Tag Archives: economics

Ageing Populations and Macroeconomy

Yunus Aksoy smiling into the camera.Professor Yunus Aksoy shares how ageing populations impact the workforce and discusses possible policy responses.

Ageing populations are a global phenomenon. They are caused by two main trends:

  1. Fertility decline: the number of children per woman in a population has been slowly decreasing since the 1990s.
  2. Declining mortality rates: people are living longer due to medical advances and lifestyle changes.

These demographic structure changes have wide-reaching impacts in the short and medium/long term. However, the fact that their impact is not visible day-to-day means that they are relatively less discussed in everyday policymaking. My work with my colleagues Professor Ron Smith at Birkbeck, Dr Henrique Basso at the Bank of Spain and Dr Toby Grasl investigates the impact of ageing populations on macroeconomy in general and brings it to the table in policy circles. I am very pleased to see that the issue has started to be taken seriously by many international organisations like the IMF, ECB, World Bank, BIS and numerous central banks. Our research has had significant impact on the debate.

Economists tend to concentrate on growth, inflation and unemployment rates, and what Central Banks and Finance Ministries can do to stabilise the economy over the short term. However, there are other deep and slowly changing forces affecting the economy about which policymakers can do little. The weak recovery after the global financial crisis has sparked renewed interest in these longer term forces, including demographics, which had often been ignored. As individuals, we are often aware of the adverse effects of ageing, as the years go by. Societies can suffer similar adverse effects from ageing, and most developed economies are ageing.

According to the UN Population Division, almost every developed economy has seen a decline in fertility rates and an increase in life expectancy. As a result, the average proportion of the population aged 60+ is projected to increase from 16% in 1970 to 29% in 2030, with most of the corresponding decline experienced in the 0-19 age group in 21 OECD economies.

In the 1960s, Simon Kuznets suggested that a society consisting of consumers, savers and producers can grow in a sustainable way if the demographic structure was a rough pyramid. Larger at the bottom, where there are the youngest – up to the age of twenty or so – the working age next – then at the top a smaller group of older people. The pyramid is now turning upside down, with the bulge at the top.

Why is the demographic structure relevant?

Our research has examined the impact of demographic structure on economic activity, productivity, and innovation. Demographic structure may affect long and short-term economic conditions in several ways. Different age groups have different savings behaviour; have different productivity levels; work different amounts (as the very young and very old tend not to work); contribute differently to the innovation process; and have different needs. Therefore, changes to the demographic structure of a society can be expected to influence interest rates and output in both the long and short-term.

Our analysis shows that the changing age profile across OECD countries has economically and statistically significant impacts and that it roughly follows a life-cycle pattern; that is, people who are likely to be dependent on state or other forms of support – generally the very young and the old populations – seem to reduce economic growth, investment and real returns in the long-run.

Demographic structure also affects innovation; the economy is less likely to develop and/or patent new innovations/inventions. Similarly, productivity, which is driven by innovation, is positively affected by young and middle aged cohorts and negatively by the dependant young and retirees.

Demographics, innovation and medium-run economic performance

When people expect to live longer, they save more for their retirement and consume less, increasing demand for investment products and causing a decline in their returns. This provides one explanation in the steady decline in real interest rates in OECD countries since the 1980s. But it leaves us with a puzzle. A decrease in long-term interest rates should increase investment, but that is not what we observe. Our estimates show that long-term investment is declining. Our solution to the puzzle is that aging has also lowered the productivity of investment, reducing the incentive to invest, because the rate of ideas production and innovation, mainly done by the young, has reduced.

With fewer younger people in the population, there will be less creativity and ideas. Thus, while the cost of investment finance may be lower due to higher savings of the aging population, there are not enough ideas worth capitalising on and so long-term investment and real output declines. An ageing population also throws up social challenges, such as the provision of care for the elderly and how this can be supported.

Are there solutions?

While immigration may address the shortage of workers in the middle of the age categories, the political problems it raises are such that governments are usually unwilling to develop immigration policies that would truly address the issue. Furthermore, as populations are aging globally, this is not an adequate long-term solution. Giving more childcare support for young parents could help increase fertility rates and this is also related to building human capital starting from a very young age.

Increases in productivity by investing in human capital, education and skills is of crucial importance, as is increased funding for research and development that could bolster a  generation of new ideas and create new innovations and investment opportunities.  At Birkbeck, we have long understood the importance of lifelong learning that is directly associated with productivity gains for the economy, which in the current climate could help to compensate for a reduced workforce and staggered productivity. Robots and AI could also address the productivity/labour supply challenges, especially if we reach a point where machines can generate innovations and robots might be used more to fill gaps in the work force and provide care for the elderly, but it might make more people unemployed.

A typical challenge is that politicians are often short sighted. Long-term investment in order to boost human capital and productivity would not be a top priority for an incumbent politician in the short term, despite the transformative effects they could have for the generations to come.  Often, what we think is happening now is the slow moving changes that started a long time back, so a long-term view is essential to tackle the economic impact of ageing populations to address the future.

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The economics of public sector employment

Our Dr Pedro Gomes has been researching public employment for nearly fifteen years. He shares why it is so important to understand how the public sector works and the key findings from his research.

Public employment is a significant consideration in any national economy. In developed countries, public employment makes up 15-30 percent of total employment and represents the large majority of government consumption. In the US, for example, the government spends 60 percent more on general government employees than on the purchase of intermediate goods and services.

The public sector also operates according to different rules than the rest of the economy, as governments do not face the same competitive forces, nor have the same objectives as private sector firms.

Considering that the public sector is responsible for delivering many key services in our society, from education to healthcare, it is essential to have a good understanding of how its employment operates. The recent COVID-19 pandemic has again put focus on the importance of having a modern public sector, with an employment force prepared to face difficult, unpredictable and unlikely crises, but its aftermath with high public debt, also puts emphasis on the costs of the public sector workforce.

Below are three of the key findings from my research into this area.

Governments hire disproportionately more educated workers

In the paper Public Employment Redux, my colleagues Pietro Garibaldi, Thepthida Sopraseuth and I explore the phenomenon whereby governments hire more educated workers than the private sector.

We noticed that governments hire very few workers with low qualifications. In the US, for example, one third of workers have a masters or a PhD qualification, and one third of these work for the government.  We documented empirical evidence for this education bias in the US, UK, France and Spain.

There are a few different explanations for this trend:

  • The government needs more educated workers to provide its highly technical goods and services, such as healthcare, education and the judicial system.
  • Higher educated workers take more of a wage penalty to work in the public sector, so are relatively less expensive to hire.
  • Public sector jobs that require low qualifications pay more than similar level jobs in the private sector, so they attract workers with more qualifications.

Within our model, we found that the technological skills needed for the public sector was the main driver of the disproportionate representation of educated workers, but that wage setting and excess underemployment explain 12-15 percent of the education bias.

Unlike other sectors, the government is able to set wages more freely, as the cost is financed from tax revenue. If the government chooses to pay very high wages, too many people will choose a skilled role in the public sector as their first choice. However, if wages are too low, too few workers will want to join the government.

In reality, a balance is needed, so the government can always attract the workers it needs, without leading to underemployment in the public sector.

Nepotism in hiring practices allows friends and family to ‘jump the queue’ for government roles

Public sector hires are often based on nepotism: Scopa (2009) found that the probability of working in the public sector is 44% higher for individuals whose parents also work in the public sector, while Colonnelli et al. (2020) found that politically connected individuals in Brazil enjoy easier access to public sector jobs.

In my research into this topic with Andri Chassamboulli, we suggest that workers can use their connections to find jobs in the public sector faster. We created a search and matching model with private and public sectors to test this theory.

Surprisingly, we encountered some positive side effects to nepotistic practices. Conditional on high public sector wages, our findings suggest that hiring through connections reduces unemployment, as people who do not have connections will instead find roles in the private sector. Conversely, if the government sets the optimal wage possible for the successful running of the public sector, nepotism is reduced.

We conclude, therefore, that nepotism is a symptom of a problem in the public sector, rather than the disease, and the problem is created when wages are set too high.

Women prefer working in the public sector

In most countries, the public sector hires disproportionately more women than men. My colleague Zoë Kuehn and I developed a model to try to make sense of this imbalance.

Our findings show that the gender imbalance in the public sector is driven by supply, meaning that women self-select to work in the public sector more than men. One explanation for this is that the type of job carried out by the government is coincidentally the type of work preferred by women, such as healthcare and education. However, even discounting these sectors, women’s public employment remains 20-25% higher than men.

This remaining imbalance can be explained by the different characteristics of public and private employment. The gender wage gap and working hours are both reduced in the public sector, making this an attractive choice for women who may be factoring family commitments alongside work opportunities in their choice of employment. Alongside reduced working hours, the public sector offers additional benefits such as more sick days, flexible hours and employer-provided childcare, ensuring an overall better work-life balance in the public sector.


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Decision making under uncertainty: Ambiguity preferences

David Schröder, Associate Professor in Finance at Birkbeck’s Department of Economics, Mathematics and Statistics, and Elisa Cavatorta, Associate Professor in the Department of Political Economy at King’s College London, have developed a questionnaire to measure how members of the public make decisions under uncertainty. Take the survey online to find out your ambiguity preferences.

Cartoon of a figure at a crossroads

This article was originally posted on the Research Outreach blog and is licensed under a Creative Commons Attribution 4.0 International License.

Every decision and action that we take in life is associated with a degree of uncertainty; whether we cross a road, what we invest our money in, what career we follow and the thousands of other decisions that we make on a daily basis. Over the years, economists and psychologists have studied different factors that affect how individuals make decisions under uncertainty so that they can better understand what drives the behaviours that we see in the world.

One of the underlying factors that explain individual behaviour under uncertainty is the different degree of tolerance anyone has for situations of uncertainty, in other words their individual preferences. To apply the behavioural models proposed in the scientific literature, it is important to accurately measure these preferences driving our behaviour. Elisa Cavatorta, Associate Professor at the Department of Political Economy at King’s College London, and David Schröder, Associate Professor in Finance at Birkbeck, University of London, noticed that existing approaches to measure uncertainty attitudes are overly complex and therefore rarely used to measure preferences outside economic laboratories. To improve the ability to measure uncertainty attitudes and make their measurement more accessible, they designed a new questionnaire to facilitate the assessment of the preferences guiding everyday decision making under uncertainty.

Understanding risk and ambiguity

The most common factor that people associate with decision making under uncertainty is risk, and how tolerant an individual is towards risky scenarios. In a risk-based situation, we have a sense of the likelihood of the different outcomes that our decisions could deliver. For example, if you roll a fair dice, you know that you have a one in six chance of getting a specific number. Likewise, outcomes of recurring situations may involve known likelihoods: if your parent cooks their usual signature dish, you know the chances that it tastes delicious.

In many situations however, there is an additional degree of uncertainty about the potential outcomes of our decisions and actions. For example, if you hear that the dice that you are about to use has a flaw that means it will not roll fairly, you can no longer accurately predict the likelihood of rolling the number four. If a stranger cooks for you, the probability that the dish is delicious can be vague. Thanks to the work of Knight (1921) and Ellsberg (1961), this degree of uncertainty over vague or unknown probabilities is referred to as ambiguity. Different people have a different “taste” for the lack of accurate information about the probabilities of given outcomes and will respond differently.

Our preferences towards ambiguity guide the decisions that we make under uncertainty. There isn’t an optimal decision that fits all. Optimal decisions for everyone depend on one’s own preferences. If we can accurately measure ambiguity preferences, then we have a powerful insight into human behaviour, that is, how people make choices subject to limited information.

Measuring ambiguity preferences

Traditionally, ambiguity attitudes have been measured within a controlled economic laboratory environment. Ambiguity tests have focused on specific decision tasks involving known and unknown probabilities, often complex to understand and requiring lengthy explanations. This method has produced very accurate results; however, the complexity of these tasks makes them impractical to roll out on a large enough scale to understand the decision-making behaviours that we see in the general population.

Elisa Cavatorta and David Schröder researched ambiguity preferences in great detail. They started from the results in a laboratory setting, but the researchers were motivated to find a more practical way of measuring ambiguity preferences outside of these experiments. They knew that an online survey questionnaire or a questionnaire that could be conducted by telephone would be a far more practical mechanism for collecting data from much larger groups of participants and be more practical for researchers who conduct field studies.

Survey design

Cavatorta and Schröder have designed a simple survey questionnaire, which accurately measures ambiguity preferences. Their work has been inspired by various studies that recommended using surveys to measure other economic preferences. In their 2019 paper, they develop a measurement for ambiguity preferences, adding to existing ones designed to elicit preferences for risk, trust and impatience.

The research team developed their questionnaire using a sample of 121 students from various colleges of the University of London. The idea was to find the best combination of survey questions that would most accurately predict ambiguity preferences elicited with the well-established approach in the laboratory setting. The challenge was to find the best combination of these survey questions. The research team selected around 50 possible candidate survey questions of various types. Some of these questions are short thought-experiments where participants make choices in some hypothetical games (e.g. selecting the preferred option between one unknown, i.e., ambiguous, probability and one with known probability, i.e. risky). Some questions are attitudinal questions from the psychology literature, in which participants assess how much they like or dislike a situation.

The researchers considered the predictive power of all combinations of the candidate questions. Using a selection process that evaluates all possible combinations and then selects the best predictors is a data-driven method that minimises forms of bias in the selection process. The result is a five survey-item questionnaire that provides an individual ambiguity preferences score that correlates well with the ambiguity preference score that would come out in a laboratory setting. This means the survey questionnaire is an accurate substitute when measurement in the laboratory is impractical or unavailable.

Possible uses of the new measurement

The professors recommend their measurement whenever incentivised experiments in a laboratory are not feasible, for example, when researchers need to gather ambiguity preferences of a large number of participants, field-studies, or in scenarios where time or money is limited.

This questionnaire provides the opportunity to conduct large-scale studies into the impact of ambiguity preferences on economic and social behaviour. Given the uncertainty surrounding many decisions in every-day life, applications of the measurement can be wide-ranging. The current pandemic demonstrates people have different preferences for ambiguity and this guides different reactions and health behaviour. Another application concerns the financial services industry: the industry has traditionally focused on risk preferences when recommending the most suitable investment options to its clients. Risk preference assessments help us to understand one element of what makes an investment a good match for an individual. However, investments often involve unknown risks (i.e. ambiguity), so the measurement can assist financial services professionals to better tailor their product recommendations to the client’s tastes and needs.

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COVID-19 induced travel restrictions are not enough to mitigate crises like climate change. Could a circular economy be the answer?

Research by the Department of Management’s Dr Fred Yamoah and colleagues points to a new way to rebuild the global economy in the wake of the coronavirus pandemic.

Image of a reuse logo

There is no doubt that COVID-19 is first and foremost a human tragedy, resulting in a massive health crisis and huge economic loss.

While the impact on life as we know it has been unthinkable, a side effect of the way of life forced upon us by the pandemic is an unprecedented reduction in global carbon dioxide emissions, which are projected to decline by 8%. If achieved, this will be the most substantial reduction ever recorded, six times larger than the milestone reached during the 2009 financial crisis.

However, these changes should not be misconstrued as a climate triumph. They are not due to the right decisions from governments, but to a temporary status of lockdown that will not linger on forever; economies will need to rebuild, so we can expect a surge in emissions in the future. Indeed, the relatively modest reduction in emissions prompted by the COVID-19 pandemic has proven that zero-emissions cannot be attained based on reduced travel alone; structural changes in the economy will be needed to meet this target.

The case for a circular economy

Before coronavirus prompted this dramatic shift in our way of life, it seemed that the world had been waking up to the need for change to protect our environment. The linear model of our industrial economy – taking resources, making products from them and disposing of the product at the end of its life – jeopardizes the limits of our planet’s resource supply. Girling (2011) found that around 90% of the raw materials used in manufacturing become waste before the final product leaves the production plant, while 80% of products manufactured are disposed of within the first six months of their life. Similarly, Hoornweg and Bhada-Tata (2012) reported that around 1.3 billion tonnes of solid waste is generated by cities across the globe, which may grow to 2.2. billion tonnes by 2025.

Against this backdrop, the search for an industrial economic model that satisfies the multiple roles of decoupling economic growth from resource consumption, waste management and wealth creation, has heightened interests in concepts about circular economy.

What is circular economy?

Circular economy emphasises environmentally conscious manufacturing and product recovery, the avoidance of unintended ecological degradation and a shift in focus to a ‘cradle-to-cradle’ life cycle for products.

In our current situation, there has never been a better time to consider how the principles of circular economy could be translated into reality when the global economy begins to recover. Strategies to combat climate change could include:

  • material recirculation (more high-value recycling, less primary material production)
  • product material efficiency (improved production process, reuse of components and designing products with fewer materials)
  • circular business models (higher utilisation and longer lifetime of products through design for durability and disassembly, utilisation of long-lasting materials, improved maintenance and remanufacturing).

Building back better

A circular economy could also act as a vehicle for crafting more resilient economies. The pandemic has forced a rethink of the way our global economy operates, revealing the inability of the dominant economic model to respond to unplanned shocks and crises. The lockdown and border restrictions have reduced employment and heightened the risk of food insecurity for millions.

To prevent a repeat of the events of 2020, it is necessary to devise long-term risk-mitigation and sustainable fiscal thinking, moving away from the current focus on profits and disproportionate economic growth. Circular economy concerns optimised cycles: products are designed for longevity and optimised for a cycle of reuse that renders them easier to handle and transform. Future innovations under this model would focus on the general well-being of the populace, alongside boosting the market and competitiveness.

This economic model would also support the achievement of social inclusion objectives, for example by redistributing surplus food from the consumer goods supply chain to the local community.

The benefits of a circular economy are therefore obvious in that it strives for three wins in terms of social, environmental and economic impact. The pandemic has instigated a focus on the importance of local manufacturing for a resilient economy; fostered behavioural change in consumers; triggered the need for diversification and circularity of supply chains and evinced the power of public policy for tackling urgent socio-economic crises.

Governments are recognising the need for national-level circular economy policies in many aspects, such as:

  • reducing over-reliance on other manufacturing countries for essential goods
  • intensive research into bio-based materials for the development of biodegradable products
  • legal frameworks for local, regional and national authorities to promote green logistics and waste management regulations which incentivise local production and manufacturing
  • development of compact smart cities for effective mobility.

Post COVID-19 investments needed to accelerate towards more resilient, low carbon and circular economies should be integrated into the stimulus packages for economic recovery being promised by governments, since the shortcomings in the dominant linear economic model are now recognised and the gaps to be closed are known. The question is no longer should we build back better, but how.

This blog was adapted from T. Ibn-Mohammed, K.B. Mustapha, J. Godsell, Z. Adamu, K.A. Babatunde, D.D. Akintade, A. Acquaye, H. Fujii, M.M. Ndiaye, F.A. Yamoah, S.C.L. Koh, ‘A critical analysis of the impacts of COVID-19 on the global economy and ecosystems and opportunities for circular economy strategies’ in Resources, Conservation and Recycling, 164. Available at: https://doi.org/10.1016/j.resconrec.2020.105169

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