The genetical theory of social behaviour.

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

Revising the Cannabis Use Disorders Identification Test (CUDIT) by means of Item Response Theory.

Cannabis use among adolescents and young adults has become a major public health challenge. Several European countries are currently developing short screening instruments to identify 'problematic' forms of cannabis use in general population surveys. One such instrument is the Cannabis Use Disorders Identification Test (CUDIT), a 10-item questionnaire based on the Alcohol Use Disorders Identification Test. Previous research found that some CUDIT items did not perform well psychometrically. In the interests of improving the psychometric properties of the CUDIT, this study replaces the poorly performing items with new items that specifically address cannabis use. Analyses are based on a sub-sample of 558 recent cannabis users from a representative population sample of 5722 individuals (aged 13-32) who were surveyed in the 2007 Swiss Cannabis Monitoring Study. Four new items were added to the original CUDIT. Psychometric properties of all 14 items, as well as the dimensionality of the supplemented CUDIT were then examined using Item Response Theory. Results indicate the unidimensionality of CUDIT and an improvement in its psychometric performance when three original items (usual hours being stoned; injuries; guilt) are replaced by new ones (motives for using cannabis; missing out leisure time activities; difficulties at work/school). However, improvements were limited to cannabis users with a high problem score. For epidemiological purposes, any further revision of CUDIT should therefore include a greater number of 'easier' items.

Toward a theory of labor market institutions

Standard economic analysis holds that labor market rigidities are harmfulfor job creation and typically increase unemployment. But many orthodoxreforms of the labor market have proved difficult to implement because ofpolitical opposition. For these reasons it is important to explain why weobserve such regulations. In this paper I outline a theory of how they may arise and why they fit together. This theory is fully developed in aforthcoming book (Saint-Paul (2000)), to which the reader is referred forfurther details.

The dynamics of US inflation: Can monetary policy explain the changes?

We investigate the relationship between monetary policy and inflation dynamics in theUS using a medium scale structural model. The specification is estimated with Bayesiantechniques and fits the data reasonably well. Policy shocks account for a part of the declinein inflation volatility; they have been less effective in triggering inflation responses overtime and qualitatively account for the rise and fall in the level of inflation. A number ofstructural parameter variations contribute to these patterns.

Capital, wages and growth: Theory and evidence

Returns to scale to capital and the strength of capital externalities play a key role for the empirical predictions and policy implications of different growth theories. We show that both can be identified with individual wage data and implement our approach at the city-level using US Census data on individuals in 173 cities for 1970, 1980, and 1990. Estimation takes into account fixed effects, endogeneity of capital accumulation, and measurement error. We find no evidence for human or physical capital externalities and decreasing aggregate returns to capital. Returns to scale to physical and human capital are around 80 percent. We also find strong complementarities between human capital and labor and substantial total employment externalities.

Slow recoveries: A structural interpretation

An analysis of the performance of GDP, employment and otherlabor market variables following the troughs in postwar U.S. businesscycles points to much slower recoveries in the three most recentepisodes, but does not reveal any significant change over time in therelation between GDP and employment. This leads us to characterizethe last three episodes as slow recoveries, as opposed to jobless recoveries.We use the estimated New Keynesian model in Galí-Smets-Wouters (2011) to provide a structural interpretation for the slowerrecoveries since the early nineties.

The implicit theory of historical change in the work of Alan S. Milward

Alan S. Milward was an economic historian who developed an implicit theory ofhistorical change. His interpretation which was neither liberal nor Marxist positedthat social, political, and economic change, for it to be sustainable, had to be agradual process rather than one resulting from a sudden, cataclysmicrevolutionary event occurring in one sector of the economy or society. Benignchange depended much less on natural resource endowment or technologicaldevelopments than on the ability of state institutions to respond to changingpolitical demands from within each society. State bureaucracies were fundamentalto formulating those political demands and advising politicians of ways to meetthem. Since each society was different there was no single model of developmentto be adopted or which could be imposed successfully by one nation-state onothers, either through force or through foreign aid programs. Nor coulddevelopment be promoted simply by copying the model of a more successfuleconomy. Each nation-state had to find its own response to the political demandsarising from within its society. Integration occurred when a number of nation states shared similar political objectives which they could not meet individuallybut could meet collectively. It was not simply the result of their increasinginterdependence. It was how and whether nation-states responded to thesedomestic demands which determined the nature of historical change.

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for U.S. output growth and inflationusing a number of commonly used forecasting models that rely on a large number ofmacroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizationsout-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have theopposite effect on higher moments. We find that normality is rejected for most modelsin some dimension according to at least one of the tests we use. Interestingly, however,combinations of predictive densities appear to be correctly approximated by a normaldensity: the simple, equal average when predicting output growth and Bayesian modelaverage when predicting inflation.