This study investigates the linkages between changes in agricultural land use and population growth in India. We have employed long-term time series and a panel dataset of 1869 samples (267 districts × 7 time points from 1961 to 2021) to determine this. We theorize that there is an inverted “U-shape” relationship between changes in population growth and agricultural land. Our findings suggest a positive impact of population growth on the change in cultivated land. However, this relationship was not static during 1961–2021. We found a two-stage split relationship with a breakpoint in 1981. Prior to the 1980s, there was a 12% expansion in cultivated land in response to a unit increase in population growth. During the post-1980s, with a unit decline in population growth, there was a 5% reduction in cultivated land. The findings were reaffirmed through several robustness checks: analyses using alternative outcome variables, alternative break points in a segmented regression model, and spatial modeling. From a policy perspective, this study advances the need for the reduction of population growth rate in high-fertility states and the adoption of superior and green technology for agricultural intensification and diversification to stop cropland expansion at the cost of environmental sustainability.

Using a polynomial cointegration technique, this paper shows that the bilateral US current account balance with China has a U-shaped relationship with the life expectancy gap between the US and China. A narrowing gap initially increases the US deficit with China, but eventually, this increased US deficit falls with the further catching-up of Chinese life expectancy. The life expectancy gap between the two countries has been below the threshold level since 2013, and this demographic trend has the potential to improve the US deficit with China. This U-shaped relationship can be theoretically reproduced. A two-country overlapping generations model indicates that the effect of life expectancy is decomposed into four components: retirement savings, social security burden, the number of elderly workers, and the productivity of elderly workers. The total effect of foreign life expectancy on the home current account balance exhibits a sign change in the catching-up of foreign life expectancy.

The construction industry is experiencing high demand for workers. Apprenticeship programmes are essential pipelines of skilled workers into the construction industry; however, apprenticeship completion rates are only around 25%. To promote apprenticeship retention and increase the number of apprentices, it is necessary to identify factors that relate to cancellation from apprenticeship programmes (i.e., leaving prior to programme completion). Using data from the Registered Apprenticeship Partners Information Database System, we descriptively characterised completion and cancellation, then conducted a time-to-event analysis of n = 335,212 construction apprentices from 2012 to 2023 to examine factors related to cancellation. Among all apprentices, 40.1% cancelled from their apprenticeship programmes, while 24.8% completed and 35.0% were actively registered at the end of the study period. Results from the time-to-event analysis show females had significantly higher odds of cancellation than males (OR: 1.11; 95% CI: 1.08, 1.15). Compared to White apprentices, American Indian/Alaska Native (OR: 1.13; 95% CI: 1.08, 1.18), Black/African American (OR: 1.41; 95% CI: 1.39, 1.44), and multiracial apprentices (OR: 1.09; 95% CI: 1.02, 1.17) had significantly higher odds of cancellation, while Asian apprentices had significantly lower odds of cancellation (OR: 0.79; 95% CI: 0.75, 0.83). Non-unionised workers were significantly more likely to cancel their apprenticeship programmes (OR: 1.77; 95% CI: 1.74, 1.80). These results indicate that individual demographic and organisational factors can influence apprenticeship cancellation. Reducing barriers to apprenticeship completion can help address the current skilled worker shortage, and identifying factors that impact entry into the industry for minoritised groups can promote equity within the industry.

This paper examines the effects of heterogeneous biased expectations between the young and old on business cycles and explores its policy implications. Empirical findings reveal that individuals, particularly the young, can have more optimistic or pessimistic views about the future state of the economy compared to the data-generating measure. This study relates these results to the learning-from-experience literature, which suggests that individuals, particularly the young, place greater weight on recent observations when forming their expectations. Incorporating household weighting schemes into a life-cycle learning model, I show that household sensitivity to recent observations amplifies the effects of economic shocks. However, the amplification effects become less extensive as the population ages due to the lower sensitivity of the old. My simulation results indicate that a 10 percentage point increase in the old population ratio leads to a 16 percent decrease in output volatility. Regarding policy implications, this paper suggests that the government spending multiplier declines by approximately 10 percent when the old population ratio rises by 10 percentage points due to weak amplification effects. Moreover, the weakened output effects deteriorate the welfare of the population, particularly that of the young.

We study whether the increased adoption of available automation technologies allows economies to avoid the negative effect of aging on per capita output. We develop a quantitative theory in which firms choose to which extent they automate in response to a declining workforce and rising old-age dependency. An important element in our model is the integration of two capital types: automation capital that acts as a substitute to human labor, and traditional capital that is a complement to labor. Empirically, our model's predictions largely match data regarding automation (robotization) density across OECD countries. Simulating the model, we find that aging-induced automation only partially compensates the negative growth effect of aging in the absence of technical progress in automation technology. One reason is that automated tasks are no perfect substitutes for non-automated tasks. A second reason is that automation raises the interest rate and thus inhibits positive behavioral reactions to aging (later retirement and investment in human capital). Moreover, increased automation generates a falling net labor share of income and rising welfare inequality. We evaluate alternative policy responses to cope with this inequality.

As the heterogeneity in life expectancy by socioeconomic status increases, many pension systems imply a wealth transfer from short- to long-lived individuals. Various pension reforms aim to reduce inequalities that are caused by ex-ante differences in life expectancy. However, these pension reforms may induce redistribution effects. We introduce a dynamic general equilibrium-overlapping generations model with heterogeneous individuals that differ in their education, labor supply, lifetime income, and life expectancy. Within this framework we study six different pension reforms that foster the sustainability of the pension system and aim to account for heterogeneous life expectancy. Our results highlight that pension reforms have to be evaluated at various dimensions. Reforms that may increase the sustainability of the pension system are not necessarily conducive to reduce the redistributive wealth transfers from short- to long-lived individuals. Our paper emphasizes the need for studying pension reforms in models with behavioral feedback and heterogeneous socioeconomic groups.

It is well-known that marital status is an important predictor for life expectancy. However, non-married individuals are often misclassified as singles which ignores the heterogeneity within the group. This paper shows the importance of distinguishing between types of singles, and in particular whether they are cohabiting, when predicting life expectancies. We use unique and detailed longitudinal register data to track marital status throughout the individual's lifetime. We find that all types of singles consistently benefit from living with a spouse, i.e., after divorce, becoming widower or being never married. This result holds for both men and women. For certain types of cohabiting singles we reject significant differences in life expectancy compared to married individuals. Finally, we use a case study to show that, like married individuals, all types of singles that cohabit also serve as informal caregivers and have the potential to limit the end-of-life long-term care expenditure levels.

Mortality shocks such as the one induced by the COVID-19 pandemic have substantial impact on mortality models. We describe how to deal with them in the period effect of the Lee–Carter model. The main idea is to not rely on the usual normal distribution assumption as it is not always justified. We consider a mixture distribution model based on the peaks-over-threshold method, a jump model, and a regime switching model and introduce a modified calibration procedure to account for the fact that varying amounts of data are necessary for calibrating different parts of these models. We perform an extensive empirical study for nine European countries, comparing the models with respect to their parameters, quality of fit, and forecasting performance. Moreover, we define five exemplary scenarios regarding the future development of pandemic-related mortality. As a result of our evaluations, we recommend the peaks-over-threshold approach for applications with a possibility of extreme mortality events.

This paper presents an empirical investigation of the hypothesis that exposure to the restrictive fertility policies of the Chinese “Later, Longer, Fewer” campaign in the 1970s contributes to the dynamics and patterns of elderly suicides in China in the period 2004–2017. We apply an identification strategy that exploits variation in exposure to this policy across birth cohorts that is based on the different timing of the implementation of the fertility policies across Chinese provinces. The results show that cohorts with a greater exposure to the restrictive fertility policy in the 1970s exhibit higher suicide rates during old ages.

Greater labor migration can establish more channels for information flows, directly contributing to faster economic growth and improved innovation and work. It can also expand international remittances, which can be invested by recipient households in home countries in education, entrepreneurship, and improved and sustainable agricultural technologies. At the same time, however, increased emigration of medical professionals and technical workers from poor countries can reduce quality of local services, innovation, health status, and productivity. This analysis attempts to quantify the economic benefits and costs of permitting an immediate 10% increase in the bilateral migration of skilled workers (physicians, engineers or science, engineering, technology, and mathematics workers, and other persons with advanced educations) among the nations of the African Continental Free Trade Area and, more broadly, among 25 global regions. Economic benefits include higher migrant incomes abroad, welfare gains in destination countries associated with higher economic efficiency, spillover productivity gains, and an improved ability of the younger and more skilled working force to support the needs of the wider population, resulting in higher national production. Benefits in source countries include productivity enhancements from two sources: (a) greater access to knowledge associated with more bilateral trade and investment and (b) the ability of local households to invest remittances in productivity-enhancing activities. Welfare losses in source nations include static efficiency reductions and a worsened demographic support capability. In Africa, the benefit-cost ratios range from 3.7 to 6.9; in the global analysis, 17 to 38.

In the first half of the twentieth century, deaths from infectious disease, especially among the very young, fell dramatically in American cities. However, as infant mortality fell and life expectancy rose, racial inequality in urban infectious disease mortality grew. In this paper, we show that the fall in mortality and the rise in racial inequality in mortality reflected two countervailing processes. The dramatic decline in infant mortality from waterborne diseases drastically reduced the total urban infectious disease mortality rate of both Black and white Americans while having a comparatively small effect on the total racial disparity in urban infectious disease mortality. In contrast, the unequal fall in tuberculosis mortality, particularly in the prime of life, widened racial inequality in infectious disease mortality in US cities.

This study develops a Malthusian model for the evolution of human society from hunting-gathering to agriculture and from agriculture to industrial production. Human society evolves across these stages as the population grows. However, under endogenous population growth, the population may stop growing at any stage. If it fails to reach the first threshold, the population remains as hunter-gatherers. If it reaches the first threshold, an agricultural society emerges. Then, if the population fails to reach the industrial threshold, it remains in an agricultural Malthusian trap without experiencing industrialization. Interestingly, high agricultural productivity triggers not only the Neolithic Revolution but also the subsequent industrialization. Using cross-country data to test this result, we employ an index of prehistoric biogeographic conditions that affect agricultural productivity as an instrument for the timing of transitions to agriculture and find that an earlier transition to agriculture has a positive effect on industrialization in the modern era.

This paper examines inter-industry patterns of the employment of older workers over the last 20 years to understand where employment opportunities have grown the most. The underlying premise is that firms strategically align their age mix depending on production function and labor cost parameters. The industries that had the largest increases in the percentage of older workers were those that had the broadest pension coverage and those that made the greatest use of high-tech capital. There also is evidence in 2001–07 that the percentage of older workers increased more in the industries most exposed to increased Chinese imports.

Disability-free life expectancy had been rising continuously in the United States until 2010, suggesting working longer as a solution for those financially unprepared for retirement. However, recent developments suggest improvements in working life expectancy have stalled, especially for minorities and those with less education. This paper uses data from the National Vital Statistics System, the American Community Survey, and the National Health Interview Survey to assess how recent trends, up to 2018, in institutionalization, physical impediments to work, and mortality have affected working life expectancy for men and women age 50, by race and education.

Human civilisation faces a series of existential threats from the combination of five global and human-engineered challenges, namely climate change, resource depletion, environmental degradation, overpopulation and rising social inequality. These challenges are arguably being manifested in both an increased likelihood and magnified impact of catastrophes like forest fires, prolonged droughts, pandemics and social dislocation/upheaval. This article argues that in understanding and addressing these challenges, important lessons can be drawn from what has repeatedly caused organisational failures. It applies the ‘Ten Pathways to Disaster’ model to a series of disasters/catastrophic events and then argues this model is salient to understanding inadequate responses to the five threats to civilisation. The article argues that because these challenges interact in mutually reinforcing ways, it is critical to address them simultaneously not in isolation.

Over the past three decades, educational attainment in Mexico has grown substantially. This increase in educational attainment may affect marriage patterns through the growing supply of individuals with higher education and changing preferences over their partner's education level. We use administrative marriage and birth certificate records to quantify changes in the relative education levels for both married and unmarried couples. Our results suggest that individuals who marry outside their education category prefer to match with a partner with a similar education level. That is, college graduates prefer to match with individuals with a secondary education rather than those with a primary education. We also find that assortativeness among pairs which include college graduates has grown considerably over this time period. Our findings hold across both marriage records and birth certificate records, indicating a parallel increase in assortativeness regardless of marital status.

Longevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field’s workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee–Carter models as special cases and inherits both frameworks’ advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method’s efficacy in both in-sample and out-of-sample performance.

This paper evaluates alternative reforms of the public pension system in an overlapping generations model for an open economy facing demographic change. We make progress compared to existing literature on pension reform by modelling individuals with heterogeneous innate ability and endogenous human capital, and by putting (the reduction of) welfare inequality effects of reform at the centre. Frequently adopted reforms such as an increase of the normal retirement age or a decrease of the pension benefit can guarantee financial sustainability, but they fail when the objective is also to avoid intergenerational or intragenerational welfare inequality. Our results prefer a reform which combines an increase of the retirement age with an intelligent linkage between the pension benefit and earlier labour earnings. First, this design conditions pension benefits on past individual labour income, with a high weight on labour income earned when older and a low weight on labour income earned when young. Second, this linkage is complemented by a strong rise in the benefit replacement rate for low ability individuals (and a reduction for high ability individuals).

This paper examines the effect of national income on the total fertility rate (children born per woman). We estimate the effects on fertility of shocks to national per capita income using plausibly exogenous variations in oil price shock as an instrument for income and using instrumental variable generalized quantile regressions (IV-GQR). Using data for a panel of 122 countries spanning the period 1965–2020, our results show that national per capita income has generally a negative and significant effect on the total fertility rate. Looking at the entire spectrum of the fertility distribution, the IV-GQR estimates exhibit considerable heterogeneity in the impact of income on fertility. The income elasticity of fertility is relatively low at the upper tail of the distribution (for countries with high fertility) compared to the value at the median.

The Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.