{"id":1681,"date":"2019-04-24T11:17:50","date_gmt":"2019-04-24T10:17:50","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=1681"},"modified":"2019-12-02T16:57:14","modified_gmt":"2019-12-02T16:57:14","slug":"gpg-yoy-trends-unilever-2","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/gpg-yoy-trends-unilever-2\/","title":{"rendered":"Pay Gap Case Study #2 – Year on year trends, the good, bad and Unilever"},"content":{"rendered":"

Welcome to my next case study where I look at the pay gap figures of Unilever Ltd.\u00a0 Unilever turn out to be a very interesting case study for analysing year on year changes in their published statistics.\u00a0 In this case I will be looking at the changes between 2017 and 2018 for the two Unilever business units that have submitted GPG data which are:-<\/p>\n

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  1. Unilever UK Ltd<\/strong><\/span><\/a><\/li>\n
  2. Unilever UK Central Resources Ltd<\/strong><\/span><\/a><\/li>\n<\/ol>\n

    Clicking on those links will take you to the government’s gender pay gap website where you can see their published figures.\u00a0 For this post, I will be using my own spreadsheet which you can download for yourselves here<\/a>.<\/p>\n

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    Why I write these case studies<\/strong><\/span><\/h4>\n

    All my case studies are intended to illustrate good and bad practice with gender pay gap statistics and to see what we can learn from them.\u00a0 It must be borne in mind that whilst we want to see the pay gap close at a national level, that can only happen if change happens within individual organisations in the first place.\u00a0 In the front line of such change will be the HR department who in my experience find basic statistics a struggle and so I hope these case studies will be illuminating.<\/p>\n

    My first case study was for my former employer Mars UK Ltd<\/strong><\/span>.\u00a0 In “Life on Mars<\/a>“, I worked out how much variation in the published gender pay gaps could be expected by chance.\u00a0 I showed that the variation in pay gaps across their 4 UK subsidiaries was within the bounds of chance and that in effect they had no gender pay gap.\u00a0 Based on my experience of working there, I explored some of the reasons why Mars did not have a pay gap.\u00a0 Mars and Unilever do compete in some markets so there should be some similarities between the two businesses.<\/p>\n

    A point that needs to be raised now is that Unilever, like Mars, is a global business with employees in many countries.\u00a0 The reported GPG data is for UK employees only and are published separately for the two business units.\u00a0 There is currently no requirement for organisations to report a single national set of figures (the group level) in addition to their figures for each subsidiary.\u00a0 They can do so if they wish but they are not required to do so.\u00a0 In Unilever’s case, they have not reported group level to the government’s website but they have reported group level data in both their 2017 narrative<\/a> & 2018 narrative<\/a>.\u00a0 In this article, I will be concentrating on the two business units only starting with Unilever UK Ltd.<\/p>\n

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    Unilever UK Ltd in 2018<\/strong><\/span><\/h4>\n

    I want to start by explaining what is being shown in the graphic below.\u00a0 This will be useful if you intend to use my spreadsheet to explore other employers<\/a>.\u00a0 If you use the dropdown box in the top left corner, you can scroll down and select Unilever UK Ltd to get this display.\u00a0 Note that more data is shown in the spreadsheet but what is shown in the graphic is just the top half of the screen.<\/p>\n

    \"\"<\/p>\n

    The first chart on the left shows how much more or less the median woman earns compared to the median man, assuming the median man earns \u00a31.\u00a0 Unilever UK pay their median woman 3p more than the median man i.e. \u00a31.03.\u00a0 This figure is repeated in the headings of the gender breakdown chart in the middle and in the table to the right.\u00a0 The chart on the left also shows that of those receiving a bonus (which is nearly all employees), the median woman gets a bonus of \u00a31.35 assuming the median man gets a bonus of \u00a31.<\/p>\n

    The chart in the middle shows the gender breakdown of the 4 income quartiles (strictly speaking quarters but I will stick with the government definition).\u00a0 Given that in each quartile, women make up similar proportions (40-44%) of the workforce, it is no surprise the median pay gap is so small since the FIQG (Female Income Quartile Gap) is only +10% (See point 4 of this article for why FIQG is a useful statistic<\/a>).<\/p>\n

    The tables to the right compare your chosen year with the previous year (assuming the employer has published data in both years).\u00a0 Both the median pay gap and the FIQG have widened a little but as I showed in “Life on Mars<\/a>“, pay gaps of +\/-5% are entirely normal with a non-discriminating organisation due to the laws of chance.\u00a0 The same criteria applies to year on year changes and these small changes are completely unremarkable and not worthy of comment.<\/p>\n

    What is definitely worthy of extensive comment is the 13 percentage point change in the gender balance with women now making up 43% of the workforce compared to 30% only 12 months earlier.\u00a0 Stop and think about what this indicates.\u00a0 Unilever UK Ltd have put themselves in the 1000-4999 employee bracket and their own narrative is not forthcoming on the exact numbers so let’s assume they have 2,000 employees.\u00a0 The 13 point change means the number of women has gone up from 600 in 2017 to 860 in 2018 whilst the number of men has gone down from 1400 to 1140.\u00a0 So 13% of the workforce has changed and more than that, the 260 that left were all men and the replacements were all women?!\u00a0 Straightaway this should be ringing alarm bells as being implausible.\"\"<\/p>\n

    When I first saw this, I decided to sense check by looking at the change in gender balance across all 10k+ employers reporting gender pay gaps.\u00a0 The table here shows that just over 9000 only saw changes of +\/- 5 percentage points and only 295 had changes of +5% to +15% which is the band that Unilever UK Ltd are in.\u00a0 Statistical theory tells us that the expected change in % female should be dependent on the number of employees so I plotted the two charts below as well.\u00a0 These show the standard deviation of the reported year on year change in the %workforce that is female against the employer size bracket.\u00a0 The larger the employer, the smaller the standard deviation and indeed the relationship is a linear one if we correlate the standard deviation with the natural logarithm of the employer size as shown by the right hand chart.<\/p>\n

    \"\"<\/p>\n

    There is a widely used branch of statistics known as SPC or Statistical Process Control<\/a> which is the basis of many quality control processes.\u00a0 I won’t go into the mathematics here but a very common criteria for flagging unusual data (known as an “action limit”) is if the observed value is greater than three times the expected standard deviation.\u00a0 If I use the fitted line above for the 1000-4999 size bracket, I find the expected standard deviation is 3.0% and therefore the criteria for flagging unusual year on year changes in the % of employees that are female is 9% (= 3 x 3%).\u00a0 This is the figure shown in the MAX column on the extreme right of the Unilever UK Summary screenshot above.\u00a0 Since Unilever saw a 13 point change, it is flagged as “Definite<\/em>” in terms of unusual data and therefore requires investigation.<\/p>\n

    Unfortunately their own narrative in 2018 is unrevealing<\/a> on this point so we need to consider possible reasons why this has occurred.\u00a0 Before I do so, let’s look at Unilever’s other business unit Unilever UK Central Resources Ltd.<\/p>\n

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    Unilever UK Central Resources Ltd in 2018<\/strong><\/span><\/h4>\n

    What we see here is very interesting indeed!\u00a0 The %female figure has dropped by 8 points from 55% to 48% (rounded) and is now flagged as being a “Possible” in terms of unusual data.\u00a0 SPC often uses a second criteria known as a “warning limit” which occurs is the observed change is greater than two times the expected standard deviation.\u00a0 In this case, this would be 6% and the observed change here is greater than this.<\/p>\n

    \"\"<\/p>\n

    I also want to point out the control limits being used for each income quartile.\u00a0 At the moment, I have set the MAX column for each quartile to be twice the value for the year on year change in the overall %Female hence the 18% shown (this is a straight application of the central limit theorem that says if the sample size is quartered, the standard error is doubled).\u00a0 This flags the lower income quartile as definitely having an unusual change with the %employees that are women in this quartile falling from 63% to 41% which again should set alarm bells ringing.\u00a0 Indeed the overall pattern for this division is that the lower the income quartile, the greater the reduction in numbers of women.<\/p>\n

    Unlike Unilever UK Ltd, the Central Resources unit is also being flagged as definitely unusual for the 18p year on year change in the median woman’s earnings from 85p to \u00a31.03 (assuming the median man is earning \u00a31).\u00a0 The maximum expected change is 15p which is also equal to three times the expected standard deviation of 5p. \"\" This time, the expected standard deviation is dependent on employer size and gender balance as shown in the chart here.\u00a0 This looks more complicated but the essential relationship is that the expected standard deviation falls as the employer size grows and is lowest when the employer is gender balanced and highest when the employer is gender dominant.\u00a0 I have built a statistical model based on this chart and it is this statistical model that generates the expected standard deviations for the year on year change in median woman’s earnings.<\/p>\n

    So the Central Resources unit has seen the opposite trend in gender balance to the UK unit.\u00a0 At the same time, the Central Resources unit no longer has a gender pay gap and the UK unit continues to have no pay gap.\u00a0 All changes here exceed what could be expected using the principles of SPC so what could explain this?<\/p>\n

    Companies are not static creatures, they grow and decline, merge and split and restructure.\u00a0 Some of the larger changes could certainly be due to Unilever acquiring a business that happens to female dominant but there is no mention of this in their narrative.\u00a0 What I think has happened here is that Unilever has transferred a large number of employees from one business unit to the other business unit.\u00a0 If this has happened, the employees transferring appear to be gender dominant.\u00a0 Either a large group of men has moved from the UK unit to the Central Resources unit or a large group of women has transferred from the Central Resources unit to the UK unit.\u00a0 Whatever the reason, Unilever really should be explaining why in their narrative but as I said earlier, their 2018 narrative makes no mention of these large year on year changes.<\/p>\n

    In June 2019, I emailed Unilever to ask for a comment on what I have written here.\u00a0 This is the reply I received from them.<\/p>\n

    “When assessing year-on-year changes, we think it\u2019s more helpful to look at the overall picture than focussing on specific figures for the individual entities.\u00a0 This is, in part, because results for our individual entities are driven not just by pay, but by changes in our workforce demographics, and over the last year we have seen a number of re-organisations as well as people leaving the business and employees moving between our two registered entities. As a result, over the last year we have seen some changes in the data collected from Unilever UK Ltd. and Unilever CR Ltd. <\/em>The companies now have a more similar workforce profile, which is reflected in the GPG figures for 2018 and helps to explain what otherwise appear to be year-on-year differences in the data.”<\/em><\/p>\n

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    Other unusual trends & figures detected with SPC<\/strong><\/span><\/h4>\n

    There are two other statistics that my spreadsheet looks at and will flag as being definitely unusual if the observed value is greater than three times the expected standard deviation.<\/p>\n

    The first one is the FIQG (Female Income Quartile Gap).\u00a0 This is closely related to the median woman’s earnings but increasingly I am coming to the view that it is a superior statistic.\u00a0 I plan to \"\"publish an article at some point on why I think this but for now I am seeking to flag organisations where the year on year change in FIQG is high.\u00a0 Again it turns out that the standard deviation of the year on year change in FIQG is dependent on employer size and %female but the direction of the curves are different this time.\u00a0 The standard deviation falls as the size of the employer increases but this time, it falls further if the employer is gender dominant and rises if the employer is gender balanced.\u00a0 Again I built a statistical model based on the observed to create the expected standard deviations.<\/p>\n

    The second one is not a year on year trend but is the difference between the FIQG and the median gender pay gap.\u00a0 Last year, I pointed out that a large difference between these two numbers was not mathematically possible<\/a> and indicated incorrect data.\u00a0 Since then I have realised that there is a specific scenario when this rule can be violated and I will write a separate case study about this but the key realisation is that the relationship is not 2-way.\u00a0 Specifically,\"\"<\/p>\n