rafmacalaba/data-use-mentions-extended huggingface dataset:
0.265|
|_Educational variable_|_Yes_|_No_|_No_|_Yes_|_No_|
|_Self-reported hazard exposure_|_Yes_|_Yes_|_No_|_No_|_Yes_|
|_Survey rounds_|_2019_|_2019_|_2019_|_2019_|_2016-2019_|

_Note: The table presents empricial estimates of the total vulnerability rate relying on different model specifications. The first colum presents the baseline estimate. Column 2 refers to a model specification which excludes the educational variable. Column 3 presents results for a model specification which relies on external data sources to measure risk exposure and excludes the educational variable. Column 4 relies on external data sources to measure risk exposure and includes the educational variable. Lastly, Column 5 considers pooled data from survey years 2016 to 2019. Source: World Bank estimates based on EHPM (2019), INFORM (2019)._

# 6.2. Accounting for Potential Measurement Errors

**The income-based measure of welfare employed in this paper is subject to important empirical limitations.** There are two main approaches to measure material well-being of the poor. One approach relies on income while a second approach draws from consumption data. Both approaches have important advantages and disadvantages (Meyer and Sullivan, 2003). While income is easier to measure, it might be inaccurately measured for the poor due to measurement error and under-reporting as well as high levels of informality among those with lower income levels. In addition, consumption might more accurately reflect material well-being. These limitations are an important caveat and could influence the results presented in this paper.

**We test if our results are robust to the inclusion of measurement errors.** One of the weaknesses of the vulnerability tool is that it assumes no measurement errors. Especially in the case of income, measurement errors are not uncommon. As the vulnerability rate draws from the idiosyncratic errors, measurement errors could inflate our vulnerability rate (measurement errors form part of the idiosyncratic error). Therefore, we assume that 25-50 percent of the overall volatility, which we observe across
For the past several years the WorldPop project,<sup>14</sup> based at the University of Southampton, has produced gridded population maps for dozens of countries, which detail the estimated population per 100 square meters.<sup>15</sup> WorldPop has also produced gridded poverty estimates at a 1 kilometer resolution for a small number of countries using a similar method based on household survey data. In collaboration with the Flowminder Foundation, WorldPop is now beginning to integrate CDR-derived features into the same framework. While relatively little information has been published on this approach, it has the advantage of being able to incorporate all of the relevant data into a single format. The resulting maps should not only be highly accurate, but also as consistent as possible with the underlying datasets. One caveat to the WorldPop approach is that the inclusion of the poverty dimension is still in an early stage, and it is not clear how these maps are validated, since by design they should closely correlate with survey-based poverty estimates.<sup>16</sup>

# **Using CDR Data for Poverty Mapping**

As technical approaches to using CDRs for poverty mapping continue to evolve, a number of challenges will need to be addressed in order to operationalize this methodology as a practical tool for policy analysis. Several of these challenges are described below. A range of other legal and ethical issues are considered in a separate World Bank/Data-Pop Alliance white paper.<sup>17</sup>

## **_Validation and Transparency_**

Validation is more complicated for CDR models than it is for survey-based techniques. Survey instruments are relatively transparent. They can be inspected, and quantitative and qualitative fieldwork performed both before and after data collection can build confidence. Spatial and temporal inconsistencies can be checked, and in rare cases overlapping surveys can be crosschecked. As a result, well-established survey programs such as the Demographic and Health Surveys and Living Standards Measurement Study are often treated as close to the ground truth.
Census m09 No, Some, Camotdo at all 4 domams.No sdf-care/communicafion<br>Europe &Viemam Central Azim Population and Housing Census 2009 If yes. How difficult is it?: alittle. very 4 dom ains. No self-care/communication yes<br>Albania Population and Housing Census 2011 yes<br>Bosnia andHezegowna HouseholdLabor ForceBudgetSurvey Survey 20152011 No,Yes\Nomina,mgr difficultes, yes<br>Georgia Population Census 2013 1=no difficniies 2-has, mma 3-tas, yes<br>Sata Population Census 2014 yes<br>Latin America and Caribbean Papulahon Census 211 yes<br>Argentina ‘National Populahon C ensas 2010 YesNo 4 dom ams_No sdf-cxe/communacafion<br>Belize Population and Housing Census 2010 Answers arenotnumbered and there is yes<br>Bolvia Popolahon<br>Brazil Brazilian Longitudinaland HousmgStudy Censusof Aging (ELSI) 2015-2016212 Different categorical answers 5 dom ams_No sdf-care_ yesyes<br>Coloma Encuesta Nacional de calidad de ada (ENCV) Yeatty from 2012-2016 Yes/No yes<br>Encuesta Nacional de calidad de vida (ENCV) 2017 yes<br>Encursta Nacional de uso del tiempo (ENUT) 2012 YesNo yes<br>Encuesta de Transicion de la escuela al trabajo (ETET) 2013, 2015 "a little" instead of "som e" yes<br>Costa Rica ‘NatNat ional DisabilityDemographic Surveyand Health Survey 20 10,18 2015 None to extreme yes<br>DommacanJamaica R_ Popolatonand Housmg Census 2010 YesNo 4 domams_No sdf-cre/communacafion<br>Mexico ‘PapulakonPopulation Censusand Housing Census 201 10 YesNo yes<br>Encuesta Nacional de Hogares (ENH) 2016, 2017 4 dom ains. No self-care/communication<br>Panama Stady om Global Apcimy and Adult Health (SAGE) 2009, 2014 None, mild, moderate, severe, creme yes<br>Pau EncPop u esla t iona Nacsonal Census De Hogares (EN AHO) 20 15,10 2016 Yes\No 5 do mains.mams_ No s elf-cadf-cu re ._ yes
“entrepreneurial spirit,” are less likely to be internal or return (international) migrants, come from wealthier households, and work longer hours. Like De Mel, McKenzie and Woodruff (2008), Grimm, Knorringa and Lay (2011) find no evidence that successful and unsuccessful entrepreneurs differ in their aversion to risk.

# **III. Data**

Like De Mel, et al (2010) and Grimm, et al (2011), we measure the proportion of own account workers who have characteristics similar to employers. Like Grimm, Knorringa and Lay (2011), we measure the proportion of unsuccessful self-employed who have a high potential to be successful, based on selected observable characteristics. Our measures of success, however, are different from that used by Grimm, Knorringa and Lay (2011). Grimm, Knorringa and Lay (2011) use a two-part measure of success based on reported capital and profit. In contrast, we use two alternative measures success: (1) whether the self-employed worker is an employer (vs. an own account worker) and (2) whether the self-employed worker belongs to a family with per capita consumption above the $2/day poverty line. Although the latter is a meaningful measure of economic position of the household, it overstates the percentage of enterprises that have the potential to grow and create jobs. Attributing household poverty to an individual member’s enterprise is challenging, and a substantial proportion of enterprises with little potential for growth or job creation are likely to be run by households that have escaped poverty due to the presence of a wage earner or non-wage income. Therefore, we consider the second measure of success as a robustness test of our results, while the first measure is our primary measure of success.

The data that we use comes from micro-level household surveys collected by the Development Economics Group (DEC) of the World Bank, the International Income Distribution Database (I2D2). This data base consists of already existing data sets that have been
_Source: World Bank’s World Development Indicators, and IEA’s Emissions data_ _<u>(2012). Data for 1960-2009 are</u> from the World Bank and data for 2010 are extrapolated using the International Energy Agency’s most recent data for emissions._ Focusing on global emissions is important because that is what matters for the atmosphere. Similarly focusing on global GDP is important to recognize the role played by energy demand in driving global emissions. As global GDP rises, demand for energy will rise in high-income countries less than proportionately. But there will be much greater increases in energy use from developing countries. These increases from very low current levels are not only unavoidable but arguably a moral imperative, since increased energy use will be crucial to lifting vast numbers of people out of extreme poverty.

How should this play out? One can ask what needs to happen by 2050 for the world to have a fighting chance of limiting temperature increases to below 2 degrees centigrade. Put more precisely, how much does the global emissions/GDP ratio need to fall to avert runaway climate change?

Suppose global GDP-per-capita growth were to average 3 percent over the next four decades, which is less than seen in recent years and assumes some under-performance by low-income countries; and global population growth to average about 0.8 percent. These assumptions, combined with the goal of limiting emissions in 2050 to about 15 gigatons of CO2 emissions, which climate scientists say is near the upper end of what might be safe, imply that the emissions/GDP ratio must fall from the current level of 0.8 to about 0.08—that is, a ten-fold decline.

In Figure 1, the barely visible bar at 2050 is the amount of emissions that the atmosphere just might be able to tolerate without rapidly overheating. For all practical purposes, we need to be in a zeroemissions world by 2050 to avert catastrophe. Note that the drop between now and 2050 will have to be dramatically steeper than anything we
|17|14|33|
|Colombia|0.19|11|23|27|12|
|Thailand|0.17|12|11|7|76|
|China|0.16|13|1|1|155|
|Brazil|0.16|14|7|5|113|
|Philippines|0.16|15|9|10|80|
|South Africa|0.14|16|16|18|62|
|Ghana|0.14|17|37|32|10|
|Russian Federation|0.14|18|6|8|122|
|Tanzania|0.13|19|45|60|4|
|Afghanistan|0.13|20|38|53|7|

Sources: Statista; Chainalysis.

Note: The panel shows the 2021 crypto adoption index calculated and published by Chainalysis. These figures should be interpreted with caution as data gaps remain significant.

# 2 Literature

## _Drivers of crypto-assets usage_

Szabo (2017) observes that economic transactions and exchanges require trust between participants. However, trust does not scale well as the number of participants in a network grows, thereby increasing transaction costs. As a result, historically, trust has been delegated to a central authority which may be susceptible to failure, fraud or rent seeking. He then posits that the technology which underpins crypto-assets can lower social transaction costs in low-trust environments without the need to delegate trust to a central third party. As such, the technology may enable more and more efficient economic interactions between large numbers of agents as 10
percent to household value added. The share of livestock is rather limited, with pigs and other livestock each contributing 0.1 percent. For this household group the total contribution of agricultural activities to value added is 59.2 percent, while the remainder is coming from engaging in off-farm activities. Hiring out its labor to other villagers contributes 1.3 percent to value added, while working in local businesses earns 19.4 percent of value added. Outside village employment is a significant income source for this household group, with a share of 18.1 percent. For this unlinked household, migration opportunities are restricted to moving inside the province, which contributes 1.8 percent of value added.

For all household types off-farm employment contributes a significant share of income, but there exist important differences in the nature of non-agricultural income sources. Comparing the two household groups that have no link outside the province, we find that households lacking draught power are oriented more towards local off-farm employment. The households with draught power obtain 71.4 percent of value-added from agriculture, which is similar to the importance of agriculture for the other household group owning draught power. The household group with access to migration but lacking draught power derives about 40 percent of value-added from outside province migration, and only derives about 45 percent of value-added from agricultural activities. Differential access to agricultural and migration opportunities is thus reflected in the composition of household value-added.

Differences in activities result in differences in income patterns across the four household groups. The bottom part of Table 2 presents income per adult consumer equivalent<sup>3</sup> to allow a direct comparison across households. As a crude poverty assessment

> 3 Adult equivalent instead of per capita consumption is used to account for differences in consumption between males and females and between age groups. Lacking survey data, conversion factors were taken from detailed consumption data of a study in Bangladesh (Zeller _et al._ , 2001). In addition to differences in age and gender, consumer equivalents were corrected
for which there were event data. The mean BAR score for dyads without treaties and those with treaties was similar, with a slightly higher value for those with treaties. Nearly 56% of all observations had alliances, while over 68% of those with treaties had some sort of military alliance ( _Alliance_ ). The mean democracy score ( _Democracy_ ) for observations with treaties was nearly triple the score for observations without treaties. There is almost no difference in the coefficient of variation of precipitation between observations with and without treaties.

13
sup>5</sup> This paper makes four main contributions. First, it assesses the gain in accuracy and precision, relative to direct survey estimates, from combining survey data with geospatial indicators to generate small area estimates of monetary poverty. Second, it compares the performance of different methods for generating estimates for both in-sample and out-of-sample municipalities –

> 5 EBPs are also known as EBLUP (Empirical Best Linear Unbiased Predictor) for linear mixed models. See Battese, Harter, and Fuller (1988), Jiang and Lahiri (2008), Molina and Rao (2010), and Van der Weide (2014) for more on their use in small area estimation.

4
# 8 Conclusion

In this paper, we address two key issues that bedevil the implementation of conditional cash transfer schemes: targeting to maximize additionality, and setting payments to minimize social cost. We analyze Mexico’s national level payments for ecosystem services program, but the methods used are broadly applicable to many conditional cash transfer settings. We first show how simple predictions of deforestation risk can be linked to existing targeting efforts to identify land with likely higher benefits. We then combine this information with estimates of willingness to accept derived from a referendum question, using a well-established contingent valuation method to improve the cost-effectiveness of CCT targeting.

We present a simple strategy for adjusting the current payment scheme to more closely align with applicants’ willingness to accept levels. We associate willingness to accept with current program payment rate-setting practices and show that the current program could be significantly expanded without sacrificing environmental and social impact. This application of the referendum approach is lower in cost and less complicated than implementing an auction to establish appropriate payment levels. Furthermore, it is significantly more likely to be practical and politically feasible where future CCTs will be scaled-up.

While implementing periodic surveys to recalibrate targeting and payment levels is not costless or without challenge, we believe that the likelihood of strategic bias in surveys used for this purpose can be minimized through sampling subsets of program applicants on an occasional basis. This is feasible in our case because (1) governments do no recalibrate prices on a yearly basis, thus allowing significant time to pass between questioning and price calibration; (2) the program is large enough to allow representative sampling without repeats. In addition, if re-sampled, repeated surveying of applicants may actually provide an opportunity to extract useful information. A mixture of new and previously treated sample members would allow researchers to assemble information to improve the cost-effectiveness of CCTs (via the new sample members) while also measuring possible strategic bias (by comparing to the older sample members). Therefore, it may be both low cost and informative to integrate this type of referendum question
supply-use table, the imbalance is below and beyond official GDP depending on the method used. GDP in our balanced table is less than 1% off official GDP, and consistent across methods.

The adjustments in the components that make up GDP are slightly larger (see Tables 3 and 4). The largest change is reported for household expenditure. The main reason is that the official data for the large manufacturing sector report 50 percent more use than is available from production and imports. To attain consistency, a large decrease in consumption of manufactured goods is required; this accounts for about half of total consumption. Similarly, our optimized balancing routine results in an increase in imports of manufacturing goods as a percentage of total imports; this is required in order to balance the officially reported excess demand for manufactured goods (see Table 5).

# **Disaggregation of the input-output table**

Two sectors in the input-output table, “Transport, storage and communications” and “Financial intermediation”, account for most business services in Armenia. Given our focus on these services in the model, these sectors are disaggregated into 9 sectors using two data sources.

For “Transport, storage and communications” we obtained unpublished national accounts data for the year 2007 from the National Statistical Office of the Republic of Armenia. Table 7 shows the data.

107
classroom, Harris and Sass (2011) incorporate in their study the teachers grade in the college admission exam as a way of controlling the effect of higher education for the pre-college skills, thus avoiding a major source of bias.<sup>10</sup> The authors found no robust evidence that pre-service teachers training affects the productivity of the future teacher.

For Brazil, Louzano (2010) did not find a significant correlation between pre-service training of teachers and the performance of students. Menezes Filho (2007), on the other hand, reported significant impact of teacher's education only on third grade secondary education students, and for teachers with a degree in mathematics. The author also reports that teachers with 50 years or more of age positively affect students' learning. Such studies, however, do not use methods capable of dealing with various issues, in particular with the existing selection bias in the allocation between students, teachers and schools.

# **4. Data and Descriptive Analysis**

In this study we use data from the National Examination of Secondary School (ENEM) and from the School Census for the years 2009 and 2015, which covers proficiency indicators and a broad set of covariates at student and school levels, for potentially all Brazilian schools.<sup>11</sup> In total, there were 20,979 public secondary schools in ENEM microdata, of which 17,214 appeared in the year 2009, 19,847 appeared in the year 2015, and 16,082 occurred in both years. Following the MEC criteria for disseminating the ENEM results, schools with fewer than 10 students from the 3rd grade participating in the exam were excluded, which resulted in 11,506 schools remaining in the sample. Of these, 151 schools with a participation rate above 100% were also excluded, leaving 11,355 schools at the base in both years.

Based on the School Census data, two school-level variables were constructed to characterize teachers’ education for each field or discipline (
- IPA, P&R. 2020. “Compiled List of Forced Displacement Impact Evaluations.” Tech. rep., Innovations for Poverty Action’s Peace & Recovery Program.

- Islam, Asad, Tanvir Ahmed Mozumder, Tabassum Rahman, Tanvir Shatil, and Abu Siddique. 2021. “Forced Displacement, Mental Health, and Child Development: Evidence from the Rohingya Refugees.” _Working Paper_ .

- ISSP. 2017. “International Social Survey Programme: Work Orientations IV - ISSP 2015.” Published: GESIS Datenarchiv, K ̈oln. ZA6770 Datenfile Version 2.1.0, https://doi.org/10.4232/1.12848.

- Jahoda, Marie. 1981. “Work, employment, and unemployment: Values, theories, and approaches in social research.” _American Psychologist_ 36 (2):184–191. Publisher: American Psychological Association tex.address: US.

- Jahoda, Marie, Paul Lazarsfeld, and Hans Zeisel. 1971. “Marienthal: The Sociography of an unemployed community.” _Social Forces_ 50 (4):550–551. Publisher: Oxford Academic.

- Kabeer, Naila. 2015. “Gender, poverty, and inequality: A brief history of feminist contributions in the field of international development.” _Gender & Development_ 23 (2):189–205. Publisher: Routledge.

- Kessler, Ronald C., J. Blake Turner, and James S. House. 1988. “Effects of Unemployment on Health in a Community Survey: Main, Modifying, and Mediating Effects.” _Journal of Social Issues_ 44 (4):69–85. Tex.copyright: 1988 The Society for the Psychological Study of Social Issues.

- Kuhn, Andreas, Rafael Lalive, and Josef Zweim ̈uller. 2009. “The public health costs of job loss.” _Journal of Health Economics_ 28 (6):1099–1115. URL `https://www. sciencedirect.com/science
and facilitated the development of information to manage drought risks. Based on these significant advances, Ethiopia is well-placed to further reduce its vulnerability to drought risks.

A key feature in sound and sustainable drought risks management is the ability to respond early. In particular, as this paper showed, the large adverse impacts of droughts are compounded when assistance is provided late. A late response to droughts is associated with reductions in consumption, long-term welfare losses, malnutrition and excess mortality. By using early warning systems to trigger early action, it is possible to reduce the negative impacts of droughts on household welfare, and to protect Ethiopian farmers from the loss of lives and livelihoods.

With the LEAP and LIAS tools, combined with hotspots assessments, Ethiopia has the building blocks for a sound framework for early warning. Based on the LEAP and LIAS tools, it is possible to define early noregrets triggers for the release of the PSNP’s FCB. In particular, the LEAP and LIAS tools produce a range of variables, such as WRSI, yield reduction, and beneficiary numbers that could be used to define early no-regrets triggers. Basing actions during the onset of a drought on clearly defined triggers could lead to an increase in the timeliness of drought response, and would enhance the objectivity and transparency of the process to respond to droughts. For instance, the LEAP tool could be used to produce early warning data as early as August / September during a given calendar year, enabling a drought response by December, thereby protecting lives and livelihoods from the adverse impacts of droughts. The integration of seasonal climate forecasts into LEAP will provide a stronger basis for applying earlier crop production and needs estimates from LEAP.

The GoE and its DPs are continually improving the existing warning tools to address current limitations. For instance, the GoE, ACCRA and Oxfam are jointly developing a tool to estimate need based on _woreda_ risk profiles. Moreover, the NDRMC is working on the development of a multi-sector early warning system based on _woreda_ risk profiles, thereby further enhancing its