Inheritances and work for pay - will the expected wave of bequests undermine active ageing policies?

It is frequently assumed that the inheritance of wealth undermines economic activity. If such an assumption is valid, the expected wave of bequests may have a negative impact on labour market activity of heirs, what might further weaken the financing of state pension systems. This paper provides a detailed review of the empirical findings on the associations of inheritances with labour market activity, that is labour force participation status and working hours, and presents own analyses based on the survey of health, ageing, and retirement in Europe. We find that the receipt of an inheritance is not related to labour force participation in general. Inheritance expectations even have a small, but statistically significant positive effect on remaining in the labour force for men. Women who expect an inheritance tend to reduce working hours, but the effect of having received an inheritance is not significant, neither for men nor for women. We conclude that the receipt of an inheritance will not affect labour market decisions, so that the expected wave of bequests will not undermine active ageing policies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10433-022-00706-1.

Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID-19 cases in the United States

Delay differential equations form the underpinning of many complex dynamical systems. The forward problem of solving random differential equations with delay has received increasing attention in recent years. Motivated by the challenge to predict the COVID-19 caseload trajectories for individual states in the U.S., we target here the inverse problem. Given a sample of observed random trajectories obeying an unknown random differential equation model with delay, we use a functional data analysis framework to learn the model parameters that govern the underlying dynamics from the data. We show the existence and uniqueness of the analytical solutions of the population delay random differential equation model when one has discrete time delays in the functional concurrent regression model and also for a second scenario where one has a delay continuum or distributed delay. The latter involves a functional linear regression model with history index. The derivative of the process of interest is modeled using the process itself as predictor and also other functional predictors with predictor-specific delayed impacts. This dynamics learning approach is shown to be well suited to model the growth rate of COVID-19 for the states that are part of the U.S., by pooling information from the individual states, using the case process and concurrently observed economic and mobility data as predictors.

The complex interplay between COVID-19 and economic activity

We introduce a dynamical system to model the complex interaction between COVID-19 and economic activity. The model introduces some novelties not accounted by SIR-like models. The equilibrium of the system is an unstable focus, with fluctuations having increasing size and periodicity. Numerical simulations of the model produce waves which reproduce the pandemic dynamics. In observing the stylized facts linking economics and pandemic and stating related reasonable assumptions, we obtain a Lotka-Volterra co-dynamics. This outcome is confirmed by extensive simulations. The outcomes obtained qualitatively replicate some important stylized facts deepening the knowledge about the role of some parameters in their origin and eventually in their shaping.

A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic

During the COVID-19 pandemic, policymakers needed to assess the impact of large monetary and fiscal policy interventions in as close to real time as possible-yet existing survey-based indicators are usually released monthly or quarterly. The use of high-frequency data to track economic activity has become widespread. This paper constructs a near real-time economic activity indicator for the Brazilian economy during the COVID-19 pandemic. Brazil's integrated national electricity sector, which covers over 98% of the population, allows us to construct an economic activity indicator based solely on electricity consumption data that are available at near real time and accounts for activity in the large informal sector of the economy. We construct our indicator by isolating the variability in electricity consumption that is not related to economic activity, then measure how well monthly and quarterly versions of our indicator track against standard economic indicators. The results show strong correlation with standard indicators, notably during economic shocks.

The impact of health and economic policies on the spread of COVID-19 and economic activity

This paper empirically investigates the causal linkages between COVID-19 spread, government health containment and economic support policies, and economic activity in the U.S. up to the introduction of vaccines in early 2021. We model their joint dynamics as generated by a structural vector autoregression and estimate it using U.S. state-level data. We identify structural shocks to the variables by making assumptions on their short-run relation consistent with salient epidemiological and economic features of COVID-19. We isolate the direct impact of COVID-19 spread and policy responses on economic activity by controlling for demand fluctuations using disaggregate exports data. We find that health containment and economic support policies are highly effective at curbing the spread of COVID-19 without leading to a long-term contraction of economic activity.

Spatiotemporal dynamic differences of energy-related CO2 emissions and the related driven factors in six regions of China during two decades

Carbon neutrality lays out a grand blueprint for carbon emission reduction and climate governance in China. How to reduce energy consumption is the key to achieving this goal. The economic development and energy consumption show a very large gap at the provincial level, and this paper divides China into six regions (North, Northeast, East, Mid-South, Southwest, and Northwest) and analyzes the dynamic changes and reveals the driving factors that have affected CO₂ emission changes from 1997 to 2017. Then, the driving forces including energy intensity, energy structure, energy efficiency, economic activity, and population scale were discussed employing the logarithmic mean Divisia index (LMDI) based on provincial panel data. The results show that CO₂ emissions from energy consumption show an upward trend, from 4145 Mt in 1997 to 13,250 Mt in 2017, with an annual average growth rate of 1.06%; coal consumption is the main source of CO₂ emission. The regions with the highest proportion of CO₂ emissions are the East and North, which account for 50% of total emissions. China's CO₂ emissions from energy consumption, coal consumption, and output have shown significant spatial autocorrelation at the provincial scale. According to coal consumption, energy consumption CO₂ emissions are divided into three stages: phase I (1997-2002), the increase in CO₂ emissions in six regions was attributed to significant and positive impacts of energy intensity, economic activity, and population scale, the effects of which exceeded those of the energy structure and energy efficiency; phase II (2003-2012), the economic activity effect on CO₂ emissions was highest in the East region, followed by the North and Mid-South regions; phase III (2013-2017), the East, Mid-South, and Southwest regions of China were dominated by the positive effects of energy intensity, economic activity, and population scale. The major driver of CO₂ emissions is economic activity; the energy efficiency effect is an important inhibitory factor. Regional economic development and energy consumption in China are unbalanced; we conclude that differentiated emission reduction measures should be of particular concern for policymakers.

Understanding the heterogeneity of COVID-19 deaths and contagions: The role of air pollution and lockdown decisions

The uneven geographical distribution of the novel coronavirus epidemic (COVID-19) in Italy is a puzzle given the intense flow of movements among the different geographical areas before lockdown decisions. To shed light on it, we test the effect of the quality of air (as measured by particulate matter and nitrogen dioxide) and lockdown restrictions on daily adverse COVID-19 outcomes during the first pandemic wave in the country. We find that air pollution is positively correlated with adverse outcomes of the pandemic, with lockdown being strongly significant and more effective in reducing deceases in more polluted areas. Results are robust to different methods including cross-section, pooled and fixed-effect panel regressions (controlling for spatial correlation), instrumental variable regressions, and difference-in-differences estimates of lockdown decisions through predicted counterfactual trends. They are consistent with the consolidated body of literature in previous medical studies suggesting that poor quality of air creates chronic exposure to adverse outcomes from respiratory diseases. The estimated correlation does not change when accounting for other factors such as temperature, commuting flows, quality of regional health systems, share of public transport users, population density, the presence of Chinese community, and proxies for industry breakdown such as the share of small (artisan) firms. Our findings provide suggestions for investigating uneven geographical distribution patterns in other countries, and have implications for environmental and lockdown policies.

Tracking economic activity in response to the COVID-19 crisis using nighttime lights - The case of Morocco

Over the past decade, nighttime lights have become a widely used proxy for measuring economic activity. This paper examines the potential for high frequency nighttime lights data to provide "near real-time" tracking of the economic impacts of the COVID-19 crisis in Morocco. At the national level, there exists a statistically significant correlation between quarterly movements in Morocco's overall nighttime light intensity and movements in its real GDP. This finding supports the cautious use of lights data to track the economic impacts of the COVID-19 crisis at higher temporal frequencies and at the subnational and city levels, for which GDP data are unavailable. Relative to its pre-COVID-19 trend growth path of lights, Morocco experienced a large drop in the overall intensity of its lights in March 2020 following the country's first COVID-19 case and the introduction of strict lockdown measures, from which it has subsequently struggled to recover. At the subnational and city levels, while all regions and cities examined shared in March's national decline in nighttime light intensity, some suffered much larger declines than others. Since then, the relative effects of the COVID-19 shock across regions and cities appear to have largely persisted. Notwithstanding these findings, however, further research is required to ascertain the exact causes of the observed changes in light intensity and to fully verify that the results are driven by anthropogenic causes.

Impact of COVID-19 on economic growth in Nigeria: opinions and attitudes

The study examined the impact of COVID-19 on economic growth in Nigeria: Opinions and attitudes. The purpose was to ascertain respondents' perception of the effect of the COVID-19 pandemic on economic growth in Nigeria. The cross-sectional survey research design was employed and a mix-method was used in collecting the research data. Content validity index and face validity served to validate the research instrument while Cronbach alpha was used to assure its reliability. The secondary data were analysed using percentage changes while the primary data were analysed using a one-sample t-test and least-squares method. Results of the respondents’ opinion indicated that the COVID-19-induced lockdown has significantly constrained economic activities and the circular flow of income. Lastly, the perceived reduction in the circular flow of income in the wake of the COVID-19 lockdown has negatively impacted on economic growth in Nigeria. The need for policymakers to take drastic measures to curtail the pandemic and forestall a recession that may be consequent upon the pandemic was suggested, among others.

Postpartum medical utilization: The role of prenatal economic activity and living costs

This study is the first to explore the extent to which prenatal economic fluctuations affect postpartum outpatient care utilization during three-month, six-month, and one-year postpartum periods in Taiwan and to document their counter-cyclical patterns for economic activity and pro-cyclical patterns for the CPI change rate. We present evidence that medical care utilization occurring during the postpartum period is sensitive to economic activity within the first trimester of pregnancy and the CPI change rate within the second trimester. The findings herein reveal that negative prenatal economic shocks lead to a higher probability of cesarean section, more outpatient visits for depressive disorders, hypertension, gestational diabetes, and anemia in the pregnancy period, and a lower number of prenatal care visits that could deteriorate postpartum maternal health. Moreover, our results are consistent with low-salary and private-sector-employed mothers who face credit constraints and experience the risk of losing their job, respectively, during a decline in economic activity and who subsequently suffer from nutritional deficits and maternal stress that lead to postpartum health deterioration. Conversely, high-salary mothers do not face credit constraints and have greater coping ability to deal with stress and nutritional problems, while public-sector-employed mothers are affected only by nutrition.

Experiences of falling and depression: Results from the Korean Longitudinal Study of Ageing

OBJECTIVE

This study estimates the association between fall experiences during the last two years and risk of depression.

METHODS

Data from 9,355 subjects of the Korean Longitudinal Study of Ageing from 2006 to 2016 were included with baseline at 2006. To analyze the association between fall experiences during the last two years and depression, a generalized estimating equation (GEE) model and chi-square tests were used.

RESULTS

At baseline 2006, the odds ratio (OR) of depressive symptoms in those with fall experiences was 1.36 times higher (p < .0001) than those with non-fall experience. In those 64 years or younger and 65 years or older, the OR of depressive symptoms in fall experience was 1.45 times higher (p =0.003) and 1.34 times higher (p =0.000) than those with non-fall experience, respectively. In males and females, the OR of depressive symptoms in those with fall experience was 1.47 times higher (p 0.008) and 1.34 times higher (p =0.000) than those with non-fall experience, respectively.

CONCLUSION

Fall experiences during the last two years are associated with depressive symptoms. Therefore, self-reported screening for fall experiences might aid in population-based prevention strategies for depressive symptoms.

Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that collapse resulted from government-imposed restrictions versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the economic slowdown using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior over the crisis within the same commuting zones but across state and county boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the massive changes to consumer behavior (and that tracking county-level policy conditions is significantly more accurate than using state-level policies alone). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 percentage points of this. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly influenced by the number of COVID deaths reported in the county; and showed a clear shift by consumers away from busier, more crowded stores toward smaller, less busy stores in the same industry. States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy. Although the shutdown orders had little aggregate impact, they did have a significant effect in reallocating consumer visits away from "nonessential" to "essential" businesses and from restaurants and bars toward groceries and other food sellers.

Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that collapse resulted from government-imposed restrictions versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the economic slowdown using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior over the crisis within the same commuting zones but across state and county boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the massive changes to consumer behavior (and that tracking county-level policy conditions is significantly more accurate than using state-level policies alone). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 percentage points of this. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly influenced by the number of COVID deaths reported in the county; and showed a clear shift by consumers away from busier, more crowded stores toward smaller, less busy stores in the same industry. States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy. Although the shutdown orders had little aggregate impact, they did have a significant effect in reallocating consumer visits away from "nonessential" to "essential" businesses and from restaurants and bars toward groceries and other food sellers.

[Occupational factors associated with suicide among French employees from the special agricultural social security scheme (MSA) working between 2007 and 2013]

BACKGROUND

Work and related exposures may play a role in suicide and there has been evidence in the literature that some occupational factors may be associated with suicide. The identification of occupational risk factors of suicide mortality among employees affiliated to the French special agricultural social security scheme (MSA), an understudied population, appears important. The objective of this study was to identify the occupational factors associated with suicide mortality among French employees from the MSA working between 2007 and 2013.

METHODS

The study population included all the employees affiliated to the MSA working between 1st January 2007 and 31st December 2013, i.e. 1,699,929 men and 1,201,017 women. The studied occupational factors included: economic activity, skill level, and work contract. Survival analyses (Cox models) stratified on gender were performed using age as time scale and region and year of contract as adjustment variables.

RESULTS

Among men, the factors associated with an elevated suicide risk were: economic activities of forestry, agriculture and related activities, and manufacture of food products and beverages (e.g. meat, wine), low-skilled level and working in the regions of Brittany, Burgundy Franche-Comté, Pays de la Loire, Normandy, Grand Est and Centre-Val-de-Loire. No association was observed among women.

CONCLUSION

These results suggest that economic activity and low-skilled level may be associated with suicide among men affiliated to the MSA and may contribute to the implementation of prevention interventions. Further studies are needed to confirm and better understand these associations.

Study of the validity of a job-exposure matrix for the job strain model factors: an update and a study of changes over time

OBJECTIVES

The objectives of the study were to construct a job-exposure matrix (JEM) for psychosocial work factors of the job strain model, to evaluate its validity, and to compare the results over time.

METHODS

The study was based on national representative data of the French working population with samples of 46,962 employees (2010 SUMER survey) and 24,486 employees (2003 SUMER survey). Psychosocial work factors included the job strain model factors (Job Content Questionnaire): psychological demands, decision latitude, social support, job strain and iso-strain. Job title was defined by three variables: occupation and economic activity coded using standard classifications, and company size. A JEM was constructed using a segmentation method (Classification and Regression Tree-CART) and cross-validation.

RESULTS

The best quality JEM was found using occupation and company size for social support. For decision latitude and psychological demands, there was not much difference using occupation and company size with or without economic activity. The validity of the JEM estimates was higher for decision latitude, job strain and iso-strain, and lower for social support and psychological demands. Differential changes over time were observed for psychosocial work factors according to occupation, economic activity and company size.

CONCLUSIONS

This study demonstrated that company size in addition to occupation may improve the validity of JEMs for psychosocial work factors. These matrices may be time-dependent and may need to be updated over time. More research is needed to assess the validity of JEMs given that these matrices may be able to provide exposure assessments to study a range of health outcomes.