rafmacalaba/data-use-mentions-extended huggingface dataset:
The 2014 Labor Force Survey shows that men earn nearly three times as much as women in wage employment, more than 2.5 times as much in non-agricultural self-employment, and nearly double in agricultural self-employment. Earnings are lowest in wage-employment in agriculture. However, the agriculture sector makes a larger contribution to poverty reduction than the mining sector (which has the highest earnings) because it provides more, albeit low-wage jobs. Holding the level of employment constant and raising the level of productivity in the agriculture sector could make a significant contribution to reducing poverty in Sierra Leone. Sierra Leone has a minimum wage established by the Regulation of Wages and Industrial Relations Act No. 18 of 1971. The Act provides for a review of the minimum wage every two years, and the most recent review in 2015 established Le500,000 (equivalent to about US$88.67) as the monthly minimum wage.

As table 5 below shows, the peak seasons for labor demand in the rice sector is around April to June (land preparation and planting) and then September to November (bird scaring and harvesting).

**Table 5: Seasonal use of labor by rice ecosystems (person-days)**

|**Season**|**Upland**|**Boliland**|**IVS**|**Riverain**|**Mangrove**|**Total**|
|---|---|---|---|---|---|---|
|January|||||0.5|**0.5**|
|February|9.8|||1.2|0.5|**1.7**|
|March|9.8|11.6|14.0|1.2||**36.6**|
|April|18.2|11.6|29.0
., 2008). Kapteyn et al. (2008) used vignettes to compare life satisfaction between respondents in the U.S. and the Netherlands.

Beegle, Himelein and Ravallion (BHR) (2012) used vignettes to provide various tests for bias due to latent heterogeneity in individual scales of subjective welfare. Their tests entailed adding vignettes to a national household survey for Tajikistan in 2007. Respondents placed these vignettes on the same six-step ladder used to report their own subjective economic welfare. BHR performed three sets of tests for bias. In their first test, they found some significant covariates for 25
introduction 7 stock, and (3) the decreasing returns in consumption–that is, the fact that people who live on $2 per day are more affected by a $1 loss than are richer individuals.

The analysis presented in this paper applies this approach to floods in Sri Lanka. It combines flood maps from a global model developed by SSBN (now Fathom) with the Sri Lanka Household Income & Expenditures Survey (HIES) to model flood impacts at the household level, using an agent-based model to represent the recovery dynamics [1]. The full methodology and detailed model description is provided in the technical appendix to this document, and in [2].

The analysis provides information to better target DRM interventions both spatially (where should we act?) and sectorally (which type of intervention is the most efficient?). Compared with previous work in Sri Lanka, the analysis innovates by measuring disaster risk with several metrics that complement asset losses (or the cost to repair or replace damaged buildings, infrastructure, productive equipment, etc.). These metrics include (1) traditional asset losses, which highlight the experience of the wealthy; (2) poverty-related measures such as poverty headcount, which highlight the experience of the poor and near-poor; (3) wellbeing losses, which provide a balanced estimate of the impact on poor and rich households; and (4) socioeconomic resilience, an indicator that measures the ability of the population to cope with and recover from asset losses. This broad perspective is intended to complement traditional, more spatially-detailed risk assessments in Sri Lanka.

One major conclusion of this work is that alternative and new metrics of disaster impacts–including poverty headcount, poverty gap, and wellbeing losses–can be used to quantify the value of interventions currently outside the traditional riskmanagement toolbox. Asset-focused risk-management strategies focus primarily on protection infrastructure, such as dikes, and the position and condition of assets, for instance with land-use and codes. can plans building Wellbeing-focused strategies utilize a wider set of available
curves show, for each of the data year, the predictions from a fractional polynomial regression of the share of top decile pairs on distance. If buildup were distributed randomly across space, we should observe a flat curve. This is not what we see: as in Figures 9 to 12 we find evidence of agglomeration, which is strongest at distances up to around 60 km, which is the range of distances covered in Figures 11 and 12. Correlation in high buildup falls after that, but only gradually – suggesting that agglomeration continues to have an effect on buildup even at long distances.

# **5 Conclusions**

Developing countries across the world experienced strong population growth during the last four decades. With population growth, urbanization also picked up pace with half of the developing world’s population now living in urban areas. The accelerated urbanization has received considerable attention in the Africa region where urbanization has not been associated as strongly with economic growth as observed historically in developed countries. Less is however known about how human settlements expanded geographically along with population growth and increased urbanization. In this paper, we use high resolution satellite data on built-up areas within micro-pixels to study the evolution of human settlements in Ghana over a 40 year period.

The empirical analysis of built-up data produces a couple of substantive findings. First, we find a strong increase in built-up area over time, mostly concentrated in the vicinity of roads, and also directly on the coast. The proportion of fully built cells increased substantially at the expense of un-built cells leaving the distribution of partially built-up cells unchanged. We find strong evidence of agglomeration effects both in the static sense – buildup in one cell predicts buildup in a nearby cell – and in a dynamic sense – buildup in a cell predicts buildup in that cell later on, and an increase in buildup in nearby cells. These effects are strongest over a 3 to 15 km radius. Within the distance windows analyzed (upto 60km), we find no evidence that human settlements are spaced more or less
The education situation of Pakistan is generally poor in absolute terms, relative to other countries in its region, and relative to developing countries at its level of per capita income (see, e.g., United Nations Educational, Scientific and Cultural Organization 2007 for a recent comparative picture). Given the present trend, Pakistan is unlikely to meet the United Nations’ Millennium Development Goal of universal primary education by 2015. The education situation of Punjab—Pakistan’s most prosperous and populous province and the site for the FAS program—is by and large comparable to the rest of the country. As Panel A in Table 1 shows, estimates using household sample survey data from 2004/05, which is just before the FAS program was initiated, show that the participation rate in formal school (grade 1+) of children ages 6–15 years was 65.7%, with a significantly lower share for children from the poorest (bottom expenditure quintile) households.

The public school system has been hampered in its ability to improve education outcomes due to, in large part, the lack of effective accountability and incentive systems which promote the legitimate and efficient use of allocated resources (Social Policy and Development Center 2003).<sup>3</sup> While the public school system has struggled to enroll children and educate them, the private school system has grown dramatically as reflected by the number of institutions and share of enrollment. In addition, responding to the broad demand for greater access and better quality, the system has evolved in character, becoming less elite and more egalitarian, increasingly

> 3 Recent evidence on relative teacher performance in private and public schools in Punjab suggests this. Andrabi et al. (2008a) find that, while public school teachers are on average significantly better paid and have higher levels of education, training, and experience than their counterparts in private schools, the teacher absenteeism rate is 15% in public schools compared to 8% in private schools. They also find that,
balance sheet and fully or partially implementing a SALM framework have well developed governance and institutional structures. As indicated by the survey respondents, countries face a number of challenges in the implementation of a SALM framework and production of a sovereign balance sheet, chief among them the complex institutional arrangements and coordination between actors that is required. As demonstrated in the results, the objectives, main actors, reporting format, and metrics used to produce the sovereign balance sheet vary widely across countries. At a fundamental level, data availability and valuation of the assets (and choosing which assets and liabilities to include) remain key constraints.

> 5 See Annex 1 for the list of responding countries.

> 6 See Annex 2 for the survey.

> 7 Among the 28 countries, one responded only to the first section of the survey covering the sovereign balance sheet. The remaining 27 responded to the full survey.

6
The theoretical and empirical literature on the impacts of pandemics originates from empirical work on global pandemics episodes such as the Spanish flu in 1918-20, SARS in 2003, MERS in 2012, among others. Varieties of this strand of economic research have recently expanded due to the ongoing COVID-19 pandemic. This section focuses on a brief literature review of the growth effects from COVID-19 across developing countries.

An assessment of the economic contraction and mortality during the Great Influenza Pandemic of 1918-20 is provided by Barro, Ursúa, and Weng (2020). Using data for 48 countries, the authors estimate that about 2.1 percent of the total population may have died due to the Spanish influenza. Contractions in economic activity and consumption for the median country were estimated at 6 and 8 percent, respectively. Additional evidence shows that the economic disruption associated with the 1918-20 Great Influenza pandemic could be mitigated by the implementation of nonpharmaceutical interventions (NPI) such as social distancing.<sup>5</sup> Recent evidence documents the impact on economic activity of five recent global pandemic and epidemic episodes in the twenty-first century; namely, Severe Acute Respiratory Syndrome (SARS) in 2003, Swine Flu (H1N1) in 2009, Middle East Respiratory Syndrome (MERS) in 2012, Ebola in 2014, and Zika in 2016.<sup>6</sup> When looking at the distribution of growth rates among two groups of countries (disease at the onset year of all affected countries vis-à-vis countries in nondisease years and unaffected countries in disease years), the evidence shows that: (i) the average growth rate of economic activity in country-year pairs unaffected by the crisis is 3.96 percent, and it slows to an average rate of 0.77 percent during pandemic episode years; (ii) the distribution of growth rates is left-skewed during pandemic years (reflecting negative outcomes) while that of non-pandemic country years is right-skewed.<sup>7</sup> Panel evidence from 210 countries from 1960 to 2018 shows that the global
of proportionality in a strongly relative poverty line based on these data using the regression coefficient it would be 0.382 (s.e.=0.021).<sup>13</sup> However, it is clear from looking at Figure 1 that neither a constant elasticity nor a (homogeneous) constant slope functional form fits these data well, as already noted. The slope (and hence elasticity) is essentially zero amongst the poorest 20 or so countries, where absolute poverty clearly dominates. The data on national poverty lines are more suggestive of model in which the elasticity starts from roughly zero but rises to something close to unity.

Quite generally one can think of the relative poverty line as a non-decreasing function of the country and date specific mean, _M it_ for country _i_ at date _t_ .<sup>14</sup> Write this function as _Z_ ( _M it_ ) .

> 12 The regression coefficient of the log poverty line on log consumption per capita is 0.653 (s.e.=0.048).

> 13 The intercept was suppressed to assure that the calculation was consistent with a strongly relative line.

> 14 We assume that the mean is the relevant parameter rather than the median. However, counterarguments can be made and there has been some debate on this choice (Saunders and Smeeding, 2002; Easton, 2002; de Mesnard, 2007). The median is more robust to measurement errors at the extremes, although poverty lines set as a constant proportion of the median can have perverse properties when the Lorenz curve shifts (de Mesnard, 2007).

8
inaccuracy in the amount of taxes remitted. Relatedly, the inability to match taxpayer-level data from PAYE to other data sets (such as income tax returns) makes it difficult to add up individuals’ total income and enforce progressive income tax rates.

A second concern revealed in the Ethiopian context, but common in many others, is that PAYE data is not fully digitized. Many returns are kept in paper form, prone to loss or deterioration with time and making it very unlikely that this data will be analyzed (Mascagni and Mengistu, 2019).<sup>7</sup>

# **5.2 Corporate and Personal Income Taxes**

Different technological interventions, such as EFDs and e-filing/e-payment services, have been introduced in Africa with the goal of facilitating compliance of self-declaring business income earners who are remitting corporate or personal income taxes. In Section 2, we discussed the potential benefits and key challenges for VAT compliance brought by the introduction of EFDs, which apply to compliance with income taxes as well. Furthermore, it is still unclear whether the revenue gains on VAT attributable to EFDs would then produce a parallel increase in income taxes. If anything, in Ethiopia, EFD users only partially improve their filing of income taxes, despite significant rises in VAT reporting (Mascagni, Mengistu and Woldeyes, 2021). Such gaps in compliance also depend on the sophistication of the IT system within the tax administration, which would limit discrepancies. In Rwanda, as discussed in more depth in Section 6, EFD data is used to benchmark self-reports from income tax returns.

Regarding the potential role of IT to facilitate compliance of income taxpayers, a first strand of the literature considers factors associated with taxpayers’ adoption of and practical experiences with tax e-services. Box 4 summarizes this evidence.

> 7Statistical analyses would be a rather frustrating task if based on analog filings, which make impossible to precisely trace and identify employees across periods and firms.

21
of district casualties and unobserved district-time-varying factors (after controlling for region-specific year fixed effects), _β3_ identifies the average treatment effect of exposure to conflict on those exposed. I also estimate variants of Equation (1) in which I add a regressor capturing specific aspects of the conflict which are likely to have affected schooling outcomes. I include, in turn, the total number of school destructions (per 1000 inhabitants) during the conflict, and the number of abductions by Maoist forces (per 1000 inhabitants) during the conflict. In addition, I consider whether Maoist control over the district had an effect on primary attainment by estimating variants of Equation (1) in which I replace the casualties measure by an indicator variable switching on when the district was under Maoist control.

> 2 In the 2001 DHS, 44.4% of 5-year olds, 24.6% of 14 year olds, and 13.6% of 15 year olds are enrolled in primary schooling.

15
.073|
|Mean Dependent Variable|0.011|8.385|8.326|
|Observations|5,592|5,453|5,558|
|_Controls in All Panels_||||
|Individual Covariates|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|

_Notes:_ The Income and Consumption Index is constructed using the outcome variables of columns (ii) to (iv) using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again. See Appendix B for details of variable’s constructions. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

46
propensity score matching estimators to control for a variety of observed characteristics measured at the municipality, barangay<sup>2</sup> , household and child levels and unobserved fixed characteristics. We examine intent-to-treat impacts by child age, and because program implementation was not instantaneous, we also consider the length of exposure to the program. Our results indicate that the program has had important positive impacts on children’s cognitive, motor, language and social development and short-run nutritional status, with some suggestion that duration of exposure increases the program impacts. The effects are particularly important for younger children (below four-years-old at the time of the evaluation).

# **Evaluations of ECD programs**

There have been few systematic evaluations of ECD programs in developing countries that also use longitudinal data to control for the behavioral aspects of investments in preschool human capital; three are discussed later below. However, several programs in the United States that have been evaluated provide important lessons. The High/Scope Perry Preschool Study (Michigan) is a study of the effects of a high-quality preschool program for children born in poverty. It randomly assigned 123 children to program or noprogram control groups, and followed these children with respect to a variety of outcome measures from age 3 to age 41. From October through May each program year, program teachers conducted daily 21⁄2-hour classes for children on weekday mornings and made weekly 11⁄2-hour home visits to each mother and child on weekday afternoons. Program evaluations have found gains in children’s readiness for school and their subsequent educational success, economic success in early adulthood (even home ownership), and reduced number of criminal arrests throughout their lives (Schweinhart and Weikart 1998; Belfield and others 2006). In addition to direct benefits to participants, the general public gains also in terms of higher tax revenues, lower criminal justice system expenditures, and lower welfare payments, repaying $12.90 for every $1 invested, easily outweighing program costs. In contrast to the Perry program, the
θθ) = γγaa (γγ + γγ∂ (h) 1(kk) + γγ0 1α (kk)<sup>2</sup> (h,kk,θθ) γγ2αθθ)<sup>−1</sup> 3 which is a linear function with wealth and wealth squared as independent ∂∂h ∂∂h h(θθ, kk) = ∂∂h γγaa (γγ + γγ (kk) + variables, In other words, the functional form of the skills cost function is based on typical, linear bb γγ (kk)<sup>2</sup> + γγ θθ) + γγ cognitive production function estimated with data on test scores (Todd and Wolpin 2003). Parameters and are changed to provide a counterfactual skills cost function as described below. They are assumed to be equal to 1 and 0, respectively, in the baseline case. aa bb γγ γγ This education cost is the monetized cost of an individual investing in skills. It might represent tuition payments or foregone earnings from attaining a higher level of education, but it would also reflect intangible costs including the disutility of effort invested by an individual into studying, for example. This cost whether tangible or intangible is monetized because of the equilibrium condition requiring that the marginal cost of skills equals the marginal benefits, which is the wage skill premium. As discussed below, in steady state, the skills cost function represents the efficiency of the education system to produce skills conditional on an individual’s level of family wealth and 8
Table 1: Exposure to agricultural advice in last 12 months

|Description|Full<br>Sample|Malawi|Nigeria|Uganda|
|---|---|---|---|---|
|Households who received agricultural advice (% all HH-year obs.)<br>_of those:_|28.7|46.5|16.7|26.0|
|Average number of contacts|4.4|4.1|5.4|4.1|
|More than 2 contacts (%)|50.6|52.8|60.9|41.1|
|More than 10 contacts (%)|8.5|5.6|12.5|9.1|
|Received advice rated as useful or very useful<sup>_a_ </sup>(%)|84.6|80.0|91.2|87.0|
|Received advice rated as useless or bad<sup>_a_ </sup>(%)|8.0|8.8|3.3|9.9|
|Paid in order to receive advice (%)|7.6|0.9|14.2|13.5|
|Households who solicited advice (% all HH-year obs.)|19.5|35.0|12.4|15.3|
|Households who solicited advice (% HH-year obs. with advice)|76.0|89.3|76.5|60.1|
|Share of solicited contacts (% all contacts)|73.6|80.8|64.2|75.8|
|Observations|24,526|7,222|8,844|8,460|

_Source:_ Authors’ calculation based on survey data from the LSMS-ISA database (World Bank, 2018). _Notes:
mechanisms through which infrastructure disruptions affect households and cover four main networked infrastructure systems: electricity, potable water, transport, and communication. When possible, it estimates the economic cost of infrastructure disruptions through the willingness-to-pay of households to avoid them. While highly uncertain, these estimates can provide a sense of the magnitude of the problem.

# 1. The impact of electricity outages

## 1.1. Electricity supply is often unreliable with measurable consequences on households

In many emerging countries, power outages are regular occurrences. As shown in Figure 1, many countries experience more than 100 hours of outage per customer and per year<sup>2</sup> , on average. Poor countries tend to experience more outages, even though the variance is very large, especially at low income levels. The figure at the bottom illustrates the number of outages per customer per year on average in relation to electricity access as percent of population. It shows no clear relationship between coverage and reliability – many of the countries with high coverage also have poor reliability. However, countries with low access to electricity more consistently tend to also have poor reliability.

Outages are not the only disruptions of the electric systems. _Brownouts_ refer to a drop in the voltage of an electric system, which constrains the use of high-voltage appliances (such as refrigerators, televisions, and air conditioners) and often result in malfunction of electric appliances. However, we explore here the impact of power _outages_ , i.e. complete interruptions of power supply through the grid.

> 2 Data retrieved from the Doing Business study http://www.doingbusiness.org/en/data

4