Push-pull factors refer to the influence of off-farm employment wages that may influence an individual’s decision to be an entrepreneur or push them to seek off farm employment. For this analysis, this influence could be considered on an individual basis or at a spousal level. The change incentives when both spouses’ incomes come from farming could change and push or pull one or both spouses into off-farm employment or to stay on the farm. Characteristics of local agriculture describe the general state of the region’s agricultural economy. This is accounted for by holding constant location and presenting statistics by state. Finally, Schmidt et al. suggest the influence that the type of farming might have, or farming characteristics may influence, as their results find that farms run by women tended to be smaller. There is some association of dairy farms being family-run, or spousal run, this claim is one that we provide evidence on for the dairy industry, specifically. The characterization of such influences provides insight into the possible impacts of female representation on farms across different industries. Again, the agricultural economic literature on the intersection of gender and agriculture has tended to be limited to developing countries. However, indoor grow rack in a recent article by USDA Economic Research Service , ERS released statistics about the characteristics of U.S. female-run farms and female operators based on the 1978 to 2007 COA .
Their results focus mostly on statistics of characteristics of overall U.S. female-run farms and female farm operators. They find that 58% of all female operators have no reported off-farm labor, and that female operators of dairy farms tend to be younger than the U.S. female operators’ average age. Griffin et al. utilize the COA data over five Census rounds and discuss the impact of farm operators’ demographics on farm exit rates. They find that larger farms are less likely to exit, and those female operators are more likely to exit than male operators. However, their study includes all farms with no industry limitations. Furthermore, research on female operators’ impact and representation within the dairy industry is a point of interest because, historically, it was not uncommon for dairy farms to be run by spouses and because off-farm employment is less likely on a dairy farm than it is on other farms. Sander finds that women working on dairy farms tend to have less off-farm employment than other farm types. He outlines the role of income variability on farms run by spouses’ decision to be both spouses’ main income with off-farm work as a possible risk mitigation strategy for farms run by spouses when farm revenue is highly variable. Schultz detailed some economic theories related to women focusing mainly on developing nations. Specifically, the role of family dynamics in economic choices on farms and female influence on such outcomes.
Rather than taking a theoretical approach, Zeuli and King provide detailed statistics of the characteristics of farmers and their commercial farms in 13 states. They find that in 1991 the average age of females relative to males is insignificant, but that the women in their sample tended to have a higher level of schooling. Interestingly, they found contradicting results, at least based on acreage, to other studies stating that women tend to manage smaller farms, with women operating more acreage on average, but this could be heavily influenced by what they grow and location. Sociology and anthropology research on female farm labor and agriculture tends to report findings based on case studies of specific regions and industries . These papers tend to discuss social incentives, norms, or barriers that influence the gender demographics of the industries of interest and, therefore, influence female representation and the impact of management decisions on the farm. Brasier et al. discuss the history of how women identify their labor on farms. Historically, female participation in farming communities was accessed through family or marriage. Typically, women involved in agriculture were either born into a family that farmed or married a farmer. In the past women often viewed their role on the farm as farm homemakers or farm helpers, following gender norms of the times, and often because they had off-farm income or only participated in farm labor seasonally .
This way of thinking about farm labor could have influenced the representation of female operators of farms. Trauger finds that women are more likely to adopt sustainable agriculture. Trauger limits itsscope to a few farms in Pennsylvania, finding that there may be a trend of female-operated farms to adopt socially minded practices, i.e., community education. This research helps build evidence that supports our claim that the presence of female operators can be considered a proxy variable for being adaptable to change. It seems like a basic assumption, but there was, and remains, a large share of women that participate in farm labor that were/are married to principal operators; this trend continues today. Therefore, the research on the relationship between gender and agriculture would not be complete without mentioning research done on agricultural spouses. A large share of female operators are the spouses of a farm operators. Barlett details the typical marriage models of agricultural spousal relationships, characterizing how farm labor related to agricultural spousal relationships is defined from a social perspective and may have influenced how women viewed their labor on the farm and subsequently the data representing farm labor, historically. The role of identity for female farmers and the professional connections can be a pivotal part of female farmer participation. This research provides evidence of the change in gender demographics based on farm size for the dairy industry. It adds to the literature detailed agricultural economic analysis on the intersection of women and agriculture for the dairy industry and discusses the change in data collection and availability by one of the most prevalent data sources for agricultural data, the COA.The survey questions asked of farmers and ranchers by the COA change slightly every Census round, although most remain the same across time. Below are descriptions of questions changes for relevant variables to the analysis. First, in 2002 and 2007, farms were asked for the total amount of dairy sales in that year, but in 2012 and 2017, this question was dropped and replaced with the total amount of milk sales. Furthermore, whether the dairy farm had any level of organic production was only asked 2007, 2012, and 2017. Second, operator characteristic questions have become more detailed over the years and allowed more operators’ data to be collected. In 2002, 2007, and 2012, the COA asked detailed operator characteristic questions about up to three operators, and only one operator was able to be identified as the principal operator. However, in 2017, the COA expanded its detailed operator questions to include up to four operators and now allows for up to four operators to be identified as a principal operator. Furthermore, in 2012, ebb and flow system the COA started asking farmers and ranchers if the secondary operators were married to the principal operator. This question was then adapted in 2017 to reflect the increase in possible principal operators identified and asked if the operator was married to a principal operator. The Census collects two categories of operators.
The first category is for which detailed operator characteristics and for which at most three operators are listed per farm in 2002-2012 and at most four operators per farm are listed in 2017. Going forward, the operators for which the number per farm is limited and detailed information is provided will be referred to the “core operators”. The second category has no limit to the number listed per farm and only gender of each operator and the number per farm is provided in the data.This section detail statistics and characteristics of female commercial dairy farm operators and their commercial dairies. The number of commercial dairies with at least one female core operator increased in every state, except New Mexico, which experienced no change from 2002 to 2017 . In 2017, every state, but New Mexico, has more than 40% of the commercial dairies reporting at least one female core operator. Although these states demonstrate significant increases in the representation of female core operators, the addition of a fourth core operator for the 2017 Census could distort these results. Table 5.2 shows the share of commercial dairies with at least one female operator by state and year. This has very interesting results with all commercial dairies reporting at least one female operator in 2017. All six states saw significant increases in the share of commercial dairies with at least one female operator. The actual share of female operators compared to the share of operators gives us a better representation of demographic changes. The share of female core operators increased from 2002 to 2017 in every state but New Mexico, for which the share of female core operators decreased in 2007 and 2012 but was the same in 2002 as in 2017 . California and New York both increased the number of across each Census year. California had a 27% increase in female core operators from 2002 to 2017 and the share of female core operators in New York increased by 33%. Idaho, Texas, and Wisconsin all had a slight decrease in female core operators in 2007 and 2012, but an increase in 2017 relative to all previous years. Interestingly, when we look at the share of female operators it follows a similar pattern. California and New York both increases in the share of female operators across each Census. Wisconsin, Idaho, and Texas all had slight decreases in 2007 and 2012 relative to the 2002 share, but the share of female operators in 2017 was larger than in 2002. However, the share of female operators in New Mexico had a small decrease from 2002 to 2017. This suggests that despite the addition of a fourth core operator in the 2017 COA the pattern is not substantially different from the trend in operators and that the trend was not only facilitated by capturing previously unmeasured management activities by women. From here characterizing the trend could be thought of in two ways: 1) this describes an actual increase in women operators playing a more prominent role and/or 2) an increase in their male associates being more likely to recognize and report female operators. Disentangling exactly what characterizes these trends is impossible, but it seems likely that the addition of a fourth core operator and the ability for more than one principal operator may have signaled a conversation about representation on the COA for many commercial dairies. Next, it is important to characterize the management characteristics of commercial dairy operators. These results are only characteristic of core operators as this data was not collected for all operators. The COA asked core operators whether their principal occupation was off farm. Overall, a larger share of female core operators had a principal off-farm occupation than male core operators . In California, less than 10% of the male core operators had an off-farm principal occupation, but about 30% of female core operators had an off-farm principal occupation with little variation over time. In other states, like Idaho and Texas, the share of core operators with off-farm principal occupation followed a similar pattern to California by gender. However, there was an 86.6% increase in male core operators with an off-farm principal occupation and an 18% decrease in female core operators in New Mexico. Along a similar thread, a very small portion of female core operators was labeled as principal operators. Now, the definition of a principal operator did change for the 2017 COA, but even with the 2017 addition of more than one core operator being labeled as a principal operator the share of female core operators that are labeled as a principal operator is relatively small. In California, 5% of female core operators are principal operators from 2002to 2012 with a jump in 2017 to 17% with the addition of the fourth core operator . Idaho, New York, and Wisconsin follow a similar pattern as California with little to no change from 2002 to 2012 and a large jump in 2017. New Mexico and Texas, however, had a decrease from 2002 to 2012 and then a large jump in 2017. In 2017, most states had about 16- 20% of female core operators listed as a principal operator, but New Mexico only had 11%.