rafmacalaba/datause-encoder-data huggingface dataset:
- 32 -

Table _7._ The Peace Dividend: Simulated Long-Run Effects of Changes in Military Spending Ratios in the Late 1980s on Capacity Output I/ Country Groups

||Industrial|||Developin|g Countries|||
|---|---|---|---|---|---|---|---|
||Countries|Asia|Eastem<br>Europe|Middle<br>East|North<br>Africa|Sub-Saharan<br>Africa|Westem<br>Hemisphere|
||||(I|n percent of G|DP)|||
|IAverage Military||||||||
|Spending Ratio,|3.97|6.35|11.75|10.34|8.13|3.12|2.16|
|1972-1985 2/||||||||
|2.Average Military||||||||
|Spending Ratio,<br>1986-19902/|3.70|3.88|14.22|9.06|4.60|3.42|1.94|
|3. Change in Military<br>Spending Ratio<br>|-0.27|-2.47|2.47|-1.28|-3.53|0.30|-0.22|
|_(2minus 1)_||||||||
|4. Associated Change in||||||||
|Ratio ofInvestment|0.14|0.66|-0.48|0.25|0.70|-0.11|0.15|
|toGDP3/||||||||
|Time||||||||
|Horizon||Simula|ted Minus Base|line Growth R|ates of Per Ca|pita GDP||
|___(Years)||||||||
||||(In<br>|percent**per**an<br>|num)<br>|||
|I|0.010|0.(071|-0.028|0.019|0.082|-0.013|0.015|
|5|0.049|0.339|-0.131|0.091|0.391|-0.063|0.073|
|10|0.043|0.297|-0.115|0.079|0.343|-0.055|0.064|
|25|0.029|0.199|-0.077|0.053|0.230|-0.037|0.043|
|50|0.015|0.103|-0.040|0.027|0.119|-0.019|0.022|
|co|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Time||||||||
|Horizon||Sim|ulated Minus B|aseline Levels|of Per Capita|GDP||
|(__<br>Years)||||||||
||||(In per<br>|cent of baselin<br>|e GDP)<br>|||
|I|0.0|0.1|-0.0|0.0|0.1|-0.0|0.0|
|5|0.1|1.0|-0.4|0.3|1.2|-0.2|0.2|
|10|0.4|2.6|-1.0|0.7|3.0|-0.5|0.6|
|25|0.9|6.2|-2.4|1.7|7.2|-1.2|1.3|
|50<br>___<br>____|1.4<br>2.0|9.8<br>13.6|-3.8<br>-5.3|2.6<br>3.6|11.3<br>15.7|-1.8<br>-2.3|2.1<br>2.9|

I/ The simulation exercise is based on the parameter estimates given in Table 5, column 4, and Table 6, column 4. Details on how the simulations were performed are given in Appendix 11.

2! Derived from data in Hewitt (1992).

3/ The change in investment ratio is produced by the total change in military spending ratio with respect to the baseline military spending ratio (average during 1972-85).
domestic market discipline that is chiefly relevant. (See Figure 1 and Table 2). To be sure, several of the jurisdictions covered in the survey with mainly foreign-owned banks are small offshore centers; nevertheless, the mean population size of countries dominated by government-owned and foreign-owned banks is larger than that of the other countries, so this is not simply a small country phenomenon. And the true figure for privatelycontrolled banks is undoubtedly lower, when account is taken, for example, of (i) banks with minority government stakes where the public sector’s total shareholding (including other public sector institutions such as social insurance funds and other state-owned financial intermediaries) exceeds 50%; (ii) joint venture banks with government and foreign partners between them holding more than 50%; and (iii) effective control by government or foreign bank albeit with less than 50%. The relevance of these cases becomes evident when we consider a few examples.

Share of banking system not majority controlled by Government or foreign banks <!-- 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 2 3 4 5 6 7 8 9 10<br>Deciles of 92 developing countries<br>% of system<br><!-- End of picture text --> Figure 1(a): _Share of banking system not majority controlled by government or foreign banks_ (Source: World Bank Regulation and Supervision Survey) 14
<!-- Start of picture text --> 40 a b<br>20<br>a<br>5<br>my 30 =<br>6 o<br>= e 15<br>= irso Le<br>~ ue we oor<br>3 we c= 10 ae<br>oS aa ee os<br>i ae 2 “<br>oO “7 ad 4<br>= - ke) ,<br>£10 Oe 2 5 /<br>e Fa<br>o7<br>7<br>0 —— 27"<br>0 ee<br>1.0 c 18 d<br>x<br>0.8 gz<br>=o 1.6<br>eoa $swn<br>SLo6> 1S}oo<br>5% 2352<br>3 S04 o®<br>£5" £Y<br>aS oN ~s} g2<br>= Z Sy a ll<br>02 off Ss ta3~ 1.2<br>‘ <<br>if Se a)<br>/ ==<br>0.04 Saee-55°"<br>1.0<br>Le | Le |<br>2020 2030 2040 2050 2060 2070 2080 2090 2020 2030 2040 2050 2060 2070 2080 2090<br>Year information is revealed Year information is revealed<br>T*=1.7°C — T*=2°C<br><!-- End of picture text -->
# **B. Debriefing survey**

- 1) Were you happy with your final rank? a. Yes (1) b. No (0)

- 2) Reflecting on the ball catching task, on a scale of 1-10, how engaging did you find it? 1 means not at all engaging and 10 means very engaging.

Not at all engaging 1. . . 2. . . 3. . . 4. . . 5. . . 6. . . 7. . . 8. . . 9. . . 10 Very engaging

- 3) You caught [autofill] balls in the catching task. What was the most important source of motivation for you? Choose one.

- a. I just like to do a good job

- b. I wanted to do better than others

- c. I was bored so did not care how many I did

- d. I got nervous about my performance, so I did not do too many

- e. I felt I had to do at least a little bit, otherwise why am I here?

- f. The rate of earnings was not high enough for me today, so I just left the basket in one place

- g. I was trying to minimize costs, so I just left the basket in one place

- h. Other: please specify

- 4) Reflecting on the investment task, on a scale of 1-10, how engaging did you find it? 1 means not at all engaging and 10 means very engaging.

Not at all engaging 1. . . 2. . . 3. . . 4. . . 5. . . 6. . . 7. . . 8. . . 9. . . 10 Very engaging

# [FOR TREATMENT – LEARNING ACTUAL RANK]

- 5) After confirming your income available for the investment task, you learned your rank. Did knowing your rank for earnings impact your choice in the investment task?

- a. Yes (1)

- b. No (0)

32
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 33 D., Wiberg, D. (2015) Securing Water, Sustaining Growth: Report of the GWP/OECD Task Force on Water Security and Sustainable Growth.

Schuerch, M., Spencer, T., Temmerman, S., Kirwan, M. L., Wolff, C., Lincke, D., McOwen C.J., Pickering M.D., Reef R., Vafeidis A.T., Hinkel J., Nicholls R.J., Brown S. (2018). Future response of global coastal wetlands to sea level rise. _Nature_ , _561_ , 231-234. DOI 10.1038/s41586-018-0476-5 Stive, M.J.F., Fresco, L.O., Kabat, P., Parmet, B.W.A.H., Veerman, C.P. (2011) How the Dutch plan to stay dry over the next century. Proceedings of the ICE-Civil Engineering, 164 (3), 114–121.

Tarrant, O. & Sayers, P. (2012) Managing flood risk in the Thames Estuary – the development of a long-term robust and flexible strategy. In Sayers, P. (ed.) _Flood Risk: Planning, design and management of flood defence infrastructure_ London, ICE Publishing, pp. 202-326.

Temmerman, S., Meire, P., Bouma, T.J., Herman, P.M.J., Ysebaert, T., De Vriend, H.J., 2013. Ecosystem-based coastal defence in the face of global change. Nature 504, 79–83.

Tol, R.S.J. (2002) Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates. Environmental and Resource Economics, 21, 47–73.

Vafeidis, A.T., Nicholls, R.J., McFadden, L., Tol, R.S.J., Hinkel, J., Spencer, T., Grashoff, P.S., Boot, G., Klein, R.J.T. (2008) A new global coastal database for impact and vulnerability analysis to sea-level rise. Journal of Coastal Research 24, 917–924 Van Alphen, J. (2015) The Delta Programme and updated flood risk management policies in the Netherlands. Journal of Flood Risk Management DOI: 10.1111/jfr3.12183 World Bank (2010) Climate Risks and Adaptation in Asian Coastal Megacities: A Synthesis Report. World Bank, Washington DC, 120pp. Available at: <u>http://documents.worldbank.org/curated/en/866821468339644916/Climate-risks-andadaptation-in-Asian-coastal-megacities-a-synthesis-report</u> (Accessed 21 March 2018) World Bank (2017) World Bank Country and Lending Groups. Available at: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-</u> - <u>lending groups (Accessed 23 March 2018)</u> Yohe, G. and Tol, R.S.J. (2002) Indicators for social and economic coping capacity—moving toward a working definition of adaptive capacity. Global Environmental Change, 12, 25-40.
# **6. Conclusion**

Comparisons of gender-neutral and gender-specific indicators of entrepreneurship highlight the importance of collecting data on female business owners. The systematic and consistent collection of such data is crucial for developing meaningful, evidence-based recommendations and public policies related to women’s economic empowerment and their contribution to international development. The lack of sexdisaggregated data in many economies limits the understanding of the issues surrounding female entrepreneurship. As previously stated, a number of national statistics authorities do not compile sexdisaggregated data on entrepreneurial activity.

This study suggests that the gender gap in business ownership remains high in many economies around the world. In the vast majority of the analyzed economies, less than one-third of new LLC owners are women. Although sole proprietorships are more frequently used by female entrepreneurs, only three economies have similar or equal number of women business owners relative to men. The gap in female entrepreneurship is especially apparent in low-income economies where women are much less likely than men to start a new business. This suggests a need to intensify efforts to boost female business ownership. Progress in this area should be tracked closely over the coming years through expanding the collection of standardized and country-comparable sex-disaggregated data on business entry and ownership within the Entrepreneurship Database.

This study also explores interlinked and reinforcing factors that impact female entrepreneurship, including women’s financial inclusion, legal rights disparities and the gender gap in education. This analysis shows that national economies should prioritize the expansion of financial inclusion, equal legal rights and access to education for women. The elimination of gender disparities in these spheres also contributes to the narrowing of the gender gap in entrepreneurship. Sustainable Development Goal (SDG) 5, which targets all forms of discrimination against women and girls, provides an opportunity to tackle these issues holistically. The achievement of SDG 5 is critically important. Without boosting women’s economic participation and moving towards gender equality, it will be significantly more challenging for economies to achieve other SDGs or the 2030 Agenda for Sustainable Development and, most importantly, to reach their full economic and social potential. In this respect, further research is needed to provide policy-relevant evidence on female entrepreneurship. Such research could measure female directorship in addition to female ownership. The development of solutions to reduce the existing gender gap in business formation would also require in-depth analysis of business demography, characteristics of women-owned businesses, and determinants of their growth.

15
# **Figure 2 Consumption Ratio to Base: Permanent versus Temporary Increase in Foreign Aid**

<!-- Start of picture text -->
1.2<br>1.15<br>1.1<br>1.05<br>1<br>0.95<br>AID-P AID-P & trade AID-T10 AID-T10 & trade<br>0.9<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>consumption ratio<br><!-- End of picture text -->

_Source: Authors’ calculations ._

# **Figure 3**

**Real Exchange Rate: Permanent versus Temporary Increase in Foreign Aid**

<!-- Start of picture text -->
1.08<br>AID-P AID-P & trade AID-T10 AID-T10 & trade<br>1.07<br>1.06<br>1.05<br>1.04<br>1.03<br>1.02<br>1.01<br>1<br>0.99<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>real exchange rate<br><!-- End of picture text -->

_Source: Authors’ calculations._

# **Figure 4**

**Debt Ratio to Base: Permanent versus Temporary Increase in Foreign Aid**

<!-- Start of picture text -->
1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2 AID-P AID-P & trade AID-T10 AID-T10 & trade<br>0<br>1 4 7 10 13 16 19 22 25 28 31 34 37 40<br>time periods (years)<br>debt ratio<br><!-- End of picture text -->

_Source: Authors’ calculations._ 17
# Figure 2: Survival Curves for Unemployed Females

<!-- Start of picture text --> SUrgwml Probsbllty<br>o 1 2 a 4 1 0 7r 0 8 10 n 1A<br>D uratlon In months<br><!-- End of picture text --> <!-- Start of picture text --> 8urylwv Probability<br>0 1 2 9 4 6 0 7 a c 10 n 12<br>Duration In months<br><!-- End of picture text --> <!-- Start of picture text --> 0 1 2 0 4 0 e r 0 9 10 in 12<br>o__.__.__._.__.__._.__.__._.__.<br>Duration In months<br>'| Cofitrla * Tra4gon* 1a lInnt2<br><!-- End of picture text -->

0 7 8 9 1 l Trainee*1t If trainee unemploy d - 6 month Tralnee2-1 If trainee & unemployed > 6 months s
Table 50

|Dependent variable: Nee|d of fiscal|adjustment|based on L|avigne(20|11)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.023***|0.024***|0.032***|0.033***|0.029***|0.026|0.021|0.032**|
|structural as % GDP|(0.006)|(0.006)|(0.006)|(0.006)|(0.011)|(0.019)|(0.018)|(0.013)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.333||-0.289|-0.328|-0.297|-0.263|-0.289|
|||(0.232)||(0.262)|(0.267)|(0.262)|(0.264)|(0.261)|
|Inflation (t-1)||0.353**||0.627***|0.642***|0.634***|0.605***|0.627***|
|||(0.155)||(0.139)|(0.138)|(0.138)|(0.138)|(0.137)|
|U.S. Interest rate||0.002||-0.014|-0.014|-0.014|-0.016|-0.014|
|||(0.011)||(0.013)|(0.013)|(0.013)|(0.013)|(0.013)|
|**Political factors**|||||||||
|Gini|||-0.006*|-0.005*|-0.005*|-0.006|-0.005*|-0.005*|
||||(0.003)|(0.003)|(0.003)|(0.004)|(0.003)|(0.003)|
|Rule of law|||0.026|0.042|0.043|0.043|0.020|0.042|
||||(0.042)|(0.045)|(0.045)|(0.045)|(0.046)|(0.045)|
|Margin of Majority|||0.038|0.057|0.058|0.060|0.056|0.053|
||||(0.114)|(0.118)|(0.117)|(0.117)|(0.120)|(0.171)|
|Election's year|||0.000|0.001|0.000|0.000|0.001|0.001|
||||(0.012)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|**Interactions**|||||||||
|EMEs * Deviation|||||0.007<br>(0.013)||||
|Gini * Deviation||||||0.000<br>(0.000)|||
|Rule of law * Deviation|||||||0.003<br>(0.004)||
|Majority * Deviation||||||||0.001<br>(0.021)|
|Constant|0.061|0.041|0.083|0.005|0.001|0.040|0.088|0.007|
||(0.046)|(0.072)|(0.186)|(0.189)|(0.188)|(0.213)|(0.188)|(0.197)|
|Observations|2,372|2,367|1,760|1,757|1,757|1,757|1,757|1,757|
|R-squared|0.040|0.046|0.067|0.085|0.085|0.085|0.086|0.085|
|Number of ifscode|124|124|95|95|95|95|95|95|
|CountryFE<br>Robust standard errors c<br>*** p<0.01, ** p<0.05, *|Yes<br>lustered by<br>p<0.1|Yes<br>country in|Yes<br>parenthes|Yes<br>es|Yes|Yes|Yes|Yes|

- 79 -
**Figure 2. absolute levels of life satisfaction and country-level attributes**

<!-- Start of picture text --> Subjective wellbeing of the employed / unemployed Subjective wellbeing of the employed / unemployed<br>ranked by quintiles of per cap GNI ranked by quintiles of rule of law index<br>1 2 3 4 5 1 2 3 4 5<br>mean of lsatisEMP mean of lsatisUNE mean of lsatisEMP mean of lsatisUNE<br>Subjective wellbeing of the employed / unemployed Subjective wellbeing of the employed / unemployed<br>ranked by quintiles of government effectiveness index ranked by quintiles of labour regulation index<br>1 2 3 4 5 1 2 3 4 5<br>mean of lsatisEMP mean of lsatisUNE mean of lsatisEMP mean of lsatisUNE<br>Subjective wellbeing of the employed / unemployed Subjective wellbeing of the employed / unemployed<br>ranked by quintiles of gov' transfers ranked by quintiles of national unempl rate<br>1 2 3 4 5<br>1 2 3 4 5<br>mean of lsatisEMP mean of lsatisUNE<br>mean of lsatisEMP mean of lsatisUNE<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>8 8<br>6 6<br>4 4<br>2<br>2<br>0<br>0<br><!-- End of picture text --> Red bars denote average life satisfaction on a 10 pt scale for the unemployed and blue bars for the employed. GNI and unemployment rates are from the World Bank WDI data base. Rule of law and government index from Kaufman et al (2010). Government transfers from Gwartney, J. et al. (2010). labor market regulation index denotes the labor laws index from Botero et al. (2004).

Similar trends emerge when we consider more general indicators of the quality of a nation’s institutions. Mapped against an index of government accountability (rule of law) and government effectiveness the level of overall life satisfaction increases for both groups, even though relative differences in welfare are more marked in countries 18
-5-

# Program Feetures

The PROBECAT program is administered through the network of state employment offices. Since 1987, it has trained a total of 251,181 unemployed persons and provided 9268 courses. During the training period, program participants receive a stipend equal to the minimum wage. Upon completion of the course, the local state employment office helps trainees find a job.Y The majority of program participants enroll in classroom training, primarily in short-term vocational courses offered through contracts with local private and public institutions. Training is provided in several occupational areas: carpentry, construction, electricity, food preparation, graphic arts and design, handicrafts, machinery, mechanics, refrigeration, services and administration, shoe repair, textiles and apparel, and welding. In principle, courses are organized to respond to the needs of the local labor market, and are designed to redress local shortages of workers with particular skills. These needs are determined through periodic studies of local labor market conditions. Courses vary in duration from one to six months, with the majority of courses lasting three months.

Not everyone is eligible to participate in the PROBECAT program. The selection procedure gives variable weights to different criteria, including the number of economic dependents, having attained certain levels of basic education, prior work experience, and having been unemployed for less than three months. The exact weighting scheme is quite complex and non-linear. Individuals with a total composite score exceeding a threshold level are eligible to join the program. In addition, participants must (in theory) be between the ages of 20 and 55, and be registered as a job-seeker at the local state employment office.&' This nnn-random selectio.i of individuals poses potentially serious measurement problems for any evaluation of the PROBECAT program.

# Data Sources

A number of surveys have been fielded to help monitor and evaluate the PROBECAT program. The first, comprising rollow-up surveys of trainees 3 and 6 l/ The follow-up survey data suggest, however, that most trainees find jobs on their own and not through the state employment service.

i/ The original age bracket was amended to allow a small number of participants between the ages of 16 and 20. The follow-up surveys also show a few participants above age 55.
9 production and assembling. Third, we look closer into patterns of CEEC integration into the EUbased networks of production and marketing.

# 3. CEECs TRADE IN PARTS

Trade in parts has been the most rapidly growing component of international trade. Yeats and Ng (1999) show that parts and components had driven the 'pre-East Asian financial meltdown' expansion in exports. Analyses pointing to a rapidly increasing level of processing in goods traded by CEEC would suggest that parts and components has been the engine of growth of their trade-see among others Aturupane, Djankov and Hoekman (1998), Baldone, Lasagni and Sdogati (1997, Dobrinsky (1995) and Kaminski, Wang and Winters (1996). Has it indeed been the case? How significant has trade in parts become in CEEC trade with the world? Who are the major partners of CEECs? What sectors play a major role in trade in parts?

# **3.1. Significance of parts in** CEEC **trade**

The collapse of central planning removed 'systemic' barriers to the development of direct horizontal links among domestic and foreign firms. The emphasis is on 'systemic' since they were a constituent component of central planning. Following its demise, one would expect the emergence of these links hinging critically on the pace of implementation of economic reforms and establishment of business friendly environment. The presence and the lack of expansion in this trade raises interesting questions about the impact of progress in economic reforms and liberalization on the integration into global networks of production and marketing.

Table 2: Structure of Exports and Imports of CEEC in 1993 and **1997** (in ndllion of US doliars and percent)

||Exports (US|**$**mill.)|(in perce|nt)|Imports (US|**$**mill.)|(in perce|nt)|
|---|---|---|---|---|---|---|---|---|
||1993|1997|1993|1997|1993|1997|1993|1997|
|Manufactures:|35,773|73,688|72|78|44,793|95,560|79|77|
|of which Chemicals|<br>4,138|8,280|8|9|6,462|13,635|11|11|
|Machinery and Transport<br>excluding parts|6,623|18,855|13|20|14,446|28,632|25|23|
|Parts|<br>2,789|8,823|6|9|5,822|16,520|10|13|
|Non-fuel primaries|9,139|14,534|19|15|<br>8,464|13,973|15|11|
|Fuels|3,781|5,099|8%|5|<br>2,518|13,238|4|11|
|All intermediate goods|28,390|58,589|57|62|<br>34.016|78,341|60|63|
|All goods|49,377|94,234|100|100|<br>56,713|124,477|100|100|

Source: Computations based on partners' data from UN COMTRADE Statistics.

Table 2 reports values of imports and exports of major product categories including intermediate goods and separately parts and components for CEEC. The value of total CEEC exports of manufactures, which rose more that total exports, doubled between 1993 and 1997. The value of imports of manufactures increased even more. Parts played major role in this
_38 How_ **_Fast HMs_** _Chiir_ **_Indamuyr_** _Grown.?_

# TABLI 14: TRIAL CALCULATION OFZNERGY CONSUWrnON AND PRODUCTITY FOR COLLCTm INDUSTRY (INCLUDING NON-INDEPENDENT UNITS AND "OTHER"OwNERmu FORMS) 1980 AND 1955

# 1. Using DERIVED GVIO figur.

||_FAwvy_c|ons.|De<br>OVIO|rived<br> Yi P80|Ton utd co|al per|%RiPe|
|---|---|---|---|---|---|---|---|
|Bralch|<br>lOOCK**t**<br>s d|coal<br>|1980|1985|<br>10000 yuan|<br>OVIO<br>|GVIO/E<br>|
||1980|1985|||1980|1955|1980/85|
|1|1325|1477|26.46|71.86|50.07|20.55|143.60|
|2|_-5556_|-6616|-1.92|7.74|2893.17<br>|_-854.85_|-438.44|
|3|13|392|21.62|35.83|0.60|10.94|-94.50|
|4|-224|-260|-6.44|48.11|34.79|_-S.40_|<br>-743.73|
|_5_|2129|2311|85.24|206.78|24.98|11.18|123.50|
|6|1663|1925|359.93|834.78|4.62|2.31|100.36|
|7|1441|2519|46.44|136.61|31.03|18.44|68.28|
|8|134|131|42.11|_65.50_|3.18|2.00|59.09|
|9|391|509|-365.47|-528.49|-1.07|-0.96|11.08|
|10|719|963|25.68|105.71|28.00|9.11|207.36|
|11|41|48|704.80|819.81|0.06|0.06|-0.65|
|12|46|_S6_|<br>106.21|172.11|0.43|0.33|33.11|
|13|300|434|-12.87|-15.07|.23.3|-28.81|-19.06|
|14|-14|66|-25.56|24.86|0.!'|2.66|-79.37|
|_15_|1254|1738|152.40|262.81|8.23|6.61|24.43|
|Sum|3662|5693|977.23|2295.72|3.7S|2.48|_51.11_|
||Pu|blishe O|VIO|PFre|nt Difference|||
|Branch|Y|i Yuan,|**P80**|(Dei|ved-publshed|||
|||||**As**per|c<br>of Derived|||
||1980||1985|1980|1985|||
|1|22.51||62.90|.14.93|-12.46|||
|2|1.65||4.44|-155.92|-42.63|||
|3|18.69||35.66|-13.56|-0.47|||
|4|1.23||3.18|-119.10|-93.39|||
|5|103.39||237.98.|21.30|15.09|||
|6|313.54||725.86|-12.89|-13.05|||
|7|79.92||163.08|72.09|19.38|||
|8|26.18||52.71|-37.84|-19.53|||
|9|35.6||108.00|-109.74|-120.44|||
|10|96.5||354.55|275.79|235.40|||
|11|100.33||154.50|-85.69|-81.15|||
|12|28.26||4S.32|-73.39|-73.67|||
|13|51.44||52.88|-499.77|-450.9|8||
|14|9.84||36.33|-138.49|247.30|||
|_I5_|71.41||87.86|-53.14|-66.57|||
|Sum|960.99||2175.25|-1.66|_-5.25_|||

Note: published **COB** OVIO we from DEPLATE.wkl.

<!-- Start of picture text --> (cmmd<br><!-- End of picture text -->