rafmacalaba/datause-encoder-data huggingface dataset:
Figure 7. Sectors in which top 20 workers are more likely to be employed have faced lower work

- stoppages

- A. Work stoppages vs. top 20 sectoral C. Work stoppages by education of respondent participation

- B. Work stoppages by sector and mobility

Note: In figure A, each dot represents a country-round-sector. Highly affected service sectors include accommodations and food and other service activities; Not as affected service sectors include retail, public administration, professional, financial, education, and health. In figures B and C, work stoppages are estimated over a pooled sample of countries and survey rounds. In figure B, higher mobility includes country-rounds with an average of the mobility index above -20 percent; lower mobility includes country-rounds with an average mobility index below -20 percent. Conditional means are taken from a regression of work stoppages on sector fixed effects and controls including country fixed effects, respondent gender, age, and head status. For figures A and B, the most recently engaged sector is used for all rounds. Source: HFPS; Google mobility reports While we are not able to observe many job characteristics that can differentiate workers within sectors, we can analyze differences according to workers’ characteristics. Indeed, within and across countries, we find that workers with tertiary education have consistently been less likely than those with below secondary or 17
-35-

rate. A slump abroad lowers borrowing potential: if growth in trading partner countries falls from, say, 4 percent to zero, the amount of feasible debt accumulation goes down by 0.3 percentage points of GNP.

# Table a: ALLOWABLE FOREIGN BORROWING: SUSTAINABLI CURRENT ACCOUNT DEFICITS (percent of GNP)

|Output<br>Growth|||||||
|---|---|---|---|---|---|---|
|of Turkey|0|1|2|3|3.5|4|
|3|1.12|1.21|1.29|1.38|1.42|1.46|
|4|1.49|1.58|1.66|1.75|1.79|1.83|
|5|1.87|1.95|2.04|2.12|2.17|2.21|
|6|2.24|2.33|2.41|2.50|2.54|2.58|
|7|2.61|2.70|2.78|2.87|2.91|2.95|

In section IV.D it is argued that a 6 percent output growth rate is feasible for Turkey if some policy adjustments are implemented. Also, the IMF and the World Bank project growth rates in the world economy that yield a combined weighted growth rate for Turkey's trading partners of 3.5 percent for the next five years. The table suggests that this implies a feasible current account deficit of 2.5 percent of GNP for Turkey. This is about the same level as in 1986 (2.6 percent of GNP) and substantially larger than the current account deficit of 1987. The conse-quences of alternative levels are pursued in Section IV.D.

How does this "allowable" current account deficit compare with the solvency constraint? At the projected average real interest rate on foreign debt of 6 percent, a current account deficit of 2.5 percent of GNP translates into a non-interest current account surplus of 0.5 percent of GNP at the ChhibberDisk/chhibber-Turkey Paper/ac:07-28-88: 1c
# **Optimal Investments in Africa’s Road Network**

Sebastian Krantz<sup>*</sup> Originally published in the Policy Research Working Paper Series in _September 2024_ . This version is updated in _November 2025_ . To obtain the originally published version, please email prwp@worldbank.org.

**Keywords:** African roads, spatially optimal investments, big data, PE and GE analysis **JEL Classification:** O18; R42; R10; O10

## **1 Introduction**

For several decades, development economists and policymakers have highlighted insufficient or inefficient transport infrastructure, coupled with border frictions and high trading costs, as a major obstacle to African economic development and regional integration. A World Bank report from 2010 states that Sub-Saharan Africa (SSA) has 31 paved roads _km_ per 100 _km_<sup>2</sup> of land, compared to 134 _km_ in other low-income countries, estimates the annual infrastructure gap at US $ 93 billion and urges SSA countries to spend 1 percent of GDP on roads (Foster & Brice ̃noGarmendia, 2010). This is echoed by the African Development Report 2010, attesting a financing gap at 5 percent of GDP to overhaul the infrastructure sector (ADB, 2010). More extensive and detailed work by Teravaninthorn & Raballand (2009) and Lamarque & Nugent (2022) unveils the nature of transportation and trucking in Africa, the determinants of transport costs and frictions, and discusses potentials and policy options for development along specific transport corridors. Yet, still little is known about the economic optimality of such (large-scale) transport investments.

This paper thus presents a comprehensive, data-driven, and quantitatively rigorous assessment of Africa’s road network and optimal investments into it at the trans-African scale. It (1) conducts a detailed empirical stocktake of Africa’s present road network; (2) characterizes global economic optimality in network investments from a market access and welfare perspective; (3) investigates the effects of cross-border frictions on optimal spatial allocations; and (4) presents cost-benefit analyses and evidence on the macroeconomic feasibility of large scale (optimal) investments. This agenda mirrors questions of international policymakers who, in theory, can invest in roads anywhere on the continent and may ask: (1) is it still important to invest in African roads?; (2) where exactly should we invest in African roads?; (3) are investments sensible with/without trade facilitation?; and (4) are they macroeconomically feasible? The paper engages this ambitious agenda by building realistic economic topographies informed by routing engines, geospatial big data, and surveys, and analyzing investments on them using tools from graph theory and spatial economic modeling.

> * Kiel Institute for the World Economy and World Bank

> _E-mail:_ sebastian.krantz@kielinstitut.de or skrantz@worldbank.org

Latest version at sebastiankrantz.com/research, data and code at github.com/SebKrantz/OptimalAfricanRoads
**Table S1.1: Summary of financial education studies by publication date and country**

||Number of studies<br>(1)|Percent of sample<br>(2)||
|---|---|---|---|
|_A By publication date_||||
|1999|2|1.59||
|2000|0|0.00||
|2001<br>|5|3.97||
|2002|1|0.79||
|2003<br>|4<br>|3.17<br>||
|2004<br>|3<br>|2.38<br>||
|2005<br>|6|4.76<br>||
|2006|5|3.97||
|2007<br>|6<br>|4.76<br>||
|2008|6|4.76||
|2009|8|6.35||
|2010|10|7.94||
|2011|7|5.56||
|2012|15|11.9||
|2013|9|7.14||
|2014|11|8.73||
|2015|15|11.9||
|2016|13|10.32||
|_B By country of intervention_|||_Income_|
|Australia|2|1.59|High|
|Bosnia and Herzegovina|1|0.79|Upper-middle|
|Brazil|1|0.79|Upper-middle|
|China|1|0.79|Upper-middle|
|Dominican Republic|1|0.79|<br>Upper-middle|
|Germany<br>|1<br>|0.79<br>|High<br>|
|Ghana<br>|1|0.79|Lower-middle<br>|
|Hong Kong, China|1|0.79|High|
|India|8|6.35|Lower-middle|
|Indonesia|2|1.59|Lower-middle|
|Italy|7|5.56|High|
|Kenya|1|0.79|Lower-middle|
|Malawi|1|0.79|Low|
|Mexico|1|0.79|Upper-middle|
|Mozambique<br>|1<br>|0.79<br>|Low<br>|
|New Zealand|2|1.59|High|
|Pakistan|1|0.79|<br>Lower-middle|
|Qatar<br>|1<br>|0.79<br>|High<br>|
|Rwanda<br>|1|0.79|Low<br>|
|Singapore|1|0.79|High|
|South Africa|1|0.79|Upper-middle|
|Spain|1|0.79|High|
|Sri Lanka|1|0.79|Lower-middle|
|Tanzania|2|1.59|Low|
|USA|83|65.87|High|
|Uganda|2|1.59|<br>Low|
|_Low inc. econ._|7|5.5||
|_Lower-middle inc. econ._|14|11.11||
|_Upper-middle inc. econ._|6|4.76||
|_High inc. econ._|99|78.57||
|Total|126|100||

Notes: Country group classifications refer to 2015 World Bank data on GNI per capita (Atlas method).

30
5 real effects on production as well as consumption.<sup>The second feature implies that the price</sup> changes that may be ultimately needed will<sup>not be marginal. The third point implies that high</sup> income families are benefitting the most<sup>from price controls. Increasing energy prices in the</sup> residential sector would, therefore, reduce<sup>the "leakages" implied by this form of "social</sup> assistance" while also providing a greater incentive<sup>to conserve energy.</sup> The purpose of this paper is to provide some empirical<sup>evidence relating to the</sup> interaction between pricing policy in the energy<sup>sector and social welfare. Section 1 describes</sup> the "stylized facts" with respect to past approaches to pricing<sup>household energy in the</sup> transition economies; some of the changes that have<sup>been introduced since the inception of</sup> the transition in 1989; and the attempts that have<sup>been made in Poland and other transition</sup> economies to raise prices. Section 2 focusses on the<sup>characteristics of energy consumption in</sup> Poland and, using the GUS (State Statistical Bureau)<sup>household budget survey, calculates</sup> what portion of households' budget is absorbed<sup>by energy outlays for various socio-economic</sup> groups and at various income levels. Section<sup>3 simulates the welfare losses and the actual</sup> income losses to different segments of the population<sup>from raising household energy prices.</sup> Section 4 examines the interaction of price policy<sup>and social policy, asking whether, and</sup> how, the impact of higher energy prices on the<sup>poor can be mitigated by a mixture of lifeline</sup> rates, cash subsidies for households or other methods<sup>such as in-kind transfers. Section 5</sup> simulates the effects of lifeline pricing on household<sup>welfare adjustments.</sup>

# **I.** Social **Pricing Policy in the Transition Economies**

Governments in the command economies used fixed<sup>low prices and wage controls as</sup> essential elements of their distributional tool-kit.<sup>Health services and education were free.</sup> As a part of the social contract, governments also<sup>provided many private goods, including</sup> subsidized dwellings, many urban services, transport,<sup>utilities, and sometimes food, at very</sup> low prices. In general, these attempts to remedy<sup>fundamental (or perceived) distributional</sup> problems through inefficient pricing of scarce<sup>resources has resulted in unsatisfactory</sup>
# _Health expenditures1_<sup>_2_</sup>

On the aggregate, expenditures on health decreased after the crisis when measured in levels or as shares of total expenditures. Health expenditures in Moldova during the period after the Russian Crisis drop on average by 7.4 lei per capita (a proportional drop of 47%). Likewise, health expenditure shares drop by about 3 percentage points, from 10.4% to 7.5% (a drop of 27.9%). These aggregate drops can also be seen from figure 3, where we have plotted estimated density functions for household health expenditure levels and shares. As for the general case of consumption, both distributions show a displacement to the left after the crisis. The following tables show, however, that this effect is not felt evenly across the population. To the contrary, the impact of the crisis on health care expenditures, follow an asymmetric pattern similar to that seen for total per capita consumption.

<!-- Start of picture text --> Figure 6.1<br>Densty at tPO - Density at t-1<br>.263472 -<br>.001535<br>_1.i4)315 ' ' 6.92762<br>Health Expenditures (log)<br><!-- End of picture text -->

**12** Because free access to basic services is widely available, the majority of households **in** our samnple report zero health expenditures for the last month, giving median values equal to zero for most of the population. We do not report proportional changes or median values in the following tables for that reason.

29
**Appendix 1: Per Capita Analysis on the Equalization Effect of Grants**

No clear criteria for the allocation of grants and ODA debt forgiveness among the 32 African countries in our sample emerge from the analysis of these countries' economic characteristics on a country by country basis. The question here, then, is whether it makes any difference if these countries are analyzed in per capita terms.

When grants are measured in per capita terms, the results are the opposite of the one discussed in the text, which concludes that grants have been allocated to richer countries. In fact, the poorest countries have received, in per capita terms, about 67 percent or more pure grants than the richer countries. Furthermore, this inequality increased marginally from 67 percent to 68 percent between 1984-88 and 1989-93 (Table A1.1).

This allocation of grants per capita implies that grants have come to compensate, at least partially, for the initial inequality that exists in per capita GNP between the high and low country groups.

**Table Al.l ODA debt forgiveness**

<!-- Start of picture text --> Total grants (incluing debt relief) Pure grants (exluding debt relief)<br>1984-S8 1989-93 1984-88 1989-93 1984-S8 19S9-93 19S44S 19L9-93<br>Average GNP per capita Average GDP per capita (const) verage GNP per capita Average GDP per capita (const)<br>High group 460 456 475 439 460 456 475 439<br>Lowgroup 277 320 311 305 277 320 311 305<br>Relative income<br>distribution 60.2% 70.2°/e 65.5% 69.5% 60.2% 70.2% 65.5% 69.5%<br>Toitl grants per cfapita Totfal grants per capita Totul grants per capita Total grants per capita<br>1984-88 1989-93 1984-88 1989-93 1984-88 1959-93 1984-88 1959-93<br>High group 14.8 27 9 14.8 27.9 14.8 23.0 14.8 23.0<br>Low group 24.7 39.4 24.7 39.4 24.7 38.6 24.7 38.6<br>(excl. STP 21.9 30.4 21.9 30.4<br>Relative income<br>distribution 167.1% 141.1% 167.1% 141.1% 167.1% 168.1% 167.1% 163.1S%<br>GNP + grants per capita GDP + grants per catpita GNP + grant per caipita GDP + griants per capita<br>1984-88 1989-93 1984-88 1989-93 1984-88 1989-93 198443 1989-93<br>High group 475 484 490 467 475 479 490 462<br>Low group 302 359 336 344 302 359 336 344<br>Relative income<br>distribution 63.5% 74.3% 68.5% 73.8% 63.5% 74.9% 68.5% 74.4%<br><!-- End of picture text --> Note: High group has received significantly more ODA forgiveness than low group STP represents **Sac** Tome Principe, a small country whose grants per capita ($185 for 1989-1993 average) is a clear outlier.

23
14 Figure 5. Export diversification by Region and Period: Intensive Margin <!-- Start of picture text --> Emerging Asia Frontier Asia Sub-Saharan Africa Western Europe<br>1965-1970 2006-2010<br>4<br>3<br>2<br>Theil Intensive<br>1<br>0<br><!-- End of picture text --> _Sources_ : UN COMTRADE, and author calculations.

Figure 6. Manufacturing Exports Share, by Region and Period <!-- Start of picture text --> Emerging Asia Frontier Asia Sub-Saharan Africa Western Europe<br>1965-1970 2006-2010<br>.8<br>.6<br>.4<br>.2<br>Share of Manufacturing Exports<br>0<br><!-- End of picture text --> _Sources_ : UN COMTRADE, and author calculations.
Table 2 shows the results and Figure 5 helps to visualize the pattern of convergence/divergence. For the period from 1970 to 1985 we observe a process of convergence, similar to the results per regions. The states of cluster 1 saw their welfare decrease in comparison with the national average, cluster 3 gained weight and the second cluster stayed stable.

**Figure 5: Per capita GDP per cluster (national average=100)**

<!-- Start of picture text --> 160<br>140<br>120<br>Cluster 1<br>100<br>Cluster 2<br>80 Cluster 3<br>60<br>40<br>1970 1985 1993 2001 2006<br>Per capita GDP (national average=100)<br><!-- End of picture text --> Source: Calculations based on information from INEGI, excluding Campeche and Tabasco.

Between 1985 and 1993, the whole process of growth “catch-up” of cluster 3 was restored to the level of 1970 and it has maintained itself at this level until 2006. Cluster 1 increased its relative standing during the 1970s to early 1980s, but later lost ground culminating in the same relative per capita GDP compared to national average in 2006 as in 1985. Cluster 2 stayed relatively stable until 1993, but later increased its relative income per capita during the last growth period.

10
# **Exhibit 3d: Evidence from cross-regional studies (IV/V)**

|**Study**|**Brief**<br>**description**|**Time**<br>**period**|**Key findings**|**Implications for market vs.**<br>**intervention question**|**Implications**<br>**for centre vs.**<br>**region**<br>**question**|
|---|---|---|---|---|---|
|Dall'erba and<br>Le Gallo<br>(2007)|• 145 EU<br>regions|1989-1999|• Two tiers of convergence, one in core richer regions<br>and one in peripheral poorer regions. Peripheral<br>regions converge faster.<br>• Non-significant impact of EU funds on the regions’<br>steady states|• Little or no impact of structural<br>funds on convergence rates|• NA|
|Fagerberg nd<br>Verspagen<br>(1996)|• 70<br>regions in<br>six<br>member<br>states|1950-1970<br>1970-1990|• Substantial convergence<br>• Poorest region grew 2.8%-4.3% faster than richest<br>• Slowing of convergence<br>• Poorest region grew 0.8-2.4% faster than richest|• EU investment began in<br>earnest in 1990, potentially<br>showing that it actually<br>exacerbates differences|• NA|
|||1980-1990|• Absence of convergence<br>• No evidence of convergence<br>• Three convergence clubs based on employment - high<br>unemployment, intermediate unemployment and low<br>unemployment<br>• EU support positive in low employment regions,<br>ineffective in the other two clubs|||

**Evidence for market         Evidence for interventionist policy         Evidence for decentralized approach Evidence for centralized approach**

28
series, to which our modification of the USCB ( _D_ = 0) series is closely anchored until 2020, is somewhat higher than that in the USCB’s ( _D_ = 0) series itself. To this extent, the series for _D_ = 1 in Table 4 represents a more conservative estimate of the level of mortality in the presence of AIDS than the USCB’s ( _D_ = 1), since by no means all those who die of AIDS would have died at the same age of other causes. The use of the series in Tables 3 and 4 as reference cases therefore involves a somewhat more conservative stance than that implicit in the USCB series where both the size of the initial ‘shock’ to mortality and the general level of mortality are concerned. With this reservation, the size of the shock to the level of premature adult mortality can be represented by the difference between the summary statistics 20 _q_ 20( _D_ = 1) and 20<sup>_q_</sup> 20<sup>(</sup><sup>_D_= 0).ThesestatisticsarereportedinTable5.</sup> Table 5: Premature adult mortality compared: 20 _<u>q</u>_ 20( _D_ = 0) and 20 _<u>q</u>_ 20( _D_ = 1)

|||1990|2000|2010|2020|2030|2040|2050|
|---|---|---|---|---|---|---|---|---|
|NO AIDS|20<sup>_q_</sup>20|0.127|0.113|0.099|0.085|0.070|0.056|0.041|
|AIDS|20<sup>_q_</sup>20|0.353|0.395|0.359|0.270|0.154|0.111|0.111|

Inspection of this wave of mortality reveals that it is large indeed, but of comparatively short duration in relation to an inter-generational period of 20 years, in which, by hypothesis, young adults make decisions with a horizon of 30 years. After the initial surge in 1990 (looking forwards), _qt_ peaks in 2000, falls a little by 2010, then more sharply by 2020, and again by 2030, by which point, the shock has essentially passed. Since the chances that a mortality shock will bring about an economic collapse depend on its duration as well as its size, it is desirable to do some sensitivity analysis. As a variation on _D_ = 1, therefore, we prolong the shock by a full decade in the following way. In 2010, we repeat the age-specific mortality profile for 2000 and push those for 2010, 2020, . . . out one decade into the future. The age pyramids in Table 4 are then recalculated accordingly. This variant of the base case is labelled _D_<sup>_∗_</sup> = 1. Note that in 31
**Polk,Panning, and Rosrch Trade Policy**

Under what macroeconomic conditions are industrial growth and financial stability - or disastermost likely in a semi-industrial country?

The question is addressed using an econometric model with the following features. Each firm's net income is a function of macroeconomic variables (such as output demand and interest rates) and firm-specific factors (such as physical asset shocks, currency exposure, and overall indebtedness). Each firm retains some - portion of its income how mi:ch depends on dividend policy and past earnings performance. Retained eamings add to net worth and are distributed among specific assets and liabilities according to the same macroeconomic and firmspecific variables. These incremental additions to assets and liabilities set the stage for the next period's adjustmtnt behavior.

Application of this model to Uruguayan raw data yielded these basic findings:

* Corporate income is very sensitive to output demand and the cost of dollar credit.

* Fluctuations in corporate income have a clear, direct effect on the rate at which net worth expands.

* Firms absorb most short-run fluctuations in net worth by adjusting assets, not debts.

* Corporate demand for peso credit is very unresponsive to the real peso interest rate.

The findings imply that rapid changes in the exchange rate or aggregate demand significantly affect leverage and liquidity in the corporate sector.

This paper is a product of the Trade Policy Division, Country Economics Division. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Carla Cabana, room N8-065, extension 61539.

The PPR Working Paper Series dissby inates the **n a** indgs of work under way in the Bank's Policy, PlWling, and Resek ch Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. T'he findings, interpretations, and conclusions in these papeTs do not necessarily represent official policy of the Bank.

Copyright 0 1988 by the Intemational Banlc for Reconstruction and Developmnenttlle World Bank