Seminars and conferences

Industrial policies, traditionally aimed at boosting the competitiveness of protected sectors, can inadvertently drive productivity growth in downstream sectors abroad. We provide evidence for this mechanism by examining the case of rare earth elements (REE) – essential inputs for many manufacturing products and the green transformation whose production is highly concentrated in China. To measure the importance of REE across manufacturing industries, we construct an input-output table that incorporates individual REE inputs. We find that exports in REE-intensive downstream sectors outside of China increased after China introduced export restrictions on REE. Using data on patents and sectoral TFP, we then show that China’s policies also triggered a surge in innovation towards more efficient usage of REE inputs and faster TFP growth in sectors relying on REE inputs in the rest of the world. To explain these findings and quantify them in general equilibrium, we develop a quantitative trade model with directed technical change. We show that input-supply restrictions shift the direction of innovation and drive growth in downstream sectors when inputs are gross complements. We also introduce a new structural method for estimating elasticities of substitution based on patent data and relative input prices and find evidence for REE being complementary production inputs.

This paper studies the pass-through from wages to producer prices using sectoral disaggregated data for the euro area. We find a positive and statistically significant wage-price pass-through that reaches 50% after three years, which differs across sectors. The wage-price pass-through in private services is significantly higher than in industry and takes longer before reaching its peak. While a higher labour intensity is a key component of the pass-through, our estimates indicate that differences in sectoral labour shares alone cannot explain the larger wage-price pass-through in private services compared to industry. Instead, the estimates hint at an important role for international competition in the domestic market for the tradeable sector. They also suggest that the sales destination matters: wage growth contributes to domestic inflation for goods but not to export inflation. Finally, we also provide evidence of an increase in the wage-price pass-through after 2020, particularly in private services.

This paper proposes an operational approach to stress testing, allowing one to assess the banking sector’s vulnerability in multiple plausible macro-financial scenarios. The approach helps identify macro-financial risk factors of particular relevance for the banking system and individual banks and searches for scenarios that could push them towards their worst outcomes. We demonstrate this concept using a macroprudential stress testing model for the euro area. By doing so, we show how multiple-scenario stress testing can complement single-scenario stress tests, aid in scenario design, and evaluate risks in the banking system. We also show how stress tests and scenarios can be optimized to accommodate different mandates and instruments of supervisory and macroprudential agencies.

We study impact of situational factors on decision making of physicians. Using detailed audit log data from emergency rooms, we access how cognitive load during the shift affects future choices of tests as well as the treatment. We draw upon theories of information acquisition to infer how much is physicians’ knowledge refined when making a particular choice. For instance, we find that high cognitive load implies that physicians make less informed choices in the future, perhaps choose to think less, and instead substitute this with more orders of diagnostic tests.

Global warming and the associated increase in physical risks are expected to lead to a greater frequency and intensity of extreme weather events, such as floods and droughts. In this paper, we assess the direct exposure of Slovak banks’ corporate loan portfolios to riverine flood risk. We propose several monitoring metrics and estimate exposures at risk due to riverine flooding. Our analysis leverages a comprehensive dataset, which integrates flood risk maps from the European Commission’s Joint Research Centre, cadastral data on firm properties, credit register data, and firms’ financial statements. While a significant share of firms are located in flood-prone areas, only a subset are likely to face flood levels that exceed critical thresholds. Consequently, the direct impact of riverine flooding on corporate credit risk appears to be relatively moderate. Our estimates suggest that the potential increase in non-performing corporate loans due to flood risk is measurable in basis points. These findings are robust across various specifications and metrics.

This seminar will explore firm-level production networks, providing both empirical and theoretical insights into their significance. Our limited understanding of granular trade networks means that detailed description and modeling of these systems can offer valuable insights for economic analysis. A notable example is how risks and crises originating within production networks can profoundly impact the financial system. By incorporating the modeling of shock propagation channels within and between the real economy and the financial sector, policy institutions can enhance the effectiveness and efficiency of their tools for maintaining financial stability.

How does reputation affect the announcement strategy for central bankers? This paper provides an answer with a novel combination of theory and computational text analysis. We study a communication game between a central bank and the public where the central bank makes announcements about their noisy forecasts of shocks and sets policy to balance its inflation and output targets. Meanwhile, the public updates beliefs about policy conditional on (1) the bank’s announcement and (2) the public’s belief about the central bank’s ability to accurately forecast, called reputation for confidence.  A tension between the central bank’s ability and their reputation induces nonmonotonic effects on how granularly the central bank communicates. Finally, we use a state-of-the-art text analysis model on internal drafts of alternative Fed policy statements to validate the model.

This article provides a microdata perspective from the Financial Situation of Households (abbreviated as FSD in  Czech) survey on the occurrence of households with elevated financial risk, defined as having critical values of debt-to-asset ratios (DTA), debt-to-income ratios (DTI), debt service-to-income ratios (DSTI), and loan-to-value ratios (LTV). To assess the overall vulnerability of households, we construct a composite indicator of financial vulnerability (CIFI).  In 2021 and 2022, about one in four to five Czech households had some form of debt, with about half consisting of mortgage loans and the rest of other loans and credits. Regression results indicate that financially vulnerable households tend to have lower income and wealth, as well as a younger age. Conversely, the probability of financial distress among indebted households decreases as the share of employed persons in the household increases.

Most countries in the world are not at the technological frontier, yet their economies grow and fluctuate. This paper sets up a quantitative model of endogenous growth with business cycle fluctuations to analyze the medium-frequency fluctuations in non-frontier countries. The growth mechanism is a Schumpeterian creative destruction framework embedded into a real business cycle dynamic stochastic general equilibrium model, with standard and non-standard features. We allow multinational firms to enter the economy and challenge existing incumbents, which permits us to study the tension between their direct positive productivity contribution and their indirect negative contribution through the expected obsolescence of domestic innovators. We estimate the model using a full-information approach and show that multinationals’ entry is both boon and bane for non-frontier economies.

We study the time-varying stock-oil correlation for the period 1986–2018. We first derive an oil futures return news decomposition following Campbell and Shiller (1988) and Campbell (1991). Then, for both stock and oil, we split unexpected returns into cash-flow news (which can be related to asset fundamentals because of its link to production) and discount-rate news (which can be driven by shocks to investors holding both assets) using a vector autoregressive (VAR) model. We find that about 75% of the time-variation in correlation is related to the comovement of cash-flow news between the two assets. This result is robust to different specification of the VAR model used to decompose returns. Thus, we provide supportive evidence that it is the real economy rather than the financialization of commodity markets the key driver for the stock-oil comovement.

This paper presents the results from a field experiment and a series of surveys conducted across multiple central banks, including the National Bank of Slovakia, the Czech National Bank, and the European Central Bank, to assess the productivity impacts of generative AI in central banking. Our field experiment randomized employees to complete tasks with and without the use of generative AI, revealing significant productivity gains in both work quality and time efficiency. While generative AI has an equalizing effect on work quality, helping lower performers close the gap with their peers, the time-saving benefits disproportionately favor high-performing employees, widening the gap in task completion speed. In parallel, our surveys highlight that while most employees believe generative AI can enhance their productivity, adoption remains limited due to a lack of clear use cases and managerial guidance. Peer effects are a strong predictor of AI adoption, but gender disparities persist, with women being less likely to use AI tools. These findings offer important insights for the implementation of generative AI in central banks and contribute to the broader conversation on skill-biased technological change.

The paper documents a slowdown in human capital accumulation in OECD countries, largely driven by the trend decline in PISA test scores. Counterfactual estimates suggest that these forces can account for roughly one-sixth of the slowdown in total factor productivity (TFP) growth since the mid-2000s. This raises the question: why have PISA scores declined and what can policy do to lean against these headwinds? The decline in PISA scores can be understood through an analytical framework that combines education policies, the widespread use of digital devices in classrooms and the COVID-19 pandemic. Simulations suggest that if the downward trend is PISA scores were to persist, long-run TFP could be 3% lower as a result. Education policy reforms, combined with a structural reform package that improves the utilisation and allocation of human capital (by supporting job reallocation and labour market fluidity and enhancing adaptability through retraining), could turn these headwinds into tailwinds and boost OECD countries’ TFP by 2% in the long run.

We study the role of production technology and credit availability for firms’ hiring practices in the wake of a labor supply shock. Our analysis uses granular firm- and loan-level data and exploits the closure of a large foreign manufacturing plant in Belgium. The closure exogenously increased labor supply to nearby firms but not to more distant firms. Comparing nearby to similar distant firms, we find that nearby firms hire significantly more after the plant closure. Yet, the effect is not homogeneous. In industries with a low capital-labor elasticity of substitution, nearby firms only hire more if credit supply enables complementary capital expenditures. In industries with a high elasticity of substitution, nearby firms hire more independent of credit supply, but (weakly) more when credit supply restricts capital expenditures. Our results have important implications, e.g., for managing unemployment during financial crises when both labor and credit supply are simultaneously affected.

This paper adds life-cycle features to a New Keynesian model and shows how this places financial wealth at the center of consumption/saving decisions, thereby enriching the determinants of aggregate demand and affecting the transmission of monetary policy. As retirement preoccupations strengthen, the potency of conventional monetary policy declines and depends more on the response of asset prices (supporting central banks closely monitoring the impact of monetary policy on asset prices). Especially “low/high for long” policies are shown to often have only muted effects on economic activity due to offsetting income and substitution effects of interest rates, in a way that can be compounded by Quantitative Easing. We also show why the presence of life-cycle forces can favor a monetary policy strategy which stabilizes asset prices in response to financial shocks. Being explicit about the role of retirement savings in aggregate demand therefore offers new perspectives on several aspects of monetary interventions.

Expectations are central for housing decisions and heterogeneity in expectations is a robust feature of survey data. We study the implications of heterogeneity in house price growth expectations for the level of house prices. We feed the joint empirical distributions of income, wealth and expectations into a calibrated heterogeneous agents housing model. We find that eliminating heterogeneity in house price growth expectations would raise average house prices and amplify house price fluctuations thereby reducing the fit of the model. Without heterogeneity, average house prices would be about 11 percent higher and the boom-bust cycle would be about 41 percent larger.

We develop a random search model of the labor market with two-sided heterogeneity and match-specific productivity shocks. Our model has two main predictions: i) there is positively assortative matching and ii) the average log wage that a worker receives is increasing in the worker’s and firm’s productivity and is submodular. Sorting and wages are driven by selection. All workers are equally likely to meet all firms, but low (high) productivity workers have a higher average log wage offer in low (high) productivity firms. The high wage offer meetings result in matches more frequently, generating positive assortative matching. Since only meetings that result in matches are observed in administrative wage data, such data contain only a selected subset of wage offers, and those turn out to be increasing and submodular. We show that our findings are consistent with results in the empirical literature and argue that the selection mechanism affects the interpretation of findings in that literature. First, firm fixed effects in wage equations do not have structural interpretation as firm wage premia because they are estimated with a bias due to selection. Second, while matching patterns are informative about productive complementarities, average log wages are not.

We develop a random search model of the labor market with two-sided heterogeneity and match-specific productivity shocks. Our model has two main predictions: i) there is positively assortative matching and ii) the average log wage that a worker receives is increasing in the worker’s and firm’s productivity and is submodular. Sorting and wages are driven by selection. All workers are equally likely to meet all firms, but low (high) productivity workers have a higher average log wage offer in low (high) productivity firms. The high wage offer meetings result in matches more frequently, generating positive assortative matching. Since only meetings that result in matches are observed in administrative wage data, such data contain only a selected subset of wage offers, and those turn out to be increasing and submodular. We show that our findings are consistent with results in the empirical literature and argue that the selection mechanism affects the interpretation of findings in that literature. First, firm fixed effects in wage equations do not have structural interpretation as firm wage premia because they are estimated with a bias due to selection. Second, while matching patterns are informative about productive complementarities, average log wages are not.

Introducing heterogeneous households into a New Keynesian model of a small open economy enables the model to fit a set of stylized empirical facts about the transmission of foreign demand shocks. In the absence of a strong labor income effect on consumption, the model counterfactually implies that domestic consumption increases in response to a shortfall of foreign demand, as the domestic central bank responds to the shock by reducing the interest rate. With plausible marginal propensities to consume, the model instead produces the observed decline in domestic consumption, including of both tradeable and non-tradeable goods. This implies that foreign demand shocks are more important for international business-cycle comovement than predicted by existing models. Our findings also have implications for stabilization policies: While monetary policy is well-suited to counteract foreign demand shocks, traditional fiscal policies are inadequate, as they do not provide sufficient stimulus to the tradeable sector. This poses a particular challenge for countries with a fixed exchange rate or in a monetary union.

This study answers three questions about central bank communication on Twitter: what was communicated, who were listeners, and how they reacted. Using various natural language processing techniques, we are able to identify seven main topics discussed by the Fed (monetary policy, economic growth, inflation, unemployment, financial risk, fiscal policy, and community) and six groups of the audience (economists, media, other academics, firm managers, financial sector, and the general public). While the Fed tweets talking about central banking topics attract greater attention from Twitter users, only the extensive margin is economically meaningful. Among all groups of users, the media accounts and economists are most active in engaging with the Fed, especially when discussing central banking related issues. Moreover, such media-Fed interactions are more likely to be noticed by other users. Further analysis shows that more positive Fed tweets are positively correlated with expected inflations during the low-interest-rate period.

While nouns in the German language are gendered toward males, the introduction of the “gender star” or “gender colon” have allowed for nouns to be interpreted as not assigning gender, or non-binary. Their usage however has become highly politically polarizing. Using over 40 million online job postings from 2016 to 2023, we document the rising prevalence of the gender star in the German labor market, particularly in German counties with greater support for the Green party. We then study how job seekers respond when the advertised job title includes such non-binary punctuation. Job seekers click substantially more often on job ads with gender-inclusive large. This relationship is larger in counties, occupations and sectors where gender-inclusive language is scarcer, suggesting a higher value for this amenity in such contexts. Finally, using a survey experiment, we investigate how such politically-charged signals in job ads affect sorting and labor market polarization.

This study investigates the determinants of euro area sovereign spreads to identify market fragmentation, employing a panel framework with interactive fixed-effects and considering non-linearities. Our framework identifies the component of the spreads explained by fundamentals and assesses the impact of different global factors on the non-fundamental dynamics. Regarding the fundamentals, we identify crucial elements influencing spreads in the euro area market. First, we identify movements in the spreads explained by contributions of the fundamentals, such as GDP growth and high-frequency indicators related to economic activity and fiscal stance. Second, we identify the movements in the spreads not explained by fundamentals and assess the effects of global factors and monetary policy on their dynamics. We find that the non-fundamental component is highly volatile and driven, among others, by market perceptions, varying heterogeneously across EA countries. These results underscore the importance of effective economic policies, sound fiscal management, and fostering financial stability within the euro area countries to prevent market fragmentation. The insights from this analysis can provide valuable guidance for policymakers and market participants in their efforts to mitigate market fragmentation and promote an integrated and resilient euro area financial market.

A fragile tail risk model is one that can only forecast tail risks accurately under a specific selection of the backtesting parameters values. In the literature, it is not yet clear how model fragility should be measured and quantified. We develop several novel measures of model fragility. Firstly, we propose indices which capture intrinsic features of forecasting models in terms of the stability of some loss function over the backtesting parameter space — ruggedness (RG), mean semi-elasticity (ME), and integrated semi-elastic radius (IE). Secondly, we develop measures which exploit comparative algorithms based on a statistical test that can either reject or not the null that a candidate model is no worse than an alternative according to the loss function — the success rate (SR), local fragility (LF), relative local fragility (RLF), and the relative area (RA). The use and relevance of all of these fragility measures are shown in a risk management application in which VaR and ES are estimated using daily S\&P 500 index returns.

This paper presents a non-linear framework to evaluate spillovers across domestic and international yield curves when the zero lower bound constrains policy rates. Based on the sample of US and UK data, we estimate a joint shadow rate model of international yield curves, accounting for the zero lower bound, no-arbitrage conditions within and between government bond markets, and the global nature of some of the bond risk factors. Results indicate that the post-2009 US monetary policy transmission mechanism and its spillover effects on the UK yield curve are non-linear and asymmetric.

We analyze how a large-scale easing of borrower-based measures affects residential mortgage credit and borrower characteristics. We exploit a case of the easing of the LTV limit and the complete abolition of DTI and DSTI limits in the Czech Republic in the first half of 2020. Our empirical evidence suggests that the households affected increased their borrowing and purchased more expensive houses while being able to decrease the collateral value. We also document a significant increase in borrowers’ debt (service) but this was softened by the concurrent growth in borrowers’ income. While exploring the heterogeneity in the transmission of the regulatory easing, we find that: (i) LTV-constrained borrowers showed signs of cash-retention behavior while DTI- and DSTI-constrained borrowers acted in line with the financial accelerator mechanism; (ii) relaxing the LTV limit had a larger effect in poorer districts while the abolition of DTI and DSTI limits affected borrowers in richer regions; (iii) younger borrowers were more affected by the easing of LTV and DTI limits, while the easing of the DSTI limit affected older borrowers; (iv) relaxing the LTV limit affected mostly first-time borrowers while abolishing the DTI and DSTI limits affected mostly second-time borrowers who obtained higher mortgages and purchased more expensive property.

Household financial decisions and their subjective expectations about macroeconomic outcomes vary within and across countries and change over time according to the new Consumer Expectations Survey of the ECB. To rationalize the role of subjective expectations for financial decisions, we estimate a structural model in which households decide how much to save under their survey-reported subjective expectations about macroeconomy. We find that households in Italy and Spain save between 4 and 14 percentage points more under subjective expectations compared to what they would have saved under rational expectations. We also use the model to determine saving rates differences between countries and show that households in Italy save between 15 and 20 percentage points less than their European neighbors. We further decompose the variation in savings rates by focusing on the impact of expectations, preferences and income risk. Notably, German households save less than their European counterparts due to income risk and French households save less than other countries due to their preferences.

We study the impact of pay transparency reform introduced in 2018 in Slovakia on employment, wages and worker-firm matching. By linking online job board data to administrative social security data, we show that before the reform less than 25% provided wage information in their advertisements. We document also that providing wage information in advertisements is beneficial in terms of leading more views and applications. Following the reform that forced all firms to post wage information, we find an increase in applications at firms that did not post before. Still, the increase in applications does not seem to benefit firms: we find that newly hired workers (but not incumbents) have 5-10% higher wages following the reform. This increase in wages mainly takes place, at low-quality firms, which did not post before. There is no change in hiring probability and in workers’ quality indicating that worker-firm match did not improve. These findings highlight that forcing firms to advertise wages helped workers to negotiate higher wages.

When taxes do not sufficiently adjust to government debt levels, the Fiscal Theory of the Price Level predicts that other variables, such as inflation and output gap, must adjust to ensure the solvency of public finances. We study the role of optimal debt maturity portfolios in this context, using a New Keynesian model with both demand and supply-side shocks. Our paper offers new analytical insights into the mechanisms through which debt maturity composition affects the trade-off between inflation and output gap: The Persistence, Discounting and Hedging channels. Our findings, based on a rich prior predictive analysis indicate that the key driving force behind optimal portfolio decisions is the Hedging channel. Moreover, the optimal maturity composition of debt is driven primarily by the supply side shocks, rather than by demand shocks. Finally, our results indicate that optimal debt management is an important margin to complement monetary policy in stabilizing inflation when debt is a binding constraint.

This paper explores the causal effect of induced inflation literacy on inflation expectations and central bank trust using a randomized controlled trial (RCT). In a two-step experiment, we first show that non-numerical information about inflation and monetary policy (the „literacy treatment”) enhances knowledge about these issues and raises both the likelihood of providing inflation predictions and trust in the central bank. In the second step, respondents exposed to the literacy treatment exhibit similar expectations updates to quantitative information about inflation projections, but respond differently to news about the ECB’s inflation target and its commitment to climate change.

Central banks wish to avoid self-fulfilling fluctuations. Interest rate rules with a unit response to real rates achieve this under the weakest possible assumptions about the behaviour of households and firms. They are robust to household heterogeneity, hand-to-mouth consumers, non-rational household or firm expectations, active fiscal policy and to any form of intertemporal or nominal-real links. They are easy to employ in practice, using inflation-protected bonds to infer real rates. With a time-varying short-term inflation target, they can implement an arbitrary inflation path, including optimal policy. This provides a way to translate policy makers’ desired path for inflation into one for nominal rates. US Federal Reserve behaviour is remarkably close to that predicted by a real rate rule, given the desired inflation path of US monetary policy makers. Real rate rules work thanks to the key role played by the Fisher equation in monetary transmission.

This paper develops a macroeconomic model that combines an incomplete-markets overlapping-generations economy with a job ladder featuring sequential wage bargaining, endogenous search effort of employed and non-employed workers, and differences in match quality. The calibrated model offers a good fit to the empirical age profiles of search activity, job-finding rates, wages and savings, so that we use the model to examine the role of age and wealth for worker flows and for the consequences of job loss. We further analyze the impact of unemployment insurance and progressive taxation for labor market dynamics and aggregate economic activity via capital, employment and labor efficiency channels. Lower unemployment benefits or a less progressive tax schedule bring about welfare losses for a newborn worker household.

We analyze how cognitive discounting and household heterogeneity affect the transmission of monetary policy. Under cognitive discounting, households’ expectations exhibit an underreaction to news about the aggregate economy, which is consistent with empirical evidence on household expectations. Our model simultaneously accounts for recent empirical findings of the transmission of monetary policy: (i) monetary policy affects consumption largely through indirect effects, (ii) households are unequally exposed to aggregate fluctuations and income risk is countercyclical, (iii) forward guidance is less powerful than contemporaneous monetary policy, (iv) and the economy remains stable at the zero lower bound. In contrast to demand shocks, supply shocks are amplified through both, cognitive discounting and household heterogeneity, such that inflation increases more than twice as strong as when abstracting from cognitive discounting and household heterogeneity.

We develop a model where temporary non-technology shocks can lead to permanent changes in the rate of growth of total factor productivity (TFP). The key ingredient of the model is a matching processes between basic researchers, product developers, and the stock of knowledge of the economy. In this context, search externalities generate vicious and virtuous cycles in R&D. The model has a unique equilibrium path but multiple balanced growth paths (BGPs). After a deep or long-lived shock, the economy can transit between these BGPs, generating “super-hysteresis” in TFP. We calibrate the model in the context of the Japanese growth slowdown and show that, quantitatively, it can explain well the TFP growth decline after the financial crisis in the 1990s. The simultaneous occurrence of demographic shocks and a persistent but temporary financial crisis gave rise to a “wretched coincidence” resulting in the growth slowdown.

Under its climate regulation, the EU is expected to become the first continent with a net-zero emissions balance. We study the pricing of climate risks, physical and transition, within European markets. Using text-analysis, we construct two novel (daily) physical and transition risk indicators for the period 2005-2021 and two global climate risk vocabularies. Applying our climate risk indices to an asset pricing test framework, we document the emergence of economically significant transition and physical risk premia post-2015. From a firm-level analysis, using firms’ GHG emissions, GHG emissions intensity, environmental, and ESG scores, we find that rises in transition (physical) risk are typically associated with an increase (decrease) in the return of green (brown) stocks. Firm-level information is used by investors to proxy firms’ climate-risks exposure, especially for transition risk since 2015, whereas the sectoral classification appears to proxy firms’ exposures to physical risk. From a country-level analysis emerges an intensified connection between European stock markets and climate risks post-2015, yet with some heterogeneity. Our results have important economic implications and show that investors demand compensation for their exposure to both climate risk types. Our novel climate risk vocabularies and indicators find several applications in identifying, measuring, and studying climate risks.

Preferences over well-being of different generations shape social, political and economic outcomes. We document systematic bias in social preferences against young adults (“youngism”), and show that it is partly due to inaccurate beliefs that young adults face relatively little hardship. In controlled experimental tasks, respondents from a Czech nationally-representative sample allocate less money to younger adults than to their own or older age groups. This bias is widespread and similar in size to discrimination against immigrants. Further, people underestimate the prevalence of mental health problems among young adults, and provision of accurate information increases prosocial behavior toward this age group.

This paper quantifies mechanisms through which heterogeneity in household finances affects the transmission of monetary policy, considering housing tenure choices over the life cycle. Our analysis focuses on the four largest economies in the euro area: France, Germany, Italy, and Spain. Across these countries, we find that responses of consumption to changes in the real interest rate and in house prices differ substantially. Our analysis links the differences in the aggregate responses to the underlying heterogeneity in household characteristics such as age, housing tenure, and asset positions. We quantify how the size of the responses depends on household expectations about future financing conditions affected by forward guidance.

Using evidence from the EU emissions trading system, we collect verified emissions of close to 4000 highly polluting and mostly non-listed firms responsible for 26% of EU’s emissions. Over the period 2013 – 2019, we find a non-linear relationship between leverage and emissions. A firm with higher leverage has lower emissions in subsequent years. However, when leverage exceeds 50%, a further increase is associated with higher emissions. Our difference-in-differences approach sheds light on the existence of a group of firms that are too indebted to successfully accomplish the low-carbon transition, even when they face a steep increase in the cost of their emissions.

In this paper, we examine how the monetary policy setting committees of the Federal Reserve, the Bank of England and the European Central Bank communicate their reaction to incoming data in their policy deliberation process by expressing confidence and surprise or uncertainty with respect to existing narratives. We use text analysis techniques to calculate forward and backward looking measures of relative surprise from the published Minutes of these decision-making bodies. We find many common patterns in this communication. Interestingly, policymakers tend to express more surprise and uncertainty with regard to developments in the real economy, whereas they are more likely to confirm their expectations with regard to inflation and monetary policy. When considering the monetary policy stance, we observe a tendency for policymakers to highlight surprise and uncertainty several meetings in advance of changes, particularly when easing monetary policy. Importantly, we document that a higher proportion of expressions of surprise and uncertainty increases the likelihood of an easier policy stance. By contrast, a higher proportion of expressions of confirmation tends to increase the likelihood of a tighter policy stance.

Industry uncertainty networks extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty, we uncover an uncertainty hub role for the communications, financials, IT and real estate industries. Conversely, shocks to industrials, materials, and utilities do not create strong linkages. The ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is constructed from uncertainty hubs which are found to be the main contributors to uncertainty shocks within the system showing a tighter link with the real economy. The stronger predictive ability of the hubs-based network is robust to a wide range of checks, the inclusion of a large set of controls, and it is also confirmed out-of-sample.

We use a dynamic factor model featuring time-varying uncertainty and asymmetry to study the relation between inflation and the real economy. We identify demand and supply factors for the euro-area economy using survey data on inflation and GDP growth. Our model allows for a trend and cycle decomposition, which enables us to study the drivers of prices and real activity at business-cycle and lower frequencies. Furthermore, by exploiting higher-order moments of survey responses, we produce (downside/upside) tail risk measures across horizons. Findings suggest that the output gap is mainly determined by demand factors, while the recent rise in inflation is attributed to negative supply factors. Moreover, uncertainty around inflation expectations is time-varying and large since the Great Recession, while asymmetry is a prominent characteristic of expectations about future inflation and real activity since the COVID-19 crisis.

On 28 September 2023, the National Bank of Slovakia, together with SUERF and Baffi Bocconi, organised a webinar on the results of the 4th wave of the Household Finance and Consumption Survey 2021. Experts from the ECB and selected central banks, from the Household Finance and Consumption Network working group, presented main results, in particular regarding the evolution of total net wealth, income as well as household indebtedness between the last two waves of the survey (2017-2021). Recording of the webinar, as well as presentations themselves, are available on the SUERF website.

Emerging market central banks, once laggards of the central banking policy scene, have made remarkable progress in improving their policy frameworks in the past two decades. They have adopted many of the principles of advanced economy central banks both in policy conduct and communication, but with modifications that reflect their specific circumstances of capital flow volatility, financial dollarization, and weaker overall policy credibility. This has served them well in the post-Covid period of high inflation. Emerging markets outperformed advanced countries in two critical respects: addressing post-Covid inflationary pressures in a timely manner and, related, avoiding up until now banking sector stress during the monetary policy tightening cycle. Our paper demonstrates these points through the prism of central bank communication, using the Federal Reserve and the European Central Bank as comparators. We conclude with policy lessons not only for emerging market central banks, but also for central banks of the advanced world.

Evidence-based policy is a golden standard for designing family and labor market policies aiming at better use of the female labour market potential. While within the last 20 years, we have observed increasing interest in the development of both estimation methods and identification strategies, little attention was paid to the measurement issues. We analyse the effects of the maternity leave extension introduced in 2013 in Poland. We use this extension as a natural experiment granting exogeneity for labour market outcomes of mothers. We show that this is not enough to estimate the true causal effect of maternity leave extension on labour market participation. In 2021 Eurostat imposed on national statistical offices (including Statistics Poland) several definition changes in Labour Force Surveys. One of them regarded females on family leave (pol. urlopy wychowawcze). Although they were absent from work for more than 3 months, they should be treated as employed. We use this change to show that the reform did not form any stronger market attachment of new mothers but rather postponed their returns to work. It follows from the analysis that the impact of family policy reforms on female labour market performance measured with LFS data, which turns out to be highly sensitive to the definitions applied. We also decompose the observed changes in activity into the true economic effect and the statistical artifacts caused by the change in definitions. We argue that our results are an important voice in the discussion on the intended goals and observed results of family policy tools.

I study the effects of the ECB Asset Purchase Program (APP) and Pandemic Asset Purchase Program (PEPP) announcements on the four largest European economies and on the aggregate of the Euro Area. Using a proxy variable of surprises for the size of announced purchases, I identify the APP shock in a TVP-SV-FAVAR using zero and sign restrictions. I document substantial heterogeneity in the responses of European countries to the policy: i) Southern European economies experienced the largest decrease in government bond yields but the smallest decrease in the cost of credit to households and non-financial corporations; ii) cross-country differences in the responses of interest rates reduced significantly over time and with subsequent packages of the policy; iii) the response of inflation has been stronger in Germany and Spain than in Italy and France; iv) responses of real activity and labor market show mixing signals across countries. Results on the aggregate of the Euro Area show that most of the channels of transmission of Quantitative Easing were active at the European level.

With the growing use of techniques from Data Science in Economics, Economic Data Science is becoming an area of study and application. However, there remain some differences about the methodological approaches of economists and data scientist. One area where this is apparent is the study of text analysis. Researchers in economics and finance apply methodologies from natural language processing (NLP), be they dictionary methods, topic models, or even state-of-the-art transformer models. However, the application of these tools in economics often does not involve careful assessment and benchmarking of which models works best in these domains (a focus of data science research). In this talk, I will explore a series of projects with Maximilian Ahrens (and others) that tries to take a more data science approach, to economic data science.

This paper studies the role of narratives for macroeconomic fluctuations. Microfounding narratives as directed acyclic graphs, we show how exposure to different narratives can affect expectations in an otherwise-standard macroeconomic framework. We capture such competing narratives in news media reports on the US yield curve inversion in 2019, using techniques from natural language processing. Linking this to data from Twitter, we show that exposure to the narrative of an imminent recession is associated with a more pessimistic sentiment, while exposure to a more neutral narrative implies no such change in sentiment. In a model with frictions in financial intermediation, these effects of narrative-driven beliefs create a novel trade-off: extended periods of quantitative easing make narrative-driven waves of pessimism more frequent, but smaller in magnitude.

Based on a representative survey of the German population in 2021 with more than 7000 observations, we investigate how perceptions about public debt figures influence attitudes towards public finances and fiscal rules. On average, people strongly underestimate the public debt-to-GDP ratio, overestimate the debt-service ratio, favor a stricter version of the German debt brake and prefer spending cuts to finance public investment or to close budget gaps. Higher education or financial literacy comes with substantially lower estimation errors, better knowledge about the debt brake plus a more lenient view on it, and a stronger preference for financing public investment or budget gaps with credit. In an information treatment experiment, people consider public debt to be a more (less) severe problem, once they learn the actual public debt figures to be higher (lower) than their estimates. However, these treatment effects vanish when participants’ beliefs are anchored with past information on public debt figures. Moreover, we find no treatment effect on attitudes towards the debt brake or financing options for public investment and budget gaps. We rationalize this behavior by psychological surprise effects and conclude that better information on public debt figures would foster a more rational debate about public finances.

This paper studies monetary policy transmission mechanisms during QE. Using high frequency yield curve event studies of monetary policy announcements in combination with a dynamic term structure model, we can identify four types of monetary policy surprises: action (working through effective policy rates), signalling (working through expected policy rates), policy uncertainty and QE-specific bond supply (both working through term premia). Applying the method to the case of the UK, we find that these channels have often operated together. Importantly, the main QE channels are transmitted to financial markets and the real economy in different ways, and only signalling is found to have ultimately affected inflation significantly.

Financial cycles are assumed to reflect the dynamics and interconnectedness between the credit, housing and stock markets, which are all important components of the overall financial stability. Estimates and accurate financial cycle forecasts could be useful for sound macro-prudential policy making and investment planning. In this study, we estimate the financial cycle for Slovakia and use machine learning techniques to predict 3- and 6-month-ahead levels of the financial cycle.  The prediction accuracy is compared across multiple models and driven by a set of 170 potential predictors, including indicators related to banks, financial market, monetary policy, labor market, economic activity, business and consumer confidence and calendar effects.

This paper evaluates the impact of an online game-based financial education tool on students’ financial literacy levels. Based on a Randomized Controlled Trial of 2587 students across four countries, our findings show that the intervention significantly increased students’ financial literacy levels. As the implementation cost of the program is low, it has a favourable cost-benefit ratio. This study contributes to the nascent academic literature about evaluations of learning-by-playing financial education interventions. The involvement of participants from four countries provides relevance in terms of cross-comparison of the results, stimulating the discussions about specific-country peculiarities in terms of the financial literacy of youth.

We provide evidence of the usefulness of alternative data-sources in nowcasting Ukrainian GDP during the early stages of the full-scale 2022 Russian military invasion. This exercise is motivated by the lack of official statistic releases for several months at a time when policymaking critically needed to evaluate the speed and depth of the contraction. The nature of the shock, of varying regional and temporal intensity, poses particular challenges for established macroeconometric nowcasting and forecasting exercises. Our preliminary results show that even with minimal modelling, remotely sensed data on nightlight intensity combined with internet search trends from Google Trends and social media activity from Twitter offer a very good in-sample fit of official GDP data and intuitive out-of-sample results after the invasion both at national and regional levels. We show a very dramatic contraction of over 30% in March at national level (driven by very large contractions in regions directly affected by violence) and a rebound in April and May (driven by lifting the siege of Kyiv). Our results also turn out to be a close match to preliminary national statistics released recently by official sources.

Macroprudential policy implementation in the mortgage market has generally involved a policy tightening, as policies have been introduced in settings where no such policies previously existed. In this paper we produce rare evidence on an episode of loosening under the macroprudential regime for mortgages. We exploit a reform of the Irish borrower-based measures in 2017 that increased LTV limits for a cohort of First Time Buyers. We show in response to the reform that borrowers bunched at the new maximum LTV of 90, increasing LTVs relative to the counterfactual. We highlight an adjustment mechanism that has important policy implications: we find no evidence that treated borrowers purchased more expensive properties; rather, we find that treated borrowers post lower downpayments after the reform, displaying a preference for cash retention once the opportunity arises. While economic intuition leads one to expect house price amplification after a policy-induced credit loosening, we show that borrowers’ choices to rebalance towards greater cash retention dampened this channel in the Irish case in 2017.

Participants to an online study in Luxembourg are presented fake real-estate advertisements and tasked to make an offer to the shown properties. A random subset is also shown sellers’ names that are strongly framed to signal their origins. Our randomised procedure allows us to conclude that, keeping everything else constant, sellers with African-sounding surnames are systematically offered lower prices. Our most conservative estimates suggest that the average racial penalty stemming from the demand-side of the housing market is equal to 22,000 euros. Last, we show that this penalty hides important differences across respondents: it is null for the youngest and most educated ones, as well as for those without some personal ties to the African diaspora, but can amount up to around 65,000 euros for those above 40 years of age and without post-secondary education.

Inflation in most western advanced economies has been rising at a fast pace since the middle of 2021. The necessary condition for central banks to maintain price stability is to prevent temporary shocks to inflation from feeding into the mechanisms of wage and price formation (Bernanke 2007, Draghi 2014). The monetary theory prescription to avoid these self-fulfilling inflation expectations and prevent long-term inflation expectations to de-anchor is to follow Taylor principle, according to which the nominal interest rate should rise more than proportionally with inflation. We show that once the inflation is high, the distribution of prices across products widens and inflation uncertainty rises, the Taylor principle is no longer sufficient for inflation stability. The anchoring of inflation expectations instead calls for strict inflation targeting and abstaining from virtually any aim to stabilize the real side of the economy.

The Covid-19 pandemic caused a sharp decline in corporate sector activity in 2020 as a series of epidemiological measures limited or even prohibited the business activity of corporates, most notably in services industries. However, the joint policy response (often including fiscal, monetary, and bank supervisory measures) contained the shock caused by a relative increase in fixed costs allowing the corporate sector to remain liquid and solvent and therefore preventing the cascade of bankruptcies but rising concerns regarding the potential zombification at the same time. After lifting the majority of the epidemiological measures, new challenges emerged for the corporate sector across the EU. Input prices increase resulting from supply chain disruptions were additionally exacerbated by the geopolitical shocks. As variable costs increased, largelly reflecting the developments in energy and material prices and availability, the interest in the corporate sector’s resilience and its ability to pass the price increase to buyers intensified.  

Given the challenges the corporate sector went through during the last three years but also its importance for the financial sector performance and economic growth, a more detailed analysis of the corporate sector is required for Financial Stability Monitoring. Apart from acknowledging that operating level of corporate activity is as important for the Financial Stability as the financial one, this also requires strengthening the current corporate sector analysis capacity, mainly by corporate finance and micro econometric techniques application. 

While there exists a rich empirical literature on the causes and consequences of home ownership (typically looking at the household main residence), there is very limited evidence on the multiple-property owners. At the same time, it is argued that (speculative) real estate investments contribute significantly to the credit cycle and market overheating. Using the most recent EU-level microdata from the 3^rd wave of Household Finance and Consumption Survey (collected at around 2017/2018) we analyze the socio-economic determinants driving the household (speculative) real estate investment appetite. Additionally, we investigate the impact of macroprudential policy tightening experienced during the period of 2014-2018 on the household real estate investments. Results on determinants of household multiple-property ownership are conventional: richer, better educated households are more likely to hold several properties. Interestingly, tightening of the borrowed based measures (BBMs) significantly decreases the propensity to engage in (speculative) real estate investments. This result is robust to possible endogeneity issue when tested by conventional 2SLS IV framework and employed few of the recently suggested instrument(s) in the financial stability literature. However, we also report a substantial heterogeneity in effects of macroprudential tightening conditional on various household characteristics. Our results are among the first to inform policy about the effectiveness of the BBMs on the multiple-property ownership in a cross-country setup.

Financial markets can support the transition to a low-carbon economy by redirecting funds from highly emissive to clean investments.  We study whether European stock markets incorporate carbon prices in company valuations and to what degree they discriminate between firms with different carbon intensities. Using a novel dataset of stock prices and carbon intensities of 338 European publicly traded companies between 2013 and 2021, we find a strongly statistically significant relationship between weekly carbon price changes and stock returns. Crucially, this relationship depends on firms’ carbon intensity: the higher the carbon costs a firm faces, the poorer its stock performance during the periods of carbon price increases. Emissions that firms cover with free allowances however do not impact this relationship, illustrating how both carbon pricing and disclosures are needed for financial markets to foster climate change mitigation. The uncovered relationship can provide an incentive for firms to decarbonize. We argue that more ambitious carbon pricing policies should be implemented in the EU, as this would strengthen the stock-market incentive channel while causing only limited financial stability risk for stocks.

Growth at risk modelling has been a cornerstone for research and policymaking recently as a way to model tail risk in the macroeconomy. However, the majority of the research has almost exclusively focused on US data using financial stress indices to capture vulnerability. Nevertheless, recent events have highlighted that financial risk is just one source of vulnerability in growth. Geopolitical events have emerged as an important alternative measure. Importantly such events impact all assets, asset classes, sectors and countries: high geopolitical risk results in a decline in real activity, plummeting stock returns, and in capital flows transferred from emerging economies towards advanced economies. It is not surprising that geopolitical risk indices are becoming attractive peers/ competitors of financial stress indices. The aim of the paper is to complete the growth at risk model for Hungary by using the global geopolitical risk index (GPRI) of Caldara and Iacoviello. When incorporating different types of risk indices it is important to only include ones that are important to our data generating process. However, these indices are highly correlated making it difficult ex-ante to select the “perfect” measure. To tackle this problem a variable selection framework is applied to identify which variables are key determinants in different parts of the Hungarian GDP distribution. Importantly this paper develops a methodology that can handle variable selection task in small sample settings, which is usually the case in emerging markets. Using the methodology the authors are able to show that the global geopolitical risk index (GPRI) has recently become a key element in expressing macroeconomic vulnerability in Hungary.

We revisit the finite-sample behavior of just-identified instrumental variables (IV) estimators, arguing that in most microeconometric applications, just-identified IV bias is negligible and the usual inference strategies likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form t1>c, where t1 is the first-stage t-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage F-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting c=0, that is by screening on the sign of the estimated first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV.

The ongoing trend in global warming needs prompt and timely policy reaction. This reaction and the consequent transformation to carbon-neutral economy results in worldwide economic and financial losses to business, households, and governments. Beside the impact on the real economy, central banks and supervisors follow the impact on the financial system as well. This paper studies the potential impact of climate change risks on banks in Slovakia over the horizon of four-years. We focus mainly on transition risks, i.e. risks posed by the transition to carbon-neutral economy. We propose an analytical framework to quantify impact of transformation policies on bank’s credit risk exposures based on scenarios prepared by Network for Greening the Financial System (NGFS). We document negative impact along credit risk channel in the form of increased non-performing loans losses from both households and non-financial corporations loan portfolio. The main drivers of credit risk are the shock to GDP in case of corporates and the increase in unemployment rate in case of households. Although losses are significantly lower compared to adverse stress testing scenarios, they are sensitive to energy price increase.

In U.S. data, inflation and output are negatively related in the long run. A Bayesian VAR with stochastic trends generalized to be piecewise linear provides robust reduced-form evidence in favor of a threshold level of trend inf;ation below which potential output is independent of trend inflation, and above which, instead, potential output is negatively affected by trend inflation. The threshold level of inflation is slightly lower than 4%, above which every percentage point increase in inflation is related to about 1% decrease in potential output per year. A New Keynesian model generalized to admit time-varying trend inflation and estimated via particle filtering provides theoretical foundations to this reduced-form evidence. The structural long-run Phillips Curve implied by the estimated New Keynesian model is not statistically different from the one implied by the reduced-form piecewise linear BVAR model.

The explosion in ESG research has led to a strong reliance on ESG rating providers. We document widespread changes to the historical ratings of a key rating provider, Refinitiv ESG (formerly ASSET4). Depending on whether the original or rewritten data are used, ESG-based classifications of firms into ESG quantiles and tests that relate ESG scores to returns change. While there is a positive link between ESG scores and firms’ stock market performance in the rewritten data, we fail to observe such a relationship in the initial data. The ESG data rewriting is an ongoing rather than a one-off phenomenon.

In this paper, we first describe which countries have implemented the counter cyclical buffer (CCyB) regime under Basel III. In a second step, we review the hypothetical performance of the Basel CCyB guidance before the great financial crisis in 2007/08. In a last step, we analyze the CCyB decision process of all countries worldwide that have implemented the CCyB under Basel III and report their aggregate credit data to the Bank for International Settlements (BIS). In contrast to opinions expressed in some of the recent literature, the hypothetical CCyB under the Basel guidance would have worked really well in many (European) countries, leading to approximately 250-300 Bn EUR additional capital requirements that could have been released during the crisis. Nevertheless, since the legal implementation of the CCyB framework in 2015, countries do not take the Basel guidance into account when setting their CCyB. We even estimate a negative loading of the suggested Basel CCyB on the CCyB decision.

We show how the Joslin, Singleton, and Zhu (2011) factor extraction approach to estimating the Gaussian term structure model can be modified to handle the interest rate lower bound. Our novel approach is exact in a sense that it does not require any approximation in the estimation process. This is important from the perspective of using the model for economic inference, such as measuring term premium or policy expectation. Compared with the standard shadow rate estimation approach based on the Kalman filter, this improves convergence and greatly reduces the computation time. It has the added advantage of producing more robust estimates of the lower bound parameter and the path of the shadow rate. We show that expected inflation and real activity are important unspanned macro factors that drive term premiums as in Joslin, Priebsch, and Singleton (2014).

How do estimates of firm-level markups that rely on production function estimations depend on common data limitations? With a tractable analytical framework, simulation from a quantitative model, and firm-level administrative production and pricing data, we study biases due to the use of revenue instead of quantity, and due to production function misspecification. Estimates from revenue mismeasure the level of markups, but do contain useful information about true markups. Conversely, misspecified production functions have little effect on the estimated average markup but reduce their information content. Finally, revenue and quantity markups display similar correlations with variables such as profitability and market share in our data.

In many countries, market power in goods markets has increased over the last decades. To examine the implications for monetary policy, we present a menu-cost model with endogenous markups. This model rationalizes the observed increases in market power via productivity increases for some firms. Aggregate productivity is procyclical as resources are reallocated across firms over the course of the business cycle. We identify a new amplification mechanism that relies on a direct effect of market concentration on the demand for individual goods. This mechanism strengthens endogenous fluctuations of aggregate productivity as well as monetary non-neutrality and thus helps to understand the flattening of the Phillips curve.