rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7.  Determinants of WTP with Normal Demand** **<u>(Robust Regression on Log (Mean WTP)</u>**

|**Variables**||**Coefficients**||
|---|---|---|---|
|worker||-0.094||
|||(0.66)||
|married||-0.011<br>||
|||(0.06)||
|smoking||-0.190||
|||(1.26)||
|Log(income)||0.418||
|||(3.99)***||
|education||0.621||
|||(2.26)**||
|Rcancer||0.355||
|||(1.42)||
|Yuncertain||-0.385||
|||(1.71)*||
|Healthcheck||0.292||
|||(2.02)**||
|religion||0.023<br>||
|||(0.10)||
|trust||0.339||
|||(2.38)**||
|need||0.187<br>(2.05)**||
|**Region-, age- and sex-**||**Guizhou**||
|**specific dummies**|**Tianjin**||**Jiangsu**|
|age15_44_male|-0.007|**Reference**|0.886|
||(0.03)|**--**|(3.98)**|
|age45_55_male|0.464|-0.087|0.686|
||(1.71)|(0.18)|(2.39)**|
|age55_65_male|-0.294|0.123|0.045|
||(0.54)|(0.14)|(0.07)|
|age65plus_male|-1.774|No Obs.|1.830|
||(2.10)**|--|(2.06)**|
|age15_44_female|-0.205|0.119|0.158|
||(0.77)|(0.47)|(0.42)|
|age45_55_female|0.085|No Obs.|0.264|
||(0.17)|--|(0.32)|
|age55plusfemale|1.578|2.296|-0.666|
|_|(2.64)***|(3.10)***|(2.23)**|
|Constant||-0.688||
|||(0.69)||
|Observations||816||
|R-squared||0.12||

Robust t statistics in parentheses, * significant at 10%, ** significant at 5%; *** significant at 1% 29
## **4.3 Discussion**

Secondary education seems to partly explain why mission areas get more aid, although the evidence is not conclusive. Even if these results are taken at face value, it is not obvious why higher levels of education should make areas attractive targets for aid. It could be that some level of human capital is necessary for successful implementation of projects, in which case this finding would be indirect evidence of the “effectiveness” hypothesis examined in Section 3. On the other hand, if the evidence of a political aid cycle is interpreted as favoritism (as argued here), human capital could play the role of a necessary condition for the formation of political connections. These are certainly questions that could be investigated empirically and which merit further research.

# **5 Conclusion**

This article has documented that 19th- and 20th-century Christian missionary activity in Africa predicts the location of present-day World Bank aid allocation. The findings suggest that the probability of receiving development projects is about 40 percent higher in areas that contain a historical Christian mission station. This result is the main contribution of the article, although attempts have been made to explain the source of the correlation and to explore policy implications.

First, the article tested whether aid effectiveness is higher in areas that hosted historical missions. As missionary activity had long-lasting positive effects on social and human capital, it is possible that donors channel aid to such areas in the hope that these endowments will facilitate successful implementation of the projects. Using data on project ratings and survey-based development indicators, the empirical analysis in this article was unable to find evidence of superior aid effectiveness in areas exposed to early missionary activity. These results are not conclusive, however, due to data limitations.

Second, the article investigated three potential mechanisms: political favoritism, religious favoritism, and education. Education is positively correlated with aid allocation, and its inclusion among the controls in the baseline regression reduces the coefficient on missions. There is no evidence of religious favoritism, but there is evidence of a political aid cycle specific to mission areas: aid commitments are reduced whenever a presidential turnover 40
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This study picks up where the previous study concluded and addresses the outstanding question of longer-term impacts. One hypothesis we previously posed for the mixed results was related to a multi-tasking problem. While the financial education curriculum urged students to save, budget, and spend wisely, it did so simultaneously, and the concurrent emphasis may have been overbearing. Furthermore, a closer examination of the course books suggests that while the curriculum offered very clear direction on actions such as saving and budgeting (both are good), there was no such indication when it came to purchasing items on expensive credit cards or installment plans. The course instead urged greater awareness and understanding of the pros and cons of spending choices but did not outright discourage the use of credit. Hence, it is likely that while students keenly followed the directions to save and budget, they did less well when such clear direction was not provided.

This type of multi-tasking problem is linked to the literature in psychology on willpower depletion. Moreover, while the school-based intervention provided the opportunity for repeated instruction and exercises that allow for sustained learning, short-term results from Bruhn et al. (2016) suggested that students tended to follow clear directions for some topics (savings, budgeting), but wavered in other aspects where direction was less clear (spending and borrowing).

In the long term, such multi-tasking problems can potentially smooth out with students having more time and opportunity to absorb and experience financial decisions, and subsequently apply learnings from the curriculum to adjust course from financial mistakes.

In this study, we test this new hypothesis on longer-term impacts by following students from the original study for the next nine years, from 2012 to 2020.

For the long-term impact evaluation, we rely on administrative data sources, housed at the BCB. Using administrative data was the most obvious choice since we do not have students’ contact information or addresses. All data for the short-term impact evaluation was collected in schools and the students would have left those schools by now (most of them would have graduated in 2012). Additionally, the administrative data are provided by authorized third parties, meaning that they do not suffer from self-reporting bias.

# **_3.b. Sample Selection for the Long-Term Impact Evaluation_**

As a starting point, we take the full sample of 35,346 students who participated in any of the surveys conducted during the short-term impact evaluation. Any given survey conducted during the short-term impact evaluation covered at most 24,473 students, but the composition of students changed throughout the study due to students rotating and repeating a year, which is why the full sample includes over 35,000 students. To match these students with the long-term follow-up data from the administrative sources housed at the BCB, we need their CPFs (the 11-digit Brazilian taxpayer identification number).

8
10

**Flgure 2. Denmark: Central Parity Against the ECU. Source: European Economy.**

<!-- Start of picture text --> ...<br>6.4-<br>6.2<br>?.6r<br>7.6-<br>7.._<br>7.§ 60 61 62 6 4 6 6 6 6 6<br><!-- End of picture text --> Figures 2 to 4 show a remarkable change in the behavior of the central parity against the ECU in all three countries. As we can see, while in France and Denmark this change seems to have taken place contemporaneously with the announcement of the stabilization plan, in Ireland this might have happened some time later, around March 1983.

The behavior of the bilateral real exchange rate vis-a-vis the German mark was consistent with the use of it as the nominal anchor. In the three cases, it experienced a severe appreciation. This was most dramatic in the case of Denmark and France, whose real exchange rates depreciated prior to stabilization.
Policy Research Working Paper 10267

# **Abstract**

High aspirations for their future motivate youth to strive toward them and achieve better outcomes. However, the influence of perceived constraints on the motivational power of aspirations is unclear: do high aspirations motivate independently of constraints or only when expected constraints seem workable? This paper explores this question with a gender lens and using a large, cross-sectional survey of adolescent students in Indonesia. The findings show that most students aspire to high education levels, but only half of the students expect to complete the level to which they aspire. Although girls have higher aspirations than boys, girls are less likely to expect to achieve their aspirations. Years of aspired education are strongly correlated with better current schooling outcomes (grades and attendance), and while expectations are also associated with better schooling outcomes, the relationship is nonlinear. Aspirations seem to motivate students despite their perceived constraints, unless there is a large gap between their aspirations and expectations. Although there are similar patterns for boys and girls, aspirations are more correlated with boys’ attendance, and expectations are more strongly related to boys’ grades and attendance. Students cite both mental barriers and economic constraints to achieving their aspirations, especially the latter for girls. The results suggest that both male and female Indonesian students could benefit from programs that boost aspirations and address psychological and economic constraints.

This paper is a product of the Gender Innovation Lab, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hjohnson1@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
light of the global economic crisis. First, why focus on the foreign market access monitoring needs of developing countries? For a number of reasons, their needs are the most neglected under the current trading system, as there is very little private sector monitoring that looks out for their interests. The economic implications of this are likely to be quite important if it results in them having less secure access to foreign markets, because they are then unable to fully exploit international trade as part of their growth strategies. If developing country exporters cannot adequately monitor the conditions of foreign market access, they are ill-informed when WTO violations occur, and they will be unable to use the threat of or actual resort to WTO dispute settlement to convince trading partners to fulfill their commitments to keep markets open.

The next section provides a more detailed discussion of the theory behind calls for additional and refocused monitoring activity on behalf of developing country exporters in the WTO system. While we describe how the WTO Secretariat makes important contributions in this area, we also identify where more efforts are needed given that much of the WTO’s monitoring effort is not targeted towards providing information to the private sector (firms and their advocates). This information is necessary, for example, if such firms are going make a rational decision on whether to self-enforce their trading interests by convincing their policymakers to engage in the dispute settlement process on their behalf. We conclude that there is a role for substantial expansion of monitoring and surveillance, and we then describe a number of monitoring initiatives that the ‘extraWTO’ community (World Bank-sponsored _Global Antidumping Database_ and _Global Trade Alert_ ) undertook in the midst of the global economic crisis in 2009. We detail how such initiatives have created useful inputs that could be repackaged to further the 6
# Refe!rences

Boskin, M.J. and L.J Lau (1990), "International and<sup>Intertemporal Comparisons of</sup> Productive Efficiencies: An Application of the Meta-Production<sup>Function," Working Paper,</sup> Department of Economics,Stanford University.

Bowman, M.J. (1980), "Education and Economic Growth<sup>An Overview," in T. King,</sup> ed., Education and Income: A Background Study for World Development<sup>Report. 1980, World</sup> Bank Staff Working Paper No. 402, Washington, D.C., 1-71.

Bowman, M.J. (1987), "The Relevance of Education," in G.<sup>Psacharopoulos, ed.,</sup> Economics of Education: Research and Studies, Oxford:<sup>Pergamon Press, 305-307.</sup> Christensen, L.R., D.W. Jorgenson and L.J. Lau (1973),<sup>"Transcendental Logarithmic</sup> Production Frontiers," Review of Economics and Statistics, 55: 2845.

Denison, E.F. (1967), Why Growth Rates Differ. Post-War<sup>Experience in Nine Western</sup> Countries, Washington, D.C.: Brookings Institution.

Denison, E.F. (1979), Accounting for Slower Economic Growth:<sup>The United States in</sup> the 1970's, Washington, D.C.: Brookings Institution.

Griliches, Z. and D.W. Jorgenson (1966), "Sources of Measured<sup>Productivity Change:</sup> Capital Input," American Economic Review, 56: 50-61.

Hayami, Y. and V.W. Ruttan (1985), Agricultural Development:<sup>An International</sup> PersDective, revised and expanded ed., Baltimore: Johns Hopkins University<sup>Press.</sup> Hicks, N.L. (1980), "Is There a Trade-Off between Growth<sup>and Basic Needs?" Finance</sup> and Development, 17: 17-20.

Hicks, N.L. (1987), "Education and Economic Growth,"<sup>in G. Psacharopoulos, ed.,</sup> Economics of Education: Research and Studies, Oxford: Pergamon Press,<sup>101-107.</sup> Jamison, D.T. and L.J. Lau (1982), Farmer Education and Farm Efficiency,<sup>Baltimore:</sup> The Johns Hopkins University Press.

Jorgenson, D.W. and Z. Griliches (1967), "The Explanation of<sup>Productivity Change,"</sup> Review of Economic Studies, 34: 249-283.

Kaneko, M. (1986), The Educational Composition of<sup>the World's Population: A</sup> Database, Discussion Paper 29, Education and Training<sup>Series, Washington, D.C.: The World</sup> Bank.

Lau, L.J. and P.A. Yotopoulos (1989), "The Meta-Production<sup>Function Approach to</sup> Technological Change in World Agriculture," Journal of Development<sup>Economics, 31: 241-269.</sup> 25
# **Figure 7. Price of Greek corporate and sovereign bonds**

<!-- Start of picture text --> 110<br>100<br>90<br>80<br>National Bank of Greece (2016 A-)<br>70<br>Hellenic Republic (2014 )<br>60<br><!-- End of picture text -->

# **_Econometric methodology and specification_**

When pricing emerging market bond securities issued internationally, investors take into account many risk factors. Investors generally make a distinction between bonds issued by public sector entities (government and government-owned companies) and those offered by private borrowers, while taking into account common factors such as the state of the home country macroeconomy, global financial market conditions, and bond- and firm-specific factors (maturity, currency of denomination, jurisdiction, covenants, sector, and the fact that corporate ratings are often subject to sovereign ceilings). Reflecting the influence of such factors, investors typically attach a higher risk premium to the private rather than public bond instruments.

Formally, our analysis of the relationship between sovereign and corporate risk centers on the following set of regressions , specifying _t_ sovereign and corporate bond spreads at issuance as a function of offering terms; currency and jurisdiction; industry; and various macroeconomic economic, financial, and institutional control variables for each issuer’s home country.

Sovereign spreads are given by: Corporate spreads are given by: 14
education and research and development (R&D). These investments gradually, and with a lag, increase labor productivity across sectors (see ENVISAGE documentation in GTAP 2018 for details of how the functional relations were estimated). These impacts of investments in R&D and human capital are introduced into the ENVISAGE model to partially endogenize the impact of policy-induced technology change.

In both diversification scenarios the level of subsidies and investments of resource rents is calibrated to match 1 percent of GDP of the baseline scenario. In this way, the two alternative diversification strategies are calibrated to have a similar fiscal burden. The direct effects of subsidizing energy-intensive industries are different from the indirect effects of a productivity boost through asset diversification: the former increase the margins of heavy industries, while the latter boost economy-wide productivity.

## **Technology Scenarios**

We run the models with two alternative assumptions about the development of green technologies – one when the progress on renewable energy, electric cars, and power storage follows recent (historical) trends, and another in which it is significantly accelerated in the future. However, to avoid confusion with too many scenarios, only those with accelerated technology development are presented here. So, the impact of the clean technology revolution is embedded in the baseline (or business-as-usual, BAU) presented here.

# **3. Results: Incentives to Cooperate and Diversify**

The FFDCs’ domestic climate mitigation policies are crucial to stabilize global climate. Cooperative climate policies combined with asset diversification is the only scenario that stabilizes domestic emissions in FFDCs (figure 3). Unilateral climate actions by LCT pioneers result in an increase of FFDCs’ carbon dioxide emissions compared with the baseline, even if they face border carbon adjustment taxes, because carbon-intensive industrial production and emissions reallocate (“leak”) from LCT Pioneers. Only cooperative domestic climate policies and asset diversification combined can stabilize fossil fuel–dependent countries’ carbon emissions and prevent carbon leakage.

_Figure 3: CO2 Emissions of Fossil Fuel–Dependent Countries’ Emissions under Noncooperative and_ 10
Shift-Share Analysis provides results for each industry that yield specific interpretations. Combinations of negative and positive values for the Mix and Regional Shift components characterize the industry’s performance relative to the national average industry, and its performance in the region relative to its performance in other regions. These are summarized in the table, below.

|M|RS|Interpretations|
|---|---|---|
|**-**|**-**|The industry was a poor performer nationally, and even poorer in the region than in the nation.|
|**-**|**+**|The industry was a poor performer nationally, but better in the region than in the nation.|
|**+**|**-**|The industry was a good performer nationally, but poorer in the region than in the nation.|
|**+**|**+**|<sup>The industry was a good performer nationally, and even better in the region than in the nation.</sup>|

This summary information can be used to supplement and reinforce (+ +) or dampen (- -) regional enthusiasm for industries that other methods might identify as key industries or industry clustering targets.

# **_Industrial Restructuring Challenges_**

Virtually all the industry targeting and industrial restructuring methods must be considered in the context of local understanding and more comprehensive knowledge of the study region. These strategies can be complicated by a number of considerations, some of which are listed below.

1. Changes in firm ownership patterns can result in organizations of production that align more strongly with aspatial competitive advantages rather than with locational advantages. Some analysts have questioned whether geographical proximity is the major source of agglomeration economies.

2. Missing supply chain linkages might reflect the lack of a sufficient volume of demand to allow the activity to reach its minimum efficient scale, and hence, make that sector viable in the region.

34