rafmacalaba/data-use-mentions-extended huggingface dataset:
(Foster and Shorrocks, 1988b); others are, to the best of our knowledge, themselves a novel methodological contribution to the literature on stochastic dominance with ordinal variables.

There has been a recent surge in the literature on multidimensional poverty measurement, especially within the counting framework. In this framework, however, it is still a usual practice to dichotomise deprivations within each dimension when using existing counting measures, ignoring the depth of deprivation across ordered categories. Future research could focus on the development of counting measures that incorporate both the depth of deprivations within dimensions and notions of precedence to the poorest among the poor.

# **References**

- Abul Naga, R. and T. Yalcin (2008). Inequality measurement for ordered response health data. _Journal of Health Economics 27_ , 1614–25.

- Alkire, S. and J. E. Foster (2011). Counting and multidimensional poverty measurement. _Journal of Public Economics 95_ (7-8), 476–87.

- Allison, R. A. and J. E. Foster (2004). Measuring health inequality using qualitative data. _Journal of Health Economics 23_ , 505–24.

- Apouey, B. (2007). Measuring health polarisation with self-assessed health data. _Health Economics 16_ , 875–94.

- Atkinson, A. B. (1987). On the measurement of poverty. _Econometrica 55_ (4), 749–764.

35
Li and K. Huang. 2019. “Building a Series of Consistent Night-time Light Data (1992–2018) in Southeast Asia by Integrating DMSP-OLS and NPP-VIIRS.” IEEE Transactions on Geoscience and Remote Sensing 58(3):1843–1856.

- Zhao, Yan, Pyae Aung, Shishao Ruan, Kyawt Win, Zifang Wu, Than Soe, Myat Soe, Yaming Cao, Jetsumon Sattabongkot, Myat Kyaw, Liwang Cui, Lynette Menezes and Daniel Parker. 2023. “Spatio-temporal Trends of Malaria Incidence from 2011 to 2017 and En-

28
**Figure 6: Goods export-employment elasticities in lower-income countries, by type of GVC specialization**

<!-- Start of picture text --> 0.15<br>0.10<br>0.05<br>0.00<br>-0.05<br>-0.10<br>Total goods Agriculture Mining Manufacturing Low-tech mfg<br>Commodity exporters Limited manufacturing<br><!-- End of picture text --> Source: Own computations. Data: ETD and Comtrade data, covering 37 countries (see Appendix 2) and 35 sectors for the period 1995-2018. Note: Export-employment elasticities at the country-sector level have been estimated using employment (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported. GVC taxonomy group by World Bank (2020).

# 3. Measuring jobs in exports: The role of supplying sectors

## 3.1 Do jobs in exports grow more strongly than other jobs?

Countries’ share of employment in exports in total employment varies considerably and is larger in small economies, but smaller in commodity-exporting countries. Analysis based on the OECD TiE database finds that smaller economies tend to show higher shares of employment in exports in their total employment, because their smaller market size dictates more openness to trade in the first place (Figure 7). The share of employment that is dependent on exports, including employment in supplying sectors, was 50 percent or higher in Ireland, Malta and Luxembourg in 2018, contrasting sharply with the United States where the share was less than 9 percent. However, commodity-exporting countries, even smaller ones, show a smaller dependence on employment in exports. Employment shares in exports range
loss faced by a district’s “typical” inhabitant as is possible in the context of a model with non-homothetic preferences and multiple types of (possibly heterogeneous) agents.

Note that equation (49) does not include any terms related to middlemen because, as we mentioned in Section V.A, their population is assumed to have measure zero. This assumption is empirically supported by the very low number of fertilizer suppliers in Sri Lanka.<sup>123</sup> Furthermore, in our model the middleman is both the richest agent type and the one who obtains an outsize financial gain from the ban (by construction, as middlemen own the domestic fertilizer endowment). Therefore, excluding middlemen from our welfare change metric makes it more representative of the overall population (as opposed to the super rich) and avoids the potential distortions that could be caused by including outliers.

> 123In 2022, the NFS listed 17 “fertilizer distributors” in Sri Lanka. It also lists six “local agents of international fertilizer suppliers”. Our Panjiva import data (see Sections III and A.B) shows that 310 firms imported fertilizer (defined as any product listed in Chapter 31 of the HS system) into the country at some point between January 2016 and October 2022. Finally, the NFS lists of fertilizer importers include 629 unique firm names. Using the highest of these numbers (i.e. 629), we obtain an average of 25.16 fertilizer suppliers per district, which is within the range that we examine in the robustness exercise described in footnote 73.
# **1. Introduction**

Security of property rights to land affects incentives for land-attached investment and female empowerment, while the ability to access formally registered documents is a precondition for transactions with strangers to foster structural change, emergence of financial markets, and transparency. Yet, the fact that legal and institutional arrangements governing land ownership and access evolved over long historical periods in response to local conditions led to enormous variation over space that limited the scope for globally comparable indicators on land tenure security that could then be targeted by specific programs. It may even have contributed a focus on rather simplistic shortcuts (e.g., “titles”) that fail to do justice to the underlying complexities and, by focusing on inputs rather than the desired outcomes, may send inappropriate signals.

To address this, the Sustainable Development Goals (SDGs) define two indicators (1.4.2 and 5.a.1) related to land.<sup>1</sup> While the latter focuses on the agricultural population, the indicators require nation-wide data covering (i) access to formally documented rights; (ii) transferability via bequest or sale; and (iii) perceived tenure security. To build local capacity and facilitate coordination in data collection and based on guidance by the inter-agency expert group on SDGs (IAEG-SDG), FAO, UN-Habitat and the World Bank designed a module for inclusion in ongoing household surveys by National Statistical Agencies (FAO _et al_ , 2018). Simultaneously, to obtain data more quickly at a larger scale, use of a ‘global poll’ has been advocated by some and considerable resources were mobilized in support of an initiative, the ‘Prindex’,<sup>2</sup> to implement it.

However, while there has been extensive conceptual discussion, empirical data have not been analyzed in any detail to explore if proposed methodologies are sound and provide a basis for actionable policy advice. This paper aims to start filling this gap by using the case of Zambia where
local VHF database. Each observation in our data represents one of these CIFs. Data from the districts were periodically transmitted to the National Ebola Response Center.

The VHF database includes four different types of cases. Two classifications result from testing: **negative** cases where Ebola has been ruled out; and **confirmed** cases per lab results. In total, the VHF contains data on 8358 confirmed cases. The average patient (reported in Fang et al. (2016)) was 30 years old; and over 70% were above the age of 18. There are two residual categories that are never confirmed with lab tests: **suspected** cases display Ebola symptoms and/or have had contact with potentially infected individuals or animals;<sup>17</sup> and **probable** cases meet the criteria for a suspected case and were either screened by a clinician or died and have an epidemiological link to a confirmed case. Given our interest in reporting, our primary outcome is total cases — the sum across these four case types.

We geocode cases using information on individuals’ residences included in the VHF database (typically, district, chiefdom, and village or parish). The full geocoding protocol is described in Section E.22. While this geocoding process introduces measurement error, we expect this to be uncorrelated with our randomized treatments and, if anything, result in a loss of efficiency. Consistent with this claim, we test whether treated sections have more or longer placenames, or placenames that are more likely to contain spaces (i.e., two word names), and find no substantial or significant differences (see Table E.27). To check the accuracy of our geocoding protocol, we look at the average number of confirmed cases in sections that hosted major Ebola-specific treatment facilities: Ebola Treatment Units (ETUs), Ebola Holding Centers (EHCs), or Community Care Centers (CCCs). We find that sections with ETUs,
# **3.1 Sector Breakdown**

The sector breakdown of ASE data is matched to the classification followed by banks in monthly reporting of sector exposures. The advantage of this method is that industry-level results could be directly matched with bank exposure and allow for integration of industry vulnerability analysis into the bank stress-testing exercise. The disadvantage is that some industry brackets end up with very few firms, while others represent fairly heterogeneous groupings. For example, in 2006 there are only 4 firms in the sector labeled “utilities and energy,” while there are 49 firms lumped together under “industry” which is fairly heterogeneous group (i.e. it includes such industries as chemicals, textiles, electronics, machinery, food processing and others).<sup>4</sup> The results for sectors with very few observations should be treated with caution, especially for the utilities and energy sector.

Table 3 reports the results of several key vulnerability ratios by sector in 2006. The last column reports averages across all industries.

Profitability is an important measure of sector performance and potential to meet its debt obligations. Among the sectors considered, the highest profitability is observed in the mining and financial sectors, which report 3.8% and 3.9%, respectively. Industry and trade on average reports ROA of about 1%, which is low by international standards, as shown above.

Transportation is the lowest, with an ROA of 0.3% in 2006. The overall ROA for all listed firms is lower than reported above for the 18 firms available in Worldscope.

In terms of liquidity, tourism and hotels, utilities and real estate companies have the highest quick ratio, i.e. highest ability to meet current liabilities with current assets. Industry, construction and transportation have lower quick ratios, just barely above one on average. These companies are less liquid, and hence more vulnerable to shocks to short-term financing or cash flows.

The overall level of indebtedness is an important factor in evaluating corporate
*23.9**|[19.2 – 28.6]|**17.6**|[13.7 – 21.6]|**23.0**|[18.4 – 27.6]|**48.3**|[42.9|– 53.7]|
|**T2**|**20.7**|[16.3 – 25.1]|**14.9**|[10.6 – 19.3]|**20.4**|[15.9 – 24.8]|**44.8**|[39.3|– 50.2]|

* _all US$ converted to local values using 2017 purchasing power parity (PPP), expressed in 2022 prices_

**Table 10b.** Prevalence of poverty at different thresholds with statistical significance tests

|**Poverty**|**T0**|**T1**|**Sig**<sup>**a**</sup>|**T0**|**T2**|**Sig**<sup>**a**</sup>|**T1**|**T2**|**Sig**<sup>**a**</sup>|
|---|---|---|---|---|---|---|---|---|---|
|National poverty line (TSh 1,876)|20.1|23.9|n/s|20.1|20.7|n/s|23.9|20.7|n/s|
|US$ 1.90 (TSh 1
Carleton, T. A., and S. M. Hsiang. 2016. “Social and Economic Impacts of Climate.” _Science_ 353 (6304): aad9837.

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situation between areas that were closer to the location of the large Rohingya camps and those areas farther away in the district. Accordingly, the sampling strategy for the CBPS had to meet two requirements: (i) be representative of both recently displaced Rohingya and host community households in Cox’s Bazar; (ii) stratify these population groups based on other key characteristics, such as the prevalence patterns for the camp and non-camp Rohingya displaced populations across Cox’s Bazar district.

# **3. Sampling Strategy**

# <u>Approach to define the survey strata</u>

# _Determining the prevalence of Rohingya displaced in camps and non-camp areas_

In this particular crisis, a large share of the Rohingya displaced located in camps. Key population statistics for camp-based populations were both frequently updated and made publicly available, and population quick counts in camps were implemented approximately every three months by the International Organization for Migration (IOM). These population counts were complemented by United Nations High Commissioner for Refugees (UNHCR) data on new arrivals. Together, the IOM and UNHCR data suggested that a significant share of Rohingya who had arrived during the 2017 influx continued to live in camps in the Ukhia and Teknaf sub-districts more than a year after the onset of the crisis **.** However, largely porous camp boundaries<sup>9</sup> and evidence from survey data<sup>10</sup> collected in the year after the crisis suggested that Rohingya displaced were not fully confined to camps. While some work opportunities could be generated within camps, data indicated that many Rohingya were either leaving and returning to camps daily for work purposes or else relocating entirely outside camps to pursue work.<sup>11</sup> For the purposes of obtaining a representative survey of Rohingya displaced it was important to determine the true prevalence of displaced Rohingya outside camps.

Due to the absence of a comprehensive sampling frame for Rohingya outside camps, IPA Bangladesh had conducted a quick population count exercise which provided
single factor that explains the scarce amount of research on refugees as documented at the outset of this paper.

In addition to the UNHCR registry, data on refugees are collected via home visits and sample surveys by a variety of organizations including the UNHCR, the World Food Program (WFP), other UN organizations and many NGOs working independently or on behalf of UN organizations. However, it is important to stress that any home visit or sample survey will have to rely on the UNHCR registry. The UNHCR registry system functions _de facto_ as the only existing population survey for refugees. By definition, refugees are not included in national censuses unless governments make a special effort to do so. Ministries of interior would generally keep records of people coming into and out of the country, including refugees, but these data are seldom shared, records are not always accurate and these records only include a handful of information strictly necessary to border police to grant entry and exit. Therefore, in many countries, the UNHCR registry system is the only system that contains population information on refugees and if this system does not exist or is managed by the host government information is scarce or not available. In a sense, the UNHCR is the statistical agency of refugees and the UNHCR registry system is the population census of refugees, which means that any other survey on refugees will have to rely on this registry, unless proper government registry systems are in place.

In addition to being scarcely available, the UNHCR registry also suffers from a number of deficiencies that span from records that are not filled or incomplete to data input and data cleaning issues to data management and data use. The quality of the UNHCR data registry varies significantly across countries with better quality usually available in middle‐income countries and lower quality data available in low‐ income countries. Moreover, where data are generally perceived to be of good quality, many records may be missing and variables not usable. In other cases, data entry errors pollute the data while in some cases existing good data
data reports outages that can be attributed to climatic shocks – in particular storms – during the period 2010-2017. The BREB publishes reports for 64 of its 79 power supply or Palli Biduyt Samity (PBS) zones<sup>8</sup> . However, due to heterogeneous reporting methods<sup>9</sup> and missing time stamps, we only used 28 PBS zones, distributed across 7 Bangladeshi divisions.<sup>10</sup> Therefore, estimates should be considered as lower bounds and conclusions taken as insights rather than a representative picture of the impact of storms on power outages in Bangladesh. A list of included districts and corresponding divisions may be found in the appendix (Appendix 4).

# 2.2. Climate data

# 2.2.1. ERA5 climate data for the US

To assess the storm exposure of Puerto Rico and all federal states of the USA, this paper uses global highfrequency climate data from the 5<sup>th</sup> global reanalysis (ERA5) conducted by the European Center for Medium-Range Weather Forecasts (ECMWF) using its Integrated Forecasting System (IFS).<sup>11</sup> As the dataset goes back as far as 1950, wind speed and temperature data could be extracted for all recorded outage events since 2000, and for all 52 centroid coordinates of the respective US states and territories.

> 6 Therefore, we only keep the outages falling within the scope of this definition. Twenty-five outages whose duration is unknown are included in the dataset.

> 7 The European Commission reports do not allow us to cross-check the number of consumers being out of power.

> 8 Details on zones can be found in maps posted by the BREB Directorate at: http://gis.brebms.com/.

> 9 Some reports only display yearly aggregates, preventing the matching of outages with climatic events.

> 10 On 14 September 2015, Mymensingh Division was added as eighth division of Bangladesh. Given the time frame of
countries to relinquish previous practices of collecting household-level land data that implied an assumption of a unitary household model. However, approaches to collecting individual-level land data are varied and different methodological choices may result in biased understandings of the importance and level of land rights by gender. A growing body of literature has shown that survey design decisions on the approach to respondent selection can significantly impact estimates across a wide range of topic areas. This paper contributes to that literature by examining the implications of respondent strategy and the level of land data disaggregation on the computation of SDGs 1.4.2 and 5.a.1 and the distribution of the underlying land rights, by gender.

Using data from the Armenia Land Tenure and Area study – a study designed specifically for this analysis – we compare the implications of the use of a proxy respondent versus the recommended self-respondent approach and the use of aggregated land data versus parcel-level land data (recommended). Understanding the implications of these design decisions on data quality is critical, as household surveys generally rely on proxy respondents and may use an aggregate- or parcel-level approach depending on the nature of the survey. In this initial comparative assessment, we find that in the case of Armenia, the measurement of legally documented rights is robust to these design decisions, both for men and women, when compared to the gold standard self-respondent, parcel-level approach. This consistency in the measurement of legal documentation also translates into consistent measurement of SDG indicators 1.4.2 (a) and 5.a.1.

Land rights that are less objective in nature and potentially more difficult for proxy respondents to answer, including the right to sell, the right to bequeath, and the perception of tenure security, are more sensitive to the data collection approach. We find that collecting data at the parcel level through a proxy respondent underestimates the share of individuals holding these types of rights to a statistically significant degree. The degree of variability in these rights across treatment arms also differs by gender. The estimates for the
# **3. Estimation Strategy**

In our estimation strategy for the first set of analyses we use pooled panel data. To estimate the impact of weather variability on consumption we estimate the following equation.

where is the logarithm of consumption expenditures per capita of household, _h_ , located in locality _l_ , in the year _t_ . is a vector describing the weather shocks in locality _l,_ at time _t_ , is a vector of other factors explaining consumption levels, such as assets, and household characteristics. are locality fixed effects which control for all local, time invariant characteristics including the agro-climatic characteristics of each locality, and control for survey year and season (wet or dry) differences, respectively, and is the error term. measures the impact of weather shocks on consumption. In the absence of insurance against income shocks any weather shock that reduces income should also reduce consumption.

In order to determine if the impact of a weather shock differs among different populations, we introduce into equation (1) an interaction term, such that it becomes, Here identifies the type of household. It could indicate, for example, whether or not the household head is female or has completed at least primary school. In this case measures the impact of the weather shock on households without the particular characteristic and measures the impact of weather on households with the particular characteristic, with denoting the difference in the impact between the two groups.

For the second set of analyses, relating weather shocks to health outcomes, we use cross sectional individual level data. The health outcome of an individual can be written as: where is the health outcome of individual _i_ in locality _l_ at time _t_ , is a vector describing the weather shocks in locality _l,_ at time _t,_ is a vector of other factors influencing the health of 9