rafmacalaba/datause-encoder-data huggingface dataset:
thresholds might not be relevant for developing countries, where small percentages of countries pupils might attain the upper benchmarks. In the future, we hope to use alternative thresholds.

# **4. Results**

We construct two complementary _Harmonized Learning Outcomes (HLO)_ datasets: (a) _Panel Data Set._ Our panel database provides over time comparable scores for education quality from 1965 to 2015. On average, our dataset includes 3.3 observations per country at the primary level and 4.5 observations at the secondary level.

(b) _Cross-Sectional Data Set_ . Our cross-sectional dataset provides measures of education quality averaged across time and subject. We obtain at least one measure of education quality for approximately 163 countries/areas, 100 of which are developing economies and 30 in SubSaharan Africa.

We provide estimates for mean scores, standard errors, and low, intermediate and advanced proficiency benchmarks. We also include disaggregated estimates across: subject, school level (primary, secondary); gender, socioeconomic status, language (if the test language is the same language spoken at home), geographic location (urban, rural), and immigration status.

_<u>4.1. International Comparison of Education Quality using the Cross-Sectional Dataset</u>_ Figures 1 and 2 present educational achievement among regions. Asian countries seem to outperform countries from other regions in the primary and secondary level, followed by North America and Europe. Latin America and the Caribbean and Northern Africa are the next best performers, followed by Sub-Saharan Africa. The regions that perform worst, Sub-Saharan Africa and Southern Asia, have larger gaps in primary education performance than secondary education performance. Among middle income countries, those in Eastern Europe and Central Asia perform the best. Developing countries perform worse in both primary and secondary education than developed countries, and have much larger variance, especially at the primary level. While variation is high, and the top-performing country in Sub-Saharan African still performs lower than the lowest performing country in developed economies.

An important note when interpreting these results is that they might capture a series of selection effects. One such selection effect is driven by immigration. If a country tends to attract the most able students from around the world, it will have high mean scores. For instance, PISA 2009 results showed that approximately 15 percent of pupils from Singapore have an immigrant background, while this proportion is more than 40 percent in Qatar, which might drive mean scores up due to immigration rather than school quality alone. We can check this empirically 23
Figure 6. Colombia: Provisions as Share of Total Credit (%) <!-- Start of picture text --> 10.0<br>9.0<br>8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>—— SPECIFIC —=— = GENERAT. —— TOTAL.<br><!-- End of picture text -->
**Table 5: Impacts of different household members’ migration on labor participation**

||**Participation**<br>**in agr.**|**Participation in**<br>**non-agr.**|**Participation**<br>**in wage labor**|**Participation in**<br>**family business**|
|---|---|---|---|---|
|**Total Sample**|||||
|Children migrated|0.092***|-0.007|0.007|-0.007|
||(0.023)|(0.031)|(0.013)|(0.030)|
|Husband migrated|0.021|-0.019|-0.051*|0.018|
||(0.038)|(0.048)|(0.027)|(0.047)|
|Other migrated|0.020|0.031|0.029|-0.011|
||(0.061)|(0.073)|(0.029)|(0.071)|
|**Age 16-20**|||||
|Other migrated|0.042|0.063|0.041|0.006|
||(0.159)|(0.190)|(0.120)|(0.141)|
|**Age 21-35**|||||
|Husband migrated|0.075|-0.036|-0.104**|0.056|
||(0.056)|(0.082)|(0.052)|(0.085)|
|Other migrated|0.080|0.078|0.017|0.040|
||(0.086)|(0.087)|(0.039)|(0.094)|
|**Age 36-50**|||||
|Children migrated|0.071***|-0.016|0.004|-0.027|
||(0.025)|(0.043)|(0.016)|(0.039)|
|Husband migrated|0.014|-0.035|-0.022|-0.020|
||(0.038)|(0.055)|(0.030)|(0.046)|
|Other migrated|0.028|-0.124|0.017|-0.045|
||(0.121)|(0.116)|(0.021)|(0.081)|
|**Age 51-60**|||||
|Children migrated|0.102**|0.042|0.024|0.042|
||(0.045)|(0.053)|(0.015)|(0.051)|
|Husband migrated|-0.212|0.055|-0.158|0.203|
||(0.227)|(0.249)|(0.125)|(0.198)|
|Other migrated|0.032|-0.056|0.015|-0.062|
||(0.127)|(0.176)|(0.013)|(0.175)|
|**Age 61-70**|||||
|Children migrated|0.245*|0.102|-0.033|0.102|
||(0.132)|(0.132)|(0.034)|(0.132)|
|Husband migrated|-0.755***|0.307|-0.034|0.307|
||(0.184)|(0.222)|(0.034)|(0.222)|
|Other migrated|-0.313|-0.314|0.032|-0.314|
||(0.445)|(0.215)|(0.035)|(0.215)|

Notes: The sample consists of all women who are present in all survey rounds. Migration variables are valued at one period lag. Other variables included but not reported are the same as are given in Table 3 along with province/year interactions. Standard errors are clustered at the village level. * denotes significance at 10% level; ** denotes significance at 5% level; and *** denotes significance at 1% level.

36
Osakwe and Schembri [1999], Flood and Hodrick [1986], Flood and Marion [1982], Dornbusch [1976]).

However, while real output _volatility_ has been addressed by the literature as a factor in the choice of optimal foreign exchange regimes, scant attention has been given to the time necessary for real output to return to its trend after an adverse external financial shock takes place, that is, _persistence._ This appears particularly relevant for emerging markets, where seamless neo-classical adjustments in labor markets are all but absent. In recent empirical research, Caporale, Kalyvitis, and Pittis (1994) find evidence of greater persistence in a number of real variables, including industrial production, in 18 OECD countries after the collapse of the Bretton Woods fixed exchange rate system. Their work, which covers no emerging economies, is based on the modified re-scaled range statistic discussed by Lo (1991), which is employed to test for long term persistence. They reject the null hypothesis of"no long-run memory" for industrial production in both fixed and floating regimes within the three month period following a shock. For a test of nine month persistence, however, they find that they cannot reject the null hypothesis of"no long run memory" for industrial production in the fixed rate period, while it can be rejected in the floating period. Though this is an important contribution to the empirical issue of persistence under alternate exchange regimes, it does not examine any emerging market economies. Additionally, the method of empirical testing can yield neither an overall picture relating to the depth of a shock nor a rough estimate of the length for which the shocks effect approaches zero, i.e., it is only able to conclude that, for example, there is evidence of persistence after 9 months, but not whether that persistence diminishes in the tenth month or when the series returns to its long run path.

This note contributes to the debate over optimal foreign exchange regimes by reporting on empirically observed persistence under alternative foreign exchange arrangements. To control for 3
# **3. Estimating Energy Delivery Costs**

We estimate the costs of universal power supply in a given country through gridconnected systems and compare them with costs of providing the same level of electricity supply with different decentralized options. We compare these options at each step of a hypothetical investment schedule that progressively adds supply areas until the entire population of a country is covered. For grid connected supply we estimate the cost of extending transmission and distribution to all populated parts of the country. We also assume a scaling up of power production with the current fuel mix. Among decentralized options we estimate the supply costs for fully decentralized power provision to currently unserved customers, in which each household generates its own electricity, and for minigrid systems that provide power to tens or hundreds of households in order to satisfy the unmet demand. We assess the use of both fossil fuels (diesel generators) and renewables (solar, wind, biodiesel) for decentralized options. Other decentralized options, such as small-scale hydro, look promising, but data on their potential are scarce and thus we were not able to include them.

In comparing the resulting cost estimates, our primary interest is in the following questions:

- Spatial partition:  Where is the optimal geographic boundary between gridconnected and decentralized provision, and what are the relative population shares supplied by each mode?

- Scale economies: How does the optimal spatial boundary change as decentralized provision moves from completely decentralized micropower to minigrids with some scale economies, thus increasing the relative economic advantage of larger scale electricity provision with renewables?

- Future costs:  How will the configuration of cost-effective energy supply options change in the future as technical change lowers the cost of renewable energy sources, or as premium values for clean technology change relative fuel prices?

We develop our model with case studies for Ethiopia, Ghana and Kenya. We primarily use Ethiopia to illustrate our approach and results. Cost comparisons for Ghana 6
is unclear whether and how the concept of shared prosperity defined in this way would resonate within a country.<sup>8</sup> The shared prosperity measure offered by Dang and Lanjouw (2016) provides a complementary angle by explicitly focusing on the poorer income groups. In particular, the 14 countries with lower poverty rate are considered as having positive pro-poor growth (i.e., marked with stars, column 8); similarly, the remaining countries with more poverty are regarded as not having pro-poor growth (i.e., marked with minuses, column 8). Furthermore, countries with less vulnerability but in the same group are judged as having better performance. For example, among the 14 countries with positive pro-poor growth, five with less vulnerability (including Mauritania, Ethiopia, Togo, Eswatini, and Malawi) are ranked as having even more positive growth. Of the seven countries with more poverty, five (including Burkina Faso, Zambia, Madagascar, Côte d’Ivoire, and Cameroon) managed to reduce vulnerability. If we combine this second measure with the first measure of shared prosperity, certain countries come out as consistently performing very well, like Botswana or Mauritania, or near the bottom, like Senegal and Nigeria.

We turn next to discussing the trade-off between economic growth and the quality of the environment. We plot in Figure 1 the global GDP per capita level against air quality, as measured by the number of micrograms per cubic meter (μg/m3) PM 2.5 using the WDI database for 1990 to 2016. Figure 1 indicates that all countries in the world as a whole almost tripled their GDP per capita during this period. While this good news should clearly be celebrated, it did not come

> 8 Other issues exist as well. For example, why should the income threshold be set at the 40th percentile, rather than say, other percentiles such as the 30<sup>th</sup> percentile or 50<sup>th</sup> percentile? Since different countries can use different national poverty lines with their concomitant poverty reduction and social protection policies, why should a blanket bottom 40 percent apply globally? Furthermore, one might also ask why we do not consider growth of the income distribution as a whole instead (see also Jolliffe et al. (2015, chapter 5) for further discussion of this last point). As an alternative, we provide in Table 2.2 (Appendix 2) estimates using the Gini coefficients, which also show inconsistency between poverty and inequality. For example, of the same 14 countries that could reduce poverty, in almost half (i.e., Chad, Ghana, Uganda, Ethiopia, Togo, and Malawi) inequality as measured by the Gini coefficient worsened.

10
substitutability in CUs are very similar to those in column (1).

Finally, there may be a concern that the effects we identify are only present in the early period, when the bulk of tariff reduction took place. In the last column, we split the Δ _PREF_ variable into a period-one effect (1990-1994) and a period-two effect (1995-2001). The Δ _PREF_ variable in period two covers the same period as the CUs. The positive effect in FTAs is present in both periods, although it is smaller in period 2. The coefficient on Δ _PREFCU_ remains negative and highly significant, so that the CU net effect, which is given by the sum of the coefficient on Δ _PREF_per2_ and the coefficient on Δ _PREFCU_ , is negative and significant, as the F-test on their sum indicates.

Now, as we highlighted in our discussion in Section III, the incentives to change external tariffs in an RTA should depend also on the importance of preferential imports relative to total imports. To account for that, we include in our regressions the share of the RTA partners in total imports (with and without the CU interaction term). If tariffs are simply moving together, there should be no effect of RTA import shares on MFN tariff changes. However, if external and preferential liberalizations are indeed complements, MFN tariffs should fall more both when preferential tariffs fall and in sectors in which imports from partners weigh heavily in total imports, provided that there is a meaningful preference margin. If the two types of liberalizations are substitutes, we should observe that preference margins are mostly protected in important RTA goods.

To address this issue, a natural variable to include would be the lagged import share of RTA partners. However, import shares are affected by preferential and MFN tariffs—a larger wedge between the two induces a higher preferential import share. This would bias the coefficient up, since increasing MFN tariffs would tend to increase RTA import shares, all else 28
# **Table 3 – Pass-through**

|Variable|Ag|riculture||Man|ufacturin|g|
|---|---|---|---|---|---|---|
|International price|1.449|***|(0.165)|0.004||(0.007)|
|Regional cpi|0.284|*|(0.149)|1.174|***|(0.247)|
|Local supply|-0.036|***|(0.011)|-0.016||(0.017)|
|Urban /Rural|0.131|***|(0.043)|0.510|***|(0.064)|
|Distance|0.002||(0.012)|-0.030|*|(0.016)|
|Tariff pass-through|0.260|*|(0.155)|0.671|***|(0.101)|
|Urban transmission|0.003||(0.033)|-0.091|***|(0.034)|
|Rural transmission|-0.054|**|(0.027)|-0.108|***|(0.027)|
|Constant|9.498|***|(1.021)|5.201|***|(0.623)|
|Obs|378|||378|||
|R-squared|0.58|||0.64|||

Note: All variables, except distance, are in log. White corrected standard errors are shown in brackets. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively.

**Table 4– Change in real income (Doha)**

|Region|Total|Urban|Rural|All<br>poor|Urban<br>Poor|Rural<br>Poor|Very<br>poor|Urban<br>Vpoor|Rural<br>Vpoor|
|---|---|---|---|---|---|---|---|---|---|
|Federal|-0.1%|-0.1%||-0.1%|-0.1%||0.0%|0.0%||
|District||||||||||
|Border|-0.3%|-0.3%|0.1%|-0.1%|-0.2%|0.1%|0.2%|0.1%|0.3%|
|North|-0.1%|-0.1%|0.1%|0.0%|-0.1%|0.1%|0.2%|0.3%|0.2%|
|Center|-0.1%|-0.2%|0.0%|-0.1%|-0.2%|0.0%|0.0%|-0.1%|0.0%|
|South|-0.1%|-0.2%|0.0%|-0.1%|-0.2%|0.0%|0.0%|-0.2%|0.0%|
|National|-0.1%|-0.2%|0.0%|-0.1%|-0.2%|0.0%|0.0%|-0.1%|0.0%|

**Table 5:  Change in real income (Doha plus)**

|Region|Total|Urban|Rural|All<br>poor|Urban<br>Poor|Rural<br>Poor|Very<br>poor|Urban<br>Vpoor|Rural<br>Vpoor|
|---|---|---|---|---|---|---|---|---|---|
|Federal|0.1%|0.1%||0.3%|0.3%||1.2%|1.4%||
|District||||||||||
|Border|0.6%|0.4%|1.5%|1.0%|0.8%|1.6%|2.0%|1.8%|2.4%|
|North|0.9%|0.7%|1.2%|1.1%|0.9%|1.2%|1.8%|2.5%|1.5%|
|Center|0.3%|0.2%|0.5%|0.4%|0.3%|0.5%|0.6%|0.7%|0.6%|
|South|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|0.1%|0.2%|0.0%|
|National|0.4%|0.2%|0.6%|0.5%|0.4%|0.5%|0.7%|0.9%|0.6%|

25
currency effects. If the US$ appreciates the<sup>country swaps other</sup> currency debt for US$ debt to maintain a constant<sup>dollar value</sup> share of other currency debt. Another hypothesis<sup>is to assume that</sup> a fixed share (a,) of the dollar flow value is borrowed in foreign currency. This allows us to determine the stocks<sup>of foreign debt</sup> by foreign currency, implicitly changing the ai<sup>for the past</sup> stocks. Both assumptions can be misleading, but<sup>in the absence of</sup> data on the ai I s or on the currency composition of<sup>flows we opt for</sup> the second assumption since Turkey does not seem<sup>to pursue an</sup> offsetting borrowing strategy. This is a temporary<sup>solution until</sup> proper data on the ai's are collected.

Table A1.2 shows the data obtained from solving the system<sup>of</sup> equations Al.l through A1.4. Boldface numbers are the<sup>variables</sup> we solved for while numbers with an asterisk indicate<sup>assumptions</sup> that we have made. All other variables are taken<sup>from available</sup> statistics, as explained above. Blank entries simply<sup>reflect the</sup> underlying economic structure as not all sectors<sup>are related to</sup> each other for all items.

# Capital Account of Balance of Payments

As a by-product of the exercise above we also obtain<sup>all the</sup> entries in the capital account of the balance of payments.<sup>Table</sup> A1.3 reproduces the balance of payments in its<sup>original format</sup> obtained from the Central Bank. Table A1.4 presents<sup>the balance</sup> of payments in the format needed for the model.<sup>As is evident, the</sup> original capital account does not contain sufficient<sup>information</sup> to fit our sectoral composition of the economy. The<sup>solution of</sup> equations Al.l to A1.4 solves this problem.

11
logarithm of quarterly real GDP (GDPQ) from a linear deterministic trend, the annualized rate of change in the quarterly GDP deflator (GDPD), the natural logarithm of the quarterly average of the monthly spot market commodity price index (PSCCOM) and the quarterly average of the Federal Funds Rate (FFR). The exogenous variable _CONNt−_ 1 contains our aggregate Measure of Connectedness. The data ranges from 1959Q1 to 2009Q1.

Four lags are included in the VAR. The identification of structural shocks is achieved by the following recursive ordering: [ PSCCOM, GDPQ, GDPD, FFR ]. Hence, the unexpected monetary policy shock is ordered last in _εt_ . The rotation matrix _H_ is obtained using Choleski ˆ decomposition of the covariance matrix of _et_ . The 95% confidence intervals are computed using 1000 bootstrap replications.

In Figure (10), we compare the impulse responses of elements in _Yt_ to an adverse monetary policy shock when the Measure of Connectedness is high and low, respectively _CONN_ = 0 _._ 98 and _CONN_ = 0 _._ 82. These are the average values of our connectedness measures for the periods 1959Q1-1983Q4 and 1984Q1-2009Q1. The impulse response with confidence bands for each level of connectedness are presented in Figures (11) and (12).

As we can see from Figure (10), at the level of connection of 0.98 the adverse monetary shock generates a decrease in output, which exhibits a hump-shaped response. The price level decreases too, but only after a few quarters (the well known price puzzle phenomenon). When we consider a level of connection of 0.82, instead, we see that the response of the GDP to the same monetary policy shock is now not statistically different from zero. Also the responses of the quarterly GDP deflator and the spot market commodity price index are muted at the lower level of connection. Interestingly, there is no evidence of price puzzle in that case.

In order to assess whether the difference in the impulse response we obtained under different levels of connection is statistically significant, we plot the difference in Figure (13), and we include confidence intervals at 90% significance level. As the figure shows, the impulse responses of GDP and GDP deflator are statistically different under the two scenarios, while the impulse responses of the commodity price index and the federal funds rate are not 18
Cleland 2004; Cleland et al. 2006; Chimbiri 2007; Hendrikson et al. 2007; Biraro et al. 2009).

Because these results are based on self-reported sexual behavior data, they should be considered with caution. Self-reported sexual behaviors, including both condom use and sexual intercourse, have been shown to be unreliable in certain instances and it is very difficult to independently validate the data because, apart from biological evidence of sexually transmitted diseases (STD), there is no outside indicator of the reported sexual behavior (Weinhardt et al. 1998). One study (Zenilman et al. 1995) done in the United States calls into question the validity of self-reported condom use by demonstrating that 15 percent of men who reported to always use condoms had acquired STD during the study period compared with 15.3 percent of those who reported never using a condom. Similarly, in the same study, 23.5 percent of women who reported always using a condom had STD compared with 26.8 percent of women who reported never using a condom. Also using biological markers, Allen and others (2003) show that reported condom use in discordant couples (where one partner has HIV and the other does not) was also not reliable by showing that sperm was present in 15.1 percent of vaginal smears when no unprotected sex was reported compared with 24.7 percent of smears when unprotected sex was reported. In contrast, by comparing reported condom use by married partners, de Boer and others (1998) find fair to good agreement in couples in Thailand regarding their reported condom use. Another study found that, based on data from 23 countries, men over-report contraception use, though to widely varying degrees (Becker and Costenbader 2001).

21
with readily available data.<sup>13</sup> While extending the sample to other countries could provide more diverse narratives, the availability of data starts getting scarce. Even within the current sample of large countries, while the data on daily exchange rate are available for all 20 of them, equity prices are available for 19 countries, and bond yields are available for just 17 of them. The exchange rates are in local currency per USD; equity prices are in local currency; and bond yields are 10-year sovereign yields in local currency (see Appendix B for the sample of countries, Appendix C for data sources, and Appendix D for their summary statistics).

**Chart 2: Treasury yields on the days of the FOMC Announcements**

<!-- Start of picture text --> 2-year Treasury yields Change in 2-year Treasury yields on FOMC days<br>Oct 2008 Sep 2010 Aug 2012 Aug 2014 Jul 2016<br>2-year Treasury yields FOMC days with large changes Oct 2008 Sep 2010 Aug 2012 Aug 2014 Jul 2016<br>Distribution of percent changes in 2-year Treasury yields Percent changes in 2-year and 10-year Treasury yields on FOMC days<br>-40 -20 0 20 40<br>FOMC days Non-FOMC days -40 -30 -20 -10 0 10 20 30 40<br>2-year<br>2 40<br>1.5<br>20<br>% 1<br>0<br>% change<br>.5<br>-20<br>0<br>-40<br>.08 15<br>10<br>.06<br>5<br>.04 0<br>Density<br>10-year -5<br>.02 -10<br>-15<br>0<br>-20<br><!-- End of picture text --> Two-year Treasury yields are depicted in the first panel of Chart 2. The period is marked by medium term trends, whereby yields declined between 2008 and 2011; stayed low during 2012-2013 and increased thereafter; as well as by short term movements at higher frequency. The vertical lines mark the FOMC days when day-to-day percentage change in yields was below or above the period average by 2 standard deviations.

> 13 Our country sample includes Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Israel, Malaysia, Mexico, Peru, Philippines, Poland, Russian Federation, South Africa, the Republic of Korea, Taiwan, China, Thailand, and Turkey. Of these, we do not have daily equity prices for Israel, and bond yields for Israel, Peru and Brazil.

8