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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
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
3 month price spells: 3 Share of 3 month: 3/13
9 month price spells: 1 Share of 9 month: 1/13
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
1 month price spells: 9 Share of 1 month: 9/13
3 month price spells: 3 Share of 3 month: 3/13
9 month price spells: 1 Share of 9 month: 1/13
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
11
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”).
12
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
13
14
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
16
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).
Introduction Data&Empirical Models Results Summary 2/31
Motivation
Note: Price statements from manufacturing sector in Germany (ifo data, monthly, averaged toquarterly frequency).
Introduction Data&Empirical Models Results Summary 3/31
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?
Introduction Data&Empirical Models Results Summary 4/31
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).
Introduction Data&Empirical Models Results Summary 5/31
Some Rudimentary Theory (2/4)
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.
Introduction Data&Empirical Models Results Summary 6/31
Some Rudimentary Theory (3/4)
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).
Introduction Data&Empirical Models Results Summary 7/31
Some Rudimentary Theory (4/4)
Payoff
Price
U*
p*
U*-
Band of inertia ( )
Introduction Data&Empirical Models Results Summary 8/31
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.
Introduction Data&Empirical Models Results Summary 9/31
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
Introduction Data&Empirical Models Results Summary 10/31
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
Introduction Data&Empirical Models Results Summary 11/31
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
Introduction Data&Empirical Models Results Summary 11/31
Data and Empirical Models
Introduction Data&Empirical Models Results Summary 12/31
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
Introduction Data&Empirical Models Results Summary 13/31
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
Introduction Data&Empirical Models Results Summary 13/31
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
Introduction Data&Empirical Models Results Summary 13/31
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
Introduction Data&Empirical Models Results Summary 14/31
Hazard Function from Raw Data
Note: Computed using the non-parametric Kaplan-Meier estimator.
Introduction Data&Empirical Models Results Summary 15/31
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.
Introduction Data&Empirical Models Results Summary 16/31
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
Introduction Data&Empirical Models Results Summary 17/31
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
Introduction Data&Empirical Models Results Summary 18/31
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
Introduction Data&Empirical Models Results Summary 18/31
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
Introduction Data&Empirical Models Results Summary 19/31
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
Introduction Data&Empirical Models Results Summary 20/31
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
Introduction Data&Empirical Models Results Summary 20/31
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
Introduction Data&Empirical Models Results Summary 20/31
Results
Introduction Data&Empirical Models Results Summary 21/31
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)
Depend. Var.: Price ↓ Price ↑ Price ↓ Price ↑ Price ↓ Price ↑
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
Includes seasonal, sector, crises, Euro and unification dummies.Introduction Data&Empirical Models Results Summary 24/31
Multinominal Logit Results (3/3): Hazard Dummies
Price ↑ Price ↓
Note: Coefficients of Hazard Dummies are plotted.
Introduction Data&Empirical Models Results Summary 25/31
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
No. of obs. 1,557,588 1,557,588 1,003,614Pseudo R-squared 0.025 0.15 0.27
*** p<0.01, ** p<0.05, * p<0.1
Includes seasonal, sector, crises, Euro and unification dummies.
Introduction Data&Empirical Models Results Summary 26/31
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)
Hazard Dummies no yes yesFirm-spec. var. no no yes
No. of obs. 1,557,588 1,557,588 1,003,614Pseudo R-squared 0.025 0.15 0.27
*** p<0.01, ** p<0.05, * p<0.1
Includes seasonal, sector, crises, Euro and unification dummies.Introduction Data&Empirical Models Results Summary 27/31
Probit Model Results (3/3): Hazard Dummies
no firm-specific state variables with firm-specific state variables
Note: Coefficients of Hazard Dummies are plotted.
Introduction Data&Empirical Models Results Summary 28/31
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)
Introduction Data&Empirical Models Results Summary 29/31
Relative Importance (2/2)
Multinomial Logit Model
(1) (2) (3)
Depend. Var.: Price ↓ Price ↑ Price ↓ Price ↑ Price ↓ Price ↑
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
Hazard Dummies no yes yesFirm-spec. var. no no yes
Sensitivity 0.14% 15.66% 37.52%
Introduction Data&Empirical Models Results Summary 30/31
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
Introduction Data&Empirical Models Results Summary 31/31