rafmacalaba/data-use-mentions-extended huggingface dataset:
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Power

## **Achievements**

Angola has been making substantial investments in the power sector since 2002 to restore and reconstruct the infrastructure that was destroyed during the civil war. Recent estimates from Empresa Nacional de Electricidade (ENE)—Angola’s major power-generating company—indicate that these investments led to an increase in generation capacity from around 830 MW in 2002 to over 1,200 MW in 2008 (World Bank 2010a) (table 2). In terms of per capita generating capacity, Angola fares better than the average African fragile state or resource-rich country. Angola has 70 MW per million people compared to resource-rich or low-income fragile countries, which have only 43 or 46 MW per million people, respectively (table 1).

Furthermore, a relatively high share of Angola’s generation capacity is actually operational. In 2008 almost 1,000 MW or 80 percent of the installed power generation capacity was operational. This is higher, on average, than resource-rich countries, where roughly 66 percent was operational and generating power. Thus, Angola has been able to rapidly ramp up its power production over the past decade. This increased generation and operational capacity facilitated a 13 percent average annual growth in power production between 1999 and 2008. As of 2008 around 4,133 GWh of power was produced, a steep increase compared to 1999 production levels of 1,295 GWh (World Bank 2010a).

The delay involved in obtaining an electricity connection has fallen dramatically since 2007. Investment climate surveys in 2007 reported that firms encountered delays of over two months in Luanda and over six months in other parts of Angola in order to obtain a new electricity connection (World Bank 2007b). But Angola has achieved tremendous progress in this area—firms in 2010 recorded only a sevenday delay, on average, in obtaining a power connection (World Bank 2010b).

## **Challenges**

But increased investments in power infrastructure have not necessarily translated into widespread electrification. As
preceding template 7) should be completed for each subsector where possible, ideally attributing costs and revenues to each sector.

All figures in the income statement, cash flow, and balance sheet should be entered as positive figures unless there is a loss.

# **Public operators’ financial data (template 11)**

Template 11 provides raw financial data from SOEs, public corporations or parastatals, regulatory bodies, or special funds. Henceforth, they are named ―public operators.‖ It should be completed for public operators include SOEs, parastatals, regulatory bodies, and special funds, which have separate accounts. For each public operator, templates should be generated for the five most recent accounting years. Where one enterprise covers more than one subsector, for example, electricity and water supply, template 11 should be completed for each subsector, if possible.

Data should be extracted from audited and/or published accounts from the sources identified in the pre-field stage. These are likely to include: (i) the central unit monitoring public corporations, if it exists; (ii) the national audit institution (auditor general, court of audit, or equivalent); (iii) the Web site of the previous institutions and/or of each of the public operators; and (iv) each of the public operators directly. Note that it is very likely that the most recent accounts are available only from the public operators themselves.

Given the variety in the forms of reporting and the use of accounting concepts, a glossary of accounting terms is provided in annex 4.

**Template 11. Public operators’ financial data**

|**Policy**<br>**code**|**Series**<br>**code**|**Variable**|Unit|**t-4**|**t-3**|**t-2**|**t-1**|**t**|
|---|--
<!-- Start of picture text --> LFP<br>Male Female<br>Q «2<br>Random sample SAE Full enumeration SAE Random sample SAE Full enumeration SAE<br>© . @ 03, 82< a3¢ é © ae<br>S . 3 we op PE<br>ao gs n 0% > $ae<br>wo wa :<br>o°<br>56 7 8 9 0 2 4 6 8<br>Actual rate Actual rate<br>Unemployment<br>Male Female<br>8<br>“ Random sample SAE Full enumeration SAE * Random sample SAE Full enumeration SAE<br>2Sg‘°oeg88<br>g6 See 8 Ss ee fe<br>oo<br>002 04 .06 .08 | 0 .01 02 .03 .04 05<br>Actual rate Actual rate<br>All unemployment variables are winsorized at the 99th percentile for the sake of presentation.<br><!-- End of picture text -->
The data are from a combination of manufacturing censuses of firms, collected by each country’s statistical agencies, and the Amadeus database, compiled by the Bureau Van Dijk, which collects the financial information of public and private firms from 34 European countries. The manufacturing censuses correspond to Ghana, Ethiopia, Kenya, Chile, Colombia, Peru, India, Malaysia, and El Salvador. In the majority of the cases, the censuses are meant to be universal for firms of a certain size, typically ten workers, with the exceptions of Peru and El Salvador, for which there are no size constraints. The Amadeus database in turn does not impose size restrictions, but it is well known that for some countries there is a bias toward overrepresenting the largest firms. Given these differences, we apply some adjustments to maximize comparability and to minimize biases in the representation of the size distributions.

The first adjustment we are forced to implement is restricting the analysis to firms with 10 workers or more. The benefit of this choice is that it maximizes comparability across all of the data sources. Of course, the downside is the lack of information on the prevalence and behavior of the smallest firms in the size distribution. Our assumption is further supported by observing that existing cross-country estimates of the costs of doing business are in effect based on the legal costs of creating a 10-plus worker firm. For example, the World Bank’s DBI (a leading source of information for the calibration of entry barriers in the literature<sup>22</sup> ) computes the cost of starting a business as the summation of all the processes entrepreneurs must undergo to obtain the necessary approvals, licenses, and permits for operating a new firm. The prototypical firm for the measurement of these costs is precisely one that features 10 to 50 employees one month after operations start. Thus, we see our analysis as providing a characterization of the business environment that rationalizes the cross-country differences in the average size distribution of firms with 10 workers or more. Of
.p.c)|0.010|-0.001***|-0.000*|-0.001*|
|||(0.000)|(0.000)|(0.000)|
|Children’s Clothing Expenditure (S.p.c)|0.008|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|
|Children’s Clothes (S.p.c.)|0.005|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|
|Children’s Footwear (S.p.c.)|0.003|-0.000|-0.000|-0.000|
|||(0.000)|(0.000)|(0.000)|

_Notes:_ Differences in means (mean of the relevant group minus mean of the rest of the population) and their standard errors (in parentheses). * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%. The data used are the 2002 LSMS, the 2005 LSMS and the 2012 LSMS. The sample consists of 7480 Albanian mothers aged 20 to 49 years old with two or more children aged 18 or younger. (S) refers to the share of a particular expenditure over total expenditure, while (S.p.c) refers to the respective share divided by the number of children aged 18 or younger still living in the household.

39
be imperfect substitutes within a CES structure. There is debate in the literature on the estimates of the elasticity of substitution between natives and immigrants. Borjas et al (2008) put it essentially at in...nity, Ottaviano and Peri (forthcoming) and Card (2009) put it around 20 and Manacorda et al. (forthcoming) put it around 6. We will analyze the wage impact of immigration and emigration on natives using each one of these parameter values. We describe the sensitivity of simulated average wages and the distributional e¤ects for non-migrant natives with di¤erent education levels to di¤erent assumptions about this elasticity.

Finally, the fourth ingredient of the model is to allow human capital (skill) intensity to have a productivity externality as immigration and emigration alter the skill composition in an economy (i.e. the ratio of highly educated to the less educated). There is some debate in the literature on this issue as well. Moretti (2004a and 2004b), based on data from the US cities, puts the elasticity of productivity to the share of college educated between 0.75 and 1. On the opposite end of the spectrum, Acemoglu and Angrist (2001) estimate essentially no productivity e¤ect of increased schooling in US states. Iranzo and Peri (2009) estimate an externality around 0.44 using US state level data. Again, we analyze the impact of immigration, emigration and net migration under each of these parameter values.

While the speci...c quantitative details of the simulations vary with the choice of parameter values, some general results emerge from this exercise. First, in general, over the period 1990-2000 immigration had zero to small positive long-run e¤ect on the average wages of non-migrant natives in the rich OECD countries (Western Europe plus the US, Canada,

> 3In section 5.5 by making assumptions on how the total migration ‡ows are distributed over the years, and on the short-run speed of adjustment of capital
The strongest correlation is between the Infrastructure Management index, which reflects quality of services, and the Accountability and Transparency index, which focuses on ease of access to information and transparency, at 0.33.

We also include data on the relationship between the survey measures and the APAs, in Appendix B. Since APAs were only collected at ULGSP LGAs, the sample is restricted to 18 observations. While for the household survey we observe overall positive correlations between outcomes measured at baseline, endline and changes over time using survey and APA indicators (Figure B3 and Figure B4), results for the government official survey are more mixed, with overall smaller correlation in magnitude and both positive and negative relationships (Figure B5 and Figure B6). We should treat these correlations with caution, not only due to the limited number of observations but also because in some indicators the amount of variation is quite limited. For example, almost all ULGSP LGAs had scored the maximum amount in the Oversight component of APA by our first-round survey, so variation between our first and second surveys is zero for the majority of them).

# **3.4 Evidence of successful project implementation**

An important initial question regards the implementation of the program: does the survey provide evidence that government officials were aware of the program and that they received training and funding as expected? The answer is a definitive yes: in the first round, officials in project LGAs recognize the project as the main source of capacity training and budget support for infrastructure. In Figure 2, panel A, we show that among officials in project areas, 65% report the project as the agency most supporting capacity building vs. 12% in comparison LGAs. For comparison LGAs, the main agencies reported as supporting capacity building are sector ministries (30%) and community-based organizations (18%).

Regarding budget support for infrastructure, panel B of Figure 2 shows that 50 percent of officials in treated LGAs choose the project as the main source for recent increases in budget
and survey characteristics, we find that the business-as-usual scenario (relative to the individual interviews) underestimates men’s and women’s participation in livestock, managing or working in an NFE, as well as wage employment for men. After controlling for demographic and survey characteristics, we find that the businessas-usual scenario significantly underestimates participation in the last 7 days in livestock, NFE management and supporting work (for both men and women), with weakly significant effects on wage employment for men. The effects are quite large, given participation rates in Table 2, and tend to be stronger for women. Among women, for example, Table 4 shows underreporting under the business-as-usual approach ranging from 3.2 percentage points for livestock (which, given mean participation for women in the IHS4, is a relative decrease of 20 percent); 3.9 percentage

> 15 The reported regression coefficients are qualitatively similar to the marginal effects derived from logit and probit regressions, which are available upon request.

> 16 Since the survey experiment/setting is exogenous, and we are focused on the conditional sample, we do not use a selection model in the analysis.

13
is Grade 12 exam score in English and Math, standardized by universe of scores in that year. Sample is students taking grade 12 exit exam in Regions 2-6, 1998-2012. Interaction with female based on individual student. Distant is dummy for above national median distance from school to main road. All other interactions are district characteristics from 1998 household survey. Low enrollment = below national median secondary-age (13-18) enrollment rate. Rural is above national median proportion of 1998 district population living in rural area. Wealthy = at or national above median household assets, by first principal component. “Treatment effect with interaction” is sum of program coefficient and coefficient on interaction term. All regressions include age, age squared, school and year fixed effects. Regression with both male and female students in sample also includes female dummy. Sample sizes differ across regressions because not all districts surveyed in 1998. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Starred significance levels are based on these p-values. * significant at 10%; ** significant at 5%; *** significant at 1%.
bootstrap replications as proposed by Romano and Wolf (2005). Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of clients to village size. Column 1 shows intervention impact on quality of the underground chamber, Column 2-3 refer to quality of the overground structure (component 1 and 2). Data source: household survey.

are being used. We analyse this in Table 7 by looking at intervention impacts on self-reported open defecation practices by study households. Column (1) studies impacts on whether everybody in the household engages in OD, and column (2) considers a broader definition of whether _anyone_ in the households still goes for open defecation. Our estimates indicate a reduction of 10-11 percentage points in open defecation along both measures. The magnitudes of these coefficients are closely in line with the impacts on toilet uptake, suggesting that households who construct a toilet also generally use it. Given that very few loans were used to repair or upgrade existing toilets, we can conduct a loan-to-toilet conversion analysis, where we regress toilet ownership on sanitation loan uptake, instrumenting the loan 29
a small share of Latin America and the Caribbean’s investments in infrastructure, hovering between a quarter and a third of a percent of GDP in the period 2000–12. Data from Foster and Briceño-Garmendia (2010) suggest that Africa spends around 0.5 percent of GDP, while Andres, Biller, and Dappe (2013) estimate that South Asia spent an average of 0.41 percent of its GDP in the period 2000–11. A more recent, and global, estimation of subsidies in the WSS sector can be inferred from Fay et al. (2019), who estimate infrastructure investments in low- and middle-income countries. Their report does not directly

> 26 It is again important to acknowledge that our estimation does not include CAPEX for infrastructure expansion. Since infrastructure expansion tends to be fully subsidized, the actual global magnitude of networked WSS subsidies is much greater than our estimation.

16
# **Previous research**

Three studies in the literature on the impact of conflict on economic activity are especially relevant for the design of the methodology in this paper. These studies deal with the impact on aggregate output, on the formal sector and on illicit activities respectively. Differences in the approaches used in each case provide a useful backdrop to guide the empirical work for Afghanistan.

At the aggregate level, Abadie and Gardeazabal (2003) use a quasi-difference-in-differences approach to assess the impact of terrorism on the GDP per capita of the Basque Country from 1955 to 1977. To do so they construct a “synthetic control region”, as a weighted combination of other Spanish regions. The economic evolution of this synthetic control region without terrorism is compared to the actual experience of the Basque Country using an autoregressive distributed lag model.

experience of the Basque Country using an autoregressive distributed lag model. The gap in per capita GDP between treatment and control is the dependent variable in the analysis, and the number of fatalities from terrorism (almost 800 over time) is the independent variable. The ∆YY study estimates the impulse-response function to conflict assuming that the output gap in year is CC determined by the number of deaths from terrorism both in the same year and in the two previous ∆YY tt years, as well as by the output gap in the two previous years: CC The error term in this equation is assumed to follow a first-order auto-regressive model. Therefore, the parameters in equation (1) are estimated using data on conflict and the output gap lagged for up to three tt uu years. No other control variables are considered in the proposed specification. The results reveal a negative impact of deaths from terrorism in the previous year, but not in the same year or two years earlier ( is statistically significant but and are not). Based on the estimated coefficients, after the outbreak of terrorism, per capita GDP in the Basque Country declined about 10 μμ<sup>1</sup> μμ<sup>
(0.0298)|(0.0349)|||
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Digital readiness|No|Yes|No|Yes|No|No|No|No|
|Observations|14532|9702|14816|9813|6391|5222|6477|5279|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01

-Parasite firms exhibit a statistically significant lower probability of investing at the 5% level of significance in Panel B, col. 3, but, controlling for initial productivity, a higher probability than other types of informal at the 1% level in col. 4.

-In col. 7, parasite firms show a lower probability vs. other types of informal at the 1% level. In col. 8, the differences are not statistically significant. -The coefficients of PCA 1 and PCA 2 are statistically different at the 1% level in Panel C, cols. 1, 5, and 6; and are not statistically different in col. 2. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
However, some marginal improvements in social support were observed in both conditions ( _d_ PI=0.09, SE=0.05, _p_ =.057; _d_ II=0.09, SE=0.05, _p_ =.077).

On an exploratory basis we assess which variables contributed to the observed effect on the composite index in the Interpersonal Initiative condition. There, we see significant improvements in women’s relationship dynamics with their households ( _d_ II=0.10, SE=0.04, _p_ =.021) and directionally but not significant improvements in dynamics with their partners ( _d_ II=0.08, SE=0.05, _p_ =.102). Specifically, women in the Interpersonal Initiative condition rated themselves as feeling closer to their households ( β =0.11, SE=0.04, _p_ =.008).

These positive impacts on household dynamics are also consistent with observed positive impacts on household businesses in the Interpersonal Initiative condition. For a randomly selected subsample of households (approximately one-fifth, N=457), we were able to collect data on off-farm businesses owned and managed by other members of the household, in addition to those owned by women participants (Table S8). Among women assigned to the Interpersonal Initiative intervention, those household-owned businesses showed greater business outcomes overall (dII=0.30, SE=0.13, p=0.023), including business engagement (dII=0.28, SE=0.14, p=0.039) and business performance (dII=0.21, SE=0.13, p=0.099). For instance, while those in the control condition saw revenues of household business amounting to approximately US$70 PPP in the past month and those in the Personal Initiative intervention saw revenues of US$83 PPP (SE=21.56, pPI=.527), those in the Interpersonal Initiative intervention
335|0.628|−542|0.011|−723|0.038|−374|0.054|
|2015|−40|0.966|−1554|0.535|−172|0.824|−307|0.033|−237|0.164|−276|0.041|
|2016|−1890|0.034|−1806|0.588|−1954|0.052|−325|0.003|−220|0.143|−251|0.007|
|2017|−2191|0.001|−1657|0.532|−1891|0.008|−419|0.001|−297|0.116|−368|0.000|

_Source:_ Based on National Household Survey data.

Note: Kernel matching is performed using the Epanechnikov Kernel and a bandwidth of 0.06. Nearest neighbor matching is performed on the closest neighbor with replacement. Radius matching imposes a caliper of 0.001. Standard errors are bootstrapped with 100 replications.

19
In order to the compare drought and flood impacts using LSMS-ISA data with those using EM-DAT data, we use two metrics: the share of individuals “affected” by the shock and the estimated total value of damages. Since the LSMS-ISA surveys run every two to three years, we only retain events in the EM-DAT database for which the start or end date is within a year containing LSMS-ISA data. The comparison for flood shocks is possible in fewer countries and years due to limitations in the microdata questionnaire’s scope in some cases (Table 18).

In order to compute the total number of individuals impacted by a shock within a specified period in the EM-DAT database, we aggregate the total number of people “affected” by the shock in the macro data. Affected persons are those that are reportedly injured, homeless, or otherwise in need of “immediate assistance.”<sup>29</sup> To estimate the total number of individuals affected by a shock in the LSMS-ISA microdata, we construct population weights by multiplying household weights by household size. These weights are then used as expansion factors, which we multiply by a dummy variable equal to one in the household report a shock on any of its cultivated plots. We then add up this product to calculate an expansion estimator.<sup>57,58</sup> To obtain shares of the total population, the numbers of individuals affected in both EM-DAT and the LSMS-ISA data are divided by total yearly population estimates drawn from a library of the World Bank World Development Indicators.<sup>53</sup> We then compute the estimated damages from both droughts and floods in the periods and years covered by LSMS-ISA data. We first aggregate the “total damages” estimated in the EM-DAT database, defined as the values of total losses “directly or indirectly related to the disaster”, in 2022 USD values. Using LSMSISA microdata, we aggregate the estimated value of crop losses for each household. In this case, the value of losses