averages - 25 % - 30 % source : office for national statistics and bank calculations chart 6 : real government investment 4 - quarter growth 2010 - 2016 2017 - 2019 2000 - 2007 4 - quarter growth period averages 60 % 50 % 40 % 30 % 20 % 10 % 0 % - 10 % - 20 % - 30 % - 40 % - 50 % - 60 % source : office for national statistics and bank calculations all speeches are available online at www. bankofengland. co. uk / news / speeches and @ boe _ pressoffice chart 7 : capital services ( d ) 4 - quarter growth 5 % 4 % 2017 - 2019 2 % 2010 - 2016 2000 - 2007 3 % 1 % 0 % - 1 % - 2 % - 3 % 4 - quarter growth - 4 % period averages - 5 % ( d ) volume index of capital services for the market sector source : office for national statistics and bank calculations all speeches are available online at www. bankofengland. co. uk / news / speeches and @ boe _ pressoffice references barrero, j m, bloom, n and davis, s j ( 2020 ), “ why working from home will stick ”, becker friedman institute, working paper, 2020 - 174 bloom, n, bunn, p, mizen, p, smietanka, p and thwaites g ( 2020 ), “ the impact of covid - 19 on productivity ”, bank of england staff working paper no. 900 broadbent, b ( 2012 ), “ productivity and the allocation of resources ” chartered institute of personnel and development ( 2020 ), “ home working set to more than double compared to pre - pandemic levels once crisis is over ” cominetti, n, henehan, k, slaughter, h and thwaites, g ( 2021 ), “ long covid in the labour market : the impact on the labour market of covid - 19 a year into the crisis, and how to secure a strong recovery ”, resolution foundation, february 2021 decision maker panel ( 2021 ), panel data – february 2021 felstead, a and reuschke, r ( 2020 ), “ homeworking in the uk : before and during the 2020 lockdown ”, wiserd report, cardiff : wales institute of social and economic research haldane, a ( 2020 ), “ is home working good for you? ” haldane, a ( 2021 ), “ inflation :

in breach of regulations or illegal. the model risk working party of the institute and faculty of actuaries have published two good papers on model risk. 5 the second one6 relates the case of the loss of the mars climate orbiter in 1998 as an example supervisory guidance on model risk management, sr letter 11 - 7, federal reserve board and office of comptroller of the currency, april 4 2011 https : / / www. actuaries. org. uk / practice - areas / risk - management / disbanded - research - working - parties / model - risk all speeches are available online at www. bankofengland. co. uk / publications / pages / speeches / default. aspx of flawed model design. the trajectory of the spacecraft had been incorrectly calculated, which meant that the spacecraft had actually been orbiting much closer to mars than had been targeted causing the space probe to disintegrate in the planet ’ s atmosphere. the problem was as basic as the use of incorrect units in part of the navigation software. an external contractor had produced software to provide thruster performance data in english units of pound - seconds. the navigation team at nasa ’ s jet propulsion laboratory mistook this data as being in the required metric units of newton - seconds. this led to errors in the spacecraft ’ s trajectory calculations. one of the root causes was nasa ’ s adoption of a ‘ faster, better cheaper ’ philosophy that had not given sufficient prominence to risk management. it had also not carried out an independent, end - to - end validation. finally, developers had had an insufficient sense of ownership of the whole project as opposed to their bit of it. the second source of model risk is misuse of models. this can arise when management take decisions based on the outputs of a model without sufficiently understanding the uncertainty around those outputs. complicated, dynamic problems are more difficult to model than simple, stable problems. model outputs are therefore more uncertain for problems that are more complex and where the underlying process in the real world is changing. calculating the capital requirements of an insurer is surely a prime example. the ifoa model risk working party has an interesting typology of corporate cultures according to their concern for model uncertainty and the legitimacy of modelling. although they explain that all are valid viewpoints, i would feel more comfortable if insurers fell into the category of conscientious modellers, with modelling having high legitimacy but management also having a high concern for model uncertainty. ( see chart 3 )