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Author:
Mariano Moszoro
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Mauricio Soto
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Publication Date:
20 May 2022
eISBN:
9798400210440
ISBN:
9798400210440
Language:
English
Keywords:
Road Quality; Sustainable Development Goals; Access to Infrastructure; MS score; road infrastructure; road network characteristic; B. geometric mean speed Score; Infrastructure; Income; Africa; Global
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Appendix I. List of Countries and Cities

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.

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*

We are thankful to Hassan Adan, Zhiyong An, Dave Coady, Hamid Davoodi, Jean-Marc Fournier, Mercedes García-Escribano, Nikolay Gueorguiev, Constant Lonkeng, Paolo Mauro, Tewodaj Mogues, Rui Monteiro, Arthur Silve, Geneviève Verdier, and Yuan Xiao for helpful comments and suggestions.

1

See, also, the World Bank’s interactive “Rural Access Index Measurement Tool” available at https://rai.azavea.com/.

2

For instance, the costing of Sustainable Development Goals (SDGs) performed by the International Monetary Fund (IMF) uses RAI as an input variable for the estimating of road stock needed by 2030 (Gaspar, Amaglobeli, García-Escribano, Prady, and Soto, 2019, p. 27).

4

For example, some people take road quality literally as potholes.

5

The World Economic Forum’s Global Competitiveness Report avouches to compute the average speed of a driving itinerary connecting the 10 or more largest cities but aggregates the results into the road connectivity index (Schwab, 2019, Appendix A: Global Competitiveness Index 4.0 Methodology and Technical Notes, p. 617).

6

See United Nations Statistics Division, Demographic Statistics Database https://unstats.un.org/unsd/demographic-social/index.cshtml.

7

The simple arithmetic mean would overweight the speed of short distances.

8

See: “The 2019 Legatum Prosperity Index,” www.prosperity.com.

9

These countries are at the low spectrum of the score. Therefore, omitting them would have made our estimates from the matched countries upwardly biased. Unfortunately, there is no official data for Kosovo.

10

Mikou, Rozenberg, Koks, Fox, and Peralta Quirós (2019) estimates are available at http://documents.worldbank.org/curated/en/759461550242864626/pdf/WPS8746.pdf. The figure for the Russian Federation comes from Roberts, KC, and Rastogi (2006), available at https://openknowledge.worldbank.org/bitstream/handle/10986/17414/360060TP100Rural0access0index01PUBLIC1.pdf.

11

Mikou, Rozenberg, Koks, Fox, and Peralta Quiros (2019) estimated the RAI using open data. The correlation of their and the World Bank’s RAI is low, though: 0.40 for primary and secondary roads, and 0.31 and 0.30 when tertiary and tracks are included, correspondingly.

12

In a few cases where variables were not available for specific countries (e.g., GDP or road network length for Cuba, Kosovo, and Syria), we procured them from various alternative sources and then cross-checked them with other data and similar counties.

13

Jaworski, Kitchens, and Nigai (2020) estimate that the US interstate highway system contributes to ca. 4 percent of GDP, a quarter of which through foreign trade.

14

The classification of countries by income levels used by the IMF follows a waterfall process. The main criteria to sort countries into advanced economies and emerging market and developing economies are: (i) income per capita, (ii) export diversification, and (iii) degree of integration into the global financial system. Further, within the emerging market and developing economies the LIDCs are countries that have per capita income levels below a certain threshold (currently set at US$2,700 in 2016 as measured by the World Bank’s Atlas method), structural features consistent with limited development and structural transformation, and insufficiently close external financial linkages to be widely seen as emerging market economies. See https://www.imf.org/external/pubs/ft/weo/2019/02/weodata/groups.htm. For this exercise, we also classified Cuba and Kosovo to the group of Emerging Market Economies on the basis of their GDP per capita.

15

Mikou, Rozenberg, Koks, Fox, and Peralta Quiros (2019) estimate that the cost of paving two lanes ranges between US$843,000 in South Asia to US$1,588,000 in Eastern Europe and Central Asia.

16

The one-way commute times before and after the road upgrade are: (i) 60 minutes × 50 km ÷ average speed 73 km/h = 41.1 minutes versus (ii) 60 minutes × 50 km ÷ 90 km/h = 33.3 minutes.

17

I.e., at the median annual GDP per capita in our sample of US$5,268 (Table 2) and assuming 1,600 working hours per year.

18

The undiscounted payback period equals 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).

19

Cf. IMF’s web page on PIMA: https://infrastructuregovern.imf.org/content/PIMA/Home/PimaTool/What-is-PIMA.html (accessed February 2021).

20

E.g., as a compliment to the accessibility framework presented by (Dijkstra, Poelman, and Ackermans 2019).

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Cover IMF Working Papers
Road Quality and Mean Speed Score
Author:
Marian Moszoro
and
Mauricio Soto
Volume/Issue:
Volume 2022: Issue 095
Publisher:
International Monetary Fund
ISBN:
9798400210440
ISSN:
1018-5941
Pages:
34
DOI:
https://doi.org/10.5089/9798400210440.001