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Understanding Brexit-Related Uncertainties Exploration of the Decision Maker Panel Survey Nick Bloom (Stanford University), Phil Bunn (Bank of England), Scarlet Chen (Stanford University), Paul Mizen (University of Nottingham), Pawel Smietanka


  1. Understanding Brexit-Related Uncertainties Exploration of the Decision Maker Panel Survey Nick Bloom (Stanford University), Phil Bunn (Bank of England), Scarlet Chen (Stanford University), Paul Mizen (University of Nottingham), Pawel Smietanka (Bank of England), Greg Thwaites (LSE Centre for Macroeconomics), Garry Young (National Institute of Economic and Social Research) ifo, Munich, December 2018 Disclaimer : Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the Bank of England or its Committees .

  2. EU-UK relations 1969 – 3 rd and successful application for membership in the EC • • 1973 – Entry to the EC • 1974 – Harold Wilson’s (Labour) commitment to renegotiate Britain's terms of membership of the EC • 1975 – National referendum on whether the UK should remain in the European Communities (67.5% voted to stay, 37.5% voted to leave) • 2013 – David Cameron’s (Conservative) promise to hold an EU referendum • 2016 – National referendum on whether UK should remain a member of the EU (48.1% voted to remain, 51.9% voted to leave) • 2016 – Cameron’s resignation as PM, succeeded by Theresa May • 2017 – Invocation of Article 50 to leave by March 2019 What next? 22

  3. Probability of UK leaving the EU was low ahead of the referendum 33

  4. Uncertainty indicators provided conflicting messages since the referendum Standard deviations from average since 1997 EU Referendum 7 6 Policy 5 uncertainty index 4 Stock market volatility 3 Bank principal component index 2 1 0 -1 Macro uncertainty -2 -3 1997 2001 2005 2009 2013 2017 44

  5. Decision Maker Panel – a new survey of UK-based companies – allows the assessment of the impact of Brexit • Decision Maker Panel was launched in August 2016 by the Bank of England, Stanford University and the University of Nottingham. • Used an approach pioneered by the Atlanta Fed (Altig, Barrero, Bloom, Davis, Meyer and Parker, 2018) • In UK, randomly contacted population of 31K UK firms with 10+ employees inviting them to join the monthly Decision Maker Panel • As of October, around 6K have been part of the panel, providing a large sample of timely firm data. 55

  6. Key messages • Brexit has been seen by most firms as large second moment (uncertainty) shock. • Firms with greater exposure to the EU, e.g. through exports, imports, and more EU workers are more heavily affected. • Uncertainties around Brexit are primarily about the impact on businesses over the longer term rather than shorter term. • Brexit-related uncertainty associated with around 1.5% lower employment and 6% less investment • Misallocation could reduce productivity by around 0.5% (likely to be negative effect within firm effects too) 66

  7. By October 2018 obtaining 2.6K responses per month spanning all industries and regions Number of responses 3000 2500 2000 1500 1000 500 0 Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18 May-18 Jun-18 Jul-18 Aug-18 Sep-18 Oct-18 Survey month 77

  8. Respondents are spread across the UK Notes: Data as of October 2018. The map shows location of businesses that have ever responded to the DMP survey since August 2016. The location of a business corresponds to the location of the registered office, hence it does not always match up with the actual location of the business. 88

  9. There is not a strong Brexit-related bias in the survey Leave Remain UK population 51.9 48.1 DMP CFOs (a) 25.0 75.0 BES managers 33.5 66.5 as by work type (b) BES managers 34.3 65.7 as by social grade (b) 0 20 40 60 80 100 Percentage of voters/respondents (a) Personal views of DMP members at the time of the June 2016 referendum taken from February to April 2018 surveys. The question asked respondents about whether they view Brexit in a positive or negative way rather than how they voted in the referendum. (b) To identify managers in the British Election Study by their stated work type, only participants doing professional or higher technical work/higher managerial work that required at least degree-level qualifications or who worked as manager or senior administrator/intermediate managerial/professional (company director, finance manager, etc.) were included. To identify managers in the BES by their stated social grade, only participants who identified themselves as in a higher managerial, administrative and professional or intermediate managerial, administrative and professional occupation were included. Only respondents working and aged 66 or lower for males or aged 60 or lower for females were included. 99

  10. Sampling frame of 31K UK firms with 10+ employees: 20% responded, uncorrelated with Brexit vote share Ever respond to a survey if in the sampling frame (1) (2) (3) (4) Leave vote share -0.022 -0.026 -0.020 -0.018 (0.019) (0.019) (0.019) (0.019) Log of employment 0.017*** 0.011*** 0.011*** (0.002) (0.003) (0.003) Log of sales 0.007*** 0.004 (0.002) (0.003) Log of assets 0.003 (0.002) Observations 29,802 29,802 29,802 29,802 R-squared 0.010 0.013 0.014 0.014 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Data as of October 2018. Two-digit UK SIC industry controls are included in all columns. Dependent variable equals 1 if a firm responded to any wave of the survey between September 2016 and October 2018 and 0 if it is part of the sampling frame but has never completed a survey. Firm characteristics are taken from Bureau van Dijk FAME data and are the latest available observations. ‘Leave vote share’ is the share of vote for leaving the EU in the local authority that a firm is headquartered in. There are 380 local authorities. Robust standard errors are given in parentheses. *** p<0.01, ** p<0.05, * p<0.1. 10 10

  11. The majority of DMP respondents are finance directors or senior managers Percentage of respondents 70 66 60 50 40 30 20 15 10 10 5 4 0 CEO CFO Finance Director Financial Other Controller/ Manager/ Executive Position of DMP respondents Notes : Data as of November 2018. The question asked ‘ Could you tell us the position of the person in your business that typically completes the Decision Maker Panel Survey? ’ 11 11

  12. Data quality looks good – for example, comparing DMP to Company Accounts 14 10 DMP: log(Emp.) in 15Q4 DMP: log(Sales) in 2015 12 8 10 6 4 8 2 6 2 4 6 8 10 6 8 10 12 14 BvD: log(Sales) in 2015 BvD: log(Emp.) in 2015 .4 .4 .3 .3 .2 .2 .1 .1 0 0 0 5 10 15 0 5 10 15 20 ln(Num. of Emp.) ln(Sales) DMP: ln(Emp) in 2015Q4 DMP: ln(Sales) in 2015 BvD: ln(Emp) in 2015 BvD: ln(Turnover) in 2015 12 12

  13. Data quality looks good – for example, comparing uncertainty to forecast errors log of error in realized Log of error in realized Employment Nominal sales employment growth at t 3 4 sales growth at t 3 2 2 1 1 0 0 -.5 .5 1.5 2.5 3.5 -.5 .5 1.5 2.5 3.5 Log of uncertainty in expected Log of uncertainty in expected sales growth at t-1 employment growth at t-1 Log of error in realized Prices 2 price growth at t Note : Uncertainty defined as 1 subjective uncertainty from the DMP 5-bin responses. Forecast 0 errors defined as ABS(forecast - -1 actual) growth over the following -2 12 month period. -1.5 -1 -.5 0 .5 1 1.5 2 Log of uncertainty in expected price growth at t-1 13 13

  14. Data quality looks good – macro aggregates and outturns Sales Percentage change Percentage change Prices on a year earlier on a year earlier 3.5 8 7 3 6 2.5 5 4 2 Past growth (DMP) Past growth (DMP) 3 Expected growth (DMP) 1.5 Expected growth (DMP) 2 CPI rate change over 12m (ONS) Total final expenditure (ONS) 1 1 2016 Q3 2017 Q1 2017 Q3 2018 Q1 2018 Q3 2019 Q1 2016 Q4 2017 Q2 2017 Q4 2018 Q2 2018 Q4 2019 Q2 Reference period Reference period 14 14

  15. Hazard functions reveal no substantial difference between different cohorts 1 .8 Probability .6 .4 .2 0 0 1 2 3 4 5 6 7 8 Quarter after joining the panel 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 Note: Based on panel members who joined between 2016Q3 and 2018Q3. 15 15

  16. Brexit important source of uncertainty for around 50% 50 Aug-Sep 16 Feb-Apr 17 Aug-Oct 17 40 Feb-Apr 18 Aug-Oct 18 Nov 18 30 20 10 0 Not important One of many One of the top 2 or The largest current drivers 3 s Note : The question asked ‘How much has the result of the EU referendum affected the level of uncertainty affecting your business?’. Respondents could select one of the options shown as response categories. 16 16

  17. In recent surveys, uncertainty was highest in wholesale & retail and manufacturing and lowest in human health & social work Wholesale & Retail Manufacturing Accom. & Food Construction Transport & Storage Prof. & Scientific Other Production Average Admin. & Support Real Estate Finance & Insurance Info. & Com. Other Services 0 10 20 30 40 50 60 70 Percentage of firms with Brexit in top 3 uncertainty sources Note : The question asked ‘How much has the result of the EU referendum affected the level of uncertainty affecting your business?’ . Industries’ employment shares are shown in square brackets. DMP data from August to October 2018 surveys. 17 17

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