Publications

As noted in recent literature, firms can run on credit lines due to fear of future credit restrictions. We exploit the 2011 stress test supervised by the European Banking Authority (EBA) and the Spanish Central Credit Register to explore: 1) the occurrence and magnitude of these runs after the release of negative stress test results; and 2) banks’ behaviour before and after the release of this information. We find that, following the release of the results, firms drew down approximately 10 pp more available funds from lines granted by banks that had a worse performance in the stress test. Moreover, before the release date, poorer performing banks were more likely to reduce the size of credit lines, while those with more significant balances of undrawn credit lines were more likely to cut term lending.

We present a house price-at-risk (HaR) model that fits the historical developments in the Spanish housing market. By means of quantile regressions we show that a model including quarterly house price growth, a misalignment measure and a consumer confidence index is able to accurately forecast the developments in the Spanish housing market up to two years ahead. We also show how the HaR model can be used to monitor the downside risk.

Buffers for systemically important institutions (SIIs) were designed to mitigate the risks posed by these large and complex banks. With a panel data model for a sample of listed European banks, we demonstrate that capital requirements for SIIs effectively reduce the perceived systemic risk of these institutions, which we proxy with the SRISK indicator in Brownlees and Engle (2017). We also study the impact of the adjustment mechanisms that banks use to comply with SII buffer requirements and their contribution to systemic risk. The results show that banks mainly respond to higher SII buffers by increasing their equity, as intended by the regulators. Once we control for the options SIIs employ to fulfil these requirements and SII characteristics (e.g. total asset size), we find a residual effect of having SII status. This result suggests that being an SII provides a positive signal to markets by further decreasing its contribution to systemic risk.

We show how policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of “conflict”, relating to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model. We show that the indicators provide useful information for forecasting GDP in the short run, even when controlling for a comprehensive set of standard high-frequency macro-financial variables. The second approach, is a SVAR model. We show that negative shocks to the selected indicators lead to economic slowdown, with a persistent drop in GDP growth and a short-lived but large increase in country risk.

Households face earnings risk which is non-normal and varies by age and over the income distribution. We show that, in the context of a structurally estimated life-cycle portfolio choice model, allowing for these rich features of earnings dynamics helps to better understand the limited participation of households in the stock market and their low holdings of risky assets. Because households are subject to more background risk than previously considered, the estimated model implies a substantially lower coefficient of risk aversion and a lower optimal risky asset share for older workers with low wealth and high earnings.

This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.

We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads to mild improvements in the point forecasts of BVARs for some variables and horizons. However, the density forecast performance considerably deteriorates. Therefore, we recommend taking into account outliers only on pre-specified dates around the onset of the COVID-19 pandemic.

This paper studies firm-level factors shaping the enforcement of financial reporting regulation on private non-financial firms and propose bank lending as a particularly important one. Our tests are based on a rare combination of datasets, which allows us to construct unique measures of misreporting, notably underreporting of debt. We observe that firms with bank debt are more likely to file mandatory financial reports and less likely to file information with irregularities. While we also find evidence that the need for bank financing can induce firms to misreport, this concern is mitigated by additional tests suggesting that banks detect reporting issues at firms’ financial statements. Critically, we observe that firms with reporting issues obtain significantly less credit, especially when the bank has previous exposure to debt misreporting and when the bank verifies debt information using the public credit registry. Collectively, our paper documents important firm-level determinants of private non-financial firms’ misreporting and highlight that banks play a significant role in the enforcement of mandatory financial reporting on these firms.

Increasing political polarization implies that each election expands the gap between the supporters of the losing side and the winning party. This asymmetry in how citizen’s feel about the outcome of elections could propagate to the institutions under partisan control but also to those designed to be isolated from electoral pressures – such as courts or central banks. Leveraging three decades of surveys covering European 27 countries, we exploit 138 cabinet shifts between 1991 and 2019 to estimate the effect of a growing divide between winners and losers on attitudes towards both types of institutions. We find that trust in either type institutions drops around elections but that the magnitude of the drop varies substantially across contexts. The polarization of parties explains most of this variance, suggesting that, in a polarized environment, partisan hostility can contaminate attitudes towards the political system as a whole creating the conditions for democratic backsliding.

Macroprudential FX regulation may reduce systemic risk; however, little is known about its unintended consequences. I propose a theoretical mechanism in which currency mismatch acts as a means for relaxing small firms’ borrowing constraints, and show that policies taxing dollar lending may increase financing disparities between small and large firms. To verify this empirically, I study the implementation of a macroprudential FX tax by the Central Bank of Peru. I construct a novel dataset that combines confidential credit register data with firm-level data on employment, sales, industry and geographic location for the universe of formally registered firms. I show that a 10% increase in bank exposure to the tax significantly increases disparities in the growth of total loans between small and large firms by 1.6 percentage points. When accounting for firms switching to soles financing from different banks, the effect on large firms’ debt is only compositional. Using a confidential dataset on the universe of FX derivative contracts, I show that firms that are mostly affected by the policy are not hedged through FX derivatives. Additional findings using survey data suggest that this policy has potential heterogeneous implications for firms’ real outcomes.

This paper investigates the role that two key methodological choices play in the construction of textual indicators: the selection of local versus foreign newspapers and the breadth of the press coverage (i.e. the number of newspapers considered). The large literature in this field is almost silent about the robustness of research results to these two choices. We use as a case study the well-known economic policy uncertainty (EPU) index, taking as examples Latin America and Spain. First, we develop EPU measures based on press with different levels of proximity, i.e. local versus foreign, and corroborate that they deliver broadly similar narratives. Second, we examine the macroeconomic effects of EPU shocks computed using these different sources by means of a structural Bayesian vector autoregression framework and find similar responses from the statistical point of view. Third, we show that constructing EPU indexes based on only one newspaper may yield biased responses. This suggests that it is important to maximize the breadth of press coverage when building text-based indicators, since this improves the credibility of results. In this regard, our first and second results are good news for researchers, given that they provide a justification for the combined use of a larger amount of data from local and foreign sources.

This paper shows that heterogeneity in bank capitalization ratios plays a crucial role in the transmission of monetary policy to bank lending. First, I offer new empirical evidence on how banks’ lending responses to monetary policy shocks depend on their capitalization ratios. Highly capitalized banks reduce their lending more after a monetary tightening, even after controlling for bank liquidity, size and market power in the deposit market. I also document how highly capitalized banks have a riskier portfolio, as measured by loan charge-off rates, and default rates on their loans increase relatively more after a tightening in monetary policy. I then construct a dynamic macroeconomic model that rationalizes the empirical evidence through the interaction of the heterogeneous recovery technologies of banks facing a risk-weighted capital constraint. In particular, after an increase in the policy rate, the model predicts that loan rates and default probabilities increase in both sectors. Highly capitalized banks with a riskier portfolio are more sensitive because the risk-weighted capital constraint affects them more, so they contract lending more. In a counterfactual analysis, I find higher capital requirements amplify the effects of monetary policy.

We provide a framework to study how different public procurement allocation systems affect firm dynamics and long-run macroeconomic outcomes. We build a new panel dataset of administrative data for Spain that merges credit-register loan data, quasi-census firm-level data and public procurement project data. We find evidence consistent with the hypothesis that procurement contracts provide valuable collateral for firms, and that they do so to a greater extent than private-sector contracts. We then build a model of firm dynamics with both asset-based and earnings-based borrowing constraints and a government that buys goods and services from private-sector firms, and use it to quantify the long-run macroeconomic consequences of alternative procurement allocation systems. We find that policies that promote the participation of small firms have sizeable macroeconomic effects, but their net impact on aggregate output is ambiguous. These policies help small firms grow and overcome financial constraints, which increases output in the long run. However, they also reduce saving incentives for large firms, decreasing output. The relative strength of these two forces and hence which of them dominates crucially depends on the type of financial frictions firms face and the specific way the policy is implemented.

It is widely accepted that episodes of social unrest, conflict, political tensions and policy uncertainty affect the economy. Nevertheless, the real-time dimension of such relationships is less studied, and it remains unclear how to incorporate them in a forecasting framework. This can be partly explained by a certain divide between the economic and political science contributions in this area, as well as by the traditional lack of availability of high-frequency indicators measuring such phenomena. The latter constraint, though, is becoming less of a limiting factor through the production of text-based indicators. In this paper we assemble a dataset of such monthly measures of what we call “institutional instability”, for three representative emerging market economies: Brazil, Colombia and Mexico. We then forecast quarterly GDP by adding these new variables to a standard macro-forecasting model in a mixed-frequency MIDAS framework. Our results strongly suggest that capturing institutional instability based on a broad set of standard high-frequency indicators is useful when forecasting quarterly GDP. We also analyse the relative strengths and weaknesses of the approach.

Carbon pricing initiatives, designed to increase the relative prices of greenhouse gas-intensive goods and services, could not only push up CPI inflation but also affect its volatility. Existing empirical literature has only found that carbon pricing schemes are generally associated to a transitory effect on the level of inflation. This paper assesses empirically the effects of carbon pricing on inflation volatility for both carbon tax and cap-and-trade schemes (also known as emission trading systems). Our work finds strong evidence that cap-and-trade schemes are associated with larger volatility in CPI headline inflation, while no significant effect is found in the case of carbon taxes. This effect seems to feed only through the energy component, and does not seem to affect the volatility of core inflation. In addition, we find that under cap-and-trade schemes, both the increase in the underlying price of emissions and the expansion in the activities covered by these initiatives are associated with greater inflation volatility. These findings have important policy implications, given that inflation volatility could complicate the conduct of monetary policy. Since the ambition to mitigate climate change in the years to come is expected to be implemented through broader coverage of carbon pricing, central banks should monitor those developments closely.

Employing an agent-based model of the Spanish housing market, this paper explores the main drivers behind the large amplitude of the Spanish house price cycle —as compared to most other European countries—, as well as the scope for macroprudential policy to reduce this amplitude. First, we exploit the availability of a previous calibration to the UK, which has a less pronounced house price cycle, to show the prominent role played by the distributions of various mortgage risk metrics: loan-to-value, loan-to-income and debt-service-to-income ratios. Second, we use the model to calibrate both a hard loan-to-value limit and a soft loan-to-income limit to smooth the Spanish house price cycle and match the amplitude of the UK equivalent. Finally, we characterise the effects of these calibrated policies over the different phases of the cycle, finding that both instruments reduce credit and price growth during the expansionary phase and also reduce their decline during the contractionary phase. Moreover, both instruments lead to a compositional shift in lending: the loan-to-value policy from first-time buyers to buy-to-let investors and the loan-to-income policy from both first-time buyers and home movers to buy-to-let investors.

We study and measure uncertainty in the minutes of the meetings of the board of governors of the Central Bank of Mexico and relate it to monetary policy variables. In particular, we construct two uncertainty indices for the Spanish version of the minutes using unsupervised machine learning techniques. The first uncertainty index is constructed exploiting Latent Dirichlet Allocation (LDA), whereas the second uses the Skip-Gram model and K-Means. We also create uncertainty indices for the three main sections of the minutes. We find that higher uncertainty in the minutes is related to an increase in inflation and money supply. Our results also show that a unit shock in uncertainty leads to changes of the same sign but different magnitude in the inter-bank interest rate and the target interest rate. We also find that a unit shock in uncertainty leads to a depreciation of the Mexican peso with respect to the US dollar in the same period of the shock, which is followed by appreciation in the subsequent period.

This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and sentiment of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning techniques. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding (implemented with the Skip-gram model) and K-Means to measure their sentiment (uncertainty). In this way, we arrive at the definition of a set of daily topic-specific uncertainty indices. These indices are then used to find explanations for the behaviour of the US financial markets by implementing a batch of EGARCH models. In substance, we find that two topic-specific uncertainty indices, one related to COVID-19 news and the other to trade war news, explain the bulk of the movements in the financial markets from the beginning of 2019 to end-April 2020. Moreover, we find that the topic-specific uncertainty index related to the economy and the Federal Reserve is positively related to the financial markets, meaning that our index is able to capture actions of the Federal Reserve during periods of uncertainty.

There is growing concern about the increase in food insecurity across the world, but little is known of its economic implications. This paper quantifies the effect of food crises on forced international migration (FIM) flows using a structural gravity model. To this end, we use a database that measures the severity, intensity and causes of food crises. The results suggest that even less severe food crises tend to increase FIM flows. More severe food crises tend to skew FIM flows towards developing countries. The results obtained appear to indicate that food crises tighten liquidity constraints on migration and that these constraints worsen as the food crisis intensifies.

Institutional investors, aware of the need to incorporate climate change as an additional risk factor into portfolio management, show a growing appetite for integrating Sustainable and Responsible Investment (SRI) criteria into their investment processes. Within a passive management context, this paper analyses, from a practical point of view, the inclusion of such criteria in the construction of corporate bond portfolios, thus incorporating a new dimension into the asset allocation process. We study the decarbonisation of a euro area corporate bond portfolio by constructing the efficient frontier, which shows the trade-off between the portfolio’s decarbonisation possibilities and the cost assumed in terms of deviation from the benchmark portfolio. We also analyse the impact of decarbonisation on the different risk-return parameters during the asset reallocation process. Finally, we present the main green investment strategies that investors can use to incorporate sustainability criteria into corporate bond portfolios’ design, introducing the Green-Parity approach as a complementary strategy to the available toolkit. The result of our empirical analysis, for the selected investment universe and sample period, shows that sustainability-conscious corporate bond investors have at their disposal different strategies that will allow them to achieve their decarbonisation objective without having to deviate significantly from their benchmark portfolio and to adequately meet the purely financial goals dictated by their investment mandate.

Using CPI micro data for 11 euro area countries, covering 60% of the European consumption basket over the period 2010-2019, we document new findings on consumer price rigidity in the euro area: (i) on average 12.3% of prices change each month, compared with 19.3% in the United States; however, when price changes due to sales are excluded, the proportion of prices adjusted each month is 8.5% in the euro area versus 10% in the United States; (ii) the differences in price rigidity are rather limited across euro area countries and are larger across sectors; (iii) the median price increase (decrease) is 9.6% (13%) when including sales and 6.7% (8.7%) when excluding sales; cross-country heterogeneity is more pronounced for the size of the price change than for the frequency; (iv) the distribution of price changes is highly dispersed: 14% of price changes are below 2% in absolute values, whereas 10% are above 20%; (v) the frequency of price changes barely changes with inflation and it responds very little to aggregate shocks; (vi) changes in inflation are mostly driven by movements in the overall size of the price change; when this effect is broken down, variations in the share of price increases have a greater weight than changes in the size of the price increase or in the size of the price decrease. These findings are consistent with the predictions of a menu cost model in a low-inflation environment in which idiosyncratic shocks are a more relevant driver of price adjustments than aggregate shocks.

We document the consequences of losing a job across countries using a harmonized research design applied to seven matched employer-employee datasets. Workers in Denmark and Sweden experience the lowest earnings declines following job displacement, while workers in Italy, Spain, and Portugal experience losses three times as high. French and Austrian workers face earnings losses somewhere in between. Key to these differences is that Southern European workers are less likely to find employment following displacement. Loss of employer-specific wage premiums explains a substantial portion of wage losses in all countries.

The highly asymmetric reaction of euro area yield curves to the announcement of the ECB’s pandemic emergency purchase programme (PEPP) is hard to reconcile with the standard “duration risk extraction” view of the transmission of central banks’ asset purchase policies. This observation motivates us to build a no-arbitrage model of the term structure of sovereign interest rates in a two-country monetary union, in which one country issues default-free bonds and the other issues defaultable bonds. We derive an affine term structure solution, and we decompose yields into term premium and credit risk components. In an extension, we endogenise the peripheral default probability, showing that the possibility of rollover crises makes it an increasing function of bond supply net of central bank holdings. We calibrate the model to Germany and Italy, showing that it matches well the reaction of these countries’ yield curves to the PEPP announcement. A channel we call “default risk extraction” accounts for most of the impact on Italian yields. The programme’s flexible design substantially enhanced this impact.

One of the biggest challenges for the application of machine learning (ML) models in finance is how to explain their results. In recent years, different interpretability techniques have appeared to assist in this task, although their usefulness is still a matter of debate. In this article we contribute to the debate by creating a framework to assess the accuracy of these interpretability techniques. We start from the generation of synthetic data sets, following an approach that allows us to control the importance of each explanatory variable (feature) in our target variable. By defining the importance of features ourselves, we can then calculate to what extent the explanations given by the interpretability techniques match the underlying truth. Therefore, if in our synthetic dataset we define a feature as relevant to the target variable, the interpretability technique should also identify it as a relevant feature. We run an empirical example in which we generate synthetic datasets intended to resemble underwriting and credit rating datasets, where the target variable is a binary variable representing applicant default. We then use non-interpretable ML models, such as deep learning, to predict default, and then explain their results using two popular interpretability techniques, SHAP and permutation Feature Importance (FI). Our results using the proposed framework suggest that SHAP is better at interpreting relevant features as such, although the results may vary significantly depending on the characteristics of the dataset and the ML model used. We conclude that generating synthetic datasets shows potential as a useful approach for supervisors and practitioners looking for solutions to assess the interpretability tools available for ML models in the financial sector.

I develop a macro model of the U.S. housing finance system that delivers an equilibrium connection between the securitization and mortgage credit markets. An endogenous securitization market efficiently reallocates illiquid assets, increases liquidity to fund mortgage lending, and lowers interest rates for borrowers. However, its benefits are hindered by originators’ private information about loan quality which leads to adverse selection in securitization. Fluctuations in household credit risk induce expansion and contractions of mortgage credit through the securitization liquidity channel. Adverse selection generates a multiplier effect of household shocks. Applying the theory to the Great Financial Crisis, I quantify that information frictions amplified the observed mortgage credit contraction by a factor of 1.5. The multiplier is an endogenous function of the severity of information frictions. A subsidy policy in the securitization market can stabilize liquidity and credit cycles. However, the policy generates inefficiently high liquidity and fails to realize meaningful welfare gains for households.

In this paper we study how 103 bilateral Regional Trade Agreements (RTAs) shape bilateral trade flows over time. The analysis of quarterly trade flows from 1982 Q4 to 2018 Q4 shows dynamic differences between RTAs and among trading partners. Results show that countries benefit unevenly from bilateral RTAs. The positive global trade trend since the 1990s and the negative effect of the 2008 economic crisis are captured by a factor analysis that allows us to study specifically how trade agreements affect bilateral trade asymmetrically. On average, RTAs lead to a 10% increase in bilateral trade flows above the mean trend, but disparities are found since Partial Scope Agreements (PSAs) tend to boost bilateral trade more than Free Trade Agreements (FTAs) (21% compared to 9%). However, most of the effects of PSAs occur during the first four quarters after the agreements come into force, whereas FTAs have effects over a longer period. The current study uses cluster analysis to classify bilateral trade flows into homogeneous groups according to the dynamic impact that RTAs have on them. This allows the heterogeneity between clusters to be explored. Moreover, the results show a difference relating to the economic crisis starting in 2008: in general, the impact on bilateral trade of agreements in force before 2008 is positive (30%), while it is negative for agreements signed after 2008 (-9%). This indicates that RTAs play a heterogeneous role as a cycle smoothing mechanism. Finally, there is evidence of an anticipation effect: we find that for more than half of the bilateral trade flows studied, the effect of the bilateral RTA is detected before it comes into force.

This papers develops a taxonomy of financially distressed and zombie firms using a rich dataset that combines detailed firm-level and bank-firm-level information in Spain. A distressed firm exhibits both cash-flow and balance-sheet insolvency whereas a zombie firm is a distressed company that has received new credit. We carry out several analyses to test the validity of these definitions. For instance, we find that being distressed is negatively correlated with the probability of receiving new credit. However, the main bank of a distressed firm is more reluctant to restrict the supply of credit to such firm than a bank with no previous exposure to the company, which may reflect the incentives of the former to engage in loan evergreening. This financial support contributes to keeping zombie firms afloat for a longer period than distressed firms. Moreover, the contraction in capital, employment and sales is much larger in distressed firms than in zombie firms.

How effective make-up strategies are depends heavily on how forward-looking agents are. Workhorse monetary models, which are much forward-looking, find them so effective that they run into the forward-guidance puzzle. Models that discount the future further find them much less effective, but imply that agents discount the very perception of future policy rates. This only evaluates make-up strategies when financial markets do not notice them, or deem them non-credible. We build on one leading solution to the forward-guidance puzzle (Woodford’s finite planning horizons) by amending the assumption that financial markets have rational expectations on policy rates, and incorporate them into the long-term nominal interest rates faced by all. Agents still have a limited ability to foresee the consequences of monetary policy on output and inflation, making the model still immune to the forward-guidance puzzle. First, we find that make-up strategies that compensate for a past deficit of accommodation after an ELB episode have sizably better stabilization properties than inflation targeting. Second, we find that make-up strategies that always respond to past economic conditions, such as average inflation targeting, do too but that their stabilization benefits over IT can be reduced by the existence of the ELB.

We develop an agent-based model of the UK housing market to study the impact of macroprudential policy experiments on key housing market indicators. The heterogeneous nature of this model enables us to assess the effects of such experiments on the housing, rental and mortgage markets not only in the aggregate, but also at the level of individual households and sub-segments, such as first-time buyers, homeowners, buy-to-let investors, and renters. This approach can therefore offer a broad picture of the disaggregated effects of financial stability policies. The model is calibrated using a large selection of micro-data, including data from a leading UK real estate online search engine as well as loan-level regulatory data. With a series of comparative statics exercises, we investigate the impact of: i) a hard loan-to-value limit, and ii) a soft loan-to-income limit, allowing for a limited share of unconstrained new mortgages. We find that, first, these experiments tend to mitigate the house price cycle by reducing credit availability and therefore leverage. Second, an experiment targeting a specific risk measure may also affect other risk metrics, thus necessitating a careful calibration of the policy to achieve a given reduction in risk. Third, experiments targeting the owner-occupier housing market can spill over to the rental sector, as a compositional shift in home ownership from owner-occupiers to buy-to-let investors affects both the supply of and demand for rental properties.

While motherhood is one of the main reasons for the persistence of gender gaps, its impact on the rising share of immigrant mothers in Europe is less well understood. This paper asks how the burden of childcare affects the labor market integration of immigrants. To identify the contribution of this burden to the native-immigrant employment gap, it exploits European Labor Force Survey (EU-LFS) microdata from 2004 to 2019. This survey collects information on respondents’ counterfactual behaviour, in the event that: a) they had no care responsibilities; b) they could find a job compatible with their care responsibilities; c) they had access to childcare services. This information allows estimates to be obtained of the impact of childcare on labor supply comparable across eleven countries. Our results show that the burden of childcare is the major obstacle to the integration of immigrant mothers. While the employment gap between non-EU immigrant and native mothers in Northern and Southern Europe is 35 and 17 percentage points (pp) respectively, two-thirds (24 pp and 12 pp) of it is explained by childcare motivated inactivity. We reject the hypothesis that the childcare gap is solely driven by immigrants’ sociodemographic traits or traditional parenting norms. Our estimates suggest that at least a quarter (5.8 pp and 2.6 pp) of the gap is due to the higher opportunity cost of paid work faced by immigrant mothers; that equal access to childcare could reduce it by 10 pp and 7 pp; and that immigrants’ exclusion from flexible time arrangements could explain the larger size and higher persistence of the gap in the North. This paper contributes to the literature on immigrant integration, highlighting that the child penalty is the main obstacle to female migrant labor supply and that differences in how European societies handle the burden of care can account for their records on the integration of immigrant households, suggesting that family policies could be central to the integration policy mix and even influence the migration decision.

So-called cryptocurrencies are becoming more popular by the day, with a total market capitalization that exceeded $3 trillion at its peak in 2021. Bitcoin has emerged as the most popular among them, with a total valuation that reached an all-time high of $68,000 in November 2021. However, its price has historically been subject to large and abrupt fluctuations, as the sudden drop in the months that followed once again proved. Since bitcoin looks all set to continue growing while largely concentrating its activity in unregulated environments, concerns have been raised among authorities all over the world about its potential impact on financial stability, monetary policy, and the integrity of the financial system. As a result, building a sound and proper regulatory and supervisory framework to address these challenges hinges upon achieving a better understanding of both the critical underlying factors that influence the formation of bitcoin prices and the stability of such factors over time. In this article we analyse which variables determine the price at which bitcoin is traded on the most relevant exchanges. To this end, we use a flexible machine learning model, specifically a Long Short Term Memory (LSTM) neural network, to establish the price of bitcoin as a function of a number of economic, technological and investor attention variables. Our LSTM model replicates reasonably well the behaviour of the price of bitcoin over different periods of time. We then use an interpretability technique known as SHAP to understand which features most influence the LSTM outcome. We conclude that the importance of the different variables in bitcoin price formation changes substantially over the period analysed. Moreover, we find that not only does their influence vary, but also that new explanatory factors often seem to appear over time that, at least for the most part, were initially unknown.

Imperfect information aggregation in secondary credit markets has significant consequences for economic cycles. As banks put more weight on mark-to-market gains, they find it optimal to refrain from revealing information about adverse shocks. Consequently, default risk is mispriced, and loan volumes, and thus investment, are not appropriately reduced. Overinvestment lowers the price of capital, leading households to increase consumption without decreasing labour supply, generating a boom. Due to mispricing, banks subsequently face bigger losses and capital depletion. Output then decreases sharply due to credit supply shortages. In a model calibrated to the US economy, these instances of market dysfunction are crucial in amplifying credit cycles.

This paper analyses the aggregate impact of industry-specific shocks and their propagation through global production networks. We focus on the case in which a common shock affects simultaneously the same industry across different countries. Thus, our analysis can be a useful tool for several policy-relevant scenarios, such as changes in environmental regulations or the implementation of new technologies. For that purpose, we highlight the importance of departing from standard linear models that assume unitary elasticities of substitution. We combine a theoretical framework of production networks with arbitrary elasticities of substitution (Baqaee & Farhi, 2019) and we make use of World Input-Output Database to account for international linkages. This setting illustrates how, in the presence of production input complementarities, the interaction between simultaneous shocks has significant non-linear effects on sectoral composition and aggregate output. The aggregate impact of negative (positive) shocks gets significantly amplified (mitigated) when they affect simultaneously industries with strong production linkages. Our results show that ignoring production complementarities leads to vastly underestimating the aggregate consequences of regulatory or technological shocks in industries like chemicals or vehicle manufacturing. In contrast, simultaneous shocks to services industries are well accounted for by standard measures.

Using granular data from the Spanish Central Credit Register, we study the contagion of financial distress via the credit quality channel in the Spanish interbank market. We propose a non-linear contagion mechanism dependent on banks’ balance-sheet structure (specifically, their leverage ratios). Moreover, we explicitly model uncertainty in lenders’ assessments of the probability of default of their borrowers, thus incorporating agents’ lack of complete information and heterogeneous expectations in their assessment of future outcomes. We perform multiple simulations across a wide range of possible levels of stress in the system, and we focus on disentangling the effects of these two key model components by comparing the results of our model with those of a linear and deterministic counterpart. We find that non-linear contagion leads to substantially larger losses than its linear counterpart for a wide range of intermediate levels of stress in the system, while its effects become negligible for very low and very high stress levels. Regarding uncertainty, we find that its effects, while smaller than those of non-linear contagion, are nonetheless relevant and most important around levels of stress at which different parts of the system become unstable. Interestingly, losses can be amplified or mitigated with respect to the deterministic case depending on the specific level of stress considered. Finally, the interaction between these two model components - non-linear contagion and uncertainty - alters the area where uncertainty matters.

This paper studies how labor market duality affects human capital accumulation and the wage trajectories of young workers in Spain. Using rich administrative data, we follow workers from their entry into the labor market to measure the experience accumulated under different contractual arrangements and we estimate their wage returns. We document lower returns on experience accumulated under fixed-term contracts compared with permanent contracts and show that this difference is not due to unobserved firm heterogeneity or the quality of the matching. Instead, we provide evidence that the gap in returns is due to lower human capital accumulation while working under fixed-term contracts. This difference widens with worker skill, suggesting that experience and skill-learning are complementary. Our results suggest that the widespread use of fixed-term work arrangements reduces the skill acquisition of highly-skilled workers, holding back life-cycle wage growth by up to 16 percentage points 15 years after their entry into the labor market.

There is no consensus in the academic literature on whether personal bankruptcy laws should be creditor-friendly or debtor-friendly in order to promote entrepreneurship and small business activity. This paper contributes to that literature by analyzing the effect of the introduction of a fresh start policy in Spain in 2015 on the performance of micro-firms as a natural experiment, using Spanish non-micro firms and Portuguese firms as control groups. We find that the reform substantially increased both the probability of filing for bankruptcy by Spanish micro-firms in financial distress (arguably to seek discharge of part of the firm owner’s debt) and the probability of these firms exiting the market, as the fresh start policy requires the liquidation of the debtor’s non-exempt assets. In addition, the reform increased investment and turnover in micro-firms but had no effect on their employment. Finally, the reform also promoted the creation of new micro-firms, especially those involved in innovation activities and in sectors with high productivity.

Between the 1940s and 1970s, Spain used a variety of economic policies that hindered international trade. Because the mix of tariffs, quotas, administrative barriers, and exchange rate regimes varied greatly over time, the quantification of the effect of the various trade policies on international trade in this period is particularly elusive. In this paper, we use historical bilateral trade flows and a structural gravity model to quantify the evolution of Spain’s border thickness, a summary measure of its barriers to international trade. We find that Spain’s borders in the period 1948-1975 were thicker than those of any other country in Western Europe, even after the liberalization of trade that started in 1959. These comparatively higher impediments to international trade implied substantial negative effects on consumer welfare. We estimate that accumulated welfare costs over the period 1948-1975 exceed 20% of a year’s total consumption.

This paper proposes a vector autoregressive model with structural shocks (SVAR) that are identified using sign restrictions and whose distribution is subject to time-varying skewness. It also presents an efficient Bayesian algorithm to estimate the model. The model allows for the joint tracking of asymmetric risks to macroeconomic variables included in the SVAR. It also provides a narrative about the structural reasons for the changes over time in those risks. Using euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks between 1999 and 2019. This variation lies behind a significant proportion of the joint dynamics of real GDP growth and inflation in the euro area over this period, and also generates important asymmetric tail risks in these macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, we found that financial stress indicators do not suffice to explain all the macroeconomic tail risks.

Concerns about climate change are now widespread, and the risks for financial assets have become more evident. Investors are increasingly aware of the need to incorporate climate-related considerations in their investment decisions. All this has had an impact on market valuations. In this paper, we extend the framework of the factor models that explain the expected return of stock models to include a climate change exposure factor. To do so, we built a portfolio that is long on companies with low carbon emissions, and short on companies with high carbon emissions. We show that this factor is relevant in the market and allows for an approximation of the climate change exposure of firms with poor disclosure of their green performance. Thus, the betas of this factor could be a useful tool for investors that wish to incorporate these aspects in the management of their portfolios and analysts interested in corporate exposure to climate change risks.

Is financial exclusion after default a relevant driver of sovereign default incentives? I find new evidence that suggests that this is not the case, and that there are substantial differences in the behavior of different lenders after a sovereign default. Private lenders tend to decrease their funding to developing countries that have defaulted to banks or to the Paris Club. But the financing from official creditors, i.e. bilateral and multilateral, remains mainly unaffected by the different sovereign defaults, only with some exceptions mostly related to defaults to multilateral lenders. This different pattern for official financing is very relevant since official loans are the main source of funds for developing economies. Official creditors continue offering funding to countries even after default, casting doubt on the relevance of one of the main assumptions in sovereign default models, the so-called financial exclusion.

In this article, we measure changes over time in the synchronization of housing price cycles across Spanish cities. In doing so, we rely on a regime-switching framework that identifies the housing price cycles of pairs of cities, and simultaneously infers the evolving relation between those cycles. These bilateral relationships are then summarized into an aggregate synchronization index of city-level housing cycles. The estimates suggest that Spanish housing prices have followed a convergence pattern, which picked in 2009 and slightly decreased afterwards. We also identify the cities that have been the main contributors to this convergence process. Moreover, we show that differences in population growth and economic structure are key factors to explain the evolution of housing price synchronization among Spanish cities.

Using two novel online surveys collected in May and November 2020, we study the consequences of the first stages of the COVID-19 pandemic on Spanish households. We document a large and negative effect on household income. By May 2020 the average individual lived in a household that had lost 16% of their pre-pandemic monthly income. Furthermore, this drop was highly unequal: while households in the richest quintile lost 6.8% of their income, those in the poorest quintile lost 27%. We also document that the pandemic deepened the gender-income gap: on average, women experienced a three-percentage-point larger income loss than men. While this is consistent with previous findings in the literature, in this paper we document that this effect is driven by women from middle-income households with kids. Finally, we provide evidence that Spanish individuals experienced moderate declines in their levels of psychological well-being. This effect is not different for individuals living in rich or poor households, but the reasons behind well-being losses do differ: richer individuals are more concerned about loss of contact with dear ones, while low-income individuals are more likely to mention loss of income and employment as a key source of emotional distress.

In this study we disentangle the effect of roots from other confounding factors to explain differences in immigrants’ outcomes in the mortgage market. Using loan-level data from the Spanish Credit Register complemented with data on securitized mortgages over a complete financial cycle, we identify that foreign-born borrowers with shallow roots to the host country pay higher mortgage rates at origination than similar debtors that are better-settled. We also find that weak roots are associated with higher default rates and with greater incentives to go into default in negative equity situations. Overall, we show that rootedness explains differential loan conditions at origination and default behavior in mortgages. From a policy perspective, our results have important implications for understanding the potential consequences of moving away from recourse mortgage regimes, and for the effectiveness of macroprudential policy.

The aim of this paper is to present the results of a novel database of sectoral regulation at a disaggregated level in Spain. Objective indicators of the volume of new regulation have been constructed for 23 sectors of activity, adopted by each Autonomous Region (Comunidad Autónoma), year by year over the period 1995-2020. In total, 206,777 norms have been identified and classified. The indicators show that new sectoral regulation in Spain is increasing over time, but there are significant differences both between sectors and between Autonomous Regions. Thus, the services and agricultural sectors are relatively more frequently regulated than the industrial ones. On a temporal scale, it is possible to observe that regulators pass new regulations more frequently in downturns. This trend is particularly noticeable in 2020, in the context of the COVID-19 pandemic, especially in the recreational, hospitality, commerce and textile industry sectors. These quantitative results, which are presented as a panel database, open the possibility of further studies on the impact and adequacy of the institutional framework, in particular its regulatory pillar, on elements such as sectoral value added, productivity by sector or business demographics.

Direct lenders, non-bank credit intermediaries with low leverage, have become increas-ingly important players in corporate loan markets. In this paper we investigate the role they play in the monetary policy transmission mechanism, using syndicated loan data covering the 2000-2018 period. We show that direct lenders are more likely to join loan syndicates whenever monetary policy announcements trigger a contraction in borrowers’ net worth irrespective of the directional change in interest rates. Thus, our findings suggest that direct lenders dampen the financial accelerator channel of monetary policy.