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Notes from Regional Strategic, Ltd.

Attempting to Offset Program Cuts with Equivalent Reductions in Taxes

I have recently posted three analyses of the Iowa economic impacts of breaking Social Security, Medicare, and Medicaid (Privatizing Social Security, Social Security – a Local (Iowa) Perspective, and Breaking Medicare and Medicaid – An Economic Perspective from Iowa).

None of these dealt directly with the typical small-government argument that an offsetting reduction in taxes will eliminate the adverse effects of eliminating programs.

This argument is not actually true in most cases. The reason is that markets are not neutral. They are created within the context of government intervention, and government intervention is required for efficient markets to function over time. Government defines and enforces property rights. Government oversees the accessibility and stability of the money supply. Government regulates financial transactions. Government influences marginal propensities to spend resources on and between categories of goods and services through taxation, investment, and program regulations and expenditures.

For better or for worse (I am not arguing one way or another), these influences shape markets, private investments, employment, and income. Making substantial changes to the way government influences the shape of markets and the economy will generally cause significant disruptions in the system. Those disruptions generally do not even out among all participants.

This analysis looks at the effects of eliminating federal Medicare and Medicaid benefits in Iowa and replacing them with equivalent increases in household income through tax reductions (see, in particular, Breaking Medicare and Medicaid – An Economic Perspective from Iowa). To develop this perspective, I

  • Set up a model of the Iowa economy
  • Removed $14.3 billion from the specific industry groups Medicare and Medicaid funding flow into
  • Added $14.3 billion to general household income

By both removing and adding $14.3 billion from/to the Iowa economy, the net initial impact on available resources is zero. The difference between where resources are removed and where resources are added, however, still results in devastating impacts upon the Iowa economy.

The change in how this $14.3 billion is allocated in the existing economic structure will result in a statewide payroll reduction of $5.6 billion reflected in the loss of over 70,000 jobs. Not all industries would lose jobs however:

  • Finance and real estate would see an increase of over 2,000 jobs
  • Wholesale and retail trade would see an increase of over 7,000 jobs
  • Education and the arts would see an increase of over 3,000 jobs
  • Accommodation and food service would see an increase in almost 2,000 jobs

On the other side of the coin

  • Health care would lose over 80,000 jobs
  • Professional services, management, and administration would lose over 7,000 jobs

These consequences would occur because markets are not neutral. They have been shaped for over 200 years by government interventions is property rights, taxation, expenditure, and regulation. An immediate and substantial change to the rules of the game can be expected to break down large segments of the economy that those rules have helped build up.

Regardless of philosophies regarding the long-term merits of one government-influenced market regime over another (and make no mistake, changes in government intervention only change the shape of government influence on the market – they do not eliminate that influence), it is important for the health of the economy that substantial changes be made slowly.

Furthermore, it is almost certain that the negative economic effects outlined above are understated. It will be worse than the results of the model shown above. It will be worse across all categories. Worse for the modeled winners as well as for the modeled losers. The reason is simple. The increases in household income (reductions in taxes) will not accrue to the same people who suffer losses of benefits.

In the model, the tax reductions were treated as increases to general personal income across Iowa. This assumes that tax reductions were proportional to incomes across the economy. That means that the people that lost Medicare and Medicaid benefits would be net losers in the transaction and everyone else would receive an unearned windfall.

A large proportion of this unearned windfall would go to high-income households with lower propensities to consume. This will result in a significant portion of the offsetting increases in income being removed from the economy as savings or financial investments. This would result in significantly lower offsetting economic activity than the model assumes. That, in turn, means the model results presented above are unrealistically optimistic.

In reality, however, this unearned windfall, these tax reductions, would not be spread proportionately across incomes within the economy. The current tax system and current proposed tax reforms heavily favor upper income households over lower income households (taxation policies are a major avenue through which government shapes the economy – see Why We Can’t Make Nice Things….). As a result, a predominant share (rather than the proportional share discussed in the previous two paragraphs) of offsetting personal income will accrue to upper income households. This will magnify the effect of lower marginal propensities to consume discussed in the paragraphs immediately above and further reduce the effect of offsetting income on benefit losses depicted in the model. For this reason, again, the economy-wide results modeled above are unrealistically over optimistic.

Regardless of the philosophical merits of any one form of government intervention over any other in shaping the economy, significant changes in these forms of intervention should not be made abruptly or haphazardly. The analysis above is clear that eliminating Medicare and Medicaid benefits in Iowa and replacing them with equivalent increases in household income through tax reductions will have a large negative impact on the Iowa economy. Markets are not neutral. They are shaped by the government. As a result, government has a responsibility to be responsible in changing the rules.

Data Disappearance and You

On February 6, 2025, I posted a note on the closure of the United States Agency for International Development (USAID). Regional Strategic, Ltd. turned down a contract to analyze the economic impact of that closure on an area of the Upper Midwest, because, in concert with the closure, the administration foreclosed access to data documenting USAID’s purchases and expenditures. The government actively denied the public the ability to evaluate government actions.

That denied my company the ability to conduct meaningful analysis for an industry group that needed to make immediate plans. That, in turn, foreclosed the generation of business incomes (and the residual personal incomes) on both sides of the potential transaction.

The note indicated that this was not the only case of data access restrictions occurring under the new administration in Washington, D.C. At that point, two weeks into the administration, data on healthcare, weather, and climate change that undercut the administration’s political positions had already been removed from public access. The note detailed some of the commercial problems these data restrictions would cause.

Yesterday, the administration moved again to restrict and/or alter major data streams available from the federal government. This time it was the Department of Commerce (USDOC). The USDOC is one of the major sources of data in the federal government. Data agencies within the USDOC include

  • The Census Bureau (Census) – which collects data on population, demographics, housing, employment, income, commercial activity, and international trade. These data streams are used to allocate congressional and state legislative seats, benchmark the National Income and Product Accounts (NIPA), manage and evaluate congressionally mandated programs, and determine the need for and effects of tariffs and trade restrictions.
  • The Bureau of Economic Analysis (BEA) – which is the national accountant. The BEA consolidates and analyzes data from the Census, the Bureau of Labor Statistics, the Department of Agriculture, and the Treasury to provide the consistent production, employment, income, and consumption data to generate the NIPA, which, in turn, is the source of national income and gross domestic product statistics.
  • The International Trade Administration (ITA) – which collects data on our international trade and the trade positions of our trading partners.

Sounds like pretty dry stuff, but this data underpins nearly every

  • Piece of market research
  • Investment decision
  • Community economic development plan
  • Interest rate
  • Bond issue
  • Congressional revenue and expenditure enactment

made in the United States.

On a personal level, this data underpins a complex integrated financial system that supports your auto loans, mortgages, and credit card transactions – all of which will get significantly more expensive as the quality and consistency of these data streams deteriorates.

The accuracy and consistency of these data streams is critical to business decisions, government action, and personal income.

On Sunday, March 2, 2025, Howard Lutnick, Secretary of Commerce, announced his intention to strip government activities from gross domestic product data. On Tuesday, March 4, 2025, he announced the disbanding of two important advisory boards:

  • The Federal Economic Statistics Advisory Committee
  • The Bureau of Economic Analysis Advisory Committee

These committees are made up primarily of professional and academic statisticians that advise the USDOC on proper data handling and increasing the quality and precision of the data and estimates the government produces and disseminates. To be effective, however, committee members need to be made aware of changes being made and how those changes are being accomplished.

Over the past five days, the federal government has, in quick succession,

  • Announced its intentions to make one of the most radical changes to federal data systems in modern memory
  • Dismissed the very experts it would need in order to accurately and successfully accomplish these changes.  

While much of the general public is not aware of these data streams on a daily basis, interrupting them is a major affair that will directly and significantly affect their livelihoods if not done correctly. It will be infinitely more disastrous if these disruptions are done politically.

This is a big deal that should command more attention than it is getting.

Post script

The list below is of posts I have made over the past 15 months that would not have been possible or accurate without the consistency of the data streams put at risk over the past five days. These are just short musings I have put up as examples of what can be done.

They do not include the extensive market reports I have generated for Midwest businesses and industry groups, economic impact studies I have done for the likes of John Deere, Des Moines University, the Iowa Off-Highway Vehicle Association, the National Balloon Classic, and others, or the policy analyses I have done for agricultural commodity groups. None of these efforts would have been possible without consistent quality data streams on the economy.

Beyond this, most people don’t spend their lives with there noses in the data. Most who do perform internal statistical analysis and do not work with the economic and social environments that underpin economic and policy analyses. Removing or corrupting the data streams discussed above will eliminate the jobs of hundreds of thousands of folks like me that connect the data to markets, the economy, development initiatives, and social and recreational initiatives.

Here are the posts:

Texas Household Income Distribution

We are doing some market analysis in Texas and surrounding states. One of the issues is to identify populations that might be potential purchasers of a particular offering. That is at least partially a function of income.

The graph below shows estimates of real per capita income trends within Texas household income quintiles.

To get this, we started with state income distributions from the U.S. Bureau of Economic Analysis (BEA) at https://www.bea.gov/data/special-topics/distribution-of-personal-income. This provided nominal incomes by household income quintiles for

  • Total personal income
  • Net earnings by place of residence
  • Proprietors’ income
  • Net compensation
  • Dividends and interest income
  • Rental income
  • Personal current transfer payments

For this graph, we didn’t work with any of the detailed categories. We stuck with total personal income.

Data came in a zip file with data for every state from 2012 to 2022. There were separate workbooks for every state. For every state there were separate worksheets for every year. Job one was to extract the data and combine all the years for Texas.

The downloaded data was not adjusted for inflation. We could easily see that some quintiles had seen income growth. With others, however, we could not immediately see if that was growth or if that was inflation. Step two was to download Consumer Price Index (CPI) data and adjust all of the years and quintile values to 2022-equivalent dollars. CPI data is available for download at https://www.bls.gov/cpi/data.htm. We used data for all urban consumers in the Southern region of the U.S. We used annual measurements that were not seasonally adjusted.

With inflation-adjusted data for quintiles of Texas households, we still could not see if individuals were gaining or losing ground. This is because every year the quintiles each give data for one-fifth of the households, but we have no idea of household or population growth.

We made a simple assumption that households averaged the same size across all five quintiles. That allowed us to take annual Texas population estimates divided by five as the number of people in each quintile. Dividing inflation-adjusted quintile incomes by population gave us the per capita income estimates shown in the graph. We utilized Texas population estimates from the BEA at https://www.bea.gov/data/by-place-states-territories, because data from the BEA is remarkably easy to locate, download, and use.

There are a few things about the data and the data manipulation that deserve note.

First, for every year the total income received by the top quintile was greater than the income received by the bottom four quintiles combined. This was not changed by any of the manipulations described above.

Second, the assumption that household sizes are the same across all quintiles was convenient and gave us the ability to normalize the data for population size but is probably not completely accurate. For any quintile where household sizes are larger than the overall average, the quintile’s per capita estimate would shift down. Conversely, for any quintile where households are smaller than the overall average, the quintile’s per capita estimate would shift up.

Our best guess is that the lower quintiles have larger households and that the higher quintiles have smaller households. This is consistent with the demographic arguments in the recent post, “The Coming Depopulation.” If so, the lines for the bottom quintiles would drop and the lines for the top quintiles would rise.

Third, the data estimates current realized income. That is pretty close to total income for the bottom quintiles. Households in the upper quintiles, however, are likely to have significant levels of unrealized unearned incomes in the form of appreciation or capital gains on investments. These streams are reported and show up in the data as they are realized. If they are realized in a constant steady stream over time, the data is probably an accurate reflection of reality. To the extent that unrealized income streams are growing over time, the data will underestimate them during any period.

This was an interesting exercise undertaken as part of a larger analysis of market potential in the Southern U.S. It is possible to replicate this for any state and to engage the data at a more specific level. While multistate regions can also be analyzed, they require additional manipulation because income ranges on household quintiles will be unique to every state. In all cases, a careful disclosure of assumptions made and the potential implications of those assumptions is required.

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