rafmacalaba/data-use-mentions-extended huggingface dataset:
limitations -- standard deviation weighing _changes_ in M2/GDP too heavily, the coefficient of variation weighing M2/GDP _levels_ too heavily -- the qualitative results of the regression analysis are insensitive to the measure selected.

The regression analysis in Table 6 has a number of policy implications. One way to interpret the results is to apportion the decision to adopt explicit deposit insurance across the decision components. Thus, if a government's desire to adopt explicit deposit insurance is due to present financial instability, one might expect that decision to meet with a reduction in financial depth (other things equal). Decreased financial depth, moreover, will likely be associated with lower real growth. In those cases where governments adopted explicit insurance when the standard deviation of M2/GDP was greater than 2%, subsequent economic growth was, on average, .3% lower than it had been in the three years prior (Table 7).

Table 7: **Real Growth Rates for Countries that Adopted Explicit Deposit Insurance**

|Volatility in Three|Real Growth Rate in|Real Growth Rate in|Observations|
|---|---|---|---|
|Years Prior to Adoption<br>of Explicit Insurance|Three Years Prior to<br>Adoption of Explicit<br>Insurance|Three Years After the<br>Adoption of Explicit<br>Insurance||
|Stan Dev M2/GDP >2%|0.4%|0.1%|10|
|Stan Dev M2/GDP <2%|2.2%|3.2%|14|

Notes: Growth rates from Summers-Heston.

By contrast, if volatility is low, and the motivation for adopting explicit insurance stems from a past crisis or is part of a general improvement in legal institutions, one might expect increased financial depth accompanied by a higher real growth rate. Indeed, in those cases where governments adopted explicit insurance when the standard deviation in M2/
population<br>4500 120<br>100<br>3000 80<br>60<br>1500 40<br>Range AE 20<br>EMDE LAC<br>0 0<br>Health Improved Improved<br>expenditure sanitation water<br>(LHS) (RHS) source<br>(RHS)<br>Guatemala Panama Costa Rica Uruguay Brazil Peru Chile Colombia Argentina Barbados Ecuador Mexico Nicaragua Bolivia Jamaica Paraguay El Salvador Haiti Honduras Dominican Republic Venezuela, RB<br><!-- End of picture text --> Sources: World Bank (2017), World Economic Forum (2016).

A. Rankings out of 138 countries.

B. Rankings out of 190 countries.

C. Blue bars denote range of unweighted regional averages across EMDE regions. Government expenditure per primary student (in percent of per capita income), unweighted averages of 87 EMDEs, 32 AEs, and 20 LAC economies. Pupil-teacher ratio in primary education (headcount basis), unweighted averages for 165 EMDEs, 31 AEs, and 23 LAC economies. Latest data available during 2011-15.

D. Blue bars denote range of unweighted regional averages across EMDE regions. Health expenditure per capita in purchasing power parity terms, unweighted averages of 199 EMDEs, 34 AEs, and 31 LAC economies. Access to improved sanitation facilities (in percent of population), unweighted averages for 150 EMDEs, 33 AEs, and 28 LAC economies. Access to improved water sources (in percent of population), unweighted averages for 148 EMDEs, 34 AEs, and 30 LAC economies. Latest data available during 2011-15.

33
# **3. PUBLIC PERCEPTIONS OF CLIMATE CHANGE IN TAJIKISTAN**

# **Survey Module on Attitudes toward Climate Change**

14. The climate change questionnaire was one of the modules of a larger Pilot Life in Transition Survey (P-LITS 2), which was organized in Tajikistan and Kazakhstan in February 2010. LITS is a joint project of the World Bank and EBRD, which was first carried out between August and October 2006 to assess the impact of transition on people in 29 ECA countries<sup>12</sup> and understand how the attitudes towards market and political reforms are related to individual and households characteristics. The second LiTS round is planned in the fall of 2010, when the updated LiTS questionnaire will be administered in all ECA countries where the LiTS 1 was canvassed in 2006. In preparation for the launch of LiTS 2, a pilot test (P-LITS 2) was carried out in the two Central Asian countries, where the draft LiTS questionnaire was administered to nationally representative samples of 500 respondents per country (i.e. 1,000 respondents in total). Table 1 presents the distribution of the samples across different regions.

**Table 1: Distribution of the P-LITS 2 sample in Tajikistan and Kazakhstan**

||**TAJIKIS**<br>**Region**|**TAN**<br>**# households**<br>**interviewed**|**KAZAKHSTAN**<br>**Region**<br>**# households**<br>**interviewed**|
|---|---|---|---|
||||Almaty|
|Dushanbe||50|South Kazakhstan|
|GBAO||20|Central Kazakhstan|
|Soghd||150|Western Kazakhstan|
|Khatlon||170|Eastern Kazakhstan|
|RSS||110|Northern Kazakhstan|
|**Total**||
# Appendix B: Journals and websites searched

## **Journals searched:**

- _American Economic Review,_

- _American Economics Journal: Applied Economics,_

- _American Economics Journal: Economic Policy,_

- _Comparative Education Review,_

- _Economic Development and Cultural Change,_

- _Journal of Development Economics,_

- _Journal of Development Effectiveness,_

- _International Journal of Educational Development,_

- _Journal of Human Resources,_

- _Quarterly Journal of Economics,_

- _The Economics of Education Review,_

- _World Bank Economic Review,_

- _Journal of Public Economics,_

- _Journal of Human Capital._

## **Websites searched** :

- 3ie impact evaluation database,

- RISE Programme Conference 2017,

- CASE 2017 Conference,

- the Abdul Latif Jameel Poverty Action Lab at MIT;

- Innovations for Poverty Action;

- the Inter‐American Development Bank;

- the National Bureau of Economic Research;

- RTI International;

- the Rural Education Action Program at Stanford University;

- the World Bank;

- the IZA Institute of Labor Economics.

43
package of skills training. This training includes life skills, such as awareness of health and reproductive issues; financial literacy; and vocational skills. Older girls can also access microfinance to start micro-enterprises.

Fieldwork for our baseline survey was completed a week prior to the first cases of Ebola being reported in May 2014. The fact that our evaluation was underway at the time of the outbreak was entirely coincidental: the ELA program is not intended as a response to the crisis. We exploit the timing of events and randomized roll out of the ELA program to document: (i) how the severity of the Ebola shock correlates to changes in the economic lives of the 4 _,_ 700 tracked girls and young women; (ii) whether the ELA intervention mitigated any of these impacts. Our work thus provides a rare opportunity to explore the overlay of a randomized intervention with an aggregate shock, in a sample tracked over pre- and post-crisis periods.

> 2Panel A of Figure A1 shows the UNDP Gender Inequality Index, that aggregates information on maternal mortality rates, adolescent fertility rates, education by gender, female held parliamentary seats, and gender inequality in labor market participation. The index ranges from 0 to 1, with 0 indicating perfect equality. Sierra Leone scores _._ 65, while the average in Sub Saharan Africa is closer to _._ 55.

> 3Reflecting these concerns, the Secretariat for the Reduction of Teenage Pregnancy was established in Sierra Leone in 2013. They developed the national strategy, _Let Girls be Girls, not Mothers!_ The Sierra Leone Sexual Offences Act 2012 provides that a person below the age of 18 is not capable of giving consent, and that it is not a defence to any of the offences in the Act to show a child has consented.

> 4Bandiera _et al._ [2018] find that four years post-intervention, ELA significantly improved the economic empowerment of women, gave them greater control over their bodies in terms of contraceptive use, pregnancy knowledge and being able to avoid
.15|17.29|25.80|30.02|
|Belgium<br>||16.42|20.55|27.20|
|Korea, Rep.<br>|4.06|10.48|19.24|24.36|
|Germnany<br>|12.73|17.63|20.68|23.82|
|Austria|||13.28|21.35|
|Spain<br>||3.21|9.87|18.78|
|South Africa|39.27|55.93|78.13|126.01|
|Singapore||153.36|115.13|93.50|
|Chile|1.00||29.28|50.61|
|Malaysia<br>|20.08|35.65|47.18|51.02|
|Thailand|||**2.10**|**4.80**|

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

The paper is organized as follows. Section II, presents the key propositions on the links between contractual savings and capital markets development. Their effects and implications for the economy as a whole are analyzed in terms of growth, term structure of interest rates, capital structure, regulation, and comparative impact on developing versus developed economies. Section III, discusses the role of contractual savings in the structure of the financial sector. In particular, it distinguishes between the effects of contractual savings, mutual funds and non-life insurance development. Section IV, presents a descriptive analysis of the data, which confirms that there is a positive relation between contractual savings and capital markets development. Section V, analyzes the causality between contractual savings and non-life insurance companies and market
**Figure 3: Changes in the Gini coefficient  - Children**

<!-- Start of picture text --> 5%<br>Categorical<br>4%<br>Targeted<br>3%<br>2%<br>1%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases Second, targeted programs are, on average, 3.2 times more effective in reducing poverty rates (and 2.3 more effective in reducing the poverty gap) than categorical programs. There is, however, considerable variation across countries: in Nicaragua, a perfectly targeted program would only be 2 times more effective in reducing poverty rates than a categorical one, while in Colombia (the other extreme) this ratio jumps to 7.1. These differences are not explained by income levels alone: effectiveness in Nicaragua and Argentina, two countries with very different income levels, is for instance very similar. Rather, differences in impact depend on a more complex combination of factors, such as how widespread are pockets of poverty with people far off the poverty line. In Colombia, not only poverty is more widespread, but the poverty gap is twice as much as in Argentina. That implies the need of higher transfer levels to the poor to lift them out of poverty, hence a categorical transfer, which dilutes available resources among more beneficiaries, remains less effective.

Targeted programs are also much more effective in reducing income inequality. On average, in our sample, a categorical transfer of 0.5 percent of GDP to children reduces the Gini coefficient by 1.4 percent, against 2.6 percent for a targeted program. However, in contrast with poverty, there is little connection between the initial level of inequality, and the higher effectiveness of targeted programs as opposed to categorical ones.

11
2017 <!-- Start of picture text --> Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br>Employed Employed and Enrolled Unemployed<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A9: Employment by type <!-- Start of picture text --> 2015<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> 2016<br>Total<br>Men<br>Women<br>Richest<br>Q4<br>Q3<br>Q2<br>Poorest<br>Non Poor<br>Poor<br>0 20 40 60 80 100<br>Percent<br><!-- End of picture text --> 21
prior to the pandemic. In IDA countries alone, the projected needs exceed the total IDA commitments that followed the pandemic by about 14%.

The result also highlights that while food insecurity was already on the rise during 2015-2019, the impact on financing resources were largely evened out by favorable developments in global food prices so that development assistance financing needs rose only modestly. This signifies the difference in the current global situation. The long-term projection highlights that even though the prevalence rate was projected to improve slightly, the continued growth in population totals offsets this, keeping the global population that is in need of assistance relatively flat through 2027. If this occurs jointly with ‘only’ a normalization of food price inflation toward the 2005-2022 long-term average, then development assistance needs are set to spiral largely out of control, reaching about US $ 150 billion set development objectives, such as improved schooling or climate change resilience, toward addressing chronic food insecurity. The shortfall in funding itself introduces a major risk for humanitarian finances. 23It is important to note that the costs of a nutrient adequate (US $ 2.33 global average) and healthy diet (US $ 3.75 global average) are higher, and unaffordable even for the populations who live above the US $ 1.90 poverty line but are vulnerable to becoming poor.

> 24The Global average is US $ 0.79, while it is as high as US $ 0.88 in lower middle-income countries, and as low as US $ 0.70 in low-income countries.

> 25The US $ 0.75 figure is indexed to the annual FAO Food Price Index. The FPI for 2023-2027 is extrapolated using the 2005-2022 geometric average rate of annual increase, thus reflecting a normalization or ‘soft landing’ of inflation pressures rather than deflation, as happened after the 2008 market collapse. In the downside scenario, the increased food price inflation of the WEO’s downside are used. At the current FPI reading (148.3, September 2022), this corresponds to
For the purposes of evaluation, a measure of benchmark welfare was constructed by predicting household per capita consumption as a function of household and demographic characteristics in the 2018 census extract using extreme gradient boosting, a popular machine learning algorithm. This model was then used to generate a prediction of per capita consumption for each household in the census extract, which we aggregate to construct a “ground truth” measure of village welfare. We refer to this model as the “census model”, because it is used to impute predicted per capita consumption into the census extract. This census model captures over half of the variation in log per capita consumption in the household survey, with an R<sup>2</sup> of 0.537. It is used both to generate “ground truth” using the full census extract and to impute welfare into the simulated partial registry, derived from a subsample of the census extract. To construct a “ground truth” measure of village welfare, the values of predicted household per capita consumption in the 2018 census extract are aggregated to the village level.

For the main set of results, the “ground truth” village welfare measure used is the average predicted per capita consumption of the poorest 50 percent of households in the census, when ranked according to their predicted per capita consumption. We select this as the main measure to generate a clean comparison with the UBR, which only collected information on the poorest 50% of households. Since the PMT scores in the UBR are based on the average predicted welfare of the bottom half of the distribution, evaluating it against a different benchmark could introduce an additional source of noise in the UBR comparison. However, since this is a non-standard measure of village welfare, we also show below that the key results are robust to using a more typical measure, namely the average predicted per capita expenditure across all households in each village.

The 20 percent census extract contains village, traditional authority (TA) and district names but not household or village geocoordinates
2015) takes all pairs of origin and destination municipalities, and designs an algorithm that flags those pairs for which the share of commuters from the origin is above some chosen commuting threshold. Around each ‘urban core’ or central city, the algorithm then identifies the commuting shed as the set of areas whose commuter share is above the commuting threshold. Both the urban core and the commuting shed thus constitute the metropolitan area. Duranton (2015) also proposes a commuting threshold of 10%, which appears to be reasonable given that Colombian municipalities tend to be fairly large. From hereon out, when we discuss cities, we are referring to the metropolitan areas as defined by this algorithm.

Next we classify a city as big if it has a population of 2.5 million or more (this would include Bogota, Medellin, and Cali), medium if it has a population of 400,000 to 2.5 million, and small if it has less than 400,000 inhabitants. To define a migratory move across cities, we first create a matrix of road distances for all municipalities. We then define an individual in our data as having moved for college if her high school city is different from her college city _and_ the road distance between those two cities is greater than 50 kilometers. Moves after college for work are defined similarly.

# **_Descriptive statistics_**

Table 1 reports summary statistics for our sample of college graduates in the formal sector. Our sample are young workers (average age is 26.6), majority female (57%), 49% of whom are first-born

> 5Our data do not allow us to identify the work status of an individual outside formal employee or formal selfemployee. We observe some individuals over a few years so we can identify them when working, even if they start their careers in unemployment or out of the labor force.

> 6Thus, mobility after college for health care workers is not voluntary and their salaries are set at the national level.

5
2020).<sup>21</sup> Bravo (2016), who studied this issue in Latin America, finds a strong correlation between SWB and the likelihood that the incumbent will win the elections using data from the Latinobarómetro. In elections held in Latin America between 1992 and 2013, support for left-wing parties was higher when the more unsatisfied voters were under a right-wing incumbent regime (Bravo, 2016).

> 21 In addition, several studies examine the relationship between political participation and SWB. Weitz-Shapiro and Winters (2011) find a causal relationship between individual happiness and the likelihood to vote in elections in Latin America and the Caribbean.

28
2

# **I. Introduction and Summary**

During 1997-1999, Latin America was subject to external and internal shocks of sizable magnitudes. Consequently, the region's growth rate in 1999 was negative for the first time since 1983. Among the external shocks, the most notable were associated with sudden stops in capital flows and terms of trade deterioration. On the internal side, real interest rates and **i** 4al exchange rates adjusted both endogenously in response to external shocks and as a result of shifts in domestic factors such as policy variables. How do these variables interact with output fluctuations in Latin America, and in particular, what is their relative importance in terms of explaining the 1998-1999 slowdown?

To answer these questions, we used two complementary approaches. In the first one, we pooled quarterly data for the eight largest economies of the region and estimated panel regressions for regional GDP growth, considering as exogenous external factors the terms of trade, US real interest rates, non-Latin Brady bonds sovereign spreads, the current account of industrialized countries (as a proxy for exogenous capital flows) and an index of El Nino effect. The role of internal factors and other non-specified external variables was captured by means of the lagged value of GDP growth, which also subsumes dynamic effects in the panel. This simple approach has several advantages: first, it permits an assessment of the relative importance of external vs. domestic factors at the regional level; second, by allowing the disturbances of the equations to be related, it captures interdependence among countries' output fluctuations; finally, by pooling the data we minimize small sample problems. The simplicity of this method, however, comes with drawbacks such as the lack of flexibility for country-specific analysis and the absence of explicit treatment of domestic factors. To overcome these limitations we estimated country-specific VARs incorporating domestic real interest rates and real exchange rates as additional explanatory variables.

The paper is divided in three parts. The first section presents the quarterly GDP panel regression exercise used to determine the
# **4.2. Estimating the NPL Elasticities**

We obtain estimates of the elasticities of NPLs to the four macroeconomic variables of interest by means of a dynamic panel data regression, using a panel of 54 high and middle income countries and controlling for the degree of development, financial deepening, and dollarization (euroization).<sup>19</sup> The sample consists of annual data over the period from 1994 to 2004.<sup>20</sup> The parameters are estimated from the following model: The variable _NPLt_ +1, _n_ is measured as the ratio of non-performing loans to total loans.<sup>21</sup> The variables ∆ _yt_ +1, _n_ , _πt_ +1, _n_ , _rt_ +1, _n_ and ∆ _et_ +1, _n_ are real GDP growth, CPI inflation, the (ex post) real interest rate and the change in the nominal US dollar exchange rate for country _n_ at time period _t_ + 1. _Zt_ +1, _n_ is a vector of variables, comprising the log of GDP per capita (constant 2000 US dollars), the credit to GDP ratio, and the share of FX loans in total loans, that control for the degree of development, financial deepening, and dollarization (euroization), respectively. The model is estimated on an unbalanced panel using the GMM 19The list of countries that was used in the computation can be obtained from the authors upon request. One has to point out that the comparability of data on NPLs across countries remains a challenge that has been also recognized by international policy makers and plans for greater harmonization in the near future are being discussed in the international fora.

20 Estimating the NPL regression on a relatively short time sample covering a period during which a country could have gone through major structural changes in the financial sector could be problematic. Similarly, using country specific historical data especially for emerging countries, covers only a rather limited number of crises periods. For these reasons