rafmacalaba/datause-encoder-data huggingface dataset:
60 zonal finalists to be zonal merit award winners. The zonal finalists who were not selected were then placed back into the pool, and were eligible to be selected as ordinary merit winners.

- **720 Ordinary Merit Winners (120 per region):** 120 ordinary winners per zone were randomly selected from among the remaining 1920 semi-finalists using the lottery procedure described below.

# **2.7 Randomization Algorithm**

Random selection of the ordinary winners was done for two main reasons. First, from an operational point of view, a competition run at this scale is not feasible to do with presentations in front of judges, and it was recognized that there would be inherent uncertainty in the scoring as to which applicants were best amongst the semi-finalists. Moreover, in many developing countries there are concerns that programs get captured by individuals with certain political or ethnic ties, and it was felt randomizing gives people a fair chance and is less subject to this perception risk. Second, random assignment enabled rigorous measurement of the program’s impacts.

The randomization procedure was designed as follows. First, among the semi-finalists, all those with business plan total scores below 30 were dropped, to maintain a minimum standard. This reduced the pool from 1,920 to 1,841 firms. Then a two-step stratified randomization was conducted in STATA to choose the ordinary winners:

1. First, within each region, half of the existing business plans were randomly chosen to be ordinary winners, with this selection stratified by gender. This reflected the preference of the government to again oversample existing business plans.

2. Then the number of slots for new firm business plans was calculated per zone as 120 minus the number of existing business ordinary winners. This was subdivided into a number of slots for new businesses operated by men and new businesses operated by women in proportion to the share of women among new businesses in the pool in each region. Then the number of new firms dictated by the number of remaining slots was randomly chosen from the number of new firm semi-finalists in each region and gender strata.

This gives an ordinary winner treatment group of 720 firms and an ordinary winner control group of 1,121 firms. After quality checking which disqualified some winners, and redrawing of 10
of being without adult supervision while their parents are at work.

The last row of panel A shows that the average propensity for violence for T and C groups is 0.038, with a standard deviation of 0.029, ranging from 0.001 to 0.215. This average propensity for violence is 14 times the mean probability that a given student will be vulnerable to violence in Chicago (Chandler et al., 2011). Even when both estimations are not completely comparable because we use fewer violence determinants than Chandler et al. (2011), this difference sheds light on the tremendous propensity for violence among the children in this study.

Panel B shows academic scores and absenteeism for the first quarter of the 2016 school year. We imputed average course grades for missing observations and present the same models using only subsamples with nonmissing information in Table A3 in the Appendix. In a grade scale of 0–10, requiring a minimum grade of 5 to pass each course, enrolled students have between 6.5 and 6.7 points, similar to average grades at national level. Additionally, 25% of enrolled children have a misbehavior report before the intervention. The mean absenteeism rate during the first quarter of the academic year—that is, before the intervention—was 4.8% (1.69 out of 35 days).

Panel C summarizes club characteristics: mean club size was 13 students and community tutors ran approximately 31% of these clubs. The average take-up, defined as the share of sessions attended by each student out of the total number, was 57%. Finally, the mean fraction of treated students by course was 42%.

## **2.6 Compliance**

In terms of compliance, attendance records indicate that no children in C group received treatment. Additionally, school coordinators were instructed to make sure that only children in T group attended the courses. However, since we did not implement an additional compliance check, and considering that treated students attended approximately 60% of all club sessions, we use an intentto-treat approach to measure the ASP impacts, as we explain in the following section. We consider all children assigned to participate in the program as T group regardless of whether they were part of the ASP at the time of the endline survey.

# **3. Estimation Framework**

In this section, we describe the empirical strategy used to measure the ASP’s effects on student behavior, violence, and academic outcomes, and to assess the heterogeneity of the intervention by individual violence levels. Additionally, we study how the intervention affects nonenrolled children’s behaviors.

16
- 40 -

The formulae from which these elasticities and<sup>those in all subsequent</sup> tables were computed can be found in appendix<sup>(5). In the above table WL stands</sup> for the price of labour, WM for the price of<sup>materials, RPK for the rental price</sup> of the services from knowledge capital, and<sup>RPP for the user cost of physical</sup> capital. These results show that the own-price elasticities<sup>both have the</sup> correct negative sign.

We first tested the pair of joint hypotheses<sup>"market power, full shifting</sup> of the CIT" and "perfect comptetition, no tax shifting"<sup>against each other. The</sup> results from these non-nested hypothesis tests were<sup>inconclusive, since neither</sup> pair of joint hypotheses was able to reject the alternative<sup>pair of hypotheses.</sup> Next we tested the joint hypotheses "market power, full<sup>tax shifting"</sup> against the pair of hypotheses "market power,<sup>partial or no tax shifting". The</sup> results from this pair of non-nested hypothesis<sup>tests are reported in the next</sup> table.

Table (10) Turkish chemical and Petroleum derivatives industries Results from the non-nested hvyothesis<sup>tests</sup>

|Specif.|HO:|market power,<br>full shifting of the CIT|Hot|market power,<br>partial or no shifting|
|---|---|---|---|---|
||S|t|S|t|
|WI=WL|1.007|3.702<br>Ho rejected|0.212|<br>0.735<br>H0 not rej.|
|Wi<br>t<br>WM|0.492|0.987<br>Honot rej.|0.969|<br>0.832<br>Ho not rej.|

The joint hypotheses "market power, partial<sup>or no tax shifting" could not</sup> be rejected by the competing pair of joint hypotheses<sup>for either specification</sup> of the model. On the other hand, the pair of<sup>hypotheses "market power, full tax</sup> shifting" was rejected by the alternative<sup>pair of joint hypotheses for the</sup> specification with labour as the numeraire input.<sup>This result agrees with what</sup> intuition predicts: While it is quite conceivable that the<sup>firms in the</sup> industries we studied had market power instead<sup>of being price takers, it is not</sup>
<!-- Start of picture text --> et<br>Ss<br>—s<br>oP]<br>3<br>Oo<br>a=)<br>= nm<br>ao<br>KS<br>g<br>E<br>wa cl<br>=E)<br>aS<br>Be<br>4<br>2<br>3)<br>em<br>SS<br>3"<br>§<br>oO<br>u<br>v<br>=<br>o<br>0 0001 .0002 0003 .0004<br>Entomological Inoculation Rate (EIR)<br><!-- End of picture text -->
**Figure 2  China: Real per capita income and Gini index, 1981-2001**

<!-- Start of picture text --> Real Mean Per Capita Income Gini index<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br>Source: Ravallion and Chen (2004)<br>Index, 1981=100<br><!-- End of picture text --> Will improved economic performance in Russia and other transition countries in Eastern Europe and the former Soviet Union come at the expense of a further widening of income disparities? Has the transition to a market economy moved these countries irreversibly to a higher inequality path, on which other factors not related to transition, such as globalization, will be superimposed, generating possibly even more unequal distributions? And is economic policy capable of influencing these processes? These are the key questions addressed in this paper. In attempting to provide answers, the paper, which is predominantly about inequality in the countries of Eastern Europe and the former Soviet Union

- reviews the extensive literature on the determinants of inequality in transition, focusing on the stylized facts on inequality in transition;

- creates a consistent and comparable consumption aggregate for the transition economies of Eastern Europe and the former Soviet Union which aims to overcome deficiencies in existing data and provide a firmer foundation for those stylized facts; and

- decomposes inequality by sources of income and household groups, with a view to understanding the role of key determinants of inequality in different countries.

The paper is organized in seven sections. Section II, following this introduction, raises the question of what is really known about inequality in the transition countries by examining the quality of available data. Section III summarizes the construction of and presents a data set more amenable to within and across country comparisons. Section IV reviews the guidance available from theoretical models of transition on the key determinants of inequality. Section V presents the decomposition of inequality by income source and by household groups, which constitutes the key contribution of the paper and assesses the outlook for inequality in the future. Section VI compares the experience of the countries of Eastern Europe and the former Soviet Union with regard to growth and inequality with what is known from 5
# **Table 4** **_Zakah_ Estimation to fill the poverty gap**<sup>**22**</sup>

|**(1)**<br>**Country name**|**(2)**<br>**Survey**<br>**year**|**(3)**<br>**GDP**<br>**PPP**<br>**Current**<br>**USD**<br>**(Billion)**|**(4)**<br>**Muslim**<br>**pop.**<br>**(%)**|**(5)**<br>**Adjusted**<br>**GDP PPP**<br>**USD**<br>**(Billion)**|**(6)**<br>**Domestic**<br>**_Zakah_**<br>**(Billion USD)**|**(7)**<br>**Incoming**<br>**remittances**<br>**(Billions**<br>**USD)**|**(8)**<br>**Domestic**<br>**savings rate**<br>**(% of GDP)**|**(9)**<br>**_Zakah_**<br>**considerin**<br>**g**<br>**Remi-**<br>**ttances**<br>**(% of**<br>**GDP)**|**(10)**<br>**resource**<br>**shortfall**<br>**under $1.25**<br>**per annum**<br>**as % of GDP**|**(11)**<br>**Does**<br>**_Zakah_**<br>**cover**<br>**(10)?**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Albania**|2008|26.45|79.9|21.13|0.38|1.50|1.60|1.44|0.01|y|
|**Algeria**|1995|129.75|98|127.16|2.29|1.12|28.11|1.77|0.14|y|
|**Azerbaijan**|2008|76.73|99.2|76.12|1.37|1.55|64.89|1.82|0.01|y|
|**Bangladesh **|2005|163.73|89.6|146.70|2.64|4.31|18.06|1.62|5.58|n|
|**Benin**|2003|9.14|24.4|2.23|0.04|0.06|5.98|0.44|5.78|n|
|**Burkina Faso**|2003|12.09|59|7.13|0.13|0.05|4.51|1.06|9.83|n|
|**Cameroon **|2007|39.77|17.9|7.12|0.13|0.17|18.53|0.32|0.26|y|
|**Comoros**|2004|0.63|98.3|0.62|0.01|0.01|0.00|1.77|8.89|n|
|**Cote d'Ivoire**|2008|34.30|36.7|12.59|0.23|0.20|17.85|0.66|2.05|n|
|**Djibouti**|2002|1.24|96.9|1.20|0.02|0.01|4.87|1.75|1.49|y|
|**Egypt **|2005|333.22|94.6|315.22|6.30|5.02|15.71|1.90|0.04|y|
|**Gabon**|2005|17.84|9.5|1.69|0.03|0.01|58.35|0.17|0.03|y|
|**Gambia**|2003|1.46|95|1.38|0.02|0.06|11.05|1.72|5.42|n|
|**Guinea **|2007|9.78|84.4|8.25|0.15|0.15|9.68|1.52|6.71|n|
|**Guinea-Bissau**|2002|1.26|42.2|0.53|0.01|0.02|0.00|0.76|8.21|n|
|**Guyana**|1998|1.54|7.2|0.11|0.00|0.01|16.94|0.13|0.88|n|
|**Indonesia**|2009|965.57|88.2|851.63|8.52|6.79|33.76|0.89|0.39|y|
|**Iran**|2005|643.50|99.4|639.64|11.51|1.03|41.09|1.79|0.02|y|
|**Iraq**|2007|94.97|99|94.02|1.69|0.00|0.00|1.78|0.09|y|
|**Jordan**|2006|26.14|98.2|25.67|0.46|2.88|0.00|1.77|0.01|y|
|**Kazakhstan**|2007|169.63|56.4|95.67|1.72|0.22|43.84|1.02|0.00|y|
|**Kyrgyz Republic**|2007|10.62|86.3|9.17|0.16|0.71|0.00|1.55|0.02|y|
|**Malaysia**|2009|384.88|60.4|232.47|4.18|1.13|36.03|1.09|0.00|y|
|**Maldives**|2004|1.20|98.4|1.18|0.02|0.00|46.15|1.77|0.02|y|

22 (4) data source: wikipedia.org. (3) (7) (8) data source: World Bank Databank. (8) Savings rate is floored to zero if negative (5) = (3) * (4) = (5)*reference ratio of Z1 from Table **2** (9) = ((6) + (7) * (8) * 2.5%) / (3) (10) = Column (10) from Table 3 (11) = Y if (9) > (10), N otherwise.

21
# <mark>3. DATA AND DESCRIPTIVE STATISTICS</mark>

# _(a) Data_

Studies on the agricultural productivity gender gap typically use one of two indicators to identify farm outcomes by gender: (i) the gender of the household head or (ii) the gender of the plot (or farm) manager. Several papers point out that the latter is likely better at capturing the gender gap, since the former does not take into account the distribution of inputs and the specific decision-making process among household members <mark>(de la O Campos, Covarrubias and Patron 2016</mark><sup>5</sup> <mark>; Peterman et al. 2011).</mark> Another problem with using the gender of the household head to compare agricultural productivity across households is that there are potential omitted variables, given that many female heads of household tend to be widowed, have a migrant husband, or are otherwise systematically different from male-headed households <mark>(Kleinjans 2013).</mark> The advantage of our household data set is that the survey captures who is the primary responsible person for each crop production. We use detailed data on individual farmers and their agricultural activities from the Household Income and Expenditure Survey 2016. The survey was conducted by the Department of Census and Statistics of Sri Lanka and the data are representative at the national and district levels. The survey includes questionnaires on demographic and socioeconomic characteristics of individuals and households, as well as relatively detailed income modules. Data on agricultural self-employment activities include information on crops cultivated, crop area, value of output, agricultural inputs, ownconsumption, and use of subsidies. While not all crops are individually listed, information is collected on paddy, other seasonal crops (chilies, onions, vegetables, cereals, yams, tobacco), cash crops (tea, rubber, coconuts, coffee/pepper/betel, banana/fruits) and livestock (meat, eggs, milk), fish, horticulture, other crops and livestock.<sup>6</sup>

> 5 In Uganda, De la O Campos et al. (2015) find a conditional agricultural productivity gender gap of about 10 percent when using female plot manager, but find no conditional gap when using gender of household head as the gender variable.

> 6 While the data are detailed enough to conduct further analysis, certain data limitations should be acknowledged. Information is available for most major crops but not all are listed separately (e.g., information on “vegetables” is collected in the aggregate). Agricultural inputs consist of seeds, fertilizers, chemicals, hired labor, agricultural equipment/rental and others, but only the aggregated cost value is available in the data. The absence of information on hours worked makes it difficult to construct detailed measures of labor input. There is also no information on technology use, cropping intensity, soil quality or actual use of extension services (though the survey asks about the distance from home to the closest Agrarian Service Center).

8
**Table 9: Poverty gaps for poor cells in poor and non-poor households**

||Nutrition<br>|poverty thre<br>|shold<br>|Basic need<br>|s poverty t<br>|hreshold<br>|
|---|---|---|---|---|---|---|
||(a1)|(b1)|(c1)|(a2)|(b2)|(c2)|
|National|32.22|28.15|14.39|40.50|30.48|14.17|
||(1.66)|(1.93)|(2.08)|(1.00)|(1.38)|(0.84)|
|Dakar|29.98|30.14|9.62|31.13|26.84|17.96|
||(10.07)|(5.97)|(3.25)|(2.02)|(2.09)|(1.71)|
|Other urban areas|28.88|29.80|17.04|37.00|32.07|10.53|
||(3.11)|(6.63)|(3.68)|(2.55)|(3.78)|(2.07)|
|Rural areas|32.61|27.75|14.19|43.48|31.31|13.13|
||(1.75)|(1.96)|(2.69)|(1.14)|(1.77)|(1.24)|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations.

Note : Poverty status based on per adult equivalent cell consumption. Bootstrap standard errors are between parentheses (250 replications). Equivalent scale: 0.5: children 0 to 14 years old; 1: adults. Columns (a1) and (a2) show the value of the poverty gap for poor cells in poor households in which all cells are found poor; columns (b1) and (b2) show the value of the poverty gap for poor cells in poor households, in which some cells are found not poor; and columns (c1) and (c2) show the value of the poverty gap for poor cells in non-poor households.

45
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KOREA, DEN. PEOPLE'S REP. OF

# 2. Projections

- NRR-1 by 1995

|Yer|**fqpuItuin**|kp.<br>ratio|Pwriod|Growth<br>**_rats_**|D|Cm|TFU|_N_**_lm_**|**F_lm**|**in**|**x1000**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|2i442|62.1|1990-94|1.48|19.7|**5.0**|2.22|67.6|75.2|24|29|
|1995|23087|52.8|IM-99|1.44|19.2|4.8|2.11|69.1|77.3|20|24|
|2000|24806|45.2|2000-04|1.42|19.0|4.8|2.10|70.6|79.2|17|20|
|2005|26632|46.2|2005-09|1.28|17.9|5.0|2.09|71.6|80.0|14|17|
|2010|28396|46.6|2010-14|1.09|16.2|5.3<br>|2.09<br>|72.6<br>|80.9<br>|12<br>|15<br>|
|2015<br>|29989<br>|46.6<br>|2014-19<br>|0.92<br>|14.9<br>|5.6<br>|2.08<br>|73.7<br>|81.8<br>|9|12<br>|
|2020|31408|46.4|2020-24|0.82|14.2|6.0|2.07|74.8|82.8|7|9|
|2025<br>|32716<br>|48.3<br>|2025-49<br>|0.53<br>|13.2<br>|7.9<br>|2.07<br>|77.3<br>|84.9<br>|4<br>|5<br>|
|2050<br>|37322<br>|69.0<br>|2050-74<br>|0.12<br>|12.2<br>|11.0<br>|2.06<br>|79.8<br>|87.0<br>|3<br>|4<br>|
|2075<br>|38423<br>|70.4<br>|2075-99<br>|0.07<br>|11.9<br>|11.2<br>|2.06<br>|81.1<br>|88.2<br>|2<br>|3<br>|
|2100<br>|39090<br>|72.7<br>|2100-24<br>|0.04<br>|11.8<br>|11.3<br>|2.06<br>|82.0<br>|89.1<br>|2<br>|3<br>|
|2125<br>2150|39511<br>39712|73.5<br>74.3|2125-49|0.02|11.7|11.5|2.06|82.4|89.5|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>& SS do~~~6-64 3 E<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>{ RD{{§§ UUgU2 0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. comparison of Country with Region and Income Group, 1985-90

# TFR

<!-- Start of picture text --> eo Growth rate<br><!-- End of picture text --> <!-- Start of picture text --> Country Region income grow<br><!-- End of picture text --> <!-- Start of picture text --> Country Region Income group<br><!-- End of picture text --> Cowatry Region Income group 67
Table 6. Measures of Agricultural Commodity Export Concentratiorn, Malawi, Tanzania, Zimbabwe.

|A. CQncnttion_Ra|tios||||
|---|---|---|---|---|
||Top- I|Top-2|Big-5"|<br>Big-7|
|1961-1973|43 (tobacco)|70 (tobacco & tea)|75|90|
|1974-1987|55 (tobacco)|74 (tobacco & tea)|89|95|
|TQA.NZAWA|||||
|1961-1973|20 (cashew)|38 (cashew & cotton)|43|70|
|1974-1987|40 (coffee)|56 (coffee & cotton)|67|80|
|ZIIVIBABW|||||
|1961-1973|53 (tobacco)|69 (tobacco & beef)|67|92|
|1974-1987|41 (tobacco)|57 (tobacco & cotton)|73|91|
|B. Or<br>Meaure o|f Concentration||||
||Big-5|Big-7|Big-|7 & Others|
|1961-1973|1.3|1.1||1.3|
|1974-1987|1.2|1.3||1.8|
|1961-1973|0.9|0.6||0.6|
|1974-1987|1.1|1.7||0.9|
|1961-1973|2.2|1.7||1.6|
|1074-1987|0.9|0.9||0.8|

'Big-5 refers to the ogive measure calculated with N equal to 5 (equation 1), Big-7 has N equal to 7 where the additional two commodities included vary from country to country (see table 1). Big-7 and others include all commodities (N equal to 8, where the 8th commodity is "all others"'.

23
Table 3. Summary statistics for key variables

||N|mean|sd|p10|p25|p50|p75|p90|
|---|---|---|---|---|---|---|---|---|
|Female-Manager|50472|0.140|0.347|0.000|0.000|0.000|0.000|1.000|
|Female entrepreneur|47702|0.109|0.312|0.000|0.000|0.000|0.000|1.000|
|OwnLargest|57574|0.792|0.265|0.400|0.500|1.000|1.000|1.000|
|Small|57574|0.553|0.497|0.000|0.000|1.000|1.000|1.000|
|Large|57574|0.139|0.346|0.000|0.000|0.000|0.000|1.000|
|Age 6-10|57574|0.229|0.420|0.000|0.000|0.000|0.000|1.000|
|Age 10+|57574|0.672|0.469|0.000|0.000|1.000|1.000|1.000|
|**City level**|||||||||
|Corruption obstacle|613|0.486|0.277|0.088|0.271|0.482|0.695|0.875|
|Overdraft facility|611|0.375|0.263|0.070|0.140|0.323|0.584|0.774|
|Trade credit|613|0.431|0.198|0.181|0.276|0.418|0.579|0.707|
|Inf competition|608|0.498|0.209|0.246|0.337|0.478|0.660|0.783|
|Outage|613|0.576|0.269|0.219|0.344|0.578|0.835|0.937|
|Web|617|0.443|0.239|0.114|0.244|0.454|0.618|0.768|
|Capacity agglo.|621|0.267|0.148|0.076|0.159|0.259|0.355|0.456|
|**Country-Industry or Country Level**|||||||||
|F_cj|1423|0.008|0.082|-0.075|-0.035|-0.004|0.034|0.102|
|F_c|122|0.105|0.078|0.025|0.057|0.096|0.130|0.186|
|Lngdppc|114|7.606|1.235|5.923|6.648|7.701|8.627|9.276|
|Education|122|3.589|0.695|2.642|3.069|3.685|4.066|4.407|
|Urbanization|116|48.718|21.196|21.934|32.213|48.938|65.252|77.186|
|Time required to start a business for females (in days)|116|22.140|26.476|5.500|8.200|14.000|25.750|48.000|
|Financial institution account, female (% for age 15+)|92|43.647|25.142|13.568|26.023|38.156|65.563|78.585|
|Fem. labor participation rate|111|53.161|15.975|31.006|45.486|52.800|62.440|75.324|
|Fem.primarycompletion rate|112|86.328|20.431|56.622|74.492|92.982|100.797|104.641|

Notes: For firm-level variables, we delete FDI firms with foreign ownership being 10% and more.

25