rafmacalaba/datause-encoder-data huggingface dataset:
-37 -

Looking more closely at primary education, we found that at all stages of economic growth, differences in budget allocations generally make only a modest contribution to richer countries' resource advantage, while the decline in teacher pay relative to the per capita GNP makes the biggest contribution, accounting for between 56 and 68 percent of the advantage. The contribution of the easing of the demographic burden shows dramatic changes over time, ranging between 17 and 32 percent in 1993 across the income spectrum represented in our exercise (from $200 to $10,000), and between 1.7 and 62 percent in 1975. The much wider range in 1975 is consistent with the fact that the transition to lower fertility rates occurred at higher income levels in 1975 than in 1993.

With their resource advantage, richer countries have the option of emphasizing an expansion of coverage or a reduction in the pupil-teacher ratio. They have emphasized the former only at the earliest stages of economic growth, when incomes rise from $200 to $400 in 1993 and from $200 to about $1,500 in 1975. At all subsequent levels of income growth, the emphasis shifted to lowering the pupil-teacher ratio. Noteworthy is that the shift occurred in contexts where coverage in primary education is still not yet universal.

# **4. What are the policy issues?**

Two kinds of policy issues arise from the foregoing analysis. The first relates to the long-term shift in favor of lowering pupil-teacher ratios that we have documented: to what extent does it represent an efficient path for the development of primary education in developing countries, particularly in the poorest countries where a significant share of children still have no access to primary education? The second issue relates to the fact that the patterns refer to the _average_ for countries at each stage of economic development: how much scope is there for deviation from the average pattern, and what difference in educational outcomes does it make?

## **4.1 Expansion of coverage versus lowering pupil-teacher ratios**

In all countries improving access and student learning are key objectives of educational policies. The accelerated decline in pupil-teacher ratios over the past two decades suggests that policy makers have succumbed to the hope that lowering this indicator would translate into gains in student learning. Yet there is persistent and widespread evidence that simply lowering pupil-teacher ratios does not generally produce the desired gains in learning.<sup>3 2</sup> While recent evidence from a randomized experiment in the United States do suggest a

> 32 See, for example, Hanushek, E. A.(1995) "Interpreting recent research on schooling in developing countries." World Bank Research Observer 10(2): 227-46.
**Table 2:** Descriptive statistics ERA5 and CHIRPS weather data

||Mean|SD|Min|Max|
|---|---|---|---|---|
|**Temperature**|||||
|Average temperature|22.59|2.29|15.93|28.52|
|Historical average temperature|22.00|1.96|16.45|27.37|
|Z-score average temperature|0.68|1.25|-2.24|4.42|
|Average maximum temperature|28.58|2.13|18.96|33.76|
|Historical average maximum temperature|27.90|1.89|21.92|32.50|
|Z-score average maximum temperature|0.64|1.04|-2.00|3.87|
|**Heat days**|||||
|Number of days above 30°C|38.76|14.43|0.00|81.00|
|Historical number of days above 30°C|34.54|12.10|0.00|74.50|
|Number of days above 35°C|8.80|6.68|0.00|39.00|
|Historical number of days above 35°C|5.63|3.37|0.00|23.52|
|**Precipitation**|||||
|Average precipitation|4.36|1.81|0.63|10.80|
|Historical average precipitation|4.15|1.13|1.27|6.01|
|Z-score average precipitation|0.14|0.95|-2.46|4.11|
|Total precipitation|392.40|163.27|56.73|972.43|
|Historical total precipitation|373.46|101.33|114.26|540.54|
|Z-score total precipitation|0.14|0.95|-2.46|4.11|
|**Anomalies**|||||
|Positive temperature anomaly (heat shock)|0.91|0.97|0.00|4.42|
|Negative temperature anomaly (cold shock)|0.23|0.44|0.00|2.24|
|Positive precipitation anomaly (food shock)|0.45|0.65|0.00|4.11|
|Negative precipitation anomaly (drought shock)|0.31|0.45|0.00|2.46|
|Observations|379,170||||

_Note:_ Authors’ calculations based on ERA5 and CHIRPS data. Averages within survey sample, estimated using individual weights. The time frame refers to a time bin of 90 days before a household’s EPH interview. Historical values are calculated based on the years 1981-2003, all other values are averaged over the years 2004-2019. The number days above 30°C/35°C are calculated based on the daily maximum temperature.

# **4.2 Methodology**

Our empirical framework builds on previous work that aims to estimate micro-level effects on poverty and inequality resulting from the observed increase in frequency and severity of extreme weather events due to climate change, such as Letta et al. (2018) and Aggarwal (2021). By exploiting exogeneity in weather shocks across districts and time we are able to estimate relationships between weather shocks and (labor) income or poverty in Paraguay during the years 2004-2019. Following Sedova and Kalkuhl (2020), we construct the regression model of the form: where _Yidt_ is our main variable of interest, which is measured in two different ways: as _lnYidt_ , where _Y_ is either monthly total or labor income of household _i_ in district _d_ and time period _t_ , log transformed using an inverse hyperbolic sine transformation (Burbidge, Magee, & Robb, 13
intermediaries and compare their profitability and leverage to the total assets of the pension<sup>funds</sup> under their management. This approach would show much lower ROA and gross income<sup>margins.In</sup> both approaches, however, the profit ratio (i.e. the net profit as a ratio of gross income) and<sup>the</sup> return on equity (ROE) would be the same.

Table 14 summarizes the financial results of the AFPs for two sub-periods, **1982-85** and **1986-90,** as well as for the whole period **1982-90.** As it can be seen, based on their own assets only, AFPs achieved high gross income margins of **85%** and **123%** of average assets in the two subperiods and **107%** for the whole period. Their ROAs were also very high in the more recent period at **26%.** However, in relation to the total assets of pension funds, their gross margins were over **7%** in the first sub-period but as low as **3.3%** in the second subperiod. Moreover, the return on total pension fund assets was less than **1%,** which is comparable to the ROAs achieved **by** banks and insurance companies in developed countries.

Table 14

||Financ|ialResults|ofAFPs|||
|---|---|---|---|---|---|
||PR|**GIM**|ROA|LEV|ROE|
|Based onPPassets||||||
|Average**1982-85**|-1.4|84.8|-1.2|**1.5**|**-1.8**|
|Average**1986-90**|21.4|**122.7**|**26.3**|1.4|**36.8**|
|Average**1982-90**|**13.7**|**106.6**|14.6|1.4|**20.9**|
|Basedonpensionfunds||||||
|Average**1982-85**|-1.4|**7.5**|**-0.1**|**18.0**|**-1.8**|
|Average**1986-90**|21.4|**3.3**|**0.7**|**52.6**|**36.8**|
|Average**1982-90**|**13.7**|4.1|**0.6**|34.8|**20.9**|

Source: Based on data reported in Habitat **(1991)** As already noted, the ROE is unaffected **by** whether the focus of analysis is the asset base of the AFPs or the pension funds under management. After a difficult first subperiod, AFPs have come to earn very high real rates of return. The high returns may attract new entry so that the threat of potential competition may force AFPs to adjust voluntarily their prices. However, if potential competition proves ineffective in lowering prices and ROEs, perhaps<sup>becauseof economiesof scale</sup> enjoyed **by** the larger institutions, then regulatory intervention may be<sup>justified.Thiscould impose</sup> price caps similar to those used in the regulation of electricity and other<sup>publicutilitiesintheUnited</sup> Kingdom and other developed countries.

Although AFPs provide a competitive service in a privately-managed system, the pension system is government-mandated and has several features of a public<sup>utility that could justify</sup> intervention, not only in regulating investment patterns but also in regulating<sup>levels of pricesand</sup> profits. However, such intervention would be advisable only if the threat of<sup>entry from potential</sup>

**31**
8 Finally, we use as weights the populations of each FUA. However, to ensure sample representativeness, we modify the weights so as to oversample richer countries.

While Web Appx. Table D.1 reports the estimated coefficients, Figure 2 shows the implied relative employment share in MFG+FIRE of each population size category for a mostly unurbanized country (urban share = 20%) and a highly urbanized country (85%). More urbanized countries have higher urban shares of MFG+FIRE. The share is higher for smaller cities, consistent with MFG moving away from larger cities as countries develop. The regression residual measures to what extent the FUA has a high, or low, MFG+FIRE share (%) given its size and its country’s level of economic development. The 5th, 10th, 25th, 75th, 90th and 95th percentile values are about -15, -10, -5, 5, 10 and 15.

**Classification** . In our classification, a _production city_ is any FUA with a residual value above 5, indicating a city with a disproportionately _high_ share of employment in urban tradables. Our definition further distinguishes production cities with a “low” (5-10), “medium” (10-15) or “high” (15+) value. A _consumption city_ is any FUA with a residual value below -5, also distinguishing consumption cities with a “low” (-5;-10), “medium” (-10;-15) or “high” (-15+) value. Cities in the [-5; 5] range are classified as _neutral_ .

Figure 1 shows the distribution of production (in blue), consumption (red), and neutral cities (grey) c. 2000. Paler shades of the blue and red colors indicate lower values for the extent to which a can be as a or city classified production consumption city. Production cities are located in China and parts of Europe, the United States, Mexico, Brazil and India, while consumption cities are located in parts of Africa, the MENA, South America, and Southeast Asia.<sup>10</sup> The patterns for Asia, Africa, Europe and the Americas are shown in Web Appx. Fig. D.1-D.5. As discussed in Web Appx. Section A, they conform with our priors.

Next, Table 1 shows the classification into production, consumption, or neutral cities for those FUAs in our sample whose population exceeds 10 million people (“Residual” is the value used to classify cities). Production cities include Ho Chi Minh, Bangalore, Istanbul and Paris as well as all Chinese megacities, while consumption cities include Kolkata and Chennai, two historically important Indian cities, as well as Lagos, Jakarta, Rio and Surabaya.<sup>11</sup> Approximately half of the mega cities are classified as neutral. In

> 10China’s cities are mostly production cities whereas India has a mix of production, consumption, and neutral cities (Appx. Fig. D.1). Different configurations within a same country are possible and frequent.

> 11Web Appx. Fig. D.7 separately considers MFG and FIRE, classifying cities according to their “best”
the period 2020-2033 all the countries, with the exception of Mauritania, are projected to have a negative contribution from price and exchange rate. Mauritania has a projected small cumulative effect of 11 percentage points (0.8 percent per year on average).

# **Section 4. Evolution of Debt Risks in SSA Countries**

This section reviews the risk assessments based on joint World Bank / IMF DSAs, and then turns to highlight particular vulnerabilities that are important for SSA policy-makers. First, we analyze how country risk ratings, as assessed by WB and IMF country teams, have changed in recent years. We extend the analysis by looking at how vulnerabilities are exacerbated across country groups under specific stress tests imbedded in the DSA. Next, we deepen the analysis of the countries’ debt vulnerabilities by undertaking tailored simulations and detailed analyses of the role played by critical variables in the DSA. The tailored simulations comprise the recent oil price shock, which is not included in DSAs undertaken before 2015, and the sluggish recovery in Europe and slower growth in China. The critical variables analyzed comprise non-debt-creating flows, contingent liabilities and the international bond issuance by some countries.

# **4.1 DSA-based risk assessments in SSA countries**

Consistent with the overall picture presented in previous sections, the risks of debt distress of SSA countries, as assessed by the joint WB-IMF DSAs, have decreased substantially in recent years. When comparing across periods, debt vulnerabilities reflected in DSAs have improved in recent years for SSA countries as a group – the conclusion is drawn after comparing DSAs undertaken in 2009-11 with those undertaken in 2012-14, for the group of 31 countries that have DSAs in both periods (Figure 4.1). Between the two periods, 20 of 31 countries improved their ratings, while 9 remained unchange (of these 4 are ‘high’, 2 ‘moderate’ and 3 ‘low’) and only two deteriorated – Mozambique and Sudan. Further, of the 20 countries that show an improvement in their risk ratings more than half (65 percent) improved by more than one step (went from ‘high’ to ‘low’), while 7 improved one step (6 from moved from ‘high’ to ‘moderate’ and 1 from ‘moderate’ to ‘low’). The rating deterioration from ‘low’ to ‘moderate’ for Mozambique is due to increased external borrowing for infrastructure investments around natural gas exploration and liquefaction. However, as noted in the DSA these investments are broadly seen as important for the country’s development, and the increased risk of debt distress is deemed appropriate.

**Figure 4.1: DSA Based Debt Distress Level Change**

(Percentage of total sample) <!-- Start of picture text --> 80 74<br>55<br>60<br>40<br>29<br>16<br>20 10 13<br>3<br>0<br>0<br>Low Moderate High In debt distress<br>2009‐2011 2012‐2014<br><!-- End of picture text --> 15
removal of tree cover canopy. The authors reported the tree cover loss data to have a false positive rate of 13 percent, a false negative rate of 12 percent, and a ratio of total forest gain to loss over 2001-2012 of 0.34. The derived data differ from statistics reported by the UN-FAO’s Forest Resource Assessment, but due to the consistent methodology and definition of forests across countries, we believe these data are better suited for a global analysis. We define forests as pixels with a minimum canopy closure density of 30 percent. Finally, we convert the data to area measures and sum the data by country to calculate tree cover loss as a percentage of tree cover extent in 2000. Our intention is to examine “natural dense forests”, but note that the data also capture forest plantations. Our loss measure is thus only a proxy for deforestation, as there may be many other natural and anthropogenic processes (storm damage, fires, mechanical harvesting) that are reflected by the data. We refrain from including the tree cover gain data as there are significant differences in methodology that limit additivity or comparison with loss.

# **3.2 Pollution data**

We use concentrations of fine particulate matter (PM2 _._ 5), coarse dust particles of 2.5 micrometers in diameter, as a proxy for broader air pollution. The (0 _._ 01<sup>_◦_</sup> _×_ 0 _._ 01<sup>_◦_</sup> resolution) data were developed by van Donkelaar et al. (2016) and include global annual ground-level PM2 _._ 5 (1999-2014) derived from a combination of satellite-, simulation- and monitor-based sources. The dataset has been developed from satellite-derived Aerosol Optical Depth reflectance values calibrated to ground-based PM2 _._ 5 observations using a Geographically Weighted Regression.

Remote sensing methods aim to observe particulate matter but are prone to capturing fine dust released from barren lands that have similar reflectance properties in the high frequency spectral wavelengths. This poses a difficulty in our analysis, countries in desert regions have high country wide average pollution levels, while large countries, or those with substantial forest cover where ambient pollution is low, have lower average concentration to what the larger population is exposed to on a regular basis. We used gridded population data that are produced using a combination of light at night data and census data, to identify patches of urban areas.<sup>10</sup> We averaged gridded pollution data that falls within urban boundaries to the country-level, defining urban areas as places where population density was higher than 300 people per square kilometer. The results in fig. 1 show that

> 10The population grids are from http://sedac.ciesin.columbia.edu/data/collection/gpw-v4, we use the 2000 grids.

13
# PREFACE

Population projections for all countries are prepared annually by the Population and Human Resources Department of the World Bank. They are published first in summary form in the Bank's _World Development Report_ and later in greater detail as technical notes or working papers and, in alternate years, as a separate book.

This set of projections was prepared for _World Development Report 1989._ It appears for the second time in the Policy, Planning, and Research (PPR) Working Papers series. The tables will also be published shortly, somewhat differently organized, in book form.

As was done last year, separate working papets cover each of the Bank's four regions: Africa (sub-Saharan); Asia (and Oceania); Europe, the Middle East, and North Africa (including the U.S.S.R.); and Latin America and the Caribbean (and Northern America). Combined, these papers cover all countries of the world.

The data format of these papers generally follows the previous edition and is consism,in the demographic indicators provided, except for minor changes, with the series of precedin5 reports.

We intend these-papers to protide a convenient and up-to-date reference on the likely demographic future of each country, to facilitate the Bank's work of assisting them in planning more productive futures. We welcome comments on their utility, and how this might be enhanced.

Anthony R. Measham Chief Population, Health and Nutrition Division
combination of taxes which minimizes the resulting economic distortions. If the government is not to be constrained in choosing an optimal tax mix, then it may not be possible to retain the hypothecation of finance from one particular tax to one particular area of expenditure.

# **II. Social Security in Transition in Moldova: Introduction**

With the breakup of the Soviet Union, Moldova experienced one of the worst terms of trade shocks of any of the FSU republics. Moldova is totally dependent on imports for its energy. As Russia pushed oil and gas prices up to world levels, the value-added of major Moldovan production activities in both agriculture and industry collapsed (World Bank 1994, p. 6). The terms of trade decline directly affected the government's budgetary position. In the social sector, housing, health, and education costs increased via the direct impact of higher energy costs on schools, hospitals and the provision of communal services. Subsidy expenditure also increased, because subsidy arrangements often committed the budget to meeting the gap between variable producer prices and a fixed consumer price. As the effect of higher prices for imported inputs fed through the economy, producer prices soared, and subsidy commitments increased accordingly.

The dismantling of the universal welfare system constituted by consumer subsidies was one of the main measures taken by the government to reduce real incomes in the face of the deterioration in Moldova's terms of trade. It is important to note that reduction in subsidies was undertaken for macroeconomic as well as microeconomic reasons, as this affected the form which compensation 9
Overall, this micro-level evidence of how one large shock to security affects firm behavior measureable in CDR data motivates our shift to a large panel data analysis.

# **4.2 District-Month Panel Results**

Next, we exploit our panel dataset of CDR measures and GTD killings to explore the generalized relationship between local changes in district-level insecurity on firm and employee presence. As our main dependent variables, we construct aggregate totals of the number of active corporate line firms and total of individual corporate subscribers in each districtmonth covered by our CDR data. We convert these into logged values to minimize the impact of extreme outliers and ease interpretation of coefficients in percentage terms.<sup>23</sup> To construct our main independent variable, we first calculate the total number of killings in a district-month and select the top 1% of this distribution (23 or more killings). We then create an indicator variable, Major Violent Event, that equals 1 if 23 or more killings took place in that month in that district; we count 111 such events distributed across 54 districts across the country and appearing in 41 of our 45 months of data.

We begin by estimating the relationship between the count of firms present in a district and recent killings using the following semi-log model: where _ln_ ( _Ydt_ ) is the log of total active firms in district _d_ and month _t_ , 1 _MajorV iolentEventdt−_ 1 is the indicator variable for 23 or more killings in district _d_ in month _t −_ 1, _θd_ and _δt_ are district and month fixed effects, respectively, and _γd ∗ t_ and _μd ∗ t_<sup>2</sup> are district linear and quadratic time trends. Throughout, we cluster our errors, _εdt_ , at the district-level. Our coefficient of interest is _β_ , which we interpret as the average treatment effect of a major

> 23A subscriber is counted as active if s/he makes at least one call from a tower in that district in that month; a firm is active in that district-month if it has at least one active subscriber. A district-month is included in the panel if it contains at least one week of mobile network coverage.

20
4 -a lable 2: The CFA Countries in Sub-Saharan Africa

||SSA Average|CFA lo|<br>CFA hi|
|---|---|---|---|
|Population density (pop/km2)|20|4|36|
|Population (million)|12|**<1**|11|
|Area (km2)|597|28|<br>1284|
|Inflation average 1980-86 %|16|5|12|
|Life expectancy at birth (yrs)|50|44|58|
|Growth avg 1980-861|0|-3|<br>8|
|8 Shr in GDP: Agriculture|36|10|<br>50|
|Industry|25|13|<br>54|
|Savings (GDS)|11|7|30|
|Investors|14|9|37|
|Export share|19|14|<br>47|
|Govt. cons.|13|9|26|
|Primary prods as%exports|57|21|<br>96|
|% of mfg exports to ind. cners|34|11|<br>82|
|Persons per physician|25|3|56|
|Sch enrlmnt: Primary 5|75|23|<br>>100|
|Secondary o|23|5|25|
|Agric. share in workforcea|75|62|<br>87|
|Urbanizati^-X||8|4|
|Infant mortality (per C00)|113|75|<br>144|

Source: World Development Report 1988; Data refers to 1986 or a recent -.ear