Geraldine McAllister

Foreign Direct Investment by Emerging Market Multinational Enterprises, the Impact of the Financial Crisis and Recession and Challenges Ahead

The global market for foreign direct investment (FDI) has undergone significant changes in recent years, with the increasingly important role played by emerging market multinational enterprises (MNEs) being one of the most important among them. While outward FDI (OFDI) from these countries, in itself, is not new, the magnitude that this development has achieved has raised a host of issues, which we will examine in this volume. This opening chapter presents the factual background of this phenomenon, the impact of the financial crisis and recession on FDI flows, and the issues and challenges related to emerging markets’ high FDI flows.

Foreign Direct Investment by Emerging Economy Multinationals: Coping with the Global Crisis

Even before the onset of the global crisis, the global market for foreign direct investment (FDI) had undergone significant changes. Foremost amongst these changes was the increasing importance of emerging market1 multinationals (MNEs). While outward foreign direct investment (OFDI) from these markets is, in itself, not new, the magnitude that this phenomenon achieved prior to the crisis and its resilience in the face of the global crisis suggest that this is not a temporary occurrence but rather a sign of a fundamental change that is taking place in the global OFDI market. However, emerging markets are not homogenous: in addition to the rise in OFDI from emerging markets, the formation of new regional groupings has led to the emergence of fresh investment patterns. This chapter examines changes taking place in global FDI flows and looks at the impact of the crisis in the context of profound structural changes; it also focuses on the response of emerging markets and the enormous risks and challenges that lie ahead. It is vital to note that this crisis is ongoing, and it is too early to predict the final contours it will leave in its wake on the FDI landscape.

Delegation and Administrative Lobbying in Rule-Making

We explore the determinants of market regulation with an analysis of the policy-making process in which the legislature delegates authority to an executive agency and special interests can lobby the executive agency. We discuss how the mere threat of administrative lobbying by the industry may be sufficient to induce the agency to set policies preferred by the industry. Our analysis also shows that policy conflict, the difference between the legislature’s preferred policy and the agency’s implemented policy, is increasing in the agency’s vulnerability to lobbying but decreasing in the interest group’s lobbying cost when the legislature prefers more extreme policies. Administrative lobbying either amplifies or mitigates the conflict between the legislature and the agency. Relatedly, our analysis shows that the “ally principle” does not hold and the legislature prefers an agency that is slightly more biased against the industry. The legislature delegates greater discretion to the agency when policy uncertainty is higher, when policy conflict between the legislature and the agency is a lower, and when administrative lobbying mitigates the policy conflict between legislature and agency.

Computational Data Sciences and the Regulation of Banking and Financial Services

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate policy processes at a scale unimaginable even a few years ago. We apply these scalable NLP and ML techniques to analyze the United States Government’s regulation of the banking and financial services sector. First, we employ NLP techniques to convert the text of financial regulation laws into feature vectors and infer representative “topics” across all the laws. Second, we apply ML algorithms to the feature vectors to predict various attributes of each law, focusing on the amount of authority delegated to regulators. Lastly, we compare the power of alternative models in predicting regulators’ discretion to oversee financial markets. These methods allow us to efficiently process large amounts of documents and represent the text of the laws in feature vectors, taking into account words, phrases, syntax, and semantics. The vectors can be paired with predefined policy features, thereby enabling us to build better predictive measures of financial sector regulation. The analysis offers policymakers and the business community alike a tool to automatically score policy features of financial regulation laws to and measure their impact on market performance.

Big Data and the Regulation of Financial Markets

The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This paper develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis. The results provide standardized coding labels of policies to assist regulators to better understand how key policy features impact financial markets.