Discussion of“Endogenous TFP, Business Cycle Persistenceand the Productivity Slowdown”by Michaela Schmöller and Martin Spitzer

Discussion by Marcin Bielecki

Narodowy Bank Polski

Annual Workshop: ESCB Research Cluster 27-9 November 2018, Banque de France

What the authors do

• Consider a model where TFP does not fluctuate aroundan exponential trend but has an endogenous componentwhich might be responsible for protracted declines in TFP

• Endogeneity of TFP due to procyclicality of R&D andadoption of technologies as in Comin and Gertler (2006)

• Estimate the model proposed by Anzoategui et al. (2016)on the Euro Area data for periods 1999Q1-2007Q4

• Provide a shock decomposition for 1999Q1-2017Q2

• Evaluate how the financial crisis (modeled via liquiditydemand shock) affected the endogenous TFPand in turn other macroeconomic variables

1

Secular decline in labor productivity

Source: Schmöller and Spitzer (2018)2

What’s responsible for the drop in productivity trend

• Hall (2014) documents a lasting fall in GDP trenddue to lower employment, physical capital and TFP

• Secular stagnation hypothesis: savings glut / ZLB• Summers (2014)→ Benigno and Fornaro (2018)

• TANK with risk premium shock as in Kollmann et al. (2016)• Cozzi et al. (2017)

• Representative household with preference toward bondsas in Fisher (2015) and dual invention-adoption R&Dsector as in Comin and Gertler (2006)• Anzoategui et al. (2016)→ Schmöller and Spitzer (2018)

3

Key mechanism in the model

• Profits of intermediate goods producers are procyclical(standard in NK / monopolistic competition models)

• Newly adopted technology is sold at price Vequal to PDV of profits (free-entry condition)

• Value of unadopted technology J depends onthe expected future values of V and J

• If the (real) skilled wages are relatively acyclicalthen technology invention and adoption are procyclical(and R&D expenditures are procyclical)

• Crucial assumption: labor markets for R&D (skilled)and production (unskilled) are separated

4

Big picture thoughts: R&D and growth

• Semi-endogenous growth• Decreasing returns to scale in R&D employment• Population growth rate determines long-run growth rate• Decline in TFP long-lasting but transitory (think Solow)

• Fully endogenous growth• Constant returns to scale in R&D employment• Employment share in R&D key for long-run growth rate• Decline in TFP permanent (think AK)

• See Cozzi (2017) for a hybrid model allowing for bothregimes (think Jones and Manuelli (1990))• The lower the population growth is, the more importantthe fully endogenous “channel” becomes

• Hard to discriminate between the two in the data• Crucial for designing growth-enhancing policies 5

Big picture thoughts: GPT and technology adoption

• In the data, technology adoption rate pertains toGeneral Purpose Technologies• Synthetic fabrics, computerization, etc.

• GPT adoption rates became higher over time• We don’t see it in faster TFP growth

• Anzoategui et al. (2016): TFP trend in decline since 2000• Gordon (2015): Are GPT’s becoming less radical over time?

• In the model, an invented technology ends up usedby a single intermediate goods producer• Concerns with mapping between data and model objects• Business R&D as a measure of “pure” R&D,adoption, or both?

• Unclear on how to treat R&D activites performed byuniversities, govt. agencies, etc.

6

Technology adoption in the US over the last century

Source: https://hbr.org/2013/11/the-pace-of-technology-adoption-is-speeding-up

7

Technology adoption in the US over the last century

Source: https://marketrealist.com/2015/12/adoption-rates-dizzying-heights8

Speed of diffusion: cycle vs long-term view

Source: Anzoategui et al. (2016)9

Main remarks

• Schmöller and Spitzer (2018) re-estimate the modelby Anzoategui et al. (2016) on EU data

• Several shortcomings relative to the “parent” paper• Shorter estimation period (start in 1999Q1 vs 1984Q1)– cannot reliably estimate the pre-2007 TFP trend

• No external validation of the technology adoption margin• No comparison of spread between model and data• ZLB episode is not modeled explicitly (Kortela (2016) andWu and Xia (2017) shadow interest rates used instead)

• Authors offer limited contribution beyondwhat Anzoategui et al. (2016) already did

10

Spreads on lending to non-financial corporations

Source: Gilchrist and Mojon (2018)11

Quantitative importance of ZLB episodes

Source: Anzoategui et al. (2016)12

Further remarks

• In the model, the relative price of investment pkfollows an exogenous AR(1) process in logs

• For the purpose of estimation both consumptionand investment series are deflated by GDP deflator

• Value of νs is not reported• Is it unimportant for the average growth rate?

• Liquidity demand shock (cf. variance decomposition)significantly more important than inAnzoategui et al. (2016) and Cozzi et al. (2017)• Explanation for this difference (US vs EA)?• How can we alleviate its effects via policy?

• Robustness checks for different calibrated valuesof parameters governing R&D processes

13

Decomposition of R&D investment growth rates

Can I get a graph like this?

Source: Cozzi et al. (2017)14

Importance of business dynamics for TFP

• Siemer (2014) (US firm-level data)• Financial constraints during GFC lowered firm entry

• Canton (2016) (EU country-level data)• Increase in the firms birth rate increases TFP growth

• Messer et al. (2016) (US regional data)• Low firm entry rates reduce labor productivity growth

• Schmitz (2016) (Spanish firm-level data)• Smaller, more innovative firms hit harder by GFC

• Bielecki (2017) (US aggregate data)• Microfounded, estimated model of business dynamicswith heterogeneous firms and endogenous TFP

15

Business dynamics in the EU

Source: Cincera and Galgau (2005)16

Business dynamics in the EU

Source: Canton (2016)17

Business dynamics in the US

Source: Decker et al. (2016)

18

R&D and business dynamics in the US

Establishments Expansions Contractions

1995 2000 2005 2010 20152.0

1.5

1.0

0.5

0.0

0.5

1.0

1.5

2.0

DaneModel

1995 2000 2005 2010 201515

10

5

0

5

10

Dane (l. o )Model (p. o )

3

2

1

0

1

2

1995 2000 2005 2010 201510

5

0

5

10

15Dane (l. o )Model (p. o )

1.0

0.5

0.0

0.5

1.0

1.5

Net entry R&D expenditures Hours worked

1995 2000 2005 2010 20151.0

0.5

0.0

0.5

1.0

1.5DaneModel

1995 2000 2005 2010 20158

6

4

2

0

2

4

6

8Dane (NSF)Model

1995 2000 2005 2010 20156

4

2

0

2

4

6

Dane (l. o )Model (p. o )

2.0

1.5

1.0

0.5

0.0

0.5

1.0

1.5

2.0

Source: Bielecki (2017) 19

Disappearing link between output gap and inflation

• Semmler and Gross (2017)• Phillips curve is steeper in booms than in recessions

• Bullard (2018)• Phillips curve in G7 became flat over time• Success of modern monetary policy?

20

Conclusions

• Very important and timely topic

• Paper competes with a rapidly growing literature

• Papers needs to differentiate itself fromAnzoategui et al. (2016) and other papers in the field• Contribute further to the output gap-inflation puzzle• Engage with “missing firms” literatureand explore business dynamics in the EU / EA

• Provide policy discussion / implications

21

Thank you!

References

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Benigno, G. and Fornaro, L. (2018). Stagnation Traps. Review of Economic Studies,85(3):1425–1470.

Bielecki, M. (2017). Business cycles, innovation and growth: welfare analysis. WorkingPapers 2017-19, Faculty of Economic Sciences, University of Warsaw.

Bullard, J. (2018). The Case of the Disappearing Phillips Curve. mimeo.Canton, E. (2016). Drivers of total factor productivity growth in the EU - the role of

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