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Speaker: Ozge Akinci

This discussion reviews a paper proposing an open-economy New Keynesian DSGE model with two frictions—sticky prices and sovereign default risk—to study how fiscal policy interacts with monetary policy when default risk is present. The core result: expectations of default act like a cost-push shock in the New Keynesian framework, making inflation stabilization harder and generating co-movements observed in data: high inflation, high sovereign spreads, high nominal interest rates, and low output.

Model and Mechanism: “Default Amplification”

• Adverse productivity shock triggers an output collapse. In a small open economy, agents borrow internationally to smooth consumption, raising external debt.
• Higher external debt increases the likelihood of default. Because default states feature low consumption and high inflation, firms expect higher future inflation and higher marginal utility of consumption.
• Via the New Keynesian Phillips curve, higher expected future inflation (and higher marginal utility) raises current pricing, lifting current inflation even without directly linking inflation to productivity.
• Under a standard Taylor rule, the central bank increases the nominal interest rate, which reduces current domestic consumption (via the Euler equation), amplifying the downturn.
• Outcome: the mechanism aligns with observed episodes of high inflation, high spreads, high nominal rates, and low output.

Scope of Shocks and Key Questions Raised

• Beyond supply/productivity shocks: Does the mechanism extend to any shock that raises default likelihood (e.g., demand shocks)?
• Global financial cycle: Can the New Keynesian default framework reconcile empirical spillovers from US monetary policy to emerging markets (EM), notably the inflation response?
• Exchange rate role: In EM contexts, how do exchange rate movements contribute to the mechanism and observed outcomes?

Global Financial Cycle Evidence and Interpretation

• Empirical results (quarterly data for 25 EM from 1965; local projections on identified US monetary policy shocks):
  • An unexpected 100 basis point increase in the federal funds rate reduces US GDP by 0.8%.
  • EM GDP falls by more than the US; EM headline inflation rises significantly, and short-term PPI (domestic prices) also increases.
  • EM central banks raise policy rates (Taylor rule) despite the output drop.
• State dependence: When EM inflation is elevated, PPI and nominal interest rates increase markedly despite a large output decline.
• Limitation of the New Keynesian Reference Model (Gali-Monecelli): It delivers spread and output spillovers but implies falling producer price inflation and lower nominal interest rates—at odds with EM inflation dynamics.
• Alternative (discussant’s prior work with Albert Queralto): Unanchored inflation expectations (partly adaptive) can explain co-movement of high inflation and low output after large depreciations; the default-risk mechanism may be complementary.

Authors’ Responses

• Generalization across shocks: The mechanism persists for any shock that raises future default risk; through the New Keynesian Phillips curve, higher default risk increases expected future inflation or raises future marginal utility, acting like a cost-push shock that lifts current inflation.
• US monetary policy shock: Not studied in the paper; in the model, tighter US policy raises international borrowing costs and default risk, triggering the same amplification. The net effect among channels merits future study.
• Exchange rate: With flexible exchange rates (as in EM inflation targeting), model-implied exchange rate movements are consistent with data, though not very volatile.

Audience Q&A Highlights

• EM specificity: The framework applies to any country with default risk and inflation targeting; the author cautioned it may not yet be a good model for France because of the monitoring unit.
• Global factor in risk pricing: For EM, about 80% of the variation in the CBS price is attributed to a global factor, underscoring the importance of global shocks (including US monetary policy and related shocks). Authors agree risk-per-membership would be another useful shock to assess.
• Debt maturity: The paper uses long-term debt following the literature; in a simple example, short-term debt also works.

Next Steps / Actions

• Extend the framework to a two-country setting to analyze US monetary policy spillovers, tracking how default likelihood transmits and affects EM inflation and output.
• Quantify the relative strength of default-amplification versus demand channels under US monetary tightening.
• Evaluate additional shocks (e.g., risk-per-membership) and assess model fit against global factor-driven movements in EM spreads.
• Further examine the role and volatility of exchange rate dynamics under flexible regimes within the model.

Fig 1: The UK’s U/V - Beveridge curve charting observations where U=>1million

Source:OpenAI ChatGPT (2025)

As I am currently working on an end-of-term assignment, I didn't realise that today’s nonfarm payrolls survey has been released, showing negative growth in job creation in key sectors of the US economy. Now, the above is a generative AI chart (above) illustrating a very profound relationship found in the literature; it projects the United Kingdom’s unemployment level by figure (>1million) and its vacancy rate expressed in hundreds of thousands, and noted in years and quarters. Can you spot the outlier?

As the plots on the chart show, when unemployment is high, vacancies are typically observed to be low (below 600,000), and vice versa. Except for a specific outlier (2020 Q2), which coincides with COVID-19, the U/V curve (also referred to as the Beveridge curve) is downward sloping (Pissarides, 2013: 292). In 2020 Q2, both unemployment and vacancies were low due to massive government job retention schemes that protected employment, combined with pandemic-related lockdowns that suppressed hiring activity and moved people into "economic inactivity". Despite a sharp drop in economic activity, the labor market was effectively frozen in place.

When inflation is high, this creates a further dilemma for businesses that post vacancies; the dilemma essentially is that unemployment tends to be low and when unemployment is low, the labour market is described as being ‘tight’ or there is a significant disparity between available vacancies and supply of workers seeking employment (assume these workers are structurally unemployed workers).

Now, although many overly-convoluted acronyms have been coined to describe aspects which the Beveridge curve reflects, that is, volatile and uncertain economics, acronyms such as VUCA and STEEPLE for example, there are still various reasons why approaching planned resourcing strategically can be beneficial for organizations.

Many strategic models outlined in my blog apply basic/hard forms of HRM, such as the Michigan or matching model authored by Fombrun, Devanna, and Tichy (1984). However, as one of my classmates who works in the UK Defence sector explained to me, organisations cannot ‘rest on laurels’ so to speak but should evolve through continuous adaptation to the challenge of new environments, such as during times of economic hardship.

References

Fombrun, C., Devanna, M. A., and Tichy, N. M. (1984). Strategic human resource management. New York, NY: John Wiley & Sons. 

Pissarides, C.A. (2013) ‘Unemployment in the Great Recession’, Economica (London), 80(319), pp. 385–403

B811 – Tutorial 1 – Human Resource Management in Context

Internal (Pricing) Decisions vs External (Supply Chain) Factors

Introducing the term: VUCA

Something very interesting has come up in the tutorial slides from Week 0 of B811. The term VUCA has emerged and is being used broadly to describe external influences on the functioning of the organisation, which translate as volatile, uncertain, complex or ambiguous (VUCA) influences. Phonetically, it may sound like a slightly graphic cross-reference to an insult and movie title where Ben Stiller was cast, but in fact, VUCA is anything but in nature, as we will come to see it is a reasonably productive abbreviation. One that may very well dictate the analysis of (external) threats in a corporate SWOT matrix.

PESTLE and STEEPLE

In Tutorial 1, the introduction to our module, we’re given the example of a high-fashion store offering clothes for sale that use fabrics sourced from ethical suppliers. Let’s refer to this store’s business as Firm X. We’re asked to critically analyse a situation where business pressures create the need to evaluate whether disbanding the value of its ethical brands, which would be categorised as being under the STEEPLE factor – which is just PESTLE with an ethical dimension – could save Firm X enough by way of its manufacturing, and production process to yield a greater level of profit.

Where do we start with this? My approach is basically to look at two aspects of the scenario and by extension, two further aspects which may inform our thinking on strategic decision making.

• Look at: Ethical constraints and the possible depreciation of brand value (Internal)
• Look at: Strategic matters concerning business decision-making (Internal)
• Observe: Market size for ethical brands in fashion (External)
• Observe: Price elasticity in ethical consumption (External)

What dynamics are at play here? Well, aside from the obvious theories that may apply (that of Ronald Coarse's theory of the firm and Milton Friedman's imperative on Firm X as a profit-seeking entity) if we focus solely on the latter two aspects of our second approach, we find various factors may come to fruition, namely, a hypothetical dichotomy arising from the timely distinction between import restrictions (relevant for US based emporiums) and logistics and supply chain synergies from the procurement of domestic ethical fabric brands.

By extension, local currency valuations also become a factor of relevance. How do foreign exchange rates for overseas ethical brands compare to procurement from domestic brands that offer similar value?

Approaching the topic like this essentially narrows the issue of effective pricing decisions to the influence of supply chain factors. How does Firm X address this practical assumption? One way is by looking at evidence-based models of price elasticity using a package such as R, where Marshallian (uncompensated) price elasticities and Hicksian (compensated) price elasticities can be estimated. Assuming a price vector that combines a dataframe reflecting sale prices and quantities.

Q: Can you think of any scenarios where there may be a conflict or tension between volatility and regulation affecting Firm X?

This is a great warmup question to get the loins gurgling. Not only is the topic of volatility a matter for globalisation, but it is also a matter for regulation. Take, for instance, the example of Credit Suisse Group and its case as a defendant in a trial where the Group and its then Chief Executive Thiam Tidjane were wrongly accused of manipulating a key market volatility benchmark (the inverse VIX). The V in VUCA also reminded me of what the Canadian environmental activist and writer, Naomi Klein, eloquently refers to as ‘Beyond Borders’ in a 2001 conference speech come New Left Review article entitled: ‘Reclaiming the Commons’. I am reminded at this juncture of Prof. Alan Barrell’s use of the same language in his presentations on ‘Entrepreneurship Without Borders’.

Q: Any crossover between external factors affecting Firm X and internal factors?

• External: PESTLE - environment (COVID-19’s HR response given treatment of racial discrimination in frontline occupations in England and Wales).
• Internal: Strategic changes/Managerialism (Al Mahameed, Yates, & Gebreiter, 2024)
• Africa and Nigeria – where studies focused on the link between 1) trade union survival strategies, 2) the new employment relations climate, and 3) the subject of globalisation (Betchoo, 2013)

References 

Al Mahameed, M., Yates, D., and Gebreiter, F. (2024). Management as Ideology: “New” Managerialism and the Corporate University in the period of Covid‐19. Financial Accountability and Management, 40(1), 34-57 (Accessed on 04 May 2025)

Betchoo, N. K. (2023) ‘Youth empowerment as a human resource development strategy in Mauritius’, Issues in Business Management and Economics, 1(8), pp. 218-229. Available at: https://journalissues.org/ibme/abstract/betchoo/ (Accessed on 04 May 2025) 

Holbrook, P. and Beadle, H. (2025) ‘B811 - Human Resource Management in Context Tutorial 1 - introduction’, Open University Business School (Accessed on 03 May 2025)

Klein, N. (2001) ‘Reclaiming the Commons’, New Left Review, Available at: https://newleftreview-org.libezproxy.open.ac.uk/issues/ii9/articles/naomi-klein-reclaiming-the-commons (Accessed on 04 May 2025)

Occasionally, you may find yourself writing a script or paper to explain a concept using mathematical models. Personally, I don’t follow a strict process, but I strive to be as systematic as possible. In hindsight, when reviewing research, you might uncover a previous article that contains a significant and unexpected correlation to what you’ve just written.

This is exactly what happened to me with my first arXiv preprint. According to OpenAI's new paid Deep Research feature, I built upon a widely popularized model based on one of the early articles by former IMF Chief Economist Olivier Blanchard. To be clear, my preprint focuses on oligopolistic pricing and the regulation of signaling by competition authorities during economic downturns.

Blanchard has been an influential figure for some time, not because he always represents the voice of reason, but because his views often challenge the prevailing consensus in contemporary thought. One particular article of his resonated with me deeply, such that I have seemingly inadvertently reiterated the key points from his 1987 paper with Kiyotaki. Specifically, his use of Ackerlof and Yellen's (1985) efficiency wages and the menu cost model. The similarities are striking.

There is sometimes unwarranted hype around menu costs, which refer to the costs associated with altering the price of a firm’s products. These costs are closely related to the concept of 'sticky' wages. Blanchard supports this view, acknowledging that there are macroeconomic effects associated with switching costs. I argue a similar point from the perspective of the switching costs related to wage changes. I introduce the switching cost as a variable, R(t), which forms a residual term and encapsulates this aspect of Blanchard and Kiyotaki’s framework within the bargaining process.

References

Blanchard, O.J., & Kiyotaki, N. (1987) 'Monopolistic Competition and the Effects of Aggregate Demand', American Economic Review, 77(4), pp. 647-666.

My question here is: How else do election manifestos affect immigration policy and wage growth?

One well-researched answer has been given by my academic advisor while at Essex Economics Department, who is now teaching at Warwick Economics Department, Prof. Francesco Squintani. He agrees with the above, and in an outstanding theoretical economics paper that focuses on the micro-theory of pandering in elections, he and 2 co-authors argue that politicians overindulge in scrupulous policy announcements, to the detrimental welfare of the electorate.

This got me thinking about the task of comparing election promises (manifestos and wider announcements) in US. UK and to a lesser extent, French incumbent electoral campaigns. In his paper, Prof. Squintani argues that a politician’s motive is essentially viewed as a singular outcome - to be elected by voters - which poses an obstacle to precise information gathering and complete information because both politicians along a Hotelling location model (which is explained in the paper) pander around policies and create informational asymmetries.

I wrote in the original draft of “Pareto-Nash Reversion Strategies” (Mayaki, 2024) that burden of ascertaining skilled immigration demand must rest in the hands of institutional employers and not left at the doorstep of central government. The figures from this report by Pierce and Selee (2017) highlight the enormity of the task at hand when identifying precise immigration policy. 

Pierce and Selee (2017) argue that in the U.S. seven executive orders signed by newly elected President Donald Trump which restricted immigration policy and led to a 3.9% reduction in tourism to the United States in the first six months of 2017, a 9% reduction in newly arriving international students, and a decline in H-1B visa applications by employers’ with only 199,000 applications received that year – the lowest number since the Great Recession. These are not mercantilist numbers. If anything, they represent exactly that which won Trump’s election against H. Clinton. Promises of this sort are usually not credible, but what makes them credible is swift action immediately after an election victory as opposed to delayed implementation.

In that sense, I am completely fascinated by the discussion surrounding this WSJ / US Bureau of Labor Statistics chart, posted by Jason Furman on X (formerly known as Twitter). As Furman's argument goes, based on this chart, foreign-born workers have been and are more likely to be in post-pandemic employment (albeit at a much lower wage) than US-born workers. Furman (Former Chief Economic Adviser to President Barack Obama) says it reflects the work ethic of immigrant labor and by extension, the positive sentiment toward favorable migration policy in the United States.

My only response as a left-leaning economist is to highlight that because of an absence of a notable contribution in the form of structural unemployment shortages in the US-born population, the consensus around the debate fails to acknowledge that the wage at which workers enter the US labor market is not only directly affected by the level of legal migration, as my latest arXiv paper outlines but this threshold is often set arbitrarily and may be interrelated to the overall price level, particularly in the most populous states in America.

References

Charles, K. K., and Stephens Jr, M. (2013). Employment, wages, and voter turnout. American Economic Journal: Applied Economics, 5(4), 111-143, Available at https://www.nber.org/system/files/working_papers/w17270/w17270.pdf (Accessed on 03 July 2024)

Kartik, N., Squintani, F. and Tann, K. (2024) “Pandering and Elections: Information Revelation and Pandering in Elections”, University of Warwick Working Paper Series, Available at https://warwick.ac.uk/fac/soc/economics/staff/fsquintani/research/pandering.pdf (Accessed on 03 July 2024)

Mayaki, A (2024) “Pareto-Nash Reversion Strategies: Three Period Dynamic Co-operative Signaling with Sticky Efficiency Wages”, SSRN: Optimisation & Control e-Journal, pp. 1-12, Available at https://dx.doi.org/10.2139/ssrn.4858795 (Accessed on 27 June 2024)

Pierce, S. and Selee, A. (2017) “Immigration under Trump: A Review of Policy Shifts in the Year Since the Election”, Migration Policy Brief, December 2017, Available at https://www.migrationpolicy.org/sites/default/files/publications/TrumpatOne_FINAL.pdf (Accessed on 03 July 2024)

Event Description

This symposium provides a forum for high-quality work in labour economics and brings together economists in the field from across Europe as well as key researchers from outside the region.

The event also provides an opportunity for researchers from different universities and countries to discuss their work in a relaxed atmosphere and to develop long-term collaborative relationships; and for young researchers to meet and discuss their work with senior economists.

Download programme.

Registration

• This event is free and open to all, to register please email Jemila Benchikh.

Organisers

• Alan Manning, CEPR and CEP, LSE
• Guy Michaels, CEPR and CEP, LSE
• Barbara Petrongolo, University of Oxford, CEPR and CEP, LSE

Venue

• SAL 1.04, Sir Arthur Lewis Building, 32 Lincoln's Inn Fields, London WC2A 3PH
  

Bayes’ application to HRM is limited to event probability but is a topic that is mentioned in passing in a paper on shirking and presenteeism by S. Brown (2004) recommended by Dr. Andrew Bryce (Sheffield), which was written over 20 years ago this year.

Brown (2004) reads as follows:

“Such ‘shirking’ is potentially costly to firms and may incite them to undertake monitoring. BST** envisage a monitoring technology in which there is some probability, α < 1, of each absentee’s true state of health being revealed to the firm.”

After deciding to initiate a brief scoping review for the B812 literature topic of choice (‘Wellbeing’). I thought I would check in with the blog and provide some justification and background for this choice of theme.

This spurious love affair with Bayes’ theorem has loomed over my educational learnings but only in its form as sub-game perfect in non-cooperative game theory. Big thanks to Melvyn Coles, Pierre Regibeau, and Franco Squintani for their lectures and classes from our days in Colchester on Economics. 

I started the HRM course in Nov 2023 and while I am still somewhat aware of some concepts surrounding Bayes, things have changed. Nowadays, Bayes’ theorem (10+ years on) is being used in combination with what we call supervised learning and algorithmic techniques such as neural networks.

So, how do we proceed? Perhaps, it is wise to proceed with caution. A brief scoping review will get me up to speed and updated with new research as much as is feasibly possible.

References

Brown, S. and Sessions, John (2004) “Absenteeism, Presenteeism and Shirking”, Economic Issues, 9(1), pp. 15-22 – Available at: https://econpapers.repec.org/article/eisarticl/104brown.htm (Accessed on 13 March 2024)

**Barmby, T. A., Sessions, J. G. and Treble, J. G. (1994) “Absenteeism, Efficiency Wages and Shirking”, Scandinavian Journal of Economics, 94(4), pp. 561-566 – Available at: https://www.jstor.org/stable/3440797 (Accessed on 13 March 2024)

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This post was written by Alfred Anate Mayaki, a student on the MSc in HRM, and was inspired by the author's previous learnings and experiences.

Dr. Andrew Bryce, the author and research academic from the University of Sheffield - who I mentioned in a previous OU Blog post, sent me this email yesterday:

"Indeed shirking and presenteeism are two sides of the same coin. As my colleague Sarah Brown shows in her paper, the problem is that true health is not observed by the employer so it is difficult to know whether a worker is shirking or genuinely too ill to work. Likewise, they don't know whether the staff who do attend work are well enough to do the work effectively. This may be even more difficult when staff habitually work remotely. So the challenge to HR practitioners is to have incentives in place to encourage sick workers to stay at home and workers in good health to come in.

I can't say much in answer to your specific question as the policies and practices adopted by firms have not been the focus of my research. You may wish to look at another paper I have recently published with the same co-authors, looking at sickness absence. In the literature review, we highlight a number of studies that look at the effectiveness of different approaches and working conditions for reducing sickness absence. I hope this will help to guide your further reading on this subject."

Lots of ways to look at this...

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This post was written by Alfred Anate Mayaki, a student on the MSc in HRM, and was inspired by the work of Andrew M. Bryce, Jennifer Roberts, and Mark L. Bryan (2021) in a European Journal of Health Economics article entitled, “The effects of long-term health conditions on sickness absence in the UK"