rafmacalaba/data-use-mentions-extended huggingface dataset:
There are at least two possible explanations for the rapid escalation in the unit costs of secondary technical education in the late 1970s. One would be a decline in the number of students enrolled in such schools. UNESCO figures do, indeed, show a dramatic decline in VTE enrollments between 1977 and 1978 (UNESCO 1986), but whether this was, in fact, real or represents some sort of statistical artifact (perhaps the result of a student reclassification) is not known. In either case, a decline in the denominator (number of students) without a commensurate decline in the numerator (total expenditure) will result in an increase in expenditure per student. Unless there is reason to believe that the decline in the number of students is only a temporary phenomenon, then there can be no justification for constructing additional schools of this type. Until the costs of technical education can be brought down again (presumably by closing some of the existing VTE schools and consolidating the rest), VTE will remain a poor investment option relative to general education.

An increase in unit costs could have occurred also within the context of educational reform. As discussed in Section 2, under the reform program introduced in 1972, a number of new technical schools (ESEPs) were constructed. Given that many of these schools did not open their doors until the late 1970s, it is likely that they were not yet fully enrolled and that the high unit costs noted above do not represent the fullcapacity costs of providing this kind of education.

Moreover, to address some of the shortcomings posited above with respect to staffing, equipment, and curriculum of VTE institutions would have required (to the extent that they were, in fact, addressed) inevitably the mobilization of additional resources. The resulting increase in expenditure per student might be justified if it led to graduates of the VTE system who were truly better prepared for the world of work and who could sustain a higher level of productivity over the period of their working lives.

We do not know to what
Paper_ No. 38 Bandyopadhyay, S., & Skoufias, E. (2015). Rainfall variability, occupational choice, and welfare in rural Bangladesh. _Review of Economics of the Household_ , 13(3), 589-634.

Banerjee, O., Cicowiez, M., Rios, A. R., & De Lima, C. Z. (2021). Climate change impacts on agriculture in Latin America and the Caribbean: an application of the Integrated EconomicEnvironmental Modeling (IEEM) Platform (No. IDB-WP-01289). _IDB Working Paper Series_ .

Bérgolo, M., Cruces, G., Gasparini, L., & Ham, A. (2010). Vulnerability to poverty in Latin America Empirical evidence from cross-sectional data and robustness analysis with panel data. _Working paper_ , 170, Chronic Poverty Research Centre.

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Chaudhuri, S., Jalan, J., & Suryahadi, A. (2002). Assessing household vulnerability to poverty from cross-sectional data: A methodology and estimates from Indonesia. _Discussion Paper_ #:0102-52, Department of Economics, Columbia University, New York.

Chaudhuri, S. (2003). _Assessing vulnerability to poverty: concepts, empirical methods, and illustrative examples_ . Mimeo. Department of Economics, Columbia University, New York, 56.

Cremen, G., Galasso, C., & McCloskey, J. (2022). Modelling and quantifying tomorrow's risks from natural hazards. _Science of The Total Environment_ , 817, 152552.

Dercon, S. (2006). Vulnerability: a micro perspective. _Securing development in an unstable world_ , 30, 117-146.

Figal Garone, L., León, S., Olarte Rodríguez, L., & Peña, X. (2019). Home durables and access to credit among the low-income in
# Annex A: Tables

Table A.1. Reported reasons among inactive Nepali women for not trying to find a job or start a business in 30 days preceding the survey

|**Reason**|**Share of females (%)**|
|---|---|
|Waitingfor results|0.9|
|Waitingfor season to work|2.9|
|Waitingto be recalled topastjob|0.1|
|Tired of lookingfor work|4.8|
|Nojobs matchingskills,lacks experience|21.4|
|Considered tooyoung/old byemployers|0.7|
|Look for oversees employment|0.6|
|In studies/ training|10.1|
|Family/ household responsibilities|34.3|
|Familymember made obstacle|2.3|
|Engaged in a subsidiaryagriculture|16.2|
|Have other sources of income|0.6|
|Disability,injury,illness|2.1|
|Other|3.2|

_Source_ : Authors’ compilation using Nepal Labor Force Survey, 2017-2018 Table A.2. Employment type for Nepali men and women

|**Employment Type**|**Share of males (%)**|**Share of females (%)**|
|---|---|---|
|Wage Formal|16.2|14.8|
|Wage Informal|45.2|26.4|
|Unpaid FamilyWorker|6.7|27.6|
|Self,Agriculture|7.1|10.8|
|Self,Non-Agriculture|24.8|20.4|

_Source_ : Authors’ compilation using Nepal Labor Force Survey, 2017-2018 34
sufficient effort by people who do not live in the village. For example, everyone in any village could be given access to a ventilated improved pit latrine in a matter of hours if a sufficient number of outsiders descended upon a village to install such latrines. An “output” is an indicator whose value does not depend entirely on the intensity of effort by outsiders and depends on a range of complex decisions by villagers, such as school attendance or child stunting.

The results are presented in two formats. A series of figures grouped by indicator show the trends by country for the Millennium Village, for the country overall, for rural areas of the same country, and for rural areas of the province or region where the Millennium Village is located (Figures 1–9).<sup>7</sup> For each country, we also present the complete results in a single table, showing standard errors for the DHS-based statistics. The tables also indicate the trend for each indicator in the Millennium Village, and the difference between this trend and the trend in the surrounding area (Tables 1–3).<sup>8</sup> In the figures we assume a linear trend in all indicators. This assumption is not highly problematic, given that under the most straightforward hypothetical deviation from linearity—exponential growth—the assumption of linearity provides a conservatively optimistic view of the intervention’s impact.<sup>9</sup> _Inputs: Relative Trends Inside and Outside Millennium Villages_ _Access to improved sanitation_ . Figure 1 shows that access to improved sanitation improved markedly across all three countries, as well as in the rural areas of the regions in which the MV in each country is located. In Nigeria, access at the MV intervention site rose at approximately the same rate as access in the surrounding region. In Ghana, access at the MV intervention site rose more rapidly than in the surrounding region. (MVP 2010c does not report this statistic for the MV in Kenya.) For this
|Idiosyncratic|26|62|15|49|46|62|
||Ratio (C/I)|0.7|0.75|0.59|0.68|0.7|0.76|

Source: Author calculations based on HCES/WMS, 2016.

# 6. Conclusion

Using the 2016 HCES/WMS data, we estimated vulnerability to poverty in the drought-prone lowlands of Ethiopia relative to other ecological zones using a two-level hierarchical regression model that includes both idiosyncratic (household level) and covariate (community level) variables. We found that vulnerability to poverty is remarkably higher in the drought-prone lowlands than in the other ecological zones (56 percent vulnerability rate for the drought-prone lowlands versus 38 percent for the whole country). The ratio of vulnerability rate to poverty rate is also significantly higher in the drought-prone lowlands – 2.1 in the drought-prone lowlands versus 1.6 in the whole nation. Furthermore, the analysis reveals important differences in the nature of vulnerability between the drought-prone lowlands and the other ecological zones including that in the lowlands: (i) though the vulnerability rate due to covariate shocks is lower than vulnerability due to idiosyncratic shocks in absolute terms, the relative importance of covariate shocks is higher in the drought-prone lowlands than in the other ecological zones; and (ii) while both poverty induced (vulnerability due to low 20
Previous work on allocative efficiency has largely avoided periods of crisis, and work on crises has rarely examined firm responses. By using firm-level data to examine the impact of a major crisis on resource allocation this paper combines and contributes to several strands of literature that have evolved fairly separately up until this point. The available evidence on the impact of crises on resource allocation typically relies on aggregate data. The few previous attempts to empirically validate the predictions of the cleansing paradigm using firm-level data – discussed in detail in section 2 – have either failed to examine changes in the reallocative process itself or suffer methodological shortcomings. Second, existing plant-level studies of reallocation dynamics have demonstrated that business cycles are important determinants of both the pattern and pace of reallocation (see the surveys by Bartelsman & Doms, 2000, Caves, 1998, and Syverson, 2010) but typically deliberately exclude crisis-periods. It is therefore not clear to what extent the conclusions based on them generalize to crisis times. Third, cross-country studies based on aggregate data suggest that plant-dynamics are a key determinant of the depth and duration of crises (Bergoeing et al. 2005, Collier and Goderis 2009), but do not examine how these dynamics matter. Fourth, micro-studies of the impact of crises on labor market outcomes mostly rely on household and labor market data (see e.g. McKenzie 2003, 2004; Fallon & Lucas, 2002; Manning, 2000, Beegle et al. 1999), which are ill-suited for analyzing the impact of crises on reallocation dynamics and labor demand. Firm-level data are better suited for this purpose and enable us to document heterogeneity in firm vulnerability and adjustment patterns.

Examining the impact of the East Asian crisis on the Indonesian manufacturing sector provides an interesting case study of how financial crises reverberate through the real economy. Lessons could be relevant for recovery from the current global crisis, which is also characterized by a sharp contraction of demand, reduced access to credit, and uncertainty (although with less dramatic depreciations). In addition, our analysis may be
5 investing firms; the minimum number of foreign investors to be included in the sample is indicated in the last row of the table. The _ExchangeRateV olatility_ is calculated from the monthly exchange rate of the local currency against the US dollar, normalized by the whole-period mean and standard deviation. For a given destination-year, _it_ , _ExchangeRateV olatility_ is proxied as the average score in the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

29
product pricing strategies are more pronounced in lending markets and for firms for which banks can exert more market power.

Finally, we contribute to the literature that studies government-driven lending. A large body of empirical work examines whether government credit crowds in or out credit by private lenders, and whether public lending promotes financial development and growth (King and Levine 1993; Demirgüç-Kunt and Maksimovic 1998; Rajan and Zingales 1998). These earlier studies explored the aggregate effects of government credit, but more recent work relies on rich firm- and loan-level data from government-owned banks to evaluate real effects (Carvalho 2014; Ru 2017). An important yet overlooked question is related to the role of private banks as intermediaries of government loans. In Brazil, private commercial banks disburse up to 40% of governmentsponsored loans. We use detailed loan-level data to provide comprehensive analysis on the allocation of credit and pricing strategies of private banks participating in the governmentsponsored lending program. Our evidence that banks engage in cross-selling strategies suggests that the design of government programs should consider the credit market structure. For example, banks might prioritize larger firms due to the potential added sales in nonlending products (e.g., investment banking services, deposit-related products, etc.). Whether an excessive focus on large firms is a desirable outcome depends on the policy objectives, but such behavior might arise naturally as banks react by spreading the cost of lending over multiple products.

The rest of the paper is organized as follows. Section 2 describes the institutional background and data. We derive our hypotheses in Section 3 and present our empirical methodology in Section 4. Section 5 documents the findings. In Section 6, we present additional evidence that provides further support for the cross-product pricing hypothesis. We conclude in Section 7.

# **2. Institutional Background and Data**

In this section, we describe the main features of the Brazilian credit market and describe our comprehensive dataset of loans to firms.

# **2.1. Brazil’s Credit Market**

The
FF FF account intra-industry spillover effects and the potential crowding out effects. Our first specification is the following: F F F E E c c (1) iic ii 2cc cc 1cc cc iic ii cc ii iic ii Here, is country-level control variables including the GDP per capita, education (as FFEE = ββ ZZ + ββ FF + δδ FF + XX ββ+ θθ + γγ + εε measured by the index of higher education and training, from World Economic Forum), cc ZZ and the urbanization rate. is a vector of firm-level controls and local business environment. Firm-level controls include the ownership share of the largest owner (as a iic ii XX 11
migrants and the level of development of the place of origin, estimating differential earnings with natives. Using the Mexican Family Life Survey data, the author finds a negative effect on adult earnings associated with migrant human capital and her network, but no influence of the place of origin. However, none of these studies explore the differential wage gaps across gender due to internal migration.

# 4. Data

**In this study, multiple data sources are used.** The primary source of information is the latest Census of Population and Housing, which was conducted in March 2020. The Census allows for the identification of internal migrants during the period 2015-2020, and it also provides information on the reasons for migration. In addition, the study uses labor market indicators (such as unemployment, employment, and informality rates) coming from the National Labor Force Survey (ENOE), information on the incidence of crimes and perceptions of public security coming from the National Survey on Victimization and Perception of Public Safety (ENVIPE), information on experiences and perceptions of the population on public procedures and services coming from the National Survey on Governmental Quality and Impact (ENCIG), and information on past migration flows coming from the Intercensal Survey of 2015.

## 4.1. Measuring internal migration

**Internal migrants are identified using microdata from the latest Census of Population and Housing in México (Census of 2020).** The 2020 Census was carried out by the _Instituto Nacional de Estadística y Geografía_ (INEGI) from March 2 to 27. This dataset allows identifying internal migrants from 2015 to 2020, defined as those who resided in Mexico five years ago and moved to another state. This information is self-reported, answering the question, _“Five years ago, in March 2015, in which state of the Republic or in which country did (NAME) live?”_ While this question enables the identification of international migrants, the analysis is limited to internal migrants contrasted with non-internal migrants, identified as those who resided in
|Turkmenistan|19,304|0.2|0.1|
|**Median**|23,446|0.3|0.3|
|**Other transition economies**||||
|Belarus|19,995|0.8|1.7|
|North Macedonia|16,359|3.4|3.4|
|Bulgaria|21,960|4.9|3.0|
|Croatia|27,580|3.5|3.9|
|Czech Republic|39,744|0.7|1.1|
|Estonia|35,974|1.2|2.0|
|Hungary|31,103|0.7|2.6|
|Latvia|30,305|2.8|4.8|
|Lithuania|35,461|2.1|3.7|
|Poland|31,337|1.6|1.4|
|Romania|28,206|0.5|2.0|
|Slovak Republic|33,736|1.2|2.1|
|Slovenia|38,049|0.8|0.8|
|**Median**|31,103|1.2|2.1|

# Source: World Development Indicators

Note: Annual data on remittance receipts begins for Kosovo in 2004, Montenegro and Serbia in 2007, Tajikistan in 2002, Turkmenistan and Uzbekistan in 2006.

9
5 percent of concerned sales) were maintained to encourage voluntary CR use. In 1996, however, even the tax credit for CR receipts was repealed as CRs had proven ineffective in reducing underreporting of cash sales.<sup>15</sup>

> 7. A news article in the _Kyunghyang Shinmum_ (September 27, 1979) reported that cash registers became useless due to the businesses’ neglect in issuing receipts and consumers’ lack of awareness of receipts. It also reported that fewer than 1 in 10 customers in restaurants obtained receipts, and no one obtained receipts in grocery stores. According to a taxpayer who equipped his business with a very expensive cash register at the urging of tax officials, tax inspectors did not assess taxes based on the cash register records but levied a 20 percent additional tax.

> 8. “Equipped with an Expensive Cash Register but Distrusted,” _Dong‐a Ilbo,_ February 20, 1979.

> 9. “Urgent to Make Receipt Exchange a Practice After One Year of Campaign,” _Mail Economy,_ February 7, 1978. 10. “Receipt Compensation Abolished from Next Year After Three‐Year Trial with Less Effectiveness,” _Dong‐a Ilbo,_ September 24, 1979.

> 11. “Lost Enforcement of Cash Registers,” _Mail Economy,_ March 19, 1980.

> 12. “Retarding VAT,” _Dong‐a Ilbo,_ April 14, 1981.

> 13. “Retailers, Allowed to Use Cash Registers Voluntarily,” _Mail Economy,_ August 2, 1988.

> 14. Article 32, VAT Law, effective as of January 1, 1994.

> 15. Paragraph 2, Article 32, VAT Law, effective as of January 1, 1996.

18
in ascending and descending order). Finally, the abstract reasoning section was modeled on the Ravens Progressive Matrices. Students were presented with an image that was missing a small section and asked to select the missing pieces from six options, based on color, pattern, and orientation.

Like in the case of health outcomes we see a strong positive gradient between child performance on each of these tests and maternal education. Figure 3 reveals a clear positive gradient between scores and maternal education. When looking at raw scores we see that children of literate mothers get rates of correct responses that are approximately 13.6 p.p. (Language), 15.8 p.p. (Math), and 10.7 p.p. (Reasoning) higher than their peers whose mothers cannot read, for example.

> 7Panel A in Table A5 presents descriptive statistics for the raw scores in the working sample (in terms of percentage of correct answers).

9
level of urbanization appears much closer to that for the world overall. They also show LAC to be less urbanized than the Middle East and North Africa region in all six measures, less urbanized than South Asia in three measures, less urbanized than Europe and Central Asia in two measures, and East Asia and the Pacific in one measure. In four of the six measures, LAC appears as the fourth most urbanized among seven regions.

> 36 Annex 3 examines the extent to which the regional and global maps of urban areas generated as a side-product of the analysis correlate with each other.  It shows that there is a generally high level of agreement between the maps generated using the AI and cluster methods and different gridded population data sets.  Interestingly, the main differences are driven less by the method adopted (AI versus cluster) and more by the choice of population layer.

> 37 In addition to these 146 countries, estimates of urban population shares were generated for a further 65 countries using either the AI and/or cluster methods.  The urban population share estimates for these countries are reported in Table A4.2 in Annex 4.

> 38 Figure 2 has been constructed using the 146-country sample for which we have complete data.  However, it also looks very similar when instead constructed using the data for all countries for which we are able to generate estimates of urbanization levels using the AI and cluster methods.

12
|61.0<br>s]; Livestock [En<br>il [Fuel Exploita|4.2<br>teric Fermentation];<br>tion – Oil]; Landfills|

# **6.3 Results by World Bank Income Group**

Our results by World Bank income group (Table 12) suggest that global accountability has begun to affect methane emissions intensity in high-income countries. They have an imbalance toward Decreasing (34.6% vs. 24.0%), but they account for only 14.5 % of EDGAR emissions for our priority GGUs. The other three income groups have very large imbalances toward Increasing. The greatest imbalance (65.2% vs. 9.0%) is in upper-middle-income countries, which also dominate methane emissions (56.5% of the total).

**Table 12: GGU estimation results by World Bank income group**

||% of E<br>|missions-Wei<br>|ghted Estimation<br>|Results<br>|% of Priority<br>|
|---|---|---|---|---|---|
|Income Group|No Trend|Mixed|Decreasing|Increasing|Emissions|
|Low|4.9|27.5|15.1|52.6|1.7|
|Lower Middle|8.0|23.8|10.4|57.8|27.3|
|Upper Middle|6.2|19.6|9.0|65.2|56.5|
|High|5.0|36.4|34.6|24.0|14.5|

27
thresholds to receive domain scores were set: 4/6 items with yes/no responses for math, 8/10 for language, and 6/9 for socio-emotional development. Overall scores were computed only if all domain scores were present. Internal consistency across the three subdomains was adequate (Cronbach’s alpha coefficients of 0.79 (math), 0.94 (language), and 0.81(socio-emotional development).

# **_Risk Factor_**

# _Parental distress_

A survey-specific measure was used to assess participants’ level of distress in the previous 15 days. This included five yes/no questions asking if the participant found it difficult to be affectionate to their child, had been more irritated or angry than usual, found it difficult to sleep, was nervous or anxious, or had been unable to control their worry. Scores ranged from 0 – 5, with 5 indicating the highest level of distress. ‘Don't know' responses were treated as missing. Based on the distribution of missingness, the minimum threshold to receive a score was 3/5 items with yes/no responses. Internal consistency was adequate (Cronbach’s alpha coefficient of 0.87).

# **_Protective factors_**

# _Psychosocial stimulation_

Psychosocial stimulation in the home environment was assessed using the Early Learning Indicator Tool (Hentschel et al., _in preparation_ ). The tool includes 14 yes/no questions regarding the availability of playthings in the home and the amount of caregiver-provided engaging interactions in the last 24 hours. The tool was previously developed and used in Pakistan, and there is evidence supporting its validity in this context (Hentschel et al., 2023; Hentschel et al., _in preparation_ ). Scores range from 0 -14, with 14 indicating the highest level of psychosocial stimulation. Internal consistency was adequate (Cronbach’s alpha coefficient of 0.85).

# _Early childhood education (ECE) enrollment_

Early childhood education (ECE) enrollment was assessed for focal children