rafmacalaba/datause-encoder-data huggingface dataset:
are damaged by drought is associated with a decline in child growth rate of approximately 0.12 cm. over a six month period in Ethiopia. Finally, Alderman, Hoogeveen, and Rossi (2008) use a panel extending for more than 10 years in the Kegara region of Tanzania to show that children who experienced weather shocks in early childhood were more likely to have low height-for-age, start school late, and have completed fewer years of schooling in adolescence. They use various simulations to show that this, in turn, translates into substantial reductions in earnings in adulthood.

Although much of the literature on the effects of droughts on investments in early childhood has focused on Africa, we close with a discussion of a recent paper by Maccini and Yang (2008) that uses data from Indonesia. Maccini and Yang match health, education and labor market outcomes for adults in a recent round of the Indonesian Family Life Survey (IFLS) with locality-specific rainfall for their birth year. Their results suggest substantial effects of rainfall in the first year of life on adult outcomes for women, but not for men. Women who were born in localities with higher rainfall have better self-reported health status, are taller, and have completed more years of schooling; these women had also accumulated more assets in adulthood. The coefficients for comparable regressions for men are never significant, suggesting that, in Indonesia, investments in boys are better protected from income fluctuations than investments in girls. These gender differences are reminiscent of the asymmetric effect of recessions on infant mortality reported by Baird, Friedman and Schady (2007).<sup>31</sup> To summarize, the picture on the effects of economic shocks on child health seems empirically clearer than that for schooling outcomes. In rich countries (effectively, in our sample of studies, for the United States), health status is counter-cyclical (i.e. mortality and morbidity fall during recessions). In terms of the conceptual framework, this is consistent with a move from point A to point B in Figure 2. Increases in adult time spent on health-promoting activities and, in the case of blacks, a reduction in the consumption of alcohol and cigarettes more than make up for reductions in the private consumption of health-increasing goods.

In developing countries, on the other hand, child health is pro-cyclical (i.e. infant mortality and malnutrition increase during recessions). In terms of Figure 2, this is consistent with a move from point A to point C during downturns or, in countries like Peru, where there

> 31 One limitation of the analysis by Maccini and Yang is that they do not separate results for positive and negative rainfall shocks. It is possible that gender interacts in important ways with the direction of the rainfall shock—see the discussion of Baird, Friedman and Schady (2007) above.

25
public sector management and institutions, including policies related to property rights, quality of public administration, and transparency. See Table 1 for a list of the 16 sub-dimensions. This table shows that there are statistically significant differences in the average of the 12 question scores across years. Trade with an average score of 3.96 has the highest score whereas the score on question 16, that deals with transparency, accountability and corruption in the public sector, with 3.05 has a score that is almost a full point lower. Both formally and in practice, the World Bank allocates more of its IDA aid to countries with higher CPIA ratings. The African and Asian Development Banks similarly allocate their aid using very similar policy indexes, and other donors take the CPIA (among other cross-country ratings) into account in allocating their aid. We therefore use the CPIA as our policy measure throughout the paper.

Table 1: Averages of CPIA components between 2004-2016

|Q1: Monetary and Exchange Rate Policies|0.39<sup>_∗∗∗_</sup><br>(0.03)|
|---|---|
|Q2: Fiscal Policy|0.06<sup>_∗∗_</sup><br>(0.03)|
|Q3: Debt Policy and Management|0.25<sup>_∗∗∗_</sup><br>(0.03)|
|Q4: Trade|0.54<sup>_∗∗∗_</sup><br>(0.03)|
|Q5: Financial Sector|-0.12<sup>_∗∗∗_</sup><br>(0.03)|
|Q7: Gender Equality|0.26<sup>_∗∗∗_</sup><br>(0.03)|
|Q8: Equity of Public Resource Use|0.11<sup>_∗∗∗_</sup><br>(0.03)|
|Q9: Building Human Resources|0.24<sup>_∗∗∗_</sup><br>(0.03)|
|Q10: Social Protection and Labor|-0.12<sup>_∗∗∗_</sup><br>(0.03)|
|Q11: Policies and Institutions for Environmental Sustainability|-0.01<br>(0.03)|
|Q12: Property Rights and Rule-based Governance|-0.33<sup>_∗∗∗_</sup><br>(0.03)|
|Q13: Quality of Budgetary and Financial Management|-0.01<br>(0.03)|
|Q14: Efciency of Revenue Mobilization|0.20<sup>_∗∗∗_</sup><br>(0.03)|
|Q15: Quality of Public Administration|-0.27<sup>_∗∗∗_</sup><br>(0.03)|
|Q16: Transparency, Accountability, and Corruption in the Public Sector|-0.36<sup>_∗∗∗_</sup><br>(0.03)|
|Constant|3.41<sup>_∗∗∗_</sup><br>(0.02)|
|N|26544|
|R-squared|0.10|
|F statistic|191.69|

The omitted category among the 16 CPIA categories is “Q6: Business Regulatory Environment.” 5
Figure 10. Growth Rate of Population Aged 9-12, 1950-2050, United Nations Medium Variant Projections <!-- Start of picture text --> Figure 10a. Selected Countries with Peak in Youth Population before 2000 Figure 10b. Selected Countries with Peak in Youth Population in 2000-2010<br>Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections<br>5.0% 6.0%<br>Costa Rica<br>4.0% Thailand 5.0% Mexico<br>China Brazil<br>Viet Nam<br>3.0% 4.0% Indonesia<br>2.0% 3.0%<br>1.0% 2.0%<br>0.0% 1.0%<br>-1.0% 0.0%<br>-2.0% -1.0%<br>-3.0% -2.0%<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Figure 10c. Selected Countries with Peak in Youth Population in 2010-2030 Figure 10d. Selected Countries with Peak in Youth Population after 2030<br>Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections<br>5.0% 6.0%<br>Philippines Kenya<br>Malaysia Democratic Republic of the Congo<br>4.0% Egypt 5.0% Iraq<br>Peru Senegal<br>India Pakistan<br>3.0% 4.0%<br>2.0% 3.0%<br>1.0% 2.0%<br>0.0% 1.0%<br>-1.0% 0.0%<br>-2.0% -1.0%<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Annual Growth Rate Annual Growth Rate<br>Annual Growth Rate Annual Growth Rate<br><!-- End of picture text --> 36
Trends are much more favorable on five measures of administrative corruption in the WEF. The average ECA _ranking_ improves on all five of these measures, although its average _rating_ on the 7-point scale fell from 4.8 to 4.4 on one of them, “irregular payments in judicial decisions.” This evidence from the WEF is remarkably consistent with the BEEPS in two major respects. First, there is evidence of improvement in administrative corruption, but not in state capture. Second, both sources “unbundle” administrative corruption in similar ways, finding more evidence of improvement for certain functions (licenses and permits, tax and customs, utilities) than for others (public contracts, judicial system).

The **Economist Intelligence Unit** (EIU) assigns countries to one of five categories, with a 1 for the least corrupt, and a 5 for the most corrupt. The average for 20 ECA countries included in the 2002 and 2005 ratings improved from 3.10 to 2.95. In contrast, the average for 63 other World Bank borrowers deteriorated from 3.21 to 3.28. The number of ECA countries with changes in their EIU corruption rating is small, as might be expected on a scale with only five categories. Corruption improvements were recorded for Latvia, Lithuania, Slovenia and Turkey, with Poland worsening, in all cases by only one category.

Both of the widely-known composite indexes of corruption show slight improvements for ECA relative to non-ECA countries. Among 152 countries (including most developed nations) with TI index values for both 2002 and 2005, the average ranking among the 28 ECA countries increased from 92<sup>nd</sup> to 88<sup>th</sup> .<sup>35</sup> Of course, evidence

> 35 The TI index provided on TI’s official web site lists somewhat fewer countries, namely only those for which at least three data sources were available.  Johann Lambsdorff lists additional countries for which only one or two data sources were available, on the web site of the Internet Center for Corruption Research (http://www.icgg.org/).

33
<!-- Start of picture text --> 14 ° Baltic Sea15 ° 16 ° Koszalin 17 ° 18 ° 19° Elblag POLAND<br>WARTA RIVER BASIN<br>SELECTED CITIES<br>POLAN D NATIONAL CAPITALS<br>Warta RiverBasin Olsztyn<br>MAJOR ROADS<br>INTERNATIONAL BOUNDARIES<br>Szczecin<br>This map was produced by the Map Design Unit of The World Bank.<br>The boundaries, colors, denominations and any other information<br>shown on this map do not imply, on the part of The World Bank<br>Bydgoszcz Group, any judgment on the legal status of any territory, or any<br>Pila ́ endorsement or acceptance of such boundaries.<br>53 ° 53°<br>Torun<br>Gorzów<br>Wielkopolski<br>Wloclawek<br>Plock<br>BERLIN<br>Poznan<br>WARSAW<br>Frankfurt<br>Konin P O L A N D<br>G E R M A N Y<br>52 ° 52°<br>Skierniewice<br>Dessau Zielona Góra<br>Leszno Lódz<br>Cottbus<br>Kalisz<br>Sieradz<br>Radom<br>Leipzig<br>Piotrków<br>Trybunalski<br>Görlitz<br>Legnica<br>Dresden<br>51 ° Wroclaw 51°<br>Jelenia<br>Góra<br>Kielce<br>Czestochowa<br>Walbrzych<br>Opole<br>CZECH<br>0 30 Kilometers REPUBLIC<br>Katowice<br>0 10 20 30 Miles 14 ° PRAGUE 15 ° HradecKralove 17 ° 18 ° 19° Kraków 21°<br>Wi<br>Wista<br>Ne<br>I<br>I I I I<br>W<br>Notec<br>I<br>art<br>r<br>I<br>a<br>I<br>O<br>I<br>ta<br>I<br>da<br>r<br>I<br>b<br>a<br>s<br>L<br>W<br>a<br>dra is<br>rta<br>O<br>d<br>ro<br>w<br>W a<br>r<br>O<br>a<br>P<br>i<br>sna<br>a<br>a<br>W<br>i<br>wk<br>r<br>w s<br>t<br>a<br>W<br>r<br>a<br>D<br>a<br>a<br>t<br>a<br>JANUARY 2005 IBRD 33721<br>tr<br><!-- End of picture text -->
**Table 10: Teaching practices (two years into intervention – 2010)**

||Probability of<br>calling students<br>by name|Probability of<br>frequent use of<br>the blackboard|Probability of<br>children asking<br>questions in class|Probability that<br>the teacher has<br>_no_lesson notes|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|WSD|0.10*|0.07*|0.03|0.03|
||(0.07)|(0.03)|(0.06)|(0.06)|
|Grant|-0.001|0.02|-0.08|-0.01|
||(0.07)|(0.04)|(0.06)|(0.06)|
|Observations|427|427|420|511|
|Mean of<br>dependent variable<br>in comparison<br>group <sup>a</sup>|39%|82%|33%|37%|

Notes: Standard deviations in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. The unit of observation is a classroom. Robust standard errors. All coefficients are marginal probabilities.<sup>a</sup> Percent of classrooms where dependent variable is 1.

**Table 11: Participation in management (two years into intervention –2010)**

|||Margin<br>|al Probability t<br>|o participa<br> <br>|te in decisio<br>|n-making<br>|<br>|
|---|---|---|---|---|---|---|---|
||Teachers|Parent|Rely on SDP|RED<br>|Fundraisers|Know<br>PTA<br>memb.|# Meetings<br>parent/school|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Model|Probit|Probit|Probit|Probit|Probit|Probit|OLS|
|WSD|0.42***|0.64***|0.18***|0.26***|<br>0.11**|-0.15**|-0.41***|
||(0.08)|(0.06)|(0.07)|(0.08)|(0.06)|(0.08)|(0.18)|
|Grant|0.37***|0.65***|0.16**|0.37***|<br>0.07|-0.04|-0.26|
||(0.08)|(0.06)|(0.07)|(0.08)|(0.06)|(0.08)|(0.18)|
|Observations|274|274|274|274|274|505|505|
|Mean of<br>dependent<br>variable in<br>comparison|3.3%|9%|1%|2%|7%|50%|1.9|

Notes: Marginal effects are reported for Probit regressions. Robust standard errors in parentheses. *** 1% Significance Level, **5% Significance Level, *10% Significance Level. The unit of observation is the school in the first four columns and the household in the remaining columns. RED = Regional Education Directorate. SDP = School Development Plan.

32
39 price had declined to only 25 cents per dollar face value. By the end of 1987, the external debt stood at $16.4 billion, with arrears on principal and interest of $6.1 billion. By early 1989 Peruvian debt could be bought at 5 cents per dollar. The low price is certainly explained by one single factor: it would be exceptionally difficult politically to achieve a stabilization in which real worker incomes were cut for the purpose of external debt service.

The First Two Years The immediate priority for the Garcia administration was to introduce a "heterodox" program of stabilization: inflation reduction via an incomes policy combined with a massive reactivation of the economy.

In the short term, the heterodox program was immensely successful. Inflation declined sharply (as shown in Table 5), employment increased, and the real wage was pushed up substantially (as shown in Figure 5). In the last quarter of 1987, the real wage stood 52 percent above the level of 1985! Growth, too, had been very substantial. In 1986, the economy grew by 9.5 percent; and, in 1987, by another 6.7 percent.

Table 5 Peruvian Macroeconomic Indicators

||1985|1986|1987|1988|
|---|---|---|---|---|
|Inflationa|158|63|115|1722|
|Growth|2.5|9.5|6.9|-8.4|
|Real Wageb|111|126|137|105|
|Trade Balance($)|1173|-67|-463|-84|
|Gov't. Revenuesc|42.7|32.1|25.9|23.5|

aDecember-December b Index July 1985-100 cPercent of GDP Source: World Bank, Ministry of Finance, National Institute of Planning.
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- **Smith, Eric.** 1994. “Taxation in a Two-Sided Search Model.” _Journal of Public Economics_ , 54(3): 423–435.

- **Sockin, Jason.** 2022. “Show Me the Amenity: Are Higher-Paying Firms Better All Around?” CESifo Working Paper 9842.

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43
sources into common units, and does so in a way that reflects the fact that different data sources cover different groups of countries with potentially different distributions of governance. However, unlike the UCM, this alternative approach assigns equal weight to all data sources. We do this in four steps:

1. We take our globally representative data sources and standardize each of them to have zero mean and unit standard deviation.  This step converts the globally representative data sources into common units (i.e., the units are z-scores).   We then compute a simple average of these standardized data sources as a preliminary estimate of governance based on only globally representative data sources.

2. As discussed in Section 3, we cannot treat globally non-representative sources in the same way, because the underlying distribution of governance in the different sets of countries covered by these sources is unlikely to be the same as the distribution across all countries in the world. Instead, we standardize each non-representative data source so that it has the same mean and standard deviation as the preliminary estimate of governance from Step 1 _in the sample of countries covered by each non-representative data source._ This step converts non-representative data sources into the same units as the globally representative data sources, allowing the distribution of governance to be different in the different sets of countries covered by non-representative data sources.

3. We construct a simple average of all the resulting standardized representative and nonrepresentative standardized data sources. This simple averaging approach assigns equal weight to all standardized data sources.

4. To make the units of this alternative indicator comparable with those of the baseline WGI estimates, we rescale the resulting average to have zero mean and unit standard deviation.

The results for this alternative aggregation method are reported in Figure 4, using the most recently available data for 2023. The six panels of the figure correspond to the six dimensions of governance. In each panel, we plot the baseline WGI estimate of governance on the horizontal axis, and the alternative equally weighted estimate on the vertical axis. The correlation between the two measures is very high, ranging from 0.97 (for Rule of Law) to 0.99 (for Political Stability and Absence of Violence/Terrorism). Averaging across all 25 years in the WGI dataset, the average correlations between the two measures remain very high, ranging from 0.98 to 0.99.

We can also use the margins of error associated with the baseline estimates in the WGI to assess the statistical significance of the differences between the baseline and alternative equally weighted 24
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36
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**Table 2: Real water tariff trends in 13 Latin American cities**

||Residenti<br>|al<br>|Industri<br>|al<br>|
|---|---|---|---|---|
||1997|2003|1997|2003|
|**Average tariffs (US$/m3)**<br>|||||
|Arequipa|0.38|0.22|1.29|0.69|
|Bogota|0.63|1.16|1.76|1.58|
|Cali|0.58|0.76|1.99|1.47|
|Cochabamba|0.43|0.32|1.29|0.83|
|Costa Rica|0.54|0.34|4.54|1.80|
|La Paz|0.24|0.22|1.52|1.19|
|Lima|0.38|0.32|1.29|0.93|
|Managua|0.21|0.31|1.37|0.57|
|Medellin|0.65|0.78|1.84|1.22|
|Pernambuco|0.67|0.51|3.89|1.35|
|Santa Cruz|0.65|0.59|0.94|0.74|
|Trujillo|0.51|0.38|1.21|1.02|
|Uruguay|0.19|0.72|1.24|1.11|
|Average|0.47|0.51|1.91|1.11|
|Median|0.51|0.38|1.44|1.11|
|Minimum|0.19|0.22|0.94|0.57|
|Maximum|0.67|1.16|4.54|1.80|
|**Cost recovery (% cases)**|||||
|Partial capital costs|62|46|100|100|
|Partial O&M costs|31|54|0|0|
|Nil|8|0|0|0|

Source: Adapted from ADERASA, forthcoming and GWI 2004 It is interesting to see how water tariffs in Latin America currently compare with those in other regions (Table 3). The comparisons are based on a sample of 132 water utilities serving major cities worldwide.

Average residential water tariffs in OECD countries currently stand at US$1.04 per cubic meter. However, even in OECD countries, only around half of water utilities have tariffs high enough to make a substantial contribution to capital costs. Among developing regions of the world, Latin America has the strongest record on cost recovery. The results show that water tariffs in Latin America are on average about 40% of those observed in OECD countries, although given higher costs of service provision in OECD countries the percentage of utilities whose tariffs make some contribution to capital costs is quite similar to that in Latin America. Latin America has the highest prices of any developing region of the world, with median water tariffs twice as high as those observed in East Asia, Eastern Europe, and the Middle East, and more than six times as high as those observed in South Asia<sup>2</sup> .

> 2 The average tariffs reported for Latin America in Tables 2 and 3 differ somewhat owing to the use of a different sample. The data in Table 2 are based on a sample of 13 water utilities for which tariff trends over time are available. The data in Table 3 are based on a sample of 23 water utilities for which only the most recent tariff is available.

7