rafmacalaba/datause-encoder-data huggingface dataset:
the capacity of those establishing and using these institutions. New institutions need to complement existing conditions in a country—that is, it needs to be consistent with the country’s income level, skills and education levels, available technology, the distribution of income, geography, and other institutions (e.g. court performance depends on how the laws are written in the country, how lawyers associations work, the presence of alternative dispute resolution systems etc.). While this “lesson” may seem obvious, it is sometimes hard to reconcile with the concept of international “best” practice. This is not to say that nothing can be learned from looking at international or foreign norms and practices. To the contrary, as is argued later.

A key question is how do we adapt institutions which are effective in high income countries for the needs of low income countries? Courts are only relevant to the extent that they approach dispute resolution in a manner that is understood by the people they serve and that meet their needs . One answer might be “ simplicity” in design.

Second, both state and private entities can benefit by being allowed to experiment /innovate in terms of finding the institutional design that “fits”. Study of different systems, however distinct in history and origin, is important because it gives us ideas and enhances the realm of the possible: moreover, some of these ideas may bear fruit.

Third, some degree of competition provides incentives for improving institutional design. The desire to do better than a peer group is often a healthy driver of change in private business—so can it be in the public domain for both individuals and organizations.<sup>5</sup> Fourth, economic openness, for example trade in goods, and services, information sharing across and within countries, can go a long way in facilitating institutional reform by creating demand for change.

> 5 World Bank, 2001.

4
financing information. The PPI also provides the current status of the project (active, concluded, distressed, or canceled).

Many projects in the PPI database lack data for all characteristics. Missing data on the variables essential for the analysis—the contract period and the level of government that granted the contract—were added for all projects using the individual project descriptions provided in the database if available.

The Polity IV data were used to identify the institutional characteristics of a country. We used yearly executive recruitment, constraints on the executive, and political competition concept variables. We linearly interpolated the values for the interruption, interregnum, and transition periods, during which the variables are coded out of their respective ranges in Polity IV.

From the WDI, we took the annual series on the per capita growth rate and nominal exchange rates, which we used to create series of detrended and demeaned series of per capita growth rate and exchange rate shocks, using the filter suggested by Hamilton (2018). The data on financial crises come from the Systemic Banking Crises dataset of Laeven and Valencia (2020).

The PPI database records 8,420 projects in the developing world, encompassing 130 countries, with financial closure dates from 1990 to 2020. The sample includes projects from five sectors: ICT, energy, transport, water and sewerage, and municipal solid waste. As the municipal solid waste data are a recent addition to the database and cover only currently active projects with financial closures starting in 2009, they were dropped from the sample to make all projects comparable. ICT projects, merchant and rental greenfield projects, management and lease projects, and divestitures were also excluded from the sample, because they are not PPPs as defined in this study.

We define cancellation as early termination of a project. The PPI database labels as cancelled any project from which the private party has exited by selling or turning over its shares back to government or by ceasing operations. It labels as distressed any project that is in international arbitration or for which either the government or the private party has requested contract termination. Using news articles and other public online sources, we determined the current status of the distressed projects and relabeled projects as canceled, concluded, or active. When no definitive information about the resolution of the distress could be found, we dropped the project from the sample.

The contract periods of some projects in the PPI database that were completed are labeled as still active. Successfully concluded projects may have gone unnoticed because they were not covered in the news or because project companies may have obtained contract extensions after the fulfillment of their initial contract terms. Projects with completed contract periods that were still labeled as active were kept in the analysis and relabeled as concluded.

10
22 values on immunization status for any of the relevant vaccines, we conservatively assumed that the particular vaccine was not given.

A child is categorized as being born in a facility if it was born in a public or private health facility.

The DHS data on antenatal care was collected for the last birth only. For a child who is not the last birth, the value was approximated by its lastborn sibling. In our final sample (4668 observations), there are 1046 children (22.4%) who are not lastborn. We consider three dummy variables of antenatal care. All of them are considered not received if the mother responded "don't know." Protection against neonatal tetanus is achieved if a pregnant woman without any previous tetanus immunization receives at least two shots of tetanus vaccine during her pregnancy. One or zero tetanus shots during the pregnancy might be sufficient if the mother received previous tetanus immunization and depending on the number and timing of the previous tetanus vaccinations (KNBS and ICF Macro 2010). Due to unavailability of the information on previous tetanus immunization in the DHS 2003, we consider a child protected against neonatal tetanus only if its mother received at least two shots of tetanus vaccine during her pregnancy. The dummy variables on intake of any type of anti-malarial drugs during pregnancy and on intake of iron supplement during pregnancy are self-explanatory. Malaria or lack of iron during pregnancy might lead to anemia of both mother and child and to low birth weight, which in turn increases both neonatal and postneonatal mortality (KNBS and ICF Macro 2010, Snow and Marsh 2002).

Information on the source of drinking water and on the type of sanitation facility is represented by a set of three dummy variables thus distinguishing three stages of quality. In both cases, there is an "improved" category. The concept of improved source of drinking water and improved sanitation facility was proposed by the WHO and UNICEF in 2004 and implemented in the DHS 2008. An attempt was made to approximate the "improved" categories in the DHS 2003 data (KNBS and ICF Macro 2010). In order to obtain time-consistent water and sanitation variables, we slightly diverge from the categorization of improved water source and sanitation proposed in the Kenya DHS 2008 report. A total of 112 observations are excluded from the analysis due to a missing value on water and sanitation quality, which is caused by the mother not being a de jure resident of the household.
# **4. Results**

Standardized scores based on the results of the PD survey are used for the following analyses: (1) overall partner country performance in 2010; (2) characteristics of FCS performance in 2010; (3) FCS partner country progress between 2005 and 2010; (4) overall development partner performance in 2010; and (5) development partner performance in FCS in 2010. The first three analyses focus on partner countries, while the remaining two analyses focus on development partners. The results of the analyses identify key aid effectiveness challenges in FCS and also provide good performance examples drawing from both FCS and development partners.

# **(1) Partner Country Performance in 2010**

The sum of standardized scores across indicators provides a comprehensive picture of a partner country‘s performance relative to that of other countries. By definition, if the sum is zero, the country‘s aid effectiveness performance is an average. If it is positive (negative), performance of the country is above (below) average. It is important to note that all indicators are treated equally. In other words, there are no different weights among indicators and correlations among indicators have not taken into account. Table 4 and figure 2 show the results of the sum of the standardized scores in all countries<sup>10</sup> .

**Table** **<u>4: Results by Regions and Country Groups</u>**

||**Total **|**IDA**|**IBRD**|**FCS**|**Non-FCS**|
|---|---|---|---|---|---|
|Africa|0.4|0.3|0.1|-1.0|1.4|
|EastAsia andPacific|1.0|0.1|0.9|-0.3|1.2|
|Europe and Central Asia|-3.4|-1.5|-1.9|-1.9|-1.5|
|Latin America and Caribbean|-1.6|-1.2|-0.4|-0.1|-1.5|
|Middle East and North Africa|3.3|0.0|3.0|0.4|3.0|
|South Asia|0.9|0.9|0.0|-1.9|2.8|
|**Total**|**0.0**|**-0.2**|**0.4**|**-3.6**|**1.0**|

_Note 1: Based on World Bank’s Classifications; sum of IDA and IBRD is not zero due to West Bank and Gaza that is not classified as IDA nor IBRD._ _Note 2: This is average sum of indicators by the number of countries in regions and country groups. Note 3: shadowed areas show negative scores Source: Author’s Calculation_

> 10 Standardized scores of all indicators by regions, country groups and countries are available in Appendix 2.

8
# **Table A.11. Predicted Poverty Rates Based on Imputation, from NPS 2020/21 to TA 1 using 2019/20 Poverty Line, Tanzania** **<u>(percentage)</u>**

|<br>**Method**|||||**TA 1**|||||
|---|---|---|---|---|---|---|---|---|---|
||**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 6**|**Model 7**|**Model 8**|**Model 9**|
|1 Nl li i dl|**18.2**<sup>**a**</sup>|**19.1**<sup>**a**</sup>|**17.8**<sup>**a**</sup>|**15.5**|**17.9**<sup>**a**</sup>|**17.6**<sup>**a**</sup>|**19.1**<sup>**a**</sup>|**20.1**|**20.3**|
|) orma near regresson moe|(2.0)|(2.1)|(2.0)|(2.0)|(2.0)|(2.0)|(2.1)|(2.1)|(2.1)|
|2) Empirical distribution of the error|**17.7**<sup>**a**</sup>|**18.8**<sup>**a**</sup>|**17.5**<sup>**a**</sup>|**14.9**|**17.5**<sup>**a**</sup>|**17.3**<sup>**a**</sup>|**18.7**<sup>**a**</sup>|**20.0**|**19.9**|
|terms|(2.0)|(2.1)|(2.0)|(2.0)|(2.0)|(2.0)|(2.1)|(2.1)|(2.1)|
|_Control variables_||||||||||
|Food expenditures|||Y|||||||
|Non-food expenditures||||Y||||||
|Furnishings and household expenses|||||Y|||||
|Health expenditures||||||Y||||
|Education expenditures|||||||Y|||
|Utilities: water, kerosene, lighting||||||||Y|Y|
|Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|R<sup>2</sup>|0.34|0.46|0.84|0.65|0.47|0.47|0.45|0.47|0.37|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|4,644|
|N2(target survey)|711|711|711|711|711|711|711|711|711|
|**True poverty rate in TA 1**|||||17.7|||||
|<br>**Note**: The standard errors in parentheses are<br>normal linear regression model with the theore<br>employ cluster random effects. The imputed p<br>The true poverty rate for TA 1 is the estimate<br>with the annual WB deflator. The estimates sh<br>underlying regression results are provided in A|adjusted for c<br>tical distribu<br>overty rates f<br>directly obtai<br>own in bold<br>ppendix A,|omplex surv<br>tion of the err<br>or TA 1 use t<br>ned using the<br>or with an “a”<br>Table A.6.|ey design. A<br>or terms and<br>he estimated<br>consumptio<br>respectively|ll estimates a<br>Method 2 us<br>parameters b<br>n data in TA1<br>fall within t|(1.9)<br>re obtained<br>es the empir<br>ased on the 2<br>and the pov<br>he 95% CI o|with the pop<br>ical distributi<br>019/20 data.<br>erty line in N<br>r one standar|ulation wei<br>on of the er<br>1000 simul<br>PS 2019/20<br>d error of th|ghts. Method<br>ror terms. Bo<br>ations are im<br>deflated to<br>e true pover|1 uses the<br>th methods<br>plemented.<br>2022 prices<br>ty rate. The|

55
# **1. Introduction**

The estimated number of people in the world that live in extreme poverty has become an increasingly important (inverse) indicator for measuring development progress. The first Millennium Development Goal (MDG) aimed to halve the share of people living in extreme poverty between 1990 and 2015.<sup>1</sup> In 2013, the World Bank adopted a goal of ending extreme poverty by 2030.<sup>2</sup> In 2015, the United Nations adopted a goal of ‘ending poverty in all its forms’ by 2030 among the Sustainable Development Goals (SDGs).<sup>3</sup> Several national governments, bilateral development agencies and non-governmental organizations are also focusing their efforts on reducing, and ultimately ending, extreme poverty. As one of the most prominent indicators of economic development, the level and trends in extreme poverty are therefore a topic of great interest to both policymakers and the public at large.

Despite the strong policy focus on ending extreme poverty globally, its definition and measurement remain challenging endeavors, subject to much debate regarding the most appropriate concepts, methods and data. At the core of this debate is how to compare the standards of living of widely different peoples, consuming vastly different goods and services, all priced in different currencies. One dimension of this question is how to define a common threshold (or poverty line) across countries and over time, which represents the same standard of living, below which a person is considered poor.

Since the early 1990s, the World Bank has monitored global extreme poverty using an international poverty line that was explicitly based upon the national poverty lines of some of the poorest countries in the world. For ease of communication, this international poverty line has always been expressed in U.S. dollars (USD), but when used for measuring poverty, the line is converted into local currencies through purchasing power parity (PPP) exchange rates, in an

> 1 Target 1.A of the United Nations Millennium Development Goals (MDGs) is to “halve, between 1990 and 2015, the proportion of people whose income is less than $1.25 a day.” For more details on the MDGs, see www.un.org/millenniumgoals/.

> 2 In April 2013, the World Bank Group announced the goal of ending extreme poverty, which it defined specifically as reducing “the percentage of people living with less than $1.25 a day [...] to no more than 3 percent globally by 2030.” For more details, see: www.worldbank.org/content/dam/Worldbank/document/WB-goals2013.pdf.

> 3Target 1.1 of the SDGs describes the goal as: “By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day.” For more details on the SDGs, see: https://sustainabledevelopment.un.org/?menu=1300

2
**<u>Table 6 Determinants of liabilities of oil-importing countries</u>**

||_FDI_|_Portfolio_|_Debt_|_Total_|
|---|---|---|---|---|
|Ln(Credit)|0.189***|0.0279***|0.108***|0.325***|
||(0.0157)|(0.00504)|(0.0401)|(0.0467)|
|Intermediate exchange rate|0.114***|–0.00821|–0.0200|0.0855|
||(0.0233)|(0.00750)|(0.0596)|(0.0694)|
|Constant|–0.346***|–0.0777***|–0.0930|–0.517*|
||(0.0931)|(0.0300)|(0.238)|(0.277)|
|Year dummies|Yes|Yes|Yes|Yes|
|Observations|292|292|292|292|
|Number of countries|10|10|10|10|

_Note_ : Standard errors in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

Regarding the balance of payments components, the existence of an SWF positively affects the accumulation of foreign reserves. From a policy perspective, this result is crucial to oilimporting countries that are currently accumulating reserves but will have to create SWFs in order to better allocate their assets. As expected, financial development and the oil exporter dummy have a positive and negative effect on reserves respectively. As in the case of assets, an intermediate exchange rate reduces assets and hence reserves, because it is less transparent than a fixed or flexible exchange rate regime (table 7). The results for the capital account are also in line with theory: The higher the level of financial development, the more the country invests abroad and hence the larger the deficit in its capital account. The same result applies for the oil dummy.

**Table 7 Determinants of reserves and the capital account – All countries**

||_Reserves_|_Capital account_|
|---|---|---|
|Sovereign wealth fund|0.373**|0.000185|
||(0.189)|(0.000172)|
|Ln(Credit)|0.160***|–0.000164**|
||(0.0567)|(6.84e–05)|
|Intermediate exchange rate|–0.260**|–5.75e–05|
||(0.104)|(0.000124)|
|Oil|1.203***|–0.000310**|
||(0.419)|(0.000145)|
|Constant|2.748***|0.000408|
||(0.400)|(0.00118)|
|Year dummies|Yes|Yes|
|Observations|638|498|
|Number of countries|21|20|

_Note_ : Standard errors in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ < 0.1.

When we distinguish between oil exporters and oil importers, table 8 shows that financial development matters more for both reserves and the capital account of non-oil-exporting countries, 16
21 period up to 12 months of age (exclusive). Neonatal mortality covers the period from birth to 28 days of age (exclusive). (World Health Organization 2005) Cumulative measures of neonatal, postneonatal, and infant mortality are calculated as the number of deaths that were observed in the respective time period per 1000 live births. The calculations are based on the actual survival data of the individuals. Neonatal and postneonatal mortality rates for a cohort sum up to the infant mortality rate. These definitions were chosen so as to be compatible with standard measures, such as those used in official Demographic and Health Survey reports. However, the use of the number of live births, rather than the population surviving to 28 days (the beginning of the postneonatal period) as the reference group for postneonatal mortality generates odd properties for the postneonatal mortality measure. (For example, the upper boundary for the postneonatal mortality rate is less than 1.) To examine the sensitivity of the results, the postneonatal analysis was repeated conditional on survival to at least 28 days. These results (not shown) are very similar to those using the unconditional measured used in the paper.

# **Definitions of explanatory variables used in the analysis**

Due to data unavailability on ITN usage for deceased children, we use a dummy variable on ITN ownership by the household at the time of the interview as a proxy of ITN usage by the child.

Vaccination records are not available for deceased children. Thus, we use immunization rate at district level as an approximation of the individual immunization status. The immunization rate is calculated as the fraction of fully immunized children at district level in our full sample (4827 observations). The definition of full immunization was developed by the World Health Organization (WHO) and is applied by the DHS. A child is considered fully immunized if it received a BCG vaccination (against TBC), three doses of the DPT-HepB-Hib (also called Pentavalent), three doses of polio vaccines (excluding polio vaccine given at birth), and a vaccination against measles. All of these vaccinations are recommended to be given before reaching the age of 12 months (KNBS and ICF Macro 2010). For children with "don't know"
|Figure 9. Urban versus rural access to water supply and sanitation, 2006|22|
|---|---|
|Figure 10. The hidden costs of water supply, as a percentage of revenue|24|
|Figure 11. Power prices, benchmarked against other Sub-Saharan African countries<br>|27<br>|
|Figure 12. ENERCA’s average revenue and total costs, 2002–09|28|
|Figure 13. Hidden costs, as percentage of revenues<br>|29<br>|
|Figure 14. Efficient market gap, benchmarked against Central African countries<br>|31<br>|
|Figure 15. Internet market, 2008<br>|32<br>|
|Figure 16. Infrastructure spending needs in the regional context|37|
|Figure 17. Transport spending needs compared to Central African peers|38|
|Figure 18. Existing infrastructure spending in a regional context|40|
|Figure 19. Patterns of capital investment in infrastructure benchmarked against comparator countries<br>|41|
|Figure 20. Patterns of public spending on infrastructure<br>|42<br>|
|Figure 21. ODA commitments by sector<br>|42|
|Figure 22. Private investment in the Central African Republic and select African countries|43|
|Figure 23. Uncollected bills and unaccounted losses in power and water utilities, 2009|45|
|Figure 24. Underpricing of power and water in the Central African Republic and comparator countries<br>|46<br>|
|Figure 25. Consumption of infrastructure services is highly differentiated by budget, 2006|46|
|List of tables<br>Table 1. Achievements and challenges in various infrastructure sectors|9|
|Table 2. Trading across borders in Central African countries|10|
|Table 3. Road indicators, benchmarked against Sub-Saharan African fragile and low-income countries|12|
|Table 4. Condition and type of road corridors|13|
|Table 5. Air transport indicators, benchmarked against select other countries|17|
|Table 6. Irrigation potential for small-scale projects|19|
|Table 7. Benchmarking water and sanitation indicators|21|
|Table 8. Evolution of operational indicators associated with SODECA, Central African Republic|23|
|Table 9. Power indicators, benchmarked against select groups of countries|25|
|Table 10. Large inefficiencies drain significant potential revenues from ENERCA|28|
|Table 11. ICT indicators, benchmarked against Sub-Saharan African fragile and low- income countries|30|
|Table 12. Players in the ICT sector|31|
|Table 13. Submarine cables and competition, ICT prices, 2008<br>|33<br>|
|Table 14. Illustrative investment targets for infrastructure|36|
|Table 15. Indicative infrastructure spending needs for 2006–15|36|
|Table 16. Transport needs|37|
|Table 17. ICT spending needs|38|
|<br>Table 18. Power spending needs|39|
|<br>Table 19. Water supply and sanitation spending needs|39|
|Table 20. Financial flows to infrastructure|40|
|Table 21. Potential gains from greater operational efficiency|44|
|Table 22. Funding gaps by sector|47|
|Table 23. The size and the composition of the funding gap by sector, O&M and capital|47|

iv