rafmacalaba/datause-encoder-data huggingface dataset:
10 of Argentina, marginal improvement in the case of Mexico and none whatsoever in the case of Brazil. Table _5_ summarizes the relevant statistics for the best models selected.

|'1~~|~~~~h1|||||
|---|---|---|---|---|---|
|Argentina'|0.03,7|0.55|37|0.83|0.65|
|<br>Brazil2|<br>0S065$|v 00<br>t0<br>R4!|<br>it0<br>$014|0s0000<br>>t<br>;<br>_0250_<br>D|l92<br> <br>4093|
|<br>Chile<sup>3</sup>|.<br>0.060|<br>.<br> <br>0.60|<br> <br>.<br>0.11|<br> <br> <br>_._<br> <br>0.27|<br> <br>.<br>0.88|
|_C3li,bia'_|004|0.|0W16|0.28|02|
|<br>Ecuador'|_0.065_|0.52|0.24|0.49|0.90|
|Mexico<sup>3</sup>|00043||_0.1_<br>0|_.3_<br>0.877||
|PeruX|0.060|0.75|0.12|0.47|0.79|

Regarding the anticipation of crises, the selected models do fairly well signaling in advance the extreme risk situations. However there are big differences across countries in the distribution of the signals within the 24-month period prior to the crisis (Table 6). In Brazil, Chile and Peru the signals are evenly distributed, with half the signals taking place in the 12 months prior to the crisis. The least anticipation is registered in Ecuador, Mexico and Venezuela, where close to 50% of the signals took place within 6 months of the crisis. The most delayed response happens in Argentina, where in the year prior to the crisis only 40% of the signals are issued.

||=.~~~~|ro<br>**toC**<br>**a**|<br>**=rii**|||
|---|---|---|---|---|---|
|**Arenin**|**1.-**<br>0X:i-d|.<br>**2.1**<br>**.**<br>**'.**|**..**||**100**|
|Brazil|18|36|54|90|100|
|Chile0|<br>t;0<br>2Xt0|_35_|56<br>-<br>X<br>G-<br>|<br>N0Nt78|t1oo00,|
|<br>Colombia|<br> <br> <br>19|<br>37|<br> <br> <br>61|<br>85|100|
|Ecuado'|\ugrrEtf0jwaSA<br>29'|El;0<br>'<ijj51|95|99|10000000040|
|<br>Mexico|<br>25|<br> <br>49|71|90|100|
|eezul|22|31<br>4|50<br>775||100|
|<br>Venezuela|<br>31|<br> <br>46|<br> <br>74|95|100|
phenomenon is that as the population ages a larger and larger share of oil revenue must be invested to make up for the lower saving caused by a smaller fraction of workers in the population.

**Figure 14. A Decline in the Population Growth Rate, Caused By Falling Fertility Rates,** **<u>Will Increase the Investment Share of Oil Revenue</u>**

<!-- Start of picture text --> 140%<br>120% 122%<br>100%<br>95%<br>80%<br>74%<br>60%<br>57%<br>40%<br>20%<br>Population growth rate over a generational period (30 years)<br>0%<br>25% 50% 75% 100%<br>10%)<br>Investment Share of Oil Revenue (Oil Revenue/GDP equals<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

Combining the two components in the aging process, life expectancy and fertility growth, yields a contradictory picture and it will be an empirical question to determine the combination of fertility and life expectancy rates that will drive the aging process in Brazil. What is clear, however, is that increased life expectancy will lead to less need for investment of oil revenue while the opposite is true for a drop in fertility rates. The net effect is ambiguous, but the increase in life expectancy is seen to have a very small, though negative, effect on the efficient investment share — while lower population growth is seen to have a much larger positive effect on the investment share. Our best judgment therefore suggests that population aging will require a larger share of oil revenue for investment.

The final robustness check is relative to the pension replacement rate. As long as that is kept high the capital stock will be crowded out to a high extent because workers do not need to save that amount they expect to receive in pension benefit when they retire. This basic mechanism leads to an increasing low investment share of oil revenue when the pension replacement rate falls. As a policy implications, therefore, lower pension replacement rates will lead to more capital accumulation and less need for oil revenue to be invested — leaving room for more consumption out of oil revenue. As Figure 15 illustrated, if the pension replacement rate is lowered, a lower efficient investment share is also obtained. Even if oil revenue is a high as 10 percent of GDP there will be potential for an efficient investment rate that is lower than 100 percent — leaving room for higher public consumption.

The robustness checks reveal that there is scope for policy to drive the economy towards its efficient capital stock, but that it will be challenged by population aging. However, these dynamics depend 27
# Table 7. Tariffs and Nontariff **Barriers on** Mercosur's Imports

||1988-94 Change in<br>Mercosur Intra-Trade<br>($000)|Avera<br>External Ta|ge<br>riff (%)|Nontariff Bar|rier Ratio|Regional Orientation<br>Index Change|
|---|---|---|---|---|---|---|
|Decile Range||1988|1996|Frequency<br>Ratio|Trade<br>Coverage||
|First Decile Products|682,680|54.4|18.1|41.9|83.5|9.63<br>|
|Second Decile Products|2,708,326|46.9|16.7|24.5*|36.6*|2.63<br>|
|Third Decile Products|815,205|56.1|17.5|42.1|51.8<br>|0.47<br>|
|Fourth Decile Products|1,001,244|46.0|9.6|17.7|28.7<br>|0.17<br>|
|Fifth Decile Products|488,863|46.0|10.3|20.4|8.3|0.05<br>|
|Sixth Decile Products|672,327|43.6|16.7|17.1|4.3|-0.21<br>|
|Seventh Decile Products|307,604|38.4|10.9|18.5|27.5|-0.37<br>|
|Eighth Decile Products|246,657|39.1|11.7|27.1|31.9<br>|-0.77<br>|
|Ninth Decile Products|142,627|38.4|9.0|4.0<br>|5.4<br>|-2.15<br>|
|Tenth Decile Products|287,531|40.7|10.9|8.2|28.2|-21.65|

*Includes Brazil's recent restrictions on automobile imports and domestic<sup>automobile assembly regulations.</sup>

Source: Computed from SMART and the WTO Integrated Data<sup>Base.</sup>
sample to sample variation. If there is a substantial lack of common support simply because of sampling error, then reweighting can also not solve this. To make progress Blanchet et al. (2022) use a replacement step, creating duplicates of observations in the surveys and assigning to each observation above the merging point the average income for its population share (the population share is determined by each individual’s weight and the population size) in the tax data. This replacement step also preserves the distribution of other variables and their relationship with income in the survey data.

Alvaredo’s (2011) analysis for Argentina assumed that the top 1% was completely missing from the household surveys, and therefore tax data should be used for the top 1%. Blanchet et al. (2022) point out that this is an extreme form of reweighting where the weights of the survey respondents are rescaled, so they represent the bottom 99% and then tax data completely represents the top 1%. Alvaredo (2011) showed that the Gini was 6-7 percentage points higher when correcting for missing top incomes using this method for Argentina.

Chancel et al. (2023) estimate inequality and provide distributional national accounts for African countries between 1990 and 2017. In doing so the authors attempt to overcome several challenges, as discussed above, including a lack of income data in many African countries, poor quality of national accounts and the assumed under-representation of the top tail of the distribution. Their starting point is not raw survey data, but tabulations on consumption per capita from the World Bank’s Povcalnet database, from which they impute consumption percentiles. They do use household survey microdata for five African countries, because these surveys asked respondents about income and consumption, and so are used to determine the relationship between the two over the distribution of consumption, meaning consumption can then be used to impute incomes where only consumption data is available from Povcalnet.

Chancel et al. (2023) then use tax data from South Africa and Côte d’Ivoire, the only African two countries where such data was available to the authors, to determine the extent of under capturing of top incomes and then to use this to correct the income distributions for the rest of the countries, themselves almost all imputed from consumption decile share data derived from surveys. Chancel et al. (2023) then parameterize the under-capturing of incomes by percentile using the tax data. They use their results to assume that the bottom 80% of the distribution is unaffected while the top 1% is adjusted upwards by between 50 and 100%.

This summary of differing methods using survey and administrative data shows that attempts to solve the under-capturing of the rich in household surveys have common elements, although the (non-) 20
than 5 years of education on average and 98.6% of mothers are currently married or living in a union. Only 16.0% of the children were from households located in urban areas and 29.3% from households with access to improved sanitation.

# _(ii) Continuous exposure_

Table 3 reports the OLS estimates for infant and neonatal death as outcome variables using groundwater electrical conductivity as a continuous explanatory variable (columns of (1) – (3) and (4) – (6) respectively). Holding all other variables constant, with a 1 mS/cm increase in drinking water EC, the probability of infant and neonatal mortality declines by 0.12% (significant at 10% level) and 0.17% (significant at 1% level) respectively for model 1 unadjusted with covariates. Gradually controlling for mode covariates, the coefficient magnitude of EC decreases and becomes insignificant (models 2 and 3). In models 2 and 3, increase in mother’s age when giving birth and years of education are associated with a significant decline in the probability of infant and neonatal deaths.

_(iii) Categorical exposure_ For the categorical model, mild-salinity with EC belonging to [0.7, 2] mS/cm is omitted as reference group (Table 4). Infants with exposure to freshwater with 0 – 0.7 mS/cm EC had 1.63% more likelihood of mortality and for neonates the corresponding figure was 1.24 %, compared to those who were exposed to mild-salinity with 0.7 – 2 mS/cm EC. Infants and neonates who were exposed to severe salinity with 10 mS/cm above EC had the highest risk of mortality in all cases. Compared to those who were exposed to mild-salinity, the probability of infant and neonatal death for children who were drinking severely saline water were 3.12% and 2.20% higher. There was no significant difference between exposure to moderate salinity 2 – 10 mS/cm and mild salinity. The impact of mother’s education and age was similar to that of the continuous exposure model.

_(iv) Restricted cubic splines (RCS) model_ Finally, for the restricted cubic splines model (Table 5), two restricted cubic terms were created among three knots at 0.7, 2, 10 mS/cm, similar to the categorical exposure model. Both restricted cubic terms are significant with negative signs for rgwec1 and positive for rgwec2 across unadjusted and fully adjusted models. This indicates a downward shaping relationship below a certain threshold and an upward shaping relationship afterwards. The positive summation value of the two restricted terms suggests an overall positive association between infant or neonatal death and salinity. The p-values reported at the bottom of the table from Wald test were less than 0.001 in model 1 and 2, and less than 0.05 in model 3. Thus, the null hypothesis of joint significance of restricted cubic terms is rejected at 1% and 5% levels for model 3, which indicates strong non-linearity. Again, the relationship between maternal age as well as mother’s years of education and mortality outcomes was same with the continuous and categorical specifications.

10
|||PRE Working PaperSerles||Contact|
|---|---|---|---|---|
||lil|Aubto|Dat|f|
|WPS373|Are Better-off Households More<br>Unequal or Less Unequal?|Lawrence Haddad<br>Ravi Kanbur|March 1990|J. Sweeney<br>31021|
|WPS374|Two Sources of Bias in Standard<br>Partial Equilibrium Trade Models|Samuel Laird<br>Alexander J. Yeats|February 1990|J. Epps<br>33710|
|WPS375|Regional Disparities, Targeting, and<br>Poverty in India|Gaurav Datt<br>Martin Ravallion|March 1990|C. Spooner<br>30464|
|WPS376|The World Economy in the<br>Mid-I 990s: Altemative Patterns of<br>Trade and Growth|Colin I. Bradford, Jr.|March 1990|C. Evangelista<br>32645|
|WPS377|Security for Development in a<br>Post-Bipolar World|John Stremlau|||
|WPS378|How Does the Debt Crisis Affect<br>Investment and Growth? A Neoclas<br>cal Growth Model Applied to Mexico|Patricio Arrau<br>si-<br>|March 1990|S. King-Watson<br>31047|
|WPS379|Some Implications of Policy Games<br>for High Inflation Economies|Miguel A. Kiguel<br>Nissan Liviatan|March 1990|R. Luz<br>39059|
|WPS380|Techniques for Railway<br>Restructuring|Lee W. Huff<br>Louis S. Thompson|March 1990|S. Shive<br>33761|
|WPS381|Trade in Banking Services:<br>Issues for Multilateral Negotiations|Alan Gelb<br>Silvia Sagar|March 1990|W. Pitayatona-<br>kam<br>37666|
|WPS382|The Indonesian Vegetable Oils<br>Sector: Modeling the Impact of<br>Policy Changes|Donald F. Larson|March 1990|D. Gustafson<br>33714|
|WPS383|On the Relevance of World<br>Agricultural Prices|Yair Mundlak<br>Donald F. Larson|March 1990|D. Gustafson<br>33714|
|WPS38|4 A Review of the Use of the Rational<br>Expectations: Hypothesis in Models<br>of Primary Commodity Prices|Christopher L. Gilbert<br>|||
|WPS385|The Principles of Targeting|Timothy Besley<br>Ravi Kanbur|March 1990|J. Sweeney<br>31021|
|WPS386|Argentina's Labor Markets in an Era<br>of Adjustment|Luis A. Riveros<br>Carlos E. Sanchez|March 1990|R. Luz<br>39059|
|WPS387|Produc vity and Externalities:<br>Models of Export-Led Growth|Jaime de Melo<br>Sherman Robinson|March 1990|M. Ameal<br>37947|
# **Appendix C: Bank Ownership, Profitability, Interest Margin and Securities (Weighted)**

Regressions are estimated via OLS at bank level for the year 2001 to 2008. Robust t statistics in brackets and bank level clustered t statistics in parenthesis. All regressions control for time and country dummies. Inverse of number of banks in a country in a given year is used as weights. ***, ** and * represent significance at 1, 5 and 10% level respectively

||1|2|3|4|5|6|
|---|---|---|---|---|---|---|
||**ROA**|**ROE**|**Net Interest**<br>**Margin (Total**<br>**Assets)**|**Interest**<br>**Income to**<br>**Assets**|**Interest**<br>**Expenses to**<br>**Assets**|**Total Securities**<br>**to Assets**|
|**Dummy Public Ownership**|**-0.281**|**-4.844**|**0.113**|**0.622**|**0.144**|**6.688**|
||[1.88]*|[2.50]**|[0.77]|[2.26]**|[0.91]|[2.95]***|
||(1.10)|(1.65)|(0.44)|(1.14)|(0.50)|(1.35)|
|**Dummy Foreign Ownership**|**-0.026**|**-0.069**|**-0.025**|**-0.156**|**-0.372**|**-3.468**|
||[0.18]|[0.04]|[0.22]|[0.79]|[2.78]***|[3.08]***|
||(0.11)|(0.03)|(0.13)|(0.42)|(1.39)|(1.54)|
|**Dummy Listed**|**0.245**|**4.319**|**0.291**|**0.206**|**-0.05**|**-1.503**|
||[1.72]*|[2.54]**|[2.72]***|[1.19]|[0.46]|[0.99]|
||(1.32)|(2.07)**|(2.37)**|(0.82)|(0.28)|(0.74)|
|**Lag Total Assets (Log)**|**-0.035**|**0.093**|**-0.052**|**-0.074**|**0.032**|**1.438**|
||[0.68]|[0.10]|[1.33]|[1.08]|[0.78]|[2.88]***|
||(0.50)|(0.09)|(0.85)|(0.65)|(0.49)|(1.48)|
|**Non-Interest Income to Assets**|**0.265**|**2.04**|**-0.271**|**-0.386**|**-0.221**|**-1.294**|
||[3.86]***|[2.78]***|[4.54]***|[3.35]***|[3.18]***|[1.81]*|
||(3.01)***|(2.22)**|(2.93)***|(2.53)**|(2.31)**|(1.24)|
|**Deposits to Assets**|**-0.007**|**0.291**|**-0.021**|**0.001**|**0.031**|**0.134**|
||[0.95]|[2.19]**|[4.01]***|[0.11]|[4.80]***|[2.20]**|
||(0.81)|(2.03)**|(2.87)***|(0.07)|(2.84)***|(1.37)|
|**Loans to Assets**|**-0.008**|**-0.069**|**0.002**|**0.011**|**0.018**||
||[2.09]**|[1.45]|[0.51]|[1.32]|[4.31]***||
||(1.40)|(1.17)|(0.32)|(0.78)|(3.06)***||
|**Observations**|517|515|556|572|562|572|
|**R-squared**|0.2|0.21|0.46|0.47|0.72|0.48|
|**Number of Countries**|9|9|9|9|9|9|
|**Number of Banks**|118|119|117|120|118|117|

45
known with certainty. In particular, it is well-known that the quality of data concerning HIV prevalence rates is rather poor. The Joint United Nations Program on HIV/AIDS (UNAIDS) uses different sets of data (e.g. surveys of pregnant women attending antenatal clinics, surveillance information, population based surveys, vital statistics, etc.) to calculate HIV prevalence. The Demographic and Health Surveys (DHS), one of UNAIDS data sources, have recently collected more accurate and reliable data on prevalence rates particularly in Africa, but the cross-country availability of such data is very limited. Measurement error can also be present in other variables (like the governance indicators) included in our regressions. However, in the case of our dependent variable (trust), OLS is suitable if the measurement error is uncorrelated with the explanatory variables.

Note that UNAIDS has warned against comparing HIV/AIDS prevalence data across time because the assumptions, methodology and data used to produce the estimates change over time with improved knowledge of the disease and superior statistical methods<sup>10</sup> . For this reason and given problems of data availability, we are unable to use panel data (or at least specify our regression in first differences) to control for possible unobserved heterogeneity (i.e. correlation between unobserved and observed country characteristics).

Omitted variables could present another serious potential problem so we try to control for the different determinants of trust identified in the literature. Perhaps more worrying is the possible endogeneity of the HIV prevalence rate. We attempt to mitigate this problem by ensuring that whenever possible, this variable is pre-determined i.e. we include values for periods before the WVS surveys took place. In addition, we also estimate the model using instrumental variables (IV). Finally, among other difficulties, the presence of multiple regimes and non-linearities in the relationships studied is a clear possibility.

Bearing those caveats in mind, table 1 presents results from a number of Ordinary Least Squares (OLS) regressions. It is remarkable that the estimated coefficient of HIV prevalence has a negative and statistically significant (at conventional levels) impact on trust through all the specifications. Specifications (2) and (3) confirm that the control of

> 10 See UNAIDS (2007).

12
# PRE Working Paoer Series

|||AuvhoDa||Contact<br>forpape|
|---|---|---|---|---|
|WPS717|Does Financial Liberalization Really<br>Improve Private Investment in<br>Developing Countries?|Jacques Morisset|July 1991|S. King-Watson<br>31047|
|WPS718|Impact of Investment Policies on<br>German Direct Investment in Develo<br>Countries: An Empirical Investigatio|Andrea Gubitz<br>ping<br>n|July 1991|S. King-Watson<br>31047|
|WPS719|How Trade and Economic Policies<br>Affect Agriculture: A Framework for<br>Analysis Applied to Tanzania and<br>Malawi|Ramon Lopez<br>Ridwan Ali<br>Bjorn Larsen|July 1991|M. Gunasekara<br>32261|
|WPS720|The Outlook for Commercial Bank<br>Lending to Sub-Saharan Africa|Ellen Johnson Sirleaf<br>Francis Nyirjesy|July 1991|S. King-Watson<br>31047|
|WPS721|The Demand for Money in Developin<br>Countries: Assessing the Role<br>of Financial Innovation|g Patricio Arrau<br>Jos6 De Gregorio<br>Carmen Reinhart<br>Peter Wickham|July 1991|S. King-Watson<br>31047|
|WPS722|Is Rice Becoming an Inferior Good?<br>FoodDemand in the Philippines|Merlinda D. Ingoc|July 1991|P. Kokila<br>33716|
|WPS723|Improving Women's and Children's<br>Nutrition in Sub-Saharan Africa:<br>An Issues Paper|Olayinka Abosede<br>Judith S. McGuire|July 1991|0. Nadora<br>31091|
|WPS724|Fiscal Issues in Adjustment:<br>An Introduction|Riccardo Faini<br>Jaime de Melo|July 1991|D. Ballantyne<br>37947|
|WPS725|How Structure of Production<br>Determines the Demand for Human<br>Capital|Indermit S. Gill<br>Shahidur R. Khandker|July 1991|A. Sloan<br>35108|
|WPS726|Perspectives on the Design of<br>Intergovernmental Fiscal Relations|Anwar Shah|July 1991|A. Bhalla<br>37699|
|WPS727|The Effects of Debt Subsidies on<br>Corporate Investment Behavior|Mansoor Dailami<br>E. Han Kim|July 1991|A. Bruce-Konuah<br>80356|
|WPS728|Does Better Access to Contracep-<br>tives Increase their Use? Key Policy<br>and Methodological Issues|Susan Cochrane<br>Laura Gibney|July 1991|0. Nadora<br>31091|
|WPS729|Is Export Diversification the Best<br>Way to Achieve Export Growth and<br>Stability? A Look at Three African<br>Countries|Ridwan Ali<br>Jeffrey Alwang<br>Paul B. Siegel|July 1991|M. Gunasekara<br>32260|
**Table 4.1 Reduction in Population-Weighted Exposure for the Analyzed Scenarios**

|_Scenario_|_Populationweighted_<br>_exposuretoPM1O _<br>_(pg/rM_<sup>_3 _</sup>_/person)_|_Populationweighted_<br>_Exposuretoozone_<br>_(ppm /person)_|
|---|---|---|
|10percent exposurereduction|6.41|0.0114|
|<br>20percentexposurereduction|12.81|0.0227|
|<br>AQScomplianceineacharea-AQS1|14.06|0.0702|
|<br>AQScomplianceinworstarea-AQS2|29.99|0.0778|

# **Environmental Contingencies**

This section estimates the number of times a contingency is declared; that is, the number of days that ozone or PM1 O concentration measured at each station exceeds the relevant standard for the reference scenario and for each of the future scenarios. Implicitly it is assumed that a contingency is declared if the measured concentration is above the concentration levels stated for the contingency (see section 2 for details on the _Contingencias Ambientales_ program). In practice this is not always the case, for instance when air quality on a specific day is expected to improve considerably because of changing meteorological conditions. Therefore, our predictions give an upper estimate of the number of contingencies.

The highest ozone concentrations in the period 1995-99 have been observed at the Pedegral station. The five-year composite frequency distribution of the highest daily 1-hour maximum ozone concentrations for the whole ZMVM is shown in figure 4.3 together with the future scenarios (see section 3). _Contingencia I_ is triggered at IMECA=240, which corresponds to 0.281 ppm. According to the frequency distribution a _contingencia I_ would be invoked for 10 days, and a _contingencia II_ would be invoked for 0 days. (In the 10 percent reduction scenario: 2 days for _contingencia I._ In the 20 percent reduction scenario: 0 days). Noted that the threshold levels from May 1998 are used to make this calculation. Table 4.2 gives the results based on pre-May 1998 threshold levels.

**Figure 4.3 Cumulative Frequency Distribution of Pedegral Station and Ozone Air Quality Scenarios, 1995-99**

<!-- Start of picture text --> Station Pedregal<br>0 .4 0 0 - . . . . . ..... . . ..... .... ...... . . . --.. ------- ~~--- - --------------------<br>0.350 - IF + Baseline<br>, t ~~~~~~~~~~~~o 10% Red<br>E0.300- o 20% Redi<br>b \ - ~~~~~~~~~~~~~~at AQS<br>0.250-<br>0.200<br>0 10<br>0.000<br>0 50 100 150 200 250 300 350 400<br>Days<br>16<br><!-- End of picture text -->