rafmacalaba/datause-encoder-data huggingface dataset:
receives in the corresponding aggregate indicator for each country. In light of the higher weights assigned to data from commercial business information providers, we find that the weighted average share of country-level data points for this type of source rises from 46 percent (unweighted) to 60 percent (weighted). Correspondingly, the weighted share of household surveys declines somewhat from 17 percent to 10 percent, and for public sector providers from 20 percent to 14 percent.

We conclude this discussion of weighting by noting that while the weighting scheme we use has the attraction in principle of reducing the variance of the overall governance estimates, in practice this effect is quite small with the standard errors of the governance estimates declining by about 10 percent relative to an unweighted benchmark. Moreover, if we compare our precision-weighted estimates of governance with an alternative set of aggregate indicators based on simple averages of the underlying indicators, we find that the two estimates of governance are very similar, with correlations of 0.99 on average across all our indicators and periods. This reflects the fact that all of our underlying data sources do, in general, provide fairly similar crosscountry ratings of governance. We have also experimented with alternative weighting schemes that equally weight each type of governance indicator (of the four types identified in Table 1). Again we find that the correlations are very high with our benchmark indicators (see Kaufmann, Kraay, and Mastruzzi (2007), Critique 8, for details).

# **3.  Estimates of Governance 1996-2006**

In Appendix C we report the aggregate governance indicators, for all countries, for each of the six indicators. The aggregate indicators, as well as almost all of the underlying indicators, are available at www.govindicators.org. The units in which governance is measured follow a normal distribution with a mean of zero and a standard deviation of one in each period. This implies that virtually all scores lie between -2.5 and 2.5, with higher scores corresponding to better outcomes.<sup>8</sup> This also implies that our aggregate estimates convey no information about trends in global averages of

> 8 These boundaries correspond to the 0.005 and 0.995 percentiles of the standard normal distribution.  For a handful of cases, individual country ratings can exceed these boundaries when scores  from individual data sources are particularly high or low.

13
# Figure A.5: Ricardian and Interest Rate Channels: Impulse Responses

<!-- Start of picture text --> A. Sub-sample: Advanced Economies Only<br>Private Consumption CDS<br>0.9 150<br>Weak fiscal position Strong fiscal position<br>0.6 100<br>50<br>0.3<br>0<br>0.0<br>-50<br>-0.3<br>-100<br>-0.6 -150<br>Weak fiscal position Strong fiscal position<br>-0.9 -200<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text -->

# B. Alternative thresholds for weak and strong fiscal positions Private Consumption CDS

<!-- Start of picture text --> 0.2 30<br>0.1 20<br>0.0 10<br>-0.1 0<br>-0.2 -10<br>Weak fiscal position Strong fiscal position Weak fiscal position Strong fiscal position<br>-0.3 -20<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text -->

# C. Lag Structure: 5 Quarters

<!-- Start of picture text --> Private Consumption CDS<br>0.3 60<br>0.2<br>0.1<br>30<br>0.0<br>-0.1<br>0<br>-0.2<br>-0.3<br>-0.4 Weak fiscal position Strong fiscal position -30 Weak fiscal position Strong fiscal position<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text -->

# D. Deviation of government debt in percent of GDP from country-specific average Private Consumption CDS

<!-- Start of picture text --> 0.2 40<br>Weak fiscal position Strong fiscal position<br>30<br>0.1<br>20 Weak fiscal position Strong fiscal position<br>0.0<br>10<br>-0.1 0<br>-10<br>-0.2<br>-20<br>-0.3 -30<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Quarters Quarters<br><!-- End of picture text --> Note: Impulse responses are based on estimates from the IPVAR model, where model coefficients are conditioned only on fiscal positions, with government debt in percent of GDP as the conditioning variable (except for the fourth row, where the fiscal position is defined as deviation of government debt in percent of GDP from the country-specific 31
The other approach uses market information. An example is the index of investor confidence provided by State Street Global Markets, which is based on institutional investors’ holdings of risky assets, particularly equities (Ken Froot, <u>www.statestreet.com/investmentconfidenceindex). The more</u> investors are willing to allocate assets to equities, the theory goes, the greater their risk appetite and confidence.

What, exactly, needs to be measured? Drawing on insights from three strands of finance literature— behavioral finance (Thaler, 1985, 1987; Loewenstein and Elster, 1992; Nisbett and Ross, 1980), investor sentiment (Barberis, Shleifer, and Vishny, 1997; Froot, O'Connell, and Seasholes, 2001; Froot and Ramadorai, 2008), and market reaction to macroeconomic news (Balduzzi, 2001; Brandt and Kavajecz, 2004; Goldberg and Leonard 2003)—we postulate four dimensions of investor confidence: _market volatility, market performance, macroeconomic news,_ and _government responses_ (see Diagram 1).

# **Diagram 1. Four dimensions of investor confidence**

<!-- Start of picture text --> Market<br>Volatility<br>Macro- Investor Equity<br>economic Market<br>Confidence<br>News Performance<br>Government<br>Responses<br><!-- End of picture text --> 5
<!-- Start of picture text --> 40 a b<br>c<br>onl or<br>hd > e<br>© 30 —— £<br>7) == o<br>Ssc > a oor 5 15<br>E25 5°<br>3fo} BSo= 10<br>5 20 vE<br>6 oe<br>% 5<br>8=<br>sre} 15 3 5<br>10 =<br>0 S<br>5 e_—=<=<—«=——=>RrevQeEeeEeEeEeEeEEEElQV—0QQVV——_—_—_et tn —eECoCoC70C(C—u_Q<br>2020 2030 2040 2050 2060 2070 2080 2090 2020 2030 2040 2050 2060 2070 2080 2090<br>Year information is revealed Year information is revealed<br>T*=1.5°C —_ T*=17°C<br><!-- End of picture text -->
# **3. Data and methods**

Our data set is constructed from the Demographic and Health Surveys (DHS) for SubSaharan Africa (DHS, 2008) and the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank, 2003). The DHS data sets are from nationally representative surveys of households with at least one woman of reproductive age, usually between 15 and 49 years of age. All eligible women in each selected household are included in the survey. The large sample sizes of the DHS surveys mean that they are particularly well suited to the aggregate level analyses that are undertaken in this paper. The details of the number of regions included in each country and the time points are provided in Table 1.

The ARTES database provides daily rainfall and temperature from 1977 to 2000, and monthly rainfall from 1948 to 2001. All weather variables are aggregated on sub-national administrative unit which corresponds, in most cases, to the sub-national indicators from the DHS.

# 3.1 Dependent variables

The diarrhea variable is the proportion of the child population under age three in the region affected by diarrhea in the survey period. At an individual level, incidence of diarrhea is defined in the DHS questionnaire as whether a child had a diarrheal episode in the two weeks prior to the survey. This short period of recall is necessary to ensure the validity of the measure of diarrhea. A drawback of the aggregate measure of the incidence of diarrhea is that respondents were asked the question throughout the survey year, and therefore the diarrheal incidence should be regarded as an average measure for the survey year.

The malnutrition variables are measures of children suffering from malnutrition as a proportion of the under age three population in the region. Two measures of malnutrition are employed, that of weight for height (wasting) and height for age (stunting). In line with established practice children are classified as malnourished if either their weight for height or height for age z-scores are two standard deviations below WHO/NCHS reference population median (WHO, 1995).<sup>4</sup> The infant mortality variable is the under five mortality rate, which is estimated based on births in the ten years preceding the survey which makes it difficult to match it with corresponding climate variables. A priori, the weather variation variables are not expected to impact significantly in a direct way on the under-five mortality rate. The analysis of the determinants of the under-five mortality rate, however, is useful to provide additional information for model specification validation purposes.

> 4 Z-score = (observed value-median value of reference population)/(standard deviation of reference population).

7
Third and last, we implement a matching estimator on geographical distance that includes controls for distance to the border (i.e., combines matching and RD features). We call this “matching RD”. This is inspired by Gibbons, Machin, and Silva (2009). We match each PSU _j_ with its nearest neighbor _ν_ ( _j_ ) on the other side of the border. We sign the differences in outcome between matches (∆ _Yj_ = _Yj − Yν_ ( _j_ )) so that positive differences designate a Cˆote d’Ivoire ( _C_ = 1) advantage. We then regress the signed difference on the distance to the border of the matched PSU, again with locally linear regressions on each side: for _−h ≤ D ≤ h_ and _h_ = 50 _,_ 75. Because different localities _j_ in the same country can share the same nearest neighbor _ν_ , we cluster the standard error _η_ by _ν_ .<sup>4</sup> In the case of the Mali border, where sample sizes do not allow the implementation of the narrowest 50 km bandwidth, we instead produce 100 km bandwidth estimates, where, for “border RD” and “matching RD”, we add the square of the distance to the border ( _D_<sup>2</sup> , interacted with the _C_ dummy as well).

When examining border discontinuities in development outcomes, we add to all specifications a few geographical controls: we use latitude in the case of the Ghana and Guinea borders and longitude in the case of Mali as well as rainfalls, elevation, and distance to the nearest river. In specification (4), we use the difference between matched neighbors for all of these variables (again signed properly).

> _a_ 03 _LON_<sup>3</sup> + _a_ 21 _LAT_<sup>2</sup> _.LON_ + _a_ 12 _LAT.LON_<sup>2</sup>

> 4We also attempted a richer model that included the distance to the border of the matches. The estimates were not significantly different from those of the simpler model, although they were sometimes more imprecise. For the 75 km bandwidth sample of PSUs, the average distance to the border of the nearest neighbor matches varies between 10 and 13 km.

11
The value of _E_ [  | _pq_ , _aq_ ) can be approximated using simulation and we use the _mixlbeta_ command in STATA to derive the individual parameter estimates (Hole 2007). For the results included in the text of the paper, we assume the preferences for attributes are the payment vehicle and fuelwood reduction attributes are random and lognormally distributed. This assumption assures that willingness to accept compensation for fuelwood reductions is always positive. As robustness checks, we present results that assume attributes are normally distributed and that only fuelwood reduction (not the payment vehicle) is lognormally distributed. As the findings are not appreciably different from those presented in the paper, we only briefly mention these results in the text.

These marginal effects are the willingness to accept payment (in Nepali Rupees) for a 1-percent reduction in fuelwood collections from common forests. As shown in Table 1, these are derived from four discrete percentage reduction possibilities. We use the percentage reduction metric, in contrast to a pure quantity-based measure, because it is relevant to all households regardless of their circumstances. It is also true, though, that different households have different family sizes, food consumption patterns, live at different altitudes, exist in environments of different qualities and characteristics, etc. They therefore will have different meanings of 1-percent reductions. We address this issue below.

Choice experiments only produce a single estimated WTP or in our case, WTA, value for each household. To examine small reductions that are greater than 1%, we must assume homothetic 16
Appemm1 DO: Ipeirsoim Im Years of Ezpeeimee imd EgMIfiuty<sup>ffoirMHGin Mezco</sup> (% under MPG eligibility requirement,<sup>by schoolinglevel</sup><sup>**)1**</sup>

|BaseRlie seema<br>|roureaRlnmte<br>|restrete<sup>=</sup> <br>|<sup>_§%,_</sup><br>irealwgE<br>|growth=2|%<br>113|
|---|---|---|---|---|---|
|Yesirsofseiool<br>|0-§|6-0|_0=12||+|
|Men_<br>||||||
|Memnexjperience<br>|45<br>|44.3<br>|44.6<br>|43.9<br>|42.8<br>|
|**%**uideir0years|0<br>|0<br>|0<br>|0<br>|0<br>|
|%nnmder20 yearo<br>|0<br>|0<br>|0<br>|0<br>|.1<br>|
|%under2§yeairs<br>|0<br>|.1<br>|0<br>|.2<br>|1.<br>|
|**%**eUlgMbleforM]?¢G|100<br>|99.9<br>|100<br>|99.8<br>|99<br>|
|**%**owM-aMImety<br><M<br>MMiinun<br>peMsaon <sup>2</sup>|0|0|0|0|0|
|WonDen||||||
|Meeanezjerneimee|20.9<br>|19.9<br>|21.9<br>|24.4<br>|31.7<br>|
|**_%_**unmderi10years<br>|25.5<br>|28.5<br>|24.5<br>|16.8<br>|5.9<br>|
|**_%_**imimder20years|47.8<br>|50.2<br>|45.6<br>|38.5<br>|19.9<br>|
|**%**unmder25vyears|59.7<br>|61.5<br>|57.1<br>|51.7<br>|31.4<br>|
|%eflgEfleforMIP¢<br>|40.3<br>|38.5<br>|42.9<br>|48.3<br>|68.6<br>|
|**%**owIMn-annSuity<br><miRnnmnmpensiomn<sup>2</sup>|47|43|34|19|4|

Notes:

1. Dispersion is calculated<sup>using coefficientsof variationof theaccumulatedyearsof</sup> experience for women and men,<sup>ages 61-65,from ENE97, more urbanareas.</sup>

2. MPG = US$99 in 1997 US$'s.<sup>Own-annuity is estimated using assumptionsgivenin</sup> Table **_5,_** for given years of<sup>experience.Probably thereis a positive correlation between</sup> years worked and size of own-annuity,<sup>sothosewhoseown-annuityarelessthanthe</sup> minimum pension are likely<sup>tobe includedamong thosewhoworked lessthan 25years</sup> and are therefore ineligible for<sup>the MPG top-up.</sup> 63
in Rio Grande do Sul and lowest in Roraima, while the concentration of registered Venezuelans is the highest in Roraima and lowest in Tocantins. Overall, it seems that integration is more in states that have lower number of Venezuelans. Venezuelans registered are also more likely to be living in urban areas than the registered Brazilians. PBF coverage rate seems to be slightly higher for Venezuelans than for Brazilians, although in numbers, there are fewer Venezuelans receiving the program compared to Brazilians. In July 2020, the relative probability index suggests that it is about 1.2 times as likely for Venezuelans registered to receive PBF as Brazilians registered, with an F-Index of 56. The figure below also shows that there has not been any di↵erential access to _Bolsa Familia_ for the Venezuelans except in 2019. Morgandi et al. (2020) reports that approximately 1.5 million families (about 10% of total families benefitted) were in the waiting list in 2019 and this deterioration in program coverage and fall in the value of the benefit opened up discussion on the need of reform of the _Bolsa Familia_ program, which resulted in the introduction of the 13<sup>_th_</sup> payment for December 2019. The rest of our analysis focuses on whether controlling for eligibility, Venezuelans still face a di↵erential coverage rate and what factors influences the coverage gap in 2019.

<!-- Start of picture text --> 2017 2018 2019 2020<br>Time<br>CadUnico PBF CadUnico PBF<br>(a) Over time (b) Over regions<br>60 80<br>50<br>60<br>40<br>F−Index 30 F−Index 40<br>20 20<br>10<br>0<br>RO AC AM RR PA AP TO MA PI CE RN PB PE AL SE BA MG ES RJ SP PR SC RS MS MT GO DF<br><!-- End of picture text --> Figure 13: Measure of Integration in Social Protection over Time and Regions

# **5.3.2 Coverage Gap and Decomposition**

Table 12 shows the results after estimating equation (13) and clustering the standard errors at the province level. It reveals that the coverage gap in 2019 remains after controlling for income per capita and family composition, which are the two main criteria for eligibility of PBF. Venezuelans are found to be 0.12 to 0.19 percentage points less likely to receive _Bolsa Familia_ than Brazilian, controlling for their income and family characteristics and conditional on them enrolling in _Cad’Unico_ . However, this coefficient should be interpreted with caution since the estimation results do not control for selection in to _Cadastro ’Unico_ and there are likely many omitted variables that the estimations do not control for, like the availability of required documents.Table 13 further shows that only 0.05 of the 0.19 percentage points di↵erence can be explained by endowment di↵erence (explained), the other 0.14 percentage points relate to unobserved characteristics or to di↵erences in returns to the characteristics. One obvious candidate to explain this unexplained gap may be propensity to register in the Single Registry, which cannot be controlled for because of unavailability of data on the 41
Vaughan, Andres. 2010. “Unconditional Cash Transfers: Will Redistribution in Bolivia Work?” Oregon State University.

Willibald, Sigrid. 2006. “Does Money Work? Cash Transfers to Ex-Combatants in Disarmament, Demobilisation and Reintegration Processes.” _Disasters_ 30 (3) (September): 316–39. doi:10.1111/j.0361-3666.2005.00323.x.

34
|Table B.8: Fiscal (pro)cyclicality of VAT rates,|whole sample (2000-|2016): Impacts of le|vel playing field,|natural resourc|es, and other fa|ctors|||||
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable: Individual income tax|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Cyclicality of real GDP|-0.082***<br>(0.022)|-0.103<br>(0.173)|-0.217***<br>(0.046)|0.001<br>(0.035)|-0.013<br>(0.036)|0.001<br>(0.035)|-0.070***<br>(0.019)|-0.111*<br>(0.057)|-3.662<br>(3.669)|-0.205***<br>(0.026)|
|**Equality of conditions**|||||||||||
|Crgdp*power.distribution|0.047<br>(0.028)||||||||||
|Crgdp*resource.distribution<br>Crgdp*ethnicandrel.stability||0.013<br>(0.295)|0.035***<br>(0.012)||||||||
|**Natural resources**|||||||||||
|Crgdp*resource.dependant (bd)||||-0.063<br>(0.040)|||||||
|Crgdp*resource.rich (bd)|||||-0.047<br>(0.041)||||||
|Crgdp*resource dependant and rich||||||-0.063|||||
|||||||(0.040)|||||
|**Other factors**|||||||||||
|Crgdp*elections|||||||0.042**<br>(0.019)||||
|Crgdp*debt.ratio||||||||0.004<br>(0.004)|||
|Crgdp*rigidity|||||||||670.223<br>(655.163)||
|Crgdp*fiscal space||||||||||6.248***<br>(0.392)|
|Constant|20.068***|19.372***|-0.731|-0.263|-0.263|-0.263|-0.262|-0.416|20.722***|-0.350|
||(0.705)|(1.708)|(1.314)|(0.249)|(0.249)|(0.249)|(0.248)|(0.334)|(0.670)|(0.287)|
|Adjusted R-squared|1.000|1.000|1.000|1.000|1.000|1.000|1.000|1.000|1.000|1.000|
|Rmse|1.003|1.007|0.997|1.009|1.009|1.009|1.009|1.014|0.865|1.007|
|Joint significance (p-value)|0.000|0.002|0.011|0.011|0.012|0.011|0.000|0.007|0.548|0.000|
|<br>CD test (p-value)|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|<br>Order of integration|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|I(0)|
|Cluster|country|country|country|country|country|country|country|country|country|country|
|Covariates|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|<br>Yes|
|Observations|687|687|743|823|823|823|823|811|410|820|
|# countries|47|47|48|48|50|50|50|50|26|50|

Notes. Standard errors (in parentheses) clustered at the country level. All interactive variables are also introduced in level but not reported to save space. Coefficients attached to cyclicality of GDP or its interactions are multiplied by 10000. All regressions include the volatility of the terms of trade, BD for missing data in real GDP and real expenditure series, BD for five years HP cycle correction. See Table A.3 for further discussion on variables definition. *** p<0.01, ** p<0.05, * p<0.1.

37
Table 4: Estimation result of second-stage analysis

||Probit|Probit|
|---|---|---|
||(1)|(2)|
|Caste: SC|_−_0_._243|_−_0_._304|
||(0_._175)|(0_._192)|
|Caste: ST|_−_1_._065<sup>_∗∗_</sup>|_−_1_._164<sup>_∗∗∗_</sup>|
||(0_._424)|(0_._437)|
|Caste: OBC|_−_0_._304<sup>_∗_</sup>|_−_0_._527<sup>_∗∗∗_</sup>|
||(0_._163)|(0_._176)|
|Caste: unknown|_−_0_._294|_−_0_._634<sup>_∗∗_</sup>|
||(0_._284)|(0_._300)|
|Religion: Muslim|0_._323|0_._260|
||(0_._243)|(0_._271)|
|Religion: other|_−_0_._329|_−_0_._451<sup>_∗_</sup>|
||(0_._237)|(0_._253)|
|Education: primary|0_._162|0_._289|
||(0_._211)|(0_._226)|
|Education: secondary|0_._217|0_._225|
||(0_._164)|(0_._178)|
|Education: higher|0_._165|0_._335|
||(0_._254)|(0_._277)|
|Duration of residence _>_15 years||0_._370<sup>_∗∗_</sup>|
|||(0_._188)|
|Monthly expenditure||0_._031<sup>_∗_</sup>|
|||(0_._016)|
|Photo-pass||0_._463<sup>_∗∗∗_</sup>|
|||(0_._147)|
|Evaluation of politicians: negative||0_._065|
|||(0_._155)|
|Evaluation of politicians: neutral||_−_0_._205<br>(0_._235)|
|Constant|0_._860<sup>_∗∗∗_</sup>|0_._159|
||(0_._164)|(0_._271)|
|Price of slum declaration (in thousand rupees)|1|1|
|Observations|429|388|
|Log Likelihood|-351.156|-281.231|

Note: Standard errors in parentheses. Dependent variable is a dummy indicator of slum declaration status. The coefficient on the implicit price is normalized to _−_ 1. _∗∗∗ ∗∗ ∗ p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1.

34
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Appreciation of Real Housing Price Index