rafmacalaba/data-use-mentions-extended huggingface dataset:
Stojanovic, M., Nieto, R., Gimeno, L., Liberato, M. L., Pauliquevis, T., ... & Ambrizzi, T. (2021). Dry and wet climate periods over Eastern South America: identification and characterization through the SPEI index. _Atmosphere_ , 12(2), 155. <u>https://doi.org/10.3390/atmos12020155</u> Guha-Sapir D., R. Below, & Ph. Hoyois (2022). _EM-DAT: The CRED/OFDA International Disaster Database_ – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium.

IPCC, 2007: Climate Change 2007. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. Van Der Linde, and C.E. Hanson (eds.)]. Cambridge University Press, Cambridge, UK, pp. 7-22.

Skoufias, E. & Baez, J. (2023). Operationalizing Vulnerability to Poverty. _Working paper_ . The World Bank Skoufias, E., Vinha, K., & Beyene, B. M. (2023). Quantifying vulnerability to poverty in the droughtprone lowlands of Ethiopia. _Journal of African Economies_ , ejad003. <u>https://doi.org/10.1093/jae/ejad003</u> Socioeconomic Database for Latin America and the Caribbean (2022). <u>https://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/. CEDLAS and The World Bank.</u> Retrieved September 2023.

ThinkHazard! Platform (2022). https://thinkhazard.org/. Global Facility for Disaster Reduction and Recovery (GFDRR Labs). The World Bank. Retrieved March 2022
. We compute the tariff rate on tariff line h by averaging over all countries from which there are positive imports in all three time periods, 2009, 2010 and 2011*. This is analogous to equations 1 and 2.

For tariff lines h in the years 2011-2020 with no match in the base period, we look for a match at the eight-digit level in the base period. If there is no match at the eight-digit level, we look for a match at the six-digit level; in a few cases, we had to find a match at the four digit level. Our calculations, however, are always based on ten-digit data. So if there is a match at the eight-digit level, we use all ten-digit tariff 9
2.19***|2.21***|
|GDP)|(-0.29)|(9.19)|(8.86)|
|Fuel exporter|1.74***|1.22*|-0.515|
|dummy * ln (real per<br>capita GDP)|(3.43)|(1.83)|(-0.96)|
|Time trend|0.0177|0.223***|0.189***|
|(=0 in 2000)|(0.65)|(4.93)|(3.93)|
|n|1,352|1,366|1,352|
|R-squared|0.151|0.345|0.270|
|RMSE|4.71|7.81|7.82|

_Note_ : Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

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<sup>3</sup> 1  Recent  work  extends  the  findings  of  Bernard  and  Jensen  (1995)  using  matched  employer-­‐employee data  for  Germany  (Schank,  Schnabel,  and  Wagner  (2007)  and  Klein,  Moser,  and  Urban  (2010)), Denmark  (Munch  and  Skaksen  2008),  and  Mexico  (Frías,  Kaplan,  and  Verhoogen  2009).  See  also Hummels,  Jorgensen,  Munch  and  Xiang  (2010)  for  analysis  of  the  impact  of  outsourcing  on  wages  and employment  using  matched  employer-­‐employee  data  from  Denmark.

2  A  vast  empirical  literature  has  examined  the  effects  of  globalization  on  the  wage  outcomes  of workers  in  the  domestic  economy  with  a  particular  focus  on  the  important  question  of  how  trade affects  the  average  wages  of  workers  with  different  levels  of  skill.  Classic  papers  in  this  literature include  Lawrence  and  Slaughter  (1993),  Leamer  (1996),  and  Feenstra  and  Hanson  (1999).  Revenga (1997),  Currie  and  Harrison  (1997),  and  Trefler  (2004)  analyze  the  effect  of  trade  liberalization  on firm-­‐level  wages  in  Mexico,  Morocco,  and  Canada,  respectively,  with  mixed  results.  Feenstra  and Hanson  (2002)  and  Goldberg  and  Pavcnik  (2007)  provide  excellent  survey  treatments.

3  An  expansion  in  exports  could  result  in  differential  labor  quality  upgrading  in  exporting  firms,  for example,  by  inducing  these  firms  to  adopt  technologies  favoring  highly  skilled  workers,  as  in  Yeaple (2005)  and  Bustos  (2011),  or  by  inducing  these  firms  to  upgrade  product  and  hence  labor  quality  as in  Verhoogen  (2008)  and  Kugler  and  Verhoogen  (2008).

2
the CIT database contains panel data on firms’ registration location, sales, cost of sales, and wage bills. It is thus the primary source of firm-level variables for output, capital, labor, and inputs used for productivity estimation (see below discussion on specific variables used for estimation). Available and comparable CIT data cover the 2014-2021 period.

The PAYE database covers over 800,000 workers that paid into PAYE tax in Zambia over the 2014-2022 period. In Zambia, every firm that offers employment is required to operate PAYE, which is a method of collecting income tax from their employees' earnings, although in practice compliance varies across firms and across workers within firms and is limited overall. The employer is obligated by law to deduct income tax from its employees' taxable salary or wages—regardless of whether they are full-time workers or casual employees—on a weekly, fortnightly, or monthly basis. The tax is deducted from all types of payments made to an employee, including salaries and wages, overtime payments, bonuses, profit sharing, fees, leave pay, commissions, allowances, cash benefits, and any benefit, advantage, or allowance (excluding non-money fringe benefits) and payments on taking up or leaving employment.<sup>6</sup> PAYE income tax calculation is based on a progressive slab system that splits the total taxable income of the employee into brackets, with each tax bracket having a corresponding tax rate. Drawing information from fields in PAYE returns, the PAYE database provides information on formal workers’ monthly gross pay and sector of operations. Thus, the PAYE database is the main source for jobs-related analyses in this paper. Available and comparable PAYE data cover the 2014-2022 period.

> 4 Mining and consultancy firms are required to register for Corporate Income Tax regardless of their turnover. 5 Turnover Tax filings are not part of the CIT database and not accounted for in this paper’s analyses because Turnover Tax filings do not contain the necessary variables on labor and costs necessary for productivity estimation
indicators of student risk and taking preventative actions tailored to students’ needs – are in place in at least 30 U.S. states and the majority of European countries (European Commission 2013; O’Cummings and Therriault 2015). As more low and middle-income countries invest in developing reliable education management information systems, student data that forms the foundation of an early warning system is becoming increasingly available. However, little evidence exists on whether and how such systems can effectively reduce dropout in countries with limited capacity to respond to students’ needs. In Guatemala, the pilot program, named ENTRE ( _Estrategia Nacional para la Transición Exitosa_ – National Strategy for Successful Transition), was designed to utilize recently developed data systems to identify students at risk and activate preventative, evidence-based measures at the school level. ENTRE focuses on three main factors identified as both particularly important for causing dropout in the primary to secondary transition in Guatemala and addressable through relatively low-cost interventions: a lack of knowledge among key primary school actors about effective measures to help students stay in school; a lack of knowledge among these same actors about which students are most at risk of dropping out; and a lack of prioritization of dropout as a problem to be addressed, as school actors have myriad responsibilities and dropout in the transition to lower secondary occurs after students have left their primary schools.

To assess the effectiveness of this early warning system approach, implementation of ENTRE was designed as a four-arm randomized experiment (three treatment arms and one control arm) among one-fifth of all the public primary schools in the country. In the first treatment arm, school principals and sixth grade teachers receive a user-friendly guidance manual and half-day training on specific, evidence-based methods to help students make the transition to lower secondary school – in essence, providing them more information on _how_ to help students stay in school. A letter signed by the Minister of Education accompanied the guidance manual, emphasizing the importance of every Guatemalan child completing at least nine years of basic education (primary and lower secondary) and
The parameter space is defined by the following 21 parameters: I partition the parameter space of the benchmark model in two groups: Ω1 and Ω2. In the first group, parameters are fixed based on previous results from micro studies or data. In the second group, parameters are estimated to match selected empirical moments, including the division of the labor force among unemployment, informal and formal-sector employment, employment and unemployment duration, and selected moments of the wage distribution.

Let Ω1 be defined as: The parameter values are chosen with a quarter as the implicit unit of time.

Table 8 summarizes the value of the fixed parameters based on data or micro studies.

The real interest rate in Colombia in 2003 was approximately 7.8 percent,<sup>72</sup> so _r_ is chosen to match the corresponding quarterly rate. Price indexes of tradable and non-tradable goods are used as proxies for the sectoral prices _PF_ and _PI_ , respectively.

The parameters _μy_ , _σy_<sup>2arechosentocoincidewiththecorrespondingempiricalmoments.73</sup> I choose the rate of arrival of the productivity shock _λ_ equal to 0.04 times per quarter. I use our micro data to test if the estimate falls within an admissible range. Using the separation rates of the formal and informal sectors, _hFF_ and _hII_ , I can express _λ_ as follows: Using duration data, I estimate that a formal-sector job lasts on average 6.89 years (27.5 quarters) while an informal-sector job lasts 7.69 years (30.77 quarters). Assuming that employment duration follows an exponential distribution, I estimate formal and informal-sector mean separation rates of 0.036 and 0.032, respectively. Given that 0 _≤ HF_ ( _RFU_ ( _y_ ) _| y_ ) _≤_ 1 and 0 _≤ HI_ ( _RIU_ ( _y_ ) _| y_ ) _≤_ 1, I have that 0 _._ 032 _≤ λ < ∞_ and 0 _._ 036 _≤ λ < ∞_
have been implemented in a number of developed countries around the world, including Italy, Germany, Netherlands, USA, and they have been shown to be strong predictors of improved financial outcomes. They have likewise been used in many emerging markets, such as Indonesia, India, Sri Lanka, and Mexico.

Despite the ubiquity of the “Big Three” questions, they are not necessarily comprehensive nor appropriate in many settings. Indeed, Lusardi and Mitchell (2009) themselves write that it is “imperative to expand the range of measures of financial literacy, so as to better evaluate the types of problems that people find most difficult” (p. 6). Developing countries in particular possess many characteristics — among others, high poverty, low access to finance, and lack of consumer finance protections — that are necessary to consider when measuring financial knowledge (Holzmann, 2010). For instance, if most households are uneducated and hold informal savings, it may be more important to evaluate understanding of bank account opening requirements than savings returns computations. Thus, the notion of financial literacy should be extended, especially in the developing country context.

To this end, we propose a broader approach to assessing financial literacy. Our conceptualization of financial knowledge focuses not on specific topics as the “Big Three” does, but rather on three general and distinct _dimensions_ . The first dimension we measure is financial _numeracy_ , which concerns the ability to add income and expenses, determine interest rates, and similar calculations. In particular, as part of our endline survey, we asked respondents the following two questions.

- (1) Let’s assume that you deposited Rs. 10,000 in a bank account at an 8% monthly interest rate.  How much money will you have in your account in one year if you do not withdraw from or add to this account any money? (a) More than 10,800; (b) Less than 10,800; (c) Exactly 10,800.

- (
decades: from those born in the 1950s to those born in the 1980s. In all regions but the Middle East and North Africa, the population coverage is greater than 90%. For the Middle East and North Africa, 83% of the population is covered (51% with retrospective questions).<sup>10</sup>

> 9 For a handful of countries, recent surveys with co-resident data and older surveys with retrospective questions were combined; the co-resident data is used for the 1980s cohort and the older survey for the 1950s through 1970s cohorts. This applies to Pakistan, Mauritania, Philippines, Rwanda, and Guinea. For two countries, neither recent co-resident surveys nor retrospective surveys were identified, while older surveys with retrospective questions exist. This concerns New Zealand (2000 survey) and Bhutan (2003 survey). To maximize coverage, these surveys are utilized as well. Tabulations show that in Bhutan most respondents born in the 1980s had completed their education by the time of the survey. For New Zealand, it is assumed that the estimate for the 1980s cohort equals the estimate for the 1970s cohort.

> 10 High-income countries are defined using the World Bank’s income classification as of July 1, 2019. High-income countries are also referred to as “the developed world” in this paper. The remaining countries, referred to as the “developing world”, are split further into geographic regions. More information on the income classifications and geographic regions is available here: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bankcountry-and-lending-groups.</u>

11
Andr ́ee In Germany, two areas stand out. First, the western part of the country, near the border with the Netherlands, Belgium, and Luxembourg, has an increased case density. This area (North Rhine-Westphalia, Rheinland-Pfalz and Baden-W ̈urttemberg) contains the major industrial regions including the Ruhr area. Second, a cluster of cases can be seen in the south-eastern part of the country near Munich where major automobile industry is found. These areas are also the most populous of the country, which makes it difficult to draw any immediate conclusions about a relationship with air quality.

In Spain, confirmed cases have the highest case density in the capital, Madrid, with an extension into neighboring Sergovia. A second cluster can be also seen northeast of Madrid. Interestingly, Spain’s population density is high along the eastern coast where the case density is lower. This suggests that case incidence in the country does not simply follow population densities, but that other factors play a role.

Finally, in Italy, confirmed cases have the highest case density in the northern part of the country, Lombardy in particular. Without a doubt, Lombardy and the Po valley as a whole has one of the highest concentrations of air pollutants of Europe. Moreover, the case density does not seem to trend strongly with Italy’s population distribution. For example, Italy’s population density is generally high along its coast, and cities like Rome and Naples do not stand out in the map.

Taken together, the maps suggest that that COVID-19 incidence clusters spatially and that environmental factors beyond population density may play a role. The analysis in the remainder of the paper confronts the relatively granular Dutch case data with possible predictors that include population density, air pollution, demographic characteristics and health related controls.

# **3. Data**

The COVID-19 data is taken from the RIVM.<sup>8</sup> The first data snapshot includes all confirmed cases as of March 22 (a total of 4,
such as better access to markets and the use of technical assistance, at mitigating these shocks. The only factor that helped households to alleviate the adverse effect of shocks was the level of education of the household head. Households with a savings account or a loan from a financial institution are not more resilient to these shocks. Similarly, enhanced access to markets where agricultural inputs are bought and where agricultural products are sold, as well as technical assistance do not make a difference in the way households cope with climate shocks and variation in prices.

Figure 6 depicts the results for education and shows that higher levels of education of the household head reduce the negative effect of rainfall shocks (measured by the WRSI index) on both crop income and per capita consumption. Having a head with some e primary education reduces the negative effect of rainfall by 2.8 percent compared to those with no education while for those with complete secondary education, the reduction increases to 4.9 percent. Something similar occurs if we look at the effect on per capita consumption, albeit the magnitude is smaller and only the results for some secondary education among the bottom 40 percent remain significant.

More education facilitates diversification by enabling increased participation in the labor market, particularly in the non-agricultural sector. In addition, more educated individuals may assess and respond to risk more successfully. For both crop income and per capita consumption, the mitigation effects of higher education level are larger for households belonging to the bottom 40 percent of the distribution.

**Figure 6: Education mitigates the impact of climate shocks**

<!-- Start of picture text --> Reduction of effect of shock in crop income Reduction of effect of shock in<br>consumption<br>0.0 0.0<br>‐1.0 ‐0.2<br>‐0.4<br>‐2.0<br>‐3.0 ‐1.8 ‐1.4 ‐0.6 ‐0.4
GTI with O*NET taxonomy<br>Occupation at 4-digit level is green if contains at least Only the share of green tasks is considered green<br>one green task employment<br>Non-green Green<br><!-- End of picture text --> _Source_ : authors calculations based on O*NET database

# Application #2: Skills demanded by online jobs add to high-skilled green occupations

Understanding the skills that are in demand for green jobs is essential for supporting the transition to a greener economy. This knowledge can then be used to shape skills policies, particularly in terms of adjusting education and training programs. It can also inform civil society about the specific skills that employers are seeking when hiring for green occupations. The BG online job vacancy data can be a valuable resource for exploring the skills that are demanded for green occupations. Green occupations

> 61 Elliot et. Al (2021) find that in the Netherlands 106 out of 436 ISCO occupations have some level of greenness when using a crosswalk from O*NET-SOC to ISCO.

29
Tax administration records provide data on whether the property owner visited the tax office in response to the notice, whether the owner paid the tax, and how much tax was paid. Visiting the tax office was the first step of the compliance process. For most property owners, this was their first interaction with the tax system, so they first had to obtain a personal tax identification number (TIN). To track responses, each notice included a unique number, and property owners were asked to bring the notice along when they came to the tax office. Data on tax payments come from the integrated tax administration systems that record all tax payments electronically and are available for this sample from 2017 to 2020.

Although tax revenue leakage may occur in several ways, this paper focuses on the extensive margin of compliance, where it is possible to determine with certainty the compliance status of a property owner— that is, whether or not the property owner made a tax payment. In addition, the paper also reports whether the property owner responded to the tax notice at all and the total amount paid.

The unit of observation for outcomes is at the owner level because payments are recorded by the property owner’s TIN; however, randomization was done at the property level. To address this discrepancy, we use the property-level randomization to infer owner-level treatment assignments using the cumulative exposure that each property owner had to different elements of the treatments, as follows: (i) property owners who have only one property are assigned to the treatment group for that property; (ii) property owners who have multiple properties that were all assigned to the same treatment group (or to a single treatment group and the control group) are assigned to that treatment group; (iii) property owners who have multiple properties and who have any property assigned to the identification and penalty treatment group are assigned to the identification and penalty treatment group because they are considered to have been exposed to both the identification and penalty messages; and (iv) property
are located in five states: Bihar, Madhya Pradesh, Rajasthan, Uttar Pradesh and West Bengal. These states are among the poorest in terms of income in 1993/94 (four of them belong to the so-called BIMARU), and also suffer from poor educational attainment and infrastructure indicators (Kingdon (2007), Deaton and Dreze (2002)).<sup>36</sup> We take the

> 35 The Ministry of Health and Family Welfare, Government of India, has defined backward districts as those having a crude birth rate of 39 per 1,000 population or higher, estimated on the basis of data from the 1981 Population Census. 36 Note that there may not be a one to one correspondence between backward districts in our sample and backward states according to income and education indicators, because there are backward districts even in developed states. The estimates of Head Count Ratio by Deaton and Dreze (2002) show that Rajasthan has a lower incidence of poverty, but the evidence reported in Kingdon (2007) shows that it lags in terms of schooling indicators, even though it has made substantial strides in primary schooling.  West Bengal, on the other hand, suffers from higher poverty, but has better schooling indicators.  As noted by Deaton and Dreze (2002), West Bengal belongs to the same ―slow growth‖ group with the BIMARU states

21