rafmacalaba/data-use-mentions-extended huggingface dataset:
choosing PSUs with resulting average scores between 4th and 8th income deciles. Within the population of middle-class PSUs, 21 sampling units were selected for the experiment via random selection with probability proportionate to size.

Within each sampling unit, the following protocol was used to recruit the needed 20 individuals (10 for treatment, 10 for control) at the same time and place. The day before the experiment in a PSU, a team of enumerators would visit the selected PSU, and the team leader would use a random walk method to select households for recruitment. Enumerators then visited this sample of households, introducing themselves with an invitation letter from the Center for Strategic Studies in Jordan (CSS), and used a Kish (1949) table to identify one eligible person who was at least 18 years old to be invited to a meeting the next day at a the reserved location (usually a nearby public school). The purpose of

> 30 The regression included the following variables, which appear in both the Household Expenditure and Income Survey and the census: average household size, owning a fixed phone, a computer, internet connection, central heating, microwave, home ownership, and having at least one family member with university education

17
farm salinity in coastal Bangladesh.</mark> _<mark>Environmental Science: Processes & Impacts</mark>_ <mark>,</mark> _<mark>17</mark>_ <mark>(6), 1127‐1136.</mark> <mark>Christen, E.W., J.E. Ayars, and J.W. Hornbuckle. 2001. “Subsurface Drainage Design and Management in Irrigated Areas of Australia.”</mark> _<mark>Irrigation Science</mark>_ <mark>21 (1): 35–43.</mark> <mark>Dam, T. H. T., Amjath‐Babu, T. S., Zander, P., & Müller, K. (2019). Paddy in saline water: Analysing variety‐specific effects of saline water intrusion on the technical efficiency of rice production in Vietnam.</mark> _<mark>Outlook on Agriculture</mark>_ <mark>, 0030727019850841.</mark> Delsman, J. R.: Saline Groundwater – Surface Water Interaction in Coastal Lowlands, PhD thesis, VU University Amsterdam, Amsterdam, the Netherlands, 2015.

<mark>Desbureaux, S., van Vliet, M., Zaveri, E., Damania, R., Russ, J., Ribeiros, G., Rodella, A.S., and Mortier, F. (2019)</mark> _<mark>A Global Assessment of Water Quality Hotspots between 1992 and 2010 for the SDGs</mark>_ <mark>.</mark> <mark>Dasgupta, S., Hossain, M. M., Huq, M., & Wheeler, D. (2014).</mark> _<mark>Climate change, soil salinity, and the economics of high‐yield rice production in coastal Bangladesh</
household-level data and defines progression and completion irrespective of the type of school in which the girl is enrolled. Therefore, the DD and RDD models using these data provide estimates of the net impacts of the FSSP that already account for the possible crowding out effect induced by the program.<sup>40</sup>

> 39 The sample size of these models is reduced in some cases by more than 50 percent.

> 40 At baseline, there were no statistically significant differences between stipend and non stipend districts in the proportion of girls switching from public to private vs. private to public at the middle school level

28
# **8. Annexes**

## **_A.1. School Productivity or Learning Gains (Grade Effect)_**

OECD (2010) suggests that “while it is possible to estimate the typical performance difference among students in two adjacent grades net of the effects of selection and contextual factors, this difference cannot automatically be equated with the progress that students have made over the last school year but should be interpreted as a lower boundary of the progress achieved. This is not only because different students were assessed but also because the content of the PISA assessment was not expressly designed to match what students had learned in the preceding school year but more broadly to assess the cumulative outcome of learning in school up to age 15. For example, if the curriculum of the grades in which 15-year-olds are enrolled mainly includes material other than that assessed by PISA (which, in turn, may have been included in earlier school years) then the observed performance difference will underestimate student progress”. In 2009, this grade effect estimate was approximately 39 PISA points for reading (see OECD (2010) Annex A1, Table A1.2), in 2012, the same exercise results in 41 points (see OECD (2014) Annex A1, Table A1.2). We have estimated using PISA 2018 data, an average effect for all participating countries of approximately 37 PISA points. For the purpose of our Global exercise we use 40 PISA points as the baseline scenario of our simulations for upper middle-income countries. We assume that learning gains will vary from 20 to 50 PISA points depending on the country’s income level.

A detailed description of the approach used by OECD to estimate this grade effects follows: _“Data on the student’s grade are obtained both from the student questionnaire and from the student tracking form. As with all variables that are on both the tracking form and the questionnaire, inconsistencies between the two sources are reviewed and resolved during datacleaning. In order to capture between-country variation
of unemployment on returns.

> 4 They are Barranquilla, Bogotá, Bucaramanga, Cali, Medellin, and Pasto.

> 5 Because of lack of longitudinal data, little is known about how labor markets respond to timing of education. Less ability to learn, less flexibility and a shorter time for companies to reap benefits from the additional education could argue for lower wages for workers that graduated mid-way in life, while experience, and maturity while studying are arguments for higher wages of those mid-life graduates.

9
income countries. They focus on the effects that trade liberalization could have on the environment and use a cross‐section of urban area pollution and country per capita GDP. Shafik and Bandyopadhyay (1992) find similar results while focusing on the decision nations make to offset the negative environmental effects associated with growth and how that decision may be affected by the lack of incentives (for example, when the burden of pollution is experienced by the poor or by other nations).

Since then, many studies have focused on this empirical phenomenon, looking at different measures of pollution and their relationship with growth. Stern (2004) and Dinda (2004) review some of these studies focusing on the theoretical and empirical approach. They find that despite the fact that many studies look at different pollutants, they all emphasize that the environment deteriorates with economic growth until a certain point beyond which the effect changes and the environmental quality starts improving. However, they do not agree on the level of per capita GDP at which pollution starts declining, partly because some studies look at emissions and others look at concentration levels. The turning point ranges from approximately $3,000 up to almost $23,000 per capita GDP (see Stern, 2004 for a list of these studies and the corresponding turning points).

More recently, studies have also tested the existence of an EKC for particular regions<sup>10</sup> or within particular countries.<sup>11</sup> However, although most studies have used urban pollution measures, they usually use national or regional per capita GDP to measure growth. The poor availability of city‐level GDP data had resulted in only a few studies that look at the EKC at the city level.<sup>12</sup> Global city‐level per capita GDP data allow us to start exploring if there is evidence of an EKC at the city level and try filling this gap in the literature.<sup>13</sup> We test if
the recall period is shortened to a week. For instance, in the health inequality literature, researchers have suggested that inequality in access explains the difference in the number of doctor‘s visits between the rich and the poor (Wagstaff and Van Doorslaer, 2000). This hypothesis makes sense in our monthly recall data where the poor visit doctors less than the rich, but is not consistent with the patterns observed in the weekly data where the poor visit doctors more than the rich. It might be possible to reconcile the results with inequality in access to _high quality health care_ , but what a measure of health inequality that accounts for quality may look like (and what measurements are required to construct it) is an open question.

The relative frequency of visits to medical providers in our data by both the rich and the poor also requires us to reconsider some commonly prescribed solutions to the health care problem in poor countries. Since the Alma Ata declaration in 1978, policymakers have assumed that they should focus on providing poor people with better ―access‖ to primary medical care.<sup>15</sup> Because the main obstacles to access are the price of care and distance, the obvious solution was to open more facilities close to poor people and heavily subsidize (or, preferably, make free) primary care. Our results show that the use of health care providers by the very poor was _higher_ than that of the somewhat better-off. Furthermore, the out-of-pocket expenditures of the very poor correspond to the higher-than-previously-thought use of health services. Again, with shorter recall periods the degree to which very poor people choose to spend on health rises accordingly. Yet, the health of the poor in Delhi—as measured through outcomes data in the National Family Health Survey or the health rounds of the National Sample Survey—is much worse than that of the rich. If the use of health care providers and the extent of health spending do not decrease gaps in health outcomes between the poor and the rich, what is the
on a selected group of students rather than all students graduating in a given year. Moreover, while the students take the decision to participate or not themselves, schools do have a possibility to try to influence this choice. For example, if a school would like to get a good average score on the EIT, it can try to persuade weaker students not to take the test.

> 29 We have 12267 schools in the EIT 2009 data and 13678 in the EIT 2008 data. The 2008 EIT database includes vocational schools, which is not the case in 2009 and 2010. In this section, we focus on the descriptive data for the 2010 EIT. The descriptive statistics for 2008 and 2009 give a qualitatively similar picture

23
931|
|Rubber, Miscellaneous|||||
|Plastic Products|585|603|23,247|15,372|
|Leather, Leather Products|<br>240|247|13,280|9,769|
|Stone, Clay, Glass|||||
|Products|589|641|25,210|17,417|
|Primary Metal Industries|1,089|1,144|27,885|23,770|
|Fabricated Metal Products|<br>1,417|1,475|24,138|18,185|
|Machinery, Except Electric|1,967|2,096|25,950|19.786|
|Electric, Electronic|||||
|Equipment|1,524|1,705|22,864|16,616|
|Motor Vehicles &|||||
|Equipment|699|787|30,709|26,522|
|Other Transportation|||||
|<br>Equipment|906|918|26,348|21,458|
|Instruments,<br>Related|||||
|<br>Goods|500|551|28,315|17,261|
|Miscellaneous<br>Manufactured|||||
|Goods|393|442|19,288|13,532|
|Total|17<sup>,</sup>174<sup>a</sup>|18,594|25,765|18,240|

a) excludes 1,128,400 employees with a payroll of **US$** 19,014.9 in administrative offices and auxiliaries located outside establishments.

Source: National Income and Product Accounts of the US 1929-76 and sources cited in table 2.6
# **2. Insights from the literature**

E-commerce, or electronic commerce, is the sale or purchase of goods or services, carried out by means of computer networks through methods specifically designed for the purpose of receiving or processing orders, regardless of whether payment and delivery of the goods or services occur online, as defined by OECD (2011). In most cases, e-commerce is not an activity that occurs entirely in an online environment. As with other processes of virtualization of human activity made possible by information communication technologies (e.g., teleworking, telemedicine, etc.), e-commerce has led to a "fragmentation of commercial activity" (Couclelis 2004), since it separates in space and time the sub-processes that are part of the purchase and sale of goods or services.

Depending on the profile of the buyer and seller, the main types of e-commerce include (i) business-to-business (B2B), or electronic commerce of goods and services between companies; (ii) business-to-consumer (B2C), or the purchase of goods and services by individual consumers from companies via the Internet; (iii) consumerto-consumer (C2C), or the sale and purchase of goods and services between private individuals over the Internet – usually associated with the so-called sharing economy platforms; and (iv) consumer-to-business (C2B), or the electronic purchase of goods and services by companies from individuals, such as the advertising of companies on blogs or social networks of individuals or the search for individuals for online panel surveys. Among these, B2B and B2C represent the main types of goods distribution flows (Cataruzza et al. 2017). With regard to the distribution mechanisms of the products purchased online, which are of particular interest for tracking the impact of e-commerce on mobility, the options include direct distribution (home delivery) and several types of indirect distribution, such as pickup at a physical establishment of the supplier (click and collect), pickup at another establishment, and collection at a box office (Zhang et al
to show that the quantitative changes in consumption and risky investments are consistent with accepted relative risk aversion parameters.

# **4.4 Recent Trends in Migrant Earnings**

Average household annual migrant earnings increased from less than 8,000 yuan in 2004 to more than 30,000 yuan in 2014 (Figure 9).<sup>22</sup> Both increases in migrant labor supply (Figure 5 above) and increased wages of low-skilled workers (Li et al. 2012) contribute to increased migrant earnings observed at the household level. Moreover, the share of migrant earnings in total household income has also increased, from about 41% in 2003 to 54% in 2014. It is important to note that “migrant earnings” ( _da gong shou ru_ ), as estimated in the RCRE surveys, are gross estimates of migrant earnings in the city and are considerably

> 21Unproductive forms of precautionary savings, in the form of grain stocks and other liquid assets, were identified as an important distortion in rural household consumption in the early 1990s  (Park, 2006).

> 22The earnings are in real terms, adjusted for inflation by a provincial consumer price index with year 2000 as the base year and Henan as the base province.

18
Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 65-108.

Carrillo, B. (2020). Early rainfall shocks and later-life outcomes: Evidence from Colombia. _The World Bank Economic Review_ , 34(1), 179-209.

Chaudhuri, S., Jalan, J., and 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.

CCKP (2023). Climate Change Knowledge Portal Temperature Country Data. Retrieved by January 30, 2023, from https://climateknowledgeportal.worldbank.org/country/ecuador/trends-variabilityhistorical.

CEPAL (2010). _Panorama Social de América Latina 2010._ Santiago de Chile: Comisión Económica para América Latina y el Caribe, CEPAL. https://www.cepal.org/es/publicaciones/1236-panoramasocial-america-latina-2010.

Damania R., Desbureaux, S., Hyland, M., Islam, A., Moore, S., Rodella, A.S., Russ, J., Zaveri, E. (2017). When Rainfall Is Destiny: The Long-Lasting Impacts of Water Shocks on Families. In: _Uncharted Waters: The New Economics of Water Scarcity and Variability._ 35-46. The World Bank. <u>https://doi.org/10.1596/978-1-4648-1179-1_ch3.</u> Drumond, A., Stojanovic, M., Nieto, R., Gimeno, L., Liberato, M. L., Pauliquevis, T., ... & Ambrizzi, T. (2021). Dry and wet climate periods over Eastern South America: identification
comparison. Ethnic identifiers were merged from the national SILC into the EU-SILC using unique ID household identifiers.<sup>39</sup>

**<mark>To analyze chronic deprivations, we focus on the two components of the AROPE measure that can be estimated and traced over time: persistent at risk of poverty and persistent severe material and social deprivation.</mark>** <mark>We follow the definitions of Eurostat, so that a person is at a persistent risk of poverty (or endures persistent material and social deprivation) if the individual is at risk of poverty (or endures persistent social material deprivation) in the current year and in at least two of the three preceding years.</mark>

IV. Assessing Multidimensional Poverty in the EU Is multidimensional poverty stagnant? Are there diverging trends across the EU countries and subregions?

**While the European Union-27 (EU-27) witnessed a marginal decrease in absolute multidimensional poverty recently, different rates of progress are observed across EU subregions.** The at-risk of poverty or social Exclusion AROPE rate (non-anchored) for EU27 (including Germany) has been stagnant since 2018. Our measure of absolute multidimensional poverty shows similar trends. From 2021 to 2022, the anchored (AROPE) rate<sup>**40**</sup> in the European Union-27 (EU-27 excluding Germany) experienced only a marginal decrease from 20.2 percent to 19.3 percent. Among the EU subregions, Southern Europe (SE) experienced the largest decline in the AROPE rate, dropping from 24.6 percent in 2021 to 22.0 percent in 2022, a decrease of 2.6 percentage points. Conversely, Central Eastern Europe (CEE) exhibited the lowest decline, with its AROPE rate decreasing only slightly from 17.8 percent to 16.4 percent, marking a reduction of 1.3 percentage points. Other subregions, such as Northern Europe (NE) and Western Europe (WE), experienced increases in their AROPE
these assessments to allocate concessional resources across countries.<sup>8</sup> This category also includes two discontinued data sources, from the European Bank for Reconstruction and Development, and the French international development agency. Finally, two data sources in this category are based on data reported by the US Department of State, on human trafficking (Trafficking in Persons Report) and on human rights violations (as coded by academic researchers in the Cingranelli-Richards Human Rights Data and the Political Terror Scale).

Finally, four WGI data sources come from commercial business information providers. Three of them are long-running data products providing subscription-based quantitative ratings of a broad range of business environment and political risks, produced by the Economist Intelligence Unit (Country Viewswire), Political Risk Services (International Country Risk Guide) and S&P Global (Country Risk Service). The fourth is Crisis24, a specialized security risk firm, which provides quantitative measures of security risks facing business travelers that feeds into the Political Instability and Absence of Violence/Terrorism measure in the WGI. All four organizations rely on a global network of experts who provide scores following a standardized methodology subject to centralized internal review within the organization.

The source data used to construct the WGI is publicly available at www.govindicators.org.<sup>9</sup> The data are available in their original form as retrieved from the original providers. We also report the data rescaled and reoriented (if necessary) to run from zero to one with higher values corresponding to better governance outcomes, using the minimum and maximum possible scores on each variable to do this rescaling. The rescaled data is comparable within a given data source over time and across countries. However, it is not necessarily comparable across different data sources, because different data sources cover different sets of countries with different underlying distributions of governance. For example, a score of 6 out of 10 might mean something different in a data source that covers only advanced

> 8 In addition, the World Bank and the
studying only.<sup>33</sup> Unfortunately, our data do not allow us to further investigate these channels.

However, the ban seems to have caused detrimental effects on a sub-group of the affected cohort, that is, those who have relative lower earnings potential. Indeed, we find that earnings are 6.2 percent lower among those affected by the law who are in the bottom 20<sup>th</sup> percentile of the log-earnings distribution.

We believe the results found in this paper has some policy implications: (1) it informs policy makers how the law affected time allocation between working and schooling activities in the short term; (2) it shows that the ban has persistent effects on time allocation between schooling and working activities; (3) it suggests that the returns to experience for the unaffected cohort were similar to the returns to education for the affected cohort; (4) it can have detrimental effects among those with lower earnings potential; and finally (5) it suggests that policies that reduce individuals’ choice-sets should be accompanied by compensating or complementary policies—for example, a conditional cash-transfer program or an apprenticeship program—for those to whom it is more likely to cause harm.<sup>34</sup>

# **Conclusions**

This is the first paper to investigate the short- and long-term effects of a child-labor ban. The increase in the Brazilian minimum legal age of entry into the labor market from 14 to 16 in December 1998 was interpreted as a natural experiment. Several years of Brazilian household surveys and a fuzzy RDD were used to estimate the ITT impact of the ban on the outcomes of interest.

> 33 Indeed, Emerson, Ponczek and Souza (2016) find that only studying improves learning as compared to studying and working in Brazil.

> 34 The Brazilian CCT _Bolsa Família_ was in a pilot stage at that time (Glewwe and Kassouf, 2012) and the Brazilian apprenticeship program was launched only in December 2000 (Corseuil
meals rose to nearly 41 percent. Also, about half the population rationed meals in both seasons, indicating a persistent form of food insecurity among the hard-core poor in the greater Rangpur region.

# **{TABLE 8 HERE}**

Does meal-consumption status vary by membership in safety-net programs or whether a household receives support during monga? At the aggregate level, 49.9 percent of recipient households and 46.1 percent of non-recipient households underwent occasional starvation during the 2006 monga (Table 9), meaning that the extent of seasonal deprivation was slightly higher among safety-net beneficiaries, compared to non-beneficiaries. This does not necessarily imply a negative effect of the safety-net programs, but merely indicates that safety-net beneficiaries are more vulnerable than their counterpart non-beneficiaries.

# **{TABLE 9 HERE}**

# **{TABLE 10 HERE}**

During the non-monga season, the rate of starvation was lower among safety-net beneficiaries, compared to non-beneficiaries (Table 10). Again, this does not necessarily imply a positive impact of the safety-net programs. An impact assessment, discussed later in this paper, is required to handle the endogeneity issues regarding program placement and participation in the safety-net programs.

# **4. Determinants of Safety-net Support in Northwest Bangladesh**

As mentioned previously, the InM survey aimed to collect information on the hard-core poor in Bangladesh’s greater Rangpur region. The targeting criteria (listed in footnote 1), according to the InM, were well enforced, but we cannot completely verify that claim as data on the monthly-income 14