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Why and when do people need credit? Answers to these two questions can help us better understand household demand for loanable funds. Most spending on goods and services in the U.S. is done by households. Household Consumption expenditures make up nearly 70% of GDP, while Business Investment spending is less than 20% of GDP. (2022: BEA) However, total private sector borrowing (private nonfinancial credit) is nearly evenly split between households and businesses. Households hold $18.5 trillion of debt, and businesses $19.5 trillion. (Q2 2022: St Louis Fed) In this post, we explore household demand for loanable funds.

Chart #1 reports household debt by major category. This reveals that the bulk of household demand for loanable funds is overwhelmingly for mortgages, representing over 70% of total household debt.

The next biggest categories include Student and Auto Loans (less than 10% each), while credit card debt makes up less than 6% of the total. Combined, these top four categories account for 95% of total household debt. This reveals why households demand loanable funds. The next question is when (or at what stages of life) does the demand for credit appear?

The Life-Cycle Income Hypothesis offers an interesting explanation of household demand for credit over time. Also known as the “permanent income hypothesis,” it assumes individuals/households make spending plans not just based on current income, but also on estimates of future (lifetime) income.

Developed in 1957 from observations by Nobel Laureate Franco Modigliani, the theory suggests people smooth consumption (housing, transportation, etc.) over their lifetimes—borrowing during times of low-income and saving during periods of high income. This suggests individuals take on debt when they are young (student loans, auto loans, mortgages, etc.), reasoning higher future incomes in prime earning years will allow them to gradually pay off the debts. They also save aggressively in peak earning years (middle age) to ensure sufficient funds are available in retirement, allowing them to “dissave” and maintain roughly the same level of consumption in old age.

The Life Cycle Income Hypothesis has important implications for credit markets since it suggests individuals may be more willing to take on debt when they are young, anticipating higher future incomes. This can lead to greater demand for credit among younger individuals and households than might otherwise be the case, reflected in higher levels of borrowing and debt among this group.

Chart #2 reveals the age distribution of U.S. household debt. (New York Fed). This suggests debt is accumulated in early years (ages 18-39), until reaching peak earning years (ages 40-59), when debt is paid down and savings increase, leaving relatively little debt in retirement (ages 60-70+). Roughly 30% of total household debt is held by the youngest cohort of 18-39 year olds. The middle-aged cohort, of 40-59 year olds in their peak earning years, hold over 47% of total debt, which is paid down leaving only 23% held by 60-70+ year olds.

A seminal study by former Bank of England Chairman Mervyn King offers more evidence in support of the permanent income hypothesis. (1981 NBER) He found life-cycle consumption patterns appeared in approximately 75% of the population studied. However roughly 20-25% of households did not have smooth consumption patterns. If lenders could identify those most likely to follow a life-cycle income (smooth consumption) pattern, they might improve credit scoring algorithms by including individuals’ future employment prospects and expected lifetime income in their models.

Credit scores use a variety of factors to evaluate borrower’s ability to repay a loan. These are typically backward looking and not forward looking. They include factors like employment history, credit history, expense-to-income ratios, and debt-to-income ratios. This information helps establish lending risks to set appropriate loan terms (interest rates, repayment periods, etc.), both for secured loans (e.g. auto loans and mortgages) and unsecured loans (e.g. student loans and credit cards). However, since models fail to incorporate future employment and income prospects, many younger households find they are credit constrained, and face stiffer loan terms. This reduces their demand for loanable funds.

Expected future employment and income prospects are important factors that could serve to improve the efficiency of credit markets and offer loans better tailored to younger individuals and households. Even if they have little current income, younger households with some wealth, good future employment prospects, and sizable expected lifetime incomes, should be considered creditworthy.

If a young household wants to borrow against expected higher future earnings to smooth consumption over time, they can only do so if lenders are willing to extend credit, but also if they have confidence in their future income/employment prospects. Most young people can only guess at their future earnings potential, so they may hesitate to accumulate debt for fear they won’t be able to pay it off.

To boost borrowing opportunities for younger households and improve credit terms, fintech companies like are exploring ways to incorporate future employment and income prospects in their models. Factors used to forecast future prospects could include Education and other qualifications; Occupation and Industry; Location and Cost of Living; Work Experience; Social, Economic, and Market Conditions; etc. Incorporating such factors in credit models and sharing the results could lower borrowing costs for younger households and boost their confidence in taking on debt early in their careers. This would increase younger households’ demand for loanable funds. More importantly, it would allow more families to maintain more comfortable levels of consumption over time.

Francois Melese, Ph.D.

Author Francois Melese, Ph.D.

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