The Unassuming Economist

From a paper by Sergio De Nardis:

“The dichotomy between Purchasing Power Parities (PPPs) and national deflators in international comparisons of real variables (GDP per capita and productivity) has returned to center stage in the recent debate on the gap between the EU and the US. This dichotomy does not only concern comparisons between structurally different countries. It is also present, to a lesser but still significant extent, in Europe and with reference to the Italian economy. This paper shows how, over the period 2005-2023, Italy’s productivity performance compared to Germany and France is better if GDP is measured in PPPs (output side) rather than deflated with implicit prices from national accounts. The two types of measurement lead to dynamics that are difficult to reconcile. Examining the PPP-deflator gap, it is clear that this difference does not depend on the composition and structure of the weights used in constructing PPPs. Analysis of the expenditure categories that make up GDP and analysis of the specific household consumption items reveal a pervasive phenomenon, affecting both tradable and non-tradable products. It is argued that the structural similarity of economies lends robustness to PPPs. It is concluded that comparisons between countries under consideration based on data deflated with implicit prices from national accounts are less reliable, even in time series, than those based on PPPs due to the heterogeneous approaches of statistical offices in accounting for the quality of goods and services, the different granularity of underlying information, and the fixed structure of relative prices across economies.”

From a paper by Camilo Granados and Zhe (Jasmine) Jiang:

“Prudential regulation is designed for financial stability, yet the credit cycles it leans against fall unevenly across households—leaving its distributional footprint an open and contested question. Tracing that footprint across advanced, emerging, and developing economies over 1999–2024, we find that tighter macroprudential policy lowers income inequality, gradually and most clearly through the instruments that bear on banks rather than borrowers, and more so for inequality measured after taxes and transfers. To understand why, we build a closed-economy model with financial frictions and capital-skill complementarity in which prudential policy taxes the returns to intermediation. A tightening reduces inequality through three channels: it rebates revenue to households, cools the credit boom feeding financial rents, and compresses the skill premium accruing to high-income agents. Inequality moderation thus arises as a by-product of stability-oriented regulation—regulators tempering the distributional consequences of the credit cycle even when they only pursue financial stability.”

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.”