Price stickiness along the income distribution and

the eects of monetary policy

Javier Cravino, Ting Lan and Andrei Levchenko

University of Michigan

July 2018
Monetary shocks have distributional consequences

I Monetary policy has dierential eect across agents:

◦ Savers vs. borrowers (Doepke and Schneider 2006)

◦ Financially constrained vs. unconstrained (Williamson, 2008)

◦ Young vs. old (Wong, 2016)

I Coibion et al. 2017: Monetary shocks increase income inequality

I Mechanism: dierential eects on agents income/wealth

I This paper: Alternative mechanism  dierential eect on prices

◦ Monetary policy dierentially aects prices of dierent goods

◦ Households with dierent income consume dierent goods
What we do

1) Document two new dierences across consumption baskets:

◦ High income households consume goods whose prices are:

i) More sticky

ii) Less volatile

2) Quantify distributional consequences of monetary shock

◦ Factor-Augmented VAR (FAVAR) model

◦ Quantitative New-Keynesian DSGE model

◦ Inequality aects eectiveness of monetary policy  modestly

Main result: Shock that ↑ π

agg by 1% → πmid −πtop = 0.2%
A simple model to guide the discussion

I Two periods. State is known at t = 1. S possible states at t = 2

I H types of households, J types of goods or 'sectors'

◦ CPI faced by household h: p

h

t

(s) ≡ ∑j ω

h

j

pj,t (s)

◦ Aggregate CPI: pt (s) ≡ ∑hs

hph

t

(s) = ∑j ωjpj,t (s)

I Cont. monopolistically competitive rms in each j, same technology

◦ t = 1: all rms set same price, p1

◦ t = 2: fraction 1−θj set prices before shocks, p

e

2 = p1

◦ t = 2: fraction θj set prices after shocks, p ̄2 (s)

I Sectoral ination:

πj (s) = θj[  ̄p2 (s)−p1]
Price rigidities and ination dierences

I Dierence in ination across households:

π

h

(s)−π

h

0

(s) = [  ̄p2 (s)−p1]∑

j

h

ω

h

j −ω

h

0

j

i

θj.

π

h

(s)−π

h

0

(s)

π (s)

=

θ ̄h −θ ̄h

0

θ ̄

where θ ̄h ≡ ∑j ω

h

j

θj; θ ̄ ≡ ∑hs

hθ ̄h

.

I More exible sectors are more volatile

σπj

σπ

=

θj

θ ̄

I More exible baskets are more volatile

σπ

h

σπ

=

θ ̄h

θ ̄
Data

I Household specic ination:

π

h

t = ∑

j

ω

h

j πj,t

◦ ω

h

j

: Consumption expenditures from the US Consumption

Expenditure Survey (CES)

◦ πj,t: Item-level price indices from BLS (178 goods)

I Household specic average frequency of price changes:

θ ̄h ≡∑

j

ω

h

j θj

◦ θj: ELI-level frequencies from Nakamura and Steinsson 2008

(265 goods)

◦ Fraction of prices that change in a month
Consumption Expenditure Survey

I Two modules: the Interview and the Dairy

◦ Expenditure les

 Collect expenditures on about 600 UCC categories

 350 UCCs in the Interview

 250 UCCs in the Dairy

◦ Income les

◦ Characteristics les

I Dairy and interview survey dierent households each year

I Percentile-level household expenditure share ω

h

j
Aggregating HHs into percentiles

I Sort households into percentiles in two steps:

◦ Aggregate HHs in the Interview survey into percentiles

◦ Use Interview income cutos to divide HHs from the Diary into

percentiles

I Imputed income before tax

◦ CES starts to include imputed income since 2004

◦ Fisher, Johnson and Smeeding (2015) imputes income back to

1984
Adjusting the expenditure values

Housing

I Owner's equivalent rent of primary residence

If someone were to rent your home today, how much do you

think it would rent for monthly, unfurnished and without

utilities?

◦ Response saves in the variable RENTEQVX in the

characteristics le

◦ Construct an articial UCC code 999999 to store the value

I Seperate consumption component from investment component

◦ Adjust expenditures on homeowner insurance, maintenance,

and major appliances

◦ Apply a factor of 0.43
Adjusting the expenditure values

Medical care

I Redistribution factors

◦ Redistribute private health insurance and Medicare premiums

to medical care services

◦ The BLS constructs redistribution factors from the National

Health Expenditure (NHE) tables

◦ Use NHE table 20 directly

I Allocate reimbursements across all HHs
Concordance

I In-scope expenditures for CPI could be divided into

◦ 8 groups

◦ 70 expenditure classes

◦ 211 item strata (item level)

◦ 303 entry level items (ELIs)

I Concordance from UCCs to item strata to ELIs

◦ Following BLS document CPI requirement for CE Appendix B
Calculating the expenditure shares

I Distinction between the survey period and the expenditure

reference period

◦ HHs surveyed in Feb.2017

◦ Reports expenditures for Nov. and Dec. 2016 and Jan. 2017

I Calculate the mean value of a calendar year

◦ Create MO_SCOPE

◦ Annualized average expenditure for each UCC category k at

percentile h

X ̄h

k =

∑i FINLWTh

i

·∑t C

h

i,k,t

∑i FINLWTh

i

·MO_SCOPEh

i

×12

I Expenditure share

ω

h

j =

 ̄X

h

j

∑j X ̄h

j
Takeaways

1 Households with dierent incomes consume dierent goods

2 Heterogeneous eects of monetary policy across goods

=⇒Distributional consequences of monetary policy

I Goods consumed by high-income households are:

◦ more sticky

◦ less volatile

I FAVAR + DSGE evidence

◦ Large eects relative to impact of monetary shocks on prices

◦ Inequality aects monetary policy eectiveness - modestly