Wolf (2000) demonstrates that trade within the U.S. appears substantially impeded by state borders. We revisit this finding with improved data. We show that much intra-national home bias can be explained by wholesaling activity. Shipments by wholesalers are much more localized within states than shipments from manufacturing establishments. Controlling for relative prices and the use of actual, rather than imputed, shipment distances also reduces home bias estimates.

David Hummels

Russell Hillberry

English

Research Papers in Economics

NBER WORKING PAPER SERIES
INTRA-NATIONAL HOME BIAS: SOME EXPLANATIONS
Russell Hillberry
David Hummels
Working Paper 9022
http://www.nber.org/papers/w9022
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
June 2002
We wish to thank, without implication, Dan Kovenock and David Meyer for helpful comments; Cathy
Buffington and Arnold Rezneck at the Center for Economic Analysis for data assistance; and Purdue CIBER
for financial support.  The views expressed herein are those of the author and not necessarily those of the
National Bureau of Economic Research, the U.S. International Trade Commission or any of its individual
Commissioners.
© 2002 by Russell Hillberry and David Hummels.  All rights reserved.  Short sections of text, not to exceed
two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is
given to the source.Intra-national Home Bias: Some Explanations
Russell Hillberry and David Hummels
NBER Working Paper No. 9022
June 2002
JEL No. F15, R12
ABSTRACT
Wolf (2000) demonstrates that trade within the U.S. appears substantially impeded by state
borders.  We revisit this finding with improved data.  We show that much intra-national home bias can
be explained by wholesaling activity.  Shipments by wholesalers are much more localized within states
than shipments from manufacturing establishments.   Controlling for relative prices and the use of actual,
rather than imputed, shipment distances also reduces home bias estimates.
Russell Hillberry David Hummels
U.S. Intl. Trade Commission Department of Economics
and Purdue University Krannert School of Management
Purdue University
West Lafayette, IN 47907-1310
and NBER
hummelsd@purdue.edu
Tel: 765-494-4495
Fax: 765-496-1778  1
I.  Introduction   
Why do political boundaries shape the geographic pattern of trade?  This question 
has attracted considerable attention since McCallum’s (1995) finding that Canadian 
interprovincial trade was twenty-two times larger than province-state trade.  While the 
magnitude of the McCallum estimate is surprising
1, one can certainly identify reasons 
why international borders impede trade.  Presumably, national borders proxy for a wide 
range of trading frictions, including tariffs and non-tariff measures imposed intentionally 
by national governments, as well as costs associated with customs clearance and currency 
exchange that inevitably arise when shipping goods across differing national 
jurisdictions.   
Such frictions are notably absent in trade between U.S. states, which are 
constitutionally enjoined from impeding interstate commerce.  Yet, it still appears that 
state borders inhibit trade flows.  Using the public sample of the 1993 US Commodity 
Flow Survey, Wolf (2000) estimated that intra-state trade was more than 4 times larger 
than trade between states.  This leaves us with two questions.  One, why do arbitrarily 
drawn political boundaries like U.S. state borders appear to pose a barrier to trade?  Two, 
is there economic significance to these borders not previously appreciated? 
An answer may be found by noting that Wolf’s data included shipments 
originating in both manufacturing and wholesale establishments.
2  One can broadly think 
of manufacturers and wholesalers as a kind of hub and spoke arrangement.  Goods are 
manufactured in the hub and dispersed, sometimes at great distances, to a number of 
wholesaling spokes spread throughout the country.  The wholesaling spokes then 
                                                 
1 The magnitudes are both surprising and in question.  See Anderson and vanWincoop (2001). 
2 This implies that any particular good may be counted twice in the shipments data; once when shipped 
from the manufacturer, and once when shipped from the wholesaler.   2
distribute, over very short distances, to retailers.  As a result, lumping wholesaling and 
manufacturing shipments together may provide a misleading picture of spatial frictions.   
  The reason that manufacturers would choose to employ this hub and spoke 
system is itself quite interesting and informative about spatial frictions.  Put another way, 
one might ask:  if manufacturers find it easy to ship over long distances, why don’t 
wholesalers?  And the answer may be that the kinds of geographic frictions wholesalers 
face are quite different from those faced by manufacturers. 
Several possibilities suggest themselves.  Wholesaling may be employed in order 
to efficiently manage inventories and respond rapidly to demand fluctuations.  Hub and 
spokes system may also be used to exploit the relative efficiency of long and short haul 
transportation modes.  That is, large trucks or rail are used for movements between 
manufacturers and wholesalers, while smaller trucks transport between wholesalers and 
retailers.  In both cases, wholesale shipments are excessively local relative to 
manufacturing shipments because the cost of some geographic frictions related to 
distribution rise sharply in stages of the value-chain immediately prior to consumption.  
Returning to Wolf’s result, it is not that state borders matter, per se, but that borders 
proxy for very short shipment lengths. 
It may also be that the political boundaries are of direct interest due to contractual 
stipulations binding on wholesale shippers.  Manufacturers are legally allowed to 
segment markets by designating explicit geographic boundaries that their wholesalers are 
not allowed to cross.  Such segmentation may be an effective way to engage in resale 
price maintenance, and state boundaries are an obvious way to divide territory.   3
All three explanations can be thought of as kinds of geographic frictions, but they 
are quite different than straightforward transportation cost frictions typically supposed.  
The question then becomes whether the responsiveness of shipments to geographic 
frictions depends as much on the nature of the shipper as on the good being shipped.  If 
yes, then this informs us about the nature, as well as the size, of the frictions in question.   
To answer these questions we employ a private use sample of the 1997 US 
Commodity Flow Survey.
3  These data provide two significant benefits relative to the 
1993 public use sample used by Wolf.  First, we can separate wholesale shipments from 
shipments by manufacturing establishments in order to distinguish their respective spatial 
characteristics.  Second, we observe actual distances shipped rather than having to impute 
distances from the physical distribution of a state’s population.  We find that actual 
shipment distances within states are much shorter than Wolf’s measure of distance 
suggests, with the effect quite pronounced within states. 
We incorporate information on shipment distances and wholesale versus 
manufacturing establishments in our estimates, and also control for goods’ prices in a 
manner consistent with the Anderson and van Wincoop (2001) critique of the gravity 
literature.  Our results suggest state border effects are still significant, but roughly a third 
as large as Wolf estimates.   
 
II.  Estimation Approach and Data 
  We use a gravity equation to estimate the volume of shipments between any state 
pair ij, including within state trade.  This equation is typically motivated by a model in 
                                                 
3 Hillberry and Hummels (2002) employ this data to link intermediate goods trade with industry 
agglomeration.    4
which goods are differentiated by origin and consumers uniquely value each variety.  
With CES preferences the value of bilateral trade M, between origin i and destination j, is 
given by: 
(1)   
1
i
ij i j ij
j
p
M YY t
P
σ
σ
−
− 
=  

 
where Y denotes income,  / ij p P  the price of output in region i relative to the consumer 
price index in region j,   ij t  the ad valorem iceberg cost of trade between regions, and σ 
the elasticity of substitution among varieties of output.  Wolf estimates a reduced form of 
(1) by taking logs and measuring the trade friction (tij) using distance shipped and a 
dummy variable, Ownstate, for whether the flow remains within the originating state 
(i=j).  Relative prices are ignored. 
 
(2)   01 2 3 4 ln ln ln ln ij i j ij ij ij M aYYD I S T O W N S T A T E e β ββ β =+ + + + + 
 
In order to make comparisons directly with Wolf, we use (2) as our baseline 
regression.  Our contributions come primarily in providing significantly improved data on 
shipment values, and on distance shipped, as detailed below.  Anderson and van Wincoop 
(2001) show that the specification in equation (2) leads to biased estimation because, in 
equilibrium, the omitted price index term from equation (1) is related to the size and 
distribution of trade barriers. We incorporate their critique in a parsimonious fashion by 
including vectors of importing and exporting state fixed effects.  This also controls for 
idiosyncrasies outside of the model such as variation in the share of state output devoted 
to nontraded services.   5
The primary data source we use is the raw data file from the 1997 U.S.  
Commodity Flow Survey (CFS).  The CFS is collected every five years by the U.S. 
Census Bureau, which chooses a stratified random sample of shipments from  U.S. 
mining, manufacturing, and wholesale establishments.
4  Reported shipment 
characteristics include the shipment’s weight, value and commodity classification, an 
establishment identifier, the shipper’s (SIC) industrial classification, the zip code of the 
shipment’s origin and destination and the actual shipping distance between them.     
  These are the best available data documenting sub-national trade because the data 
are drawn from stratified random samples of actual shipments.  This is in sharp contrast 
with the Statistics Canada data, which are imputed from multiple data sources. The 
private use sample provides two improvements relative to the publicly available CFS data 
used by Wolf (2000) and Anderson and van Wincoop (2001):  the ability to 
include/exclude wholesale shipments and the ability to measure actual shipment 
distances. 
  Wholesale shipments are important for reasons addressed above.  Estimates of the 
Ownstate coefficient in (2) can be highly sensitive to the measure of internal state 
distances because these distances set the baseline against which actual Ownstate trade 
flows are compared.  Wolf relies on a simple measure of internal distance (Dii): 
(3)  
,1
,12
,1 ,2
21
i
ii i
ii
Pop
DD
Pop Pop

=− 
+  
 
where Popi,1 and Popi,2 are the populations of the first and second largest cities in i, 
respectively, and Di,12 is the distance between the two largest cities in i. 
                                                 
4 The procedure samples a set of establishments, then randomly samples shipments for the chosen 
establishments.   6
 Several authors construct more sophisticated distance measures by linking them 
directly to a model of spatial activity which treats output as proportional to population 
and fixed in space.
5  What Wolf and subsequent authors have in common is that their 
measures overstate actual distances if firms move to be near to sources of idiosyncratic 
demand.  For example, suppose a food processing firm is the primary customer for a 
particular kind of glass jar.  If the glass jar manufacturer moves proximate to its customer 
within the same state the actual shipping distance of the jars may be minute.  Yet, Wolf’s 
measure applies a statewide distance as the measure of frictions applied to this 
transaction.  The problem is most acute for internal state distances, but also applies to 
distances between adjacent states.   
  We sidestep the debate on theoretical constructions of distance by using shipment 
level data on actual distance shipped taken from the Commodity Flow Survey.
6  This 
draws on Department of Transportation impedance calculations on actual transport miles, 
that is, mileage that shipments must have traveled given the system of highways and rail 
lines connecting any two points.  This distance can be much larger than straight line miles 
when highways and rail lines do not permit direct transit.  It can also be much smaller 
than equation (3) if the location of output responds to trade costs.
7 
  To see the difference, we compare Wolf’s measure of distance to the shipment 
level data in the 1997 CFS.    We regress actual distances for each shipment s on the 
distance measure used by Wolf, along with an own state dummy variable, an adjacent 
state dummy variable and a commodity fixed effect 
                                                 
5 Nitsch (2000) and Helliwell and Verdier (2001) provide the most exhaustive measures of internal 
distance. 
6 The calculations apply the distance between zip code of origin and zip code of destination.  These are still 
central place distances, but they are applied within a very fine grid.    
7 Direct evidence on this point is provided in Hillberry and Hummels (2001).   7
(4)   12 3 ln ln
s
ij ij ij ij k ij ACTDIST WOLFDIST OWNSTATE ADJ a e ββ β =+ + + +  
 
Table 1.  Actual versus imputed distances 
 OLS  Fixed  Effects 
Wolf distances  0.939 
(0.001) 
0.821 
(0.001) 
0.931 
(0.001) 
0.816 
(0.001) 
Ownstate   -0.498 
(0.004) 
 -0.488 
(0.004) 
Adjacent   -0.404 
(0.002) 
 -0.395 
(0.002) 
        
Observations 303086  303086  303086  303086 
Adj R
2 0.865  0.878  0.869  0.881 
Dependent variable is logged shipment distance reported in CFS.  Standard errors 
in parentheses.  All variables are significant at the 1% level. 
 
We find that the Wolf distances slightly overstate actual distances for all pairs,  
but dramatically overstate distance within own-state and between adjacent state pairs.  
Actual shipments in-state are half as large as the Wolf measure suggests, while adjacent 
state shipments are 40 percent smaller.  These differences affect our estimates of home 
bias within states.   
 
III.  Results 
We estimate equation (2) as follows.  First, we aggregate shipments over 
commodity categories to yield total bilateral trade flows between states.  We regress these 
shipment values on output in each state, distance and an own-state dummy variable.  The 
columns in Table 2 reflect differences in the dataset employed.  Column 1 reports Wolf’s 
(2000) results from the 1993 CFS public use sample.  The remaining columns use the 
1997 data.  Columns 2-4 use Wolf’s distance measure.  Columns 5-7 use actual shipment 
distances from the CFS.  The other adjustments are excluding wholesale shipments, and   8
using vectors of origin-state and destination-state fixed effects to control for output 
levels, relative prices, and other state-level idiosyncracies outside of the model. 
Table 2.  Aggregate domestic shipments 
 1993 
CFS 
(Wolf 
2000) 
Basic 
regression
Excluding 
wholesale 
shipments 
Using 
origin and 
destination 
fixed 
effects 
Basic 
regression
Excluding 
wholesale 
shipments 
Using 
origin and 
destination 
fixed 
effects 
yi 
 
 
1.02 1.04 
(0.02) 
1.01 
(0.02) 
 1.04 
(0.02) 
1.01 
(0.02) 
 
yj 
 
 
0.98 0.98 
(0.02) 
1.03 
(0.02) 
 0.99 
(0.02) 
1.05 
(0.02) 
 
distij  
(Wolf 
measure) 
 
-1.00 -0.96 
(0.02) 
-0.88 
(0.02) 
-0.95 
(0.02) 
   
distij  
(observed) 
 
      -1.06 
(0.02) 
-1.00 
(0.02) 
-1.05 
(0.02) 
Ownstate 1.48  1.13 
(0.14) 
0.74 
(0.15) 
0.53 
(0.11) 
0.99 
(0.13) 
0.61 
(0.14) 
0.44 
(0.10) 
           
Observations  2137  2304 2304 2304  2304 2304 2304 
Adj R
2  0.836  0.825 0.812 0.893  0.848 0.832 0.911 
Dependent variable is the logged value of aggregate shipments from state i to j.  Standard errors in 
parentheses.  All variables significant at the 1% level.   
 
We focus primarily on the Ownstate dummy variable.  There are four main 
findings.  First, the estimated Ownstate coefficient is lower in 1997 than in 1993.
8  
Second, using actual distances shipped reduces the Ownstate coefficient in all 
specifications.  Third, excluding wholesale shipments considerably reduces the effect of 
the Ownstate dummy, as well as slightly lowering the distance elasticity.  This suggests 
that wholesale shipments are highly localized.  Fourth, using origin and destination fixed 
effects to control for prices, among other things, reduces the Ownstate coefficient further.  
                                                 
8 This may simply reflect differences in sample coverage between the private and public use data.  Also, the 
1997 data includes all state pairs within the continental US, while the 1993 data exclude some pairs.   9
Our final specification, which incorporates all three adjustments, shows the sizable 
border coefficient estimated by Wolf to be reduced to one-third its former size.
9 
To check robustness, we also estimated a version of equation (2) separately for 
each 4 digit SIC category using a dependent variable that first included, then excluded, 
wholesale shipments.  We employed origin-state and destination-state fixed effects, 
distance shipped, and an own-state dummy as dependent variables.  Ownstate coefficients 
were, on average, twice as large for the shipment regressions that included wholesale 
shipments.  Distance coefficients were also systematically larger for wholesale 
shipments. 
  
IV.  Conclusions 
By documenting the effect of state borders on internal U.S. trade patterns, Wolf 
(2000) suggested an important puzzle for the economic geography literature.  Home bias 
in trade among U.S. states is surprising, given the relative absence of obvious trading 
frictions.  We provide three possible explanations for measured home bias in state 
commodity flows.  Taken together, we find home bias one-third as large as Wolf. 
  First, average distance calculations based on the geographic distribution of 
population overstate actual distances that shipments travel.  This is likely explained by 
firms locate near to idiosyncratic demand for their output.  Second, wholesale shipments 
are highly localized, and more sensitive to state borders.  This is consistent with the view 
that wholesale shipments serve a substantially different economic function (e.g., 
inventory holding) than shipments by manufacturers.  Wholesale shipments may also be 
                                                 
9 The coefficient implies that the ratio of actual to predicted Ownstate trade is 1.55.  Manufacturers’ 
Ownstate shipments are 55% higher than is predicted by the model.  Wolf’s coefficient estimates imply a 
ratio of 4.39.   10
directly affected by state borders if distribution contracts assign exclusive state territories 
to wholesalers.  Finally, controlling for variation in relative prices, as suggested by 
Anderson and van Wincoop (2001), reduce measured home bias.  
 
 
References: 
 
Anderson, James and Eric van Wincoop 2001, “Gravity with Gravitas: A Solution to the 
Border Puzzle,” NBER Working Paper 8079. 
 
Helliwell, John F. and Genevieve Verdier 2001, “Measuring Internal Trade Distances: A 
New Method Applied to Estimate Provincial Border Effects in Canada,” Canadian 
Journal of Economics 34, pp. 1024-1041. 
 
Hillberry, Russell  and David Hummels 2002, “Explaining Home Bias in Consumption:  
The Role of Intermediate Input Trade,” Purdue University mimeo. 
 
McCallum, John T. 1995, “National Borders Matter: Canada-U.S. Regional Trade 
Patterns,” American Economic Review 85, pp. 615-623. 
 
Nitsch, Volker 2000, “National Borders and International Trade: Evidence from the 
European Union,” Canadian Journal of Economics 33, pp. 1091-1095.  
 
Wolf, Holger C. 2000, “Intra-national Home Bias in Trade,” The Review of Economics 
and Statistics 82(4), pp. 555-563. 
 

Explaining Home Bias in Consumption: The Role of Intermediate Input Trade,” Purdue University mimeo.

Gravity with Gravitas: A Solution to the Border Puzzle,”

Intra-national Home Bias in Trade,”

Measuring Internal Trade Distances: A New Method Applied to Estimate Provincial Border Effects in Canada,”

National Borders and International Trade: Evidence from the European Union,”

Intra-national Home Bias: Some Explanations

Intra-national Home Bias: Some Explanations

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