quantitative research title about unemployment

• Inflation Topics Topics: 117
• Macroeconomics Topics Topics: 112
• Microeconomics Topics Topics: 75
• Recession Research Topics Topics: 86
• Financial Crisis Research Topics Topics: 127
• Managerial Economics Research Topics Topics: 50
• Economic Growth Paper Topics Topics: 80
• Economic Crisis Research Topics Topics: 76
• Cryptocurrency Research Topics Topics: 65
• Taxes Paper Topics Topics: 176
• Minimum Wage Research Topics Topics: 77
• Monopoly Research Topics Topics: 95
• Banking Topics Topics: 196
• Globalization Paper Topics Topics: 272
• Trade Essay Topics Topics: 263

140 Unemployment Essay Topics

🏆 best essay topics on unemployment, 🔎 easy unemployment research paper topics, 👍 good unemployment essay topics to write about, 🎓 most interesting unemployment research titles, 💡 simple unemployment essay ideas, ❓ unemployment research questions.

• Causes of Youth Unemployment
• Artificial Intelligence and Unemployment
• The Impact of Unemployment on Crime Rates
• Economics: Unemployment, Its Causes and Types
• The Problem of the Unemployment
• Unemployment Rates Among Young College Graduates
• The Relationship Between Unemployment and Economic Growth
• Building a Business to Address Youth Unemployment An opportunity to build a business based on the youth unemployment problem has both strengths and weaknesses, also opportunities for further development.
• Natural Rate of Unemployment In determining the natural rate of unemployment, analysts focus on evaluating price and wage settings in the labour market.
• Unemployment, Its Types and Government Intervention Unemployment is among the most significant challenges that influence contemporary economies. Indeed, even global economic giants suffer from the problem.
• Unemployment and Political Regime Unemployment should be considered one of the critical factors influencing the economy of states and political stability. This paper discusses unemployment and political regime.
• Unemployment Rate During COVID-19 COVID-19 and subsequent lockdown measures significantly affected the civilian labor force participation and unemployment rates.
• Social Problems and Policy: Youth Unemployment and Mental Health In the history of the US, the federal and state governments have been at the forefront to facilitate effective social programs.
• Unemployment Rate in Leisure and Hospitality Sector For leisure and hospitality businesses, low unemployment rates tend to be related to a higher average salary, owing to the lower labor supply.
• The Impact of Government Spending on GDP Growth, Unemployment, and Inflation Real GDP Refers to every financial activity done by the government, including consumption, investment, and transfer payment.
• Unemployment as an Imperfect Economic Measure Unemployment has been an essential aspect of the country’s economy because the unemployed labor force cannot pay taxes or perform other activities beneficial to the economy.
• Domestic Violence in Melbourne: Impact of Unemployment Due to Pandemic Restrictions The purpose of this paper is to analyze to what extent does unemployment due to pandemic restrictions impact domestic violence against women in Melbourne.
• Unemployment and Its Macroeconomic Implications In the process of learning about macroeconomic trends, one obtains an opportunity to expand their knowledge about particular factors and their outcomes for the economy.
• Counter-Terrorism and Unemployment Approaches A more novel approach to unemployment that considers the needs of a disenchanted youth is vital to reducing the draw towards terrorist activities.
• The Long-Term Unemployment Positive Tendency The article argues that the level of long-term unemployment has fallen significantly compared to the previous years, reaching the lowest point in 9 years.
• The Unemployment and Inflation Causes in Australia The change in the Australian 2021 indicator of unemployment is the representation of cyclical unemployment since it lasted less than a year.
• Why the Unemployment Rate Needs Fixing in the US The article Latest Jobs Report Shows Why the Unemployment Rate Needs Fixing regards the issue of the inadequateness of the currently established formal indicator in the US.
• Unemployment After the US Industrial Revolution Since the commencement of the industrial revolution, the process of automation, or more broadly the replacement of human employees by machines, has piqued widespread interest.
• The Hispanic Unemployment Issue in the US A Hispanic person in the US is more likely to be unemployed than an average American. People of color have historically been one of the most discriminated groups.
• The US Fiscal Policy and Unemployment Rate The problem to be discussed will be centered around the relationship between fiscal policies in regard to the unemployment rate in the United States.
• Unemployment Rates in the State of Georgia In this essay, the author will present the current unemployment statistics and job outlook in the state of Georgia.
• The High Unemployment Rate as a Most Serious Threat to Americans Although the United States has one of the highest economic indicators globally, thousands of Americans are unemployed across the country.
• AI Development, Unemployment, and Universal Basic Income The theme of AI-human relationships takes an important place in science fiction literature, movies, and video games, but it is not limited by them.
• Unemployment Rates in the United States due to COVID-19 The increase in unemployment in the United States is associated with the country’s epidemiological situation and the tightening of quarantine measures taken by states.
• Homelessness Due to Unemployment During COVID-19 This paper is a research on how unemployment resulting from the Covid-19 pandemic has left many homeless in the United States.
• An Article Review: “Metropolitan Area Employment and Unemployment” The U.S. Bureau of Labor Statistics published a short article that reports the results of the analysis of the changes in the “nonfarm payroll employment” in metropolitan areas.
• The Effects of the Minimum Wage on Overall Unemployment The raised minimum wage would create more jobs for low-wage workers, as this rise would prompt the goods and services demand of such workers who would now be able to afford more.
• Unemployment Rates in the United States Unemployment is unevenly distributed across the US population, with regards to race, age, gender, and education.
• Inflation and Unemployment in Bavaria Considering the normal state of the economy and the existing level of employment close to full, the President of Bavaria is not recommended to pursue an expansionary fiscal policy.
• Federal Poverty, Welfare, and Unemployment Policies In the paper, the federal policies regarding the above mentioned areas of public interest will be scrutinized and discussed at length.
• Unemployment: Types And Factors Unemployment is one of the greatest social evils in our society today. This is because of the unfriendly impacts it has on the economy.
• Unemployment and Rosenberg’s Self-Esteem Scale The concept of self-esteem is derived from self–theory. A basic assumption of self-theory is the need to appreciate oneself and be appreciated by others.
• “Unemployment Checks: Keep ‘Em Coming” by Owens and Stettner: Article Review In the article, Owens and Stettner underline that current unemployment affects both the economy and employers, the government financial resources, and the jobless population.
• Youth Unemployment in the United Kingdom Over the years, there have been remarkable unemployment rates among the youths all across the globe as compared to the age brackets that are regarded as adults.
• Economics for Management. Unemployment in Spain Spain has the potential to reduce the unemployment rate, especially since it has already decreased significantly from 2016.
• Unemployment Rates in the US The state of the American economy is getting closer to full employment, whereas the unemployment rates (as of 2017) remain to be approximately 4.4%.
• “Unemployment and Terrorism” TED Talk by Mohamed Ali In this TED talk, Mohamed Ali explores the relationship between unemployment and terrorism. Ali incorporates stories from his native country to support his arguments.
• Frictional Unemployment and Hyperinflation Frictional unemployment is also known as voluntary unemployment. It cannot be eliminated from the economy. There are some economic benefits associated with it.
• Youth Unemployment Rates in Canadian Society The problem under investigation is the fact that the unemployment rate among people in the 18-25 age group is higher than any other age group in Canadian society.
• Social and Economic Aspects of Unemployment in the UAE Despite the UAE having the lowest level of unemployment in the world, the number of foreign workers exceeds its native employees.
• 2008 Great Recession, Unemployment and Stagnation This paper is looking into the case of the financial crisis, which results in an economic recession and the further sustained effects.
• Unemployment’ Nature and Possible Causes Unemployment rate refers to the percentage of people within the available labour force who do have jobs and are actively looking for one. Unemployment rates cannot be reduced to zero.
• Unemployment and the Labour Market in Australia The paper studies forces of supply and demand in the Australian labor market, the labor force participation rate and the trends in labour force participation of older workers.
• Reduced Unemployment in the UK In order to understand why there has been a decline in unemployment rate in the UK, it is essential to understand the reasons affecting UK unemployment.
• Earnings-Related Unemployment Security, Employment and Lifetime Income
• Employment, Unemployment and Real Economic Growth
• Business Cycles and Compositional Variation in US Unemployment
• Crime, Earnings Inequality, and Unemployment in England and Wales
• European Unemployment: Cause and Cure
• Demographic and Education Effects on Unemployment in Europe: Economic Factors and Labour Market Institutions
• Centralized Wage Bargaining and Regional Unemployment
• Capital Shortages and Asymmetries in UK Unemployment
• Disarmament, Unemployment, Budgets, and Inflation
• Demography, Capital Flows, and Unemployment
• Duty-Free Zone, Unemployment, and Welfare a Note
• Factors Affecting the Adjustments to Unemployment
• Capital, Wages, and Structural Unemployment
• Earnings, Unemployment, and Housing in Britain
• Canada’s Interwar Unemployment From 1919 Until 1939
• Aging and the Labor Market: Age Structure, Cohort Size, and Unemployment
• Community Unemployment and Immigrants’ Health in Montreal
• Employment, Unemployment, and Underemployment in Africa
• Correlation Between Crime and Unemployment
• Equilibrium Labor Turnover, Firm Growth and Unemployment
• Changing Identity: Retiring From Unemployment
• Equilibrium Unemployment and Retirement
• Employment Turnover and Unemployment Insurance
• Embodied Technical Change and the Fluctuations of Wages and Unemployment
• Eligibility for Unemployment Benefits in Great Britain
• Capital Immobility, Informal Sector, and Urban Unemployment
• Age Structure and the UK Unemployment Rate
• Economics Instability Increases the Unemployment Rate in Malaysia
• Australian Unemployment, Inflation, and Economic Growth
• Broadband Infrastructure and Unemployment – Evidence for Germany
• Economic Recession, Skilled Unemployment, and Welfare
• Construction Industry Growth Economic Unemployment
• Agglomeration, Job Flows, and Unemployment
• Entrepreneurship, Asymmetric Information, and Unemployment
• Economic Freedom and Unemployment in Emerging Market Economies
• Absenteeism, Unemployment and Employment Protection Legislation: Evidence From Italy
• Environmental Policy, Efficient Taxation, and Unemployment
• Dynamic Contracts and Equilibrium Unemployment
• Agro-Manufactured Export Prices, Wages and Unemployment
• Banking Crises, Labor Reforms, and Unemployment
• Environmental Policy, Pollution, Unemployment, and Endogenous Growth
• Demographic Evolutions and Unemployment: An Analysis of French Labour Market With Workers Generations
• Employment and Unemployment Insurance Schemes
• Disability, Unemployment, and Poverty
• Business Volatility, Job Destruction, and Unemployment
• Aggregate Demand, Productivity, and Disguised Unemployment in the Chinese Industrial Sector
• Child Support and Involuntary Unemployment
• Efficiency-Wage Unemployment and Endogenous Growth
• Addressing Education, Inequality, and Unemployment in Uganda
• Economic Freedom and Unemployment Around the World
• Dual Labor Markets, Urban Unemployment, and Multicentric Cities
• Employment, Unemployment, and Problem Drinking
• Correlations Between Recessions and Unemployment
• Employment and Unemployment Effects of Unions
• Collective Bargaining, Firm Heterogeneity and Unemployment
• Equilibrium Unemployment During Financial Crises
• Capital, Heterogeneous Labour, Global Goods Markets and Unemployment
• Economic Policy, Industrial Structure, and Unemployment in Russia’s Regions
• Capital Stock, Unemployment and Wages in the UK and Germany
• Environmental Fiscal Reform and Unemployment in Spain
• Why Did Unemployment Persist Despite the New Deal?
• Can More FDI Solve the Problem of Unemployment in the EU?
• What Is the Current Rate of Unemployment in the UK 2022?
• Who Can Get Unemployment Benefits in Germany?
• What Are Relationships Between Short-Term Unemployment and Inflation?
• Does Broadband Internet Reduce the Unemployment Rate?
• Are Education Systems Modern as Well as Practical Enough to Eliminate Unemployment, and Thus Poverty?
• What Us State Has the Lowest Unemployment Rate?
• Does High Unemployment Rate Result in a High Divorce Rate?
• Does Culture Affect Unemployment?
• Why Unemployment Is a Problem?
• What Is the Unemployment Rate in Canada?
• Are Early Educational Choices Affected by Unemployment Benefits?
• How Long Does Unemployment Take To Get Approved?
• Which Country Has the Lowest Unemployment Rate?
• What’s the Lowest You Can Get From Unemployment?
• Why Is the Us Unemployment Rate So Low?
• How Does Unemployment Rate Affect Everyone?
• Are Interest Rates Responsible for Unemployment in the Eighties?
• Does Employment Protection Lead To Unemployment?
• Are Searching and Non-searching Unemployment Distinct States When Unemployment Is High?
• What Are the Solutions to Unemployment?
• Can Google Econometrics Predict Unemployment?
• How Far Was Unemployment the Most Important Reason for the Rise of the Nazis in Germany Between 1918 and 1933?
• Are Protective Labor Market Institutions at the Root of Unemployment?
• What Is China’s Unemployment Rate?
• What Are the Five Causes of Unemployment?
• What Are the Main Causes of Unemployment in an Economy?
• What City Has the Lowest Unemployment Rate?
• Can Insider-Outsider Theories Explain the Persistence of Unemployment?

Cite this post

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2022, March 1). 140 Unemployment Essay Topics. https://studycorgi.com/ideas/unemployment-essay-topics/

"140 Unemployment Essay Topics." StudyCorgi , 1 Mar. 2022, studycorgi.com/ideas/unemployment-essay-topics/.

StudyCorgi . (2022) '140 Unemployment Essay Topics'. 1 March.

1. StudyCorgi . "140 Unemployment Essay Topics." March 1, 2022. https://studycorgi.com/ideas/unemployment-essay-topics/.

Bibliography

StudyCorgi . "140 Unemployment Essay Topics." March 1, 2022. https://studycorgi.com/ideas/unemployment-essay-topics/.

StudyCorgi . 2022. "140 Unemployment Essay Topics." March 1, 2022. https://studycorgi.com/ideas/unemployment-essay-topics/.

These essay examples and topics on Unemployment were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on June 25, 2024 .

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

A quantitative analysis of unemployment benefit extensions

2011, Available at SSRN 1758047

Related Papers

Brookings Papers on Economic Activity

Stephanie Aaronson

SSRN Electronic Journal

Kurt Mitman

Abstract The last three recessions in the United States were followed by jobless recoveries: while labor productivity recovered, unemployment remained high. In this paper we propose and quantitatively evaluate a new explanation for this fact, namely that extensions of unemployment benefits in recessions slow down the recovery of employment.

The Quarterly Journal of Economics

Johannes Schmieder

Wayne Vroman

Mario Centeno

Economics Letters

Phanindra Wunnava

Haydory Ahmed

PurposeThis paper explores the evidence of a long-run co-movement between aggregate unemployment insurance spending and the labor force participation rate in the USA. The unemployment insurance (UI) program tends to expand during an economic downturn and contract during an expansion. UI may incentivize unemployment and may also facilitate better matching in the labor market. Statistical evidence of the presence of a co-movement will thus shed new light on their dynamics.Design/methodology/approachThis research applies time-series econometric approach using monthly data from 1959:1 to 2020:3 to test threshold cointegration and estimate a threshold vector error-correction (TVEC) model. The estimates from the TVEC model investigating the nature of short-run dynamics.FindingsThe Enders and Siklos (2001) test find evidence of threshold cointegration between the two indicating the presence of long-run co-movement. The estimates from the TVEC model investigating the nature of short-run dyn...

Claudio Michelacci

We analyze how unemployment, job finding and job separation rates react to neutral and investment-specific technology shocks. Neutral shocks increase unemployment and explain a substantial portion of unemployment volatility; investment-specific shocks expand employment and hours worked and mostly contribute to hours worked volatility. Movements in the job separation rates are responsible for the impact response of unemployment. Movements in the job finding rates account for its adjustment path. Our evidence qualifies the conclu- sions by Hall (2005) and Shimer (2007) and warns against using search models with exogenous separation rates to analyze the effects of technology shocks.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Stephen Woodbury

Andrew Johnston

Discussion …

Lars Osberg

Journal of Applied Econometrics

Silvia Vannutelli

Christopher Pissarides

Harald Lang

Journal of Labor Economics

Marios Michaelides

Bruno Van der Linden

Juha Kettunen

Pedro Portugal

IMF Working Papers

Bharat Trehan

Economic Review

Tomer Blumkin

Working Papers

Miquel Faig

Applied Economics

Bertrand Candelon , Arnaud Dupuy

The Economic Journal

Cepremap Working Papers

Etienne Lehmann

Eva Sierminska

The Australian Economic Review

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

Analysis of the COVID-19 impacts on employment and unemployment across the multi-dimensional social disadvantaged areas

This is the study of economic impacts in the context of social disadvantage. It specifically considers economic conditions in regions with pre-existing inequalities and examines labor market outcomes in already socially vulnerable areas. The economic outcomes remain relatively unexplored by the studies on the COVID-19 impacts. To fill the gap, we study the relationship between the pandemic-caused economic recession and vulnerable communities in the unprecedented times. More marginalized regions may have broader economic damages related to the pandemic. First, based on a literature review, we delineate areas with high social disadvantage. These areas have multiple factors associated with various dimensions of vulnerability which existed pre-COVID-19. We term these places “ multi-dimensional social disadvantaged areas ”. Second, we compare employment and unemployment rates between areas with high and low disadvantage. We integrate geospatial science with the exploration of social factors associated with disadvantage across counties in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. We disagree with a misleading label of COVID-19 as the “great equalizer”. During COVID-19, marginalized regions experience disproportionate economic impacts. The negative effect of social disadvantage on pandemic-caused economic outcomes is supported by several lines of evidence. We find that both urban and rural areas may be vulnerable to the broad social and economic damages. The study contributes to current research on economic impacts of the COVID-19 outbreak and social distributions of economic vulnerability. The results can help inform post-COVID recovery interventions strategies to reduce COVID-19-related economic vulnerability burdens.

1. Introduction: social disadvantage

Pandemics create severe disruptions to a functioning society. The economic and social disruptions intersect in complex ways and affect physical and mental health and illness ( Wu et al, 2020 ). Additionally, loss of jobs, wages, housing, or health insurance, as well as disruption to health care, hospital avoidance, postponement of planned medical treatment increase mortality, e.g., premature deaths ( Kiang et al., 2020 ; Petterson et al., 2020 ). The COVID-19, misleadingly labelled the “great equalizer” implies everyone is equally vulnerable to the virus, and that the economic activity of almost everyone is similarly impacted regardless of social status ( Jones & Jones, 2020 ). We set out to answer whether economic vulnerability is equally distributed during the COVID-19-caused economic recession or whether is it based on structural disadvantages? Is the social distribution of economic vulnerability magnified in regions with pre-existing social disparities, thus, creating new forms of inequalities? Knowledge of what areas experience the greater economic burden will help identify the most economically vulnerable communities relevant to post-COVID recovery interventions ( Qian and Fan, 2020 ).

Current studies on the impacts of COVID-19 largely focus on medical aspects including the COVID diagnosis and treatment ( Cai et al., 2020 ; Kass et al., 2020 ; O’Hearn et al., 2021 ; Price-Haywood et al., 2020 ). Non-medical urban research primarily concentrates on the impact of COVID on cities by studying factors related to environmental quality including meteorological parameters, and air and water quality ( Sharifi and Khavarian-Garmsir, 2020 ). COVID-related socio-economic impacts on cities are relatively less well studied, especially during the later stages of the recession.

Many pre-pandemic disparities unfold during COVID-19. To illustrate, residents of Black and Latino communities are suffering disproportionately higher unemployment rates, greater mortality due to the COVID-19 ( Thebault, Tran, & Williams, 2020 ; Wade, 2020 ), higher hospitalizations ( O’Hearn et al., 2021 ) and financial troubles. In contrast, some attributes make persons and communities more resilient. In China’s context, these include higher worker education and family economic status, membership in Communist Party, state-sector employment, and other traditional markers. These factors protect people from the pandemic-related financial stress and diminish its adverse economic effects ( Qian and Fan, 2020 ). Building on these recent studies on economic impacts, this social justice research focuses on areas with pre-existing social disadvantages. We study the role of social disadvantage and its impact on labor market during the COVID.

The distribution of economic vulnerability may potentially be related to COVID-19 conditions including those of economic burdens for people living in the pandemic epicenters ( Creţan and Light, 2020 ). Similarly, socio-economic disruptions create “a characteristic mosaic pattern in the region” ( Krzysztofik et al., 2020 , p. 583). The disruptions are strongly correlated with the spatial distribution of the COVID-19-related health effects. This study is set in Tennessee which is part of coronavirus “red zone” states of the US southern Sunbelt region. It is among the U.S. states with the highest rates of cases per capita, with 137,829 cases per 1 million people, or the 6th highest as of August 13, 2021 ( Worldometers, 2020 ; https://www.worldometers.info/coronavirus/country/us/ ). The study seeks to explore the impacts of social disadvantage on economy. The impact is measured by employment and unemployment in unprecedented times in the US context of prolonged disruptions to the health system, society, and economy intersecting in complex ways ( Kiang et al., 2020 ). We answer the following questions: (1) Do communities with high social disadvantage already burdened pre-COVID-19 by the lack of income, healthcare access, lacking resources, have less jobs available during the COVID-19 pandemic? (2) Do these areas simultaneously experience higher unemployment compared with other areas in the context of the pandemic?

The paper is organized as follows: Section 1 introduces the topic, provides the background information on social disadvantage and a brief description of the study implementation. It further discusses the links between employment and unemployment, and coronavirus, respectively, and introduces the study area. Section 2 describes in detail materials and methods used in the study. Section 3 provides the theory and calculations. Section 4 reports the results, and Section 5 offers a discussion. Finally, the paper concludes with conclusions found in Section 6 .

1.1. Background

Certain socio-economic and demographic conditions burden some communities more than others including racial and ethnic minorities, lower-income groups, and rural residents. The conditions include lacking economic opportunities and other inequalities ( Petterson et al., 2020 ) caused by social environment. Prior to the pandemic, it was challenging to live in areas with high social disadvantage where residents already have increased vulnerability to poor health due to greater psychosocial stress such as discrimination, unhealthy behaviors, and poorer health status ( Hajat et al., 2015 ). This is true for poor, marginalized communities elsewhere as spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with broader communities ( Méreiné-Berki et al., 2021 ). Within the context of studying disadvantaged urban communities, a recent work by Creţan et al. (2020) focused on the everyday manifestations of contemporary stigmatization of the urban poor using the case study of the Roma people who have been historically subject to state discrimination, ghettoization, inadequate access to education, housing, and the labor market for many decades in the past in multicultural urban societies of Central and Eastern Europe. The inequalities may persist and even increase if left unaddressed during pandemics ( Wade, 2020 ) leading to stark COVID-19-related health and economic disparities. Indeed, during the COVID-19, economic impacts of the pandemic disproportionately affect marginalized groups. The impact of coronavirus was harsh for those people as many of the already existing disparities unfold during COVID-19: black communities in the United States are disproportionately affected by higher death rates due to the COVID-19 virus ( Thebault et al., 2020 ), unemployment, and financial stress. Other growing COVID-19 research similarly suggests that elsewhere outside of the United States, areas that were disadvantaged prior to the pandemic with high rates of poverty and unemployment tended to be affected the strongest by the COVID-19 with the largest concentration of cases, while other spatially segregated ethnicity-based communities (e.g., the Roma) that have been vulnerable decades prior to COVID-19, saw an increase in the existing discrimination and stigmatization experiencing greater marginalization even during the current COVID-19 pandemic period ( Crețan & Light, 2020 ).

To achieve greater economic stability, and secure a dynamic labor market, countries in the global north and south for several decades have been increasing service employment much of which is low wage. The recent book Corona and Work around the Globe ( Eckert and Hentschke, 2020 ) describes the tremendous impact of the pandemic on human life and livelihoods as it sheds light on various experiences of workers during COVID-19 in various countries. Among the dramatically different cases worldwide, Germany which for decades has been promoting the low-wage sector to combat unemployment, provides a good example. The official approach to handling a disease differed substantially depending on whether the infected individuals were working people from the low- or upper-wage sector of the economy: applying a strict lockdown to the entire high-rise building where ethnic workers lived and preventing them from going to work in the former case and granting permission to work from home in the latter ( Mayer-Ahuja, 2020 ). The plight of the agricultural migrant workers who come to Germany from Eastern and Southeastern Europe, subjected during the pandemic to low wages or no payments and poor working and living conditions, however, is shared among the workers of low-wage sector across all countries who are more likely to get infected due to higher exposure and direct contact, but often experience unfair treatment based on ethnicity, migration and class status.

In yet another case set in the U.K., disadvantaged households have experienced intensified disadvantage during the COVID-19 as they could not access vital necessities, already stretched for resources pre-COVID-19. As provision of services or employment was discontinued due to their closure, disadvantaged households had significant impacts on their income level, mental health and wellbeing, education, nutrition, and domestic violence. In the absence of the key support of public institutions including schools, community centers, and social services, care for the most vulnerable members such as elderly, children, the disabled, have been absorbed by households ( Bear et al., 2020 ).

Another aspect experienced by workers during the pandemic is the total loss of earnings which is especially harsh in places with precarious employment even under normal circumstances. Informal workers in India who represent the vast majority of working population (over 93%), with no social security benefits and absent job security, experienced prolonged periods of time of no work due to lockdown and suspended transport services preventing them from getting to their workplaces, many on the verge of starvation ( Banerjee, 2020 ). This study looks into this aspect of COVID-19 economic impacts and confirms the findings of the growing COVID-19 research.

However, not only the poorest and marginalized people, but also marginalized regions are more likely to suffer from broader social and economic damages related to the pandemic compared with more privileged areas ( Creţan and Light, 2020 ; Krzysztofik et al., 2020 ). When disadvantages combine, it may lead to environment-driven COVID-19-related disparities in health. Besides a direct health effect, disadvantaged communities are disproportionally experiencing other side effects of COVID-19 such as negative labor market outcomes including forced unemployment, loss of income and social isolation. Studies found the extreme vulnerability of cities and urban areas exposed during the global pandemic ( Batty, 2020 ; Gössling et al., 2020 ). We argue that rural areas may be equally vulnerable to the broad range of social and economic damages if there is a spatial concentration of factors related to various dimensions of vulnerability.

This study is situated in the context of social disadvantage. Prior studies developed the methodology of the delineation of disadvantaged residential communities proxied by low-income workers ( Antipova, 2020 ). Disadvantaged low-income workers can be defined as those with inadequate access to material and social resources in the study area. However, this is a narrow approach which uses only a single dimension of a disadvantage, that of worker low earnings and misses other social inequality indicators. Accordingly, an approach adopted in this study identifies areas where socio-economic and demographic attributes each associated with multiple dimensions of social disadvantage are spatially co-locating. Spatial segregation of disadvantaged and marginalized communities decreases life opportunities for their members who have limited relationships with wider communities ( Méreiné-Berki et al., 2021 ). We identify these attributes based on a thorough literature review. Thus, we simultaneously consider multiple factors associated with disadvantage capturing a multi-dimensional social disadvantage. To meet the objective, we integrate geospatial science with the exploration of predictive geographic and social factors associated with disadvantage across counties in TN. The geospatial analysis includes point interpolation within the Geographic Information System (GIS) environment for the generation of a surface from a sample of social disadvantage values. This allowed us to visualize the spatial extent of disadvantaged communities. The focus is on labor market outcomes which are important indicators of society well-being. We study the association between pre-existing inequalities and COVID-19-related employment and unemployment rates. Thus, we identify the role of social disadvantage on labor market conditions in the context of the ongoing pandemic-caused economic recession.

Prior research determined the key metrics of social disadvantage. Conditions contributing to various aspects of disadvantage include poverty, occupations with low earnings, low rent, segregation and discrimination-related residential concentrations of minorities, and exposure to poor air quality ( Bullard, 2000 ). The recent COVID-19-related literature focuses on the separate effect of minorities, Hispanics, crowded households, dense areas, obesity, poverty, air pollution exposure and identifies those as important COVID-19 health risk factors ( Finch & Hernández Finch, 2020 ; Golestaneh et al., 2020 ; Han et al., 2020 ; Millett et al., 2020 ). These community-level variables result in neighborhood disadvantage comprising sub-standard housing quality, crowded conditions, poverty- and violence-caused stress which combined increase the risk of disease and other negative outcomes in life among socially disadvantaged groups ( Malhotra et al., 2014 ). The demographic and socio-economic attributes selected to represent the various aspects of social disadvantage in this research include minorities and ethnicities, poverty, housing crowdedness, educational attainment, underlying population health conditions, and pre-COVID-19 unemployment which may collectively drive a greater vulnerability to the COVID-19 infection and mortality as well as loss in employment and higher unemployment. It is challenging to isolate the separate effects of the multiple risk factors. By “critically analyzing the theoretically intended meaning of a concept” ( Song et al., 2013 ), a composite variable can be created to logically represent a multi-dimensional social disadvantage .

The following subsection briefly describes study implementation. First, we locate areas of disadvantage where multiple factors associated with various aspects of disadvantage co-locate spatially and term these places “multi-dimensional social disadvantaged areas”. Then, we examine how employment and unemployment were impacted in these already socially vulnerable areas. We map geographical inequalities in employment and unemployment rates during the period of COVID-19-related economic recession. For the first objective, we identify socially disadvantaged counties within TN which is part of coronavirus “red zone” states of the US southern Sunbelt region applying consistent criteria. For the second objective, we compare employment and unemployment outcomes between areas with high and low disadvantage.

1.1.1. Employment and coronavirus

This subsection discusses the role of employment and how it was impacted by the COVID-19-caused economic recession. The literature recognizes the complex interrelationship between employment and overall health and well-being. Negative COVID-19 impacts on urban economy include loss of citizens' income, while movement restrictions and ‘stay home’ measures adversely impacted tourism and hospitality and small- and medium sized businesses due to the closure of markets, food outlets and social spaces ( Wilkinson et al., 2020 ).

Millions of essential or blue-collar workers are still doing their jobs out of necessity and because they cannot telecommute and work jobs that cannot be done from home and have higher exposure to the virus. Some racial groups disproportionally have jobs that do not allow them to work from home and where social distancing is a challenge. Prior studies find that workplaces of low-income individuals tend to be close to their residential spaces, and disproportionately concentrated in lower-wage industries such as hospitality and retail services ( Antipova, 2020 ). These industries commonly represent essential services experiencing higher exposure to the COVID virus through workplaces. At the same time, minorities and lower-income groups often live in inner-ring suburbs with older housing and aging infrastructure ( Antipova, 2020 ) in multiunit structures and in multigenerational households which inhibit the ability to practice social distancing increasing the risks of disease occurrence and deaths ( Qualls et al., 2017 ). In addition, minorities and lower-income groups have fewer options for protecting both their health and economic well-being ( Gould and Wilson, 2020 ). Nearly two-thirds of Hispanic people (64.5%) considered at high risk for coronavirus live with at least one person who is unable to work from home, compared to 56.5% of black and less than half (47%) of white Americans, according to a recent study ( Selden and Berdahl, 2020 ).

Despite the pandemic-induced layoffs, job hires have occurred by major retailers such as Walmart and e-commerce giant Amazon, and takeout and delivery-based services such as Domino’s Pizza and Papa John’s which may become permanent positions. These workplaces may match the job skill sets of low-income residents of vulnerable communities. However, oftentimes many low-income workers benefitted less, even when jobs were created during the COVID-19. To illustrate, big technology companies (i.e., communication services: Netflix, Tencent, Facebook, T-Mobile; information technology: Microsoft, Nvidia, Apple, Zoom Video, PayPal, Shopify; consumer discretionary: Amazon, Tesla, Alibaba, etc.) prospered in the pandemic with the financial success measured by equity value added ( Financial Times, 2020 ). Workers who lost jobs in low-income segment such as hospitality sector may be hired by retailers such as Kroger or CVS. However, many others from the communities with high social disadvantage may not have a skill set needed at technology firms that benefit from the working from home trend and hire skilled workers including software engineers and product designers. Cross-industry employment shifts plays a minor role in total job creation, while employer-specific factors primarily account for job reallocation ( Barrero et al., 2020 ).

1.1.2. Unemployment and coronavirus

This subsection discusses how unemployment was impacted by the COVID-19-caused economic recession. An economic recession occurs when there is a substantial drop in overall economic activity diffused throughout the economy for longer than a few months. While past recessions were driven by an inherently economic or financial shock, the current recession is caused by a public health crisis ( Weinstock, 2020 ). COVID-19 caused a drop in consumer demand across all industrial sectors resulting in economic recession and massive unemployment where not only hourly workers but salaried professionals lost their jobs ( Petterson et al., 2020 ). A range of factors contributed to the spatial variation in economic damage including the share of jobs in industries delivering non-essential services to in-person customers ( Dey and Loewenstein, 2020 ), declines in personal consumption caused by individual fears of contracting COVID-19 ( Goolsbee and Syverson, 2020 ), and the implementation of social policies including stay-at-home orders and business shutdowns ( Gupta et al., 2020 ).

Unemployment rate is defined as a percentage of unemployed workers in the total labor force. The rate is published monthly by the Bureau of Labor Statistics (BLS) which uses both the establishment data (captured by the Current Employment Statistics program) and household surveys (Current Population Survey) to generate the labor market data ( Bureau of Labor Statistics (BLS), 2020b ). A person is unemployed if they were not employed during the survey’s reference week and who had actively searched for a job in the 4-week period ending with the reference week, and were presently available for work ( BLS, 2020b ).

Caused by the COVID-19, the unemployment rate reached a peak in April 2020 at 14.7% nationwide, an unprecedented joblessness amount since employment data collection started in 1948. It exceeded the previous peaks during the Great Recession and after ( Falk et al., 2020 ). The official unemployment rate may have been over 20%, since the actual level of joblessness could have been understated due to local unemployment rate measurement errors ( Coibion et al., 2020 ). In addition, the unemployment rate was understated due to a geographically widespread misclassification of those who was not at work but considered employed and non-inclusion of labor force non-participants who still counted as employed ( Bureau of Labor Statistics (BLS), 2020a ). Further, the COVID-19 caused the rapid rate of change in unemployment at the national level challenging accurate forecast of the monthly unemployment rate ( Weinstock, 2020 ).

Overall, current unemployment (using the most recently available county-level data at the time of writing for December 2020) is still elevated and is almost twice as high as it was back in February 2020 which represented the business cycle peak with the peak of payroll employment. March 2020 was the first month of the subsequent current economic recession as declared by The National Bureau of Economic Research (NBER, 2020) caused by the COVID-19 pandemic which turned out the worst downturn after the Great Recession. As Fig. 1 shows using the Current Population Survey data (Series ID: LNS14000000) from the BLS, during the prior recessions the unemployment rate rose gradually reaching its peak, and in the pandemic-caused recession it increased unprecedentedly to its peak over one month, from March 2020 to April 2020 by 10.3% (from 3.5% in February 2020 to 4.4% in March 2020 to 14.7% in April). After that, the rate declined as workers continued to return to work to 6.3% in December 2020.

U.S. Historical unemployment rate for workers 16 years and over, January 1948 to December 2020, % (seasonally adjusted).

Some communities can absorb the impact of economic downturns due to more favorable economic and social factors protecting residents from adversity. Yet other communities are witnessing the effect of rising unemployment in the time of COVID-19. Loss of income and livelihood has further effects: as wages drop, more people are forced into poverty while simultaneously people's health is impacted. Unemployment impacts all-cause mortality. Fig. 2 presents the dynamics of unemployment distribution across counties in TN for the selected months. Shown are pre-COVID-19 unemployment rates as of August 2019 ( Fig. 2 a), followed by May 2020 ( Fig. 2 b) where even the lowest levels of unemployment exceed the highest rates of the pre-pandemic period even in wealthy counties around Nashville (seen in the legend entries), August 2020 ( Fig. 2 c), and September 2020 ( Fig. 2 d). The overall unemployment abates somewhat during the later stage, and the general spatial pattern resembles that of the pre-COVID-19 period with higher unemployment concentrated in the southwestern corner of the state around Memphis.

Dynamics of unemployment rate across counties in TN for selected months: (a) August 2019, (b) May 2020; (c) August 2020; (d) September 2020.

1.1.3. Study area

Tennessee is home to large cities including Nashville (the county seat), Memphis, Knoxville and Chattanooga. Despite urban diversified economy, there was a steep decline in the number of international and domestic tourists impacting urban economy. Among cities listed above, Memphis, located in Shelby County, is a shrinking city with a declining population base. Urban shrinkage makes cities more vulnerable due to very negative impacts on urban economy. Shrinking cities are characterized by higher unemployment rates, depopulation (as people with higher economic and social status leave elsewhere), and a higher share of older people (increasing a share of individuals with underlying health conditions) ( Haase et al., 2014 ; Hartt 2019 ; Hoekveld 2012 ; Krzysztofik et al., 2020 ). The shrinking cities have higher exposure to extreme socioeconomic phenomena, including financial stress due to the decreases in the city’s budget. Decreasing budget in its turn has further urban development implications since implementation of some plans deemed of lesser priority such as environmental and cultural may be delayed and cancelled altogether ( Kunzmann, 2020 ; Sharifi and Khavarian-Garmsir, 2020 ).

Tennessee is one of the US southern Sunbelt states which had infection surges since summer 2020 due to the aggressive push for economy opening by then-President Trump administration. The pandemic has affected unemployment for every state in the United States ( Falk et al., 2020 ). Fig. 3 portrays selected industries impacted by the economic recession in Tennessee using seasonally adjusted data on employees on nonfarm payrolls for November 2019 (as a base period), September–November 2020. Unemployment rates concentrate disproportionately in sectors providing in-person non-essential services where some demographic groups are overrepresented. This results in substantially higher unemployment rates for those workers ( Cortes and Forsythe, 2020 ; Fairlie, 2020 ). Accordingly, it can be seen in Fig. 3 that in Tennessee, among the reported industries, leisure and hospitality has suffered the most, followed by jobs in government, education and health services, professional and business services, and trade, transportation, utilities. There was a slight increase in jobs in financial activities from 2019 to 2020 ( Bureau of Labor Statistics (BLS), 2020a ). The hardest hit industries tend to employ demographic groups such as women, minorities, low-income workers, and younger workers who have experienced greater job losses ( Murray and Olivares, 2020 ).

Employees on nonfarm payrolls by selected industry sector, seasonally adjusted, in TN.

2. Materials and methods

In the absence of fine-scale monthly data on employment and unemployment, we sourced county-level data from the Bureau of Labor Statistics (BLS) to track monthly changes in employment and unemployment in Tennessee (retrieved from https://www.bls.gov/lau/ ). Labor force data were extracted from this official primary source.

We used a comparative assessment approach to analyze the COVID-19-based labor market outcomes including the rates of COVID-19-related employment and unemployment attributable to social disadvantage conditions. For this, we stratify data based on community disadvantage status, and combine data in a comparative assessment framework. We proceed and identify disadvantaged communities using the methodology described below. Next, we test the hypothesis that in areas with high social disadvantage where more essential workers are more likely to reside, the unemployment is higher while employment opportunities are lower by comparing unemployment and employment rates within these communities to those of more privileged communities.

3. Theory/calculation

We focus on the areas where the multiple risk factors identified in the recent literature co-locate spatially and term these places “ multi-dimensional social disadvantaged areas ”. We carried out a rigorous literature review of the variables to stand in for social disadvantage in this research. The following demographic and socio-economic factors have been selected to represent community’s vulnerability: (1) Minorities and ethnicity; (2) Crowded households; (3) Poverty; (4) Education; (5) Underlying medical conditions (obesity); and (6) Unemployment. For the 1st variable, minorities and ethnicity , we used percent minority population and Hispanic ethnicity as studies commonly use race and ethnicity as vulnerability metrics (as explained in Section 2 Background information). For the 2nd variable, crowded households , we used percent households that are multigenerational as an indicator of crowdedness, and thus, indicating area’s disadvantage with a high share of such households. For the 3rd variable, poverty , we chose percent of households below 100% of federal poverty level which is also known as the poverty line. It is an economic measure of income. The poverty guidelines are updated annually by the US Department of Health and Human Services to indicate the minimum income needed by a family for housing, food, clothing, transportation, and other basic necessities and to determine eligibility for certain welfare benefits. This measure was used because less affluent and less privileged households have fewer means and less access to various resources to cope with the effects of financial crises ( Pfeffer et al., 2013 ). Low-income households may be especially vulnerable to wage losses during the outbreak ( Qian and Fan, 2020 ). For the 4th variable, education , we used percent of population with less than high school diploma since lower educational attainment is an indicator of poverty and thus captures social disadvantage, while workers with better education have higher economic resilience when challenged with a large-scaled social shock ( Cutler et al., 2015 ; Kalleberg, 2011 ). For the 5th variable, underlying medical conditions , we used percent population with obesity as the top risk for COVID-19-related hospitalization. Supported by several lines of evidence, both domestically and internationally, obesity may predispose to more severe COVID-19 outcomes ( O’Hearn et al., 2021 ). Finally, for the 6th variable, unemployment , unemployment rate (averaged from August 2019 to January 2020 to adjust for seasonality) was used as a marker of overall vulnerability as it is linked to overall mortality. Further, regions with higher unemployment are more susceptible to business-cycle fluctuations, and thus, are more socially and economically vulnerable.

These socio-economic and demographic attributes (minority population, Hispanic ethnicity, federal poverty level, crowded households, adult obesity, lower educational attainment, and unemployment) have been used in this research to create a composite variable to represent a multi-dimensional social disadvantage (also referred to as vulnerability). Due to different variances in the original variables, we standardized them to prevent a disproportionate impact which may be caused by any one original variable with a large variance. The z-score transformation was applied by averaging the original variables and computing z scores with a mean of 0 and values ranging from negative to positive numbers ( Song et al., 2013 ).

Thus, the original variables were converted to z-scores to preserve the distribution of the raw scores and to ensure the equal contributions of the original variables. Next, we created a composite variable capturing a multi-dimensional social disadvantage. It was calculated by summing standardized z-scores of the original risk factors. The higher value can be interpreted as higher disadvantage while the lower value means more privileged communities. Based on the frequency distribution of values of the composite variable, we established a cut-off value for the composite variable to designate communities with high or low exposure to social disadvantage. We used the following method to determine the cut-off value of the composite variable. The values greater than 3.38 correspond to 1 standard deviation above the mean (or, the 88th percentile in the value distribution) indicating communities in the top 12 percent of social disadvantage and therefore, a higher share of factors contributing to disadvantage. This value was used to differentiate communities according to their disadvantage status. We identified twelve counties with high social disadvantage (N high  = 12), and other counties represent more privileged communities (N low  = 83). To test whether the taken approach correctly identifies disadvantaged communities, we conducted a Wilcoxon two-sample test for the variables of interest ( Table 1 ). We report the results of the estimates in the following section. The above socio-economic and demographic population characteristics come from the 2018 American Community Survey (ACS) 5-year data, an annual nationwide survey conducted by the US Census Bureau, available for various geographic units and applied for areal units within the study area ( U. S. Census Bureau, 2020 ).

Descriptive statistics.

The basic descriptive demographic and socio-economic characteristics of the TN population are shown in Table 1 . It includes the summaries for communities with high and low social disadvantage allowing to compare the variables of interest between these communities. The following variables are reported: percent African American, percent Hispanic, median income, percent of people over 25 years who are less than high school graduates, estimated percent of obese adults, percent households below 100% of federal poverty level, and percent of multi-generation households. The factors comprising social disadvantage were statistically significantly different than those extant in more privileged counties. Compared with the general TN population, the disadvantaged cohort was generally more likely to be of non-Hispanic Black race; more impoverished; with less educational attainment, more obese, and had more households with crowded conditions.

To visualize social disadvantage and show how it varies across the space, we used our sample of social disadvantage measurements and created a surface of social disadvantage within the study area using the Geographic Information System (GIS). The interpolated surface was derived from an Inverse Distance Weighted technique ( Watson and Philip, 1985 ). Fig. 4 presents the surface illustrating that both urban and rural counties in Tennessee are subject to social disadvantage.

Social disadvantage within the study area.

We examined how unemployment changed from August 2019 to December 2020. Currently, all counties have substantially higher unemployment compared with that prior to COVID. Fig. 5 presents the results of the Nonparametric One-Way ANOVA test showing the distribution of Wilcoxon scores for unemployment rate for all counties in Tennessee combined, regardless of social disadvantage status, for 17 months. A statistically significant difference is found for unemployment rates between the pre-COVID period and the period since April 2020, with current unemployment rates although decreased but still significantly higher compared with those prior to the recession.

Nonparametric One-Way ANOVA and distribution of Wilcoxon scores for unemployment rate for all counties combined for 17 months (August 2019–October 2020), regardless of social disadvantage status.

We compared employment and unemployment rates for Tennessee counties stratified by the type of social disadvantage separately for each month. Fig. 6 presents the average employment and unemployment rates by community disadvantage from August 2019 to December 2020 in a graphical form. The results of the non-parametric Wilcoxon test for employment and unemployment rates are presented in Table 2 . Pre-COVID and before the unemployment peak in April 2020, communities with high social disadvantage consistently had less jobs and greater unemployment, which we tested statistically and found a significant difference for both outcomes of the labor market between communities by their disadvantage status ( Table 2 ). Shown in Table 2 , in April and May 2020, during the peak of unemployment and immediately after, unemployment rates observed in both types of communities were high with no statistical difference. In June, the differences again became prominent, when there were more jobs available in more advantaged areas and employment rate remained consistently greater in areas with less disadvantage. Also in June, unemployment rate remained consistently greater in areas with higher disadvantage. This month saw the greater difference in both outcomes since the COVID-19 than pre-pandemic (supported by higher p-values). Compared with all TN population, residents of disadvantaged counties had less jobs available and were more likely to be unemployed during all periods except for April and May.

Mean employment and unemployment stratified by community disadvantage status.

Wilcoxon Two-Sample Test: Distribution of Wilcoxon scores in employment and unemployment rates by community disadvantage status by month (August 2019–December 2020).

We examined the percent change in both labor market outcomes. Fig. 7 presents the percent change in mean employment ( Fig. 7 a), and mean unemployment by community disadvantage ( Fig. 7 b). The percent change in employment and unemployment was relatively small in both types of community during the pre-COVID period. However, the overall fluctuations in both conditions were greater in communities with high social disadvantage (evidenced by a greater range between ups and downs for disadvantaged communities shown with the black-colored symbols). On the other hand, employment and unemployment were more stable in more privileged communities (shown with the grey-colored symbols in the Fig. 7 ). During the unemployment peak in April 2020, the change in percent employment was −11.5 points from the previous month even in more advantaged counties, while the unemployment in April increased by 10.42 percentage points in disadvantaged counties.

Percent change in (a) mean employment; (b) mean unemployment by community disadvantage.

We show how various factors of social disadvantage intersect and combined impact economic vulnerability measured by unemployment rate. Fig. 8 reports the link between unemployment and social disadvantage pre-COVID (unemployment rate was averaged over August 2019–January 2020 in Fig. 8 a), and during COVID (unemployment rate for November 2020 is shown in Fig. 8 b). During the COVID pandemic, its impact is even stronger as evidenced by a greater slope of the line of fit, larger coefficients, and a greater R-squared value ( Fig. 8 b). The strong relationship between these factors of social disadvantage and economic outcomes in COVID-19 might inform post-COVID recovery intervention strategies to reduce COVID-19-related economic vulnerability burdens. For example, in the light of findings on socio-economic and demographic subpopulations at a higher risk for economic damages, prioritization of economic relief distribution might be based on community disadvantage status targeting individuals from areas with existing inequalities to increase economic resilience of marginalized communities.

Unemployment and Social disadvantage: (a) pre-COVID (averaged August 2019–January 2020); (b) during COVID (November 2020).

5. Discussion

Current studies on the impacts of COVID-19 tend to focus on medical aspects while non-medical urban research mostly analyzes the role of environmental quality. To better understand the full effects of pandemics on communities and minimize the various impacts as well as to improved response, other aspects need to be examined. This includes studying less researched themes including socio-economic impacts consisting of both social impacts and social factors making individuals and communities less resilient and more vulnerable to the effects of the COVID. Additionally, economic impacts of the pandemic-caused recession so far remain relatively underexplored and need to be investigated ( Sharifi and Khavarian-Garmsir, 2020 ).

Communities are often severely segregated along wealth and social lines in developing and developed world ( Wilkinson et al., 2020 ). We study the role of social factors and the impact of the COVID on labor market conditions in Tennessee. Specifically, we studied the impacts of social environment on employment and unemployment through the concept of a multi-dimensional social disadvantage by using geospatial science.

A recent study identified factors which can make a community more vulnerable to the pandemic’s effects using as a case study the province of Silesia in Poland, one of the largest industrial and mining regions in Europe. Specialized functions such as mining-oriented industries, large care centers, polycentricity, and urban shrinkage make communities most at risk due to very negative impacts on urban economy ( Krzysztofik et al., 2020 ). Since vulnerability is always very context-specific, we found a combination of different causal factors of social disadvantage captured by a composite variable making communities most at risk during the COVID reflected in broader social and economic outcomes. In creating a composite variable to capture social disadvantage logically and meaningfully, the following variables were used: % African American, % Hispanic, % below 100% federal poverty level, % population with less than high school diploma (an indicator of poverty), % multi-generation households (an indicator of crowdedness), % estimated obese adults reporting to be obese with the BMI 30 or greater, % unemployed. The proposed method can be generalized beyond the study area and used as a tool by policy makers using consistent criteria for the delineation of areas carrying a greater risk for the more severe impact by the pandemic due to co-existence and co-location of the multi-dimensional social disadvantage factors which are more likely to experience further socio-economic disruptions.

Current urban research on COVID economic impacts found that some cities are more vulnerable than others and are most at risk. Cities with an undiversified economic structure with industries where a large number of workers are shoulder-to-shoulder share cramped spaces for a prolonged time and where social distancing is challenging (e.g., meat-packing and poultry processing plants), cities relying on tourism as well as cities that have large care centers, polycentric cities, and shrinking cities are the most vulnerable to negative impacts on urban economy. The urban hotel market, city tax revenues, citizens' income, tourism and hospitality, small- and medium sized firms, urban food supply chain, and migrant workers are all impacted ( Krzysztofik et al., 2020 ). Other recent studies similarly concluded that the COVID has revealed the extreme vulnerability of cities and urban areas disrupting tourism and affecting supply chains in cities ( Batty, 2020 ; Gössling et al., 2020 ). We support this statement but also find that rural areas can experience a broad range of social and economic damages related to COVID.

Before and during the COVID-19 period, money laundering, limitations of economic development, environmental pollution and uncontrolled deforestation, population displacement, institutional incompetence, and corruption of political elites have been debated including corruption and conflagration in Bucharest before the pandemic ( Creţan & O’Brien, 2020 ), as well as other contestations on selling masks and different medical products highlighted in different countries during the pandemic period. Following catalytic events, the affected community may respond to long-held concerns with demands to address these problems bringing about important changes to the systems. Marginalized stigmatized minorities may effectively overcome discriminatory laws, higher poverty and other constraints and influence public opinion and politics in their favor through collective action via various strategies including protests against corruption and the inaction of the political leaders in Romania in 2015 forcing the resignation of the Government, and protests in the US in the aftermath of police violence against black people have been documented ( Creţan & O’Brien, 2020 ; Fryer, 2019 ). During the COVID-19, the non-payment of wages and poor working and living conditions caused seasonal workers in Germany to protest against this unfair treatment, however, generating low coverage in the national press ( Mayer-Ahuja, 2020 ).

6. Conclusions

Some socio-economic and demographic conditions consistently and significantly impact some communities more often than others, particularly based on ethnic minority status, low income, and rural location. The conditions include systemic issues such as fragmented health care system (within which some individuals do not get health care in a timely fashion), racism and structural disparities in education, income, wealth, a consistent lack of economic opportunity, environmental factors, transportation and housing ( Petterson et al., 2020 ). These factors interact in complex ways resulting in persisting social environment-driven health and other inequalities which if left unaddressed will only increase.

Respectively, among policies goals across the Global North enhancing wellbeing and social mobility for disadvantaged and marginalized families, creating socially mixed, heterogeneous neighborhoods (that is, desegregation) is promoted to avoid spatial segregation based on racial and ethnic membership and class while supporting social cohesion ( Méreiné-Berki et al., 2021 ). Importantly, a marginalized community is not a homogeneous group as the lived experience of disadvantage within the communities is variegated: respectively, policies to improve socio-spatial integration and addressing the various causes of extreme poverty including social, economic, and cultural that improve social equity have been suggested since desegregation on its own is insufficient (( Méreiné-Berki et al., 2021 ). Sustainable planning may mitigate consequences of urban sprawl noted in the urban studies literature including urban blight which is the greatest in poorest areas entrapping the low-income residents in the inner city where they have only limited regional mobility and access to job opportunities at the urban edge. Understanding the links between a development of a metropolitan-wide blight remediation strategy toward a sustainable urban form and welfare enhancing among the disadvantaged populations needs to be further investigated.

During public health crises, the importance of the central role of the community has been highlighted especially when some state-based social services may be less available due to lockdown. Rather than inventing new solutions, voluntary informal social networks that have been generated by communities utilize local assets and resources ( Bear et al., 2020 ). Community-based initiatives may rely on the voluntary sector, faith- and charities-based organizations, and social enterprises for various services including help with visiting housebound people, or using them as a distribution hub for food distribution to families in need.

In conclusion, in this study, we situated the research on economic impacts of the COVID in the broader context of social disadvantage with findings both domestically and from other countries in line with those in our study. The earlier misleading view of the global epidemic representing a systematic disadvantage that may affect and limit everyone’s economic activity, with any socioeconomic status or from any geographic location, was rejected. Our finding indicates that certain factors may increase people's vulnerability to the financial stress related to COVID-19. We find support that the social distribution of economic vulnerability is magnified in regions with pre-existing social disparities, creating new forms of disparity ( Qian and Fan, 2020 ).

This work was supported by the UTHSC/UofM SARS-CoV-2/COVID-19 Research CORNET (Collaboration Research Network) Award.

CRediT authorship contribution statement

Anzhelika Antipova: Conceptualization, Formal analysis, Methodology, Visualization, Writing – original draft, Writing – review & editing.

Declaration of competing interest

The author declares no conflict of interest.

• Antipova A. Analysis of Commuting Distances of Low-Income Workers in Memphis Metropolitan Area. TN. Sustainability. 2020; 12 (3):1209. doi: 10.3390/su12031209. [ CrossRef ] [ Google Scholar ]
• Banerjee S. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Skill, informality, and work in pandemic times: Insights from India; pp. III–IX. 2020. [ CrossRef ] [ Google Scholar ]
• Barrero J.M., Bloom N., Davis S.J. COVID-19 also a reallocation shock. 2020. https://bfi.uchicago.edu/wp-content/uploads/BFI_WP_202059.pdf Working paper NO. 2020-59. At:
• Batty M. The coronavirus crisis: What will the post-pandemic city look like? Environ. Plan. B: Urban Anal. City Sci. 2020; 47 (4):547–552. [ Google Scholar ]
• Bear L., James D., Simpson N., Alexander E., Bhogal J.K., Bowers R.E., Cannell F., Lohiya A.G., Koch I., Laws M., Lenhard J.F., Long N.J., Pearson A., Samanani F., Wuerth M., Vicol O., Vieira J., Watt C., Whittle C., Zidaru-Barbulescu T. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Changing care networks in the United Kingdom; pp. VVIII–VVX. [ CrossRef ] [ Google Scholar ]
• Bullard R. Westview Press; 2000. Dumping in Dixie: Race, class, and environmental quality, third edition: Bullard, Robert D.: 9780813367927: Amazon.com: Books. [ Google Scholar ]
• Bureau of Labor Statistics (BLS) Economic news release. State employment and unemployment —NOVEMBER 2020. December 18, 2020. 2020. https://www.bls.gov/news.release/laus.nr0.htm USDL-20-2267. At:
• Bureau of Labor Statistics (BLS) Frequently asked questions: The impact of the coronavirus (COVID-19) pandemic on the Employment Situation for April 2020. 2020. https://www.bls.gov/cps/employment-situation-covid19-faq-april-2020.pdf May 8, 2020. At:
• Cai Q., et al. Obesity and COVID-19 severity in a designated hospital in shenzhen, China. Diabetes Care. 2020; 43 (7):1392–1398. [ PubMed ] [ Google Scholar ]
• Coibion O., Gorodnichenko Y., Weber M. NBER working paper No. 27017. 2020. Labor markets during the COVID-19 crisis: A preliminary view. April 2020. [ Google Scholar ]
• Cortes G.M., Forsythe E. Upjohn Institute Working Paper; May 2020. The heterogeneous labor market impacts of the Covid-19 pandemic. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Creţan R., Light D. COVID-19 in Romania: Transnational labour, geopolitics, and the Roma ‘outsiders. Eurasian Geography and Economics. 2020; 61 (4–5):559–572. [ Google Scholar ]
• Creţan R., Málovics G., Méreiné-Berki B. On the perpetuation and contestation of racial stigma: Urban Roma in a disadvantaged neighbourhood of Szeged. Geographica Pannonica. 2020; 24 (4):294–310. [ Google Scholar ]
• Creţan R., O’Brien T. Corruption and conflagration: (in)justice and protest in bucharest after the colectiv fire. Urban Geography. 2020; 41 (3):368–388. doi: 10.1080/02723638.2019.1664252. [ CrossRef ] [ Google Scholar ]
• Cutler D.M., Huang W., Lleras-Muney A. When does education matter? The protective effect of education for cohorts graduating in bad times. Social Science & Medicine. 2015; 127 :63–73. 2015. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dey M., Loewenstein M.A. How many workers are employed in sectors directly affected by COVID-19 shutdowns, where do they work, and how much do they earn? Monthly Labor Review. April 2020 https://www.bls.gov/opub/mlr/2020/article/covid-19-shutdowns.htm [ Google Scholar ]
• Eckert A., Hentschke F. Andreas Eckert and Felicitas Hentschke. De Gruyter; Berlin, Boston: 2020. Introduction: Corona and work around the Globe". Corona and work around the Globe; pp. XVII–XXII. 2020. [ CrossRef ] [ Google Scholar ]
• Fairlie R. NBER working paper No. 27309, June 2020. 2020. The impact of covid-19 on small business owners: Evidence of early-stage losses from the April 2020 current population survey. [ Google Scholar ]
• Falk G., Carter J.A., Nicchitta I.A., Nyhof E.C., Romero P.D. Unemployment rates during the COVID-19 pandemic. 2020. https://fas.org/sgp/crs/misc/R46554.pdf Brief. Nov. 2020. Prepared by the Congressional Research Service (CRS). CRS Report R46554. At:
• Financial Times “Prospering in the pandemic: The top 100 companies,” 18 June. 2020. https://www.ft.com/content/844ed28c-8074-4856-bde0-20f3bf4cd8f0 At:
• Finch W.H., Hernández Finch M.E. Poverty and covid-19: Rates of incidence and deaths in the United States during the first 10 Weeks of the pandemic. Front. Sociol. 2020; 5 :1–10. June. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fryer R.G.J. An empirical analysis of racial differences in police use of force. Journal of Political Economy. 2019; 127 (3):1210–1261. [ Google Scholar ]
• Golestaneh L., et al. The association of race and COVID-19 mortality. EClinicalMedicine. 2020; 25 :100455. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Goolsbee A., Syverson C. NBER working paper No. 27432. June 2020. Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gössling S., Scott D., Hall C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism. 2020:1–20. [ Google Scholar ]
• Gould E., Wilson V. 2020. Black workers face two of the most lethal preexisting conditions for coronavirus — racism and economic inequality. [ Google Scholar ]
• Gupta S., et al. NBER working paper No. 2780. May 2020. Effects of social distancing policy on labor market outcomes. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Haase A., Rink D., Grossmann K., Bernt M., Mykhnenko V. Conceptualizing urban shrinkage. Environment and Planning A. 2014; 46 (7):1519–1534. doi: 10.1068/a46269. [ CrossRef ] [ Google Scholar ]
• Hajat A., Hsia C., O’Neill M.S. Vol. 2. Springer; 2015. Socioeconomic disparities and air pollution exposure: A global review; pp. 440–450. (Current environmental health reports). 4. 01-Dec. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Han J., Meyer B.D., Sullivan J.X. 2020. Income and poverty in the COVID-19 pandemic. [ Google Scholar ]
• Hartt M. The prevalence of prosperous shrinking cities. Annals of the Association of American Geographers. 2019; 109 (5):1651–1670. doi: 10.1080/24694452.2019.1580132. [ CrossRef ] [ Google Scholar ]
• Hoekveld J.J. Time-space relations and the differences between shrinking regions. Built Environment. 2012; 38 (2):179–195. doi: 10.2148/benv.38.2.179. [ CrossRef ] [ Google Scholar ]
• Jones B.L., Jones J.S. Gov. Cuomo is wrong, covid-19 is anything but an equalizer. Washington Post. 2020 https://www.washingtonpost.com/outlook/2020/04/05/gov-cuomo-is-wrong-covid-19-is-anything-an-equalizer/ Accessed from. [ Google Scholar ]
• Kalleberg A.L. Russell Sage Foundation; New York, NY: 2011. Good jobs, bad jobs: The rise of polarized and precarious employment systems in the United States, 1970s-2000s. 2011. [ Google Scholar ]
• Kass D.A., Duggal P., Cingolani O. Obesity could shift severe COVID-19 disease to younger ages. Lancet. 2020; 395 (10236):1544–1545. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kiang M.V., Irizarry R.A., Buckee C.O., Balsari S. Every body counts: Measuring mortality from the COVID-19 pandemic. Annals of Internal Medicine. Sep. 2020:M20–M3100. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krzysztofik R., Kantor-Pietraga I., Spórna T. Spatial and functional dimensions of the COVID-19 epidemic in Poland. Eurasian Geography and Economics. 2020; 61 (4–5):573–586. 10.1080/15387216.2020.1783337. [ Google Scholar ]
• Kunzmann K.R. Smart cities after covid-19: Ten narratives. disP - Plan. Rev. 2020; 56 (2):20–31. [ Google Scholar ]
• Malhotra K., Baltrus P., Zhang S., Mcroy L., Immergluck L.C., Rust G. Geographic and racial variation in asthma prevalence and emergency department use among Medicaid-enrolled children in 14 southern states. Journal of Asthma. 2014; 51 (9):913–921. Nov. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Mayer-Ahuja N. In: Corona and work around the Globe. Eckert A., Hentschke F., editors. De Gruyter; Berlin, Boston: 2020. Solidarity’ in times of Corona? Of migrant Ghettos, low-wage heroines, and empty public coffers; pp. XVII–XXII. 2020. [ CrossRef ] [ Google Scholar ]
• Méreiné-Berki B., Málovics G., Crețan R. “You become one with the place”: Social mixing, social capital, and the lived experience of urban desegregation in the Roma community. Cities. 2021; 117 :103302. [ Google Scholar ]
• Millett G.A., et al. Assessing differential impacts of COVID-19 on black communities. Annals of Epidemiology. 2020; 47 :37–44. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Murray S., Olivares E. 2020. Job losses during the onset of the COVID-19 pandemic: Stay-at-home orders, industry composition, and administrative capacity (June 18, 2020) https://ssrn.com/abstract=3633502 Available at SSRN: [ CrossRef ] [ Google Scholar ]
• O’Hearn M., Liu J., Cudhea F., Micha R., Mozaffarian D. Coronavirus disease 2019 hospitalizations attributable to cardiometabolic conditions in the United States: A comparative risk assessment analysis. Journal of the American Heart Association. 2021 doi: 10.1161/JAHA.120.019259. https://www.ahajournals.org/doi/abs/10.1161/JAHA.120.019259 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Petterson S., Westfall J.M., Miller B.F. Well Being Trust; 2020. Projected deaths of despair from COVID-19. Well being trust. [ Google Scholar ]
• Pfeffer T., Danziger S., Schoeni R.F. Wealth disparities before and after the great recession. The Annals of the American Academy of Political and Social Science. 2013; 650 (1):98–123. 2013. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Price-Haywood E.G., et al. Hospitalization and mortality among black patients and white patients with Covid-19. New England Journal of Medicine. 2020; 382 (26):2534–2543. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Qian Y., Fan W. Research in social stratification and mobility. JAI Press; 2020. Who loses income during the COVID-19 outbreak? Evidence from China. [ Google Scholar ]
• Qualls N., et al. Community mitigation guidelines to prevent pandemic influenza — United States, 2017. MMWR. Recomm. Reports. Apr. 2017; 66 (1):1–34. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Selden T.M., Berdahl T.A. COVID-19 and racial/ethnic disparities in health risk, employment, and household composition. Health Affairs. Sep. 2020; 39 (9):1624–1632. [ PubMed ] [ Google Scholar ]
• Sharifi A., Khavarian-Garmsir A.R. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. The Science of the Total Environment. 2020; 749 :1–3. 2020. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Song M.K., Lin F.C., Ward S.E., Fine J.P. Composite variables: When and how. Nursing Research. Jan. 2013; 62 (1):45–49. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• The National Bureau of Economic Research https://www.nber.org/cycles.html At:
• Thebault Reis, Tran Andrew Ba, Williams Vanessa. “African Americans are at higher risk of deathfrom coronavirus”, The Washington Post, 07-Apr-2020. The Washington Post. 2020 https://www.washingtonpost.com/nation/2020/04/07/coronavirus-is-infecting-killing-black-americans-an-alarmingly-high-rate-post-analysis-shows/ [ Google Scholar ]
• U. S. Census Bureau 2018 American Community Survey (ACS 2018 5-year) 2020. https://www.census.gov/programs-surveys/acs/data.html [Online]. Available:
• Wade L. An unequal blow. Science. 2020; 368 (6492):700–703. [ PubMed ] [ Google Scholar ]
• Watson D.F., Philip G.M. A refinement of Inverse distance weighted interpolation. Geo-Processing. 1985; 2 :315–327. [ Google Scholar ]
• Weinstock L.R. Prepared by the congressional research service (CRS). CRS report IN11460. 2020. COVID-19: How quickly will unemployment recover? https://crsreports.congress.gov/product/pdf/IN/IN11460 AT: [ Google Scholar ]
• Wilkinson A., Ali H., Bedford J., Boonyabancha S., Connolly C., Conteh A., Dean L., Decorte F., Dercon B., Dias S., Dodman D., Duijsens R., D’Urzo S., Eamer G., Earle L., Gupte J., Frediani A.A., Hasan A., Hawkins K.…Whittaker L. Local response in health emergencies: Key considerations for addressing the COVID-19 pandemic in informal urban settlements. Environment and Urbanization. 2020; 32 (2):503–522. https://journals.sagepub.com/doi/full/10.1177/ [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Worldometers COVID-19 coronavirus pandemic. 2020. https://www.worldometers.info/coronavirus/country/us/ At:
• Wu D., Yu L., Yang T., Cottrell R., Peng S., Guo W., Jiang S. The Impacts of Uncertainty Stress on Mental Disorders of Chinese College Students: Evidence From a Nationwide Study. Frontiers in Psychology. 2020; 11 :243. [ PMC free article ] [ PubMed ] [ Google Scholar ]

• Privacy Policy

Home » 500+ Quantitative Research Titles and Topics

500+ Quantitative Research Titles and Topics

Table of Contents

Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

• “Statistical Analysis of Supply Chain Disruptions on Retail Sales”
• “Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
• “Predicting Stock Market Trends Using Machine Learning Algorithms”
• “Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
• “Impact of Economic Policies on Small Businesses: A Regression Analysis”
• “Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
• “Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
• “Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
• “Price Elasticity of Demand for Luxury Goods: A Case Study”
• “The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
• “Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
• “Examining the Correlation Between Interest Rates and Consumer Spending”
• “Standardized Testing and Academic Performance: A Quantitative Evaluation”
• “Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
• “The Relationship Between Extracurricular Activities and Academic Success”
• “Influence of Parental Involvement on Children’s Educational Achievements”
• “Digital Literacy in Primary Schools: A Quantitative Assessment”
• “Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
• “Correlation Between Teacher Experience and Student Success Rates”
• “Analyzing the Impact of Classroom Technology on Reading Comprehension”
• “Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
• “The Relationship Between Homework Load and Academic Burnout”
• “Assessment of Special Education Programs in Public Schools”
• “Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

• “The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
• “Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
• “Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
• “Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
• “Correlations Between Stress Levels and Immune System Functioning”
• “Smoking and Lung Function: A Quantitative Analysis”
• “Influence of Physical Activity on Mental Health in Older Adults”
• “Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
• “The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
• “Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
• “Impact of Hospital Design on Patient Recovery Rates”
• “Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

• “Examining Social Inequality through Wage Distribution: A Quantitative Study”
• “Impact of Parental Divorce on Child Development: A Longitudinal Study”
• “Social Media and its Effect on Political Polarization: A Quantitative Analysis”
• “The Relationship Between Religion and Social Attitudes: A Statistical Overview”
• “Influence of Socioeconomic Status on Educational Achievement”
• “Quantifying the Effects of Community Programs on Crime Reduction”
• “Public Opinion and Immigration Policies: A Quantitative Exploration”
• “Analyzing the Gender Representation in Political Offices: A Quantitative Study”
• “Impact of Mass Media on Public Opinion: A Regression Analysis”
• “Influence of Urban Design on Social Interactions in Communities”
• “The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
• “Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

• “Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
• “Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
• “Impacts of Data Center Cooling Solutions on Energy Consumption”
• “Analyzing the Reliability of Renewable Energy Sources in Grid Management”
• “Optimization of 5G Network Performance: A Quantitative Assessment”
• “Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
• “The Relationship Between Software Complexity and Bug Frequency”
• “Machine Learning in Predictive Maintenance: A Quantitative Analysis”
• “Wearable Technologies and their Impact on Healthcare Monitoring”
• “Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
• “Analysis of Noise Pollution from Urban Transportation Systems”
• “The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

• The effects of social media on self-esteem among teenagers.
• A comparative study of academic achievement among students of single-sex and co-educational schools.
• The impact of gender on leadership styles in the workplace.
• The correlation between parental involvement and academic performance of students.
• The effect of mindfulness meditation on stress levels in college students.
• The relationship between employee motivation and job satisfaction.
• The effectiveness of online learning compared to traditional classroom learning.
• The correlation between sleep duration and academic performance among college students.
• The impact of exercise on mental health among adults.
• The relationship between social support and psychological well-being among cancer patients.
• The effect of caffeine consumption on sleep quality.
• A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
• The relationship between physical attractiveness and job opportunities.
• The correlation between smartphone addiction and academic performance among high school students.
• The impact of music on memory recall among adults.
• The effectiveness of parental control software in limiting children’s online activity.
• The relationship between social media use and body image dissatisfaction among young adults.
• The correlation between academic achievement and parental involvement among minority students.
• The impact of early childhood education on academic performance in later years.
• The effectiveness of employee training and development programs in improving organizational performance.
• The relationship between socioeconomic status and access to healthcare services.
• The correlation between social support and academic achievement among college students.
• The impact of technology on communication skills among children.
• The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
• The relationship between employee turnover and organizational culture.
• The correlation between job satisfaction and employee engagement.
• The impact of video game violence on aggressive behavior among children.
• The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
• The relationship between bullying and academic performance among middle school students.
• The correlation between teacher expectations and student achievement.
• The impact of gender stereotypes on career choices among high school students.
• The effectiveness of anger management programs in reducing violent behavior.
• The relationship between social support and recovery from substance abuse.
• The correlation between parent-child communication and adolescent drug use.
• The impact of technology on family relationships.
• The effectiveness of smoking cessation programs in promoting long-term abstinence.
• The relationship between personality traits and academic achievement.
• The correlation between stress and job performance among healthcare professionals.
• The impact of online privacy concerns on social media use.
• The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
• The relationship between teacher feedback and student motivation.
• The correlation between physical activity and academic performance among elementary school students.
• The impact of parental divorce on academic achievement among children.
• The effectiveness of diversity training in improving workplace relationships.
• The relationship between childhood trauma and adult mental health.
• The correlation between parental involvement and substance abuse among adolescents.
• The impact of social media use on romantic relationships among young adults.
• The effectiveness of assertiveness training in improving communication skills.
• The relationship between parental expectations and academic achievement among high school students.
• The correlation between sleep quality and mood among adults.
• The impact of video game addiction on academic performance among college students.
• The effectiveness of group therapy in treating eating disorders.
• The relationship between job stress and job performance among teachers.
• The correlation between mindfulness and emotional regulation.
• The impact of social media use on self-esteem among college students.
• The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
• The impact of renewable energy policies on carbon emissions
• The relationship between employee motivation and job performance
• The effectiveness of psychotherapy in treating eating disorders
• The correlation between physical activity and cognitive function in older adults
• The effect of childhood poverty on adult health outcomes
• The impact of urbanization on biodiversity conservation
• The relationship between work-life balance and employee job satisfaction
• The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
• The correlation between parenting styles and child behavior
• The effect of social media on political polarization
• The impact of foreign aid on economic development
• The relationship between workplace diversity and organizational performance
• The effectiveness of dialectical behavior therapy in treating borderline personality disorder
• The correlation between childhood abuse and adult mental health outcomes
• The effect of sleep deprivation on cognitive function
• The impact of trade policies on international trade and economic growth
• The relationship between employee engagement and organizational commitment
• The effectiveness of cognitive therapy in treating postpartum depression
• The correlation between family meals and child obesity rates
• The effect of parental involvement in sports on child athletic performance
• The impact of social entrepreneurship on sustainable development
• The relationship between emotional labor and job burnout
• The effectiveness of art therapy in treating dementia
• The correlation between social media use and academic procrastination
• The effect of poverty on childhood educational attainment
• The impact of urban green spaces on mental health
• The relationship between job insecurity and employee well-being
• The effectiveness of virtual reality exposure therapy in treating anxiety disorders
• The correlation between childhood trauma and substance abuse
• The effect of screen time on children’s social skills
• The impact of trade unions on employee job satisfaction
• The relationship between cultural intelligence and cross-cultural communication
• The effectiveness of acceptance and commitment therapy in treating chronic pain
• The correlation between childhood obesity and adult health outcomes
• The effect of gender diversity on corporate performance
• The impact of environmental regulations on industry competitiveness.
• The impact of renewable energy policies on greenhouse gas emissions
• The relationship between workplace diversity and team performance
• The effectiveness of group therapy in treating substance abuse
• The correlation between parental involvement and social skills in early childhood
• The effect of technology use on sleep patterns
• The impact of government regulations on small business growth
• The relationship between job satisfaction and employee turnover
• The effectiveness of virtual reality therapy in treating anxiety disorders
• The correlation between parental involvement and academic motivation in adolescents
• The effect of social media on political engagement
• The impact of urbanization on mental health
• The relationship between corporate social responsibility and consumer trust
• The correlation between early childhood education and social-emotional development
• The effect of screen time on cognitive development in young children
• The impact of trade policies on global economic growth
• The relationship between workplace diversity and innovation
• The effectiveness of family therapy in treating eating disorders
• The correlation between parental involvement and college persistence
• The effect of social media on body image and self-esteem
• The impact of environmental regulations on business competitiveness
• The relationship between job autonomy and job satisfaction
• The effectiveness of virtual reality therapy in treating phobias
• The correlation between parental involvement and academic achievement in college
• The effect of social media on sleep quality
• The impact of immigration policies on social integration
• The relationship between workplace diversity and employee well-being
• The effectiveness of psychodynamic therapy in treating personality disorders
• The correlation between early childhood education and executive function skills
• The effect of parental involvement on STEM education outcomes
• The impact of trade policies on domestic employment rates
• The relationship between job insecurity and mental health
• The effectiveness of exposure therapy in treating PTSD
• The correlation between parental involvement and social mobility
• The effect of social media on intergroup relations
• The impact of urbanization on air pollution and respiratory health.
• The relationship between emotional intelligence and leadership effectiveness
• The effectiveness of cognitive-behavioral therapy in treating depression
• The correlation between early childhood education and language development
• The effect of parental involvement on academic achievement in STEM fields
• The impact of trade policies on income inequality
• The relationship between workplace diversity and customer satisfaction
• The effectiveness of mindfulness-based therapy in treating anxiety disorders
• The correlation between parental involvement and civic engagement in adolescents
• The effect of social media on mental health among teenagers
• The impact of public transportation policies on traffic congestion
• The relationship between job stress and job performance
• The effectiveness of group therapy in treating depression
• The correlation between early childhood education and cognitive development
• The effect of parental involvement on academic motivation in college
• The impact of environmental regulations on energy consumption
• The relationship between workplace diversity and employee engagement
• The effectiveness of art therapy in treating PTSD
• The correlation between parental involvement and academic success in vocational education
• The effect of social media on academic achievement in college
• The impact of tax policies on economic growth
• The relationship between job flexibility and work-life balance
• The effectiveness of acceptance and commitment therapy in treating anxiety disorders
• The correlation between early childhood education and social competence
• The effect of parental involvement on career readiness in high school
• The impact of immigration policies on crime rates
• The relationship between workplace diversity and employee retention
• The effectiveness of play therapy in treating trauma
• The correlation between parental involvement and academic success in online learning
• The effect of social media on body dissatisfaction among women
• The impact of urbanization on public health infrastructure
• The relationship between job satisfaction and job performance
• The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
• The correlation between early childhood education and social skills in adolescence
• The effect of parental involvement on academic achievement in the arts
• The impact of trade policies on foreign investment
• The relationship between workplace diversity and decision-making
• The effectiveness of exposure and response prevention therapy in treating OCD
• The correlation between parental involvement and academic success in special education
• The impact of zoning laws on affordable housing
• The relationship between job design and employee motivation
• The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
• The correlation between early childhood education and social-emotional learning
• The effect of parental involvement on academic achievement in foreign language learning
• The impact of trade policies on the environment
• The relationship between workplace diversity and creativity
• The effectiveness of emotion-focused therapy in treating relationship problems
• The correlation between parental involvement and academic success in music education
• The effect of social media on interpersonal communication skills
• The impact of public health campaigns on health behaviors
• The relationship between job resources and job stress
• The effectiveness of equine therapy in treating substance abuse
• The correlation between early childhood education and self-regulation
• The effect of parental involvement on academic achievement in physical education
• The impact of immigration policies on cultural assimilation
• The relationship between workplace diversity and conflict resolution
• The effectiveness of schema therapy in treating personality disorders
• The correlation between parental involvement and academic success in career and technical education
• The effect of social media on trust in government institutions
• The impact of urbanization on public transportation systems
• The relationship between job demands and job stress
• The correlation between early childhood education and executive functioning
• The effect of parental involvement on academic achievement in computer science
• The effectiveness of cognitive processing therapy in treating PTSD
• The correlation between parental involvement and academic success in homeschooling
• The effect of social media on cyberbullying behavior
• The impact of urbanization on air quality
• The effectiveness of dance therapy in treating anxiety disorders
• The correlation between early childhood education and math achievement
• The effect of parental involvement on academic achievement in health education
• The impact of global warming on agriculture
• The effectiveness of narrative therapy in treating depression
• The correlation between parental involvement and academic success in character education
• The effect of social media on political participation
• The impact of technology on job displacement
• The relationship between job resources and job satisfaction
• The effectiveness of art therapy in treating addiction
• The correlation between early childhood education and reading comprehension
• The effect of parental involvement on academic achievement in environmental education
• The impact of income inequality on social mobility
• The relationship between workplace diversity and organizational culture
• The effectiveness of solution-focused brief therapy in treating anxiety disorders
• The correlation between parental involvement and academic success in physical therapy education
• The effect of social media on misinformation
• The impact of green energy policies on economic growth
• The relationship between job demands and employee well-being
• The correlation between early childhood education and science achievement
• The effect of parental involvement on academic achievement in religious education
• The impact of gender diversity on corporate governance
• The relationship between workplace diversity and ethical decision-making
• The correlation between parental involvement and academic success in dental hygiene education
• The effect of social media on self-esteem among adolescents
• The impact of renewable energy policies on energy security
• The effect of parental involvement on academic achievement in social studies
• The impact of trade policies on job growth
• The relationship between workplace diversity and leadership styles
• The correlation between parental involvement and academic success in online vocational training
• The effect of social media on self-esteem among men
• The impact of urbanization on air pollution levels
• The effectiveness of music therapy in treating depression
• The correlation between early childhood education and math skills
• The effect of parental involvement on academic achievement in language arts
• The impact of immigration policies on labor market outcomes
• The effectiveness of hypnotherapy in treating phobias
• The effect of social media on political engagement among young adults
• The impact of urbanization on access to green spaces
• The relationship between job crafting and job satisfaction
• The effectiveness of exposure therapy in treating specific phobias
• The correlation between early childhood education and spatial reasoning
• The effect of parental involvement on academic achievement in business education
• The impact of trade policies on economic inequality
• The effectiveness of narrative therapy in treating PTSD
• The correlation between parental involvement and academic success in nursing education
• The effect of social media on sleep quality among adolescents
• The impact of urbanization on crime rates
• The relationship between job insecurity and turnover intentions
• The effectiveness of pet therapy in treating anxiety disorders
• The correlation between early childhood education and STEM skills
• The effect of parental involvement on academic achievement in culinary education
• The impact of immigration policies on housing affordability
• The relationship between workplace diversity and employee satisfaction
• The effectiveness of mindfulness-based stress reduction in treating chronic pain
• The correlation between parental involvement and academic success in art education
• The effect of social media on academic procrastination among college students
• The impact of urbanization on public safety services.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

300+ Political Science Research Topics

200+ Funny Research Topics

500+ Criminal Justice Research Topics

500+ Cyber Security Research Topics

300+ Chemistry Research Topics

500+ Computer Science Research Topics

• Research article
• Open access
• Published: 10 December 2019

Living in the shadow of unemployment -an unhealthy life situation: a qualitative study of young people from leaving school until early adult life

• Anne Hammarström   ORCID: orcid.org/0000-0002-4095-7961 1 , 2 &
• Christina Ahlgren   ORCID: orcid.org/0000-0001-5965-5368 1 , 3  

BMC Public Health volume  19 , Article number:  1661 ( 2019 ) Cite this article

8528 Accesses

9 Citations

1 Altmetric

Metrics details

Despite the magnitude of youth unemployment there is a lack of studies, which explore the relations between health experiences and labour market position in various contexts. The aim of this paper was to analyse health experiences among young people in NEET (not in education, employment or training) in relation to labour market position from leaving school until early adult life.

The population consists of everyone (six women, eight men) who became unemployed directly after leaving compulsory school in a town in Northern Sweden. Repeated personal interviews were performed from age 16 until age 33. The interviews were analysed using qualitative content analysis.

Health experiences can be viewed as a contextual process, related to the different phases of leaving school, entering the labour market, becoming unemployed and becoming employed. Perceived relief and hope were related to leaving compulsory school, while entering the labour market was related to setbacks and disappointments as well as both health-deteriorating and health-promoting experiences depending on the actual labour market position. Our overarching theme of “Living in the shadow of unemployment – an unhealthy life situation” implies that it is not only the actual situation of being unemployed that is problematic but that the other phases are also coloured by earlier experiences of unemployment .

A focus on young people’s health experiences of transitions from school into the labour market brings a new focus on the importance of macroeconomic influence on social processes and contextualised mechanisms from a life-course perspective.

Peer Review reports

Since the great depression around 1930 quantitative research has demonstrated that unemployment is related to deteriorated health, with both mental and somatic symptoms, deteriorated health behaviour and increased mortality [ 1 , 2 ].

However, there is still a lack of understanding of why these relations exist. What is it in unemployment that increases the risk of ill health? What are the mediating mechanisms? Various models have been suggested in order to explain the relationships between unemployment and ill health. Six main models can be identified. The economic deprivation model suggests that the financially strained situation of the unemployed is the actual cause of ill health [ 3 ]. The stress model implies that unemployment is a chronic stressor with negative impact on health status [ 4 ]. The model of latent functions was developed by Marie Jahoda [ 5 ] based on her research about what needs, besides the economic ones, a good job should fulfil. These needs are called “latent functions” and they imply that employment should give:

* a time structure of the day

* regularly shared experiences and contacts with others

* goals and purposes that transcend those of the employed

* personal status and identity

* enforced activity

The model implies that unemployment causes ill health due to the absence of these five needs (or functions) which (to a smaller or larger extent) are provided by a job.

The control model states that the potential to have control over one’s life is crucial for health and well-being. The model has been developed in a variety of theoretical traditions, and empirical research has shown the importance for health of maintaining control during unemployment [ 6 ]. The work involvement model means that low work involvement during unemployment is protective of one’s health status [ 7 ] while the lack of support model implies that unemployment is related to deteriorated health due to increased social isolation [ 7 ].

A quantitative testing of these six models shows that the capacity of the models to explain the connection between unemployment and ill health varied [ 8 ]. The model of latent functions was most successful, followed by the economic deprivation model. The social support and the control models were also fairly good. The work involvement scale and the stress model demonstrated the smallest explanatory power.

Even though the theoretical development and testing of models is a step forward for understanding why unemployment is related to deteriorated health, quantitative methods are based on prefixed answer alternatives, leaving no space for new understandings to be discovered. In order for research to develop knowledge about a poorly understood phenomena, qualitative methods are needed. The results from qualitative research can later on can be tested with quantitative methods to draw conclusions about cause and effect. But there is an almost total lack of inductive qualitative research about health experiences among those who are unemployed. Some of the few qualitative studies in the field are summarized below.

A qualitative study among participants over age 18 from the time of economic recession in Sweden illustrates the importance of gaining better understanding of the experiences of hardship and perceptions of health among those who lose their jobs [ 9 ]. Another qualitative study of unemployed persons, aged 18 and above with mental health problems, was performed in Germany [ 10 ]. The study illustrates barriers and facilitators for help seeking on three levels: mental health literacy, stigma and discrimination of them as help-seekers and structures and conditions of health care such as miscommunication and GPs lack of interest in mental health problems.

Many young people, who do not have jobs, are not officially registered as unemployed. The concept of NEET – not in employment, education or training – has been increasingly used in order to define a group of mostly young people who are disengaged from the labour market [ 11 ]. This group is difficult to reach in both quantitative and in qualitative studies and thus there is a need for more knowledge about their experiences and life situation [ 12 ].

A qualitative study from Sweden about health experiences of young people in NEET demonstrates the importance of contextualisation of health experiences [ 13 ]. Health was created in relation to others and the participants felt good when closely connected to others. Health was created in relation to severe hardship (like drugs and various forms of violence) as well as in relation to the participants’ ability to adapt and respond to challenges. Thus, health among NEETs was viewed as constructed within the social and cultural context in which the ability to adapt and respond to challenges are crucial. The participants had a diverse background, but still shared common experiences related to feelings of inclusion and exclusion. Thus, health was viewed as created both on an individual and collective level.

The transition from school to work can be influenced by macroeconomic factors such as recession also among young people who are not in NEET. A qualitative study of students [ 14 ] showed that the crisis had negative impact on the participants’ mental health by creating feelings of instability as well as difficulties in planning for the future.

The participants clearly experienced disengagement from community participation manifested in feelings of isolation, lack of interest, and distrust. Nevertheless, the respondents described a proactive attitude to deal with the problems they were facing, without giving up their commitment to personal fulfilment.

These qualitative studies demonstrate that open interviews about health experiences among different groups of unemployed can provide a deeper insight into why these health experiences occur as well as in which context they develop. Therefore, qualitative methods are needed in order to analyse such experiences. All research, especially qualitative, must be contextualised in order to increase the trustworthiness and the transferability of the findings.

We have demonstrated in a review that most quantitative research on unemployment and ill health lacks such contextualisation as very few studies have analysed the results separately per subgroup of population in relation to, for example, age and gender [ 15 ]. Most empirical and theoretical research on this topic has been performed in adults, in spite of the growing evidence of both the short-term [ 16 , 17 , 18 ] and the long-term [ 19 , 20 , 21 , 22 ] negative health consequences of unemployment early in life. The aim of this paper was to analyse health experiences among young people in NEET in relation to labour market position from leaving school until early adult life.

Interviews for this qualitative study were performed within the Northern Sweden Cohort [ 23 ]. The cohort was created in 1981 in a medium-sized (with about 70,000 inhabitants) industrial town in Northern Sweden. The town is representative of medium-sized industrial towns in Sweden as regards the sociodemographic factors and labour market conditions. The cohort has also been shown to be representative of the country as a whole in relation to socio-demographics and socioeconomic factors as well as in relation to health status and health behaviour [ 23 ]. The labour market is dominated by manufacturing and mineral extraction, with a steel company and a large harbour. Other major employers are the public sector and a technical university. At the time of the interviews, unemployment in the town was twice as high rate as in the rest of Sweden and there was more workforce immigration from Finland. The social democrats had ruled the area for decades together with smaller socialist parties.

In order to tackle the growing rate of youth unemployment the Swedish government introduced active labour market policy measures for young people from the beginning of the 1980s. These measures included educational and vocational activities directed towards unemployed young people with the objective that no one aged 18 years or below should be outside education or employment. Subsidised employment, with work tasks that did not compete with regular jobs, was the most common labour market measure for young people at the time when the cohort participants were young. Labour market policy measures for people below 18 years were financed by the state with study assistance instead of salary and were intended to guarantee activity for 8 h a day for 40 weeks for unemployed in the age-group 16–18 years. In 1982, the time for reimbursement for these programmes was somewhat increased beyond 40 weeks and in 1984, a law was introduced stating that unemployed aged 18–19 should participate in labour market measures at least 4 h a day and for a minimum wage ([ 16 ] pp 4). This meant that, by international standards, Sweden had a very active labour market policy aimed at young people during the 1980s. For the participants in our study, these active labour market policy programmes meant that they did not become permanently long-term unemployed but rather moved between unemployment, various labour market programmes and short periods of temporary employment. However, lack of resources due to regional differences in number of unemployed could have been the reason why cohort participants were not contacted by the youth centre and instead became unemployed.

Design and ethics

A qualitative study design was chosen. Repeated interviews were conducted with young women and men aged from 16 to about 30, concerning their experiences of work and unemployment and associated health experiences. The interviews were analysed with qualitative content analysis.

Participants

The interviews were conducted with a subsample of participants in the Northern Swedish Cohort – a prospective longitudinal cohort study. The cohort consists of all pupils ( n  = 1083) in their last year of compulsory school (age 16) in all nine schools in a medium-sized town in Northern Sweden [ 23 ]. The cohort has been followed over time with questionnaires about their health status and life circumstances. A subsample of the cohort has been followed with personal interviews at the age intervals 16–17, 21–23 and 28–33 years. This subsample constitutes the base for this study.

The subsample consists of all cohort participants who became unemployed directly after compulsory school (six women and eight men). They had all left school at the age of 16 (some of them without full grades) and were all of working-class background. They were interviewed two to four times per individual during a time period from age 16 until age 33 (between 1981 and 1998).

Data collection

Individual audio-recorded structured interviews were performed by the PI (principle investigator) of the cohort (AH, Anne Hammarström), a medical doctor and specialist in social medicine. The interviews dealt with daily activities in various spheres, possibilities of influencing one’s life, dreams for the future and experiences of health and unemployment/work (see Interview Guide in the Additional file  1 ). The interviews were performed in the informant’s home or at the PI’s workplace and lasted for about 1 to 1½ hours. The same informants were interviewed two to four times at different ages. In total 37 interviews constituted the base for the analysis. Health at age 30 was collected both from the interviews and from the informants’ answers to questions about health in a questionnaire to the whole cohort.

Data analysis

The interviews were analysed using qualitative content analysis as described by Graneheim and Lundman [ 24 ]).Qualitative content analysis is used in systematic analysis of verbal communication [ 25 ] and is useful in analysing people’s experiences and reflexions [ 26 ]. Furthermore, it is a suitable method for focusing on similarities and differences in the material [ 24 ].

The interviews were read through several times by both authors to get a sense of the whole. Thereafter, the analysis was performed in several steps using the software package Open Code [ 27 ]. In each step CA (Christina Ahlgren) did a preliminary analysis, which then was discussed between the authors. In cases of disagreement, we reread the transcripts in order to catch the initial meaning and adjusted the interpretations of codes and categories’ until agreement was reached. In the first step of the analysis, the text was divided into meaning units, each comprising several words, sentences, or paragraphs containing aspects of health during employment and unemployment. After that the meaning units were labelled with codes and sorted into content areas. A content area is a rough structure of content that can be identified with little interpretation. The content areas in this study were parts of the text dealing with the informants’ experiences of unemployment and employment periods at different ages, which resulted in four phases defined as leaving the school phase, entering the labour market phase, the unemployment phase and the employment phase. Thirdly, codes with similar meanings were summed into categories with subcategories. Categories can be seen as manifest interpretations of the text and should answer the question “What?” [ 25 ]. Categories can include subcategories. In this study categories and subcategories were formed through interpretations of codes representing the informants’ experiences of unemployment and employment and associated aspects of health. Fourthly, a theme was suggested, through interpretation of the underlying meaning of health and health problems related to the experiences described in the categories. A theme could be seen as an interpretation of the latent content of the text and answers the question “How?” [ 24 ]. Sub-categories, categories and a theme are presented in Table  1 .

In order to further, achieve trustworthiness the two researchers from different fields of expertise (family medicine/social medicine and physiotherapy) worked in parallel during the whole analysis process and discussed the findings until agreement was reached. This is in accordance with the process of triangulation [ 24 , 28 ]. The findings were also discussed with other researchers in the field and found trustworthy.

The analysis resulted in a tentative process of the development of positive health or health problems by interpreting the informant’s feelings and experiences of unemployment and employment from leaving school at age 16 to adulthood at age 32. The process is described in the text and in Fig.  1 .

The figure describes a tentative process of the relationship between the informants’ experiences and feelings (illustrated in sub-categories and categories) during phases of unemployment and employment from leaving school at 16 year of age until 30 years of age and how their experiences could be related to positive health symptoms and deteriorating health. + denotes experiences or feelings associated with positive health -denotes experiences or feelings associated with deteriorating health

The informant’s experiences during each phase are described in six categories, distributed over the four phases. Leaving the school phase is characterised by the category “Perceiving relief and hope”. Entering the labour market phase by the category “Noticing setbacks and disappointments”. The unemployment phase by “Perceiving hopelessness and resignation” and “Perceiving caring responsibilities” and the employment phase by “Perceiving pride and social worthiness”, and “Accepting adverse work situations”. Each category includes subcategories. The theme “Living in the shadow of unemployment – an unhealthy life situation” describes a tentative process between the categories and experiences of health

“Living in the shadow of unemployment – an unhealthy life situation”

The theme is built upon the interviewees’ expressions of experiences and feelings during employment and unemployment phases and our interpretations of how this could be associated with health and health problems. Experiences during unemployment phases were often expressed in terms of passivity, resignation and poverty and were linked to feelings of deteriorated mental health, i.e. dysphoria, depression and low self-confidence. Experiences and feelings counter-balancing deteriorated health during unemployment phases were mostly expressed by women. Employment phases, on the other hand, were mostly mentioned in relation to positive health experiences, i.e. being trusted and a sense of being needed. However, the short-term contracts with a constant risk of being unemployed again induced stress. The fear of unemployment also made them accept non-optimal work environments, although they could affect future health.

Perceiving relief and hope

This category corresponds to leaving the school phase and embraces the first half year after leaving compulsory school at 16 years of age and is dominated by the informants’ positive thoughts directly after leaving compulsory school. They felt relieved at quitting the school they did not like or even hated during the last school years. The reasons for the dislike were multifactorial and included “ teachers are unfair ”, “ school is of no use in real life ”, or “ I was bullied ”. However, some participants also said that school was okay, but that they were “ bored ”. They expressed a desire to become mature by getting a job and earning one’s own money, which also meant being less dependent on parents. Their dreams about the future included both specific occupations and standards of living, for example being able to afford to buy a car of one’s own, or a motor bike, to travel abroad and to buy clothes. The occupations they dreamt of were partly gendered, in that young men dreamt of driving their own lorry or starting a delivery firm while young girls dreamt of working in caring occupations or in shops.

Health issues were not on their agenda, and the feelings expressed were primarily positive.

Noticing setbacks and disappointments

Leaving the school phase gradually passed into Entering the labour market phase at about 17 years of age, when the feelings of an extended holiday faded away and the informants still were out of work.

The category describes the situation most of them faced when their dreams about life after quitting school were slowly crushed. They told about the disappointment when job applications were rejected repeatedly with the notification that they were too young and had no work experience. They had lot of leisure time but no money, which made them dependent on parents’ economic contributions.

Due to the prescribed social/labour market policy, the informants in our study became dependent on employment officers at the youth centre to get work. However, the informants expressed distrust against them and said that the only information they gave was that there were no jobs available unless they were prepared to move south. Neither the 16-year-old participants nor their parents appreciated this advice, because it meant sending a 16-year-old son or daughter to a large city almost 500 miles away. When job training courses were offered via the youth centre, some of the informants found them very positive, while others rejected them as being low-paid and not real jobs. Some of the informants had succeeded by themselves in getting jobs for short periods, for example in workplaces they had come into contact with during trainee jobs when still at school.

In this phase as well, all informants assured that they were in good health, although some suffered from health problems, for example congenital hip disease, allergy and headache. These were not considered as health problems, but just something they had. On the other hand, they expressed feelings of disappointment when they talked about the conflicting situation between their hopes for a job and the setbacks with repeated rejections of job applications.

Perceiving hopelessness and resignation

This category describe the informants’ experiences during unemployment. However, neither the unemployment nor the employment situation was permanent over the years. Instead they altered between the unemployment phase and the employment phase and the time spent in each phase varied between individuals. Although informants perceived the first months of unemployment as an extended school holiday, when they were free to do whatever they liked, it became boring after a while. With time and still alternating between short temporary employment and unemployment, it was experienced as extremely discouraging. The unemployment situation led to a very passive lifestyle among informants of all ages, although somewhat differently expressed with age. The common response to the question What do you do during the days? was “I do nothing ”. Among the young informants still living with their parents, it led to a reversed structure of the day. Many of them slept long in the mornings to make time pass, “ There’s no point getting up in the morning, because there is nothing to do” and stayed out late in evenings, drinking beer, and carrying on with friends. This resulted in awakening late next day and the vicious circle continued.

Informants who had children or a partner with a job had to get up in the morning, but idleness during the days was experienced as very hard. Despite having a lot of time they found it difficult to find the energy to engage in activities. Much time was spent watching television.

“ You don’t do anything when you’re unemployed. I was restless. When I had money I could drive around in the car and busy myself with that, and a bit of everything. I looked for work, but I didn’t really do anything useful, if I put it like that. ”

“ Unemployment periods are extremely difficult. … The days are endless. … I sometimes help my sister with her children, when she is at school .”

Their social contacts were solely with other unemployed friends, while those who were employed were busy at work. This made our participants feel even more outside the labour market.

Lack of money and small financial resources were experienced by all informants during the phases of unemployment, although it was more often emphasised in the interviews with men. They talked about their wish to get a driver’s licence and buy a car. Women’s wishes concerned having an apartment of their own and being able to buy nice clothes.

“ The most negative thing about unemployment is the shortage of money. You can’t do anything and when you start a new job, you have to wait a month for the salary”.

Several men said that they had missed jobs due to lack of money to pass a driving test.

“ They needed people and I could have gone on working there if I’d had a licence to drive a heavy lorry .”

Their poor financial situation restricted their possibilities to make use of the spare time which they had too much of. Some participants were entitled to unemployment benefit for a while, but felt stressed about finding a new job during this time. Others were dependent on parents or on social welfare, which was experienced as a defeat.

One of the men admitted that he had stolen when he was out of money. However, he was caught, which ended his criminal career, but this also ruined his economy further, due to the fines he had to pay.

Deteriorating self-confidence and feelings of low worth were expressed in relation to rejections of job applications even several years after leaving school and when employment officers told them that there were no jobs available for them because they lacked formal education. This was even more apparent with longer periods in unemployment, which is illustrated with quotations from a man and a woman in their early twenties.

“ You feel worthless when unemployed. You become work-shy .”

“ Being unemployed gives you low self-confidence. I feel that I know nothing and am not capable of anything. ”

Feelings of irritation and aggression were experienced and handled differently. Some turned to more excessive drinking to hide their low self-confidence while others were more depressed.

“ You don’t get self-confidence from being unemployed. You do nothing but sleep all day and hang around. I was quite aggressive when unemployed .”

The repeated rejections of job applications led to resignation in some of the informants, who thought there was no point in keeping on trying to find a job, which is illustrated with quotations from a young man and a young woman.

“ There’s no point applying for a job, because there are no jobs .”

“ I find it tiresome to be unemployed, but you get used to it and become more and more apathetic. You want to do something, and then you realise that there’s no point in trying, that’s what makes it tiresome .”

“I think it’s no use exerting yourself, you are nothing. You get fed up being out of money .”

Intense smoking and drinking was common among both young men and women during unemployment. In order to counteract the boring passivity during the days, they spent evenings and nights out with friends. This was often accompanied by drinking alcoholic beverages, and some told of excessive drinking. By being in such situations some of the men had been involved in fights and been physically abused, which had given one of them persistent harm, with psychological problems and memory loss. Other men had been involved in criminality and had been accused of theft and assault and drinking and driving, which had resulted in accidents and fines.

Bad eating habits were common when unemployed. The informants told about not having breakfast or lunch and only coffee and cakes during the day and fast food in the evenings. Reasons cited for bad eating habits were passivity and resignation.

“ After a period you didn’t bother to cook at home. You didn’t bother to go out to buy food .”

One young woman was on a constant diet, because she had been bullied in school for being too fat. She also continued dieting as an adult and this affected her little daughter, who was given the same diet food.

Stress-related health symptoms during unemployment phases took physical expressions as stomach-ache and headache. In addition mental health symptoms such as anxiety depression and sleeping problems were often experienced as well as negative moods such as restlessness, aggressiveness, dysphoria and low self-confidence. One young woman talked of several health-damaging feelings during unemployment.

“ Unemployment is very stressful. I feel restless and depressed when I can’t get a job and I get stomach pains and sleeping problems .”

Perceiving caring responsibilities

Gendered caring responsibilities were a recurrent subcategory during the whole follow-up period and seemed to counterbalance some of the negative experiences of unemployment. Although the young women also talked about being passive during unemployment, many of them said that their parents required them to do tasks at home when unemployed. For example, they were expected to help their mothers to take care of younger siblings or to clean the house, something none of the young men talked about.

Several of the women became pregnant in their late teens and thus they found a purpose in life. Being responsible for children and family gave them a more structured day and reduced their patterns of night-life drinking. Also when growing older women told about becoming a mother as a positive interruption of unemployment. To be on parental leave was experienced to give a respectable position in society in contrast to being unemployed.

“ I was out drinking and smoking a lot the last year at school. Nowadays I hardly drink any alcohol at all, I have to take care of the children .”

Unemployment could also be experienced as a relief from strenuous work tasks and a possibility to stay at home with the child, as this woman on parental leave said:.

“ Earlier when unemployed I felt irritated, which I’m not now. Instead I find it a relief not to have to leave my son in day care. ”

Although men did not talk about taking care of children, one man said he had to support his mother financially and therefore had to take on all kinds of jobs he was offered.

Perceiving pride and social worthiness

The category includes the informants’ experiences during periods of employment (employment phases). With time most of the participants managed to find a job on shorter or longer contract basis. Having a job could refer to participation in labour market measures arranged by the youth centre, long- or short-term contracts on the labour market or work tasks arranged by relatives. Mainly positive feelings were expressed about getting employed, although negative feelings were also mentioned.

Having a job was experienced as a sense of being needed, of being a valuable citizen and doing something valuable. This was strengthened by a sense of belonging to a team at the workplace and accompanied with feelings of happiness and pride. This was acknowledged in informants of all ages.

“ The best thing about having a job is being with nice workmates .”

“ The best thing about the job at the steel company is the lads. They are so cool. ” To be treated with respect and expected to be a capable worker was a positive experience which made them feel needed and made them endeavour to do their best.

“ It feels nice, when they come and give you a new task which they think you can manage. It’s nice that they trust you to pull it off so you exert yourself to do it well .”

A woman at age 32, working as an assistant nurse in a home for the elderly, expressed her responsibility for the care recipients:

“ We have this discussion about cleaning the rooms or not. We take care of everything and have great responsibility, both caring and cleaning. But we are there for the residents, aren’t we ?”

A feeling of being trusted and given responsibility was conveyed by some of the 30-year-old informants who had been offered jobs. They had either approached the employers earlier and asked for a job or had previously had temporary employment at the workplace. Being offered employment without having applied for it was accompanied by a sense of having a reputation for being competent, which made them extremely proud.

“ I didn’t have to apply for this position. They had heard of me and came and asked me to work there .”

“I had contacts from before and phoned and introduced myself. I was offered lots of jobs. I think it’s up to you. No jobs comes in your mailbox, you have to get involved. ”

Having a job meant a structuring of the day, getting up in the morning and working 5 days a week. Although some of the young informants found it tiresome it was still seen as part of maturing and taking responsibility. In their early twenties the participants still felt the need for a job to structure their days:

“ A job means that you get up in the morning .”

Having a job also meant that leisure time was felt to be more valuable.

“ When you are unemployed, you have a lot of time, but you don’t do anything. When you have a job, you do something meaningful during the days and also have time to do something in the evenings .”

Having a job was mostly associated with positive feelings. The interviewees’ expressed feelings of pride and being a respectable citizen, someone who took responsibility and was trusted. In order to manage the job, they had to restrict their late nights out drinking and instead living the life of an ordinary “Svensson”. Being in employment phases seemed to be health-promoting. This is understood from the answers “ I’m fine, could not be better ”, “ I have a good life ” and” I feel like a prince ”, which were given by informants with jobs to the question of how they perceived their health.

Social support seemed to be crucial for longer times in employment phases, in terms of parents’ help to find jobs or persuading the participants to study, or partners’ help to create a secure social situation.

Accepting adverse work situations

Many informants also described negative experiences of their labour market situation and had health problems related to their jobs. Informants who worked on short-term contracts for long periods experienced job insecurity . The joy they felt from having a job was accompanied by a perception of stress, as they knew that they would soon have to quit. The insecurity in a temporary job made some informants feel that they had to abstain from complaints, even justified ones, in order to make a good impression and be eligible for a new employment.

“ I have this job and love it, everybody is so nice to me. I can’t continue as the ordinary staff want full-time employment. It feels very sad .”

“ I don’t know but it feels harsh, you have been there for six months and then … just leave .”

Those who still were in temporary employment when approaching 25 years and had family responsibilities perceived the future as very uncertain. Although the temporary jobs gave a salary for the moment and contacts with future employers, they worried about their financial situation in the future. They had to move between workplaces and repeatedly adopt to new work teams, which contributed to the feelings of insecurity. To accept some of the jobs offered, they had to move away from family and friends.

“ You get a chance to get a job in a company, but you never know how long you’re allowed to be there and what next month will be like. This makes me anxious, which is not good. ”

Health problems in relation to jobs also included workplace injuries and illnesses due to physically heavy work tasks and working in other unhealthy environments. By being eager to take on all kinds of jobs the participants accepted unhealthy and dangerous work situations or set security aside. For example, a young man who took on all jobs he was offered was hit by an overhead crane during work at a steel company. This damaged his shoulder and caused him persistent pain which in turn made it harder for him to find another job in the future. Similarly, a woman avoided unemployment by engaging in shoeing horses. She was kicked in the shoulder by a horse and became partly impaired for a long period of time. Another example was a young man with asthma from childhood, who experienced a worsening of his symptoms when he worked in a very dusty environment sorting newspapers for recycling.

This qualitative study showed that the health experiences among young people in NEET can be viewed as a contextual process, related to the different phases of leaving school, entering the labour market, becoming unemployed and becoming employed. Perceived relief and hope were related to leaving compulsory school, while entering the labour market was related to setbacks and disappointments as well as both health-deteriorating and health-promoting experiences depending on the actual labour market position. Our overarching theme of “Living in the shadow of unemployment – an unhealthy life situation” implies that it is not only the actual situation as unemployed that is problematic but that the other phases are also coloured by earlier experiences of unemployment. The results should be interpreted in the light of the Nordic welfare system, with extensive active labour market policies during the period when the cohort members were young. These policies contributed to keeping the levels of unemployment on a quite low level but still the labour market measures were not enough to prevent everyone from unemployment during certain periods. In the case of this study, the youth unemployment rate of the county was about 13% as compared to 4% in Stockholm [ 16 pp 6 ].

In our inductive analysis, we identified various health experiences among young people in NEET. Periods of unemployment were related to stress related symptoms (such as stomach and headache) as well as to mental health symptoms, such as restlessness, aggressiveness, anxiety, dysphoria, depressiveness and sleeping problems and deteriorating health habits. The participants described a vicious circle, with symptoms adding to each other and to intense drinking. An interesting gendered finding was that while both boys and girls described intensified drinking related to unemployment only girls described that when becoming a parent their caring responsibilities for the child made it impossible for them to be out drinking. These findings provide a deeper understanding of our earlier publications of quantitative analyses of alcohol consumption among unemployed youths, were we found that unemployment generally increased the risk of drinking, but not among young women who had children [ 29 ].

In this inductive approach, we also identified other experiences which we interpret as possible pathways or mechanisms between the health experiences and the labour market experiences. These mechanisms could partly be related to the models mentioned in the Introduction, mainly the economic deprivation model (no money, being dependent), the model of latent functions (passive lifestyle, deteriorated self-confidence) and the stress model (stress, stress-related health symptoms). Other mechanisms were not related to these models such as being dependent, experiencing distrust, feeling disappointed, resignation, gendered caring responsibilities and deteriorated health behaviour (intense drinking, bad eating habits). In this regard our findings are in accordance with one of the few qualitative studies in the field [ 30 ]. The “traditional” models for explaining health effects of unemployment do not fully account for people’s own interpretation and experiences. Thus, our study contributes new understandings about why unemployment is related to ill health among young people.

In earlier research, we have identified a “scarring” mechanism of early unemployment [ 19 , 20 , 21 , 22 ]. In this context, scarring means that, while youth unemployment has well-known direct effects on health (“wounds”), the wounds remain as scars in adult age (measured in relation to blood pressure and various measures of mental health). Could the mechanisms identified in this study explain scarring? May feelings related to unemployment in young age – such as disappointment, dependence, distrust and resignation – become embedded in the mind and in the patterns of reactions so that those who have been early unemployed respond to stressful life-events later in life with similar reactions and feelings, instead of with an active engagement against injustice and disappointments in life? Feelings of disappointment, dependence, distrust and resignation are closely embedded in depressive states and may be a key to our understanding of unemployment scarring.

We also identified mechanism for improved health during periods of employment. One of these mechanisms was related to the model of latent functions (structuring the day), while several were partly related to the model (being needed, being trusted and given responsibility and feelings of pride). Improved health behaviour was another positive mechanism during employment.

The participants experienced pride and worthiness related to getting employment. Pride could be viewed as a dimension of inner strength, well-being and thus as a positive aspect of health [ 31 ]. In addition, in order to manage the job they had to restrict their late nights out drinking.

However, the employment phase during a context of living in the shadow of unemployment was also hazardous to health. The participants, who all experienced a shorter or longer period of NEET, entered the labour market from a marginalised position. Thus, the kinds of jobs and employment they received were on the margin of the labour market where contracts are insecure and short-term. The participants described stress, injuries and adverse working conditions, which are well-known adverse health consequences of precarious employment [ 18 ]. Earlier quantitative research has demonstrated the significance of high levels of unemployment in society not only for the unemployed [ 22 ] but also for young people in work as well as in studies [ 32 ]. Thus, the positive effects on health of finding employment are threatened by macroeconomic influences such as recession. Then, the work environment is deteriorated due to increased stress in several sectors in society, such as health care, social services etc. The work-related demands increase on these women-dominated workplaces due to deteriorated health in the population and increased need for social security benefits for unemployed, while at the same time there is a reduction in the number of employees, resulting in increasing job strain among those who still are employed [ 32 ]. In addition, other macroeconomic influences such as the increasingly flexible labour market may hamper the positive experiences of getting employed, as insecure employment contracts are becoming increasingly common, especially among young people. There is increasing evidence of the negative health impact of temporary employment [ 33 ].

Our findings emphasise the need to analyse the labour market situation of young people as a contextualised continuum, rather than as a dichotomy, from leaving school to entering the labour market. Even when young people have entered the labour market, they move between the phases of unemployment and training or employment due to the lack of available jobs in society. The importance of bringing a life-course perspective into the research is in accordance with research by Dooley and Prause [ 34 ] who (focusing mainly on adults) have highlighted the need to conceive employment status as a continuum, from adequate employment to inadequate employment and to unemployment. Another well-known theoretician within the field of unemployment and health, Douglas Ezzy [ 35 ], also proposed a middle-range theory of status passage in order to deal with the inadequate temporal aspects of earlier research. He explained changes in mental health among adults during unemployment derived from identity theory. Inspired by a middle-ranged theory developed by Ezzy [ 35 ], Giuntoli et al. [ 30 ] have performed a qualitative study among a group of wide age range who had all lost their jobs. Despite the different sample in their study as compared to ours, the mechanisms they identified were similar (unemployment and welfare stigma) or the same as in our study (financial strain, difficulties in finding a new job, personal identity crises, loss of time structure). Giuntoli et al. [ 30 ] propose a middle-range theory, which suggests that the effects of employment transitions on people’s mental health are linked with people’s experiences of these passages. This is similar to the results from our inductive qualitative analyses.

In one of the few qualitative studies within the field, Simmons et al. [ 36 ] conclude that the broader social structures have a significant impact on becoming and or remaining in NEET. These processes are deeply integrated into a framework of “agency within structures”. In earlier research on the young men who are part of this study we have developed a model of agency within structures in relation to health experiences [ 37 ]. There we interpret our findings as constructions of masculinities within certain structures, in relation to choices, habitus and healthy practices. Agency is surely important in relation to the context of this study, for example from the identified mechanisms of wanting a job, searching for jobs in the first phase of entering the labour market, to resignation due to experiences of unemployment (lack of agency). Like us, Simmons et al. [ 36 ] found that repeated negative labour market experiences had a de-motivating effect on job-searching among the young people. As a result of continued failure to secure employment, the young people became disillusioned. Again, dissolution may help explain the long-term scarring of yearly unemployed as discussed above.

On the methods

The strength of this study is the repeated interviews with young people from 16 to 32 years of age and with the same focus. This gives us unique and rich data together with insight into how the young informants’ experiences of health varied with contextual factors, e.g. job/unemployment and changes in their family situation.

In qualitative content analysis one aspect of trustworthiness is dependability. Dependability concerns the stability of data over time and the researchers’ decisions during the analysis process [ 24 ]. In our study the same researcher (AH) did all the interviews and used the same interview guide. This might raise questions about dependability but could also be seen as a strength because a trustful relation between researcher and interviewees was created and might have made it easier to talk about sensitive matters for example drug use and criminal activities.

In our study, the interviewees were disfavoured young people with early unemployment due to interrupted school attendance, and the context in which data were sampled was the working class in an industry-dominated city in northern Sweden. Therefore, the findings might be related to similar groups in similar contexts. However, regulations for unemployment insurance greatly affect the possibilities for job training, subsidised employments and so on and have to be considered when generalising.

Conclusions

Availability of data and materials.

Data are not freely available. The Swedish Data Protection Act (1998:204) does not permit sensitive data on humans (like in our interviews) to be freely shared. After ethical approval the anonymous data set could be obtained on request to Umeå University after their secrecy examination.

Abbreviations

Anne Hammarström

Christina Ahlgren

Not in education, employment or training

Principle investigator

McKee-Ryan FM, Song ZL, Wanberg CR, Kinicki AJ. Psychological and physical well-being during unemployment: a meta-analytic study. Appl Psychol. 2005;90(1):53–76.

Article   Google Scholar  

Paul KI, Moser K. Unemployment impairs mental health: meta-analyses. J Vocat Behav. 2009;74(3):264–82.

Tøge AG. Health effects of unemployment in Europe (2008-2011): a longitudinal analysis of income and financial strain as mediating factors. Int J Equity Health. 2016;15:75.

Article   PubMed   PubMed Central   Google Scholar  

Sumner RC, Gallagher S. Unemployment as a chronic stressor: a systematic review of cortisol studies. Psychol Health. 2017;32(3):289–311.

Article   PubMed   Google Scholar  

Jahoda M. The impact of unemployment in the 1930s and the 1970s. Bull Br Psychol Soc. 1979;32:309–14.

Google Scholar  

Infurna FJ, Gerstorf D, Ram N, Schupp J, Wagner GG, Heckhausen J. Maintaining perceived control with unemployment facilitates future adjustment. J Vocat Behav. 2016;93:103–19.

Winefield AH. Unemployment: its psychological costs. In: Cooper CL, Robertson IT, editors. International review of industrial and organizational psychology; 1995. p. 169–212.

Janlert U, Hammarström A. Which theory is best? Explanatory models of the relationship between unemployment and health. BMC Public Health. 2009;9:235.

Hiswåls AS, Marttila A, Mälstam E, Macassa G. Experiences of unemployment and well-being after job loss during economic recession: results of a qualitative study in east central Sweden. J Public Health Res. 2017;6(3):995.

Staiger T, Waldmann T, Rüsch N, Krumm S. Barriers and facilitators of help-seeking among unemployed persons with mental health problems: a qualitative study. BMC Health Serv Res. 2017;17(1):39. https://doi.org/10.1186/s12913-017-1997-6 .

Eurofound. NEETs – young people not in employment, education or training: characteristics, costs and policy responses in Europe. Luxembourg: Publications Office of the European Union; 2012.

Holte B. Counting and meeting NEET young people. Methodology, perspective and meaning in research on marginalized youth. Young. 2018;26(1):1–16.

Lögdberg U, Nilsson B, Kostenius C. “Thinking about the future, what’s gonna happen?”-How young people in Sweden who neither work nor study perceive life experiences in relation to health and well-being. Int J Qual Stud Health Well-being. 2018;13(1):1422662.

Di Blasi M, Tosto C, Marfia A, Cavani P, Giordano C. Transition to adulthood and recession: a qualitative study. J Youth Stud. 2016;19(8):1043–60.

Norström F, Janlert U, Hammarström A. Is unemployment in young adulthood related to self-rated health later in life? Results from the northern Swedish cohort. BMC Public Health. 2017;17(1):529.

Hammarström A. Youth unemployment and ill-health. Results from a two year follow-up study. Solna and Sundbyberg: Karolinska Institute, The National Institute of Psychosocial Factors and Health and the Department of Social Medicine; 1986.

Novo M, Hammarström A, Janlert U. Health hazards of unemployment – only a boom phenomenon? A study of young men and women during times of prosperity and times of recession. Public Health. 2000;114:25–9.

Article   CAS   PubMed   Google Scholar  

Vancea M, Utzet M. How unemployment and precarious employment affect the health of young people: a scoping study on social determinants. Scand J Public Health. 2017;45(1):73–84.

Brydsten A, Hammarström A, San Sebastian M. The impact of economic recession on the association between youth unemployment and functional somatic symptoms in adulthood: a difference-in-difference analysis from Sweden. BMC Public Health. 2016;16:230.

Nygren K, Gong W, Hammarström A. Is hypertension in adult age related to unemployment at a young age? Results from the northern Swedish cohort. Scand J Public Health. 2015;43(1):52–8.

Strandh M, Winefield A, Nilsson K, Hammarström A. Unemployment and mental health scarring during the life course. Eur J Pub Health. 2014;24(3):440–5.

Virtanen P, Hammarström A, Janlert U. Children of boom and recession and the scars to the mental health--a comparative study on the long-term effects of youth unemployment. Int J Equity Health. 2016;15:14.

Hammarström A, Janlert U. Cohort profile: the northern Swedish cohort. Int J Epidemiol. 2012;41(6):1545–52.

Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today. 2004;24:105–12.

Krippendorf K. Content analysis. An introduction to the methodology. London: The Sage commtext series, Sage publications Ltd.; 1980.

Downe-Wamboldt B. Content analysis: method, applications, and issues. Health Care Women Int. 1992;13(3):313–21.

UMDAC & Division of Epidemiology and Public Health Sciences (2007). Open Code (version 3.4) [Software]. March 2019 freely available at https://www.umu.se/en/department-of-epidemiology-and-global-health/research/open-code2/

Alvesson M, Karreman D. Qualitative research and theory development. London: Sage; 2011.

Janlert U, Hammarström A. Alcohol consumption among unemployed youths. Br J Addict. 1992;87:703–14.

Giuntoli G, Hughes S, Karban K, South J. Towards a middle-range theory of mental health and well-being effects of employment transitions: findings from a qualitative study on unemployment during the 2009-2010 economic recession. Health. 2015;19(4):389–412.

Lundman B, Aléx L, Jonsén E, Norberg A, Nygren B, Santamäki Fischer R, Strandberg G. Inner strength: a theoretical analysis of salutogenic concepts. Int J Nurs Stud. 2010;47:251–60.

Novo M, Hammarström A, Janlert U. Do high levels of unemployment influence the health of those non-employed? A gendered comparison of young men and women during boom and recession. Soc Sci Med. 2001;53(3):293–303.

Waenerlund AK, Virtanen P, Hammarström A. Is temporary employment related to health status? Analysis of the northern Swedish cohort. Scand J Public Health. 2011;39(5):533–9.

Dooley D, Prause J. The social costs of underemployment: inadequate employment as disguised unemployment. Cambridge: Cambridge University Press; 2004.

Ezzy D. Unemployment and mental health: a critical review. Soc Sci Med. 1993;37(1):41–52.

Simmons B, Russel L, Thompson R. Young people and labour market marginality: findings from a longitudinal ethnographic study. J Youth Stud. 2014;17:577–91.

Hammarström A, Lundman B, Ahlgren C, Wiklund M. Health and Masculinities Shaped by Agency within Structures among Young Unemployed Men in a Northern Swedish Context. PLoS One. 2015;10(5):e0124785. https://doi.org/10.1371/journal.pone.0124785 .

Download references

Acknowledgements

The authors would like to thank the participants of the study. Open access funding provided by Karolinska Institute

The study was financed by The Swedish Research Council Formas dnr 259–2012-37, The Cutting Edge Medical Research VLL-355661 granted by The County Council of Västerbotten as well as by the Swedish Research Council for Health, Working Life and Welfare dnr 2011–0445.

The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and affiliations.

Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Anne Hammarström & Christina Ahlgren

Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden

Department of Community Medicine and Rehabilitation, Umeå University, Umeå, Sweden

You can also search for this author in PubMed   Google Scholar

Contributions

AH is the PI of the Northern Swedish Cohort. She performed all interviews and took initiative to the manuscript. CA made the analyses in close discussion with AH. CA wrote the Result and part of the Method. AH wrote the rest of the manuscript. Both authors have read and approved the manuscript.

Corresponding author

Correspondence to Anne Hammarström .

Ethics declarations

Ethics approval and consent to participate.

The study was approved by the Regional Ethical Review Board in Umeå, Sweden (2012–69-31 M). At that time in Sweden written consent was not needed. Thus, the Board accepted oral consent and did not require written consent. Oral informed consent was received and the participants were informed about the strict confidentiality in relation to the interviews as well as about their rights to withdraw from the study at any time without motivation. Written consent was not requested from the board.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1..

Interview guide.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Hammarström, A., Ahlgren, C. Living in the shadow of unemployment -an unhealthy life situation: a qualitative study of young people from leaving school until early adult life. BMC Public Health 19 , 1661 (2019). https://doi.org/10.1186/s12889-019-8005-5

Download citation

Received : 08 November 2018

Accepted : 26 November 2019

Published : 10 December 2019

DOI : https://doi.org/10.1186/s12889-019-8005-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

BMC Public Health

ISSN: 1471-2458

• General enquiries: [email protected]

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

Publications

• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Unemployment

After dropping in 2020, teen summer employment may be poised to continue its slow comeback.

Last summer, businesses trying to come back from the COVID-19 pandemic hired nearly a million more teens than in the summer of 2020.

Most in the U.S. say young adults today face more challenges than their parents’ generation in some key areas

About seven-in-ten say young adults today have a harder time when it comes to saving for the future, paying for college and buying a home.

Some gender disparities widened in the U.S. workforce during the pandemic

Among adults 25 and older who have no education beyond high school, more women have left the labor force than men.

Immigrants in U.S. experienced higher unemployment in the pandemic but have closed the gap

With the economic recovery gaining momentum, unemployment among immigrants is about equal with that of U.S.-born workers.

During the pandemic, teen summer employment hit its lowest point since the Great Recession

Fewer than a third (30.8%) of U.S. teens had a paying job last summer. In 2019, 35.8% of teens worked over the summer.

College graduates in the year of COVID-19 experienced a drop in employment, labor force participation

The challenges of a COVID-19 economy are clear for 2020 college graduates, who have experienced downturns in employment and labor force participation.

U.S. labor market inches back from the COVID-19 shock, but recovery is far from complete

Here’s how the COVID-19 recession is affecting labor force participation and unemployment among American workers a year after its onset.

Long-term unemployment has risen sharply in U.S. amid the pandemic, especially among Asian Americans

About four-in-ten unemployed workers had been out of work for more than six months in February 2021, about double the share in February 2020.

A Year Into the Pandemic, Long-Term Financial Impact Weighs Heavily on Many Americans

About a year since the coronavirus recession began, there are some signs of improvement in the U.S. labor market, and Americans are feeling somewhat better about their personal finances than they were early in the pandemic.

Unemployed Americans are feeling the emotional strain of job loss; most have considered changing occupations

About half of U.S. adults who are currently unemployed and are looking for a job are pessimistic about their prospects for future employment.

REFINE YOUR SELECTION

• Drew DeSilver (17)
• Rakesh Kochhar (15)
• Jens Manuel Krogstad (5)
• Amanda Barroso (4)
• Jesse Bennett (4)
• Kat Devlin (3)
• Kim Parker (3)
• Pew Research Center Staff (3)
• Rich Morin (3)
• Richard Fry (3)
• Anna Brown (2)
• George Gao (2)
• Jeffrey S. Passel (2)
• Juliana Menasce Horowitz (2)
• Mark Hugo Lopez (2)
• Rachel Minkin (2)
• Ruth Igielnik (2)
• Stella Sechopoulos (2)
• Alec Tyson (1)
• Ana Gonzalez-Barrera (1)
• Antonio Flores (1)
• Bruce Drake (1)
• Bruce Stokes (1)
• D’Vera Cohn (1)
• J.J. Moncus (1)
• Jacob Poushter (1)
• John Gramlich (1)
• Luis Noe-Bustamante (1)
• Sousan Arafeh (1)

Research Teams

• Social Trends (39)
• Global (13)
• Global Migration and Demography (12)
• Politics (9)
• Race and Ethnicity (7)
• Data Labs (4)
• Internet and Technology (4)
• Journalism (4)
• Methods (4)
• Pew Research Center (4)
• Religion (4)
• Science (4)

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder.

© 2024 Pew Research Center

Federal Reserve Economic Data

Unemployment Rate in Tampa-St. Petersburg-Clearwater, FL (MSA) (TAMP312URN)

Observation:

Data in this graph are copyrighted. Please review the copyright information in the series notes before sharing.

• Page short URL
• Embed in website
• Image short URL
• Add to data list
• Get email notification

Units:   Percent , Not Seasonally Adjusted

Frequency:   Monthly

Suggested Citation:

U.S. Bureau of Labor Statistics, Unemployment Rate in Tampa-St. Petersburg-Clearwater, FL (MSA) [TAMP312URN], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/TAMP312URN, .

RELEASE TABLES

• Unemployment Rate by Metropolitan Statistical Area, Monthly, Not Seasonally Adjusted: Florida

RELATED DATA AND CONTENT

Data suggestions based on your search.

Content Suggestions

Other Formats

Related categories, permalink/embed, subscribe to the fred newsletter.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Research models and methodologies on the smart city: a systematic literature review.

1. Introduction

2. theoretical background and previous studies, 2.1. definition of smart city, 2.2. previous studies, 3. research method, 3.1. research subject, 3.2. systematic review, 4. results and discussion, 4.1. results of research method analysis, 4.2. results of research content analysis, 4.2.1. infrastructure/monitoring, 4.2.2. citizen/sustainability, 4.2.3. big data/algorithm, 4.2.4. smart grid, 4.2.5. the internet of things/cloud, 4.2.6. governance, 4.2.7. transportation, 5. conclusions and discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• KAIA. Planning and Research to Establish a Foundation for International Standardization of Smart Cities. 2019. Available online: http://www.kaia.re.kr/portal/cmm/fms/FileDown.do?atchFileId=FILE_000000000025140&fileSn=1&bbsId= (accessed on 1 January 2022).
• Technavio Smart City Market by Application and Geography—Forecast and Analysis 2021–2025. Available online: https://www.technavio.com/report/smart-cities-market-industry-analysis (accessed on 1 January 2022).
• Cocchia, A. Smart and Digital City: A Systematic Literature Review. In Smart City ; Dameri, R., Rosenthal-Sabroux, C., Eds.; Springer: Berlin/Heidelberg, Germany, 2014; pp. 13–43. [ Google Scholar ] [ CrossRef ]
• Thomas, V.; Wang, D.; Mullagh, L.; Dunn, N. Where’s Wally? In search of citizen perspectives on the smart city. Sustainability 2016 , 8 , 207. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Souza, J.T.d.; Francisco, A.C.d.; Piekarski, C.M.; Prado, G.F.d. Data mining and machine learning to promote smart cities: A systematic review from 2000 to 2018. Sustainability 2019 , 11 , 1077. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yigitcanlar, T.; Han, H.; Kamruzzaman, M.; Ioppolo, G.; Sabatini-Marques, J. The making of smart cities: Are Songdo, Masdar, Amsterdam, San Francisco and Brisbane the best we could build? Land Use Policy 2019 , 88 , 104187. [ Google Scholar ] [ CrossRef ]
• Laufs, J.; Borrion, H.; Bradford, B. Security and the smart city: A systematic review. Sustain. Cities Soc. 2020 , 55 , 102023. [ Google Scholar ] [ CrossRef ]
• Abusaada, H.; Elshater, A. Competitiveness, distinctiveness and singularity in urban design: A systematic review and framework for smart cities. Sustain. Cities Soc. 2021 , 102782. [ Google Scholar ] [ CrossRef ]
• Nam, T.; Pardo, T.A. Smart city as urban innovation: Focusing on management, policy, and context. In Proceedings of the 5th International Conference on Theory and Practice of Electronic Governance, New York, NY, USA, 26–29 September 2011; pp. 185–194. [ Google Scholar ]
• Caragliu, A.; Del Bo, C.; Nijkamp, P. Smart cities in Europe. J. Urban Technol. 2011 , 18 , 65–82. [ Google Scholar ] [ CrossRef ]
• Kourtit, K.; Nijkamp, P.; Arribas, D. Smart cities in perspective—A comparative European study by means of self-organizing maps. Innov. Eur. J. Soc. Sci. Res. 2012 , 25 , 229–246. [ Google Scholar ] [ CrossRef ]
• Verducci, P.; Desideri, U. Architecture, Urban Design, Energetic System. Design of Eco-Sustainable Industrial Parks with Very Low Environmental Impact, Reduced Use of Water and Energy ; Morlacchi Editore: Perugia, Italy, 2012. [ Google Scholar ]
• Dameri, R.P. Searching for smart city definition: A comprehensive proposal. Int. J. Comput. Technol. 2013 , 11 , 2544–2551. [ Google Scholar ] [ CrossRef ]
• Svítek, M. Telematic approach into program of smart cities. In Proceedings of the 7th Euro American Conference on Telematics and Information Systems, Valparaiso, Chile, 2–4 April 2014; pp. 1–2. [ Google Scholar ]
• Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things J. 2014 , 1 , 22–32. [ Google Scholar ] [ CrossRef ]
• Piro, G.; Cianci, I.; Grieco, L.A.; Boggia, G.; Camarda, P. Information centric services in smart cities. J. Syst. Softw. 2014 , 88 , 169–188. [ Google Scholar ] [ CrossRef ]
• Monzon, A. Smart cities concept and challenges: Bases for the assessment of smart city projects. In Proceedings of the 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Lisbon, Portugal, 20–22 May 2015; IEEE: Piscataway, NJ, USA, 2015; pp. 1–11. [ Google Scholar ]
• Marsal-Llacuna, M.-L.; Colomer-Llinàs, J.; Meléndez-Frigola, J. Lessons in urban monitoring taken from sustainable and livable cities to better address the Smart Cities initiative. Technol. Forecast. Soc. Chang. 2015 , 90 , 611–622. [ Google Scholar ] [ CrossRef ]
• Snow, C.C.; Håkonsson, D.D.; Obel, B. A smart city is a collaborative community: Lessons from smart Aarhus. Calif. Manag. Rev. 2016 , 59 , 92–108. [ Google Scholar ] [ CrossRef ]
• Lara, A.P.; Da Costa, E.M.; Furlani, T.Z.; Yigitcanla, T. Smartness that matters: Towards a comprehensive and human-centred characterisation of smart cities. J. Open Innov. Technol. Mark. Complex. 2016 , 2 , 8. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Azevedo Guedes, A.L.; Carvalho Alvarenga, J.; Dos Santos Sgarbi Goulart, M.; Rodriguez y Rodriguez, M.V.; Pereira Soares, C.A. Smart cities: The main drivers for increasing the intelligence of cities. Sustainability 2018 , 10 , 3121. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Anthopoulos, L.G. Understanding the smart city domain: A literature review. Transform. City Gov. Success. Smart Cities 2015 , 9–21. [ Google Scholar ]
• Daneva, M.; Lazarov, B. Requirements for smart cities: Results from a systematic review of literature. In Proceedings of the 2018 12th International Conference on Research Challenges in Information Science (RCIS), Nantes, France 29–31 May 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–6. [ Google Scholar ]
• Esashika, D.; Masiero, G.; Mauger, Y. An investigation into the elusive concept of smart cities: A systematic review and meta-synthesis. Technol. Anal. Strateg. Manag. 2021 , 33 , 957–969. [ Google Scholar ] [ CrossRef ]
• Kim, S.T. A review and suggestion on the methodology of domestic content analysis research. Commun. Theory 2005 , 1 , 39–67. [ Google Scholar ]
• Chung, S.; Yu, P.J. Analysis on E-Participation Research Trend in Korea. Informatization Policy 2014 , 21 , 3–23. [ Google Scholar ] [ CrossRef ]
• Kim, G.J. Meta-analysis of research on the spread of innovation: Focusing on the field of journalism. Korea J. 2010 , 54 , 31–56. [ Google Scholar ]
• Mok, J.-W.; Kang, K.-B.; Oh, C.-H.; Choi, Y.; Hong, H.; Kim, H.; Park, Y. An Empirical Study on the Trends of Korean Policy Research: Focused on Lasswell’s Policy Paradigm. Korean Policy Stud. Rev. 2005 , 14 , 131–154. [ Google Scholar ]
• Kim, K. Understanding the Social Science Research Methodology ; B&M Books: Seoul, Korea, 2011. [ Google Scholar ]
• Park, K.C.; Lee, C.H. A study on the research trends for smart city using topic modeling. J. Internet Comput. Serv. 2019 , 20 , 119–128. [ Google Scholar ]
• Alablani, I.; Alenazi, M. EDTD-SC: An IoT sensor deployment strategy for smart cities. Sensors 2020 , 20 , 7191. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Munjal, R.; Liu, W.; Li, X.J.; Gutierrez, J. A Neural Network-Based Sustainable Data Dissemination through Public Transportation for Smart Cities. Sustainability 2020 , 12 , 10327. [ Google Scholar ] [ CrossRef ]
• Alwajeeh, T.; Combeau, P.; Aveneau, L. An Efficient Ray-Tracing Based Model Dedicated to Wireless Sensor Network Simulators for Smart Cities Environments. IEEE Access 2020 , 8 , 206528–206547. [ Google Scholar ] [ CrossRef ]
• Reddy, K.H.K.; Luhach, A.K.; Pradhan, B.; Dash, J.K.; Roy, D.S. A genetic algorithm for energy efficient fog layer resource management in context-aware smart cities. Sustain. Cities Soc. 2020 , 63 , 102428. [ Google Scholar ] [ CrossRef ]
• Deveci, M.; Pekaslan, D.; Canıtez, F. The assessment of smart city projects using zSlice type-2 fuzzy sets based Interval Agreement Method. Sustain. Cities Soc. 2020 , 53 , 101889. [ Google Scholar ] [ CrossRef ]
• Elsaeidy, A.A.; Jagannath, N.; Sanchis, A.G.; Jamalipour, A.; Munasinghe, K.S. Replay attack detection in smart cities using deep learning. IEEE Access 2020 , 8 , 137825–137837. [ Google Scholar ] [ CrossRef ]
• Kirimtat, A.; Krejcar, O.; Kertesz, A.; Tasgetiren, M.F. Future trends and current state of smart city concepts: A survey. IEEE Access 2020 , 8 , 86448–86467. [ Google Scholar ] [ CrossRef ]
• Malik, A.W.; Mahmood, I.; Ahmed, N.; Anwar, Z. Big data in motion: A vehicle-assisted urban computing framework for smart cities. IEEE Access 2019 , 7 , 55951–55965. [ Google Scholar ]
• Jia, J.; Wu, X. A Multidimensional Assessment Model Using RE–3DSG Sensors on Net ES and GVR for Sustainable and Smart Cities. Sensors 2020 , 20 , 1259. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Santana, J.R.; Sánchez, L.; Sotres, P.; Lanza, J.; Llorente, T.; Munoz, L. A privacy-aware crowd management system for smart cities and smart buildings. IEEE Access 2020 , 8 , 135394–135405. [ Google Scholar ] [ CrossRef ]
• Li, C.; Liu, X.; Zhao, Z. Smart City: A Shareable Framework and Its Applications in China. Sustainability 2019 , 11 , 4346. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Balasubramaniam, A.; Paul, A.; Hong, W.-H.; Seo, H.; Kim, J.H. Comparative analysis of intelligent transportation systems for sustainable environment in smart cities. Sustainability 2017 , 9 , 1120. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Muvuna, J.; Boutaleb, T.; Mickovski, S.B.; Baker, K.; Mohammad, G.S.; Cools, M.; Selmi, W. Information integration in a smart city system—A case study on air pollution removal by green infrastructure through a vehicle smart routing system. Sustainability 2020 , 12 , 5099. [ Google Scholar ] [ CrossRef ]
• Aldelaimi, M.N.; Hossain, M.A.; Alhamid, M.F. Building dynamic communities of interest for internet of things in smart cities. Sensors 2020 , 20 , 2986. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Moch, N.; Wereda, W. Smart Security in the Smart City. Sustainability 2020 , 12 , 9900. [ Google Scholar ] [ CrossRef ]
• Oh, J. Smart city as a tool of citizen-oriented urban regeneration: Framework of preliminary evaluation and its application. Sustainability 2020 , 12 , 6874. [ Google Scholar ] [ CrossRef ]
• Yu, C.; Ye, B.; Lin, C.; Wu, Y.J. Can Smart City Development Promote Residents’ Emotional Well-Being? Evidence From China. IEEE Access 2020 , 8 , 116024–116040. [ Google Scholar ] [ CrossRef ]
• Singh, S.; Sharma, P.K.; Yoon, B.; Shojafar, M.; Cho, G.H.; Ra, I.-H. Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustain. Cities Soc. 2020 , 63 , 102364. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Zhang, Z.; Zhang, L.; Yang, Y.; Kang, Q.; Sun, D. Object tracking for a smart city using IoT and edge computing. Sensors 2019 , 19 , 1987. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rojek, I.; Studzinski, J. Detection and localization of water leaks in water nets supported by an ICT system with artificial intelligence methods as a way forward for smart cities. Sustainability 2019 , 11 , 518. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Anisetti, M.; Ardagna, C.; Bellandi, V.; Cremonini, M.; Frati, F.; Damiani, E. Privacy-aware Big Data Analytics as a service for public health policies in smart cities. Sustain. Cities Soc. 2018 , 39 , 68–77. [ Google Scholar ] [ CrossRef ]
• Apanaviciene, R.; Urbonas, R.; Fokaides, P.A. Smart Building Integration into a Smart City: Comparative Study of Real Estate Development. Sustainability 2020 , 12 , 9376. [ Google Scholar ] [ CrossRef ]
• Nicolas, C.; Kim, J.; Chi, S. Quantifying the dynamic effects of smart city development enablers using structural equation modeling. Sustain. Cities Soc. 2020 , 53 , 101916. [ Google Scholar ] [ CrossRef ]
• Ramos, F.; Trilles, S.; Muñoz, A.; Huerta, J. Promoting pollution-free routes in smart cities using air quality sensor networks. Sensors 2018 , 18 , 2507. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hurst, W.; Montanez, C.A.C.; Shone, N.; Al-Jumeily, D. An ensemble detection model using multinomial classification of stochastic gas smart meter data to improve wellbeing monitoring in smart cities. IEEE Access 2020 , 8 , 7877–7898. [ Google Scholar ] [ CrossRef ]
• Suartika, G.A.M.; Cuthbert, A. The Sustainable Imperative—Smart Cities, Technology and Development. Sustainability 2020 , 12 , 8892. [ Google Scholar ] [ CrossRef ]
• Vidiasova, L.; Cronemberger, F. Discrepancies in perceptions of smart city initiatives in Saint Petersburg, Russia. Sustain. Cities Soc. 2020 , 59 , 102158. [ Google Scholar ] [ CrossRef ]
• Oliveira, T.A.; Oliver, M.; Ramalhinho, H. Challenges for connecting citizens and smart cities: ICT, e-governance and blockchain. Sustainability 2020 , 12 , 2926. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Alotaibi, S.S. Registration center based user authentication scheme for smart E-governance applications in smart cities. IEEE Access 2018 , 7 , 5819–5833. [ Google Scholar ] [ CrossRef ]
• Yao, F.; Wang, Y. Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data. Sustain. Cities Soc. 2020 , 63 , 102448. [ Google Scholar ] [ CrossRef ]
• Lavalle, A.; Teruel, M.A.; Maté, A.; Trujillo, J. Improving sustainability of smart cities through visualization techniques for big data from iot devices. Sustainability 2020 , 12 , 5595. [ Google Scholar ] [ CrossRef ]
• Avanzato, R.; Beritelli, F. Hydrogeological Risk Management in Smart Cities: A New Approach to Rainfall Classification Based on LTE Cell Selection Parameters. IEEE Access 2020 , 8 , 137161–137173. [ Google Scholar ] [ CrossRef ]
• Curiale, M. From smart grids to smart city. In Proceedings of the 2014 Saudi Arabia Smart Grid Conference (SASG), Jeddah, Saudi Arabia, 14–17 December 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1–9. [ Google Scholar ]
• Pei, P.; Huo, Z.; Martínez, O.S.; Crespo, R.G. Minimal green energy consumption and workload management for data centers on smart city platforms. Sustainability 2020 , 12 , 3140. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Yao, T.; Huang, Z.; Zhao, W. Are smart cities more ecologically efficient? Evidence from China. Sustain. Cities Soc. 2020 , 60 , 102008. [ Google Scholar ] [ CrossRef ]
• Jiang, D. The construction of smart city information system based on the Internet of Things and cloud computing. Comput. Commun. 2020 , 150 , 158–166. [ Google Scholar ] [ CrossRef ]
• Garcia Alvarez, M.; Morales, J.; Kraak, M.-J. Integration and exploitation of sensor data in smart cities through event-driven applications. Sensors 2019 , 19 , 1372. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Falco, G.; Viswanathan, A.; Caldera, C.; Shrobe, H. A master attack methodology for an AI-based automated attack planner for smart cities. IEEE Access 2018 , 6 , 48360–48373. [ Google Scholar ] [ CrossRef ]
• Purnomo, F.; Prabowo, H. Smart city indicators: A systematic literature review. J. Telecommun. Electron. Comput. Eng. (JTEC) 2016 , 8 , 161–164. [ Google Scholar ]
• Westraadt, L.; Calitz, A. A modelling framework for integrated smart city planning and management. Sustain. Cities Soc. 2020 , 63 , 102444. [ Google Scholar ] [ CrossRef ]
• Azgomi, H.F.; Jamshidi, M. A brief survey on smart community and smart transportation. In Proceedings of the 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, 5–7 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 932–939. [ Google Scholar ]
• Zhou, L.; Li, Q.; Tu, W. An efficient access model of Massive spatiotemporal Vehicle trajectory data in Smart City. IEEE Access 2020 , 8 , 52452–52465. [ Google Scholar ] [ CrossRef ]
• Gohar, M.; Muzammal, M.; Rahman, A.U. SMART TSS: Defining transportation system behavior using big data analytics in smart cities. Sustain. Cities Soc. 2018 , 41 , 114–119. [ Google Scholar ] [ CrossRef ]
• Ang, L.-M.; Seng, K.P.; Ijemaru, G.K.; Zungeru, A.M. Deployment of IoV for smart cities: Applications, architecture, and challenges. IEEE Access 2018 , 7 , 6473–6492. [ Google Scholar ] [ CrossRef ]
• Chithaluru, P.; Al-Turjman, F.; Kumar, M.; Stephan, T. I-AREOR: An energy-balanced clustering protocol for implementing green IoT in smart cities. Sustain. Cities Soc. 2020 , 61 , 102254. [ Google Scholar ] [ CrossRef ]
• Jararweh, Y.; Otoum, S.; Al Ridhawi, I. Trustworthy and sustainable smart city services at the edge. Sustain. Cities Soc. 2020 , 62 , 102394. [ Google Scholar ] [ CrossRef ]
• Qureshi, K.N.; Rana, S.S.; Ahmed, A.; Jeon, G. A novel and secure attacks detection framework for smart cities industrial internet of things. Sustain. Cities Soc. 2020 , 61 , 102343. [ Google Scholar ] [ CrossRef ]
• Sun, Y.; Su, Z.; Zhao, Y.; Deng, D.; Zhu, F.; Xia, J. Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities. IEEE Access 2020 , 8 , 150750–150758. [ Google Scholar ] [ CrossRef ]
• Dong, X.; Shi, T.; Zhang, W.; Zhou, Q. Temporal and spatial differences in the resilience of smart cities and their influencing factors: Evidence from non-provincial cities in China. Sustainability 2020 , 12 , 1321. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Guo, Y.; Tang, Z.; Guo, J. Could a smart city ameliorate urban traffic congestion? A quasi-natural experiment based on a smart city pilot program in China. Sustainability 2020 , 12 , 2291. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, K.; Jung, J.-K.; Choi, J.Y. Impact of the smart city industry on the Korean national economy: Input-output analysis. Sustainability 2016 , 8 , 649. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Lytras, M.D.; Şerban, A.C. E-government insights to smart cities research: European union (EU) study and the role of regulations. IEEE Access 2020 , 8 , 65313–65326. [ Google Scholar ] [ CrossRef ]
• Perboli, G.; Rosano, M. A taxonomic analysis of smart city projects in North America and Europe. Sustainability 2020 , 12 , 7813. [ Google Scholar ] [ CrossRef ]
• Šulyová, D.; Vodák, J. The Impact of Cultural Aspects on Building the Smart City Approach: Managing Diversity in Europe (London), North America (New York) and Asia (Singapore). Sustainability 2020 , 12 , 9463. [ Google Scholar ] [ CrossRef ]
• Amer, H.; Salman, N.; Hawes, M.; Chaqfeh, M.; Mihaylova, L.; Mayfield, M. An improved simulated annealing technique for enhanced mobility in smart cities. Sensors 2016 , 16 , 1013. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Encalada, L.; Boavida-Portugal, I.; Cardoso Ferreira, C.; Rocha, J. Identifying tourist places of interest based on digital imprints: Towards a sustainable smart city. Sustainability 2017 , 9 , 2317. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Garcia-Font, V.; Garrigues, C.; Rifà-Pous, H. A comparative study of anomaly detection techniques for smart city wireless sensor networks. Sensors 2016 , 16 , 868. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Khan, M.S.; Woo, M.; Nam, K.; Chathoth, P.K. Smart city and smart tourism: A case of Dubai. Sustainability 2017 , 9 , 2279. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Li, X.; Lv, Z.; Hijazi, I.H.; Jiao, H.; Li, L.; Li, K. Assessment of Urban Fabric for Smart Cities. IEEE Access 2016 , 4 , 373–382. [ Google Scholar ] [ CrossRef ]
• Hořejší, P.; Novikov, K.; Šimon, M. A smart factory in a Smart City: Virtual and augmented reality in a Smart assembly line. IEEE Access 2020 , 8 , 94330–94340. [ Google Scholar ] [ CrossRef ]
• Luque-Vega, L.F.; Carlos-Mancilla, M.A.; Payán-Quiñónez, V.G.; Lopez-Neri, E. Smart cities oriented project planning and evaluation methodology driven by citizen perception—IoT smart mobility case. Sustainability 2020 , 12 , 7088. [ Google Scholar ] [ CrossRef ]
• Mahapatra, C.; Moharana, A.K.; Leung, V. Energy management in smart cities based on internet of things: Peak demand reduction and energy savings. Sensors 2017 , 17 , 2812. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Saadi, M.; Noor, M.T.; Imran, A.; Toor, W.T.; Mumtaz, S.; Wuttisittikulkij, L. IoT enabled quality of experience measurement for next generation networks in smart cities. Sustain. Cities Soc. 2020 , 60 , 102266. [ Google Scholar ] [ CrossRef ]
• Villegas-Ch, W.; Palacios-Pacheco, X.; Luján-Mora, S. Application of a smart city model to a traditional university campus with a big data architecture: A sustainable smart campus. Sustainability 2019 , 11 , 2857. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Calavia, L.; Baladrón, C.; Aguiar, J.M.; Carro, B.; Sánchez-Esguevillas, A. A semantic autonomous video surveillance system for dense camera networks in smart cities. Sensors 2012 , 12 , 10407–10429. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Won, J.; Seo, S.-H.; Bertino, E. Certificateless cryptographic protocols for efficient drone-based smart city applications. IEEE Access 2017 , 5 , 3721–3749. [ Google Scholar ] [ CrossRef ]
• Weber, M.; Podnar Žarko, I. A regulatory view on smart city services. Sensors 2019 , 19 , 415. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bracco, S.; Delfino, F.; Laiolo, P.; Morini, A. Planning & open-air demonstrating smart city sustainable districts. Sustainability 2018 , 10 , 4636. [ Google Scholar ]
• Pinna, F.; Masala, F.; Garau, C. Urban policies and mobility trends in Italian smart cities. Sustainability 2017 , 9 , 494. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Choque, J.; Diez, L.; Medela, A.; Muñoz, L. Experimentation management in the co-created smart-city: Incentivization and citizen engagement. Sensors 2019 , 19 , 411. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kuru, K.; Ansell, D. TCitySmartF: A comprehensive systematic framework for transforming cities into smart cities. IEEE Access 2020 , 8 , 18615–18644. [ Google Scholar ] [ CrossRef ]
• Braun, T.; Fung, B.C.; Iqbal, F.; Shah, B. Security and privacy challenges in smart cities. Sustain. Cities Soc. 2018 , 39 , 499–507. [ Google Scholar ] [ CrossRef ]
• Hasbini, M.A.; Eldabi, T.; Aldallal, A. Investigating the information security management role in smart city organisations. World J. Entrep. Manag. Sustain. Dev. 2018 , 14 , 86–98. [ Google Scholar ] [ CrossRef ]
• Rahman, M.A.; Asyhari, A.T.; Leong, L.; Satrya, G.; Tao, M.H.; Zolkipli, M. Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain. Cities Soc. 2020 , 61 , 102324. [ Google Scholar ] [ CrossRef ]
• Andrade, R.O.; Yoo, S.G.; Tello-Oquendo, L.; Ortiz-Garcés, I. A Comprehensive Study of the IoT Cybersecurity in Smart Cities. IEEE Access 2020 , 8 , 228922–228941. [ Google Scholar ] [ CrossRef ]
• Yu, S.; Lee, J.; Park, K.; Das, A.K.; Park, Y. IoV-SMAP: Secure and efficient message authentication protocol for IoV in smart city environment. IEEE Access 2020 , 8 , 167875–167886. [ Google Scholar ] [ CrossRef ]
• Sifah, E.B.; Xia, H.; Cobblah, C.N.A.; Xia, Q.; Gao, J.; Du, X. BEMPAS: A decentralized employee performance assessment system based on blockchain for smart city governance. IEEE Access 2020 , 8 , 99528–99539. [ Google Scholar ] [ CrossRef ]
• Gheisari, M.; Pham, Q.-V.; Alazab, M.; Zhang, X.; Fernandez-Campusano, C.; Srivastava, G. ECA: An edge computing architecture for privacy-preserving in IoT-based smart city. IEEE Access 2019 , 7 , 155779–155786. [ Google Scholar ] [ CrossRef ]

Share and Cite

Myeong, S.; Park, J.; Lee, M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability 2022 , 14 , 1687. https://doi.org/10.3390/su14031687

Myeong S, Park J, Lee M. Research Models and Methodologies on the Smart City: A Systematic Literature Review. Sustainability . 2022; 14(3):1687. https://doi.org/10.3390/su14031687

Myeong, Seunghwan, Jaehyun Park, and Minhyung Lee. 2022. "Research Models and Methodologies on the Smart City: A Systematic Literature Review" Sustainability 14, no. 3: 1687. https://doi.org/10.3390/su14031687

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

St. Petersburg Florida Unemployment Office Locations

If you are searching for a Florida, unemployment office, you can find one in your community. At your nearest facility, you can speak with unemployment insurance (UI) representatives about your claim. In fact, you can come to these locations no matter where you are in the enrollment process. Some of the most common reasons that people with the Florida unemployment claim visit these locations is because they have application, interview or renewal questions.

No matter where you live in St. Petersburg , there is an unemployment facility near you. All applicants should know where these facilities are in case they have questions about their claims or are contacted by a caseworker.

How to Find Your St. Petersburg Unemployment Office in Florida

If you need to find an unemployment insurance (UI) office in St. Petersburg , Florida , you can do so easily. A list of all Florida UI offices throughout the city are included below. When you find a location, click to learn more. On the individual office pages, you can find out specific information, such as:

• Where the UI office is located.
• The office’s phone number.
• The facility’s hours of operation.

If you have a address in mind, you can also find facilities by searching with this information. There is a box on the right of the page where you can enter street addresses to find St. Petersburg offices near you.

Find Unemployment Office Locations

Leave a Reply Cancel reply

You must be logged in to post a comment.