Estimating the Price Effects of Globalization:
The Case of U.S. Imports from China∗
Liang Bai†
Sebastian Stumpner‡
March 29, 2016
PRELIMINARY AND INCOMPLETE
Abstract
This paper investigates the size of U.S. consumer gains from the recent growth in Chinese
imports. Using barcode-level price data and exploiting cross-product variation in import penetration between 2004 and 2012, we find that prices declined by more in product categories with
higher Chinese import penetration: a one standard-deviation increase in import penetration
leads to a 1.9 percentage point reduction in the annual inflation rate, or roughly 60% of one
standard deviation. We address the potential endogeneity of imports by using Chinese exports
to European countries as an instrument. We find that the effect of import penetration on prices
is driven by both the intensive and extensive margins, suggesting the presence of pro-competitive
effects for old goods as well as variety gains from new goods. We find, however, no evidence for
net gains in the number of consumed varieties, as newly introduced goods appear to strongly
displace old varieties. Finally, we also find evidence for reductions in final goods prices induced
by imported intermediate inputs from China.
∗
We are grateful to Jason Garred, Andres Rodriguez-Clare, Thomas Sampson, Catherine Thomas and seminar
participants at the LSE and Université de Montréal for their helpful comments. Liucija Latanauskaite, Matus Luptak
and Tomaz Norbutas provided excellent research assistance.
†
School of Economics, University of Edinburgh
‡
Department of Economics, Université de Montréal
1
1
Introduction
A common prediction of international trade models is that consumers benefit from efficiency
gains through lower prices. While there exists a rich theoretical literature, empirical work
studying the magnitude of these gains remains limited. In this paper, we revisit this classic
question in international economics by combining barcode-level data on U.S. consumer goods
prices with detailed data on international trade flows. Focusing on the recent rise of U.S. imports
from China, we ask: how much does the U.S. gain from Chinese import penetration through
lower consumer prices? And what are the main channels underlying this effect?
Focusing on the years 2004-12, we first show that U.S. consumer prices declined by more in
product categories with higher Chinese import penetration. This effect is not only statistically
significant, but also economically large: across product categories, a one-standard deviation
increase in Chinese import penetration leads to a 1.9 percentage point reduction in the annual
inflation rate, or roughly 60% of one standard deviation. To allow for a causal interpretation
of these results, we address the endogeneity of imports by using Chinese exports to European
countries as an instrument (Autor, Dorn and Hanson 2013). We find that the effect of import
penetration on prices is driven by both the intensive and the extensive margins, suggesting the
presence of pro-competitive effects for old goods as well as variety gains from new goods (Broda
and Weinstein 2006). We find, however, no evidence for net gains in the number of consumed
varieties, as newly introduced goods appear to strongly displace old varieties. Finally, we also
find evidence for reductions in final goods prices induced by imported intermediate inputs from
China.
We focus on the years of 2004-2012, a period in which U.S. imports from China grew rapidly,
from $196bn in 2004 to $425 bn in 2012.1 For manufactured goods, the share of total US imports
accounted for by China went from 17% to 25%, overtaking Canada and Mexico as well as the
European Union (Figure 1). In our empirical strategy, we make use of the heterogeneity in
Chinese import penetration across different product categories. For instance, while imports of
clocks and watches have remained almost stagnant during this period, those of electric machinery
and sound/TV equipment have increased by more than 4-fold. This variation allows us to
compare the evolution of prices in industries where trade with China has grown rapidly to that
of prices in industries where trade with China has not.
1
In comparison, the increase in U.S. exports to China over the same time period only equaled 1/3 of the increase
in U.S. imports from China.
2
To study the effect of Chinese import penetration on U.S. consumer prices, we bring together
data from the AC Nielsen consumer panel and international trade flows at the 6-digit HS level
from BACI. The AC Nielsen consumer panel provides us with data on the purchases of roughly
60,000 U.S. households, where products are defined at the barcode-level. We construct a concordance between Nielsen "product modules" and HS 6-digit codes and define a set of roughly 280
product categories. Examples of product categories include coffee, computer software, creams
and cosmetics, etc. For each category, we then compute inflation rates using the method developed by Sato (1976) and Vartia (1976) for a non-symmetric CES function, and extended by
Feenstra (1994) to allow for entry and exit of products.
To address the potential endogeneity of Chinese import penetration to local demand or supply shocks, we follow work by Autor et al. (2013) and instrument for Chinese import penetration
to the U.S. using Chinese import penetration to major European countries. The objective of
using this instrument is to isolate variation in Chinese import penetration that can be explained
by changes in supply conditions in China, as opposed to changes in demand conditions in the
U.S. The primary threat to this identification strategy is negatively correlated demand shocks
between the US and EU, which we think is extremely unlikely.
Our first main result indicates that the rise of Chinese imports substantially reduced prices
of consumer goods in the U.S. Comparing two product categories with a difference in Chinese
import penetration equal to one standard deviation caused a cumulative decline in U.S. prices
of 16.4 percentage points (or 1.9 percentage points per year), which corresponds to 0.58 of a
standard deviation in inflation rates across categories. These estimates are robust to different
values of the within-category elasticity of substitution, to the weighting of each category by
initial expenditure, and to the inclusion of additional controls such as import penetration from
Mexico, Canada, and Germany (the three next largest countries in terms of import penetration
in the U.S. in 2004-12).
Next, we investigate the channels by which Chinese import penetration affect domestic prices.
We show that the effect of rising imports on prices works through both the extensive and
intensive margins, that is both through the availability of new goods as well as through procompetitive effects on previously available products. Chinese import penetration does not,
however, result in a net increase in the number of consumed varieties. Instead, our results
suggest that Chinese imports displaced previously consumed varieties one-for-one. Consistent
with this interpretation, we observe a significantly higher degree of product turnover in categories
3
with high Chinese import penetration.
Finally, we also assess the role of imports of intermediate inputs from China for U.S. consumer
prices. We use information from the BEA’s detailed input output table for more than 300
manufacturing industries to compute the average import penetration in intermediate goods for
each of our product categories. These results show that higher imports of intermediate inputs
also significantly reduced final goods prices.
In ongoing work, we aim to complement the empirical exercise with a model of US-China
trade. In the model, we will calibrate productivity shocks to match the rise in Chinese import
penetration across product categories. The model would then allow us to identify the aggregate
gains to U.S. consumers that are implied by our cross-sectional empirical estimates.
This paper fits into a large and diverse literature on quantifying the effects of trade integration (see Costinot and Rodriguez-Clare (2013) for a recent survey). Within the micro
approaches of this literature, there has been work examining the implications of international
trade for productivity, inequality and labor markets. There has been surprisingly little work
studying the effects on prices and consumer surplus. The most closely related papers to ours are
Broda and Romalis (2008) and Broda and Weinstein (2006). In particular, Broda and Romalis
(2008) showed that Chinese import penetration was associated with lower consumer prices in
the U.S. Our work differs, however, in several important respects: First, we go beyond previous
work by investigating the channels through which Chinese import penetration affects domestic
prices, such as intensive margin price growth, variety effects, and the role of imported intermediate goods. Second, we will be able to quantify the aggregate benefits to U.S. consumers
by integrating the empirical results with a model of U.S.-China trade. This seems particularly
relevant in light of the work by Autor et al. (2013) that focused on the negative employment
consequences of Chinese import penetration. Finally, we adopt a different empirical strategy,
using Chinese import penetration to Europe as an instrument for Chinese import penetration
in the U.S.
This paper is also related to a much more recent and smaller literature on the implications
of China’s rise for the global economy (Autor et al. (2013), Hsieh and Ossa (2011), Costa et al.
(2014)). Most closely related to our paper, Autor et al. (2013) and Autor et al. (2014) focus on
the effect of rising Chinese imports on U.S. manufacturing employment and the long-run effects
on workers’ careers. We build on their work and, in particular, on their identification strategy,
but consider instead the effects of Chinese imports on U.S. product markets and consumer prices.
4
The rest of the paper is organized as follows. Section 2 will describe in detail our data
sources and the construction of key variables. Section 3 will outline our empirical strategy and
discuss summary statistics. Section 4 will present our results on inflation, product variety and
turnover, as well as robustness checks. Section 5 offers some concluding remarks.
5
2
Data Sources and Methodology
2.1
Data Sources
We use data from several sources. The two main datasets we employ are data on international
trade flows at the HS 6-digit level, and data on household purchases and product prices from
AC Nielsen.
The data on international trade flows come from the BACI dataset, which is based on UN
Comtrade. It provides us with information on the volume of all bilateral trade flows for 5226
different HS 6-digit products (e.g. microwave ovens, electrically-operated alarm clocks, etc.)
during our period of study.
The data on the prices of consumer goods and volume of purchases come from a proprietary
dataset from AC Nielsen. These "Homescan" data contain information on purchases by a
nationally-representative sample of around 60,000 U.S. households at the barcode level and at
quarterly frequency. We observe prices and purchases of a total of roughly 1.8 million different
products. We use the versions of this dataset for the years 2004-12.
In order to merge the two aforementioned datasets, we constructed a concordance between
the 5226 6-digit HS codes and 1200 Nielsen "product modules". A large part of the HS codes
are accounted for by intermediate goods, and therefore do not match directly to Nielsen product
modules. We match slightly under 1000 HS codes to the Nielsen data. In several cases, complex
merges are required in which several HS codes and several Nielsen product modules are combined
to form one product category.2 This procedure resulted in a total of 280 product categories.
Examples of such categories include coffee, computer software, and creams and cosmetics.
2.2
Constructing Category-Specific Inflation Rates
To construct category-specific inflation rates, we focus on the non-symmetric CES consumption
function. Consumption in product category i at time t is given by an aggregate over different
varieties k:
Cit =
X
1
aki σ ckit
σ−1
σ
σ
! σ−1
k
2
For example, the Nielsen products "Milk chocolate" and "Dark chocolate" were combined with the HS 6-digit
codes "Chocolate in blocks weighing less than 2kg" and "Chocolate in blocks weighing more than 2kg" to form one
category named "Chocolate".
6
The terms aki denote unobserved product quality, which is constant for a barcode-level product
over time. The ideal price index for this consumption bundle is given by:
X
Pit =
1−σ
aki pkit
1
! 1−σ
k
When the set of goods is constant over time, then we can compute the inflation rate as
follows. First, define inflation as
Pit
Pit−1
πit =
From Sato (1976) and Vartia (1976), we can write inflation as the weighted geometric sum of
individual price changes:
πit =
k
!ωit
Y
pkit
k
pkit−1
k
The weights ωit
sum to one and can be expressed as a function of expediture shares skit =
pk ck
P it kit k :
k pit cit
k
sk
it −sit−1
k
log(sk
it )−log(sit−1 )
k
ωit
=P
k
sk
it −sit−1
k
k log(sk
)−log(s
it
it−1 )
For very small changes of the market share, this weight equals the market share skit .
With a changing set of goods, Feenstra (1994) shows that inflation can be written as:

πit = 
Y
k∈ItE
k 
!ωit
pkit
pkit−1

λit
λit−1
1
σ−1
E
where Iit
denotes the set of goods that are present in both time periods. The full inflation rate
is now the product of the inflation rate at the intensive margin, multiplied by a correction factor
that measures the contribution of the extensive margin (new and disappearing varieties).
The term λit of the correction factor can be expressed as follows:
λit =
E
Xit
,
Xit
which is the fraction of expenditure at time t that is going towards previously available varieties.
Intuitively, when the share of expenditure going towards new varieties in period t exceeds the
share of expenditure going towards old varieties in period t − 1, the adjustment term reduces
7
overall inflation. The extent to which the introduction of new goods affects the inflation rate depends on the elasticity of substitution σ. In particular, if the new goods are highly substitutable,
the effect on inflation will be more muted.
In order to compute inflation rates, we need a value for the elasticity of substitution between
product varieties. We assume a value of 5 for the elasticity of substitution, which is roughly the
mean of the elasticities of substitution estimated by Broda and Weinstein (2006) for product
groups at a similar level of aggregation. In robustness exercises, we also consider different values
of the elasticity of substitution, and find that the results are qualitatively unchanged.
2.3
Measures of Import Penetration
We follow Autor et al. (2013) in measuring Chinese import penetration as the change in U.S.
imports from China from 2004-12, which we normalize by total U.S. expenditure in that product
category for 2004.3
ImpP eni =
i
i
XCHN,12
− XCHN,04
i
X04
Total U.S. expenditure on product category i are computed as total production plus imports
minus exports.4
We similarly compute a measure for Chinese import penetration of intermediate inputs
(IPII). To do so, we collect input shares from the BEA input-output tables, and compute IPII
as the weighted average of input-level import penetration ratios, with weights γij given by input
shares:
IP IIi =
S
X
γij ImpP enj
j=1
3
P
For small changes, the growth of the price index can be written as P̂i = j bij p̂ij , where p̂ij is the growth of the
price index of goods purchased from country j, and bij is the share of expenditure on goods from country j. Focusing
only on price changes in goods from China, and ignoring indirect feedback effects through changes in the aggregate
1
1 ∆XiC
price index, we can write: P̂i = − σ−1
biC b̂iC = − σ−1
Xi
4
We compute production at the category-level using production data for 6-digit NAICS industries, and using a
crosswalk from NAICS to HS.
8
3
Empirical Strategy and Summary Statistics
Our main empirical estimation will exploit cross-product variation in the import penetration
measures to identify the effect of trade shocks on consumer prices and product varieties in the
US between 2004 and 2012. Using barcode-level data on prices, as well as household-level data
on expenditures, we construct price indices for 280 product categories (e.g. school/office supplies, musical instruments, cameras, etc.). We then compare inflation rates between categories
with stronger vs. weaker import penetration originating from China, controlling for import
penetration from Mexico, Canada and Germany, the three next most important countries in
terms of US import growth during our study period. That is, our main estimation is as follows.
S,Chn
S,M ex
S,Can
S,Deu
yi = α + β1 mU
+ β2 m U
+ β3 mU
+ β4 mU
+ i
i
i
i
i
(1)
To begin with, we use as the dependent variable the cumulative inflation rate in product
category i, πi (i.e. the ideal cost of living inflation as calculated from a CES demand system). In
subsequent specifications we will decompose the effect on inflation into intensive and extensive
margins. Specifically, we will have two measures of inflation at the intensive margin: first using
the same set of goods in consecutive years, second using the same set of goods in all years. For
the extensive margin, we use as the dependent variable the Feenstra (1994) correction factor
outlined in Section 3, which captures the contribution to inflation from new product varieties.
We then turn our attention to the effect of import penetration on the growth in consumer
expenditure X̂i , the change in the number of goods purchased as well as a measure of product
S
turnover. The variable mU
is the import penetration measure for category i.
i
3.1
Identifying Trade Shocks
The main challenge for identification is to isolate trade shocks that affect Chinese exports to
the US, but are uncorrelated with changes in demand conditions in the US or supply conditions
elsewhere at the product level. To do this, we follow Autor, Dorn and Hanson (2013) in using
EU imports from China as an instrument for US imports from China. Intuitively, if the growth
in US imports from China during 2004-2012 is driven either by productivity growth in China
or a reduction in trade barriers as a result of China’s accession to the WTO, we should observe
an increase in Chinese exports to other developed countries, such as the EU. In practice, we
restrict our EU sample to its five largest economies: Germany, France, United Kingdom, Italy
9
and Spain, due to insufficient data on industrial output at the ISIC - Rev 3 sector level for the
other EU countries.5
The first set of potential confounding factors are local demand shocks. Specifically changes
to demand in the US in product category i could drive both US prices and imports from China.
This is our main motivation for using the instrument. The exclusion restriction is that demand
shocks in Europe and the US are uncorrelated. Notice that OLS estimates should be biased
towards zero in this case, because rising imports from China would be the result of growing US
demand, which would push up prices, ceteris paribus.
The second set of potential confounding factors are global or correlated demand shocks.
In particular, consider a change in demand in the US and Europe in product category i (e.g.
demand increases in both regions). In that case, imports from China by both regions would
increase, and prices would increase as well. If demand shocks in EU and US are positively
correlated, the IV should still be biased towards zero. It would only be biased away from zero
if demand shocks are negatively correlated, which we think is unlikely.
The third set of potential confounding factors are local supply shocks. If productivity
growth in the US differs across product categories, then US prices will change differentially
across categories, and imports may be affected differentially as well (e.g. if productivity rises in
the US, US products will be used to substitute for foreign products). This would explain the
results only if, across product categories, US supply shocks happen to be positively correlated
with Chinese import penetration.
Finally, we may be concerned about global or correlated supply shocks. If there is a
supply shock that is the same for all producers of a particular product in all countries (e.g. a
new production technology), then we would see US prices decline in that category, but we would
not see a rising share of Chinese imports. The share of Chinese imports would only rise if China
becomes relatively more productive than competitors. While this is theoretically possible, the
primary change in the composition of US imports during this period is the role of China (See
Figure 1). Moreover, we can control for import penetration from the next three fastest-growing
exporters to the US - Mexico, Canada and Germany - during our study period.
With these points in mind, we run the following first-stage regression:
5
Data on output at the sectoral-level is needed in order to compute total expenditure at the product level. This,
in turn, is needed to construct our import penetration measures.
10
S,Deu
S,Can
S,M ex
S,Chn
+ i
+ β4 mU
+ β3 mU
+ β2 mU
= α + β1 mEU,Chn
mU
i
i
i
i
i
(2)
is the China import penetration measure for the EU in category i.
where mEU,Chn
it
3.2
The Role of Intermediate Inputs
Besides the direct effect of import penetration on final consumer goods, there could also be an
indirect effect via intermediate inputs. For example, the price of PCs in the US may be affected
by imports of PCs, as well as by imports of semi-conductors. To capture this indirect effect, we
construct a measure called import penetration in intermediate inputs (IPII), for each of our 280
product categories. In our regressions, we include this as an additional explanatory variable,
instrumented similarly by an analogous measure for the five-EU economies.
3.3
Summary Statistics
Table 1 shows summary statistics for some of our key outcomes and regressors. Assuming an
elasticity of substitution of 5, the average cumulative inflation rate during 2004-2012 was 4.2%,
or 0.52% per annum. Using only goods that are present in consecutive years, and therefore
disregarding the contribution of new varieties through the Feenstra correction factor, the average
cumulative inflation rate becomes 15.1% during 2004-2012, or 1.8% per annum. There is a great
deal of variation across product categories for each of these measures, with standard deviations
of 28 and 23 percentage points respectively.
At the same time, Chinese imports to both the U.S. and Europe grew rapidly. As a share of
total expenditure on a given product category in 2004, the increase in Chinese imports accounted
for 5.7% and 5.8% respectively. This is much larger than the import penetration measure for
each of the next three fastest-growing exporters to the US - Mexico, Canada and Germany - at
1.3%, 0.9% and 0.3% respectively.
11
4
Results
In this section we discuss the estimated effects of import penetration from China, first in final
goods and then in intermediate inputs, on inflation and product variety in the United States
between 2004 and 2012. We will examine both the intensive and extensive margins of inflation,
separately identifying the contribution of new varieties. We will also split our sample period
into three sub-periods (2004-2007, 2007-2009 and 2009-2012), corresponding to pre-, duringand post-recession respectively. Finally, we will check for robustness of our results to alternative
values of the elasticity of substitution.
4.1
Effect on Inflation Rates
Before discussing the results on inflation, Table 2 presents the first-stage of the IV regression.
Columns 1 and 2 are bivariate specifications, first unweighted and then weighted using 2004
expenditure levels. Columns 3 and 4 also include Mexico, Canada and Germany’s import penetrations in the US as additional explanatory variables. The main coefficient on China’s import
penetration in the five-largest EU economies is positive, close to one, and highly statistically
significant across all specifications. This is in stark contrast to coefficient estimates on the other
import penetration variables, suggesting that sectoral patterns of comparative advantage are
different between China on the one hand, and Mexico, Canada and Germany on the other.
Table 3 presents our estimated effects of import penetration from China on US inflation
rates. Columns 1 and 2 are simple OLS estimates, while columns 3-6 are results when China’s
import penetration in the US is instrumented by its import penetration in the EU.
As discussed in the previous section, if demand shocks in the U.S. affect both prices and
imports from China, then the least squares specifications would underestimate the true effect.
We see some suggestive evidence of this in our results. While the OLS and IV coefficient
estimates are both negative and highly statistically significant, the IV estimates are slightly
larger in magnitude. For instance, in the simple unweighted OLS specification (Column 1), a
one standard-deviation change in Import Penetration from China leads to a 16.4 percentage
point cumulative difference in the price index, or roughly 1.9% per year. This corresponds to
0.58 of a standard deviation in the inflation rate, a very large effect indeed. Compared to the
OLS estimates, the IV ones are approximately 15% larger still. Using 2004 expenditures as
weights doesn’t change the results, but does decrease the precision of estimates.
12
4.2
Intensive vs. Extensive Margins of Inflation Rates
Given the formulation of our price index, inflation rates could be affected either by (i) changes
in the prices of existing goods (the intensive margin), or (ii) the introduction of new goods (the
extensive margin). As discussed in the previous section, Feenstra (1994) contains a convenient
decomposition of the inflation rate into these two margins, which allows us to estimate the effect
of import penetration on them separately. The results are presented in Table 4.
There are two different ways of computing the intensive margin, depending on the definition
of "existing" goods. First, we can compute the year-on-year inflation rate, using only the set
of goods that are consumed in both periods. We then update the set of "existing" goods for
each consecutive pairs of future years, before compounding these annual inflation rates into
a cumulative value. This is the procedure followed in constructing our short-stay inflation
measure (columns 1 and 2). Alternatively, we can focus exclusively on the set of goods which
are consumed in all of the years in our sample. This is the approach followed in constructing
our long-stay inflation measure (columns 3 and 4). Finally, columns 5 and 6 present results on
the extensive margin, which captures the contribution of new goods.
Both the OLS and IV coefficient estimates suggest that there is a sizeable effect on the
intensive, as well as the extensive margin. Results are smaller in magnitude, but not in statistical
significance, if we use the long-stay, rather than the short-stay inflation measure. Consistent
with Table 3, there doesn’t appear to be an inflation effect coming from either Mexican or
Canadian imports during this period, while German imports does appear to have an important
effect, but only at the intensive margin.
4.3
Number of Varieties and Product Turnover
We now test explicitly for the effect of China’s import penetration on the number of different
product varieties (as proxied by UPC/bar-codes) consumed by households. Results are presented
in Table 5. Overall, we do not find any evidence for a changing number of varieties, either in
the OLS or the IV specifications.
One potential reason why we may not see an increase in the number of varieties consumed is
that Chinese products are replacing other products one-for-one. In that case, we should observe
a high degree of product turnover as a response to Chinese import penetration. We construct a
13
measure of product turnover inspired from the labor literature on worker turnover as follows:
Turnoverct =
Entry
Exit
Nct
+ Nct−1
− |∆Nct |
0.5(Nct + Nct−1 )
That is, we measure the degree of product turnover between periods t and t − 1 as the ratio
of turnover in excess of the absolute change in the number of products, divided by the average
number of products in t and t − 1. We compute this turnover variable for each category and
year, and then take an average within a category over time. Results are in columns 3-4 and
show a higher degree of turnover in categories where Chinese import penetration was higher.
4.4
Effect of Intermediate Inputs
Having discussed the effect of Chinese import penetration of final goods on US inflation, product
variety and turnover, we turn our attention next to the effects of Chinese imports in intermediate
inputs. Our analysis proceeds in an analogous way. First, Table 6 presents the first-stage results,
where China’s IPII in the US is instrumented by its IPII in the EU. Much like the results for
final goods in Table 2, the coefficient estimates are positive, close to one, and highly significant.
Armed with this first-stage relationship, we proceed to estimate the effect of Chinese IPII on
US inflation, product variety and turnover. We do so by adding this as an additional regressor
in our regression equation. Results are presented in Table 7. Here we first note that the effects
of Chinese import penetration in final goods remain unchanged, while growth in the imports
of intermediate inputs from China during the study period does appear to have an additional
effect on US inflation and product turnover.
4.5
Sub-Period Analysis and Robustness
Table 8 presents results by sub-period. We split up our sample into the period prior to the
Great Recession (2004-07), during- (2007-09), and post-recession (2009-12). The results are
strong for Chinese import penetration on U.S. prices for the pre- and post-recession periods.
The point estimates are naturally lower, since we always estimate the effect on cumulative
inflation. For the time during the recession, however, we find that the instrument does not have
sufficient predictive power. The F-test value of 2.97 is substantially below acceptable levels.
The irrelevance of the instrument during the recession period suggests that supply shocks in
China were not the main driver of changes in trade flows to the U.S. and the EU during that
14
time period. This is consistent with evidence by, for instance, Eaton et al. (2013).
Table 9 presents results from a robustness check, using alternative values of the elasticity of
substitution in computing cumulative inflation rates. Specifically, we experimented with values
of 2 and 10 (while the main regressions used a value of 5). In both cases our results are essentially
unchanged, both qualitatively and quantitatively (although the point estimates are necessarily
larger for smaller values, as the contribution of new goods is more important in these cases).
5
Concluding Remarks
In this paper we have tried to investigate the size of U.S. consumer gains from the recent growth
in Chinese imports by adopting a micro-econometric approach. Using barcode-level price data
from AC Nielsen and exploiting cross-product variation in import penetration between 2004 and
2012, we find that prices declined by more in product categories with higher Chinese import
penetration: a one standard-deviation increase in import penetration leads to a 1.9 percentage
point reduction in the annual inflation rate, or 0.58 of one standard deviation.
We addressed the potential endogeneity of imports by using Chinese exports to European
countries as an instrument. We find that the effect of import penetration on prices is driven by
both the intensive and extensive margins, suggesting the presence of pro-competitive effects for
old goods as well as variety gains from new goods. We find, however, no evidence for net gains
in the number of consumed varieties, as newly introduced goods appear to strongly displace old
varieties. Finally, we also find evidence for reductions in final goods prices induced by imported
intermediate inputs from China.
Taken together, these results suggest substantial gains to U.S. consumers from the recent
growth in trade with China. These have the potential to offset at least some of the negative
labor market consequences due to import competition.
15
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Feenstra, R. C. (1994). New product varieties and the measurement of international prices.
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Hsieh, C.-T. and Ossa, R. (2011). A global view of productivity growth in china. Technical
report, National Bureau of Economic Research.
Sato, K. (1976). The ideal log-change index number. Review of Economics and Statistics,
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Vartia, Y. (1976). Ideal log-change index numbers. Scandinavian Journal of Statistics, 3:121–
126.
16
Figure 1: US Import Composition
Figure 2: Industry Heterogeneity
17
Figure 3: China Import Penetration
Figure 4: Effect on Inflation
18
Table 1: Summary statistics
Variable
Mean
Std. Dev.
Min
Max
Obs
0.954
1.042
1.1
0.328
0.283
0.253
0.017
0.063
0.132
1.919
1.912
1.909
279
279
279
1.151
1.208
0.891
0.229
0.207
0.111
0.239
0.188
0.265
1.906
2.127
1.033
279
278
279
0.057
0.058
0.097
0.093
-0.049
-0.001
0.566
0.485
287
287
0.011
0.011
0.021
0.023
-0.001
0
0.147
0.179
287
287
0.013
0.009
0.003
0.059
0.061
0.042
-0.185
-0.603
-0.176
0.512
0.459
0.619
287
287
287
Panel A: Inflation (category level, 2004-2012)
Inflation (Elasticity of Substitution = 3)
Inflation (Elasticity of Substitution = 5)
Inflation (Elasticity of Substitution = 10)
Panel B: Inflation (intensive vs. extensive margin, 2004-2012)
19
Inflation (using only goods present in consecutive years)
Inflation (using only goods present in all years)
Inflation (Feenstra Correction Factor, Elasticity of Substitution = 5)
Panel C: China Import Penetration in Final Goods, 2004-2012
Import Penetration (US)
Import Penetration (Europe)
Panel D: China Import Penetration in Intermediate Inputs, 2004-2012
Import Penetration in Intermediate Inputs (US)
Import Penetration in Intermediate Inputs (Europe)
Panel E: Import Penetration in Final Goods (other countries, 2004-2012)
Mexico Import Penetration (US)
Canada Import Penetration (US)
Germany Import Penetration (US)
Table 2: First-Stage: Import Penetration (US and Europe, 2004-2012)
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
0.904
(0.038)***
0.780
(0.072)***
0.912
(0.039)***
0.778
(0.073)***
Mexico Import Penetration in U.S.
0.032
(0.024)
0.018
(0.071)
Canada Import Penetration in U.S.
0.017
(0.026)
-0.009
(0.058)
Germany Import Penetration in U.S.
0.133
(0.091)
-0.128
(0.176)
Dep. Variable: China Import Penetration in US
China Import Penetration in EU
20
Weights
No
Yes
No
Yes
R2
N
0.80
279
0.79
279
0.81
279
0.79
279
F-statistic
571.6
117.1
558.9
112.9
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. The instrument used is China import penetration in the five
largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used in columns 2 and 4 are total US expenditure
in a given category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of both China import
penetration and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in 2004)/expenditure in
2004. We control for Mexico, Canada and Germany’s import penetration during this period, as these are the next three countries whose
exports to the US grew the most after China.
Table 3: Import Penetration and Inflation (EoS = 5, 2004-2012)
(1)
(5)
(6)
-2.016
(0.203)***
-3.657
(1.262)***
Mexico Import Penetration in U.S.
-0.132
(0.257)
0.514
(0.559)
Canada Import Penetration in U.S.
-0.341
(0.181)*
-0.241
(0.515)
Germany Import Penetration in U.S.
-1.329
(0.634)**
-2.298
(0.808)***
Dep. Variable: Cumulative Inflation
China Import Penetration in U.S.
(2)
(3)
(4)
OLS
-1.691
(0.166)***
IV
-2.372
(0.558)***
-1.924
(0.201)***
-3.507
(1.207)***
21
Weights
No
Yes
No
Yes
No
Yes
R2
N
0.24
241
0.18
241
0.24
241
0.14
241
0.25
241
0.15
241
1st-stage F-stat
n/a
n/a
571.6
117.1
558.9
112.9
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. Columns 1 and 2 are OLS regressions, while columns
3-6 are IV regressions. The instrument used is China import penetration in the five largest EU economies (Germany, France, United
Kingdom, Italy and Spain). The weights used in columns 2, 4 and 6 are total US expenditure in a given category in 2004. The sample
includes all product categories that fall within the 1st and 99th percentiles of both China import penetration and cumulative inflation. All
import penetration measures are defined as (imports in 2012 - imports in 2004)/expenditure in 2004. We control for Mexico, Canada and
Germany’s import penetration during this period, as these are the next three countries whose exports to the US grew the most after China.
Table 4: Import Penetration and Inflation (Intensive vs. Extensive Margins, 2004-2012)
Dependent Variable:
(1)
(2)
Inflation: Short-Stay
OLS
IV
(3)
(4)
Inflation: Long-Stay
OLS
IV
(5)
(6)
Feenstra Correction Term
OLS
IV
China Import Penetration in U.S.
-1.692
(0.453)***
-2.750
(1.077)**
-0.910
(0.325)***
-1.578
(0.670)**
-1.021
(0.234)***
-1.499
(0.572)***
Mexico Import Penetration in U.S.
0.410
(0.402)
0.656
(0.362)*
0.063
(0.516)
0.201
(0.492)
-0.147
(0.297)
-0.035
(0.292)
Canada Import Penetration in U.S.
0.219
(0.336)
0.095
(0.342)
0.139
(0.267)
0.055
(0.270)
-0.251
(0.214)
-0.307
(0.232)
-1.606
(0.713)**
-1.755
(0.649)***
-1.779
(0.771)**
-1.871
(0.767)**
-0.380
(0.410)
-0.447
(0.394)
Weights
Yes
Yes
Yes
Yes
Yes
Yes
R2
N
0.14
241
0.09
241
0.07
239
0.04
239
0.22
240
0.18
240
1st-stage F-stat
n/a
112.9
n/a
112.9
n/a
112.9
Germany Import Penetration in U.S.
22
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. Columns 1-4 present results on the intensive margin of
the inflation effect, while columns 5-6 the extensive margin. Specifically, columns 1-2 study the effect on an inflation rate constructed using
the same set of goods in consecutive years, while columns 3-4 use an inflation rate constructed using the same set of goods throughout
the sample period. In other words, we have excluded the effect of new goods on inflation in columns 1-4. The instrument used is China
import penetration in the five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used are total
US expenditure in a given category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of
both China import penetration and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in
2004)/expenditure in 2004. We control for Mexico, Canada and Germany’s import penetration during this period, as these are the next
three countries whose exports to the US grew the most after China.
Table 5: Import Penetration, Number of Varieties and Product Turnover (2004-2012)
(1)
(2)
Number of Varieties
OLS
IV
(3)
(4)
Excess Product Turnover
OLS
IV
China Import Penetration in U.S.
0.452
(0.474)
0.828
(0.540)
1.035
(0.270)***
1.525
(0.565)***
Mexico Import Penetration in U.S.
1.223
(1.234)
1.164
(1.252)
0.113
(0.544)
0.011
(0.533)
Canada Import Penetration in U.S.
0.683
(1.147)
0.729
(1.189)
-0.345
(0.250)
-0.283
(0.263)
Germany Import Penetration in U.S.
-0.505
(1.013)
-0.505
(1.010)
-0.293
(0.599)
-0.236
(0.596)
Weights
Yes
Yes
Yes
Yes
R2
N
0.04
240
0.03
240
0.11
240
0.09
240
1st-stage F-stat
n/a
112.9
n/a
112.9
Dependent Variable:
23
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. Columns 1-2 present results on the growth rate in the
number of varieties (i.e. UPC codes) consumed, while columns 3-4 results on excess product turnover. The instrument used is China
import penetration in the five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used are total
US expenditure in a given category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of
both China import penetration and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in
2004)/expenditure in 2004. We control for Mexico, Canada and Germany’s import penetration during this period, as these are the next
three countries whose exports to the US grew the most after China.
Table 6: First-Stage: Import Penetration in Intermediate Inputs (US and Europe, 2004-2012)
(1)
OLS
(2)
OLS
(3)
OLS
(4)
OLS
0.890
(0.055)***
0.954
(0.128)***
0.889
(0.055)***
0.964
(0.123)***
China Import Penetration in EU
-0.002
(0.002)
0.004
(0.014)
Mexico Import Penetration in U.S.
0.008
(0.007)
-0.092
(0.048)*
Canada Import Penetration in U.S.
0.004
(0.004)
0.032
(0.049)
Germany Import Penetration in U.S.
0.005
(0.004)
-0.010
(0.025)
Dep. Variable: China IPII in US
China IPII in EU
24
Weights
R2
N
F-statistic
No
Yes
No
Yes
0.88
279
262.9
0.89
279
55.5
0.88
279
133.4
0.90
279
49.5
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. The instrument used is China import penetration in
intermediate inputs for the five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used in columns
2 and 4 are total US expenditure in a given category in 2004. The sample includes all product categories that fall within the 1st and 99th
percentiles of both China import penetration and cumulative inflation. All import penetration measures are defined as (imports in 2012 imports in 2004)/expenditure in 2004. We control for Mexico, Canada and Germany’s import penetration during this period, as these are
the next three countries whose exports to the US grew the most after China.
Table 7: Import Penetration in Intermediate Inputs (Inflation, Varieties and Product Turnover 2004-2012)
(1)
Dependent Variable:
(2)
Inflation
(3)
(4)
Number of Varieties
OLS
IV
(5)
(6)
Product Turnover
OLS
IV
OLS
IV
China Import Penetration in U.S.
-1.686
(0.320)***
-2.594
(0.503)***
0.286
(0.474)
0.870
(0.634)
0.723
(0.168)***
1.087
(0.293)***
China IPII in U.S.
-3.900
(1.252)***
-3.507
(1.395)**
1.212
(1.379)
-1.130
(3.016)
2.015
(0.486)***
1.738
(0.602)***
Mexico Import Penetration in U.S.
0.195
(0.602)
0.383
(0.588)
1.180
(1.249)
1.110
(1.246)
0.253
(0.530)
0.192
(0.515)
Canada Import Penetration in U.S.
-0.335
(0.470)
-0.412
(0.476)
0.755
(1.202)
0.677
(1.151)
-0.126
(0.210)
-0.102
(0.218)
-3.010
(0.705)***
-3.057
(0.709)***
-0.620
(1.050)
-0.840
(1.000)
0.426
(0.360)
0.410
(0.372)
Weights
Yes
Yes
Yes
Yes
Yes
Yes
R2
N
0.35
238
0.33
238
0.05
237
0.01
237
0.34
237
0.32
237
Germany Import Penetration in U.S.
25
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. The instrument used is China import penetration in the
five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used are total US expenditure in a given
category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of both China import penetration
and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in 2004)/expenditure in 2004. We
control for Mexico, Canada and Germany’s import penetration during this period, as these are the next three countries whose exports to
the US grew the most after China.
Table 8: Import Penetration and Inflation (sub-period analysis)
Dependent Variable:
(1)
(2)
Inflation 2004-07
OLS
IV
(3)
(4)
Inflation 2007-09
OLS
IV
(5)
(6)
Inflation 2009-12
OLS
IV
China Import Penetration in the US
-1.305
(0.311)***
-1.893
(0.356)***
1.840
(0.859)**
3.138
(3.068)
-1.212
(0.327)***
-1.347
(0.475)***
China IPII in the US
-0.552
(0.237)**
-0.527
(0.230)**
1.683
(2.842)
11.204
(8.356)
-0.773
(0.704)
0.300
(1.543)
Mexico Import Penetration in the US
0.474
(0.286)*
0.538
(0.264)**
-0.098
(0.478)
-0.371
(0.613)
0.492
(0.595)
0.526
(0.601)
Canada Import Penetration in the US
-0.579
(0.265)**
-0.626
(0.276)**
0.266
(0.969)
-0.210
(1.063)
0.187
(0.345)
0.195
(0.342)
-0.251
(0.294)
-0.209
(0.281)
0.336
(0.275)
0.191
(0.298)
-0.310
(0.218)
-0.296
(0.197)
Weights
Yes
Yes
Yes
Yes
Yes
Yes
R2
N
0.15
240
0.13
240
0.05
236
.
236
0.20
238
0.18
238
Germany Import Penetration in the US
26
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. The instrument used is China import penetration in the
five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used are total US expenditure in a given
category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of both China import penetration
and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in 2004)/expenditure in 2004. We
control for Mexico, Canada and Germany’s import penetration during this period, as these are the next three countries whose exports to
the US grew the most after China.
Table 9: Robustness Check: Alternative Values for the Elasticity of Substitution
Dependent Variable:
(1)
(2)
Inflation: sigma = 2
OLS
IV
(3)
(4)
Inflation: sigma = 10
OLS
IV
China Import Penetration in U.S.
-2.517
(0.390)***
-3.404
(0.638)***
-1.383
(0.307)***
-2.246
(0.456)***
China IPII in U.S.
-4.423
(0.880)***
-3.893
(1.098)***
-3.519
(1.277)***
-3.182
(1.401)**
Mexico Import Penetration in U.S.
0.021
(1.051)
0.198
(1.036)
0.302
(0.463)
0.482
(0.448)
Canada Import Penetration in U.S.
-1.009
(0.741)
-1.073
(0.748)
-0.131
(0.389)
-0.207
(0.392)
-4.764
(1.027)***
-4.777
(1.015)***
-2.598
(0.617)***
-2.650
(0.623)***
Yes
0.33
238
n/a
Yes
0.32
238
49.5
Yes
0.33
238
n/a
Yes
0.31
238
49.5
Germany Import Penetration in U.S.
27
Weights
R2
N
1st-stage F-stat
Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05,* p<0.10. The instrument used is China import penetration in the
five largest EU economies (Germany, France, United Kingdom, Italy and Spain). The weights used are total US expenditure in a given
category in 2004. The sample includes all product categories that fall within the 1st and 99th percentiles of both China import penetration
and cumulative inflation. All import penetration measures are defined as (imports in 2012 - imports in 2004)/expenditure in 2004. We
control for Mexico, Canada and Germany’s import penetration during this period, as these are the next three countries whose exports to
the US grew the most after China.