One in six young people will not be in education, employment or training within five years unless urgent action is taken, a major review has warned. The education, health and welfare systems are no longer fit for purpose in preparing young people for adult life, said its author former minister Alan Milburn. We are at risk of a lost generation, he warned, with the number of 16 to 24-year-olds out of work, education or training set to rise to 1.25 million by 2031. The first rung of the career ladder has thinned and that for too many young people it is now simply out of reach, Milburn is set to say in a speech later. That places them in a hopeless catch-22 where employers ask for work experience but the opportunities for young people to gain it have narrowed or gone, he will say.

There's Never Been a Better Time to Study Computer Science Even as AI progresses, coders aren't doomed. It's a weird time to be studying computer science. Recent grads have a higher unemployment rate than those in just about every other major--yes, even philosophy. The internet is littered with rants from newly minted programmers who can't find work. On one such YouTube video, the top comment reads: "Your first mistake is not being born earlier."

'I've applied for 500 jobs in two months since graduating' You have to work 10 times harder to work for a role that 10 years ago you could have got very easily straight out of university, says 22-year-old business management graduate Charlotte Briggs. Within two months she had applied for 500 roles. It's quite upsetting because I've worked really hard for the last three years to achieve a 2:1 just to be rejected for not having experience. Although her job search sounds extreme, it may not be that unusual. According to latest ONS figures, 22.5% of people aged 16 to 24 cannot find work, putting London as the UK region with the second highest rate of youth unemployment.

What is the Insurrection Act? Why is the US Fed chair criminal probe causing alarm? Which 75 countries are on Trump's travel ban list? The US economy seems strong after a year of Trump, but is it really? Over the past year, United States President Donald Trump has unleashed a slew of policies that have upended businesses, supply chains and jobs.

During training, generative large language models (LLMs) are exposed to vast amounts of information, including data relevant to economic modelling, such as geospatial statistics and firm-level financial metrics. If LLMs can effectively retrieve and utilise this knowledge, they could reduce dependence on external data sources that are time-consuming to access, clean, and merge, or that incur financial costs. Moreover, if LLMs accurately represent data, they could support downstream tasks like data imputation and outlier detection. In this study, we evaluate whether and how LLMs can be used for typical economic data processes. Not all knowledge within an LLM may be explicit and retrievable in natural language by prompting the model.

The Synthetic Control method (SC) has become a valuable tool for estimating causal effects. Originally designed for single-treated unit scenarios, it has recently found applications in high-dimensional disaggregated settings with multiple treated units. However, challenges in practical implementation and computational efficiency arise in such scenarios. To tackle these challenges, we propose a novel approach that integrates the Multivariate Square-root Lasso method into the synthetic control framework. We rigorously establish the estimation error bounds for fitting the Synthetic Control weights using Multivariate Square-root Lasso, accommodating high-dimensionality and time series dependencies. Additionally, we quantify the estimation error for the Average Treatment Effect on the Treated (ATT). Through simulation studies, we demonstrate that our method offers superior computational efficiency without compromising estimation accuracy. We apply our method to assess the causal impact of COVID-19 Stay-at-Home Orders on the monthly unemployment rate in the United States at the county level.

Methods for forecasting time series adhering to linear constraints have seen notable development in recent years, especially with the advent of forecast reconciliation. This paper extends forecast reconciliation to the open question of non-linearly constrained time series. Non-linear constraints can emerge with variables that are formed as ratios such as mortality rates and unemployment rates. On the methodological side, Non-linearly Constrained Reconciliation (NLCR) is proposed. This algorithm adjusts forecasts that fail to meet non-linear constraints, in a way that ensures the new forecasts meet the constraints. The NLCR method is a projection onto a non-linear surface, formulated as a constrained optimisation problem. On the theoretical side, optimisation methods are again used, this time to derive sufficient conditions for when the NLCR methodology is guaranteed to improve forecast accuracy. Finally on the empirical side, NLCR is applied to two datasets from demography and economics and shown to significantly improve forecast accuracy relative to relevant benchmarks.

We present SimCity, a multi-agent framework that leverages LLMs to model an interpretable macroeconomic system with heterogeneous agents and rich interactions. Unlike classical equilibrium models that limit heterogeneity for tractability, or traditional agent-based models (ABMs) that rely on hand-crafted decision rules, SimCity enables flexible, adaptive behavior with transparent natural-language reasoning. Within SimCity, four core agent types (households, firms, a central bank, and a government) deliberate and participate in a frictional labor market, a heterogeneous goods market, and a financial market. Furthermore, a Vision-Language Model (VLM) determines the geographic placement of new firms and renders a mapped virtual city, allowing us to study both macroeconomic regularities and urban expansion dynamics within a unified environment. To evaluate the framework, we compile a checklist of canonical macroeconomic phenomena, including price elasticity of demand, Engel's Law, Okun's Law, the Phillips Curve, and the Beveridge Curve, and show that SimCity naturally reproduces these empirical patterns while remaining robust across simulation runs.

Large Language Models have spread rapidly since the release of ChatGPT in late 2022, accompanied by claims of major productivity gains but also concerns about job displacement. This paper examines the short-run labor market effects of LLM adoption by comparing earnings and unemployment across occupations with differing levels of exposure to these technologies. Using a Synthetic Difference in Differences approach, we estimate the impact of LLM exposure on earnings and unemployment. Our findings show that workers in highly exposed occupations experienced earnings increases following ChatGPT's introduction, while unemployment rates remained unchanged. These results suggest that initial labor market adjustments to LLMs operate primarily through earnings rather than worker reallocation.

A dismal jobs report affirms earlier warnings about the economic impact of Donald Trump's tariffs, immigration restrictions, and -led firings. At the start of last week, I watched a big cargo ship stacked high with containers enter New York Harbor. As the vessel approached the Verrazzano-Narrows Bridge, it appeared to stop, but that was an illusion created by its size and the slowness of its advance. Fifteen minutes later, it had managed to push its way under the bridge. Throughout the years, I've often compared the U.S. economy to a giant freighter that is tough to deflect from its course, and, since Donald Trump was elected for a second time, this metaphor has become particularly apt.