rafmacalaba/datause-encoder-data huggingface dataset:
average across regions, the standard deviation of rainfall losses is quite constant across regions (with the exception of Benishangul-Gumuz which experiences very low levels of variation). This suggests that although rainfall losses are often high on average in Afar and Somali regions, they are not more variable than in other regions.

**Figure 1: Meher rainfall losses across regions during 1997-2012 period**

<!-- Start of picture text --> Gambella<br>SNNP<br>Benishangul Gumuz<br>Somali<br>Oromia<br>Amhara<br>Afar<br>Tigray<br>0 10 20 30 40 50 60 70<br>Mean Median Standard Devation<br><!-- End of picture text --> Note: the rainfall loss is measured in terms of the amount of yield that is calculated to have been lost as a result of rainfall shortfalls during the season. Source: LEAP from 1997 to 2012 To explore the temporal variation in rainfall losses in the country, we present annual average and standard deviation of rainfall losses from 1996 to 2012 (Figure A.2 in the Annex). There is a considerable year-to-year variation in rainfall losses. Some years particularly standout as ones in which bad droughts were experienced: 1999-2003, 2008, and 2012. The share of _Meher_ rainfall lost was the highest in 2001, and the mean rainfall loss in the country was 18 percent. Another notable result is that the spatial variation in rainfall losses tends to increase in drought years. This evidence suggests that rainfall losses tend to be localized, and some areas are particularly hit hard. This notion of localized droughts is perhaps different to drought in other countries that may have larger widespread effects and arises on account of the significant variation in agroclimatic conditions in different parts of Ethiopia. These spatial differences are leveraged to study the effects of localized weather shocks on grain prices.

There are also major year-to-year variations in rainfall losses in each region. Figure A.3 shows the average rainfall losses in each region from 1996 to 2012. In almost all regions, 2001 has been the year when the highest rainfall loss was experienced. For instance, in Afar and Somali regions in 2001 it was calculated that they would lose more than three-quarters and half of their yields as a result of poor rainfall. Afar and Somali regions also had the highest rainfall losses in each year.

12
pandemic and the recent inflationary pressures (Flugge et al., 2021). SL debt instruments lower borrowing costs by unlocking new sources of capital for emerging and developing markets, especially when paired with credit enhancement that lowers the credit risk of such instruments (such as guarantees, as discussed above). In 2022, issuances from Chile and Uruguay demonstrated proof of concept for SL sovereign financing, and a surge of these bonds was expected in 2023 (Heng, 2023). However, the corporate and sovereign markets for SL financing are currently experiencing a slowdown amid mounting doubts that issuers can credibly select the right targets and appropriate sustainable metrics. IFC research has also pointed out loopholes related to late target dates and callable options embedded in many of the corporate bonds (Ul Haq and Doumbia, 2022).

The measurement challenge is central to the development of this asset class. Indeed, while the use of proceeds framework relies on public financial and accounting reports, which are easily verifiable, the SL targets are based on non-financial metrics that are self-reported by issuers. The World Bank is therefore actively providing its technical assistance in writing these contracts and establishing the appropriate metrics and targets (Wang et al., 2023). There is an obvious trade-off between specifying a slack target (low hanging fruit) and targets that are too ambitious, and which therefore increase the risk of paying a higher price on debt if a sustainability target is missed. This risk is particularly costly for sovereign issuances from emerging and developing economies, where debt management offices are mandated to raise funds at the lowest possible cost, and some are therefore not willing to accept a potential predetermined increase in interest payments that comes into effect if the issuer is unable to achieve sustainability performance targets (a so-called coupon step-up).

35
more. Despite the high correlations, it is clear that changing the weights makes a sizable difference to aid allocations.

Data and methodological revisions also provide a clue to the robustness of mashup indices. An independent evaluation of the DBI by the World Bank (2008) pointed to a number of concerns about the robustness of country rankings to data revisions. The evaluation found 2,200 changes to the original data posted in 2007; the data revisions changed the country rankings by 10 or more for 48 countries. Wolff et al (2010) use data revisions to measure the imprecision in the HDI, and find standard deviations that vary from 0.03 (for the United States) to 0.11 for Niger (recall that the HDI is scaled to the (0,1) interval). Poorer countries tend to have less accurate HDIs.

In the case of mashup indices that use expert assessments, such as the CPIA, we can learn about robustness by comparing the assessments of different experts. The same CPIA questionnaire administered to the World Bank‘s country experts was also completed by experts at the African Development Bank (though only for Africa of course). Kaufmann and Kraay (2008) compared the two and found many notably differences in the CPIA ratings for 2005. The overall correlation coefficient in the two institution‘s scores on governance across the countries of Africa was significantly positive, with a correlation coefficient of 0.67, but still suggests a good deal less than full agreement. Of course, the source of these differences is unclear. Experts may disagree on the facts about a country, or they may disagree about how those facts are to be weighted in forming the various sub-indices that go into the CPIA.

I repeated this test for the 2009 CPIA ratings of governance by both institutions, and found that the correlation has risen to 0.87. The correlations are similar for other CPIA components: for economic management the coefficient is 0.88, for structural policies it is 0.85, while it is 0.87 for social inclusion/equity.<sup>32</sup> The correlation coefficient between the overall CPIA indices is 0.94. While their expert assessments cannot be considered independent, these correlations point to a high level of agreement, with signs that this has risen over time. However, as already noted, the aid allocations based on these indices may well be sensitive to even small differences, depending on the allocation formulae.

> 32   These calculations use the 2009 CPIA ratings available at relevant World Bank and African <u>Development Bank</u> (ADB) web sites. There are 39 countries with CPIA ratings from both institutions.

21
There are two very interesting and important findings from this emission intensity analysis. First, developing countries like China, India and even Africa are expecting higher percentage drops in their CO2 intensities than developed countries in future. Secondly, developing countries’ future CO2 intensities would remain, as in the past, much smaller than that of most developed countries by 2030 (Please see Table 2). These findings could provide an important insight during the negotiations for post-Kyoto commitments.

**Table 2: CO2 Intensities and their Average Annual Growth Rates**

|Country/Region|CO2 <br>(Metric tons p|Intensities<br>erthousand d|ollar)|AAGR of CO<br>( perc|2Intensities<br>ent)|
|---|---|---|---|---|---|
||1980|2004|2030|1980-2004|2004-2030|
|United States|0.92|0.55|0.35|-2.1|-1.7|
|Canada|0.87|0.58|0.41|-1.7|-1.4|
|Mexico|0.41|0.38|0.27|-0.3|-1.2|
|OECD EU|0.67|0.39|0.24|-2.2|-2.0|
|Japan|0.48|0.36|0.29|-1.1|-0.8|
|South Korea|0.87|0.69|0.39|-0.9|-2.2|
|ANZ|0.69|0.62|0.40|-0.5|-1.7|
|Russia|0.88|0.88|0.44|0.0|-2.6|
|OECA|1.24|0.80|0.34|-1.8|-3.2|
|China|1.96|0.72|0.28|-4.1|-3.5|
|India|0.31|0.30|0.14|-0.1|-3.0|
|Other Asia|0.40|0.36|0.22|-0.4|-1.9|
|Middle East|0.45|0.89|0.55|2.8|-1.9|
|Africa|0.40|0.43|0.22|0.3|-2.4|
|Brazil|0.21|0.23|0.17|0.4|-1.1|
|OLA|0.39|0.37|0.23|-0.2|-1.8|
|Global|0.71|0.49|0.28|-1.5|-2.2|

Source: EIA (2007a) for historical intensities and IEA (2007b) for projected intensities.

-17-
# WPS3610

## **Effects of Primary, Secondary and Tertiary Education on Economic Growth**

### **Evidence from Guatemala**

#### Josef L. Loening<sup>1</sup>

The World Bank, Washington D.C. and Ibero-America Institute for Economic Research, University of Goettingen

**Abstract:** This paper investigates the impact of human capital on economic growth in Guatemala during 1951-2002 using an error-correction methodology. The results show a better-educated labor force having a positive and significant impact on economic growth. Consistent with micro studies for Guatemala, primary and secondary education are most important for productivity growth. These findings are robust while changing the conditioning set of the variables, controlling for data issues and endogeneity. Due to an environment of social and political conflict, however, total factor productivity has been slightly negative for the past decades, and there is evidence of a missing complementarity between the country’s skills and its technology base. A growth-accounting framework is presented, which takes into account quality changes of physical capital, and differentiates by level of education. It shows that the human capital variables explain more than 50 percent of output growth. Of these, secondary schooling was the predominant determinant of growth.

**Key words:** Economic Growth, Education, Econometrics, Guatemala.

**JEL Classification:** I20, C22, C51, O54.

World Bank Policy Research Working Paper 3610, May 2005 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 Address for correspondence: <u>jloening@worldbank.org.</u>
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**Table 4:  Simulated net revenues per hectare (US $) by climate**

|District|Province|Current|CCSR|HAD3|PCM|CSIRO|
|---|---|---|---|---|---|---|
|Kandy|Central|178.42|248.72|182.28|205.00|189.05|
|Matale|Central|115.31|113.38|118.74|165.61|91.24|
|Nuwara Eliya|Central|430.29|514.47|465.90|413.04|419.09|
|Anuradhapura|North Central|101.00|99.21|37.58|56.13|84.48|
|Polonnaruwa|North Central|107.04|121.59|41.96|38.22|87.39|
|Kurunegala|North Western|62.62|80.03|33.89|119.65|80.31|
|Puttalam|North Western|210.19|210.95|134.03|235.37|214.79|
|Kegalle|Sabaragamuwa|67.09|76.17|42.73|166.47|88.31|
|Ratnapura|Sabaragumuwa|345.85|404.11|302.27|581.33|435.09|
|Galle|Southern|425.07|477.78|368.91|717.66|533.79|
|Hambantato|Southern|184.95|172.74|130.34|152.14|150.17|
|Matara|Southern|306.20|336.59|251.77|515.80|372.88|
|Badulla|Uva|258.84|258.71|184.73|94.70|200.00|
|Moneragala|Uva|108.74|134.96|87.87|35.64|77.74|
|Gampaha|Western|117.84|107.75|51.01|166.32|124.10|
|Kalutara|Western|112.45|128.79|65.13|344.86|182.03|
|Sri Lanka||186.49|207.55|143.76|228.29|196.31|
|||(4.67)|(5.06)|(4.53)|(6.61)|(5.22)|

Note: North and Eastern Provinces are excluded. The district of Colombo has been omitted from the table because only 16 households in the survey are agricultural smallholders in Colombo. Standard errors in parentheses.

29
(1986), Wall (1995), Alesina and Weder (2002), and Collier and Dollar (2002). In particular, we use the following statistical measures.

We take as our indictor of need, average per capita income for the period, measured in 1995 $US. Because large countries are expected to have strategic importance, average hostcountry population is included. Motivated by the presumption that donor countries want to influence the policies of neighbors and important trade partners, we include two fixed effects: one based on proximity and a second to indicate that the host and investor country have entered into bilateral or regional trade agreements.<sup>24</sup> Because most donors have voiced support for democratic principles, we include an indicator of the openness and accountability of host governments to their citizens, referred to in the tables as “voice and accountability”. Poorly performing bureaucracies and corruption are expected to diminish the efficacy of aid and discourage donor support. To capture this we include measures to indicate host countries with bureaucracies of above-average quality (government effectiveness) and where public and private sectors levels of corruption are below average (corruption). Political instability due to external or internal violence and armed conflict is expected to diminish the effectiveness of aid and we include this measure in our selection equation. At the same time, donors may also take a lack of civil order as an additional measure of need so that relationship between aid and civil order is ambiguous. The measures on income and population are averages for 1992-2001 (World Bank, 2006). The trade agreement and proximity variables are constructed. The institutional measures are described in Kaufmann, Kraay and Mastruzi (2005).

# **6. Empirical Results**

A simple prediction of the conceptual model is that, because donor-country agencies prefer projects that help achieve national policy objectives that are also pursued through bilateral aid, AIJ projects will be observed together with bilateral aid flows. At the same time, because firms are motivated to invest for separate reasons, not all countries receiving aid are expected to host projects. Among the 2,992 investor-host relationships in our sample, this simple prediction holds over study period averages. (See table 2.) Moreover, in all but five of the 147 AIJ projects, bilateral aid was given during the year that the project was launched.<sup>25</sup> Even so, it is possible that

> 24 A dummy variable was set to one for investor-host dyads when both countries were members of the Commonwealth, ASEAN or NAFTA and for dyads containing an EU investor-country and a host country participating in the Phare program.

> 25 See annex table 2 for more detail.

15
A natural consequence of these patterns is that host countries may end up with high concentrations of high‐skilled immigrants in particular occupations. For example, immigrants account for some 57 percent of scientists residing in Switzerland, 45 percent in Australia, and 38 percent in the United States (Franzoni et al. 2012). In the United States, 27 percent of all physicians and surgeons and over 35 percent of current medical residents were foreign born in 2010. Immigrants also accounted for over 35 percent of recent enrollments in STEM fields, with very high proportions in specific areas like Electrical Engineering (70 percent), Computer Science (63 percent) and Economics (55 percent) reported by Anderson (2013). In the United Kingdom in 2014, the _Guardian_ newspaper reported that the National Health Service (NHS), the world’s fifth largest employer, employed workers from some 200 countries worldwide and 26 percent of NHS doctors were not originally British citizens.<sup>6</sup> The global migration of inventors and the resulting concentration in a handful of countries have been particularly well documented. Kerr (2016) reviews the literature and we focus here on patent records taken from the World Intellectual Property Organization (WIPO) database. Miguelez and Fink (2013) calculate that the global migration rate of inventors in 2000 stood at 8.6 percent, at least 50 percent greater in share terms than the average for high‐skilled workers as a whole. Figure 4 builds on WIPO global patent filings from 2001‐2010. The United States has received an enormous net surplus of inventors from abroad, while China and India have been major source countries. Some countries like Canada and the United Kingdom are major destinations but still experience a negative net flow due to even larger emigration rates for inventors, usually to the United States.

Similarly striking levels of immigrant concentration are evident in sports, where global outliers in terms of talent and income are the norm. Spectators in the English Premier League, the most popular and profitable soccer league in the world, have now witnessed the fielding of players from 100 different nationalities during the 2013‐14 season. As of 2015, the English Premier League featured more players of different nationalities than any other league in Europe, followed by Germany’s Bundesliga (55 nationalities), Italy’s Serie A (51), Spain’s La Liga (50) and France’s 6https://www.theguardian.com/society/2014/jan/26/nhs‐foreign‐nationals‐immigration‐health‐service.

9
results are consistent with recent evidence on how some interventions in CBT can positively impact behavior (Blattman et al., 2017; Heller et al., 2017).

Although valid EEG recordings were approximately 52% of the total subsample, we showed in the attrition subsection that this rate was not different across T and C groups. Therefore, assuming that the process governing recordings validity follows a standard monotonicity property, we can compare outcomes across both groups, which will be valid estimates of the impact of treatment on outcomes for the full randomly selected subsample. However, as a check, we computed Heckman selection-corrected regression results for the same subsample outcomes (Heckman, 1979). These results are presented in Table A10 in the Appendix. We find these estimated ITT effects using the 598 observations. After correcting for the selection bias—using a dummy that indicates if the student was selected or not selected to participate in the neurophysiological experiments as selection variable—we notice they are very similar to the results obtained from the 308 valid EEG recordings. We also estimated Lee’s treatment effects bounds for nonrandom sample selection (Lee, 2009) and present these results in Table A11 in the Appendix. We find that zero belongs to the treatment effects interval for all variables except for valence, positive valence difference, and locus of control. Thus, attrition is unlikely to have biased the results reported above.

There is a lack of evidence of heterogeneous effects of ASPs on socioemotional skills and no evidence of differences in the impact on ER by participants’ initial propensity for violence. Therefore, we contribute to the literature testing if ASPs affect high- and low-violence children’s neurophysiological outcomes differently. In Table 6, we present the estimated effects using specification (2) for ER measures. Coefficients in row [i] show the ASP’s effects on low-violence treated students compared to low-violence children in the C group. Coefficients in row [ii] show the differences in effects between highly violent treated students and similar children in the control group. Then, coefficients in row [iii] point to the difference in effects between high- and low-violence treated students. Finally, row [iv] indicates _p_ -values for the test of difference in effects between high- and low-violence treated students.

When we compare low-violence students assigned to T or C groups (row [i]), we find treated children drive both the reductions in the valence-at-resting-state measure and in the perception that they can control their circumstances by 0.57 and 0.47 standard deviations, respectively.

Then, looking at the estimations on the ASP’s differential effects on arousal, we unexpectedly find that treating highly violent children increases their stress levels compared to both highly violent nontreated peers and with low-violence treated students. What can be causing this unexpected result? Due to the novelty of the complete experimental design, evidence from Dinarte (2018) on group composition in terms of participants’ initial propensity for violence helps to pro30
# **Annex 1. Imputations of skills requirements for occupations unrepresented by job postings**

# **data**

## **Table A.1.1. Employment share of the 27 imputed occupations**

|||**Occupation**|<br>|**Employmen**<br>|**t share (%)**<br>||
|---|---|---|---|---|---|---|
|**#**|**Occupation name**|<br>**code**|**Cambodia**|**Malaysia**|**Thailand**|**Vietnam**|
|1|Legislators and senior officials|111|0.5|0.1|0.7|0.4|
|2|Traditional and complementary medicine professionals|223|0.0|0.0|0.0|0.0|
|3|Traditional and complementary medicine associate professionals|323|0.0|0.1|0.0|0.0|
|4|Veterinary technicians and assistants|324|0.0|0.0|0.0|0.0|
|5|Secretaries (general)|412|0.0|0.1|0.0|0.0|
|6|Street and market salespersons|521|4.8|4.2|3.5|7.1|
|7|Market gardeners and crop growers|611|9.6|5.1|21.7|4.8|
|8|Animal producers|612|6.8|0.2|2.5|1.8|
|9|Mixed crop and animal producers|613|0.0|0.0|0.2|0.0|
|10|Forestry and related workers|621|0.4|0.0|0.2|0.0|
|11|Fishery workers, hunters and trappers|622|1.3|0.7|0.8|1.8|
|12|Subsistence crop farmers|631|12.2|0.0|3.1|1.1|
|13|Subsistence livestock farmers|632|3.5|0.0|0.0|0.1|
|14|Subsistence mixed crop and livestock farmers|633|0.0|0.0|0.0|0.1|
|15|Subsistence fishers, hunters, trappers and gatherers|634|1.1|0.2|0.2|0.0|
|16|Wood treaters, cabinet-makers and related trades workers|752|0.9|0.5|0.3|0.8|
|17|Other craft and related workers|754|0.0|0.1|0.5|0.5|
|18|Metal processing and finishing plant operators|812|0.0|0.2|0.3|0.2|
|19|Rubber, plastic and paper products machine operators|814|0.1|0.6|0.6|0.3|
|20|Food and related products machine operators|816|0.3|0.3|0.5|0.2|
|21|Wood processing and papermaking plant operators|817|0.0|0.5|0.1|0.2|
|22|Locomotive engine drivers and related workers|831|0.0|0.0|0.0|0.0|
|23|Ships'deck crews and related workers|835|0.0|0.1|0.1|0.2|
|24|Vehicle, window, laundry and other hand cleaning workers|912|0.7|0.2|0.4|0.1|
|25|Street and related service workers|951|0.0|0.0|0.0|0.1|
|26|Street vendors (excluding food)|952|0.4|0.3|1.1|0.2|
|27|Refuse workers|961|0.4|0.5|0.3|0.3|
|||**Total**|43.0|14.0|37.1|20.3|

_Sources_ : Employment shares are from Cambodia 2020 Socioeconomic Survey, and 2017 employment surveys of Malaysia, Thailand, and Vietnam.

40