rafmacalaba/datause-encoder-data huggingface dataset:
# **4. IDENTIFICATION**

# **a. Experimental Design**

The evaluation design took advantage of the phased implementation of the program at the district level over a 23-month period. Rwanda manages its health care system at the district level and P4P is no exception. As a result the government mandated that all facilities in a district must be incorporated into the P4P scheme at the same time. Hence, the evaluation employed a stratified cluster randomized designed where districts were first grouped into blocks with common characteristics and then randomly assigned to treatment and comparison groups. Administrative districts with pre-existing P4P schemes were excluded from the experimental design. The remaining districts were grouped into blocks based on similar characteristics for relief, rainfall, and predominant livelihoods as per the 2002 Census.

However, just before implementation, administrative district boundaries were redrawn in the context of a government-wide decentralization effort (MINALOC 2004). As a result, some of the experimental areas were combined into new districts with areas that already had the pilot P4P schemes. Because P4P could not be “removed” from health facilities that were already implementing the schemes, and because P4P is managed at the district level, the MoH required that all facilities within those new districts be in the treatment group. This led the evaluation team to switch the assignment of treatment and comparison for eight districts. In the end, the study included 19 districts, of which 10 belonged to the treatment group and 9 to the comparison group.

The sample included 166 of Rwanda’s 401 primary care facilities, 80 in treatment districts and 86 in comparison districts. The facilities in the treatment group started receiving P4P in 2006, while the facilities in the comparison group continued with traditional input-based financing for an additional 23 months. P4P was introduced in treatment districts over a 5-month period, yielding a minimum program exposure of at least 18 months.

# **b. The Incentive Effect**

Since a primary objective of the evaluation was to isolate the impact of the P4P incentives separately from the effect of increased resources, it was necessary to hold the level of resources constant across treatment and comparison facilities. To accomplish this, comparison 12
Policy Research Working Paper 5130

# **Abstract**

Based on nationally representative samples from 13 Sub-Saharan African countries, this paper reinforces and expands previous findings that condom use in general is low in this region, men report using condoms more frequently than women, and unmarried individuals report they use condoms more frequently than married individuals with their spouse. Based on descriptive, bivariate, and multivariate analyses, the authors also demonstrate to a degree not previously shown in the current literature that married men from most countries report using condoms with extramarital partners about as frequently as unmarried men. However, married women from most countries included use condoms with extramarital partners less frequently than unmarried women. This result is especially troubling because marriage usually ensures regular sexual intercourse, providing more opportunities to pass HIV from extramarital partner to spouse than an unmarried person who may also have multiple partners but not as regular sexual intercourse.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to understand the determinants of the HIV/AIDS epidemic. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ddewalque@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
# **References**

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Figure 8. Bank Account Ownership – Individual Level <!-- Start of picture text --> Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>100 100<br>90 90 83.4<br>77.7<br>80 80 72.0<br>68.2<br>706050 55.9 47.1 56.2 47.2 57.6 44.0 58.6 48.1 59.8 48.7 706050 61.5 52.6 44.5 56.5 48.3 64.2<br>40 40<br>30 15.6 16.0 23.7 17.9 18.6 30<br>20 20 9.7 15.3 14.6 10.9<br>10 10 6.9<br>0<br>0<br>Brazil Bosnia and Turkey Belarus United Arab Brazil Bosnia and Turkey Belarus United Arab<br>Herzegovina Emirates Herzegovina Emirates<br>Coverage Dissimilarity EACR<br>Shapley Decomposition of the Dissimilarity Index, (Individual level, FINDEX)<br>2011 2014<br>100% 100%<br>5.3<br>90% 20.2 13.8 15.6 90% 21.0 13.2 16.8 14.8<br>0.7 20.8 32.3 0.0<br>80% 37.8 80%<br>10.4<br>70% 18.3 33.8 16.5 70% 6.6 39.4 37.6 37.3<br>60% 8.8 58.4 60%<br>50% 50% 25.8 33.2<br>40% 20.3 33.9 40% 18.2 17.8<br>30%<br>30% 57.4<br>20% 33.0 13.8 49.9 20% 35.4 35.4 29.2 45.6 30.1<br>27.6 10%<br>10%<br>14.0 0%<br>0%<br>Brazil Bosnia and Turkey Belarus United Arab Brazil Bosnia and Turkey Belarus United Arab<br>Herzegovina Emirates Herzegovina Emirates<br>Age Education Gender Income<br>Notes:. FINDEX is an individual level data base. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage<br>rates are shown. See Appendix for all countries. The variable control list for LITS and FINDEX varies.<br>Sources : FINDEX 2011 and 2014<br><!-- End of picture text --> Using the LITS, which is a household level database, Turkey’s coverage rate of bank account ownership decreases by over 17 percentage points; and the contribution of gender in the dissimilarity index is greatly reduced (Figure 9). The reduction in the role of gender can be explained by the low number of households with female head of household as well as the presence of male household members with bank accounts. In the Shapley decomposition of the dissimilarity index, it is also important to note that the effect of gender, at almost 18 percent, is still an important part of dissimilarity at the household level.

10
**Table 2.A Variables for analysis of fiscal procyclicality**

|**Variable**|**Description**|**Source**|
|---|---|---|
|Government spending|Real government expenditure|IMF_World Economic Outlook_<br>(WEO)|
|Political risk|Index based on 12 components with varying weights:<br>government stability, socioeconomic conditions,<br>investment profile, internal conflict, external conflict,<br>corruption, military in politics, religious tensions, law<br>and order, ethnic tensions, democratic<br>accountability, and bureaucracy quality.|PRS Group (database)|
|Bureaucracy quality|<br>Institutional strength and quality of the bureaucracy<br>is a shock absorber that tends to minimize revisions<br>of policy when governments change. In low-risk<br>countries, the bureaucracy is somewhat autonomous<br>from political pressure. Index measures the quality of<br>bureaucracy from 0 to 6.|PRS Group (database)|
|Control of corruption|<br>A measure of corruption within the political system<br>that is a threat to foreign investment by distorting<br>the economic and financial environment, reducing<br>the efficiency of government and business by<br>enabling people to assume positions of power<br>through patronage rather than ability, and<br>introducing inherent instability into the political<br>process. Index ranges from 0 to 6.|PRS Group (database)|
|Government stability|A measure of both the government’s ability to carry<br>out its declared program(s) and to stay in office. The<br>risk rating assigned is the sum of three<br>subcomponents: government unity, legislative<br>strength, and popular support|PRS Group (database)|
|Law and order|“Law” assesses the strength and impartiality of the<br>legal system, while the “Order” element is an<br>assessment of popular observance of the law.|PRS Group (database)|
|Capital account openness|The Chinn-Ito index (KAOPEN) measuring a country’s<br>degree of capital account openness; available from<br>1970-2019 for 182 countries.|Chinn and Ito (2006)|
|Exchange rate regime|A dummy indicator, where “1” is a floating exchange<br>rate regime and “0” is fixed. Classification codes 1-3<br>in the database have been classified as “0” and codes<br>4-6 as “1.”|Ilzetzki, Reinhart, and Rogoff<br>(2021)|
|Openness|Sum of exports and imports (percent of GDP)|World Integrated Trade<br>Solution (World Bank)|
|Fiscal rules|Covers four types of rules: budget balance rules, debt<br>rules, expenditure rules, and revenue rules, applying<br>to the central or general government or the public<br>|Davoodi et al. (2022)|
|SWF (sovereign wealth<br>fund)|sector.<br>Dummy variable: “0” if a country does not have an<br>SWF and “1” if it has an SWF.|https://www.swfinstitute.org/<br>fund-rankings/sovereign-<br>wealth-fund|

45
Fofack Hippolyte (2008). “Technology-Trap and Poverty Trap in Sub-Saharan Africa”, World Bank Policy Research Working Paper No. 4582, The World Bank Group.

Fofack Hippolyte and Leonce Ndikumana (2009). “Potential Gains from Capital Flight Repatriation for Sub-Saharan African Countries”, Forthcoming World Bank Policy Research Working Paper.

Friedman Thomas (2005). The World is Flat: A Brief History of the Twenty-First Century. Farrar, Strauss and Giroux, USA.

Gerchenkron Alexander (1962). _Economic Backwardness in Historical Perspective_ . Cambridge, MA: Belknap Press.

Goldstein Andrea, Nicolas Pinaud, Helmut Reisen and Xiaobao Chen (2006). “The Rise of China and India: What’s in it for Africa?” OECD Development Centre Studies. The Organization for Economic Cooperation and Development, Paris, France. World Economic Outlook International Monetary Fund (2006). World Economic Outlook: Globalization and Inflation. (April) International Monetary Fund (2007). “World Economic Outlook: Globalization and Inequality”, The International Monetary Fund, Washington DC.

International Monetary Fund (2009). “World Economic Outlook: Financial Stress, Downturns, and Recoveries”, The International Monetary Fund, Washington DC.

Jovanovic Boyan and Yaw Nyarko (1996). “Learning-by-Doing and the Choice of Technology”, Econometrica, **64** , 1299-1310.

Knack S. and P. Keefer (1995). “Institutions and Economic Performance: Cross-Country Test using Alternative Institutional Measures”, Economics and Politics, 7, 207-227.

Kneller R. and P. Stevens (2002). ”The Role of Efficiency as an Explanation of International Income Differences”, National Institute Discussion Paper No. 206.

Leamer Edward (2007). “A Flat, A Level Playing Field, a Small World After All, or None of the Above?” _Journal of Economic Literature_ , Vol. 45, No. 1.

Lucas Robert E. (1988). “On the Mechanics of Economic Development”, Journal of Monetary Economics, Vol. 22 (1) 3-42.

Lucas Robert E. (2007). “Trade and the Diffusion of the Industrial Revolution”, NBER Working Paper No. 13286.

Maddison Angus (1995). “Monitoring the World Economy, 1820-1992”. Paris: Organization for Economic Cooperation and Development.

Maddison Angus (2001). “The World Economy: A Millennial Perspective”. Paris: Organization for Economic Cooperation and Development.

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Mathews John and Mei-Chih Hu (2007). “Universities and Public Research Institutions as Drivers of Economic Development in Asia”, in S. Yusuf and K. Nabeshima, eds, “How Universities Promote Economic Growth”, The World Bank.

Montiel, Peter J. (2005). “Public Debt Management and Macroeconomic Stability: An Overview,” the _World Bank Research Observer_ , Vol. 20(2), pp. 259-81.

Mundell Robert (1997). “The International Monetary System of the 21<sup>st</sup> Century: Could Gold Make a Comeback?”, Columbia University, New York.

26
<!-- Start of picture text --> 2 HM Had only one peak<br>[-] Had a second peak<br>w<br>2<br>r=<br>Sa<br>fe)<br>oO<br>51<br>®<br>2<br>=)<br>Zz<br>0<br>-50 0 50<br>Early Late<br>Reopening wait<br>Date of reopening start minus date of first outbreak peak<br><!-- End of picture text -->
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 1*** 0.92***<br>H<br>Kendall tau-b 0.89*** 1*** 0.78***<br>Spearman 0.92*** 0.87*** 0.88***<br>A<br>Kendall tau-b 0.83*** 0.78*** 0.72***<br>Spearman 0.98*** 1*** 0.93***<br>M0<br>Kendall tau-b 0.94*** 1*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.2<br>1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C13. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Civil participation indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C13.1. FOSD: Counting Vector

<!-- Start of picture text --> C13.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0 -.1<br><!-- End of picture text --> C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations 62
Table 5. Poverty headcount, poverty gap, and poverty severity for different income concepts

||Poverty headcount<br>($3.20/day)|Poverty gap<br>($3.20/day)|Poverty severity<br>($3.20/day)|
|---|---|---|---|
|Gross market income||||
|plus pension|8.2|1.6|0.5|
|Disposable income|8.2|1.6|0.5|
|Consumableincome|9.3|2.2|1.0|

_Source_ : World Bank staff calculation using BLSS 2017.

Figure 4. The impact of fiscal systems on poverty reduction across countries (change in $3.20 poverty rate, percentage points)

|Bhutan (2017)<br>Turkey (2016)<br>Armenia (2011)|
|---|
|Moldova (2017)|
|Brazil (2008)|
|Peru (2011)|
|El Salvador (2011)|
|Dominican Republic (2006)|
|Romania (2016)|
|Jordan (2017)<br>Chile (2013)|
|Colombia (2017)|
|Mexico (2014)|
|Uruguay (2009)<br>Panama (2016)|
|-4<br>-2<br>0<br>2<br>4|

Note: A positive number indicates an increase in poverty. Pensions are treated as deferred income. Source: World Bank staff calculation using BLSS 2017.

# 4.2. Incidence, Progressivity, and Marginal Contribution of Taxes and Social Spending

Besides the overall impact of the net fiscal system, a relevant policy question could be which fiscal instrument is equalizing or not, and to what extent. For this purpose, we examine how the individual fiscal interventions contribute to the observed changes in poverty and inequality. Figure 5 shows the impact of broad categories of tax and benefit programs as a share of market income by decile.

There are a few notable findings in the results. First, Bhutanese households, except for those in the top 20 percent, are net receivers of fiscal interventions, as indicated by the net benefit position in Figure 5. Fiscal benefits occur overwhelmingly through education and health benefits which are progressive (that is, poorer 18
**Table 4.6 Stayers Vs. Movers (Probit)**

||**Stayers Wage=1**|**Stayers Non Wage=1**|
|---|---|---|
||**Movers Wage-Non Wage=0 **<br>|**Movers Non Wage-Wage=0 **<br>|
|**Log Disp. Income Per Capita**|**0.103**<br>**(25.45)****|**-0.001**<br>**(0.25)**|
|sex|0.083<br>(13.71)**|0.067<br>(9.66)**|
|age|-0.006<br>(3.89)**|0.028<br>(17.93)**|
|age2|0.000|-0.000|
||(3.83)**|(21.32)**|
|EDUC_Secondary|-0.008|0.646|
||(1.13)|(30.35)**|
|EDUC_Primary|0.246<br>(22.56)**|0.455<br>(20.59)**|
|EDUC_Inc. prim.or ill.||0.771<br>(22.21)**|
|HH SIZE|0.008|-0.099|
||(2.77)**|(26.99)**|
|HH head female|-0.033<br>(4.90)**|0.135<br>(16.69)**|
|HH No. of child.<16|0.027|0.005|
||(6.15)**|(1.00)|
|Rural|-0.168|0.005|
||(27.95)**|(0.47)|
|Constant|0.003|-1.024|
||(0.06)|(20.95)**|
|_Observations_|_230334_|_150488_|

Source: Moldova HBS 1998-2002. Absolute value of z statistics in parentheses. * significant at 5%; ** significant at 1%

# **5. JOB CREATION POLICIES IN SEGMENTED LOW-INCOME COUNTRIES**

Despite the deindustrialization process observed in the course of transition, agriculture and industry together remain the largest sectors in the CIS-7 and contribute to in between 30% and 60% of total output and in between 50% and 60% of total employment which would make these sectors the primary concern of governments. Yet, agricultural and industrial policies have been marginalized in the 1990s and today do not appear in any substantial form in government policies plans and programs.

Agricultural policies during the 1990s have focused on land reforms and privatization and in this respect they have been relatively successful. Armenia distributed more than 80% of the arable land to more than 320,000 families starting in 1995 and the 2002 World Bank Poverty Update for this country found a clear inverse correlation between land ownership and poverty. Georgia implemented land reforms in 1992 and 1996 distributing full land rights covering about a third of arable land with an additional 20% of the land leased to large producers. By April 1999, 918,000 hectares had been transferred to 1,026,000 rural households, while 825,000 additional hectares had been leased to 46,000 entities. Despite two severe droughts in the late 1990s, poverty rates in rural areas in Georgia have been maintained below urban levels<sup>25</sup> . In Kyrgyzstan, land reforms occurred in 1995 and 1997 and received a further boost after the 1998 referendum on land ownership with the introduction of a 99 years lease system. Several studies have pointed out that the

> 25 World Bank (2002) Georgia Poverty Update, Report No. 22350-ge

32
|Flood Rehabilitation Project<br>Abel Mejia|
|---|
|Flood Rehabilitation Project<br>Abel Mejia|
|Flood Rehabilitation Project<br>Abel Mejia|
|Flood Rehabilitation Project<br>Abel Mejia|
|Flood Rehabilitation Project<br>Abel Mejia|
|Flood Rehabilitation Project<br>Abel Mejia|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|Flood Protection Project<br>Manuel G. Marino|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|MINING SECTOR DEVELOPMENT T.A.<br>Gotthard Walser|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR-Decentralization & Improvement of SecondaryEduc. & PolymodJuan Prawda Witenberg|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR: Fourth Social Protection Project<br>Theresa Jones|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|
|AR-Maternal and Child Health and Nutrition 2(PROMIN)<br>Fernando Lavadenz|