Abstract:We show that in successful episodes of export market entry, there are statistically and economically significant post-entry dynamics of quantities, but no post-entry dynamics of markups. This suggests that shifts in demand play an important role in successful entry, but that firms do not use dynamic manipulation of markups as an instrument to shift demand. We structurally estimate two competing models of customer base accumulation to match these moments. In the first model, firms use marketing and advertising … Show more
“…The first column of Table 7 reports the results from estimating our baseline export revenue equation, equation (12). We focus on the elasticities for observations with low predicted exit probability for which selection bias is less likely to be an issue.…”
Section: Revenuementioning
confidence: 99%
“…Firms with 3 or more persons engaged are required to file returns. 12 We make use of data for the years 1996 through 2009, and for NACE Revision 1.1 sectors 10-40…”
Section: Census Of Industrial Productionmentioning
confidence: 99%
“…The sample period for this estimation is 1980-2014. Notes: Table reports average across 100 simulated data sets of estimated coefficients on low exit probability observations in revenue equations similar to equation (12).…”
mentioning
confidence: 99%
“…To obtain the quantity and markup elasticities from equations (14) and (15), we estimate equation (12), but with log quantity (i.e. tonnes) and log unit value (i.e.…”
“…The first column of Table 7 reports the results from estimating our baseline export revenue equation, equation (12). We focus on the elasticities for observations with low predicted exit probability for which selection bias is less likely to be an issue.…”
Section: Revenuementioning
confidence: 99%
“…Firms with 3 or more persons engaged are required to file returns. 12 We make use of data for the years 1996 through 2009, and for NACE Revision 1.1 sectors 10-40…”
Section: Census Of Industrial Productionmentioning
confidence: 99%
“…The sample period for this estimation is 1980-2014. Notes: Table reports average across 100 simulated data sets of estimated coefficients on low exit probability observations in revenue equations similar to equation (12).…”
mentioning
confidence: 99%
“…To obtain the quantity and markup elasticities from equations (14) and (15), we estimate equation (12), but with log quantity (i.e. tonnes) and log unit value (i.e.…”
“…At the retail level, Hottman, Redding, and Weinstein (2014) use scanner price and expenditure data on individual consumer products to show that demand differences-reflecting variation in quality and taste among consumers-are the principal reason why some firms succeed in the marketplace while others fail. In addition, as documented by Roberts et al (2012); Eaton et al (2015);and Fitzgerald, Haller, and Yedid-Levi (2016), customer markets considerations also shape the pricing decision of exporting firms in both advanced and emerging market economies.…”
In spite of substantial and persistent economic slack, the United States experienced only a mild disinflation during the Great Recession and its aftermath. Consumer price inflation, measured by the core personal consumption expenditures price index, averaged 2 percent between 2003 and 2007 and only declined to an average annual rate of about 1.5 percent over the following eight years, a period that saw the deepest contraction in economic activity since the Great Depression, followed by an uneven and weak recovery. Among many economists, the absence of more pronounced deflationary pressures during this period has cast doubt on the empirical relevance of the Phillips curve-a central tenet of most standard macroeconomic models-which posits that a high level of resource underutilization should cause inflation to fall over time (Hall 2011;King and Watson 2012). NW, Washington, DC 20551 (e-mail: egon.zakrajsek@frb.gov). We thank Rudi Bachmann, Mark Bils, Marco Del Negro, Etienne Gagnon, Marc Giannoni, Yuriy Gorodnichenko, Jim Kahn, Pete Klenow, Emi Nakamura, Joe Vavra, and three anonymous referees for helpful comments and suggestions; we also benefited from comments from participants at numerous conferences and seminars. We are especially grateful to Kristen Reed, Ryan Ogden, and Rozi Ulics of the Bureau of Labor Statistics for their invaluable help with this project and to Jonathan Weinhagen for sharing his expertise with the PPI data.
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