The Unassuming Economist

From a paper by Domenico D’Ausilio & Massimiliano Cerciello:

“This paper investigates the relationship between monetary policy and income inequality across 32 OECD countries over the period 1995 to 2023. We estimate a dynamic panel model using the system Generalised Method of Moments estimator, accounting for persistence in inequality, unobserved country heterogeneity, and potential endogeneity between monetary policy and distributional outcomes. To capture non-linear transmission, the short-term nominal interest rate enters the model in both linear and quadratic form. Income inequality is measured through the Gini index and the income shares held by the top 10%, middle 40%, and bottom 50%, distinguishing between gross and net income to account for fiscal redistribution. The results show that monetary policy tightening and sufficiently large monetary easing are both associated with lower inequality, mainly through a decline in the top income share and an increase in the bottom 50% share. Smaller rate cuts, by contrast, are associated with higher inequality. These patterns suggest that the distributional effects of monetary policy vary with the intensity of the policy stance and the segment of the income distribution considered, while fiscal redistribution attenuates but does not eliminate the underlying non-linear relationship.”

From a paper byNitiphong Songsrirote & Nalinee Himpong:

“Why do economies that undertake broadly similar industrial and institutional reforms follow markedly different growth trajectories? This study reassesses the manufacturing-led growth hypothesis by examining whether the gains from structural transformation vary across growth regimes and are shaped by institutional quality and resource dependence. Using an annual panel of 217 economies for 1981–2024, the analysis combines MICE imputation, two-state Markov-switching AR(1) regime classification, regime-specific two-step System GMM, observed-WGI validity checks, conservative instrument tests, lambda-sensitivity analysis, and an MI20 consistency check. Given the observational macro-panel design and the use of internal instruments, the estimates are interpreted as dynamic conditional associations, rather than causal effects. Manufacturing intensity does not emerge as a robust predictor of growth performance: in the adjusted baseline model, it is negatively associated with growth in the high-growth regime and weakly negative in the low-growth regime, while losing statistical significance in the observed-WGI, conservative-instrument, post-2000, and MI20 checks. Institutional quality is a more consistent growth-supporting correlate, operating primarily through a direct institutional growth channel rather than through manufacturing-based moderation.”

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 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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