Credit data: firms with fewer well-paid women are riskier

David Carruthers
04 May 2018

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A burgeoning area of research in the past two decades has been the relationship between gender balance and corporate performance. Studies have variously found that companies with more women in their senior management ranks outperform – but only if the firm’s strategy is focused on innovation; that replacing a senior man with a senior woman produces an uplift of around 8–13 basis points in return on assets, and that greater gender diversity at board level results in improved corporate governance.

Now it appears there may be a relationship between diversity and default risk.

When UK companies’ recent disclosures on their gender pay gaps are matched up with bank probability of default (PD) estimates, it reveals that companies with the highest proportion of men in their top pay quartile are riskier than those with more balance in their top earners.

It’s not close. For the 15 companies where the top quartile pay band is more than 50% female, bank PDs translate into a median rating of a+. The 35 companies with a very high (>80%) proportion of males in their top quartile pay band have the highest credit risk, with a median of bbb. What’s more, there is a neat curve between those two groups.

It’s difficult to explain the relationship precisely, but credit risk modellers may have a new proxy to explore.

Elsewhere in this month’s data, we look at the recent trends in US retail and US merchant generation. We also analyse the risks of a consumer backlash for big tech companies.

Global credit industry trends

Figure 1 shows industry migration trends for the most recent published data.

Figure 1 shows:

In global corporates, consensus upgrades outnumber downgrades. Upgrades dominate downgrades in four out of nine industries. Three industries are dominated by downgrades and two are balanced.
Overall, 4% of the global corporates obligors have improved and 3% show a deterioration. Compared with the previous month, the imbalance favouring upgrades has increased.
Basic materials and oil and gas show a balance in favour of upgrades, continuing their recent patterns.
Technology is now tending towards upgrades after a few months of mainly downgrades.
Consumer goods has shown no clear trend for a number of months, but upgrades currently outweigh downgrades.
Healthcare has returned to its recent pattern of downgrades outnumbering upgrades.
Utilities show a balance of downgrades outweighing upgrades for the second month in a row.
Telecommunications are now biased towards downgrades after three months that were dominated by upgrades.
Consumer services and industrials are close to balanced.

US general retail

US retail sales have continued to decline; February was the third month in a row to show a drop as consumers cut back on big-ticket purchases. Retailers have also signalled that US president Donald Trump’s tariffs – aimed at China – may hit their business by driving up their import costs. Figure 2 provides an update on recent trends for the US general retail subsectors.

Figure 2 shows:

The left-hand chart shows specialised consumer services with the highest average credit quality and apparel retailers as the lowest. The rank order of these subsectors has not changed since February 2017.
Apparel retailers were downgraded from bb- to b+ during the last year.
The right-hand chart shows a significant deterioration for apparel retailers in the first half of 2017, but a stabilisation in the second half of the year. Average credit risk increased by nearly 30% between January 2017 and February 2018.
Broadline retailers continue to deteriorate; credit risk increased sharply by 7% in December 2017, and the cumulative increase since January 2017 is 17%.
Specialty retailers have only shown a slight (4%) deterioration.
Specialised consumer services is the only subsector where credit quality has improved since January 2017.

US conventional electricity generation

The conventional electricity generation sector in the US continues to face major challenges. The main issue is that US electricity demand growth has been flat for 10 years. This is not just because of technology-led efficiency; it is also because GDP growth is increasingly driven by electricity-light service industries. The market share for conventional power is also shrinking because renewable energy is increasingly affordable and natural gas supplies are growing. Legislation and regulation have also not been favourable for this sector; there have been calls for a complete overhaul of the utility pricing model that originally assumed ever-growing electricity demand.

Figure 3 shows cyclical changes in the credit consensus of a subset of the largest US companies in the conventional electricity generation sector.

Figure 3 shows:

In conventional electricity generation, credit risk changes are highly cyclical.
The left-hand chart shows that deterioration dominated in 2016 and in the first half of 2017, but the second half of 2017 favoured improvements. Recent data has been trending towards deterioration again.
The cumulative effect plotted in the right-hand chart is close to zero. The cumulative index recovered to its initial value after strongly negative period; recently the index has stabilised and a future reversal in trend is a strong possibility.

Big tech

Big tech is having a rough time in 2018. After significant 2017 stock market gains, the recent so-called “tech-lash” against Silicon Valley has seen senators and MPs lining up to grill Facebook CEO Mark Zuckerberg, and President Trump flaming Amazon for alleged abuse of the US Postal Service.

The privacy and anti-trust issues raised have also focused public concerns on Google (Alphabet), Apple and new tech in general; tougher regulation may be on the horizon. In this environment some commentators have pointed to “old tech” (Intel, IBM, Microsoft, etc.) and even traditional, non-tech companies as safe havens for investors.

Figure 4 compares current credit risk for the big four or “GAFA” (Google, Apple, Facebook and Amazon) companies and the constituents of the Dow Jones Industrial index, excluding Apple.

Figure 4 shows:

The four GAFA giants have a healthy average Credit Benchmark consensus of aa–.
The consensus for the Dow Jones, excluding Apple, is very close to a. Including Apple, the Dow Jones has an average Credit Benchmark consensus of a+.
There is a significant credit gap between the GAFA companies and the Dow – if the backlash continues and increased regulation is rolled out, this gap has scope to narrow.

Credit risk vs gender pay gap

Companies increasingly face a legal requirement to report their gender pay gap. Recent disclosures from more than 10,000 UK companies showed UK businesses currently pay more to men than to women with an average median difference of almost 12%. The average male/female percentage split in each company is close to 50/50, but the top quartile is 61% male. This is a global issue: in the US, the Congress Joint Economic Committee reports that women earn 79 cents for every dollar paid to men.

Figure 5 plots credit risk ranges for bank-sourced data across 128 companies in the UK financial sector.

Figure 5 shows:

There is an apparent relationship between gender-based pay measures and credit risk.
The 15 companies where the top quartile pay band is more than 50% female show significantly lower credit risk, with a median a+. The 35 companies with a very high (>80%) proportion of males in their top quartile pay band have the highest credit risk with a median of bbb.
The critical threshold seems to be 50–60%; it has a very large interquartile range and a median of a–. Where males represent more than 60% of the top quartile pay band, the median ratings are bbb+ or bbb, and the ranges are narrower.
This relationship obviously reflects a complex set of factors, including the employee age distribution. But it suggests the gender pay gap may provide a partial proxy for credit risk in UK financials. It is worth noting that this relationship also seems to hold in modified form in parts of the UK corporate sector. The gender pay gap could be a useful additional input to credit risk modelling.

About this data

The Credit Benchmark dataset is based on internally modelled credit ratings from a pool of contributor banks. These are mapped into a standardised 21-bucket ratings scale, so downgrades and upgrades can be tracked on a monthly basis. Obligors are only included where ratings have been contributed by at least three different banks, yielding a total dataset of roughly 15,000 names, which is growing by 5% per month.

David Carruthers is head of research at Credit Benchmark.