Geospatial Heterogeneity in Inflation:

A Market Concentration Story

Seula Kim1 Michael Navarrete2

1Princeton University

2University of Maryland

2023 FCSM Research & Policy Conference

October 26, 2023
Motivation

● Spatial income inequality has been on the rise in the United States

▸ The dispersion of MSA-level (log) income has increased from 5.36 to 11.40 from 2000 to 2019

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 1 / 18
Motivation

● Spatial income inequality has been on the rise in the United States

▸ The dispersion of MSA-level (log) income has increased from 5.36 to 11.40 from 2000 to 2019

● If inflation varies b/w rich and poor regions → “real” income inequality might have a different story

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 1 / 18
Motivation

● Spatial income inequality has been on the rise in the United States

▸ The dispersion of MSA-level (log) income has increased from 5.36 to 11.40 from 2000 to 2019

● If inflation varies b/w rich and poor regions → “real” income inequality might have a different story

● Inflation is typically measured at the national level and presumed to be uniform across regions

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 1 / 18
Research Questions

1 Do inflation rates systematically vary across MSAs having different income level?

2 How is it related to local retailer market structure and power?

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 2 / 18
This Paper

● Uses Nielsen Retail Scanner and Business Dynamic Statistics

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 3 / 18
This Paper

● Uses Nielsen Retail Scanner and Business Dynamic Statistics

● Finds the following evidence:

1 Food inflation rates vary across regions with different income level

2 The poorest decile experiences about 10 p.p. ↑ inflation than the richest decile over 2006-2016

3 The pattern holds for both aggregate and disaggregated food categories

4 Larger (smaller) share of large (small) retailers in poorer areas

5 The degree of market concentration is higher in poorer areas

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 3 / 18
This Paper

● Uses Nielsen Retail Scanner and Business Dynamic Statistics

● Finds the following evidence:

1 Food inflation rates vary across regions with different income level

2 The poorest decile experiences about 10 p.p. ↑ inflation than the richest decile over 2006-2016

3 The pattern holds for both aggregate and disaggregated food categories

4 Larger (smaller) share of large (small) retailers in poorer areas

5 The degree of market concentration is higher in poorer areas

● Documents suggestive evidence about heterogeneous market power acting as a potential source

behind the differential inflation rates

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 3 / 18
Data and Measures
Nielsen Retail Scanner (RMS)

● Nielsen contains detailed information for retail chains across U.S. markets

▸ Covers 100 chains and over 40,000 individual stores

▸ Weekly pricing, volume, store location, and merchandising conditions, etc.

▸ Total sales worth over $200 billion/yr; 50% of total sales in grocery stores; 55% in drug stores; 32% in

mass merchandisers; and 2% in convenience stores

▸ Over 2.6 million 12-digit universal product codes (UPCs), aggregated to product modules and groups

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 4 / 18
Nielsen Retail Scanner (RMS)

● Nielsen contains detailed information for retail chains across U.S. markets

▸ Covers 100 chains and over 40,000 individual stores

▸ Weekly pricing, volume, store location, and merchandising conditions, etc.

▸ Total sales worth over $200 billion/yr; 50% of total sales in grocery stores; 55% in drug stores; 32% in

mass merchandisers; and 2% in convenience stores

▸ Over 2.6 million 12-digit universal product codes (UPCs), aggregated to product modules and groups

● Build on the BLS concordance and construct a mapping b/w Nielsen and PCE food categories

● Map MSAs into into deciles based on income per capita

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 4 / 18
Business Dynamic Statistics (BDS)

● BDS provides annual measures of business dynamics

● Public version of the Census firm data (Longitudinal Business Dynamics)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 5 / 18
Business Dynamic Statistics (BDS)

● BDS provides annual measures of business dynamics

● Public version of the Census firm data (Longitudinal Business Dynamics)

● Contains detailed information about establishment and firm characteristics

▸ Total number of firms, employment, job creation and destruction

▸ By different firm age/size bins, industry, and regions (MSAs)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 5 / 18
Business Dynamic Statistics (BDS)

● BDS provides annual measures of business dynamics

● Public version of the Census firm data (Longitudinal Business Dynamics)

● Contains detailed information about establishment and firm characteristics

▸ Total number of firms, employment, job creation and destruction

▸ By different firm age/size bins, industry, and regions (MSAs)

● We use it for retail trade sector (NAICS 44-45)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 5 / 18
Business Dynamic Statistics (BDS)

● BDS provides annual measures of business dynamics

● Public version of the Census firm data (Longitudinal Business Dynamics)

● Contains detailed information about establishment and firm characteristics

▸ Total number of firms, employment, job creation and destruction

▸ By different firm age/size bins, industry, and regions (MSAs)

● We use it for retail trade sector (NAICS 44-45)

● Use employment size and define large (500+) and small (20-) firms

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 5 / 18
Price Indices Demand-based

ln ΨG

t = ∑

k∈Ct−1,t

wkt ln pkt

pkt−1

,

● wkt is a weight assigned to product k (typically based on the product’s market share) in quarter t

● The set Ct−1,t

is the set of all “continuing” goods that are sold both in period t and in period t − 1

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 6 / 18
Price Indices Demand-based

ln ΨG

t = ∑

k∈Ct−1,t

wkt ln pkt

pkt−1

,

● wkt is a weight assigned to product k (typically based on the product’s market share) in quarter t

● The set Ct−1,t

is the set of all “continuing” goods that are sold both in period t and in period t − 1

● Our main focus is the Laspeyres index

▸ Laspeyres index uses lagged expenditure shares as weights (wkt = skt−1)

▸ Paasche index uses current expenditure shares (wkt = skt)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 6 / 18
Price Indices Demand-based

ln ΨG

t = ∑

k∈Ct−1,t

wkt ln pkt

pkt−1

,

● wkt is a weight assigned to product k (typically based on the product’s market share) in quarter t

● The set Ct−1,t

is the set of all “continuing” goods that are sold both in period t and in period t − 1

● Our main focus is the Laspeyres index

▸ Laspeyres index uses lagged expenditure shares as weights (wkt = skt−1)

▸ Paasche index uses current expenditure shares (wkt = skt)

● As robustness check, we have used demand-based indices

e.g. Sato-vartia (wkt =

(s

k,t

−s

k,t−1

)

(ln s

k,t

−ln s

k,t−1

)

∑k∈Ct−1,t

(s

k,t

−s

k,t−1

)

(ln s

k,t

−ln s

k,t−1

)

)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 6 / 18
Main Findings
Spatial Heterogeneity in Inflation: Aggregate Food

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 7 / 18
Spatial Heterogeneity in Inflation: Aggregate Food

● Food price has been growing faster in poorer areas

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 7 / 18
Spatial Heterogeneity in Inflation: Eggs Demand-based

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 8 / 18
Spatial Heterogeneity in Inflation: Eggs Demand-based

● The pattern stays robust

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 8 / 18
Spatial Heterogeneity in Inflation: Eggs Demand-based

● The pattern stays robust

● There was a bird flue episode in 2015 causing the price spike

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 8 / 18
Spatial Heterogeneity in Retailer Dynamics Regression

Figure: Share of large firms (emp≥ 500) Figure: Share of small firms (emp< 20)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 9 / 18
Spatial Heterogeneity in Retailer Dynamics Regression

Figure: Share of large firms (emp≥ 500) Figure: Share of small firms (emp< 20)

● More (less) large firms located in the bottom (top) decile

● Less (more) small firms located in the bottom (top) decile

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 9 / 18
Spatial Heterogeneity in Retailer Dynamics

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 10 / 18
Spatial Heterogeneity in Retailer Dynamics

● Less (more) number of chains are located in poorer (richer) areas

● Consistent pattern across time

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 10 / 18
Spatial Heterogeneity in Retailer Dynamics

Figure: Distribution of store numbers

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 11 / 18
Spatial Heterogeneity in Retailer Dynamics

Figure: Distribution of store numbers

● Higher fraction of large (small) chains in the bottom (top) decile (in terms of # of stores)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 11 / 18
Market Concentration across Income Deciles

HHIidt = β0 + β1Deciledt + δi + δt + εidt

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 12 / 18
Market Concentration across Income Deciles

HHIidt = β0 + β1Deciledt + δi + δt + εidt

● HHIidt is the Herfindahl–Hirschman index of retailer sales for PCE food category i, MSAs in income

decile d in quarter t

● Deciledt is an indicator for income decile

● δi

, δt : PCE food category, year fixed effects

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 12 / 18
Market Concentration across Income Deciles

HHI

Decile -0.004***

[0.000]

Constant 0.145***

[0.001]

Observations 10,920

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

● Higher retailer concentration is observed in lower income decile

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 13 / 18
Potential Mechanism: Retailers’ Market Power
Simple OLS Estimator

Pst = β0 + β1HHIst + δs + δ

yr

t + δ

qtr

t + εst

● Pst is the (geometric) Laspeyres index of eggs in MSA s, quarter t

● HHIst is the HHI of retailer sales in MSA s, quarter t

● δs, δ

yr

t

, δ

qtr

t

: MSA, year, quarter fixed effects

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 14 / 18
OLS Estimation Results

Price

HHI 0.011*

[0.006]

Constant 1.009***

[0.003]

Observations 9,484

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

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 15 / 18
OLS Estimation Results

Price

HHI 0.011*

[0.006]

Constant 1.009***

[0.003]

Observations 9,484

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

● HHI increases price level

● Potential endogeneity issue exists

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 15 / 18
Triple Difference Estimator

Pst = β0 + β2HHIst + β4(Treateds × HHIst )

+ β5(Treateds × HHIst ) + β6(Postt × HHIst )

+ β7(Treateds × HHIst × Postt ) + δs + δ

yr

t + δ

qtr

t + εst

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 16 / 18
Triple Difference Estimator

Pst = β0 + β2HHIst + β4(Treateds × HHIst )

+ β5(Treateds × HHIst ) + β6(Postt × HHIst )

+ β7(Treateds × HHIst × Postt ) + δs + δ

yr

t + δ

qtr

t + εst

● Treateds is a binary variable indicating whether MSA s is near to where egg layers were culled

during the 2015 Bird Flu according to the USDA report.

● Postt is a binary variable equal to 1 if quarter t is after 2015q1

● Pst , HHIst , and fixed effects are the same as before

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 16 / 18
Triple Difference Estimation Results

Price Price Price

Bird Flu × HHI × Post 0.033*** 0.018**

[0.011] [0.008]

Bird Flu × Post -0.006*** -0.023*** -0.017***

[0.002] [0.007] [0.005]

HHI × Post -0.014** -0.008*

[0.006] [0.005]

Bird Flu × HHI -0.003 -0.030**

[0.009] [0.015]

HHI 0.013*** 0.014***

[0.002] [0.005]

Fixed Effects Yes No Yes

Observations 9,484 9,484 9,484

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

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 17 / 18
Conclusion
Concluding Remarks

● Systematic diffs. in inflation rates and retailer market structure observed b/w poor and rich MSAs

● The poorest decile of MSAs faces (than the richest)

1 Higher inflation rates for both aggregate and disaggregated food items

2 Higher (Smaller) fraction of large (small) retailers

3 Higher concentration rate of retailers

● Exploiting the 2015 bird flu episode, we find that more concentrated retailers charge higher prices

● Future work:

1 Structural estimation of market power and its contribution to price growth

2 Identify and quantify the impact on spatial inequality

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 18 / 18
THANK YOU! ,

sk6285@princeton.edu / mnav@umd.edu
Appendix
Price Indices (Demand-based) Main

● Potential issues with Laspeyres or Paasche: no consideration on substitution effects

● Demand-based indices with CES assumption

▸ Sato-Vartia: based on common goods (b/w t and t − 1)

ln ΨSV

t = ∑

k∈Ct−1,t

wkt ln

pkt

pkt−1

, where wkt =

(sk,t−sk,t−1)

(ln sk,t−ln sk,t−1)

∑k∈Ct−1,t

(sk,t−sk,t−1)

(ln sk,t−ln sk,t−1)

▸ Feenstra-adjusted Sato-Vartia: further take into account product turnover

ln ΨFeenstra−SV

t = ln ΨSV

t +

1

σ − 1

ln

λt,t−1

λt−1,t

, where λt,t−1 =

∑k∈Ct−1,t

pk,tqk,t

∑k∈Ωt

pk,tqk,t

, λt−1,t =

∑k∈Ct−1,t

pk,t−1qk,t−1

∑k∈Ωt−1

pk,t−1qk,t−1

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 18 / 18
Spatial Heterogeneity in Inflation: Eggs (Demand-based) Main

Figure: Sato Vartia Figure: Feenstra-adjusted Sato Vartia

● The patterns stays robust (even after considering product turnover)

● Entering goods have larger sales value than exiting goods across all deciles (more so in decile 1)

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 18 / 18
Spatial Heterogeneity in Large Firm Activity Main

LargeFirmst = β0 + β1Incomest + δs + δt + εst

● LargeFirmst is the (employment) share of large firms in MSA s, year t

▸ Large firms: firms with 500+ employees

● Incomest is income per capita in MSA s

● δs, δt : MSA, year fixed effects

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 18 / 18
Spatial Heterogeneity in Large Firm Activity Main

Large firm share Large firm emp. share

Income -0.040*** -0.040***

[0.006] [0.009]

Constant 19.896*** 61.713***

[0.214] [0.345]

Observations 7,620 7,620

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

● Larger firms are more active in lower income decile

Seula Kim (Princeton), Michael Navarrete (UMD) Geospatial Heterogeneity in Inflation: A Market Concentration Story Oct 26, 2023 18 / 18