Main Problems Presented by the UK Statistics on Incomes and Well-being

Main Problems Presented by the UK Statistics on Incomes and Well-being

1. Definition

The statistics on the UK’s income and well-being have unreliability and ambiguity in the definition of essential terms and display summary statistics for straightforward interpretation by the people who have little or no knowledge of statistics. The statistics on the UK’s income fails to define terms like disposal income and income statistics clearly. It works on the assumption that everyone that explores the data has a prior understanding of the words. Therefore, the lack of comprehension on how to display the income statistics is the leading cause of the problems. Statistics on well-being only give the abridged version of summary statistics and fail to provide a clear understanding of concepts.

1. Classification

The statistics on the UK’s income and well-being has an equivocal addition of valuable information to the data and lacks appropriate classification, which makes the interpretation of the data onerous. The statistics are not provided in great detail as the data provided is generalised. Generalised data does not give a clear presentation and interpretation of the data. The improper classification in the UK’s income and well-being statistics is evident through generalising income. Lack of proper classification does provide comprehensible data on the rudimentary statistics (Altman et al. 1983).

1. Accuracy

The statistics on the UK’s income and well-being does not provide adequate details concerning the data acquired. The data provides only summarized statistics, which is evidence of inaccuracy in the statistics presented on the UK’s income and well-being. A broad approach should be employed to explain the essential features of the data that constitutes the statistics of the data. Lack of addition of vital data makes the presentation of statistics on the UK’s income and well-being inaccurate. You cannot rely on inaccurate data. Therefore, the statistics on the UK’s income cannot be used to make economic decisions.

1. Bias

The statistics on the UK’s income and well-being are statistically biased. There are human and coverage bias. Human bias is introduced when certain features of the data are treated with partiality compared to others. The statistics on the UK’s income uses only private household. It is an example of bias because to believe that only individual households are instrumental in the immense growth of the economy apart from communal homes is a statistical bias (Ons.gov.uk, 2019). Lack of clear presentation of the data introduces coverage bias, and this is brought by the use of estimates on essential aspects of the data. The statistical bias can be submitted by inaccuracy in the presentation of vital statistics of the data. For example, when the descriptive statistics are wrongly presented, there is inaccuracy, which leads to statistical bias. Statistically biased data cannot be relied upon to make sound judgments (Mirkin, 2019).

1. Validity

The generalisation of the vital statistics and lack of extensive and exhaustive data makes the presentation of the statistics invalid. Summarisation of the statistics interprets critical information as a problem hence the introduction of validity problems. Validity problems are introduced when there are no legal institutions to asses and validate the documents presented about the collected data, and to supervise the process adopted to collect data. When the collected data is not well specified, and essential details lack, it also creates validity problems (Summers, 2019). The gaps in the collected data interpret the data difficult, and therefore, critical conclusions cannot be drawn to deduce recommendations. The statistics on the UK’s income and well-being has a lot of gaps in the collected data and lacks the exhaustive and extensive information on the data. Therefore, they cannot be used to make economic decisions.

Discussion of the Statement

1. Modification of the Statement

The statement is, ‘Because the growth in average earnings in the UK has been over three times the increase in consumer prices over the 1947-2017 period people are now over three times better off than they were just after the Second World War.’ The statement given cannot be used to analyse the data that we are going to use statistically. We should often check the accuracy of the statement before any statistical analysis (Birt et al., .2016). We shall, therefore, use the statement; there has been growth in average earnings and consumer prices in the UK since the end of the Second World War.

1. Finding out Some Facts

The data to be used will be from the Office of National Statistics on average weekly earnings in Great Britain. We shall use the data of seasonally adjusted Average Weekly Earnings (AWE), and seasonally adjusted Average Weekly Earnings (adjusted for consumer price inflation). The data on seasonally adjusted Average Weekly Earnings shows the total pay, bonus pay, and regular pay series for the nine sectors (“whole economy,” “private sector,” “public sector,” “services,” “finance and business services,” “public sector excluding financial services,” “manufacturing,” “construction” and “wholesaling, retailing, hotels & restaurants”). For each sector, three measures are presented. The level of earnings (in pounds per week), the percentage change between the current month and the same month in the previous year, and the percentage change between the three months ending in the current month and the same three months in the last year. The data on seasonally adjusted Average Weekly Earnings (adjusted for consumer price inflation) shows Average Weekly earnings for the whole economy, for both total and regular pay, in real terms (adjusted for consumer price inflation). The Average Weekly Earnings is calculated as unadjusted Average Weekly Earnings divided by the consumer price index, including the owner’s occupier’s housing costs. The owner’s occupiers’ housing costs are used as a measure of consumer price inflation.

The statistics in Average Weekly Earnings are of good quality. Estimates are available for both total pays (which includes bonus payments) and for regular pay (which excludes bonuses). Forecasts are available in both nominal terms (not adjusted for inflation) and real terms (adjusted for inflation) (Office for National Statistics, 2019). Many variables are accurately measured in the dataset. The information on wages and salaries is distributed monthly to around 9,000 employers covering approximately 12.8 million employees (Office for National Statistics, 2019). There is no statistical bias. Therefore, the data is reliable, valid, and can be used to make economic conclusions.

1. Explaining one Set of Facts by Another.

The following line graph shows the Average Weekly Earnings in the UK, including bonuses for the whole economy. The earnings estimates are not just a measure of pay rises as they also reflect changes in the number of paid hours worked and changes in the structure of the workforce; for example, more high-paid jobs would have an upward effect on earnings growth rates (Office of National Statistics, 2019).

From the line graph above, there is an upward trend in the Average Weekly Earnings. Therefore, the Earnings for employees are continually growing.

The mean from the descriptive table is 429.0072, maximum and minimum Average Weekly Earnings are 542.1196 and 300.4836, respectively.

The following is a line graph for seasonally adjusted Average Weekly Earnings including bonuses and excluding arrears (adjusted for consumer price inflation) for the whole economy, private sector, public sector, services, finances and business services, and public sector excluding financial services.

The descriptive statistics of seasonally adjusted Average Weekly Earnings (adjusted for consumer price inflation) for the whole economy is;

The mean is 136.872. The maximum and minimum earnings are 172.9598 and 95.86737, respectively.

From the above line graphs and descriptive statistics tables, we can deduce that an increase in the Average Weekly Earnings is associated with an increase in the consumer price. Therefore, there is an increase in average earnings and consumer prices in the UK.

Altman, D.G., Gore, S.M., Gardner, M.J. and Pocock, S.J., 1983. Statistical guidelines for contributors to medical journals. British medical journal (Clinical research ed.), 286(6376), p.1489.

Birt, L., Scott, S., Cavers, D., Campbell, C., and Walter, F., 2016. Member checking: a tool to enhance trustworthiness or merely a nod to validation?. Qualitative health research, 26(13), pp.1802-1811.

Hinton, P.R., 2014. Statistics explained. Routledge.

Kirby, S., Carreras, O., Meaning, J., Piggott, R., and Warren, J., 2015. Prospects for the UK Economy. National Institute Economic Review, 231(1), pp.F49-F75.

Mirkin, B., 2019. Core Data Analysis: Summarization, Correlation, and Visualization. Springer International Publishing.

Office for National Statistics, 2019. Average Weekly Earnings in Great Britain. [Online] [updated 15 Oct. 2019] Available at : https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/averageweeklyearningsingreatbritain/october2019 [Accessed 10 Dec. 2019]

Ons.gov.uk. (2019). Average household income, UK – Office for National Statistics. [online][updated 15 Oct.2019] Available at: https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/bulletins/householddisposableincomeandinequality/financialyearending2019provisional [Accessed 7 Dec. 2019].

Ons.gov.uk.,2019. Well-being – Office for National Statistics. [Online] [updated 15 Oct. 2019] Available at: https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing [Accessed 10 Dec. 2019].

Summers, J.O., 2019. Guidelines for conducting research and publishing in marketing: From conceptualization through the review process. In How to Get Published in the Best Marketing Journals. Edward Elgar Publishing.

Tinkler, L., 2015. The Office for National Statistics experience of collecting and measuring subjective well-being. Statistics in Transition. New Series, 16(3), pp.373-396.