The Unassuming Economist

From The Economist:

“DONALD TRUMP’S popularity may be plummeting in America and around the world, yet India’s appetite for buildings bearing his name remains undiminished. This month a local developer licensing the Trump name announced the details of the latest such project. Promising “every comfort, convenience and indulgence” and spread across two towers of 65 storeys each, it will be the country’s largest Trump-branded property. But those who are expecting to find this monument to the tastes of India’s new rich in high-rise Mumbai, brand-obsessed Delhi or tech-fuelled Bangalore will be disappointed. The home of the newest Trump towers is, surprisingly, Hyderabad.

The capital of the wealthy southern state of Telangana, Hyderabad is little known outside India. Domestically its reputation rests on its distinct culture (it serves as a meeting point for north and south India), wonderful cuisine (it produces the best biryani known to humanity, no matter what those misguided souls in Lucknow tell you) and thriving tech industry (Google, Microsoft and Amazon all have huge offices there). It is not famous for skyscrapers. Yet it has more of them than any Indian city bar Mumbai, which was forced into vertical growth by its island geography.”

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From a paper by Nauro F. Campos, Corrado Macchiarelli, and Fotios Mitropoulos:

“This paper provides new, structural estimates of Okun’s unemployment-output relationship for euro area countries between 1979 and 2019. We show that these structural estimates are stable over time and yet substantially smaller than the reduced-form estimates that tend to characterise the literature. We also find that country specific factors largely shape how output responds to unemployment in both core and periphery economies. Specifically, for the euro periphery we find that product market regulation plays a major role in explaining the significance of Okun’s estimates. Our results are robust, inter alia, to conditioning on diverse institutional set-ups.”

From an article by Daron Acemoglu:

“Artificial intelligence is reshaping the world in front of our eyes. The concerns around AI, however, extend beyond the uncertainty and disruption that accompany any other radical technological change. They stem in part from the direction AI development has taken: many leading companies are engaged in a single-minded pursuit of artificial general intelligence (AGI).

AGI is generally understood as an AI system that can match or exceed human capabilities on most economically valuable tasks. In such a world, the automation of work would not be limited to specific industries or routine jobs. Instead, machines would take over many forms of work, including highly skilled professions. The consequences of this displacement of human labour would be unparalleled.

To draw attention to this urgent public-policy challenge, I joined thousands of economists and AI researchers in signing We Must Act Now, a statement urging policymakers and technology leaders to steer AI towards complementing, rather than replacing, human labour.”

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From a paper by Gazmend Dehari, and Sindise Salihi:

“There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specific goals of a monetary policy. There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specifics goals of a monetary policy. This paper will start by comparing the actual and forecasted inflation, based on quarterly data from OECD for the period 2010 – 2026. Price data is derived from Harmonized core inflation for the 17 member of the Euro area (EA17). For the most part of the period, inflation stud firmly below the 2% level i.e. in accordance with the objectives of the ECB, before spiking to historic levels during the COVID 19 pandemic, peaking around 5.5 percent, and stabilizing afterword’s at the 2 percent level, until the end of 2026. Evaluating the forecast error, three metrics will be employed, based on the work of Czekaj et al. (2024). These methods are: mean absolute forecast error (MAE), root mean squared forecast errors (RMSE) and mean absolute percentage error (MAPE). In the case of MAE for yearly data inflation forecast was off by only 0.83 percent, meaning that inflation targeting by the ECB was in line with the expectations, because predictive inflation error was inferior of 2 percent, lower than the target set by the central bank. The same thing cannot be said for the quarterly data, where mean absolute error is 2.89 percent, considerably greater than the ECB objective. MAPE, method is used to calculate the average forecast error, with yearly and quarterly data, the results show that on average the prediction is off by 0.54 percent and 0.58 percent respectively. Considering the RMSE method, it produces on average a prediction for yearly and quarterly data, off by 1.27 and 1.34 respectively. In the case when forecast accuracy measures are considered separately, q3 and q4 quarters are the quarters where prediction accuracy is the lowest for the three methods. Examining the accuracy of forecasted measure for inflation targeting economies, the results are in line with Czekaj et al. (2024) and Schnabel (2024). Inflation targeting has contributed in stabilizing price increases with one exception, during a sudden economic shock, i.e. COVID 19 crises. Moreover, through forecasted accuracy methods this study showed that in q3 and q4 quarters the errors where the highest and volatility of errors in predicting inflation was the greatest during the shock of COVID 19 crises.”

From a paper by Christian Glocker, and Thomas Url:

“The gradual rise in temperatures motivates conceptualizing climate change as a phenomenon shaping the propagation of macroeconomic shocks, rather than as an independent shock. We formalize this in a theoretical model, showing that climate change induces a structural shift by steepening the aggregate supply curve, exacerbating the price effects of demand shocks while dampening the output response. Our empirical evidence is consistent with this prediction: higher temperatures raise the share of inflation variation attributable to demand shocks by up to 10 percentage points, underscoring the role of climate change in intensifying stagflationary dynamics rather than independently driving business cycles.”