rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The provision of early childhood education services has become a central policy debate in many countries. Massive long-term socioeconomic benefits from early childhood interventions coupled with the potential to encourage maternal employment suggest that expanding access to preschools may kill two birds with one stone: increasing intergenerational mobility and closing gender inequality (Blau and Currie 2006). While the benefits for children enrolled in an early childhood program are well-documented (see Garcia et al. 2016 for a review of this literature), the causal evidence of preschool expansion on maternal employment is more of a mixed bag.

The effect varies across contexts, particularly over two dimensions: (1) current rate of preschool utilization and (2) current rate of maternal employment. When both rates are high, preschool expansion may no longer be effective at the extensive margin of inducing women to enter the workforce and enrolling their children in preschools (Lundin et al. 2008, Bauernschuster and Schlotter 2015). It may not induce greater preschool enrollment if the public expansion of preschools simply crowds-out private provisions (Baker et al. 2008). Furthermore, it may not induce maternal employment if it re-allocates children from informal non-parental childcare to formal childcare services (Havnes and Mogstad 2011b, Havnes and Mogstad 2011a). In the United States, past evidence (Gelbach 2002, Cascio 2009) may no longer apply today, because female labor supply is not as elastic as it once was (Fitzpatrick 2010).

We contribute to this literature by studying preschool expansion and maternal employment in a context that might yet be relevant in policy discussions. We study an emerging economy where preschool attendance and maternal employment are low, there is a large informal labor market, and individuals have a large network of kin who can, presumably, provide informal childcare services in the absence of formal providers. Indonesia is the fourth most populous country and is one of the fastest-growing economies in the world. However, in 2016, female labor force participation (FLFP) in Indonesia stood at 50.9 percent, lagging other countries in East Asia and the Pacific where the average FLFP is 60.8 percent, and stagnating from 50.2 percent participation in 1990. Prior to the recognition of pre-primary education in the national education system in 2003, only a quarter of children were enrolled in preschools, lagging behind the world’s average of 32.1 percent, the regional average of 43.3 percent, and the OECD average of 73.3 percent (World Development Indicators). We complement the growing body of research on low- and middleincome countries (see Mateo Diaz and Rodriguez-Chamussy 2016 for a survey of studies in Latin 2
<!-- Start of picture text --> © ———————SSSSSSSSSS<br>v<br>or<br>aS<br>(a)<br>Cc<br>Soc<br>3<br>£<br>=,8 2<br>=o<br>The<br>ow<br>£<br>3 .<br>—Fo eeee H . ee e i e| |<br>wy | Trtctrrcoche ------F5- 555-52 ---5----<br>7 14 21 28 35 42 49 56 63<br>Top of Income Bin<br>° Placebo Estimates —---—- Treatment Effect R$(63,70]<br>——— Treatment Effect R$(70,77]<br><!-- End of picture text -->

> ()
(“Statistical Capacity Indicator Dashboard” 2017). Further, the Source Data Available in the World Bank Microdata Library indicate that the surveys included in the analysis, with the exception of “complete vital registration system” survey, are unavailable; additionally, poverty data - particularly the poverty gap ratio – are listed on Egypt’s country profile as also being unavailable, and the data available for education are quantitative (i.e. primary completion rate) as opposed to qualitative (i.e., teacher training). In spite of these deficiencies, the cumulative score of 83.3 suggests that Egypt has the capacity to collect and manage more comprehensive data that will allow for better coverage for the purposes of monitoring the SDGs. Considering the evidence suggesting Egypt’s potential in this arena, further investigation to address the current limited availability of internationally comparable data as well as issues related to regional disparities should be assessed.

12. Figure 2 offers a visual representation of SDG data coverage for Egypt. In addition, Table 1 provides a summary of the availability of data for the SDG indicators (Egypt’s statistical capacity will be further discussed later in this note). To overcome existing data limitations, the UN SDG data have been complemented with alternative indicators available mainly through the World Bank (organized by theme, i.e. health, education, etc.).

**Figure 2:** Egypt’s SDGs Data Availability by Tier and Compared to the World.<sup>8</sup>

<!-- Start of picture text --> Coverage<br><!-- End of picture text --> Source: World Bank Staff predictions with UN-SDG Data. March 22, 2017.

> 8According to the UN (https://unstats.un.org/sdgs), each SDG indicator is classified into three tiers based on their level of methodological development and data availability; the number of indicators for each SDG varies. The classification of tiers is as follows: (a) **Tier I:** indicator is conceptually clear, established methodology and standards available and data regularly produced; (b) **Tier II:** indicator conceptually clear, established methodology and standards available by data are not regularly produced; and (c) **Tier III:** indicator for which there is no established methodology and standards and methodology/standards are being developed/tested. Figure I shows all data categorized by tier available to the world, all Tier I and Tier II data, and then compares it to the available Tier I and Tier II data available in Egypt as well as recent Tier I + Tier II data, measured at =<5 years.

6
# Figure 8 Malaysia MCY, M1Y, QMY and DCPY over time

<!-- Start of picture text --> 0.9<br>0.8-<br>07<br>0.6<br>1:<br>~0.5 B~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~........ '.. ...<br>0.4<br>0.3-<br>0.2-<br>0.1* I<br>1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990<br>year<br>| MCY -M1Y . QMY --- DCPY<br><!-- End of picture text -->
Like reverse mortgages these transactions essentially involve a "bet" on when death will occur. The experience with these instruments is of some interest in determining whether the market, by itself, will be able to bring a sufficient number of buyers and sellers together so that transactions occur.

In this instance, until there were a large number of people with reasonably certain and shorter than had been expected life spans, as well as high needs for current income, there was no market for such trades. Until the establishment of this secondary market for their policies many U.S. life insurance beneficiaries held assets which yielded a return which could be realized only after it would have been useful for addressing current, often catastrophic, income concerns. In effect, this secondary market permitted policy-holders to react to changing circumstances. With it, they could opt out of the perhaps ultimate "deferred gratification" implied by life insurance pay-outs after their death. Like homeowners with reverse mortgages they could use some of their illiquid future wealth to pay for current needs.

The viatical experience suggests that if left to their own devises, markets do eventually develop. However, it also suggests that development may require a catalyst to reach some threshold level, such as the simultaneous appearance of many demanders of a product. For elderly Russian homeowners there is no question that housing privatization there has lead to a rather sudden creation of a potential market demand. What is considerably less clear is whether these potential demanders understand the possibilities implied by their wealth, and whether they trust market-based financial mechanisms to help realize these possibilities. In such a context, and particularly given the informational complexities involved, a public role would appear to be justified.

# **VI.** Discussion **and Conclusion**

The answers to the questions raised at the beginning of the paper all appear to be positive, suggesting that reverse mortgages could contribute to an effective safety net for pensioners in Moscow. They might also be expected, according to a recent Bank of Japan Report, Nakagawa (1999) to help address pension reform issues in aging societies. Moreover, given the potential scale of the demand implied by current housing wealth, the situation of the elderly in Moscow or even Russia is by no means an isolated event.

14
additional risk premium due to the inherent risk. This question can be debated at length. The stance taken here is that a premium on the order of 5% on equity is acceptable, which yields a normal profit rate of 20%.

Table 9. Appropriated Rent on Licensed Oil - Sensitivity Analysis

|Profit<br>rate*||19<br>No<br>transfer<br>pricing|99<br>Transfer<br>pricing|20<br>No<br>transfer<br>pricing|00<br>Transfer<br>pricing|
|---|---|---|---|---|---|
|25%|Normalprofit(US$/ton)|19.9|19.9|19.1|19.1|
||Appropriatedrent(US$/ton)|-0.5|10.7|14.2|33.7|
||Foregonerevenues(US$mn/yr)|-143|3,278|4,537|10,778|
|30%|Normalprofit(US$/ton)|23.9|23.9|22.9|22.9|
||Appropriated rent(US$/ton)|-4.4|6.8|10.4|29.9|
||Foregone revenues(US$mn/yr)|-1,357|2,064|3,314|9,555|

* Normal return on equity defined as market capitalization.

2.32 Since normal rates of return are controversial, a sensitivity analysis is necessary, which conveys the change in profitability and rent in response to changes in normal profit rates. Table 9 indicates that even with a high normal profit rate (30%, i.e. Russia Eurobond 2007 + 15%), the total appropriated rent still amounted to US$ 7 per ton in 1999 and $US 30 per ton in 2000. The normal profit rate that brings the appropriated rent to zero in 2000 (assuming transfer pricing) is around 70%. Assuming a 20% normal profit rate and transfer pricing, Table 10 reconstructs the total current oil rent per ton of oil produced in Russia in 1999. The total average rent was estimated at US$ 95.4, consisting of the following parts: the bulk is unrealized rent, as defined above, which amounted to US$ 40.5 per ton, or 42% of the total; normal profit equaled US$ 15.9 or 17%, which was a little less than either the tax take or captured rent (US$ 24.2 or 25%) and a little more than the appropriated rent (US$ 14.7 or _15%)._ Table 10. Total Current Oil Rent in 1999 (US$/ton)

|Unrealized rent (implicitsubsidy)|40.5|
|---|---|
|Entrepreneurialrent(normal profit)|15.9|
|Captured rent (notionaltaxtake)|24.2|
|Appropriated rent(excessprofit)|14.7|
|Totalrent|95.4|

# **Estimation of Gas Rents**

2.33 The firm OAO Gazprom, a natural and legal monopoly, accounts for 92% of natural gas production in Russia. It is the largest natural gas producer in the world.'<sup>4</sup> Gazprom 14 Plans are discussed to break up the monopoly into Gazprom's core functions (production, transportation, distribution, _etc.)._ 19
Also, these scores are normalized in children’s age. That is, the scores are adjusted on children who have the same age (in months). _InfoAssetsih_ is the variable that includes assets owned by individual _i_ in household _h_ through which they receive media information such as TV, Radio, VCR, and PC. The effects of these information assets are given by coefficient ��. The variable _X_ is a vector of household characteristics including income per capita, age of mother, age of mother, sex, and mother’s education. Given that the localities and the households were assigned randomly to the treatment and control group, the groups are not balanced in some indicators, so performing a simple comparison of means might yield inconsistent and biased estimate of the causal average treatment effect. To correct for this problem, we employ the panel data to estimate the causal Average Treatment Effect. The Education Production Function is denoted as,

# ������h ��� ��������������h�����������h�∗����h�������h��∈�h

where ������h is the test score for individual _i_ in household _h_ . The variable ����������h is a dummy variable (1 = group received intensified treatment, 0 = otherwise) that captures the difference between the control and treatment group. The variable, _PostTreatment (PostT)_ , is the dummy variable (1= if area was under the intensified campaign at the moment of follow up, 0=otherwise) that captures the effect of the treatment after 2 years of the program. The average treatment effect of the program on these developmental skills is β3, which is the coefficient of interest. _InfoAssetih_ is the variable that includes information systems such as, TV, Radio, PC, and VCR owned by individual _i_ in household _h_ through which they receive media information. X is a vector of household characteristics described above.

The term _εih_ represents the error term for individual _i_ in household _h_ . The regression method answers the following questions: Does hand-washing intervention (i) increase communication skills? (ii) increase motor skills? (iii) increase socio-emotional skills? (iv) does the initial imbalance of the information assets affect the treatment and therefore impact the control and treatment group equally? And if these initial imbalances are taken into consideration v) what is the program’s net effect?

> children at early ages. Social skills refer to when the child produce behaviors that elicit their environment and information transmitted by close family members or other means of information. For further review of these skills see (WHO, 2006).

17
The negative interaction effect is still evident, though less precisely estimated (which is not too surprising given the tendency for ICP non-participants to be poorer). A significant difference between the benchmark and non-benchmark models is still evident.<sup>26</sup> The mystery remains. The non-benchmark countries are quite heterogeneous.<sup>27</sup> It appears that all countries were invited to participate in the 1993 ICP, but the (unknown) process determining why some countries chose not too was based on variables that are correlated with the strength of DPE. When interpreted in terms of the Balassa-Samuelson model, it appears that the countries that chose not to participate had latent characteristics that made their productivity differences for tradable goods less responsive to the differences in their GDP per capita.

One can speculate on one possible explanation. Some countries undoubtedly chose not to participate in the 1993 ICP for idiosyncratic, possibly political, reasons. But it can be expected that many non-participants lacked the public-institutional capacity for implementing the ICP’s surveys. Furthermore, it can be conjectured that weak statistical capacity is probably correlated with weak institutions more generally, including weak states. Suppose now that institutional capacity is cooperant with labor in the production of traded goods—such that the marginal product of that labor is lower when institutions are weaker. Then we can see that nonparticipation in the ICP could signify a weaker relationship between GDP and the relative productivity of labor in the traded-goods sector, and (hence) a weaker Penn effect in the data.

The ICP participation effect helps explain why the increases in the price level index tended to be higher in countries with lower GDP per capita in 1993, as seen in Figure 3. Simply adding a control for benchmark countries brings the slope of the regression line in Figure 3 down from -0.059 (s.e.=0.014) to -0.037 (s.e.=0.014). Adding the 1993 GDP to equation (6) gives:<sup>28</sup>

> 26 The difference also persisted when I allowed for non-linearity in the underlying cross-sectional double log model for the Penn effect, by adding ln( _Y_ 05 _i_ ) 2  ln( _Y_ 93 _i_ ) 2 to the differenced model in (1).

> 27 The specific countries are Albania, Angola, Bhutan, Bosnia and Herzegovina, Brunei, Burkina Faso, Burundi, Cambodia, Cape Verde, Central African Republic, Chad, China, Colombia, Comoros, Democratic Republic of the Congo, Cote d'Ivoire, Cyprus, Djibouti, Equatorial Guinea, Ethiopia, Gambia, Ghana, Guinea-Bissau, India, Israel, Lesotho, Macao China, Macedonia, Malta, Mauritania, Mozambique, Niger, Paraguay, Rwanda, South Africa, Sudan, Togo, and Uganda.

> 28 The weighted mean coefficient on GDP in 1993 is -0.039 (s.e.=0.002), as compared to a regression coefficient without controls of -0.059 (s.e.=0.014).

14
<!-- Start of picture text --> < 29°C @ |<br>[29°C - 31°C) e !<br>[31°C - 33°C) e<br>s|<br>&[33°C - 35°C) e |<br>5I<br>=<br>-~ >= 35°C e<br>5|<br>S <0.5l/m2 e |<br>=<br>)<br>S(0.5, 1) m2 | ©<br>©I<br>©S|<br>®o [1,5)/m2 ; @<br>oc<br>[10, 15) I/m2 | @<br>[15, 20) m2 ° |<br>>= 20 Iim2 e |<br>-.3 -.2 -.1 0 | 2<br>Impact on diarrhea incidence<br><!-- End of picture text -->
variables for Sri Lanka’s districts.

To prevent model over-fitting in using the entire set of satellite imagery in _Xv_ , we employ the least absolute shrinkage and selection operator (LASSO) regularization which estimates a regression model with an added constraint that enforces parsimony. We follow a two-step procedure. First, a full set of variables is included to conduct LASSO regularization to choose variables. Second, the final Poisson model is estimated using the chosen variables. Equation 2 presents the objective function for LASSO regularization in a model using intercept _β_ 0 and the vector of predictor coefficients _τ_ (of length _p_ ) in step 1.

where _l_ is the value of the log likelihood function of the Poisson model using parameters _β_ 0 and _τ_ , and _λ_ is a non-negative regularization parameter. While setting _λ_ = 0 yields unconstrained Poisson regression estimates, a large _λ_ penalizes the absolute values of the coefficients, _β_ .<sup>16</sup> Fivefold cross validation is applied to choose the value of _λ_ that minimizes the root-mean squared error (RMSE) across the folds. The in-sample _R_<sup>2</sup> from this step indicates the goodness of the model fit. Since LASSO also generally shrinks the magnitude of all coefficients towards zero (Varian, 2014), we avoid biased predictors by running a simple Poisson model based on the Lasso selected variables. From this, we obtain the out-of-sample _R_<sup>2</sup> , mean absolute error (MAE), and RMSE, all of which indicate out-of-sample predictive accuracy.

First, we implement the above model using the village level census population density as the dependent variable. We conduct this exercise for the entire country separately using different imagery resolution types: (1) low-resolution publicly available indicators; (2) higher resolution publicly available indicators plus the indicators mentioned in (1). Doing this enables us to compare prediction accuracy between higher versus lower resolution data in the national sample. In the 55 sub-districts where the features from Engstrom et al. (2017) are available, we implement a model using: (3) spectral and texture-features plus indicators used in (2); and (4) object-features plus the indicators used in (3). For the 55 sub-districts, we compare prediction accuracy successively from including the set of imagery-based indicators in (1)

> 16 For estimations, we use the glmnet package in _R_ that estimates the model parameters by minimizing the penalized log likelihood function. Glmnet provides an option to penalize the minimand based on the sum of the absolute values of the parameters (LASSO), or based on the sum of squares of the parameters (ridge), or a combination of both (Hastie and Qian, 2018). Glmnet’s minimization function is presented in Equation 3. We set _α_ = 1 to use the LASSO method.

8
What share of green goods do these affect? Conformity assessment related to technical barriers to trade not elsewhere specified (B89) affects only a small share of green goods (2.4 percent value and 6.2 percent products), with no category being particularly affected. The highest share is water supply with 3 percent. Therefore, reforms in this measure is unlikely to have any significant impact. This is the case for the rest of the measures, suggesting they may be costly but not widely applied. However, Technical barriers to trade regulations on transport and storage (B42) affect just over 40 percent of both value and product share and is likely to have more impact if reviewed for possible reform.

In addition, considering the tariff equivalent on the types of green goods, Figure 11 (B) reveals that the relative cost of green goods types is also significantly higher in the Philippines compared to the other ASEAN countries. This is the case for the case of B42 and B85 (of which B853 is a subset), notably on products such as Cleaner or More Resource Efficient Technologies and Products and Environmentally Preferable Products based on End-Use or Disposal Characteristics. However, these estimates are done on only 12 NTMs with the highest coverage ratios for the PH (Figure 7 shows the top 12, this estimation drops the bottom 12 from this list) and then compared to ASEAN countries, mainly due to computational practicality. Nevertheless, this reveals that B42 is indeed an important measure whose underlying regulations may need to be reviewed when considering climate-friendly trade reform.

18
that self-employment in agriculture is still the dominant activity in rural areas. Yet, about 36 percent or 38 million people living in rural areas across these five countries are involved in offfarm employment.

# _[Figure 1 here]_

Even over the (short) time horizon that the longitudinal LSMS-ISA data currently inform, we find evidence for the ongoing structural transformation in Africa and the resulting job creation in offfarm employment activities (Reardon et al., 2007). We observe significant increases in off-farm employment rates in some countries; specifically, in Ethiopia (from 26 percent in 2012 to 34 percent in 2016),<sup>11</sup> Malawi (from 51 percent in 2010 to 58 percent in 2013), and Tanzania (from 49 percent in 2011 to 52 percent in 2013). In other countries the employment rates have decreased over time; from 50 percent in 2013 to 47 percent in 2016 in Nigeria, and from 55 percent in 2010 to 40 percent in 2014 in Uganda.<sup>12</sup> The decreasing trend in Nigeria has been documented before and is due to a combination of renewed investments in the agricultural sector and a declining oilsector-driven economy (Kwame Yeboah and Jayne, 2018). The national trends are similar in both rural and urban areas, although it remains country-specific whether the pace of change in employment rates is higher in rural areas (Tanzania and Uganda) or in urban areas (Ethiopia, Malawi and Nigeria).

# _[Figure 2 here]_

We find that women are less likely to participate in off-farm employment, and this difference is consistent over time, and across and within countries. The gender difference in off-farm employment in the latest survey round stands at 4 percentage points (pp) in Nigeria, 12 pp in Ethiopia, 14 pp in Tanzania, 18 pp in Malawi, and 19 pp in Uganda, which corresponds to a crosscountry weighted gender gap of 9 pp. If women have the same probability to participate in offfarm employment as men, about 7.8 million women would be employed in addition to the 33 million that are currently employed. The number of ‘missing women’ in off-farm employment is

> 11 In 2012, the ESS was representative only for the rural and small towns, while in 2014 and 2016, an urban stratum was incorporated into the survey design, ensuring that the survey is representative at the national-level. The surge in off-farm employment rates from 2012 to 2016 may, therefore, be in part due to expanded spatial coverage of the ESS sample, as the off-farm employment rate in 2014 was 35 percent, which stagnated between 2014 and 2016. 12 The observed decline in Uganda is driven specifically by the decline in off-farm self-employment. There are otherwise no changes in the UNPS survey design, including the household questionnaire instrument.

9