This paper proposes a measure of financial fragility that is based on economic welfare in a general equilbrium model calibrated against UK data. The model comprises a household sector, three active heterogeneous banks, a central bank/regulator, incomplete markets, and endogenous default. We address the impact of monetary and regulatory policy, credit and capital shocks in the real and financial sectors and how the response of the economy to shocks relates to our measure of financial fragility. Finally we use panel VAR techniques to investigate the relationships between the factors that characterise financial fragility in our model, i.e. banks' probabilities of default and banks' profitsto a proxy of welfare.
Pandemics have historically had a significant impact on economic inequality. However, official inequality statistics are only available at low frequency and with considerable delay, which challenges policymakers in their objective to mitigate inequality and fine-tune public policies. We show that using data from bank records it is possible to measure economic inequality at high frequency. The approach proposed in this paper allows measuring, timely and accurately, the impact on inequality of fast-unfolding crises, like the COVID-19 pandemic. Applying this approach to data from a representative sample of over three million residents of Spain we find that, absent government intervention, inequality would have increased by almost 30% in just one month. The granularity of the data allows analyzing with great detail the sources of the increases in inequality. In the Spanish case we find that it is primarily driven by job losses and wage cuts experienced by low-wage earners. Government support, in particular extended unemployment insurance and benefits for furloughed workers, were generally effective at mitigating the increase in inequality, though less so among young people and foreign-born workers. Therefore, our approach provides knowledge on the evolution of inequality at high frequency, the effectiveness of public policies in mitigating the increase of inequality and the subgroups of the population most affected by the changes in inequality. This information is fundamental to fine-tune public policies on the wake of a fast-moving pandemic like the COVID-19.
Official statistics on economic inequality are only available at low frequency and with considerable delay. This makes it challenging to assess the impact on inequality of fast-unfolding crises like the COVID-19 pandemic, and to rapidly evaluate and tailor policy responses. We propose a new methodology to track income inequality at high frequency using anonymized data from bank records for over three million account holders in Spain. Using this approach, we analyze how inequality evolved between February and November 2020 (compared to the same months of 2019). We first show that the wage distribution in our data matches very closely that from official labor surveys. We then document that, in the absence of government intervention, inequality would have increased dramatically, mainly due to job losses and wage cuts experienced by low-wage workers. The increase in pre-transfer inequality was especially pronounced among the young and the foreign-born, and in regions more dependent on services. Public transfers and unemployment insurance schemes were effective at providing a safety net to the most affected segments of the population and at offsetting most of the increase in inequality. Increased inequality is primarily driven by differential changes in employment rate. Indeed, using individual-level regressions, we find that, over the course of the pandemic, the probability of being employed decreased drastically for workers in the lower part of the pre-COVID wage distribution, young cohorts, and foreign-born.
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