Organizational behavior and climate change in the financial sector.
The financial sector has the potential to catalyze an economic transformation towards sustainability. Recently, these “sleeping financial giants” have begun to stir due to rising pressures from various stakeholders, and growing awareness of climate risks. These murmurs have come in the form of statements, disclosures and a proliferation of coalitions. However, it is unclear which pressures are most effective and how the financial system responds. We use a mosaic of publicly available data—policy scores, shareholder resolutions, activist movements, and coalition membership—to examine the relationship between internal and external pressures on banks. We show that private politics, including shareholder resolutions and activist movements, has increasingly turned its attention to banks. In response, banks have increased the number of commitments to divest from fossil fuel and invest in renewables. The banking sector is highly competitive and is quick to react to stresses in the market. This is formalized by systems to evaluate risk and its integration into the decision making process. The banking sector is also governed by strict norms and competition between banks increases the risk of diverging from a norm. Consistent with existing work on social norms and social change, norms are slow to change but effective and lasting when they do. We collect all publicly available text data that the six largest US banks have published (for example, reports, web pages, press releases, and position papers) to map out the norm diffusion network to answer the question: how do climate norms diffuse through the banking sector? Using text analysis, we examine how norms about climate have changed in the banking sector since the Paris Agreement was signed in 2015. We build diffusion networks to evaluate the flow of norms between the six largest US banks and identify leaders, adaptors, and laggards. The diffusion network compares norm similarity between bank statements and other publicly available at different points in time. Leaders are identified based on the magnitude of their influence on other banks. We extend this analysis of bank messages to climate action by looking at divestment from fossil fuels and investment in renewables for each group of banks. Next, we examine the influence of external pressures in changing banking sector norms on climate. The pressures include non-bank actors, including stakeholders, activist groups, industry leaders, and major financial coalitions, and world events. This work has important implications for the study of policy networks, and for climate governance by non-state actors.
Surfacing social norms in naturally occuring text data using machine learning and rule-based algorithms.
Motivation: A common set of questions when studying norms are, "how do you know a norm exists?" and "how do you know that you are considering all the relevant norms?". The proposed method will offer a more complete and rigorous way to surface norms from text.
The secondary motivation for this work is to improve the tools researchers have to study organizations. Organizations, and corporations in particular, are very challenging to study because access is restricted as a result of confidentiality and competition. This method uses publicly available text data to offer insight into organizational norms and attitudes. Better understanding in this domain can improve future experimental designs and studies on organizational behavior and change.
Purpose: Build an algorithm that will identify text (sentences) that contain norms and values in natural language.
Evaluate multiple modeling methods to develop the most accurate algorithm. The two methods that I will test are: rule-based modeling and machine learning algorithms (using the bag of words method).
Collaborators: Elke U. Weber
Meta-analysis of social norm mechanisms.
Collaborators: Gregg Sparkman