r1 1 ij r>um ~m TV(r, T( ( ;fi r Y' ! y1 .1 1 7 *1 LE 0 All2~NYO .2~2$(~ a ~7J49~ N a~+ @1MllL I'.( (_IR(1ILxR 290 AI NF 1987 *(RICUt L Al, IXPI FRIMF;NT STATION AUBU~1RN [;NIX I Rl~l IX ELL. '1. I R( )ISIl D.IIRECTOR At BI RN t NIX ERSICIY, ALABIAM A XI.R11 \I\\ Farm level data were collected from these four geographical regions of Alabama. SUMMARY AND IMPLICATIONS Income, expense, investment, finance, and consumption trends in the data of 65 ongoing Alabama farms during 1978-82 indicate deepening financial stress. * Analysis of income stability and enterprise earnings reveals cash rates of return to assets that tended to be quite variable and for most farm types averaged less than 4 percent. " Negative economic income, for the study period as a whole, occurred for several one- and two-enterprise farm types. " Debt management factors of particular interest that are positively related to increases in reliance on debt include machinery investment rates, interest expense, sales per dollar of farm assets, cash withdrawals for personal use, and labor expense. " Factors related to lower farmer reliance on debt include improved profitability, increases in sales of commodities covered by government programs, increases in the share of expense of custom machinery expense, and increases in the share of expense for purchase of fertilizer, fuel, chemicals, and other operating inputs. The relationship of the statistical results to investment and expense decisions during the study period points out elements of both effective management and limitations on the part of management to control the growth of debt. Percent response analyses of the debt-toasset ratio indicate marked sensitivity to the ratio of sales to farm assets, enterprise specialization, government program participation, and profitability. Critical level/benchmark analyses point out the "ceiling" and "floor" effects of important management factors on debt management. Up-to-date, comprehensive data from ongoing farms are especially useful in a time of marked turbulence and change in the agricultural sector. Alabama farmers are particularly encouraged to compare their experience with the key economic factors identified in this study: machinery investment rates, profitability, turnover of farm resources into commodity sales, enterprise specialization, participation in farm programs, and control of labor and custom expenses. Finally, farmers are encouraged to develop their own investment, profitability, and cost-control management goals. CONTENTS Page SUMMARY AND IMPLICATIONS..................................... 3 ....................... 5 INTRODUCTION ........... DESCRIPTION OF STUDY ......................................... 8 ........... 8 Data Collection ........... RESULTS....................................................... ........... 8 ............... 8 Evidence of Economic Stress ........... .......... 11 Enterprise Income and Variability .......... ................ 12 Single-Enterprise Returns ........... ................. 13 Two-Enterprise Returns ............ 15 ..................... Debt Management May Affect Debt Usage ..................... 16 How Management 17 Results of Analysis .................................. 19 ............... Response and Critical Levels ............ 20 Study Update and Validation ............................ 22 Suggestions for Farm Management ........................ BIBLIOGRAPHY ................................................. 24 FIRST PRINTING 3M, JUNE 1987 Results reportedherein are available to all without regard to race, color, sex, or national origin. Financial Stress, Debt Use, and Cost Control Among Sample Alabama Farms GREGORY D. HANSON, KENNETH H. MOSS, WILLIAM E. HARDY JR.* INTRODUCTION come variability continue to be major concerns in U.S. agriculture. Extensive research efforts have focused on these issues in attempts to find possible solutions to the financial problems of farmers. Some general research conclusions identifying problems and management alternatives have gained considerable acceptance. One such problem is farm income fluctuations, which may substantially alter desired investment schedules. Farmers may cut back on purchases of assets or disinvest following one or more low return years. A series of high return years, as experienced in the mid1970's, may lead farmers to drive up input prices as they compete for limited short run supplies. Farm income variability often interferes with desired product marketing schedules, and this represents another problem. Farmers postpone crop and livestock sales because of low current prices and the expectation of improved future prices. However, they may be forced to sell before an optimal price is reached to cover current debt obligations. Family consumption patterns are often adversely affected by farm income fluctuations, thereby creating another problem. Results of specific studies have suggested various solutions to financial problems. Hanson and Thompson (2), in a study of members of farm management associations in southern Minnesota, found debtcarrying capacity increased substantially with: 1. Flexible default arrangements (permitting recovery from worst case years). 2. Farm size increases (unless small farms earned above average rate of returns). *Respectively, former Assistant Professor (now Section Leader, Economic Indicators Branch of the Economic Research Service, USDA), former Graduate Research Assistant, and Professor of Agricultural Economics. DEPRESSED INCOME, high expense levels, and price and in- 3. Diversification into labor intensive operations (primarily relying on operator and family labor). 4. Diversification with equal asset shares in two enterprises. 5. Two to 3 percent increases in rate of return to assets. 6. Increase of real estate mortgage term from 20 to 30 years. 7. Decreases in interest rates of 4 or more percentage points. In a similar study of low equity farmers in Wisconsin, Harris and Saupe (3) found that many of these farmers had debt burdens almost as large as the value of the farms they operated. They typically did not generate enough revenue to cover family living expenses, farm operating expenses, interest costs, principal payments on their farm debts, and replacement of capital items. Their conclusion is that several potential solutions lie in the market place. These include (1) using contracts to purchase land or other farm resources, with the seller providing credit and loan supervision; (2) renting instead of purchasing farmland; and (3) making loans through financial markets for operations with the greatest chance of success. An analysis of debt payment ability and debt carrying capacity of New York dairy farms by Knoblauch (4) found that debt carrying capacity is largely determined by profitability of the farm business, the ratio of long term to short term debt, and interest rate and term of loan. As would be expected, profitability of the farm business was the most dominant factor. The level of short term debt and the interest rate were also important. Concerning alternatives to debt financing, Penson and Duncan (5) state that increased capital needs projected for the farm sector during the 1980's will place increased pressures on farmers' credit reserves. They emphasized that farmers can lease farmland, tractors, trucks, combines, irrigation equipment, silos, and even cows. Among the advantages of leasing are acquiring the use of an asset without making sizable initial outlays of equity capital and reducing income tax liabilities by deducting the lease payment as an expense. Leasing also has some serious disadvantages. The interest rate on financial leases can be 3 or more points higher than the interest rate on debt financing. Also, any residual value of the asset at the conclusion of the lease period may belong to the lessor and not the farmer. Other alternatives to debt financing are limited partnerships, venture companies, joint ventures, and incorporation. Farmers generally welcome new sources of capital, but have viewed outside equity capital with distrust. Boehjle and Eidman (1)indicate that traditional approaches to risk reduction and farm survival have concentrated on production or marketing strategies to restructure debt and reduce leverage. They list 6 four options to explore for farm survival during periods of cash flow shortfalls. 1. Asset liquidation is a viable alternative since most farmers have real and personal property which could be sold if necessary At least six factors warrant consideration if this option is to be implemented. a. The net cash generated by the sale may be reduced substantially or even completely eliminated and deficiency judgements filed if the proceeds are less than the secured debt. b. The property may have to be sold at a discount because of the forced sale. The amount of the loss is a function of the selling method, timing of sale, financing terms, asset quality, and local market conditions. c. The type of return the asset earns is critical. Inventories and equipment generate higher cash rates of return than farm real estate. d. Unique and favorable financing terms may be given up as well as the asset. e. The extent to which proceeds from the sale will be taxed is important. f. Assets which are encumbered with mechanics liens and other security interests may be difficult to sell because of title problems. 2. Sale-leaseback options may be good for farm operations which can efficiently utilize the resources but cannot handle the cash flow costs of ownership. For example, real estate cash flow requirements can frequently be reduced from 10 to 15 percent for annual debt servicing to 6 to 8 percent for cash rents. 3. Equity infusion can be used in businesses with financial problems by bringing in equity capital from outside the business. Several sources are available for this capital-a family member, a non-family investor, or a present lender who may incur a loss if the note is called. 4. Bankruptcy may involve immediate liquidation of assets and discharging of the debt obligation. It can also involve rehabilitating the business under Chapter 11 or 13 of the bankruptcy law. The increased financial difficulties of the 1980's have required many farmers to carefully re-examine debt management and other financial practices, focusing on the options described above. Close cooperation between the farmer and the farm lender (whether a commercial bank, a member of the Farm Credit System, the Farmers Home Administration, or an individual) is essential in this difficult process. The results reported herein may contribute to knowledge of both financial difficulties and options to improve returns to farming by presenting statistical evidence of cost control strategies. 7 DESCRIPTION OF STUDY This study examined the inter-relationship between financial stress and debt management in a sample of ongoing Alabama farms during 1978-82. The deepening financial difficulties experienced by the sample farmers were identified. In addition, expense, earnings, farm size, and other management factors relating to debt usage were analyzed. Research results were also compared to farm performance in a Southern region, including Alabama, during 1984. At the beginning of this project, empirical data were collected for the development of practical research information that can be utilized by farmers, lenders, and other researchers. The broad objective of this study was to examine the financial condition of selected Alabama farm producers and to analyze management strategies to improve returns. Specific objectives were to (1) explore income variability effects on selected Alabama farms, (2) develop a series of indices relating levels of debt usage to general financial, expense, and investment ratios, and (3) present ideas for alleviation of short term cash flow difficulties. Data Collection The farm level data are largely from the four geographical regions in Alabama identified by the map (page 2). Data sources include Production Credit Association financial records, Tennessee Valley Authority/Alabama Cooperative Extension Service farm management records service, and a mail survey in one region. The sample contains 65 farms; 47 had data from 1978 through 1982 and 18 had records from 1979 through 1982, providing 292 useable observations. Farm income, expense, liability, and asset data and selected personal information, including consumption expense (for 115 observations) and number of dependents, were collected. The sample farms are larger and more representative of "commercial" size agriculture than the typical Alabama farm in the 1982 Census of Agriculture. Primary enterprises include peanuts, cotton, soybeans, poultry, and beef cattle.' RESULTS Evidence of Economic Stress Annual changes in several income, expense, investment, consumption, and finance measures are presented in tables 1 and 2. Ev'The representativeness of the farm sample cannot be readily determined. 8 TABLE 1. DEFLATED MEASURES OF INCOME AND EXPENSE, ALABAMA FARM SAMPLE, 1978-82 Section B: selected expenses Section A: selected measures of income' Year Unit of measure Gross farm Cash operating Farm net cash flow Farm income Operating livestock expense Rent expense Labor expense Machine hired Interest expense purchases)2 $, 19723 Pct. change Pct. change Pct. change Pct. change $, 1972 Percent change, 1978-82 1978 1979 1980 1981 1982 83,465 - 5.0 -13.9 23.4 -11.0 74,984 - 10.1 13,357 28.5 - 78.7 181.8 -66.7 3,423 - 74.3 - 18,003 89 -461 96 -1,440 -6,793 62.2 565 1,343.9 - 165.1 145.9 -281.2 -4,423 -882.4 67,146 - 7.4 4.0 14.4 -3.3 71,561 6.5 4,530 7.3 - 7.5 7.9 24.1 6,024 32.9 7,174 - 18.5 4.2 -1.9 0.1 5,988 -16.5 1,924 12.2 - 16.9 31.8 -15.3 2,000 4.0 7,252 -10.6 21.4 43.2 -1.4 11,109 53.1 'Not all income and expense components are listed in this table. Income and expense measures are defined as follows: gross farm income: the sum of crop and livestock sales; cash operating income: gross farm income less operating expense less nonbreeding livestock purchases plus miscellaneous farm income; farm net cash flow: cash operating income less expenditures for breeding and nonbreeding livestock, buildings, machinery and land; farm income: cash operating income less depreciation. 2Operating expense other than rent, labor, machine work, and interest increased from $46,266 to $46,439 between 1978 and 1982. Depreciation is not included in this expense measure. 3Deflated by the Net National Product Implicit Deflator. Percent change is from the previous year. TABLE 2. DEFLATED MEASURES OF INVESTMENT, CONSUMPTION, ASSETS, DEBT, AND NET WORTH, ALABAMA FARM SAMPLE, 1978-82 Unit of Section A: investment and consumption Machinery purchases 17,733 - 37.8 -26.5 - 27 -9.2 5,365 - 69.7 Building purchases 5,112 - 65.4 -39.5 -39.2 -28.3 466 - 90.9 Livestock purchases 3,917 -11.2 -11.1 - 55.1 -29.8 1,800 - 54 Family living Average farm expenses assets 8,197 1.02 -4.6 -3.5 -9.4 6,901 - 15.8 289,726 - 16.2 0.0 -1.0 -2.3 234,035 -19.2 Year 1978 1979 1980 1981 1982 measure Section B: assets, debt, and net worth Farm debt Sample debt Average farm ratio' debt ratio' 116,678 - 21 14 3 1 108,916 -6.6 40.3 - 11.6 13.2 15.2 -10.9 41.1 2.6 40.2 -6.0 14.5 3.7 3.5 46.5 15.6 Farm net worth 173,048 - 13 -9 -4 -5 125,118 -27.7 $, 19723 Pct. change Pct. change Pct. change Pct. change $, 1972 Percent change, 1978-82 2 'Total farm debt divided by total farm assets for the entire sample. The average of individual farm debt ratios observed in the sample. 3Deflated by the Net National Product Implicit Deflator. Percent change is from the previous year. TABLE 3. AVERAGE ANNUAL RATES OF RETURN AND VARIABILITY OF DOMINANT ENTERPRISES, ALABAMA FARM SAMPLE, 1978-82 Farm type Cotton ..... Poultry ..... Swine ...... Peanuts ..... Soybeans ... Dairy ...... Beef ....... Total sales due to dominant enterprises Pct. (1) 61.5 70.9 67.3 54.5 63.0 82.2 61.4 Mean sample rate of return (ROR) Pct. (2) 27.5 12.0 10.2 9.8 9.8 4.9 4.2 Cash income Coefficient Range of of the annual variability mean ROR Pct. Pct. (3) (4) 105.1 37.9 37.3 3.5 166.0 23.6 87.2 9.3 166.9 17.3 83.6 4.0 156.0 4.5 Average annual Mean sample change in rate of the mean ROR return (ROR) Pct. Pct. (5) (6) 19.3 18.1 1.8 2.9 9.7 3.3 2.2 3.5 8.4 1.9 2.3 -1.5 1.6 -1.5 Economic income Coefficient Range of Average annual of the annual change in variability mean ROR the mean ROR Pct. Pct. Pct. (7) (8) (9) 159.5 42.3 21.2 186.5 4.2 2.3 523.7 20.6 8.8 212.0 10.3 4.7 857.9 18.0 7.9 -298.4 6.6 2.5 -359.2 4.0 1.3 ident in section A of table 1 are strong downward trends for cash operating income and farm income, and moderate downward trends in real farm sales. Farm net cash flow, which consists of cash operating income (excluding livestock purchase expense) plus cash flow relating to farm investment and family consumption, increased from a negative $18,003 in 1978 to a negative $6,793 in 1982. This reflects cost control behavior relating to reductions in real consumption and especially investment, table 2, section A. Among the expense categories shown in section B of table 1, a decline occurs only for labor costs. In view of the large increases in rent (33 percent) and interest (53 percent), the 6.5 percent increase in deflated total farm expense is relatively moderate. Investment and consumption measures, shown in the first section of table 2, declined without exception between 1980 and 1982. The one annual increase occurred for consumption in 1979, the highest real income year for both Alabama and U.S. agriculture during the study period. Financial effects of the 1980-82 recessionary period are evident in section B of table 2. Although farm debt declined 6.6 percent in real terms between 1978 and 1982, this was entirely due to relatively high 1979 income. While the 2.6 percent increase in the debt ratio (total debt divided by total assets) for the combined sample is minimal, average debt ratios increased nearly 16 percent on a per farm basis. It is apparent from positive trends in land values in the data that the large reduction in deflated net worth from $173,048 to $125,118 does not reflect declines in land values observed in U.S. agriculture since 1981. An overview of the income, expense, investment, and finance trends of tables 1 and 2 indicates increasing financial difficulties for the sample farmers. Real income levels tended to be negative or fluctuated in the vicinity of zero. There were substantial increases in several expense categories. Marked declines in real investment occurred, while average ratios of debt to assets increased. The bottom line was a clear erosion of the financial well-being of the sample farms. Enterprise Income and Variability Rates of return for farms dominated by the sales of one enterprise are shown in table 3. The enterprise that contributed the large percentage of total sales was defined as "dominant." A minimum of 20 observations was required for each dominant enterprise. The large variability values for cash rates of return, shown in column 3 of table 3, indicate that considerable income variability occurred in the sam11 ple. (The variability measures need to be cautiously interpreted when the rate of return is close to zero.) Rates of return (ROR) are calculated as follows:2 Cash rate of return = cash farm income to total farm assets, where Cash farm income = Total farm assets = Economic rate of return = Adjusted farm income = summation of crop sales, livestock sales, and miscellaneous farm income, less cash production expense, purchases of non-breeding livestock, and interest expense; summation of year-end market value of land, buildings, improvements, machinery, equipment, livestock, crop, seed, and feed; adjusted farm income to total farm assets; where, cash farm income less depreciation of buildings and machinery. Single-Enterprise Returns The largest cash and economic rates of return for a dominant enterprise were achieved by cotton, 27.5 and 18.1 percent, table 3. The second largest cash return was posted by poultry, 12 percent, followed by swine, peanuts, and soybeans with return rates of approximately 10 percent. 3 Dairy and beef exhibited cash returns between 4 and 5 percent, and negative economic rates of return. The measure of income variability ranged from 37.3 to 166.9 for poultry and soybean cash rates of return, respectively. There is no clear trade-off between level of returns and income variability. However, beef, soybeans, and swine had much higher relative variability than dairy, peanuts, and poultry. The estimated rates of return include interest expense. Thus, farms with little or no interest expense would tend to have higher 2 The income concepts in table 1, as well as the enterprise rates of returns in tables 3 and 4, include interest expense. To the extent that debt ratios tended to differ substantially between farm types, this may introduce a bias in results. (Inclusion of interest expense paid is also consistent with USDA cost of production budgets and published returns to operators series for farm management associations in many states.) 3Inventory and income tax effects were estimated to change the economic rate of return between 0.0005 and 0.0146 for beef, dairy, peanuts, and soybeans, to lower cotton and soybeans by 0.026 and 0.089, respectively, and to raise swine by 0.062. The average combined effect of inventory and income taxes was estimated to be a decline of 0.0102 in the economic rates of return for the seven dominant enterprises. These factors were not included in the economic rates of return because personal deductions, off-farm income, and investment behavior were not indicated in most records. 12 rates of return than the sample average. The general lack of profitability of beef cows is often observed with the suggestion that beef is profitable no more than 2 or 3 years in 10. The only year exhibiting a positive economic rate of return for beef in the sample was 1979. Correspondingly, USDA enterprise budgets for cow-calf herds in the Southeast indicate negative returns for 1978 and 1980-82 and positive cash returns for 1979. The current decline of the dairy sector in Alabama is consistent with the low level of profits shown in table 3. The strong profitability shown for cotton and swine is primarily due to a single year of high returns for each (cotton, 1979; swine, 1981). For enterprises with cash or economic rates of return near zero, variability is perhaps more effectively portrayed by the measures in columns (4), (5), (8), and (9) of table 3. The range of the cotton mean rate of return is 37.9 and 42.3 percentage points, respectively, for cash and economic incomes. The comparable measures for beef are 4.5 and 4.0 percentage points, however, the absolute values of the measure of variability are higher for beef. In most cases, the range in column (8) slightly exceeds that of column (4). Returns for most crops were highly variable, poultry was relatively stable (with positive returns), and beef was also stable (with negative returns). The economic return appears to be "adequate" only for cotton, but both high income and variability shown for this crop were primarily due to strong returns in 1979. The return levels for dairy and beef were disturbingly low. Two-Enterprise Returns Two-enterprise returns and variability results are shown for 11 farm types in table 4. The enterprises are the largest and next largest in terms of farm sales. Because fewer observations are available in the data for this additional level of farm type specification, increased caution is required in assessing the reliability of these findings. Compared to single enterprises, the two-enterprise combinations in table 4 exhibited less difference in levels and ranges of cash and economic rates of return. Cotton/soybeans (the only combination with cotton) demonstrated the "diversification effect" of greater relative declines in variability than in returns. Soybeans in combination with swine, beef, or "miscellaneous" provided higher returns and generally lower variability than single enterprises, but there was a rapid fall-off in returns after cotton/soybeans. Economic income levels (including depreciation) were particularly low. Examination of the diversification effects in table 4 raises the man13 TABLE 4. RATES OF RETURN AND VARIABILITY OF Two-ENTERPRISE Cash income ofR variability Pct. (4) 114.45 155.63 80.90 175.66 69.39 156.76 76.44 33.42 241.20 COMBINATIONS, ALABAMA FARM SAMPLE, 1978-82 Farm type to each enterprise Total sales due Mean sml reur Coefficient samplecnd Economic income Range of the annual Mean sample rate Pct. (1) Cotton/soybeans............ 53.37 Soybeans/misc.'............ 64.84 Peanuts/soybeans........... 48.84 Soybeans/swine............ 44.80 Swine/broilers............. 53.61 Soybeans/beef ............. 48.91 Swine/peanuts............. 46.07 Beef/broilers .............. Soybeans/wheat............ 48.57 50.23 Scn Frt Pct. (2) 36.14 24.35 30.75 43.83 42.39 35.51 35.91 41.34 34.82 reun(O) Pct. (3) 23.6 14.6 11.6 10.7 10.5 10.2 8.7 7.4 6.7 mean ROR Pct. (5) 19.6 32.0 14.3 34.0 7.5 21.0 9.4 4.635.2 of return (ROR) Pct. (6) 14.6 5.5 3.5 4.5 1.2 2.8 4.3 1.5 Coefficient of Range of the annual Peanuts/beef .............. Soybeans/corn............. 47.85 64.71 35.04 27.41 5.7 5.0 114.27 133.86 14.6 15.4 -3.8.3 -1.0 variability Pct. (7) 86.0 403.75 230.63 419.07 772.95 59J6.26 170.38 -1,-475.26 042.74 mean ROR Pct. (8) 21.2 26.4 14.2 31.1 6.0 20.0 12.4 3.7 40.3 -925.11 452.73 15.3 18.7 'Miscellaneous refers to sales from enterprises not included because of insufficient observations, e.g., vegetables. Certain combinations were not prevalent, such as corn/wheat or combinations with the dairy enterprise. agement issue of specialization vs. diversification. This is one of the issues examined in the next section, which analyzes debt management in the sample and relates trends among the farms to levels of economic stress. Debt Management Several management/cost control strategies were analyzed with statistical methods. Size, investment, specialization, profitability, and expense variables were analyzed because of their impacts on important economic, technical, and growth factors in farming. The statistical analysis is based on the economic relationships identified as follows: Debt DEBTRTO-the ratio of total farm debt to total farm assets Investment MCHINVRT-annual gross machinery investment divided by total assets LSTINVRT-annual gross livestock investment divided by total assets Size TASSET-total farm assets GSALESRT-gross farm sales divided by total assets (the asset turnover ratio) Finance, profitability, taxes INTPDRTO-interest expense divided by sales FRMPFTRT-net profit divided by assets WITHDRAW-this is a "disappearance" variable that is the net of cash farm income, off-farm income, operating expense, capital expense, and/or principal transactions. A primary component of withdrawals from the farm business would be family living expense. state, and self-employment taxes on income (includes carry-overs of net operating losses and investment credits) TTAXCOVR-federal, Expense LABHIRRT-labor expense paid divided by total sales MACHIRRT-machinery lease or custom expense divided by total sales OTHEXPRT-other cash expense (e.g. chemicals, fuels) divided by total sales RTPDRTO-rent expense divided by total sales 15 Specialization CONINDEX-concentration index recognizing enterprises contributing more than 10 or more than 50 percent of sales 4 TOTPCTGP-the percentage of total sales in commodities with strong government programs (dairy, peanuts, cotton, corn) How Management Factors May Affect Debt Usage In developing this analysis, the debt-to-asset ratio was expected to be positively related to the following variables: TASSET, LSTINVRT, MCHINVRT, INTPDRTO, WITHDRAW, GSALESRT, AND LABHIRRT. That is, an increase in these factors was thought to be associated with increases in reliance on debt, as explained by the following statements: " Asset, livestock, and machinery expansion are typically facilitated through an increase in financial leverage (i.e., use of debt). " An increase in the ratio of interest expense to sales, e.g. due to increasing variable interest rates, was expected to result in increases in debt leverage. However, outstanding efficiency in production could result in a highly leveraged farm with a low interest-to-sales ratio. " The "disappearances" variable (WITHDRAW) was formulated because of the often cited phenomenon of"vanishing" loan funds that were used for family expense rather than farm expense. Damage to a farm's debt position can become substantial if such "disappearances" continue to be large. " Increases in the turnover ratio of sales to assets, especially if due to capital investment in highly specialized facilities, were expected to be associated with increased debt-to-asset ratios. · Increases in the ratio of labor expense to farm sales were also expected to increase debt usage. Increases in TTAXCOVR, MACHIRRT, TOTPCTGP, FRMPFTRT, CONINDEX, OTHEXPRT, and RTPDRTO were expected to lower the debt-to-asset ratio, in accordance with the following: " Management strategies to lower tax liabilities, e.g., generation of investment credit and depreciation deductions, were expected to lower debt usage. 4 For example, a farm with enterprise sales of 15, 25, and 60 percent of total sales would have an index value of [(15 + 25 + 60)/3 + (60- 50)] = 43.3. A single-enterprise farm would be given an index of 150 [100 + (100 - 50)]. This measure does recognize and differentiate between what were viewed to be critical levels of enterprise contributions. While many other formulations are possible;, this is the only one examined in this study. 16 " Leased machinery can substitute for owned machinery of which much can be expected to be debt financed. " Commodity programs are believed to reduce downward price variability and increase farm income levels. Over the long run, one would expect government program participation to lower debt financing requirements. " Increased profitability would be expected to lessen debt financing requirements. " Efficiency gains due to enterprise specialization are expected to increase profitability, and thus decrease the use of debt financing. * Increases in other expense, such as chemicals and fertilizer, may result in lower debt usage through increased production efficiency of operating inputs. * Increases in rental expense, compared to land ownership costs, would be associated with decreasing debt-to-asset ratios. Results of Analysis The results in table 5 indicate that the following 10 factors were statistically important (1-6 relate to general production, investment, and financial factors and 7-10 to input costs): 1. Machinery investment rates. 2. Profitability. 3. Sales compared to farm asset value. 4. Enterprise specialization. 5. Participation in government commodity programs. 6. Withdrawals from the firm for personal/family uses. 7. Interest. 8. Labor. 9. Custom machinery expense. 10. Other expense (fertilizer, chemicals, fuel, etc.). Several statistically important relationships are of particular interest to management. Higher rates of machinery custom expense and other expenses, such as fertilizer and chemicals, were associated with lower debt usage. Relatively high labor expenses were related to high debt usage. High firm withdrawals for personal uses tended to be associated with high debt usage. Strong farm profitability and participation in government commodity programs fostered low debt usage. Enterprise specialization promoted better control of debt loads. Comparison of the statistical results in table 5 with the trends in investment, expense, and earnings in tables 1 and 3 provides an in17 TABLE 5. STATISTICALLY IMPORTANT ECONOMIC FACTORS INFLUENCING DEBT USAGE Variable name (1) MCHINVRT (machinery investment) INTPDRTO (interest paid) FRMPFTRT (profitability) GSALESRT (asset turnover) CONINDEX (enterprise concentration index) TOTPCTGP (government program sales) LABHIRRT (labor expense) MACHIRRT (custom machinery expense) OTHEXPRT (other expense) WITHDRAW (firm withdrawals) Description (2) Machinery investment to assets ratio Interest paid to sales ratio Net profit to assets ratio Gross farm sales to assets ratio An index of farm enterprise specializationGovernment commodity program participationHired labor expense to sales ratio Hired machinery expense to sales ratio Other farm expense to sales ratio Cash withdrawals for consumption or other purposes Level of statistical significance (3) 2.62 4.19 7.90 3.56 4.31 2.15 Effect on debt usage (4) increase increase decrease increase decrease decrease increase decrease decrease increase -7.38 -2.04 2.48 4.61 'T-value, significant at the .05 level. dication of the latitude of farmers to respond to worsening financial conditions. Lowering interest expense would be anticipated to lower the debt ratio. Considering that average effective Production Credit Association interest rates in Alabama increased from 9.7 percent in 1978 to an average of 16.2 percent during 1980-82, the imperative to reduce debt was evident. Because interest expense increased in real terms from $7,252 to $11,109 between 1978 and 1982, table 1, management of interest cost in the farm sample was not effective. Obviously this cost is difficult to control for many farmers, a fact that farmers and lenders need to bear in mind when they enter agreements to increase debt. Response to interest rate increases would be expected to occur through investment behavior and the growth or decline in total farm assets. Both investment and asset data in table 2 indicate substantial declines in these trends. Thus, the response of farmers to decrease investment was consistent with the statistical finding that this results in lower debt usage. The debt management model also found lowering the labor share of expense reduced the need for debt. Farmers lowered labor expense from $7,174 to $5,988 (in real terms) between 1978 and 1982, table 1, while the other expense categories were not reduced. The statistical results in table 5 point out the importance of investment, interest costs, profitability, sales levels, enterprise spe18 cialization, participation in government programs, and the control of labor, machinery, and other expenses for effective debt management. Response and Critical Levels The sensitivity of the debt ratio to a 1 percent increase in labor usage, investment, etc. can be called a "percentage response" measure. Percentage response results for variables found to be statistically important are provided in column 3 of table 6. A 1 percent increase in the machinery investment-to-asset ratio was estimated to result in 0.06 percent increase in debt-to-asset ratio. Similarly, a 1 percent increase in sales to assetswas estimated to result in nearly 0.6 percent increase in the debt ratio. The relatively high percentage response for the sales-to-assets ratio is difficult to interpret. If firms with large sales-to-assets ratios tend to be capital intensive, purchasing expensive production facili- ties or farm land should be carefully evaluated with respect to financial risk. Also, the high percentage responses of the concentration index, government program usage, and profitability indicate the sensitivity of debt usage to these key management variables. An alternative, "benchmark" model relationship can be calculated with "critical levels" of management variables. A positive relationTABLE 6. CALCULATED DEBT RATIO "PERCENTAGE RESPONSE" AND CRITICAL LEVELS FOR SIGNIFICANT VARIABLES AT THE MEAN DEBT RATIO Variable name (1) MCHINVRT (machinery investment) INTPDRTO (interest paid) FRMPFTRT (profitability) GSALESRT (asset turnover) CONINDEX (enterprise concentration index) LABHIRRT (labor expense) OTHEXPRT (other expense) TOTPCTGP (government program sales) MACHIRRT (custom machinery expense) Description (2) Machinery investment to assets ratio Interest paid to sales ratio Net profit to assets ratio Gross farm sales to assets ratio An index of farm enterprise specialization Hired labor expense to sales ratio Other farm expense to sales ratio Government commodity program participation Hired machinery expense to sales ratio Percent Critical eicing effect valu response floor effect (-) level levelvalue (3) 0.06 .10 - .17 .56 -. 33 .08 -. 12 (4) 0.0431 .1731 .06 .3868 56.81 .0788 .6431 .08 .0264 (5) (+ ) (+) (-) (-) (+)' (-) (+ ) (-) (-) (-) -. 26 -. 04 'The "ceiling" indicates that values above the critical level are related to higher than average levels of debt to assets. The "floor" indicates that values below the critical level are related to higher than average debt burdens. 19 ship indicates a "ceiling" or maximum amount. This suggests that if the critical level is exceeded, an increase in the debt ratio is anticipated. A negative relationship indicates a "floor" or minimum value. If the variable in question falls below the critical level, an increase in the debt-to-asset ratio will occur. Ratios of annual machinery investment to assets exceeding 0.04 are associated with debt ratios greater than the sample average of 40 percent. Ratios of interest paid to sales exceeding 0.17 are associated with above average debt ratios. Profitability rates above 6 percent are indicative of debt ratios below the average. While this rate of return may appear modest, only the cotton and cotton/soybeans enterprises exceeded this level for the sample period, tables 3 and 4. From a managerial perspective, the labor hired to sales critical value of 0.08 provides a "benchmark," or goal, that many farmers may try not to exceed to limit the growth of debt. Large cash withdrawals not accounted for in the records are also associated with high debt ratios. The importance of percentage response estimates and critical values is that they aid the formulation of financial and business management objectives based on empirical evidence. They also indicate the latitude available in working towards attainment of objectives. Study Update and Validation A comparison of the critical values derived from the sample of Alabama farms with mean values of the variables from a larger sample of farms points out the continued importance of investment, interest, and profitability in debt management in 1984. While the mean values from the 1984 USDA Farm Cost and Returns Survey (FCRS) regional data are not directly comparable to "critical" values, they illustrate major financial and investment behavior differences by debt level. Definitions of factors vary slightly between the two sources of farm level data, particularly with respect to government program participation. Factors not shown were not comparable between the two data sources. The FCRS results available for analysis had 371 and 760 observations in, respectively, the Southeast and Delta regions with sales of $20,000 or more and assets of $10,000 or more. To provide a more consistent comparison with the Alabama commercial farms in the study sample, farms with sales exceeding $1 million or assets exceeding $5 million were not included. The FCRS survey is probabilistically based and represented approximately 8,000 high debt and 2,800 low debt commercial size farms in the Southeast. Similar projections are for 12,000 high and 21,000 low debt farms in the Delta. Both critical and mean values are-shown in table 7. The mean debt ratio of the Alabama sample is 0.4. 20 TABLE 7. COMPARISON OF 1978-82 CRITICAL VALUES WITH MEANS OF DEBT MANAGEMENT FACTORS Factor 1984 FCRS survey data' 1978-82 Alabama farm record data Southeast Region Delta Region t Hg Critical Effect, High Low High Low debt debt debt debt value ceiling (+) (D/A>.4)(D/A<.4)(D/A>.4)(D/A<.4) value floor(-) 0.04 .17 06 .39 .08 .64 .08 .03 + + + + 0.06 .2 .02 .73 .07 .8 .44 .01 0.02 .09 .06 .57 .08 .74 .23 .02 0.15 .22 .10 .83 .07 .73 .32 .02 0.04 .09 .17 .54 .06 .66 .20 .02 Machinery investment Interest paid .......... Profitability ............ Asset turnover ........ Labor expense ........ Other expense ........ Government programs participation ........ 8. Custom machinery expense............. 1. 2. 3. 4. 5. 6. 7. 'Southeastern States are South Carolina, Georgia, Florida, and Alabama. Delta States are Mississippi, Arkansas, and Louisiana. Definitions of factors vary slightly between the two sources of farm level data, particularly with respect to government program participation. Factors not shown were not comparable between the two data sources. The USDA Farm Costs and Returns Survey (FCRS) results made available for analysis had 371 and 760 observations in, respectively, the Southeast and Delta regions with sales of $20,000 or more and assets of $10,000 or more. To provide a more consistent comparison with the Alabama commercial farms in the study sample, large farms with sales exceeding $1,000,000 or assets exceeding $5,000,000 were not included. The FCRS survey is probabilistically based and represented approximately 8,000 high debt and 2,800 low debt commercial size farms in the Southeast.: Similar projections are for, respectively, 12,000 and 21,000 high and low debt farms in the Delta. Since real estate asset values declined markedly and the "farm crisis" became more severe between the study period and 1984, differences between the two surveys can be anticipated. Sales-to-turnover ratios are higher from FCRS data reflecting the decline in asset values by 1984 (the denominator of the asset turnover ratio fell). Compared to the critical values, mean profitability decreased in the Southeast among high debt farms. The high debt farms tend to participate more in government programs. This large difference compared to the critical value may reflect sampling differences relating to the distinct time periods or a weakening of commodity program effects. The differences in mean values of factors between high and low debt farms in the Southeast suggest quite contrasting behavior. Farmers need to recognize the requirement for higher sales (asset turnover) that is associated with high debt usage. On the other hand, low debt usage may require (or force) low machinery investment rates. Results for the Delta region show similar relative values but different absolute values of management factors. These results indicate benchmark or critical levels based on performance ratio analysis may vary by region, enterprise type, and farm size. 21 Suggestions for Farm Management Several of this study's specific findings can be summarized into general suggestions or "benchmarks." Farmers and farm lenders are cautioned that these benchmarks need to be viewed as general trends and cannot be applied strictly to any specific farm situation. These results vary by enterprise type, farm size, operator and family goals, and management approach. The guidelines shown below were estimated based on average farm characteristics. Particular strengths of a farm operation, e.g., increased efficiency of labor, can compensate for the cost of higher interest expense. Thus, the guidelines can be viewed to be "flags" that may caution the manager, although the guideline "flagged" may not apply to the farm situation. Factor 1. Profitability Guideline Low debt usage was supported by rates of return on assets of 6 percent or higher after payment of all cash expense including interest. Go for "quality" sales. The study suggests that high sales rates that are accompanied with heavy expense burdens can cause debt to increase. Maximizing sales (by itself) may not strengthen a firm's financial position without strong management. Interest levels of 10-17 percent (or less) of sales were associated with low debt burdens. When limited to 6-8 percent of sales, debt levels tended to be low. 2. Sales-to-assets 3. Interest expense 4. Labor expense 5. Custom machinery expense 6. Machinery investment Levels of 2-3 percent or more of sales, if accompanied by lowered machinery investment and ownership costs, were consistent with low debt usage. When limited to 4 percent or less of sales annually, debt use was low. 22 Levels of 65-75 percent of sales were associated with low debt usage. Concentrate on highly productive inputs such as fertilizer, pesticides, and fuels. 8. Specialization Enterprise specialization in two main enterprises may improve efficiency levels while not substantially increasing the risk of "overspecialization." Specialization that fully utilizes available labor is also suggested. 9. Farm program participation Participation was associated with lower debt in the sample. However, low debt farms participated less than high debt farms in the last year studied, 1984. Consider each program carefully and participate when returns are improved. 10. Firm withdrawals Cost control and family budgeting tools are recommended. Study results suggest either poor accounting of expenses and other withdrawals or else a spending "discipline" problem on many farms. This factor is significantly related to high debt burdens. An additional factor of consideration is that certain expense categories appear to have more "slack" and are prime candidates for cost reduction. These include custom machine work, labor, investment, and family consumption. Other categories of expense, especially interest, appear to have little "slack." The need is to develop cost reduction strategies that recognize where cost-cutting is possible without reducing productivity and profit. 7. Other expenses 23 BIBLIOGRAPHY (1) BOEHLJE, M. AND V. EIDMAN. 1983. Financial Stress in Agriculture: Implica- tions for Producers. Amer. J. Agr. Econ. 65:937-944. (2) HANSON, G.D. AND J.L. THOMPSON. 1980. A Simulation Study of Maximum Feasible Farm Debt Burdens by Farm Type. Amer. J. Agr. Econ. 62:727-733. (3) HARRIS P.E. AND W.E. SAUPE. 1981. Debt Repayment Capacity of Low-Equity Farms. Univ. of Wisconsin-Madison, Dept. of Agr. Econ, No. 62. (4) KNOBLAUCH, W.A. 1979. Debt Payment Ability and Debt Carrying Capacity of New York Dairy Farms. J. Amer. Soc. Farm Managers and Rur. Appraisers. 43:29-34. (5) PENSON, JOHN B., JR. AND MARVIN DUNCAN. 1981. Farmers: Alternatives to Debt Financing. Agr. Fin. Rev. 41:83-90. (6) U.S. DEPARTMENT OF AGRICULTURE. 1980 and 1983. Economic Indicators of the Farm Sector: Costs of Production. Nat. Econ. Div., Econ. Res. Serv., ECIFS 3-1. . 1983. Economic Indicators of the Farm Sec(7) tor and Review. Nat. Econ. Div., Econ. Res. Serv., ECIFS 3-2. . 1983. Economic Indicators of the Farm Sec(8) tor: State Income and Balance Sheet Statistics. Nat. Econ. Div., Res. Serv., ECIFS 3-4. . 1985. The Current Financial Condition of (9) Farmers and Farm Lenders. Nat. Econ. Div., Econ. Res. Serv., Ag Info. Bull. 490.