rafmacalaba/data-use-mentions-extended huggingface dataset:
constructed: 1,751, the most in half a century (Aboldornoz, 2015). The investment in ICT has also been impressive, representing as much as 50 percent of the infrastructure investment in the years 2011 and 2012 (Albornoz, 2015).

# _3.2 Trends in PISA Results_

The OECD’s Program for International Student Assessment (PISA) was first carried out in 2000, and since then a new edition has come out every three years. More than 70 countries have participated in PISA to date, making it the preferred dataset for cross-country comparisons. PISA is a standardized test assessing three subject areas: mathematics, reading, and science. It is not based on the curriculum of any particular education system. Rather, it evaluates students’ ability to apply their knowledge and competences to problem solving. By construction, PISA had a mean of 500 and a standard deviation of 100 among OECD countries in 2000, in each subject area (see Annex A for more details on PISA’s design).

This study uses micro data at the student level from 2000, 2006, 2009, and 2012, years in which Argentina participated in the PISA survey. PISA results are composed of a random sample of 15-year-olds who are attending school. The students are selected by a two-stage sampling strategy—first, by randomly selecting schools, and then by selecting 15-year-olds within the schools, also at random. In the case of Argentina, samples are representative at the national level only (DINIECE, 2004).<sup>3</sup>

> 3 A look at the sample suggests different degrees of representativeness in the editions considered. Using the survey’s expansion factors, the weighted sample accounted in 2000 for more than 90 percent of the total 15-year-old population enrolled. Nevertheless, there is an important number of missing values. Fortunately the missing values are not for the learning outcome results but for variables capturing school characteristics and socioeconomic background where, in some cases, more than 40 percent of the observations
the salience of marriage and dowry in the Indian context, it is unsurprising that the recall error is not substantial.

External validity of dowry data: We corroborate the cross-sectional patterns by caste and religion in 2006 REDS using dowry data from 2005 IHDS. Unlike REDS, IHDS elicits dowry data indirectly by asking respondents how much money is usually spent at the time of the marriage by a groom’s or a bride’s family that is similar to the respondent’s family; this information is only collected for the survey year. Table A.4 shows the sample means of the net dowry paid by the bride’s family calculated using these responses for various caste and religious groups. Although our paper focuses on rural India because REDS does not cover urban areas, we also report urban dowries from the IHDS for comparison. Reassuringly, the patterns in Table A.4 are identical to those in the REDS data.<sup>24</sup> The similarities between REDS and IHDS also assuage concerns about differential under- or over-reporting of dowry payments and receipts by the sampled households.

# **2.3 Savings**

Using the detailed information available in REDS, we construct the following measures of householdlevel saving: savings in financial institutions,<sup>25</sup> savings in jewelry, savings in livestock, market investments,<sup>26</sup> and savings in durable goods. The _flow_ saving variables are constructed based on the value of each item purchased (deposits) and sold (withdrawals) during the year before the survey.

The literature describing saving behavior of households in developing countries is quite limited, primarily due to the lack of micro-level data. The bulk of the existing research examines micro-savings for small samples that are not nationally representative (typically, in the context of randomized control trials) or is based on incomplete data on the various forms of savings (see Karlan et al. (2014) for a literature review). For India, we are not
, reading comprehension on EGRA maps directly onto one of the levels in Levels 1-8 on SACMEQ, the RSAT for East and Southern Africa. Analysis and work done by ACER for the UIS Reporting Scale – a global effort to compare underlying proficiencies across assessments – focuses on reading comprehension partially for this reason (Turner et al., 2018). Abadzi (2011) also cites reading comprehension as a likely proficiency link between EGRA and ISATs and RSATs. This is born out in the data. For example, when we use reading comprehension scores, Tanzania outperforms Kenya, consistent with SACMEQ. When using ORF scores, Kenya does not outperform Tanzania, which is no longer consistent with SACMEQ. Moreover, reading comprehension was the most available subtask across all EGRA datasets we collected.

To this end, we primarily analyze reading comprehension, which is available in nearly all EGRA datasets, is less sensitive to differences in context and implementation, and has a strong conceptual link to RSATs and ISATs. To ensure robustness to language effects, we only include data when students took the test in their language of instruction.

5
Rainfall is the most important dimension of weather variation in Nigeria. Because of its tropical location, temperature shows very little variation in Nigeria, either within years or across them. As noted by Nieuwolt (cited in Odekunle, 2004) temperatures are not a critical factor in tropical agriculture. Therefore, in this study we concentrate on the effects of precipitation shocks.

Perhaps one of the key aspects of this study is to give proper weight to spatial considerations. Although many studies have linked health outcomes with climate, the use of insufficiently matched climate and disease data can lead to biased results. Thus, we carefully interpolate climate and weather data from the network of stations to the precise location of each DHS clusters. Relatively few studies have matched climate variables to outcomes at fine resolution. The usual practice in the economic literature is to match each locality –a cluster or the geographic center of an administrative unit– to the geographically closest weather station (for example Yang and Choi 2007, Maccini and Yang 2009). While simple, this interpolation method may yield poor results if missing records are prevalent in the weather data, stations have been affected by site relocation, or considerable differences exist in terms of topographic characteristics –for example altitude- between the location of the cluster and the location of the closest station. The replacement of missing data with data from the second closest weather station will introduce artificial variation in weather, whereas the differences in topography will introduce a bias for the interpolated values.

Another alternative is to predict climate using statistical methods, similar to those used in climate modeling (for an application in the economic literature see Nordhaus et al, 1994). This approach requires estimating climate (or weather) based on a station’s observable characteristics –latitude, longitude, altitude, distance to the sea, etc.– and then using the regression coefficients to predict the corresponding values to each DHS clusters. We have opted for this approach because it better fits the observed climate at the weather station site.<sup>
et al., 2021). B ̈ohme et al. (2020) show how geo-referenced online search data on migration-related terms can be used to measure migration intentions in origin countries and to predict bilateral migration flows. Adema et al. (2022) uses individual-level data from 112 countries to provide causal evidence that an increase in mobile internet access increases the desire to migrate and actual migration by lowering the cost of acquiring information on potential destinations. Other studies look at the effect of TV and newspaper articles on migration. For instance, Farr ́e and Fasani (2013) find that exposure to TV reduces internal migration in Indonesia. Wilson (2021) shows that access to newspaper articles and TV news providing information about potential labor market opportunities increases migration to areas mentioned in the news.<sup>6</sup> More generally, our paper is related to the literature on the effect of information on migrants’ decisions. Shrestha (2019) shows that information on migrants’ mortality rates decreases migration flows from Nepal to Malaysia and to the Gulf countries. Baseler (2023) documents that providing information on urban earnings in Kenya increases migration to the capital. Some papers use randomized controlled trials to understand the role of risk information and perceptions in the decision-making process of migrants. Bah et al. (2023) show that providing information

> 6Another important source of information is migration networks at destination. These provide prospective migrants with information about the migration process and economic opportunities, helping them to shape their migration decision (McKenzie and Rapoport, 2010; Beine et al., 2015; Giulietti et al., 2018).

3
As an illustration, Figure 1 shows the relationship found by Ravallion and Lokshin (2010) in survey data for one of the world’s poorest countries, Malawi. The Figure gives the nonparametric regression for responses to the ―satisfaction with life‖ question against household consumption per person. The Figure also shows how the relationship varies with the respondent’s assessment of the economic welfare of their friends and neighbors. We see that SWL increases with consumption, but higher economic welfare of friends or neighbors tends to come with higher satisfaction with life at given own consumption. So this does not support the theory of relative deprivation (as this would imply lower welfare with richer friends or neighbors, at given own consumption). The parametric models presented in Ravallion and Lokshin (2010) did, however, indicate that concerns about relative deprivation emerged amongst the richer Malawians. Ravallion (2008b) calculated the implied (iso-welfare) poverty lines for Malawi, and showed that they exhibit a small weakly relativist gradient, whereby higher owneconomic welfare is needed to compensate people for having richer social comparators.

These findings are echoed in the responses to survey questions on whether poverty is perceived as absolute or relative reported by Corazzini et al. (2010), which suggest that respondents coming from richer backgrounds see poverty as more relative, though not to the point that absolute poverty is seen as unimportant.

Some of the literature has also thrown light on the role played by expectations. Ravallion and Lokshin (2002) find strong lagged income effects on subjective welfare in Russia, consistent with the view that that those on a positive (negative) income trajectory are likely to rate their welfare higher (lower) than others at given current income (and other covariates).<sup>23</sup> Knight and Gunatilaka (2012) argue that ―aspirations‖ for higher income also matter negatively to subjective welfare, and that this mitigates appreciably the direct income effect. Using data for rural China they proxy ―aspiration income‖ by answers to a version
drought-related stochastic catalogs of synthetic simulations for Malawi at a spatial resolution of 0.05° and dekadal temporal resolution. Stochastic catalogs are designed to provide a view of hazard that goes beyond the data available in the historical record. AIR used historical data on rainfall to produce three such catalogs: for precipitation, soil moisture, and vegetation index.

To generate the stochastic catalogs, AIR used data similar to that used by the World Bank team in generating the historical hazard time series. CHIRPS data were used for precipitation; the Soil Water Index at a depth of 40 cm from the Copernicus Global Land Service was used for soil moisture; and the NDVI for vegetation cover was from MODIS. The resulting catalogs were validated by comparing the synthetic time series with the historical data in Malawi. The validation results show that the catalogs preserve the intrinsic properties in terms of spatial and temporal correlations, produce a realistic view of hazard, and are consistent with historical observations while allowing for the creation of new extremes.

For the purposes of this paper, the approach was tested using the stochastic catalog for soil moisture in Malawi. Further details on the methodology used to generate the 10,000-year catalog and the validation conducted are in appendix C.

11
|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each outcome. Clustered standard errors by municipality are shown in parentheses. Columns 1 and 3 estimates Equation (1) with no controls, Columns (2) and (4) include controls for gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

21
outcome equation (2) to estimate the effect of microenterprise on household welfare.<sup>7</sup> To operationalize, we use instrumental variable techniques in a fixed-effects model.<sup>8</sup> The results are shown in Table 8. We present the endogeneity test statistics, which show that adoption of microenterprise activity as an additional activity is endogenously determined (at least for two outcomes as per capita expenditure and extreme poverty) by the same characteristics affecting the outcomes itself. Thus, we find that household adoption of a microenterprise activity <u>causally</u> increases both income and expenditure, and thus reduces poverty. For example, the adoption of microenterprise activities increases household per capita expenditure by over 50 percent. If we assume that the growth in income or expenditure has been accumulated over the duration of microenterprise operation, which is an average of 10 years, we can infer that a household’s adoption of an enterprise activity increases per capita income by 6 percent per year and per capita consumption by 5 percent per year. Consequently, the reductions in moderate and extreme poverty have been 2.4 percent and 3.6 percent per year, respectively, because of microenterprise adoption. These findings bolster our hypothesis that besides farm income growth, microenterprise expansion is an effective way to accelerate poverty reduction in rural Bangladesh.

# <u>4. Rates of return to investment using productivity measures</u>

To understand whether income and consumption gains (or alternatively poverty reduction) are substantial with microenterprise growth, we must analyze the profitability of microenterprise investment, or alternatively the rates of return to investment in microenterprise, as it is the return

> 7 The instruments are the program placement of microcredit, commercial and other development banks in a village. We also interact these variables with household characteristics (head’s age, education, land and non-land assets) to create additional instruments. It is assumed that the availability of a credit program in a village is expected to influence
an impact on wages (Kuhn and Mansour, 2014; Shahiri and Osman, 2014). However, as Shahiri and Osman (2014) point out, many people in developing countries—including both employers and job seekers—cannot take advantage of employment websites due to limited internet infrastructure, high user fees, and/or a lack of knowledge regarding information technology. Studies have also found that greater cellphone use improves employment outcomes in developing countries (Klonner and Nolen, 2010 for South Africa; Aker, 2011 for Niger; Burga and Barreto, 2014 for Peru), lending further support to the conclusion that information constraints are a significant, though often unrecognized, source of inefficiency in the labor market.

There are important challenges involved in using big data for labor market analysis. These include accessing and analyzing data in ways that respect the privacy and confidentiality of individuals, designing creative and scalable approaches to summarizing, describing and analyzing unstructured data sets, and dealing with the partial representativeness of the data. Methodological challenges and the ownership of data sets are also important considerations. One of the principal critiques of big data analytics is that the underlying empirical 5
|Yes|Yes|Yes|Yes|
|**Additional controls**|No|No|No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank) and WDI (World Bank). Notes: Robust standard errors in brackets, clustered at the country level for models 3-10. * significant at 10%; ** significant at 5%; *** significant at 1%. The additional control variables include the log of GDP per capita, the log of the real exchange rate against the US Dollar (both from WDI), and the log of a tariff index. Column 8 includes the log of a minimum wage index compiled by ILO (available only for 1995-2009). The regressions in column 10 only include the nine countries for which there is a relatively long, continuous and homogeneous data source: Argentina, Brazil, Chile, Costa Rica, Mexico, Panama, El Salvador, Uruguay and Venezuela.

32
355|
||_(0.155)_||_(0.174)_||||
|Father Lives at|||||||
|Home|0.952||0.943||0.009*|10,355|
||_(0.214)_||_(0.233)_||||
|Mother College|||||||
|<br>Educated|0.303||0.304||-0.001|10,355|
||_(0.460)_||_(0.460)_||||
|Father College|||||||
|Educated|0.288||0.284||0.005|10,355|
||_(0.453)_||_(0.451)_||||

* p < 0.05, ** p < 0.01, *** p < 0.001

_Notes:_ Standard deviations in parentheses. T-tests with robust standard errors clustered at the school level. Sample of students with non-missing information in all the socio-emotional variables analyzed (i.e. the sample of students used in the regressions analysis). All figures rounded to three decimal places.

_Source:_ Own survey data, administrative data, school year 2015/2016, own calculations.

Table 2 Panel A focuses on students' socio-emotional skills taken from surveys and GPAs taken from official administrative records, whereas Panel B focuses on student’s socio-economic characteristics. The sample includes all students with non-missing information on all outcomes and controls in the regression analysis of Table 3. As can be seen, in contrast to GPAs taken from official records, we encountered a significant amount of missing information on socio-emotional skills and characteristics, mainly due to some surveys 10
variation in year-on-year growth rates, thus highlighting the need for spatial planners to be highly responsive. Second, it explicitly distinguishes the growth dynamics of safe versus flood-exposed spatial development. This allows us to document a divergence in flood exposure – regions that are either increasing or decreasing their flood exposure as they urbanize over time. Third, rather than focusing on a certain flood hazard type, we combine different flood types (fluvial, pluvial, coastal floods) and assess overall flood exposure. Fourth, by using global data sets, we document trends with complete global coverage and within one consistent methodology. Finally, while past studies (both global and country-specific) have documented a general increase in flood exposure, this study corroborates and expands these findings based on data with high spatio-temporal resolution.

# 3. Data and methods

## 3.1. Global flood hazard and settlement data

### Flood hazard data

To obtain complete estimates of urban exposure to flood hazards, this study considers the three most common flood types:

- _Fluvial flooding:_ when intense or excessive precipitation or snow melt causes rivers to overflow

- _Pluvial flooding:_ when surface water builds up beyond the absorptive capacity of soil, due to extended precipitation and insufficient drainage

- _Coastal flooding:_ due to tidal or storm surges, or sea level rise.

Country-level pluvial and fluvial flood data are based on the 2019 global Fathom flood hazard data set<sup>40,41</sup> . These provide gridded information on flood extents and depths at a 3-arcsecond resolution (equivalent to 90 meters at the equator), simulating 5, 20, 50, 100, 250, and 500-year flood events, and are available for all countries. The maps are based on the DEM MERIT elevation model that corrects for multiple errors, including absolute bias, stripe noise, speckle noise, and tree and building height biases<sup>42</sup> . We consider flooding with a 100-year return period.

5
categories: training and curriculum (T&C),

> 4 Throughout this paper, “determinants” refers to practices and inputs that programs can choose. Other program or institutions characteristics (for example, institution’s age or public/private status) are not quality determinants because they are not equally flexible. 5 We initially set out to collect individual-level data for each of these five countries. However, most countries in the region either do not collect this type of data, merge datasets as needed (for example, those from higher education and social security), or make datasets available to researchers. Building good information systems and facilitating data access remains a key task in developed and developing countries alike.

3
2.106)|
|Accounts are computerized (yes=1)|0.103||0.396*|
||(0.208)||(0.207)|
|Demand: Village Size and Organization||||
|Village has a market (yes=1)|-0.196||-0.395**|
||(0.195)||(0.195)|
|Demand: Household Characteristics||||
|Household assets (excluding land)|-0.150||-0.294*|
||(0.154)||(0.155)|
|Loan terms and conditions||||
|VF borrows to on-lend to members|0.796***<br>(0.274)||0.961***<br>(0.273)|
|Memo items:||||
|Number of observations||1,919||
|p-value for significance||0.00||

Source: Village Fund Survey 2010 and SES 2009. Robust standard errors in parentheses. Stepwise regression uses p=0.2 cutoff for dropping variables. For fuller regression, see Appendix 1. * p<0.05, ** p<0.01, *** p<0.001. Omitted rating is “A”.

Village Funds that borrow to on-lend have higher credit ratings. The effect is strong and also unsurprising, although the direction of causality is unclear: VFs may borrow because their ratings are good enough, but borrowing VFs are also likely to be careful to maintain good credit ratings. The credit ratings are stronger for 34
Figure 22. SSA: Obstacles to economic growth and reforms to accelerate potential growth A. Networked Readiness Index <!-- Start of picture text --> Index<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>C. Potential GDP growth<br>Percent 2000-10 2011-21<br>8 2022-30 2000-21<br>6<br>4<br>2<br>0<br>EMDEs SSA<br>E. Reform scenarios<br>Percent Social benefit reform<br>6 Labor market reforms<br>Education and health improvements<br>5 Investment surge<br>Baseline<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDEs SSA<br>Advanced economies EMDEs China SSA South Africa Kenya Nigeria Cameroon Benin Burundi<br><!-- End of picture text --> B. Climate change vulnerability and readiness index <!-- Start of picture text --> Index<br>60 Region's median EMDE median<br>40<br>20<br>ECA EAP LAC MNA SAR SSA<br>D. Per capita potential GDP growth<br>Percent Potential growth<br>Actual growth<br>2<br>2000-2021 potential growth<br>1<br>0<br>SSA<br>F. Climate change investment scenarios<br>Percent Investment boost (climate change)<br>6 Disasters (climate change)<br>Baseline<br>5<br>4<br>3<br>2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>EMDE SSA<br>2000-10 2011-21 2022-30<br><!-- End of picture text -->