Periodic Reporting for period 2 - IllegalPharma (Competitive Dynamics in the Informal Economy: The case of Illegal Pharmaceutical Drugs)
Reporting period: 2018-11-01 to 2020-04-30
According to the World Health Organization (WHO), the sale of illegal pharmaceuticals amounts to about 30€ billion annually (WHO, 2003). Furthermore, the WHO also estimates that a large amount of people (around 1 million) may die globally because of counterfeit drugs. Yet, as explained above, there is a lack of understanding about (a) which medicines are more frequently traded through illegal channels, (b) which ones represent a greater concern in terms of public health, and (c) how could governments and regulators fight against this. Consequently, beyond the academic contribution explained above, this research will also provide a better understanding of the magnitude and nature of the illegal trade of pharmaceutical drugs, as a means to fight this fundamental threat for society.
The main objective of this project is to develop and test a conceptual model that explains the competitive dynamics in illegal markets. This project will focus on three specific competitive outcomes: (1) market entry decisions (i.e. entry by an illegal pharmacy into a particular drug-market) (2) price decisions (i.e. the price gap between drugs sold in illegal and legal pharmacies), and (3) drug quality decisions (i.e. quality differences among drugs sold in illegal pharmacies).
To explain variance along these three competitive dimensions, we will develop our theoretical model around the concept of legitimacy. According to Suchman (1995), legitimacy is the perception that “actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions” (Suchman, 1995: 574). Our main conceptual proposition relies on the claim that the illegal trading of medical drugs will be perceived as more legitimate in those cases where individuals perceive that the existing laws and regulations are “unfair”. Specifically, we expect that illegal pharmacies are (1) more likely to enter new niches, (2) increase drug price and (3) invest on drug quality, when there is a greater perception among agents that illegal trade of such drugs is a legitimate activity and is justified from a moral standpoint.
To capture perceptions of legitimacy, we look at whether a certain law or regulation is perceived as unfair and unreasonably strict. Specifically, we will look at three laws: (1) prescription law, (2) manufacturing law and (3) intellectual property law. In each of these 3 laws there is a conflict between a value that is left in the legal side of the spectrum and another value that remains in the legal side: tension between patient safety versus patient privacy (prescription law), tension between patient safety and timely access for a drug (manufacturing law), and tension between rewarding innovators and drug affordability (intellectual property law). Accordingly, for each pharmaceutical drug, we argue that the more salient the value left in the illegal side is compared to the value remaining in the legal side, the more strict and unfair a regulation will be perceived, meaning that the more illegal trade will be perceived as a legitimate and morally justified activity.
In sum, the main objective is to show how perceptions of legitimacy explain illegal pharmacies’ decisions with respect to (1) new market entry, (2) prices and (3) quality.
Conceptual model: The model described above has already been developed and a theoretical draft is under preparation. The objective is to combine this conceptual model with the empirical tests described below, in the form of a series of academic papers, which will be submitted to conferences and peer-review journal publication.
Empirical analysis: So far, the main progress in this front has been in the collection, cleaning and preparation of the final database that will be used to test the predictions derived from the conceptual model. Let me summarize the progress made in each of these three activities:
1. Data collection: data has been collected to create two of the three outcome variables already (market entry and drug legal-illegal prices) and to develop the construct of perceived legitimacy in each of the three examined regulations/laws. In addition, additional data has been collected to create control variables in order to avoid omitted variable bias in our estimations.
2. Data cleaning and preparation of the final dataset: All the datasets collected so far have been cleaned using a machine learning algorithm. The cleaning process has been homogenized for all these datasets to assure that the same unique drug identifier is extracted for each data point, so that it can be later used to merge these datasets together. The selection of this unique identifier is obtained from the Orange Book database provided by the FDA. The Orange Book provides information at the drug level on all the drugs approved by the FDA in the United States (https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic-equivalence-evaluations-orange-book). In all the files, a single drug is determined by the following attributes: Trade Name, Active Ingredient, Route of Administration, and Strength. Thus, the designed machine learning algorithm is able to assign these unique drug identifiers in all the databases collected so far. The next step is to merge all these different datasets to obtain the final database that will be used to test the proposed theoretical model.
1. Theoretical contribution: The multidisciplinary perspective of the conceptual model developed so far is expected to provide important academic contributions to different fields:
a. Business literature: This project will have a strong impact on the management field in two dimensions. On the one hand, most of the management literature examines the drivers of firms’ competitiveness focusing on the legal economy. Thus, this research will fill this gap by developing a new theory of the competitive dynamics in the informal economy. On the other hand, management research has not yet deeply explored the role of legitimacy in the illegal sector. In this sense, this research will contribute to prior literature in this field by examining the role of legitimacy in the illegal pharmaceutical sector. Furthermore, by analyzing how perceptions of legitimacy may influence patients’ willingness to pay for illegal drugs, this research could also contribute to behavioral marketing and social psychology academic research.
b. Social Sciences literature: This research could also contribute to research in economics, psychology and sociology. As a matter of fact, the data and methodology proposed in this research could foster further empirical and conceptual research in all these areas.
c. Medical literature: Medical research examining the illegal pharmaceutical sector, and the risks that it entails for public health, could also benefit from this project.
2. Novel data and methodology: In this project we collected a large amount of data using a very unique methodology. Accordingly, we expect that the construction of this database will provide the following advantages once completed.
a. Unique data on pharmaceutical drugs. As explained above, we are about to develop a unique data using a range of very novel and rich sources (e.g. legal and illegal drug prices). This, we believe, will turn into a very unique resource that could be very useful to develop empirical research on both illegal and legal sectors. In a similar line, this research has also collected data to capture perceptions of legitimacy from a quantitative point of view. More specifically, this project will measure the tensions between values in a range of regulatory and legal environments. Up to our knowledge, this has not been done so far in prior academic studies. Therefore, the collected data on perceived legitimacy is a key resource as well.
b. Unique methodology. We also believe that the methodology used to capture perceptions of legitimacy is in itself an important useful resource that comes out of this project. In a similar fashion, the methodology we develop to capture disease stigma using a machine learning algorithm for text analysis in online community forums, also provides a unique and very valuable resource for academic research in multiple disciplines.