rafmacalaba/datause-encoder-data huggingface dataset:
even higher capital-labor ratio, higher wages, and lower capital returns (see ...gures 2a and 2b). Therefore, by examining the intertemporal budget constraint in (5) we can analyze the substitution, income, and wealth e¤ects on leisure<sup>20</sup> .

Figure 2a. Wage rate Figure 2b. Return to capital <!-- Start of picture text --> Response to a one percent decline in fertility, lagged Response to a one percent decline in fertility, lagged<br>0.35 0<br>Wage rate<br>0.3 -0.1<br>0.25 -0.2<br>0.2 -0.3<br>0.15 -0.4<br>0.1 -0.5<br>0.05 -0.6<br>Returns<br>0 -0.7<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Generational periods after shock Generational periods after shock<br>Figure 2c. Workers’leisure Figure 2d. Retirees’consumption<br>6 x 10-4 Response to a one percent decline in fertility, lagged 0.05 Response to a one percent decline in fertility, lagged<br>0<br>5<br>Leisure -0.05<br>4<br>-0.1<br>3 -0.15<br>-0.2<br>2<br>-0.25<br>1<br>-0.3<br>Second period consumption<br>0 -0.35<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br>Generational periods after shock Generational periods after shock<br>Percent deviation from steady state Percent deviation from steady state<br>Percent deviation from steady state Percent deviation from steady state<br><!-- End of picture text --> The substitution e¤ect on leisure comes from a shrinking labor force that alters factor payments: wages increase; the return to capital falls. The price (opportunity cost) of leisure thus increases so the substitution e¤ect on leisure is negative. A given level of income can now buy less resulting in negative income e¤ects on all goods, including leisure. The wealth e¤ect is positive for all goods, because the increased wage rate appears in lifetime income<sup>21</sup> . The dynamics of leisure, as well as retirees’consumption, are illustrated by the simulated trajectories in ...gures 2c and 2d, respectively<sup>22</sup> .

In an in‡uencial paper, Weil (2006) ...nds that a key mechanism through which aggregate income and welfare are a¤ected by population ageing is the distortion from taxes to fund PAYG pension systems. This mechanism is also present here: the price on leisure depends on �; i.e. the (‡at) PAYG contribution rate. With labor supply being endogenous, this distorting tax rate implies that the positive wealth

> 20 In the case where labor supply is exogenous (see, e.g., Jensen and Jorgensen, 2008), the only e¤ect on the capital-labor ratio originates from the lower fertility rate.

> 21 See the right-hand side of the intertemporal budget constraint in equation (5).

> 22 The dynamics of ...rst-period consumption is identical to the simulated trajectory for leisure, though larger numerically.

10
<mark>Obstfeld, M., Taylor, A.M., 2003. Sovereign risk, credibility and the gold standard: 1870– 1913 versus 1925–31. Econ. J. 113, 241–275.</mark> <mark>Ravallion, M., 2011. On multidimensional indices of poverty. The Journal of Economic Inequality volume 9, pages 235–248. https://doi.org/10.1007/s10888-011-9173-4</mark> <mark>Reinhart, C. M., Rogoff, K. S., 2009. This time is different: Eight centuries of financial folly. - Princeton University Press. https://press.princeton.edu/books/paperback/9780691152646/this</mark> <u><mark>time-is-different</mark></u> <mark>Reinhart, C.M., Trebesch, C., 2016a. Sovereign debt relief and its aftermath. J. Eur. Econ. Assoc. 14, 215–251.</mark> <mark>Rewilak, J., 2018. The Impact of Financial Crises on the Poor. Journal of International Development, 30(1), pp.3–19. https://doi.org/10.1002/jid.3334.</mark> <mark>Rose, A.K., 2005. One reason countries pay their debts: renegotiation and international trade. J. Dev. Econ. 77, 189–206</mark> <mark>Sandleris, G., 2008. Sovereign defaults: information, investment and credit. J. Int. Econ. 76, 267–275.</mark> <mark>Sandleris, G., Wright, M.L.J., 2014. The costs of financial crises: resource misallocation, productivity, and welfare in the 2001 Argentine crisis. Scand. J. Econ. 116,</mark> <mark>Sandleris, G., 2016. The costs of sovereign default: Theory and empirical evidence. Economia, 16(2), pp.1-27. https://ideas.repec.org/a/col/000425/014408.html</mark> <mark>Sarkees, M. R., Wayman, F., 2010. Resort to War: 1816 - 2007. Washington DC: CQ Press. Souma, W., 2001. Universal structure of the personal income distribution. Fractals, 9(04), 463-470. https://doi.org/10.1142/S0218348X01000816</mark> <mark>Standard and Poor’s, 2006. Sovereign Defaults and Rating Transition Data: 2006 Update. Standard and Poor’s.</mark> <mark>Stiglitz, J. E., Fitoussi, J. P., Durand, M., 2018. Measuring What Counts for Economic and Social Performance. OECD. https://doi.org/10.1787/9789264307292-en</mark> <mark>Strauss, J. and Duncan, T., 1998. Health, nutrition, and economic development. Journal of economic literature, 36(2), pp.766-817.</mark> <mark>https://EconPapers.repec.org/RePEc:aea:jeclit:v:36:y:1998:i:2:p:766-817</mark> 39
stakes brinksmanship, and so forth. Second, even if the existing creditors agree to refinance a significant proportion of payments that are due, new creditors are still blocked from participating in new lending on a decentralized basis, for fear of having their new loans become part of the overall bad debt. And third, refinancing (rather than debt reduction) causes a build-up of future debt that may adversely affect the incentives of current investors, mainly the debtor governments. Let us consider each of these points in turn.

First, the rescheduling process has all but broken down. There are at least 42 countries that have rescheduled their debts with commercial banks in recent years.<sup>20</sup> Of these. only four countries were able to negotiate new money agreements in 1987-88: Argentina (1987). Brazil (1988). Ecuador (1987). and Ivory Coast (1988). Every one of these agreements has come to naught. As of January 1989, the Argentine agreement has completely collapsed, 20 These are listed in Table III-5 of the World Bank World Debt Tables. 1988-89 Edition. The countries, in alphabetical order, are as follows: Argentina, Bolivia, Brazil, Central African Republic, Chile, Congo, Costa Rica, Cote d'Ivoire, Cuba, Dominican Republic, Ecuador, Gabon, Gambia, Guyana, Honduras, Jamaica, Liberia, Madagascar, Malawi, Mexico, Morocco, Mozambique, Nicaragua, Niger, Nigeria, Panama, Peru, Philippines, Poland, Romania, Senegal, Sierra Leone, Somalia, Sudan, Togo, Turkey, Uganda, Uruguay, Venezuela, Yugoslavia, Zaire, Zambia. One country, Turkey, has reestablished normal market creditworthiness after falling into a debt crisis. One other country, Colombia, has not rescheduled, but does not have normal market access to new lending. After a prolonged negotiation, Colombia and the creditor banks agreed in late 1988 to a new money package of $1.7 billion. Notably, to the consternation of the Colombian government, this new money package does not even fully refinance amortization payments (which are about $2.1 billion), and therefore refinances none of the interest payments due.

17
<!-- Start of picture text --> a)<br>Household utility: -43.5%<br>Aggregate land<br>value: +0%<br>Not urbanized<br>MH Flood prone area:<br>20% damage<br><!-- End of picture text --> <!-- Start of picture text --> b)<br>Household utility: -33.7%<br>Aggregate land<br>value: +0%<br>Not urbanized<br>Flood prone area:<br>10% damage<br><!-- End of picture text --> <!-- Start of picture text --> c)<br>Household utility: -3.4%<br>Aggregate land<br>value: +0%<br>Not urbanized<br>HB Urbanized - no flood<br>MME Flood prone area:<br>20% damage<br><!-- End of picture text --> <!-- Start of picture text --> d) .<br>Household utility: -3.3%<br>Aggregate land<br>value: +0% ;<br>Not urbanized<br>HB Urbanized - no flood<br>Flood prone area:<br>10% damage<br><!-- End of picture text --> <!-- Start of picture text --> e) f)<br>Household utility: -0.008% Household utility: -0.007%<br>Aggregate land Aggregate land<br>value: +0.10% value: +0.10%<br>Not urbanized Not urbanized<br>HM Urbanized - no flood HM Urbanized - no flood<br>M8 Flood prone area: Flood prone area:<br>20% damage 10% damage<br>9) h)<br>Household utility: -34% Household utility: -29.4%<br>Aggregate land Aggregate land<br>value: -12.2% value: -7.55%<br>Not urbanized Not urbanized<br>HMB Urbanized - no flood HMB Urbanized - no flood<br>M8 Flood prone area: Flood prone area:<br>20% damage 10% damage<br><!-- End of picture text -->
# Does Improved Local Supply of Schooling Enhance Intergenerational Mobility in Education? Evidence from Jordan

_Ragui Assaad and Mohamed Saleh_ JEL codes: I24, I28 Keywords: Education, inequality of opportunity, intergenerational mobility, Middle East, supply of schooling.

Assaad (Corresponding author): Professor at Humphrey School of Public Affairs, University of Minnesota, 301 19th Ave. S., Minneapolis, MN 55455; E-mail: assaad@umn.edu. Saleh: Assistant Professor at Toulouse School of Economics and Institute for Advanced Study in Toulouse; E-mail: <u>mohamed.saleh@tse-fr.eu. This article was</u> supported by a grant from the Economic Research Forum as part of the research project on Inequality of Opportunity and Inequality of Outcomes in the Arab Region. Saleh thanks Norhan Muhab for her excellent research assistance and gratefully acknowledges the financial support from the ANR-Labex IAST.
**Table 13. Employment outcomes (ITT), disaggregated by** **<u>gender, 2010, 2011 and 2012 cohorts</u>**

|||Pooled|2010‐2012 Cohorts<br>IPSW model||Difference<br>between men|
|---|---|---|---|---|---|
||Baseline mean for men|Baseline mean for women|Men|Women|and women|
||[Std Dev]|[Std Dev]|(1)|(2)|(3)|
|Any IGA (1=Yes)|0.774|0.518|0.025|0.130***|‐0.106**|
||[0.418]|[0.500]|(0.035)|(0.028)|(0.045)|
|Any non‐farm IGA (1=Yes)|0.471|0.195|0.105**|0.192***|‐0.087*|
||[0.499]|[0.396]|(0.044)|(0.028)|(0.051)|
|Trade‐specific IGA (1=Yes)|[0.499]|[0.396]|0.147***|0.209***|‐0.062|
||0.295|0.113|(0.046)|(0.028)|(0.053)|
|Hours worked in past month|107.772|46.887|11.564|26.287***|‐14.723|
||[99.126]|[70.525]|(8.796)|(4.242)|(9.795)|
|Total monthly earnings (NRs)|2137.947|774.683|681.698**|1036.088***|‐354.390|
||[2539.479]|[1796.025]|(300.488)|(173.214)|(341.802)|
|Logged earnings|4.796|2.428|0.281|1.688***|‐1.407***|
||[3.917]|[3.476]|(0.341)|(0.237)|(0.414)|
|Earnings > 3000 NRs. (1=Yes)|0.350|0.098|0.091**|0.166***|‐0.075|
||[0.477]|[0.297]|(0.039)|(0.027)|(0.047)|
|Self‐Employed (1=Yes), if any|0.324|0.313|0.020|0.086**|‐0.066|
|IGA|[0.468]|[0.464]|(0.048)|(0.034)|(0.058)|
|Works outside of home|0.736|0.430|‐0.005|0.038|‐0.043|
|(1=Yes), if any IGA|[0.441]|[0.495]|(0.054)|(0.074)|(0.091)|
|Any Savings (1=Yes)|0.484|0.666|0.047|0.039|0.008|
||[0.500]|[0.472]|(0.037)|(0.029)|(0.047)|
|Total Savings (NRs).|2328.389|3644.997|1823.869**|812.143|1011.726|
||[6704.381]|[8506.123]|(808.152)|(583.697)|(1004.908)|
|Logged Savings|3.619|5.021|0.537*|0.463**|0.074|
||[3.874]|[3.770]|(0.301)|(0.219)|(0.369)|
|Taken out loan (1=Yes)|0.379|0.322|‐0.008|0.013|‐0.021|
||[0.485]|[0.467]|(0.036)|(0.025)|(0.043)|
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level.

36
10 percent (Table 4).

Note also, that even if there were an international agreement on the value of the climatechange externality, _E_<sup>_PF_</sup> , the impact of this component on the overall tax on motor fuel, _tF_ , varies in each country, as equations (1)–(3) make apparent.

# **4. Conclusions**

The taxation of motor fuel displays a great variability across different countries. While these products are generally subject to broadly similar consumption taxes, the rates applied by individual countries, especially on the excise component, vary substantially. Parry and Small (2005) develop a model balancing the level of motor fuel taxes against all other taxes. They show that the second-best optimal tax on motor fuel can be broken into several components: an adjusted Pigovian tax to account for the external effects of motor fuel; a general Ramsey-type consumption tax component; and a reduced-congestion feedback.

In this paper, we apply the Parry-Small framework to estimate the second-best optimal level of taxation of motor fuel for the OECD and BRICs, which jointly account for about 80 percent of fossil-fuel world GHG emissions. In the benchmark specification, we find that six countries, China, Russia, the U.S., Brazil, Mexico and Canada, accounting for more than 40 percent of transport oil GHG emissions, would be undertaxing fossil fuels. Increasing the shadow value on GHG emissions to $100 per ton of carbon (from the $25 benchmark value) and the size of the labor-leisure substitution response to wages result in more than half the considered countries undertaxing fossil fuels.

We evaluate the sensitivity of the results to the values of the elasticities and externalities that we use by computing numerical derivatives evaluated with each country’s specific parameter values. We find that varying the values of these parameters—within the level of uncertainty reasonably associated with them—significantly affects the quantitative results although the qualitative results are more robust. This implies that, while informative, the results must be taken as indicative. Further analysis for a particular country must rely on well-informed choices for the values of the country-specific parameters.

11
<!-- Start of picture text --> 1995 11<br>East . . 2000 13<br> Asia & Pacific 2005 -<br>2010 14<br>1995 14<br>Europe & Central Asia. 20002005 17 on<br>2010 18<br>1995 17<br>. . 2000 21<br>Middle East & North Africa 2005 on<br>2010 25<br>1995 11<br>South . 2000 14<br> Asia 2005 -<br>2010 14<br>0 5 10 15 20 25<br>%<br><!-- End of picture text -->
than emerging and marginal products; however, the latter are more difficult to develop and scale‐up, as reflected in their lower _density_ values (Table 1).

Table 1: Product Space Measures for Merchandise Exports in Morocco

|**ProductClassification**|**Number of**|**Exports,**|**Average**|**Average**|**Average**|
|---|---|---|---|---|---|
||**Products**|**% of total**|**PRODY**|**Path**|**Density**|
|Classics|46|51.7|9,641|114|0.263|
|Emerging|49|36.7|10,607|129|0.237|
|Marginal|22|1.1|8,684|110|0.215|
|Disappearing|93|3.4|16,060|150|0.183|

Classics are products with RCA>1 in both periods 1980‐1984 and 2005‐09. Emerging champions are products with RCA>1 from 2005‐09, but not during the earlier period 1980‐84 Disappearing products had RCA>1 during the period 1980‐84, but RCA<1 by 2005‐09 Marginals didn’t have RCA >=1 either in the past or in the recent period, but they have relatively high PRODY and density.

Source: Authors' calculations using UN‐Comtrade Data The relationship between potential income gain (ln _PRODY_ – ln _EXPY_ ) and lack of capability (1/ _density_ ) for Morocco’s goods exports in 2010 is shown in Figure 7 and Figure 8 below. Products above (below) the zero line on the vertical axis have a higher (lower) potential income than the average for Morocco’s 2010 export basket of goods (measured by _EXPY_ ). The higher the value for a product along the horizontal axis, the lower the capability of Morocco to develop an RCA > 1 in the product. As expected, the upward‐ sloping relationship in both Figures indicates that the larger the potential income gain, the more severe the lack in the capabilities needed for competitive production. Interestingly, compared to classical and emerging products, marginal products show a stronger potential income gain and a more severe lack of capabilities (Figure 7). Figure 8, which scales the size of product bubbles by export values, shows that a subset of the emerging products and most marginal products have a _PRODY_ well above Morocco’s _EXPY_ while the opposite holds for the bulk of classical products; as a result of small export values, the bubbles for the marginal products are indeed very small. In sum, Figures 7 and 8 both suggest that the marginal products offer payoffs that may be worth exploring.<sup>9</sup>

> 9 Annex 2 presents average _PRODY_ , density, and _PATH_ for all Moroccan exports at the 3‐digit level (SITC Rev. 2). Calculations at the 4‐digit level are available upon request. Product Complexity Index can be downloaded from The Observatory of Economic Complexity.

13
new theories and methods on the one hand, and their application to real-world problems on the other. There is a further lag between application of new research approaches to gain additional understanding, and the translation of such understanding into operational terms. The Bank can play an important role in reducing the costs of increased understanding and its application, even with the most sophisticated policy analysis, if technical capabilities also increase among key stakeholders. Through innovative uses of technology, DEC is particularly well positioned to help broaden access to data, information, and experience, so as to better inform development policy debates and to better engage the direct stakeholders in those debates.

Building on the _Open Data Initiative_ , the Bank will need to become more deeply involved in producing tools to facilitate better development analysis and policy making by nonBank researchers. These tools include development data, in particular survey data on households, enterprises and facilities using questionnaires that are geared towards addressing the most important development issues. They also embrace software tools for accessing and using existing data. The aforementioned data tool _PovcalNet_ is an example. Another can be found in a new product of the Bank’s research department, _<u>ADePT.</u>_ This is an innovative software program designed to simplify and speed-up the production of standardized tables and graphs in many areas of economic analysis, focusing particularly on the Bank’s analytic work at country and regional levels. ADePT is a free, stand-alone program, available for download to anyone in the world. Another example is the _<u>Apps for Development Competition</u>_ <u>, which aims to bring together</u> the developer and the development communities to create innovative tools, applications, and mashups. Such tools allow practitioners in developing countries to easily access the most powerful analytic methods available.

Looking ahead, the challenge is in combining more effective “retail research”— addressing the key knowledge gaps emerging from development practice—with well-chosen applications of this new wholesale model for how we do research. New research products outside the Bank will also start to emerge from open access to the Bank’s data and analytic tools. Our vision is that the data, knowledge and tools that provide the solutions to development problems will ultimately be generated collaboratively through active peer-to-peer participation by those who have the most to gain from the success of those solutions.

37
Stiglitz, J. (1990), “Peer Monitoring and Credit Markets,” **World Bank Economic Review** , vol. 4.

Stiglitz, J. (1992) “Banks versus Markets as Mechanisms for Allocating and Coordinating Investment,” **in The Economics of Cooperation: East Asian Development and the Case for Pro-Market Intervention** , J.A. Roumasset and S. Barr (eds.), Westview Press, Boulder, 1992, pp. 15-38. Reprinted in **The Selected Works of Joseph E. Stiglitz, Volume II: Information and Economic Analysis: Applications to Capital, Labor, and Product Markets** , Oxford: Oxford University Press, 2013, pp. 258-272.

Stiglitz, J. (2002), **Globalization and Its Discontents** , Norton & Co., New York.

Stiglitz, J. (forthcoming) “Monetary Policy in a Multipolar World,” forthcoming in the proceedings of the International Economics Association’s 2012 Izmir roundtable.

Stiglitz, J. and Yun, J. (2013), “Optimal Provision of Loans and Insurance against Unemployment from a Lifetime Perspective,” NBER Working Paper No. 19064.

Tirole, J. (2012), “Country Solidarity, Private Sector Involvement and the Contagion of Sovereign Crises,” mimeo: Toulouse School of Economics.

Villanger, E. (2005), “Company Interest and Foreign Aid Policy: Playing Donors Out Against Each Other,” **European Economic Review** , vol. 49.

29
were also sharp reductions in public health expenditures at the time of the crisis, to point D. The only exception to this pattern, among the studies we have surveyed, are the results in Miller and Urdinola (2007) on pro-cyclical infant mortality in Colombia.

# **4. Conclusions and policy implications**

The simple framework in Section 2 suggests that the effect of aggregate economic shocks on child health and education outcomes is theoretically ambiguous—and therefore, that we should expect it to vary across countries and episodes. But the framework also suggests some elements of _how_ we should expect it to vary.

Richer countries, and those with deeper and better-functioning credit markets, are more likely to see improvements in both health and education during downturns. School enrollment increases because the income effect of a crisis is weaker for initially richer countries, and for households with greater access to credit markets, leading to a dominance of the “pro-schooling” substitution effect. Health improves because the marginal product of public and private expenditures in health is lower in rich countries and households, relative to the health contribution of additional parenting time (and, in some cases, lower consumption of goods with negative externalities).

In the poorest countries, we would expect to see exactly the opposite pattern: education and health outcomes should be pro-cyclical, with things getting worse in recessions. Middleincome countries should fall somewhere in between, and the resolution of the (different) tradeoffs in health and education are an empirical matter. The review of the literature in section 3 is highly supportive of the predictions for rich and poor countries—largely the United States at one extreme, and Africa and a few low-income Asian countries at the other. It is also quite informative of the empirical outcomes for middle-income countries, largely in Latin America. Table 5 provides a schematic summary of these findings.

As Table 5 illustrates, recessions, droughts and other economic downturns tend to have negative effects on both health and education outcomes for children in poor countries. This was apparent for school enrollment and child health in a variety of African countries. To some extent, the picture that emerges for Indonesia during the 1998 crisis is similar, although the magnitude of the negative effects is much smaller—perhaps because Indonesia was wealthier than the African countries that were analyzed, or perhaps because public expenditures on education and 26
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