## Appendix I. List of Countries and Cities

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---

### Methodology and Mean Speed (MS) score construction
- MS score definition:
  - MSi = (sum distance_i_j) / (sum time_i_j) (harmonic mean speed between the largest city and other large cities > 80 km away).
- Alternative specifications:
  - gMSi = geometric mean of (distance/time) across routes.
  - aMSi = MS adjusted by crow-flies ratio (travel time divided by sqrt(crow-flies ratio)); crow-flies ratio winsorized at the right tail at the 5 percent level.
- Sample and exclusions:
  - 760 cities in 162 countries, minimum of three and maximum of six cities per country.
  - Routes shorter than 80 km by road excluded; tiny countries and archipelagos omitted.
  - Cities in all capital letters are state capitals.
  - Data are publicly available from Google Maps.

### Key summary statistics (MS and related variables)
- Mean Speed [MS] score:
  - Mean: 73
  - Std. Dev.: 17
  - 25%: 60
  - 50%: 73
  - 75%: 87
  - Min.: 38
  - Max.: 107
  - Observations: 162
- Geometric Mean Speed [gMS] score:
  - Mean: 73
  - Std. Dev.: 16
  - 25%: 59
  - 50%: 73
  - 75%: 85
  - Min.: 38
  - Max.: 107
- Adjusted Mean Speed [aMS] score:
  - Mean: 83
  - Std. Dev.: 17
  - 25%: 70
  - 50%: 84
  - 75%: 98
  - Min.: 49
  - Max.: 119
- GDP per capita 2018 (USD):
  - Mean: 13,572.59
  - Std. Dev.: 18,873.62
  - 25%: 1,539.90
  - 50%: 5,268.20
  - 75%: 16,415.19
  - Min.: 307.46
  - Max.: 82,756.02
  - Observations: 161
- Road density (road km / area km2):
  - Mean: 0.42
  - Std. Dev.: 0.71
  - 25%: 0.03
  - 50%: 0.13
  - 75%: 0.38
  - Min.: 0
  - Max.: 4.13
- Quality of Road Infrastructure (QRI) survey score:
  - Mean: 3.86
  - Std. Dev.: 1.05
  - 25%: 3.0
  - 50%: 3.8
  - 75%: 4.5
  - Min.: 2.0
  - Max.: 6.37
- Rural Access Index (RAI):
  - Mean: 64.36
  - Std. Dev.: 25.47
  - 25%: 43
  - 50%: 69
  - 75%: 84
  - Min.: 5
  - Max.: 100
  - Observations: 161

### Validation and relationships with other measures
- Correlations and comparative patterns:
  - MS score is strongly positively correlated with GDP per capita (log GDP per capita); relationship rises exponentially for wealthier countries.
  - MS score positively correlates with road density (log road density); association stronger for countries with denser road networks.
  - MS score positively correlates with QRI and RAI; relationship stronger for countries with low RAI.
  - Cross-correlation: MS, gMS, and aMS scores have cross-correlation coefficients above 0.96.
- Interpretive distinctions:
  - RAI measures rural household access to all-weather roads within two kilometers.
  - MS focuses on expeditiousness in moving people and goods between major urban centers.
- Endogeneity caution:
  - RAI, road density, road quality, and GDP per capita are endogenous, simultaneous, and autocorrelated.

### Ordered logistic regressions: income levels on RAI, road density, and MS score (Table 5)
- Dependent variable:
  - Income classification encoded as: 1 = Low-Income Developing Countries (LIDC), 2 = Emerging Market Economies (EME), 3 = Advanced Economies (AE).
- Sample and estimation:
  - Balanced data for 162 countries. Heteroskedasticity-robust standard errors used.
- Reported results (coefficients and significance; ∗ p<10%, ∗∗ p<5%, ∗∗∗ p<1%):
  - Model (1) univariate RAI:
    - RAI: 0.0891 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.342
  - Model (2) univariate Road density:
    - Road density: 0.928 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.244
  - Model (3) univariate MS score:
    - MS score: 0.103 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.250
  - Model (4) multivariate (RAI, Road density, MS score simultaneously):
    - RAI: 0.0575 ∗∗∗
    - Road density: 0.554 ∗∗∗
    - MS score: 0.0946 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.506
- Comparative robustness (univariate → multivariate):
  - RAI coefficient falls by 35 percent (from 0.0891 to 0.0575).
  - Road density coefficient falls by 40 percent (from 0.928 to 0.554).
  - MS score coefficient falls by 8 percent (from 0.103 to 0.0946).
- Interpretation:
  - MS score outperforms RAI and road density in explanatory power for country income classification—higher resilience to collinearity and stronger association when included with other road variables.

### Ordered logistic regressions: road quality (QRI) on RAI, road density, and MS score (Table 4) — comparative point
- Coefficient changes (univariate → multivariate):
  - RAI coefficient falls by 54 percent.
  - Road density coefficient falls by 48 percent.
  - MS score coefficient falls by 25 percent.
- Conclusion:
  - MS score is economically meaningful, statistically significant, and more resilient than other covariates to explain perceived road quality.

### Welfare and cost-benefit illustration using MS score
- Counterfactual example parameters:
  - Increase MS score from median 73 by one standard deviation to 90.
  - Bypass assumptions:
    - Length: 100 km
    - Cost of paving two lanes: US$1,000,000 per km
    - Commuters affected: 100,000
    - Average commute reduction: ca. 8 minutes each way (one-way commute time reduced from 41 minutes to 33 minutes for a 50-km commute)
    - Hourly GDP per capita: US$3.30
  - Payback:
    - Road investment would pay back in ca. five years.
- Additional numerical details and references:
  - Cost estimate reference range: US$843,000 to US$1,588,000 per km for paving two lanes (Mikou, Rozenberg, Koks, Fox, and Peralta Quirós, 2019).
  - Key calculation excerpts:
    - Travel time comparison: "41.1 minutes versus (ii) 60 minutes × 50 km ÷ 90 km/h = 33.3 minutes."
    - Median annual GDP per capita in the sample: "US$5,268".
    - Assumption for working hours: "1,600 working hours per year."
    - Example undiscounted payback period calculation: "US$100 million investment in the bypass ÷ (US$3.30 median hour rate × 16 minutes two-way shorter commute ÷ 60 minutes × 250 working days × 100,000 commuters)."

### Public investment management linkages
- MS score correlations:
  - MS score correlates strongly with good public investment management practices across 14 PIMA dimensions, except investment protection during budget implementation.
- PIMA scope:
  - PIMA surveys evaluate 15 institutions across planning, allocation, and implementation (45 variables), assessing institutional strength and effectiveness.

### Applications, limitations, and recommended uses
- Key uses:
  - Frequent replication by local authorities for monitoring and evaluation.
  - Calibration into cost-benefit analysis for public road investments (ex-ante and ex-post).
  - Use as an output metric in public investment management diagnostics and reforms.
  - Applications and extensions: quantification of different transport policies; sub-national and regional rankings; travel time between major cities in different countries; determinants of efficient investment in infrastructure; event studies (e.g., the effects of natural disasters).
- Caveats and limitations:
  - MS score reflects the fastest times in a day (usually at night); may not match mean speed during high economic activity for locations with high congestion variation.
    - Consequence: two countries with similar MS score may have different mean speeds during commute times.
    - Recommendation: collect high-low and variance MS scores in future work.
  - MS score uses speed between a minimum of three and a maximum of six cities.
    - Small countries have fewer large cities than large countries, and large cities tend to be better connected.
    - City count truncation may bias upwards the MS estimate towards large countries.
    - Recommendation: compare countries with peers by size and population rather than unconditionally across the board.
- Instrumental variable potential:
  - MS score can serve as a robust instrumental variable for the quality of road infrastructure because:
    - (i) it strongly affects the perception of road quality (cf. Figure 3 and Table 4).
    - (ii) it is unlikely to suffer from the same measurement problems as the other connectivity indicators.
- Calibration:
  - MS score can be calibrated for local/regional networks and distance ranges (e.g., 20–50 km) for ex-post and ex-ante cost-benefit analyses.
- External complements:
  - Accessibility framework reference: Dijkstra, Poelman, and Ackermans 2019.

### Appendix: Cities and distances
- Notes on the appendix table:
  - The table lists the cities by country in the sample used to compute the MS score.
  - The first city by country is the city of reference (start), usually the largest metropolitan area; remaining cities are the destinations in alphabetical order.
  - Distance is the distance between the city of reference and the destination.
  - Tiny countries, archipelagos, and cities within 80 km by road from the city of reference are omitted.
- Representative entries (country: city of reference — destinations and distances):
  - Afghanistan: KABUL — Herat 817; Kandahar 497; Kunduz 336; Mazari Sharif 427
  - Albania: TIRANA — Kukes 145; Sarande 279; Shkoder 103; Vlore 152
  - Algeria: ALGIERS (EL DJAZAIR) — Batna 427; El Djelfa 297; Stif 268; Wahran 413
  - Angola: LUANDA — Benguela 542; Huambo 604; Lubango 898; Malanje 381
  - Argentina: BUENOS AIRES — Cordoba 696; Mendoza 1050; Rosario 297; Salta 1466
  - Australia: Sydney — Adelaide 1375; Brisbane 916; Melbourne 878; Perth 3934
  - Canada: Toronto — Edmonton 3472; Montreal 541; Ottawa 456; Vancouver 4373
  - China: Shanghai — BEIJING (PEKING) 1214; Chongqing 1684; Guangzhou 1436; Wuhan 839
  - India: Mumbai — Ahmedabad 531; Bangalore 984; Delhi 1422; Hyderabad 709
  - United States: New York (NY) — Chicago (IL) 1272; Houston (TX) 2618; Los Angeles (CA) 4490; Phoenix (AZ) 3873
  - (Full country-by-city listing is included in the appendix table of the source.)
- Data provenance:
  - Distances and cities as listed were used to compute MS, gMS, and aMS scores; data drawn from Google Maps.

### Main conclusions and policy implications
- The Mean Speed (MS) score:
  - Is a computationally-efficient, repeatable proxy for road network expeditiousness covering 162 countries.
  - Is highly correlated with existing measures (RAI and QRI) but provides a distinct and robust dimension—speed—relevant for economic outcomes.
  - Exhibits stronger and more stable explanatory power for perceived road quality (QRI) and country income classification than RAI and road density when included jointly.
- Policy recommendations and operational uses:
  - Replicate MS frequently at local and national levels for monitoring and evaluation.
  - Calibrate MS for local/regional distance ranges for ex-ante and ex-post cost-benefit analysis of road investments.
  - Use MS as an output metric in public investment management diagnostics and reforms to strengthen project appraisal and prioritization.

*Italic: Source — IMF Working Paper excerpt “Road Quality and Mean Speed Score,” ordered logistic regressions and MS score analysis.*

### Appendix I. List of Countries and Cities ...............................................................................

### Appendix I. List of Countries and Cities

### Contents Overview
- Appendix I. List of Countries and Cities .................................................................................................. 18
- References .................................................................................................................................................. 30

### Figures
- 1. Histograms of Mean Speed Scores ........................................................................................................... 7
- 2. MS Scores on the World Map .................................................................................................................. 10
- 3. OLS and Quantile Best Fit Lines of MS Score and GDP per Capita, Road Density, Quality of Road Infrastructure, and Rural Access Index
 .................................................................................................... 13

### Tables
- 1. Summary Statistics of MS Scores .............................................................................................................. 6
- 2. Mean Speed Scores by Country ................................................................................................................ 7
- 3. Summary Statistics of GDP per Capita, Road Density, QRI, and RAI ..................................................... 12
- 4. Ordered Logistic Regressions of Road Quality on Road Network Characteristics .................................. 14

*Source: wpiea2022095-print-pdf - Appendix I. List of Countries and Cities*

### 5. Ordered Logistic Regressions of Income Levels on RAI, Road Density, and MS Score ......................... 15

### 5. Ordered Logistic Regressions of Income Levels on RAI, Road Density, and MS Score

### Methodology and Mean Speed (MS) score construction
- MS score definition:
  - MSi = (sum distance_i_j) / (sum time_i_j) (harmonic mean speed between the largest city and other large cities > 80 km away).
- Alternative specifications:
  - gMSi = geometric mean of (distance/time) across routes.
  - aMSi = MS adjusted by crow-flies ratio (travel time divided by sqrt(crow-flies ratio)); crow-flies ratio winsorized at the right tail at the 5 percent level.
- Sample:
  - 760 cities in 162 countries, minimum of three and maximum of six cities per country.
  - Routes shorter than 80 km by road excluded; tiny countries and archipelagos omitted.

### Key summary statistics (MS and related variables)
- Mean Speed [MS] score:
  - Mean: 73
  - Std. Dev.: 17
  - 25%: 60
  - 50%: 73
  - 75%: 87
  - Min.: 38
  - Max.: 107
  - Observations: 162
- Geometric Mean Speed [gMS] score:
  - Mean: 73
  - Std. Dev.: 16
  - 25%: 59
  - 50%: 73
  - 75%: 85
  - Min.: 38
  - Max.: 107
- Adjusted Mean Speed [aMS] score:
  - Mean: 83
  - Std. Dev.: 17
  - 25%: 70
  - 50%: 84
  - 75%: 98
  - Min.: 49
  - Max.: 119
- GDP per capita 2018 (USD):
  - Mean: 13,572.59
  - Std. Dev.: 18,873.62
  - 25%: 1,539.90
  - 50%: 5,268.20
  - 75%: 16,415.19
  - Min.: 307.46
  - Max.: 82,756.02
  - Observations: 161
- Road density (road km / area km2):
  - Mean: 0.42
  - Std. Dev.: 0.71
  - 25%: 0.03
  - 50%: 0.13
  - 75%: 0.38
  - Min.: 0
  - Max.: 4.13
- Quality of Road Infrastructure (QRI) survey score:
  - Mean: 3.86
  - Std. Dev.: 1.05
  - 25%: 3.0
  - 50%: 3.8
  - 75%: 4.5
  - Min.: 2.0
  - Max.: 6.37
- Rural Access Index (RAI):
  - Mean: 64.36
  - Std. Dev.: 25.47
  - 25%: 43
  - 50%: 69
  - 75%: 84
  - Min.: 5
  - Max.: 100
  - Observations: 161

### Validation and relationships with other measures
- MS score is strongly positively correlated with:
  - GDP per capita (log GDP per capita): relationship rises exponentially for wealthier countries.
  - Road density (log road density): stronger association for countries with denser road networks.
  - QRI (Quality of Road Infrastructure) and RAI (Rural Access Index): MS correlates positively; relationship stronger for countries with low RAI.
- Cross-correlation:
  - MS, gMS, and aMS scores have cross-correlation coefficients above 0.96.
- Interpretive distinction:
  - RAI measures rural household access to all-weather roads within two kilometers.
  - MS focuses on expeditiousness in moving people and goods between major urban centers.
- Endogeneity note:
  - RAI, road density, road quality, and GDP per capita are endogenous, simultaneous, and autocorrelated.

### Ordered logistic regressions: income levels on RAI, road density, and MS score (Table 5)
- Dependent variable:
  - Income classification encoded as: 1 = Low-Income Developing Countries (LIDC), 2 = Emerging Market Economies (EME), 3 = Advanced Economies (AE).
- Sample: balanced data for 162 countries. Heteroskedasticity-robust standard errors used.
- Results (coefficients and significance; ∗ p<10%, ∗∗ p<5%, ∗∗∗ p<1%):
  - Model (1) univariate RAI:
    - RAI: 0.0891 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.342
  - Model (2) univariate Road density:
    - Road density: 0.928 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.244
  - Model (3) univariate MS score:
    - MS score: 0.103 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.250
  - Model (4) multivariate (RAI, Road density, MS score simultaneously):
    - RAI: 0.0575 ∗∗∗
    - Road density: 0.554 ∗∗∗
    - MS score: 0.0946 ∗∗∗
    - Observations: 162
    - Pseudo R2: 0.506
- Comparative robustness:
  - From univariate to multivariate regressions:
    - RAI coefficient falls by 35 percent (from 0.0891 to 0.0575).
    - Road density coefficient falls by 40 percent (from 0.928 to 0.554).
    - MS score coefficient falls by 8 percent (from 0.103 to 0.0946).
- Interpretation:
  - MS score outperforms RAI and road density in explanatory power for country income classification (higher resilience to collinearity and stronger association when included with other road variables).

### Ordered logistic regressions: road quality (QRI) on RAI, road density, and MS score (Table 4) — key comparative point
- Single and simultaneous regressions indicate:
  - Coefficients for RAI and road density fall by 54 percent and 48 percent, respectively, when moving from univariate to multivariate specifications.
  - MS score coefficient falls by 25 percent in the analogous comparison.
- Conclusion: MS score is economically meaningful, statistically significant, and more resilient than other covariates to explain perceived road quality.

### Welfare and cost-benefit illustration using MS score
- Counterfactual example:
  - Increase MS score from median 73 by one standard deviation to 90.
  - Bypass assumptions:
    - Length: 100 km
    - Cost of paving two lanes: US$1,000,000 per km
    - Commuters affected: 100,000
    - Average commute reduction: ca. 8 minutes each way (one-way commute time reduced from 41 minutes to 33 minutes for a 50-km commute)
    - Hourly GDP per capita: US$3.30
  - Payback:
    - Road investment would pay back in ca. five years.
- Notes:
  - Cost estimate reference range: US$843,000 to US$1,588,000 per km for paving two lanes (Mikou, Rozenberg, Koks, Fox, and Peralta Quirós, 2019).
  - MS score can be calibrated for local/regional networks and distance ranges (e.g., 20–50 km) for ex-post and ex-ante cost-benefit analyses.

### Public investment management linkages
- MS score correlates strongly with good public investment management practices across 14 PIMA dimensions (Public Investment Management Assessment), except investment protection during budget implementation.
- PIMA surveys evaluate 15 institutions across planning, allocation, and implementation (45 variables), assessing institutional strength and effectiveness.

### Main conclusions and policy implications
- The Mean Speed (MS) score:
  - Is a computationally-efficient, repeatable proxy for road network expeditiousness covering 162 countries.
  - Is highly correlated with existing measures (RAI and QRI) but provides a distinct and robust dimension—speed—relevant for economic outcomes.
  - Exhibits stronger and more stable explanatory power for perceived road quality (QRI) and country income classification than RAI and road density when included jointly.
- Policy uses:
  - Frequent replication by local authorities for monitoring and evaluation.
  - Calibration into cost-benefit analysis for public road investments (ex-ante and ex-post).
  - Use as an output metric in public investment management diagnostics and reforms.

*Italic: Source — IMF Working Paper excerpt “Road Quality and Mean Speed Score,” ordered logistic regressions and MS score analysis.*

### 41.1 minutes versus (ii) 60 minutes × 50 km ÷ 90 km/h = 33.3 minutes.

### Road Quality and Mean Speed Score

### Key calculations and illustrative numbers
- Travel time comparisons given: "41.1 minutes versus (ii) 60 minutes × 50 km ÷ 90 km/h = 33.3 minutes."
- Median annual GDP per capita in the sample: "US$5,268".
- Assumption for working hours: "1,600 working hours per year."
- Example undiscounted payback period calculation: "US$100 million investment in the bypass ÷ (US$3.30 median hour rate × 16 minutes two-way shorter commute ÷ 60 minutes × 250 working days × 100,000 commuters)."
- Reference to PIMA web page: "https://infrastructuregovern.imf.org/content/PIMA/Home/PimaTool/What-is- PIMA.html (accessed February 2021)."

### Findings on the Mean Speed (MS) score
- The MS score outperforms other variables describing road network characteristics in predicting the perception of road quality.
- The MS score correlates more strongly with country income level than road access and road density.
- The MS score provides a robust instrumental variable for the quality of road infrastructure because:
  - (i) it strongly affects the perception of road quality (cf. Figure 3 and Table 4).
  - (ii) it is unlikely to suffer from the same measurement problems as the other connectivity indicators.

### Caveats and limitations
- The MS score reflects the fastest times in a day (usually at night), so it may not necessarily correlate with mean speed during high economic activity for locations with high congestion variation.
  - Consequence: two countries with similar MS score may have different mean speeds during commute times.
  - Future work: guided towards collecting the high-low and variance MS scores.
- The MS score draws on speed between a minimum of three and a maximum of six cities.
  - Small countries have fewer large cities than large countries, and large cities tend to be better connected.
  - City count truncation may bias upwards the MS estimate towards large countries.
  - Recommendation: countries should be compared with peers by size and population rather than unconditionally across the board.

### Applications and further uses
- The MS score can be used as an instrument of road investment efficiency and for cost-benefit analysis of road investments.
- The MS score is easy to replicate locally and can be run periodically to create rich panel data.
- Further applications and extensions include:
  - quantification of different transport policies,
  - sub-national and regional rankings,
  - travel time between major cities in different countries,
  - determinants of efficient investment in infrastructure,
  - event studies (e.g., the effects of natural disasters).
- Example external complement: accessibility framework presented by (Dijkstra, Poelman, and Ackermans 2019).

*Source: IMF Working Papers — Road Quality and Mean Speed Score (excerpts provided).*

### Appendix I. List of Countries and Cities

### Appendix I. List of Countries and Cities

### Notes
- The table below lists the cities by country in our sample used to compute the Mean Speed [MS] score.
- The first city by country in the list is the city of reference (start), usually the largest metropolitan area; the remaining cities are the destinations in alphabetical order.
- Distance is the distance between the city of reference and the destination.
- Cities in all capital letters are state capitals.
- Tiny countries, archipelagos, and cities within 80 km by road from the city of reference are omitted.
- Data are publicly available from Google Maps.

### Cities and distances (city of reference first; destinations and distances)
- Afghanistan: KABUL
  - Herat 817
  - Kandahar 497
  - Kunduz 336
  - Mazari Sharif 427
- Albania: TIRANA
  - Kukes 145
  - Sarande 279
  - Shkoder 103
  - Vlore 152
- Algeria: ALGIERS (EL DJAZAIR)
  - Batna 427
  - El Djelfa 297
  - Stif 268
  - Wahran 413
- Angola: LUANDA
  - Benguela 542
  - Huambo 604
  - Lubango 898
  - Malanje 381
- Argentina: BUENOS AIRES
  - Cordoba 696
  - Mendoza 1050
  - Rosario 297
  - Salta 1466
- Armenia: YEREVAN
  - Gosh 118
  - Gyumri (Leninakan) 120
  - Kapan 303
  - Vanadzor (Kirovakan) 116
- Australia: Sydney
  - Adelaide 1375
  - Brisbane 916
  - Melbourne 878
  - Perth 3934
- Austria: WIEN
  - Graz 199
  - Innsbruck 476
  - Linz 184
  - Salzburg 296
- Azerbaijan: BAKU
  - Balakan 394
  - Ganja 360
  - Lankaran 248
  - Mingachevir 317
- Bangladesh: DHAKA
  - Chittagong 248
  - Khulna 271
  - Mymensingh 112
  - Rajshahi 248
- Belarus: MINSK
  - Gomel 311
  - Grodno 280
  - Mogilev 198
  - Vitebsk 291
- Belgium: BRUXELLES (BRUSSEL)
  - Arlon 193
  - Bastogne 153
  - Liege (Luik) 97
  - Malmedy 150
  - Ostend 111
- Belize: BELIZE CITY
  - Corozal 136
  - Punta Gorda 270
  - San Ignacio 115
- Benin: Cotonou
  - Bohicon 125
  - Djougou 458
  - Kandi 628
  - Parakou 414
- Bhutan: THIMPHU
  - Gelephu 244
  - Jakar 258
  - Phuntsholing 147
  - Samdrup Jongkhar 420
- Bolivia: Santa Cruz
  - Cochabamba 480
  - El Alto 851
  - LA PAZ 853
  - Oruro 688
- Bosnia and Herzegovina: SARAJEVO
  - Banja Luka 190
  - Mostar 129
  - Tuzla 119
- Botswana: GABORONE
  - Francistown 433
  - Selibe Phikwe 406
  - Serowe 310
- Brazil: Rio de Janeiro
  - BRASILIA 1202
  - Belo Horizonte 441
  - Fortaleza 2587
  - Salvador 1632
- Brunei Darussalam: BANDAR SERI BEGAWAN
  - Kuala Belait 113
  - Seria 101
- Bulgaria: SOFIA
  - Burgas 383
  - Plovdiv 146
  - Ruse 309
  - Varna 441
- Burkina Faso: OUAGADOUGOU
  - Bobo Dioulasso 356
  - Koudougou 117
  - Ouahigouya 182
  - Solenzo 333
- Burundi: BUJUMBURA
  - Gitega 99
  - Ngozi 125
- Cambodia: PHNOM PENH
  - Battambang 293
  - Kampong Cham 124
  - Serei Saophoan 422
  - Siem Reap 318
- Cameroon: Douala
  - Bafoussam 258
  - Bamenda 321
  - Garoua 1370
  - YAOUNDE 266
- Canada: Toronto
  - Edmonton 3472
  - Montreal 541
  - Ottawa 456
  - Vancouver 4373
- Central African Republic: BANGUI
  - Bambari 377
  - Berbérati 520
  - Bouar 435
- Chad: Carnot N’DJAMENA
  - Abéché 748
  - Kélo 379
  - Moundou 480
  - Sarh 561
- Chile: SANTIAGO
  - Antofagasta 1336
  - Temuco 679
  - Valparaiso 116
  - Vina del Mar 122
- China: Shanghai
  - BEIJING (PEKING) 1214
  - Chongqing 1684
  - Guangzhou 1436
  - Wuhan 839
- Colombia: BOGOTA, D.C.
  - Barranquilla 1001
  - Cali 461
  - Cartagena 1072
  - Medellin 415
- Congo, Republic of: BRAZZAVILLE
  - Dolisie 361
  - Kindamba 144
  - Nkayi 281
  - Pointe-Noire 515
- Costa Rica: SAN JOSE
  - Liberia 210
  - Limon 159
  - San Carlos 156
- Croatia: ZAGREB
  - Osijek 283
  - Rijeka 160
  - Split 409
  - Zadar 285
- Cuba: HAVANA
  - Guantánamo 910
  - Holguín 736
  - Pinar del Rio 164
  - Santiago de Cuba 867
- Cyprus: NICOSIA
  - Famagusta 82
  - Limassol 85
- Czech Republic: PRAHA
  - Brno 205
  - Liberec 110
  - Ostrava 371
  - Plzen 95
- Côte d’Ivoire: Abidjan
  - Bouake 343
  - Daloa 379
  - Korhogo 565
  - YAMOUSSOUKRO 236
- Democratic Republic of the Congo: KINSHASA
  - Kananga 1121
  - Kisangani 2324
  - Lubumbashi 2291
  - Mbuji-Mayi 1297
- Denmark: KOBENHAVN
  - Alborg 304
  - Arhus 187
  - Esbjerg 298
  - Odense 168
- Djibouti: DJIBOUTI
  - Ali Sabieh 151
  - Dikhil 173
  - Obock 120
- Dominican Republic: SANTO DOMINGO
  - Las Matas de Farfan 221
  - Monte Cristi 272
  - Puerto Plata 231
  - Punta Cana 194
  - Santiago de los Caballeros 155
- Ecuador: Guayaquil
  - Cuenca 197
  - Machala 182
  - QUITO 425
  - Santa Elena 130
- Egypt: CAIRO
  - Alexandria 218
  - Port Said 197
- El Salvador: SAN SALVADOR
  - San Miguel 138
  - Usulutan 115
- Equatorial Guinea: BATA
  - Aconibe 192
  - Anísoc 161
  - Ebebiyín 221
- Eritrea: ASMARA
  - Assab 1062
  - Keren 94
  - Massawa 118
- Estonia: TALLINN
  - Narva 212
  - Parnu 128
  - Tartu 185
  - Voru 252
- Eswatini: MBABANE
  - Lavumisa 176
  - Lomahasha 143
  - Nhlangano 130
- Ethiopia: ADDIS ABABA
  - Bahir Dar 490
  - Gondar 656
  - Hawassa 279
  - Mek’ele 934
- Finland: HELSINKI
  - Kuusamo 798
  - Oulu 607
  - Tampere 180
  - Turku 168
  - Utsjoki Village 1262
- France: PARIS
  - Lyon 466
  - Marseille 774
  - Nantes 385
  - Toulon 839
- Gabon: LIBREVILLE
  - Franceville 737
  - Moanda 680
  - Oyem 371
- Gambia: BANJUL
  - Bansang 269
  - Farafenni 117
  - Fatoto 353
  - Sintet 135
- Georgia: TBILISI
  - Batumi 374
  - Gori 89
  - Kutaisi 230
- Germany: BERLIN
  - Frankfurt am Main 545
  - Hamburg 289
  - Koln 573
  - Munchen 585
- Ghana: ACCRA
  - Kumasi 249
  - Ashiaman 268
  - Tamale 382
  - Tema 276
- Greece: ATHINAI
  - Larissa 355
  - Patrai 211
  - Thessaloniki 502
- Guatemala: CIUDAD DE GUATEMALA
  - Coban 211
  - Huehuetenango 232
  - Puerto Barrios 292
  - Quetzaltenango 201
- Guinea: CONAKRY
  - Kankan 638
  - Labe 353
  - Nzerekore 864
- Guinea-Bissau: BISSAU
  - Bafatá 141
  - Catio 285
  - Gabu 191
- Guyana: GEORGETOWN
  - Bartica 674
  - Linden 108
  - New Amsterdam 111
- Haiti: PORT-AU-PRINCE
  - Cape-Haitien 199
  - Jacmel 94
  - Les Cayes 200
  - Port-de-Paix 218
- Honduras: TEGUCIGALPA
  - Choloma 349
  - Danli 94
  - La Ceiba 394
  - San Pedro Sula 267
- Hungary: BUDAPEST
  - Debrecen 231
  - Miskolc 186
  - Nyiregyhaza 231
  - Szeged 175
- Iceland: REYKJAVIK
  - Akureyri 388
  - Fjardabyggd 668
  - Hof, Iceland 342
- India: Mumbai
  - Ahmedabad 531
  - Bangalore 984
  - Delhi 1422
  - Hyderabad 709
- Indonesia: JAKARTA
  - Bandung 151
  - Medan 1913
  - Surabaya 783
- Iran: TEHRAN
  - Esfahan 448
  - Mashhad 900
  - Shiraz 932
- Iraq: BAGHDAD
  - Erbil 365
  - Mosul 401
- Ireland: DUBLIN
  - Cork 259
  - Galway 208
  - Limerick 203
  - Waterford 171
- Israel: JERUSALEM
  - Beersheba 118
  - Eilat 314
  - Haifa 150
  - Mitzpe Ramon 203
- Italy: ROMA
  - Milano 573
  - Napoli 226
  - Palermo 924
  - Torino 690
- Jamaica: KINGSTON
  - Montego Bay 170
  - Savanna-la-Mar 192
- Japan: TOKYO
  - Kumamoto 1188
  - Niigata 318
  - Osaka 499
  - Sendai 370
- Jordan: AMMAN
  - Al-Jafr 222
  - Aqaba 333
  - At-Tafilah 184
  - Irbid 90
  - Kerak 130
- Kazakhstan: Almaty
  - Astana 1214
  - Aktobe Province 2184
  - Karaganda 1002
  - Shimkent 682
- Kenya: NAIROBI
  - Eldoret 324
  - Kisumu 351
  - Mombasa 488
  - Nakuru 171
- Korea: SEOUL
  - Busan 391
  - Daegu 279
  - Daejeon 157
  - Gwangju 293
- Kosovo: PRISTINA
  - Gjakova 89
  - Pec 85
  - Prizren 85
- Kuwait: Salmiya
  - Abdali 125
  - Al Wafrah 103
  - Al-Nuwaiseeb 97
- Kyrgyz Republic: BISHKEK
  - Karakol 403
  - Naryn 316
  - Osh 610
  - Talas 291
- Laos: VIENTIANE
  - Luang Prabang 324
  - Paxce 670
  - Savannakhet 462
  - Thakhek 337
- Latvia: RIGA
  - Daugavpils 223
  - Liepaja 218
  - Rezekne 238
  - Ventspils 189
- Lebanon: BEIRUT
  - Qaa 132
  - Qoubaiyat 138
  - Tripoli 82
  - Tyre 83
- Lesotho: MASERU
  - Qachas Nek 224
  - Quthing 176
  - Rafolatsane 294
- Liberia: MONROVIA
  - Buchanan 142
  - Ganta 265
  - Gbarnga 198
- Libya: TRIPOLI
  - Al Baida 1226
  - Al Khums 122
  - Benghazi 1022
  - Misrata 210
- Lithuania: VILNIUS
  - Kaunas 103
  - Klaipeda 307
  - Panevezhis 137
  - Shauliai 213
- Madagascar: ANTANANARIVO
  - Antsirabe 171
  - Fianarantsoa 413
  - Mahajanga 572
  - Toamasina 355
- Malawi: LILONGWE
  - Blantyre City 312
  - Chitipa 665
  - Mzuzu 355
  - Zomba 288
- Malaysia: KUALA LUMPUR
  - Johor Bahru 329
  - Kuantan 237
  - Majlis Perbandaran Ipoh 205
- Mali: BAMAKO
  - Kayes 618
  - Koutiala 392
  - Segou 235
  - Sikasso 375
- Mauritania: NOUAKCHOTT
  - Atar 439
  - Kaedi 411
  - Kiffa 599
  - Nouadhibou 480
- Mexico: MEXICO, CIUDAD DE
  - Guadalajara 551
  - Juarez 1793
  - Monterrey 910
  - Puebla-Tlaxcala 121
- Moldova: CHISINAU
  - Balti 135
  - Cahul 167
  - Ribnita 105
  - Soroca 156
- Mongolia: ULAANBAATAR
  - Darkhan-Uul 245
  - Hovsgol 866
  - Selenge 378
- Montenegro: PODGORICA
  - Bijelo Polje 121
  - Pljevlja 175
- Morocco: CASABLANCA
  - Agadir 466
  - Fez 294
  - Marrakech 242
  - Tánger 338
- Mozambique: MAPUTO
  - Beira 1216
  - Chimoio 1147
  - Nampula 2075
- Myanmar: Yangon
  - Hpa-an 289
  - Mandalay 626
  - NAY PYI TAW 367
- Namibia: WINDHOEK
  - Henties Bay 357
  - Omaruru 208
  - Swakopmund 352
  - Walvis Bay 396
- Nepal: KATHMANDU
  - Bharatpur 149
  - Biratnagar 377
  - Pokhara 201
- Netherlands: AMSTERDAM
  - Enschede 162
  - Groningen 183
  - Maastricht 215
  - Nijmengen 121
- New Zealand: Auckland
  - Gisborne 480
  - Hamilton 124
  - WELLINGTON 644
- Nicaragua: MANAGUA
  - Bluefields 354
  - Chinandega 138
  - Leon 97
  - Puerto Cabezas 517
- Niger: NIAMEY
  - Agadez 951
  - Maradi 661
  - Tahoua 551
  - Zinder 891
- Nigeria: Lagos
  - Benin City 315
  - Ibadan 130
  - Kaduna 771
  - Kano 978
- North Macedonia: SKOPJE
  - Bitola 174
  - Gevgelija 154
  - Kriva Palanka 100
  - Struga 174
- Norway: OSLO
  - Bergen 463
  - Kristiansand 318
  - Stavanger 547
  - Trondheim 494
- Oman: As Seeb
  - Salalah 1012
  - Sohar 183
- Pakistan: Karachi
  - Faisalabad (Lyallpur) 1114
  - Gujranwala 1267
  - Lahore 1211
  - Rawalpindi 1392
- Panama: CIUDAD DE PANAMA
  - David 445
  - Las Tablas 284
  - Yaviza 282
- Papua New Guinea: PORT MORESBY
  - Abau 221
  - Kerema 303
  - Kupiano 185
  - Maopa 157
  - Vuru 157
- Paraguay: ASUNCION
  - Ciudad del Este 320
  - Encarnacion 367
  - Hernandarias 337
  - Mariscal Estigarribia 522
  - Salto del Guaira 407
- Peru: LIMA
  - Arequipa 1012
  - Chiclayo 774
  - Cusco 1102
  - Trujillo 558
- Philippines: Quezon City
  - Baguio 240
  - Laoag 479
  - Naga 393
  - Tuguegarao 475
- Poland: WARSZAWA
  - Krakow 294
  - Lodz 130
  - Poznan 310
  - Wroclaw 348
- Portugal: LISBOA
  - Braga 364
  - Porto 314
  - Vila Nova de Gaia 308
- Puerto Rico: SAN JUAN
  - Aguadilla Pueblo 132
  - Boqueron 190
  - Mayaguez 191
  - Ponce 117
- Qatar: DOHA
  - Abu Samra 96
  - Al Ruwais 110
  - Dukhan 84
  - Zubara Fort 104
- Romania: BUCURESTI
  - Cluj-Napoca 453
  - Iasi 389
  - Sibiu 279
  - Timisoara 548
- Russia: MOSCOW
  - Ekaterinburg 1786
  - Nizhny Novgorod 422
  - Novosibirsk 3357
  - St. Petersburg 706
- Rwanda: KIGALI
  - Butare 124
  - Gisuma 229
  - Rubavu 138
- Saudi Arabia: RIYADH
  - Ad-Dammam 409
  - Al-Madinah 839
  - Jiddah 954
  - Makkah 869
- Senegal: DAKAR
  - Ballou 716
  - Kaolack 191
  - Mbour 96
  - St Louis 289
  - Tambacounda 466
- Serbia: BEOGRAD (BELGRADE)
  - Kragujevac 139
  - Nis 237
  - Novi Sad 94
  - Subotica 190
- Sierra Leone: FREETOWN
  - Bo 239
  - Kambia 126
  - Kenema 306
  - Makeni 186
- Slovak Republic: BRATISLAVA
  - Kosice 404
  - Liptovsky Mikulas 287
  - Poprad 328
  - Zilina 203
- Slovenia: LJUBLJANA
  - Koper 106
  - Maribor 130
  - Metlika 98
  - Murska Sobota 179
- Somalia: MOGADISHU
  - Borama 1522
  - Bosaso 1395
  - Galkayo 721
  - Hargeisa 1289
- South Africa: Johannesburg
  - Cape Town 1403
  - Durban 568
  - Port Elizabeth 1051
  - Upington 794
- South Sudan: JUBA
  - Ezo 579
  - Raga 963
  - Wau 645
- Spain: MADRID
  - Barcelona 621
  - Sevilla 535
  - Valencia 357
  - Zaragoza 314
- Sri Lanka: COLOMBO
  - Batticaloa 320
  - Galle 145
  - Jaffna 360
  - Trincomalee 265
- Sudan: OMDURMAN
  - El-Obeid 409
  - Kassala 637
  - Nyala 1210
  - Port Sudan 839
- Suriname: PARAMARIBO
  - Matapi 376
  - Moengo 108
  - Nieuw Nickerie 229
  - Pokigron 185
- Sweden: STOCKHOLM
  - Goteborg 469
  - Linkoping 200
  - Malmo 613
- Switzerland: Zurich
  - Basel 84
  - Geneve 277
  - Lausanne 221
  - Lugano 207
- Syria: Aleppo
  - Ar Raqqah 210
  - DAMASCUS 356
  - Homs 185
  - Latakia 175
- Taiwan: NEW TAIPEI
  - Kaohsung 346
  - Taichung 151
- Tajikistan: DUSHANBE
  - Khorog 522
  - Khujand 303
  - Kulob 196
  - Panjakent 234
- Tanzania: Dar es Salaam
  - DODOMA 443
  - Mwanza 1129
  - Tanga 331
  - Zanzibar 92
- Thailand: BANGKOK
  - Chiang Mai 690
  - Chon Buri 84
  - Nakhon Ratchasima 259
  - Songkhla 968
- Timor-Leste: DILI
  - Maliana 153
  - Suai 176
- Togo: LOMÉ
  - Atakpame 161
  - Kara 414
  - Kpalime 128
  - Sokode 340
- Trinidad and Tobago: PORT OF SPAIN
  - Icacos 134
  - Mafeking 98
  - Toco 89
- Tunisia: TUNIS
  - El Kef 168
  - Gafsa 363
  - Monastir 169
  - Sfax 267
- Turkey: ISTANBUL
  - Adana 935
  - Ankara 450
  - Bursa 154
  - Izmir 479
- Turkmenistan: ASHKHABAD
  - Balkanabat 425
  - Dashoguz 599
  - Tashauz 599
  - Turkmenabat 620
- Uganda: KAMPALA
  - Fort Portal 296
  - Gulu 334
  - Mbarara 270
  - Mbarara 270
- Ukraine: KYIV
  - Dnepropetrovsk 477
  - Donets’k 732
  - Kharkiv 526
  - Odessa 475
- United Arab Emirates: DUBAI CITY
  - Abu-Dhabi City 139
  - Fujairah 147
  - RAK City 106
- United Kingdom: LONDON
  - Birmingham 202
  - Glasgow 663
  - West Midlands 188
  - West Yorkshire 326
- United States: New York (NY)
  - Chicago (IL) 1272
  - Houston (TX) 2618
  - Los Angeles (CA) 4490
  - Phoenix (AZ) 3873
- Uruguay: MONTEVIDEO
  - Chuy 326
  - Melo 398
  - Rivera 504
  - Salto 492
- Uzbekistan: TASHKENT
  - Andizhan 353
  - Namangan 294
  - Nukus 1136
  - Samarkand 308
- Venezuela: CARACAS
  - Barquisimeto 365
  - Ciudad Guayana 671
  - Maracaibo 696
- Vietnam: HO CHI MINH CITY
  - Can Tho 171
  - Da Nang 852
  - Ha Noi 1611
  - Hai Phong 1672
  - Valencia HO CHI MIN CITY 167 (entry ordering preserved as in source)
- Yemen: SANA’A
  - Adan 385
  - Al-Hudaydah (Hodeidah) 251
  - Al-Mukalla 799
  - Ta’izz 269
- Zambia: LUSAKA
  - Chipata 568
  - Kasama 855
  - Kitwe 358
  - Ndola 317
- Zimbabwe: HARARE
  - Bulawayo 442
  - Gweru 276
  - Mutare 266

*IMF WORKING PAPERS Road Quality and Mean Speed Score — Appendix I. List of Countries and Cities*

### References

### References

### Cited works on rural access, roads, and transport policy
- Asher, Sam and Paul Novosad, 2020, “Rural Roads and Local Economic Development,” American Economic Review, Vol. 110, No. 3, pp. 797–823.
- Berg, Claudia N., Uwe Deichmann, Yishen Liu, and Harris Selod, 2015, “Transport Policies and Development,” Policy Research Working Papers No. 7366 (Washington: The World Bank Group).
- Danish International Development Assistance (DANIDA), International Development Cooperation, 2010, “Impact Evaluation of DANIDA Support to Rural Transport Infrastructure in Nicaragua,” Ministry of Foreign Affairs of Denmark.
- Dijkstra, Lewis, Hugo Poelman, and Linde Ackermans, 2019, “Road Transport Performance in Europe, Introducing A New Accessibility Framework,” DG REGIO Working Paper (Luxembourg: European Commission).
- Iimi, Atsushi, Farhad Ahmed, Edward Charles Anderson, Adam Stone Diehl, Laban Maiyo, Tatiana Peralta-Quirós, and Kulwinder Singh Rao, 2016, “New Rural Access Index: Main Determinants and Correlation to Poverty,” Policy Research Working Paper No. 7876 (Washington: The World Bank Group).
- Mikou, Mehdi, Julie Rozenberg, Elco Koks, Charles Fox, and Tatiana Peralta Quiros, 2019, “Assessing Rural Accessibility and Rural Roads Investment Needs Using Open-Source Data,” Policy Research Working Paper No. 8746 (Washington: The World Bank Group).
- Roberts, Peter, Shyam KC, and Cordula Rastogi, 2006, “Rural Access Index: A Key Development Indicator,” Transport Paper Series No. TP-10 (Washington: The World Bank Group).
- World Bank, 2016, “Measuring Rural Access: Using New Technologies,” Working Paper No. 107996 (Washington: The World Bank Group).

### Empirical studies on infrastructure productivity and investment needs
- Calderón, César, Enrique Moral-Benito, and Luis Servén, 2015, “Is Infrastructure Capital Productive? A Dynamic Heterogeneous Approach,” Journal of Applied Econometrics, Vol. 30, No. 2, pp. 177–98.
- Calderón, César and Luis Servén, 2010, “Infrastructure in Latin America,” Policy Research Working Paper No. 5317 (Washington: The World Bank Group).
- Calderón, César and Luis Servén, 2004, “The Effects of Infrastructure Development on Growth and Income Distribution,” Policy Research Working Paper No. 3400 (Washington: The World Bank Group).
- Fay, Marianne and Tito Yepes, 2003, “Investing in Infrastructure: What is Needed from 2000 to 2010?,” IMF Policy Research Working Paper No. 3102 (Washington: The World Bank Group).
- Karpowicz, Izabela, Carlos Góes, and Mercedes García-Escribano, 2018, “Filling the Gap: Infrastructure Investment in Brazil,” Journal of Infrastructure, Policy and Development, Vol. 2, No. 2, pp. 301–18.
- World Bank, 2019, “World Development Index” (Washington: The World Bank Group).

### Studies on travel time, valuation, and accessibility in transport appraisal
- Mackie, P.J., Sergio Jara-Dıaz, and Anthony Fowkes, 2001, “The Value of Travel Time Savings in Evaluation,” Transportation Research Part E: Logistics and Transportation Review, Vol. 37, No. 2–3, pp. 91–106.
- Martens, Karel and Floridea Di Ciommo, 2017, “Travel Time Savings, Accessibility Gains and Equity Effects in Cost–Benefit Analysis,” Transport Reviews, Vol. 37, No. 2, pp. 152–69.
- Goodin, Ginger, Mark Burris, Tina Geiselbrecht, and Nick Wood, 2013, “Application of a Performance Management Framework for Priced Lanes,” Technical Report No. 5-6396-01-1 (College Station: Texas A&M Transportation Institute).
- Wood, Nick, Jordan McGee, Tuba Geiselbrecht, and Chris Simek, 2020, “Emerging Challenges to Priced Managed Lanes,” NCHRP Synthesis 559, National Academies of Sciences, Engineering, and Medicine.
- Jaworski, Taylor, Carl Kitchens, and Sergey Nigai, 2020, “Highways and Globalization,” NBER Working Paper 27938 (Cambridge: National Bureau of Economic Research).

### Policy, evaluation, and international organization sources
- Gaspar, Vitor, David Amaglobeli, Mercedes García-Escribano, Delphine Prady, and Mauricio Soto, 2019, “Fiscal Policy and Development: Human, Social, and Physical Investments for the SDGs,” IMF Staff Discussion Notes No. 19/03 (Washington: International Monetary Fund).
- International Monetary Fund (IMF), 2019, “World Economic Outlook Database” (Washington: International Monetary Fund).
- International Monetary Fund (IMF), 2018, “Public Investment Management Assessment—Review and Update” (Washington: International Monetary Fund).
- International Monetary Fund (IMF), 2015, “Making Public Investment More Efficient,” IMF Policy Paper No. 25 (Washington: International Monetary Fund).
- OECD, 2020, “Transport Bridging Divides,” OECD Urban Studies (Paris: OECD Publishing).
- United Nations (UN), 2015, “Transport for Sustainable Development - The case of Inland Transport,” Transport Trends and Economics Series, United Nations.
- Schwab, Klaus (Ed.), 2019, “The Global Competitiveness Report 2019,” World Economic Forum.
- Central Intelligence Agency (CIA), The World Factbook, accessed June 2020 (New York: Skyhorse).

### Technical and methodological contributions
- Berg, Claudia N., Uwe Deichmann, Yishen Liu, and Harris Selod, 2015, “Transport Policies and Development,” Policy Research Working Papers No. 7366 (Washington: The World Bank Group).
- Mikou, Mehdi, Julie Rozenberg, Elco Koks, Charles Fox, and Tatiana Peralta Quiros, 2019, “Assessing Rural Accessibility and Rural Roads Investment Needs Using Open-Source Data,” Policy Research Working Paper No. 8746 (Washington: The World Bank Group).
- Dijkstra, Lewis, Hugo Poelman, and Linde Ackermans, 2019, “Road Transport Performance in Europe, Introducing A New Accessibility Framework,” DG REGIO Working Paper (Luxembourg: European Commission).

*Road Quality and Mean Speed Score; Working Paper No. WP/2022/095*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022095-print-pdf.pdf_