rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 8 – Growth incidence curves by average of completed years of formal education**

(All provinces, 1989-2004) <!-- Start of picture text --> 0 25 50 75 100<br>Percentile of the population (ranked by real per capita household income)<br>0-3 years 3-6 years<br>6-9 years 9 years or above<br>500<br>400<br>300<br>200<br>100<br>0<br>Real per capita household income increase (%)<br>-100<br><!-- End of picture text --> The distribution of income changes between households with various human capital stocks differs across urban areas and rural areas (Figure 9). The GIC patterns for urban areas are similar to those for the entire sample. In urban areas, a higher initial income is closely associated with a higher income growth, for example, the increase in real per capita household income was about 50 percent for the poorest quartile of households with in average nine years of education or less and 100 percent for the poorest quartile of households with in average nine years of education or above; while the increase were more than 100 percent and 200 percent for the respective two richest quartiles. This suggests that urban labor market is sensitive to difference in skilled and returns to education are monotonically increased with years of schooling. A widening income gap can be observed in all households with any level of initial human capital endowment. In rural areas, the GIC patterns are different – it is virtually flat around 100 percent for all households with in average nine years of education or less, which suggest everyone more or less double income in 1989-2004 so income gaps remained unchanged in relative sense; while it is flat around 200 percent for all households with in average nine years of education or above of the three poorest quartile and positively sloped quite steeply for the richest quartile, which suggest that everyone except the richest quartile tripled income in 1989-2004 while the richest quartile gained substantially more up to about quintupled income. Hence, income gaps widened between all households with less education and those with better education but remained unchanged within rural households with less education, and income gaps widened within rich households with better education. One reason that may explain the differentiated pattern of distribution of income change in rural areas is that most rural labor with less education participates mainly in farm activities and the marginal returns to education do not significantly differ as human capital stock remains below a certain threshold. However, for those with better education, a large proportion of rural labor may participate in non-farm activities, where returns to education may be significantly higher. Rural households with higher initial income may have higher income growth for two reasons: first, many rural households with higher income in 1989 were those in coastal provinces, who had better opportunities in working in TVEs and can earn higher non-farm 13
promising sector-level investment. In the case of the electrical industry, this finding is driven by an entirely different channel - weak complementarity between energy and capital.

<!-- Start of picture text --> 4.00%<br>Labor<br>2.00%<br>Capital<br>0.00%<br>-2.00%<br>-4.00% Materials<br>-6.00%<br>Energy<br>-8.00%<br>-10.00%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> Figure 5: Effect of $ 30 Carbon Tax on Energy Consumption in the U.K. Electrical Industry

# **6 Concluding Remarks**

We have expanded the traditional estimation of energy, materials, and labour responses to input price changes by including vintages of the capital stock. The model accounts for transitional dynamics of capital in response to energy prices, and allows for more efficient use of inputs to production by choosing more efficient technologies at the time of investment.

In order to test the model, we develop a new dataset for 19 OECD countries and five manufacturing industries over the period 1990-2005. At the industry level, the explanatory value of the model with vintage capital stock is significantly improved. The conventional dynamic factor demand model of energy demand is rejected for all industries in the sample. The estimated elasticities of energy input efficiency with respect to energy prices vary between 0.015 and 1.1. The investment response to energy prices thus varies significantly across manu29
<!-- Start of picture text --> 100%<br>8545<br>80%<br>64%<br>60% __<br>20% 19% [—_ -—<br>9094 55<br>O65 i a NeenEEE<br>Standardized Ratings-based Baslell not Standardized Ratings-based Basle ll not<br>Approach Approach adopted yet Approach Approach adoptedyet<br>aGCcc Non-GCC<br>100%<br>80%<br>80% —_<br>60% [_>_<br>46%<br>42%<br>40% po<br>20% —_—_—hi——<br>9%<br>0 T rr a |<br>Standardized Ratings-based Baslell not Standardized Ratings-based \Baslell not<br>Approach Approach adopted yet Approach Approach adopted yet<br>= State Private<br><!-- End of picture text -->
that are clearly distinguishable from one another in statistical terms. Figure 18 shows that at a conventional significance rate of 95% approximately 35% of municipalities can be ranked and distinguished in a statistical sense from one another. At a less stringent significance level of 75%, the number of significant rankings increases to 43%.<sup>15</sup> Figure 17: ELL simulation at municipality level and 95% confidence intervals <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 101 201 301 401 501 601 701 801<br>Municipalities<br>FGT(0)<br><!-- End of picture text --> Source: Authors’ Calculation.

15 Does 35% represent an unacceptably low percentage of statistically significant rankings? It is not obvious that if municipality level, representative, household survey data were available, the proportion of statistically significant rankings would be much higher. For many pairwise comparisons of poverty, estimated poverty rates are very close and would require extremely precise estimates in order to yield statistically significant rankings. To illustrate, our 12.5% sample of the Census generates poverty measures at municipality level that yield statistically significant rankings for only 37% of all municipalities (considering, here, that the sample Census is associated with sampling error). Back of the envelope calculations that impose the ‘typical’ precision of stratum-level poverty estimates in Brazil’s PNAD surveys on our municipality level poverty map estimates, indicate that the proportion of statistically significant rankings of municipalities (at 95% confidence levels) would be even less than the 35% reported here.

28
In PovcalNet, rather than directly adjusting the PPP conversion factors to the rural and urban income distributions, the adjustments are built into the displayed consumption values. For the purposes of estimating the count of the poor, adjusting either the PPP or consumption data has the equivalent effect. But, when making this adjustment directly in PovcalNet, users of the data obtain PPP denominated values that have urban-bias adjustments built in. If producing poverty estimates using the 2005 PPP data, and wishing to follow the methodology of Chen and Ravallion (2010), then these are the correct values to use. But, once the analysis shifts to the 2011 PPP data, using these adjustments only makes sense if the analysts wishes to assume that the data continue to need a correction for urban bias and that the magnitude of this adjustment is the same as in 2005.<sup>11</sup> To date, there is no evidence that the particular adjustments that were made to the 2005 PPP data should be carried over to the 2011 PPP data. Even if evidence points to an urban bias in some countries in the 2011 ICP round, there is no reason to assume that the correction based on the 2005 PPP data is the correct adjustment to be used in the 2011 data.<sup>12</sup> In contrast, the report of the International Comparison Program (World Bank, 2015) suggests that data from the 2011 ICP round was much better at capturing rural prices than the 2005 ICP round, particularly with respect to China, India and Indonesia -- three large-population countries in Asia where there was particular concern about urban bias.

For example, the Asian Development Bank (2014) reports that the 2011 ICP saw the full and complete participation of China, accomplishing national coverage with price collection in

> 11 Not all researchers were in agreement about the validity of these adjustments to the data. Deaton and Heston (2010), both of whom served on the ICP technical advisory board, argue that the 2005 PPP index almost certainly underestimated the price level in China by at least 20 percent.

> 12 Adjustments may have to be made to the 2011 data, but it is difficult to know how because sampling data for the ICP data are not readily available for a number of countries.

10
additional capital is higher. It may also serve as an instrument for tax avoidance in jurisdictions with inheritance taxes. However, the products could also pose a risk to the vulnerable elderly, if forced into products they do not fully understand and to pass on an inheritance when they still have their own financial needs to plan and care for.

# 4. International experience

RM have been introduced mainly in advanced economies, and RM markets there tend to be small. RM are a rather bespoke financial product that caters to elderly people only. It is thus not surprising that RM mostly exist in countries with a relatively high median age and an advanced financial market. The United States features the oldest and largest RM market to date. Smaller RM markets exist in Canada; Australia; Hong Kong SAR, China; Spain and the United Kingdom (Ong, 2008). Some European countries have introduced buy-and-lease-back agreements, where the buyer pays the seller an annuity until death to receive the home in return afterwards. The following paragraphs present the findings of a selection of studies related to RM from the United States; United Kingdom; Australia; Taiwan, China; the Republic of Korea; Singapore; and India.

## **_United States_**

The United States is home to the oldest and largest RM market. A savings and loan company in Maine reportedly originated the first RM in 1961. But it was not until the late 1980s that RM received regulatory endorsement. In 1988 The US Department of Housing and Urban Development created Home Equity Conversion Mortgages (HECM), by far the most popular product with a market share of 95% of the RM market to date. The loans are originated by banks and insured by the Federal Housing Administration.

Over the last 20 years, the RM market in the United States has grown considerably. In 1990, HECM origination was capped at 2,500. RM grew steadily over the years to reach 55,000 in 2016. Growth rates picked up in the 2000s as housing prices continued to rise. While low-income areas were overrepresented in the RM market in the 1990s, residents living in high-income zip codes were equally likely to close an RM as poorer ones in the 2000s. Recent data suggest that the trend may have reverted again in the 2010s, since RM market penetration is currently much higher in the bottom income quintile than the elderly homeowner population at large (Nakajima & Telyukova, 2017). In the wake of the 2008 11
- IPCC: 2006a, Chemical Industry Emissions, in H. Eggleston, L. Buendia, K. Miwa, T. Ngara and K. Tanabe (eds), IPCC Guidelines for National Greehnouse Gas Inventories, Volume 3, The Intergovernmental Panel on Climate Change, pp. 3.13.110.

- IPCC: 2006b, _N_ 2 _O_ Emissions from Managed Soils, and _CO_ 2 Emissions from Lime and Urea Application, in H. Eggleston, L. Buendia, K. Miwa, T. Ngara and K. Tanabe (eds), IPCC Guidelines for National Greehnouse Gas Inventories, Volume 4, The Intergovernmental Panel on Climate Change, pp. 4.1 4.54.

- Jones, J. W., Hoogenboom, G., Porter, C., Boote, K., Batchelor, W., Hunt, L., Wilkens, P., Singh, U., Gijsman, A. and Ritchie, J.: 2003, The DSSAT Cropping System Model, European Journal of Agronomy 18(3), 235265.

- Jorgenson, D. and Vu, K.: 2010, Potential Growth of the World Economy, Journal of Policy Modeling 32(5), 615631.

- Lee, H., Hertel, T., Rose, S. and Avetisyan, M.: 2009, An Integrated Global Land Use Data Base for CGE Analysis of Climate Policy Options, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 7288.

- Licker, R., Johnston, M., Foley, J. A., Barford, C., Kucharik, C. J., Monfreda, C. and Ramankutty, N.: 2010, Mind the Gap: How do Climate and Agricultural Management Explain the 'Yield Gap' of Croplands around the World?, Global Ecology and Biogeography 19(6), 769782.

- Lobell, D. B., Cassman, K. G. and Field, C. B.: 2009, Crop Yield Gaps: their Importance, Magnitudes, and Causes, Annual Review of Environment and Resources 34(1), 179.

- Long, S. P., Ainsworth, E. A., Leakey, A. D., Nösberger, J. and Ort, D. R.: 2006, Food for Thought: Lower-Than-Expected Crop Yield Stimulation with Rising CO2 Concentrations, Science 312(5782), 19181921.

- Moss, R. H., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J. A., Elgizouli, I., Emori, S., Lin, E., Hibbard, K. et al.: 2008, Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies, Technical Report PNNL-SA-63186, Pacic Northwest National Laboratory (PNNL), Richland, WA (US).

38
much bigger (around 12 percentage points per year) because it includes the bounce back in trade after the crises from the depressed growth rate during the crises period 1997-2001. In any case an export growth rate of 20% per year is unusually high both in terms of China’s own record, which registered real export growth of 16.3% per annum in the period 1978-1993, and in terms of the record of the newly industrializing countries in Asia, which achieved average annual growth rates of just 12.1% over the course of their development from 1965 to 1993 (Yang, 1996).

**Table 3 Comparison of actual and simulate real export growth rates**

||Average annual real export growth<br>rate|
|---|---|
|1990-1997|13.3|
|1997-2005|16.0|
|Implied WTO accession effect including<br>effect of crises|2.4|
|1997-2001|7.1|
|2001-2005|20.0|
|Implied WTO accession effect including crisis<br>recovery|12.0|
|Simulation effects||
|Wang (1997, 2003)|4.4-5.1|
|Ianchovichina and Martin (2004)|3.4|
|Walmsley and Hertel (2001)|6.2|
|Yang (1996)|1.4-4.7|

Source: Data on exports come from Amiti and Freund (2007) based on China Customs Statistics and export values deflated using 1992 US Consumer Price Index. Note: The accession-related effect is computed as the difference in growth rates calculated as (1+g1/100)/(1+g2/100)-1)*100. The annualized rates are obtained from the cumulative effects reported in some studies the formula ((1+gc/100)^(1/n)-1)*100, where n is the number of years associated with reform.

We next turn to the annualized simulation results from the studies presented in Table 2. They serve as an alternative source of information on the magnitude of the accession-related export effect and can help us narrow the range of possible export outcomes. Several points come out clearly and are worth emphasizing. First, the range of the simulated effects is smaller than the range obtained from 18
# **ii. SAVINGS**

Figure 25. Savings at a Financial Institution across Gender <!-- Start of picture text --> 2011 2014<br>50% 50%<br>45% 45%<br>40% 40%<br>35% 35%<br>30% 30%<br>25% 25%<br>20% 20%<br>15% 15%<br>10% 10%<br>5% 5%<br>0% 0%<br>Turkey BRIC Rest of High Income Rest of ECA Turkey BRIC Rest of High IncomeRest of ECA<br>Developing Developing<br>World World<br>male female<br><!-- End of picture text --> _<u>Source</u>_ : FINDEX 2011 and 2014 Figure 26. Savings at a Financial Institution across Education Levels <!-- Start of picture text --> 2011 2014<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High Income Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Developing<br>World World<br>Primary Secondary Tertiary<br><!-- End of picture text --> _<u>Source</u>_ : FINDEX 2011 and 2014 34
|**TableAIII.4Probability**|**ofExittoEmployment in**|**thePrivateSector(Ma**|**rginalEffects)**|
|---|---|---|---|
|**Variables**|**Baseline**|**I**|**II**|
|**ReceivesUl**||0.005<br>(0.015)|-0.021<br>(0.015)|
|**ReceivesSA**||0.007<br>(0.017)|0.007<br>(0.017)|
|**Duration ofspell**||-0.006<br>(0.002)|-0.006<br>(0.002)|
|**PastUI**||___ __ __ __ __ __ __ __ __ __ __ _ _<br>_(0.027)|-0.009<br>_ __ __ __ __ __|
|**Female(=1)**|0.005<br>(0.016)|0.003<br>(0.016)|0.005<br>(0.015)|
|**Married(=I)**|-0.020|-0.022|-0.020|
||(0.021)|(0.021)|(0.019)|
|**26-35**|-0.018|-0.019|-0.021|
||(0.019)|(0.019)|(0.017)|
|**36-45**|-0.030<br>(0.023)|-0.034<br>(0.022)|-0.024<br>(0.021)|
|**46-55**|-0.015<br>(0.028)|-0.017<br>(0.028)|-0.010<br>(0.027)|
|**Apprenticeship**|0.052"|0.052"|0.044|
||(0.024)|(0.024)|(0.022)|
|**Secondary**|0.030<br>(0.024)|0.028<br>(0.024)|0.025<br>(0.022)|
|**College(+)**|0.199<br>(0.064)|0.189"<br>(0.064)|0.184<br>(0.061)|
|**Nopreviousexp.**|-0.073<br>(0.014)|-0.070"<br>(0.014)|-0.071'<br>(0.013)|
|**HeadofHH**|**0.010**|0.016|0.010|
||(0.028)|(0.029)|(0.026)|
|**Spouseofhead**|-0.008|-0.001|-0.007|
||(0.029)|(0.030)|(0.027)|
|**Occupation**|YES|YES|YES|
|**Sector**|YES|YES|YES|
|**Region**|YES|YES|YES|
|**Year**|YES|YES|YES|
|**Quarter**|YES|YES|YES|
|**Observedprob.**|15.84|15.84||
|**Predictedprob.**<br>|10.73|10.59|__|
|**Prob**>**Chi**<sup>**2**</sup>|0.000|0.000|0.000|
|**Numberifindividuals**|2,465|2,465|2,465|
|**Numberofpseudo-spells**|2,465|2,465|2,680|

Source: Labor Force Survey 1996:1-1996:4, 1999:1-1999:4, and 2000:1-2000:2. Coefficients represent marginal effects evaluated at sample means.

Robust standard errors in parentheses (errors clustered at the individual level). Significant at the 5 percent level. Significant at the 10 percent level. Baseline comparison: Single male, 16-25 years old, with primary studies and previous employment experience. Only exit to employment in the private sector was considered as a successful exit. Qualitatively and quantitatively equivalent results are obtained when exit to both employment in the private sector and employment in a public employment program are considered instead.

47
Tabl- 20: Other assets (as **a** percentage of **agsets)**

||1970|1975|1980|1985|1990*|
|---|---|---|---|---|---|
|UK|0|0|2|4|6|
|Us|3|5|8|6|2|
|Germany|11|6|3|1|1|
|Japan|-|-|-|-||
|Canada|8|1|2|2|2|
|Netherlands|-|-|-|-|_|
|Sweden|3|0|0|0|0|
|Switzerland|I|1|1|1|1|
|Denmark|23|25|28|24|25|

* 1989 for Canada 1987 for Denmark

30b
The market is composed of a number of relatively small, inefficient firms. Despite economies of scale in the industry, as illustrated in Figure 4, large firms coexist with medium sized and smaller firms, which tend to have higher costs (discussed in Section 3).

**TABLE 6:  EXTERNAL TRADE SHARE OF FREIGHT AND TOTAL TRADE VALUES**

||Percentage of Total<br>Freight to and from<br>Ports (Trucking<br>Survey)|External Trade<br>Percent of Total<br>Trade Tonnage|External Trade as<br>Percent of Total<br>Value of Trade|USD/Ton all<br>Merchandise Trade by<br>Country|
|---|---|---|---|---|
|**Costa Rica**|58|85|88|$1,727.38|
|**El Salvador**|19|63|72|$1,871.55|
|**Guatemala**|45|82|80|$1,328.46|
|**Honduras**|72|75|76|$1,087.62|
|**Nicaragua**|51|67|78|$1,556.54|
|**Panama**|98|89|92|$4,889.09|

Source: CA Trucking Survey and World Integrated Trade Solution, The World Bank Although the presence of over 100 operators per country (Table 5) would seem to provide effective competition, companies serve different routes and clients. Of all routes trucking companies list as among their main routes (including return trips), 57 percent are listed by only one sampled firm – as shown in Figure 5.

Adjusting for the appropriate sampling weights, this would indicate that on over half of all major routes, on average only 2.7 firms count the route as among their main ones served. Moreover, 87 percent of routes had five or fewer firms in the survey claiming it as a main route.

Viewed by market segment, market concentrations are fairly high. Revenue shares of the top five trucking firms by country range from 17 percent to an estimated 33 percent of total revenues in the sector for the Nicaraguan and Honduran markets, respectively (Table 7.) Since international routes are not as concentrated, this suggests higher revenue concentration on national routes. As shown Table 8, the top three trucking firms perform 77 of trips on a given route (comprising and origin and destination) and 26 percent of all trips from a given origin. This percentage is significantly higher for national routes by origin at 34 percent, as compared with 16 percent for international routes.

**TABLE 7:  ESTIMATES OF MARKET CONCENTRATION: REVENUE SHARES TOP 5 AND 10 FIRMS** **<u>BY COUNTRY OF DOMICILE</u>**

|**Country**|**Top 5**|**Top 10**|
|---|---|---|
|Costa Rica|20.8%|29.8%|
|El Salvador|22.7%|32.1%|
|Guatemala|19.6%|29.4%|
|Honduras|33.5%|37.7%|
|Nicaragua|17.0%|24.7%|
|Panama|19.3%|25.4%|

Note: Estimates use survey sampling weights.

11
product of environmental damage and the appropriate exponential of per-capita income, divided by the sum of products for all partner countries.

Equations _(5')_ and (8') lend themselves to a variety of uses and interpretations, For example, _(5')_ can be applied to the Bank's loan portfolio, in which case si* is each country's optimal investment share. It can also be applied to the Bank's policy dialogue and technical assistance activities, in which case si* is each country's annual budget share; Bi* is a relevant measure of Bank activity (staff time, etc.), and ci is a countryspecific cost index. Poverty weights ((oi) can be assigned explicitly, or simply assumed to be the same across countries (implying that the Bank assigns equal value at the margin to damage abatement in any country, ceteris paribus).

In equation (8'), the optimal country share depends on environmental damage (Di) and income per capita (yj). If the income elasticities of the poverty weight (0) and project success probability (o1) are equal in absolute value (while opposite in sign), then the optimal budget share in (8') is simply the country's share of total environmental damage.

# 3. **Measures of Environmental Problems (DI)**

# 3.1 **Health Damage from Water Pollution**

Our estimates of health damage from water pollution are based on recent econometric work by Wang, et al. (2003). This approach models health outcomes (measured by the under-five mortality rate) as a function of income; social and environmental variables (female education, immunization coverage, and access to safe water); and policy variables (e.g., share of public health expenditure to GDP). To project lives lost, the econometric estimates are combined with country-level demographic data 7