rafmacalaba/datause-encoder-data huggingface dataset:
deviation observed in the control group.

In addition, we access separate administrative data on students’ records that we use to construct an indicator for enrollment in seventh grade, which is the first grade in lower secondary school. The sample reduces to 468 sixth graders in 182 schools, who are deciding whether to transit to secondary school. This sample reduction is due to the multi-grade aspect of the schooling system, where student composition among grades in each school is not homogeneous in size. Missing schools in this analysis are balanced among treatment arms. The choice of this cohort of students is meant to maintain the same length of exposure to the API program of the main sample of the analysis.<sup>5</sup> Finally, we conducted a series of in-depth interviews in the spring of 2022 for a small and representative subsample of 16 mentors and 12 community instructors who were part of our study.<sup>6</sup> This qualitative evidence proves useful to complement the quantitative analysis and to shed further light on the mechanisms through which the mentoring intervention affects students and parents as discussed in Section 3.3, as well as on the role of parents as means of scalability as discussed in Section 5.2.

# **3 Experimental Evidence**

In this section, we report OLS estimates from separate regression models for each experiment on the treatment assignment indicators for the _Original_ and _Plus_ modality after two years of exposure to the mentoring program. All models include the strata indicators that account for the block randomization design (see Section 2.2) as well as few individual characteristics such as students’ age and ethnicity. We control for interview week fixed effects, which account for changes in weather and political conditions, as well as indicators for the different teams of enumerators who administered the survey across the communities in our sample. The error terms are clustered at the school level, which represents the unit of randomization of the treatments. We complement the usual asymptotic inference with two alternative procedures. First, we display _p_ -values based on randomization inference, which are accurate

> 5The distribution of missing schools in the analysis of transition to secondary school is 18 schools in the control group, 14 in the _Original_ group and 16 in the _Plus_ group. Due to the different individual identifiers, we are not able to match this dataset to the survey data. The estimates reported in Table B-6 document no program effects on grade repetition and attrition, which suggest that conditioning on grade attainment is not problematic in our context.

> 6Appendix A.3 reports more details about these interviews. Tables B-4 and B-5 show that the characteristics of these survey respondents are broadly comparable to those of the mentors and the local instructors in our main sample.

10
**Figure 6: Effects of CHECC Preschool on Inequality Aversion, Separately by Gender**

_Notes_ : This figure shows averages for the control and preschool groups in Cappelen et al.’s (2020) inequality and merit experiments. The left column shows averages for boys. The right column shows averages for girls. The outcome, “Mean Inequality s.e.m.”, is the absolute difference between the number of stickers of each child. It is normalized by subtracting its mean (among all participants) and dividing it by 10, which is the total number of stickers. We followed all of Cappelen et al.’s (2020) conventions so this figure can be compared to the bottom panel of Cappelen et al.’s (2020) Figure 2. The brackets show 95% confidence intervals. _N_ = 69 (Control Boys); 39 (Preschool Boys); 57 (Control Girls); and 44 (Preschool Girls).

Figure 6 shows figures that are comparable to the bottom panel of Cappelen et al.’s (2020) Figure 2. The only distinction is that separate graphs are shown for boys and girls. The top panel shows results for the luck experiment.<sup>37</sup> The bottom panel shows results for the merit experiment.<sup>38</sup> The left column shows results for boys while the right column shows results for girls. The variable shown on the vertical axis, mean inequality s.e.m., is the absolute difference between the number

> 37 In the merit experiment, the participant was informed that the two other children had completed a memory task and that the child who had performed better in the task had earned 8 stickers while the other child had earned 2 stickers. The participant was then given the option to reallocate the stickers between these two children. 38 In the luck experiment, the inequality was the result of luck rather than merit. The experimenter flipped a coin in front of the participant, which determined which of the two other children was the “lucky” child who would earn 10 stickers, and which was the “unlucky” child who would earn no stickers. The participant was then given the option to reallocate the stickers between the “unlucky” and the “lucky” child.

23
1. _y_ and _ε_ are realized. All agents observe _y_ , but only the government observes _ε_ .

2. Lenders use their information to post (i) a monitoring rule, _m ∈{_ 0 _,_ 1 _}_ , and (ii) price schedules _q_ m and _q_ nm for the cases of monitoring and non-monitoring, respectively.

3. The government observes all states and lenders policy functions, and it makes a default decision: _d ∈{_ 0 _,_ 1 _}_

- If default ( _d_ = 1): no coupons are paid, all hidden debt is revealed, the country does not obtain any flows from _ε_ , and it suffers from income losses and exclusion while the default status persists.

- If repay ( _d_ = 0): the government can borrow again by choosing _b_<sup>_′_</sup> . When it evaluates possible values for _b_<sup>_′_</sup> it takes into account the fact that different borrowing levels imply different prices and different monitoring policies from lenders.

- If there is monitoring ( _m_ = 1), all discovered debt is added to _b_ , and the hidden debt going forward is zero ( _h_<sup>_′_</sup> = 0).

- If there is no monitoring ( _m_ = 0), hidden debt is _h_<sup>_′_</sup> = _h_ (1 _− δ_ ) + _ε_ .

4. Consumption and coupon payments (if relevant) take place.

For a country that ended period _t −_ 1 in financial exclusion, the first event that occurs is a realization of a reentry shock. With probability 1 _− θ_ the country remains excluded and has no decision in the period (it consumes its reduced income level). If reentry occurs (with probability _θ_ ), then the country receives a realization of _ε_ , its initial debt level gets reduced to _bD_ (according to equation 4), and its initial hidden debt is set to zero (since it had been revealed in the prior default event). The country then finds itself at step 2 above, and the timing of events continues as specified there.<sup>29</sup>

# **5.2 Foreign lenders**

We follow the modeling of lenders presented in Gu and Stangebye (2023); however, to use the full extent of our novel dataset of hidden debt revelations and to retain tractability, we simplify the information acquisition problem. We assume that foreign lenders are risk averse and that they arrive in overlapping generations, each with wealth _W_ . They have access to a risk-free asset that yields a net return of _r_ . When the sovereign starts the period in good standing and has not defaulted, the problem for a foreign lender is to decide whether to monitor the country’s debt: _m_ = 1 denotes monitoring, and _m_ = 0 denotes not monitoring. Monitoring is the only way in which lenders can acquire information about the true debt level of the country and it is costly: lenders have to pay a fee of _f_ .

When they make this decision they know _b_ , _y_ , and the number of periods since the last revelation ( _τ_ ), and they evaluate their value under monitoring or no monitoring for different candidate levels of _B_<sup>_′_</sup> . After this decision is made, hidden debt either continues to be

> 29Implicit in our timing is the assumption that when the government makes decisions ( _d_ and _b′_ ), lenders cannot change their monitoring policies or pricing schedules.

25
# **Table 23b.  Piecemeal Sensitivity Analysis – Carbone Dioxide Emissions by Region from WTO Accession (percentage change from base year)**

||**Overall**||**Far**||||||**St.**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Parameter being changed**|**average**|**Central**|**East**|**North**|**Siberia**|**South**|**Urals**|**Moscow**|**Peters.**|**Tumen**|**Northwest**|
|**Central Results for reference**|4.3|6.1|4.9|5.2|4.6|4.4|3.3|2.4|8.9|3.7|8.2|
|**esubc = 0.5**|4.3|5.9|5.0|5.2|4.6|4.3|3.3|2.6|9.3|3.7|8.0|
|**esubc = 1.5**|4.3|6.2|4.9|5.2|4.6|4.5|3.3|2.3|8.9|3.7|8.2|
|**esub = 2**|3.6|5.3|4.8|5.0|3.6|3.8|2.6|1.8|7.9|3.2|8.2|
|**esub = 4**|4.4|6.3|4.7|5.1|4.9|4.5|3.6|2.5|9.0|3.7|8.0|
|**esubs = 0.5**|3.0|4.8|3.0|3.3|3.3|3.1|2.5|1.4|6.2|2.2|6.0|
|**esubs = 2.0**|6.3|8.4|8.1|8.4|6.6|6.5|4.6|3.9|10.8|6.0|12.1|
|**esubva = 0.5**|4.3|6.0|5.0|4.7|4.8|4.5|3.4|2.3|8.4|3.6|7.8|
|**esubva = 1.5**|4.3|6.2|4.9|5.2|4.5|4.4|3.3|2.4|9.1|3.7|8.3|
|**etad = 10**|4.1|6.1|4.7|4.9|4.3|4.2|3.0|2.2|9.6|3.5|7.8|
|**etad = 5**|4.6|6.3|5.2|5.7|5.0|4.8|3.9|2.7|8.2|4.0|8.8|
|**etadx = 3**|3.9|5.6|4.8|4.3|4.3|4.0|2.9|2.3|7.7|3.6|7.5|
|**etadx = 7**|4.6|6.6|5.1|5.5|5.0|4.8|3.7|2.4|10.2|3.7|8.7|
|**etaf = 12.5**|3.6|5.3|4.2|4.4|3.6|3.7|2.6|1.9|7.6|3.1|7.3|
|**etaf = 17.5**|5.0|6.9|5.4|5.8|5.5|5.0|4.0|2.8|10.0|4.2|8.9|
|**sigmadm = 4**|4.5|6.4|4.8|5.2|4.9|4.5|3.6|2.5|9.2|3.7|8.4|
|**sigmadm = 2**|3.7|5.4|4.7|4.9|3.7|3.8|2.6|1.9|7.8|3.3|7.8|
|**Source: Authors' calculations.**||||||||||||
|**Notes:**<br>a. Thepiecemeal sensitivityanalysis e|mploys cent|ral values for|allparamet|ers(see belo|w)other than|the testedp|arameter.|||||

|**Source: Authors' calculations.**<br>**Notes:**<br>a. Thepiecemeal sensitivityanalysis e|mploys cen|tral values for allparameters(see below)other than the testedparameter.|
|---|---|---|
|**Parameter**|**Central**|**Definitions of theparameter(see figures, especially figure 5)**|
||**value**|**for moreprecise elasticity structure).**|
|esubc|1.25|Elasticityof substitution in consumer demand|
|esubs|1.25|Elasticityof substitution between value-added and business services|
|sigmadm|3|"Armington" elasticityof substitution between imports and domesticgoods|
|etaf|15|Elasticityof multinational service firm supplywith respect toprice of output|
|etad|7.5|Elasticityof Russian service firm supplywith respect toprice of output|
|esub|3|Elasticityof substitution between firm varieties in imperfectlycompetitive sectors|
|esubva|1|Elasticityof substitution betweenprimaryfactors ofproduction in value added|
|etadx|5|Elasticityof transformation(domestic output versus exports)|

68
**Coefficient Plot: Personal Economic Impact by Date**

<!-- Start of picture text --> mencarmeluz neg_emoticontortillmenhaycarmeluzkwmi menhaycarmeluzmi meluz mencarmeluz menme<br>dollar_amountat_mentionpreguntcomprenergielectrsocialfamilitarjetaltosacabaplictengvergestajajajmaspartquitquehubhayhoynadsubhacnosdarfaltpertenvalleñsinsalsolyami dollar_amountat_mentiontransportcomedorpreguntconsumaumentgobiernurl_linknecesitpropancanastsistemfocalizcomprenergicambiminecjajajajelectrordencuantseñorporqutendrfamilimismdinercocinbasicgracipuedrecibacabhechaplicgenttenggranverdgustgastfinalestajajajtantmasunicpartsalvplazpaisquitquedebhubaguhoynadsubhacnosdarfaltcospertenfuetodfunquividdejadivalleñdicsinsigsalsolasiyahaja dollar_amountneg_emoticonat_mentioncomedorgobiernpropancanastcomprenergicambijajajajelectrcuantporqutendrfamilinoticidinerbasicgracirecibacabdesdgenttenggranfmlngustgastestatantmassufrquehoypodnadsubhacnosdarcospertenfuetodfunadivalleñdicsinkwsalsolasiyaja neg_emoticonat_mentioneconomhistorijajajajelectrentrarecibtengalgojajajquitquesubpertensinsolasiyami neg_emoticonat_mentionaumentgobierncambielectrporqubuserahorrmismdinerrecibtwitttengverdjajajtantmaspartquitqueaguhaypodnadsubdoshacnosfaltcospertodsinsalsolasiyamiha dollar_amountneg_emoticonat_mentionconsumemprescomprelectrfamiligracipuedrecibacabmasunicquehayhoyperdicluzsinkwsolmi<br>January 2011 April 2011 May 2011 August 2011 May 2012 August 2012<br><!-- End of picture text --> Figure 11: The top 100 or fewer coefficients associated with “personal economic impact” over time. The blue indicates positive coefficients and the red negative. This graph demonstrates topic drift during the sample period. The scales of each graph are not constant over time. Source: Author’s calculations.

– indicate an information tweets what we captured in the “other” category. Instances of some manifestation of laughing (e.g., “Jajaja” or “Jaja”) could be replaced by a LAUGHING concept. This may help capture instances of positive emotion or sarcasm better.

With respect to the sentiment classification, we could use untagged tweets that contain positive emoticons to help identify the features that indicate positive sentiment. Similarly, we are exploring the use of a Spanish language sentiment lexicon, in which psychologists or linguists have identified the psychological valence of a list of emotionally charged words.

The use of bigrams could be further improved. They did not turn out to be very important informationally, beyond marginally improving the cross-validation results. We might instead identify collocations, words that are juxtaposed frequently and have their own meaning, and keep those instead of all bigrams. Similarly, there are still some purely functional stop words that appear in our results that could be removed so that we rely less on the regularization of the algorithm. We could include time fixed effects<sup>11</sup> , time

> 11Or perhaps seasonal effects given our observations for August.

34
<!-- Start of picture text --> 200TM®,<br>150<br>3<br>z<br>Pred°3°<br>3z£S 100<br>50<br>0<br>1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023<br>@ Vertically Integrated Utility Single Buyer Model m@ Wholesale/Retail Competition<br><!-- End of picture text -->
**Table 1: Summary statistics**

|||Brazil|||China|||India||
|---|---|---|---|---|---|---|---|---|---|
||1981|1993|2005|1981|1993|2005|1981|1993|2005|
|**Average income or**<br>GDP per capita|**consumpt**|**ion**||||||||
|($PPP per year)|7072.8|7241.0|8471.0|543.5|1505.5|4076.3|901.4|1274.1|2233.9|
|PCE per capita||||||||||
|($PPP per year)|3727.3|3711.1|4408.6|248.9|635.4|1336.6|642.5|790.3|1208.8|
|Survey mean||||||||||
|($PPP per year)|2367.5|3091.4|3344.2|300.2|571.8|1294.8|494.5|560.3|642.2|
|Mixed mean||||||||||
|($PPP per year)|2323.7|2473.0|3030.0|382.3|597.5|1219.6|613.9|699.3|841.0|
|**Inequality and hum**|**an develo**|**pment**||||||||
|Gini index (%)<br>Infant mortality<br>rate (deaths per<br>1000 births)|57.5<br>72.2|59.7<br>49.2|57.6<br>21.8|29.1<br>45.8|35.5<br>36.3|41.5<br>21.4|35.1<br>113|30.8<br>80|33.4<br>57.7|
|Life expectancy at||||||||||
|birth (years)|62.8|66.6|71.81|65.5|68.3|72.6|55.7|59.7|64.0|
|Primary||||||||||
|<br>enrollment rate|136.7|141.0|136.9|111.7|127.5|111.2|81.6|93.6|114.6|
|(Female/male, %)*||||(86.9)|(93.3)|(99.5)|(67.6)|(76.7)|(97.6)|
|Secondary<br>enrollment rate|47.2|54.2|105.5|43.2|37.7|75.5|33.1|41.3|54.0|
|(Female/male, %)*||||(73.2)|(75.1)|(100.8)|(49.3)|(59.7)|(82.3)|
|Literacy (% of||||||||||
|people age 15+)<br>(Female/male, %)*|74.6|86.4|89.6|65.5<br>(64.6)|77.8<br>(78.2)|93.3<br>(93.3)|40.8<br>(48.7)|48.2<br>(54.7)|66.0<br>(70.9)|
|**Poverty**||||||||||
|Headcount index<br>($1.25; %)|17.1|13.0|7.8|84.0|53.7|16.3|59.8|49.4|41.6|
|Headcount index<br>using mixed<br>method ($1.25; %)|17.6|18.1|9.7|73.0|45.0|12.1|42.3|30.4|20.3|
|Headcount index||||||||||
|($2.00; %)|31.1|24.7|18.3|97.8|78.6|36.9|86.6|81.7|75.6|
|Headcount index<br>using mixed||||||||||
|method ($2.00; %)<br>Notes<br>:GDP, per capita|31.7<br>consumpti|31.5<br>on expendi|21.1<br>ture (PCE)|95.4<br>and the sur|78.4<br>vey mean|33.9<br>are all at PP|77.0<br>P for 2005|68.4<br>and 2005|57.0|

<u>Notes: GDP, per capita consumption expenditure (PCE) and the survey mean are all at PPP for 2005 and 2005</u> constant prices and annual. Survey means relate to household income per person for Brazil and China and household consumption expenditure per person for India. Adult literacy rate for Brazil is 2006 and 2007 for China and India. Enrollment rates are 1980 and 2006 for China. *: Enrollment and literacy rates have been approximately equal for Brazil since the 1970s, and so are omitted. <u>Sources: Poverty and inequality measures are from</u> _PovcalNet_ . All other data are from the World Bank’s Development Data Platform.

31
varied the _α_ parameter and the regularization penalty function, trying the _l_ 1, _l_ 2, and the elastic net. We set _α_ to a grid of size 25 from 1 _e −_ 6 to 1000 in log space. For the elastic net, we let the _ρ_ be [ _._ 05 _, ._ 15 _, ._ 25 _, ._ 5 _, ._ 75 _, ._ 85 _, ._ 95]. Finally, we also varied the weights for each class. We tried both without weights and setting the weight of each class to the inverse of its observed frequency given that we do not observe a uniform distribution of classes in either the categories or the sentiment. For the Naive Bayes classifier, we used the same feature vector transformation options, and we used a grid of size 10 from _._ 1 to 1 for _α_ .

Using the _f_ 1 _−_ measure to evaluate performance, we select a feature vector transformation that uses only the frequency of unigrams rather than _tf-idf_ for the subject matter classification. The chosen classifier is the SGD classifier with the modified Huber loss function and an _l_ 2 regularization penalty with _α ≈ ._ 1. We also select to use the class weights that are inversely proportional to the observed class frequency.

For the sentiment feature vector transformation, we select the _tf-idf_ of unigrams with an _l_ 2 normalization for each tweet. For the classifier, we select the SGD classifier with the hinge loss function and an _l_ 2 regularization penalty with _α_ = _._ 01. All classification was performed using the scikit-learn library for Python version 0.15.2.[Pedregosa et al., 2011]. Any parameter not mentioned was left at its default value.

# **6.2 General Results**

Table 9 shows the results of using the classifier to predict the subject of the tweets over time. It is similar to Table 1 with a notable exception. There is a smaller percentage of tweets categorized as “distrust of institutions.” We do not put too much weight on the August 2011 and August 2012 months due to the small sample<sup>10</sup> . Only a few misclassified results will change the percentage considerably. However, we do not have these problems with the other months. We also still observe the increase in the months of April and May of 2011, confirming the survey results from _La Prensa Grafica_ reported in Calvo et al. [2014].

Similarly, we observe a drop-off in the predicted “lack of information” and “personal economic impact” categories with the same caveat about August 2011 and 2012. This gives us confidence that these results are capturing general public opinion about the gas subsidy reform. Table 10 presents select data from the _La Prensa Grafica_ [Calvo et al., 2014]. We see a similar trend when we look at the “distrust of institutions”, “lack of

> 10We speculate that perhaps August is month of low Twitter usage in general owing in part to the _Fiestas Agostinas_ .

23
# CONTENTS

|1.|<br>INT|RODUCTION .....................................<br>1|
|---|---|---|
|2.|<br>THE<br>DES|PHN PROGRAM TASK AND ITS CONTEXT:<br>IGN AND MANAGEMENT IMPLICATIONS<br>............... 2|
|3.|<br>REV|IEW OF BANK WORK IN PHN .......................<br>8|
||A.|Lending .....................................<br>8|
||B.|Sector Work ................................<br>17|
||C.|Policy and Research Work ...................<br>17|
||D.|Institutional Issues in the Bank ........... 19|
|4.|CON|CLUSIONS AND RECOMMENDATIONS ................. 22|
||ANN|EXES|
||A.|Summary of Quintitative Analysis|
|||of Project Content .........................<br>28|
||B.|Working Papers Cited in the Text ...........<br>34|
||C.|Case Studies ...............................<br>35|
|||1.<br>Zimbabwe Family Health Project:|
|||Targets and Indicators ................ 35<br>2.<br>Tamil Nadu Nutrition Project:<br>The Use of Growth Monitoring for<br>Targeting Services to the|
|||Disadvantaged .........................<br>36<br>3.<br>Ethiopia Family Health Project:<br>The Use of Rapid Anthropological<br>Approaches in Project Preparation .....<br>37<br>4.<br>Nigeria Imo State Health Project:<br>The Use of Focus Group Interviewing<br>in Project Preparation ................<br>40<br>5.<br>Ethiopia Family Health Project:<br>Functional Task Analysis of Service<br>Providers .............................<br>44<br>6.<br>Indonesia Nutrition Development<br>Project: An Application of Social<br>Marketing .............................<br>46|
|||7.<br>The Training and Visit (T&V) System|
|||of Agricultural Extension .............<br>48|
<!-- Start of picture text --> Lorenz Curves for informal firms<br>=<br>So<br>ao<br>2<br>&<br>ge<br>g<br>[o)<br>ot<br>=<br>g<br>Sa<br>Se<br>|<br>(S)<br>o<br>oO ee 4 6 8 1<br>Cumulative % of entrepreneurs<br>————_ line of 45° (perfect equality) —————— Top-performers<br>——— Constrained gazelles ———— Survivalists<br><!-- End of picture text -->
# **Table (5): Tests on the significance of each variable**

|**Variable**|**F-test Value**|**[Prob]**|
|---|---|---|
|**rm2**|**F(15,72) = 6.2812**|**[0.0000]****|
|**rgdp**|**F(15,72) = 6.2104**|**[0.0000]****|
|**INFL**|**F(15,72) = 3.0388**|**[0.0008]****|
|**Interestrt**|**F(15,72) = 1.5745**|**[0.1000]***|
|**DISC**|**F(15,72) = 2.1325**|**[0.0174]***|
|dumm1986|F(5,26) = 1.5760|[0.2018]|
|**dumm1992**|**F(5,26) = 2.4659**|**[0.0589]**|
|**dumm2008**|**F(5,26) = 4.7243**|**[0.0033]****|
|**dumm2009**|**F(5,26) = 3.8180**|**[0.0100]**|
|dumm2010|F(5,26) = 0.40268|[0.8425]|
|**dumm2011**|**F(5,26) = 2.1938**|**[0.0857]**|
|**dumm2012**|**F(5,26) = 7.0776**|**[0.0003]****|
|**dumm2013**|**F(5,26) = 2.1997**|**[0.0850]**|
|**dumm1991**|**F(5,26) = 17.779**|**[0.0000]****|
|Constant|F(5,26) = 1.7887|[0.1503]|

P-values are in brackets

*** Significance level at 1%;  ** Significance level at 5%; * Significance level at 10%

45