rafmacalaba/datause-encoder-data huggingface dataset:
# Figure 3. Strategic Planning Framework im Vietnam

<!-- Start of picture text --> Ten-Year Socic-Economic<br>Development Strategy<br>4 \/<br>. Comprehensive Poverty<br>manieheiianeae Re p| Reduction and Growth<br>——fo——<br>[seasons<br><!-- End of picture text -->

# Source: Socialist Republic of Vietnam, 2002

The Ministry of Planning and Investment (MPI) was assigned the task of drafting the CPRGS and MPI, in turn, established an inter-ministerial drafting committee to generate broad-based input from across Government into the drafting process. An important role was played by the Government-donor-NGO Poverty Task Force (PTF). The PTF was initially established to coordinate detailed analysis of poverty in Vietnam. But it then — at the request of the Government of Vietnam - continued as a mechanism for coordinating support to the development of the CPRGS (Box 1).

Box I. Key actors involved in the work on the CPRGS and detining the Vietnam Development Goals and targets

- © The Government of Vietnam, particularly the Ministry of Planning and Investment (MPI) who has led work on developing a CPRGS and the members of the 17-agency CPRGS Drafting Committee

- © The Government of Vietnam, particularly the Ministry of Planning and Investment (MPI) who has led work on developing a CPRGS and the members of the 17-agency CPRGS Drafting Committee, line ministries, but particularly the Ministries of Health, Education and Training, Transport, and Agriculture and Rural Development

- © Sub-national levels of government, who have interacted with the CPRGS Drafting Committee to respond to early drafts of the CPRGS

- © The Poverty Task Force (PTF), which provides a mechanism for Government-donor-NGO collaboration on strategy formulation for poverty reduction. Other Government-donor-NGO partnership groups have also been involved in the target-setting work in certain sectors

- © The Consultative Group (CG) Meeting, which over the last few years has allowed for formal dialogue between the Government of Vietnam and the donor community (including NGOs) on poverty diagnostics, strategy development and (in December 2001) target-setting for poverty reduction; and

° In the future, two government bodies, a CPRGS Steering Committee and a Poverty Unit, are likely to play an important role in overseeing the implementation of the CPRGS, including monitoring progress against defined targets and intermediate indicators 6
rounds adjusting for inflation between midline and endline, which was particularly important for food items (Online Appendix Text 3 presents details on real value estimates).

2. _Household durable assets._ An index proxy for wealth generated using principal component analysis of the number and types of durable household assets (excluding land/property), following Filmer & Pritchett (2001), for the durable assets available in the DHS (2015) data. The index is then normalized to the unit standard deviation of control, with control mean equal to zero.

3. _Value of livestock._ Total value of livestock is calculated as the total number of TUP livestock (cows, goats, sheep) and chickens owned by the household, multiplied by unit price, for each livestock type. The unit price comes from the valuation given by the households for each type of livestock in nominal USD.

4. _Financial inclusion index._ A standardized index including the following outcomes: (i) anyone in the household has savings; (ii) household value of total savings; and (iii) the household’s ability to access formal credit (i.e., from a bank or microfinance institution) for emergency purposes.

5. _Psychological well-being index._ A psychological well-being index is computed for women using the standardized weighted average of scores on the Center for Epidemiologic Studies Depression (CES-D) seven-point scale (Radloff, 1977) (negatively coded), and the World Values Survey (WVS) questions on happiness and life satisfaction.<sup>15</sup> The index is computed using the procedure outlined in Anderson (2008), by subtracting the mean and dividing by the standard deviation of the control group for each unit of analysis, computing the covariance matrix, inverting the matrix, adding up the rows of the matrix, and weighting each variable with its corresponding entry in the summed inverted covariance matrix. As a last step, the index is normalized again by dividing by the sum of the weights.

6. _Women’s empowerment index._ A 3-dimension index of women’s empowerment including (i) decisionmaking, (ii) economic empowerment, and (iii) political and social involvement. The decision-making index includes 12 measures of women’s agency through questions of whether women’s views are taken into consideration and/or followed on decisions related to household finances and expenditures (food, home improvements, household finances, buying or selling property, other non-food expenditures); their children (school enrollment, health visits, and marriage), and themselves (how many children to have, taking a loan, opening a business, working outside home). The women’s economic opportunities index includes whether the primary woman participates in market work, whether she is the owner or manager of a self-employment enterprise other than livestock or agriculture, as well as effective access to inputs

> 15Some questions were adapted to the local context as in Bedoya et al. (2019).

14
—―――, _Using Matched Employer-Employee Data to Study Labor Market Discrimination,_ IZA Discussion Papers 1555, Institute for the Study of Labor (IZA), 2005.

- Jarrell, S.B. and T.D. Stanley, “Declining Bias and Gender Wage Discrimination? A MetaRegression Analysis,” _Journal of Human Resources_ , 39(3), 828–38, 2004.

- Lemos, S., “Minimum Wage Effects in a Developing Country,” _Labour Economics_ , 16(2), 224–37, 2009.

- Magruder, J. “Can Minimum Wages Cause a Big Push?: Evidence from Indonesia,” _Journal of Development Economics_ , 100(1), 48–62, 2013.

- MacPherson, D.A. and B.T. Hirsch, “Wages and Gender Composition: Why Do Women’s Jobs Pay Less?” _Journal of Labor Economics_ , 13(3), 426–471, 1995.

- Maloney, W.F. and J. Nuñez-Mendez, “Measuring the Impact of Minimum Wages,” in J. Heckman and C. Pagés (eds) _Law and Employment: Lessons from Latin America and the Caribbean_ , University of Chicago Press, Chicago, IL, 2004.

- Manning, C., “The Political Economy of Reform: Labour after Soeharto,” Indonesian Studies Working Papers No. 6: University of Sydney, Sydney, Australia, 2008.

- Mincer, J. and S. Polachek, “Family Investments in Human Capital,” _Journal of Political Economy_ , 82, 76–108, 1974.

Neumark, D. and W. Wascher, _Minimum Wages_ , MIT Press, Cambridge, MA, 2008.

- Pirmana, V., “Earnings "Differential Between Male-Female in Indonesia: Evidence from Sakernas Data, Working Papers in Economics and Development Studies (WoPEDS) No. 200608, Department of Economics, Padjadjaran University, 2006.

- Rama, M., “The Consequences of Doubling the Minimum Wage,” _Industrial and Labor Relations Review_ , 54(4), 864–88, 2001.

- Rubery, J., D. Grimshaw, and H. Figueiredo, “How to Close the Gender Pay Gap in Europe: Towards the Gender Mainstreaming of Pay Policy,” _Industrial Relations Journal,_ 6(3), 184–213, 2005.

- Stanley, T. D. and S.B. Jarrell, “Gender Wage Discrimination Bias? A Meta-Regression Analysis,” _Journal of Human Resources,_ 33(4), 947–73, 1998.

- Suryahadi, A., W. Widyanti, D. Perwira, and S. Sumarto, “Minimum Wage Policy and its Impact on Employment in the Urban Formal Sector,” _Bulletin of Indonesian Economic Studies_ , 39(1), 29–50, 2003.

- Thomas, D., J.P. Smith, K. Beegle, G. Teruel, and E. Frankenberg, “Wages, Employment and Economic Shocks: Evidence from Indonesia,” _Journal of Population Economics,_ 15(1), 161–93, 2002.

28
Table 2 summarizes the information in Table 1 and shows some clear patterns emerging in the direction of trade for various countries and groups. First, there is a group of countries which includes the Baltics and the Caucasus countries which shifted their trade orientation away from the FSU and towards the rest of the world early on and continued to increase their dependence on foreign markets and sources through the period. Second, there is another group, which includes Kazakstan, the Kyrgyz Republic, Moldova and Ukraine which increased their dependence on trade with the rest of the.world somewhat less rapidly, but also quite steadily.Then there is Uzbekistan which increased its dependence until 1996 but reduced it later. Russia is,as in many other things, different. It increased its orientation to the rest of the world very rapidly, from an already large base. But in 1998 the share of its trade with the rest of the world as a proportion of the total was roughly the same as in 1993. Finally, Belarus is a case totally to itself: its share of trade with the rest of the world was **less in 1998 than in 1991.V**

# III. Trade Policies in the Late 1990's

# A. The CEE Countries

The trade regimes that have evolved after a decade of reform in the CEE are characterized by higher protection in agriculture and selected manufacturing sectors. Tariffs are typically low in most sectors, except agriculture and consumer manufactures. Tariff exemptions are often widespread and sometimes non transparent (Kaminski, 1999). In manufactures, the tariff regimes established have tended to provide higher protection than in OECD countries; but not siginificantly different than that provided by countries in Latin America. Non- tariff barriers (NTB) appear to be more prevalent in Poland and Hungary and are focused on consumer products. For example, Hungary has a global consumer goods quota affecting a variety of textile and clothing products (except for WTO members), automobiles, and leather products (WTO, 1998). Poland's NTBs focus on motor vehicles, beverages and tobacco (Kaminski, 1999).

_5 The trade statistics of Tajikistan and Turkmenistan are far too unreliable to make ajudgement about the evolution of their trade patterns over time._ 10
- Sohngen, B., Tennity, C., Hnytka, M. and Meeusen, K.: 2009b, Global Forestry Data for the Economic Modeling of Land Use, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 4971.

- Steinbuks, J. and Hertel, T. W.: 2013, Energy Prices Will Play an Important Role in Determining Global Land Use in the Twenty First Century, Environmental Research Letters 8(1), 014014.

- Taheripour, F., Hurt, C. and Tyner, W. E.: 2013, Livestock Industry in Transition: Economic, Demographic, and Biofuel Drivers, Animal Frontiers 3(2), 3846.

- Taheripour, F. and Tyner, W.: 2011, Introducing First and Second Generation Biofuels into GTAP Data Base Version 7, GTAP Research Memorandum 21, Center for Global Trade Analysis, Purdue University.

- United Nations Department of Economic and Social Aairs Population Division: 2011, World Population Prospects: The 2010 Revision, http://esa.un.org/ unpd/wpp/index.htm. [Online; accessed 10-June-2011].

- USGAO: 2003, Natural Gas: Domestic Nitrogen Fertilizer Production Depends on Natural Gas Availability and Prices, Report to the Ranking Democratic Member, Committee on Agriculture, Nutrition and Forestry, U.S. Senate GAO-03-1148, United States General Accounting Oce.

- Winston, A.: 2009, Enhancing Agriculture and Energy Sector Analysis in CGE Modelling: An Overview of Modications to the USAGE Model, Centre of Policy Studies/IMPACT Centre Working Paper G-108, Monash University, Centre of Policy Studies/IMPACT Centre.

- Wise, M. and Calvin, K.: 2011, GCAM 3.0 Agriculture and Land Use: Technical Description of Modeling Approach, Technical report, Pacic Northwest National Laboratory (PNNL), Richland, WA (US).

- World Energy Council: 2008, Energy Eciency Policies around the World: Review and Evaluation, World Energy Council, London, UK.

- Yu, W., Hertel, T., Preckel, P. and Eales, J.: 2004, Projecting World Food Demand Using Alternative Demand Systems, Economic Modelling 21(1), 99 129.

40
page 17 Table 3. Estimation of the Average Number of Life-Days Saved Per Encounter with a Village Health Worker.

|<br>Disease<br>(1)|Life-Days<br>Lost<br>If Sick<br>(2)|VH W<br>Effectiv-<br>ness<br>(3)|Life-Day<br>Saved/<br>Encntr<br>(4)|s<br>Incidence<br>Per Thou.<br>(5)|Est'ed<br>"Need"<br>(6)|Life-<br>Days<br>Svd.<br>(7)|Est'ed<br>"Demand"<br> <br>(8)|Life-<br>Days<br>Savad<br>(9)|
|---|---|---|---|---|---|---|---|---|
|1. Cold|0.6|0.10*|0.04|1000.0|1600|0.02|800|0.02|
|2. Skin|||||||||
|Tnfection|6|0.10*|0.4|470.0|752|0.09|752|0.17|
|3. Malanra|815|0.26|29.3|40.0|289|2.58|0|0.00|
|4. Malnutrition,<br>Severe|11667|0.63|1016.8|1.5|11|3.40|0|0.00|
|5. Gastro-<br>enteritis|207|0.38|49.2|70.0|112|1.68|56|1.68|
|6. Accidenrs|1935|0.20*|241.9|7.7|12|0.91|12|1.82|
|7. Schisto-<br>somiasis|629|0.69|271.3|7.0|11|0.92|6|0.93|
|8. Pneumonia<br>- Child|7750|0.37|1792.2|2.4|4|2.09|2|2.10|
|9. Pneumonia<br>- Adult|1300|0.15|121.9|7.0|11|0.42|11|0.83|
|10. Premature<br>Birth|1750|0.10|18.7|9.6|90|0.51|0|0.00|
|11. Complication<br>of Pregnancy|s<br> <br>1229|0.39|51.1|4.8|45|0.70|0|0.00|
|12. Birth<br>Injury|10250|0.21|229.6|1.6|15|1.05|0|0.00|
|13. Other<br>Diseases|786|0.01*|4.9|209.0|334|0.50|0|0.00|
|TOTALS|38,325|||1,830.6|3,287|14.86|1,639|7.53|

- NOTES: Column (2): Denved by dividing the life-days-losc calculated by the Ghana Health Assesment Project (1981) by the estimated incidence rate from column (5).

- Column (3): The National Health Planning Unit (1978, Table 6) of Ghana estimated healthy days of life currently lost from each disease, LDL, the life days that would be saved by the fully implemented primary health care system including VHWs, LDS, and the portion of these savings that would be achieved without the VHW system, LDSt. Figures without asterisks are derived by the formula: (LDS-LDSt)/(LDL-LDSt). Figures with asterisks are the author's estimates for diseases omitted in the National Health Planning Unit document.

- Column (4): Column (2) x Column (3) divided by an estimate of number of encounters per episode, which is given by the ratio of column (6) to column (5).

- Column (5): From Ghana Health Assessment Project (1981).

- Column (6): Prevention of malaria and malnutrition on the one hand and birth problems on the other requires frequent encouters (e.g. five per year) between the VHW and the target groups of children under three and pregnant women respectively. Assuming there are 60 children under three and 30 pregnant women per thousand population, the two groups would require 300 encounters and 150 encounters respectively. These totals are distributed &cross diseases 3 and 4 on the one hand and diseases 10, 11 and 12 on the other according to the incidence ratios. Other diseases are assumed to average 1.6 encounters per episode, the ratio observed in a sample of VH W huts in Senegal in 1979 (Over, 1980).

- Column (7): Column (4) x Column (6) divided by the sum of Column (6). Column (8): Assume the VH W performs no preventitive or screening services and, for lack of demand, sees only half the episodes of diseases 1, 5, 7 and 8.

- Column (9): Column (4) x Column (8) divided by the sum of (8).
<!-- Start of picture text --> 8<br>7 MX OM “ 3B — .<br>Brazil . PA pe<br>6 we EC L sl cy iw<br>Average a oe °<br>SWB om © row<br>5 TJ MA a he 18<br>& hl ‘ ° ma GA *<br>3<br>0 10000 20000 30000 40000 50000 60000<br>GDP per capita<br>8<br>L ri ets<br>7 aE e GB AE OM MX “<br>a , on Brazil °<br>6 « sI - EC BZ SY -<br>Average gm - .<br>SWB : Uy K8 ly JM<br>(0-10) vn Ke ‘0 Ru . GR JO PH b<br>5 Z wah MA<br>:<br>4 ohne > HT GN OBJ on -<br>3<br>1.25 1.5 1.75 2 2.25 2.5 2.75 3<br>SWB inequality (standard deviation)<br><!-- End of picture text -->
# **3.1 Efficiency Implications of Higher Oil Revenue**

A main finding of this paper is that if oil revenue as a share of GDP reaches, for example, 10 percent then 97.8 percent should be invested while 2.2 percent could be transferred for consumption whilst still maintaining an optimal allocation and achieving efficiency (Figure 3). The key issue is, though, that oil revenue as a share of GDP has to almost reach 10 percent before any revenue should be consumed. If oil revenue as a share of GDP increases to as much as 15 percent, for example, almost a quarter of the oil revenue could be consumed — so any percentage point increase in oil revenue as a share of GDP over 10 percent yields a larger and larger share that can be consumed.

Conversely, if oil revenue falls below 10 percent of GDP — even with the additional revenue that the Pre-Salt discoveries will bring — there is a need to invest all the revenue leaving nothing for consumption. This is simply because the investments from all the available oil revenue will still not be sufficient for Brazil to reach its optimal capital stock because Brazil is relatively far from that point. However, if oil revenue reached 10 percent or more of GDP there is scope for both an optimal (efficient) capital stock, increased consumption levels from the fraction of oil revenue that can be optimally consumed, as well as increased income growth (per capita) as the capital-labor ratio enhances the growth potential of the labor force.

**Figure 4. Oil Revenue Crowds Out Private Saving When Such Revenue is** **<u>Transferred Equally Among Workers and Retirees</u>**

<!-- Start of picture text --> 0.0651<br>0.0650<br>0.0649<br>0.0648<br>0.0647<br>0.0646<br>0.0645<br>0.0644<br>0.0643<br>Oil revenue as a share of output<br>0.0642<br>1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15%<br>Private Saving<br><!-- End of picture text --> Source: Author’s simulations based on a general equilibrium OLG model calibrated for Brazil.

Going more into depth on this result, Figure 4 illustrates the falling private saving as oil revenue increases. The economic intuition is stated above, but this result depends crucially upon whether any share of the oil revenue is invested or not. If 100 percent of oil revenue is transferred for consumption, then the assumption on how it is distributed matters.

17
|**Table 5 Fuelwood use, source**<br>variable|**, expendi**<br>Non-<br>Poor|**ture and co**<br>Poor|**llection time**<br>North|**by househ**<br>Center|**olds**<br>South|Less<br>biomass<br>Areas|More<br>Biomass<br>Areas|Overall|
|---|---|---|---|---|---|---|---|---|
|**Percent of households using fuelwood as coo**|**king fuel:**||||||||
|Fuelwoodis primary cookingfuel|95.9%|98.6%|98.2%|97.5%|96.6%|96.9%|97.6%|97.2%|
|Fuelwoodis secondary cookingfuel|2.6%|1.1%|0.6%|1.7%|2.3%|2.1%|1.3%|1.9%|
|Fuelwoodis primarily collected|78.5%|90.5%|88.5%|82.8%|83.9%|82.2%|88.0%|84.2%|
|Fuelwoodis sometimes collected|7.8%|5.4%|2.6%|7.2%|7.4%|7.9%|4.2%|6.6%|
|**Percent of households with different sources**<br>|**of fuelwo**<br>|**od:**<br>|||||||
|Ownwoodlot|9.8%|8.7%|4.0%|9.7%|10.6%|11.6%|4.9%|9.3%|
|Community woodlot|4.2%|3.9%|1.5%|7.6%|2.0%|4.4%|3.5%|4.1%|
|Forestreserve|12.5%|17.9%|13.4%|10.1%|19.8%|14.3%|16.5%|15.1%|
|Unfarmed areas ofcommunity|50.8%|58.9%|71.4%|47.7%|55.2%|51.8%|59.9%|54.6%|
|Other|9.3%|6.7%|2.4%|15.1%|4.0%|8.4%|7.5%|8.1%|
|**Distance, hours, and expenditure**|||||||||
|One way walkto the source (Hours)|0.6|0.6|0.5|0.7|0.6|0.6|0.6|0.6|
|Hours spent (women) yesterday collecting<br>firewood|1.5|1.5|1.2|1.5|1.5|1.5|1.5|1.5|
|Hours spent onagriculturalactivities perday|2.9|2.8|2.7|2.8|3.0|2.9|2.8|2.9|
|Expenditure on Fuelwood (MK 2004)<sup>@</sup>|3484|1536|1557|3205|2327|2731|2233|2558|
|TotalConsumption Expenditure (MK 2004)<sup>@</sup>|31139|10946|21742|23809|19683|21850|21115|21595|
|Percent of Expenditure on Fuelwood|11%|14%|7%|13%|12%|12%|11%|12%|
|**Of those who collected fuelwood:**|||||||||
|Percent womenage10 and above|81.6%|86.8%|94.9%|81.6%|83.4%|83.5%|86.3%|84.4%|

@ Per capita per year.

40
|_ijst_<br>_supply_<br>∆<br>×<br>||||0.023<sup>_b_</sup>|
|---|---|---|---|---|
|_Low edu_||||(0.011)|
|_e_<br>_ij_<br>_FE_|yes|yes|yes|yes|
|_e_<br>_st_<br>_FE_|yes|yes|yes|yes|
|_e_<br>_it_<br>_FE_&<br>_e_<br>_jt_<br>_FE_||yes|yes|yes|
|Observations|4614|4614|4614|4209|
|0 <sup>:</sup><br>=<br>_H_<br>_L_<br>_H_<br>β<br>β|0.000|0.000|0.000|0.000|

# (p-value)

Heteroskedasticity-robust standard errors clustered at the state of origin-year level appear in parentheses.<sup>_a_</sup> ,<sup>_b_</sup> and<sup>_c_</sup> indicate significance at the 1%, 5% and 10% confidence levels. Source: Authors’ analysis based on data described in the text.

Table 3 reports results on the heterogeneous impact of market access across educational groups. As in table 1, we display first estimation results for a less restrictive specification. Column 1 does not include the state-year fixed effects, but instead the relative population size, the unemployment gap, and the difference in homicide rates. Column 2 contains our second benchmark specification (equation 15) and column 3 excludes the wage variable to obtain the joint effect of market access on migration via both channels. Column 4 adds the gap of the sector-state specific export supply capacity.

In all specifications, the coefficients of the control variables, including wages, are similar across educational groups. However, the coefficient of market access is significant at conventional levels only for low-qualified workers. The t- tests reported at the bottom of the table clearly reject the hypothesis of a uniform impact of market access across educational groups.

In light of our findings, the differences in the observed migration patterns across educational levels can be partly explained by a different sensitivity to foreign market access.

Economic opportunities associated with international trade seem most important for the location choice of low educated individuals. The strong impact 26
<!-- Start of picture text --> iy<br>“oO of 03 Od OS 06 O07 O8 09 1<br>Benchmark parameters<br>Nonresource primary balance to nonresource output<br>Minimized Loss function 61.94 56.87 52.18 47.67 43.56 39.63 35.99 32.48 29.20 26.17 23.30<br>Optimal value of y 0.50 0.50 0.40 040 040 0.30 0.30 0.20 0.10 0.10 0.00<br>Nonresource primary balance to total output<br><!-- End of picture text -->

|||||||iy<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||“o|O|of|03|Od|OS|06|O07|O8|09|1|
|Benchmark parameters<br>Nonresource primary balance to nonresource output||||||||||||
|Minimized Loss function|61.94|56.87|52.18|47.67|43.56|39.63|35.99|32.48|29.20|26.17|23.30|
|Optimal value of y|0.50|0.50|0.40|040|040|0.30|0.30|0.20|0.10|0.10|0.00|
|Nonresource primary balance to total output||||||||||||
|Minimized Loss function|121.48|104.48|89.67|76.66|65.36|55.49|46.91|39.56|33.23|27.86|23.30|
|Optimal value of 7|0.60|0.50|0.50|0.40|0.30|0.30|0.20|0.10|0.10|0.00|0.00|
|‘Overall primary balance to total output||||||||||||
|MinimizedLossfunction|21.25|21.64|22.02|22.41|22.81|23.16|23.42|23.63|23.70|23.59|23.30|
|<br>Optimal value of 7|0.50|0.40|0.40|o.40|040|0.30|0.30|0.20|0.20|0.10|0.00|
|General index ofmacroeconomic volatility 2||||||||||||
|MinimizedLossfunction|12.95|13.90|14.93|16.01|17.09|18.19|19.28|20.33|21.35|22.36|23.30|
|<br>Optimalvalueof|0.50|0.50|0.50|0.40|O40|0.30|0.20|0.20|0.10|0.00|0.00|

<!-- Start of picture text --> Optimal value of 7 0.60 0.50 0.50 0.40 0.30 0.30 0.20 0.10 0.10 0.00 0.00<br>‘Overall primary balance to total output<br>Minimized Loss function 21.25 21.64 22.02 22.41 22.81 23.16 23.42 23.63 23.70 23.59 23.30<br>Optimal value of 7 0.50 0.40 0.40 o.40 040 0.30 0.30 0.20 0.20 0.10 0.00<br>General index of macroeconomic volatility 2<br>Minimized Loss function 12.95 13.90 14.93 16.01 17.09 18.19 19.28 20.33 21.35 22.36 23.30<br>Optimal value of 0.50 0.50 0.50 0.40 O40 0.30 0.20 0.20 0.10 0.00 0.00<br><!-- End of picture text -->