rafmacalaba/data-use-mentions-extended huggingface dataset:
during the retail tariff year, its annual return from operating the electricity distribution concession will be equal to a contractually allowed return on its capital investments of 20 percent. This contractual return is set through the life of the concession.

**Table 6: Utility Management index of utilities in Uganda and comparators, 2015**<sup>16</sup>

||Kenya<br>Kenya Power|Senegal<br> SENELEC|Tanzania <br>TANESCO|Uganda<br> UMEME|International<br>benchmark|
|---|---|---|---|---|---|
|**Utility Management**|**80%**|**56%**|**65%**|**76%**|64%|
|_Financial Discipline_|_76%_|_57%_|_64%_|_77%_|59%|
|_Human Resource_|_64%_|_57%_|_71%_|_71%_|62%|
|_Information and Technology_|_100%_|_53%_|_60%_|_80%_|71%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform Umeme’s human resource framework reflects and is in line with the 2006 Employment Act. There is a strong focus on staff development and the company runs a training school, the Umeme Academy in Jinja, which offers various programs through which employees can develop their professional skills. Umeme’s salary package is competitive in the industry and the company is a popular employment destination for University graduates and professionals. The company offers performance-based bonus plans, yearly salary increments, and employee stock schemes rewards. Umeme’s staff comprise a diversity of professions, including engineering, operations, finance, IT, HR,
In statistical terms, a child is stunted if his/her height-for-age Z-score (HAZ) is more than 2 standard deviations (SD) below the median height of a healthy reference population (that is, HAZ <-2).<sup>1</sup> The measure of HAZ and thus stunting used in this study is based on the new WHO child growth reference population. The incidence of stunting among children ages 36-59 months is a consequence of the growth faltering in first 1,000 days from conception. Growth faltering is the rapid decline in height- and weight-for-age of children in the first two years of life and is common in many developing countries. Growth faltering among children was first documented in a study by Shrimpton et al. (2001) and to a large extent, it is in response to these finding that several global health policy and information campaigns with emphasis on the first 1,000 days window have been initiated (Prentice et al., 2013).

Figure 1 presents the cross-sectional age profile of growth faltering (HAZ scores) for children between 0 and 59 months of age in the two countries analyzed in this paper. As is easily apparent, growth faltering in the first 1,000 days is prevalent among children in both the Republic of Congo and São Tomé and Príncipe, with growth faltering (or HAZ decreasing) accelerating rapidly with age in the first 21-24 months of life, somewhat recovering at a slower rate afterwards with minor fluctuations.<sup>2</sup>

**Figure 1: Growth faltering in the Republic of Congo and in São Tomé & Príncipe**

<!-- Start of picture text --> Congo: Average HAZ Sao Tome & Principe: Average HAZ<br>Children under 60 months Children under 60 months<br>0 20 40 60 0 20 40 60<br>Age in months Age in months<br>Source: Rep. of Congo MICS 2014-15 and São Tomé and Príncipe MICS 2014-15<br>
%|NAP|57%|

Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform

# _3.4.2.4 Quality Regulation_

As regards quality regulation, there are specific quality of service standards formally written and publicly available for quality of product, supply, and commercial service. Utilities are legally required to meet these standards and they take them seriously and generally comply with them. There are rewards and penalties incorporated in the performance incentive scheme under the performance-based rate setting. Penalties are incorporated in the next tariff setting thus, all users are affected, and they are always enforced in practice. Also, it is disclosed to the public whether utilities have met the standards. As regards quality of service enforcement regulated entities are required to report on a periodic basis on different indicators, such as SAIDI, SAIFI, other service interruption indicators, and voltage indicators. According to the survey, these indicators are independently validated and reviewed by ERC.

All measurements of the quality or reliability of the power supply are made publicly available. The information, broken down monthly, has to be collected and supplied to the ERC annually. In addition to the monthly figures, the cumulative performance total against each index must also be provided, from the start of the corresponding calendar year until the end of the month for which each index was measured. The ERC intends to annually publish the information disclosure data for all Regulated Distribution Systems. Finally, formal measurements of overall customer satisfaction are required and are indeed undertaken in practice through customer surveys carried by utilities (more than once a year).

Summing up, the Philippines’ performance on quality regulation is very good (92 percent), with its weakest area being Quality of service enforcement (83 percent). There is a comprehensive quality regulatory framework, with specific and publicly available require information, including processes and consequences of non-compliance. The main limitations are the fact
permit.<sup>18</sup> Unfortunately, the law of 1998 is not clear about this issue and we cannot observe in our data if that was the case or not. Figure C.2 in the appendix shows that in 1999 the formalization rate was small but positive among the control cohort and null among the eligible cohort. In one of our placebo tests, we use December 1997 as cutoff and find statistically significant difference in formalization rates between children who turned age 14 before and after December 1997.<sup>19</sup> To check robustness, eq. (1) is estimated with two different bandwidth sizes, six and nine months. We use nine-month bandwidth to perform analysis when only one cross-sectional data is used. Estimates with six-month bandwidth are provided when several waves of the survey data are pooled. We perform two placebo tests in the short term and one in the long term to check robustness.

# **5. Data**

We use several years of the Brazilian household survey PNAD. Data from 1999 and 2001 are used for descriptive statistics and short-term estimates. Data from 1998 are used in one of the placebo analyses. We use all PNADs from 2001 to 2014 to look both at persistency effects in the outcomes used in the short-term analysis and at trajectories of the outcomes used in the long-term analysis.

IBGE has annually conducted the PNAD since the late 1970s. It covers around 100,000 households and 320,000 individuals. The survey is conducted between October and December each year and constitutes one of the main sources of microdata in Brazil.<sup>20</sup> The PNAD is nationally representative, containing information on household socio-economic characteristics, demographic data, household sources of income, and labor-force status.

The sample of interest is given by two cohorts of individuals aged 14 by the time the law passed. The first cohort, which we consider the comparison group, includes individuals who turned 14 before the ban of December 1998. The second cohort,
marginal effect of 1.1 percentage points. We also estimated a random effects model on the full set of observations (118 countries). The results are similar to those obtained when including country fixed effects<sup>12</sup> and are reported in Appendix 3.

# **Violent uprisings**

Can a decrease in subjective well-being also explain violent resistance? We test whether this is the case using three different measures of violent political resistance, showing the results of the regressions in Table 5 (Columns 2-4). In Column 2, the dependent variable is the number of riots obtained from the CNTS data set. Riots are only included if they involve a clash of more than 100 citizens who use physical force. We transform the number of riots using the inverse hyperbolic sine transformation and estimate an OLS model with country and year fixed effects. In this regression, _suffering_ has a significant effect on riots, but the coefficient is only significant at the 10% level and is considerably smaller than when the dependent variable is nonviolent resistance. Whereas a percentage point increase in suffering increases nonviolent resistance by 2.1%, it only increases the number of riots by 1.3%.

To measure armed violent conflict, we use a dummy variable which is one if there is “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in one calendar year” (Pettersson & Wallensteen, 2015, p. 1). We also measure the intensity of violent political conflict with the number of battle-related deaths. This variable is left censured at 25 battle-related deaths, as deaths are only recorded for recognized violent conflicts. We transform the number of battle-related deaths using the inverse hyperbolic sine transformation. Data for both violent conflict variables are derived from UCDP/PRIO (see Pettersson & Wallensteen, 2015).

> 12 A Hausman test does not find evidence of
Because advanced countries feature higher trade and financial integration (see Kose, Terrones and Prasad (2004)) than other countries, which likely affects their growth comovement, we consider separately a sub-sample of 21 advanced economies. The full list of countries is given in Table A1 in the appendix.

Real GDP is measured in constant U.S. dollars (expressed in international prices, base 2010), and annual real output growth is computed as the first difference of the log of real GDP.

The spatial weight matrix that connects cross-sectional units (countries) is an important element in the empirical implementation of the model. We measure the economic distance between each pair of countries by the magnitude of their bilateral trade, following the view that bilateral trade intensities capture economic interactions and shock spillovers across countries, so that countries that trade more are economically more connected, see e.g. Frankel and Rose (1998).

To construct the bilateral trade weight matrix, we use information on bilateral trade taken from the IMF Direction of Trade Statistics (DOT). Specifically, for a pair of countries _i_ and _j_ , _i_ = _j_ , entry _i_ , _j_ of the trade spatial weight matrix _W_ is defined as where _Exportsij_ denotes the exports from country _i_ to country _j_ , and _Importsji_ are the imports of country _i_ from country _j_ . Once _W_ has been computed, it is rescaled dividing each of its elements by the sum of its corresponding row, so that the rows of the rescaled matrix sum to unity.<sup>7</sup> Alternatively, following Ertur and Koch (2007), we use a weight matrix _WD_ based on inverse squared distance. The elements of _WD_ are defined (before row normalization) as where _dij_ is the great-circle distance between the capital cities of countries _i_ and _j_ .<sup>8</sup> To assess the covariates of the common factors, we consider three candidate variables, namely, (i) total factor productivity; (ii) policy uncertainty, and
practices. Nevertheless, the actual amount of schooling of the student cohort affected by COVID19 will be compromised if no mitigation or remediation takes place. In addition, the economic shock is likely to affect student drop out, and we should expect long term consequences.

We used microdata from the latest available household survey for 130 countries to estimate country specific dropout-income elasticities using the observed cross-sectional variation between educational enrollment and welfare. Following the HCI framework, we estimated this relationship for pre-school and primary-age students (4-11) and secondary-age students (12-17) separately (for more information see Annex A.2). If a country did not have a household survey, we used the average values from the countries in the same income level classification. In alignment with the existing literature, on average, older-age students seem to be more vulnerable to income shocks than younger students. The patterns for high and upper middle income countries are distinct from those of low- and lower-middle income countries.

The primary pathway for this is the income shock _(γ)_ from reduced economic activity. Given the dropout-income elasticity _(d),_ this will lead to more families pulling their children out of school to work (which particularly affects children in the secondary school age group), or because they cannot afford schooling.

There are important differences across countries in terms of gender and being out of school. In low- and lower-middle income countries, girls are more likely to be out-of-school especially at the 12-17 age range. In contrast, in upper-middle and high-income countries boys are more likely to be out of school. Despite this, the estimations of dropout-income elasticities show no systematic differences between boys and girls (see Figure A.2.1).

As more data become available in terms of policy actions taken by government, the behavior of households, and the effectiveness of the proposed alternative modalities, we will be able to refine some of these assumptions. The availability of additional data is particularly important to better understand the intrahousehold dynamics and school safety
Dx = x( _x_ t) - x( _x_ t-1) = x( _x_ t) - x( _xt_ ,t-1) + x( _x_ t,t-1) - x( _x_ t-1), (4) or in condensed form Thus, the total change in the inequality index of the nominal consumption vector _x_ (henceforth “nominal inequality” for short) can be broken down into a component DxQ that captures the effects of changing consumption _quantities_ (i.e. changing real inequality) and another component DxP that captures the effects of changing consumption _prices_ (i.e. inflation inequality). Notice that, holding constant real inequality, nominal inequality can change in response to price changes. In fact, if price changes are anti-poor then DxP < 0: prices rise faster at the lower tail of the distribution than at the upper tail, and hence for a fixed bundle of goods the relative spending of the poor rises. In such case, it follows that Dx < DxQ. Thus, _ceteris paribus_ , for given real inequality, anti poor price changes _reduce_ nominal inequality, giving the (false) appearance of an improving distribution. Similarly, anti-rich price changes imply DxP > 0 and, _ceteris paribus_ , lead to higher nominal inequality, giving the (again false) impression of a worsening distribution.

# **III. Empirical results**

# _III. 1 Data_

Inflation differentials across consumers are driven by two factors. The first is the difference in consumption patterns across individuals. The second is the difference in inflation rates across goods. Hence, for our empirical analysis we need two ingredients: data on the consumption patterns of different households, and data on the trends in the prices of different groups of goods. We take the data on consumption patterns from household expenditure surveys, which contain relatively detailed information on the type of goods and services consumed by different households. Depending on the country under consideration, consumption is disaggregated into 7 or 8 major categories, although finer partitions are usually
to eligible households who do not report receiving PBS. This negative wealth effect in a sample of individuals above 65 is qualitatively consistent with our simulations. The larger magnitude is not surprising since the estimate corresponds to the subsample of the elderly whose increase the most.

> 40Packard (2002) was the first to study the frequency of contributions over the life-cycle and their determinants in Chile, using limited dependent variable models of self-reported contribution densities elicited in PRIESO, a cross-sectional household survey.

> 41An implicit assumption of Behrman et. al.’s (2011) difference-in-difference approach is that households who do not qualify for the program at a point in time do not anticipate that they may qualify at some future time period, which could affect their current behavior even if they are not actively receiving benefits. The dynamic structural modeling framework used in this study explicitly incorporates such possible anticipatory

27
2014). A third and more pessimistic view considers informal businesses as parasites engaged in unfair competition with formal businesses that pay tax and abide by regulations (Farrell, 2004). Finally, a more nuanced view argues that informality is a rational decision where businesses choose to stay informal when the perceived benefits of formally registering are outweighed by costs such as paying taxes, registration fees, and meeting regulatory requirements (Maloney, 2004; Perry et al., 2007; Ulyssea, 2020; Kanbur, 2017).

Informal Sector Enterprise Surveys ask respondents questions about some of the reasons why businesses chose to remain informal and the perceptions about any potential benefits from formally registering. This may provide useful information for policy makers to understand the potential barriers faced by these businesses to formalize. Figure 15 shows the reasons given (not mutually exclusive), for each country. In Somalia, Mozambique, and Lao PDR, the lack of any perceived benefit from formally registering is the most common reason cited, for Iraq it is taxes, and in Zambia and Zimbabwe, it is lack of information and the time and fees required respectively.

In three of the surveys (Lao PDR, Somalia, and Zambia), an additional question asks owners to choose the main reason, out of seven possible options, for not registering. Figure 16 displays the relative importance of each reason per sampled city. The results largely support the view that businesses decide to remain informal due to the perception that formalizing imposes additional costs and confers few, if any, benefits. By far, the two most common main reasons cited for not registering were: first, no perceived benefit, and second, the fees and time associated with formalizing. Each option was chosen by around 30% of businesses. Costs associated with registering, along with taxes and meetings or inspections with government officials were reported as the main reason for 12% and 3% of businesses, respectively. Overall, the primary reasons for not registering appear to be related to either a lack of benefit
**Figure 6: Changes in the Gini Coefficient - Elderly**

<!-- Start of picture text --> 5%<br>Categorical<br>4%<br>Targeted<br>3%<br>2%<br>1%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases The first, striking contrast with respect to transfers to children is that in all countries (with the exception of Nicaragua, and under some circumstances Guatemala), both targeted and categorical transfers to the elderly have a significantly lower impact on poverty than an equivalent program for children. The difference in poverty impact is large: on average, categorical transfers to children are 1.6 times more effective in reducing poverty than categorical transfers of equal budget to the elderly, and targeted transfers twice more effective. The reasons are straightforward: poverty rates among the elderly are, on average, lower than for children; and poorer families have more children, but not more elderly people. The simulations also suggest that the common belief that cash transfers to the elderly can reduce substantially poverty by trickling down to all family members has limited validity: with fewer elderly than children living in poor households, for the trickledown effect to be effective money should be transferred across family members living in different households, a much less likely event.

There is nevertheless considerable variation across countries in the poverty impact of transfers to the elderly. The maximum reduction in poverty rates (close to 20 percent) is achieved, as expected, in countries with high poverty incidence among the elderly, such as Mexico and Colombia. Effectiveness of a targeted program as opposed to a categorical one also varies substantially across countries, being 0.7 times higher in Nicaragua, to 1 in Guatemala, to 2 in Colombia and 3.2 times in Costa Rica, and 2.8 percent on average across countries. Similar conclusions apply for the reduction
naire, but a limitation is that respondents only make up a relatively small share of all formal firms and some sectors are excluded (e.g. the mining sector). In contrast, tax administrative data captures all firms in all sectors that file VAT and PAYE returns. However, not all firms file returns each month, and they are only required to provide limited information about their activities when they do (e.g. total monthly sales and number of employees paid each month). By comparing the findings across these different data sources, it is possible to have far greater confidence in what actually took place regarding the recovery (or lack thereof) of formal firms.

We show, in both the survey and tax administrative data, that the sales of formal firms in Zambia recovered from the pandemic far more strongly than their employment levels. The order of magnitude of the initial decline and subsequent recovery of firms varied between the survey and tax administrative data, with the latter presenting a more positive picture. However, the overall trend was the same. In June 2020, on average, formal firms reported a large decline in sales and a moderate fall in employment. In contrast, by July 2021, most firms had experienced a complete recovery in their sales, while the share of firms reporting that they had decreased their number of employees doubled. Econometric analysis examining the factors associated with an initial decline and subsequent recovery illustrates structural factors (e.g., the type of business activity a firm was involved in) were more closely correlated with changes in sales than changes in employment, whereas the latter was more closely correlated with firm-specific factors (e.g. the level of experience of the top manager).

These results provide several insights about how formal firms have recovered from the COVID-19 crisis. First, there has been a ‘decoupling’ between the levels of sales and employment among some formal firms in Zambia. Formal firms appear to have adjusted their operations in a way that reduced their need for