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Research DivisionFederal Reserve Bank of St. LouisP.O. Box 442St. Louis, MO 63166-0442Fax: (314) 444-8731Email: Serdar.Ozkan(at)gmail.com
Herhangi bir finansal danışmanlık veya yatırım tavsiyesi vermiyorum. Benim ismimi kullanarak sizinle iletişime geçenler sizi dolandırıyorlar!
Why Are the Wealthiest So Wealthy? New Longitudinal Empirical Evidence and Implications for Theories of Wealth Inequality UPDATED DRAFT! (revision submitted to Econometrica)
(with Joachim Hubmer, E. Halvorsen, and S. Salgado )Hubmer et al 2024 Draft. Early version with extensive empirical results.
Media Coverage and Blogs: Bloomberg Knowledge at Wharton Regional Economist Timely Topics Podcast OTE Blog
Abstract. We study the lifecycle dynamics of wealth inequality using 1993-2019 Norwegian administrative panel data on wealth and income. Employing a novel budget-constraint approach, we decompose the excess wealth of the top 0.1% households relative to the median between ages 45 and 64 into higher saving rates (36%), inheritances (31%), returns (28%), and labor income (5%). One-quarter of the wealthiest—the “New Money”—start with negative wealth on average but accumulate rapidly through high labor income and exceptionally high saving rates and returns. The “Old Money” inherit substantial wealth and grow it through above-average though more modest saving and returns. We use these dynamic facts to evaluate five standard wealth inequality models. Although these models match cross-sectional wealth concentration, they fail to reproduce the distinct dynamics of New and Old Money. A heterogeneous entrepreneurship model with decreasing returns to scale technology and nonhomothetic preferences is consistent with the observed dynamics.
Media Coverage and Blogs: Bloomberg Knowledge at Wharton Regional Economist Timely Topics Podcast OTE Blog
Abstract. We study the lifecycle dynamics of wealth inequality using 1993-2019 Norwegian administrative panel data on wealth and income. Employing a novel budget-constraint approach, we decompose the excess wealth of the top 0.1% households relative to the median between ages 45 and 64 into higher saving rates (36%), inheritances (31%), returns (28%), and labor income (5%). One-quarter of the wealthiest—the “New Money”—start with negative wealth on average but accumulate rapidly through high labor income and exceptionally high saving rates and returns. The “Old Money” inherit substantial wealth and grow it through above-average though more modest saving and returns. We use these dynamic facts to evaluate five standard wealth inequality models. Although these models match cross-sectional wealth concentration, they fail to reproduce the distinct dynamics of New and Old Money. A heterogeneous entrepreneurship model with decreasing returns to scale technology and nonhomothetic preferences is consistent with the observed dynamics.
Determinants of the Top 0.1% Wealth Accumulation
We follow the 50-year-old top 0.1% wealth owners over the past 30 years and decompose their excess wealth relative to the median households into several factors on Figure (A). Figure (B) shows the same decomposition for the New Money households, who start their working lives below median wealth and manage to join the top 0.1% group by age 50. The values on y-axes are in multiples of the economy-wide average wealth (AW). See Hubmer et al 2025 for details.
We develop a novel measure of firm-level marginal labor cost and investigate its pass-through to inflation. To construct this measure, we apply textual analysis to earnings calls to identify discussions of labor-related topics such as higher costs, shortages, and hiring. Leveraging the theoretical principle that cost-minimizing firms equate marginal costs across variable inputs, we project changes in firms’ intermediate input revenue shares onto the intensity of labor-related discussions to quantify their contributions to marginal labor costs. This approach provides an economically-motivated way to reduce the multidimensional qualitative textual information into a single quantitative measure. A sales-weighted average of this measure tracks closely with conventional aggregate slack variables and outperforms them in forecasting inflation. When aggregated at the industry level, we find a significant but heterogeneous pass-through of marginal labor costs to PPI inflation, with the pass-through highest for service sector and near-zero for manufacturing. Consistent with the latter fact, firm-level data reveal that investment in automation mitigates the effects of higher labor cost pressures in manufacturing.
Mortgage Market Structure and the Transmission of Monetary Policy During the Great Inflation (revision requested by RESTUD)
(with A. Hedlund, K. Mitman and K. Larkin)We examine the impact of mortgage market structures on shaping economic responses to the unprecedented interest rate and inflation dynamics of 2021-2024. We first empirically document that economies with a larger share of variable-rate mortgages exhibit stronger responses in house prices to monetary policy shocks. We then develop and calibrate a structural model of the housing market to demonstrate that these mortgage structures can account for a substantial portion of the divergent house price paths observed across the U.S., Canada, Sweden, and the U.K. during the Great Inflation. Our analysis reveals that early pandemic mortgage rate cuts drove 45% of the U.S. house price boom. Economies dominated by adjustable-rate mortgages (ARMs) show greater price sensitivity to monetary tightening, while fixed-rate mortgage (FRM) regimes exhibit more pronounced path dependence due to a lock-in effect. These dynamics have significant distributional consequences, with low-income homeowners benefiting most from the initial low-rate environment, especially in FRM regimes. Finally, we show that the preferred monetary tightening path is regime-dependent, as a policy counterfactual reveals that FRM-dominant economies benefit more from a shorter and sharper tightening schedule.
Scalable versus Productive Technologies UPDATED DRAFT!
(with Mons Chan, Guangbin Hong, Joachim Hubmer, and S. Salgado)Are larger firms more productive, more scalable, or both? We use firm-level panel data from thirteen countries and employ a broad set of methods to estimate factor elasticities—capturing returns to scale (RTS)—and total factor productivity (TFP). We find substantial RTS heterogeneity within industries, with larger firms exhibiting higher RTS driven by greater intermediate input elasticities. TFP, by contrast, rises with firm size only up to the top decile before declining. Incorporating RTS heterogeneity into a standard model of entrepreneurship more than doubles the efficiency losses from financial frictions compared with a conventional calibration with only TFP differences.
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