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- 1. Uses of ARMS Data James MacDonald USDA Economic Research Service
- 2. Major ERS Uses of ARMS Data • Financial reporting & other data releases • On farm sector, farm businesses, farm households • Via ERS webinars, web data-tool, & postings • ERS reports on policy-relevant issues • Posted on website and available to all • ERS custom reports (staff analyses) • Unpublished, for policymakers; Quick turnaround
- 3. ARMS Uses: Financial reporting 2015 Net Farm Income to fall 36 percent Note net farm income vs. net cash income Net farm and net cash hit records in 2013
- 4. ERS Farm Financial Reporting • That’s the 2015 forecast. ERS also provides estimates of what did happen. – For the headline numbers, as well as for component expense and revenue items. – For farm sector, and breakouts • ARMS provides about ¾ of the data used in the farm sector accounts.
- 5. ARMS Detail Allow Us to Break Down Changes in Net Farm Income Notice: declining cash receipts, from falling commodity prices, drive this decline But prices haven’t fallen enough to trigger large increases in government payments
- 6. Farm income has fallen from record levels... Net cash income is still near long-term average Net farm income is well below 10-year average The difference? Depreciation (little changed) and inventories, which fell sharply as farmers reduced stocks.
- 7. ERS Provide Forecasts for Regions Net cash income is forecast to fall in all regions, but there is wide variation.
- 8. Compare to 2012... Particularly the Northern Great Plains, Prairie Gateway, and Mississippi Portal What happened? Drought, but price increases and crop insurance offset production declines for crops. Livestock hit by drought and feed price increases.
- 9. ARMS Data Also Underlie ERS Balance Sheet Analyses What’s different between 2015 and 1983? Can we use ARMS to identity what types of farms are at risk?
- 10. Who Wants This Information? Not Just Policymakers • Input providers – Cash income drives equipment purchases. What will equipment/chemical/seed/feed demand look like? • Lenders & Investors – What are the risks? What guidelines should I use? – Poor information is worse than pessimistic info • Extension and farm advisors – They are how information and advice get to farmers
- 11. Use in Policymaking • Congress, USDA, farm groups, the media, and others use ARMS-based data – Easy access to fundamental finance data: can look it up for themselves – ERS reports are widely available; Congress and USDA also ask for custom reports • Informs Farm Bill discussions, & implementation – And other agriculture-related policy
- 12. ERS Also Uses ARMS to Estimate Farm Household Income Net of farm expenses, and including income from off-farm sources Provides a direct measure of how farmers are doing, not just farm businesses Household income needed to assess: 1) How tax proposals work 2) Full impacts of farm policies 3) How changes in the farm economy—from crop prices, drought, an export boom--affect farm households
- 13. Farm Type Share of all farms (%) Median Household Income ($) From all sources From farming Small farms (Sales<$350,000) Operator is retired from farming 12.5 57,470 -380 Primary occupation is non-farm 42.8 89,686 -2,773 Primary occupation is farming: Sales<$150,000 28.1 51,719 -2,145 Sales of $150,000-$349,999 5.1 91,745 50,602 Midsize farms ($350,000-$999,000) 5.4 181,086 129,126 Large-scale farms $1,000,000-$4,999,999 in sales 3.2 410,751 349,400 $5,000,000 of more in sales 0.3 1,249,654 1,079,750 All family farms 97.6 71,697 -1,141 Farms vary a lot; detail, provided by ARMS, matters Note the role of off-farm income (all sources minus from farming) Note the huge range in income from farming Source: 2013 ARMS Phase III
- 14. Using Household Income: An Example from the 2008 Farm Bill • Proposal: Limit EQIP only to farmers realizing at least 2/3 of household income from farming • Aside: why would Congress care about this? – What could the goal of this proposal be? • How could ARMS be used to evaluate this proposal?
- 15. Item Farm Share of Household Income Percent of: <0 0-66% >66% EQIP recipients 44 27 29 Production 27 17 56 Payments 30 22 48 Average farm sales $204,987 $215,318 $654,193 What Did We Find? The proposal would have eliminated nearly 70 percent of recipients. But the eliminated groups weren’t necessarily “lawyers with horses’; on average, they were real commercial farmers, with a lot of production What’s the <0 group? Is this important in agriculture? In 2015-16? Note: we used 2006 ARMS data for this 2008 analysis, prices, and sales, would be higher now
- 16. ARMS & Household Income • We used almost the whole survey for this – Farm production and sales: Sections B-H – EQIP payments: Section H – Household income from farming: Sections B-I, L – Household income, not from farming: Section N • These issues are common – Policy that uses household income – Policy that relies on understanding farm incomes
- 17. Uses: ARMS in National Economic Accounts • ERS farm income estimates enter into: – National Economic (GDP) accounts – State Personal Income & Local Area Income estimates • GDP estimates used for national economy measurement and policymaking • Farm income is small share of national GDP – But an important source of year-to-year variation
- 18. ARMS Uses: State & Local Income Estimates • Formula allocation of federal funds – Medicaid, Supplemental Security Income – Agricultural research & extension, USDA ag lending • Local planning of public investment – Public utilities, highways, hospitals • Private investment – Local retail & wholesale facilities
- 19. ARMS Uses: NASS Reports • Farm Production Expenditures report • Vegetable Chemical Use data • Fruit Chemical Use data • TOTAL report
- 20. ARMS Uses: ERS Reports
- 21. Research & Policy Topic: Tax Policy • We are frequently asked for analyses of tax policy proposals that might affect farmers: – Depreciation rules – Changes in tax rates – Estate taxes • ARMS provides data for ERS tax models
- 22. Example: Assessing the Likely Incidence of Estate Taxes Under present law, the estate of a decedent, who at death owned assets in excess of the estate tax exemption amount ($5.43 million in 2015), must file a Federal estate tax return; those estates are subject to a 40 percent tax rate on the nonexempt amount. Based on simulations for the 2014 tax year an estimated 2.7 percent of farm estates would be required to file an estate tax return, with a much smaller share of estates (about 0.8 percent) owing any Federal estate tax. On average, a farm estate that owed Federal estate tax had net worth of $11.1 million and a tax liability of $1.68 million, paying an average tax rate of 15 percent. Among estates that would owe tax, estates of small family farms (those with gross cash farm income (GCFI) below $350,000) faced the lowest average effective tax rate, while estates of large-scale family farms (those with GCFI of $1 million or more) were taxed at an average effective rate of 18 percent.
- 23. Policy Issue: Aging Farmers • Average age of principal operators is increasing – 58 in the 2012 census; 50 in the 1982 census • Is this meaningful, or a statistical artifact? – Why is it happening? – Should government try to do something about it?
- 24. Looking Behind the Average Why do that? We count retired farmers. We count people are retired, with some farming. Issue: are commercial farmers also aging?
- 25. Larger commercial farms are frequently multi-generational That’s one reason to become large; a younger generation came back to the farm. Why does that matter? Census reports the age of the principal operator? Who’s that?
- 26. USDA Has Programs to Assist Beginning Farmers • USDA/FSA loan programs • Conservation assistance • Crop insurance coverage • Value-added grants; certification services • Outreach, training, education programs
- 27. ARMS Assists in Managing Those Programs • Where are beginning farmers? What do their businesses look like? Where is assistance likely to be effective? • How do we identify beginning farmers in ARMS? – Once we identify them, how does ARMS data help? • Agriculture is big and complex – ARMS helps identify beginning farmers most in need
- 28. Uses: Analyzing Data from TOTAL • Last year’s ARMS/TOTAL survey – Tenure, Ownership, and Transition of Agricultural Land – Surveyed operators and non-operator landlords – Detailed questions on land ownership (by type of owner), rental arrangements, plans to transfer • What are we learning?
- 29. Who Owns U.S. Farmland? See how non-operator landlords are broken out. Individuals still account or nearly half, and trusts for a fifth. Corporate landlords are 3% of all land, and 10% of land rented by non-operator landlords.
- 30. What Types of Leasing Arrangement are Used?
- 31. What’s New: A Hog Version in 2015 • Previous ARMS Hog Versions—2009, 2004, 1998, 1992 (in FCRS) – Gives baseline for annual cost and returns estimates – Used to understand changing hog sector • This is the first done with initial mail-out – A shortened questionnaire to accommodate mail – Can we maintain the value of this data?
- 32. In past years, larger farms had lower costs, and production shifted to bigger farms Cost of production, by enterprise size, for hog finishing operations (2009) Will this Continue in 2015? ARMS demonstrates, and explains, the industry’s striking changes Head removed Average cost ($/cwt) <500 64.81 500-1,249 46.34 1,250-4,999 44.52 5,000-12,499 35.88 12,500 or more 32.79
- 33. Hog Finishing is Persistently Profitable Over 2004-2014 Can that continue? How will 2015 be? Persistent losses before 2004 Persistent profits since Why? Now look at trend for costs, Before 2009 and after. What happened?
- 34. Issues in Hogs • Is structural change over? – Will industry-average costs therefore depend more on feed expenses, and less on changing structure? • How are changing regulations affecting the industry’s practices and finances? – CAFO environmental regulation; antibiotics limits
- 35. Also New in ARMS 2015 • Conservation practices & environmental regulations (Section A, items 14-17) – Item 14: baseline data on conservation practices that USDA seeks to encourage – Item 17: environmental regulations • Many and varied. We seek to identify burden on farmers • Note that Don’t Know (DK) is an option • Probe for federal vs. state and local rules • EQIP/CSP funding
- 36. What Else is New in ARMS 2015? • Agritourism (Section H, item 3k) – An increasingly important source of income for some farms. What’s most important, and how does if fit in to farm businesses? • Use of health insurance (Section M, items 7-8) – Only source of info on how farmers are getting coverage, and who’s not. • Updated Cotton & Oats COPs
- 37. • Farm Financial (Net Farm Income) reporting and forecasts • Custom Reports for policy makers who affect farmers everyday • Special Reports that answer questions on current hot topics • Major information source for Farm Bills and Ag Policy • Agricultural Component of GDP • Part of Formulas to Allocate Tax Dollars • Crop Insurance and Disaster damage estimates • Lenders, Manufacturers, Suppliers, & Retailers decisions • Farm Commodity groups, for analysis and advocacy • Data Summaries Available to all through the web tool A Summary: Major Uses/Users of ARMS data are …
- 38. Why is ARMS Valuable? • It’s Representative, Comprehensive, Objective • Links Enterprise, Whole Farm, & Household • Tracks Income Statement & Balance Sheet Items – Links to production and marketing decisions
- 39. That Value Comes from a Full Team • ERS – Objective analyses & economic expertise • NASS – Survey design, management, & production expertise • NASDA enumerators – Producer cooperation & guidance, ground truthing • Producers – Time, knowledge, thoughtfulness
- 40. • Policymakers… • Some have farm backgrounds, most don’t • Those that do can’t just rely on background, experience • They’re all busy, so they rely on others for information • ARMS provides accurate data on U.S. agriculture • Better information makes for better decisions Policy Decisions Will be Made with or Without ARMS
- 41. Additional Information • The Phase III Interviewers Manual • ERS website: www.ers.usda.gov • Charts of Note: read and sign up for free distribution at – http://www.ers.usda.gov/data-products/charts-of-note.aspx • Farm Sector Income Forecast: – http://www.ers.usda.gov/topics/farm-economy/farm-sector-income- finances.aspx#.Up4zByfQxnE • Thanks!!
Editor's Notes
- Net farm income adds depreciation expenses & nonmonetary benefits paid to farmworkers to cash expenses, and adds changes in inventories and accounts receivable and home consumption of farm products, and the imputed rental value of the farm dwelling to cash receipts (if the home is part of the farm business, and because home expenses are then counted as farm business expenses).
- The latest available ARMS data are for 2014. How does that contribute to a 2015 forecast? ARMS provides baseline expense and revenue estimates, and we forecast with current estimates of price and production changes from that baseline.
- Start with 2014 estimates, then use NASS and other sources on production, stocks, and prices to project changes in receipts, inventories, expenses for 2015.
- Why did inventories fall? Farmers expected low prices in 2015-16, and reduced crop inventories before prices fell further.
- These are ERS resource regions. Declines are projected in all regions, but there’s wide variation. The biggest declines are in regions with livestock and feed grain dominance (heartland, northern crescent, southern seaboard)
- A good year overall, but enormous variation across regions. Use it to emphasize that the details count
- Note that debt (left chart) is measured in 2009 dollars to adjust for inflation and allow for comparisons over time. By 2015, reached debt reached levels in 1980, before farm financial crisis of early 1980’s. But (right chart) ratio of debt to equity and to assets is still well below levels in 1980’s crisis. With farm-level data, we can identify the types of farms (by commodity orientation, size, location, operator age and other attributes) that are most at risk from a decline in land values.
- We often focus on policymakers, but these groups rely heavily on the estimates. The third group uses our (and others) estimates to formulate strategies for farmer-clients, who may not realize that our data are being used for their advice
- During the farm financial crisis of the 1980’s we could not provide policymakers and their staffs with useful and timely farm sector financial information; that’s what gave rise to ARMS. Today, policymakers and their can access up-to-date farm finance, structure, and practices measures on their computers (via the ARMS webtools); they get regular briefings and webinars; and they can engage ERS to do custom analyses of the data to answer specific questions.
- Farm household income combines net income flowing to the household from the farm business with income earned from off-farm employment as well as income from pensions and savings.
- Note use of ERS farm typology. “Sales” is gross cash farm income (revenue from the sales of commodities, fees from production contracts, government payments, and farm-related income like income from land rentals or custom services). Remind them of the median: half of households above, half below. These calculatons are for the principal farm operator’s household.
- EQIP is environmental quality incentives program, through which USDA shares the costs of certain production practices. This proposal was brought up by Senator Coburn (OK). Senator Harkin (IA) brought up the ERS staff analysis during floor debate. Why would Congress care? There are lots of farms with substantial off-farm income and limited agricultural production: “lawyers with horses”, is the phrase sometimes used. The proposal aimed to raget money to real farmers. ARMS: we have household income, and ARMS collects data on EQIP $ received, so we can evaluate what types of farms currently receive EQIP funding, and what types would be lose eligibility.
- We broke EQIP recipients into 3 groups by household income, and then used ARMS to calculate total EQIP payments received, total value of production, and gross farm cash income (farm sales). These calculations rely on virtually all sections of ARMS. What do we find? First, farms that derive 2/3 of household income from farming are pretty large. Second, farms that receive EQIP funding tend to do a lot of farming even if farming accounts for less than 2/3 of household income. Third, some farms have negative income from farming (<0 column); they may have had a bad year, or they may be deferring income to another year. But on average those that get EQIP do a lot of farming Aside: is there a better way to limit payments. Yes, use farm sales.
- This was a simple policy proposal, but we needed data from all across the survey—revenues from all farm sources, expenses, ownership of the farm, and off-farm income—to analyze the issue. People rarely get that farm net income is frequently negative, even on commercial farms, so for policymaking or understanding the sector they need guidance on how it’s organized and how outcomes look. ARMS provides that.
- GDP is Gross Domestic Product. The Commerce Department’s Bureau of Economic Analysis produces all of these series.
- State and Local Income estimates (total income received, by all persons in an area) are used in formulas that allocate federal funds across states and metro areas. ERS net income estimates are a component of those. The same data are used to evaluate business activity in states and local areas, and to support forecasts of future business activity, used in planning investment in public and private facilities.
- This is a case that also relies heavily on household income data. ERS has a large and complex model of taxes, for different types of farm households (household composition, farm size, location, etc), and the model uses ARMS data to accurately characterize typical farm businesses and households.
- Cover pages from recent ERS reports that use ARMS
- This is a case that also relies heavily on household income data. ERS has a large and complex model of taxes, for different types of farm households (household composition, farm size, location, etc), and the model uses ARMS data to accurately characterize typical farm businesses and households.
- Where does ARMS fit in? With data on farm and farm household assets and debt, we can make estimates of the number of estates of different sizes, and their likely tax liability. We can also tie those findings back to farm attributes (such as size and location).
- We use the ERS farm typology (also in slide 14) to show that older operators are heavily represented in retirement and low-sales small farms. Operators of retirement farms (one sixth of all farms) say that they are retired from farming. They have enough sales, acres of cropland, or head of livestock to still qualify as a farm, but they do little farming. Large-scale and midsize farms account for most production and have far fewer older operators; nonetheless, older operators are certainly common on these farms, and their numbers are growing.
- ARMS, and the census, reports the age of the principal operator, who is frequently the senior operators. Most large-scale farms have multiple operators, and many are multiple generation farms, with a younger generation nearing the time to take over. When we account for multiple generation and multiple operator farms, the “aging farmer” problem looks less severe. But there are still many commercial farms with older operators and no obvious successor. USDA is trying to make policies that can assist in transition and in new farmer entry.
- FSA is Farm Service Agency
- Section L, item 10, helps to identify a beginning farmer. One we know that, we use the rest of ARMS data on farm location, production and financial performance; operator attributes; and farm participation in various programs.
- TOTAL was part of ARMS last year. I thought we should talk a little about what we’re doing with the data
- ARMS gives baseline values for ERS hog costs and returns estimates for 1992, 1998, 2004, 2009, and 2015. We use data on changes input and product prices to extend baseline estimates to other years.
- Notes: cost include all costs, including those borne by contract growers and by contractees (integrators) Costs include opportunity costs of unpaid farm labor, and replacement costs of capital Between 1992 and 2009, the industry shifted to much larger enterprises, and to enterprises specialized in a single stage of production It also shifted to greater use of production contracts between integrators and contract growers, and to marketing contracts between integrators and packers
- Production shifted to larger and lower cost operations over 1992-2009, with substantial improvements in feed conversion, labor productivity, and output per unit of capital (housing and equipment). Consequently, industry-average costs didn’t rise even though Feed prices rose after 2003. Moreover, as smaller, higher cost farms left the business, industry-average net returns turned positive (after 2004). ERS research indicates that structural change and associated productivity growth was largely completed by 2009. Estimated costs rose sharply after that, reflecting feed price increases (and feeder pig cost increases due to disease outbreaks) against slower productivity growth. Profits stayed positive because demand continued to grow sharply. The 2015 ARMS hog data will help us see if productivity growth and structural change has indeed slowed.
- The conservation practices (A, 14) relate to things that can limit/reduce greenhouse gas emissions, and USDA leadership wants to track them in order to see if we are making progress in response to policy initiatives. As for regulations, farmers face a wide array of regulations in different areas, administered by different levels of government. Some EQIP and CSP funding is aimed at assisting compliance with regulations. We’re trying to identify the range of regulations that farmers face, and to see how funding is used.
- The first distinguishes ARMS from university and private surveys of farm finances, which rely on samples of convenience (volunteers, with no particular attempt to make them representative). The next 2 bullets distinguish ARMS from other USDA surveys.