rafmacalaba/data-use-mentions-extended huggingface dataset:
healthy financial intermediation among the low- and middle-income groups.

BANSEFI is a descendent of the “ _Patronato del Ahorro Nacional_ ”, commonly identified by its main product the “ _Bonos del Ahorro Nacional_ ” which collected savings but did not make private-sector loans. BANSEFI continues to provide this savings service, but as noted above it also functions as a development bank, or “second floor” bank, to assist and support programs that could help popular banks to increase their income, reduce costs, improve their management, and broaden the services that popular intermediaries could provide to their members. One of the first tasks of BANSEFI was to develop a database on all popular banking institutions. For this purpose in 2002 BANSEFI conducted a census of the entities ( _Entidades de Ahorro y Crédito Popular_ ),<sup>18</sup> including those financial institutions that were not recognized officially by the financial authorities.

Table 26 presents an overview of the results from this census. As shown in the table, the cooperatives with the largest membership are the savings and loan associations ( _Sociedades de Ahorro y Préstamo_ ), which are formally regulated, and the non-regulated savings and loan cooperatives ( _Cooperativas de Ahorro y Prestámo_ ). The third largest membership is that of the _Cajas Solidarias_ . These were originally rural savings institutions which were instituted in a number of

> 17 It continues playing its old role of enhancing the savings culture through its deposit instruments, but does not make privatesector loans as all of its funds are channeled to the public sector.  To enhance its ability to collect funds and increase savings, the institution can offer tax-exempt interest and/or premiums in order to increase savings.

> 18 BANSEFI Diagnóstico de la Situación Financiera, Equipamiento Tecnológico y censo de las Entidades de Ahorro y Crédito Popular (EACP) Informe Versión Pública Junio de 2002.

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**Table 1: Descriptive Statistics of Key Variables**

||Mean|S.D.|
|---|---|---|
|Hourly Wage|1.4|1.6|
|Log(wage)|0.0838|0.7307|
|Schooling (years)|12.8|2.3|
|Experience (years)|20.6|12.5|
|Experience-squared|581.5|560.4|
|_Level of education (percent)_|||
|No schooling|0.3|0.1|
|Primary|10.2|3.0|
|Secondary|69.4|46.1|
|Tertiary|20.1|40.1|

Source: AMSSW 2015 On average, the population of Azerbaijan has completed 13 years of schooling. That is, 69 percent of this sample has completed high school, 20 percent higher education, and only 10 percent of the population did not complete high school. This country has made a significant achievement in terms of high school graduation, but this accomplishment is not translated into higher graduation in university. On average, the labor force in Azerbaijan has 21 years of experience, suggesting a middle-aged workforce population. In this sample, the male employment rate is higher (see Tables A1 and A2 in the Annex). However, female workers have half a year more schooling, and almost two years of more experience than men do.

9
the means provided by pensions.

**Table A.1: Main results under the PGT scenario**

||**Brazil 2019**|**Market**<br>**Income**|**Market Income +**<br>**Pensions**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|**Final**<br>**Income**|
|---|---|---|---|---|---|---|
||**Gini**|0.616|0.585|0.521|0.521|0.429|
|**Inequality**|**Theil Index**|0.745|0.674|0.533|0.533|0.376|
||**90/10**|1185.218|39.345|12.886|12.709|5.597|
||**Headcount Index**|24.2%|18.4%|10.5%|13.3%||
|_National Extreme PL_|**Poverty Gap**|17.7%|12.2%|4.6%|5.7%||
||**Squared Poverty Gap**|15.4%|10.1%|2.5%|3.2%||
||**Headcount Index**|36.7%|32.8%|26.0%|32.2%||
|_National Moderate PL_|**Poverty Gap**|24.3%|19.1%|11.5%|14.4%||
||**
ii

# <u>Abstract</u>

This paper uses a large, nationally-representative household survey to analyze the impact of internal remittances (from Ghana) and international remittances (from African and other countries) on poverty in Ghana. With only one exception, it finds that both types of remittances reduce the level, depth and severity of poverty in Ghana. However, the size of the poverty reduction depends on how poverty is being measured. The paper finds that poverty is reduced more when international, as opposed to internal, remittances are included in household income, and when poverty is measured by the more sensitive poverty measures: poverty gap and squared poverty gap. For example, the squared poverty gap measure shows that including international remittances in household expenditure (income) reduces the severity of poverty by 34.8 percent, while including internal remittances in such income reduces the severity of poverty by only 4.1 percent. International remittances reduce the severity of poverty more than internal remittances because of the differential impact of these two types of remittances on poor households. Households in the poorest decile group receive 22.7 percent of their total household expenditure (income) from international remittances, as opposed to only 13.8 percent of such income from internal remittances. When these “poorest of the poor” households receive international remittances, their income status changes dramatically and this in turn has a large effect on any poverty measure – like the squared poverty gap – that considers both the number and distance of poor households beneath the poverty line.
repositories (Ravallion and Wagstaff 2011).

Using citation data from the Google Scholar platform, this study revealed the heterogeneous quality of the research products selected. A large share of them – including several _World Development Reports_ – had little discernable influence. However, within specific geographic areas such as Africa, China and India, and specific topics such as education, growth, inequality, health, and poverty, World Bank research products were ranked highly. And a few of them were among the most cited articles in several of the top economics journals. However, beyond noting that ratings tend to rise with years of publishing, this study did not attempt to explain what drove these stark differences in visibility.

6
our theory, however, that difference is not statistically significant.

Finally, Table 8 investigates interaction effects related to financial flows from China, using data obtained from AidData (Dreher, Fuchs, Parks, Strange, and Tierney, 2017).<sup>5</sup> China’s aid and investment policies follow a principle of non-interference in the politics and policy choices of recipient countries (Dreher, Fuchs, Parks, Strange, and Tierney, 2016). Furthermore, flows from China to aid-recipient countries have become very sizeable in recent years. Comparing its aid amounts with those of other bilateral and multilateral donors, China has been among the top 10 donors in most years. A notewor-

> 5China does not provide reliable data on its financial investments in developing countries. The methodology used for the data we use here relies on identifying aid projects through a media database, followed by targeted online searches to gather more detailed information. For a full description of the methodology see Strange, Parks, Perla, and Desai (2015) and Custer, Rice, Masaki, Latourell, and Parks (2015).

21
................................ 15|
|Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB) ................................................ 16|
|Table 7: National poverty estimates based on nominal and spatially adjusted welfare ............................ 17|
|Table 8:Ratio of survey household consumption to Private Consumption and GDP ................................. 19|
|Table 9: Average annual growth rate 2003-2009 ....................................................................................... 20|
|Table 10: Comparison between baseline poverty estimates and projections in 2009 ............................... 24|
Leach, Andrew, Nic Rivers, and Blake Shaffer. 2020. “Canadian Electricity Markets during the COVID-19 Pandemic: An Initial Assessment.” Canadian Public Policy 46 (S2): S145–59. https://doi.org/10.3138/cpp.2020-060.

Liu, Zhu, Philippe Ciais, Zhu Deng, Ruixue Lei, Steven J. Davis, Sha Feng, Bo Zheng, et al. 2020. “COVID-19 Causes Record Decline in Global CO2 Emissions,” May. https://escholarship.org/uc/item/2fv7n055.

McWilliams, B. and G. Zachmann (2020) ‘Bruegel electricity tracker of COVID-19 lockdown effects’, Bruegel Datasets, first published 25 March, available at https://www.bruegel.org/2020/03/covid-19-crisis-electricity-demand-as-a-real-timeindicator/ Menut, Laurent, Bertrand Bessagnet, Guillaume Siour, Sylvain Mailler, Romain Pennel, and Arineh Cholakian. 2020. “Impact of Lockdown Measures to Combat Covid-19 on Air Quality over Western Europe.” Science of The Total Environment 741 (November): 140426. https://doi.org/10/gg5swp.

Nakada, Liane Yuri Kondo, and Rodrigo Custodio Urban. 2020. “COVID-19 Pandemic: Impacts on the Air Quality during the Partial Lockdown in São Paulo State, Brazil.” Science of The Total Environment 730 (August): 139087. https://doi.org/10/ggvctc.

Sharma, Shubham, Mengyuan Zhang, Anshika, Jingsi Gao, Hongliang Zhang, and Sri Harsha Kota. 2020. “Effect of Restricted Emissions during COVID-19 on Air Quality in India.” Science of The Total Environment 728 (August): 138878. https://doi.org/10/ggvcd3.

23
Table 10 presents the welfare statistics associated with the 4-1-1-1 schedule. The analysis suggests that there are positive welfare gains from adopting the Brunei calendar, with expected welfare gains of 3.5 percent.

# **6 Conclusion**

There are several challenges associated with assessing the efficiency properties of the 5-2 week and quantitatively evaluating the tradeoffs associated with altering its structure. This paper attempts to formalize these challenges and make preliminary steps towards addressing them.

The first challenge relates to evaluating the relationship between work duration and productivity on a daily frequency. Conceptually, increasing the duration of consecutive work periods may be associated with some productivity losses due to fatigue, or with some productivity gains due to learningby-doing. Empirically evaluating the quantitative relevance of each of these channels presents a challenge given that daily output measures are unavailable. Under the assumption that, on a daily frequency, electricity and labor are required in fixed proportions, the structural parameters of the model can be estimated based on movements in electricity usage. The findings suggest that both channels are quantitatively relevant for the evolution of labor productivity over the workweek. However, a permanent reduction in the ratio of workdays to rest days unambiguously results in lower output.

A second challenge relates to calibrating the preference towards leisure. Evaluating alternative schedules requires taking a stance towards the preference for leisure smoothing on a relatively high frequency. Here, I follow the macro literature and assume that agents have a preference for smoothing leisure. However, the institutional practice of “long weekend” holidays (e.g. Labor Day, Presidents’ Day, Memorial Day etc) suggests that there may be some preference for leisure-bunching at high frequencies, or some fixed costs associated with vacation. These plausible channels have the potential to 35
047|305|152,179|0.093|||0.020|35.05|7,431|0.061|
|Summergrowing23|0.066|236|166,796|0.104|||0.023|37.22|9,305|0.064|

_Source:_ Interpretation of satellite imagery as described in the text (columns 1 to 3); computations using data from https://liveuamap.com/ru/time and https://novy.tv/ru/news/2022/06/02/karta-bojovyh-dij-v-ukrayini/ (column 4); ACLED battles and fatalities (columns 7 to 10). _Note:_ Data are for 10,666 VCs where 10,556 and 10,002 VCs that were under Ukrainian control in 2015 (excluding Crimea and areas occupied in 2014) that had a positive cultivated area in 2019–23 for summer and winter crops, respectively. The stages for winter and summer crops are as follows: P = planting; D = dormancy; G= growing. ACLED = <mark>Armed Conflict Location and Events Data; ha = hectares; indic. = indicator of conflict; VC = village council.</mark> 18
area level random effects, with clusters nested within areas, entails virtually no loss of efficiency when estimating area level welfare-based indicators.

Under design-based validation, where the sampling strategy mimics real world scenarios such as those implemented under LSMS surveys, SAE methods present improvements over direct estimators. We have also investigated estimators based on unit-context models, originally proposed by Nguyen (2012), which can be applied when census auxiliary data at the household level is not valid. Given that under the two-stage samples used, many municipalities, which are the target areas, are represented by a small number of observations (or even zero), these data are not always suitable for FH area level models. Despite model-based simulations yielding poor results in terms of bias for unit-context models the CensusEB variant does considerably better than the ELL variant under the design-based simulations. The reason for the positive results is shown to be due to the bias that is inherent in the unit-context model, which was being offset by bias due to deviations from normality. This offset is something that is not guaranteed to occur in all scenarios because the direction of the bias of unit-context models is not known _a priori_ . In simulations where deviations from normality are not an issue, the bias of the unit-context method becomes quite clear. Additionally, the method’s performance is contingent upon the number of subdomains present in the domain. Moreover, PSUs or subdomains must be matched across census and survey for unit-context models, something that is not always feasible. Finally, since the direction and size of the bias of the unit-context model are not known beforehand, the models may be considered an alternative to FH and other area level models only when area level models are not applicable, like in our design-based experiment with the more realistic two-stage sampling.

38
-1.875<br>(2.213)||||-1.365<br>(2.414)||
|Party of executive controls all houses in<br>non-democraciesi,t|||-6.579<sup>~~**~~</sup><br>(3.045)||||-6.241<sup>~~*~~</sup><br>(3.346)||
|Herfindahl Index of government party<br>seats inparliament in democraciesi,t||||-5.055<br>(3.574)||||-5.079<br>(4.105)|
|Herfindahl Index of government party<br>seats inparliament in non-democraciesi,t||||-17.748<sup>~~***~~</sup><br>(3.769)||||-15.018<sup>~~***~~</sup><br>(4.714)|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R<sup>~~2~~</sup>within|0.591|0.591|0.609|0.594|0.571|0.572|0.605|0.587|
|R<sup>~~2~~</sup>overall|0.578|0.578|0.606|0.603|0.530|0.532|0.593|0.537|
|Time dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,077|1,077|940|1,024|959|959|839|
||||
|Cragg-Donald Wald F statistic||176.40|9.91|49.86|
|<br>||14883|172|3671|
|Kleibergen-Paap Wald F statistic<br>||.<br>{0.00}|.<br>{0.25}|.<br>{0.00}|
|HansenJ-statisticp-values<br>Notes: Panel A of this table shows first-stag<br>all exogenous variables included in the seco|e regression estim<br>nd-stage. Panel B|ates. These first-sta<br>shows OLS (colum|{0.72}<br>ge regressions<br>n 1) and IV re|{0.02}<br>also include<br>gression|

Notes: Panel A of this table shows first-stage regression estimates. These first-stage regressions also include all exogenous variables included in the second-stage. Panel B shows OLS (column 1) and IV regression (columns 2-4) estimates. Column (2) uses sector-region averages of uncertainty as the instrument, (3) uses the three variables on obstacles as instrument, (4) uses all four variables. Standard errors are in parentheses. calculated assuming clustering at the level of strata. All regressions use sampling weights. Braces are used for -values. Significance levels: .01 - ***; .05 - **; .1 - *. pp 3
Kirchberger, 2021). These recent experiences, coupled with the increasing demand for more and better data on migration and the growing interest of scholars and policy makers in the climate-migration relationship, make this a very opportune time to rethink the role of household surveys in this domain and identify potential areas of improvement.

Building on recent insights from Letta et al. (2023), this paper proposes a three-part solution for advancing the climate migration agenda by leveraging the use of household surveys. First, it offers a critical reflection on the current challenges and opportunities for household surveys to fill data gaps and contribute to shedding light on some crucial aspects of the climate-migration nexus and its implications for household and individual welfare. Second, it proposes a conceptual framework that identifies key relationships, focusing on burning issues such as the direct and indirect contribution of climate change to cross-border mobility, and assesses the corresponding data needs to operationalize this empirically and the potential of multi-topic household surveys to address these needs. Third, the paper provides practical empirical guidelines for researchers interested in leveraging existing household surveys to investigate the climate-migration nexus, with special emphasis on how to assess and reduce the rare event problem, explore the migration-adaptation relationship, and address issues related to migration definition, variable construction, and identification strategy. For the sake of concreteness, we tailor our recommendations to one of <mark>the largest and longest-running household survey programs worldwide,</mark> namely, the Living Standards Measurement Study (LSMS) of the World Bank. Among academics, LSMS surveys are often referred to as vested candidates for contributing to an increasing demand for migration data (Bilsborrow, 2016). Using LSMS surveys as an example, the paper identifies priority entry points for enhancing the value of household surveys as a source for accurate and reliable climate migration data and proposes some concrete actions that can be undertaken for achieving this goal in the short- and medium-term.

The remainder of the paper is organized as follows. Section 2 provides essential information on data sources and characteristics of climate-related
>(0.89)|0.69<br>(0.96)|8.14**|5.49**|
|Mean number of<br>household members over<br>age 15 with secondary<br>education|0.94<br>(1.07)|1.01<br>(1.19)|1.09<br>(1.17)|-2.07*|-4.17**|
|Household has electricity<br>(1=yes, otherwise zero)|0.71<br>(0.45)|0.78<br>(0.41)|0.83<br>(0.38)|-5.44**|-8.54**|
|Area (1 = urban, 2 = rural)|1.58<br>(0.49)|1.49<br>(0.50)|1.52<br>(0.49)|6.51**|5.02**|
|Mean annual per capita<br>income (excluding<br>remittances) in<br>Guatemalan quetzals|6,798.98<br>(14,021.5)|6,523.24<br>(8,351.7)|7,042.01<br>(13,307.4)|0.45|-0.12|
|N|5,665|1,063|593|||

Notes: N = 7,276 households; 88 households receive both internal remittances (from Guatemala) and international remittances (from USA). All values are weighted; standard deviations are in parentheses.

In 2000, 1 Guatemalan quetzal = US$0.128.

Source: Guatemala 2000 ENCOVI Survey, Instituto Nacional de
and conflicts. Studies suggest that if there are fewer surviving men after conflicts, women’s position in the marriage market deteriorates due to the scarcity of grooms, which increases men’s bargaining power (La Matthina, 2017; Abramitzky et al., 2011). On the contrary, when a shock leads to fewer women relative to men, there is an increase in female bargaining power in the marriage market (Angrist, 2002). Therefore, when sex ratios are distorted, the more prevalent sex is generally induced to delay marriage, marry individuals of less appropriate age, or remain unmarried due to greater competition for spouses (Hacker et al., 2010). We test this hypothesis using Census data of 2001 (before Gujarat riots in 2002) and 2011 (post-riots), using the following difference-in-difference model to examine if the sex ratios changed after the riots for specific age groups:

16
particularly true when social insurance programs, even for formal sector workers, do not properly function or provide adequate protection. Self-insurance may be equally difficult regardless of the sector due to credit constraints and myopia. If family members’ vulnerability to risks in the informal sector stems from limited access to social insurance, protection, or information, measures need to be taken to delink social insurance and relevant information from the work sector and expand insurance coverage and accessibility. However, if informal sector is no more vulnerable than formal sector due to other informal safety net, the welfare gain from expanding social insurance may be small.

This paper investigates the informal households’ vulnerability using Yemen’s Household Budget Survey focusing on health.<sup>5</sup> Health is a critical determinant of household well-being, especially in low income countries like Yemen where labor and human capital is usually the main source of income.<sup>6</sup> Using a unique data set that includes information on labor market activities and health variables, this paper investigates whether Yeminis’ health, access to care, and burden of health spending systematically differ by employment sector, and if so, the underlying reasons for these differences. This paper contributes to a better understanding of informality and its implications on household welfare in health in Yemen.

Informality in this study is defined based on access to social insurance.<sup>7</sup> Wage employed workers provided with pension or health insurance or both are formal, whereas workers without access to social insurance coverage are informal. Among the formal wage employed, those with both health and pension coverage are separately categorized with those with pension coverage only, as the role of health insurance in health outcomes is also of interest. Informal workers include the wage

> 5 Ideally, in order to investigated households’ vulnerability to external shock, one needs panel data to track the same individuals or households, showing how their consumption, investment in health and education, or their labor market behavior change in response to
Columns (1)-(3), we can see that EMDE increase government spending in response to a rise in commodity export prices, indicating a procyclical fiscal policy. In fact, a 10 percent increase in commodity export prices leads to an increase in government spending of about 0.6—0.8 percent.<sup>45</sup> In contrast, advanced economies respond countercyclically. We can see, from Columns (4)-(6), that an increase of 10 percent in commodity export prices elicits a reduction in government spending of about 0.7-1.2 percent.

# 5.4 Fiscal multiplier

The third stage consists in computing an average fiscal multiplier for commodityexporting countries. To this effect, we use quarterly data for GDP and government expenditure from IFS (IMF) for the period 1990-2019. We estimate a panel SVAR and use Blanchard-Perotti as our identification method.<sup>46</sup> Figure 17 shows the resulting cumulative multiplier. In computing the pours component, we will use the value of the multiplier after 4 quarters (given by 0.88).<sup>47</sup>

# 5.5 Computation of pours component

With the above calculations in hand, we can now proceed to compute the pours component for each country in the sample. The pours component is given by where CEP is the commodity export price, coefficient FR is the estimated coeffi cient in the fiscal regression, and g/y is the ratio of government spending to GDP.<sup>48</sup> Below the different terms, we have noted the units to show how the right-hand side of the

> 45For a sample of 48 countries (including 4 AE), Cespedes and Velasco (2014) compute elasticities of 0.08 and 0.11 pre- and post-2000, respectively, which is comparable to our results.

> 46The availability of quarterly data is critical for relying on Blanchard and Perotti’s (2002) identification method since Blanchard-Perotti