rafmacalaba/data-use-mentions-extended huggingface dataset:
Uruguay, but larger than what is found in cross-country studies** .  While cross-country studies do find a link between water deficits and GDP growth (see for example Zaveri et al. (2023) or Kalkuhl and Wenz (2020)), their estimated impacts are milder. There are a few reasons behind these differences. First, the temporal definition of a drought. Cross-country studies typically identify droughts at an annual frequency: there is a drought if the average annual rainfall is below a certain threshold. This hides the fact that intra-annual patterns are of first-order importance to determine agricultural yields. In Uruguay, low summer precipitations ruin soybean and maize yields, no matter how much it rains over the winter or spring. This study, building on IDB (2019), uses a three-month drought index instead. Second, this study complements precipitation data with temperature data to account for evapotranspiration, which jointly correlates with agricultural yields. Third, this study uses a bottom-up approach following IDB (2019) and MEF (2023). That is, it builds from estimated impacts at the micro level, to then account for indirect impacts and general equilibrium effects. Finally, cross-country studies find average effects across countries that have different structural characteristics and, as such, for which the effect of droughts may vary significantly. This study is instead intended to capture Uruguay’s specificities.

**The analysis in this paper supports broad policy messages around Uruguay’s development agenda** . Climate shocks on average lower GDP, but most importantly add significant uncertainty to economic outcomes, compounded by other macroeconomic shocks, increasing the likelihood of bad years from a macro and fiscal perspective. This stresses the importance of institutional arrangements that help

29
between landlord control and industry employment, we compare average (firm size adjusted) industry employment in our three industry categories in landlord and nonlandlord districts (Table 3). We find significantly lower levels of industry concentration in landlord districts for all industry types. This suggests that the differences between these two categories is not simply an industry composition effect, but truly reflects lower levels of industrial activity in landlord districts. We also ran OLS estimations and find that having a historic landlord based land revenue system is negatively associated with industry concentration.

# **4 Results**

# A. <u>Data</u>

To estimate the models set out in the preceding section, we have combined industry survey data from the World Bank’s Investment Climate Survey (ICS) of India along with establishment level data from the Annual Survey of Industries (ASI).<sup>15</sup> The ICS survey was conducted from March to July 2003 on a random selection of 1,860 manufacturing establishments sampled from 40 cities in 12 of India’s 14 major states.<sup>16</sup> A list of cities covered in the ICS is provided in Appendix 1. These cities are representative of the main industrial centers in the country as each state is represented by its top 3 or 4 industrial cities accounting for the bulk of its manufacturing Gross State Domestic Product (GSDP). These 12 states themselves account for 94 % of India's

> 15 The World Bank and the Confederation of Indian Industry (CII) have jointly carried out the ICS, and the Indian Central Statistical Office annually carries out the ASI. The ASI covers factories registered under sections 2m(i) and 2m(ii) of the Factories Act 1948, employing 10 or more workers and using power, and those employing 20 or more workers but not using power on any day of the preceding 12 months. 16 These are Andhra Pradesh, Delhi, Gujarat, Haryana, Kerala, Karnataka, Madhya Pradesh, Mahrashtra
Table 6: Effect of participation in NEP on parental practices and beliefs

||Obs.|NEP-B|NEP-I|P value for B=I|
|---|---|---|---|---|
|Parental Practices|||||
|Home Index|2545|0.464<br>(0.396)|0.659**†<br>(0.311)|0.532|
|PBC Affection|2545|0.234<br>(0.254)|0.358*<br>(0.199)|0.536|
|PBC Interaction|2545|0.082<br>(0.251)|0.059<br>(0.197)|0.907|
|PBC Negative discipline|2545|-0.262<br>(0.257)|-0.332*<br>(0.201)|0.731|
|PBC Positive discipline|2545|0.294<br>(0.262)|0.246<br>(0.205)|0.817|
|Parental Beliefs, Attitudes, Perceptio|ns||||
|Perceived Self-efficacy|2543|0.205<br>(0.256)|0.411**†<br>(0.202)|0.311|
|Perceived Parental Impact of own|2545|0.372|0.446**†|0.714|
|behavior on child development||(0.254)|(0.199)||
|Perceived Social Support – Family|2545|-0.423<br>(0.263)|-0.033<br>(0.206)|0.059|
|Perceived Social Support – Friends|2545|0.389<br
main issues are the availability and affordability of services such as the Internet and mobile telephones. Over the past 10 years the Philippine telecommunications sector has grown exponentially, with services once available only in Manila and other major cities now covering more remote areas—reaching the point that will enable the Expanded Small Investors Program to reach to most prospective clients. Still, limited competition and weaknesses in the industry’s ownership structure pose challenges in terms of the affordability of these services.

# **Electronic security issues**

E-security is a major—and often neglected—concern for systems like the Expanded Small Investors Program. In recent years the incidence and cost of e-fraud and hacking cases have skyrocketed (KPMG Global Information Security Survey 2002, Ernst & Young Global Information Security Survey (2002), 3i White Paper (2002), PWC Information Security Breaches Survey 2002). Any e-security threat could undermine the trust of retail investors and reduce demand.

30
13. **A multidimensional approach to informality should be taken to have a deeper understanding of informality in Indonesia.** As summarized by Elgin et al. (2021a), there are three dimensions of informality, with most of the available data capturing the following three dimensions: output informality, employment informality, and firm/perceived level of informality.

# **2.2 Informality data  and estimation methods description: Cross-country and Indonesiaspecific databases**

14. **The paper carries out two sets of analyses.** The first set of analyses (section 3) compares the levels and trends of informality in EMDEs with those in Indonesia. The EMDE estimates are taken from a comprehensive informality database provided by Elgin et al. (2021a) and updated to 2021. The estimates for Indonesia mainly use data from the labor force surveys, referred to as SAKERNAS, which is provided by the official statistics agency BPS over the period 2000-22. In particular, estimates on output and employment informality at the regional and sectoral levels are also estimated using SAKERNAS surveys. The second set of analyses (section 4) explores policy and development challenges associated with informality across EMDEs. The findings from crosscountry analyses will be compared with specific features of informality in Indonesia.

15. **The cross-country analyses rely on one of the most comprehensive cross-country databases of informal economic activity.** The updated database (from Elgin et al 2021a) covers more than 160 economies for the period 1990-2021. The database includes model-based and survey-based measures of informality, which capture the three dimensions of informality: output informality, employment informality, and perceived level of informality. The model-based measures cover output informality (often expressed in percent of official GDP), while surveybased estimates capture employment informality (in percent of total employment) and the perceived level of informality (especially by firms and by households). Annex 3 provides a detailed description of the three dimensions of informality and their construction. In this paper, when conducting cross-country analyses, preference is given
IASC. _IASC Framework on Durable Solutions for Internally Displaced Persons._ Brookings Institution, 2010.

IDMC. _Displacement figures in Ukraine fail to reflect a complex reality._ September 3, 2015. http://www.internal-displacement.org/europe-the-caucasus-and-central-asia/ukraine/newarchive/displacement-figures-in-ukraine-fail-to-reflect-a-complex-reality.

IDMC. _Global Overview 2015: People Internally Displaced by Conflict and Violence._ Internal Displacement Monitoring Centre, 2015.

IDMC. _Global Report on Internal Displacement 2016._ Internal Displacement Monitoring Centre, 2016.

IDMC. _Guidance on Profiling Internally Displaced Persons._ Internal Displacement Monitoring Centre, 2008.

IDMC. _Innovation at IDMC: Evidence to prevent and respond to internal displacement._ Internal Displacement Monitoring Centre, 2015.

Khawaja, Marwan. "The Fertility of Palestinian Women in Gaza, the West Bank, Jordan and Lebanon." 2003.

Morrison, Leah. "The vulnerability of Palestinian refugees from Syria." _Forced Migration Review_ , September 2014.

Reid, Claire. "International Law and Legal Instruments ." 2005.

UNHCR. "Handbook and Guidelines on Procedures and Criteria for Determining Refugee Status." 2011. UNHCR. "Handbook for Registration." 2003.

UNHCR. "Statistical Yearbook 2014." 2015.

UNHCR. "Statistical Yearbook 2015." 2016.

UNHCR. "UNHCR Global Trends 2015." 2016.

UNHCR. "UNHCR Mid Year Trends 2015." 2016.

—. _Vietnamese refugees well settled in China, await citizenship._ May 10, 2007. http://www.unhcr.org/464302994.html.

UNHCR. "World at War: Global Trends Forced Displacement in 2014." 2015.

UNRWA. "Historical Regfugee Registration Data From Year 1952 - 2005." 2016.

UNRWA. "Syria Regional Crisis Emergency Appeal 2016." 2016.

UNSD. "Decisions (Rapporteur’s text, subject to editing)." 2016.

UNSD. "Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and internally
ajor economies in the world have pledged to become carbon neutral by 2060. Electrifying the transportation sector coupled with a cleaner electricity grid is Mconsidered a key pathway to reach carbon neutrality. The diffusion of electric vehicles (EVs) faces multiple economic and technological challenges including the high upfront purchase cost of EVs, limited driving range, the lack of adequate charging infrastructure, and the inherent and perceived uncertainty about this new technology (Krutilla and Graham, 2012; Carley et al., 2013; Li et al., 2017a). Despite these challenges, global EV sales reached 4.2 percent of the new vehicle market (or 3.2 million units) in 2020 after a decade of growth beginning from the introduction of the Chevrolet Volt and Nissan Leaf as the first mass-market EV models into the U.S. market in late 2010. Nevertheless, the diffusion process needs to accelerate significantly in order to reach the EV adoption goals set by many countries and regions (Figure 1).

What are the key market and policy drivers behind the spatial disparities of global EV diffusion? What are the important lessons from the first decade of the market growth? Understanding these questions has important implications for the next phase of development in the global EV market and for addressing pressing climate change and local air pollution challenges. This study aims to answer these questions based on, to our knowledge, the most comprehensive data ever complied on the global EV market including model-level EV sales by country, vehicle attributes, charging infrastructure, detailed EV policies, and demographic and socio-economic variables by country from 2013 to 2020. Our analysis focuses on 13 countries with the highest EV sales, accounting for 95% of total global EV sales during this period.

# **Global EV Patterns**

After rapid growth in the past decade, the global EV stock reached 10 million in 2020. Between 2013 and 2018, the global EV market saw rapid expansion - sales increasing by over 50% each year. In 2019, the growth rate stalled temporally but
multiple of four, then we can allocate the same number of PSUs to all quarters. Yet, if the number of PSUs allocated to a stratum in a specific district is not a multiple of four, there is

> 19 Note that we are not changing the sample size of districts. This is certainly suboptimal. Ideally, we can shift samples from districts with very low projected standard errors to districts with the problematic strata; however, how to design such an allocation requires more careful analysis and is beyond the scope of this paper. 20 The HIES 2010 sample frame was based on the IMPS, which in urban areas was constructed using the concept of Statistical Metropolitan Areas (SMA). The concept of SMA was replaced by the concept of Rural/Urban/CC (RUC) in the 2011 Census of Population and Housing. Of the 64 districts, only in three does the old SMA concept not match perfectly with the new RUC: in the districts of Gazipur and Narayanganj in the Dhaka division (districts 33 and 67, respectively), and the district of Khulna in the Khulna division. For Gazipur and Narayanganj, a perfect match can be achieved by replacing all SMA areas to Other Urban areas. For Khulna district, however, a match is not straightforward as the SMA area was divided into CC and Other urban areas. In addition, all of the PSUs from the Khulna district available from the HIES 2010 come from SMA areas, and there is therefore no baseline for Other Urban Khulna district. To conduct all the analysis for the design of the sample for the HIES 2016/17, we use the same sub-stratum values for both CC and Other Urban areas.

18
-94-

# Confrontation of Our Results with Proxy PPPs Derived Manufacturing

# from ICP for

One can use the ICP PPPs in order to derive a crude proxy PPP estimate for the manufacturing sector. The authors of the ICP have not themselves ever tried to do this, but several other investigators have done so (see table 1.2 in chapter I). Using the same technique as such analysts, we derived the proxy ?PPs for manufacturing presented in table 8.2.

In fact our average PPP result in the Brazil/USA comparison is identical with the proxy PPP, and not very different for Mexico/Brazil. but for the Mexico/USA comparison they are substantially different. It should be noted that the preliminary results of Van Ark's binary comparisons for India/USA and Brazil/UK also show differences from similarly derived ICP proxies, with the "industry of origin" PPPs being nearer to the exchange rate.

Apart from the possible shortcomings of the proxy PPPs, there is also the substantial problem that they are applied (sc- D.J. Roy, 1987) to the respective national accounts at national prices, without adjustment for differences in the coverage of such accounts. As we found in chapter II, the Mexican national accounts make a very large imputation for manufacturing activity in the informal sector, whereas the Brazilian accounts make virtually no adjustment for this. As there is no reason to expect the relative size of the informal sec_or to be much different in the two countries, use of inconsistent national accounts can have serious results. The typical shortcut proxy procedure would overstate Mexico's output position relative to Brazil's for two reasons: a) by overstating the relative PPP of the peso, and b) overstating Mexico's output in national currency terms vis-&-vis Brazil. Substantive Results for Output and Productivity. Brazil/USA and Mexico/USA The most striking feature of our quantitative results (table 8.3) is the relatively high levels of productivity
# **C. Measurement Issues and Possible Threats to Identification**

The absence of baseline data across the treatment and control communities is not considered a serious limitation on causal identification. Power calculations suggest that a randomized cluster design that is clustered by communities (CfW subprojects), with 276 communities, is sufficient to ensure a balance between the treatment and control households, thereby guaranteeing the comparability of the two groups for the purpose of causal attribution. Nonetheless, the variables collected during the endline surveys that were not expected to change over the short duration of the program provide evidence— albeit limited—of pre‐intervention balance between the treatment and control groups. As shown in Table 1, the overall treatment group is statistically comparable with the control group in most of the variables tested (see Annex 2 for all tables).

Another measurement issue relates to the practice of rotation (described in section II) that was applied in an informal and largely undocumented manner among the treatment communities. This has implications for the measurement of program impacts because program impacts were measured through registered beneficiaries who had been identified on the first day of subproject implementation. If this informal rotation occurs, the impacts captured by the impact evaluation (IE) may be under‐ or overestimated. For instance, registered beneficiaries may be more well‐informed or have stronger networks than nonregistered beneficiaries. This may lead them to exhibit better outcomes even in the absence of the program, and the IE would then overestimate the program’s true effects. Conversely, nonregistered beneficiaries may be poorer and may enjoy higher returns to receiving the transfer (for instance, through lower substitution effects). In this case, the IE may underestimate the true impact of the program. Another cause of potential underestimation is the fact that, owing to informal rotation, some beneficiary households worked fewer days than intended in program design, leading to an underestimation of the welfare impacts of the program. Because of informal rotation, actual program impacts may have been diluted given that the same total
# **1 Introduction**

For many children worldwide, particularly those from low-income family backgrounds, schooling represents the only avenue for escaping the intergenerational transmission of poverty. However, in the developing world, the common occurrence of aggregate income shocks threatens children’s pursuit of schooling, which their parents view as a costly investment. In these countries, evidence shows that aggregate income shocks increase children’s vulnerability to child labor or child marriage (Corno and Voena (2016), Corno et al. (2020))—two cultural practices known to undermine children’s schooling outcomes (Canagarajah and Coulombe, 1999; Field and Ambrus, 2008). It is therefore not surprising that, when in early 2020 the COVID-19 pandemic erupted, concerns immediately arose over possible setbacks in terms of progress achieved on education outcomes (UNICEF, 2021b). The justification behind these concerns is that this outbreak led to the enactment of protracted economy-wide lockdown measures, with implications for household income and food security. However, since school closure was only temporary, and in some environments schoolchildren were allowed to maintain contact with their schools through distance learning, it is unclear whether such concerns were justified.

This paper uses a unique data set from Nigeria to test the hypothesis that COVID-19 school lockdown measures undermine children’s school resilience by reducing the number of those who returned to school after these measures were lifted. The literature on the effect of COVID-19 on children’s learning outcomes is growing but still predominantly focused on developed countries where distance learning resources are more easily accessible during the pandemic. Yet, of the 142 million children predicted to fall into poverty due to COVID-19, nearly two-thirds live in Sub-Saharan Africa and South Asia,<sup>1</sup> where access to distance learning is already severely limited. Indeed, many children from lowincome backgrounds do not have internet access.<sup>2</sup> Moreover, evidence from past covariate income shocks such as droughts shows that Sub-Saharan African countries, where risk-insurance markets
could “afford” (that is, pay no more than 5 percent of total household expenditures to purchase) more electricity. The number of countries in which electricity consumption becomes unaffordable (exceeding the 5‐percent threshold) to the poor, based on total household expenditures (including imputed values), nearly doubles from 7 to 13 at 50 kWh. At 100 and 250 kWh a month, electricity is unaffordable to the poor in all but Angola and South Africa (where the poor are entitled to varying amounts of free electricity, depending on the municipality). The number of countries in which electricity becomes unaffordable to urban households doubles from three at 30 kWh to six at 50 kWh, and more than doubles to 14 at 100 kWh a month.

Table A.10 in the annex enables a limited degree of comparison with the findings of Briceño‐Garmendia and Shkaratan (2011). That study covered 18 countries, 14 of which are also in this study. Briceño‐Garmendia and Shkaratan found that monthly consumption of 50 kWh would be affordable to about one‐third of unconnected households on average, whereas this study found that the median for 50 kWh being affordable would be two‐thirds of unconnected households and the average would be three‐fifths. These results suggest that electricity tariffs became more affordable in the intervening years.

# Grid‐electricity poverty measures

The electricity poverty gaps for different levels of consumption are shown in Figure 5. In the figure, each rectangle for different monthly consumption is incremental. For example, the poverty gaps in Rwanda for 50, 100, and 250 kWh are 46, 67, and 85 percent, respectively. Table A.17 in the annex provides the results by location (urban and rural). Figure 6 contrasts the poverty gap and headcount. The highest poverty gap for 30 kWh is less than 30 percent, but the corresponding poverty headcount is in excess of 70 percent.
To study the degree to which there is selection, we use the „student participation ratio‟, that is, for those schools which have students that take the exam on a specific subject, we compute the ratio of students who take the exam divided by the total number of students in the highest grade (for a vast majority of schools this is the 11<sup>th</sup> grade, for some it is the 12<sup>th</sup> grade). Given that all students that participate in the EIT take the Ukrainian exam, a school‟s ratio of the number of students that take the Ukrainian exam on the number of students in the highest year is a good proxy for the extent of selection. If this ratio is 1, it means all students participate, if it is 0 it means nobody participates.

In about 7 % of the cases, the participation ratio indicator is bigger than one, i.e. there are more students taking the test than there are in the 11<sup>th</sup> /12<sup>th</sup> grade. One explanation for this is that the MoE data were recorded at a different time than the EIT exams, and that some students might have changed schools between the recording of the MoE data and the EIT exams. A second possible explanation is that some graduates of previous years were allocated to the school were they graduated in the past. A third explanation is simply that there are mistakes in the database. In the regression analysis presented below, we include in the sample only schools with a participation ratio that is less than 101 percent.

Table 5 gives the 2010 participation ratio statistics by region – we see a substantial variation among oblasts with especially low rates of participation in the Western regions of Zakarpatia and Chernivtsi (between 60 and 70 percent) and high rates of participation in Sevastopol and especially Kyiv (over 90 percent).

Table 6 tries to explain the differences between schools