rafmacalaba/datause-encoder-data huggingface dataset:
of asset sales is added to net monthly earnings. The corresponding figure in the groups receiving UCTs is $0.60, smaller because of reduced consumption from liquidation of group assets. These numbers correspond with the evidence from the household data, an insignificant average increase in consumption of 72 cents per person per month over a baseline mean of $19. Hence the combination of preserved (not consumed) group asset values and low profits limit the scope for increases in household income.

These somewhat bleak results help to inform three distinct literatures in the debate over how to build effective economic inclusion programs. First, they add a data point to the literature on group-based business investment programs in Sub-Saharan Africa. In many ways TASAF is similar to the NUSAF Youth Opportunities Program evaluated in neighboring Uganda by Blattman et al. (2014, 2020), which saw impressive short-term gains followed by long-term catch-up by the control.<sup>4</sup> However, as we discuss in more detail in the next section, key differences exist; NUSAF selected youth at the beginning of their working careers, invested mostly in training for skilled trades, allowed more scope for individuals to own enterprises, and avoided low-skilled sectors such as livestock that make up the majority of TASAF investment.<sup>5</sup> These findings echo those of Haushofer et al. (2022), who find that cash transfers targeted based on deprivation (rather than, say, potential in the form of high expected conditional average treatment effects) may not be attractive for increasing social welfare – even when the social planner cares about redistribution. In Appendix B, we provide a more detailed comparison to related social protection programs.

> 4 A similar increase in earnings in the short run followed by convergence a few years later is observed in an entrepreneurship program in Ethiopia that provided a cash grant and business training to young people (Blattman and Dercon 2018a & 2018b).

> 5 In a more individualized context, Argent, Augsburg, and Rasul (2014) evaluate Rwanda’s _Girinka One Cow_ policy and find that a short and inexpensive livestock management training caused large and long-lasting economic benefits up to six years after the asset transfer on milk production and yields, household earnings, and assets.

5
with the self-reported ethnicity data from the 2006 DHS (Samak ́e, Traor ́e, Ba, Demb ́el ́e, & Diop, 2006) as an inverted Herfindahl-Hirschman Index (HHI). The HHI, first developed to study levels of market competition, is commonly used in the literature to measure ethnic fractionalization indices. It is simply an inverse of the sum of the squares of the share of each ethnic group per population in each commune.<sup>7</sup> Figure 2 shows the distribution of the index across the country for communes where we have DHS survey data. In the figure a greater index value and darker color indicates a greater level of ethnic diversity. Additionally, we gather the 2009 commune population from the Malian census reported on the Wikipedia pages (Wikipedia, n.d.).<sup>8</sup> Lastly, all maps are made with the administrative geographical boundaries made available by the Database of Global Administrative Areas (University of California Berkely, n.d.).

For outcomes measures, we use per-capita household non-agricultural income and consumption from EMOP and then calculate commune-level poverty measures and the Gini coefficient as a measure of inequality from the consumption data. One important caveat on the EMOP income data is that it captures non-agricultural household income rather than total household income. The EMOP questionnaires are structured in a way that is more suitable to collect data on income with stable flows. We believe that this likely ignores most agricultural income. In fact, about a quarter of observations on household income is reported to be zero, while there is no report on zero consumption. Therefore, we interpret the EMOP income data to be reflective of non-agricultural household income, rather than total household income.

> 7Please refer to Equation 5 in the online appendix for details on the creation of the index.

> 8The Wikipedia page offer both circle- and commune-level population counts. We checked its internal consistency by comparing each circle-level value to a sum of commune-level values for each circle. After we verified internal consistency, we took the sum of all the commune population values to obtain the country population value, and compared it to Mali’s population count available on the World Bank’s DataBank. The data aggregated up accurately and we concluded that the data posted on the Wikipedia page is the valid, true data.

16
Table 9. EFFECT OF IE ON RATE OF DISBURSEMENT ADJUSTED BY SECTOR AVERAGE

|||DID|-OLS|||DID|-RE|||DID|-FE||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**T (quarters with IE==1)**|0.74***|0.48***|0.74***|0.48***|0.46***|0.40***|0.44***|0.40***|0.42***|0.41***|0.40***|0.40***|
||(5.60)|(4.03)|(5.54)|(4.00)|(3.88)|(3.03)|(3.68)|(2.93)|(3.62)|(3.04)|(3.39)|(2.91)|
|**D (project with IE==1)**|-0.46***|-0.21|-0.46***|-0.21|-0.30|-0.21|-0.29|-0.21|||||
||(-3.84)|(-1.53)|(-3.81)|(-1.53)|(-1.44)|(-1.02)|(-1.38)|(-0.99)|||||
|**_TTL switch dummies_**|Y|N|Y|N|Y|N|Y|N|Y|N|Y|N|
|**_TTL UPI dummies_**|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|**_Polynomial of sector averages_**|N|N|Y|Y|N|N|Y|Y|N|N|Y|Y|
|**_Observations_**|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|17784|
|**_Outcome mean_**|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|1.00|
|**_Effect in %_**|74.0%|48.0%|74.0%|48.0%|46.0%|40.0%|44.0%|40.0%|42.0%|41.0%|40.0%|40.0%|

_Note:_ Standard errors are clustered (sector and quarter level). T-stats are in parenthesis. All specifications include fixed effects for quarters of project life cycle, sector, fiscal year when project became effective and country.

Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent 36
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

## **_III.D Estimated key indicators for 2003/2004_**

Table 8 reports the main pension indicators estimated for the base year 2003/2004. The indicators are derived from the data provided by our Ugandan counterparts. The total expenditure is calculated as what was due to system participants during the base year, without considering past arrears.<sup>7</sup>

**<u>Table 8: Main pension system indicators (2003/04)</u>**

|**Indicators**|**Civil Servants**|**Teachers**|
|---|---|---|
|**System dependency ratio***|29.1%|8.2%|
|**Estimated total expenditure****<br>**(UGS mln)**|71,180|48,831|
|**Of which:**|||
|**for old-age pensioners**<br>**(regular pensions)**|30,358|19,054|
|**for old-age pensioners**<br>**(CPG payments)**|25,000|25,000|
|**for survivors**<br>**(regular pensions)**|5,145|870|
|**Other payments*****<br>**(non-CPG gratuities, etc.)**|10,677|3,907|
|**Total expenditure as % of GDP**|0.59%|0.40%|
|**Average replacement rates for old-**<br>**age pensioners:**<br>**Av. pension / Av. covered wage******|79.7%|83.4%|
|**Memo items:**|||
|GDPUGS12,121,000mln|||
|Population 23,443,052|||

*Number of all beneficiaries / Number of active employees

**Due to be paid in 2003/2004, excluding past arrears

***MPS experts’ estimates ****Average covered wage = average wage received by active employees in the respective covered population groups (government or teachers)

# **IV THE BASELINE PROJECTIONS**

The analysis of the long term sustainability of a pension system involves assessing the adequacy of the pension promise defined in legal provisions and the cost associated with these pension promises. As far as adequacy of benefits is concerned, replacement rates in terms of average wage provide a useful measure of what individuals may expect to receive when they retire. As far as system costs are concerned, key benchmarks would be the time path of system expenditure, the contribution rate required to balance the system if the system were contributory, the financing gap (as an overall measure of system future liabilities), and the implicit pension debt (liabilities accrued by the system at a given point in time).

Apart from the generosity of benefits, the cost of the system is largely driven by the system demographics summarized in the system dependency rate.

> 7 We have previously mentioned, but it is worth repeating it, that pension arrears do not affect our projections as the totality of new retirees elects to receive the reduced pension which is paid in full.

- 36 -
R-squared 0.2109 0.2378 0.211 0.238 0.211 0.238 0.211 0.238 _Table A- 5: Employment growth and government-owned banks_

|VARIABLES|emp_gr|emp_gr|emp_gr|emp_gr|emp_gr|emp_gr|
|---|---|---|---|---|---|---|
|SOB|-6.464*|-6.005**|-7.077|-6.152**|-8.281*|3.046|
||(3.490)|(3.055)|(4.794)|(3.013)|(4.537)|(4.917)|
|INTER||-0.591|1.022|-0.888|6.145|0.260|
|||(0.953)|(6.540)|(1.467)|(7.716)|(0.664)|
|INTCC||||0.822|-12.361|-1.143|
|||||(1.549)|(10.834)|(0.910)|
|INT1||||1.791|5.054||
|||||(2.585)|(3.892)||
|INT2||||0.162|-1.876|0.561|
|||||(0.640)|(4.124)|(0.678)|
|lsize|79.407***|79.217***|79.185***|79.155***|79.426***|65.481***|
||(5.419)|(5.498)|(5.509)|(5.483)|(5.428)|(8.414)|
|lage|-1.051|-1.085|-1.035|-1.092|-1.192||
||(1.781)|(1.768)|(1.780)|(1.708)|(1.831)||
|SOE|5.940*|5.725*|5.977**|5.018*|5.268*||
||(3.047)|(3.004)|(2.976)|(2.998)|(3.046)||
|lagempl|-78.810***|-78.587***|-78.560***|-78.370***|-78.741***|-66.160***|
||(5.451)|(5.546)|(5.550)|(5.543)|(5.407)|(8.626)|
|FEM|1.911|1.843|1.871|1.806|1.697||
||(2.550)|(2.554)|(2.522)|(2.509)|(2.504)||
|Constant|-1.858|-2.210|-1.557|-3.151|-2.277|-0.306|
||(5.741)|(5.761)|(5.116)|(6.027)|(5.345)|(17.877)|
|Observations|2,505|2,438|2,438|2,438|2,438|326|
|R-squared|0.804|0.802|0.802|0.803|0.806|0.842|

37
# _A different way of aggregating the non-pecuniary incentives_

From a policy perspective, it is useful to look at the impact on teacher effort of each nonpecuniary variable. However, since there are several such variables, there is likely to be a multicollinearity problem if these variables reflect just different aspects of the same enabling environment in a school. It could be useful then to aggregate the two types of non-pecuniary incentive mechanisms. For this purpose, we create a teacher autonomy index and a monitoring index by adding up the corresponding variables with the former ranging from 0 to 3 and the latter from 0 to 4.<sup>28</sup> A higher value for either index indicates a higher level of teacher autonomy or monitoring in the school.

We estimate again Model 3 in Table 2 and show estimation results of Model 4 in Table 3. Again, results are consistent with our two main results in Model 3 in Table 2: higher levels of teacher autonomy and monitoring induce teachers to substitute effort away from the unobservable activity of after-school work hours into the more observable activity of providing tutoring lessons.

# _Other hetoregenity analyses_

Teachers who are civil servants may behave differently from teachers on shorter-term contracts. Studies on India (Banerjee et al., 2007) and Kenya (Duflo, Dupas, and Kremer, 2012) find that short-term contract teachers hired by the local community work harder and more efficiently than their civil-service colleagues. This could be because contract teachers are likely to be subject to more local monitoring and face greater pressure to work harder. Since around one percent of teachers in our sample are hired by the local community, to examine the robustness of results against this hypothesis we exclude from our estimation sample those

> 28 Another way to aggregate the variables is to use, say, the first component obtained by the principal component method. We also tried this and found that estimation results are very similar. Thus to make results easier to interpret, we use the simpler arithmetic aggregation instead.

21
|||<br>**Mass**<br>**Provinc**|<br>**media treat**<br>**e level inter**|<br>**ment**<br>**vention**|||**Commun**<br>|**ity treatmen**<br>|**t sample**<br>|**District level**|**intervention**<br>|<br>**School**<br>|**component**<br>|**sample**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contro|l mean|Treatm|ent mean||Contro|l mean|Treatm|ent mean||Treatm|ent mean|Treatm|ent mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|**Percentage of employed HH**||||||||||||||||
|**heads**|707|0.945|714|0.948|-0.003|707|0.945|762|0.963|-0.018|682|0.965|705|0.956|0.009|
|**Last week activity: HH head**||||||||||||||||
|Looking for work|39|0.231|37|0.135|0.096|39|0.231|28|0.286|-0.055|24|0.167|31|0.226|-0.059|
|Taking care of home<br>Not working and not looking|39|0.462|37|0.324|0.138|39|0.462|28|0.357|0.105|24|0.542|31|0.484|0.058|
|for job|39|0.128|37|0.216|-0.088|39|0.128|28|0.25|-0.122|24|0.208|31|0.161|0.047|
|Other|39|0.179|37|0.324|-0.145|39|0.179|28|0.107|0.072|24|0.083|31|0.129|-0.046|
|**Percentage of employed**<br>**other HH members**|1055|0.344|1273|0.346|-0.002|1055|0.344|1289|0.38|-0.036|1151|0.381|1119|0.35|0.031|
|**Last week activity: other HH**<br>**members**||||||||||||||||
|Looking for work|692|0.01|832|0.006|0.004|692|0.01|799|0.005|0.005|713|0.011|727|0.003|**0.008***|
|Studying|692|0.136|832|0.137|-0.001|692|0.136|799|0.14|-0.004|713|0.181|727|0.169|0.012|
|Taking care of home|692|0.803|832|0.802|0.001|692|0.803|799|0.801|0.002|713|0.75|727|0.779|-0.029|
|Not working and not looking||||||||||||||||
|<br>for job|692|0.04|832|0.038|0.002|692|0.04|799|0.038|0.002|713|0.046|727|0.03|0.016|
|Other|692|0.01|832|0.017|-0.007|692|0.01|799|0.016|-0.006|713|0.011|727|0.019|-0.008|
|**Primary work: position**||||||||||||||||
|Self-employed|1290|0.557|1421|0.55|0.007|1290|0.557|1462|0.538|0.019|1325|0.546|1294|0.59|-0.044|
|Employee|1290|0.302|1421|0.265|0.037|1290|0.302|1462|0.325|-0.023|1325|0.303|1294|0.268|0.035|
|Employer or boss|1290|0.003|1421|0.001|0.002|1290|0.003|1462|0.005|-0.002|1325|0.004|1294|0.003|0.001|
|Worker without remuneration|1290|0.123|1421|0.165|-0.042|1290|0.123|1462|0.126|-0.003|1325|0.137|1294|0.134|0.003|
|Day laborer|1290|0.015|1421|0.018|-0.003|1290|0.015|1462|0.005|0.01|1325|0.008|1294|0.005|0.003|
|Other|1290|0.001|1421|0.001|0|1290|0.001|1462|0|0.001|1325|0.002|1294|0|0.002|
|**Monthly salary**|1082|369.64|1148|334.22|34.42|1082|369.64|1245|391.42|-21.78|1101|351.01|1087|336.88|14.13|
|**Hours per week**|1287|42.933|1404|42.551|0.382|1287|42.933|1448|41.823|1.11|1322|40.735|1287|42.045|-1.31|
|**Months worked in last 12**||||||||||||||||
|**months**|1279|9.177|1405|9.731|-0.554|1279|9.177|1446|9.577|-0.4|1307|9.431|1271|9.862|-0.431|

40
step-by-step nationwide rollout in 2001, with implementation in most provinces in 2002 and 2003.

In introducing the RFR in 2000, Premier Zhu Rongji explained the reform in this way: “Promoting the RFR is the fundamental strategy for protecting the legal rights of farmers and reducing their burdens. The basic approach in the RFR is to raise – by an appropriate amount – the existing rates of the agricultural tax and the agricultural special products tax, while abolishing all administrative fees that are levied on farmers, including the unified township levy and the village accumulation levy.”<sup>43</sup> The reform began with twin objectives: to reduce the overall fiscal burden on farmers and to impose a more structured framework for financing government at the grassroots levels by bringing previously extra-budgetary revenues and expenditures on budget. These objectives were intertwined: the fact that grassroots governments were financed mostly off-budget prior to the RFR meant that they generally operated free of oversight from higher levels and unrestrained by tax laws. The proliferation of fees and their growing burden on farmers were a major cause of widespread rural protests in the late 1990s.<sup>44</sup> Thus, the RFR was an important component of the nationwide reform of extra-budgetary fees beginning in the late 1990s that sought to curb the growth of extra-budgetary revenues and bring them onto the budget as much as possible (World Bank 2002).

According to official statements of the time, it appeared that the RFR began as a tax rationalization and budget reform measure that sought to bring township and village finance more fully into the budget **.** By banning extra-budgetary levies, the government aimed to harden the budget constraints and curb the excesses in “taxing and spending” at the grassroots level. Prior to the RFR, there was a common saying in the countryside: “the first tax is light, the second tax is heavy, and the third tax is a bottomless pit” ( _toushui qing, ershui zhong, sanshui shi ge wudidong_ ). The “first tax” referred to the agricultural tax. The “second tax” was the

> 43 “Speech at the CCP Economic Work Conference, November 28, 2000.” Reprinted in State Council RFR Work Group (2001).

> 44 These are well documented in two popular books on the plight of farmers. Cao (2000) and Chen and Chun (2004). See also Bernstein and Lu (2003).

45
- For capex, industry sources were used for generation asset data. The allocation of public to state owned assets may not be accurate because the privatization process was underway during 2014, the reference year for Nigeria, in which case the capex estimates may be overestimated. T&D capex estimates followed the same methodology as that used in the general methodology.

- For opex, the numbers found in the 2015 MYTO model were taken.

# A1.6 Assumptions used in the overstaffing analysis

This annex describes the approach and assumptions used to estimate overstaffing in state-owned utilities by using benchmark employment in generation, transmission, and distribution separately.

## Definitions used for overstaffing analysis

- **Actual number of staff is the number** of staff reported by state-owned utilities supplying electricity, including temporary staff, sub-contractors, and consulting staff.

- **The efficient number of staff** is estimated using reference values for generation, transmission and distribution.

- **Overstaffing ratio** is calculated as the estimated number of excess staff as a proportion of total number of actual staff.

## Data availability

- Employee and customer data are available for 36 countries. Employee data are not available for Lesotho, Nigeria, and Sudan. Ghana can be analyzed only partially because employee data are not available for staff working in state-owned generation utilities.

- A breakdown of staff between generation and T&D was available for 12 countries. These are typically the countries where there is partial or full unbundling in the sector.

- For the 8 utilities providing services other than electricity, such as water and sewerage services, the number of customers taken was the number of electricity customers. If a breakdown of employees is not provided by services, the number of staff working in electricity was pro-rated in proportion to the breakdown of revenues between electricity and other services.

- Fifteen utility companies stated owned with data publicly available on employees and customers in Latin America were studied to obtain reference values for T&D staff. Most also had data available on km of T&D infrastructure greater than 1 kV.

## Limitations of overstaffing analysis

This benchmarking analysis is necessarily simplified to be able to cover dozens of countries for crosscountry comparison. Results for any individual country should not be used as a substitute for an indepth country-level analysis. Limitations are discussed below.

- Latin America may not be a fair comparison to use for optimal employment in T&D:

- Density of customers may be much higher in Latin America with much higher per capita income and urbanization and electrification rates compared to SSA.

- Outsourcing of operations and maintenance is common practice for electricity utilities but the degree of outsourcing varies significantly from utility to utility and there is no “optimal” level of outsourcing. Some privatized utilities in Latin America have very high levels of

77
# **I. Introduction**

The specific needs of young people are an increasingly debated subject in the development community. A large literature has emerged to inform the debate in Latin America, principally by mapping youth behaviors, positing policy and program interventions to address the behaviors, and, to a lesser extent, to identifying the (economic) factors driving young people’s decisions (Lloyd 2005, World Bank 2006). A primary shortcoming of the research is that it treats youth as a homogenous group and reports average behaviors, thereby not capturing the complexities of the youth population across its many dimensions.

Even if we do understand the heterogeneity in the youth population, appropriate policy requires an understanding of why young people make the decisions they do. The evidence for Latin American youth is limited. The role of economic incentives and budget constraints affecting decision-making by young people in developing countries has been examined (World Bank 2006 and the sources within), building on Gruber’s (2001) work in the United States. A second line of work considers the broader context in which young people form their preferences and make their decisions (World Bank 2003, 2007), building on the public health and psychology research in the United States. While this literature gives good insights as to _how_ to modify behaviors, it is silent on _whom_ prevention programs should target.

This paper aims to construct a more complete picture of the heterogeneous youth population in LAC by quantifying and describing the at-risk youth population and by identifying easily observable variables can be used to identify, ex ante, those youth who have a higher propensity of engaging in risky behaviors. We use non-parametric methodologies and special cross sectional youth surveys from Mexico and Chile that permit us to identify the factors that today’s risk-taking youth had in their childhoods, which are then used to identify who should be the target of prevention policies and programs.<sup>2</sup> The paper finds that over 20 percent of 18 to 24 year olds in Chile and 40 percent in Mexico have influences in their early and current lives that predispose them to negative behaviors, they have engaged in these behaviors, and they are suffering the consequences. Conversely, 40 percent of Chileans age 18 to 24 and 16 percent of Mexicans of the same age do not display any of these risks, behaviors, or consequences. While many of the factors that identify if a person will engage in risky behavior are unobservable, we find two observable variables that are highly correlated with these underlying factors and can be used

> 2 Causality can only be determined using panel data that includes information on behaviors and household factors, which do not exist in LAC, to the best of our knowledge.

2