The Dynamics of UK and US Inflation Expectations

This paper investigates the relationship between short term and long term in ation expectations in the US and the UK with a focus on iflation pass through (i.e. how changes in short term expectations affect long term expectations). An econometric methodology is used which allows us to uncover the relationship between in ation pass through and various explanatory variables. We relate our empirical results to theoretical models of anchored, contained and unmoored inflation expectations. For neither country do we find anchored or unmoored inflation expectations. For the US, contained inflation expectations are found. For the UK, our ndings are not consistent with the specifi =c model of contained inflation expectations presented here, but are consistent with a more broad view of expectations being constrained by the existence of an inflation target.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Estimating Phillips Curves in Turbulent Times using the ECB’s Survey of Professional Forecasters

This paper uses forecasts from the European Central Bank's Survey of Professional Forecasters to investigate the relationship between inflation and inflation expectations in the euro area. We use theoretical structures based on the New Keynesian and Neoclassical Phillips curves to inform our empirical work. Given the relatively short data span of the Survey of Professional Forecasters and the need to control for many explanatory variables, we use dynamic model averaging in order to ensure a parsimonious econometric speci cation. We use both regression-based and VAR-based methods. We find no support for the backward looking behavior embedded in the Neo-classical Phillips curve. Much more support is found for the forward looking behavior of the New Keynesian Phillips curve, but most of this support is found after the beginning of the financial crisis.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Model Switching and Model Averaging in Time- Varying Parameter Regression Models

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

The Political Economy of Resource Rent Distribution

I model the link between political regime and level of diversification following a windfall of natural resource revenues. The explanatory variables I make use of are the political support functions embedded within each type of regime and the disparate levels of discretion, openness, transparency, and accountability of government. I show that a democratic government seeks to maximize the long-term consumption path of the representative consumer, in order to maximize its chances of re-election, while an authoritarian government, in the absence of any electoral mechanism of accountability, seeks to buy off and entrench a group of special interests loyal to the government and potent enough to ensure its short-term survival. Essentially the contrast in the approaches towards resource rent distribution comes down to a variation in political weights on aggregate welfare and rentierist special interests endogenized by distinct political support functions.

(Optimal) Spatial Aggregation in the Determinants of Industrial Location

Empirical studies on the determinants of industrial location typically use variables measured at the available administrative level (municipalities, counties, etc.). However, this amounts to assuming that the effects these determinants may have on the location process do not extent beyond the geographical limits of the selected site. We address the validity of this assumption by comparing results from standard count data models with those obtained by calculating the geographical scope of the spatially varying explanatory variables using a wide range of distances and alternative spatial autocorrelation measures. Our results reject the usual practice of using administrative records as covariates without making some kind of spatial correction. Keywords: industrial location, count data models, spatial statistics JEL classification: C25, C52, R11, R30

Cultural competency of health-care providers in a Swiss University Hospital: self-assessed cross-cultural skillfulness in a cross-sectional study.

BACKGROUND: As the diversity of the European population evolves, measuring providers' skillfulness in cross-cultural care and understanding what contextual factors may influence this is increasingly necessary. Given limited information about differences in cultural competency by provider role, we compared cross-cultural skillfulness between physicians and nurses working at a Swiss university hospital. METHODS: A survey on cross-cultural care was mailed in November 2010 to front-line providers in Lausanne, Switzerland. This questionnaire included some questions from the previously validated Cross-Cultural Care Survey. We compared physicians' and nurses' mean composite scores and proportion of "3-good/4-very good" responses, for nine perceived skillfulness items (4-point Likert-scale) using the validated tool. We used linear regression to examine how provider role (physician vs. nurse) was associated with composite skillfulness scores, adjusting for demographics (gender, non-French dominant language), workplace (time at institution, work-unit "sensitized" to cultural-care), reported cultural-competence training, and cross-cultural care problem-awareness. RESULTS: Of 885 questionnaires, 368 (41.2%) returned the survey: 124 (33.6%) physicians and 244 (66.4%) nurses, reflecting institutional distribution of providers. Physicians had better mean composite scores for perceived skillfulness than nurses (2.7 vs. 2.5, p < 0.005), and significantly higher proportion of "good/very good" responses for 4/9 items. After adjusting for explanatory variables, physicians remained more likely to have higher skillfulness (β = 0.13, p = 0.05). Among all, higher skillfulness was associated with perception/awareness of problems in the following areas: inadequate cross-cultural training (β = 0.14, p = 0.01) and lack of practical experience caring for diverse populations (β = 0.11, p = 0.04). In stratified analyses among physicians alone, having French as a dominant language (β = -0.34, p < 0.005) was negatively correlated with skillfulness. CONCLUSIONS: Overall, there is much room for cultural competency improvement among providers. These results support the need for cross-cultural skills training with an inter-professional focus on nurses, education that attunes provider awareness to the local issues in cross-cultural care, and increased diversity efforts in the work force, particularly among physicians.

History matters: relating paths and rates of landscape change to butterfly species decline

Western European landscapes have drastically changed since the 1950s, with agricultural intensifications and the spread of urban settlements considered the most important drivers of this land-use/land-cover change. Losses of habitat for fauna and flora have been a direct consequence of this development. In the present study, we relate butterfly occurrence to land-use/land-cover changes over five decades between 1951 and 2000. The study area covers the entire Swiss territory. The 10 explanatory variables originate from agricultural statistics and censuses. Both state as well as rate was used as explanatory variables. Species distribution data were obtained from natural history collections. We selected eight butterfly species: four species occur on wetlands and four occur on dry grasslands. We used cluster analysis to track land-use/land-cover changes and to group communes based on similar trajectories of change. Generalized linear models were applied to identify factors that were significantly correlated with the persistence or disappearance of butterfly species. Results showed that decreasing agricultural areas and densities of farms with more than 10 ha of cultivated land are significantly related with wetland species decline, and increasing densities of livestock seem to have favored disappearance of dry grassland species. Moreover, we show that species declines are not only dependent on land-use/land-cover states but also on the rates of change; that is, the higher the transformation rate from small to large farms, the higher the loss of dry grassland species. We suggest that more attention should be paid to the rates of landscape change as feasible drivers of species change and derive some management suggestions.

Quantiles of the Realized Stock-Bond Correlation

Abstract: We scrutinize the realized stock-bond correlation based upon high frequency returns. We use quantile regressions to pin down the systematic variation of the extreme tails over their economic determinants. The correlation dependence behaves differently when the correlation is large negative and large positive. The important explanatory variables at the extreme low quantile are the short rate, the yield spread, and the volatility index. At the extreme high quantile the bond market liquidity is also important. The empirical fi…ndings are only partially robust to using less precise measures of the stock-bond correlation. The results are not caused by the recent …financial crisis. Keywords: Extreme returns; Financial crisis; Realized stock-bond correlation; Quantile regressions; VIX. JEL Classifi…cations: C22; G01; G11; G12

R&D cooperation between Spanish firms and scientific partners: what is the role of tertiary education?

This paper explores the factors that determine firm’s R&D cooperation with different partners, paying special attention on the role of tertiary education (degree and PhDs level) in facilitating the connection between the firms and the to scientific bodies (technology centres, public research centres and universities). Here, we attempt to answer two questions. First, are innovative firms that carry out internal and external R&D activities more likely to cooperate on R&D projects with other partners? Second, do Spanish innovative firms with a high participation of researchers with degrees or PhDs tend to cooperate more with scientific partners? To answer both questions we apply a three-dimensional approach on a firm level Panel Data with a sample of 4.998 manufacturing and services Spanish firms. First, we run a complementary test between external R&D acquisition and skilled research workers and find that firms which carry out external R&D activities obtain a greater return on R&D cooperation when they have skilled workers in R&D, especially in high-tech manufactures and KIS services. Second, we carry out a 2-step tobit model to estimate, in the first stage, the determinants that explain whether Spanish innovative firms cooperate or not; and in the second stage the factors that affect the choice of partners. And third, we apply an ordered probit model to test the marginal effects of explanatory variables on the different partners. Here we contrast some of the most interesting empirical hypotheses of previous studies, and which emphasize the role of employees with degrees and PhDs in facilitating cooperative R&D between firms and scientific partners.

Prevalence of chronic conditions – Stroke

IPH has estimated and forecast clinical diagnosis rates of stroke among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke in the previous 12 months. Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke at any time in the past. Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Diabetes

IPH has estimated and forecast clinical diagnosis rates of diabetes among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed diabetes in the previous 12 months (annual clinical diagnosis).  Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. Note that an adjustment was made for diabetes medication use recorded in the SLÁN physical examination sub-group of 45+ year olds. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06 . The data describe the number of people who report that they have experienced doctor-diagnosed diabetes at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland.Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. Differences between IPH estimates and reference study estimates: The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.  

Prevalence of chronic conditions – Hypertension

IPH has estimated and forecast clinical diagnosis rates of hypertension among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed hypertension in the previous 12 months (annual clinical diagnosis). Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of people who report that they have experienced doctor/nurse-diagnosed hypertension at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past.   The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Coronary Heart Disease

IPH has estimated and forecast clinical diagnosis rates of CHD (heart attack and/or angina) among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007 . The data describe the number of people who report that they have experienced doctor-diagnosed heart attack and/or angina in the previous 12 months (annual clinical diagnosis). Data is available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06 . The data describe the number of people who report that they have experienced doctor-diagnosed heart attack and/or angina at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.