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.
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.
We have recently been engaged in some demographic analysis of voter data here at Regional Strategic, Ltd. While a lot of news is made with exit polls on election day, those polls are seldom representative of the population as a whole. News organizations position pollsters at select stations, but they don’t have the resources to cover the gamut of socio-economic areas represented by precincts nationwide. They rely on sampling frames (see our November 24, 2024 blog at www. regionalstrategic.com/wp/the-moveable-middle-statistics-information-progress/ for some thoughts on sampling frames) which rely on expert insights that may have as much to do with news value as with statistical coverage.
After the fact, substantial voter analysis can be done with official statistics. Regional Strategic, Ltd. is supporting some analysis in Iowa utilizing data from the Iowa Secretary of State’s office:
The January 6, 2025 release of the Iowa Voter Registration Database. This includes all Iowa voter registrations on January 6 and information on their voting history. January 6 is the first release that includes 2024 general election information for all current registrants.
The Official Canvass by County, which includes vote totals, undervotes, and overvotes for every state and federal office contested in the election on a county level.
Precinct Results by County, which brings vote counts down to a precinct level.
The Iowa Voter Registration Database is the largest of these. It comes in at more than 2.2 million records with 132 fields. It offers information on voter age, sex, location (address, county, precinct) and voting history. It comes in multiple files for each of four congressional districts.
The first step is to combine files by district. The entire state is too big to conveniently handle in Excel. The 3rd Congressional District was consolidated and cleaned up. There are always some broken and/or incomplete files. This isn’t due to malfeasance or incompetence. In a world where taxpayers insist on paring government functions to the bone, there simply is not enough help to adequately process the masses of data and sources of data that must be reconciled. This scarcity of resources is also evident in the period of two months that is necessary to release files after an election.
The 2025 registration file is an improvement over recent periods. Only 10 damaged records were encountered in the 569,000 records for the 3rd Congressional District. These were all successfully reconciled into 4 complete records. As a result, the 3rd Congressional District file for analysis contained 568,994 registration records. The fields were checked to make certain all general 2024 election results were in the proper field (this year they were – another improvement). At this point, we had a data file for analysis.
Original fields allow data to be separated by county and precinct. These generate fields for national and statewide offices, and local election districts. Age, sex, and political affiliation (if any) are also recorded. In areas where political parties organize on the basis of neighborhood groups, a field can be inserted to identify these if they are defined in terms of groups of precincts.
The graph below shows the number of registered voters and the number of votes cast by sex and party as percentages of total registrations and votes for Iowa’s 3rd Congressional District.
Similar representations can be made by age group, sex by age group, or age group by sex. Any of these can be done statewide or by
Congressional district
Any state legislative district
County
Precinct
Any other jurisdiction that can be created with these groups
The graph below represents the same data splits as the graph above. This time, however, the area is Polk County. Polk is by far the most populous of the 21 counties that make up Iowa’s 3rd Congressional District. It accounts for approximately 61 percent of registered voters in the district and approximately 61 percent of district votes cast in the 2024 general election.
In both Polk County and the 3rd Congressional District, Democrats are dependent upon female voters and Republicans are dependent upon male voters. Both of these groups are significantly more likely to vote than any other groups depicted in the graphs.
Also apparent is the size of the independent group. In the 3rd Congressional District, Independents are the largest registered voter block. In Polk County, they are the second largest block. Independents do not turn out at the same rates as Republicans and Democrats, but the potential size of the block means it has significant impacts on elections.
We can take the voter results derived from the Iowa Voter Registration Database and blend them with candidate results from the Official Canvass by County and Precinct Results by County to get a pretty good estimate of the number of Independents who voted for candidates of either party. Without accounting for undervotes (registrants that voted in the election but did not vote in this contest) or overvotes (registrants that voted for too many candidates in this contest and, thus, had their votes voided), we can roughly estimate that 47.5% of voting Independents voted for the Democratic candidate for congress and 44.4% voted for the Republican candidate in the race for Iowa’s 3rd Congressional District seat. In Polk County, 51.2% voted for the Democratic candidate and 41.8% voted for the Republican candidate in the race. In neither area do the totals sum to 100%. Accounting for overvotes and undervotes (which could be done with available data) would push up all of these percentages. It is also nearly certain that some Independents (as well as some Republicans and Democrats) placed write-in votes for unlisted candidates.
This work is ongoing as inquiries for election analysis come in. Regional Strategic, Ltd. has the data in-house to work on 2024 general election results for Iowa. Data for other states can be obtained. Analysis is possible by age, sex, political affiliation and region to the extent that any individual state’s database will support.
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.
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.
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.
Regional Strategic, Ltd. engages in issues of regional economics: economic impact studies for existing enterprises and new initiatives, business planning and pro forma financial projections for new and growing businesses, and market and policy analyses (they are pretty much the same animal, really). We like project work. Every challenge is somewhat unique.
Almost all of our clients are looking to persuade someone: investors, taxpayers, development boards, potential clients, etc. We help them find, create, and interpret the information and data needed to support their interests. Upon occasion, we are asked to evaluate analytical studies done by others that our clients would like to challenge. It is still a persuasion game. It is just sometimes adversarial.
A term that often comes up when I talk to clients is the “Movable Middle.” The idea is that if a client can pull some of the middle of a population in their direction, they can generate sustainable growth or drive sustainable changes in policy.
In marketing situations where the client is only a small fraction of an industry made up of similar small players, this is healthy competition. It is reflective of Adam Smith’s 18th century observation of the “Invisible Hand” – where the individual actions of many small participants will continually move the marketplace towards a better solution for all – a mutually beneficial movement of the middle as a whole. In this case, small movements at the middle or average will be offset by multiple other small movements. The overall middle may actually move, but trends in the middle will reflect trends in the population as a whole. The population remains stable.
The situation can be very different where there are major players with market power. They can peel off so many participants in the middle that other market participants cannot accommodate within the existing population distribution. In these cases, moving participants away from the moveable middle affects the existence of the middle, itself. This can make the population less stable.
This is a particular issue in politics, where individual parties are actively attempting to split the middle in order to attain dominance. In political contexts the idea is that a group will never win over the core members of the other persuasion, but they should be able to whittle away at the population in the middle – to draw these denizens of the middle towards the clients’ points of view. This becomes increasingly critical as the number of competing entities diminishes.
You don’t have to remember much of your college statistics course to visualize this. Most of you remember the graphic below – the infamous bell curve. It is a standard normal population distribution where the mean (average) and the median are the same. They sit in the center (at the peak) and two-thirds of the population sits in close proximity to the mean. This is the starting point for an introduction to statistics course, and this type of population is the basis for most of the statistical analysis any of us has done.
In a marketing context, it may represent public perceptions of an industry-standard product where most people are generally satisfied with the product, some people really love it, and a few people really hate it. In a political context, it might represent a population where the majority of members generally wants the same things but sees multiple ways of getting to those things. It is easy to survey a standard normal population. With a normal population, a random survey of the population is sufficient as long as enough inquiries are made to assure reasonable representation of the central core and the outliers.
The center of the bell is the “Moveable Middle.” The term generally does not mean moving the entire middle. It means moving some members of the population away from the middle. A client might want to move someone slightly right of center to slightly left of center or move someone slightly left of center to farther left of center. This flattens the bell curve, increasing the variance and, perhaps, moving the mean away from the median and the highest point on the bell. The graphic below shows what this might look like.
Our population is no longer uniformly distributed around the mean. We have encouraged distinctions among the members that may depend on several specific factors (age, income, sex, gender, religion, etc.) We can no longer look at the population as homogenous. Also, since we actively advocated for and encouraged this movement, we can no longer look at population members as independent. The answer or perspective of any member we survey may be directly dependent upon the attitudes and perspective of other members we survey. We can no longer assume that each member sampled generates an independent data point for analysis.
At this point, we can no longer just pull a random sample from the population and survey the sample because members of our population are no longer randomly distributed or independent. We need to develop a sample frame. A sample frame is a set of rules regarding what we think we know about the distribution of the population. Do we expect that suburban middle-aged males, in general, have a common worldview? If so, we might treat them as a pool to sample. Do we expect that retired folks living on Social Security have the same interests and needs? Ditto. The more observable actionable distinctions we see within the population, the more detailed or complex the sample frame becomes.
Once we identify our frames of reference, we decide what a given group’s weight is within the population. We sample each group in accordance with the perceived weights. Then we combine our sampling responses to simulate the interests and opinions of the entire population.
Setting up and weighting the sample frame is not scientific. It is based on insights derived from science and observable facts on the ground, but it is not scientific. It requires some special insights and knowledge regarding the non-normal non-independent populations to be surveyed. This is why multiple seemingly redundant surveys of what appear to be the same populations regarding what appear to be the same questions and preferences often generate different results.
So-called “Gold Standard” pollsters or survey firms are “Gold Standard” because they have a track record of correctly setting up and weighting sample frames for evaluating non-normal non-independent populations. None of them are perfect, but they are regarded as consistently better than the rest. This is based on their ability to translate insights and facts on the ground into sampling frames that consistently mirror results revealed after the fact.
In any event, the difficulty in constructing sampling frames increases as we continue to move people away from the middle of the population. The graphic below might show a continuing population movement away from the middle. It may actually be a case where the population has split into two populations. In that case, if each population has a normal distribution and its members are independent, surveying either population becomes simpler, again. If both populations are of interest, however, from a political or marketing perspective, we still have the growing problem of developing accurate and informative sampling frames to evaluate the interests of the two populations together.
These issues are real regardless of whether we are marketing goods and services or political ideas. The wider implications are different in the two cases, however.
If we are trying to move the middle when marketing goods, we want to differentiate the market into two populations (ours and everyone else’s). For decades, Mercedes Benz attempted to split the automobile market. They tried to create a distinction between driving a car and driving a Mercedes Benz. Splitting the population would have made their information gathering easier. Two distinct populations could be polled separately, because they would be clearly identied. Mercedes drivers could be polled to find out what would enhance their experience. Car drivers could be queried regarding what it would take to move up. Separate polls would drive separate marketing campaigns. Successfully splitting the market would, in many ways, simplify the marketing game.
This has very few society-wide downsides when marketing goods and services, because we can each acquire the goods and services we want, regardless of the purchases of others. In the political marketplace, however, the population as a whole decides to buy one package of policies. Determining which package of society will “Buy” would ideally be made by an educated and informed electorate, similar to the assumption of perfect information in the theory of competitive markets. Unfortunately, the attempt to “Move the middle” in politics effectively reduces the amount of useful information in two ways.
The first of these is passive. As we move from a normal distribution in the first graphic above to the double-peaked population of the last graphic, it becomes harder to effectively poll or survey the population with regard to their community interests or to weight the importance of these interests across groups within the population. As discussed above, this is a result of the split in the population no matter how the split came about. The result, however, is that different polls regarding the same issue or group of issues will give significantly different results depending upon how the pollster designed the sample frame. Individual poll results will increasingly depend on the subjective insights of the individual pollster. The information provided to the electorate and to policy makers will be inconsistent. Decisions made on the basis of that information will become increasingly inconsistent. Governance on the basis of these decisions will become increasingly inconsistent.
The second of these is active. If we assume the population is differentiating because of an active information campaign to draw members of the population away from the middle, we almost have to assume this information is not representative of the population as a whole. We also might assume that some of this information is developed in the form of opinion surveys where sample frames were defined in such a manner as to generate favorable results. As we discussed above, in a differentiated or split population, the accuracy of polls has a lot to do with the pollster’s definition of the sample frame. All-star pollsters are those who are adept at correctly reading the population and developing appropriate sample frames. Policy influencers, however, can also be adept at defining sample frames that generate directed results, even while utilizing acceptable statistical techniques and avoiding the use of leading questions.
Regardless of whether the active or the inactive effects predominate, the erosion of useful information caused by moving population members away from the “Moveable Middle” will decrease the value of information provided to the electorate and policy makers. As a result, decisions made by the electorate and policy makers will be increasingly inconsistent. Governance based on these decisions will become increasingly erratic and ineffective.
This will go beyond politics. It will inevitably affect economic planning, investment, and growth.
Increasingly erratic and ineffective governance generates environments where it is increasingly difficult to plan. An inability to plan generally results in a reluctance to take risks. Increasing risk aversion tends to retard productive investment. A dearth of productive investment reduces production. Reduced production restricts income.
Remember that the expected return on any investment is the product of potential returns and the probability of attaining potential returns. A major determinant in that probability is government stability. Long-term productive investment tends to be made where there is an expectation of government stability over the lifetime of the investment.
In uncertain environments, investable funds tend to fine their way into financialization (placing bets on future policy direction by buying and leveraging shares or derivatives of existing productive assets), or the pursuit of returns directly through the exploitation of favorable government policies (rent seeking). While both may generate income for the participants involved, neither generates productive value or wealth for the economy as a whole.
None of these observations changes the fact that it is in the client’s best interest to try peeling the population in the middle away and towards the client’s ends. The society-wide implications, however, particularly in a political context, are important issues to consider.
P.S.
All of the images in this post were taken from an online article by Andrey Akinshin, “Normality is a Myth,” dated October 9, 2019 and downloaded from https://aakinshin.net/posts/normality-is-a-myth/ on November 20, 2024.
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.
We all talk about employment and quote statistics on a regular basis, but few of us even imagine the complexity of the employment data out there or the substantial differences between sources.
Technically, “Employment” is the commitment of personal effort to the process of producing goods and services. In common usage, a person is said to be “Employed” if the person works for another person, a person is said to be “Self-employed” if the person works for him or herself, and a person is said to be “Unemployed” if the person has no job. This terminology works fine in most casual conversations but breaks down when dealing with data collection and maintenance efforts and utilizing data in analysis.
To maintain more precision and consistency, these terms are more tightly controlled in the official statistical process. Understanding the terms and the sources of data is important to understanding and correctly using the information the data provides. The link below is to a paper that provides definitions of terms and an understanding of data collection and content for major statistics on “Employment” in its most common contexts:
Employment and unemployment rates for the residential labor force
Employment by Industry
Employment by occupation
Employment by place of residence
Employment by place of work
There are several sets of employment statistics available for any specified area. These might originate from multiple statistical agencies and refer to different measures of “Employment.” It is important to understand what the statistics are and what they tell us.
About 20 years ago, I wrote the original version of the linked narrative to assist students and researchers engaged in regional analysis work. Its first home was the Department of Economics website at Iowa State University. When I left the department in 2007, the document stagnated due to a lack of updates. It was eventually taken down. I recently stumbled across the original text and decided it would still serve a purpose if updated. Here is the update –
