Women on the Field and Money in the Bank: The Business of the All-American Girls Professional Baseball League
Bibliographic record
Abstract
Women on the Field and Money in the BankThe Business of the All-American Girls Professional Baseball League Lisa Giddings (bio) and Michael Haupert (bio) Sports economics is a field that benefits from an abundance of production data. Scholars have long exploited this bounty to make contributions to the field of economics in general, and sports economics in particular. The existence of financial data to go along with the production data, however, is much harder to come by. Moreover, research on women in professional sports is even scarcer. This article is an early contribution to the literature on the business of professional women's baseball. We make use of a largely unexploited data set to explore the financial performance of one franchise in the All-American Girls Professional Baseball League (AAGPBL), which existed from 1943–54, with franchises located primarily in midsized Midwestern cities. We also highlight some of our findings from previous work on baseball labor markets to put into perspective the labor market of professional women's baseball players, and we further exploit that data set to investigate the determinants of the demand for women's professional baseball. Sports economics literature is rich in labor studies, the bulk of which focus on MLB salaries. Far less attention has been paid to women's sports. Here we rely on some of our earlier work1 to look at salaries in a different light, by focusing on what economists call marginal revenue product (MRP). With our data set we compiled the first measures of labor exploitation in women's professional sports. It is well established that men were paid more than women during the 1940s and 1950s, and baseball players were no exception. But our study goes beyond salary comparisons to calculate MRP and exploitation rates for both female and male professional ballplayers. Sports literature has also seen several studies of the demand for male sports, particularly baseball. But there is an embarrassing gap when it comes to our understanding of the demand for women's professional sports. We know that women earn less money, play before smaller crowds, and earn lower television ratings than men playing the same sports. But we do not [End Page 146] know what determines the demand for women's sports despite the existence of several professional women's leagues, including the Women's National Basketball Association (WNBA), the National Women's Soccer League (NWSL), National Pro Fastpitch (NPF), and the National Women's Hockey League (NWHL). Our data set includes daily attendance records for some AAGPBL teams, along with detailed financial records that include player salaries, ticket prices, advertising expenditures, and park expenditures to analyze the determinants of attendance. We also look at the scheduling of games and weather conditions. This work is the first of its kind to study the market for women's professional baseball. a brief history of the aagpbl In the fall of 1942, with America at war and men subject to the military draft, the rosters of professional baseball teams at both the minor and major league levels were being rapidly depleted. More than five hundred MLB players would ultimately enlist, and the minor leagues, the chief source of new talent for MLB, had already been decimated by the demand for soldiers and war industry labor. In 1938 there were thirty-eight minor leagues supporting 261 teams. The 1943 season opened for only ten of those leagues, consisting of sixty-six teams.2 Philip K. Wrigley, owner of the Chicago Cubs, feared the day was near when there would not be enough players left to populate the rosters of MLB teams. With both patriotism and profits in mind, he assigned Assistant General Manager Ken Sells to head up a task force to consider the role of professional baseball for the duration of the war. The task force recommended professional women's softball as a substitute for professional baseball. Softball was a popular sport in the 1940s, and women were not subject to the draft.3 The notion of women playing professional sports was not as radical as it might have been just a few years earlier, as women were entering the paid labor market in record numbers. Labor...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".