11 resultados para happiness, utility functions, correlation analysis, personal income, economic models

em Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom


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This paper considers the characterisation and measurement of income-related health inequality using longitudinal data. The paper elucidates the nature of the Jones and Lopez Nicholas (2004) index of “health-related income mobility” and explains the negative values of the index that have been reported in all the empirical applications to date. The paper further questions the value of their index to health policymakers and proposes an alternative index of “income-related health mobility” that measures whether the pattern of health changes is biased in favour of those with initially high or low incomes. We illustrate our work by investigating mobility in the General Health Questionnaire measure of psychological well-being over the first nine waves of the British Household Panel Survey from 1991 to 1999.

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This paper elaborates the approach to the longitudinal analysis of income-related health inequalities first proposed in Allanson, Gerdtham and Petrie (2010). In particular, the paper establishes the normative basis of their mobility indices by embedding their decomposition of the change in the health concentration index within a broader analysis of the change in “health achievement” or wellbeing. The paper further shows that their decomposition procedure can also be used to analyse the change in a range of other commonly-used incomerelated health inequality measures, including the generalised concentration index and the relative inequality index. We illustrate our work by extending their investigation of mobility in the General Health Questionnaire measure of psychological well-being over the first nine waves of the British Household Panel Survey from 1991 to 1999.

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This paper develops an accounting framework to consider the effect of deaths on the longitudinal analysis of income-related health inequalities. Ignoring deaths or using inverse probability weights (IPWs) to re-weight the sample for mortality-related attrition can produce misleading results, since to do so would be to disregard the most extreme of all health outcomes. Incorporating deaths into the longitudinal analysis of income-related health inequalities provides a more complete picture in terms of the evaluation of health changes in respect to socioeconomic status. We illustrate our work by investigating health mobility in Quality Adjusted Life Years (QALYs) as measured by the SF6D from 1999 till 2004 using the British Household Panel Survey (BHPS). We show that for Scottish males explicitly accounting for the dead, rather than using IPWs to account for mortality-related attrition, changes the direction of the relationship between relative health changes and initial income position, while for other population groups it increases the strength of this relationship by up to 14 times. When deaths are explicitly incorporated into the analysis it is found that over this five year period for both Scotland and England & Wales the relative health changes were significantly regressive such that the poor experienced a larger share of the health losses relative to their initial share of health and a large amount of this was related to mortality.

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There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

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There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

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This paper has three contributions. First, it shows how field work within small firms in PR Chinese has provided new evidence which enables us to measure and calibrate Entrepreneurial Orientation (EO), as ‘spirit’, and Intangible Assets (IA), as ‘material’, for use in models of small firm growth. Second, it uses inter-item correlation analysis and both exploratory and confirmatory factor analysis to provide new measures of EO and IA, in index and in vector form, for use in econometric models of firm growth. Third, it estimates two new econometric models of small firm employment growth in PR China, under the null hypothesis of Gibrat’s Law, using our two new index-based and vector-based measures of EO and IA. Estimation is by OLS with adjustment for heteroscedasticity, and for sample selectivity. Broadly, it finds that EO attributes have had little significant impact on small firm growth, and indeed innovativeness and pro-activity paradoxically may even dampen growth. However, IA attributes have had a positive and significant impact on growth, with networking, and technological knowledge being of prime importance, and intellectual property and human capital being of lesser but still significant importance. In the light of these results, Gibrat’s Law is generalized, and Jovanovic’s learning theory is extended, to emphasise the importance of IA to growth. These findings cast new empirical light on the oft-quoted national slogan in PR China of “spirit and material”. So far as small firms are concerned, this paper suggests that their contribution to PR China’s remarkable economic growth is not so much attributable to the ‘spirit’ of enterprise (as suggested by propaganda) as, more prosaically, to the pursuit of the ‘material’.

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The breakdown of the Bretton Woods system and the adoption of generalized oating exchange rates ushered in a new era of exchange rate volatility and uncer- tainty. This increased volatility lead economists to search for economic models able to describe observed exchange rate behavior. In the present paper we propose more general STAR transition functions which encompass both threshold nonlinearity and asymmetric e¤ects. Our framework allows for a gradual adjustment from one regime to another, and considers threshold e¤ects by encompassing other existing models, such as TAR models. We apply our methodology to three di¤erent exchange rate data-sets, one for developing countries, and o¢ cial nominal exchange rates, the sec- ond emerging market economies using black market exchange rates and the third for OECD economies.

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The regional economic impact of biofuel production depends upon a number of interrelated factors: the specific biofuels feedstock and production technology employed; the sector’s embeddedness to the rest of the economy, through its demand for local resources; the extent to which new activity is created. These issues can be analysed using multisectoral economic models. Some studies have used (fixed price) Input-Output (IO) and Social Accounting Matrix (SAM) modelling frameworks, whilst a nascent Computable General Equilibrium (CGE) literature has also begun to examine the regional (and national) impact of biofuel development. This paper reviews, compares and evaluates these approaches for modelling the regional economic impacts of biofuels.

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The regional economic impact of biofuel production depends upon a number of interrelated factors: the specific biofuels feedstock and production technology employed; the sector’s embeddedness to the rest of the economy, through its demand for local resources; the extent to which new activity is created. These issues can be analysed using multisectoral economic models. Some studies have used (fixed price) Input-Output (IO) and Social Accounting Matrix (SAM) modelling frameworks, whilst a nascent Computable General Equilibrium (CGE) literature has also begun to examine the regional (and national) impact of biofuel development. This paper reviews, compares and evaluates these approaches for modelling the regional economic impacts of biofuels.

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The standard approach to the economics of climate change, which has its best known implementation in Nordhaus's DICE and RICE models (well described in Nordhaus's 2008 book, A Question of Balance) is not well equipped to deal with the possibility of catastrophe, since we are unable to evaluate a risk averse representative agent's expected utility when there is any signi cant probability of zero consumption. Whilst other authors attempt to develop new tools with which to address these problems, the simple solution proposed in this paper is to ask a question that the currently available tools of climate change economics are capable of answering. Rather than having agents optimally choosing a path (that differs from the recommendations of climate scientists) within models which cannot capture the essential features of the problem, I argue that economic models should be used to determine the savings and investment paths which implement climate targets that have been suggested in the physical science literature.

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The aim of the paper is to identify the added value from using general equilibrium techniques to consider the economy-wide impacts of increased efficiency in household energy use. We take as an illustrative case study the effect of a 5% improvement in household energy efficiency on the UK economy. This impact is measured through simulations that use models that have increasing degrees of endogeneity but are calibrated on a common data set. That is to say, we calculate rebound effects for models that progress from the most basic partial equilibrium approach to a fully specified general equilibrium treatment. The size of the rebound effect on total energy use depends upon: the elasticity of substitution of energy in household consumption; the energy intensity of the different elements of household consumption demand; and the impact of changes in income, economic activity and relative prices. A general equilibrium model is required to capture these final three impacts.