New evidence of granular business cycles from German cities

Federica Daniele ’13 shares a paper accepted to Review of Economics and Statistics.

journal cover

My paper “The Micro-Origins of Business Cycles: Evidence from German Metropolitan Areas” joint with Heiko Stueber has been accepted to the Review of Economics and Statistics. Here is a summary of our work:

Cities compete to attract large firms. When Amazon announced in 2017 the opening of its second headquarters, 238 US cities signed up for it. Large firms bring jobs and can boost local productivity through spillovers. However, the downside is that they generate excessive local volatility.

We leverage quarterly data on size of all German establishments from 1990 to 2014 to show that a buildup of concentration of economic activity in the hands of few sizable firms is systematically associated with higher volatility in local labor markets in subsequent months.

The reason is granularity. When concentration is high, shocks to large firms do not average out with shocks to smaller ones and the evolution of local employment ends up mimicking the evolution of employment in the large firm. The economy experiences “granular” business cycles.

Our paper is the first to provide solid time-series support to granular business cycles as in Carvalho and Grassi. However, we show that large firms do not seem capable to trigger both booms and busts alike. Our evidence points in favor of granularity-driven recessions only.

Finally, we calibrate the parameters governing local firm dynamics to match the local employment law of motion, because we want to see what are the causes of the disproportionate presence of large firms in big cities. We find that it’s because of higher growth opportunities in big cities.

Bottom line: the volatility externality imposed by large firms encourages short-time work schemes as opposed to layoffs and may justify using size-dependent forms of public support for crisis management, but the benefit might have to be weighed against potential moral hazard.

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Federica Daniele ’13 is an economist at the Bank of Italy. She holds a PhD from UPF and Barcelona GSE and is an alum of the Barcelona GSE Master’s in Economics.

Tackling domestic violence using large-scale empirical analysis

New paper in Journal of Empirical Legal Studies co-authored by Ria Ivandić ’13 (Economics)

A woman holds a sign in front of her face that reads, "Love shouldn't hurt."
Photo by Anete Lusina from Pexels

In England, domestic violence accounts for one-third of all assaults involving injury. A crucial part of tackling this abuse is risk assessment – determining what level of danger someone may be in so that they can receive the appropriate help as quickly as possible. It also helps to set priorities for police resources in responding to domestic abuse calls in times when their resources are severely constrained. In this research, we asked how we can improve on existing risk assessment, a research question that arose from discussions with policy makers who questioned the lack of systematic evidence on this.

Currently, the risk assessment is done through a standardised list of questions – the so-called DASH form (Domestic Abuse, Stalking and Harassment and Honour- Based Violence) – which consists of 27 questions that are used to categorise a case as standard, medium or high risk. The resulting DASH risk scores have limited power in predicting which cases will result in violence in the future.  Following this research, we suggest that a two-part procedure would do better both in prioritising calls for service and in providing protective resources to victims with the greatest need. 

In our predictive models, we use individual-level records on domestic abuse calls, crimes, victim and perpetrator data from the Greater Manchester Police to construct the criminal and domestic abuse history variables of the victim and perpetrator. We combine this with DASH questionnaire data in order to forecast reported violent recidivism for victim-perpetrator pairs.  Our predictive models are random forests, which are a machine-learning method consisting of a large number of classification trees that individually classify each observation as a predicted failure or non-failure. Importantly, we take the different costs of misclassification into account.  Predicting no recidivism when it actually happens (a false negative) is far worse in terms of social costs than predicting recidivism when it does not happen (a false positive). While we set the cost of incurring a false negative versus a false positive at 10:1, this is a parameter that can be adjusted by stakeholders. 

We show that machine-learning methods are far more effective at assessing which victims of domestic violence are most at risk of further abuse than conventional risk assessments. The random forest model based on the criminal history variables together with the DASH responses significantly outperforms the models based on DASH alone. The negative prediction error – that is, the share of cases that would be predicted not to have violence yet violence occurs in the future – is low at 6.3% as compared with an officer’s DASH risk score alone where the negative prediction error is 11.5%.  We also examine how much each feature contributes to the model performance. There is no single feature that clearly outranks all others in importance, but it is the combination of a wide variety of predictors, each contributing their own ‘insight’, which makes the model so powerful.

Following this research, we have been in discussion with police forces across the United Kingdom and policy makers working on the Domestic Abuse Bill to think how our findings could be incorporated in the response to domestic abuse. We hope this research acts as a building block to increasing the use of administrative datasets and empirical analysis to improve domestic violence prevention.

This post is based on the following article:

Grogger, J., Gupta, S., Ivandic, R. and Kirchmaier, T. (2021), Comparing Conventional and Machine-Learning Approaches to Risk Assessment in Domestic Abuse Cases. Journal of Empirical Legal Studies, 18: 90-130. 

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Ria Ivandić ’13 is a Researcher at LSE’s Centre for Economic Performance (CEP). She is an alum of the Barcelona GSE Master’s in Economics.

Eliciting preferences for truth-telling in a survey of politicians

Publication in PNAS by Katharina Janezic ’16 (Economics) and Aina Gallego (IBEI and IPEG)


Honesty is one of the most valued traits in politicians. Yet, because lies often remain undiscovered, it is difficult to study if some politicians are more honest than others. This paper examines which individual characteristics are correlated with truth-telling in a controlled setting in a large sample of politicians. We designed and embedded a game that incentivizes lying with a non-monetary method in a survey answered by 816 Spanish mayors. Mayors were first asked how interested they were in obtaining a detailed report about the survey results, and at the end of the survey, they had to flip a coin to find out whether they would be sent the report. Because the probability of heads is known, we can estimate the proportion of mayors who lied to obtain the report.

We find that a large and statistically significant proportion of mayors lied. Mayors that are members of the two major political parties lied significantly more. We further find that women and men were equally likely to lie. Finally, we find a negative relationship between truth-telling and reelection in the next municipal elections, which suggests that dishonesty might help politicians survive in office.

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Cross-border effects of regulatory spillovers: Evidence from Mexico

Forthcoming JIE publication by Jagdish Tripathy ’11 (Economics)

Economics alum Jagdish Tripathy ’11 has a paper forthcoming in the Journal of International Economics on “Cross-border effects of regulatory spillovers: Evidence from Mexico.”

Paper abstract

This paper studies the spillover of a macroprudential regulation in Spain to the Mexican financial system via Mexican subsidiaries of Spanish banks. The spillover caused a drop in the supply of household credit in Mexico. Municipalities with a higher exposure to Spanish subsidiaries experienced a larger contraction in household credit. These localized contractions caused a drop in macroeconomic activity in the local non-tradable sector. Estimates of the elasticity of loan demand by the non-tradable sector to changes in household credit supply range from 1.2–1.8. These results emphasize cross-border effects of regulations in the presence of global banks.

Key takeaways

Loan-loss provisions introduced in Spain in 2012 imposed a significant burden on the balanced sheet of Spanish banks. This regulation was unrelated to the Mexican financial system or the credit conditions of Mexican households. However, Mexican subsidiaries of two large Spanish banks, BBVA and Santander, reduced lending to Mexican households in response to the regulation (Fig. 1).

Fig. 1. Growth in credit lending by Spanish and non-Spanish banks in Mexico.

Mexican municipalities with a higher exposure to Spanish banks (Fig. 2) experienced a larger contraction in lending to households. This drop in lending to households (i.e. a drop in credit supply) was associated with a reduction in lending to the local non-tradable sector driven by a drop in local demand. This shows (1) cross-border effects of a macroprudential regulation on lending and economic activity, and (2) the macroeconomic effects of shocks in lending to households in an emerging economy.

Fig. 2. Share of Spanish banks in the household credit market across Mexican municipalities.

About the author

Jagdish Tripathy ’11 is an Advisor at Bank of England. He is an alum of the Barcelona GSE Master’s in Economics and has his PhD from GPEFM (UPF and Barcelona GSE).

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Accounting for Mismatch Unemployment

JEEA publication by Benedikt Herz ’08 and Thijs van Rens (former BGSE professor)


Benedkit Herz (Economics ’08, GPEFM ’13), has published a paper in the Journal of the European Economic Association. His co-author is former Barcelona GSE Professor Thijs van Rens (now at Warwick).

Paper abstract

We investigate unemployment due to mismatch in the United States over the past three and a half decades. We propose an accounting framework that allows us to estimate the contribution of each of the frictions that generated labor market mismatch. Barriers to job mobility account for the largest part of mismatch unemployment, with a smaller role for barriers to worker mobility. We find little contribution of wage-setting frictions to mismatch.

Benedikt Herz ’08 is member of the Chief Economist’s Team, European Commission DG for Internal Market and Industry. He is an alum of the Barcelona GSE Master’s in Economics.

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Backlash: The Unintended Effects of Language Prohibition in US Schools after World War I

Review of Economic Studies publication by Vicky Fouka ’10 (Economics)

The paper Backlash: The Unintended Effects of Language Prohibition in U.S. Schools after World War I by Economics alum Vicky Fouka ’10 has been published in the Review of Economic Studies (REStud).

Her research on the topic was also featured in The Washington Post last year!

Paper abstract

Do forced assimilation policies always succeed in integrating immigrant groups? This paper examines how a specific assimilation policy – language restrictions in elementary school – affects integration and identification with the host country later in life. After World War I, several US states barred the German language from their schools. Affected individuals were less likely to volunteer in WWII and more likely to marry within their ethnic group and to choose decidedly German names for their offspring. Rather than facilitating the assimilation of immigrant children, the policy instigated a backlash, heightening the sense of cultural identity among the minority.

“Banning immigrants’ languages can backfire. Just ask Ohio and Indiana.”
The Washington Post. May 11, 2019.

Vicky Fouka ’10 is Assistant Professor of Political Science at Stanford University. She is an alum of the Barcelona GSE Master’s in Economics and earned her PhD in Economics at GPEFM (UPF and BGSE).

Specific Human Capital and Wait Unemployment

Publication in the Journal of Labor Economics by Benedikt Herz ’08 (Economics)


The main chapter of the PhD dissertation by Benedkit Herz (Economics ’08, GPEFM ’13), “Specific Human Capital and Wait Unemployment,” has been published in the Journal of Labor Economics and is now available online.

Paper abstract

A displaced worker might rationally prefer to wait through a long spell of unemployment instead of seeking employment at a lower wage in a job he is not trained for. I evaluate this trade-off using micro data on displaced workers. To achieve identification, I exploit the fact that the more a worker has invested in occupation-specific human capital, the more costly it is for him to switch occupations and therefore the higher is his incentive to wait. I find that between 9% and 17% of total unemployment in the United States can be attributed to wait unemployment

Benedikt Herz ’08 is member of the Chief Economist’s Team, European Commission DG for Internal Market and Industry. He is an alum of the Barcelona GSE Master’s in Economics.

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Sectoral risk-weights and macroprudential policy

Publication in Journal of Banking and Finance by Alex Hodbod (ITFD ’12) and Steffi Huber (Economics ’10, GPEFM ’17)

We have a forthcoming article “Sectoral Risk Weights and Macroprudential Policy” in the Journal of Banking & Finance with our co-author Konstantin Vasilev (Essex).

The authors!

Paper abstract

This paper analyses bank capital requirements in a general equilibrium model by evaluating the implications of different designs of such requirements regarding their impact on the tendency of banks to amplify the business cycle.

Interest rate spreads structure. This figure gives an overview of the different interest rate spreads within the model and the factors that affect them. The asset-specific interest rate spreads determine the borrowing costs of households and firms and hence the quantities of specific loan types in the economy.

We compare the Basel-established Internal Ratings-Based (IRB) approach to risk-weighting assets with an alternative macroprudential approach which sets risk-weights in response to sectoral measures of leverage. The different methods are compared in a crisis scenario, where the crisis originates from the housing market that affects the banking sector and is then transmitted to the wider economy.

Variance decomposition – real consumption during the Great Recession. This variance decomposition shows that the model identifies the productivity shock and the shock to mortgage lending risk to be the main drivers of the crash in real consumption during the Great Recession. In our model, the main channel through which the shock to mortgage risk has a procyclical effect on consumption is through lending and house prices.

We investigate both boom and bust phases of the crisis by simulating an unrealized news shock that leads to a gradual build-up and rapid crash in the economy. Our results suggest that the IRB approach creates procyclicality in regulatory capital requirements and thereby works to amplify both boom and bust phases of the financial cycle. On the other hand, our proposed macroprudential approach to setting risk-weights leads to counter-cyclicality in regulatory capital requirements and thereby attenuates the financial cycle.

Impulse Response Function – Unrealised news shock. Here, we model both the build-up and crash phases of the crisis and use this to examine how different policy approaches perform in handling the boom phase of the cycle. In periods 1-4 agents start with expectations that a housing boom will occur, but at period 4 a shock arrives as this boom does not materialise. At the top of the chart one sees that the policy setup based on the IRB approach (red) generates the biggest macroeconomic consequences from this shock; it is the most procyclical. The macroprudential approach to risk-weighting (in green) is the least procyclical. An unweighted “leverage ratio” approach (blue) is less procyclical than the IRB approach, but more so than our macroprudential approach.

Conclusions in brief

  • We show that IRB risk-weights can induce procyclicality of capital requirements and amplify both boom and bust phases of the business cycle. This is particularly concerning because procyclical risk weights could undermine other macroprudential tools, as these other tools are themselves based on risk-based measures of capital requirements e.g. Counter Cyclical Capital Buffers.
  • Our alternative approach of macroprudential risk weights could induce countercyclicality of capital requirements, which may offer benefits in terms of smoothening financial cycles. Targeting macroprudential intervention on bank risk-weights is likely to be more effective when it is sector-specific. This will alter banks’ incentives in a sensitive way – thereby tending to attenuate sectoral asset booms.
  • The results complement the ongoing debate about the potential merits of a Sectoral Counter Cyclical Capital Buffer, which is ongoing internationally.

About the authors


Alexander Hodbod ’12 is Adviser to representatives on the ECB Supervisory Board. He is an alum of the Barcelona GSE Master’s in International Trade, Finance and Development.

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Stefanie J. Huber ’10 is Assistant Professor at the University of Amsterdam. She is an alum of the Barcelona GSE Master’s in Economics and GPEFM PhD Program (UPF and Barcelona GSE).

LinkedIn | Website

If you are an alum and would like to share your work on the Barcelona GSE Voice, please reach out!

Structural Change and the Fertility Transition

Forthcoming paper in Review of Economics and Statistics by Philipp Ager ’08 and Benedikt Herz ’08 (Economics)

Paper abstract

This paper provides new insights on the relationship between structural change and the fertility transition. We exploit the spread of an agricultural pest in the American South in the 1890s as plausibly exogenous variation in agricultural production to establish a causal link between earnings opportunities in agriculture and fertility. Households staying in agriculture reduced fertility because children are a normal good, while households switching to manufacturing reduced fertility because of the higher opportunity costs of raising children. The lower earnings opportunities in agriculture also decreased the value of child labor which increased schooling, consistent with a quantity-quality model of fertility.

See this paper on the REST website.

Also featured on VoxEU!

A more in-depth summary of the paper is available in the VoxEU column “From the farm to the factory floor: How the structural transformation triggered the fertility transition.”

Check it out on VoxEU!

About the authors

Philipp Ager ’08 is an Associate Professor of Economics, University of Southern Denmark and CEPR Research Affiliate. He is an alum of the Barcelona GSE Master’s in Economics.


Benedikt Herz ’08 is member of the Chief Economist’s Team, European Commission Directorate-General for Internal Market and Industry. He is an alum of the Barcelona GSE Master’s in Economics.


Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models

Publication in “Journal of Business & Economic Statistics” by
Gergely Ganics ’12 (with A. Inoue and B. Rossi)


In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be consistently estimated, the confidence intervals for the strength of identification are straightforward and computationally easy to calculate, as they are obtained from inverting a chi-squared distribution. Furthermore, they provide more information to researchers on instrument strength than the binary decision offered by tests. Monte Carlo simulations show that the confidence intervals have good, albeit conservative, in some cases, small sample coverage. We illustrate the usefulness of the proposed methods in two empirical situations: the estimation of the intertemporal elasticity of substitution in a linearized Euler equation, and government spending multipliers. 

Supplementary materials for this article are available online. The online appendix contains the proofs, further theoretical and Monte Carlo results, and the description of the datasets used in the present article. Replication code is available on the journal’s website.