rafmacalaba/datause-encoder-data huggingface dataset:
their ability to provide skilled labor.

The introduction of NYSC was met initially with some resistance and public protests (Sanda 1976, Marenin 1979) but over time, it became established as a core institution in Nigeria. It is now enshrined in the 1999 Constitution of Nigeria (Section 315) and its influence extends to all local governments of the country through the presence of corps members. Nevertheless, NYSC remains a controversial topic in Nigeria and there are regular demands for its termination in the popular press from critics decrying its large costs and limited impact. On the other hand, supporters claim it plays a significant role in promoting intergroup harmony. However, there has been limited empirical evidence on the impact of the program.<sup>11</sup> The service year is divided into four components: orientation, primary assignment, community development and passing out ceremony. It begins with a regimented three-week orientation camp in each state. Corps members are required to wear uniforms throughout the camp and are not permitted to leave. Orientation exercises, such as physical training and drills, sports competitions, lectures on different social issues, language classes, and social activities, are designed to internalize the ideals of the program, build camaraderie among corps members and introduce them to their host state.

At the end of the orientation, corps members receive posting letters to a “place of primary assignment” in a local government within the state. They work at this position for the remainder of the year, mostly as school teachers but also as staff in hospitals, government offices, private companies and non-profit organizations. Corps members receive a monthly stipend comparable to the federal minimum wage (equivalent to about $100 in 2015) from the government, and often, a supplemental stipend and/or housing from their employer.

> 11Existing studies (e.g Marenin 1979, 1989, Obadare 2005, Sanda 1976) are very informative but there are selection concerns in the choice of comparison groups.

9
22 members or assist its participants with loan application. In the case of joint voluntary liability, individuals are only responsible for the repayment of their own loan, but all group members are denied access to future loans if one or several group members fail to repay. In the case of mandatory joint liability, each group member is responsible for the repayment of all loans made to group members and access to new loans is denied to everybody as long as not all outstanding loans have been repaid. Mandatory joint liability normally prevails if the loan is made to the group as a whole. Experience does not clearly indicate whether lending to individuals or lending to a group as a whole yields better results. On the other hand, practice has shown that joint liability haa positive effects on loan repayments if certain conditions are met. In Bangladesh and Malawi, where loan recovery rates have been 98.6% and 97.4% respectively, loans have been made to and must be repaid by the irdividual (Hosaain, 19881 Schaefer-KRhnert, 1983). However, the group is jointly liable for default by any of its members and future access to credit is denied to the entire group in case of default. The BAAC's program in ThaLland has achLeved repayment rates of 82% (vs. an average rate of 66% for comparable loans wlth individual liability) by lending to individnals under mandatory joint liability. Its experience has also shown that lending to groups collectively results in higher default rates because noone accepts responsibilLty (Tohtong, 1988). A comparative study of different group lending schemes in Zimbabwe has found that in normal years current recovery rates for loans made to a group were up to 40% higher (92% vs 53%) than those of loans to Lndividuals and up to 20% higher than
# **<u>Table A.24. The district fixed-effect RDD-DD regressions of individual-level outcomes using region-specific time trend</u>**

|||||Dependen|t variables||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1531***|0.0510***|0.0601|0.5362**|0.2655*|0.2872**|-0.0087|0.7291|
||(0.0570)|(0.0168)|(0.0493)|(0.2695)|(0.1533)|(0.1435)|(0.0469)|(0.5125)|
|(Poverty rate – 50) * Post-program|0.0097|-0.0054***|-0.0060|-0.0456*|-0.0510**|-0.0483**|0.0068|-0.0195|
|period|(0.0059)|(0.0018)|(0.0047)|(0.0274)|(0.0228)|(0.0221)|(0.0050)|(0.0512)|
|Program * (Poverty rate – 50) * Post-|-0.0047|0.0043**|0.0045|0.0129|0.0501**|0.0467**|-0.0095*|0.0293|
|program period|(0.0064)|(0.0018)|(0.0052)|(0.0298)|(0.0229)|(0.0221)|(0.0052)|(0.0540)|
|Age|0.0243***|0.0040***|0.0104***|0.1130***|-0.0058|-0.0059|0.1570***|0.3932***|
||(0.0013)|(0.0005)|(0.0006)|(0.0065)|(0.0039)|(0.0038)|(0.0093)|(0.0473)|
|Age squared|-0.0003***<br>(0.0000)|-0.0000***<br>(0.0000)|-0.0001***<br>(0.0000)|-0.0015***<br>(0.0001)|0.0003***<br>(0.0001)|0.0002***<br>(0.0001)|-0.0081***<br>(0.0004)|-0.0199***<br>(0.0021)|
|Male|-0.0655***|-0.0076**|0.0867***|0.9970***|-0.1497***|-0.1383***|0.0371***|0.1042***|
||(0.0072)|(0.0030)|(0.0051)|(0.0567)|(0.0263)|(0.0253)|(0.0105)|(0.0322)|
|Ethnic minorities (yes=1, Kinh=0)|0.1854***|-0.0987***|-0.0333**|-0.2281|-0.2455**|-0.2388**|-0.1294***|0.8573***|
||(0.0260)|(0.0110)|(0.0163)|(0.1575)|(0.0994)|(0.0976)|(0.0259)|(0.1159)|
|Region-specific time trend|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2182***|0.0400***|-0.0979***|-0.6343***|0.6536***|0.5768***|0.2783***|-1.3268***|
||(0.0359)|(0.0104)|(0.0188)|(0.1919)|(0.0829)|(0.0804)|(0.0537)|(0.2875)|
|Observations|128,446|128,446|128,446|128,446|86,980|86,980|50,645|50,645|
|R-squared|0.193|0.066|0.178|0.156|0.097|0.092|0.196|0.337|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of older members in households.

Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

74
Table 5: Sample balancing after matching—Average school characteristics in 2007, by treatment status

||Panel A: F|ifth-grade s|chools|Panel B: N|inth-grade s|chools|
|---|---|---|---|---|---|---|
||Schools in|||Schools in|||
||_Mais_<br>_Educação_<br>(2008−2011)|Control<br>group|_P_value|_Mais_<br>_Educação_<br>(2008−2011)|Control<br>group|_P_value|
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆Portuguese grades|1.04|0.950|0.91|5.04|3.27|0.03**|
|∆ Mathematics grades|10.49|11.357|0.24|1.68|-0.19|0.01**|
|Portuguese grades|164|165|0.37|222|222|0.62|
|Mathematics grades|180|181|0.25|230|229|0.25|
|Dropout rates|5.89|6.72|0.08*|11.71|11.45|0.65|
|IDEB Index|3.51|3.50|0.91|2.87|2.80|0.14|
|∆IDEB|0.36|0.43|0.06*|0.34|0.27|0.05*|
|Municipal population|1,200,000|850,000|0.00***|1,400,000|1,400,000|0.92|
|Students' enrollments|924|850|0.06*|1,160|1,084|0.01**|
|IDHM|0.76|0.75|0.11|0.76|0.76|0.39|
|Urban|1.00|1.00|0.16|0.99|1.00|0.18|
|Basic sanitation|0.98|1.00|0.01***|0.99|1.00|0.03**|
|Library|0.75|0.73|0.33|0.88|0.88|0.71|
|Sports court|0.55|0.55|0.91|0.69|0.74|0.07*|
|Number of classrooms|13.02|12.50|0.21|14.33|14.38|0.87|
|Number of computers|0.92|0.94|0.19|0.96|0.98|0.08*|
|Science lab|0.14|0.16|0.38|0.27|0.33|0.03**|
|Computer lab|0.50|0.45|0.06*|0.57|0.54|0.30|
|Teachers room|0.90|0.88|0.28|0.97|0.98|0.08*|
|DVD|0.90|0.92|0.37|0.92|0.94|0.34|
|Overhead projector|0.74|0.69|0.04**|0.88|0.90|0.38|
|Number of employees|65.29|62.73|0.38|76.27|69.96|0.0***|
|School meals|0.92|0.93|0.52|0.92|0.88|0.03**|
|Number of classrooms used|13.57|12.79|0.10|14.46|14.49|0.94|
|Morning lessons|0.53|0.53|0.60|0.51|0.50|0.66|
|<br>Child care|0.04|0.04|0.56|0.03|0.03|0.86|
|Kindergarten|0.40|0.35|0.04**|0.21|0.21|0.94|
|State schools|0.27|0.23|0.09*|0.59|0.63|0.19|
|High school|0.09|0.08|0.40|0.41|0.41|0.85|
|Share classes in school with full-time day (7 or more hours)|0.02|0.03|0.50|0.01|0.01|0.88|
|<br>Total duration of school day (hours)|4.31|4.34|0.53|4.30|4.30|0.83|
|Number of observations|611|385||546.00|319.00||

_Sources:_ MEC/FNDE, INEP, _Prova Brasil,_ and School Census.

_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 control group selected by propensity score matching (columns (2) and (5)). Columns (3) and (6) report the _p_ value of the _t_ test performed to check mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. In control group, the means are weighted using propensity score matching weights. 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.This table compares treated to control schools in 2007, after propensity score matching.
10 economies, growth rates two years (or, eight quarters) before a trough, _t-8_ , are similar between credit-less and credit-with events. However, the growth gap gets wider and becomes quite noticeable at least a year (i.e., four quarters) prior to the trough _t_ . In the year the recovery occurs, credit-less episodes experience slow growth rates; however, this gap narrows after a year from the trough. By eight quarters after the trough, growth in credit-less recoveries is 1.5 percentage point lower than that in credit-with recoveries in developed countries. For emerging markets, the growth gap is even wider four and five quarters after the trough, but it also narrows during the rest of the second year following the trough. As a result, the growth differential in the group of emerging markets ends up at broadly the same level as in advanced economies.

Figure 4 shows the investment performance difference between credit-less and credit-with events following the format in Figure 3. As with growth patterns, there is lower investment growth in credit-less episodes in general, especially in emerging markets, and the differential in investment growth between credit-less and credit-with recoveries is more pronounced than in the case for output growth; for example, emerging markets incur a negative investment growth of around 27 percent at the trough date. Although investment growth rates eight quarters before the trough are similar to each other in advanced economies and, indeed, are higher in credit-less events than in credit-with episodes among emerging markets, this deterioration is more rapid during credit-less episodes. Still, once economies bottom out, investment recovers relatively fast and rapidly reaches the same level as is the case in credit-with events. For advanced economies, by eight quarters after the trough, the gap between two types of investment growth patterns is exhausted. Because of this upward trend in investment growth in credit-less events, especially for advanced economies, the GDP growth gap between credit-less and credit-with recoveries is likely to disappear within the following two years that follow the _t+8_ quarter. In fact, Figure 4 also shows that investment growth two years after the trough is above the level before the trough.

**Figure 4. Investment Performance, eight quarters before and after trough**

<!-- Start of picture text --> Advanced Economies Emerging Market Economies<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>t-8 t-6 t-4 t-2 t t+2 t+4 t+6 t+8 t-8 t-6 t-4 t-2 t t+2 t+4 t+6 t+8<br>Credit-less Credit-with<br>Real Investment Growth (%)<br><!-- End of picture text --> Note: A trough date is set _t_ . Median values of year-on-year real investment growth rates within each country group are shown, and they are smoothed by calculating three-quarter moving averages ( _t-1_ , _t_ , and _t+1_ ). Due to this smoothing adjustment, the value at the trough date is not necessarily the lowest. Source: Authors’ calculations based on data from the IMF, OECD, UNECE, Haver Analytics, Datastream and country sources.
The demographic structure of Uruguay has changed significantly in the past decade and Uruguay’s aging process is the most advanced in the LAC region. Uruguay’s population in 2010 is estimated at 3.4 million. A comparison between 2000 and 2010 shows that the demographic pyramid has narrowed in the infant population segment (under age 10) and widened significantly in the population segment corresponding to age 30 and up. Uruguay’s population is aging, with a high percentage in the middle population segment. Uruguay has the oldest population in LAC, followed by Argentina and Chile (Cotlear, 2011). Uruguay also has a low fertility rate, which with 2.1 children is just at the replacement rate.

**Figure 9: Demographic structure of Uruguay, 2000 and 2010**

<!-- Start of picture text --> 90-94 90-94<br>80-84 80-84<br>Men Women Men Women<br>70-74 2000 70-74 2010 2010<br>2000<br>60-64 60-64<br>50-54 50-54<br>40-44 40-44<br>30-34 30-34<br>20-24 20-24<br>10-14 10-14<br>0-4 0-4<br>-10 -5 0 5 10 -10 -5 0 5 10<br><!-- End of picture text --> Source: INE

# **4 Macroeconomic projections**

Two main scenarios are considered for this analysis. While the <u>benchmark scenario assumes</u> ‘business as usual’ without major disruptions to economic development, the crisis scenario simulates a situation similar to that during the Argentine crisis in 2001/02. Projections for the benchmark/nocrisis scenario are formulated based on forecasts from the Ministry of Economy and Finance, local consultancy companies as well as internal World Bank models. The crisis scenario is mainly based on developments during the crisis a decade ago, assuming external shocks hitting the economy leading to negative real growth and double digit inflation. GDP as well as employment shares are divided into the following four sectors: (i) primary sector, including agriculture, fishing and mining; (ii) manufacturing, including gas, water, electricity and construction; (iii) commerce, tourism and transport; and (iv) other services, including financial and real estate services, public administration, health and education. In addition, an alternative crisis scenario has been formulated that assumes conditions similar to the 2008/09 crisis, which is presented briefly in section 5. The scenarios are developed for two consecutive years, which will be identified as year 1 and year 2 in the remainder of the document.

8
balance and the current account of the balance of payments). Capital stock data (both public and private) were derived using the perpetual inventory method, using relatively small depreciation rates, as indicated earlier. In solving the model, we use the net output price as the numéraire, and therefore keep its value fixed in all the experiments that are reported below.

To calculate the poverty effects of policy shocks, we first linked the model to a household survey, using the methodology outlined earlier. The data that we use are from the 1999/2000 _Household Income, Consumption, and Expenditure Survey_ (HICES) conducted by the Ethiopian Central Statistical Authority. The survey covers 17,332 households, of which 8,660 are from rural areas and 8,672 from urban areas. Given an initial poverty line (at current prices), we calculated the headcount index for the survey year. For 2003 onward, based on the projections of the model, each observation in the sample is adjusted using the rate of growth of nominal consumption per capita, whereas the poverty line is adjusted using the growth rate of composite prices. Given these projections, a new poverty rate is calculated for each period. We used the same procedure (using actual data on consumer prices and consumption per capita) to update our estimates of the poverty rate for 2001 and 2002.

We also used the partial growth elasticity approach mentioned earlier to relate the “base” poverty rate and the rate of growth of real consumption per capita. Three different values for that elasticity are specified: -1.0 (which corresponds to the case where growth is distribution neutral), -0.5, and -1.8. The elasticity of -0.5 is close to the elasticity of the poverty headcount index with respect to the change in (mean) expenditure estimated by Christiansen, Demery, and Paternostro (2003, Table 4, p. 326) for Ethiopia. The elasticity of -1.8 (which is within the two-standard error interval of the point estimate provided by Ravallion (2001)) reflects the case where consumption growth is “pro-poor”, in the sense that it entails a more than proportional effect of increases in consumption on poverty—or, equivalently, the case where consumption growth is accompanied by lower income inequality.

32
**Table 4: São Tomé and Príncipe: Effects on Early Childhood Development (children 36-59 months)**

|||**(1)**<br>**Ad-hoc**<br>|||**(2)**<br>**DML approach**<br>||
|---|---|---|---|---|---|---|
|**Dependent variable: ECD Index**<br>**(standardized)**|**Coeff**|**95% CI**|**P-value**|**Coeff**|**95% CI**|**P-value**|
|**Specification A**|||||||
|Child HAZ score|0.118|[0.051,0.186]|0.001|0.108|[0.034,0.183]|0.004|
|Mother’syears of education|0.0429|[0.024,0.059]|0.000|0.034|[0.0160,0.053]|0.000|
|**Specification B**|||||||
|Whether child is stunted|-0.205|[-0.404,-0.006]|0.044|-0.145|[-0.349,0.060]|0.166|
|Mother’syears of education|0.042|[0.025,0.059]|0.000|0.036|[0.018,0.054]|0.000|
|**Dependent variable: Binary (0,1)**<br>**variable on whether child is on track**<br>**in ECD**|**OR**|**95% CI**|**P-value**|**OR**|**95% CI**|**P-value**|
|**Specification A**|||||||
|Child HAZ score|1.188|[1.027,1.374]|0.020|1.090|[0.920,1.292]|0.318|
|Mother’syears of education|1.069|[1.025,1.115]|0.002|1.058|[1.004,1.115]|0.036|
|**Specification B**|||||||
|Whether child is stunted|0.895|[0.561,1.427]|0.640|0.984|[0.563,1.723]|0.956|
|Mother’syears of education|1.070|[1.027,1.116]|0.001|1.071|[1.017,1.127]|0.009|
|Shaded cells denote variables with sign<br>Confidence intervals reported are base<br>The control variables used in the ad-ho<br>squared, household size, the dependen<br>set of binaryvariables for region of resi|ificant coeff<br>d on robust<br>c model inc<br>cy ratio, the<br>dence.|icient estimates (P<br>standard errors<br>lude: the child gen<br>household’s weal|-value < 0.05<br>der, child ag<br>th quintile, a|)<br>e in month<br>binary var|s and age in mont<br>iable for rural are|hs<br>as and a|

The DML estimates, on the other hand, reveal that only maternal education has a consistent and statistically significant effect irrespective of the dependent variable used to measure ECD (standardized EDC index, vs. dichotomous indicator for being on track in ECD) and the specification for child undernutrition (HAZ vs. stunting) (see column 2 Table 4). A child being stunted does not appear to have a statistically significant effect on the ECD index or on the odds ratio of being on track in development (see specification B in column 2 Table 4). Moreover, the absence of any relationship between stunting and being on track in development appears to hold irrespective of the method used (ad-hoc vs. DML) (compare columns 1 and 2 in specification B of Table 4).

As for the Republic of Congo, the relationship between HAZ and stunting and maternal education controlling for covariates using the alternative approaches (ad-hoc vs. DLM) was also carried out for São Tomé and Príncipe. Table 5 presents estimates of the relation between years of maternal education and different child anthropometric measures for the sample of all children less than 5 years of age (0-59 months) commonly used in the empirical literature as well as separately for the sample of children less than 36 months and the sample of children 36-59 months of age for which ECD indicators are also 18
# **Table 3. Multivariate FSE estimates (three variables or more).**

First variable is log GNI per capita.

The second variable is liner shipping connectivity index (maximum value in 2004 =100).

|# Variable(third and more)|count|information|
|---|---|---|
|3. Governance: Political Stability and Absence of Violence<br>2011|96|0.290|
|3. tons equivalentper unit ofgdp|89|0.328|
|3. Physical Capital Stockper Worker|88|0.322|
|3. Physical Capital Stock per Worker<br>4. Governance: Political Stability and Absence of Violence<br>2011<br>5. tons equivalentper unit ofgdp|83|0.382|
|3. Natural Capital, $ per worker|78|0.367|
|3. Natural Capital, $ per worker<br>4. Physical Capital Stock per Worker<br>5. tons equivalentper unit ofgdp|69|0.455|
|3. Natural Capital, $ per worker<br>4. Governance: Political Stability and Absence of Violence<br>2011<br>5. tons equivalentper unit ofgdp|72|0.411|

**Table 4. Correlation of Revealed Factor Intensity vs. Factor elasticity**

|GDPper capita(current US$)|0.536|
|---|---|
|GDP,PPP(current international$)|0.605|
|Liner shippingconnectivityindex|0.657|
|Time required to start a business(days)|0.101|
|Natural Capital, $ per worker|0.283|
|Physical Capital Stockper Worker|0.540|
|Governance: Political Stabilityand Absence of Violence 2011|0.597|
|Governance: Government Effectiveness 2011|0.645|
|Governance: Regulatory quality2011|0.530|
|Governance Rule of Law 2011|0.584|
|Governance: Control of Corruption 2011|0.604|
|Governance: Voice and Accountability2011|0.465|
|LPI score 2010|0.751|
|tons equivalentper unit ofgdp|0.814|

18