rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A6: Reasons of well‐being worsening**

|||**Job**|**loss**|||**Salary re**|**duction**|||**Infla**|**tion**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2014**|**2015**|**2016**|**2017**|**2014**|**2015**|**2016**|**2017**|**2014**|**2015**|**2016**|**2017**|
|Average|2.74|3.55|4.71|3.93|3.19|7.16|7.83|4.49|10.13|15.87|17.68|20.83|
||||Poor|and vuln|erable p|opulation|||||||
|Vulnerable|2.76|4.04|5.47|3.75|5.03|8.16|9.99|5.37|8.86|13.17|13.92|18.47|
|Official poverty|6.67|10.35|6.13|4.78|3.07|6.31|10.37|5.44|11.73|10.69|11.17|15.04|
|Moderatepoverty|4.35|5.16|6.12|4.57|3.49|7.39|9.15|4.67|10.60|14.03|14.95|19.35|
|||||Place o|f residen|ce|||||||
|Minsk|2.04|4.10|6.25|6.28|1.91|8.08|7.46|5.41|11.88|17.96|19.92|22.77|
|Large cities|3.16|4.09|5.01|4.08|3.79|9.00|9.36|4.91|10.35|17.84|18.29|22.13|
|Small cities|3.35|2.38|4.30|3.11|3.74|4.81|7.39|4.91|10.57|18.82|24.04|24.84|
|Rural area|2.20|3.11|3.40|2.51|2.90|5.34|6.15|2.92|8.18|9.50|10.86|14.89|
||||||Age||||||||
|Child|2.93|3.66|5.18|4.30|4.53|8.87|9.74|5.92|8.94|16.26|16.75|19.50|
|Working age|3.11|4.39|5.53|4.37|3.59|9.19|9.85|5.54|9.64|15.39|16.50|19.50|
|Retiree|1.83|1.79|2.80|2.93|1.39|1.94|2.60|1.74|11.98|16.56|20.60|23.99|
|||Empl|oyment|status fo|r workin|g age po|pulation||||||
|Employed|2.45|3.65|4.70|3.70|3.85|8.69|9.46|5.65|9.39|15.47|17.14|19.27|
|Partly employed|4.96|7.60|13.49|10.18|3.00|9.33|13.78|5.16|10.65|14.52|7.60|18.63|
|Nonemployed|4.61|5.70|7.08|6.38|2.22|8.53|7.66|3.77|10.11|14.23|13.73|18.57|
|||Edu|cation l|evel for|working|age popu|lation||||||
|High education|2.04|4.26|5.61|6.09|3.54|7.59|7.43|5.65|11.65|18.95|18.40|17.84|
|Secondary specialized|3.49|4.52|5.39|4.37|3.60|9.08|10.20|6.27|9.07|14.77|16.13|21.21|
|Other|3.48|4.41|5.60|3.09|3.65|10.30|11.29|4.86|8.97|13.20|15.45|19.26|
||||T|ype of ho|usehold|(HH)|||||||
|HH without children|2.42|3.67|4.51|2.82|2.16|5.71|4.87|3.13|11.89|15.75|19.02|22.27|
|Retiree HH|1.32|0.97|1.78|0.71|0.39|0.89|0.57|1.00|12.70|16.80|21.94|26.41|
|HH with children|3.05|3.42|4.91|5.04|4.22|8.63|10.81|5.84|8.36|16.00|16.33|19.40|
|1 child|3.12|3.50|5.37|6.34|3.64|8.34|12.23|5.15|8.24|17.19|16.45|19.85|
|2 children|3.21|3.05|3.75|2.99|3.81|8.97|8.93|7.40|8.50|14.64|16.55|18.56|
|3 and more children|2.12|4.31|6.06|3.21|8.89|9.44|7.50|4.78|8.60|12.41|14.53|19.33|
|singleparent|2.22|4.71|8.00|2.18|2.31|8.43|5.64|6.18|14.42|21.15|17.75|23.15|

Source: Authors’ estimates based on HSS data.

33
top earnings bracket due to the calibration, the corrections make a substantial difference to trends over time in the top tail. Without imputation and reweighting the earnings for the top 10 percentiles supposedly shrank by between 25% and 30%, whereas with these corrections they rose by between 1 and 8 percent.

# Replacement

Replacement using household and administrative data means that some of the incomes in the household surveys are replaced with incomes in the administrative data. There are various methods that have been used in this broad approach. One distinction is between those that use parametric and non-parametric replacement (Lustig 2020). Parametric replacement involves estimating the Gini coefficient or some other inequality measure for the top x% of the income distribution from tax or other administrative data by assuming the top incomes follow a Pareto distribution, estimating the Pareto parameter and using this to obtain the implied Gini coefficient of the top x%. This is very similar to the methods described above using survey data only. One can then estimate the Gini coefficient for the bottom (1-x) % of the population using survey data and combine them to obtain an overall measure of inequality, using the results of Alvaredo (2011) that the Gini can be decomposed using shares of the top (say) 1% and the bottom (say) 99%. Alvaredo (2011) used tax data and the Current Population Survey for the US to show that the increase over time in the Gini coefficient doubled when adjusting for missing top earners. This method was also used by Alvaredo and Londono Velez (2013) for Colombia, who showed that when using tax data to replace the top 1% of incomes in the surveys, inequality levels were substantially higher, and the trend of declining inequality was much smaller than when using uncorrected household surveys.

Non-parametric replacement with admin data involves either replacing higher income individuals in the household surveys with individuals from admin data or imputation of incomes in surveys using incomes in admin data observably similar individuals. Bach et al. (2009) undertook this this for Germany, using unit record tax data and the German Socio-Economic Panel to match individuals in the survey and tax data and replace the survey respondents with similar individuals in the tax data. They found that the Gini increased by 6 percentage points, and that increases among individuals at the very top of the income distribution drove the increase in inequality in Germany between 1992 and 2003. Non-parametric replacement was also undertaken by Burkhauser et al. (2018), who used UK household surveys but replaced top incomes with cell means from tax data for the UK, which is a method applied by the UK Department of Work and Pensions. This increased Gini coefficients in most years between 1994 and 2014 and reduced volatility over the different surveys compared to the 18
Hsiao, eds., _Efficient, equity-oriented strategies for health: International perspectives-focus on Vietnam._ Melboumne: C]MH.

- Desai, J. 1998. "Poverty and fertility in Vietnam." In D. Dollar, P. Glewwe and J. Litvack, eds., _Household Welfare and Vietnam 's Transition._ Washington, D.C.: World Bank.

- Esrey, S. A., and J. P. Habicht. 1988. "Maternal literacy modifies the effect of toilets and piped water on infant survival in Malaysia." _Am. J. Epidemiol._ 127(5): 1079-87.

- Filmer, D., and L. Pritchett. 1999. "The effect of household wealth on educational attainment: evidence from 35 countries." _Population and Development Review_ 25(1): 85-120.

- Furquim De Almeida, M., L. C. Rodrigues, G. Pereira Alenca, and H. M. Dutilh Novaes. 2000. "Neonatal mortality by place of delivery in Sao Paulo, Brazil." _J. Epidemiol Community Health_ 54(11): 879.

- Glewwe, P., M. Gragnolati, and H. Zaman. 2000. "Who gained from Vietnamn's boom in the 1990s?" Policy Research Working Paper No. 2275. Development Research Group, World Bank, Washington, D.C.

- Government of Vietnam. 1999. _Vietnam Demographic and Health Survey 1997._ Hanoi: National Committee for Population and Family Planning.

- Government of Vietnam. 2000. _Analysis of the Multipie Indicator Cluster Survey II (MICSII)._ Hanoi: Statistical Publishing House.

- Greene, W. (1997). _Econometric Analysis._ Upper Saddle River, NJ, Prentice-Hall Inc.

- Grossman, M. 1972. _The Demandfor Health: A Theoretical and Empirical Investigation._ New York: National Bureau of Economic Research.

- Guilkey, D., and R. Riphahn. 1998. "The determinants of child mortality in the Philippines: Estimation of a structural model." _Journal of Development Economics_ 56: 281-305.

- Gwatkin, D., S. Rutstein, K. Johnson, R. Pande, and A. Wagstaff. 2000. "Socioeconomic Differences in Health, Nutrition and Population." The World Bank, Health, Nutrition & Population Discussion Paper, Washington DC,

- Hill, K., R. Pande, M. Mahy, and G. Jones. 1999. _Trends in Child Mortality in the Developing World: 1960 to 1996._ New York: lUNICEF.

33
Tables 9 and 10 report the conditional factor demand functions for early season (i.e., preplanting and planting period) fertilizer and labor, respectively, the former estimated at both the intensive and extensive margins since there are many zero-valued observations.<sup>22</sup> The results highlight four intuitive findings. First, the provision of true plot size information has zero effect on fertilizer use reporting at the extensive margin. Farmers know whether they used fertilizer or not and that self-report is unaffected by corrective information about plot size. Fertilizer use, as a binary variable, is positively associated with plot size only and unrelated to either pre-treatment plot size measurement error or corrective information.

Second, the plot size information treatment significantly affects farmers' self-reported fertilizer use at the intensive margin, by roughly 7 percent of mean fertilizer use at mean plot size and pre-treatment measurement error (-0.298/4.244). Since the average farmer overestimates his plot size, corrective information on average told him that he was overestimating his area. This finding indicates that informational treatment induces farmers to reduce their estimates of fertilizer use intensity, too. Since farmers were asked to report their fertilizer use minutes after they witnessed the GPS measurement of their plot and were told the true plot size and the measurement error in their self-report of plot area, there was no opportunity for any actual adjustment in fertilizer use. These are pure spillover effects on beliefs, or at least on self-reports, caused by corrective information on a related but different variable. This raises concerns about the quality of selfreported data on fertilizer and labor inputs in agricultural household surveys, an issue that has received limited attention in the evolving discussion of improved household survey methods. NCME in non-land inputs may affect inferences about the determinants of and marginal returns to inputs (e.g., Abay, 2020).

Third, the treatment effects are systematically heterogeneous, varying especially with plot size. Farmers owning larger plots adjusted their self-reported fertilizer use intensity downwards by a larger amount. Without treatment, we find only a mild inverse relationship between input use per acre and plot size – the so-called Boserup hypothesis (e.g., Boserup, 1965; Ruthenberg, 1980; Binswanger-Mkhize and Savastano, 2017; Abay et al., 2021) – statistically significant at only the

> 22 We emphasize that the objective of this estimation is to explore the extensive and intensive margins separately. We have no instrument to identify the selection component, thus identification in the second stage comes solely through the nonlinearity of the first-stage probit estimator. Estimation results involving triple interaction terms are given in Appendix Tables A5 and A6.

26
pressing need for alternative mechanisms to ensure that conservation stewardship responsibilities are both delegated and effectively monitored. This may involve the creation of ad hoc agreements between neighboring countries or the establishment of transboundary conservation initiatives that focus specifically on biodiversity protection. These initiatives could help fill the gaps where formal governance structures are lacking, promoting a more coordinated approach to conservation across national borders.

Ultimately, the results from Table 3 underscore the urgent need for international cooperation to ensure the protection of species in transboundary river basins. Without adequate governance frameworks, both at the treaty and RBO level, the survival of many species in these regions remains uncertain. Efforts to strengthen governance, prioritize species protection within existing agreements, and develop new mechanisms for regions without formal frameworks are critical for the long-term success of biodiversity conservation in these ecologically important areas.

**Table 3: Transboundary river basins:  species covered by treaties and river basin organizations**

||Total<br>Species|%<br>Without<br>Treaty|%<br>Without<br>RBO|% Without<br>RBO or<br>Treaty|
|---|---|---|---|---|
|Species Presence|369,211|4.93|12.78|4.87|
|Endemic|78,800|7.01|21.99|6.97|
|Small Occurrence Region|40,951|8.16|23.91|8.01|
|Extinction Threat Indicator (ETI) > 80|21,362|8.08|23.76|7.93|
|Endemic with Small Occurrence Region|24,288|9.38|29.85|9.28|
|Small Occurrence Region with ETI > 80|11,513|9.28|26.88|9.04|
|Endemic with ETI > 80|12,326|8.84|28.20|8.75|
|Endemic, Small Occurrence Region with ETI>80|8,339|9.57|29.57|9.44|

# **Discussion**

The data provided by the Global Biodiversity Information Facility (GBIF) can play a pivotal role in identifying and protecting vulnerable species, especially those located in fragile regions and ecosystems that span multiple political jurisdictions. One of the primary benefits of GBIF-type 19
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textbooks daily mid day meal for all students. The unit of analysis is the individual student.

Because the entitlements can differ across caste categories and by gender, results are presented separately for these sub groups wherever applicable. These analyses are valid since the sample selection was stratified by caste categories and by gender. Tables 11A and 11B present difference in differences analysis of the impact of the campaign.

**Scholarship:** In UP, SC/ST and OBC students in primary schools are eligible for scholarship. In addition, high caste (i.e., those in the _other_ category) students below the poverty line are also eligible for scholarship. There is no effect of the intervention on scholarships for SC/ST and OBC category students. But there is a positive and significant impact on scholarship received by high caste students. The magnitude of this effect is 33 percentages and the associated p value is .04.

In MP, girls from SC and ST caste categories are eligible for scholarship. There is a significant impact of the intervention on scholarship received by SC and ST girls. The intervention is associated with 26 percent higher scholarship received and the p value for this effect is .02.

**Uniform for girls:** In both states, in educationally backward blocks which are defined as blocks with low female literacy rate, all girls in grades 1-5 are entitled to free school uniform annually. In UP, in the districts with educationally backward blocks, there is a significant impact of the campaign on the percentage of girls who received school uniform. The magnitude of this increase is 18 percentages and has a p value of .05.

In MP, there is a no overall impact on girls who receive school uniform but there is a significant impact on the percentage of girls from high castes who received school uniform. The size of this effect is about 14 percentages and the associated p value is .02.

**Mid day meal and textbooks:** In both states, there is no impact on the delivery mid day meal and whether textbooks were received.

# **5.4 Change in Processes: Is There an Observed Increase in Community Participation in Oversight of the School?**

In UP, the unit of analysis is the individual VEC member. Tables 12A and 12B present difference in differences analysis of the impact of the campaign. The intervention is associated with a 25 percentage increase in the number of VEC meetings and a 25 percentage increase in members’ participation in school inspections. Both these effects are significant at p value below .03. The campaign significantly influences the knowledge of VEC members regarding teacher aspects of roles and responsibilities.

In MP, the difference in differences analysis is not possible at the individual member level since new rules for PTA election came into effect just as the campaign was starting. The PTA committees were re elected based on these new rules during or after the 16
Readiness” are two important lagging policy areas that have relatively few numbers of laws and decrees. In contrast, “Diversification”, also a lagging policy area, has received more laws and decrees. We test this hypothesis formally in Appendix A2 with different econometric specifications and confirm the robustness of this finding.

One possible explanation for the finding is that countries enter a 'virtuous cycle' whereby they have more capacity to implement reforms in areas of pre-existing strength. Another argument is related to the sequencing of reforms. The ‘right’ reforms are not necessarily on weak policy areas but the most logical next step (Edwards, 2009; <mark>Feltenstein and Nsouli, 2003</mark> ). More studies and thoughtful debates are needed to understand the countries’ current reform efforts. They will likely help policy makers boost the engagement and support for economic reforms, which we will discuss in the next section.

**Figure II.2: Number of Laws and Decrees Passed and Lagged of Distance to Policy Frontier**

<!-- Start of picture text --> Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Distance to Frontier, Lagged (100: farthest away from the frontier)<br>Diversification Financial Market Development Goods Market Efficiency Health and Primary Education<br>Higher Education and Training Infrastructure Innovation Institutions<br>Labor Market Efficiency Macroeconomic Environment Technological Readiness<br>150<br>100<br>50<br>0<br>150<br>100<br>50<br>0<br>Number of Laws and Decrees Passed<br><!-- End of picture text --> _Note_ : A higher ‘Distance to Policy Frontier’ implies being farther away from policy best practices. GCI data are from 2007 to 2017 due to data constraints from WEF’s GCI database (the GCI scores are only methodically consistent and comparable across years during this period).

5
# **1. Introduction**

Polluted surface water is encountered across most of China, as a byproduct of its rapid economic development over the past three decades. However, few valuation studies have been conducted in China to estimate the economic damage of the water quality deterioration or to measure the potential economic value of water quality improvement in the lakes and rivers. Many observers believe that massive and serious surface water clean-up efforts are urgently needed in China. However, the lack of information of the economic values of cleaner waters in the rivers and lakes and the damages imposed to the society by water pollution is a handicap for making informed judgments on clean-up investments.

This paper reports a contingent valuation (CV) study conducted in Yunnan Province of China in 2007 on a lake water quality improvement project that is proposed to be financed by the World Bank. The multiple bounded discrete choice (MBDC) value elicitation method and the stochastic valuation modeling strategy are employed in this study in order to deal with the potential uncertainties involved in the valuation process. As the investment project under valuation is real, the potential hypothetical bias associated with the CV method is expected to be at its minimum. This study also provides analysis on the potential influences of project information provision, project benefit perception, and interviewer’s presentation, on the value estimation. The total economic value of the project benefits is compared to the project cost and the economic rate of return of the project is computed. It is expected that the study can contribute to the policy debates on investments in water pollution clean-up in China.

The paper is organized as follows. In the following section, we briefly review the existing literature on CVM application to water quality improvements in China. In section 3 we introduce the Lake Puzhehei water quality improvement project and the potential benefits this project may generate. The survey development, implementation and summary statistics are reported in section 4. In section 5, we present the analytical framework and the estimation results. Conclusions and discussion are provided in section 6.

# **2. Policy Context and Previous Research**

The water resources in China are overall abundant, but scarce in per capita terms and in some regions. China’s renewable water resources at per capita basis is estimated at 2,156 m3/year in 2007, only onefourth of the world average of 85,49 m3/year. In northern part of China, the water availability is even worse, only 757 m3/year per person (World Bank, 2009). At the same time, water demand is increasing due to the continuing economic growth, population increase, industrialization and urbanization.

2
primary school teachers were absent at the time of a surprise visit during school hours (see also Alcázar, Rogers, Chaudhury, Hammer, Kremer, and Muralidharan, 2006). At the other end of the spectrum, 25 and 27 percent of teachers were absent from Indian and Ugandan schools, respectively. The other three countries fell between these extremes, with the unweighted average absence for the six countries coming in at a high 19 percent. These results were consistent with roughly contemporaneous estimates from two other countries where survey teams also directly observed teacher attendance: Papua New Guinea, where primary school absence was estimated at 15 percent<sup>3</sup> (World Bank, 2004), and Zambia, at 18 percent (Das, Dercon, Habyarimana, and Krishnan, 2007).

It is important to note that this was the absence rate from the school, rather than only from the teacher’s assigned classroom. If the teacher was present in the school but out of the classroom, or in the classroom but not teaching, he or she was nonetheless marked as being in attendance.<sup>4</sup> Hence these are actually conservative definitions of absence, in that the actual rate of absence from teaching duties was sometimes considerably higher.<sup>5</sup> Some regions within these countries suffered from even more serious absence problems. Among the states of India, the variation in absence was even greater than across the six countries. Teacher absence ranged from a low of 15 percent in Maharashtra, one of the most developed Indian states, to a high of 38 percent in Bihar and 42 percent in Jharkhand. Similar patterns were found within Peru: while Lima had an absence rate of only 7 percent, teachers in remote schools averaged 20 percent absence.

# **_Teacher absence at the secondary level_**

More recently, a handful of other World Bank studies have used the direct‐ observation methodology to measure teacher absence at the lower secondary level.

> 3 According to the authors of the PNG study, “the rate is an underestimate of the true rate since schools that were closed ‘because there weren’t enough teachers’ were replaced by schools that were open” (World Bank, 2004).  By contrast, in other studies such as the India teacher study reported in Kremer _et al_ (2005) and Chaudhury _et al_ (2006), teachers at schools that were closed simply due to teacher absence were in fact counted as absent.

- 4 See Chaudhury _et al_ (2006) for further details on the assumptions and calculation methods.

- 5 One commenter noted that some schools, typically in desirable urban areas, have too many teachers, and suggested that this could cause some of the recorded absence.  But because only teachers scheduled to be in classrooms at the time of the visit were included in the absence calculations, such underutilization of teachers should not affect the calculated absence rates.
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