rafmacalaba/data-use-mentions-extended huggingface dataset:
2. Two factors for Google search terms related to labor: money, salary, LinkedIn, unemployment, employment 3. Two factors for all Google mobility data 4. One factor for food and assistance search terms: assistance, money, bread, milk, wheat 5. One factor for education and sport search terms: school, student, exercise 6. One factor for religion search terms: prayer, bible A key question in the regression results is what share of the total r-squared is attributable to different combinations of variables. To shed some light on this issue, following Shorrocks (1982), we investigate the contribution of each group of real-time indicators to the overall goodness of fit of each regression, using Shorrocks–Shapley decomposition of r-squared.<sup>8</sup> The resulting decomposed values of r-squared allow checking the fraction of the goodness-of-fit contributed separately by each group of real-time indicators and fixed effects, with the latter especially pertinent in the quarterly results where we include both quarter and country fixed effects.

# 3. Findings

# 3.1 Satellite indicators

- a. Nitrogen dioxide

Figure 2 presents trends in median NO2 emissions across seven different geographic regions: East Asia and the Pacific, Europe and Central Asia, Latin America and the Caribbean, the Middle East and North Africa, South Asia, Sub-Saharan Africa, and North America.

At first glance, there is a noticeable decrease in NO2 emissions in all regions – Latin America being a possible exception – with declines in Europe/Central Asia and Sub-Saharan Africa starting in February. Interestingly, the largest decrease is in Europe and Central Asia, and it began in February, even though the large changes in human behavior began in March. The decreases in the Middle East/North Africa, South Asia, and East Asia and the Pacific took place in March and generally did not recover until much later in the year. Trends in North America and East Asia and the Pacific are very comparable in the first quarter. However, North America faced a steeper
### **Definition 1** **_Equilibrium Definition._**

_An equilibrium in this model specifies the set of child care providers available to households as well as their prices and qualities, and the decisions made by households with respect to work and child care, such that: (1) the child care providers in parental choice sets are indeed available in the market; (2) every child care provider in the market makes non-negative profits, and (3) neither parents nor child care providers wish to alter their decisions._ Due to the discreteness of household and firm choices, as well as the finite number of household types and potential firms, the equilibrium does not have a closed-form solution and must be solved numerically for a given set of parameter values. Appendix B.1 describes the quantitative version of our model, and Appendix B.2 describes the equilibrium computation.

# **3 Data**

## **3.1 Sources**

We estimate the model with data from the US Early Childhood Longitudinal Study birth cohort (ECLS-B), which follows individuals from birth through kindergarten for a nationally representative sample of children born in 2001 and includes several waves to capture children’s development as they grow. We use the second wave, collected between January and December 2003, when the children are two years old. In this wave the ECLS-B uses instruments to assess child development in the physical, cognitive, and socio-emotional domains. We focus on the cognitive domain, assessed with the Bayley-Short Form instrument (Research EditionMental), which contains measures of general cognitive ability such as problem solving and language acquisition.

The ECLS-B also collects household demographic data (household members; parents’ education, age, and marital status; living arrangements) and parental labor market information (labor force participation, hours of work, and hourly wages). It contains detailed information on child care modes (parental care, relative care, non-relative care, center-based care), hours of use for each
is based on the aggregate food expenditure deflated by the Paasche index, computed with the median aggregate price at the level of each data collection stratum (A. Deaton and S. Zaidi, 2002). Following Deaton and Zaidi (2002), we compute the Paasche index for each sample household with the following equation: where _PP_ is the Paasche index;<sup>_s_</sup> _i_<sup>is the budget share for commodity i;</sup><sup>_p_</sup> _i_<sup>is the</sup> aggregate price for commodity i; and<sup>_p_</sup> _i_ 0<sup>is the reference aggregate price for commodity i, which</sup> is set to the median aggregate price in each stratum.

**<u>Table 8: Welfare Loss from the VAT by Food Expenditure Quintile</u>**

||**Bottom**|**2nd**<br>**quintile**|**3rd**<br>**quintile**|**4th**<br>**quintile**|**Top**|
|---|---|---|---|---|---|
|||8 % Increase in<br>|cereal, meat, a<br>|nd other foods<br>|<br>|
|Compensating variation (x1000)|-39.138|-67.136|-91.616|-123.922|-213.267|
||[0.145]|[0.109]|[0.148]|[0.208]|[1.241]|
|Share of aggregate loss|0.073|0.125|0.171|0.232|0.399|
|||12
**Figure 4: Five-Year Raw Domestic (Internal) Migration Rates**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Indonesia7 Uzbekistan160 Indonesia26 Philippines16 Nicaragua17 Portugal22 Philippines83 Canada11 Argentina24 Indonesia280 Colombia33 Honduras18 Philippines1610 Australia8 Venezuela24 Mexico2438 Greece54 Ecuador22 Ghana110 Colombia10 Barbados11 Dominican Republic225 Vietnam1203 USA9 Portugal308 Argentina511 Vanuatu6 Uruguay19 Senegal34 Mongolia21 Peru25 Chile44 Brazil1520 Costa Rica81 New Zealand16 Peru1833 South Africa52 Portugal4000 Australia69 Chile178 New Zealand73<br><!-- End of picture text --> _Source: Bell and Charles-Edwards(2013) and the L2CU baseline survey (2018). Note: Migration rates for the circa 2000 round of Censuses. Values in country labels indicate the number of territorial units._ For the small amount of domestic migration present in Uzbekistan, the city of Tashkent has remained the dominant destination over the past several decades.<sup>9</sup> Figure 5 (left) shows that according to L2CU baseline data, about 27 percent of all households that had moved within Uzbekistan at all resided in Tashkent city at the time of their interview. Samarkand and Tashkent region (excluding the city) are the most common “origin” provinces. There are several prominent migration “spikes” in migration flows (Figure 5 - right), including in 1999/2000 when Tashkent was declared a closed city (see following subsection for more detail). This was followed by a steep decline in self-reported migration in 2001.

> 9 There is also substantial international migration from Uzbekistan, described in detail in Seitz (2019).

15
# **References**

Alessandri, P., & Bottero, M. (2020). Bank lending in uncertain times. _European Economic Review_ , _128_ . https://doi.org/10.1016/j.euroecorev.2020.103503

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Aterido, R., Beck, T., & Iacovone, L. (2013). Access to Finance in Sub-Saharan Africa: Is There a Gender Gap? _World Development_ , _47_ , 102–120. https://doi.org/10.1016/j.worlddev.2013.02.013 Aterido, R., Hallward-Driemeier, M., & Pagés, C. (2011). Big Constraints to Small Firms’ Growth? Business Environment and Employment Growth across Firms. _Economic Development and Cultural Change_ , _59_ (August), 609–647. https://doi.org/10.1086/658349 Bae, K. H., & Goyal, V. K. (2009). Creditor rights, enforcement, and bank loans. _Journal of Finance_ , _64_ (2), 823–860. https://doi.org/10.1111/j.15406261.2009.01450.x Banerjee, A. V., & Duflo, E. (2014). Do firms want to borrow more? Testing credit constraints
**Table 7. Tax compliance: "cell" approach, women (measured at median income)**

|Age/region|<30|30 – 39|40 - 49|50-59|
|---|---|---|---|---|
|RO1 (North)|111.38%|92.07%|93.91%|91.52%|
|RO2 (East)|96.84%|95.00%|96.80%|111.93%|
|RO3 (South)|105.33%|101.04%|103.25%|109.95%|
|RO4 (West)|127.60%|97.92%|98.90%|110.19%|

_Source: Own estimation based on the imputed EU-SILC data._

**Our results show that tax compliance is highest among workers younger than 30 and older than 60, possibly due to the selection bias where people with better jobs tend to stay longer in the labor market** . The research also reveals that tax compliance varies across regions, with Macro region One (North) having the lowest compliance rate. In contrast, the compliance rate is medium in Macro region.

factors affecting tax compliance, such as the size of the informal economy or the quality of tax administration, may exist. Further investigation may be necessary to understand these regional differences and address the compliance issues.

**Finally, we discuss the impact of non-compliance on the share of minimum-wage earners.** Estimating the share of minimum wage earners is important for several reasons. Firstly, it allows for assessing the prevalence of low-wage work in the economy, a crucial indicator of income inequality and poverty. Secondly, it can inform policy decisions regarding minimum wage setting and targeting social protection programs for low-income households. Additionally, understanding the characteristics of minimum wage earners, such as their age
al., 2018; Uribe-Carvajal et al., 2018). Despite the high prevalence of overweight and obesity in 1999, the situation has worsened over time. In the 2006, 2012, and 2016 National Health and Nutrition Survey (ENSANUT in Spanish), datasets in which the PROSPERA population could be identified, the difference between the prevalence of overweight and obesity among the program's population and among the general population with an equivalent socioeconomic status who did not participate in the program are not significant.

We are able to identify important trends in the health conditions of populations with lower socioeconomic levels by analyzing the 2000 National Health Survey (ENSA) and the 2006 and 2012 ENSANUT, and by narrowing down our analysis to the 20 to 24 age group and the villages with relatively greater presence of the program. (Valdespino et al., 2003; Olaiz-Fernández et al., 2006; Gutiérrez et al., 2012).

Figure 1 shows that between 2000 and 2012, there is a decrease in the percentage of the population 20-24 years of age with a healthy (normal) body mass index (BMI between 18.5 and 24.9) for both males and females. At the same time, there is an increase in the proportion of the population categorized as obese. Over this time period, the overweight category remains relatively constant.

5
, while upper bound estimates of impact leave no unaccounted transfer income (Table 6, Columns 5-7). Reported savings are rare in this population and not differential across groups, therefore unlikely to account for the gap between our impact estimate and the expected gain in net income. One explanation, especially early on, is the possibility that treatment households purchased durable assets with the transfers – such as refrigerators, TVs, air conditioners, smartphones, and the like – which are not captured by our surveys.<sup>29</sup> Another possibility is that the aggregate expenditure categories in the phone survey (such as food, rent, utilities, and so on) did not fully capture the extent of the impacts on household expenditures. Specifically, total food expenditures can be substantially lower when households are asked to report them in collapsed categories rather than each food item separately. Furthermore, this underreporting can be differential

> 27 ESSN operations have been found to be carried out with relatively high implementation fidelity. Program records show that more than 99.5% of cash transfers to eligible beneficiaries were redeemed (World Food Program 2020). In addition, an external assessment of the program found that ESSN adequately disseminated information about the program and conducted efficient distribution of ATM cards and timely delivery of cash transfers (Oxford Policy Management 2018).

> 28 The unaccounted amount is approximately the same at the 18-month follow-up, but substantially higher at the 6-month follow-up, primarily because the eligibility gap between the two groups was larger early on.

> 29 One question was asked to households, to better understand access to communications and social media, on ownership of smartphones. Over the course of the 18-month study, the treatment group was 3.2 percentage points more likely (83.8% vs. 87%; p=value<0.01) to own a smartphone. Similar modest gains in asset ownership over a number of household durables could plausibly add up to explain a substantial part of the unaccounted transfer income. However
(in percent of total employment) and the perceived level of informality (especially by firms and by households). Annex 3 provides a detailed description of the three dimensions of informality and their construction. In this paper, when conducting cross-country analyses, preference is given to measures that are constructed using the same statistical standard (and definition) and have better country-and-year coverage.<sup>4</sup> 16. **The paper considered two model-based estimates to compute output informality and three survey-based estimates for employment informality.** First, two model-based estimates are considered for the cross-country comparisons. One is the multiple indicators multiple causes model (MIMIC), which is a type of structural equation model that takes into account both the multiple possible causes of informal activity and the outcome indicators of it. The other one is a dynamic general equilibrium model (DGE) created by Elgin and Oztunali (2012) where production in the informal sector is determined by how households optimize their labor allocation between formal and informal economies in each period and how the allocation changes over time. Both models provide estimates of informal output in percent of official GDP. Second, three surveybased informal employment measures are considered here to capture employment informality:

> 4 For instance, self-employment shares are often used as a proxy for informal employment here, given its wide country-and-year coverage and consistency across countries.

6
18 (15) Ln _Y_ = 5.805 + 0.351 _S_ , R<sup>2</sup> = 0.62, N = 128 ( _t_ =14.4) Variants of the above Mincer-macro function using per capita income as the dependent variable in cross-country data have been fitted by Krueger and Lindahl (2001), Heckman and Klenow (1997) and Hanushek and Woessmann (2008). Our estimate of a 35 percent ―return‖ to one additional year of schooling is nearly identical to theirs. After fitting such a function, Heckman and Klenow (1997) concluded that: _“each additional year of schooling attainment in a country is associated with about 30% higher GDP per capita, whether one looks at 1990, 1985 or 1960..... The macro-Mincer coefficients....are consistent with large positive external productivity gains to economy-wide schooling attainment.”_ Barro and Lee (2010), among many others, have fitted the same function using output per worker (rather than per capita income) as the dependent variable and adding a measure of physical capital stock as an independent variable. Since their estimate of the _b_ coefficient is about one-half of ours, we adopted a 50 percent reduction of our estimated welfare loss as a lower bound. Table 7 and Figure 8 present estimates the welfare loss in terms of potentially higher per capita income had Pakistan a higher level of educational attainment as indicated in Col. 2, Table 7.

**Table 7.  Pakistan: Welfare loss** **<u>as percent of per capita income</u>**

|Year<br>(1)|Enhanced<br>years of<br>schooling<br>(2)|Per capit<br>(constan<br>Actual<br>(3)|a income<br>t 1990 $ )<br>Simulated<br>(4)|Welfa<br>(<br
- Aarethun, V., Sandal, G. M., Guribye, E., Markova, V., & Bye, H. H. (2021). Explanatory models and help-seeking for symptoms of PTSD and depression among Syrian refugees. _Social Science & Medicine_ , _277_ , 113889.

- Chen, W., Ling, L., & Renzaho, A. M. (2017). Building a new life in Australia: an analysis of the first wave of the longitudinal study of humanitarian migrants in Australia to assess the association between social integration and self-rated health. _BMJ open_ , _7_ (3), e014313.

- Conley, T. G., Hansen, C. B., & Rossi, P. E. (2012). Plausibly exogenous. _Review of Economics and Statistics_ , _94_ (1), 260-272.

- Dagnelie, O., Mayda, A. M., & Maystadt, J. F. (2019). The labor market integration of refugees in the United States: Do entrepreneurs in the network help? _European Economic Review_ , _111_ , 257-272.

- Department of Social Services. (2017). Building a new life in Australia (BNLA): The longitudinal study of humanitarian migrants–Data users guide release 3.0. Australian Government, Canberra, Australia.

- Department of Social Services. (2018). Sample sizes and response rates for the Centre studies. Australian Government, Canberra, Australia. Retrieved from: <u>https://www.dss.gov.au/about-the-department/national-centre-for-longitudinalstudies/growing-up-in-australia-the-longitudinal-study-of-australian-childrenlsac/sample-sizes-and-response-rates-for-the-centre-studies</u>

- LoPalo, M. (2019). The effects of cash assistance on refugee outcomes. _Journal of Public Economics_ , _170_ , 27-52.

- Roodman, D. (2011). Fitting fully observed recursive mixed-process models with cmp. _Stata Journal_ , _11_ (2), 159-206.

63
The regression coefficients are estimated using the generalized two stage least squares random-effects instrumental variables regression estimation procedure (models 1.1, 3.1-5.1), two stage least squares estimation procedure with instrumental variables on cross-sectional samples (models 2.2 and 2.3) and two stage least squares estimation with instrumental variables on pooled panel data (for models 1.2, 3.2-5.2 and 1.3, 3.3-5.3). The feasibility of utilization of these approaches was tested by Durbin-Wu-Hausman tests (which were replaced by xtoverid test procedure in Stata application, introduced by Schaffer and Stillman, 2006, for testing coefficients estimated in the RE and FE panel estimation procedure in case that covariance matrix in Hausman test was not positive definite) and Breusch-Pagan Lagrange Multiplier test for presence of random effects.

The regression equation has following form for all samples: _X ti = α + β1 EPLti + β2 MWti + β3 TUt + β4 TAXti + β5 LMPAti + β6 LMPPti +_ _+ β7 ln GDPPCti + β8 FISFti + β9 BUSFti + β10 CORRti + β11 REGQUALti + εti (1),_ where _X_ takes the form of _SHEC, CONTRIB, SELFEMPL_ , _LESS10,_ and _CONTRACT_ in consequent regressions (Model 1, 2, 3, 4 and 5 respectively). The detailed description of all the models applied in our analyses is given in Annex 4.

The independent variables and their expected effects were described in the previous section. In our samples S1 and S3, we used the OECD’s employment protection legislation index, version 2 ( _EPL_ ). In the sample S2, this variable was replaced by inverse value of indicator of labor freedom as the employment protection index is not available for non-OECD countries over a longer time period. Minimum wage ( _MW_ ) is a cluster variable constructed according to minimum wage level as a share of median wage in the economy in the basic sample S1 and according to minimum wage level in purchasing power parities in sample S2 and S3.
educational attainment are available, age bounds are identical to those of 2000. The concept of citizenship must only be used for Germany and for all other countries where Census and register data are not available.

# 2.2 Step 2 - Emigration rates

The second step consists in comparing the emigration stocks to the number of remaining residents in the source country for the same three educational categories mentioned above. Calculating the brain drain as a proportion of the total educated labor force is a better strategy to evaluate the pressure imposed on the local labor market. It is indeed obvious that the pressure exerted by 1,096 thousand Indian skilled emigrants (4.5% of the educated total labor force) is less important than the pressure exerted by 76 thousand Surinamese (90.1% of the educated labor force).

This step requires using data on the skill structure of the adult population in the countries of origin. Denoting Nt;s<sup>jasthestockofindividualsaged25+,ofskillsin</sup> country j, at time t, we de...ne the emigration rates by In particular, m<sup>j</sup> t;h<sup>providessomeinformationabouttheintensityofthebrain</sup> drain in the source country j. It measures the fraction of skilled agents born in country j and living in other OECD countries<sup>10</sup> .

Data for Nt;s<sup>jare taken from Barro and Lee (2000). For 1990 and 2000, they provide</sup> the adult population structure by educational attainment for about 120 countries. Barro and Lee’s educational attainment measures are based on the highest level of schooling attained. Their measures are based on Census data when available. They provide perpetual inventory estimates based on historical schooling enrollment ...gures when Censuses are not available.

Combining Census and Survey data on immigration, we obtain the emigration stock for almost all the countries of the world (191 countries in 2000, 170 in 1990). For countries
**Folha de Pagamentos do Bolsa Familia (BF payment records)** The payroll dataset of BF contains information on the monthly payments received by every family. It does not allow for distinguishing between the different benefits but gives the total sum of transfers received. In addition, datasets on the exit reasons and people in the benefit graduation/transition rules ( _Regra de Permanencia_ ) are available from 2016 onwards.

**Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS)** RAIS is an administrative dataset that contains information on formal dependent employment in Brazil. Every company needs to fill in the information on its employees’ occupation, monthly compensation, contract type, date of entering the company, date of exiting the company, and dismissal reason. In addition, information on the firm, including the economic sector, the location, and the size of the establishment (defined by the number of employees), is available.

# **2.3 Sample Construction and Key Variables**

We construct a panel to longitudinally track all individuals aged 18 to 59 who were newly registered in CadUnico and either belonged to a BF recipient family as of December 2012 or joined an already recipient BF family at some point during the observation period. BF participation is determined based on payment records, which are registered at the household head level. To identify all family recipients, we expand the payment information using the family identification number, ensuring coverage of all household members. Individual records in CadUnico are then matched to RAIS using the anonymized _Programa Integra ̧c ̃ao Social_ (PIS) number. The initial set of individuals is followed monthly until 2019. We then further collapse the data to a semester basis – enabling us to track individuals over 14 semesters and examine their transitions into and out of the formal labor market – and at the family level – to examine family-level movements in and out of the BF program. Figure A.1 in the Annex provides an overview of the data merging process