Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

Impacts of improved rural roads on the well-being of
Cambodian villagers
TIEN MANH VU, Hiroyuki Yamada
18 June, 2024
DP2024-016
https://ies.keio.ac.jp/en/publications/23970/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
18 June, 2024

Impacts of improved rural roads on the well-being of Cambodian villagers
TIEN MANH VU, Hiroyuki Yamada
Keio-IES DP2024-016
18 June, 2024
JEL Classification: O15, R11, R58, R42
Keywords: Improved rural roads, wealth, rice, education, pollution, Cambodia

Abstract
We examine impacts of improved rural roads on villagers, using panel data of
Cambodian villages (2006-2021). We find an association between the wealth ofvillagers and the improvement of rural roads. A higher minimum price for rice at farm
gate may be one of the reasons for the increase in wealth. We also find impacts ofimproved rural roads on (reducing) illiteracy rates among villagers. However, we do
not find any statistically significant impacts on school enrollment rate, structuralchange, or internal migration. Instead, improved rural roads lead to a higher share offamilies being subjected to environmental pollution.

TIEN MANH VU
Faculty of Global Management, Chuo University742-1 Higashinakano, Hachioji, Tokyo
mvu264@g.chuo-u.ac.jp
Hiroyuki Yamada
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyohyamada@econ.keio.ac.jp

Acknowledgement: This work was supported by JSPS (Japan Society for the Promotionof Science) KAKENHI Grant Numbers 21K01455, 22K18534 and Chuo University.

Impacts of improved rural roads on the well-being of Cambodian villagers

TIEN MANH VU
Corresponding author. Faculty of Global Management, Chuo University742-1 Higashinakano, Hachioji, Tokyo 192-0393, Japan
Email: mvu264@g.chuo-u.ac.jp
https://orcid.org/0000-0002-2251-0795
HIROYUKI YAMADA
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
E-mail: hyamada@econ.keio.ac.jp
https://orcid.org/0000-0003-3709-1914
Abstract: We examine impacts of improved rural roads on villagers, using panel data of
Cambodian villages (2006–2021). We find an association between the wealth of villagers andthe improvement of rural roads. A higher minimum price for rice at farm gate may be one of
the reasons for the increase in wealth. We also find impacts of improved rural roads on(reducing) illiteracy rates among villagers. However, we do not find any statistically significant
impacts on school enrollment rate, structural change, or internal migration. Instead, improved
rural roads lead to a higher share of families being subjected to environmental pollution.

Keywords: Improved rural roads, wealth, rice, education, pollution, Cambodia
JEL classification: O15; R11; R58; R42
Acknowledgements: This work was supported by JSPS (Japan Society for the Promotion of
Science) KAKENHI Grant Numbers 21K01455, 22K18534 and Chuo University.
Conflict of interests: None.

11. Introduction
Rural roads connect large proportions of low-income areas in developing countries, but
few studies have investigated the impacts of improvements to rural roads. If there is a positive
externality, existing rural roads might be in an under-investment stage. Therefore, research on
improved rural roads would generate insightful policy implications with the potential toenhance the well-being of local residents, especial low-income residents.
Herein, we examine the possible impacts of improved roads under the purview of theCambodian Improved Rural Roads project overseen by the Asian Development Bank (ADB)
on various well-being outcomes of villagers, including education, health, income, wealth, andmigration. Specifically, we combine panel data of (annual) village-level censuses during 2006–
2021 with detailed GPS maps of rural roads that were targeted for improvement as part of the
project. Following the method of Callaway and Sant’Anna (2021), which relies on event-study
and inverse probability weighting (doubly-robust) methods, we examine the possible impactsof the improved rural roads on villagers with variations in treatment timing and multiple time
periods.
We find that the share of families who upgrade their houses with better materials after
the completion of the improved roads increases. Villagers receive a higher price of rice at farmgate. In addition, we find that illiteracy rates among villagers aged 36–45 years decreased
significantly in association with improved rural roads. However, we do not find any significant
impacts on junior high or high school enrollment rates, structural changes, or internal migration.
As a negative externality, we find that improved rural roads lead to increased environmental
pollution.
Our research makes several important contributions to the literature. First, our results
are from data having among the highest resolution over a long period of time (up to 15 years
2before the treatment and 8 years after) within the theme of (the impacts of) improved ruralroads in developing countries. Second, our findings provide possible answers regarding theshort- and medium-term impacts of improved rural roads. Third, our findings support the few
studies that show a new negative externality of improved rural roads—environmental pollution.
The remainder of this paper is organized as follows. Section 2 provides the background
literature of our study. Section 3 describes the data as well as the division of the treatment andcontrol groups. Section 4 outlines the methodology. Section 5 reports the results and section 6
concludes the paper.

2. Related literature
Numerous studies have examined whether investments in roads in developing countries
have causal effects on economic growth, opening up trade, reallocation of firms along the roads,
structural changes, rural poverty reduction, higher school enrollment, and reducing crime aswell as environmental externalities such as deforestation and biodiversity loss (Berg et al.,2017). However, the impacts may vary depending on the type of roads (highway, major roads,
rural roads), type of investment (newly built, improved/repaired roads), and duration ofobservation (short-, medium-, and long-term effects). The literature reports conflicting findings,
suggesting that this is a complex issue.
Specifically, the findings regarding newly constructed roads in rural areas are mixed.
New roads help Indian villagers to access work in sectors other than agriculture but changes inagricultural outcomes, income, and assets are not significant (Asher & Novosad, 2020). New
roads also lead to higher enrollment rates in middle school and higher test scores among
students in response to higher returns of schooling from newly available markets (Adukia,
Asher, & Novosad, 2020). Aggarwal (2018) finds that in India, rural households close to newly
constructed roads have lower prices, greater variety of non-local goods, increased usage of3

agricultural technologies, and more teenagers leaving school to enter the workforce. In Ethiopia,
newly constructed rural roads connecting agricultural villages help to increase agricultural
income (Kebede, 2024), consumption, and wage employment, particularly for women andyoung people (Nakamura et al., 2020). Also in Ethiopia, combining roads with agriculturalextension services is suggested to increase productivity by offering a better market and greater
access to public services (Gebresilasse, 2023).
Although few studies have examined the impacts of improved rural roads, the findingsof such research provide new perspectives. Shimamura et al. (2023a) find that in Morocco,
improved rural roads have different associations with some impacts according to gender.
Specifically, more females had higher secondary school attainment. However, the proportionof males with wage employment increases (Shimamura et al., 2023a). Improved rural roads in
Vietnam have impacts on access to markets, leading to a change from agriculture to services(Mu & van de Walle, 2011). In Tanzania, households transition from agricultural to wage
employment and pay lower prices for rice (consumption) as a result of lower transportation
costs from improved major roads (Dumas & Jativa, 2024).
Transportation costs can also account for the impacts of improved roads in Cambodia.
Using Demography and Health Surveys and a differences-in-differences (DID) approach, Anti
and Zhang (2023) count the number of built roads within 15 km from the respondents’
residence to estimate the impacts on women’s employment and gender equity. Similarly, using
survey data of 400 residents in 2016, Takada et al. (2021) evaluate the perception of
respondents on the impacts of improved rural roads in Cambodia during 2010–2016. Takada
et al. (2021) suggest that improved roads lead to an increase in the frequency of visiting local
markets and time savings, which eventually leads to higher income growth.
However, there are some methodological issues concerning these previous studies. First,
is the endogeneity of the selected roads. Newly constructed roads in rural areas have two

4

endogenous issues: the direction of the roads and where they are located. In contrast, researchon improvements to rural roads have only one of these issues: selection of the road(s) to be
improved.
Solving endogeneity issues is not straightforward. The problem lies in the existence ofcounterfactuals. Some research uses unique methods such as (fuzzy) regression discontinuity(Asher & Novosad, 2020; Adukia et al., 2020), counterfactual roads based on cost conditions(Kebede, 2024), and distance to historical ports (Banerjee et al., 2020), which can be difficult
to apply to other countries. Other studies use propensity score matching methods (incombination with DID ) (Mu & de van Walle, 2011; Nakamura et al., 2020; Dumas and Jativa,2024). However, propensity score matching methods are known to be subject to the selectionof observables and the algorithm methods in use.
Meanwhile, the majority of studies (Takada et al., 2021; Aggarwal, 2018, 2021;
Gebresilasse, 2023; Anti & Zhang, 2023) rely on a DID approach. However, recent researchon this method (de Chaisemartin and D’Haultfœuille, 2020; Goodman-Bacon, 2021; Callaway& Sant’Anna, 2021; Sun & Abraham, 2021) reveals many drawbacks of using simple DID (e.g.,“negative” weighting of two-way fixed effects, different treatment timings). Roth (2022) and
Rambachan and Roth (2023) show that conventional parallel trend tests in simple DID (event
study) may lack statistical power and lead to wrong conclusions.
Second, most of the studies face a limitation in terms of the window of observation inassociation with the available data. The main question is when the possible impacts might
appear. Takada et al. (2021) reports on outcomes 1 year after road construction. However, somepossible impacts may appear over the medium term rather than the short term. Specifically, Mu
and van de Walle (2011) and Asher and Novosad (2020) suggest 27 months and 4 years,
respectively, for impacts to be realized. The “resolution” of the data, especially survey data,
might raise concerns on the real impacts of improved roads. Low “resolution” also includes

5cases in which the distance between rural roads and objects (e.g., people in developing
countries) is far (≥15 km) and when the (integrated data level) observations are large
geographical areas (>150 km2).

3. Panel data and division of treated and control groups
We construct panel data of Cambodian villages for an event study of 2006–2021, using
two important sources of information. The first is the Commune Village Database (CVD) fromthe Cambodian National Institute of Statistics. The institute collects data for the CVD annually.
The location of each village is identifiable (as map points), with approximate latitude andlongitude.
The second source of information is global information system data on rural roads under
the purview of ADB-supported projects. The ADB has provided loans for the Cambodian
government to improve the country’s existing rural road system in three phases. As of 2021,
phase 1 (started in 2011, targeting 505.4 km of rural roads) and phase 2 (started in 2014,
targeting 1,200 km of rural roads) have been completed. 1 However, none of the roads in phase
3 (started in 2023, targeting 360 km of rural roads) had been completed as of 2021, which is
the most recent year in the available CVD data.
We obtain detailed information on the road locations and year of completion from the
ADB rural roads improvement project office. Phase 1 covered seven provinces, mainly in the
Tonle Sap Basin. Phase 2 covered ten provinces: Banteay Meanchey, Battambang, Kampong
Cham, Kampong Chhnang, Kampong Speu, Kampong Thom, Pursat, Siem Reap, Takeo, andThe details of phases 1, 2, and 3 can be found on the ADB website (https://www.adb.org/projects/42334013/main, https://www.adb.org/projects/42334-014/main, and https://www.adb.org/projects/42334-018/main).
We cover all roads improved during 2011–2021 (i.e., phases 1 and 2).
1

6

Tbuong Khmum. Phase 3 covers five provinces: Kampong Cham, Kratie, Prey Veng, Svay
Rieng, and Tboung Khmum. Although the roads targeted for improvement are rural roads inrural areas, the reasons these roads were selected are not documented. From the geographic
locations of the roads, we find that about 2,787 (4,686) villages of Cambodia’s (approximately)
14,000 villages are located within 2 km (4 km) of the roads to be improved in phases 1, 2, and
3. A map of the roads targeted by the project is shown in Figure 1.
[Insert Figure 1 here]
The distance between the centroid of the villages and the improved rural roads is a key
factor for deciding the possible influences of improved roads. As suggested by findings in
China by Banerjee, Duflo, and Qian (2020), the closer an area is to the transportation network,
the higher the causal impacts of the roads on the area’s income in the long term. Therefore, foreach village, we calculate the nearest distance to each road targeted in phases 1, 2, and 3 to (themap point of) the village. Among rural roads with a fully identified date of completion, we
limit the closest distance to 2 km to construct our sample, 2 giving us 16 years’ worth of data
on 1,569 for analysis. The descriptive statistics of the data are presented in Appendix 1.
We assign villages close to the roads that were improved in phase 1 and 2 as thetreatment group and those near roads to be improved in phase 3 as the control group, under theassumption that roads targeted for improvement in phase 3 were selected in a similar way tothose targeted in the first two phases. However, villages near the roads targeted for
improvement in phase 3 did not receive any “treatment” during the observation window. If avillage is close to roads targeted by more than one phase, with different completion timings,
we use the earliest year of completion. Furthermore, because villages in the control group are2

We also construct the sample with 4 km and report the results in the Appendices.

7

close to the roads in phase 3 but not to those in phase 1 and 2, there should be no spillover
effects from the treatment group to the control group.
We acknowledge that our assumption and selection may be subject to biasness. Our
division between treatment and control groups may be the second-best choice. The best choice
would be villages that are completely identical to villages in the treatment group but cannot be
impacted by the road improvement. However, the best choice is not practical in the context of
Cambodia. In addition, matching methods based on observable characteristics of the villages
do not guarantee homogeneity of unobservables. Meanwhile, the selection of roads under thepurview of ADB-supported projects is not arbitrary; they were selected because policymakers
had specific targets/outcomes in mind for the given areas and villagers.
To quantify the biasness that we would need to address, we perform simple balance
checks between the treatment and control groups, using information from 2006, about 8 years
before any of the roads were improved (Appendix 2). The balance tests in Appendix 2 suggestthat villages in the control group tend to have a better starting point in 2006 with a lower
illiteracy rate, slightly higher wealth (better housing conditions), and a smaller population.
Therefore, any evidence showing that the treatment group had better outcomes compared withthe control group (because of the improved roads) would underestimate the real impacts of the
improved roads.

4. Methods and econometric specification
Our identification strategy is based on a sophisticated event-study with different
treatment timings. Recent advanced studies of DID methodology support our empirical
implementation. Specifically, we apply the methods by Callaway and Sant’Anna (2021) for
our final reports, and Sun and Abraham (2021) for robustness checks. The approach can be
explained as follows.
8First, standard DID can be used to estimate the average treatment effects ( 𝛽𝛽 ) ofimproved roads on villages i𝑜𝑜𝑜𝑜𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖 = 𝛽𝛽 × 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑑𝑑𝑖𝑖 × 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖𝑖𝑖 + 𝜛𝜛𝑖𝑖 + 𝛾𝛾𝑡𝑡 + 𝜆𝜆 × 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 ,(1)

where two-way fixed effects (TWFE) are 𝜛𝜛𝑖𝑖 (village fixed effects) and 𝛾𝛾𝑡𝑡 (time fixed effects),

and 𝑋𝑋𝑖𝑖𝑖𝑖 are the control variables. 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑑𝑑𝑖𝑖 equals 1 if village 𝑖𝑖 is in the treatment group, and 0

otherwise. 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖𝑖𝑖 is 1 if the roads corresponding to the village are improved, and 0 otherwise.

The canonical identification of the standard DID is that the parallel trend assumption must be
satisfied (the pre-treatment trend of the outcome would be parallel between the treatment andcontrol groups). Furthermore, the canonical DID assumes no spillover effects from thetreatment group to the control group.
However, because each road was constructed and completed on different timelines,
equation (1) is subject to at least four problems. The first is that some treated villages were partof the control group in years when the treated villages had not yet received the treatment (deChaisemartin & D’Haultfœuille, 2020; Goodman-Bacon, 2021). Meanwhile, the desired
estimation is the difference between treated units and control units that will never receive anytreatments. The second problem is when the treatment effect is not homogenous (i.e., changes
over time). The estimands from the standard DID (or TWFE) can be contaminated when earlier
treated observations serve as the basis for later treated observations (the trend of treated effects
in earlier treated observations enter to the estimand with a negative-sign weight component)(de Chaisemartin & D’Haultfœuille, 2020). The bias would be worse if the remaining
observations of the untreated group were few or nonexistent in the last window of time
(Goodman-Bacon, 2021). The parallel trend cannot be properly tested for the same reason,
which is the third problem. The fourth problem lies in fact that the (calendar) timing oftreatment may be associated with different magnitudes of impact. For example, if the village

9receives the treatment 2 calendar years earlier, the impacts would have been higher than if it
received the treatment in the present year.
To solve the second problem, a panel event-study might help to separate the treatment
effects into each period after the event (intervention), using the following equation (Miller,2023),
𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑒𝑒𝑖𝑖𝑖𝑖 = �∑𝑗𝑗∈{−15,…,0,…,8) 𝛼𝛼𝑗𝑗 × 𝐷𝐷𝑖𝑖,𝑡𝑡−𝑗𝑗 � + 𝛾𝛾𝑖𝑖 + 𝜑𝜑𝑡𝑡 + 𝜇𝜇 × 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝜖𝜖𝑖𝑖𝑖𝑖 ,(2)

in which 𝛾𝛾𝑖𝑖 and 𝜑𝜑𝑡𝑡 are panel fixed effects and 𝑋𝑋𝑖𝑖𝑖𝑖 are the control variables. 3 The term
𝛼𝛼𝑗𝑗 × 𝐷𝐷𝑖𝑖,𝑡𝑡−𝑗𝑗 is for each j event period (measured in year unit). 𝐷𝐷𝑖𝑖,𝑡𝑡−𝑗𝑗 indicates the event time.
The coefficients after the event occurred, 𝛼𝛼𝑗𝑗 if 𝑗𝑗 ≥ 0. A year before the event is often used as

the baseline. To claim the causal effects from the event, the parallel trend condition should be
met. Specifically, and ideally, all 𝛼𝛼𝑗𝑗 before the event (𝑗𝑗 < −2) should be the same as thebaseline (𝛼𝛼−1 ). 4 However, the standard event study cannot solve the previous two problems in
the TWFE. In addition, the parallel trend is difficult to test because of the differences intreatment timings.
Fortunately, recent literature (de Chaisemartin & D’Haultfœuille, 2020; GoodmanBacon, 2021; Callaway & Sant’Anna, 2021; Sun & Abraham, 2021) generously provides manysolutions to the above issues. We apply the approach of Callaway and Sant’Anna (2021) 5 on
using an event-study because the settings are closest to the context of the problem settings,
allowing for variation in treatment timings and multiple time periods as well as for flexible

From the data, the maximum of treatment period is 8 and pre-treatment one is 15. Our graphical results report
only 7 periods of pre-treatment from the numeric estimations.
4
Recent studies such as Rambachan and Roth (2023) suggest researchers can do more even when the ideal parallelassumption is not met. If the post-trend can be predicted from the pre-trend, researchers may still come to aconclusion on the impacts (Rambachan and Roth, 2023).
5
We also use Sun and Abraham (2021) for robustness checks.
3

10

covariates. 6 Specifically, Callaway and Sant’Anna (2021) propose an estimation procedure
based on the outcome regression and inverse probability weighting (doubly-robust) methods.
The procedure disaggregates causal parameter–identified group-time average treatment effects
by comparing treated observations with never-treated observations, and treated observations
with “not-yet-treated” observations. Then, the procedure aggregates group-time averagetreatment effects. Using a bootstrap procedure, inference procedures simultaneously accountfor balances such as those across times and estimands. Thus, the estimations from Callaway
and Sant’Anna (2021) can show treatment effects that change with the length of exposure to
the treatment, variations in average treatment effects across treatment groups, and the evolutionof cumulative average treatment effects over the calendar year.
We apply the estimation procedure from Callaway and Sant’Anna (2021) to equation(2) with three different outcome groups. The first group covers wealth, income, structuralchange, and internal migration, including share of families having a house with a zinc/fibro
roof (or a TV set), the minimum farm-gate price of rice per kilogram, share of families usingchemical fertilizer in previous year, share of families who irrigated their paddy field, share of(fe)males 18 years of age or older, (Chamkar) rice farmer, share of (fe)males above 18 yearsof age whose main occupation is long crop growing, share of (fe)males above 18 years of age
whose main occupation is wage employment in agriculture/construction, and share of (fe)males
above 18 years of age whose main occupation is migrating to work in the country. The second
group covers educational attainment, including illiteracy rates in the village by age cohort (18–24, 25–35, and 36–45 years) and by gender (males and females), and school enrollment rate

For example, the staggered treatment adoption assumption of Callaway and Sant’Anna (2021) is exactly true inour context. Once the villages are subjected to the treatment, the roads exert the treatment effect over time. Also,
their approach ensures that time-varying control variables are not affected by the intervention. In our context, thecontrol variable is the village population.
6

11

per age cohort (12–14 and 15–17 years) for junior and high school and by gender. The thirdgroup covers pollution (share of families who are affected by environmental pollution) andsanitation (share of families who regularly boil water for drinking). We use village population
as a control covariate because village population might account for the spatial agglomeration
perspective.
The parallel trend assumption is important for the claim of causal effects. Specifically,
the trend before the event would be parallel between the treatment and control groups (H0: All
𝛼𝛼𝑗𝑗 with 𝑗𝑗 < 0 should be all zero). Callaway and Sant’Anna (2021)’s procedure assumes

conditional parallel trends based on never-treated and later-treated observations. However,recent studies have raised some concerns about the validity of the test even though all
coefficients of the pre-trend are (jointly) statistically insignificant. The first is the lower powerof conventional tests (Freyaldenhoven, Hansen & Shapiro, 2019; Kahn-Lang & Lang, 2020;
Roth, 2022). For example, each coefficient of the pre-trend cannot be rejected for being
different non-zero values with the same p-values that are observed in similar tests where theconclusion is that H0 cannot be rejected (H0: having a value of 0). Therefore, the pre-trend
may exist in either linear or non-linear form, which continues and even vibrates after the eventand causes biasness in the estimated coefficients of interest. In addition, the draws of the data
for the tests are a selected sample, which induces additional statistical issues (Roth, 2022).
Fortunately, Roth (2022) and Rambachan and Roth (2023) also provide someprocedures to detect the pre-trend and test its sensitivity. The key point of the procedure is tofind the probability of encountering the issues and to determine how sensitive it is, by accepting
the violation of the pre-trend assumptions in different scenarios and using helpful informationfrom the pre-event coefficients. We apply these procedures for each reported outcome.

5. Results

125.1 Wealth, income, structural change, and internal migration
We find that improved roads are associated with more families having houses with azinc or fibro roof in the village, as seen in Figure 2 (2.1). This is an indicator of more wealth.
Villagers would have become richer in association with improved roads. Similarly, we find theshare of families having a TV (Figure 2.3) also increases in association with improved roads.
[Insert Figure 2 here]
We checked the sensitivity of the pre-trend assumption shown in Appendix 8, using
procedures from Roth (2022) and Rambachan & Roth (2023). Regarding the share of families
having a house with a zinc or fibro roof shown in Appendix 8 (8.1), the test using proceduresfrom Roth (2022) indicates a 50% probability that we would find a positive upward trend witha slope of 0.0008. The test result, illustrated in the graph on the lefthand side, uses thesmoothness restriction of Rambachan & Roth (2023). 7 The critical value for the bound M isapproximately 0.001. If the extrapolation of the pre-trend does not change more than 0.001
percentage points across consecutive periods, we can reject the null effect. Therefore, we areconfident that the effects on the share are more likely to exist even when there is a linear pretrend and a nonlinear pre-trend (up to M≈0.001). Meanwhile, the right-hand side of the graph,
which illustrates the results from using the procedure of Roth (2022), shows the estimatedcoefficients during the treatment time position far above the expectation after pre-testing,suggesting that the effects of improved roads are significant regardless of the pre-trend and the
expectation after pre-testing. A similar interpretation can be applied to the share of families
having houses with a zinc or fibro roof with TV set.

The graphs corresponding to Rambachan and Roth (2023) show the confidence interval of a bound M, which isthe slope of the difference in trend changes from pre-treatment to treatment. When M=0, the difference in trends
is linear. A large M is associated with greater non-linearity of the post-event trend compared with the pre-event
trend. However, as M becomes larger, the impacts fade away.
7

13

We seek evidence for a possible mechanism. We note that the roads already existed
before the treatment and that road construction was carried out for the purpose of improving
the quality of these roads. There are two hypotheses for increasing wealth in association withimproved roads. One is that the cost of building or renovating a house decreases because oflower transportation costs. Another is that the annual income of the villages increases becauseof structural changes, enabling villagers to build or renovate houses such that the overall
condition of houses in the village improves. A potential reason for structural changes due toimproved roads might be that villagers can access (labor) markets that are farther away at lower
costs. Villagers can earn a higher income than that from agriculture and thus begin to transition
away from agricultural production.
Unfortunately, we do not have any information on transportation costs and housing
construction costs to test the first hypothesis directly. However, several outcomes related to theprice of rice at farm gate, share of workers by industry, and internal migration in the data canbe used to test the other hypotheses. For example, one hypothesis is that the probability ofselling rice outside the villages would have been limited by high transportation costs likely due
to poor road conditions. Therefore, the minimum price of rice at farm gate would be depressedby intermediate traders. However, owing to the improved roads, farmers can access a variety
of markets and negotiate for a higher price because transportation costs have been reduced.
Another hypothesis is that structural change in the village leads to higher income, that is, moreincome owing to increased wage employment. In addition, having better roads may help
underemployed workers reach better job markets outside their villages. As they migrate to work
elsewhere (enabled by lower travel costs), they would earn a higher income and use part oftheir earnings to upgrade their housing conditions. We test the hypotheses with the available
outcomes in the data, as shown in Figures 2 (2.4, 2.5, 2.6) and 3.
[Insert Figure 3 here]14

We find evidence to support one of these hypotheses. The minimum price of rice atfarm gate would have been increased in association with the improved roads, as shown in
Figure 2 (2.4). The higher price potential incentivizes rice production (more workers, morechemical fertilizer, and more paddies irrigated). However, as shown in Figure 2 (2.5 and 2.6)
and Figure 3 (3.1 and 3.2), the effects on rice production are statistically insignificant. In
addition, the analyses in Appendix 8 (8.4, 8.5, and 8.6) suggest that the statistical results might
be sensitive to the pre-trend assumption.
However, we do not find evidence to support the other hypotheses. We do not find that
improved roads lead to structural change or more (internal) migration, as shown in Figure 3.
There is no significant change in the share of people with wage employment (Figure 3.5 and3.6), agriculture (long crop growing; Figure 3.3 and 3.4), and internal migration (Figure 3.7
and 3.8). Additional pre-trend sensitivity analyses refute any possible effects. Also, unlike
Shimamura et al. (2023a), our findings suggest no statistically significant differences by gender.
5.2 Education
We find that improved roads have significant effects on illiteracy rates. As shown in
Figure 4, we find the illiteracy rate among people aged 36–45 years decreases after roads are
improved. In Appendix 10 (10.5 and 10.6), the test using procedures from Roth (2022)
indicates a 50% probability that we would find a positive upward trend with a slope of 0.0006(0.0007). In the sensitivity analysis using the smoothness restriction of Rambachan and Roth(2023), the critical value for the bound M in the case of males (Appendix 10.6) is about 0.002.
If the extrapolation of the pre-trend does not change more than 0.002 percentage points acrossconsecutive periods, we can reject the null effect. Therefore, we are also confident that theeffects on the illiteracy rate are more likely to exist even when there is a linear pre-trend and anonlinear pre-trend (up to M≈0.002). However, we come to no clear conclusion on the15

statistically significant average effects on the illiteracy rates of people aged 18–24 and 25–35
years in Figure 4 (4.1, 4.2, 4.3 and 4.4).
[Insert Figure 4 and 5 here]
However, we do not find evidence for the effects on school enrollment rates amongpeople aged 12–14 (junior high school) and 15–17 (high school) years, as shown in Figure 5.
The pre-trend assumptions are violated. Further sensitivity analyses, as shown in Appendix 11,
do not give any evidence to support rejecting the null effects.
5.3 Pollution and sanitation
The results shown in Figure 6 (6.1) suggest that after the roads are improved, a higher
share of families in the village are subjected to environmental pollution. Although it does notappear immediately, the share of families subjected to environmental pollution increases andbecomes statistically significant from the third year after the event. Although the pre-trend
assumption is not held, as shown in Appendix 12, sensitive analyses of the pre-trend suggestthat the effects would be sustained in the existence of the pre-trend.
[Insert Figure 6 here]
Meanwhile, we do not find statistically significant effects on the share of families who regularly
boil water for drinking, as shown in Figure 6 (6.2).
5.4 Robustness checks
First, we apply the methods suggested by Sun and Abraham (2021). The estimation
results can be found in Appendices 3–7. The interpretation of the results remains similar.
Second, we alter the distance that would constitute the influence scope of the improved
roads to 4 km. Although the effects on the share of families having a house with a zinc/fibro
roof (or a TV) tend to be magnified, as shown in Appendix 12 (12.1), the previously foundeffects with the setting of 2 km are no longer statistically significant in the setting of 4 km (see16Appendices 13–17). These results agree with Shimamura et al. (2023b). The possible impactsof improved rural roads may be limited to within a few (≤5) kilometers of the site.

6. Conclusions
We use panel data of Cambodian villages during 2006–2021 to investigate the possible
impacts of improved rural roads on nearby villagers. We find that improved rural roads help toincrease the wealth of villagers, perhaps via higher income (higher price of rice) channel andreduce illiteracy among villagers. However, we do not find any increase in school enrollment
rates, structural changes, or internal migration. In addition, we find that improved rural roads
increase environmental pollution. Finally, the impacts of improved roads in Cambodia may belimited to within a short distance (2 km).
Our study shows that rural road improvement might have welfare-enhancing impacts
as well as welfare-damaging impacts (in our context, environmental pollution). Hence,
policymakers should take into account both the pros and cons of such projects. Conducting a
cost-benefit analysis would be preferable. However, due to data limitations, this will be a targetof future research.

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Figure 1 Improved roads under ADB supported road projects

Notes: Black curves: Rural roads under phase 1. Yellow curves: Rural roads under Phase 2. Red

curves: Rural roads under Phase 3.

22

Figure 2 Wealth and income

2.1 Share of families having houses with zinc/fibro roof
P-value (pretrend test): 1.000
ATT on Treated: 0.0329 (0.0085)***

2.2 Share of families having houses withzinc/fibro roof & electricity
P-value (pretrend test): 0.0000
ATT on Treated: 0.047 (0.031)

2.3 Share of families having houses with zinc/fibro roof
& TV
P-value (pretrend test): 0.9999.
ATT on Treated: 0.042 (0.011)***

2.4 Minimum farm gate price of rice
P-value (pretrend test): 0.9992
ATT on Treated: 15.638 (9.078)*

2.5 Share of families using chemical fertilizer in previous
year
P-value (pretrend test): 0.9970
ATT on Treated: 0.0067 (0.0062)

2.6 Share of families who irrigated paddy field
P-value (pretrend test): 1.000
ATT on Treated: 0.0042 (0.0032)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

23

Figure 3 Structural change and internal migration

3.1 Share of females over 18 years old whose main
occupation as rice farmers
P-value (pretrend test): 0.5152
ATT on Treated: 0.0169 (0.014)

3.2 Share of males over 18 years old whose main
occupation as rice farmers
P-value (pretrend test): 0.0000
ATT on Treated: 0.0131 (0.018)

3.3 Share of females aged 18 and over whose main
occupation as long crop growing
P-value (pretrend test): 0.000
ATT on Treated: 0.0015 (0.0009)*

3.4 Share of males aged 18 and over whose main
occupation as long crop growing
P-value (pretrend test): 0.000
ATT on Treated: 0.00252 (0.0011)**

3.5 Share of females aged 18 and over with wage
employment
P-value (pretrend test): 0.000
ATT on Treated: 0.0038 (0.0033)

3.6 Share of males aged 18 and over with wage
employment
P-value (pretrend test): 0.6833
ATT on Treated: 0.0013 (0.0029)

3.7 Share of females aged 18 and over whose main 3.8 Share of males aged 18 and over whose main
occupation as migrating to work in the country
occupation as migrating to work in the country
P-value (pretrend test): 0.000
P-value (pretrend test): 0.000
ATT on Treated: 0.0033 (0.0036)
ATT on Treated: 0.0012 (0.0062)
Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

24

Figure 4 Illiteracy rate

4.1 Share of illiterate females aged 18-24
P-value (pretrend test): 0.000
ATT on Treated: −0.01 (0.008)

4.2 Share of illiterate males aged 18-24
P-value (pretrend test): 0.0000
ATT on Treated: −0.0036 (0.0078)

4.3 Share of illiterate females aged 25-35
P-value (pretrend test): 0.002
ATT on Treated: −0.012 (0.01)

4.4 Share of illiterate males aged 25-35
P-value (pretrend test): 1.000
ATT on Treated: −0.019 (0.014)

4.5 Share of illiterate females aged 36-45
P-value (pretrend test): 0.9831
ATT on Treated: −0.013 (0.0075)*

4.6 Share of illiterate males aged 36-45
P-value (pretrend test): 0.000
ATT on Treated: −0.018 (0.01)*

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

25

Figure 5 School enrolment rate

5.1 School enrolment rate in junior high school(females aged 12-14)
P-value (pretrend test): 0.0000
ATT on Treated: 0.055 (0.0294)*

5.2 School enrolment rate in junior high school(males aged 12-14)
P-value (pretrend test): 0.000
ATT on Treated: 0.0527 (0.03)*

5.3 School enrolment rate in high school (females
aged 15-17)
P-value (pretrend test): 0.1687
ATT on Treated: −0.0025 (0.0157)

5.4 School enrolment rate in high school (males aged
15-17)
P-value (pretrend test): 0.0000
ATT on Treated: 0.028 (0.020)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

26

Figure 6 Sanity and pollution

6.1 Share of families who are affected byenvironment pollution
P-value (pretrend test): 0.000
ATT on Treated: 0.059 (0.031)*

6.2 Share of families who regularly boil water for
drinking
P-value (pretrend test): 0.8035
ATT on Treated: 0.0383 (0.027)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

27

Online Appendix 1: Descriptive statistics
VariableYear
Population
Distance to to-be-improved roads
Treated group
Treated group * treated
Minimum farm gate price of rice per kilogram
Share of families
Having house with zinc/fibro roof
Having house with zinc/fibro roof and electricity
Having house with zinc/fibro roof and TV
Using chemical fertilizer in previous year
Effected by environmental pollution
Regularly boiling water for drinking
Who irrigated paddy field
Share of people among their gender and cohort (aged ≥18) whose main occupation as*(Chamkar) Rice farmers (Females)(Chamkar) Rice farmers (Males)Long crop growing (Females)Long crop growing (Males)
Wage employment in agriculture/construction (Females)
Wage employment in agriculture/construction (Males)
Migrating to work in the country (Females)
Migrating to work in the country (Males)
Illiterate rate
Female aged 18-24
Male aged 18-24
Female aged 25-35
Male aged 25-35
Female aged 36-45
Male aged 36-45
School enrolment rate*
Female aged 12-14 to jr. high school
Male aged 12-14 to jr. high school
Female aged 15-17 to high school
Male aged 15-17 to high school
Note: * The data only have such information from 2011.

Obs.
25,104
25,104
25,104
25,104
25,104
24,732

Mean
2013.5902.46
0.813
0.725
0.123
769.36

Std. dev.
4.609864
633.2282
0.634
0.446
0.329
169.820

Min
2006
69
0.00
0
0
300

Max
2021
8378
1.99
1
1
2000

25,104
25,104
25,104
25,104
25,104
25,104
25,104

0.439
0.213
0.320
0.143
0.011
0.348
0.034

0.217
0.254
0.223
0.075
0.088
0.298
0.062

0
0
0
0
0
0
0

1
1
0.99
0.71
1
1
0.56

15,69015,69015,69015,69015,69015,69014,121
14,121

0.508
0.575
0.009
0.012
0.025
0.063
0.076
0.087

0.274
0.253
0.042
0.048
0.054
0.084
0.101
0.114

0
0
0
0
0
0
0
0

1
1
0.79
0.93
0.91
1
0.96
0.95

23,53423,53523,531
23,53323,532
23,533

0.045
0.045
0.072
0.069
0.116
0.112

0.113
0.112
0.138
0.137
0.176
0.174

0
0
0
0
0
0

1
1
1
1
1
1

15,687
15,68815,686
15,682

0.402
0.405
0.242
0.251

0.310
0.314
0.255
0.259

0
0
0
0

1
1
1
1

28

Online Appendix 2: Balance checks between the treated and control groups in 2006
Variables

Control

Treated

(C)

(Tr)

Diff.

H0:
Diff.=0

Mean

Obs.

Mean

Obs.

(C−Tr)

Pr(|T| > |t|)

Population

705

431

861

1138

−156***

0.000

Distance to health center (km)

4.3

431

3.9

1138

0.356

0.146

Zinc/fibro roof

0.392

431

0.328

1138

0.065***

0.000

Zinc/fibro roof and electricity

0.172

431

0.024

1138

−0.007

0.178

Zinc/fibro roof and having TV

0.271

431

0.179

1138

0.091***

0.000

Minimum farm gate price of rice per kilogram

474.13431

442.241138

31.89***

0.000

Using chemical fertilizer in previous year

0.141

431

0.139

1138

0.003

0.447

Who irrigated paddy field

0.045

431

0.027

1138

0.018***

0.000

Effected by environmental pollution

0.008

431

0.002

1138

−0.0008

0.811

Regularly boiling water for drinking

0.475

431

0.316

1138

0.159***

0.000

Female aged 18-24

0.065

431

0.098

1138

−0.033***0.000

Male aged 18-24

0.058

431

0.098

1138

−0.04***

0.000

Female aged 25-35

0.116

431

0.154

1138

−0.039***0.000

Male aged 25-35

0.100

431

0.134

1138

−0.033***0.000

Female aged 36-45

0.177

431

0.209

1138

−0.033***0.007

Male aged 36-45

0.157

431

0.183

1138

−0.026**

0.018

Share of families having houses with

Share of families

Share of illiterate29Online Appendix 3: Repeated Figure 2’s specification using Sun and Abraham (2021)

3.1 Share of families having houses with zinc/fibro roof

3.2 Share of families having houses withzinc/fibro roof & electricity

3.3 Share of families having houses with zinc/fibro roof
& TV

3.4 Minimum farm gate price of rice

3.5 Share of families using chemincal fertilizer inprevious year

3.6 Share of family who irrigated paddy field

Note: The settings on the event are similar to our equation (2).

30Online Appendix 4: Repeated Figure 3’s specification using Sun and Abraham (2021)

4.1 Share of females over 18 years old whose main
occupation as rice farmers

4.2 Share of males over 18 years old whose main
occupation as rice farmers

4.3 Share of females aged 18 and over whose main
occupation as long crop growing

4.4 Share of males aged 18 and over whose main
occupation as long crop growing

4.5 Share of females aged 18 and over with wage
employment

4.6 Share of males aged 18 and over with wage
employment

4.7 Share of females aged 18 and over whose main
occupation as migrating to work in the country

4.8 Share of males aged 18 and over whose main
occupation as migrating to work in the country

Note: The settings on the event are similar to our equation (2).

31Online Appendix 5: Repeated Figure 4’s specification using Sun and Abraham (2021)

5.1 Share of illiterate females aged 18-24

5.2 Share of illiterate males aged 18-24

5.3 Share of illiterate females aged 25-35

5.4 Share of illiterate males aged 25-35

5.5 Share of illiterate females aged 36-45

5.6 Share of illiterate males aged 36-45

Note: The settings on the event are similar to our equation (2).

32Online Appendix 6: Repeated Figure 5’s specification using Sun and Abraham (2021)

6.1 School enrolment rate in junior high school(females aged 12-14)

6.2 School enrolment rate in junior high school(males aged 12-14)

6.3 School enrolment rate in high school (females
aged 15-17)

6.4 School enrolment rate in high school (males aged
15-17)

Note: The settings on the event are similar to our equation (2).

33

Online Appendix 7: Repeated Figure 6’s specification using Sun and Abraham (2021)

7.1 Share of families who are affected by environment
pollution

7.2 Share of families who regularly boil water for
drinking

34

Online Appendix 8: Sensitive analyses on the pre-trend assumption (Figure 2)
8.1 Share of families having house with zinc or fibro roof

Sensitive analyses of pretrend (Rambachan & Roth,Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0008 (50% power).
8.2 Share of families having house with zinc or fibro roof with electricity

Sensitive analyses of pretrend (Rambachan & Roth,Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0011 (50% power).
8.3 Share of families having house with zinc or fibro roof with TV set

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0009 (50% power).

35

Online Appendix 8: (Cont.)
8.4 Minimum farm gate price of rice

Sensitive analyses of pretrend (Rambachan & Roth,Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 1.276 (50% power).
8.5 Share of families using chemical fertilizer in previous year

Sensitive analyses of pretrend (Rambachan & Roth,Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.00025 (50% power).
8.6 Share of families who irrigated paddy fieldSensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.00015 (50% power).

36

Online Appendix 9: Sensitive analyses on the pre-trend assumption (Figure 3)
9.1 Share of females over 18 years old whose main occupation as rice farmers

Sensitive analyses of pretrend (Rambachan & Roth,Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0006 (30% power).
Share of males over 18 years old whose main occupation as rice farmers

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0014 (50% power).
9.2 Share of females aged 18 and over in long crop

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.00019 (50% power).
9.3 Share of males aged 18 and over in long crop

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.00019 (40% power).

37

Online Appendix 9 (Cont.)
9.4 Share of females aged 18 and over with wage employment

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0005 (50% power).
9.5 Share of males aged 18 and over with wage employment

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0005 (50% power).
9.6 Share of females aged 18 and over whose main occupation as migrating to work in the country

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0006 (50% power).
9.7 Share of males aged 18 and over whose main occupation as migrating to work in the country

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0009 (50% power).

38

Online Appendix 10: Sensitive analyses on the pre-trend assumption (Figure 4)
10.1 Share of illiterate females aged 18-24

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0005 (50% power).
10.2 Share of illiterate males aged 18-24

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0004 (50% power).
10.3 Share of illiterate females aged 25-35

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0006 (50% power).
10.4 Share of illiterate males aged 25-35

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0006 (50% power).

39

Online Appendix 10 (Cont.)
10.5 Share of illiterate females aged 36-45

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0006 (50% power).
10.6 Share of illiterate males aged 36-45

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0007 (50% power).

40

Online Appendix 11: Sensitive analyses on the pre-trend assumption (Figure 5)
11.1 School enrolment rate in junior high school (females aged 12-14)

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.005 (50% power).
11.2 School enrolment rate in junior high school (males aged 12-14)

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0045 (50% power).
11.3 School enrolment rate in high school (females aged 15-17)

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.002 (50% power).
11.4 School enrolment rate in high school (males aged 15-17)

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0025 (50% power).

41

Online Appendix 12: Sensitive analyses on the pre-trend assumption (Figure 6)
12.1 Share of families who are affected by environment pollution

Sensitive analyses of pretrend (Rambachan & Roth, Sensitive analyses of pretrend (Roth, 2022)
2023)
Slope pretrend: 0.0003 (50% power).
12.2 Share of families who regularly boil water for drinking

Sensitive analyses of pretrend (Rambachan & Roth,2023)

Sensitive analyses of pretrend (Roth, 2022)
Slope pretrend: 0.0015 (50% power).

42

Online Appendix 13: Repeating estimations in Figure 2 with 4km as the potential influence
scope of the improved rural roads.

13.1 Share of families having houses with zinc/fibro roof
P-value (pretrend test): 0.000
ATT on Treated: 0.0335 (0.0085)***13.2 Share of families having houses withzinc/fibro roof & electricity
P-value (pretrend test): 0.0000
ATT on Treated: 0.0437 (0.0268)

13.3 Share of families having houses with zinc/fibro roof
& TV
P-value (pretrend test): 0.0436.
ATT on Treated: 0.046 (0.012)***13.4 Minimum farm gate price of rice
P-value (pretrend test): 0.000.
ATT on Treated: 10.15 (7.3)

13.5 Share of families using chemical fertilizer inprevious year
P-value (pretrend test): 0.0000
ATT on Treated: 0.005 (0.005)

13.6 Share of families who irrigated paddy field
P-value (pretrend test): 0.000
ATT on Treated: 0.0038 (0.003)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

43

Online Appendix 14: Repeating estimations in Figure 3 with 4km as the potential influence
scope of the improved rural roads.

14.1 Share of females over 18 years old whose main
occupation as rice farmers
P-value (pretrend test): 0.000
ATT on Treated: 0.015 (0.009)

14.2 Share of males over 18 years old whose main
occupation as rice farmers
P-value (pretrend test): 0.0000
ATT on Treated: 0.015 (0.014)

14.3 Share of females aged 18 and over whose main
occupation as long crop growing
P-value (pretrend test): 0.000
ATT on Treated: −0.00008 (0.0007)

14.4 Share of males aged 18 and over whose main
occupation as long crop growing
P-value (pretrend test): 0.000
ATT on Treated: 0.0008 (0.0007)

14.5 Share of females aged 18 and over with wage
employment
P-value (pretrend test): 0.000
ATT on Treated: 0.00065 (0.0025)

14.6 Share of males aged 18 and over with wage
employment
P-value (pretrend test): 0.000
ATT on Treated: −0.003 (0.002)

14.7 Share of females aged 18 and over whose main 14.8 Share of males aged 18 and over whose main
occupation as migrating to work in the country
occupation as migrating to work in the country
P-value (pretrend test): 0.000
P-value (pretrend test): 0.000
ATT on Treated: 0.0025 (0.0035)
ATT on Treated: 0.006 (0.0066)
Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

44Online Appendix 15: Repeating estimations in Figure 4 with 4km as the potential influence
scope of the improved rural roads.

15.1 Share of illiterate females aged 18-24
P-value (pretrend test): 0.000
ATT on Treated: −0.0087 (0.0055)

15.2 Share of illiterate males aged 18-24
P-value (pretrend test): 0.0000
ATT on Treated: −0.0049 (0.0054)

15.3 Share of illiterate females aged 25-35
P-value (pretrend test): 1.000
ATT on Treated: −0.0075 (0.0074)

15.4 Share of illiterate males aged 25-35
P-value (pretrend test): 0.000
ATT on Treated: −0.013 (0.009)

15.5 Share of illiterate females aged 36-45
P-value (pretrend test): 1.000
ATT on Treated: −0.009 (0.0067)

15.6 Share of illiterate males aged 36-45
P-value (pretrend test): 0.000
ATT on Treated: −0.014 (0.007)*

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from 0.

45Online Appendix 16: Repeating estimations in Figure 5 with 4km as the potential influence
scope of the improved rural roads.

16.1 School enrolment rate in junior high school(females aged 12-14)
P-value (pretrend test): 0.0000
ATT on Treated: −0.039 (0.029)

16.2 School enrolment rate in junior high school(males aged 12-14)
P-value (pretrend test): 0.000
ATT on Treated: 0.045 (0.032)

16.3 School enrolment rate in high school (females
aged 15-17)
P-value (pretrend test): 0.0000
ATT on Treated: −0.003 (0.022)

16.4 School enrolment rate in high school (males
aged 15-17)
P-value (pretrend test): 0.0000
ATT on Treated: 0.019 (0.019)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

46

Online Appendix 17: Repeating estimations in Figure 6 with 4km as the potential influence
scope of the improved rural roads.

17.1 Share of families who are affected byenvironment pollution
P-value (pretrend test): 0.000
ATT on Treated: 0.019 (0.025)

7.2 Share of families who regularly boil water for
drinking
P-value (pretrend test): 0.000
ATT on Treated: 0.031 (0.03)

Notes: Due to the default settings on the event of software package for Callaway and Sant’Anna (2021), the baseyear is not shown in the graph. The intervention starts from j=0.

47