We analyse the relationship between unemployment and self-assessed health using the European Community Household Panel (ECHP) for Finland over the period 1996–2001. Our results reveal that the event of becoming unemployed does not matter as such for self-assessed health. The health status of those that end up being unemployed is lower than that of the continually employed. Hence, persons who have poor health are being selected for the pool of the unemployed. This explains why, in a cross-section, unemployment is associated with poor selfassessed health. However, we are somewhat more likely to obtain the negative effects of unemployment on health when long-term unemployment is used as the measure of unemployment experience
Level and Determinants of Medical Expenditure and Out of Pocket Medical Expen...inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document is a thesis submitted by Yi Liu to the University of North Dakota in partial fulfillment of the requirements for a Master of Science degree. The thesis examines the relationship between health care expenditures and various health outcomes using data from 12 Midwestern U.S. states over an 11-year period from 1999-2009. Prior studies on this topic have found mixed results, which the author attributes to differences in data and methodological approaches. The thesis will analyze the effect of health care spending and other determinants like income, lifestyle factors, and demographics on several measures of health outcomes.
The document discusses how population aging is changing healthcare systems. It notes that the percentage of people over age 65 and over age 80 in Europe is projected to increase significantly by 2030 and 2060. Integrated care models that coordinate services across care settings are presented as an important approach to address the needs of older patients with multiple chronic conditions. The document also describes a community-based care program for senior citizens in Baselland, Switzerland that aims to implement an integrated care model through risk screening, comprehensive geriatric assessments, and care coordination.
This study examines how welfare policies across countries impact the time married men and women spend on paid work, unpaid housework, and childcare, and how these differences in time allocation relate to variations in self-reported health between genders. The study uses data from time use surveys in 5 countries to analyze time use patterns among married individuals and health outcomes. The goal is to help explain observed gender differences in health benefits of marriage across welfare regimes and inform policies to reduce health inequalities.
This document discusses the role of different types of evidence in informing public health policy and actions related to obesity. It makes three key points:
1. Public health aims to understand population-level causes of disease and take actions to reduce disease burden. Epidemiology provides essential evidence but considers different types of evidence.
2. Descriptive epidemiology provides difference-making evidence about risks and populations affected, which informs targets for public health actions. Mechanistic evidence from analytic epidemiology provides insights into causal pathways and how diseases work.
3. A causally-based approach to public health recognizes that different evidence serves different roles - difference-making evidence identifies what works for whom, while mechanistic evidence provides insights into potential
Background; Social Class has shown relation with admissions at Emergency Departments. To assess whether there is a relationship between the level of triage and the social class of patients who attend the emergency department and whether there are other variables that can modulate this association. Methods Observational study with 1000 patients was carried out between May and July 2018 in the Emergency Department of the University Hospital Arnau de Vilanova in Lleida. Sociodemographic variables such as age, gender, country of origin and marital status were analyzed. The triage level and the main explanatory variable was social class. Social class was calculated based on the CSO-SEE 2012 scale. Results 49.4% were male and the average age was 51.7 years. Most of the patients (66.6%) attended the emergency department under their own volition and the most common triage levels were level III or Emergency (45%). There is a significant relationship between age and triage level. The younger patients had a lower triage level (p <0.001). The percentage of patients with lower social class who attended the emergency department for minor reasons was 42% higher compared to the rest of the patients (RR = 1.42; 1.21-1.67 95% CI, p <0.001). Conclusions; Patients with a lower socioeconomic class go to the Emergency Department for less serious pathologies.
This document provides an introduction to epidemiology, including definitions of key terms, the history and scope of epidemiology, study designs, and methods of measuring disease frequency and distribution in populations. It defines epidemiology as the study of disease patterns in human populations and the application of this study to disease control. The summary discusses the origins of epidemiology in Hippocrates' work and its development through pioneers like John Graunt, William Farr, and John Snow. It also outlines common study designs like cross-sectional and longitudinal studies and how epidemiology is used to describe, analyze, and prevent disease.
The Link Between Health Care Expenditure and Life Expectancy: Turkey (1975-2015)inventionjournals
This document analyzes the link between health care expenditure and life expectancy in Turkey from 1975-2015. It finds that there are two long-term cointegrating relationships between health spending and life expectancy variables. Some variables, including GDP, health care expenditure, and life expectancy measures granger cause each other, indicating directional relationships. The results show health spending and life expectancy are stable over time and correlated in Turkey.
Level and Determinants of Medical Expenditure and Out of Pocket Medical Expen...inventionjournals
International Journal of Humanities and Social Science Invention (IJHSSI) is an international journal intended for professionals and researchers in all fields of Humanities and Social Science. IJHSSI publishes research articles and reviews within the whole field Humanities and Social Science, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document is a thesis submitted by Yi Liu to the University of North Dakota in partial fulfillment of the requirements for a Master of Science degree. The thesis examines the relationship between health care expenditures and various health outcomes using data from 12 Midwestern U.S. states over an 11-year period from 1999-2009. Prior studies on this topic have found mixed results, which the author attributes to differences in data and methodological approaches. The thesis will analyze the effect of health care spending and other determinants like income, lifestyle factors, and demographics on several measures of health outcomes.
The document discusses how population aging is changing healthcare systems. It notes that the percentage of people over age 65 and over age 80 in Europe is projected to increase significantly by 2030 and 2060. Integrated care models that coordinate services across care settings are presented as an important approach to address the needs of older patients with multiple chronic conditions. The document also describes a community-based care program for senior citizens in Baselland, Switzerland that aims to implement an integrated care model through risk screening, comprehensive geriatric assessments, and care coordination.
This study examines how welfare policies across countries impact the time married men and women spend on paid work, unpaid housework, and childcare, and how these differences in time allocation relate to variations in self-reported health between genders. The study uses data from time use surveys in 5 countries to analyze time use patterns among married individuals and health outcomes. The goal is to help explain observed gender differences in health benefits of marriage across welfare regimes and inform policies to reduce health inequalities.
This document discusses the role of different types of evidence in informing public health policy and actions related to obesity. It makes three key points:
1. Public health aims to understand population-level causes of disease and take actions to reduce disease burden. Epidemiology provides essential evidence but considers different types of evidence.
2. Descriptive epidemiology provides difference-making evidence about risks and populations affected, which informs targets for public health actions. Mechanistic evidence from analytic epidemiology provides insights into causal pathways and how diseases work.
3. A causally-based approach to public health recognizes that different evidence serves different roles - difference-making evidence identifies what works for whom, while mechanistic evidence provides insights into potential
Background; Social Class has shown relation with admissions at Emergency Departments. To assess whether there is a relationship between the level of triage and the social class of patients who attend the emergency department and whether there are other variables that can modulate this association. Methods Observational study with 1000 patients was carried out between May and July 2018 in the Emergency Department of the University Hospital Arnau de Vilanova in Lleida. Sociodemographic variables such as age, gender, country of origin and marital status were analyzed. The triage level and the main explanatory variable was social class. Social class was calculated based on the CSO-SEE 2012 scale. Results 49.4% were male and the average age was 51.7 years. Most of the patients (66.6%) attended the emergency department under their own volition and the most common triage levels were level III or Emergency (45%). There is a significant relationship between age and triage level. The younger patients had a lower triage level (p <0.001). The percentage of patients with lower social class who attended the emergency department for minor reasons was 42% higher compared to the rest of the patients (RR = 1.42; 1.21-1.67 95% CI, p <0.001). Conclusions; Patients with a lower socioeconomic class go to the Emergency Department for less serious pathologies.
This document provides an introduction to epidemiology, including definitions of key terms, the history and scope of epidemiology, study designs, and methods of measuring disease frequency and distribution in populations. It defines epidemiology as the study of disease patterns in human populations and the application of this study to disease control. The summary discusses the origins of epidemiology in Hippocrates' work and its development through pioneers like John Graunt, William Farr, and John Snow. It also outlines common study designs like cross-sectional and longitudinal studies and how epidemiology is used to describe, analyze, and prevent disease.
The Link Between Health Care Expenditure and Life Expectancy: Turkey (1975-2015)inventionjournals
This document analyzes the link between health care expenditure and life expectancy in Turkey from 1975-2015. It finds that there are two long-term cointegrating relationships between health spending and life expectancy variables. Some variables, including GDP, health care expenditure, and life expectancy measures granger cause each other, indicating directional relationships. The results show health spending and life expectancy are stable over time and correlated in Turkey.
CHD Secondary Prevention Clinics in Primary Care; a critical assessmentJosep Vidal-Alaball
There is a need for CHD secondary prevention in primary care. This need has been addressed providing specialized clinics run by nurses or GPs. Whether with this clinics we are meeting this need is a question to be answered.
This document summarizes a study on violence against staff at primary health clinics and specialist clinics in Israel. The study found high rates of reported verbal and physical violence against staff in the past year. Specifically, 75% of staff reported experiencing verbal violence at least once in the past year, while 36% reported physical or spatial violence. Additionally, 85% of staff reported witnessing some form of violence. The study aimed to examine the nature and frequency of violence, identify risk factors, and make recommendations to better prevent and cope with violence in healthcare settings.
This document summarizes research on obesity among night shift nurses. It finds that night shift work disrupts circadian rhythms and leads to unhealthy behaviors like poor eating and lack of exercise, which can increase obesity rates. A study is proposed to examine if night shift nurses have higher BMIs than day shift nurses and to see if shift work contributes to higher weight. The theoretical framework is based on social cognitive theory, which says personal, environmental, and behavioral factors influence each other. For nurses, night shift work creates stress and disrupted sleep that can lead to using food as an unhealthy coping mechanism and weight gain.
This document provides a literature review on economic factors that influence obesity rates. Several factors are examined, including food prices, technological changes reducing physical activity, television viewing, portion sizes, and reliance on automobiles. Empirical studies have found relationships between obesity and lower food prices, more sedentary lifestyles, television viewing over 3 hours per day, larger portion sizes outside the home, and greater car reliance. The author aims to analyze the significance of several economic factors on obesity rates in males and females across 55 countries using econometric models.
This document provides an overview of epidemiology, including its basic concepts, principles, scope, and measurement tools. Some key points:
- Epidemiology is the study of disease distribution and determinants in populations, and is used to prevent and control health problems. It describes disease patterns and identifies risk factors.
- Epidemiological principles are applied in various areas like clinical research, disease prevention, and health services evaluation. Measurement tools include rates, ratios, and proportions to quantify disease frequency and burden.
- The scope of epidemiology includes measuring mortality, morbidity, disability, births, risk factors, and assessing health needs in populations. Different study designs are used to investigate disease etiology and evaluate interventions.
Burnout among Health Workers: Case of the Military Hospital of Ouakam, Senegalinventionjournals
This study assessed burnout among workers at the Military Hospital of Ouakam in Senegal. A survey was conducted using the Maslach Burnout Inventory tool, involving 66 hospital employees. The results found that 68.2% of workers showed signs of burnout, with 46.9% experiencing mild burnout and 21.2% moderate burnout. Emotional exhaustion was observed in 30.3% of participants, depersonalization in 21.2%, and reduced personal achievement in 36.3%. Burnout was more common among older workers and paramedics. The high prevalence of burnout indicates the need for measures to improve workers' social and professional environments.
Diseases and economic performance evidence from panel data, is a journal article that accesses the co-integration (long-run) relationship and effect of some selected communicable diseases i.e. Dengue, HIV/AIDS and TB on GDP in the south-east Asia... by estimating their coefficient using Fully modified ordinary least square (FMOLS) and confirmed by Dynamic ordinary least square (DOLS).
Pattern of medical admissions in a tertiary health centre in abakaliki south ...Alexander Decker
This study analyzed 1,247 medical admissions to a tertiary hospital in Abakaliki, Nigeria over one year. The majority of patients (70%) were between ages 30-69. Infectious diseases accounted for 20.45% of admissions, while cardiovascular and neurological disorders each accounted for around 20%. Non-communicable diseases made up 57.02% of cases. There was a double burden of both communicable and non-communicable diseases. Males had a higher prevalence of neurological and liver diseases, while females had more infectious diseases such as HIV/AIDS. The study highlights the need for preventative health strategies and health planning to address this significant disease burden.
Vital statistics in India provide information on births, deaths, marriages and other demographic events. The collection and dissemination of vital statistics involves several organizations under different ministries. Key organizations include the Central Statistical Organization, National Sample Survey Organization, and Registrar General and Census Commissioner of India. Vital statistics data is used for administrative, legal and public health purposes like analyzing disease patterns and justifying health programs. Factors like population census, civil registration and sample registration systems contribute to India's vital statistics.
Social epidemiology in public health researchPoope รักในหลวง
This document discusses social epidemiology in public health research and the social determinants of health. It presents the Commission on Social Determinants of Health conceptual framework which shows how socioeconomic and political context, socioeconomic position, and structural determinants impact health inequities through intermediary determinants. The document also discusses how public health research integrates health and social epidemiology concepts to analyze risk factors related to public health problems. Finally, it presents the SOCIPID model for social epidemiology research and provides an example of how it was applied to research on coronary heart disease in women.
DayOne aims to create a world-leading hub for healthcare innovation in Basel, Switzerland focused on precision medicine and digital health. It brings together over 1500 professionals, entrepreneurs and researchers from industry and academia. The hub implements various activities to accelerate innovation including an acceleration program for startups, experts talks, and an annual conference. Its mission is to build on the strengths of the Basel region to become a top hub for healthcare innovation through collaboration across disciplines and industries with a focus on precision medicine.
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
The document discusses principles of epidemiology and causal inference. It provides examples of public health questions that hinge on causal relationships. It then discusses several key concepts in causal inference, including that causation cannot be directly observed and must be inferred, the importance of temporal relationships between cause and effect, and criteria for evaluating causal relationships such as strength of association, consistency, and biological plausibility. The document also discusses challenges in causal inference and the application of epidemiological evidence in legal settings.
Introduction: Migraine is a chronic disease evolving through recurrent attack; it constitutes a frequent reason of consultation in
neurology. It has a signifi cant impact that can affect all spheres of life. Thus, it is one of the most disabling primary headaches.
Objective: To evaluate the impact of migraine in population of Brazzaville
Introduction
Uses and aims of epidemiology
Qualification
Jobs included
List of skills
Role of epidemiologists
Specializations
Courses offered
Public health significance
The document provides lecture notes on epidemiology for environmental and occupational health students, covering topics such as the definition of epidemiology, the history of epidemiology, disease causation, levels of disease prevention, infectious disease epidemiology, descriptive epidemiology, and measurements of morbidity and mortality. The notes were developed by Yigzaw Kebede of the University of Gondar in Ethiopia in collaboration with various Ethiopian government ministries and international partners.
Acute Telogen Efluvium is associated with the presence of female androgenetic alopecia. Potential Therapeutic Implications - A retrospective study of 503 female patients
This study examines factors that influence sickness absenteeism and presenteeism using survey data from 725 Finnish union members in 2008. The study finds that presenteeism is much more sensitive to working time arrangements than absenteeism. Permanent full-time work, mismatch between desired and actual working hours, shift/period work, and overlong weekly working hours increase the likelihood of presenteeism. Regular overtime decreases absenteeism but increases presenteeism. The ability to take 3 days of paid sick leave without a doctor's note and easing of efficiency demands decrease presenteeism. Presenteeism is more common in the public sector and among those in shift/period work.
This paper explores the potential role of adverse working conditions in the determination of workers’ sickness absences. Our data contain detailed information on the prevalence of job disamenities at the workplace from a representative sample of Finnish workers. The results from reduced-form models reveal that workers facing adverse working conditions tend to have a greater number of sickness absences. In addition, reduced-form models clearly show that regional labour market conditions are an important determinant of sickness absences. Hence, sickness absences are more common in regions of low unemployment. Recursive models suggest that the prevalence of harms at the workplace is associated with job dissatisfaction and dissatisfaction with workers’ sickness absences.
Quantitative Analysis Template !Instructions When analyzing.docxamrit47
Quantitative Analysis Template !
Instructions: When analyzing a journal article, first focus on the title of the article and/or the abstract.
Determine the independent variables (IVs) or the dependent variables (DVs) from these sections. If the
IVs and DVs are what you are looking for, then go ahead and read the whole article and fill out the following
information below. The purpose of filling out this template is to organize the most relevant information in a
journal article.
Reference (APA style) !!!
Background of the problem {e.g., According to Jonson et al. (2008), low self-esteem is correlated to lower
academic performance and behavioral problems in young adolescents.} !!!!!
Rationale (Key phrase: …few studies…) !!!!
Purpose (Key phrase: to determine or to examine…) !!!!
Past Studies{i.e., facts that are relevant to the IV and DV with citations (APA style)} !!!!!
Participants !
Age group(s) !
Gender !
Ethnic group(s)
SES !!
Quantitative Methods (survey, causal-comp, experimental, single-subject, mixed)
Assessments !
IV:
Measure !
Example question !
Likert-scale !
Reliability !
DV:
Measure !
Example question !
Likert-scale !
Reliability !!
(If there are more IVs and DVs that you feel are relevant, then you may note them here) !!
Treatment (Intervention) !!!
Hypothesis or research question(s) (Focus on the IVs and DVs from your definitions section) !!!!
What was significant (results) in the study? Focus on the IVs and DVs that you have picked. You don’t have
to state all the IVs and DVs from the article. What reasons do the authors give for the significant data (key
phrase in the discussion section: may be, might be)? !!!!!!!
Implications (What is the study recommending to educators? How can educators apply what they have
learned to their children/students/clients?)
RESEARCH ARTICLE
Mental Health and the Life Span
Depression and Retirement in Late
Middle-Aged U.S. Workers
Jalpa A. Doshi, Liyi Cen, and Daniel Polsky
Objective. To determine whether late middle-aged U.S. workers with depression are
at an increased risk for retirement.
Data Source. Six biennial waves (1992–2002) of the Health and Retirement Study, a
nationally representative panel survey of noninstitutionalized 51–61-year-olds and their
spouses started in 1992.
Study Design. Workers aged 53–58 years in 1994 were followed every 2 years there-
after, through 2002. Depression was coded as lagged time-dependent variables mea-
suring active depression and severity of depression. The main outcome variable was a
transition to retirement which was measured using two distinct definitions to capture
different stages in the retirement process: (1) Retirement was defined as a transition out
of the labor force in the sample of all labor force participants (N 5 2,853); (2) In addition
a transition out of full time work was used as the retirement definition in the subset of
labor force participants who were full time workers (N 5 2,288).
Princip ...
The Effect of Working Hours on Health Care Expenditure in the United StatesNick Shepherd
This document discusses a study investigating the relationship between working hours and health care expenditure in the United States. It provides background on trends showing Americans working increasingly long hours without using vacation time. The study aims to quantify how working hours affects health and whether this correlates with higher health care costs. Literature is reviewed showing relationships between long work hours and poorer health outcomes like cardiovascular issues. Descriptive statistics are provided on data from the Current Population Survey used in the analysis.
CHD Secondary Prevention Clinics in Primary Care; a critical assessmentJosep Vidal-Alaball
There is a need for CHD secondary prevention in primary care. This need has been addressed providing specialized clinics run by nurses or GPs. Whether with this clinics we are meeting this need is a question to be answered.
This document summarizes a study on violence against staff at primary health clinics and specialist clinics in Israel. The study found high rates of reported verbal and physical violence against staff in the past year. Specifically, 75% of staff reported experiencing verbal violence at least once in the past year, while 36% reported physical or spatial violence. Additionally, 85% of staff reported witnessing some form of violence. The study aimed to examine the nature and frequency of violence, identify risk factors, and make recommendations to better prevent and cope with violence in healthcare settings.
This document summarizes research on obesity among night shift nurses. It finds that night shift work disrupts circadian rhythms and leads to unhealthy behaviors like poor eating and lack of exercise, which can increase obesity rates. A study is proposed to examine if night shift nurses have higher BMIs than day shift nurses and to see if shift work contributes to higher weight. The theoretical framework is based on social cognitive theory, which says personal, environmental, and behavioral factors influence each other. For nurses, night shift work creates stress and disrupted sleep that can lead to using food as an unhealthy coping mechanism and weight gain.
This document provides a literature review on economic factors that influence obesity rates. Several factors are examined, including food prices, technological changes reducing physical activity, television viewing, portion sizes, and reliance on automobiles. Empirical studies have found relationships between obesity and lower food prices, more sedentary lifestyles, television viewing over 3 hours per day, larger portion sizes outside the home, and greater car reliance. The author aims to analyze the significance of several economic factors on obesity rates in males and females across 55 countries using econometric models.
This document provides an overview of epidemiology, including its basic concepts, principles, scope, and measurement tools. Some key points:
- Epidemiology is the study of disease distribution and determinants in populations, and is used to prevent and control health problems. It describes disease patterns and identifies risk factors.
- Epidemiological principles are applied in various areas like clinical research, disease prevention, and health services evaluation. Measurement tools include rates, ratios, and proportions to quantify disease frequency and burden.
- The scope of epidemiology includes measuring mortality, morbidity, disability, births, risk factors, and assessing health needs in populations. Different study designs are used to investigate disease etiology and evaluate interventions.
Burnout among Health Workers: Case of the Military Hospital of Ouakam, Senegalinventionjournals
This study assessed burnout among workers at the Military Hospital of Ouakam in Senegal. A survey was conducted using the Maslach Burnout Inventory tool, involving 66 hospital employees. The results found that 68.2% of workers showed signs of burnout, with 46.9% experiencing mild burnout and 21.2% moderate burnout. Emotional exhaustion was observed in 30.3% of participants, depersonalization in 21.2%, and reduced personal achievement in 36.3%. Burnout was more common among older workers and paramedics. The high prevalence of burnout indicates the need for measures to improve workers' social and professional environments.
Diseases and economic performance evidence from panel data, is a journal article that accesses the co-integration (long-run) relationship and effect of some selected communicable diseases i.e. Dengue, HIV/AIDS and TB on GDP in the south-east Asia... by estimating their coefficient using Fully modified ordinary least square (FMOLS) and confirmed by Dynamic ordinary least square (DOLS).
Pattern of medical admissions in a tertiary health centre in abakaliki south ...Alexander Decker
This study analyzed 1,247 medical admissions to a tertiary hospital in Abakaliki, Nigeria over one year. The majority of patients (70%) were between ages 30-69. Infectious diseases accounted for 20.45% of admissions, while cardiovascular and neurological disorders each accounted for around 20%. Non-communicable diseases made up 57.02% of cases. There was a double burden of both communicable and non-communicable diseases. Males had a higher prevalence of neurological and liver diseases, while females had more infectious diseases such as HIV/AIDS. The study highlights the need for preventative health strategies and health planning to address this significant disease burden.
Vital statistics in India provide information on births, deaths, marriages and other demographic events. The collection and dissemination of vital statistics involves several organizations under different ministries. Key organizations include the Central Statistical Organization, National Sample Survey Organization, and Registrar General and Census Commissioner of India. Vital statistics data is used for administrative, legal and public health purposes like analyzing disease patterns and justifying health programs. Factors like population census, civil registration and sample registration systems contribute to India's vital statistics.
Social epidemiology in public health researchPoope รักในหลวง
This document discusses social epidemiology in public health research and the social determinants of health. It presents the Commission on Social Determinants of Health conceptual framework which shows how socioeconomic and political context, socioeconomic position, and structural determinants impact health inequities through intermediary determinants. The document also discusses how public health research integrates health and social epidemiology concepts to analyze risk factors related to public health problems. Finally, it presents the SOCIPID model for social epidemiology research and provides an example of how it was applied to research on coronary heart disease in women.
DayOne aims to create a world-leading hub for healthcare innovation in Basel, Switzerland focused on precision medicine and digital health. It brings together over 1500 professionals, entrepreneurs and researchers from industry and academia. The hub implements various activities to accelerate innovation including an acceleration program for startups, experts talks, and an annual conference. Its mission is to build on the strengths of the Basel region to become a top hub for healthcare innovation through collaboration across disciplines and industries with a focus on precision medicine.
The IOSR Journal of Pharmacy (IOSRPHR) is an open access online & offline peer reviewed international journal, which publishes innovative research papers, reviews, mini-reviews, short communications and notes dealing with Pharmaceutical Sciences( Pharmaceutical Technology, Pharmaceutics, Biopharmaceutics, Pharmacokinetics, Pharmaceutical/Medicinal Chemistry, Computational Chemistry and Molecular Drug Design, Pharmacognosy & Phytochemistry, Pharmacology, Pharmaceutical Analysis, Pharmacy Practice, Clinical and Hospital Pharmacy, Cell Biology, Genomics and Proteomics, Pharmacogenomics, Bioinformatics and Biotechnology of Pharmaceutical Interest........more details on Aim & Scope).
All manuscripts are subject to rapid peer review. Those of high quality (not previously published and not under consideration for publication in another journal) will be published without delay.
The document discusses principles of epidemiology and causal inference. It provides examples of public health questions that hinge on causal relationships. It then discusses several key concepts in causal inference, including that causation cannot be directly observed and must be inferred, the importance of temporal relationships between cause and effect, and criteria for evaluating causal relationships such as strength of association, consistency, and biological plausibility. The document also discusses challenges in causal inference and the application of epidemiological evidence in legal settings.
Introduction: Migraine is a chronic disease evolving through recurrent attack; it constitutes a frequent reason of consultation in
neurology. It has a signifi cant impact that can affect all spheres of life. Thus, it is one of the most disabling primary headaches.
Objective: To evaluate the impact of migraine in population of Brazzaville
Introduction
Uses and aims of epidemiology
Qualification
Jobs included
List of skills
Role of epidemiologists
Specializations
Courses offered
Public health significance
The document provides lecture notes on epidemiology for environmental and occupational health students, covering topics such as the definition of epidemiology, the history of epidemiology, disease causation, levels of disease prevention, infectious disease epidemiology, descriptive epidemiology, and measurements of morbidity and mortality. The notes were developed by Yigzaw Kebede of the University of Gondar in Ethiopia in collaboration with various Ethiopian government ministries and international partners.
Acute Telogen Efluvium is associated with the presence of female androgenetic alopecia. Potential Therapeutic Implications - A retrospective study of 503 female patients
This study examines factors that influence sickness absenteeism and presenteeism using survey data from 725 Finnish union members in 2008. The study finds that presenteeism is much more sensitive to working time arrangements than absenteeism. Permanent full-time work, mismatch between desired and actual working hours, shift/period work, and overlong weekly working hours increase the likelihood of presenteeism. Regular overtime decreases absenteeism but increases presenteeism. The ability to take 3 days of paid sick leave without a doctor's note and easing of efficiency demands decrease presenteeism. Presenteeism is more common in the public sector and among those in shift/period work.
This paper explores the potential role of adverse working conditions in the determination of workers’ sickness absences. Our data contain detailed information on the prevalence of job disamenities at the workplace from a representative sample of Finnish workers. The results from reduced-form models reveal that workers facing adverse working conditions tend to have a greater number of sickness absences. In addition, reduced-form models clearly show that regional labour market conditions are an important determinant of sickness absences. Hence, sickness absences are more common in regions of low unemployment. Recursive models suggest that the prevalence of harms at the workplace is associated with job dissatisfaction and dissatisfaction with workers’ sickness absences.
Quantitative Analysis Template !Instructions When analyzing.docxamrit47
Quantitative Analysis Template !
Instructions: When analyzing a journal article, first focus on the title of the article and/or the abstract.
Determine the independent variables (IVs) or the dependent variables (DVs) from these sections. If the
IVs and DVs are what you are looking for, then go ahead and read the whole article and fill out the following
information below. The purpose of filling out this template is to organize the most relevant information in a
journal article.
Reference (APA style) !!!
Background of the problem {e.g., According to Jonson et al. (2008), low self-esteem is correlated to lower
academic performance and behavioral problems in young adolescents.} !!!!!
Rationale (Key phrase: …few studies…) !!!!
Purpose (Key phrase: to determine or to examine…) !!!!
Past Studies{i.e., facts that are relevant to the IV and DV with citations (APA style)} !!!!!
Participants !
Age group(s) !
Gender !
Ethnic group(s)
SES !!
Quantitative Methods (survey, causal-comp, experimental, single-subject, mixed)
Assessments !
IV:
Measure !
Example question !
Likert-scale !
Reliability !
DV:
Measure !
Example question !
Likert-scale !
Reliability !!
(If there are more IVs and DVs that you feel are relevant, then you may note them here) !!
Treatment (Intervention) !!!
Hypothesis or research question(s) (Focus on the IVs and DVs from your definitions section) !!!!
What was significant (results) in the study? Focus on the IVs and DVs that you have picked. You don’t have
to state all the IVs and DVs from the article. What reasons do the authors give for the significant data (key
phrase in the discussion section: may be, might be)? !!!!!!!
Implications (What is the study recommending to educators? How can educators apply what they have
learned to their children/students/clients?)
RESEARCH ARTICLE
Mental Health and the Life Span
Depression and Retirement in Late
Middle-Aged U.S. Workers
Jalpa A. Doshi, Liyi Cen, and Daniel Polsky
Objective. To determine whether late middle-aged U.S. workers with depression are
at an increased risk for retirement.
Data Source. Six biennial waves (1992–2002) of the Health and Retirement Study, a
nationally representative panel survey of noninstitutionalized 51–61-year-olds and their
spouses started in 1992.
Study Design. Workers aged 53–58 years in 1994 were followed every 2 years there-
after, through 2002. Depression was coded as lagged time-dependent variables mea-
suring active depression and severity of depression. The main outcome variable was a
transition to retirement which was measured using two distinct definitions to capture
different stages in the retirement process: (1) Retirement was defined as a transition out
of the labor force in the sample of all labor force participants (N 5 2,853); (2) In addition
a transition out of full time work was used as the retirement definition in the subset of
labor force participants who were full time workers (N 5 2,288).
Princip ...
The Effect of Working Hours on Health Care Expenditure in the United StatesNick Shepherd
This document discusses a study investigating the relationship between working hours and health care expenditure in the United States. It provides background on trends showing Americans working increasingly long hours without using vacation time. The study aims to quantify how working hours affects health and whether this correlates with higher health care costs. Literature is reviewed showing relationships between long work hours and poorer health outcomes like cardiovascular issues. Descriptive statistics are provided on data from the Current Population Survey used in the analysis.
PUBH 380 Measures of Morbidity – Indirect Age AdjustmentHomew.docxwoodruffeloisa
PUBH 380: Measures of Morbidity – Indirect Age Adjustment
Homework 3 (35 points)
NAME:
Based on the information given in the following table, and knowing that there were 4,500 deaths in Population X and 25,000 deaths in Population Y in 2009, and that crude death rate of standard population is 50 deaths per 1,000 persons, calculate and interpret the following (including any comparisons between X and Y in your interpretation):
NOTE: When you do indirect adjustment, all comparisons are done with the referent (standard) population, NOT between “adjusted” populations.
1. Complete the table below (20 pts)
Age
Standard Death Rates per 1,000
Population X
Expected Deaths in Pop X
Population
Y
Expected Deaths in Pop Y
<1
18
6000
50000
1-4
3
7000
70000
5-14
2.5
7000
25000
15-24
1.5
8000
30000
25-34
1.5
10000
70000
35-44
3.5
10000
25000
45-54
10
25000
20000
55-64
15
15000
25000
65+
80
30000
20000
Total
XXX
118,000
335,000
2. Calculate the crude death for each population (7 points – 3 for each crude, 1 for interpretation)
3. Calculate the standardized mortality ratios (SMRs) for each population (interpretation should include what the SMRs mean) (3 points – 1 for each SMR, 1 for interpretation)
4. Age-adjusted death rates for populations X and Y, using the indirect adjustment method. Please explain why your results might have occurred in your interpretation. (3 points – 1 for each IAR, 1 for interpretation)
5. Rates are adjusted for age to: (Please choose one answer) (2 pt)
a) Remove the bias of dissimilar age group distribution between populations.
b) Minimize the health problems of a community as compared to standard.
c) Determine how well a community compares with acceptable national births, deaths, and disease rate standards.
d) Allow valid comparison between populations with similar age distributions but different race and sex makeup.
e) All of the above.
NM 208 – Nursing Informatics
Telehealth Assignment
PART 2 – 100 points
This will be a research paper at least 3 pages in length, in APA format, that includes:
· The information from part 1.
· An outline of your telehealth effort.
· How will you determine if the information presented was understood?
· What resources will you need? Consider technological resources, physical space, interpreters, etc.
· How will you make up for the fact that you will not be face to face with the client(s)?
.
Running head: HOMELESS POPULATION 1
HOMELESS POPULATION 2
Application of Telehealth in Homeless Population
Name:
Course: Nursing Informatic
University:
Date of Submission: January 19, 2020
Application of Telehealth in Homeless Population
In the advent of technology, telehealth has greatly ...
BioMed CentralPage 1 of 9(page number not for citation pChantellPantoja184
BioMed Central
Page 1 of 9
(page number not for citation purposes)
BMC Health Services Research
Open AccessResearch article
Prevalence and associated factors in burnout and psychological
morbidity among substance misuse professionals
Adenekan Oyefeso*1, Carmel Clancy2 and Roger Farmer3
Address: 1Division of Mental Health, Medical School, St George's, University of London, London SW17 0RE, UK, 2School of Health and Social
Sciences, Middlesex University, F Block, Holborn Union Building, Archway Campus, Highgate Hill, London N19 3UA, UK and 3South West
London and St George's Mental Health NHS Trust, Richmond Royal Hospital, Kew Foot Road, Surrey TW9 2TE, UK
Email: Adenekan Oyefeso* - [email protected]; Carmel Clancy - [email protected]; Roger Farmer - [email protected]
* Corresponding author
Abstract
Background: Studies of psychological stress among substance misuse professionals rarely
describe the nature of burnout and psychological morbidity. The main aim of this study was to
determine the extent, pattern and predictors of psychological morbidity and burnout among
substance misuse professionals.
Methods: This study was a cross-sectional mail survey of 194 clinical staff of substance misuse
services in the former South Thames region of England, using the General Health Questionnaire
(GHQ-12) the Maslach Burnout Inventory (MBI) as measures of psychological morbidity and
burnout, respectively.
Results: Rates of psychological morbidity (82%: 95% CI = 76–87) and burnout (high emotional
exhaustion – 33% [27–40]; high depersonalisation – 17% [12–23]; and diminished personal
accomplishment – 36% [29–43]) were relatively high in the study sample. High levels of alienation
and tension (job stressors) predicted emotional exhaustion and depersonalisation (burnout) but
not psychological morbidity. Diminished personal accomplishment was associated with higher
levels of psychological morbidity
Conclusion: In the sample of substance misuse professionals studied, rates of psychological
morbidity and burnout were high, suggesting a higher level of vulnerability than in other health
professionals. Furthermore, pathways to psychological morbidity and burnout are partially related.
Therefore, targeted response is required to manage stress, burnout and psychological morbidity
among substance misuse professionals. Such a response should be integral to workforce
development.
Background
Since the introduction of the United Kingdom Govern-
ment's Drug Strategy in 1998, substance misuse services
have expanded with increases in funding available from
central government as part of implementation of the drug
strategy [1]. The targets set in the strategy may have put
extra demands on substance misuse services with a likely
increase in job-related stress, burnout and associated psy-
chological morbidity.
Studies of stress and burnout in various occupational
groups and settings have been widely reported [2-4].
Published: 8 February 2008
BMC Health Servic ...
The document discusses social class, health, and health inequalities in the UK. It defines social class and examines ways it has been measured, such as the Registrar General's scale and NS-SEC classification. It also discusses the relationships between social class, poverty, health outcomes like life expectancy, and access to healthcare services. Studies show health inequalities exist and are widening between socioeconomic groups in the UK despite increasing average life expectancy.
Tutkimuksesssa tarkastellaan stressiä aiheuttavien elämänmuutosten vaikutuksia työmarkkinamenestykseen Suomessa. Tutkimus perustuu suomalaiseen kaksoisaineistoon, joka on yhdistetty kattavaan palkkatuloja ja työllisyyttä kuvaavaan rekisteriin. Työmarkkinamenestystä mitataan 20 vuoden seurantajakson aikana. Kaksoisaineiston avulla huomioidaan yhteiset perhetekijät sekä genetiikan vaikutus tulemiin. Tutkimus paljastaa kolme tulosta. Ensinnäkin stressiä aiheuttavat elämänmuutokset heikentävät merkittävästi työmarkkinamenestystä. Toiseksi miesten menestykseen työmarkkinoilla vaikuttavat enemmän taloudelliseen tilanteeseen ja työhön liittyvät stressitekijät, kuten ristiriitojen lisääntyminen töissä. Naiset reagoivat sitä vastoin vahvemmin perhepiirissä tapahtuviin muutoksiin, kuten perheenjäsenen kuolemaan tai sairastumiseen. Kolmanneksi stessiä aiheuttavien elämänmuutosten vaikutukset liukenevat ajan myötä.
A Study on Work Environment, Work-life Balance and Burnout among Health Worke...Yolanda Ivey
This document summarizes a study on the work environment, work-life balance, and burnout among health workers in Bangalore during the COVID-19 pandemic. The study investigated how workplace factors and family support impacted health workers' work-life balance based on a survey of health professionals in Bangalore from November to December 2020. The results showed that workplace settings had a significant impact on work-life balance. Unmarried women were found to balance work with less burnout, and health workers in higher positions received better support from hospital management. The introduction provides background on how health workers faced disrupted lives, physical exhaustion, fear of infection, and lack of protective equipment while responding to the pandemic.
This document discusses a proposed study to identify factors associated with mental stress among new immigrants in New Zealand. The study aims to examine what stressors are most common among different age groups of immigrants who have lived in New Zealand for less than 3 years. A questionnaire would be administered to immigrants accessing mental health services in Auckland. Responses would be analyzed by age group to determine the average ratings of stressors like difficulties finding work, low wages, housing pressures, education levels, and lack of local experience. This could help mental healthcare providers better understand differences in mental health needs between age groups of new immigrants.
This document discusses a proposed study to identify factors associated with mental stress among new immigrants in New Zealand. The study aims to examine what stressors are most common among different age groups of immigrants who have lived in New Zealand for less than 3 years. A questionnaire would be administered to immigrants accessing mental health services in Auckland. Responses would be analyzed by age group to determine the average ratings of stressors like difficulties finding work, low wages, housing pressures, education levels, and lack of local experience. This could help mental healthcare providers better understand differences in mental health needs between age groups of new immigrants.
Using Finnish data this paper examines to what extent the share of a cohort being unemployed, in health care employment and being out of the labour market after the graduation of the cohort have affected the long-run labour market outcomes. The dependent variables are formed by standardising the shares for all cohorts and years. This is done by dividing the cohort specific share with the average share for all nurses in each year. The long-term effects are significant for all three variables. According to the estimates the deep recession of the 1990s raised the long-run unemployment by more than 2 percentage points for the nurses graduating then.
Commentary _social_epidemiology__questionable answers and answerable questionsBsie
This document summarizes a paper that was presented at the Third North American Congress of Epidemiology in 2011. The paper discusses the changing field of social epidemiology, which examines relationships between health and social factors like socioeconomic status, gender, and social policies. While social epidemiology was once a fringe field, it is now a prominent area of epidemiology. However, some argue that social epidemiology often answers descriptive questions about associations rather than causal questions about interventions, and thus provides "policy-free evidence" that does not directly inform policies to reduce health inequalities. The paper examines studies that have tried to answer more causal questions through alternative study designs like natural experiments and randomized trials.
This report was produced by Peter Butterworth, Liana S. Leach and Kim M. Kiely of the Centre for Research on Ageing, Health and Wellbeing, The Australian National University under commission from Safe Work Australia.
Objectives: We examine the predictors of sickness presenteeism in comparison with sickness absenteeism. The paper focuses on the effects of working-time match and efficiency demands and differentiates the estimates by a respondent’s self-assessed health. Methods: We use survey data covering 884 Finnish trade union members in 2009. We estimate logit models. All models include control variables such as the sector of the economy and the type of contract. Results: Working-time match between desired and actual weekly working hours reduces both sickness absence and presenteeism in the whole sample that consists of workers with all health levels. The point estimates reveal that working-time match decreases the prevalence of sickness absence by 7% and presenteeism by 8%. However, the estimates that differentiate by a respondent’s health show that this pattern prevails only for those workers who have poor health. Hence, the point estimates for those who have poor health are much larger than the ones for the whole sample. Working-time match reduces the prevalence of sickness absence by 21% and presenteeism by 20% for those workers who have poor health. In contrast, working-time match has no influence whatsoever on the prevalence of work-related sickness for those who have good health. We also find that efficiency demands increase presenteeism in the whole sample. However, additional results reveal that this pattern prevails only for those workers who have good health. Conclusions: The effects of working-time match and efficiency demands on the prevalence of sickness absence and presenteeism are strongly conditional upon a worker’s self-assessed health level. Therefore, the worker’s initial health is an important attribute that has to be taken into account when one is designing appropriate policies to reduce sickness absence and presenteeism.
The document discusses a proposed quantitative study on the effects of a psychosocial intervention on depression among caregivers of elderly people with dementia. The study would collect depression scale scores from 1,100 caregiver participants before and after receiving the intervention. The mean scores would be analyzed to compare changes in depression levels between groups and visually display trends over time. While the research is a first attempt, positive results could encourage promoting psychosocial interventions for caregivers given their lack of side effects compared to medications.
Ireland has had one of the highest unemployment rates in the EU since joining in 1973. There is substantial evidence that unemployment is causally linked to poor mental health among the Irish, including increased risks of suicide, depression, and psychological distress. This study analyzed literature on the relationship between unemployment and mental health in Ireland. It recommends raising awareness of unemployment's effects, improving support services, and enhancing collaboration to address poverty and social exclusion.
Eduprof Expertmeeting 14-15 April 2011 Groningen.
Presentation on German Cohort Study on Work, Age and Health by Angela Rauch, Anita Tisch, Silke tophoven and Stefan Bender, Institut für Arbeitsmarkt- und Berufsforschung, Germany
Eduprof Expertmeeting 14-15 April 2011 Groningen
Workshop Applied Sports Sciences
Presentation by Angela Rauch, Anita Tisch, Silke Tophoven and Stefan Bender of the Institute für Arbeitsmarkt- und Berufsforschung, Germany
Similar to Unemployment and self-assessed health: Evidence from panel data (20)
Talous & Yhteiskunta -lehden numero 4/2019 sisältää artikkeleita ja haastattelun, jotka kertovat alueellista keskittymistä käsitelleistä tutkimuksista. Suomen seitsemän suurimman kaupunkiseudun väestö kasvaa nopeimmin, kun taas pienempien kaupunkien ja maaseudun väestöosuus supistuu. Muutos on kuitenkin verrattain hidasta, ja sille on myös vastavoimia.
Talous & Yhteiskunta -lehden numeron 3/2019 teemana on työ ja terveys. Artikkeleissa tarkastellaan Suomen terveydenhuoltojärjestelmän toimivuutta ja pohditaan mitä voitaisiin oppia Ruotsissa jo tehdyistä terveydenhuollon uudistuksista. Muissa artikkeleissa käsitellään terveyskäyttäytymisen ja työmarkkinamenestyksen yhteyttä, työttömien aktivointia, työikäisten eritasoisia terveyspalveluja, työaikajoustojen vaikutusta terveyteen sekä informaatioteknologian ja tekoälyn käyttöä mielenterveyspalvelujen tukena. Haastateltavana on THL:n tutkimusprofessori Unto Häkkinen. Hänen mielestään sote-uudistus on tehtävä, vaikka se vaatiikin vielä monen yksityiskohdan ratkaisemista.
Opiskelijavalinta ylioppilaskirjoitusten nykyarvosanojen perusteella ei ole täysin perusteltua, todetaan Aalto-ylipiston ja Palkansaajien tutkimuslaitoksen uudessa tutkimuksessa. Ylioppilaskirjoitusten arvosanoilla on pitkän ajan vaikutuksia. Hienojakoisempi arvosteluasteikko tekisi opiskelijavalinnasta nykyistä reilumman.
Esimerkkiperhelaskelmissa tarkastellaan seitsemää kotitaloutta. Laskelmat kuvaavat ansiotulojen, tulonsiirtojen sekä verojen ja veronluonteisten maksujen kehityksen vaikutusta perheiden ostovoimaan. Perheille lasketaan Tilastokeskuksen tietoihin perustuvat perhekohtaiset kulutuskorit, jotka mahdollistavat perhekohtaisten inflaatiovauhtien ja reaalitulokehitysten arvioinnin. Ensi vuonna eläkeläispariskunnan ostovoima kasvaa eniten ja työttömien vähiten. Esimerkkiperhelaskelmia on tehty Palkansaajien tutkimuslaitoksella vuodesta 2009 lähtien.
Palkansaajien tutkimuslaitos ennustaa Suomen talouskasvuksi tänä vuonna 1,3 prosenttia ja ensi vuonna 1,1 prosenttia. Kasvua hidastaa eniten yksityisen kulutuksen kasvun hidastuminen. Toisaalta vienti kasvaa tänä vuonna hieman ennakoitua nopeammin, neljä prosenttia, ja ensi vuonnakin vielä kaksi prosenttia. Tuotannollisten investointien kasvu jatkuu maltillisena, mutta rakentamisen vähentyminen kääntää yksityiset investoinnit kokonaisuutena pieneen laskuun ensi vuonna. Hallituksen vuoteen 2023 mennessä tavoittelemien 75 prosentin työllisyysasteen ja julkisen talouden tasapainon toteutumista on vaikea arvioida, koska nämä tavoitteet on määritelty rakenteellisina ja niiden eri arviointimenetelmät saattavat tuottaa hyvin erilaisia tuloksia.
Suomen palkkataso oli 2015 ylempää eurooppalaista keskitasoa. Suomen suhteellinen asema ei ole juurikaan muuttunut 2010-luvun alun tilanteesta. Hintatason huomioiminen kuitenkin heikentää asemaamme palkkavertailussa. Palkkaerot meillä olivat vertailumaiden pienimpiä ja pysyivät melko samalla tasolla koko tarkastelujakson 2007–2015 ajan. EU-maissa havaittiin erisuuntaista kehitystä palkkaeroissa. Suurin osa palkkojen kokonaisvaihteluista selittyi taustaryhmien sisäisillä palkkaeroilla.
Suomessa toteutettiin vuonna 2005 laaja eläkeuudistus, jossa vanhuuseläkkeen alaikärajaa laskettiin. Tutkimuksessa havaitaan, että ikärajan lasku aikaisti eläkkeelle jäämistä. Kun alaraja laskettiin 65:stä 63:een, myös yleinen eläköitymisikä laski. Taloudellisten kannustimien muutosten vaikutukset eläköitymiseen jäivät paljon heikommiksi alaikärajan muuttamiseen verrattuna. Eläköitymisikään voidaan siis vaikuttaa tehokkaasti ja vähäisin kustannuksin lakisääteistä eläkeikää muuttamalla.
Talous & Yhteiskunta -lehden numeron 2/2019 artikkelit ja haastattelu kertovat tutkimuksista, joita on tehty Suomen Akatemian strategisen tutkimuksen neuvoston hankkeessa "Osaavat työntekijät - menestyvät työmarkkinat". Keskeinen kysymys on, miten sopeudutaan teknologisen kehityksen mukanaan tuomaan työn murrokseen.
Tutkimuksessa tarkastellaan ammattirakenteiden polarisaatiota sekä sitä, että mihin supistuvissa ja rutiininomaisissa ammateissa olevat työntekijät päätyvät hyödyntämällä kokonaisaineistoa vuosille 1970-2014. Ammattirakenteiden polarisaatio on jatkunut Suomessa jo vuosikymmeniä. Ammattirakennemuutoksen kehityskulku on pääosin tapahtunut siten, että keskitason tuotanto- ja toimistotyöntekijät ovat nousseet urapolkuja pitkin asiantuntijatöihin. Viimeaikaista palveluammattien osuutta on puolestaan kasvattanut se, että nuoret siirtyvät työmarkkinoille palvelutöihin. Rutiininomaisia ja kognitiivisia taitoja vaativien ammattien työntekijöillä on kuitenkin suurempi todennäköisyys nousta korkeammille palkkaluokille rutiininomaista ja fyysistä työtä tekeviin työntekijöihin verrattuna. Rutiininomaista ja fyysistä työtä tekevät tippuvat puolestaan suuremmalla todennäköisyydellä matalapalkka-aloille, ja heidän ansiotason kehitys on myös heikompaa.
Palkansaajien tutkimuslaitos on alentanut Suomen talouskasvun ennustettaan kuluvalle vuodelle viimesyksyisestä 2,3 prosentista 1,4 prosenttiin. Kansainvälisen talouden näkymien epävarmuus hidastaa Suomen talouskasvua etenkin kuluvana vuonna. Jos pahimmat uhkakuvat jäävät toteutumatta, kasvu piristyy ensi vuonna hivenen 1,5 prosenttiin. Viime vuonna pysähtynyt viennin kasvu elpyy, ja myös yksityisen kulutuksen kasvu tukee talouskasvua. Suomi on sopeutunut ammattirakenteiden murrokseen yleisesti ottaen hyvin, mutta etenkin perusasteen koulutuksen varassa olevien varttuneiden työntekijöiden työllistämiseen voi olla vaikea löytää työkaluja.
The Labour Institute for Economic Research has lowered its forecast of Finland’s economic growth for the current year from last autumn’s 2.4 per cent to 1.4 per cent. Uncertainty in the international economic outlook will slow Finland’s economic growth, particularly this year. If the worst threats do not materialise, growth will pick up slightly next year to 1.5 per cent. Export growth, which came to a halt last year, will recover and growth in private consumption growth will also provide support to economic growth. In general, Finland has adjusted well to occupational restructuring, but it may be difficult to find means to employ older workers who only have basic education.
Palkansaajien tutkimuslaitos on alentanut Suomen talouskasvun ennustettaan kuluvalle vuodelle vii-mesyksyisestä 2,3 prosentista 1,4 prosenttiin. Kansainvälisen talouden näkymien epävarmuus hidastaa Suomen talouskasvua etenkin kuluvana vuonna. Jos pahimmat uhkakuvat jäävät toteutumatta, kasvu piristyy ensi vuonna hivenen 1,5 prosenttiin. Viime vuonna pysähtynyt viennin kasvu elpyy, ja myös yksityisen kulutuksen kasvu tukee talouskasvua. Suomi on sopeutunut ammattirakenteiden murrokseen yleisesti ottaen hyvin, mutta etenkin perusasteen koulutuksen varassa olevien varttuneiden työntekijöiden työllistämiseen voi olla vaikea löytää työkaluja.
Tämä PT Policy Brief tuo esiin havaintoja Suomen tuloerojen kehityksestä 1990-luvun puolivälin jälkeen. Tällä ajanjaksolla tuloerot ovat kasvaneet. Aluksi kasvu oli hyvin nopeaa, kunnes kehitys tasaantui finanssikriisin myötä. Tämä näkyy tarkasteltaessa kehitystä viiden vuoden ajalta lasketuissa keskituloissa. Taloudessa on tuloliikkuvuutta, ts. tulot vaihtelevat vuodesta toiseen. Havaitsemme, että liikkuvuus tuloportaikossa on vähentynyt. Samalla kun tuloerot ovat kääntyneet kasvuun, on tuloverotuksen progressiivisuus alentunut. Valtion tuloveron alennusten ohella tähän on erityisesti tulojakauman huipulla vaikuttanut pääomatulojen voimakas kasvu.
Julkisen budjetin sopeuttamistoimia toteutetaan usein etuuksien indeksileikkauksina tai tuloverojen korotuksina. Näillä toimenpiteillä on tulonjako- ja työllisyysvaikutuksia. Tämä PT Policy Brief esittää SISU-mallilla lasketut vaikutukset käytettävissä oleviin tuloihin tuloluokittain, jos valtion tuloveroasteikkoa korotettaisiin 0,4 prosenttiyksiköllä tai jos kansaneläkeindeksiä leikattaisiin. Molemmissa toimenpiteissä budjetti vahvistuisi 180 miljoonalla eurolla mutta tulonjakovaikutukset ovat huomattavan erilaiset. Oheisen kuvion mukaisesti indeksileikkaukset kohdistuvat voimakkaasti alempiin tulonsaajakymmenyksiin, kun taas tuloveron korotukset kohdistuvat ylempiin kymmenyksiin. Kun huomioidaan muutosten aiheuttamat työllisyysvaikutukset, kokonaiskuva muuttuu vain hieman.
Makeisvero otettiin käyttöön makeisille ja jäätelölle vuoden 2011 alusta. Virallinen perustelu oli kerätä verotuloja, mutta poliittisessa keskustelussa selvä tavoite oli ohjata kulutusta terveellisempään suuntaan. Makeisvero nosti selvästi makeisten kuluttajahintoja, mutta se ei vaikuttanut makeisten kysyntään. Toisaalta vuonna 2014 virvoitusjuomavero nousi sokerillisille juomille, mutta sokerittomat juomat jäivät alemmalle verotasolle. Tämä muutos alensi sokerillisten juomien kulutusta ja ohjasi kulutusta sokerittomiin juomiin. Onnistunut terveellisiin tuotteisiin ohjailu näyttääkin vaativan riittävän läheisen terveellisempien tuotteiden ryhmän olemassaolon.
Talous & Yhteiskunta-lehden "Suuren vaalinumeron" 1/2019 jutut käsittelevät aiheita, jotka voivat nousta esille kevään vaalikeskusteluissa. Pääpaino on ilmastonmuutoksessa: haastateltavana on Maailman ilmatieteen järjestön pääsihteeri Petteri Taalas, ja kahdessa eri artikkelissa pohditaan metsien hiilinielujen ja yhdyskuntarakenteen merkitystä pyrittäessä hillitsemään ilmaston lämpenemistä. Muut artikkelit käsittelevät tuloerojen kasvua, sotea, eläkkeiden riittävyyttä, maahanmuuttajien työllistymistä, EMUn uudistamista, eurooppalaista palkkavertailua ja kestävyysvajeen sopimattomuutta talouspolitiikan suunnitteluun.
Raportissa tehdään laskelmia korkeakouluopiskelijoille suunnatun opintotuen tulorajojen muutosten vaikutuksista. Opintotuen tulorajojen tavoite on, että suurituloisille opiskelijoille ei makseta opintotukea. Samalla nykyiset tulorajat kuitenkin estävät opiskelijoita tienaamasta niin paljon kuin he haluaisivat. Laskelmissa hyödynnetään simulaatiomallia, jonka avulla voidaan arvioida miten opiskelijoiden tulojakauma muuttuisi eri vaihtoehtoisissa opintotuen tulorajojen muutoksissa. Tulosten mukaan nykyisiä tulorajaoja voisi nostaa esimerkiksi 50 prosentilla, jolloin yhdeksän kuukauden ajan opintotukea nostavan opiskelijan vuosituloraja olisi 18 000 euroa nykyisen noin 12 000 euron sijaan. Laskelmien mukaan tällöin päästäisiin opintotuen nykyisten tulorajojen haitallisista tulovaikutuksista laajasti ottaen eroon, koska vain harva opiskelija tienaisi tätä tulorajaa enempää. Ottaen huomioon verotulot ja tulonsiirrot tämä vaihtoehto lisäisi julkisyhteisöjen nettotuloja arviolta 5,9 miljoonaa euroa vuodessa.
Tässä tutkimuksessa tutkitaan diskreettien valintajoukkojen vaikutusta palkansaajien työn tarjonnan reagoimiseen tuloveroihin. Artikkelin empiirisessä osiossa hyödynnetään opintotuen tulorajojen aiheuttamaa tuloveroissa tapahtuvaa äkillistä nousua, ja reformia, jossa tulorajoja nostettiin. Tulosten mukaan vuoden 2008 reformi, jossa tulorajaa nostettiin 9 opintotukikuukautta nostaneille 9000 eurosta 12000 euroon, aiheutti merkittäviä muutoksia opiskelijoiden tulojakaumassa. Tulojakauma siirtyi korkeammalle tasolle lähtien noin 2000 euron tuloista. Koska opiskelijoiden verojärjestelmässä ei tapahtunut muutoksia näin alhaisella tasolla vuoden 2008 reformissa, eivät työn taloustieteen normaalit mallit pysty selittämään tätä siirtymää. Artikkelissa esitetään empiirisiä lisätuloksia, teoreettisia argumentteja ja simulaatiomalli, jotka kaikki viittaavat siihen, että tuloksen pystyy selittämään diskreettien valintajoukkojen mallilla. Lisäksi artikkelissa esitetään, että verotuksen hyvinvointitappiot voivat olla suuremmat kuin empiirisesti estimoidut, jos valintajoukot ovat diskreettejä, mutta niiden ajatellaan olevan jatkuvia.
Talous & Yhteiskunta -lehden uuteen numeroon sisältyy artikkeleita veropohjasta ja -vajeesta, liikenteen veroista, naisista tulojakauman huipulla, työllisyyden kasvusta, mikrosimulaatiomalleista ja digitalisaatiosta. Lehdessä on myös Helsingin yliopiston professori Uskali Mäen haastattelu, jossa käsitellään tieteenfilosofista näkökulmaa taloustieteeseen.
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2. PALKANSAAJIEN TUTKIMUSLAITOS •TYÖPAPEREITA
LABOUR INSTITUTE FOR ECONOMIC RESEARCH • DISCUSSION PAPERS
* An earlier version of this paper was presented at the UNU-WIDER Conference on Advancing
Health Equality, Helsinki. We are grateful to the seminar audience for comments. In addition,
we are grateful to Eija Savaja for her help with the data. The usual disclaimer applies.
** Labour Institute for Economic Research. Address: Pitkänsillanranta 3A, 6. krs.
FIN-00530 Helsinki, FINLAND. Phone: +358-9-25357330. Fax: +358-9-25357332.
E-mail: petri.bockerman@labour.fi
*** Helsinki School of Economics and HECER. E-mail: pekka.ilmakunnas@hse.fi
Helsinki 2007
227
Unemployment
and self-assessed
health: Evidence
from panel data*
Petri Böckerman**
Pekka Ilmakunnas***
4. 1
Tiivistelmä
Tutkimuksessa tarkastellaan työttömyyden vaikutusta itse koettuun terveyteen Suomessa käyt-
täen hyväksi eurooppalaista elinolotutkimusta (ECHP), joka on saatavissa vuosilta 1996-2001.
Tulosten valossa työttömyyden ja terveyden välisessä yhteydessä ei ole niinkään kyse työttö-
mäksi joutumisen vaikutuksesta koettuun terveydentilaan vaan siitä, että heikko terveydentila on
yhteydessä työttömäksi joutumiseen. Työttömäksi joissain vaiheessa joutuvat kokevat tervey-
dentilansa keskimäärin heikommaksi kuin jatkuvasti työssä olevat. Kun ko. henkilöt todella jou-
tuvat työttömiksi, koettu terveys ei kuitenkaan oleellisesti muutu. Pitkäaikaistyöttömyydellä
näyttäisi sitä vastoin olevan jonkin verran itsenäistä vaikutusta koettuun terveyteen.
Abstract
We analyse the relationship between unemployment and self-assessed health using the European
Community Household Panel (ECHP) for Finland over the period 1996-2001. Our results reveal
that the event of becoming unemployed does not matter as such for self-assessed health. The
health status of those that end up being unemployed is lower than that of the continually
employed. Hence, persons who have poor health are being selected for the pool of the
unemployed. This explains why, in a cross-section, unemployment is associated with poor self-
assessed health. However, we are somewhat more likely to obtain the negative effects of
unemployment on health when long-term unemployment is used as the measure of
unemployment experience.
JEL Codes: C30, J28, J31
Keywords: health, subjective well-being, unemployment
5. 2
1. Introduction
The welfare effects of unemployment have been considered in many strands of research. Several
empirical studies ranging from research papers in medicine to those in the social sciences and
economics have shown that unemployment is associated with adverse health outcomes (e.g.
Björklund and Eriksson, 1998; Mathers and Schofield, 1998). Both cross-sectional and panel
data sets and both objective and subjective measures of health have been used in this literature.
Furthermore, the relationship between health and subsequent unemployment has been examined
(e.g. Arrow, 1996; Riphahn, 1999). There is some evidence that poor health is associated with
subsequent unemployment. On the other hand, the growing research on the determinants of
happiness in economics reports that the unemployed are very unhappy if they are evaluated by
standard subjective measures (e.g. Clark and Oswald, 1994; DiTella et al. 2001). There are also
some recent studies that have looked at unemployment and the subsequent evolution of the
subjective measures of well-being, most notably happiness and life satisfaction, in a panel data
setting (e.g. Lucas et al. 2004; Clark, 2007), but the available empirical evidence is still sparse
in this respect.
In this paper, our purpose is to analyse the evolution of self-assessed health in a panel data
setting before and after the event of unemployment occurs and also when unemployed persons
become employed again in order to disentangle the causal effect of unemployment on health. In
contrast to most of the earlier studies, we apply difference-in-differences models and matching
methods. In particular, the use of matching methods allows us to take into account the selection
for unemployment and the possibility of reverse causality from poor health to unemployment.
Previously, this selectivity issue has mainly been tackled by using plant closings as instruments
for unemployment (e.g. Kuhn et al. 2004; Browning et al. 2006).
Our most important empirical finding is that the event of unemployment does not matter as such
for self-assessed health in a panel data setting. The health status of those that end up being
unemployed is lower than that of the continually employed before their unemployment episodes
actually start. Hence, persons who have poor health are being selected for the pool of the
unemployed. This explains why, in a cross-section, unemployment is associated with poor self-
assessed health. However, we are somewhat more likely to obtain the negative effects of
unemployment on health when long-term unemployment is used as the measure of
unemployment experience.
We take advantage of the European Community Household Panel (ECHP) for Finland, which is
a representative household survey. The ECHP has not been much exploited in the literature on
the determination of self-assessed health (see, however, Hildebrand and van Kerm, 2004, and
6. 3
Cantanero and Pascual, 2005). The data span the period 1996-2001. As a result, it covers a
period long enough reliable results about the adverse effects of unemployment on health to be
obtained. The effect of unemployment on a subjective measure of health is interesting in the
current Finnish context, because the national unemployment rate surged very rapidly from 3 to
17 per cent in the early 1990s.1
Such an increase has been unprecedented among the industrial
countries. High unemployment may reduce the negative subjective health effects that are
associated with the personal experience of unemployment, because less stress and social stigma
may arise from being unemployed in times of high unemployment (e.g. Lindbeck et al. 1999;
Clark, 2003). This point is relevant, because the relatively high unemployment rate has persisted
in Finland since the great depression of the early 1990s. Importantly, persistent unemployment
is helpful when investigating the relationship between health and unemployment, because there
are a great number of unemployment episodes that start at any given point of time that allow us
to analyse the causal effect of unemployment on health in detail. In addition, long-term
unemployment rose a great deal in Finland during the 1990s. This is useful when investigating
the habituation effects on unemployment.
Measures of self-assessed health are widely used in empirical research. Despite this, there is still
some amount of scepticism regarding the use of self-reported data on health. In particular,
subjective measures of health that often originate from household surveys can be criticised on
the ground that they provide potentially biased information about persons’ health for the very
reason that they are self-reported. Accordingly, self-reported information on health cannot be as
reliable as that based on the objective measurement of health. However, various subjective
measures of health have been proven to have substantial value in predicting objective health
outcomes, including morbidity and mortality (e.g. Idler and Benyamini, 1997; Franks et al.
2003; Van Doorslear and Jones, 2003).
2
For that reason alone they are worth analysing.
The paper proceeds as follows. Section 2 presents a summary of the earlier Finnish studies on
the effect of unemployment on health. Section 3 provides a description of the data. Section 4
reports our results. Section 5 concludes the paper.
7. 4
2. Earlier evidence from Finland
There is a large international literature on the connections between unemployment and health.
Since we are using Finnish data in the empirical study, we illustrate different kinds of
approaches by using previous Finnish studies as examples.
Many of the earlier studies are based on the simple comparison of population averages. The
results concerning the connection between unemployment and health are, for the most part,
mixed (e.g. Martikainen and Valkonen, 1996; Lahelma et al. 1997). Based on these studies, it
seems that the health status of the unemployed slightly improved in Finland during the 1990s.
There are two potential explanations for this pattern. First, there are more healthy individuals in
the pool of unemployed persons during the times of high unemployment, because there may be
less selection in the process to become unemployed. For instance, unemployment incidence rose
a great deal across all education levels during the great depression of the early 1990s. Second, it
is possible that the social stigma from being unemployed has somewhat decreased during the
times of high unemployment and has thus mitigated the potential negative effects of
unemployment on health.
From the macroeconomic perspective, Jäntti et al. (2000) by exploiting municipal data over the
period 1987-1994 discover, that regional unemployment was not associated with an increase in
mortality among Finns during an episode of rapidly increasing average unemployment. One
possible way to interpret this result is to say that the welfare state managed to insulate many of
the negative effects of rising aggregate unemployment on health.
Closest to our paper are the earlier Finnish studies that follow the same individuals over time
through the use of panel data. In contrast to papers that rely solely on the comparison of
population averages, these studies provide somewhat more robust empirical evidence for the
view that unemployment leads to deterioration in measures of health (Lahelma, 1989; Leino-
Arjas et al. 1999; Nyman, 2002). In contemporaneous work, Martikainen et al. (2007) discover
that workplace downsizing and workplace closures increase mortality among the affected
workers, but the effects are modest in the context of high unemployment or rapid downsizing.
Still, the underlying causal relationship between unemployment and health remains largely
unsolved and has not been examined in the same way as in some papers in the international
literature (e.g. Kuhn et al. 2004; Browning et al. 2006), where plant closings have been
exploited as exogenous shocks that cause unemployment. Furthermore, some of the earlier
studies (e.g. Leino-Arjas et al. 1999) have been based on very restricted samples covering
special groups of workers and this makes it rather difficult to generalize the results obtained.
8. 5
Related to our research are also earlier empirical findings (Böckerman and Ilmakunnas, 2006,
using the World Values Surveys 1991, 1996 and 2000; Ervasti and Venetoklis, 2006, using the
European Social Survey 2002/2003) according to which experiencing unemployment does not
have a significant negative effect on the level of happiness (conditional on income) in Finland.
However, in these studies the data have been separate cross-sections, making it hard to isolate
the causal influences.
3. Data
Our paper takes advantage of the European Community Household Panel (ECHP) for Finland
over the period 1996-2001. The ECHP is based on a standardised questionnaire that involves
annual interviews of a representative panel of households and individuals in each European
Union country (e.g. Peracchi, 2002). Co-ordination of the national surveys is conducted by
Eurostat. The fact that the ECHP is representative over population is an important advantage
with respect to some earlier studies on the relationship between unemployment and health that
have used panel data sources. The ECHP is composed of a separate personal file and a separate
household file that can be linked with each other. In this paper, we use data from the personal
file, because it is the file that contains information on self-assessed health.
The ECHP’s questions include various topics such as income, health, education, housing, living
conditions, demographics and employment characteristics, among other things. Finland was
included in the ECHP for the first time in 1996 after she joined the European Union. The
European Union stopped gathering the ECHP in 2001, which means that Finland is included in
six waves of the data.3 The ECHP data allow us to record the health status of individuals before
their unemployment episodes actually start. This constitutes an important advantage over cross-
section data sources that have been more frequently used in research to compare population
averages, because we are in a better position to analyse the underlying causal effect of
unemployment on health. In this paper, we focus on transitions between work and
unemployment or vice versa. Hence, we exclude persons who are out of the labour force, like
retirees and students.
One’s self-assessed health status is an answer to the question: ”How is your health in general?”.
This question aims to summarise an individual’s general state of health at the moment of
interview. Self-assessed health is measured on an ordinal 5-point Likert scale with alternatives 5
(‘very good’), 4 (‘good’), 3 (‘fair’), 2 (‘bad’) or 1 (‘very bad’). Hence, a higher value on this
scale means that a person feels currently healthier.4 A similar question on self-assessed health
9. 6
appears in many other well-known household surveys such as the British Household Panel
Survey (BHPS) and the German Socio-Economic Panel (GSOEP). There are two recent papers
that have analysed the determination of self-assessed health by using the ECHP for various
countries. Hildebrand and Van Kerm (2004) focus on the connection between income inequality
and self-assessed health by using the ECHP for 11 countries. Cantarero and Pascual (2005)
investigate the relationship between socio-economic status and health by using the ECHP for
Spain. To our knowledge, the effect of unemployment on self-assessed health has not been
examined previously through the use of the ECHP.
We study persons that are unemployed at least once over the period 1996-2001. The reference
group consists of those that are continually at work. This means that unemployed persons are
compared with persons with a strong attachment to the labour market. The ECHP does not
incorporate direct information about the unemployment duration for the persons interviewed.
However, the data record monthly activity statuses (unemployed being one possible alternative)
for each person for the whole year before the interview. In addition, the data contain
information on the month in which the interview took place in each wave. This piece of
information is important, because the main month of interview in the ECHP for Finland has
changed from the beginning of the year in the first waves towards the end of the year in the last
waves. By combining information on the monthly activity statuses and the month of interview,
it is possible to construct a measure for each person’s unemployment duration in months at the
time of the interview. Following the conventions of the Ministry of Labour, we define the term
‘long-term unemployed’ to include those persons that have been unemployed continuously at
least for six months. In this way, we avoid problem of “top-coding” in the unemployment
duration mentioned, in the context of the ECHP, by Clark (2007).
5
4. Findings
4.1. Descriptive evidence
Figure 1 documents the evolution of the average level of self-assessed health in the years 1996-
2001. The health status of those who are employed has slightly deteriorated over the period. The
overall change is not large by any reasonable standards. More interestingly, the population
averages reveal that the health status of the unemployed is clearly lower than that of the
employed and it also deteriorated somewhat during the 1990s. Furthermore, the health status of
the long-term unemployed is lower than that of all unemployed.
=== FIGURE 1 HERE ===
10. 7
Table 1 reports a cross-tabulation of the self-assessed health and unemployment status. This
simple characterization of the data provides some evidence that the health status of those who
are currently unemployed is lower than that of the employed. In particular, long-term
unemployment seems to damage self-assessed health. However, it is important to keep in mind
that this kind of purely descriptive analysis that exploits solely the cross-sectional variation in
the data is not able to reveal anything about the underlying causal relationship between
unemployment and health.
=== TABLE 1 HERE ===
To shed light on the causal relationship, we need to take advantage of the panel dimension of
the ECHP. Accordingly, it is useful to illustrate the changes in health around the beginning of
unemployment episodes. Figure 2 shows a Galton squeeze diagram (see Campbell and Kenny,
1999) of the development of self-assessed health before and after becoming unemployed. The
starting points of the lines on the left-hand side of the figure show the initial levels of health
while the individuals were still working. The end-points of the lines on the right-hand side of
the figure show the average level of health after becoming unemployed. The figure summarizes
changes in health using all two-year pairs in the panel. For example, those who had self-
assessed health equal to 1 while still working had, on average, level 1.5 after becoming
unemployed. The pattern of the lines shows that there is a regression towards the mean: Those
with poor or good health tend to converge towards the average. Clearly, we cannot say that on
average becoming unemployed leads to a fall in health.6
Figure 3 shows the same kind of
diagram drawn using data from all two-year periods where the individuals were employed in
both periods. We can see that even in this case there is a regression towards the mean. The main
difference between Figures 2 and 3 is that in Figure 2 those who have low self-assessed health
in the first period do not converge as much to the average as those with low health in Figure 3.
That is, those with low health while employed tend to stay at a relatively low health level when
they become unemployed. However, in general, we cannot say that those becoming unemployed
converge to a different mean health level than those in continuous employment.
== FIGURES 2 AND 3 HERE ===
The relationship between health and unemployment can also be evaluated in another way by
looking at changes in the subjective perception of health status when a person switches from
unemployment to employment. Figure 4 shows the Galton squeeze diagram for those who are
unemployed in the first period, but employed in the second period. The pattern in Figure 4 is
similar to the previous figures, except for the line starting from 1 which is, however, based on
only two observations.7
11. 8
=== FIGURE 4 HERE ===
One problem clearly revealed by Figures 2-4 is that the regression towards the mean is partly
driven by the fact that health cannot improve beyond level 5 and hence for those at level 5, the
level in the next period is likely to be on average below 5. Similarly, health cannot fall below
level 1 and hence for those at level 1, the level in the next period is likely to be on average
above 1. It is therefore likely that controlling for the initial level of health is necessary when one
is studying changes in the health scores.8
4.2. Difference-in-differences estimates
To analyse the relationship between unemployment and health more closely, we estimate
difference-in-differences models in which an individual’s self-assessed health is explained with
a dummy variable for the ”unemployment target group” that consists of persons that become
unemployed at least once during the period 1996-2001, a dummy variable for those currently
unemployed after a period of employment (the dummy is equal to one in all the years of
unemployment after an employment spell), a dummy for the “employment target group” that
consists of those who become employed at least once, a dummy for those employed after a
period of unemployment (the dummy is equal to one in all the years of employment after an
unemployment spell), a dummy for those who are unemployed for the whole data period, and
year dummies to capture the effect of business cycle fluctuations. In some of the models we also
include individual-level control variables X, age and its square, gender and the level of
education in three categories, which capture the ‘usual suspects’ that should have a bearing on
the self-assessed level of health,9
and the health status in the previous period. This analysis of
changes in health status assumes that self-assessed health is measured on a cardinal scale, and
not on an ordinal scale, and that both experiencing unemployment and becoming employed after
a period of unemployment are exogenous events. The estimated model is
Healthit = α + β(becomes unemployed at least once)i + γ(unemployed after employment)it +
φ(becomes employed at least once)i + µ(employed after unemployment)it + η(always
unemployed)i + Xitθ + Σtτt(year t) + ρhealthi,t-1 + εit (1)
The average health level for those in continuous employment is α, for those who become
unemployed at some stage but are currently employed α + β, for those who become
unemployed α + β + γ, for those who become employed at some stage but are currently
unemployed α + φ, for those who become employed α + φ + µ, and for those who are
unemployed for the whole period α + η. It is possible that some individuals become
12. 9
unemployed in some period and employed in some other period (or vice versa), so that they
belong to both “target groups”. For them, the “basic” level of health is α + β + φ and it is
changed by γ (µ) when they become unemployed (employed). The coefficients of the indicator
variables from the OLS estimation of model (1) are reported in the first two columns of Table 2.
=== TABLE 2 HERE ===
We first consider the results without controls and without lagged health as an explanatory
variable. The results in Column 1 reveal that the unemployed tend to have lower health than
those who are continuously employed (the reference group). Those who are always unemployed
in the data period have clearly lower health (the indicator is significant at the 1% level), but also
those who become unemployed at some stage have a lower self-assessed health level. However,
those who are unemployed but become employed again at some stage have a somewhat higher
health level than the reference group. On the other hand, when those working become
unemployed, their self-assessed health status does not deteriorate and when those who are
unemployed find a job, their health status does not improve.10
Taken together, our results show
that unemployment as such does not seem to worsen the level of self-assessed health. It is more
the case that the persons who experience poor health are being selected for the pool of
unemployed persons and those who manage to escape unemployment tend to have better health
in the first place. When we include the observable characteristics of the individuals (gender, age
and its square, and educational level), the coefficients of the indicators for the employment
status become lower in absolute value, and the indicator for those who at some stage become
employed is no longer significant.
There may be unobserved attributes of the individuals that affect both the level of health
experienced and the probability of being unemployed. To account for this, we estimate the
model with fixed effects using the within transformation. In this case, the time-invariant group
indicators are left out (and the gender dummy is excluded from the controls). The estimated
model is
Healthit = αi + γ(unemployed after employment)it + µ(employed after unemployment)it + Xitθ +
Σtτt(year t) + ρhealthi,t-1 + εit (2)
The results with and without control variables and without lagged health are shown in Columns
3 and 4 of Table 2. Again, the indicators for being unemployed after employment and for being
employed after unemployment are not statistically significant. Hence, there is no clear impact of
unemployment on health.
13. 10
The above results are based on treatment of the health scores as cardinal variables.11
It is likely,
however, that the respondents do not treat health level 3, for example, as three times as good as
level 1. We therefore estimated the difference-in-differences model (1) also using ordered logit.
In this case we assume that Health is a continuous latent variable that is observed as a discrete
ordinal variable. The results are shown in Columns 5 and 6 of Table 2. The results regarding the
signs and significance of the coefficients are quite similar to those obtained with the OLS
estimation, although the magnitudes of the coefficients are, of course, not comparable.
To include fixed effects in the ordered logit estimation, we follow the suggestion of Ferrer-i-
Carbonell and Frijters (2004). They show that an ordered logit model with fixed effects can be
estimated as a fixed effect logit (conditional logit) model, where the ordered data are collapsed
to binary data with individual-specific thresholds. In our case, the recording of observations to
“high” and “low” health is individual-specific, based on the individuals’ average health scores
in the panel. In this case, only individuals with changes in health status over time can be
included. Columns 7 and 8 of Table 2 show the estimation results. Again, the labour market
status indicators that are time-invariant have been left out. The indicators for becoming
unemployed or becoming employed are clearly not significant.12
As another way of taking fixed
effects into account, we used Chamberlain’s random effect estimation in an ordered probit
model. The individual means of the control variables were included as additional explanatory
variables to proxy the fixed effects and the model was estimated with random effect ordered
probit. The estimates are shown in Column 9 of Table 2. The results are fairly close to those
obtained with ordered logit (column 6). Those who are always unemployed in the data period or
become unemployed at some stage have poorer health.
The descriptive analysis of the data suggested that it may be worthwhile to include lagged
health status as an explanatory variable. In addition, past health may have an impact on
becoming unemployed, which can be controlled by including the lagged health variable in the
regression. Table 3 shows the estimation results for various models with a lagged dependent
variable. Columns 1 and 2 show the OLS estimates where it is again assumed that health is a
cardinal measure. The lagged health variable has a significant positive coefficient.
=== TABLE 3 HERE ===
Adding lagged health to the model reduces the significance of the indicators for the groups
“becomes unemployed at least once” and “becomes employed at least once” and “always
unemployed”. This is what one would expect, since if the individuals that experience
unemployment have poor health in the first place, it should be picked up by the lagged variable.
The lagged health variable has a positive coefficient of 0.588. Therefore, deducting lagged
health from both sides, changes in the health scores are negatively related to previous health.
14. 11
This is exactly what regression towards the mean implies: those with a high initial health level
are likely to experience a fall in health and those with a low initial level a gain in health.13
Inclusion of control variables again reduces the absolute values of the coefficients of the
indicator variables, but does not change our conclusions.
Including fixed effects in the model with lagged health would lead to inconsistent estimates. We
therefore use the Anderson-Hsiao estimator, where the data are first differenced and the lagged
difference of the health score is instrumented with the lagged level of health two periods
previously. Again the indicators for changing the labour market status are non-significant. The
negative sign of the lagged differenced health variable in these fixed effects results is consistent
with regression towards the mean.
When the health variable is treated as an ordinal measure, we face an initial condition problem
when the lagged health score is included. If the initial health status is fixed, we can simply
include lagged health in an ordered logit model. We do this by including separate dummy
variables for different health scores in the previous period (denoted Health (t-1) = j, with
j=2,…,5). The lagged values for Levels 3 to 5 have significant coefficients. Without observable
individual characteristics the results are qualitatively similar to the OLS results. When the
personal characteristics are included, only the indicator for being always unemployed is
significant. When the initial condition is treated as stochastic we use the Chamberlain type of
approach and include individual means of the characteristics (to proxy fixed effects) and initial
levels of the lagged health status dummies in a random effect ordered probit estimation (see
Wooldridge, 2002). The results are relatively similar to the ordered logit estimates.
4.3. Propensity score matching estimates
To evaluate the robustness of the basic results that are based on regression-based models
further, we estimate propensity score matching models.
14
Persons with certain observable
characteristics are much more likely to be unemployed. For instance, the less educated face
disproportionate difficulties in the labour market. The key idea of propensity score matching is
to construct a control group from the group of untreated individuals and to ensure that the
control group is as similar as possible to the treatment group with respect to available
observable characteristics. In our case, the treatment is becoming unemployed (or becoming
employed) and we study its effect on self-assessed health. In particular, we need not worry
about the endogeneity of becoming unemployed (or employed).
15. 12
Matching has some important advantages over regression-based methods that were used to
produce the basic results. Being a non-parametric method, matching does not impose any
specific linearity assumptions on the evaluated effects that are inherent in regression-based
modelling. Furthermore, matching explicitly tries to find for each untreated unit a similar treated
unit to evaluate the counterfactual, i.e. what would happen to the treatment group without the
treatment. As a drawback, it has to be assumed that there are no unobservable factors that affect
the individuals’ probability of becoming unemployed. Controlling for the observable factors, the
outcome (health) is assumed to be independent of the treatment status (conditional
independence or unconfoundedness assumption). One need not control for all the observable
factors at the same time, but it suffices to condition on the propensity score, i.e. the probability
of treatment. In using the propensity score, one has to further rule out the perfect predictability
of the treatment (overlap or common support assumption). Corresponding assumptions apply
when the treatment is becoming employed.
We first estimate a probit model for the probability of becoming unemployed (i.e. the
probability that the person is unemployed, given that he or she was employed in the previous
year). The explanatory variables include personal factors such as age, age squared, and the level
of education (dummies for medium and high levels). We also include the employer’s
characteristics, a dummy for small firms (less than 20 employees), and a dummy for the public
sector. These variables are lagged by one period. In addition, we include lagged health status
and year dummies. For simplicity, we include the lagged health score directly, rather than
separate dummies for different health levels. The probit model is estimated using pooled data
for the whole period. Since the aim is to model selection on observables, we do not model
unobservable individual characteristics in the probit model. The data set in the probit estimation
consists of year pairs for those who are employed both in the current year and in the previous
year and those who are unemployed in the current year, but were employed in the previous year.
The propensity scores are used with nearest-neighbour matching (one-to-one matching with
replacement) and kernel (Epanechenikov kernel) methods when calculating the average
treatment effect on the treated (ATT).15
In particular, the self-assessed health status of those that
have become unemployed, i.e. the treatment group, is compared with those employed that have
a similar propensity to be in the pool of unemployed persons, but are not currently in that pool,
i.e. the control group. Assuming that the health status is a cardinal outcome measure, we then
calculate the average treatment effect on the treated.
An alternative measure of health impact, which takes better account of the ordinal nature of self-
assessed health, is constructed using the probabilities of different levels of health. Using the
same set of individuals as in the probit model, we estimate an ordered probit model for health,
16. 13
using age, age squared, gender, educational levels, and lagged health as the explanatory
variables. Using the estimates, we calculate the probabilities of all five health levels for each
individual. Using these probabilities we obtain the expected health score
∑ =
==
5
1
)Pr()( j
jHealthjHealthE . This is then used as the outcome in propensity score
matching.
The above measures essentially treat the data as separate cross-sections and compare the health
status of the treated and controls in each year. As an alternative, we utilize the panel aspect and
use changes in health scores or expected health scores as the outcome measures, i.e. we use
difference-in-differences matching (e.g. Blundell and Costa Dias, 2000).
The first column of Table 4 shows the estimates for the probit model for becoming unemployed.
As expected, higher education decreases the probability of becoming unemployed, other things
being equal, and employees in small firms are more likely to become unemployed. The age
effect is U-shaped with young and old employees more likely to become unemployed; the
minimum is at the age of 43. Public sector employees are more likely to face unemployment,
which may be related to a large share of temporary employees in this sector. In addition, lagged
self-assessed health in the previous year has a negative and significant coefficient when
explaining the probability of becoming unemployed in the current year. The propensity score
matching is performed using the region of common support for the propensity scores, which
included 405 cases of a person becoming unemployed (none off support) and 11006 control
cases.16
Figure 5 plots the distributions of the propensity scores before matching. The figure
shows that for the controls the probability of becoming unemployed tends to be smaller. To
check the validity of the matching, covariate balancing is tested. The results are shown in Table
5. For all the variables the matching succeeds in making the means of the covariates close to
each other for the treated and controls.17
=== FIGURE 5 HERE ===
=== TABLES 4-5 HERE ===
Table 6 reports the estimated treatment effects on the treated, with standard errors computed by
bootstrapping. When nearest-neighbour matching with replacement is used, the average
treatment effect of becoming unemployed on self-assessed health is small and not statistically
significant. The same result is obtained when expected health is the outcome. As a robustness
check, nearest-neighbour matching was also conducted for each year separately. Although the
results slightly varied over time, all of the estimated ATTs were non-significant. (The results are
not reported in the table.) To check the robustness of the result further, kernel matching is also
17. 14
used. Now there is a significant effect, when expected health is the outcome. Unemployment
causes a somewhat lower expected health level according to kernel matching.
=== TABLE 6 HERE ===
However, a closer way to compare the matching results with those obtained with simple
parametric regression methods (difference-in-differences) is to use the change in health as the
outcome. Now both nearest-neighbour and kernel matching give the same conclusion: changes
in health or expected health are not statistically significantly different in the treatment group and
control group.18
Taken together, the results based on matching confirm our earlier conclusions
that the experience of unemployment as such does not have an independent influence on the
self-assessed level of health, but those persons with a low perception of their health are more
likely to become unemployed in the first place. In fact, it is likely that using the health level as
the outcome picks up the health difference, even when lagged health is used as a variable in the
estimation of the propensity scores.
A corresponding matching analysis is done for the treatment of becoming employed. In this case
the data set is restricted in each year pair to those who are unemployed in both the current and
past periods and those who become employed in the current period. The second column of
Table 4 shows the estimates from the probit models of becoming employed. Age has an inverted
U-shaped relationship with the probability of becoming employed, with maximum at the age of
35, and higher education increases the employment probability. In addition, self-assessed health
in the previous year has a positive and significant coefficient. Note that because all the
individuals in this analysis are unemployed in t-1, information about the employer is not
available. The propensity score matching is performed using the region of common support for
the scores, which includes 542 treated (18 off support) and 793 controls. Figure 6 shows the
distribution of the propensity scores for the treated and controls. The treatment group tends to
have a higher probability of becoming employed, which is understandable, since the control
group also includes those who are unemployed for the whole data period 1996-2001. To check
the validity of the matching, covariate balancing is tested. According to Table 7, the matching
again succeeds in making the distributions of the covariates similar.19
=== FIGURE 6 HERE ===
=== TABLE 7 HERE ===
Table 6 reports the results. When nearest-neighbour matching is used, the average treatment
effect of becoming employed is 0.061, which is statistically significant at the 10 per cent level.
The ATTs with expected health as the outcome or the ones from kernel matching are not
18. 15
significant.20
When we use change in health as the outcome, we again obtain the result that
becoming employed does not improve health in a statistically significant way. This is consistent
with our findings using difference-in-differences models. As a robustness check, we re-run the
nearest-neighbour matching analyses for each year separately. (The results are not reported in
the table.) The conclusions were, otherwise, similar to those obtained with the pooled data, but
with health as the outcome ATT was 0.360 and significant at the 1 per cent level (t-value 8.55)
in 2000, driving the result for the pooled data.
4.4. Effects of long-term unemployment
In the above analysis we have treated all kinds of unemployment in the same way. Now we
examine whether the definition of “experiencing unemployment” matters for the robustness of
the basic results. In this case, we define long-term unemployment as the relevant measure of
unemployment experience.21
The long-term unemployed are those persons who have been
unemployed continuously at least for six months. Because selection by observable
characteristics such as education is arguably more important in the case of long-term
unemployment than for overall unemployment, we focus on the results that stem by using
matching methods.22
We drop the short-term unemployed from matching. Hence, we compare the health level of the
long-term unemployed with the health of those who are continuously employed. The treatment
is in this case being long-term unemployed in t conditionally on having been employed in t-1,
and the outcome variable is alternatively health, expected health, or changes in them in t. The
probit models for becoming long-term unemployed contain the same explanatory variables as
earlier for overall unemployment. As expected, persons with a low perception of their health are
more likely to become long-term unemployed. In addition, the corresponding analysis as earlier
is done for the treatment of becoming employed after experiencing long-term unemployment.
Table 8 summarizes the results. When nearest-neighbour matching is used with the health level
in t as the outcome variable, the average treatment effect of becoming long-term unemployed is
-0.16. The effect is statistically significant at the 10 per cent level. Accordingly, there is some
evidence that becoming long-term unemployed leads to a deterioration in self-assessed health.
However, the results differ by using different measures of health, i.e. health vs. expected health
and health level vs. change, and between different matching methods, nearest-neighbour vs.
kernel matching. Additionally, becoming employed after long-term unemployment does
improve self-assessed health in a statistically significant way when using nearest-neighbour
19. 16
matching, but not when using kernel matching. The result on health is weaker when the panel
dimension of the data is taken into account in difference-in-differences matching.
=== TABLE 8 HERE ===
5. Conclusions
We have explored the relationship between unemployment and self-assessed health. Our results
show that the event of unemployment does not matter as such for the level of self-assessed
health, when evaluated in a panel data setting, since the health status of those who end up being
unemployed is already lower than that of the continually employed before their unemployment
episodes actually start. Importantly, the matching results are similar to those obtained with
simple parametric regression methods (difference-in-differences models) when the change in
health is used as the outcome. Hence, persons who have poor self-assessed health, for some
reason or another, are being selected for the pool of the unemployed. This explains why, in a
cross-section, unemployment is associated with poor self-assessed health. Accordingly,
unemployment may merely be a veil that hides the underlying causes of poor self-assessed
health. Furthermore, we discover that the definition of “experiencing unemployment” matters
somewhat for the findings. In particular, we are more likely to obtain the negative effects of
unemployment on health when we use long-term unemployment as the relevant measure of
unemployment experience.
Our basic finding, according to which unemployment does not appear to have a significant
negative effect on self-assessed health, is consistent with the results by Lucas et al. (2004) for
unemployment and life satisfaction. They show that individuals tend to shift back towards their
baseline levels of life satisfaction after unemployment has lasted for some time. This pattern
demonstrates that the unemployed become mentally accustomed to their situation rather
quickly. This may arise, because unemployment has fewer stigma effects in the presence of high
aggregate unemployment. From the policy perspective, the findings of this paper imply that the
allocation of resources to improve the health status of those that are currently unemployed is not
enough. It is equally important to put resources into the improvement of health of those persons
currently employed, but who are more likely to experience unemployment at some point of
time.
20. 17
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23. 20
Table 1. Distribution of the level of self-assessed health.
Health Employed Unemployed Long-term
unemployed
Total
population
1 32 18 11 50
(0.18) (0.66) (0.90) (0.24)
2 339 143 83 482
(1.89) (5.28) (6.83) (2.34)
3 3779 771 439 4550
(21.12) (28.46) (36.10) (22.09)
4 9807 1296 522 11103
(54.92) (47.84) (42.93) (53.90)
5 3933 481 162 4414
(21.98) (17.76) (13.24) (21.43)
Total 17890 2709 1216 20599
(100) (100) (100) (100)
Note: Percentage shares of column totals in parentheses. Long-term unemployed are those who have been
unemployed continuously at least for six months.
24. 21
Table 2. Effect of labour market status on self-assessed health.
OLS OLS Fixed effects Fixed effects Ordered logit Ordered logit FE ordered
logit
FE ordered
logit
RE ordered
probit
-0.207 -0.097 -0.541 -0.273 -0.228Becomes unemployed at
least once (0.024)*** (0.023)*** (0.064)*** (0.064)*** (0.080)***
-0.028 0.028 0.032 0.032 -0.076 0.067 0.198 0.199 0.073Unemployed after
employment (0.037) (0.035) (0.027) (0.027) (0.099) (0.100) (0.130) (0.130) (0.068)
0.095 0.009 0.262 0.032 0.012Becomes employed at
least once (0.024)*** (0.022) (0.063)*** (0.064) (0.072)
-0.030 -0.018 -0.009 -0.008 -0.098 -0.064 0.010 0.011 -0.031Employed after
unemployment (0.031) (0.029) (0.024) (0.024) (0.082) (0.083) (0.112) (0.112) (0.059)
-0.457 -0.263 -1.150 -0.694 -0.558Always unemployed
(0.026)*** (0.025)*** (0.068)*** (0.071)*** (0.093)***
Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Control variables No Yes No Yes No Yes No Yes Yes
N 19206 19206 19206 19206 19206 19206 12891 12891 19206
Note: Standard errors in parentheses. Significance: *** 1 %, ** 5 %, * 10 %. Reference group: continuously employed. Unreported control variables include age
and its square, gender and the level of education in three categories. In the fixed effects models gender is excluded. In FE ordered logit age is also excluded. In the RE
model, individual means of control variables are also included.
25. 22
Table 3. Effect of labour market status and past health on self-assessed health.
OLS OLS
First difference
IV
First difference
IV
Ordered logit Ordered logit
RE ordered
probit
-0.084 -0.047 -0.264 -0.134 -0.084
Becomes unemployed at least once
(0.023)*** (0.023)** (0.083)*** (0.083) (0.064)
-0.004 0.020 0.056 0.032 -0.021 0.051 0.026
Unemployed after employment
(0.033) (0.033) (0.044) (0.027) (0.118) (0.119) (0.074)
0.066 0.033 0.232 0.118 0.078
Becomes employed at least once
(0.025)*** (0.025) (0.088)*** (0.089) (0.065
-0.044 -0.038 0.065 -0.008 -0.164 -0.147 -0.081
Employed after unemployment
(0.029) (0.029) (0.042) (0.024) (0.104) (0.105) (0.068)
-0.188 -0.120 -0.615 -0.401 -0.234
Always unemployed
(0.025)*** (0.025)*** (0.088)*** (0.091)*** (0.070)***
0.588 0.535 -0.437 -0.437
Health(t-1)
(0.006)*** (0.007)*** (0.011)*** (0.011)***
Health(t-1) = 2 0.406 0.437 0.195
(0.423) (0.426) (0.245)
Health(t-1) = 3 2.354 2.322 0.661
(0.410)*** (0.414)*** (0.240)***
Health(t-1) = 4 4.613 4.400 1.219
(0.411)*** (0.415)*** (0.244)***
Health(t-1) = 5 6.536 6.199 1.631
(0.413)*** (0.417)*** (0.250)***
Year dummies Yes Yes Yes Yes Yes Yes Yes
Control variables No Yes No Yes No Yes Yes
N 14524 14524 6447 6447 14524 14524 14524
Note: Standard errors in parentheses. Significance: *** 1 %, ** 5 %, * 10 %. Reference group: continuously employed. Unreported control variables include age and its
square, gender and the level of education in three categories. In the differenced models gender is excluded. In the RE model, individual means of control variables and
starting values of lagged health categories are also included.
26. 23
Table 4. Probit models for change in labour market status.
Becomes unemployed Becomes employed
Age -0.074 0.158
(0.016)*** (0.025)***
Age squared 0.001 -0.002
(0.0002)*** (0.0003)***
Middle education (t-1) -0.186 0.196
(0.057)*** (0.087)***
High education (t-1) -0.507 0.438
(0.065)*** (0.109)***
Small firm (t-1) 0.408
(0.046)***
Public sector (t-1) 0.080
(0.048)*
Health (t-1) -0.062 0.178
(0.034)** (0.051)***
Year dummies Yes Yes
N 11411 1353
Note: Standard errors in parentheses. Significance: *** 1 %, ** 5 %, * 10 %.
27. 24
Table 5. Test of covariate balancing for becoming unemployed.
Variable Sample Mean
treated
Mean
control
% bias % reduction
of bias
t-test
Age Unmatched 42.541 42.306 2.2 0.47
Matched 42.541 42.849 -2.9 -31.6 -0.38
Age squared Unmatched 1938.2 1886.1 5.9 1.24
Matched 1938.2 1968.9 -3.5 40.8 -0.46
Middle education (t-1) Unmatched 0.4666 0.3933 14.8 2.96***
Matched 0.4666 0.4814 -3.0 79.8 -0.42
High education (t-1) Unmatched 0.2172 0.4335 -47.4 -8.67***
Matched 0.2172 0.2074 2.2 95.4 0.34
Small firm (t-1) Unmatched 0.5876 0.3509 48.8 9.79***
Matched 0.5876 0.5851 0.5 99.0 0.07
Public sector (t-1) Unmatched 0.4271 0.4485 -4.3 -0.85
Matched 0.4271 0.3728 10.9 -153.7 1.58
Health (t-1) Unmatched 3.842 3.9654 -16.9 -3.40***
Matched 3.842 3.8568 -2.0 88.0 -0.29
Note: Year dummies not reported. Significance: *** 1 %, ** 5 %, * 10 %.
Table 6. Average treatment effect on the treated for overall unemployment.
Outcome
Treatment Matching method Health E(health) Change in
health
Change in
E(health)
Becomes unemployed Nearest-neighbour 0.006
(0.046)
-0.008
(0.018)
0.020
(0.045)
0.015
(0.030)
Kernel -0.061 -0.059 0.014 -0.001
(0.047) (0.017)** (0.044) (0.030)
Becomes employed Nearest-neighbour 0.061
(0.032)*
0.004
(0.008)
0.037
(0.034)
-0.003
(0.019)
Kernel 0.030 0.009 0.008 0.009
(0.031) (0.007) (0.032) (0.021)
Note: Bootstrap standard errors (150 replications) in parentheses. Significance: *** 1 %, ** 5 %, * 10 %.
28. 25
Table 7. Test of covariate balancing for becoming employed.
Variable Sample Mean
treated
Mean
control
% bias % reduction
of bias
t-test
Age Unmatched 37.929 44.889 -60.9 -10.89***
Matched 38.094 37.738 3.1 94.9 0.56
Age squared Unmatched 1549.4 2165.2 -66.1 -11.72***
Matched 1564.5 1529.3 3.8 94.3 0.72
Middle education (t-1) Unmatched 0.5035 0.3934 22.3 4.04***
Matched 0.5166 0.5258 -1.9 91.6 -0.30
High education (t-1) Unmatched 0.2392 0.1374 26.2 4.84***
Matched 0.2177 0.2158 0.5 98.2 0.07
Health (t-1) Unmatched 4.0161 3.6532 45.8 8.19***
Matched 3.9926 3.9686 3.0 93.4 0.52
Note: Year dummies not reported. Significance: *** 1 %, ** 5 %, * 10 %.
Table 8. Average treatment effect on the treated for long-term unemployment.
Outcome
Treatment Matching method Health E(health) Change in
health
Change in
E(health)
Becomes long-term
unemployed
Nearest-neighbour
Kernel
-0.156
(0.081)*
-0.273
-0.034
(0.031)
-0.154
-0.099
(0.080)
-0.084
0.105
(0.055)*
0.028
(0.085)** (0.030)** (0.088) (0.055)
Becomes employed after
long-term unemployment
Nearest-neighbour
Kernel
0.154
(0.054)**
0.057
(0.055)
0.025
(0.014)*
-0.011
(0.013)
0.109
(0.057)*
0.074
(0.055)
-0.039
(0.036)
0.000
(0.032)
Note: Bootstrap standard errors (150 replications) in parentheses. Significance: *** 1 %, ** 5 %, * 10 %.
30. 27
Figure 3. Galton squeeze diagram for those employed in two consecutive periods.
12345
12345
Self-assessedhealth
Employed Employed
Figure 4. Galton squeeze diagram for those becoming employed.
12345
12345
Self-assessedhealth
Unemployed Employed
31. 28
Figure 5. Distribution of propensity scores of becoming unemployed in the region
of common support.
0102030
0 .1 .2 .3 0 .1 .2 .3
Untreated Treated
Density
Propensity score: Becoming unemployed
Figure 6. Distribution of propensity scores of becoming employed in the region of
common support.
0123
0 .2 .4 .6 .8 0 .2 .4 .6 .8
Untreated Treated
Density
Propensity score: Becoming employed
32. 29
Notes
1
Koskela and Uusitalo (2006) provide an overview of the Finnish unemployment problem.
2
Crossley and Kennedy (2002) show that the response reliability of self-assessed health is related to age,
income and occupation. In total, around 28% of those that respond twice to the same health question
changed their response on self-assessed health. McFadden et al. (2005) note that one problem with the
questions on self-assessed health is that different respondents can interpret the response scales differently.
This may be a particular problem in cross-country comparisons of self-assessed health status when using
data sources such as the ECHP. In our case, that problem is less severe, because we use information from
the ECHP only for one country.
3
As in all panel data sets, there is some amount of attrition between waves.
4
We have reversed the scale of the health measure in the ECHP survey to emphasise that higher numbers
correspond to better health.
5
For instance, in the first wave of the ECHP from the year 1996, ”top-coding” means that it is not
possible to observe unemployment durations of over 12 months.
6
The same conclusion would be reached from a reverse Galton squeeze diagram where the end-point of
each line is the final health level of some unemployed and the starting point is their average health while
they were still employed.
7
There are only two persons who were initially at health level one. After becoming employed both of
them were at level 4.
8
See e.g. Henning et al. (2003) for a discussion on this issue in another context.
9
For instance, it is a well-known fact that better educated persons are usually healthier both by subjective
and objective measures (see Martikainen, 1995, for evidence from Finland). The three education
categories in the ECHP are third level education (ISCED 5-7), second stage of secondary level education
(ISCED 3) and less than second stage of secondary level education (ISCED 0-2).
10
Our result that becoming unemployed is not related to self-assessed health is in accordance with the
results reported by Browning et al. (2006). They discover by using a random 10% sample of the male
population of Denmark for the years 1981-1999 that being displaced does not cause hospitalization for
stress-related disease.
11
As a robustness check of the basic results, we estimated the models (1) and (2), but proxied the discrete
health scores with a continuous variable that is based on the observed shares of the scores (following
Terza, 1987). The estimates using the converted scores (not reported in tables) were quite similar to the
ones obtained treating the scores directly as cardinal measures of health.
12
Due to poor convergence of the estimates, the age variable is left out of the estimation with control
variables.
13
The correlation between change in the health score and initial health can be positive only if the variance
of the health scores increases over time (Campbell and Kenny, 1999).
14
See e.g. Blundell and Costa Dias (2000), Caliendo and Kopeinig (2007), and Lee (2005) for surveys of
the matching methods.
15
In our analysis we use the programs written by Becker and Ichino (2002), and Leuven and Sianesi
(2006) for Stata.
16
We follow the definition of common support in the Leuven-Sienesi program, where this is defined to
include all controls and those treated whose propensity score is below the maximum or above the
minimum propensity score of the controls. In the Becker-Ichino program the common support option
keeps all treated and those controls with a propensity score below the maximum or above the minimum of
that of the treated. In practice, there are small differences in the results, but the significance of the
estimated ATTs is not affected.
17
If the data are divided into 7 blocks (based on an algorithm; see Becker and Ichino, 2002), the
balancing property holds in all of the blocks.
18
When change in expected health is used as the outcome, the number of observations in matching is
smaller than in the other cases. In the estimation of the ordered probit models for health we use lagged
health as an explanatory variable, so we lose one year and in differencing we lose another year. We have
287 treated and 7773 controls in the region of common support (no treated off support).
33. 30
19
When the data are divided to 6 blocks, the balancing holds in all other cases, except for the age variable
in the first block (lowest end of the distribution of propensity scores). If the squared age is left out, the
balancing property holds. We have kept squared age in the model, but examined the sensitivity of the
results to its exclusion. This has practically no effect on the estimated treatment effects.
20
Again, the number of observations is slightly smaller when we have change in expected health as the
outcome. There are 353 treated (17 off support) and 413 controls on common support.
21
Gordo (2006) discovers, using the GSOEP data with random effects models, that being unemployed
for a long period of time has a significant negative effect on health satisfaction while short-term
unemployment does not always cause negative effects.
22
We have made some experiments with parametric models by using long-term unemployment as the
relevant measure of unemployment. The results that stem from parametric models are somewhat mixed.