rafmacalaba/datause-encoder-data huggingface dataset:
schools at the district level and act as conduit of funds and grants from the center. The funds flow from the Ministry of Finance (MoF) to schools through the MoE, DoE, and DEOs. Each school has an elected School Management Committee (SMC) that manages day-to-day operation and makes budgetary allocations.<sup>3</sup> Teacher salaries, textbooks, scholarships, and construction expenditure constitute more than 90 percent of the budget on basic education (grades 1 – 8) (Thapa, 2011; The World Bank, 2014).

Education access has expanded and outcomes have improved significantly in the past decades in Nepal. Between 1999 and 2015, the primary net enrollment rate increased from 66 percent to 97 percent and the proportion of out-of-school children of primary school age dropped from 34 percent to 3 percent. Over the same period, the primary completion rate went from 64 percent to 105 percent (World Development Indicators; The World Bank, 2014)<sup>4</sup> . Gender parity has been attained up to the secondary level and disparities in access to schooling across geographical areas, income groups, and ethnicity and caste groups have also been eliminated or substantially reduced compared to a few decades ago.

Despite the progress in schooling access and outcomes, however, quality of education remains low. A national assessment of grade 10 students in 2011 showed poor performance of students in the core subjects, with particularly low scores in Nepali (33 percent) and Mathematics (37 percent) (Educational and Developmental Service Centre, 2011).

Liberal promotion policy was introduced late 1990s in response to the high degree of inefficiency in the system. While the primary net enrollment ratio in 1999 was 66, the gross enrollment ratio was 117 due to high repetition rates. It was estimated that the system had to deliver 11.4 years of schooling to complete five years of primary education (The World Bank, 1999). To improve efficiency at the primary level, the Government of Nepal introduced the twin policies of continuous assessment and liberal promotion starting with the Basic and Primary Education Project (BPEP) in 1999. Under this policy, continuous assessment of student learning achievement would be conducted to provide regular feedback to both students and teachers on student learning levels. At the same time, to reduce school dropout and repetition, students would be granted automatic promotion to the

> 3 Institutional or private schools are privately funded and privately managed.  Community schools, or public schools, are managed by the local community, but the government controls teacher recruitment and management in these schools and they also receive government funding and support.  Public schools are known as community schools after the Education Act 2001 handed over the management of publicly-funded schools to local communities.

> 4 Accessed: March 3, 2016.

4
# **5.2.Mortality**

Table 6 presents results from a Cox proportional hazard model of mortality following equation (6). Panel A reports results for men who did not serve in Vietnam, our main analysis sample. The results of specification 1 and specification 2 (which adds controls for marital status and family size) suggest a slight, statistically significant protective role of induction risk on the hazard of death. However, in specification 3, when we include educational attainment and income variables, this association becomes smaller and statistically insignificant.

The negative correlation between exposure to stress and later-life mortality is the opposite of what one would expect. In the next section, devoted to falsification tests, we will provide evidence that suggests that these mortality results, but not the results for weight and selfassessed health, are due to cohort effects.

# **5.3.Falsification Tests: Male Vietnam War Veterans and Women**

In Table 7 we present results for the same models that were estimated using samples of: male Vietnam veterans (results shown in Panel A) and women (Panel B). In the interests of being concise, only the coefficients on induction risk are shown in the table, but full results of the models are available upon request. The results from these models are substantively different from those for male non-Vietnam-veterans in Tables 3-5. In Panel A for male Vietnam veterans, the coefficient on induction risk for BMI and the marginal effect of induction risk for obesity are very small and not statistically significant. In addition, induction risk is consistently associated with _better_ self-assessed health among veterans, which is the opposite of the pattern observed for non-veterans. For example, in specification 2 for veterans, a one percentage point increase in induction risk is associated with a 0.005 unit (0.1%) increase in self-reported health, a 0.14 18
age refugees has been increasing in recent years. While financing data is only available for 2021, the total cost of providing inclusive education to all school-age refugees in LICs and MICs was estimated to be US$ 9.3 billion in 2022 (Silva, 2023). This represents a US$ 1.6. billion increase in cost on the previous year, driven almost entirely by growing refugee displacement.

<!-- Start of picture text --> Total financing for and costs of refugee Total financing for refugee education in<br>education in LICs and MICs, 2021 LICs and MICs, 2021<br>2022 cost increase<br>(1.6 bn)<br>Host Gov.<br>36%<br>0.4<br>7.7<br>1.7 ODA<br>55%<br>2.6<br>Philanthropy<br>9%<br>Total Financing Total cost (2021)<br>ODA Host Gov. Philanthropy Total cost<br>USD billions<br><!-- End of picture text -->

# _Source: REFD (2024)_

refugee education. Over-stretched education systems and/or significant fiscal constraints limit state capacity to finance refugee education in many host countries. As shown in Figure N, philanthropic financing for basic refugee education in LICs and MICs contributed approximately 8 percent of total financing for refugee education in 2021. However, as discussed in Key fact 5, the majority of philanthropic financing was delivered to nongovernment and civil-society organizations, and did not finance inclusive refugee education. While philanthropic organizations should be encouraged to finance inclusive education, it is clear that significantly more resources, beyond those delivered by the philanthropic sector, are required to meet the current financing gap. With ODA for refugee education representing 10 percent of aid to refugee situations in LICs and MICs, and less than 1 percent of all ODA in 2021, increasing ODA for refugee education in LICs and MICs is feasible. However, ODA for refugee education can arguably be improved to ensure a more efficient use of limited donor resources and improve refugee education outcomes in LICs and MICs.

considerations, the analysis presented in this paper clearly demonstrates that ODA for refugee education targets specific countries. An estimated 75 percent of all ODA for refugee education in 2021 went to Syrian refugees residing in just four countries, while several other host 22
attending elementary school. In addition, we show (using a different sample) that our distance variable is uncorrelated with test scores (Math, Bahasa, Science, and Social Studies) in elementary school. These are very important dimensions of the pre-secondary school experience that are measures of early ability and early home environments, and which we would expect to be correlated with distance to the nearest secondary school if this variable was endogenously determined.

Our instrumental variable estimates of the returns to schooling are higher than the returns to schooling for Indonesia found in Duflo (2000) with the qualification that the dataset, the instrumental variable, and the time period are not the same. Petterson (2010) finds similar rates of return using the same year, same data but a different sample and a different instrument variable.

This paper proceeds as follows. Section 2 discusses the data. Section 3 reviews the econometric framework. Section 4 presents our empirical results. Section 5 concludes.

# **2. Data**

We use data from the third wave of the Indonesia Family Life Survey (IFLS) fielded from June through November, 2000<sup>3</sup> . The IFLS is a household and community level panel survey that has been carried out in 1993, 1997 and 2000. The 2000 sample was drawn from 321 randomly selected villages, spread among 13 Indonesian provinces containing 83% of the country‟s population containing over 43,000 individuals. The sample we used consists of males aged 25-60 who are employed in the labor market and who have reported non-missing wage and schooling information. We consider only salaried workers, both in the government and in the private sector. We exclude females from the analysis because of low labor force participation, and we exclude self-employed workers because it is difficult to measure their earnings. The dependent variable in our analysis is the log of the hourly wage. Hourly wages are constructed from self-reported monthly wages and hours worked per week. The final sample contains 2608 working age males.

> 3 For a description of the survey see Strauss, J., K. Beegle, B. Sikoki, A. Dwiyanto, Y. Herawati and F. Witoelar. "The Third Wave of the Indonesia Family Life Survey (IFLS): Overview and Field Report", March 2004. WR-144/1-NIA/NICHD. In the appendix we list the main variables we use.

5
heading: first, cases where more than two years have passed since the panel has been established and its composition has yet to see the light of the day; second, cases where the WTO adjudicating body has exonerated the defendant from any legal responsibility. (We regard it as irrelevant whether exoneration occurs at the panel stage and no appeal has been submitted or at the appellate level.) These cases are listed in Annex Table 1.

2. Ongoing disputes. These are listed in Annex Table 2 and are sub-divided in: Pending Consultations, Pending The Panel’s Outcome, Pending the Appellate Body’s (AB) Outcome, Pending the Art.21.5 DSU Panel’s Outcome; Pending the Appeal Against A Report By An Art. 21.5 DSU Panel; and Other Cases.

3. Disputes With Uncertainty As To The Outcome. These are listed in Annex Table 3.

4. Disputes that were settled before the end of the process (consultations + panel/AB proceedings). These cases are listed in Annex Table 4, and are subdivided into two categories: cases of unilateral withdrawal of the contested measure, and cases where mutually agreed solutions (MAS) were concluded before the end of process.

5. Disputes where Panel or AB reports were implemented (Annex Table 5). These are sub-divided in cases of unilateral implementation, and cases where a MAS was concluded following a multilateral finding of inconsistency.

6. Cases that led to countermeasures (suspension of concessions).

In this paper, we focus on the last category of cases as our interest is in the effectiveness of countermeasures to induce compliance. This implies that we need to first discard all cases that fall into categories 1-5. In terms of collecting data on cases, our “workhorse” is WTO Doc. WT/DS/OV/14 of 30 June 2003. This document describes the official WTO 9
questions, choice of the sample and the demand features such as user‐charges and connection costs provides the ‘control’ that mimics an experiment. The ‘sterility’ of laboratories is replaced with field relevance by implementing the survey in the home environment of a typical user of the piped water service with questions that reflect service delivery programs that are both familiar to respondents and being considered by the service provider ‐ the National Water Supply and Drainage Board (NWSDB).

The questions used in WTP survey can be either open‐ended or closed‐ended. In an open‐ ended question, the respondent is asked to state the monetary amount that he or she is willing to pay for the service that is being valued. With a closed‐ended CV question (also referred to as a “dichotomous choice” or “referendum” question), the respondent is asked whether he or she is willing to pay a specified rupee amount for the good being valued. The respondent then answers yes or no. Open‐ended questions, despite the fact that they provide more information than closed‐ended questions, can be especially difficult for respondents to answer because individuals are typically not accustomed to performing such tasks in daily‐life decision making. Therefore, we will use a closed‐ended approach to elicit WTP for improved WSS services. Although easier for respondents to answer than open‐ended questions, this type of question may not reveal the respondent’s maximum WTP. The response to the closed‐ended question provides a bound on the respondent’s WTP, and the average WTP for respondents is estimated using a random utility modeling (RUM) framework and econometric models (Hanemann, 1984; Cameron and James, 1987; Cameron, 1988).

As stated above, we ask households to consider two scenarios: the improved service with certain quality and cost attributes, and the status quo. RUM model assumes the utility associated with a particular choice alternative is expressed as a function of individual characteristics and the attributes of the alternative, including costs. Under the assumptions of the RUM model, individual indirect utility is expressed as a function of WSS service attributes and personal characteristics: 7
survey for at least two reasons. By pure chance we might end up with an imbalance in the distribution of user‐charges such that, for example, poor households were consistently asked about high consumption charges in comparison to their rich counterparts. Moreover, user‐ charges (often the key design element in a WTP survey) may not be the only factor that impacts demand. Other ‘demand shifters’ such as connection costs, income, education, etc. are likely to matter.<sup>2</sup> Some of these ‘policy levers’ such as connection costs and service quality can be evaluated through a more general class of SP surveys – conjoint experiments (see for example, Sills et al., [2004], Yang et al. [2005], and Mansfield et al. [forthcoming]). That still leaves a large residual heterogeneity. Our paper shows how regression modeling can be an important first step in accounting for, incorporating, and using this additional information on the population of interest.

In our analysis, the regression modeling shows that a complex combination of factors drives expressed demand for service improvements. While poverty and costs are found to be key determinants of demand, we also find that location, self‐provision, and perceptions matter as well, and that sub‐sets of these factors matter differently for sub‐samples of the population. For example, these models suggest that poor households are more influenced by the proposed monthly consumption charge. We also find that perceptions, water quality, and safety nets affect poor households’ WTP differently than non‐poor. The regression models also show that all else equal, fewer households in the overall sample will connect to the network if this service improvement is provided by the private sector, in contrast to a reformed public sector.

The estimated coefficients from the regression modeling can also be used to further evaluate the uptake of piped water connections. In this paper, we use the model to evaluate the uptake of private piped water connections under various scenarios – demand in sub‐groups, institutional decision to rely on private sector provision, and various financial incentives targeted to the poor.

> 2 In principle, you could construct a survey experiment that considers all the important features of demand. In reality, you would run into the curse of curse of dimensionality such that the individual cells will contain too few or zero observations to allow for reliable inference. Regression modeling gets around this curse by making some assumptions about the functional form of the model and the error.

5
# **Smoking prevalence in Russia**

Despite the gradual decrease in recent years, smoking is still common in Russia (figure 1, panel a). According to RLMS-HSE data, 29 percent of the population ages 16–75 were smoking in 2016. Smoking is three times more popular among men (45 percent) than among women (15 percent). Smoking prevalence among men has been falling over the last decade and has declined by 10 percentage points since 2010, while, among women, prevalence has remained stable.

Smoking is common across the entire expenditure distribution (figure 1, panel b). While there is no specific pattern in smoking among men across deciles, smoking among women is clearly more popular among the more well-off population. Smoking in Russia involves mostly filter cigarettes; the share among all cigarettes smoked is between 90 percent among the lower deciles to 100 percent among the richer deciles. Men smoke an average of five or six packs a week, while women smoke an average of four packs a week; these numbers are rather stable across the distribution.

<!-- Start of picture text --> Figure 1 – Smoking Prevalence, Russian Federation<br>(ages 16–75)<br>a. Over time b. By decile<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 1 2 3 4 5 6 7 8 9 10<br>both men women both men women<br>c. By age-group d. By educational attainment<br>60 60<br>50 50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10 0<br>0 below general above higher<br>16-24 25-34 35-44 45-54 55-64 65+ secondary secondary general<br>secondary<br>both men women both men women<br><!-- End of picture text --> _Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2010–16. _Note_ : Deciles have been established using per capita household expenditures.

# **Tobacco price elasticity by decile**

Although several models have been tested to estimate elasticities by decile, the pooled cross-sectional model with year fixed effects and demographic controls at the household level is preferred (table 1; see 5
DMO such as the required minimum level of concessionality. The DMS should also alert the debt manager to borrowing scenarios that would breach these rules.

# **Resource mobilization**

In most countries, the central bank acting as the agent of the Ministry of Finance undertakes the domestic bill and bond issuances. Some DMOs, however, prefer to run the auctions themselves, using their DMS as the bidding platform and recording the results directly.

# **Integration with other systems**

Although not essential for core debt management, a DMS’s ability to interact with other IT systems to exchange data is essential to streamlining debt management functions. Systems integration enables access to information essential to forecasting cash flows, such as exchange rates and interest rates from external data providers. It can also enable a DMS to supply timely and accurate information to other Public Financial Management (PFM) systems for budget preparation or debt related payments. Integration also facilitates the efficient and accurate updating of the debt database with information on realized transactions, such as disbursement proceeds provided by disbursement management systems and payments made through central bank systems or treasury systems.

# **Box 2: Financial Management Information Systems (FMIS)**

A Financial Management Information System (FMIS) supports the automation and integration of public financial management (PFM) processes. These focus on (i) budget planning, usually consisting of the formulation of the government’s budget; (ii) budget execution, covering functions such as budget authorizations, commitment controls, payments through treasury accounts, cash flow forecasting and management, and revenue collection; and (iii) budget monitoring, consisting of accounting and reporting.

Debt management operations constitute an important part of PFM processes, and a highquality DMS supports the automation and integration of these activities. An effective DMS can provide vital input for budget formulation, execution and monitoring, thus contributing to the governance and efficiency of PFM operations.

Ideally, an FMIS integrates essential and satellite PFM functions using a single database and user interface. However, in practice, this integration is difficult to achieve because it requires powerful technology, strong coordination among all actors implicated in the PFM domain, standardized information, and well-aligned workflows. A more practical solution is to establish connections between the systems.

Information exchange through connections between the DMS and the Financial Management Information Systems (FMIS) (see Box 2) strengthens the budgeting and accounting processes and at the same time, reduces operational risk.

Of the FMIS modules, the most essential and typically well-established two-way connection is between the treasury system and the DMS, especially in cases where the DMO is separate from treasury operations or there is no STP (see Page 15). Even though it is best to have an integrated 12
Table 14: Effects on main primary caregivers (duration in hours where)

||(1)<br>mother cares<br>for the child|(2)<br>father cares<br>for the child|(3)<br>sister cares<br>for the child|(4)<br>brother cares<br>for the child|(5)<br>child in<br>childcare center|(6)<br>child with<br>no care|
|---|---|---|---|---|---|---|
|**Full sample**|||||||
|ITT estimate|-0.258|-0.072|-0.050*|-0.022|0.352***|0.710|
||(0.886)|(0.186)|(0.029)|(0.017)|(0.121)|(0.982)|
|Observations|2968|2968|2968|2968|2968|2968|
|_R_<sup>2</sup>|0.107|0.248|0.546|0.524|0.326|0.161|
|Control mean|15.982|1.570|0.242|0.158|0.407|1.615|
|**Have child aged 0-2**|||||||
|ITT estimate|-0.204|-0.185|-0.034|0.006|0.398***|0.884|
||(0.887)|(0.198)|(0.033)|(0.020)|(0.079)|(1.081)|
|Observations|1206|1206|1206|1206|1206|1206|
|_R_<sup>2</sup>|0.101|0.232|0.488|0.483|0.317|0.184|
|Control mean|17.032|1.602|0.256|0.148|0.184|1.254|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

41
**Table 1. Educational and labor characteristics of occupied youths between 18-35 years of age, ENIGH 2016 and ENCEL 2017**

|Variables of interest|ENIGH 2016<br>Ruraland urban|ENIGH 2016<br>Rural|ENCEL 2017<br>(n=3,310)|
|---|---|---|---|
|Average age|26.84|26.45|25.98|
|Females (%)|40.79|37.49|38.55|
|Single (%)|42.07|36.83|38.88|
|Average years of schooling|10.85|9.00|9.27|
|Studies and works (%)|9.44|5.29|3.66|
|Only works (%)|90.56|94.71|96.34|
|Labor position (%)||||
|Employers|3.86|6.22|0.85|
|Wage-earning|83.14|69.16|79.91|
|Self-employed|8.23|13.28|15.85|
|Without pay|4.77|11.33|3.39|
|Average labor income|5159.71|4703.50|3405.68|
|Average ISEI|34.30|23.92|23.70|
|With health services (%)*|40.92|19.40|16.49|

* Includes medical services of IMSS, ISSSTE, Local ISSSTE, PEMEX, Ministry of Defense, Ministry of Navy, universities and private. Source: Own elaboration from ENIGH 2016 and ENCEL 2017.

When comparing different ethnic backgrounds, the educational and labor characteristics of both subgroups were similar, with the exception being that, although possessing occupations of similar status, indigenous individuals seem to receive lower labor income and a lesser proportion have access to health services, which also indicates an inequality in remunerations. It is possible that, in addition to discrimination due to their ethnic background, the disadvantages of indigenous individuals are associated with their context. Indigenous individuals not only come disproportionately from localities of very high marginalization (89.5% in comparison to 52.8% of non-indigenous), but also their current localities of residence are, in greater proportion, of very high marginalization (23.9% in comparison to 3.1% of non-indigenous).<sup>31</sup>

> 31 The marginalization of the localities of origin data correspond to those from CONAPO in 1997 and the localities of current residence to those from 2010.

18