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news, local-news, Andrew Woodward, Cowper, Federal Election, Bob Hawke, dead, Peter Alley
LOCAL Labor candidate Andrew Woodward has given a heartfelt tribute to former Prime Minister Bob Hawke, who passed away on May 16, aged 89. The Bellingen-based Mr Woodward is contesting the seat of Cowper at the Federal Election, but like so many others, has paused to reflect on the contributions of Labor's longest serving Prime Minister. "In my 30 years as a senior political journalist and later as a member of the Labor Party, I had many encounters with Bob Hawke. I saw him at the top of his game as PM," Mr Woodward said. "I also saw him at the other end of the spectrum, helping and mentoring Labor candidates and acting as a guardian of the party. Read more: "In any of these capacities, he brought with him the same vigour, passion and intellect that he is famous for. "But more than anything else, he was a top bloke who gave his time freely to anyone." Mr Woodward noted the irony that he had come 'full circle' - in one of the legacies of Mr Hawke. "Bob was someone who indoctrinated me with the need to care for the environment," Mr Woodward said. "I remember as a journalist in 1989 when I was a bureau chief at Parliament House in Canberra, travelling with Mr Hawke to Wentworth where he announced a Labor Government would plant one billion trees to deal with soil erosion. "That struck a chord with me and here I am 30 years on actively campaigning on initiatives similar to this. "Thanks Bob. Good on you, mate." President of the Camden Haven branch of the Labor Party Peter Alley met Mr Hawke during the 1983 election campaign when he was then opposition leader. Mr Alley was a 22-year-old university student living in Sydney at the time. "He was a person who was so full of passion for the Australian community," Mr Alley said. The meeting was a formative moment in Mr Alley's life. Shortly after he joined the Australian Labor Party. Mr Alley believes Mr Hawke's legacy is far-reaching. "His legacy is the economic reforms that were brought about during that era in terms of floating the Australian dollar, the reductions in company tax rates, the introduction of imputation credits throughout that era and creating a much fairer tax system. "He also reached out and embraced the community and worked with his colleagues, business, unions and industry to create a fairer Australia."
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May 17 2019 - 12:00PM
Mid North Coast residents reflect on the legacy of Bob Hawke
Christian Knight, Carla Mascarenhas
Former Prime Minister Bob Hawke has died aged 89.
LOCAL Labor candidate Andrew Woodward has given a heartfelt tribute to former Prime Minister Bob Hawke, who passed away on May 16, aged 89.
The Bellingen-based Mr Woodward is contesting the seat of Cowper at the Federal Election, but like so many others, has paused to reflect on the contributions of Labor's longest serving Prime Minister.
"In my 30 years as a senior political journalist and later as a member of the Labor Party, I had many encounters with Bob Hawke. I saw him at the top of his game as PM," Mr Woodward said.
"I also saw him at the other end of the spectrum, helping and mentoring Labor candidates and acting as a guardian of the party.
Former Prime Minister Bob Hawke
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"In any of these capacities, he brought with him the same vigour, passion and intellect that he is famous for.
"But more than anything else, he was a top bloke who gave his time freely to anyone."
Mr Woodward noted the irony that he had come 'full circle' - in one of the legacies of Mr Hawke.
"Bob was someone who indoctrinated me with the need to care for the environment," Mr Woodward said.
"I remember as a journalist in 1989 when I was a bureau chief at Parliament House in Canberra, travelling with Mr Hawke to Wentworth where he announced a Labor Government would plant one billion trees to deal with soil erosion.
"That struck a chord with me and here I am 30 years on actively campaigning on initiatives similar to this.
"Thanks Bob. Good on you, mate."
President of the Camden Haven branch of the Labor Party Peter Alley met Mr Hawke during the 1983 election campaign when he was then opposition leader.
Mr Alley was a 22-year-old university student living in Sydney at the time.
"He was a person who was so full of passion for the Australian community," Mr Alley said.
It was a time when there was great energy and hope for the future.
Peter Alley
The meeting was a formative moment in Mr Alley's life.
Shortly after he joined the Australian Labor Party.
Mr Alley believes Mr Hawke's legacy is far-reaching.
"His legacy is the economic reforms that were brought about during that era in terms of floating the Australian dollar, the reductions in company tax rates, the introduction of imputation credits throughout that era and creating a much fairer tax system.
"He also reached out and embraced the community and worked with his colleagues, business, unions and industry to create a fairer Australia."
The day before Australians go to the polls, Labor is mourning the loss of favourite son Bob Hawke
What else is making news?
Youth Decide: Cowper's youngest voters share their perspective
Inside the battle for Cowper and the tactics used to win
Discuss "Local Labor candidate on Hawke: "I saw him at the top of his game as PM""


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After controlling for various individual and job characteristics, on average there is a positive earnings (including pensions) differential in favour of the public sector.
The positive differential in favour of the public sector is mainly concentrated on low-skilled workers and on workers in smaller organisations when the organisation size is considered.
High-skilled employees in the public sector tend to have lower earnings than their counterparts employed in the knowledge-intensive services and primary sectors in the private sector.
Relatively low-paid employees have the largest earnings advantage from employment in the public sector; for high-paid employees, the gap still exists in favour of the public sector for those working in smaller organisations, however, employment in the large private sector organisations has a positive impact on the earnings of those employees.
The headline figures of median and mean earnings for the public and private sector are published in Annual Survey of Hours and Earnings: 2017 provisional and 2016 revised results. Direct comparison between these sectors can be difficult for many reasons.
The composition of the public and private sectors is different. They consist of different industries, occupations and employees with different education, experience and skill levels. For example, the human capital of private and public sector differs. In the public sector, a higher proportion of employees have a degree-level education or higher. Additionally, jobs in the private sector vary significantly compared with the public sector. The private sector has some of the lowest paid workers, but also has some of the highest paid, whereas in the public sector, wages are spread more evenly across the distribution. It is therefore difficult to make direct comparisons between the two sectors using a simple mean (or median) earnings, without considering a range of factors associated with a person's earnings, such as job type, employee characteristics and job location1. Regression modelling can be used to account as far as possible for the differences between the sectors. For more information on regression analysis and its limitations, see the Quality and methodology section.
Another reason why public-private earnings comparison is not straightforward is that employee compensation consists of more than just wages. An employee's reward package may include additional monetary benefits, such as bonuses and pensions, and non-monetary benefits, such as company cars, uniforms, health insurance and workplace quality of life. These benefits are not included in gross pay. This analysis focuses on gross pay and pensions because pension contribution is a significant part of an employee's remuneration. It also represents a major cost to public and private sector employers.
The primary source of data is the Annual Survey of Hours and Earnings (ASHE) for all employees whose earnings in April were not affected by absence and were paid adult rates.
There are several important points to note about this analysis and the approach used in the regression model (further information on the regression methodology and definitions is given in the Quality and methodology section), which are detailed in this section.
The ASHE dataset does not include all factors that affect pay such as education, employee ability or motivation.
The ASHE dataset only covers the earnings of paid employees in the UK and does not include data on self-employed earnings (who are often some of the highest paid workers, as well as some of the lowest paid workers).
Three variables are estimated using regression analysis: hourly earnings, hourly total reward including pensions and hourly total reward including pensions and salary sacrifice.
The ASHE survey asks how much the employer and the employee contributed to the pension if the employee belonged to a workplace pension scheme. We used this information and defined "total reward" as the sum of hourly earnings plus hourly employer pension contribution2. This can also be seen as the sum of current and deferred earnings.
Employers can contribute to a pension on behalf on an employee through a process called "salary sacrifice". Where such arrangements exist, the member and employer both benefit from lower National Insurance payments. Therefore, employer contributions levels can be affected by salary sacrifice arrangements, whereby higher contributions are made by the employer with the member in return accepting a lower rate of pay. ASHE 2017 survey questions 5e and 5g ask whether employee pensions contributions were made through a salary sacrifice arrangement. Additionally, we estimated a third measure of earnings where the employees made their pension contributions through salary sacrifice. For more information on how this is calculated, see the Quality and methodology section.
Therefore, to calculate the true value of defined benefit scheme pensions requires a series of assumptions about individuals and pension plans – something that is not possible using the ASHE dataset nor is it possible to do so using a single source of data.
When modelling total reward using regression analysis, we included a variable indicating whether the employees receive any benefits in kind (see 2017 ASHE questionnaire – Question 6c) to control for the effects of fringe benefits on earnings4.
Overtime paid at a higher rate would increase an employee's hourly earnings whereas working unpaid overtime would effectively reduce hourly earnings. The standard ASHE measure of hourly earnings does not include overtime because excluding overtime gives a more like-for-like (in terms of working pattern) comparison between workers. However, overtime payments can be an important consideration in the analysis of "total reward" as sometimes people actively seek out jobs with overtime due to the opportunity to increase their total pay. Overtime payments and total hours worked by employees are included in ASHE, so we adjusted the hourly earnings for overtime payments in this article.
Bonus payments can also be a significant part of the employee remuneration. In this article, a bonus adjustment is made to hourly earnings in the ASHE dataset using the Average Weekly Earnings series (published monthly based on the Monthly Wages and Salaries Survey), to better account for the timing of bonus payments throughout the year. Estimates in this article are not therefore directly comparable with those published in ASHE provisional results 2017, which do not make this adjustment for the timing of bonus payments.
For consistency over time, employees of those banks classified to the public sector in 2008 have been treated as if they were in the private sector throughout.
Analysis of earning differentials should also consider non-random sorting of individuals between sectors as they may have different characteristics (both observed and unobserved) that cause them to select themselves into different sectors and these characteristics may be significantly correlated with their earnings.
Note that ASHE (Q-6b) only asks whether the employee was a member of any pension scheme run or facilitated by the organisation he or she works for, however, the individual may belong to another (private) pension scheme.
The true value of the pensions can also be overestimated as employees could lose money in a defined contribution (DC) scheme with poor investment strategy or in an unfavourable economic climate.
The expected sign of this variable is negative after controlling for personal and job characteristics affecting earnings, however, in cases where the benefits increase the productivity of employees, the sign of the variable can be positive as earnings have a positive relationship with productivity.
This section will consider a number of factors that are available in the Annual Survey of Hours and Earnings (ASHE) dataset and are considered in the earnings models presented in this article. Simple averages are considered to gain an initial insight into earnings differences amongst individuals with different characteristics, such as age and gender and job-related characteristics, such as public or private sector, industry groups in the private sector, occupational group, region of employment, job tenure, job status, and size of employer.
Pension contributions make up a significant proportion of employee remuneration, therefore it is important to consider this in any analysis of earnings. The composition of pension entitlement varies significantly between sectors.
Table 1 shows that 93% of employees in the public sector belong to a defined benefit pension scheme, whereas in the private sector, only 13% of employees belong to a defined benefit scheme. Pension enrolment also differs greatly between sectors, with 33% of private sector employees not belonging to any workplace pension scheme. In the public sector, only 11 % of employees do not belong to a pension scheme. This figure may decline in years to come with the introduction of auto-enrolment. For more information, see workplace pensions.
Table 2 shows that in 2014 contribution rates for employees and employers tended to be higher in the public sector, where participations rates were also relatively high and the majority of schemes were defined benefit. Note that these are the latest published estimates, which are available in the Median Contribution Rates to Workplace Pensions by Pension Type and Sector dataset. For more on contribution rates, see Pensions short stories: Employees eligible for automatic enrolment: contributions to workplace pensions, 2005 to 2014.
1. All employees with a workplace pensions.
2. As a percentage of all employees aged 16 to 64.
3. Data for Group Self-invested personal pensions (GSIPP) are included within the category Group Personal Pensions (GPP).
4. Percentages may not sum up to 100 due to rounding.
5. Data for 2017 are provisional.
Table 3 displays the proportion of employees who have a salary sacrifice arrangement. It shows that private sector employees are more likely to have a salary sacrifice arrangement than those in the public sector.
1. Data for 2017 are provisional.
Age is an important factor affecting earnings, as this tends to be a proxy for experience and the build-up of skills over time. Figure 1a shows that the distribution of jobs held within the private sector is more skewed towards the younger age groups, while the distribution of the jobs held within the public sector is more skewed towards the older age groups.
Figure 1b also shows current average hourly earnings for different age groups in the public and private sectors. Average hourly earnings regardless of sector rise sharply at younger age groups as job-related skills and experience are rewarded. Peaking in both sectors at ages 40 to 44 years, the average private sector earnings levels (excluding pensions) remain slightly higher than the public sector between the ages 35 to 39 and 55 to 59 years. In both sectors average earnings begin to decline at ages 50 to 54 years, with average earnings declining faster in the private sector.
Average hourly earnings (including pensions) for all employees also rise sharply with age. On average, public sector earnings (including pensions) remain above the private sector at every age group.
Data for 2017 are provisional.
Table 4 shows the proportion of men and women differs between the two sectors. In the public sector, around two-thirds of employees are women, whereas in the private sector only 42% of employees are women.
There are also differences in "total reward" between sectors. Our estimates using ASHE 2017 survey data show that women earn less than men on average in both sectors. In the public sector, adding pensions to mean hourly earnings increases average earnings by around £3 for men and £2 for women. In the private sector, including pensions increases average hourly earnings by around £1 for men.
Earnings are likely to increase as the skill level of the job increases. Thus, differences in earnings by sector may be partly explained by compositional differences in the share of employees at each skill level. The ASHE survey does not include employees' educational attainment or other skill-related attributes, however, it records the occupation of employees using the Standard Occupational Classification 2010: SOC 2010, which provides an indication of the types of job undertaken.
SOC 2010 allocates jobs to occupations, based on a description that considers the level of qualification and the type of tasks to be carried out. This makes SOC occupations a good proxy measure for skill levels. Therefore, for this analysis skill is defined solely by occupation and is grouped into four broad skill levels using SOC 2010.
Professional or upper-skill group jobs include occupations such as scientists, IT engineers, and health and educational professionals, while elementary occupations or lower-skill group occupations include farm workers, window cleaners, waiters, waitresses and processing operatives. The Quality and methodology section provides more detail of occupations in each skill group.
Figure 2 shows that a larger proportion of public sector employees are in the professional and upper middle-skilled group (63%) compared with the private sector (49%).
Table 5 shows the mean, median and standard deviations of hourly earnings (including pensions) of disaggregated occupations by sector. Mean hourly earnings for different occupations is relatively similar across most sectors. However, the medians and the standard deviations1 suggest that the private sector has a more dispersed earnings distribution than the public sector, indicating that the differential between top and bottom earners is much larger in the private sector.
The biggest difference in standard deviations is observed in the "managers, directors and senior officials" occupations, where standard deviation is around £28 in the private sector compared with around £17 in the public sector. This could be due to the factors affecting earnings such as age, however, jobs within the same occupational group can vary significantly in terms of the associated level of educational qualification, skills or responsibility and may not be distributed evenly between the public and private sectors. For example, jobs in a narrowly defined occupational category of "teaching and educational professionals" includes all teaching professionals such as university lecturers, secondary education teaching professionals or nursery teachers and the distribution of these jobs between the public and private sectors are not perfectly balanced.
Even within a more narrowly defined occupation such as "solicitors" there can be a range of different types of skills, specialisation and productivity levels of workers. For example, a lawyer working in a multinational law firm in London may have a different specialisation (hence different labour productivity levels affecting their earnings) to a lawyer working in a small local law firm, but both will have the same narrowly defined occupational classification.
2. The standard deviation is a statistic indicating how the values for a group are spread from the mean. A low standard deviation means that most of the values are very close to the mean. A high standard deviation means that the values are spread out.
Our previous publications have undertaken analysis of the earnings in the public and private sectors in a broad manner, that is, each sector was treated as a whole. However, the private sector is very diverse consisting of different types of industries with different working conditions, skills, productivity levels and job contents of the seemingly similar occupations.
Table 6a shows the mean, median and standard deviations of earnings for aggregated industry groups in the private sector and for the public and private sectors as a whole. It suggests that average earnings in the private sector vary considerably between different industry groups. For example, in knowledge-intensive services sectors, the mean hourly earnings (including pensions) are £12 higher than mean hourly earnings in the less knowledge-intensive services sectors.
Table 6a also shows that average hourly earnings in the private sector as a whole are lower than the public sector. Table 6b suggests that one of the reasons for this is that the relatively high share (around 40%) of the employees in the private sector are working in the relatively less-skilled and low-productivity services sectors (less-knowledge intensive services sectors) and this pulls down the average earnings of the private sector as a whole. This reflects the fact that many of the lower-skilled jobs have been outsourced from the public sector to the private sector.
Additionally, several studies have shown that in many countries there are large and persistent earnings differentials for seemingly similar workers and jobs across industries.
2. Less knowledge-intensive services include: Private sector Wholesale and retail trade; repair of motor vehicles; transport and storage; accommodation and food services; administrative services, education, human health and social work activities and other service activities.
4. Primary Services include: Private sector agriculture forestry and fishing, mining and quarrying, and utility services.
Geographic location is another factor that can influence earnings. Local labour market conditions and cost of living can be among the geographic factors affecting wages.
Figure 3 shows the mean hourly earnings including pensions for employees in selected occupation groups – professional occupations and elementary occupations. Average earnings of the employees in the elementary occupations were broadly similar across the regions, however, professionals in London earned on average around £7 more than in Northern Ireland in 2017.
Many empirical studies have shown a strong and positive relationship between employer size and earnings.
However, firms can also become too large and suffer from diseconomies of scale.
Figure 4 shows that in 2017, nearly 60% of all employee jobs were in organisations with over 500 employees, with noticeable differences between the private and public sector. In the public sector, around 90% of employee jobs are in organisations with over 500 employees, whereas this is just 46% in the private sector. Nearly half of the employee jobs in the private sector are in organisations with less than 250 employees, whereas in the public sector, only 7% of employee jobs are in organisations with under 250 employees.
Table 7 shows that without controlling for the factors affecting earnings, on average employees in large organisations tend to earn more than employees in small organisations. Average earnings of public sector employees are closer to the earnings of private sector employees in larger organisations.
1. Public sector as a whole (includes all sizes).
2. Data for 2017 are provisional.
Other job-related factors such as full- or part-time or temporary status of the job can also affect the earnings of an employee. The regression analysis controls for those factors available in the ASHE dataset (for more information, see regression model specifications in the Regression analysis modelling earnings differences section).
Figure 5 shows that mean hourly earnings (including pensions) were generally higher for public sector employees across the earnings distributions, apart from the top 10% where employees in the private sector earn more. Mean hourly public sector earnings exceeded private sector earnings by between £0 to £5 per hour until the 91st percentile of the earning distribution. At this point, both mean public and private sector earnings (including pensions) were around £32 per hour. In April 2017, private sector employees at the 99th percentile of earnings were paid around £12 per hour more than employees at the 99th percentile of hourly earnings in the public sector.
Only data between the first and 99th hourly earnings percentile have been used.
When the median is lower than mean, the distribution is skewed to the left with a tail stretching toward the right (high values), so a long tail of low values (on the left) pulls the mean down. The standard deviation is a statistic indicating how the values for a group are spread from the mean. A low standard deviation means that most of the values are very close to the mean. A high standard deviation means that the values are spread out.
The previous section considered one characteristic at a time to compare average earnings between the private and public sectors. This creates a partial picture of the earnings as employees have multiple personal and job characteristics that can impact on their earnings. Regression modelling can be used to account for some of these differences by including controls for them. The technical notes provide more detail on regression analysis. This regression analysis estimates the earnings difference between types of individuals when controlling for the modelled factors.
Log of bonus-adjusted hourly earnings.
Log of bonus-adjusted hourly earnings including pensions (employer contribution).
Log of bonus-adjusted hourly earnings including pensions (employer contribution) and salary sacrifice (if employee pension contributions made through salary sacrifice).
For more information, see the Quality and methodology section.
For more information on how to interpret the coefficients of interacted variables, see the Quality and methodology section.
There are arguments for and against including organisation size in the regression model. The argument for including it is that there is clear evidence from the private sector that employees in large organisations on average earn more than employees in small organisations for similar jobs. The previous section has outlined the potential reasons for this. If organisation size impacts on earnings, then it should be controlled for in the model as the aim of the regression model is to calculate public sector earnings differentials after all other influencing factors have been controlled for.
However, another view is held that public sector employees should earn the same as private sector employees, irrespective of organisation size. In this case, it would be useful to see the results without organisation size included.
Occupational group multiplied by industry sector – the effect of industry may differ for different occupation groups, for example, labour supply and demand for the same occupations or incentive conditions may differ between industries.
The Analysis of factors affecting earnings using the Annual Survey of Hours and Earnings linear regression dataset gives the coefficients generated by the linear regression model and provides metadata for the variable names and model specifications. The Quality and methodology section explains how to interpret these results.
Given that we have split private sector firms into six different size groups, and that earnings vary significantly across private sector firms of different size, it is useful to compare the modelled private sector wages in organisations of different sizes with those in the public sector.
Since the impact of organisation size in the private sector can also vary according to occupational groups (as modelled by the interaction term "occupational group multiplied by organisation size"), we present the comparison for each of our categories of occupation. Figures 6a to 6d show the percentage difference in average hourly earnings of employees working in the public sector compared with employees working in different sized private sector organisations for four occupational groups and for different definitions of earnings.
The results are for the linear model, which interacts occupation with sector and firm size. The results in Figures 6a to 6d are obtained by using the estimated coefficients from the individual effects of occupational groups, firm size and the interaction effect between occupational groups and firm size. For more information on how results are calculated, see the Quality and methodology section.
Figure 6a suggests that employees with lower and lower-middle skills working in the public sector on average earn more than similarly-skilled employees in the private sector regardless of the size of their organisation. However, employees in upper-middle and upper skill groups in firms with over 50 employees on average tend to earn more in the private sector than employees in the same skill groups in the public sector. For example, upper-skilled workers in firms with 251 to 500 employees earn on average 11% more than their public-sector counterparts.
Bars show the percentage by which public sector earnings are higher or lower than private sector earnings.
Figures 6b and 6c show that the public sector receives a larger remuneration package on average in almost every occupation grouping, regardless of private sector company size, when pensions (Figure 6b) and both pensions and salary sacrifice (Figure 6c) are included in the hourly earnings. The exceptions are for employees in the upper-middle occupations working for firms with over 500 employees and employees in upper-skilled occupations in firms with 251 to 500 employees. They earn slightly more on average than their public-sector counterparts when pensions are included in the hourly earnings. The biggest difference between public and private sector total remuneration, is found for upper-skilled occupations in smaller firms, where the public sector employees earn on average 42% more than private sector employees in firms with less than 11 employees.
Differences in private and public sector earnings (including pensions) can be difficult to interpret because 32% of private sector workers do not belong to a pension scheme run or facilitated by their employer, whereas this figure is only 11% in the public sector. To carry out a like-for-like comparison between the two sectors, one more regression has been run looking at only those with pensions.
Figure 6d shows that when examining only those who belong to a workplace pension scheme, the differences between the public and private sector are still largely in favour of the public sector. However, the total remuneration differences are less than before between the public sector workers and those with upper-middle and upper skills working in small organisations. For example, those in upper-skilled occupations in the public sector earn on average 31% more than their private-sector counterparts in firms with less than 11 employees. Only employees in the upper-middle-skilled occupations working for firms with over 500 employees and employees in upper-skilled occupations in firms with 251 to 500 employees earn slightly more on average than their public-sector counterparts when comparing those in a pension scheme.
Data include employees belonging to employer run or facilitated pension schemes only.
Figure 7 compares the earnings differential of the job tenure groups with those who have been in their job for six months or less for both with and without pensions. It suggests that the longer a person is in a job, the more they earn, even after accounting for all other individual and job-related factors such as gender, occupation, industry and region. These results are calculated using the estimated coefficients produced in the regression model.
The model also suggests that each subsequent group earns more than the previous group, with those in the same job for 20 years or more earning 25% more than those newly started in a job.
The Annual Survey of Hours and Earnings (ASHE) 2017 estimates and Figure 3 suggest that employees in every occupational group working in London earn more than similar employees located in other regions and countries of the UK. Figure 8 shows that this was still the case after controlling for various individual and job characteristics affecting earnings, with the higher-skilled occupations having the largest earnings differential. For example, after controlling for the factors affecting earnings, employees in upper-skilled occupations in the UK regions and countries (except the South East) earn around 25% (including pensions) less than their counterparts working in London. Employees working in the South East have the second-highest earnings in every occupational group in the UK.
The regression output for each of the previous estimates is available in the datasets, including for previous years.
Controlling for all factors affecting earnings, for the model that includes pension contributions, part- time workers in 2017 earned 9% less than full-time workers when controlling for other factors and temporary workers earned 2% less than permanent workers. In the model that excludes pension contributions, part-time workers earned 8% less than full-time workers and temporary workers earned 1% less than permanent employees.
Figures 9a and 9b show the percentage difference in average hourly earnings of employees working in the public sector compared with employees working in different private sector industry groups for four occupational groups.
The results are for the linear model, which interacts occupational groups with different industry groups. The results are obtained by using the estimated coefficients from the individual effects of occupational groups, industry groups and the interaction effect between the two groups.
Figure 9a suggests that employees in all the occupational groups working in knowledge-intensive services and primary industries in the private sector tend to earn more than their public-sector counterparts, with the largest difference occurring for the upper-middle-skilled occupations. For all the occupational groups, private sector employees earn on average less than their public-sector counterparts if they are working in less knowledge-intensive services sectors, with the largest difference occurring for the upper-skilled occupational groups.
Figure 9b shows that when pensions are included in the earnings, employees in the lower-middle and upper-skilled occupational groups in the knowledge-intensive services sectors still earn more than their public-sector counterparts. However, there was no difference in earnings for those in the lower-skilled occupational groups in the knowledge-intensive services sectors between the public sector and the private sector. For all the workers in the less knowledge-intensive services sectors, the differences shown in Figure 9a become larger when the pensions are included in the earnings.
This analysis suggests that, excluding pensions, public sector workers in the knowledge-intensive services sectors earn on average less than those in the private sector. On the other hand, private sector employees in the less knowledge-intensive services sectors earn on average less than public sector employees; however, these differences increase on average when pensions are included in earnings.
Bars show the percentage by which public-sector earnings higher than private-sector.
As detailed in the Results 1 section, which included organisation size, to carry out a like-for-like comparison between the two sectors without the organisation size, one more regression has been run looking at only those who belong to a workplace pension scheme.
Figure 9d shows when examining only those who belong to a workplace pension scheme, the differences in earnings between the public and private sector are still largely in favour of the employees in the knowledge-intensive services sectors. For all the workers in the less knowledge-intensive services sectors, the differences shown in Figures 9b and 9c become smaller when we compare employees with pensions only.
The linear regression model considers the difference in the mean earnings of public and private sector workers. This does not take account of the fact that the distribution of pay tends to be narrower in the public sector than the private sector, as shown in Table 6a, and so does not give a complete picture. It is possible to use quantile regression methods to estimate the difference in the median pay of public and private sector workers, as well as the difference at each percentile, for example, the fifth or tenth percentile. This is useful as it indicates if the pay gap is different at different points of the pay distribution, effects which cannot be captured by mean regressions.
It should be noted that estimates across different quantiles of the income distribution compare the average hourly pay for a certain distribution of the public sector workforce with the average pay for a certain distribution of the private sector workforce. For example, if we observe a positive public sector premium at the lower end of the distribution, this does not necessarily imply that if an individual in this part of the income distribution working in the public sector was to move to the private sector, they would earn a lower hourly pay.
Instead, it implies that individuals in the lower end of the public sector income distribution – conditional on observed characteristics – on average earn an hourly premium compared with the individuals in the lower end of the private sector income distribution. The pay gap between private and public sector workers has been estimated for the second, fifth and tenth percentiles, the median and the 75th, 90th, 95th and 98th percentiles using the quantile regression model both excluding and including organisation size.
Table 8a shows that that the earnings differential between public and private sector employment varies substantially along the earnings distribution, with relatively low-paid employees having the largest earnings advantage from employment in the public sector. For high-paid employees, the gap still exists in favour of the public sector for those working in smaller organisations, however, employment in the large private sector organisations has a positive impact on the earnings of those employees.
Without controlling for the organization size, Table 8b shows that employees working in the less knowledge-intensive services sectors have a negative earnings differential at all levels of the earnings distribution, with the differential being the largest for the employees at the lower end of the distribution. Employees in knowledge-intensive services and primary industries generally benefit more from working in the private sector if they are earning above the median. In the knowledge-intensive services, the negative earnings differentials for those earning below the median are relatively small.
The analysis in this article finds that after controlling for various individual and job characteristics, on average there is a positive earnings differential in favour of the public sector. The positive differential in favour of the public sector is mainly concentrated on low-skilled workers and on workers in smaller organisations, when the organisation size is considered.
Without controlling for the organisation size, the public sector also has a positive wage differential on average, however, high-skilled employees in the public sector tend to have lower earnings than their counterparts employed in the knowledge-intensive services and primary sectors in the private sector.
Quantile regression analysis provides a more complete picture of the distribution of the public-private sector earnings differential than the estimates of the mean earnings differentials. The analysis shows that the earnings differential between public and private sector employment varies substantially along the earnings distribution, with relatively low-paid employees having the largest earnings advantage from employment in the public sector. For high-paid employees, the gap still exists in favour of the public sector for those working in smaller organisations; however, employment in the large private sector organisations has a positive impact on the earnings of those employees.
Without controlling for the organisation size, employees working in the less knowledge-intensive services sectors have a negative earnings differential at all levels of the earnings distribution, with the differential being the largest for the employees at the lower end of the distribution.
However, the earnings differentials are not fully explained by employees' observable individual or job characteristics included in the regression models. The literature provides some theoretical explanations to account for these, such as monopolistic power of governments in the provision of public services, which may result in non-competitive wage settlements, different objectives of government (for example, political or income distribution) and private sector (for example, profit maximising) or differences in wage bargaining powers.
Alternatively, the regression estimates can be misleading for various technical reasons. These include data limitations, sampling bias, omitted variables, non-sampling errors, and self-selection issues. For a detailed explanation of the technical issues, please see the Quality and methodology section.
These nine major groups can be combined further into four skill groups (levels one through to four, where level one indicates relatively low skill requirements and level four indicates relatively high skill requirements). Table 9 describes some of the important features of each skill group.
In March 2012, the 2011 ASHE estimates were published on a SOC 2010 basis (they had previously been published on a SOC 2000 basis). Since the SOC forms part of the methodology by which ASHE data are weighted to produce estimates for the UK, this release marked the start of a new time series and therefore care should be taken when making comparisons with earlier years.
Similarly, methodological changes in 2004 and 2006 also resulted in discontinuities in the ASHE time series. On 28 February 2014, we published a methodological note explaining the impact of the change in Standard Occupational Classification on the estimates of public and private sector pay.
ASHE aims to provide high-quality statistics on the structure of earnings for various industrial, geographical, occupational and age-related breakdowns. However, the quality of these statistics varies depending on various sources of error.
ASHE statistics are also subject to non-sampling errors. For example, there are known differences between the coverage of the ASHE sample and the target population (that is, all employee jobs). Jobs that are not registered on PAYE schemes are not surveyed. These jobs are known to be different to the PAYE population in the sense that they typically have low levels of pay. Consequently, ASHE estimates of average pay are likely to be biased upwards with respect to the actual average pay of the employee population.
Non-response bias may also affect ASHE estimates. This may happen if the jobs for which respondents do not provide information are different to the jobs for which respondents do provide information. For ASHE, this is likely to be a downward bias on earnings estimates since non-response is known to affect high-paying occupations more than low-paying occupations.
Further information about the quality of ASHE, including a more detailed discussion of coverage and non-response errors, is available on our archive website.
The dependent variable is expressed in log form. If the distribution of a variable has a positive skew, taking a natural logarithm of the variable sometimes helps fitting the variable into a model. Also, when a change in the dependent variable is related with percentage change in an independent variable, or the other way around, the relationship is better modelled by taking the natural log of either or both the variables.
Gross weekly earnings for the reference period equals basic weekly earnings plus incentive pay paid in this period relating to the pay period plus additional premium payments during the pay period for shift work and night or weekend work not treated as overtime plus weekly overtime pay for the pay period plus pay received in the pay period for other reasons.
Total paid hours worked during the reference period equals basic weekly paid hours worked plus weekly paid overtime hours worked during the reference period.
Hourly earnings including pensions equals (gross weekly earnings divided by weekly total paid hours worked) plus hourly employer pension contributions.
Pensions are provided in ASHE in the form of weekly employer and employee contributions. To get an hourly amount, weekly employer pension contributions are divided by total paid weekly hours.
Hourly earnings including pensions and salary sacrifice equals hourly earnings including pensions plus hourly employee pension contributions if they are made through salary sacrifice.
In the salary sacrifice scheme, the salary sacrificed is recorded in employee pensions contributions. To get a value for earnings including pensions and salary sacrifice, both employee and employer pension contributions are added to gross pay as an hourly amount1.
All earnings variables have been adjusted to account for bonus payments using data from the Average Weekly Earnings (AWE) series. Using AWE data, we have calculated an average bonus amount, as a percentage of average earnings for each (Standard Industrial Classification 2007: SIC 2007 two digit) industry in both the public and private sector. Hourly earnings from ASHE are then multiplied by this bonus percentage to get bonus-adjusted hourly earnings.
When accounting for the age of employees in the regression model, we have incorporated a variable for both age and age squared; this is due to the Taylor series approximations. Taylor series approximations tell us that for many smooth functions, they can be approximated by a polynomial, so including terms like x^2 or x^3 let us estimate the coefficients for the approximation for a known or unknown non-linear function of Χ, or in this case age.
As well as the suite of independent variables observed in the model, a number of interaction terms are included. These are added to account for the assumption that some characteristics interact with one another.
Adding an interaction term to a model drastically changes the interpretation of all the coefficients. If there were no interaction term, β1 would be interpreted as the unique effect on the Y (in this case earnings). But the interaction means that the effect of α1 on Y is different for different values of α2. So, the unique effect of 1 is not limited to β1, but also depends on the values of β3 and α2.
β1 + β3* α2. β1 is now interpreted as the unique effect of α1 on Y only when α2= 0.
When interpreting the outputs of the model, care needs to be taken with the coefficients of variables. When the independent variables are in their original state and the dependent variable is in its log-transformed state (a log-linear model) each one unit increase in the independent variable multiplies the expected value of Y by (exp*β).
For small values of β, approximately exp*β ≈ 1+β. We can use this for the following approximation for a quick interpretation of the coefficients: (100 * β) is the expected percentage change in Y for a unit increase in the independent variable. For example, for β = .06, exp*.06 ≈ 1.06, so a one unit change in the independent variable corresponds to (approximately) an expected increase in Y of 6%.
In analysis that looks at the relationship between two variables, it can be tempting to infer that one variable is directly related to the other. For example, a conclusion such as "the average earnings of the employees in the public sector is higher than the average earnings of the employees in the private sector" would only be justified if we could show that there were no other important differences between the two groups of employees that might affect the findings, such as differences in skills or job characteristics.
Regression analysis allows us to do this by holding constant all the variables in the model while measuring the size and strength of the relationship between two specific variables. If the regression results show a significant relationship between the two groups, then this means that two people who are identical in every way apart from their employment sector would indeed have different average earnings. This implies a direct relationship between earnings and employment sector, even when the other variables included in the analysis are considered. Therefore, the main benefit of regression analysis is that it provides a better method than analysis looking at the relationship between only two variables at a time of identifying those factors that can influence the earnings of an individual.
However, every analytical method has its limitations and regression analysis is no exception. The following sections summarise some important considerations which should be borne in mind in terms of the statistical assumptions underlying the techniques used here and the types of inference that can be drawn from the findings.
The explanatory power of the regression models (that is, the amount of variance in earnings that has been explained by the model) used in this analysis is around 55%.
It is important to note that the explanatory power of the model can be influenced by leaving out important factors, both observable and non-observable, which contribute to earnings, such as training on the job, other observable variables not included in ASHE such as education, or individual motivation or productivity, which can only be observed by the employer.
In an ideal world, a regression model should include all the relevant variables that are associated with the outcome (that is, the variable being analysed such as earnings). In reality, however, we either cannot observe all the potential factors affecting earnings (such as the productivity of the employee) or are limited by whatever information is collected in the survey data used in the regression analysis.
If a relevant factor is not included in the model, this may result in the effects of the variables that have been included being mis-estimated. When the omitted variables are correlated with the included variables in the model, the coefficient estimates of those variables will be biased and inconsistent. However, the estimated coefficients are less affected by omitted variables when these are not correlated with the included variables (that is, the estimates will be unbiased and consistent). In the latter case, the only problem will be an increase in the estimated standard deviations of the coefficients, which are likely to give misleading conclusions about the statistical significance of the estimated parameters.
Regression analysis based on cross-sectional observational data cannot establish with certainty whether relationships found between the independent and dependent variables are causal. For example, the usual assumption is that individual characteristics, such as occupation, are independent variables that may affect earnings (viewed here as a dependent variable). However, variables such as occupational choice could be jointly determined with earnings (that is, some of the association between occupational choice and earnings may be caused by the impact of earnings on occupational choice).
A non-random selection of workers into different sectors may also affect the outcome of the regression results. A self-selection bias will affect the outcome if the characteristics of the people that cause them to select themselves into a group are different and these characteristics are correlated with earnings.
If two or more independent variables in the regression model are highly correlated with each other, the reliability of the model as a whole is not reduced but the individual regression coefficients cannot be estimated precisely. This means that the analysis may not give valid results either about individual independent variables, or about which independent variables are redundant with respect to others. This problem becomes increasingly important as the size of correlations between the independent variables (that is, multi-collinearity) increases.
The analysis compared the aggregate average earnings of a group such as occupations or industries between the public and private sectors. However, the jobs, skills and responsibilities in these groups can be very diverse and the distribution of these jobs between the public and private sectors may not be balanced. For example, jobs in a narrowly defined occupational category of "teaching and educational professionals" includes all teaching professionals such as university lecturers, secondary education teaching professionals or nursery teachers and the distribution of these jobs between the public and private sectors is not perfectly balanced.
Even within a more narrowly defined occupation such as "solicitors" there can be a range of different types of skills, specialisation and productivity levels of workers. For example, a lawyer working in a multinational law firm in London may have a different specialisation (so different labour productivity levels affecting their earnings) to a lawyer working in a small local law firm, but both will have the same narrowly defined occupational classification.
Note that the employer contribution can be overestimated as the total amount may include employee contributions.
We would like to thank Aaron Walton, Ed Benham, Roger Smith, Ed Moskalenko and Mark Chandler for their contributions and methodological advice.


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If You Read One Article About Resources, Read This One
A Brief History Of Semitic Tribe
The Semites, Semitic tribe or Semitic culture was a term for cultural, racial or ethnic group who spoke or speak Semitic languages. In archaeology on the other hand, the term is informally used sometimes as a type of shorthand for the ancient Semitic speaking individuals.
In racialist classification presented by Carleton S. Coon, Semitic tribe were deemed to be members of Caucasian race and with same appearance to neighboring Northwest, Indo-European, Kartvelian and Berber speaking peoples of the said region. As the language studies are interwoven with the cultural studies, the term came to describe religions as well as Semitic speaking ethnicities and histories as associated by linguistic distribution and close geographic.
As a matter of fact, some genetic studies that were done recently uncovered that they have a common ancestry. Despite the fact that there's no significant common mitochondrial outcomes have been discovered, the Y-chromosome links between Semitic speaking individuals of the Middle East (M.E) such as Hebrews, Arabs, Samaritans, Assyrians and Mandaenas showed links despite the differences contributed from the other groups.
In a DNA study of Palestinian Arabs and Jews including Bedouins, it has unearthed that these were related more to each other compared to the ones of Ethiopian Semitic tribe, Arabian Peninsula and Arabic speakers of the North Africa.
Genetic studies indicate that modern Jews similar to Mizrahi, Sephardic and Ashkenazi, Arabs, Assyrians, Levantine, Maronites, Samaritans, Mandaeans, Mhallami and Maronites have an ancient indigenous common near system heritage which may be mapped genetically back to the ancient Fertile Crescent but usually displaying genetic profiles distinctive from each other that indicates different histories of such individuals.
Whether you believe it or not, the Semitic languages of Semitic tribe is a branch of the Afroasiatic language operating in M.E. These languages are spoken by roughly 330 million people in North Africa, Western Asia as well as the Horn of Africa and also, even in the large expatriate communities in Europe and North America with smaller communities found in Central Asia as well as Caucasus.
There are actually a number of proposed locations as feasible sites for the prehistoric origins of the Semitic tribe including The Levant, Arabian Peninsula, North Africa and Mesopotamia with most recent Bayesian studies which indicate Semitic Originated in Levant Circa around 3800 BC and later introduced to Horn of Africa in around 800 BC.
Semitic languages of Semitic tribe were spoken in much of Asia Minor as well as M.E during the Iron and the Bronze age and in fact, the earliest attested was in the East Semitic Akkadian of Mesopotamian as well as the South Eastern Anatolian polities of Assyria, Babylonia and Akkad and the East Semitic Eblaite language of kingdom of Ebla in North Eastern Levant.
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Sheldon in the Big Bang Theory often has trouble knowing when Leonard or his other friends directed a sarcastic remark at him. New research shows that perhaps it's his lack of empathy for others that is causing that.
According to a new study in the open-access journal Frontiers in Psychology, the greater the empathy skills of children, the easier it is for them to recognize sarcasm.
For children, sarcastic language can be difficult to understand. They generally begin to recognize sarcasm between ages 6 and 8, especially familiar sarcastic praise such as "Thanks a lot!" and "Nice going!" But some children take much longer to begin to understand sarcasm, with detection improving even through adolescence.
The researchers looked at empathy specifically because they thought that in order to understand sarcasm children must be able to adopt the perspective of the speaker - to understand the speaker's attitude and emotions.
The study involved 31 children between 8 and 9 years old in a task that required them to recognize sarcasm. After children watched a series of puppet shows that included either sarcastic or non-sarcastic praise, they were asked to pick up a "mean" toy shark if they believed that the puppets had spoken sarcastically, or a "nice" duck otherwise. Each child was tested 12 times, with different puppets and scenarios. The empathy skills of the children were measured separately.
Children detected the puppets' sarcasm about half of the time, and children with relatively strong empathy skills did so more accurately. Children with stronger empathy skills were nearly twice as accurate as children with less advanced empathy skills. Initially, the researchers analyzed a group of 6-7 year olds, but this age group revealed almost zero accuracy for sarcasm.
The researchers also quantified children's eye gaze and reaction time during the sarcasm recognition task, to quantify subtle clues about their understanding. They measured whether the children looked towards the duck or the shark, as well as the time it took them to choose either. This gave more subtle clues about the children's understanding.
"Sarcastic language, especially in unfamiliar forms, is a real challenge for most children," explains Prof. Pexman. "Even when children did not recognize a remark as sarcastic, there was evidence in their reactions that the children with stronger empathy skills were sensitive to the speaker's intent."
"This study helps us understand why some children deal better with this challenge than others and provides new insights about development of this complex aspect of emotion recognition," adds Pexman. "It also puts us in a better position to help children who are struggling with this challenge".


Gave a 7-minute talk, with slides, at Coquitlam's PechaKucha Night on Feburary 14, 2013.
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Folding Ladder – A Stow-Away Ladder That Conceals Its Purpose – p. 20, issue 74 (March 2012) of Australian Wood Review.
Cuckoo Calamity – p. 75, April 2005 of Canadian Home Workshop.


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text:
module Tarot
  module Entities
    class Correspondence < Entity
      attr_reader :golden_dawn

      def initialize(options)
        @golden_dawn = set_attribute options, :golden_dawn
      end

      private

      def set_attribute(options, name)
        options.fetch(name) do
          msg = "Missing required correspondence: #{name}"
          raise ArgumentError, msg
        end
      end

    end
  end
end

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In 2012 a group of elders in the Northern Rivers area of NSW Australia started to meet. We had all been exposed to the way our society regards older people, but we had also started to recognise our own responsibility in how our lives were lived and that perhaps there was more to life than was ordinarily accepted by today's society. We spent our meeting times in exploring these ideas with some of our friends overseas and we realised that we all had a story to tell about how our lives had been changed.
We felt that by sharing these diverse stories in a book we could perhaps help others to remember how it felt for them when they were children. How life can be wondrous and joyful, and if we can let go of all the impositions that we have allowed life to put upon us, we can have a joy-filled older age.
This book is dedicated to all those who feel that perhaps there is more to life – that they can delve more deeply within and find that joy of which our writers speak. It has become clear to many of the writers that there is far more to life than the five senses reveal and that our bodies have an amazing ability to show us how to live if we choose to listen.
"At 55 years old I feel that life has just begun. . . and the funny thing is that I feel this every morning!!! I wake up and I find that there is always more of ME to connect to and express from and as I continue to do this I feel more lovely, more vital, more aware and more worthy every single day!" Kathleen B.
"The 60s have been an amazing time for me and I have a feeling that the 70s might be even better. I've laid a strong foundation in my 60s for the years ahead. Where once I felt uncertain about the future, I now look forward to what's ahead of me, whatever it may be. And, I've learned it's never too late to make changes in your life. I'm living proof of that!" Judy F.
"So here at seventy-three years I have a choice. Am I going to go on living my old patterns into my old age and pass on miserably, or am I at last going to accept the reality of life, my age, my body, and above all the divine woman I am inside, and live this stage of my life from inside out, not outside in, accept the reality of myself and let myself live that, and so pass from it lovingly? Something fundamental has changed and allowed me this choice, whereas before I could not see it." Joan C.
"I am still young at heart, while recognising and accepting that my body is not as strong as it used to be and that my short-term memory is not as good as I would like it to be. I still have a zest for living. I certainly have more wisdom than forty or even twenty years ago. And there is more harmony and stillness in my life; more time for reflection. More gentleness. More serenity. More love. More joy. I am love. I express love." Tony P.
I came across The Joy of Ageing, Esoterically last year and was so inspired by the elder women and men's stories, ageing from their 50s through to the 80s that I purchased several more copies to give to dear friends. Each and every story has a uniqueness and quality that reached out and offered me new insights into my own life. To feel the joy, zest, love and commitment they each have to life was glorious. It clearly showed me how we all have the ability to turn our lives around, no matter our age, or what we have experienced thus far.
I now realise that we indeed have a purpose and a very important purpose at that. I had been led to believe that we become useless, have nothing to offer and are pretty much just a burden on society as we enter our elder years.
This book turns that belief completely on its head, in fact it is imperative that we live fully and engage with life right up until our last breath!!


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text:
We must do better to curb discrimination against working parents
July 27, 2014 4.22pm EDT
Diann Rodgers-Healey, James Cook University
Diann Rodgers-Healey
Adjunct Professor, James Cook University
Diann Rodgers-Healey does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
James Cook University provides funding as a member of The Conversation AU.
One in two Australian women report discrimination, despite existing protection laws. Flickr/Ryan, CC BY
A landmark study by the Australian Human Rights Commission reporting that one in two mothers has experienced work discrimination shows employers clearly believe they have legitimate reasons to discriminate against women raising families, and their partners.
But with a solid legislative framework in place clearly mandating how employers must behave towards pregnant women and their partners, is this sort of behaviour really excusable?
The Supporting Working Parents: Pregnancy and Return to Work National Review surveyed 2000 mothers and 1000 fathers and partners on their experiences of discrimination.
One in five (18%) mothers told the AHRC they were made redundant, their jobs were restructured, they were dismissed or their contract was not renewed because of their pregnancy, taking parental leave, or family responsibilities that included the need to breastfeed or express milk at work.
There was significant negative impact of this discrimination on women's health, finances, career and their family. An alarming 72% reported that the discrimination impacted on their mental health. Some 42% reported that the discrimination had a financial impact on them, while 32% of mothers went to look for another job or resigned.
The report found fathers and partners also experienced discrimination in the form of negative attitudes through to dismissal. This group also reported similar impacts on mental health, finances, career and their family.
"Nothing will change"
Strikingly, their responses to the discrimination also did not vary. The survey found 91% of mothers and 95% of fathers and partners did not make a formal complaint either within their organisation or to a government agency.
Primarily mothers did not complain because they "perceived that the discrimination was not serious enough, that it was too hard, stressful or embarrassing to take action, or that they would not be believed or nothing would change."
Researchers Byron and Roscigno in their analysis of US pregnancy based employment discrimination claims stated that what "explains the relative infrequency at which pregnant women win favorable judgments…" is that when women do contest employer actions, "the structural and cultural playing fields … are quite uneven."
The AHRC's findings showed that employers are confused about their own legal obligations, and about employee rights related to pregnancy, parental leave and return to work issues. It recommends assistance for employers and employees to understand the legislative framework.
Australian laws
Australia already has a suite of federal laws that protect pregnant women and new parents from workplace discrimination in Australia, including the Sex Discrimination Act 1984, the Fair Work Act 2009, and Work Health and Safety laws.
The Sex Discrimination Act 1984 for example, specifically prohibits discrimination on the grounds of pregnancy, potential pregnancy, breastfeeding and pregnancy/return to work discrimination. So to claim confusion and uncertainty about workplace legal obligations is difficult to understand.
Although the report cites that there are many employers who support and retain pregnant employees and working parents, other employers identified challenges managing the uncertainty around pregnancy, parental leave and return to work issues.
In response, the report advises that workplaces need a solid foundation of the right policies and practices which are effectively communicated and supported by leaders.
Effectively implementing the strategies and practices requires developing a plan from the time an employee announces her pregnancy; through to preparing for an employee's parental leave; staying connected during parental leave; reintegrating after parental leave; and career acceleration upon their return.
Financial bottom line
It is clear that financial interests are also a concern for employers. One of the challenges identified by employers in managing pregnancy/return to work issues was "limiting the direct costs associated with training a temporary replacement employee." Seeing pregnant women as "economic liabilities" could be one of the motivations perpetuating pregnancy-based discrimination.
However there are more primal reasons underlying the "hidden, insidious, and often intentionally carefully concealed" discrimination.
The report found "harmful stereotypes and attitudes in the workplace about "the pregnant employee" and "the employee with family or caring responsibilities", "the flexible worker", as well as stereotypes about "the ideal worker", are pervasive in Australian workplaces."
What is clear is that pregnancy continues to be associated with gender stereotypes and organisations are continually recreating gender inequalities related to working parents.
Pregnant presenteeism
Patriarchal stereotypes that "women cannot be good mothers and good workers" persist. There is even evidence that challenges the "common belief" that pregnant employees are prone to take sick leave. The term "pregnant presenteeism" was coined to describe pregnant employees who resist taking sick leave.
Given that increasing women's workforce participation in Australia by 6% could increase the national GDP by A$25 billion, there is a compelling reason to curb pervasive discrimination that has changed little in the 15 years since the first Inquiry by the AHRC.
The report concludes that we are all implicated in ensuring that this form of discrimination against working parents is curbed:
"It is up to all of us – government, employers, unions, peak bodies, community organisations and men and women in workplaces around Australia – to play a role in addressing such discrimination and preventing its continuation."


Lena Emir-Kozzi, MD, presents a laser cataract extraction with Nd-YAG laser 1.44 um. The patient is a Caucasian 61-year-old female. Laser cataract extraction was accomplished with an Nd-YAG laser 1.44 um combined with forepump. The laser cataract surgery technology is certified by the Department of Public Health (Russia). The depth of YAG laser energy permeation into the tissues is 0.5-0.7 mm. The laser works in pulse mode with a frequency up to 25 per second. The more dense is the cataract nucleus, the more effective is the laser destruction. Laser is less effective in patients under 40 years old, but in these patients cataract removal can be performed at the expense of vacuum. This is the surgeons first experience with this device. In postoperative period the patient has minimal corneal edema and no complications in posterior eye segment.
What. Is the actual development of this type of laser??
Posterior capsule rupture is possible only in case of laser tip contact with the capsule. The red He-Ne guidance beam helps to avoid the contact, He-Ne beam shows the depth of laser permeation. Experiments showed it is not possible to create anterior capsulorhexis with low laser energy, low laser energy just coagulates the anterior capsule. Capsule rupture is possible with high laser energy. There is a special laser tip for porterior capsulorhexis, it helps to create a round posterior capsulorhexis. I have not tried it yet, I am going to try it when I have an occasion for it.
any comparision with femto and phaco energy and what is the risk on endothelium apart from other potential PCR risks. amount of heat energy produced with Nd-Yag is quite high, so there is a possibility of IOP rise POP also. could you share your experience and study reports?
what Is a possibility of PC rent with this Laser ?


I received email notice that NCRiviera posted "cool first gen tail light treatment" when I clicked the link there was no post. What happened? It was a pretty neat photo.
Does anyone know what taillights Barris used in the original Villa?
I looked on O'Reilly's website and found at least three choices for 63 425. Murray temp control plus, Temp control Ultra (larger than OEM opening) and Moto-Rad Fail Safe. I plan to run 180* Suggestions? I tried a search but results were for the entire AACA site. I couldn't figure out how to narrow it down to Nailhead/Riviera. P.S. Car currently has a 15# cap, is that correct or should it be 16#....?
They didn't always drain well, dirt and sand would collect and plug the drain. I used to remove, clean, flush panel, and reinstall on my Pontiac Grandville convertible.
Thanks steelman. Cstang, did you beef up the control arm to mount the Shockwave? I've been told the SW puts undo stress on the original shockmount.
Do you have any installation pics?
64Rivvy, where did you purchase all the bushings etc. for the front end rebuild?
Review from Amazon: http://smile.amazon.com/gp/customer-reviews/RK7NNKJRYYDPD/ref=cm_cr_pr_viewpnt?ie=UTF8&ASIN=B000CPZVTS#RK7NNKJRYYDPD 4 of 4 people found the following review helpful " Thank goodness for these blades. Thanks Trico!, June 25, 2011 By Bobby B. This review is from: Trico 33-150 Classic Wiper Blade, 15" (Pack of 1) (Automotive) I have a 1960 Buick LeSabre. As any enthusiast knows, the new-style, now-ubiquitous black-finished wiper blades of today look ridiculous on such automobiles. My car arrived with this type of blade. I was resigned to digging up a set of old blades on Ebay or in a junkyard and fitting new refills. But when I read about the Trico Classic I decided to do a little research. I could not figure out from my reading if these blades were actually stamped stainless(?) like the originals. Decided to take a flyer on these anyway and am very glad I did. The stampings are not *quite* as well polished and finished as the originals, but they look right and fit properly. I may, just may, sand them down a bit with a very fine grit and run them over a buffing wheel/metal polish. We'll see. Even if I don't I am very happy with the look." I will go to O'Reillys to look at them prior to purchase.


If you've ever found a Bible in a hotel room, they're the ones who put it there. Visit their website today for the wonderful testimonies of how God's Word is changing lives through the generosity and commitment of Gideons members all over the world.
And any time you need to send a gift or $5.00 or more, why not send Gideon Bibles? What a thoughtful gift in lieu of flowers for a funeral, for any occasion, or for the person who has everything. Each Bible placement is only $5.00, so it's a gracious and meaningful gift that's within reach of those with limited incomes.
Just go to the website to order. Select a card, fill in the "In Honor of" certificate and the address info, and make your donation by credit card. It couldn't be easier! Then the Gideons will mail the gift recipient the card you chose showing how many Bibles will be distributed in their honor. You'll also receive a confirmation of your donation.
This is a gift from the heart, and it's convenient and affordable too!
An interesting post that I enjoyed reading. Glad you have Jesus in your life.
When my husband passed away I received a Sympathy Acknowledgment Gideon Card saying.... five New Testaments had been given as a memorial for his life. (I was so blest). It's the gift that keeps on giving, truth is always alive.
Nice to hear from you both today, and Dodi, what a sweet memory and memorial. I love your ALIVE acrostic. thanks.


text:
The Seoul International Marathon, also known as the Dong-A Ilbo Seoul Marathon or Seoul Marathon is an annual marathon race that takes place in Seoul, South Korea. It is one of two annual races over the 42.195 km classic distance in the city, alongside the JoongAng Seoul Marathon which is held in November. It holds World Athletics Platinum Label Road Race status. First held in 1931, it is the third longest-running road running competition in Asia after the Hakone Ekiden and Chugoku Yamaguchi Ekiden in Japan.

The race has been integral to the elite level of the sport in Korea, as ten of the 28 South Korean national records in the marathon have been set at the competition. The day's races attract around 35,000 people on a yearly basis.

History
The genesis of the competition is traced back to the Youngdungpo Marathon, which was first held in 1931. It was not a true marathon and the looped course in the city measured roughly 50 ris (around  or 23.3 km). The race was contested between fourteen of the country's top male runners and Seoul's Kim Eun-Bae won the first edition. Kim and the 1933 winner Sohn Kee-chung both went on to compete at the Olympic marathon, although they did so under the flag of Japan as Korea was part of the Japanese empire at that time.

This fact contributed to the suspension of the 1937 race: the event's sponsor, Korean broadsheet the Dong-A Ilbo, censored the Japanese flag in its reports of Sohn's marathon victory at the 1936 Berlin Olympics and the ruling Japanese military junta responded by suspending both the newspaper and the race. The race returned as an annual fixture from 1938 to 1940 but was again discontinued, initially due to World War II and later because of the Korean War. The race returned in April 1954 and Im Jong-Woo became the first person to win twice, taking back-to-back victories in 1954 and 1955.

The competition's first official full-length marathon was contested in 1964 and the change brought about improvements in the national standards as Lee Myeong-Jeong set a South Korea record to win in 1965 and Kim Bong-Nae became the first Korean to run under two hours twenty minutes a year later. At the 1970 edition, Canadian Ron Wallingford and two Japanese runners provided the race with its first international competitors. The quality of the field saw domestic runner Kim Cha-Wan react with a national record run of 2:17:34.4 to win the race. Kim improved his mark again in 1973 and went on to become the Dong-A Marathon's most prolific winner, scoring four victories in the 1970s. Japanese marathoner Toyoichi Masuda became the first foreign winner in 1977 and he was soon joined by his countryman Makoto Matsuzaki, who won two years afterwards. A women's 10 km race was added to the programme in 1979 and Moon Ki-sook became the first women's Dong-A champion.

Im Eun-Joo won a women's 30 km race in 1981 and female runners were allowed to take on the full marathon distance the following year. She went on to claim three straight marathon titles from 1983 to 1985. The 1982 race hosted a dual domestic and international race, with results kept separate for the divisions. In anticipation of the 1988 Seoul Olympics, the city also hosted separate international races from 1983 up to 1987 (which was the IAAF World Marathon Cup race). The Dong-A competition in the 1980s saw the men's record reach 2:12 territory through Jang-hee Lee in 1987.

With the advent of another decade, Won-Tak Kim brought the men's record down to 2:11:38 in 1990, winning the Asian Marathon Championship which was hosted within the race that year. Hwang Young-cho, Seoul's winner in 1991, went on to take the gold medal at the 1992 Barcelona Olympics. Kim Wan-gi reduced the national record further to 2:09:25 in 1993. The marathon was moved to Chuncheon for the 1992 event and had a seven-year stint in Gyeongju from 1993 to 1999. It returned to Seoul in 2000, but its impact in the latter city led to the creation of the Gyeongju International Marathon.

The event took on a more international nature from 1994 onwards, renaming itself the Dong-A International Marathon; following the invitation of runners from twelve countries, Manuel Matias of Portugal became the first winner from outside of Korea and Japan. The marathon reached a landmark of over 10,000 starters in 1999 as 11,303 runners signed up for the full distance in the elite and popular races.

China's Wei Yanan brought the women's record down to 2:25:06 in 2002, knocking over five minutes off the previous course best. Gert Thys had two consecutive victories in 2003 and 2004 (recording a record time of 2:07:06 in the latter), but a third win in 2006 was erased after he failed a drugs test for the banned steroid norandrosterone.  The runner-up Jason Mbote was declared the winner, but after a protracted legal battle Thys had his ban overturned in 2012 due to a breach of the rules in the way his sample was handled. At the 2002 edition, Zhou Chunxiu of PR China became the seventh woman to ever finish under two hours and twenty minutes as she set the women's record at 2:19:51 hours. Kenyan runner Sylvester Teimet beat the men's record in 2010 with his win in 2:06:49 hours. Three Kenyans went under this time in 2012, with Wilson Loyanae's time of 2:05:37 hours knocking more than a minute off the record.

The 2020 edition of the race was cancelled due to the coronavirus pandemic, with all runners receiving a full refund.

Course

The competition begins at Gwanghwamun Plaza in the city centre and finishes within the Olympic Stadium. The course has a point-to-point format and traces a south-easterly path through the city centre. After departing from the main plaza, the route flows into Sejongno thoroughfare and passes the statue of Yi Sun-sin. The runners then pass the Namdaemun gateway and head through Cheonggyecheon park. The route traces a pass through Dongdaemun-gu district before crossing the Han River to head towards the stadium finishing point.

Past winners

Pre-marathon

Key:

Note: All other years approx

Marathon

Key:

''In 1992, the race took place in Chuncheon, South Korea; and from 1993 to 1999, the marathon was run in the city of Gyeongju, South Korea

Notes
An international race and a domestic race were held simultaneously in 1982, with results being kept separately for each category.
 At the 2006 edition, Gert Thys was initially disqualified as the winner for a doping infraction. Second placed Jason Mbote of Kenya was considered the winner until 2012, when Thys had his ban overturned by the Court of Arbitration for Sport.

References

List of winners
Seoul International Marathon. Association of Road Racing Statisticians (2010-03-23). Retrieved on 2011-03-19.

External links

Official website

Athletics in South Korea
Marathons in South Korea
Sport in Seoul
Recurring sporting events established in 1931
Spring (season) events in South Korea

redpajama_set_name: RedPajamaWikipedia


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50 Best Crime Movies on Netflix Argentina
Love watching crime movies, look no further. Here's the list of 50 best crime movies on Netflix Argentina right now from classic noirs to high-stakes capers and thrilling modern dramas.
Director(s): Denis Villeneuve
An FBI agent takes part in an undercover operation targeting a Mexican drug lord, but her ethics are challenged when the sting crosses the line.
Actor(s): Benicio Del Toro, Bernardo Saracino, Daniel Kaluuya, Emily Blunt, Jeffrey Donovan, Jon Bernthal, Josh Brolin, Julio Cedillo, Raoul Trujillo, Victor Garber
His marriage crumbling, Nick comes home one day to find his wife has vanished. And as the police turn up the heat, shocking truths come to light.
Actor(s): Ben Affleck, Boyd Holbrook, Carrie Coon, Casey Wilson, David Clennon, Emily Ratajkowski, Kim Dickens, Lisa Banes, Lola Kirke, Missi Pyle, Neil Patrick Harris, Patrick Fugit, Rosamund Pike, Sela Ward, Tyler Perry
Director(s): Quentin Tarantino
In the dead of a Wyoming winter, a bounty hunter and his prisoner find shelter in a cabin currently inhabited by a collection of nefarious characters.
Actor(s): Bruce Dern, Channing Tatum, Dana Gourrier, Demian Bichir, James Parks, Jennifer Jason Leigh, Kurt Russell, Michael Madsen, Samuel L. Jackson, Tim Roth, Walton Goggins, Zoe Bell
I Don't Feel at Home in This World Anymore
Director(s): Macon Blair
A timid nursing assistant gets a new lease on life when she and a neighborhood loner track down the degenerates who broke into her house.
Actor(s): Chris Doubek, Christine Woods, David Yow, Devon Graye, Elijah Wood, Gary Anthony Williams, Jane Levy, Jared Roylance, Marilyn Faith Hickey, Melanie Lynskey, Robert Longstreet
Director(s): Coralie Fargeat
A woman left for dead in the desert after a harrowing assault embarks on a brutal and bloody quest for revenge against the three men who betrayed her.
Actor(s): Guillaume Bouchede, Kevin Janssens, Matilda Lutz, Vincent Colombe
A War
Director(s): Tobias Lindholm
When a decision he makes in combat results in the deaths of civilians in Afghanistan, a Danish officer is called home to face war crimes charges.
Actor(s): Alex Hogh Andersen, Charlotte Munck, Dar Salim, Dulfi Al-Jabouri, Pilou Asbaek, Soren Malling, Tuva Novotny
A Man in the Dark
Genre: Crime, Horror, Thriller
Director(s): Fede Alvarez
He's blind and rich the perfect target for robbery. But the tables turn when three teen thieves find themselves in a lethal game of cat and mouse.
Actor(s): Daniel Zovatto, Dylan Minnette, Jane Levy, Sergej Onopko, Stephen Lang
Director(s): Chad Stahelski, David Leitch
When a gangster's son steals his car and kills his dog, fearless ex-hit man John Wick takes on the entire mob to get his revenge.
Actor(s): Alfie Allen, Bridget Moynahan, Dean Winters, Ian McShane, Jason Isaacs, Keanu Reeves, Michael Nyqvist, Omer Barnea, Willem Dafoe
Director(s): Zak Hilditch
A simple yet proud rancher in the year 1922 conspires to murder his wife for financial gain, convincing his teenage son to participate.
Actor(s): Bob Frazer, Brian d'Arcy James, Dylan Schmid, Kaitlyn Bernard, Molly Parker, Neal McDonough, Thomas Jane