Import Competition and Multi Product
Firms’ Productivity
Richard Bräuer
Matthias Mertens
Viktor Slavtchev
Abstract
Both increasing market integration and declining productivity growth defined the European
economic experience of the last decades. Against this background, we revisit the productivity
effects of increasing foreign competition. In contrast to the previous literature, we take the
heterogeneous effects predicted by theory into account. We find substantially different effect
in line with endogenous growth models: Effects are more positive for technologically
advanced competitors and the more important a product is to the firm. However, while
competition from emerging countries favors productive firms, competition from
industrialized countries leads to technology upgrading by previously unproductive firms. This
is consistent with technology diffusion models. In contrast to previous studies, we also find
negative productivity effects which stem from adjustment frictions: Firms cannot downsize as
fast as necessary. Our estimates at the product level indicate that productivity increases due
to new product launches, not improvements of existing ones. Both negative productivity and
employment effects point towards substantial welfare losses for some participants in the
economy. To arrive at these results, we propose adaptations to estimators for firm and firmproduct productivity.
1 Introduction
Throughout the economic literature, fierce competition is credited with beneficial effects on
partaking firms. This consensus has also been acted upon in a number of policy fields,
ranging from competition policy to trade deals. However, it is grounded on a thoroughly
static analysis. Recent models of endogenous investment in productivity or innovation view
1
competition much more ambiguously. This is because new entrants depress rents and thus
returns on successful innovation. Conversely, they can also induce incumbents to escape
competition through innovation (Aghion et al. 2004).
We contribute to this discussion by measuring the causal effects of increased competition on
firms’ productivity evolution. We indeed find both positive and negative effects, a first in the
literature. We find that technologically advanced entrants cause relatively unproductive
incumbents to increase their productivity and have an ambiguous effect on firm growth. This
seems to indicate a technology transfer channel where laggards imitate innovative products
and as a result do not suffer markedly. Contrarily, we find that entrance by technological
laggards improves the efficiency of already productive incumbents but hurts relatively
unproductive firms. Across the board, such entry causes substantial reductions in
employment. To identify exogenous entrance and technology levels, we rely on imports
from foreign countries and instrument them with productivity shocks to that country.
We demonstrate that the declining productivity of some firms is caused by hysteresis: Firms
losing market shares faster than they can cut back their input use, notably capital and labor.
Thus, flexible input markets are necessary if an economy intends to capture the full
productivity benefits, i.e. prevent productivity declines. This, however, comes at costs to
workers outside the scope of our analysis: Workers suffer enduring losses when dismissed in
general, a large part of which are picked up by state transfer programs (Fackler, Hank 2016).
Their earnings also diminish when shrinking employment due to import competition forces
them to switch sectors (Müller, Stegmaier, Yi 2016). In the aggregate, such shocks depress
local labor markets, leading to persistent drag on employment (Autor, Dorn, Hansen 2013).
Hence, there is convincing evidence that these costs can be severe. While there is no
evidence of such hysteresis after competition shocks effects yet, there are several
theoretical discussion of adjustment processes in general and their contribution to factor
misallocation (Foster et al. 2016; Haltiwanger 2016).
We also document asymmetric effects along different product lines within a firm. State of
the art models of industrial competition and trade concern themselves with precisely these
questions, too. Our results provide a much more nuanced picture of competition and can
thus inform future modeling. In all of these ways, the paper thus contributes to our
understanding of the microeconomic effects of competition in general and of international
2
integration in particular, about which our knowledge is still rather incomplete and
fragmented (De Loecker, Goldberg 2014).
To be able to analyze the evolution of efficiency, not market power, we propose and
implement several improvements of the currently most commonly used TFPR estimators:
We deflate output with a firm level price index, implement a price control function (De
Loecker et al. 2016), estimate product level TFP (Dhyne et al. 2016) and propose a solution
to the identification problem of TFP estimators analyzed by Gandhi et al. (2013).
Fundamentally, the common estimators are not identified because intermediate inputs
enter both as productivity proxy and production input. We propose a different productivity
proxy constructed from all flexible inputs of the firm, which by construction differs from the
intermediate inputs. This solves the identification problem without assuming a specific
functional form or invoking the stronger assumptions of Gandhi et al. 2013. We also use our
product level data to construct a much finer measure of import competition not at the
industry, as is frequently done, but at the product and the firm level. This allows us to
identify our effects within an industry, not between industries.
With this, we contribute directly to the literature on endogenous firm innovation. Innovation
is usually modeled in steps up a productivity/quality/technology ladder (see e.g. Grossman,
Helpman 1999; Crepón, Duguet & Mairesse 1998; Aghion et al. 2004). Firms endogenously
decide on whether to take up R&D in order to increase their chances of moving further up
the ladder. Typically, in such models, firms innovate to stay ahead of their rivals while
technology diffusion erodes their advantage. Because competition depresses potential
monopoly rents earned through innovation, it also depresses R&D. On the other hand, too
little competition would allow firms to reap monopoly rents even without continuously
innovating to deter competition. Importantly, the effect of entrance in such a setup also
relies on the technological ranking of entrants and incumbents: Too advanced new products
make it implausible to overtake them and become technology leader, while too basic
products do not threaten incumbents’ technologically grounded monopolies. Other types of
models predict technology transfer as firms copy the designs of technologically advanced
new products.
We test these different models by distinguishing between high and low tech imports. Using
quantile regression, we detect heterogeneous effects across the productivity distribution.
3
We find evidence for the imitation channel in the case of imports from industrialized
products and support for ladder models in imports from middle income countries.
This paper is also related to a growing body of empirical academic literature that investigates
the effects of competition on firm level outcomes. Usually, using industry level productivity
measures, the empirical literature finds significant positive effects of competition.1
Nevertheless, increases in foreign competition seem to have significant negative effects on
local labor markets (see e.g. Autor, Dorn & Hanson 2016; Dauth, Findeisen, Suedekum 2014;
Dauth, Findeisen, Suedekum 2016). Several studies have discussed the effects of trade
liberalization in general on productivity. Note that this also includes new export
opportunities for firms and that this usually accounts for the bulk of effects (e.g. Melitz,
Treffler 2012). De Loecker (2011) gives a good overview over this literature and the
difference between revenue and quantity productivity. He finds that in studies of this kind,
price changes lead to dramatic overestimates of the productivity effects. We would also
argue that competition increases inherently bias the evolution of revenue TFP: First,
increased competition will depress prices, leading to a reduction in revenue. Second, trade
has been shown to lower firm’s input prices, reducing costs and increasing mark-ups (De
Loecker et al. 2016). Third, competition leads to redistribution of market shares from low to
high mark-up firms and products (Melitz, Ottaviano 2008). De Loecker & Goldberg (2014)
provide an overview of the identification challenges associated with this research set up.
Compared to this literature, we offer both new findings and improvements of the
methodology. Last, but not least, the literature puts a disproportionate emphasis on small
open economies like Belgium (e.g. De Loecker et al. 2014; Dhyne et al., 2016). Thus, previous
results may not be generalizable: Theory suggests that the measured effects are different for
each combination of trading partners, depending on country characteristics (e.g. Melitz,
Ottaviano 2008). To tackle such representation problems, we study Germany, a large
industrialized economy. Such countries form a large part of the global economy, which
makes our results transferable to a larger part of the global economy. Furthermore, we also
conduct a comprehensive analysis of imports from several heterogeneous German trading
partners and indeed find important differences in their effects. Our results thus provide a
much clearer picture of the effects of market integration on productivity.
The remainder of the paper is structured as follows:
1
See e.g. Holmes and Schmitz (2010) for a review of this literature.
4
Since our data has not yet been used in the related literature, Section two gives an overview
over the data and our treatment of it. Section three describes our productivity estimation
and discusses its assumptions, as well as advantages over the frequently used TFPR
estimators. Section four presents and discusses our estimates of the effect of competition on
productivity. Section five gives a summary of our results, details their implications and
highlights areas of future research.
2 Data preparation and import competition measure
Parts of the methodological advancements that we make are highly contingent on our data
set. In the following, we will thus give an overview over our data sources and how
representative they are, as well as over their scope and coverage.
2.1 The Comtrade Database
To measure incoming import competition, we use data from the UN statistics division. The
Comtrade database contains information on trade flows between countries. It theoretically
represents the full universe of trade transactions. National statistical offices or other
government sources form its basis, usually from customs data. For each product as classified
by the Harmonized System or SITC, it contains the value of exports, imports, re-exports and
re-imports for each combination of countries. Data quality varies according to who is
actually providing the data, however, we take care to only use data provided by the most
reliable reporters.2 Using classification provided by Eurostat, we reclassify these products
into Europe’s PRODCOM classification. In ambiguous cases, we split the trade value
proportionally the German market size for this product.
2.2 The German AfiD data
For our analysis, we use the AfID-database gathered by Germany’s statistical offices, which
so far has never been used for this kind of analysis. It is of higher quality than data sets
typically used throughout the literature, both in reliability and in scope. Our data consists of
two complementary panels, the product module and the firm module. Both span the full
universe of German manufacturing firms for the years 1995-2014 with more than 20
employees. They contain information on revenue and quantity by product, wage bill,
2
Our analysis requires data reported from Germany, Great Britain, Norway, Sweden, Japan, New Zealand,
Australia, Israel and Singapore.
5
investment and location for each plant of each firm. Firms are obliged to answer all
questions by law. The negligible amount of missing values allows ruling out significant
selection bias.
However, to reduce the administrative burden for firms, some questions are only asked of a
representative subsample of firms – e.g. firm level intermediate input use, research and
exporting activities, the number of workers and non-industrial revenue. This unbalanced
panel encompasses about 40% of revenue and employment of German manufacturing. Every
four years, it is drawn anew. Because the sample is stratified according to industry and
employment, which we observe for all variables, we can reweight this subsample to
represent the whole population.
With such detailed information on products, cost structure and research & exporting
activities, we can take a closer look at firms than is usually possible.
2.3 Reclassification of products
One of the key advantages of the AFiD data is the availability of revenue and quantity data
on the product level. We use this data to assign industry codes and import competition to
each firm. Products are classified in the German GP schematic, the first eight digits being
equivalent to the European system PRODCOM. A ninth digit allows for an even finer
categorization of products. The tenth digit shows whether the produce is sold or produced
on commission. About 3500 different eight-digit product codes are found in the data each
year. PRODCOM and thus GP changed several times during the 20 years in consideration. For
comparability, we transform all codes both into the GP 2002 and the GP 2009.
To do this, we use the concordances provided by the German statistical offices, which are
usually unambiguous. In case of ambiguity, we compare the product portfolios before and
after the changes and settle on the only code that can also be found after the change. If this
leaves us with multiple or no options, we do not make additional assumptions and do not
switch the code. In most such cases, we are still able to assign the first few digits of the
classification unambiguously, which we exploit for the reclassification of industries (see 2.4).
All in all, the problem of changing classifications is negligible: We are able to construct
consistent ten digit product codes for about 96% of all products in question. When looking at
the first few digits, the problem disappears entirely: We were able to assign the first four
digits of the product code in 99.5% of all cases (99.9% for the first two digits).
6
2.4 Reclassification of industries
In 2002 and even more fundamentally in 2009, the European industry definitions were
reworked. Thus, new and old industry codes are generally considered to be incomparable
due to these dramatic changes in the underlying classification. However, in order to estimate
a production function for each industry and to merge industry level deflators for inputs, we
need a time consistent industry classification. We circumvent this problem by inferring the
firm specific industry code from the product data. This exploits the fact that the first four
digits of a PRODCOM code equal the industry which produces the product. When assigning
the industry classification to a firm, we closely follow the general methodology of the
German statistical office for picking the industry code most representative of a firm’s
product portfolio.3 The codes frequently used throughout the literature tend to be selfreported by the firms. Frequently, there is an unenforceable recommendation to pick the
industry with the most workers or value added. To the contrary, our self-constructed
measure determines the center of gravity of production in terms of revenue, arguably more
appropriate for a revenue-based production function. In any case, we arrive at very similar
results: 4% of firms provide a different code than the one we assign. In rare cases, a firm had
revenue in a product we could not assign to our time consistent industries. If this product
was important enough to potentially change the industry assigned, we did not assign any
industry. However, such cases were negligible.
We use these time consistent industry codes (which follow NACE Rev1.1) to estimate
separate production functions for each industry. We merge price indices for production,
depreciations, intermediate inputs and various capital goods prices by two digit industry
sector.
2.5 Firm Level Price Indices
In productivity analysis, it is generally acknowledged that price movements confound the
estimates: If researchers only have access to revenue data and industry specific output
deflators, they cannot distinguish between price increases and additional production within
3
First, we consider the two digit industry of the firm: Here, we pick the one that combines the most product
revenue. Second, with this two digit industry, we determine the three digit industry that contains most product
revenue. Third, within this smaller industry, we define the 4 digit code with most production.
This is basically done by defining that the main activity of a firm lies in the industry in which it produces most of
its revenue.
7
firms of the same industry.4 Thus, both will lead to higher productivity estimates. However,
we are able to eliminate this source of bias by using firm-product level data: Following Eslava
et al. (2004), we construct a Törnqvist price index for firms. This index is computed as
𝑛
1
(𝑠𝑖𝑔𝑡 +𝑠𝑖𝑔𝑡−1 )
𝑝𝑖𝑔𝑡 2
𝑃𝑖𝑡 = ∏ (
)
𝑝𝑖𝑔𝑡−1
𝑃𝑖𝑡−1 ,
𝑔=1
where 𝑝𝑖𝑔𝑡 is the price of good 𝑔 and 𝑠𝑖𝑔𝑡 is the corresponding share of this good in the
production at firm 𝑖 in period 𝑡. Thus, the growth of the index value is the product of the
individual products’ price growths, each weighted with the average revenue share of that
product over this year and the last. For our start year we set 𝑃𝑖𝑡=2001 = 100. For firms
entering after the start year we use an industry average as starting price index. This
methodology has the advantage that new products slip into the index calculation seamlessly:
In this case, 𝑠𝑖𝑔𝑡−1 = 0, 𝑠𝑖𝑔𝑡 ≠ 0. In fact, the weights of the index are adjusted every year.
When computing the average price growth like proposed by Laspeyres, new products have
zero weight. Thus, practitioners have to change the underlying basket of goods manually.
This forces them to make many arbitrary decisions, because firms’ product portfolios change
relatively fast.
We use this price index to deflate industrial revenue. However, we do not have a single firm
in our sample with zero revenue outside of manufacturing. Firms also sell services like
transportation, produce capital goods (for own use mostly), sell products from their
warehouses, etc. The assertion that product prices move in tandem with prices for these
activities seem dubious, so we deflate each of them with the best deflators available from
the statistical offices.5
We want to emphasize that our data also represents the full data basis for constructing the
official industry 2-digit deflator supplied by the statistical office of Germany, which regularly
4
Thus, a 5% increase in prices and a 5% increase in output would create the same observation for estimation.
When estimated, the result is often called TFPR, or revenue TFP. This contains improvements in production
technology and changes in market power. For most analyses, this creates severe measurement problems.
Needless to say, prices within the 27 broad industries that form the manufacturing sector do not move in
perfect synchrony.
5
Specifically, we use the capital goods deflator for self-produced capital goods, we use the BIP deflator for the
very diverse category of service revenue and we use firms’ manufacturing goods price deflator for revenue
from warehouse sales. Main results are robust to different treatments of non industrial revenue.
8
is used to estimate revenue based productivity (TFPR). However, TFPR captures undesired
price fluctuations between firms within the same industries, which may lead to serious
mismeasurement of true firm level productivity effects. Especially when considering the
effect of import competition, there is ample evidence that markups may rise due to lower
intermediate input prices (see De Loecker et al. 2016, De Loecker 2011), which would
wrongly be considered as true efficiency gains when estimating productivity as TFPR instead
of TFPQ. Using a firm level price index, like we described, will lead to a TFPQ measure of
productivity which measures TFP in quantity instead of revenue terms, as this price index
purges the output variable form all price variation. Importantly, when constructing our price
index, we do not make any additional assumptions, compared to using the industry deflator
supplied by the statistical office and therefore also compared to other studies that use TFPR
as their productivity measure. This is essentially true because we use exactly the same data
base as the statistical office when it creates its industry deflator. In a sense, the only thing
we do different compared to the statistical office is that we aggregate product level prices to
a lower level, i.e. the firm level. Therefore, in any case, our firm specific price index is
superior compared to using an industry deflator when one wants to make statements about
the evolution of true technological efficiency.
3 Methodology:
Estimating
TFP
and
import
competition
This section explains our empirical strategy for estimating the effect of import competition
on firm- and firm-product-level TFP. Chapter 3.1 starts with describing our framework for
estimating a production function at the firm level. Importantly, we will take care of the fact
that firms are multi-product firms by deflating revenue-based output with a self-created firm
specific price deflator. Section 3.2 then shows the methodology for estimating TFP at the
firm-product-level. Section 3.3 and 3.4 follow with explaining the construction of our import
competition measure.
9
3.1 Estimating a firm-level production function for multi-product
firms
3.1.1 Setting and general notes
To illustrate our approach we start with a standard Cobb-Douglas firm-level production
function in logs:
𝑞𝑖𝑡 = 𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝜔𝑖𝑡 + 𝜀𝑖𝑡 , (1)
where 𝑞𝑖𝑡 is the log of properly deflated revenue from all firm activities. 𝑙𝑖𝑡 is the log of full
time equivalents used by the firm, our preferred measure of labor input. 𝑚𝑖𝑡 represents the
log of intermediate inputs, 𝑘𝑖𝑡 symbolizes the log of the capital stock6, 𝜔𝑖𝑡 is the productivity
shock, 𝜀𝑡 is an i.i.d. disturbance term that is per assumption uncorrelated with all inputs, and
𝛽𝑙 , 𝛽𝑘 , 𝛽𝑚 are the output elasticities of the production function. The indices 𝑖 and 𝑡 symbolize
the firm and time dimension respectively.7
Note that in order to arrive at a consistent estimate of 𝜔𝑖𝑡 the production variables above
should represent all inputs used and outputs produced. E.g. consider a case where due to
data availability, service revenues are excluded from 𝑞𝑖𝑡 , which is not uncommon in the
literature. In this case, a firm which uses a lot of its inputs to produce service revenue would
receive a much lower TFP than is actually warranted. Since this is an endogenous choice
made by the firm (e.g. large firms might find it optimal to provide relatively more services),
this introduces not only measurement error but systematic bias. This also means one cannot
simply leave out a part of the inputs and assume that both problems “cancels each other
out”.8
However, assuming all of the above variables are well defined, the equation is still not
identified econometrically. This is because the firm potentially observes its own productivity
𝜔𝑖𝑡 and choses its inputs accordingly. Hence, simple OLS suffers from an omitted variable
6
See Appendix A for the construction of the capital variable.
When choosing which cost categories to include in which input variable, we remained consistent with the
definition of the statistical offices. This consistency allows us to aggregate firm level results to the country level.
8
E.g. intermediate inputs are often only defined as raw materials, while outputs may be defined as the sum of
the product based production value. This would only then be correct if the ignored inputs were only used for
the production of the ignore output, an unlikely hypothesis.
7
10
and we cannot back out productivity as the residuum of this estimation. In the following we
will detail our solution to this problem.
3.1.2 Identification strategy
Our empirical strategy for estimating a production function at the firm level is based on the
well known framework of Olley, Pakes (1996) and Levinsohn, Petrin (2003), which was
repeatedly modified and improved by other scholars of this field. Our version of this
approach is best described as an extension of the popular Wooldridge-LP approach
(Wooldridge 2009; Petrin, Levinsohn 2012). For an overview of these techniques, see e.g.
Ornaghi and Van Beveren (2012). First, we augment this backbone by incorporating the
treatment of input price bias described in De Loecker et al. (2016). Second, we make use of
our high quality data to implement a simple but effective way to deal with the criticism of
Gandhi et al. (2013) regarding the identification problem of the intermediate input
coefficient in gross output based production functions.
As it is standard in the literature, we assume that 𝜔 is a state variable unobserved by the
econometrician but observed by the firm and described by a first-order Markov process, i.e.
follows the law of motion:
𝜔𝑖𝑡 = ℎ(𝜔𝑖𝑡−1 ) + 𝜉𝑖𝑡 , with 𝜉𝑖𝑡 being the innovation in
productivity. We assume that intermediate inputs are freely adjustable, i.e. that firms
choose their intermediate input consumption only after observing the productivity shock. 9
Contrary, Capital and Labor are assumed to be “quasi-fixed variables” and to be determined
before the shock. Therefore 𝑙𝑖𝑡 and 𝑘𝑖𝑡 are part of the state variable space of the firm.
Treating 𝑙𝑖𝑡 as quasi-fixed input seems to be a reasonable assumption for the German labor
market.10 Thus, both 𝑙𝑖𝑡 and 𝑘𝑖𝑡 are assumed to be correlated with past productivity
ℎ(𝜔𝑖𝑡−1 ), but uncorrelated with the productivity innovation 𝜉𝑖𝑡 .
As argued in the literature, this allows to identify all parameters using a control function: The
firm’s choice of intermediate inputs can be described by a function of productivity and all
state variables describing the decision problem of the firm 𝑚𝑡 = ℎ𝑡 (𝜔𝑖𝑡 , 𝑘𝑖𝑡 , 𝑙𝑖𝑡 , 𝑧𝑖𝑡 ). Ornaghi
and Van Beveren (2012) showed that ℎ𝑡 is a monotone function. In this case, we can
9
To be exact, it is sufficient to assume that some parts of 𝑚𝑖𝑡 are flexible. E.g. raw materials and energy are
flexible, while intermediate services are quasi-fixed.
10
The OECD indicators of employment protection (OECD, 2013) show that the labor market regulations in
Germany are relatively strict compared to other countries. Therefore treating 𝑙𝑖𝑡 as fixed input seems
justifiable, especially when considering the fact that agency workers are part of the intermediate input
variable.
11
rearrange it to obtain: 𝜔𝑖𝑡 = 𝑔(𝑘𝑖𝑡 , 𝑙𝑖𝑡 , 𝒛𝑖𝑡 , 𝑚𝑖𝑡 ). Substituting this and the law of motion of
productivity into the original equation, we arrive at
𝑞𝑖𝑡 = 𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝑔(𝑘𝑖𝑡−1 , 𝑙𝑖𝑡−1 , 𝒛𝑖𝑡−1 , 𝑚𝑖𝑡−1 ) + 𝜉𝑖𝑡 + 𝜀𝑖𝑡
The vector 𝒛𝑖𝑡−1 should provide a description of the decision making problem of the firm. It
should thus be as broad as possible (De Loecker and Warzynski (2012)). Here we utilize the
number of variables in our data set and incorporate export status, research activities,
location of firm headquarters, industry dummies and the number of products. The results
are robust to different operationalization of these variables, as well as to different
approximations of the functional form of 𝑔𝑡 . All flexible variables are instrumented with
their own lags.
The problem raised by Gandhi et al. (2013) is that using 𝑚 simultaneously as proxy for 𝜔𝑖𝑡
and as an argument in the production function gives rise to another identification problem:
Since 𝑚𝑖𝑡 must be flexible in order to serve as a proxy, it has to be instrumented with its lag.
However, in the above setting, 𝑚𝑖𝑡 lacks any exogenous variation that could be recovered
via this instrument. To see this, insert the control function into the law of motion of
productivity. This gives:
𝑚𝑖𝑡 = ℎ𝑡 (𝜔𝑖𝑡 , 𝑘𝑖𝑡 , 𝑙𝑖𝑡 , 𝑧𝑖𝑡 ) = ℎ𝑡 (𝑔(𝑚𝑖𝑡−1 , 𝑘𝑖𝑡−1 , 𝑙𝑖𝑡−1 , 𝒛𝑖𝑡−1 ) + 𝜉𝑖𝑡 , 𝑘𝑖𝑡 , 𝑙𝑖𝑡 , 𝑧𝑖𝑡 )
This shows that 𝑚𝑖𝑡 is determined by a function of its own lag, contemporaneous and past
values of the state variables, and a random disturbance 𝜉𝑖𝑡 that is by assumption
uncorrelated with anything. Therefore 𝑚𝑖𝑡 lacks any exogenous variation after conditioning
on the arguments of ℎ𝑡 .11
However, this problem can be circumvented by postulating a slightly different reaction of
the firms to its state variables. Notice that 𝑚𝑖𝑡 is not a term actually observed, but rather an
aggregate of very different inputs: It contains the services of the firms cleaning the office
buildings as well as raw material input. While the firm probably choses the later in response
to TFP fluctuations, this is probably not true for other components. Since the firm’s flexible
11
We refer the interested reader to Gandhi et al. (2013) for an illuminating discussion on the identification
problem associated with flexible inputs.
12
adjustment of the control variable in response to productivity shocks is the backbone of the
methodology, one should only include really flexible inputs into the control function.
Examples include energy or raw material usage, use of interim staffing and perhaps
investment variables. All of these individual items have the advantage of not being part of
the production function, so there is no need for instruments and no identification problem.
Any firm level data set should include at least some of the above variables, so this approach
is open to other researchers working with different data. For this application, we stick with
raw materials plus energy usage, 𝑒𝑖𝑡 , because of their familiarity from other uses in firm
level data. However, we found that the results are robust to different definitions of the
proxy.
Having dealt with the identification problem inherent to the standard approach, we could
proceed to estimate the equation if all of our above variables were observed in real terms.
However, while we can built an exact deflator for output, we do not observe input prices.
The seminal paper by De Loecker et al. (2016) shows how to deal with this problem common
to virtually all firm level data sets.
However, such quantity data is typically not available, and furthermore, even if such a
dataset would exist, one would have to conduct an aggregation of many different inputs and
many different outputs of a firm into a few variables, that allow formulating a (meaningful)
production function. Therefore, the standard approach in the existing literature is to use the
best possible deflator, i.e. the most disaggregated, to deflate nominal values of inputs and
outputs into quasi-quantities.12 Normally researchers therefore use industry level deflators.
For our dataset we are, however, able to improve on this practice by constructing a firmspecific output price index from the available information on product quantities and prices.
Regarding the input side, we do not have to deflate the labor input as it is reported in
quantities, while intermediate inputs and capital are deflated by a 2-digit-industry deflator
supplied by the statistical office of Germany.
De Loecker et al. (2016) show how using industry-deflators for inputs creates a price-bias
when estimating a production function like the one in (1).13 We follow their solution to this
12
They are obviously not real quantities, because the price deflator is not equal to the price. Deflating inputs or
outputs with a deflator, normalized in year 𝑡 implicitly assumes that the price differences between the inputs
or outputs of the firms in year 𝑡 stay constant, which could for example be interpreted as assuming that quality
differences between products in year 𝑡 are constant over time, i.e. last forever.
13
The underlying reason is that firms producing the same good(s) may have different prices, e.g. due to quality
differences of their products. Industry price deflators ignore these differences within industries. Not controlling
13
problem, assume a vertical differentiation model of demand, and include the output price of
∗
a firm, 𝑝𝑖𝑡
, as separate price control function. This demand structure implies that output
prices will be perfect proxies for input prices. In order to make the output price comparable
∗
between firms within the same 2-digit-industry, we define the price of a firm product, 𝑝𝑖𝑔𝑡
,
for every good 𝑔 as the deviation from the average product price. For the firm-level
equation, we then sum up all products inside a firm using revenue share weights.
∗
Implementing 𝑝𝑖𝑡
and the control function approach detailed above leads to:
∗
𝑞𝑖𝑡 = 𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝛾𝑝𝑖𝑔𝑡
+ 𝑔(𝑒𝑖𝑡−1 , 𝑘𝑖𝑡−1 , 𝑙𝑖𝑡−1 , 𝒛𝑖𝑡−1 ) + 𝜉𝑖𝑡 + 𝜀𝑖𝑡 . (5)
To translate this equation into an estimation, we use the following moment conditions,
which come from our assumptions about the two stochastic error processes:
𝑙𝑖𝑡
𝑘𝑖𝑡
𝑚𝑖𝑡−1
∗
𝑝𝑖𝑡−1
𝐸 (𝜉𝑖𝑡 + 𝜀𝑖𝑡 ) 𝑒𝑖𝑡−1
=0 ,
𝑙𝑖𝑡−1
𝑘𝑖𝑡−1
𝐳𝑖𝑡−1
[
( 𝚪𝑖𝑡−1 ) ]
(6)
where 𝚪𝒊𝑡−1 colletcts higher order interaction terms of 𝑔(. ).
We proceed to estimate this with a simple moment estimator, following the standard
procedure of Wooldridge (2009) and Petrin, Levinsohn (2012).14 This means that we
∗
instrument 𝑚𝑖𝑡 and 𝑝𝑖𝑡
with their lagged values while all static variables are exogenous to
current TFP and thus serve as their own instruments. After having consistent estimates for
the production function we can calculate productivity:
∗
𝜔𝑖𝑡 = 𝑞𝑖𝑡 − (𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝛾𝑝𝑖𝑡
) (7)
With this estimate, we have a state of the art productivity measure including input price
controls, circumventing the identification problems of the standard approach. This is thus
arguably a consistent estimate of the real productivity of firms, which is often called TFPQ.
for these price differences then obviously generates biases in the input coefficients. We refer the reader to De
Loecker et al. (2016) for further details.
14
To absorb the high dimensional dummies we use the command reghdfe by Correia (2015).
14
3.2 Estimating a firm-product level production function
For estimating a firm-product level production function we follow the novel framework by
Dhyne et al. (2016) and shortly note that their approach is generalizable to single product
firms if one uses more than production based outputs for the output variable definition (as
we propose).
The core idea of Dhyne et al. (2016) is based on work of Diewert (1972) and Lau (1976).
Dhyne et al. (2016) show that it is possible to formulate a firm-product level production
function which can be used to estimate productivity of single products of a multi-product
firm. We do not rewrite their derivations and instead recommend their working paper for
further elaborations. Again, we draw from the insights of De Loecker et al. (2016) and
∗
include a product level price control function, constituted from the product price, 𝑝𝑖𝑔𝑡
, and
∗
product market share, 𝑚𝑠𝑖𝑔𝑡
, into the estimation to purge our productivity measure from
undesirable input price variation between firms and products (we want to recover a pure
∗
∗
quantity measure of productivity).15 We define the price control function as 𝜑(𝑝𝑖𝑔𝑡
, 𝑚𝑠𝑖𝑔𝑡
)
and write our estimation equation as:
∗
∗
𝑞𝑖𝑔𝑡 = 𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝛽−𝑔 𝑟𝑖(−𝑔)𝑡 + 𝜑(𝑝𝑖𝑔𝑡
, 𝑚𝑠𝑖𝑔𝑡
) + 𝜔𝑖𝑡𝑔 + 𝜀𝑖𝑔𝑡 ,
(8)
where 𝑔 is an index for variables referring to product 𝑔. It follows that 𝑞𝑖𝑔𝑡 is the log
quantity of good 𝑔, while 𝑟𝑖(−𝑔)𝑡 symbolizes the log of all other revenue of a firm 𝑖 at time 𝑡
and is deflated with an index which heeds the exclusion of product 𝑔 from 𝑟𝑖(−𝑔)𝑡 . Equation
(8) needs some discussion. First note that the input coefficients 𝑙𝑖𝑡 , 𝑘𝑖𝑡 , and 𝑚𝑖𝑡 are firm
level variables. This means that the approach by Dhyne et al. (2016) requires that after
including a control variable for all revenue but the revenue of good 𝑔 one can recover a
productivity estimate for good 𝑔. In this sense the individual input coefficients have to be
interpreted as the percentage change of output 𝑔 after increasing the specific input by one
percent, conditional on all other inputs and all other revenue sources being constant.
Second, as Dhyne et al. (2016) show, equation (8) is only correctly specified when 𝛽−𝑔 < 0
15
At the firm level estimation we could not simply implement a market share variable because firms are
simultaneously engaged in very different markets. We also omitted the product market share from the product
level estimation which leads to very similar results.
15
and 𝛽𝑥 > 0 with 𝑥 = (𝑙, 𝑘, 𝑚) holds. The returns to scale for product 𝑔 are somewhat
difficult to interpret in the presence of coefficient 𝛽−𝑔 . One has to consider all 𝛽-coefficients
simultaneously. Third, we want to mention that equation (8) is only a general production
function when a firm produces at least two outputs. This means that neglecting the output
sources besides the product production output will lead to an exclusion of single products
firm when estimating equation (8), because log( 𝑟𝑖(−𝑔)𝑡 ) = log( 0) will be not defined. Like
before, we use all revenue sources a firm generates in period 𝑡 (i.e. abstracting from
inaccuracies
of
deflating,
in
our
case
it
theoretically
holds
that
exp(𝑞𝑖𝑔𝑡 ) + exp(𝑟𝑖(−𝑔)𝑡 ) = exp(𝑞𝑖𝑡 ) ). This ensures that equation (8) is defined for single
and multi-product firms.
The steps of recovering a product level productivity estimate from equation (8) are basically
identical to the firm level framework discussed in chapter 3.1. The only differences are that
∗
we now also have to instrument 𝑟𝑖(−𝑔)𝑡 with its first lag and that the vector 𝐳𝑖𝑔𝑡
now
includes product level control variables.16 For completeness we rewrite (8) as:
𝑞𝑖𝑔𝑡 = 𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝛽−𝑔 𝑟𝑖(−𝑔)𝑡
∗
∗
∗
+ 𝜑(𝑝𝑖𝑔𝑡
, 𝑚𝑠𝑖𝑔𝑡
) + 𝑔(𝑒𝑖𝑡−1 , 𝑘𝑖𝑡−1 , 𝑙𝑖𝑡−1 , 𝐳𝑖𝑔𝑡−1
) + 𝜀𝑖𝑔𝑡 + 𝜉𝑖𝑔𝑡 ,
(9)
which is identified by the moment conditions:
𝑙𝑖𝑡
𝑘𝑖𝑡
𝑚𝑖𝑡−1
∗
𝑝𝑖𝑔𝑡−1
∗
𝑚𝑠𝑖𝑔𝑡−1
𝑒𝑖𝑡−1
𝐸 (𝜉𝑖𝑔𝑡 + 𝜀𝑖𝑔𝑡 )
= 0.
𝑙𝑖𝑡−1
𝑘𝑖𝑡−1
∗
𝐳𝑖𝑔𝑡−1
𝚪𝑖𝑡−1
[
(𝑟𝑖(−𝑔)𝑡−1 ) ]
(10)
16
We include the rank of the product within the firm (in terms of revenue shares), product dummies and
location dummies of the firms headquarter.
16
After having consistent estimates for the input coefficients, firm-product level productivity
can be calculated:
∗
∗
𝜔𝑖𝑔𝑡 = 𝑞𝑖𝑔𝑡 − (𝛽𝑙 𝑙𝑖𝑡 + 𝛽𝑘 𝑘𝑖𝑡 + 𝛽𝑚 𝑚𝑖𝑡 + 𝜑(𝑝𝑖𝑔𝑡
, 𝑚𝑠𝑖𝑔𝑡
) + 𝛽−𝑔 𝑟𝑖(−𝑔)𝑡 ). (11)
When using equation (9) we follow the advice of Dhyne et al. (2016) and only consider the
products that represent at least 5% of a firms’ revenue.
3.3 Trade data and import competition measure
In this chapter, we lay out a methodology to deal with two recurrent problems in the
literature. First, previous studies on import competition have often suffered from an
imprecise measurement of competition, usually utilizing firms’ industry branches. All firms
within an industry thus had the same import competition by assumption. Apart from this
being imprecise, it forced researchers to compare firms in different industries with each
other to gauge the effect of competition.
Second, import shocks from individual countries are often considered independently.
However, it is very likely that they are correlated: E.g. trade negotiations that lead to WTO
ascendancies are linked via the diplomatic and political climate around trade negotiations in
general. Therefore, we consider import shocks from most German trading partners jointly.
We further sort countries in terms of their GDP per capita into high income and low income
countries. This separation functions as a proxy for high and low wage countries and aims at
investigating potential differences in the effects of import competition on productivity for
each country group.17
We are able to assemble such a precise and comprehensive data set by using the UN
Comtrade database in the Harmonized System (version of 2002) and relate it to the
PRODCOM 8-digit classification of 2002. Whenever this concordance is ambiguous, we split
the import volume according to the domestic production of the ambiguous goods. This
allows us to compute both total production and total import exposure on the level of
17
Our high income country group encompasses USA, Canada, Japan, and South Korea, while our middle-low
income country group is constituted of China, India, Russia, Brazil, South Africa, Argentina, Chile, Mexico,
Malaysia, Turkey, Thailand, Tunisia, Bangladesh, Indonesia, Philippines, Vietnam, and Pakistan. Note that we
consider only countries that contribute at least 0.001% to the total imports in the manufacturing sector of
Germany and that are not directly linked to Germany via common currency or geographical neighborhood.
17
individual products. Following the work of Dhyne et al. (2016) and Dauth et al. (2014) we
compute our measure of import competition, the import share as
𝑛→𝐺𝑒𝑟𝑚𝑎𝑛𝑦
𝑀𝑔𝑡
𝑛
𝐼𝑆𝑔𝑡
𝑛→𝐺𝑒𝑟𝑚𝑎𝑛𝑦
=
𝑀𝑔𝑡
𝑀𝑔𝑡 +𝑌𝑔𝑡
∗ 100.
denotes the imports of good 𝑔 from country (-group) 𝑛 to Germany, 𝑀𝑔𝑡
denotes all imports of good 𝑔 into Germany and 𝑌𝑔𝑡 equals total production within Germany.
As evident from the index 𝑛, we can compute this for all trading partners of Germany. We
compute the import exposure of the individual firm as the revenue-weighted average
pressures across its’ product portfolio. This allows for the fact that even within an industry,
firms face different import competition, based on their product portfolio.
3.4 Instrument and Identification
While computable for every product and trading partner, the import competition itself is
probably endogenous. We use an instrument similar in spirit to Autor, Dorn & Hanson 2013,
which has already been used in the context of Germany by Dauth, Findeisen & Suedekum
2016: Fluctuations in the exogenous competitiveness of the foreign country. An example
would be Chinese firms picking up semiconductor manufacture or becoming much better in
it as a result of internal Chinese productivity developments. This constitutes an exogenous
rise in competition from the viewpoint of German firms producing semiconductors. We
measure this exogenous competition by looking at third markets: If China is capturing a big
share of the import markets of other countries, we conclude that its competitiveness has
indeed risen. Specifically, we look at China’s market share among imported goods in a set of
control countries. We take countries whose markets for industrial products we consider
roughly comparable to Germany, but which are not directly related to Germany via common
currency or geographical neighborhood.18 We compute our instrument for each product as
𝑛
𝐼𝑁𝑆𝑔𝑡
=
𝑛→𝐼𝑁𝑆
𝑀𝑔𝑡
𝐼𝑁𝑆
𝑀𝑔𝑡
𝑛→𝐼𝑁𝑆
∗ 100 . 𝑀𝑔𝑡
stands for imports from the country (-group) of interest into
𝐼𝑁𝑆
any of the instrument countries, while 𝑀𝑔𝑡
is just total imports into these countries. One
can describe it as the market share of e.g. China in the joint import market of the instrument
countries. Again, to arrive at a measure for each firm, we aggregate over all 𝐼𝑁𝑆𝑔𝑡 , weighted
with firm specific revenue weights. For the estimation we aggregate over all instruments,
leading to one instrument per endogenous import competition shock.
18
This list currently entails Norway, New Zealand, Israel, Australia, Great Britain, Sweden, and Singapore.
Various robustness checks have shown that the results are not altered greatly by choosing a different list.
18
4 Empirical Results
This Chapter presents our estimation results as well as some descriptive statistics regarding
the evolution our measures of import competition and productivity. In section 4.1 we
present the results from our firm level and firm-product level production function
estimation. Both production functions are estimated at the NACE rev. 1.1 2-digit-level in
order to allow the coefficients of the production functions to vary between industries. In
section 4.2 we present results from our regression of firm and firm-product level
productivity on import competition. We find that qualitatively and quantitatively the effects
of import competition on productivity depend on the country of origin and that productivity
effects are partly disguised at the firm level due to an asymmetric effect of import
competition over the product portfolios of firms. We emphasize the importance of product
market analyses to measure the impact of import competition on domestic productivity.
4.1 Production function estimates
Table 1 presents the output elasticities for capital, labor, and intermediate inputs from
estimating the firm level equation (5) for every NACE rev. 1.1 2-digit-industry for which we
have more than 500 observations.
19
Table 1
Output elasticities from estimating equation (5)
for every nace rev. 1.1 2-digit-industry with more than 500 observations
Labor
Capital
Intermediate
Returns to
inputs
scale
Sector
15 (Food products and
beverages)
17 (Textiles)
18 (Wearing apparel; dressing
and dyeing of fur)
19 (Leather and leather
products)
20 (Wood and wood
products)
21 (Pulp, paper, and paper
products)
24 (Chemicals and chemical
products)
25 (Rubber and plastic
products)
26 (Other non-metallic
mineral products)
27 (Basic metals)
Number of
observations
(1)
0.14
(0.03)
0.31
(0.05)
0.14
(0.07)
(2)
0.23
(0.07)
0.14
(0.1)
0.27
(0.14)
(3)
0.75
(0.02)
0.73
(0.04)
0.81
(0.03)
(4)
1.13
(5)
9913
1.18
2622
1.22
650
0.30
(0.07)
0.04
(0.10)
0.76
(0.04)
1.1
0.20
(0.06)
0.17
(0.05)
0.29
(0.05)
0.18
(0.04)
0.18
(0.05)
0.22
(0.05)
0.06
(0.06)
0.06
(0.04)
0.05
(0.06)
0.06
(0.05)
-0.05
(0.04)
0.01
(0.04)
0.76
(0.03)
0.8
(0.03)
0.77
(0.03)
0.71
(0.03)
0.74
(0.02)
0.76
(0.03)
1.02
1627
1.02
2248
1.11
4407
0.95
4874
0.87
4547
0.99
3907
-0.01
(0.04)
0.68
(0.02)
0.88
7207
0.09
(0.06)
0.16
(0.08)
0.11
(0.13)
0.06
(0.09)
0.02
(0.08)
0.11
(0.06)
0.77
(0.05)
0.60
(0.04)
0.79
(0.05)
0.66
(0.04)
0.75
(0.04)
0.74
(0.03)
1.05
4136
1.1
2623
1.14
695
1.02
1539
0.86
1081
1.11
2396
28 (Fabricated metal
0.21
products, except machinery,
(0.04)
and equipment)
29 (Machinery and
0.18
Equipment n.e.c.)
(0.06)
31 (Electrical machinery and
0.33
apparatus n.e.c.)
(0.06)
32 (Radio, television, and
0.24
communication)
(0.13)
33 (Medical, precision, and
0.31
optical instruments)
(0.09)
34 (Motor vehicles, trailers
0.09
and semi-trailers)
(0.08)
36 (Furniture; manufacturing
0.25
n.e.c.)
(0.07)
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
515
Our coefficients are in line with our expectations, although we find relatively high standard
errors for the capital coefficients. This is not surprising as using firm specific price indices
forces us to reduce our sample noticeably. Further, firms within a broadly defined 2-digit
industry are still characterized by a high degree of heterogeneity. Due to using firm specific
output price indices while simultaneously controlling for input prices, we relate output
quantities to input quantities. Compared to using industry deflators (a revenue based
20
concept) this should amplify the variation in the output elasticities as firms, even in the same
2-digit industry, produce very different outputs. For this reason, we would ideally like to
estimate a production function for each firm separately, which, however, is impossible due
to the known data limitations. Evidently from table 1, output elasticities with respect to
production inputs vary enormously between industries, which is in line with the theory and
emphasizes the importance of allowing different production functions for separate
industries. When calculating productivity according to equation (7) we drop industries for
which we computed negative output elasticities with respect to capital, labor, or
intermediate inputs. We do this because the production function itself is not well defined in
those cases.
Table 2 presents our estimates for our firm-product level production function, which is
represented by equation (9). This takes the whole productivity estimation one step further
and allows for heterogeneous productivity levels among products within the same firm.
21
Table 2
Output elasticities from estimating equation (9)
for every nace rev. 1.1 2-digit-product with more than 500 observations
Labor
Capital
Intermediate
Other
inputs
revenue
Sector
15 (Food products and
beverages)
17 (Textiles)
18 (Wearing apparel; dressing
and dyeing of fur)
19 (Leather and leather
products)
20 (Wood and wood products)
21 (Pulp, paper, and paper
products)
23 (Coke, refined petroleum
products and nuclear fuel)
24 (Chemicals and chemical
products)
25 (Rubber and plastic
products)
26 (Other non-metallic
mineral products)
27 (Basic metals)
28 (Fabricated metal products,
except machinery, and
equipment)
29 (Machinery and
Equipment n.e.c.)
31 (Electrical machinery and
apparatus n.e.c.)
32 (Radio, television, and
communication)
33 (Medical, precision, and
optical instruments)
34 (Motor vehicles, trailers
and semi-trailers)
36 (Furniture; manufacturing
n.e.c.)
(1)
0.09
(0.07)
0.82
(0.16)
1.08
(0.45)
1.26
(0.49)
0.57
(0.14)
0.47
(0.10)
0.54
(0.35)
0.4
(0.09)
0.4
(0.07)
0.44
(0.14)
0.43
(0.09)
(2)
0.21
(0.12)
0.23
(0.2)
-0.78
(0.91)
0.41
(0.49)
0.06
(0.13)
0.18
(0.09)
0.06
(0.16)
-0.12
(0.16)
0.09
(0.08)
0.51
(0.16)
0.07
(0.11)
(3)
0.76
(0.85)
0.41
(0.14)
0.32
(0.31)
0.52
(0.25)
0.65
(0.12)
0.4
(0.08)
0.36
(0.17)
0.72
(0.08)
0.6
(0.06)
0.77
(0.09)
0.72
(0.08)
(4)
-0.43
(0.04)
-0.36
(0.04)
-0.9
(0.18)
-0.43
(0.09)
-0.33
(0.04)
-0.22
(0.02)
-0.56
(0.12)
-0.31
(0.03)
-0.30
(0.01)
-0.34
(0.06)
-0.33
(0.02)
0.52
(0.09)
0.41
(0.15)
0.48
(0.10)
0.52
(0.3)
0.29
(0.17)
0.66
(0.18)
0.54
(0.16)
0.01
(0.09)
0.19
(0.11)
0.06
(0.15)
0.04
(0.32)
0.19
(0.24)
0.27
(0.1)
0.24
(0.14)
0.65
(0.07)
0.64
(0.11)
0.59
(0.08)
0.65
(0.21)
0.57
(0.09)
0.59
(0.13)
0.62
(0.08)
-0.3
(0.02)
-0.28
(0.02)
-0.32
(0.02)
-0.29
(0.06)
-0.28
(0.03)
-0.26
(0.03)
-0.34
(0.03)
Number of
observations
(5)
32792
4997
2807
711
2697
2878
731
11221
8480
7934
7942
12555
7328
4992
1119
2444
1866
4034
Again, we estimated (9) for every PRODCOM 2002 2-digit product category (equivalent to
NACE rev. 1.1 2-digit industry) with more than 500 observations separately in order to allow
for different input coefficients for products from different industries. Once again, our results
coincide with our expectations and show huge variations in the production function
coefficients between products from different 2-digit industries. Also within 2-digit product
categories we find high levels of variation in the coefficients, which is in line with our
expectations as individual products naturally differ a lot despite the fact that they are
22
classified into the same 2-digit product category. Further, across firms individual products
may experience a very different importance within their firms, leading to even more
heterogeneity.
Note that contrary to the firm level estimation, the coefficients have to be interpreted
conditional on the output of other goods being constant. This does not allow to define the
returns to scale straightforward as the sum of the input coefficients. Again we drop
industries for which we computed negative output elasticities with respect to labor, capital,
and intermediate inputs. Further, we would have to drop industries for with we computed
positive values for 𝛽−𝑔 , which is never the case in our estimation.19
4.2 The effects of competition on the German manufacturing sector
4.2.1 Evolution of import competition and productivity
Figure 1 shows the evolution of the value of imports from high income, low income, and the
entire world to Germany. Throughout the time period in question, both TFP and import
pressure have generally risen. This rise, however, is largely attributable to imports from low
income countries, while the market share of our set of developed countries stayed virtually
constant. Nonetheless, products originating from industrialized partners make up the bulk of
German imports. Note that a large chunk of imports has been left out of the analysis, since
we excluded direct neighbors and Eurozone members to eliminate potential bias from
common productivity shocks to both countries.
19
As mentioned in section 3.2, equation (9) is only well defined when 𝛽−𝑔 < 0 and 𝛽𝑥 > 0 ,with 𝑥 = (𝑙, 𝑘, 𝑚),
holds.
23
Figure 1
year
c
World
c
High-income countries
Low-income countries
c
When we relate the import competition measures we derived from the above share of
imports with our TFP estimates, we find substantial co-movement over time. Figure 2
presents the corresponding graphs were we normalized all measures to unity in our base
year 2001.
24
Figure 2
year
𝐼𝑚𝑝𝑜𝑟𝑡industrialized
industrialized
𝐼𝑚𝑝𝑜𝑟𝑡
𝑇𝐹𝑃
𝐼𝑚𝑝𝑜𝑟𝑡emerging
Eyeballing the above graph, one could propose a positive relationship between lagged
import pressure and TFP: E.g., both types of import pressures peak 2006-2007, which
coincides with a peak in TFP in 2007-2008. After the crisis, both TFP and import competition
rise again. These fluctuations around the business cycle, however, provide only limited
evidence of the efficiency enhancing effects of competition. One can easily argue that both
have a common source and their causal relationship need not exist. This already points to
the problems of identification over time, which we will discuss in more detail when we
present our identification strategy. We should also note that, in any case, the correlation in
the above graph should not be interpreted causally.
4.2.2
Estimating the effect of import competition on firm level productivity
Theoretically, increased competition should have a strong disciplining effect on firms: To
combat their market share decreases, firms should be forced to lower prices and either
accept lower mark-ups or decrease their costs per unit. Additionally, firms which cannot
adapt will fall prey to their more productive competitors. This can be called the selection
effect of competition. Together, they provide the rationale for competition policy as well as
25
market integration. To identify these channels, we rely on exogenous competitiveness
increases in other countries, measured at the product level. This fine-grained measure of
import shocks allows us to improve upon the previous literature: We can compare firms
within the same industry, but with a different product mix, instead of relying on intraindustry comparisons prone to omitted variable bias. However, we will refrain from
identifying our effects via firm fixed effects, i.e. over time, for three reasons: First, this
specification would eliminate the selection channel: If a firm is forced out of the market due
to a competition shock, a fixed effects estimator would drop the firm at precisely the
moment we are most interested in. Because the selection mechanism is ignored, this will
result in an underestimate of the economy-wide effect. Second, the pressure a firm
experiences over time is also a choice variable: It likely fluctuates as firms adjust their
product portfolio to move away from attacked products. In this case, even if this is the result
of competition and even if it increases productivity, the resulting correlation might be
negative: As new, uncompetitive products gain importance for the firm, the measured
import pressure declines. The exploration of how increasing competition drives structural
change of this sort lies beyond the scope of this paper and may be better addressed in a
subsequent research project, but again, it leads to an underestimation of the true
coefficients. Last, but not least, extensive pressure might induce firms to switch industries.
This poses a problem for the estimation since the production function is different for each
industry and TFP measures are thus not comparable across them. This would force us to
restrict the sample to those firms which do not switch. Again, this would lead to a downward
bias since firms reacting in a strong way are excluded, although they are especially
interesting.
For this reason, we stick to identification over different firms. In fact, one could call our
baseline specification a repeated cross section within each year and industry:
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝑇𝐹𝑃𝑖𝑡 = 𝐼𝑆𝑖𝑡−1
𝑖𝑛𝑐𝑜𝑚𝑒
+ 𝐼𝑆𝐿𝑜𝑤
+ 𝐷𝑗∗𝑡 + 𝑁𝑢𝑚𝑃𝑡−1 + 𝐸𝑥_𝐼𝑡−1 (YX)
𝑖𝑡−1
Here, 𝐷𝑖∗𝑡 represents a year times 4-digit industry ( 𝑗 ) dummy. We additionally control for
past values of the number of products ( 𝑁𝑢𝑚𝑃𝑡−1 ) and the export intensity ( 𝐸𝑥_𝐼𝑡−1 ), both of
which insulate companies from competition in the domestic market. Table 3 shows the
associated results.
26
Table 3
The effect of import competition on firm level productivity
All firms
Dep. variable: 𝑇𝐹𝑃𝑖𝑡
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
𝐸𝑥_𝐼𝑡−1
𝑁𝑢𝑚𝑃𝑡−1
Obs.
R-squared
Wald-F
# of Clusters
Split by firm type
Single-product
(3)
(4)
OLS
IV
(1)
OLS
(2)
IV
0.0031***
(0.0009)
0.001*
(0.0005)
-0.0002*
(0.0001)
-0.0045***
(0.0007)
0.0043***
(0.0015)
-0.0008
(0.0009)
-0.0002*
(0.0001)
-0.0045***
(0.0007)
0.00246*
(0.00127)
0.0004
(0.0007)
0
(0.0001)
0.003
(0.002)
-0.0027**
(0.0013)
0
(0.0001)
-
-
42,397
0.962
11452
42,397
0.962
351.2
11452
15,297
0.948
4658
15,297
0.948
195.6
4658
Multi- product
(5)
(6)
OLS
IV
0.0032***
(0.0011)
0.0015*
(0.0008)
-0.0003**
(0.0001)
-0.0037***
(0.0006)
0.0064***
(0.0023)
0.0002
(0.0013)
-0.0004**
(0.0002)
-0.0038***
(0.0006)
26,885
0.968
7235
26,885
0.968
214.3
7235
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Evidently, the beneficial effects of competition are almost exclusively derived from import
competition from developed countries. The size of the measured effects is economically
meaningful, but not improbably large. For example, our results indicate that if foreign firms
from industrialized countries capture an additional percentage point of the market, this
leads domestic firms to increase their productivity by 0.4%. Throughout our observation
period, the average pressure from foreign industrialized countries rose by 2 percentage
points, to which we would attribute a modest 0.8% TFP growth contribution.
It is noteworthy that OLS and IV estimates differ, and sometimes greatly. This illustrates the
importance of instrumenting import competition shocks properly. In this case, OLS seems to
view competition in a more benign light than is actually warranted. Thus, positive selection
seems to be dominant here: In order to make it into our sample, firms must exist for one
year after experiencing a significant competition shock. This means they likely are more
productive than their counterparts from the start, because more productive firms are more
resilient in general. However, as we will see in future applications, there is also an opposite
effect: Competition likely targets especially uncompetitive, i.e. unproductive sectors. This
leads to a negative correlation between productivity and import competition shocks and
biases coefficients downwards.
27
Competition from low income countries seems to have, if any, a negative effect –
concentrated among single product firms. This is in line with a whole class of IO models,
which all postulate some sort of ladder competition: Both the quality ladders of Grossman,
Helpman (1999) or Crepón, Duguet & Mairesse (1998) and the technology ladders of Aghion
et al. (2004) suggest that only competition from firms on the same ladder step or a bit below
that can spur firms to innovate. This is because firms give up on catching competitors very
far ahead. Only if they think that innovation will give them a perceivable chance to become
technology leader will they start to innovate.
An additional argument would be one of market size: If innovation carries fixed sunk costs,
but gives a per-unit benefit to the producer, only big firms have an incentive to innovate. In
this case, increasing competition, by redistributing market shares towards the top, will
increase the innovation incentives of some firms, while hurting that of others. Such models
are prominent in the literature on export and innovation (Bustos (2011)).
However, the evidence is also in line with a technology transfer model of trade where it is
often argued that the emergence of technologically rich products on the market facilitates
imitation.
To better understand the mechanisms behind the effects, we split the sample of firms into
productivity quantiles. Both competing explanations predict different patterns of effects in
this exercise: If German firms learn from technology rich imports, we would expect to see
the biggest effects for very unproductive firms. If, however, the effect works through the
fixed innovation costs or through firms competing to become technology leader, we would
expect positive effects for productive and negative effects for unproductive firms. Table 4
presents the corresponding results, where 𝑄1 represents a dummy for the lowest
productivity quintile, while 𝑄5 symbolizes a dummy for the highest productivity quintile.
28
Table 4
The effect of import competition on firm level
productivity per productivity quantiles
(1)
OLS
(2)
IV
0.0013
(0.0011)
0.0005
(0.0008)
0.0006
(0.0012)
0
(0.0004)
0.0012
(0.0014)
-0.0024***
(0.0005)
-0.0004
(0.0003)
0.0005*
(0.0003)
0.0008***
(0.0003)
0.0014***
(0.0006)
0.159***
(0.0036)
0.261***
(0.0036)
0.374***
(0.0039)
0.534***
(0.0061)
0
(0)
-0.0014***
(0.0002)
0.0037**
(0.0015)
0.0013
(0.0012)
0.00088
(0.0009)
-0.0005
(0.0008)
0.0004
(0.0017)
-0.0027***
(0.0007)
-0.0009*
(0.0005)
0
(0.0004)
0.0003
(0.0004)
0.0005
(0.0008)
0.162***
(0.004)
0.264***
(0.0041)
0.380***
(0.0044)
0.542***
(0.0068)
0
(0)
-0.0014***
(0.0002)
42,397
0.981
11452
42,397
0.981
73.42
11452
Dep. variable: 𝑇𝐹𝑃𝑖𝑡
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑄1
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑄2
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑄3
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑄4
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑄5
𝐼𝑆𝑖𝑡−1
𝐼𝑆𝑖𝑡−1
𝐼𝑆𝑖𝑡−1
𝐼𝑆𝑖𝑡−1
𝐼𝑆𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
∗ 𝑄1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
∗ 𝑄2
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
∗ 𝑄3
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
∗ 𝑄4
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑖𝑡−1
∗ 𝑄5
𝑄2
𝑄3
𝑄4
𝑄5
𝐸𝑥_𝐼𝑡−1
𝑁𝑢𝑚𝑃𝑡−1
Obs.
R-squared
Wald-F
# of Clusters
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
The estimation again points to significant heterogeneity between different sources of
competition: For industrialized countries, learning from imported goods seems to be the
relevant channel, evident from the positive coefficients for low productivity firms. To the
contrary, for low income countries, we find negative coefficients for unproductive ones. This
is inconsistent with technology imitation and points to competition inducing investments in
29
productivity. This difference in channels seems plausible: Only highly developed countries
will export technology that German firms will on average find worthwhile to innovate,
Germany being a technology leader itself in many cases.
While the products of low income countries are internationally competitive through lower
wages, their quality and technology content is on average lower (Khandelwal (2010);
Hummels, Klenow (2005)). Attacked domestic firms seemingly do not feel that investing in
productivity helps them to cope with this. There are two theoretical explanations for this:
First, perhaps there are in fact two separate markets for cheap and for technologically
sophisticated products. In this case, if the consumers evidently prefer the cheap variant,
investing in even more technological sophistication will not help firms. Second, endogenous
growth models usually feature decreasing return to productivity investments. If this is the
case, it might be simply to costly to innovate enough to be competitive with very low cost
producers. In an Heckscher-Ohlin fashion, firms would cede products which can be produced
with cheap labor to foreign competition. In fact, this is a prediction that also comes out of
ladder-type models.
All of the above arguments indicated that competition is a driver of productivity. However,
somewhat unintuitive, we also find relatively strong negative effects. However, there is
actually a compelling reason for these: If firms loose market share fast, they have to shrink.
However, as already standardly argued when estimating a production function, labor and
capital cannot be considered as fully flexible inputs. As a result, unexpectedly shrinking firms
might end up with declining TFP, as unused inputs pile up. This implies that to reap the
productivity benefits of sudden moves in competition, input market would have to be
flexible. To our knowledge, this channel was not yet shown in the literature.
When looking at multi-product firms, we can use their product portfolio to get a clearer
picture of these effects. In the table below, we present an estimate where we compute the
import pressure for the first product (𝐼𝑆_1𝑠𝑡𝑖𝑡𝑛 ) and all other products (𝐼𝑆_𝑂𝑡ℎ𝑖𝑡𝑛 ) separately, to
find out through which channels product pressure affects the firm.
30
Table 5
The effect of import competition of firm level
productivity separately
Dep. variable: 𝑇𝐹𝑃𝑖𝑡
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_1𝑠𝑡𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_1𝑠𝑡𝑖𝑡−1
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_𝑂𝑡ℎ𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_𝑂𝑡ℎ𝑖𝑡−1
𝐸𝑥_𝐼𝑡−1
𝑁𝑢𝑚𝑃𝑡−1
(1)
OLS
(2)
IV
0.0014
(0.001)
0.0001
(0.0006)
0.0028***
(0.001)
0.0005
(0.0006)
-0.0003**
(0.0002)
-0.0036***
(0.0026)
0.0016
(0.0020)
0
(0.001)
0.0094***
(0.0023)
0.0006
(0.0009)
-0.0004**
(0.0002)
-0.0036***
(0.0068)
Obs.
26,254
R-squared
0.969
Wald-F
# of Clusters
6965
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
26,254
0.969
93.36
6965
Unexpectedly, we find that the productivity effect is concentrated among multi-product
firms whose secondary product portfolio is threatened. However, it is consistent with the
view that rigidities hinder firms from adjusting their size efficiently: We would expect that
pressure on the first product generates the bulk of revenue losses. Without a hit to the first
product, it seems unlikely that firms will be forced to lay off inputs at such a rate that input
market frictions become a serious problem for them.
However, we caution against interpreting this result too confidently for two reasons: One is
theoretical: While intuitively, we would like to measure whether the effect of import
competition is more pronounced on the first product, this is not equivalent of the above
equation. To see this, take a big multinational firm as an example: If such a firm receives
pressure on its core product, this might still only amount to a minuscule pressure overall,
because the core product does not represent a big share of revenue. On the other hand, the
same pressure over the whole product range would be felt by the firm. The second reason is
that we observe some volatility in the estimated coefficients across specifications: While the
results are always consistent with an overall positive effect of competition from
31
industrialized countries and a insignificant or negative effect of poorer trading partners, the
coefficients for the first product and the rest are not as stable as we would like.
While illustrative, this discussion only scratches the surface of the potential product data has
in order to better understand the channels through which competition work. Thus, we will
deepen the discussion of these effects by incorporating our product level results.
4.2.3 Firm-product TFP and import competition
At the product level we use the following baseline specification:
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝑇𝐹𝑃𝑔𝑖𝑡 = 𝐼𝑆𝑔𝑡−1
𝑖𝑛𝑐𝑜𝑚𝑒
+ 𝐼𝑆𝐿𝑜𝑤
+ 𝐷𝑔∗𝑖+𝑡 ,
𝑔𝑡−1
(11)
were 𝐷𝑔∗𝑖+𝑡 captures time dummies and an interaction between product and firm dummies.
Consequently, for identification we explore variation over time within a specific firmproduct. Like explained in chapter 3.4, we instrument import competition from country
group 𝑛 to Germany with the share of imports on total imports in other markets with
characteristics similar to Germany. Table 6 shows the associated results.
Table 6
The effect of import competition of product level
productivity
(1)
OLS
(2)
IV
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
0.0023
(0.0024)
-0.0064***
(0.0011)
0.0018
(0.0087)
-0.0124***
(0.0033)
Obs.
R-squared
Wald-F
# of Clusters
139,452
0.995
13676
100,476
0.995
75.90
10367
Dep. Variable: 𝑇𝐹𝑃𝑔𝑖𝑡
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
As one can immediately see, instrumenting the import competition greatly improves the
quantitative dimension of the coefficient for import competition from low income countries,
while the significance of the import competition measure for high income countries stays
unchanged. This results imply that among surviving products (products produced in 𝑡 and
32
𝑡 − 1) import competition from low income countries lead to negative productivity effects,
while import competition from high income countries show no significant effects in this
respect. The negative effects is in line with the results found at the firm level for single
product firms and implies that a one percent increase in import competition from low
income countries, measured by the share of imports on the domestic market, reduces the
productivity of the average domestic firm-product by a considerable 1,2 percentage points.
Notably, import competition from high income countries does not significantly effect the
productivity of firm-products. Intuitively, when foreign goods are imported, market shares
are transferred from domestic products towards foreign products. This forces firms to
produce at a lower scale which already by itself can lead to negative productivity effects and
which can be emphasized by frictions that prevent the firm from efficiently reorganizing
their production activities, which in line with very recent findings that stress long labor
market adjustment processes after import competition shocks from China at the sector level
(Autor, Dorn, and Hanson (2016)).
Further, seemingly firms cannot overcome those market share losses associated with import
competition, at least not within one year, by investing in product productivity increasing
process innovations. As low income countries, compared to a high income country like
Germany, posses a production cost advantage due to lower wages, one can easily imagine
that German firms want to avoid competing with producers from low wage countries and try
to escape into other production activities. However, our results at the firm level show that
such adjustment processes do not lead to an increase in productivity within the first year
after the competition shock.
To investigate further the effects of product level import competition on product level
productivity, we interacted the import competition measure with the rank of a product
within its firm (in terms of revenue shares), were products with a rank of 4 or higher are
consolidated. Results are reported in Table 7.
33
Table 7
The effect of import competition on product level productivity
conditional on the rank of a product
Dep. Variable: 𝑇𝐹𝑃𝑔𝑖𝑡
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘1
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘2
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑅𝑎𝑛𝑘3
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
∗ 𝑅𝑎𝑛𝑘4+
𝐼𝑆𝑔𝑡−1
𝐼𝑆𝑔𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘2
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘3
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
∗ 𝑅𝑎𝑛𝑘4+
Time FE
Firm * product FE
Product * time FE
Product * firm* product rank FE
Obs.
R-squared
Wald-F
# of Clusters
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(1)
OLS
(2)
IV
0.0094**
(0.0038)
-0.0024
(0.0032)
-0.0057*
(0.0033)
-0.0103***
(0.0034)
-0.0013
(0.0012)
-0.0074***
(0.0013)
-0.0092***
(0.0015)
-0.0143***
(0.0018)
YES
YES
NO
NO
0.0314***
(0.0107)
0.0074
(0.0101)
-0.0152
(0.0106)
-0.0345***
(0.0124)
0.0012
(0.004)
-0.0133***
(0.0039)
-0.0227***
(0.0042)
-0.0317***
(0.0049)
YES
YES
NO
NO
118,244
0.995
11474
85,372
0.996
14.02
8666
(3)
OLS
(4)
IV
-0.004*
(0.0023)
-0.0075**
(0.0033)
-0.0109***
(0.0032)
-0.0064
(0.0046)
-0.0178***
(0.0063)
-0.0253***
(0.0071)
-0.0022**
(0.001)
-0.0024*
(0.0013)
-0.0045***
(0.0016)
NO
NO
YES
YES
-0.0074***
(0.0016)
-0.0094***
(0.0023)
-0.0124***
(0.0026)
NO
NO
YES
YES
100,340
0.998
10745
72,389
0.998
65.12
8055
The first two columns show results from a regression that includes the same fixed effects as
the baseline specification, i.e. time and firm times product fixed effects, which can be
referred as the within estimator. Column (3) and (4) complete this picture by looking across
products within a firm and excluding all variation coming from products that switch their
rank. Column (3) and (4) therefore, represent a comparison between the productivity effects
of lagged import competition on product ranks within a firm, while column (1) and (2) are
showing the absolute productivity effects of import competition. First of all, again we find
that instrumenting the import competition shock amplifies the associated coefficients
remarkably.
We further see from Column (2) that import competition from high wage countries leads to
highly positive productivity effects when the targeted product is the core product of a firm.
34
Quantitatively we find that a one percentage point increase in import competition form high
wage countries leads to a product level productivity increase of roughly 3 percentage points
when the core product of a firm is targeted. Interestingly, in both specifications we find that
the lower the revenue share of a product subjected to import competition, the more
negative is the productivity effect. This implies that firms skew their product mix towards
their core products, which confirms theoretical models (e.g. Mayer, Melitz, Ottaviano 2014)
and recent empirical findings (Dyhne et al. 2016). Furthermore, combining this results with
our firm level estimates, positive productivity effects of import competition from high
income countries at the firm level are asymmetrically distributed over the product mix of
firms and indeed are partly associated with negative productivity effects. This in an
important finding as it emphasizes distributional consequences. Workers associated with
peripheral production processes may lose from import competitions shocks originating from
high income countries, while workers associated with production processes that lie in the
focus of the firm may benefit. Therefore our results stress the importance of product level
analyses, as only investigating the firm level may overlooks important parts of the effects
from import competition that are highly relevant for welfare questions as well as for the
evaluation of trade policies.
In line with our previous findings we see that import competition from low income/wage
countries is only associated with negative productivity effects. Similar to the high
income/wage case, those effects become increasingly negative when we move down the
product ranking of the targeted product.
4.2.4 Labor reactions to import competition shocks
During the analysis of the effects of intensifying competition on TFP, we came across several
negative estimates, i.e. findings that TFP decreased with increasing competition. We argued
that decreasing market shares force firms to shrink, which they cannot do as fast as would
be appropriate due to input market frictions. This might cause the short-term TFP decline
observed in the data. To test this hypothesis, we study the firms’ employment reaction in
the wake of an import competition shock (Columns 1-4 of the below table). To estimate it,
we swapped the dependent variable from Frim TFP to full-time-equivalents, but left the
methodology unchanged otherwise, i.e. we control for industry times year fixed effects.
35
Table 8
Labour adjustments after import competition shock
Dep. Variable: Full Time
Equivalent of Labor
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆𝑔𝑡−1
(1)
(2)
(3)
(4)
(5)
(6)
OLS
IV
OLS SP
IV SP
OLS
IV
-0.0116***
0.00501
-0.00542*
(0.00240)
(0.00518)
(0.00315)
-0.00765*** -0.00575** -0.00556***
(0.00141)
(0.00254)
(0.00178)
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_1𝑠𝑡𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_1𝑠𝑡𝑖𝑡−1
𝐻𝑖𝑔ℎ 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_𝑂𝑡ℎ𝑖𝑡−1
𝐿𝑜𝑤 𝑖𝑛𝑐𝑜𝑚𝑒
𝐼𝑆_𝑂𝑡ℎ𝑖𝑡−1
l_NumP
l_Ex_I
0.0468***
(0.00645)
0.0114***
(0.000736)
79,636
Obs.
0.287
R-squared
Wald-F
24247
# of Clusters
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.0469***
(0.00647)
0.0113***
(0.000731)
0.122***
(0.0213)
0.0102***
(0.000534)
79,636
0.285
581.6
24247
30,394
0.255
9752
0.00925
(0.00655)
-0.00533*
(0.00319)
-0.0107***
0.00798
(0.00278)
(0.00690)
-0.00730*** -0.00667**
(0.00165)
(0.00319)
-0.00631**
-0.00175
(0.00262)
(0.00709)
-0.00432*** -0.00325
(0.00156)
(0.00266)
0.122***
0.0383***
0.0383***
(0.0214)
(0.00546)
(0.00547)
0.00999*** 0.0120***
0.0118***
(0.000539)
(0.00128)
(0.00127)
30,394
0.252
271.3
9752
47,668
0.325
14651
47,668
0.322
212.6
14651
We can see that indeed, firms seem to lay off people in response to increased competition.
However, while this correlation does appear to be causal in the case of low income
countries, instrumentation reveals that the correlation breaks down in the case of high
technology competition. Again, this points to learning effects as a central driver behind the
productivity increase: Increased imports from industrialized countries seem to induce firms
to become more productive. However, we should probably not put to much weight on the
positive effect of competition on employment, given the big standard errors. Still, it seems
pretty clear that negative employment effects from import competition are mainly
originating from imports from low income countries which is in line with our previous results
on firm and product level productivity and also reported in other studies (Acemoglu et al.
(2013); Autor, Dorn, and Hanson (2016)).
To quantify this effect: A one percentage point increase in competition from low wage
countries leads to a more than 0.5% reduction of employment. This effect is remarkable
stable across OLS and IV, as well as across single product firms and the whole population. In
the last two columns, to better understand the dynamics for multi-product firms, we split
36
import pressure into pressure originating from the first product of the firms and the
pressure resulting from the rest of the product portfolio. Thus, we can see that even for
multiproduct firms, the effects work primarily through the core products of the firm. The
very high skewness of the revenue distribution across firms and products documented
throughout the literature in this case works against multi-product firms: If they had a
portfolio of three equally important products, they would be able to mitigate the effects of
lost revenue much better. As it is, they suffer to the same degree that single product firms
do.
From this evidence we can conclude that input market frictions which prevent efficient
downsizing may indeed explain the negative effect of competition on productivity. This is
plausible because negative productivity reactions coincide with downsizing, while growing
productivity does not. This provides strong support for the idea that flexible input markets
are a prerequisite to realize gains from trade. It should however also be pointed out that
such flexibility comes at a cost not modelled here, which would have to be weighted against
any additional productivity gains.
5 Conclusion
This paper examines the casual effects of import competition on firm and firm-product level
productivity of German manufacturing firms. We exploit our high level administrative data
base to construct a quantity based measure of total factor productivity (TFPQ) which,
contrary to revenue based measures is purged from undesired price effects that may bias
true technical efficiency measures. We further, propose a simple intuitive solution to the
critique of Gandhi et al. (2013), which normally would forbid using popular control function
techniques like in Olley, Pakes (1996), Levinsohn, Petrin (2003) or Wooldridge (2009) to
estimate a production function. Our study presents the first casual evidence on the potential
different effects from import competition from different country sources on firm level and
firm-product level productivity. We indeed find that the origin of international competition
matters. While import competition from high wage countries is associated with firm
productivity enhancing effects, import competition from low wage countries depresses firm
productivity. To deepen our understanding on potential channels, we go one step further
and look at the reactions at the firm-product level. Our results show that import competition
37
form high income countries leads to product productivity enhancing effects only if the
targeted product is at the core of the production activities of a firm. Low wage import
competition in general causes productivity decreases at the product level. In general, the
more peripheral a product is, the more negative becomes the effect of import competition
on productivity. Even where we find positive firm level effects, we find much more
ambiguous results at the product level. This indicates that generally, firms do not achieve
these productivity gains from improving their product lines. Instead, product portfolio
adjustment seems to play the most important role.
Our results imply that firms skew their product mix towards their core products when
foreign competition shocks hit the firm, which confirms modern trade theories (e.g. Mayer,
Melitz, and Ottaviano 2014). Furthermore, our study emphasizes the importance of product
market analyses as firm level reactions partly disguise product market adjustments which
have important welfare implications, especially for workers associated with peripheral
production processes.
We also investigate firm level labor adjustments and find that especially low wage import
competition leads to a shrinking of firms. For international competition originating from high
wage countries we cannot find any significant effects on the firms’ labor adjustment. This is
in line with our results for total factor productivity and reinforces the strong contrast
between the effects of import competition from different country sources. We therefore
stress the great importance of country asymmetries when analyzing firm level consequences
from international competition – an aspect which lately had remarkably sparse attention in
empirical economic science.
38
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Appendix A: Construction of capital series
Our dataset does not contain direct information on the (accounting) capital stock. However,
once we have a start capital stock, we can use investment data for the perpetual inventory
method to construct a series of capital stocks. To estimate a start capital stock, we combine
information on the value of the yearly depreciation of firms, 𝜋𝑖𝑡 , which is available in our
data set, with information on the 2-digit industry ( 𝑗 ) specific average lifetime of capital
goods bought in year 𝑡, 𝐷𝑡𝑗 (𝜃). Latter is provided by the statistical office of Germany and is
subdivided in the categories, 𝜃, which are equipment and buildings.
The expected lifetime of capital goods contains information about their real depreciation
rate.20 We assume a constant depreciation rate of capital and that capital is destroyed
(depreciated) at the end of the production period. Both are standard assumptions in the
literature. To ease notation we suppress the index 𝜃, noting that it makes no difference for
our derivation. We define the number of machines which were destroyed during the
production process in industry 𝑗 and period 𝑡 as:
𝜑𝑗𝑡 ≡ 𝛿𝑡 ′ 𝑗 ∗ 𝐾𝑗𝑡 ,
where 𝛿𝑡 ′ 𝑗 is the depreciation rate of capital purchased at time 𝑡 ′ . The average lifetime of a
capital stock purchased in year 𝑡 = 0 then equals:
20
Using this relationship rests on an idea of Müller (2008). We augment his approach by specifying an exact
functional form for the depreciation of capital that is consistent with assuming a linear depreciation of capital
(i.e. a regressive function of the surviving capital stock).
41
∞
𝐷0𝑗
∞
1
1
=
∑ 𝜑𝑗𝑡 ∗ 𝑡 =
∑(𝛿0𝑗 𝐾𝑗𝑡 ) ∗ 𝑡 .
𝐾𝑗0
𝐾𝑗0
0
(𝑋𝑋𝑋)
0
By using the law of motion for a linear capital depreciation, 𝐾𝑗𝑡 = 𝐾𝑗0 ∗ (1 − 𝛿0𝑗 )𝑡 , and
substituting it into (XXX), one can show with some algebra, that the following equation
holds:
−𝛿 (𝜃)
0𝑗
𝐷0𝑗 (𝜃) = ln(1−𝛿
.21
0𝑗 (𝜃))
We solve this expression numerically for each year and each capital type (equipment and
buildings). This generates two depreciation rates for each industry and point in time. We
then define a single depreciation rate by using weights from the industry wide stocks of
equipment and buildings at time 𝑡. Further, we simplify by assuming that the depreciation
rate for the whole capital stock in each period equals the depreciation rate of newly
purchased capital in this period, i.e. 𝛿𝑡 ′ 𝑗 = 𝛿𝑡𝑗 .
Having 𝛿𝑡𝑗 , we can calculate a start capital stock for firm 𝑖 by using the data on yearly
depreciations, deflated by an industry specific capital depreciation deflator coming from the
statistical office of Germany:
𝜋
𝜋𝑖𝑡 = 𝛿𝑡𝑗 ∗ 𝐾𝑖𝑡 → 𝐾𝑖𝑡 = 𝛿 𝑖𝑡 .
𝑡𝑗
After having the start capital stock, we construct our capital series by using the following law
of motion:
𝐾𝑖𝑡 = 𝐾𝑖𝑡−1 (1 − 𝛿𝑗𝑡−1 ) + 𝐼𝑖𝑡−1 ,
Where 𝐼𝑖𝑡−1 is last years firm specific investment, deflated by an industry specific investment
deflator (Source: statistical office of Germany). We therefore assume that Investment takes
one year to be available in production.
21
The prove is available on request.
42
Our capital stock has several advantages over the usual ones used in literature. First we do
not need to assume a arbitrary depreciation rate.22 Second, our depreciation rate is industry
and time specific, taking care of the fact, that capital stocks deviate between industries.
Third, our capital stock is closer to the productive capital stock, i.e. the capital used in
production, rather than the usual capital stocks used throughout the literature, which are
normally based on accounting data.23
22
Often one simply says that 𝛿𝑡𝑗 is time constant and equals 8%, which, in our case, would be a noteworthy
deviation from the calculated depreciation rate.
23
Capital stocks from accounting data can be a poor approximation of the real capital used in the production
process, because firms’ have an incentive to depreciate their accounting capital excessively high.
43