As I am close to the end of my research experience, I would like to discuss some challenges, successes, and my overall thoughts about this opportunity. In my past posts, I tried to keep discussing details of my research (such as the actual researching process and technical details) to a minimum, but here I wanted to elaborate more to discuss some specific findings and learning opportunities.
The past couple weeks, I have been learning about multinationals. As I was reading about sweatshops and child labor, a common question was asked throughout various papers: what is the cause of sweatshops and child labor? Why does it happen, even though these forms of labor are undesirable? Something that kept being brought into the conversation was poverty, which makes sense: if individuals had the option to take a higher paying job with far better working conditions, the logical idea is that they would. Likewise, if people had the option to have their children enrolled in school for all school-aged children, the logical idea is that they would. The problem is that these people are in poverty. What I wanted to know is what the exact relationship between poverty and multinationals was. But, what I learned is that there’s endogeneity within this issue, where not only do multinationals affect poverty, but poverty can affect the presence of a multinational. Here, I wanted to learn more about the specifics of multinationals and poverty, in particular how they can impact the average manufacturing wage across countries. I hoped that figuring out this relationship would contribute to the poverty conversation in this context.
I hypothesized that poverty was the solution to these problems because if a given government had more resources invested in its people — better public schooling, higher minimum wage legislation, and the creation of more welfare programs to support families in need —there would be less labor supply to multinationals, both in the form of sweatshops and child labor. However, trying to find the solution to poverty has been a struggle to researchers for years and I wasn’t about to begin trying to come up with a solution in a one-month period. Instead, I thought it would be more wise to allocate my time towards the relationship between multinationals and poverty. In researching multinationals, there are some key characteristics that complicate things. One is that multinationals not only impact wages, but they can have an influence on democracy as well, making the problem not only existing between poverty and multinationals but also politics and multinationals. In addition, child labor is difficult to measure in survey data, but some data has shown that a significant proportion of child labor is domestic (ie, caring for siblings) or family work (ie, working on the family farm). So, part of the problem here, as often is the case, is the ability to find data and clearly define variables such as child labor.
I began by reading some papers that approached this relationship, then began searching for data. Once I found data, I had to figure out what the data meant by looking at individual variables and what they mean across countries. The data I found for manufacturing wages showed average hourly manufacturing wages across different countries for a period of 7 years. To me, this sounded like time series data. However, what I didn’t learn in econometrics was about panel data, as it is not often taught in undergraduate courses. Luckily, I was able to consult my textbook from a couple semesters before and now understand (somewhat) what panel data is, another example of learning valuable aspects of economic research. Then, I had to discover how to properly manipulate data to be used in Stata. This was an entirely new concept to me, but it also pushed me to learn more about it, as it is something necessary for graduate-level, real-life data analysis.
This research process that I went through above is what I have been doing all summer long, teaching myself along the way the scientific process in economic research by running into more roadblocks than I thought there would be. Something that sounded like such a simple task ended up teaching me all these new skills, none of which I had anticipated but all of which I know will carry over into any future experiences in economics.
This opportunity has been such a great learning experience for me. I’m still very new to economic research, so coming into this experience I had never manipulated data on my own, I hadn’t learned what some data structures even were, and I had not yet had the opportunity to use course material in real-life applications. At first, I was disappointed in myself for not having been able to find as many results as I would have liked to, but given that I had to learn many aspects of a research position, I am actually proud of myself for taking on the challenge. Even though I may not have gotten the data I needed or the results I wanted, I am extremely thankful that I had the experience of working with such a fantastic PI and for being given the opportunity to gain all this knowledge.
I went into this experience with high expectations of what I would be able to accomplish over the summer and that doing so would come easily to me. Boy, was I wrong. Finding data was by far the most difficult aspect of this experience, but by the end, at least I now know where to look for data, how to find out which measurements to use, and how to manipulate and analyze it. I learned first-hand the importance of paying close attention to detail within a data set and that analyzing data isn’t as simple as finding it and regressing different variables. I think this experience was much more advanced than I was anticipating but even so, I am so grateful to have worked alongside such knowledgeable faculty and for giving me a taste of what furthering my education in economics would be like.
Learning all about fair trade certification, sweatshops, child labor, and other problems the developing world faces helped change my perspective on trade and consumer behavior. It taught me that I can make a difference not only on these people’s lives, but on the environment just through making small adjustments in where I purchase products from. It strengthened by passion for human rights issues and allowed me to explore career options related to these real-world problems people experience every day. I hope to find a career at a non-profit organization which focuses on these issues and to continue learning about what challenges these agricultural and manufacturing employees are facing globally and how I can make a difference half-way across the world.