rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Climate change is a threat to the objective of sustainably eradicating poverty. Poor people are more often and harshly affected by climate change as they have fewer mechanisms to cope, more losses, and less support to recover. But even those not in poverty can suffer negative impacts that condition their future welfare prospects. Climate change can affect people's wellbeing through various channels, and it can also have an impact on natural disasters, leading to further negative consequences for people. The identification of the specific impact of climate change on natural disaster events such as floods, landslides, droughts, and storms is challenging and complex to estimate. However, as Hallegatte (2014) mentions, climate change has played an increasing role over time, dramatically escalating natural hazards, and displaying heterogeneous effects on people's well-being depending on the type of hazard. In Latin America and the Caribbean (LAC), the acceleration of climate change has shown a sharp increase in the frequency and intensity of extreme weather events (World Bank, 2022). In recent years, some of the deadliest natural catastrophes have occurred, including the earthquakes in Haiti (230,000 deaths in 2010 and 2,248 deaths in 2021), the Brumadinho mudslide in Brazil (300 deaths in 2019), and hurricanes Maria (3,059 deaths in 2017) and Eta (183 deaths in 2020) (UNDP, 2022). The number of people in the region affected by natural disasters has also increased substantially over the last decades. Between 2000 and 2019, the Emergency Events Database (EM-DAT) indicates that 41 million people have been affected by floods, while droughts have impacted the lives of approximately 53 million people (GuhaSapir, et al., 2022). In this study, we look at natural disasters and the mechanisms of households to cope with them.

This study is framed within a climate risk framework, with a specific focus on the household perspective. As per the terminology of the Intergovernmental Panel
score. Quantity and quality are linked to prevent providers from providing higher volumes of low-quality services. Quantity indicators are reported by health facilities and verified by district health teams and the State Health Activities Supervision Services (SHASS) agency by review of registers and patient records. Quality of care is scored by a quality checklist conducted during the SHASS agency visits. The UNICEF country office was contracted to conduct additional external independent counter-verification to confirm performance indicators. A maximum of 70% of the quarterly PBF payments to the facilities can

> 1 World Bank, World Development Indicators.

> 2 World Bank, World Development Indicators, the latest estimation available is for 2016.

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Policy Research Working Paper 5905

# **Abstract**

This paper provides a methodological tool to support the collection and preparation of standardized, comprehensive data regarding public spending on infrastructure services that can be rigorously compared across countries. Infrastructure is defined to cover six sectors: irrigation, energy (primarily power), transport, communication, wastewater management, and water supply.

The guide is designed to provide a much richer and more complete measurement of infrastructure spending than the limited highly aggregated data currently available through the IMF Government Financial Statistics. Originally developed for Africa, the methodology is relevant and readily applicable to any developing country.

With the aim of being as comprehensive as possible, the methodology covers central and sub-national government expenditures, non-budgetary vehicles (such as road funds), state-owned enterprises (SOEs), and publicprivate partnerships (PPPs). While the methodology focuses on collecting quantitative data on the level and composition of spending, this is complemented with qualitative data that provides the institutional context. Importantly, the methodology allows for crossclassification of infrastructure spending by purpose (power, roads, etc) and by function (operational versus capital spending).

This guide provides practical guidance—including concepts, definitions, and classifications—for each of the three stages of work, namely: (i) pre-field, (ii) field, and (iii) back office.

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

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry
sup>26</sup> Taking into account the counterfactual families associated with migration suggests several directions and implications for policy efforts. First, many of the details of migration policies will have impacts on decisions of families to form and stay together or dissolve, and if policies take the family as fixed, there may be unintended consequences. The most obvious is spousal or family reunification visa categories, which are typically much easier to migrate through than obtaining work visas into many countries. They have the good intention of wanting

> 26To give just two telling examples, the Mexican census does not provide information on the marital status of current migrants, and the ACS does not provide information on children that do not co-reside with their parents, or on the timing of marriage dissolution.

30
each country’s research agenda, since these infections are not only important in and of themselves but also have significant relevance to the spread of HIV.

## **Bolivia**

In Bolivia, a total of 25 documents were included in the characterization and assessment phase, most of which were technical reports. A quarter of them had not been published at the time of assessment.

Almost all of the studies had a cross-sectional design with different variations, with KAP surveys being the most frequent. In both Bolivia and Peru, the methodological quality of the KAP surveys was inferior to the HIV serological surveys.

Only a third of the studies in Bolivia were done in most-at-risk populations. Among those groups, MSM and female sex workers were prioritized. The conclusion is that the overall prioritization of HIV-related studies in Bolivia does not match the type of concentrated epidemic that is present in Bolivia. This suggests a need to re-orient research objectives to the groups where the epidemic is most concentrated.

Approximately 70% of the studies had high methodological quality. However, this is not true for all population groups—the quality of the studies done in most-at-risk groups is significantly worse than those done in the general population. Of the three Andean countries, Bolivia is the one where this difference is most significant. The conclusion is that the studies done in key Bolivian populations are proportionally less and of lesser methodological quality. As discussed above, this suggests the need to re-prioritize the country’s research agenda towards those groups where the burden of the epidemic is more pronounced.

This analysis did not aim to evaluate aspects related to the standardization of the operational variables. All the same, it is important to point out that a wide variation in the operative definitions used was observed in the region. This variation limits the possibility of comparing the studies either across time or across geographical regions. Each of these countries needs to consider adopting a more consistent use of indicators and stronger prioritization of UNGASS indicators in their respective research agendas. It is important to establish
Further, addressing Markusen and Zahniser’s concern, it does not seem to be the case that the internal migrant population is so significantly distinct from that migrating to the US. Table 3 tabulates the educational attainment, age and gender of non-movers in Mexico and within Mexico migrants tabulated from the Mexican census, and that of recent migrants to the US as tabulated by the US census.<sup>8</sup> Men have similar age profiles although women tend to be somewhat older when they migrate to the US, perhaps reflecting a delayed migration after their spouses. For both genders, however, what emerges is that, though not radically different, the domestic migrants are somewhat less educated than those internationally. Markusen and Zahniser’s concern that FDI and trade would not prove a disincentive to migration because it did not affect the relevant migratory population seems unfounded.

# _Simulations of US impact_

Ideally, we could use the estimates above to offer some guess at the magnitude of the impact on US migration. Clearly, for such an exercise to be relevant, it must be the case that the decision to migrate to the US, or alternatively, the types of people who decide to migrate to the US do not differ too much between the two destinations. As noted above, table 3 suggests that the demographics of the two groups are similar, and the literature is mixed although not necessarily inconsistent with this assumption.<sup>9</sup> It does appear, however, that relative to men, women are more likely to migrate within Mexico, than to the US. This may reflect the strategy of households that migrate to the border to send the husband across to engage in the riskier and more intensive work while the wife

> 8 These findings are very similar to those of Chiquiar and Hanson (2003) using the US census.

> 9 Rivera-Batiz (1986) in an often cited article implicitly suggests that migrants’ indifference between destinations:  “The impact of the maquiladoras on migration to the United
drawing on World Bank's Social Protection and Jobs Policy Inventory data, it offers a descriptive analysis of the UB and ALMP reforms implemented during the Covid-19 pandemic.

## **3.1. Description of the data**

UB and ALMP data were collected through questionnaires distributed to 900 experts in 191 economies in 2019 and 2020.<sup>5</sup> To ensure the comparability of data across economies and over time, the questionnaires are based on a standard assumption that the UB recipient is a 19-year-old former full-time cashier in a supermarket or a grocery store in the largest business city, who has been made redundant after one year of work due to economic reasons. S/he is single with no dependents, not a member of a labor union, unless membership is mandatory, and is not granted more benefits than those mandated by law, regulations, or (if applicable) collective bargaining agreements.

> 5 See Table A1 in the appendix for detailed information on the questions. The cutoff date for data collection cycles in both 2019 and 2020 was May 1. Most of the 900 respondents were the same in both years. In cases where the original respondents were not available, we found new respondents. These experts included labor lawyers, government officials, and other professionals routinely administering or advising on legal and regulatory requirements related to unemployment protection schemes and PES. Several rounds of due diligence were carried out on the data collected through questionnaires, including follow-up questions to the experts through phone calls, videoconferencing, and face-to-face interviews, as well as verification against the laws and regulations of each country. Data were also reviewed by several experts in the World Bank Group.

9
br>|22.0|39.1|

<mark>Vegetable and animal oils 1.8 3.0 5.2 11.5 18.6 34.4</mark> **<mark>Food</mark>** <mark>4.0 7.4 12.9 12.9 22.0 39.1</mark> Note: The formula used to compute the changes in prices is: d i =<sup>∆QQppccddIIppIIQQii</sup> �E . Where i is the importer and k is the product. Direct price effects are calculated at the country-product level using HS4 digit product elasticities from Fontagné, Guimbar and DDppIId II PPIIppd dde II ii Orefice (2019) and Kee and Nicita (2020) and are assumed to be constant. For each product-group, results are aggregated using the share of ccI ppddppIIQQ country-products imports of global imports as weights.

The initial shock of Covid-19 and containment policies on the food export supply in the previous section has been informed by the early data for Chinese exports in 2020 (Section 3). These data indicate a decline in food exports in January and February 2020 year on year of 17 percent. While this is a logical first scenario, there is significant uncertainty on the size of the Covid-19 shock. On the one hand, there are reasons to believe that the shock could be larger for other countries as the health crisis now affects a large fraction of the world economy simultaneously. First, the more global nature of the shock could imply that supply effects through food value chains could be larger, for instance as key imported inputs could be delayed or foreign seasonal workers could be denied entry. Second, the global nature of the shock could also drive a larger number of firms into financial problems or bankruptcy. Both may lead to larger effects on food supply (upward scenario). On the other hand, a downward scenario where the initial shock could be smaller than the baseline is also
households per stratum and 375 replacements were drawn, from which 1,326 households were interviewed in total in the first wave, 1,687 in the second wave, 1,469 in the third wave, 1,357 in the fourth wave and 1,536 in the fifth wave.<sup>12</sup>

# **3.2 Variable definitions**

We measure the extensive margin by an employment indicator which equals one for any refugee aged 18 to 64 who was employed or self-employed in agriculture or other nonagricultural household businesses and equal to zero if an individual was not working in a given period. To construct a pre-COVID-19 employment indicator we use a set of retrospective questions available from waves 2 to 4 which ask for every household member if they were working in either wage work, household businesses or agriculture in February 2020.<sup>13</sup> We also use work stoppages measured by the proportion of individuals who were employed before COVID-19 and who stopped working afterwards. We construct an indicator equal to one if an individual worked in February 2020 and did not work at time the time of interview. Work stoppage equals to zero when an individual was working both before COVID-19 as well as at the time of interview.

For measuring the intensive margin of labor supply, we consider hours worked per week and monthly per capita household income. Total hours worked is only available for wave 1 to 5 (without retrospective questions for before wave 1) and defined for each adult member in

> 10 The Kenya Integrated Household and Budget survey is nationally representative of the population of Kenya and is administered by the Kenyan Bureau of Statistics (KNBS) using computer assisted personal interviewing (CAPI) methodology.

> 11 We multiply weights with 1/(number of phone numbers the household uses) such that households with more phone numbers, which are more likely to be called randomly in the RDD, receive a lower weight.

> 12 For a detailed discussion on the construction of survey weights for refugees,
households, suggesting the optimal sample size to be around 1,000 households.

# _PMT performance and survey period_

Another important question in the design of PMT pilots is the survey period. Ideally, the survey would be fielded uniformly across the quarters so that the consumption estimates (which often rely on a recall or diary of consumption in the previous week or two weeks) are not biased by seasonality. However, doing this in practice may not be feasible due to logistical or budgetary reasons. This raises the following question: how does the performance of the PMT model change across different calendar quarters? We address this question by implementing Model 1 on quarterly subsamples of HIES 2016. In other words, we do not change the variables in the PMT model, but only estimate its coefficients for the quarterly subsamples and predict consumption that can be rescaled to obtain the PMT scores. These PMT scores and the actual consumption values are then used to measure the performance metrics of undercoverage, leakage, and eligibility.

14 The Law of Large Numbers suggests that the estimate of the consumption based on larger samples will have a smaller variance than estimates based on smaller samples.

15
|0.325|0.021|0.068|0.074|0.071|
|# of EAs|433|433|433|433|290|143|
|N|13391|13391|13062|12937|9337|3600|

_Notes:_ This table provides estimates of the impact of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita. Household sample weights from the ESS2 applied. The first four columns report results for all households while columns (5) and (6) report results for rural and urban households, respectively. All columns control for round FEs. Further, column (1) controls for woreda FEs, column (2) controls for EA FEs, and all other columns control for household (HH) FEs. Columns (4) to (6) additionally control for 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. The overall and the within R-squared are given in the first two and other columns, respectively. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

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# **1 Including direct air capture technologies**

In this Section, we include direct air capture technologies (DAC) in our modeling framework. We treat DAC like an additional economic sector with zero annual emissions (i.e., _a_ ̄ _DAC_ = 0) and some abatement potential, that we call _Z >_ 0 (in GtCO2 yr<sup>_−_1</sup> ). DAC is unique in that its emissions rate is not the same as its abatement potential, as was assumed in every other sector.

## **1.1 Augmented model framework**

A social planner tasked with decarbonization with DAC technologies at their disposal solves the following cost-minimization problem, Subject to : _ai,t_ +1 = _ai,t_ + ∆ _t_ ( _xi,t − δiai,t_ ) _,_ Extending this formulation to a model with uncertainty about the RCB is straightforward. We take the marginal abatement cost of DAC from International Energy Agency (2022) as $100–$300 tCO<sup>_−_</sup> 2<sup>1.</sup> In each simulation we show below, we assume a marginal abatement cost of $200 tCO<sup>_−_</sup> 2<sup>1.Anylower</sup> marginal cost we would judge to be unrealistically cheap, and any higher cost causes model behavior to be completely unchanged in all of our simulations. We translate this marginal abatement cost to a marginal investment cost using (2.12). We assume the capital depreciation rate of DAC is 5% yr<sup>_−_1</sup> and that, at maximum, the abatement potential from DAC technologies is 5 GtCO2 yr<sup>_−_1</sup> (Terlouw et al., 2021).

## **1.2 Results**

We find that the impact of including DAC in our model for abatement investment is
vacancy database and job placement are most frequently used, followed by counseling and internship/apprenticeship programs. Support for geographic mobility is the least frequently used.

- The eligibility conditions most likely to be used together are periodic reporting on active job search and participating in training offered by PES, followed by periodic reporting and registration with PES. As for the services offered by PES, job placement and access to the vacancy database are most likely to be offered together, followed by job placement and counseling services.

- According to the World Bank's Social Protection and Jobs Policy Inventory that gathered data from March 2020 to January 2022, 223 economies implemented 3856 policies to safeguard workers, vulnerable groups, and economic activities from the Covid-19 crisis. Among these reforms, 159 were related to UB, 865 were related to ALMP and the rest was on social assistance and other labor market policies.

- High-income economies led 62 percent of UB reforms, with upper middle-income and lower middle-income countries contributing 26 and 11 percent, respectively.

- A larger proportion of low and lower middle-income economies implemented ALMP reforms during this period, with 8 percent and 17 percent, respectively, compared to 47 percent in highincome countries and 27 percent in upper middle-income countries.

- Across all income groups, Covid-19 related UB reforms primarily targeted increasing UB amounts, followed by expanding coverage and extending payment durations.

- Labor regulation adjustments were the most prevalent ALMP reform in all income groups. In upper-middle and high-income economies, wage subsidy programs and job search/training initiatives were the second and third most common reforms, respectively. Conversely, public works, while the least common overall, emerged as the second most popular reform in low and lower middle-income economies.

The econometric analysis of the relationship between labor market outcomes and UB/ALMP reveals distinct patterns across income groups:

- In low- and lower-middle-income countries, ALMP show a stronger correlation with labor market outcomes compared to upper middle and high-income countries. Specifically, they exhibit positive associations with productivity growth and employment rate in the
# **3. Methodology**

In this paper, we sought a methodology for estimating the magnitude of subsidies that could be adapted to data scarce environments while better accounting for differences among service providers, such as population served (to account for economies of scale), coverage of water and sanitation services individually, and their level of operational efficiency in terms of water losses and staffing. Accounting for these considerations, Chile’s model firm approach provides a solid methodology through which costreflective tariffs are calculated for each utility, which can then be used to estimate their magnitude of subsidy.

Superintendencia de Servicios Sanitarios (SISS), the Chilean water regulator, uses an efficient (optimized) bottom-up model<sup>9</sup> that provides valuable information on the greenfield capital investment<sup>10</sup> needed for the provision of WSS services that, for the purposes of the present study, we extrapolate to utilities in other countries. It is important to note that our methodology does not seek to benchmark these utilities against Chilean utilities. Instead, our decision to use the efficient model developed by the Chilean regulator is predicated on its sophisticated and novel framework for estimating costs and measuring performance.

To compute an efficient WSS tariff for each utility with sufficient data for the period 2010–15 in the World Bank’s International Benchmarking Network for Water and Sanitation Utilities (IBNET) database,<sup>11</sup> we develop a methodology that complements utility-specific data with estimates of the long-term incremental costs of efficient model firms, as determined by the Chilean regulator.<sup>12</sup> We then adjust this tariff to account for the relative losses and labor inefficiencies of each utility to obtain a full tariff, which allows a return compatible with a utility’s economic opportunity cost of capital (i.e., to be economically sustainable). The subsidies for each firm can then be computed as the difference between this costreflective full tariff and the effective tariff that a utility