October 11, 2019 - Caroline
RPP #6: Quantitative Data Sources
I am proposing to research agribusiness because I want to find out what explains the corporate circumvention of regulations that enables the production and sale of “banned pesticides” to help my readers better understand whether or not the business decisions behind the use of agrochemicals is for the public good.
My large-n analysis research question is:
What explains the variation in regulating pesticide use within a country?
I plan on creating my own dataset for the dependent variable—the regulation of pesticide use by country (my unit of analysis)—using data sources like the Food and Agriculture Organization of the UN (FAO) and Organization for Economic Co-operation and Development (OECD). I would operationalize the dependent variable using an ordinal level of measurement in the form of a scale ranging from “low” to “moderate” to “high.” Because there is no one concise dataset for this particular variable and my analysis would be based on studying a range of other, more specific datasets, I am using an ordinal rather than interval level of measurement and would then try to see if there is any correlation among them.
One independent variable that I would include is the interval measurements of foreign direct investment regulatory restrictiveness from the OECD. These numbers could hint at how much power the government of a host country of a foreign MNC might have versus the company itself concerning pesticide use. It could also provide insight into how much foreign market involvement a country has impacts its own control on certain sectors like agriculture. Another dataset that I plan on using for an independent variable is government expenditure on environmental protection. Seeing how much money or attention the government is putting towards the environment could be a good indicator of the strength of resulting regulations. Another indicator is the Environmental Performance Index which uses ordinal levels of measurement via rankings and scores to demonstrate how well or poorly a country is doing in things like lead exposure (which could be an indicator of a larger environmental toxicology problem). In terms of cases covered, my dataset would definitely include China, the top exporter of pesticides, and Brazil, the top importer. I would focus on countries in the European Union, Asia, and Latin America since they are the ones covered in the datasets I found. As for limitations of the dataset, because they are from different organizations they vary in scope and format. Another possible limitation is missing values, especially from the 90s. I would like the time frame I use to include the 90s, however, because that is when the major international chemical conventions occurred and I want to include any interesting increases or decreases in regulation before or after their ratification by countries.
 “OECD FDI Regulatory Restrictiveness Index.” OECD.Stat. Accessed October 10, 2019,
 “Government Expenditure (subsection: Environmental protection).” FAOSTAT. Accessed October 10, 2019, http://www.fao.org/faostat/en/#data/IG
 “Lead Exposure Results.” Environmental Performance Index. Yale Center for Environmental Law & Policy. Accessed October 11, 2019, https://epi.envirocenter.yale.edu/epi-indicator-report/PBD
 “OEC – Pesticides (HS92: 3808) Product Trade, Exporters and Importers,” accessed October 11, 2019, https://oec.world/en/profile/hs92/3808/.