rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Summary Statistics by Treatment Group**

**Panel A: Assignment to Treatment**

||Treatment<br>Group1|Treatment<br>Group2|Treatment<br>Group3|Treatment<br>Group4|Control|F-test of equality<br>p-value|
|---|---|---|---|---|---|---|
|_Variables stratified or matched on_|||||||
|Retail|0.23|0.22|0.24|0.23|0.24|0.998|
|Manufacturing|0.34|0.34|0.32|0.31|0.31|0.993|
|Services|0.43|0.44|0.44|0.46|0.45|0.996|
|Colombo|0.50|0.50|0.50|0.52|0.49|0.993|
|More than 2 paid workers|0.58|0.58|0.60|0.56|0.60|0.971|
|Says would register and sees benefit to doing so|0.49|0.48|0.48|0.49|0.48|0.999|
|Sales in last month|218570|153184|180691|160159|213310|0.497|
|Median of sales last month|90000|92500|100000|100000|90000||
|_Variables not stratified or matched on_|||||||
|Doesn't keep records|0.49|0.54|0.55|0.55|0.47|0.649|
|Years of Education of Owner|11.07|10.68|10.89|11.43|10.54|0.082|
|Number of Paid employees|2.93|3.13|3.10|3.00|3.15|0.905|
|Registered at the Pradeshiya Saba|0.71|0.66|0.71|0.67|0.62|0.572|
|Profits in last month|29679|32822|32634|36705|37585|0.662|
|Given Treatment Offer (or eligible for it if control)|0.75|0.63|0.63|0.58|0.61|0.070|
|**Sample Size**<br>**Panel B: Given Treatment Offer(or Eligible for it if Con**|**104**<br>**trol)**|**104**|**105**|**102**|**105**||
||Treatment|Treatment|Treatment|Treatment||F-test of equality|
||Group1|Group2|Group3|Group4|Control|p-value|
|Retail|0.19|0.23|0.20|0.19|0.23|0.941|
|Manufacturing|0.38|0.37|0.38|0.39|0.36|0.997|
|Services|0.42|0.40|0.42|0.42|0.41|0.998|
|Colombo|0.42|0.43|0.33|0.39|0.39|0.790|
|More than 2 paid workers|0.50|0.48|0.50|0.41|0.53|0.707|
|Says would register and sees benefit to doing so|0.56|0.45|0.61|0.54|0.59|0.379|
|Sales in last month|138985|136883|146917|150559|196391|0.780|
|Doesn't keep records|0.54|0.60|0.56|0.64|0.52|0.609|
|Years of Education of Owner|10.81|10.38|10.65|11.19|10.19|0.241|
|Number of Paid employees|2.64|2.98|3.05|2.61|2.77|0.695|
|Registered at the Pradeshiya Saba|0.64|0.60|0.62|0.54|0.59|0.834|
|Profits in last month|26449|31270|30945|30759|33754|0.800|
|**Sample Size**|**78**|**65**|**66**|**59**|**64**||

Note: Treatment Group 1 is the information and reimbursement only group, Groups 2, 3 and 4 were offered 10,000 Rs, 20,000 Rs, and 40,000 Rs respectively.

26
37 school-level factors which are shown to promote positive attitudes and achievement especially for female students. In Nigeria, boys seem to benefit from sharing their school experiences with females, while females are benefited by excluding males from their primary educational environment.

Perhaps Nigerian boys' schools should be examined more closely for particular curricular or organizational structures which might impede their students, rather than suggesting that single-sex education for young men be curtailed in Nigeria. Unfortunately, the IEA study was short on details about school organization.

As mentioned earlier, the threat of physical abuse, rape, and pregnancy are seen as particular barriers which discourage school attendance for females who have reached the age of puberty in some developing countries. Certainly such threats are minimized in schools with a faculty and student body that are primarily female. It could be the case that girls' schools are seen as safe and orderly environments where learning may be the primary focus of a young woman's life, without the distraction of potentially disruptive social encounters. Under that view, the small number of Nigerian females in single-sex schools is regrettable, for both in methodological and substantive reasons.4i2 Of course, parental concerns for safety could be met by other forms of re- structuring education: employing more female teachers and administrators in coeducational schools; building additional local secondary schools instead of requiring parents to send their daughters to coeducational secondary boarding schools (in which other students, teachers, and administrators were male), developing high quality correspondence courses for
understanding the impact of HIV/AIDS.<sup>3</sup> Women are more likely to be caregivers, both of sick adults as well as children fostered into the household. For example, a survey in South Africa showed that two-thirds of caregivers were women with a third of them above age 60 (Steinberg _et al._ 2002 cited in UNICEF 2004). Gender inequalities in access to off-farm income opportunities make remittance income and (dis)inheritance of assets more salient issues for women. Women in many African settings have less secure rights to land and assets, raising concerns about women being dispossessed of assets when widowed or when another (male) household member dies.

Evidence of such events often comes from case studies; evidence from sample survey efforts is lacking. One exception from Malawi is somewhat contrary to expectations. The national household survey in 2004/05 found that the average value of land and assets lost after a male death in the household was actually lower in the patrilineal North region, where women traditionally have less rights to assets, compared to the South and Central regions (Republic of Malawi and the World Bank 2007). This finding likely reflects rapid changes in national laws and local customs. For example, for the 51 villages in the Tanzania data used in this study, in 1991, 39 percent of village informants reported that the norm in the community allowed a woman to inherit land when her husband dies. In 2004, 86 percent report that this is the norm. While male deaths, including husbands and sons, may be more salient for women than men with respect to assets and income, deaths of prime-age women may matter for non-monetary reasons. Adult daughters are more likely to be caregivers to their elderly parents, making older adults potentially vulnerable to a lack of care if they lose daughters. Combined, the speculation is that faced with the death of a working adult family member, older adults in Africa, especially women, will be left with fewer income sources and assets, will need to work more, and will face greater poverty and poorer health.

Despite well-developed theories about the pathway and speculation of magnitude of impacts (see, for example, Ferreira 2004), there exists little systematic, empirical evidence about the effects of adult death on older household members (Knodel _et al_ .

> 3 Gender inequalities may also be a driver of the transmission of HIV. The related literature is not discussed here. Likewise, children may be differentially impacted by the loss of their parent along gender lines. Several studies find that maternal mortality impacts schooling more than paternal mortality (Beegle and De Weerdt 2008).

3
hazards if not properly maintained. For example in Zambia, Ashraf et al.(2017 <mark>) found that service interruptions were associated with an increased incidence of diarrheal disease, upper respiratory infections and typhoid fever</mark> [22 <mark>]. Equally, sewer networks are considered to be the top of the sanitation service ladder according to the Joint Monitoring Program of the WHO (JMP-WHO, 2020) with risks from onsite facilities more well documented in the literature (</mark> Bancalari & Martinez, 2018 **)** [23 <mark>]. Nevertheless, when bursts or leakages are not properly addressed, liquid sewage can potentially be even more hazardous than waste from onsite sanitation facilities [</mark> 24].

This paper uses geo-spatial analysis to explore the role of water and sanitation infrastructure in cholera risk. The study focuses on Harare, Zimbabwe, using the cholera outbreak of September - December 2018 as a case study. We use spatial modeling, detailed data on infrastructure and mobility, and georeferenced case data to identify factors associated with cholera risk. Second, we model different scenarios of infrastructural improvements and compare which of them contributes the most to reducing such risk. Third, we look at how the implementation of infrastructural improvements can be selectively targeted by ward throughout the city in order to potentially reduce cost and maximize health impacts.

# Methods

## Ethics Statement

<mark>This study was approved by the Medical Research Council of Zimbabwe. The approval number is MRCZ/E/302 and it was assessed as exempt. All data obtained and herein reported were anonymized prior to analysis.</mark>

## The Study Area

Zimbabwe is a landlocked Sub-Saharan African country with a population of just under 15 million people (World Bank, 2021). It is divided into 60 districts and bordered by Zambia, Mozambique, Botswana and South Africa. Its capital, on which this analysis is focused, is Harare, a city with a population of 1.5 million inhabitants (UN, 2012) and 46 wards. Zimbabwe has experienced cholera outbreaks dating back to 1971 with the worst occurring in 2008/9 which resulted in 4,288 reported deaths over 60 of its 62 districts [25]. The focus of this paper is a more localized but severe outbreak which took place between September and December 2018 and was concentrated in the capital, Harare. Of almost 10,000 cases, 93% (9,755) were reported in Harare, resulting in 46 deaths. According to the latest DHS data, 30% of Harare’s population is connected to an existing sewer network, compared to just 12% in neighboring country capital Lusaka, Zambia (DHS, 2015 & 2018).

All the data detailed in the paragraphs below was cleared for use by the Medical Research Council of Zimbabwe (MRC-Z). The approval letter can be found in the supporting information.

3
**Figure 5 (continued): Transport Sector CO2 Emissions Growth and Driving Factors in Bolivia, Caribbean, Cuba, Ecuador, Guatemala, Honduras, Other Latin America, Panama and Paraguay**

<!-- Start of picture text --> h) Panama<br><!-- End of picture text --> <!-- Start of picture text --> g) Other Latin America<br><!-- End of picture text --> <!-- Start of picture text --> 5.3<br>CO2 CO2<br>4.8 Fuel Mix Fuel Mix<br>Modal Mix 2.8 Modal Mix<br>4.3 Emission Coefficient Emission Coefficient<br>3.8 Energy Intensity Energy Intensity<br>GDP 2.3 GDP<br>3.3<br>2.8 1.8<br>2.3<br>1.8 1.3<br>1.3<br>0.8 0.8<br><!-- End of picture text --> i) Paraguay <!-- Start of picture text --> 3.3<br>CO2<br>Fuel Mix<br>2.8 Modal Mix<br>Emission Coefficient<br>Energy Intensity<br>2.3 GDP<br>1.8<br>1.3<br>0.8<br><!-- End of picture text -->

**Table 6: Average Annual Growth Rate of GDP and Transport Sector Energy Consumption for the 1980-2005 Period (%)**

|Country|GDP|Energy Consumption|
|---|---|---|
|Bolivia|2.18|2.44|
|Caribbean|2.49|2.60|
|Cuba|1.53|-3.12|
|Ecuador|2.61|4.19|
|Guatemala|2.51|5.48|
|Honduras|3.03|5.27|
|Other Latin America|2.84|7.86|
|Panama|3.51|3.62|
|Paraguay|2.20|4.60|

Source: IEA (2007) 26
- Narayanan, B. and Walmsley, T.: 2008, Global Trade, Assistance, and Production: The GTAP 7 Data Base, Center for Global Trade Analysis, Purdue University.

- National Research Council: 2005, Valuing Ecosysten Services: Toward Better Eenvironmental Ddecision-Making, National Academies Press.

- Nordhaus, W. D. and Boyer, J.: 2000, Warming the World: Economic Models of Global Warming, MIT press.

- Pindyck, R. S.: 2011, Fat Tails, Thin Tails, and Climate Change Policy, Review of Environmental Economics and Policy 5(2), 258274.

- Plantinga, A. J.: 1996, The Eect of Agricultural Policies on Land Use and Environmental Quality, American Journal of Agricultural Economics 78(4), 1082 1091.

- Rust, J.: 2008, Dynamic Programming, The New Palgrave Dictionary of Economics, London, UK, Palgrave Macmillan, Ltd.

- Rutherford, T.: 2002, Lecture Notes on Constant Elasticity Functions, http: //www.gamsworld.org/mpsge/debreu/ces.pdf. [Online; accessed 14-April2011].

- Sedjo, R.: 1983, The Comparative Economics of Plantation Forestry: A Global Assessment, Johns Hopkins University Press.

- Sohngen, B., Golub, A. and Hertel, T.: 2009a, The Role of Forestry in Carbon Sequestration in General Equilibrium Models, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 279303.

- Sohngen, B. and Mendelsohn, R.: 2007, A Sensitivity Analysis of Forest Carbon Sequestration, in M. Schlesinger (ed.), Human-Induced Climate Change: An Interdisciplinary Assessment, Cambridge University Press, pp. 227237.

- 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.

- Stavins, R.: 1999, The Costs of Carbon Sequestration: A Revealed-preference Approach, The American Economic Review 89(4), 9941009.

25
decomposition revealed that the endowment component favored wives by 70% whilst husbands had a coefficients advantage of 170%. A similar pattern emerged when selfemployed workers were considered. In the case of single workers the national wage gap was only 18% but the decomposition revealed that 21% of the differential was not explained by endowment differences. However, there seemed to be a regional difference, namely that in the Northeast, women not only had an earnings advantage but also a coefficient advantage in the decomposition.

In the second case study Tiefenthaler used PNAD 1989 and estimated male and female earnings functions in three sectors: formal employment, informal employment and selfemployment.<sup>9</sup> Ratios of married female to male earnings in each sector were 0.7, 0.85 and 0.7 respectively. Ratios for single women were almost identical. The decomposition analysis revealed that, depending on specification of the model, between 81-89% of the earnings differential of men versus women in the formal sector, 72-75% in the informal sector and 83-84% in the self-employment sector could be attributed to the structure of rewards. Thus, Tiefenthaler concluded that discrimination played a more important role in explaining male-female wage differentials in Brazil in 1989 than differences in characteristics.

More recently, Loureiro and Carneiro investigated the existence of discrimination in the urban and rural labor markets in Brazil.<sup>10</sup> The authors used the Blinder-Oaxaca decomposition analysis on data from the 1998 PNAD and concluded that there are substantial wage differentials between men and women by gender and race.

5
Table 12: Comparing candidate feature choices

||(<br>Basic v|1)<br>ariables|(<br>MOSAIK|2)<br>S variables|(<br>MOSAIKS an|3)<br>d interactions|
|---|---|---|---|---|---|---|
||EBP|XGB|EBP|XGB|EBP|XGB|
|**Panel A: Assets**|||||||
|Burkina Faso|0.743|0.850|0.699|0.809|0.689|0.840|
|Madagascar|0.875|0.910|0.872|0.905|0.866|0.907|
|Malawi|0.664|0.844|0.541|0.811|0.544|0.833|
|Mozambique|0.897|0.917|0.893|0.917|0.902|0.919|
|Sri Lanka|0.919|0.935|0.923|0.934|0.930|0.937|
|Tanzania|0.868|0.899|0.879|0.906|0.891|0.912|
|Vietnam|0.868|0.904|0.911|0.916|0.911|0.930|
|Average|0.833|0.894|0.817|0.886|0.819|0.897|
|**Panel B: Poverty**|||||||
|Malawi|0.654|0.868|0.631|0.878|0.613|0.858|
|Tanzania|0.836|0.858|0.845|0.858|0.855|0.861|
|Average|0.745|0.863|0.738|0.868|0.734|0.860|

Note: The first two columns present the simple mean of pearson correlation across simulations for each country using all candidate features as well as interactions between features and urbanity. The last two columns present the same correlations but restricting the candidate feature set by excluding all mosaiks features and interactions. The results are based on 100 simulations for each country and method.
capabilities and functionings can be assessed from a good household survey of llving standards. Certain, now quite familiar, poverty measures can be interpreted this way: in the abstract, their objective is to assess the extent of some need fulfillment. (Elsewhere Sen hae argued compellingly, in my view, that "poverty" should be viewed as "lack of capability"; see Sen 1984, Chapter 12, and 1987.) Here Dreze and Sen may reasonably question the sole use of the conventional consumption expenditure indicator of well-being in poverty measurement. But then we need not be confined to that particular indicator, and there are now many household surveys that allow other possibilities. Some of these surveys have also tracked the same households over two or more years, and can thus be used to help quantify the distinction made by Dreze and Sen (and others) between "chronic" and "transitory" poverty or hunger (Ravallion, 1988).

In discussing progress in reducing chronic hunger in Part 3 of HPA, Dreze and Sen rely almost exclusively on a few "social indicators", such as aggregate child mortality and average life expectancy. I found this surprising. These indicators are useful up to a point, but it must be recognized that they can be rather uninformative about the quality of the - lives that people live their capabilities, if you wish. One could hardly contend that they are good indicators of the attainment of the most basic capabilities, beyond being alive. In making comparisons over time, they may also be deceptively sensitive to even a limited degree of progress in primary health care, particularly when starting from a high mortality rate. While preventing the common infectious and parasitic diseases such as malaria is undeniable progress, there is a lot mora to the elimination of chronic hunger and related deprivations. Furthermore, though these indicators are probably 12
13 to 17.<sup>6</sup> Kerckhoff (2002) also observes that Americans are more likely than Europeans to shift back from full-time work to full-time school before settling into stable employment. Duryea, et al. (2003) shows the high incidence of working students in Latin America while UCW (2010) suggests that this dual activity is increasing across the world.

The degree to which labor markets are segmented seems to change over the lifecycle. A segmented labor-market perspective assumes that the informal sector is a disadvantaged one and all informal workers are really queuing for formal sector jobs.<sup>7</sup> However, several authors have found flows of comparable magnitudes between formal and informal jobs at different times of the life cycle: Gong et al (2004) can not reject the hypothesis that formal and informal sector jobs have the same entry and access rates, using 1999 – 2000 panel data in urban Mexico; Bosch and Maloney (2010) find similar conditional flows in Mexico, Argentina and Brazil, without separating young from adult workers; and Egel and SalehiIsfahani (2008) observe that young men switch frequently between formal and informal sectors in Iran.<sup>8</sup> This suggests that the informal sector may play a transitory role for youth rather than being a dead-end career path.

# **3. Methodology and Data**

Following the work of Bosch and Maloney (2007), three methodologies are used to understand the dynamics of youth employment. First, transition matrices<sup>9</sup> are estimated to understand the magnitude of turnover, namely the share of people who move into one state of employment or out of it every period. This allows us to determine, for example, if young people enter the informal sector or leave it at different rates than do adults at any one period of time.

Second, as proposed in Bosch and Maloney (2007), we decompose the transitions matrix into two separate components. The first component represents the transition probabilities independent of the rate at which different age groups leave any sector, and is

> 6 Entwisle et al (2000) also found that the majority of African Americans either worked inconsistently or did not work at all, as an indicator of how less advantage groups face their transition to adulthood.

> 7 Here we define an informal job as a salary job where the employer does not pay into the social security system on behalf of the employee.

> 8 Using retrospective life stories collected as part of the 2005 Iranian School to Work Transition Survey, the authors define formal jobs as those in the public sector or those with a contract of unlimited or fixed duration. 9 Transition matrices are referred to as “intensity matrices” in Bosch and Maloney (2007).

5
Table 1C. Correlations between asset prices and output <u>(correlation coefficient)</u>

|||**Lags**||||**Leads**||
|---|---|---|---|---|---|---|---|
||-3|-2|-1|0|1|2|3|
|**Equity Prices**||||||||
|1971:1-2016:4|0.44|0.49|0.47|0.37|0.22|0.07|-0.05|
|1971:1-1984:4|0.47|0.49|0.44|0.26**|0.05***|-0.17***|-0.32***|
|1985:1-2016:4|0.44|0.50|0.50|0.41|0.27|0.12|0.01|
|**House Prices**||||||||
|1971:1-2016:3|0.34|0.41|0.46|0.47|0.44|0.39|0.35|
|1971:1-1984:4|0.06***|0.17***|0.27*|0.37|0.37|0.35|0.30|
|1985:1-2016:3|0.39|0.45|0.48|0.47|0.44|0.39|0.35|

Note: The average within-country correlation between the year-over-year growth rates of asset prices and output is presented. The sample consists of 18 advanced economies. Lags (leads) indicate that output is shifted one or more quarters forward (backward) relative to asset prices. ***, ** and * indicate that the average correlations for the period Q1 1971 to Q4 1984 are statistically different from those for the period Q1 1985 to Q4 2016 (or Q3 2016) at the 1%, 5% and 10% levels, respectively.

49
|**Y**|**= RIF estim**|**ate of Log [**|**Self-reporte**|**d Productivit**|**y (XOF/ha)]-**|**Export crop**|**s**|||
|---|---|---|---|---|---|---|---|---|---|
||Q10|Q20|Q30|Q40|Q50|Q60|Q70|Q80|Q90|
||<br>(0.009)|<br>(0.009)|<br>(0.007)|<br>(0.006)|<br>(0.006)|<br>(0.007)|<br>(0.006)|<br>(0.009)|<br>(0.007)|
|Household Male Labor Use (Number)|0.058|0.146**|0.091**|0.042|0.035|0.028|-0.015|-0.065|-0.050|
||(0.066)|(0.060)|(0.045)|(0.039)|(0.042)|(0.041)|(0.042)|(0.048)|(0.054)|
|Household Female Labor Use|0.010|-0.014|-0.009|-0.010|-0.014|0.004|0.009|0.013|0.009|
|(Number)|(0.027)|(0.018)|(0.016)|(0.016)|(0.015)|(0.015)|(0.015)|(0.016)|(0.016)|
|<br>Household Child Labor Use (Number)|0.001|0.001|0.000|0.001|0.002|0.002|0.001|0.001|0.001|
||(0.002)|(0.003)|(0.001)|(0.003)|(0.005)|(0.004)|(0.003)|(0.002)|(0.003)|
|Hired Labor Use (Number)|0.012|0.008|0.010|0.009|0.006|0.007|0.003|0.001|0.003|
||(0.011)|(0.008)|(0.009)|(0.008)|(0.006)|(0.006)|(0.004)|(0.004)|(0.004)|
|Number of Fields Managed by the|0.036*|0.019|0.010|0.014|0.003|-0.005|-0.008|-0.008|-0.001|
|<br>household|(0.020)|(0.013)|(0.009)|(0.010)|(0.009)|(0.010)|(0.010)|(0.010)|(0.009)|
|Total number of crops produced|0.009|0.009|0.009|0.005|0.009|0.013|0.012|0.013|0.009|
||(0.011)|(0.010)|(0.011)|(0.006)|(0.010)|(0.015)|(0.014)|(0.014)|(0.010)|
|Household used any purchased seed|-0.000|0.000|0.001|0.001|0.001|0.001|0.001|0.000|0.002|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.005)|
|Plots that Use Chemical Fertilizer (%|0.029|0.038**|0.023**|0.031***|0.034***|0.034***|0.025**|0.031**|0.037**|
|<br>of total)|(0.020)|(0.015)|(0.011)|(0.011)|(0.012)|(0.013)|(0.012)|(0.013)|(0.015)|
|<br>Plots that Use Pesticide (% of total)|0.107***|0.076***|0.081***|0.060***|0.052**|0.041*|0.036*|0.029|0.028|
||(0.039)|(0.029)|(0.024)|(0.022)|(0.022)|(0.021)|(0.021)|(0.022)|(0.022)|
|**unexplained**||||||||||
|<br>Log [Total land area cultivated in ha]|-0.086|0.108|0.133|0.211|0.076|0.098|0.020|0.201|0.061|
||(0.213)|(0.222)|(0.204)|(0.184)|(0.151)|(0.134)|(0.134)|(0.137)|(0.155)|
|Household head age (years)|-0.473|0.036|0.085|0.185|-0.164|0.028|-0.195|-0.045|-0.387|
||(0.534)|(0.651)|(0.612)|(0.528)|(0.394)|(0.393)|(0.329)|(0.277)|(0.319)|
|Married|0.286**|0.328**|0.309**|0.320**|0.295**|0.296***|0.173|0.119|0.215|
||(0.146)|(0.139)|(0.130)|(0.147)|(0.117)|(0.105)|(0.114)|(0.119)|(0.135)|
|Divorced/Separated|0.039|0.022|0.030|0.026|0.021|0.043|-0.002|0.001|0.013|
||(0.035)|(0.038)|(0.034)|(0.035)|(0.028)|(0.026)|(0.025)|(0.026)|(0.026)|
|Widowed|<br>0.408|<br>0.279|<br>0.260|<br>0.238|<br>0.233|<br>0.292*|<br>0.030|<br>-0.081|<br>0.169|
||(0.328)|(0.289)|(0.247)|(0.255)|(0.193)|(0.174)|(0.177)|(0.177)|(0.197)|
|Household head has Primary|<br>0.026|<br>0.111|<br>0.144**|<br>0.232***|<br>0.192***|<br>0.120**|<br>0.036|<br>0.005|<br>0.074|
|<br>education|(0.077)|(0.080)|(0.068)|(0.067)|(0.053)|(0.048)|(0.044)|(0.042)|(0.049)|
||<br>-0.005|<br>0.066|<br>0.067|<br>0.053|<br>0.033|<br>0.029|<br>0.029|<br>0.022|<br>0.013|

45