Anders Björklund

Bio: Anders Björklund is an academic researcher from Lund University. The author has contributed to research in topics: Transplantation & Dopamine. The author has an hindex of 165, co-authored 769 publications receiving 84268 citations. Previous affiliations of Anders Björklund include University of Washington & Institute for the Study of Labor.

Failure of Neonatally Administered Reserpine To Affect the Hypothalamic Catecholamines in the Adult Rat

Abstract: Young rats were treated with reserpine for various periods during the first 15 days of life and killed at the age of 4–6 months. Compared with their litter-mate controls, no effect of the early reserpine treatment was observed in the distributional pattern and intensity of the catecholamine fluorescence in the histochemical preparations, and no obvious difference was found in the whole-brain catecholamine content between reserpine-treated and control animals. No effect of the early reserpine treatment on the incidence of vaginal cornification could be established. (Endocrinology 85: 788, 1969)

Family Background and Income During the Rise of the Welfare State: Brother Correlations in Income for Swedish Men Born 1932-1968

Abstract: The goal of this study is to examine trends in the importance of family background in determining adult income in Sweden. We investigate whether the association between family background and income in Sweden has changed for cohorts born 1932-1968. Our main finding is that the share of the variance in long-run income that is attributable to family background, the so-called brother correlation in income, has fallen by some 11 percentage points from 0.34 for the cohorts of brothers born in the early 1930s to below 0.23 for the cohorts born around 1950. From then on, the correlations have been more or less stable and are in line with earlier estimates. When we adjust income for the income return to years of schooling, we find constant brother correlations in income. The main effect is coming from changes in the distribution of schooling across cohorts. This finding is consistent with the hypothesis that education policies have been a key factor in equalizing life chances in Sweden.

Risks in using CFD-codes for analytical fire-based design in buildings with a focus on FDS:s handling of under-ventilated fires

Abstract: The use of CFD-models as an engineering tool for fire based analytical design of buildings has increased over the last few years. The reason for this is partly that the legislation allows for analytical dimensioning but also because the legislation demands a high degree of verification for the analytical dimensioning. The biggest reason is, however, that the computer power has increased and reached a point where it is applicable for engineering problems in terms of both time and money. The most common CFD-program in Sweden is FDS (Fire Dynamics Simulator) which is developed by NIST (National Institute of Standards and Technology), today’s version is the fifth large release. To ensure the correctness of the program it needs to be validated against experimental data. In this report, FDS is validated for under-ventilated fires. It is, however, not only how the program simulates the reality that is associated with risks but also how the program is handled by its users and their reviewers. If incorrect results are used and reviewed in an incorrect way that means that fire safety design in buildings may be incorrectly dimensioned which can have a negative impact on people’s health during evacuations in case of fire. The process to decide if incorrectness in how FDS simulates under-ventilated fires is made in four steps. First, under-ventilated fires are described, how and when they arise and what consequences they may have for people’s health during fires, which is made through a literature study. The next step is to validate how FDS works for under-ventilated fires, which is made by comparing FDS output data with experimental tests performed by the SP Technical and Research Institute of Sweden. The test is a part of a larger research project (BRANDFORSK) financed by the Swedish Fire Research Board. The next step is to decide how the users handle under-ventilated in FDS. This is done through a series of telephone interviews with fire and safety design consultants in Sweden. The fourth step is to investigate how the reviewer (rescue services) handles their role as a reviewer. The results show that the empirical expression concerning when a fire is allowed to burn or not together with the mixture fraction combustion makes the heat release rate and thereby the temperatures very sensitive for changes of oxygen level. Visibility and toxicity (carbon monoxide level), which is based on the soot and carbon monoxide yields, are much harder to apply and the FDS output for these parameters should be used very cautiously. The results also show that the fire and safety design consultants generally have little understanding about how FDS treats under-ventilated fires but that the basic use is handled in a good way. It is clear that although the rescue services in many cases can do a good review of an analytical dimensioning, they lack the knowledge and the resources for doing a good review of an analytical dimensioning with an under-ventilated fire involved. In total this means that incorrectness in how FDS simulates an under-ventilated fires do constitute a risk when it is used in analytical dimensioning of a buildings fire and safety design. The consequence can be that people in the building may be exposed to conditions dangerous (in a higher degree) to their health. Measures to reduce the risk are for example that the CFD course given at the Department of Fire and Safety Engineering and Systems Safety at Lund University contains more elements about how FDS handles under-ventilated fire and how the output is affected. This is however no guarantee for that the students/users actually learn more and there should also exist certified CFD/FDS able users. A suitable party for this is a trade organisation like BIV. It is also appropriate to improve the conditions for the rescue service in their reviewing role

Intergenerational Top Income Mobility in Sweden – Capitalist Dynasties in the Land of Equal Opportunity?

Abstract: This paper presents new evidence on intergenerational mobility in the top of the income and earnings distribution. Using a large dataset of matched father-son pairs in Sweden, we find that intergenerational transmission is very strong in the top, more so for income than for earnings. In the extreme top (top 0.1 percent) income transmission is remarkable with an IG elasticity above 0.9. We also study potential transmission mechanisms and find that sons’ IQ, non-cognitive skills and education are all unlikely channels in explaining this strong transmission. Within the top percentile, increases in fathers’ income are, if anything, negatively associated with these variables. Wealth, on the other hand, has a significantly positive association. Our results suggest that Sweden, known for having relatively high intergenerational mobility in general, is a society where transmission remains strong in the very top of the distribution and that wealth is the most likely channel.

Equality of Opportunity and the Distribution of Long-Run Income in Sweden

Abstract: Equality of opportunity is an ethical goal with almost universal appeal. The interpretation taken here is that a society has achieved equality of opportunity if it is the case that what individuals accomplish, with respect to some desirable objective, is determined wholly by their choices and personal effort, rather than by circumstances beyond their control. We use data for Swedish men born between 1955 and 1967 for whom we measure the distribution of long-run income, as well as several important background circumstances, such as parental education and income, family structure and own IQ before adulthood. We address the question: in Sweden, given its present constellation of social policies and institutions, to what extent is existing income inequality due to circumstances, as opposed to 'effort'? Our results suggest that several circumstances, importantly both parental income and own IQ, are important for long-run income inequality, but that variations in individual effort account for the most part of that inequality.

Econometric Analysis of Cross Section and Panel Data

Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Econometric Analysis of Panel Data

Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.