metadata: authors: 0: author: Mikou,Mehdi
1: author: Rozenberg,Julie
2: author: Koks,Elco Eduard
3: author: Fox,Charles James Edward
4: author: Peralta Quiros,Tatiana
count: World
docna: 0: docna: Assessing Rural Accessibility and Rural
            Roads Investment Needs Using Open Source Data
docty: Policy Research Working Paper
subtopic: Rural Transport,Rural Roads & Transport,Intelligent Transport Systems,Hazard Risk Management,Global Environment Facility,Biodiversity
teratopic: Rural Development,Transport,Urban Development,Environment
abstracts: cdata!: Rural accessibility is the only metric
            used in the Sustainable Development Goals to track progress
            toward better transport services in low- and middle-income
            countries. This paper estimates the rural accessibility
            index, defined as the proportion of the rural population who
            live within 2 kilometers of an all-season road, in 166
            countries using open data. It then explores the cost of
            increasing the rural accessibility index in 19 countries,
            using an algorithm that prioritizes rural roads investments
            based on their impact on rural access and connectivity.
            Investment costs quickly balloon as the rural accessibility
            index increases, questioning the affordability of universal
            access to paved roads for many countries by 2030. If
            countries spent 1 percent of their gross domestic product
            annually on the upgrade of rural roads, even under
            optimistic assumptions on growth of gross domestic product,
            rural accessibility would only increase from 39 to 52
            percent by 2030 across all developing countries. Alternative
            solutions to rural integration must thus be implemented in
            the short run until countries can afford to increase
            significantly access to all weather roads. For example,
            drones that supply regular food and medicine supply to
            remote communities are much more affordable than roads in
            the short term.
repnme: repnme: Assessing Rural Accessibility and Rural
            Roads Investment Needs Using Open Source Data
display_title: Assessing Rural Accessibility and
            Rural Roads Investment Needs Using Open Source Data
listing_relative_url: /research/2019/02/30837721/assessing-rural-accessibility-rural-roads-investment-needs-using-open-source-data
topicv3: Biodiversity m1326214 2084,Rural Roads(RR) m1327606 786,Rural Accessibility m1504911 4178,Secondary Roads m1505488 4164,Drones m1505732 4200
publishtoextweb_dt: 2019-02-19T00:00:00Z
historic_topic: Rural Development,Transport,Urban Development,Environment
no_of_pages: 29
author_keys: 0: author_key: 1621778
1: author_key: 1296276
2: author_key: 1618196
3: author_key: 1436860
4: author_key: 1271177
model_extractions: Policy Research Working Paper 8746

**Beyond the Gap** How Countries Can Afford the Infrastructure They Need While Protecting the Planet

_Background Paper_ Assessing Rural Accessibility and Rural Roads Investment Needs Using Open Source Data _Mehdi Mikou_ _Julie Rozenberg Elco Koks Charles Fox Tatiana Peralta Quiros_ Sustainable Development Practice Group Office of the Chief Economist February 2019
Policy Research Working Paper 8746

# **Abstract**

Rural accessibility is the only metric used in the Sustainable Development Goals to track progress toward better transport services in low- and middle-income countries. This paper estimates the rural accessibility index, defined as the proportion of the rural population who live within 2 kilometers of an all-season road, in 166 countries using open data. It then explores the cost of increasing the rural accessibility index in 19 countries, using an algorithm that prioritizes rural roads investments based on their impact on rural access and connectivity. Investment costs quickly balloon as the rural accessibility index increases, questioning the affordability of universal access to paved roads for many countries by 2030. If countries spent 1 percent of their gross domestic product annually on the upgrade of rural roads, even under optimistic assumptions on growth of gross domestic product, rural accessibility would only increase from 39 to 52 percent by 2030 across all developing countries. Alternative solutions to rural integration must thus be implemented in the short run until countries can afford to increase significantly access to all weather roads. For example, drones that supply regular food and medicine supply to remote communities are much more affordable than roads in the short term.

This paper was commissioned by the World Bank Group’s Chief Economist office for Sustainable Development Practice Group and is a background paper for the World Bank Group’s report: _“Beyond the Gap: How Countries Can Afford the Infrastructure They Need While Protecting the Planet.”_ 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 jrozenberg@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
# **Assessing Rural Accessibility and Rural Roads Investment Needs Using Open Source Data**

Mehdi Mikou<sup>1</sup> , Julie Rozenberg<sup>1</sup> , Elco Koks<sup>2</sup> , Charles Fox<sup>1</sup> , Tatiana Peralta Quiros<sup>1</sup> 1 World Bank, Washington DC, USA; 2Environmental Change Institute, University of Oxford, Oxford, United Kingdom JEL: O1; O2; R0; R4 Keywords: Rural accessibility index; Open Street Map; WorldPop; Sustainable Development Goal 9; transport investments; drones.
# **1. Introduction**

Worldwide, billions of people are unable to access work and educational opportunities because transport services remain either unavailable or unaffordable. The Sustainable Development Goals (SDGs) include a goal to increase rural accessibility, but they do not set a target. Specifically, SDG indicator 9.1.1 refers to the proportion of the rural population who live within 2 kilometers of an all-season road. An " _all season road"_ refers to _"a road that is motorable all year round by the prevailing means of rural transport_ ." In countries with a significant monsoon season, this implies paved roads; but in drier countries, all-season roads can also include gravel roads.

Access to an all-season road is believed to significantly increase households’ welfare if it can help rural populations access new job markets and social services. For example, in Bangladesh, a road paving project implemented from 1997–2001 increased household expenditure by 9-10 percent on average (Khandker and Koolwal, 2011). Similarly, in Ethiopia, access to an all-season road reduced poverty by 7 percent, and increased household consumption by 16 percent (Dercon et al., 2008). However, a road may bring little economic benefit in areas with no market to sustain non-agricultural jobs.

One measure of rural accessibility is the Rural Access Index (RAI), defined by (Roberts et al., 2006) as "the number of rural people who live within two kilometers (typically equivalent to a walk of 20-25 minutes) of an all-season road as a proportion of the total rural population". (Roberts et al., 2006) assessed the RAI in a small number of countries using household survey data. However, this approach was costly, the surveys lacked spatial representativeness, and the resulting RAIs were not necessarily comparable between countries. Ten years later, (Iimi et al., 2016) proposed a new method based on GIS data and applied it in eight countries. They assessed the RAI using WorldPop data for rural population distribution and digitized road network data, including road condition, provided by national road agencies.

However, to be able to inform global policy for improving road access in rural areas, global-wide estimates are required. This is the first global study that attempts to provide such estimates. We go on to explore how rural access might be optimally increased via a sequential road upgrading strategy.

In this paper, we build on the approach developed by (Iimi et al., 2016) and use road networks extracted from OpenStreetMap (OSM) for 166 countries, to assess road access globally. Population data for these countries is, similar to Limi et al. (2016), taken from WorldPop.<sup>1</sup> We find that while most European countries have an RAI close to 100 percent, most developing countries are below 60 percent, all SubSaharan countries are below 51 percent, and 24 countries are below 20 percent.

We then explore the cost of increasing rural access in 19 countries with good quality data, using an algorithm that prioritizes investment in individual rural road segments based on their impact on rural access and connectivity. The results show that costs quickly balloon as the RAI increases, but also that they depend on geography, road network connectivity, population distribution, and road unit costs.

Finally, as a thought experiment, we assess the cost of providing regular food and medicine supply by drones to remote areas in Sierra Leone and compare the cost of doing so with the cost of paving roads. We find that even though drones cannot generate the same socio-economic benefits as roads, they can be a good short-term solution to increase rural integration until roads become affordable.

> 1 <mark>www.worldpop.org</mark>

2
# **2. Methods and data**

The RAI is calculated by overlaying a road network with a population layer, removing the urban areas, drawing a buffer around the roads, and counting the number of people in the buffer (World Bank, 2016). The code and documentation are available at <u>http://rai-wb.readthedocs.io/index.html.</u> We construct a GIS composed of a model of the distribution of rural population, and a geospatial model of rural roads (including their location and type). This first section describes the data that were used and the assumptions that were made to estimate rural accessibility with open source data.

## **2.1. Population and urban data**

We calculate rural accessibility for a subset of countries using different data sets for population distribution (WorldPop, LandScan, GHS-Pop) and urban area boundaries (Grump, EC LandScan and EC GHS). The results, displayed in Table 1, show a wide spread of values (RAI ranges from 29% to 51% for the Lao People’s Democratic Republic, depending on the choice of data sets, for example), and different rankings of countries depending on the data sets used. Overall, the RAI is lower when using WorldPop, and higher when using GHS-Pop. This is because WorldPop is the population data set with the least concentrated population in rural areas, while GHS-Pop is the most concentrated of those tested. The impact of the urban/rural area definition is very dependent on the country, though overall EC LandScan tends to lead to higher RAIs.

_Table 1 RAI calculated using the same roads but different population distribution datasets for a selection of countries with diverse geographies and population distribution_

|Population||WorldPop|||LandScan|||GHS-Pop||
|---|---|---|---|---|---|---|---|---|---|
|Urban/Rural limit|Grump|ECLandScan|EC GHS|Grump|ECLandScan|EC GHS|Grump|ECLandScan|EC GHS|
|Belize|27%|<br>45%|<br>41%|41%|<br>53%|<br>48%|35%|62%|<br>51%|
|Lao PDR|31%|<br>31%|<br>29%|39%|<br>33%|<br>36%|51%|49%|<br>45%|
|Panama|23%|<br>43%|<br>35%|34%|<br>37%|<br>35%|32%|49%|<br>35%|
|Sierra Leone|28%|<br>29%|<br>23%|31%|<br>29%|<br>25%|44%|46%|<br>33%|
|Nicaragua|30%|<br>41%|<br>37%|42%|<br>41%|<br>41%|55%|62%|<br>54%|
|Lesotho|20%|<br>21%|<br>21%|46%|<br>43%|<br>45%|43%|43%|<br>40%|
|Togo|30%|<br>26%|<br>23%|33%|<br>27%|<br>30%|54%|49%|<br>43%|
|Guinea|26%|<br>29%|<br>25%|27%|<br>25%|<br>23%|45%|44%|<br>37%|
|Benin|36%|<br>31%|<br>27%|45%|<br>36%|<br>37%|60%|59%|<br>59%|
|Kyrgyz Republic|25%|30%|31%|45%|45%|48%|54%|62%|66%|

We eventually chose WorldPop for our population layer. The computational process underlying the WorldPop data is fully transparent (Stevens et al., 2015), and the model is considered to be the most accurate and robust among the currently available data sets (World Bank, 2016). In addition, to maintain global comparability and methodological simplicity, we rely on the GRUMP (Global Rural-Urban Mapping Project) data set for urban-rural delimitation, as in (World Bank, 2016). Note that WorldPop uses roads as a factor to model population distribution, so there is an endogeneity in our analysis which could lead to an overestimation of rural accessibility. However, WorldPop is still the data set with the lowest concentration of population in rural areas and thus the one that gives the lowest rural accessibility in most countries (Table 1). These caveats call for high caution in the interpretation of the results presented here, and for further 3
<!-- Start of picture text --> SSP1 Projections for RAI : SSP3 Projections for RAI: Regional<br>Sustainability Rivalry<br>65% 65%<br>60%Pa 60% —————<br>55% 55% Country:<br>@ Benin<br>50% 50% TM Burkina Faso<br>® Burundi<br>45% 45% TM Central African Republic<br>@ Eritrea<br>40% 40% ® Guinea<br>(heen @ Kenya<br>=z 35% =z 35% @ Liberia<br>& ~ @ Mali<br>30% 30% @ Niger<br>@ Sierra Leone<br>25%, SB5 somata<br>20%SS 08 O———————————_—_—— Togo<br>10%— — 10% —_——__—_———————<br>5% 5%<br>0% 0%<br>2010 2020 2030 2040 2050 2010 2020 2030 2040 2050<br><!-- End of picture text -->
# **2.2. Road data**

In the original formulation of the Rural Accessibility Index, the “all-seasonality” of rural roads is based on the surface type and roughness of each road. The RAI only considers the roads serviceable if they are paved roads in ‘good’ or ‘fair’ condition, and unpaved roads in ‘good’ condition.

National road agencies typically have a database of their road network with some information on paving and condition. However, in many low-income countries, these databases are incomplete and rarely updated (Espinet Alegre et al., 2018). An alternative to these databases is OpenStreetMap (OSM), a collaborative project to create a free map of the world. OSM allows any user to edit the map anywhere in the world, without restriction. It represents perhaps the largest map in the world, though completeness varies from region to region. OSM users classify roads based on their function, using their knowledge of the local area or satellite imagery. We reduce this classification down to four categories (primary, secondary, tertiary, and tracks, see Annex 2 for the detailed classification).

Unfortunately, OSM does not allow users to provide information on the type of pavement or the quality of the road; and yet information on road quality is essential for the calculation of rural accessibility. To overcome this issue, the RAI is calculated three times, assuming that all-season roads can be loosely defined from the OSM ‘highway’ tag as (i) primary or secondary roads only; (ii) primary, secondary and tertiary roads; (iii) all roads. We then compare the results with previous RAI estimates (Figure 2) and with the fraction of paved roads provided by the International Roads Federation<sup>2</sup> and conclude that the best proxy for all-season roads are primary and secondary roads.

> 2 https://worldroadstatistics.org/

5
<!-- Start of picture text --> =<br>uw /O%<br>Tc<br>oc<br>2<br>oo> 60% Burundi87%<br>wT<br>c<br>8<br>vo Nepal<br>Y com ® 100%<br>fs Uganda ©<br>> 55% B Bangladesh<br>5 - e 6 19%<br>i= 40% e Ey Kenya<br>= Malawi : Liberia 71%<br>5 329 Somalia =f<br>s 98% BL<br>10)<br>O 30% a<br>=)) Tanzani. s<br>5 59% Sierra Leone<br>FS: aa ie<br>s 2U% Lesotho99% . \ Mozambique ue<br>55%<br>mH<br>10% Zambiaid Ethiopia429%<br>@ 72%<br><!-- End of picture text --> RAI using detailed road data (%)
|Eastern Europe<br>and Central Asia|1,718,000|1,588,000|567,000|27,000|4,000|2,000|15,000|23,000|
|---|---|---|---|---|---|---|---|---|

## **2.4. Prioritization of rural investments**

We built a model to prioritize rural road investments based on two simple criteria: (i) maximizing the RAI increase per kilometer paved; and (ii) candidate roads for upgrade must be connected to the existing primary and secondary network. The only investment option available in our model is to upgrade existing roads labeled as either ‘tertiary’ or ‘track’ in OSM to an all-season road where those roads are directly adjacent to the network of current primary and secondary roads. The analysis was done in 19 countries in which the potential for increasing the RAI is high, and for which the data were complete enough.<sup>3</sup> The algorithm for investment prioritization takes a two-step approach, which can be described as follows:

**Step 1** : The OSM road data are processed to identify the largest current connected network of primary, secondary, tertiary roads and tracks (the largest connected sub-graph of the network). In many countries, a significant number of road segments are disconnected or misaligned in OSM. A quick fix is to generate a buffer of 200 meters around each road and remove the roads that are not connected to the network outside the buffer. In addition, all roads with a length of less than 500 meters were omitted, as well as the roads serving less than 0.01% of the total rural population.

**Step 2** : The network of connected roads is then overlaid with the population layer, and the number of people living within 2km of each tertiary road or track is calculated. Tertiary roads and tracks are then ranked according to the number of people per kilometer of road.

The first road from this list is recorded and marked as upgraded to all-season, the new RAI is calculated, and the algorithm starts at Step 2 again, until no more improvement to accessibility can be achieved given the restrictions outlined in Step 1.

# **3. Results and discussion**

## **3.1. Global Rural Accessibility Index estimates**

Results for rural accessibility are displayed in Figure 3. While most European countries have an RAI close to 100 percent, most developing countries are below 60 percent, all Sub-Saharan countries are below 51 percent, and 24 countries are below 20 percent.

> 3 Data were considered good enough if the completeness score on Open Street Map was higher than 75 percent.

7
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<!-- Start of picture text --> 70<br>éo<br>—<br>z 50<br>S<br>40<br>30<br>0 20 40 60 a wo 120<br>Cumulative cost (% GOP)<br><!-- End of picture text --> <!-- Start of picture text --> é y wn<br>£5 re =,<br>One a:<br>Ne,ae jhe<br>C a ee eG ae|<br>— PrimaryTertiary and Roads Secondary Roads ~~ 3 if~<br><!-- End of picture text -->

<!-- Start of picture text --> 6<br>.<br>5 ‘ “e<br>a 4 . *<br>. tad<br>= 3 Ls . . °<br>nada . td]<br>Pn a<br>1 J<br>*<br>o *<br>30 4g co 60 70<br>RAI (96)<br><!-- End of picture text --> <!-- Start of picture text --> o : . Y .<br>Ee vn ie? ee Sy,<br>e ca a A; Ne: Og<br>, rad a,<br>Ae, APR ee<br>“oe , Q ee yo<br>— Primary and Secondary Roads ‘ a<br>Tertiary Roads aig<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>ee ename + os te<br>80<br>x=. 0<br>s<br>3&S<br>EB 4<br>Fd<br>20 ee BoliviaMorocco<br>e Cameroon<br>e Bulgaria<br>e@ Benin<br>0 e@ Togo<br>0 500 1000 1500 2000 2500<br>Population Density (people/km*)<br><!-- End of picture text --> <!-- Start of picture text --> 70 .<br>60<br>i/<br>50<br>>z<br>FE<br>40<br>30 @@ BoliviaMorocco<br>@ Cameroon<br>@ Bulgaria<br>@ Benin<br>20 @ Togo<br>0 1000 2000 3000 4000 5000<br>Cumulative cost($ million)<br><!-- End of picture text -->
**_Table 4_** _Capital and maintenance costs of reaching a given RAI objective_

|**Country**|**RAI objective**<br>**(percent)**|**Total cost of upgrading Tertiary**<br>**Roads (percent of current GDP)**|**Total annual maintenance costs (paved**<br>**roads) (percent of GDP)**|
|---|---|---|---|
|Sierra Leone|51|30|1.1|
||70|85|1.8|
|Togo|56|25|2|
||69|65|2.2|
|Burkina Faso|24|16|1.7|
||32|42|2.1|
|Guinea|40|17|1.1|
||47|39|1.4|

Given that goals and costs are so country-dependent that it is impossible to cost overall rural access, we can reverse the question and ask: How much access could countries achieve by 2030 if they each spent 1 percent of their GDP on new rural roads every year? Our results show that with optimistic assumptions on GDP growth, the increase in access could go from 9 percentage points on average in East Asia to 18 percentage points on average in Sub-Saharan Africa (Table 5). Table 5 also shows that most of the gains can be obtained by spending only 0.5 percent of GDP every year, since the cost of incrementally improving access quickly escalates as the served population increases.

**_Table 5_** _Universal access to paved roads is not within countries’ reach by 2030._

||**Share of rura**<br>**Current RAI**|**l population within 2km of a primary**<br>**RAI if all countries spend 0.5**<br>**percent of their GDP per year to**<br>**2030**|**or secondary road (percent)**<br>**RAI if all countries spend 1**<br>**percent of their GDP per**<br>**year to 2030**|
|---|---|---|---|
|East Asia & Pacific|52|59|61|
|Europe & Central Asia|29|37|40|
|Latin America & Caribbean|34|42|45|
|Middle East & North Africa|39|49|51|
|South Asia|43|54|57|
|Sub-Saharan Africa|29|42|46|

Note: GDPs for each country grows according to the Shared Socio-Economic Pathway 5, which has the highest growth rate (Dellink et al., 2017).

# **3.3. Environmental impact of rural roads**

A growing body of literature is sounding the alarm on the potential negative impacts of opening or upgrading roads on forest cover and biodiversity, although the degree of impact strongly depends on the local context and road type. (Asher et al., 2018) do not find a significant impact of rural roads on forests in India, while (Pfaff et al., 2018) and (Damania et al., 2018) find that impacts can be significant in the Amazon rainforest and in Africa.

To understand the possible order of magnitude of the impact, we run three scenarios: (i) newly paved rural roads lead to deforestation in an area of 25 meters on each side of the road (minimum impact, mainly due to road works), (ii) the impact spreads to 2 kilometers on each side of the road; and (iii) the impact extends to 10 kilometers on each side of the road (this is not the most extreme scenario we could consider, since Desbureaux and Damania (forthcoming) find that in Kenya the blast of a road on biodiversity can go as far as 25km). If the impact is 2km or more of deforestation, as Figure 6 shows, the consequences for 11
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<!-- Start of picture text --> 60<br>35<br>50<br>#45<br>%<br>4p<br>5<br>@ Paved Roads<br>0 ® Gravel Roads<br>@ Paved and Gravel Roads<br>0 1000 2000 3000 - 4000 5000<br>Cumulative cast (§ million)<br><!-- End of picture text --> <!-- Start of picture text --> 475<br>45.0<br>425<br>g 0.0<br>4375<br>35.0<br>RS e@ Paved Roads<br>® Gravel Roads<br>30.0 @ Paved and Gravel Roads<br>0 500 1000 1500 2000 3500<br>Cumulative cost (§ million)<br><!-- End of picture text --> <!-- Start of picture text --> MED.<br>/VeQuee, \opes<br>é [7 @ Rural Area of Sierra Leone<br>G@@ 20km Buffer around PS Roads<br>@ 30km Buffer around PS Roads<br>¢<br><!-- End of picture text -->
As a thought experiment, the cost of supplying low-density rural areas by drones on a weekly basis was estimated for Sierra Leone. New high-end commercial drones have a range of 40 - 60km, but cheaper ones, like the ones used by nongovernmental organizations, have a range of 20km (Raptopoulos, 2013; USAID Global Health Supply Chain Program-Procurement and Supply Management, 2017). In Sierra Leone, 75 percent of the rural population that lives more than 2km away from a primary or secondary road is within 10km of a primary or secondary road, 16 percent are between 10 and 20km, and 8 percent are between 20 and 40km (only 1 percent lives further than 40km from a primary or secondary road). If drone deliveries target only low-density areas (less than 150 people per square kilometer), the cost of delivering 1kg of supplies for 10 people every week would be between $1 per person per year and $26 per person per year, depending on their distance to the closest all-season road and the drone technology used.

**Table 6** presents the results based on the assumptions that a $3,000 drone flies at 40 kilometers per hour, can carry 1 kilogram, and can fly for 8 hours. A $50,000 drone flies at 100 kilometers per hour, can carry 2 kilograms, and flies for 8 hours. Costs include the capital cost of the drones plus operating costs (including labor). Isolated population is the share of the rural population living further than 2 kilometers away from an all-season road. This results in total estimated cost of $5 million to $9 million per year.

**_Table 6_** _Estimated annual cost of delivering weekly supplies to low density areas in Sierra Leone for different maximum population densities and different drone technologies_

|**Max density served**<br>**(Population/km2)**|**Share of isolated rural**<br>**population served (percent)**|**Total annual cost in milli**<br>**3 000$ Drones**|**on $ (average over 5 years)**<br>**50 000$ Drones**|
|---|---|---|---|
|150|69|5|9|
|100|51|4|7|
|75|37|3|5|

Based on the most conservative assumptions in this analysis for Sierra Leone, bringing weekly supplies to remote areas by drone costs 3 times less every year than increasing rural accessibility from 30 to 31 percent; and 22 times less than increasing accessibility from 69 to 70 percent. Moreover, these costs are likely to go down quickly, as drone markets mature, and the number of service providers increases. Thus, at least in the short term, drone delivery could help increase social integration in remote rural areas.

# **4. Conclusion**

This study presented first estimates of global road access in rural areas, combined with road upgrade cost and strategies to explore how rural access can be increased. To do so, we used the latest state-of-the-art publicly available data sets: road network data from OpenStreetMap and global population estimates from WorldPop.

The results showed that for some countries, universal access to paved roads is not a realistic goal in the short to medium term. We thus reversed the question and asked how much incremental access can be achieved if countries spend 1% of their GDP annually on the upgrade of rural roads, beginning with those that have the highest impact. We found that, under optimistic assumptions on GDP growth, rural accessibility would only increase from 39 to 52 percent by 2030 across all developing countries. We also found that the deforestation effect that accompanies paving these roads can be significant in many countries.

14
Finally, as a thought experiment, we also assessed the cost of providing regular food and medicine supply by drones to remote areas in Sierra Leone. We found the cost is dramatically lower than the cost of paving the required roads in the same areas. While we do not claim that drones can replace roads, we argue they can be a useful short-term solution to improve the welfare of remote populations.

For future developments, measures of rural accessibility must be used with caution when it comes to planning investment in rural roads at the country level. Upgrading rural roads to all weather roads does not guarantee welfare returns if connectivity with the rest of the network and with economic opportunities is not ensured. Second, the investments can have significant negative impacts on biodiversity. This means that the RAI alone cannot be used in the absence of a broader network model to prioritize investments based on a broader list of criteria. Avenues for improvement include using more sophisticated measures of accessibility based on travel time to nodes of interest for social or economic goals, using criticality analysis that will prioritize roads with the highest impact on travel cost or economic activity (Espinet Alegre et al., 2018), and adding penalties when roads have negative impacts on forests and biodiversity.

# **References**

Asher, S.E., Garg, T., and Novosad, P.M. (2018). The ecological impact of transportation infrastructure (The World Bank).

Damania, R., Russ, J., Wheeler, D., and Barra, A.F. (2018). The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction. World Development _101_ , 351–376.

Dellink, R., Chateau, J., Lanzi, E., and Magné, B. (2017). Long-term economic growth projections in the Shared Socioeconomic Pathways. Global Environmental Change _42_ , 200–214.

Dercon, S., Gilligan, D., Hoddinott, J., and Woldehanna, T. (2008). The Impact of Agricultural Extension and Roads on Poverty and Consumption Growth in Fifteen Ethiopian Villages. American Journal of Agricultural Economics 91 (2009) 4 _91_ .

Espinet Alegre, X., Rozenberg, J., Rao, K.S., and Ogita, S. (2018). Piloting the use of network analysis and decision-making under uncertainty in transport operations : preparation and appraisal of a rural roads project in Mozambique under changing flood risk and other deep uncertainties (The World Bank).

Fay, M., Han, S., Lee, H.I., Mastruzzi, M., and Cho, M. (2019). Hitting the Trillion Mark -- A Look at How Much Countries Are Spending on Infrastructure (The World Bank).

Gao, J. (2017). Downscaling Global Spatial Population Projections from 1/8-degree to 1-km Grid Cells.

Iimi, A., and Rao, K. (2018). Spatial Analysis of Liberia’s Transport Connectivity and Potential Growth (Washington DC, USA: The World Bank).

Iimi, A., Ahmed, A.K.F., Anderson, E.C., Diehl, A.S., Maiyo, L., Peralta Quiros, T., and Rao, K.S. (2016). New rural access index : main determinants and correlation to poverty (The World Bank).

Khandker, S.R., and Koolwal, G.B. (2011). Estimating the Long-Term Impacts of Rural Roads: A Dynamic Panel Approach (Rochester, NY: Social Science Research Network).

Laurance, W.F., Clements, G.R., Sloan, S., O’Connell, C.S., Mueller, N.D., Goosem, M., Venter, O., Edwards, D.P., Phalan, B., Balmford, A., et al. (2014). A global strategy for road building. Nature _513_ , 229–232.

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Pfaff, A.S.P., Robalino, J., Reis, E.J., Walker, R., Perz, S., Laurance, W., Bohrer, C., Aldrich, S., Arima, E., Caldas, M., et al. (2018). Roads & SDGs, tradeoffs and synergies: Learning from Brazil’s Amazon in distinguishing frontiers. Economics: The Open-Access, Open-Assessment E-Journal _12_ , 1–26.

Raballand, G., Macchi, P., and Petracco, C. (2010). Rural road investment efficiency : lessons from Burkina Faso, Cameroon, and Uganda (The World Bank).

Raptopoulos, A. (2013). No roads? There’s a drone for that.

Roberts, P., KC, S., and Rastogi, C. (2006). Rural Access Index: A Key Development Indicator (Washington DC, USA: World Bank).

Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLOS ONE _10_ , e0107042.

USAID Global Health Supply Chain Program-Procurement and Supply Management (2017). Unmanned Aerial Vehicles Landscape Analysis : Applications in the development context (Washington DC, USA: Chemonics International Inc).

World Bank (2016). Measuring rural access : using new technologies (The World Bank).

World Bank (2018). Road Costs Knowledge System (ROCKS) – Doing Business Update (Washington DC: World Bank).

# **Annex 1: OSM roads classification**

Below is a list of OSM road labels and classification in primary, secondary, tertiary, and track. All other types of roads are in “other”.

|‘highway’ tagin OSM|Meta‐Classification|
|---|---|
|unsurfaced<br>services<br>corridor<br>escape<br>emergency_access_point<br>emergency_bay<br>construction<br>bridleway<br>path<br>track|**track**|
|living_street<br>residential<br>service<br>tertiary<br>tertiary_link<br>unclassified|**tertiary**|

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|road<br>secondary|**secondary**|
|---|---|
|secondary_link||
|primary||
|primary_link||
|trunk<br>motorway|**primary**|
|trunk_link||
|motorway_link||

# **Annex 2: All RAI results**

|Country|RAI PS|RAI PST|RAI PSTT|OSM<br>completeness|
|---|---|---|---|---|
|Afghanistan|20.3|38.8|56.8|10%|
|Albania|66.7|98.5|99.8|75%|
|Algeria|72.6|86.6|87.8|98%|
|Angola|19.6|40.9|42.2|60%|
|Argentina|43.2|73.1|74.7|100%|
|Armenia|17.7|28.9|29.5|94%|
|Azerbaijan|27.2|58.1|65|71%|
|Bangladesh|47|77.3|77.6|19%|
|Belize|26.7|70|76.2|98%|
|Benin|36.1|80.7|81.7|82%|
|Bhutan|34.2|64.1|67.6|17%|
|Bolivia|20.5|70.1|76.6|100%|
|Bosnia and Herzegovina|79.2|98.7|99|97%|
|Botswana|28|56|66.1|61%|
|Brazil|35.5|65.5|67.5|100%|
|Bulgaria|37.7|99.8|99.9|97%|
|Burkina Faso|15.7|51.7|56.3|93%|
|Burundi|65|93.6|93.8|87%|
|Cambodia|38.1|89.2|90.8|75%|
|Cameroon|31.1|66.6|73.5|100%|
|Cabo Verde|56.8|86|95.2|100%|
|Central African Republic|22.8|33.6|40.8|99%|
|Chad|13.9|28.5|35.3|58%|
|China|57.4|71.4|71.8|24%|
|Colombia|23.9|52.9|63.5|73%|
|Congo,Dem. Rep.|21.8|46.3|50.6|34%|

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|Congo,Rep.|12.5|39.7|46|92%|
|---|---|---|---|---|
|Costa Rica|45.5|93|93.8|93%|
|Côte d'Ivoire|22.7|72.6|74.2|61%|
|Djibouti|28.3|40.9|53.5|42%|
|Dominica|53.1|83|97.6|100%|
|Dominican Republic|49.3|82.4|85.9|76%|
|Ecuador|49.1|81.8|83.9|98%|
|Egypt,Arab Rep.|78.8|95.5|95.7|17%|
|El Salvador|46.1|92.3|92.9|75%|
|Eritrea|23.3|33|34.5|82%|
|Ethiopia|14.1|48|53.6|42%|
|Gabon|19.7|32.3|63.6|100%|
|Gambia. The|48.8|84.9|96.1|61%|
|Georgia|37.4|76|85.4|97%|
|Ghana|43.8|93.9|94.2|45%|
|Guatemala|30.4|71.8|73.1|47%|
|Guinea|26.2|71|78.7|100%|
|Guinea‐Bissau|15.3|53.3|65.2|27%|
|Honduras|30.7|63.3|65.2|67%|
|Hungary|83.9|99.8|100|100%|
|India|42.8|69.2|69.6|36%|
|Indonesia|47.9|71.6|72.6|52%|
|Iran,Islamic Rep.|20.3|46|49.3|25%|
|Iraq|40.8|73.8|74.7|65%|
|Jamaica|48.1|93.2|94.2|100%|
|Jordan|47.2|84.9|87.6|96%|
|Kazakhstan|26.3|50.9|57|29%|
|Kenya|41.7|83.6|86.2|71%|
|Korea,Dem. Rep.|57.7|82.4|83.5|66%|
|Kosovo|82.1|99.9|100|92%|
|Kyrgyz Republic|25.4|58.2|64.9|85%|
|Lao PDR|31.5|63.5|70|76%|
|Lebanon|78|97.5|98.6|79%|
|Lesotho|20.2|72.6|92.4|99%|
|Liberia|39.2|77.8|84.3|87%|
|Libya|26.6|57.6|59|75%|
|Macedonia. FYR|84.9|99.8|99.8|100%|
|Malawi|36.5|90.4|91.8|32%|
|Malaysia|50.1|65.7|68.8|99%|
|Mali|27.2|64.1|69.5|94%|

18
|Mauritania|9.4|19.1|26.9|80%|
|---|---|---|---|---|
|Mexico|40.8|74.7|76.4|80%|
|Mongolia|18.9|36.1|42.7|38%|
|Montenegro|64|98.9|99.8|100%|
|Morocco|31.4|80.3|88.8|80%|
|Mozambique|17.8|53|55.7|55%|
|Myanmar|38.3|68.4|69.8|47%|
|Namibia|52|68.2|73.4|93%|
|Nepal|51.6|89.5|93.7|100%|
|Nicaragua|30.1|61.7|68|86%|
|Niger|20.5|35.6|42.8|91%|
|Nigeria|41.5|85|85.9|36%|
|Pakistan|39.9|59.2|60.1|14%|
|Panama|22.7|58.4|61.2|89%|
|Paraguay|26.8|79.3|89.1|100%|
|Peru|28|47|49.9|100%|
|Philippines|64.1|87.1|89.5|100%|
|Romania|82.1|99.9|100|100%|
|Rwanda|44.2|92.3|92.7|44%|
|São Tomé and Príncipe|46|71.4|80.6|99%|
|Senegal|24.1|63.1|75.1|70%|
|Serbia|69|98.9|99.1|100%|
|Sierra Leone|27.6|90.6|94.2|84%|
|Somalia|38.2|57.8|92.1|98%|
|South Africa|26.5|72.2|77|81%|
|South Sudan|14.4|26.2|34.5|58%|
|Sri Lanka|68.7|95.5|96.8|57%|
|Sudan|10.5|24.3|26.9|87%|
|Eswatini|52.4|97.1|97.9|82%|
|Syrian Arab Republic|54.4|95.6|96.5|100%|
|Tajikistan|26.3|55|59|30%|
|Tanzania|29.9|73.2|74.9|59%|
|Thailand|46.1|92|93.3|77%|
|Togo|30.4|85|87.8|77%|
|Tunisia|36.8|72.9|79|91%|
|Turkmenistan|5.1|10.8|13.2|77%|
|Uganda|45|91.2|91.9|55%|
|Ukraine|47.7|94.1|97.5|95%|
|Uzbekistan|19.3|57.2|61.4|26%|
|Venezuela,RB|22.2|42.6|46|68%|

19
|Vietnam|66.7|88.5|88.8|47%|
|---|---|---|---|---|
|West Bank and Gaza|71.8|91.8|99.7|100%|
|Yemen,Rep.|28.4|51.1|53|73%|
|Zambia|12.5|36.3|38.7|72%|
|Zimbabwe|15.8|65.9|72.7|68%|

20
° Ss 8s 3 & 3 3 3S 8s 8 =Ss beS iceland nn25% Norway nn 100% Croatia nnSSl; Germany nn 100% Austria nn Lithuania ne979622%; Switzerland a 100% Netherlands nn9755 Belgium nnSS; Italy nnS95 Greece nn9.496 France nn Hungary a<sup>100%</sup> 2995 Portugal nn9295 United Kingdom nnn 100% Kosovo en Romania nn<sup>100%</sup> 2255 Ireland a 100% Lebanon nn 759; Slovenia EEE 100% Denmark III 100% Spain nn295; Serbia E/E Slovakia en Albania nn<sup>75%;</sup> SS; Montenegro mI 100% Finland nn 100% Poland mm 100% Sweden en Estonia en24%.2555 Burundi nn S755 Swaziland ee S255 Algeria nl Philippines aS555<sup>100%</sup> Ukraine 25%; Bulgaria Rn9756 Jamaica EE 100% Nepal ee Ol Dominica 10026 Costa Rica nl 9355 Thailand nn7795 El Salvador en 7595 Jordan nn Cambodia nn265;<sup>759;</sup> Ecuador nn9555 Sierra Leone a 8.490 Togo nn 77%. Liberia en S79: Morocco a 80% Georgia rs Benin a<sup>82%</sup> 27%; Paraguay a 100% Namibia nn235° Mexico a S020 Argentina EEE 100% Tunisia ee 22155 Somalia nnS255 Lesotho S25; Malaysia en Guinea a9295<sup>100%</sup> South Africa a 81% Belize nn Cameroon 1.00%95% Brazil a 210026 Bolivia EE 100% Mali aaa 24% EE 2 Nicaragua ee Soo. RABB Libya nn 755. 47 = Panama ne S950 ° Peru a 100% Burkina Faso ee93% Gabon a 100% Niger ee9190 Congo, Rep ee929° Central African ee99% Eritrea es 8290 Armenia EE 9490 Sudan a 8700 Mauritania EE Turkmenistan a 77° 690
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