rafmacalaba/datause-encoder-data huggingface dataset:
- Dahlbom, L., Jakobsson, A., Jakobsson, N., & Kotsadam, A. 2011. Gender and overconfidence: are girls really overconfident? _Applied Economics Letter_ , **18(4)** , 325–327.

- De Paola, M., Scoppa, V., & Nistico, R. 2012. Monetary incentives and student achievement in a depressed labor market: results from a randomized experiment. _Journal Hum. Cap._ , **6(1)** , 56–85.

- Dobriyoni, C.R., Oreopoulos, P., & Petronijevic, U. 2017. Goal Setting, Academic Reminders, and College Success: A large-scale Field Experiment. _NBER Working Paper No. 23738_ .

- Elliot, A. J. 1999. Approach and avoidance motivation and achievement goals. _Educational Psychologist_ , **34** , 169–189.

- Fiala, N., Garcia-Hernandez, A., Narula, K., & Prakash, N. 2019. Wheels of Change: Impact of Bicycles on Female Education and Empowerement in Zambia. _Working Paper_ .

- Fryer, R. 2011. Financial Incentives and Student Achievement: Evidence from Randomized Trials. _Quarterly Journal of Economics_ , **126(4)** , 1755–1798.

- Gneezy, U., Meier, S., & Rey-Biel, P. 2011. When and Why Incentives (Don’t) Work to Modify Behavior. _Journal of Economic Perspectives_ , **25** , 191–210.

- Gutman, L.M., & Akerman, R. 2008. _Determinants of Aspirations_ .

- Hanushek, E.A. 2009. The Economic Value of Education and Cognitive Skills. _Pages 39–56 of:_ Sykes, G., Schneider, B., & Plank, D.N. (eds), _Handbook of Education Policy Research_ . New York: NY: Routledge.

- Heath, C., Larrick, R., & Wu, G. 1999. Goals as Reference Points. _Cognitive Psychology_ , **38(1)** , 79–109.

- Heckman, J.J., Stixrud, J., & Urzua, S. 2006. The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior. _Journal of Labor Economics_ , **24(3)** , 411–482.

- Henry, G.T., Rubenstein, Ross, & Bugler, Daniel T. 2004. Is HOPE enough? Impacts of receiving and losing merit-based financial aid. _Education Policy_ , **18(5)** , 686–709.

- Hershleifier, D., Low, A., & Teoh, S.H. 2012. Are Overconfident CEOs Better Innovators? _The Journal of Finance_ , **67(4)** , 1457–1498.

- Heyman, J.E., & Ariely, D. 2004. Effort for Payment. _Psychological Science_ , **15(11)** , 787–793.

- Hwang, S., Feltz, D.L., Kietzmann, L.A., & Diemer, M.A. 2016. Sport Involvement and educational outcomes of high school students: A longitudinal study. _Youth and Society_ , **48(6)** , 763–785.

- Kahneman, D., & Tversky, A. 1979. Prospect Theory: An Analysis of Decision under Risk. _Econometrica_ , **47(2)** , 263–292.

- Kosfeld, M., & Neckermann, S. 2010. Getting More Work for Nothing? Symbolic Awards and Worker Performance. _IZA DP No. 5040_ .

- Kruger, J., & Dunning, D. 1999. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. _Journal of Personality and Social Psychology_ , **77(6)** , 1121–1134.

15
_Role of NGOs in Healtd Delivery Afrta Health Polljy Study_ .7.e altruism, rather than purely flnpncial Incentives, tends to motivate emFr yees in NGOs _Cyr_ missions. This characteristic may instil trust In the clients of NGO services which may be lacking among clients of private health services where profit motives may pre-empt quality concerns.

# _Community Based_

17. Consistent with thelr ideology, NGOs are typically community based, with their personnel residing and working in proximity to the populadon they serve. Their &afs are tnerefore much more likely than remote bureaucrats In capital cities to understand the soctal processes within their constituent communities, and to empathize with community members' perceived needs Such proximity also avoids value or lifestyle conflict of government bureaucrats who are socialized in a milieu foreign to that of the beneficiaries of the programs they design. Staff of NGOs therefore tend, on the whole, to be better equipped to adapt their programs to local circumstances. Moreover, because they know the members of the community, they are often able to identify their poorer beneficiaries and apply a sliding scale In fee collection. This flexibility may indeed be the key to the successful cost recovery systems prevailing in mission services.

# _small Scale_

18. NGOs' localautonomy-ortheirability makeIndependentdecddlons-enables them to respond to expressed community needs, and Is critical to their success. The limited resource base of NGOs means that fewwield the cumbersome bureaucracieswhich slow government responses. Autonomy of operations enables NGOs to be more flexible and innovative in their activities. In addidon their modest scale allows them to take risks which larger institutions are now allowec to take. It is therefore not surprising that NGOs pioneereck some of the new technologies, such as oral rehydration therapy, which have become .e hallmarks of PHC and have been adopted on a wide scale (Cumper 1986). Many fear, however, that if the resource base of NGOs is expanded too quickly, without the necessarily lengthy process of institution building, the price to be paid may be a loss of such valuable flexibility.

19. NGOs' small size and flexibility may also allow them to deliver services where government cannot, or for political reasons will not, and to respond quickly to emergency demands. Increasing the size of such NGOs would be likely to increase the threat they pose to government. Moreover, when NGOs function as part of an 3nternational network of NGOs, as most do In Africa, tension between the local branch and ' #.'-,loped-country headquarters may arise over agendas. As funds are Increasingly cdaitneled to NGOs from bilateral and multilateral organizations, constraints on their room for maneuver are likely to tighten. Indeecd Hellinger has observed a downward trend in local NGO autonomy in the 1980s (Hellinger 1988).
than work. Marriage is the dominant reason for moving among women; study is the dominant reason for moving among children and youths. In contrast, of the adult males who migrated during last 5 years, 69% moved for work reasons.

Because our focus is on work migration, we restrict our attention to adult males. Among those, 16,850 are recorded as having moved in the fi ve years preceding the census speci fi cally for work reasons. These individuals are the focus of our analysis. We note that, by construction, this approach excludes those who have migrated outside Nepal. Our focus is thus on internal migrants. We do not have data on India but since there is no big Indian city within 200 km of Nepalese border, commuting to India for work while residing in a Nepalese district is rare, making it unlikely that economic opportunities in neighboring India a ff ected the choice of migration destination within Nepal.

Figures 1 and 2 show the geographical distribution of work migrants in terms of district of residence and origin. Districts with a high concentration of work migrants relative to nonmigrant adult males appear in red, those with a low concentration appear in blue. We see that a small number of destination districts have a high proportion of work migrants. In contrast, districts of origin are distributed widely across the country. This re fl ects the fact that much work migration is from remote rural areas to towns and cities.

The main characteristics of work migrants are reported in Table 1, together with those of nonmigrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ethnicity’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35% of 13
# **Table 1: Effect of the Islamic Inheritance Rule on the Ideal Proportion of Sons and the Ideal Number of Children**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|||Depend<br>|ent Variable:<br>||
|Panel A: Ideal Proportion of Sons<br>|Women’s<br>|Stated Ideal<br>|Men’sS<br>|tated Ideal<br>|
|Muslim* At least one brother at|0.12**|0.17**|0.073|0.12*|
|childbearing|(0.042)|(0.058)|(0.040)|(0.055)|
|Muslim * At least one brother at|-0.13*|-0.060|0.043|-0.72***|
|childbearing * Childbearing after 1994|(0.052)|(0.21)|(0.076)|(0.21)|
|Mean, ideal proportion of sons|0.41|0.40|0.38|0.36|
|R-squared|0.07|0.10|0.08|0.14|
|Observations|6419|1614|6530|1671|
|Panel B: Ideal Number of Children|||||
|Muslim* At least one brother at|-0.022|-0.012|0.36|0.45|
|childbearing|(0.27)|(0.42)|(0.30)|(0.46)|
|Muslim * At least one brother at|0.042|-0.51|-0.28|-6.89***|
|childbearing * Childbearing after 1994|(0.31)|(1.16)|(0.34)|(1.08)|
|Mean, ideal number of children|2.84|3.06|3.02|3.24|
|R-squared|0.18|0.24|0.19|0.22|
|Observations|6419|1614|6530|1671|
|Sample|All couples|Wife age<br>40 to 49|All couples|Wife age<br>40 to 49|

* p<0.05,  ** p<0.01,  *** p<0.001.

Note: OLS estimates of interactions between indicators of Muslim religion, having at least one surviving brother at the time of first child birth, and starting childbearing in the post-compilation period. Parentheses contain standard errors clustered at the PSU-level. Regressions use survey weights. Baseline sample excludes mixed-religion and polygamous couples. Columns (1) and (3) report estimated coefficients for the full baseline sample; columns (2) and (4) report estimated coefficients for a subsample of couples in which women are 40 to 49 years old. All regressions include fixed effects for the men’s total number of siblings, women’s age, the year of first child birth, and area and region of residence. Other controls included are men and women’s educational attainment and indicator of household wealth. Data summary is provided in Appendix 1.

32
The revenues of fuel exporters respond immediately to changes in the real price of oil, as one might expect, and expenditures respond with a lag of approximately one year. There is no evidence of asymmetry between rises and falls in oil prices, or that the effect varies with political instability.

# **6. Country Studies**

In this section we briefly consider some individual resource-rich countries whose recent fiscal performance might be of interest. After a foreign investment boom in the mining sector, Mongolia relies heavily (more than 50 percent) on the export of ores and metals, but its fiscal position has recently deteriorated. Ghana has been a major exporter of oil only since 2010, but has seen a rapidly expanding fiscal deficit in that time. We also discuss three countries with a relatively sound fiscal position: Paraguay, which like Ghana exported oil in large quantities since 2010 only; Bolivia, which for many years relied on exports of gas (about 50 percent) and ores and metals (30 percent); and Tanzania, where the share of ores and metals has increased to about 30 percent of total exports in recent years.

In Mongolia total revenues have fluctuated between 30 percent and 40 percent of GDP in recent years. They reached a peak of 40.3 percent in 2011, but fell to 35.5 percent of GDP in 2012 and 33.7 percent in 2013. Meanwhile total expenditure has grown rapidly from 26.2 percent of GDP in 2006 to 45.1 percent in 2011, 46.3 percent in 2012, and 43.8 percent in 2013. Mongolia moved from a budget surplus in four out of the six years between 2005 and 2010, to a deficit of over 10 percent of GDP in 2012 and 2013. The 2014 IMF Country Report characterizes both fiscal and monetary policy in Mongolia as extremely expansionary (IMF 2014c); however, a breakdown of expenditure is not available. The model does not explain these developments: the residual for 2012 is -8.2 percent of GDP, after being -2.2 percent in 2011 (the model is not estimated for 2013).

Ghana has had a fiscal deficit of over 4.0 percent of GDP every year since 2006. From 2010 to 2013 revenue was in the range 16 to 20 percent of GDP, and expenditure between 25 percent and 31 percent of GDP. As a percentage of GDP, the fiscal deficit was 9.4 percent in 2010, 6.5 percent in 2011, 11.8 percent in 2012, and 10.0 percent in 2013. General government capital expenditure actually fell from 7.6 percent of GDP in 2010 to 4.6 percent in 2013, but in the same period consumption expenditure increased from 18.5 percent of GDP to 22.2 percent. The 2014 IMF Country Report attributes this to the public sector wage bill and energy subsidies (IMF 2014b). Ghana’s negative residual in every year since 2005 shows a consistently worsening fiscal position than the model predicts. The negative residuals have gotten larger in every year except 2009—they reached -6.6 percent of GDP in 2010, -8.1 percent in 2011, and -13.5 percent in 2012. As was the 23
# **Figure 3: Post-Reform Cost Recovery Levels A1 and A2 in 16 Case Studies (23 utilities)**

<!-- Start of picture text --> a) Average Financial Operating Cost Recovery b) Average Financial Operating and Limited<br>Level in 2010-17 (Country-level; A1; %) Capital Cost Recovery Level in 2010-17<br>(Country-level; A2; %)<br>Colombia Uganda<br>Egypt, Arab Rep. Pakistan<br>Peru Colombia<br>Pakistan 8 out of 16<br>Kenya ≥ 100 %<br>Uganda 11 out of 16<br>Ukraine<br>Tajikistan ≥ 100 %<br>Peru<br>Kenya<br>Tajikistan<br>Ukraine<br>Philippines<br>Philippines<br>Vietnam<br>Vietnam<br>Senegal India - Odisha 13 out of 16<br>≥ 80%<br>India - Odisha 14 out of 16 Egypt, Arab Rep.<br>Tanzania ≥ 80% Senegal<br>India - Andhra Pradesh Tanzania<br>India - Rajasthan India - Andhra Pradesh<br>Dominican Republic India - Rajasthan<br>0% 50% 100% 150% 200% 0% 50% 100% 150%<br><!-- End of picture text -->

## **c) Average Financial Operating Cost Recovery Level in 2010-17 (Utility-level; A1; %)**

**d) Average Financial Operating and Limited Capital Cost Recovery Level in 2010-17 (Utility-level; A2; %)**

<!-- Start of picture text --> Colombia Uganda<br>Egypt, Arab Rep. Colombia<br>Peru (Luz del Sur) Kenya<br>Peru (Hidrandina) Peru (Luz del Sur) 8 out of 22<br>Uganda Ukraine ≥ 100 %<br>Tajikistan 14 out of 23 Tajikistan<br>Kenya ≥ 100 % Philippines (Meralco)<br>Ukraine<br>Philippines (Beneco)<br>Philippines (Meralco) Vietnam<br>India - Andhra Pradesh (APSPDCL)<br>India - Odisha (WESCO)<br>Philippines (Beneco)<br>Peru (Hidrandina) 17 out of 22<br>Vietnam<br>India - Odisha (CESCO) ≥ 80%<br>Senegal<br>Pakistan (LESCO)<br>India - Andhra Pradesh (APEPDCL)<br>Egypt, Arab Rep.<br>India - Odisha (WESCO)<br>Senegal<br>India - Odisha (CESCO)<br>Pakistan (LESCO) 21 out of 23 India - Andhra Pradesh (APEPDCL)<br>Tanzania ≥ 80% Tanzania<br>India - Rajasthan (JVVN) Pakistan (KE)<br>India - Rajasthan (AVVN) India - Rajasthan (JVVN)<br>India - Rajasthan (JDVVN) India - Andhra Pradesh (APSPDCL)<br>Pakistan (KE) India - Rajasthan (JDVVN)<br>Dominican Republic India - Rajasthan (AVVN)<br>0% 100% 200% 0% 100% 200%<br><!-- End of picture text -->

# _Source:_ World Bank staff.

26. It is notable that cost recovery appears unrelated to the absolute level of costs. The cost of electricity service differs by an order of magnitude between the case studies, but under-recovery of costs is an issue at both ends of the cost spectrum (see Figure 4). When costs are high, either due to the legacy of expensive generation sources and high-cost take or pay contracts (Senegal, Philippines, Uganda) or high system losses (Dominican Republic, Kenya, Odisha, Rajasthan, Tanzania), it is difficult to raise tariffs to 13
**Table 9: Simulated anchored poverty rates (percent)**

||2008|2020<br>[assuming<br>national<br>goals are<br>met]|2020<br>[assuming<br>2000-11<br>growth<br>rate]|2020<br>[assuming<br>2000-08<br>growth<br>rate]|2020<br>(assuming<br>CEDEFOP's<br>forecast)|2020<br>[assuming<br>2008-11<br>growth rate]|
|---|---|---|---|---|---|---|
|Bulgaria|20.6|9.0|10.1|5.0|8.2|10.6|
|Czech Rep|9.3|7.1|9.4|9.0|5.9|8.9|
|Estonia|19.5|11.3|11.7|9.1|11.3|11.6|
|Hungary|12.4|9.7|13.6|13.3|12.4|13.2|
|Latvia|25.8|12.5|12.4|9.7|12.3|12.0|
|Lithuania|20.2|14.8|14.3|12.0|14.7|14.2|
|Poland|16.9|3.5|3.6|3.1|4.0|5.1|
|Romania|23.4|13.6|15.3|15.1|14.4|17.9|
|Slovakia|11.6|7.5|7.2|5.2|7.6|6.8|
|Slovenia|11.6|7.6|11.6|11.3|9.1|11.3|

_Source:_ World Bank staff calculations using EU-SILC data. _Note:_ We assume no deterioration in employment and education indicators by 2020 relative to their rates in 2008.

# **_Activating specific groups_**

Labor market activation measures are often targeted at specific groups. In this section, we simulate the impact on poverty and social exclusion that might result from activating lone (single) mothers, inactive family members, and people with disabilities before activating other groups into the labor force in order to achieve the national 2020 employment target. Regardless of the scenario, prioritizing any of the three groups in labor market activation measures reduces the anchored poverty rate, often significantly. The impact on at-risk-of-poverty rates, however, is ambiguous.

Prioritizing single mothers, inactive family members, and people with disabilities in labor force activation measures before other groups are activated, but yet achieving the 2020 national employment rate target, has a particularly large and favorable impact on anchored poverty reduction in Bulgaria, Latvia, Poland, and Romania (Table 10). The impact on at-risk-of-poverty rates is significant and favorable for Latvia, Lithuania, Poland, and Romania. However, in Bulgaria, the Czech Republic, and Estonia the at-risk-of-poverty rates _increase_ if single mothers, inactive family members, and people with disabilities are activated before activating other groups in order to achieve the national 2020 employment target.
**<u>Table 4: Determinants of participation in land purchase and land sale (ordered probit)</u>**

||**Specification**|
|---|---|
||**(1)**<br>**(2)**|
|Agricultural ability (technical efficiency)|0.164**<br>0.163**|
||(2.30)<br>(2.27)|
|Household Size in 1982|0.008<br> <br>0.008<br>|
||(1.60)<br>(1.60)|
|No. of unmarried sons below 25 in 1981|0.067***<br> <br>0.067***<br>|
||(3.73)<br>(3.77)|
|Dynasty land endowment|-0.004***<br>-0.004***|
||(3.71)<br>(3.71)|
|Landless dynasty dummy|0.122***<br>0.122***|
||(2.84)<br>(2.82)|
|Total asset value (log)|0.030<br>0.032|
||(1.56)<br>(1.62)|
|Years of independence in 1999|0.007**<br>0.007**|
||(2.42)<br>(2.36)|
|**Lower bound equation**||
|**(sale to autarky)**||
|<br>No.of climatic shocks|0.106***<br>0.234***|
||(3.58)<br>(4.46)|
|Employment guarantee scheme (EGS) 1982|-0.147**<br>0.001|
||(2.09)<br>(0.01)|
|EGS in 1982×No. of climatic shocks|-0.062*|
||(1.76)|
|Bank access 1982|<br>0.169***<br>0.399***|
||(2.68)<br>(3.56)|
|Bank access in 1982×No. of climatic shocks|-0.084**<br>|
||(2.37)|
|Village income growth rate 1982-1999|1.127<br>1.219*|
||(1.62)<br>(1.73)|
|Land Gini in village|<br> <br>0.916***<br>0.917***|
||(4.70)<br>(4.67)|
|ST/SC dummy|-0.418***<br>-0.405***|
||(5.40)<br>(5.21)|
|**Upper bound equation**<br>||
|**(autarky to purchase)**||
|No.of climatic shocks|-0.129***<br>-0.132***|
||(4.49)<br>(2.70)|
|Employment guarantee scheme (EGS) 1982|0.136**<br>0.199**|
||(2.31)<br>(2.03)|
|EGS in 1982×No. of climatic shocks|-0.028<br>|
||(0.84)|
|Bank access 1982|<br>-0.130***<br>-0.235***|
||(2.58)<br>(2.61)|
|Bank access in 1982×No. of climatic shocks|0.040|
||(1.33)|
|Village income growth rate 1982-1999|<br>-1.867***<br>-1.885***|
||(3.10)<br>(3.10)|
|Land Gini in village|<br> <br>0.147<br>0.149|
||(1.01)<br>(1.02)|
|ST/SC dummy|<br> <br>0.200***<br>0.197***|
||(3.50)<br>(3.44)|
|Observations|4583<br>4583|

Robust z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies included throughout but not reported.

22
intercept and the absolute slope of marketing margin following the reform are much larger in this case compared with those in columns 1 and 2 in Table 1. The estimates from the matched pre-reform sample in the last two columns also support the conclusions that the implementation of the reform increased both the intercept of the margin equation and the absolute slope with respect to world market crude oil price. However, the estimated announcement effects are no longer statistically significant at the 10 percent level.

# **(7.2) Results from the DID Analysis**

In this section, we present the empirical evidence from the difference-in-difference design. Again, the focus of our econometric analysis is on the world-wholesale trading margin. We, however, also report estimates of the effects of the reform on world-retail trading margin in a subsection later.

# **(7.2.1) Evidence on the Validity of the DID Design**

We provide evidence on the validity of the DID design with two tests of the parallel trend assumption between palm and wheat margins. First, we report graphical evidence on the behavior of palm and wheat margins in the pre-reform sample. Figure 3 plots the marketing margins (world to wholesale) for palm and wheat for the pre-intervention sample; the marketing margin for wheat closely tracks the changes in the marketing margin for palm (the phase of the market, and the peaks and the troughs). This can be interpreted as strong evidence in favor of the parallel trend assumption. We present additional evidence on the evolution of trading margins for palm oil and wheat over the pre-reform sample period in the online appendix (please see figures A.1- A.4b in the appendix).

As an additional (and more formal) test of the parallel trend assumption we use a placebo policy implementation date before the enactment of the law in March 23 of 2011). If the DID identifying assumption is violated, we should find significant DID effect for the fictitious policy reform date. For this exercise, we take the matched pre-intervention sample, and divide it into two halves. We then define a policy intervention dummy which takes a value of unity during the first half of the sample and zero otherwise. We then estimate the effect of this placebo policy sample seems more appropriate for a before-after design instead of the full sample covering a longer time period.

27