rafmacalaba/data-use-mentions-extended huggingface dataset:
individuals not in the labor force work to be engaged in the home sector. Table 2 lists the 1-digit occupations together with some examples of narrower sub-groups that constitute them.<sup>11</sup> I make two more adjustments to the data. First, I classify the bottom five percentile of workers in terms of their hours worked in the market occupations to be engaged in the home sector. Depending on the country and the year, the cut-off for being classified in the marker occupations ranges from 21–24 hours. Second, I drop the bottom five percentile of workers in terms of their incomes when using such data. The income of a worker includes earnings from supplying labor in market occupations together with business and farm income in the previous year. A useful feature of the SEDLAC data is that the data on income are converted to real income in local currency with base year in 2011. Finally, I use sampling weights provided by the surveys to aggregate variables at the group-occupation-cohort level, or any combination thereof.

The next section explains how I calibrate the model using the survey data to recover the parameters of the model including the barriers operating in the labor markets and in the accumulation of human capital. Obtaining the estimates of barriers allows me to conduct counterfactual experiments that quantify their aggregate impact.

|Tab|le 2: Occupational classification<br>|
|---|---|
|Occupation|Sub-groups|
|Managers (MNG)|Chief executives, senior officials and legislators; Administrative and<br>commercial managers; Production and specialized services managers<br>etc.|
|Professionals (PRF)|Science and engineering professionals; Health professionals; Teaching<br>professionals etc.|
|Technicians (TCH)|Science and engineering associate professionals; Health associate<br>professionals; Business and administration associate professionals<br>etc.|
|Clerks (CLR)|General and keyboard clerks; Customer services clerks; Numerical and<br>material recording clerks etc.
1.2 cross-country evidence on revenue recovery

5 robustness of results with different parameter assumptions of elasticities for product substitution among exporters, between exporters and domestic producers, and overall demand. Section 6 highlights two additional aspects of tariff reform. Section 6 concludes.

# 1.2 cross-country evidence on revenue recovery

To set the stage for a detailed country case study subsequently, I examine in this section the cross-national evidence from a sample of 40 low-income countries on their record of replacing trade taxes with domestic sources over time. As trade taxes as a share of GDP have altered, how have poor countries fared in terms of domestic tax collection? In other words, for every dollar “lost” in trade taxes, how many cents have they recouped through domestic sources? A cross-national estimation of this nature requires a dynamic panel regression involving detailed tax data that are not always publicly available. I, therefore, use internally compiled IMF data and the estimation strategy of Baunsgaard & Keen (2010). I make three major changes to their data and specification (explained later) to derive results for revenue recovery by low-income countries that are comparable to, if not stronger than the estimations in Baunsgaard and Keen (2005, 2010).

# 1.2.1 _Econometric Model_

The basic econometric specification is as in equation (1.1) where the dependent variable is total domestic tax revenue (net of trade taxes) as a share of GDP (DTit). Subscripts i and t indicate country and time, respectively.

olies, raising of low tariffs, elimination of export taxes, removal of foreign exchange rationing and import licensing regimes, among others. Often these are coupled with macro-economic reforms to influence exchange rates, inflation, and incentives for investment.
midline survey. The project interventions were implemented from November 2016 to the first week of November 2017, when data collection for the midline survey started. So, none of the health- and nutrition-related impacts would have materialized by then. As has been noted, a follow-up survey has been planned for 2021 to measure these impacts.

The same household and community questionnaires were used for both the baseline and midline surveys, though the midline survey included some additional questions to assess the implementation of the project interventions. A school survey was included at midline. The community survey and the school survey were used mostly to assess implementation.

# 4.5 Measurement of Outcomes

The main indicators of interest for this study are hygiene awareness and hygienic behaviors, toilet construction, and toilet use. All these outcomes of interest were measured using data collected from the household survey. A brief description of the indicators used to measure these outcomes follows.

15
Fig. 9 <!-- Start of picture text --> Cohort Survival Rate and Unit Cost, 1993<br>Poor<br>Guizhou T -<br>Guangxi I -.<br>Anhui<br>Henan .-.<br>Gansu<br>Sichuan<br>Jiangxi i<br>Yunnan<br>Shaanxi -<br>Hunan<br>Tibet ---. . .- . -<br>Ningxia<br>Qinghai<br>Shanxi U<br>Inner Mongolia<br>Hubei I<br>Hebei<br>Jilin ......... 3<br>X injiang -_____ _____ _____ _____ _____<br>Fujian<br>Heilongjiang<br>Shandong<br>Jiangsu .<br>Zhejiang U , .:<br>Liaoning u<br>Guangdong Tianjin '<br>Beijing<br>Shanghai<br>Rich 120 100 80 60 40 20 0 5 10 15 20 25 30 35 40<br>U CSR 1. Unit cost<br><!-- End of picture text --> Note: Provinces are ranked by per capita income.

CSR = Cohort Survival Rate at the end of primary cycle. Unit cost = per-pupil government expenditure in primary educat!r. as a percentage of provincial GNP per capita.

<!-- Start of picture text --> Fig. 10: Rate Of Return to Education<br><!-- End of picture text --> <!-- Start of picture text --> ROR (%)<br>o 5 10 15 20<br>E<br>E~~~~~~~~~~~~~~~~~ PRVT ROR<br>.0:<br>I-~~~~~~~~~~~~~~~~~~~
Chirwa, E. W., & Dorward, A. (2013). _Agriculture Input Subsides; the recent Malawi experience_ (First Edit). Oxford: Oxford University Press.

- Dillon, A. (2010). _Do differences in the scale of irrigation projects generate different impacts on poverty and production?_ (IFPRI Discussion Paper No. 01022). _IFPRI Discussion Papers_ . https://doi.org/10.1111/j.1477-9552.2010.00276.x

- Ellis, F., & Maliro, D. (2013). Fertiliser Subsidies and Social Cash Transfers as Complementary or Competing Instruments for Reducing Vulnerability to Hunger: The Case of Malawi. _Developemnt Policy Review_ , _31_ (5), 575–596.

- Fan, S., Gulati, A., & Thorat, S. (2008). Investment, subsidies, and pro-poor growth in rural India. _Agricultural Economics_ , _39_ (2), 163–170. https://doi.org/10.1111/j.15740862.2008.00328.x

- FAO. (2011). _Guidelines for measuring household and individual dietary diversity_ . Rome, Italy.

- Fisher, M., & Kandiwa, V. (2014). Can agricultural input subsidies reduce the gender gap in modern maize adoption? Evidence from Malawi. _Food Policy_ , _45_ . https://doi.org/10.1016/j.foodpol.2014.01.007

- Garbero, A., & Songsermsawas, T. (2018). _Impact of modern irrigation on household production and welfare outcomes_ (IFAD RESEARCH SERIES No. 31). Rome, Italy.

- Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2016). _Impact Evaluation in Practice_ (Second Edi). Washington, D.C.: World Bank. https:
another job (including exiting self-employment) to take this job, and the net employment creation impact of these training programs on people other than the worker trained is extremely minimal.

The one study to show a stronger employment effect is Glaub et al. (2012), which hints at the possibility of employment impacts when training larger firms. They find that employment in their treatment group grows from 7.9 employees at baseline to 10.7 at follow-up, whilst employment in the control group falls from 6.6 employees at baseline to 5.0 at follow-up, with this difference significant at the 5 percent level. Their sample is small, and they drop non-compliers to their treatment, so this is likely at overstatement of the effect, and more studies with larger firms are needed.

# _Impacts on Microfinance Institution Outcomes_

Since many of the studies have worked with microfinance clients, they have also looked at outcomes using administrative data from the microfinance organization. These data have the advantage of potentially being available with less attrition and over longer time periods, and are useful for assessing whether offering training is cost-effective for the microfinance organization. But they are less useful for telling us about how such training impacts firms. Karlan and Zinman (2011) find training resulted in a 4

> 12 If we account for opportunity costs and risk aversion, the desired returns would be higher. But a 25 percent increase in profits would provide a very reasonable return to microenterprise training, even after accounting for these costs.

17
competition. As a result, the structure of production is expected to change according to relative comparative advantages—defined by both factors of production and institutional arrangements. This effect can either increase or decrease relative output in pollution-intensive sectors (composition effects). Finally, changes in production technologies (including pollution intensity by unit of output) tend to follow trade liberalization (technique effects). Technique effects can result from different forces: while trade facilitates the access to more efficient (and cleaner) technologies, stronger competition can trigger a race to the bottom of environmental standards, favoring the adoption of cheaper and dirtier technology in the short run. Nevertheless, as income grows, the demand for environmental quality tends to increase. By adopting both tighter environment policies and more advanced, cleaner technologies, countries can afford to reduce emissions after reaching a certain level of income. This inverted-U relationship between per capita income and pollution is known as the environmental Kuznets curve<sup>2</sup> .

Previous empirical studies on the relationship between trade and the environment have found varying results. For example, Dean (2002) uses province-level data on water pollution from China and finds support for the idea that trade liberalization has both a direct and an indirect effect on emissions growth

> 2 Its proponents argue that environmental degradation is just a matter of ―growing pains‖ that will disappear with prosperity.

2
, the share of Afghan households who receive aid, transfer, and free goods are respectively 3.2%, 6.6%, and 14.4%, which is significantly lower than these shares for Iranians.

## **4.2.4. Measuring economic shock**

In order to measure shocks, we need a variable that both reflects the exogenous nature of macro shocks in Iran’s economy and has at least a monthly frequency to be merged with HEIS data. In this regard, our best candidate is the exchange rate (US dollar to IR rial) which is available daily and, as shown in Figure 3, quickly reveals the effect of sanctions on Iran’s economy. In addition, since we aim to study the effect of shock on social cohesion, it is also important that exchange rate shock captures not only the direct economic impact of exogenous shocks but also people’s perceptions and expectations about the status of the economy. Besides, many Afghan refugees send remittances back home and regularly consider returning home or remigrating to other places. Such considerations are mostly affected by the exchange rate that is instantly visible to them. In contrast, other shock measures like GDP growth capture the economic shocks to all sectors of the economy, many of which are irrelevant to work for refugees.

Since the recall period for most of our dependent variables is the past year, our primary variable to − measure monthly shock is the year-on-year difference in log of USD to IRR rate, i.e. ( log Xrate�,� log Xrate�,��� ). However, because the exchange rate in Iran jumps in bad economic times and remains almost constant in good times, which is evident in Figure 3, the variation in this measure of shock mainly reflects bad economic shocks. For this reason, when we examine the asymmetric effect of good and bad shocks in section 5.4, we apply a Hodrick-Prescott filter to extract the cyclical component of the log of the
). The tool consists of up to 100 caregiver-reported items developed according to typical abilities by age within the birth to age 3 range in 6-month increments (e.g., for children 0-6 months, 6-12 months, and so forth) and administration ends based on 5 consecutive incorrect answers so as not to distress the child. Items measured children’s developmental status across four primary domains: motor, language, cognition and social-emotional development. An age-standardized Z-score, created by uploading deidentified data to the CREDI application, allows comparison of a given sample to a global, advantaged, sample based on children from 15 low- and middle-income countries.<sup>5</sup> A child scoring more than 2 standard deviations below the mean is typically considered to be developmentally “off-track."

> 5 <mark>The reference sample contains 19,165 children who all have a mother who completed secondary school or higher education and live in a household where least one adult had engaged in 4 or more of the 6 “Play activities” from the Family Care Indicators with the child.</mark>

6
Bone district in South Sulawesi Province is one of the first districts that introduced the Community Based Development Information System (CBDIS) program, a community-based mapping mechanism to identify OOSC by name, address and other details. Using CBDIS data, the local government organizes the “Back to School Movement” ( _Gerakan Masyarakat Lisu Massikolah or Gemar Limas_ ) to provide necessary support for these children. During the period of 2017-19, the initiative has returned 4,998 OOSC to school, either formal or non-formal schooling. This model has now been taken to scale through the government’s village development planning system to enable its wider and sustained use by village governments nationwide.

# 3.3.1. Child Marriage

Another determinant of enrollment is child marriage, often linked to poverty and gender, and also contributing to dropping out. The rate of child marriage in Indonesia has been declining in the recent years, though it is still higher than in neighboring countries (Figure 6). Indonesia’s child marriage rate— defined as the proportion of women aged 20-24 married before age 18—is lower than India’s but twice Vietnam’s (Figure 6). And <mark>because of the huge size of the Indonesian population, the country is among the top 10 countries with the highest absolute number of child brides, ranking seventh globally with 1.4 million women aged 20-24 married before age 18. S</mark> ince 2008, the rate has been relatively stagnant at 25 percent. More recent data in 2015 show a modest improvement to 22 percent.<sup>29</sup> While overall early marriage rates are high across the country, prevalence varies by district. At the provincial level, West Sulawesi had the highest prevalence of early marriage in 2015, with 34.2 percent of ever-married women aged 20-24 married before age 18. In contrast, the Riau Islands had a rate of 11.7 percent. East Java, South Kalimantan and Central Java have the greatest number of high prevalence
|**Export restraints**<br>|**Import reforms**<br>|
|---|---|---|
|Medical|134.6|165.2|
|Food|39.4|42.2|
||Panel b: Medicalgoods only||
|**Trade restrictive measures**||Worldwide export coverage|
|All included instruments||134.6|
|Of which:|||
|Export ban||101.8|
|Export licensing requirement||66.2|
|Export quota||1.4|
|**Trade liberalizing measures**|||
|All included instruments||165.2|
|Of which:|||
|Import ban||0|
|Import licensing requirement||6.3|
|Import quota||0|
|Import-related non-tariff measure|, nes|7.5|
|Import tariff||145.7|
|Import tariff quota||12.5|
|Internal taxation of imports||88.6|

> 12 Trade data are for 2019 given 2020 trade data are not yet available. The intention is simply to give a sense of the coverage of the trade measures used. The figures in Table 2 estimate the total value of 2019 trade in essential goods that were affected at least once according to the GTA data. The percentages reported in Table 3 are based on al interventions in force at the end of the given month, using as denominator the total value of 2019 world trade in the food or medical essential goods, respectively.

6
School participation was observed repeatedly over 2000 and 2001. Prior to the implementation of the program, there was one round of school participation checks in all preschools<sup>26</sup> . After the start of the program, there were ...ve rounds of visits in 2000 and eight rounds in 2001. The NGO ...eld sta¤ visited the schools unannounced and in di¤erent order at each round of visits. Two of the 2001 rounds of visits included school participation checks in grade 1 of primary school. Though each round was meant to include all schools, in practice some schools were missed<sup>27</sup> . The average number of actual attendance checks in preschool is 5.75 in 2000 and 6.12 in 2001, and the di¤erence between treatment and comparison schools is not statistically signi...cant<sup>28</sup> .

We use an intention-to-treat estimator to estimate the e¤ect of the program on school participation. We de...ne the set of children that the program intends to treat as the baseline children, and we de...ne the treatment as the availability of free breakfast at the school of a child’s older sibling. As explained before, school participants were systematically matched to their school’s baseline. Therefore if no school participant was matched to a particular baseline child, we assume that baseline child did not attend school. The school participation measure that we use does not make any distinction between length of absence from school or reason for absence. Children who are temporarily absent from school and children who permanently drop out of school are counted as absent. This makes sense because the line between prolonged absenteeism and de...nitive dropout is subjective. A child that has dropped out of preschool is very likely to return to school later on, either to the preschool or to primary school, so in that sense most absences are temporary. In addition, most
1. Introduction

The world is rapidly losing biodiversity. Pimm et al. (2014) find that the current rate of species extinction is at least 1,000 times the background rate. Corroborating evidence is provided by the Living Planet Index (LPI), an indicator of global biodiversity based on population trends for vertebrate species in terrestrial, The LPI informs the Convention on Biological Diversity (CBD) and its Conference of the Parties (COP). In response to such indicators of rapid decline, 188 governments in the COP ratified the Kunming-Montreal Global Biodiversity Framework (GBF) at the COP 15 meeting in December 2022. Among other measures, the Framework commits participants to protecting 30 percent of the planet by 2030.

Effective implementation of the Framework will require addressing two difficult questions: (1) What is the spatial distribution of global biodiversity that should be protected? (2) How can protecting 30% of the This paper attempts to contribute by revisiting the spatial distribution of global biodiversity with species occurrence records from the Global Biodiversity Information Facility (GBIF). During the past 15 years, the GBIF’s global reporting network has expanded to include occurrences for over 2 million species. During of these records include locational coordinates, the GBIF data can enable new estimates of spatial distributions for previously-unmapped species, as well as improved estimates for species with existing Species maps underpin spatial analyses of global biodiversity. If all species are treated equally, then a species; (2) Overlay all chosen maps on a high-resolution global grid; (3) Count the total species incidence in each grid cell. In practice, global biodiversity analyses can modify species counting in three dimensions. life” that describes overall genetic variation in the global biome. Second, species may have different conservation priority weights because their vulnerability to human encroachment differs greatly. Third, species’ distributions may be far from uniform in the spaces enclosed by their maps. If a species’ spatial distribution density is known, its counting weight for each cell can be made proportional to
India|7.6|12.1|17.0|10.4|30.2|5.4|5.0|23.0|7.2|
|Korea, Rep.|18.8|20.1|23.5|5.5|15.9|4.8|0.7|13.0|3.0|
|Malaysia|53.1|58.1|45.8|7.3|9.2|6.3|1.0|13.6|-4.4|
|Mexico|42.3|78.4|69.6|15.2|7.7|8.3|6.9|9.8|-2.2|
|South Africa|16.1|20.1|27.0|3.1|19.9|0.8|2.3|14.2|5.7|
|Turkey|17.2|30.0|31.0|7.9|24.2|2.1|5.8|23.6|0.6|
|Total: above<br>developingco.|17.2|25.4|18.7|10.4|17.6|6.2|4.2|23.7|-6.1|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Sources: Computations based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Developing country import behavior in some ways is similar to that of the industrial countries. Import growth rates accelerate from already high rates of 10 percent p.a. during the first period to almost 18 percent p.a. during the second. The acceleration of demand growth is much more dramatic; from an average of 6
.S. credit scoring bureaus, reported that it examined loan application data from "tens of thousands" of individuals applying for installment loans between July 1992 and December 1994 (Martell et al). Using its proprietary data base, Fair Isaac assigned scores for each applicant ranging from 0 to 240, with a higher score indicating a lower credit risk. The report (p. 14) notes that, “Many lenders set their cutoff score – the score...below which they decline applicants – around 200.” The study found that that 54 percent of low- and moderate-income individuals (defined in the study as individuals with annual incomes under $21,000 a year) had scores below 200, but only 33 percent of individuals with incomes over this amount had scores below this level. Similarly, the Freddie Mac Corporation (1999), a large government-sponsored housing enterprise, conducted a survey of 20,000 households with incomes under $75,000. The survey focused on the households' credit histories and financial behaviors. It classified a household as having a "bad" credit record if the household reported that:

- it had been at least 90 days late on a payment in the previous two years,

- it had been 30 days late on a payment more than once in the previous two years, or if

24
# **Table 3**

# **Determinants of growth amid the COVID-19 pandemic: Sensitivity Analysis II Changes in the set of explanatory variables**

_Dependent variable: Change in the predicted 2020 growth rate (June 2020 vs. October 2019) Least Squares, Effective sample of 110 countries across the world_

|||**Financial O**|**enness inde**||**201 **|**Human Ca**|**ital Index**||**Sens**|**tivitto Tr**|**de Variable**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variables|**[1]**|<br>**[2]**|<br>**[3]**|**[4]**|<br>**[5]**|<br>**[6]**|<br>**[7]**|**[8]**|<br>**[9]**|**y **<br>**[10]**|<br>**[11]**|**[12]**|
|_International Trade_|||||||||||||
|Trade Openness<br>_(% GDP)_|-0.0168<br>(00148)|..|..|-0.0186<br>(00148)|-0.0164<br>(00115)|..|..|-0.0117<br>(00112)