ESCoE Research Seminar · ESCoE Research Seminar . The Flexibility of German Manufacturing Prices...

ESCoE Research Seminar

The Flexibility of German Manufacturing Prices 1980-2015: Micro and Macro Determinants

Presented by Huw Dixon (Cardiff Business School)

30 October 2018

1

The Flexibility of German Manufacturing Prices

1980–2015: Micro and Macro Determinants

Huw Dixon,

Cardiff Business School

Christian Grimme,

Ifo Institute, University of Munich.

Escoe, 29th October 2018.

2

Q: What do we mean by price flexibility?

Q: How do we measure it?

Q: How is it related to the frequency of price changes (proportion of firms changing price per month)?

Part 1: “Masterclass” in this topic – statistics.

Q: How has the frequency of price behaved in German Industry 1980‐2015?

Q: Is it more influenced by macroeconomic factors (inflation, output growth)

Q: Is it more influenced by microeconomic (firm specific) factors?

Part 2: empirical analysis of German data.

3

Survival analysis.

Price spells.

Survival function S(t). What proportion of price spells last at least i periods?

Hazard function. Conditional on surviving i periods, what is the probability that the price spell will end in period i? (Failure or death rate).

Distribution of durations. The proportions of price spells that last i periods.

Discrete time, but also continuous time.

4

General Functions in discrete and Continuous time.

Examples: Exponential and Weibull.

Survival function

Hazard function Durations PDF Frequency CDL PDF (Dixon) Mean Duration Mean CDL (Dixon)

CT

( )S t

( )( )( )

S th tS t

( ) ( )da t S t

0

1

( )FhS t dt

0

. ( )( )( )

F

t S ta tS t dt

0

( )

1

F

d S t dt

h

2

0

2

0

1 ( )

( )

F

F

T t S t dtd

h t S t dt

DT ( )S t

( ) ( 1)( )

( )S t S th t

S t

( ) ( ) ( 1)da t S t S t

0

1

( )F

t

hS t

( ) . ( )da t t ha t 0

( )

1

F

t

d S t

h

2

1

. ( )F

d

t

T h t a t

5

Exponential

The discrete time geometric (Bernoulli or “Calvo”) extra results were first derived by Dixon and Kara JMCB 2006 (both the distribution and the mean).

Survival function

Hazard function Durations PDF Frequency CDL PDF (Dixon) Mean Duration Mean CDL (Dixon)

CT

te

( )h t h

( )d ta t e

h

2( ) ta t t e

1d

2T

DT

1 ( 0,1.. )

tht

( )h t h

1( ) 1 tda t h h

h h

12( ) . 1 ta t t h h

1dh

2 1Th

6

Weibull distribution (two parameter, scale and shape p.)

Discrete time: not useful. Durations vs CDL.

Generic formulas in discrete time: All discrete time distributions can be described by either survival, hazard function, distribution of durations or CDL. Used to calibrate pricing models

General Calvo: sequence of duration dependent hazard rates.

1 2 3, , .... Fh h h h

General Taylor: CDL

1 2, ,... Fa a a

Survival function

Hazard function Durations PDF Frequency CDL PDF (Dixon) Mean Dur.

Mean CDL (Dixon)

CT

( )pte

1( ) p ph t pt

1 ( )( )( )

pp tda t p t e

(1 (1 / ))h

p

( )

1 (1 / )( )

pp tp ta t ep

1d h

2 /21 /

pT

p

7

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.10.20.30.40.50.60.70.80.9 1 1.11.21.31.41.51.61.71.81.9 2 2.12.22.32.42.52.62.72.82.9 3 3.13.23.33.43.53.63.73.83.9 4 4.14.24.34.44.54.64.74.8

Comparison pdf and CDL when p=1 (exponential)

pdf p=1

CDL p=1

8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4

Comparison p=1.5

pdf

CDL

9

So, what about h ?

CPI “panel” of prices (100,000+)

Jan Feb March April May June July August SeptemberFirm 1 1 1 1 1 1 1 1 1 1Firm 2 9Firm 3 3 3 3

9 months, 3 price setters (“firms”). 9x3 = 27 observations.

Price spells: periods with the same price (durations) 13

1 month price spells: 9 Share of 1 month: 9/13



10

Jan Feb March April May June July August SeptemberFirm 1 1 1 1 1 1 1 1 1 1Firm 2 9Firm 3 3 3 3

Price spells: periods with the same price (durations) total 13




Mean price spell d (to two decimal places):

9 3 1 271 3 9 2.08

13 13 13 13 d

Hence 13 0.4827

h

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Jan Feb March April May June July August SeptemberFirm 1 Firm 2 Firm 3 h 100% 33% 33% 66% 33% 33% 66% 33% 33%

h is the average frequency:

1 2 2 6 1 3 4 6 131 0.489 9 3 9 3 27 27

h

CDL: cross‐sectional distribution of lifetimes. Take any month (or “representative month”): you observe 3 spells, a one month, a three month and a nine‐month

1 1 1 131 3 9 4.333 3 3 3

T

In this example, 2T d . This is the correct measure of nominal rigidity (price flexibility). It averages across prices (price‐setters, retailers or “firms”).

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h is not closely related to nominal rigidity. CDL 1 2, ,... Fa a a

1

1 / 3 1/ 3 1/ 31 3 9

Fi

i

ai

h

There are lots of CDLs (and pdfs) consistent with a given h .

Theorem (Dixon and Tian OBES 2017): minimum T d iff all spells have the same length. Upper bound ∞.

Lots of distributions of durations have exactly same h and d

Lots of different CDL have exactly same h but DIFFERENT T , T d

15

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

CDL and PDF Germany 1980‐2015

Durations PDF CDL

Mean spell: 4.4hbar: 23%Mean CDL: 17.4

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The distributions “cross” when i d : . di ia i ha German data, first twelve months

length i Durations i.hbar CDL

1 0.4255 0.2299 0.0978

2 0.1820 0.4597 0.0837

3 0.0894 0.6896 0.0617

4 0.0553 0.9194 0.0508 i<d=4.4

5 0.0403 1.1493 0.0464 i>d=4.4

6 0.0307 1.3791 0.0423

7 0.0229 1.6090 0.0369

8 0.0190 1.8388 0.0349

9 0.0168 2.0687 0.0348

10 0.0151 2.2985 0.0346

11 0.0163 2.5284 0.0413

12 0.0206 2.7582 0.0568

Structure of the Talk

1 Introduction

2 Data & Empirical Models

3 Results

4 Conclusion

Introduction Data&Empirical Models Results Summary 1/31

Motivation

Note: Price statements from manufacturing sector in Germany (ifo data, monthly, averaged toquarterly frequency).


Motivation

In a large class of monetary models:frequency of price adjustment / degree of nominal rigidity affectsmonetary non-neutrality

Research Question:What are the determinants behind the changing flexibility of prices?Aggregate vs. firm-/sector-specific information?State vs. time dependent elements in the pricing behaviour of firms?


Some Rudimentary Theory (1/4)

Alternative theoretical approaches:

1 Time dependent:I Timing of price change exogenous, size of change endogenousI Calvo vs. TaylorI Simple or general (Dixon and Le Bihan 2012: calibrate to micro

data)

2 State-dependent:Timing and size of change endogenous(Mankiw 1985, Golosov and Lucas 2007, Alvarez and Lippi 2014).



In both time and state dependent pricing:prices can persist through time (nominal rigidity or sticky prices):

1 Sticky information and rational inattention:Prices change every period, but may be based on old information(Mankiw and Reis 2002, Mackowiak and Wiederholt 2009)

2 Indexation:Prices adjust every period (Smets and Wouters 2003) when notexplicitly reset.



Optimal flexible prices (log linear):

P∗t = Pt + γYt

γYt: marginal cost variableIf marginal cost is flat, γ = 0 and output does not matter.

Menu-costs:“Band of inertia” around P∗t

Inflation: increases P∗t by increases in Pt; output growth: the same.Higher inflation moves more firms “out” of band of inertia, increasesproportion of firms changing price. Firms at bottom increase prices(more price increases). However, firms that would have been aboveband of inertia move into it (less price decreases).



Payoff

Price

U*

p*

U*-

Band of inertia ( )


Literature ReviewLein (2010): Quarterly Swiss firms over the period 1984 to 2007.“Macroeconomic factors are significant, but add little in terms ofgoodness of fit”. Both time-dependent and state-dependentfactors were important in the decision to change prices.Carlson and Skans (2012): Annual survey of Swedish firms1990-2002, specific product prices linked to unit labour costs at theplant level. Time-dependent Calvo model (without indexation)outperformed alternative models of Sticky information andrational inattention.Bachmann et al (2018): Focus on the effect of firm leveluncertainty on price-setting using the same German data set asthis study. They find that firm level uncertainty has a positiveeffect on the frequency of price change.Gagnon (2009) and Berardi et al (2016): CPI data, no microleveldata. Mexico 1994-2002 and French data 2003-2011. Inflationmatters.


Results (1/2): Time-Series Evidence

Frequency of price changes: not linked to aggregate information

Frequency of price increases: strongly determined by aggregateinflation; to a smaller extent by aggregate production

Frequency of price decreases: determined to a smaller extend byoil price inflation and aggregate production


Results (2/2)

Frequency of price changes: strongly determined by aggregateinflation; aggregate output is important as long as there is nofirm-specific information

Frequency of price increases: strongly determined by aggregateinflation and oil price inflation; aggregate output is important aslong as there is no firm-specific information

Frequency of price decreases: strongly determined by aggregateinflation and aggregate output as long as there is no firm-specificinformation

Order of importance:Price change/decrease:firm-specific > time-dependence > aggregatePrice increase:time-dependence > firm-specific > aggregate


Data and Empirical Models


Data

IFO Business Climate Survey (IFO-BCS):monthly firm survey for Germany⇔ CBI Business Optimism Index in the U.K.⇔ Business tendency survey by INSEE in FranceHighly confidential: can only be accessed under strictnon-disclosure agreements→ less likely to suffer from strategic behaviorOur paper: Manufacturing sector from 1980–2015(∼2500 firms of all sizes in each month)Contains information on price setting of firmsIncludes many firm-specific variables that allow us to control foridiosyncratic factors


Description of Dependent Variables

Binary dependent variable:→ price change

→ equals 1 if a firm states that it changes its price from previousto current month

Discrete dependent variable:→ statement of firm how its price changed from previous tocurrent month

→ equals 1 if price is increased→ equals 0 if no price change→ equals -1 if price is decreased


Hazard Function from Raw Data

Note: Computed using the non-parametric Kaplan-Meier estimator.


Representativeness

Note: Fraction: Balance Statistics of price statements from manufacturing sector in Germany (Ifodata, monthly). PPI: Prices in manufacturing sector (excludes energy) in Germany,year-over-year-rates. All series are averaged to quarterly frequency.


Description of Firm-specific Controls

Label Variable Response Scale

Price Expectations Expprice change BinaryExpprice+ increase BinaryExpprice− decrease Binary

Business Situation Statebus+ good BinaryStatebus− unsatisfactory Binary

Business Expectation Expbus+ increase BinaryExpbus− decrease Binary

Orders Order+ increase BinaryOrder− decrease Binary

Firm-Level Uncertainty Uncertainty Variance FE IntervalCost of Input Goods ∆Costs −0.42. . . 0.87 Interval


Description of aggregate variablesAggregate inflation (Producer Prices without energy,Manufacturing):

inflm: monthly growth of pricesinfly: annual growth of prices

Production growth (Manufacturing):

mpm: monthly growth of productionmpy: annual growth of production

Oil price inflation (Brent Crude Oil in Euro):

oilm: monthly growth of oil pricesoily: annual growth of oil prices

Problems of endogeneity:use lagged values of aggregate variables


Producer and Oil Prices, and ManufacturingProduction

Note: PPI: Prices in manufacturing sector (excludes energy) in Germany, year-over-year-rates. Production: Production inmanufacturing sector (excludes energy) in Germany, year-over-year-rates. Oil: Crude oil prices in Euro (Brent),year-over-year-rates. Series are on quarterly basis. Shaded regions: recessions


Empirical Models

1 OLS time series model: price change, increase, and decrease –separately & cross-sectional averages;includes aggregate variables and recession & unification & Eurodummies

2 Pooled multinominal logit model: price increase and decrease;includes:

I Aggregate variables (Inflation, Oil, Production)I Firm-specific (state) variablesI Firm-specific hazard dummiesI Seasonal dummiesI Sector dummiesI Recession & unification & Euro dummies

3 We also estimate Pooled probit models: price change, increase,and decrease – separately


Results


Time Series ResultsDependentVariable Price Change Price Increase Price Decrease

Inflm 0.500 2.001*** 0.080(0.689) (0.476) (0.253)

Infly 0.158 0.298** -0.061(0.105) (0.099) (0.071)

Oilm 0.012 0.012 0.008(0.017) (0.015) (0.005)

Oily 0.001 0.007* -0.005**(0.004) (0.004) (0.002)

Mpm -0.024 0.025 0.001(0.048) (0.037) (0.026)

Mpy -0.052 0.057*** -0.026**(0.032) (0.015) (0.010)

Exp Price Change 0.420***(0.046)

Expprice+ 0.506***(0.028)

Expprice- 1.031***(0.042)

No. of obs. 440 440 440R-squared 0.79 0.89 0.96

*** p<0.01, ** p<0.05, * p<0.1

Includes seasonal, crises, Euro and unification dummies.Introduction Data&Empirical Models Results Summary 22/31

Multinominal Logit Results (1/3)

(1) (2) (3)

Depend. Var.: Price ↓ Price ↑ Price ↓ Price ↑ Price ↓ Price ↑

Inflm -2.042*** 6.077*** -0.600*** 2.572*** -0.050* 0.310***(0.213) (0.345) (0.053) (0.141) (0.024) (0.047)

Infly -0.152*** 1.317*** 0.018** 0.349*** 0.005 0.017*(0.027) (0.075) (0.007) (0.022) (0.004) (0.007)

Oilm -0.001 -0.022*** -0.003*** -0.005* -0.001** -0.003***(0.002) (0.004) (0.001) (0.002) (0.000) (0.001)

Oily -0.007*** 0.023*** -0.003*** 0.009*** -0.001*** 0.001***(0.001) (0.002) (0.000) (0.001) (0.000) (0.000)

Mpm -0.046*** 0.110*** -0.021*** 0.044*** -0.002 -0.005(0.008) (0.015) (0.004) (0.008) (0.002) (0.004)

Mpy -0.127*** 0.174*** -0.028*** 0.068*** 0.002** 0.001(0.012) (0.011) (0.002) (0.005) (0.001) (0.002)

Hazard Dummies no no yes yes yes yesFirm-spec. var. no no no no yes yes

No. of obs. 1,557,588 1,571,588 1,003,614Pseudo R-squared 0.044 0.24 0.38

*** p<0.01, ** p<0.05, * p<0.1

Includes seasonal, sector, crises, Euro and unification dummies.Introduction Data&Empirical Models Results Summary 23/31

Multinominal Logit Results (2/3)(1) (2) (3)


Expprice+ -0.003*** 0.115***(0.000) (0.006)

Expprice- 0.017*** -0.003***(0.001) (0.001)

Order+ -0.001*** 0.007***(0.000) (0.001)

Order- 0.006*** -0.002***(0.000) (0.000)

Statebus+ -0.002*** 0.004***(0.000) (0.000)

Statebus- 0.004*** -0.002***(0.000) (0.000)

Expbus+ -0.000 0.002***(0.000) (0.000)

Expbus- 0.004*** -0.001*(0.000) (0.000)

∆ Costs -0.009*** 0.028***(0.001) (0.002)

Uncertainty 0.001*** 0.001**(0.000) (0.000)

Hazard Dummies no no yes yes yes yes

*** p<0.01, ** p<0.05, * p<0.1


Multinominal Logit Results (3/3): Hazard Dummies

Price ↑ Price ↓

Note: Coefficients of Hazard Dummies are plotted.


Probit Model Results (1/3)

Dependent Variable: Price Change

Inflm 2.423*** 1.662*** 0.301***(0.200) (0.134) (0.075)

Infly 0.787*** 0.301*** -0.037***(0.047) (0.023) (0.011)

Oilm -0.009* -0.007** -0.004*(0.004) (0.003) (0.002)

Oily 0.006*** 0.003*** -0.000(0.002) (0.001) (0.000)

Mpm 0.043** 0.019 -0.005(0.014) (0.012) (0.007)

Mpy -0.098*** -0.043*** -0.004(0.009) (0.005) (0.003)

Hazard Dummies no yes yesFirm-spec. var. no no yes


*** p<0.01, ** p<0.05, * p<0.1

Includes seasonal, sector, crises, Euro and unification dummies.


Probit Model Results (2/3)Dependent Variable: Price Change

Exp Price Change 0.141***(0.004)

Order+ 0.012***(0.001)

Order- 0.010***(0.001)

Statebus+ 0.005***(0.001)

Statebus- 0.014***(0.001)

Expbus+ 0.003***(0.001)

Expbus- 0.011***(0.001)

∆ Costs 0.021***(0.002)

Uncertainty 0.002***(0.000)



*** p<0.01, ** p<0.05, * p<0.1


Probit Model Results (3/3): Hazard Dummies

no firm-specific state variables with firm-specific state variables

Note: Coefficients of Hazard Dummies are plotted.


Relative Importance (1/2)

Relative Importance of time vs. state (micro vs. macro) factors:

compute “sensitivity”: measure of fit of each model

ratio of two values:numerator: number of predicted price changes (increases /decreases) that are correctly predicteddenominator: total number of actual price changes (increases /decreases)


Relative Importance (2/2)

Multinomial Logit Model

(1) (2) (3)


Hazard Dummies no no yes yes yes yesFirm-spec. var. no no no no yes yes

Sensitivity 0.031% 0.002% 9.384% 30.219% 35.489% 43.340%

Probit Model

(1) (2) (3)

Depend. Var.: Price Change


Sensitivity 0.14% 15.66% 37.52%


Summary and Conclusion

Factors explaining the pricing decision of firmsI State-dependent:

F Aggregate inflation, oil prices, and aggregate outputF Firm-specific information

I Time-dependent

Hazard dummies and firm-specific state variables explain morevariation in the dependent variable than macro variables


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