rafmacalaba/datause-encoder-data huggingface dataset:
**Table F3: Descriptive Statistics of Key Variables (Nepal)**

|**Variable**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|
|_Z-Scores_|||||
|HAZ|-1.78|1.5|-6|5.28|
|WAZ|-1.474|1.16|-5.86|3.01|
|_Food Security Measures_|||||
|PC kcal|2487.85|802.55|628.75|6457.53|
|1-SSR|0.37|0.13|0.07|1|
|1-SSEXR|0.63|0.15|0.16|1|
|FCS|70.76|21.47|0|112|
|DDS|8.8|1.81|0|12|
|PC consumption|71.18|54.41|12.47|1403.11|
|Food Share|0.66|0.14|0.06|0.92|
|Child Age ( in months)|30.5|17.08|0.1|60.25|
|Age squared|1222.01|1063.22|0.01|3630.063|
|Child is female|0.49|0.5|0|1|
|Household size|7.95|3.46|2|20|
|Share of kids under 5|0.28|0.11|0.0625|0.75|
|_HH Head Characteristics_|||||
|Female-headed HH|0.17|0.38|0|1|
|_HH Head’s Education_|||||
|No education*|0.48|0.5|0|1|
|Literate, or < 5 yrs of schooling|0.2|0.4|0|1|
|Completed [5-7] yrs of schooling|0.13|0.34|0|1|
|Completed [8-10] yrs of schooling|0.1|0.29|0|1|
|Completed 11 + yrs of schooling|0.1|0.3|0|1|
|Head occupation (SE in agriculture)|0.55|0.5|0|1|
|_Ethnicity/Caste_|||||
|Brahmin/Chhetri|0.27|0.44|0|1|
|Newar*|0.03|0.17|0|1|
|Terai Middle Class|0.21|0.41|0|1|
|Janajati (Indigenous groups)|0.26|0.44|0|1|
|Dalit (untouchable caste)|0.17|0.37|0|1|
|Muslim/Others|0.06|0.24|0|1|
|_Mother’s Education_|||||
|No education*|0.53|0.5|0|1|
|Literate, or < 5 yrs of schooling|0.15|0.36|0|1|
|Completed [5-7] yrs of schooling|0.14|0.35|0|1|
|Completed [8-10] yrs of schooling|0.09|0.28|0|1|

55
NFA treatments into one pooled treatment indicator.

When analyzing data at the clinic-level, we drop the indexes for households and villages, estimating: These models include a single observations for each clinic, so there is no need to cluster our standard errors.

In addition to conventional standard errors, we report q-values that control for the proportion of incorrectly rejected null hypotheses (Anderson 2008). Specifically, we control for the false discovery rate within treatment arm (1) across outcome families; and (2) across sub-components within each family.<sup>21</sup> As noted above, we follow Kling et al. (2007) to construct outcome families for the medium-run results. If an individual only has data for some sub-components within a family, this procedure involves imputing those missing sub-components using the mean from that individual’s treatment arm. This leads to high rates of imputation when a family includes sub-components that are not well-defined for a large share of observations (e.g., complications with childbirth can only be measured for mothers). To confirm that such imputation does not influence our results, we follow Kling et al. (2007) and Casey et al. (2012) and employ seemingly unrelated regressions (SUR). In short, we estimate treatment effects on each sub-component of a family without any imputation and then average the coefficients across these stacked regressions, using the delta method to compute standard errors. The tables in Appendix Section D.2 include results for our pre-specified families using SUR, as well as the results from the stacked sub-component regressions.

# **3.5.2 Ebola Case Data**

We assess the impact of the CM and NFA interventions on reported cases for the 160 sections in the Ebola sample (described in Section 3.1.2). We observe counts of reported cases in each section in every week from 10 August 2014 to 18 October 2015. However, we restrict attention to the period from September through April 2015, when Ebola transmission was a real threat in our study area; less than 1 percent of confirmed cases (3 in total) occur in the seven months between May and October 2015.<sup>22</sup>

> 21 In the analysis plan, we specified controlling for the false discovery rate (FDR) only across some families denoted primary families and within those families, only across some outcomes. However, since we examine all outcomes, and several key to our overall account (e.g., satisfaction) were designated such that they would not have been accounted for in the FDR adjustment, we deviate from the analysis plan in the conservative direction by instead, correcting across all outcome families, and within each family, across all composite outcomes.

> 22 In Table E.9 we extend the panel back to August 2014 and replicate our primary results from Table 3.

19
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**Figure 14: Changing composition of livestock in ECA by country.**

|**Chang**|**e in livestock from 1995 to 2008**<br>Cattle|
|---|---|
|Albania|Chickens|
|Armenia<br>Azerbaijan|Pigs<br>Sheep|
|Belarus||
|Bosnia & Herzegovina||
|Bulgaria||
|Croatia||
|Czech Republic||
|Estonia||
|Georgia||
|Hungary||
|Kazakhstan||
|Kyrgyzstan||
|Latvia||
|Lithuania||
|Macedonia, FYR||
|Moldova||
|Poland||
|Romania||
|Russian Federation||
|Serbia & Montenegro||
|Slovakia||
|Slovenia||
|Tajikistan||
|Turkey||
|Turkmenistan||
|Ukraine||
|Uzbekistan||
|-15%|-5%<br>5%<br>15%<br>25%<br>35%|
||Annualized percentage change|

Source: FAO (2011) 68
Most of the difference, however, is “unexplained.” However, “unexplained” in this case refers to the returns to observable characteristics. That is, for the same level of resources, Jordan’s schools are producing more output (student test scores); or put another way, Jordan’s teachers are able to add more value with a given level of resources. More than two-thirds of the improvement is due to improved effectiveness of the use of resources, or increased value-added of Jordan’s teachers. This is reflected in the fact that the urban advantage disappeared over time. Moreover, while the student: teacher ratio increased slightly over time, the effectiveness of teachers to handle a large class improved; that is, the system became much more efficient, thus being able to educate more children, and to improve their test scores at the same time. Female advantage more than doubled over this short period of time.

Table 2: TIMSS scores decomposition (1999-2003)

||||||Determinants of test sc<br> <br>|ore differentials<br>||
|---|---|---|---|---|---|---|---|
||||||Explained<br>Unexplained|as % of total te|st score diff.|
||_b1999_|_b2003_|_X1999_|_X2003_|_b2003(X2003-X1999)_<br>_X1999(b2003-b1999)_|Explained|Unexplained|
|Constant|450.3|476.8|1.0|1.0|0.0<br>26.5|0.0%|38.7%|
|Public school|-36.2|-32.6|0.8|0.8|0.3<br>2.9|0.5%|4.2%|
|Urban|15.7|-14.3|0.7|0.6|0.6<br>-19.8|0.8%|-28.9%|
|Student-teacher ratio|0.6|1.2|23.5|24.6|1.3<br>14.1|1.9%|20.6%|
|School size<br>|0.0|0.0|749.1|753.1|0.0<br>0.0|0.0%|0.0%|
|Teacher qualification||||||||
|<br>(univ)|14.3|17.7|0.9|0.9|0.0<br>3.1|0.0%|4.5%|
|Training certificate|4.6|6.2|0.5|0.7|1.2<br>0.8|1.8%|1.2%|
|School resources||||||||
|(shortage)|-4.2|-5.6|0.7|0.6|0.6<br>-1.0|0.8%|-1.4%|
|Total hours teaching|1.4|1.9|21.9|21.3|-1.1<br>11.0|-1.7%|16.0%|
|High morale|9.3|19.7|0.5|0.6|2.0<br>5.2|2.9%|7.6%|
|Homework per week|7.1|6.4|3.6|3.9|1.9<br>-2.5|2.8%|-3.7%|
|Computer for instruction|2.6|2.8|0.2|0.2|0.0<br>0.0|0.0%|0.1%|
|High teacher confidence<br>Emphasis problem|9.4|22.7|0.3|0.5|4.5<br>4.0|6.6%|5.8%|
|<br>solving|7.7|11.5|0.2|0.5|3.3<br>0.8|4.9%|1.2%|
|Female|7.6|18.6|0.5|0.5|0.0<br>5.5|0.0%|8.1%|
|Self-confidence|11.1|18.3|0.2|0.4|3.7<br>1.4|5.3%|2.1%|
|Mother - lower secondary|5.6|7.5|0.1|0.2|0.7<br>0.2|1.1%|0.3%|
|Mother - upper secondary|13.7|12.9|0.3|0.4|1.3<br>-0.2|1.9%|-0.4%|
|Mother – university|19.6|21.2|0.1|0.2|2.1<br>0.2|3.1%|0.2%|
|11–100 books|6.3|1.8|0.7|0.7|0.0<br>-3.2|0.0%|-4.6%|
|101-500 books|15.2|1.3|0.2|0.1|-0.1<br>-2.8|-0.2%|-4.1%|
|Total|||||22.3<br>46.1|32.6%|67.4%|
|Overall|||||68.4|100.0|%|

But perhaps the greatest proof that the Jordanian reforms paid off is reflected in the large size of the returns to total hours teaching. There is no real difference in amount of hours devoted to 11
**Table 6. Impacts of the EU Green Deal and CBAM on selected sectors in EU trading** **<u>partners</u>**

|**Sector**|**Change in**<br>**EU imports,**<br>**%**|**Key source region**<br>**(share in total**<br>**imports)**|**Change in exports**<br>**to EU for key**<br>**source region, %**|**Change in**<br>**total exports**<br>**for key source**<br>**region, %**|**Change in**<br>**output for key**<br>**source region,**<br>**%**|
|---|---|---|---|---|---|
|Coal|-69.1|United States (38.9%)|-70.8|-19.7|-5.4|
|Electricity|-28.2|Europe and Central<br>Asia (58.2%)|-34.3|-15.6|-1.2|
|Non-metallic<br>minerals|-28.0|China (48.7%)|-33.8|-4.2|-0.2|
|Wood and paper<br>products|-28.0|China (33.9%)|-37.5|-7.4|-0.6|
|Chemicals|-23.2|China (18.4%)|-38.4|-5.8|-0.7|
|Metals|-23.0|Europe and Central<br>Asia (26.6%)|-38.7|-9.2|-3.2|
|Natural gas|-22.8|Europe and Central<br>Asia (45.9%)|-20.9|-9.1|-1.5|
|Petroleum<br>products|-11.5|Europe and Central<br>Asia (47.1%)|-12.6|-5.4|-1.2|
|Crude oil|-9.3|Europe and Central<br>Asia(52.5%)|-5.6|-1.6|-0.4|

Notes: Share in total imports for key source region is estimated for total imports excluding intra-EU trade. For the case of chemical products (CHM), the second most important EU trading partner is reported (i.e. CHN), as for the first most important partner (United States), observed changes in flows were non-significant. Selected sectors with the largest reduction in EU imports are reported. Source: ENVISAGE simulations.

EU trade with the rest of Europe and Central Asia are likely to be hardest hit by the CBAM, with carbonintensive sectors most vulnerable. The most impacted sectors in terms of output changes are reported in Figure 24. The impact of the CBAM on changes in the output of the exporting country depends on two key drivers – carbon intensity of exports and the share of total output that is exported to the EU. ECA commodities that have both high carbon intensity and a large share of production that is exported to the EU (metals and chemicals) suffer the largest reductions in output. At the same time, output of highly carbon-intensive commodities, like electricity, is not impacted significantly due to the low share of exports to the EU in total output.

The potential negative impacts on output of selected sectors in developing countries can be mitigated by own policy responses. By pursuing more ambitious climate mitigation policies themselves, developing countries could transform potential income losses into income gains by supporting the use of more efficient and cleaner technologies, and facilitating the green transition. In addition, developing countries would benefit from the environmental and health co-benefits of more stringent climate action. Even in the case of energy exporters, a group of countries that is substantially hit by the EU Green Deal implementation due to the reduction in the global prices of fossil fuels, transition from a traditional diversification to proactive asset diversification (with increasing R&D investments) within ambitious climate mitigation scenarios, could be an important welfare-improving channel, as shown by Peszko et al. (2020).

37
- vii -

individuals demanding pension services will be able to obtain more diversification if they are allowed to operate internationally; and

- (d) Finally, in many small countries securities markets are not liquid and are often characterized by a lack of independent interests. The paper suggests that it may even be necessary to _mandate_ offshore investments _in addition to_ permnitting greater foreign competition in the provision of investment management services.

7. From the perspective of World Bank policy advice and actions, four key implications emerge from ithis study:

- (a) The need to encourage small open economies with a small base of contributors to permit much greater international competition in pension services, which implies that establishment of a multi-pillar pension system (see James et al., 1994) may not always be advisable.

- (b) The need to encourage much greater harmonization in certain key regulations and coordinated supervision across countries to facilitate greater cross-border provision of pension services.

- (c) The need to evaluate how World Bank financial products such as convertibility guarantees might be used to permit development of more diversified pension products involving investments in multi-currency portfolios, for demanders of pension services. Such guarantees are needed given that authorities would need to commit to unrestricted convertibility of pension incomes into domestic currency without exchange or capital controls.

- (d) The need to carefully analyze the links between the design of pension reforms and the form of prudential regulatory or other legal/regulatory reform being advocated. For example, minimum capital regulations for investment management companies may not create significant fixed costs to new entrants if collection of contributions is vertically disintegrated and processing services are subcontracted. This result holds, because such a pension reform design, involving vertical disintegration of collections, would imply the need for less initial capital by prospective entrants offering investment management services.

8. The more specific conclusions and recommendations of the paper can be summarized under four categories: institutional infrastructure to support pension services, supply of pension services, demrand for pension services, and other considerations in pension policy and reform.

# 1. **Institutional Infrastructure to Support Pension Services**

9. _The preconditions for the institutional infrastructure needed in small countries to permit partial or total privatization of pension services are no greater and can even be less than those required in large countries._ Some important types of infrastructure can be imported by permitting freer trade in international financial services, including: custody services, offshore markets in which to invest in local or foreign currency-denominated securities, and account processing services. Some services, such as registries of contributors and pensioners, are far more difficult to import, but other
which include 36 OECD economies and 110 non-OECD countries.<sup>5</sup> Educational attainment of the adult population in the database is broken down into seven levels of schooling: no formal education, incomplete primary, complete primary, lower secondary, upper secondary, incomplete tertiary, and complete tertiary.

To describe time trends in educational attainment by gender, we focus on three main levels of educational attainment: primary school completion, secondary school completion, and enrollment in tertiary education. As the data is reported every five years from 1950 to 2010, we have a maximum of 13 data points per country, for a given level of educational attainment. We exclude eight countries from the original Barro-Lee dataset, due to highly inconsistent data and missing values on educational attainment.<sup>6</sup> We compute completion and enrollment rates for 5-year-band birth cohorts born from 1921 to 1990, by gender.<sup>7</sup> We measure the enrollment rate in tertiary education at time t as the share of the population age 25-29 that has ever enrolled in tertiary education. The secondary education completion rate is measured as the share of the population age 20-24 at time t that has completed secondary education and above. Finally, primary education completion rates are measured as the share of the population age 15-19 at time t that has completed primary education and above.<sup>8</sup> The female-to-male ratio is measured as the enrollment or completion rate of females over that of males for a given level of educational attainment.

Table I displays the summary statistics of enrollment and completion rates for primary, secondary and tertiary education in OECD and non-OECD countries from the Barro-Lee database in 1950 and 2010. As documented in Barro and Lee (2013) and Lee and Lee (2016), the data shows a large increase in educational attainment over the period covered. In addition, and more importantly in the context of the paper, enrollment and completion rates have increased more rapidly for females

> 5In case of missing observations over time, data points were obtained by forward and backward extrapolation of the census/survey observations on attainment. See Barro and Lee (2013) and Lee and Lee (2016) for a complete description of the dataset construction and methodology.

> 6Excluded countries are Bostwana, Burundi, Cameroon, Gabon, Guyana, Mozambique, Libya and Yemen.

> 7For primary education, we compute completion rates for individuals born between 1936 to 1995, from 1931 to 1990 for secondary school completion and from 1921 to 1980 for tertiary education.

> 8The choice of these age brackets was motived by the fact that individuals would be expected to have enrolled in or completed the corresponding level of education by these ages, if they ever did.

6
demand for workers with different characteristics, and trade liberalization have all been mentioned as possible explanations (Blom and Velez 2001; Blom, Pavcnik, and Schady 2001).

<!-- Start of picture text --> Figure 5.4: Poverty Headcount (P0) and Education Level Attained,<br>Bahia, 1993-2001<br>7 0 .0<br>N o n e<br>P rim a ry<br>6 0 .0 L o w e r S e c o n d a ry U p p e r S e c o n d a ry<br>T e rtia ry<br>5 0 .0<br>4 0 .0<br>3 0 .0<br>2 0 .0<br>1 0 .0<br>0 .0<br>1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 1<br>Source: Calculations based on PNAD 2001.<br><!-- End of picture text --> _The younger the household head, the poorer the household_ . Data reveal that 64 percent of the households headed by a person younger than age 25 are extremely poor in Bahia. Moreover, it is worrisome that they are getting worse off. The poverty rate for these households rose 5 percentage points during 1995–2001 and nearly doubled in the last two decades. This is slightly better than the Northeast as a whole, where 66 percent of the households headed by a person younger than age 25 are extremely poor. However, P0 increased by 22 percentage points during 19812001 in the Northeast region, hence less than in Bahia. Targeted social protection measures that relate to youth employment, family planning, and pre-school programs could help improve employment prospects of young people (see also below).

_Elder household heads are far less likely to experience poverty than younger household heads_ . In Bahia, only 13 percent of those households headed by a member older than age 65 are below the indigent poverty line—a decline of 20 percentage points since 1993. Additionally, the latter group has the highest average income of any age group, which may be explained in part by pension reforms. The P0 of the population groups aged 25 to 44 and 45 to 64 fell by 10 and 18 percentage points, respectively, during 1981–01. In 2001, 50 percent of the younger group was extremely poor as compared to 35 percent of the older group. Thus the younger the head of household, the more likely it is to be poor. This life-cycle profile of poverty illustrates that many households are born poor (mainly due to inadequate assets), with some escaping poverty as they accumulate more assets or as their household size shrinks.

23
were access to the service in neighborhoods currently without access, some households would still not connect to the network. They therefore argued that in areas without access, there was for some households a combined or mixed problem with both demand and supply-side problems. Next, for those households who would probably connect to the network if there were access in their neighborhood to the service, the authors argued that there was a genuine pure supply-side problem. Overall, the authors thus decomposed the lack of coverage of the network in the sum of a pure demand-side problem, a pure supply-side problem, and a combined demand and supply-side problem. Others, including Angel-Urdinola et al. (2006), Angel-Urdinola and Wodon (2007) and Komives et al. (2005; forthcoming) have expanded on the work of F&A in order to analyze factors determining not only who benefits or not from a connection to the network, but also who benefits (or is likely to benefit) from various connection or consumption subsidies for modern infrastructure services.

However, a weakness with the simple statistical approach used by F&A lies in the fact that there are limitations in the surveys used to assess empirically the magnitude of demand-side and supply-side problems, and that this may lead to biases in the estimates of demand as opposed to supply-side problems. As already mentioned, some households may live in an area where there is access to the service, but may still be located too far from the electric line or water pipe to be able to be connected (or perhaps the capacity of the electric line or water pipe may be designed to support a specific and limited number of households). Under the simple empirical procedure for estimating demand-side and supply-side problems proposed by F&A, these households would be considered as suffering from a demand-side problem, while the true nature of the issue may be a supply-side constraint. To some extent, this type of biases can be dealt with by using regression techniques. In this paper we suggest how this can be done, and we show that using an econometric as opposed to a statistical approach to the estimation can make a significant difference in the results.

The rest of the paper is structured as follows. In section 2, we describe and formalize in simple mathematical notations the methodology used by F&A for assessing the relative role of demand and supply-side problems to explain lack of coverage of modern infrastructure services. Results obtained with this methodology for African countries in the case of urban coverage of piped water and electricity are then provided. The next section presents our alternative
# Trade and Financial Development<sup>∗</sup> WPS 3347

Quy-Toan Do Andrei A. Levchenko The World Bank MIT April, 2004

## Abstract

The differences in financial development between advanced and developing countries are pronounced. It has been observed, both theoretically and empirically, that these differences in countries’ financial systems are a source of comparative advantage and trade. This paper points out that to the extent a country’s financial development is endogenous, it will in turn be influenced by trade. We build a model in which a country’s financial development is an equilibrium outcome of the economy’s productive structure: in countries with large financially intensive sectors financial systems are more developed. When a wealthy and a poor country open to trade, the financially dependent sectors grow in the wealthy country, and so does the financial system. By contrast, as the financially intensive sectors shrink in the poor country, demand for external finance decreases and the domestic financial system deteriorates. We test our model using data on financial development for a sample of 77 countries. We find that the main predictions of the model are borne out in the data: trade openness is associated with faster financial development in wealthier countries, and with slower financial development in poorer ones.

> ∗We are grateful to Daron Acemoglu, Abhijit Banerjee, Thorsten Beck, Olivier Blanchard, Simon Johnson, Roberto Rigobon, and workshop participants at MIT for helpful comments. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily reflect the view of the World Bank, its Executive Directors, or the countries they represent. E-mail: qdo@worldbank.org, alev@mit.edu.

1