rafmacalaba/datause-encoder-data huggingface dataset:
macroeconomic benefits in Case 2 are smaller than Case 1. Another reason could be that in Case 2 the coverage of the sectors that have technical progress and efficiency gains is smaller.

# _Trade Diversion_

In theory, trade facilitation reform measures tend to create less trade diversion effects. Table 6 summarizes the impacts on the direction of nominal trade. Overall, exports from and to APEC members increase. The largest gains are associated with exports from APEC members to APEC members, followed by that from non-APEC to APEC members, and that from APEC to non-APEC. One may note also that the exports from non-APEC economies to non-APEC economies are lowered by about 3 percent or less. The trade relations between the APEC members has been strong, and even non-discriminatory trade liberalization measures, such as transparency improvements, would intensify intra-regional trade ties. However, these somewhat negative impacts on the nominal trade between the non-APEC economies are generally much smaller than the bilateral preferential trade liberalization, such as regional trade agreements.

**Table 6: Impacts on Directions of Nominal Exports (%)**

|Case 1|importers||
|---|---|---|
||APEC<br>non-APECW|orld|
|APEC|12.7<br>3.3|9.3|
|non-APEC<br>exporters|9.5<br>-3.4|0.4|
|world|11.6<br>-1.4|4.3|
|C 2|importers||
|ase|APEC<br>non-APECW|orld|
|APEC|10.9<br>0.4|7.1|
|non-APEC<br>exporters|7.7<br>-2.1|0.5|
|world|9.8<br>-1.4|3.5|

(Note) Authors’ calculation using the GTAP model and Gempack.

29
- 6a -

_TAILS_ 1: C4pacitz of Resions bw Clilmtic **Areas** with Prgsent Input.

|ftrconu/a<br>Growing Days<br>COP. Ptrsonal/a|Desuert<br>0<br>0.005|Arid<br>1-75<br>0.05|Sea<br>Arid<br>7-.20<br>0.1|Dr<br>Subhdtid<br>lWV<br>0.5|moiste<br>Subboudd<br>IS-zIu<br>1.0|Rudd<br>_ZIwt_<br>1.5|Total<br>Capacity.<br>at preceat<br>land use|Prouat<br>population<br>(1980data)|ieerw<br>Surport<br>Capaeity|
|---|---|---|---|---|---|---|---|---|---|
|Haditerranean<br>ArldNorth|1.0|2.0|1.7|6.0|15.6|4i|railfd<br>26.3<br>+ irrigtiom<br>40.0|90<br>(grati<br>imported)|-<br>24|
|Sudano SBabeian|1.3|14.7|8.4|8.9|7.9|3.6|44.8|53|-<br>10|
|Humid and<br>Sub-Rumid<br>test|0|0|0.3|7.4|46.6|31.5|103.8|117|-<br>11|
|Hudid Cantral|0|0|0|3.0|80.2|292.7|375.9|42|+ 334|
|Sub-Huid<br>and<br>bfantalm East|0.1|3.1-|3.2|4.4|21.2|15.5|47.5|81|-<br>33|
|Sub-Hudd<br>and<br>Smd Arid South|0.2|4.2|8.9|21.8|91.9|6.9|133.9|62|*72|
|Totals|2.6|24.0|22.5|51.5|263.4|370.2|734.2|U?|+ 328|

Sourcet Caleated from tAO data. Quoted ia -hw Capacity of the African tUnvroment to fuatain ropulatio Grwtbh by H. Charles etreira, p. 37 (smmo).
everyone knows that2'. But the implication of our positive model is that while "money matters" to educational outcomes, money also matters to those who receive it as income and that "mattering" will have implications for publicly determined outcomes, like the allocation of expenditures.

_Budgets and educational outcomes when objectives differ._ If non-experimental data are taken from jurisdictions (or time periods within the same jurisdiction) that differ in either the degree of educational output concern (1-a) or in the degree of professionalism (6) of the teachers then equal budgets could produce very unequal outcomes due to the allocation across inputs". For any given level of a, higher budgets lead to better outcomes (the sense in which money matters). But a higher a implies worse outcomes for any given budget. Any estimation of the relationship between expenditures and budgets from non-experimental data that differ in a which do not control for these differences can produce any relationship at all: positive, zero, or negative. For instance, Hanushek and Kim (1996) estimate the relationship between the internationally comparable test scores of achievement in math and science derived from the various IEA and IEAP studies and expenditures. They find that both public expenditure per student and the ratio of total educational expenditure to GDP are significantly 25 There has been a vociferous debate in the US around the question "does money matter?" In this political debate the rejoinder "the only ones who say resources do not matter have their children in schools with adequate resources" carries a good deal of common sense appeal.

26Differences in the relative weight of teachers also happen over time within a given jurisdiction as across jurisdictions. For instance, in Paraguay after the end of a long period of dictatorship teacher wages more than tripled in a six year period, with an almost complete lack of complementary reforms either in the school system or even in the structure of teacher pay. Given the total lack of objective comparable testing it is impossible to say for sure, but many doubt this increase led to commensurate increases in student performance.

35
- [33] Markandya, A., M. González-Eguino, and M. Escapa. (2013): From shadow to green: Linking environmental fiscal reforms and the informal economy. Energy Economics, 40, S108–S118.

- [34] Marron, D., and A. Morris (2016): How to Use Carbon Tax Revenues. Washington DC: Tax Policy Center.

- [35] Mazzucato, M. (2013): The Entrepreneurial State: Debunking Public vs. Private Sector Myths. London: Anthem Press.

- [36] Mazzucato, M. (2015): The Green Entrepreneurial State, in Scoones, I., Leach, M., and Newell, P. (eds.) The Politics of Green Transitions. Abingdon: Routledge.

- [37] Mazzucato, M. (2016): From Market Fixing to Market-Creating: A New Framework for Innovation Policy. Industry and Innovation, 23 (2), 140-156.

- [38] Mazzucato, M. and M. McPherson, (2018). The Green New Deal: A bold, mission-oriented approach. UCL Institute for Innovation and Public Purpose Working Paper IIPP PB 04.

- [39] Mazzucato, M. and G. Semieniuk (2017): Public financing of innovation: new questions, Oxford Review of Economic Policy, 33(1), pp. 24-48.

- [40] Mazzucato, M. and G. Semieniuk (2018a): Financing renewable energy: Who is financing what and why it matters, Technological Forecasting and Social Change, 127, 8-22.

- [41] Mazzucato, M. and G. Semieniuk, (2018b): Bridging the gap: The role of innovation policy and market creation, in Christensen et al (eds.) Emissions Gap Report 2018, Nairobi: United Nations Environment Programme.

- [42] Moody‘s. (2018): Sovereign green bond market on course for critical mass, but challenges remain. Sector in Depth: Sovereign and Supranational. Press Release 09/07/2018.

- [43] Norberg-Bohm, V. (2000): Creating Incentives for Environmentally Enhancing Technological Change: Lessons From 30 Years of US Energy Technology Policy. Technological Forecasting and Social Change, 65(2), 125-148.

- [44] Nordhaus, W. (2008): A Question of Balance. New Haven: Yale University Press.

- [45] Organisation for Economic Cooperation and Development (2017): Mobilising Bond Markets for a Low-Carbon Transition, Green Finance and Investment. Paris: OECD Publishing

- [46] Organisation for Economic Cooperation and Development (2018): Taxing Energy Use 2018: Companion to the Taxing Energy Use Database. Paris: OECD Publishing.

- [47] Orlov, S., E. Rovenskaya, J. Puaschunder, and W. Semmler (2018): Green Bonds, Transition to a Low-carbon Economy, and Intergenerational Fairness --- Evidence from the DICE Model. . IIASA Working Paper, Laxenburg, Austria.

15
# **5. Conclusion**

This paper has reviewed levels and trends in education and skill premiums, and skilled labor force, across eight East Asian countries, and over time, sector and sub-sector, representing the most comprehensive comparative exercise so far on this topic. Several main trends have emerged. Main trends include:

-there is evidence of increasing proportions of skilled/educated workers over the long run across the region

-this evidence combined with stable or increasing education/skill wage premiums (in regressions with only basic controls) indicates generally increasing demand for skills in the region (and that education is also leading to increasing inequalities in several countries)

-sector and sub-sector analysis of skill wage premiums and proportion of skilled labor, combined with changes in labor force composition in favor of the service sector, confirm that the service sector has become the most important driver of demand for skills for all countries (except Thailand)

-beyond these general trends, there is evidence that countries can be broadly categorized into three groups in relation to trends and patterns of demand for skills:

-Indonesia, Philippines and Thailand – where demand for skills is on the rise but only moderately so; and increasingly industry-specific (in particular for the Philippines and Thailand)

-Vietnam and China – where demand for skills is sharply on the rise and involving most sectors/sub-sectors

-Cambodia and Mongolia – where skill premiums are sharply on the rise across most sectors/sub-sectors, but accompanied with slow increases or even some declines in the proportion of skilled labor which point to less clear cut trends in demand and the importance of shortage of skills

-there is also evidence of rising industry premiums – and related possible labor market segmentation – in three countries of the region (Philippines, Thailand and Cambodia).

These results point to several **policy implications** . The main ones are: 68
# Table A.12: Attitudes toward Violence

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Women’s attitudes index**|||
|Fraction of female leaders<br>0.281<br>(0.257)|0.169<br>(0.282)|0.552<br>(0.350)|
|Observations<br>62,060|43,936|18,124|
|Outcome mean<br>-0.006|-0.049|0.098|
|**Panel B: Men’s attitudes index**|||
|Fraction of female leaders<br>-0.290|-0.353|-0.100|
|(0.288)|(0.331)|(0.347)|
|Observations<br>67,065|47,088|19,977|
|Outcome mean<br>0.036|0.003|0.115|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are an index of women’s attitudes towards IPV in Panel A and an index of men’s attitudes towards IPV in Panel B. Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the victory margin is less that 3 percent of votes. All regressions include state fixed effects and control for the share of seats in the district that had close elections between women and men. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC/ST population in the district, and the female and male literacy rates. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

39
new school between the years 1998 and 2009. We then investigate the timing of the introduction of schools across municipalities. For municipalities that receive a school, the outcome is an ordinal variable equal to the number of years from 1997 that a school expansion is realized.<sup>18</sup> These two outcomes are regressed on 1997 municipal characteristics and a set of state dummy variables (Table 2).

Consistent with our identifying assumption, the correlation between the teenage birthrate and both the introduction of a secondary school or the timing of the expansion is neither significant nor large in magnitude. Rather, we find that supply-demand factors are important in explaining the school expansion. Municipalities with high secondary school density in 1997, which we interpret as a supply factor, are less likely to receive a school in the preceding periods and, if they do receive a school, the expansion takes place later than in municipalities with low initial school density. The municipality’s enrolment rate in the final year of primary school, which we interpret as a demand factor, is positively correlated with how early the municipality receives a new secondary school, but does not determine the receipt of a school. This is consistent with a policy objective to equalize secondary school access across Brazilian municipalities, while giving priority to those municipalities that have a higher immediate demand (Soares 1998).

Identification also depends on the independence between school introductions and trends in birth outcomes. We investigate pre-trends in birth outcomes for municipalities that received secondary schools by performing an event study-style analysis,<sup>19</sup> constructing visual plots reflecting time-demeaned births relative to the periods just before and after a school introduction:

> 18 In the case of municipalities that receive more than one school, this outcome reflects receipt of the first school. 19 The complexity in conducting an event study in this framework is that some municipalities experience multiple ‘events’ by having schools introduced at multiple points in time. We simplify the analysis and focus on only the first ‘event’ (i.e. the first observed change in the number of secondary schools). We do not attempt to infer a causal relationship from this exercise, the purpose is only to examine pre-trends.

14
of use for some metals, such as in alloys. Third, in several cases, especially coal and tin, the results of the model improve considerably when cross‐prices are included.

As a final extension and check, the control variables were included in the com‐ modity‐specific demand equations. A key result emerging from this extension is that un‐ like those from the aggregate equations, the results for individual commodities are sen‐ sitive to the inclusion of control variables. Detailed parameter estimates are reported in Appendix E (Tables E1‐E9). Even in the cases of oil and copper (two of the four equations that performed well in the base specification), the inclusion of population density or trend in the former, and the inclusion of urbanization in the latter, weakens the performance of the model. This implies that estimating the demand functions for group aggregates may capture substitutabilities and complementarities that are not accounted for by simply in‐ cluding cross prices.

# **5. Conclusions**

This paper has examined the relationship between commodity demand and per capita income level for energy and metal commodities both at the aggregate and commodity‐ specific levels. Three energy commodities (oil, coal, and natural gas) and six metals (alu‐ minum, copper, lead, nickel, tin, and zinc) were featured in the analysis. These commod‐ ities account for more than 85 percent of global energy consumption and for nearly all base metal use as measured by volume. The analysis was based on an autoregressive distributed lag model utilizing annual data for 1965‐2018 for up to 63 countries.

Several findings emerged. First, the estimation of demand equations for individual commodities gave highly heterogeneous results, consistent with most of the literature; only four of the nine commodities showed significant results consistent with theory (i.e. that demand increases as income rises and prices decline) and _a priori_ expectations (i.e. that demand increases at a decelerating pace). Second, the group aggregates gave results that are consistent with theory and _a priori_ expectations, i.e., income elasticities are in‐ versely proportional to income levels. Elasticities for both energy and metals were close to unitary at the median income level, but close to 2 for low‐income countries and as little as 0.2 for advanced economies. Third, the inclusion of cross‐price effects confirmed sub‐ stitutability for several commodities (and, for some metals, complementarity), which is consistent with the superior performance of the aggregate estimates compared to the in‐ dividual ones. Fourth, the size of the elasticities at current median income levels (based on the sample) is close to unity for both commodity groups and around 0.2 for advanced economies, implying the plateau stage is reached at a very slow pace.

_— 15 —_
# **2 Overlap in Aid Disbursements**

In analyzing overlap of donors’ aid allocations, we use data provided by the Creditor Reporting System (CRS) published by the OECD on a regular basis. This detailed dataset reports aid disbursements and sectoral attributions (through “purpose” codes) at the project level. We use the data from 2007 onwards, as the data are more comprehensive in recent years, and sectoral data are missing for many observations in the years before 2007.

In our analysis we include 16 sectors: education; health and population policy; water and sanitation; government and civil society; other social infrastructure and services; transport and storage; communication; energy generation and supply; banking, financial services, business and other services; agriculture, forestry, and fishing; industry and construction; mineral resources and mining; trade; environmental protection; other multi-sector; and finally general budget support. We exclude debt relief efforts, humanitarian and emergency aid efforts, administrative Table 1: Budget Shares across Sectors

|Sector|Average Bu<br>2010|dget Shares:<br>2011|2012|
|---|---|---|---|
|Government and Civil Society|17.6 (14.4)|17.9 (15.7)|18.4 (15.5)|
|Education|16.8 (11.7)|16.4 (11.1)|18.7 (11.5)|
|Health and Population Policy|12.4 (13.7)<br>|13.2 (15.1)<br>|14.0 (15.7)<br>|
|Other Multisector|7.9 (6.4)|6.4 (5.5)|6.4 (5.2)|
|Transport and Storage|6.9 (12.2)|7.9 (12.6)|5.8 (13.5)|
|Agriculture, Forestry, and Fishing|6.9 (6.7)|8.6 (6.8)|8.5 (6.1)|
|General Budget Support|6.7 (5.0)|5.6 (4.2)|4.5 (3.4)|
|Water and Sanitation|6.5 (7.7)|6.1 (8.2)|5.8 (8.0)|
|Other Social Infrastructure and Services|5.6 (4.7)|6.0 (3.6)|6.1 (3.8)|
|Energy Generation and Supply|4.4 (7.3)|4.3 (7.5)|4.4 (8.5)|
|Environmental Protection|3.3 (5.5)|3.1 (5.2)|2.7 (3.6)|
|Banking, Financial Services, Business and Other Services|2.2 (2.5)|1.8 (1.7)|1.7 (2.3)|
|Industry and Construction|1.2 (1.0)|1.2 (1.7)|1.2 (1.7)|
|Communication|0.8 (0.4)|0.6 (0.4)|0.8 (0.5)|
|Trade|0.6 (0.4)<br>|0.8 (0.4)<br>|0.7 (0.6)<br>|
|Mineral Resources and Mining|0.3 (0.3)|0.3 (0.2)|0.3 (0.2)|

Budget shares across sectors is calculated for 25 DAC members. This table reports the average of these shares across donors for the years 2010, 2011, and 2012. Shares of total disbursements across sectors reported in parenthesis. Data Source: Credit Reporting System (OECD).

costs, and refugee costs in donor countries, as these forms of aid do not match the forms of aid conceptualized in our model. We are interested in forms of aid that directly affect the public good in recipient countries in non-emergency situations. Table 1 shows budget shares across sectors for the average bilateral donor 5
Figure 6: Distribution of the labor force (ages 15-64) by region <!-- Start of picture text --> Sub-Saharan Africa 7 ....<br>(labor force 214 million) . ..<br>South Asia<br>(labor force 440 million)... . ..<br>East Asqi a nd Pacific...<br>(labor force 963 million)<br>Middle East and North Africa<br>2<br>(labor force SO million) U<br>Europe and Central Asia<br>3<br>(labor force 239 million) 00<br>Latin America and Cari'bbean . .....<br>(labor force 166 million) V<br>O.E C.D. 2<br>(labor force 372 million)<br>0 10 20 30 40 50 60 70 80 90 100<br>1 7<br><!-- End of picture text --> <!-- Start of picture text --> U Workers in<br>* services(%<br>Work erq in<br>industry<br>U Workers in<br>arcltr306<br>* Unemployed (3⁄4)<br><!-- End of picture text -->
Syroka, J., Calmati, S., Carfagna, F. and Gelassie, T. Y. 2013. _African Risk Capacity_ presented at the International Conference on Regional Climate ‐ CORDEX 2013, Brussels, Belgium Syroka, J. and Nucifora, A. 2010. _National Drought Insurance for Malawi_ , The World Bank, Policy Research Working Paper 5169 Dinku, T. and Sharoff, J. 2013. _ENACTS Ethiopia: Partnerships for Improving Climate Data Availability,_ ‐ _Accessibility and Utility_ , available at http://www.climate <u>services.org/sites/default/files/ENACTS_Case_Study.pdf</u> United Nations 2000, _United Nations Millennium Declaration_ , available at <u>http://www.unmillenniumproject.org/documents/ares552e.pdf</u> United Nations 2013. _Millennium Development Goals and Beyond 2015 Fact Sheet_ , available at <u>http://www.un.org/millenniumgoals/pdf/Goal_1_fs.pdf</u> WFP and FAO 2013. _WFP/FAO Shock Impact Simulation Model for Food Security Analysis and Monitoring_ WFP 2009 _. Emergency Food Security Assessment Handbook_ ‐ second edition, World Food Programme, Food Security Analysis Service WFP 2011. _Market Analysis Framework, Tools and Applications for Food Security Analysis and_ Decision‐Making WFP 2013. _Comparative Review of Market Assessments Methods, Tools, Approaches and Findings_ World Bank (2011). _Weather Index Insurance for Agriculture: Guidance for Development_ _Practitioners_ , Agriculture and Rural Development Discussion Paper 50, World Bank, Washington, DC, 102 pp.

World Bank 2014. _World Development Indicators_ , available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐ development‐indicators#</u> 24