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Press Release: 2021 FinHealth Spend Report Shows Financially Coping and Vulnerable Households Spent $ 255 Billion for Everyday Financial Services

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Press Release: 2021 FinHealth Spend Report Shows Financially Coping and Vulnerable Households Spent $ 255 Billion for Everyday Financial Services

Posted | Updated by Insights team:

Publication | Update:

May 2021
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Struggling households shoulder greater burden of fees and interest, highlighting the need for innovative solutions that reduce economic pressures

The Financial Health Network, the nation’s authority on financial health, in partnership with Prudential Financial, today released The FinHealth Spend Report 2021, the evolution of our annual market sizing report showing Financially Coping and Vulnerable households account for 84% of spending on fees and interest for financial services (for a total of $ 255B) despite representing less than two thirds of the population. In particular, Financially Vulnerable households spent, on average, 13% of their annual income on the products studied, compared with 5% for Financially Coping households and just 1% for Financially Healthy households. Financially Coping and Vulnerable households are those who struggle to spend, save, borrow, and plan.

The refreshed report, formerly called the Financially Underserved Market Size Study, combines primary and secondary research to provide unprecedented insight into the fees and interest paid by consumers on dozens of everyday financial services in 2020. The report’s innovative methodology enables new insights into market spending by several key lenses, including financial health, income, race, and ethnicity.

Overall, this landmark new data found that consumers struggling with financial health, Black and Latinx households, and low- to moderate-income (LMI) households spent a greater share of their income on interest and fees for financial services, contributing to a widening gap in consumer financial health as the nation recovers from the pandemic.

“Ideally, financial services can help individuals and families manage their financial lives and build better financial health, but the data shows those least able to afford them are disproportionately shouldering the costs,” said Financial Health Network president and CEO Jennifer Tescher. “This exacerbates the financial distress felt by many families and contributes to a well documented and growing financial gap in this country.”

Key insights into household spending by income, race, and ethnicity include:

  • LMI households – households with income below 80% of area median income – spent $ 127 billion in interest and fees on everyday financial services;
  • The report estimates that LMI households spent, on average, 7% of their income on the financial services studied, compared to 3% for higher income households;
  • Black and Latinx households spent a collective $ 101B on the financial services studied;
  • It is estimated that Black households spent, on average, 6% of their income on the financial services studied, and Latinx households spent 5%, while White households spent 3%.

Additional key findings of the 2021 report by population segments include:

  • Just under two-thirds of households drove 84% of spending on everyday financial services;
  • Financially Vulnerable households – who comprise 14% of those surveyed – pay 24% of all fees and interest;
  • Financially Coping and Vulnerable households spent $ 123B on interest and fees on short-term credit, $ 91B on long-term credit, $ 24B on single payment credit, and $ 17B on payments and accounts.

Analysis of spending by product found:

  • Credit cards and auto loans are the largest drivers of spending:
    • Interest and fees from revolving balances on general purpose credit cards are estimated at $ 90B from Financially Coping and Vulnerable households;
    • Spending by the Financially Coping and Vulnerable on used auto loans totals $ 63B;
    • Regardless of the type of auto loan, Financially Coping and Vulnerable populations are paying higher rates for vehicle financing, due in part to lower credit scores (57% of Vulnerable respondents have credit scores classified as “deep subprime”).
  • The consumer segments discussed in this report are more likely to utilize high-cost, single payment credit products and other alternative financial services, reflective of the economic and racial disparities in access to mainstream services:
    • Black households are 2.7x more likely to use pawn loans and 3.8x more likely to use payday loans than White households, while Latinx households are 3.1x more likely to use payday loans than White households;
    • LMI households are 7 times more likely to use pawn loans than higher-income households;
    • Forty-three percent (43%) of Vulnerable households with checking accounts report having overdrafted in the past year, with 9.6 overdrafts on average.

Prudential partnered with the Financial Health Network to produce the FinHealth Spend Report in 2021. The research builds upon Prudential’s ongoing commitment to close the financial divide by deeply analyzing the financial health of American households.

“We have a long history of addressing the unique financial challenges of our customers while providing steps towards improving their financial health and lives,” said Sarah Keh, vice president, Inclusive Solutions at Prudential. “This partnership with the Financial Health Network helps us further our mission by collaborating with innovators and policymakers committed to designing solutions that contribute to improved resilience and financial wellness for all.”

For more information regarding the FinHealth Spend Report, please view the 2021 Report.

About the Financial Health Network

The Financial Health Network is the leading authority on financial health. We are a trusted resource for business leaders, policymakers and innovators united in a mission to improve the financial health of their customers, employees and communities. Through research, advisory services, measurement tools, and opportunities for cross-sector collaboration, we advance awareness, understanding and proven best practices in support of improved financial health for all. For more on the Financial Health Network, go to www.finhealthnetwork.org and follow us on Twitter at @FinHealthNet.

Photo courtesy of geralt.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

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Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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