Information overload, choice deferral, and moderating role of need for cognition: Empirical evidence

ABSTRACT Choice deferral due to information overload is an undesirable result of competitive environments. The neoclassical maximization models predict that choice avoidance will not increase as more information is offered to consumers. The theories developed in the consumer behavior field predict that some properties of the environment may lead to behavioral effects and an increase in choice avoidance due to information overload. Based on stimuli generated experimentally and tested among 1,000 consumers, this empirical research provides evidence for the presence of behavioral effects due to information overload and reveals the different effects of increasing the number of options or the number of attributes. This study also finds that the need for cognition moderates these behavioral effects, and it proposes psychological processes that may trigger the effects observed.

Political mobilisation and models of trade unionism : Southern Europe in comparative perspective

The relationship between union membership and political mobilization has been studied under many perspectives, but quantitative cross-national analyses have been hampered by the absence of international comparable survey data until the first round of the European Social Survey (ESS-2002) was made available. Using different national samples from this survey in four moments of time (2002, 2004 and 2006), our paper provides evidence of cross-country divergence in the empirical association between political mobilisation and trade union membership. Cross-national differences in union members’ political mobilization, we argue, can be explained by the existence of models of unionism that in turn differ with respect to two decisive factors: the institutionalisation of trade union activity and the opportunities left-wing parties have available for gaining access to executive power.

Measurement with Some Theory: a New Approach to Evaluate Business Cycle Models

We propose a method to evaluate cyclical models which does not require knowledge of the DGP and the exact empirical specification of the aggregate decision rules. We derive robust restrictions in a class of models; use some to identify structural shocks and others to evaluate the model or contrast sub-models. The approach has good size and excellent power properties, even in small samples. We show how to examine the validity of a class of models, sort out the relevance of certain frictions, evaluate the importance of an added feature, and indirectly estimate structural parameters.

Exploring models for the length of stay distribution.

Diagnosis Related Groups (DRG) are frequently used to standardize the comparison of consumption variables, such as length of stay (LOS). In order to be reliable, this comparison must control for the presence of outliers, i.e. values far removed from the pattern set by the majority of the data. Indeed, outliers can distort the usual statistical summaries, such as means and variances. A common practice is to trim LOS values according to various empirical rules, but there is little theoretical support for choosing between alternative procedures. This pilot study explores the possibility of describing LOS distributions with parametric models which provide the necessary framework for the use of robust methods.

Business Cycles, Unemployment Insurance, and the Calibration of Matching Models

This paper points out an empirical puzzle that arises when an RBC economy with a job matching function is used to model unemployment. The standard model can generate sufficiently large cyclical fluctuations in unemployment, or a sufficiently small response of unemployment to labor market policies, but it cannot do both. Variable search and separation, finite UI benefit duration, efficiency wages, and capital all fail to resolve this puzzle. However, both sticky wages and match-specific productivity shocks help the model reproduce the stylized facts: both make the firm's flow of surplus more procyclical, thus making hiring more procyclical too.

Value and depreciation of mineral resources over the very long run: An empirical contrast of different methods

The paper contrasts empirically the results of alternative methods for estimating thevalue and the depreciation of mineral resources. The historical data of Mexico andVenezuela, covering the period 1920s-1980s, is used to contrast the results of severalmethods. These are the present value, the net price method, the user cost method andthe imputed income method. The paper establishes that the net price and the user costare not competing methods as such, but alternative adjustments to different scenariosof closed and open economies. The results prove that the biases of the methods, ascommonly described in the theoretical literature, only hold under the most restrictedscenario of constant rents over time. It is argued that the difference between what isexpected to happen and what actually did happen is for the most part due to a missingvariable, namely technological change. This is an important caveat to therecommendations made based on these models.

An assessment of empirical Bayes and composite estimators for small areas

We compare a set of empirical Bayes and composite estimators of the population means of the districts (small areas) of a country, and show that the natural modelling strategy of searching for a well fitting empirical Bayes model and using it for estimation of the area-level means can be inefficient.

On heuristic and linear models of judgment: Mapping the demand for knowledge

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from as if linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of lens model research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human andheuristic performance in the same tasks. Our results highlight the trade-off betweenlinear models and heuristics. Whereas the former are cognitively demanding, the latterare simple to use. However, they require knowledge and thus maps of when andwhich heuristic to employ.

Gender specialization in households: An empirical analysis

This paper studies the effect of parental education on the educational attainmentof children in the US for cohorts born after 1910. Importantly, we allow for cohort-specificdifferences by gender. Our estimates show that paternal education has been more importantfor the attainment of male children (paternal specialization on sons). However, maternalspecialization (on daughters) seems to have appeared only for cohorts born after 1955. Weinterpret these results as evidence that fathers are more important role models for sonswhile mothers are a more important reference for daughters. We argue that our results arerobust to the presence of hereditary unobserved ability and conjecture that both types ofgender specialization may have been present in earlier cohorts too.

Estimating cross-industry cross-country models using benchmark industry characteristics

International industry data permits testing whether the industry-specific impact of cross-countrydifferences in institutions or policies is consistent with economic theory. Empirical implementationrequires specifying the industry characteristics that determine impact strength. Most of the literature has been using US proxies of the relevant industry characteristics. We show that usingindustry characteristics in a benchmark country as a proxy of the relevant industry characteristicscan result in an attenuation bias or an amplification bias. We also describe circumstances allowingfor an alternative approach that yields consistent estimates. As an application, we reexamine theinfluential conjecture that financial development facilitates the reallocation of capital from decliningto expanding industries.

Growth and externalities across economies : an empirical analysis using spatial econometrics

Recent theoretical models of economic growth have emphasised the role of external effects on the accumulation of factors of production. Although most of the literature has considered the externalities across firms within a region, in this paper we go a step further and consider the possibility that these externalities cross the barriers of regional economies. We assess the role of these external effects in explaining growth and economic convergence. We present a simple growth model, which includes externalities across economies, developing a methodology for testing their existence and estimating their strength. In our view, spatial econometrics is naturally suited to an empirical consideration of these externalities. We obtain evidence on the presence of significant externalities both across Spanish and European regions.

Tax reforms and inequality: theoretical and empirical implications

In this paper we examine the effect of tax policy on the relationship between inequality and growth in a two-sector non-scale model. With non-scale models, the longrun equilibrium growth rate is determined by technological parameters and it is independent of macroeconomic policy instruments. However, this fact does not imply that fiscal policy is unimportant for long-run economic performance. It indeed has important effects on the different levels of key economic variables such as per capita stock of capital and output. Hence, although the economy grows at the same rate across steady states, the bases for economic growth may be different.The model has three essential features. First, we explicitly model skill accumulation, second, we introduce government finance into the production function, and we introduce an income tax to mirror the fiscal events of the 1980¿s and 1990¿s in the US. The fact that the non-scale model is associated with higher order dynamics enables it to replicate the distinctly non-linear nature of inequality in the US with relative ease. The results derived in this paper attract attention to the fact that the non-scale growth model does not only fit the US data well for the long-run (Jones, 1995b) but also that it possesses unique abilities in explaining short term fluctuations of the economy. It is shown that during transition the response of the relative simulated wage to changes in the tax code is rather non-monotonic, quite in accordance to the US inequality pattern in the 1980¿s and early 1990¿s.More specifically, we have analyzed in detail the dynamics following the simulation of an isolated tax decrease and an isolated tax increase. So, after a tax decrease the skill premium follows a lower trajectory than the one it would follow without a tax decrease. Hence we are able to reduce inequality for several periods after the fiscal shock. On the contrary, following a tax increase, the evolution of the skill premium remains above the trajectory carried on by the skill premium under a situation with no tax increase. Consequently, a tax increase would imply a higher level of inequality in the economy

Growth and externalities across economies : an empirical analysis using spatial econometrics

Recent theoretical models of economic growth have emphasised the role of external effects on the accumulation of factors of production. Although most of the literature has considered the externalities across firms within a region, in this paper we go a step further and consider the possibility that these externalities cross the barriers of regional economies. We assess the role of these external effects in explaining growth and economic convergence. We present a simple growth model, which includes externalities across economies, developing a methodology for testing their existence and estimating their strength. In our view, spatial econometrics is naturally suited to an empirical consideration of these externalities. We obtain evidence on the presence of significant externalities both across Spanish and European regions.

Tax reforms and inequality: theoretical and empirical implications

In this paper we examine the effect of tax policy on the relationship between inequality and growth in a two-sector non-scale model. With non-scale models, the longrun equilibrium growth rate is determined by technological parameters and it is independent of macroeconomic policy instruments. However, this fact does not imply that fiscal policy is unimportant for long-run economic performance. It indeed has important effects on the different levels of key economic variables such as per capita stock of capital and output. Hence, although the economy grows at the same rate across steady states, the bases for economic growth may be different.The model has three essential features. First, we explicitly model skill accumulation, second, we introduce government finance into the production function, and we introduce an income tax to mirror the fiscal events of the 1980¿s and 1990¿s in the US. The fact that the non-scale model is associated with higher order dynamics enables it to replicate the distinctly non-linear nature of inequality in the US with relative ease. The results derived in this paper attract attention to the fact that the non-scale growth model does not only fit the US data well for the long-run (Jones, 1995b) but also that it possesses unique abilities in explaining short term fluctuations of the economy. It is shown that during transition the response of the relative simulated wage to changes in the tax code is rather non-monotonic, quite in accordance to the US inequality pattern in the 1980¿s and early 1990¿s.More specifically, we have analyzed in detail the dynamics following the simulation of an isolated tax decrease and an isolated tax increase. So, after a tax decrease the skill premium follows a lower trajectory than the one it would follow without a tax decrease. Hence we are able to reduce inequality for several periods after the fiscal shock. On the contrary, following a tax increase, the evolution of the skill premium remains above the trajectory carried on by the skill premium under a situation with no tax increase. Consequently, a tax increase would imply a higher level of inequality in the economy

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.