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1. 2. Remove minor roads, or other roads not represented on the most recent (2019) Michelin road maps. 3. Categorize all remaining roads as in the most recent Michelin road maps.
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2. 4. Add all settlements from Africapolis which include all agglomerations which have a population of at least 10,000 in 2015. 5. Using settlement level population estimates from Africapolis and back-dated locality level population estimates from Census data, calculate the remaining locality-level population not covered by the Africapolis settlements. Add this population to a location at the centre of each locality.
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3. 6. Make small adjustments to the 2019 road network so that roads hit the centroid of each settlement and form a connected network. To do this I used the topology tool in ArcGIS. 7. Iteratively delete or downgrade roads using maps increasingly in the past. In this way, for each year a map is available, create the complete road network.
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12. Comparing the baseline and alternative parameterization (1) (2) Baseline With alternative parameters Benin × 1970 5.092∗∗∗ 9.472∗∗∗ Remoteness (1.357) (1.300) Cameroon × 6.995∗∗∗ 9.596∗∗∗ 1970 Remoteness (2.409) (1.664) Mali × 1970 5.468∗∗∗ 7.491∗∗∗ Remoteness (1.299) (0.876) Observations 156 156 R2 0.334 0.642 Notes: This table correlates 1970 remoteness with the total impact of road building since 1970 on local opportunity. Column one replicates results from figure 7 in the main text. Column two presents analogous results under the alternative parameterization.
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32. Figure 12 Locality level primary completion rates (a) Benin (2013) (b) Mali (2009) (c) Cameroon (2005) Notes: this figure shows the spatial distribution of primary education completion rates for all those above the age of 12 in each of Benin, Cameroon and Mali. Each figure has it’s own scale and corresponding legend where darker orange/red indicates higher completion rates. The data for Benin comes from the 2013 census, for Mali the 2009 census and for Cameroon the 2005 census.
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33. Figure 13 Variation in paved roads Notes: This figure shows the proportion of the 2000 total paved road stock in place in each given year for Benin, Cameroon, and Mali. It uses data from Canning and Pedroni [2008].
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34. Figure 16 Estimates of local educational opportunity from Heath Milsom [2021] (a) Benin 2013 (b) Cameroon 2005 (c) Mali 2009 Notes: These maps show estimates from Heath Milsom [2021] of the spatial distribution of local educational opportunity. Darker colors indicate areas of higher opportunity. Numerical values can be interpreted as the causal effect (relative to the country’s mean) of spending an additional year of childhood in a given location on the probability of completing primary school.
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35. Figure 16 shows the spatial variation in local educational opportunity as recovered in Heath Milsom [2021]. These figures show considerable variation: moving to a one standard deviation better location at birth increases a child’s probability of completing primary school by 15 percentage points.
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36. Figure 21 Aggregate distributional shifts due to road building since 1970 Notes: This figure shows the aggregate effects of changes to the road network since 1970 on spatial inequality of opportunity in each of Benin (yellow dashed), Cameroon (blue dash-dot), and Mali (red small dash-dot), including mean shifts.
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37. Figure 22 Distribution of effects Notes: This figure shows the (centered) distribution over locations of effects of road building since 1970 on local education opportunity, pooled over Benin, Cameroon, and Mali. In blue (solid line) baseline results are plotted. In red (dashed line) movement costs are no longer allowed to adjust in response to road building since 1970. In green (dash-dot line) changes in goods trade costs are shut down. In orange (long dash dot) the utility value of education is set to 0.
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38. Figure 26 Distribution of effects Notes: This figure shows the (centered) distribution over locations of effects of road building since 1970 on local education opportunity, pooled over Benin, Cameroon, and Mali. In blue (solid line) baseline results are plotted. In green (dash-dot line) changes in goods trade costs are shut down. In orange (long dash dot) the utility value of education is set to 0.
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39. Figure 28 “Experiments” identifying variation similar to that used in Faber [2014] Notes: These figures depict the variation isolated to replicate the analysis in Faber [2014] in my setting. Black lines are the routes between two main cities, where main cities are defined as having a population over 100,000. Darker colors represent treated locations, ones which the roads pass through. Lighter colors represent control locations, ones adjacent to treated areas. Locations containing the main city are removed from the analysis. Roads between main cities where it wasn’t possible to create reasonable treated and control groups have also been omitted.
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40. Figure 29 Original maps (a) Mali 1969 (b) Mali 2019 Notes: This figure shows pictures of the original Michelin road maps for Mali in 1969 on the left hand side and in 2019 on the right hand side.
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41. Figure 30 Digitized road networks from historical Michelin maps (a) Benin 1969 ! ! !
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42. Figure 33 Compare the distribution effects of road building since 1970 under the baseline and alternative parameterization Notes: This figure shows the distribution over locations of the causal effect of road building since 1970 on local opportunity. In gray I plot baseline results, replicating figure 6 pooling over all three countries. In orange I plot the analagous results with the model estimated using the alternative parameters.
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43. Figure 34 Comparing the distributional effects of road building since 1970 under various values of β Notes: This figure shows the distribution over locations of the causal effect of road building since 1970 on local opportunity. Each line gives the results for a different value of β, darker orange lines correspond to lower values (starting at 0.01) and higher lines to higher values (ending at 0.95). Lines move in increments of 0.05.
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96. Table 20 Relationship with remotness, comparing baseline and generalising consumption extension results (1) (2) Benin × 5.092∗∗∗ 3.478∗∗∗ Log(Expected travel time) (1.357) (1.031) Cameroon × 6.995∗∗∗ 5.527∗∗∗ Log(Expected travel time) (2.409) (1.591) Mali × 5.468∗∗∗ 3.555∗∗∗ Log(Expected travel time) (1.299) (0.888) Observations 156 156 R2 0.334 0.343 Notes: This table shows the results from estimating the relationship between the counterfactual change in opportunity due to road building since 1970 and locality remoteness in 1970. Regressions include country fixed effects, and weighted by 1970 population, have robust standard errors, and drop departments in the Extreme-Nord province of Cameroon. Column one shows the baseline results and column two shows results from estimating the model with generalized consumption patterns.
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97. Table 22 Relationship between the total relative effect of roads built since 1970 and locality remoteness in 1970.
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100. The correlation between baseline results and results under the alternative parameterization is .87. Figure 33 compares the distribution of effects using the baseline and alternative parameters. Table 22 then compares the relationship between total affects and baseline 1970 remoteness in both parameterisations. Although differences are apparent in both table and figure, they are not sufficient to alter the qualitative conclusions from either presented in the text.
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101. The final complication is that the main outcome variable of interest it, is itself an estimated quantity from an earlier paper Heath Milsom [2021]. As a result of this, it will exhibit measurement error. However, there is little reason to suppose that this measurement error is not classical in nature, and therefore will only attenuate coefficients, thus imposing stricter requirements on rejecting the null of 0 effect.
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102. The results are exactly inline with the main findings from Faber [2014]. Locations that happened to be on the road between two main cities had worse outcomes relative to those just off the road. This may be surprising as I consider a very different setting, and my modeling framework doesn’t allow for economies of scale. This exercise highlights that the type of variation used can lead one to come to very different conclusions as to the effect of changes in connectivity on local outcomes. The advantage of my framework is that I can capture all of the potentially complex effects of different changes in connectivity on outcomes.
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103. The World Bank, 2011.
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104. Then the expected travel time of a randomly chosen household in i traveling to a randomly chosen household in j in year t is given by the following. tijt = X p∈i Pp,1970 Pi,1970 X q∈j Pq,1970 Pj,1970 dpqt (18) This can be seen as a coarse discretisation of equation 17, the best that can be done with the data available. C.1.2 Calculating incomes Y s it Census data does not provide information about wages or the total income/ output of localities.
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113. Turning to comparing the counterfactual estimates of how road building since 1970 affected the inequality of local educational opportunity across space, I find that in this extension the variance of opportunity across space due to road building increased by 0.24, 1.85, and-0.48 percent in each of Benin, Cameroon, and Mali respectively vs 0.04, 5.81, and-1.44, in the baseline. Finally, table 20 compares the correlation if the estimated effects from the baseline and extension to pre-period remoteness. I find unsurprisingly given the strong correlation but already noted lower dispersion, that the previously uncovered relationship remains, in all three countries more remote locations saw larger gains, but effects are somewhat attenuated.
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8. Infrastructure development and population growth on economic growth in South Africa. (2021). Stungwa, Sanele ; Daw, Olebogeng David.
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9. Productive government investment and the labor share. (2021). Bom, Pedro ; Erauskin, Iaki.
   In: MPRA Paper.
   RePEc:pra:mprapa:108381.

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10. Pork, infrastructure and growth: Evidence from the Italian railway expansion. (2021). Tarasov, Alexander ; Facchini, Giovanni ; Bonfatti, Roberto ; Tedeschi, Gian Luca ; Testa, Cecilia.
    In: Discussion Papers.
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11. The Long Shadow of Infrastructure Development: Long Run Effects of Railway Construction in Colonial India. (2021). Maitra, Pushkar ; Yu, William.
    In: Monash Economics Working Papers.
    RePEc:mos:moswps:2021-01.

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12. The Short-Run Effect of a Local Fiscal Squeeze on Pollution Abatement Expenditures: Evidence from China’s VAT Pilot Program. (2021). Lu, Peng ; Peng, Langchuan ; Mao, Jie.
    In: Environmental & Resource Economics.
    RePEc:kap:enreec:v:78:y:2021:i:3:d:10.1007_s10640-021-00539-z.

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13. Sweet Unbinding: Sugarcane Cultivation and the Demise of Foot-Binding. (2021). Fan, Elliott ; Wu, Tsong-Min ; Cheng, Nora.
    In: IZA Discussion Papers.
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14. Paving the way to modern growth. Evidence from Bourbon roads in Spain. (2021). Viladecans-Marsal, Elisabet ; Tassinari, Filippo ; Herranz-Loncán, Alfonso ; Garcia-López, Miquel-Àngel ; Garcia-Lopez, Miquel-Angel ; Herranz-Loncan, Alfonso.
    In: Working Papers.
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15. Transportation Infrastructure Construction and High-Quality Development of Enterprises: Evidence from the Quasi-Natural Experiment of High-Speed Railway Opening in China. (2021). Xiao, Xiang ; Zhao, Tianjiao ; Dai, Qinghui.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:23:p:13316-:d:692926.

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16. Can Rural Road Construction Promote the Sustainable Development of Regional Agriculture in China?. (2021). Zhou, Zhou ; Li, Wenxing ; Geng, Shaoqing ; Duan, Jianqiang.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:19:p:10882-:d:647242.

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17. Does Transport Infrastructure Inequality Matter for Economic Growth? Evidence from China. (2021). Li, Yueran ; Nie, Tianyi ; Liu, Rui ; Ye, Kunhui ; Chen, Anyu.
    In: Land.
    RePEc:gam:jlands:v:10:y:2021:i:8:p:874-:d:618377.

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18. High-speed Rail and the Spatial Distribution of Economic Activity: Evidence from Japans Shinkansen. (2021). Tabuchi, Takatoshi ; Koster, Hans ; Hayakawa, Kazunobu ; Takatoshi, Tabuchi ; Thisse, Jacques-Franois.
    In: Discussion papers.
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19. Spatial spillovers of pollution via high-speed rail network in China. (2021). GUO, Huanxiu ; Li, Hao.
    In: Transport Policy.
    RePEc:eee:trapol:v:111:y:2021:i:c:p:138-152.

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20. Global evidence on productivity effects of road infrastructure incorporating spatial spillover effects. (2021). Takeuchi, Wataru ; Kiguchi, Riku ; Kato, Hironori ; Konno, Akio.
    In: Transport Policy.
    RePEc:eee:trapol:v:103:y:2021:i:c:p:167-182.

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21. The impact of green energy infrastructure on firm productivity: Evidence from the Three Gorges Project in China. (2021). Shi, Xunpeng ; Cheng, Dong ; Yu, Jian.
    In: International Review of Economics & Finance.
    RePEc:eee:reveco:v:71:y:2021:i:c:p:385-406.

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22. “Ghost cities” versus boom towns: Do Chinas high-speed rail new towns thrive?. (2021). Zheng, Siqi ; Kahn, Matthew ; Du, Rui ; Dong, Lei ; Ratti, Carlo.
    In: Regional Science and Urban Economics.
    RePEc:eee:regeco:v:89:y:2021:i:c:s0166046221000429.

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23. Property rights and prosocial behavior: Evidence from a land tenure reform implemented as randomized control-trial. (2021). Fabbri, Marco.
    In: Journal of Economic Behavior & Organization.
    RePEc:eee:jeborg:v:188:y:2021:i:c:p:552-566.

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24. The unintended harms of infrastructure: Opium and road construction in Afghanistan. (2021). Wigton-Jones, Evan.
    In: Journal of Comparative Economics.
    RePEc:eee:jcecon:v:49:y:2021:i:2:p:405-424.

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25. The nexus between industrial growth and electricity consumption in China – New evidence from a quantile-on-quantile approach. (2021). Yuan, Weihong ; Li, KE.
    In: Energy.
    RePEc:eee:energy:v:231:y:2021:i:c:s0360544221012391.

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26. How do VAT reforms in the service sectors impact TFP in the manufacturing sector: Firm-level evidence from China. (2021). Wang, Zheng ; Peng, Langchuan.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:99:y:2021:i:c:s0264999321000663.

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27. Industrialization from scratch: The “Construction of Third Front” and local economic development in Chinas hinterland. (2021). Zou, Ben ; Fan, Jingting.
    In: Journal of Development Economics.
    RePEc:eee:deveco:v:152:y:2021:i:c:s0304387821000754.

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28. A structural estimation of the return to infrastructure investment in China. (2021). Wu, Guiying ; Feng, Qu ; Wang, Zhifeng.
    In: Journal of Development Economics.
    RePEc:eee:deveco:v:152:y:2021:i:c:s0304387821000511.

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29. How productive is public investment? Evidence from formal and informal production in India. (2021). Lebesmuehlbacher, Thomas ; Chatterjee, Santanu ; Narayanan, Abhinav.
    In: Journal of Development Economics.
    RePEc:eee:deveco:v:151:y:2021:i:c:s030438782100002x.

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30. Need for speed: High-speed rail and firm performance. (2021). Kuang, Chun ; Liu, Zijie ; Zhu, Wenyu.
    In: Journal of Corporate Finance.
    RePEc:eee:corfin:v:66:y:2021:i:c:s0929119920302741.

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31. Take the Highway? Paved Roads and Well-Being in Africa. (2021). DJEMAI, Elodie ; D'Ambrosio, Conchita ; Clark, Anrew E.
    In: CEPREMAP Working Papers (Docweb).
    RePEc:cpm:docweb:2110.

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32. Railways and cities in India. (2021). Fenske, James ; Kala, Namrata ; Wei, Jinlin.
    In: CAGE Online Working Paper Series.
    RePEc:cge:wacage:559.

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33. Pork, Infrastructure and Growth: Evidence from the Italian Railway Expansion. (2021). Tarasov, Alexander ; Facchini, Giovanni ; Bonfatti, Roberto ; Tedeschi, Gian Luca ; Testa, Cecilia.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_9228.

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34. Take the highway? Paved roads and well-being in Africa. (2021). DJEMAI, Elodie ; D'Ambrosio, Conchita ; Clark, Andrew.
    In: CEP Discussion Papers.
    RePEc:cep:cepdps:dp1761.

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35. Can Trade Facilitation Prevent the Formation of Zombie Firms? Evidence from the China Railway Express. (2021). Yang, LU ; Hu, Jun ; Li, Juncheng.
    In: China & World Economy.
    RePEc:bla:chinae:v:29:y:2021:i:1:p:130-151.

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36. Infrastructure investment and marginal spending behaviour of households. (2021). Li, Tao ; Hu, Jingxin ; Ran, Guanghe.
    In: Asian-Pacific Economic Literature.
    RePEc:bla:apacel:v:35:y:2021:i:2:p:3-17.

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37. Rural Infrastructure and Poverty in China. (2021). Qin, Xiaodi ; Wu, Haitao.
    In: 2021 Conference, August 17-31, 2021, Virtual.
    RePEc:ags:iaae21:314996.

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38. The Evolution of Built-up Areas in Ghana since 1975. (2020). Shilpi, Forhad ; Fafchamps, Marcel.
    In: Policy Research Working Paper Series.
    RePEc:wbk:wbrwps:9314.

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39. Railroads, specialization, and population growth in small open economies: Evidence from the First Globalization. (2020). Tapia, Matias ; González, Felipe ; Gallego, Francisco ; Gonzalez, Felipe ; Forero, Andrea.
    In: Documentos de Trabajo.
    RePEc:ioe:doctra:548.

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40. Does High-Speed Rail Network Access Enhance Cities’ Innovation Performance?. (2020). Yang, Tianle ; Du, Qunyang ; Yu, Hangdong.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:19:p:8239-:d:424440.

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41. Transport Infrastructure Development and Economic Growth in China: Recent Evidence from Dynamic Panel System-GMM Analysis. (2020). Wang, Yong ; Lin, Justin ; Fu, Caihui.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:14:p:5618-:d:383739.

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42. Economic effect and its disparity of high speed rail in China: A study of mechanism based on synthesis control method. (2020). Wu, Zongfa ; Zhao, Xingchen ; Li, Xiaolong.
    In: Transport Policy.
    RePEc:eee:trapol:v:99:y:2020:i:c:p:262-274.

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43. Impact of transportation infrastructure on industrial pollution in Chinese cities: A spatial econometric analysis. (2020). Shi, Xunpeng ; Zhang, Jie ; Yu, Jian ; Huang, Guobin.
    In: Energy Economics.
    RePEc:eee:eneeco:v:92:y:2020:i:c:s0140988320303133.

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44. How does manufacturing agglomeration affect green economic efficiency?. (2020). Lee, Chien-Chiang ; Feng, Yidai ; Cen, Yan ; Yuan, Huaxi.
    In: Energy Economics.
    RePEc:eee:eneeco:v:92:y:2020:i:c:s014098832030284x.

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45. Expressways, GDP, and the environment: The case of China. (2020). Zhang, Bing ; He, Guojun ; Xie, Yang.
    In: Journal of Development Economics.
    RePEc:eee:deveco:v:145:y:2020:i:c:s0304387820300602.

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46. The construction of high-speed railway and urban innovation capacity: Based on the perspective of knowledge Spillover. (2020). Cai, Siyuan ; Wang, Jiating.
    In: China Economic Review.
    RePEc:eee:chieco:v:63:y:2020:i:c:s1043951x2030136x.

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47. Train to Opportunity: the Effect of Infrastructure on Intergenerational Mobility. (2020). Mohnen, Myra ; Guerra, Jose-Alberto ; Costas-Fernndez, Julin.
    In: Documentos CEDE.
    RePEc:col:000089:018591.

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48. Railroads, specialization, and population growth in small open economies: Evidence from the First Globalization. (2020). Tapia, Matias ; González, Felipe ; Gallego, Francisco ; Gonzalez, Felipe ; Forero, Andres.
    In: Working Papers Central Bank of Chile.
    RePEc:chb:bcchwp:887.

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49. Did railways affect literacy? Evidence from India. (2020). Fenske, James ; Chaudhary, Latika.
    In: CAGE Online Working Paper Series.
    RePEc:cge:wacage:529.

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50. Do changes in air transportation affect productivity? A cross‐country panel approach. (2020). Aitbihiouali, Laila ; Carbo, Jose M ; Graham, Daniel J.
    In: Regional Science Policy & Practice.
    RePEc:bla:rgscpp:v:12:y:2020:i:3:p:493-505.

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