rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure 15: State Level Share of Children of School-going-age Not in School – Southern Regions<br>a) b) c)<br>Not in School at School going Age Deprivation - Not in School at School going Age Deprivation - Not in School at School going Age Deprivation -<br>by State in South East - MICS by State in South South MICS by State in South West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Delta Edo Rivers Ekiti Lagos Ogun Ondo Osun Oyo<br>d) e) f)<br>Urban Rural - Boy Girl - Not in School at School Urban Rural - Boy Girl - Not in School at School Urban Rural - Boy Girl - Not in School at School<br>going Age Deprivation - by State in South East - going Age Deprivation - by State in South South going Age Deprivation - by State in South West -<br>MICS - MICS MICS<br>80% 80%<br>80%<br>70% 70%<br>70%<br>60% 60%<br>60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Abia Anambra Ebonyi Enugu Imo Akwa Ibom Bayelsa Cross River Delta Edo Rivers Ekiti Lagos Ogun Ondo Osun Oyo<br>Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17<br><!-- End of picture text -->

# Source: Authors’  estimates based on MICS 2016/17 data

Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age. For Ebonyi the shares for urban boys and girls are excluded because of too limited sample size. The threshold for exclusion is set at less than 30 observations per demographic group.

29
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|---|---|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Agricultural Competitiveness and Diversification Project|Yeyande Kasse Sangho|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|Total War Against HIV and AIDS(TOWA)Project|Wachuka W. Ikua|
|PovertyReduction Support Credit|Maria Carmen Del Rocio Castro Arce|
|PovertyReduction Support Credit|Maria Carmen Del Rocio Castro Arce|
|Chile Hornitos Project(Chacabuquito II)|Lucia Spinelli|
|Chile Hornitos Project(Chacabuquito II)|Lucia Spinelli|
|Chile Hornitos Project(Chacabuquito II)|Lucia Spinelli|
|Chile Hornitos Project(Chacabuquito II)|Lucia Spinelli|
|Chile Hornitos Project(Chacabuquito II)|Lucia Spinelli|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
|Hubei Shiman HighwayProject|Fei Deng|
will incrcase the temperature and irreversible damages, shortening the waiting period in comparison to that suggested by equation (8). In that case, we will determine the optimal timing by simulating the Cline's proposal with different dates of intervention, starting from 1990 (as proposed by Cline). The optimal timing will be determined by the furst date when the benefit to cost ratio is greater tnan the critical ratio (Y > Y*). This exercise will be done in the next section.

In short, the model can thus be used (i) to determine if governments should intervene to reduce global warming; (ii) to examine the influencc of uncertainty on the process; (iii) to determine the optimal date of intervention; and (iv) to identify the optimal level of CO2 cutback to be implemented today.

# 3 An Application

The objective of this section is to apply the option-pricing model developed in the preceding section. In order to proceed with application we need two types of data:

* The estimated costs and benefits associated to the abatement in global warming, as well as the relevant discount rate. This data will be extracted from Cline's analysis.

* Variables relevan **t** o the option model, such as quantitative measures for the underlying uncertainty (on costs and benefits), specifically the sandard deviation of uncertain variables and the correlation between these variables.

7
- 29 -

# **Table** **<u>5. Nested logit model with predicted crop prices</u>**

||Model (1)||Model(2)|||
|---|---|---|---|---|---|
||||Export cro|ps|Domestic crops|
|Choice-specific variables:||||||
|Transport cost|-0.013|***|-0.027|***|-0.040<br>**|
||(0.003)||(0.004)||(0.020)|
|Crop suitability|-0.106|**|0.175||-0.010|
||(0.049)||(0.264)||(0.008)|
|Crop price hat|-1.230|**|0.163||-0.121|
||(0.548)||(0.109)||(0.102)|
|Access to fertilizer|0.229|*|0.407||0.020|
||(0.134)||(0.278)||(0.020)|
|Access to irrigation|0.125|**|||0.013|
||(0.050)||||(0.010)|
|Access to improved seed|0.989|**|3.844|***|0.078|
||(0.504)||(0.469)||(0.066)|
|Type-specific variables:<sup>1</sup>||||||
|Area of plot|0.090|**|0.070|**||
||(0.045)||(0.028)|||
|Steep slope|-0.080||0.010|||
||(0.186)||(0.202)|||
|Land title|-0.151||0.020|||
||(0.306)||(0.327)|||
|Electricity access|-0.906||-0.728|||
||(0.615)||(0.658)|||
|Mobile access|-0.014||0.044|||
||(0.180)||(0.188)|||
|Distance from road|-0.008||0.024|||
||(0.030)||(0.030)|||
|Male household head|-0.105||-0.041|||
||(0.213)||(0.230)|||
|HH head age|-0.018|***|-0.021|***||
||(0.006)||(0.007)|||
|HH head's year of schooling|-0.094|***|-0.109|***||
||(0.034)||(0.038)|||
|Household size|0.064|***|0.017|||
||(0.023)||(0.026)|||
|HH wage income|-0.045||-0.123|||
||(0.039)||(0.085)|||
|Obs.|17325||17325|||
|No. of cases|2475||2475|||
|Wald chi2|544.03||385.59|||
|Dissimilarity parameters:||||||
|Export crops|1.875||1.071|||
||(0.633)||(0.245)|||
|Domestic crops|0.507||0.043|||
||(0.247)||(0.036)|||

1/ The export crop specific coefficients are shown, while domestic crops are used as a baseline.
48

# **Figures**

## Figure I: Dynamic Ban Effects on Imports

<!-- Start of picture text --> −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12 −4 −3 −2 −1 0 1 2 3 4 5 6 7 8 9 10 11 12<br>Month relative to period when treatment first changes (t=0) Month relative to period when treatment first changes (t=0)<br>(a) Extensive Margin (b) Intensive Margin<br>.5 3<br>.4 2<br>.3 1<br>0<br>.2<br>−1<br>.1 −2<br>0 −3<br>−.1 −4<br>−.2 −5<br>−.3 −6<br>Effect on Pr(imports>0) −.4 Effect on log(imports) −7<br>−8<br>−.5 −9<br>−.6 −10<br>−.7 −11<br><!-- End of picture text --> _Notes_ : The figures show De Chaisemartin and d’Haultfoeuille (2022) estimates of the effects of import bans on the extensive and intensive margins of imports, relative to the last month before the ban takes effect, using an HS8 product-month level panel. The sample covers the January 2019September 2022 period (but only coefficients in January 2021-May 2022 are reported) and excludes non-fertilizer HS8 products whose imports were ever banned. The treatment variable is a set of dummies _ban_<sup>_τ_</sup> _ct_<sup>indicating whether fertilizer product</sup><sup>_c_was first banned</sup><sup>_τ_months before month</sup><sup>_t_, and</sup> the not-yet-treated products serve as the control group, which includes both non-banned fertilizers and other products. The y-axis shows the effects of fertilizer import bans on the probability of nonzero imports (panel Ia) or on the log value of imports (panel Ib), where negative values correspond to a decrease in probabilities/values. The p-values of the joint significance tests for the placebo estimators are 0.70 (panel Ia) and 0.0001 (panel Ib).
In India, the evidence in section (5.1) shows that the long-term variance is substantially higher in the nonfarm households because of a large family background multiplier. The estimates of the long-term (steady state) means also show a higher mean for the nonfarm households. When we plug in the estimates from sections (5.1) and (5.2) for rural India in equation (18) above, we get (using _ω_ = 0 _._ 40 from the summary statistics table in online appendix): Thus, the evidence suggests that structural change in favor of the nonfarm sector contributed to higher cross-sectional variance in rural India during our study period.

# **(6) Economic Mechanisms: Towards an Explanation of the Differences between Rural China and Rural India**

A major concern in the literature has been whether the observed pattern of intergenerational linkages is primarily driven by omitted variables bias due to unobserved genetic correlations between parents and children. An obvious approach to this question is to try to correct the estimates for possible positive bias due to genetic correlations in cognitive ability. We develop a simple but plausible approach by taking advantage of the recent evidence on intergenerational correlation in cognitive ability from economics and behavioral genetics. There is substantial evidence that intergenerational correlation in cognitive ability (denoted as _ρ_ ) falls in a narrow interval, _ρ ∈_ [0 _._ 20 _,_ 0 _._ 40]; see, for example, Black et al. (2009), Bjorklund et al. (2010) on economic literature, and Plomin and Spinath (2004) on behavioral genetics literature. We use this information in a biprobit sensitivity analysis as developed by Altonji et al. (2005) to check if the estimates of intergenerational persistence in schooling remain positive and statistically significant for plausible values of intergenerational correlation in ability. We call this approach augmented AET (AAET) sensitivity analysis, and it requires binary indicators of educational attainment instead of years of schooling. We use a dummy for higher than primary schooling for fathers. For sons in rural China a dummy for higher than 9 years of schooling (higher middle school), and for sons in rural India, a dummy for more than 10 years of schooling (called SSC or matriculation) are used. The details of this approach are provided in online appendix B, and the estimates are reported in Table 3.

The evidence from the AAET sensitivity analysis suggests that, the estimated intergenerational schooling persistence in India is very strong, and the estimates remain statistically 21
Table 5. Sectoral productivity growth, percent per year.

||Agriculture|Manufacturing|Mining|
|---|---|---|---|
||%|%|%|
|OECD|2.6|1.9|-0.2|
|Low income countries|1.99|0.69|na|
|Middle income countries|2.9|0.97|na|
|All developing countries|2.6|0.9|na|
|Industrial countries|3.5|2.8|na|
|Overall average|2.9|1.6|na|

Sources: OECD results from Bernard and Jones (1996). All other results from Martin and Mitra (2001). TFP estimated <u>using factor shares.</u> The apparently robust finding of relatively rapid technical change in agriculture suggests that the decline in developing countries’ dependence on agricultural exports cannot simply be explained by higher rates of productivity growth in manufactures. This difference, alone, would seem to increase the importance of the other possible explanations of increased exports of manufactures from developing countries— Rybczynski effects and reductions in protection.

There is a possibility of a strong positive interaction between increased export orientation and productivity growth in manufacturing exports. This does not appear to result from the traditional anecdotal model in which exporters “learn by doing” or from their interactions with their foreign customers. Rather, recent studies suggest that the firms that choose to export generally have higher productivity and produce higher quality products when they begin exporting (Tybout 2001; Hallward-Dreimeyer, Iarossi, and Sokoloff 2001). In this situation, it becomes particularly important to have a policy environment that encourages entry of firms—whether new or old—into exporting activities. Further, the productivity gains from entry can be compounded by the expansion of these firms, at the expense of less efficient firms, following entry. Finally, as noted in the discussion of technical change, the gains from technical change may be much greater when firms have the opportunity to expand than in cases where their market size is restricted.

27
understandable that the ratio would be higher for a teaching training program implemented once with some refresher sessions than for the provision of an additional year of education.

Panel B uses the more conservative discount rate of 5 percent to translate lifetime earnings into a present value (Haacker et al., 2020). We still find benefit-to-cost ratios all above 1. The minimum value for this ratio is 14.2 for the Gallego et al. (2021) study, which assessed the impact of a change in the math curriculum. The maximum value, which is for the Wolf et al. (2019a) study, is still quite large at 49.7.

# **5. Discussion and conclusion**

This study investigates whether current investments in preprimary education are too low through a systematic review of quasi-experimental evidence from around the world. Using 798 outcomes extracted and standardized from 54 studies, we use robust variance meta-regression to assess the returns to investment in preprimary education by establishing whether on average there is demand for preprimary programs and the extent to which preprimary education improves children’s skills during the preprimary period. To more concretely gauge returns, we also combine estimates from the literature on the relationship between improvements in cognitive skills and future earnings with study-specific estimates of improvements in these skills in a subset of studies from low- and middle-income countries that report per-child costs.

We find that interventions that expand access to preprimary education led to significant increases in the take-up of preprimary education services and school participation during the preprimary period. On average, preprimary education programs significantly improve children’s cognitive and social-emotional skills and executive functions in the short run, suggesting that these kinds of services promote learning and skill development better than children’s alternative care options during the preprimary period. A translation of these effects into gains in children’s earnings suggest sizable benefit-to-cost ratios, ranging from 1.7 to 103.5.

Although fewer studies track children beyond the preprimary years, we find evidence of smaller but statistically significant persistent effects of preprimary interventions in both cognitive skills and non-cognitive skills. Not only do these estimates highlight the potential for preprimary education programs to generate lasting learning gains among young children, but they also estimate wage returns only for these gains in social emotional skills with a discount rate of 3 percent, we still find sizable benefit-to-cost ratios of 8.1 (lower bound) to 24.5 (upper bound).

31
so. Another reason is that some country pairs have zero recorded traffic due to data suppression. Again, qualitatively, the Poisson specification preserves the data patterns identified in the prior specifications.

The final row of Table 2 reports estimates of a simple counterfactual exercise in which we calculate the quantitative impacts of policy changes on passenger traffic that are implied by the model. Specifically we consider changes in the ACAC commitments that would raise the ALI score from its current level (39) to that of the most liberal of the bilateral agreements (50). These estimates imply outcomes that would have occurred if more liberal policy had been in place in 2010. Our estimates suggest that traffic between pairs of ACAC countries would have been 27 to 35 percent higher, depending on the specification. These are our best estimates of the likely impact of a significant liberalization of commitments within the existing ACAC membership.

To summarize, the results from the aggregate gravity model reported in Table 2 indicate that aviation liberalization has a direct and positive effect on air traffic. The estimated response of passengers to the ALI policy index is stable across several specifications. There does not seem to be sufficient evidence to conclude that, conditional on their policy provisions, the plurilateral and bilateral agreements differ substantially in their effect on passenger flows. The estimated effects of policy on passenger travel imply that implementation of policies consistent with the most liberal bilateral agreements would imply an increase in passenger travel of roughly 30 percent.

# _V.2 City Level Estimates_

The available cross-country evidence on the effects of air services agreements on air passenger traffic relies on measures of traffic between countries, as in Table 2. Studies of the US open skies agreements exploit data documenting flights between US and international cities. Cristea et al (2012), for example, show that the US agreements led to a shift of traffic away from primary hubs, so that passengers now enjoy many more direct international flights. In this section we estimate the impacts of policy on city level flows, in two ways. Initially, we estimate a model of traffic along existing city-to-city routes. Next we turn to a model that predicts the existence of an international flight between two cities.

19
-4-

The structure of the paper is **as** follows. In Section II we discuss some basLc methodological **issues as** regards the eWpirical measure_ent of wages and non-wage costs. Section III presents **a** brief description of the prevLous work in the area, which indicates the lack of comparative studies on labor costs in LDCs. Section IV discusses the evidence vith regard to the ratios of non-wage costs to wags aand non-wage costs to per capita income, wbile in Section V similar analysis is done in connection vith total labor cost levels. The enforc ment of non-wage cost regulations in the countries is analyzed in Section VI, with their like-y distortionary role in terms of time trends and across countries examined in Section VII. Section VIII **examines** international dLfferences in labor cost levels and in labor costs ezpressed in units of percapita output. _Finally,_ **some** conclusions are presented in Soction IX.

II. Basic Methodological Isues Neasurntg **Wage** and **Non-Wage Costs Use** of a simllar concept of labor costs across the countries covered in this study is crucial to **a** proper Interpretation of the results. In fact, in comparing labor costs across countries, consideration of the definitional content of the statistical information on wages and non-wage coats and its coverage in term of industries is Important.

The empirical data used In this study for all the countries correspond to the mantifacturing sector. The basic Information **vas** obtained from national sources and originates in manufacturing surveys containing wages and data on other paymants to labor. Given that segmentatLon is a distinctLve feature of labor markets in LDCs, it should be noted tbat **these data** correspond to **the** urban formal sector of the labor market; i.e., the one covered by different types of regulations and labor protection laws. Thus, this information does not necessarily describe the level -- nor probably the
## liV INTRODUCTION

# Table 11 Long-Run and Stationary Population, Momentum, Doubling Y,er, and Year Whtn Net Reproduction Rate Reaches 1 by Geographic Region and by Income Group

|||Populat|ion<br>(mill|ions)|Station<br>ary over|-<br>Popu-|Year<br> <br>when||
|---|---|---|---|---|---|---|---|---|
|Region<br>or<br>income<br>group|1985|<br>2050|<br>2100|Statio<br> <br>ary|1985<br>n- popula<br>tion|lation<br>- mo-<br>mentun|popula-<br>tion<br>doubles|Year<br>when<br>NRR=1|
|World|4,844|10.035|11.33|0 11.514|<br>2.4|<br>1.4|<br>2044|<br>2060|
|Lessdeveloped<br>(a)|3.666|8,716|10020|10,200|<br>2.8|<br>1.5|<br>2028|<br>2060|
|More developed<br>(a)|1,179|1.319|1,31|0 1,314|<br>1.1|<br>1.1|<br>(c)|<br>2030|
|Africa|559|<br>2,275|2,962|3.049|<br>5.5|<br>1.6|<br>2009|<br>2060|
|East Africa|231|<br>1.034|1,38|3 1,424|<br>6.2|<br>1.6|<br>2008|<br>2055|
|West Africa|204|<br>873|<br>1,152|1,190|<br>5.8|<br>1.5|<br>2008|<br>2060|
|North<br>Aff<br>ica|124|<br>368|<br>427|<br>435|<br>3.5|<br>1.6|<br>2016|<br>2050|
|America|667|<br>1,146|1,19|2 1,201|<br>1.8|<br>1.4|<br>cc)|<br>2030|
|Latin<br>America<br>and Caribbea|n 399|<br>814|<br>864|<br>872|<br>2.2|<br>1.6|<br>2043|<br>2030|
|Northern<br>America|268|<br>332|<br>328|<br>329|<br>1.2|<br>1.2|<br>tc)|<br>2030)|
|Asia|2,823|5,728|6.288|6,374|<br>2.3|<br>1.5|<br>2047|<br>2045|
|East and Southeast<br>Asia|1,650|2,719|2.822|2,842|<br>1.7|<br>1.4|<br>(c)|<br>2030|
|South<br>Asia|1.058|2.614|2,977|3,034|<br>2.9|<br>1.5|<br>2025|<br>2040|
|Southwest<br>Asia|115|<br>396|<br>490|<br>498|<br>4.3|<br>1.6|<br>2013|<br>2055|
|Europe<br>and U.S.S.R.|771|<br>843|<br>842|<br>846|<br>1.1|<br>1.1|<br>Cc)|<br>2030|
|Oceania|25|<br>42|<br>44|<br>45|<br>1.8|<br>1.3|<br>tc)|<br>2030|
|Income<br>group (b)|||||||||
|Low|2.711|6,343|7.315|7,458|<br>2.8|<br>1.5|<br>2030|<br>2060|
|Lower-middle|580|<br>1.416|1.60|9 1,632|<br>2.8|<br>1.6|<br>2026|<br>2055|
|Upper-middle|422|<br>891|<br>989|<br>999|<br>2.4|<br>1.5|<br>2040|<br>2050|
|High|767|<br>869|<br>869|<br>871|<br>1.1|<br>1.2|<br>(c)|<br>2030|
|Nonreporting<br>norinember|365|<br>515|<br>548|<br>554|<br>1.5|<br>1.2|<br>(c)|<br>2030|

a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealandr-and Japan. "Less developed" comprises the rest of the world. b. Criteria for income groups are given in World Bank 1989. See listing in Exhibit 1.

c. Population will not double with projected rates.