Creative Destruction,Economic Insecurity,Stress, and Epidemic Obesity

The percentage of Americans who are obese has doubled since 1980. Most attempts to explain this “obesity epidemic” have been found inadequate, including the “Big Two” (the increased availability of inexpensive food and the decline of physical exertion). This article explores the possibility that the obesity epidemic is substantially due to growing insecurity, stress, and a sense of powerlessness in modern society where high‐sugar and high‐fat foods are increasingly omnipresent. Those suffering these conditions may suffer less control over other domains of their lives. Insecurity and stress have been found to increase the desire for high‐fat and high‐sugar foods. After exploring the evidence of a link between stress and obesity, the increasing pace of capitalism's creative destruction and its generation of greater insecurity and stress are addressed. The article ends with reflections on how epidemic obesity is symptomatic of a social mistake—the seeking of maximum efficiency and economic growth even in societies where the fundamental problem of material security has been solved.

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Multiple capital inputs, Q, and investment spending

Despite their explicit treatment of dynamics and solid theoretical basis, investment models based on the Brainard-Tobin Q have recorded a generally disappointing empirical performance. When the Q model is expanded to recognize the possibility that the value of the firm depends on two or more capital inputs with differing adjustment cost technologies, the econometric equation following from optimizing behavior includes Q as well as a set of additional explanatory variables. The importance of these omitted variables is assessed, and the capital homogeneity assumption for equipment and structures implicit in Conventional Q models is rejected. The Multi-Capital Q model is then extended in two ways: (i) adding inventory, research and development, and labor as quasi-fixed factors and (ii) exploring the sensitivity of the instrumental variables estimates to normalization. We conclude that the Multi-Capital Q model is a useful extension that overcomes an important omitted variables problem in the Conventional Q framework.     

Continuous Dependent Variables and Organizational Ecology: Toward a more Perfect Union

In this paper, I review all previously published organizational ecology research which utilizes continuous dependent variables. I unearth twenty-one such studies, half of which were published within the past four years. The broadening array of dependent variables in this field is a most welcome development. However, each of these papers has at least one methodological limitation, in specification of cross-unit effects and/or controls for autocorrelation. Perhaps the most serious problem is the assertion that the fixed effects research design solves the problem of autocorrelation. I demonstrate that this assertion is untrue. I conclude with advice on the proper way to model continuous dependent variables in organizational ecology research, as follows: (1) Consider omitting all organizations which do not exist for more time periods than the number of independent variables. (2) Test for autocorrelation, report the results, and correct for autocorrelation if the test indicates that it is a problem. (3) Use a fixed effects model, and justify it based on the nonrandomness of the data.     

On multiple regression analysis

The sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in the literature to avoid this inconvenience: "forward selection" or "backward elimination".
With forward selection the independent variables are introduced in successive stages. The order is not predetermined but at each stage that variable is taken as the next one which produces the highest reduction in the residual sum of squares of the dependent variable.
With backward elimination on the other hand, we start with the complete regression equation and eliminate the independent variables from it in the order in which they produce the smallest increases in the residual sum of squares.
This paper describes a simple and convenient computational lay-out which can be used for both procedures. In forward selection we start with the matrix of product sums, and in bacward elimination we work from the inverse matrix.
In addition these techniques are applied to a variety of practical examples in order to see what results they lead to and what pitfalls may be encountered.     

Consumption, house prices, and collateral constraints: a structural econometric analysis

If borrowing capacity of indebted households is tied to the value of their home, house prices should enter a correctly specified aggregate Euler equation for consumption. I develop a simple two-agent, dynamic general equilibrium model in which home (collateral) values affect debt capacity and consumption possibilities for a fraction of the households. I then derive and estimate an aggregate consumption Euler equation, and estimate its structural parameters. The results provide robust support for housing prices as a driving force of consumption fluctuations.     

Smoking, self-selection and absenteeism

How much, if any, does smoking contribute to absenteeism? Separate samples of employed men and women are drawn from the 1986 wave of the Panel Study of Income Dynamics (PSID) to answer this question. The 1986 wave is the most recent one with information on smoking. In the first analysis, single equation Tobit regressions are run explaining the absence rate. Independent variables include a binary smoking variable together with others for age, race, marital status, and so on. In the single equation models, smoking appeared to raise absence rates by 42 and 232 percent for women and men, respectively. In the second analysis, decomposition techniques, which also adjust for smoking and employment hazard rates, are applied to four separate samples of smokers, non-smokers, men, and women. The decomposition techniques lowered these differentials. Smoking appears to make a moderate contribution to absenteeism for men, but only a slight contribution for women. The decomposition suggests that it is the observed and unobserved personal characteristics of female smokers that account for most of the simple positive correlations between absenteeism and smoking among women found in the single equation models.     

Simple solutions to the initial conditions problem in dynamic,nonlinear panel data models with unobserved heterogeneity

I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three leading dynamic, nonlinear models: probit, Tobit and Poisson regression. I treat the general problem of estimating average partial effects, and show that simple estimators exist for important special cases. Copyright © 2005 John Wiley & Sons, Ltd.     

Regional growth and interstate migration

The current study examines a simultaneous equation model of interstate migration using income growth, employment growth, unemployment growth, population growth, gross migration, and employment in manufacturing as endogenous variables. The results show that neither the growth of employment nor the growth of income in the destination location has been directly important determinants of migration flow. However, an indirect effect through the population variable can be discerned for these variables, and this has important policy implications. A key feature of this migration model is that it incorporates most of the determinant factors as rates of change over time. The study sheds light on the joint and indirect effects of migration and other endogenous variables and draws some important policy implications pertaining to growth.     

Unemployment,disequilibrium and the short run phillips curve: An econometric approach

The paper specifies a disequilibrium model for the aggregate labour market consisting of demand and supply functions for labour, an adjustment equation for wages as well as for prices, a transactions equation and, finally, an equation that relates measured unemployment to vacancies and to excess demand. The model has a more sophisticated treatment of dynamics than earlier disequilibrium models. The parameter estimates and the goodness-of-fit are satisfactory and the model's implications for the behaviour of several important variables are sensible. In particular, excess demand estimates computed in various ways are reasonable.     

Residential choice,hedonic prices,and the demand for urban air quality

This paper uses a two-step estimation procedure suggested by Sherwin Rosen to estimate structural demand and supply equations for urban air quality. In the first step, a hedonic price equation is estimated for residential property values for the Washington, D. C., SMSA for 1970. In the second step, a set of marginal hedonic prices is generated. These prices and the quantity of clean air (reciprocal of air pollution) are used as endogenous variables in a simultaneous equation model. Empirical results indicate a price elasticity of demand between ?1.2 and ?1.4 and a unitary income elasticity.     

Occupational change,employer change,internal migration,and earnings

"In this paper I use microdata from the [U.S.] Panel Study of Income Dynamics to measure the financial returns to intercounty and interstate migration for individuals in a temporal framework accounting for gains that accrue over time....To account for the indirect effects of migration on earnings, explanatory variables are created by interacting migration status with: (1) occupational change, (2) employer change and (3) changes in both occupation and employer. These interaction terms are then included in the earnings functions. Earnings are estimated for three years subsequent to the migration decision to account for the financial returns to migration accruing over time. Results indicate that, when estimating earnings, the use of a simple migration dummy variable will mask the indirect effects of migration on earnings."     

Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach

The identification of structural parameters in the linear instrumental variables (IV) model is typically achieved by imposing the prior identifying assumption that the error term in the structural equation of interest is orthogonal to the instruments. Since this exclusion restriction is fundamentally untestable, there are often legitimate doubts about the extent to which the exclusion restriction holds. In this paper I illustrate the effects of such prior uncertainty about the validity of the exclusion restriction on inferences based on linear IV models. Using a Bayesian approach, I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. I illustrate these points using several prominent recent empirical papers that use linear IV models. An accompanying electronic table allows users to readily explore the robustness of inferences to uncertainty about the exclusion restriction in their particular applications. Copyright © 2010 John Wiley & Sons, Ltd.     

Urban spatial structure,suburbanization and transportation in Barcelona

I investigate the effect of improvements to the transportation infrastructure on changes in location patterns of population in Barcelona, Spain between 1991 and 2006. At a census tract level, I verify and extend the finding of Baum-Snow (2007a) that transportation improvements cause suburbanization: (1) improvements to the highway and railroad systems foster population growth in suburban areas; (2) the transit system also affects the location of population inside the central business district (CBD). To estimate the causal relationship between the growth of population (density) and transportation improvements, I rely on an instrumental variables estimation that uses distances to the nearest Roman road, the nearest 19th century main road, and the nearest 19th century railroad network as instruments for the 1991–2001 changes in distance to the nearest highway ramp and the distance to the nearest railroad station.     

Classification and Discrimination Problems with Applications, Part IIa

Abstract  In Part I exact results for univariate (" p = 1") two-group ("k = 2") classification problems were derived assuming normality and equality of the variances. In Part IIa asymptotic results for multivariate (" p > I") two-group classification and discrimination problems are based on the corresponding assumptions of multivariate normality and equality of the covariance matrices. The results (4.6.5), (4.6.6) and (4.6.7) are believed to be new.
The asymptotic results in Section 4.6, together with results presented elsewhere in the literature, constitute the basis of various detailed proposals to deal with problems from actual statistical practice. Most of these proposals are modifications or specifications of existing ones. We shall pay some attention to (I) testing whether differences exist. But we are mainly interested in: (II) constructing a discriminant function, (III) assigning the individual under classification, and in (IV) constructing a confidence interval for "the" posterior probability that the individual under classification belongs to Population 2.
An important part in our theory is played by various techniques for selecting variables in discriminant analysis. The need for such techniques follows from Section 4.10. The consequences of building-in a selection technique are discussed in Section 4.12. One of our proposals motivates the theory presented in Chapter 3 and is mentioned here for that reason: employ a large part of the data, say 70%, in order to construct a discriminant function (via a selection of variables); by applying this function to the rest of the data, the exact univariate theory of Part I becomes of application. Part IIb will contain a chapter on applications.     

Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables

In this paper, I discuss three issues related to bias of OLS estimators in a general multivariate setting. First, I discuss the bias that arises from omitting relevant variables. I offer a geometric interpretation of such bias and derive sufficient conditions in terms of sign restrictions that allows us to determine the direction of bias. Second, I show that inclusion of some omitted variables will not necessarily reduce the magnitude of bias as long as some others remain omitted. Third, I show that inclusion of irrelevant variables in a model with omitted variables can also have an impact on the bias of OLS estimators. I use a running example of a simple wage regression to illustrate my arguments.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

Adaptive learning,endogenous uncertainty,and asymmetric dynamics

I present a simple model where forecasting confidence affects aggregate demand. It is shown that this model has similar stability properties, under statistical and evolutionary learning, as a model without a confidence affect. From this setup, I introduce “Expectational Business Cycles” where output fluctuates due to learning, heterogeneous forecasting models and random changes in the efficient forecasting model. Agents use one of two forecasting models to forecast future variables while heterogeneity is dictated via an evolutionary process. Increased uncertainty, due to a shock to the structure of the economy, may result in a sudden decrease in output. As agents learn the equilibrium, output slowly increases to its equilibrium value. Expectational business cycles tend to arrive faster, last longer and are more severe as agents possess less information.     

Industrial Productivity Growth Linkages Between OECD Countries, 1970–90

Technology ‘spillovers’ are increasingly being recognized as sources of productivity growth. International ‘convergence’ in productivity levels has also been noted in recent studies. This paper reports a study of international total factor productivity (TFP) growth for 11 industrial sectors in seven OECD countries. Spillover variables are defined based on interindusty and international invention input–output (I(IO)) weights. These variables are tested against import-weighted variables. The study concludes that I(IO)-weighted R&D ‘spill-ins’ are important determinants of TFP growth and that convergence is dependent on domestic RBD.     

Estimating predictions,prediction errors and their standard deviations using constructed variables

Increasing use has been made of predictive tests for assessing model adequacy, but it is sometimes difficult to generate predictions and their standard errors in dynamic or simultaneous equation models. Following earlier suggestions by Salkever and Fuller, this paper shows how the requisite information may be obtained by the use of specially constructed variables in a regression framework. The main use of the method will be in those situations where prediction information is not available as a standard option in econometric packages.     

Education,Occupation and Career Expectations: Determinants of the Gender Pay Gap for UK Graduates*

Despite anti‐discrimination policies, women are paid 20% less then men in the UK. A large proportion of this wage gap is usually left unexplained. In this paper, I investigate whether the unexplained component is due to mis‐specification. Using a sample of recent UK graduates, I examine the role of choice variables (subject of study and occupation) as well as career expectations and aspirations. The evidence indicates that women are more altruistic and less career‐oriented than men. Career break expectations, for example, explain 10% of the gender wage gap in the favoured model. By omitting attitudinal variables, most studies are likely to overestimate the unexplained component of the gender wage gap. Women with a more traditional view concerning childrearing are also found to have less intensive search behaviour. Since aspirations may reflect perceived discrimination or social pressure, current legislations are unlikely to reduce the gender wage gap.