rafmacalaba/datause-encoder-data huggingface dataset:
US, we assume that Mexican migrants in the US are employed in the private sector (not working for the federal, state, or local government, and not in the armed forces). We also assume that all migrants are Hispanic, single (for work migration), and have no US citizenship or permanent residency status.

The expected earnings in the US of a Mexican migrant is thus obtained given that and where _σ_ ˆ<sup>2</sup> is the unbiased estimator of _σ_<sup>2</sup> as estimated from (10) (see Wooldridge, 2013).

We postulate that a migrant makes a migration decision assuming that his earnings in the US are functions of his specific human capital characteristics and his occupation in Mexico. Here, the migration decision is also a function of the migrant’s professional experience in his home country. A Mexican electrician plans to work in that occupation in the US, forming his wage expectations ( ˆ _wMX_<sup>_US_)basedoninformationaboutwagesofelectriciansintheUS.</sup> The other explanatory variables ( _Xi_<sup>_US_)includeinformationaboutage,gender,marital</sup> status, race, the highest level of education, citizen status, job classification, and whether a worker works full- or part-time. Figure 1 shows the distribution of predicted yearly earnings produced by the Mincer regression. As apparent in the graph, the expected earnings of Mexican migrants are heavily concentrated in the low range, with the distribution of wages for US nationals first-order stochastically dominating that of would-be Mexican migrants.<sup>4</sup> Three sources of limitation of the Mincer regressions are relevant to our simulations. First, there are likely to be unobservables that influence choices about participating in this market. A US citizen-worker with high latent productivity will expect good career prospects

> 4Note that, strictly speaking, first-order stochastic dominance is violated at the bottom of the earnings distribution. In the US, these consist of part-time jobs, which will be replaced by full-time jobs occupied by migrants in a market for work permits.

10
0.261 9.545 Adj. R2 Mean of the dependent variable

## **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-30% across the board)**

||Coeff.|Std.<br>Error|Sig.|
|---|---|---|---|
|(Constant)|5.854|0.289|0.0%|
|LN full-time employees|0.149|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.213|0.022|0.0%|
|Share of activities completely outsourced|0.457|0.087|0.0%|
|Share of activities partially outsourced|0.895|0.105|0.0%|
|Adj. R2|0.267|||
|Mean of the dependent variable|9.455|||

## **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-40% across the board)**

|(Constant)|Coeff.<br>5.686|Std.<br>Error<br>0.291|Sig.<br>0.0%|
|---|---|---|---|
|LN full-time employees|0.150|0.029|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.214|0.022|0.0%|
|Share of activities completely outsourced|0.592|0.087|0.0%|
|Share of activities partially outsourced|0.977|0.106|0.0%|
|Adj. R2|0.276|||
|Mean of the dependent variable|9.353|||

## **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (-50% across the board)**

|(Constant)|Coeff.<br>5.487|Std.<br>Error<br>0.293|Sig.<br>0.0%|
|---|---|---|---|
|LN full-time employees|0.151|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.216|0.023|0.0%|
|Share of activities completely outsourced|0.755|0.088|0.0%|
|Share of activities partially outsourced|1.080|0.107|0.0%|
|Adj. R2|0.289|||
|Mean of the dependent variable|9.237|||

# **<u>Sensitivity analysis: Base model</u>**

## **Dependent Variable: Sensitivity analysis: LN Total cost of tax compliance (+50% across the board)**

||Coeff.|Std. Error|Sig.|
|---|---|---|---|
|(Constant)|7.042|0.298|0.0%|
|LN full-time employees|0.126|0.030|0.0%|
|LN of q55 after imputed values were introduced (imputed "no answer")|0.164|0.023|0.0%|
|Share of activities completely outsourced|-0.136|0.086|11.4%|

36
# **Table 11: Cross-country covariates associated with changes in loan penetration**

The results of OLS regressions of the changes in the natural logarithm of number of loans per 1,000 adults in commercial banks on different country characteristics.

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita (log)|0.008<br>(0.015)|0.011<br>(0.017)||||||||
|Population density (log)||0.007<br>(0.023)|0.001<br>(0.023)|||||||
|Expected real economic growth||0.053<br>(0.043)|0.060*<br>(0.031)|0.060**<br>(0.030)||||||
|Exected GDP (lo)|||0.026**|0.026**|0.035***|0.028**|0.043***|0.046***|0.049***|
|p g|||(0.010)|(0.010)|(0.013)|(0.014)|(0.014)|(0.013)|(0.012)|
||||||-0.033*|-0.039**|-0.054**|-0.049***|-0.055***|
|# outstanding commercial bank loans in 2008 (log)|||||(0.017)|(0.018)<br>|(0.020)|(0.013)|(0.013)|
|Deposit insurance||||||0.065<br>(0.058)||||
|EAP|||||||0.038<br>(0.104)<br>|||
|ECA|||||||0.019<br>(0.102)|||
|High income|||||||0.022<br>(0.128)|||
|LAC|||||||0.084<br>(0.108)|||
|MENA|||||||0.155<br>(0.095)|0.121**<br>(0.046)|0.107**<br>(0.045)|
|South Asia|||||||-0.216*<br>(0.108)|-0.245***<br>(0.085)|-0.253***<br>(0.085)|
|Offshore financial center|||||||||0.104<br>(0.068)|
|Constant|-0.065|-0.174|-0.155|-0.151***|0.039|0.058|0.087|0.086|0.098*|
||(0.132)|(0.165)|(0.109)|(0.049)|(0.078)|(0.077)|(0.055)|(0.053)|(0.053)|
|N<br>|58|58|58|58|58|58|58|58|58|
|Adjusted R<sup>2</sup>|-0.0144|-0.0420|0.0335|0.0510|0.116|0.123|0.238|0.268|0.277|
|Robust standard errors in parentheses||||||||||
|***p<0.01,**p<0.05,*p<0.1||||||||||

36
On the opposite direction, the most important contributor are the indirect taxes, which increase poverty by 6.1 percentage points (Figure 12).

**Figure 12. Marginal contributions to poverty reduction (percentage points)**

|Indirect Taxes|**-6.1**<br>_-1.9_<br>Extreme Poverty|
|---|---|
|Other (DT)<br>Direct Taxes<br>IPTU/ITR (DT)|**0.1**<br>**0.0**<br>**-0.1**<br>_0.0_<br>_0.0_<br>_0.0_<br>Moderate Poverty|
|Unemployment Benefits (DT)|**0.2**<br>_0.1_|
|Salario Familia (DT)|**0.2**<br>_0.1_|
|Bolsa Familia (DT)|**0.4**<br>_0.1_|
|Abono Salarial (DT)|**0.5**<br>_1.9_|
|BPC (DT)|**1.9**<br>_2.0_|
|Rural Pension (DT)|**4.0**<br>_4.2_|
|-8|.0<br>-6.0<br>-4.0<br>-2.0<br>0.0<br>2.0<br>4.0<br>6.0|
||**Marginal contribution at Consumable Income (percentage points)**|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: IPTU stands for Imposto sobre a Propriedade Territorial Urbana, while ITR stands for Imposto sobre a Propriedade Territorial Rural. BPC stands for Benefício de Prestação Continuada. Notes: Moderate and extreme poverty are based on poverty lines of BRL 499 and BRL 178, respectively.

# 4. What are the implications for vulnerable demographic groups?

We complement this incidence analysis by disaggregating the results by the demographic composition of households. In particular, we look into how the headship structure, and the presence of both children and elderly can be determinants of the net effects of fiscal policies in Brazil. In this exercise, children are considered all members aged 15 or less who are neither the household head nor the household head’s spouse, while an elderly member is an individual aged 60 or more.

Table 4 presents the households’ demographic characterization. There are twelve categories, four for each type of household headship. The rationale for such categorization is three-fold. First, headship structure is likely to be deeply connected with economic agency, as it is a major determinant of since the benchmarks are not the same. Bolsa Família’s evaluations focus on families that are potentially poor after (potentially) receiving other government transfers. Here, the relative contribution to poverty reduction uses as a benchmark a pre-fiscal situation so that larger transfers (such as pensions) can have larger effects on poverty reduction than programs like Bolsa Familia.

29
the respondent believes malaria is a problem in the village. Table B2 shows that, in spite of the fairly low levels of parasite prevalence in the region<sup>6</sup> , malaria is still (correctly) perceived as a problem in the community by a large majority of the population, both in treatment and control villages. However, notice that around 30 percent of respondents report that malaria is not a problem in their community, despite the fact that our survey was conducted in the most malarious villages in Eritrea.<sup>7</sup> A third set of variables focus on whether the respondent believes a certain category of individuals is most affected by malaria. Even though almost everyone agrees that children are especially at risk from malaria, only about a third of respondents believe that pregnant women suffer greatly from having malaria.

To check whether the index captures pre-existent differences in exposure to malaria, we complement our dataset with sub-zone level panel data on a vegetation index called Normalized Difference Vegetation Index (NDVI), which is obtained from the analysis of the color spectrum of satellite imagery and which generally measures the overall propensity of an area to harbor mosquito populations<sup>8</sup> . To build a measure of past exposure to malaria, for each sub-zone we counted the number of 2-week periods in which NDVI exceeded 0.361 during the period 2000-2009 and we divided sub-zones into three different groups: “very limited vegetation (low past exposure)”, “some vegetation (middle past exposure)” and “significant vegetation (high past exposure)”. Table B3 shows that past exposure, as proxied by the NDVI index, is positively correlated with higher concern and knowledge of malaria. At the same time, the treatment effect of providing IRS is unaffected by introducing controls on average past exposure.

6Keating et al. (2011) document a prevalence rate below 1 percent (October, 2009).

> 7The Global Malaria Action Plan of the Roll Back Malaria initiative (http://www.rbm.who.int/gmap/) explains that the situation whereby villagers lose interest in malaria and in prevention, in areas where malaria has been dramatically reduced by successful control efforts, is referred to as “malaria fatigue”. It can lead the public to reduce use of available preventive and treatment measures.

> 8For Sudan, Gaudart et al. (2009) find that the seasonal pattern of _P. falciparum_ incidence is significantly explained by NDVI from 1981-2006 and they identify a threshold NDVI value of 0.361, above which an increase in the incidence of parasitemia is predicted. Similarly, for Indochina Peninsula, Nihei et al. (2002) find that _P. falciparum_ Malaria is most prevalent in regions with NDVI _>_ 0.4 for at least 6 consecutive months.

10
> TABLE 1: SUMMARY STATISTICS AND SOURCES FOR MACROECONOMIC AND POLITICAL

VARIABLES.

|_Variable_|_Description_|_Source_|_Mean_|**_Std._**_Dev._|
|---|---|---|---|---|
|NumberofCellularCompanies|Number|Authors'Calculations<br>(seetext).|2.815|1.543|
|ElectricityDistribution Privatized|Dummy|Bacon(1999)|0.424|0.494|
|CountryhasParliamentarySystem|Dummy|Beck_etal._(2001)|0.246|0.431|
|YearsofDemocracy<br>Years of Democracy|Yearsthatcountryhasscoredsixorhigheron<br>legislativeandexecutiveindexofelectoral<br>~~~competitiveness(IEC)sincestartoftransition.<br>Seebelow.|Beck_etal._(2001)<br>Bc<br>ta.(01|3.779<br>.7|2.751<br>_.5_|
||Index(0-7). Avg.scoreonlegislativeand||||
|DemocracyIndex|executive indexofelectoralcompetitiveness<br>(fIEC).Highnumbersmean greaterlevel of<br>democracy|Beck_etal._(2001)|6.760|0.539|
|EBRDIndexfor large-scale<br>privatization|Index(1-4)--Higherscoresmeangreater<br>extentofprivatization|EBRD(1999)|3.041|0.676|
|PoliticalRights|Index (1-7)--Higherscoresmean greater<br>democracy|Freedom House (2000)|3.184|1.804|
|Decentralization|LocalandStateSpendingasshareoftotal<br>govemment spending|Intemational Monetary<br>Fund(2001)|0.326|_0.115_|
|RegulationIndex|Index_(1-5)._Higherscoresmeanregulationis<br>morerestrictive|HeritageFoundation<br>(1997)|3.379|0.780|
|CorporateTaxIndex|Index(1-5).Higherscoresmean corporate tax<br>ratesarehigher|HeritageFoundation<br>(1997)|3.079|0.651|
|Corruption(Overall)|Index(1-6).Intemational Country RiskGuide<br>IndexofCorruption.Higherscoresmeanless<br>corruption.|PRSGroup(1999)|3.200|1.097|
|NumberofFixedLines|Per100inhabitants|Intemational<br>Telecommunications<br>Union(2000)|20.987|9.930|
|Fuel,MineralandMetalExports|As%ofGDP|WorldBank(2001)|24.075|22.494|
|Population|Natural Log|WorldBank(2001)|16.414|1.384|
|Inflation|Averagebetween1996and1999|World Bank(2001)|36.939|67.545|
|Openness|Imports aspercentofGDP|World Bank(2001)|46.675|17.913|
|GovernmentExpenditures|As%ofGDP|WorldBank(2001)|16.900|5.115|
|GDPGrowth.|Averagebetween1996and1999|WorldBank(2001)|1.978|4.244|
|PerCapita GDP|000sofUS$|WorldBank(2001)|5.906|3.175|
|FixedLineTelecommunications<br>Operator Privatized|Dummy|WorldBank<br>Telecommunications<br>Department|0.502|0.500|
|CountryisLandlocked|Dummy<br>__**__**_**_**_ ___ ___**___** **__**_ ___ ___ ___<br>_<br>_<br>____<br>Growth|WorldBankGlobal<br>DevelopmentNetwork<br>Database|0.388|0.487|

Note: Data is for 1998, except where noted and for data from Beck _et al_ (2001) and Heritage Foundation (1997), which are from 1997 since data for 1998 were not available.

29
# deliveries attended by TBAs.

**Program focus:** enhancing awareness; training TBAs, upgrading facilities; improving referral system.

3. Urban area; many public and private facilities but low quality and underutilized.

**Program focus:** IEC to build demand for services, training to improve quality of care.

Indica*-s of success in these programs were suggested by the Columbia Unive.sity model. Program impact in the three areas can be assessed by the following measures:

- **reducing maternal mortality by preventing pregnancy:** increased contraceptive prevalence or effectiveness, reduced fertility rates;

- **reducing maternal mortality by preventing complications:** improved hygiene and delivery practices, improved knowledge, attitudes and practices, reductions in complication rates;

- and **reducing maternal mortality by improving treatment of complications:** improvements in women's condition on arrival at referral facilities, reduced case fatality rates;

The workshop also included a general discussion about the importance of perinatal mortality and other possible health indicators that one could include in the evaluation of Safe Motherhood programs. Programs that aim to reduce maternal mortality and morbidity will also have an impact on other health indicators, most notably perinatal mortality, or those deaths that occur between 28 weeks pre-birth and seven days postpartum. Twenty-five to 30 percent of the 2.5 million perinatal deaths that take place each year could be prevented through quality maternity care, and more in those areas where maternal mortality is highest. Data from Bangladesh, for example, indicate that training traditional birth attendants can reduce neonatal mortality by as much as 60 percent. In addition, women who experience and survive severe maternal complications will frequently have asphyxiated infants. Approximately one million infants die a year due to asphyxia, and as many as two to three million survive with severe disabilities, including cerebral palsy, seizure disorders, and learning disabilities.

Rw ;owing are examples of other Safe Motherhood program components that have an impact on perinatal mortality (as well as on infant and child mortality, and overall child health and welfare): * family planning - - particularly through birth spacing; 9
care law was not enacted or effective, i.e., for those with _Postct_ = 0. To understand the magnitude of these estimated effects, we compare them with the estimated effects of childcare interventions implemented in LMICs on mothers’ labor market outcomes. We rely on a recent evidence review of 22 studies conducted by Halim et al. (2023). Out of these studies, half evaluate the impacts of childcare services targeted to children aged three years or younger on women’s labor market outcomes. In sum, the effects of childcare services on FLFP are between 11 percent to 29 percent in urban China (Du and Dong, 2013; Du et al., 2019) and 37 percent in Colombia (Attanasio and Vera-Hernandez, 2004).<sup>8</sup> Thus, our estimated effects of childcare laws on FLFP are one-fifth of the estimated effects of childcare services in these studies. This smaller effect of the childcare laws, relative to the provision of childcare services, is not surprising as the regulation of childcare precedes the actual provision of these services in most contexts. Moreover, enactment of legislation does not always translate into effective implementation.

In order to test for parallel trends and to study the dynamic effect of the childcare law enactment on FLFP rate, we estimate an event-study version of the TWFE model with indicators for different periods before and after the enactment of the law. The estimates from specification (2) are presented in Figure 1. First, the estimates are consistent with the parallel trend assumption: the coefficients for the years prior to the enactment of the childcare law in each country are all close to zero and exhibit no discernible differential pre-trends. Second, the treatment effect of childcare laws on FLFP increases over time. In

> 8One study finds no effects of public daycare on women’s labor market outcomes in Chile (Medrano Vera, 2009). Other studies included in the review find effects of childcare services on women’s employment that range from 10 percent (Barros et al., 2013) to 22 percent (Rosero and Oosterbeek, 2011).

15
30 Table 6 Food Cost as Percent of Total Cost. Poeding Programs: Summary

|Category|Range|Median|Mean|Number of Programs|
|---|---|---|---|---|
|All Programs|11-95|75|69.7|81|
|Asia|22- 95|76|74.4|26|
|North Africa|(69 -92)|(v-)|(80.5)|<br>2|
|Sub-Saharan Africa|80- 90|84|84.4|5|
|Latin America|11-<br>90|71|65.1|48|
|School Feeding|25- 95|71|71.5|19|
|MCH|11- 95|63|68.4|39|
|Small(<br><100,000)|43- 90|79.5|74.5|6|
|Medium (1- 500,000)|58- 90|85|82.0|8|
|Large(<br>2 500,000)|31- 92|69|70.7|7|
|Gov't. Operated|11- 95|77|70.4|67|
|NGO Operated|54- 90|74.5|77.3|14|
|[Targeted Food<br>Subsidies|63- 88|83|78.8|5]|
|[Micronutrient<br>interventions|29|||11|

Source: Calculated from Appendix Table 1 (excluding references from Mateus) and Appendix Table 2 (including references from Mateus).

Food costs include external transport where applicable.

Food costs bracketed in appendix are not included in the calculation. Micronutrient data from iodine program in Bangladesh, Mills (1983).
company and the client government.<sup>6</sup> However one empirical regularity was that capacity building (the transfer of knowledge by private surveillance companies to customs) was typically the weak point of PSI. Yang (2005) studied the Philippines’ PSI program and showed that when a new origin country was covered, imports from that country were deflected to the Philippines’ export-processing zones and then fraudulently brought into the domestic market. Thus, the program was generating new forms of fraud rather than curbing it. Anson et al. (2006) studied four PSI programs and showed that they had two effects working at crosspurposes. On one hand, PSI generates valuable information that the client government can use to curb fraud. On the other hand, it de-motivates customs administrations, so that part of the information brought in by the private company simply substitutes for slackening customs effort. Using a simple model they showed that the “perverse” effect (customs de-motivation) can sometimes more than offset the direct effect, resulting in less duty collection rather than more, and verified that this indeed seemed to be the case in Argentina and Indonesia. Yang (2008) was more positive, showing that PSI did have some effect in curbing customs corruption.

This literature leaves an important question unanswered. Improving tax collection for budget purposes is one thing; encouraging trade is another. A vast literature suggests that trade openness is correlated with economic growth (for a recent empirical investigation of this link, see Wacziarg and Welsh 2008). Another strand of literature highlights the importance of trade facilitation in encouraging trade, relative to the reduction in traditional tariff and nontariff barriers (see e.g. Wilson, Mann and Otsuki 2003, Portugal-Perez and Wilson 2010). Does private inspection encourage trade? Private surveillance companies typically bring with them best practices in terms of customs and transit procedures, potentially resulting in shorter and less variable clearance times. This could encourage trade, partly or totally offsetting the slowing-down effect discussed above.

In their study analyzing the relationship between trade facilitation, trade flows and GDP per capita in the Asia-Pacific region (for the Asia-Pacific Economic Cooperation’s members), Wilson et al,(2003) define and measure trade facilitation using four different indicators for port efficiency, customs environment, regulatory environment, and electronic-business usage. They further introduce these indicators in an augmented gravity equation specification. They estimate their model solely in a cross-section context though, for the Asia-Pacific countries. In the absence of specific empirical data, the authors rely on survey information in order to construct the four indicators. Their sources are as diverse as to include the World Economic

> 6 Ramirez (1992) and Byrne (1995) also provide brief discussions as to the pros and cons of PSI. Page | 5
Table 10: Fixed Effects OLS Dependent Variable Satisfactory Index (0/1)

|log(GDP per capita growth)|-0.04|-0.04|-0.04|-0.04|-0.05|
|---|---|---|---|---|---|
||[0.02]*|[0.02]|[0.03]*|[0.03]*|[0.03]*|
|CPIA Rating|0.23|0.20|0.20|0.19|0.16|
||[0.09]**|[0.09]**|[0.14]|[0.15]|[0.16]|
|TTL Quality||0.17|0.17|0.17|0.17|
|||[0.08]**|[0.09]*|[0.09]*|[0.10]*|
|log(Supervision Costs per Yr - $’000s)|||-0.05|-0.05|-0.04|
||||[0.04]|[0.04]|[0.04]|
|log(Total Staff Weeks per Yr)|||0.03|0.04|0.04|
||||[0.05]|[0.05]|[0.05]|
|log(No. of Consultant Days per Yr)||||0.00|0.00|
|||||[0.01]|[0.01]|
|log(Size of Country Offce)|||||-0.00|
||||||[0.05]|
|IRS Ratio of Country Offce|||||-0.04|
||||||[0.14]|
|Constant|-0.05|-0.10|0.05|0.08|0.13|
||[0.30]|[0.31]|[0.49]|[0.51]|[0.52]|
|_R_<sup>2</sup>|0.01|0.02|0.02|0.02|0.02|
|_N_|1,864|1,709|1,454|1,454|1,332|

41