rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.93*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.83*** 0.83***<br>Spearman 0.92*** 0.8*** 0.87***<br>A<br>Kendall tau-b 0.78*** 0.67** 0.72***<br>Spearman 0.95*** 0.93*** 0.93***<br>M0<br>Kendall tau-b 0.89*** 0.83*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C3. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Level of education indicator removed (weights within the Social Inclusion<br>dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C3.1. FOSD: Counting Vector C3.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C3.3. SOSD: Social Sustainability Index C3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.95*** 0.88***<br>H<br>Kendall tau-b 0.81*** 0.82*** 0.75***<br>Spearman 0.93*** 0.83*** 0.86***<br>A<br>Kendall tau-b 0.81*** 0.65*** 0.76***<br>Spearman 0.98*** 0.96*** 0.88***<br>M0<br>Kendall tau-b 0.89*** 0.86*** 0.74***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .1<br>.8 .05<br>.6 0<br>CCDF<br>.4 Difference -.05<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C3. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Level of education indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C3.1. FOSD: Counting Vector C3.2. Dominance Analysis 49
# **Table S2: Descriptive statistics for inventors residing in United States**

|||||E|thnicity of inv|entor||||
|---|---|---|---|---|---|---|---|---|---|
||Anglo-Saxon|Chinese|European|Hispanic|Indian|Japanese|Korean|Russian|Vietnam.|
|||A|. Ethnic Invent|or Shares Esti|mated from US|Inventor Reco|rds, 1975-200|4 (%)||
|1975-1979|74.8|2.1|15.6|2.7|2.0|0.6|0.3|1.9|0.1|
|1980-1984|73.4|2.9|15.1|2.7|2.6|0.7|0.4|2.0|0.1|
|1985-1989|72.2|3.6|14.6|2.9|3.1|0.8|0.5|2.1|0.2|
|1990-1994|70.0|4.8|14.1|3.2|3.9|0.9|0.6|2.2|0.4|
|1995-1999|66.4|6.7|13.6|3.5|5.2|0.9|0.7|2.5|0.5|
|2000-2004|63.1|8.8|13.0|3.8|5.9|1.0|0.9|2.8|0.6|
|Chemicals|65.8|7.3|14.4|3.2|4.9|0.9|0.7|2.5|0.3|
|Computers|62.9|8.4|12.6|3.4|7.5|1.0|0.7|2.7|0.7|
|Pharmaceuticals|64.8|7.2|14.8|3.9|4.6|1.1|0.8|2.6|0.3|
|Electrical|64.3|8.3|13.3|3.3|5.3|1.0|0.9|2.8|0.7|
|Mechanical|72.8|3.3|14.2|3.3|2.8|0.7|0.5|2.2|0.2|
|Miscellaneous|74.1|2.9|13.9|3.6|2.3|0.6|0.5|1.9|0.2|
|Top Cities as a|WS (84)|SF (14)|MIL (21)|MIA (16)|SF (8)|SD (2)|BAL (2)|NYC (4)|AUS (2)|
|Percentage of|SLC (83)|LA (8)|NOR (19)|SA (9)|AUS (7)|SF (2)|LA (1)|BOS (4)|SF (1)|
|City’s Patents|NAS (82)|AUS (6)|STL (19)|WPB (6)|PRT (6)|LA (2)|DC (1)|HRT (4)|LA (1)|
|||B. Im|migrant Scientis|t and Engineer|Shares Estim|ated from 1990|US Census R|ecords (%)||
|Bachelor's Share|87.6|2.7|2.3|2.4|2.3|0.6|0.5|0.4|1.2|
|Master's Share|78.9|6.7|3.4|2.2|5.4|0.9|0.7|0.8|1|
|Doctorate Share|71.2|13.2|4|1.7|6.5|0.9|1.5|0.5|0.4|

Source: Author's analysis based on data described in text. Notes: Panel A presents descriptive statistics for inventors residing in the US at the time of patent application. Inventor ethnicities are estimated through inventors' names using techniques described in the text. Patents are grouped by application years and major technology fields. Cities, defined through Metropolitan Statistical Areas, include AUS (Austin), BAL (Baltimore), BOS (Boston), DC (Washington), HRT (Hartford), LA (Los Angeles), MIA (Miami), MIL (Milwaukee), NAS (Nashville), NOR (New Orleans), NYC (New York City), PRT (Portland), SA (San Antonio), SD (San Diego), SF (San Francisco), SLC (Salt Lake City), STL (St. Louis), WPB (West Palm Beach), and WS (Winston-Salem). Cities are identified from inventors' city names using city lists collected from the Office of Social and Economic Data Analysis at the University of Missouri, with a matching rate of 99%. Manual recoding further ensures all patents with more than 100 citations and all city names with more than 100 patents are identified. Panel B presents comparable statistics calculated from the 1990 Census using country of birth for scientists and engineers. Anglo-Saxon provides a residual in the Census statistics. Many US inventors with European names are native citizens.
# **II. Literature Review and Empirical Strategy**

# **_An earnings model with an expanded measure of human capital_**

The basic Mincer (1965) model is in which the earnings ( _yi_ ) of individual _i_ are a function of that person’s human capital ( _Hi_ ) and a stochastic term ( _εi_ ) that represents idiosyncratic earnings differences, presumed to be orthogonal to _Hi_ . Due to the lack of direct measures of human capital, _H_ has been typically measured by years of schooling attained. This human capital earnings function has been the basis of a large empirical literature, including on developing countries.<sup>8</sup> We expand this basic earnings function to include other measures of human capital—in particular, cognitive skills and noncognitive skills—besides years of schooling and experience.<sup>9</sup> Glewwe (1996) and Hanushek et al. (2015), among others, find that including cognitive skills as a measure of human capital lowers the return to schooling estimated using just the basic earnings function.<sup>10</sup> A growing literature shows statistically significant earnings returns to noncognitive skills.<sup>11</sup>

> 8 A summary of the estimates for developing countries is provided by Psacharopoulos (1984) and Psacharopoulos and Patrinos (2004).

> 9 Even when measures of skills are included, we expect schooling years to affect earnings because of measurement error (that is, our skills measures are not likely to be a comprehensive set) and because years of schooling itself may be valued in the labor market where it is perceived to signal useful information about the individual (Spence, 1973; Belman and Heywood, 1991; Riley, 2001).

> 10 These two studies illustrate the types of data sources used for this growing literature. Glewwe (1996) uses a household survey in Ghana which administered tests to household members of abstract reasoning (Raven's Coloured Progressive Matrices), mathematics, and (English) reading comprehension. Using the multi-country PIAAC data, Hanushek et al. (2015) find that in 22 countries a one-standard deviation increase in numeracy skills is associated with an average 18 percent wage increase among prime-age workers. They note that due to measurement errors in skills, these estimates may be lower bounds on the return to cognitive skill.

> 11 Among relatively recent studies are Kuhn and Weinberger (2005), Nyhus and Pons (2005), Wanberg et al. (2005), Heckman, Stixrud, and Urzua (2006), Mueller and Plug (2006), Losoncz (2007); Schmitt et al. (2007), Fortin (2008), Borghans, ter Weel, and Weinberg (2008), Heineck and Anger (2010), Lindqvist and Vestman (2011), and Segal (2013). Table 1 summarizes the studies on low- and middle-income countries.

6
Table 2 Vietnam Aquaculture by Province in the Mekong

|Province|Sh|are in 20|02|Sh|are in 20|03|Catfsh|
|---|---|---|---|---|---|---|---|
||Freshwater|Brackis|h & Marine|Freshwater|Brackis|h & Marine|Production|
|||total|shrimp||total|shrimp|2003|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Long An|76.7|23.3|19.7|69.2|30.8|28.1|–|
|Dong Thap|100.0|0.0|1.8|100.0|0.0|1.5|15.8|
|An Giang|100.0|0.0|0.3|100.0|0.0|0.3|40.2|
|Tien Giang|38.9|61.1|6.4|43.0|57.0|9.3|6.4|
|Vinh Long|100.0|0.0|0.7|100.0|0.0|0.3|7.4|
|Ben Tre|7.6|92.4|16.2|13.5|86.5|20.7|–|
|Kien Giang|33.2|66.8|45.9|28.3|71.7|49.3|–|
|Can Tho|100.0|0.0|0.3|100.0|0.0|0.2|35.5|
|Tra Vinh|48.9|51.1|13.1|49.5|50.5|19.9|–|
|Soc Trang|29.3|70.7|67.4|29.6|70.4|69.0|3.1|
|Bac Lieu|2.2|97.8|76.4|0.8|99.2|76.3|–|
|Ca Mau|6.4|93.6|68.6|7.3|92.7|67.6|–|

Source: Ministry of Fisheries ( `www.fistenet.gov.vn` ) and Seafood from Vietnam Magazine ( `www. seafoodfromvietnam.com.vn` ). For 2002 and 2003, the figures show the shares of total aquaculture production from freshwater, brackish & marine, and shrimp aquaculture. The last column reports the fraction of total catfish (tra and basa) production from each province in the Mekong region, calculated from data on total production as well as production by province. Provinces for which the fraction is not reported account for 1.6 percent of total production in the Mekong region.

38
Source: Tables 4, 5, and 6 Note: Figure 1 plots a histogram of the distribution of estimates of empty mileage shares. When estimates are provided of backhaul probabilities, this figure assumes that all fronthaul trips are loaded. The figure also plots a kernel density estimate with bandwidth 0.05.

Figure 1: Distribution of Empty Mileage Estimates Source: Table 7 Note: Figure 2 plots kernel density estimates of the distribution of estimates of empty mileage shares, broken up by High Income countries and non-High Income countries. When estimates are provided of backhaul probabilities, this figure assumes that all fronthaul trips are loaded. The kernel density estimates use a bandwidth of 0.05.

Figure 2: Distribution of Empty Mileage Estimates by WB Income Status 8
# **1. Introduction**

In the wake of the recent global financial crisis, many policy makers have argued that lack of financial education was a contributing factor to the market turmoil (Lusardi and Mitchell, 2013). As a result, financial education programs are currently at the forefront of the post-crisis reform agenda.<sup>2</sup> Other macroeconomic and technological trends are also prompting such demand. With the explosive growth of microfinance and the emergence of mobile money in many parts of the world, policymakers and NGOs are urging more widespread financial awareness and knowledge to enable the poor to better understand and utilize the new financial products at their disposal.<sup>3</sup> Thus, a consensus has emerged around the need to expand financial education in the developed and developing world. Such policies may be critical not only to improving household welfare, but also to ensuring a stable financial system. Despite the theoretical benefits of greater financial education, however, the empirical evidence supporting such benefits is limited and offers mixed results.

While an abundance of survey data indicates a positive correlation between financial education and better financial decisions, causal analysis remains scarce. Moreover, what causal evidence exists does not provide a clear picture of the impact of financial education on ultimate financial outcomes. The results vary from positive, such as the improved business practices of microentrepreneurs in response to “rule of thumb” financial education (Drexler et al., 2013), to zero, shown by the lack of impact on savings from financial education in Indonesia (Cole et al., 2011), to outright failure, such as a video and radio based financial education course that was discontinued

> 2 The United States adopted a President’s Advisory Council on Financial Literacy in 2008 to help promote financial education at all levels of the economy; the UK government mandated compulsory financial education in schools from 2012 onwards; the Indonesian government declared 2008 as the year of financial education; the Reserve Bank of India launched a series of financial literacy and counseling centers across the country in 2007; Brazil and many other developing countries have incorporated national strategies for improving financial education; and private and multilateral agencies such as Citibank and the World Bank have multi-million dollar programs on financial education throughout the developed and developing world.

> 3 See for example, “Financial Literacy: A Step for Clients towards Financial Inclusion,” Global Microcredit Summit Commissioned Workshop Paper, November 14-17, 2011.

2
|Share of HH with improved floor|0.18<br>|0.02<br>|0.16|
|---|---|---|---|
|Share of HH with cellphone|0.45<br>|0.01<br>|0.73|
|Share of HH with fridge|0.02<br>|0.00<br>|0.27|
|Share of HH with stove|0.02<br>|0.00<br>|0.32|
|Share of HH with computer|0.02<br>|0.00<br>|0.35|
|Share of HH with oxcart|0.03<br>|(0.01)<br>|0.24|
|Share of HH with bicycle|0.33<br>|(0.02)<br>|0.28|
|Share of HH with motorcycle|0.04<br>|(0.00)<br>|0.77|
|Share of HH with car|0.01<br>|(0.00)<br>|0.74|
|Share of HH with radio|0.28<br>|0.01<br>|0.12|
|Share of HH with television|0.06<br>|0.00<br>|0.50|

Notes: the difference in means are calculated using a regression of each variable against the indicator variable equal to 1 if the village was matched and zero otherwise. They are weighted by the number of households in each village and the standard errors are clustered at district level.

# 3. Methodology

We propose four different targeting methods using census and survey data combined with geospatial data to understand the most effective way to target the poor population: (1) the PMT scores calculated in the UBR data of 2017; (2) the Relative Wealth Index from Meta (3) combining survey data and geospatial indicators to predict average welfare; and (4) a census sample to simulate a _partial registry_ data set used to train models using satellite data.

We rely on rank correlations, Area Under the Curve coefficients, and R-squared coefficients to compare the accuracy of each targeting method. In each case, we compare the predictions with a measure of benchmark welfare constructed from the census model.

## 3.1 Construction of Benchmark Welfare using the Census model

We define a benchmark welfare measure of “ground truth” in order to evaluate different targeting methods. As noted above, the primary welfare measure is the average predicted log per capita consumption of the poorest 50 percent of households in each village. This measure echoes the UBR structure limited to the bottom half of households in each village according to the average Malawi poverty rate. This measure takes advantage of the rich data in the census sample, and it is easier to predict than measured consumption due to reduced measurement error and its inability to capture temporary shocks.<sup>15</sup> To construct the benchmark welfare in the census, we use the IHS 2016 to estimate a model and then impute welfare in the census.

The variables included in the model are selected so that both data sets contain the same information. All the variables are measured at the household level. We include education variables such as the literacy of the household head, the maximum level of education achieved by men and women in the household, dependency ratios, household size and overcrowding,

> 15 Due to data availability, we use the per capita consumption as a welfare measure. However, we observe a high rank correlation with other potential measures such as absolute poverty rate (correlation of -0.86) or extreme poverty (correlation -0.796).

10
_Figure 6.4: Total emission reduction as % from base case_ <!-- Start of picture text --> Total Emission reduction as % from base case<br>2018 2022 2026 2030<br>(5.0 USD/ton) (13.3 USD/ton) (21.7 USD/ton) (30.0 USD/ton)<br>0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4<br>DTAX STAX SAVINGS CORPOR TRANS<br>%age reduction from base case<br><!-- End of picture text -->

# **6.2 Impacts on the Economy**

The assumed time path of carbon prices has a fairly modest effect on the growth of GDP. In BAU, GDP is much higher in 2030 than in 2015 given the assumed growth rates for the economy. The same is true in the policy scenarios; the different tax and recycling combinations only slow growth by a little over the study period. GDP in 2030 is about 1 percent lower compared to BAU in almost all scenarios. Average GDP growth is from 0.38 to 0.52 percentage points lower with the carbon pricing than under BAU, depending on the chosen scenario. This is a small decrease in the growth rate of 7.6 percent per year in BAU.

A cut in either sales or corporate tax leads to smaller negative effects on GDP compared to the other policy scenarios. The reason is that lower sales tax reduces cost of goods and services and stimulates demand across income groups. Lower business tax reduces the cost of capital which encourages investment and therefore employment and production. Both tax reductions end up reducing existing distortions in the economy. That, in turn, will lessen the amount of emission reduction from the carbon tax.

The scenario in which personal income tax is reduced leads to a larger decrease in the growth of economic — activity. The reason is that professional and educated labor the types of workers paying the personal income tax—is assumed to be fully employed. Reducing income tax therefore increases neither labor supply nor economic activity.

17
counterpart in the _Doing Business_ or Global Competitiveness data.<sup>7</sup> We can match four categories of the business environment indicators from Global Competitiveness data and Enterprise Survey data as follows (see table 1 for how each theme of the BE is represented in the three data sets). For Government Protection in the Enterprise Survey data—which includes Corruption Obstacle and Security Cost—we use “Corporate Ethics/Corruption” (based on the survey question, “How do you rate the corporate ethics of companies?”) and “Police System” (based on the survey question, “To what extent can police services be relied upon to enforce law and order?”). For Infrastructure in Global Competitiveness data, we choose “Electricity Quality” (from the International Energy Agency) and “Broadband” (from the International Telecommunications Union). For Human Capital, we choose “Quality of Education System” (based on the survey question, “How well does the education system meet the needs of a competitive economy?”). For Access to Finance, we choose “Financial Development” (the eighth pillar of the Global Competitiveness indicator). For Barriers to Entry, we could not find an appropriate indicator from Global Competitiveness data. All Global Competitiveness indicators range between 0 and 100 points. A higher value of a Global Competitiveness indicator for a country indicates a better business environment.

For _Doing Business_ indicators, we can match three categories of the regulatory environment: for basic Infrastructure (in the Enterprise Survey), we choose the “Getting Electricity” score (from _Doing Business_ ). For Access to Finance, we choose the “Getting Credit” score. For access to land (in the Enterprise Survey) in Barriers to Entry, we choose the “Dealing with Construction Permits” score.

In addition, a relevant indicator outside the scope of _Doing Business_ that comes from the World Bank is the newly minted measure of Human Capital (3), the Harmonized Learning Outcome (HLO), a country-level index for education quality (for more details, see Angrist et al. 2019). A higher value of a _Doing Business_ (and HLO) indicator implies a better BE. Table 3 reports the summary statistics of all the BE indicators from various sources. Because the scale of these indicators differs greatly, to facilitate comparison, we standardize them into variables with means of zero and variances of one (that is, the z-score) in our regression analysis.

These three groups of indicators are different and complementary. First, all the _Doing Business_ indicators and the Global Competitiveness indicators are at the country level and

> 7 The _Doing Business_ project does not capture some BE aspects by design, including the quality of the labor force, incidence of bribery and corruption, and lack of security. See table 2.2 of the most recent _Doing Business_ report (World Bank 2019).

10
|||Source|Mean<br>S|td.Dev<br>M|in<br>Min Importer)|Max|Max<br>Importer|MEXICO<br>|MEXICO!<br>MEAN —|MEXICO!<br> Min|MEXICO/<br>Wax|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Port Efficiency|Part Facilities|GCR|0.636|0.168|0.261 Bolivia|1.000|Singapore|0.478|0.75|1.63|0.48|
||Air Transport|GCR|0.710|0.165|0.229 Slovak Rep ||1.000|(Singapore|0.656|0.97|3.00|0.69|
|Aggregate Index|||0.673)|0.168|0.345 Bolivia|1.000|Singapor|0.582|0.87|1.69|0.58|
|Custom Environment|[Hidden Import Barners|GCR|0.702|0.165|0.368 Paraguay|1.000|Finland|0.706|1.01|1.92|071|
||Bribery|GCR|0.669)|0.174|0.343Bangladesh|=1.000—|Finland|0.614|0.89|1.79|0.61|
|Aggregate Index|||0.695,|0.162|<br>0.384 Paragua|<br>0.979|<br>Finland|0.660|0.95|1.72|0.67|
|Regulatory Environmey|Transparency ofGovernment Policies|=WYC|0.619|0.203|0.089 Argentina|1.000|Finland|0.631|1.02|71|0.63|
||Control ofCorruptan|KKZ|0.744|0.140|0.530 South Africa ||1.000|Finland|0.651|0.88|1.23|0.65|
|Aggregate Index|||0.708|0.157|0.353 Venezuela|1.000|Finland|0.641|0.91|1.82|0.64)|
|Senice sector infrastry|Speed and Costs of Intemet Access|GCR|0.629|0.161|0.346 Vietnam|1.000|Finland|0.550|0.92|1.67|0.58|
||Effact of Intemet on Business|GCR|0.719|0.104|0.401 Greece|1.000||Finland|0.712|0.99|1.48|0.74|
|AggregateIndex|||0.674|0.120|0.482Mauritius|1.000|Finland|0.646|0.96|1.34|0.65|
# **Appendix Figure 3. Comparison between bilateral and balance of payments account data on FDI investment**

<!-- Start of picture text --> Panel A. Flows to North Countries Panel B. Flows from North Countries<br>1600 2500<br>1400<br>2000<br>1200<br>1000 1500<br>800<br>600 1000<br>400<br>500<br>200<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2003 2004 2005 2006 2007 2008 2009 2010 2011<br> Panel C. Flows to South Countries Panel D. Flows from South Countries<br>1600 700<br>1400 600<br>1200 500<br>1000<br>400<br>800<br>300<br>600<br>200<br>400<br>200 100<br>0 0<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Bilateral Data BoP Data<br>Billions of U.S. Dollars Billions of U.S. Dollars<br>Billions of U.S. Dollars Billions of U.S. Dollars<br><!-- End of picture text --> This figure compares the FDI flows between the bilateral data used in the paper (SDC Platinum for M&A and Financial Times’ FDI Markets for greenfield investments) and the aggregated country-level BoP data. The BoP data come from the IMF. Panel A shows the cross-border flows to North countries and Panel B shows the flows from North countries. Panel C shows the flows to South countries and Panel D shows the flows from South countries. Results are expressed in billions of current U.S. dollars. North countries are the G7 and Western Europe. South countries are all the others (including LAC countries). Offshore centers are excluded from the sample.