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Politics, Real and Perceived Commodity Prices, and Their Effects upon Farm and Non-farm Incomes in Iowa

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Over the past several weeks, I have heard multiple farmers say that, while prices are really bad right now, they are still a buck or two higher than when Biden was president. I didn’t believe them for a minute, but, at the same time, I don’t believe they were lying to me.

Our minds all work to make sense out of the thousands of bits of information we encounter each day. We unconsciously build a story that makes consistent sense of our current experiences and our deeply held beliefs. As a result, we all firmly believe some things that are somewhat less than true. We truly believe them because they are consistent with our understanding of ourselves. If you care to delve into this phenomenon, I suggest picking up a copy of Daniel Kahneman’s Thinking, Fast and Slow.

Anyway, while these gentlemen were not lying, they were passing on untruths. It is likely that presenting facts in response will have no effect upon what they believe, but facts are available, and I am going to run through a few of them here.

This is about more than prices for a bushel of corn or soybeans. A buck or two on Iowa’s 2.7 billion bushels of corn and 600 million bushels of soybeans is 3.3 to 6.6 billion dollars, or 2-3 percent of Iowa’s personal income. The gain or loss of a buck or two on a bushel of corn or soybeans is important to the state’s economy as a whole.

As farmers spend or don’t spend this level of money, revenues surge or dry up for local car dealers, dance instructors, restaurants, and grocers. This determines whether kids wear new shoes to school – farm kids and non-farm kids alike. Voting on the basis of falsely held beliefs regarding commodity prices affects the entire community – not just the farmer. The discussion that follows attempts to put some values on these impacts for Iowa.

USDA Market Year Prices

We start with market year average prices from the United States Department of Agriculture (USDA). Market years run from September 1 of the harvest year to August 31 of the following year. They coincide with the expected period during which a producer markets the current harvest.

The table below shows USDA average market year prices for corn and soybeans from the 2019 harvest year to the 2025 harvest year in Iowa. The table includes market year dates for reference. It also indicates who was president during the market year. The lists of market year prices for both corn and soybeans were given a color-gradient scale with Excel. Red indicates the lowest prices. Green indicates the highest prices. All of the red occurs in Trump presidencies. All of the green occurs during the Biden presidency.

The price range for corn over the seven market years is from $3.50 per bushel to $6.62 per bushel. The range for soybeans is from $8.48 per bushel to $14.20 per bushel.

Adjusting for Inflation, Adding Production Levels, and Generating a Weighted Average Inflation Adjusted Price for the Period

The discussion above is sufficient, in and of itself, to address the price comments we have heard from farmers. These differences, however, have significant effects on Iowa’s non-farm economy. To show that, we need to demonstrate the effect that these price swings have on the overall income – farm and non-farm.

Inflation Adjustment

We start by adjusting the market year prices for inflation. At a per-bushel level over seven years this is not hugely important. If we are to look at overall income, however, we have to multiply per-bushel prices by total production. A ten-cent price difference multiplied times 2.5 billion bushels of corn is 250 million dollars. To avoid this variance, we used the Consumer Price Index (CPI) for all Midwestern urban consumers to standardize dollar values across the period.

We made the assumption that USDA market year prices were representative of prices at the midpoint of the market year. We then averaged CPI indices for February and March of the years 2020 to 2026 to coincide with the midpoints of the 2019 to 2025 market years. We then used those averages to standardize our USDA market year prices on the dollar value at the midpoint of 2021 market year (February-March of 2022). The year was chosen because it matches the current set-up of the economic impact model we will be using below. It has no effect on results.

Inflation adjusted prices are shown in the table below. Once again, the prices were placed on a color-gradient scale.

Adding Production, Realized Income, and a Weighted Average Price

To get to total incomes related to the price variations we see, we add bushel output for both corn and soybeans through the period. Output numbers are from the USDA and represent the harvest years coinciding with the market year prices utilized above.

To generate income or realized revenue, we simply multiplied inflation adjusted prices times output levels to get inflation adjusted revenue for each crop year.

Then we divided the sum of inflation adjusted revenue by the sum of output across the seven years to generate a weighted average price for the overall period. Our weighted average price for corn is $4.68 per bushel. For soybeans, $11.09 per bushel. Both weighted average prices are denominated in dollar values current at February-March 2022.

The Difference Between Realized Revenue and Baseline Revenue

We used realized income in the table above to generate a weighted average price for both corn and soybeans across the seven-year period. The weighted average price can be used as a baseline of sorts to reflect expectations. We can multiply the weighted average price times annual production values to get a baseline revenue value or expected revenue relative to any year’s production volume.

We can then subtract our calculated value of baseline revenue from our calculate value of actual inflation adjusted revenue to generate a measure of realized farm revenue divergence.

We have done that in the table below. We have used Excel to place a color gradient on the series. Red represents a large negative divergence (expectations are much higher than realizations). Green represents large positive divergence (expectations are much lower than realizations).

For corn, our divergences of realized to expected revenues ran from a deficit of over $3 billion to a surplus of nearly $4 billion – a spread of nearly $7 billion.

For soybeans, our divergences of realized to expected revenues ran from a deficit of $1.3 billion to a surplus of nearly $1.5 billion – a spread of nearly $2.8 billion.

These variations all represent farm income. At this point, we have made no assumptions or estimates on how these farm income fluctuations will affect the surrounding non-farm economy. They will have an effect, however. When farm incomes are flush, farmers spend extra money on new vehicles, recreation, goods, and services. When farm incomes are short, farmers economize on these expenditures.

Non-farm Impacts of Farm Revenue Variations

Because we are working only with final price variations and production is fixed, we can look at this variation in farm revenue as variations in farm family income. By the time commodity prices are established, all farm production activities and expenses have been realized. The economic impact of production is fixed. The economic impact of these income variations are entirely due to changes in the expenditures of farm families in the economy around them.

To get at this, we first combined the corn and soybean variations for every year (the far-right column of numbers in the table above). Then we ran these summed variations in farm income for every year through an economic impact model generated with Regional Input-Output Multiplier System coefficients from the U.S. Bureau of Economic Analysis (BEA). We then used the CPI to adjust the dollar values to reflect dollar values at the midpoint of the 2025 market year (February-March 2026) to bring the values into line with current price experience.

The results of this are shown in the table below. The first column of numbers (under the green-shaded heading) is the sum of the corn and soybean income variations from the table above after adjusting to February-March 2026 dollar values. This is the effect that revenue surpluses or deficits from our weighted average price directly affect farm families in Iowa. In the 2025 line, this figure reflects a shortfall of slightly over $5 billion in Iowa farm income relative to expectations based on our weighted average prices for corn and soybeans.

The next five columns (under the plum-shaded headings) show the off-farm impact of these farm income variations. The first of these columns shows changes in non-farm expenditures by farm families. In the 2025 row, for example, we see that a farm income shortfall of just over $5 billion is expected to result in a nearly $4.8 billion reduction in farm family expenditures. This is money that is not received by businesses like auto dealers, grocers, dance studios, and restaurants around Iowa.

Of this $4.8 billion that Iowa businesses do not receive from farm families, nearly $2.8 billion is value that would have been created within the Iowa economy – “Value Added” in the language of economists. The remaining $2 billion would have consisted of goods and services imported from outside of Iowa.

The $2.8 billion in lost Iowa economic activbity would be split nearly evenly between labor income ($1.39 billion that would have supported over 28,000 jobs) and business income ($1.38 billion worth of business earnings, interests, rents, and a very small sliver of indirect business taxes).

You can run across any of the crop-market year lines and build a similar story. The column with the green-shaded heading represents the direct effect of expected price shortfalls on farm income. The columns with the plum-shaded headings shows how reduced farm income translates into reduced off-farm expenditures, payrolls, jobs, and business income.

Conclusion

This all started with a few farmers remarking that farm prices, while bad, are a couple of dollars higher than they were under President Biden. Even though it is easy to demonstrate that they are wrong, these farmers are sincere in their beliefs in this regard.

As a talking point, a couple of dollars on a bushel of corn or soybeans passes most of us without generating an awareness of billions of dollars in lost economic activity. This is unfortunate.

When we can promote simple erroneous statements as political talking points and ignore the fact that those erroneous statements mask billion-dollar swings in personal income and economic activity, we are encouraging people to vote in ways that are in direct opposition to their own self interests and the interests of their neighbors.

Nothing is more detrimental to faith in government than false political narratives that are demonstrably harmful to the very people they are targeted at.

The Federal Shutdown and Iowa

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It’s October 3, 2025, and the federal government shut down two days ago for lack of budget legislation. It was not a surprise. Republicans holding power in the House of Representatives, the Senate, and the White House have made it a point not to negotiate a resolution, either among themselves (they had the power to resolve this all by themselves) or with the opposition party.

Citizens in the United States are becoming so used to dysfunctional federal government that the whole affair was met with a collective yawn on October 1. The pain will not hit immediately, and, when it does, most citizens believe it will not fall directly upon themselves.

Citizens in the United States have become so self-indulgent that pain which does not fall directly upon themselves does not matter.

In that context, all the follows may be a waste of time and computational effort. What follows is a quick look at the ongoing costs of a federal government shutdown on the state of Iowa. Iowa was picked because I am familiar with the data and I have a recently constructed economic impact model for Iowa. Similar calculations could certainly be done for any state.

The analysis will be done on the basis of a one-day shutdown. Results can be multiplied by the number of days the shutdown lasts to approximate total costs to the Iowa economy. Wherever possible, I will try to bring effects back to numbers of jobs lost.

To put this in perspective, over the past ten years, from December 2014 to December 2024, Iowa had a net gain of just 10 nonfarm jobs per day. Over the past year, from December 2023 to December 2024, Iowa had a net loss of 15.5 nonfarm jobs per day.

Federal Jobs

The first loss Iowa will see from a shutdown is the idling of federal employees. Iowa has about 18,400 federal civilian employees (Bureau of Labor Statistics – BLS). Most of them will be idled as nonessential workers. These employees generate an Iowa payroll of a little over $524,000 per workday (Bureau of Economic Analysis (BEA) and BLS). Like the rest of us, they spend their earnings on groceries, cars, clothes, dance lessons, and what-not.

When that payroll money does not get spent, someone else in Iowa doesn’t receive it. In general, taking half a million dollars out of Iowa payrolls will result in a loss of about 3.5 jobs. That means Iowa can expect to lose 3.5 jobs for every day the shutdown lasts. This is in addition to the 18,400 federal workers idled by the shutdown. This is a loss to the rest of us because those federal workers are not being paid and are not spending their earnings in the local economy.

Remember, over the past ten years, Iowa has generated only ten new nonfarm jobs per day.

Finally, this is not just workers. Idling federal workers will cost Iowa nearly $150,000 in business earnings (profits, rents, interest, etc.) for every day the shutdown continues. This is entirely from the effects of the unspent federal payrolls. This does not include the losses of federal contract or supply receipts or federal direct payments.

Direct Farm Payments

According to the Environmental Working Group, Iowa farmers received $43.5 billion dollars in direct payments over the past 30 years. This works out to an average of nearly $4 million per day, ever day, over the period. Because crops have already been cultivated this year, this is also best looked at as a subtraction from farm family incomes.

If we remove these sums from farm family incomes, farm families, like the federal employees above, will not be able to spend their funds on cars, houses, groceries, and what-not. This drop in expenditures means other Iowa families will not receive these expenditures as income.

The result of all of this is that reducing farm family expenditures by $4 million will reduce Iowa’s total employment by 26 jobs. It will reduce Iowa business earnings by $1.1 million.

On average, this will happen every day of the shutdown. The model is linear. The results can be multiplied by the number of days the shutdown lasts.

Remember, this will be in addition to the initial $4 million daily loss in farm family income.

Social Security Benefits

According to the BEA, Iowans received $15.5 billion in Social Security payments during 2024. This works out to an average of $42.5 million per day. This, too, is an addition to family income. When it does not arrive, recipients do not spend it on groceries, medical care, vacations, cars, and what-not. This reduces the incomes of Iowans who would normally supply these things to the recipients.

Reducing Iowa household income by the loss of daily potential Social Security payments would cost Iowa 278 jobs for every day that a shutdown stops Social Security payments. It would also cost Iowa businesses $11.7 million in lost earnings – every day that Social Security payments are not received. This is on top of and completely separate from the hardships imposed on Social Security recipients.

Medicare and Medicaid

The BEA reports that Medicare and Medicaid pumped $19.5 billion into the Iowa healthcare industry in 2024. This averages $53.2 million per day.

Unlike the impacts calculated above, these sums are not properly looked upon as changes to household income. Medicare and Medicaid payments are direct purchases of services from the healthcare industry.

Splitting these expenditures across the segments of the healthcare industry in Iowa results in a loss of 612 jobs for every day’s loss in Medicare and Medicaid expenditures in Iowa. It also results in the lose of approximately $21.7 million in business earnings across the state.

These estimates can be multiplied over the days payments are eliminated during a government shutdown.

These estimates are above and beyond the hardships imposed on recipients who are denied healthcare, and they are above and beyond additional costs that result from healthcare being denied.

Roll It All Together

Summing up the impacts laid out above, each day’s loss of the federal funding would cost 920 jobs and $34.6 million in business earnings. This would be above and beyond the direct loss of incomes and services to the initial recipients of the funds.

This would be multiplied every day receipts and services are lost due to a federal government shutdown.

Unlike a layoff at John Deere or some other manufacturer, however, these losses will be spread across businesses throughout Iowa and will not be reported to the Iowa Department of Workforce Development as mass layoffs. They will not be reported in the media the same way a mass layoff would be reported.

It will start as reduced hours, lost shifts, and scattered individual job losses, but the steady march of reduced expenditures, incomes, and employment will be insidious. The victims will be largely invisible except to their own small circles of family and friends.

And On and On and On We Go

Without going on ad nauseum, this is not all of it. According to Common Good Iowa, in 2024

  • The USDA spent $916.6 million in Iowa beyond direct farm payments  
  • The Department of Commerce spent $1.4 million 
  • The Department of Education spent $676 million 
  • The Department of Energy spent $7.6 million 
  • The Environmental Protection Agency spent $139.1 million 
  • The Department of Health and Human Services spent $795.4 million in addition to its Medicaid outlays 
  • Homeland Security spent $11 million 
  • Housing and Urban Development spent $77.2 million 
  • The Department of the Interior spent $30.5 million 
  • The Justice Department spent $17.3 million 
  • The Department of Labor spent $60.2 million 
  • The National Endowment for the Arts and Humanities spent $1.9 million 
  • The Department of Transportation spent $934.6 million

It all totals another $3.77 billion in federal spending in Iowa – an average of over $10 million per day. All of this could also be allocated to industries and run through an economic impact model, but you get the idea.

Most of us won’t directly feel the effects of a federal shutdown unless it lingers for some time. Although scattered, however, those impacts are larger on a daily average basis than the largest mass layoff reports that regularly make headlines in Iowa.

The invisibility of the victims magnifies the cruelty of these impacts and the irresponsibility of the people who made the shutdown a reality.

Pro Forma Financials and Small Business Development

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We do a bit of business planning work here at Regional Strategic, Ltd. Our president, Mark Imerman, cut his teeth on business planning as a market development economist at the Iowa Department of Agriculture and Land Stewardship. It that role, he assisted farm operators and investors develop marketing facilities for alternative crops. It was a strategy pursued by the State of Iowa to reduce dependence on traditional row crops during the farm crisis.

In that role, he generally produced complete business plans:

  • Business description and objectives 
  • Management qualifications 
  • Personnel biographies and job descriptions 
  • Sources of technical support 
  • Market analysis 
  • Competitive analysis 
  • Pro forma financial projections 
  • Investment and capital needs

At Regional Strategic, Ltd., however, we seldom get requests for the whole ball of wax. We primarily get contracts to do the market and competitive analyses. It is challenging and satisfying work. Every product, region, and delivery channel presents a different situation. All of these efforts are unique.

One thing we seldom see, anymore, is a full pro forma financial layout in small business development. This is true whether we are asked to help with small business planning or we are looking at plans done by others. Instead of detailed breakdowns of revenues, expenditures, and investment needs by month, we see rough annual estimates of total payrolls, start-up costs, production volume, and revenue.

This is unfortunate. The estimates might be well-informed and accurate, but the lack of detail is unfortunate, nonetheless. Annual estimates don’t match up revenues and expenditures as they occur. This is important, particularly in a small business startup. Without this, initial cash needs might be substantially underestimated simply because of mismatches between the accrual of expenditures and the realization of revenue. Annual estimates also hazard the risk of overlooking costs that might be incidental in and of themselves. These costs, in aggregate, can often be consequential. Missing them can result in material differences in overall estimates. There are often underestimation issues with startup costs, as well.

All of these factors increase the financial risk of business startup investors. They also increase uncertainty upon the part of potential lenders. This often results in elevated interest and insurance costs. In combination, these result in fewer small business startups in any given community. This reduces community income which reduces community tax revenues and impedes the improvement of community services. In short, they retard a community’s growth potential.

A five-year monthly pro forma financial layout encourages a business owner to walk through the first 60 months month-by-month. Properly set up, it will allow the investors and lenders to do what-if scenarios easily.

The table below is the fifth-year cash flow layout of one of Mark Imerman’s first business planning exercises. This particular plan specified financial requirements for a contract service provider engaged in planting, harvesting, packaging, and selling an alternative perishable food crop in Iowa. The spreadsheet analysis was set up with a single sheet of annual assumptions regarding acreage, service costs, output, waste, and revenues. Numbers in the cash-flow layouts were calculated from these assumptions. As a result, basic changes could be made concerning expected costs, acreages, yields, and sales prices, and those changes would automatically change the annual cash-flow layouts, which, in turn, would ripple through the annual operating and balance statements.

Pro forma financials are powerful tools. They result in better informed and prepared business owners, more confident investors and lenders, and increased potential for business growth within a community.

This was a relatively small investment project. It required a direct investment of about $130,000 and long-term loans of about $220,000 at the time. Given inflation, that is nearly equivalent to a total investment of $700,000 today. Long-term loans to accomplish this investment would likely have not been available in the absence of the pro forma analysis.

The second table shows a more recent example. It represents a potential frozen food manufacturer serving a limited geographic market in the upper Midwest. Figure Two shows the third-year expenditure portion of a five-year pro forma. Along the tabs at the bottom of the screen shot, you can see sheets for five years’ cash flows, balance sheets, and operating statements. There is also an assumptions sheet that drives cash-flow calculations. Cash flow calculations drive operating statements and balance sheets.

A set-up like this allows developers and investors to walk through alternative scenarios. You can see that production expenditures increase across the year. This reflects a scenario where output is still growing as the initial investment matures. Delivery expenses do not grow on the same schedule as production expenses. This reflects the lumpiness of delivery investments. All of these things become obvious when working through monthly pro forma cash flow projections. They are easily missed in annual lump sums.

Overall, the more recent operation is larger than that represented in the first table. The more recent table is part of the projections for a $5,000,000 investment. Generally, Regional Strategic, Ltd. works with business planning projects with investments between $500,000 and $15,000,000. It is a market segment that almost always benefits from a rigorous estimation of expenses and revenues.

Terminating Federal Funding Flows – An Iowa Example

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Increasingly, we live in a world where the federal funding we have integrated into our local economies cannot be relied upon. At the same time, there are no guarantees that the lost funds will be returned to the economy in other forms if they are removed. There is substantial talk of deficit reduction and of selective tax cuts, but there is no sign that funds held at the federal level will be broadly distributed to the local economies which will bear the loss.

This is a simple analysis of what the Iowa economy would look like if four major flows of federal funding were cut off:

  • Agricultural Subsidies
  • Social Security
  • Medicare
  • Medicaid (the federal share only)

No assumptions are made of any alternative flows that would replace these losses. This is simply a look at general expectations assuming these funding flows simply disappear.

Data for this exercise were collected for 2023. This is the last year for which the full range of data could be obtained. All data except the level of agricultural subsidies was sourced from the United States Bureau of Economic Analysis (BEA). Agricultural subsidy totals were obtained from the Environmental Working Group, because the BEA has recently stopped publishing detailed agricultural industry statistics at the local level.

The effects of removing each of the four funding flows were analyzed using an impact model built with Iowa economic coefficients obtained from the BEA Regional Input-output Modeling System (RIMS II). Each of the four major funding sources was run separately, sums were taken, and a comparison was made to Iowa totals for actual 2023 gross domestic product and employment. The table below shows the results. Dollar values are in billions.

What all falls out is a loss in federal funding of almost $30 billion. As these losses percolate through the Iowa economy, they will result in

  • Lost economic transactions totaling $42 billion
  • Lost economic value added (GDP) totaling $24 billion
  • Lost business income, interest payments, rents, and direct production taxes of $10 billion
  • Lost labor income (payrolls) of nearly $14 billion
  • Over 268,000 jobs lost

At the end of the day, Iowa can expect to see its GDP drop by almost 12 percent and its employment totals to drop by 12.5 percent if these funding flows are terminated without replacement. Iowa is not unique among states with respect to the expected impacts if major federal funding streams dry up.

Additionally, we can use payrolls as a proxy for production and income to roughly estimate Iowa tax losses resulting from this. Iowa collects approximately 8.75 cents in general revenue for every dollar in statewide payroll. At this rate, the loss of payrolls resulting from losing federal flows of funds would result in a reduction of state general tax revenue by over $1.2 billion. This would further cut expenditures throughout the state and magnify the losses listed above.

Regardless of the pros and cons of government interventions in the economy, the economy has been built up over decades on the incentive systems driven by those interventions. It would behoove us all to be a patient and cautious in making changes.

Attempting to Offset Program Cuts with Equivalent Reductions in Taxes

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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

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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:

USAID and the Business Implications of Data Disappearance

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Yesterday, Regional Strategic, Ltd. was asked to evaluate the effect shutting down the United States Agency for International Development (USAID) would have on demand for agricultural commodities in a specific area of the Midwest. We had to decline the project. After looking at available data, we found that, in shutting down the USAID website, the administration had denied citizens and the business community the ability to evaluate what had been lost and plan for the alternatives that remained.

The question is not trivial. It appears that USAID acquired approximately $1.8 billion in U.S. food products to support its activities in 2022. Every $100 million spent on food production and processing in the upper Midwest generates approximately

  • $100 to $120 million in value-added economic activity within the Midwest
  • $55 to $70 million in labor income
  • $30 to $65 million in corporate profits and tax revenue
  • 1,000 jobs

Any of these estimates could be increased 18 times to accommodate the $1.8 billion demand loss from eliminating USAID. All of these totals would go up if the impact was evaluated across the entire United States.

Clearly, local regions that are heavily invested in commodity production and processing would like to evaluate what portion of existing demand is being taken off the table:

  • Every $100 million reduction in 2022 Iowa corn purchases in Iowa would have been equivalent to idling over 75,500 acres of 200-bushel corn
  • A similar reduction for wheat in Kansas would have been equivalent to idling over 310,000 acres of 37-bushel wheat

The sudden lack of data with which to evaluate these impacts on local areas is a business issue. It is a family welfare issue. It is an employment issue. It is a public policy issue.

This is not limited to the situation involving USAID. In the first two weeks of the present administration, data access has been restricted in the areas of health care, climate, and weather forecasting where those data run counter to the administration’s political inclinations. This is bad for business, and it is dangerous.

Health data is being restricted at a time when the United States is experiencing a growing bird flu epidemic, Africa is experiencing renewed Ebola outbreaks, and drug-resistant tuberculosis is becoming more prevalent worldwide. Any one of these situations could rapidly become an international health problem. Any one of these is a personal safety issue. Each of these could rapidly become a workforce issue.

Weather and climate data are critical for construction, shipping, food production, tourism, energy distribution, and many other industries. Data on income, trade, consumption expenditures, and demographics are critical to any business doing market, workforce, or facility siting analysis. In any of these cases, businesses that rely on private vendor subscriptions are not immune, as their private vendors all depend upon public data sources as foundations for their models.

Given the rapidity of data “Disappearances” in the first two weeks of the administration, we don’t expect it to stop. There is plenty of information that contradicts the administrations political proclivities in the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census, the Energy Information Administration, the International Trade Administration, the Department of Agriculture and other agencies. We anticipate that many of these sources will disappear or become restricted in the coming months. Restriction of any one of these would have major implications for significant portions of the economy.

The situation is made more critical by online data access and delivery. Thirty years ago, data histories for all these sources were published and available in libraries across the country. That is no longer the case. Unless restrictions are anticipated and data is downloaded, catalogued, and stored, even data histories will be unavailable. The reduction in publication and distribution costs has resulted in more and better data over the intervening period, but it has also put citizens and business at risk under the current administration.

There has always been public data that made elected officials uncomfortable. The current difference is that the administration is not willing to address and live with its discomforts – opting instead to eliminate the evidence of its contradictions.

THIS IS A BUSINESS ISSUE. It is time for businesses to step up to help resolve it.

Those are my two cents. Spend them as you will.

Stick to the Voodoo You Do

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We all get our best results if we stick to things we are good at and interested in, but every enterprise involves a lot of tasks that don’t fit into any team member’s, “Voodoo set.”

Many economic development staff, business entrepreneurs, and community advocates are vision people. They must be to keep teams of volunteers, employees, and stakeholders together, focused on the goal, and moving forward.

It takes a lot of marketing, a good bit of dreaming, and a whole bunch of optimism.

That doesn’t leave a lot of time for analysis – whether that is the quantitative analysis of hard data or the qualitative analysis of personal feedback, surveys, and community discussions.

A lot of this very important stuff gets done at the frustration level. That is a recipe for lost opportunities.

Regional Strategic, Ltd. specializes in the analysis of data and community input. We can help you build a solid foundation under your vision. We are data experts. We are stakeholder input experts.

THAT IS THE VOODOO WE DO.

Texas Household Income Distribution

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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.

Why We Can’t Make Nice Things…

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This all started with some import-export data from the United States Bureau of Economic Analysis (BEA) and a couple of import-export graphs from the Economic Research Service (ERS) of the United States Department of Agriculture (USDA). It is not really about imports and exports in general, however, or even agricultural imports and exports in particular. It is about how our international transactions are influenced by the United States’ system of taxation. In particular, it is a discussion of how the United States’ tax system disadvantages labor relative to other factors of production and how that disadvantage affects our transactions with the world.

The graph below is “Export value share of production, 2013-22” for U.S. agricultural and food production. While we are just looking at industry production shares and not total volumes, it is clear that U.S. agricultural and food exports are heavily weighted towards relatively nonperishable commodities, low value food products. The majority of U.S. agricultural products are non-manufactured and non value added. Since 2008, the export share of U.S. agricultural production has remained relatively constant at about 20 percent.

The second graph is “Import share of U.S. food consumption, 2011-21.” The accompanying explanatory notes indicate that imports accounted for 15 percent of U.S. food consumption for the period, and that they steadily grew as a consumption share over the period. The unstated takeaway is that imports must be significantly above 15 percent of U.S. food consumption now.

As in the export graph, we are looking at industry shares rather than total volumes or values. It is still clear, however, that U.S. agricultural and food imports are skewed towards perishable and value-added food products – high value stuff. The explanatory notes also suggest that this is due to, “…numerous factors – including relative competitiveness in production…,” but no explanation for the, “…relative competitiveness of production…,” is given. It is assumed that relative competitiveness is a given – a state of nature.

It is not.

One of the reasons the U.S. exports nonperishable, non-manufactured, low value-added agricultural products and imports perishable, manufactured, and high value-added products is the competitive position of labor within the U.S. We often hear about the competitive position of labor between the U.S. and other countries, but that is not what we are discussing here.

The uncompetitive position of labor within the U.S. is largely a creature of the U.S. tax system. The tax system penalizes labor utilization within the U.S. in a number of ways. In general, taxes on labor are high. Income taxes, which are levied on wages and salaries, earned income, are substantially higher than taxes on capital gains, which are levied on incomes derived from physical and financial capital. This artificially raises the cost of labor in production. It also artificially lowers the cost of capital in production.

Income taxes are also generally levied on gross earned income and are collected immediately upon payment. Labor has very few means of minimizing or deferring their share of taxes. Labor pays gross rates. In addition, the U.S. government funds very large components of its social expenditure package (Social Security and Medicare) with direct taxes on labor. All of these increase the production cost of utilizing labor. It also increases the participation threshold of labor in production, making it less likely that labor will participate in the production process.

Conversely, recipients of receipts from physical and financial capital benefit from multiple incentives that can reduce rates that are already favored over labor (and further distort investment decisions). Among these is a very favorable schedule of depreciation, allowing owners of physical capital to claim a significant portion of receipts during the depreciation cycle as expense deductions. Recipients of returns from capital also have substantial leeway in determining when and how to realize those returns. This allows them to time and combine their receipts in tax-advantaged ways. When they do realize those returns, the recipients pay taxes at filing time rather than upon receipt. They pay net rates rather than gross rates. Furthermore, a large proportion of receipts from returns on capital is self-reported, generating substantial opportunities for tax avoidance.

Taken together, U.S. tax policy raises the relative cost of utilizing labor and lowers the relative cost of utilizing capital in the production process. At this point, one might ask, “What the heck does that have to do with the industry distribution of agricultural imports and exports?”

The answer is relatively simple. High-value food products, perishable, manufactured, and specialty foods, are generally more labor intensive than low-value foods. Through its tax policies, the U.S. disadvantages local production of high-value foods and encourages the production of low-value foods. This is mirrored in the types of food the U.S. exports and imports. There are other factors, but U.S. tax policy is a significant factor in this imbalance.

Tax policy doesn’t just affect employment and production in agriculture. Its effects are economy-wide. Luxury cars and watches are not generally products of the United States. Premium handmade shoes are generally imported, as are handmade suits. We export relatively capital-intensive goods and services. We import relatively labor-intensive goods. Both trends are supported by a domestic tax system that penalizes labor (earned incomes) and rewards physical and financial capital (unearned income).

If you recall your international trade course in undergraduate economics, trade is determined by relative input cost differentials within countries. Movement between countries equalizes internal cost differentials for both partners regardless of single-factor cost differentials between them. That means we can alter our import and export mix with the rest of the world by reducing the tax policy distortions between earned and unearned income. We are often told that unfair competition is stealing American jobs, but before anyone can be accused of cheating, we need to stop driving American jobs away with a distortionary tax regime.

In addition to the artificial cost differentials between labor and capital, the practice of funding social benefits through taxes on labor builds the cost of pensions and health care into the cost of goods on the market. This directly penalizes domestic consumers, and it increases the prices of U.S. exports, making labor-intensive exports even less competitive on the world markets. In most industrial nations, pensions are paid through general taxation and do not directly translate into export prices. In many, such benefits are paid for through value-added taxes that are only levied on goods sold domestically. This makes exports from these countries more competitive than exports from the U.S.

The tax distortions also cause fundamental economic problems and political distortions. While producers face artificially high costs of labor due to taxes, labor gets artificially low returns due to those same taxes. As a result, productive labor is often not an attractive trade-off with respect to the informal economy or household production. High labor costs due to taxes are coupled with low returns to labor due to taxes. We end up with:

  • A labor to capital cost differential that distorts our production and international trade
  • Stagnating labor incomes
  • A shortage of labor

All tied to a tax regime that penalizes earned income and rewards unearned income.

Recipients of unearned income recognize there are distortions in the labor market, but they have no interest in giving up their tax advantages. As an alternative, they insist that U.S. labor is paid too much. They fight to reduce the rights of labor to organize. They fight to reduce labor regulations that address fair payroll practices, overtime payments, working hours, and child labor. In order to get labor costs back in line, they fight to further reduce returns to labor while also defending and expanding the tax differentials that are the root of the problem.

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