Impact of early economic activity loss on all-cause mortality in gastric cancer survivors following curative treatment: a nationwide study in Korea.

BACKGROUND: The impact of economic engagement on the health of cancer survivors is notable. Our study aims to explore the association between early loss of economic activity (EA) and the risk of all-cause mortality among gastric cancer survivors. METHODS: This retrospective cohort study utilized data from Korea's National Health Insurance Service, focusing on 30-59-year-old gastric cancer patients who received either surgery or endoscopic procedures from January 2009 to December 2013. The primary outcome measure was all-cause mortality. Early loss of EA was identified when a patient's insurance status shifted to dependent within one year following treatment. Adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for all-cause mortality were estimated using multivariable Cox proportional hazards models, conducting separate analyses for surgical and endoscopic groups. RESULTS: Among 24,159 patients (median follow-up, 9.9 years), 2976 (12.3%) experienced all-cause mortality. Specifically, 2835 of these deaths occurred in patients who underwent surgery, while 141 were in the endoscopic procedure group. Early loss of EA was recorded in 14.4% of the surgery group and 7.7% of the endoscopic procedure group. Adjusted HRs (95% CI) for all-cause mortality associated with early loss of EA were 1.39 (1.27-1.54) for the surgery group and 2.27 (1.46-3.52) for the endoscopic procedure group. CONCLUSIONS: This study highlights a significant association between the early loss of EA and an increased risk of all-cause mortality in those who have undergone curative treatments for gastric cancer. It underscores the crucial role of sustaining EA in enhancing the health outcomes of these survivors.

Association Between Economic Activity and Depressive Symptoms Among Women With Parenting Children.

BACKGROUND: Balancing parenting and work life poses challenges for women with children, potentially making them vulnerable to depression owing to their dual responsibilities. Investigating working mothers' mental health status is important on both the individual and societal levels. This study aimed to explore the relationship between economic activity participation and depressive symptoms among working mothers. METHODS: This study was a cross-sectional study and used data from the Korea National Health and Nutrition Examination Survey collected in 2014, 2016, 2018, and 2020. The participants in the study were women aged 19 to 50 who were residing with their children. In the total, 3,151 participants were used in the analysis. The independent variable was economic activity, categorized into two groups: 1) economically active and 2) economically inactive. The dependent variable was the depressive symptoms, categorized as present for a Patient Health Questionnaire-9 score of ≥ 10 and absent for a score < 10. Multiple logistic regression analysis was performed to assess the association between economic activity and depressive symptoms, and sensitivity analyses were performed based on the severity of depressive symptoms. RESULTS: Among women with children, economically active women had reduced odds ratio of depressive symptoms compared with economically inactive women (odds ratio [OR], 0.54; 95% confidence interval [CI], 0.36-0.80). In additional analysis, women working as wage earners had the lowest odds of depressive symptoms (OR, 0.43; 95% CI, 0.28-0.66). Women working an average of 40 hours or less per week were least likely to have depressive symptoms (OR, 0.42; 95% CI, 0.25-0.69). CONCLUSION: Economic activity is significantly associated with depressive symptoms among women with children. Environmental support and policy approaches are needed to ensure that women remain economically active after childbirth.

Modeling green energy and innovation for ecological risk management using second generation dynamic quantile panel data model.

Ecological risk management has emerged as a critical research and policy development area in energy and environmental economics. Sustained ecology is crucial for the standard of living and food security. As the adverse impacts of environmental degradation and climate change become increasingly apparent it is imperative to understand ecological risk and its interconnectedness with environmental pressure, clean energy, economic activity, globalization, and green technology. Ecological risk is assessed using the environmental performance index which is a holistic indicator of climate change, environmental pressures and human actions in which most of these indicators have spatial effects. This paper explores the multifaceted relationship between identified anthropogenic critical factors and their role in effectively managing ecological risk globally. This study has developed the second-generation dynamic panel quantile regression considering spatial effects of economic activities on ecology across borders of 55 countries between 1995 and 2022. This innovative hybrid estimation scheme that integrated theoretical and econometric aspects makes the model robust to major regression issues. Several implications ranked in decreasing order of its effectiveness are reducing environmental pressure, expediting energy transition, and embracing economic integration while there is a need to work on rejuvenating green technology and green growth.

Anthropogenic Landforms Derived from LiDAR Data in the Woodlands near Kotlarnia (Kozle Basin, Poland).

Unlike farmland or urban areas, forests have long been regarded as environments that favour the preservation of valuable geological and historical sites. However, due to invasive forestry methods, the implementation of large investment projects and the development of mining, they are increasingly no longer safe spaces for the relics of human activities recorded in landforms. Data collection, including using LiDAR technology, presents an opportunity to preserve knowledge about these landforms. Through the analysis of shaded images of a 37-hectare woodland area near Kotlarnia, landforms of various ages documenting 21 different human activities were identified, including remnants of reforestation activities, the expansion and modification of hydraulic structures and road infrastructure, charcoal burning and tar distilling, exploitation of mineral resources and military activities. The results of the remote sensing work were verified in the field.

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.

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.

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.

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.

The Daily Economic Indicator: tracking economic activity daily during the lockdown.

The SARS-CoV-2 outbreak made clear the urgent need to depart from traditional statistics, typically released with a lag and available at a relatively low frequency. This led to unparalleled efforts to put forward high-frequency indicators to track economic developments timely. By resorting to non-traditional data sources, we propose a novel daily economic indicator to track economic activity in Portugal. It corresponds to the latent variable of a set of daily series within a factor model framework. We find a sudden and sharp drop in economic activity in mid-March 2020, when the lockdown of several activities was declared due to the COVID-19 pandemic. Since in this approach we address the complexities of high-frequency data without further smoothing, we are able to identify sudden changes of economic activity in a timely and daily manner in contrast with other approaches.

[Occupational factors associated with suicide among French employees from the special agricultural social security scheme (MSA) working between 2007 and 2013]. / Facteurs professionnels en lien avec le suicide au sein des salariés affiliés à la Mutualité sociale agricole (MSA) et en activité entre 2007 et 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.

Spatial distribution and determinants of PM2.5 in China's cities: fresh evidence from IDW and GWR.

While numerous studies have explored the spatial patterns and underlying causes of PM2.5 at the urban scale, little attention has been paid to the spatial heterogeneity affecting PM2.5 factors. In order to enrich this research field, we collected PM2.5 monitoring data from 367 cities across China in 2016 and combined inverse distance weighted interpolation (IDW) and geographically weighted regression (GWR) model. As a result, we could dynamically describe the spatial distribution pattern of urban PM2.5 at monthly, seasonal, and annual scales and investigate the spatial heterogeneity of the influential factors on urban PM2.5. Furthermore, in order to make the result more scientific and reasonable, the paper used selection.gwr function and bw.gwr function, respectively, to optimize model, thereby avoiding local collinearity caused by independent variables. The main results are as follows: (1) PM2.5 in Chinese cities is characterized as time-space non-equilibrium pattern. The Beijing-Tianjin-Hebei region, the Yangtze River corner region, the Pearl River Delta region, and the northeast region have formed a pollution-concentrating core area with Beijing-Tianjin-Hebei region as the axis, which brings greater difficulties and challenges to PM2.5 governance. (2) The effects of various factors of socio-economic activities on the concentration of PM2.5 have significant spatial heterogeneity among Chinese cities. (3) There is an inverted "U" curve between economic growth and PM2.5. When the per capita income reaches 47,000 yuan, the PM2.5 emission reaches the peak, which proves the existence of environmental Kuznets curve (EKC). These findings could provide a significant reference for policy makers in China to facilitate targeted and differentiated regional PM2.5 governance measures.

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.

Bank ownership, lending, and local economic performance during the 2008-2009 financial crisis.

Although government banks are frequently associated with political capture and resource misallocation, they may be well-positioned during times of crisis to provide countercyclical support. Following the collapse of Lehman Brothers in September 2008, Brazil's government banks substantially increased lending. Localities in Brazil with a high share of government banks received more loans and experienced better employment outcomes relative to localities with a low share of government banks. While increased government bank lending mitigated an economic downturn, we find that this lending was politically targeted, inefficiently allocated, and reduced productivity growth.

Proxying economic activity with daytime satellite imagery: Filling data gaps across time and space.

This paper develops a novel procedure for proxying economic activity with daytime satellite imagery across time periods and spatial units, for which reliable data on economic activity are otherwise not available. In developing this unique proxy, we apply machine-learning techniques to a historical time series of daytime satellite imagery dating back to 1984. Compared to satellite data on night light intensity, another common economic proxy, our proxy more precisely predicts economic activity at smaller regional levels and over longer time horizons. We demonstrate our measure's usefulness for the example of Germany, where East German data on economic activity are unavailable for detailed regional levels and historical time series. Our procedure is generalizable to any region in the world, and it has great potential for analyzing historical economic developments, evaluating local policy reforms, and controlling for economic activity at highly disaggregated regional levels in econometric applications.

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.

JUE insight: Demand for transportation and spatial pattern of economic activity during the pandemic.

Using traffic data from Taiwan for 2020, we quantify how the COVID-19 outbreak affected demand for public and private transportation. Despite there being no governmental restrictions, substantial shifts in travel modes were observed. During the peak of the pandemic in Taiwan within the study period (mid-March 2020), railway ridership declined by 40% to 60%, while highway traffic volume increased by 20%. Furthermore, railway ridership was well below pre-pandemic levels, though there were no locally transmitted cases in the eight-month period from mid-April to December. These changes in traffic patterns had implications for spatial patterns of economic activity: retail sales and nighttime luminosity data show that during the pandemic, economic activity shifted away from areas in the vicinity of major railway stations.

Live longer, retire later? Developments of healthy life expectancies and working life expectancies between age 50-59 and age 60-69 in Europe.

Europe's population is ageing. Statutory retirement ages are commonly raised to account for continuous increases in life expectancy. In order to estimate the potential to increase statutory and consequently effective retirement ages further, in this study, we investigate the relationship between partial working life expectancy (WLE) and three health expectancies that represent health aspects important for work ability and employability between ages 50 and 59 as well as 60 and 69 for women and men in Europe. We also explore the association between these four indicators and the highest level of educational attainment. We apply Sullivan's method to estimate WLE and three selected measures that capture general, physical, and cognitive health status of older adults for 26 European countries since 2004. Over time, WLEs increased significantly in the younger age group for women and in the older age group for both sexes. The expected number of years in good physical health have continuously been higher than any of the other three indicators, while the expected number of years in good cognitive health have shown a noticeable increase over time. The investigation of the relationship between education and each life expectancy confirms the well-established positive correlation between education and economic activity as well as good health. Our results indicate potential to extend working lives beyond current levels. However, significant differences in the expected number of years in good health between persons with different levels of education require policies that account for this heterogeneity.

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.

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.