rafmacalaba/datause-encoder-data huggingface dataset:
planations. In this section, we discuss and examine the extent to which these alternative factors could be driving our results. Importantly, the fact that we use community-level data in the main analysis makes it possible to account for competing explanations that operate at this level of aggregation. This would be unfeasible with individual-level data: since black people are highly concentrated in space, any factor influencing community-level income that is correlated with employment in mining might confound individual-level estimates; and since individual-level data lack precise geographic identifiers, these factors could not be accounted for in the estimation.

# **7.1 Access to infrastructure**

The period of analysis witnessed numerous government interventions aimed at addressing the historically uneven access to infrastructure. In line with historical accounts, Figure 8 documents large initial gaps between communities just-inside and just-outside the former homelands with regard to access to the electric grid, refuse, and piped water. However, this figure also documents considerable convergence in access and use of these infrastructure items over the period of analysis. If improvements in access to infrastructure are correlated with initial employment in the mining industry, our estimates would be biased.

In order to account for these factors we include the respective infrastructure variables for the years 1996 and 2011, both interacted with the former homeland indicator.<sup>26</sup> Table 6 presents the results, where from column 1 to 3 we include each infrastructure variable separately, and in column 4 we include all of them together (though these are not reported to avoid clutter). The corresponding estimates for the coefficients of those variables as well as our initial coefficients of interest are reported. Note that when accounting for changes in access to infrastructure, the point estimates for our coefficients of interest do not change substantially relative to our original estimates.

When considering the estimates for the correlation between our infrastructure variables and income growth, we find that our results are consistent with the existing evidence. These estimates have to be interpreted with caution since there may be other unobserved heterogeneity related to the expansion of infrastructure. Dinkelman (2011) evaluates the effect of rural electrification in KwaZulu-Natal, a former homeland, on employment. The study finds negative correlations between electrification and employment rates for males when estimating by OLS. This is consistent with our results, where greater access to electricity does not have a positive relation with income trajectories for former homeland communities. On the other hand, increasing access to having a refuse or piped water do not appear to be significantly related to income growth.

> 26Using the absolute change or the percentage change in access yields similar results.

18
17 have made remarkable economic progress in recent years—the share of wage earners in the labor force (with or without permanent contracts) is usually around 2 or 3 percent.

**Figure 13. Fragility of Labor Markets in Sub-Saharan Africa: Distribution of Employment in Selected Countries**

**Including agriculture**

<!-- Start of picture text --> Female Male<br>Tanzania 2006 Rwanda 2006<br>employer 0.9 2.2 employer 0.3 1.0<br>wage with permanent contract 2.0 4.8 wage worker secure contract 2.3 5.1<br>wage without permanent contract 2.7 6.7 wage worker without secure contract 3.7 14.9<br>HE without outside of HH employees 9.7 11.9 HE without outside HH employees 5.7 10.3<br>non-ag family worker 7.0 2.6 non-ag. family worker 2.3 0.7<br>agriculture 77.7 71.7 agriculture 85.7 67.9<br>100 80 60 40 20 0 20 40 60 80 100 80 60 40 20 0 20 40 60 80<br>Uganda 2005 Ghana 2005<br>0.0 0.2<br>employer<br>employer 0.3 3.4<br>wage permanent contract 2.6 6.4<br>wage permanent contract 2.3 18.9<br>wage temp/casual 4.2 11.5<br>wage temp/casual 3.7 1.5<br>HE without outside HH employees 10.0 14.3 HE without outside HH employees<br>5.7 12.6<br>non-ag. family worker 2.1 0.5 non-ag. family worker 2.3 0.4<br>agriculture 81.2 67.0 agriculture 85.7 63.2<br>100 80 60 40 20 0 20 40 60 80 80 60 40 20 0 20 40 60 80<br><!-- End of picture text -->

**Excluding agriculture**

<!-- Start of picture text --> Female Male<br>Tanzania 2006 Rwanda 2006<br>employer 3.9 7.9 employer 2.3 3.2<br>wage with permanent contract 8.8 17.1 wage worker secure contract 16.3 16.0<br>wage without permanent contract 12.1 23.5 wage worker without secure contract 25.6 46.3<br>HE without outside of HHemployees 43.8 42.2 HE without outside HH employees 39.9 32.1<br>non-ag family worker 31.4 9.3 non-ag. family worker 15.9 2.3<br>50 40 30 20 10 0 10 20 30 40 50 50 40 30 20 10 0 10 20 30 40 50 60<br>Uganda 2005 Ghana 2005<br>employer 0.0 0.6 employer 2.3 9.2<br>wage permanent contract 13.7 19.4 wage permanent contract 16.3 51.4<br>wage temp/casual wage temp/casual<br>22.4 35.0 25.6 4.1<br>HE without outside HE without outside<br>HH employees 53.0 43.5 HH employees 39.9 34.2<br>non-ag. family worker 10.9 1.4 non-ag. family worker 15.9 1.1<br>60 40 20 0 20 40 60 100 80 60 40 20 0 20 40 60<br><!-- End of picture text --> Source: Fox 2011. Note: HE = household enterprise. HH = household.
18% (Chen and Ravallion, 2007).<sup>2</sup> Brazil’s disappointing record in poverty reduction was due both to very low growth rates, and to a low growth elasticity of poverty reduction. Annual growth in per capita GDP averaged just under 0.5% over this period, while the average growth elasticity of poverty reduction was -1.09; just over half the norm for developing countries.<sup>3</sup> In this paper, we do not address the question of why Brazil’s growth rate was so low. Although this is a matter of first-order importance for poverty reduction, it has been discussed elsewhere and is not our object here.<sup>4</sup> We focus, instead, on the second piece of the puzzle, and seek to shed light on why Brazil’s growth _elasticity_ of poverty reduction was also so disappointingly low. This is not a second-order issue either: had Brazil had the world’s average elasticity, even the low growth rates in GDP per capita over 1985-2004 would have reduced the poverty rate from 33% to 23%; a ten percentage-point reduction, instead of the four points actually observed. While there are reasons to be cautious about this calculation, it is at least suggestive.<sup>5</sup> The paper tries to help understand why this elasticity was so low. One explanation concerns the role played by the pattern of growth—notably its sector composition—in determining the pace of poverty reduction and how this has changed over time with changes in the overall policy regime. As indicated, we also analyze the influence of different initial conditions at the state level. Brazil is of special interest for this enquiry given the diversity found in both the policy environment (over time) and initial conditions (over space). Intertemporally comparable household survey data exist for Brazil since 1976, on a quasi-annual basis. GDP growth data disaggregated by sector and state are available since 1985. Combining the two sources of data with information on initial conditions from the 1970 Census, we model poverty dynamics in Brazil at state level over the period 1985-2004.

We find that growth in the services sector was substantially more poverty-reducing than growth in either agriculture or industry, echoing the findings of Ravallion and Datt (1996) for

> 2  Note that the figure for Brazil uses its national poverty line, which is more than $1 a day. In 1985, 8% of Brazil’s population lived below $1 a day, which fell to 7% by 2004. However, the comparison made in the text is still of interest given that the initial poverty rate is the same.

> 3 Using cross-country comparisons for developing economies, Ravallion (2001) estimates an elasticity of poverty incidence (based on the “$1 a day” poverty line) to growth in mean household incomes of -2.1. This was computed using an instrumental variable approach exactly analogous to the one used to estimate the Brazilian figure. See Table A2 in  Appendix II for details.

> 4 See, e.g. Bacha and Bonelli (2001) and Ferreira and Rossi (2003).

> 5 It is not clear that the elasticity can be changed independently of the growth rate. It should also be noted that the international “$1 a day” poverty line is lower than Brazil’s own poverty line.

3
# **Table AB.1: Empirical evidence of the impact of resource abundance on local growth**

|**Paper**|**Explanatory variable**|**Outcome**<br>**variable**|**Sign**|<sup>**Country, data**</sup><br>**level**|**Identification**<br>**strategy**|
|---|---|---|---|---|---|
|Douglas & Walker<br>(2013)|Share of coal revenue in total<br>county personal income|||U.S., county level|Cross-sectional OLS|
|James & Aadland<br>(2011)|Share of earnings in resource-<br>extraction industries|Income<br>growth|_|U.S., county level|Cross-sectional OLS|
|Papyrakis &<br>Gerlagh (2007)|Share of primary sector in<br>local GDP|rate||U.S., state level|Cross-sectional OLS|
|Zuo & Jack (2014)|Provincial annual energy<br>production per capita, or<br>provincial annual energy<br>production, or ratio of the<br>regional energy production<br>over GDP|||China,<br>province level|Fixed effect panel<br>model|

_Note_ : + = positive relation; - = a negative relation; 0 = statistically insignificant. OLS = ordinary least squares.

53
<!-- Start of picture text --> 6 4<br>4<br>0)<br>E<br>-4<br>-6<br>-8<br>2016 2018 2020 2022 2024 2026 2028 2030<br>60<br>50<br>40<br>30<br>20<br>10<br>)<br>2016 2018 2020 2022 2024 2026 2028 2030<br>=== CEMAC+rule<br><!-- End of picture text --> <!-- Start of picture text --> 3<br>4<br>)<br>“1<br>-2<br>3<br>2016 2018 2020 2022 2024 2026 2028 2030<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9<br>:<br>7<br>6<br>5<br>4<br>7<br>2<br>1<br>0<br>2016 2018 2020 2022 2024 2026 2028 2030<br><!-- End of picture text --> <!-- Start of picture text --> === CEMAC+rule — — Baseline<br><!-- End of picture text --> <!-- Start of picture text --> 99<br>8 8<br>7 7h<br>66<br>55<br>44<br>33<br>22<br>11<br>2019 2021 2023 2025 2027 4«2029 2019 2021 2023 2025 2027 2029<br>90% 70% 040% 20% 90% 70% 40% 20%<br><!-- End of picture text -->
Promoting Democracy in Fragile States Table 5: Effects of programs on actual vote for national versus parochial candidates

|L|P voteshare|UP voteshare|CDC voteshare|Other voteshare|
|---|---|---|---|---|
|(Control mean)|0_._10|0_._32|0_._26|0_._33|
||(0_._01)|(0_._01)|(0_._01)|(0_._01)|
|Civ. Ed. ATE|_−_0_._01|0_._11<sup>_⇤⇤_</sup>|<sup>_⇤_</sup><br>_−_0_._02|_−_0_._09<sup>_⇤⇤⇤_</sup>|
||(0_._01)|(0_._03)|(0_._02)|(0_._02)|
|Sec. Com. ATE|0_._02|0_._02|0_._01|_−_0_._04<sup>_⇤_</sup>|
||(0_._01)|(0_._02)|(0_._01)|(0_._02)|
|Combined ATE|_−_0_._02|0_._03|0_._04<sup>_⇤⇤_</sup>|_−_0_._05<sup>_⇤_</sup>|
||(0_._01)|(0_._03)|(0_._01)|(0_._02)|
|_N_|142|142|142|142|
|_R_<sup>2</sup>|0_._90|0_._88|0_._91|0_._95|

WLS estimates account for blocking using centered block interactions (coefficients omitted). Party share outcomes are compositional and sum to one. Standard errors account for clan-level clustering. _†_ significant at _p < ._ 10; _⇤p < ._ 05; _⇤⇤p < ._ 01; _⇤⇤⇤p < ._ 001 Table 6: Effects of programs on vignette vote for national versus parochial candidates

||None|Regional|Ethnic|National|Materialist|Religious|No resp.|
|---|---|---|---|---|---|---|---|
|(Control mean)|0_._01|0_._08|0_._23|0_._59|0_._02|0_._01|0_._01|
||(0_._00)|(0_._01)|(0_._02)|(0_._02)|(0_._00)|(0_._01)|(0_._01)|
|Civ. Ed. ATE|0_._00|_−_0_._04<sup>_⇤⇤⇤_</sup>|_−_0_._06|0_._08<sup>_⇤_</sup>|_−_0_._00|_−_0_._02<sup>_†_</sup>|0_._02|
||(0_._00)|(0_._01)<br>|(0_._04)|(0_._04)<br>|(0_._01)|(0_._01)|(0_._02)|
|Sec. Com. ATE|0_._01|_−_0_._03<sup>_†_</sup>|_−_0_._07<sup>_⇤_</sup>|0_._06<sup>_†_</sup>|0_._01|_−_0_._00|_−_0_._02|
||(0_._01)|(0_._02)|(0_._03)|(0_._04)|(0_._01)|(0_._01)|(0_._01)|
|Combined ATE|0_._01|_−_0_._06<sup>_⇤⇤⇤_</sup>|_−_0_._06<sup>_⇤_</sup>|0_._10<sup>_⇤_</sup>|_−_0_._00|_−_0_._01|0_._00|
||(0_._00)|(0_._01)|(0_._03)|(0_._04)|(0_._01)|(0_._01)|(0_._01)|
|_N_|1426|1426<br>|1426|1426|1426|1426|1426|
|_R_<sup>2</sup>|0_._44|0_._44|0_._44|0_._44|0_._44|0_._44|0_._44|

Outcomes are compositional and sums to one. Standard errors account for clan-level clustering. _†_ significant at _p < ._ 10; _⇤p < ._ 05; _⇤⇤p < ._ 01; _⇤⇤⇤p < ._ 001 by respondents reporting that they witnessed voter intimidation and threats and that they received help in filling election day ballots. At the same time, we see that the positive aggregate effect masks the fact that for some items, such as whether respondents thought that they would get in trouble for voting the “wrong” way or whether or not respondents reported receiving gifts, we estimate substantial _negative_ effects. The civic education program and the combined program are also associated with substantial reductions in invalid votes recorded in the polling place results; no such pronounced effect was evident for the 29
Policy Research Working Paper 10472

# **Abstract**

As the COVID-19 pandemic led to a historic and widespread shutdown of schools across the world, including in Sub-Saharan Africa, there were general concerns that girls would be disproportionately affected. This study analyzes the effects of the pandemic on the school attendance of adolescent girls and boys in six African countries. The study uses individual-level data on children’s school attendance collected as part of high-frequency phone surveys. Contrary to expectations, the study reveals that there is no evidence to suggest that gender gaps widened during the pandemic.

If anything, gender gaps appear to have narrowed in some countries. Further in-depth analysis shows that while being a descendent of the household head, having parents with at least primary education, and above-median household wealth were associated with a higher probability of school attendance among adolescents before the pandemic, these factors lost their salience in explaining school attendance in the aftermath of the pandemic. These results suggest that some traditionally protective forces were eroded during the COVID-19 crisis.

This paper is a product of the Gender Innovation Lab, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at igaddis@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
effects of observable and unobservable characteristics at the level of country-policy cluster pairs and isolate the _within-pair_ variation that is explained by the receipt of World Bank lending and non-lending services.<sup>17</sup>

# **Model**

Our baseline statistical model is an ordered probit model summarized in Equation (1).

In Equation (1), _i_ denotes a survey respondent. Our primary outcome variable— _Influence Rating_ , which represents an individual respondent’s assessment of the World Bank’s influence on his or her government’s decision to pursue reforms in a specific policy domain and time period (the respondent’s self-identified area of policy expertise during his or her period of service in a self-identified public sector position and institution)—is drawn from the _2014 Reform Efforts Survey_ . This variable takes integer values from 0 to 5.<sup>18</sup> _β_ 0 is the intercept. _β_ 1 is the coefficient for a given form of _Assistance_ . **_α_ 1** is a vector of partial effects for a matrix of respondent-country-year-level control variables denoted by **X1** _i_ . While these control variables do not vary within respondents (since there is only one observation per respondent who evaluated the World Bank), they do vary within countries based on the time periods in which respondents held their public sector positions. These measures also vary within country-policy-cluster pairs (i.e., specific policy areas within countries) whenever there

> 17By way of illustration, assume that there are two respondents in a given country-policy cluster pair (e.g. Madagascar-Human Development) and the first respondent reports that the World Bank had a high level of influence on the host government’s policy priorities and the second respondent reports that the World Bank had a low level of influence on the host government’s policy priorities. If the first respondent worked during a period of time (say, 2004-2007) when the World Bank provided a higher level of assistance, analysis or advice to the human development sector and the second respondent worked during a period of time (say, 2008-2011) when the World Bank provided a lower level of assistance, analysis, or advice to the human development sector, a model that uses country-policy cluster pair fixed effects would make it possible to isolate this source of variation. Therefore, our approach allows to isolate variation in the timing of treatment and variation in the timing of when respondents evaluated the policy influence of the World Bank _within_ country-policy cluster pairs.

> 180 denotes no influence. 5 denotes maximum influence.

14
independent regulatory commissions, not subject to Presidential oversight, such as those involving nuclear plant safety, consumer product safety, and most financial regulations.<sup>2</sup> Before a new regulation in the U.S. is adopted, it must be published in the _Federal Register_ in proposed form, with an opportunity for public comment and, in some cases, a formal public hearing. When it is published in the _Federal Register_ as a final rule, written explanations of any important revisions and responses to public comments are included. Major regulations must be cleared by OIRA.

Figure 1 categorizes the major rules submitted to OMB by agency over the period 20002010. As shown, the EPA accounts for the most rules issued by any single agency, or about 30 percent of the total. In terms of economic impact, EPA rules account for about half the total costs and 60-80 percent of the total benefits. (OMB 2011, page 13).

> 2 For a discussion of rules excluded from RIA requirements, see Fraas and Lutter 2011).

8
**Table 5: Expected visitation and benefit estimates**

|**_Measure_**|**_Household:_**<br>**_NB_**|**_Household:_**<br>**_ZINB_**|**_On-site:_**<br>**_TRNB_**|**_On-site:_**<br>**_TRNBES_**|
|---|---|---|---|---|
|)<br>|<br>(<br>_<br>_X_<br>_y_<br>_E_<br>_i_|0.8926|0.5787|5.8822|6.9664|
|CS ($USD per day-trip)|8.16|8.82|8.73|8.21|
|Total WTP<sup>1</sup>($USD)|6,362,295|6,875,160|6,802,126|6,399,840|

_Note_ : _X_ is evaluated at the sample mean. 1 – Calculated for households as: CS * 779,230 households in 2001.

# **V. Conclusion**

In this paper, a population-based household sample and an on-site sample are modeled in a travel cost framework to compare estimated consumers surplus for the value of site access. If each model is corrected for several dependent variable issues, we expect the models to produce similar welfare estimates. In the household model, we account for the potential for over-dispersion (variance>mean) by the use of a negative binomial distribution function, and for the possibility of observing a large number of zero visits (a recreation participation decision) by splitting the participation and quantity decisions directly in one censored model, the zero-inflated negative binomial (ZINB). For the onsite survey, there is a possibility of over-sampling those who recreate quite often, thus the truncated distribution function is augmented for endogenous stratification (e.g. the likelihood of surveying respondents whose characteristics are associated with higher trip frequencies). To compare the effect of ES, we model the on-site sample as a truncated negative binomial with and without endogenous stratification (TRNB and TRNBES, respectively).

Each of these models are then applied to a unique water-based recreational site in Armenia, Lake Sevan. The site has few, if any, alternatives facilitating a comparative welfare exercise. In addition, as the surveys measured only current revealed preference behavior, no quality changes are present to confound the measurement of expected trips outside the current experience.

17
Figure 2.3 <!-- Start of picture text --> Gross Domestic Savings and Gross Capital Formation - 1999<br>50<br>47'~~~~~~~~~~~~4<br>40 __<br>z 30 -MD 33 53~~~~~~~~2 F- S<br>24 ~ ~ ~ ~ ~ .Ž<br>201<br>p 4~~~~<br>Carnbda Indornsa LaaPDR Malaysia Myanrnar PNlippines Sigapore Thailand Vietnam China Japan Korea<br>NOTE: Data for Brunei not available<br>Source: The World Bank, World Development Indicators 2001<br><!-- End of picture text --> Investment rates (GCF/GDP)<sup>aresomewhatmoreconsistent thansavings rates</sup> economies<sup>-generallyin the rangeof 20 to 30percent(exceptfor</sup> among individual Cambodia and Myanrmar).<sup>China'srateis remarkableat37 percent,the highestamong</sup> the countries included in Figure<sup>2.3.While it can financeits current(1999) levelof</sup> investnent with domestic<sup>savings,it does not have a large savingssurplus, as do</sup> Malaysia, Singapore, Thailand<sup>and Indonesia.For a poorereconomy,even a high</sup> savings rate has little impact<sup>asit can raiseonly small amountsfor investmentso that</sup> these economies need to rely<sup>on othersourcesof funds, includingdevelopment</sup> assistance. 1<sup>46</sup> East Asia's savings and<sup>investmentrates are higher than those of other regions,as</sup> shown in Figure 2.4. In particular,<sup>ASEAN'ssavings rateof 35percent isnearlydouble</sup> that of NAFTA and one and<sup>a half timesthat of the EMIU.Of course,this is not</sup> surprising given the low savings<sup>ratesin westerncountries;forexample18 percent</sup> (GDS/GDP) in the U.S. (1999)<sup>and16 percentin theU.K.Theinvestmentratefor</sup> ASEAN is more in line with<sup>that of NAFTAandthe EMNUbut therate forASEAN+3(28</sup> percent) is pushed up by<sup>the inclusion of Chinain this grouping.</sup> 39