rafmacalaba/datause-encoder-data huggingface dataset:
In addition to achieving better social equity and improved cost recovery, increasing the water price is also recognized as an effective way to promote water conservation by the National People’s Congress (NPC). Article 24 of the Pricing Regulation establishes the principles for water tariff adjustment. To improve cost recovery, any water tariff adjustment should take into account the development of the water supply industry, the need for economic development, the public’s daily need for water, water conservation, affordability for consumers, and the pricing mechanism.

The water tariff has been adjusted several times in many areas in China. For instance, in Beijing the water price increased from 0.80 yuan per ton in 1997 to 3.70 in 2004. In the central area of Chongqing, it rose from 0.85 yuan/ton in 1999 to 2.80 in 2006 (WB 2007). However, more water price reforms are still needed to achieve true efficiency in the domestic water market.

# **The Chongqing Survey**

Chongqing, located in western China, is one of the largest and most populous municipalities in China. The population of the urban area of Chongqing was 4.1 million in 2005. In 2006, the nominal GDP of the Chongqing municipality was 348.62 billion yuan ($45.2 billion), an increase of 11.5 percent from 2005. Its per capita GDP was about 12,000 Yuan (or $1,610) (NSB 2006).

Due to its location, the city has very limited surface and underground water resources. The city’s household water supply service has been monopolized and under heavy subsidy by the government. Current water prices in survey areas in Chongqing range from 1.8 yuan/ton to 2.8 yuan/ton. This price does not include sewage treatment costs, which are entirely absorbed by the municipal government.

Improving water supply service in several regions outside of Chongqing urban proper, which are characterized by high water scarcity, has been under discussion. In the summer of 2006, the Chongqing World Bank Project Management Office (CQPMO) hired professors and graduate students of Chongqing University to conduct in-person interviews of the heads of selected households, with technical support by World Bank experts. The major purposes of the survey was to better understand domestic water use and to assess household willingness to pay for improved domestic water service, so that sound policy proposals on water tariff structure and water service improvement could be provided by the municipal government.

The survey focused on five areas where water supply improvement projects were under consideration.<sup>5</sup> In each survey area, several communities were selected to balance the income and water usage level within each region. A random sampling process was run

> 5 The areas include part of Shapingba District, Jiulongpo District, Dianjiang County,  Yongchuan County, Tongnan County, and Dianjiang County.
<!-- Start of picture text --> Sri Lanka: Poverty Headcount (%)<br>°<br>FS] 40"<br>°<br>8<br>3<br>S 21,<br>N<br>13.4<br>° 12.8<br>S<br>2015 2020 2025 2030<br>— <= Baseline === Full Reform<br><!-- End of picture text --> <!-- Start of picture text --> Growth Incidence Curve with respect to Baseline, 2028<br>ite)<br>ov<br>£<br>3<br>Bo<br>3<br>a<br>6Sg<br>ee<br>oO<br>0 20 40 60 80 100<br>Percentiles of Per Capita Income<br>= Paratariffs = CHN = IND = Full<br><!-- End of picture text -->
education of the mother, and state of residence. Note that the effect of each of these covariates is allowed to change across cohorts.<sup>17</sup> Table 4 shows estimated coefficients on a selective group of variables of equation (1). Panel A is a copy of Table 3, and presents the expected impact of the program as a function of the cohort of the child, the year height was measured and the type of locality of residence. Panel B shows the estimated results for rural areas, and Panel C presents the results for urban areas. Each cell presents the estimated coefficient corresponding to the i1 , i2 , and i3 terms in equation (1). In Panel B, for example, the cell corresponding to the older cohort in community-group 1 in 2002 (-0.52) αα αα αα shows the estimated value of 1 , when equation (1) is restricted to the rural sample. oo02 αα In the absence of selective program placement, the coefficients in equation (1) would already represent impact effects. But we know that is not the case. Looking at the rural areas, results in Table 4 are consistent with the fact that Oportunidades was first implemented in the poorest communities of Mexico, and reinforces descriptive analysis done with census data on a broader set of socioeconomic variables. As expected, rural communities incorporated to Oportunidades before 2005 are statistically different from communities incorporated later (or never incorporated) in terms of average children’s height: 2 and 3 are negative and significant at the 5 and 10 oo02 oo02 percent level, when they should be zero in the absence of selection in program placement. This αα αα analysis cannot provide direct evidence of placement selection for the first group of communities but the literature documents they are among the poorest communities in Mexico. As a result, we see that Oportunidades was introduced first to communities where children’s nutritional status was

> 17 We tried specifications that allow for the covariates to be cohort-time specific and they essentially produce the same results (except that some coefficients have larger standard errors).

16
arable land, 23% forest, and close to 4% construction and residential land. Certificates for collective construction land were issued to the village whereas those for contracted arable and forest land and residential land and actual structures were awarded to households.

We note that in more than 85% of cases, rules were made at the village level, by either the assembly (48%), economic organizations (23%), representatives (14%) or leaders (1%). Organization came more often from above (47% of township or above; 26% village leaders), and actual measurement was done by village representatives in 55% of cases. In 55% of villages, land registration led to dispute and, where this was the case, an average of 14.9 disputes per village emerged. Disputes was most pervasive for arable and forest land (which attracted disputes in 50% and 35% of villages with 9.4 and 8.8 disputes, respectively) and least frequent with regard to collective and construction land (18.7% and 22.6% of villages with a mean of some 6 disputes). Even where disputes emerged, most cases were resolved by local institutions: in villages with conflict, a total of 1.2 cases required intervention by institutions above the village.

After certification, contracts with a length exceeding 30 years were issued for all construction land and more than 95% of all other land use types. Permanent land use contracts were given in close to 72% of cases overall, from 85% of residential and construction land to 80% of collective land, and some 50% and 48% of arable and forest land. Survey data point toward a total cash cost for the program of about Y 8.6 per mu of which close to half (38%) was contributed by the village and the remainder from outside. Villagers contributed close to 2,000 man-days of labor (about 3 days per household) and, with somewhat more than 10 person-months of labor by outsiders, contributions from above the village remained limited.

# **4. Econometric results**

The reform is estimated to have led to a significant increase of 7.7 percent in per capita consumption that was most pronounced for less educated and less wealthy households, and an increment in net income of almost equal size. Average annual benefits exceeded program cost. Reforms contributed to job creation with an increase in labor supply by males and a shift from migration to agricultural activities by the young and from farming to off-farm activities by the old. It also resulted in increases of agricultural yields by 55% and of profits by 38%, more intensive input use, a shift of crop composition towards higher-value crops, and higher rental market activity to transfer land from less to more productive users.

# **4.1 Welfare impacts of property rights reform**

Estimates of reform-induced impacts on consumption and income as well as shifts in the contribution of different sources to total income are reported in table 3. Here and in subsequent tables, estimated mean impacts are in panel A while impacts that are allowed to vary by initial level of education and physical assets are in panels B and C, respectively. Columns 1 and 2 of table 3 panel A point towards a reform11
9 The unit cost data presented in the tables here are drawn from compilations by other authors. For the feeding programs the costs were standardized by this author. In addition, the feeding program data were supplemented by costs of 13 WFP feeding programs, calculated by the author from a sample of WFP project documents. The WFP allots between 20 and 30% of its resources for feeding programs for what they term vulnerable groups (MCH and primary school). In 1989 for example such feeding programs accounted for 28% of their commitments (WFP Annual Report, 1990). The 13 projects examined here are those which were reviewed at the 25th and 26th sessions of the WFP donors' review group (costs were calculated for any phase of these projects for which data were on file at CIDA - Canadian International Development Agency, not necessarily the exact phase under review at those sessions). Costs were calculated from project proposal documents, excluding non-recurrent costs. According to the sample of evaluation documents available, most projects have beneficiary numbers quite similar to those projected in the proposals (evaluation data are more scanty and less standardized than proposal data). In those cases in which project size increases unexpectedly, additional budget requests are usually made. Thus the WFP are prospective not actual project data, but projects usually operate pretty much on the planned scale (if not necessarily the planned schedule).

Table 1 presents information on education-type interventions, both nutrition education, and breastfeeding promotion. Mass media nutrition education campaigns are quite inexpensive, ranging from $1-5 per beneficiary. Breastfeeding promotion efforts (including changed legislation on infant formula, education efforts in hospitals for both mothers and hospital staff, and policies encouraging "rooming in") are also estimated to cost $1-5 per mother. Levine and Huffman (1990) present additional data on costs and savings of breastfeeding promotion, although not in the unit cost format used here. Costs of face-to-face nutrition education programs such as that in the Dominican Republic (integrated with growth monitoring) are higher ($23 per beneficiary), and costs of other programs to
Policy Research Working Paper 9208

# **Abstract**

This paper analyzes the factors that affect countries’ graduation from International Development Association (IDA) assistance and develops a statistical model of graduation. IDA provides concessional financing (credits, grants, and guarantees) to the world’s poorest countries to help reduce poverty and improve living standards. IDA’s eligibility criteria include (a) absence of International Bank for Reconstruction and Development (IBRD) creditworthiness and (b) gross national income (GNI) per capita below the IDA operational cutoff. Following several ‘reverse graduations’ during the 1990s, the set of factors affecting graduation decisions broadened to include an assessment of the country’s macroeconomic prospects, risk of debt distress, vulnerability to shocks, institutional constraints, and levels of poverty and social indicators. Through a pooled logit estimation using panel data covering IDA-eligible countries for 1987–2016, the authors determine the factors that influenced IDA graduation decision making for FY1989–FY2018. They find that throughout the sample the probability of being a graduate is positively and statistically significantly associated with income per capita, creditworthiness, and country size. They account for the shift of policy after 1998. Using an interaction dummy variable to capture the pre-FY1999 period, they find that life expectancy, reduced poverty, urbanization, and institutional development have been positive and significant predictors of graduation status in the later period.

This paper is a product of the Development Finance Vice-Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at adobronogov@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# CONTENTS

|Preface .....................................<br>'<br>v|
|---|
|Acknowledgements.<br>vii|
|Introduction.<br>ix|
|Projection Results .ix|
|Data and Methods .x|
|Total population and age-sex composition.<br>x|
|Mortality level and trend.<br>x|
|Fertility level and trend<br>i.xi|
|Migration level and trend .xviii|
|Summary of Results .xxi|
|World and continents..<br>xx|
|Geographic regions and income groups .xxxi|
|World Bank regions.<br>xxxi|
|Some country results .xxxvi|
|References .xxix|
|Appendix: Sources of Population Data .xl|
|Tables .lii|
|Detailed Population<br>Projections .|
|World, Geographic<br>Regions, and Income Groups .1|
|World.<br>2|
|Less developed countries.<br>4|
|More developed countries .6|
|Geographic Regions|
|Africa.<br>8|
|East Africa .10|
|West Africa .12|
|North Africa .14|
|America ....<br>16|
|Laiin America and the Caribbean ......<br>..........<br>18|
|Northern America ...........................<br>20|
|Asia................................<br>22|
|East and Southeast Asia .........<br>.............<br>24|
|South Asia ...............................<br>26|
|Southwest Asia ............................<br>28|
|Europe and U.S.S.R . ...........................<br>30|
|<br>Oceania ...................................<br>32|
|Income Groups|
|<br>Low-income economies<br>.<br>........................<br>34|
|Lower-middle-income economies .......<br>............<br>36|
|Upper-middle-income economics .........<br>.<br>.....<br>38|
|High-income economics .........................<br>40|
|<br>Nonreporting nonmember economics .......<br>.........<br>42|
# Estimates of the Opportunity Costs of Public Funds

|Country|Estimation|Source|
|---|---|---|
|Australia|125||
|Canada|12-13|Ruggeri (1999)|
|Philippines|248|Jones, Tandon, Vogelsang (1990)|
|France|1.3 and 1.5|Commissariat Général du Plan (1973 and 1985)|
||1.1-1.4|Applying Snow and Warren’s formula (1996)|
||12-13|Bernard (1976)|
||1.12|Bernard and Vieille (2003)|
||L.13-L3|Lebégue et al. (2005)|
||1.05=1.2<br>1.3|Quinet (2006) quoting literature<br>Auriol and Blane (2007)<br>applying Snow and Warren's formula (1996)|
|Japan|1.03|BernardandVieille(2003)|
|Malaysia|1.20|<br>Jones, Tandon, Vopelsang (1990)|
|Westem countries|1=1.5|Browning (1987)|
||1.3=1.5|Laffont (1009)|
|United Kingdom|1.3<br>1.3|Florio (2002)|
|Russia|1.23|BernardandVieille(2003)|
|Thailand|1.19-].54|<br>Jones, Tandon, Wogelsang (1990)|
|USA|L.17=1.56|Ballard (1990)|
||1.02|BernardandWieille(2003)|
Figure 2: Alternative Fiscal Rules

## Spend as You Go (SAYG)

Permanent Income Hypothesis (PIH) <!-- Start of picture text --> 10 10<br>9 9<br>8 8<br>Oil Revenues Oil Revenues<br>7 7<br>6 6<br>5 5 PIH Annuity<br>4 Spending 4 Savings<br>3 3<br>2 2 Use of interest<br>Borrowing earnings<br>1 1<br>0 0<br>-1 -1<br>2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070<br>Bird in Hand (BIH) Moderate Frontloading (MF)<br>10 10<br>9 9<br>8 8<br>7 Oil Revenues 7 Oil Revenues<br>BIH Annuity<br>6 6 Savings<br>5 5<br>Savings<br>4 4<br>32 Use of interest earnings 32 Borrowing Use of interest earnings and other resources<br>1 1<br>0 0<br>-1 -1<br>2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 2015 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070<br>Billion USD Billion USD<br>Billion USD Billion USD<br><!-- End of picture text --> education ( _εE_ = 0 _._ 1) relative to health ( _εH_ = 0 _._ 03).

# **5.3 Spending under alternative fiscal rules**

At the beginning of the resource boom, the PIH leads to a deficit that needs to be financed externally. However, once the production of natural resource comes to an end, PIH and BIH imply an equivalent annuity that is equal to the returns on financial assets. Using the baseline revenue projections for Kenya, and assuming a 2.7 percent real interest earnings on savings, annuities under each fiscal rule is calculated and shown in the following figures.

The SAYG approach does not lead to any savings; therefore, transfers to the budget from the resource boom diminish over time, following the resource revenue depletion.

Under PIH, the government transfers about $2.7 billion to the fiscal budget annually (figure 2, black line in panel b). In the short term, this is financed by borrowing from 21
**Table 5: Mobile Telephone Diffusion in Sub-Saharan Africa**

||Popu<br>(Milli|lation<br>ons)|Mob|ile Phone S<br>(Thousan|ubscribers<br>ds)||
|---|---|---|---|---|---|---|
||||||2009|Pop % With|
|Region/Country|1997|2009|1998|2009<br>(Baseline|<br>(Growth 5%;<br>) Policy = 5)|<br>Mobile<br>Phones<br>(5/5 Forecast)|
|SUB-SAHARAN<br>AFRICA|||||||
|South Africa|38|47|2,500|<br>3,651|<br>14,574|<br>31.3|
|Angola|11|16|10|<br>58|<br>726|<br>4.6|
|Benin|6|8|6|<br>77|<br>230|<br>2.7|
|Botswana|1|1|23|<br>191|<br>484|<br>37.7|
|Burkina Faso|11|15|**3**|<br>40|<br>183|<br>1.2|
|Burundi|7|10|1|<br>3|<br>40|<br>0.4|
|Cameroon|14|20|7|<br>701|<br>4,518|<br>22.9|
|Cote d'lvoire|15|**21**|91|<br>252|<br>1,012|<br>4.8|
|Ghana|18|25|49|<br>804|<br>2,590|<br>10.5|
|Guinea|7|10|22|<br>132|<br>412|<br>4.3|
|Kenya|28|38|11|<br>298|<br>1,783|<br>4.7|
|<br>Lesotho|2|3|10|<br>49|<br>102|<br>4.0|
|Malawi|10|14|**11**|<br>39|<br>139|<br>1.0|
|Namibia|2|3|**20**|<br>114|<br>276|<br>10.2|
|Niger|10|15|1|<br>26|<br>167|<br>1.1|
|<br>Nigeria|118|166|<br>**20**|<br>732|<br>3,640|<br>2.2|
|<br>Senegal|9|13|**22**|<br>138|<br>554|<br>4.4|
|<br>Tanzania|31|44|**38**|<br>90|<br>**483**|<br>1.1|
|Togo|4|6|**8**|<br>31|<br>217|<br>3.8|
|<br>Uganda|20|29|**30**|<br>250|<br>321|<br>1.1|
|<br>Zambia|**9**|13|**5**|<br>59|<br>**296**|<br>2.4|
|Zimbabwe|11|14|**55**|<br>200|<br>1,027|<br>7.1|
|Total Outside South<br>Africa|<br>344<br>I|482<br>I|<br>441<br>|<br>4,285<br>I<br>I|<br>19,199<br>|.<br>4.0|

In Sub-Saharan Africa (Table 5), the baseline forecast is 3.7 million South African subscribers and 4.3 million in the other Sub-Saharan countries. Cameroon, Ghana and Nigeria figure most prominently in these projections. In the optimistic case, subscriptions outside of South Africa grow to 19.2 million.

While these African projections large in absolute terms, they still imply very low percapita subscriber rates. At least another decade of rapid expansion after 2009 would be required for convergence with current OECD penetration rates, even under optimistic assumptions. To illustrate the problem, the last column in Table 5 provides estimates of mobile phones per capita in the optimistic scenario. Only a few countries have sizable 12
For the implementation and analysis, we use 2500 replications, with a sample size of 1,000 observations. Replications using sample sizes of 500 and 2,000 are provided in the appendix, with results that are qualitatively similar. We focus on the comparison of conditional quantile regressions for the 10<sup>th</sup> , 50<sup>th</sup> and 90<sup>th</sup> quantiles, as well as for the 10<sup>th</sup> , 50<sup>th</sup> and 90<sup>th</sup> unconditional quantiles. Quantile regressions were estimated using the fast algorithm developed in Chernozhukov et al (2022) and implemented via the Stata command -qrprocess-, whereas the unconditional quantile regressions were estimated following Firpo, Fortin and Lemieux (2009) and implanted via the Stata command -rifhdreg- (Rios-Avila, 2020). Finally, the simulation was implemented using -parallel- (Vega Yon and Quistorff, 2019). Finally, our imputation method is implanted with a new user-written program -intreg_mi-, which is available upon request.

While population parameters for conditional quantile regressions for some of the data generating exists, there are no close form solutions for the population parameters corresponding to the RIF regressions. Because of this, we assume that average estimates using fully observed data represent the population parameters, for the calculation of the relevant statistics. Thus, by construction, the bias of the model estimations using fully observed data is zero.

# **3.2. Results**

Tables 1 to 3 provide a summary of the results for the Monte Carlo simulations using the different data generating processes. In each table, we present the bias of our imputation procedure compared to the average parameters using fully observed data as if they were the asymptotic population parameters. For both conditional and unconditional quantile regressions, the bias observed using the multiple imputation data is small when the homoscedastic error is assumed to follow a symmetric bell curve distribution, regardless of the type of heteroskedasticity implied by the dgp.

When the errors follow a chi2 distribution or uniform distribution, we observe some bias, especially for the lower quantile coefficients. The bias, however, is considerably smaller if the data generating process assumes an functional form with exponential heteroskedasticity. Finally, the results using the varying coefficient structure reveal low bias in both cases. In all simulations, the bias magnitude did not depend on the sample size (see appendix).