<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Susanto Basu | Macro Paper Warehouse</title><link>https://macropaperwarehouse.com/authors/susanto-basu/</link><description>Susanto Basu</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><atom:link href="https://macropaperwarehouse.com/authors/susanto-basu/index.xml" rel="self" type="application/rss+xml"/><item><title>Are Technology Improvements Contractionary?</title><link>https://macropaperwarehouse.com/papers/are-technology-improvements-contractionary/</link><guid>https://macropaperwarehouse.com/papers/are-technology-improvements-contractionary/</guid><description>&lt;p&gt;This 2006 &lt;em&gt;American Economic Review&lt;/em&gt; paper (circulated as NBER Working Paper 10592, June 2004) asks a question its own abstract answers in one word &amp;ndash; &amp;ldquo;Yes&amp;rdquo; &amp;ndash; are technology improvements contractionary? The authors construct a &amp;ldquo;purified&amp;rdquo; measure of aggregate technology change that corrects the standard Solow residual for varying utilization of capital and labor, non-constant returns to scale, imperfect competition, and aggregation effects, building on the production-function methodology of Basu and Fernald (1997) and Basu and Kimball (1997) &amp;ndash; an identification strategy that relies on entirely different assumptions from, and is explicitly offered as a complement to, Galí&amp;rsquo;s (1999) long-run-restriction structural VAR approach to the same question. Using this measure on postwar U.S. data, the paper&amp;rsquo;s central finding is that when technology improves, input use (hours, utilization, and especially non-residential investment) falls sharply on impact while output barely moves, and only with a lag of one to several years do inputs, investment, and output recover and then rise strongly &amp;ndash; a short-run contraction followed by a long-run expansion. The authors show this pattern is &amp;ldquo;clearly inconsistent with standard parameterizations of real-business-cycle models,&amp;rdquo; which predict inputs and output rise immediately after a technology improvement, but is qualitatively consistent with sticky-price models, in which a positive technology shock that is not immediately offset by expansionary monetary policy reduces firms&amp;rsquo; demand for labor and capital in the short run even as it raises full-employment output. The paper argues this amounts to a &amp;ldquo;robust stylized fact&amp;rdquo; because four methodologically distinct approaches &amp;ndash; their own production-function measure, Galí&amp;rsquo;s SVAR, estimated structural DSGE models, and Shea&amp;rsquo;s (1998) patent/R&amp;amp;D-based technology proxy &amp;ndash; all independently reach the same short-run-contraction conclusion.&lt;/p&gt;</description></item><item><title>Returns to Scale in U.S. Production: Estimates and Implications</title><link>https://macropaperwarehouse.com/papers/returns-to-scale-in-u.s.-production-estimates-and-implications/</link><guid>https://macropaperwarehouse.com/papers/returns-to-scale-in-u.s.-production-estimates-and-implications/</guid><description>&lt;p&gt;This 1997 &lt;em&gt;Journal of Political Economy&lt;/em&gt; paper (circulated as Federal Reserve International Finance Discussion Paper 546, March 1995) asks how large deviations from constant returns to scale and perfect competition really are in the U.S. economy &amp;ndash; a question central to models in which increasing returns and markups amplify business-cycle shocks or generate multiple equilibria. Using Dale Jorgenson&amp;rsquo;s 34-industry dataset of the U.S. private business economy for 1959-89, the authors estimate that &amp;ldquo;a typical (roughly) two-digit industry in the United States appears to have constant or slightly decreasing returns to scale,&amp;rdquo; which at face value implies small markups &amp;ndash; but this finding comes bundled with three puzzles: returns-to-scale estimates rise sharply when industries are aggregated into broader sectors; several individual industries show statistically significant &lt;em&gt;decreasing&lt;/em&gt; returns, which (absent large observed profits) implies the economically nonsensical conclusion that firms price below marginal cost; and estimates built from value-added data diverge substantially from estimates built from gross-output data. The paper&amp;rsquo;s central theoretical contribution is to show that all three puzzles can be artifacts of aggregating over heterogeneous, imperfectly-competitive producing units: because firms with different returns to scale have different market power and hence different marginal products for the same input, reallocating inputs toward high-return-to-scale, procyclically-growing firms during a boom raises &lt;em&gt;measured&lt;/em&gt; aggregate productivity and &lt;em&gt;apparent&lt;/em&gt; aggregate returns to scale even when every individual firm&amp;rsquo;s own technology is unchanged. After deriving and subtracting an estimated &amp;ldquo;reallocation&amp;rdquo; correction term from the aggregate data, the authors find the private economy as a whole is consistent with essentially constant returns to scale (around 1.01-1.03 across gross-output and value-added measures), with durable-goods manufacturing still showing statistically significant mild increasing returns and non-durables mild (statistically insignificant) decreasing returns. A simple stylized two-firm model in the paper&amp;rsquo;s final section shows this reallocation is not merely an econometric nuisance but can itself be a real macroeconomic mechanism &amp;ndash; though whether it is safe to ignore heterogeneity and calibrate a representative-firm model with aggregate data depends on the economy&amp;rsquo;s non-production institutions (e.g., how labor and capital markets allocate factors across heterogeneous firms), so the paper explicitly declines to offer a general answer to that calibration question.&lt;/p&gt;</description></item></channel></rss>