rafmacalaba/datause-encoder-data huggingface dataset:
|Child benefits?<br>Public Services Prov<br>Does the law allow<br>|Yes<br>ided byC<br>civil socie<br>|Constitution of the<br>Republic of Bulgaria<br>(1991), Art. 6 Para. 2<br>Protection Against<br>Discrimination Act<br>(2004), Art. 4 Paras. 1,<br>37<br>Social Assistance Act<br>(1998), Art. 3<br>ivil Society<br>tyorganizations(CSOs)to<br>|<br> <br>No<br> provid<br>|e social services specifically <br>|No<br>to LGBTI individuals, including:<br>|Yes<br>|Equal Treatment Act<br>(1994), Secs. 7 (1)(b)–(c)<br>|No<br>|No<br> <br>|
|---|---|---|---|---|---|---|---|---|---|
|Vaccination?|Yes|Bulgarian Non-Profit<br>Legal Entities Act<br>(2000), Art. 38<br>Health Act (2004) Art.<br>222|<br>Yes|Ley Federal de Fomento<br>a las Actividades realiza-<br>das por Organizaciones<br>de la Sociedad Civil<br>(2004), Art. 5 Paras. I,<br>V, XI|N/A|Yes|Not explicit but should be<br>covered by Constitution<br>of the Kingdom of The<br>Netherlands (2008), Art.<br>8|N/A|Yes<br>Not explicit but should<br>be covered by Decree<br>No. 12/2012/ND-CP<br>on registration and<br>management of opera-<br>tion of nongovernmen-<br>tal organizations in<br>Vietnam(2012), Art. 4|
|Water?|Yes|Bulgarian Non-Profit<br>Legal Entities Act<br>(2000), Art. 38|Yes|Ley Federal de Fomento<br>a las Actividades realiza-<br>das por Organizaciones<br>de la Sociedad Civil<br>(2004), Art. 5 Paras. I,<br>V, XI|N/A|Yes|Not explicit but should be<br>covered by Constitution<br>of the Kingdom of The<br>Netherlands (2008), Art.<br>8|N/A|Yes<br>Not explicit but should<br>be covered by Decree<br>No. 12/2012/ND-CP<br>on registration and<br>management of opera-<br>tion of nongovernmen-<br>tal organizations in<br>Vietnam(2012), Art. 4|
|Sanitation?|Yes|Bulgarian Non-Profit<br>Legal Entities Act<br>(2000), Art. 38|Yes|Ley Federal de Fomento<br>a las Actividades realiza-<br>das por Organizaciones<br>de la Sociedad Civil<br>(2004), Art. 5 Paras. I,<br>V, XI|N/A|Yes|Not explicit but should be<br>covered by Constitution<br>of the Kingdom of The<br>Netherlands (2008), Art.<br>8|N/A|Yes<br>Not explicit but should<br>be covered by Decree<br>No. 12/2012/ND-CP<br>on registration and<br>management of opera-<br>tion of nongovernmen-<br>tal organizations in<br>Vietnam(2012), Art. 4|
|Family planning?|Yes|Bulgarian Non-Profit<br>Legal Entities Act<br>(2000), Art. 38|Yes|Ley Federal de Fomento<br>a las Actividades realiza-<br>das por Organizaciones<br>de la Sociedad Civil<br>(2004), Art. 5 Paras. I,<br>V, XI|N/A|Yes|Not explicit but should be<br>covered by Constitution<br>of the Kingdom of The<br>Netherlands (2008), Art.<br>8|N/A|Yes<br>Not explicit but should<br>be covered by Decree<br>No. 12/2012/ND-CP<br>on registration and<br>management of opera-<br>tion of nongovernmen-<br>tal organizations in<br>Vietnam(2012), Art. 4|

Anti-Discrimination Law and Shared Prosperity 75
- c. Anthropometrics:

- i. **Height for age z-score, weight for height z-score, nutritional status (based on weight for height using Malawi standards)**

- ii. Stunting, wasting (binary, less than 2 std z-score)

- d. Educational Testing:

- i. **MDAT scores**

- ii. **SDQ scores on behavior towards others.** Use the following to score: <u>http://www.sdqinfo.com/c3.html</u>

# **Detailed Description of Construction of Competencies Index** :

Moderator variable for fertilizer application: S11bQ22

**_Fertilizer_** (Q23-26):

Quantity index: time taken to complete (Q23), categorize it as 1 ‘below median’ (in seconds); 2 ‘above median’; and 3 ‘did not complete/did not complete in time’. Median is calculated among those who completed under the allocated time.

Quality index: Each Q (24-26) coded as 1 if Yes 0 if No and then added up to create an index between 0-3 of the quality of the application of fertilizer.

Normalize each by subtracting the control mean and dividing by the control SD.

# **_Making change_** (Q27-28):

Same as above: (quantity index, Q27) and quality index (Q28).

Use the same procedure for Q29-30, Q31-32. Then, add the quality indices (Q28, 30, and 32). Add quantity indices (Q27, 29, and 31).

Normalize each by subtracting the control mean and dividing by the control SD.

**_Sending a text message_** (Q35-37) – moderator variables to be used for adjustment (Q33-34):

Same as above, then normalize each index.

# **_Use the calculator on mobile phone_** (Q38-39):

Same as above, then normalize each index.

# **_Calculate profits from trade_** (Q-40-42):

Same as above, then normalize each index.

**Finally, average the normalized quantity indices and the quality indices separately to produce two final competency indices.**

58
set 3 strategies, 383 females (48.5%) set 2 strategies, and 293 females (37.1%) set only one strategy. The strategies identified by participants mostly focused on the adoption of safe sexual behaviors. Figure 2 highlights that the most commonly identified strategy was to use a condom, followed by abstinence and being faithful. Females also wrote about why this goal is important for their future and what obstacles they might face in following through with their specific strategies. We returned four months later in December 2017 to check-in and remind participants of the goal and their strategies. We collected additional information at that time on the female’s perception of whether she was successfully adhering to her strategies.

In Table A3 we use characteristics from the baseline survey and information that was collected during the initial goal setting activity visit in August 2017 to predict the total number of strategies developed and the number of strategies ultimately reported as achieved at endline (both of which range from 0-3). Interestingly, females who likely suffer from depression set and achieved fewer strategies.<sup>8</sup> Consistent with the psychological concept of self-efficacy, females with higher general self-efficacy scores set and achieved more strategies.<sup>9</sup> Females from households with earthen floors (reflecting a relatively poor household) set and achieved fewer strategies.

# **3 Data Collection and Outcomes**

# **3.1 Data Collection**

**Sample.** We conducted a baseline census of members of all 149 ELA clubs in operation in Dodoma, Iringa, and Mbeya from August to October 2016. The census was taken by meeting with the club leader of each club and obtaining a complete list of active members. Members were considered active if they were attending ELA clubs at least two times per week if they were enrolled in school or were attending at least three times per week if they

> time of the intervention or had moved away from the study area.

> 8Likely depression is measured using the Patient Health Questionnaire-2 (PHQ-2), where a score of 3 or higher is indicative of depression. The PHQ-2 includes the first two items of the PHQ-9 (Kroenke, Spitzer and Williams, 2003).

> 9Self-Efficacy is measured using the General Self-Efficacy Scale developed by Schwarzer and Jerusalem (1995). A total self-efficacy score that ranges from 10-40 was calculated. We then standardized this score using the mean and standard deviation of the score among females in control communities.

11
**Table 1. Social Sustainability Indicators in the Social Sustainability Global Dataset**

|**Component**|**Indicators**|**Obs**|**Source**|
|---|---|---|---|
|<br>**Social Inclusion**||||
|**Access to markets**|Labor force participation rate|113|GMD|
||Unemployment rate<br>|109<br>|GMD<br>|
||Percentage of people that work and are self-employed|79|GMD|
||Percentage of people that work and have a contract|36|GMD|
||Share of population that owns a bank account|105|FINDEX|
|**Access to basic**<br>**services**|Percentage of households that have access to water|135|GMD|
||Percentage of households that have access to sanitation|137|GMD|
||Percentage of households that have access to electricity|104|GMD|
||Percentage of households that have access to internet|50|GMD|
||Share of population that uses the internet|42|AF, AB, ASB|
||Share of population that uses the internet as a source of information|73|WVS|
|**Access to human**<br>**capital services**|Percentage of people that are attending primary school/people of school age|114|GMD|
||Percentage of people that completed primary education|107|GMD|
||Percentage of people that are attending secondary school/people of school age|114|GMD|
||Percentage of people with health insurance|28|GMD|
||Percentage of people that have social security|30|GMD|
|**Access to political**<br>**and civic spaces**|Percentage of women in parliament|190|WDI|
|**(voice and agency)**||||
||Share of population that agrees it is a problem if women earn more than their<br>husbands|75|WVS|
||Share of population that agrees that when jobs are scarce, men should have<br>more right to a job than women|76|WVS|
||Share of women that are the chief earner in their households|18|LB|
||Share of population that agrees or strongly agrees men make better political<br>leaders than women|76|WVS|
||Percentage of women 25 years or older that finished secondary school|144|GMD|
||Share of women who believe a husband is justified in beating his wife|39|WB|
|**Resilience**||||
||Percentage of households that own a computer|98|GMD|
||Percentage of households that own a cellphone|92|GMD|
||Percentage of households that have a TV|76|GMD|
||Percentage of households that have a radio|55|GMD|
||Percentage of households that have a washing machine|59|GMD|
||Percentage of households that have a sewing machine|41|GMD|
||Percentage of households that have a motorcycle|62|GMD|
||<br>Percentage of households that have a fridge|68|GMD|
||<br>Percentage of households that have a car|72|GMD|
||Percentage of households that own their land|13|GMD|
||Average share of assets in the household|101|GMD|
||Share of population that receives government transfers (that is, individual is<br>beneficiary of a state aid program)|18|LB|
||Share of population that receives remittances|34|AF|
||Share of population that saves some money|85|ASB, LB, WVS|
||Share of population that has gone without enough food to eat in the past year|110|LB, AF, WVS|

9
6 sensible for three reasons: (1) only about one-half of all migrants in Ghana remit;<sup>8</sup> (2) about 50 percent of all remittance-receiving households in the survey do not have a migrant; and (3) if we attempted to measure differences according to migration cum remittances behavior, the number of observations for each cell would be very small. In Ghana, where family ties are very strong, migration is different from remittances because households without migrants can receive internal or international remittances from relatives (e.g. cousins, aunts, uncles) and close friends.<sup>9</sup> Table 1 presents summary data from the 2005/06 Ghana GLSS 5 Survey (subsample). Since we want to work with three exclusive groups of households, in this table and in all subsequent tables, we have dropped the 57 households that receive remittances from both internal and international sources. Of the remaining 3,884 households, 2,515 households (64.7 percent) receive no remittances, 1,159 households (29.8 percent) receive internal remittances (from Ghana) and 210 (5.4 percent) receive international remittances (from African or other countries).

Table 1 reveals several interesting contrasts between the three groups of households, that is, those receiving no remittances, those receiving internal remittances (from Ghana) and those receiving international remittances (from African or other countries). With respect to human capital, the table shows that households receiving international remittances generally have more human capital than households with no remittances, while households receiving internal remittances usually have less. The table also shows that households receiving international remittances have the highest mean per capita expenditure, while households receiving internal remittances have the lowest mean expenditure.
Inspection of the series for GDP reveals that the period from 1960 to 1975 was one of fairly steady and appreciable growth (see Figure 2). By this yardstick, performance thereafter has been at best mediocre. Among the contributing factors were the two oil shocks, the sanctions against the _Apartheid_ regime and the restructuring following its collapse. We therefore concentrate on the early sub-period, viewing it as plausible initial basis for assessing how the post- _Apartheid_ economy ought to be able to perform over the long haul. Denoting calendar years by the subscript _k_ , and ignoring child labor, we obtain GDP in year _k_ from (5) as where _Lk_ and λ _k_ year, respectively. Since the labor force series begins in 1965, that year is the initial starting point for the calibration procedure.

Table 2: Values of _Yk_ , _Lk_ and _e_<sup>_B_</sup> _<u>k</u>_

|Year|_Yk_(_US_$1995)|_e_<sup>_B_</sup><br>_k_|_Lk_|_Yk/Lk_|
|---|---|---|---|---|
|1960|49_._2_·_10<sup>9</sup>|0_._406|n.a.|n.a.|
|1965|68_._4_·_10<sup>9</sup>|0_._410|7_._42_·_10<sup>6</sup>|9_._22_·_10<sup>3</sup>|
|1970|90_._6_·_10<sup>9</sup>|0_._447|8_._24_·_10<sup>6</sup>|10_._99_·_10<sup>3</sup>|
|1975|113_._0_·_10<sup>9</sup>|0_._453|9_._25_·_10<sup>6</sup>|12_._23_·_10<sup>3</sup>|
|1980|127_._4_·_10<sup>9</sup>|0_._461|10_._34_·_10<sup>6</sup>|12_._32_·_10<sup>3</sup>|
|1985|132_._4_·_10<sup>9</sup>|0_._495|11_._93_·_10<sup>6</sup>|11_._10_·_10<sup>3</sup>|
|1990|144_._7_·_10<sup>9</sup>|0_._500|13_._58_·_10<sup>6</sup>|10_._65_·_10<sup>3</sup>|
|1995|151_._0_·_10<sup>9</sup>|n.a.|15_._29_·_10<sup>6</sup>|9_._88_·_10<sup>3</sup>|

Sources: World Bank (2002) and Barro and Lee (1996) The next step is to anchor the system to λ 65. This can be accomplished by progressive substitution using eq. (3), which involves the unknown parameter _z_ and the series for _et_ . Although only quinquennial data on educational attainment are available (Barro and Lee, 1996), this is a small drawback in the present setting; for annual fluctuations are of no real significance. The data take the form of the average years of schooling among the population 64
**Table 2: Olley-Pakes (1996) Decomposition of Allocative Efficiency (i.e. Aggregate TFP minus Average TFP) by NACE 2-digit Industry Classification of Manufacturing Firms.**

|**NACE 2 Industry**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|---|---|---|---|---|---|---|
|Food Product|4.64|3.66|4.43|5.21|4.27|5.19|
|Beverages|6.23|7.02|7.79|8.44|8.39|5.83|
|Tobacco|5.96|3.80|4.66|5.94|6.38|4.76|
|Textiles|1.26|1.49|1.59|2.10|2.89|3.93|
|Apparel|3.21|2.56|2.75|3.52|3.77|6.55|
|Leather|2.49|2.52|2.52|3.40|3.54|3.37|
|Wood|3.08|2.91|2.92|4.39|4.52|5.21|
|Paper|4.24|4.52|4.92|4.62|3.97|4.85|
|Printing|2.76|2.82|3.16|3.15|3.12|3.66|
|Coke & Refined Petrol Products|6.52|5.80|8.92|10.37|12.68|11.87|
|Chemicals|4.43|5.81|6.13|5.90|6.87|4.79|
|Basic Pharmaceutical|9.27|9.48|9.94|11.74|10.62|9.80|
|Rubber|4.18|3.69|3.29|4.20|4.48|5.84|
|Non-metallic|3.23|3.07|3.11|4.46|4.77|4.92|
|Basic Metal|10.52|12.52|9.40|10.72|9.63|6.29|
|Fabricated Metals|3.62|3.38|2.99|3.66|4.58|4.43|
|Computer|2.37|2.24|2.24|3.07|3.51|3.35|
|Electrical|3.20|3.06|2.84|4.38|4.12|4.25|
|Machinery & Equipment|2.12|2.92|2.90|4.01|4.27|4.72|
|Motor Vehicles|3.89|4.03|4.72|6.70|8.77|7.71|
|Other Transport|4.14|2.36|3.12|3.50|2.77|3.26|
|Furniture|2.20|2.26|2.17|3.06|3.00|3.32|
|NEC|4.74|3.38|3.65|5.04|5.80|6.00|
|Machine Repair & Installation|1.55|1.25|1.90|2.94|2.45|3.37|
|Utilities|4.30|4.39|4.21|5.60|7.89|6.97|

Beverages, Tobacco, Basic Metal, and other Transport – had declined through 2008, by 6.4%, 20.2%, 40.2%, and 21.1%, respectively, suggesting a decrease in the share of output of the most productive firms in these sectors. On the other hand, efficiency in some sectors, like Textiles, Apparels, Machinery and Equipment (including Repair and Installation), more than doubled over the period.<sup>5</sup>

> 5 See the Annex for results using the full sample.

10
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**Table 6.  Household Spending on Energy Consumption, 2008**

||**Am**<br>|**ount**_(in_<br>|_drams per mo_<br>|_nth)_<br>|**Share**<br>|**of Tot**<br>|**al Expenditu**<br>|**res**(%)<br>|
|---|---|---|---|---|---|---|---|---|
|_Quintile_|_Energy_|_Gas_|_Electricity_|_Heating_|_Energy_|_Gas_|_Electricity_|_Heating_|
|**Armenia**|||||||||
|_Poorest_|4,655|2,043|2,572|3,948|7.0|3.0|4.0|6.5|
|_2_|7,344|3,759|3,543|4,814|7.8|3.9|3.9|5.4|
|_3_|8,397|4,659|3,576|4,909|8.6|4.8|3.7|5.4|
|_4_|9,778|5,533|4,022|5,299|8.2|4.6|3.4|4.6|
|_Richest_|12,441|6,922|5,366|5,869|6.9|3.9|2.9|3.4|
|_All_|8,743|4,727|3,886|5,018|7.7|4.1|3.5|5.0|
|**Yerevan**|||||||||
|_Poorest_|5,392|2,216|3,176|4,048|8.1|3.1|4.9|6.1|
|_2_|9,202|4,252|4,950|5,151|8.6|3.9|4.7|5.0|
|_3_|9,776|4,794|4,983|4,665|8.8|4.4|4.4|4.3|
|_4_|11,600|5,667|5,789|5,169|8.2|3.9|4.2|3.9|
|_Richest_|13,559|6,658|6,888|5,673|6.4|3.2|3.2|2.6|
|_All_|10,064|4,806|5,225|4,959|8.0|3.7|4.3|4.3|
|**Other Urban**|||||||||
|_Poorest_|4,370|2,117|2,250|3,788|7.0|3.2|3.7|6.8|
|_2_|7,017|3,992|2,956|4,443|8.2|4.6|3.6|5.6|
|_3_|8,094|4,936|3,132|4,918|9.2|5.5|3.7|5.9|
|_4_|9,127|6,032|3,063|5,150|9.0|5.9|3.1|5.4|
|_Richest_|12,115|7,823|4,211|5,755|7.8|5.0|2.6|3.9|
|_All_|8,396|5,176|3,176|4,879|8.3|4.9|3.3|5.4|
|**Rural**|||||||||
|_Poorest_|4,295|1,707|2,474|4,103|6.2|2.5|3.7|6.7|
|_2_|6,293|2,987|3,288|4,941|6.5|3.0|3.5|5.2|
|_3_|7,275|4,079|2,992|4,879|7.2|4.1|3.0|5.3|
|_4_|9,188|5,577|3,222|5,528|7.9|4.6|2.9|5.0|
|_Richest_|10,624|5,945|3,971|6,280|6.6|3.5|2.3|3.9|
|_All_|7,749|4,216|3,222|5,209|6.9|3.6|3.0|5.1|

_Source:_ Author’s calculations based on 2008 ILCS

# **_Distributional impact of the gas price hike_**

The analysis suggests that the welfare losses are higher for the poorest than the richest in Yerevan, but neither progressive nor regressive for the rest of the country. Table 7 presents the results of the analysis of welfare losses, expressed in percent of household expenditure, 13
_Changes in “between” inequality._ In general, consumption in rural areas is lower than in urban areas. Capital cities have much higher living standards--in most cases about 40 percent higher consumption than the national average, even after controlling for price differences -- but also higher inequality. Over time, the relative position of rural areas has deteriorated in most countries, sometimes quite sharply. This indeed is common across all countries in Table 7A. As a result, the “between” component of consumption inequality went up everywhere except in Moldova, where it remained unchanged and in Hungary where it fell, the only case of clear convergence between locations.

_Changes in “within”inequality_ . Within capital city inequality increased everywhere, again with the exception of Hungary and stayed virtually the same in Russia. In rural areas, withinlocation inequality fell for the most part but remained broadly the same in Hungary and Moldova. There was no clear pattern for changes in inequality within other urban areas.

Some part of the decline in inequality within rural areas may be linked to land ownership or use rights reforms. A broadly similar redistribution of land occurred in low income CIS countries such as Armenia and Moldova, which also have labor-intensive agriculture and where it is reasonable to expect the effects to have been equitable. Indeed, Table 7A also shows a large fall in the Theil entropy index in rural Moldova, as opposed to rural Tajikistan, where land reform has been much less comprehensive.

# _Regional factors_

While the urban/rural dichotomy is small, the role of regional differences may be much greater. Thus, Yemtsov (2002), using official per capita income data series, shows that between-regional factors among Russia’s eighty-plus regions accounted for about a third of the overall inequality in that country by the year 2000, with the increase in the betweenregions component being a key driver of the change in inequality between 1995 and 2000. However, direct survey measurements on consumption find the role of regional variation to have been much smaller: only about 15 percent of overall inequality can be ascribed to the between regional differences in means, with stability between 1997 and 2002 (World Bank 2005 d). Thus, while the persistence of regional factors is evident, their role as drivers of inequality change is not. Lack of convergence across Russian regions in mean real incomes is also presented as a major factor influencing the outlook for inequality going forward by Dolinskaya and by Fedorov.

Summing up, while locational factors play a role as a driver of inequality, it is unlikely that they will strongly influence the dynamics of inequality going forward. It is therefore necessary to focus on within-urban and, for low income CIS countries at any rate, within – rural drivers as key factors that will determine the evolution of inequality in the future.

24
_Table 4. Effect of different treatment arms on school enrollment_

|Dependent variable: “Is th|e child enrolled<br>(1)|andgoingto sch<br>(2)|ool”. Dprobit,m<br>(3)|arginal effects.<br>(4)|
|---|---|---|---|---|
||All|children|Children|aged 8 to 12|
||All<br>sample|Only treated<br>villages|All<br>sample|Only treated<br>villages|
|Endline * 0 GNF|0.0771<br>(0.0438)*|_reference_|0.112<br>(0.0408)***|_reference_|
|Endline* 70k GNF|0.110|0.0341|0.132|0.0221|
||(0.0418)***|(0.0258)|(0.0402)***|(0.0350)|
|Endline * 140k GNF|0.170<br>(0.0473)***|0.0983<br>(0.0349)***|0.176<br>(0.0422)***|0.0744<br>(0.0443)*|
|Endline|0.241|0.327|0.222|0.332|
||(0.0339)***|(0.0300)***|(0.0338)***|(0.0320)***|
|Girl|-0.0640|-0.0606|-0.0666|-0.0690|
||(0.0134)***|(0.0169)***|(0.0178)***|(0.0218)***|
|Observations|16,851|8,782|7,912|4,185|
|Individual controls|yes|yes|yes|yes|
|HH controls|yes|yes|yes|yes|
|Fixed-Effects|Villages|Villages|Villages|Villages|
|Clustered-SE|Village|Villages|Village|Village|
|P- value 0GNF vs 70GNF|0.196||0.564||
|P-value 0GNF vs 140GNF|0.006||0.109||
|P-value 70GNF vs 140GNF<br>Robust standard errors in pa|0.039<br>rentheses. ***|0.039<br>p<0.01, ** p<0.05|0.118<br>, * p<0.1|0.133|

<mark>Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1</mark> Individual controls include: age, age squared, ethnicity dummies (sousou, malinke, peul, other). Household controls include household size <mark>Standard errors are clustered at the village level, for 148 villages.</mark>