The average for 2024 based on 11 countries was 6.39 points. The highest value was in Costa Rica: 6.96 points and the lowest value was in Dominican Republic: 5.82 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

This statistic shows the results of a survey, conducted in 2013 among adult Americans, on whether they are as happy now as they expected to be at this stage of their life. 28 percent of respondents said they are even happier than expected now.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

This statistic shows the results of a survey conducted in the United States in 2017 regarding how happy Americans are with their current relationship, on a scale from 1 (not happy at all) to 10 (very happy), by gender. Some 38 percent of female respondents stated they are very happy.

Surveys measuring happiness or preferences generate discrete ordinal data. Ordered response models, which are used to analyze such data, suffer from an identification problem. Their conclusions depend on distributional assumptions about a latent variable. We propose using response times to solve that problem. Response times contain information about the distribution of the latent variable through a chronometric effect. Using an online survey experiment, we verify the chronometric effect. We then provide theoretical conditions for testing conventional distributional assumptions. These assumptions are rejected in some cases, but overall our evidence is consistent with the qualitative validity of the conventional models.

Dataset Card for World Happiness Report

  Dataset Summary





  Context

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/world-happiness.

This statistic shows the results of a Popsugar survey conducted in 2015 among American women, asking them to define happiness. During the survey, **** percent of respondents said that family and spending time with family equaled happiness for them.

Happiness index of United States of America went up by 0.37% from 6.95 index in 2020 to 6.98 index in 2021. Since the 1.53% downward trend in 2017, happiness index improved by 1.32% in 2021.

Do people who have more money feel happier during their daily activities? Some prior research has found no relationship between income and daily happiness when treating income as a continuous variable in OLS regressions, although results differ between studies. We re-analyzed existing data from the United States and Germany, treating household income as a categorical variable and using lowess and spline regressions to explore nonlinearities. Our analyses reveal that these methodological decisions change the results and conclusions about the relationship between income and happiness. In American and German diary data from 2010 to 2015, results for the continuous treatment of income showed a null relationship with happiness, whereas the categorization of income showed that some of those with higher incomes reported feeling less happy than some of those with lower incomes. Lowess and spline regressions suggested null results overall, and there was no evidence of a relationship between income and happiness in Experience Sampling Methodology (ESM) data. Not all analytic approaches generate the same results, which may contribute to explaining discrepant results in existing studies about the correlates of happiness. Future research should be explicit about their approaches to measuring and analyzing income when studying its relationship with subjective well-being, ideally testing different approaches, and making conclusions based on the pattern of results across approaches.

The statistic above provides information about the income level in the United States at which money won't make you happier. In 2010, a household in Hawaii needs to make about 122 thousand U.S. dollars per year to reach the happiness plateau, in which more income doesn't provide better emotional well-being. The state-by-state comparison takes into account the disparity in cost of living between the states.

About this dataset

Context

The dataset tabulates the Happy population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Happy. The dataset can be utilized to understand the population distribution of Happy by age. For example, using this dataset, we can identify the largest age group in Happy.

Key observations

The largest age group in Happy, TX was for the group of age 10 to 14 years years with a population of 127 (16.75%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Happy, TX was the 75 to 79 years years with a population of 5 (0.66%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Happy is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Happy total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Happy Population by Age. You can refer the same here

We conduct a random-assignment experiment to investigate whether positive affect impacts time preference, where time preference denotes a preference for present over future utility. Our result indicates that, compared to neutral affect, mild positive affect significantly reduces time preference over money. This result is robust to various specification checks, and alternative interpretations of the result are considered. Our result has implications for the effect of happiness on time preference and the role of emotions in economic decision making, in general. Finally, we reconfirm the ubiquity of time preference and start to explore its determinants. (JEL D12, D83, I31)

This statistic shows the results of a 2014 Popsugar survey among American women asking them which three brands make them the happiest. During the survey, **** percent of female respondents said MAC make them the happiest.

About this dataset

Context

The dataset tabulates the data for the Happy, TX population pyramid, which represents the Happy population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Happy, TX, is 47.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Happy, TX, is 12.4.
• Total dependency ratio for Happy, TX is 60.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Happy, TX is 8.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Happy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Happy for the selected age group is shown in the following column.
• Population (Female): The female population in the Happy for the selected age group is shown in the following column.
• Total Population: The total population of the Happy for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Happy Population by Age. You can refer the same here

Quality of Life by State 2024

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This dataset provides insights into the quality of life across different states in the United States for the year 2024. Quality of life, encompassing aspects like comfort, health, and happiness, is evaluated through various metrics including affordability, economy, education, and safety. Dive into this dataset to understand how different states fare in terms of overall quality of life and its individual components.

Columns Description

• State: The name of the U.S. state.
• QualityOfLifeTotalScore: The total score representing the overall quality of life for the respective state. This score is calculated based on various quality of life metrics.
• QualityOfLifeQualityOfLife: The score representing the quality of life aspect for the respective state. This aspect may include subjective factors related to happiness, satisfaction, and overall well-being. Higher scores may indicate a higher level of subjective well-being, happiness, or overall satisfaction among residents. Lower scores could suggest lower levels of subjective well-being.
• QualityOfLifeAffordability: The score representing the affordability aspect of the quality of life for the respective state. This aspect evaluates factors such as cost of living, housing affordability, and income levels. Higher scores typically indicate greater affordability of housing, cost of living, and basic necessities. Lower scores may suggest that these essentials are less accessible or more expensive for residents.
• QualityOfLifeEconomy: The score representing the economic aspect of the quality of life for the respective state. This aspect assesses factors such as employment opportunities, economic growth, and income distribution. Higher scores may reflect a stronger economy with more job opportunities, higher incomes, and lower levels of poverty. Lower scores might indicate economic challenges such as unemployment or income inequality.
• QualityOfLifeEducationAndHealth: The score representing the education and health aspect of the quality of life for the respective state. This aspect considers factors such as access to quality education, healthcare facilities, and overall public health indicators. Higher scores generally signify better access to quality education, healthcare services, and overall public health. Lower scores may indicate deficiencies in these areas, such as limited access to healthcare or lower educational attainment levels.
• QualityOfLifeSafety: The score representing the safety aspect of the quality of life for the respective state. This aspect evaluates factors such as crime rates, public safety measures, and community well-being initiatives. Higher scores suggest lower crime rates, better community safety, and a higher sense of security among residents. Lower scores may indicate higher crime rates or concerns about safety.

These descriptions provide an overview of what each column represents and the specific aspects of quality of life they assess for each U.S. state.

This paper proposes foundations and a methodology for survey-based tracking of well-being. First, we develop a theory in which utility depends on "fundamental aspects" of well-being, measurable with surveys. Second, drawing from psychologists, philosophers, and economists, we compile a comprehensive list of such aspects. Third, we demonstrate our proposed method for estimating the aspects' relative marginal utilities—a necessary input for constructing an individual-level well-being index—by asking ~4,600 U.S. survey respondents to state their preference between pairs of aspect bundles. We estimate high relative marginal utilities for aspects related to family, health, security, values, freedom, happiness, and life satisfaction.

About this dataset

Context

The dataset tabulates the population of Happy by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Happy. The dataset can be utilized to understand the population distribution of Happy by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Happy. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Happy.

Key observations

Largest age group (population): Male # 10-14 years (73) | Female # 35-39 years (45). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Happy population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Happy is shown in the following column.
• Population (Female): The female population in the Happy is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Happy for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Happy Population by Gender. You can refer the same here

Job Satisfaction: What Is It? Job Satisfaction Statistics: The term job "job contentment," also known as "employee satisfaction," is used in companies to understand the state of workers' unhappiness or happiness in their jobs. Organizations that want to have the best results can use job satisfaction statistics. They strongly correlate with staff output, retention, and overall happiness in the workplace. In addition, employers who wish to succeed with the best talent at their side need to comprehend the entire scope and importance of employee satisfaction stats. In this article, we will discuss the most important job satisfaction statistics. Job Satisfaction Statistics for 2023 (Editor's Choice) 77% of employees believe their coworkers remained very satisfied with overall job satisfaction. 55% of US workers are expected to search for new jobs within the next 12 months. 36% of employees globally claimed that they are in love with their current job. 75% of workers quit their jobs due to managerial conflicts. Remote work is preferred by 32% of respondents across the world. In the United States, 60% of workers are happy with their jobs in 2023. By the end of 2023, around 61% of American employees will leave their current jobs.

Since 2020, the world has faced two unprecedented shocks: lockdowns (regulation) and the invasion of Ukraine (war). Although we realise the health and economic effects of these shocks, more research is needed on the effect on happiness and whether the type of shock plays a role. Therefore, in this paper, we determine whether these macro-level shocks affected happiness, how these effects differ, and how long it takes for happiness to adapt to previous levels. The latter will allow us to test whether adaptation theory holds at the macro level. We use a unique dataset of ten countries spanning the Northern and Southern hemispheres derived from tweets extracted in real-time per country. Applying Natural Language Processing, we obtain these tweets’ underlying sentiment scores, after which we calculate a happiness score (Gross National Happiness) and derive daily time series data. Our Twitter dataset is combined with Oxford’s COVID-19 Government Response Tracker data. Considering the results of the Difference-in-Differences and event studies jointly, we are confident that the shocks led to lower happiness levels, both with the lockdown and the invasion shock. We find that the effect size is significant and that the lockdown shock had a bigger effect than the invasion. Considering both types of shocks, the adaptation to previous happiness levels occurred within two to three weeks. Following our findings of similar behaviour in happiness to both types of shocks, the question of whether other types of shocks will have similar effects is posited. Regardless of the length of the adaptation period, understanding the effects of macro-level shocks on happiness is essential for policymakers, as happiness has a spillover effect on other variables such as production, safety and trust.

According to a survey conducted in the United States and released in 2021, nearly ** percent of respondents agreed that houseplants make them happier, ** percent of which strongly agreed. Only two percent of respondents disagreed with the statement.