rafmacalaba/datause-encoder-data huggingface dataset:
Table C1: Summary Statistics of pooled household-level data

||N|Mean|Median|SD|Min|Max|
|---|---|---|---|---|---|---|
|**Consumption (per day USD in 2017 PPP terms):**|||||||
|Real per capita consumption expenditures|453,632|5.62|3.56|9.48|0.00|2289.78|
|Nominal per capita consumption expenditures|454,631|5.35|3.21|10.05|0.00|2521.83|
|**Spatial defator**<br>|||||||
|Spatial defator<br>|453,659|1.00|0.99|0.13|0.76|1.93|
|**Demographic:**<br>|||||||
|Household size|454,728|5.70|5.00|3.26|1.00|62.00|
|Age of household head|454,623|43.15|43.00|17.04|0.00|119.00|
|Household head is male|454,674|0.81|1.00|0.39|0.00|1.00|
|**Marital status (of household head):**|||||||
|Married|454,664|0.80|1.00|0.40|0.00|1.00|
|Never married|454,664|0.03|0.00|0.17|0.00|1.00|
|Living together|454,664|0.05|0.00|0.21|0.00|1.00|
|Divorced/Separated|454,664|0.04|0.00|0.20|0.00|1.00|
|Widowed|454,664|0.08|0.00|0.28|0.00|1.00|
|**Education (of household head):**|||||||
|No education|438,551|0.33|0.00|0.47|0.00|1.00|
|Primary incomplete|438,551|0.20|0.00|0.40|0.00|1.00|
|Primary complete|438,551|0.15|0.00|0.36|0.00|1.00|
|Secondary incomplete|438,551|0.20|0.00|0.40|0.00|1.00|
|Secondary complete|438,551|0.06|0.00|0.24|0.00|1.00|
|Post secondary but not university|438,551|0.02|0.00|0.13|0.00|1.00|
|University incomplete and complete|438,551|0.03|0.00|0.18|0.00|1.00|
|Can read and write|442,262|0.56|1.00|0.50|0.00|1.00|
|**Employment (of household head):**|||||||
|Not in labor force|432,935|0.14|0.00|0.34|0.00|1.00|
|Unemployed|432,935|0.02|0.00|0.14|0.00|1.00|
|Employed in Agriculture|432,935|0.44|0.00|0.50|0.00|1.00|
|Employed in Industry|432,935|0.13|0.00|0.33|0.00|1.00|
|Employed in Services|432,935|0.28|0.00|0.45|0.00|1.00|
|**Access to basic services**|||||||
|Improved water|454,639|0.80|1.00|0.40|0.00|1.00|
|Improved sanitation facility|454,637|0.40|0.00|0.49|0.00|1.00|
|Access to electricity|443,585|0.64|1.00|0.48|0.00|1.00|
|**DOU urban classifcation:**|||||||
|Urban center|454,728|0.27|0.00|0.44|0.00|1.00|
|Urban cluster|454,728|0.33|0.00|0.47|0.00|1.00|
|Rural|454,728|0.40|0.00|0.49|0.00|1.00|
|**DB urban classifcation:**|||||||
|Core|454,728|0.28|0.00|0.45|0.00|1.00|
|Suburb|454,728|0.21|0.00|0.41|0.00|1.00|
|Town|454,728|0.13|0.00|0.34|0.00|1.00|
|Other rural|454,728|0.50|1.00|0.50|0.00|1.00|

Source: International Urban Poverty Database. Note: The statistics above are based on the household-level data pooled for 19 countries. Real per capita consumption expenditures are deflated using a spatial deflator calculated using the WorldPop 250m dataset and the DOU method.

84
clothed for meeting the physical needs of survival and normal activities. On top of this, a person must also satisfy certain social inclusion needs, which are assumed to be directly proportional to mean consumption in the country of residence. The Atkinson and Bourguignon proposal is that one should only be deemed “not poor” if one is neither absolutely poor nor relatively poor. The generalization proposed by Ravallion and Chen is to allow for a cost of social inclusion that can be non-negligible for very poor people, i.e., that cost does not go to zero in the limit as consumption or income goes to zero, as in strongly relative measures and in the AtrkinsonBourguignon proposal.<sup>31</sup> Figure 3 illustrates the difference between weakly and strongly relative poverty lines. Notice that the strongly relative line has an intercept of zero, implying that the costs of social inclusion goes to zero as income goes to zero. The weakly relative line avoids this property. (The Atkinson and Bourguignon proposal is the absolute poverty line up to income _y_ * and the strongly-relative line above _y_ *.) In setting weakly relative lines, Ravallion and Chen (2010) calibrate them to the observed empirical relationship across countries between national poverty lines and mean consumption (as in Figure 2(b)). Their schedule starts at an absolute line of $1.25 a day and then rises with a gradient of 1/3 when consumption per capita exceeds $2 a day; thus the line for country _i_ is:<sup>32</sup> _zi_  max[$.125, $.060  _~~y~~ i_ / ]3 (9) (where<sup>_~~y~~_</sup> _i_<sup>is consumption per capita). This provides an excellent fit to the data on national lines;</sup> indeed, (9) fits better than the non-parametric regression in Figure 2(b) at the chosen bandwidth (though a sufficiently less smooth non-parametric function would of course fit better).

Using data from almost 700 household surveys for 116 countries to measure relative poverty in the developing world Ravallion and Chen found that 47% of the population was poor in 2005, though the proportion has fallen over time, from 63% in 1981 to 53% in 1990. By contrast, Chen and Ravallion (2010) estimate an absolute measure using an international line of $1.25 a day—the lower bound of the Ravallion and Chen schedule of weakly relative lines—and find that 25% of the population lived below $1.25 a day in 2005. Twenty-five years earlier (in

> 31 Foster (1998) also proposed a “hybrid line” given by the weighted geometric mean of an absolute and a (strongly) relative line. While this is also “weakly relative,” it has a constant elasticity, whereas the elasticity rises from zero to unity in the Ravallion and Chen (2010) proposal—consistently with the data on national lines.

> 32 In calibrating their schedule of weakly relative lines, Ravallion and Chen use a suitably constrained version of Hansen’s (2000) method for estimating a piece-wise linear (“threshold”) model. The variation is that, in their application, the slope of the lower linear segment is zero and there is no potential discontinuity at the threshold.

19
_e_ ˆ _k,t_ +1 _≡ yk,t_ +1 _−_ E _t_ [ˆ _yk,t_ +1] (10) for model _k_ . We compute the relative MSE for each series combination method defined in section 4.3. That is, for the original data _Xi,j,t_ we computed the results for each of reduction methods of the _i_ sampling dimension – mean, median, applying the CF – filter after taking the mean, and ETS smoothing after taking the mean and for each _i_ – reduction we also computed the three linear indices over the _j_ keywords linear OLS, lasso, and elastic net. We found first that the linear OLS trend preformed unambiguously the worst. We were we unable to beat both the benchmark model and the ARMA model even once regardless of the smoothing technique that we applied. This is not wholly suprising given that we did not apply any variable selection of the keywords beforehand. Inclusion of inappropriate keywords appears to have led to overfitting and poor out of sample performance.

Moving to the lasso and the elastic net, for each respective estimator the ETS smoothed results performed best. Between the classifiers, the elastic net performed marginally better, beating both the benchmarks and the ARMA models in a few more cases. Again, this is not wholly surprising given the documented better empirical performance of the elastic net estimator when there is high pairwise correlations among the regressors. Due to the large number of results, Table 3 contains only the results of the ETS smoothed data and the trend computed via the elastic net estimator.

Using our best performing method, the ARMAX model outperforms the benchmark in 28% of the cases or for 16 out of the 58 series. In each of these cases, the ARMAX also outperforms the ARIMA model. The ARMA model fairs only slightly better versus the benchmark, outperforming the benchmark in 22% of the series or for 13 out of 58 series. However, the ARIMA model is only the best model versus the ARIMAX model in 7 of these cases. The food price series appear to be particularly difficult to forecast. If we consider only the consumer price series, then the ARMAX model is the best model in 24% of the cases while the ARMA model is only the best in 14% of the cases. The difficulty in forecasting food prices is likely due to the food price crisis during the period. The price US dollar price fluctuations during the time under consideration were due largely to events external to the countries of Central America.

These results, though only partially successful, indicate that there may be some benefit to exploring the further use of Google Trends data in forecasting economic series in Central America. We use the concluding section to speculate on some of the reasons for this success or lack thereof and give suggestions for future research.

24
|timation<br>(6)<br>�LSR<br>-0.212<br>(3.44)***<br>0.270<br>(1.77)*<br>0.235<br>(3.19)***<br>0.019<br>(0.30)<br>0.414<br>(6.08)***<br>-0.481<br>(0.63)<br>Yes<br>Yes<br>IV<br>30.07<br>0.747<br>0.506<br>3486<br> proxy for diaspora<br> (2) , the proxy is<br>d as|
|---|
|): IV es<br>(5)<br>�LSR<br>-0.203<br>(3.22)***<br>0.249<br>(1.61)<br>0.226<br>(3.04)***<br>0.028<br>(0.43)<br>0.415<br>(6.09)***<br>-0.567<br>(0.73)<br>Yes<br>Yes<br>IV<br>29.49<br>0.562<br>0.509<br>3486<br>ents and a<br>i:In column<br>s compute|
|-skill ratio<br>(4)<br>�LSR<br>-0.215<br>(3.50)***<br>0.277<br>(1.82)*<br>0.238<br>(3.24)***<br>0.016<br>(0.25)<br>0.414<br>(6.08)***<br>-0.450<br>(0.59)<br>Yes<br>Yes<br>IV<br>30.16<br>0.574<br>0.506<br>3486<br>theses<br>...cant at 1%<br>rker agreem<br>j)�Conf1<br>the proxy i<br> 2i):|
|diaspora on selection (log high-skill/low<br>(1)<br>(2)<br>(3)<br>Log-skill ratio<br>Log-skill ratio<br>Log-skill ratio<br>-0.218<br>-0.207<br>-0.215<br>(3.01)***<br>(2.78)***<br>(2.95)***<br>0.092<br>0.068<br>0.085<br>(0.52)<br>(0.37)<br>(0.48)<br>0.469<br>0.459<br>0.466<br>(5.41)***<br>(5.20)***<br>(5.37)***<br>0.057<br>0.067<br>0.060<br>(0.75)<br>(0.87)<br>(0.79)<br>0.536<br>0.536<br>0.536<br>(6.38)***<br>(6.38)***<br>(6.38)***<br>-0.468<br>-0.573<br>-0.501<br>(0.42)<br>(0.50)<br>(0.44)<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>IV<br>IV<br>IV<br>30.16<br>29.49<br>30.07<br>0.974<br>0.244<br>0.812<br>0.599<br>0.600<br>0.599<br>3486<br>3486<br>3486<br>Absolute values of robust t statistics in paren<br>igni...cant at 10%; ** signi...cant at 5%; *** signi<br>umns include a dummy for bilateral guest-wo<br>e proxy is computed as ln(popi�immstj=disti<br>opi�immstj=distij)�Conf2i;in column (3), <br>ln(popi�immstj=distij)�(conf1i+Conf|
|Table 5. Impact of <br>Lagged diasp<br>Col links<br>linguage<br>Log(dist)<br>Schengen<br>Constant<br>Orig Dum<br>Dest Dum<br>Method<br>F-stat First stage<br>Hansen J-test (p-value)<br>R2<br>Observations<br>* s<br>Instrument sets for Mij in all col<br>size in 1960. In column (1) , th<br>computed as ln(p|

29
Real GDP growth is maintained at the 8 percent target until 2014 and then declines although it stays above 6 percent until the end of the decade. Eventually, it settles at a rate which is similar to Sri Lanka‘s historical average growth rate of 5 percent.

## **Figure 8. Growth Scenario 1 With Government Savings of 1.5% of GDP**

TFP Growth 1.75 percent <!-- Start of picture text --> (a). (b).<br>0.30 8.0%<br>0.25<br>6.0%<br>0.20<br>0.15<br>4.0%<br>0.10<br>0.05 2.0%<br>0.00<br>2010 2015 2020 2025 2030 0.0%<br>-0.05<br>2010<br>National Saving Private Saving Public Saving<br><!-- End of picture text --> <!-- Start of picture text --> 8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2010 2015 2020 2025 2030<br>Per Capita GDP Growth GDP growth rate<br><!-- End of picture text -->

**Figure 9. Growth Scenario 2**

TFP Growth 2.5 percent

# Public Saving / GDP = 1.5% by 2013

<!-- Start of picture text --> (a). (b).<br>0.30 10.0%<br>0.25<br>8.0%<br>0.20<br>6.0%<br>0.15<br>0.10 4.0%<br>0.05<br>2.0%<br>0.00<br>2010 2015 2020 2025 2030 0.0%<br>-0.05<br>National Saving Private Saving Public Saving<br><!-- End of picture text --> <!-- Start of picture text --> 10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2010 2015 2020 2025 2030<br>Per Capita GDP Growth GDP growth rate<br><!-- End of picture text --> _Source_ : Hevia and Loayza (2011) 23
by an abnormal subpopulation of two percent of children with absent mothers. Overall, these results confirm mild positive selection into private schools but also suggest that, controlling a host of other observables typically not available in other datasets (such as child height and household assets) does not alter significantly the size of the private schooling coefficient.

## 4.1.2 Graphical evidence

Figure 1 plots learning levels in the tested subjects (English, mathematics, and the vernacular, Urdu) over three years. While, levels are always higher for children in private schools, there is little difference in learning gains (the gradient) between public and private schools. This illustrates why a specification that uses learning gains (i.e., assumes perfect persistence) would conclude that private schools add no greater value to learning than their public counterparts.

Many of the dynamic panel estimators that we explore identify the private school effect using children who switch schools. Figure 2 illustrates the patterns of achievement for these children. For each subject we plot two panels: the first containing children who start in public school and the second containing those who start in private school. We then graph achievement patterns for children who never switch, switch after third grade, and switch after fourth grade. For simplicity, we exclude children who switch back and forth between school types.

As the table at the bottom of the figure shows, very few children change schools. Only 48 children move from public to private schools in fourth grade, while 40 move in fifth grade. Consistent with the role of private schools serving primarily younger children, 167 children switch to public schools in fourth grade, and 160 switch in fifth grade. These numbers are roughly double the number of children available for our estimates that include controls, since only a random subset of children were surveyed regarding their family characteristics.

Even given the small number of children switching school types, Figure 2 provides preliminary evidence that the private school effect is not simply a cross-sectional phenomenon. In all three subjects, children who switch to private schools between third and fourth grade experience large achievement gains. Children switching from private schools to public schools exhibit similar achievement patterns, except reversed. Moving to a public school is associated with slower learning or even learning losses. Most gains or losses occur immediately after moving; once achievement converges to the new level, children experience parallel growth in public and private schools.

# 4.2 OLS and Dynamic Panel Value-Added Estimates

Tables 4 (English), 5 (Urdu), and 6 (mathematics) summarize our main value-added results. All estimates include the full set of controls in the child survey sample, the survey date, round (grade) dummies, and village fixed effects. For brevity, we only report the persistence and 13
|**Appendix Table 3. Family planning and reproductive**|**outcomes**|||||||Wife reports|Husbands|
|---|---|---|---|---|---|---|---|---|---|
||Ever had<br>terminated<br>pregnancy|Ever<br>used<br>prenatal<br>help|Received<br>assistance<br>with the<br>delivery|Received<br>any<br>antenatal<br>care visit|Number of<br>antenatal<br>care visits|Total<br>number of<br>children<br>who died|Unmet<br>need for<br>family<br>planning|<br>use of any<br>type of<br>modern<br>contraceptive|reports use<br>of any type<br>of modern<br>contraceptive|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Wife gives herself more power than husband [A]|0.009**|0.036***|0.006*|0.025***|0.155**|-0.025***|-0.010*|0.019***|0.019***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.07)|(0.01)|(0.01)|(0.00)|(0.00)|
|Husband gives herself more power than herself [B]|0.004|0.031***|0.001|0.024***|0.056|-0.031***|-0.011**|0.014***|0.023***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.07)|(0.01)|(0.01)|(0.00)|(0.00)|
|Wife and husband agree she is main dm or dm is joint||||||||||
|[C]|-0.006|0.044***|0.005|0.027***|0.248***|-0.029***|-0.028***|0.026***|0.041***|
||(0.00)|(0.00)|(0.00)|(0.01)|(0.07)|(0.01)|(0.01)|(0.01)|(0.01)|
|Constant|0.080***|0.928***|1.024***|0.591***|4.900***|-0.526***|0.423***|0.117***|0.095***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.13)|(0.02)|(0.01)|(0.01)|(0.01)|
|_Controls_||||||||||
|Women's status and empowerment proxies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Individual/Household-level characteristics|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|CountryFixed Effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|66,735|52,736|52,788|66,735|45,487|71,274|71,274|62,114|58,543|
|Adjusted R-squared|0.04|0.36|0.25|0.21|0.15|0.33|0.09|0.17|0.19|
|Mean Control|0.14|0.65|0.82|0.68|4.35|0.55|0.62|0.17|0.15|
|P-value A=B|0.29|0.18|0.10|0.90|0.12|0.41|0.75|0.30|0.34|
|P-value A=C|0.00|0.02|0.89|0.56|0.08|0.53|0.00|0.13|0.00|
|P-value B=C|0.01|0.00|0.09|0.49|0.00|0.80|0.00|0.01|0.00|

42
**Figure 1. Schooling Composition of the Labor Force, 1996-2015**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 2015 1996 2015 1996 2015<br>WAP EAP Sample<br>Incomplete Prim Complete Prim Incomplete JH Complete JH<br>Incomplete SH Complete SH University<br><!-- End of picture text --> _Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note_ : _Prim_ , _JH_ , and _SH_ refer to Primary, Junior High, and Senior High, respectively. _Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

Second, the growth rates of the EAP largely mimic those of the WAP, in contrast with the sample, where employment of workers with incomplete junior high or less schooling falls in absolute terms. Third, as a result, the schooling composition of the sample changes rapidly: in 1996, the share with at most primary education equaled that with at least high school (27.7 percent); by 2015, the first share had fallen to 12.9 percent, and the second risen to 47.4 percent. Finally, the sample has more years of schooling: in 2015, workers who had, at most, completed primary school 17
Figure 6 _Seasonal weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

# _a. Seasonal rain S1 (Jan‐Apr)_

# _b. Seasonal temperature S1 (Jan‐Apr)_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>c. Seasonal rain S1 (May‐Aug) d. Seasonal temperature S2 (May‐Aug)<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>e. Seasonal rain S3 (Sep‐Dec) f. Seasonal temperature S3 (Sep‐Dec)<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>35<br>1,000<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br>35<br>1,000<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br>1,000 35<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br><!-- End of picture text -->

# _c. Seasonal rain S1 (May‐Aug)_

# _e. Seasonal rain S3 (Sep‐Dec)_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of rainfall levels and mean temperature in the respective months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

14
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