Information and Financialization: Credit Markets as a New Source of Inequality

• Author: Torben Iversen,Philipp Rehm
• DOI: http://doi.org/10.1177/00104140221074286
• Published date: 01 December 2022
• Date: 01 December 2022
• Subject Matter: Articles

Article

Comparative Political Studies

2022, Vol. 55(14) 2349–2381

© The Author(s) 2022

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DOI: 10.1177/00104140221074286

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Information and

Financialization: Credit

Markets as a New

Source of Inequality

Torben Iversen

1

and Philipp Rehm

2



Abstract

Driven by f‌inancialization and rising demand for credit, household sector debt

in OECD countries has risen sharply. We argue that this rise in private debt

has become a signif‌icant driver of inequality because access to, and the terms

of, credit vary by the risk of default, which is closely tied to income. The effect

is magnif‌ied by a trove of new data that allow lenders to more accurately

assess individual risks, thereby linking interest rates more closely to the

underlying risk distribution. This inequalizing logic is conditioned by social

transfers and by government regulation of f‌inancial markets. We test our

model with data on mortgage interest rates and access to credit, using the

government takeover of Fannie Mae and Freddie Mac (FM/FM) in the United

States (resulting in regulatory change) and the Hartz-IV reform in Germany

(resulting in changes to social transfers) as exogenous changes in important

parameters of our model.

Keywords

Political economy, social welfare programs, Big Data, credit, lending,

f‌inancialization, income inequality, home ownership, f‌inancial regulation,

welfare state, Freddie Mac, Fannie May

1

Department of Government, Harvard University, Cambridge, MA, USA

2

Department of Political Science, Ohio State University, Columbus, OH, USA

Corresponding Author:

Philipp Rehm, Department of Political Science, Ohio State University, 154 N. Oval Mall, 2140

Derby Hall, Columbus, OH 43210, USA.

Email: rehm.16@osu.edu

Introduction

1

At the turn of the last century, banking was personal. Banks made lending

decisions based on personal knowledge of borrowers; a fact that made credit

often haphazard and not infrequently biased toward friends and family, and

against minorities. The small-town banker and horse trader David Harum, the

main character in Edward Noyes Westcott’s 1898 novel by the same name,

described his approach to lending in the 1932 movie adaptation of the novel

(played by Will Rogers): “I go a long way on a man’s character. And then I go

a longer way on his collateral. And if he’s got character and collateral both, I

let him have about half what he asked for …anybody can get along on half of

what they think they can.”

The use of information has come a long way since then, but the objective is

the same: separate good risks from bad and lend to the former on the best

possible terms (for the bank). The massive improvement in data, a large

expansion of risk-sharing f‌inancial instruments, securitization, and a huge

increase in demand have resulted in loans and credit to the household sector

expanding exponentially (Figure 1). In less than 25 years, from 1995 to 2019,

private debt in advanced democracies increased from an average of 90% to

about 150% of disposable income (with some notable exceptions), extending

a trend that started in the 1980s in the United States and the United Kingdom

(the rise in consumer lending mostly occurred earlier in these countries). A

growing portion of personal income now goes to servicing debt, and this has a

sizable effect on discretionary income. With an average interest rate of 10%, it

would amount to 15% of disposable income, but obviously with huge var-

iation across countries, time, and individuals.

Moreover, access to credit has become an important determinant of in-

dividual welfare in a new economy where credit is used to smooth income

across increasingly nonlinear life-cycles, with frequent changes in jobs, time

off for retraining and additional schooling, and moves back and forth between

work and family (Iversen & Soskice, 2019, Chapter 4; Wiedemann, 2021).

Owning a home has also become a marker of middle-class success in many

countries, and access to mortgage f‌inance is therefore increasingly seen as an

important tool for aspirational voters. For all these reasons, access to credit

and the cost of such access are emerging as important determinants of in-

dividual welfare. This paper explores the consequences of f‌inancialization of

the household sector for economic inequality.

Specif‌ically, we argue that credit has become a signif‌icant—and largely

overlooked—driver of inequality. This is because terms of access to credit

vary with individual risks of default, which is tied to socio-economic status.

Risk assessments in turn depend on individual data on the likelihood of

experiencing catastrophic life events—signif‌icant loss of income due to

unemployment, illness, or involuntary job switches—and ability to f‌inancially

2350 Comparative Political Studies 55(14)

weather such events. Such data have been greatly facilitated by the infor-

mation revolution. “Big Data”combine information disclosed by borrowers

with a trove of data on residence, demographic indicators, credit history,

income, employment history, and so on, which are often kept in central credit

registries. As Hurley and Adebayo (2017, p. 151) note, the credit-scoring

industry takes an “all data is credit data”approach, “combining conventional

credit information with thousands of data points mined from consumers’

off‌line and online activities.”

As the data available to lenders improve, they can make more differentiated

risk-of-default assessments, which means that interest rates increasingly re-

f‌lect the underlying risk distribution. As interest payments come out of

disposable income, and insofar as disposable income is negatively correlated

with default risk, the distribution of discretionary income (which is disposable

income net of debt service) becomes more unequal. Those deemed too risky

will not qualify for large loans (such as home mortgages) in the f‌irst place, and

Figure 1. Household debt as a percentage of disposable income. Note: Second data

point refers to 2018 in JPN, NOR, USA, and 2020 in CAN. Source: OECD National

Accounts Statistics: National Accounts at a Glance (https://doi.org/10.1787/f03b6469-en,

last accessed June 3, 2021 [https://perma.cc/HE5R-NR7X]).

Iversen and Rehm 2351