rafmacalaba/datause-encoder-data huggingface dataset:
# **6.  Policy Implications**

Our results indicate that competition policy orientation is a powerful determinant of cellphone coverage in Sub-Saharan Africa. To assess the potential implications of further reform, we perform a policy simulation using the probit estimates from our full spatial dataset (Table 7, column 2). For all 993,401 grid cells, using the observed values for our demand and supply variables, we simulate strongly pro-competitive policy by setting the CPIA index at 4.0 for all countries with CPIA index below 4.0. While this would require substantial reform in many SubSaharan countries, it has already been attained by Benin, Botswana, Namibia, Uganda and South Africa<sup>9</sup> . Our simulation increases the probability of cell-tower location in all grid squares that are in countries whose current CPIA scores are below 4.0. Map b in Figure 2 plots the full results for Sub-Saharan Africa, showing the probability of cell-tower location in each grid square. Higherprobability squares are colored blue; lower-probability squares green. The clustering pattern in the map reveals the critical roles of population, infrastructure and topography in determining coverage, even with uniformly-high levels of competition policy.

Numerically, our policy simulation results translate to very large changes in area coverage for cell phone towers. For the two cases (current CPIA, CPIA=4), Table 8 presents country-level percent differences in grid squares with cell-tower probabilities greater than 0.5. These are equivalent to expected percent differences in area coverage by country. For nine countries, the policy improvement translates to a coverage increase greater than 100%, and for all countries whose CPIA was below 4 in 2004, the predicted increase in coverage is 96%.

> 9 For these countries, actual CPIA values were used for the simulation.

11
These data are similar to those from Chile; however they are slightly stronger on protective factors and more limited on risk factors. The Mexican dataset does not have information about connectedness with other adults, abuse in home, victimization or violence in community, but it does have questions on attitudes towards drugs, the number of parents in the household and parental response to good/bad behavior. As with the Chile survey, there is information on individual earnings and three family poverty variables – parental earnings, parental education and durable goods in household – as well as regional/community level information. Although youth aged 12 to 29 were surveyed, those aged 12 to 14 were not asked questions about sexual activity or practices.

To the extent possible, similar variables are used in our analysis for both countries, although the Chilean data are more robust with the protective factors and the Mexican data include more risk factors. Table 1 describes each variable and identifies which variables are used in which clustering exercise and which are treated exogenously.

# **B. Descriptive Statistics**

Descriptive statistics for continuous and binary variables are provided for risky behaviors and factors in Table 2.<sup>19</sup> Sample means for all of the normalized variables are presented in Annex A to provide a baseline of the average status of the population against which to compare the variable values in each distinct cluster. The Chileans are 1.7 years older in the sample than the Mexicans, but this is primarily due to the inclusion of 12 to 14 years olds in the Mexican survey. Thus, it is important to compare characteristics by age group, as presented in Table 2. Both surveys are slightly more weighted toward women. About 11 percent of the Chilean sample self-identifies as indigenous; ethnicity was not

> tracked in the dataset:  they did not want to participate, were not at home at the time of the interview and would not return within the week, were on vacation, were working or at school in another city, were disabled, and other.  Comparing the poverty variables (education level and monthly earnings of heads of households) and rural means of this dataset before and after dropping the data showed no significant difference at α=.01 (However, at α=.05, the difference between the education level of heads of households the original and remaining datasets is not zero). An additional 5% of the observations were dropped in creation of the variables. Since this was such a small amount of the sample, the resulting dataset was not statistically different from the original. Finally, the data were restricted to youth aged 12 to 24, thus further decreasing the sample size by 8903.

> 19 Variables with categorical responses cannot be summarized, but the mean of the variables created from the categorical responses can be.  Thus, the categories are not summarized in Table 2, but the mean of the variable created from the categorical responses is summarized in Annex A.

11
WPS3321

**Education and Its Poverty-Reducing Effects: The Case of Paraíba, Brazil**

by

# **Dorte Verner**<sup>1</sup>

## World Bank Policy Research Working Paper 3321, June 2004

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 This paper was prepared for a sector report on education in Brazil, managed by Madalena dos Santos. The author would like to thank the Secretariat of Education in Paraíba for helpful assistance collecting information and Madalena dos Santos and Alberto Rodrigues for helpful comments and suggestions.
**Figure 2: Equity bliss points across groups of stakeholders**

<!-- Start of picture text --> 0 1<br>1 2 3 4 5<br>Total<br>1 2 3 4 5<br>Stakeholders' perceptions of equity of the Benefit Plan<br>Graphs by civilsoc<br>Likert scale: Very inequitable Inequitable Satisfactory Equitable Very equitable<br>Values: 1 2 3 4 5<br>60<br>40<br>20<br>0<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Hypothesis 2 of the model predicted that unorganized groups are more concerned about equity effects than organized groups <sup>*</sup><sup>_j_</sup><sup>_L_</sup> <sup>*</sup><sup>_j_</sup><sup>_L_</sup> . If this is empirically correct, then lobbying healthcare producer groups (such as pharmaceutical companies, doctors groups, insurers) should have less divergence from the government’s assessments of equity compared to patient/consumers groups.

The two histograms below show the distribution of respondents representing organized lobbies on the equity dimension.

**Figure 3: Organized lobbies’ comparative assessments of the efficiency and equity of the Benefit Plan and priority setting strategies**

<!-- Start of picture text --> 2 2.5 3 3.5 2 2.2 2.4 2.6 2.8<br>Producer groups' perceptions of equity of the Benefit Plan Producer groups' perceptions of efficiency of the Benefit Plan<br>Likert scale: Very low Low Satisfactory High Very high<br>Values: 1 2 3 4 5<br>50<br>60<br>40<br>40<br>30<br>20<br>20<br>10<br>0 0<br><!-- End of picture text --> 27
rates, underemployment, and food insecurity (e.g., WFP 2012; World Bank 2017a; etc.). Furthermore, there was a broad agreement between household heads and spouses in the average levels of satisfaction, which is also slightly surprising given significant evidence of intra-household inequities (e.g., World Bank 2012).

However, there is an important data issue to highlight that might affect estimating the impact of capturing Sana‘a. Ramadan occurred primarily in July in 2014, and this might have biased peoples’ subjective assessment of their welfare given different consumption patterns and household choices during that month (e.g., Jolliffe and Serajuddin 2012; World Bank 2017a). Thus, baseline specifications exclude households surveyed during Ramadan.<sup>17</sup><sup>_,_18</sup>

# **5. The Impacts of the Capture on Subjective Assessments of Welfare**

Using the 2014 HBS, we estimate how much more the share of household heads reporting to be either “very satisfied” or “fairly satisfied” increased in Sana’a relative to the rest of the Republic of Yemen following the capture of the city. Specifically, for each welfare dimension, we estimate the following specification: where _Satisfaction_<sup>_j_</sup> _i_<sup>isanindicatorequalingoneiftheheadofhousehold</sup><sup>_i_iseither</sup> very or fairly satisfied with dimension _j_ ; _Sana_<sup>_′_</sup> _ai_ is an indicator if the household lives in the capital city; _Posti_ is an indicator if the household completed their final interview before Sana’a was captured by Houthi forces; and _Xi_ is a set of control variables.<sup>19</sup> The estimate of _α_ is by how much the share of households satisfied with welfare dimension

> 17However, all results are qualitatively identical when the excluded households are included, and these estimates are included in the Appendix.

> 18This reduces the total sample size from 8772 households to 8006.

> 19Controls include household size, and indicators equaling one if the household was urban, the head was married, whether the head ever attended primary school, whether the head smoked, whether the head chewed qat, whether there were children in the household, whether the house was built with stone, whether the roof of the house was reinforced concrete, whether the roof of the house was made of wood, whether the roof of the house was made with mud, whether the floor of the house was made with concrete, whether the floor of the house was made with tile, and whether the house had a flush toilet.

12
<!-- Start of picture text --> 1300<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br>Year<br>GDP per capita (PPP, constant 17 int. USD)<br><!-- End of picture text --> _Source: GDP per capita series are from the World Development Indicators_ (35) _, from 1990-2017. Note: The grey bar indicates the beginning of the civil war up to the departure of Séléka rebel coalition from Bangui (from December 2012 to January 2014). The subsequent period translates into localized conflicts areas in Northwestern and Eastern parts of the country, outside of major cities._ 9
In terms of the models discussed above, the limited ability of Saath members to pledge savings as a collateral prevented Saath from expanding, and it saw group liability as a way to solve this problem. In the year following this change, Saath gained almost 800 new clients and increased its reach from 11 to 20 wards.

The transition from individual liability to group liability loans was implemented with the following rule. Beginning in November 2007, all new loans disbursed were group liability loans; Saath would no longer disburse individual liability loans. However, existing loans whose term lasted beyond November 2007 were unaffacted. For example, individual liability loan clients who completed their loan in February 2008 continued under the individual liability contract until then, and following February 2008 would receive a group liability loan should they borrow again. The date of switching from individual to group liability was therefore determined by individual liability loan completion dates. These completion dates and subsequent conversion to group liability loans were distributed relatively uniformly throughout the year.

Although Saath’s loan product moved from individual to group liability beginning in late 2007, repayment collection protocol (e.g. place of repayment, frequency of collection) and salaries for field officers remained similar across time in our dataset. An empirical concern for the analysis to follow is whether Saath’s policy shift from individual to joint liability was also accompanied by a shift in their loan collection techniques. A survey of field officers conducted in June 2010 confirms that in both individual and group liability settings, field officers collected loan installments at the client’s household or workplace every month. For group liability borrowers, Saath does not require groups to designate one of its members as a “group leader,” but in practice, almost all groups have a leader who is in charge of collecting repayments from other members. Hence, among individual borrowers, field officers visited each borrower, while among group borrowers, field officers typically visit only the group leader’s household. If any member of a particular borrower group fails to make a scheduled payment, the field officer assembles all group members together, and collects the installment amount from the other members as stipulated in the group liability contract. Both individual liability borrowers and joint liability borrower groups in default are not granted any loans in the future. In addition, over the period we study, there was no change in wages among field officers, who continued to receive a fixed monthly sum.<sup>5</sup>

> 5After March 2009 field officers received 1% of the loan interest they collect. Surveys of field officers indicate that

10
Table 4: Interaction results using institutions from baseline specification

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐TFP Ac|kerberg_et al_.‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.361***||||||
||(0.000)||||||
|Overallt‐2* Inst|0.030||||||
||(0.296)||||||
|Entryt‐2||0.014|||‐0.094|‐0.078|
|||(0.846)|||(0.259)|(0.348)|
|Entryt‐2* Inst||0.001|||0.032|0.055|
|||(0.982)|||(0.441)|(0.206)|
|Conductt‐2|||‐0.198***||‐0.203***|‐0.213***|
||||(0.000)||(0.000)|(0.000)|
|Conductt‐2* Inst|||‐0.001||‐0.025|‐0.011|
||||(0.971)||(0.684)|(0.860)|
|FDI Overallt‐2||||0.085*||‐0.110**|
|||||(0.074)||(0.013)|
|FDI Overallt‐2* Inst||||0.001||‐0.179***|
|||||(0.984)||(0.000)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐YES|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4983642|5431331|5064175|5848487|4983642|4983292|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

32
Table G5. Rule of Law (cont.)

|**Code**|**Table**<br>**(Appendix 1)**|**Row**|**Concepts measured**|
|---|---|---|---|
|**Non-rep**|**resentative sour**|**ces (con**|**t.)**|
|GCSA|A 1.8|20|Legal system effectiveness at enforcing commercial contracts|
|||26|Private business has recourse to independent and impartial<br>courts for challenging the legality of<br>government actions|
|||36|Private business can readily file and try lawsuits against other|
||||business, foreign or domestic, at independent<br>and impartial courts|
|||25|Citizens' willingness to accept legal means to adjudicate<br>disputes rather than depending on physical|
||||force or illegal means|
|||31|Costs of organized crime for business|
|||44|Costs of petty crime and theft for business|
|||30|Police effectiveness in safeguarding personal security|
|||8|Extent of tax evasion|
|||_65_|Crime and theft as an obstacle to business development|
|WCY98|A 1.12|1|The parallel economy as an obstacle to business development|
|||8|Extent of tax evasion|
|||18|Confidence in the fair administration of justice in the society|
|||19|Confidence among people that their person and property<br>is protected|
|||21|Protection of intellectual property|

59
Additional sample restrictions are as follows:

- Among the 259,618 records corresponding to the universe of World Bank Group headquarters employees between 1987 and 2015, we exclude the 4,689 records of Executive Directors and their staff, and of “secondments”.<sup>16</sup>

- We also exclude 229 records because they had missing or anomalous grades (6 records); recorded gender changed over time (97 records); recorded salary was 0 (88 records) and; recorded salary is clearly outside the grade range in the corresponding year (38 records).<sup>17</sup>

Finally, given that different data are available in different time periods and that we will examine career gaps after 15 years of service, the samples for our analysis will differ depending on the specific analysis. In particular:

- For results on the aggregate gap, we use the entire sample subject to the restrictions discussed previously.

- For results on the career gap, we use data on staff who entered as GF between the years of 1987 and 2000. As discussed in the introduction, this is the only group with sufficient gaps and hiring in each subgroup to allow for meaningful decompositions. The time period is determined by the need for data 15 years after entry, which limits the last entry date to 2000.

- For questions related to pay and performance, we focus on staff who entered the WBG between 2000 and 2005. The performance system that is used started in 2000 and the last entry date of 2005 allows us to examine staff performance over a 10- year period.

# 2. Institutional Features

Like other multinational firms, the WBG is a large institution with central headquarters in Washington DC and country offices in over 100 countries. Unlike other multinational firms, however, special arrangements with the U.S. government allow the WBG to hire and bring in staff to central headquarters from all around the world. In fact, as Figure 1 shows, U.S. citizens are a minority in the Washington D.C. office. Compensation at the WBG therefore reflects multiple objectives, balancing the need to incentivize performance, allow managerial discretion and ensuring equity. For instance, at the WBG, salary bands for different grades as well as mean increases each year are decided with reference to a “comparator group”

> 16 Executive Directors are shareholder-appointed members of the supervisory board of the World Bank Group. The United States government, for example, appoints an Executive Director. Although EDs and their staff are paid by the WBG, their special role and manner of appointment sets them apart from staff. People on secondments are also excluded because they are not paid by the WBG.

> 17 This was defined as (i) less than half the 10th percentile for that year and grade or (ii) more than twice the 90th percentile for that year and grade.

10
in the key studies, while focusing on a shorter but more recent growth period. Our data cover on average 87 per cent of the global imports from our 99 developing country exporters reported in Direction of Trade Statistics for 2004 (after adjusting for oil). Hence, there can be a relatively high degree of confidence that the majority of trade flows from our developing country exporters are being captured by our data set.

# **Growth of developing country exports 1995 to 2004**

Over this 10 year period exports from the 99 developing countries in our sample to the 102 import markets increased by almost 140 percent. Excluding China, exports from these developing countries grew by 107 percent. Thus, this has been a period of substantial growth in developing country exports. However, there is considerable variation across countries. Table 1 shows the growth performance by income group and then by region.

|Table 1 Export growth|of developing countri<br>Growthofexports|es by incom<br>Minimum|e group and<br>Maximum|by region: 1995 t<br>Average<br>country growth|o 2004<br>Dispersion<br>ofgrowth|
|---|---|---|---|---|---|
|Low-income|119.8|-85.7|744.7|77.4|154.7|
|Lower-middleincome|184.6|-7.9|690.1|131.6|136.7|
|Upper-middleincome|132.5|0.7|328.2|148.3|96.2|
|High Income|83.1|43.0|122.0|75.7|33.5|
|Africa|79.1|-85.7|471.1|49.0|103.7|
|S. Asia|115.1|45.6|152.8|95.3|46.8|
|E.Asia|146.4|38.9|744.7|190.1|200.2|
|LAC|105.1|-4.0|328.2|89.8|77.9|
|ECA|188.1|0.3|690.1|190.9|159.2|
|MENA|141.4|59.8|202.2|135.2|57.8|

There are 38 low-income countries in the sample, 34 lower-middle-income countries, 23 upper-middle-income countries and just 4 high-income countries. The data and results for the latter should therefore be treated with circumspect. Total exports from all income groups have grown strongly but dispersion across countries tends to be higher for the lower income groups. For both the low income and lower-middle income countries the mean growth across countries is substantially below the overall growth of the group.

Our sample of countries is not evenly divided among regions. There are 34 African countries, 5 countries from South Asia, 12 East Asian countries, 21 countries from Latin 7
The model could have two corner solutions: _x_ * = 0 is always an equilibrium (though it need not be stable), since at _x =_ 0 the _political switch line_ lies above, or is coincident with, the _stripping ability curve. x*_ = 1 is also an equilibrium if the _political switch line_ lies below the _stripping ability curve_ at _x_ = 1. If this point is an equilibrium, then the society is trapped in the non-rule-of-law state.<sup>20</sup> The model clarifies the effect of changes in the expected recapture fraction, λ .<sup>21</sup> A decrease in λ makes asset stripping more attractive (the _economic switch line_ shifts down), but also increases the demand for rule of law by asset strippers (the _political switch line_ shifts up). The net effect on support for the establishment of the rule of law thus depends on the nature of the original equilibrium. Starting from a stable equilibrium in which the marginal “voter” is a wealth-creator,<sup>22</sup> a reduction in λ _increases the opposition_ to the establishment of the rule of law, since it increases the fraction of agents who strip assets. However, starting from a stable equilibrium in which the marginal “voter” is an asset stripper, the effect is the opposite: a reduction in λ _increases the demand for_ the rule of law, since it lowers exit costs from a lawless state.

# 1.8. _A Numerical Example_

In the standard model of “political and economic losers” who block reform (see references in _n._ 1), dysfunctional institutions serve the interests of narrow groups at the expense of everyone else. In our model, in contrast, _the victims of the dysfunctional_ 20 As emphasized by Greif (1994), culture is an equilibrium selection device, and so it is interesting to consider within this model the role that culture might have played in Russia. Two facts suggest that the “good” corner equilibrium, with _x*_ = 0, would not be a focal point: (a) managers commonly engaged in asset stripping well before the mass privatization of 1992-94 (see Grigoriev 1992), and (b) most beneficiaries of the mass privatization of large state enterprises in Russia were the managers. For instance, Varese (2001, Appendix B), finds in his survey of one Russian city that 51 percent of the 92 full-time officials of the Communist Party in 1988 were top managers of economic enterprises in 1993.

21 We discuss other comparative statics results in Hoff and Stiglitz (2004a).

> 22 Or, more accurately, if the intersection of θ _*(x;_ λ _)_ and the _stripping ability curve_ lies along θ _a_ , not θ _p._

15