The Unassuming Economist

From a paper by by Adela Karapici and Arjeta Vokshi:

“This paper estimates the short-run relationship between real output growth and unemployment in Albania over 1992–2023 and asks how sensitive that relationship is to the way crisis episodes are dated. Albania offers a demanding test: a transition economy with high informality, extensive self-employment and sustained emigration, whose sample contains three major shocks in three decades. We estimate first-difference specifications in which the change in the unemployment rate is regressed on output growth, with a lagged dependent variable capturing persistence and separate indicators for the three crisis episodes. Because conventional standard errors are unreliable at this sample size, we report alternative estimators alongside bootstrap inference and a full set of residual and stability diagnostics. Our main practical result concerns crisis dating. Crisis dummies are conventionally dated to the year a crisis began internationally, but for a small open economy whose transmission is indirect, the year in which a shock reaches domestic activity need not be that year. Albania did not contract in 2008 or 2009. We date the financial-crisis indicator from an external chronology of transmission—credit growth, non-performing loans, exports, investment, remittances and the fiscal stance—assembled from contemporaneous institutional sources and fixed before estimation, which places the arrival of the crisis in the Albanian economy in 2009 and 2010. The estimated crisis coefficient is highly sensitive to that choice: a 2008-dated indicator produces the best-fitting specification in this paper on every information criterion together with a large negative coefficient; the conventional 2008–2009 window straddles the turning point and returns an estimate indistinguishable from zero; the 2009–2010 window returns a positive one. These coefficients are individually imprecise, and their intervals overlap, so we do not claim that the data identify any one dating as correct, and we do not treat the sign of an estimate as evidence for or against a window. Our claim is narrower: the substantive conclusion a reader would draw about the crisis is determined by a specification choice that the data cannot settle, so the window has to be fixed ex ante on transmission evidence and its consequences reported. We demonstrate this for Albania and do not assert that published work on other countries is affected; we specify the test that would settle it. The dating choice leaves the estimated Okun coefficient itself almost unchanged. That coefficient is negative in every specification, estimator, subsample and phase definition we report, with a normal-times impact estimate of approximately −0.21, smaller in absolute value than the range reported for advanced economies. Its statistical significance is sensitive to the mode of inference: under the null-imposed bootstrap we regard as most appropriate at this sample size, the coefficient is not significant at conventional levels in the specification that carries that estimate. The evidence for the sign of the relationship is considerably stronger than the evidence for its magnitude, and comparisons of level with advanced-economy estimates should be read against that. The three crisis coefficients are large but imprecisely estimated, and restrictions of equality between them are not rejected, so we report them as descriptive magnitudes and do not rank the episodes. We validate the modeled unemployment series against the national Labour Force Survey over the years in which both exist and re-estimate the model on the national series; the crisis coefficients and the crisis-dating pattern are essentially unchanged. We do not detect a structural break at the 1997 pyramid-scheme collapse once dynamics and crisis indicators are jointly modeled, and we do not detect cyclical asymmetry, though the confidence intervals are wide enough that both are failures to detect rather than evidence of stability or symmetry.”

From Torsten Slok:

“We have updated our 127-page US housing outlook, available here.

There are 10 conclusions:

1. High mortgage rates at 6.7% combined with a median home price of $400,000 are holding down traffic of prospective homebuyers, see the first chart.
2. 75% of US households can only afford a home priced below $300,000, well under the current median sales price, see the second chart.
3. The share of first-time homebuyers is at the lowest level in decades, and the median first-time buyer is now 40 years old, up from 30 in 2008, see the third chart.
4. Nobody is moving. The structural decline in the share of the US population changing address continues, and the share of households planning to move over the next 12 months has fallen to a record low of approximately 7%, see the fourth chart.
5. Household formation has slowed sharply as immigration has declined, removing a key source of underlying housing demand, see the fifth chart.
6. The typical American home is now 42 years old, and we are not building fast enough to replace the aging stock, see the sixth chart.
7. US homes are getting smaller, with the median size of new single-family homes declining over the past decade as builders chase the price points buyers can still reach, see the seventh chart.
8. House price inflation has stalled near 1% overall, but it is turning higher again for the most expensive homes, where buyers are least dependent on mortgage financing, see the eighth chart.
9. Households’ equity in real estate totals $35 trillion, an average of roughly $400,000 per owner-occupied home, see the ninth chart.
10. Delinquency rates on multifamily housing have climbed above their post-GFC peak to the highest level since at least 2004, see the tenth chart.”

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.”

Continue reading here.

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.”

Continue reading here.