The Use of Enterprise Units in Crop Insurance

The 2008 Farm bill provided for an alternative level of crop insurance subsidies for Enterprise Units relative to Basic and Optional Units.  As you can see in Table 1 the subsidy for enterprise units are sometimes as much as 20% higher than for the same coverage with basic and optional unit structures.

Coverage Level Basic & Optional

Subsidy %

Enterprise Unit Subsidy %
50% 67% 80%
55% 64% 80%
60% 64% 80%
65% 59% 80%
70% 59% 80%
75% 55% 77%
80% 48% 68%
85% 38% 53%

A brief review of unit structures is as follows:

  • Basic unit – All insurable acreage of the insured crop in the county on the date coverage begins for the crop year: (1) In which a producer has 100 percent crop share; or (2) Which is owned by one person and operated by another person on a share basis.
  • Optional Unit – Subdivision of basic unit.
  • Enterprise unit – All insurable acreage of the same insured crop or all insurable irrigated or non-irrigated acreage of the same insured crop in the county in which a producer has a share.

Importantly, because enterprise units are aggregated from basic units the base rates for enterprise units are generally lower than for the basic units from which they are aggregated.  Thus, higher subsidies and lower rates lead to significantly lower producer paid premiums for enterprise units.

A review of RMA participation data from 2009-2016 reveals the choices farmers have made.  The results are reported by crop.  For the six major row crops enterprise units have covered at least 27% of acres since 2009.  However, it appears enterprise units are far more popular for corn and soybeans than the other four crops.  More than ½ of corn and soybean acres have been insured with enterprise units.  In contrast, ½ of wheat acres have been insured at the optional unit level.

Percent of 2009-2016 Acres Insured with Basic, Enterprise, or Optional Units
Crop Optional Unit Basic Unit Enterprise Unit
Corn 28% 18% 53%
Cotton 42% 25% 33%
Rice 12% 54% 33%
Sorghum 34% 38% 29%
Soybeans 28% 20% 52%
Wheat 50% 23% 27%

Source: USDA RMA County Summary Data

Will farm programs be cut even before the next farm bill starts?

I had the privilege of serving as a chief economist for the U.S. Senate Committee on Agriculture, Nutrition, and Forestry during the last farm bill debate.  The cost of legislation was paramount with the mandated goal of reducing the Congressional Budget Office (CBO) score of the bill.  Roughly speaking all the program crops were asked to take about a 30% reduction in the CBO estimate of farm program spending.  Note that CBO looks forward 10 years and compares the cost of new legislation to a continuation of existing legislation.  One must also understand that the CBO agricultural baseline changes from year to year as market conditions change.  One should understand predicting program costs that far into the future is a highly imprecise process.

Many groups have turned their attention to the expiration of this bill.  So it is fair to ask where will the baseline for the big three farm support programs (ARC, PLC, crop Insurance) be at when CBO is asked to score a new farm bill.  Figure 1 shows the March 2016 CBO baselines for County ARC, PLC, and crop insurance.  Examining this chart, one can see that from 2018 on:

  • CBO assumes that at expiration of this bill producers will be allowed a ‘do over’ on the ARC vs PLC choice. Some switch from ARC to PLC is expected.
  • Crop insurance is projected to increase slowly and average around $9 billion/year.
  • PLC a program widely adopted by rice, peanuts and a substantial amount of wheat is projected to remain at around $3 billion/year in out years.
  • County ARC annual cost is expected to slide 85% from a peak of $6 billion to around $1 billion. This especially affects corn, soybeans and some wheat producers. This is primarily due to the fact that the 5-year Olympic average price used in ARC will fall dramatically as the high prices of a few years ago are dropped.
  • The baseline is likely to shrink in the next 24 months primarily due to dropping the recent high ARC payment levels and replacing them with out-years with lower payment levels. For example, the $6,099 Billion for ARC in 2017 will likely be replaced with a value near one billion. This reduces money available for the next farm bill.

 

CBO Baseline 2016

 

So what does this mean?

  • Crop insurance will likely remain a focal point for policy because it is the biggest pot of funds. That also means it will be attached as a source of funds for other programs.
  • Three commodities are facing dramatic declines in baseline funding – corn, soybeans, and to a lesser degree wheat.
  • Participation rates affect these outcomes. For example, the fact that actual STAX participation has been below what CBO expected, means expected increases in cotton crop insurance program cost in the last farm bill did not materialize.

 

The ARC County Yield Problem – Not if, But When and Where

The county yields plugged into the Agricultural Risk Program (ARC) calculation have come under fire recently for perceived inaccuracy and varying dramatically across nearby counties.  I was there when the last farm bill was written, there were concerns about the county yields, but a lot of people – Hill staffers, farm groups, and political appointees said surely the USDA can find a way to do this.  Basically, this is a statistical problem and most of us hate statistics. I will try to shed some light here.

Be careful what you wish for

NASS uses a statistically survey approach to estimating yield. It is probably about as cost effective as one can approach the task.  Pollsters, marketing firm, and researchers use these techniques all the time. But here is what you must know.  NASS reported hundreds of county corn yields and then crop reporting districts, state, and national aggregates for 2015.  Note also NASS did not report other counties due to small samples.  The fundamentals of statistical surveying imply the accuracy of NASS estimates increases with each higher level of aggregation.  The bottom line is while state and national numbers are highly credible in most cases, lower levels will simply be less accurate.  Well-established rules of survey sampling dictate the primary way to get better NASS county yield estimates is to send more surveys into the county which will cost money and will necessitate greater respondent burden.  This is an increasing problem over time as there are fewer farmers to be surveyed.  So even if attempted it might not work.

RMA data is great – where there is a lot of it

What about RMA data?  RMA does collect yields from participants and in many locations reaches 80 to 90 percent participation rates.  But participation is not as randomized like the NASS survey, and in a county with an 80% participation rate one may ask what are the characteristics of the 20% not in the program.  Are they the best yielding farms or the worst or neither?  I am unaware of research that answers this question.  I will note the RMA has develop their own county yield estimates for use in area insurance products including STAX and SCO.  But they also encounter counties with limited crop insurance participation and thin data.

Statistical stews

Note that RMA does not mix NASS and their own data so that the historical benchmark is consistent with the covered year.  I have looked at this some in the past and the RMA and NASS data often do not seem to match up.  The RMA data was sometimes lower and sometimes higher than NASS yields.  When NASS and RMA are mixed, you get a statistical stew that probably no one can sort out.

Farmers prefer individual protection if they can get it

Even if we get amazingly accurate county estimates will it be enough? I doubt it.  It is pretty clear farmers want protection that is very highly correlated with their own yield – so the county triggers when the farm needs it.  In 2014 before the introduction of STAX and SCO only two percent of crop insurance acres where insured with area insurance plans.  Why? In part, there are real and perceived variation of yields within counties.  A grad student in our department just defended a thesis showing a dramatic lack of correlation of farm yield with county yields in some counties.  In one county, she found the farm-county yield correlation ranged from 0.18 to 0.93 (perfect would be 1.0).  At 0.93 you have a pretty good risk management tools.  At 0.18 you are pretty close to having payments with no relationship with farm losses.  Remember ACRE with a state trigger was adopted in 2008.  The fix was county yield in 2014.  What next?

A jumpy clutch  

My Dad started me on a Farmall Super C tractor.  It had what he called a jumpy clutch, which meant it went from disengaged to engaged in what felt like a ¼ inch of release.  Many fail to recognize that ARC goes from no payment to maximum payment with a 10% change in revenue.  This mimic a design that my colleagues Barry Barnett and Steve Martin drew up for Steve’s dissertation many years ago.  This differs from crop insurance the triggers at a given coverage and then reaches maximum payment at zero yield.  The 10% range in ARC makes payments react quickly to slight differences in county yield. So county A has a revenue 14% below average and neighboring county B has a revenue 5% worse.  County A gets zero ARC payment and County B gets half the maximum.

‘Fair Boundaries’

The average county in the United States is 997 square miles while the largest county in the lower 48 states is San Bernardino County California at 20,105 square miles.  In Oregon the largest county is 23 times larger than the smallest county. All this just points out that counties in the U.S. are not defined in anything like equitable agricultural regions.  This impacts the magnitude of payments and the correlation of farm-county yields. County size matters but so does crop acreage and heterogeneity within the area.

So what next?

Does USDA need to produce three slightly different county yields – the NASS, RMA, and FSA number?  Compromises in the farm bill probably created some of this confusion.

Georeferenced data may help a lot someday. That day is nearing as USDA migrates to using more common land unit information for RMA and FSA.  Layering of soil, crops and other information may give us the ability define areas based more sophisticated grouping.  Here at Mississippi State we are working with National Commodity Crop Productivity Index (NCCPI) data that makes me hopeful.

But in the end, declining price guarantees in the ARC Olympic average for 2017 and beyond may make this a less important issue anyway.  The Congressional Budget Office projects a dramatic decline in ARC payments for many crops for the 2017 year and beyond.  This means less likely payments and smaller payment if they do occur.

Regional Differences in Crop Insurance Base Rates

Base county rates reflect the starting point for crop insurance rates for an insured crop in a particular county.  A particular insured unit’s rate will be derived by adjusting the base county rate to reflect yield versus revenue coverage, coverage level, unit structure, and unit APH relative to base county yield.  Differences in base county rates reflect differences in yield risk derived from historical crop insurance losses.  Figures 1-4 reflect the 2016 base rates for corn, cotton, rice, and soybeans.  A lower base rate reflects a less risky region.  Often large contiguous areas have similar risk levels.  Also, a low yield risk crop such as rice has generally lower rates than other crops.

Figure 1
Figure 1
Figure 2
Figure 2
Figure 3.
Figure 3.
Figure 4.
Figure 4.

Crop Insurance Subsidy Per Policy

It is crop insurance sign up time and I did a quick analysis of 2015 crop insurance subsidy per policy by crop for the entire U.S.  Note subsidy is a function of rates, coverage levels, unit structure, quantity and value of the crop.  What jumps out of this analysis is that specialty crops tend to top the list and that row crops are generally fairly far down the ranking.  But some other special crops also fall near the bottom of the list

 

Commodity Name Average Subsidy/Policy
Strawberries

$39,169

TOMATOES (FRESH MARKET)

$39,064

ONIONS

$36,845

Whole Farm Revenue Protection*

$34,666

COTTON EX LONG STAPLE

$27,797

POTATOES

$27,780

TOBACCO – CIGAR WRAPPER

$27,436

PRUNES

$25,498

MACADAMIA TREES

$24,928

PEPPERS

$24,302

APPLES

$23,881

MACADAMIA NUTS

$23,533

NURSERY – FIELD GROWN & CONTAINER

$22,292

Pistachios

$21,428

BANANA TREE

$19,029

MANDARINS/TANGERINES

$18,277

TABLE GRAPES

$17,388

TOBACCO – CIGAR BINDER

$16,363

ALMONDS

$16,324

SWEET POTATOES

$15,583

CULTIVATED WILD RICE

$14,133

CITRUS (TX) – RIO RED & STAR RUBY GRAPEFRUIT

$14,066

CABBAGE

$13,919

APRICOTS (PROCESSING)

$12,994

TOMATOES

$12,183

ALFALFA SEED

$12,066

CHERRIES

$11,535

PEACHES

$11,044

COTTON

$10,152

TOBACCO – FLUE CURED

$9,979

CLAMS

$9,840

BLUEBERRIES

$9,115

CUCUMBERS

$9,063

APRICOTS (FRESH)

$8,689

FIGS

$8,562

PECANS

$8,118

CITRUS TREES (FL) – ORANGE

$8,105

APICULTURE

$7,760

NECTARINES (FRESH)

$7,530

BEANS (DRY)

$7,104

LEMONS

$7,043

BEANS (FRESH MARKET)

$6,777

AVOCADOS

$6,696

GRASS SEED

$6,406

SWEET CORN (FRESH MARKET)

$6,294

PLUMS

$6,197

GRAPES

$6,116

SUNFLOWERS

$5,931

CORN

$5,847

CANOLA

$5,780

ORANGES

$5,575

PASTURE,RANGELAND,FORAGE

$5,552

CITRUS TREES (FL) – GRAPEFRUIT

$5,392

POPCORN

$5,344

PEANUTS

$5,175

WALNUTS

$5,068

PEAS (DRY)

$5,019

ANNUAL FORAGE

$5,002

TOBACCO – BURLEY

$4,887

Olives

$4,819

MINT

$4,661

BEANS (PROCESSING)

$4,459

CITRUS TREES (FL) – AVOCADO

$4,353

PAPAYA

$4,323

WHEAT

$4,277

BARLEY

$4,208

RAISINS

$4,173

MUSTARD

$4,160

SUGARCANE

$4,080

GRAPEFRUIT

$3,975

PEACHES (CLING PROCESSING)

$3,936

HYBRID SORGHUM SEED

$3,927

RICE

$3,902

BUCKWHEAT

$3,792

SUGAR BEETS

$3,715

CITRUS (TX) – LATE ORANGES

$3,605

CITRUS TREES (FL) – CARAMBOLA

$3,589

SAFFLOWER

$3,531

SOYBEANS

$3,525

GRAIN SORGHUM

$3,464

PEACHES (FREESTONE FRESH)

$3,392

SILAGE SORGHUM

$3,195

FORAGE PRODUCTION

$3,064

HYBRID CORN SEED

$2,917

CRANBERRIES

$2,826

SESAME

$2,684

PEAS (GREEN)

$2,670

TOBACCO – FIRE CURED

$2,664

TANGELOS

$2,529

PEARS

$2,468

PEACHES (FREESTONE PROCESSING)

$2,406

FLAX

$2,224

RYE

$2,099

CITRUS (TX) – RUBY RED GRAPEFRUIT

$1,991

MILLET

$1,929

BANANA

$1,858

CITRUS TREES (FL) – ALL OTHER CITRUS TREES

$1,645

COFFEE

$1,590

CHILE PEPPERS

$1,565

TANGORS

$1,483

SWEET CORN

$1,475

PUMPKINS

$1,317

CITRUS (TX) – EARLY & MIDSEASON ORANGES

$1,283

COFFEE TREE

$1,233

CITRUS TREES (FL) – MANGO

$954

Tangerine Trees

$837

Camelina

$831

FORAGE SEEDING

$786

OATS

$776

TOBACCO – DARK AIR

$748

CITRUS TREES (FL) – LIME

$497

TOBACCO – CIGAR FILLER

$373

PAPAYA TREE

$333

TOBACCO – MARYLAND

$44

* Note Whole Farm Revenue Insurance covers multiple commodities.