rafmacalaba/data-use-mentions-extended huggingface dataset:
(Figure 3).<sup>5</sup> Despite these improvements, Sudan’s labor force participation rate lags behind many neighboring and post-conflict African countries such as Chad (71 percent), Eritrea (81 percent), and Ethiopia (82 percent). The employment-to-population ratio in Sudan is the lowest (41 percent) among the average share of Sub-Saharan Africa and low-income countries (63 and 68 percent, respectively) (WDI 2020, data for 2019).

# **_Labor market indicators by gender and age_**

Women’s economic activity lags substantially behind that of men, even though their increased participation in the workforce is a key driver behind the overall increase in the labor force participation rate in Sudan **.** The country’s increase in the labor force participation rate between 2009 and 2014 (from 48 to 53 percent) was mainly due to the substantial increase in female participation rate from 24 percent in 2009 to 33 percent in 2014. This positive change took place in both urban and rural areas. However, while the female labor force participation rate increased by nearly 10 percentage points (compared to only 2 percentage points among men), it remains less than half the male rate: the labor force participation rate is 76 percent among men compared to 33 percent among women (World Bank 2019b). In addition, among the third of women who are economically active, few women actually work: they are more than twice as likely as men to be unemployed (19 percent and 8 percent, respectively).

## **Figure 3. Labor force participation rates and unemployment rate by sex, quintile, locality, and year**

<!-- Start of picture text --> (a) Labor force participation rate (b) Unemployment rate<br>80% %<br>60%<br>%<br>40%<br>%<br>20%<br>0% %<br>
>social sectors, from income and employment<br>programmes to human development<br>programmes. Concludes that there is an urgent<br>need to increase social spending.|
|Corbacho and<br>Schwartz 2001|Mexico|1980-02|Descriptive statistics,<br>benefit incidence analysis|Expenditure on education,<br>health, social security and other<br>social expenditure programs<br>(including targeted spending for<br>human capital devt, basic social<br>infrastructure, and productivity)|Primary education spending is progressive,<br>considerable access improvement in health, no<br>significant impact from social security,<br>promising results from some recent initiatives<br>that target specific groups.|
|Mosley and<br>Hudson 2001|57 Countries|1994-98|Regression analysis|Expenditure on education, social<br>services, and agriculture|<br>Aid raises pro-poor spending in poor<br>countries but not in middle-income countries.|

In some cases, simple first-stage cross-country regressions of the poverty headcount (or some other measure of poverty) on each of the major expenditure categories, including education and health care, are estimated, to determine which expenditures are “pro-poor” (e.g., Gomanee, Girma, and Morrissey 2003). Without taking into account the complexities discussed in the main text, these simple, bivariate regressions provide some evidence that education and health care spending are pro-poor. In the second-stage, the statistical relationship between, for example, aid flows and pro-poor expenditure items is evaluated.

28
of governance is constant or not. We take this issue up in Section 5.3 where we show that there is little evidence of trends over time in global averages of governance as measured by the WGI data sources, implying that the relative changes observed in the baseline WGI estimates also correspond to absolute changes.

# 5. Methodological Issues

In this section we explore in greater depth three potential issues with the WGI source data and aggregate indicators. We first consider the possibility that measurement error may be correlated across data sources, contrary to our baseline assumption in the WGI that each data source brings independent information on the dimension of governance to which it is assigned. This issue is relevant because it has implications for both the margins of error in the WGI as well as the relative weights assigned to different data sources. We then consider the robustness of the WGI to alternative weighting schemes, focusing 19
it is assumed that 50% of Mustangs were produced with 4 cylinder engines.

- b) This compares with 9,192,000 units given in the US Annual Survey of Manufacturers for 1975. The reasons for the understatement of total output by Automotive News are not known.

# Source: Automotive News, 1975 Almanac icsue.

Next we calculated unit values for the two major types of passenger car. For Mexico this information was available in the census. For Brazil we could not find any price quotation by model. We assumed therefore that the Mexican price differential was also representative for Brazil. The price ratio for the USA was derived from information on retail prices in Automotive News. The average 1975 retail price for 4 cylinder cars was USS 3,079, and an average of USS 4,079 for the sample of 6 and 8 cylinder cars (see table A.3).
Yes<br>Yes<br>re and code<br>ss survey mo<br>e by survey m<br>r “Interactive|
|Sample<br>Recruitment|**RDD**<br>RDD, National surveys<br>National surveys<br>RDD<br>Survey frm<br>National surveys<br>National surveys<br>RDD<br>RDD<br>National surveys<br>Social enterprise<br>Recruitment team<br>RDD<br>Social enterprise<br>RDD, Midwestern university<br>e the description of each featu<br>ntact mode was the same acro<br>tact participants were the sam<br>ed Self-Interviewing; “IVR” fo|
|Sample<br>Size|**599**<br>2016421<br>450<br>2196<br>1043<br>I<br>895<br>8273<br>6056<br>2265<br>1579<br>2603<br>1821<br>3673<br>1057<br>156<br>re. We includ<br>(iv) if the co<br>empts to con<br>puter-Assist|
|Compared<br>Methods|**CATI, SMS**<br>CATI, IVR, SMS,FTF<br>CATI, SMS, SMS modular design<br>CATI, IVR<br>E-mail web link, FTF<br>Monthly - Weekly FTF, Weekly CAT<br>FTF, ACASI, List Experiment<br>IVR, SMS<br>IVR, CATI: Sequential<br>FTF, CATI<br>SMS, Web link<br>CATI, FTF<br>SMS, Web link<br>CATI,FTF: Sequential<br>SMS, Web link<br>eriment we identifed in the literatu<br>was
# **5.3. Alternative measures of educational outcomes**

Figure 5.3 Proportion completing Primary schooling (CEPE) <!-- Start of picture text --> 10 15 20 25 30 10 15 20 25 30<br>Age Age<br>Others Income declined Others Displaced<br>10 15 20 25 30 10 15 20 25 30<br>Age Age<br>Others Lost Ownership Others Lost job<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>% completed CEPE<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>.1 .1<br>% completed CEPE<br>0 0<br><!-- End of picture text --> In the previous analysis we used only total years of education as an educational outcome variable. As a sensitivity analysis, we propose to look at another potential outcome variable that measures the percentage of the population that completed CEPE (six years of primary education). This is justified by the fact that the average years of education based on the ENV-2008 data is recorded as being a little over 7 years and almost 40 percent of the population fail to complete the CEPE. We estimate nonparametric kernel-weighted local polynomial regressions of percent ever completing six years of primary education against age using Epanechnikov kernel. We ran the regressions separately for the war victims and the rest of the sample as identified by the victimization indicators. Figure 5.3 provides the outcomes of non-parametric estimation for four victimization categories. Of the victimized groups, individuals whose family members suffered from income loss or job loss are less likely to complete CEPE. This is evident particularly in the age-cohort with individuals aged between 10 and 22. The internally displaced individuals do not show a different trend in the successful completion of CEPE. Among households that suffered
Table 1: Distribution of Foreign Affiliates by Region and Country, 2016

|Region/Country|# of Subsidiaries|Share|
|---|---|---|
|_Panel A. Asia_|3,237|78.89|
|China|1,851|45.11|
|Vietnam|488|11.89|
|Indonesia|174|4.24|
|India|165|4.02|
|Japan|81|1.97|
|Thailand|80|1.95|
|Hong Kong SAR, China|68|1.66|
|Malaysia|60|1.46|
|Philippines|58|1.41|
|_Panel B. North America_|356|8.68|
|United States|343|8.36|
|_Panel C. Europe_|315|7.68|
|Poland|44|1.07|
|Russian Federation|40|0.97|
|Germany|40|0.97|
|_Panel D. Latin America_|161|3.92|
|Mexico|91|2.22|
|Brazil|39|0.95|
|_Panel E. Rest of the World_|34|0.83|
|Total|4,103|100.00|

_Notes:_ The data comes from the Survey of Business Activities (SBA) in 2016. The sample is restricted to Korean parent firms in manufacturing. There are 1,939 Korean manufacturing firms that have 4,103 foreign affiliates. Regions are classified into five areas (Asia, North America, Europe, Latin America, and Rest of the World). The top 15 countries are also presented.

Table 2: MNCs and Non-MNCs Observable Characteristics, 2016

||MNCs|Non MNCs|Difference|
|---|---|---|---|
|Employees (number)|602|153|449|
|
The _H_ and _AH_ statistics also can evaluate the impact of weights on risk measure-implied correlations. The sign of the difference is not important any more since the interest here is to test whether using different weights could lead to different implied correlations. Thus I use the absolute value of the difference to construct statistics. The new _H_ statistics do not distinguish between downside and upside, and take the supremum across all probability levels except the interval (0.3, 0.7) to avoid the unstable VaR-implied correlations. That is, and where ρˆ<sup>(1)</sup> _VaR_ ,α<sup>andρˆ(1)</sup> _ES_ ,α<sup>areimpliedcorrelationsforagivenchoiceofweights,whileρˆ(2)</sup> _VaR_ ,α<sup>and</sup> ρˆ<sup>(2)</sup> _ES_ ,α<sup>are implied correlations for another choice of weights.The corresponding</sup><sup>_AH_are</sup> and Since the VaR- and ES-implied correlations are invariant with respect to weights for elliptical distributions,<sup>7</sup> the difference in implied correlations from choosing different weights should be insignificant when data are from the multivariate normal distribution. Thus this paper simulates data from the multivariate normal distribution and calculates the statistics using simulated data to test the significance of the statistics from the empirical data. The process is similar to the test of correlation asymmetries and thus is not repeated.

# **3 Simulation**

In order to study the possible sacrifice and gains of using the ES-implied correlation, I design three cases in the simulation. In the first two cases, correlation is constant and the linear correlation is appropriate in order to judge whether allowing extra generality embodies a large sacrifice; in the

> 7See Mittnik (2014) and Campbell et al. (2002)

14
to differentiate between, these two forms of profit shifting and the tax effects that operate through them.

The paper is structured as follows. Section 2 sets out the broad framework that articulates potential channels for tax spillovers on real investment and guides the empirical work. Section 3 elaborates on the fundamental differences between FAI and FDI data and Section 4 then develops an estimation strategy. Section 5 sets out baseline results, explores their robustness underlying investment which just yields the investor their required post-tax return (conventionally expressed as a proportion of the former).

> 10As, notably, Mintz (2018) and Bazel and Mintz (2020).

5
that are difficult to reach from the national capital are also hotbeds of rebellion and political conflict. Using a map of African road networks to calculate access to ethnic homelands from national capitals and ethnic group interconnectivity, they estimate the effects of these two variables on political conflicts, showing that political conflicts occurred in areas where the state had a weak physical presence. To demonstrate the robustness of this finding, they created a theoretical network comprised of hypothetical road segments that ensure the shortest travel time between the busiest ethnic groups; using these hypothetical road networks, they build instrumental variables estimating the accessibility of ethnic homelands from political centers, and the degree of interconnectivity of ethnic groups that would occur if these networks were to exist. Their findings are robust to the use of these instruments, confirming the role of road networks in both extending political control and reducing political instability; they show that political authority and stability are strengthened in ethnic groups that are accessible from political capital.

Thus, to construct our instrumental variables to examine the interaction of the proximity to main roads and high-speed internet, we rely on this historical literature, which highlights political leaders’ motivation to build roads to expand their capacity to govern. In doing so, we follow a large body of literature that shows that non-economic factors influence African governments’ willingness to invest in public infrastructure (Herbst, 2014; Burgess et al., 2015; Bonfatti et al., 2019). We assume that African leaders build roads to facilitate territorial administration by easing the movement of administration officials and military troops from the capital city to the rest of the country (Herbst, 2014; Muller-Crepon et al., 2020).

Following these assumptions, we use a map of African ethnolinguistic divisions created by an American anthropologist in 1959 (Murdock, 1959) to identify the ethnic homelands within all countries of our sample. For all purposes, these ethnic homelands are generally regarded as being exogenous. Using the least-cost-path algorithm, we create hypothetical roads that connect the centroids of different ethnic homelands to the
*

<!-- Start of picture text --> 80 20.0% 16.4%<br>Chile<br>9.6%<br>70 10.0% 0.1% 1.8%<br>60 -2.5% -3.5% -2.3% 1.1% 1.1%<br>0.0%<br>50<br>Colombia<br>40 -10.0%<br>Mexico<br>30 Peru -20.0%<br>20 -21.0% -20.7%<br>-30.0%<br>10 Venezuela<br>-32.6%<br>0 -40.0%<br>1998 1999 2000 2001 2002 2003<br>Colombia Chile Peru Mexico Venezuela Return on Average Equity Return on Average Assets<br>Credit to GDP (%)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> _Source: World Bank calculations based on SIMA and Superintendencia Bancaria data_

> 11 The term financial sector refers to all financial intermediaries supervised by the Superintendencia Bancaria, including credit institutions (private and public banks including mortgage banks), financial cooperatives, corporate finance companies and leasing companies.

19
nearest road is computed as the distance between the centroid of the city where this firm is located and the nearest road. In our sample, the average distance from a household or a firm to the closest main road differs according to the data set used: 2.31 kilometers (WBES), 9.70 kilometers (DHS),and 15.2 kilometers (LSMS). The average distance from a main road to a household is 13.43 kilometers in rural areas and 3.60 kilometers in urban areas (DHS). There is heterogeneity in terms of households’ proximity to a main road by level of development; in less-developed countries, the average distance is 11.46 kilometers; by contrast, the average distance is 7.50 kilometers in more-developed countries.

# **3.1.5 Internet Data**

We use the Hamilton data from Africa Bandwidth Maps on long-distance opticalfiber nodes from 2009 to 2019. The map depicts Africa’s terrestrial, satellite, and submarine cable transmission networks in great detail. The Hamilton map includes fiber-optic networks (operational, under construction, planned, and proposed), microwave networks (operational and planned), and submarine cables (operational, under construction, planned, or proposed) for over 300 network operators in more than 50 African countries, and for 72 submarine cable systems. Geographic coordinates of fiber-optic nodes are included in the data set. We have information on the year each fiber node became operational. Figure (A2) in the Online Appendix depicts the evolution of the optical-fiber network over time, showing a significant increase in the number of optical-fiber backbones from the year 2009 (see Figure (A3) too). In fact, by 2009, a large majority of African countries already had their terrestrial internet backbones connected to submarine cables. We also use information from Mahlknecht (2014) on submarine internet-landing points and arrival dates. Figure (A4) shows the gradual arrival of submarine internet on the African coast since 2008, with the East
farm and nonfarm enterprises, which include access to finance, market, and transportation. Business service constraints are relatively perceived to be less critical (about 20%) by both farm and nonfarm entrepreneurs. Most farm and nonfarm entrepreneurs expect no support from any agencies/institutions in rural areas because there are almost no business service agencies exists in rural Yemen. This may be one of the reasons they did not perceive business services as a major constraint or obstacle. Our survey data shows that entrepreneurs recognize basic investment climate such as access to finance, access to market, and market demand as major obstacles.

# **Figure 3.2 Comparison of Business Constraints, Farm vs. Nonfarm**

<!-- Start of picture text --> Nonfarm<br>Licenses and permits Farm<br>Electricity Exports and Imports<br>Financing Land-use policy<br>Market Court<br>Transportation Postal/Delivery Services<br>Corruption Tax system<br>Telecommunication Business services<br>Water Security/Safety<br>Technology and IT Labor issue<br><!-- End of picture text --> _Source:_ Yemen RIC Survey 2010

# **3.1.3** **_Gender Difference in Obstacles Perceived by Entrepreneurs_**

Another interesting finding emerged regarding constraints to doing business in Yemen revealed a significant difference along gender lines. Because only one woman farm entrepreneur was represented in the sample data, the gender results were limited to nonfarm women entrepreneurs. While men and women entrepreneurs are confronted with similar types of constraints, those which women face are much larger. The investment climate constraints that men and women in Yemen appear to share in common include access to finance, electricity, markets, and transportation. Yet there is at least one major difference in the types of constraints that men and women face: labor and security/safety represent serious constraints to women whereas they do not appear to be major constraints for male entrepreneurs (figure 3.3).
The BBR and SSR can either keep the composition of spending constant or try to increase investment as in Table 1. As in the Default submodel, the highinvestment rule is called a Hartwick Rule (HR), though its application here is more complicated and closer to how it is applied in practice. The principle behind HRs is to prevent extra revenues earned from exhaustible natural resource from being used to finance government consumption. Accordingly, under an HR, all cyclical resource revenue must be invested either in physical or financial assets.<sup>23</sup> The rule is implemented by adding the following inequality to the model: ⁄ 0 ����� (usually, ����� = 1) gg this inequality states that the Sustainable Budget Index (SBI, the ratio of tt tt tt tt tt SSBBII ≡cc zz ≤SSBBII SSBBII government consumption to non-resource revenues) must be equal or lower than an exogenously determined threshold ����� (typically set to one for all ). An >1 means that government consumption is being financed at least partially by resource tt tt SSBBII tt SSBBII revenues. An <1 means that resource revenues are being invested either in physical or financial assets, while consumption is being financed only from non-tt SSBBII resource revenues. Capping the to one ensures that assets are being preserved (for a detailed discussion, see Lange and Wright 2004).tt<sup>24</sup> SSBBII The standard SBI rule (with ����� = 1) works well for governments with moderate dependence on resource revenues but might be too restrictive for tt SSBBII countries where resource revenues represent a large part of the budget. For example, Angola’s fiscal oil revenues account for more than 80 percent of total revenues. In this case, it is inevitable that oil revenues are partially consumed to run basic functions of the government. For that reason, the user of the LTGM-NR can choose any positive value for ����� .
- _Marginal incidence analysis_ . The usefulness of gauging the current _average_ benefit incidence of public spending is generally acknowledged. However, there is some evidence that the marginal gains of the poor may be high, even for interventions that may not currently have a pro-poor average incidence (Lanjouw and Ravallion 1999). For example, tertiary education spending may not be pro-poor because it benefits mostly the top household quintiles but the marginal increase in tertiary education spending may primarily benefit the poorer quintiles. Ajwad and Wodon (2001) have recently used marginal incidence analysis to compare the impact of basic infrastructure

# **V. Other quantitative tools.**

- _Service delivery surveys_ . Some studies employ various quantitative techniques to assess the efficiency of service provision. For a number of reasons, some of them alluded to in the main text, information on the allocation of expenditures by sectors is a poor indicator of the quantity and quality of services _actually_ provided in the frontlines. A number of empirical tools have been developed in recent years—including expenditure tracking surveys and quantitative service delivery surveys—designed to collect information at the service provider level and assess the quality and quantity of service provision. Dehn, Reinikka, and Svensson (2003) review the recent literature.

- _Using household survey to derive allocation choices._ Collier, Dercon, and Mackinnon (2002) illustrate a method for deriving a simple decision rule for allocating expenditures within the health sector. In particular, they present a model—within a framework that incorporates a utility function that depends on health status, a health production function, and a government budget constraint—that yields optimality conditions for allocating between recurrent (in this case, spending to improve quality) and capital spending (building more facilities). They use household survey data for Ethiopia to test their decision rule and find that it would be more efficient to allocate additional spending to improve the quality of health services rather than build more facilities.

37