Franco Peracchi, together with Francesco Bartolucci and Valentino Dardanoni, in: “Ranking scientific journals via latent class models for polytomous item response data” proposes a strategy for ranking scientific journals starting from a set of available quantitative indicators representing imperfect measures of the unobservable “value” of the journals of interest. After discretizing the available indicators, the authors estimate a latent class model for polytomous item response data and use the estimated model to classify each journal. They apply this approach to data from the Research Evaluation Exercise carried out in Italy in the period 2004-10, focusing on the sub-area of Statistics and Financial Mathematics. They derive a complete ordering of the journals according to their latent value, starting from four quantitative indicators of the journals’ scientific value (IF, IF5, AIS, h-index), and show that their strategy is simple to implement and that the obtained ranking is robust with respect to different discretization rules.
Franco Peracchi, together with Francesco Bartolucci and Federico Belotti, in: “Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data” proposes a computationally convenient test for the null hypothesis of time-invariant individual effects in generalized linear models for panel data, a wide class of models that includes the Gaussian linear model and a variety of nonlinear models typically employed for discrete or categorical outcomes. The basic idea of the test is to compare fixed effects estimators defined as the maximand of full and pair wise conditional likelihood functions. Thus, this approach requires no assumptions on the distribution of the individual effects and, most importantly, it does not require them to be independent of the covariates in the model. The finite sample properties of the test are illustrated through a set of Monte Carlo experiments. The results show that the test performs quite well, with small size distortions and good power properties. An example based on data from the Health and Retirement Study is used to illustrate the test.
Franco Peracchi, together with Valentino Dardanoni, Giuseppe De Luca and Salvatore Modica, in: “Bayesian model averaging for generalized linear models with missing covariates” addresses the problem of estimating a broad class of nonlinear models (generalized linear models or GLMs) when the outcome of interest is always observed, the values of some covariates are missing for some observations, but imputations are available to fill-in the missing values. This situation is becoming quite common, as public-use data files increasingly include imputations of key variables affected by missing data problems, and specialized software for carrying out imputations directly is also becoming available. Under certain conditions on the missing-data mechanism and the imputation model, this situation generates a trade-off between bias and precision in the estimation of the parameters of interest. Following the generalized missing-indicator approach originally proposed by Dardanoni et al. (2011) for linear regression models, the authors characterize this bias-precision trade-off in terms of model uncertainty, so that the problem can be handled through Bayesian model averaging (BMA). In addition to applying the generalized missing-indicator method to the wider class of GLMs, two extensions are proposed: a block-BMA strategy that incorporates information on the available missing-data patterns and has the advantage of being computationally simple; second, the observed outcome is allowed to be multivariate, thus covering the case of seemingly unrelated regression equations models, and ordered, multinomial or conditional logit and probit models. The new proposed approach is then illustrated through an empirical application using the first wave of the Survey on Health, Aging and Retirement in Europe (SHARE).
In “Monetary Shocks with Observation and menu Costs” Francesco Lippi and Luigi Paciello, together with Fernando Alvarez, compute the impulse response function of output to a monetary shock in a general equilibrium model when firms set prices under two frictions: a standard fixed cost of adjusting the price (menu cost) and a fixed cost of observing the state of nature (observation cost). They then analyze how the effects on output depend on these costs. First, they find that the larger the observation cost relative to the menu cost, the larger and the more persistent the output response is to a monetary shock. Second, over a wide range of values for observation and menu costs, the shape of the impulse response function resembles that in a model with observation cost only and is flatter than the impulse response function in a model with menu cost only. Finally, they show that, for monetary shocks of moderate size, the assumptions about the information structure (i.e. perfectly observed monetary shocks vs. unobserved monetary shock) have negligible consequences for the prediction of the model about the output effects of such shocks.
When and why does finance cease to be the “lifeblood” of the real economy and turn into a “toxin”? In “Finance: Economic Lifeblood or Toxin?“, Marco Pagano argues that this metamorphosis occurs when finance grows past the funding needs of the real sector. At this point it stops contributing to economic growth and comes to threaten the solvency of banks and systemic stability. In principle regulation should be designed as to contain financial development within bounds that ensure positive benefits. However, the author argues that, because of the vast political consensus created by economy-wide asset bubbles, any effective supervisory action to counteract them is often prevented.
In “E-commerce as a Stockpiling Technology: Implications for Consumer Savings” Andrea Pozzi documents a previously neglected benefit of the introduction of e-commerce. Since online orders are generally home delivered, shopping on the Internet spares customers the discomfort of carrying around heavy and bulky baskets of goods. This makes e-commerce a technology well suited to helping consumers to buy in bulk or to stockpile items on discount. Exploting scanner data provided by a supermarket chain selling groceries both online and through traditional stores he shows that the introduction of e-commerce leads to an increase in bulk purchase and stockpiling behavior by customers. Since bulk and discounted items are sold at a lower price per unit, this allows consumers to obtain substantial savings.
In “The Demand of Liquid Assets with Uncertain Lumpy Expenditures” Francesco Lippi, together with Fernando Alvarez, analyze how unexpected large-sized expenditures, such as the purchase of durable goods by households, impact on the management of liquid assets in the context of inventory theoretical models. The paper shows that lumpy purchases give rise to the possibility that liquidity gets withdrawn and spent immediately, thus changing the relationship between the size of liquidity withdrawals and the average liquidity holdings compared to canonical models. By using two novel datasets, the authors summarize the main patterns in the data concerning the households’ currency management in Austria and the management of demand deposits by a large sample of Italian investors and show that their model can explain some empirical regularities that traditional models cannot account for.
Would citizens coordinate to punish a government when they observe suspicious behaviour? In “Coordination, Efficiency and Policy Discretion” Facundo Piguillem, together with Anderson Schneider, show that under some circumstances such coordination is impossible. The authors set up a model with incomplete information where the fundamental (aggregate productivity) is stochastic and observed only by the government, while every private agent receives a noisy signal about it. In this environment coordination among private agents is harder to achieve and punishing the government when it deviates from optimal policies may become impossible. The results of the paper support the arguments in favor of strong institutions that tie the hands of policymakers, as to endow governments with full discretion and to impose the right incentives to avoid deviation from optimal policies could be too costly. At the same time, some doubts are cast on policy prescriptions arising from models with complete information, as it is shown that even arbitrarily small departures from the complete information assumption render the results invalid.
In “Inequality and Relative Ability Beliefs” Jeffrey V. Butler documents a novel channel causing inequality persistence. In a sequence of experiments it is shown that i) individuals respond to salient (earnings) inequality by adjusting their performance beliefs to justify the inequality; ii) it is beliefs about relative ability – an ostensibly stable trait – rather than effort provision that are affected; and iii) unequal pay on an initial task affects willingness to compete on a subsequent task. Taken together, the results provide evidence for a novel mechanism perpetuating inequality: initial inequality colors beliefs about one’s own ability relative to others, lowering the ex-ante expected return to courses of action which require, at some point, ability-based competition. As the latter is a feature of many paths to upward mobility, initial inequality may become persistent inequality.
Cross-countries differences in firm growth are typically analysed under the closed economy assumption, in spite of the empirical evidence of strong links between firm size and international trade. In “Barriers to Firm Growth in Open Economies” Facundo Piguillem (with Loris Rubini) develops a tractable, open economy, dynamic framework where the firm size distribution is endogenous and is affected by both innovation costs and trade barriers. The authors show that, by neglecting the latter, the estimates of innovation costs are biased downwards; as this bias largely differs across countries, the true ranking of innovation costs is altered; moreover, the predicted effects of changing innovation costs on welfare and aggregate productivity are biased upwards. The open economy model, calibrated to a set of European countries, successfully captures between 54 and 87 per cent of the differences in value added per worker across countries while the closed economy model over-predicts the effects of innovation costs on welfare by between 31 and 64 per cent relative to the open economy model.
Banks and financial institutions are blamed for compensation packages that reward managers generously for making investments with high returns in the short run but large “tail risks” that emerge only in the long run. As governments have been forced to rescue failing financial institutions, politicians and media have stressed the need to cut executive pay packages, making them more dependent on long-term performance. Whether this is the right policy response crucially depends on what is the root of the problem. In “Seeking Alpha: Excess Risk Taking and Competition for Managerial Talent” Marco Pagano (with Viral Acharya and Paolo Volpin) present a model where the root of the problem is the difficulty of rewarding managerial talent when projects can have tail risk, as is typically the case in the financial sector, and the market allows executives to move from firm to firm before that risk materializes. The paper shows that managers who take tail risks while moving rapidly between firms raise their short-term performance and pay, while reducing their accountability for failures. In this situation managerial talent, that is the ability to generate high returns without incurring in high risks, can be identified by firms only in the long run, so that the efficient allocation of managers to projects cannot be achieved and too many projects fail; at the same time, managers’ pay is not commensurate with their actual performance. In this setting, efficiency can be improved by implementing measures that discourage managerial mobility (i.e. taxing managers who switch jobs at a higher rate than loyal ones) or by capping the pay of top financial managers.
Francesco Lippi (with Fernando Alvarez) in “Price Setting with Menu Cost for Multi-product Firms” develops an analytically tractable model of the optimal price setting decisions of a firm facing a fixed cost of price adjustment common to all goods it produces. The authors solve the firm’s decision problem, derive the steady state predictions for a cross-section of firms and study the response of the aggregate economy to a monetary shock. While previous literature has often resorted to numerical methods, the novel contribution of the paper is to present an approximate analytical solution to the general equilibrium of an economy where firms face a multidimensional and non-convex control problem. Two sets of results are worth mentioning. First, the model substantially improves the ability of state-of-the-art menu cost models to account for observed price setting behavior: as the data display a large mass of small price changes, the size distribution of price changes appears bell-shaped, which is what the model produces provided that the number of goods produced by each firm is larger than six. Second, as regards the characterization of the response of the economy to a monetary shock, the size of the output response and its duration increase with the number of products; actually they more than double as the number of products goes from one to ten, quickly converging to the standard results of Taylor’s staggered price model.
What shapes individuals’ attitudes toward risk and uncertainty? This question is of fundamental importance to economists. Prior research shows that those who more readily rely on their intuition when making decisions are also significantly more tolerant of risk and ambiguity. Still, the direction of causation in these findings can be debated: are more uncertainty-tolerant individuals more likely to rely on their intuition, or does reliance on intuition reduce aversion to risk and ambiguity? Jeffrey Butler and Luigi Guiso (with Tullio Jappelli) in “Manipulating Reliance on Intuition Reduces Risk Ambiguity Aversion” provide the first experimental evidence directly addressing the question of whether variation in reliance on intuition causes shifts in aversion to risk and ambiguity. In the experiment they directly manipulate participants’ predilection to rely on intuition and find that enhancing reliance on intuition lowers the probability of being ambiguity averse by 30 percentage points and increases risk tolerance by about 30 per cent among male participants.