rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table S2. Description of SPI Dimensions**

|Dimension|Brief Description|
|---|---|
|Dimension 1.1: Data<br>use by national<br>legislature|Not included because of lack of established methodology. In principle it may be<br>possible to utilize websites of national legislatures but this will require further work and<br>assessment.|
|Dimension 1.2: Data<br>use by national<br>executive branch|Not included because of lack of established methodology. There are some usable data<br>sources (as used by (PARIS21 2019)) but gaps in data across countries have prevented<br>full adoption.|
|Dimension 1.3: Data<br>use by civil society|Not included because of lack of established methodology. There are some usable data<br>sources with good coverage, for example from social media but more data is required to<br>help assess and allow for likely biases between and within countries.|
|Dimension 1.4: Data<br>use by academia|Not included because of lack of established methodology. We have not been able to<br>find usable data sources with global coverage on which a new methodology could be<br>developed.<br>Five measures usefulness or reliability of country produced measures for international<br>organizations have been included. First, on comparability of poverty estimates for the<br>World Bank reporting on international poverty usingthe Poverty and Inequality Portal|
|Dimension 1.5: Data<br>use by international<br>organizations|(PIP). Second on usable surveys for statistics on child mortality for theUN Inter-<br>agency Group for Child Mortality Estimation. Third on accuracy of debt reporting as<br>classified by the World Bank (Source: World Bank WDI metadata). Fourth, on<br>availability of safely managed drinking
20

# **S2 Table. Intervention effects on WASH institutions index and index sub-components**

|||Control||In|tervention|||CI<br>Lower|95%<br>Upper|
|---|---|---|---|---|---|---|---|---|---|
|Outcomes|n|Mean|SD|n|Mean|SD|ITT|Bound|Bound|
|WASH institutions index|185|0.00|1.00|144|0.46|0.75|0.40|0.16|0.65|
|Committee (y/n)|185|0.70|0.46|144|0.97|0.16|0.21|0.10|0.32|
|Committee mtg freq*|88|2.91|1.49|104|2.62|1.61|-0.29|-0.79|0.20|
|WASH expenditures (CDF<br>per month, IHS)|130|2.12|4.07|140|3.28|4.58|1.14|0.07|2.20|
|Track health (y/n)|185|0.69|0.46|144|0.84|0.37|0.16|0.07|0.25|
|Track sanitation(y/n)|185|0.74|0.44|144|0.78|0.41|0.05|-0.06|0.15|

ITT = intention-to-treat effect estimate. CDF = Congolese francs. IHS = inverse hyperbolic spline. Effects are estimated with models that include controls for randomization blocks based on province and number
these are subtle changes. For instance, our results for Brazil show that after the introduction of inflation targeting some agents appear to start negotiating wage changes as a function of expected inflation, while others remain pegged to changes in the minimum wage. Understanding the evolution of the focal point of wage negotiations is important for the design of monetary policy, and to guide the need for possible structural reforms in the labor market. However, these changes may be blurred by aggregation at the macro level. The use of micro data instead allows us to examine time-varying worker and establishment heterogeneity in wage setting.

5
2004) conflict database records 71 conflicts from 1975 – 2000 for which data on competitive elections (from the Database of Political Institutions) are available. In only 15 of these cases were countries governed by competitively elected leaders in the year before the conflict started. In the remainder, governments were not competitively elected or not elected at all. Indeed, this empirical regularity is as striking as the association of conflict with income: of these same 71 conflicts, again only 15 (not the same 15) occurred in the richest 50 percent of countries.

Although the paucity of elections in conflict countries is an indication that leaders in conflict countries impose limits on collective action by citizens, elections are not a sufficient condition for effective collective action. Even citizens with unfettered voting rights are handicapped in their ability to hold leaders to account when they are constrained by imperfect information (the inability to observe leader actions or the effects of those actions on their welfare). More importantly for the analysis here, though unfettered voting rights lower the individual costs of holding leaders accountable, they do not guarantee that challengers will emerge who can credibly commit to pursuing different policies than those of the incumbent.

Electoral accountability of leaders depends on the existence of such challengers. Ferejohn (1986) and Persson and Tabellini (2000) examine the case where credible challengers do not exist. Their absence substantially attenuates incumbent incentives to pursue the public interest (e.g., to provide public goods). These results extend immediately to the conflict setting: such leaders, even if elected, have limited incentives to win hearts and minds through the provision of public services.
round and 14.4 percent in the sixth round. Unlike the Ethiopia HFPS, this variable indicates the income status of the respondents, who are household heads, instead of their households. Unfortunately, a household-level indicator is not available in the survey. Thus, even if the variable indicates the household head’s income reduction, the household’s incomes may have remained at the same level because of increased income from other household members.

> 7 Attrition is explained by the weariness of some households to be surveyed on a regular basis. To reduce this attrition, a telephone call credit is offered to each household that answered the questionnaire as an incentive. On the other hand, data collection teams sometimes reach households that could not be reached in the previous round, which explains why in some cases there is an addition in a round over the previous round.

> 8 In addition, we exclude respondents who have changed their residential locations since the baseline surveys, as their current locations are not known in the HFPS data. The HFPS asks whether the respondent has changed residential locations, although it does not ask the exact location.

8
flood maps used here are based on a model of HCMC, and are not the same map as used in the figurative example in Section 4.1.

Exposure to flooding was again evaluated using flood extent (we also evaluate flood depth, for full results, see Appendix 2). We examine the flood extent in three areas: for all urban areas (the whole HCMC province), for those areas defined as potential slums (from the PUMA data set), to examine how exposure to floods is different in slum areas. We do the same for areas defined as urban expansion locations (also from the PUMA data set) to evaluate whether new urban developments within the province of HCMC take place in flood prone areas.

Again we use a number of events, from the case of regular flooding (10‐year event) to more extreme flooding events (1000‐year event). Moreover, we examine how this exposure changes due to climate change (proxied by sea level rise changes), by running the analysis with flood hazard maps taking into account a 30 cm sea level rise. In each district and across the whole city, we examine the percentage of area within each of the three categories (all urban areas, slums, and urban expansion areas) that is exposed to floods (that is, where flood depth > 0cm) and the percentage which is not exposed to floods (that is, where flood depth = 0cm). We then compare these values across the three categories.

# **5. Results**

> 2 There is also  a good reason for examining extent over depth, in terms of the hazard modeling; flood depths within a large scale flood model are very uncertain, and there is much more certainty about extents.

10
havens (Switzerland, Luxembourg, Belgium, Jersey, Guernsey and the Isle of Man) and 55% can be assigned to 11 non-havens. Among the havens, for which bilateral deposit information is publicly available, Switzerland is by far the most important. Around 16% of the total foreign deposits cannot be assigned to individual banking centers. Even if all these unallocated deposits are held in havens like Cayman Islands, Singapore and the Bahamas where public data is not available at the bilateral level, the public series still allocate almost two thirds of all haven deposits to individual havens for this particular sample.<sup>19</sup> We first re-estimate the baseline model with the (incomplete) measures of haven and nonhaven deposits based on publicly available information while using the same sample period as in the baseline analysis, 1990-2010. As shown in Column (1) of Table 7, an aid disbursement equivalent to one percent of GDP in a given quarter induces a statistically significant increase in haven deposits of around 2.5% and, as shown in Column (2), an insignificant decrease in non-haven deposits of around 2%. The increase in haven deposits, measured over and above the increase in non-haven deposits, is around 4.1%, as shown in Column (3). These estimates are similar to the baseline estimates based on restricted deposit information (Columns 1-3 in Table 2), but somewhat smaller. A possible interpretation is that the havens not allowing for public release of bilateral deposit data are also the havens where deposit responses to aid disbursements are largest. Extending the sample period to include the most recent observations in the public data yields almost identical results, as shown in Columns (4)-(6).

Next, we show results by individual banking centers; an exercise we are not allowed to conduct with the restricted dataset due to confidentiality requirements.<sup>20</sup> As shown in Columns (7)-(10), the overall increase in haven
# **5.1. National‐level analysis for poverty and exposure to floods**

# **5.1.1. Flood risks (with and without climate change)**

For the entire country of Vietnam, at the district level, we estimate the total number of people and the share of the population who are exposed to floods. In the results presented, we examine the four scenarios for the 25‐year, 50‐year, 100‐year and 200‐year return period flood – a historical scenario, and three scenarios representing future climate: a low, medium, and high scenarios.

We aggregate the results at the country level.<sup>3</sup> A third (33%) of today’s population is already exposed to a 25‐year flood in Vietnam, assuming no protection (such as dikes and drainage systems), which can make a large difference in the flood hazard particularly in well‐protected areas. In these well‐protected areas, our flood maps may over‐estimate the flood hazard.

When including climate change, this percentage increases by 13‐27%, depending on the severity of sea level rise. This increase in exposure is due to the concentration of the population in coastal areas. For the 50‐year flood, more than a third (38%) of today’s population is already exposed. Given climate change, this number is expected to increase by 7‐21% (resulting in overall exposure of between 40 and 48%) for the same return period (50‐year). For a 100‐ and 200‐year flood under a high climate scenario, more than half of the population is exposed.

Climate change impacts can be seen in these exposure numbers – for instance, a 50‐year flood with medium climate change impacts has the same exposure of a 200‐year historical flood (at 44%), while almost half the country’s population (48
Birth in western medicine facility<br>12 †|1|0.862|0.345|0|1|877|Yes|

†: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.3:** Health Outcomes

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) U5 Child death per HH<br>6 †|0|0.033|0.178|0|1|5053|Yes|
|(2) Maternal death per HH<br>6 †|0|0.001|0.034|0|1|5053|Yes|
|(3) Illness/injury in HH<br>1 †|1|0.583|0.493|0|1|5053|Yes|
|(4) Child weight for length|0.600|0.621|1.633|_−_4.910|4.980|1991|No|
|(5) Vaccine completion index (Under 2)|4|3.117|2.589|0|7|1457|Yes|
|(6) Child birth complication index<br>12|_−_0.619|_−_0.002|0.975|_−_0.619|2.649|856|Yes|
|(7) Child illness index<br
firms. Details are given below.

## **3.1 Egyptian Household Income, Expenditure and Consumption Survey**

The Egypt HIECS 2008/9 is conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS). We were given a 50% sample of the survey (approximately 24,000 observations).<sup>9</sup> Throughout the paper, our welfare aggregate is expenditure per capita which is consistent with standard practice in most developing countries. Household expenditures have been adjusted for spatial differences in prices by deflating nominal values by a spatial price index following

> 9Hlasny and Verme (2013) were able to access the 100% sample on site at CAPMAS.

13
11 that place sufficient weight on the accuracy of the information on the marginal distribution would choose to analyze each margin separately, in each case drawing from the best survey.<sup>6</sup> In many cases, of course, reliable information on relevant dimensions - such as health status, anthropometrics, education and consumption - does exist in the same survey. This is the case, for instance, of the Russian RLMS survey used by Decancq (2009), the Indonesia Family Life Survey, and various LSMS-type surveys. In those cases, it is difficult to find an excuse for not complementing the marginal analysis with information on the dependency structure. Furthermore, one should not underestimate the power of the demand for better data. If there are certain aspects of well-being and deprivation which are not regularly captured in household surveys, but whose joint distribution with other dimensions (such as income) is of real policy interest, then more frequent analysis of the kind we suggest might well encourage statistical institutes, or other data providers, to collect information on them.

# _3. What about scalar multidimensional poverty indices?_

Finally, we turn briefly to the question of whether the analytical approaches we propose should completely preclude the computation of scalar multidimensional poverty indices. It is already clear that they do not preclude the “dashboard approach”, in so far as looking at the marginal distributions is inherent in looking at the joint. The three approaches described above are best seen as complementing the “dashboard approach” with information on the dependency structure between the dimensions, when that is available.

But what room do we see for the definition and computation of _scalar_ multidimensional poverty _indices_ ? We see this question as just another instance of choice given a trade-off. A scalar multidimensional index buys you a complete ordering, i.e. the ability to rank two years, or countries, or regions, even then their joint distributions (or copulas) cross. Just as in unidimensional poverty or inequality analysis, that ability to generate
**Figure 3: Distribution of SBM Efficiency across hotels (without sampling weights)**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>SBM Efficiency<br><!-- End of picture text --> Note: SBM Efficiency is shown for each hotel in the baseline sample. Source: Authors’ own calculations based on ES data for hotels in Malaysia in 2019.

36
uncertainty are combined with a major credit crunch one can expect a high initial discrepancy between durable and non-durable consumption due to both financial constraints and elevated uncertainty levels.

The final set of results shown in Figure 5 documents the response of international trade to uncertainty. We augment Bloom’s original specification with the effective exchange rate and aggregate imports and exports. We do not include consumption expenditures in this estimation. Because of monthly data availability the sample is almost 20 years shorter, starting only in January 1980. Reassuringly, despite using a shorter sample and omitting some of the shocks, the response of industrial production is almost unchanged. As shown in Figure 5 the reactions of aggregate imports and exports to a rise in domestic uncertainty differ considerably. Exports experience a short sharp increase after which they drop almost immediately to their previous path. In contrast, imports drop dramatically and the negative response is protracted. We do not have sufficiently long monthly time series to estimate the impact on the durables and non-durables components of trade separately.<sup>5</sup> Therefore, based on previous findings, one could speculate that most of the initial drop is driven by the durable component of imports while the protracted nature is primarily due to the non-durable part. Both imports and exports respond very similarly to changes in the stock market index. The increases in wealth leads to a rise of both above trend for an extended period. A contractionary monetary policy leads to an initial short lived rise in aggregate imports that soon converts into a protracted drop. A potential explanation for this is that initially the appreciation brought by a rise in interest rates makes foreign goods cheaper, however, over time the restrictive effects induced by financial constraints take over and reduce imports. Surprisingly, exports also rise in response to a hike in the federal funds rate. Even though, one would expect a negative effect on exporters via financial constraints and appreciation, a potential explanation for this result is that the biggest exporters responsible for most of the aggregate exports are less financially constrained
which are typically used in cross-national studies, fail to capture the role of roads in connecting cities, and are subject to a great deal of error.<sup>26</sup> A recent World Bank project on infrastructure in Africa has improved the state of georeferenced roads data for the continent so that they can be used to assign infrastructure to specific cities and routes between cities (World Bank, 2010).<sup>27</sup> The resulting dataset combines information on road location and surface assigned to specific (and recent) years from each country’s Transport Ministry or equivalent, or a consultant specific to the project.<sup>28</sup> It contains information on over a million kilometers of roads in 39 countries.<sup>29</sup> For over 90 percent of this length, a measure of the surface type is recorded. The comprehensiveness of the coverage varies by country, but only in that some countries contain more minor roads. Intercity roads are available for all countries. Figure 8 shows these roads data for Tanzania. Roads go through all the populated cities shown. Most roads are unpaved, and most paved roads are found along a few major corridors.

The shortest path along the road network was calculated between the centroid of each city-light and three destinations: (1) its country’s primate city, defined as the light with the largest associated population in 1992, (2) the nearest city in the same country with a 1992 population of at least one hundred thousand, and (3) the nearest city in the same country with a 1992 population in the top quintile of the population distribution for that country. Plausible primate city routes were found for 287 out of 299 cities in the 15-country sample.<sup>30</sup> Figure 9 shows all roads and primate routes for Tanzania. Descriptive statistics are in Table 2.

> 26Countries such as Canada, Australia, and Botswana have low road density relative to their economic
**Figure 6** : Percent of facilities with handwashing facilities, toilets, and acceptable waste disposal by urban/rural location in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Kenya Sierra Leone<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>0% 0%<br>Facilities with Facilities with Facilities with Facilities with Facilities with Facilities with<br>handwashing for toilets acceptable waste handwashing for toilets acceptable waste<br>patients disposal patients disposal<br>Urban Rural Urban Rural<br>Tanzania<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Facilities with Facilities with Facilities with<br>handwashing for toilets acceptable waste<br>patients disposal<br>Urban Rural<br><!-- End of picture text -->

# Availability of personal protective equipment

Personal protective equipment (PPE) is imperative to ensure that health care workers are protected during a time of high risk. As described previously, health workers and health facility staff experience high rates of exposure to disease during a pandemic, and PPE can help protect them from infection. While information on protective eyewear and face shields is not available, the Sierra Leone SDI collected data on availability of disposable gloves, apron, face mask, and protective boots. Encouragingly, disposable gloves, face mask, and apron were each available in more than 95% of facilities. Protective boots were less common, occurring in just 71% of facilities. All four pieces of protective equipment were present together in only 66% of facilities. Figure 7 shows that the average availability of these four PPE items varies based on facility level, most notably for boots. Notably, the Sierra Leone survey occurred in 2018, following the
sup> <mark>, 2021. Each wave had over 1000 “completed” surveys per country. A “completed” survey is one where respondents answered until the eighteenth question, which included all questions related to employment status and characteristics. For the analysis, we use only completed surveys, and ignored partial-response surveys. The sample size for these surveys in each wave is presented in panel A in table 3.1.</mark> <mark>Second, we use the Global Monitoring Database, a probability-based survey, to compare demographic variables and individual attributes with our survey. The GMD is the repository of multitopic income and expenditure household surveys used by the World Bank to monitor global poverty and shared prosperity. The household survey data are typically collected by national</mark> 11
Overall, the differences in questionnaire design do not significantly affect the poverty predictors required for Models 1, 2, 8 and 9. However, differences in food and non-food consumption aggregates collected with the short questionnaires can have a significant impact on the predictive accuracy of Models 3 and 4, highlighting the importance of questionnaire design for poverty imputation and survey research in general.

# **3. Analytical framework**

The analytical framework features the poverty imputation method developed in Dang _et al._ (2017), which builds on the survey-to-census poverty mapping method in Elbers _et al._ (2023).<sup>10</sup> The method has been applied to fill poverty data gaps for general household populations in various low- and middle-income countries from different regions, including India, Jordan, Tunisia, Viet Nam, and Sub-Saharan African countries (Beegle _et al._ , 2016; Dang and Lanjouw, 2023), as well as to estimate poverty within refugee sub-populations (Dang and Verme, 2023). Most recently, the method is employed in Dang _et al._ (forthcoming) to analyze data from 14 multi-topic household surveys from Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam. We briefly describe the method below.

Building on a household utility budget model (Deaton and Muellbauer, 1980), (log) household consumption per capita ( ) is typically estimated using the following reduced-form linear model jj for survey _j_ , for _j_ = 1, 2<sup>11</sup> yy

> 10 Previous studies offer various refinements of certain features of the poverty mapping technique, such as imposing a parametric probit functional form on the error term (Tarozzi, 2007) or offering a different formula to estimate the standard errors (Mathiassen, 2009). Dang _et al._ (2017) offer a well-defined framework for survey-to-survey imputation with simpler variance formulas and formulas for standardization of variables from surveys with different sampling designs (e.g., imputing from a household consumption survey