The Propensity to Consume Income from Different Sources and Implications for Saving: An Application to Norwegian Farm Households

Traditionally, farm households have relatively high saving and low marginal propensity to consume (MPC). In the last decades, this seems to have changed. To investigate these matters, a dynamic consumption model is estimated using a GMM-system estimator and a panel of 258 Norwegian farm households followed from 1976-1997. The main findings are that the MPC of farm income is lower than for off-farm income and that average MPC is low but increasing over time in these households. This may imply that some of the observed reduction in farm saving is explained by reduced need for precautionary saving.saving, consumption, dynamic panel model, Consumer/Household Economics,

The Role of Immigration in Sustaining the Social Security System: A Political Economy Approach

In the political debate people express the idea that immigrants are good because they can help pay for the old. The paper explores this idea in a dynamic political-economy setup. We characterize sub-game perfect Markov equilibria where immigration policy and pay-as-you-go (PAYG) social security system are jointly determined through a majority voting process. The main feature of the model is that immigrants are desirable for the sustainability of the social security system, because the political system is able to manipulate the ratio of old to young and thereby the coalition which supports future high social security benefits. We demonstrate that the older is the native born population the more likely is that the immigration policy is liberalized; which in turn has a positive effect on the sustainability of the social security system.

Migration-Regime Liberalization and Social Security: Political-Economy Effect

The pay-as-you-go social security system, which suffers from dwindling labor force, can benefit from immigrants with birth rates that exceed the native-born birth rates in the host country. Thus, a social security system provides effectively an incentive to liberalize migration policy. The paper examines a political- economy, inter-generational, mechanism through which the social security system influences voter attitudes in favor of more liberal immigration regime. We demonstrate that the Markov equilibrium, with social security, consists of more liberal migration policies, than the corresponding Markov equilibrium with no social security.

The Political-Economy Positive Role of the Social Security System in Sustaining Immigration (But Not Vice Versa)

In the political-economy debate people express the idea that immigrants are good because they can help pay for the old, thus help sustaining the social security system. In addition, the median voter whose income derives from wages will wish to keep out the immigrants who will depress his/her wage. Therefore the decisive voter will keep migrants out. The paper addresses these two accepted propositions. For this purpose we develop an OLG political economy model of social security and migration to explore how migration policy and a pay-as-you-go (PAYG) social security system are jointly determined. The sub-game perfect Markov , depends on the different patterns of fertility rates among native born and migrants. Our analysis demonstrates that a social security system may change the first proposition significantly because the median voter may opt to bring in migrants to help him/her during retirement. As for the second proposition we get a significantly nuanced version. Not always immigration helps sustain the social security.

Modeling Temporal Structure in Music for Emotion Prediction using Pairwise Comparisons

The temporal structure of music is essential for the cognitive processes related to the emotions expressed in music. However, such temporal information is often disregarded in typical Music Information Retrieval modeling tasks of predicting higher-level cognitive or semantic aspects of music such as emotions, genre, and similarity. This paper addresses the specific hypothesis whether temporal information is essential for predicting expressed emotions in music, as a prototypical example of a cognitive aspect of music. We propose to test this hypothesis using a novel processing pipeline: 1) Extracting audio features for each track resulting in a multivariate "feature time series". 2) Using generative models to represent these time series (acquiring a complete track representation). Specifically, we explore the Gaussian Mixture model, Vector Quantization, Autoregressive model, Markov and Hidden Markov models. 3) Utilizing the generative models in a discriminative setting by selecting the Probability Product Kernel as the natural kernel for all considered track representations. We evaluate the representations using a kernel based model specifically extended to support the robust two-alternative forced choice self-report paradigm, used for eliciting expressed emotions in music. The methods are evaluated using two data sets and show increased predictive performance using temporal information, thus supporting the overall hypothesis