rafmacalaba/data-use-mentions-extended huggingface dataset:
.5|3.0|3.4|3.0|
|**Sub-Saharan Africa**|||||||||
|Output|0.3|0.3|0.2|3.2|1.0|4.0|3.7|3.8|
|Output per capita|-2.3|-2.6|-2.6|0.4|-1.8|1.2|1.0|1.1|
|<br>Industrial production|...|...|...|...|...|...|...|...|
|<br>Trade|6.4|-10.3|4.5|-9.9|-2.3|4.7|2.7|4.3|
|Output (PPP)|0.3|0.4|0.3|3.6|1.1|4.0|3.8|3.9|
|Output per capita (PPP)|-2.4|-2.5|-2.6|0.8|-1.6|1.3|1.1|1.1|

Note: All variables show percent changes. See footnote of Table 3. Regional aggregates include EMDEs only.

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impacts on total household expenditures, as we did not anticipate the potentially large spillover effects with respect to household composition.

# _a) Total and Per Capita Household Expenditure_

Households were asked about their monthly expenditures on the following categories: rent, utilities (electricity/heating/gas for cooking), hygiene items, health, education, water, telecommunications (telephone/internet), transportation, debt repayments, celebrations (social events, births, weddings, religious ceremonies etc.) and other expenditures (clothing, tobacco and others). In addition, households were asked about food expenditure using last week as the reference period. After baseline, an additional category was added into the expenditure module on remittances sent abroad.

> 18 For the remaining 6% of those lost to 18-month follow-up, the reasons for not being reached included households being available only after working hours, terminating the interview before completion, households not wanting to be called again or refusing to participate in the household survey, enumerators not having the relevant language skills to conduct the interview, households moving back to Syria or to a third country.

> 19 For instance, at the 12-month follow-up, 8.6% of the households from the baseline had numbers that were changed or belonged to someone else, 6.5% had not picked up their phones, and a further 6.5% had their mobile phones turned off. 2.3% of the baseline households were busy or not available to participate in the survey. 5% of the baseline sample was not reached due to the other reasons enumerated above.

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male to female ratio on marital outcomes, but do not find evidence that it increased marital instability.

# **IV.  Health and Disability**

Since the Khmer Rouge period was a period of war, starvation and violence, it is reasonable to expect that it had an impact not only on mortality but also on morbidity. This section attempts to measure the long term consequences of the period on the health status of Cambodians in 2000.

Poor nutrition during childhood and adolescence are likely to result in stunting. The Cambodian Demographic and Health Survey contains anthropometric measurements of women aged 15 to 49. Figure 12 plots the average height and the proportion of women above 150 cm (women under 150 cm are considered to be at nutritional risk). Women

> 13 The variable is the age of the current partner, not necessarily the first husband. The data is reported in table 4.

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Violence in the United States.” _Journal of Family Violence_ 33 (4): 239–49. https://doi.org/10.1007/s10896-018-9954-7.

IRB (Immigration and Refugee Board of Canada). 2017. “Brazil: Domestic Violence, Including Legislation, Recourse and Support Services Available to Victims (2014– January 2017).” Document 1036584 (January 20), Research Directorate, IRB, Ottawa. https://www.refworld.org/docid/589440134.html.

- Kendall, Tamil. 2020. “A Synthesis of Evidence on the Collection and Use of Administrative Data on Violence against Women.” Background Paper (January), UN Women, New York.

- Kotsadam, Andreas, and Espen Villanger. 2020. “Jobs and Intimate Partner Violence: Evidence from a Field Experiment in Ethiopia.” CESifo Working Paper 8108, Munich Society for the Promotion of Economic Research, Center for Economic Studies, Ludwig Maximilian University and Ifo Institute for Economic Research, Munich.

- Litwin, Ashley, Elizaveta Perova, and Sarah Anne Reynolds. 2019. “A Conditional Cash Transfer and Women’s Empowerment: Does Bolsa Família Influence Intimate Partner Violence?” _Social Science and Medicine_ 238 (October): 112462. https://doi.org/10.1016/j.socscimed.2019.112462.

- Macmillan, Ross, and Rosemary Gartner. 1999. “When She Brings Home the Bacon: Labor-Force Participation and the Risk of Spousal Violence against Women.” _Journal of Marriage and Family_ 61 (4): 947–58. https://doi.org/10.2307/354015.

- Manser, Marilyn, and Murray Brown. 1980. “Marriage and Household Decision-Making:

- A Bargaining Analysis.” _International Economic Review_ 21 (1): 31–44. https://doi.org/10.2307/2526238.

- Mele, Marie. 2009. “The Time Course of Repeat Intimate
6

- a. Use a natural language processing tool called nlp4dev<sup>14</sup> to tag each dataset in the WB MDL by thematic areas and keep those ones that are flagged as relevant to the issues of migration, displacement and refugees;<sup>15</sup>

- b. Keep those datasets that mention “refugee(s)”, “internally displaced”, “internal displacement”, “IDP(s)”, or “Venezuelan(s)” in either the title or sampling section of a dataset in the metadata;

- c. Keep those datasets that have UNHCR as one of the data producers in the metadata.

If a given dataset passes at least one of these filters, it is flagged as “potentially relevant.”<sup>16</sup> It is worth highlighting that these filters have been developed in an iterative process. We consulted with a number of country experts both from UNHCR and the World Bank to avoid _false negatives_ (or those datasets that do in fact sample from refugees or IDPs but are not correctly identified).<sup>17</sup> Applying these filters identifies 358 potentially relevant datasets from the WB MDL.

Fourth, we perform a manual verification to remove those datasets that are also cross-listed in the UNHCR MDL. It is worth noting that most of the potentially relevant datasets taken from the WB MDL are also already cross-listed in the UNHCR MDL and thus are removed to avoid double-counting. After removing these cross-listed datasets, we have 62 datasets left from the WB MDL. Fifth, we apply the same manual verification to identify false positive datasets from the WB MDL following the same procedure as done for the datasets from the UNHCR MDL, which ultimately leaves us with 6 datasets from the WB MDL.<sup>18</sup> Finally, we combine the identified 369 datasets from the UNHCR MDL and 6 datasets from the WB MDL to arrive at our final list of 375 FDP micro-level datasets.
allocation method is imputation. In the first case, the CASEN allows to identify who attends an educational institution, the educational level attended, and the financing scheme of the institution —i.e., public, subsidized or private—, so that it is possible to impute the average cost of education disaggregated by level of education, financing scheme and, in the case of tertiary education, if the benefit is either received by the institution or the student. If the student is the recipient, the imputation is disaggregated by benefit, scholarship or credit, with the latter considering only the fee paid for the credits bought by the government under the _Crédito con garantía estatal_ scheme (credit guaranteed scheme). In the case of health, the CASEN identifies who is affiliated to FONASA, DIPRECA or CAPREDENA systems, so that the analysis imputes the average cost based on the use of health services.

The assessment presented in this paper offers the most comprehensive tax-benefit incidence analysis available for Chile to date, and allows for the results to be comparable with other developing countries applying the same methodology. Yet, the results presented are point-in-time and do not account for behavioral, general equilibrium, or lifecycle effects, therefore overlooking the long-term effects of fiscal policy on wellbeing indicators. In addition, the analysis acknowledges the potential presence of measurement errors due to under-reporting of certain income categories and under-sampling of the top incomes in the household surveys.

The evidence presented in the next section, as mentioned before, corresponds to the scenario that considers contributory pensions as deferred income instead of as government transfer, and for

> 16 For a detailed description of the scaling down procedure, see Higgins and Lustig (2017).

> 17 The magnitude of VAT evasion was estimated by Chile’s Internal Revenue Service (Servicio de Impuestos Internos, 2015).

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fluctuations <mark>(Ezran et al. 2023)</mark> . And satellite images may not be sufficient to disentangle the impact of disasters across population groups.

On the other hand, the approach proposed in this paper is closely linked to the existing literature on disasters through its reliance on _microsimulations_ . These are computational exercises that use disaggregated data on assets and earnings from previous household surveys or population and housing censuses to assess how each household in the sample may be affected by specific disasters.

In this spirit, Rozenberg and Hallegate (2016) harmonized representative household surveys for 89 emerging and developing countries, and estimated that “altogether earthquakes, storm surges, tsunamis and cyclones bring between 300,000 and 2.9 million people into extreme poverty on average per year”. Similarly, Skoufias et al. (2020) developed a microsimulation model for the Philippines to estimate household vulnerability to natural disasters of different types and intensities.

An important strength of microsimulations is their ability to generate impact estimates by population groups and geographic areas. However, an analytical framework aggregating the various impacts into a welfare loss is often missing. For example, a given fraction of assets of different types and of labor earnings in different sectors may be deemed lost. But the impact on specific households is bound to vary with their endowments and occupations prior to the disaster.

5
# **1 Introduction**

The first Sustainable Development Goal (SDG) is to reduce global poverty.<sup>1</sup> As a custodian agency responsible for monitoring progress, the World Bank measures and monitors global poverty across countries based on income or consumption expenditures reported in household budget surveys and the international poverty lines (Ferreira et al., 2016; Ravallion et al., 1991; World Bank, 1990; World Bank, 2022). However, the current practice of measuring global poverty implicitly adopts official national definitions of urban areas, which have been shown to vary widely across countries (Dijkstra et al., 2021; Roberts et al., 2017; Satterthwaite, 2007; World Bank, 2009). This variation poses a challenge in disaggregating poverty by urban and rural areas in a globally comparable way. The lack of globally consistent information on urban poverty hinders efficient and effective resource allocation to achieve the SDGs. National governments may struggle to allocate resources optimally between urban and rural areas, as well as between different types of urban areas within their countries. Unfortunately, only a few attempts have been made to measure and analyze global poverty from an urban/rural perspective, with Ravallion et al. (2007) being an exceptional early example examining the urbanization of global poverty. At best, cross-country comparisons have been made based on poverty measures using national poverty lines (for example; Ferré et al., 2012).

Building on recent work by Combes et al. (2023), this paper aims to provide new evidence on poverty distributions within and between countries based on globally consistent urban and rural poverty measurements.<sup>2</sup> Combes et al. (2023) present a new effort to consistently delineate urban areas across countries using two different approaches: the Degree of Urbanization (DOU) and Dartboard (DB) approaches. The DOU approach classifies cells in a gridded population layer into different urban and rural categories by applying uniform population and population density thresholds
85. In the _Czech Republic_ , self-employed also benefit from a lower labor tax wedge due to a lower tax base for social insurance contributions, which is only half that of employees. According to a survey by the Research Institute for Labor and Social Affairs (Výzkumný Ústav Práce a Sociálních Vĕcí, RILSA), the number of ―false‖ self-employment (in Czech labelled as ̳švarcsystém‘) amounted to 100,000 in 2006-2007. Experts from the Czech Chamber of Commerce (Hospodářská komora ČR, HK ČR) estimate that this figure could be significantly higher at 200,000 workers. These figures represented between 13 and 26 percent of the 750,000 self-employed individuals in the Czech Republic in 2007. This type of work contract is not common among large firms but more among medium-sized and smaller companies. According to the survey by RILSA, despite short-term benefits for the firms, over the longer-term this type of labor contract also carries risks as competitiveness could decline due to insufficient training and lack of loyalty. Furthermore, due to lower social insurance benefits, the self-employed face a higher poverty risk over the longer-term.

86. In the _Slovak Republic_ , self-employed workers also benefit from a lower labor tax wedge, as their tax base for social security contributions is only half of the average monthly taxable income of the previous year. This encourages bogus self-employment and has led to a nationwide discussion on false self-employment during the preparation of the amendments to the Labor Code in 2006/2007. The main critique was the insufficient social protection of involuntary self-employed workers. The policy objective is to eliminate involuntary selfemployment.

87. In _Latvia_ , there is an ongoing debate about the need to simplify the tax regime for SMEs and thereby remove incentives for undeclared work. Self-employed workers have to register with the State Revenue Service (Valsts Ieņēmumu Dienests, VID) and to record all economic activities (income, costs) in a special record book. Self-employed persons, who work as individual businesspersons, are required to register themselves with the local government. They
Our results suggest several areas for consideration in a public policy context. One implication of the new standard on aflatoxins in Europe is the potential application of the risk reduction level to other contaminants in food. The EU directive was developed based on the JECFA risk assessment used by CODEX to establish a less stringent international standard. The fact that the EU decided to regulate aflatoxin B 1 directly to achieve deaths risk reduction is not without cost. The JECFA risk assessment can suggest that 0.2 death per billion risk reduction will be achieved by reducing the aflatoxin B 1 maximum allowable level by 1 ppb<sup>8</sup> , which implies for the case of cereals, dried and preserved fruits and edible nuts that 1.4 deaths per billion risk reduction will be achieved under the Commission's new standard (2 ppb) as opposed to the level that follows the CODEX guideline (9 ppb). This estimated reduction of liver cancer is small compared to the total number of deaths of liver cancer in the EU. WHO estimates approximately 33,000 people die from liver cancer every year in EU which has population of half billion.

The standard is also relevant in consideration of obligations in the WTO SPS Agreement. The Agreement recognizes the rights of member countries to determine the "appropriate levels of protection" of human health. The level set by Europe and our findings on the magnitude of the trade effect, however, raise important questions for consideration. These include the costs of a proliferation of national standards set in absence of CODEX setting an internationally agreed level for B 1 directly, as well as how the WTO addresses the economic trade-off of individual interpretations of "appropriate protection" and "least trade distorting" in SPS cases with the type of empirical work now being conducted in analyses such as this one.

Finally, our results suggest several areas for further empirical research and extension of the analysis. A gravity-equation model is unable to disentangle demand and supply 18
income, net market income, disposable income, consumable income or: YY = II TT BB YY final income. _Ii_ is the market or pre-fiscal income for individual _I_ ; _j_ =1,2,3...n is the type of tax paid, direct or indirect, by individual _i_ ; and m=1,2,3...n is the type of transfers, including subsidies received by individual _i_ . Our starting point is disposable income, which is proxied in the 2018/19 Ethiopia Socioeconomic Survey (ESS) data by total consumption spending. In addition, we construct two intermediate income concepts following market income, namely gross income and net market income. _Gross income_ is the cash available when the government has distributed direct transfers. _Net market income_ is cash available after direct taxes. _Consumable income_ is calculated by subtracting direct and indirect taxes from the sum of market income, subsidies, and direct transfers received; for this study, it is derived from disposable income by adding subsidies and deducting indirect taxes, which are simulated using data on purchased consumable items. Similarly, indirect subsidies are estimated using ESS wheat and kerosene consumption data. _Final income_ is equal to consumable income plus the monetized value of in-kind health and education services, less any co-payments, user fees, and participation costs for those services. Moving from consumable to final income highlights the distributional effects of public spending on health and education.

> 2 Spending on non-assignable goods consumption includes domestic food consumption (food grown by the household, purchases, and gifts); consumption of food away from home; and some non-food consumption and utilities expenditures. Based on ESS data, spending on non-assignable food consumption averages about 80 percent of total non-assignable goods.

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Table 2: First stage

||Mean PM2_._5|Mean PM2_._5|Mean PM2_._5|
|---|---|---|---|
|Winter inversion, low|2.3865<sup>_∗∗∗_</sup><br>|2.4005<sup>_∗∗∗_</sup><br>||
||(0.4930)|(0.4931)||
|Summer inversion, low|-1.2599||-1.5167|
||(1.4048)||(1.4015)|
|N|8,823|8,823|8,823|
|**Relative Efects:**||||
|100% winter days as inversion_→_X% ∆PM2_._5|5.16|5.13||
|**F-test of inversions:**|12.47|23.70|1.17|
|**Weather controls:**||||
|Temperature|Y|Y|Y|
|Precipitation|Y|Y|Y|
|Relative humidity|Y|Y|Y|
|Surface pressure|Y|Y|Y|
|Wind speed|Y|Y|Y|
|Wind direction|Y|Y|Y|
|**Fixed efects:**||||
|Fiscal year|Y|Y|Y|

Notes: The outcome is the first difference within a district of the annual average of PM2 _._ 5 measured at the district level during a fiscal year from April to March and weighted by population. Pollution data comes from the Van Donkelaar group. Inversion data comes from the MERRA re-analysis data and measures the incidents when temperature at atmospheric levels above the surface is higher than at the surface by district for each fiscal year. Inversions are measured as the share of
analysis stage. However, we argue that these solutions do not full address the issues with both idiosyncratic and systematic choice error in MPL. They often misattribute the individual error to variation in WTP, fail to account for order biases, and potentially even introduce new sources of bias, for example through selection. Table 1 summarizes these issues. In this section, we discuss how these solutions can lead to biased WTP measurement or incorrect standard errors. In Section 3.6, we illustrate this using data from South Africa.

At the elicitation stage, MPL design approaches often focus on avoiding or suppressing MSB by enforcing a single switch. The problem is that these solutions may not change the incidence of error, only its visibility. As a result, errors are never observed; variation that may simply be due to error is instead attributed to heterogeneity in WTP. For example, a “switching MPL” or sMPL shows subjects the full MPL and ask them at which point they would switch from option A to option B (e.g., Tanaka et al. (2010), Andersen et al.

13
Both FIF and WIF groups had similar types of proposed activities. These activities included crop production, natural resource management, livestock and poultry rearing, apiculture, and off-farm activities.

# **4. Data and Baseline Discussion**

In order to study the effect of these two interventions on agricultural productivity and income, three rounds of data were collected to form a panel data series from 1,595 households, of which 1,214 are used for the analysis in this paper. Round 1, the baseline, was collected from August to September 2010 and covered 2,675 households. Randomization only occurred after the baseline had been collected. Only 1,806 households that were members of a FIF group with an eligible proposal were kept in the study and followed up through to the endline. One and a half years after FIF was implemented, and a year after WIF, midline data were collected (March to April 2012). Finally, in June 2013 endline data were collected. In this section, we discuss existing baseline differences in our main outcome variables.

From the original sample of 1,806 FIF eligible households, 1,595 households were interviewed in all three rounds and 1,214 of them are used for this study. In our analysis, we only used households that satisfied our selection criterion: married couples.<sup>9</sup> Our sample has a total of 634 FIF and 158 WIF households. From these two groups, there are a total of 76 households that receive both FIF and WIF treatments. In addition, we have a total of 498 pure control households. Figure A1 in the appendix lays out the study treatment arms.

Our study sample is primarily male-headed, as shown in Table A1 in the appendix, where 98 percent of our sample at baseline was male-headed. At baseline, the average age of the household head was 44.5, with an average household size of 6.4 members and 0.03 members enrolled in school.

One critical issue
indicated that they use electric fans in their homes. Most BISP beneficiaries and _katchi abadi_ residents (the poorest groups interviewed) reported using washing machines, fridges, irons, TVs and mobile phone chargers. Lower middle‐income households reported using a variety of electrical appliances, including personal computers, evaporative coolers, juicers, vacuum cleaners and sewing machines. More than half of the research participants mentioned using electric pumps to fill household water tanks. Although these water pumps consume a limited amount of electricity, the household water supply is interrupted if load shedding occurs when tanks are empty. Respondents engaged in farming also mentioned using electric pumps for tube‐well irrigation. While farmers have relatively higher electricity use on average for this reason, households residing in rural areas use slightly less electricity on average than their urban counterparts due to lower rates of appliance use (Figure 7). This is not just due to lower living standards: during the summer months, rural families can sleep outside at night, and thereby avoid the need for electric cooling.

> 12 The average monthly income of participants varies based on province and location. Overall, the reported average monthly income of low‐income respondents was between Rs 13,050 and Rs 14,675, representing the first and second quintiles according to 2011‐2012 PSLM. The average monthly income of lower middle income participants ranged between Rs 19,700 and Rs 22,000, corresponding to the 3<sup>rd</sup> and 4<sup>th</sup> quintiles according to 2011‐2012 PSLM/HIES.

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