Serdar Ozkan
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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!
Scalable vs. Productive Technologies NEW PAPER!!!
(with Mons Chan, Guangbin Hong, Joachim Hubmer, and S. Salgado )Abstract. Do larger firms have more productive technologies or are their technologies more scalable, or both? We use administrative data on Canadian and US firms to estimate flexible nonparameteric production functions. Our estimation results in a joint distribution of output elasticities of capital, labor, and intermediate inputs—therefore, returns to scale (RTS)—along with total factor productivity (TFP). We find significant heterogeneity in both RTS and TFP across firms. Larger firms operate technologies with higher RTS, both across and within industries. Higher RTS for large firms are entirely driven by higher intermediate input elasticities. Descriptively, these align with higher intermediate input revenue shares. We then incorporate RTS heterogeneity into an otherwise standard incomplete markets model with endogenous entrepreneurship that matches the observed heterogeneity in TFP and RTS. In this model, we find that the efficiency losses of financial frictions are more than twice as large relative to the conventional calibration that loads all heterogeneity on TFP and imposes a common RTS parameter.
Media Coverage and Blogs: Bloomberg Knowledge at Wharton Regional Economist Timely Topics Podcast OTE Blog
An earlier version of this paper focused on the empirical part and was circulated under the title "Why Are the Wealthiest So Wealthy? A Longitudinal Empirical Investigation."
Abstract. We use 1993—2015 Norwegian administrative panel data on wealth and income to study lifecycle wealth dynamics. By employing a novel budget constraint approach, we show that at age 50 the excess wealth of the top 0.1%, relative to mid-wealth households, is accounted for by higher saving rates (38%), inheritances (34%), returns (23%), and labor income (5%) (Figure A). One-fourth of the wealthiest—the "New Money"—start with negative wealth but experience rapid wealth growth early in life. Relative to the "Old Money," the New Money are characterized by even higher saving rates, returns, and labor income (Figure B). We use these dynamic facts to test six commonly used models of wealth inequality. Although these models can generate the high concentration of wealth seen in the cross-section, they tend to put too much weight on (accidental) bequests and fail to capture the contribution of the New Money. A model with heterogeneous returns that decrease in wealth, and non-homothetic preferences is consistent with the new facts on the dynamics of wealth accumulation.
An earlier version of this paper focused on the empirical part and was circulated under the title "Why Are the Wealthiest So Wealthy? A Longitudinal Empirical Investigation."
Abstract. We use 1993—2015 Norwegian administrative panel data on wealth and income to study lifecycle wealth dynamics. By employing a novel budget constraint approach, we show that at age 50 the excess wealth of the top 0.1%, relative to mid-wealth households, is accounted for by higher saving rates (38%), inheritances (34%), returns (23%), and labor income (5%) (Figure A). One-fourth of the wealthiest—the "New Money"—start with negative wealth but experience rapid wealth growth early in life. Relative to the "Old Money," the New Money are characterized by even higher saving rates, returns, and labor income (Figure B). We use these dynamic facts to test six commonly used models of wealth inequality. Although these models can generate the high concentration of wealth seen in the cross-section, they tend to put too much weight on (accidental) bequests and fail to capture the contribution of the New Money. A model with heterogeneous returns that decrease in wealth, and non-homothetic preferences is consistent with the new facts on the dynamics of wealth accumulation.
Determinants of the Top 0.1% Wealth Accumulation
We follow the 50-year-old top 0.1% wealth owners over the past 22 years and decompose their excess wealth relative to the median households into several factors on Figure (A). We also merged them with their younger "twins", so the part before age 28 (separated by the vertical dashed line) on Figure (A) corresponds them. 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 2024 for details.
(A) Top 0.1% Owners
(B) "New Money" Households