The age of the operator is likely to influence the size of the dairy operation because it is likely that as an operator gets older and remains in the dairy industry as a dairy operator, they expand their business. Since most dairy farm operators enter the industry when they are young, age is likely to be highly correlated dairy farm experience and often with specific experience at a specific farm in a particular location. Therefore, it is reasonable to suggest that age is heavily correlated with on-farm experiences which is a form of human capital. High level of human capital at the farm level could be hypothesized to be attributed to a farm’s success and growth. The trend of increasing farm size as the age of the operator increases is likely to occur until they reach the age of retirement, maybe decreasing slightly as they get closer to retirement age. Table 4.3 shows the share of dairy operators by age range, state, and year. We can see that the average age of dairy farm operators is increasing for both female and male operators. Based on the information available, I include the following variables in my analysis: the average age of operators and maximum age of any one operator . There are no COA questions directly asking about the farm’s level of sales diversification . However, I created a variable intended to capture sales diversification by taking the share of milk or dairy sales divided by total sales revenue.
This gives an idea of the level of sale diversification on the dairy farm with dairies with little to no sale diversification being near one and those with significant sales diversification with lower values. I also included the share of operators that have off farm employment . These are not clear independent variables, vertical grow rack system as there appears to simultaneity bias between sales diversification and other variables. For the farm size variables, of the individual farm at time , are the dependent variables including Cowsit number of milk cows , TMDit total sales revenue from dairy or milk, and TVPit total value of production.Table 4.4 shows the regression results for Equation 1 with the maximum age selected as the age variable. First starting with the farm size variable, number of milk cows, the sales diversification is significant and with a 1% increase in share of sale diversification relates to about 124% increase in the number of milk cows. Whereas a 1% increase in the share of operators with off farm employment would suggest a decrease by 31.1% of the number of milk cows. Finally, age has relatively little relationship with the number of milk cows on the farm but does show that a year increase in the max age does correspond with an increase by about 0.7%. Next, using the milk sales or dairy sales as the farm size variable, there are very similar results to those for the number of milk cows. The relationship of the maximum age of the operator remains the same. I find that a 1% increase in the share of operators with off farm employment relates to a decrease in the total milk or dairy sales of about 32.4%.
Interestingly, a 1% increase in sales diversification suggests an increase of 215% in total milk or dairy sales. Finally, when we consider the farm size variable total value of production, the relationship of the maximum age of the operator remains similar to the results of the other farm size variables with a year increase in the maximum age there is a decrease of 0.6% in the total value of production. I also find that a 1% increase in the share of operators with off farm income corresponds to a decrease by 32.2% of the total value of production. In contrast with the other two farm size variable specifications, a 1% increase in sales diversification relates to a decrease in the total value of production by 34.1%. Table 4.5 shows the regression results for Equation 1 with the mean age selected as the age variable. First starting with the farm size number of milk cows, I find that the coefficient on the mean age variable is not significant. A 1% increase in the share of operators with off farm employment suggests a decrease in the number of milk cows by 30.8%. Whereas a 1% increase in sales diversification corresponds with an increase of 107% in the number of milk cows. Now looking at the farm size variable total milk or dairy sales, the mean age variable is now significant. A year increase in the mean age of dairy operators relates to a decrease of 0.1% in the total milk or dairy sales. Sales diversification level has a relatively strong relationship with a 189% increase in the total milk or dairy sales given a 1% increase in the level of sales diversification. Finally, when we consider the total value of production as the farm size variable, a year increase in the mean age of dairy operators corresponds to a decrease in the total value of production by 0.1%. Also, a 1% increase in the share of operators with off farm employment relates to a decrease in the total value of production by 32% and a 1% increase in sales diversification suggests a decrease the total value of production by 39.3%.Dairy farms have long been run by men, with relatively few women acknowledged as farm operators. Women have played a substantial role on farms, even when their contribution was often not classified as contributing to the farm operation or management. The role of women on farms has likely changed along with changes in agriculture itself. With the rapidly changing dairy industry, it is important to document the validity of assumptions we have about the demographics of farm operators. Successful farms have high quality management, and women have become a crucial part of the supply of farm management expertise. Based on recent U.S. Department of Agriculture Census of Agriculture data, there appears to be both an increase in the share of female dairy farmer operators and an increase in the share of dairies with at least one female operator. There are two confounding factors that influence these statistics, but fundamentally it implies that farms that have been successful have tended to include female operators. Furthermore, the current data support the previously held assumption that there are a significant number of dairies that are run by spouses with a large share of female farm operators married to a principal operator. Understanding the correlation between the presence and the share of female operators, as well as operations run by spouses on farm size provides insight to a previously limited section of agricultural economics literature. Furthermore, by providing evidence and understanding of dairy farm management demographics this research is able to add to discussions about the future of the dairy industry and a better understanding past patterns.Very little agricultural economics literature has addressed the intersection of gender and agricultural industry in developed countries, but there has been some work on this topic for developing countries .
Historically, being a farm operator has been thought of as a male profession with the work done by women on farms tending not to be labeled as farm management. Interest in the role of women on farms is prevalent across several disciplines with some sociology and anthropology research on women in agriculture claiming that women farmers tend to run smaller farms and adopt more sustainable practices than their male counterparts . There has been no agricultural economics research on the role and impact of female operators in agriculture for the dairy industry, specifically. An increase in the share of commercial dairy farms with a female operator suggests that farms that have not exited, during a trend of consolidation, are likely to have a female operator as compared those with only male operators. However, the increase in shares of women may also reflect a change in the practice of reporting to data collectors in addition to a change in actual farm practices. This chapter explores the hypotheses that the presence of a female operator on the dairy farm may indicate that the dairy farm is more adaptable or more open to change in management practices. Listing a female farm operator among all the farm operators may be at least correlated with a willingness to adopt new technology, diversify sales, grow rack with lights or increase vertical integration on the dairy farm. This is a feasible hypothesis because the presence of a female operator may indicate that the farm is more open to change than many peers in the industry. Part-time farming is common in crop and beef cow-calf operations, whereas commercial dairy farm operators tend to be full-time operators. Also, in the dairy industry, a female operator of dairy farms is likely to be married to a principal operator. Having both spouses as farm operators likely implies less off-farm income and, therefore, higher financial reliance on the dairy farm’s success than for families with more diversified income sources. Moreover, dairy farms tend to have more concentrated farm incomes with crop and dairy enterprises vertically integrated rather than the diversification common among crop farms. This changes the incentives of the spousal operators to remain economically viable because it likely increases risk aversion leading to diversification of sales and mitigation of feed price volatility risk by increasing economies of scope. The COA finding of an increase in the share of women dairy operators and farms with women operators reflects three things: an actual increase in women operators playing a more prominent role, their male associates being more likely to recognize and report female operators, and changes in COA questions that better collect previously unmeasured management activity by women. It is important but difficult to disentangle how these factors affect the data. The increase in the share of female dairy farms must be considered against the broader pattern of dairy farm consolidation, changes in dairy farm size distribution, farm characteristics, and geographic shifts . This research seeks to provide statistical evidence of differences in farm size of dairies operated by dairies with at least one female operator relative to all male operators, the share of female operators, and those operated by spouses. By considering farms with at least one female operator and/or married operators as a “treatment” group, I compare the herd size, milk or dairy sales, and total value of production, between the two treatment groups, while holding location and year constant. This chapter is structured as follows: a brief overview of previous literature on the intersection of women and agriculture, a description of COA data related to women and farm operators, a discussion of statistics, empirical method, and results, and then a brief conclusion.Research on the intersection of women and agriculture has tended to be limited in scope and by academic discipline. Previous research on the topic from an agricultural economic perspective has focused on the intersection of women and agriculture in developing countries or limited its analysis to some demographic statistics on female farm operators without much commodity distinction within the agricultural industry. Industry distinction is important because of generally held assumptions about particular commodity farms, including that dairy farms are run by spouses. Moreover, although there have been many anthropology and sociology research studies that have been done on the intersection of women and agriculture in both developing and developed countries, these have tended to be on a case study basis that are limited in geographic scope. I found little empirical agricultural economics research on the patterns over time and across states of female farmers, and I found no prior research on the economics of patterns of female operators in the dairy industry, specifically. A recent article by Schmidt et al. summarizes the current literature on the intersection of women and agriculture, specifying that most economic literature on this subject focuses on developing nations. The article calls for further research on this topic to further characterize the change in gender demographics and collect information on influences in the economy that may have impacted or continue to impact the number of female farm operators in agriculture. Schmidt et al. outline three possible influences on the share of female farmers, including push-pull factors, characteristics of local agriculture, and the type of farming practiced.