rafmacalaba/datause-encoder-data huggingface dataset:
percent. Corporate tax – at a 30 percent rate – is levied on profits<sup>12</sup> and is mostly payable by resident limited companies and cooperatives.

_Figure 3. Tax structure in different countries a. Indirect taxes as percent of GDP b. Direct taxes as percent of GDP_ <!-- Start of picture text --> 25% 20%<br>20% South<br>15%<br>Africa<br>15% Tanzania<br>Kenya<br>Tanzania 10%<br>10% Ethiopia Ghana<br>South<br>5% Uganda Africa 5%<br>Ghana Uganda<br>Kenya<br>Ethiopia<br>0% 0%<br>1,000 10,000 1,000 10,000<br>GDP per capita ( 2011 PPPs) GDP per capita (2011 PPPs)<br>% of GDP % of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers._ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, for Kenya 2015._ In the past 15 years, major reforms have been implemented to increase the revenue from the VAT and PAYE taxes, such as an increase of the standard VAT rate from 17 percent to 18 percent in 2005 (SEATINI, 2017). Even so, the fiscal authority has consistently missed tax revenue targets by 0.5 percent of GDP in the last few years (IMF, 2019).

# 2.4. Expenditures

Public expenditures in Uganda reached 19 percent of GDP in fiscal year 2016/17, a slight decline from 20.1 percent in 2015/16. This share is slightly higher than that of Ethiopia (18.6 percent) but considerably lower than that of Tanzania (31.4 percent), Kenya (24.3 percent), Ghana (28.4 percent) and South Africa (34.5 percent), as seen in Figure 4a. Uganda’s levels of public expenditure are constrained by low levels of tax revenue.

The fiscal incidence analysis in this report covers three types of expenditures in addition to the public pension system: i) direct transfers, ii) indirect subsidies, and iii) in-kind transfers. The direct transfers consist mainly of social protection programs, while in-kind transfers refer to education and health expenditures. Together direct transfers and in-kind transfers comprise social spending and represent 22 percent of total expenditure. Indirect subsidies include water and agricultural inputs.

> 12 Dividends of companies controlled by other resident companies are exempt.

10
consistent pattern of children from poorer households3 to be more likely to engage in harmful, i.e., human capital inflicting, child labor. Similarly, an almost similar pattern is seen for school attendance, in that children from wealthier households are more likely to attend school (even though the incidence "jumps" a bit in the case of children from the next-to-lowest poverty quintile).

**Table 3.2 Incidence of Child Labor and Primary Education**

||School only|Work only|Both|Neither|
|---|---|---|---|---|
|_Across rural/urba_|_n location_||||
|Rural|95.7|2.1|0.4|1.9|
|Urban|96.6|1.0|0.4|1.9|
|_Across gender_|||||
|Girls|95.6|2.1|0.4|1.9|
|Boys|96.4|1.4|0.4|1.8|
|_Across poverty qui_|_ntile_||||
|Lowest|94.1|3.0|0.6|2.3|
|Next-to-lowest|96.0|1.9|0.3|1.8|
|Middle|95.3|1.8|0.3|2.7|
|Next-to-highest|96.9|1.2|0.4|1.6|
|Highest|97.7|0.9|0.4|1.1|
|_A crossfull sample_|||||
||96.0|1.8|0.4|1.9|

_Source:_ Core Welfare Indicators Questionnaire 1997 (Ghana).

Based on these initial results on the incidence of child labor and schooling, we choose to divide the sample in two dimensions in the econometric analysis in section five. First, we will only include children 9-14 years of age (both included).<sup>4</sup> Nine years of age is chosen as the lower cut-off value, since at age nine the incidence of harmful child labor for the first time passes one percent. Second, the preliminary findings induce us to focus exclusively on the work/no-work decision, since almost all children attend school. Hence, there is not much of a schooling "choice" to model. Also, including the schooling "choice" would bias the results in the "no-harmful child labor" direction.

3Households are weighted according to various poverty predictors, e.g., how frequent they get meat to eat, whether the household uses toothpaste, etc, and subsequently divided into poverty quintiles, based on their ranking- see Fofack (1998) for details.

> 4 This is because that if _not_ doing so, the results of the econometric analysis of the next section would become biased in the "no-harmful child labor" direction.

8
_Table 2: Estimated Results of Different Models_

|Dependent Variable: Female-to-Male Labor<br>Force Participation Ratio|(1)|(2)|(3)|
|---|---|---|---|
|Method|GLS|GLS|2SLS|
|Main Independent Variable|Internet<br>Inclusive<br>Index|Gender Gap<br>in accessing<br>internet|Gender Gap<br>in accessing<br>internet|
|Inclusive Internet Index|0.781***|||
||(0.139)|||
|Gender Gap in Internet Access||-0.0176|-0.501*|
|||(0.0440)|(0.297)|
|Population Growth|0.243|-0.894**|-1.290|
||(0.467)|(0.423)|(1.325)|
|Educational Attainment (Years of||||
|education)|-2.049***|-2.026***|-4.044***|
||(0.299)|(0.379)|(1.032)|
|Gender Parity Index in||||
|<br>Tertiary Education||||
|Unemployment Rate (%)|-0.180|-0.0571|-0.735|
||(0.177)|(0.173)|(0.704)|
|Share of Industry Value Added in GDP (%)|0.624***|0.818***|0.240|
||(0.0798)|(0.0963)|(0.456)|
|Share of Service Value Added in GDP (%)|0.528***|0.566***|0.103|
||(0.0436)|(0.0672)|(0.338)|
|Share of Trade in GDP (%)|0.171***|0.192***|0.128**|
||(0.0151)|(0.0161)|(0.0497)|
|year=2019|-1.129*|0.382|-0.423|
||(0.647)|(0.675)|(3.596)|
|Year=2020|-3.739***|0.904|-2.214|
||(1.310)|(0.900)|(4.175)|
|Year=2021|-6.700***|-0.409|-3.534|
||(1.505)|(0.950)|(4.283)|
|Length of Paid Paternal Leave|-0.201|0.0465|-0.348|
||(0.133)|(0.116)|(0.383)|
|Women Can Travel Outside Their Homes in<br>the Same Way as Men|6.842***|4.350***|-0.588|
||(1.334)|(1.475)|(3.078)|
|Law that Prohibits Discrimination in Access<br>to Credit Based on Gender|-2.629**|-0.00311|2.348|
||(1.076)|(0.987)|(1.595)|
|Constant|-60.18***|-23.11**|60.69|
||(7.876)|(9.092)|(54.08)|
|Observations|42|48|42|
|R-squared|||0.750|
|chi2|6856|2215||

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 12
in the follow-up survey beginning in Wave 2. For example, respondents are asked about their understanding of deposit insurance, health insurance cover, or the purpose of a household budget. Results for such financial literacy questions are presented in Tables 5 and 6.

We find that the financial literacy training has a large, positive impact on basic financial awareness. Column (1) of Table 5 shows that relative to health training participants – respondents who receive financial literacy training are 4.5 percentage points more likely to know that both income and expenses are included in a household budget. In column (2), we examine the effects of both financial literacy and pay for performance treatments and find that participants who received both treatments are 5.5 percentage points more likely to know that both income and expenses are included in a household budget, compared to participant who received neither financial literacy nor pay for performance on financial literacy questions. The F-test of adding up all three coefficients has a p-value of 0.078, however the marginal impact of pay for performance is not significant.

The remaining columns in Table 5 and Table 6 continue to show this positive treatment effect for most basic awareness questions. Notably, financial literacy training participants are 17 percentage points more likely to know the minimum amount necessary to open a bank account, and 20 percentage points more likely to understand that borrowing for consumption is an unproductive loan. These effects are quite large and represent a 20-30 percent increase over the control group means, since less than two-thirds of the pure control group was able to answer such questions correctly.

Finally, columns 7 and 8 of Table 6 use as the dependent variable a composite measure of all basic financial literacy questions. The results show that aggregate basic financial awareness increases by 7.7 percentage points due to financial literacy training; the combined effect of training and pay for performance is also statistically significant, though pay for performance does not provide any additional, statistically significant improvements.

14
_Table 3_ - Years of life lost related to dietary risks in Brazil

|Diseases related to dietaryrisks|Years of life lost|
|---|---|
|Ischemic heart disease|1,700,337|
|Intracerebral hemorrhage|371,371|
|Diabetes mellitus type 2|358,077|
|Total cancers|335,480|
|Ischemic stroke|281,207|
|Colon and rectum cancer|204,317|
|Subarachnoid hemorrhage|148,322|
|Hypertensive heart disease|60,017|
|Stomach cancer|39,746|
|Esophageal cancer|38,375|
|Breast cancer|32,387|
|Tracheal, bronchus, and lungcancer|20,656|
|Other cardiomyopathy|19,691|
|Chronic kidneydisease due to hypertension|11,773|
|Chronic kidneydisease due to diabetes mellitus type 2|11,361|
|Aortic aneurysm|11,041|
|Chronic kidneydisease due to other and unspecified causes|11,022|
|Chronic kidneydisease due toglomerulonephritis|10,726|
|Other cardiovascular and circulatorydiseases|9,911|
|Atrial fibrillation and flutter|6,022|
|Non-rheumatic calcific aortic valve disease|3,035|
|Endocarditis|2,909|
|Chronic kidneydisease due to diabetes mellitus type 1|2,733|
|Rheumatic heart disease|2,209|
|Peripheral arterydisease|1,389|
|Total|3,694,114|

Source: Global Burden of Disease 2019

# 4.3. Hospital Information System - SIH/SUS

The average expenditures for the treatment of each disease are derived from the Hospital Information System of the Brazilian Unified National Health System (SIH/SUS), which adheres to the International Classification of Diseases, 10th revision (ICD-10). It is important to note that these expenditures primarily reflect the public sector (based on reimbursement rates).

To establish the medical expenditures associated with each risk factor-related disease, we link them to the diseases outlined in the Global Burden of Disease dataset. For each specific disease, we deflate the monthly treatment expenditures incurred during 2018-2022 using Brazil’s National Consumer Price Index and calculate the average, as shown in Table 4. Subsequently, the average medical expenditure associated with each disease related to the risk factor “dietary risks” is determined by computing the average cost across all related diseases and dividing it by the average number of approved Hospital Admission Authorizations (AIH). In other words, the average cost for each disease is calculated as the ratio between the total medical costs for that disease and the number of admitted patients due to that disease, using the ICD classification.

12
possible channels linking behavior to test scores – in particular, measures of student and teacher effort, as well as other factors (e.g., student attitudes toward school) that are not explicitly modeled above – to better understand the mechanisms underlying the reduced form estimates. We do not attempt to definitively disentangle the relative contributions of each channel in the overall program impact, as a number of possibly important channels (i.e., paying more attention in class) are difficult to measure in practice, and since this would likely require strong, untestable structural econometric assumptions.

# **<u>4. Data and Estimation</u>**

# **<u>4.1 The Dataset</u>**

The test score data were collected from the District Education Offices (DEO) in Busia district and Teso district. Test scores were normalized in each district such that scores in the comparison school sample (girls and boys together) are distributed with mean zero and standard deviation one. The complete dataset with both cohort 1 and cohort 2 students is called the _baseline sample_ (Table 1, Panel B). In the main analysis, we focus mainly on students with complete age, school attendance, and gender information, in schools that did not pull out of the program and for which we have baseline 2000 test scores and school ethnic composition, and call this the _restricted sample_ (Panel C). Note that average test scores are slightly higher in the restricted sample than in the baseline sample, since the students dropped are typically somewhat below average in terms of academic achievement, as discussed below.

Attendance data are based on four unannounced checks, one conducted during September to October 2001, and one in each of the three terms of the 2002 academic year. These were collected by NGO enumerators and they recorded baseline students actually in school on the day of the unannounced check as “present”. Attendance rates are below 80% for the baseline sample and a bit over 80% for the restricted sample (Table 1, Panels B and C). These data from unannounced checks are a substantial improvement over data from official school attendance registers, which are often thought to be unreliable in less developed countries.

14
Table 4a: Summary statistics: environmental polygons 2011-2014

|||**2011-2012**|||**2013-201**|**4**|
|---|---|---|---|---|---|---|
||(1)<br>Mean<br>enrollees|(2)<br>Mean non-<br>enrollees|(3)<br>Normalized<br>diference|(4)<br>Mean<br>enrollees|(5)<br>Mean non<br>enrollees|(6)<br>Normalized<br>diference|
|% baseline forest (Hansen)|0.891|0.883|0.036|0.904|0.897|0.032|
|% forest 2000 (NFI)|0.918|0.876|0.126|0.908|0.900|0.025|
|% agricultural land (NFI 2000)|0.046|0.055|-0.037|0.055|0.038|0.081|
|Area of Tiny polygon|393|257|0.193|284|193|0.161|
|Average Elevation (in meters)|1455|991|0.342|1385|1123|0.186|
|Average Slope (in degrees)|15|13|0.136|14|13|0.010|
|Km to major city|94|106|-0.182|96|108|-0.157|
|Km to city _>_ 5000 people|29|29|-0.004|28|31|-0.102|
|Meters to any road|3918|4420|-0.088|3859|4872|-0.170|
|Meters to highway with speed _>_ 60 km/h|7096|7253|-0.018|6638|8293|-0.184|
|Meters to highway with speed _>_ 80 km/h|19468|17369|0.098|18798|17868|0.044|
|Deforestation risk|0.040|0.050|-0.124|0.044|0.039|0.077|
|Marginality 2000|0.173|0.138|0.029|0.238|0.166|0.059|
|Applied as ejido|0.819|0.611|0.334|0.785|0.662|0.196|
|Obs|2456|5028|.|2110|5070|.|

> 1 The table shows means for enrolled and non-enrolled polygons and normalized differences in means.

Table 4b: Summary statistics: household characteristics

|||**2011-201**|**2**||**2013-201**|**4**|
|---|---|---|---|---|---|---|
||(1)<br>Mean<br>enrolees|(2)<br>Mean non-<br>enrolees|(3)<br>Normalized<br>diference|(4)<br>Mean<br>enrolees|(5)<br>Mean non<br>enrolees|(6)<br>Normalized<br>diference|
|Household (HH) head is female|0.086|0.088|-0.004|0.097|0.091|0.016|
|HH head age|57.003|58.170|-0.059|57.154|57.011|0.007|
|HH head can read and write|0.800|0.855|-0.103|0.806|0.782|0.041|
|HH head went to sec. school or above|0.178|0.171|0.013|0.161|0.150|0.022|
|# hh members|3.896|3.737|0.057|3.945|3.819|0.044|
|Indigeneous language spoken in hh|0.371|0.275|0.146|0.402|0.380|0.033|
|Receives PROCAMPO support (gov prog. for agric)|0.614|0.626|-0.018|0.618|0.594|0.035|
|Receives PROGAN support (gov prog. for livestock)|0.164|0.183|-0.035|0.175|0.144|0.059|
|Experienced a shock to husbandry prod (last 12m)|0.673|0.676|-0.004|0.674|0.707|-0.050|
|HH member ill and unable to work _>_1w (last 12m)|0.427|0.402|0.036|0.430|0.385|0.064|
|HH member had a large medical expense (last 12m)|0.468|0.446|0.031|0.487|0.458|0.041|
|Observations|2038|1428|.|2828|2119|.|

> 1 The table shows means for households in enrolled and non-enrolled communities and normalized in means.

36
at the border. We further assume that any changes in tariffs and charges are perfectly passed through to the final consumer.

On the basis of this Armington (1969)-style model, price changes impact demand for a product from a given supplier through three channels: through an exporter substitution effect, which is the consumption response to relative price changes between different national suppliers; through a demand—or income—effect, as consumption of a product changes in response to a change in its overall price; and through a domestic substitution effect (which due to data limitations we do not capture). For our purposes, then, the strength of the first two effects is determined by the vector � _σ_<sup>_P_</sup> _σ_<sup>_ES_�</sup> of elasticity parameters. The precise calculation steps have been documented in Brenton _et al._ (2009) and are, for completeness, reproduced in the technical appendix.

Finally, implicit in our empirical model is at least one additional technical assumption: since demand responses are based on elasticities, there will never be market entry by new exporters as a result of price changes (zero trade flows will always remain unchanged at zero).

This paper extends the standard trist model described in Brenton _et al._ (2009) in two ways. First, we advance the static simulations associated with a single year to include sequential simulations that take into account multi-year tariff liberalization scenarios. Second, we include scenarios that allow for variations in other categories of border-related charges, such as the spending tax and the revenue tax, as well as the implementation of a VAT. It is important to remember, however, that TRIST has been designed with the specific task of providing policy makers with important insights into the short-term effects of trade reform. It has not been designed for making longer-term predictions about the economy wide impact of trade reform; TRIST only looks at the import side of the economy whereas trade reform will also have an impact on exporting sectors by reducing bias against exporting.

# **3 Analysis of Trade-Related Adjustment Costs**

## **3.1 Description of the data**

We use proprietary data provided by Syrian Customs, which were collected via the newlyimplemented Automated System for Customs Data (asycuda) system, for Syrian customs posts employing the system over the period between January and end-July 2009. The data set comprises information on all import transactions at the 8-digit harmonized system (HS) level, including information on trading partners, c.i.f. import values, col8
of the top ten countries are the same in both surveys. Moreover, for most countries the EWCS-2005 total informality rate is very close either to both ESS-2004 and ESS-2006 rates or at least to one of them. Situation with the dependent informality rates is broadly similar. The exceptions in both cases include Slovenia, Norway and the Netherlands.

The coherent findings from ESS and EWCS raise concerns about the quality of field work performed in the countries of Southern Europe, as well as UK, Ireland and Poland for the Special Eurobarometer Survey on Undeclared Work in the European Union (Riedmann and Fischer (2008), - according to this survey, even after adding together positive responses and non-response, the level of informal employment in these countries is significantly lower that it follows from the ESS data (detailed comparisons are available on request).

When recent informal employment is accounted for, the largest increases in the informality level are found in countries where it was already high. As the result, the total level of informal employment is [at least] around 50% in Ireland, Israel, Greece, and Cyprus, around 25% in Austria, the UK, Poland, Portugal, and Italy, and close to 20% in Spain, Ukraine, Slovenia, and Bulgaria. Most of the other European countries feature informality level from 14 to 16%, whilst it is 11% to 13% in France, Hungary, Finland, Belgium, and Denmark, and just 9% in Sweden and Lithuania.

In terms of relative size of dependent and self-employed informal workforce, three groups of countries emerge:

- (i) In Cyprus, Israel, and Ireland both groups are large, but employees without contracts dominate the informal sector (even despite seasonal migrant workers are likely to be not covered, see footnote 13);

- (ii) In Russia, Ukraine, Bulgaria, Romania, Slovenia, Latvia, the United Kingdom, and Austria  the two groups are of comparable size;

- (iii) In remaining countries (i.e., Portugal, Spain, and Italy; the four Central European countries; Estonia and Lithuania; the Nordic countries; as well as Switzerland, Netherlands, Germany, France, and Belgium) the informal sector is dominated by the self-employed.

The latter finding calls for a closer look, given that, according to anecdotal evidence, in countries with more restrictive Employment Protection Legislation a large share of selfemployed are hired as „self-employed service providers‖ and doing work that is in every way identical to a formal dependent worker‘s. However, even if this is the case, in a survey such workers might describe their status as „an employee without a contract‖ or even as „an employee with a contract‖ (the contract being not an employment one though). Fortunately, ESS data allow to distinguish between ̳true self-employed‘ and ̳quasi-self-employed‘, using 15
district, determined when computing runoff (Section 2.4). Examples of the percentage of area irrigated are shown for districts within Burkina Faso in Figure 13.

# **3. Modeling climate change impacts on streamflow**

# **_3.1. Generation of climate change scenarios_**

To ascertain the possible impacts of climate change within the districts being investigated in this study, it is proposed to use synthetic or GCM-based climate change scenarios as input to the WatBal model. This would provide insight into the changes in hydroclimatic variables that can be expected under different climate change scenarios. It is recommended that the project use the 16 scenarios (or a subset of the 16) produced by the CRU for which data are available at 0.5o x 0.5o for the globe. These scenarios represent a range of equally plausible future climates (expressed as anomalies of the baseline 1961–1990 climate) with differences attributable to the different climate models used and to different emission scenarios that the world may follow. The ten scenarios are derived by using five different models (i.e. CSIRO2, HadCM3, CGCM2, ECHAM and PCM) in conjunction with two different emission scenarios (A2, B2, Figure 14). The WatBal model can be used to determine the impact of these different scenarios on runoff and actual evaporation and hence flow in the districts.

The analysts used the TYN SC 2.0: Global 0.5° x 0.5° Gridded Climate Change Dataset. The primary purpose for which TYN SC 2.0 was constructed was to provide environmental modelers with some of the inputs they require to run their models. The control scenario represents the evolution of surface climate over the 21st century under the assumption that the mean climate remains fixed at 1961–1990 levels. The 20 climate change scenarios are made up of all permutations of five **<u>models</u>** 3 with four SRES emissions scenarios (A1FI, A2, B2, B1, Figure 14).

The month-to-month and year-to-year variations are superimposed on top of the averaged climate changes taken from the models; these variations are taken from the gridded observations in **<u>CRU TS 2.0</u>** <u>.</u><sup>4</sup>

> 3 http://www.cru.uea.ac.uk/%7Etimm/grid/TYN_SC_2_0_text.html#Models. 4 http://www.cru.uea.ac.uk/%7Etimm/grid/CRU_TS_2_0.html.

17
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