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- Among high-tech plants, declining responsiveness produces a counterfactual that is broadly similar—both qualitatively and quantitatively—with the TFPR-based results from Figure 6, with a productivity “drag†that is only slightly smaller under RPR than under TFPR. Among non-tech plants, the counterfactual produces somewhat different results from those reported in Figure 6, with a gap opening up early in the sample then remaining stable (and negative) after the late 1990s. In general the RPR results confirm the TFP-based findings suggesting a quantitatively significant change in the contribution of reallocation to aggregate productivity growth. We conduct an additional robustness check addressing the concerns of Gandhi et al.
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- Appendix A. Figures and tables to supplement the main text Figure A1: Exit selection on TFP has weakened (manufacturing) Note: Young firms have age less than 5. High-tech is defined as in Hecker (2005). Exit probability of plant with TFPR one std. dev. above industry mean vs. industry mean. Author calculations from the Longitudinal Business Database, the Annual Survey of Manufacturers, and the Census of Manufacturers.
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- Appendix C. Alternative TFP calculation While our TFPR measure as a measure of TFP is common in related literature, as we discus in the main text we also consider an estimate of RPR using the proxy method of Wooldridge (2009). As we show in equation (3), RPR is only a function of exogenous TFPQ and demand shocks (even if plant-level prices are endogenous) because the elasticities recovered by revenue function estimation are revenue elasticities (not factor elasticities) capturing both production and demand parameters (Foster et al. (2017)). In this appendix, we discuss the estimation of RPR and the results using the RPR measure of TFP. Given the possible presence of demand shocks, RPR should be interpreted as reflecting both TFPQ and demand shocks.
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- B. Globalization Globalization may be playing a role in declining responsiveness since increased exposure to foreign trade facilitates adjustment by scaling international operations. That is, it may be that rather than growing domestically, productive firms are more likely to expand and produce in other countries, a dynamic that could eliminate or even reverse the standard positive correlation between growth and productivity (since we do not observe employment outside the U.S.). There is substantial evidence already that the decline in US manufacturing employment is closely linked to rising import penetration of production activity from low wage countries (see, e.g., Bernard, Jensen, and Schott (2006), Schott (2008) and Pierce and Schott (2016)).
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- Bernard, Jensen and Schott (2006) and Schott (2008) develop measures of import penetration ratios from low wage countries. Their measures vary by 4-digit SIC industry from 1972-2005 and by 6-digit NAICS industry from 1989-2005; we extend the time series using the public domain information from Census on imports by country and industry.55F 56 We integrate 56 To construct low-wage import penetration data by year and industry, Bernard, Jensen and Schott first construct domestic absorption for each industry. Next, they construct total imports of goods produced by each industry that are
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- But Figure B5 shows that the OP covariance for labor productivity initially rises as labor productivity begins to be dispersed, continuing to rise over the range of adjustment frictions that produce reallocation rates above 30 percent (compare Figure B5 to Figure B4). But since productivity responsiveness declines monotonically as adjustment costs rise, the OP covariance eventually declines as labor is increasingly “trapped†in unproductive firms while productive firms are starved of resources (i.e., employment weight). Thus, the OP covariance for labor productivity is decreasing in adjustment costs (and increasing in misallocation) across the plausible range of costs. This pattern is related to that found in Bartelsman, Haltiwanger, and Scarpetta (2013), in whose model distortions reduce the OP covariance for labor productivity as long as the benchmark is characterized by sufficient frictions; we explore the OP covariance in more detail below.
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- Figure 1: Job reallocation patterns vary by sector Note: Y axis does not start at zero. HP trends using parameter set to 100. Industries defined on a consistent NAICS basis; high-tech is defined as in Hecker (2005). Data include all firms (new entrants, continuers, and exiters). Author calculations from the Longitudinal Business Database (LBD).
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- Figure 10: Changing contribution of reallocation to aggregate labor productivity (economywide) Note: Figure depicts diff-in-diff counterfactual as described in the text. High-tech is defined as in Hecker (2005). Author calculations from the RE-LBD. Finance, Insurance, and Real Estate (NAICS 52-53) omitted. Figure 11: Within-firm productivity growth in the average industry (economywide) Note: Average within-firm productivity growth, with and without employment weights. Author calculations from the RE-LBD. 0.10 0.15 0.20 0.25 0.30 0.35 0.40 High-tech young High-tech mature -0.07 -0.06 -0.05 -0.04 -0.03 -0.02 -0.01 0 High-tech Non-tech -0.10 -0.05 0.00 0.05 0.10 Unweighted within, high-tech Weighted within, high-tech Unweighted within, non-tech Weighted within, non-tech
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- Figure 2: Young firm share patterns vary by sector Note: Young firms have age less than 5. Industries are defined on a consistent NAICS basis; high-tech is defined as in Hecker (2005). Data include all firms (new entrants, exiters, and continuers). Author calculations from the LBD. 10 15 20 25 30 35 40 45 Information Manufacturing Retail Services High-tech High-tech manufacturing Economywide 0 5 10 15 20 25 30 Information Manufacturing Retail Services High-tech High-tech manufacturing Economywide Figure 3: Most variation in job reallocation is not explained by changing startup rates Note: Sectors are defined on a consistent NAICS basis. Author calculations from the LBD.
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- Figure 4: Within-industry TFP dispersion has risen (manufacturing) Note: Y axis does not start at zero. Young firms have age less than 5. Standard deviation of within-detailed industry log TFPR. High-tech defined as in Hecker (2005). Author calculations from the LBD, the Annual Survey of Manufacturers (ASM), and the Census of Manufacturers (CM). HP Trends.
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- Figure 5: Establishment job growth has become less responsive to TFP (manufacturing) Note: Young firms have age less than 5. High-tech is defined as in Hecker (2005). Growth rate of plant with TFPR one std. dev. above industry mean vs. industry mean. Author calculations from the LBD, the ASM, and the CM. 0.00 0.10 0.20 0.30 0.40 0.50 High-tech young High-tech mature Non-tech young Non-tech mature 0.00 0.05 0.10 0.15 0.20 High-tech young High-tech mature Non-tech young Non-tech mature 1980s 1990s 2000s
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- Figure 6: Changing contribution of reallocation to aggregate TFP (manufacturing) Note: Figure depicts diff-in-diff counterfactual as described in the text from TFPR concept. High-tech is defined as in Hecker (2005). Author calculations from the LBD, the ASM, and the CM.
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- Figure 7: Establishment investment rates have become less responsive to TFP (manufacturing) Note: Young firms have age less than 5. High-tech is defined as in Hecker (2005). Investment rate of plant with TFPR one std. dev. above industry mean vs. mean. Author calculations from the LBD, the ASM, and the CM.
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- Figure 8: Within-industry labor productivity dispersion has risen (economywide) Note: Y axes do not begin at zero. Standard deviation of log labor productivity deviated from industry by year means. Young firms have age less than five. High-tech is defined as in Hecker (2005). Author calculations from the RE-LBD. Finance, Insurance and Real Estate (NAICS 52-53) omitted. -0.025 -0.020 -0.015 -0.010 -0.005 0.000 0.005 0.010 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 High-tech Non-tech 0.00 0.02 0.04 0.06 0.08 High-tech young High-tech mature Non-tech young Non-tech mature 1980s 1990s 2000s 0.70 0.80 0.90 1.00 1.10 1.20 High-tech young High-tech mature Non-tech young Non-tech mature Figure 9: Firm growth has become less responsive to labor productivity (economywide) Note: Y axis does not start at zero. Growth rate of firm with labor productivity one std. dev. above industry mean vs.
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- Figure A2: Exit selection on labor productivity has weakened (economywide) Note: Annual coefficients constructed from Table 3. Young firms have age less than five. High-tech defined as in Hecker (2005). Exit probability of plant with labor productivity one std. dev. above industry mean vs. industry mean. Author calculations from the RE-LBD. Finance, Insurance and Real Estate (NAICS 52-53) omitted.
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- Figure A3: Average industry-level productivity growth, BLS and aggregated microdata Source: BLS and author calculations from RE-LBD. -0.08 -0.06 -0.04 -0.02 0.00 High-tech young High-tech mature Non-tech young Non-tech mature 1980s 1990s 2000s -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 High-tech young High-tech mature Non-tech young Non-tech mature 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 Micro-based high-tech Micro-based non-tech BLS non-tech BLS high-tech
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- Figure A5: Standard deviation of innovations to plant-level TFPR Note: High-tech is defined as in Hecker (2005). For the set of years where we can estimate the AR(1) process (see note for Figure A4), we can also recover the distribution of innovations to plant-level TFP for continuing plants.
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- Figure C1: Within-industry dispersion in RPR (standard deviation), manufacturing Note: The standard deviation is the based on within-detailed industry log revenue productivity residual. High-tech is defined as in Hecker (2005). Manufacturing is defined on a consistent NAICS basis. Author calculations from the Longitudinal Business Database, the Annual Survey of Manufacturers, and the Census of Manufacturers. HP trends.
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- Figure C2: Persistence of plant-level RPR: High-tech vs. non-tech Note: High-tech is defined as in Hecker (2005). Author calculations from the Longitudinal Business Database, the Annual Survey of Manufacturers, and the Census of Manufacturers.
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- Figure C3: Relative employment growth rates, high-productivity vs. average-productivity plant (RPR) Note: Young firms have age less than 5. High-tech is defined as in Hecker (2005). Author calculations from the Longitudinal Business Database, the Annual Survey of Manufacturers, and the Census of Manufacturers. 0 0.2 0.4 0.6 0.8 1 1980s 1990s 2000s High tech Non-tech 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Tech young Tech mature Non-tech young Non-tech mature 1980s 1990s 2000s
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- Figure D4 shows the implied changing responsiveness over time due to composition effects within high-tech manufacturing. There is no implied increase in responsiveness due to composition effects from the 1980s to the 1990s (which would have been expected if generalpurpose producers were more responsive on average), and there is actually a modest increase in responsiveness from the 1990s to the 2000s rather than a decline. Declining responsiveness must therefore be a within-category phenomenon with respect to the general-purpose/special-purpose taxonomy and other industry characteristics. 58 We thank Christopher Foote for this insight. 59 We use employment weights given our interest in the implications of changing responsiveness for job reallocation.
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- standard deviation of TFPR or RPR in U.S. manufacturing during the 1980s; and we set ðœŒðœŒ = 0.65, broadly consistent with the AR(1) coefficient on TFPR and RPR that we find among manufacturing establishments in the 1980s (see Figure A4 and Appendix C). These values of ðœŽðœŽð‘Žð‘Ž and ðœŒðœŒ imply that innovations to TFP have a standard deviation of ðœŽðœŽðœ‚𜂠= 0.26. Strictly speaking, if plant-level prices are endogenous (which this model permits) the appropriate empirical moments are those from RPR.
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- The additional regressors added are the 6-digit NAICS import penetration ratio for each year and the interaction of this ratio with lagged TFP. We permit the coefficients on this interaction effect to differ between plants belonging to young and mature firms. The main effect of the import penetration (not reported) is negative and significant: Consistent with Bernard, Jensen, and Schott (2006), plants in industries with especially large increases in import penetration have lower net employment growth.
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- The last two rows of Table D1 show that the interaction effect for young plants of lagged TFP and the import penetration ratio is estimated to be negative and significant. This implies that young-firm plants in industries with especially large increases in import penetration ratios have larger decreases in responsiveness. In Figure D2, we quantify the effect of changing import penetration ratios using the estimated effects from Table D1. The overall effects show, consistent with Table 1, that the marginal effect of productivity on employment growth among young high-tech firms increased from the 1980s to 1990s then declined in the post-2000 period.
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- these public domain data into our data infrastructure from 1981-2010. Our ability to integrate this is facilitated by our having 4-digit SIC codes in the micro level data from 1981-1996 and 6digit NAICS codes from 1981-2010; hence, we need not rely on aggregate SIC/NAICS concordances.56F 57 Figure D1 shows aggregate import penetration ratios in and out of high-tech manufacturing. Table D1 presents results of a modified version of our main regressions in equation (3).
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Vavra, Joseph. 2014. “Inflation Dynamics and Time-Varying Volatility: New Evidence and SS Interpretation.†Quarterly Journal of Economics 129(1): 215-58.
- We also perform these analyses using the Wooldridge (2009) RPR productivity measure (unreported), finding no significant role for import penetration, so we consider this evidence mixed. More research on globalization and dynamism is needed; promising avenues include sourced in a low-wage country, which are defined as countries whose GDP per capita is less than 5 percent of the U.S. Import penetration is the ratio of low-wage imports to total domestic absorption, by industry and year. We thank Peter Schott for providing the import data and guidance necessary for extending the dataset. 57 We integrate the SIC-based import penetration ratios from 1981-88 and the NAICS-based ratios from 1989-2010 into the micro data. We use the internally consistent NAICS codes in the micro data from 1981-2010 to conduct our analysis. (see Fort and Klimek (2016)). specific policy variation, distinction between intermediate and final goods competition, and differences between TFP concepts.
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- We compute the fraction of these patterns accounted for by the changing import penetration ratios by using the coefficients from Table D1 along with the aggregate pattern of import penetration ratios for high-tech manufacturing. The role of rising penetration is very modest in the 1980s to 1990s. However, the rapid rise in import penetration during the 2000s accounts for a substantial share (about 16 percent) of the overall decline in responsiveness over that period.
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