rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2: Impact of Family Background on Children's Years of Schooling**

|**(primary schooling as the threshold forparental education)**|
|---|

|||**(1)**<br>**Parents'**<br>**education**|**(2)**<br>**Parents**<br>|**(3)**<br>**education and O**<br>|**(4)**<br>**ccupation**<br>|**(5)**<br>**Test of e**|**(6)**<br>**quality**|
|---|---|---|---|---|---|---|---|
|**Estimates**|||**Non-farm, low**|**Farm, high**|**Non-farm, high**|||
|||**Full sample**|**edu**|**edu**|**edu**|||
|||||||**Z score**|**Z score**|
|**_Daughters_**||||||||
||OLS|**1.10**<br>10.78***|**0.57**<br>3.32****|**1.05**<br>8.26***|**1.53**<br>11.07***|-2.24*|-2.56**|
|**Matching**<br>|A-I<br>matching||**0.72**<br>3.62***|**1.04**<br>7.18***|**1.31**<br>6.25***|-1.30|-1.06|
|**Estimators**|BC-RM||**0.66**<br>3.03***|**1.00**<br>7.03***|**1.40**<br>9.11***|-1.31|-1.91*|
|**Propensity**<br>**score**<br>**weighted**<br>**estimators**|NIPW||**0.59**<br>(0.31,0.87)|**1.01**<br>(0.76,1.22)|**1.58**<br>(1.33,1.83)|-1.97*|-2.87**|
|**_Sons_**||||||||
||OLS|**1.02**<br>12.72****|**0.8**<br>5.42***|**1**<br>10.74***|**1.67**<br>15.42***|-1.14|-4.69***|
|**Matching**|A-I<br>matching||**0.70**<br>4.25***|**1.05**<br>10.31***|**1.72**<br>12.38***|-1.69|-3.87***|
|**Estimators**|BC-RM||**0.62**<br>3.54***|**0.91**<br>8.21***|**1.74**<br>11.63***|-1.39|-4.45***|
|**Propensity**<br>**score**<br>**weighted**<br>**estimators**|NIPW||**0.82**<br>(0.60,1.09)|**1.04**<br>(0.86,1.19)|**1.74**<br>(1.53,1.92)|-1.17|-4.91***|

## **_Notes_**

- 1) Dependent variable=children's years of schooling;

- 2) For the column (1), the interest variable=parents' education dummy, dummy =1 if at least one of parents more than primary schooling,

- 0=otherwise;

- 3) For all columns, the control variables are children's age, children's age squared, mother's age, mother's age squared, father's age, father's age squared;

- 4) For the columns (2) - (4), the interest variable is parents' education and occupation status dummy. The dummy=1 if treatment group,

- =0 otherwise; base group is (farm, low edu)

- 5) A-I matching=Abadie and Imbens (2002)

- 6) BC-RM = Biased corrected radius matching ;

- 7) NIPW = normalized inverse probability weighted propensity score estimator of Hirano and Imbens (2001);

- 8) The computation for NIPW uses STATA program written by Millimet and Tchernis (2013);

- 9) Estimates and t statistics are shown. For NIPW, 90% confidence interval is given in parentheses, which is boostrapped using 250 replications;

- 10) "high edu" stands for more than primary schooling, "low edu" stands for primary schooling or less;

- 11) ***, **, and * Denotes statistical significance at the 1%, 5%, and 10% level respectively.
**<u>Table 1: Summary statistics</u>**

|||**All**<br>|**Before**<br>**Compute**<br>|**After**<br>**rization **<br>|
|---|---|---|---|---|
|**Total**|Credit|0.24|0.10|0.35|
||Sales|2207|1310|2818|
||Mortgages|664|650|673|
|**Rural**|Credit|0.08|0.05|0.11|
||Sales|1854|1203|2342|
||Mortgages|637|631|644|
|**Urban**|Credit|2.00|1.00|2.37|
||Sales|5815|3922|6244|
||Mortgages|697|672|796|

_Note:_ Nominal values of credit are deflated using the RBI whole sale price index with 1993-94 as the base year. Credit is in billions of 1993 Rupees.

17
the ACASI module to suit this study population’s needs, considering their illiteracy and minority languages. Questions were recorded on electronic tablets in multiple ethnic languages and participants were taught during survey administration how to select answers by pressing the appropriate color-coded symbols (e.g. green star = yes). A test module on non-sensitive questions included at the beginning of the GBV module confirmed that the participants had learned it correctly. Participants listened to the questions with headphones and selected their own answers, ensuring confidentiality. Studies that have compared the likelihood of revealing victimization experience suggest that the likelihood of reporting GBV is either higher or at the same level with ACASI, as with traditional face-to-face direct questioning, commonly used in demographic and health surveys (Cullen 2020; Dervisevic et al. 2020).

# 5. Empirical Approach

The analytical approach was registered as a pre-analysis plan with 3ie RIDIE (study ID: RIDIESTUDY-ID-5e61e44365ed9). This paper focuses only on the GBV outcomes, and we note deviations from the pre-analysis plan when they occur.

We measure the impact of the RMG program on women and their households through intent-to-treat (ITT) and treatment on the treated (ToT) models. ITT allows us to estimate the population-level effectiveness of the intervention when participants may not comply perfectly with the intervention activities, while ToT identifies the results on those who engaged in the intervention and shows the efficacy that can be achieved if participants comply. We estimate the ITT effect on GBV through the following estimating equation: where is the value of the outcome of interest at endline, β β is a dummy that takes the value of 1 for the treatment group (RMG) as assigned at baseline, and ii1 contains a set of baseline characteristics ii0 yy tt (age, marital status (1/0) ever went to school, ethnicity dummies, household size, winsorized household ii0 XX monthly income and baseline level outcome , when available (as for labor outcomes confirming participation). ii0 yy In the case of perfect compliance with treatment assignment, we can interpret the ITT as a Treatment-on-Treated (ToT) effect and this would be equal to the Average Treatment Effect (ATE). However, in our case, 14 women dropped out of the program and were replaced by 12 women from the waitlist. We have information on the women who dropped out, which allows us to track actual treatment. This allows us to instrument for take-up using the random assignment in a two-stage regression to estimate a ToT. Since this is only relevant for women who take up the intervention, it should be interpreted as a 8
**Figure 7:** Intergenerational Educational Mobility in LAC - Urban/rural

<!-- Start of picture text --> PANURYPRYGTM BOLHNDCOLPANPRYMEXCHLGTMHTIBRACRIURY BOLBRACOLVENGTMHNDPRYARGPANCHL gap = 10.692 + -.0053 cohort (3.1274) (.0016)BOLPERGTMHNDBRACOLVENMEXPRYARG HTINICBRAPANBOLGTMHNDVENCOL NICPAN PANURYBRA CRIPANURYBOLMEXPRYHNDGTMCHLBRAHTICOL CRIARGURYPANCHLGTMPRYVENBOLCOLBRA LCAURYCRIARGECUCHLMEXPRYVENPERGTMCOLHNDBRABOL JAMCHLECUARGPERPRYMEXPAN C SLVDOMBOLVENNICHNDBRAGTMHTIRIOL CRIMEXECUPANBOLDOMPERBRACOLSLVNIC<br>BRA URYCRI CRICHLLCAECU DOMARGPERSLVCRIPRYMEXECU PERCOLSLVBRADOMBOL PRY HND<br>URY CHLJAM CRIECUMEX GTM gap = -9.263 + .0046 cohort (2.2662) (.0012)<br>1940 1950 1960 1970 1980 1990 1940 1950 1960 1970 1980 1990<br>Birth decade Birth decade<br> countries with data for 1950s, 1960s, 1970s, 1980s countries with data for 1950s, 1960s, 1970s, 1980s<br> other countries other countries<br> mean among countries with data 1950s-1980s mean among countries with data 1950s-1980s<br>(a) Upward mobility (b) Downward mobility<br>.5 0<br>.4 -.1<br>.3 -.2<br>.2 -.3<br>.1 -.4<br>urban upward IGM - rural upward IGM 0 -.5<br>urban downward IGM - rural downward IGM<br><!-- End of picture text --> Notes: These estimates correspond to the probability of completing at least primary education for those whose parents did not finish primary school in the case of upward mobility and probability of not completing primary education for those whose parents completed primary school in the case of downward mobility. They are estimated for individuals aged 14-18 years by country, birth decade of the “children” and urban/rural status of the household residence.

in IGM between these populations and document how they have evolved across birth cohorts. I do so by estimating upward and downward mobility<sup>16</sup> by country, birth decade of the “children” and urban/rural status of their residence. Figure 7 reports the gap between the upward/downward mobility in urban-rural areas over birth cohort. I find a positive gap that has been declining from 36 percentage points (i.e., upward mobility in urban areas is on average 36 percentage points higher than in rural areas for the cohort born in years 19501959) to 20 percentage points as one moves towards older birth cohorts. Similarly, the gap in downward mobility is closing from below moving from 29 percentage points for 1950 birth decade to 15 percentage points for 1980 birth decade. Figure A14 and Figure A15 in the Appendix show estimates by sub-population rather than the gap between them for countries with data for at least 4 decades, suggesting that the gap has been decreasing because of an increase (decrease) in upward (downward) mobility.

> 16The probability of completing at least primary education for those whose parents did not and the probability of not completing primary for those whose parents complete primary school, respectively.

19
recall of these variables collected in surveys long after the visit. The data used in the analysis are extracted from these clinic records and contain information on the universe of patients for the 36 clinics in the study. The records also include the individual’s national identity number, which is used to link the individual clinic medical records from primary health facilities with the registry of health insurance coverage, the registry of Plan Nacer beneficiaries, and hospital medical records. In all, 97% of the primary clinic medical records were merged with the data on insurance status and program beneficiary status. In addition, 75% of these were successfully merged with medical records data from hospitals. Therefore our analysis is able to evaluate the impact of the intervention for those women who initiated their prenatal care in one of the primary care clinics of the sample.

# **4.1 ANALYSIS SAMPLE**

Figure 2 depicts the timeline of the study and the availability of data divided into 4 different sub‐periods: (i) a 16‐months pre‐intervention period from January 2009 to April 2010, (ii) an 8‐ month intervention period from May 2010 to December 2010, (iii) a 15‐month “post‐intervention period I” from January 2011 to March 2012 and (iv) a 9‐month “post‐intervention period II” from April 2012 to December 2012.

Prenatal care data was consistently collected for the first 3 periods from January 2009 through March 2012. Starting in April 2012, however, Misiones adopted a new information system and as a result data from post‐intervention period II cannot easily be compared to data from the earlier periods. In particular, the new system changed the codes used to classify the reason for visits in order to facilitate billing. If in the first visit the attending physician requested an ultrasound to confirm a pregnancy, this first visit was labeled as a “care visit” while the subsequent (second) visit, was labeled as the first prenatal visit, if indeed the ultrasound confirmed the pregnancy. On average, this would led to a reduction in the share of women who had a visit labeled as “first prenatal visit” before week 13 and an increase in the weeks pregnant at the time of this visit. If the new coding system affected the treatment and control groups in the same way, the differences between the treatment and control groups would still capture the impact of the incentives, albeit possibly with some measurement error. Therefore, we analyze the data from post‐intervention period II separately, and interpret the results with caution.

9
from the 2006 Doing Business Report (World Bank, 2006) on the number of documents required to export and import ( _docs_ ), summing across the exporting and importing countries.<sup>4</sup> We prefer this measure to the more commonly used indicator of time to export and import (Djankov et al. 2006; Nordås et al. 2006) because it does not suffer from endogeneity to trade flows in the same way: while an unexpectedly large trade flow this year might lead to congestion and thereby increase trading time, the same is not true for the number of documents required by customs authorities.<sup>5</sup> Another appealing feature of our measure is that it bears a close relationship to the core interpretation given to the term “trade facilitation” at the WTO level, which emphasizes the streamlining of trade-related administrative procedures and formalities (Wilson 2005).

# **5 Estimation Results**

We use (5) as our baseline gravity model for estimation purposes. We first estimate using standard OLS. Next, we present the results of a number of alternative estimation methods drawn from the recent literature.<sup>6</sup> In all cases, we gauge robustness by considering three different specifications of (5). Model 1 is our preferred specification, and takes the exact form of (5) above. Model 2 drops _paved_min_ and uses _paved_ave_ only, while Model 3 does the opposite. Finally, we briefly discuss some additional robustness checks.

The first three columns of Table 9 contain our OLS estimates. The dependent variable is _log(trade)_ , with zero or missing observations simply dropped from the dataset; this is an issue we

> 4 Due to lack of data availability, we use customs formalities in 2005 as a proxy for 2003. Similarly, when TRAINS data are missing for a given year, we take the most recent available data prior to 2003.

> 5 Our argument is indirectly supported by the results of Djankov et al. (2006), who instrument for trading time using two related variables, namely the number of signatures required to export and import. Their model is overidentified and does not reject the relevant restriction, which suggests that the proposed instruments are valid.

> 6 All calculations were performed using Stata 9.2SE.

- 12 -
The disaggregation of MAMS varies widely across different applications depending on data availability and the kinds of questions the model is called upon to analyze. For the Liberian application, the database is disaggregated into some 60 accounts (see Table A2.1), indicative of the aspects of Liberia’s economy that the model is able to consider. Most importantly, the database includes 22 production sectors as well as two commodities without domestic production (refined petroleum and non-competitive imports). Among the sectors, government production is represented by 7 services, covering HD, infrastructure and other areas. The factors of production are split into labor, different types of capital (mining, other private, and one capital type for each government service), and land factors specific to agricultural sectors.

25
The economy could benefit from other parts of the reforms, such as reflecting the power supply costs in the retail pricing of electricity. This could be a natural extension of the study in future.

# **References**

Chen, Wenying. 2005. “The costs of mitigating carbon emissions in China: findings from China MARKAL-MACRO modeling” Energy Policy, 33:885-896.

China Electricity Council (CEC). Electricity industry statistics 2010-2015.

Dupuy, M., and F. Weston and A. Hove (2015). Stronger Markets, Cleaner Air and Power Sector: Deepening Reform to Reduce Emissions, Improve Air Quality and Promote Economic Growth. The Paulson Institute. Chicago.

- Ho, M.S, Z. Wang, and Z. Yu (2017). China’s Power Generation Dispatch. Discussion Paper, Resource for the Future (RFF), Washington, DC.

Jamasb, T. R. Nepal and G.R. Timilsina (2017). A Quarter Century Effort Yet to Come of Age: A Survey of Electricity Sector Reform in Developing Countries. The Energy Journal, Vol. 38 (3), pp. 195-234.

Joskow, P.L. (1998). “Electricity Sectors in Transition.” The Energy Journal, Vo. 19, No. 2, pp. 25–52.

Lei, N., L. Chen, C. Sun and Y. Tao (2018). Electricity Market Creation in China: Policy Options from Political Economics Perspective. Sustainability, Vol. 10, pp. 1481.

Max Dupuy, M., F. Weston and A. Hove (2015). Power Sector: Deepening Reform to Reduce Emissions, Improve Air Quality and Promote Economic Growth. Paulson Dialogue Paper. The Paulson Institute, Chicago.

Newbery, D.M. (1999). Privatization, Restructuring and Regulation of Network Utilities, MIT Press.

Teng, F., F. Jotzo, X. Wang, 2017. “Interactions between Market Reform and a Carbon Price in China’s Power Sector.” Economics of Energy & Environmental Policy, 2017, 6(2): 39-53.

Timilsina, G.R., S. Pargal, M. Tsigas and S. Sahin (2018). How Much would Bangladesh Gain from the Removal of Subsidies on Electricity and Natural Gas. World Bank Policy Research Working Paper. No. 8677, World Bank, Washington, DC.

Timilsina, Govinda R. and Erika Jorgensen (2018). The Economics of Greening Romania’s Energy Supply System. Mitigation and Adaptation Strategies for Global Change, Vol. 23, pp.123-144.

19
Table III compares the balance between treatment and control households with respect to 31 variables on which data were collected in the 2005 survey. These variables are grouped into six different categories: household demographics, children’s characteristics, housing characteristics, hygienic environment, economic variables and benefits from public social programs. As in Table II, we also present the means and standard errors for these variables for both treatment and control groups, as well as their mean differences and respective standard errors. Notably, almost all of these variables are statistically balanced. No variables present statistically significantly mean differences at 5 percent or better levels and only 2 of the 31 variables present significant differences at the 10 percent level, something that is likely even when the true mean values of these variables were the same between both groups.

We start by noting that there were no differences in the share of rooms with cement floors in 2000, prior to program intervention. This is important because it implies that treatment and control groups are comparable in terms of the key measure of _Piso Firme’s_ objective, replacing dirt floors, at baseline. We also note that households are well balanced in terms of other housing characteristics, such as number of rooms, water and electricity connections.

Households are also reasonably well balanced in terms of household demographics and the characteristics of young children, number of household members and characteristics of the head and spouse of the head. The characteristics of young children are on average the same in terms of own age, mother and father age and education, as well as the probability the mother is present. Of note, however, is that the proportion of male children in the control group is .517 compare to .492 in the treatment group and the difference is statistically significant at the 10 percent level. Additionally, the proportion of children whose father is present in the household is .763 in the control versus .797 in the treatment and the difference is also statistically significant at the 10 percent level. We control for these characteristics in the analysis to test the robustness of the estimates of these minor deviations.

The health and hygienic environments of the control and treatment households are also well balanced. Households in both groups have the same share of municipal water connections on their land plot and inside their houses, as well a similar access to 15
provides the most information. As discussed in the text, our final design did include three choice situations in which one alternative likely strictly dominated the other. While these three situations provided little information about trade-offs, they did serve as a check on whether respondents were in fact making reasonable decisions.

In practice, there are a variety of ways in which efficient designs can be developed. We selected the most commonly used efficiency method – the _D-efficient_ design – which aims to minimize the determinant of the estimated asymptotic variance-covariance (AVC) matrix of the experimental design. To estimate the AVC matrix, we need to have priors about the parameter estimates (Hensher et al. 2015). While it is possible to assume that the parameters equal zero, using reasonable, non-zero priors can improve efficiency (Carlsson and Martinsson 2003). Thus, to design the experiment for the pilot phase of the survey, we used reasonable estimates based on theoretical priors about the signs and likely magnitudes of the parameters. For the design of the final survey, we relied on the parameter estimates from the pilot survey as priors.

We used Ngene software by Choice Metrics to create the efficient design for our choice experiment. Below, we outline the specific design choices that we made, and we provide sufficient information so that other researchers can produce a similar design. However, as Hensher et al. (2015) note, given the number of potential choice situations, it is generally not possible to examine all potential designs, so the software uses an algorithm to cycle through many different experimental designs, and to select the one with the smallest D-error. Thus, the exact choice situations produced may differ to some extent, even if the same approach is followed.

_Model type:_ We selected the standard discrete choice model, namely the multinomial logit (MNL).
5 shows the absolute mobility in height (cm) by comparing the value of the mobility indicator between youth and providers of the same gender, in the total population, and for each of the three exposure groups (groups 1-3). Group 0 is the comparison group. Mobility in the total population is estimated as 2.83 cm for males on average, and 4.05 cm for females on average. The absolute intergenerational mobility in years of schooling is 5.2 and 5.4 for males and females, respectively (Table 6).

|Table 5. Mean of absolute intergenerational mobility in health by gender and analysis group|
|---|

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|Total|Group 0|Group 1|Group 2|Group 3|
|Males|2.83|1.22|2.92***|3.33***|2.63**|
||(0.18)|(0.45)|(0.39)|(0.27)|(0.37)|
|Females|4.05|2.25|4.95***|4.32***|4.05**|
||(0.25)|(0.61)|(0.66)|(0.36)|(0.48)|
|Observations|2,199|288|401|1,035|509|

Notes: The units are cm of height. Standard errors in parentheses. *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1 indicate statistical significance comparing the mean of each group to the mean of the reference group, group 0. Source: estimations based on ENCEL 2017.

|Table 6. Mean of absolut|e intergenerationa<br>|l mobility in educ<br>|ation, by gende<br>|r and analysis<br>|group<br>|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|All|Group 0|Group 1|Group 2|Group 3|
|Males|5.20|4.05|5.43***|5.26***|5.56***|
||(0.10)|(0.20)|(0.19)|(0.14)|(0.17)|
|Females|5.40|4.00|5.52***|5.69***|5.44***|
||(0.09)|(0.22)|(0.20)|(0.13)|(0.16)|
|Observations|3,816|432|685|1,819|880|

Notes: The units are years of schooling. Standard errors in parentheses. *** _p<_ 0.01, ** _p<_ 0.05, * _p<_ 0.1 indicate statistical significance comparing the mean of each group to the mean of the reference group, group 0. Source: estimations based on ENCEL 2017.

In order for the comparison of absolute intergenerational mobility of individuals whose households received transfers during critical periods for development with the proposed control group to be valid, the two groups must be comparable in the pre-treatment period. To ensure this comparability, the matching analysis described in the methods section was carried out. Validation of the matching results is included in Appendix C.

Table 7 presents the comparison of each exposure group with the control group. The first two columns show estimates of absolute intergenerational mobility of height and years of schooling, based on the double difference estimator. This estimator summarizes the difference between the exposed group and the comparison group and the difference observed between the youth and their providers.

The change in intergenerational mobility presented by the comparison group (Group 0) can be interpreted as the average change expected among the rural population in the villages attended by PROSPERA between 1997 and 2000 for the group of individuals who did not receive the intervention during their critical human capital formation periods. The additional mobility observed in the differential exposure groups (Groups 1-3), in relation to the comparison group, is the change resulting from PROSPERA.

27