rafmacalaba/datause-encoder-data huggingface dataset:
The difference from our predicted and original scores ranges from 1-23 points for math and from 0-9 for reading and science. This shows a clear pattern that our index predicts reading and science scores more accurately than math scores.

However, even a difference of 23 points for Chile in math - our largest recorded discrepancy - constitutes just a fifth of a standard deviation taken for all country adjusted test scores. This indicates that even our larger discrepancy in math is insignificant.

Most differences are much lower than our ranges indicate, with some differences netting zero, which is the most accurate result. This holds true even for TIMSS where the adjustment Index is based on just two countries (Colombia and El Salvador) which, as outlined before, usually aggravates the computation of a precise exchange factor. This alleviates our concern that we can only rely on indexes that use a large number of doubloon countries to produce reliable predicted scores. Even our estimates, which use limited doubloon countries, produce reliable results. However, we do benefit from having more _doubloon countries_ overall in our dataset than the original Altinok and Murseli dataset, which improves the general accuracy of our index.

An additional robustness check we conduct involves comparing projections for _doubloon countries_ that participate in both PISA and TIMMS achievement tests. Studies show that achievement on international tests is highly correlated (Rindermann, Heiner and Stephen, 2009). Therefore, by comparing projections for _doubloon countries_ that have both PISA and TIMMS scores, we can verify that our estimates are consistent across achievement tests.

The results from our robustness check are detailed in Table 1.0 and verify the reliability of our predicted scores.

18"
|Fraction of poor HHs that have some welfare-relevant assets|33%|68%|
|---|---|---|
|Fraction of poor HHs that have a private car/motorcycle or bike|15%|24%|
|Fraction of poor HHs that have any washing machine (electric, semi-<br>automatic, or automatic)|16%|53%|
|Fraction ofpoor HHs that have a smartphone|8%|26%|

**Source** : Authors’ calculations based on HIECS 2015.

**Note** : “Low education” includes all illiterate individuals. “Currently not working” includes the unemployed and people not actively looking for a job. “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

# **3.2 Poverty figures in 2017/18**

Table 3 presents our estimated poverty rates for all regions in 2017/18. Our estimate of the national rate in 2017/18 is 31 percent. As in 2015, the two regions with the highest poverty rates are Upper Urban and Upper Rural Egypt, while the two regions with the lowest poverty rates are Lower Urban Egypt and the Metropolitan Areas.

_Table 3: Contrasting 2017/18 poverty estimates based on total expenditures with official poverty rates_

|Statistic|National|Metropolitan|<sup>Lower</sup><br>Urban|Lower<br>Rural|Upper<br>Urban|Upper<br>Rural|Borders|
|---|---|---|---|---|---|---|---|
|% of the population*|100%|6%|8%|30%|14%|39%|3%|
|Per capita total<br>expenditures*|7366|9626|8560|6886|7507|5817|5924|
|Poverty rate (total<br>expenditures) *|31.0%|25.3%|12.6%|25.3%|28.2%|51.4%|40.7%|

**Source** : *Authors’ calculations based on HIECS 2015. **CAPMAS (2019).

To get a better sense of the types of households that are poor, we partition the population of households into eight categories that differ in composition. These categories differ in terms of household size and number of dependents (either children or elderly individuals). Table 4 provides summary statistics on these household types as well as their average total expenditures per capita and their respective poverty rates in 2017/18. Total expenditures per capita decrease sharply with household size and number of dependents. As a result, households with larger size and more numerous dependents have higher poverty rates. For instance, households without dependents have an average size of 2.7 members, average total expenditures per capita close to 11,000 Egyptian pounds (deflated in 2015 prices), and a poverty rate of 16 percent. In contrast, households with two adults and at least four dependents have an average size of 6.4 members, average total expenditures per capita close to 5,000 Egyptian pounds (deflated in 2015 prices), and a poverty rate of 48 percent.

_Table 4: Decomposition of 2017/18 poverty estimates by household types_

|HH category|% of<br>population|Age|HH<br>size|Total<br>expenditure<br>pc|Poverty<br>rate (total<br>exp.)|
|---|---|---|---|---|---|
|HHs without dependents|14%|43|2.7|10926|16%|
|HHs with only 1 adult and at least 1 dependent|6%|38|2.8|9144|11%|
|HHs with 2 adults and 1 or 2 dependents|14%|28|3.6|8301|14%|
|HHs with 3 or more adults and 1 or 2 dependents|20%|31|5.0|7711|35%|
|HHs with 2 adults and 3 dependents|16%|20|5.0|6069|27%|

7
hypothesizes that the difference in the wage adjustment between the two groups can be explained in the following way. If, after the acquisition, total profits increase and highly educated workers have greater bargaining power (due to the accumulation of firm specification skills), their wage adjustment may be higher. However, somewhat surprisingly, there is no statistically significant wage adjustment following foreign acquisition when the sample includes services industries. No explanation is provided for why this may be the case.

Hijzen et al. (2013) rely on linked employer-employee data from Brazil, Germany, Portugal, and the United Kingdom. Similar to Heyman, Sjöholm, and Tingvall (2007), they define foreign acquisitions as a change from having no or less than 50 percent foreign ownership to more than 50 percent of assets being foreign owned. In their most stringent specification, they use propensity score matching at the firm-worker level.<sup>6</sup> The matching procedure is conducted separately for a combination of year, manufacturing vs. services, and skill group (unskilled, semiskilled, skilled) and is combined with a difference-in-differences approach. The average estimated effect is equal to approximately 3 percent in Germany and 6 percent in Brazil. Both estimates are significant only at the 10 percent level. The lack of statistically significant effects for the United Kingdom and Portugal may be due to smaller samples (9,348 and 923 matched workers, respectively).

Earle, Telegdy, and Antal (2013) benefit from a very long panel encompassing 4,926 foreign acquisitions in Hungary. They also rely on linked 7
However, during the final years of the Ali regime and the recent post-revolution period, the spending and composition of Tunisia’s subsidies have notably changed (Figure 1). During the last 10 years, the combined spending on energy, food, and transportation has more than tripled, rising from 2% of GDP in 2005 to 7% in 2013. Energy subsidies, in particular, increased even more, fourfold, during that period. Energy subsidies reached 4.7% of GDP in 2013, with sustained increases since 2010, reflecting the partial (rather than the full) pass-through of international oil prices to domestic prices sought by the government (IMF 2014). Regarding the composition of subsidies, during the post-revolution period, energy subsidies increased both in absolute and relative terms. As a result, energy subsidies went from one-third of total public subsidies prior to the revolution to about two-thirds in 2013. In contrast, food and other basic needs’ subsidies have lost relative weight in the total subsidy bill despite having notably increased in absolute terms. With respect to other public expenditures, energy subsidies in Tunisia account for one-fifth of all public spending, or 7% of the GDP in 2013, the latest available figure. Because they are considered the backbone of the social protection strategy, it is not surprising that public spending on subsidies exceeds that of social assistance, health and education, and individual programs on youth, children, or women (Figure 2).

Figure 1: Evolution of the Composition and Level of Subsidies by Type, 2005–13 <!-- Start of picture text --> 7.0<br>6.0<br>5.0<br>4.7<br>4.0<br>3.0 3.3<br>2.4<br>3.0 1.5<br>2.0 0.9 0.7 0.9<br>1.2 1.1 1.9 1.7 1.8 1.8 1.9<br>1.0 1.3 1.4 1.2<br>0.6 0.7<br>0.0<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2013<br>Transportation Basic Products Energy (Apr) (Nov*)<br><!-- End of picture text --> _Source:_ WB staff calculations using data from Ministère des Finances (October 2013). _Notes:_ Basic products refer to food products such as cereals, bread, sugar, and vegetable oil. * = forecast.

4
**From the national accounts data we see that the share of labor out of GDP at factor costs is 30 percent, while out of net domestic product at factor costs it is 33 percent on average for the period.** Correspondingly, the income accruing to capital, as per the national accounts, comes out to 67 percent of net domestic product as an average for the period. In this case, labor income only includes labor employed by corporate entities, not the self-employed or those working for unincorporated employers (see Table 2.1).

# **Table 2.1 Mexico: Labor income** **<u>shares, 2003–2011</u>**

|Year|Gross value<br>added at<br>basic prices<br>[million<br>pesos]|<br>Labor income<br>over GDP at<br>factor costs|<br>Net domestic<br>product at<br>factor costs<br>(GVA –<br>depreciation)<br>[million<br>pesos]|Labor income<br>over net<br>domestic<br>product at<br>factor costs|Mixed<br>income<br>[million<br>pesos]|Net domestic<br>product at<br>factor costs<br>less mixed<br>income<br>[million<br>pesos]|<br>Labor income<br>over [NDP at<br>factor prices<br>less mixed<br>income]|
|---|---|---|---|---|---|---|---|
|2003|7,162,773|33%|6,479,420|37%|1,359,937|5,119,483|46%|
|2004|8,171,095|31%|7,411,912|34%|1,497,569|5,914,343|43%|
|2005|8,825,085|31%|8,020,770|34%|1,875,614|6,145,156|44%|
|2006|9,943,093|30%|9,059,396|33%|2,049,152|7,010,244|42%|
|2007|10,854,384|29%|9,881,647|32%|2,260,332|7,621,315|42%|
|2008|11,837,772|29%|10,742,185|32%|2,505,541|8,236,644|41%|
|2009|11,394,220|30%|10,129,619|34%|2,384,723|7,744,896|45%|
|2010|12,485,511|29%|11,179,435|33%|2,674,661|8,504,774|43%|
|2011|13,843,758|29%|12,461,358|32%|2,945,621|9,515,737|42%|

_Source:_ SHCP; for mixed income: UN and SHCP.

**Once we exclude mixed income earned in self-employed and unincorporated activities, the labor income out of net value added at factor prices increases by about 10 percent.** If the labor income contained in mixed income is assumed to be the same share as in the corporate sector, then the labor income in the corporate sector can be adjusted to include this same share of mixed income. This is shown in Table 2.2 for method LS4, such that the adjusted labor share averaged 43 percent of the net domestic product (NDP) at factor costs over the period. This is expected to underestimate the labor share of the unincorporated sector, so the alternative estimate under method LS5 assumes that the wage rates in the unincorporated sector are the same as in the corporate sector. Table 2.2 shows that this raises the adjusted labor income share to an average of 51 percent over the period. Given that wages in the unincorporated sector are expected to be lower on average than in the corporate sector, using the average of these two estimates results in an average labor share of 47 percent (last column in Table 2.2). This adjusted labor income is used in the subsequent estimates.

15
# **References**

- Ahmad, Saad, Christopher Montgomery, and Samantha Schreiber. 2020. "A Comparison of Armington Elasticity Estimates in the Trade Literature." Economics Working Paper 2020-04-A, US International Trade Commission, Washington, DC.

- Alston, Julian M., and Richard Green. 1990. "Elasticities in AIDS Models." _American Journal of Agricultural Economics_ . 72(2): 442-45.

- Alston, Julian M., Colin A. Carter, Richard Green, and Daniel Pick. 1990. "Whither Armington Trade Models." _American Journal of Agricultural Economics_ . 72(2): 455-67.

- Anderson, James E., and Eirc van Wincoop. 2004. “Trade Costs.” _Journal of Economic Literature_ 42 (3): 691-751.

- Arbache, Jorge, Delfin S. Go, and John Page (2008). "Is Africa's Economy at a Turning Point?" In Delfin S. Go and John Page, eds., _Africa at a Turning Point? Growth, Aid, and External Shocks._ Washington, DC: The World Bank.

- Armington, Paul. 1969. "A Theory of Demand for Products Distinguished by Place of Production." _IMF Staff Papers_ , Vol. 16, pp. 159-178.

- Arrow, Kenneth J., Hollis B. Chenery, Bagicha S. Minhas, and Robert M. Solow. 1961. "Capital-Labor Substitution and Economic Efficiency." _Review of Economics and Statistics,_ 43(3):225-50.

- Bajzik, Josef, Tomas Havranek, Zuzana Irsova, Jiri Schwarz (2020). "Estimating the Armington Elasticity: The Importance of Design Study and Publication Bias.” _Journal of International_ Economics 127 103383. See also _VOX, CEPR Policy Portal, September 23, 2020. https://voxeu.org/article/armington-elasticity-and-international-trade-models-fifty-years._

- Boehm, Christoph E., Andrei A. Levchenko, and Nitya Pandala-Nayar. 2023. “The Long and Shor (Run) of Trade Elasticities.” _American Economic Review_ 113 (4): 861-905.

- Bond, Marian. 1987. "An Econometric Study of Primary Commodity Exports from Developing Country Regions to the World. _IMF Staff Papers_ 34: 191-227.

- Branson, William H. 1972. "The Trade Effects of the 1971 Currency Realignments." _Brookings Papers on Economic Activity I:_ 15-58.

- Corsetti, Giancarlo, Luca Dedola, and Sylvian Leduc. 2008. "International Risk Sharing and the Transmission of Productivity Shocks." _Review of Economic Studies_ 75: 443-75.

- de Melo, Jaime, and Sherman Robinson. 1989. Product Differentiation and the Treatment of Foreign Trade in Computable General Equilibrium Models of Small Economies." _Journal of International Economics_ 27: 47-67.

Deardorff, Alan V. 2006. Glossary of International Economics. World Scientific Publishers.

Devarajan, Shantayanan and Sherman Robinson. 2013. “Contribution of Computable General Equilibrium Modeling to Policy Formulation in Developing Countries.” In P.B. Dixon and D.W.

20
-61 -

As Indicated In Table 2, the average rate of return for the period 1966-85 was 7.9%. However, the deviation about the mean was substantial, as can be seen from Table 3.

Table 3

# Distribution of the Rate of Return on Assets

|Rate of Return<br>At Project Complel'on<br>|For Projects<br>Approved in 1966-78<br> <br>|For Projec<br>Approved in 1<br>|ts<br>979-83<br> <br>|
|---|---|---|---|
|%<br>_|No. of Projects<br>%|No.ofProiect|s<br>%|
|Below<br>4.0<br> <br>|23<br>22<br> <br>|13<br>|40<br>|
|4.1<br>-<br>6.0<br> <br>|20<br>10<br> <br>|5<br>|15<br>|
|6.1<br>-<br>8.0<br> <br>|17<br>17<br> <br>|4<br>|12<br>|
|8.1<br>- 10.0<br> <br>|13<br>13<br> <br>|7<br>|21<br>|
|10.1<br>- 12.0<br> <br>|12<br>12<br> <br>|0<br>|0<br>|
|12.1<br>- 14.0<br> <br>|7<br>7<br> <br>|0<br>|0<br>|
|Over<br>14.0|10<br>10|4|12<br>|
|_ _ __||33|1<br>X|

For projects approved between 1966-78, 22% of utilities had rates of return equal to or less than 4% at project completion, and 17% had rates of return of over 12%. The situation has deteriorated for ongoing projects In 1984, when 40% of utilities had rates of return of 4% or less and only 12% had rates of return over 12.0%. Part of this deterioration was due to more rates of return belrig based on revalued assets.

Fur orojects approved between 1968-78, about 18% had the same return as forocast at project completion and another 26% achieved higher rates of return than forecast. However, 55% of projects had lower returns than forecast and 43% of projects were below the targeted rate of return by more than three percentage points. For projects approved In the late 1960s and 1970s there appears to have been an Increasing deviation between actual and forecast returns over time, particularly for those projects Implemented during or after the oil crisis. This trend also appears to have continued for those projects Implemented during the 1980s. The situatlon appears to have been better for projects Implemented during the 1950s and 1960s, when a higher percentage achieved the targeted rate of return.
To study the degree to which there is selection, we use the „student participation ratio‟, that is, for those schools which have students that take the exam on a specific subject, we compute the ratio of students who take the exam divided by the total number of students in the highest grade (for a vast majority of schools this is the 11<sup>th</sup> grade, for some it is the 12<sup>th</sup> grade). Given that all students that participate in the EIT take the Ukrainian exam, a school‟s ratio of the number of students that take the Ukrainian exam on the number of students in the highest year is a good proxy for the extent of selection. If this ratio is 1, it means all students participate, if it is 0 it means nobody participates.

In about 7 % of the cases, the participation ratio indicator is bigger than one, i.e. there are more students taking the test than there are in the 11<sup>th</sup> /12<sup>th</sup> grade. One explanation for this is that the MoE data were recorded at a different time than the EIT exams, and that some students might have changed schools between the recording of the MoE data and the EIT exams. A second possible explanation is that some graduates of previous years were allocated to the school were they graduated in the past. A third explanation is simply that there are mistakes in the database. In the regression analysis presented below, we include in the sample only schools with a participation ratio that is less than 101 percent.

Table 5 gives the 2010 participation ratio statistics by region – we see a substantial variation among oblasts with especially low rates of participation in the Western regions of Zakarpatia and Chernivtsi (between 60 and 70 percent) and high rates of participation in Sevastopol and especially Kyiv (over 90 percent).

Table 6 tries to explain the differences between schools in terms of the 2010 participation ratio. We first run a simple OLS regression (1) of the participation ratio on our main variables of interest and a set of dummies reflecting the settlement status (urban versus rural), the oblast in which the school is located and the type of the school. Then (2) we add a set of additional explanatory variables including the gender composition of the school‟s EIT participant, their language choice, the school‟s location population in 2001, and the average wage and the unemployment rate in the rayon where the school is located in 2009. Next (3) we allow for nonlinear effects in our main input variables, adding squared terms of the total number of students, the student-teacher ratio at the school and the class size of the graduating class. Finally, we use tobit regression (4) to control for the fact that the participation ratio is constrained to be between 24
**Panel D: Stock of Capital**

<!-- Start of picture text --> Figure 22: Levels Figure 23: 10 Year Growth Rates Figure 24: Annual Growth Rates<br>18 120 20<br>100<br>16<br>16<br>80<br>12<br>60<br>14<br>40 8<br>12 20<br>4<br>0<br>10<br>0<br>-20<br>8 -40 -4<br>6 8 10 12 14 16 18 -200 -100 0 100 200 -100 -50 0 50 100 150 200<br>ln(R*AoL) 10 Year Change ln(R*AoL) Dln(R*AoL)<br>ln(Capital Stock) Dln(Capital Stock)<br>10 Year Change ln(Capital Stock)<br><!-- End of picture text --> The physical capital hypothesis cannot be rejected but nights-lights data are not a suitable proxy. The growth elasticities are significant and positive, and they are stable in the case of annual growth rates, but the sum of the elasticities is not sufficiently large to be useful. As is true for GDP and electricity use, the elasticities for the ten-year intervals are larger than those for the annual intervals. Unlike those cases, however, c2 is no longer consistently larger than c1, in fact they are almost equal in each of the tests of the annual data.

||**Table 15**|**: Stock of**|**Capital**||||
|---|---|---|---|---|---|---|
||**Log-Leve**|**ls 1/**|**10 Year Growth**|**Rates 2/**|**Annual Growth**|**Rates 2/**|
||**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|**Coeff.**|**Prob.**|
|**Full Sample**|||||||
|_Constant_|-1.411|0.000|0.035|0.000|0.038|0.000|
|_AoL_|1.026|0.000|0.151|0.000|0.019|0.000|
|_R_|1.417|0.000|0.076|0.021|0.017|**0.052**|
|Adjusted R-squared|0.873||0.918||0.622||
|F-statistic|4,755||98.233||25.528||
|**FullSample, Excluding Later Years**|||||||
|_Constant_|||0.035|0.000|0.035|0.000|
|_AoL_|||0.088|0.000|0.021|0.000|
|_R_|||0.153|0.000|0.023|0.000|
|Adjusted R-squared|||0.964||0.679||
|F-statistic|||124.942||19.413||
|**Excluding High Income**|||||||
|_Constant_|||0.038|0.000|0.042|0.000|
|_AoL_|||0.169|0.000|0.026|0.000|
|_R_|||0.081|0.000|0.027|0.027|
|Adjusted R-squared|||0.920||0.617||
|F-statistic|||97.588||23.493||
|**Excluding High Income and Later Years**|||||||
|_Constant_|||0.037|0.000|0.038|0.000|
|_AoL_|||0.103|0.000|0.027|0.001|
|_R_|||0.174|0.001|0.027|**0.074**|
|Adjusted R-squared|||0.963||0.683||
|F-statistic|||120.812||19.055||

1. Panel regression, no fixed effects.

2. Panel regressions with fixed effects across time and space.

3. Adjusted R-squared for constant and fixed effects only.
<mark>improved sanitation. Using a district-level household survey (2007-2008), the authors found significant direct benefits and concave positive externalities</mark><sup>1</sup> <mark>for both improved sanitation and fixed-point defecation (Andres et al., 2014). Few studies have thus explored the link between handwashing and cognitive development. However Bowen et al (2012) use developmental quotients and children’s growth as outcomes to measure the effect of a handwashing campaign, joined to a water disinfection intervention.</mark> <mark>While recent studies demonstrate the direct effect of childhood infections on cognitive test scores (Venkataramani 2012), to our knowledge, no prior study has yet estimated the effects of hygiene and hand-washing information on socio-emotional skills of children. Considering the importance of health in childhood as a crucial factor for the development of cognitive and other critical abilities (Heckman 2008), this paper estimates the effects of intensified hand-washing information (through the presence of communication assets in the household) may influence cognitive development of children.</mark> <mark>From the time the child is born until age four, the brain is not fully developed and an extended period of postnatal maturation is still occurring. For this reason, governmental and parental interventions are crucial for the child’s cognitive and socioemotional development, as well as the</mark> environmental conditions the child is exposed to. <mark>Employing Impact Evaluation Surveys collected by the Water and Sanitation Program (WSP) in Senegal, which administered a battery of communication, motor, and socioemotional tests on infants, this study examines the potential effects of hand-washing interventions through the change in health behavior and parental awareness about their children’s development.</mark> The causal links between health or hygiene information and better developmental outcomes of children are based on evidence produced over the last few years (Conti and Heckman, 2010; Conti, et al., 2010). Some studies have shown that reducing the incidence of diarrhea, through delivering intensified hygiene information, affects mostly

> 1 The relationship shows positive slopes between fixed‐point open defecation and diarrhea prevalence with an increasing slope as open‐defecation rates increase.

3