搜索结果：Fertility and sterility

This paper develops a general equilibrium overlapping-generations model with endogenous fertility, in which firms accumulate both physical and artificial intelligence (AI) capital, and uses it to study the macroeconomic transmission of two structural disturbances: an AI technology shock and a longevity shock. The AI shock acts as a capital-demand disturbance: it raises all rates of return, most sharply the return to AI capital, reallocates investment from physical to AI capital, and produces a front-loaded output expansion that decays monotonically. The longevity shock acts as a saving-supply disturbance: it deepens the aggregate capital stock, compresses returns and the real interest rate, and generates hump-shaped, persistent dynamics. The two shocks move fertility in opposite directions: AI raises it modestly through an income effect, while longevity lowers it by strengthening the life-cycle saving motive and the cost of childrearing. A forecast-error variance decomposition attributes most aggregate volatility to the longevity shock, while the AI shock dominates the variance of the return to AI capital. Fertility is strongly countercyclical and almost perfectly negatively correl

The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predictive ability on new data. This prevents us from systematically comparing studies, hindering the evaluation and accumulation of knowledge. In this paper, we present two datasets which can be used to study the predictability of fertility outcomes in the Netherlands. One dataset is based on the LISS panel, a longitudinal survey which includes thousands of variables on a wide range of topics, including individual preferences and values. The other is based on the Dutch register data which lacks attitudinal data but includes detailed information about the life courses of millions of Dutch residents. We provide information about the datasets and the samples, and describe the fertility outcome of interest. We also introduce the fertility prediction data challenge PreFer which is based on these datasets and will start in Spring 2024. We outline the ways in which measuring the predictability of fertility outcomes using these datasets and combinin

Accurate fertility estimates at fine spatial resolution are essential for localized public health planning, particularly in low- and middle-income countries (LMICs). While national-level indicators such as age-specific fertility rates (ASFR) and total fertility rate (TFR) are often reported through official statistics, they lack the spatial granularity needed to guide targeted interventions. To address this, we develop a framework for subnational fertility estimation using small-area estimation (SAE) techniques applied to birth history data from household surveys, in particular Demographic and Health Surveys (DHS). Disaggregation by geographic area, time period, and maternal age group leads to significant data sparsity, limiting the reliability of direct estimates at fine scales. To overcome this, we propose a suite of methods, including direct estimators, area-level and unit-level Bayesian hierarchical models, to produce accurate estimates across varying spatial resolutions. The model-based approaches incorporate spatiotemporal smoothing and integrate covariates such as maternal education, contraceptive use and urbanicity. Using data from the 2021 Madagascar DHS, we generate distr

The sterile insect technique controls mosquito-borne diseases such as malaria, dengue, and yellow fever through either eradication or depressing the associated vector population. We formulate a three-dimensional delayed mosquito population suppression model with a saturated release rate to explore the interactive dynamics between wild, sterile, and non-sterile mosquitoes, focusing on the delay and residual fertility in the interactive dynamics among insects. We investigate the stability of the positive equilibrium and derive the Hopf bifurcation conditions. We establish the stability conditions for the positive equilibrium and examine how the time delay ($τ$) and residual fertility affect the non-sterile insects' dynamics. Below the critical values of the delay, the system remains stable, while beyond that, the Hopf bifurcation is guaranteed under certain circumstances. However, analysis shows a clear band of non-sterile insect population values as residual fertility varies within a very narrow range. This suggests that within this interval, the system exhibits sensitive dependence on the fertility parameter, likely due to underlying nonlinear dynamics. Numerical simulations are pr

Declining fertility is one of the defining policy questions of the next decade, and increasingly, what policymakers know about it is shaped by AI-synthesising the evidence base. But ask such a tool about reproduction and the answer depends on the word you use. The same phenomenon, framed clinically (e.g, infertility, IVF) or socially (e.g., childlessness, fertility intentions), is catalogued with radically different completeness. And the catalogue as much, if not more than the underlying scholarship, is what AI synthesis begins with (Bolaños et al., 2024). That databases under-index the social sciences, books, and grey literature is well established (Visser et al., 2021). What is new here is holding the topic fixed and asking whether metadata gaps act as a hidden policy filter on a single contested issue: the determinants of (in)fertility. We use two OpenAlex queries on the same phenomenon: a clinical basket (infertility, subfertility, ART, IVF, fecundity; n=101,645) and a social basket (childlessness, social infertility, fertility intentions, reproductive decision-making; n=3,646). We compare them on metadata completeness, open access, output type, and institutional provenance. Th

When humans translate, not every word depends equally on the surrounding context. Some tokens, particularly function words like pronouns and auxiliaries, rely heavily on preceding or following sentences, while others, such as proper nouns, do not. Understanding this inherent context sensitivity is essential for evaluating whether machine translation systems use context in human-like ways. However, existing approaches to analysing context usage rely on discourse-specific test sets or model internals, making them narrow or model-dependent. We propose a post-hoc, model-agnostic framework to quantify context sensitivity at lexical and syntactic levels using two measures derived from word alignments: fertility (number of target tokens generated per source token) and entropy (stability of fertility patterns across contexts). Using reference translations for three language pairs (German $\leftrightarrow$ English, English $\rightarrow$ Hindi) under four context conditions, we show that context selectively redistributes generative responsibility from source to context tokens without altering overall fertility. Function words show the largest fertility reductions, while content words remain

There has long been an apparent consensus in the literature on intra-household allocation and fertility that greater paternal involvement in childcare relaxes maternal time constraints, enabling mothers to increase their labor supply or leisure. Recent evidence, particularly from South Korea, challenges this view: increases in fathers' childcare time have coincided with a further increase in mothers' time dedicated to child-rearing. This paper develops an Overlapping Generations (OLG) growth model to address such a puzzle. The central mechanism and our main innovation hinge on the functional form of the childcare technology. When maternal and paternal time are substitutes, the conventional result holds. However, when they are complements, greater paternal involvement necessarily raises maternal childcare time, depressing fertility and redirecting household resources toward child quality. We further argue that the elasticity of substitution should not be interpreted as a pure preference parameter, as it also reflects the social and institutional norms, the skills each parent brings to child-rearing and their intergenerational transmission. The model is extended to study the effectiv

We show that translation quality can be predicted with surprising accuracy \textit{without ever running the translation system itself}. Using only a handful of features, token fertility ratios, token counts, and basic linguistic metadata (language family, script, and region), we can forecast ChrF scores for GPT-4o translations across 203 languages in the FLORES-200 benchmark. Gradient boosting models achieve favorable performance ($R^{2}=0.66$ for XX$\rightarrow$English and $R^{2}=0.72$ for English$\rightarrow$XX). Feature importance analyses reveal that typological factors dominate predictions into English, while fertility plays a larger role for translations into diverse target languages. These findings suggest that translation quality is shaped by both token-level fertility and broader linguistic typology, offering new insights for multilingual evaluation and quality estimation.

We study fibres of the fertility map $Φ$ from decorated rooted trees to decorated multi-index monomials. For a multi-index $\mathbf{k}$ of weight $-1$, the fibre $\mathcal F_{\mathbf{k}}=\{\,t:Φ(t)=\xx^{\mathbf{k}}\,\}$ consists of all rooted trees with decoration--fertility profile $\mathbf{k}$. We consider its ordinary cardinality $F_{\mathbf{k}}$, its symmetry-weighted cardinality $W_{\mathbf{k}}$, and the coefficient mass $J_{\mathbf{k}}$ appearing in the tree expansion of the transposed embedding $\jmath$. We obtain an explicit formula and a functional equation for the weighted counts, and an exact multiset recursion together with a cycle-index functional equation for the ordinary counts. We also introduce coefficient generating functions for the lowering derivation $\bar\partial$, derive recursive and transport-array formulas for the corresponding coefficients, and use them to refine the admissible-cut formula for the coproduct in the LOT Hopf algebra.

This paper examines the determinants of fertility among women at different stages of their reproductive lives in Uruguay. To this end, we employ time series analysis methods based on data from 1968 to 2021 and panel data techniques based on department-level statistical information from 1984 to 2019. The results of our first econometric exercise indicate a cointegration relationship between fertility and economic performance, education and infant mortality, with differences observed by reproductive stage. We find a negative relationship between income and fertility for women aged 20-29 that persists for women aged 30 and over. This result suggests that having children is perceived as an opportunity cost for women in this age group. We also observe a negative relationship between education and adolescent fertility, which has implications for the design of public policies. A panel data analysis with econometric techniques allowing us to control for unobserved heterogeneity confirms that income is a relevant factor for all groups of women and reinforces the crucial role of education in reducing teenage fertility. We also identify a negative correlation between fertility and employment

Demographic forecasting remains a fundamental challenge for policy planning in rapidly evolving nations such as India, where fertility transitions, policy interventions, and age structured dynamics interact in complex ways. In this study, we present a hybrid modelling framework that integrates policy-aware fertility functions into a Physics-Informed Neural Network (PINN) enhanced with Long Short-Term Memory (LSTM) networks to capture physical constraints and temporal dependencies in population dynamics. The model is applied to India's age structured population from 2024 to 2054 under three fertility-policy scenarios: continuation of current fertility decline, stricter population control, and relaxed fertility promotion. The governing transport-reaction partial differential equation is formulated with India-specific demographic indicators, including age-specific fertility and mortality rates. PINNs embed the core population equation and policy-driven fertility changes, while LSTM layers improve long-term forecasting across decades. Results show that fertility policies substantially shape future age distribution, dependency ratios, and workforce size. Stricter controls intensify agei

A novel fertility model based on Thom's nonlinear differential equations of morphogenesis is presented, utilizing a three-dimensional catastrophe surface to capture the interaction between latent non-catastrophic fertility factors and catastrophic shocks. The model incorporates key socioeconomic and environmental variables and is applicable at macro-, meso-, and micro-demographic levels, addressing global fertility declines, regional population disparities, and micro-level phenomena such as teenage pregnancies. This approach enables a comprehensive analysis of reproductive health at aggregate, sub-national, and age-group-specific levels. An agent-based model for teenage pregnancy is described to illustrate how latent factors -- such as education, contraceptive use, and parental guidance -- interact with catastrophic shocks like socioeconomic deprivation, violence, and substance abuse. The bifurcation set analysis shows how minor shifts in socioeconomic conditions can lead to significant changes in fertility rates, revealing critical points in fertility transitions. By integrating Thom's morphogenesis equations with traditional fertility theory, this paper proposes a groundbreaking

This paper investigates the conditions under which the Easterlin hypothesis holds within a neoclassical overlapping generations model with endogenous capital accumulation, wages, interest rates, and fertility. We develop a tractable analytical framework that maps economic transitions into utility space via a continuously differentiable first-order difference equation for cohort lifetime utilities. This reformulation allows for a transparent normative evaluation of non-steady-state paths without requiring explicit solutions to the underlying nonlinear system. Within this framework, we show that when fertility cycles emerge and children are normal goods, the utility of small cohorts strictly exceeds that of large cohorts. Crucially, this cohort-welfare asymmetry is driven by fertility preferences and is independent of the economy's position relative to the golden rule.

Fertility choices are linked to the different preferences and constraints of individuals and couples, and vary importantly by socio-economic status, as well by cultural and institutional context. The meaning of childbearing and child-rearing, therefore, differs between individuals and across groups. In this paper, we combine data from Google Correlate and Google Trends for the U.S. with ground truth data from the American Community Survey to derive new insights into fertility and its meaning. First, we show that Google Correlate can be used to illustrate socio-economic differences on the circumstances around pregnancy and birth: e.g., searches for "flying while pregnant" are linked to high income fertility, and "paternity test" are linked to non-marital fertility. Second, we combine several search queries to build predictive models of regional variation in fertility, explaining about 75% of the variance. Third, we explore if aggregated web search data can also be used to model fertility trends.

Fertility differentials by urban-rural residence and nativity of women in Australia significantly impact population composition at sub-national levels. We aim to provide consistent fertility forecasts for Australian women characterized by age, region, and birthplace. Age-specific fertility rates at the national and sub-national levels obtained from census data between 1981-2011 are jointly modeled and forecast by the grouped functional time series method. Forecasts for women of each region and birthplace are reconciled following the chosen hierarchies to ensure that results at various disaggregation levels consistently sum up to the respective national total. Coupling the region of residence disaggregation structure with the trace minimization reconciliation method produces the most accurate point and interval forecasts. In addition, age-specific fertility rates disaggregated by the birthplace of women show significant heterogeneity that supports the application of the grouped forecasting method.

A rapid decline in mortality and fertility has become major issues in many developed countries over the past few decades. A precise model for forecasting demographic movements is important for decision making in social welfare policies and resource budgeting among the government and many industry sectors. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function and a spectral mixture covariance function for mortality and fertility modelling and forecasting. Unlike most of the existing approaches in demographic modelling literature, which rely on time parameters to decide the movements of the whole mortality or fertility curve shifting from one year to another over time, we consider the mortality and fertility curves from their components of all age-specific mortality and fertility rates and assume each of them following a Gaussian process over time to fit the whole curves in a discrete but intensive style. The proposed Gaussian process regression approach shows significant improvements in terms of preciseness and robustness compared to other mainstream demographic modelling approaches in the short-, mid- and

The accelerating shift toward low and ultra-low fertility has intensified the debate over whether countries now undergoing rapid decline are approaching stabilization or entering a more persistent low-fertility regime. Existing projection systems answer that question differently because they embed different assumptions about recovery and about the role of external drivers. To provide an empirical benchmark in this debate, we introduce NeuralTFR, an endogenous global forecasting framework based on a recurrent neural network. Drawing on a harmonized panel of historical fertility series from 196 countries and territories, the model pools cross-country information to learn demographic momentum and generate empirical prediction intervals via multi-quantile regression. Evaluated on a held-out period (2009--2023), NeuralTFR achieves lower point-forecast errors than a Naive Drift baseline and BayesTFR, the United Nations' Bayesian Hierarchical Model, while maintaining competitive uncertainty calibration. In forward projections to 2040, NeuralTFR points to broader exposure to low and very low fertility than BayesTFR, suggesting weaker support for near-term stabilization while still falling

We characterize the outcomes of a canonical deterministic model for the intergenerational transmission of capital that features differential fertility. A fertility function determines the relationship between parental capital and the number of children, and a transmission function determines the relationship between the capital of a parent and that of their children. Together these functions generate an evolving cross-sectional distribution of capital. We establish easy-to-verify conditions on the fertility and transmission functions that guarantee (a) that the dynamical system has a steady state distribution that is either atomless (exhibiting inequality) or degenerate (not exhibiting inequality), and (b) that the system converges to such states from essentially any initial distribution. Our characterization provides new insights into the link between differential fertility and long-run cross-sectional inequality, and it gives rise to novel comparative statics relating the two. We apply our results to several parametric examples and to a model of economic growth that features endogenous differential fertility.

Tokenization is a crucial but under-evaluated step in large language models (LLMs). The standard metric, fertility (the average number of tokens per word), captures compression efficiency but obscures how vocabularies are allocated across languages and domains. We analyze six widely used tokenizers across seven languages and two domains, finding stable fertility for English, high fertility for Chinese, and little domain sensitivity. To address fertility's blind spots, we propose the Single Token Retention Rate (STRR), which measures the proportion of words preserved as single tokens. STRR reveals systematic prioritization of English, strong support for Chinese, and fragmentation in Hindi, offering an interpretable view of cross-lingual fairness. Our results show that STRR complements fertility and provides practical guidance for designing more equitable multilingual tokenizers.