rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4.** Effect of the IST intervention at 9 and 24 months follow-up on sustained attention outcomes for younger (class 1) and older (class 5) children.

|**Outcome**<br>**C**<br>**(50**|**ontrol**<br>**schools)**|**Inte**<br>**(51**|**rvention**<br>**schools)**|**Mean difference**<sup>**a**</sup><br>**(95% CI)**|**p-value**|**Cluster-**<br>**size; range**<br>**(mean)**|
|---|---|---|---|---|---|---|
|**9 months follow-up**|**Mean (sd)**<sup>**b**</sup>||**Mean (sd)**<sup>**b**</sup>||||
|**CLASS 1**<br>N=1210||N=1281|||||
|**(median age:8, range: 5-15)**|||||||
|**Sustained Attention**<sup>**c**</sup>**(score:0-20)**|||||||
|Unadjusted<br>1070|8.48 (3.63)|1162|8.43 (3.76)|-0.04 (-0.58,0.51)|0.895|8-27 (22.1)|
|Adjusted <br>1030|8.52 (3.65)|1144|8.43 (3.77)|-0.13 (-0.66,0.39)|0.623|5-27 (21.7)|
|**CLASS 5**<br>N=1283||N=1365|||||
|**(median age:12, range: 8-18)**|||||||
|**Sustained Attention**<sup>**d**</sup>**(score:0-20)**<br> <br>|||||||
|Unadjusted<br>1180|13.38 (5.45)|1231|13.35 (5.13)|-0.09 (-0.77,0.56)|0.799|8-30 (23.9)|
|Adjusted<br>1178|13.38 (5.45)|1221|13.40 (5.10)|-0.21 (-0.81,0.39)|0.490|8-30 (23.8)|
|**24 months follow-up**|||||||
|**CLASS 1**<br>N=1201||N=1269|||||
|**(median age:8, range: 5-15)**|||||||
|**Sustained Attention**<sup>**c**</sup>**(score:0-20)**|||||||
|Unadjusted<br>960|13.45 (5.15)|1059|13.20 (4.96)|-0.26 (-0.95,0.43)|0.456|8-26 (20.0)|
|Adjusted<br>923|13.49 (5.15)|1041|13.18 (4.96)|-0.44 (-1.09,0.21)|0.180|4-25 (19.6)|
|**CLASS 5**<br>N=1267||N=1350|||||
|**(median age:12, range: 9-18)**|||||||
|**Sustained Attention**<sup>**d**</sup>**(score:0-20)**|||||||
|Unadjusted<br>1007|14.22 (4.90)|1052|14.66 (4.60)|0.40 (-0.14,0.94)|0.144|6-31 (20.4)|
|Adjusted<br>1006|14.21 (4.90)|1044|14.70 (4.58)|0.28 (-0.23,0.79)|0.283|6-29 (20.3)|

Results presented (i) for all children with outcome data (unadjusted) and (ii) for those with baseline measurements of each outcome and accounting for age, sex and stratification effects as the primary pre-specified analysis. N=number of children eligible for follow up (not withdrawn or deceased) a Mean difference (intervention-control) are obtained from GEE analysis accounting for school-level clustering. b Mean score and sd at follow-up c Pencil tap test was conducted at baseline and single digit code transmission task was conducted at 9 and 24 months follow-ups. d Double digit code transmission was conducted at baseline and both follow-ups. **Unadjusted** : All children with outcome measures, not adjusted for any baseline or study design characteristics. **Adjusted** : for baseline age, sex, school mean exam score and literacy group (to account for stratification) and baseline measure of the outcome, where available.

23
**Figure A.1. Headcount Poverty Rates in Self-Reported Male-Headed Households (%), by Number of Female Adults**

<!-- Start of picture text --> Egypt Iraq<br>2012<br>2007<br>2015<br>2017<br>2012<br>2020<br>0 10 20 30 40<br>0 50 100<br>Jordan Mauritania<br>2004<br>2007<br>2008<br>2014<br>2012<br>2019<br>0 10 20 30 40 0 20 40 60 80<br>Palestine Tunisia<br>2007 2005<br>2009 2010<br>2011 2015<br>2017 2021<br>0 20 40 60 80 100 0 20 40 60<br>0 1 2 3 4 >4<br><!-- End of picture text -->

**Note:** Headcount poverty rates are estimated using per capita household expenditures. The numbers of female adults are shown for 0, 1, 2, 3, 4, and more than 4 adults. The years are shown on the y-axis and the poverty rates are shown on the x-axis.

60
able in Mexico. Table 2 provides estimates of the mobility coefficient over one quarter, one year, two year, and five year time periods. Since not all cohorts are aged between 25 and 49 in every quarter, less cohort-period observations are available for longer intervals. Table 2 presents results from the balanced pseudopanel, where the same cohort-quarter observations are used for estimation over different time lags.<sup>8</sup> Columns 1 through 4 provide the estimates of absolute mobility, while Columns 5 through 8 include cohort fixed effects and therefore give measures of conditional mobility. Absolute mobility increases slightly as one increases the time frame, but the estimate of β is still 0.933 over two year intervals and 0.950 over five year intervals.<sup>9</sup> Thus while poorer households experience slightly faster income growth than richer households, a household which has 10 percent higher income than another household today is estimated to still have 9.5 percent higher income five years later.

In contrast, Table 2 shows a high degree of conditional mobility. A ten percent difference in income between two households with the same fixed effect is reduced to a 8.3 percent difference after one quarter, a 5.5 percent difference after one year, and only a 0.5 percent difference after two years. By five years, the households have reversed rankings.

# 5.1 Mobility and Attrition

Measurement error will result in both OLS and IV methods giving inconsistent measures of mobility. However, a second source of potential bias in mobility estimates based on genuine panel data is that of non-random attrition. This is particularly likely to be a concern in many developing country contexts in which panel surveys track dwelling units, rather than households, over time. Thomas, Frankenberg and Smith (2001) note that this is the standard protocol for follow-up surveys conducted as part of the World Bank’s Living Standards Measurement Study, with second round follow-up rates of 87 percent in Cote

> 8 The point estimates for the unbalanced pseudo-panel are very similar to those for the balanced pseudo-panel and are available upon request.

> 9 The five-year coefficient is not statistically significant from the two-year coefficient.

19
variables that perfectly predicted CONAFE participation and other variables that could not have equal sample means in p-score divisions of the CONAFE and non-CONAFE samples.

These three criteria gave us five context variables for primary schools and four context variables for secondary schools. For primary schools, we considered the Mexican state in which the school was located, the community’s principal economic activity (primary, secondary or tertiary), the school modality (urban public, urban private, rural or indigenous), the average literacy of students’ fathers, and the presence of sewage facilities. All background information came from student surveys except for information on community economic activity, which came from a school director’s survey. For secondary schools, we considered the state of Mexico in which the school is located, the community’s principal economic activity (primary, secondary or tertiary), the presence of a telephone in the student’s community, and the average literacy of students’ fathers. All indicators for both primary and secondary school were expressed as a series of dummy variables.

We then formed combined databases of schools for which we had data in any year. We considered including only schools for which data appeared in every year of the 1998-2002 sample. For the primary schools, that limit would have included about 70 percent of all the schools that appeared in any year. Because it is unlikely, however, that the schools surveyed in every year are randomly selected from the schools surveyed in any year, we instead formed a combined database of schools present in any year. We collapsed that database to include one entry for each school, including mean values for the selected background variables. That collapse produced one database of schools with codes and background information for each school.

13
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Heig|ht-for-age-z-s|cores|||
|Within 30min of health post|0.055|0.069|0.072|0.065|0.062|0.067|0.065|
||(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|(0.060)|
|Within 30min of hospital|-0.009|-0.017|-0.019|-0.014|-0.012|-0.015|-0.013|
||(0.081)|(0.081)|(0.081)|(0.081)|(0.081)|(0.081)|(0.081)|
|Within 30min of dirt road|0.004|0.012|0.008|0.020|0.007|0.028|0.022|
||(0.062)|(0.062)|(0.062)|(0.062)|(0.062)|(0.062)|(0.062)|
|Within 30min of paved road|0.214***|0.203***|0.200**|0.193**|0.187**|0.199**|0.184**|
||(0.078)|(0.078)|(0.078)|(0.078)|(0.078)|(0.078)|(0.078)|
|Piped water in house|-0.030|-0.042|-0.046|-0.035|-0.026|-0.045|-0.038|
||(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|
|Flush toilet|0.139*|0.133*|0.124|0.107|0.112|0.116|0.112|
||(0.080)|(0.081)|(0.081)|(0.080)|(0.080)|(0.080)|(0.081)|
|Latrine|-0.012|-0.017|-0.023|-0.011|-0.008|-0.003|-0.008|
||(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|
|Child exclusive-ly breastfed|0.153**|0.153**|0.152**|0.138**|0.137**|0.145**|0.148**|
||(0.067)|(0.067)|(0.067)|(0.066)|(0.066)|(0.067)|(0.066)|
|Months breastfed|-0.007***|-0.007***|-0.007***|-0.006**|-0.006**|-0.007***|-0.007***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Complementary food|-0.017**|-0.017*|-0.017**|-0.016*|-0.016*|-0.016*|-0.017*|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|
|Suffer from chronic illness|-0.561*|-0.584*|-0.584*|-0.497|-0.487|-0.482|-0.487|
||(0.303)|(0.304)|(0.303)|(0.306)|(0.307)|(0.303)|(0.308)|
|Fell sick in the last 30 days|-0.044|-0.042|-0.041|-0.043|-0.041|-0.044|-0.040|
||(0.058)|(0.058)|(0.058)|(0.058)|(0.058)|(0.058)|(0.058)|
|Asset index 2|0.147*|0.167*|0.166*|0.152*|0.147*|0.165*|0.160*|
||(0.087)|(0.087)|(0.087)|(0.088)|(0.088)|(0.087)|(0.088)|
|Asset index 3|0.252***|0.259***|0.256***|0.239**|0.233**|0.251***|0.250***|
||(0.095)|(0.095)|(0.096)|(0.096)|(0.097)|(0.096)|(0.096)|
|Asset index 4|0.333***|0.355***|0.350***|0.323***|0.316***|0.337***|0.339***|
||(0.105)|(0.105)|(0.105)|(0.107)|(0.108)|(0.107)|(0.108)|

63
2011 PPP). The poverty severity index, which is the square of the poverty gap and thus places more weight on people with consumption levels further below the poverty line, was equal to 0.31 (p<0.001).

**Figure 3: Poverty headcount in low and lower middle‐ Figure 4: Gini index in SSA countries. income countries.**<sup>**33**</sup>

<!-- Start of picture text --> 100% 0.7 South Sudan<br>0.6<br>90% (2009)<br>SSD ‐ 2016 0.5<br>80% SSA<br>70% SSD ‐ 2015 0.4 average<br>0.3<br>60% SSD ‐ 2009 South Sudan<br>0.2<br>50% (2015,2016)<br>0.1<br>40%<br>0<br>30%<br>20%<br>10%<br>0%<br>0 2,000 4,000 6,000 8,000 10,000<br>GDP per capita (2011 PPP)<br>Figure 5: Cumulative consumption distribution. Figure 6: Consumption distribution, 2016.<br>100% 0.025<br>90%<br>Urban<br>80% 0.02<br>Rural<br>70%<br>National<br>60% 0.015<br>50% $1.90 USD PPP<br>40% 0.01<br>Rural<br>30%<br>National<br>20% 0.005<br>Urban<br>10%<br>US$1.90 PPP<br>0% 0<br>0 50 100 150 200 0 50 100 150 200<br>Total cons. per capita per day (Oct. 2016 SSP) Total cons. per capita per day (Oct 2016 SSP)<br>Poverty headcount<br>South AfricaNamibiaBotswanaZambiaCentral African RepublicLesothoSwazilandGuinea‐BissauRwandaCongo, Rep.BeninSeychellesSouth SudanCameroonMalawiMozambiqueComorosDjiboutiChadZimbabweNigeriaTogoAngolaMadagascarGhanaCongo, Dem. Rep.Cote d'IvoireUgandaSouth SudanSenegalBurundiTanzaniaMauritiusSudanBurkina FasoNigerSierra LeoneGuineaEthiopiaLiberiaMaliMauritaniaSao Tome and Principe<br>Kernel Density<br><!-- End of picture text -->

**Note: Figure 3 includes low income and lower middle‐income countries with poverty data post‐2008. All data for South Sudan refers to the seven states covered by the HFS.**

# **Source: Authors’ own calculations based on NBHS 2009, HFS 2015‐2017, World Bank WDI, and IMF World Economic Outlook.**

Such high levels of deprivation translate into widespread hunger and food insecurity. Disruptions to agricultural production and the near hyperinflationary increases in prices of most staple foodstuffs have left most households struggling to find enough food to sustain themselves (World Bank, 2018). Widespread fighting and large‐scale displacement over several consecutive planting seasons have disrupted many households’ normal agricultural activities, resulting in increasingly large production deficits each year and widespread food insecurity (FAO and WFP, 2017). This has had a devastating effect on livelihoods, given that except for a few oil enclaves the productive structure of South Sudan is one of a rural pastoralist society where more than 4 in 5 people practice subsistence agriculture (World Bank, 2018, 2016). Food security has continuously deteriorated since late 2012, sometimes even reaching famine conditions in certain vulnerable counties. During the most recent harvest season in 2017, a time

> 33 Data for real GDP per capita in 2011 PPP for South Sudan were obtained from the IMF World Development Outlook Database.

14
may be able to maintain a portion of their consumption through the year of the drought through accessing some form of savings. But in smoothing consumption in the current year there is a negative impact in their productivity in the following year. It may be a reduction in their ability to invest in productive activities (e.g., fertilizer or other inputs) or a reduced appetite for investment after suffering a year of loss. Therefore, the impacts of a climate anomaly such as drought may be expected to affect the current year and the following year’s economic activities negatively. This effect is investigated through the lagged regressions.

**Table 6a: Lagged GDP per Capita Growth With Country and Year FE’s**

Dependent variable: GDP per Capita Growth

||(Precip)|(Temp)|(P, T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip_1|-0.00<br>(0.00)||-0.01<br>(0.00)|||||||
|Temp_1||0.05|0.06||0.10||0.01||-0.06|
|||(0.05)|(0.05)||(0.08)||(0.07)||(0.10)|
|WASP_minus1|(lag)|||||0.48|0.48|-1.82|-1.78|
|||||||(0.47)|(0.47)|(1.00)*|(1.01)*|
|WASP_plus1(l|ag)|||-0.42|-0.38|||-2.90|-2.86|
|||||(0.54)|(0.54)|||(1.11)**|*(1.11)**|
|Observations|4897|4897|4897|2782|2782|2955|2955|1677|1677|
|Countries|181|181|181|178|178|180|180|137|137|
|R-squared|0.047|0.047|0.048|0.048|0.049|0.065|0.065|0.065|0.065|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1).

In general, the results of the lagged regressions are consistent with the results of the simultaneous regressions. For per capita GDP growth (Table 6), the WASP variables, both drought and flood, were the sole statistically significant factors. The signs of the regression coefficients were negative in all cases, another sign of consistency. The results for WASP(+1) were slightly stronger (99%) than for WASP(-1) (90%) in the lagged regressions, a reversal in ordering from the concurrent regressions. This result may indicate that flood effects, which WASP(+1) may be indicative of, have greater longer term effects due to 18
a closer look reveals that it is outdoor pauperism that declined. The mean number of paupers relieved in the vorkhouse as percentage of the population in England and Wales was almost unchanged from 0.77% in 1850 to 0.78% in 1910 (Rose, 1972, p.50). Williams concludes that the overall decline is evidence of the authorities' success in limiting access to outdoor relief rather than an improvement in the standard of living of the extreme poor. Nonetheless, the decline in overall pauper rates after the 1840s is consistent with the inequality and unskilled wage trends discussed in Section II.

So far, we have discussed trends in poverty following the late 1840s. What about the early 'ndustrialization period, years of greater interest to contemporary analysis of Third World problems. Here we are on even shakier ground. The original "social tables" by Gregory King (1688), Joseph Nassie (1759), Patrick Colquhoun (1801/3, 1812), and Dudley Baxter (1868) took very different approaches to estimating pauperism, but they did supply some wellinformed guesses. The revised social tables imply the following trends for the percent in poverty (Williamson, 1985, p.70):

|1759|12.5%|
|---|---|
|1801/03|19.9|
|1812|14.8|
|1850|10.0|
|1867|6.2|

The shares in poverty rose in the late 18th century, a period of rising inequality and stable real wages of the poor. The rate of fall between 1812 and 1850 was slow, 0.3% per decade, while it accelerated thereafter, 2.2%, a result consistent with Figure 6 and with the inequality and unskilled real wage trends docusented in Section II. However, changes in supply of relief may 41
a credit at an agricultural input supplier for the financed production inputs. For further details on CP, MRFC, and the loan particulars, please see Online Appendix A.<sup>5</sup> At the time of the study, the vast majority of farmers in the sample had no access to formal-sector credit. In our baseline survey, only 6.7% of farmers had any formal loans in the previous year. Among these few farmers with formal-sector credit, MRFC was the largest single lender, providing 34% of loans (more than twice the share of the next largest lender).<sup>6</sup> Farmers therefore had a strong interest in maintaining good credit history with MRFC so as to maintain access to what would likely be their primary source of formal credit going forward.

In the absence of fingerprinting, farmer identification relies on the personal knowledge of loan officers (who may also rely on local informants such as village and locality leaders). While loan officers could build up reliable knowledge of borrowers over time, this identification “technology” is imperfect. Loan officers are sometimes promoted and routinely rotated to other localities. Among the 11 loan officers who handle our study areas, the median number of years at the branch is only two, while the median number of years working for the lender is 13.<sup>7</sup> In the absence of an independent mechanism for identifying borrowers, the institutional memory is lost when the loan officer is transferred to another location. Even when loan officers remain in a given location over time, the large number of borrowers can lead them to make mistakes in identification. In this project, loan officers issued an average of 104 loans, and also handled other loan customers not associated with the project.

The timeline of the experiment is presented in Appendix Figure 1. Our study sample consists of 249 clubs with approximately 3,500 farmers in Dedza, Mchinji, Dowa and Kasungu districts. Farmer clubs in the study were randomly assigned to be fingerprinted (the treatment group) or not (the control group), with an equal probability of being in either group.

Randomization of treatment status was carried out after stratifying by locality and week of club

> 5 The Online Appendix can be found in:

> http://siteresources.worldbank.org/DEC/Resources/84797-1114437274304/ginegoldbergyangWPSApp092110.pdf 6 Across study areas, access to formal credit varies from 4% to 10%. In Dedza, the region with highest access to formal loans, MRFC provides almost half of these formal loans.

> 7 Because soft information about borrowers is important, one may be surprised by the high loan officer turnover rate. MRFC, like other lenders, rotates credit officers for many reasons. For example, rotation is thought to improve morale and help minimize corruption. Promotion of successful individuals within the organization also leads to replacement of loan officers at the local level and some loss of soft information on borrowers.

6
Uzbekistan, Tajikistan and Turkmenistan, where the trade regimes continue to be restrictive, in good part because of the slow introduction of overall market reforms. In between, one finds the remaining countries, Azerbaijan, Kazakstan, Russia and Ukraine.

It is interesting to compare the above grouping to the most recent EBRD ranking of these countries according to its trade and foreign exchange system indicator as of 1998 (EBRD, 1998). This is done in Table 3, which also includes the EBRD ;ankings on enterprise governance and competition. The last two columns show the EBRD ranking on foreign exchange and trade regimes and a ranking prepared by the author. The two rankings are quite consistent with the exception of Kazakstan and Russia. In the latter's case, the EBRD seems to penalise Russia too much for exchange restrictions it imposed on capital account. This leads Russia to have a lower ranking on this indicator than Tajikistan, which does not seem plausible. On the other hand, the EBRD seems not to have taken into account the restrictions imposed (and recently lifted) by Kazakstan. Also, Estonia and Latvia as well as the Kyrgyz Republic, probably have more liberal trade regimes than Hungary, Poland and Romania.

Table 3

# EBRD TRANSITION INDICATORS 1998

Progress in transition in Central and Eastern Europe, the Baltic States and CIS

|_Countries_|_Governance &_<br>**_Enterprise_**<br>_restructuring_|_Competition_<br>_Policy_|_Trade_<br>_&_<br>_System_|_Foreign_|_Exchange_|
|---|---|---|---|---|---|
||_EBRD_|_EBRD_|_EBRD_|_Au_|_thor_|
|_Albania_|_2_|_2_|_4_|||
|_Armenia_|_2_|_2_|_4_|_4_||
|_Azerbaijan_|**_2_**|**_1_**|_3_|_3_||
|_Belarus_|**_1_**|_2_|**_1_**|**_1_**||
|_Bosnia and Herzegovina_|_2-_|**_1_**|_2_|||
|_Bulgaria_|_2+_|_2_|_4_|||
|_Croatia_|**_3-_**|_2_|_4_|_4_||

17
Table S1: Updated Data and Assumptions in Henderson et al. (2012) Baseline.

||ln(GDP)<br>HSW|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|ln(GDP)<br>(5)|ln(GDP)<br>(6)|
|---|---|---|---|---|---|---|
|ln(lights/area)|0.277**<br>(0.031)|0.287***<br>(0.059)|0.284***<br>(0.058)|0.334***<br>(0.037)|0.237***<br>(0.045)|0.201***<br>(0.032)|
|PWT Version|6.1|10.0|10.0|10.0|10.0|10.0|
|Raw OLS|✓|✓|||||
|Inter-Calibrated OLS|||✓|✓|✓|✓|
|Satellite Average|✓|✓|✓|✓|||
|Satellite Fixed Efects|||||✓|✓|
|Observations|3,015|3,034|3,034|3,934|6,169|2,205|
|Countries|188|180|180|180|180|180|
|Years|92-08|92-08|92-08|92-13|92-13|05-13|
|Within Country R-Sq.|0.769||||||
|Within R-Squared|0.209|0.139|0.137|0.192|0.128|0.118|

Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes._ “HSW” denotes Henderson et al. (2012)’s baseline result, which appears in Table 1, Column 1 (p. 1,012) and Table 2, Column 1 (p. 1,015). All specifications include country and year fixed effects. Robust standard errors, clustered by country, are in parentheses. Both reported R-Squared statistics are adjusted for sample size; “Within R-Sq.” is within all fixed effects. Note, Penn World Tables v. 6.0 had a country count of 188, while v. 10.0 has a reduced count of 183. In this analysis, Hong Kong, Macao, and Taiwan are included as part of China for consistency with existing shape files commonly used to process worldwide data. We adjust all Penn World Tables numbers accordingly, and thus obtain a final data set of 183-3=180 countries with both GDP and night lights data available.

S10