rafmacalaba/datause-encoder-data huggingface dataset:
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Construction materials that are normally unavailable locally are very expensive due to the large costs associated with shipping them to a construction site (World Bank 2009a). This is exacerbated by the limited information on the potential of infrastructure development and upcoming investments, and by poor procurement practices. Added to this are questions of political stability and security, factors that tend to improve as a country moves forward or, as in the case of South Sudan, consolidates into itself. Meanwhile, costs are being pushed up by a shortage of skilled operators and technicians and the extraordinarily high cost of living faced by the mobilized labor force. The domestic

**Table 4. Cost of rehabilitation and reconstruction of two-lane interurban roads**

|**Country**<br>**Average unit cost ('000**<br>**US$/km)**|
|---|
|Congo, Dem. Rep. of<br>229|
|Ghana<br>261|
|Mozambique<br>279|
|Nigeria<br>330|
|Ethiopia<br>388|
|Malawi<br>421|
|South Sudan<br>760-1000|
|_Source:_Taken from World Bank (2009a), originally in Alexeeva,|
|Padam, and Queiroz (2008), and World Bank staff estimates.|

construction industry is very underdeveloped and, last but not least, the widespread incidence of land mines that need to be cleared prior to construction further increases costs. Until the institutional capacity needed to handle these and other challenges is established, the road sector is not on track to improve.

**Table 5. South Sudan’s road indicators benchmarked against those of African peers**

|**Indicator**|**Unit**|**South**<br>**Sudan**|**Low-**<br>**income**<br>**countries**|**East**<br>**Africa**|**Middle-**<br>**income**<br>**countries**|**Resource-**<br>**rich**<br>**countries**|
|---|---|---|---|---|---|---|
|Classified road density|Km per 1,000 sq-km of arable<br>land area|15|88|101|278|57|
|Paving ratio|% of primary network paved|2|71.6||32|82.1|
|GIS rural accessibility|% of rural population within 2<br>km of an all-season road||23|21|32|20|
|Paved road traffic|AADT|0|1,341|1,549|3,798|1,408|
|Unpaved road traffic|AADT|53|39|47|75|54|
|Classified network condition|Percentage in good or fair<br>condition|0|86|59||80|
|Overengineering|Percentage of main road<br>network overpaved relative to<br>traffic flows||30||18|15|
|Paved network condition|% of paved roads in good or<br>fair condition|0|86|79|82|68|
|Unpavednetworkcondition|% of unpaved roads in good or<br>faircondition|30|56||58|61|

_Source:_ Derived from AICD calculations.

_Note:_ For South Sudan: the paving ratio is calculated based on the classified road network that is paved, the paved network condition refers to regional and national roads, the paved road network does not include roads of fair quality because the length of roads with fair quality is unknown.

GIS = geographic information system; AADT = average annual daily traffic.

16
use of private vehicles is the preferred price instrument for restoring economic efficiency (Anderson & Sallee, 2016). Additionally, even though the Pigouvian ideal is rarely achievable in practice, it still serves as useful intuition and guiding principle.

The direct effect of a fuel tax is straightforward. It encourages drivers to consume less fuel, whether by driving less or by using more fuel-efficient vehicles. However, a fuel tax also has several indirect effects on both drivers and vehicle manufacturers that are slightly less obvious. The first indirect benefit relates to road injuries and fatalities. In case of a vehicle collision, being hit by a heavier vehicle increases both the injury severity and fatality risk, while being the driver of a heavy vehicle is safer for the occupant. In other words, driving a heavier vehicle further externalizes the social costs of road injuries and fatalities. Over the past half century, the average vehicle weight has grown continuously in the U.S. (U.S. EPA, 2022). Some authors hypothesize that, all else equal, this favors the purchase of increasingly heavy vehicles, and has likely contributed to creating an “arms race" effect in vehicle weight. A simple distance-based tax can price in the social cost of road injuries and fatalities at average incident rates, i.e., the average cost of road incidents per kilometer driven. However, such a tax fails to account for vehicle weight. Conveniently, a vehicle’s fuel consumption scales positively with its weight. Hence, a fuel tax can simultaneously internalize mileage and weight externalities. This can be another argument for raising fuel taxes (Anderson & Auffhammer, 2014). A second indirect benefit of fuel taxes relates to their saliency and the signal they send to consumers. Although changes to fuel taxes and the tax-exclusive components of fuel prices affect consumer wallets the same, empirical evidence suggests that consumers respond more strongly to the former (Li et al., 2012). Specifically, a fuel tax increase yields larger reductions in fuel consumption and larger improvements to fuel economy of sold vehicles than a commensurate increase to the taxexclusive components of fuel price. The difference can possibly be attributed to fuel tax changes being more salient, e.g., due to news coverage, than changes to the tax-exclusive components of the price. Additionally, for deciding fuel economy when purchasing a vehicle, consumers might see fuel taxes as a reflection of a longer-term policy direction, whereas changes to the taxexclusive components are more likely interpreted as short-term price fluctuations. In other words, the strength of fuel taxes as a policy instrument is amplified by its saliency and signaling effects. Consequently, evaluating the impact of a fuel tax policy using the total price elasticities could actually underestimate the behavioral response but overestimate the tax revenue. Last but 13
- ii. Absolute value for the base case and ten sets of decadal average _changes_ from the _base_ for values of precipitation (% change from base), temperature (delta T in degrees from base ) and streamflow (% change from base).

Data are provided for all districts, not just those identified for analysis using the Ricardian approach (Section 1). Consequently, data are provided for 1421 political districts in total. The data are stored in individual Excel spreadsheets for each country. Within each spreadsheet the different data types (e.g. runoff, relative soil moisture storage, etc.) are stored in separate sheets, with the districts identified by a unique district number. The district numbers were obtained from the ArcView coverages provided by each country.

All of these data are available from the following website for each case study nation and for the remaining African Nations and subset of Middle Eastern nations:

# **<u>http://www.joyfullyfit.net/downloads/GEF/</u>**

For each nation and the rest of Africa and the Middle East there are 60 files: ten scenarios, three variables and two formats (absolute and changes). With each file approximately one megabyte in size, the total size of the directory is above 600 megabytes.

# **3.3 Africa-wide analysis of climate change results**

With data provided for 1421 political districts in total it is difficult to present any results or make any general conclusions. The authors therefore undertook an Africa-wide analysis with average results for 49 African nations. The results for decadal average changes for 2050 and 2100 in annual values for precipitation, temperature and streamflow are presented in Tables 8, 9, and 10.

Even with this aggregation the data is overwhelming so to give some idea of the spatial variability of the climate change impacts a series of four figures was produced. These figures represent the Africa-wide highest and lowest average impact on streamflow for 2050 and 2100 (see Figures 15 to 18).

21
(2) where the dependent variable is the ratio of public investment to GDP or to total investment; is quality of governance or political checks and balances; is the natural logarithm of population in millions; denotes a left-leaning party as the largest party in the legislature; is the price of investment relatively to the United States; is the growth rate of GDP per capita in PPP terms<sup>19</sup> ; is a set of unchanging country specific effects (proxied by country dummies); are effects common to all countries in period (time dummies); and is the error term. All the controls are added once at time to check whether results are robust to the progressive loss of observations.

In order to eliminate the country-effects, we take first differences of equation (1), which yields: (3) The differentiated error term is correlated with the lagged dependent variable by construction. Thus, the difference GMM estimator uses a set of lagged explanatory variables to address this problem and the endogeneity of some covariates.<sup>20</sup> However, in presence of high persistence in the levels of the explanatory variables (with levels being weak instruments of the first differences) and small samples, the estimation of equation (3) would produce biased estimates. Differently, the system GMM assumes a further moment restriction, namely that although explanatory variables might be correlated with the unobservable component , the first differences are uncorrelated with . Therefore, lagged first differences can be used as instruments for the levels of equation (2). In this setting, we can estimate a system GMM with the level equation (2) and its differenced form (3), under the mentioned moment assumption.

As suggested in Roodman (2006), we include year effects to control for global factors. The autocorrelation test and the robust estimates of the coefficient standard error assume no correlation across individuals in the idiosyncratic disturbances and the introduction of time dummies makes such assumption more likely to hold.

> 19 Fisher panel data unit root test could not reject the null hypothesis that all panels contain unit roots for GDP per capita in PPP terms. On the contrary, the null hypothesis is rejected when the growth rate of the same variable is considered.

> 20 The estimation procedure exploits lagged instruments that are weakly exogenous if they are not correlated with future error terms. The lagged dependent variable is “predetermined” as it is correlated with past error terms, but uncorrelated with the current and future error terms. However, the other variables are potentially endogenous given that they are correlated with the current and past error terms, but are assumed to be uncorrelated with future errors. In other words, predetermined and endogenous variables are uncorrelated to unanticipated shocks (future error terms), albeit expected future dynamics may affect them. Therefore, a set of instruments is given by the lagged levels.

12
> **I Policy Research**

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# **Education and Employment**

# **WPS 837**

This paper -- a product of the Education and Employment Division, Population and Human Resources - Department is part of a larger effort in the Department to study the operation of labor markets and their impactonhumanresourcesdevelopment. Copiesofthispaperare Available free from the World Bank, 1818 H Street, NW, Washington, DC 2(433. Please contact Selina Kh.n. room S6-228, extension 33651 (29 pages). January 1992.

Thc movement from centrally planned to market economies will not eliminate the need for manpower planning. Rather, it will substantially change the roles manpower planners play and the techniques they use.

* To identitiy impediments to competitive lahor markets.

* To help rationallize public investments in education antid trainino.

Manpower planniers must become analysts oi thc labor market. **In** a market economy, thev wvill be asked for i.iformation:

* To guide private decisions about training.

* To improve the manlagemcnit of traiininig systems,

Adams, Middleton. anld Zidermanl introduce tcchniques for manpo\Aer planninlg that ackno\wNledge the d\ nami- nature of market economies. They reject the idea of forecasting manpo"er requircnents. proposing instea;d to use sionals from thie labor market picked up by monitoritng movemcnts in **\k** ages and employment and evaluatin- training prog,rams.

> Th> Pto]icy Recaic. WrklVVi.ng Paper< Sci r 1s **(Iis sCm 0nalte"** it **IC** I iidrz ing of ork **it!it** i .¶! In IttEniiB **Cl** Ba!e Kl e oh **C| 1k C. hC CTs**

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P'rodus edI h\ iOwl 1 **RPCoah** h Dissemination (XnICT
# **2.3 Additional data**

In our heterogeneity analysis, we draw on a wide range of additional data sources to classify countries across several economic, institutional, and political dimensions. Detailed definitions and data sources are listed in Table C1.

**Income** Countries are grouped by their level of economic development, following the World Bank classification.

**Trade, agriculture, and natural resources shares in GDP** Countries are classified based on three indicators from the World Bank: i) merchandise trade as a share of GDP, capturing countries’ engagement in international trade; ii) the share of agriculture in GDP; and iii) the share of natural resources in GDP.

**Fragility** We use the World Bank classification to identify fragile countries—those experiencing deteriorating governance, prolonged political crises, post-conflict transitions, or gradual but still unstable reform processes. In our sample, 16% of countries fall into this category.

**Economic Complexity** Countries are categorized according to the complexity of their production structure using the Economic Complexity Index (ECI, Hidalgo and Hausmann, 2009). The ECI has been shown to explain income differences across countries and to predict future growth (see, for instance, Hausmann et al., 2014). **Drug producing** Major illicit drug-producing and/or drug-transit countries are identified based on the _2006 International Narcotics Control Strategy Report_ , published by the U.S. Bureau for International Narcotics and Law Enforcement Affairs. **Bureaucracy quality** The quality of a country’s bureaucracy is measured using data from the International Country Risk Guide (ICRG, Political Risk Services Group). This index captures the institutional strength and quality of the civil service, including its ability to function without political interference, ensure continuity in government policy, and effectively implement reforms.

**Corruption** Country-level corruption is measured using an index from the ICRG that captures perceptions of corruption within the political system. This includes the misuse of public office for private gain through practices such as bribery, patronage, and nepotism. **Peace/conflict status** Countries are grouped based on their peace or conflict status at the beginning and throughout the entire period of analysis. A country is classified as _initially at peace_ if, at the time of the first survey in that country, there are no major episodes of political violence (such as civil conflicts, ethnic violence, riots, popular protests, or repression of dissidents). Countries not meeting this criterion are classified as _initially in conflict_ . We also categorize countries based on their conflict status throughout the entire analysis period. A country is classified as _always in conflict_ if major episodes of political violence begin before the first survey wave and persist throughout all subsequent waves. See Table C1 for details.

8
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|---|---|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Conservation of Medicinal and Herbal Plants Project|Kanta K. Rigaud|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Wuhan Urban Transport Project|Ke Fang|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Tuberculosis and HIV/AIDS Control Project|Maria E. Gracheva|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Investment Fund Project|Menahem M. Prywes|
|Social Sector Adjustment Loan Project|Vicente B. Paqueo|
|Agricultural TechnologyTransfer Project|Ulrich K. H. M. Schmitt|
|Agricultural TechnologyTransfer Project|Ulrich K. H. M. Schmitt|
|Agricultural TechnologyTransfer Project|Ulrich K. H. M. Schmitt|
|Agricultural TechnologyTransfer Project|Ulrich K. H. M. Schmitt|
|Agricultural TechnologyTransfer Project|Ulrich K. H. M. Schmitt|
size SOEs were restructured through privatization, mergers, and bankruptcies. The state’s portion of nonagricultural employment fell from 52% to 13% between 1978 and 2004 (Xu, Zhu, and Lin 2005). By 2001, the number of SOEs had declined from 110,000 to 53,489 (Cai, Park, and Zhao 2008), accompanied with massive layoffs: at least 10 million workers were laid off by 1997, and an additional 27 million between 1998 and 2004. Growth of the private sector cushioned the displacements from the downsizing of the public sector. By 2002, job destruction rates had fallen to be at par with job creation rates (Dong and Xu 2009). Moreover, following SOE restructuring, real wages grew in the late-1990s and early 2000s (Cai, Park, and Zhao 2008; Dong and Xu 2008), and laid off workers were more likely to be reemployed. Another piece of evidence in favor of functioning labor market is seen in returns to education. By 2001, each additional year of schooling raised annual income by 10.2%, compared to 4% in 1988. College graduates earned 37.3% more than high school graduates in 2001, compared to 12.2% in 1988. Zhang et al. (2005) find that returns to education were no different between nonpublic enterprises and SOEs, suggesting that labor is mobile across sectors.

# **D. Product Market Integration**

The PRC’s internal markets of goods and commodities were segmented in the early days of the reform, but there has been emerging evidence of increasing integration. Perhaps the most influential of this literature, Young (2000, p. 1128) argues that local protectionism has led to ―a fragmented internal market with fiefdoms controlled by local officials whose economic and political ties to protected industry resemble those of the Latin American economies of past decades.‖ His explanation is that fiscal decentralization links local government revenue with local industry protection since local firms are stable sources of taxes and jobs. Based on five fairly aggregated sectors, he finds that industrial composition became more similar between 1980 and 1997 while prices of goods diverged. Moreover, resource allocation seemed to be deviated from the principle of comparative advantage.

Two studies reach similar conclusions. Zhou et al. (2000) find a general lack of integration among the indica rice market in the PRC based on monthly rice data from 12 cities between 1992 and 1996. They attribute this lack of integration to transport costs and government intervention in the grain markets. Similarly, Poncet (2003 and 2005) also suggest that trade barriers within the PRC 9
might be more likely to become displaced (e.g., a house, etc.).

# **Section 3b. Data Patterns and Challenges**

In order to better identify the evolution of food security indicators following displacement, there are five important data issues to address. First, the data corroborate reports of humanitarian surveys and news reporting and demonstrate that displacement in the Republic of Yemen’s conflict is strongly associated with living in close proximity to violence (e.g., TFPM 2016; OCHA 2019). Figure 2 reports the evolution of violence in the district of origin and average food security outcomes in the six months prior to displacement. In the first two panels, both the number of fatalities and the number of violent incidents were escalating leading up to displacement.

In the month before displacement, the number of fatalities was 37 percent higher than the number six months before displacement, and the number of violent incidents was over 28 percent higher. Alternatively, Figure 2 demonstrates that there was little-tono change in food access outcomes leading up to displacement. The FCS was one percent higher<sup>16</sup> in the month before displacement than six months before displacement, and the rCSI was less than four percent higher.<sup>17</sup><sup>_,_18</sup> Importantly, for both food security measures, the confidence intervals for each monthly estimate overlap the confidence interval of the estimate six months before displacement.

Second, it is important to investigate whether displacement interferes with a household’s ability to respond to the survey, i.e., whether food access effects are driven by changes in the composition of respondents rather than by displacement. For example, if we observe food access improving following an immediate drop during the month of displacement, it is possible that the struggling displaced households dropped out of the survey at higher rates as opposed to an actual improvement in post-displacement food security outcomes.

This, however, does not appear to be the case. Table 1 reports results from specifica-

> 16A higher FCS implies better food access.

> 17A higher rCSI implies worse food access.

> 18See Appendix 4 for similar graphs for all remaining food security and other well-being indicators collected in the survey. The patterns are nearly identical to those of the FCS and rCSI.

12