The Nexus between Loan Portfolio Size and Volatility: Does Bank Capital Regulation Matter?
Franziska Bremus, Melina Ludolph
Journal of Banking and Finance,
June
2021
Abstract
This paper analyzes the effects of bank capital regulation on the link between bank size and volatility. Using bank-level data for 27 advanced economies over the 2000–2014 period, we estimate a power law that relates the volume of a bank’s loan portfolio to the volatility of loan growth. Our analysis reveals, first, that more stringent capital regulation weakens the size-volatility nexus. Hence, in countries with more stringent capital regulation, large banks show, ceteris paribus, lower loan portfolio volatility. Second, the effect of tighter capital requirements on the size-volatility nexus becomes stronger for the upper tail of the bank size distribution. This is in line with capitalization decreasing with bank size, such that larger banks tend to be more affected by increasing capital requirements. Third, in countries with higher sectoral capital buffers, the size-volatility nexus is weaker.
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Measuring and Accounting for Innovation in the Twenty-First Century
Carol Corrado, Jonathan Haskel, Javier Miranda, Daniel Sichel
Studies in Income and Wealth,
2021
Abstract
Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies.
This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution.
The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.
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Does Capital Account Liberalization Affect Income Inequality?
Xiang Li, Dan Su
Oxford Bulletin of Economics and Statistics,
No. 2,
2021
Abstract
By adopting an identification strategy of difference‐in‐difference estimation combined with propensity score matching between liberalized and closed countries, this paper provides robust evidence that opening the capital account is associated with an increase in income inequality in developing countries. Specifically, capital account liberalization, in the long run, is associated with a reduction in the income share of the poorest half by 2.66–3.79% points and an increase in that of the richest 10% by 5.19–8.76% points. Moreover, directions and categories of capital account liberalization matter. The relationship is more pronounced when liberalizing inward and equity capital flows.
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Introduction to "Measuring and Accounting for Innovation in the Twenty-First Century"
Javier Miranda
Measuring and Accounting for Innovation in the Twenty-First Century,
NBER Studies in Income and Wealth, Vol 78 /
2021
Abstract
Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies. This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution. The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.
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HIP, RIP, and the Robustness of Empirical Earnings Processes
Florian Hoffmann
Quantitative Economics,
No. 3,
2019
Abstract
The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.
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The Income Elasticity of Mortgage Loan Demand
Manthos D. Delis, Iftekhar Hasan, Chris Tsoumas
Financial Markets, Institutions & Instruments,
Special Issue: 2016 Portsmouth – Fordham Conferenc
2019
Abstract
One explanation for the emergence of the housing market bubble and the subprime crisis is that increases in individuals’ income led to higher increases in the amount of mortgage loans demanded, especially for the middle class. This hypothesis translates to an increase in the income elasticity of mortgage loan demand before 2007. Using applicant‐level data, we test this hypothesis and find that the income elasticity of mortgage loan demand in fact declines in the years before 2007, especially for the mid‐ and lower‐middle income groups. Our finding implies that increases in house prices were not matched by increases in loan applicants’ income.
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