GROWING FARMS AND INCREASING INCOMES BY OTHER MEANS
November 30, 2015
GROWING FARMS AND INCREASING INCOMES BY OTHER MEANS
The revenue generated from the off-farm work has been an important factor in increasing the farm household income. Since 1960, the farm household income had always been below the national average, until 2002, when it exceeded the national average by $8,000 (Dimitri, Effland & Conklin, 2005). Fifty-two percent of farm operators worked off the farm in 2004 (up from 44 percent in 1979). The share of spouses working off farm grew from 28 percent in 1979 to 45 percent in 2004 (Mishra & Paudel, 2011).The amount of income received from off-farm activities varies by the level of education, the geographical area, and age of the farm operators.
One hypothesize that there is an inverse relationship between the size of the farms and off-farm incomes; the smaller farms would have more income from off-farm activities than the larger operations. Using time series data and regression analysis, and this paper shall examine the relationship between farm size and off-farm income.
The USDA compiles many facts and puts together a unique database. Some of these facts were gathered and compared on a range of variables, including farm operator age. The age would indicate a more skilled and efficient operator, thus allowing for more time and expertise to devote towards income away from the farm. The age of the operator could include additional family members (spouses) and the possibility of a two income, which would increase the off-farm income.
The average median annual income would indicate the opportunity cost of farming. The lower incomes from farm activities would be the reason for the operators to seek additional income. The following literature is a review of different theory about farm incomes.
These studies are on the different aspects of the incomes available to the farmer and the purposes for obtaining it. There are different perspectives as to the need for and the allocation of these incomes for the farm households. They use the types of employment available in various areas of the country and the levels of employment best suited to the farm operator and spouse to measure the farm’s total amount of incomes. These studies detail the importance of the additional incomes and the relationships between different variables to help estimate their conclusions.
Jackson-Smith & Jensen (2009) measured the farm income by using the classification of Farm Dependent (FD) and Agricultural Importance (Al). The article represents the division of the farms by regions and the amount of farm agricultural activity within these areas. The majority of the Corn Belt falls into a category of farm-dependent and agricultural importance. Their approach allowed for a different look at farming; thus by ruling out the urban influences and excluding the counties with less than 50 farms from the analysis. This allowed for the conclusion of their study; there was no significant in using the different classifications to measure incomes because there were too many limitations due to the complexity of the American landscape. It did provide a new approach to studying farm economics at the county level. This knowledge will aid in the proposed study by recognizing that all data has limitations (Smith & Jensen, 2009). A quantitative analysis between farming, local socioeconomic conditions, demographic trends, and policy by the USDA Economic Research Service acted as a screening tool to subdivide agriculture into these classified regions.
Mishra and Paudel (2011) measured permanent incomes using the areas of regional differences because certain areas of the country offered better employment opportunities. The Corn Belt lies in three general regions; Northern Great Plains, Prairie Gateway, and the Heartland, but these areas also have differences in climate, soil type, and cropping practices. Using these differences as variables to describe the off-farm employment opportunities: they concluded that farm operators and spouses had dual sources of income from both farm and off-farm activities. Some of these activities offered higher incomes for the US farm households, which gave them a higher average wealth the non-farm households. The regression analysis concluded that the occupation of the farm operator has a positive and significant impact on only the farming income of farm households (Mishra and Paudel 2011). Their results confirmed that this permanent income relates to the age of the operator, education of the operator, occupation, farm size, geographical location and the number of earners in the household. The proposed study will give insight into the relationship between farm size and the overall income.
Brown and Weber (2013) concluded the majority of the farms household income is from off-farm activities by measuring farm household earnings from non-farm sources. They suggested that the labor of the typical farm household was more skilled than the typical US household laborer. They produced evidence through comparative analysis that the skills required to operate a successful farm business may also influence the off-farm job opportunities. Although, this high salary does not offset the differences of two wage earner per unit. This higher wage earner would have a positive effect on the amount of income as per the size of operation (Brown and Weber, 2013). The information provides a useful analysis of the different aspects of income sources as per the individual states and national data.
Briggeman (2011) explored off-farm incomes and its importance as the main source of farm household income. The article examines the geographical areas where the economic activity is concentrated in a struggling industrial zone, and how the income loss could significantly reduce a farmer’s ability to service their debt. It describes how the dependence on the off-farm income also varies by the type of agricultural enterprise, and this is particularly vital when the incomes from the crop and livestock move in the opposite directions. This is also significant in areas where there is a lack of industrial jobs. Briggman’s article shows 90 percent of farm households derive their income from off-farm employment (2011). Using ARMS data, a regression analysis was run. They examined the relationship between a farm household’s Debt Repayment Capacity Utilization Ratio and the country unemployment rates while controlling for geographical areas and farm characteristics. These characteristics include; farm sales, operator age, a primary commodity produced, and the primary occupation of the producer. They used a comparison analysis with weight averages of the 2010 ARMS, and two surveys; one in 2004 and again in 2010 asking the respondents to indicate the industry associated with their off-farm income.
It was very apparent, throughout the previous articles how important off-farm incomes has become. This income is a valuable asset to the farm unit. It supplies a method to provide the farm household with a higher standard of living. It also enables the farm unit to pay down the debt associated with farming. The loss of this extra income would be a critical blow to the economic well-being of the farm unit. By constructing the data sources used in the explanation of this theory, we provide a focused study on incomes and farm sizes.
DATA AND METHODS
The focus of this study is to examine the relationship between farm sizes and its effect on off-farm incomes using regression analysis. The following discussion explains the economic theory of the problem.
Since the 1920’s, farms have steadily grown in acres thus increasing in size. This increase is generally due to the mechanization of the farm industry; machines have allowed one man to do more per day. This increase in size added to the accumulation of more debt. Technology has also made improvements in the households by adding convenient solutions to every day struggle. This freed up the farm women’s time, no longer a slave to cooking and cleaning, this time allowed them ventured out into the general workforce. This move brought new wealth into their homes, which allowed them to purchase more labor saving devices, which freed up more time. These factors along with the low commodity prices have led to the dependence on off-farm income to bring the farms total incomes above poverty levels. The economic benefit to the farm family from the extra income has given them more purchasing power by adding to their disposable income. The regression formula to examine this hypothesis would resemble the following:
Y1 OI = B0 + B1 FSt + B3NUt + B4ANIt + B5 AFIt + B6 AAFt + e
The dependent variable OI is Off-farm income; it indicates the total off-farm annual income in dollars. FS indicates the farm size by acres over the time from 1960 to 2014. There are four other independent variables: NU signifies the national yearly annual average unemployment rate. ANI is the average national median yearly income. AFI is the actual annual farm income; AAF is the national average age of the farmers, and “t” would be the times of years thru 1960 to 2014.
Table 1: shows a summary of the variables in regression models and provides the descriptions of the variables and the expected results.
Table 1 Variable Descriptions Expected Values & Scores
Sign Dependent Variable Descriptions Expected Outcomes Source
OI Off-farm Income Total Off-farm Income ARMS
FS Farm Size Acres per Operation – USDA
NU National unemployment National unemployment Rates – Bureau of Labor Statistics
ANI Average national Median Income Median National Income + Bureau of Labor Statistics
AFI Average Income from Farming Farm Income – ARMS
AAF Average age The Age of Average Farmer – Bureau of Labor Statistics
Using the theory of economic relationships, it predicted that the farm size variable would show a negative relationship. As the size of the farms increase, the amount of farm income from off-farm income will decrease. It is a negative relationship due to the theory that as the farm grows in acres, it encompasses all the working capacity for one of the family members. This limits the earning to one off-farm income rather than two, not both spouses working off-farm. The National unemployment (NU) variable will cause incomes to increase as the unemployment rate decreases, a negative relationship. The reason for this would be that as local economic conditions deteriorate; jobs become scarce and farmers will take lesser paying activities or possibly lose their job completely. The average National median income variable (ANI) is the lost opportunity cost for a person to work a job rather than farming as an occupation. As this average increases, it would have a positive effect on off-farm incomes. The higher median income would raise the off-farm income.
Average Income from Farming (AFI) variable indicates the actual income arising from farming. This would have a negative effect on the amount of incomes from off-farm activities. The theory is as this income increases the need for extra income would decrease. The average farmer age (AAF) would have a negative relationship to the off-farm income. We expect that age would have a direct bearing on the amount of extra work a farmer would want to take on.
The size of the operation would also be an indication of the age of the farm operator; the variable AAF would account for the relationship for operators of different ages. Farmers 35 years of age and younger are more dependent on the off-farm income. This is due to the operators over 35 years as having the larger operations. This variable would be positive for the younger farmers and negative for those over 35 years old. This off-farm income is a valuable asset to the younger and smaller operations; this allows for the ability to pay down there debt. The time line will show a steady increase in the size of the farm operations, which, I hypothesis has a negative effect on off-farm incomes. I obtained the some data for this analysis from the USDA and ARMs databases.
The data included the years spanning from 1960 to 2014; this was the most reliable period. There was a problem with the collection method of their data before 1960. Off-farm income is from ARMS. The Bureau of Labor Statistics has the data needed for input of the other variables. The Bureau of Labor Statistics collected data on unemployment, all types of wages for different occupations, and the average ages by occupations. We use USDA data to measure and diagnose trends related to agriculture by using descriptive statistics and regression analysis.
The average farm size has been on a steady increase during the last fifty-five years. The data shows that both farm’s size and income has increased. In 1960, the average farm size was 297 acres, which grew to 438 acres in 2014. The real annual farm income in 1960 was $11,897, which grew $22,692 in 2014. The poverty level for a family of three in 2014 was $20,090.
Fifty-five years of National data statistics’ used to evaluate the increase in farm size and the farm income. Off-farm income has also increased during this period. The average income received from off-farm is over 50,420 dollars while the average farm income is just over 14,000 dollars. The median annual income, would be the opportunity cost of not farming, for the same period is 34,206 dollars, this shows a wide disparity between the three income examples. Off-farm income is well above the median income while the farm income is less than half the median income. Table 2 shows the results of the Descriptive Statistics.
TABLE 2 Descript Statistics
Variable n Mean St. Dev Min Max.
OI 55 50,423.44 23,737.65 13,315.22 93,928.00
FS 55 413.07 43.01 297.00 464.00
NU 55 6.11 1.58 3.49 9.71
ANI 55 34,296.43 5,929.55 24,920.91 47,230.00
AFI 55 14,008.65 7,018.24 3,811.70 37,549.22
FAA 55 52.88 2.46 50.30 58.30
The regression analyzes indicate there was a significant increase in off-farm income. The R-squared value of 0.977 explains almost 98% of the variation in off-farm income is accountable by the variables in the model and a relationship exists between the variables. The model shows an F-statistic value of 415.27; the F-statistic is significant. The regression model predicts the response variable better than the mean of the response, with a p-value of less than 0.00 indicates that there is a significant relationship in the model.
Regression coefficients represent the mean change in the response variable for one unit of change in the predictor variable while holding other predictors in the model constant. The statistical control that regression provides is important because it isolates the role of one variable from all of the others in the model.
Table 3 displays these regression results.
Table 3. Regression Results Variable Coefficient Intercept -201,693.20 **
(33076.2541) FS 107.59 **
(20.8126) NU -1450.73 **
(361.0149) ANI 2.52 **
(0.3641) AFI -0.07 (0.0752) FAA 2478.16 **
(752.6678) R-Squared 0.9770 F-Statistic 415.27 **
Observation 55 Standard errors are in parenthesis *p-value<0.05 (two-tailed) **p-value<0.01 (two- tailed)
This model shows, for every acre a farm increases in size the off-farm income advances by 107.59 dollars. The original hypothesis was, as the farm grew the off-farm income would decrease. The opposite was true; as the farms increase its size, they gain economies of scale. This allows them to become more efficient, because of the size and complexity of the equipment used. They spend less time farming.
The national unemployment rate has a negative result on the function. The Unemployment (NU) rate does have a negative effect on the outcome, when the unemployment rate is high there are fewer opportunities for employment. The national median income has a positive effect on the model as this income rises it adds value it off-farm income. Average median national income variable (ANI) had a positive influence on the results, which indicates that as the median income increases off-farm income would increase. The variable Average Farm Income (AFI) has a negative effect on the result, as the farm income increases it would have a negative effect on other incomes although the p-value (0.36) indicated this was not a good indicator for the model. The farm income fluctuations would force farmers to seek other sources of income.
The age of the farmer had a larger than expected positive influence on the outcome, as a farmer ages his level of experience and knowledge increases he becomes more of an access to the work force allowing for higher wages. The difference value than expected could be due to primarily to a more educated and skilled worker to devote towards a higher off-farm source of income.
We notice that the relationship between the independent (X) variable and dependent (Y) variable looks like it follows a curved line. Squaring the FS value, we conducted a regression analysis and found it formed a non-linear model with a u-shaped line. The model showed a decline in off-farm income until the farm size reached 322 acres. The off-farm income will increase after the farm size reaches 322 acres. The general specification for the equations that tests for a U-shaped regression is by adding, the “B (FS)2” value allowed to examine the relationship closely. The equation is as follows:
Y1 OI = B0 + B1 FSt + B2 (FSt)2 + B3NUt + B4ANIt + B5 AFIt + B6 AAFt + e
In the next section, we summarize the data and the report the conclusions.
SUMMARY & CONCLUSIONS
The goal of the study was to determine the amount of income generated by the farm operators through non-farm related activities as it relates to the different size operations. The results are in-line with the finding of these other articles. This study shows there is a considerable amount of income generated through activities not related to farming. If the study could separate the farms into sizes by actual farm income levels, it may help to understand the real value farm operators place on the off-farm incomes. It was also determined in the earlier articles that some of these operations classified as farms, produce little or no products. These small farms would rely completely on non-farm income; this would cause the average farm size to decrease and off-farm incomes to increase.
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