rafmacalaba/datause-encoder-data huggingface dataset:
|**Intervention Category**<br>**(down) vs. Outcomes**<br>**(across)**|**Increasing Educational Attainment**|**Delaying Marriage**|**Delaying Fertility**|
|---|---|---|---|
|**services and modern**<br>**contraceptives**|interventions to teens seeking services<br>might be highly effective at reducing<br>pregnancy-related school dropouts.|interventions to teens seeking services<br>might be highly effective at delaying<br>marriages related to unintended<br>pregnancies.|promising results in HIC settings.<br>Evidence suggests teens more price<br>sensitive w.r.t. FP services. Provision of<br>subsidies for high quality contraceptives<br>to adolescents_seeking_these services<br>highly effective in terms of take-up and<br>thus likely effective in reducing teen<br>pregnancies. Challenges still remain to<br>increase take-up of those not seeking<br>services but sexually active. Caveat: if<br>offering to married teens, consider<br>complementing with interventions that<br>include and/or inform partners.|
|**Information on SRH**<br>**and FP**|**Mixed.**Evidence on effects on schooling<br>limited. No effect of abstinence-until-<br>marriage focused HIV training in Kenya,<br>while mixed results on dropouts in<br>Cameroon. We conjecture that, given the<br>mixed results on pregnancy (and risky<br>sexual behavior more generally), there are<br>unlikely to be strong flow-on effects to<br>education.|**Unknown.**Very little evidence on effects<br>on marriage. We conjecture that, given the<br>mixed results on pregnancy, there are<br>unlikely to be strong flow-on effects to<br>marriage.|<br>**Mixed.**School-based sex education<br>programs have mixed results on risky<br>sexual behavior and, in turn, teen<br>pregnancy. Evidence suggests the way in<br>which the information is delivered, what<br>information is presented, and potentially<br>who presents it matters (e.g., no effect of<br>abstinence until marriage interventions<br>while age-HIV-risk profile interventions<br>promising; outside consultants delivering<br>information sessions promising as are in-<br>class questionaries that make risks<br>salient). Interventions for married couples<br>on FP services and maternal risks,_while_<br>_limited_, appears effective at reducing<br>pregnancies and desired number of<br>children as well as improving happiness<br>within marriages. Finally, no effect of<br>goal-setting SRH intervention for girls or<br>SRH information intervention for boys in<br>Tanzania.|
|**Cash transfers**|**Effective.**Cash transfers conditioned on<br>schooling and well-targeted are very<br>effective at increasing enrollment &|**Mixed (context-dependent).**Depends on<br>setting (dowry vs. bride-price),<br>conditionalities, and targeting. In dowry|**Mixed (context-dependent).**Depends on<br>setting (dowry vs. bride-price),<br>conditionalities, and targeting. In dowry|

11
<!-- Start of picture text --> Schanae Export competitiveness Schanae Demand for exports<br>20 30<br>PX/PC Real exports<br>10 —— REER 20 — Foreign demand<br>0<br>10<br>-10<br>0<br>-20<br>-30 -10<br>-40 -20<br>98 oo 02 04 06 08 10 12 14 16 18 98 00 02 04 06 08 10 12 14 16 18<br><!-- End of picture text --> =at+é0

|+B A(pxkey )
# Figure 21. SSA: Economic growth and drivers of potential growth

A. GDP Growth <!-- Start of picture text --> A. GDP Growth B. Contribution to potential GDP growth<br>Percent SSA Median Percent TFP Capital Labor Potential growth<br>10 8<br>6<br>8<br>4<br>6<br>2<br>4 0<br>2<br>0<br>-2 EMDEs SSA SSA excl. South<br>2000-21 2000-10 2011-21 2022-23 Africa<br>C. Investment growth D. Potential TFP growth<br>Percent Percent<br>SSA Median SSA Median<br>14<br>4<br>12<br>10 3<br>8<br>2<br>6<br>4<br>1<br>2<br>0 0<br>2000-21 2000-10 2011-21 2000-21 2000-10 2011-21<br>E. Secondary education attainment F. Working-age population growth<br>Percent Percent<br>SSA Median 3 SSA Median<br>100<br>80<br>2<br>60<br>40<br>1<br>20<br>0 0<br>2000-20 2011-20 2000-2021 2000-2010 2011-2021<br>2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30 2000-21 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables; UN Population Prospects; World Bank; World Bank, World Development Indicators database.

Note: EMDEs = emerging market and developing economies; SSA = Sub-Saharan Africa. Shaded areas indicate forecast. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21.

A.C.D.F. Bars show period averages of annual GDP-weighted averages. Markers show median of GDP-weighted averages of six EMDE regions; vertical lines denote range of regional averages.

B. Period averages of annual GDP-weighted averages. Estimates based on production function approach. Sample includes 53 EMDEs (14 from SSA).

C. D. Sample includes 14 SSA economies (where potential growth estimate is available for both investment growth and TFP growth measures for the period 2000-19).

E. Period averages of simple annual averages. Percentage of population ages 25 and above that completed at least lower secondary education.

F. Working-age population refers to population ages 15-64. Sample includes 14 SSA economies.
**Table 2: Convergence test**<sup>**1**</sup>

|**Period**|**Rice**|**Wheat**|**Maize**|
|---|---|---|---|
||(1)|(2)|(3)|
|2005-2017|1.724|-0.316 **|-0.454***|
||(0.194)|(0.153)|(0.127)|
|2005-2011|2.864|0.452|-0.382***|
||(0.017)|(0.038)|(0.132)|
|2012-2017|-0.959 ***|-1.610 ***|-1.696***|
||(0.042)|(0.016)|(0.013)|

1Standard errors in parentheses. *Rejecting convergence at 10 percent, **Rejecting convergence at 5 percent, ***Rejecting convergence at 1 percent.

**Table 3: Club convergence test: 2012-2017**<sup>**1**</sup>

|**Period**|**Rice**|**Wheat**|**Maize**|
|---|---|---|---|
||(1)|(2)|(3)|
|Club 1|0.882|-0.061|1.468|
|Club 2|0.619|0.040|0.123|
|Club 3|0.625|0.098|-0.072|
|Club 4||0.234|-0.915|
|Club 5||0.094||
|Club NC|-1.526 ***||-2.284 ***|

1*Rejecting convergence at 10 percent, **Rejecting convergence at 5 percent, ***Rejecting convergence at 1 percent.

31
# **Figure 2**

Marginal Effect of Access to Electricity by Intensity of COVID <!-- Start of picture text --> 2 3 4 5 6 7 8 9 10 11 12 131415<br>COVID Growth Shock (2020)<br>.4<br>.3<br>.2<br>.1<br>Change in Access to Electricity<br>Change in Digital Payments from<br><!-- End of picture text --> Notes. Figure derived from Table 5, model 2. Horizontal scale is logarithmic. The line reflects average marginal effects, the whisker endpoints mark the 90% confidence intervals.

41
**Table 12. Large hidden costs at associated with the power sector in Uganda, 1998-2009**

||Power<br>billings|System<br>losses|Implicit<br>Collection<br>Ratio|Cost recovery<br>benchmark|Average<br>Revenue<br>Average<br>effective tariff|<br>Total hidden<br>costs<br>Total hidden<br>costs|
|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|(US$/kWh)|(US$/kWh)<br>(US$/kWh)|(US$million/year)<br>(%revenues)|
|1998|865|36|81|0.09|0.08<br>0.08|153<br>249|
|1999|876|42|81|0.08|0.08<br>0.07|167<br>287|
|2000|1094|36|81|0.07|0.07<br>0.06|207<br>308|
|2001|1019|41|81|0.08|0.08<br>0.10|174<br>227|
|2002|1139|41|81|0.08|0.08<br>0.09|199<br>212|
|2003|1252|35|81|0.08|0.08<br>0.09|48<br>52|
|2004|1226|41|81|0.08|0.07<br>0.10|56<br>61|
|2005|1139|43|86|0.13|0.08<br>0.09|120<br>122|
|2006|1043|38|85|0.20|0.12<br>0.12|162<br>134|
|2007|1204|40|93|0.23|0.17<br>0.18|159<br>74|
|2008|1345|39|90|0.26|0.18<br>0.16|277<br>115|
|2009|1483|40|94|0.24|0.15<br>0.17|234<br>103|
|_Source:_<br>**Figure**<br>**relativ**|Briceño-Garme<br>**s 22a and 22b**<br>**e to several n**<br>350|ndia, Smits, a<br>**.** **Hidden co**<br>**eighbors**|nd Foster 2009<br>**sts in revenu**|; World Bank 2008<br>**e terms decreas**|and 2010a._Note:_kWh = kilowat<br>**ed between 1998 and 2005**<br><br>|t-hour; GWh = gigawatt-hour.<br>**but are on the higher side**|
|**%)**|||||Malawi<br>**of**||
|**ue (**|300||||Botswana<br>**are**||
|**ven**|250||||Tanzania<br>**sh**<br>**)**||
|**a share of re**|150<br>200||||<br>Ethiopia<br>Uganda<br><br>**osts as a**<br>**evenue(%**||
|**en costs as**|0<br>50<br>100||||Rwanda<br>Kenya<br>DRC Congo<br>**Hidden c**<br>**r**||
|**Hidd**|1998<br>1999|2000<br>2001<br>2002|2003<br>2004<br>2005<br>|2006<br>2007<br>2008<br>2009||0<br>50<br>100<br>150<br>200<br>250|
|U|naccounted Loss|es<br>Under-P|ricing<br>Collec|tion Inefficiencies|Losses<br>U|nderpricing<br>Collection Inefficiencies|

_Source:_ Briceño-Garmendia, Smits, and Foster 2009; and World Bank 2008 and 2010a. **[[Remove hyphen from under-pricing in figure.]]** _Note:_ DRC = Democratic Republic of Congo.

37
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37
|P128605|2017|2019|10/2/2018|4 Active|Turkey|
|---|---|---|---|---|---|
|P128605|2017|2019|5/1/2019|5 Active|Turkey|
|P128605|2017|2020|10/31/2019|6 Active|Turkey|
|P128605|2017|2020|5/9/2020|7 Active|Turkey|
|P128628|2014|2014|12/30/2013|1 Active|Congo,Republic of|
|P128628|2014|2014|6/22/2014|2 Active|Congo,Republic of|
|P128628|2014|2015|12/23/2014|3 Active|Congo,Republic of|
|P128628|2014|2015|6/22/2015|4 Active|Congo,Republic of|
|P128628|2014|2016|12/20/2015|5 Active|Congo,Republic of|
|P128628|2014|2016|6/19/2016|6 Active|Congo,Republic of|
|P128628|2014|2017|12/21/2016|7 Active|Congo,Republic of|
|P128628|2014|2017|6/17/2017|8 Active|Congo,Republic of|
|P128628|2014|2018|1/25/2018|9 Active|Congo,Republic of|
|P128628|2014|2019|1/8/2019|10 Active|Congo,Republic of|
|P128628|2014|2019|6/24/2019|11 Active|Congo,Republic of|
|P128628|2014|2020|12/30/2019|12 Active|Congo,Republic of|
|P128628|2014|2020|5/13/2020|13 Active|Congo,Republic of|
|P128634|2012|2013||0 Closed|India|
|P128644|2013|2013||0 Closed|Sudan|
|P128644|2013|2014|12/7/2013|1 Closed|Sudan|
|P128644|2013|2014|5/14/2014|2 Closed|Sudan|
|P128644|2013|2015|11/9/2014|3 Closed|Sudan|
|P128644|2013|2015|4/9/2015|4 Closed|Sudan|
|P128644|2013|2016|10/7/2015|5 Closed|Sudan|
|P128644|2013|2016|1/10/2016|6 Closed|Sudan|
|P128644|2013|2016|6/30/2016|7 Closed|Sudan|
|P128644|2013|2017|1/11/2017|8 Closed|Sudan|
|P128644|2013|2017|6/30/2017|9 Closed|Sudan|
|P128644|2013|2018|5/1/2018|10 Closed|Sudan|
|P128644|2013|2019|1/18/2019|11 Closed|Sudan|
|P128653|2012|2012||0 Closed|Sierra Leone|
|P128653|2012|2013||0 Closed|Sierra Leone|
|P128654|2015|2016|1/6/2016|1 Closed|Morocco|
|P128654|2015|2017|12/22/2016|2 Closed|Morocco|
|P128654|2015|2018|9/19/2017|3 Closed|Morocco|
|P128654|2015|2019|10/19/2018|4 Closed|Morocco|
|P128654|2015|2021|7/22/2020|5 Closed|Morocco|
|P128662|2012|2013|4/23/2013|1 Closed|Turkey|
|P128662|2012|2014|12/7/2013|2 Closed|Turkey|
|P128662|2012|2015|11/29/2014|3 Closed|Turkey|
|P128680|2012|2020|7/22/2019|1 Closed|Kenya|
|P128685|2012|2012||0 Closed|Philippines|
|P128685|2012|2013||0 Closed|Philippines|
|P128689|2012|2012|12/13/2011|1 Closed|Jordan|
|P128689|2012|2013|10/30/2012|2 Closed|Jordan|
|P128689|2012|2014|7/9/2013|3 Closed|Jordan|
|P128689|2012|2014|6/8/2014|4 Closed|Jordan|
# Figure 11.B. Global expansions by country group

## A. Output per capita, advanced economies

## B. Output per capita, EMDEs

<!-- Start of picture text --> Percent Percent<br>4 Excluding the initial year of recovery 4 Excluding the initial year of recovery<br>3 3<br>2 2<br>1 1<br>0 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br>C. Trade, advanced economies D. Trade, EMDEs<br>Percent Percent<br>8 Excluding the initial year of recovery 9 Excluding the initial year of recovery<br>6<br>6<br>4<br>3<br>2<br>0 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br>E. Credit, advanced economies F. Credit, EMDEs<br>Percent Percent<br>8 Excluding the initial year of recovery 12 Excluding the initial year of recovery<br>6 9<br>4 6<br>2 3<br>0 0<br>1976-81 1983-90 1992-2008 2010-19 1976-81 1983-90 1992-2008 2010-19<br>Sources: Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-<br>operation and Development; World Bank.<br><!-- End of picture text --> Note: Each bar represents average growth during the periods of global expansions. The growth rates of per capita output and credit are market-weighted (by GDP in U.S. dollars).

47
argument that aid has a positive effect, but that its effect is conditional on having good institutions, has additional support from theory, case studies, and project level evidence. Also convincing is the recent poll of “opinion makers” in developing countries quoted above; it found overwhelming support for the view that corruption often limits the effectiveness of aid [PSRA (2003)]. We would like to think that all this evidence, together, has encouraged a shift in the allocation of aid, away from an indiscriminate distribution toward one that favors developing countries with reasonably good institutions and policies.

# **_1. Shifting Aid Allocation from the 1980s to the 1990s_**

In studying the relationship between institutions and growth, researchers have used a number of different measures of institutional quality. Several recent studies [Easterly and Levine (2003); Kaufmann, Kraay, and Zoido-Lobaton (1999)] use an overall index of institutional quality that is compiled by standardizing and averaging all of the different institutional variables available in the second half of the 1990s (hereafter referred to as KKZ). This overall measure is ideal for our growth analysis in the next section, because we need a summary measure of growth-enhancing institutions and policies to interact with aid and this particular measure is available for more than 120 countries in the 1990s. However, in this section we want to look at how the allocation of aid has changed between the 1980s and 1990s, and for this we need institutional quality measures that are available over a longer span of time.

Some of the other institutional quality measures that have been used in the empirical growth literature are: the rule of law component from the KKZ aggregation [Dollar and Kraay (2003); Rodrik, Subramanian, and Trebbi (2002)]; the International Country Risk Guide (ICRG) expropriation risk index [Acemoglu, Johnson, and Robinson (2001)]; composite ICRG and Business Environmental Risk Intelligence (BERI) indexes [Knack and Keefer (1995)]; an index combining five ICRG indicators with the SachsWarner openness index [Hall and Jones (1999)]; the bureaucratic efficiency, political stability and institutional efficiency indexes composed of nine Business International (BI) indicators [Mauro (1999)]; and the composite ICRG index and the Freedom House 8
Gregorio Inpavido, Alberto R. Musalem, and Dimitri Vittas simultaneously available for M2, GDP, population, imports of goods and services and export of goods and services

**Table** 1: **Countries with small financial sectors,** 1999

|**Country**|**M2~**<br>**(US$**<br>**00000**<br>oloooo)|<br>**GDP**<br>**(US$**<br>**0y**<br>**000000)**|**Percapita**<br>**GDP**<br>**(us$)**|<br>**Population**<br>**(000)**|**Openness.**<br>(X+MyGD|**M2IGDP**<br>P<br>(0/a)|
|---|---|---|---|---|---|---|
|SaoTomeandPrincipe|11.46|46.40|319.44|145.26|119.31|24.71|
|Comoros|40.49|209.10|384.18|544.28|66.74|19.36|
|Equatorial Guinea|57.11|873.39|1,972.97|442.68|188.21|6.54|
|Guinea-Bissau|66.22|239.57|202.22|1,184.67|69.57|27.64|
|Guinea|71.74|1,280.36|176.59|7,250.52|44.88|5.60|
|SierraLeone|107.62|775.80|156.75|4,949.34|34.13|13.87|
|Gambia,The|129.03|415.50|332.14|1,251.00|117.14|31.05|
|Mauritania|135.47|968.08|372.58|2,598.33|87.77|13.99|
|Niger|150.34|2,162.50|206.04|10,495.60|38.35|6.95|
|Chad|166.93|1,510.31|201.76|7,485.61|47.33|11.05|
|Burundi|169.35|850.92|127.42|6,677.95|27.09|19.90|
|KyrgyzRepublic|169.74|1,429.68|293.89|4,864.60|99.20|11.87|
|CentralAfricanRepublic|175.96|1,107.98|313.00|3,539.81|40.90|15.88|
|Dominica|195.96|278.49|3,814.89|73.00|124.97|70.36|
|Bhutan|196.60|500.80|640.62|781.75|75.00|39.26|
|Armenia|204.56|1,965.31|515.98|3,808.86|70.67|10.41|
|Georgia|220.77|2,961.94|543.28|5,452.00|73.01|7.45|
|St.KittsandNevis|236.33|306.26|7,491.70|40.88|118.14|77.17|
|Moldova|239.64|1,365.33|318.93|4,281.02|114.82|17.55|
|St.VincentandtheGrenadines|244.09|353.58|3,099.43|114.08|122.76|69.03|
|Malawi|271.60|2,022.20|187.45|10,787.80|69.63|13.43|
|Rwanda|295.58|2,030.63|_244.36|83140.00|26.71|-14.56|
|Grenada|338.44|389.01|4,010.39|97.00|125.40|87.00|
|Congo,Rep.|342.49|2,417.89|845.78|2,858.76|148.43|14.16|
|Togo|347.08|1,461.54|320.03|4,566.94|70.35|23.75|
|CapeVerde|348.85|620.80|1,451.17|427.79|73.51|56.19|
|Swaziland|364.24|1,311.76|1,286.71|1,019.47|205.20|27.77|
|Belize|400.31|783.86|3,175.45|246.85|107.26|51.07|
|St.Lucia|453.94|680.14|4,410.78|154.20|126.23|66.74|
|Guyana|458.54|712.70|832.67|855.92|206.11|64.34|
|Turkmenistan|490.86|3,641.08|761.84|4,779.33|103.53|13.48|
|Antiguaand Barbuda|512.96|669.80|9,933.25|67.43|158.36|76.58|
|Azerbaijan|529.93|4,390.58|549.99|7,983.00|84.45|12.07|
|Seychelles|544.62|599.36|7,489.17|80.03|159.36|90.87|
|Zambia|585.38|3,533.03|357.55|9,881.21|63.11|16.57|
|Mali|589.69|<br>2,583.30|244.08|<br>10,583.70|60.90|22.83|
|Burkina Faso|593.33|2,612.71|237.61|10,995.70|40.71|22.71|
|Benin|618.54|2,720.24|444.92|6,114.05|44.75|22.74|
|Macedonia,FYR|661.51|3,904.75|1,932.22|2,020.86|97.21|16.94|
|Gabon|724.09|4,286.11|3,546.90|1,208.41|83.47|16.89|
|Madagascar|740.15|4,045.69|268.81|15,050.50|57.41|18.29|
|Fiji|784.98|1,874.50|2,340.19|801.00|131.39|41.88|
|Uganda|878.02|6,383.84|297.21|21,479.30|34.21|13.75|

**-3** -
response to the recent wave of banking crises mirrors that of previous ones: Laeven and Valencia (2012) document that most countries provided significant funds for bank recapitalization, liquidity support as well as guarantees on bank liabilities, while bank nationalizations were less common.

Table 1. Banking Crises in the ECA region 2008 - 2011

|Country|Output<br>Loss|Fiscal Costs|Liquidity<br>Support|Peak NPLs|Increase in<br>Public Debt|
|---|---|---|---|---|---|
|Austria|14|5|8|3|15|
|Belgium|19|6|14|3|19|
|Denmark|36|3|11|5|25|
|France|23|1|7|4|17|
|Germany|11|2|4|4|18|
|Greece|43|27|42|15|45|
|Iceland|43|44|17|61|72|
|Ireland|106|41|16|13|73|
|Italy|32|0|6|11|9|
|Luxembourg|36|8|4|1|15|
|Netherlands|23|13|4|3|27|
|Portugal|37|0|17|7|34|
|Spain|39|4|6|6|31|
|Sweden|25|1|13|2|11|
|Switzerland|0|1|3|1|0|
|United Kingdom|25|9|6|4|24|
|Western Europe|32|10|11|9|27|
|Hungary|40|3|1|13|0|
|Kazakhstan|4|5.5|31.9|9.1|3.3|
|Latvia|106|6|3|16|28|
|Russia|0|2.3|23.9|9.6|6.4|
|Slovenia|38|4|10|12|18|
|Ukraine|2|4.5|9.2|15.5|28.9|
|Eastern Europe/Central Asia|32|5|13|12|16|

Source: Laeven and Valencia (2012) and LiTS. _Output loss:_ Cumulative sum of the differences between actual and trend real GDP over the period [T,T+3], expressed as a percentage of trend real GDP, with T the starting year of the crisis. _Fiscal costs:_ Fiscal costs are defined as the component of gross fiscal outlays related to the restructuring of the financial sector in percent of GDP. They include fiscal costs associated with bank recapitalizations but exclude asset purchases and direct liquidity assistance from the treasury. Liquidity support: is measured as the ratio of central bank claims on deposit money banks and liquidity support from the Treasury to total deposits and liabilities to non-residents (in%). Peak Non Performing Loans (NPL) are measured in percentage of outstanding private sector credit. _Increase in Public Debt:_ is computed as the difference between pre- and post-crisis debt projections (in % GDP). See Laeven & Valencia (2008, 2012) for detailed definitions and sources.

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