rafmacalaba/data-use-mentions-extended huggingface dataset:
13 areas. While the first result does not differ from that obtained using Kakwani's formulae, the second does, so that marginal proportional rates of substitution obtained using (11) would differ from Kakwani's. What is the net impact of growth on poverty? It can be found by using (10) or by estimating: LogPkt =g+LogWkt + k +lkt (12) The results are still given in Table 6 and they are illustrated in Figure 2, 4, and 6 for the headcount with the lower poverty lines. Nationally, when factoring in the impact of growth on inequality, a one percentage point increase in growth reduces the headcount index by 1.98 to 2.03 percentage points with the lower poverty lines, and 1.29 to 1.37 points with the upper poverty lines. The impact of growth on higher order poverty measures is similarly reduced as compared to what was obtained holding inequality constant. About one fourth of the potential gains from growth for poverty is lost due to higher inequality.

W.Whilth **e** results for the headcount index are similar in urban and rural areas, they differ for higher order poverty measures which are more sensitive to inequality. Consider the squared poverty gap with the upper poverty lines. The net elasticity of poverty to growth obtained with the fixed and random effects models in urban areas are -2.51 and -2.53, much below the gross elasticities at -3.53 and -3.52. In rural areas the net elasticities, at -3.50 and -3.59, are virtually equal to the gross elasticities, at -3.62 with the two models. This confirms that rural growth reduces inequality-sensitive poverty measures more than urban growth simply because growth is more associated with inequality in urban than in rural areas.

# _IV 3 Possibilitiesforfurther work_

The regressions used above remain descriptive in that we did not attempt to investigate the potentially complex relationships between past, current, and
24|13|13|18|14|23|
|Percentage of electricity from generator|**18**|—|19|3|3|11|6|11|19|11.3|
|Delay in obtaining an electrical connection<br>(days)|**25**|14|92|98|19|13|9|16|97|42.6|
|Percentage of firms identifying electricity as<br>a major constraint|**7**|44|55|60|43|25|6|21|12|30.3|

Source: Enterprise Survey Database (www.enterprisesurveys.org). _Note:_ Year of the survey is in brackets.

In this context, the BPC was able to attain high efficiency and profitability while providing low-cost service. Collection-to-billings ratio has been nearly one to one. System losses have been also quite good—barely above the 10 percent efficient power utility benchmark. Tariffs are somewhat below cost recovery levels with the gap having widened slightly in recent years (table 11).

The BPC is one of the most efficient utilities in southern Africa, as measured by the hidden costs of its inefficiencies in past years (box 2). These inefficiencies represent about 14 percent of the BPC’s revenues, just around one-seventh of those of Zambia (figure 12a).

**Table 11. Evolution of hidden costs associated with the Botswana Power Corporation (BPC)**

||Volume of<br>electricity<br>produced /<br>purchased|System<br>losses|Collection<br>ratio|Average total<br>cost|Average<br>effective tariff|Total hidden<br>costs|Total hidden<br>costs|
|---|---|---|---|---|---|---|---|
||
implemented here follows closely the methodology suggested by Fay and Herriot (1979), in which a linear mixed model establishes the relationship between the welfare parameter and auxiliary subgroup level information.

> ′ (2) dd dd dd where is the mean income, YY = XX is a vector of characteristics, and  ββ+ uu ,         dd= 1, ... , DD, is a vector of random effects with mean zero and variance YYdd XXdd2 that account for additional variability that is not explained uudd uu by in subgroup _d_ . These characteristics are found in the most recent census (or the one closest σσ dd to the most recent available household survey) and are country-specific. However, most countries XX provide information of characteristics at the household and individual level such as geography, access to utilities, ownership of appliances (and/or amenities), dwelling features, demography, fertility, mortality, education, and disabilities.

Given that the household income parameter, , is not available in the census, it must be dd estimated from the household survey by, YY (3) dd dd dd where is the sampling error associated with the estimation of YY<sup>�</sup> = YY + ee , such that the direct dd dd estimator is assumed to be unbiased, ee ) = 0, and the variances YY<sup>�</sup> , are assumed to be dd dd dd known, . Thus, by combining equations (2) and (3) we obtain the following mixed EE(ee |YY φφ dd dd dd linear model, VV(ee |YY ) = φφ ′ (4) dd dd dd dd To obtain the _empirical best linear unbiased predictor_ YY<sup>�</sup> = XX ββ+ uu + ee(EBLUP) of , which is unbiased and dd minimizes the mean squared error (MSE) of the model, Fay and Herriot (1979, 270) suggest YY<sup>�</sup>

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size estimates, our review also did not capture emerging approaches to assessment of unhealthy diets and associated outcomes that may be well-suited to monitor the rapid nutrition transition faced by several countries. For example, two relatively new metrics, the Global Dietary Recommendations Score (Herforth et al. 2020) and Global Diet Quality Score (Bromage et al. 2021), have been validated in several countries and are increasing in use in LMICs (Angulo et al. 2021; H. Wang et al. 2022; P. H. Nguyen et al. 2023). Both approaches utilize two sub-scores to monitor dietary factors protective against NCDs and those associated with increased risk of NCDs.

The results of our study provide an overview of the effect sizes for some of the dietary ‘culprits’ in both highincome and LMIC contexts. While consumption of UPF was not historically a major concern, the average dietary share of UPFs has already reached over 50% of total energy intake in some high-income countries and consumption of these foods has been rising quickly in LMICs over the past 30 years (Scrinis and Monteiro 2022). The summary of effect size ranges from SRM that include data from LMICs provide a starting point for better understanding what links exist and how these could be used to inform modeling assumptions on the nutrition and health costs associated with unhealthy diets in LMICs.

No meta-analysis for UPF as a risk factor for overweight/obesity in children <5 years was found in our search, however. UPF consumption among young children has been an active area of investigation and there are several systematic reviews of observational studies. For example, Pries and colleagues (2019) conducted a systematic review of snack food and SSB intake and nutritional status of children 0-23 months in LMICs, noting mixed results, with only one of three studies reporting a positive association with overweight. Two other systematic reviews also explored the risk of obesity (De Amicis et al. 2022) and body fat (Costa et al. 2018) associated with UPF consumption in children. In a
To expand the data coverage to the 19th century, we supplement this source with information from the National Center for Environmental Information, which lists events of earthquakes, volcano eruptions, floods, tsunamis, and storms going back until 1800. In addition, we make use of numerous historical and country studies on famines and epidemics during the 19th century, as listed in Appendix I.

Table 1. Number of disaster events in different eras: 1800-2015

|1800 - 1913<br>Long 19th Century|1914 - 1945<br>Inter-War Period|1946 - 1973<br>Bretton Woods|Modern Era<br>1973 - 2015|
|---|---|---|---|
|Financial crises<br>254|235|164|416|
|Severe natural<br>disasters<br>44|28|20|16|
|Military conflict<br>157|70|104|154|
|Total number of<br>disaster events<br>455|333|288|586|

_Sources:_ See text and Appendix I for details.

The resulting dataset identifies several thousand natural disasters since 1800. Given our focus on disasters that warrant the provision of international financial assistance, we focus only on the most severe events. To do so, we match our list of natural disasters with population data from Mitchell (2007) and include only those natural disasters that result in casualties of at least 0.1 percent of the population. This yields 108 severe natural disaster events over the course of the past 200 years. While the timing of the disaster event is straight-forward with respect to earthquakes, floods or hurricanes, it can be difficult to accurately measure the duration of famines and epidemics. For these episodes, we consider the initial two years of the outbreak.

Table 1 summarizes the number of disasters and their frequency across different eras. Figure 11 also plots the incidence of disasters but takes a different perspective. It shows the share of
“ VL 'g<br>Sr 7 os<br>=oO - +o$<br>©~ / \ A sor < -- v : | *N=<br>21 4 1 Pet ba db,<br>1995 2000 2005 2010 2015<br>year<br>——— Hearing-aid —---—- Synthetic hearing-aid<br>[ p-value (RHS)<br><!-- End of picture text -->

<!-- Start of picture text --> RCA<1 Countries (weighted)<br>re} a7 2<br>a y aN \-sQNU- N<br>ca8xD Z Z oos3<br>& , $<br>£ it =o<br>re} fo- 7 -,<br>° __7 N<br>2<br>foe) , | o-<br>1995 2000 2005 2010 2015<br>year<br>——— Hearing-aid —---—- Synthetic hearing-aid<br>[ p-value (RHS)<br><!-- End of picture text -->
similar as possible (i.e. high intra-class similarity) whereas objects from other clusters are as dissimilar as possible (i.e. high inter-class dissimilarity). The clustering algorithm uses Euclidean distance D(x, y) to measure the degree of similarity between two variables, x and y over N dimensions such that: This resulted in a list of 3 clusters which are reported in Table 2.<sup>15</sup> Even a cursory examination of Table 2 is sufficient to establish that the clusters, whether based on GDP per capita or HCI ranking, are essentially similar. Cluster 1 countries have HCI scores around .80, Cluster 2 countries generally have scores above .60 and Cluster 3 countries have scores below .60. There are, however, some anomalies. Vietnam is clearly a striking anomaly. Vietnam’s per capita GDP places it in Cluster 3 but its HCI score of .69 should easily place it in Cluster 2. The reason for Vietnam’s high HCI score has to do with its superior performance on harmonized test scores and LAYS. Vietnam’s harmonized test score of 519 and LAYS score of 10.70 places it just below Cluster 1 countries. Uzbekistan also has a HCI score of .62 due to its test scores but its per capita GDP places it among lower middle-income countries.

Among the clusters, Cluster 3 contains the largest sample of similar countries. In the remainder of the paper, we focus on this cluster.

> 15 It is interesting to note that countries in Cluster 1 held the top positions in the HCI 2020 index. Singapore (1), Korea, Rep. (2), Japan (3) and Hong Kong SAR, China (4).

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Once the hurdle rates has been estimated, i.e. the cost of capital and its components including the cost of equity, it is useful to try to generate estimates of the rates of returns on assets and on equity to get a sense of the extent to which risks are consistent with the returns earned. To do so we rely on accounting data to measure each company’s overall return on the capital invested in each infrastructure project since 1998.<sup>12</sup> In theory, several measures of the effective returns earned by the operators can be used such as the return on assets, the internal rate of return or the return on equity. The first two measures can however not be estimated due to data problems. We have to rely instead on the return on equity (RoE) which is the least complete indicator of the three. The return on equity (RoE) is a measure of the after tax return the company is earning on its equity capital. It reflects the profits a company is able to generate given the resources provided by its shareholders. It is the ratio of the project’s net income divided by the shareholders’ equity investment in the project and can be expressed as: where: Shareholders’ equity = total assets minus total liabilities ; and, Net income = after-tax profit The main problem with this measure is that it is unclear whether it underestimates or overestimates the returns of a business. First, in the short run, it tends to overestimate returns because it assumes that all the income generated by a project represents compensation for shareholders. Indeed, at least in the early years of a project, investors receive only a portion of a project’s net income. The rest is reinvested in the company and produces income for investors only when the company is sold or transferred back to the government—provided that transaction occurs at a market price higher than the initial investment and shareholders are compensated for the value they created by reinvesting what otherwise would have been compensation
used to incorporate macroeconomic and demographic information and address data constraints, such as missing data from non-survey years (ILO 2010). However, labor market forecasts by occupation, training type or educational attainment level are not standard, primarily due to a lack of data and difficulties in consistently integrating data into economic projections (Maier, Monnig and Zika 2015).<sup>18</sup> In this context, the use of online data for “nowcasting”<sup>19</sup> and forecasting offers new ways to analyze labor market trends and skills demand, which can largely overcome the subjectivity and time-lag issues associated with workforce surveys. For example, Askitas and Zimmermann (2009) used Google keyword searches to conduct a predictive analysis of unemployment rates in Germany. In the wake of the 2008 global economic crisis, this innovative method for using online data proved to be a valuable tool to predict economic behavior, and strong correlations were discovered between keyword searches and unemployment rates. More recently, Vicente, Lopez-Menendez and Perez (2015) examined the possibility of using internet search data and business-confidence indicators to predict unemployment trends in Spain.

# **_Application of the Analysis to Babajob Data_**

Online job-portal data can provide a continuous source of up-to-date, objective information on the demand for and supply of skills in different industries, occupations or locations. A simple predictive analysis using Babajob data has the potential to forecast trends in three variables, including the demand for skills as listed in job postings, the supply of skills as reported in job seeker profiles, and the wages being offered for new positions. However, without the presence of relevant and timely external data for cross-checking the composition of skills demand and supply against, nowcasting or forecasting the skills demand and supply in quantity is constrained.<sup>20</sup> The analysis of wage offers is the most robust among the three variables, considering that the wage offers posted on Babajob are influenced by the labor market for its competitiveness.

# **
reports represent the most relevant antecedents to the novel indicators proposed and tested in this report. First, the World Bank has created a measure of legislative predictability for Romania and Türkiye that focuses on the count of amendments and repeals over time (World Bank 2019, 2021). In the case of Romania, the World Bank relied on the number and frequency of amendments and repeals to show changes per year, trends over time, comparisons between different year intervals, as well as amendment patterns across different sectors. For Türkiye, the World Bank analyzed the number of annual changes and average changes over larger time periods with a further breakdown of these patterns by type of legislation. One of the key insights of these analyses was that they allow for an identification of the sectors where unpredictability was the highest. Second, Brenner & Fazekas (2023) uses legislative data on France, Hungary, Italy and the UK, to develop new indicators of legislative predictability focusing on the timing of first modification and number of total modifications.

While our main innovation is the development of legislative predictability indicators, we also propose a number of variables which are essential for exploring the prevalence, distribution and trends of legislative predictability (Table 1).

**Table 1. Indicators used in the analysis (unit of analysis=individual law)**

|**Name**|**Description**|
|---|---|
|Modification count|Total count of modifications a law/regulation received until 2022|
|First modification date|Date when the law/regulation was first modified|
|Category|Type: law or regulation|
|Date passing|The date a law/regulation was published|
|Title|Title of a law/regulation|
|Text|Full text of a law/regulation|
|Text size|Size of the text (expressed in number of characters)|
|Policy Production|Number of laws/regulations passed in a year (level of country-year)|
|Related laws
this credit for sanitation investments. This could be explained by differences in availability of information at different times in their sequential decision making process. The household needs to make two sequential decisions. First, they need to decide whether or not to take up a sanitation loan. Second, they need to decide whether or not to use the loan for sanitation investment. The former decision is made before the start of the toilet construction works, at a time when the household does not necessarily have full information about either the true costs of toilet construction and/or about the actual delay in subsidy disbursement that it will face. The decision therefore depends on the household’s ex-ante beliefs about these benefits and costs, and on the risks they take by taking up micro-credit whilst potentially getting these beliefs wrong. The latter risk depends, in turn, on the extent to which the household expects to be penalised if it ends up using the loan for non-sanitation purpose. In a context where loan use is not strictly monitored and not enforced, households might decide to apply for the sanitation loan - and more so if larger delays in subsidy disbursement are expected to allow for bridge funding - with the prospect to give up on their plans if the actual sanitation investment costs (toilet costs and interest payments) turn out to be high relative to the perceived benefits. This could explain the finding of (i) relatively high impacts on sanitation loans and (ii) zero impacts on toilet uptake in areas with short delays in subsidy disbursement. In the next section, we explore the extent to which variation in toilet prices confirm these results.

# **Provision of supplementary funding to subsidy eligibles**

The second channel we consider for eligible households relates to the fact that most Indian households prefer to construct expensive toilet types, typically sceptic tanks and toilets with a nearby bathroom (Coffey et al., 2014, Coffey and Spears, 2017). Coffey et al. find that the typical household in their survey of 5 Northern Indian states would
one question on interest rate caps and pricing limits on loans (GFICPS 2017);<sup>7</sup> and with Reifner et al. (2010), who collected comprehensive data on interest rate caps in the EU. We gave preeminence to citations to laws and regulations, creating a repository for these as a complement to our survey. In total, we received responses from 108 countries, which were provided by 91 central banks and financial supervisory authorities, 20 local banking associations and 9 World Bank financial sector experts. These 108 countries represent 88 percent of the global GDP and the sample is balanced in terms of levels of income and geographical representation. The information from the responses refers to 2019. The full list of surveyed countries is presented in the annexes.

IRCs are common in countries across different income levels and legal systems though some regional differences emerge. Table 1 presents a general overview of IRCs in the 108 countries grouped by region, income level and legal system.<sup>8</sup> A total of 63 countries responded that they have IRCs in place. Both high income countries and low and middle income countries show a similar prevalence of IRCs at about 60 percent of their respective surveyed samples.<sup>9</sup> This even distribution across income levels is also reflected in a GDP-weighted average of 66 percent of the sample with IRCs, though this hinges on the fact that China had unwound its IRCs by 2019 and our survey does not consider the recent policy response to Covid-19.<sup>10</sup> The share of countries with _de jure_ IRCs also remains roughly unaltered when countries are divided according to their legal system, though we observe a relatively higher prevalence of IRCs in common law countries.<sup>11</sup> Differences appear when the regional dimension is introduced, with a bigger fraction of high income relative to low and middle income countries in Europe & Central Asia reporting IRCs, while the opposite is observed in East Asia &
1,102|
|Index of improvements|0.01|(0.04)|[1.00]|0.16|(0.23)|1,102|
|FR childcare and chore hours|-3.66|(2.86)|[1.00]|19.51|(18.70)|1,070|
|HH childcare and chore hours|1.25|(5.31)|[1.00]|32.70|(25.24)|681|
|Any domestic employees|0.01|(0.02)|[1.00]|0.02|(0.15)|1,102|
|N domestic employees|0.02|(0.03)|[1.00]|0.02|(0.15)|1,102|
|N Syrian domestic employees|-0.00|(0.01)|[1.00]|0.01|(0.07)|1,102|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022 neighbor survey data. Each row is its own dependent variable.

- The outcomes that require definitions are: Housing expenditure is the sum of monthly rent paid, mortgage paid and money spent on housing upgrades, divided by household size. Rent paid corresponds to the monthly rent paid by households who rent. Dwelling value corresponds to “If you were to sell your house in the next month, how much money do you think it would sell for? In other words, what is the current market value of your home?” =1 if .. paid for any improvements corresponds to “ Have you or an NGO made (or paid for) housing improvements where you live since October 2019?” Index of improvements is an index of structural, cosmetic, mold
by the report’s peer reviewers: Kirsten Hommann, Emmanuel Skoufias and Eric Dickson.

# 5 References

Erman, A. E., Motte, E. G., Goyal, R., Asare, A. B., Takamatsu, S., Chen, X., ... Hallegatte, S. (2018). The road to recovery: the role of poverty in the exposure, vulnerability and resilience to floods in Accra. Policy Research Working Paper Series.

Hambati, H. (2013). Weathering the storm: disaster risk and vulnerability assessment of informal settlements in Mwanza City, Tanzania.

Kebede, A. S. and Nicholls, R. J. (2012) Exposure and vulnerability to climate extremes: population and asset exposure to coastal flooding in Dar es Salaam, Tanzania. Reg Environ Change 12:81-94 Kiunsi, R. (2013) “The constraints on climate change adaptation in a city with a large development deficit: the case of Dar es Salaam” Environment and Urbanization 23(2):321-337 Maxwell, D., Caldwell, R. (2008) The Coping Strategies Index – Field Methods Manual. 2<sup>nd</sup> Edition. USAID, WFP, CARE International, Feinstein International Center, TANGO.

Picarelli, N., Jaupart, P., Chen, Y. (2017) “Cholera in times of floods – Weather shocks and health in Dar es Salaam” International Growth Centre Working Paper. Reference number: C-40404-TZA-1 Ramani Huria (2018). Community flood map. Data collected in 2014-2015.

Ramani Huria 2.0 (2018). Maps and data collected by Ramani Huria team managed by the Humanitarian Open Street Map team.

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