rafmacalaba/datause-encoder-data huggingface dataset:
a summertime system to withstand the burden of a 1-in-10 year<sup>1</sup> extreme event. The method entails taking the event size from the historical record with a 1-in-10 year return period to be the design event. Since this paper is focused on the impact of climate change on the reliability of the power grid, we do not explicitly consider changing population dynamics, major changes in electricity demand patterns, the effect of energy efficiency investments in buildings that make cooling more efficient and increases in air conditioning system penetration (linked to an increase in income and demand for comfort). Neglecting population changes should not have an impact on the results of this analysis because changes in population would be reflected on both the demand and supply side and thus would not impact the difference between demand and supply, which is the most critical quantity. Increasing efficiency in buildings to lower the power demand for cooling and increases in the penetration of air conditioning will have opposing impacts on this analysis, though the magnitude of their impacts is yet unknown and will likely be heterogenous across regions and urban centers. As such, we hold these values fixed for the time being but note that a follow-on analysis should investigate the extent to which these impacts themselves can be estimated.

We repeat the system sizing simulation for each climate model (to estimate the impact of climate model uncertainty in our projections and subsequent design and system performance simulations), for each shared socioeconomic pathway (to estimate the dependence of our simulations on the future emissions scenario), and for each urban center separately. We have also included two alternative methods for estimating the system capacity to illustrate the uncertainty (or lack thereof) that is inherited from the design methodology (see Supplementary Material).

Lastly, we investigate the impact of varying two design parameters to understand the extent to which power system disruptions due to under-supply can be expected to accelerate into the future and the extent to which issues of potential under-design are related to the historical record length used in the capacity analysis. For all simulations, the system redesign is conducted every ten years.

- used in the capacity analysis. For all simulations, the system redesign is conducted every ten years. • **Design year (** d **)** : defined by the year in which the updated design and system updates are assumed to have occurred.

> 1This is a common design standard in power system capacity adequacy planning (https://www.esig.energy/five-principles-of-resource-adequacy-for-modern-powersystems/).

10
Table 5: Channel-level Inequality

||Lo|g(DPR)|Log(land|value per acre)|
|---|---|---|---|---|
||mean|98<sup>_th_ </sup>percentile|mean|98<sup>_th_ </sup>percentile|
|_β_1 : _H ×_log_θ_|0.213***|0.185***|0.0997***|0.0928***|
||(0.0520)|(0.0455)|(0.0291)|(0.0314)|
|_H×_ Gini|0.0229|0.0439|-0.165|-0.138|
||(0.327)|(0.309)|(0.311)|(0.330)|
|_R_<sup>2</sup>|0.642|0.643|0.781|0.781|
|Observations|3,510|3,510|7,844|7,844|

_Notes:_ See notes to Table 2. Compare columns 1 and 2 to, respectively, columns 1 and 4 of Table 3, column 3 to column 4 of Table 4, and column 4 to column 8 of Table 4. Gini coefficient of family landholdings is computed at the channel level (i.e., across head and tail outlets).

– cannot explain variation in collective action or, rather, in its converse water theft. This finding is consistent with our presumption that cooperation between head and tail outlets of a channel is practically nonexistent.

Another threat to our lobbying power interpretation revolves around the role of groundwater. As suggested above, the market premium on land at the head of a channel is partly attributable to better groundwater recharge at the head (due to more plentiful canal water supplies). Recovering this groundwater, however, requires costly private investments in tubewells. Only about a third of farmers in our data-set actually own a tubewell (or tubewells) and ownership is heavily skewed toward the larger landowners; around half of all tubewells are owned by the top 25% of farmers ranked by landholdings. Thus, one reason why the head-of-channel premium is higher along channels with larger average landholdings at the head might be that along these channels farmers at head outlets are better able to exploit existing groundwater potential than those at tail outlets. In other words, our relative influence variable log _θ_ may be picking up differential groundwater _access_ . To address this concern, which is obviously not relevant for DPR, we include the head-tail difference in tubewell density (total number of tubewells per acre CCA), ∆TW/acre, in the land value 35
# Policy Esearh Working Paper Series

||_lb_|Author|DOWor|Contact<br>pae r|
|---|---|---|---|---|
|WPS800|The Legal Framework for Private<br>Sector Development in a Transitional<br>Economy: The Case of Poland|Cheryl W. Gray<br>Rebecca J.<sup>Hanson</sup><br>Michael A. Heller<br>Peter lanachokov<br>YoussefL)hane|November 1991|CECSE<br>37188|
|WP3801|Unraveling the Mysteries of China's<br>Foreign Trade Regime: A View from<br>Jiangsu Province|Arvind Panagariya|November 1991|D. Ballantyne<br>37947|
|WPS802|Strengthening ths Bank's Population<br>Work in the Nineties|Steven<sup>W. Sinding</sup>|November 1991|0. Nadora<br>31091|
|WPS803|Financial Regulation: Changing the<br>Rules of the Game|Millard Long<br>Dimitri Vittas|November 1991|W. Pitayatonakarn<br>37666|
|WPS804|Global Trends in Raw Materials<br>Consumption|Boum-Jong Choe|November 1991|S. Lipscomb<br>33718|
|WPS805|Privatization in the Soviet Union:<br>The Beginnings of a Transition|Sergei Shatalov|November 1991|CECSE<br>37188|
|WPS806|Measuring Commercial Bank<br>Efficiency: Use and Misuse of Bank<br>Operating Ratios|Dimitri Vittas|November 1991|W. Pitayatonakarn<br>37666|
|WPS807|Moderate Inflation|Rudiger Dornbusch<br>Stanley Fischer|November 1991|S. Moussa<br>33490|
|WPS808|The New Trade Protection: Pric~e<br>Effects of Antidumping and<br>Countervailing Measures in the United<br>States|Ann Harrison<br>|November 1991|D. Ballantyne<br>37947|
|WPS809|Openness and Growth: A Time<br>Series, Cross-Country Analysis for<br>Developing Countries|Ann Harrison|November 1991|WDR Office<br>31393|
# III. THE DEBT MODULE

External debt and creditworthiness are key issues for the Bank's operational work. Therefore, it is necessary to provide an adequate treatment of debt flows and creditworthiness issues in the RMSM-X system. This is accomplished by linking the RMSM-X projection model to a debt module (DM) that supplies detailed information on debt stocks, interest payments and capital flows by creditor.

The debt module allows the country economist to use the wealth of external debt information contained in the Debt Reporting System (DRS). In addition, it permits the user to investigate the effects of alternative debt strategies, including the use of debtrestructuring techniques such as reschedulings, buybacks, accumulation of arrears, and debt forgiveness. The DM and the RMSM-X macroeconomic, model have an interactive relationship through which both the impact of debt management on the macroeconomy and of macroeconomic policies on external debt accumulation can be analyzed.

# III.1 A Concise Description of the Debt Module

The DM projects debt stocks, capital flows and interest payments by creditor. Stocks and flows that result from loans already committed are named "existing". Stocks and flows that result from projected commitments are labeled "new". As a rule, each variable representing a total will be the addition of existing 38
# 2. Data & estimation

## 2.1 Data

The sample is a panel including 179 importing countries and 170 exporting countries over 1980-2005, or a notional total of 791,180 one-way trade observations, of which 321,348 have positive trade (41%).

The dependent variable is the aggregate one-way trade value reported in the IMF’s Direction of Trade Statistics (DOTS). It is customary in trade-volume studies to “mirror” export statistics, i.e. to disregard direct export statistics from the exporting country and instead to use import data from its partners. The reason is that customs typically monitor imports (on which duties are based) better than exports (rarely taxed). However our study purports to measure only the trade-facilitation effect of PSI, not its effect on the capacity of customs to record imports correctly. Mirrored import data would confound these two effects and would thus potentially bias results upward, generating a statistical illusion of increased volumes. In order to avoid this source of bias, we use direct export statistics, at the cost of having noisier data than if we had used mirroring.

Standard gravity regressors include GDPs in constant 1995 dollars, taken from the World Bank’s World Development Indicators; “great-circle” distances between the main industrial agglomerations of countries in the sample, taken from CEPII,<sup>9</sup> and dummy variables for common land borders, common official languages, and formal colonial ties.

The “treatment variable” is equal to one when an inspection program is in force in the importing country _j_ of a pair ( _i,j_ ) at time _t_ . It covers programs run by the largest four firms in the industry, Société Générale de Surveillance (SGS) and Cotecna Inspection SA, both based in Geneva; BIVAC International (a subsidiary of Paris-based Bureau Veritas), and Intertek, based in London. The list of programs is given in Appendix 1.

Table 1 shows descriptive statistics. For dummy variables, the mean is the proportion of the sample’s observations for which the variable is equal to one, i.e. the incidence of the variable.

> 9 So-called “great-circle” distances are simply the shortest routes along the earth’s surface, irrespective of actual terrain. Page | 8
- 11 -

# _Table 4 Regression Results for Transition Rate, 1992_

# Dependent Variable = Transition rate, 1992

||_Primary_|_Junior Secondary_|
|---|---|---|
|_Right hand_<br>_side variables_|_to_<br>_Secondary_|_to_<br>_Senior Secondary_|
|constant|-86.4*|-0.17|
||(-9.23)|(-0.77)|
|log Income per capita|12.91*<br>(2.02)|-0.571*<br>(-2.49)|
|log Expenditure per pupil|10.33*<br>(3.05)|-0.077<br>(-0.56)|
|log Agricultural income|1.69<br>(0.95)|-0.106<br>(-1.74)|
|Deviation of Grade 6 Achievement Score|0.112*|-NA-|
|From National Mean|(2.55)||
|N|29|29|
|Adj. R|0.655|0.305|

# Note: The T-statistics are in parenthesis.

(*) Significant at 5 % level.

**(b)** External **Efficiency.** Ascertaining a closer match between the education system's future output and the demand for educated labor requires rate of return analysis. Such analysis also assesses whether educational investment had any impact on household welfare, and where should resources be allocated in the sector so as to make it more externally efficient? A summary of economic rates of return to three levels of education, based on "elaborate" or "complete" method with the data from 1993 Labor Survey by the Ministry of Labor, is presented in Table 5. Both the private and social returns to each level of education were high in 1993, making investments in education socially profitable in China.
# 3 Data

## 3.1 Survey data

The data come from surveys of households and leaders implemented in 12 Mexican states in 2016. In total, we surveyed 862 communities and 8,413 households. The data were collected in order to evaluate social and environmental impacts of the PSA program for cohorts enrolled between 2011 and 2014 (Alix-Garcia, Sims, Orozco-Olvera & et al. 2018). To determine the sample, we divided the country into zones (North, Center, South and Yucatan Peninsula) and selected the states with the highest number of applicants. The sample was stratified by “medium” (2011-2012) and “shortterm” (2013-2014) cohorts, although for the purposes of this paper we combine all cohorts, as the program rules and deforestation pressures were substantially similar in all these years. Because the design of the impact evaluation exploits a threshold in the enrollment criterion for the program – based upon the point score assigned by CONAFOR – sampling of accepted and rejected applicants occurred around the acceptance threshold for the point score. The basic sampling strategy was to select treatment and control communities with scores adjacent to the cut-off for each cohort year and state, and continue selecting farther away from the cut-off until arriving at a sample size that represented the proportion of program applicants for that state and cohort. Figure A3 shows the spatial distribution of all applicants to the program between 2011 and 2014, and highlights the states in which interviews took place. The survey instrument was field-tested by project PIs and then interviews were conducted by a team of enumerators from Chapingo University in Mexico. While CONAFOR facilitated access to all communities, no CONAFOR employees were present for interviews and enumerators clearly identified themselves as coming from an independent third party.

An individual was defined as a leader if they were currently in a leadership position within the ejido – i.e., they were in one of the main elected positions of president, secretary, treasurer, or oversight committee. Surveyors asked first to speak with the president, but if the president was absent, they would speak with his designated representative. Community leaders are accustomed to such interactions and generally have systems in place for other council members to stand in for the president when necessary.

For the household survey, 10 households were randomly selected to be interviewed for the pro11
43 Table 5: Natural Resources, Structural Change & Tall Building Construction, 1960-2020

|**Type of Buildings:**|All|Resid.|Non-Res.|Offce|Hotel|Retail|Gvt|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|**Panel A:**<br>Core Controls|**L**|**ong-Differe**|**nce:** Dep. Va|r.: Log Urba|n Height D|ensity c. 202|0|
|MFGSERV2020|0.08***|0.09**|0.10***|0.11***|0.07**|0.07*|0.02|
||[0.024]|[0.040]|[0.029]|[0.029]|[0.034]|[0.043]|[0.029]|
|NRXGDP1960_−_2020|0.05|0.10*|0.06**|0.06*|0.02|0.05|0.04|
||[0.031]|[0.057]|[0.030]|[0.031]|[0.051]|[0.052]|[0.038]|
|DEINDU1980_−_2020|-0.01|-0.04|-0.00|-0.05|0.04|-0.06|-0.03|
||[0.028]|[0.050]|[0.035]|[0.036]|[0.039]|[0.054]|[0.047]|
|NRXGDP - MFGSERV|-0.03|0.01|-0.04|-0.05|-0.04|-0.02|0.01|
||[0.03]|[0.06]|[0.03]|[0.03]|[0.05]|[0.06]|[0.04]|
|DEINDU - MFGSERV|-0.08***|-0.13**|-0.10**|-0.16***|-0.03|-0.13**|-0.06|
||[0.03]|[0.06]|[0.04]|[0.04]|[0.05]|[0.06]|[0.05]|
|Obs; Controls|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|115; Y|
|**Panel B:**<br>+ Urban Dvt Ctrl|s<br>**L**|**ong-Differe**|**nce:** Dep. Va|r.: Log Urba|n Height D|ensity c. 202|0|
|MFGSERV2020|-0.01|-0.02|0.01|0.02|-0.05|0.01|-0.02|
||[0.023]|[0.043]|[0.022]|[0.024]|[0.029]|[0.040]|[0.035]|
|NRXGDP1960_−_2020|0.02|0.03|0.05**|0.07**|-0.02|0.07|0.01|
||[0.027]|[0.050]|[0.023]|[0.026]|[0.046]|[0.056]|[0.051]|
|DEINDU1980_−_2020|-0.02|-0.04|-0.03|-0.06**|0.03|-0.09|-0.03|
||[0.026]|[0.042]|[0.024]|[0.025]|[0.031]|[0.056]|[0.048]|
|NRXGDP - MFGSERV|0.03|0.05|0.05**|0.04*|0.02|0.06|0.03|
||[0.02]|[0.05]|[0.02]|[0.02]|[0.04]|[0.07]|[0.06]|
|DEINDU - MFGSERV|-0.01|-0.01|-0.04|-0.09***|0.07*|-0.10|-0.00|
||[0.04]|[0.06]|[0.03]|[0.03]|[0.04]|[0.06]|[0.06]|
|**Panel C:**<br>+ Urban Dvt Ctrl|s<br>**10-**|**Yr Panel (w**|**/ 4 Lags):** De|p. Var.: Log|Urban Heig|ht Density i|n_t_|
|Sum for MFGSERV Lags|0.05|0.08|-0.01|0.01|-0.03|-0.11|0.05|
||[0.06]|[0.09]|[0.06]|[0.06]|[0.08]|[0.12]|[0.12]|
|Sum for NRXGDP Lags|0.10**|0.09|0.04|0.04|0.01|0.10|0.07|
||[0.04]|[0.06]|[0.04]|[0.05]|[0.05]|[0.07]|[0.05]|
|Sum for DEINDU Lags|-0.08|0.09|-0.06|-0.05|-0.03|0.40*|-0.79***|
||[0.07]|[0.14]|[0.07]|[0.07]|[0.15]|[0.23]|[0.30]|
|Obs; Cntry & Yr FE, Ctrls|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|346; Y|

_Notes:_ Robust SE in parentheses (clust. at the country level in Panel C). Summary statistics for LUHT in Panels A-B: All: Mean = 4.6; SE = 1.7; Min = -2.0; Max = 9.6. Resid: Mean = 3.3; SE = 2.7; Min = -3.7; Max = 9.3. Non-Res.: Mean = 3.9; SE = 1.8; Min = -2.4; Max = 8.6. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.
Table 9: cross-sectional 2SLS number of conditions

|equation no.|(1)|(2)|(3)|
|---|---|---|---|
||OLS|First stage|Second Stage|
|log of cumulative conditions 1996-2008|.097|.|.274|
||(.027)<sup>_∗∗∗_</sup>||(.057)<sup>_∗∗∗_</sup>|
|CPIA 1996|-.588|.146|-.630|
||(.062)<sup>_∗∗∗_</sup>|(.220)|(.068)<sup>_∗∗∗_</sup>|
|average annual GDP per capita growth|.016|.003|.017|
||(.009)<sup>_∗_</sup>|(.012)|(.007)<sup>_∗∗_</sup>|
|log of GDP per capita 1996|.047|-.160|.129|
||(.053)|(.195)|(.066)<sup>_∗∗_</sup>|
|ethnic fractionalization|.120|.086|.070|
||(.177)|(.571)|(.200)|
|Political Rights 1996|-.063|-.048|-.035|
||(.029)<sup>_∗∗_</sup>|(.093)|(.033)|
|change in Political Rights|-.118|.031|-.103|
||(.033)<sup>_∗∗∗_</sup>|(.112)|(.037)<sup>_∗∗∗_</sup>|
|average annual aid over GDP|-1.706|7.420|-.316|
||(1.126)|(5.021)|(1.289)|
|log of 1996 population|.|.419<br>(.096)<sup>_∗∗∗_</sup>|.|
|average fraction of votes with G-7|.|2.963|.|
|||(.847)<sup>_∗∗∗_</sup>||
|No. observations|126|126|126|
|_R_<sup>2</sup>|.56|.331|.368|
|F test of excluded instruments|.|27.0558|.|
|_p-value_|.|0.00001|.|
|test of endogeneity|.|.|12.8265|
|_p-value_|.|.|0.0003|
|Overidentifcation test|.|.|.141|
|_p-value_|.|.|0.906|

Note: Dependent variable is the change in policy quality over the period 1996-2008, as measured by the CPIA cluster A and B average. Robust standard errors in parentheses. * significance at 10%; ** significance at 5%; *** significance at 1%. Argentina excluded from the sample.

30
continues to develop from an upper-middle to high income country.<sup>25</sup> The experiences of high income countries reveal that their agricultural output growth tends to rely increasingly on TFP growth rather than input growth (Fuglie, 2012).<sup>26</sup> The increase in productivity would also generally have the desirable effect of increasing the incomes of farmers. In particular, it has very powerful poverty-reducing impacts because so many of the poor—especially in China—live in rural areas and depend on agriculture for their livelihoods (Christiaensen, Demery and Kuhl,2011; Christiaensen, 2012).

Continued investment in R&D is likely to be a key factor for sustained growth of agricultural output in China. However, as China’s land productivity has already attained a high level, there is a possibility that further intensification of the use of cropland would result in diminishing returns and environmental degradation (Brown, 1994; Wirsenius, 2010). Technological developments beyond the focus on yield growth, in particular, to explore sustainable management of natural resources and to address environmental concerns, seem likely to become increasingly important. In contrast to China’s high land productivity, the labor productivity of farmers in China remains low given China’s development level and relative to other sectors of its economy. The shift of labor out of agriculture is likely to continue, imposing challenges on China’s current labor-intensive, family based agricultural production system.

Some scholars point out that farm size in China is too small to reap economies of scale necessary for domestic production to satisfy domestic demand (Otsuka, 2013). As China’s comparative advantage has been shifting from the farm to the non-farm sector and the

> 25 According to the World Bank’s income classification, China <mark>moved up from “low” to “lower middle income” status in 1996 and further advanced to “upper middle income” status in 2010</mark>

> (http://data.worldbank.org/about/country-classifications).

> 26 Decomposing agricultural output growth into contributions from inputs and TFP for the period 1960-2009, Fuglie (2012) shows that developed countries as a whole have relied increasingly on TFP growth to keep output from falling. Total agricultural inputs for developed countries as a group have been declining since the 1980s (Fuglie, 2012, Table 16.4).

26
Figure 1a: Non-anonymous Growth Incidence Curve - Synthetic vs. Actual Panel Data Chile 1996 and 2006 <!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>0 5 10 15 20<br>Ventiles<br>Truth Lower Bound Upper Bound<br>Growth in Consumption Per Capita (%)<br><!-- End of picture text --> Data source: CASEN, 1996 - 2006.

Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

32
pension funds, including those insured and/or administered by insurance<sup>companies,</sup> represent 75 percent of contractual savings.

Other developing countries with large contractual savings<sup>sectors includeSouth</sup> Africa, Malaysia and Chile alongside most high income countries and<sup>some island</sup> economies like Cyprus and Malta. The vast majority of developing countries in Africa, Asia and Latin America as well as most transition countries<sup>of EasternEuropeare well</sup> below this level.

Table 1: Assets of Contractual Savings ]Institutions, 1997-2001

||1997|1998<br>(M|1999<br>URmillion)|2000|2001|
|---|---|---|---|---|---|
|RegisteredPensionFunds|1704|2095|2368|2720|2959|
|<br>SugarIndustryPensionFunds|1180|1750|2005|2500|2196|
|InsuredandAdministered*"|5502|6460|7210|8120|8904|
|TotalOccupationalPensionFunds|8386|10305|11583|13340|14059|
|NPF|12174|14266|16464|18899|21772|
|NSF|948|1366|1825|2385|2849|
|InsuranceCompanies|14130|16672|19200|21123|23971|
|Total|35638|42609|49072|55747|62651|
|LessDouble Countng**|5502|6460|7210|8120|8904|
|ContractualSavingsAssets|30136|36149|41862|47627|53747|
|||(perc|entofGDP)|||
|Registered PensionFunds|1.93|2.10|2.21|2.30|2.25|
|Sugar IndustryPension Funds|1.34|1.75|1.87|2.11|1.67|
|Insured&Administered**|6.23|6.47|6.72|6.85|6.77|
|Total OccupationalPensionFunds|9.50|10.32|10.80|11.26|10.69|
|NPF|13.78|14.28|15.33|15.95|16.56|
|NSF|1.07|1.37|1.70|2.01|2.17|
|Insurance Companies|15.99|16.69|17.88|17.83|18.23|
|Total|40.34|42.66|45.71|47.05|47.65|
|Less DoubleCounting"*|6.23|6.47|6.72|6.85|6.77|
|ContractualSavingsAssets|34.11|36.19|38.99|40.20|40.88|
|||(percent|oftotalass|ets)||
|RegisteredPensionFunds|5.65|5.80|5.66|5.71|_5.51_|
|SugarIndustryPensionFunds|3.92|4.84|4.79|5.25|4.09|
|Insured&Administered**|18.26|17.87|17.22|17.05|16.57|
|TotalOccupationalPensionFunds|27.83|28.51|27.67|28.01|26.16|
|NPF|40.40|39.46|39.33|39.68|40.51|
|NSF|3.15|3.78|4.36|5.01|5.30|
|InsuranceCompanies|46.89|46.12|45.86|44.35|44.60|
|<br>Total|118.26|117.87|117.22|117.05|116.57|
|LessDoubleCounting**|18.26|17.87|17.22|17.05|16.57|
|ContractualSavingsAssets<br>|100.00<br>|100.00<br>|100.00<br>|100.00<br>|100.00|
|*estimates;**ins|uredandadm<br>Source: FSC|ninisteredp<br>,NPF,NS|ensionfund<br>F.|s||

2
43 <!-- Start of picture text --> Flgure 3 The Duration of 0<br>Education, the Rate of<br>Growth and the 075<br>Fertility Rate I<br>j0.7 ..........................<br>0......... .............<br>i) The Fraction of 'Youth' spent In W<br>Education and the Rate of per C apia 0 .. , . ........... . ._<br>Growth____________ __<br>1row8s 1.9 2 21 22 .3 2.i .s 2.6<br>Pase d G,owth % pA.<br>ii)Fertility and Growth in Output and Growth in Output per ll<br>Capita 0<br>1.....................,.......... .. ... ....<br>1a8 1.9 2 2:1 2.2 23 24 2.5 2.6<br>Rate d Gwh % p.a<br>lifi)be Fertility Rate and the Fraction of<br>'Youth' spent in Education<br>Lt ......... ,_ ,,,,,,,,,,<.....................<br>0.5 ......<br>O i5 066 0.65 0.7 ON 0.8<br>Ageatwt*ch EtuaUfonm Cmos<br><!-- End of picture text --> ii)Fertility and Growth in Output and Growth in Output _per ll Capita_ lifi)be Fertility Rate and the Fraction of 'Youth' spent in Education