Working Papers
2021
June 4, 2021
How Have IMF Priorities Evolved? A Text Mining Approach
Description: This paper assess how priorities of the IMF’s membership have evolved over the past two decades, by using text mining techniques on a unique dataset combining IMFC communiqués and constituency statements. Our results reveal significant variation in priorities across time and constituencies. Statements can be characterized by the weight which they place on three key priorities: (i) growth; (ii) debt and development; and (iii) crisis management and quota reform. Sentiment analysis techniques also show that addressing climate change is a topic which is viewed positively by an increasing number of constituencies.
June 4, 2021
Are Passive Institutional Investors Engaged Monitors or Risk-Averse Owners? Both!
Description: We differentiate the effects of passive institutional investors, which mainly refer to index funds that adopt a passive portfolio strategy, on firms’ innovation activities and innovation strategies. Relying on plausibly exogenous variation in passive institutional ownership generated by Russell 1000/2000 index reconstitutions, we find that, with larger passive institutional ownership, while firms’ countable innovation activities increase, they shift their innovation strategies by focusing more on exploitation of existing knowledge instead of exploring new technology. Enhanced monitoring by passive institutional investors through active votes could explain their positive effects on firms’ innovation activities. Increasing risk aversion on the part of passive institutional investors appears the underlying force that drives firms’ shift to incremental innovation. Our paper uncovers a subtle relation between institutional investors and innovation, which is largely ignored by earlier studies.
June 4, 2021
COVID-19 Containment Measures and Expected Stock Volatility: High-Frequency Evidence from Selected Advanced Economies
Description: We study the effect of COVID-19 containment measures on expected stock price volatility in some advanced economies, using event studies with hand-collected minute-level data and panel regressions with daily data. We find that six-month-ahead volatility indices dropped following announcements of initial or re-imposed lockdowns, and that they did not drop significantly following the easing of lockdowns. Such patterns are not as strong for three-month-ahead expected volatility and generally absent for one-month-ahead expected volatility. These results provide suggestive evidence for the existence of an intertemporal trade-off: although stringent containment measures cause short-term economic disruptions, they may reduce medium-term uncertainty (reflected in expected stock volatility) by boosting markets’ confidence that the outbreak would be under control more quickly.
June 4, 2021
The Long-Run Impact of Sovereign Yields on Corporate Yields in Emerging Markets
Description: We analyze the long-run impact of emerging-market sovereign bond yields on corporate bond yields, finding that the average pass-through is around one. The pass-through is larger in countries with greater sovereign risks and where sovereign bonds are more liquid. It is also greater for corporate bonds with lower ratings, shorter maturities, and for those issued by financial companies and government-related firms. Our results support theoretical arguments that corporate and sovereign yields are linked together through credit risks and liquidity premiums. Consequently, high sovereign risks may slowdown growth by persistently increasing private sector borrowing costs.
May 27, 2021
The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
Description: The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.
May 27, 2021
Restructuring and Insolvency in Europe: Policy Options in the Implementation of the EU Directive
Description: The Directive on Restructuring and Insolvency sets minimum standards for restructuring and certain insolvency matters, but its harmonization effect will be limited given multiple options for implementation, likely leading to divergent restructuring models in Europe. These options reveal different policy approaches to the regulation of restructuring and insolvency. The analysis in this paper aims to illustrate the breadth of the policy choices and their consequences for restructuring activity. States should carefully design restructuring procedures to avoid the negative economic effects of certain options that could undermine creditors’ rights or result in unpredictable outcomes, particularly in cross-border cases.
May 27, 2021
Do Lenders Make Less-Informed Investments in High-Growth Housing Markets?
Description: Nonlocal mortgage lenders with greater exposure to high-growth housing markets accept fewer loan applications in these markets and experience greater stock return volatility. When these lenders expand to high-growth markets, they also ration credit to a significantly greater degree than when they ex-pand to other markets. Mean-variance analyses show that nonlocal lenders’ exposure to high-growth markets is associated with more risk, more efficiency, and more return on mortgage portfolios. Overall, these results imply that expansion to high-growth markets leads to a decline in screening and riskier investment by nonlocal lenders, which may reflect a risk–return tradeoff in their portfolio strategy.
May 27, 2021
Predicting Fiscal Crises: A Machine Learning Approach
Description: In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.
May 27, 2021
When They Go Low, We Go High? Measuring Bank Market Power in a Low-for-Long Environment
Description: We examine trends in bank competition since the early 2000s. The Lerner index—arguably the most commonly used measure—shows evidence of a marked increase in market power in advanced economies, especially after the global financial crisis. But other frequently used indicators of banking sector competition seem much more muted. We show that the significant drop in policy rates that occurred in the aftermath of the crisis could explain the seeming disconnect. Adjusting the Lerner index for the impact of policy rates reveals that market power has been fairly constant in advanced economies—consistent with the other signals and similar to the pattern observed in emerging markets.
May 27, 2021
Chile: A Role Model of Export Diversification Policies?
Description: Largely because of its vast copper reserves, Chile’s exports are highly concentrated on this low complexity product and this is often cited as a major drawback of its economic policy framework. However, its exogenous copper abundance conceals the country’s success in developing non-mineral and complex exports. This achievement is remarkable considering its remoteness from the large international economic centers, which limits its integration to global value chains. As suggested in this paper, this accomplishment reflects Chile’s strength in policy areas that foster non-mineral exports (including complex exports), making the country a role model in export diversification and complexity policies among emerging market countries.