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Rising Inequality and Falling Property Tax Rates

by Mason Gaffney, 15 June 92
Chapter 10 of Ownership, Tenure, and Taxation of Agricultural Land,
Gene Wunderlich (ed.), Westview Press

This paper is available in pamphlet form from the Robert Schalkenbach Foundation

A priori, a tax on buildings works to suppress building and to penalize smaller farmers, whose building to land ratio is higher than that of bigger farmers. The findings seem to show, therefore, a stronger counter effect, pro-incentive and pro-subdivision, of the other part of the property tax, the part based on land value.

The national average of farm property tax rates peaked in 1930 at 1.32 percent. It fell to 0.77 percent in 1945 and stabilized at about that level -- it was 0.85 percent in 1987. Sales and income taxes, which bear heavier on urban activities, replaced the missing property taxes as sources of revenue.

Mean acres per farm had remained fairly constant for 65 years (1870-1935) at about 155 acres, despite two major industrial merger movements. After 1935 the mean took off and had tripled to 462 acres by 1987. As the number of farms was falling, national population was on the rise. In 1900 there was one farm per 11 Americans; in 1987 only one per 113. Farms became unaffordable. Real wage rates have not risen as fast as real land prices since 1955, and not at all since about 1975, which has raised the labor-price of land. Coupling this with rising acres per farm, the labor-price of a farm roughly tripled, from about 6 years' wages (before payroll deductions) in 1954 to about 17 years' wages in 1987.

The Vanishing Middle Class

In 1900 the Census Bureau began publishing farm data ranked by acres per farm. Using those data, the Gini Ratio (GR) was .58 in 1900, and it rose only slowly, to .63 in 1930. After that it rose faster, to .70 by 1950, plateaued there for 15 years, then rose again to .76 by 1987. (By comparison, GRs for personal income are much lower, about .40, and are much more stable over decades.) The accelerated rise since 1930 coincided with the rise of mean acres per farm, and both followed the fall of property tax rates.

As a measure, GR deals only with concentration among existing farms. Industrial economists fault it for not reflecting the loss of farms. Acknowledging the critics, GR can be modified to combine both effects. Simply add the ghosts of 4.5 million farms that died between 1935 and 1988 to the lowest bracket, as farms with zero acres in 1988. This raises GR for 1988 from .76to .92, a radical rise of inequality since 1930 (.63).Calculating the GR this way gives one a better sense of how concentration shot up after 1930-1935. In the Great Depression(1930-1941), millions of small farms provided a refuge for the jobless and homeless. Today, that refuge is closed, with explosive social consequences in urban slums.

The Rise of Land Quality in Vast Farms

The concentration of the value of farm real estate is growing faster than that of farm acres. The value of land and buildings ($L+B) per acre in the top bracket (farms of 1,000 acres and over) has risen relative to all farms. For easy recall and reference, I label this ratio GAMMA. Gamma is the top bracket acre value divided by the mean acre value. In 1910, Gamma for $L+B was .35. By 1930 it had dropped to .29, after 20 years of high farm property tax rates. By 1987, after 57 years of low property tax rates, it had doubled to .61.

Accordingly, the share of $L+B in farms of 1,000 acres and over rose faster than the share of acreage from 1930-1987. The share of acreage rose from .28 to .62, a rise of 123 percent. The share of $L+B rose from .08 to .38, a rise of 375 percent.

The land share of real estate value (LSREV) in the top bracket (1,000 acres and over) has probably risen faster than overall. LSREV is an acronym for $L/($L+B), that is the value of land as a fraction of the value of land and buildings together. Gamma for $L alone always stood higher than for $L+B during the years 1900-1940 when the census published separate data on $L and$B for all farms. There is no comparable later data published to test directly whether Gamma for $L has risen even higher over Gamma for $L+B (for 1988, AELOS separates $L and $B only for owner-occupied farms, not for all farms.) There is indirect evidence, however, that LSREV in the top bracket may have risen faster than overall.

One indicator is the share of harvested cropland, by size of farm. This share in the top bracket has risen relative to the share for all farms. This ratio in 1925 was 8.5 percent over 37.3 percent, or .23. It rose to .37 in 1950 and to .69 by 1987.

Another confirming indicator is the rising concentration of irrigated land. When irrigation was young in Anglo-America (1890-1914), it was the recourse of small farmers struggling for land against bonanza wheat farmers and ranchers (the familiar grist of horse operas). Then, vast spreads were subdivided to create small irrigated farms. There was drastic subdivision and intensification (1900-1930). Land in farms of 1,000 acres and over actually dropped (nationally) by 15 percent from 1900 to 1910, the only drop on record. Now, however, 34 percent of all irrigated land is in the top bracket, farms of 2,000 acres and over. Control of irrigated land means control over water. Control of water gives control over arid lands round about. Ownership and control based on water have become highly concentrated. For farms with irrigated land, GR = .82, substantially higher than the GR of .76 for all farms.

An independent study by Villarejo illustrates the trend from 1940 to 1982 in a specific area intensively studied by Wilson and Clawson in 1940. The study area was the irrigable and irrigated land in Kern and Tulare counties, California. Replicating the study in 1982, Villarejo found that "land ownership has become more concentrated as more land has been placed in irrigated farms."

A last confirming indicator is concentration by sales. Data for this are available from 1950. GR for farms ranked by sales has risen faster from 1950-1987 (.67 to .80) than GR for farms ranked by acres (.70 to .76). Sales were less concentrated than acres; now they are more concentrated. That is consistent with the hypothesis that land quality in large holdings is rising.

Rising Land Share and Rising Ratio of Price to Cash Flow

The LSREV almost certainly rose from 1940, when land prices were depressed, to 1987. By splicing disparate tables to get comparable data, I estimate LSREV for all farms rose from .69 to about .80. Higher LSREV means a higher price to cash flow (P/C) ratio. That is because cash flow (C) from buildings and other capital includes allowance for depreciation (D). Depreciation is part of cash flow, but not part of earnings; price is capitalized only from earnings (C-D). Thus, the price of capital is capitalized from less than its cash flow. The price of land, oppositely, is capitalized from more than its cash flow. It is from cash flow plus the current appreciation (C+A). Land price also captures expectations of cash and service flows from various non-farm elements, many of which are deferred and speculative.

High ratios of farm price to cash flow are another barrier to farm entry. Direct data on farmland P/C ratios are not available, but a rough surrogate (to show trend, not value) is the ratio of farm real estate values to gross farm revenues. This ratio was low (2.56) in 1945, when a gloomy market looked for another postwar farm depression and land buyers got lucky. It rose to 4.0 in 1954, and to 5.9 in 1982. Farmland prices dropped sharply in the mid-1980s, but still left the ratio at 4.35 in 1987, much higher than for 1945-1950.

A high P/C ratio shows a higher share of $L in farm wealth. A common belief is that high capital costs of machinery and equipment ($M&E) are peculiar to modern farm technology and are the main barrier to farm entry. That belief doesn't wash, however. Cyrus McCormick was mass marketing mechanical reapers before the Civil War. Today, $M&E is about 10 percent of all farm assets, much smaller than $L+B. In addition, loans invested in $M&E are usually self-liquidating from the excess of cash flow over interest, but loans for buying land mean negative cash flow for several years. Meeting a negative cash flow requires pumping in still more outside capital, an added barrier to entry.

To sum up, rising acreages mean there are fewer farms overall. Rising labor prices per farm mean aspiring farmers who lack prior wealth can no longer buy in. Rising Gini Ratios mean acreage is less equally shared among a given number of farms. Rising Gamma factors mean the higher quality land is moving into bigger farms. The Gamma data are confirmed by rising shares of cropland and irrigated land in vast farms. Rising P/C ratios reflect a higher LSREV, and they mean it is harder for a newcomer to acquire any farm acres. The combination means the agricultural ladder has been pulled up. Entry is nearly impossible for farmers lacking outside finance; exit and latifundiazation proceed apace. These changes accompanied and followed a 40 percent drop in farm property tax rates.

The Lesser Improvement of Bigger Farms

A result of rising concentration is the separation of land from capital. With some exaggeration, American latifundia are now lands without buildings, but buildings cluster on smaller farms, many without enough land. This implies at least three points. First, building wealth is more equally distributed than land wealth. Second, the property tax would be more progressive if changed to a pure land tax, exempting buildings. Third, many latifundia are not being used to their potential, while capital on some small farms is under-complemented with land. I support the case first using national data, and then by comparing states.

It is awkward that the 1987 Census of Agriculture defines "farm size," and ranks farms, only by acres rather than value. I used the acreage rankings above for intertemporal comparison because they are comparable with each other over time and are all that is available over time. Data on value per acre is available by acreage brackets, but a proper data set to test my thesis cross-sectionally would rank farms by land value ($L), rather than acres. This would reshuffle and rerank individual farms. For example, in the brackets from 260 acres to 1999 acres there are now more farms worth $1 million and over than there are in the top bracket (2,000 acres and over). Half the farms in the top bracket are worth less than $1 million each. If all farms were properly reranked by value, the degree of inequality and the effect of "size" on factor proportions would change. In what follows, I use available data to simulate what those changes might be. One available data set, although partial, is ranked by value, and it confirms my thesis with startling force.

National Data [on] Concentration of irrigated land

The yield per acre of most crops stays level or rises with harvested acres per farm. At the same time, sales per dollar of real estate fall somewhat. The most likely reason is that the quality of harvested land rises with quantity. There is, to be sure, a trade-off between quality and quantity, but there is also a bond. Whoever can afford more can afford better. Which effect is stronger? The question must be resolved by data.

The 1987 Census of Agriculture does not provide overall land-value data (separate from $B), but it does provide one surrogate for land quality: land irrigated. Irrigated land is generally flatter, lower, and warmer; in addition, the water supply itself is an easement over more land (the watershed, whose acreage is not counted with acres per farm). Farms of 2,000 acres and over have 34 percent of all irrigated land, but only 24 percent of $L+B. That indicates higher land quality coupled with lesser improvement.

The 1987 census ranks farms by "acres harvested," not in the aggregate, but crop by crop. For almost all crops, the share irrigated rises steeply with acres per farm. Alfalfa is an example. Sixty-seven percent of acres in the top bracket are irrigated, compared with 23 percent for all farms producing alfalfa. The ratio of those percentages forms an index, Zeta (share of land irrigated on vast acreages, relative to all farms). For example, for alfalfa, Zeta = .67/.23 = 2.9. The Zetas for other crops are given in the end note. This finding is very strong because it runs against the ranking bias. These farms are ranked by all acres harvested; this bias alone would make Zeta < 1 if the scatter of points was perfectly symmetrical about both axes. If the census ranked these same data by acres irrigated, instead of acres harvested, the Zetas would be much higher.

Comparing different crops, high values of GR go with crops that are mostly irrigated. For example, 85 percent of tomato acres and 14 percent of silage corn are irrigated. For tomatoes,GR = .91; for silage corn, GR = .52.

It is easy to presume that in a state of extremes, like California, high GRs result simply from consolidating high-priced irrigated land with vast arid ranches, "the cattle on a thousand hills." Several of the older Wright Act irrigation districts are strikingly egalitarian, it is true, with small mean farm sizes. These older districts have become, however, exceptional. An intensive study of the huge Westlands Water District, 100 percent irrigated with cheap, subsidized federal water, shows GR= .77. Villarejo and Redmond consolidated data from 10 districts receiving among them 48 percent of all Central Valley Project water, for GR = .69. These high GR values come from 100 percent irrigated lands. These and other data on irrigated acres support the thesis that quantity and quality of cropland are mates more than alternates. The vaster farms also get more water per acre.

Land Concentration for Farms Ranked by Sales

The census of agriculture now also ranks farms by sales per farm. This yields higher GR values: .80 in 1987, compared with .76 by acres. Sales are a measure of dollar values. This suggests, without proving, that GR-by-$L > GR-by-acres. In 1950, the top class (at that time, farms with sales >$25,000 per year) comprised 1.9 percent of all farms, 26 percent of the sales, and 41 percent of the irrigated land. Again, this finding is very strong because it runs against the ranking bias, which is to put a higher share of sales in the top bracket.

I used 1950 above because the current census does not consolidate this information. It does, however, show it on a crop-by-crop basis. For example, for cash-grain corn, in the top group (highest sales per farm), 37 percent of the acres are irrigated versus 14 percent for all groups. It goes on like that for all crops (except rice, all of which is irrigated). This finding is unaffected by ranking bias, pro or con.

Lack of buildings on latifundia

The 1940 Census of Agriculture was the last to separate $L from $B, overall. In 1940 the building share of real estate value ($B/[$L+B], or BSREV) was .69 in the lowest acreage bracket, .31 for all farms, and .12 for farms of 1,000 acres and over.

AELOS (1988) gives no comparable comprehensive data, but it does give two series that test the point and have the advantage of disaggregation. One is for "owner-operators" and one for "landlords with debt." For the owner-operators, ranked by acres per farm, BSREV was .63 for farms under 10 acres; .29 for all farms; and .12 for farms of 2,000 acres and over. Building values are much more equally distributed among these farms than land values.

For "landlords with debt," the BSREVs are lower overall (.11) than for owner-operators (.29), but the immediate interest here is how the shares fall with size of holding. Ranking by acres per farm, BSREV is .11 overall, and falls gently to .07 in the top bracket. These data, however, are also ranked by $L+B. Ranking thus, BSREV is still, of course, .11 overall, but -- here is the shocker -- BSREV falls to an astonishingly low .01 in the top bracket.

A share of .01 is breathtaking in any such scatter, but more so here because the ranking variable includes $B. When a scatter of points is loose, the choice of ranking variable (i.e., the definition of "size") biases the findings to show the share of the ranking variable rising with size. However, the present data are ranked by $L+B, which is neutral between $L and $B. Thus, BSREV = .01 in the top bracket is free of ranking bias and fully significant without adjustment. This is an uncommonly strong relationship. The biggest landlord holdings, in dollar value, are 99 percent pure land.

Lack of family labor on latifundia

Lack of buildings reveals lack of family labor, because so many farm buildings are operator dwellings, whose economic function is to house operator labor near the job site. The census of agriculture no longer publishes data on family labor. As a surrogate, one can assume that operator labor inputs are roughly in proportion to operator housing, which the census reports separately. In 1988 operators' dwellings were 48 percent of farm real estate assets in the smallest acreage bracket, 16.4 percent for all farms, and falling steadily, 4.4 percent on farms 2,000 acres and over. For family-held corporate farms (of all sizes), the share is 6.3 percent; for other corporate farms, 3.2 percent. These data support the common impression that smaller and unincorporated farms are better supplied with operator family labor.

In 1950 the census reported more detail than it does now on inputs used by farms ranked by sales. Class I farms (the largest) had 22 percent of the land in farms, and 7 percent of the farm labor (at that time, family labor was included). Class VI farms (the smallest) had 5 percent of the land in farms, and 11 percent of the farm labor. This contrast would be much greater if farms were ranked by acres or $L+B, because sales reflect the presence of labor inputs, as well as feeder livestock and purchased feed. Those contrasts of people to land ratios were brought out in many studies in that more socially conscious era.

To sum up what national data show, there is evidence that land quality rises with acreage harvested, using irrigated acres as a surrogate for quality, and that BSREV falls. Ranking farms by sales, the same rule holds. For all owner-operated farms, ranked by acres, BSREV falls steeply with size. For landlords with debt, ranked by $L+B, BSREV falls even more steeply with size, nearly to zero. The last point distills my thesis to its essence in one datum.

Comparisons Among States

AELOS provides a third set of separate land and building values. These are aggregates by state. Grouping data by areal units, as Reid did in her study of housing and income, is one way to overcome regression fallacy. The idea is to group data on some basis other than the variables being studied and then to compare those variables among the groups. States serve the purpose, just as neighborhoods served Reid in her housing studies.

Lesser Improvement of Land in States with Larger Farms

One method of testing how $B grows with $L is to compare their dispersions. The result is unbiased because the two variables are treated the same -- neither ranking is given priority over the other. The egg-shaped envelope of scatter points is standing on its end if the Y variable is more dispersed, and leaning on its side if the X variable is more dispersed. Any standard measure of dispersion is acceptable. I use two. One is the mean deviation, dividing each by its respective mean to standardize it for comparison with others. I also calculated coefficients of variation (CV), which are standard deviations divided by the respective means.

My results support the hypothesis that farmland values are much more concentrated than farm building values. The CVs are .44 for land value, and .24 for building value. See Table 10.1 (in appendix to this chapter) for details.

My overall findings are displayed in Figure 10.1, [not shown here] a scatter-plotting of LSREV against $L per farm, by states. Land value per-farm ranges from $71,000 (West Virginia) to $630,000 (Arizona). Arizona and other big-farm states have higher LSREVs than West Virginia and other small-farm states. Overall, the scatter displays a strong positive relationship between $L per farm and LSREV, state to state. This supports the basic finding which is, otherwise put, that land is much more concentrated than buildings among farms.

Urban Influence

Data by states also provide new insights into interstate and interregional differences. I divide states into three groups: 9 small urban states, 7 arid ranching states, and 34 rural and rural-urban states (See Table 10.1). For the small urban states, the CV values for $L and $B are .42 and .16; for the arid ranching states, .32 and .29; for the 34 rural states, .67 and.32. Thus, $L is more concentrated than $B among the states within each of the three groups, but the difference is greatest among the small urban states, where farm values are most affected by urban speculation. This suggests that the effect of urban land speculation is toward higher concentration of landholdings, a point made earlier by Gray, and by Goldenweiser and Truesdell, and observable today around growing cities.

Association of Property Taxation and Land Improvement

The specific contrast of two states, Wisconsin and Florida, illustrates and exemplifies my general findings. In Table 10.2, I rank the 50 states by LSREV. The complement of LSREV is BSREV. Wisconsin has the highest BSREV, .47; Florida has the lowest, .15. Yet, Wisconsin's farm property tax rate (PTR) exceeds Florida's 4 to 1. Wisconsin, the high-tax state, leads Florida 3 to 1 in farm output per dollar of farmland value, 5 to 1 in farm buildings per dollar of farmland value, and (surprisingly) 7 to 3 in machinery/livestock. Florida, the low-tax state, leads Wisconsin in GR (2 to 1); in $L per farm (5.5 to 1); in acres per farm (3 to 2); in $L per acre (4 to 1); and in real estate/all assets (11 to 8) (Table 2).

Florida and Wisconsin are not exceptions or outliers, but bellwethers. Extending the data to 8 states below Florida, and 8 above Wisconsin, the differences persist and accumulate consistently. The "Florida 9" are Florida, Arizona, New Mexico, Hawaii, Montana, North Dakota, Wyoming, California, and Texas. The "Wisconsin 9" are Wisconsin, Delaware, Maine, Pennsylvania, New York, New Hampshire, North Carolina, Oklahoma, and Ohio. There are two contrasting Gestalts along the lines shown.

The Wisconsin 9 have higher PTRs overall than the Florida 9. To the extent that the PTR is a cause of the effects with which it is associated, its effect is not so much to abort farm capital, as expected. It is associated with high BSREV. High PTR is also associated with small farms (low $L per farm, low $L per acre), low GR values, high ratios of M&E to livestock and to real estate, low shares of leased land, and fuller land usage, as measured by sales per $L.

The inverse relationship between PTR and GR is particularly consistent and noteworthy. In this respect North Dakota and Delaware, otherwise nonconforming members of their respective groups, fall into line. Delaware has a low PTR and a high GR; North Dakota the opposite. The egalitarian effects of a high PTR seem stronger than its negative incentive effects, even though buildings are part of the tax base. These egalitarian effects would be stronger if the tax base was limited to naked land value, because LSREV rises steeply with size of farm. Untaxing buildings would also eliminate negative incentive effects.

Conclusion

One may at least firmly conclude that large farm units are less improved and less peopled than small and medium-sized farms.There are two possible interpretations. One is that big farms are more efficient, getting more from less, but that is refuted by their getting less output per $L. The other is that Veblen was right, many of them are oversized stores of value, held first to park slack money and only secondly to produce food and fiber, and complement the owner's workmanship. The Florida 9 may represent a home grown rural "third world" of large, underutilized landholdings that preempt the best land and force median farmers onto small farms on low-grade land.

The issue cannot be settled in a few words, but the implications for tax policy are the same either way. If large units are more efficient, they can bear heavier taxes. If they are less efficient, heavier PTRs will induce them to release surplus land for others, which will tend at the margins to equalize factor proportions, moving more states from the Florida model toward the Wisconsin model.