rafmacalaba/datause-encoder-data huggingface dataset:
|Table A2: School Chara|<br>Panel<br>cteristics for th|Schools dropped<br>A: Fifth-grade scho<br>e sample of schools|ols<br>that quit_Mais_|<br>Panel<br>_Educação_betwe|Schools dropped<br>B: Ninth-grade sc<br>en 2008 and 2011|<br>hools<br>|
|---|---|---|---|---|---|---|
||Schools in|<br>||Schools in|<br>||
|Variable|<br>_Mais_<br>_Educação_<br>(2008–2011)|from_Mais_<br>_Educação_<br>between 2008 and<br>2011|Difference<br>(1)−(2)|<br>_Mais_<br>_Educação_<br>(2008–2011)|from_Mais_<br>_Educação_<br>between 2008<br>and 2011|Difference<br>(4)−(5)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Portuguese grade|164|162|1.6|222|221|1.0|
|Mathematics grade|180|178|2.0|230|229|0.6|
|Dropout rate|5.9|7.3|-1.4**|12.0|12.2|-0.2|
|∆Portuguese grade|1.0|0.3|0.8|5.0|4.3|0.7|
|∆ Mathematics grade|10.5|8.8|1.7|1.6|0.8|0.8|
|IDEB Index|3.5|3.3|0.2**|2.9|2.6|0.2***|
|∆IDEB|0.4|0.4|-0.1|0.3|0.2|0.1**|
|Municipal population|1,180,426|1,286,878|-106,452|1,364,439|1,558,995|-194,556**|
|<br>Students' enrollments|924|999|-75|1159|1330|-171***|
|IDHM|0.8|0.8|0.0|0.76|0.76|0.0**|
|Urban|1.0|1.0|0.0**|1.0|1.0|0.0|
|Basic sanitation|1.0|1.0|0.0|1.0|1.0|0.0|
|Library|0.8|0.7|0.0|0.9|0.8|0.1**|
|Sports court|0.5|0.6|0.0|0.7|0.7|0.0|
|Number of classrooms|13.0|12.8|0.2|14.3|14.4|-0.1|
|Number of computers|0.9|0.9|0.0|1.0|1.0|0.0|
|Science lab|0.1|0.1|0.0|0.3|0.3|0.0|
|Computer lab|0.5|0.5|0.0|0.6|0.6|0.0|
|Teachers room|0.9|0.9|0.0|1.0|1.0|0.0|
|DVD|0.9|0.8|0.1**|0.9|0.9|0.0|
|Overhead projector|0.7|0.8|0.0|0.9|0.9|0.0|
|Number of employees|65.3|64.9|0.4|76.2|75.3|0.8|
|School meals|0.9|0.9|0.0|0.9|0.9|0.0|
|Number of classrooms used|13.6|13.7|-0.1|14.4|14.6|-0.1|
|Morning lessons|0.5|0.5|0.0|0.51|0.48|0.0**|
|Child care|0.0|0.0|0.0|0.0|0.0|0.0|
|Kindergarten|0.4|0.3|0.1|0.2|0.1|0.1***|
|State schools|0.3|0.7|-0.4***|0.6|0.9|-0.3***|
|High school|0.1|0.2|-0.1***|0.4|0.6|-0.2***|
|<br>% of classes with full-time education|0.0|0.0|0.0|0.0|0.0|0.0|
|Duration of school day|4.3|4.4|-0.1|4.3|4.4|-0.1**|
|N|611|84||555|127||

_Sources: Prova Brasil,_ School Census, and INEP. _Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2008 and remained until 2011 (columns (1) and (4)) and for the schools that joined the program in 2008 but left the program over the period (columns (2) and (5)). Columns (3) and (6) report the mean differences of the variables between the two groups. _T_ tests were performed to detect the mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample. Our sample include only schools that have information for all variables over all the analyzed period.
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# **8 Appendix**

Appendix Table 1: Control variables at baseline (1435 observations)

||Pure con<br>|trol (508)<br>|<sup>Individua</sup><br>(44<br>|<sup>l Training</sup><br>9)<br>|Couples<br>(47<br>|Training<br>8)<br>|Contro<br>train<br>|l - Ind.<br>ing<br>|Control -<br>train<br>|Couples'<br>ing<br>|Ind. tra<br>Couples'<br>|ining -<br>training<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Mean|SD|Mean|SD|Mean|SD|Diff.|P-val|Diff.|P-val|Diff.|P-val|
|Age of household<br>head|46.09|11.04|45.50|11.87|45.27|10.75|0.59|0.43|0.82|0.24|0.23|0.76|
|Household size|6.47|3.43|6.12|3.53|6.08|3.35|0.35|0.12|0.39*|0.07|0.04|0.86|
|Number of adults|3.14|1.72|2.93|1.70|2.87|1.67|0.20*|0.06|0.27**|0.01|0.06|0.56|
|Number of<br>children|3.15|2.35|2.98|2.44|3.06|2.27|0.17|0.26|0.09|0.55|-0.08|0.58|
|Dependency ratio|1.16|0.85|1.17|0.89|1.23|0.90|-0.02|0.76|-0.07|0.19|-0.06|0.34|
|Married planter|0.91|0.29|0.89|0.31|0.89|0.31|0.02|0.40|0.02|0.40|-0.00|0.99|
|Polygamous<br>relationship|0.15|0.42|0.11|0.34|0.10|0.31|0.04*|0.07|0.05**|0.03|0.01|0.76|
|Planter has IGA|0.62|0.49|0.64|0.48|0.71|0.45|-0.02|0.46|-0.10***|0.00|-0.07**|0.02|
|Asset index|9.84|1.98|9.84|2.23|9.83|2.07|0.00|0.97|0.01|0.91|0.01|0.95|
|Credit constrained|0.13|0.34|0.13|0.34|0.13|0.34|0.00|0.98|0.00|0.92|0.00|0.94|
|Share of plots<br>owned|0.95|0.19|0.94|0.22|0.93|0.22|0.01|0.44|0.02|0.22|0.01|0.68|
|Average distance<br>to plots (minutes)|65.53|49.66|64.23|48.12|70.18|58.96|1.30|0.68|-4.65|0.18|-5.95*|0.09|
|Number of plots|2.46|1.44|2.23|1.34|2.39|1.35|0.24***|0.01|0.07|0.42|-0.17*|0.06|
|Averageplot size|3.86|3.70|3.66|3.40|3.52|3.23|0.20|0.39|0.34|0.12|0.14|0.52|

3
prior to the household survey. As shown in Table 1, parental schooling is low, with only 13 percent of the children having a parent that ever attended school. Fifty-four percent of this children’s sample is male and the average age is 9.4 years old. On average, these children have 3.8 siblings under age 15, including 1.8 sisters. They live in households with an average of 8.9 individuals, including a head of household, 1.5 wives, 4.8 biological children of the household head under age 15, 0.4 children under age 15 that are not the biological children of the head, and 1.2 other members that include grandparents, aunts, uncles, and other extended family members. Parents were directly asked about the chance of future success they believe each of their children will have in formal employment, a reasonable measure of parental perceptions about the investment return on their child’s education, since most jobs in “formal employment” in Burkina Faso require a level of education beyond primary school and in particular French skills.<sup>4</sup> This parental perception measure is based on everything the parent knows about the child and about the labor market, whether right or wrong, and was asked about every child in the household. For each child, the parents responded whether that particular child had a ‘small’, ‘medium’, ‘large’, or ‘very large’ chance of future success in formal employment. Parents considered 25 percent of these children to have a ‘small’ chance of future success and only 8 percent to have a ‘very large’ chance of future success. Parents viewed most children (67 percent) to have a ‘medium’ (38 percent) or ‘large’ (29 percent) chance of future success.

To corroborate these parent perceptions, we also consider externally validated measures about a child. We use the Raven’s CPM and the WISC Digit Span to measure a child’s cognitive ability; both are tests that do not require formal schooling to be able to answer the questions. The Raven’s CPM is a measure of fluid intelligence or problem solving ability. The test does not

> 4 Schultz (2004) and Kazianga (2004) using Burkina Faso nationally representative data report substantial returns (9 to 16 percent) for an additional year of primary school, highlighting the importance of schooling in this context. Returns to secondary (14 to 26 percent) and tertiary (13 to 23 percent) schooling are even higher.

7
- Korinek, A. (2010). Regulating capital flows to emerging markets: An externality view. Technical report.

- Korinek, A., A. Roitman, and C. Vegh (2010). Decoupling and recoupling. _American Economic Review Papers and Proceedings_ .

- Lane, P. R. and G. M. Milesi-Ferretti (2010). The cross-country incidence of the global crisis. _Mimeo_ .

- Mendoza, E. G. (2010). Sudden stops, financial crises and leverage. _American Economic Review forthcoming_ .

- Paasche, B. (2001, December). Credit constraints and international financial crises. _Journal of Monetary Economics 48_ (3), 623–650.

- Pavlova, A. and R. Rigobon (2008). The role of portfolio constraints in the international propagation of shocks. _Review of Economic Studies 75_ (4), 1215–1256.

21
Table 7: Effects on employment composition (restricting to employed individuals only)

|||_Dependen_|_t variable:_||
|---|---|---|---|---|
|||_Rural_||_Urban_|
||Low-skill|Hired Farm<br>Worker|Self-Employed<br>Farmer|Low-skill|
||(1)|(2)|(3)|(4)|
|Dry shock (SPEI _< −_1_._5)|-0.0465***|-0.146***|0.0201|-0.0250|
||(0.0117)|(0.0155)|(0.0213)|(0.0215)|
|Wet shock (SPEI _>_ 1_._5)|-0.0104|-0.0247|-0.0874|0.0538**|
||(0.0196)|(0.0290)|(0.0599)|(0.0254)|
|Controls|Yes|Yes|Yes|Yes|
|Full set of FEs|Yes|Yes|Yes|Yes|
|Observations|224,233|224,233|224,233|131,354|
|R<sup>2</sup>|0.171|0.508|0.409|0.128|

_∗ ∗∗ ∗∗∗_ **_Note:_ p** _<_ **0.1; p** _<_ **0.05; p** _<_ **0.01** . This table reports the coefficient estimates on the type of employment outcomes attaines by individuals for rural and urban areas, using our preferred specification but restricting to individuals who reported being employed. These outcomes include whether an individual (i) has a low-skill job; (ii) is employed are hired labor on a farm;(iii) is self-employed in farming activities. Standard errors clustered at the grid cellmonth-year level are reported in parenthesis.

34
4. Movements in and out of NEET condition as well as immobility probabilities are calculated using (13) and the observed NEET/non-NEET condition of individuals in round 2 via Equations (4) to (7).

In this case, steps 1 to 3 do not have to be replicated since the prediction errors for each individual are used.

In summary, the only difference between the lower- and upper-bound estimates arises from the residual that is added to the prediction of the NEET/non-NEET condition, as can been seen by comparing Equations (9) and (12). The lower-bound estimate simply adds the same residual to the linear prediction that an individual has in round 2, thereby inducing perfect correlation between the residuals. The upper-bound estimate takes a random draw from all individual residuals in round 1, resulting in no correlation between the residuals in the first and second round.

# 5. Results

## 1. Characteristics of the NEET Category

<mark>Results of the logit regression analysis for all individuals in the age group 15-24 are presented in the following figures</mark><sup>9</sup> <mark>and the marginal effects are presented in the Appendix. We estimate a logit model for the NEET binary dependent variable (NEET = 1, non-NEET = 0) assuming that the probability of a positive outcome is determined by the standard normal cumulative distribution function. Results show a clear profile of the NEETs, already provided in the descriptive analysis. In particular, the NEET category is composed mainly of young women 20 to 24 years old, with low levels of education, and typically married. This profile will be analyzed in greater detail when gender-specific results are presented.</mark> <mark>With reference to Figure 5, results show that the likelihood of being a NEET increases with age because of the complexity of transitioning from school to the labor market that all too frequently leads</mark>

> 9 For the logit regression model, we used the 2010, 2011, 2015, 2017 and 2018 survey rounds. The choice depended on whether of a complete set of data required to perform the analysis was available. Complete regression results are saved in the appendix. In the following graphs, significant results are those where the bar is away from the zero value and the symbol describing the confidence interval does not include the zero value. All the independent variables are dummies or categorical variables: female (1 = female, 0 = male); age group (1 = 19-24, 0 = 15-19); marital status (married or widower/divorced, with being single as the omitted reference case); education attainment (Koranic school, primary school, secondary school and tertiary education, with no-education as the omitted reference case); housing characteristics (villa, apartment, traditional Moroccan house, modern Moroccan house, with rural house as the omitted reference case) and two geographical areas, namely urban or rural regions (with urban as the omitted reference case), and macro-regions (less developed regions and more developed regions, with the former as the omitted reference case).

17
to report business disruptions from anti-government groups and to adapt their business practices in response to such experiences. For this analysis, we divide our sample into above and below the median number of current employees (12 in our data), and refer to those above the median as “large firms” and those below the median as “small firms.” In Panel A, nearly four out of five firms are likely to report being adversely affected by antigovernment group activity during the past three years, with large firms are 9 percentage points more likely to do so ( _p_ = _._ 02). Large firms also report significantly higher likelihoods of employees being threatened, injured, or killed, and firm assets being threatened or critical public infrastructure destroyed. In Panel B, we find that large firms are also more likely to report taking steps to respond to government violence, including being 26 percentage points more likely to invest in private security ( _p < ._ 01), 24 percentage points more likely to make protection payments ( _p < ._ 01), and significantly more likely to take additional steps including relocating staff from a district or stopping operations permanently. While not causal evidence of the role of firm size, this correspondence between self-reported behavior in the survey data and the patterns in the administrative data from Table 5 suggests large firms respond differentially more to insecurity.

# **4.3 Discussion**

We use a novel data source, corporate cell phone account records, to test the hypothesis that firms and employees operating in conflict-affected settings respond to increased local insecurity by decreasing their local presence. Using our GTD measure of major violent events in which 23 or more media-confirmed fatalities from terrorism were recorded, we find the number of active firms decreases by approximately 5-6% in the next month, and that these effects can persist at similar magnitudes for up to six months.

We find evidence that our effects are concentrated in large firms as proxied by number of unique subscribers; a result consistent with existing work suggesting that large firms are more vulnerable to predation. Theoretically, the direction of heterogeneity by size is am23
# **III. What is the extent and distribution of tax compliance in Romania?**

**The imputation results show that the final imputed values using predictive mean matching (PMM) are reasonably close to those observed on the tax admin data** . The distribution of income in the EUSILC, tax data, and imputed EU-SILC is presented in Table 3. The mean income value in tax data and imputed EU-SILC are reasonably close.<sup>11</sup> The mean tax income is 1.2% lower than the mean survey income, and the mean imputed tax income is 6.6% higher than the mean survey income. As discussed below, our estimate of tax evasion in Romania is close to that of other countries in the region.

**Table 3. The distribution of survey income, tax income, and imputed tax income (2020 €).**

|**Statistic**|**Annual income based on**<br>**Survey data**<br>**(EU – SILC, gross labor**<br>**income)**|**Tax**<br>**annual**<br>**income-**<br>**observed value**<br>**(July 2020 * 12)**|**Imputed tax annual gross**<br>**income**<br>**(imputed EU-SILC)**|
|---|---|---|---|
|**Mean (without**<br>**censoring)**|11,300|11,164|12,051|
|**Mean(with censoring)**|10,863|.|10,191|
|**P1**|2,968|322|1,364|
|**P5**|5,223|1,374|5,531|
|**P10**|5,741|2,321|5,531|
|**P25**|7,419|5,531|6,186|
|**P50**|9,539|7,813|8,644|
|**P75**|13,779|13,376|13,862|
|**P90**|18,019|21,446|21,823|
|**P95**|22,258|28,416|29,108|
|**P99**|29,678|57,253|56,975|

_Note: To limit the impact of the underrepresentation of top incomes in survey data on the estimate of mean tax compliance, we censor the distribution of survey income (“true income”) and imputed tax income (“reported income”) at the 99_<sup>_th_</sup> _percentile of the distribution of survey income._ _Source: Observed and imputed values based on EU-SILC and tax admin data. P1-P99 refers to the first and 99_<sup>_th_</sup> _income percentiles based on the uncensored distribution._ 11 Theoretically, we can imagine a scenario in which the distribution of imputed tax income is completely different from the distribution of income observed in this survey. In such a case, the outcomes of the imputation would be difficult to interpret.

18
We tested the empirical implications of our model on a new dataset of stakeholders’ perceptions of the efficiency and equity attributes of the public health care system and perceptions of the impact of health litigation in Uruguay. Since the early 1990s, there has been an increasing trend in the overall number of litigation cases brought to Latin American courts on the basis of constitutionally guaranteed rights to health and life. For example, the phenomenon of health litigation took shape around 1995 in Colombia (Contreras 2008).<sup>30</sup> The number of litigation cases brought to the Colombian courts reached a total of 33,328 in only two years. This example is not singular, as Brazil and Argentina started to follow the escalating trend. Given budget constraints, the individual or group claims against governments caused distortions in terms of equity, efficiency and financial sustainability of the publicly funded healthcare plans. Compared to such regional standards, the number of healthcare litigation cases in Uruguay is small. According to a study that includes a selected sample of litigations from 2001 to 2009, there were nine (9) cases of judicial proceedings that demanded access to certain medications<sup>31</sup> and five (5) cases from 2009 to 2010.<sup>32</sup> According to additional estimates of one of the main executive agencies affected by judicialization, the number of cases has been around 20 a year.<sup>33</sup> Taking into considerations the relatively small scale of the judicialization phenomenon, as well as the lack of issue polarization among key stakeholders, Uruguay offers a compelling and feasible pilot case study of actors’ perceptions of the efficiency and equity consequences health litigation.

# _Methodology_

In order to solicit perceptions of relevant actors, we followed the main steps and procedures of stakeholder analysis. Stakeholder analysis and mapping are methods widely used in management and social sciences to identify the key actors in a policy process, as well as their incentives, power and positions within a given policy domain. More specifically, this method has been extensively used in

> 30 Contreras, Juan. “La tutela como manifestación de algunas fallas del mercado y de gobierno en el sistema general de seguridad social en salud de Colombia que comprometen la eficiencia y la equidad del mismo.” Revista de Derecho y Economía. 25 (2008).

> 31 Campos, Santiago Pereira, Rafael García Martínez, et al. Estudios Multidisciplinarios sobre Derecho Medico y Organizaciones <u>de la Salud. Uruguay: La Ley Uruguay, 2011.  pg. 285.</u>

> 32 Campos, Santiago Pereira, Rafael García Martínez, et al. Estudios Multidisciplinarios sobre Derecho Medico y Organizaciones <u>de la Salud. Uruguay: La Ley Uruguay, 2011.</u>

> 33 Sartori, Maria  az. “Fondo de Recursos sufre presiones jurídicas importantes” debido a laboratorios que sustentan las demandas, dijo experto de la O S” Seminario Búsqueda. June 9, 2011.

16
There have been substantial shifts in the age structure of the population (Table 2.3), similar to those taking place in Brazil as a whole. The share of children aged 0-14 years declined from 38 percent in 1991 to an estimated 31 percent in 2000. Furthermore, the absolute number of children in this group fell during 1991-2000.

# **3. Education**

In the area of education, the poverty profile indicates that household heads that have completed secondary or more education are much better off than those with no or primary education only. Hence, education is a key in raising earning potentials of individuals and productivity of the economy. A more educated workforce produces high value output, which contributes to growth and poverty reduction. Education, apart from human capital, also creates social capital as it may transmit cultural messages and build social cohesion. Although the positive effects on economic growth of education investment is gradual and takes time to appear, the benefits to the individuals that receive the investment are more instant.

As shown in the previous section, Paraíba has achieved considerable progress in reducing income poverty and improving social indicators. With decisive action, Paraíba could achieve targets for further improvements in social indicators.

State expenditures on education have increased considerably in the last decade, which is appropriate given their importance for poverty reduction. Since the number of those aged 14 and under is not growing, Paraíba has been presented with an excellent opportunity to improve the quality of education. Paraíba, as other states of Brazil, has seized the opportunity effectively. At both the national and state level, governments have raised education’s share of the public budget and introduced policy changes aimed at improving access to primary education, increasing enrollment in secondary school, and diminishing regional disparities. Paraíba has been particularly aggressive and has seen enormous improvements in enrollment rates and school attainment.

While the state is spending considerable resources on education, the Brazilian growth analyses show that education has not played a major role in the determination of economic growth in Brazil.<sup>8</sup> Similarly, analyses show that education, rather than labor market segmentation, explains most of the wage differential in the Northeast. This section suggests two possible reasons: (a) the quality of education provided in schools is not very high and (b) most students do not remain in school long enough to learn skills that will help them obtain better jobs. The latter may itself be due to the low quality of education. Another worrisome finding is that the substantial achievements in education have not led to the extremely poor catching up to the nonpoor in education attainment.

The section on education is organized in three subsections. Section 3.1 outlines the education structure. Section 3.2 addresses educational status and Section 3.3 analyzes expenditures, policies, and programs.

> 8 See Abreu and Verner (1997) on Brazil.

15