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

The Gendered Impact of Rural Road Improvement on Schooling Decisions
and Youth Employment in Morocco
Yasuharu Shimamura、Satoshi Shimizutani、Eiji Yamada、
Hiroyuki Yamada
4 January, 2022
DP2022-001
https://ies.keio.ac.jp/en/publications/15633/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
4 January, 2022

The Gendered Impact of Rural Road Improvement on Schooling Decisions and Youth
Employment in Morocco
Yasuharu Shimamura、Satoshi Shimizutani、Eiji Yamada、Hiroyuki Yamada
Keio-IES DP 2022-001
4 January, 2022
JEL Classification: I25; O18; J24
Keywords: rural road improvement;schooling outcomes;youth employment;market
integration;Morocco

Abstract
This paper examines the impact of a rural road improvement project on schoolingdecisions and youth employment in Morocco. Paved rural roads are expected to reduce
travel time and costs, allowing additional school choices and increasing the motivation
for youth to enter higher education in response to higher returns. On the other hand,
immediate earnings opportunities created by new connections may encourage youth toseek paid employment. Thus, the impact of rural road improvement on schooling andyouth employment warrants empirical investigation. We employ a difference-indifferences estimation using a household-level dataset with a five-year interval collectedunder a quasi-experimental setting. First, we do not observe any positive effect on
primary school completion for either sex, but we find a positive and significant effect on
secondary school attainment or above only for females. Moreover, the higher
educational attainment of females is associated with a lower proportion of earlymarriage. Second, we do not observe any significant effects on self-employment foreither sex, but we find a positive and significant effect on wage employment only formales, which is pronounced among the better educated. Our findings reveal sharp
gendered differences in the impact of the rural road improvement project, withincreased motivation toward better education for females and paid work for males.

Yasuharu Shimamura
Graduate School of International Cooperation Studies, Kobe University
2-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo
yshima@harbor.kobe-u.ac.jp

Satoshi Shimizutani
JICA Ogata Sadako Research Institute for Peace and Development
10-5 Ichigaya Honmuracho, Shinjuku-ku, Tokyo
Shimizutani.Satoshi@jica.go.jp

Eiji Yamada
JICA Ogata Sadako Research Institute for Peace and Development
10-5 Ichigaya Honmuracho, Shinjuku-ku, Tokyo
Yamada.Eiji@jica.go.jp
Hiroyuki Yamada
Faculty of Economics, Keio University
2-15-45 Mita, Minato-ku, Tokyohyamada@econ.keio.ac.jp

The Gendered Impact of Rural Road Improvement on
Schooling Decisions and Youth Employment in Morocco

By
Yasuharu Shimamura*, Satoshi Shimizutani**, Eiji Yamada***and Hiroyuki Yamada****January 2022

* Professor, Graduate School of International Cooperation Studies, Kobe University; 21 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan, Tel: +81-78-803-7158, E-mail:yshima@harbor.kobe-u.ac.jp.
** (Corresponding author) Executive Senior Research Fellow, JICA Ogata Sadako
Research Institute for Peace and Development; 10-5 Ichigaya Honmuracho, Shinjukuku, Tokyo 162-8433, Japan, Tel: +81-(0)3-3269-2911, FAX: +81-(0)3-3269-2054 Email: Shimizutani.Satoshi@jica.go.jp.
*** Research Fellow, JICA Ogata Sadako Research Institute for Peace andDevelopment; 10-5 Ichigaya Honmuracho, Shinjuku-ku, Tokyo 162-8433, Japan, Email: Yamada.Eiji@jica.go.jp.
**** Professor, Faculty of Economics, Keio University; 2-15-45 Mita, Minato-ku,
Tokyo 108-8345 Japan, Tel (Direct): +81-(0)3-5427-1271 E-mail:hyamada@econ.keio.ac.jp.

1

Acknowledgments
This study was conducted as part of the project “Empirical Research in Africa” at the
JICA Ogata Sadako Research Institute for Peace and Development. The data used in thisstudy was collected by the Evaluation Department of JICA. We thank the Evaluation
Department and JICA Ogata Institute for allowing us to use the data for this study. We
also appreciate the cooperation from JICA Morocco Office in assisting with datacollection, especially from Ryoma Harasawa, Khadija Kili, and Ayman Hafid as well as
Etsuko Masuko and Shimpei Taguchi for their constructive comments on the draft. The
views expressed in the paper are those of the authors and do not represent the official
positions of either the JICA Ogata Sadako Research Institute for Peace andDevelopment or JICA. The authors are responsible for any errors or omissions.

2

Abstract
This paper examines the impact of a rural road improvement project on schoolingdecisions and youth employment in Morocco. Paved rural roads are expected to reduce
travel time and costs, allowing additional school choices and increasing the motivation
for youth to enter higher education in response to higher returns. On the other hand,
immediate earnings opportunities created by new connections may encourage youth toseek paid employment. Thus, the impact of rural road improvement on schooling andyouth employment warrants empirical investigation. We employ a difference-indifferences estimation using a household-level dataset with a five-year interval collectedunder a quasi-experimental setting. First, we do not observe any positive effect on primaryschool completion for either sex, but we find a positive and significant effect on secondary
school attainment or above only for females. Moreover, the higher educational attainmentof females is associated with a lower proportion of early marriage. Second, we do not
observe any significant effects on self-employment for either sex, but we find a positiveand significant effect on wage employment only for males, which is pronounced among
the better educated. Our findings reveal sharp gendered differences in the impact of therural road improvement project, with increased motivation toward better education forfemales and paid work for males.
Keywords: rural road improvement, schooling outcomes, youth employment, market
integration, Morocco, gender.
JEL Classification Codes: I25, O18, J24.

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1． Introduction
Access to transportation infrastructure is essential for daily life in both developed
and developing countries, serving as a driving force for economic development.
However, for many people, access to transportation infrastructure remains very
disappointing, particularly in developing countries. Rural Access Index (RAI) rankings
indicate that transport access continues to pose serious policy challenges for poorer
countries.1 Access to transportation infrastructure in rural areas is particularly impairedin the African continent, where the majority of smallholder farmers suffer from limited
connections with outside markets, and many poorer people endure limited access tosocial services.
This study examines the impact of a rural road improvement project on schooling
outcomes and youth employment in Morocco. Despite substantive efforts being madeover the past several decades, there have been calls for the upgrading of rural roads in
Morocco. Limited access to road infrastructure impedes economic opportunities andsocial services provision in rural areas. Moreover, the country suffers from lower
educational enrollment, particularly in post-primary education for girls in rural areas.
This can be partly attributed to poor road infrastructure (JICA, 2011). Under thesecircumstances, rural road improvement is expected to favorably affect both schoolingand work decisions among youth.
However, the impact of rural roads improvement on schooling outcomes andyouth employment is a priori indeterminate and calls for empirical investigation(Adukia et al., 2020). The improvement of road transportation is expected to reduce

travel time and costs as well as expand the availability of transport options. As a result,1

https://rai.azavea.com/
4access to improved roads may increase students’ school choices, which is critical for
decisions to advance beyond primary school education. Moreover, people living in rural
areas may gain access to outside markets and enjoy new employment opportunities.
New connections to markets may encourage educational attainment if they increasefinancial returns from education. If this is the case, households will be motivated toinvest more in human capital to seek higher educational attainment.
On the other hand, integration with outside markets through new connections may
activate local economic development and create new jobs in rural areas. Renewed ruralroads may create new wage employment opportunities by transforming agricultural
production and creating small businesses. Further, renewed rural roads may create new
employment opportunities through access to outside markets. Hence, the transformationof regional economic activities may lead to new immediate employment opportunities
in rural areas. As opportunity costs for schooling are higher than before, youth may
choose to take an earlier exit from school to obtain a paid job. If this is the case, road
improvements may crowd out educational investment in rural areas.
In comparison to the large volume of literature on new connections tointernational markets, the impacts of domestic market integration have been studiedless.2 Among the limited available literature, some studies have found that better
accessibility has a positive effect on education outcomes in terms of increasing school
attendance, greater school choices and school completion rates in Morocco (Levy et al.,1996), Vietnam (Mu & van de Walle, 2007), Nigeria (Porter (1997)) and India(Mukherjee, 2012). Moreover, Khandker et al. (2009) and Khandker and Koolwal

2

For example, schooling increase and child labor reduction were mitigated in areas with employment
concentrated in industries losing tariff protection in India (Edmonds et al., 2010). Districts with lower
costs of learning English benefited more from globalization in India (Shastry, 2012).
5

(2011) showed that small-scale road construction is associated with a higher school
enrollment in Bangladesh. On the other hand, other studies found no significant effect inGhana (Hine et al., 1983)) and in Vietnam (Cuong, 2011).3
Recently, Aggarwal (2018) used district-level data in India to show that ruralroads enhance school enrollment for younger children, while teenagers are motivated todrop out of school and join the labor force as urban markets become accessible. Adukia
et al. (2020) found children are more likely to stay in school longer and perform betteron standardized exams in India. They argue that a standard human capital investment
model supports their finding: school enrolment increases most when nearby labormarkets offer high returns to education.
A central concern for empirical analysis is the crowding out of higher education:
the trade-off for individuals between long-run investment in human capital and
immediate economic opportunities. In order to interrogate this concern, this study
utilizes a unique data set collected under a quasi-experimental setting during the 2010sin Morocco. The data consists of a panel sample of households that gained access to allweather roads through a road improvement project and households that remained
inaccessible to paved roads. We employ a difference-in-differences estimation to
explore the impact of rural road improvement on schooling outcomes and youth
employment during the period.
We attempt to contribute to the literature in three ways. First, we provide
evidence on the impact of rural road improvement in a country where gender disparities
in educational and economic opportunities are large. In Morocco, females face greater
restrictions on opportunities to attain higher education or employment than males. Thus,3

See Hine et al. (2016) for a systematic survey on the impact of rural road extension.
6

we focus on the difference in the impact of rural road improvement between young
females and males, which may shed light on the gendered impacts of infrastructure on
beneficiaries. Second, we examine the impact of community road improvement in rural
areas, an area that has been largely neglected in the literature, in contrast to manystudies on the impact of highways and their feeder roads. This study focuses on
residential roads dispersed over rural areas, including the coastal and mountainous areasthat are relevant to decisions on schooling and employment for youth. Third, we utilize
rich household-level data to provide new evidence on the impact of rural road
improvement, allowing us to identify the impact after controlling for heterogeneity ofhouseholds. Our dataset contains rich information on the characteristics of individuals,
households, and villages, and the negligible attrition rate enhances the precision of ourestimates.
Our empirical results show that road improvement has a positive and significant
impact on youth, which differs between males and females. For young females, accessto paved roads encouraged them to advance to secondary school or above. Higher
educational attainment is also associated with a decline in early marriage, suggestingthat improved road access has eased commuting to secondary schools or above and led
young females to postpone marriage. In contrast, for young males, rural road
improvement did not motivate them to pursue better education. Instead, young males
exploited new employment opportunities to enjoy a significantly higher chance offinding wage employment and the impact is larger for better-educated males. In
summary, we show the heterogeneous impact of road access improvement acrossgender, reflecting the difference in return on education between females and males.
This article is comprised of the following sections. The next section describes the7target project and the research design. Section 3 explains the data set and Section 4
describes our empirical strategy. Section 5 shows the estimation results and Section 6
provides those using alternative analyses. We discuss the findings in Section 7. The
final section summarizes our main findings and offers some considerations for future
research.

2． The project and research design
With a population of 37 million in 2020, Morocco is located in the west of the
Maghreb region of North Africa that looks toward both the Mediterranean Sea and theAtlantic Ocean. The transportation sector occupies a substantial portion of economic
activities in the country, accounting for 6% of its total GDP, creating 10% of urban
employment and contributing to 15% of national tax revenue (JICA, 2011). However,until recently, there was a substantial gap in transportation activities between urban andrural areas. At the beginning of the 2010s, more than 80% of major roads in the country,
such as highways and national/state roads, were paved, and road transportation
conveyed 90% of intercity land passengers and 75% of inter-city land freight (JICA,
2019). In contrast, the development of rural road infrastructure was slow: only 62% ofprefectural roads were paved in 2009, and only 54% of the rural population enjoyedaccess to road infrastructure (JICA 2011).4
The large economic disparity between urban and rural areas has been a major
challenge for Morocco, where the latest rural poverty headcount ratio is 36.8% against

4

At the beginning of the 2010s, the public roads in Morocco consisted of highways (1.6%), nationalroads (18.3%), state roads (17.6%) and prefectural roads (62.5%). In addition to public roads, “notclassified roads” concentrated in rural areas play an important role in rural transportation (JICA, 2011).
Accessibility to rural roads is defined as the proportion of the rural population who live in “douars”(villages) of at least 50 households located within one kilometer from an all-weather road.
86.4% for urban areas in 2019 (Observatoire National du Développement Humain,2021). For decades, the government has placed a top priority on reducing disparities
between urban and rural areas, a goal repeatedly stated in the country’s five-year
economic development plans. The government acknowledged that the limited access toroad infrastructure was an impediment to economic opportunities and social services
provision in rural areas and that rural economic activities can be activated through ruralroad improvement and better access to transportation services for remote populations(JICA, 2011).
Under these circumstances, the government launched the National Rural Roads
Plan (Plan National des Routes Rurales: PNRR) I in 1995 and improved rural roads by
11,200 kilometers to increase rural road accessibility to 50% in 2005. Subsequently, thegovernment began to implement the National Rural Roads Plan II in 2005, aiming to
improve 15,500 kilometers of rural roads to ensure that 80% of the rural population had
access to the paved road network by 2012.
In addition to economic indicators, urban-rural inequality is predominant amongsocial indicators and prominently observed in school attainment. In Morocco,compulsory education starts at the official age of 6 and covers both primary (age 6 to11) and junior secondary education (age 12 to 14). This is followed by senior secondary(age 15 to 17) and tertiary education (age 18 and over). The enrolment rate for primaryeducation has gradually increased from 93.2% in 2011 to 99.5% in 2019.5 The
enrolment rate for secondary education also increased from 53.8% in 2011 to 66.2% in
2019.

5

The data is available from UNESCO Institute of Statistics (http://uis.unesco.org/country/MA).
During the same period, the gross enrolment ratio increased from 110.7 in 2011 to 114.76 in 2019 witha higher ratio for boys. The gross enrolment ratio for secondary education was 66.5% in 2011, which
increased to 81.2% in 2019 and the rate was consistently higher for boys.
9However, there is a large discrepancy in school enrolment by region. Among
primary-school-age children, 4% of rural children were not enrolled, compared to 1% inurban areas, and the exclusion rate in rural children was higher for girls (UNICEF,
2014). Among children in the junior-secondary-school age, 16% were not in primary or
secondary school, and the exclusion rate exceeded 30% for rural children. Moreover,
11% of rural children in this age group were working, contrasting to only 1% forchildren living in urban areas.
Furthermore, the educational disparity by gender among rural youth is large,
especially in secondary education. Girls comprise 78% of children excluded from juniorsecondary education in rural areas. According to the High Planning Commission (HautCommisariat au Plan), in 2016, 14.8% of rural women aged 15 – 24 years old were
illiterate against 7.2% for the rural men in the same age range. Furthermore, theproportion of NEET (Not in Education, Employment, or Training) among rural womenof 15 – 24 years old was 44%, almost four times that of men. Young rural women tend
to marry earlier; the average age of first marriage is 25.5 years old for women and 29.5
years old for men (LANDINFO 2017).
Given this harsh reality, the Japan International Cooperation Agency (JICA) has
supported rural road improvement initiatives in the National Rural Roads Plan II
through two concessional loans (“Rural Road Improvement Project” I and II), expecting
to improve the living standards of the rural population and stimulate the rural economy
by ensuring access to road infrastructure.6 The Rural Road Improvement Project II, thetarget of this study, financed the improvement of rural roads (530km in total) in 5

6

The Rural Road Improvement Project I financed the construction of 67 sections in 9 provinces witha total length of approximately 835km between 2008 and 2015. The program of the National Rural
Roads Plan II was financed by the World Bank, African Development Bank, European Investment
Bank, French Development Agency, and JICA.
10

provinces (Al Hauze, Chefchaouen, Essaouira, Safi, and Settat). The project paves 30
sections of rural unpaved roads (on average 17.7km per section) and widens the roads toallow two-way traffic so that more than 163,000 people in rural areas can gain access toan all-weather road (JICA 2011). The construction work commenced at different times
depending on the section. The first work started in 2012 and the last in 2014. The
construction period also varies, and the final construction work related to this study wascompleted in June 2016. The population started to use each road once construction work
was completed.

3． Data description
In the Rural Road Improvement Project II, 30 of the target (treatment) roads to beconstructed or rehabilitated were selected as follows. First, the provincial offices of“Direction des Routes” (Roads Directorate within the Ministry of Equipment,
Transport, Logistics and Water) in each of five provinces prepared a priority list of
potential roads to be constructed. The selection criteria included the size of the
population covered by each road as well as the number of markets, schools, and healthfacilities along roads and connectivity to existing road networks (JICA, 2019). The list
was scrutinized by communal and provincial governments and the Ministry assemblies
to accommodate local needs and preferences. The participation of local authorities was
crucial to ensure the sustainability of the roads since communes are responsible for the
maintenance of roads after construction.
The target (treatment) roads for data collection were drawn from this list by JICA,
considering the preparedness of the construction plan, coverage of other donors, and thenecessity of land acquisition and/or resettlements. Seventeen treatment roads were11

identified for the survey. Then, comparison (control) roads that were not improved wereselected. The “Direction des Routes” in each province listed several roads that weremost similar to the target roads based on the same criteria considered in the selection
process of the target roads. Among these listed roads, the candidates for comparison
roads were further identified by the evaluation team. Finally, 18control roads wereidentified.
The baseline survey was conducted from September to November 2011 before thefirst construction work started. The end-line survey was implemented from March toJune 2017, 2 – 5 years after the completion of construction.7 The primary sampling unit
(PSU) was “douar,” a cluster of houses (small villages) in a rural area. Hereafter, we use“village” for “douar.” The catchment area of each road, which is defined as an area
within a one-kilometer distance on both sides from each road, is used in the sampling ofvillages, following the national definition of rural road accessibility. A village isregarded as located within a road’s catchment area if at least a part of a village is within
one kilometer from the road. For each road, five located villages were randomly
selected in the catchment area and all villages were sampled if the number of villages in
the catchment area was less than five. In each village, 10 households were randomly
selected.
Both household and village surveys were conducted at the baseline and end-line
surveys. The household survey collected a variety of socio-economic variables ofhouseholds and individuals, including demographic characteristics, access to facilities,
economic activities, education, health, household assets, and consumption. The village
survey collected information on basic village characteristics, availability of transport
7

It was impossible to conduct the end-line survey between September and November, as done in thebaseline survey, for logistical reasons.
12services and infrastructure. At the end-line survey, the same households and villages
were surveyed again to construct panel data.
For both baseline and end-line surveys, the number of villages in the treatment
road areas was 76 from 27 communes, with 70 from 21 communes in the control roadareas. The number of households in the treatment group was 760 in the baseline survey,
which reduced to 748 at the end-line survey. The number of households in the control
group was 694 in the baseline survey, which reduced to 669 in the end-line survey. The
attrition rate is surprisingly negligible given the five-year interval, 1.6% for thetreatment group and 3.6% for the control group. The number of individuals in thetreatment group was 4,710 in the baseline survey, which increased to 4,974 in the endline survey and the number of individuals in the control group was 4,271 in the baseline
survey, which increased to 4,404 at the end-line survey. The sample of individuals
includes those who migrated out from original villages but were recovered by the phonesurvey.
We can define the treatment and control group more precisely using the actual
distance from the road based on the coordinates (longitude and latitude) of each sample
household. Since some households in the sample were accessible from both treatment
and control roads and we are interested in the impact of treatment roads, we regroupedhouseholds into treatment and control groups using two definitions. First, we define thehouseholds as belonging to the treatment group if a household is located within 2
kilometers from a treatment road and the households in the control group if a household
is located within 2 kilometers from a control road and (at least 2 kilometers) away froma treatment road. Second, we define the households as being in the treatment group if ahousehold is located within 5 kilometers from a treatment road and the households in13

the control group if the household is located within 5 kilometers from a control road and(at least 5 kilometers) away from a treatment road. The thresholds are arbitrary but wesucceed in splitting the households into treatment and control groups through thebalance test (see Table 1 below). We also confirm that the main results do not dependon the choice of the threshold. We perform the analyses based on these two definitions
below.
Table 1 provides a balance test of the outcome variables at the baseline before theintervention of the project to compare the variables used in the estimation between thetreatment and control groups. We employ different age groups for different outcomes
since the age group that is supposed to be most affected depends on outcomes. First, we
see the balance of the schooling outcomes at the time of the baseline survey. School
attendance for girls aged 7 to 18 is 59% for the treatment group and 54% for the control
group when setting the threshold as 2 kilometers and that for boys is 69% for thetreatment and 70% for the control groups. The differences of those variables between
treatment and control groups are not statistically significant. The figures are comparable
when we take the other threshold as 5 kilometers and we do not see any statisticaldifference between treatment and control groups.
Further, we take two forms of educational attainment outcomes. The proportionof primary school completion for girls aged 13 to 18 is 41% for both the treatment andcontrol groups, and that for boys aged 13 to 18 is 61% for the treatment group and 66%
for the control group when the threshold is 2 kilometers. The proportion of having everattained secondary school or above for females aged 13 to 25 is 14% for the treatmentgroup and 10% for the control group, while that for young males aged 13 to 25 is 36%
for the treatment group and 31% for the control group. The difference of those variables14

is not statistically significant between treatment and control groups, which is also thecase when the threshold is 5 kilometers.
Turning to labor market outcomes, the proportion of the self-employed for young
females aged 13 to 25 is 2% for the treatment group and 1% for the control group whilethat for males in the same age range is 42% for the treatment group and 37% for thecontrol group when the threshold is 2 kilometers. The proportion of the wage
employment for females aged 13 to 25 is less than 1% for both groups, while that foryoung males in the same age group is 33% for the treatment group and 43% for thecontrol group. In both cases, the proportion of young females with work is very small
across the country, which is particularly the case for work outside the home. The
proportion of the wage employment of males is statistically different when the thresholdis 5 kilometers, but the proportion is higher for the control group than the treatmentgroup.
Lastly, the proportion of being married for young females aged 14 to 25 is 40%
for the treatment group and 38% for the control group while that for males in the sameage range is 8% for the treatment group and 11% for the control group when thethreshold is 2 kilometers.8 The difference of those variables is not statisticallysignificant between treatment and control groups, which is also the case when thethreshold is 5 kilometers.
Moreover, we performed a balance test for underlying characteristics of thesampled households and villages at the baseline survey (Appendix 1 and 2). The
average age among the youth is statistically different between the treatment and control

8

While the minimum marital age is 18 for both sexes, child marriage is prevalent in the country
corresponding to 11% of the total number of marriages in 2013 which is considered to beunderreported (LANDINFO 2017).
15

group, but the difference in the magnitude is marginal (less than one year difference).
While the proportion of male head/spouse and female spouse is slightly different
between the treatment and control groups, the average ages of the male head/spouse andfemale spouse are not statistically different between the treatment and control groups
regardless of the threshold. Education attainment of the female spouse does not differ,
whereas that of the male head/spouse differs in some categories, but we control thesedifferences in the regression analyses below. We do not see any significant difference inhousehold characteristics (household size, the ratio of dependents to household size,
holding of agricultural lands, agricultural land, assets, consumption, and Palmer
Drought Severity Index (PDSI)9 or village characteristics (population, average
agricultural land per household, and so on) except marginally statistical differences in
population in the case of the threshold of 2 kilometers.
In sum, we confirm that there was no statistically significant difference inoutcome variables except the wage employment of males in the case of the threshold of
5 kilometers between the treatment and control groups. Regarding the covariates,
household and village characteristics at the baseline are reasonably balanced, whilesome individual characteristics differ between the treatment and control groups that wecontrol in the regression analyses below.

4． Empirical strategy

9Palmer Drought Severity Index (PDSI) shows the level of drought of a location based on the data of
precipitation, temperature, and water balance information. This variable at the household level is
included in the balance test to capture the climate impact for their agricultural production. We use the
1/24 degree gridded (approximately 4km X 4km) raster image of monthly PDSI retrieved fromhttps://www.climatologylab.org/terraclimate.html. We construct the household level PDSI index byassigning each household the value of PDSI of the cell it is located (12-month average before thesurvey month).
16

This study employs a difference-in-differences (DID) approach to estimate theimpact of the project. The central assumption for the DID methodology to be valid is the“parallel trend.” We assume that any change between baseline and end-line surveys
without the intervention caused by unobserved characteristics is common between thetreatment and control groups. We confirmed the observed characteristics were notbiased at the baseline survey, showing that people in both groups lived in similar
circumstances before the project started.
We examine the impact of the rural road improvement project on the outcome
variables by using pooled panel data of the individuals belonging to a specific age group(corresponding to each outcome variable) at each round of the survey. The basic
specification is as follows:𝑌= 𝛽 + 𝛽 ∙ 𝑡 + 𝛽 ∙ 𝑆 + 𝛽 ∙ (𝑆 ∗ 𝑡) + 𝜖(1)where: i refers to an individual, j points to a village, and t is time (t = 0 for baseline andt = 1 for end-line). Yijt is the dependent variable and takes several forms. The first three
are related to schooling outcomes and the latter two are employment outcomes. All ofthem are binary. For educational outcomes, Yijt is a binary variable (a) to take 1 if ayouth attends school and 0 otherwise, (b) to take 1 if a youth completed primaryeducation and 0 otherwise, and (c) to take 1 if a youth has ever attained secondary or
above education and 0 otherwise. For labor outcomes, Yijt is a binary variable to take thevalue of 1 if a youth is self-employed and 0 otherwise, and to take the value of 1 if ayouth is employed with wages and 0 otherwise. We also take marital status to take 1 if
married and 0 otherwise as the dependent variable.
Turning to the right-hand side variables, Sj is a binary variable that takes the value17

1 for the treatment group and 0 for the control group. 𝛽 to 𝛽 are the parameters tobe estimated. 𝛽 is the parameter of our interest and measures the impact of the projecton the outcomes. 𝜖

is an iid error term. We employ an ordinary least squared (OLS)

estimation to obtain the coefficients. Since the dependent variables are binary, our
specification is a linear probability model (LPM).
The parallel trend assumption in the DID methodology may be violated ifchanges in covariates are not common between the treatment and control groups. Thus,
we also employ an empirical model with some covariates. The covariates take four
forms: 𝑋

comprises age cohort dummies (individual characteristics) and 𝑋

is avector to include the characteristics of male heads/spouses and female spouses as well
as a set of household characteristics such as household size, ratio of dependents tohousehold size and holding of agricultural land. PDSI (12-month average before thesurvey month) is also included to capture labor environmental conditions for labormarket outcomes. Moreover, we include 𝑋 , a vector containing a set of time variant
village j’s characteristics. Lastly, 𝜇 is a dummy variable to indicate village-level fixed
effect to capture village 𝑗’s time invariant characteristics. By adding those covariates,
we write another version of our empirical model:𝑌=𝛽 +𝛽 ∙𝑡+𝛽 ∙𝑆 +𝛽 ∙ 𝑆 ∗𝑡 +𝑋

𝛾 +𝑋

𝛾 +𝑋 𝛾 +𝜇 +𝜖(2)where the notations are the same as in (1) except that 𝛾 to 𝛾 are vectors of theparameters to be estimated.
All the regression models in the following section control for the covariates at the18

individual, household, and village level as well as with village-level fixed effects. All
standard errors are clustered at the village level.

5． Empirical results
Table 2 reports the estimation results on primary and secondary school attendancefor youth aged 7 to 18. The parameter of interest is the coefficient on the interaction
term between the treatment group dummy and the year 2017 (end-line) dummy.
Columns (A) and (B) report the results for girls. The coefficient on the interaction term
is positive but not significant in Column (A), but it is positive and significant in Column(B), suggesting that the rural road development project enhances school attendance
significantly for girls. The coefficient on the interaction terms is positive but notsignificant for boys, as reported in Columns (C) and (D). These results indicate thatroad improvement might encourage school attendance for girls but not for boys in theage group.
Table 3 shows the estimation results of primary school completion for youth aged
13 to 25. The coefficient on the interaction term of the interest is not significant for girls
regardless of the threshold of the treatment group (2 or 5 kilometers). This is also thecase for boys. The coefficient on the interaction term is not significant for boys either.
These results reveal that road improvement did not stimulate primary school completionfor either girls or boys. Those results are natural because most primary schools thatstudents attend are located near each household and most students do not use roadtransportation to commute to their schools. We note that the coefficient of the year
dummy for girls is largely positive and statistically significant at the one percent level,19

reflecting a series of government policy measures to promote girls’ education.10
Table 4 shows the estimation results of having ever attained secondary school
education or above for youth aged 13 to 25. The coefficient on the interaction term ofinterest is positive and significant for girls. The coefficient is 0.104 in Column (A),showing that the rural road development project increased the probability of having everattained secondary education or above by 10%. The positive coefficient is comparable
at 0.079 (7.9%) if we take a threshold of 5 kilometers to reach treatment roads (Column(B)). In contrast, the corresponding coefficient for boys is positive but much smaller andimprecisely estimated, indicating the rural road improvement did not increase theprobability of ever attaining secondary education or above of males. The coefficient of
the year dummy for girls is positive and significant, for the same reason ascribed toTable 3.
We find that the rural road improvement encouraged youth to pursue substantiallybetter educational attainment by around 10% only for girls but not for boys, (given thatmales’ education attainment was much higher than females at the baseline survey. The
clear gender gap in the impact of road improvement on completion of higher schools
may reflect the lower completion rate of higher schools for young females; theproportion of secondary school or above attainment for girls is about 10 – 14% at thebaseline. While most primary schools are located within walking distance of theirhouses, and road improvement is less relevant to primary school attendance, most
students need road transportation to commute to secondary schools or above. Thus,

10

In 2006, the government enacted the “National Strategy for Gender Equality,” providing a practical
approach to the reduction of gender disparities in all sectors and seeking to promote girls’ education
and women’s rights to counter the multiple inequalities they experience. Subsequent to this strategy, a
new program, the “National Strategy for Equity and Equality,” was adopted for the period 2012 –2016. It includes equal access to education as one of eight pivotal areas (Boutieri, 2016).
20

rural road improvement reduces the travel time and costs of commuting to school andprovides a wider range of options for transportation, which contributes to the higher rateof completion in higher education.
In contrast, we do not see any positive impact on schooling outcomes for young
males, partly because the completion rate of higher education was higher than that ofgirls, and there was less room for them to increase. In the aspect of school attainment,rural road improvement did not benefit young females and males evenly, and it might betempting to conclude that the road project did not alter the behavior of young males.
However, the little impact on schooling does not mean that paved roads did not affectyoung males.
Now, we turn to labor market outcomes for youth. Table 5 reports the estimation
results of the impact on self-employment. For young females, the coefficient on theinteraction term between the treatment group dummy and the end-line year dummy isinsignificant. Similarly, for young males, the coefficient is not significant. These results
indicate that rural road improvement did not stimulate self-employed work in rural areasfor either females or males.
Table 6 shows the estimation results of the impact on wage employment. The
upper part finds that the coefficient on the interaction term is small and insignificant foryoung females. In contrast, the coefficient is positive and statistically significant foryoung males. Column (E) shows that rural road improvement increased wage
employment by 11.7%. The point estimate is similar at 11.3% in Column (D). These
results demonstrate that rural road improvement increased paid jobs for young males,
but the positive impact is not found for young females.

216． Further analyses
We turn to further analyses.11 First, we considered the distance from eachhousehold to a paved (treatment) road since the impact of access to paved roads may
depend on the location of each household. We utilized geographical data on the locationof households and paved roads to measure the distance and included an interaction termamong the treatment group dummy, the end-line survey dummy and distance to roadfrom the household in the treatment group. We confirmed that our estimation results are
not largely altered and that the impact is less affected by the distance to the paved roadsin the treatment group.
Second, we incorporated the duration in service of each treatment road since thetime of completing construction differs between roads (one to three years). We included
interaction terms among the treatment group dummy, the end-line survey dummy, andeach of the three-year dummies of completion of a treatment road (year=2014, 2015, or2016). For female educational attainment for secondary high school or above, themagnitude of the coefficients of the three interaction terms is comparable, and only theinteraction term including the year 2015 dummy is statistically significant. For malewage employment, all the interaction terms are positive, with the year 2014 dummy thelargest in magnitude and statistically significant, implying that earlier completion of theroad benefits potential wage earners more in later periods.
Third, we adjusted the threshold to distinguish between the treatment and control
groups other than two and five kilometers (i.e., 3 kilometers and 4 kilometers) andconfirmed that the main results are unchanged.
Finally, we estimated the main specifications of education and employment by

11

All the results in this section are available upon request from the authors.
22

applying the seemingly unrelated regressions (SUR) because the decisions onundertaking education or engaging in work are likely to be jointly determined. We
confirm that the main results are robust to this joint estimation.

7． Discussion
Our findings uncover a sharp gendered impact resulting from the rural road
improvement project, motivating higher education for females and paid work for males.
Adukia et al. (2020) find that road construction did not have positive effects on primaryschool children but significantly increased middle school enrollment in India for both
girls and boys.12 While the insignificant effect of road improvement on primary school
children with fewer labor market opportunities is common in the literature, the effect on
secondary schooling among young males is noteworthy because we do not find any
positive impact from the improved rural road on males having ever attained somesecondary education or above.
In the case of rural Morocco, young males are more likely to get a paid job easily,
thanks to the improvement of the road. However, this does not necessarily mean a tradeoff between a higher level of schooling and immediate employment opportunities. To
investigate this aspect, we estimate the main specification on the wage employment
regression focusing on the young male sample age 16 to 25, splitting them into theirlevels of educational attainment (primary or some secondary or above). The lower partof Table 6 shows that the coefficient on the interaction term of interest is notstatistically significant for male youth with primary education but only positive and

12Adukia et al. (2020) also found that school performance improved, but we do not examine this due
to a lack of performance data.
23

significant for males with some secondary school or above if the threshold is 5
kilometers. Even in the case of the threshold of 2 kilometers, the coefficient is positive
and the magnitude is similar to the threshold of 5 kilometers. Hence, the improved ruralroads encourage relatively more educated males to find wage employment
opportunities.13 We confirmed that the most popular occupation types that young males
are engaged in for wage employment require proficiency in Arabic or French (i.e.,
construction and manufacturing workers, and employees repairing cars or motorbikes),
while most of the individuals in the sample speak the local Amazigh language as theirmother tongue. This may explain why rural road improvement is effective for the wage
employment of better educated young males.
Compared to males, under conservative social norms related to Muslim traditions,young females are restricted from entering the labor market outside of their households.
We find that young females are more motivated to complete higher (secondary or
above) education following the rural road improvement, which reduces travel time andcosts of commuting to school and provides a wider range of options for transport. At thesame time, we find suggestive evidence that early marriage among young females
decreased in the treatment group. Table 7 shows the impact of improved rural roads on
the probability of marriage among young females, which includes those who had
migrated out at the end-line survey.14 We use the same specification as our main
specification (2), replacing the dependent variable with a binary to take 1 if married and0 otherwise. The coefficients on the interaction term of interest in columns (A) and (B)13This “complementarity” between schooling and roads is consistent with the case of rural Indonesia(Yamauchi et al., 2011).
14 Marriage involves separation from the original household, especially for females in Morocco. The
end-line data includes the data from the phone tracking survey for individuals who have separated
from the original household to track individuals who have separated from their households.
24are negative and statistically significant. Hence, the higher educational attainment ofyoung females might coincide with the decrease in early marriage. The coefficient is not
significant for boys. Assaad and Krafft (2015) show that assortative mating in terms ofeducation is likely to be at work in the marriage market in Morocco: there is a clear
pattern of trade-offs between women’s education and later marriages for higher
educated men. Such underlying mechanisms could be at work in the marriage market to
explain our findings.

8． Conclusion
This paper examines the impact of a rural road improvement project on schoolingdecisions and youth employment in Morocco. We find contrasting results between young
females and males. First, we find a positive and significant effect on secondary school
attainment or above only for females. Higher educational attainment is associated with alower proportion of early marriages. Second, we observe a positive and significant effecton wage employment only for males. Our findings reveal a sharp gender difference in theimpact of the rural road improvement project, which motivates better education forfemales and paid work for males.
Our results show that the impact of an infrastructure improvement project may beheterogeneous between young females and males. Thus, we argue that not only the total
impact but also the gendered impact should be considered for policymaking. The
contrasting impact revealed in this study was made possible by rich longitudinalhousehold-level data. Future research should address the impact of infrastructure using
micro-level data to reveal heterogeneous effects across different beneficiaries withdifferent characteristics, which are not limited to gender. Such efforts are essential to25

understand the gradation of the total impact that the previous literature has largely
neglected by identifying the full range of beneficiaries and exploring whether roadinfrastructure has contributed to reducing inequality and poverty.

26

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Observatoire National du Développement Humain. (2021). Dynamiques des Niveaux deVie et de la Pauvreté au Maroc: Une Analyse Longitudinale.
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29

Table 1 Balance test of outcome variables
Treatment (within 2km)
TreatmentgroupsControlgroups

(A)

(B)

n=311

n=203

0.595

0.542

n=338

n=222

0.692

0.703

n=141

n=98

0.411

0.408

n=174

n=111

0.609

0.658

n=360

n=228

0.139

0.101

n=408

n=229

0.360

0.306

n=294

n=175

0.017

0.006

n=316

n=163

0.418

0.368

n=294
0.007
n=316
0.335

Diff.

Treatment (within 5km)
Diff.

(A)-(B)

TreatmentgroupsControlgroups(D)-(E)

(C)

(D)

(E)

(F)

n=393

n=255

0.598

0.565

n=439

n=253

0.699

0.684

n=184

n=124

0.435

0.411

n=220

n=127

0.591

0.646

n=494

n=265

0.154

0.132

n=532

n=250

0.346

0.332

n=404

n=204

0.012

0.010

n=417

n=176

0.050

0.451

0.398

0.053

n=175
0.006
n=163

0.001

n=404
0.007
n=417

n=204
0.010
n=176

-0.0020.429-0.094

0.317

0.415

0.098**

School attendance (=1)
Girls aged 7 to 18
Boys aged 7 to 18

0.053-0.010

0.033
0.016

Primary completion (=1)
Girls aged 13 to 18
Boys aged 13 to 18

0.003-0.0480.023-0.055

Some secondary or above (=1)
Females aged 13 to 25
Males aged 13 to 25

0.038
0.055

0.022
0.014

Self-employment (=1)
Females aged 16 to 25
Males aged 16 to 25
Wage employment (=1)
Females aged 16 to 25
Males aged 16 to 25

Marriage (=1)
Females aged 14 to 25

0.011

0.003

n=341
n=212
n=468
n=246
0.396
0.377
0.019
0.385
0.346
0.039
Males aged 14 to 25
n=372
n=210
n=488
n=231
0.078
0.114
-0.0360.084
0.091-0.007
Note: Fisher's exact test results are shown: * Significant at 10%, ** Significant at 5%, *** Significant
at 1%.

30

Table 2 Impact on school attendance
Dependent variable:
School attendance (=1)

Female youths aged 7 to 18
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Male youths aged 7 to 18
Treatment
Treatment(within 2km) (within 5km)
(C)
(G)

0.048
0.094*
0.062
0.046(0.056)(0.049)(0.061)(0.053)
Year 2017 (=1)
0.042
0.003
-0.082-0.076(0.047)(0.042)(0.051)(0.048)Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.416
0.391
0.401
0.390
No. of observations
1042
1291
1080
1340
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

31

Table 3 Impact on primary school completion
Dependent variable:
Primary completion (=1)

Female youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Male youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(C)(G)

-0.001
0.053-0.041-0.028(0.090)(0.084)(0.124)(0.106)
Year 2017 (=1)
0.305***
0.250***
0.174
0.172*(0.063)(0.065)(0.107)(0.098)Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.378
0.372
0.306
0.313
No. of observations
488
620
544
676
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

32

Table 4 Impact on secondary school or above attainment
Dependent variable:
Some secondary or above (=1)

Female youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Male youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(C)(G)

0.104**
0.079*
0.020
0.023(0.045)(0.041)(0.062)(0.057)
Year 2017 (=1)
0.083***
0.084***
0.054
0.049
(0.029)(0.027)(0.055)(0.052)Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.299
0.321
0.254
0.272
No. of observations
1063
1355
1203
1471
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

33

Table 5 Impact on self-employment
Dependent variable:
Self-employment (=1)

Female youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Male youths aged 13 to 25
Treatment
Treatment(within 2km) (within 5km)
(C)(G)

-0.0010.006-0.0120.016(0.014)(0.015)(0.064)(0.065)
Year 2017 (=1)
0.028
0.021
-0.259*-0.266**(0.037)(0.031)(0.138)(0.131)
Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.147
0.142
0.278
0.257
No. of observations
969
1246
983
1204
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

34

Table 6 Impact on wage employment(1) All youth aged 16 to 25
Dependent variable:
Wage employment (=1)

Female youths aged 16 to 25
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Male youths aged 16 to 25
Treatment
Treatment(within 2km) (within 5km)
(C)
(G)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

0.007
0.003
0.117*
0.113*(0.012)(0.010)(0.063)(0.063)
Year 2017 (=1)
0.009
-0.009-0.185-0.144(0.010)(0.017)(0.123)(0.121)
Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.173
0.148
0.334
0.316
No. of observations
969
1246
983
1204
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

(2) Young males aged 16 to 25 by educational level
Dependent variable:
Wage employment (=1)

Male youths aged 16 to 25(primary education)

Male youths aged 16 to 25(some secondary)

Treatment(within 2km)(A)

Treatment(within 2km)
(C)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Treatment(within 5km)
(B)

Treatment(within 5km)
(G)

0.098
0.121
0.162
0.180*(0.075)(0.077)(0.115)(0.108)Year 2017 (=1)-0.371**-0.294*-0.039-0.151(0.175)(0.175)(0.187)(0.185)Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.415
0.410
0.407
0.389
No. of observations
644
787
339
417
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

35

Table 7 Impact on marriage
Dependent variable:
Marriage (=1)

Female youths aged 14 to 25
Treatment
Treatment(within 2km) (within 5km)
(A)
(B)

Treatment and year dummy variables
Treatment * Year 2017 (=1)

Male youths aged 14 to 25
Treatment
Treatment(within 2km) (within 5km)
(C)(G)

-0.104*-0.082*-0.063-0.048(0.053)(0.044)(0.043)(0.042)
Year 2017 (=1)
0.023
0.020
0.071*
0.056(0.040)(0.037)(0.037)(0.037)Age cohort fixed effects
Yes
Yes
Yes
Yes
Male head/spouse characteristics
Yes
Yes
Yes
Yes
Female spouse characteristics
Yes
Yes
Yes
Yes
Household characteristics
Yes
Yes
Yes
Yes
Village characteristics
Yes
Yes
Yes
Yes
Village fixed effects
Yes
Yes
Yes
Yes
R sq.
0.422
0.417
0.196
0.185
No. of observations
1140
1456
1167
1430
Note: Village-level cluster-adjusted standard errors are in parentheses: * Significant at 10%, **Significant at 5%, *** Significant at 1%.

36

Appendix 1 Balance test (threshold= 2 kilometers)

Treatmentgroups(A)n=1102
0.481
16.3
n=447
0.982
54.7
0.738
0.148
0.060
0.054
n=422
0.927
45.2
0.950
0.040
0.009
n=455
6.36
0.336
3.32
20.7

Treatment (2km)Difference
Controlgroups(A)-(B)
S.E.
(B)
(C)
(D)
n=673
0.495
-0.014(0.024)15.8
0.47*(0.26)
n=263
0.953
0.030**(0.013)53.9
0.86(1.13)
0.821-0.083**(0.033)0.122
0.026(0.027)0.034
0.026(0.017)0.023
0.031**(0.016)n=267
0.967-0.040**(0.018)44.3
0.87(1.01)
0.951-0.001(0.017)0.041-0.001(0.015)0.007
0.002(0.007)
n=276
6.33
0.03
(0.233)0.352-0.016(0.016)3.54-0.23(0.55)
14.6
6.09(5.30)

Individual characteristics (aged 7 to 25)
Female (=1)
Age
Male head/spouse characteristics
Present (=1)
Age
No education (=1)
Primary education (=1)
Secondary education or above (=1)
Islamic studies (=1)
Female spouse characteristics
Present (=1)
Age
No education (=1)
Primary education (=1)
Secondary education or above (=1)
Household characteristics
Household size
Ratio of dependents to household size
Agricultural land (ha)Household assets (thousand MAD)
Annual consumption per capita
6.07
6.40
-0.33
(0.38)(thousand MAD)
Palmer Drought Severity Index (PDSI)
2.64
2.59
0.06
(0.08)
Village characteristics
n=48
n=29
Population
439
320
119.0*(62.1)
Average agricultural land per household
3.38
3.58
-0.194(0.865)(ha)
Average assets per household (thousand
20.8
15.0
5.85
(6.30)MAD)
Distance to permanent market (km)-0.33
(4.88)
19.5
19.8
Distance to periodic market (km)-1.30
(4.69)
11.8
13.1
Distance to trade center (km)8.5
(6.88)
28.6
20.1
Note: t-test or Fisher's exact test results are shown (for binary variables, Fisher's exact tests also
support the results); * Significant at 10%, ** Significant at 5%, *** Significant at 1%.
Monthly consumption per capita is adjusted by using adult equivalence scales. Assets perhousehold include the value of productive and durable assets. 1USD was worth approximately
9MAD in 2011.

37

Appendix 2 Balance test (threshold= 5 kilometers)

Treatmentgroups(A)n=1454
0.483
16.4
n=592
0.979
54.2
0.730
0.145
0.061
0.064
n=560
0.926
44.8
0.948
0.041
0.011
n=605
6.23
0.328
3.31
20.7

Treatment (5km)Difference
Controlgroups(A)-(B)
S.E.
(B)
(C)
(D)
n=772
0.513
-0.029(0.022)15.6
0.83***(0.24)
n=300
0.952
0.026**(0.012)53.9
0.30(1.02)
0.830-0.100*** (0.030)
0.120
0.025
(0.024)0.023
0.037**(0.015)0.027
0.038**(0.016)n=306
0.971-0.046*** (0.016)
44.5
0.40
(0.92)
0.951-0.003(0.016)0.039
0.002(0.014)0.010
0.001(0.007)n=315
6.27-0.040(0.206)0.349-0.022(0.015)4.03-0.73(0.55)
14.3
6.40(4.62)

Individual characteristics (aged 7 to 25)
Female (=1)
Age
Male head/spouse characteristics
Present (=1)
Age
No education (=1)
Primary education (=1)
Secondary education or above (=1)
Islamic studies (=1)
Female spouse characteristics
Present (=1)
Age
No education (=1)
Primary education (=1)
Secondary education or above (=1)
Household characteristics
Household size
Ratio of dependents to household size
Agricultural land (ha)Household assets (thousand MAD)
Annual consumption per capita (thousand
6.31
6.23
0.08
(0.38)MAD)
Palmer Drought Severity Index (PDSI)
2.64
2.72
-0.08
(0.07)
Village characteristics
n=62
n=34
Population
441
374
66.7
(57.4)
Average agricultural land per household (ha)
3.29
3.97
-0.679(0.780)
Average assets per household (thousand
20.7
14.3
6.38
(5.47)MAD)
Distance to permanent market (km)-1.67
(4.52)
20.1
21.8
Distance to periodic market (km)-2.01
(4.05)
12.5
14.5
Distance to trade center (km)8.6
(7.23)
31.7
23.1
Note: t-test or Fisher's exact test results are shown (for binary variables, Fisher's exact tests also
support the results); * Significant at 10%, ** Significant at 5%, *** Significant at 1%.
Monthly consumption per capita is adjusted by using adult equivalence scales. Assets perhousehold include the value of productive and durable assets. 1USD was worth approximately
9MAD in 2011.

38