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Biden’s executive order on care work is an important step for an industry in crisis

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Biden’s executive order on care work is an important step for an industry in crisis

Posted | Updated by Insights team:

Publication | Update:

May 2023
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For three years, I have been waiting for good news from Washington to share with Yvette Beatty.

Beatty, 63, is a home health aide in Philadelphia who has provided care for elderly adults and people with disabilities for nearly 40 years. Like most direct care workers, she earns very low wages despite the increasing demand for her essential work.

I first met Beatty in April 2020, just as the COVID-19 pandemic began. In our interviews, she shared with me the hardships she endured: the fear she faced as she risked her life going to work, and her daily struggle to afford basics like food and medicine for her family. “It is very hard.” she told me. “Thank God for noodles.” (You can listen to Beatty in her own words in this profile.)

Beatty questioned why leaders in Washington were not doing more to support underpaid yet essential care workers—the vast majority of whom, like her, are women and workers of color.

“With home health aides, we are struggling out here,” Beatty told me in April 2020. “This is a field everyone needs. They need our service. Why can’t we get wages that help us?”

Yvette Beatty

Yvette Beatty: Listen to her own words in this profile.

Two weeks ago, President Joe Biden took important steps toward heeding Beatty’s call. In a Rose Garden ceremony, he signed a historic executive order with more than 50 directives for improving care jobs and expanding access to affordable child care and long-term care. In his remarks, President Biden thanked care workers like Beatty and said they “deserve jobs with good pay and good benefits.”

The new executive order is important for two key reasons. First, it demonstrates the Biden administration’s commitment to addressing three interrelated and critical challenges in the care sector: 1) the struggle that millions of American families face trying to access high-quality, affordable child care and long-term care for their loved ones; 2) the dire shortage of care workers who provide these services; and 3) the inadequate pay, benefits, and job quality that plague the sector. Because pay for care workers is so low, turnover is high, waitlists are long, and families are unable to find the care they need. In his Rose Garden remarks, President Biden framed the stakes in moral and economic terms, calling the issue “fundamental to who we are as a nation” and important to the entire economy.

To this end, the executive order directs the Department of Health and Human Services (HHS) and the Department of Education to use their regulatory power to enhance the job quality and wages for long-term care workers, early educators, and child care workers. For instance, HHS could increase the pay and benefits for Head Start personnel, and the Education Department could encourage its grantees to increase wages for child care staff.

The second key reason for the executive order’s importance is that it harnesses the power of the executive branch at a time when progress in Congress has stalled. Early in his administration, President Biden proposed historic investments in the care sector as part of the Build Back Better agenda, including $ 400 billion for long-term care, $ 225 billion for child care, and $ 200 billion for early childhood education. However, the final version of this legislation signed into law (the Inflation Reduction Act) was ultimately a slimmed down version of Build Back Better that was stripped of any investments in care.

Now, with Republicans in control of the House of Representatives, any major legislation investing in care work seems unlikely for the foreseeable future. But by issuing an executive order on care work, President Biden was able to bypass Congress to make some progress through the executive branch and demonstrate continued support—albeit without bringing any new money to the issue.

Unfortunately, that lack of new money means that America’s care crisis will continue, despite the positive steps outlined in the executive order. Sizable federal and state investment is required to simultaneously improve care jobs and expand access to affordable, quality child care and long-term care. Unlike other low-wage sectors such as retail or fast food (where wages are responsive to labor market demand, as evidenced by fast-growing wages for in-demand leisure and hospitality workers), the hourly pay for workers providing direct long-term care is mainly financed through Medicaid (funded by both the federal and state governments) and restricted by (often inadequate) Medicaid reimbursement rates set by states. Even when demand for workers is high, as it is today, employers in the care sector have little room to raise pay or improve benefits to attract and retain staff, unless states increase Medicaid reimbursement rates and Congress and state governments invest additional money to finance pay bumps.

Recently, several states have made financial commitments to raise pay for care workers permanently. Colorado, Michigan, North Carolina, and New York have funded pay increases for direct care workers providing long-term services, including home health aides. And New Mexico, Washington, D.C., Maine, and Louisiana boosted pay for child care workers. While promising, these examples of state leadership remain the exception. A comprehensive and national solution to the care crisis requires federal action.

To Yvette Beatty, this issue isn’t partisan. “It isn’t a Democrat or a Republican thing,” she told me back in 2020. “It’s a ‘we’ thing.” She noted the appeal to voters in helping both families and workers. “Help the home health aides so we can continue to help our patients. If we can’t keep ourselves together, how are we going to keep our patients together?”

President Biden’s executive order and instances of state action show hopeful signs of progress on solving America’s care crisis. But major federal investment is needed, and I’m still waiting to call Beatty with good news that it’s coming. Three years ago, when I asked her how she would feel if leaders in Washington invested in care workers, she responded: “It would give us hope if they supported us. It would let us know we are appreciated. If the government could help us right now, it would feel beautiful.”

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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.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

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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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