rafmacalaba/datause-encoder-data huggingface dataset:
- 2 Low Top income tax rate greater than 0 percent and less than or equal to 25 percent.  Marginal rate for the average taxpayer greater than 0 percent and less than or equal to 10 percent

- 3 Moderate Top income tax rate greater than 25 percent and less than or equal to 35 percent.  Marginal rate for the average taxpayer greater than 10 percent and less than or equal to 15 percent.

- High Top income tax rate greater than 35 percent and less than or equal to 50 percent.  Marginal rate for the average taxpayer greater than 15 percent and less than or equal to 20 percent.

- 4

- 5 Very high Top income tax rate greater than 50 percent.  Marginal rate for the average taxpayer greater than 20 percent

- Corporate Tax Grading Scale

- 1 Very low Corporate tax rate less than or equal to 20 percent

- 2 Low Corporate tax rate greater than 20 percent and less than or equal to 25 percent.

- 3 Moderate Corporate tax rate greater than 25 percent and less than or equal to 35 percent.

- High Corporate tax rate greater than 35 percent and less than or equal to 45 percent.

- 4

Based on: Ernst & Young, 2002 The Global Executive and 2002 Worldwide Corporate Tax Guide; International Monetary Fund Staff Country Report, Selected Issues and Statistical Appendix, 2000 to 2002; Economist Intelligence Unit, Country Commerce, Country Profile, and Country Report for 2001 and 2002; U.S. Department of State, Country Commercial Guide 9 ; and official government publications of each country. Sources other than Ernst & Young are noted in the text. For information on government expenditures, the authors’ primary sources were Organisation for Economic Co-operation and Development data (for member countries); International Monetary Fund, Government Finance Statistics Yearbook for 2001, and International Monetary Fund Staff Country Report, Selected Issues and Statistical Appendix, 2000 to 2002; Standard & Poor’s, Sovereigns Ratings Analysis; Asian Development Bank, Key Indicators of Developing Asian and Pacific Countries 2001; African Development Bank, ADB Statistics Pocketbook 2002; European Bank for Reconstruction and Development, Country Strategies; Inter-American Development Bank; U.S. Department of State, Country Commercial Guide 10 ; and official government publications of each country. Sources other than the OECD and the IMF are noted in the text.

- 5 Very high Corporate tax rate greater than 45 percent.

- Government Expenditures Scale for Developed Countries

- 1 Very low Less than or equal to 15 percent.

- 2

- Low Greater than 15 percent but less than or equal to 25 percent

- 3 Moderate Greater than 25 percent but less than or equal to 35 percent

- 4 High Greater than 35 percent but less than or equal to 45 percent 5 Very high Greater than 45 percent

- Government Expenditures Scale for Developing Countries

- 1 Very low Less than or equal to 15 percent 2 Low Greater than 15 percent but less than or equal to 20 percent.

- 3 Moderate Greater than 20 percent but less than or equal to 25 percent

- 4 High Greater than 25 percent but less than or equal to 30 percent 5 Very high Greater than 30 percent

41
<!-- Start of picture text --> 0% 0%<br>*<br>-<br>-10% -% A<br>°° e<br>ee e<br>ee e<br>20% -19% Ae °<br> @ -22% ©<br>a . e<br>30% aa ‘<br>32% A oe “<br>- -35%<br>Pe e 0<br>-40% =* -39% 39 6<br>e e e e e e<br>“6% e < 7%<br>A < *e °. © ef © Data Adjusted for Initial Month of Exporting<br>50% e<br>° ° © ® © Raw Data Based on Calendar Year<br>: 54%6<br>° e © el © Data Adjusted for Final Month of Exporting<br>e 58%<br>-60% : eee e<br>:.*ye“<br>°<br>-70% “2-68%7<br>ee<br>ray -75% eee<br>-80% e<br>e<br>e<br>e<br>e<br>-90% 82%<br>-100%<br>Entry Entry+1 Entry+2 Baseline Last Year-2_ Last Year-1 _ Last Year<br><!-- End of picture text -->
Anchoring civil registration in a regulatory framework is the first step towards not only completeness and accuracy of information about the life events of each citizen, but also towards ensuring access and ample registration to all and safeguarding basic human rights.

_Living Life’s_ Civil Registration and Identification focus area includes data on registering births and deaths as well as data on obtaining IDs. The topic covers the legal framework for each of the civil registration processes including procedures and requirements, time and cost to complete certain steps, the type of information collected at the time of registration, late registration, and the digitalization of registration mechanisms.

The study revealed that all four economies have mandatory legal provisions for civil registration, although the sanctions for non-compliance and procedures vary. While India and Chile have moved towards including digital components to civil registration and identification systems, Vietnam and Ghana rely on more traditional processes. The efficiency of identification systems measured by time taken to obtain a national identity card (NID) or passport also varies across economies.

# **3.1.1. Legal requirements**

Birth and death registration is legally required in all four economies. Relevant laws also contain a specific time period within which records on births and deaths should be registered. These periods vary across different countries (Figure 3.1). Generally, countries establish shorter timeframes for death registration as opposed to birth registration. While a death should be registered within one day in Ghana, in India this time limit is 21 days. A birth should be registered within 21 days in both Ghana and India and within 60 days in Chile and Vietnam.

**Figure 3.1: Time Limits for Birth and Death Registration (in days)**

<!-- Start of picture text --> Time limits for birth and death registration<br>70<br>60 60<br>60<br>50<br>40<br>30<br>21 21 21<br>20 15<br>10<br>3<br>1<br>0<br>Time limit for birth registration Time limit for death registration<br>Chile Ghana India Vietnam<br>Time in days<br><!-- End of picture text --> _Source: Living Life data_ Linking legally compulsory birth and death registration to incentives or penalties for non-compliance is a good regulatory practice according to the World Health Organization (WHO) (2013). Legislation can help establish the authority and duties of the registrar, which include enforcing and promoting registration of the population. Incentives or penalties can encourage people to come forward and register births and deaths (World Health Organization 2013). The 1969 Registration of Birth and Death Act in India details provisions for late 11 | P a g e
# **I Robustness Test**

Table I.1. Robustness Analysis for the Socioeconomic Well-being Index

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|0.928***|0.830***|1.021***|0.867***|0.895***|0.905***|0.928***|0.830***|1.021***|0.867***|0.895***|0.905***|
||(0.305)|(0.289)|(0.339)|(0.295)|(0.293)|(0.312)|(0.305)|(0.289)|(0.339)|(0.295)|(0.293)|(0.312)|
|Obs. left|445|434|411|447|434|415|445|434|411|447|434|415|
|Obs. right|848|788|731|1,214|848|1,108|848|788|731|1,214|848|1,108|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|1.282***|0.899**|1.200***|1.065***|0.819**|1.035***|1.609***|1.702***|1.671***|1.335***|1.294***|1.328***|
||(0.472)|(0.439)|(0.457)|(0.361)|(0.400)|(0.364)|(0.585)|(0.603)|(0.611)|(0.410)|(0.450)|(0.429)|
|Obs. left|415|441|419|415|445|419|415|441|419|415|445|419|
|Obs. right|741|836|746|1,788|1,322|1,597|741|836|746|1,788|1,322|1,597|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|_Panel C: Polynomial of order tw_|_o_||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|1.523**|1.593*|1.752**|1.440***|1.274**|1.557***|1.524**|0.796|1.671***|1.415***|0.950*|0.965*|
||(0.637)|(0.873)|(0.811)|(0.493)|(0.597)|(0.563)|(0.677)|(0.649)|(0.611)|(0.540)|(0.535)|(0.584)|
|Obs. left|458|348|367|454|348|363|458|348|419|454|348|363|
|Obs. right|901|576|677|1,789|1,663|1,563|901|576|746|1,789|1,663|1,563|
|Mean values (Non-RAMV)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
32 and future increases in the employment of Senegalese nationals will have to come from new employment. Unfortunately, aside from the 1980-1982 period, little permanent employment was generated for the Senegalese in the first half of the 1980s. As the data in Table 4.8 show, the number of permanent Senegalese workers in the sampled firms increased from 18,266 in 1980 to 19,653 in 1982 but has been decining slightly since then.

TABLH 4.7

||Smp:<br>of To|*<br>NoalCo<br>tal Induara|ipouiton<br>l Employm|ent||
|---|---|---|---|---|---|
||1963|1975|1980|1984|<br>1985|
|Senegalese|87.0%|93.0%|96.0%|97.1%|<br>97.6%|
|Other Africans|4.7%|23%|1.2%|09%|0.7%|
|Non-Afican|83%|4.7%|2.8%|2.0%|1.7%|
|TOTAL|100.0%|100.0%|100.0%|100.0%|P 100.0%|

Source: Pfeffermann (19§8Y fbf`1963 and Svqjnar (1984) for 1975. Enterprise sample for 1980, 1984rand 1985 TABLE 4.8

|Se.eal<br>Empl:ment<br>Wouas by<br>1duty|of Pemanet<br>in the lnzeqm|Seealew<br>si Sample||
|---|---|---|---|
|Industly<br>1980|1982|1984|1985|
|Food<br>9,546|9,852|9,618|9,837|
|Tobacco and Matches<br>541|453|S00|465|
|Textilcs, Clotwing<br>and Leather<br>4,424|4,523|4,460|4,594|
|Wood<br>222|192|219|200|
To compare differences in the fraction of children who are on-track across years, I limit the sample to 8 to 10 year-olds. This is because 6 to 7 year-olds are on-track by construction, while children aged 11 and older in 1997 were exposed to the crisis during their school-aged years. Tables 1 a and I b show that, cond itional on attendance at school, the fraction of 8 to 10 year-olds who have completed the minimum appropriate number of school years for their age is higher in 1991 than in either 1985/86 or 1997. In the whole sample, 87% of 8 to 10 year-olds were on-track in 1991, but only 72% and 79% in 1985/86 and 1997, respectively. Here too this difference across years holds for all regions, for girls and boys, and for the poorest and richest income quintiles. Note that these comparisons could suffer from selection bias because I condition on attendance-in particular, if "marginal" students who are less likely to make satisfactory progress in school are more likely to start school late or drop out early. I do not think that this is an important source of concern, however, given that there are only very small variations in attendance rates across years.

Figure 4 graphs the mean years of schooling completed by age for children exposed and unexposed to the crisis, without conditioning on attendance. The graph also includes a 45 degree line which corresponds to on-track school progression. The results in Figure 4 are very similar to the findings for the probability of being on-track in Tables la and lb. Mean years of schooling are below the ideal on-track progression, especially at higher ages, because of school desertion and the cumulative effects of late school entry and repetition. However, children exposed to the crisis have an average of between .1 and .2 more years of schooling. There is no clear pattem whereby the unexposed children fall further behind or catch up with age.

# **4. Econometric specification and results**

I next pool the data from the three surveys, and turn to multivariate regressions to estimate differences in school attendance, employment, and school attainment. First, I estimate logit regressions with a dependent variable which takes on the value of one if children are both attending school and working, and zero if they are attending school only. These regressions include dummy variables for the two "non-crisis" years (1985/86 and 1997), as well as other controls. The omitted category is therefore the "crisis" year of 1991. I report the odds-ratios for the 1985/86 and 1997 dummies, and interpret these as year effects conditional on changes over time in household characteristics. Pervasive, predictable differences across years could be consistent with a causal effect of the 1988-92 crisis on attendance and employment. Second, I _estimate_ OLS regressions in which the dependent variable is the number of years of schooling completed. These regressions include dummy variables for two survey years, as well as a 10
# **2 Argentina**

Argentina is one of the wealthiest countries in Latin America and is well positioned in terms of educational indicators. In 2000 (the year of the data used in this paper), per capita GDP was around PPP$ 10 _,_ 000, the literacy rate was nearly 98%, primary net enrollment was close to 100%, and secondary school enrolment was just above 80%.<sup>1</sup> However, as in much of Latin America, wealth was distributed extremely unequally and poverty was far from eradicated. According to official figures, the Gini index of per capita household income circa 2000 was 0.50, 14% of people lived below the 2.5-dollar-a-day poverty line, and 29% below the national poverty line.

In the last couple of decades, the composition of primary school pupils in Argentina has changed (Llach 2006). Enrolment into primary schooling has risen significantly since 1980, particularly among less well-off students, and drop-out rates have fallen appreciably (Rivas 2010b). Along with these changes, the quality of education received is much less equal. The difference in achievement between private and public school pupils has broadened in the areas of the country with weak technical capacities following the school decentralization process in the early nineties (Galiani, Gertler & Schargrodsky 2008), and the inequality of achievement remains sizeable in the poorest provinces (Etchart, Gasparini, Bohorquez, Curia, Ferroni & Hontakly 2004).<sup>2</sup> Disparities in test scores are driven by differences at three levels: the school, the class, and the pupil. Schools differ in their physical, human and ‘social’ resources. Spending per student may greatly vary across schools. As an example, the private sector in Buenos Aires province spends 30 percent more per pupil than the public sector (Rivas 2010b). There are also large disparities between spending in the public sector across provinces – for instance, in 2009 Tierra del Fuego public schools spent five times more per pupil than Salta (Rivas 2010b).<sup>3</sup> In addition, teacher quality, pedagogy and

> 1Source: _World Development Indicators and Censo Nacional de Poblaci ́on y Vivienda_ , INDEC (2001).

> 2The ratio between the first decile and the tenth decile of performance is above four for the poorest provinces, and only slightly better for the other ones.

> 3Public schools are mostly funded by the provincial governments (approximately 70% of the consolidated expenditure in education, Rivas et al 2011). The federal government gives some restricted resources to specially disadvantage schools and students which can only be used to pay for infrastructure work, provision of didactic material, support of special schools initiatives, and training (Fiszbein 1999, p. 11). In a few cases, the local government may also contribute to the school financing, through funding special construction work or extra-curriculum activities (Veleda 2005). Parents contribute voluntarily with a very

4
<!-- Start of picture text --> 70 i<br>an l<br>S<br>6<br>68 oy<br>a=<br>550<br>Z& &<br>= 66 2o 0.2<br>)s<br>3-l<br>64 sg<br>S=<br>262 6 <<br>62 a9 “I<br>1990 2000 2010 2020 1990 2000 2010 2020<br>—— Historical numbers --*-- COVID-19 projection --*-- Counterfactual projection<br><!-- End of picture text -->
- 1.53 -

# Figure I.C.I0 LABOR USES UNDER POPULATION GROWTH, 2015-19

<!-- Start of picture text --> (littions of Nmn-Do")<br>1.4-<br>to<br>0.9 -<br>0.8 -<br>0.7<br>0.6<br>0.5_<br>120 140 160 180 200<br>Cocoa Price (1987 C.&/Kg)<br>3 POuUAL + ANUAL FAMIY LABOR<br><!-- End of picture text -->
Table 5: Estimates of the determinants of renegotiations

||**Firm-led Renegotiations**|**Government-led**<br>**Renegotiations**|
|---|---|---|
||-1.09***|-1.40***|
|Existence of regulatory body|(0.22)|(0.34)|
||0.68*|-0.46*|
|Price cap|(0.38)|(0.40)|
||0.96**|-0.70***|
|Investment requirements|(0.40)|(0.24)|
||0.35|-1.23***|
|Private financing|(0.28)|(0.24)|
||-0.35**|-0.57***|
|Bureaucratic quality|(0.15)|(0.16)|
||0.31|0.21|
|Elections -1|(0.20)|(0.19)|
||-0.06***|-0.05**|
|Growth -1|(0.02)|(0.03)|
||-0.14***|-0.08**|
|Growth -2|(0.02)|(0.03)|
||0.53|-0.38|
|Transport dummy|(0.36)|(0.36)|
|Log likelihood|-251.1||
|Numberofobservations|1132||

Source: Guasch, Laffont and Straub (2003, 2005). Significance at the 1%, 5%, and 10% level is noted by ***, **,* respectively.

# **3.3 Regulation and renegotiations**

First, the existence of a regulator at the time the concession contract is signed appears to be crucial in avoiding failures during the early life of concession projects. This aspect has the strongest marginal effect of all variables found to be statistically significant. Comparing three specific contracts out of the initial sample, and using the probabilities predicted by the empirical model, Guasch, Laffont and Straub (2003) show that had a regulator been in place at the time of awarding the contract, the respective probabilities of renegotiation in the last year of existence of the contract would have been reduced from 29.7, 9.9, and 3.1 percent, to 5.3, 0.3, and 0.2 percent respectively.

18
We find average income gains from import tariff liberalization in 45 countries and average income losses in the remainder 9 countries. On average, the developing countries in our study enjoy gains from trade equivalent to 1.9 percent of real household expenditure. This is mostly because the consumption gains from lower prices dominate the income losses from reduced protection.

The distributional impacts of import tariff liberalization are highly heterogeneous, across both countries and households. We find that the equality gains, the change in social welfare associated with these distributional impacts, are negatively correlated with the average income gains. Inequality costs arise primarily because trade exacerbates nominal income inequality, while the consumption gains tend to be more evenly spread. This creates trade-offs between the income gains and the equality gains in 45 of the 54 countries in our sample. The income gains typically more than offset the increase in inequality. In 39 countries, liberalization of import tariffs would result in inequality-adjusted welfare gains for a wide range of empirically plausible values of inequality aversion (between 1 and 2). In 9 countries that face trade-offs, protectionism would instead be welfare enhancing for plausible values of inequality aversion. Finally, there are 6 countries where the trade-offs are acute, in which the presence of welfare gains or losses depends crucially on the presumed level of inequality aversion and policy prescriptions are consequently more equivocal. These results imply that in the majority of developing countries in our study, the prevailing pattern of protection induces sizeable welfare losses.

The rest of the paper is organized as follows. Section 2 sets up the model and derives the formulas for the welfare effects of trade policy. Section 3 uses the tariff data and the survey data to estimate those welfare effects in 54 countries. Section 4 discusses the gains from trade and their distribution. Section 5 evaluates and quantifies the trade-off between income gains and inequality costs of trade. It also decomposes equality gains into consumption equality gains and income equality gains, presents robustness tests, and assesses the trade-offs that would arise if countries were to undertake protectionist trade reforms instead of liberalizing. Section 6 concludes.

3
of including a mine. Nevertheless, since size may correlate with potential unobserved district characteristics, in the analysis we control for it.

# **Methodology**

Our identification relies upon comparing producing and non-producing districts that are spatially close and institutionally similar. We do so by means of several exercises. Our baseline case considers all districts from departments with mining activity (excluding the department of Lima), and compares socioeconomic outcomes of producing districts with, in turn, outcomes of non-producing districts in non-producing provinces and outcomes of non-producing districts in the same province of each mining district. We use information from the 1993 Census to control for district characteristics in 1993, prior to the mining boom, so that our baseline regression is as follows: where, _p_ denotes the province, _d_ the district, Ipd[PD] a binary variable that takes a value of one if the district is producing, Ipd[NPDPP] a binary variable that takes a value of one if the district is nonproducing in a producing province, Xpd a set of time invariant and 1993 district characteristics, and νd, νp, εpd department, province, and district error terms.

Under this specification, we consider two types of “treatment” districts: producing districts and non-producing districts in producing provinces; and one type of “control districts”: non-producing districts in non-producing provinces. Therefore, without province fixed effects, the estimates of β0 and β1 refer to the respective impacts on the two types of treatment districts with respect to control districts. When we introduce province fixed effects, however, the dummy Ipd[NPDPP] (associated with β1 ) drops out, and the estimate of β0 refers to the additional impact on producing districts with respect to non-producing districts in the same province.

Using the specification in (1), we first perform a diagnostic analysis to check the possibility of pre-treatment differences. Drawing from the 1993 Census, we use the 1993 district averages of illiteracy rate and index of basic necessities uncovered (the two dependent variables for which information is available for 1993) and test whether, everything else being equal, the two treatment and one control sets of districts had statistically different levels before the mining boom. Then, we perform a set of exercises to evaluate the impact of the treatment. In the benchmark exercise, we estimate regression (1) by Ordinary Least Squares (OLS) using outcome variables corresponding to 2007.

10