rafmacalaba/datause-encoder-data huggingface dataset:
> Financing Lifelong Learning

> Miguel Palacios

Advantages: Income-contingent loans (ICLs), like HCCs and the graduate tax, decrease the risk for the student in terms of their earnings-net-of-education-payments. Also, like HCCs and the graduate tax, ICLs eliminate default risk due to financial distress. Thus, they retain the most important advantage of income-contingent repayment schemes.

ICLs also satisfy a concept of fairness based on dynamic equity, since students end up paying less than the value of the loan only if they end up with low incomes during the repayment period. Dynamic equity is not perfect, since once the value of the loan has been repaid, students who enjoy high incomes during the repayment period end up much better off than the rest. However, from a cost-recovery perspective, tuition cost is forgiven only for students with low lifelong incomes.

Another advantage ICLs have is the lack of a strong adverse selection problem. Students who see in themselves high future incomes will not have an unlimited liability and will not have a disincentive to join. However, there will be some adverse selection as long as students have to pay a "premium" for protection against low-income. Still, a reduction in adverse selection is very important for the proper functioning of ICLs.

Also related to the fact that payments are capped, ICLs do not create such a high incentive to hide income. Hiding income only pays off for those with such low incomes that they see the possibility of not repaying the loan entirely. 1<sup>9</sup> Thus, an ICL system might require less control on the amounts that students are reporting as their incomes, eventually reducing administration costs. For instance, as Johnstone (1972) proposed, a scheme where fixed payments are expected from all _except_ from those who prove low income would shift attention from focusing on income generated by all students to income generated by some students (those who cannot complete their payment). This is the way Chile's University credit ("Credito Universitario") works. Students pay the maximum of 5% of their income and a fixed amount (see Camhi and Latuf, 2000).

> To conclude, ICLs only create a disincentive to work for those who have low-incomes. High-income earners will repay their debt completely, and thus do not perceive a

> difference between different jobs with different wages. This contrasts with the effect of HCCs and the graduate tax, where the disincentive exists for any salary range.

# Weaknesses and Obstacles to Implementation:

ICLs share with HCCs and the graduate tax some common weaknesses and obstacles to implementation. They require the measurement of income, are subject to some adverse selection and distort the labor market, though to a _lesser_ degree. By far their greatest disadvantage, when contrasted with HCCs, is that they do not reflect purely expectations

> **19** This "disincentive" to hide income decreases if the interest rate on the loan is subsidized. In that case, the longer that an individual takes to repay the loan, the higher the value of the subsidy received.

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Policy Research Working Paper 8296

# **Abstract**

This paper introduces a new, publicly available database for tracking merchandise trade in the global value chains for apparel/textile/footwear, motor vehicles and parts, and electronics, developed originally for in-depth analysis at the country level. The data enable tracking of intermediate and final goods trade by country. Among other results, the study finds that East Asia and Pacific is the most global value chain–intensive region, and Sub-Saharan Africa is the least global value chain–intensive region. Final assembly of motor vehicles is significantly more sophisticated (takes place in higher income countries), while final apparel is the least sophisticated global value chain category and is geographically dispersed. In general, intermediate goods are more likely to be traded within regions, and final goods are more likely to be traded between regions. South Asia produces a large value of relatively unsophisticated global value chain exports, focusing on apparel and textiles; North American exports are the most sophisticated. Within categories, the degree of product sophistication varies markedly.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mferrantino@worldbank.org and gschmidt1@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
bias by significantly diverging from the consensus view in climate science that humans contribute to climate change” (2008). Similar studies have been done in U.S. print media.

This trend seems to be most prominent in the United States. For example, comparative work mentioned earlier by Brossard, et al. (2004) between the U.S. and France found that American coverage emphasized conflicts between scientists and politicians, whereas this was not observed in France. Boykoff’s work comparing the United States and United Kingdom shows the same. The prevalence of “balanced” reporting in newspaper coverage of climate change in the United States and United Kingdom from 2003 and 2006 shows a significant “divergence” from scientific consensus in the U.S. in 2003–4, but no major “divergence” in UK reporting. Boykoff speculates that his findings “inform ongoing considerations about the spatially-differentiated media terms and conditions through which current and future climate policy is negotiated and implemented.” The questioning of scientific certainty in the media is a recent phenomenon, and one worth devoting some attention to. The scientific process provides a mechanism for falsification, but not “proof.” This quality, together with the lack of public understanding of science has been manipulated in recent years. Given that all science contains a measure of uncertainty, if agencies can be prevented from imposing regulations until they are unambiguously “justified,” regulations can be defeated or postponed, often for decades. For example, sociologist Reiner Grundmann compares the relationship between scientific certainty and policy prescriptions in the cases of ozone layer protection and climate change (2006). Although scientific expertise is obviously a requirement for policy development in either case, Grundmann notes that highly successful policies to combat ozone depletion were agreed upon under conditions of far less scientific certainty than currently exists for climate change. And despite the greater scientific consensus, climate agreements attempted by world political leaders have been far lass successful.

Authors Michaels and Monforton provide a historical view into the process of "manufacture uncertainty" in the United States by opponents of public health and environmental regulations (2005). Michaels and Monforton provide historical data from the tobacco and other industries and document the rise of the label "junk science" to de-legitimize research that threatens powerful interests. This process is carried out as industries question the validity of scientific evidence on which the regulations are based. They highlight that the industry strategy 41
between U.S. and UK skills, on the one hand, and Singapore, on the other, fell sharply. Singapore also built an increasing skill gap relative to the developing economies in our sample.

Thus, throughout our period Singapore was small in size but shifted its endowment ratio from being labor-abundant to skill-abundant, even with respect to many of the industrialized countries. This development path should then be reflected in the data from a positive impact of skill differences on inward investment early in our period, reflecting the tendency of foreign investment to seek low-cost labor in Singapore. Put differently, a rise in Singapore’s skill ratio would diminish incentives for vertical investment, which would not be offset by a gain in horizontal investment incentives. However, late in the period inward foreign investment from industrialized partners should expand with a rise in Singapore’s skill endowment because that investment would be skill-seeking in nature.

As an initial study, Figure 3 shows the growth of inward FEI stocks from the same set of industrialized countries over the period. In all cases, as skill differences decreased investment in Singapore expanded, but this increase accelerated late in the period. _2c. Prior Estimation_ In CMM (2001) the initial empirical estimation of the KK model was performed, using a 1986-1994 panel dataset of bilateral country-level affiliate sales in manufacturing, involving both U.S. affiliates abroad and foreign affiliates in the United States. In their econometric work, the authors found that a convergence in income (GDP) between the United States and any investment partner (holding the sum of their incomes constant) increased affiliate sales in both directions. There was substantially greater evidence of horizontal FDI, with affiliate sales rising in host countries with skill endowments closer to 8
_How_ **_Fast HIt_** _Chinse Industy Grown? 37_

# TABLE 13: RouGH CONVERSON OF SOE ENERGY .1ATA TO _1S_ BRANCH FORMAT

||Energ|y Cona.|Derived|GV1O,P80|Ton aid c|oal per|% Rui|
|---|---|---|---|---|---|---|---|
|Bmnch|lOOOt|stid<br>coal|Yi|yuan|10000<br>yua|n GVIO<br>|GVIO/E<br>|
||1980|1985|1980|1985|1980|1985|1980,85|
|1. Using|DEFIVED GV|IO DATA||||||
|1|6459|7329|446.59|592.18|14.46|12.38|16.86|
|2|7431|9127|191.08|265.01|38.89|34.44|12.92|
|3|2583|2873|138.16|172.59|18.70|16.65|12.31|
|4|2644|2760|296.51|368.28|8.92|7.49|18.99|
|_5_|6265|6787|479.84|719.92|13.06|9.43|38.49|
|6|1987|2298|761.84|1400.32|2.61|1.64|58.93|
|7|2824|3802|149.36|214.00|18.91|17.77|6.42|
|8|256|325|63.41|67.59|4.04|4.81|-16.04|
|9|1087|1635|477.79|741.66|<br>2.28|2.20|3.20|
|10|1273|1715|586.55|846.13|<br>2.17|2.03|7.08|
|11|10|13|22.73|<br>20.95|0.44|0.42|4.76|
|12|42|48|22.46|<br>26.97|1.87|1.78|<br>5.06|
|13|594|746|64.50|<br>91.53|9.21|8.15|13.01|
|14|44|58|51.69|<br>83.19|0.85|0.70|22.09|
|15|49|77|15.78|<br>46.73|3.11|1.65|88.46|
|Sum|33548|39593|3768.28|5667.07|8.90|6.99|27.43|
|2. Using P|1WULISHE<br>G|D<br>VIO data||||||
|1|6459|7329|447.90|.594.50|14.42|12.33|<br>16.97|
|2|7431|9127|14.71|263.85|40.23|34.59|16.30|
|3|2583|2873|136.79|169.03|18.88|17.00|11.10|
|4|2644|2760|283.99|368.64|9.31|7.49|24.35|
|_5_|6265|6787|451.95|661.83|13.86|10.25|<br>35.18|
|6|1987|2298|768.28|1390.66|2.59|1.65|56.51|
|7|2824|3802|101.09|159.09|27.94|23.90|16.89|
|8|256|325|67.S6|<br>69.88|3.79|4.65|-18.53|
|9|1087|1635|493.22|746.11|2.20|2.19|0.57|
|10|1273|1715|610.92|859.30|2.08|2.00|4.41|
|11|10|13|20.47|31.71|0.49|0.41|19.16|
|12|42|48|21.88|26.74|1.92|1.80|6.94|
|13|594|746|120.38|83.69|4.93|8.91|-44.64|
|<br>14|<br>44|<br>_58_|0.00|83.65||0.69||
|_15_|49|77|82.28|148.08|0.60|0.52|14.53|
|Sum|33548|39593|3791.42|5656.76|8.85|7.00|26.42|

Note: 'published GVIO for 1S branches is from DEFLATE.wkl. Derived GVIO daa comes<sup>from collpeing</sup> 40-branch GVIO figure implicit in th eergy and energy/GVIO figur. into 15 bmnch.s following the algorithm contained in workshoot DATA86.wkl, omitting ub-branch adjuatmets for which no data are available. *Yi yuan - 100,000,000 Yuan.
# **3. Methodology and Data**

_This section discusses the methodology and data we use in producing estimates of the equilibrium peso exchange rate using the BEER approach._ We employ a model-based approach known as the behavioral equilibrium exchange rate to compute estimates of the equilibrium exchange rate for Argentina. This methodology (MacDonald, 1997) identifies the main determinants of the equilibrium real exchange rate and assesses exchange rate misalignment taking into account Argentina’s fundamentals. A reduced-form vector error correction model is estimated using Johansen’s (1995) cointegration procedure, from which the equilibrium real exchange rate is inferred as a function of macroeconomic fundamentals.

The equilibrium real exchange rate depends on a vector of fundamental variables, ��, as follows: Changes in the actual real exchange rate can be expressed in the following form, where θ denotes the adjustment coefficient: Combining these two equations we obtain: The equilibrium exchange rate can therefore be measured as a function of macroeconomic fundamentals via reduced form model estimation of a vector error correction model. Predicted values of the real exchange rate (smoothed using an HP filter) constitute the so-called behavioral equilibrium exchange rate, which can be expressed compactly as: ����� ������ We explore the long-run relationship between the real exchange rate and fundamentals found to be important in the literature (see Table 2). We consider a large selection of fundamental indicators representing terms of trade, productivity differentials, net foreign assets, trade openness, government consumption, active foreign exchange interventions, and money supply. Real interest rate differentials are not used because they are found to be stationary I(0). We use monthly data for the period 1980:m1 to 2015:m5 and all variables are expressed in natural logarithms. A detailed description of the data is provided in Appendix Table A.1.

We employ Johansen’s (1995) cointegration approach to estimate the BEER as follows. First, we test for stationarity and confirm I(1) processes for all series we consider as fundamentals driving the long-term REER. Second, we choose the number of lags to include based on various information criteria. Third, we use trace and maximum eigenvalue tests for the number of cointegration relations between the REER and fundamentals (Johansen, 1991). This three-step procedure is repeated for different combinations of fundamentals and only those yielding a single cointegrating relation are retained. Finally, we estimate the cointegration relation using a vector errorcorrection (VEC) model with an unrestricted constant, which allows for a linear trend in the data and a cointegrating equation that is stationary around a nonzero mean. The VEC model is equivalent to a restricted VAR designed for use with non-stationary 11
the included variables. Over the years, one strand of the literature has focused on panel data methods to draw inferences on causality. Studies such as Levine, Loayza, and Beck (2000) and Beck, Levine, and Loayza (2000) use a panel GMM estimator to establish a positive relationship between the exogenous component of financial development and economic growth, productivity growth, and capital accumulation.

Other researchers have tried to address causality using micro data at the industry and firm level. In an influential study, Rajan and Zingales (1998) argue that industries that are naturally more heavily dependent on external finance should benefit disproportionately more from greater financial development than industries that are not naturally heavy users of external finance. As discussed in section 3 of this paper, using data from the U.S. as a measure of industries‟ technological dependence on external finance, they find that financial development has a substantial impact on industrial growth, both through the expansion of existing establishments and formation of new establishments, by influencing the availability of external finance.

Demirguc-Kunt and Maksimovic (1998) use a different approach to examine the relations between external financing and institutions. They directly estimate the external financing needs of each individual firm by using a financial planning model. The model permits them to calculate how fast firms could be expected to grow without external finance but instead only with retained earnings and cash from operations. The extent to which firms are able to grow faster than this internally financed growth rate is a function of the dependence of firm‟s growth on external finance. They then show that the proportion of firms that grow at rates exceeding the nonexternally-financed rate is positively associated with stock market liquidity, banking system size and the perceived efficiency of the legal system.

In a similar vein, Wurgler (2000) also employs industry-level data and computes investment elasticity that shows that countries with higher levels of financial development are better able than countries with lower levels at increasing (decreasing) investment in growing (declining) industries. Love (2003) shows that the sensitivity of investment to cash flow depends negatively on financial development, which is consistent with the evidence in the industry and firm-level studies.

24
- Easterly, W. (1999), ‘The Ghost of Financing Gap: Testing the Growth Model of the International Financial Institutions’, _Journal of Development Economics_ **60** (2), 423–438.

- Easterly, W., Levine, R. and Roodman, D. (2004), ‘New Data, New Doubts: A Comment on Burnside and Dollar’s ‘Aid, Policies and Growth (2000) ́’, _American Economic Review_ **94** (3), 774–780.

- Eisensee, T. & Strömberg, D. (2007), ‘News Floods, News Droughts, and U.S. Disaster Relief’, _The Quarterly Journal of Economics_ **122** (2), 693-728.

- Fink, G. & Redaelli, S. (2009), Determinants of International Emergency Aid: Humanitarian Need Only?, World Bank Policy Research Working Paper No. 4839, The World Bank.

- Frey, B. S. and Schneider, F. (1986), ‘Competing Models for International Lending Activity’, _Journal of Development Economics_ **20** (2), 225–245.

- Gahvari, F. and Mattos, E. C. (2007), ‘Conditional Cash Transfers, Public Provision of Private Goods and Income Redistribution’, _American Economic Review_ **97** (1), 491–502.

- Gassebner, M., Keck, A. and Teh, R. (2009), ‘Shaken, Not Stirred: The Impact of Disasters on International Trade’, _Review of International Economics_ forthcoming.

- Gibson, C. C., Andersson, K., Ostrom, E. and Shivakumar, S. (2005), The Samaritan’s Dilemma. The Political Economy of Development Aid, Oxford University Press.

- Grün, C. and Klasen, S. (2003), ‘Growth, Inequality, and Well-Being: Intertemporal and Global Comparisons’, CESifo Economic Studies 49(4), 617–659.

- Isopi, A. and Mattesini, F. (2008), Aid and Corruption: Do Donors Use Development Assistance to Provide the ’Right’ Incentives?, CEIS Tor Vergata Reserach Paper Series No. 121, Center for Economic and International Studies.

- Jepma, C. J. (1991), The Tying of Aid, Development center studies, Organisation for Economic Co-Operation and Development (OECD).

- Kahn, M. E. (2005), ‘The Death Toll from Natural Disasters: The Role of Income, Geography and Institutions’, _The Review of Economics and Statistics_ **87** (2), 271–284.

- Kaufmann, D., Kraay, A. and Mastruzzi, M. (2008), Governance Matters VII: Aggregate and Individual Governance Indicators 1996-2007, World Bank Policy Research Paper 4654, The World Bank.

- Khan, H. A. and Hoshino, E. (1992), ‘The Impact of Foreign Aid on the Fiscal Behaviour of LDC Governments’, _World Development_ **20** (10), 1481– 1488.

18
# **S3 Table and Figures**

Table S1: Robustness Mangrove Protection

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|_f_ 0-1 km<br>mangrove width|-5.849<br>(1.860) <br>[2.307]<br>{2.676}|-5.378<br> (1.875)|-1.768<br> (0.640)|-4.619<br> (1.807)|-1.89_×_10<sup>_−_17</sup><br> (5.22_×_10<sup>_−_18</sup>)|-9.049<br> (1.899)|-6.487<br> (2.946)|-5.852<br> (1.861)|
|_f_ 1-2 km<br>mangrove width|0.185|0.188|0.022|-0.231|5.04_×_10<sup>_−_19</sup>|-1.964|-0.793|0.166|
||(0.842) <br>[1.241]<br>{1.975}|(0.842)|(0.277)|(0.883)|(2.91_×_10<sup>_−_18</sup>)|(0.825)|(2.419)|(0.842)|
|_f >_2 km<br>mangrove width|0.195|0.198|0.029|0.363|1.55_×_10<sup>_−_18</sup>|-2.042|-0.828|0.254|
||(0.879) <br>[1.295]<br>{2.105}|(0.879)|(0.291)|(0.830)|(2.91_×_10<sup>_−_18</sup>)|(0.863)|(2.419)|(0.857)|
|Observations<br>|50,089|50,089|50,089|50,089|50,089|24,011|50,076|50,089|
|Implied mangrove<br>protection, cat. 3|<sup>1.209</sup>|1.209|0.961|1.111|1.658|1.401|1.132|1.221|
|V threshold (km)|92.6|92.6|92.6|75.63|-|92.6|92.6|92.6|
|V half (km)|277.8|277.8|203.72|277.8|-|277.8|277.8|277.8|

_Notes:_ Dependent variable nightlights. Estimates from OLS regressions, cell and year fixed effects included but not reported. Mangrove protection is the difference in damage (measured in nightlights) between cells with more and less than 1 km of mangrove width when they are affected by a category three hurricane (209 km/h). Robust standard errors clustered at the municipal level in parentheses (120 clusters); clustered at state level (35 clusters) in brackets; Conley errors (12 lags and a 500 km bandwidth) in crochets. Column 2 uses the highest wind speed for every every cell-year regardless of whether is generated by the strongest storm in that year. Column 3 uses a lower _vh_ parameter as suggested by Emanuel (2011).Column 4 uses gale level winds to set a lower _vT_ parameter. Column 5 follows Nordhaus (2006) and uses wind to the power of 8 instead of the _f_ damage index. Column 6 restricts the sample to countries that experience at least a category one hurricanes during our sample period (Guatemala, Honduras, and Nicaragua). Column 7 includes state-year fixed effects. Column 8 uses only dense mangrove (at least 50% forest cover) to calculate mangrove width on the path to the coast.

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