DMSP-OLS dataset.<sup>5</sup> One key difference between the calibrated DMSP-OLS data and the simulated DMSP-OLS-like data (i.e., VIIRS data simulated to be like DMSP-OLS) is that the DMSP-OLS-like data captures small values of

> 5The stepwise calibration approach refers to a four-step process, where DMSP-OLS satellites are individually calibrated to match earlier years; the process starts with earlier satellites and moves sequentially to later satellites. The method produces nighttime lights trends that more accurately track trends in electricity consumption compared to DMSP-OLS calibration methods developed by Zhang et al. (2016) and Elvidge et al. (2014). See Li and Zhou (2017) for details on how the dataset is created.

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nighttime lights where the DMSP-OLS data observes no light (see Appendix S3 for a comparison of DMSPOLS data and DMSP-OLS-like data). This difference results from VIIRS’s ability to capture low-level light that DMSP-OLS cannot capture; when simulating DMSP-OLS-like data from VIIRS data, some of the lowlevel light is retained. However, these differences should not have a large impact on results. First, these differences are concentrated in low-lit regions; Appendix S4 shows that average nighttime light trends are relatively consistent across DMSP-OLS and DMSP-OLS-like data. Second, as the empirical strategy section later describes, we control for factors common to all units in a given time period, such as a difference in the satellite used to capture nighttime lights.

While nighttime lights are now a staple of the economic literature on roads programs, including in Africa (Donaldson and Storeygard, 2016), the GlobCover data is a more recent addition to the toolbox. This dataset is produced by the European Space Agency (ESA), and is available annually from 1992 to 2018 at 300m resolution (European Space Agency, 2017, 2019). It classifies each pixel into one of the 36 land cover classes defined in the United Nations Land Cover Classification System. From this dataset, we use two land cover classes. First, we use land classified as urban, defined as 300m pixels covered mostly ( _>_ 50%) by artificial surfaces and associated areas. Second, we create a cropland category, defined as 300m pixels where over 50% of the pixel is cropland (this aggregates three cropland categories in the underlying data).

# **3.2 Aggregating Data**

We rely on Kebeles (Ethiopia’s smallest administrative unit) as our primary unit of analysis. We use a dataset that includes the boundaries of Kebeles for all regions in Ethiopia except Somali Region (15,670 Kebeles).<sup>6</sup> For Somali Region, we rely on boundaries of administrative units one higher than Kebeles—Woredas (N=44). However, for simplicity, we refer to the units of the combined dataset as ”Kebeles” throughout the paper. Kebeles are small enough to capture granular changes in the road network; 15,714 Kebeles cover Ethiopia, and the average size of a Kebele is 70km<sup>2</sup> (the median size is 24km<sup>2</sup> ). Appendix S5 shows a map of Kebeles. When aggregating data to Kebeles, we take the average values of nighttime lights and the total number of urban and cropland pixels.

To test the sensitivity of results to the unit of analysis, when relevant, we also estimate models using 1x1km pixels—the original resolution of DMSP-OLS data (1.1 million 1x1km pixels cover Ethiopia). As GlobCover landcover data are captured at a 300m resolution, we aggregate cropland and urban to the 1x1km pixels by creating binary variables indicating whether any portion of the 1x1km pixel contains urban or cropland area. However, we use Kebeles as the primary unit of analysis because they represent a more

> 6The Kebele shapefile is available at `https://ethiopia.africageoportal.com/datasets/africageoportal::kebeles-level-4/ about`

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policy-relevant unit than pixels.

For continuous outcome variables (nighttime lights at the 1km grid and Kebele level, and cropland and urban land at the Kebele level), we transform the variables using the inverse hyperbolic sine (IHS) transformation, which is defined as _Yi_ = _log_ ( _yi_ + � _yi_<sup>2+ 1).Theinterpretationisequivalenttothatof</sup> logarithms. The transformation is defined at zero and provides a way to use a transformation that is suited for cases where there are a large number of zeros (Mitnik et al., 2018).

# **3.3 Separating Units by Baseline Economic Activity**

Across analyses, we explore heterogeneity of results according to baseline levels of economic activity. We group units (1x1km pixels and Kebeles) according to the baseline level of nighttime lights of the Woreda in which the unit is located. We divide Woredas into four groups. The first group consists of Woredas that registered no nighttime lights at baseline. We then divide Woredas that registered nighttime lights into three similarly sized groups using 33<sup>_rd_</sup> and 66<sup>_th_</sup> percentile cut-offs, based on the maximum nighttime lights value recorded within the Woreda. There are 557 dark Woredas, 74 Woredas with low light (max. 1–5 NTL value), 78 Woredas with medium light (max. 6–8 NTL value), and 71 Woredas with high light (max. _>_ 8 NTL value). Appendix S4, which presents trends in dependent variables for Woredas in each category, shows that Woredas in each category experienced growth in nighttime lights and urbanization. Using the baseline value of nighttime lights at the Woreda level—not the level of the individual unit (e.g., Kebele)—ensures that two nearby units from the same town or city are not separated into different analyses. For example, the approach ensures that a brightly lit pixel in a city’s center and a dimly lit pixel in the same city’s periphery are included in the same group. To test the sensitivity of these groupings, we also divide Woredas into two groups: those that had zero baseline nighttime lights, and those that had some positive baseline lights.

# **3.4 Descriptive Statistics**

Table 1 shows descriptive statistics of outcome variables at the Kebele level, and Figure 2 shows maps of dependent variables and road upgrades. Nighttime lights and urban land are heavily concentrated in Ethiopia; most of the country is neither lit nor settled. However, during the study period, Ethiopia experienced significant growth in both urban area and the intensity of lit pixels. The number of Kebeles with some positive nighttime lights increased 103% from 1996 to 2013 and further increased by 414% from 2013 to 2016.<sup>7</sup> In addition, the number of Kebeles with some urban land grew by 96% (from 542 to 1,062 out of 15,714). In contrast to nighttime lights and urban area, much of Ethiopia is classified as cropland, and

> 7Much of the increase from 2013 to 2016 is likely driven by 2013 using DMSP-OLS data and 2016 data using DMSP-OLS-like data, which is derived from VIIRS and better captures low-level light.

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cropland experienced less change over time. For example, 15,081 Kebeles (95.9%) contained some cropland in 1996 and 15,045 (95.7%) contained some cropland in 2016—a 0.2 percentage point change.

When considering only areas near (within 5km of) an improved road, urban land and luminosity are also heavily concentrated. Table 1 shows that out of the 14,933 Kebeles near an improved road, 626 (4%) registered positive nighttime light values at baseline, and this value grew to 1,267 by 2013 (a 102% increase) and further grew to 6, 214 (a 390% increase from 2013); however, much of that increase may be due to differences in underlying nighttime lights data (2013 uses DMSP-OLS data and 2016 uses DMSP-OLS-like data, which better captures low values of nighttime lights). Urban area saw similar growth over time, from 520 Kebeles with some urban area to 1,035 (a 99% increase). While a small proportion of Kebeles near an improved road contain lit or urban cells, a high proportion of lit or urban cells are near an improved road.

**Table 1:** Summary Statistics of Outcome Variables Across Kebeles

|Variable||All||(Within|Treated A<br> 5km of Im|reas<br>proved Road)|C<br>(_>_5km|ontrol Ar<br>of Improv|eas<br>ed Road)|
|---|---|---|---|---|---|---|---|---|---|
||1996|2013|2016|1996|2013|2016|1996|2013|2016|
|**_Average across Kebeles_**||||||||||
|Avg. NTL|0.35|0.57|1.88|0.37|0.6|1.9|0.01|0.02|1.45|
|Avg. Urban Area (km<sup>2</sup>)|0.072|0.156|0.189|0.066|0.156|0.189|0.159|0.207|0.225|
|Avg. Cropland Area (km<sup>2</sup>)|40.365|40.854|40.557|40.86|41.334|41.031|30.918|31.647|31.512|
|**_N Kebeles with Positive _**|**_Value_**|||||||||
|NTL|628|1,278|6,472|626|1,267|6,214|2|11|258|
|Urban|542|910|1,062|520|884|1,035|22|26|27|
|Cropland|15,081|15,044|15,045|14,368|14,333|14,332|713|711|713|
|N|15,714|||14,933|||781|||

Nighttime lights data come from Li et al. (2020), who provides an intercalibrated nighttime lights dataset derived from DMSP-OLS and VIIRS from 1992 to 2018; DMSP-OLS data from 1992–2013 is used, and VIIRS is used to simulate DMSP-OLS-like data from 2014 to 2018. Urban and cropland categoriescome from the ESA Globcover dataset. The table shows values in 1996 (first year of road data available), 2013 (last year of DMSP-OLS data available), and 2016 (last year of road data available, and where VIIRS is used to simulate DMSP-OLS-like data).

# **4 Empirical Strategy**

As shown in the previous two sections, Ethiopia experienced substantial changes in its road network (Figure 1) and in economic activity (Table 1). In this section, we exploit the temporal and spatial variation in roads and economic activity to estimate the effect of road upgrades.

We rely on three complementary empirical approaches to estimate the impact of road upgrades on economic activity and changes in land cover (urban land and cropland). First, we rely on a difference-indifference design that focuses on the impact of road upgrades on surrounding areas. Second, we rely on a long-difference approach that focuses on the benefits of the program for incidentally treated areas compared to the areas that did not benefit from the program. Third, we examine how improvements in market access 12
<!-- Start of picture text --> Baseline<br>Nighttime Lights Urban Cropland<br>— TaeSo <ae% ; bee)Pe<br>eg wo a= i oben Tape Se, Oe ca<br>Endline<br>Nighttime Lights Urban Cropland<br>okas ou ee<br>9 ES oe FERS peel TM<br>Honea beta UES ea<br>Road Improvements<br>ffhireea HWS)a mA<br>a is, RED<br>8 sr ue < as at 4<br>SEG ae<br>a at BR Beark, ori |ARES Improvement<br>Nee aferNepel se *MR¢is eas © 4 Year2015<br>ath, SREa, ONESaaa htoe ¥ oe r} SBD» RLADpea EST § 2010<br>Patt etsy 4 PDEbk ‘ oh Lee ;<br>A SE nee: 2005<br>Bs ax ray = 2000<br><!-- End of picture text -->
due to road upgrades contributed to changes in economic activity and land cover.

# **4.1**

First, we implement a differences-in-differences approach to look at the short-term effects of the road upgrades. We use the method proposed by Callaway and Sant’Anna (2021) that is designed for applications with multiple time periods and where the timing of treatment varies across units. Here, the control group dynamically changes and is comprised of units not-yet-treated. Average treatment effects are computed for each group and each time period; in our setting, the treated “group” corresponds to units that were near roads improved in the same year, while the control groups are units yet to be treated. Group-time average treatment effects are estimated using a doubly robust approach, which uses weighted least squares for estimating regressions and inverse probability tilting for estimating propensity scores (Callaway and Sant’Anna, 2021; Sant’Anna and Zhao, 2020). We rely on an unconditional difference-in-differences approach.

Following the method proposed by Callaway and Sant’Anna (2021), we recover average treatment effects by length of exposure to improved roads, which generates coefficients for both before and after treatment. Treatment effects in post-treatment periods allow an understanding of how long impacts took to materialize after improvements, while treatment effects in pre-treatment periods allow testing for whether treatment and control units had parallel trends in outcome variables before treatment. This strategy is valid as long as the trends in the control and treated groups are parallel for each pair of control and treated group and there is no correlation between the timing of the road construction or upgrade and one of the outcomes of interest. To assess a potential violation of the underlying hypothesis, we examine the pre-trends in our data in the results section. Specifically, a primary threat to our identification would be if factors affected localities near upgraded roads with the same timing as RSDP. However, given the significant variation in upgraded roads across time and geographic areas, this risk is relatively small. To this point, we find that another large program—a national electrification program—that was implemented at a similar time as RSDP was rolled out differently. In 2005, Ethiopia initiated the Universal Electricity Access Program (UEAP). At the start of the program, approximately 6% of the population was connected to electricity, and only about 15% of the population lived in electrified areas (i.e., areas with some form of electricity supply for residences and businesses). The program, as of 2015, reportedly added connections to over 5,000 towns and villages, connecting 60% of towns and villages to the grid (MoWIE, 2019). The program added a large share of these connections in urban areas, mainly Addis Ababa (World Bank Group, Independent Evaluation Group, 2015). The timing of UEAP overlapped with part of RSDP. However, areas targeted by the program differed (see Appendix S6, which compares roads improved through RSDP and new grid lines from the Electricity 14
Access Rural Expansion Project—which was a key component of UEAP).

Moreover, we find that road projects were often affected by idiosyncratic delays in road construction. Consequently, the timing of actual road completion at the regional level within a phase should be as good as random. Delays in planned road construction were reported in RSDP and the Universal Rural Roads Access Program (URRAP) due to gaps in project handover and quality issues in locally assembled construction equipment. These delays are reflected in the road construction accomplishment rates, per RSDP.

Our base model includes all road types and units. We explore heterogeneity along two dimensions: initial level of economic activity and road type. To examine differences across initial levels of economic activity, we separate units according to baseline nighttime lights. To examine differences across road type, we separate roads by whether the road’s speed limit was above or below 50km/h after improvement (50km/h and above corresponds to federal roads, highways, and expressways) and estimate the baseline model separately for each case. While this approach is indicative of the differential effects of different types of investments, different road types are likely to be affected by selection into treatment, such that these results are mainly indicative of correlation. To explore sensitivity to Addis Ababa-driven effects, we also estimate models excluding units within 100km of Addis Ababa. To check the robustness of results using this specification, we also estimate results using a two-way fixed effects model (see appendix S7).

# **4.2 Long Difference Estimation**

Second, we estimate the effects of RSDP from its inception to its end, comparing areas that were included in the program and those that were not. For this analysis, we account for selection issues, which arise from the fact that RSDP was targeted to areas based on specific criteria. For Phases I–III, regional states submitted proposals to ERA, which then evaluated the proposals against five selection criteria and funding constraints (Shiferaw et al., 2015). Selection criteria for new roads placed a 40% weight on economic potential (20% for areas with economic development potential and another 20% for areas with surplus food and cash crop orientation), and another 40% focused on reducing inequities in road accessibility.<sup>8</sup> To address the selection issue, we focus on areas that were incidentally connected, which provides a source of exogenous variation in network connections. This approach is common in the literature on transport networks (Michaels, 2008; Datta, 2012; Faber, 2014; Banerjee et al., 2020). Our strategy largely follows Faber (2014), who examines the impact of a highway system in China designed to connect provincial capitals with larger cities. The key idea is that areas that happened to lie between targeted cities were incidentally, rather than purposefully, connected, generating quasi-random variation in road access in the sample that excludes the targeted cities.

> 8See Shiferaw et al. (2015) for a list of selection criteria and the process for identifying road projects.

15
For this analysis, we focus only on RSDP I–III, which concentrated on trunk and link roads. In RSDP IV, Ethiopia began URRAP, which had the objective of connecting all of Ethiopia’s Kebeles to the nearest all-weather road (Ethiopian Roads Authority, 2015b; Iimi et al., 2018). As of 2016, the latest year in our roads dataset during RSDP IV, not all Kebeles were connected. However, it does not make sense to include RSDP IV when using this instrumental variable strategy, as all Kebeles were targeted for treatment as part of URRAP, and this analysis focuses only on non-targeted treated areas.

We rely on RSDP road improvement data to identify areas targeted by RSDP. The dataset is organized by road link and lists the names of cities at each end of the link. We use this list of cities and Ethiopia’s nine regional capitals as the targeted areas. This process yields 919 targeted locations (Appendix S8 shows sample sizes after removing targeted locations and the number of units near an RSDP road).

We estimate the following long-difference model using Kebeles as our main model. The same model is run at using the 1x1km grid to check the sensitivity of our main results, where we exclude units within 5km of targeted areas. ∆ _y_ is the change in the outcome variable from baseline (1996) to endline (2009, the last year of RSDP III), and _Near Improved Road_ is a binary variable that indicates whether a unit is near a road improved from RSDP. To explore heterogeneity in impacts, we estimate alternative versions of equation 1 where we interact _Near Improved Road_ with variables indicating whether the unit was in a Woreda with zero, low, medium, or high baseline nighttime lights.

Identifying the impact of RSDP from OLS (model 1) assumes that non-targeted areas that were connected to road projects were as good as randomly selected. However, this assumption is violated if regional states also choose to connect politically or economically important areas between the primarily targeted locations. To this point, non-targeted treated and control areas are not balanced across baseline levels and pre-trends of outcome variables (see Appendix S9).

To address this concern, we follow Faber (2014) and construct two minimum spanning trees to use as instruments for road placement. The MSTs are hypothetical road networks developed under the sole goal of connecting all targeted destinations subject to cost minimization. The first MST connects targeted areas in a way that minimizes the total Euclidean distance of the network, and the second MST minimizes the total construction cost of the network. Construction costs are proxied using elevation and land cover (additional construction costs are assigned to areas that are built-up or that contain wetlands or water; see Appendix S10, which provides details on how construction costs are computed and how the MSTs are constructed).

We compute two variations of the Euclidean distance and least-cost MSTs. First, we construct MSTs that 16
# Minimum Distance MST

# Least Cost MST

<!-- Start of picture text --> . SD 1<br>4 ( xy<br>4 Ly os<br>izCAdSep© *oh] ~raat “y;as N — Improved Roads<br>paarwhet ua? a ek\ OD mtBN& thotTd¢ bth wee "RS. — MST<br>g OS 5 OShe<br>= y+8 > oye >»<br><!-- End of picture text -->
in the context of transportation infrastructure, because such investments might also affect locations further away. The market access measure captures such spillovers. As with our previous empirical approaches, we rely on Kebeles as our primary unit; however, to test the sensitivity of results, we also repeat the analysis using Woredas (N = 780). The 1x1km pixels are less suitable for market access analysis.

The measure of market access is constructed using the following equation (Donaldson and Hornbeck, 2016): where population of the other markets ( _popj_ ) is held constant at 2000 levels and where we measure the travel time _ttij,t_ between the origin unit _i_ and the destination _j_ at time _t_ (see Appendix S11 for details on estimating travel time using the RSDP road network data). We use the most populous pixel within each unit as the location for estimating travel times. In our primary models, we use an elasticity ( _θ_ ) of 3.8, following Donaldson and Hornbeck (2016); however, we test the robustness of our results to alternate measures.

We aim to understand whether market access growth is associated with changes in economic activity, urbanization, or cropland. As our primary model, we use a long-difference framework adopted from Alder (2017), who estimates how changes in market access are associated with changes in nighttime lights in India. We regress changes in our outcome variables on changes in market access, as specified in the following equation: where ∆ _IHS_ ( _yi_ ) represents changes in outcome variables as specified in the above section, and we take the inverse hyperbolic sine transformation before differencing; _MAi_ is the measure of market access; _Ni_ is a vector of indicator variables for different levels of baseline nighttime lights; _Ci_ is a vector of controls, including initial market access, average nighttime lights at baseline, pre-trends in luminosity, and pre-trends in the number of urban pixels; and _γz_ are Zone fixed effects (where these represent Zone-year fixed effects in a panel framework). In computing the long difference, baseline values are from 1996 and endline values are from 2016. In all models, we cluster standard errors on Woredas (when estimating the model using Woredas as the unit of analysis, we cluster using Zones, Ethiopia’s second-level administrative division, of which there are 64). We explore heterogeneity in the impact of market access by interacting market access with indicator variables ( _Ni_ ) for different baseline levels of nighttime lights.

An identification challenge is that market access may be endogenous. For example, road upgrades may 18
have been targeted to areas that would have experienced growth anyway. Following Alder et al. (2018), we address this endogeneity concern by controlling for pre-trends in urban growth and luminosity. In addition, following Donaldson and Hornbeck (2016), Blankespoor et al. (2017), and Jedwab and Storeygard (2016), we implement a doughnut instrumental variables (IV) strategy. Here, we instrument market access with an alternative version of market access that excludes units within a certain buffer. Specifically, when calculating market access for each unit, units within a specified buffer are excluded. For our primary model, we use a 50km buffer, but we also test 20km and 100km buffers. This instrument helps address endogeneity concerns by excluding units more prone to non-random road placement (Blankespoor et al., 2017). When including interaction terms with market access, we instrument using the interaction of the “doughnut” market access variable and the interaction terms. For example, we instrument _MA_ and _MA × Distance Addis_ with _MAdoughnut_ and _MAdoughnut × Distance Addis_ .

To take full advantage of the panel nature of our data, we test the robustness of the long-difference results using a two-way fixed effects model. This approach follows Blankespoor et al. (2017), who measure the impact of road improvements in Mexico. The methodology is discussed in more detail in Appendix S12.

# **5 Results**

## **5.1**

Figures 4 and 5 show results after recovering the average treatment effect by length of exposure to road improvements using Kebeles as the unit of analysis. We find that road upgrades contributed to growth in both nighttime lights and urban land, and a decrease in cropland. Urban areas grow immediately after the upgrades, while nighttime lights growth takes place five years after the upgrades. Most of the impact is driven by proximity to roads with higher speed limits, but this might be the result of selection of locations into treatment. In addition, pre-treatment coefficients generally appear flat throughout all results, suggesting no-to-minimal pre-trends.<sup>10</sup> Comparing places with different initial luminosity levels, coefficients on road improvements for nighttime lights show relatively similar magnitudes in areas with low and high baseline nighttime lights; road improvements lead to approximately 10–20% growth in nighttime lights. In models with urban land as the dependent variable, the coefficient on road improvements is notably larger in areas with high initial nighttime lights compared to areas with low initial lights. Results using only two groups of baseline nighttime lights show similar results, with gains concentrated in Woredas with positive nighttime lights (see Appendix S13).

> 10Across most regressions, the p-value of the Wald pre-test of the parallel trends assumption is generally near zero; however, only a small number of pre-treatment coefficients appear significant in any regression, and the coefficients are small in magnitude.

19
The results thus suggest that road upgrades may have contributed to a transition from cropland to urban land. Examining the land cover classes that transitioned to urban supports this point; of all pixels that transitioned to urban, 52% were previously cropland (see Appendix S14, which shows the distribution of land cover classes that transitioned to urban).

As robustness checks, we also implement the traditional two-way fixed effects estimator and change the unit of analysis to 1x1km pixels. When estimating models using 1x1km pixels, we remove pixels within 1km of improved roads; doing so helps to remove impacts that may be driven by simply the addition of new roadside lights. Our results do not change significantly using two-way fixed effects, albeit the coefficients on leads are significant for higher-speed roads for cropland. However, in areas with high baseline nighttime lights, we observe a reduction in cropland. This is consistent with the finding that road improvements are strongly correlated with growth in urban land (see Appendix S7). Results using 1x1km pixels as the unit of analysis generally show similar patterns (see Appendix S13).

20
<!-- Start of picture text --> Dependent Variable: Nighttime Lights<br>1.0<br>0.5<br>00 t oft t {<br>-0.5<br>-1.0<br>Dependent Variable: Urban<br>1.0<br>O Baseline<br>S05 Nighttime<br>8 Lights<br>: Ll ts<br>E 00 + Medium<br>2 { Low<br>g + Dark<br>O<br>-0.5<br><!-- End of picture text --> Dependent Variable: Cropland <!-- Start of picture text --> 0.0 *<br>r} @ e 5 ° . . . i<br>-0.1<br>-0.2<br>-0.3<br>-10 -5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
<!-- Start of picture text --> Dependent Variable: Nighttime Lights<br>0.2; dil<br>0.0<br>-0.1<br>Dependent Variable: Urban<br>0.15<br>O<br>in 0.10 Road Type<br>$ $ All<br>(0)<br>‘O 50 km/h<br>= 0.05 | | | | $ >=50 km/h<br>4 0.00<br>O<br>-0.05<br>Dependent Variable: Cropland<br>0.000 1<br>-0.050<br>-0.075<br>-10 -5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
# **5.2 Long Difference and Instrumental Variable Results**

Table 2 shows long-difference OLS (columns 1–6) and instrumental variable (columns 7–12) results on areas incidentally connected to RSDP I–III.<sup>11</sup> Roads are associated with growth in nighttime lights and urban land, primarily in areas with medium-to-high levels of baseline nighttime lights. First-stage results of the MSTs are all highly significant. IV results show roughly similar coefficients compared to the OLS model and, similar to OLS results, show the largest gains from roads occurring in areas with high baseline nighttime lights. Focusing on the IV results, road upgrades are associated with a 17% increase in nighttime lights in Kebeles with high initial light and an 18% increase in Kebeles with medium initial nighttime lights. In addition, road upgrades are associated with a 27% increase in urban land in Kebeles with high initial light. Results using different versions of the MST instrument (least-distance MSTs and MSTs constructed using all targeted areas at once—not first within regions, then appended together) show similar results (see Appendix S8). In addition, results using two groups of baseline nighttime lights show gains concentrated in Woredas with positive nighttime lights.

**Table 2:** Long Difference, OLS and IV Results - RSDP I - III

||N|TL|Ur|ban|Cro|pland|NT|L|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._03<sup>_∗∗_</sup>|0_._003|0_._01<sup>_∗_</sup>|_−_0_._001|0_._005|0_._01|0_._08<sup>_∗∗_</sup>|0_._04|0_._01|_−_0_._01|0_._04|0_._05|
||(0_._01)|(0_._01)|(0_._01)|(0_._005)|(0_._01)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Low||0_._02||0_._02||_−_0_._01||0_._02||0_._02||_−_0_._06|
|||(0_._02)||(0_._01)||(0_._03)||(0_._03)||(0_._02)||(0_._06)|
|Imp Rd._×NTL_96 Med||0_._13<sup>_∗∗_</sup>||0_._02||0_._01||0_._18<sup>_∗∗_</sup>||0_._04||_−_0_._004|
|||(0_._06)||(0_._02)||(0_._02)||(0_._09)||(0_._03)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._22<sup>_∗∗_</sup>||0_._20<sup>_∗∗_</sup>||_−_0_._04<sup>_∗_</sup>||0_._17<sup>_∗_</sup>||0_._27<sup>_∗∗_</sup>||_−_0_._05<sup>_∗_</sup>|
|||(0_._10)||(0_._08)||(0_._02)||(0_._09)||(0_._13)||(0_._03)|
|Constant|_−_0_._02|_−_0_._01|_−_0_._10<sup>_∗∗_</sup>|_−_0_._09<sup>_∗∗_</sup>|_−_0_._02|_−_0_._02|_−_0_._18|_−_0_._16|_−_0_._09|_−_0_._07|_−_0_._15|_−_0_._16|
||(0_._06)|(0_._06)|(0_._05)|(0_._04)|(0_._07)|(0_._07)|(0_._13)|(0_._13)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|
|Type<br>|_OLS_|_OLS_|_OLS_|_OLS_<br>|_OLS_|_OLS_|_IV_|_IV_|_IV_|_IV_|_IV_|_IV_|
|1st Stage F-Stat|_N/A_|_N/A_|_N/A_|_N/A_|_N/A_|_N/A_|84_._5|25_._4|84_._5|25_._4|84_._5|25_._4|
|Observations<br>|10,220|10,220|10,220|10,220|10,220|10,220|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.02|0.001|0.02|-0.0001|-0.0001|-0.01|0.02|0.001|0.02|-0.003|-0.004|

Unit of analysis is Kebeles. Standard errors are clustered on Woredas. For models (6)–(12), the least-cost-distance MST is used, where the MST is first constructed for each region, then the regional MSTs are appended. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a Kebele was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime light within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01

> 11The table shows IV results using the least-cost MST, where MSTs are constructed at the region level, then appended together. Appendix S8 shows results using alternate constructions of the MST.

23
# **5.3 Impact of Market Access**

Tables 3 and 4 show the correlation between changes in market access and changes in outcome variables using Kebeles and Woredas as units of analysis, respectively. Both tables show OLS results (columns 1–6) and results when instrumenting market access with the 50km doughnut market access variable (columns 7–12). Focusing on IV results using Kebeles as the unit of analysis, results show that market access is primarily associated with gains in nighttime lights across non-dark Kebeles (i.e., Kebeles with some positive nighttime light pixels at baseline), with gains largest in areas with the highest baseline nighttime lights. Nighttime light results are consistent across OLS results and results using Woredas as the unit of analysis.

Results using urban land as the dependent variable tend to be sensitive to using OLS or IV models and to the unit of analysis. Results using Kebeles as the unit of analysis show that gains in market access are associated with a small reduction in urban land in areas with low initial light but no significant association between market access and urban land in areas with medium or high initial light. Results using Woredas as the unit of analysis—particularly the IV models—show a positive association between market access and urban land in areas with higher initial light.

Results showing that gains from road upgrades tend to be concentrated in areas with higher baseline nighttime lights is consistent with our results on the impacts of road upgrades on the immediate areas surrounding the upgrades. Results examining the impact on areas incidentally connected to RSDP similarly show the greatest benefits to areas with the highest initial light. In addition, difference-in-difference results do show benefits to areas with below-median initial nighttime lights, but coefficients tend to be larger in areas with higher initial lights.

Results using different values of _θ_ when calculating market access, different doughnut market access sizes (using 20km and 100km), and two groups of baseline nighttime lights (Woredas with no baseline light versus Woredas with some baseline light) all show similar results (see Appendix S15). Results using a panel, two-way fixed effects approach also show similar results; however, here results show a significant association between market access and urban land using both Kebeles and Woredas as the unit of analysis (see Appendix S12).

Overall, the results using market access show that the gains in terms of nighttime lights from road upgrades are concentrated in areas with moderate-to-high initial levels of economic activity. This finding is consistent with the results based on long-differences specifications. Similarly, the areas with higher initial light show a positive association between market access and urban land, which is consistent with the difference-in-difference and long-differences results.

24
**Table 3:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Kebeles]

||NT|L|Urb|an|Crop|land|NT|L|Urba|n|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|_−_0_._004<sup>_∗∗_</sup>|0_._003|0_._002|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._004<sup>_∗_</sup>|0_._002|0_._001|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._01)|(0_._01)|(0_._003)|(0_._002)|(0_._003)|(0_._004)|
|MA_×NTL_96 Low||0_._04<sup>_∗∗∗_</sup>||_−_0_._01<sup>_∗∗∗_</sup>||0_._004||0_._05<sup>_∗∗_</sup>||_−_0_._01<sup>_∗∗_</sup>||0_._002|
|||(0_._01)||(0_._003)||(0_._003)||(0_._02)||(0_._004)||(0_._004)|
|MA_×NTL_96 Med||0_._07<sup>_∗∗∗_</sup>||_−_0_._003||_−_0_._001||0_._08<sup>_∗∗∗_</sup>||_−_0_._002||0_._002|
|||(0_._01)||(0_._004)||(0_._004)||(0_._02)||(0_._01)||(0_._004)|
|MA_×NTL_96 High||0_._15<sup>_∗∗∗_</sup>||0_._02||0_._01||0_._16<sup>_∗∗∗_</sup>||0_._04||0_._01|
|||(0_._02)||(0_._02)||(0_._01)||(0_._03)||(0_._03)||(0_._01)|
|MA, 1996|0_._03<sup>_∗∗∗_</sup>|0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|0_._07<sup>_∗∗∗_</sup>|0_._06<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._004<sup>_∗_</sup>|_−_0_._004<sup>_∗∗_</sup>|
||(0_._003)|(0_._003)|(0_._001)|(0_._001)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|Log mean light, 1996|_−_0_._01|_−_0_._16<sup>_∗∗∗_</sup>|0_._45<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|0_._02|_−_0_._14<sup>_∗∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._42<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|
|Pre-trend: log mean light|0_._06|0_._13<sup>_∗_</sup>|0_._15|0_._17|_−_0_._001|0_._01|0_._08|0_._16<sup>_∗∗_</sup>|0_._16|0_._19|_−_0_._002|0_._01|
||(0_._08)|(0_._08)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|(0_._08)|(0_._08)|(0_._15)|(0_._16)|(0_._03)|(0_._03)|
|Pre-trend: log N urban pixels|0_._34<sup>_∗∗_</sup>|0_._34<sup>_∗∗∗_</sup>|0_._56<sup>_∗∗∗_</sup>|0_._56<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|0_._35<sup>_∗∗_</sup>|0_._35<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|
||(0_._13)|(0_._13)|(0_._22)|(0_._21)|(0_._12)|(0_._12)|(0_._14)|(0_._13)|(0_._21)|(0_._21)|(0_._12)|(0_._12)|
|Model<br>|_OLS_<br>|_OLS_<br>|_OLS_<br>|_OLS_<br>|_OLS_|_OLS_|_IV_|_IV_|_IV_|_IV_|_IV_|_IV_|
|Observations<br>|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|
|Adjusted R<sup>2</sup>|0.18|0.19|0.22|0.23|0.14|0.14|0.17|0.18|0.22|0.22|0.14|0.14|

The unit of analysis is Kebeles. Standard errors are clustered on Woredas and all models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. IV indicates that MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, Kebeles within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

**Table 4:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Woredas]

||NT|L|Urb|an|Cropl|and|NT|L|Ur|ban|Cropl|and|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._03|_−_0_._02|_−_0_._03<sup>_∗_</sup>|_−_0_._004|_−_0_._004|_−_0_._05|_−_0_._07|0_._03|0_._01|_−_0_._002|_−_0_._003|
||(0_._02)|(0_._02)|(0_._01)|(0_._02)|(0_._004)|(0_._004)|(0_._04)|(0_._04)|(0_._03)|(0_._03)|(0_._01)|(0_._01)|
|MA_×NTL_96 Low||0_._06<sup>_∗∗_</sup>||0_._002||_−_0_._002||0_._12<sup>_∗∗∗_</sup>||0_._04<sup>_∗_</sup>||0_._003|
|||(0_._03)||(0_._02)||(0_._004)||(0_._04)||(0_._02)||(0_._01)|
|MA_×NTL_96 Med||0_._13<sup>_∗∗∗_</sup>||0_._04||0_._001||0_._20<sup>_∗∗∗_</sup>||0_._12<sup>_∗∗∗_</sup>||0_._01<sup>_∗_</sup>|
|||(0_._03)||(0_._03)||(0_._005)||(0_._05)||(0_._03)||(0_._01)|
|MA_×NTL_96 High||0_._20<sup>_∗∗∗_</sup>||0_._20<sup>_∗_</sup>||0_._02||0_._32<sup>_∗∗∗_</sup>||0_._40<sup>_∗∗∗_</sup>||0_._05<sup>_∗∗_</sup>|
|||(0_._04)||(0_._11)||(0_._01)||(0_._06)||(0_._11)||(0_._02)|
|MA, 1996|0_._02<sup>_∗∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗_</sup>|<sup>_∗_</sup><br>0_._001|0_._001|0_._04<sup>_∗∗∗_</sup>|0_._02|0_._01|_−_0_._01|_−_0_._001|_−_0_._003|
||(0_._004)|(0_._01)|(0_._003)|(0_._002)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._01)|(0_._01)|(0_._002)|(0_._003)|
|Log mean light, 1996|_−_0_._15<sup>_∗_</sup>|_−_0_._29<sup>_∗∗∗_</sup>|0_._29|0_._15|_−_0_._12|_−_0_._13|_−_0_._11|_−_0_._35<sup>_∗∗∗_</sup>|0_._32<sup>_∗_</sup>|0_._03|_−_0_._11|_−_0_._15|
||(0_._08)|(0_._07)|(0_._19)|(0_._16)|(0_._10)|(0_._10)|(0_._07)|(0_._09)|(0_._19)|(0_._19)|(0_._10)|(0_._11)|
|Pre-trend: log mean light|0_._11|0_._10|_−_0_._20|_−_0_._19|0_._12|0_._13|0_._11|0_._08<br>|_−_0_._20|_−_0_._18|0_._13|0_._13<sup>_∗_</sup>|
||(0_._13)|(0_._11)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|(0_._13)|(0_._10)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|_−_0_._24<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._33<sup>_∗_</sup>|0_._34<sup>_∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._26<sup>_∗∗_</sup>|_−_0_._23<sup>_∗∗_</sup>|0_._36<sup>_∗_</sup>|0_._40<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._10)|(0_._10)|(0_._19)|(0_._20)|(0_._02)|(0_._02)|(0_._10)|(0_._10)|(0_._21)|(0_._23)|(0_._02)|(0_._02)|
|Model<br>|_OLS_|_OLS_<br>|_OLS_|_OLS_|_OLS_<br>_O_|_LS_|_IV_|_IV_<br>|_IV_|_IV_|_IV_|_IV_|
|Observations|780|780|780|780|780|780|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.33|0.36|0.28|0.31|0.37|0.37|0.33|0.35|0.27|0.26|0.37|0.36|

The unit of analysis is Woredas. Standard errors are clustered on Zones and all models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. IV indicates that MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, Woredas within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Woredas, Urban is the total urban area within Woredas, and Cropland is the total cropland area within Woredas; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 25
# **6 Conclusion**

Since 1997, Ethiopia has implemented RSDP, a countrywide program of road construction and improvements. As in other contexts, a primary purpose of these transportation investments is to accelerate economic growth. For example, a key objective of RSDP is to lower transport costs to support the production and distribution of goods across the country and for export. From 1997 to 2016, 17.4 billion USD was invested across 128,470 km of roads. This paper evaluates the impact of RSDP on satellite-based measures of local economic activity and land use.

Identifying the impact of transportation investments is difficult, as road placements may be endogenously related to outcome variables of interest (e.g., roads targeted to areas already experiencing growth in economic activity or urban land). We therefore rely on several complementary empirical approaches to uncover impacts of road upgrades. First, we exploit temporal and spatial variation of road upgrades; we employ a differencein-difference model that controls for unobserved time-invariant heterogeneity in locations as well as common temporal trends throughout Ethiopia. Second, we focus the analysis on localities incidentally connected to RSDP roads as a potential source of exogenous variation in networks connections; here, following Faber (2014), we instrument road upgrades with a hypothetical road network that would have been constructed if connecting areas targeted by RSDP in the least-cost way was the only priority. Third, we examine the impact of gains in market access, rather than whether a locality was near an upgraded road; we follow Alder (2017) and use a long-difference approach to understand how changes in market access are associated with changes in satellite-based outcome variables, controlling for baseline-level characteristics and pre-trends in outcome variables. To check the robustness of these results, we take advantage of the panel data nature of the model and, following Blankespoor et al. (2017), also employ a two-way fixed effects model.

Altogether, we find that gains from road upgrades in nighttime lights and urbanization were concentrated in areas with higher levels of initial economic activity. In examining impacts of upgrades on surrounding areas, the difference-in-difference models show impacts on local economic activity and urbanization across all areas, but impacts were largest in areas with above-median levels of nighttime lights at baseline. Long difference IV results show some positive impacts in areas with the least initial light; however, the coefficients are small in magnitude for the darkest areas and are significantly larger for areas with the highest baseline nighttime lights. Similarly, market access models tend to show the strongest impacts among areas with medium-to-high levels of baseline nighttime lights. In addition, the results show either no positive impact of upgrades in the darkest areas or that upgrades resulted in a reduction of economic activity or urbanization in these areas.

The result showing that gains from road improvements are concentrated in larger markets is in line 26
with existing literature. For example, BenYishay et al. (2018) find that road improvements in the West Bank shifted nighttime lights from darker areas to brighter areas; road improvements led to a reduction in nighttime lights in areas with the darkest baseline nighttime lights but led to gains in nighttime lights for other targeted areas. Faber (2014) finds that highways connecting major metropolitan areas caused a reduction in GDP growth in peripheral counties also connected to the new highways. In addition, Mitnik et al. (2018) find that poor communities do not benefit from road improvements, but they also find that the richest communities do not benefit either; namely, gains are concentrated in communities in the middle of a ranking according to unsatisfied basic needs.

Road upgrades primarily benefiting larger markets also fits within the context of new migration trends in Ethiopia. In particular, since the early 2000s, Ethiopia has experienced increased rural-to-urban migration and a decrease in rural-to-rural migration (OECD and Policy Studies Institute, 2020).<sup>12</sup> Moreover, most migration has taken place within regions. Consequently, the size and importance of regional market centers is growing. Increased growth due to road improvements in larger markets could have made them more attractive places to migrate to, and improved connections could have facilitated migration. While our study cannot confirm this mechanism, existing literature has shown that transportation investments can encourage migration. For example, Morten and Oliveira (2018) find that new highways in Brazil intended to connect Brasilia with other state capitals led to increased migration between states that became better connected. They argue that improved connectivity encourages migration because it lowers both the costs of migration itself and the utility costs of being away from friends and relatives (improved connectivity reduces costs to travel back and visit the original location). However, while improved connectivity may lower costs to migration, Castaing Gachassin (2013) finds that road upgrades improving local conditions reduced migration in Tanzania.

Our findings also show a trade-off between urban land and cropland. About half of all pixels that transitioned to urban land were previously cropland. In addition, difference-in-difference models show that larger roads in areas with above-median baseline nighttime lights contributed in particular to a reduction in cropland area. This finding is in line with Fiorini and Sanfilippo (2019), who show that RSDP increased employment in the service sector but reduced agricultural employment.

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32
The Impact of Ethiopia’s Road Investment Program on Economic Development and Land Use: Evidence from Satellite Data

# Supplementary Information

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. The views expressed in this paper are those of the authors and do not necessarily reflect those of the SNB. In addition, they do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **Contents**

|**S1**|**Road Improvements by Baseline Woreda Nighttime Lights**|**S2**|
|---|---|---|
|**S2**|**RSDP Road Dataset**|**S3**|
|**S3**|**Nighttime Lights Data**|**S4**|
|**S4**|**Trends in Woreda-Level Outcome Variables Over Time**|**S5**|
|**S5**|**Kebele Information**|**S8**|
|**S6**|**Universal Electricity Access Program versus RSDP**|**S9**|
|**S7**|**Two-Way Fixed Efect Models**|**S10**|
|S7|.1<br>Results using Kebeles as Unit of Analysis<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|S10|
|S7|.2<br>Results using 1x1km Pixels as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . .|S11|
|**S8**|**Additional IV Results Analyzing the Impact of Areas Incidentally Connected to**<br>**the RSDP**|**S12**|
|S8|.1<br>Treated and Control Summary Stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.2<br>OLS Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S12|
|S8|.3<br>First Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S13|
|S8|.4<br>Second Stage Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S14|
|S8|.5<br>Second Stage Results - Diferent Baseline Nighttime Light Groups<br>. . . . . . . . . . . . . .|S16|
|**S9**|**Balance Across Non-Targeted Treated and Control Areas**|**S18**|
|**S10**|**Constructing Minimum Spanning Trees**|**S19**|
|**S11**|**Estimating Travel Time from Road Network Data**|**S21**|
|**S12**|**Market Access Two-Way Fixed Efect Model**|**S23**|
|**S13**|**Diference in Diferences - Additional Results**|**S26**|
|S1|3.1 Results using 1x1km Grid as Unit of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .|S26|
|S1|3.2 Results when excluding Kebeles within 100km of Addis Ababa<br>. . . . . . . . . . . . . . . .|S27|
|S1|3.3 Heterogeneity of impacts across Woredas with zero and positive baseline nighttime lights . .|S28|
|**S14**|**What land cover type does urban replace?**|**S29**|
|**S15**|**Market Access, Long Diference - Additional Results**|**S30**|
|S1|5.1 Varying Doughnut Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S30|
|S1|5.2 Varying Values of Theta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|S32|
|S1|5.3 Varying Baseline Nighttime Light Groups<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|S34|

S1
<!-- Start of picture text --> Average Lengh of Road Above Different Speed Limits<br>Across Woredas with Different Baseline Nighttime Lights<br>Road Length = 30km/h Road Length 2 50km/h Road Length 2 70km/h<br>= Maximum Value of<br>@ 90- Nighttime Lights<br>w 30- 20- Within Woreda<br>@ 70- m= Dark: 0<br>oO — 4!<br>2 20- 10- a °<br>S 50- === Medium: 6-8<br>< == High: 9+<br>30, ’ ; ’ — OI y y y | o7 Y y | |<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text -->

<!-- Start of picture text --> Length of Roads by Estiamted Speed Limit<br>Across Woredas by Baseline Nightime Lights<br>Dark: Max NTL in Woreda, 0 Low: Max NTL in Woreda, 1 - 5<br>1.00 . Speed<br>0.75 (km/h)<br>x<br>5<br>Fs® 0.50 MisMo<br>2no)& 0.250.00 iWsx<br>a Medium: Max NTL in Woreda, 6 - 8 High: Max NTL in Woreda, 9+ || 30<br>= 1.00<br>S Ms<br>S 0.75 | | 45<br>& 0.50 BW x<br>0.25 [_| 70<br>0.00 1 BB 120<br>1995 2000 2005 2010 2015 1995 2000 2005 2010 2015<br><!-- End of picture text -->
# **S2 RSDP Road Dataset**

Table S1 shows the assumed travel speed information by road type and condition that the Ethiopia Road Authority used in constructing the RSDP panel dataset. The dataset provided by ERA contains the speed of the road in each year (where a speed of 0km/h is assigned to roads that had not been constructed yet). As the table shows, an increase in speed limit indicates that a road was improved except for town roads. Consequently, as our analysis relies on increased speeds to indicate improvement, our analysis focuses on roads connecting towns rather than within town roads.

|Pavement type and condition|Speed before reha-<br>bilitation<br>or<br>con-<br>struction|Speed after rehabil-<br>itation or construc-<br>tion|
|---|---|---|
|Asphalt road (expressway)||120km/h|
|Asphalt roads (Highway - as-<br>phalt concrete or surface treat-<br>ment 50km/h|70km/h||
|Federal gravel Road (high class<br>gravel roads)|35km/h|50km/h|
|Regional gravel Road (intermedi-<br>ate class gravel roads)|25km/h|45km/h|
|URRAP roads - lower district<br>level<br>roads<br>(low<br>class<br>gravel<br>roads)|20km/h|35km/h|
|Earth surfaced roads (very low<br>class public roads)|20km/h|30km/h|
|Federal gravel or regional rural<br>roads to asphalt roads|25km/h to 35km/h|70km/h|
|Town roads (asphalt)|30km/h|30km/h|
|Town roads (cobbled)|20km/h|20km/h|
|Town roads (gravel)|15km/h|15km/h|
|Town roads (earth)|10km/h|10km/h|

**Table S1:** Road types, conditions, and speeds. Information provided by the Ethiopian Roads Authority

S3
<!-- Start of picture text --> 1996 - DMSP-OLS<br><!-- End of picture text --> <!-- Start of picture text --> 2013 - DMSP-OLS<br><!-- End of picture text --> 2014 - Simulated DMSP-OLS 2018 - Simulated DMSP-OLS
# **S4 Trends in Woreda-Level Outcome Variables Over Time**

This section shows trends in outcomes variables over time. Trends are shown across different groupings of Woredas, where Woredas are grouped by baseline levels of nighttime lights into dark Woredas (Woredas with no positive nighttime lights), and those with low, medium, and high baseline levels of nighttime lights (we use 3-quantiles of the maximum value of nighttime lights across Woredas with some positive nighttime lights to form the low, medium, and high groups).

Figure S4 shows average trends over time. Woredas across all groupings saw growth in nighttime lights and urban land; in addition, all followed a general pattern of increasing then decreasing cropland area. The figure shows a sharp increase in nighttime lights among Woredas with a maximum nighttime value of 0 at baseline. This increase is due to a difference in the underlying nighttime lights data; from 1992-2013, DMSP-OLS data is used; and from 2014 onwards, simulated DMSP-OLS data is used, which captures more low-level light.

Figure S5 shows the distribution in growth rates in outcome variables from 1992 to 2016. The figure shows that most Woredas saw growth in nighttime lights; while many Woredas saw growth in Urban area, a notable proportion saw no change in urban area. In addition, most Woredas saw no change in Cropland area. Among Woredas that did see change in cropland, Woredas were roughly equally split in seeing growth or a reduction in cropland, except for Woredas with high initial nighttime lights, where Woredas tended to see a reduction in cropland.

S5
<!-- Start of picture text --> Outcome variables over time, by baseline levels of nighttime lights<br>Woredas, Max Light: 0 [N = 557]<br>NTL Urban Cropland<br>2.0 2.75<br>2050<br>1s; 2.50 2040<br>1.0 2.25 2030<br>0.5 2.00 2020<br>0.0 1.75 2010<br>2000 2010 2000 2010 2000 2010<br>Woredas, Max Light: 1-5 [N = 74]<br>NTL Urban Cropland<br>3<br>46 2220<br>2 2210<br>4.4<br>1 2200<br>4.2<br>0 2190<br>2000 2010 2000 2010 2000 2010<br>Woredas, Max Light: 6-8 [N = 78]<br>NTL Urban Cropland<br>1.7<br>4 2100<br>1.5<br>3<br>2080<br>2 1.3<br>1 14 2060<br>2000 2010 2000 2010 2000 2010<br>Woredas, Max Light: 9+ [N = 71]<br>NTL Urban Cropland<br>20.0 895<br>17.5 30 890<br>15.0 885<br>12.5 20<br>880<br>10.0<br>7.5 10 875<br>2000 2010 2000 2010 2000 2010<br><!-- End of picture text -->
Growth rate from baseline to endline, by baseline levels of nighttime lights Woredas, Max Light: 0 [N = 557] <!-- Start of picture text --> NTL Urban Cropland<br>500<br>150 400 400<br>100 300<br>200 200<br>50 100<br>0) ) + 0 — +<br>-4 -2 0) 2 4 -4 -2 ) 2 4 -4 -2 0) 2 4<br>Growth Rate<br>Woredas, Max Light: 1-5 [N = 74]<br>NTL Urban Cropland<br>12.5 60 60<br>10.0<br>7.5 40 40<br>i wii —_ ;<br>0) 1 2 3 ) 1 2 3 ) 1 2 3<br>Growth Rate<br>Woredas, Max Light: 6-8 [N = 78]<br>NTL Urban Cropland<br>12.5 60<br>10.0 60<br>75 40 40<br>5.0 20<br>2.5 20<br>-2 -1 ) 1 2 -2 -1 ) 1 2 -2 -1 ) 1 2<br>Growth Rate<br>Woredas, Max Light: 9+ [N = 71]<br>NTL Urban Cropland<br>60<br>15 30<br>40<br>10 20<br>5 10 20<br>0 = 0 han 0 —_<br>-2 ) 2 4 -2 0) 2 4 -2 0) 2 4<br>Growth Rate<br><!-- End of picture text -->
<!-- Start of picture text --> Lip<br>f .INToeans A<br>Nee EAN, ff<br>STS eee!<br>Ks ,<br>RseNS a — Sea eke]or vy) A<br>a7<br>;<br>sais see ann<br>eleooSee PSaety<br>wy ——_=oe oe fas<br>VRS<br>oe<br>om.<br>ee BORER Say<br>Bich) ARG: Opes De PASwoosa ty 47<br>SED yoynae) Sor e 2 pes S\<br>ralSeetee a ~ ec Y<br>vealoa enuy\ SEBLIXPeeBer S Reyeraatf<br>seeictSRL&l AYy<br><!-- End of picture text -->
<!-- Start of picture text --> See<br>SHV *Peeectitien, 2 ne<br>Wes:"4 eyNe ieseeof: if eo — RSDPImproved Roads<br>a Ce ee fe gt — _UEAP Grid Lines<br>44 ES y a, VCore<br>oe ?<br><!-- End of picture text -->
<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>06 0.75<br>ro Baseline<br>se 0.4 0.50 00 Nighttime<br>41 . | Lights<br>¥F+ 02 0.25 + HighHo<br>c + Medium<br>22°oo tft 02 {Low<br>87 02 0.00 + Dark<br>-0.25 04<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>0.3<br>0.15<br>S)<br>%<br>3 “ ! 0.10; 0.00 Road oa Type<br>404 t | t + all<br>=3 0.0 } t | 0.05 | | } | | $$ >=50<60 km/h km/h<br>3 fet! | ald -0.05<br>9 } | 0.00 nie<br>9 04 f } fs {<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->

<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>rase 060.4 0.750.50 | BaselineNighttime<br>fey 0.0 Lights<br>F+ 02 0.25 + Hohi<br>5 + Medium<br>g 0.0 00 } } -0.2 } Low<br>©8 02 , + Dark<br>-0.25 04<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->

<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>0.3<br>0.15<br>S)<br>fazo ! 0. 1 0 0.00 Road‘oad TType<br>$04 t | | t $ all<br>A3 0.0 { j { 0.05 . t | | $$ >=50<50 km/h km/h<br>O043 Hae 0.00. ee allt! -0.05<br>f {tf fet {<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
# **S8 Additional IV Results Analyzing the Impact of Areas Incidentally Connected to the RSDP**

Our main specification examining the impact of the RSDP on areas incidentally connected to RSDP roads uses Kebeles as the unit of analysis and uses a minimum spanning tree (MST) constructed from regional least cost distance MSTs appended together as an instrument. In this section, we test the sensitivity of results to using 1x1km pixels as the unit of analysis and using different versions of the MSTs. Section S8.1 shows sample sizes and the number of units near the RSDP and MSTs. Section S8.2 shows OLS results, section S8.3 shows first-stage results from IV models, section S8.4 shows second-stage results, and section S8.5 shows second-stage results when using two groups of baseline nighttime lights as opposed to four.

## **S8.1 Treated and Control Summary Stats**

For instrumental variable models, units that are near targeted areas are removed. Table S3 shows the number of units after removing targeted areas. In addition, S3 shows the number of units treated (near an RSDP road) and near the MST networks.

**Table S3:** Number of Treated and Control Units for IV Analysis Across Different Units

||Sam|ple Size||N Units|Near RSDP and D|iferent MSTs||
|---|---|---|---|---|---|---|---|
|Unit|N Total|N, Targeted|RSDP|MST Least|MST Least|MST Least|MST Least|
|||Areas Removed||Dist|Dist: Regional|Cost|Cost: Regional|
|1x1km Grid|1,337,567|113,908|1,271,094|95,603|106,534|124,674|136,769|
|Kebeles|15,714|3,157|10,220|1,956|2,233|2,781|3,020|

## **S8.2 OLS Results**

Table S4 shows results from OLS models; models (1)–(6) show results using 1x1km pixels as the unit of analysis and models (7)–(12) show results using Kebeles as the unit of analysis. The results show road upgrades associated with gains in nighttime lights and urban land, particularly in areas with medium-tohigh initial baseline nighttime lights.

S12
**Table S4:** Long Difference, OLS Results - RSDP I - III

||NT|L|Urba|n|Crop|land|NT|L|Urb|an|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._02<sup>_∗∗∗_</sup>|0_._005|0_._001<sup>_∗∗∗_</sup>|_−_0_._0001|0_._001|0_._002|0_._03<sup>_∗∗_</sup>|0_._003|0_._01<sup>_∗_</sup>|_−_0_._001|0_._005|0_._01|
||(0_._01)|(0_._004)|(0_._0002)|(0_._0001)|(0_._001)|(0_._002)|(0_._01)|(0_._01)|(0_._01)|(0_._005)|(0_._01)|(0_._01)|
|Imp Rd._×NTL_96 Low||0_._001||0_._001||_−_0_._004||0_._02||0_._02||_−_0_._01|
|||(0_._01)||(0_._001)||(0_._003)||(0_._02)||(0_._01)||(0_._03)|
|Imp Rd._×NTL_96 Med||0_._08<sup>_∗∗_</sup>||0_._001<sup>_∗_</sup>||0_._002||0_._13<sup>_∗∗_</sup>||0_._02||0_._01|
|||(0_._04)||(0_._001)||(0_._004)||(0_._06)||(0_._02)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._19<sup>_∗∗∗_</sup>||0_._01<sup>_∗∗_</sup>||_−_0_._0001||0_._22<sup>_∗∗_</sup>||0_._20<sup>_∗∗_</sup>||_−_0_._04<sup>_∗_</sup>|
|||(0_._06)||(0_._005)||(0_._01)||(0_._10)||(0_._08)||(0_._02)|
|Constant|0_._04<sup>_∗∗∗_</sup>|0_._04<sup>_∗∗∗_</sup>|0_._002<sup>_∗∗∗_</sup>|0_._002<sup>_∗∗∗_</sup>|0_._004|0_._004|_−_0_._02|_−_0_._01|_−_0_._10<sup>_∗∗_</sup>|_−_0_._09<sup>_∗∗_</sup>|_−_0_._02|_−_0_._02|
||(0_._01)|(0_._01)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._06)|(0_._06)|(0_._05)|(0_._04)|(0_._07)|(0_._07)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._<br>|_Keb._|_Keb._|
|Observations<br>|1,271,082|1,271,082|1,271,094|1,271,094<br>|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.004|0.01|0.0002|0.002|0.0000|0.0001|0.003|0.02|0.001|0.02|-0.0001|-0.0001|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

# **S8.3 First Stage Results**

Table S5 shows first stage results of IV models for different variations of the minimum spanning trees and using both 1x1km pixels (models 1–4) and Kebeles (models 5–8) as the unit of analysis. The MSTs are all strongly associated with roads improved from RSDP I-III; the F-Stat on models using 1x1km pixels range from 194-250, and the F-Stat on models using Kebeles range from 31-88.

**Table S5:** First Stage, RSDP I-III

|||||Near Impro|ved Road||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Near Least Cost MST|0_._29<sup>_∗∗∗_</sup><br>||||0_._24<sup>_∗∗∗_</sup><br>||||
||(0_._02)||||(0_._03)||||
|Near Min. Distance MST||0_._27<sup>_∗∗∗_</sup><br>||||0_._15<sup>_∗∗∗_</sup><br>|||
|||(0_._02)||||(0_._03)|||
|Near Least Cost MST: Regional|||0_._26<sup>_∗∗∗_</sup><br>||||0_._22<sup>_∗∗∗_</sup><br>||
||||(0_._02)||||(0_._02)||
|Near Min. Distance MST: Regional||||0_._25<sup>_∗∗∗_</sup>||||0_._15<sup>_∗∗∗_</sup>|
|||||(0_._02)||||(0_._03)|
|Constant|0_._89<sup>_∗∗∗_</sup><br>|0_._98<sup>_∗∗∗_</sup><br>|0_._92<sup>_∗∗∗_</sup><br>|0_._99<sup>_∗∗∗_</sup><br>|2_._67<sup>_∗∗∗_</sup><br>|3_._06<sup>_∗∗∗_</sup><br>|2_._69<sup>_∗∗∗_</sup><br>|3_._02<sup>_∗∗∗_</sup><br>|
||(0_._08)|(0_._08)|(0_._08)|(0_._08)|(0_._15)|(0_._14)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|250_._2|213_._3|208_._6|194_._8|88_._1|31_._3|84_._5|35_._9|
|Observations<br>|1,271,082|1,271,082|1,271,082|1,271,082|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.21|0.19|0.21|0.19|0.22|0.20|0.22|0.20|

Standard errors are clustered on Woredas. “Near” indicates that the unit was within 5km of the MST or improved road. Least Cost MSTs minimize the estimated construction cost of connecting targeted areas (construction cost is a function of slope and land cover), while Min. Distance MSTs minimize the Euclidean distance between targeted areas. Regional MSTs first compute targeted areas within Ethiopia’s regions, and are then appended together.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S13
# **S8.4 Second Stage Results**

Tables S6-S9 show results from the second stage of IV models using different versions of the MSTs. Across different versions of the MSTS, results show road upgrades associated with growth in NTL and urban area, particularly in areas with medium-to-high baseline nighttime lights.

**Table S6:** Long Difference, IV Results using Least Euclidean Distance MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|N|TL|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._03<sup>_∗_</sup>|0_._01|0_._001|0_._0000|0_._004|0_._004|0_._01|_−_0_._005|0_._04|0_._04|_−_0_._05|_−_0_._06|
||(0_._02)|(0_._01)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._06)|(0_._05)|(0_._04)|(0_._04)|(0_._07)|(0_._07)|
|Imp Rd._×NTL_96 Low||_−_0_._02<sup>_∗∗_</sup>||0_._0001||_−_0_._01||0_._01||_−_0_._01||_−_0_._06|
|||(0_._01)||(0_._0003)||(0_._01)||(0_._03)||(0_._01)||(0_._09)|
|Imp Rd._×NTL_96 Med||0_._13<sup>_∗∗_</sup>||0_._004<sup>_∗∗_</sup>||0_._001||0_._19<sup>_∗∗_</sup>||0_._06||0_._06|
|||(0_._06)||(0_._002)||(0_._01)||(0_._10)||(0_._05)||(0_._05)|
|Imp Rd._×NTL_96 High||0_._25<sup>_∗∗_</sup>||0_._03<sup>_∗_</sup>||0_._01||0_._22<sup>_∗_</sup>||0_._50<sup>_∗_</sup>||_−_0_._06|
|||(0_._11)||(0_._01)||(0_._03)||(0_._13)||(0_._27)||(0_._05)|
|Constant|0_._04<sup>_∗∗_</sup>|0_._03<sup>_∗_</sup>|0_._001|0_._0003|0_._001|0_._0004|0_._03|_−_0_._005|_−_0_._20|_−_0_._28|0_._18|0_._19|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._20)|(0_._21)|(0_._13)|(0_._17)|(0_._25)|(0_._25)|
|Unit|1_km_|1_km_<br>|1_km_|1_km_<br>|1_km_|1_km_<br>|_Keb._|_Keb._|_Keb._|_Keb._<br>|_Keb._|_Keb._|
|1st Stage F-Stat|213_._31|58_._81|213_._32|58_._81|213_._32|58_._81|31_._34|8_._19|31_._34|8_._19|31_._34|8_._19|
|Observations<br><br>|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.004|0.01|0.0001|-0.001|-0.0000|-0.0000|0.002|0.02|-0.003|-0.04|-0.01|-0.01|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S14
**Table S7:** Long Difference, IV Results using Least Euclidean Distance Regional MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|N|TL|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._02|_−_0_._01|0_._001|0_._0000|0_._01|0_._01|_−_0_._01|_−_0_._04|0_._02|0_._02|_−_0_._03|_−_0_._03|
||(0_._01)|(0_._01)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._05)|(0_._05)|(0_._03)|(0_._04)|(0_._06)|(0_._06)|
|Imp Rd._×NTL_96 Low||_−_0_._01||0_._0002||_−_0_._01<sup>_∗_</sup>||0_._01||_−_0_._004||_−_0_._05|
|||(0_._01)||(0_._0003)||(0_._01)||(0_._03)||(0_._01)||(0_._08)|
|Imp Rd._×NTL_96 Med||0_._14<sup>_∗∗_</sup>||0_._004<sup>_∗∗_</sup>||_−_0_._0003||0_._19<sup>_∗∗_</sup>||0_._05||0_._04|
|||(0_._06)||(0_._002)||(0_._01)||(0_._08)||(0_._04)||(0_._05)|
|Imp Rd._×NTL_96 High||0_._27<sup>_∗∗_</sup>||0_._03<sup>_∗_</sup>||0_._004||0_._19||0_._44<sup>_∗_</sup>||_−_0_._05|
|||(0_._11)||(0_._02)||(0_._02)||(0_._12)||(0_._25)||(0_._05)|
|Constant|0_._05<sup>_∗∗∗_</sup>|0_._04<sup>_∗∗_</sup>|0_._001|0_._0003|_−_0_._001|_−_0_._001|0_._12|0_._10|_−_0_._14|_−_0_._20|0_._09|0_._10|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._18)|(0_._18)|(0_._11)|(0_._15)|(0_._22)|(0_._22)|
|Unit|1_km_|1_km_<br>|1_km_|1_km_|1_km_|1_km_<br>|_Keb._|_Keb._<br>|_Keb._|_Keb._<br>|_Keb._|_Keb._|
|1st Stage F-Stat|194_._84|55_._31|194_._84|55_._31|194_._84|55_._31|35_._92|10_._65|35_._92|10_._65|35_._92|10_._65|
|Observations|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.004|0.01|0.0002|-0.002|-0.0001|-0.0001|-0.003|0.02|0.001|-0.01|-0.002|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

**Table S8:** Long Difference, IV Results using Least Cost MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._05<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗_</sup>|0_._002<sup>_∗_</sup>|_−_0_._0000|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._09<sup>_∗∗_</sup>|0_._07<sup>_∗∗_</sup>|0_._01|0_._01|0_._02|0_._02|
||(0_._02)|(0_._01)|(0_._001)|(0_._0004)|(0_._005)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Low||_−_0_._003||0_._001||_−_0_._02<sup>_∗∗∗_</sup>||0_._03||0_._02||_−_0_._05|
|||(0_._02)||(0_._001)||(0_._01)||(0_._04)||(0_._02)||(0_._06)|
|Imp Rd._×NTL_96 Med||0_._13<sup>_∗∗_</sup>||0_._003<sup>_∗∗_</sup>||_−_0_._01||0_._17<sup>_∗_</sup>||0_._04||_−_0_._005|
|||(0_._07)||(0_._001)||(0_._01)||(0_._10)||(0_._03)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._27<sup>_∗∗_</sup>||0_._03<sup>_∗_</sup>||_−_0_._001||0_._20<sup>_∗_</sup>||0_._38<sup>_∗_</sup>|<sup>_∗_</sup>|_−_0_._06|
|||(0_._11)||(0_._02)||(0_._01)||(0_._11)||(0_._20)||(0_._04)|
|Constant|0_._01|0_._01|0_._001|0_._0002|_−_0_._01|_−_0_._01|_−_0_._23<sup>_∗_</sup>|_−_0_._26<sup>_∗∗_</sup>|_−_0_._10|_−_0_._16<sup>_∗_</sup>|_−_0_._07|_−_0_._07|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._13)|(0_._13)|(0_._07)|(0_._09)|(0_._14)|(0_._14)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|250_._25|69_._65|250_._25|69_._65|250_._25|69_._65|88_._07|24_._73|88_._07|24_._73|88_._07|24_._73|
|Observations<br>|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.002|0.01|0.0001|-0.002|-0.001|-0.001|-0.01|0.01|0.001|0.001|-0.001|-0.001|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S15
**Table S9:** Long Difference, IV Results using Least Cost Regional MST - RSDP I - III

||NT|L|Ur|ban|Crop|land|NT|L|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._04<sup>_∗∗∗_</sup>|0_._02|0_._002<sup>_∗_</sup>|_−_0_._0000|0_._01<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._08<sup>_∗∗_</sup>|0_._04|0_._01|_−_0_._01|0_._04|0_._05|
||(0_._01)|(0_._01)|(0_._001)|(0_._0004)|(0_._004)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Low||0_._0002||0_._001||_−_0_._02<sup>_∗∗∗_</sup>||0_._02||0_._02||_−_0_._06|
|||(0_._01)||(0_._001)||(0_._01)||(0_._03)||(0_._02)||(0_._06)|
|Imp Rd._×NTL_96 Med||0_._15<sup>_∗∗_</sup>||0_._003<sup>_∗∗_</sup>||_−_0_._01||0_._18<sup>_∗∗_</sup>||0_._04||_−_0_._004|
|||(0_._07)||(0_._001)||(0_._01)||(0_._09)||(0_._03)||(0_._02)|
|Imp Rd._×NTL_96 High||0_._25<sup>_∗∗∗_</sup>||0_._03<sup>_∗_</sup>||_−_0_._005||0_._17<sup>_∗_</sup>||0_._27<sup>_∗∗_</sup>||_−_0_._05<sup>_∗_</sup>|
|||(0_._09)||(0_._01)||(0_._01)||(0_._09)||(0_._13)||(0_._03)|
|Constant|0_._02|0_._02|0_._0005|0_._0003|_−_0_._01<sup>_∗_</sup>|_−_0_._01|_−_0_._18|_−_0_._16|_−_0_._09|_−_0_._07|_−_0_._15|_−_0_._16|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._13)|(0_._13)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|208_._63|62_._22|208_._63|62_._22|208_._63|62_._22|84_._51|25_._44|84_._51|25_._44|84_._51|25_._44|
|Observations<br>|1,271,082|1,271,082|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.01|0.0001|-0.002|-0.001|-0.001|-0.01|0.02|0.001|0.02|-0.003|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

# **S8.5 Second Stage Results - Different Baseline Nighttime Light Groups**

Our primary models examine heterogeneity of impacts of road upgrades across four groups of baseline nighttime lights. To test the sensitivity of the results to different groups of baseline nighttime lights, we also estimate models using just two groups: Woredas with no nighttime lights and Woredas with some positive nighttime lights at baseline. Here, results are generally similar to when using four groups: road upgrades are associated with growth in nighttime lights and urban land, particularly in areas with higher baseline nighttime lights.

S16
**Table S10:** Long Difference, OLS and IV Results - RSDP I - III

||NT|L|Urb|an|Cropl|and|N|TL|Ur|ban|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Imp Rd.|0_._04<sup>_∗∗∗_</sup>|0_._02|0_._002<sup>_∗_</sup>|0_._0000|0_._01<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._08<sup>_∗∗_</sup>|0_._05|0_._01|_−_0_._01|0_._04|0_._05|
||(0_._01)|(0_._01)|(0_._001)|(0_._0004)|(0_._004)|(0_._01)|(0_._04)|(0_._03)|(0_._02)|(0_._02)|(0_._04)|(0_._04)|
|Imp Rd._×NTL_96 Lit||0_._11<sup>_∗∗∗_</sup>||0_._01<sup>_∗∗∗_</sup>||_−_0_._01<sup>_∗∗_</sup>||0_._12<sup>_∗∗∗_</sup>||0_._08<sup>_∗∗∗_</sup>||_−_0_._03|
|||(0_._03)||(0_._003)||(0_._01)||(0_._04)||(0_._03)||(0_._03)|
|Constant|0_._02|0_._02|0_._0005|0_._0001|_−_0_._01<sup>_∗_</sup>|_−_0_._01|_−_0_._18|_−_0_._17|_−_0_._09|_−_0_._08|_−_0_._15|_−_0_._16|
||(0_._02)|(0_._02)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._13)|(0_._13)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|
|Unit|1_km_|1_km_|1_km_|1_km_|1_km_|1_km_|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|
|1st Stage F-Stat|208_._63|104_._26|208_._63|104_._26|208_._63|104_._26|84_._51|42_._55|84_._51|42_._55|84_._51|42_._55|
|Observations<br>|1,271,082|1,271,082<br>|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.005|0.0001|-0.001|-0.001|-0.001|-0.01|0.01|0.001|0.01|-0.003|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S17
# **S9 Balance Across Non-Targeted Treated and Control Areas**

We check balance in outcome variables across non-targeted treated and control areas. We use both levels of outcome variables at baseline (1996) and compare differences in pre-trends (using the difference between 1992 and 1996). Levels and trends of outcome variables tend to be unbalanced, emphasizing that simple comparisons between treated and non-treated areas are insufficient to understand impacts of road upgrades.

**Table S11:** Summary Statistics Across Non-Targeted Treated and Control Areas: Kebeles, RSDP I-III

|Variable|(1)<br>Treatment<br>(_<_5km RSDP)<br>Mean/SE|(2)<br>Control<br>(_>_5km RSDP)<br>Mean/SE|T-test<br>Diference<br>(1) - (2)|
|---|---|---|---|
|NTL (1996)|0.06<br>(0.02)|0.02<br>(0.01)|0.04<sup>_∗∗_</sup>|
|Urban (1996)|0.03<br>(0.01)|0.22<br>(0.04)|-0.19<sup>_∗∗∗_</sup>|
|Cropland (1996)|134.21<br>(2.66)|130.81<br>(1.78)|3.4|
|NTL (Pre-trends)|0.03|0.01|0.02<sup>_∗_</sup>|
||(0.01)|(0)||
|Urban (Pre-trends)|0<br>(0)|0.02<br>(0.01)|-0.02<sup>_∗∗_</sup>|
|Cropland (Pre-trends)|0.69<br>(0.09)|0.59<br>(0.07)|0.1|
|N|3,157|7,063||

**Table S12:** Summary Statistics Across Non-Targeted Treated and Control Areas: 1km Grid, RSDP I-III

||(1)<br>Treatment<br>(_<_5km RSDP)|(2)<br>Control<br>(_>_5km RSDP)|T-test<br>Diference|
|---|---|---|---|
|Variable|Mean/SE|Mean/SE|(1) - (2)|
|NTL (1996)|0.0496<br>(0.0024)|0.0056<br>(2e-04)|0.044<sup>_∗∗∗_</sup>|
|Urban (1996)|0.001<br>(1e-04)|8e-04<br>(0)|2e-04<sup>_∗∗∗_</sup>|
|Cropland (1996)|0.6086<br>(0.0014)|0.2338<br>(4e-04)|0.3748<sup>_∗∗∗_</sup>|
|NTL (Pre-trends)|0.016|0.0022|0.0138<sup>_∗∗∗_</sup>|
||(0.0012)|(1e-04)||
|Urban (Pre-trends)|1e-04<br>(0)|1e-04<br>(0)|0|
|Cropland (Pre-trends)|0.0013<br>(1e-04)|3e-04<br>(0)|0.001<sup>_∗∗∗_</sup>|
|N|113,908|1,157,186||

S18
# ily
# Minimum Distance MST

# Least Cost MST

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# **S12 Market Access Two-Way Fixed Effect Model**

Our primary model to measure the impact of changes in market access uses a long difference framework. To test the robustness of long difference results, and to take advantage of the panel nature of our data, we implement a two-way fixed effect model. This approach follows from Blankespoor et al. (2017) who use a similar two-way fixed effects approach to measure how changes in market access from 1986 to 2014 impact employment and specialization in Mexico. Our model is described by the below equation: where _Y_ is our outcome variable (nighttime lights, urbanization or cropland) for unit (Kebeles or Woredas) _i_ in time _t_ , _MA_ is the log of market access, _N_ is a vector of interaction terms where values are constant over time (low, medium, or high nighttime lights at baseline), _C_ is a vector of time-varying unit-level characteristics (temperature and precipitation), _γt_ are time fixed effects, _δi_ are Woreda fixed effects and _εi,t_ is the error term. Our outcome variables include average nighttime lights, the number of pixels with values above 2 and 6 and the number of urban pixels within each Woreda. The coefficients of interest are _βM_ and _βI_ , which capture the impact of market access and how the impact varies across different variables. As in the panel data models, we also implement a version of 3 that instruments market access with the market access “doughnut“ variable.

Figures S13 and S14 show results when using four groups of nighttime lights, and figures S15 and S16 test the sensitivity of these results using two groups of nighttime lights. Results show that road upgrades are associated with growth in nighttime lights and urban land, particularly in areas with medium-to-high baseline nighttime lights. Coefficients tend to be insignificant for cropland.

S23
**Table S13:** Association of market access on outcome variables using OLS, panel data

||N|TL|Urb|an|Cropl|and|N|TL|U|rban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._003|_−_0_._0003|0_._001|0_._02|_−_0_._02|_−_0_._02<sup>_∗_</sup>|_−_0_._07<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._003|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._02)|(0_._03)|(0_._01)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 Low||0_._06<sup>_∗∗∗_</sup>||0_._02<sup>_∗∗∗_</sup>||0_._004||0_._10<sup>_∗∗_</sup>||0_._02||0_._01|
|||(0_._02)||(0_._01)||(0_._004)||(0_._04)||(0_._02)||(0_._01)|
|MA_×NTL_96 Med||0_._12<sup>_∗∗∗_</sup>||0_._03<sup>_∗∗∗_</sup>||_−_0_._003||0_._15<sup>_∗∗∗_</sup>||0_._09<sup>_∗_</sup>||0_._01|
|||(0_._04)||(0_._01)||(0_._005)||(0_._05)||(0_._05)||(0_._01)|
|MA_×NTL_96 High||0_._20<sup>_∗∗∗_</sup>||0_._25<sup>_∗∗∗_</sup>||_−_0_._06<sup>_∗∗_</sup>||0_._14<sup>_∗_</sup>||0_._48<sup>_∗∗∗_</sup>||_−_0_._07|
|||(0_._04)||(0_._05)||(0_._02)||(0_._08)||(0_._14)||(0_._07)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Year FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Unit FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Observations<br>|329,973|329,973|329,973|329,973|329,973|329,973|16,380|16,380|16,380|16,380|16,380|16,380|
|R<sup>2</sup><br>|0.70|0.70|0.90|0.90|0.99|0.99|0.91|0.91|0.96|0.97|1.00|1.00|
|Adjusted R<sup>2</sup>|0.69|0.69|0.89|0.90|0.99|0.99|0.90|0.90|0.96|0.96|1.00|1.00|

Standard errors clustered on Woredas when using Kebeles as the unit of analysis and standard errors clustered on Zones when using Woredas as the unit of analysis. MA is the logged value of market access. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within units (Kebeles or Woredas), Urban is the total urban area within units, and Cropland is the total cropland area within units; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

**Table S14:** Association of market access on outcome variables using 50km doughnut IV, panel data

||N|TL|Urb|an|Crop|land|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._002|_−_0_._04<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|_−_0_._002|_−_0_._001|0_._003|0_._04|_−_0_._04|0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._01|0_._004|
||(0_._01)|(0_._01)|(0_._004)|(0_._003)|(0_._005)|(0_._01)|(0_._03)|(0_._04)|(0_._03)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 Low||0_._08<sup>_∗∗∗_</sup>||0_._02<sup>_∗∗∗_</sup>||0_._005||0_._19<sup>_∗∗∗_</sup>||0_._03||0_._01|
|||(0_._03)||(0_._01)||(0_._01)||(0_._06)||(0_._03)||(0_._01)|
|MA_×NTL_96 Med||0_._18<sup>_∗∗∗_</sup>||0_._05<sup>_∗∗∗_</sup>||_−_0_._01||0_._22<sup>_∗∗∗_</sup>||0_._12<sup>_∗∗∗_</sup>||_−_0_._001|
|||(0_._05)||(0_._01)||(0_._01)||(0_._07)||(0_._05)||(0_._01)|
|MA_×NTL_96 High||0_._25<sup>_∗∗∗_</sup>||0_._37<sup>_∗∗∗_</sup>||_−_0_._09<sup>_∗∗_</sup>||0_._21<sup>_∗_</sup>||0_._71<sup>_∗∗∗_</sup>||_−_0_._09|
|||(0_._06)||(0_._08)||(0_._04)||(0_._11)||(0_._17)||(0_._08)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Year FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Unit FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Observations|329,973|329,973|329,973|329,973|329,973|329,973|16,380|16,380|16,380|16,380|16,380|16,380|
|R<sup>2</sup>|0.70|0.70|0.90|0.90|0.99|0.99|0.91|0.91|0.96|0.96|1.00|1.00|
|Adjusted R<sup>2</sup>|0.69|0.69|0.89|0.90|0.99|0.99|0.90|0.90|0.96|0.96|1.00|1.00|

Standard errors clustered on Woredas when using Kebeles as the unit of analysis and standard errors clustered on Zones when using Woredas as the unit of analysis. MA is the logged value of market access. Market access is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, units within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within units (Kebeles or Woredas), Urban is the total urban area within units, and Cropland is the total cropland area within units; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S24
**Table S15:** Association of market access on outcome variables using OLS, panel data

||N|TL|Urb|an|Crop|land|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._004<sup>_∗_</sup>|_−_0_._0003|0_._001|0_._02|_−_0_._02|_−_0_._02<sup>_∗_</sup>|_−_0_._08<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._002|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._02)|(0_._03)|(0_._01)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 L|it|0_._11<sup>_∗∗∗_</sup>||0_._06<sup>_∗∗∗_</sup>||_−_0_._01<sup>_∗_</sup>||0_._13<sup>_∗∗∗_</sup>||0_._18<sup>_∗∗∗_</sup>||_−_0_._02|
|||(0_._02)||(0_._01)||(0_._004)||(0_._03)||(0_._04)||(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Year FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Unit FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Observations|329,973|329,973|329,973|329,973|329,973|329,973|16,380|16,380|16,380|16,380|16,380|16,380|
|R<sup>2</sup>|0.70|0.70|0.90|0.90|0.99|0.99|0.91|0.91|0.96|0.96|1.00|1.00|
|Adjusted R<sup>2</sup>|0.69|0.69|0.89|0.89|0.99|0.99|0.90|0.90|0.96|0.96|1.00|1.00|

Standard errors clustered on Woredas when using Kebeles as the unit of analysis and standard errors clustered on Zones when using Woredas as the unit of analysis. MA is the logged value of market access. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within units (Kebeles or Woredas), Urban is the total urban area within units, and Cropland is the total cropland area within units; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01

**Table S16:** Association of market access on outcome variables using 50km doughnut IV, panel data

||N|TL|Urb|an|Cropl|and|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._002|_−_0_._03<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._0001|_−_0_._001|0_._003|0_._04|_−_0_._04|0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._01|0_._004|
||(0_._01)|(0_._01)|(0_._004)|(0_._003)|(0_._005)|(0_._01)|(0_._03)|(0_._04)|(0_._03)|(0_._02)|(0_._01)|(0_._01)|
|MA_×NTL_96 Li|t|0_._15<sup>_∗∗∗_</sup>||0_._09<sup>_∗∗∗_</sup>||_−_0_._02<sup>_∗_</sup>||0_._20<sup>_∗∗∗_</sup>||0_._28<sup>_∗∗∗_</sup>||_−_0_._02|
|||(0_._03)||(0_._02)||(0_._01)||(0_._04)||(0_._06)||(0_._03)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Year FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Unit FE|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|_Y_|
|Observations<br>|329,973|329,973|329,973|329,973|329,973|329,973|16,380|16,380|16,380|16,380|16,380|16,380|
|R<sup>2</sup><br>|0.70|0.70|0.90|0.90|0.99|0.99|0.91|0.91|0.96|0.96|1.00|1.00|
|Adjusted R<sup>2</sup>|0.69|0.69|0.89|0.89|0.99|0.99|0.90|0.90|0.96|0.96|1.00|1.00|

Standard errors clustered on Woredas when using Kebeles as the unit of analysis and standard errors clustered on Zones when using Woredas as the unit of analysis. MA is the logged value of market access. Market access is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, units within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within units (Kebeles or Woredas), Urban is the total urban area within units, and Cropland is the total cropland area within units; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S25
<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>1.0<br>0.04 ar) oe<br>=5 0.0000 op arue stats Baseline<br>xeS@ 0.5 0.03 -0.0025 7 * ao NighttimeLights<br>£ 0.02 . } High‘<br>2~3-0.0050 — . $ Medium.<br>E 00 0.01 . $ Low<br>8 -0.0075 § } Dark<br>S) . °<br>0.00 oye<br>-0.0100<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br>Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>O 02 0.0000 7<br>xe 0.004 el.<br>io ° Road Type<br>$0 -0.0025<br>0.002 } >=50 km/h<br>o=z 0.0 | ~» ooso }t al<50 km/h<br>(S); ) { it+ -0.0075<br>ot 0.000 rz)<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>0.6 .<br>= 0.20 0.05<br>3 ee Baseline<br>xe 0.4 . Nighttime<br>‘9 0.15 . Lights<br>2 .° 0.00 + igh<br>= 0.2 ] 0.10 . . ig)<br>= " . . er) + Medium<br>4 i$ °<br>: se eTy | <= i rrrpot ee<br>3 0.0 q brio ee aMUIPLAIIEL of ITIL TT . } Dark<br>0.00 “s “ -0.10<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br>Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>0.06 0.02<br>O 02<br>xsig 0.04. °.00 Road Type<br>$2$0.0. -0.02 } all<br>~ >=50 km/h<br>o | | | 0.02 t<br>Z 00 $ <50 km/h<br>® { -0.04<br>8 0.00 ii<br>-0.1 -0.06<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
<!-- Start of picture text --> Dependent Variable: Nighttime Lights Dependent Variable: Urban Dependent Variable: Cropland<br>0.3<br>~ 0.000<br>oO%° 02 0.15 -0.025 Baseline.<br>> Nighttime<br>= 0.10 -0.050 Lights<br>&a } NTL>O<br>€2 0.05 | -0.075 } NTL=0<br>o 0.0 { ; -0.100<br>0.00 clot ete<br>-10 5 0 5 10 -10 5 0 5 10 -10 5 0 5 10<br>Years Since Road Improved<br><!-- End of picture text -->
# **S14 What land cover type does urban replace?**

The Globcover data shows 6,956 300-meter pixels that transitioned to Urban at some point from 1992 to 2018. Table S17 shows the frequency of land cover classes that transitioned to urban areas. Cropland is the most frequent land cover class that transitioned to urban; considering all cropland classes (which includes (1) cropland, rainfed, (2) mosaic cropland ( _>_ 50%), (3) Cropland, rainfed - Herbaceous cover, and (4) Cropland, irrigated or post-flooding), cropland makes up 52.6% of landcover that transitioned to urban during the study period.

**Table S17:** Summary Statistics of Dependent Variables

|Land Cover Class|N|Proportion|
|---|---|---|
|Cropland, rainfed|1825|0.262|
|Mosaic cropland (_>_50%); natural vegetation (tree, shrub, herbaceous cover) (_<_50%)|1540|0.221|
|Mosaic natural vegetation (_>_50%); cropland (_<_50%)|1429|0.205|
|Shrubland|731|0.105|
|Grassland|584|0.084|
|Cropland, rainfed - Herbaceous cover|151|0.022|
|Cropland, irrigated or post-fooding|148|0.021|
|Mosaic herbaceous cover (_>_50%); tree and shrub (_<_50%)|124|0.018|
|Mosaic tree and shrub (_>_50%); herbaceous cover (_<_50%)|123|0.018|
|Tree cover, broadleaved, deciduous, open (15-40%)|111|0.016|
|Bare areas|44|0.006|
|Consolidated bare areas|41|0.006|
|Tree cover, fooded, saline water|33|0.005|
|Tree cover, broadleaved, evergreen, closed to open (_>_15%)|19|0.003|
|Sparse vegetation (tree, shrub, herbaceous cover) (_<_15%)|19|0.003|
|Tree cover, broadleaved, deciduous, closed to open (_>_15%)|10|0.001|
|Sparse shrub (_<_15%)|9|0.001|
|Water bodies|8|0.001|
|Deciduous shrubland|4|0.001|
|Sparse herbaceous cover (_<_15%)|2|0|
|Shrub or herbaceous cover, fooded - fresh, saline or brakish water|1|0|
|TOTAL|6956|1|

S29
# **S15 Market Access, Long Difference - Additional Results**

This section examines the sensitivity of long difference market access results. Section S15.1 tests different doughnut sizes when instrumenting market access, section S15.2 tests different values of _θ_ (travel elasticity) when constructing market access, and S15.3 estimates models using two groups of baseline nighttime lights.

## **S15.1 Varying Doughnut Sizes**

In this section, we explore the sensitivity of market access results to using different doughnut sizes for the market access instrument; i.e., excluding Woredas within different distances. Our main specification uses 50km; figure S18 shows results using a 20km buffer and S19 using a 100km buffer. The coefficient values change slightly from the main specification; however, the magnitude and significance of the coefficients generally stay the same.

**Table S18:** Association of changes in market access on changes in outcome variables using a long difference, 20km doughnut IV results

||NT|L|Urb|an|Crop|land|N|TL|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._09<sup>_∗∗∗_</sup>|_−_0_._11<sup>_∗∗∗_</sup>|_−_0_._0005|_−_0_._002|0_._003|0_._002|_−_0_._10|_−_0_._15<sup>_∗_</sup>|0_._005|_−_0_._03|_−_0_._0001|_−_0_._005|
||(0_._02)|(0_._02)|(0_._01)|(0_._005)|(0_._01)|(0_._01)|(0_._07)|(0_._08)|(0_._05)|(0_._06)|(0_._02)|(0_._02)|
|MA_×NTL_96 Low||0_._07<sup>_∗∗_</sup>||_−_0_._02<sup>_∗∗∗_</sup>||0_._005||0_._24<sup>_∗∗∗_</sup>||0_._04||0_._01|
|||(0_._03)||(0_._01)||(0_._01)||(0_._07)||(0_._04)||(0_._01)|
|MA_×NTL_96 Med||0_._13<sup>_∗∗∗_</sup>||_−_0_._02<sup>_∗∗_</sup>||0_._004||0_._36<sup>_∗∗∗_</sup>||0_._19<sup>_∗∗∗_</sup>||0_._02<sup>_∗∗_</sup>|
|||(0_._03)||(0_._01)||(0_._01)||(0_._09)||(0_._06)||(0_._01)|
|MA_×NTL_96 High||0_._29<sup>_∗∗∗_</sup>||0_._04||0_._03||0_._58<sup>_∗∗∗_</sup>||0_._67<sup>_∗∗∗_</sup>||0_._08<sup>_∗∗∗_</sup>|
|||(0_._04)||(0_._04)||(0_._02)||(0_._11)||(0_._20)||(0_._03)|
|MA, 1996|0_._13<sup>_∗∗∗_</sup>|0_._11<sup>_∗∗∗_</sup>|0_._03<sup>_∗∗∗_</sup>|0_._03<sup>_∗∗∗_</sup>|_−_0_._01<sup>_∗∗_</sup>|_−_0_._01<sup>_∗∗_</sup>|0_._07<sup>_∗∗∗_</sup>|0_._03<sup>_∗_</sup>|0_._04<sup>_∗∗_</sup>|0_._01|_−_0_._002|_−_0_._005|
||(0_._01)|(0_._01)|(0_._004)|(0_._003)|(0_._003)|(0_._003)|(0_._01)|(0_._02)|(0_._02)|(0_._01)|(0_._01)|(0_._01)|
|Log mean light, 1996|_−_0_._001|_−_0_._13<sup>_∗∗∗_</sup>|0_._45<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._12|_−_0_._32<sup>_∗∗∗_</sup>|0_._31|0_._08|_−_0_._11|_−_0_._14|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._08)|(0_._08)|(0_._19)|(0_._19)|(0_._10)|(0_._10)|
|Pre-trend: log mean light|0_._08|0_._15<sup>_∗∗_</sup>|0_._15|0_._18|_−_0_._002|0_._01|0_._12|0_._10|_−_0_._20|_−_0_._17|0_._12|0_._13<sup>_∗_</sup>|
||(0_._08)|(0_._08)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|(0_._13)|(0_._11)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|0_._34<sup>_∗∗_</sup>|0_._34<sup>_∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|_−_0_._26<sup>_∗∗∗_</sup>|_−_0_._23<sup>_∗∗_</sup>|0_._34<sup>_∗_</sup>|0_._37<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._14)|(0_._13)|(0_._21)|(0_._21)|(0_._12)|(0_._12)|(0_._09)|(0_._09)|(0_._20)|(0_._22)|(0_._02)|(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Observations|15,714|15,714|15,714|15,714|15,714|15,714|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.17|0.18|0.22|0.23|0.14|0.14|0.33|0.36|0.28|0.30|0.37|0.37|

Standard errors are clustered on Woredas when using Kebeles as the unit of analysis and are clustered on Zones when using Woredas as the unit of analysis. All models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. MA is instrumented with a 20km MA Doughnut variable; that is, in calculating market access, units within 20km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01 S30
**Table S19:** Association of changes in market access on changes in outcome variables using a long difference, 100km doughnut IV results

||NT|L|Urb|an|Crop|land|NT|L|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._10<sup>_∗∗∗_</sup>|_−_0_._11<sup>_∗∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._01<sup>_∗_</sup>|0_._003|0_._002|_−_0_._17|_−_0_._21<sup>_∗_</sup>|_−_0_._11<sup>_∗_</sup>|_−_0_._12<sup>_∗∗_</sup>|_−_0_._01|_−_0_._01|
||(0_._03)|(0_._03)|(0_._01)|(0_._01)|(0_._01)|(0_._01)|(0_._12)|(0_._11)|(0_._06)|(0_._06)|(0_._04)|(0_._04)|
|MA_×NTL_96 Low||0_._08<sup>_∗∗_</sup>||_−_0_._02<sup>_∗∗∗_</sup>||0_._002||0_._25<sup>_∗∗∗_</sup>||0_._04||0_._01|
|||(0_._04)||(0_._01)||(0_._01)||(0_._08)||(0_._04)||(0_._01)|
|MA_×NTL_96 Med||0_._16<sup>_∗∗∗_</sup>||_−_0_._01||_−_0_._001||0_._40<sup>_∗∗∗_</sup>||0_._17<sup>_∗∗∗_</sup>||0_._03<sup>_∗_</sup>|
|||(0_._04)||(0_._01)||(0_._01)||(0_._10)||(0_._06)||(0_._02)|
|MA_×NTL_96 High||0_._32<sup>_∗∗∗_</sup>||0_._06||0_._03||0_._60<sup>_∗∗∗_</sup>||0_._57<sup>_∗∗∗_</sup>||0_._10<sup>_∗∗_</sup>|
|||(0_._06)||(0_._04)||(0_._03)||(0_._11)||(0_._17)||(0_._04)|
|MA, 1996|0_._20<sup>_∗∗∗_</sup>|0_._16<sup>_∗∗∗_</sup>|0_._04<sup>_∗∗∗_</sup>|0_._04<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._01|0_._10<sup>_∗∗∗_</sup>|0_._04|0_._09<sup>_∗∗_</sup>|0_._05<sup>_∗_</sup>|_−_0_._003|_−_0_._01|
||(0_._02)|(0_._02)|(0_._01)|(0_._004)|(0_._01)|(0_._01)|(0_._02)|(0_._03)|(0_._04)|(0_._03)|(0_._01)|(0_._01)|
|Log mean light, 1996|0_._03|_−_0_._12<sup>_∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._11|_−_0_._32<sup>_∗∗∗_</sup>|0_._31<sup>_∗_</sup>|0_._11|_−_0_._11|_−_0_._15|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._08)|(0_._08)|(0_._18)|(0_._19)|(0_._10)|(0_._11)|
|Pre-trend: log mean light|0_._10|0_._17<sup>_∗∗_</sup>|0_._16|0_._19|_−_0_._003|0_._01|0_._12|0_._10|_−_0_._20|_−_0_._18|0_._12|0_._13|
||(0_._08)|(0_._08)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|(0_._13)|(0_._11)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|0_._35<sup>_∗∗_</sup>|0_._34<sup>_∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|_−_0_._24<sup>_∗∗∗_</sup>|_−_0_._23<sup>_∗∗_</sup>|0_._34<sup>_∗_</sup>|0_._36<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|
||(0_._14)|(0_._13)|(0_._22)|(0_._21)|(0_._12)|(0_._12)|(0_._08)|(0_._09)|(0_._17)|(0_._19)|(0_._02)|(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Observations<br>|15,714|15,714|15,714|15,714|15,714|15,714|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.16|0.17|0.22|0.22|0.14|0.14|0.31|0.35|0.28|0.31|0.37|0.37|

Standard errors are clustered on Woredas when using Kebeles as the unit of analysis and are clustered on Zones when using Woredas as the unit of analysis. All models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. MA is instrumented with a 100km MA Doughnut variable; that is, in calculating market access, units within 100km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01 S31
# **S15.2 Varying Values of Theta**

In this section, we explore the sensitivity of market access results to using different values of _θ_ (travel elasticity). Our main specification uses 3.8; S20 uses a value of 2, S21 uses a value of 5 and S22 uses a value of 8. The coefficient values change slightly from the main specification, however the magnitude and significance of the coefficients generally stay the same.

**Table S20:** Association of changes in market access on changes in outcome variables using a long difference. _θ_ = 2, using a 50km doughnut IV

||NT|L|Urb|an|Crop|land|NT|L|Ur|ban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._27<sup>_∗∗∗_</sup>|_−_0_._32<sup>_∗∗∗_</sup>|0_._04<sup>_∗_</sup>|0_._03|0_._01|0_._01|_−_0_._36|_−_0_._50<sup>_∗_</sup>|_−_0_._12|_−_0_._20|_−_0_._02|_−_0_._03|
||(0_._10)|(0_._09)|(0_._02)|(0_._02)|(0_._03)|(0_._03)|(0_._24)|(0_._25)|(0_._11)|(0_._12)|(0_._09)|(0_._09)|
|MA_×NTL_96 Low||0_._18<sup>_∗∗_</sup>||_−_0_._03<sup>_∗∗_</sup>||0_._002||0_._45<sup>_∗∗∗_</sup>||0_._07||0_._01|
|||(0_._07)||(0_._01)||(0_._01)||(0_._15)||(0_._07)||(0_._02)|
|MA_×NTL_96 Med||0_._31<sup>_∗∗∗_</sup>||_−_0_._02||_−_0_._0004||0_._71<sup>_∗∗∗_</sup>||0_._31<sup>_∗∗∗_</sup>||0_._04|
|||(0_._07)||(0_._02)||(0_._02)||(0_._17)||(0_._10)||(0_._03)|
|MA_×NTL_96 High||0_._62<sup>_∗∗∗_</sup>||0_._10||0_._05||1_._07<sup>_∗∗∗_</sup>||1_._04<sup>_∗∗∗_</sup>||0_._17<sup>_∗∗_</sup>|
|||(0_._11)||(0_._08)||(0_._04)||(0_._21)||(0_._31)||(0_._07)|
|MA, 1996|0_._30<sup>_∗∗∗_</sup>|0_._23<sup>_∗∗∗_</sup>|0_._07<sup>_∗∗∗_</sup>|0_._07<sup>_∗∗∗_</sup>|_−_0_._01|_−_0_._01|0_._17<sup>_∗∗∗_</sup>|0_._06|0_._15<sup>_∗∗_</sup>|0_._08<sup>_∗_</sup>|_−_0_._003|_−_0_._02|
||(0_._04)|(0_._04)|(0_._01)|(0_._01)|(0_._01)|(0_._01)|(0_._03)|(0_._06)|(0_._07)|(0_._04)|(0_._01)|(0_._01)|
|Log mean light, 1996|0_._03|_−_0_._11<sup>_∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._11|_−_0_._32<sup>_∗∗∗_</sup>|0_._31|0_._10|_−_0_._11|_−_0_._14|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._08)|(0_._09)|(0_._18)|(0_._18)|(0_._10)|(0_._10)|
|Pre-trend: log mean light|0_._11|0_._16<sup>_∗∗_</sup>|0_._16|0_._19|_−_0_._003|0_._005|0_._12|0_._11|_−_0_._20|_−_0_._17|0_._12|0_._13<sup>_∗_</sup>|
||(0_._08)|(0_._08)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|(0_._13)|(0_._10)|(0_._23)|(0_._20)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|0_._34<sup>_∗∗_</sup>|0_._33<sup>_∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|_−_0_._24<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._35<sup>_∗_</sup>|0_._36<sup>_∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|
||(0_._14)|(0_._13)|(0_._21)|(0_._21)|(0_._12)|(0_._12)|(0_._09)|(0_._09)|(0_._19)|(0_._20)|(0_._02)|(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Observations<br>|15,714|15,714|15,714|15,714|15,714|15,714|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.16|0.17|0.22|0.22|0.14|0.14|0.32|0.37|0.29|0.32|0.37|0.37|

Standard errors are clustered on Woredas when using Kebeles as the unit of analysis and are clustered on Zones when using Woredas as the unit of analysis. All models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, units within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01 S32
**Table S21:** Association of changes in market access on changes in outcome variables using a long difference. _θ_ = 5, using a 50km doughnut IV

||NT|L|Urb|an|Crop|land|NT|L|U|rban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._06<sup>_∗∗∗_</sup>|_−_0_._08<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._002|0_._001|_−_0_._08|_−_0_._11|0_._03|0_._01|_−_0_._001|_−_0_._004|
||(0_._02)|(0_._02)|(0_._01)|(0_._004)|(0_._01)|(0_._01)|(0_._07)|(0_._07)|(0_._04)|(0_._05)|(0_._02)|(0_._02)|
|MA_×NTL_96 Low||0_._07<sup>_∗∗_</sup>||_−_0_._01<sup>_∗∗_</sup>||0_._003||0_._19<sup>_∗∗∗_</sup>||0_._05<sup>_∗_</sup>||0_._01|
|||(0_._03)||(0_._01)||(0_._01)||(0_._06)||(0_._03)||(0_._01)|
|MA_×NTL_96 Med||0_._13<sup>_∗∗∗_</sup>||_−_0_._004||0_._003||0_._32<sup>_∗∗∗_</sup>||0_._18<sup>_∗∗∗_</sup>||0_._02<sup>_∗_</sup>|
|||(0_._03)||(0_._01)||(0_._01)||(0_._08)||(0_._04)||(0_._01)|
|MA_×NTL_96 High||0_._25<sup>_∗∗∗_</sup>||0_._06||0_._02||0_._50<sup>_∗∗∗_</sup>||0_._58<sup>_∗∗∗_</sup>||0_._08<sup>_∗∗_</sup>|
|||(0_._04)||(0_._04)||(0_._02)||(0_._09)||(0_._16)||(0_._04)|
|MA, 1996|0_._11<sup>_∗∗∗_</sup>|0_._10<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|_−_0_._01<sup>_∗_</sup>|_−_0_._01<sup>_∗_</sup>|0_._06<sup>_∗∗∗_</sup>|0_._03|0_._02|_−_0_._01|_−_0_._002|_−_0_._01|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._003)|(0_._003)|(0_._01)|(0_._02)|(0_._02)|(0_._01)|(0_._004|)<br>(0_._005)|
|Log mean light, 1996|0_._02|_−_0_._13<sup>_∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._11|_−_0_._34<sup>_∗∗∗_</sup>|0_._32<sup>_∗_</sup>|0_._06|_−_0_._11|_−_0_._15|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._07)|(0_._08)|(0_._19)|(0_._19)|(0_._10)|(0_._11)|
|Pre-trend: log mean light|0_._09|0_._16<sup>_∗∗_</sup>|0_._16|0_._19|_−_0_._003|0_._01|0_._11|0_._09|_−_0_._20|_−_0_._18|0_._13|0_._13<sup>_∗_</sup>|
||(0_._08)|(0_._08)|(0_._15)|(0_._16)|(0_._03)|(0_._03)|(0_._13)|(0_._10)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|0_._35<sup>_∗∗_</sup>|0_._34<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|_−_0_._25<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._36<sup>_∗_</sup>|0_._39<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._14)|(0_._13)|(0_._21)|(0_._21)|(0_._12)|(0_._12)|(0_._10)|(0_._09)|(0_._21)|(0_._23)|(0_._02)|(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Observations|15,714|15,714|15,714|15,714|15,714|15,714|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.17|0.18|0.22|0.22|0.14|0.14|0.32|0.35|0.27|0.28|0.37|0.36|

Standard errors are clustered on Woredas when using Kebeles as the unit of analysis and are clustered on Zones when using Woredas as the unit of analysis. All models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, units within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01

**Table S22:** Association of changes in market access on changes in outcome variables using a long difference. _θ_ = 8, using a 50km doughnut IV

||NT|L|Urb|an|Crop|land|N|TL|U|rban|Cro|pland|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._06<sup>_∗∗∗_</sup>|_−_0_._08<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._002|0_._001|_−_0_._08|_−_0_._11|0_._03|0_._01|_−_0_._001|_−_0_._004|
||(0_._02)|(0_._02)|(0_._01)|(0_._004)|(0_._01)|(0_._01)|(0_._07)|(0_._07)|(0_._04)|(0_._05)|(0_._02)|(0_._02)|
|MA_×NTL_96 Low||0_._07<sup>_∗∗_</sup>||_−_0_._01<sup>_∗∗_</sup>||0_._003||0_._19<sup>_∗∗∗_</sup>||0_._05<sup>_∗_</sup>||0_._01|
|||(0_._03)||(0_._01)||(0_._01)||(0_._06)||(0_._03)||(0_._01)|
|MA_×NTL_96 Med||0_._13<sup>_∗∗∗_</sup>||_−_0_._004||0_._003||0_._32<sup>_∗∗∗_</sup>||0_._18<sup>_∗∗∗_</sup>||0_._02<sup>_∗_</sup>|
|||(0_._03)||(0_._01)||(0_._01)||(0_._08)||(0_._04)||(0_._01)|
|MA_×NTL_96 High||0_._25<sup>_∗∗∗_</sup>||0_._06||0_._02||0_._50<sup>_∗∗∗_</sup>||0_._58<sup>_∗∗∗_</sup>||0_._08<sup>_∗∗_</sup>|
|||(0_._04)||(0_._04)||(0_._02)||(0_._09)||(0_._16)||(0_._04)|
|MA, 1996|0_._11<sup>_∗∗∗_</sup>|0_._10<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗∗_</sup>|_−_0_._01<sup>_∗_</sup>|_−_0_._01<sup>_∗_</sup>|0_._06<sup>_∗∗∗_</sup>|0_._03|0_._02|_−_0_._01|_−_0_._002|_−_0_._01|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._003)|(0_._003)|(0_._01)|(0_._02)|(0_._02)|(0_._01)|(0_._004|)<br>(0_._005)|
|Log mean light, 1996|0_._02|_−_0_._13<sup>_∗∗_</sup>|0_._46<sup>_∗∗∗_</sup>|0_._43<sup>_∗∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._06<sup>_∗∗_</sup>|_−_0_._11|_−_0_._34<sup>_∗∗∗_</sup>|0_._32<sup>_∗_</sup>|0_._06|_−_0_._11|_−_0_._15|
||(0_._05)|(0_._05)|(0_._12)|(0_._13)|(0_._02)|(0_._03)|(0_._07)|(0_._08)|(0_._19)|(0_._19)|(0_._10)|(0_._11)|
|Pre-trend: log mean light|0_._09|0_._16<sup>_∗∗_</sup>|0_._16|0_._19|_−_0_._003|0_._01|0_._11|0_._09|_−_0_._20|_−_0_._18|0_._13|0_._13<sup>_∗_</sup>|
||(0_._08)|(0_._08)|(0_._15)|(0_._16)|(0_._03)|(0_._03)|(0_._13)|(0_._10)|(0_._23)|(0_._21)|(0_._08)|(0_._08)|
|Pre-trend: log N urban pixels|0_._35<sup>_∗∗_</sup>|0_._34<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|_−_0_._25<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._36<sup>_∗_</sup>|0_._39<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._14)|(0_._13)|(0_._21)|(0_._21)|(0_._12)|(0_._12)|(0_._10)|(0_._09)|(0_._21)|(0_._23)|(0_._02)|(0_._02)|
|Unit|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Keb._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|_Wor._|
|Observations|15,714|15,714|15,714|15,714|15,714|15,714|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.17|0.18|0.22|0.22|0.14|0.14|0.32|0.35|0.27|0.28|0.37|0.36|

Standard errors are clustered on Woredas when using Kebeles as the unit of analysis and are clustered on Zones when using Woredas as the unit of analysis. All models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, units within 50km are excluded. _NTL_ 96 Low, Medium, and High are dummy variables for different groups of baseline nighttime lights; groups are formed using 3-quantiles of the maximum value of nighttime lights within Woredas that had some positive nighttime lights. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1; _∗∗ ∗∗∗_ p _<_ 0.05; p _<_ 0 _._ 01 S33
# **S15.3 Varying Baseline Nighttime Light Groups**

In this section, we explore heterogeneity of impacts of changes in market access across two groups of baseline nighttime lights—Woredas with no positive nighttime lights at baseline, and Woredas with some positive nighttime lights at baseline. Tables S23 and S24 show results using Kebeles and Woredas respectively. Results are consistent to models that use four groups of nighttime lights; road upgrades are associated with growth in nighttime lights and urban land, particularly in areas with positive baseline nighttime lights. As with results using four groups of baseline nighttime lights, coefficients on models explaining urban land tend to be more significant when using Woredas as the unit of analysis.

**Table S23:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Kebeles]

||NT|L|Urb|an|Crop|land|NT|L|Urba|n|Crop|land|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|_−_0_._003<sup>_∗∗_</sup>|0_._003|0_._003|_−_0_._04<sup>_∗∗∗_</sup>|_−_0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗_</sup>|0_._01<sup>_∗_</sup>|0_._002|0_._002|
||(0_._01)|(0_._01)|(0_._002)|(0_._002)|(0_._002)|(0_._002)|(0_._01)|(0_._01)|(0_._003)|(0_._003)|(0_._003)|(0_._003)|
|MA_×NTL_96 Lit||0_._23<sup>_∗∗∗_</sup><br>||_−_0_._005<br>||0_._002<br>||0_._23<sup>_∗∗∗_</sup><br>||0_._01<br>||0_._01<br>|
|||(0_._04)||(0_._01)||(0_._01)||(0_._04)||(0_._02)||(0_._01)|
|MA, 1996|0_._03<sup>_∗∗∗_</sup>|0_._03<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|_−_0_._002<sup>_∗∗_</sup>|0_._07<sup>_∗∗∗_</sup>|0_._06<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗∗_</sup>|_−_0_._004<sup>_∗_</sup>|_−_0_._004<sup>_∗∗_</sup>|
||(0_._003)|(0_._003)|(0_._001)|(0_._001)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._001)|(0_._001)|(0_._002)|(0_._002)|
|Log mean light, 1996|_−_0_._01<br>|_−_0_._08<br>|0_._45<sup>_∗∗∗_</sup><br>|0_._45<sup>_∗∗∗_</sup><br>|_−_0_._05<sup>_∗_</sup><br>|_−_0_._05<sup>_∗_</sup><br>|0_._02<br>|_−_0_._05<br>|0_._46<sup>_∗∗∗_</sup><br>|0_._46<sup>_∗∗∗_</sup><br>|_−_0_._05<sup>_∗_</sup><br>|_−_0_._05<sup>_∗∗_</sup><br>|
||(0_._05)|(0_._05)|(0_._12)|(0_._12)|(0_._02)|(0_._02)|(0_._05)|(0_._05)|(0_._12)|(0_._12)|(0_._02)|(0_._02)|
|Pre-trend: log mean light|0_._06<br>|0_._04<br>|0_._15<br>|0_._15<br>|_−_0_._001<br>|_−_0_._001<br>|0_._08<br>|0_._06<br>|0_._16<br>|0_._16<br>|_−_0_._002<br>|_−_0_._003<br>|
||(0_._08)|(0_._07)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|(0_._08)|(0_._07)|(0_._15)|(0_._15)|(0_._03)|(0_._03)|
|Pre-trend: log N urban pixels|0_._34<sup>_∗∗_</sup>|0_._32<sup>_∗∗_</sup>|0_._56<sup>_∗∗∗_</sup>|0_._56<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|0_._35<sup>_∗∗_</sup>|0_._33<sup>_∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|0_._57<sup>_∗∗∗_</sup>|_−_0_._04|_−_0_._04|
||(0_._13)|(0_._13)|(0_._22)|(0_._22)|(0_._12)|(0_._12)|(0_._14)|(0_._13)|(0_._21)|(0_._22)|(0_._12)|(0_._12)|
|Model|_OLS_|_OLS_|_OLS_|_OLS_|_OLS_|_OLS_|_IV_|_IV_|_IV_|_IV_|_IV_|_IV_|
|Observations<br>|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|15,714|
|Adjusted R<sup>2</sup>|0.18|0.19|0.22|0.22|0.14|0.14|0.17|0.18|0.22|0.22|0.14|0.14|

The unit of analysis is Kebeles. Standard errors are clustered on Woredas and all models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. IV indicates that MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, Kebeles within 50km are excluded. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S34
**Table S24:** Association of changes in market access on changes in outcome variables using a long difference, OLS results [Woredas]

||NT|L|Urb|an|Crop|land|NT|L|Ur|ban|Cropl|and|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|MA|_−_0_._01|_−_0_._004|_−_0_._02|_−_0_._02|_−_0_._004|_−_0_._003|_−_0_._05|_−_0_._05|0_._03|0_._03|_−_0_._002|_−_0_._002|
||(0_._02)|(0_._02)|(0_._01)|(0_._01)|(0_._004)|(0_._004)|(0_._04)|(0_._04)|(0_._03)|(0_._03)|(0_._01)|(0_._01)|
|MA_×NTL_96 Lit||0_._41<sup>_∗∗∗_</sup>||0_._18<sup>_∗∗_</sup>||0_._03||0_._46<sup>_∗∗∗_</sup>||0_._29<sup>_∗∗∗_</sup>||0_._03<sup>_∗_</sup>|
|||(0_._09)||(0_._07)||(0_._02)||(0_._07)||(0_._09)||(0_._02)|
|MA, 1996|0_._02<sup>_∗∗∗_</sup>|0_._01|0_._01<sup>_∗∗∗_</sup>|0_._01<sup>_∗∗_</sup>|0_._001|0_._0004|0_._04<sup>_∗∗∗_</sup>|0_._02<sup>_∗∗_</sup>|0_._01|_−_0_._001|_−_0_._001|_−_0_._002|
||(0_._004)|(0_._01)|(0_._003)|(0_._002)|(0_._001)|(0_._001)|(0_._01)|(0_._01)|(0_._01)|(0_._01)|(0_._002)|(0_._002)|
|Log mean light, 1996|_−_0_._15<sup>_∗_</sup>|_−_0_._24<sup>_∗∗∗_</sup>|0_._29|0_._26|_−_0_._12|_−_0_._12|_−_0_._11|_−_0_._23<sup>_∗∗∗_</sup>|0_._32<sup>_∗_</sup>|0_._25|_−_0_._11|_−_0_._12|
||(0_._08)|(0_._07)|(0_._19)|(0_._18)|(0_._10)|(0_._10)|(0_._07)|(0_._07)|(0_._19)|(0_._18)|(0_._10)|(0_._10)|
|Pre-trend: log mean light|0_._11|0_._08|_−_0_._20|_−_0_._22|0_._12|0_._12|0_._11|0_._07|_−_0_._20|_−_0_._22|0_._13|0_._12|
||(0_._13)|(0_._12)|(0_._23)|(0_._22)|(0_._08)|(0_._08)|(0_._13)|(0_._11)|(0_._23)|(0_._22)|(0_._08)|(0_._07)|
|Pre-trend: log N urban pixels|_−_0_._24<sup>_∗∗_</sup>|_−_0_._20<sup>_∗∗_</sup>|0_._33<sup>_∗_</sup>|0_._34<sup>_∗_</sup>|_−_0_._05<sup>_∗∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._26<sup>_∗∗_</sup>|_−_0_._22<sup>_∗∗_</sup>|0_._36<sup>_∗_</sup>|0_._38<sup>_∗_</sup>|_−_0_._05<sup>_∗_</sup>|_−_0_._04<sup>_∗_</sup>|
||(0_._10)|(0_._09)|(0_._19)|(0_._18)|(0_._02)|(0_._02)|(0_._10)|(0_._09)|(0_._21)|(0_._20)|(0_._02)|(0_._02)|
|Model|_OLS_|_OLS_|_OLS_|_OLS_<br>|_OLS_|_OLS_|_IV_|_IV_|_IV_|_IV_|_IV_|_IV_|
|Observations<br>|780|780|780|780|780|780|780|780|780|780|780|780|
|Adjusted R<sup>2</sup>|0.33|0.37|0.28|0.29|0.37|0.37|0.33|0.36|0.27|0.28|0.37|0.37|

The unit of analysis is Woredas. Standard errors are clustered on Zones and all models include Zone fixed effects. MA is the logged difference of MA from 1996 to 2016. IV indicates that MA is instrumented with a 50km MA Doughnut variable; that is, in calculating market access, Woredas within 50km are excluded. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. NTL is the average nighttime lights within Woredas, Urban is the total urban area within Woredas, and Cropland is the total cropland area within Woredas; we use the inverse hyperbolic sine transformation on all outcome variables, which has a similar interpretation as logs.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0 _._ 01 S35