rafmacalaba/datause-encoder-data huggingface dataset:
occurred over the period March 2012 - October 2015 with a temperature above 1300K, or (ii) two detections occurred regardless of temperature. The resulting set of pixels was then visually inspected and manually edited to remove low temperature sites (under 1300 K) located in agricultural settings, deemed to be the outcomes of biomass burning.

To further ensure that the flaring sites identified by the algorithm described above did correspond to natural gas flaring associated with oil production, we verified the presence of oil production infrastructure around the detected flares. Daytime satellite imagery were obtained from Google Earth and Esri’s World Imagery Map. Google Earth combines imagery dating to as early as 2004 from a range of providers at varying resolutions over the area. Esri’s World Imagery Map also collates satellite and aerial imagery from a range of providers; however, Digital Globe provided the majority of images in the ISIS region of interest at 0.5 meter resolution during 2010-2012.<sup>6</sup> A site was classified as having infrastructure if a flare stack was observed within 750 meters (the size of the pixels in the VIIRS data) of the detection coordinates in any of the available images. Included images typically showed production facilities with a pipeline to the flare stack, though even sites with primitive infrastructure, such as simply a stack with a wall, were also included. Excluded sites fell into two categories. Either the site had no infrastructure, suggestive of agricultural burning or bombing, or the site had industrial facilities inconsistent with oil production, such as oil refineries. Figure A.8 shows an example of a site with infrastructure and a site without.

> 6The Esri data is available at http://www.arcgis.com/home/item.html?id= 10df2279f9684e4a9f6a7f08febac2a9. The list of providers by region is documented at http: //help.arcgis.com/en/communitymaps/pdf/WorldImageryMap_Contributors.pdf

29
structure related to supply and demand of firm and household credit, (ii) elements of the contractual, information and macroeconomic environments shown to be important for banking sector development, (iii) banking market structure and (iv) bank regulatory policies. We will test the relationship between credit composition and different groups of country traits before performing a horserace among these different groups. Table 6 provides descriptive statistics of the different variables.<sup>16</sup>

# **Economic and financial structure**

We include several country traits that proxy for supply and demand factors associated with household vs. firm credit. A large number of similar loans can help lenders overcome diseconomies of scale, as can geographic concentration of borrowers and we therefore conjecture that the **share of urban population** in a country is positively associated with a higher share of household credit in total credit. A higher share of the population working in the **informal economy** , on the other hand, is likely to be negatively associated with household credit, as households working in the informal sector lack the necessary formal documentation to access formal banking services. A higher **share of manufacturing** is also expected to be negatively associated with the share of household credit in total bank credit, as it might indicate a higher demand from enterprises. Finally, as capital markets gain in importance and thus the opportunities for enterprises to access alternative external finance sources rather than being limited to bank finance (Demirguc-Kunt and Levine, 2001), banks are forced to look for alternative lending opportunities. We therefore conjecture that the share of household credit in total bank lending increases as markets gain in importance vis-à-vis banks, as measured by **financial structure** , the log ratio of value traded to bank credit **,** using data from Beck, Demirguc-Kunt and Levine (2000).

> 16 While we have already shown a positive relationship between the share of household credit and economic development, we will not include GDP per capita in our regression analysis because of multicollinearity concerns. First, GDP per capita is highly correlated with many other country characteristics; however, we are interested, which of these country traits explain the composition of credit.  Second, once controlling for other country traits, GDP per capita often does not enter significantly anymore.

13
data.<sup>15</sup> Each country-level distribution is summarized by the average income of the ten decile groups. We first use a parametric Lorenz curve to obtain a continuous within-country distribution of 10,000 points. Second, we simulate these base year distributions until 2030 under four different growth scenarios and five shared prosperity premiums . We will now explain each of these steps in more detail. mm The latest line-up data consist of the income or consumptions shares of the ten decile groups and the overall mean in 2011. We use a lognormal Lorenz curve to generate a distribution of 10,000 points, or ‘myriantiles’, for each country. The use of a parametric Lorenz curve is very similar to what is done in PovcalNet to calculate poverty when microdata are not directly available.<sup>16</sup> Figure 5 compares our poverty estimates at the $1.25 poverty line against those from PovcalNet. The difference has a median of 0pp and standard deviation of 0.78pp. For more than half of the observations, the modeled estimates fall within +/- 0.2pp of the PovcalNet estimates. Our estimate of global poverty in 2011 is 14.7%, compared with 14.5% in PovcalNet. The differences are largest for countries where PovcalNet relies on microdata for estimating poverty (mostly LAC region) and the cases where PovcalNet uses separate distributions for rural and urban (China, India and Indonesia), but where our method relies on the national distribution. Furthermore, our simulated headcounts for 2030 under distribution-neutral growth are very similar to World Bank (2015), which used the same line-up and growth scenarios but the PovcalNet method.

The income distribution in the base year is simulated forward in the following way. In the first year (from 2011 to 2012), we apply a growth rate to each of our 10,000 fractile groups, following the stepfunction GIC where the bottom 4000 groups (bottom 40 percentile groups) grow at a rate that is pp above the growth rate of the mean. This implies that the top 60% grow at a somewhat lower rate mm depending on their income share, as discussed in detail in the conceptual framework.

> 15 See World Bank (2015, box 6.4) for an explanation of the line-up method and estimating poverty rates for countries for which there is no or little survey coverage. Following World Bank (2015) we assume the headcount ratio in rich countries to be zero at the $1.25 a day poverty line. We mix income and consumption surveys to construct the global headcount as done in PovcalNet.

> 16 From two parametric Lorenz curves – the General Quadratic and the Beta Lorenz – PovcalNet chooses the one with the best fit. Instead, Shorrocks and Wan (2008) suggest that a lognormal functional form fits better. Minoiu and Reddy (2012) show that for global poverty estimates a parametric Lorenz curve should be preferred to estimating kernel densities. We use the ungroup command included in the DASP Stata Package (Abdelkrim and Duclos, 2007) to fit a separate lognormal Lorenz curve for every country. This command implements the Shorrocks and Wan (2008) approach which ensures that the fitted Lorenz curve matches the observed shares.

11
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6. Mobile money accounts are identified by respondents who reported having used a mobile money account to make a payment in the past year.

7. Some might argue that the potential for expanding financial inclusion is limited because 59 percent of unbanked adults cited lack of enough money to use an account as a reason for not having one. But the data show that poor people do make financial transactions, and research has shown that even the very poor save when they are provided with a savings mechanism (Dupas and Robinson 2013b). This underlines the importance of providing basic low-cost accounts.

8. Allen and others (2012) show that such policies can expand account ownership especially among the groups most likely to be unbanked, such as poor people and those living in rural areas.

9. See World Bank (2014b) for a more detailed discussion of the benefits and challenges of digitizing payments.

10. See, for example, Aker and others (2013); Babatz (2013); and CGAP (2011).

11. Wright and others 2014.

12. Muralidharan, Niehaus, and Sukhtankar 2014.

13. See Aportela (1999); Prina (2012); and Batista and Vicente (2013).

14. Zimmerman, Bohling, and Rotman Parker (2014) describe the challenges of moving cash payments into accounts in the context of digitizing government transfer payments in four developing economies. See also World Bank (2014b).

15. Globally, 2 percent of unbanked adults receive government wages in cash only.

16. Globally, 6 percent of unbanked adults receive government transfers in cash only.

17. Globally, less than 1 percent of unbanked adults receive both government wages and government transfers in cash only.

18. These benefits must be weighed against the potential public costs of the improvements to the payments infrastructure necessary to digitize government payments. But growth in mobile money and card-based accounts—through mobile agents and point-of-sale merchants—provides a private sector solution for cash-out points (see, for example, CGAP 2012; and Zimmerman, Bohling, and Rotman Parker 2014).

19. CGAP 2014.

20. For international remittances, data from the World Bank’s Remittance Prices Worldwide database show that on average it costs less to send the remittances through a money transfer operator than through an account at a commercial bank (World Bank 2015).

21. Globally, 13 percent of adults with an account, 420 million in total, receive wages or government transfers in cash only. But most of them (57 percent) actually have a high-use account, used for saving, for paying utility bills or school fees, for making purchases through a debit card, or for three or more monthly withdrawals. Only 18 percent of those receiving wages or government transfers only in cash have a dormant account.

22. In high-income OECD economies 10 percent of adults with an account, almost 80 million people, pay utility bills in cash. The question about payments for school fees was not asked in this group of economies.

> THE GLOBAL FINDEX DATABASE 69
poverty levels in each of the three survey years can be read from the distribution functions at the point where the function crosses the vertical line that indicates 100 percent of the poverty line (DENEAP, 2010). The distribution can also be compared in welfare terms: any distribution appearing to the right of another is statistically dominant and can be considered a welfare improvement.

**Figure 6: Distribution Function for Revised Real Consumption**

<!-- Start of picture text --> National North<br>20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 200<br>% of Poverty Line % of Poverty Line<br>1997 2003 2009 1997 2003 2009<br>Center South<br>20 40 60 80 100 120 140 160 180 200 20 40 60 80 100 120 140 160 180 200<br>% of Poverty Line % of Poverty Line<br>1997 2003 2009 1997 2003 2009<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

**Revised:** national 1996/97 based basket PL with 13 location price deflators and 2002/03 and 2008/09 data partially re-estimated (Sets A and B, Table1)

These conclusions regarding poverty trends hold true at both the national and consolidated macroregion levels. The 2002/03 survey recorded a dramatic reduction in poverty and a general improvement in overall welfare. However, income growth was fastest at higher income levels, and inequality consequently increased. It is worth noting that the unequal growth of consumption has a clear spatial correlation: welfare improvements in the South between 1996/97 and 2002/03 (Figure , bottom right graph) were twice as high as those in the Central and Northern regions.

From 2002/03 to 2008/09 the rate of poverty reduction decreased and became less even, but poverty reduction itself did not stop. The consumption distribution in 2008/2009 is superior to 2003/04, but only 24
Figure 7: Brazil: 2002–2005–2008 Type Opportunity Growth Incidence Curve <!-- Start of picture text --> 0 5 10 15<br>types<br>type OGIC 2002−2005<br>type OGIC 2005−2008<br>30<br>20<br>10<br>0<br>% yearly growth<br>−10<br>−20<br><!-- End of picture text --> _Authors’ calculation from PNAD (Instituto Brasileiro de Geografia e Estat ́ıstica)_ As is reasonable to expect, the comparison of the two processes highlights an unambiguous dominance of the second period growth over the first. The difference in the OGIC coordinates is statistically significant for almost all types, as shown in Table 5 in the appendix. For robustness purposes, we also test the difference of the respective cumulative type OGICs coordinates, which is clearly statistically significant for all types, and the difference, which is again significant, of their aggregate index of growth (see Table 6 ).

Finally, Figure 8, reporting the within-type GICs, explains how the progressive growth of Brazil between 2002 and 2005 is the joint effect of a reduction of between- and-within type inequality. The four within-type GICs are downward sloping, and the average growth rate in the poorest seven types is higher in both cases than the same rate in the eight richest types.

32
# **V. Results**

Capitalizing on existing studies (e.g. Lin & Ye, 2007; Tapsoba, 2012), we compare tax revenues in Non-Cooperative and Cooperative countries—matched based on their propensity scores— using various matching methods: the Nearest-Neighbor (NN) matching, the Radius matching— with a large (r=0.05), a medium (r=0.01), and a small (r=0.005) radius, the regression-adjusted Local Linear matching (Heckman et al., 1998), and the Kernel matching.

Estimations of the ATT are reported in Table 2. Before presenting our main results, we report at the bottom of the table several tests to evaluate the quality of our fitting. First, we check if our estimations are affected by an eventual hidden bias arising from unobservables (Rosenbaum, 2002). The value of the Rosenbaum sensitivity test—performed at the common 5% significance level—is around 1.5, namely above the value used by other studies to conclude to the lack of such a hidden bias (for example, Guerguil et al., 2017, conclude with values around 1.2). Second, following Rosenbaum & Rubin (1985), the high p-values of the standardized bias confirm the statistical equality between the means of the various observed variables among countries from the treated and the control group. Finally, the low values of the pseudo R2 reveal that our matching procedure leads to balanced scores (see Sianesi, 2004). Altogether, these results support the conditional independence assumption and the common support assumption.

Given the quality of our estimations, we can now discuss our main results reported in the top part of Table 2. As illustrated by line [1], all eight ATTs are significant and negative irrespective of the matching method. Consequently, we reveal that being a Non-Cooperative country—included on the FATF list—is associated on average with significantly lower tax revenues, between—in absolute value— 2.066 (2-Nearest Neighbor Matching) and 2.778 (Local Linear Regression Matching) percentage points. Moreover, this effect is economically meaningful, as it represents between one-third and almost half of the standard deviation of our measure of tax revenues (which is slightly above 6, see Table A2 in the Online Appendix). Finally, as shown by lines [2]-[6], using different measures of institutional quality does not affect ATTs, who remain significant and negative in all cases, with an adverse effect climbing up to (in absolute value) 3.589 percentage points (1-Nearest Neighbor Matching, line [4]).

10
for most surveys of citizens or entrepreneurs we have the mean response across respondents of discrete categorical scores. We re-orient data from each source so that higher values correspond to better outcomes (i.e. stronger rule of law, more effective govemment, and less graft). In addition, we rescale each indicator by subtracting the minimum possible score and dividing by the difference between the maximum and minimum scores, so that each indicator is on a possible scale from zero to one. Since we rescale each indicator using the maximum and minimum possible scores (rather than the maximum and minimum actual scores in the sample of countries covered by each indicator), this is nothing more than a convenient choice of units.

In Table 2, we report the pairwise correlations among indicators within each of the three governance clusters. The great majority of these are positive and substantial, frequently greater than 0.6. In the empirical work which follows, we will interpret these large correlations within clusters as reflecting the common component of govemance in these indicators. It is interesting to note that despite the strong pairwise correlations among these indicators, and despite the favourable interpretation that these correlations reflect the common component of governance rather than correlated perception errors, we nevertheless find that governance is not very precisely measured. We provide some intuitions for this in the following section.

7
(1) ii,tt ii,tt−1 ii ii,tt where YY = ββ is a crisis indicator (either sovereign debt, banking, or currency crisis) for<sup>′</sup> XX + μμ + εε country in year , and takes the value of 1 if it is in a crisis, and 0 otherwise; is the ii,tt vector of determinants of a crisis; YY is the vector of coefficient estimates common across ii,tt all countries; ii captures the unobserved country heterogeneity; and tt is the stochastic XX error term. ββ ii ii,tt μμ εε The probability of a crisis is given by: P ) (2) ii,tt ii,tt−1 ii ii ii,tt �YY = 1 | XX , ββ , μμ �= Ψ(μμ + ββ<sup>′</sup> XX

where assumptions about the distribution of the error terms, that is, the form of Ψ(. ) renders the estimation of the logit (logistic distribution) or probit (normal distribution) discrete choice panel data model. The parameters can be estimated by maximizing the panel-level likelihood function.

Selection of explanatory variables . The variables are chosen from the empirical findings from the early warning crisis literature.<sup>5</sup> This literature has identified the following correlates of higher crisis probabilities:

- Factors that increase rollover risk : These are particularly relevant during periods of elevated financial stress; they include high short-term external debt and high or rapidly growing total, government or private debt.

- Factors that restrict policy room to respond: These include low international

- reserves, large fiscal or current account deficits, and weak institutions.

- Factors that suggest overvaluation of assets : These indicate potential for large asset price corrections; they include exchange rate misalignments, and credit and asset price booms.

We include a large number of variables (and various data transformations, such as levels, growth rate, percentage point change, deviation from trend) that can be characterized into several groups:

- Debt profile: public debt, private debt, short-term debt, variable interest rate debt, concessional debt, multilateral debt, commercial debt, IMF credit, debt service

- Capital account : international reserves, currency mismatch, portfolio flows, FDI

- Current account : current account balance, exchange rate overvaluation, exchange

- rate regime, terms of trade

- Foreign : U.S. interest rate, advanced economies’ GDP growth

- Domestic macro: GDP growth, inflation, unemployment, fiscal balance

- Financial sector : credit to private sector, money supply, interest rate

- Banking sector: liquidity, leverage, banking concentration, non-performing loans

- Structural : trade openness, export diversification, capital account openness

> 5  See Kaminsky, Lizondo, and Reinhart 1998; Frankel and Saravelos 2012; Chamon and Crowe 2012; and Moreno Badia et al. 2020 for an extensive review.

10
**Table 5. Daily Wages (in birr) by OSD Severity, Gender, and Age Category for Temporary Workersa**

||N|_Female D_<br>Age 15-25|_aily Wages_<br>N<br>Age 25+|N|_Male Daily_<br>Age 15-25|_Wag_<br>N|_es_<br>Age 25+|
|---|---|---|---|---|---|---|---|
|Non-OSD|30|<br>3.11<br>(0.11)|8<br>3.2<br>(0.09)|39|3.5<br>(0.11)|11|<br>4.3<br>(0.22)|
|Severe-OSDb|4|3.1<br>(0.33)|7<br>3.2<br>(0.06)|13|3.5<br>(0.12)|9|4.3<br>(0.29)|
|Differencebc||0.0<br>(0.31)|0.0<br>(0.02)||0.0<br>(0.20)||0.0<br>(0.36)|
|Wilcoxon Rank-Sum<br>Statistic||-0.107|-0.847||-0.328||0.190|

a. Daily wages equal average monthly income over average days worked per month for latest 12 months.

b. Standard errors in parentheses.

c. "*" represents statistical significance at 5% based on a t-test with unequal variances.

d. "*" represents statistical significance at 5% based on the Wilcoxon rank-sum test for equal medians.

As can be seen, there is no OSD-related statistical difference in the daily wages of temporary male and female workers across age categories. There are several factors to note before we limit the ensuing analysis to permanent workers. First, temporary workers may have alternate income sources that we are unable to control for. Secondly, the decision regarding labor supply for temporary workers are determined in part by plantation labor demand. Thirdly, the sample size is small for temporary workers, therefore causing possibilities of bias.

_Estimation of Wagae Equation:_ Given the preliminary indications in Tables 3 through 5, we estimate the wage equation for permanent workers at the plantation. We regress the logarithm of daily wages on the prevalence and severity of OSD controlling for other pertinent variables. As mentioned earlier, the model we estimate is: _In W = a,_ **_X_** _+ a2 0 + a3 I + e_ where, _W:_ Daily wages earned. _0:_ Severe OSD = 1. _I:_ Intermediate OSD = 1. X: Vector of control variables: age, age squared, and gender of individual.

Table 6 summarizes the regression results.

8