rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 11. Gradual global warming risks and macro‐financial risks**

## A. Gradual warming risk vs. macroeconomic risk, 2017

## B. Gradual warming risk & macroeconomic risk by region

<!-- Start of picture text --> 70 100% 0<br>6560 MWIZMB SDNSLE 90%80% 4 9 6 4 1<br>55 KEN MOZ 70% 0<br>50 KHMSYCAGOCOG 60% 2 0 7 0 12<br>45 SGP JAM 50% 4<br>40 CHN SYR 40%<br>35 SWENORISR KOR 30% 7 12 2 1 2<br>30 LUX NZL 20%<br>25 CHE AUS 10% 4 5 1<br>1<br>20 0% 0 0<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>EIU economic risk index (100=High), 2017 low both high climate high macro high both<br>C. Gradual warming risk vs. banking risk, 2017 D. Gradual warming risk & banking risk by region<br>70 100% 0<br>6560 ZMBPNGSLEMWI SDN 90%80% 4 7 5 4<br>55 KENMOZ 70% 1 0 1 2<br>50 COG AGO 60% 11<br>45 SGP KHM 50% 2 3 8<br>40 SYR 40%<br>35 SWENZLAUSISRKOR LBY 30%20% 6 14 7 1 0 0<br>30 1 2<br>NOR 10% 4<br>25 LUX<br>CHE 0% 0 1<br>20<br>EAP ECA LAC MENA SA SSA<br>10 30 50 70 90<br>low both high climate high banking high both<br>EIU banking risk index (100=High), 2017<br>E. Gradual warming risk vs. public debt risk, 2017 F. Gradual warming risk & public debt risk by region<br>70 100% 0<br>65 SDN 90% 4<br>4 5 1<br>60 PNG 80% 8<br>MWI<br>55 KENSLE MOZ 70% 0 0<br>50 ZMB COG 60% 2 0 3 11<br>AGO 7<br>45 SGP PER 50%<br>4035 NZL LTUCHL LBY VEN SYR 40%30% 7 13 3 1 2<br>SWE AUS<br>30 ISR 20% 0<br>5 5 2<br>25 LUX 10%<br>CHE 1<br>20 0% 0<br>10 30 50 70 90 EAP ECA LAC MENA SA SSA<br>low both high climate high debt high both<br>EIU debt risk index (100=High), 2017<br>NotreDame Vulnerability (0 ‐ 100,high), 2017<br>Notre Dame Vulnerability (0 ‐ 100,high), 2017<br>Notre Dame Vulnerability (0 ‐ 100,high), 2017<br><!-- End of picture text -->

## F. Gradual warming risk & public debt risk by region

Note: Panels A, C, and E: Countries above (below) country median in Notre Dame Readiness indicator in green (red). Panel A: average losses (% GDP) between 2005 and 2017.

Panels B, D, and F: columns show proportion of countries in each category (i.e., low climate and macro‐financial risks, high climate‐low macro‐ financial risks, low climate‐high macro‐financial risks, high climate and macro‐financial risks) by region. Thresholds are country median values of EIU indicator or climate variable as of 2017 (historical country median for the case of fuel exports). Numbers in columns are the number of countries in each category. EIU data for Saudi Arabia as of 2016.

Source: EIU Credit Risk Model, GermanWatch, Notre Dame GAIN, WDI. Staff calculation 37
# Research Components of World Bank Education Projects, since 1982.

|Are,jCountry and Loan/Credi<br>Number|t1<br>Approved|<br>Research Components|Current Status of Studies, including<br>Completed Documents/Data|
|---|---|---|---|
|||(ii) provide comprehensive<br>long-term financing<br>ptan;<br>(iii) evaluate teacher upgrading;<br>(iv) prepare a school location map (pare. 3.1<br>p.10).<br>(v) demand for primary education|Oiii)<br>ongoing - no document as yet<br> <br>(iv) to start early 1990<br>(v) study in progress- being prepared by<br>M. Diambobma<br>Related document:<br>W.W. Koch Pubtlishers<br>Association (1985).<br>"Textbook publishing,<br>distribution and printing<br>report" (doc. U 220.276)|
|AFRICA Uganda CR 1329-UG|1983|Third Education (Rehabilitation).<br>Special<br>studies in:(i)<br>technical education;<br>(ii)<br>distribution (equity issues)<br>and management of resources for educational<br>financing;|No in,oramtion<br>suppLied.|
|||(iii)<br>teacher supply and deand<br>(paras.3.B3-6<br>p.20).||
|AFRICA UgandaCR1965-UG|1989|Fourth Education Project. Evaluation of pilot<br>scheme (June 1990);<br>(ii)<br>evatuation of textbook fund(June 1991);<br>vii)<br>pre-investment<br>studies inctuding:<br>financing and efficiency of education,<br>feasibility<br>study of textbook publishing,<br>feasibility<br>study on provision of water to<br>rural primary schools.|Feasibility<br>study of textbook publishing and<br>printing in Uganda (1989) (d c.#033655)|

35
employment creation from changes in land management and agriculture in developing countries, where these economic sectors are relatively more important.

Schwartz et al. (2009) considers direct employment effects of fiscal stimulus projects in three Latin American countries, Honduras, Brazil and Peru. Again, the variation in jobs created per dollar across the different projects considered is large. Water network rehabilitation and expansion in Honduras is much more effective (by a factor of more than ten) in creating jobs than hydroelectric schemes in Brazil, with rural electrification in Peru falling in between (the Honduran projects seem to be a lot more labor intensive than water resource management in South Korea).

Rutovitz (2010) carries out a detailed analysis of employment opportunities from a switch towards renewable energy and greater energy efficiency in South Africa, in the spirit of Rutovitz and Atherton (2009), a global study for Greenpeace. The focus is on direct employment but allowance is made for learning effects with new technologies that increase labor productivity (reduce the number of jobs supported) over time. Different scenarios are considered for how much of the plant and equipment needed is manufactured in South Africa and for how much South Africa supplies the rest of Africa with renewables technologies. Job losses in the coal industry are factored into the projections. A business as usual scenario from the International Energy Agency (IEA) is compared with (i) a South African government ‘Growth Without Constraints’ (GWC) scenario designed to reflect South Africa’s energy future in the absence of climate change, with no oil constraints, and if no effort was made to internalize externalities, and (ii) an Energy [R]evolution scenario designed to reduce South African emissions by 60% by 2050 (compared with 2005). The authors estimate that the Energy [R]evolution scenario creates 27% more jobs than the IEA reference scenario and 5% more than the GWC scenario. Macroeconomic feedbacks are not analyzed and nor are costs. Yet the costs of job generation, including opportunity costs, can be substantial once macroeconomic consequences are taken into account.

Upadhyay and Pahuja (2010) examine the employment potential of renewable energy, especially wind and solar power, in India. Once again, its scope is limited to an estimate of direct employment effects, because it is difficult to assemble input-output tables with an appropriate breakdown of activities and to model induced macroeconomic effects. Unlike the developing country studies discussed above, the authors concentrate on estimates of 17
The structure of the paper is as follows. Details of the variables included in the analysis and a discussion of the dataset is provided in Section 2. The importance of firm turnover to short-run industry performance is explored in Section 3. Sections 4 and 5 explore the influence of the timing and spillover biases respectively. Finally, we conclude in Section 6 with a summary of the findings and their policy implications.

# **_2. Data and Variable Construction_**

# _2.1 The dataset_

The dataset used in this analysis is a census of manufacturers conducted annually by the Moroccan Ministry of Industry, Commerce and Productivity (formerly the Ministry of Industry and Telecommunications) from 1985-2003.<sup>3</sup> The survey has almost universal coverage of manufacturing firms across all sectors and areas of the country, with approximately 90% of firms responding.<sup>4</sup> The high response rate can be attributed to the rigorous manner in which the survey is conducted. Each year, firms are sent (via post) a questionnaire to complete. Firms failing to complete this questionnaire are then visited by officials from the Ministry of Industry, Commerce and Productivity (MICP) in order to conduct a face-to-face interview. Haddad, de Melo and Horton (1996) and Fafchamps (2004) have used components of this dataset to analyse the determinants of firm turnover.

The manufacturing firm survey covers a range of basic information on these firms such as sales, production, exports, investment, number of permanent and temporary workers, sector, year of establishment, legal status and location.

> 3 Fraser Thompson thanks Marcel Fafchamps for making the data available for his dissertation and related work.

> 4 Only very small or informal firms are not captured by the sample. The coverage of medium and large enterprises is virtually universal. The Ministry of Industry, Commerce and Productivity imputes values for all non-responding firms, usually based on the previous year’s information. The regression analysis that follows is restricted to the non-imputed observations, however the productivity decompositions require all observations to be used.

5
makers to focus on the most important characteristics in assessing the potential dissemination of stoves. To our knowledge only two recent studies, van der Kroon et al. (2014) and Jeuland et al. (2015), have attempted to elicit preferences for attributes of generic improved fuelwood stoves.

In their study van der Kroon et al. (2014) compare preferences across multiple fuel sources and cooking technologies. In our work we focus solely on improved biomass stoves, an energy source that is projected to be the main fuel source for 40% of the global population out to 2030 (IEA 2012). Jeuland et al. (2015) conduct a comprehensive analysis of factors influencing the adoption of improved stoves using both revealed and stated preference data from India. Our analysis, which focuses on Ethiopia, complements their work in that it is the first study to explicitly elicit preferences for improved fuelwood stoves in Africa. Our sample differs from Jeuland et al. (2015), as their sample includes many households that already use other (non‐ biomass) fuel technologies (20% LPG). Only 24% of their respondents were using traditional three stone stoves, whereas in the Ethiopian context over 90% of the households, and in our rural sample 100%, use traditional three stone tripod stoves.

By using a choice experiment that presents respondents with the ability to make choices over stoves with different combinations of characteristics, we contribute to the literature on improved stove adoption by analyzing the attributes of stoves that are most important for households. We also examine how socio‐demographic factors impact preferences for stoves. Results from this study also contribute to the growing body of research about global sustainable development, as clean cookstoves provide a solution for both human health and environmental issues and may promote development in many parts of the world.

# **3. Choice Experiment Design**

Choice experiments are a stated preference valuation tool used to determine someone’s marginal willingness to pay (WTP) or willingness to accept (WTA) for goods, or characteristics of goods, when market data are not available for assessing these valuations. A choice experiment survey presents the respondent with choice scenarios where each choice scenario has alternatives with varied levels of different attributes of the good or policy being evaluated. The, 7
<!-- Start of picture text -->
1.4 0.02<br>——Firm profit<br>= = =Tax revenue<br>1.3 4 0.015<br>a “7<br>¢ ¢<br>4<br>1.2 , 0.01<br>a<br>¢<br>¢<br>¢ ¢<br>1.1 w-cr7” 0.005<br>’<br>-—=—7<br>1 0<br>Oo 0.1 0.2 0.3 0.4<br><!-- End of picture text -->

<!-- Start of picture text --> 3 0.<br>—— Firm profit<br>prot = Tax revenue<br>yn" “TS.<br>2 Pan ‘ ae ‘ 0.<br>- on”<br>1 0.<br>0 °<br>oO 0.05 0.1 0.15 0.2 0.25 0.3<br><!-- End of picture text --> <!-- Start of picture text --> 12 0.25<br>11 0.2<br>‘ s\<br>10 ‘\ 04s<br>‘<br>s<br>\<br>‘<br>9 . * Jo<br>—Firm profit ‘<br>= = -Tax revenue y<br>8 0.05<br>ie} 0.02 0.04 0.06 0.08 0.1<br><!-- End of picture text --> <!-- Start of picture text --> 4 0.1<br>Tate ~.<br>2+ ——Firm- profitre aan ‘. ie)<br>Tax revenue x<br>-0.1<br>1<br>-0.2<br>0 0.15 0.2 0.25 0.3 0.35<br><!-- End of picture text --> <!-- Start of picture text --> 2 01<br>‘<br>‘ 005<br>1<br>——— Firm profitre ‘\<br>== =Tax revenue<br>0<br>0 0.05 0.1 0.15 0.2<br><!-- End of picture text --> <!-- Start of picture text --> 3 01<br>——Firm Profit<br>--=-=Taxrevenue o — __ ~.<br>o” . .<br>¢ x<br>¢ x<br>o ¢<br>o*<br>2 e“7 0.05<br>a<br>-<br>‘<br>‘<br>1 ()<br>0 0.05 0.1 0.15<br><!-- End of picture text -->
import price �a<sup>t</sup> nn<sup>< 0</sup> �, then the implied import demand is inelastic, so that an increase in import price induces a less than proportionate decrease in import quantities. Finally, if the share of import in GDP increases with import prices<sup>�</sup> a<sup>t</sup> nn<sup>> 0�,thentheimpliedimportdemandmustbeelastic</sup> such that an increase in the price of import induces a more than proportionate decrease in import quantity.<sup>8</sup>

# 3 Empirical Strategy

With data on output shares, unit values and factor endowments, Equation (10) is the basis of our estimation of import elasticities. In principle, we could fi rst estimate the own price effects, a<sup>t</sup> nn<sup>, for</sup> every good according to Equation (10) , and apply Equation (11) to derive the implied estimated elasticities, since the own price elasticity is a linear function of own partial effects. There are, however, at least three problems with the estimation of the elasticities using (10). First, there are literally thousands of goods traded among the countries in any given year. Moreover, there is also a large number of non-traded commodities that compete for scarce factor endowments and contribute to GDP in each country. Thus the number of explanatory variables in Equation (10) could easily exhausts our degrees of freedom or introduce serious collinearity problems. Second, even after solving this fi rst problem, we could also run out of degrees of freedom given the short time span of trade data available at the six digit of the HS —which only started being used in the late 1980s. Finally, there is nothing so far to ensure that the estimated elasticities satisfy second order conditions of GDP maximization, i.e., there are negative. We tackle these three problems in turn.

## 3.1 From an N good economy to N sets of two-good economies

Given that our objective is to estimate the own price effect, a<sup>t</sup> nn<sup>,foreverygoodn,were-express</sup> the N-good GDP function into N “two-good economies”producing goods n and −n :

> 8 Kohli (1991) found an inelastic demand for the aggregate US imports with ann < 0, while highly elastic demand for the durables and services imports of the US, with the corresponding ann > 0, when the aggregate import is broken down into 3 disaggregate groups.

9
<!-- Start of picture text --> 6,135 women aged 16 —59<br>administered a multi-topic<br>survey by a female enumerator<br>50% randomized to receive two 50% randomized to receive two<br>F2F questions on IPV before F2F questions on IPV after<br>ACASI module ACASI module<br>F2F questions (last 6 months): ACASI questions (last 6<br>1) partner slaps months): Full IPV module<br>7) cuts, bruises or aches including 13 questions<br>ACASI questions (last 6 6 F2F questions (last 6 months):<br>months): Full IPV module 1) partner slaps<br>including 13 questions 2) cuts, bruises or aches<br><!-- End of picture text --> <!-- Start of picture text --> ACASI questions (last 6 6<br>months): Full IPV module<br>including 13 questions<br>All other components of the<br>survey administered as usual<br><!-- End of picture text --> All other components of the survey administered as usual
# **Appendix for Online Publication**

## **A Benefits of the PEP Program**

Table A.1.

||All Refugees|Refugees with RAMV|Refugees with PEP|
|---|---|---|---|
|Education|Nursery, primary and secondary<br>Food and school bus service|Nursery, primary and secondary<br>Food and school bus service|Nursery, primary and secondary<br>Food and school bus service|
||No|No|**Promotion across education levels**|
||No|No|**Degree recognition**|
|SISBEN*|No|No|**Yes**|
|Health|Emergency care|Emergency care|Emergency care|
||Public health programs|Public health programs|Public health programs|
||Vaccines|Vaccines|Vaccines|
||Prenatal care|Prenatal care|Prenatal care|
||Prevention campaigns|Prevention campaigns|Prevention campaigns|
||No|No|**Access to subsidized regime**|
|Childhood Services (ICBF**)|No|No|**Childcare**|
||No|No|**Early childhood services**|
|Labor Market|No|No|**Possibility to be (formally) employed**|
|Financial Services|No|No|**Access to the banking sector**|

_Notes:_ *SISBEN: score used to target social safety net programs in Colombia and ** ICBF: Colombian Family Welfare Institute, which is in charge of all early childhood programs and services.

51
results in the literature that condom use overall is low but also separates the results by different groups in the population.

The literature consistently describes more condom use by males than females. In 1997, a study in Tanzania finds significantly more men (34.1 percent) than women (14.1 percent) reported having ever used a condom (Mnyika et al. 1997). Approximately ten years later, 18.1 percent of rural males in Uganda reported condom use compared to 9.9 percent of rural females (Biraro et al. 2009). Various other studies report similar differences (Gardner et al. 1999; Kamali et al. 2000; Ahmed et al. 2001; Fylkesnes et al. 2001; Hartung et al. 2002; Mumtaz et al. 2005; Pullum et al. 2005; Gregson et al. 2006; Chimbiri 2007). Hendrikson and others (2007) show that condom use is higher among youth than adults but the differences between the genders are maintained with 59 percent of young males and 48 percent of young females reporting condom use at their last intercourse. Another study finds that in discordant couples, where one spouse is HIV positive and the other is not, condom use is higher when the man is uninfected (17.1 percent) than when the woman is uninfected (9.5 percent) (Serwadda et al. 1995). The results from our analysis also show that significantly more men than women report using condoms in all countries included.

Another consistent result in the literature is that condom use within marriage is very low. Based on nationally representative data sets from around the world, Ali and others (2004) report that only 2 percent of married couples used condoms. The level of condom use for unmarried individuals was more than double that of the married respondents in a study done in Kenya (Bauni and Jarabi 2003). Cleland and others (2006) compare condom use for single and married women in 1993 and 2001 using nationally 5
_Table 11. Coefficients (Standard Errors) and Variance Explained or Combinations of Student-level and School-type Variables Regressed on Mathematics Achievement_

|||_Coeffi_|_cient (St. error)_||||
|---|---|---|---|---|---|---|
|_Variable_|_IM_|_2M_|_3M_|_4M_|_5M_|_6M_|
|ConstantV|4.84|4.40|4.51|5.07|5.10|4.77|
||(.14)|(.20)|(.20)|(.11)|(.11)|(.17)|
|Log ageb|-3.63|-|-3,47|-|-3.44|-3.54|
||(.74)||(78)||(.79)|(.79)|
|Repeater|-.40|-|-.44|-|-.42|-.42|
||(.07)||(.07)||(.07)|(.07)|
|Family sizeb|-.02|-|-.03|-|-.03|-.03|
||(.01)||(.01)||(.01)|(.01)|
|Father primary|0.09|-|.07|-|.06|0.07|
||(.07)'||(.08)0||(.08)0|(.08)'|
|Father J. C.|0.06|-|.05|-|.04|0.04|
||(.07)'||(.07)'||(.07)-|**(.07)0**|
|FatherO|0.05|-|.06|-|.08|0.09|
||(-1)'||(.10)'||(.10)'|**(. 1)0**|
|Father A|0.33|-|.29|-|.28|0.30|
||(.13)||(.14)||(.14)|(.12)|
|Female teacher|-|_.50_<br>|0.41|-|||
|||(.19)|(.18)||||
|Age of teacherb|-|.02|0.019|-|-|-|
|||(.01)|(.0 1)||||
|Standard trained|-|<br>.60|<br>0.57|-|-|.39|
|teacher||(.22)|(.21)|||(.19)|
|Log Instr. Timeb|-|1.14|1.00|-|-|1.09|
|||(.52)|(.49)|||(.47)|
|Grade 7M Textsb|-|1.83|1.68|-|-||
|||(.36)|(.34)||||
|Supervised Studyb|-|.15|.14|-|-|.13|
|||(.05)|(.05)|||(.04)|
|Texts (class)b|-|-|-|-|-|1.88|
|||||||(.29)|
|Pupil-teacher ratio|-|-|-|-.054|**-.04**|**_-0.05_**|
|||||(.02)|(.02)|(.01)|
|School Texts|-|-|-|1.33|1.13|1.48|
|||||_(.50)_|(.47)|(.53)|
|Trained teachers|-|-|-|.02|0.02|0.02|
|||||(.005)|(.004)|(.005)|
|Percent Variance|11|21|24|19|22|27|
|explained (total)|||||||
|School|30|76|80|76|81|93|
|Class|2|3|11|-|-||
|Student|4|1|3|2|3|3|

_Notes:_ 'Constant Average for non-repeat. r, father no education, at grand means **ab** uoted **b** Measured from grand mean Non-significant (p > .50) 23
_-_ 32 _-_ Table 12a.: Impact of AGOA: Inputs Sourced from South Africa (Growth in exports relative to 2000, in percent)

||**Mauritius**|
|---|---|
|_Parametervalues_|_Growthinexports_|
||_2001-2004_<br>_2005-2008_|
||_WithRuleofOrigin_|
|_e= _1|4.9<br>-26.3|
|_e= _2|10.6<br>-44.7|
|_e= 5 _|21.4<br>-64.7|
||_Withoutruleoforigin_|
|_e= _1|35.5<br>-18.4|
|_e=2_|53.6<br>-21.1|
|_e=5_|112.3<br>18.3|

||**Madagascar**<br>||
|---|---|---|
|_ParameterValues_|_Gro_|_wthinexports_|
||_2001-2004_|_2005-2008_|
|||_WithRuleoforigin_|
|_e_=1|29.5|-19.2|
|_e=2_|44.8|-25.2|
|_e=5_|92.3|-38.8|
|||_Withoutruleoforigin_|
|_e =_1|Same asabove|-3.2|
|_e= _2|Sameasabove|-6.7|
|_e= 5 _|Sameasabove|5.6|

Source: Authors' calculations.
# **Table 4: Tax Changes and GDP**

# Dependent Variable: Change in GDP

||Full S|ample|Develop|ingcountries|
|---|---|---|---|---|
||OLS|Arellano-Bond|OLS|Arellano-Bond|
|PIT|**-0.14**<br>**(.055)**|**-0.16**<br>**(.081)**|**-0.20**<br>**(.080)**|<br>**-0.37**<br>**(.091)**|
|CIT|**0.043**<br>**(.028)**|**-0.084**<br>**(.063)**|**0.018**<br>**(.060)**|<br>**0.034**<br>**(.061)**|
|VAT|**0.38**<br>**(.129)**|**0.51**<br>**(.55)**|**0.44**<br>**(.24)**|<br>**0.55**<br>**(.33)**|
|Lagged<br>GDP||0.35<br>(.043)||0.37<br>(.10)|
|UR|-00079<br>(.00036)|0.0035<br>(0.0011)|-0.015<br>(.0012)|<br>0.003 (.0017)|
|Trading<br>Partners'<br>Growth<br>|1.52 (.31)|2.95 (.23)|1.62<br>(.34)|<br>2.77<br>(.44)|
|n|335<br>|308|188|166|
||Significant at 1%<br>Significant at 5%<br>Significant at 10%||||
# Table **II.** Exporting Firms Only

||Supply|Side Only|Joint Sup|ply and Demand|
|---|---|---|---|---|
|Parameter|CRTS|Non-CRTS|CRTS|Non-CRTS|
|_b*v_|7.32*|6.64*|54.29*|35.41*|
||(1.93)|(1.98)|(1.68)|(1.72)|
|_bet_|-1.75*<br>|-1.40*<br>|-2.39*|-3.41*|
||(0.35)|(0.36)|(0.47)|(0.25)|
|_bag_|-3.90*|-3.72|-40.49*|-25.93*|
||(1.42)|(1.43)|(1.26)|(1.33)|
|_bi._|1.04*|0.84*|0.97*|0.66*|
||(0.13)|(0.13)|(0.11)|(0.04)|
|_big_|0.16|0.08|0.63|2.20*|
||(0.26)|(0.13)|(0.41)|(0.21)|
|_b9 s_|3.23'|3.27*|31.81'|19.35*|
||(1.13)|(1.14)|(1.34)|(1.20)|
|_bxt_|0.65*|0.73*|2.16*|1.10*|
||(0.16)|(0.19)|(0.27)|(0.23)|
|_bs_||14.81*||42.44|
|||(5.56)||(46.55)|
|ox||-2.07**||16.13|
|||(1.17)||(10.16)|
|ox||-2.14E-6||5.80E-5|
|||(8.68E-5)||(1.31E-4)|
|7s||-0.01||-0.014|
|||(0.02)||(0.029)|
|_bit_|0.56*<br>|0.44'|0.85*|0.65*|
||(0.14)|(0.15)|(0.15)|(0.11)|
|_b,_||-8.80||15.95|
|||(2.91)||(18.74)|
|at||0.68<br>||-1.11E+l*<br>|
|||(0.61)||(4.10)|
|f31||1.70E-5||3.70E-5|
|||(4.54E-5)||(5.20E-5)|
|71||0.01||0.013|
|||(0.01)||(0.012)|
|_bgt_|0.40|0.31|9.04*|5.39*|
||(0.48)|(0.50)|(0.93)|(0.75)|
|_bg_||-7.99||1.76E+2|
|||(8.30)||(1.39E+2)|
|||||994E1*|
|ag||0.77<br>||-.+<br>|
|||(1.74)||(30.12)|
|_I39_|||||
|||3.06E-5||2.30E-4|
|||(1.30E-4)||(3.9E-4)|
|78||-0.01||0.036|
|||(0.04)||(0.088)|

- 17 -