Prolific Pigs? Mating Reproductive Capacity with Market Price in Early Twentieth Century Pig Breeding

Project Summary

Yanmin (Mike) Ma, mathematics/economics major, and Manchen (Mercy) Fang, electrical and computer engineering/computer science major, spent ten weeks studying historical archives and building a model to predict the price of pigs, relative to a number of interesting factors.

Themes and Categories
Year
2015
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results

Berkshire pig transaction price is influenced by factors including the age of the pig, the number of offspring and the prevailing pork price. And we can conclude that within 1910 - 1920, breeders and buyers did price Berkshire pig rationally to a large extent.

Download the executive summary (PDF).

Disciplines Involved

  • Economics
  • History

Project Team

Undergraduates: Yanmin (Mike) Ma, mathematics/economics major, and Manchen (Mercy) Fang, electrical and computer engineering/computer science major

Client: Gabriel Rosenberg, Assistant Professor, Women's Studies Program

Graduate student mentor: Chris Glynn, Department of Statistical Science

From left to right: Manchen (Mercy) Fang; Chris Glynn; Yanmin (Mike) Ma

"Working with Mercy Fang and Mike Ma has been a pleasure. Throughout the Data+ experience, Mike and Mercy demonstrated creativity, diligence, and technical ability in their research on Berkshire pigs. Our primary objective was to build a data set that would cross-reference genealogical, market, and geographic data on individual Berkshire pigs. To collect this data, Mike and Mercy worked with original scans of archived primary source publications. Together, they developed efficient algorithms and error-correcting mechanisms to extract data from PDF documents. Mike and Mercy taught me a lot about working with text-data, the perils of optical character recognition, and the factors that drove hog markets in the early 1900s.

The pride that they took in their research is evident in their Data+ seminar talks and their final poster. Most impressively, Mike and Mercy were extremely independent and self-reliant. They formulated their own questions, developed strategies for answering them, and implemented solutions on their own. They are very promising young researchers." — Chris Glynn, graduate student mentor

Related People

Related Projects

In this two-day, virtual data expedition project, students were introduced to the APIM in the context of stress proliferation, linked lives, the spousal relationship, and mental and physical health outcomes.

Stress proliferation is a concept within the stress process paradigm that explains how one person’s stressors can influence others (Thoits 2010). Combining this with the life course principle of linked lives explains that because people are embedded in social networks, stress not only can impact the individual but can also proliferate to people close to them (Elder Jr, Shanahan and Jennings 2015). For example, one spouse’s chronic health condition may lead to stress-provoking strain in the marital relationship, eventually spilling over to affect the other spouse’s mental health. Additionally, because partners share an environment, experiences, and resources (e.g., money and information), as well as exert social control over each other, they can monitor and influence each other’s health and health behaviors. This often leads to health concordance within couples; in other words, because individuals within the couple influence each other’s health and well-being, their health tends to become more similar or more alike (Kiecolt-Glaser and Wilson 2017, Polenick, Renn and Birditt 2018). Thus, a spouse’s current health condition may influence their partner’s future health and spouses may contemporaneously exhibit similar health conditions or behaviors.

However, how spouses influence each other may be patterned by the gender of the spouse with the health condition or exhibiting the health behaviors. Recent evidence suggests that a wife’s health condition may have little influence on her husband’s future health conditions, but that a husband’s health condition will most likely influence his wife’s future health (Kiecolt-Glaser and Wilson 2017).

Alexa Goble (Finance) joined Econ majors Chavez Cheong and Eli Levine in a ten-week exploration of mortgage enforcement actions related to the financial crisis from earlier in this century. Using NLP techniques on mortgage data from Ohio and Massachusetts, the team validated a new experimental approach to understanding the dynamics between state regulatory agencies, mortgage lenders, brokers, and loan originators. This project was a continuation of two previous Data+ projects:

https://bigdata.duke.edu/projects/american-predatory-lending-global-financial-crisis

https://bigdata.duke.edu/projects/american-predatory-lending-and-global-financial-crisis-year-2

 

View the team's project poster here

Watch the team's final presentation on Zoom:

 

Project Lead: Lee Reiners

Project Manager: Malcolm Smith Fraser

Stats/Sociology major Mitchelle Mojekwu joined Neuroscience majors Kassie Hamilton and Zineb Jaidi in a ten-week exploration of data relevant to an upcoming public school zone redistricting in Durham County. Using information acquired from the General Social Survey and the US Census, the team applied modern mathematical and statistical methods for generating proposed redistricting plans, with the aim of providing decision-makers with information they can use to produce school districts that are equitable and reflective of the Durham County student population.

View the team's project poster here

Watch the team's final presentation on Zoom:

 

Faculty Lead: Greg Herschlag

Project Manager: Bernard Coles