New Dimensions of Regulatory Complexity and their Economic Cost. An Analysis using Text Mining (435 KB)
Juan de Lucio and Juan S. Mora-Sanguinetti
Spain has adopted 386,850 new norms since the beginning of the democratic period (1979-2020) and its rate of adoption has quadrupled since 1978 (12,250 new regulations were published in the year 2020). Notwithstanding, in addition to the problems that may arise from the "quantity” of regulations, the legal framework can also be complex due to “qualitative” reasons such as (1) the linguistic ambiguity of the norms or (2) their relational structure (i.e. references between legal documents). Our article innovates by analysing these new dimensions of complexity at the regional level using text mining and explores their relationship with productivity (as a relevant economic variable) and with judicial efficacy (as a relevant institutional-structural variable).
Market polarization and the Phillips curve (542 KB)
Javier Andrés, Óscar Arce y Pablo Burriel
The Phillips curve has flattened out over the last decades. We develop a model that rationalizes this phenomenon as a result of the observed increase in polarization in many industries, a process along which a few top firms gain an increasing share of their industry market. In the model, firms compete à la Bertrand and there is exit and endogenous market entry, as well as optimal up and downgrading of technology. Firms with larger market shares find optimal to dampen the response of their price changes, thus cushioning the shocks to their marginal costs through endogenous countercyclical markups. Thus, regardless of its causes (technology, competition, barriers to entry, etc.), the recent increase in polarization in many industries emerges in the model as the key factor in explaining the muted responses of inflation to movements in the output gap witnessed recently.
Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation (184 KB)
Andrés Alonso and José Manuel Carbó
We study the economic impact for financial institutions of using machine learning (ML) models in credit default prediction. We do so by using a unique and anonymized database from a major Spanish bank. We first measure the statistical performance in terms of predictive power, both in classification and calibration, comparing models like Logit and Lasso, with more advanced ones like Trees (CART), Random Forest, XGBoost and Deep Learning. We find that ML models outperforms traditional ones, although more complex ML algorithms do not necessarily predict better. We then translate this into economic impact by estimating the savings in regulatory capital that an institution could achieve when using a ML model instead of a simpler one to compute the risk-weighted assets following the Internal Ratings Based (IRB) approach. Our benchmark results show that implementing XGBoost instead of Lasso could yield savings from 12.4% to 17% in capital requirements, depending on the type of underlying assets.
We quantify the aggregate, distributional, and welfare consequences of setting an optimal progressivity level in the Spanish personal income tax scheme. For that purpose, we use a heterogeneous households general equilibrium model featuring both life cycle and dynastic elements which is calibrated to replicate some aggregate and distributional characteristics of the Spanish economy. The findings suggest that increasing progressivity would be optimal, even though it would involve an efficiency loss. The optimal reform of the tax schedule would reduce wealth and income inequality at the cost of negative effects on capital, labor, and output. The evaluation of these theoretical results with tax microdata describes a current scenario where the income-top households typically face suboptimal effective average tax rates.
Urban air pollution and sick leaves: Evidence from social security data (169 KB)
Felix Holub, Laura Hospido and Ulrich J. Wagner
We estimate the impact of air pollution on the incidence of sick leaves in a representative panel of employees affiliated to the Spanish social security system. Using over 100 million worker-by-week observations from the period 2005-2014, we obtain that higher air pollution increases the incidence of sick leaves, controlling for weather, individual effects, and a wide range of time-by-location controls. The effect is stronger for workers with pre-existing medical conditions, and weaker for workers with low job security. Our estimates are instrumental for quantifying air pollution damages due to changes in labor supply. Improved air quality in urban Spain between 2005 and 2014 saved at least €503 million in foregone production thanks to 5.55 million fewer worker absence days.
Raising Markups to Survive: Small Spanish Firms during the Great Recession (159 KB)
Pilar Garcia-Perea, Aitor Lacuesta, Pau Roldan-Blanco
Markups and other measures of concentration have been on the rise in the United States in the last few decades, driven by the behavior of large and productive firms. We document that markups rose during the financial crisis in Spain, and that this increase was led by a behavioral response of small and unproductive firms: in response to a drop in sales, these firms were unable to increase their productive efficiency when average costs increased and, in order to escape a sharp decline in profit rates, they increase their markups to survive in the market.