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Predicted and observed subjective well-being (united states aggregate level). Graphs show the estimates (with confidence intervals) for subjective wellbeing at the US level, constructed using the US-model, in red, alongside estimates from the benchmark (seasonality only) model in yellow and the Gallup series in blue. Confidence intervals are constructed using 1000 draws. Training data is inside the red lines, and Testing data is outside the red lines. Correlations are given in Table 6.
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We build models to estimate well-being in the United States based on changes in the volume of internet searches for different words, obtained from the Google Trends website. The estimated well-being series are weighted combinations of word groups that are endogenously identified to fit the weekly subjective well-being measures collected by Gallup A...
Citations
... In past years, GDP was used to measure a nation's happiness, but the GDP cannot measure qualities such as family and friendship, moral values, happiness, or life purpose. Therefore, there is an increasing trend of using subjective measures of happiness beyond the classical income-based approach [30]. ...
The COVID-19 pandemic has recently caused the loss of millions of lives, and billions of others have been deeply affected. This crisis has changed the way people live, think about life, and perceive happiness. The aim of this study is to reveal differences between geographical regions by investigating the effect of the happiness variable on different countries during the international COVID-19 pandemic. The primary purpose is to demonstrate how such a pandemic may affect different countries in terms of happiness at the individual level and to identify possible strategies for the future. With this aim, both static and dynamic panel data models were used while applying fixed effects, random effects, and the generalized method of moments (GMM). A basic assumption in panel data models is that the coefficients do not change over time. This assumption is unlikely to hold, however, especially during major devastating events like COVID-19. Therefore, the piecewise linear panel data model was applied in this study. As a result of empirical analysis, pre- and post-COVID differences were seen between different geographical regions. Based on analysis conducted for three distinct geographical regions with piecewise linear models, it was determined that the piecewise random effects model was appropriate for European and Central Asian countries, the piecewise FGLS model for Latin American and Caribbean countries, and the piecewise linear GMM model for South Asian countries. According to the results, there are many variables that affect happiness, which vary according to different geographical conditions and societies with different cultural values.
... Some studies also pointed to long-term effects [7]. Indeed, even before the COVID-19 pandemic, Ref. [23] considered a weighted averaging constructed index based on some specific word search queries addressed to Google Search in analyzing well-being in the United States. In conclusion, they indicated that data from Internet search engines can be a significant supplement to traditional survey research. ...
... For example, Refs. [23,48] applied averaging procedures to Google Trends indices and showed the usefulness of such data in analyzing well-being in the United States. They showed that data from Internet search engines can be a significant supplement to traditional survey research. ...
... Google Trends data [92] were downloaded with the help of the "gtrendsR" R package [93]. Subsequent to previous studies [4,23,24] the following search queries were considered: "afraid", "apathetic", "boredom", "contentment", "depression", "divorce", "fear", "frustration", "impairment", "irritability", "loneliness", "nervous", "panic", "pissed", "sadness", "scared", "sleep", "stress", "suicide", "tension", "well-being", "worry", and "worthless". They were translated into a given country's dominant language. ...
Lockdowns introduced in connection with the COVID-19 pandemic have had a significant impact on societies from an economic, psychological, and health perspective. This paper presents estimations of their impact on well-being, understood both from the perspective of mental health and considering economic security and similar factors. This is not an easy task because well-being is influenced by numerous factors and the changes happen dynamically. Moreover, there are some obstacles when using the control group. However, other studies show that in certain cases it is possible to approximate selected phenomena with Google search queries data. Secondly, the econometric issues related to the suitable modeling of such a problem can be solved, for example, by using Bayesian methods. In particular, herein the recently gaining in popularity Bayesian structural time series and Bayesian dynamic mixture models are used. Indeed, these methods have not been used in social sciences extensively. However, in the fields where they have been used, they have been very efficient. Especially, they are useful when short time series are analyzed and when there are many variables that potentially have a significant explanatory impact on the response variable. Finally, 15 culturally different and geographically widely scattered countries are analyzed (i.e., Belgium, Brazil, Canada, Chile, Colombia, Denmark, France, Germany, Italy, Japan, Mexico, the Netherlands, Spain, Sweden, and the United Kingdom). Little evidence of any substantial changes in the Internet search intensity on terms connected with negative aspects of well-being and mental health issues is found. For example, in Mexico, some evidence of a decrease in well-being after lockdown was found. However, in Italy, there was weak evidence of an increase in well-being. Nevertheless, the Bayesian structural time series method has been found to fit the data most accurately. Indeed, it was found to be a superior method for causal analysis over the commonly used difference-in-differences method or Bayesian dynamic mixture models.
... Withdrawal from rural homesteads (WRH) is a land policy which attempts to organize idle and abandoned homesteads to optimize the use of rural areas to provide residents with living and production spaces to meet basic needs [2], including the happiness of local residents. Algan [3] pointed out that happiness reflects an individual's satisfaction with life as well as the quality of the social system in which they live. Research shows that rural residents are happier than urban residents [4], likely due to high levels of urbanization in urban areas and contemporary social networking in rural areas [5]. ...
Urbanization and aging populations are threatening the sustainability of rural development around the world. Improving the happiness of rural residents is closely related not only to rural development but also to the harmony and stability of a country. Sustainable development has become an important strategy for China’s rural areas. Although withdrawal from rural homesteads is an important issue in rural land policy, few researchers have examined the determinants of the subjective well-being of farmers following withdrawal. The current paper investigated 315 rural residents under three models of the “withdrawal from homestead” policy in Jinjiang City, Fujian Province, China. The application of the orderly probit model revealed how satisfaction with economic, social, environment, cultural, and policy factors impacted their subjective well-being. The pooled results showed that satisfaction with cultural and policy factors had no significant impact; however, the other aspects significantly promoted their subjective well-being. The empirical model with interaction terms indicated the significant positive impact of economic, environmental, and social factors on subjective well-being under the index replacement model, while only environment and social factors exerted a significant positive impact under the asset replacement and monetary compensation models. Corresponding policy implications are discussed.
... The work of Bryson et al. (2016) and Piekalkiewicz (2017) show that happiness can act as a determinant of economic outcomes: increases in productivity predict increases in an individual's future income and affects labor market performance. On the other hand, recent studies by Algan et al. (2019) show that GDP cannot be an ideal indicator for measuring individuals' happiness because it does not take into account non-market social interactions, such as friendship, family, moral values, and the cost of living. In work conducted by Leigh-Hunt et al. (2017), Verne (2009), Clark & Oswald (1994, unemployment, social isolation, and lack of freedom, are considered risk factors that can deteriorate mental health and happiness. ...
... Our most significant results are those related to touristic activity, the number of deaths This result is consistent to some extent with recent work by Algan et al. (2019), WHO (2020). Prob > F 0.0000 0.0001 Notes: Significance codes are *, **, and ***, 10%, 5% and 1% statistical significance, respectively. ...
The outbreak of the COVID-19 pandemic has caused a great slump in the world. There has been an alarming increase in mortality rate, number of infections, and deterioration of macroeconomic indicators such as GDP, which have worsened the happiness and well-being of the population, despite the multi-faceted restraint and supports measures implemented by several governments to curb the spread of the virus. Using sample of 31 European countries as a dataset, this paper analyzes the effect of the worsening COVID-19 budget deficit on people's happiness in the year 2020. The main idea is to determine, independently of a country's characteristics, the duration of the pandemic or the containment regulations; whether the effect of the pandemic on happiness is increased by the worsening of the budget deficit. The Generalized Least Squares (GLS) estimation shows that death tolls and infected cases of COVID-19 are key determinants that significantly worsen individuals' happiness. In addition, its interaction with fiscal deficit further decreases people's life satisfaction, since the budgetary situation of these countries was already unsustainable long before the pandemic. Hence, there is urgency for States to provide for a community solidarity fund for the resources necessary for crisis management in their budget line.
... According to Algan et al. [12], there is an increasing demand to use well-being measures to move beyond the classical income-based approach to measuring human development and progress. GDP does not measure non-market social interactions, such as friendship, family, happiness, moral values or the sense of purpose in life. ...
Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state’s mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states’ evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand’s daily happiness data for May 2019 –November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.
... We used the previous three queries separately for the in-sample analysis to examine the effect of each query on the migration flow. For forecasting purposes, we also considered the average of these three time series to reduce the number of variables involved, and to improve the forecasting efficiency; see e.g., [4,69] for details. ...
This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.
... During 2008-2013, Algan et al. (2019) used search queries in Google to analyze them with Google Trends. As search queries, the authors used two lists of words related to subjective wellbeing. ...
The article offers a new method of quality of life assessment based on online activities of social networks users. The method has obvious advantages (quickness of research, low costs, large scale, and detailed character of the obtained information) and limitations (it covers only the “digital population,” whereas the rural population is not included). The article dwells on the potential of social networks as a data source to analyze the quality of life; it also presents the results of an empirical study of online activities of the users of VK, the most popular Russian social network. Using the obtained data, the authors have calculated the quality of life index for 83 regions of the Russian Federation based on 19 parameters of economic, social, and political aspects of life quality.
... Then, the specialist can combine the discovered information with the patient's evaluation in a face-to-face service (limited by the hour) to improve the evaluation of the patient's mental state. In this context, changes in online users' behavior can provide meaningful mental illness indicators and trigger health care systems to produce automatic analysis and reports to inform specialists and allow early interventions, even before feelings worsen [10], [11]. ...
Depression is one of the most growing health disorders, generating social and economic problems globally. The affective computing models focus on analyzing unique user posts, not observing temporal behavior patterns, which are essential to track changes and the evolution of emotional behavior and user context, that involves the persistent analysis of feelings and characteristics over time. This article proposes the TROAD framework for longitudinal recognition of sequential patterns from depressive users on social media. The framework identifies the best interval to analyze every user activity, extracts emotional and contextual features from user data, and models the features into time windows to recognize sequential patterns from depressive user behavior. The main characteristics of the users found in the top-10 rules are negative emotions: violence, pain, shame, depression, sadness, and silence. We obtained strong sequence patterns with a minimum of 70% of support, 81% of confidence, and 69% sequential confidence, considering periods of silence between users’ posts. Without considering silent periods, the rules showed 70%, 86%, and 38% of support, confidence, and sequential confidence. TROAD computational approach is a promising tool for clinical specialists in human behavior.
... Often in the past, GDP was erroneously used to measure the well-being of a nation; however, it cannot measure non-market social interactions, such as family and friendship, or attributes such as moral values, happiness or the sense of purpose in life. As such, there is an increasing demand to use subjective measures of well-being and to move beyond the classical income-based approach to measuring human development and progress (Algan et al. 2019). ...
Background: Amid the rapid global spread of the coronavirus disease 2019 (COVID-19), many governments enforced country-wide lockdowns, likely with severe well-being consequences. The actions by governments triggered a debate on whether the costs of a lockdown, economically and in well-being, surpass the benefits perceived from a lower infection rate.
Aim: To use the Gross National Happiness index (GNH), derived from Big Data, to investigate the determinants of happiness before and during the first few months of a lockdown in a country as an extreme case, South Africa (a country with low levels of well-being and stringent lockdown regulations). Next, to estimate (1) the probability of being happy during a pandemic year, before and after the implemented lockdown, relative to the mean happiness levels of the previous year, and (2) to utilise simulations to estimate the probability of being happy if there were no lockdown.
Setting: This study considers the effect of government-mandated lockdown on happiness in South Africa.
Methods: We use Big Data in the forms of Twitter and Google Trends to derive variables and ordinary least squares and ordered probit estimation methods.
Results: What contributes to happiness under lockdown, except for COVID-19 cases, are the factors linked to the implemented regulations themselves. If we compare scenarios pre- and post-lockdown, we report a happiness cost of 9%. The simulations indicate that assuming there were no lockdown in 2020, the relative well-being gain is 3%.
Conclusion: If policymakers want to increase happiness levels and the probability of achieving the same happiness levels as in 2019, they should consider factors related to the regulations that can increase happiness levels.
... Sivarajah, Kamal, Irani, and Weerakkody (2017) reveal how organisations apply sentiment analysis in order to assess how consumers perceive their brands and actions from a sustainability viewpoint, therefore offering insights into more emotional and personal feelings of individuals. Similarly, there is a rising trend towards the use of digital footprints of social media users as a set of metrics to measure major determinants of well-being, as it offers a more detailed level of insights across time and space dimensions (Algan, Murtin, Beasley, Higa, & Senik, 2019;Huang et al., 2019;Lai, Hsieh, & Zhang, 2019). Chen, Chiang, and Storey (2012) explored the role of user-generated data and the digital traces resulting from social media sites such as Facebook and Twitter to measure, study, and even change SWB. ...
Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people’s well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people’s personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.
