Author: Nicholas Vasilakos

Posted on

Navigating the storm: understanding the socioeconomic impacts of climate change



Climate change is not just an environmental challenge: its socioeconomic impacts are profound and far-reaching, touching every aspect of society. From agriculture to health, from urban infrastructure to coastal communities, the effects of climate change are evident and escalating.

The far-reaching effects

In agriculture, rising temperatures, more intense and frequent heatwaves and changing precipitation patterns pose significant threats to food security.1, 2 Crop yields decline as extreme weather events become more frequent and unpredictable, leading to increased food prices and economic instability. Smallholder farmers, who often lack the resources to adapt, are particularly vulnerable, exacerbating rural poverty and food insecurity.3

Coastal communities face the dual threats of sea-level rise and more intense storms.4 Erosion and inundation damage homes, infrastructure and livelihoods, displacing populations and disrupting local economies. The loss of coastal ecosystems further compounds these challenges, reducing natural defences against storm surges and exacerbating the impacts of climate-related disasters.

Health systems strain under the burden of climate-change-induced heatwaves, air pollution and the spread of vector-borne diseases.5, 6 Heat-related illnesses increase as temperatures rise, particularly affecting vulnerable populations such as the elderly and outdoor workers. Air pollution exacerbates respiratory conditions, leading to higher healthcare costs and decreased productivity. Vector-borne diseases, such as malaria and dengue fever, expand into new regions, placing additional strain on already overburdened health systems.

Displacement due to climate-related disasters amplifies social inequalities and challenges urban planning and infrastructure.7 Vulnerable communities, often located in low-lying areas or informal settlements, bear the brunt of climate impacts, facing the loss of homes, livelihoods and community cohesion. Inadequate housing and infrastructure increase the risks associated with extreme weather events, perpetuating cycles of poverty and vulnerability.

Furthermore, climate change exacerbates existing socioeconomic disparities, disproportionately affecting marginalised and vulnerable populations. Indigenous communities, women, children and people living in poverty are often the hardest hit, lacking access to resources, information, and adaptive capacity.8

Policy responses

Addressing the socioeconomic impacts of climate change requires co-ordinated action across sectors and scales. Policy interventions, such as investment in climate-resilient infrastructure and the promotion of sustainable agriculture practices, are essential for building resilience and reducing vulnerability. Community-led initiatives that prioritise local knowledge and empower marginalised groups are also critical for fostering adaptive capacity and promoting social equity.

To address these challenges, projects like CROSSEU, the new €5 million Horizon Europe project (that I have the pleasure to be part of), play a crucial role in enhancing our understanding of these impacts and developing actionable strategies for resilience and adaptation. One of the key contributions of CROSSEU lies in its development of a Decision Support System (DSS) that integrates tools, measures, and policy options to address these risks in a cross-sectoral and cross-regional perspective. This DSS will support (and hopefully improve) decision-making processes at various levels, from local to EU-wide, and facilitate the adoption of evidence-based policies and measures to enhance resilience and mitigate the impacts of climate change.

Would you like to know more about CROSSEU? Follow our journey and be informed of our publications and events in our new webpage: https://crosseu.eu/9

Articles/References

  1. Global food security under climate change
    Proceedings of the National Academy of Sciences, Josef Schmidhuber and Francesco N Tubiello (11/12/2007)

  2. Reducing risks to food security from climate change
    Global Food Security, Bruce M Campbell et al. (2016: 11, pp 34–43)

  3. The value-add of tailored seasonal forecast information for industry decision making
    Climate, Clare Mary Goodess et al (16/10/2022)

  4. Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen
    Climatic change, Stéphane Hallegatte, Nicola Ranger, Olivier Mestre, Patrice Dumas, Jan Corfee-Morlot, Celine Herweijer and Robert Muir Wood (7/12/2010)

  5. Health risks of climate change: An assessment of uncertainties and its implications for adaptation policies
    Environmental Health, J Arjan Wardekker, Arie de Jong, Leendert van Bree, Wim C Turkenburg and Jeroen P van der Sluijs (19/9/2012)

  6. Climate Change and Temperature-related Mortality: Implications for Health-related Climate Policy
    Biomedical and Environmental Sciences, Tong Shi Lu, Jorn Olsen and Patrick L Kinney (2021: 34(5) pp 379–86 )

  7. Climate Change, Inequality, and Human Migration
    IZA Discussion Paper No. 12623, Michał Burzyński, Christoph Deuster, Frédéric Docquier and Jaime de Melo (23/9/2019)

  8. The trap of climate change-induced “natural” disasters and inequality
    Global Environmental Change, Federica Cappelli, Valeria Costantini and Davide Consoli (30/7/2021)

  9. Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe
    UEA Research Project, Nicholas Vasilakos, Katie Jenkins and Rachel Warren

Questions

  1. How do the socioeconomic impacts of climate change differ between rural and urban communities? What factors contribute to these disparities, and how can policies address them effectively?
  2. In what ways do vulnerable populations, such as indigenous communities and those living in poverty, bear the brunt of climate change impacts? How can we ensure that climate adaptation strategies prioritise their needs and promote social equity?
  3. The blog mentions the importance of community-led initiatives in building resilience to climate change. What examples of successful community-based adaptation projects can you identify, and what lessons can be learned from their implementation?
  4. How can governments and organisations collaborate to address the socioeconomic impacts of climate change while also promoting economic growth and development? What role do cross-sectoral partnerships play in building resilience and fostering sustainable practices?
Posted on

Unlocking happiness: how jobs shape young lives in developing nations

Have you ever wondered how your job affects your happiness? We all know that not all jobs are created equal. Some are awesome, while others … not so much. Well, it turns out that employment status and the type of work you do can have a big impact on how you feel – especially in developing countries where labour markets are usually tighter and switching between jobs can be more difficult.

A recent study by Carmichael, Darko and Vasilakos (2021) uses survey data from Ethiopia, Peru, India and Vietnam to answer this very question. The study found that the quality of work is a big deal when it comes to how young people feel. Not all jobs are ‘good jobs’ that automatically make you feel great. Although your wellbeing is likely to be higher when you’re in employment than when you’re not, there are certain job attributes that can push that ‘employment premium’ up or down. This is especially important to understand in countries like many in sub-Saharan Africa, where there aren’t many formal jobs, and people often end up overqualified for what they do.

What job attributes lead to higher wellbeing?

What then are the job attributes that are correlated with higher levels of wellbeing? The first is money: Okay, we know money can’t buy happiness, but it can certainly make life easier. We were therefore hardly surprised to find a positive and statistically significant association between hourly earnings and wellbeing.

We were also not surprised to find that a ‘poor working environment’ has a strong and highly significant negative effect on wellbeing.

Finally, feeling proud of your work is also found to be a strongly significant determinant of your wellbeing. After all, people tend to excel in things they like doing, which is probably part of the ‘transmission mechanism’ between ‘work pride’ and ‘subjective wellbeing’.

Which one of these attributes did you think had the greatest effect on wellbeing? Let me guess, many of you will say ‘earnings’. But then you would be wrong. Earnings were indeed positively associated with wellbeing and statistically significant at just about the 10% level, whereas work pride was very strongly statistically significant at the 1% level and had an effect on wellbeing that was four times greater than hourly earnings.

Putting yourself in a poor working environment on the other hand would reduce your wellbeing by almost twice as much as the earnings coefficient.

Policy implications

What does all this mean for policy-makers? If we want to make life better for young people in low-income countries, we need to tackle the problems from multiple angles.

First, young people need to be helped to get the skills they need for the job market. This can be done through things like training programmes and apprenticeships. However, not all of these programmes are created equal. Some have great results, and others not so much.

But that’s not the whole story. In many countries, there’s a massive informal job market. It’s a place where people work but often don’t have the rights or protections that formal employees do. So, even if young people get trained, they might not find the ‘good’ jobs they’re hoping for.

Changes also need to be made on a much bigger scale. This often includes decentralising public investment to include rural areas, improving infrastructure, and encouraging private investment. Strengthening labour market rules and social protection can help too, by making sure that work is safe and fair.

In a nutshell, where you work and what kind of work you do can make a big difference to how you feel.

Conclusions

If policy-makers want to help young people in low-income countries, they need both to give them the skills they require and to create better job opportunities. But policy-makers also need to make bigger changes to the way things work, like boosting production and making sure jobs are safe and fair.

In the end, it’s about making life better for young people around the world. Let’s keep working on it!

Articles

Questions

  1. How does the quality of work impact the happiness and wellbeing of young people in low- and middle-income countries (LMICs), and why is this significant in the context of job opportunities in sub-Saharan Africa?
  2. What are some potential solutions and strategies discussed in the article for improving the wellbeing of young people in LMICs, particularly in the context of employment and job opportunities?
  3. Have you ever experienced a job that significantly (positively or negatively) impacted your wellbeing or happiness? Reflect on your experience and how it influenced your overall life satisfaction?
  4. How is AI likely to affect the wellbeing of young professional workers?
  5. How is the pandemic likely to have affected job satisfaction?
Posted on

Till death do us part? (updated)

You’ve had a busy day at work. You check your watch; it’s almost 5pm. You should be packing soon – except, your boss is still in their office. You shouldn’t really be seen leaving before your boss, should you? You don’t want to be branded as ‘that guy’ – the one who is ‘not committed’, ‘not willing to go the extra mile’, ‘not flexible enough’, first out of the door’ – you don’t want to have that label pinned on your performance appraisal. After all, your boss is still hard at work, and so are your other colleagues.

So you wait, pretending to work, although you do not really do much – perhaps you’re checking Facebook, reading the news or similar. And so does your boss, not wanting to be seen leaving before anyone else. But what example is this going to set for you and your other colleagues. You all wait for someone to make the first move – a prisoner’s dilemma situation. The only difference is that it’s you who is the prisoner in this situation.

Presenteeism

What we have described is an example of presenteeism. But how would we define it? If you search the term on Google Scholar or Scopus, you will come across a number of articles in the fields of health and labour economics that define presenteeism as a phenomenon in which employees who feel physically unwell choose to go to work, or stay on at work, rather than asking for time off to get better (see, for instance, Hansen and Andersen, 2008 and several others). This is also known as sickness presenteeism.

According to Cooper and Lu (2016), however, the use of the term can be extended to describe a wider situation in which a worker is physically present at their workplace but not functioning (by reason of tiredness, physical illness, mental ill-health, peer pressure or whatever else). As explained in Biron and Saksvik (2009):

Cooper’s conceptualisation of presenteeism implied that presenteeism was a behaviour determined by specific determinants (i.e. long working hours and a context of uncertainty). This tendency to stay at work longer than required to display a visible commitment is what Simpson (1998) calls ‘competitive presenteeism’ where people compete on who will stay in the office the longest.

The effect of presenteeism

Unsurprisingly, the effect of presenteeism on the wellbeing of workers and the economic performance of firms has been looked at extensively from different angles and disciplines – including health economists, organisational behaviour and labour economists – for a recent and comprehensive review of the literature on this topic see Lohaus and Habermann (2019).

Most of these studies agree that the effects of presenteeism are negative; in particular, they identify significant negative effects on the physical health of workers (Skagen and Collins, 2016); emotional exhaustion and mental health issues (Demerouti et al, 2009); persistent productivity loss (Warren et al, 2011); lower work engagement and negative feelings (Asfaw et al, 2017) – among several others. There seems, therefore, to be plenty of convincing evidence that presenteeism is bad for everyone – business owners, managers and staff.

So next time that you find yourself stuck at work working silly hours, feeling totally unproductive and just staying to be seen, email this blog to your boss and other colleagues – and ask them if they wish to join you for a drink or a walk.

You’re welcome!

(By the way, there’s a saying that in the UK the last one to leave the office is seen as the hardest working, whereas in Germany the last one to leave the office is seen as the least efficient!)

Articles

References

Questions

  1. ‘Presenteeism leads to lower productivity and firm performance and should be discouraged by business owners and managers’. Discuss.
  2. Jack Ma, the Chinese billionaire and owner of Ali Baba, has defended his ‘996 work model’ (working 9am to 9pm for 6 days a week) as a ‘huge blessing’. Find and review some articles on this topic, and use them to write a response. Your response should be substantiated using relevant economic theory and empirical research.
  3. Have you or anyone you know found yourself guilty of presenteeism? Share your experience with the rest of the class, focusing on effects on productivity and your attitude towards your employer and work colleagues.
Posted on

Automation revisited

It’s been a while since I last blogged about labour markets and, in particular, about the effect of automation on wages and employment. My most recent post on this topic was on the 14th of April 2018 and it was mostly a reflection on some interesting findings that had been reported by Acemoglu et al (2017). More specifically, Acemoglu and Restrepo (2017) developed a theoretical framework to evaluate the effect of AI on employment and wages. They concluded that the effect was negative and potentially sizeable (for a more detailed discussion see my blog).

Using a model in which robots compete against human labor in the production of different tasks, we show that robots may reduce employment and wages … According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18–0.34 percentage points and wages by 0.25–0.5 percent.

Since then, I have seen a constant stream of news on my news feed about the development of ever more advanced industrial robots and artificial intelligence. And this was not because of some spooky coincidence (or worse). It has been merely a reflection of the speed at which technology has been progressing in this field.

There are now robots that can run, jump, hold conversations with humans, do gymnastics (and even sweat for it!) and more. It is really impressive how fast change has been happening recently in this field – and, unsurprisingly, it has stimulated the interest of labour economists!

A paper that has recently come to my attention on this subject is by Graetz and Michaels (2018). The authors put together a panel dataset on robot adoption within seventeen countries from 1993 to 2007 and use advanced econometric techniques to evaluate the effect of these technologies on employment and productivity growth. Their analysis focuses exclusively on developed economies (due to data limitations, as they explain) – but their results are nevertheless intriguing:

We study here for the first time the relationship between industrial robots and economic outcomes across much of the developed world. Using a panel of industries in seventeen countries from 1993 to 2007, we find that increased use of industrial robots is associated with increases in labor productivity. We find that the contribution of increased use of robots to productivity growth is substantial and calculate using conservative estimates that it comes to 0.36 percentage points, accounting for 15% of the aggregate economy-wide productivity growth.
 
The pattern that we document is robust to including various controls for country trends and changes in the composition of labor and other capital inputs. We also find that robot densification is associated with increases in both total factor productivity and wages, and reductions in output prices. We find no significant relationship between the increased use of industrial robots and overall employment, although we find that robots may be reducing the employment of low-skilled workers.

This is very positive news for most – except, of course, for low-skilled workers. Indeed, like Acemoglu and Restrepo (2017) and many others, this study shows that the effect of automation on employment and labour market outcomes is unlikely to be uniform across all types of workers. Low-skilled workers are found again to be likely to lose out and be significantly displaced by these technologies.

And if you are wondering which sectors are likely to be disrupted most/first by automation, the rankings developed by McKinsey and Company (see chart below) would give you an idea of where the disruption is likely to start. Unsurprisingly, the sectors that seem to be the most vulnerable, are the ones that use the highest share of low-skilled labour.

Articles

Questions

  1. “The effect of automation on wages and employment is likely to be positive overall”. Discuss.
  2. Using examples and anecdotal evidence, do you agree with these findings?
  3. Using Google Scholar, put together a list of 5 recent (i.e. 2015 or later) articles and working papers on labour markets and automation. Compare and discuss their findings.
Posted on

Stop and smell the roses

Spring has already made its appearance here in Norfolk. Our garden is in full bloom and I am in a particularly spring-philosophical mood today – especially so as I should soon be hearing news from the editorial office of a coveted economics journal. This concerns a paper that I submitted for publication what feels like months ago.

And just as I was reflecting on this thought, a paper by Firmuc and Paphawasit (2018) landed on my desk, evaluating the impact of physical attractiveness on academic research productivity in the field of economics. More specifically, the authors pull together information about the research productivity of about 2000 published economics researchers. They then find photos of them and rate their attractiveness (yes, seriously!) using an online survey. In particular:

Besides collecting some basic information on the authors, we also rated their attractiveness. To this effect, we circulated a number of online survey links to potential participants at Brunel University and elsewhere, using direct communication, email and social networks. Each online survey collected basic background information on the assessor (gender, age, ethnicity, highest education, and whether they are currently enrolled as a student) followed by 30 randomly-chosen and randomly-ordered photos, with each picture placed on a separate page.

…Each rater was asked to rate the attractiveness of the person in the photo on an 11-point scale, from 0 (unattractive) to 10 (very attractive). No information on the photographed individuals was provided and the raters were told that the survey studies the formation of perceptions of beauty. The raters were also asked whether they recognised the person in the picture, or whether the picture did not load properly: in such instances, their scores were excluded from the analysis.

The average beauty score was 3.9, with the most attractive academic scoring 7.6

They even attach photographs of the three most attractive male authors in their sample in an appendix (thankfully the other end of the distribution was left out – I had to check to make sure, as I was worried for a few minutes I would find my photo posted there!).

Their results show that there is a link between authors’ attractiveness and quality of journals where their papers are published, as well as number of citations that they receive. According to their findings, this association matters most for more productive authors (‘of intermediate and high productivity’), whereas there seems to be very small or no effect for less productive authors. Some of these effects disappear once controlling for journal quality:

…attractive authors tend to publish their research in better journals, but once their work is published, it does not attract more citations than other papers published in the same journal by less good-looking authors.

Although there are many methodological parts of this paper that I do not quite understand (probably because it is not my area of specialisation), it does remind us that looks do matter in labour markets. There is a well-established literature in labour economics discussing the association between appearance/beauty and wages and the so-called ‘halo effect’ (referring to the physical attractiveness premium that more attractive workers are likely to command in labour markets – see also Langlois et al., 2000; Zebrowitz et al., 2002; Kanazawa and Kovar, 2004; for a detailed discussion on this).

I was also surprised to read that this beauty bias can be also gender specific. For instance, Cash et al (1977) and Johnson et al. (2010) find that the effect goes the other way (negative impact) when considering female candidates applying for jobs traditionally perceived as ‘masculine’ ones. By contrast, male candidates are more likely to experience a positive return on good looks, irrespective of the type of job that they do (see also Johnson et al., 2010).

No surprise then that ‘guyliners’, ‘make up for men’ and other male beauty products are becoming increasingly popular amongst younger workers – in Europe it is not as common yet as it is in parts of Asia (Japan comes to mind), but I imagine it is a matter of time, as more workers realise that there are positive returns to be made!

References

Article

Questions

  1. Read some of the papers posted above and explain the main argument about the link between physical attraction and wages. What does the empirical evidence show on this?
  2. Using examples and anecdotal evidence, do you agree with these findings?
  3. If these findings are representative of the real world, what do they suggest about the functioning of modern labour markets?