Generational Connections, Population Aging, and Sustainable Consumption

Gender Differences in Economic Behavior - Expectations and Social Norms

Can social preferences explain gender differences in economic behavior?

This article provides a methodological contribution to the study of the effect of changes in population age structure on carbon dioxide (CO2) emissions. First, I propose a generalization of the IPAT equation to a multisector economy with an age-structured population and discuss the insights that can be obtained in the context of stable population theory. Second, I suggest a statistical model of household consumption as a function of household size and age structure to quantitatively evaluate the extent of economies of scale in consumption of energy-intensive goods, and to estimate age-specific profiles of consumption of energy-intensive goods and of CO2 emissions. Third, I offer an illustration of the methodologies using data for the United States. The analysis shows that per-capita CO2 emissions increase with age until the individual is in his or her 60s, and then emissions tend to decrease. Holding everything else constant, the expected change in U.S. population age distribution during the next four decades is likely to have a small, but noticeable, positive impact on CO2 emissions.

Economic behaviour as well as economic resources of individuals vary with age. Swedish time series show that the age structure contains information correlated to medium‐term trends in growth and inflation. GDP gaps estimated by age structure regressions are closely related to conventional measures. Monetary policy is believed to affect inflation with a lag of 1 or 2 years. Projections of the population's age structure are comparatively reliable several years ahead and provide additional information to improve on 3–5 years‐ahead forecasts of potential GDP and inflation. Thus there is a potential scope for using age structure based forecasts as an aid to monetary policy formation. Copyright © 2004 John Wiley & Sons, Ltd.

Because of the importance of economic decisions, researchers have looked into what factors influence them. Gender has received a lot of attention for explaining differences in behavior. But how much can be associated with gender, and how much with an individual’s biological sex? We run an experimental online study with cis- and transgender participants that (1) looks into correlational differences between gender and sex for competitiveness, risk-taking, and altruism by comparing decisions across these different subject groups. (2) we prime participants with either a masculine or feminine gender identity to examine causal gender effects on behavior. We hypothesize that if gender is indeed a primary factor for decision-making, (i) individuals of the same gender (but different sex) make similar decisions, and (ii) gender priming changes behavior. Based on 780 observations, we conclude that the role of gender (and sex) is not as decisive for economic behavior as originally thought.

Chapter 2 - Social Norms and Preferences, Chapter for the Handbook for Social Economics edited by J. Benhabib, A. Bisin and M. Jackson

PurposeNonfamily farms are responsible for a disproportionate amount of US agriculture production. The importance of these operations to the volume of agriculture production in the United States has led researchers and policymakers to understand nonfamily farms as large commercial operations. This paper examines whether the distinction between family and nonfamily helps explain the financial outcomes of farm operations and households.Design/methodology/approachWe test for differences in financial outcomes of the household and operations of family and nonfamily farms using an Oaxaca-Blinder decomposition. We compare these results to a decomposition of other possible typologies.FindingsWe present evidence that nonfamily farms are a heterogeneous group with a majority of small operations that are dominated by a small number of large operations. We discover that differences associated with the family-nonfamily distinction are largely explained by observable farm and operator characteristics that arise mechanically from the definition. However, we find suggestive evidence that family-nonfamily classification captures differences in economic behavior that lead to higher profitability measures to nonfamily farms. We find little evidence of any inherent structural differences between family and nonfamily farms that helps explain financial outcomes related to leverage or household finances.Practical implicationsWe conclude that including nonfamily farms in official statistics of farm households may provide a more comprehensive overview of the farm sector, as our results suggest that family and nonfamily farms do not have innate differences that help explain many of their financial outcomes.Originality/valueWe incorporate previously unused data on nonfamily farm households and test the difference in mean financial outcomes between family and nonfamily farms.

Ross, M. S., T. L. SHARIK, and D. WM. SMITH. (Dept. Forestry, Virginia Polytechnic Inst. and State Univ., Blacksburg, VA 24061, U.S.A.). Age structure relationships of tree species in an Appalachian oak forest in southwest Virginia. Bull. Torrey Bot. Club 109: 287-298. 1982.-The complete age and diameter structures were determined for all tree species in seven Appalachian oak stands arranged along a topographic moisture gradient in southwestern Virginia. All stands but one exhibited a prominent gap in the age distribution in the 20-40 year age classes. No gap was evident in the corresponding diameter distributions. Total and juvenile age distributions were described for selected species. Age structures differed among species, and reflected the different spatial and temporal niches which they occupy. It was hypothesized that the shape of the total age distribution in individual stands is related to the disturbance sequence, and to site through its influence on the species present. Coupling of age and diameter distributions of individual populations provided insights into the functional roles of species along the moisture gradient.

Problem setting. In the list of indicators, pursued by the State Strategy of Regional Development for the period up to 2020, inter alia, the demographic burden on population aged 16 to 59 was identified. The aging process of the Ukraine’s population and dwindling of the working population have not shown the reduction of those indicators value. Recent research and publications analysis. Forecasting of changes in the population structure of Ukraine was a subject of research in many institutions, organizations, projects etc. During the implementation of the project “Building Capacity in Evidence-Based Economic Development Planning” in a few cities of Ukraine there were prepared their “profiles”. There are also publications, dedicated to the problems, concerning the impact of the Ukrainian population aging on its structure, and comparative analysis of the impact of demographic processes on the labor market in Ukraine and European countries as well. In 2009 the Institute for Demography and Social Studies named after Mykhailo Ptukha of the National Academy of Sciences of Ukraine formulated demographic forecasts in the regions of Ukraine. However, there is a lack of publications, dedicated to forecasts indicators of demographic burden on working population in countryside. Highlighting previously unsettled parts of the general problem. The purpose of article is to compare the regions of Ukraine with the projected values of demographic burden per 1 000 of working-age population, including persons residing in rural areas, and to compare results of related indicators, provided in the State Strategy of Regional Development for the period up to 2020 as well. Paper main body. Demographic burden is calculated as quotient of the two components (population aged under 15 and over 60 years) to third component-population aged 16 – 59 years. In order to obtain projections of demographic burden, we made the calculations based on analytical model for forecasting the population structure of different age groups. Total fertility rate (TFR) is the basis of the first cohort population projection (aged to 4 years). For each further 5-year cohort (5 years or more) the evolution coefficient of its number is calculated. The website of the State Statistics Service of Ukraine provides the information of the population by age groups in regions. This information is the basis of forecasting of population structure changes in the future. The age structure is very irregular, caused by joint actions of various processes, that had led to the “demographic holes” in the past (World War II and Holodomor had deep influence). In order to obtain projections as of 2020 (date of expiration of the State Strategy of Regional Development), statistics on the structure of the Ukrainian regions population in 2015 were taken into consideration. The gap of 5 years derives from the fact that mentioned analytical model uses five-year age cohorts. We calculated the projections of demographic burden per 1 000 of working-age population in the regions of Ukraine in the countryside. On the average, around the country it will reach: in 2020 – 709, and in 2025 – 760 persons per 1 000 of population aged 16 to 59. The highest value of demographic burden per 1 000 of working-age population in the countryside is expected in 2020 in Chernihiv, Khmelnytskyi and Rivne Regions (over 750). Its highest value in 2025 is being projected in Chernihiv, Khmelnytskyi, Donetsk, Dnipro, Luhansk and Zaporizhia Regions (over 790). The results may be taken into account for strategic documents concerning village development at national and regional levels. Conclusions of the research and prospects for further studies. The results of calculation showed the deviations of demographic burden on the working-age population from the indicators, given in the State Strategy of Regional Development for the period up to 2020. The projections, concerning changes of the population of countryside, illustrate the tendency of significant increase of demographic burden on the working-age population in Ukraine in general (by 7%) and in all regions (by 4 – 11%) as well. The prospects of further research are prediction of changes in the population structure in the countryside in the regions of Ukraine according to the All-Ukrainian Population Census, due to be conducted in 2020.

Objective: To summarize recent progress in research of burden of disease attributed to population ageing and provide reference for relevant research in the future. Methods: We conducted a systematic literature review of quantitative studies about the impact of population ageing on burden of disease published from 2009 to 2019 according to the inclusion and exclusion criteria through PubMed, Web of Science, Embase, Cochrane Library, Wangfang database and China National Knowledge Infrastructure (CNKI) databases and extracted basic information and key results of the searched literature. Results: A total of 65 studies were included in the analysis, in which 29 (44.6%) studies used death number or mortality rate as outcome measures, 43 (66.2%) studies focused on a single country, such as China, United Kingdom and United States, 39 (60.0%) studies quantified the impact of population ageing on single disease, such as diabetes, lung cancer and coronary heart disease, 44 (67.7%) studies used decomposition methods to quantify the impact of population ageing, and 10 (15.4%) studies evaluated the effect of interventions on alleviating the impact of population ageing. Most studies found that population ageing increased the burden of some diseases, such as cancer, cardiovascular disease and dementia, while a few studies reported that population ageing decreased the burden of some diseases, such as neonatal disease and malaria. Various decomposition methods were adopted in 65 included studies, but several common methods were sensitive to the preconditions that were assumed, the decomposition order of three factors (population size, age structure, and age-specific rate) and the choice of control group, resulting incomparable and unstable results. Conclusions: The published decomposition studies adopted various methods and only evaluated the impact of population ageing in very limited countries and for several diseases. Therefore, a systematic evaluation using robust decomposition method is very necessary to evaluate the impact of population aging on disease burden across countries and diseases.

Investing in health to improve healthy life expectancy (HLE) is fundamental to create a demographic dividend. However, how dramatic population ageing affects HLE remains unknown. This study aims to quantify and project the major diseases and injuries attributed to changes in population size and age structure that contributed to substantial losses in HLE. Using data from 188 countries in the Global Burden of Disease Study 2021 and World Population Prospects 2024, we assessed the correlation between HLE and total dependency ratios. Furthermore, we decomposed the mortality and disability burden attributable to changes in population size as well as age structure for 22 disease and injury categories and then quantified the loss of HLE due to the attributable burden. Additionally, we projected the loss of HLE due to priority diseases in 2030, while considering the impact of population ageing. From 2010 to 2019, globally, the mortality and disability burden attributable to age structure caused 0.40 years and 0.71 years of HLE loss, while for population size, these two estimates were 1.18 years and 1.00 years. By 2030, the mortality and disability burden attributable to age structure may lead to 0.76 years and 0.89 years of HLE loss, while for population size, these two predictions will be 1.21 years and 1.17 years. Population size growth is a consistent and crucial contributor to HLE losses. Reaping the second demographic dividend requires eliminating the double burden of premature death caused by infectious and chronic diseases, whereas gaining the sustainable third demographic dividend requires investments in healthy and successful ageing.

Have age profiles of consumption and labor income in the Philippines changed from 1994 to 2002? What are the implications of the changes observed in the lifecycle patterns? The National Transfer Accounts (NTA) methodologies are applied to estimate the per capita age profiles of current consumption and labor income for the Philippines for the years 1994, 1999, and 2002. Age profiles estimated include those for public and private consumption for three broad types, i.e., education, health, and other, and two types of labor income, i.e., earnings from paid employment and self employment income._x000D_ Some of the main findings include: (1) Consumption pattern by age: The age profile for mean per capita current consumption is strongly influenced by the profile of private other consumption being the single largest item of consumption. The pronounced sharp rise in per capita mean consumption observed up to age 18 and the subsequent decline is due to the age pattern of public and private education spending. And the gradual increase in per capita consumption after age 45 may be attributed to the increasing per capita public and private spending for health care as age increases. (2) Consumption age profile over time: The age profiles for current consumption have generally remained unchanged from 1994 to 2002, with mean age of consumption staying at about 27 years. (3) Labor income pattern by age: The age profile of labor income has the expected inverted-U shape, peaking at around age 40. (4) Labor income age profile over time: The overall shape of the age income profiles have generally remained the same, but a gradual shift of the position of the profiles toward the right was observed from 1994 to 2002. The mean age of labor income was 35 in 1994, 38 in 1999, and 39 in 2002. The implications of the consumption and labor income lifecycle patterns and changes observed over time for the Philippines include the following: increase in the deficit age cut-off at older ages; increase in the span of productive or surplus ages; and increase in the lifecycle surplus to deficit ratio.

Background: The typical contraceptive–fertility relationship has not held in some countries witnessing rapid increases in contraceptive uptake. With increasingly more diverse population age structures among developing countries, one explanation may be the different measurement approaches of total fertility rate (TFR), an age-adjusted measure, and contraceptive prevalence rate (CPR), an unadjusted measure. Objective: To examine whether the association between contraception and fertility has changed over time in less developed settings and whether the relationship is better explained with an age-adjusted measure of contraceptive use. Methods: Using data from 259 Demographic and Health Surveys, we examine associations between CPR and TFR in two 15-year periods. We then develop age-adjusted CPR and explore the relationship between TFR and unadjusted versus age-adjusted CPR, using linear regression analyses with country-level fixed effects. Results: A TFR decrease of 1 was associated with a CPR increase of 15.4 percentage points during 1985–2000 and of 17.2 percentage points during 2001–2016. On average, across 259 surveys, age-adjusted CPR was higher than unadjusted CPR by about 3% with significant regional variation. Regression model fit reveals that age-adjusted CPR better explains the CPR–TFR relationship. Contribution: Age-adjusted CPR addresses varying age structures and age patterns of contraceptive use across populations, facilitating cross-country and cross-temporal comparisons of contraceptive use. Updated demographic shorthand for the CPR–TFR relationship is that a TFR decrease of 1 is associated with a CPR increase of 17 percentage points overall and a 20 percentage point increase in sub-Saharan Africa.

The article explores the impact of the Russo-Ukrainian war on sustainable consumption and production systems, with a focus on adaptive strategies of industrial enterprises and consumer behaviour. The study analyses how wartime conditions have caused significant changes in resource management, supply chain reconfiguration, and industrial efficiency. The research highlights a shift in consumption patterns towards essential goods, a reduction in energy consumption, and a prioritization of resource conservation. The war has led to increased industrial relocation, transformation of production processes, and a reassessment of sustainable development principles, including the traditional 3R model of ‘Reuse-Reduce-Recycle,’ supplemented by ‘Rethink.’ The findings of the study contribute to a better understanding of sustainable production and consumption strategies in crisis conditions, emphasizing innovative solutions for maintaining industrial operations and achieving sustainable development goals in extreme environments.

In a realm of literature on influence factors on fertility rates; this paper innovatively introduces capitalism being associated with lowered fertility rates. Based on a 180 country strong worldwide data set and cross sectional correlation studies, this paper outlines that hallmark pillars of capitalism are all negatively associated with fertility rates. The 2017 Economic Freedom Index -comprising of input variables such as property rights protection, judicial effectiveness, government integrity, fiscal health, freedom of business, labor, monetary policy, trade, investment, finance, taxation, GDP freedoms as well as FDI inflows -is significantly negatively correlated with fertility rates around the globe. Based on a 139 country strong worldwide dataset on industrialization as measured by the UNIDO in the Industrialization Intensity Index of 2014 and fertility rates, a highly significant negative relation is found between industrialization and fertility rates around the world. Urban areas around the world tend to have higher fertility rates and access to markets within rural communities lowers fertility rates measured by the World Bank Rural Access Index for 64 countries around the world. The inverse relation of economic freedom and fertility was also found for 50 U.S. states based on the 2017 Economic Freedom Index and fertility rates in the United States. The historic examples of communism imploding giving way to free market mechanisms but also an Islamic regime changing towards a Western free market approach captures capitalism to crowd out fertility in a meta-analysis, which also finds higher education levels not being stringently related to fertility rates. Industrialization, globalization and capitalism lead to vanishing populations. Being occupied by production and consumption but also the entertainment of capitalist markets, economic mobility and international trade may distract societies to prosper regarding fertility. Focus on competing in markets may hinder from procreation. Equilibria und markets may have an undocumented negative effect on fertility. Unruled capitalism may lead to a falling rate of fertility, decimating the populace and eventually also the reserve pool of economic agents. The paper concludes with proposals how to use these novel insights as (1) birth control mechanism in those parts of the world, where overpopulation is currently demanding governments to find ways how to lower the fertility rate and (2) means to avert the falling rate of fertility in capitalist societies with a shrinking, aging population. Infusing capitalistic market freedoms in birth control needed territories but also taxing capitalist activities to fund parenthood through direct investment in social benefits and/or via subsidized parenthood to alleviate the tendency of the falling rate of fertility in the eye of capitalism is recommended concurrently, depending on the starting level on the overpopulation-low capitalism versus under-reproduction-high-capitalism spectrum. Revealing the found mechanism has also implications for advocacy to strengthen fertility and the intergenerational glue in light of vanishing populations in the Western world. In addition, the findings have innovative and futuristic prospects in the age of artificial intelligence. Robotics and artificial intelligence slowly taking over human capital labor activities but not being able to reproduce a human DNA is argued to increase the future value of humanness -and with that human fertility -in the artificial age.