rafmacalaba/datause-encoder-data huggingface dataset:
are cut by a smaller percentage than the aggregate spending; and "highly protected" if real education expenditures increase while aggregate government expenditures decline.

# Table 111.4: Government expenditure cuts in education. 1979-1983 (, of cases b region)

|Region<br>No. of countries|All<br>(57)|Africa<br>(16)|Asia<br>(8)|EMENA<br>(13)|LAC<br>(20)|
|---|---|---|---|---|---|
|Vulnerable|45|38|25|38|65|
|Protected|22|24|50|27|_5_|
|Highly Protected|33|38|25|35|30|
|Total|100|100|100|100|100|

Source: Cornia, et.al. (1987).

3.08 Because the data predate all but the earliest adjustment loans, they reflect government priorities in making budget cuts during economic recession rather than during adjustment. Most countries (67 percent) reduced education spending during the recession period from 1979-1983. In 22 percent, the education reduction was less in percentage terms than the aggregate reduction. However, in 45 percent, education was vulnerable (Cornia, et.al., 1987). Did these patterns persist during the adjustment period? Why was education particularly vulnerable in LAC, but particularly protected in Asia?V Were there systematic differences in adjustment processes between regions?

B. Coverage 3.09 Several authors link Bank adjustment lending to declining coverage in the short term. Gallagher (1990) argues that "trends in primary school enrollments very much rerlect spending trends, rising in most instances between the mid 1970s to the beginning of the 1980s and slowing or declining since then." However, the evidence he presents is unclear: fiscal duress is negatively with gross primary enrollment rates in Africa and Asia, but not in EMENA or LAC (annex 3). Kakwani, et.al. (1990) find that gross primary enrollment rates increased in all country groups from 1970 to 1985 except among intensely adjusting (IAL) countries. Furthermore, the rate of growth of primary enrollments declined in TAL countries after 1980, although total enrollments continued to increase in all country groups.

i/Asian countries, compared to LAC countries, tend to have smaller debt service obligations, higher economic growth, and smaller shares of the public budget devoted to education.

I/Nonpublic sources of financing tend to constitute a significant proportion of education expenditures in EMENA and LAC (but less so in Africa and Asia), and can compensate for declining public expenditures.

25
to measure a country’s statistical _performance_ through objective and comparable indicators. (This challenge is highlighted by a large number of indicators with missing data that we discuss later. Also see Cameron _et al._ (2021) for further discussion on this and the _desiderata_ .) We identify five key pillars of a country’s statistical performance, as shown in Figure 7. These are data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. This figure shows these pillars and dimensions in the form of a dashboard, which can help countries identify areas for development in their statistical system. We briefly describe these pillars below and provide more details on the dimensions of the SPI, including ongoing data work, in Appendix A, Table S1.

Since statistics have no value unless they are used, the first pillar of the SPI is data use. In order to meet user needs, the statistical system must develop a range of services that connect data users and producers and facilitate dialogue between them. The second pillar of the SPI is therefore data services that are trusted by users. The dialogue between users and suppliers in turn drives the design of statistical products that are to be created including the quality of product needed for the country requirement. This will incorporate accuracy, timeliness, frequency, comparability, and levels of disaggregation. The third pillar of the SPI is therefore data products. In order to create the products required, the statistical system needs to make use of a variety of sources from both inside and outside the government. This includes making use of typical data collection methods like censuses and surveys, but also administrative data, geospatial data, and data generated from the private sector and from citizens. The fourth pillar of the SPI is therefore data sources. For the cycle to be complete, capability needs continuously to be reviewed to ensure that it is enough to deliver the products, services and ultimately data use required. The fifth pillar of the SPI is therefore data infrastructure.

In summary, a successful statistical system offers highly valued and well-used statistical services, generates high quality statistical indicators that can also track progress for the SDGs, draws on all types of data sources relevant to the indicators that are to be produced, develops both hard infrastructure (including legislation, governance, standards) and soft infrastructure (including skills, partnerships), and has the financial resources to deliver. Figure A.1 (Dang _et al._ , 2023) offers an alternative visual description of the beneficial interactions of the different data pillars, which reinforce each other through stakeholders’ partnership, joint accountability, better capacity, 17
middle class for three Latin American countries. The conceptual basis follows the vulnerability to poverty approach, by which, the middle class is defined the level of income that allows individuals to protect themselves from falling into poverty over time.

**Table 2: Incidence of health shocks; 5-year period: Chile 2001-06, Mexico 2000-05, and Peru 2001-06**<sup>**/a**</sup>

_Percentage of households_

|**Income**<br>**quintiles**|**Chile**|**Mexico**|**Per**<br>**At least one**<br>**member**|**u**<br>**Head**|
|---|---|---|---|---|
|1|11.1|11.5|30.7|7.2|
|2|11.6|12.0|39.8|10.0|
|3|14.2|12.2|44.3|12.5|
|4|15.1|12.1|46.3|15.0|
|5|18.2|11.0|43.6|17.1|
|**Total**|**14.0**|**11.8**|**40.9**|**12.4**|

_Source_ : Author‘s calculations based on data from CASEN, MxFLS and ENAHO panel databases. /a The differences in the incidence for Chile and Mexico with respect to Peru is probably due, among other factors, to the way the surveys collects the information (first three columns). In Chile and Mexico the surveys in 2006 and 2005, respectively, asks for the occurrence of health shocks during the previous 5 years for which household members required hospitalization. The 5-year period probably underestimates the incidence as individuals are more likely to forget the occurrence of shocks. In Peru, however, the surveys in 2002, 2003, 2004, 2005 and 2006 ask for the occurrence of these shocks during the last 12 months, making it more likely to capture all the events.

# **3. Measurement of the middle class**

Existing income or consumption thresholds used to measure and analyze trends for the middle class have been defined using ad-hoc criteria. Milanovic and Yitzhaki (2002) have defined the middle class as those individuals living with a per capita income on $12-50 a day at 2005 PPP (roughly the mean per capita incomes of Brazil and Italy, respectively), while Kharas and Gertz (2010) have used a range of $10 and $100 daily expenditure per person, obtained after adjusting survey distributions to national accounts. These thresholds were defined by excluding those individuals who would be considered rich in Portugal and poor in Luxembourg (the poorest and richest among the industrialized countries, respectively).

Moving to a developing world perspective, Birdsall (2010) defined the middle class to include those with income between $10 day and at or below the 95th percentile of the income distribution. She argues that $10 a day is a high income compared to the global absolute poverty line of $1.25 a day and, although it is still low by OECD standards, it is close to setting a standard for a minimum income threshold for ―economic security‖. In an influential work, B&D have defined middle class as those living with a per capita expenditure on $2-10 a day at PPP, while Ravallion proposed thresholds ranging between $2 a day (the median poverty line in 70 developing countries), and $13 a day (the poverty line in the United States). Both Ravallion and B&D‘s measures have triggered an important debate on internationally comparable measurement of the middle class. The use of these ad-hoc criteria, when analyzing

- 6 -
. 12-

# Estimated net flows for maternal care in 1988 were S13 million.

The ffnancial outlook beyond 1989 is favorable for support to UNFPA, UNICEF. and NoOs. Marginal declines will affect hospital-related maternal care funds in Zaire, although total health investments are expected to remain at a level of about 9 percent of sectoral assistance.

# Health and Maternal Care Policy

Maternal care has long been a major component of Belgian assistance, with particular concentration on Africa (Zaire. Burundi, and Rwanda). BADC supports WIHO's primary health care programs by working to expand the network of rural health centers and enlarge the infrastructure for referral to secondary or tertiary hospitals. BADC is sensitive to the specific problems of maternal mortality in Africa and attempts to address the complications of childbearing through hospital and laboratory services.

In supporting services at the village, urban and research institute levels with WHO and Belgian NGOs, health sector policy singles out high-risk populations. Mothers are specifically included in this group'. BADC considers it counterproductive, however, to initiate separate vertical programs for maternal health or Safe Motherhood. These views mirror those of a number of other European donors that are committed to Safe Motherhood practices within the context of primary health care. For example, the Belgian Government has assisted Senegal's Ministry of Health, with a study of the Project Pikine on maternal health (Senegal, Ministry of Hlealth 1989). In the Belgian view it is not only important for the donor to recognize the risks of childbearing but to help recipient countries identify high-risk populations. The implication is how to strengthen recipient country demand.

I The principles of health sector assistance. including support for maternal health, are stated in the February 1988 (second edition) of La Belgique et la Cooperation au Ddveloppement.
percentile ratios, namely the 90/10 ratio, the 80/20 ratio, and the 90/40 ratio (i.e., the Palma ratio). All income measures are converted to real terms using 2011 PPP dollars. Panel B of Figure 1 provides a global map of income inequality at the subnational level, which shows substantial variation of inequality across regions within a country.

We match our poverty and inequality data with the ERA5 satellite reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ECMWF). The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). An advantage of the ERA5 data is that it combines information from ground stations, satellites, weather balloons, and other inputs with a climate model, and therefore is less prone to station weather bias.<sup>5</sup> For robustness tests, we use the global gridded data from Climate Research Unit of the University of East Anglia (CRU) available at 0.5<sup>◦</sup> resolution. We provide a more detailed description of the data sources, including the list of the countries in each dataset and the summary statistics of the main variables in Appendix B.

# **3. Empirical Specifications**

Different identification strategies were employed to estimate the effects of climate change on economic outcomes (Burke and Emerick, 2016; Dell _et al._ , 2014; Kolstad and Moore, 2020). Early studies mostly used a cross-sectional approach utilizing spatial variation at a point of time, comparing outcomes between hot and cold areas (Mendelsohn _et al._ , 1994; Schlenker _et al._ , 2005). Yet, a key assumption when estimating the coefficients of the climate-related variable from cross-sectional models is that climate change is not correlated with other unobservable factors. Violation of this assumption could result in an omitted variables problem,

> 5 Auffhammer _et al._ (2013) find high correlations between ERA5 reanalysis data and weather station data for temperature, which further supports our study focus on temperature. However, we appear not to have similar supportive evidence for rainfall, particularly in poorer countries with limited ground station data.

8
# **Descriptive Statistics**

Tables 1 and 2 summarize the information used in the econometric models presented in the Results section. As mentioned above, about 6% of PPPs that reached financial closure before 2011 were canceled. But remarkable differences were observed when disaggregating by sector, type of PPP, and region. First, the lowest PPP contract cancellation rate is observed in the energy sector (3.2%) – a rate that is less than half the cancellation rates in transport and water and a fifth of that observed in the ICT sector.

Second, the cancellation rate for new infrastructure projects or 'greenfield' projects (4.3%) is about 60% of the brownfield concession project cancellation rate (7.1%) and 31% of the management and lease contract cancellation rate (13.7%). Finally, from a regional perspective, the highest cancellation rate was registered in Africa (9.6%), with a rate almost 70% higher than those observed in East Asia and the Pacific (EAP), East Europe and Central Asia (ECA) and Middle East and North Africa (MENA). Compared with South Asia (SAR), Africa has a cancellation rate seven times higher (Table 1).

Table 1: Cancellation rates by sector, type of PPP and Region

|Variable|Distribution(%)|Cancellation Rate(%)|
|---|---|---|
|Sector|||
|ICT|0.031|0.154|
|Energy|0.442|0.032|
|Transport|0.333|0.069|
|Water and sewerage|0.195|0.074|
|Type of PPP|||
|Brownfield|0.338|0.071|
|Greenfield|0.619|0.043|
|Management & Lease|0.043|0.137|
|Region|||
|AFR|0.055|0.096|
|EAP|0.360|0.056|
|ECA|0.057|0.057|
|LAC|0.325|0.072|
|MENA|0.026|0.056|
|SAR|0.177|0.013|
|Obs. (PPPs reaching financ|ial closure before 2011)|3,400|
|Clusters (Countries)||110|

In terms of the size of the PPP projects under study, the average investment committed to a PPP project at the time of financial closure was US$258 million, though half of these projects did not exceed US$78 million in size. Only a quarter of the PPPs that reached financial closure before 2011 surpassed the average investment size (Table 2).

Table 2: Investments, GDP and Population

|Variable|Obs.|min|Average|max|
|---|---|---|---|---|
|Total investment (US$m)|3,106|0.04|257.57|6,693.07|
|GDP (constant 2005 US$)|3,385|192.17|2,799.52|8,942.85|
|Population (millions)|3,399|0.10|521.39|1,337.71|

10
Table A7: Relationship between management and energy intensity (value) (split by energy intensity for manufacturing and knowledge intensity for service firms).

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||High EI|Moderate EI|Low EI|High KI|Low KI|
|General|-0.211|0.085|0.111|0.213|-1.050|
||(0.804)|(0.340)|(0.369)|(0.341)|(0.708)|
|No. Employees|0.921<sup>_∗∗_</sup>|0.909<sup>_∗∗∗_</sup>|1.029<sup>_∗∗∗_</sup>|1.251<sup>_∗∗∗_</sup>|1.021<sup>_∗∗_</sup>|
||(0.422)|(0.245)|(0.308)|(0.233)|(0.417)|
|Capital Stock|0.490<sup>_∗∗_</sup><br>(0.198)|0.335<sup>_∗∗∗_</sup><br>(0.087)|0.458<sup>_∗∗∗_</sup><br>(0.132)|||
|Log(Sales)|-1.536<sup>_∗∗∗_</sup>|-1.727<sup>_∗∗∗_</sup>|-1.672<sup>_∗∗∗_</sup>|-1.972<sup>_∗∗∗_</sup>|-1.576<sup>_∗∗∗_</sup>|
||(0.311)|(0.173)|(0.281)|(0.195)|(0.312)|
|3-digit sector|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|
|Observations|586|1693|666|1301|758|
|Adjusted _R_<sup>2</sup>|0.189|0.237|0.268|0.189|0.444|
|df<br>~~m~~|43|46|54|43|52|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 49
change, crop yields and Mexico–US cross-border migration’, _Proceedings of the National Academy of Sciences USA_ , **107** (32), 14257–62.

Germenji, E. and J. Swinnen (2005), ‘Human capital, market imperfections, poverty and migration: evidence From Albania’, Licos Discussion Papers, No. 175, Leuven: Centre for Institutions and Economic Performance.

Gibson, J. and D. McKenzie (2011), ‘The microeconomic determinants of emigration and return migration of the best and brightest: evidence from the Pacific’, _Journal of Development Economics_ , **95** (1), 18–29.

Gibson, J., D. McKenzie and S. Stillman (2009), ‘The impacts of international migration on remaining household members: omnibus results from a migration lottery program’, World Bank Policy Research Working Paper No. 4956, Washington, DC: The World Bank.

Gibson, J., D. McKenzie and S. Stillman (2011), ‘What happens to diet and child health when migration splits households? Evidence from a migration lottery program’, _Food Policy_ , **36** (February), 7–15.

Girma, So. and Z. Yu (2002), ‘The Link between immigration and trade: evidence from the U.K.’, _Weltwirtschaftliches Archiv_ , **138** (1), 115–30.

Glewwe, P. (2000), ‘Human capital’, in M. Grosh and P. Glewwe (eds), _Designing_ _Household Survey Questionnaires for Developing Countries: Lessons from 15 years of the Living Standards Measurement Study Volume 1_ . Washington, DC: World Bank, pp. 143–76.

Hanson, G. (2007), ‘Emigration, labor supply, and earnings in Mexico’, in G. Borjas (ed.), _Mexican Immigration_ , Chicago, IL: University of Chicago Press 52
The key question is how the financier can manage its exposure to the credit risk. When the Facility draws a loan from the credit line to purchase health products, who can the financier count on to pay back the loan? There are four options to manage the credit risk, either separately or in combination. Under Option A, donors (e.g., high-income countries, foundations) make a legally binding commitment to repay the loan after the next pandemic. This option would be equivalent to donors pledging in advance of COVID-19 to fully fund the ACT-A and a financier lending against this commitment. Under Option B, a group of low- and middle-income countries make a legally binding commitment to repay the loan, without involvement from high-income countries. This option would be like low- and middle-income countries forming their own Facility to make purchases on their behalf. Under Option C, the private sector assumes the risk by buying pandemic-linked bonds. The financier would issue these bonds and repay the principal only if no pandemic is declared before its maturity. Donors and/or low- and middle-income countries themselves would have to provide the financier with funds annually to service interest on the bonds if a pandemic is not declared (akin to paying an insurance premium).<sup>4</sup> Under Option D, no advance commitments are made by anyone, but the financier is empowered by its shareholders to retain the full risk of loans drawn from the credit line on its balance sheet. In this case, the financier’s shareholders may have to replenish its capital after the pandemic (if no grants are raised ex-post). In addition, ahead of the pandemic, carrying such risk may impact lending activities if the financier’s capital constraint is binding.

Of course, the political economy could constrain a financier’s ability to manage risk in these ways. Low- and middle-income countries may lack the fiscal space to guarantee the credit line themselves (Option B). High-income countries may be unwilling to commit in advance to fund a Facility that would compete with them to buy health supplies for their own populations in the next pandemic (Option A). Further, donor countries backing the Facility may nudge its management to make purchases from their own developers, potentially trading off speed of delivery or quality of health products to promote national interests. Options C and D may help in part to overcome these challenges, though if the financier is an international financial institution owned by national governments, political economy constraints would still be relevant.

> 4 Section 3 discusses this option from the perspective of the World Bank’s pilot Pandemic Emergency Financing facility, which issued pandemic-linked bonds and used the proceeds to respond to the COVID-19 pandemic.

4
Table 4: _Stock Market Liquidity Measures: Selected Countries, Annual Averages 1976-1993_

||Turnover Ratio|Value Traded Ratio|Per Capita GDP Growth|
|---|---|---|---|
|_Low-income_||||
|Bangladesh|0.015|0.000|1.89%|
|Cote d'lvoire|0.028|0.001|-2.50%|
|Egypt|0.060|0.030|3.56%|
|India|0.537|0.036|2.43%|
|Nigeria|0.006<br>|0.000|-0.11%|
|Pakistan|1<br>0.105|0.008|3.13%|
|Zimbabwe|0.059|0.010|-0.97%|
|_lower-middleincome_||||
|Colombia|0.087|0.004|1.95%|
|Costa Rica|0.013|0.001|0.89%|
|Indonesia|0.193|0.010|4.18%|
|Jordan|0.154|0.085|3.01%|
|Philippines|0.250|0.026|0.21%|
|Thailand|0.739|0.144|5.90%|
|Turkey|0.207|0.026|2.32%|
|_uWWer-mLddLe-incom_<br>||||
|Argentina<br>|0.266|0.013|0.22%|
|Brazil|0.355|0.041|0.65%|
|Chile|0.060|0.021|3.61%|
|Korea|0.832|0.186|9.67%|
|Malaysia|0.230|0.243|4.27%|
|Mauritius|0.059|0.003|1.76%|
|Mexico|0.498|0.044|0.85%|
|Portugal|0.108|_0.014_|2.85%|
|_high-income_||||
|0<br>Australi<br>=|a<br> <br>0.256|0.124|1.57%|
|Germany|0.704|0.156|0.95%|
|Great Britain|0.349|0.253|1.75%|
|Hong Kong|0.372|0.471|6.20%|
|Israel|0.669|0.144|1.72%|
|Italy=|<br>0.253|0.028|2.68%|
|Japan|0.469|0.406|3.42%|
|Netherlands|0.490|0.123|1.43%|
|Norway|0.318|0.059|2.48%|
|Spain|0.216|0.045|1.75%|
|Switzerland|0.467|0.442|1.16%|
|United Statesi|0.493|0.299|1.67%|

Tumover Ratio = Value of Domestic Equities Traded on Domestic Exchanges Divided by Market Capialization Value Traded Ratio = Value of Domestic Equities Traded on Domestic Exchanges Divided by GDP Income classifications from the World Bank's 1995 World Development Report Low-income economies= average GNP per capita of $380 in 1993 Lower-middle-income economies = average GNP per capita of $1,590 in 1993 Upper-middle-income economies = average GNP per capita of $4,370 in 1993 High-income economies = average GNP per capita of $23,090 in 1993 _Sources: Intemational Finance Corporation, and Moigan Stanley Capital lntemational_