Michael Kumhof, Phurichai Rungcharoenkitkul and Andrej Sokol
Understanding gross capital flows is crucial for both macroeconomic and financial stability policy. However, theory is lagging behind empirical work, as much of the literature continues to rely on net capital flow models developed many decades ago. Missing from these models is an explicit tracking of the financial records underlying all goods and asset purchases, namely gross balance sheet positions, which in turn requires modelling the principal medium of exchange, bank deposits. Our new model features gross capital flows and offers a fresh perspective on important policy debates, such as the role of current accounts as indicators of financial fragility, the nature of the global saving glut, Triffin’s current account dilemma, and the synchronisation of gross capital inflows and outflows.
The SIR model, first developed by Kermack and McKendrick (1927), remains the canonical epidemiological model. It a natural choice for economists seeking to model the interplay between economic and epidemiological dynamics. This briefing surveys some of the many adaptations to the basic SIR setup which have emerged in the epi-macro literature over the past six months. These have all been used to analyse issues such as lockdown policies, super-spreaders, herd immunity, hospital capacity and ‘test- and-trace’.
The Coronavirus pandemic and measures to contain contagion had far reaching consequences on economic activities, which also led to a sharp fall in CO2 emissions. This has sparked new debate about how the recovery from the crisis could be made compatible with the Paris climate goals. In this post, I survey the emerging literature on the link between the economic recovery from the aftermath of the pandemic and climate change.
Since the tumultuous events of 2007, much work has suggested that financial shocks are the main driver of economic fluctuations. In a recent paper, I propose a novel strategy to identify financial disturbances. I use the evolution of loan finance relative to bond finance to proxy for firms’ credit conditions and single out the shocks born in the financial sector. I apply and test the method for the US economy. I obtain three key results. First, financial shocks account for around a third of the US business cycle. Second, these shocks occur around precise events such as the Japanese crisis and the Great Recession. Third, the financial shocks I obtain are predictive of the corporate bond spread.
Arthur Turrell, Eleni Kalamara, Chris Redl, George Kapetanios and Sujit Kapadia
Every day, journalists collate information about the world and, with nimble keystrokes, re-express it succinctly as newspaper copy. Events about the macroeconomy are no exception. So could there be additional valuable information about the economy contained in the news? In a recent research paper, we ask whether newspaper stories could help to predict future macroeconomic developments. We find that news can be used to enhance statistical economic forecasts of growth, inflation and unemployment — but only by using supervised machine learning techniques. We also find that the biggest forecast improvements occur when it matters most — during stressed periods.
Average first-time buyer (FTB) house prices have risen by 60% over the past 15 years and homeownership has fallen. How did those who bought their first home finance it and how has this changed? i) We find that average incomes of FTBs have risen. ii) But age-cohorts with the most FTBs (e.g. millennials) have recently experienced below-average income growth. iii) FTBs are therefore increasingly richer than their classmates: in 2018 they had 1.8x the mean cohort income vs. 1.5x in 2006. iv) FTBs are also taking on bigger mortgages. v) But monthly FTB mortgage payments have actually remained flat as lower interest rates and longer mortgages mean the same monthly payment can service more debt.
During the current pandemic, economic variables have moved quickly and by large magnitudes. Given the publication lags for official data this has led to a greater emphasis on higher-frequency and/or more timely measures to track the economic impact of the pandemic and gauge the state of the economy in real time. This post looks at the emerging body of work in this area, with a particular focus on real-time measures of consumer expenditure and activity in the labour market.
The COVID-19 pandemic has rapidly spawned a literature analysing its impact on macroeconomic aggregates. But there’s also been work that seeks to look at heterogeneity of impacts across industries, households and individuals. This post summarises this literature which seeks to better understand the heterogeneous effects of the pandemic and associated policy responses on income, hours worked and employment status.
It has been well established that macroeconomic outcomes, such as recessions and unemployment, can have important impacts on households’ well-being. So it follows that monetary policy decisions can affect happiness too. In a recent working paper we use a novel approach to assess how the unprecedented loosening in monetary policy in response to the 2008 global financial crisis affected the well-being of UK households. The framework we use could be used to assess the welfare implications of other monetary policy responses, including to the spread of Covid-19 during 2020.