metadata: authors: 0: author: Iimi,Atsushi
1: author: Mengesha,Haileyesus Adamtei
2: author: Markland,James
3: author: Asrat ,Yetmgeta
4: author: Kassahun,Kefargachew
count: Ethiopia
docna: 0: docna: Heterogeneous impacts of main and feeder
            road improvements : evidence from Ethiopia
docty: Policy Research Working Paper
subtopic: Transport Services,Climate Change and Agriculture,Crops and Crop Management Systems,Food Security,Inequality,Pest Management
teratopic: Agriculture,Poverty Reduction,Transport
abstracts: cdata!: Rural access is among the most important
            infrastructure constraints in rural Africa. Using the
            results from comprehensive household surveys and other data
            from Ethiopia, the paper recasts light on the heterogeneous
            impacts of road accessibility on agriculture and
            nonagricultural growth. It is found that crop production is
            increased by major and feeder road improvements. Significant
            synergy is also found. When investigating further into this
            effect, there are two impacts: farmers' access to the
            input market, especially fertilizer, was improved mainly by
            major corridor improvement. And output market access was
            improved by feeder road improvement. In addition, the
            household's nonagricultural income is somehow increased
            by improved road connectivity. There must be secondary
            effects. The transport demand function estimated with
            additional data indicates that as the road network improves,
            people's mobility increases. Furthermore, local
            business employment is found to increase with road
            improvements. To meet the increasing demand for mobility,
            efficiency and frequency of transport services are important.
geo_reg_key: 517191
repnme: repnme: Heterogeneous impacts of main and feeder
            road improvements : evidence from Ethiopia
display_title: Heterogeneous impacts of main and
            feeder road improvements : evidence from Ethiopia
listing_relative_url: /research/2018/08/30343969/heterogeneous-impacts-main-feeder-road-improvements-evidence-ethiopia
topicv3: Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Transport m1326661 135,Poverty m1326687 695,Pesticides m1504403 2914
publishtoextweb_dt: 2018-08-13T00:00:00Z
historic_topic: Agriculture,Poverty Reduction,Transport
no_of_pages: 36
author_keys: 0: author_key: 914707
1: author_key: 1037431
2: author_key: 1133263
model_extractions: WPS8548 Policy Research Working Paper 8548 Heterogeneous Impacts of Main and Feeder Road Improvements Evidence from Ethiopia _Atsushi Iimi Haileyesus Mengesha James Markland Yetmgeta Asrat Kefargachew Kassahun_ Transport and Digital Development Practice August 2018
Policy Research Working Paper 8548

# **Abstract**

Rural access is among the most important infrastructure constraints in rural Africa. Using the results from comprehensive household surveys and other data from Ethiopia, the paper recasts light on the heterogeneous impacts of road accessibility on agriculture and nonagricultural growth. It is found that crop production is increased by major and feeder road improvements. Significant synergy is also found. When investigating further into this effect, there are two impacts: farmers’ access to the input market, especially fertilizer, was improved mainly by major corridor improvement. And output market access was improved by feeder road improvement. In addition, the household’s nonagricultural income is somehow increased by improved road connectivity. There must be secondary effects. The transport demand function estimated with additional data indicates that as the road network improves, people’s mobility increases. Furthermore, local business employment is found to increase with road improvements. To meet the increasing demand for mobility, efficiency and frequency of transport services are important.

This paper is a product of the Transport and Digital Development Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Heterogeneous Impacts of Main and Feeder Road Improvements:**

# **Evidence from Ethiopia**

Atsushi Iimi<sup>¶†</sup> Haileyesus Mengesha<sup>†</sup> James Markland<sup>†</sup> Yetmgeta Asrat<sup>§</sup> Kefargachew Kassahun<sup>§ ‡</sup> Key words: Rural access, Agricultural production, Transport service demand, Local job creation.

JEL classification: R41; Q12; C33; C34.

> ¶ Corresponding author

> † Transport & Digital Development Global Practice, World Bank Group

> § Ethiopian Roads Authority

> ‡ We would like to acknowledge both TPO consultant and URRAP Impact Assessment teams for their efforts to carry out various surveys and data collection.
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# **I. INTRODUCTION**

Rural access is among the most important infrastructure constraints to development in rural Africa. A spatial analysis by Dorosh et al. (2012) indicates that crop productivity in Africa is closely related to transport connectivity. Dercon et al. (2012) show that access to an allweather road has a significant impact on farmers’ welfare in Ethiopia. Similar results were found by Bachewe et al. (2017). In general, although causality is always a difficult issue, the literature is supportive of a wide range of socioeconomic impacts of improved rural accessibility around the world. In the short term, transport costs and travel time can be reduced by improved road conditions, and resilience to drought is improved (e.g., Lokshin and Yemtsov 2005; Danida 2010). Over the longer term, agricultural productivity would likely be increased (Khandker et al. 2009; Bell and Van Dillen, 2014), and firms would become more profitable with more jobs created (Mu and van de Walle, 2011). Poverty could then be alleviated (Dercon et al. 2009; Khandker and Koolwal, 2011).

However, there are still divergent views on results chains for rural access improvement. For example, there has long been a debate on the relative importance of agricultural and nonagricultural impacts. Bell and van Dillen (2014) show that a higher level of road accessibility increased crop output prices by 5 percent in India. Improved rural road connectivity can also increase agricultural employment and working hours in Nicaragua. (Rand, 2011). According to Diao et al. (2012), agricultural growth has a greater impact on poverty reduction in many African countries. This is primarily because the vast majority of the poor engage in agricultural production in rural Africa.

However, there are also spillover effects on non-agricultural sectors. Manufacturing growth, especially in the agro-processing subsector, is often found to be critical for poverty reduction (Dorosh and Thurlow, 2017). Some studies, such as Shirley and Winston (2004) and Datta (2012) show that improved road accessibility can reduce firms’ inventory costs, though the studies are focused on connectivity to primary roads. But firms are generally likely to flock together where good transport infrastructure is available (e.g., Cieślik and Ryan, 2004;
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Boudier-Bensebaa, 2005). New local businesses may also emerge (Mu and van de Walle, 2011). Given the rapid urbanization trend, non-agricultural activities, regardless of whether they are formal or informal, are certainly becoming more and more important even in Africa.

The current paper examines the question of whether improved rural accessibility has promoted agricultural or non-agricultural growth, or both. It attempts to identify the relative importance of different growth channels. For the agricultural growth channel, it investigates whether the road improvements impacted on people’s access to markets for inputs or outputs. The literature is supportive of both cases. While proximity allows farmers to use more advanced inputs, such as fertilizer (e.g., Sheahan and Barrett, 2017; Damania et al., 2017), farmers’ participation in the output market would be stimulated by lower crop transport costs (e.g., Key et al., 2000). For the non-agricultural growth channel, the paper investigates further the causes for non-agricultural income growth. The analysis will be extended to the estimation of people’s demand for transportation services and the indirect impacts on local businesses, especially, job creation.

Of particular note, the paper examines the impacts of different types of road improvement works. Though often forgotten, different roads fulfill different functions. While feeder roads provide last-mile connectivity to local farmers and businesses, the higher level of the road network, such as trunk roads and regional corridors, connects major cities and economic activities, carrying consolidated traffic between urban and rural areas. There is little evidence of the interaction effects between them. One of the few studies is Bell and van Dillen (2014), which indicate some heterogeneous impacts between highways and feeder roads: Crop prices decrease with distance along a major road link but increase with distance along a stretch of feeder road. By contrast, Khandker et al. (2009) show that rural road investment can increase output prices, while reducing farmgate prices of inputs. In the general equilibrium context, Warr (2008) shows the particular importance of the minimum level of accessibility to reduce poverty in the Lao People’s Democratic Republic. By contrast, Escobal and Ponce (2002) find that households in villages receiving motorable roads had an average increase in
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household income of 35 percent, with no effect for treated households served by nonmotorable roads.

The paper will cast light on such heterogeneity and complementarities across different types of roads. Our data come from five rounds of household surveys carried out in Ethiopia for the period 2012 to 2016. In recent years, the Government of Ethiopia has been investing heavily in both primary and feeder roads. Under the Road Sector Development Program (RSDP), many Trunk and Link Roads have been rehabilitated. There are also rural road initiatives, such as Universal Rural Road Access Programme (URRAP), which is focused on feeder road improvement at the village (kebele) level. Different levels of connectivity are likely to contribute to the economy in different ways.

The paper is organized as follows: Section II provides a brief overview of the country context, specifically focused on the agriculture and road sectors. Section III develops our empirical strategy. Section IV provides more details on our data. Section V presents main results and discusses policy implications. Then Section VI concludes.

# **II. AGRICULTURE AND ROAD SECTORS IN ETHIOPIA**

In Ethiopia, agriculture remains among the most important economic sectors. It produces about one-third of GDP, employs 70 percent of the workforce and accounts for 80 percent of the country’s merchandise exports (Ethiopian Agricultural Transformation Agency, 2014). Ethiopia produces about US$6 billion of crops a year, including teff, maize, coffee, beans, sorghum and wheat. Coffee is one of the traditional export crops of the country. Agricultural productivity is generally low but compares favorably to neighboring countries. Earlier studies (e.g., Taffesse et al., 2012; Spielman et al., 2012) show that agricultural growth was largely attributed to expansion of land area cultivated during the 1990s, but productivity growth has also been contributing since the early 2000s.
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Still, the use of advanced inputs, such as fertilizer and improved seeds, remains limited. Fertilizer was applied to less than 40 percent of cereal acreage, and pesticides to about 20 percent of land. The use of improved seeds and irrigation is limited to several percent on average (Taffesse et al. 2012). The poor rural road network has long been recognized as a crucial constraint preventing the timely delivery of agricultural inputs. Transport costs are estimated to account for 64-80 percent of fertilizer farmgate prices (Rashid et al. 2013).

In Ethiopia, there are more than 110,000 km of roads, of which only 14,000 km or about 13 percent are paved. In recent years, the government has been making significant efforts to develop both main corridors and rural feeder roads. About 26,000 km of federal roads are generally well-maintained. In 2010, the Universal Rural Road Access Program (URRAP) was embarked upon, aimed at connecting all communities (kebeles) by all-weather roads. Under the program, about 46,000 km of rural roads have been rehabilitated, and more than 5,600 villages (kebeles) were connected. The total costs in the first 5 years of the program amounted to 28 billion Ethiopian birr (ETB) or about US$1.4 billion. Still, there are a number of feeder roads that are in poor condition. Only about 29 percent of the total road network is good condition in Ethiopia.<sup>1</sup> The paper is focused on assessing the impacts of rehabilitation works along four major roads that were rehabilitated under the Road Sector Development Program (RSDP) and a number of feeder roads that were simultaneously improved under a separate program, URRAP, in the same areas. The four main roads are: (i) Aposto-Wendo-Negele Road (268 km), (ii) Mekenajo-Dembidolo Road (181 km), (iii) Kombolcha-Bati-Mille Road (133 km), and (iv) Ankober-Awash Arba Road (89 km). While the first two roads are located in Oromia and Southern Nations, Nationalities and Peoples’ Regions, the last two connect Amhara and Afar Regions ( **Figure 1** ).

1 See Nakamura (2017) for more details.
<!-- Start of picture text --> ¥<br>/ ¢<br>own? Le = Main roads<br>; “ ) Road condition<br>i 4 a ms —— Poor ;<br>ee Kilometers<br><!-- End of picture text -->
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Household income is assumed to depend on two types of road accessibility, _D_<sup>_RSDP_</sup> and _D_<sup>_URRAP_</sup> , as well as agricultural inputs used ( _X_ ) and other household-specific characteristics ( _Z_ ). _DitRSDP_ is set to be one if the main road closest to household _i_ was rehabilitated by the point of time _t_ . Similarly, _DitURRAP_ is set to be one if the closest feeder road was rehabilitated by URRAP. _β3_ is expected to capture the interaction effect between the two interventions. As our data are panel, _ci_ and _ct_ are the individual- and time-specific fixed-effects, respectively.

Our identification strategy is based on the phased nature of the two road programs. Even along the same road, some segments may have been completed earlier than others. Therefore, some groups benefit from the programs earlier than others. The current paper takes advantage of such time lags in project implementation and applies the difference-in-differences (DID) method with various covariates taken into account. Among the RSDP roads, for example, the Aposto-Wendo-Negele Corridor was divided into three contract lots ( **Table 1** ). While two lots were completed by 2013, the final lot was handed over in 2014. The household surveys were carried out in the almost same season every year. This creates an opportunity to distinguish beneficiaries and nonbeneficiaries at a certain point of time. The Kombolcha-Bati-Mille and Ankober-Awash Arba Corridors can be considered as a full comparison group because no road work had yet been completed when the last round of the survey was carried out.

**<u>Table 1. Timeline of project implementation of the main road works and household surveys</u>**

|<br>Road|<br>Lot|<br>2009|<br>2010|<br>2011<br>2012|<br>2013<br>2014|<br>2015<br>|2016<br>2017|
|---|---|---|---|---|---|---|---|
|Aposto-Wendo-Negele|Aposto-Irbamoda|||||||
||Irbamoda-Wadera|||||||
||Wadera-Negele|||||||
|Mekenajo-Dembidolo|Mekenajo-Ayira|||||||
||Aiyra-Chanka|||||||
||Chanka-Dembidolo|||||||
|Kombolcha-Bati-Mille|Kombolcha-Burka|||||||
||Burka-Mille|||||||
|Ankober-Awash Arba|Ankober to Dulesa|||||||
||Dulesa to Awash Arba|||||||
<!-- Start of picture text -->
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<!-- Start of picture text --> © Househokis surveyed<br>RSDP Road<br>—24<br>— m5<br>— a8<br>—_— a)<br>2 ongoing<br>8 Road network<br>: URRAP roads<br>: 5 —— 214aae<br>o 20 —— ats<br><!-- End of picture text -->
<!-- Start of picture text --> j<br>‘ —pu<br>; ) ——oi— a5a6<br>S, wilt — a<br>\ 5 Be<br><!-- End of picture text --> <!-- Start of picture text --> 5 a<br>.<br>— 14<br>—a5 D4<br>— nw f<br>—ariT /<br>f<br>— a sa “<br>a4 :<br><!-- End of picture text -->
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and if that road was rehabilitated at the point of time _t_ . _DitURRAP_ 2 _km_ is defined in the same way. To allow this identification strategy, our surveys covered both households who live close to and far from each project road.

This identification strategy has the advantage of controlling for unobserved local conditions since we have both beneficiaries and nonbeneficiaries within the same local area along a given road. Though, it is a naïve approach because household placement may be selfselected. It is plausible that those who live closer to the road network may be systematically more productive than remote households. The individual fixed-effects are expected to remove such time-invariant factors.

**Figure 3. Distribution of distance traveled from households to market**

<!-- Start of picture text --> 0 10 20 30<br>Distance to market (km)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text --> Under the same identification strategies, two additional models are examined. First, the farmers’ technology adoption decision is analyzed to understand the reasons for possible agricultural income growth. As in earlier studies (e.g., Taffesse et al., 2012; Rashid et al., 2013), access to advanced inputs is a critical constraint in Ethiopia. To this end, the following specification is considered:
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Two inputs, _k_ , are considered for _X_ : {Fertilizer ( _F_ ) and Pesticide ( _P_ )}.<sup>2</sup> When estimating the equation, one empirical issue is that many input variables are likely to be zero in developing countries. According to Taffesse et al. (2012), fertilizer was applied to less than 40 percent of cereal acreage, and pesticides to about 20 percent of land in Ethiopia. Our sample data are consistent and show even more limited use: About 20 percent and 10 percent of households surveyed used fertilizer and pesticides, respectively. To deal with this problem, the truncated regression model with fixed effects is applied: where _ε_ is a very small positive number to avoid taking the logarithm of zero. Except for the truncated observations, the distribution of the amount of fertilizer or pesticides used is close enough to a normal distribution, which is needed to perform the truncated regression model ( **Figure 4** ). Other covariates are the same as the above except for one of the input variables that is now used as a dependent variable.

**Figure 4. Distributions of fertilizer and pesticides used**

<!-- Start of picture text -->
-5 0 5 -5 0 5 10<br>ln(Fertilizer used, kg) ln(Pesticides used, litre)<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text -->

2 No data that show the use of improved seeds are available, which is minimal anyway in the current Ethiopian context.
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The other model is the crop sales equation. The road improvements are expected to enhance farmers’ participation in output markets. To this end, the following specification is considered with the crop production value replaced with the total amount of crops sold at market, _SALE_ : Our data cover 28 crops, such as teff, maize, coffee and beans. To aggregate them, producer price data are used from FAOSTAT. Note that as in the case of advanced inputs used, not many farmers have yet participated in market transactions in rural Ethiopia. In our survey data, about one-fourth of households went to market and sold some crops that they produced. Therefore, again, the truncated regression model is used: The total value of crop sales at market in logarithm is distributed closely to a normal distribution, which validates the truncated regression model ( **Figure 5** ).

**Figure 5. Distributions of total crop sales**

<!-- Start of picture text -->
-5 0 5 10 15<br>ln(Total crop sales, US$)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->
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# **IV. DATA**

The data were collected through five rounds of household surveys carried out by the Ethiopia Road Authority (ERA), along the four main corridors that have been recently rehabilitated or are currently being rehabilitated. As discussed above, they are major Link Roads that share the similar functionality connecting major regional cities to Addis Ababa, the country’s capital. According to the preliminary screening, these four roads share certain common characteristics and are broadly comparable with each other ( **Table 2** ). The baseline survey was conducted in 2012 before the first major construction work was started along Aposto-WendoNegele Road. Each round of the survey was targeted at the same, approximately 250 households along each main RSDP road. For each road, 15 to 20 villages were selected around the starting, middle and ending point of each road. The villages were also stratified based on distance to the project roads.<sup>3</sup> Attrition turned out fairly small over time ( **Table 3** ).

**<u>Table 2. Household characteristics (sample means) by RSDP road</u>**

|<br>Variable|<br>Aposto-Wendo-<br>Negele|Mekenajo-<br>Dembidolo|Kombolicha-<br>Bati-Mille|Ankober-<br>Awasharba|
|---|---|---|---|---|
|Size of household|6.00|5.08|5.02|4.24|
|Dummy variable for male household head|0.81|0.84|0.68|0.74|
|Age of household head|44.0|44.6|46.3|44.0|
|Dummy for the level of education attained by hou|sehold head:||||
|1 to 6 grade|0.27|0.14|0.22|0.13|
|7 to 8 grade|0.08|0.19|0.06|0.08|
|9 to 12 grade|0.18|0.19|0.10|0.11|
|Technical/ vocational certificate|0.02|0.00|0.00|0.01|
|University/ college diploma|0.06|0.08|0.02|0.03|
|University/ college degree|0.02|0.04|0.02|0.02|
|Adult education program/ Non-formal|0.05|0.02|0.05|0.06|
|Dummy variable for infrastructure services:|||||
|Electricity for cooking|0.02|0.01|0.02|0.01|
|Electricityfor lighting|0.54|0.78|0.49|0.63|

> 3 The normal cutoff point was originally set to 5km, which is considered to be a normal distance allowing people to walk to a road in Ethiopia. In the actual sample, as shown in the above figure, half of the sample households live less than 2km of the project roads, and another half live 5 to 20km from the roads.
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# **<u>Table 3. Number of households surveyed</u>**

||<br>Road contract lot|<br>Survey|<br>year:|||||
|---|---|---|---|---|---|---|---|
|||2012|2013|2014|2015|2016|Total|
|A1|Aposto-Irbamoda|70|64|68|67|66|335|
|A2|Irbamoda-Wadera|61|58|58|57|56|290|
|A3|Wadera-Negele|109|106|106|103|103|527|
|B1|Mekenajo-Ayira|78|77|78|77|77|387|
|B2|Aiyra-Chanka|73|73|73|73|73|365|
|B3|Chanka-Dembidolo|89|86|87|88|87|437|
|C1|Kombolcha-Burka|138|139|139|136|130|682|
|C2|Burka-Mille|102|100|99|97|103|501|
|D1|Ankober-Awash Arba|240|233|227|203|210|1,113|
|Total||960|936|935|901|905|4,637|

The summary statistics indicate that the surveyed households are significantly heterogeneous ( **Table 4** ). The household income from crop production ranges from ETB50 to ETB800,000, with an average of ETB9,700 or about US$450. Recall that per capita GDP in Ethiopia was US$706 in 2016. Livestock seems to be as important as crop production in the survey areas. The average income from livestock is about ETB8,000. Many households earn income from other activities, such as forest product sales and seasonal employment. This reflects our sampling strategy: Half of the survey villages were located close to the RSDP roads. Note that remittance income is excluded from our non-agricultural income variable because it is perhaps less relevant to our research context. In the sample, about half of the farmers participate in transactions to sell their produce at market: The average value of crop sales among those farmers is US$573, with a wide variation from only US$1 to over US$130,000. This implies that, not surprisingly, some larger-volume producers are more likely to participate in market sales and earn more than smallholder farmers.

For the crop income equation, five inputs are considered for _Xk_ : _k_ = { _L, R, I, F,P_ }, following the agricultural economics literature (e.g., Gyimah-Brempong, 1987; Bravo-Ortega and Lederman,2004). _L_ denotes the number of labor force in each household, which is assumed to include household members between the ages of 16 to 60. While _R_ denotes rain-fed areas used for crop production, _I_ represents irrigated land areas. Two advanced inputs are included: fertilizer ( _F_ ) and pesticides ( _P_ ).
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In the livestock income equation, land areas for grazing ( _G_ ) is used instead of rain-fed or irrigated areas. Fertilizer and pesticides are, of course, irrelevant and removed. To estimate the impacts on nonagricultural income, all these agricultural input variables are excluded.

# **<u>Table 4. Summary statistics</u>**

|<br>Variable|Abb.|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Household income from (Birr):|||||||
|Crop production (including own consumption)|Y<sup>Crop</sup>|1,877|9,722|22,162|50.00|800,000|
|Livestock (including own consumption)|Y<sup>Livestock</sup>|1,094|8,072|8,962|40.00|111,000|
|Nonagricultural activities (exc. remittance)|Y<sup>NonAG</sup>|3,157|19,519|30,251|36.00|754,000|
|Crop sales at market (US$)|SALE|1,044|573|4,318|1.03|133,919|
|Dummy variable for program beneficiaries:|||||||
|RSDP|D<sup>RSDP</sup>|4,639|0.20|0.40|0|1|
|URRAP|D<sup>URRAP</sup>|4,639|0.67|0.47|0|1|
|RSDP within 2km distance|D<sup>RSDP2mk</sup>|4,639|0.11|0.31|0|1|
|URRAP within 2km distance|D<sup>URRAP2km</sup>|4,639|0.33|0.47|0|1|
|Household labor force (16< age <60)|L|4,639|2.64|1.37|0|10|
|Rain fed crop production area (ha)|R|1,721|1.32|2.38|0.025|75.00|
|Irrigated land area (ha)|I|137|0.62|1.14|0.013|10.00|
|Land area for grazing (ha)|G|365|0.57|2.26|0.013|25.00|
|Fertilizer used (kg)|F|927|4.40|17.99|0.005|200.00|
|Pesticides used (litre)|P|441|5.89|57.78|0.003|1200.00|
|Size of household|SIZE|4,639|5.00|2.28|1|14|
|Dummy variable for male household head|MALE|4,639|0.78|0.42|0|1|
|Age of household head|AGE|4,639|45.72|15.00|14|99|
|Dummy for the level of education attained by hou|sehold head:||||||
|1 to 6 grade||4,639|0.19|0.39|0|1|
|7 to 8 grade|EDU2|4,639|0.11|0.31|0|1|
|9 to 12 grade|EDU3|4,639|0.14|0.35|0|1|
|Technical/ vocational certificate|EDU4|4,639|0.01|0.10|0|1|
|University/ college diploma|EDU5|4,639|0.05|0.22|0|1|
|University/ college degree|EDU6|4,639|0.03|0.17|0|1|
|Adult education program/ Non-formal|EDU7|4,639|0.04|0.19|0|1|
|Dummy variable for infrastructure services:|||||||
|Electricity for cooking|COOK|4,639|0.03|0.18|0|1|
|<br>Electricity for lighting|LIGH|<br>4,639|0.61|0.49|0|1|
|Time fixed effects:|||||||
|_t_=2013|T2013|4,639|0.20|0.40|0|1|
|_t_=2014|T2014|4,639|0.20|0.40|0|1|
|_t_=2015|T2015|4,639|0.19|0.40|0|1|
|_t_=2016|T2016|4,639|0.20|0.40|0|1|
- 16 -

|Distance to the nearest RSDP road (km)|4,637|5.79|10.86 0.0003|89.45|
|---|---|---|---|---|
|Distance to the nearest URRAP road (km)|4,637|44.51|67.92 0.0005|188.89|

# **V. MAIN RESULTS AND DISCUSSION**

First of all, the fixed-effect panel regression is performed for different types of income sources. When project beneficiaries are defined irrespective of distance from the roads, it is found that only major road improvements have an impact on crop production growth ( **Table 5** ). The coefficient of _D_<sup>_RSDP_</sup> is estimated at 0.73, which is statistically significant. On the other hand, the coefficient of _D_<sup>_URRAP_</sup> is negative and insignificant. There are a number of possible reasons for this unexpected result. For instance, the URRAP may actually have had no impact on agricultural income, possibly because of the low technical standards of URRAP roads or the relatively small incremental changes in road condition caused by the URRAP investment. The URRAP roads are normally gravel roads with no significant structures, such as bridges, culverts and drainage. Therefore, sustainability is also a challenge unless systematic maintenance regimes are put in place. This may be another reason for the estimated insignificant effect.

However, the more likely reason is that feeder road beneficiaries may be overestimated. Given such low technical standards, it is unlikely that the URRAP roads would benefit those who live far from the project roads. This view is supported by the results shown in the last four columns, which are our preferred models. When beneficiaries are limited to the 2km areas of the project roads, both major and feeder roads are found to have a significant impact. The coefficients of _D_<sup>_RSDP2km_</sup> and _D_<sup>_URRAP2km_</sup> are 0.60 and 0.56, respectively. Both are statistically significant. In addition, the significant interaction term suggests the important synergy between major corridors and feeder roads. The coefficient of _D_<sup>_RSDP2km_</sup> • _D_<sup>_URRAP2km_</sup> is positive and strongly significant.<sup>4</sup> This is one of the most important findings in this paper, reconfirming that the road sector is a network: Trunk and feeder roads must be connected

> 4 The interactive effect between _DRSDP_ and _DURRAP_ cannot be examined because of multicollinearity.
- 17 -

with one another. This may be an intuitively obvious finding, but there has been little evidence to support this in the literature.

One may wonder whether this conclusion is robust regardless of the selection of the threshold (i.e., 2km). When project beneficiaries are defined by an alternative threshold of 5km, the significant impact of _D_<sup>_RSDP5km_</sup> remains unchanged, and the coefficient of _D_<sup>_URRAP5km_</sup> is found to be positive but not significant ( **Table 6** ). Thus, the results are consistent: the impact of a major road improvement is sustained over a broader area, but the impact of feeder road improvements is geographically limited.

When distance between households and roads is taken into account, this finding can be reconfirmed more clearly. Let _D_<sup>_RSDP_</sup> be replaced with _D_<sup>_RSDP_</sup> •ln _KM_ , and _D_<sup>_URRAP_</sup> with _D_<sup>_URRAP_</sup> •ln _KM_ , respectively. _KM_ represents the distance between households and the nearest project road. The results are shown in the last two columns of the table. The coefficient of _D_<sup>_RSDP_</sup> •ln _KM_ is negative but not significant, meaning that the impact of RSDP roads does not depend on the distance. On the other hand, the coefficient of _D_<sup>_URRAP_</sup> •ln _KM_ is significantly negative at -0.15, meaning that the impact of improved URRAP roads decreases with distance. Numerically, the magnitude of the coefficient implies that the impact would taper off quickly, especially when distance is less than 2km. All the indications are that the impact of major road improvements is broader, while the impact of feeder roads is more areaspecific. Hence, the following discussion will be focused on the results based on the 2km distance identification strategy. But all the results are quite robust, as will be shown.
**<u>Table 5. Fixed-effect panel regression on household crop production income</u>**

||<br>Coef.|<br>Std.Err.<br>|<br>Coef.|<br>Std.Err.|<br>Coef.<br>|<br>Std.Err.|<br>Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|D<sup>RSDP</sup>|0.734|(0.232) ***|||0.735|(0.232) ***|||||||||
|D<sup>URRAP</sup>|||<br>-0.206|(0.237)<br>|<br><sup>-0.207</sup>|<br><sup>(0.237)</sup>|||||||||
|D<sup>RSDP2km</sup>||||<br>|||0.746|(0.286) ***|||0.607|(0.286) **|-0.633|(0.512)|
|D<sup>URRAP2km</sup>|||||||||0.690|(0.277) **|0.564|(0.278) **|0.470|(0.281) *|
|D<sup>RSDP2km</sup>•D|<sup>URRAP2km</sup>||||||||||||1.472|(0.580) ***|
|ln_L_|0.049|(0.079)|0.049|(0.079)|0.048|(0.079)|0.050|(0.079)|0.052|(0.079)|0.051|(0.079)|0.049|(0.079)|
|ln_R_|1.030|(0.072) ***|1.032|(0.073) ***|<br>1.027|(0.072) ***|1.026|(0.073) ***|<br>1.034|(0.073) ***|<br>1.028|(0.073) ***|<br>1.025|(0.073) ***|
|ln_I_|0.418|(0.095) ***|0.424|(0.095) ***|<br>0.420|(0.095) ***|0.421|(0.095) ***|<br>0.420|(0.095) ***|<br>0.420|(0.095) ***|<br>0.420|(0.095) ***|
|ln_P_|<br>0.100|<br>(0.042) **|<br>0.095|<br>(0.043) **|<br> <br>0.098|<br>(0.042) **|<br>0.099|<br>(0.042) **|<br> <br>0.102|<br>(0.043) **|<br> <br>0.102|<br>(0.042) **|<br> <br>0.101|<br>(0.042) **|
|ln_F_|0.046|(0.043)|0.049|(0.043)|0.049|(0.042)|0.047|(0.043)|0.034|(0.043)|0.037|(0.043)|0.042|(0.043)|
|ln_SIZE_|0.364|(0.221) *|0.338|(0.223)|0.363|(0.221) *|0.332|(0.222)|0.355|(0.223)|0.346|(0.222)|0.331|(0.222)|
|ln_MALE_|0.561|(0.294) *|0.570|(0.294) **|0.568|(0.295) *|0.556|(0.294) *|0.551|(0.294) *|0.548|(0.294) *|0.549|(0.294) *|
|ln_AGE_|<br>0.606|<br>(0.361) *|<br>0.633|<br>(0.364) *|<br>0.608|<br>(0.362) *|<br>0.614|<br>(0.362) *|<br>0.627|<br>(0.362) *|<br>0.613|<br>(0.361) *|<br>0.599|<br>(0.361) *|
|EDU2|-0.084|(0.325)<br>|<sup>-0.066</sup>|<sup>(0.322)</sup><br>|<sup>-0.080</sup>|<sup>(0.324)</sup><br>|<sup>-0.081</sup>|<sup>(0.325)</sup><br>|<sup>-0.076</sup>|<sup>(0.323)</sup><br>|<sup>-0.085</sup>|<sup>(0.325)</sup>|<sup>-0.091</sup>|<sup>(0.326)</sup>|
|EDU3|-0.465|(0.304)<br>|<sup>-0.435</sup>|<sup>(0.303)</sup><br>|<sup>-0.450</sup>|<sup>(0.303)</sup><br>|<sup>-0.479</sup>|<sup>(0.303)</sup><br>|<sup>-0.483</sup>|<sup>(0.301) *</sup>|-0.500|(0.302) *|-0.501|(0.302) *|
|EDU4|-0.074|(0.648)<br>|<sup>-0.140</sup>|<sup>(0.642)</sup><br>|<sup>-0.051</sup>|<sup>(0.647)</sup><br>|<sup>-0.101</sup>|<sup>(0.646)</sup><br>|<sup>-0.143</sup>|<sup>(0.644)</sup><br>|<sup>-0.096</sup>|<sup>(0.647)</sup>|<sup>-0.096</sup>|<sup>(0.648)</sup>|
|EDU5|-0.617|(0.454)<br>|<sup>-0.618</sup>|<sup>(0.453)</sup><br>|<sup>-0.597</sup>|<sup>(0.452)</sup><br>|<sup>-0.630</sup>|<sup>(0.452)</sup><br>|<sup>-0.649</sup>|<sup>(0.451)</sup><br>|<sup>-0.640</sup>|<sup>(0.450)</sup>|<sup>-0.638</sup>|<sup>(0.450)</sup>|
|EDU6|-0.706|(0.592)<br>|<sup>-0.668</sup>|<sup>(0.588)</sup><br>|<sup>-0.700</sup>|<sup>(0.592)</sup><br>|<sup>-0.703</sup>|<sup>(0.585)</sup><br>|<sup>-0.677</sup>|<sup>(0.586)</sup><br>|<sup>-0.701</sup>|<sup>(0.584)</sup>|<sup>-0.681</sup>|<sup>(0.583)</sup>|
|EDU7|0.012|(0.407)<br>|<sup>-0.006</sup>|<sup>(0.408)</sup>|0.020|(0.409)|0.004|(0.407)|0.030|(0.403)|0.036|(0.404)|0.042|(0.405)|
|COOK|0.263|(0.453)|0.192|(0.453)|0.264|(0.453)|0.220|(0.453)|0.178|(0.452)|0.204|(0.453)|0.191|(0.454)|
|LIGH|<br>-0.680|<br>(0.302) **|<br>-0.694|<br>(0.301) **|<br>-0.684|<br>(0.302) **|<br>-0.688|<br>(0.301) **|<br>-0.633|<br>(0.304) **|<br>-0.641|<br>(0.304) **|<br>-0.632|<br>(0.304) **|
|T2013|-0.419|(0.187) **|-0.385|(0.190) **|-0.395|(0.189) **|-0.415|(0.187) **|-0.473|(0.190) **|-0.466|(0.191) **|-0.460|(0.191) **|
|T2014|-0.531|(0.214) **|-0.318|(0.218)<br>|<sup>-0.455</sup>|<sup>(0.224) **</sup>|-0.467|(0.209) **|-0.576|(0.223) ***|-0.602|(0.224) ***|-0.590|(0.224) ***|
|T2015|0.652|(0.210) ***|1.047|(0.255) ***|<br>0.795|(0.259) ***|0.754|(0.202) ***|<br>0.663|(0.224) ***|<br>0.584|(0.224) ***|<br>0.587|(0.224) ***|
|T2016|-0.606|(0.217) ***|-0.080|(0.249)<br>|<sup>-0.464</sup>|<sup>(0.263) *</sup>|-0.434|(0.203) **|-0.458|(0.214) **|-0.589|(0.220) ***|-0.553|(0.221) **|
|Constant|4.837|(1.629)***|4.856|(1.635)***|4.892|(1.626)***|4.863|(1.631)***|4.644|(1.635)***|4.723|(1.630)***|4.813|(1.628)***|
|Obs.|4639||4639||4639||4639||4639||4639||4639||
- 19 -

|No. of<br>groups|1004|1004|1004|1004|1004|1004|1004|
|---|---|---|---|---|---|---|---|
|R-square||||||||
|within|0.2169|0.215|0.217|0.2163|0.2163|0.2172|0.218|
|between|0.8295|0.8275|0.8307|0.8285|0.8228|0.8244|0.8247|
|overall|0.6302|0.628|0.631|0.6274|0.6244|0.6245|0.6242|
|F-stat.|24.05|23.22|23.3|23.37|23.29|22.5|21.68|

The dependent variable is the log of total crop production value. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
**<u>Table 6. Fixed-effect panel regression on household crop production income</u>**

|<br>|<br>Coef.|<br>Std.Err.||<br>Coef.|<br>Std.Err.||
|---|---|---|---|---|---|---|
|D<sup>RSDP5km</sup><br>|0.641|(0.275)|**||||
|D<sup>URRAP5km</sup><br>|0.175|(0.271)|||||
|D<sup>RSDP</sup>•KM<br>||||-0.037|(0.077)||
|D<sup>URRAP</sup>•KM||||-0.152|(0.058)|***|
|ln_L_|0.052|(0.079)||0.050|(0.079)||
|ln_R_<br>|1.027|(0.073)|***|1.025|(0.072)|***|
|ln_I_|0.418|(0.095)|***|0.424|(0.095)|***|
|ln_P_|0.099|(0.042)|**|0.095|(0.043)|**|
|ln_F_|0.044|(0.043)||0.042|(0.043)||
|ln_SIZE_<br>|0.336|(0.222)||0.343|(0.222)||
|ln_MALE_|0.558|(0.294)|*|0.579|(0.295)|**|
|ln_AGE_<br>|0.626|(0.361)|*|0.638|(0.363)|*|
|EDU2<br>|-0.091|(0.325)||-0.069|(0.324)||
|EDU3|-0.485|(0.304)||-0.444|(0.302)||
|EDU4<br>|-0.105|(0.646)||-0.044|(0.648)||
|EDU5<br>|-0.638|(0.451)||-0.593|(0.453)||
|EDU6<br>|-0.695|(0.586)||-0.668|(0.588)||
|EDU7<br>|0.019|(0.405)||0.080|(0.409)||
|COOK|0.231|(0.453)||0.203|(0.451)||
|LIGH<br>|-0.657|(0.304)|**|-0.631|(0.306)|**|
|T2013<br>|-0.433|(0.191)|**|-0.409|(0.187)|**|
|T2014|-0.535|(0.225)|**|-0.390|(0.207)|*|
|T2015<br>|0.664|(0.226)|***|1.074|(0.211)|***|
|T2016|-0.517|(0.220)|**|-0.044|(0.204)||
|Constant|4.736|(1.634)|***|4.700|(1.632)|***|
|Obs.|4639|||4639|||
|No. of groups|1004|||1004|||
|R-square|||||||
|within|0.2163|||0.2166|||
|between|0.8282|||0.8269|||
|overall|0.6278|||0.6255|||
|F-stat.|22.33|||23.16|||

The dependent variable is the log of total crop production value. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

For other covariates, the estimated results are generally consistent with our prior expectation: The coefficient of _L_ is positive but small and indifferent from zero. Thus, labor is not so productive. This may be attributed partly to our data issue that we assume the whole household members would engage in agricultural activities. But this may not be the case,
- 21 -

especially when people work on non-agricultural activities. Thus, our labor variable may be overestimated, resulting in underestimating the true impact.

The coefficient of rain-fed land, _R_ , is always positive and strongly significant. This is consistent with the fact that Ethiopian agriculture is still land intensive. Advance inputs are generally found to be productive. Particularly, the impacts of irrigation and pesticide use are significant. The coefficient of fertilizer, _F_ , is also positive, though not statistically significant. The findings are consistent with the fact that subsistence farming contributes the majority of agricultural production in Ethiopia.

Did the road improvement programs not contribute to improving access to advanced inputs? Equation (3) is estimated using the truncated regression model.<sup>5</sup> Proximity to major roads enables farmers to use more fertilizer: the coefficient of _D_<sup>_RSDP2km_</sup> is positive and significant ( **Table 7** ). On the other hand, feeder roads do not seem to impact on the farmers’ use of fertilizer. Thus, as far as fertilizer is concerned, the main road network, and not feeder roads, is of particular importance to ensure timely and more affordable availability for farmers. This may be attributed to the fact that Ethiopia imports almost all fertilizer and other advanced inputs through the Port of Djibouti, about 800 km away from the capital city, Addis Ababa. Imported inputs are normally distributed through the main road network to regional cooperatives and farmers. This component of transportation cost contributes a substantial proportion of the fertilizer price to farmers (Rashid et al., 2013).

Neither RSDP nor URRAP roads have a significant impact on pesticide use. The coefficient of _D_<sup>_RSDP2km_</sup> is positive but not significant. The coefficient of URRAP is also insignificant. The results are inconclusive, possibly because the use of pesticide is still highly limited in Ethiopia. Recall that pesticides are used by only 10 percent of the surveyed households.

> 5 It may be open to debate whether the other input variable should be included in the equation. From an empirical point of view, there may be omitted variables that affect both simultaneously, for example, proximity to agrobusinesses, possibly making the result biased. On the other hand, from a practical point of view, there must be complementarities between the different inputs used. Both cases were checked. The results were almost the same.
- 22 -

For other coefficients, unlike crop production income, the time-specific fixed-effects are estimated to be positive for fertilizer use, meaning that the fertilizer use among farmers systematically increased in comparison with the baseline year, 2012. In addition, the level of education attained by household-heads may also be important. Some education coefficients are significantly positive. This is also a different result from the above crop income regression.

**<u>Table 7. Truncated regression on input use with fixed-effects</u>**

|<br>Dependent var.|<br>ln_F_||<br>ln_F_||ln_P_||ln_P_||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|D<sup>RSDP2km</sup><br>|0.419|(0.205) **|0.695|(0.220) ***|0.226|(0.499)|0.532|(0.569)|
|D<sup>URRAP2km</sup><br>|0.035|(0.151)|0.106|(0.147)|<sup>-0.369</sup>|<sup>(0.284)</sup>|<sup>-0.277</sup>|<sup>(0.281)</sup>|
|D<sup>RSDP2km</sup>•D<sup>URRAP2km</sup>|||-0.804|(0.391) **|||-1.069|(0.942)|
|ln_L_|0.020|(0.055)|<br>0.021|<br>(0.055)|<sup>-0.107</sup>|<sup>(0.168)</sup>|<br> <sup>-0.108</sup>|<br><sup>(0.164)</sup>|
|ln_R_|1.084|(0.079) ***|1.083|(0.080) ***|1.035|(0.165) ***|1.081|(0.163) ***|
|ln_I_|0.019|(0.027)|0.020|(0.027)|0.052|(0.076)|0.051|(0.075)|
|ln_P_|0.050|(0.014) ***|0.050|(0.014) ***|||||
|ln_F_|||||0.058|(0.030) **|0.049|(0.029) *|
|ln_SIZE_|-0.038|(0.204)|<sup>-0.044</sup>|<sup>(0.203)</sup>|<sup>-0.652</sup>|<sup>(0.425)</sup>|<sup>-0.688</sup>|<sup>(0.425) *</sup>|
|ln_MALE_|-0.340|(0.256)|<sup>-0.345</sup>|<sup>(0.258)</sup>|0.058|(0.300)|0.034|(0.307)|
|ln_AGE_|0.245|(0.230)|0.243|(0.229)|<sup>-0.582</sup>|<sup>(0.484)</sup>|<sup>-0.628</sup>|<sup>(0.489)</sup>|
|EDU2|0.393|(0.273)|0.374|(0.270)|<sup>-0.569</sup>|<sup>(0.519)</sup>|<sup>-0.663</sup>|<sup>(0.524)</sup>|
|EDU3|0.491|(0.288) *|0.484|(0.286) *|1.393|(0.627) **|1.384|(0.633) **|
|EDU4|0.031|(0.417)|0.220|(0.560)|<sup>-0.453</sup>|<sup>(0.377)</sup>|<sup>-0.096</sup>|<sup>(0.454)</sup>|
|EDU5|0.115|(0.310)|0.111|(0.327)|<sup>-0.296</sup>|<sup>(0.554)</sup>|<sup>-0.508</sup>|<sup>(0.610)</sup>|
|EDU6|0.936|(0.338) ***|1.099|(0.387) ***|-7.335|(0.832) ***|-7.337|(0.833) ***|
|EDU7|-0.071|(0.217)|<sup>-0.056</sup>|<sup>(0.215)</sup>|0.424|(0.316)|0.450|(0.321)|
|COOK|0.379|(0.426)|0.409|(0.429)|0.367|(0.386)|0.393|(0.356)|
|LIGH|-0.144|(0.248)|<sup>-0.185</sup>|<sup>(0.251)</sup>|0.337|(0.509)|0.270|(0.518)|
|T2013|0.157|(0.141)|0.162|(0.141)|0.085|(0.254)|0.116|(0.255)|
|T2014|1.173|(0.179) ***|1.160|(0.177) ***|-0.366|(0.229) *|-0.378|(0.230) *|
|T2015|0.152|(0.166)|0.139|(0.165)|<sup>-0.626</sup>|<sup>(0.221) ***</sup>|-0.618|(0.221) ***|
|T2016|0.365|(0.170) **|0.343|(0.169) **|-0.376|(0.306)|<sup>-0.352</sup>|<sup>(0.303)</sup>|
|Constant|-1.471|(1.126)|-1.445|(1.125)|4.137|(2.206)*|4.373|(2.254)*|
|Obs.|4639||4639||4639||4639||
|Of which, truncated|3712||3712||4198||4198||
|No. of groups|390||390||287||287||
|Wald chi2|.||.||.||.||

The dependent variable is the log of fertilizer or pesticide used. Cluster standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
- 23 -

As shown above, one likely results chain from road improvements to household income is that improved main roads contributed to farmers’ access to input markets, particularly for fertilizer. How about access to output markets? Equation (5) is estimated using the truncated regression model. Although the improvement of feeder roads was found to have little influence on farmers’ access to input markets, feeder roads do play an important role in providing access to the output market. The coefficient of _D_<sup>_URRAP2km_</sup> is estimated at 0.36 to 0.39, which is significant at least at the 10 percent level ( **Table 8** ).

By contrast, major roads do not have any impact on farmers’ output sales. This can be interpreted to mean that farmers are selling extra produce at their neighboring markets. This is in line with the above observation that most farmers travel fairly short distances to market (see **Figure 3** ). Therefore, feeder roads are far more critical than main roads, allowing the consolidation of produce which can then by distributed to larger market centers.

|**Table 8. Truncated**|**regressio**|**n of market**|**crop sales**|**with fixed-e**|**ffects**||||
|---|---|---|---|---|---|---|---|---|
|Dependent var.|ln_SALE_||||||||
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|D<sup>RSDP2km</sup>|-0.183|(0.245)|||-0.244|(0.243)|<sup>-0.548</sup>|<sup>(0.415)</sup>|
|D<sup>URRAP2km</sup>|||0.366|(0.220) *|0.392|(0.223) *|0.326|(0.236)|
|D<sup>RSDP2km</sup>•D<sup>URRAP2km</sup>|||||||0.539|(0.494)|
|ln_L_|0.040|(0.074)|0.051|(0.076)|0.052|(0.077)|0.048|(0.077)|
|ln_R_|0.022|(0.036)|0.026|(0.037)|0.028|(0.036)|0.027|(0.037)|
|ln_I_|0.007|(0.042)|0.003|(0.043)|0.005|(0.043)|0.005|(0.043)|
|ln_P_|-0.009|(0.019)|<sup>-0.007</sup>|<sup>(0.020)</sup>|<sup>-0.007</sup>|<sup>(0.020)</sup>|<sup>-0.008</sup>|<sup>(0.020)</sup>|
|ln_F_|0.031|(0.019) *|0.024|(0.019)|0.025|(0.019)|0.025|(0.019)|
|ln_SIZE_|-0.090|(0.246)|<sup>-0.106</sup>|<sup>(0.246)</sup>|<sup>-0.103</sup>|<sup>(0.245)</sup>|<sup>-0.095</sup>|<sup>(0.247)</sup>|
|ln_MALE_|0.240|(0.301)|0.226|(0.293)|0.237|(0.295)|0.217|(0.294)|
|ln_AGE_|0.241|(0.326)|0.239|(0.322)|0.231|(0.321)|0.234|(0.321)|
|EDU2|0.082|(0.251)|0.079|(0.249)|0.062|(0.250)|0.068|(0.249)|
|EDU3|-0.064|(0.363)|<sup>-0.060</sup>|<sup>(0.365)</sup>|<sup>-0.077</sup>|<sup>(0.365)</sup>|<sup>-0.065</sup>|<sup>(0.364)</sup>|
|EDU4|0.875|(0.504) *|0.936|(0.524) *|0.961|(0.517) *|0.900|(0.523) *|
|EDU5|-0.036|(0.511)|<sup>-0.007</sup>|<sup>(0.513)</sup>|<sup>-0.006</sup>|<sup>(0.510)</sup>|<sup>-0.012</sup>|<sup>(0.510)</sup>|
|EDU6|0.078|(0.578)|0.093|(0.600)|0.137|(0.575)|0.076|(0.598)|
|EDU7|0.070|(0.328)|0.146|(0.334)|0.155|(0.337)|0.136|(0.335)|
|COOK|-0.128|(0.467)|<sup>-0.111</sup>|<sup>(0.477)</sup>|<sup>-0.120</sup>|<sup>(0.471)</sup>|<sup>-0.140</sup>|<sup>(0.467)</sup>|
|LIGH|0.390|(0.228) *|0.448|(0.223) **|0.442|(0.224) *|0.476|(0.220) **|
- 24 -

|T2013|0.537|(0.209) ***|0.507|(0.211) **|0.502|(0.211) **|0.503|(0.211) **|
|---|---|---|---|---|---|---|---|---|
|T2014|1.018|(0.198) ***|0.904|(0.212) ***|0.909|(0.213) ***|0.927|(0.217) ***|
|T2015|0.134|(0.216)<br>|<sup>-0.053</sup>|<sup>(0.240)</sup><br>|<sup>-0.035</sup>|<sup>(0.242)</sup><br>|<sup>-0.020</sup>|<sup>(0.244)</sup>|
|T2016|0.379|(0.214) *|0.176|(0.235)|0.218|(0.237)|0.246|(0.241)|
|Constant|1.928|(1.440)|1.703|(1.428)|1.702|(1.423)|1.710|(1.424)|
|Obs.|4639||4639||4639||4639||
|Of which, truncated|3595||3595||3595||3595||
|No. of groups|444||444||444||444||
|Wald chi2|.||.||.||.||

The dependent variable is the log of crop sales at market. Cluster standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Turning to other types of household income, no impact is found on livestock ( **Table 9** ). This may be able to be understood because pastoralism has a certain mobile aspect and is less transport-intensive. In Ethiopia, many pastoralists are scattered in remote areas. Thus, the quality of transport infrastructure may not matter much, though livestock can be reared using intensive methods in non-pastoral areas and obtaining feed and forage for the stock and delivering the stock to slaughter and urban markets is potentially more dependent on good trunk roads.

On the other hand, non-agricultural income seems be affected positively by improved RSDP roads and negatively by URRAP roads. This can be interpreted to mean that trunk road improvements generated some indirect impacts in the project areas. Given the increase in crop production and market sales, local economies may be boosted by increased activity in non-agricultural sectors. Interestingly, this impact seems to have intensified over time: the time-specific fixed-effects are significant and positive. The coefficients of _T_ become larger the more time passes after the road improvement. The current household survey data do not allow the possible reasons behind this non-agricultural growth to be identified. Some additional data will now be examined.
- 25 -

**<u>Table 9. Fixed-effect panel regression on livestock and nonagricultural income</u>**

||<br>Livesto<br>Coef.|<br>ck income<br>Std.Err.|<br>Livesto<br>Coef.|<br>ck income<br>Std.Err.|<br>NonAG<br>Coef.|<br>income<br>Std.Err.|NonAG<br>Coef.|income<br>Std.Err.|
|---|---|---|---|---|---|---|---|---|
|D<sup>RSDP2km</sup><br>|-0.638|(0.304) **|0.564|(0.997)|<sup>2.018</sup>|<sup>(0.396) ***</sup>|<sup>-1.105</sup>|<sup>(0.982)</sup>|
|D<sup>URRAP2km</sup><br>|0.119<br>|(0.325)|<sup>0.206</sup>|<sup>(0.325)</sup>|<sup>-1.414</sup>|<sup>(0.379) ***</sup>|<sup>-1.640</sup>|<sup>(0.383) ***</sup>|
|D<sup>RSDP2km</sup>•D<sup>URRAP2km</sup>|||-1.425|(1.010)|||3.701|(1.042) ***|
|ln_L_|0.166|(0.091) *|0.168|(0.091) *|0.160|(0.109)|<sup>0.155</sup>|<sup>(0.109)</sup>|
|ln_G_|0.056|(0.094)|<sup>0.056</sup>|<sup>(0.094)</sup>|||||
|ln_SIZE_|0.332|(0.265)|<sup>0.347</sup>|<sup>(0.264)</sup>|<sup>0.421</sup>|<sup>(0.320)</sup>|<sup>0.383</sup>|<sup>(0.319)</sup>|
|ln_MALE_|0.472|(0.353)|<sup>0.471</sup>|<sup>(0.352)</sup>|<sup>-0.475</sup>|<sup>(0.417)</sup>|<sup>-0.472</sup>|<sup>(0.415)</sup>|
|ln_AGE_|0.684|(0.464)|<sup>0.698</sup>|<sup>(0.464)</sup>|<sup>-0.646</sup>|<sup>(0.502)</sup>|<sup>-0.681</sup>|<sup>(0.501)</sup>|
|EDU2|-0.175|(0.364)|<sup>-0.169</sup>|<sup>(0.364)</sup>|<sup>-0.087</sup>|<sup>(0.430)</sup>|<sup>-0.102</sup>|<sup>(0.430)</sup>|
|EDU3|-0.612|(0.369) *|-0.613|(0.369) *|0.919|(0.455) **|0.919|(0.453) **|
|EDU4|-0.440|(0.683)|<sup>-0.440</sup>|<sup>(0.682)</sup>|<sup>1.463</sup>|<sup>(0.699) **</sup>|1.463|(0.697) **|
|EDU5|-0.973|(0.398) **|-0.975|(0.401) **|0.507|(0.560)|<sup>0.512</sup>|<sup>(0.553)</sup>|
|EDU6|-1.648|(0.546) ***|-1.667|(0.545) ***|<br>1.339|(0.757) *|1.388|(0.748) *|
|EDU7|0.000|(0.533)|<sup>-0.006</sup>|<sup>(0.533)</sup>|<sup>0.181</sup>|<sup>(0.542)</sup>|<sup>0.196</sup>|<sup>(0.543)</sup>|
|COOK|-0.537|(0.403)|<sup>-0.523</sup>|<sup>(0.403)</sup>|<sup>0.355</sup>|<sup>(0.445)</sup>|<sup>0.319</sup>|<sup>(0.443)</sup>|
|LIGH|-0.597|(0.331) *|-0.607|(0.331) *|1.597|(0.422) ***|1.621|(0.420) ***|
|T2013|2.134|(0.240) ***|<br>2.125|(0.239) ***|<br>4.909|(0.296) ***|4.933|(0.297) ***|
|T2014|2.270|(0.270) ***|<br>2.255|(0.269) ***|<br>4.649|(0.308) ***|4.688|(0.308) ***|
|T2015|2.109|(0.280) ***|<br>2.104|(0.280) ***|<br>6.529|(0.323) ***|6.541|(0.324) ***|
|T2016|2.140|(0.305) ***|<br>2.102|(0.305) ***|<br>6.110|(0.335) ***|6.210|(0.338) ***|
|Constant|-7.580|(1.867)***|-7.652|(1.865)***|0.788|(1.920)|0.986|(1.917)|
|Obs.|4639||4639||4639||4639||
|No. of groups|1004||1004||1004||1004||
|R-square|||||||||
|within|0.0406||0.0412||0.1861||0.189||
|between|0.2514||0.2507||0.2791||0.2785||
|overall|0.1145||0.1164||0.1912||0.1949||
|F-stat.|8.8||8.39||38.2||37.32||

The dependent variable is the log of livestock or nonagricultural income. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

To investigate the indirect effects of improved road accessibility more deeply, two additional pieces of data are used, which were collected together with the household surveys. First, the demand for transport services is estimated from passenger survey data. Note that the surveyed passengers may not be the same persons over time, although the surveys were carried out at the same locations that are normally village centers where formal and informal transport services are offered. Thus, the data are cross-sectional at five periods of time and reflect overall demand for transport.
- 26 -

The sample size is much smaller than the above household data: 30 passengers were surveyed along each RSDP road every year. While the total sample size is 600 over five years, the following analysis uses only 498 observations because of some missing values. Each passenger was asked about their travel time, frequency, costs and mode, as well as some basic individual characteristics, such as age and education attained ( **Table 10** ). In the sample, about half of the surveyed passengers travel twice to four times per month. About 35 percent travel only once a month.

|**Table 10. Summary statistics of passenger survey data**|
|---|

|<br>Variable|<br>Abb.|Obs.|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Frequency of travel per month|||||||
|Once||172|||||
|2 to 4 times||243|||||
|5 to 8 times||83|||||
|Dummy variable for program beneficiaries|D<sup>RSDP</sup>|498|0.227|0.419|0|1|
|from RSDP roads|||||||
|Distance to all weather road (minutes):|||||||
|15 to 30 minutes|FEED15-30m|498|0.235|0.424|0|1|
|31 to 60 minutes|FEED30-60m|498|0.098|0.298|0|1|
|1 to 2 hours|FEED1-2hr|498|0.056|0.231|0|1|
|2 to 3 hours|FEED2-3hr|498|0.044|0.206|0|1|
|More than 3 hours|FEED>3hr|498|0.052|0.223|0|1|
|Average travel time (minutes)|TIME|498|103.886|128.402|0|850|
|Average transport fares (EHB)|FARE|498|36.040|45.574|0|551|
|Average waiting time before boarding|WAIT|498|77.966|84.962|0|480|
|(minutes)|||||||
|Dummy variable for occupation:|||||||
|Farmers|FARM|498|0.112|0.316|0|1|
|Traders, bakery and other small businesses|SME|498|0.173|0.378|0|1|
|Government employees, nurse, teachers|GOVT|498|0.205|0.404|0|1|
|Students|STUD|498|0.122|0.328|0|1|
|Dummy variable for transport mode:|||||||
|Minibus|MMinibus|498|0.514|0.500|0|1|
|Bajaj, motorcycles|MBajaj, Motor|498|0.008|0.089|0|1|
|Age|AGE|498|33.558|11.717|14|87|
|<br>Dummy variable for male|MALE|498|0.624|0.485|0|1|
|<br>Dummy variable for education attained: Higher|<br>2EDU|498|0.390|0.488|0|1|
|<br>than secondary school|||||||
|<br>Dummy variable for living areas: Urban|URBAN|498|0.729|0.445|0|1|
|Time fixed effects:|||||||
|_t_=2013|T2013|498|0.175|0.380|0|1|
- 27 -

|_t_=2014|T2014|498|0.175|0.380|0<br>1|
|---|---|---|---|---|---|
|_t_=2015|T2015|498|0.237|0.426|0<br>1|
|_t_=2016|T2016|498|0.229|0.421|0<br>1|

The censored regression is performed where the dependent variable is the number of journeys per month. Our dependent variable is censored to (i) once, (ii) 2 to 4 times, (iii) 5 to 8 times, and (iv) more than 8 times, because of our simplified survey design.<sup>6</sup> The results show that the mobility of people is crucially dependent on proximity to the nearest feeder road in good condition ( **Table 11** ). It is significantly reduced when people live more than 1 hour from an all-weather road. The coefficients of _FEED1-2hr_ and _FEED2-3hr_ are estimated to be significantly negative. On the other hand, the proximity to RSDP roads does not seem to affect people’s demand for transportation. To examine the interaction effect between RSDP and feeder roads, a dummy variable representing those who have a good feeder road in less than 1 hour is created. As expected, this variable has a positive and significant coefficient of 0.563. However, there is no significant interaction effect on the transport demand between the main and feeder road accessibility.

The estimated demand equation also indicates what constraints on mobility people face in rural Ethiopia. One of them is efficiency of transport services: The coefficient of travel time, _TIME_ , is significantly negative, meaning transport speed is important to meet people’s demand for transportation. The waiting time variable ( _WAIT_ ) also has a negative and weakly significant coefficient. That is, transportation demand would be reduced when waiting time is longer. The finding is in line with the positive coefficient of bajaj, which is a three-wheeler taxi now widely used in Ethiopia. Frequency of transport services really matters. This is why people seem to prefer to use bajajs, because they do not have to wait for a taxi or bus to be fully laden with passengers before departing. On the other hand, transport fares have a negative coefficient, as expected, however, there is no statistical significance.

> 6 In our sample, no one responded that she traveled more than 8 times. Thus, we have only 3 discrete choices that are actually observed.
- 28 -

The transport service demand is found to be significantly high when people engage in retail and other local businesses, such as bakery and carpentry: The coefficient of _SME_ is significant. Farmers’ demand seems to be weak: the coefficient of _FARM_ is small and statistically insignificant. This is broadly consistent with the above. The estimation results suggest that there is an important indirect effect on local businesses. As the feeder road network improves, agricultural production and market sales increase. Together with such agricultural growth, local economies will grow, with more transport services provided. This is a much broader impact that improved roads can generate.

**<u>Table 11. Censored regression for transportation demand</u>**

||<br>Coef.|<br>Std.Err.|<br>Coef.|<br>Std.Err.|Coef.|Std.Err.|
|---|---|---|---|---|---|---|
|D<sup>RSDP</sup>|-0.265|(0.223)|-0.161|(0.214)|0.066|(0.339)|
|FEED15-30min|0.192|(0.210)|||||
|FEED30-60min|-0.323|(0.292)|||||
|FEED1-2hr|-0.611|(0.282) **|||||
|FEED2-3hr|-1.131|(0.327) ***|||||
|FEED>3hr|-0.086|(0.364)|||||
|D<sup>Less than 1hr</sup>|||0.563|(0.209) ***|0.642|(0.231) ***|
|D<sup>RSDP</sup>•D<sup>Less than 1hr</sup>|||||-0.280|(0.390)|
|TIME|-0.0018|(0.0007) **|-0.0018|(0.0007) **|-0.0018|(0.0007) **|
|FARE|-0.0041|(0.0026)|<sup>-0.0041</sup>|<sup>(0.0025)</sup>|-0.0040|(0.0025)|
|WAIT|-0.0014|(0.0008) *|-0.0010|(0.0008)|-0.0010|(0.0008)|
|FARM|0.029|(0.250)|0.043|(0.244)|0.025|(0.246)|
|SME|0.941|(0.245) ***|0.926|(0.246) ***|0.922|(0.247) ***|
|GOVT|0.022|(0.196)|0.002|(0.197)|-0.010|(0.197)|
|STUD|0.422|(0.246) *|0.409|(0.251) *|0.410|(0.252) *|
|MMinibus|-0.187|(0.177)|<sup>-0.135</sup>|<sup>(0.172)</sup>|-0.140|(0.173)|
|MBajaj, Motor|0.812|(0.528)|0.924|(0.558) *|0.946|(0.556) *|
|AGE|-0.004|(0.007)|<sup>-0.003</sup>|<sup>(0.007)</sup>|-0.003|(0.007)|
|MALE|0.262|(0.161) *|0.207|(0.162)|0.211|(0.162)|
|2EDU|-0.033|(0.160)|<sup>-0.026</sup>|<sup>(0.159)</sup>|-0.024|(0.159)|
|URBAN|-0.046|(0.211)|0.056|(0.196)|0.049|(0.196)|
|T2013|-0.119|(0.250)|<sup>-0.028</sup>|<sup>(0.246)</sup>|-0.026|(0.246)|
|T2014|0.828|(0.271) ***|0.809|(0.274) ***|0.811|(0.275) ***|
|T2015|-0.036|(0.246)|<sup>-0.012</sup>|<sup>(0.246)</sup>|-0.027|(0.245)|
|T2016|0.360|(0.292)|0.346|(0.295)|0.355|(0.298)|
|Constant|3.037|(0.419)***|2.289|(0.402)***|2.231|(0.412)***|
|Obs.|498||498||498||
|Wald chi2|102.05||91.53||91.98||
- 29 -

The dependent variable is the (censored) frequency of travel per month. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

Another set of complementary data covers local businesses. Along with the household surveys, local enterprises were also interviewed. Again, the sample size is limited; Only 30 firms, such as retailers and restaurants, were surveyed along each RSDP road every year. Each firm was asked about the number of employees and their basic business environment. In the sample, the average size of firms is about 5 employees ( **Table 12** ).

Logarithmic panel regression was performed on the size of firms. It was found that the improvement of RSDP roads had a positive and significant impact on the firm size ( **Table 13** ). This is consistent with the above local business hypothesis: as the local economies pick up, local businesses are hiring more people. The demand for local services increases along the main corridors. Feeder road accessibility is also important: The size of firms is significantly reduced when they are located more than 30 minutes from an all-weather road. The coefficients of _FEED30-60min_ and _FEED>3hr_ are significantly negative.<sup>7</sup> Though, there is no clear synergy between the RSDP and feeder road proximity.

By type of business, hotels, restaurants and pharmacies are among the rapidly growing subsectors, compared with a baseline, which is retailers. As the traffic increases, these businesses along the improved roads are likely to expand, generating more local jobs. While supply increases, prices are also likely to decline. These indirect effects are considered as a main reason why the road programs have a positive and significant impact on nonagricultural household income.

7 Note that in our sample data, there are no firms that are located 1 to 2 hours from an all weather road.
- 30 -

# **<u>Table 12. Summary statistics of local business survey data</u>**

|<br>Variable|<br>Abb.|<br>Obs.|<br>Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Number of employees||474|4.989|6.139|1|53|
|Dummy variable for program|D<sup>RSDP</sup>||||||
|beneficiaries from RSDP roads||474|0.234|0.424|0|1|
|Distance to all weather road (mi|nutes):||||||
|15 to 30 minutes|FEED15-30min|474|0.139|0.347|0|1|
|31 to 60 minutes|FEED30-60min|474|0.034|0.181|0|1|
|1 to 2 hours||474|0|0|0|0|
|2 to 3 hours|FEED2-3hr|474|0.053|0.224|0|1|
|More than 3 hours|FEED>3hr|474|0.015|0.121|0|1|
|Business perspective compared t<br>sales:|o last year||||||
|No change|NOCH|474|0.095|0.293|0|1|
|Increasing|INCRE|474|0.859|0.349|0|1|
|Business type:|||||||
|Shop||474|0.367|0.483|0|1|
|Hotel, restaurant, café|HOTL|474|0.340|0.474|0|1|
|Pharmacy|PHAM|474|0.293|0.456|0|1|
|Time fixed effects:|||||||
|_t_=2013|T2013|474|0.224|0.417|0|1|
|_t_=2014|T2014|474|0.184|0.388|0|1|
|_t_=2015|T2015|474|0.184|0.388|0|1|
|_t_=2016|T2016|474|0.173|0.379|0|1|

**<u>Table 13. OLS regression on size of firms</u>**

|<br>Coef.|<br>Std.Err.<br>Coef.|Std.Err.|Coef. Std.Err.|
|---|---|---|---|
|D<sup>RSDP</sup><br>0.310|(0.074) ***<br>0.304|(0.077) ***|0.197 (0.142)|
|FEED15-30min<br>-0.056|(0.077)|||
|FEED30-60min<br>-0.499|(0.155) ***|||
|FEED2-3hr<br>-0.026|(0.113)|||
|FEED>3hr<br>-0.468|(0.112) ***|||
|D<sup>Less than 30min</sup>|0.244|(0.089) ***|0.227 (0.102) **|
|D<sup>RSDP</sup>•D<sup>Less than 30min</sup>|||0.116 (0.151)|
|NOCH<br>0.067|(0.126)<br> <sup>0.057</sup>|<sup>(0.123)</sup>|<sup>0.060 (0.124)</sup>|
|INCRE<br>0.191|(0.119) *<br>0.191|(0.117) *|0.194 (0.118) *|
|HOTL<br>1.062|(0.071) ***<br>1.059|(0.071) ***|1.060 (0.071) ***|
|PHAM<br>0.336|(0.046) ***<br>0.346|(0.046) ***|0.347 (0.046) ***|
|T2013<br>0.254|(0.087) ***<br>0.251|(0.085) ***|0.251 (0.085) ***|
|T2014<br>0.101|(0.089)<br> <sup>0.097</sup>|<sup>(0.092)</sup>|<sup>0.092 (0.093)</sup>|
|T2015<br>0.047|(0.092)<br><sup>0.044</sup>|<sup>(0.096)</sup>|<sup>0.039 (0.097)</sup>|
- 31 -

|T2016|0.237|(0.102) **|0.255|(0.103) **|0.253 (0.103) **|
|---|---|---|---|---|---|
|Constant|0.469|(0.125)***|0.216|(0.158)|0.230 (0.163)|
|Obs.|474||474||474|
|R-squared|0.440||0.430||0.430|
|F-stat.|25.69||30.77||27.85|

The dependent variable is the log of the number of employees. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.

# **VI. CONCLUSIONS**

Rural access is among the most important infrastructure constraints in rural Africa. Despite a relatively large number of earlier studies in this area, there are still divergent views on the results chains generated by rural access improvement. The paper examines the heterogeneous impacts of road accessibility on agriculture and non-agricultural growth. It is found that it is important to distinguish between different types of road to understand these heterogeneous effects.

Using the comprehensive household and other data from Ethiopia, the paper examined the impacts of corridor improvement and feeder road rehabilitation. It was found that crop production is increased by both major and feeder road accessibility. Significant synergy between them was also found. When investigating further into this effect, there are actually two different impacts: Farmers’ access to input markets, especially fertilizer, was improved mainly by major corridor improvement. On the other hand, output market access was improved by feeder road improvement, though this impact seems to be relatively weak. These findings may make sense given the current Ethiopian context. The timely distribution of fertilizer relies on the main road network, because many agricultural inputs are imported from abroad. Farmers may sell extra produce in their neighboring markets, but the market participation is generally still limited.

It is also found that there is no significant impact of accessibility on household income from livestock. By contrast, the households’ non-agricultural income is increased by improved
- 32 -

road connectivity. There seem to be other indirect effects. With additional data, further investigation was carried out: It is found that along the improved roads, significant transport demand was generated by local business activity. As the road condition was improved, the local economies grew with more local jobs created. This is the main source of the secondary effects of road improvements.

To meet the increasing demand for transport services, efficiency and frequency are found to be important. The elasticities are small but statistically significant. By contrast, transport costs do not seem to be critical. Thus, not only physical infrastructure, regardless of whether it is a main road or a feeder road, but also transport services are also important to promote economic growth in rural Ethiopia.