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Category Archives: Ag and Natural Resources

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.

Potential Implications of ICE Interventions in Midwest Detasselling

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It is detasselling season in Iowa. Detasselling is a critical process in hybrid seed production. Hybrid seed is critical to Midwest corn production. Detasselling in Iowa is heavily dependent upon immigrant labor.

During detasseling season, Iowa utilizes 8,000 to 10,000 hours per day of immigrant labor on H-2A visas in crop production The exact numbers for any year can be compiled from public data at the U.S. Department of Labor. That is 1,000 to 1,250 workers per day. Adding in other immigrants, whether documented or undocumented, immigrant detasseling labor might reasonably be double that.

ICE has arrested over 450 people in Iowa since President Trump has taken office. That is over 50% more than in all of 2024. An active intervention by ICE during detasselling season could decimate the immigrant labor supply needed to complete the task, regardless of immigrant status. This isn’t far-fetched. Farms all over California, Florida, and Texas are reporting that immigrant labor is not coming to work due to of fear of ICE.

If the average detasseller covers about 2 acres a day, ICE could prevent 4,000 to 5,000 acres of seed corn per day from being detasseled. Over a ten-day season, that is 40,000 to 50,000 acres of seed corn fields that won’t produce useable hybrid seed. That seed won’t be there for farmers to plant next year.

At 30-50 bushel of seed per acre, this could cut 1.2 million to 2.5 million bushels of seed from the available supply in 2026. At 2.5 planted acres per bushel of seed, this is 3.0 to 6.25 million acres of Iowa’s anticipated corn acreage that will not have seed. That’s 25% to 50% of anticipated Iowa corn acreage in 2026.

A Simple Land-value Appreciation Model and Ascension to the Top One-percent

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People talk about the “1%” as if it was something of the outer world. It is assumed to be a small cadre of individuals with seemingly infinite wealth who live in the economic stratosphere. This is not actually the case.

In 2024, there were only 813 billionaires in the United States. These people are the ones we often think of when we think of the “1%.” However, because there are 340 million people in the United States there are 3.4 million people in the “1%.” If you took away the 813 billionaires in the United States, there are still 3.4 million non-billionaire people in the “1%.” Almost all of us know some of them.

According to Investopedia, the minimum net worth to be among the “1%” was $13.7 million in 2023. This essay looks at one possible journey into this cohort. It starts with a simple model of farmland acquisition initiated by an inheritance. It shows how, over the course of 30 years, this can provide entrance into the “1%.” It concludes with some insights into the results of the process illustrated.

The Situation – Inheriting the Average Iowa Farm

Assume that one inherited an average Iowa farm in 1992. It consisted of 325 acres valued at $2,559 per acre in 2022-equivalent dollars. It was inherited it complete with the equipment and facilities required for operation. Assume it was inherited free and clear. It was paid off, and any inheritance and estate taxes and any other transfer costs were covered by the previous owner’s life insurance or a cash component in the estate. As a result, one acquired real estate (farmland) assets valued at $831,643 in 2022-equivalent dollars. (Some calculations may not be exact due to slight rounding errors in the presentation.)

We can build a model of the land value account of this operation utilizing publicly available data:

  • Number of farms, land in farms, and average farm sizes from the United States Department of Agriculture
  • Average Iowa farmland values per acre from Iowa State University
  • Average Iowa ag land rents per acre from the United States Department of Agriculture
  • Farm income and government payments information from the United States Bureau of Economic Analysis

The model is driven by land value appreciation and rental income (savings) per owned acre. Beyond the initial inheritance, additional land acquisitions are entirely funded by either capitalizing land value appreciation or in the form of cash purchases supported by accrued rental income (savings) in excess of mortgage payment needs. The amount of land value appreciation that can be capitalized is limited by the availability of rental income (savings) to fund mortgage payments. All dollar-denominated data is adjusted to 2022-dollar equivalents to facilitate comparisons and trends.

The rules to the model are listed below:

  1. The land value account is isolated from other income. Land acquisition is supported by nothing other than the value appreciation and rental income (savings) generated by the initial 325-acre inheritance.
  2. Rental income (savings) must be sufficient to cover mortgage payments at the time of any purchase or refinance.
  3. Land can be purchased on terms of 40 percent down, 7.5 percent interest, and 20-year amortization with payments once a year throughout the period modeled.
  4. The land is inherited in 1992, the first year in which we have complete single-source data.
  5. The model runs through 2022, the last year in which we have complete data from the same sources.
  6. The unencumbered value of the initial inheritance is utilized as collateral to purchase as much land as possible in 1993.
  7. Following 1993, land is purchased and/or debt is refinanced every five years to whatever extent land value appreciation and rental income (savings) supports under the assumptions above.
  8. At purchase points, accrued surplus rental income (savings) over mortgage payments is used to purchase land for cash as long as total purchases (cash and amortizations) total 10 acres or more.
  9. Farm operating income and expenses, including property taxes are ignored.

From 1992 to 2022, the average acre of Iowa farmland appreciated at an average of $282.75 per acre per year in 2022-equivalent dollars. Over the same period, average farmland rents increased from $182 per acre to $256 (reaching a peak of $314 in 2014). These increases varied from year to year but have been significant over time. This farmland appreciation and increasing rental value provides significant leverage to increase wealth.

The table below presents the major items of interest. It covers the starting point (1992), the ending point (2022), and the purchase points throughout the period (1993, 1998, 2003, 2008, 2013, and 2018).

Our inheritor inherited 325 acres in 1992. It was valued at $831,643 and was free and clear. There were no loan or loan payments. At 1992 rental rates, it generated $59,006 in rental income (if rented out) or rental savings (if farmed by the inheritor) annually.

In 1993, the inherited acreage was fully leveraged to purchase an additional 491 acres. Our inheritor then had land assets of $2,070,614 and a mortgage of $1,246,158. Expected rental income (savings) of $150,175 annually is more than enough to cover the mortgage payment of $122,238. Any surplus of rental income (savings) over mortgage payments is accumulated. If sufficient, it is used for cash purchases of land at subsequent five-year transaction points. Any future appreciation in land values is capitalized at five-year transaction points to purchase more land up to the point where expected rental income (savings) is sufficient to cover mortgage payments.

Our inheritor will continue to make land purchases with capitalized appreciation and excess cash every five years to the extent possible. In 2013, the final land purchase is made. At that point, land holdings are 2,132 acres. This is 656 percent of the initial inheritance of 325 acres. In terms of dollar values, 2013 holdings are worth $22,934,292. This is 2,758 percent of the original inherited value of $831,643.

Land value increased substantially more than acreage because we constrained our mortgage obligations to levels where expected rental income (savings) could cover mortgage payments. Land values increased significantly faster than rental rates, so our inheritor was not able to capitalize a significant portion of land value appreciation. Had the inheritor faced lower interest rates or longer mortgage terms, it would have been possible to accumulate significantly more land and generate a significantly higher net worth.

In 2013, the land value account has a net worth of over $16 million. That is well over the $13.7 million needed to be a member of the “1%” in 2023. It took only 20 years of land value appreciation and rental income (savings) to raise a 325-acre inheritance to the “1%” level.

Land values hit a peak in 2013 and fell through 2018. Rental rates also fell. As a result, our inheritor could not purchase land in 2018. In fact, falling rental rates were insufficient to service the existing mortgage, so property needed to be refinanced in 2018 in order to spread remaining debt and lower payments to rent-serviceable levels. Refinancing was possible under model rules, but, unfortunately, net land value fell below $12 million.

By 2022, land valuations had recovered, but rental rates had continued to fall. This increased net worth but decreased the level of mortgage payments rental income (savings) could support. It is assumed that 2023 (which is just beyond the model) will require another refinance to spread remaining debt obligations and lower payments to a rent-supportable level. The net worth of the land value account is over $18 million. That put our inheritor solidly back into the “1%.”

It should be noted that there is nothing here about farming the land. Our inheritor could have rented the entire accumulated acreage out. While not of interest here, farming the land – no matter who farmed it – would have generated average of

  • $54.75 per acre per year in direct government payments
  • $120.72 per acre per year in operating income
  • $175.47 per acre per year in total farming income

Over the period from 1992 to 2022, farming these holdings would have generated a total of nearly $9 million. That would have averaged nearly $300,000 per year.

Things to think about

There are several things going on, here. First and foremost, these things are not at all unique to the farming industry. Ag land was used in the example because existing historical data makes it easy to consistently separate asset values and incomes from operational incomes in farming. The asset-value accumulations and the inheritance value advantages illustrated here are common across nearly all industries in the United States.

Contrasting Purchase to Inheritance

In the example above, we closed off the land value account from all other sources of funds. The acquisition process was initiated by the inheritance of 325 acres of unencumbered land. That inheritance was critical to the land value accumulation observed.

Suppose someone wanted to initiate this process by buying 325 acres of land on the same credit terms outlined above. That purchase would require collateral. Suppose that collateral was in the form of an unsecured loan at 7.5% interest (the same interest used in amortization throughout this analysis). We would initiate the model with

  • 325 acres of land in 1992
  • $831,643 in land value
  • $498,986 in a bank mortgage with payments of $48,947 annually
  • $332,657 in unsecured debt

Whereas our inherited land in the example above was unencumbered and could immediately by leveraged to purchase more land, the land purchased here is completely leveraged and cannot support any additional purchases.

Because the land value account is closed, we consider the unsecured debt as an obligation that must be retired before additional land can be purchased. Funds for this can be acquired in two manners:

  1. Land value appreciation can be capitalized to pay the unsecured debt. In effect, this trades increased mortgage balances for decreased unsecured balances. This is limited by both the amount of land value appreciation and the amount of mortgage payments that can be supported by rental income (savings).
  2. Increases in rental income (savings) generate surplus cash that can be used to pay off the unsecured debt.

In 1998, 2003, and 2008, increased rental income (savings) is used to restructure the bank mortgage, capitalizing all the land value appreciation that rental income (savings) can service as mortgage payments. The entire value of this increased mortgage in each period is utilized to buy down the unsecured debt. There are no additional funds for land acquisition.

Restructuring in 2013 is sufficient to completely clear the remaining unsecured debt and to purchase an additional 27 acres of land. This is the only land acquisition that will be made in this scenario. As in the discussion above, land valuations and rental values fell from 2013 to 2018. In 2018, mortgage debt had to be refinanced to spread remaining debt and lower payments to a level supportable by rental income (savings). Rental values continued to fall through 2022. It is assumed that mortgage debt will have to be restructured again in 2023 (which is beyond the model) to lower payments so they can be supported by rental income (savings).

At the end of the model period, our purchaser has

  • 352 acres of land, versus 2,132 acres accumulated by our inheritor
  • $3 million net worth in the land value account, versus $18 million accumulated by our inheritor

Recall that, in both scenarios, the land value account is closed. It is driven by land value appreciation and market rental rates regardless of whether our owner farms the land or rents it out. Had our land purchaser farmed these acres, it would have generated $1.77 million in farm income over the period, or an average of almost $59,000 per year. Our inheritor, on the other hand, would have generated nearly $9 million in farm income for an average of nearly $300,000 per year.

Intergenerational Implications

In the current tax environment, neither of our landowners would be in a position where their assets were subject to estate taxes when they die. In most jurisdictions, their heirs would not be subject to inheritance taxes. This has tremendous intergenerational implications.

Suppose our original purchaser and inheritor both die in 2022. Sufficient land is sold to cover their remaining mortgage debts and rental income (savings) shortfalls, and their land is bequeathed to heirs prior to the end of 2022.

We assume that property sold to cover existing debts is subject to capital gains taxes totaling 23.98% at the national and state level. We assume that any remaining property passes to the next beneficiary tax free. We also assume that the basis of any inherited land is stepped-up upon inheritance, freeing the beneficiary from any capital gains that had accrued to the deceased owner.

In order to clear $5.4 million in outstanding debts, our inheritor’s estate liquidates a total of 515 acres of land. Disposing of the highest-basis land acquisitions to minimize capital gains taxes covered both the outstanding debts and a $273,863 capital gains tax obligation (an average of $530 per acre sold).

At the conclusion of these transactions, our inheritor’s beneficiary is bequeathed 1617 acres of land valued at nearly $17.9 million. Of this, $12.9 million was acquired as capital gains by the previous owner. Stepping up the beneficiary’s basis is equivalent to a gift of $3.1 million in foregone capital gains taxes (over $1,900 per bequeathed acre).

It should be noted that, even had the estate or beneficiary paid or retained liability for these taxes, the beneficiary would still have received land of sufficient value to remain among the top “1%.” All of this results from the simple appreciation of the inheritance of an average Iowa farm over 30 years. Within this land appreciation model it has not even been necessary for the original inheritor to farm the land. Rental values and appreciation were all that were required to generate this result.

Alternatively, consider our other example, the individual who bought 325 acres in 1992. At the point of death in 2022, the value of land owned is nearly $3.9 million. Debts are $779,863. In order to clear debt, this estate liquidates 81 acres of land. This includes the 27 acres purchased in 2013 and 54 of the original 325 acres purchased in 1992.

The sales result in a capital gains tax obligation of $112,180 or $1,385 per acre sold. Note that this is over 250% of the per-acre capital gains tax obligations for our original inheritor. This is because our inheritor was able to continue acquiring land as land appreciated. This increased his basis on land sold, which reduced his capital gains on transactions. Our original buyer was able to acquire only 27 additional acres over the model period. The majority of his sales came from land with 30 years of capital gains.

After the land sales, our original owner’s beneficiary is bequeathed 271 acres valued at nearly $3 million. Of this, $2.3 million accrued as capital gains to our original purchaser. Stepping up the basis of this farmland at inheritance is effectively a gift of a little over $551,000 to our original purchaser’s beneficiary ($2,034 per acre bequeathed).

Questions of Equity

If we were able to continue the model, we would start with two beneficiaries:

  • Heir A: our original inheritor’s heir bequeathed 1617 acres
  • Heir B: our original purchaser’s heir bequeathed 271 acres

In 2023, our two heirs would be able to leverage their unencumbered inheritances. Heir A could purchase 2,425 acres for a total of 4,048 acres with 40% equity. Heir B could purchase 406 acres for a total of 677 acres with 40% equity. This is where cycle number two begins. 

There are clearly issues of equity revealing themselves. Recall that we did not expect any of the owners to farm their land. They did nothing other than fully exploit their passive income from rents and their passive appreciation in land values. There is no difference in behavior or apparent merit between our participants, yet their opportunities at the outset of any cycle are vastly different and are diverging.

In addition to the disparity noted above, there are serious concentration problems. To begin the first cycle, our two participants each acquired one average sized farm (325 acres of land). At the end of the cycle, they bequeathed 1,888 acres. In effect, they concentrated nearly 4 additional average sized farms into their two operations. To start the second cycle, our two heirs immediately purchased an additional 2,831 acres of land. Even given an increased average farm size of 345 acres, this transaction concentrated another 8.2 average-sized farms into their two operations. The relative ease with which inheritors can multiply their holdings stands in stark contrast to the opportunities available to new purchasing entrants (recall our first generation, above).

Assuming that this cycle of land value increases over 30 years is consistent, in less than 6 generations (30-year cycles) our two current heirs will have accumulated land in excess of the current farmland total in Iowa. This is unstable just between the two of them. Assuming they are not alone, the individual advantages proffered by inheritance will almost certainly lead to an unstable and unsupportable farmland market in a couple of cycles.

The situation our model illustrates is a market failure. We have two individuals who cannot be distinguished with respect to market performance yet see inevitably diverging economic outcomes. In addition to that, we have a rapidly concentrating land market that inhibits market competition and competitor entrance. Furthermore, we have inheritance and capital gains tax environments that exacerbate the problems illustrated.

Conclusion

At the outset, we noted that the majority of the top “1%” is not otherworldly. Our model demonstrates that a relatively common inheritance and equity appreciation can move an individual into the “1%” in the course of a single generational cycle if that individual diligently exploits and capitalizes unearned incomes and equity appreciation. Our model also shows that individuals starting from scratch with similar assets cannot match the results of inheritors, even if no distinction can be made between them in terms of economic merit.

This points to economic failures that could and should be addressed by the estate, inheritance, and capital gains tax environments. If we are going to provide opportunity to every individual, we need to assure that the accumulations of previous generations do not override the potential of current participants.

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.

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.

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.

An Inquiry into Farmland Value Streams

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We are doing some work on farm and farmer value streams here at Regional Strategic, Ltd. The pilot work is using Iowa, but the intent is to take what is found and expand the work across the Upper Midwest.

One of the first major questions regards farmland valuation and appreciation. The graph below shows a simple relationship that leads to a number of complex questions. The graph shows cumulative inflation-adjusted value streams for ag land appreciation (from Iowa State University’s Farmland Value Survey), direct government payments (from the Bureau of Economic Analysis), and farm income net of government payments (derived from the Bureau of Economic Analysis) per acre of farmland (from the Census of Agriculture).

The period runs from 1993 to 2022. The scenario assumes that an acre of land is purchased in 1992 and the purchaser initiates production in 1993. The three lines show accumulations of income and land appreciation over a 30-year period. The endpoint is set as the last year in which complete stable information was available from the Bureau of Economic Analysis.

The first thing that jumps out is that accumulated land value appreciation outruns operating income and direct government payments. Accumulated land appreciation separates from the other two streams in 2002. In addition, Operating income breaks out above direct government payments in 2007.

Over the thirty years, the three inflation-adjusted value streams generated an average of $458 per year. Averages for each of the components were

Of this average value stream, only 38 percent came from income, and nearly a third of this income was in the form of direct government payments. Operating income accounted for only a little over 26 percent of the value stream generated by an average acre of Iowa agricultural land.

Average farm earnings net of government payments (operating income) was only sufficient to pay a 4.72 percent return on the 1992 purchase price of $2,559. Operating income plus direct government payments were only sufficient to pay 6.86 percent return to purchase price. This is all barely enough to cover interest or carrying cost on the investment.

Given these low production returns, what makes land price appreciation average 11.5 percent per year?

What caused land appreciation rates to break away from operating income and direct government payments in 2002?

What caused operating income to break away from direct government payments in 2007?

A portion of these relationships might simply be the result of the period being observed, but the size and consistency of the breaks suggest there is something more. There appears to be a confidence in the value of Iowa farmland that overrides observed farmland productivity. Why is that?

  • Is it due to indirect subsidies?
  • Is it due to the conviction that subsidies and relief will always maintain farm income?
  • Is it because of a belief that the removal or reduction of farm subsidies, both direct and indirect, will inordinately affect other production areas and concentrate production and value in Iowa?

We honestly don’t know the answers to these questions. That is the point of the inquiry. More will come as we noodle this out.

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