rafmacalaba/datause-encoder-data huggingface dataset:
As a first pass to testing whether these changes across birth cohorts are significant, we regress DSB on a linear cohort trend, separately by region. Each observation is weighted by the number of women in that cohort-year cell, which gives greater weight to the more precisely calculated cell averages. The coefficient on the cohort trend in this regression for South Asia is highly significant (0.007, with a standard error of 0.002), which suggests that DSB has been increasing by about 0.7 percentage points for each successive cohort. The corresponding coefficient for Southeast Asia is also significant (0.005, with a standard error of 0.02). None of the other coefficients is close to standard levels of significance.

Figure 2, and the corresponding regression analysis described above, has two potential problems. The first of these is that a linear cohort trend may not do justice to the data; this is particularly apparent for the ECA due to its inverted U-shaped pattern. To address this concern we next turn to a regression of DSB on 5-year birth cohort dummies, again separately by region. The results from these calculations are presented in the top panel of Table 5 where these coefficients are the regression analogue of the pattern observed in Figure 2. Once again, the clearest pattern is apparent for South Asia, where DSB rises monotonically across five-year birth cohorts. The increase in DSB is almost tenfold, from 0.018 (for the cohort born in 1941-45) to 0.170 (for the cohort born in 1961-65).

The second, more difficult problem is that the regional averages for different birth cohorts may be driven by different countries, depending on the years in which they conducted DHS. For example, the data from Sri Lanka, where the only DHS was carried out in 1987, enters the average for South Asia for the early birth cohorts, but not the later ones, while Nepal, where DHS were carried out in 1996, 2001, and 2006 enters the regional averages for the later birth cohorts, but not earlier ones. In an attempt to address this concern, we limit the sample to 14
20 and emphacizes the point that rapid increase in immunization coverage is most easily obtained where the health infrastructure Is already developed."16 A country by country analysis reveals, however, the fragility of achievements in the field of immin.,zation. Results aee precarious as the target population fully changes from one year to another. In Africa, for example, Benin's poliomyelitis coverage fell from 93 per cent in 1986 to 16 per cent a year later. 61 per cent of Cameroon's Infants were protected against diphtheria and tetanus in 1987 for only 20 per cent in 1988. Yemen's example is probably the most dramatic. Having reached an impressive average coverage of 80 per cent in 1986, it fell to a low 18 per cent in 1987, only to fall further to 16 per cent in 1988.

In addition to the fluctuations in coverage, there remains a handful of countries, all of them among the least developed, where immunization is still waiting to take off. These include the following countries which, by 1988, were still below 20 per cent global coverage: Cameroon, Chad, Equatorial Guinea, Ethiopia, Guinea, Mali, Bangladesh, and Yemen.

The global spread of vaccination in developing countries, a relatively low cost health intervention, offers a plausible explanation as to why infant and under five mortality rates did not generally respond to economic recession in the 1980s. While living conditions may have been close to hardship for most children, a simple intervention such as immunization, most probably 1<sup>6</sup> Keja, Chan, Brenner, and Henderson. Effectiveness of the Expanded Programme on Immunization, World Health Statistics Ouarterlv, 12 (1986).
certain sense. The reader is refrred to Hill (1982) and Diewert (1986) for excelent discussions of this issue.

22. After selecting the sample of prices and specifing each item in the sample, the quality of goods selected from both sides are compared carefidly to ensure that qualities are equivalent between China and the United States. For some goods, such as foods, quality can be assumed to be the same; while for others, no brief specification can define the product with sufficient precision to ensure the matching of qualities. Each specification still covers a variety of difrent goods.

23. A usefil way of coping with ftis problem is to consult engineering perts m this field. Sometimes, they can give the price rdatives for products whose qualities are matched, or they can tell the specification for products that are matched. By these chdes, more pnce ratios could be found for the sample. Tlhs approach was used in aicmft (14.400), ships and boats (14.500), and metalwng mhineuy (I15.400).

24. The criteia used in the matching process are:

- a) Phvsical idenity: The preferred method is to find goods that are physically identical in both countries. Ths is possible where the same goods of a given brand or btademark are sold. Many durable conamer goods and other goods can be included in this group, beause imported goods were selected for these specfications

- b) Equivalene in qualiit: In some cases, eactly identical conmodiies cannot be found. However, there are products in both countries that confom with the same genral specifications, but have slight dfferenc i deign or composition that seem to be relativy unmportan For these goods, it is sail to ignore the dfifeces and include the items in the comparison

12
- Osterman, Paul, and Beth Shulman. 2011. _Good Jobs America: Making Work Better for Everyone_ . New York: Russell Sage Foundation.

- Osypuk, Theresa L., Lisa M. Bates, and Dolores Acevedo‐Garcia. 2010. “Another Mexican birthweight paradox? The role of residential enclaves and neighborhood poverty in the birthweight of Mexican‐origin infants.” _Social science & medicine_ 70.4: 550‐560.

- Pastor, Manuel. 2000. _Regions That Work: How Cities and Suburbs Can Grow Together_ . Minneapolis: University of Minnesota Press.

- PolicyLink. “Changing the face of Silicon Valley start‐ups.” _America’s Tomorrow: Equity is the Superior Growth Model._ May 23<sup>rd</sup> , 2013.

- Raphael, Steven, and Michael A. Stoll. 2003. Modest progress: The narrowing spatial mismatch between blacks and jobs in the 1990s. Brookings Institution Center on Urban and Metropolitan Policy.

- Reardon, Sean F., and Kendra Bischoff. 2011. “Income Inequality and Income Segregation.” American Journal of Sociology 116.4: 1092‐1153.

- Shapiro, Thomas, Tatiana Meschede and Sam Osoro. 2013. “The Roots of the Widening Racial Wealth Gap: Explaining the Black‐White Economic Divide.” Research and Policy Brief, Institute on Assets and Social Policy, Brandeis University

- Sharkey, Patrick, and Felix Elwert. 2011. “The legacy of disadvantage: Multigenerational neighborhood effects on cognitive ability.” _AJS; American journal of sociology_ 116.6: 1934.

- Sharkey, Patrick. 2013. _Stuck in Place: Urban Neighborhoods and the End of Progress Toward Racial Equality_ . Chicago: University of Chicago Press.

- Stoll, Michael A. 2001 “Race, neighborhood poverty, and participation in voluntary associations.” Sociological Forum. 16.3: 529‐557.

- Treuhaft, Sarah, Angela Glover Blackwell, and Manuel Pastor. 2011. “Equity: The Superior Growth Model.” _Race, Poverty & the Environment_ 18.2.

- U.S. Census. 2013a. March Current Population Survey. Historical Income Tables: Income Inequality, table H‐1 All Races. Machine‐readable data accessed by the authors November 25, 2013 from

- http://www.census.gov/hhes/www/income/data/historical/household/2012/H01 AR_2012.xls.

- U.S. Census. 2013b. March Current Population Survey. Historical Income Tables: Income Inequality, table H‐3 All Races. Machine‐readable data accessed by the authors November 25, 2013 from

- http://www.census.gov/hhes/www/income/data/historical/household/2012/H03 AR_2012.xls .

- Warner, Mildred E., and Amir Hefetz. 2008. “Managing markets for public service: the role of mixed public–private delivery of city services.” _Public Administration Review_ 68.1: 155‐166.

- Wilkerson, Isabel. 2010. _The Warmth of Other Suns: The Epic Story of America’s Great Migration_ . New York: Random House.

- Wodtke, Geoffrey T. 2013. “Duration and Timing of Exposure to Neighborhood Poverty and the Risk of Adolescent Parenthood.” Demography 50.5: 1765‐1788.

41
current branch lending, such as digital credit. Digital credit can both reduce the cost of serving existing markets, and make it profitable to serve consumers outside the current financial system.

# **_Implementation_**

As demonstrated, this approach can be used to extend telecom-specific credit, within the firms that already possess the necessary data.<sup>14</sup> However, the applications are much broader. Mobile money makes it cheap to deliver a loan and collect general payment. With regulatory approval, telecoms may connect to the banking sector, and offer loans to consumers.<sup>15</sup> Alternately, telecoms can package these data into a credit score that can be used by third parties, either through mobile banking platforms or an independent credit bureau.<sup>16</sup> A third implementation, a smartphone app, allows third parties to access usage data independently of telecom operators, and is being explored by several startups.<sup>17</sup> These apps ask for permission to view call history and other behavioral data, and can collect real-time data for a set period.

Our performance estimates are derived from provision of postpaid credit. If phone usage is particularly informative about repayment of this form of credit, performance could differ when predicting default of other forms of loans.<sup>18</sup> But general loans extended over mobile phones have similarities to the postpaid credit we study. Both are extended remotely, without human interaction. In case of nonpayment of a general loan, telecoms can also report borrowers to a credit bureau, and may also be able to freeze a phone account or garnish funds from mobile money balances (as permitted by regulation).<sup>19</sup>

> 14 In addition to assisting with the transition to postpaid plans, this method can be used to extend credit for handset purcases, or to maintain consistent airtime balances. Many developing country operators offer small airtime loans like this; a scoring model could improve their provision.

15 See for example, Jumo.

16 See for example, Cignifi.

17 See for example, Tala and Branch.

> 18 One formulation of this concern would be if heavy phone users are willing to do more to avoid phone account closure, and the method simply picked up the level of phone usage. In that case, it may perform better predicting when repayment of a phone bill than a general loan. However, as mentioned in the text and in Figure S2, the method achieves similar performance in each quartile of usage, and in our sample that has a sufficient amount of usage, the correlation between airtime usage and repayment is very small (-0.03).

> 19 Alternately, it could be that bureau information is less predictive for this form of credit than a general loan, which would deflate the benchmark. Bureau information would represent the status quo for the extension of postpaid credit. There is not much evidence on the performance of bureau information in low income populations; the performance observed here is in the lower end of the range of published estimates for general loans from more developed settings (Table S4). However, bureau information

19
personality is shown to be more important for job performance. In this study, we focus on a specific population - rural farmers in Western Kenya - and consider outcomes that are specific for their main occupation, farming. Beyond the advantage of focusing on decision making in a specific domain, understanding the importance of skills for agricultural decisions and productivity is important in its own right, given that the majority of the world’s poor continue to live in rural areas, where agriculture remains the most important source of employment (World Bank, 2008). Differences in the willingness to exert effort are often considered key to understand heterogeneity in agricultural outcomes (de Janvry, Sadoulet, and Suri, 2016). More generally, farmers face many different tasks and decisions, some of which may depend more on knowledge, others on problem solving ability, and yet others on effort. In the predictive regressions, we consider a variety of outcomes to capture those potential differences, and analyze to what extent different skills explain a meaningful part of the variation in those outcomes.

Our first set of results show that cognitive skills, using both standardized scales and tests developed for the specific context can be measured with high levels of reliability and validity, similar indeed to those found in developed country settings. Cognitive skills also show good predictive validity, even after controlling for educational levels. On the other hand, we find that standard application of commonly used scales of noncognitive and technical skills suffer from large measurement error, resulting in low reliability and validity. For technical skills, factor analysis and item response theory results in a construct with higher predictive validity, even if large measurement error remains. Repeated measurement further helps to improve predictive power, and overall most of the measurement error in technical skills appears to be random measurement error. For noncognitive skills, we find evidence of systematic measurement error related to acquiescence bias and show that combining questions according to pre-existing scales leads to low internal consistency. Related, the latent noncognitive construct resulting from the factor analysis does not map in the personality domains typically found in developed countries. While the corrected noncognitive constructs are predictive of agricultural productivity, the estimates do not allow drawing clear conclusions about the relevance of specific noncognitive skills. Overall, the best predictions obtained after corrections of different sources of measurement error show that the three types of skills together explain up to 17 % of the variation in yield, with all three skill constructs being significant and with similar point estimates. Technical and noncognitive skills also help predict agricultural practices, though with varying degrees.

In the last part of the paper, we further analyze the different challenges related to measuring skills in household surveys in developing countries, and discuss guidelines on how to address them.

4
# **Algorithm** Augment <u>gazetteer</u>

**Input** Landmark gazetteer, where for each landmark entry includes: (1) name, (2) types and (3) coordinates **Output** Augmented landmark <u>gazetteer</u>

# **A. Split landmarks at select punctuation**

1. If a landmark has a slash, open parentheses, dash or comma, add landmarks to the gazetteer that separate at the character.

# **B. Clean landmark names**

1. Everything lowercase, only keep alphanumeric characters (eg, remove punctuation)

# **C. Remove certain landmarks**

1. Remove landmarks that are just one character in length

2. Remove landmarks that have certain types (eg, where the type indicates that the landmark actually represents a large area). We remove landmarks with the type: route, road, political, locality or neighborhood except if the landmark also contains “flyover“ or “roundabout“ in the name<sup>1</sup>

# **D. Create N-grams and skip-grams**<sup>2</sup>

1. Generate 2-3 N-grams and add to gazetteer

2. Generate 2-3 skip-grams, skip 1-4, restrict so that the first and last word match and add to gazetteer<sup>3</sup>

# **E. Create parallel landmarks**

1. If a word begins/ends with a certain word/phrase, remove the word or phrase

- (a) If it begins with a stopword or preposition, create parallel landmark with word removed

- (b) If ends with: bar, shops, restaurant, hotel, stage, bus stop or bus station, create parallel landmark with word removed

2. If word contains certain word/phrase, swap with another

- (a) (stage, bus stop, bus station) – make interchangeable. So if someone says “X stage”, create “X bus stop” and “X bus station”

1 We treat flyovers and roundabouts as landmarks, even though they are roads, as they represent a unique location 2 Other geoparses such as LNEx only add the n-grams and skip-grams if the name does not already exists in the gazetteer. Our algorithm differs, and we add all n-grams and skipgrams. However, in the algorithm to locate events, we preference locations where the landmark name associated with the location was not a derived n/skip-gram, but still consider the n- gram/skip-gram version as the nonderived landmark location may be removed from consideration if it is not near a mentioned road.

- 3 For example, from the original land-

- mark ‘Prestige Plaza Shopping Mall‘, this generates ‘Prestige Mall‘, ‘Prestige Plaza Mall‘, and ‘Prestige Shopping Mall‘

3. Different spellings of words

- (a) British/English spellings (Eg,: center vs centre, theater vs theatre)

S5
**Table 10: Identifying the $3.20 poor based on covariates of poverty**

_<u>(Pooled data across countries with equal weights for each country)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|0.013***|-0.112***|0|-0.324***|-0.449***|0|683,533|
|Asset index|-0.020***|-0.044***|0.0008|-0.667***|-0.691***|0.0008|431,436|
|Asset ownership|-0.010***|-0.026***|0|-0.187***|-0.204***|0|264,680|
|Literacy|0.001|-0.012***|0.0006|-0.220***|-0.233***|0.0006|694,463|
|Not employed in the<br>agricultural sector|-0.008***|-0.038***|0|-0.141***|-0.171***|0|450,521|
|Access to electricity|0.015***|0.021***|0.3081|-0.351***|-0.346***|0.3081|697,574|
|Piped drinking<br>water|0.000|0.023***|0.0002|-0.140***|-0.117***|0.0002|595,934|
|Improved sanitation|0.000|-0.017***|0|-0.224***|-0.241***|0|592,937|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: This table shows the results of regressing an indicator variable of being poor on (ranked) residuals of covariates</u> of poverty (see Section 3b in the paper for more details). The residuals are determined by conditioning out household size from the covariates of poverty, including years of schooling, asset ownership/index, literacy, access to electricity, etc. Ranked residuals are used for only the first two covariates, years of schooling and asset index, which are continuous variables by construction. The remaining covariates are binary variables (e.g., literacy, ownership of a computer, etc.). With limited asset categories (e.g., only three assets for Colombia, Mali, and Pakistan), it makes more sense to create a binary variable than an asset index. The regressions are based on all countries with data for each covariate of poverty, with each country having a weight of 1. Table 2 above has summary statistics on the various covariates of poverty, thus providing information on the availability of data from the different countries pooled together in these regressions. The per capita poor are defined using the $3.20 line for all countries, and the root N poor are defined using the root N-equivalent poverty line, which is country-specific (see Table 7). The per capita poor only are those individuals who are only poor by the per capita allocation rule but not the root N allocation rule. The regressions run on a subsample of household heads only. Column 4 indicates the results of a test of equality between the coefficients reported in columns 2 and 3 under the assumptions of seemingly unrelated regressions. Column 7 indicates the results of a test of equality between the coefficients reported in columns 5 and 6 under the assumptions of seemingly unrelated regressions.

39
**Figure 11: log** **_Service EXPY_ against log GDP per capita, for 1992 and 2007.**

<!-- Start of picture text --> 6 7 8 9 10 11 6 7 8 9 10 11<br>Log Per capita GDP 1992 Log Per capita GDP 2007<br>Note: black - all countries, red - EU-15, blue - NMS, green - Acc Note: black - all countries, red - EU-15, blue - NMS, green - Acc<br>Source : Authors’ calculations.<br>9.5<br>9<br>8.5<br>8<br>7.5<br>10.5<br>10<br>9.5<br>9<br>8.5<br><!-- End of picture text --> Another interesting question is the relationship between _Services EXPY_ and _Goods EXPY_ , and to what extent Europe is in line with the global pattern. Figure 12 shows that in general, countries with high Service EXPY have also high Goods EXPY, which is also true for Europe. Compared to the median for both categories, all European countries have higher sophistication.

**Figure 12: Service EXPY vs Goods EXPY, 2008.**

<!-- Start of picture text --> 8 8.5 9 9.5 10<br>Log Sophistication in Goods Exports 2007<br>Note: black - all countries, red - EU-15, blue - NMS, green - Acc<br>Source : Authors’ calculations.<br>10<br>9.5<br>9<br>8.5<br><!-- End of picture text --> Finally, in Table 4 we compare export sophistication in different years, controlling for the stage of development and size, to check whether any one of the regions was significantly different from the global norm. We make the observation that ACC was a significant and positive outlier in service export sophistication in 1991. In more recent time, 2006, ACC is instead a significant negative outlier. Put another way, given the stage of development, ACC has a lower level of Service EXPY than expected.

15
<!-- Start of picture text --> Absolute gains in consumption expenditure (fractile groups), 1988-2012<br>)<br>at<br>”n<br>=)<br>a<br>a<br>Qa<br>=O<br>=o<br>on<br>x<br>G<br>co)<br>><br>Bo<br>ea<br>ee<br>ne}<br>e<br>®<br>a<br>x<br>=2<br> ___ TT<br>=<br>=<br>oO<br>&<br>cS<br>io)<br>© fo}<br>10 20 30 40 50 60 70 80 90 100<br>Percentile groups of EAP distribution of consumption expenditure<br>Absolute gains in annual average consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Evaluated at ventile groups (e.g. bottom 5%).<br>Horizontal line shows absolute gain in EAP mean of USD88.3 per year (2011, PPP-adjusted).<br><!-- End of picture text --> <!-- Start of picture text --> Distribution of consumption gains by percentile group: 1988-2012<br>21.4<br>20<br><o<br>& 15<br>£<br>oO> 12.3<br>g<br>=<br>3 10 9.4:<br>2 7.7<br>= 6.7<br>Yn 52 5.9<br>5 4.2 =<br>=<br>3.0: 34 28<br>1.6 1.8 2-1 2.4 21<br>0.8<br>0<br>12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Share of total gain in annual consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Evaluated at ventile groups (e.g. bottom 5%).<br><!-- End of picture text -->
<!-- Start of picture text --> Grid expansion and replacements Grid substation capacity growth<br>£ 6 Serer tit te eee eed 60% VO CGE UeOe EO ee SOE Eee EO eee ee 12 2<br>§ — G)2<br>S|8<br>2 oe<br>2 Petia eign wenn ne oi ere Tt be ist oe ana ei 20% — © Be icce cua | 4<br>2016- 2021- 2031- 2041- 2016- 2021- 2031- 2041-<br>70 30 40 50 20 30 40 50<br>@Refurbishments Renewables and demand increase © Share of digitalisation (right axis)<br><!-- End of picture text -->