The Goldin specification is:  (5) ln  ∝  Where  ∗  ∗  =  1 0  female male  We regress the natural log of average gross hourly pay on a binary female coefficient and a set of controls, wider than the ones used in Goldin (2014). The set of controls comprises both individual and job-specific characteristics. It includes: age, female-age group interactions, usual hours worked, public or private sector, full time or part time, contract type, whether born in the UK, has a child under 2, female and child under 2  26  All speeches are available online at www.bankofengland.co.uk/speeches  26  interaction, occupation, female and occupation interaction, educational qualifications, tenure, region of home and sector (Annex Table 1A).11 Once we take account of these factors, Table 2 suggests that there was, on average, a conditional gender pay gap of just under -15% between 1994 and 2019.12 This gender pay gap is highly statistically significant. To understand how it has changed over time, we split the sample into five periods (1994-1998, 1999-2003, 2004-2008, 2009-2013, 2014-2019) and re-estimate (5). As Chart 22 shows, the gender pay gap has fallen over time, though it remains around -10% and is statistically significant by the end. Table 2: Gender pay gap for specification (5)  Female Observations  Coefficient  Pay gap, per cent  Number of observations for females  -0.159***  -15%  291,859  (-11.17) 537,177  Source: ONS Labour Force Survey and Bank of England calculations.
Chart 20: Ethnicity pay gap Oaxaca-Blinder decomposition, sample average  Per cent  15  10  Unaccounted for  5  0  ‐5 Sex  Qualification  Sector  Age  Tenure  Region  Occupation  Public sector  FT/PT  Job type  Total  Accounted for  ‐10  ‐15 Total gap  Source: ONS Labour Force Survey and Bank of England calculations. Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts. ‘Job type’ captures if the job is temporary or permanent. Looking over time (Chart 21), the unconditional ethnicity pay gap was broadly stable from the mid-1990s until 2015, since when it has declined. That decline can to a large extent be explained by the rise in the relative educational performance of ethnic minorities which, by the end of the sample, is subtracting 2 percentage points from the pay gap. By contrast, the unexplained ethnicity pay gap has been largely unchanged over the sample. Not only is the unexplained (“pay bias”) part of the ethnicity pay gap as large as for gender; it appears also to have been more persistent and has shown fewer signs of falling over time. The ethnicity pay gap problem in the UK is every bit as acute as the gender pay gap problem, contrary to the message from the raw pay gap data.