rafmacalaba/datause-encoder-data huggingface dataset:
# SUPPLEMENT TABLE II:

LEARNING SCORES WITH ALL COUNTRY AND TIME PERIODS VS. RANDOM SUBSET

||(1)|(2)|(3)|(5)|(6)|
|---|---|---|---|---|---|
||EGRA|LLECE|PASEC|PISA|SACMEQ|
|HLO|368.6|420.4|410.5|497.9|390.6|
|HLO - Random Set of Countries and Time Intervals|361.6|422.2|433.1|497.3|409.5|
|Correlation|1.000|0.990|0.975|1.000|0.955|

_Notes_ : We compare Harmonized learning Outcomes (HLO) using all county and time periods over the fixed linking function period with HLO scores computed using a random subset of half of available countries for each test-subjectlevel.

Next we explicitly account for linking errors by including measures of uncertainty to quantify the degree of confidence around our estimates by test. We capture two sources of uncertainty: scores on the original test and uncertainty in the estimation of linking parameters across tests. We calculate the variance by bootstrapping. We consider each average country score on a given subject, test, and schooling level as a random variable with a mean – the score itself – and a standard deviation which captures the sampling variation across students. This distribution of scores is asymptotically normal by virtue of the central limit theorem. We take 1,000 draws from the distribution of subject-level average test scores for each testing regime. We do this as a computational shortcut, rather than bootstrapping subsamples of students from each test. We derive the linking function and scores from each bootstrapped sample. We take the 2.5<sup>th</sup> and 97.5<sup>th</sup> percentiles of the distribution and use this to construct lower and upper bounds of uncertainty.

We find small uncertainty intervals overall, as shown in Supplement Figure II with an average of 3.5 points and ranging from 1 to 11 points. This is consistent with original standard errors from each respective testing regime. Consistent with sensitivity tests, we find larger uncertainty for our estimates relative to original scores when testing regimes have fewer countries participating in a given pair of tests. Supplement Figure II decomposes standard errors due to within-test sampling variation as well as variance in the linking function. This figure shows that for tests where there is no need to produce a linking function, or many pair-wise countries which we can use to produce this linking, the final standard errors remain similar to standard errors from the original test. For tests with fewer pair-wise countries, the linking has more uncertainty, such as PASEC, where the average standard error increases from 5.3 on the original test to 7.5 for the HLO. By quantifying the degree of uncertainty, we can more reliably bound our estimates.

27
3 country like Bangladesh where half the population is poor, poverty lines also represent valid price indices faced by the population. Hence, they can also be used to estimate real rather than nominal consumption and inequality. Consumption will be the preferred indicator of well-being because it incorporates the life cycle hypothesis, and because it is measured more precisely. Therefore, all measures of poverty, growth and inequality in this paper will be based on consumption rather than income, and they are all computed using successive rounds of the nationally representative Household Expenditure Surveys (HES) Regional poverty lines were estimated according to the cost of basic needs method. Details on the implementation of the method can be found in Wodon (1997), hence the exposition will be brief here. Three steps were followed for the estimation of the poverty lines. First, the country was divided into fourteen geographical areas. The list of areas and their sample size for the various years is given in Table Al in appendix. A food bundle representative of actual consumption patterns in the country and providing 2,122 kcal per day and per person was chosen. The bundle is given in Table A3, and the same bundle applies to all areas. In each area, the price of each item in the food bundle was estimated, using regressions to control for the impact of household characteristics on the quality of the food consumed. The resulting prices for 1995-96 by area are given in Table A3. Given the estimates of the food prices by area, the cost of the food bundle (the food poverty line denoted by ZF in Table A4) was computed in each area. The second step consisted in computing a cost of basic non-food needs. The non-food expenditures of households whose food or total consumption is equal to their area food poverty line were estimated as, respectively, lower and upper bounds for the cost of non-food needs. Third, lower and upper poverty lines were obtained by summing up the food poverty line with respectively the lower and upper allowance for non-food consumption. The resulting poverty lines by area<sup>4</sup> , denoted by ZL and ZU, are given in Table A4.

4 There are minor differences in the poverty lines appearing in Table A3 and those computed for the years 1983-84 to 1991-92 by Wodon (1997). This is because the composition of the food bundle was changed slightly as well as
of group j in the initial period. In period t = 0, fj0 is thus equal to 1/N for each household.

# 3.3 The Use of Distribution Functions

## 3.3.1 Methodology

The third approach uses specific parametric distributions to describe the dispersion of income within each group. The parameters of these distributions are generally estimated using real household survey data. We use again our fictitious sample of household incomes and consumption expenditure. In contrast to the two approaches presented above, once the parameters are estimated, the survey data are not used anymore to evaluate the distributional effects of shocks. Following a shock, the fitted distributions are only shifted according to the changes of the group-specific mean incomes; to the right (increase in income or consumption) or to the left (decrease in income or consumption) without modifying the shape of the distributions. The poverty indicators are also computed using the estimated shape parameters, without once again relying on the survey data. The overall distribution of income is generated empirically by summing the separate within-group distributions and is then used to generate overall measures of poverty and inequality.

It is important to note that in this approach and in the two former approaches changes in income are not distributed in the same way within groups. Whereas in the first two, we suppose usually that the relative increase is uniform over the whole distribution (that is, the absolute change is proportional to the initial income), in the distribution function approach the absolute increase is assumed to be uniform over the whole distribution (that is, the relative increase is the higher the lower the initial income). Nevertheless, with the distribution function approach we also assume that intra-group inequality remains unaffected by a shock to income or consumption levels, as in the other two approaches.

As in Decaluwé, Patry, Savard and Thorbecke (1999) and Decaluwé, Dumont and Savard (1999), we use Beta distribution functions to describe the within-group distributions. Of course, it would be more logical to use the log-normal distributions from which our data is drawn, but in a real country case the assumption of Beta distributions may be more convenient, because of their higher degree of flexibility.<sup>9</sup> 9Boccanfuso, Decaluwé and Savard (2002) compared six alternative functional forms to 12
**Table** **<u>S3. Change in behavior since COVID-19 outbreak</u>**

||Handwashed<br>with soap more<br>often|Avoided<br>physical<br>greetings|Avoided crowds|
|---|---|---|---|
|Ethiopia|0.079***|0.238***|0.231***|
||(0.014)|(0.019)|(0.024)|
|Nigeria|0.050***|0.196***|0.329***|
||(0.016)|(0.022)|(0.023)|
|Uganda|0.061***|0.232***|0.301***|
||(0.014)|(0.019)|(0.023)|
|Ethiopia-Nigeria|0.001|0.001|0.000|
|Ethiopia-Uganda|0.005|0.485|0.000|
|Nigeria-Uganda|0.235|0.004|0.037|
|Observations|9,138|9,138|9,131|
|R-squared<br>_Note_: Each column re|0.011<br>ports results from a sin|0.045<br>gle regression of a|0.064<br>binary variable equal|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1if the respondent changed their behavior and 0 otherwise. The binary variable is then regressed on indicators for each country, with Malawi as the base case. The second panel reports p-values for Wald tests of the simple linear hypothesis that the coefficient on one country indicator equals the coefficient on an indicator for a different country. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

30
- 8 -

# **F. Estimating Benefits from Removing Transport Barriers**

1.22 In this study the net economic benefit and the improvement in the balance of payments that would result from eliminating trade barriers were computed. The balance of payments impact was estimated with the SMART simulation model, (a partial equilibrium model) using the ad valorem equivalence between transport costs and tariff barriers. This model was based on demand elasticities for products imported into industrial countries substitution and supply elasticities for products exported by the Maghreb countries.<sup>2</sup> This design made it possible to estimate the transport elasticity, that is the ratio of the percentage change in export earnings to the percentage change in freight costs following the removal of a barrier.

1.23 Positive economic effects from imports take the form of lower domestic prices following the elimination of excess costs. The net economic gain equals the increase in the value of imports multiplied by the average variation in ad valorem excess costs (before and after the elimination of the distortions). This gain is also equal to consumer surplus.

1.24 The total effect from exports is the sum of trade created and trade diverted. Created trade is the increase in demand from country j for product i exported by country k, resulting from a reduction in the price of this product when distortions are lessened or eliminated and assuming that price variations are automatically passed on. The value of trade created is calculated using the elasticity of demand for an imported product with respect to domestic prices and the elasticity of supply of an exported product with respect to world prices.

1.25 Importers divert trade when they switch from one exporting source to another because relative prices have changed. In the model used, suppliers' substitution elasticity is assumed to be 1.5. The value of this parameter could be made slightly higher if products are more homogeneous or slightly lower if they are more differentiated. The elasticity of supply of exports was assumed equal to infinity, implying a limitless supply of the exported good. Finally, the demand elasticities from the European Union for each tariff line were used for import elasticities.

1.26 The SMART model computes the increase in international trade as the sum of trade created and trade diverted **(Tables A1.1, A1.2, and A1.3).**

1.27 In 1990, the reference year deficit, removal of excess costs would have reduced Tunisia's Current Account deficit by 23%, reduced Morocco's by 21% and raised Algeria's surplus by 9% (Table 1.4). Although the base estimates are conservative (because they are not exhaustive), they are significant enough to warrant corrective measures.

2 **The** SMART model was joindy developed by UNCTAD and **the World** Bank.
Some benchmarking suggests that this estimation represents a conservative scenario. Tobacco-attributable medical expenses are much lower than the calculation by the UNDP et al. (2018) of GEL 327.3 million in 2016 reviewed above.

Tobacco-related mortality Data on smoking-attributable death events in Georgia are taken from the Global Burden of Disease project (GBD 2017). It is estimated that 6,799 people died in Georgia from smoking-attributable causes in 2017 (Table 5).<sup>14</sup> Ischemic heart disease accounts for over one-third of those deaths. And, consistent with the prevalence rates, 91 percent of smoking-related deaths occur among males.

According to the GBD data, smoking-attributable diseases would be responsible for 13 percent of all premature death in Georgia in 2017.<sup>15</sup> This figure represents a conservative scenario on the health burden of tobacco, relative to previous estimates. The NCDCPH reports that tobacco is the leading cause in cardiovascular disease, cancer and respiratory diseases in Georgia, accounting for up to one-fifth of all premature deaths in the country (NCDCPH et al. 2016).

Years of working life lost (WYLL) The smoking-attributable years of life lost among the working population (YWLL) in Georgia are calculated based on the mortality data from the GBD (2017). For this calculation, the retirement age is assumed at 65 years old for both males and females.<sup>16</sup> The resulting years of productive life lost at the national level totaled 28,794 years in 2017 (Table 6).

> 14 Includes death events from smoking-attributable causes. The GBD reports smoking-attributable death events only for adults over 30 years old. Deaths related to second-hand smoking and chewing of tobacco are not included.

> 15 The overall number of death events reported by the GBD was compared with national records to test consistency. According to Geostat, 50,771 people died in the country in 2016 (Geostat 2016).

> 16 Hence, 2,972 smoking-attributable death events for the population between 30 and 64 years old are considered. This is equivalent to only 44 percent of all smoking-attributable deaths. Currently, life expectancy in Georgia is 69 years for males and 78 years for females (WDI 2017). An analysis based on the Labor Force Survey (LFS) 2017 determined that there is not a clear age cutoff in labor force participation in Georgia. (Results are available upon request to the authors.) Hence, the analysis applies the standard assumption of retirement at age 65. This threshold represents a conservative assumption, as the WYLL would be higher (and hence, the effects of taxing tobacco higher) if workers continue earning labor incomes after age 65.

13
**Table 4: Method of fertilizer acquisition in 2004, (% of total)**

||**Organic**<br>**Fertilizer**|**Chemical Fertilizer**|
|---|---|---|
|Cash|10.3|37.7|
|Credit|10.2|61.5|
|Left over stock|..|0.1|
|Provided by land owner<br>to tenants|13.5|0.1|
|Provided for free|9.7|0.6|
|Own animal dung|56.4|..|

Source: Authors’ calculations based on EEA-World Bank survey.

32
**_Figure 4:  Share of Disaggregated Services Sub-Sectors in Value Added_**

<!-- Start of picture text --> Wholesale Trade Retail Trade Inland Transport<br>Latvia Poland Slovak Rep.<br>Poland Lithuania Lithuania<br>Lithuania Slovak Rep. Latvia<br>Estonia Latvia Czech Rep.<br>Czech Rep. Estonia Estonia<br>Slovak Rep. Hungary Poland<br>EU-15 EU-15 Hungary<br>Slovenia Czech Rep. Slovenia<br>Hungary Slovenia EU-15<br>0% 2% 4% 6% 8% 10% 12% 0% 2% 4% 6% 8% 10% 0% 1% 2% 3% 4% 5% 6% 7%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Post & Telecommunications Financial Intermediation Real Estate Activities<br>Latvia EU-15 Estonia<br>Lithuania Slovenia EU-15<br>Hungary Estonia Slovak Rep.<br>Estonia Poland Hungary<br>Czech Rep. Latvia Slovenia<br>Slovak Rep. Slovak Rep. Poland<br>Poland Czech Rep. Lithuania<br>EU-15 Hungary Latvia<br>Slovenia Lithuania Czech Rep.<br>0% 1% 2% 3% 4% 5% 6% 0% 1% 2% 3% 4% 0% 2% 4% 6% 8% 10% 12% 14%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br>Computer & Related Activities R&D Legal, Technical & Advertising Activities<br>Slovenia Slovenia<br>EU-15<br>Latvia Hungary EU-15<br>Czech Rep. Poland Hungary<br>Hungary EU-15 Latvia<br>Slovenia Latvia Czech Rep.<br>Poland Slovak Rep. Estonia<br>Slovak Rep. Czech Rep. Poland<br>Estonia Estonia Slovak Rep.<br>Lithuania Lithuania Lithuania<br>0.0% 0.5% 1.0% 1.5% 2.0% 0.0% 0.2% 0.4% 0.6% 0% 1% 2% 3% 4% 5%<br>1997-2000 2001-2004 1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: KLEMS database 2007. Note: In this figure, financial intermediation is NACE 65 category.

# **3. Services Sector Performance: Patterns**

To understand the potential for services-based growth in the ECA region, it is important to examine the recent performance of the sector. Performance will be measured by labor productivity defined to be the ratio of output to labor input. Defining physical 11
<u>30(16)</u> 2015 12,000 <u>4</u> A small number of data entries are missing. Because we are concerned mainly with household income, Table 3 summarizes the income data gaps related to households that are represented in only some quarters. Of course, a household may move out of the domain or the country or be dissolved by death, so some of the missing data cannot be regarded as nonresponse. In any case, because there are so few gaps, we make no provisions to address this issue. Similarly, we cannot establish whether some changes in the composition of the households are a result of a genuine change or nonresponse.

**Table 2: The Regions**

||**Sample Si**|**zes (2013 – 15)**||**Samp**|**le Sizes**|
|---|---|---|---|---|---|
|**Region**|**Rural**|**Urban**|**Region**|**Rural**|**Urban**|
|Akmola|480|360|Karaganda|360|600|
|Aktobe|480|360|Kostanay|450|360|
|Almaty|480|240|Kyzylorda|360|240|
|Almaty City|—|898|Mangystau|270|330|
|Astana City|—|660|North Kazakhstan|390|270|
|Atyrau|240|300|Pavlodar|480|360|
|East Kazakhstan|480|420|South Kazakhstan|480|300|
|Jambyl|420|270|West Kazakhstan|420|240|

**Table 3: Missing Household Income Data**

|||**Households**|**with data for**|||
|---|---|---|---|---|---|
||**1 quarter**|**2 quarters**|**3 quarters**|**4 quarters**|**All**|
|2003|_90_|_160_|_96_|11 645|11 991|
|2006|12 000|—|—|—|12 000|
|2010|_183_|23 816|—|—|23 999|
|2013|_45_|_107_|_49_|11 799|12 000|
|2014|_54_|_81_|_54_|11 809|11 998|
|2015|_123_|_93_|_56_|11 713|11 985|

_Note_ : Gap counts are in italics.

The income of a household consists of the total of the income of each 5
will likely sort into better schools (where ���� is higher). Estimating the equation by simple OLS would therefore likely lead to biased results because there are unobserved student (and school) characteristics that are correlated both with the teacher quality measures and the left-hand side variable. In addition, if there are other dimensions of teacher quality that matter for student learning that are unobserved but correlated with effort, knowledge and skills, then the estimated coefficients will be biased.

If we want to retain all the teacher quality measures in the estimation, the best we can do is to try to control for all the relevant inputs in the education production function. Since the SDI data contain a wealth of information on students, teachers and schools, we can make some progress here. In particular, we include a student’s non-verbal reasoning score, a (admittedly) crude measure of innate ability, whether they had breakfast and their age and gender in the regression. For the teacher we include the same set of characteristics used in the previous section, namely gender, age, experience, and whether they have a university and/or education degree. For the school, we include its location, the pupil-teacher ratio and indices for teaching equipment and infrastructure. In addition, we add country-district fixed effects and day of week and month of survey fixed effects.

If we consider only a subset of the teacher quality measures, namely subject content and pedagogy knowledge, the prospect for identification is improved, because we can now exploit within-student variation. Specifically, we take first differences of equation (1) to arrive at where the dependent variable, ��� ����, is now the difference in student test scores in language and math. Note that content knowledge ��� differs both across and within teachers while pedagogical knowledge is teacher specific. Thus, �� is identified from the sub-sample of students with different teachers in the two subjects, while �� is identified off of variation across and within teachers.

This first-differencing approach removes any unobserved student (and school) characteristics that are fixed across subjects. Estimation of equation (2) will generate unbiased estimates of the teacher vector � if (a) the difference in the student-by-subject-specific error terms ���� ����� and (b) the difference in the teacher-specific error term ���� ����� are uncorrelated with the first differenced teacher quality variables. Condition (a) allows for sorting of better students to better teachers, as long as this sorting is not subject-specific. Put simply, 19
# **1. Introduction**

Improving the socio-economic outcomes for girls and young women is of central importance, not only to the beneficiaries themselves, but also to their communities and the next generation. Many of the 600 million girls who live in the developing world do not have the opportunity to become fully functioning members of society. Approximately one-quarter of girls in developing countries are not in school (Lloyd 2005) and onequarter to one-half of girls in developing counties become mothers before age 18 (United Nations Population Fund 2005).

Although girls are half the youth population (aged 15-24) of the developing world, little attention has been given to the specific challenges facing adolescent girls as they develop into adult members of families, the workforce, and society. General statistics and sector-specific studies point to the merits of investing in girls, including lower infant mortality, healthier families (Bicego and Boerma 1993), and greater labor market earnings, but policy often does not explicitly target development opportunities for adolescent girls. This may be due to insufficient understanding of the actual social benefits of investing in adolescent girls.

Most work that considers girls’ contribution to economic development or poverty reduction focuses on investments in girls’ education and health (see for instance Levine et al. 2008, Lloyd and Young 2009, Temin and Levine 2009). Returns to girls’ education in developing countries are substantial, and in most cases they exceed those observed in developed countries and those of boys. A cross country study on the effect of education on average wages (a proxy for productivity) estimates that primary education increases girls’ earnings by 5 to 15 percent over their lifetimes, while boys experience a rate of return between 4 and 8 percent (Psacharopoulos and Patrinos 2002).<sup>4</sup> And, for example, returns to female secondary education are 15 to 25 percent higher for women than men in Thailand, Ghana and Cote d’Ivoire (Schultz 2002).

More than 14 million girls ages 15-19 give birth every year (United Nations Population Fund 2005), which puts them at risk. While there is an overall trend of decreasing birth rates for girls, maternal causes kill more 15- to 19-year old girls than any

> 4 This is partly due to greater entry to formal labor markets by educated girls, where earnings exceed informal or home-based work (Malhotra, Grown, and Pande 2003).

2
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