Market Selection in Global Value Chains
Philipp Mundt, Uwe Cantner, Hiroyasu Inoue, Ivan Savin and Simone Vannuccini

Working Paper No. 170
April 2021

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Market Selection in Global Value Chains
Philipp Mundt∗ Uwe Cantner†‡ Hiroyasu Inoue§
Ivan Savin¶‖∗∗ Simone Vannuccini††

Abstract
The idea that market selection promotes survival and expansion of the “fittest” producers
is a key principle underlying theories of competition. Yet, despite its intuitive appeal, thehypothesis that companies with superior productivity also exhibit higher growth lacks empirical support. One reason for this is that companies are not “islands” that produce goods
and services in isolation but depend on their suppliers in value chains, implying that excessive growth can also originate in the superior productive performance of these value-chain
partners. Neglecting these dependencies in empirical tests of the selection hypothesis leads
to measurement errors and may impair the identification of competition for the market.
In this paper, we use data from the World Input-Output Database to capture these global
value-chain relationships in an empirical test for market selection, studying competition
between country-sectors for a global market share in different economic activities. Compared to the conventional view that focuses on individual productivities, our value-chain
perspective on the productivity-growth nexus provides stronger empirical support for market selection. This suggests that the scope of selection reaches beyond the level of individual
producers and requires a systemic analysis of production networks. Our findings contributeto a better understanding of the determinants of selection in competitive environments andalso represent a novel application of global value-chain data.
Keywords: competition; country–sector dynamics; input–output analysis; replicator dynamics; productivity decomposition
JEL Classification: C67; D22; L14; L16; L20∗ Department of Economics, University of Bamberg, Feldkirchenstraße 21, D-96052 Bamberg† Faculty of Economics and Business Administration, Friedrich Schiller University of Jena
‡ Department of Marketing and Management, University of Southern Denmark, Denmark§ Graduate School of Simulation Studies, University of Hyogo, Japan¶ Institute of Environmental Science and Technology, Universitat Autónoma de Barcelona, Spain‖ Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russia∗∗ Corresponding author: ICTA-ICP Building (Z), UAB Campus, Cerdanyola del Vallés, Barcelona, Spain, Tel:

+34 935-868779 Ivan.Savin@uab.cat†† Science Policy Research Unit (SPRU), University of Sussex Business School, BN1 9SL Falmer, United Kingdom

1

1

Introduction

What determines the market success of producers in a competitive environment? One way toapproach the process leading to economic success — usually measured in terms of market shares— roots in the evolutionary hypothesis of the “survival of the fittest” that is consistent with
Darwinian principles. According to this view, market selection ensures that the fittest producers
will see their share in the market grow, while the others will shrink and are eventually driven outof the market. This idea has inspired studies of firm-level market selection using variants of theso-called replicator dynamics model (Taylor and Jonker, 1978, Metcalfe, 1994). Yet, although
this approach offers an intuitive explanation of differential growth driven by competition, theliterature has until now barely succeeded in the task to turn analytical outcomes into compelling
empirical evidence.
The reasons why the empirical evidence does not lend much support to the selection hypothesis
are manifold. Possible explanations include the naïve interpretation of how selection works,
misconceptions about the subjects and boundaries of selection (e.g., industry vs. (sub-)market),and the choice of irrelevant fitness measures. Typically, at the firm level, fitness is boiled down to asingle indicator that reflects technological and economic characteristics of the individual producer(e.g., productivity, unit costs, or combinations of product characteristics, Cantner et al., 2012).
The firm-specific (idiosyncratic) realization of this indicator is then employed to explain the
success of the individual firm in the market. In this paper, we argue that the complexity of value
chains, where producers buy intermediate inputs from upstream suppliers and sell their output
to downstream customers, may undermine the selection mechanism at the level of individual
producers. This is because less efficient producers can gain share in their own markets if they
are linked to superior value chain partners in related markets, even if their own productive
performance is relatively poor (and vice versa), as argued theoretically by Cantner et al. (2019).
Put differently, it is not only the idiosyncratic fitness that determines the market success of anindividual producer but also the fitness of the value chain the producer belongs to.
Against this background, the purpose of the present study is to empirically test the selection
hypothesis extended to value chains. To map these value chains, we employ network panel
data on production input linkages from the World Input Output Database (WIOD; see Timmer
et al., 2015), providing sector-level data for 43 major economies over the period 2000-2014.
Consequently, the unit of observation in our sample is not a firm in a given sector but a sector
in a certain country (country-sector, henceforth), which competes with corresponding sectors inother countries on the global market. Compared to the prototypical example of firm-level value
chains, for which we could not obtain suitable data, the advantage of the WIOD data is not
only its global coverage but also the fact that it captures the full range of economic activities inthe value chain, including all pre- and post-production phases before the output is sold to final
consumers.
Building on these data, we use standard input-output accounting (Leontief, 1936) and a modelof vertical production in the spirit of Pasinetti (1973) to compute direct and indirect input
and value-added contributions of all suppliers participating in the global value chain (GVC) of
a given country-sector. We then use these quantities to estimate (labor) productivity of theentire value chain, which represents a more thorough measure of productive performance than
the idiosyncratic productivity of the individual country-sector. The explicit comparison between
the two productivity measures enables us to assess the gains in explanatory power arising fromthe shift of attention away from individual producers towards value chains. Building on thisapproach, we conduct three different tests for market selection.
First, we consider an aggregate labor productivity index for each sector and decompose itschange over time into a within and a between component. The latter captures the change in2

aggregate productivity arising from the transition of market shares from less to more productive
producers. Under the hypothesis that the selection mechanism works properly, we expect thatthe between effect is positive and can explain a considerable fraction of the change in aggregate
productivity. In other words, a country-sector operating above (below) the average productivity
will experience an increase (decrease) of its share in global markets. We run this test based on
the individual labor productivity and the productivity of the entire value chain and find that theconsideration of the latter productivity leads to a higher median and mean between effect acrossall sectors, supporting the hypothesis that selection forces are stronger at the value-chain level.
Second, we turn to a more direct test for market selection by assessing the explanatory powerof idiosyncratic versus value-chain productivity for output growth in regression analysis. Again,
our results support the extended selection hypothesis as the productivity of the entire value chain
can better explain variation in producers’ growth in end-consumer markets than the individual
productivity.
Third, we employ a spatial regression approach to assess the direct influence of the upstream
suppliers’ productivity on the growth of the focal country-sector. This model incorporates the
productivity level and productivity change of the focal producer as well as the spatial lags ofthese variables for its direct and indirect suppliers, which enables us to separate the effect of the
producer’s idiosyncratic characteristics and the influence of the value-chain partners. Consistent
with the extended selection hypothesis, our results suggest that the relation between the individual productivity change and its spatial lag on the one end and the output growth on the otherend is significantly positive. We also find that the joint explanatory power of the individual andspatial productivity terms is almost identical to that of the value-chain productivity measure in
our second test, and that the effect pertaining to the spatially lagged productivity terms is atleast as strong as the direct influence of the individual productivity. Therefore, neglecting theformer leads to a systematic underestimation of the strength of market selection, and can explain
the weak empirical support for the market selection hypothesis in prior work. Overall, our results testify to the importance of value chains in shaping the success of producers in competitivemarkets.
The remainder of this paper is structured as follows. Section 2 provides a review of thepertinent literature. Section 3 describes the data and explains the measurement of productivity
in GVCs based on world input–output tables. Section 4 introduces our empirical strategy anddiscusses the results of our analysis, while Section 5 concludes.

2

Related literature

Our work relates to different strands of literature. First and foremost, it is connected with studies on market selection and industry dynamics. Fueled by the growing availability of detailed
firm and establishment level data in the last few decades (Bartelsman, 2010), a diverse literature
has started to consider that there is “a great variability in the fate of similar firms over time”(Ericson and Pakes, 1995), evoking the question of how firm heterogeneity and market selection
shape the evolution of industries. Models in this vein pertain to different research traditions,
ranging from Jovanovic’s (1982) classic selection model for industry evolution with passive learning to so-called Markov-perfect industry dynamics (Ericson and Pakes, 1995, Weintraub et al.,2008, Doraszelski and Satterthwaite, 2010, Hopenhayn, 1992) and evolutionary models (Winteret al., 2003). Yet, despite these different theoretical perspectives, researchers have applied similar
empirical strategies to measure selection based on productivity decomposition (Maliranta and
Määttänen, 2015) and evolutionary accounting (Metcalfe, 2008). Evolutionary models of market selection rely on the replicator dynamics model (Metcalfe, 1994, Mazzucato, 1998) as their3

standard workhorse. It predicts that firms whose fitness is above a benchmark (typically conceived as a market-share-weighted performance indicator) will gain market shares at the expenseof their less fit competitors. The replicator dynamics model can be tested empirically either
by assessing the “between” term in productivity decompositions (see, e.g., Foster et al., 2001),
which captures the change in the aggregate productivity index originating in the reallocation ofmarket shares between firms, or by directly estimating the relationship between the fitness offirms and their sales growth in regressions (Bottazzi et al., 2010). Yet, prior empirical tests of thereplicator dynamics mechanism have largely failed to provide conclusive support to the theory.
For example, Dosi et al. (2015) conduct an extensive empirical analysis of selection forces basedon both decomposition and regression analysis for France, Germany, the United Kingdom, andthe United States. They conclude that aggregate productivity gains originate predominantly ina learning process occurring within the firm, while the effect pertaining to the reallocation ofmarket shares towards fitter producers is small and in some countries even negative. Although
the results of their complementary regression analysis are slightly more optimistic about thestrength of selection effects, attributing on average 14-19% of the variance in sales growth toidiosyncratic productivity terms, the overall evidence for the selection mechanism is relatively
weak. Our idea to extend the scope of the empirical analysis to value chains is most closely related to the recent model by Cantner et al. (2019) that suggests that theories of market selection
must incorporate interdependencies between producers. In particular, their work shows that thesurvival and growth of the fittest actors may hold at the value-chain level, while the empiricallyobserved “regressive” selection dynamics may occur at individual layers of the value chain in thesense that firms with low fitness experience high growth due to the influence of their value-chain
partners. The present study links to this model by providing the first empirical test of market
selection in value chains.
Since our work highlights the influence of supply-chain partners on the market success of individual producers, the present study also relates to the broader literature explaining how network
structure shapes economic outcomes. Related effects have been explored in different research trajectories, ranging from abstract network games in which individual payoffs depend on the peergroup (Galeotti et al., 2010) to concrete applications in the fields of the economics of innovation (Savin and Egbetokun, 2016, Korzinov and Savin, 2018), suggesting that network structure
determines the firm’s propensity to innovate or the technology one should innovate upon. The influence of peer groups and limited interactions between certain individuals also determine opinion
dynamics in populations (van den Bergh et al., 2019), and the recent macroeconomics literaturedescribes how input-output linkages facilitate the propagation of idiosyncratic shocks and thus
contribute to aggregate fluctuations (Acemoglu et al., 2012, Carvalho and Tahbaz-Salehi, 2019,
Carvalho and Grassi, 2019, Di Giovanni et al., 2014). Accounting for vertical relationships in production processes is also important for industrial policy, development, and international trade.
Liu (2019) describes how sales distortions may compound through input-output linkages, thereby
providing an incentive to subsidize upstream sectors because they are prone to these distortions.
In a similar vein, King et al. (2019) show that carbon tax reforms targeting specific sectors basedon their position in the production network may lead to a greater reduction in emissions, while
Laursen and Meliciani (2000) explain sectors’ export shares based on upstream and downstream
industrial linkages, finding that these linkages are an important channel for innovation spillovers
that determine the competitiveness in international markets. Last but not least, the relevance of
production linkages is also stressed in the field of corporate strategy. For example, Wan and Wu(2017) propose a model of value distribution in vertical relationships and explain how suppliers
can “climb” a value chain to compete with their downstream partners in the product market.
The present paper contributes to this literature by analyzing the influence of network structureon the competitive process.

4Last but not least, our study relates to the growing literature that explores and explains GVCs.
Recent years have seen a surge of interest in GVCs because production processes increasingly rely
on foreign value added, typically exhibiting complex “spider-like” configurations that go beyond
the prototypical example of “snake-like” structures (Antràs, 2020). Analytical models of GVCs
are variations of trade models with sequential production (Antràs and Chor, 2013, Costinot et al.,2013). These models shed light on the mechanisms behind the allocation of production stages
to firms or countries (Chor, 2019, Alcacer and Delgado, 2016) and on the geographical locationof given stages of production in the presence of trade barriers (Antràs and De Gortari, 2020).
Parallel to the theoretical study of GVCs, the literature has progressed on the fronts of developing
GVC governance models (Gereffi et al., 2005), empirical investigations into the determinants andconsequences of participating in GVCs (Amador and Cabral, 2016, Antràs, 2020), and on themeasurement of GVCs (Johnson, 2018). In this respect, the growing availability of global inputoutput data from sources such as WIOD (Timmer et al., 2015) has been particularly valuable.
A main research topic in this field has been that of “slicing up” GVCs in order to understandthe distribution of value added across countries (Timmer et al., 2014), and the measurement of“upstreamness” and “downstreamness” of producers to assess their distance to final consumers.
(Antràs et al., 2012, Antràs and Chor, 2018). Moreover, López González et al. (2019) study
the interplay between inter-industry linkages and patterns of specialization. Using WIOD data,
they show that countries’ participation in business services depends on the presence of domestic
industries that are backward-linked to business services. In a recent publication, Amoroso andMartino (2020) use WIOD data to compute firms’ distance to the technology frontier and study
the efficacy of capital, labor, and product market policies in the technological catch-up process.
Our work contributes to this literature by discussing a novel application of input-output data in
the field of competition analysis and market selection.

3

Data and productivity measurement

Our analysis employs network panel data on global production input linkages for the period
2000-2014 across N = 43 countries. These include the current 27 member states of the European
Union and 16 additional large economies such as the United States, China, Japan, India, Russia,
Brazil, United Kingdom, and Canada, which together account for approximately 85% of world
GDP at the end of the sample period. This data are obtained from the 2016 release of theWorld Input Output Database (WIOD) that reports annual trade flows in intermediate goods
between S = 56 sectors (see Timmer et al., 2015, for a comprehensive introduction to the WIOD
database). Therefore, our entire sample consists of SN = 2408 country-sectors such as, forinstance, crop and animal production in Australia or manufacturing of pharmaceutical productsin Spain.
The input-output network in Figure 1 illustrates intra- and inter-sectoral trade in intermediate
inputs within and across countries. From 2000 to 2014 the number of production input linkages
increased by almost 69% from 370,912 to 625,937, testifying to the rising complexity of the global
production network over time. The high relevance of GVCs is also reflected in the share of total
output sold to downstream sectors. Averaging across all sectors, this share increased from 52% in2000 to 58% in 2014, suggesting that trade in intermediate inputs accounts for the major shareof trade and became evidently more important over time, which motivates our interest in GVCs
and their effect on competition in the first place.
In addition to these input-output data, WIOD provides information on the quantity of goods
and services sold to end consumers domestically and abroad, which enables us to distinguishsector output by its utilization. It also includes data on total gross output, value added, and5

Figure 1: The global input-output network in 2014.
Note: Nodes represent country-sectors and edges illustrate trade in intermediate inputs. Colors represent the 43
countries. Edges with a weight below 1 billion USD have been removed to simplify the graphical exposition.

employment via the supplementary Socio-Economic Accounts that we use to assess the productivity of individual country-sectors and entire value chains. All these data are converted into US
Dollars and adjusted for inflation using national price indexes with base year 2010.
Throughout this study our measure of productivity is value added per hour of labor. To test
for market selection in value chains and to compare our network approach to prior literature,
we consider two variations of labor productivity: (i) the individual labor productivity of eachcountry-sector (idiosyncratic productivity), and (ii) the productivity of the entire value chainof each country-sector, including the focal producer that sells output to final consumers andall its direct and indirect suppliers (value-chain productivity). For the value-chain productivity
measure, we compute the ratio of the sum of value added across all layers of the GVC to thesum of both direct and indirect labor demand for producing a particular final good or service.
Since the accounting identity implies that the sum of value added across all contributing countrysectors equals the output sold to end consumers, we measure the former as final demand. To
compute the total labor demand across all layers of the GVC, we combine standard methodology
in input-output analysis (Leontief, 1936) with the model of vertical production initially proposed6

Table 1: Mean and median growth rate and idiosyncratic and value chain labor productivity.
Growth

Productivity
Idiosyncratic

Obs.

Mean

MedianObs.

Mean

Value-chainMedianObs.

MeanMedian30,7640.036810.04193
32,97962.39
35.76
32,61949.04
40.91
Note: Productivity is in USD per labor hour and is measured in constant prices as of 2010 as explained in themain text.

by Pasinetti (1973) and later applied to productivity measurement by Timmer and Ye (2017).1
Building on this approach, the labor requirements matrix is given by
L = l(I − A)−1 f ,(1)where (I − A)−1 is known as the Leontief-inverse with the identity matrix I. A is a SN ×
SN matrix of technical coefficients, with the element in row m and column n quantifiying theoutput shipped from country-sector m to country-sector n, divided by the output of the receiving
country-sector. l in Eq. (1) is an SN × SN matrix with the direct labor coefficients on themain diagonal and zero otherwise. These direct labor coefficients reflect the labor demand ofa particular country-sector, measured in terms of labor hours, per unit of gross output. If wepost-multiply these direct labor coefficients with the Leontief inverse, we obtain the direct andindirect labor requirements of each country-sector per unit of final output. Finally, we derive
the matrix of direct and indirect labor requirements, L, by post-multiplying this result with the
diagonal matrix f with dimension SN × SN , capturing the final demand of each country-sector.
For the country-sector in a given column of L, the pertinent column sum measures the absolute
quantity of direct and indirect labor that is necessary to produce the final output of this countrysector, while its row sum quantifies the total hours worked in this country-sector. Notice thatthe latter also includes labor to produce intermediates for other domestic and foreign sectors.
In addition to the value-chain productivity measure, we also consider the idiosyncratic laborproductivity of the focal country-sector to compare our results to the case where value chain
linkages are ignored, i.e. following the strategy adopted in prior studies (cf. Section 2).2 To
this end, we proceed in the standard way and compute the ratio of the country-sector’s gross
output minus its intermediate use to the total hours worked in this particular country-sector.
The crucial difference between the value-chain and idiosyncratic productivity measures is thatthe former sums up the labor and value added contributions of all domestic and foreign sectors
in the value chain to produce output for the target country-sector, while the latter focuses on
the target country-sector and thus disregards that a significant fraction of output is sold to otherproducers as an intermediate input. The explicit comparison of the two measures enables us toassess the change in explanatory power of productivity on growth arising from the considerationof value-chain linkages in the replicator dynamics model.
Table 1 reports summary statistics for the output growth rate and the two alternative productivity measures calculated across all time periods and country-sectors in our dataset. The results
suggest that country-sectors grow with an annual rate of 3.7% on average, while the median
1 Unlike our study, Timmer and Ye (2017) construct a measure of total factor productivity (TFP) for value
chains. Yet, for the sake of consistency with previous work, and because TFP might be biased in the presenceof technologically heterogeneous firms and input complementarities (Dosi and Grazzi, 2006), we focus on laborproductivity.
2 Yet, here we consider the country-sector instead of the firm-sector level.

7

growth rate is 4.2%, indicating a negatively skewed distribution. Comparing the two productivity measures, we find that the idiosyncratic productivity has a mean of 62 USD per hour oflabor, while the value-chain productivity is smaller with a value of 49 USD per hour of labor.
One potential explanation of the larger idiosyncratic productivity measure is that end-consumer
markets in some sectors have very high profit margins, which renders the entry of upstream
suppliers into these markets so attractive (Wan and Wu, 2017).
Table 2 shows the standard deviations of growth rates and (the log of) idiosyncratic and value
chain productivity. Consistent with the view that idiosyncratic shocks are averaged out at thehigher level of aggregation due to diversification, we find that the variability of growth rates is
considerably smaller on the country-sector level than what is usually found for individual firms.
For example, Dosi et al. (2015) report median standard deviations for firm growth in France,
Germany, UK, and the US in the range 0.2-0.4, while for our data the median standard deviation
across all sectors amounts to 0.15.
Comparing the dispersion of labor productivity to estimates obtained for firm-level data, wefind that the dispersion of sector-level productivity is typically larger. While our estimatesare between 0.8 and 1.1, Dosi et al. (2015) report standard deviations between 0.5 and 0.6 fordeveloped economies. One way to rationalize this disparity is that our global sample also includes
developing countries or economies in transition, e.g. Russia, for which the standard deviationof productivity is much higher (around 1.7 on average; see Savin et al., 2020). Our estimates
imply that a country in a given sector which is one standard deviation above the mean is about
seven times more productive than a country whose productivity is one standard deviation below
the mean.3 It is worth noting, however, that the consideration of value-chain linkages reduces
productivity dispersion to some extent, consistent with the view that the global perspective
on production processes leads to convergence in productivities because the vast majority of
producers are connected via input-output linkages. Yet, even at the value-chain level, there islarge variation of productivity across countries to warrant a meaningful analysis of its effect on
differential growth. We will turn to this question in the next section.

4

Empirical strategy and results

In this section, we outline our empirical strategy and then present three related sets of results
testing market selection in value chains. The empirical strategy is geared to the recent study
by Cantner et al. (2019), which extends the replicator dynamics model to value chains. In theirmodel, firms in a value chain are connected to each other by contracts that regulate the deliveryof intermediate products. The authors show that value-chain fitness, which results from theaggregation of the performance of all firms in a given value chain, governs the success of the firmin the end-consumer market and its upstream suppliers. In other words, the model predicts thatthe selection dynamics works properly at the bottom layer of the value chain under the condition
that the performance of all upstream suppliers is incorporated, while it may fail when solely theindividual performance of the firm at the end of the value chain is considered.
In the present study, the unit of observation is not a firm operating in a given sector but acountry-sector in a global market. The latter are connected by input linkages characterizing value
chains. To test whether market selection dynamics should be assessed based on the idiosyncratic
performance of a country-sector or the performance of the whole value chain, we compute andcompare the explanatory power of the two productivity measures introduced in Section 3. If
the value-chain labor productivity measure yields stronger evidence for selection dynamics, we
3 For comparison, US firms whose productivity is one standard deviation above the mean are about three times
more productive than companies which operate one standard deviation below the mean (Dosi et al., 2015).

8

Table 2: Standard deviations of growth and (log) labor productivity.
SectorGrowth

Productivity
Idiosyncratic

Value-chain

Crop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

0.12
0.19
0.17
0.15
0.10
0.13
0.14
0.14
0.14
0.24
0.19
0.13
0.14
0.15
0.18
0.16
0.19
0.18
0.17
0.21
0.21
0.16
0.17
0.15
0.12
0.14
0.14
0.15
0.12
0.11
0.15
0.19
0.22
0.15
0.25
0.11
0.15
0.15
0.12
0.13
0.12
0.15
0.22
0.10
0.12
0.15
0.17
0.19
0.16
0.13
0.09
0.09
0.09
0.11
0.14

1.12
1.19
1.20
1.30
0.84
1.06
0.95
0.85
0.80
1.33
0.98
1.04
0.89
0.90
0.93
0.86
1.07
0.92
0.91
0.84
0.98
1.03
0.75
1.07
1.14
0.85
0.86
0.83
0.86
0.76
0.81
1.23
1.07
0.81
0.83
0.80
0.96
0.84
1.02
0.72
0.81
0.90
0.83
1.35
0.88
0.77
0.93
0.77
0.85
0.83
0.84
1.02
0.90
0.79
1.05

0.95
1.04
1.05
0.97
0.77
0.86
0.83
0.73
0.69
0.95
0.74
0.74
0.70
0.75
0.69
0.66
0.79
0.71
0.69
0.64
0.71
0.82
0.64
0.80
0.92
0.63
0.73
0.73
0.73
0.72
0.72
0.85
0.75
0.75
0.77
0.77
0.70
0.69
0.85
0.66
0.75
0.78
0.57
0.98
0.79
0.69
0.77
0.61
0.68
0.75
0.79
0.97
0.82
0.74
1.01

Weighted mean
Median

0.13
0.15

0.93
0.90

0.80
0.75

Note: Weights for the weighted mean have been computed as the (relative) size of each sector in terms of direct(idiosyncratic productivity) or the sum of direct and indirect labor (value-chain productivity), respectively.

9conclude that value-chain linkages carry additional and so far neglected information on the interplay of competition and selection outcomes. We now turn to three distinct but complementary
approaches to test the selection hypothesis and assess the effect of value-chain linkages in thisprocess.

4.1

Decomposition of global sector-level productivity change

We start to assess the strength of market selection by decomposing the aggregate (global) laborproductivity index for sector j at time t,XΠj,t =
si,t πi,t ,(2)i∈jwhere πi,t denotes the labor productivity of country i in that sector. si,t is the country’s market
share in that sector, measured as the quantity of labor hours employed in that country in percentof the global number of labor hours in that sector. To decompose this productivity index, we
employ the method proposed by Griliches and Regev (1995). It splits the change in aggregate
productivity into two components
XX
s̄i ∆πi,t +∆si,t π̄i ,(3)∆Πj,t =i∈ji∈jwhere a bar over a variable stands for average of that variable over two consecutive years t − 1
and t. The first term on the right-hand side of Eq. (3) represents the so-called within effect, i.e.
the sum of country-specific changes in productivity in a given sector, weighted by the share ofeach country. This component captures the change in productivity resulting from idiosyncraticefforts (e.g. due to innovation). It measures how much of the global productivity growth insector j originates in improvements in productivity within countries. The second term on theright-hand side of Eq. (3) is the so-called between effect, i.e. the sum of the changes in countries’
market shares in a given sector, weighted by the productivity levels of these sectors. Since thesum of shares is constant and equal to unity by construction, a positive between term reflects towhat extent market shares reallocate to country-sectors operating above the average productivity
level. Therefore, it can explain why a rise in aggregate productivity is consistent with a situationin which individual productivities remain unchanged, but global competition reallocates market
shares from less to more productive producers.
A formal test of the selection hypothesis can be conducted based on the between effect: a large
and positive between effect indicates that a substantial fraction of aggregate productivity growthoccurs because market shares reallocate from less to more productive producers that compete forthe global market in a given sector. Therefore, if the between effect is larger for the value-chain
than for the idiosyncratic productivity measure, this sustains the presumption that value-chain
effects are relevant for market selection between individual country-sectors and thus must be
taken into consideration. Notice that a negative between term would speak against the selection
mechanism in the sense that less productive producers gain a larger share in the global marketof the respective sector. On the other hand, a negative sign of the within effect would imply thatcountries shift their production from more to less valuable (e.g., less technologically-intensive)
activities.4
Given that the WIOD dataset covers a period of 15 years, we are interested in the aggregate
4 It is noteworthy that our decomposition does not separate out the so-called covariance effect that sometimes
appears in dynamic decompositions based on Foster et al. (2001). Yet, it is straightforward to show that Eq. (3)splits the covariance term equally between the within and the between component (see Savin et al., 2019).

10

4

2

WithinBetween0-2-4
Idiosyncratic productivity

Value-chain productivity

Figure 2: Distributions of sectoral between and within components.

effects over multiple years. To this end, we compute the overall contribution of the within andbetween components by running the decomposition for different pairs of consecutive years andsumming the results over the years, which yields
XXXXX∆Πj,t =s̄i ∆πi,t +∆si,t π̄i .
(4)
t

t

t

i∈j

i∈j

Moreover, to ease the comparison of the relative importance of between and within effects obtained from Eq. (4), we follow Dosi et al. (2015) and report percentage shares of the two components for total productivity change. For example, for the between effect we have


!
P
XX
X
X  i∈j ∆si,t π̄i   ∆Πj,t P∆si,t π̄i /∆Πj,t =.
(5)∆Πj,tt ∆Πj,tt i∈jttPrior empirical tests of the replicator dynamics model on the firm-level identified a dominant
within effect.5 What we find in Table 3 at the country-sector level is, first, that the between effectis considerably higher, with a median of 64% for the idiosyncratic productivity measure. Second,and more importantly, there is a rise of the median between effect to 79% if we employ the valuechain instead of the idiosyncratic productivity measure. The consideration of the (weighted)
mean instead of the median between effect indicates a smaller increase due to extreme realizations
in some sectors. Yet the improvement is still positive, consistent with the extended selection
hypothesis. The violin plots in Figure 2 confirm the increase in the relative importance of the
between effect and further suggest that the distributions of the within and between effects for thevalue-chain measure become less dispersed, concentrating around 0.9 for the between and 0.1 forthe within effect. Overall, these results are consistent with the hypothesis that the considerationof GVC linkages in a productivity-based fitness indicator facilitates the identification of selection
effects.
5 For example, Dosi et al. (2015) report that less than 10% of the aggregate sector productivity growth can be
explained by market share reallocation.

11

Table 3: Decomposition of productivity change.
SectorIdiosyncratic

Value-chain

WithinBetweenWithinBetween

Crop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

0.09
0.13
0.17
1.17
-12.01
0.80
1.27
-1.19
19.73
1.07
0.68
0.58
24.36
1.01
0.16
-6.58
-0.30
-1.96
-1.91
-0.61
-0.18
1.69
2.12
1.05
1.83
-2.03
1.35
-1.88
-0.23
4.05
1.58
0.78
0.36
-0.81
2.34
4.15
0.18
0.46
0.84
-0.76
0.41
0.24
0.64
1.28
0.21
-0.17
-1.03
-1.25
1.75
-4.01
-2.93
1.61
-12.43-33.94
0.47

0.91
0.87
0.83
-0.17
13.01
0.20
-0.27
2.19
-18.73
-0.07
0.32
0.42
-23.36
-0.01
0.84
7.58
1.30
2.96
2.91
1.61
1.18
-0.69
-1.12
-0.05
-0.83
3.03
-0.35
2.88
1.23
-3.05
-0.58
0.22
0.64
1.81
-1.34
-3.15
0.82
0.54
0.16
1.76
0.59
0.76
0.36
-0.28
0.79
1.17
2.03
2.25
-0.75
5.01
3.93
-0.61
13.43
34.94
0.53

0.06
0.11
0.09
1.34
-8.30
0.54
0.85
-0.05
-1.81
1.04
0.74
0.54
-0.39
0.15
1.03
-0.43
0.20
0.03
-0.16
0.07
0.11
1.46
2.50
1.07
1.63
0.19
0.76
-0.31
0.21
-42.14
1.00
0.82
0.36
-0.09
2.87
26.14
0.28
0.56
0.77
-0.09
0.50
0.38
0.63
1.19
0.32
0.12
0.14
0.14
0.18
-1.17
-0.73
1.47
-1.87
-1.25
0.55

0.94
0.89
0.91
-0.34
9.30
0.46
0.15
1.05
2.81
-0.04
0.26
0.46
1.39
0.85
-0.03
1.43
0.80
0.97
1.16
0.93
0.89
-0.46
-1.50
-0.07
-0.63
0.81
0.24
1.31
0.79
43.14
0.00
0.18
0.64
1.09
-1.87
-25.14
0.72
0.44
0.23
1.09
0.50
0.62
0.37
-0.19
0.68
0.88
0.86
0.86
0.82
2.17
1.73
-0.47
2.87
2.25
0.45

Weighted mean
Median

-1.41
0.36

2.41
0.64

-1.49
0.21

2.49
0.79

Note: Weights for the weighted mean have been computed as the (relative) size of each sector in terms of direct(idiosyncratic productivity) or the sum of direct and indirect labor (value-chain productivity), respectively.

12

4.2

Regression analysis of the output growth-productivity relationship

One of the main drawbacks of the decomposition analysis is that market shares are measured interms of the number of labor hours to warrant consistency with the labor productivity measure.
Yet it is more realistic to assume that competition is reflected in sales (output) instead of labor(input). To account for this shortcoming of the decomposition analysis and to measure thestrength of competition directly, we next turn to regression analysis and estimate the effect ofsector-level productivity on output growth. To this end, we follow Bottazzi et al. (2010) andestimate the growth equation
gi,t = a + bt + β∆ ∆πi,t + βm π̄i,t + ci + i,t ,(6)where gi,t denotes the (log) growth rate of output of country-sector i from year t − 1 to t. bt isa time dummy. ∆πi,t stands for the (log) growth rate of labor productivity. π̄i,t is the level ofproductivity, measured in terms of the time average over the years t and t−1. ci represent country
fixed effects, and i,t is the error term. Since we estimate Eq. (6) individually for each sector,
the presence of time dummies is equivalent to consider the deviation of individual productivity
from the sectoral average in a given year, implying that countries’ relative efficiency within those
sectors serves as the explanatory variable for output growth (Dosi et al., 2015).
Using firm-level data, Dosi et al. (2015) showed that out of the two productivity terms in Eq. (6)the change in productivity is the main driver of output growth. Here we revisit this hypothesis at
a higher level of aggregation and consider the level of productivity and the productivity change
as two alternative drivers of growth for country-sectors on the global markets. An additional andultimately more important question for us is, however, whether value-chain productivity exhibits
higher explanatory power on growth than idiosyncratic productivity. Put differently, does a morefine-grained econometric test of the mechanism behind the replicator dynamics model provide
additional empirical support for the hypothesis that market selection is stronger on the level ofentire GVCs? To answer these questions, we estimate Eq. (6) for the two different measuresof productivity and rely on the Shapley decomposition of the pertinent R2 to determine theexplanatory power of π̄i,t and ∆πi,t fromS2 =Var(β∆ ∆πi,t + βm π̄i,t ),Var(gi,t )(7)which measures the share of the growth variance explained by the two productivity terms.
Table 4 summarizes the estimation results. While the influence of the level of productivity
is small and mostly insignificant, we find that the coefficient of the change in productivity is
positive and statistically significant at the 0.1% level across all sectors. The stronger influenceof the dynamic productivity component on growth is confirmed by the Shapley decompositionin Table 5, which shows that the explanatory power of the change in productivity is twice ashigh as that of the level of productivity across all sectors. The median share of the growthvariance explained by Eq. (6) amounts to 27% for the idiosyncratic productivity measure, and12 percentage points of which pertain to the two productivity terms.
Turning to the value-chain productivity measure, our regression results confirm the findings ofthe decomposition exercise obtained earlier. In particular, we obtain stronger empirical support
for selection in the context of value chains than for individual producers. The median shareof the variance explained by Eq. (6) across all sectors rises to 35%, and 18 percentage pointsof which must be attributed to the value-chain-based productivity terms. Figure 3 provides agraphical illustration of the Shapley decomposition, confirming that the focus on value chainsleads to a rise in explanatory power of productivity on output growth.

13

Table 4: Estimation results for the fixed effects model in Eq. (6).
SectorIdiosyncratic

Crop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

Value-chain

β∆

βm

β∆

βm

0.42∗∗∗
0.46∗∗∗
0.29∗∗∗
0.38∗∗∗
0.41∗∗∗
0.38∗∗∗
0.42∗∗∗
0.24∗∗∗
0.34∗∗∗
0.16∗∗∗
0.45∗∗∗
0.31∗∗∗
0.32∗∗∗
0.44∗∗∗
0.26∗∗∗
0.46∗∗∗
0.45∗∗∗
0.43∗∗∗
0.45∗∗∗
0.46∗∗∗
0.37∗∗∗
0.57∗∗∗
0.53∗∗∗
0.26∗∗∗
0.19∗∗∗
0.33∗∗∗
0.62∗∗∗
0.61∗∗∗
0.49∗∗∗
0.67∗∗∗
0.61∗∗∗
0.24∗∗∗
0.08∗∗∗
0.46∗∗∗
0.57∗∗∗
0.52∗∗∗
0.29∗∗∗
0.37∗∗∗
0.28∗∗∗
0.33∗∗∗
0.5∗∗∗
0.37∗∗∗
0.34∗∗∗
0.28∗∗∗
0.4∗∗∗
0.36∗∗∗
0.26∗∗∗
0.14∗∗∗
0.31∗∗∗
0.54∗∗∗
0.58∗∗∗
0.64∗∗∗
0.62∗∗∗
0.44∗∗∗
0.15∗∗∗

000
0
0−0.01∗∗−0.01−0.02∗∗−0.03∗∗∗
0
−0.01
0.01
−0.02∗∗∗−0.01∗
0−0.01∗∗−0.04∗∗∗−0.03∗∗∗−0.01∗−0.03∗∗∗−0.01−0.02∗∗∗
−0.01
0
0.01
0
−0.01∗−0.01∗
0
0
−0.01
0
0
0
−0.02∗−0.01∗∗
0−0.01∗−0.01−0.02∗∗−0.01∗∗∗
−0.01
0.01
0
−0.01∗∗−0.01
0.01−0.05∗∗∗−0.02∗∗−0.01∗∗
0
0
0
0
−0.01

0.66∗∗∗
0.66∗∗∗
0.51∗∗∗
0.52∗∗∗
0.63∗∗∗
0.66∗∗∗
0.65∗∗∗
0.63∗∗∗
0.62∗∗∗
0.54∗∗∗
0.5∗∗∗
0.44∗∗∗
0.74∗∗∗
0.71∗∗∗
0.31∗∗∗
0.81∗∗∗
0.91∗∗∗
0.8∗∗∗
0.72∗∗∗
0.73∗∗∗
0.49∗∗∗
0.76∗∗∗
0.85∗∗∗
0.53∗∗∗
0.34∗∗∗
0.55∗∗∗
0.77∗∗∗
0.82∗∗∗
0.72∗∗∗
0.79∗∗∗
0.8∗∗∗
0.47∗∗∗
0.45∗∗∗
0.7∗∗∗
0.96∗∗∗
0.69∗∗∗
0.51∗∗∗
0.66∗∗∗
0.46∗∗∗
0.48∗∗∗
0.59∗∗∗
0.58∗∗∗
0.57∗∗∗
0.3∗∗∗
0.51∗∗∗
0.56∗∗∗
0.38∗∗∗
0.55∗∗∗
0.32∗∗∗
0.59∗∗∗
0.74∗∗∗
0.69∗∗∗
0.68∗∗∗
0.64∗∗∗
0.22∗∗∗

0.01∗∗∗
00
0.01∗
0.01∗∗∗
00−0.01−0.02∗∗
0.02
0
0.01∗
−0.01∗∗∗
0
0.01
0
−0.01
−0.02∗
0−0.01
0−0.02∗∗
00.01∗
0.01∗∗
00−0.02∗∗∗
0
0
0
0.01
0
0.01∗∗
000−0.01∗
0
−0.01
0
0
0
0
0
−0.01∗
0.02∗−0.04∗∗
00.01∗∗
0.01∗∗
0∗0.01∗∗∗
0−0.01∗

Note: Entry 0 stands for values < 5 × 10−3 . ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 0.1%, 1%, and
5% level, respectively.

14

Table 5: Shapley decomposition results for the fixed effects model in Eq. (6).
SectorIdiosyncratic

Value-chain

R2

2
S∆π

Sπ̄2

R2

2
S∆π

Sπ̄2

Crop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

0.38
0.36
0.21
0.28
0.32
0.23
0.34
0.17
0.24
0.10
0.24
0.23
0.22
0.28
0.15
0.31
0.32
0.36
0.32
0.32
0.21
0.36
0.25
0.12
0.14
0.22
0.34
0.50
0.43
0.60
0.27
0.16
0.03
0.32
0.35
0.42
0.20
0.26
0.25
0.23
0.43
0.34
0.23
0.23
0.29
0.21
0.11
0.09
0.22
0.40
0.48
0.55
0.50
0.29
0.09

0.15
0.13
0.06
0.07
0.10
0.05
0.11
0.04
0.07
0.02
0.08
0.09
0.03
0.10
0.04
0.08
0.07
0.13
0.08
0.13
0.07
0.12
0.06
0.02
0.03
0.05
0.09
0.27
0.19
0.27
0.11
0.06
0.00
0.13
0.16
0.14
0.07
0.07
0.07
0.08
0.17
0.15
0.09
0.05
0.10
0.06
0.03
0.01
0.09
0.18
0.14
0.16
0.21
0.07
0.01

0.07
0.06
0.05
0.06
0.05
0.07
0.04
0.01
0.04
0.01
0.03
0.02
0.07
0.01
0.01
0.05
0.09
0.04
0.08
0.01
0.03
0.09
0.06
0.03
0.03
0.05
0.08
0.02
0.02
0.11
0.04
0.01
0.00
0.02
0.03
0.10
0.00
0.05
0.04
0.01
0.06
0.02
0.00
0.05
0.02
0.00
0.02
0.03
0.01
0.01
0.13
0.16
0.09
0.07
0.03

0.52
0.50
0.32
0.49
0.47
0.34
0.37
0.34
0.31
0.30
0.12
0.25
0.43
0.39
0.15
0.48
0.46
0.44
0.40
0.31
0.15
0.35
0.41
0.39
0.22
0.30
0.36
0.53
0.52
0.62
0.33
0.24
0.13
0.38
0.47
0.51
0.16
0.39
0.30
0.27
0.43
0.39
0.33
0.15
0.28
0.29
0.14
0.19
0.10
0.29
0.61
0.59
0.51
0.42
0.17

0.24
0.27
0.14
0.17
0.14
0.08
0.13
0.11
0.14
0.11
0.03
0.10
0.12
0.13
0.02
0.21
0.19
0.17
0.13
0.13
0.06
0.16
0.21
0.14
0.07
0.12
0.13
0.24
0.27
0.28
0.17
0.08
0.04
0.18
0.17
0.20
0.03
0.17
0.11
0.10
0.19
0.17
0.15
0.04
0.11
0.10
0.04
0.06
0.03
0.15
0.22
0.20
0.19
0.14
0.02

0.06
0.01
0.00
0.08
0.12
0.10
0.06
0.08
0.02
0.01
0.01
0.04
0.12
0.08
0.03
0.06
0.05
0.06
0.08
0.01
0.00
0.07
0.04
0.05
0.03
0.02
0.06
0.12
0.03
0.13
0.04
0.04
0.01
0.02
0.07
0.11
0.04
0.05
0.03
0.01
0.04
0.04
0.01
0.03
0.01
0.02
0.01
0.03
0.01
0.01
0.15
0.16
0.11
0.09
0.06

Weighted mean
Median

0.38
0.27

0.14
0.08

0.07
0.04

0.45
0.35

0.17
0.14

0.08
0.04

Note: Weights for the weighted mean have been computed as the (relative) size of each sector in terms of direct(idiosyncratic productivity) or the sum of direct and indirect labor (value-chain productivity), respectively.

15

0.6

0.4

R2
S2
S2 Δπ
S2π_

0.2

0.0

Idiosyncratic productivity

Value-chain productivity

Figure 3: Explanatory power of idiosyncratic and value-chain productivity terms.

4.3

Spatial regression analysis

Our third test considers whether the idiosyncratic fitness of any supplier j of the focal producer
i 6= j has a direct effect on the output growth of i. It builds on a spatial panel model with fixed
effects for growth that incorporates the spatial lags of the two productivity terms in Eq. (6)as additional explanatory variables. Therefore, this augmented regression model does not only
test whether the output growth of country-sector i at time t is determined by its own (idiosyncratic) productivity but also by the weighted average idiosyncratic productivity of its value-chain
partners. Formally, we estimate
gi,t = a + bt + β∆ ∆πi,t + βm π̄i,t + γ∆ SL(∆πi,t ) + γm SL(π̄i,t ) + ci + i,t ,(8)where the spatial lag SL(∆πi,t ) represents the weighted average productivity change and SL(π̄i,t )is the weighted average productivity level, respectively, of the direct and indirect suppliers of the
focal country-sector i. Following the methodology outlined in Section 3, we determine the weights
for the productivities of all direct and indirect suppliers based on the suppliers’ labor contributions to the focal country-sector. Specifically, the weights are obtained from Eq. (1) by dividing
the elements in each column of the matrix L by the respective column sum. Since the typicalcountry-sector consumes a significant portion of its own output, however, idiosyncratic productivity and the spatially lagged productivity terms obtained from these weights would be highly
correlated without further corrections. To avoid biased results due to collinearity, we therefore
set the elements on the main diagonal of L equal to zero before computing the column sum andthe respective labor shares. This correction excludes domestic intra-industry transactions andthus separates the focal country-sector from the set of its suppliers. The resulting shares sum
up to one for each focal producer and thus serve as appropriate weights in the spatial regression
model.
Table 6 presents the estimation results for the spatial regression model in Eq. (8). We findthat the essence of the results from Section 4.2 remains valid as the change in productivity
is quantitatively still more important for growth than the productivity level, testifying to therobustness of our previous findings. The spatial lag of the productivity change is positive and16

Table 6: Estimation results for the spatial panel regression model in Eq. (8).
Sectorβ∆Crop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

0.34∗∗∗
0.43∗∗∗
0.25∗∗∗
0.30∗∗∗
0.25∗∗∗
0.26∗∗∗
0.33∗∗∗
0.14∗∗∗
0.24∗∗∗
0.15∗∗∗
0.42∗∗∗
0.25∗∗∗
0.12∗∗∗
0.22∗∗∗
0.20∗∗∗
0.27∗∗∗
0.38∗∗∗
0.37∗∗∗
0.35∗∗∗
0.38∗∗∗
0.30∗∗∗
0.47∗∗∗
0.51∗∗∗
0.20∗∗∗
0.14∗∗∗
0.27∗∗∗
0.33∗∗∗
0.49∗∗∗
0.32∗∗∗
0.50∗∗∗
0.48∗∗∗
0.20∗∗∗
0.06∗∗
0.38∗∗∗
0.54∗∗∗
0.27∗∗∗
0.26∗∗∗
0.27∗∗∗
0.19∗∗∗
0.21∗∗∗
0.39∗∗∗
0.32∗∗∗
0.31∗∗∗
0.17∗∗∗
0.30∗∗∗
0.25∗∗∗
0.22∗∗∗
0.13∗∗∗
0.27∗∗∗
0.41∗∗∗
0.36∗∗∗
0.37∗∗∗
0.32∗∗∗
0.23∗∗∗
0.15∗∗∗

βm

γ∆

γm

0
−0.01
0
0
0.01
0
0
0
−0.01
0.01
0
0.01
−0.02∗
00−0.01−0.04∗∗∗−0.01
0−0.02∗
0.01−0.02∗
0
0
0.01
0
0.01
0
0.01
0.01
0.01
0
0.02
0.01
0
0
0.01
−0.02∗−0.01∗−0.01−0.01∗−0.01
0.01
0−0.02∗−0.01
0−0.06∗∗∗
−0.01
0
0
0
0
−0.02∗−0.01

0.47∗∗∗

0.01
0.03∗
−0.01
0
−0.01
−0.03∗∗−0.02∗−0.02∗−0.03∗
−0.02
−0.02
−0.01
−0.02
−0.02
−0.01
−0.02
0.01
−0.03∗−0.01−0.02−0.04∗
0
−0.01
0
0
0
−0.03∗−0.03∗∗−0.02∗∗−0.02∗∗−0.03∗
0.01
−0.03
−0.02
−0.02
−0.01∗
−0.03
0.01
0
−0.02
0
−0.01
−0.01
−0.01
0.01
0
0.02
0.02
−0.02
−0.02∗∗
0.01
−0.01
0.01
0.02∗
0.01

0.36∗∗∗
0.52∗∗∗
0.56∗∗∗
0.57∗∗∗
0.62∗∗∗
0.47∗∗∗
0.78∗∗∗
0.65∗∗∗
0.36∗
0.34∗∗∗
0.56∗∗∗
0.98∗∗∗
0.78∗∗∗
0.56∗∗∗
0.78∗∗∗
0.54∗∗∗
0.58∗∗∗
0.53∗∗∗
0.69∗∗∗
0.76∗∗∗
0.42∗∗∗
0.23
0.48∗∗∗
0.55∗∗∗
0.53∗∗∗
0.76∗∗∗
0.53∗∗∗
0.57∗∗∗
0.41∗∗∗
0.45∗∗∗
0.47∗∗∗
0.8∗∗∗
0.45∗∗∗
0.21∗
0.59∗∗∗
0.36∗∗∗
0.65∗∗∗
0.64∗∗∗
0.73∗∗∗
0.4∗∗∗
0.45∗∗∗
0.58∗∗∗
0.54∗∗∗
0.45∗∗∗
0.56∗∗∗
0.67∗∗∗
0.44∗∗
0.43∗∗∗
0.43∗∗∗
0.47∗∗∗
0.4∗∗∗
0.5∗∗∗
0.68∗∗∗−0.05

Note: Entry 0 stands for values < 5 × 10−3 . ∗∗∗ , ∗∗ , and ∗ indicate statistical significance at the 0.1%, 1%, and
5% level, respectively.

17

statistically significant at the 0.1% level in more than 96% of all industries. Merely two sectors(repair and installation of machinery and equipment and activities of households as employers)
exhibit no significant spatial effects of productivity on output growth. It turns out that these two
sectors exhibit the weakest dependence on suppliers across all 43 countries in the entire sample,
with a ratio of direct and indirect labor contributions from upstream suppliers in percent of totallabor demand of merely 12% and 23%, respectively, which explains the insignificant influence ofthe suppliers on the focal actor in these sectors.6 Thus, our third test provides direct empiricalevidence for the argument that production units may grow not only because their own productive
performance is above the average but also because they have suppliers with superior performancein their value chain.
Considering the results for goodness of fit and the Shapley decomposition in Table 7, themedian share of the growth variance explained by the spatial model amounts to 36% across all
sectors, which is nearly identical to the result obtained for the value-chain productivity measurein Table 5. We thus conclude that the explanatory power of the value-chain productivity measureused in the previous two tests can be decomposed into two terms: (i) idiosyncratic productivityand (ii) the productivity of upstream suppliers in the value chain. Neglecting the latter inprior empirical studies must have led to a systematic underestimation of the strength of market
selection, contributing to the weak evidence for the replicator dynamics mechanism obtainedin the extant literature, especially because Table 7 suggests that the spatial productivity terms
explain at least as much variation in growth as the individual productivity terms in the majority
of sectors.
Finally, we also studied the relationship between the influence of the suppliers on the focal
actor as the dependent variable, measured in terms of the variance explained by the spatial lags
divided by the variance explained by all productivity terms in Eq. (8), and the aforementioned
measure of dependence on the upstream suppliers as the explanatory variable. Fitting a linear
model to this relationship using (weighted) ordinary least squares yields a slope of 0.66 ± 0.20
with a p-value of 0.002 (R2 = 0.17), implying that the explanatory power of the spatially lagged
productivity terms increases with the dependence on the direct and indirect suppliers.7 Thesefindings further testify to our argument that the performance of the value-chain partners is ofutmost importance for the market success of an individual producer.

5

Conclusions

This paper demonstrates empirically that market success of a producer is not only determined by
its individual productive performance but also by the performance of its upstream suppliers inglobal value chains. Using WIOD data on production input linkages to map these relationships
between individual producers, we employ productivity decomposition and regression analysis to
study the influence of idiosyncratic productivity, the productivity of upstream suppliers, and the
productivity of the entire value chain on the growth of a producer. Our study thus contributes
to the literature on market selection and industry dynamics by providing the first empirical testof the replicator dynamics model extended to GVCs.
Our empirical tests provide several important and noteworthy findings. First and foremost,they suggest that the productivity of entire value chains is a more thorough performance indicator
than idiosyncratic productivity when it comes to testing the market selection hypothesis, which
carries additional information on the nexus between productivity and growth. In particular, ourspatial regression approach shows that the influence of upstream suppliers on individual growth
6 The median realization of this ratio across all sectors is 53%.
7 Observations are weighted by the (relative) size of each sector in terms of labor.

18

Table 7: Shapley decomposition results for the spatial panel model in Eq. (8).
Sector

R2

2
S∆π

Sπ̄2

2
SSL(∆πCrop and animal production
Forestry and logging
Fishing and aquaculture
Mining and quarrying
Manufacture of food products
Manufacture of textiles
Manufacture of wood
Manufacture of paper
Printing and reproduction of recorded media
Manufacture of coke and refined petroleum products
Manufacture of chemicals and chemical products
Manufacture of basic pharmaceutical products
Manufacture of rubber and plastic products
Manufacture of other non-metallic mineral products
Manufacture of basic metals
Manufacture of fabricated metal products
Manufacture of computer
Manufacture of electrical equipment
Manufacture of machinery and equipment n.e.c.
Manufacture of motor vehicles
Manufacture of other transport equipment
Manufacture of furniture
Repair and installation of machinery and equipment
Electricity, gas, steam and air conditioning supply
Water collection, treatment and supply
Sewerage
Construction
Wholesale and retail trade and repair of motor vehicles
Wholesale trade, except of motor vehicles and motorcycles
Retail trade, except of motor vehicles and motorcycles
Land transport and transport via pipelines
Water transport
Air transport
Warehousing and support activities for transportation
Postal and courier activities
Accommodation and food service activities
Publishing activities
Motion picture, video and television
Telecommunications
Computer programming and consultancy
Financial service activities
Insurance, reinsurance and pension funding
Activities auxiliary to financial services and insurance
Real estate activities
Legal and accounting activities
Architectural and engineering activities
Scientific research and development
Advertising and market research
Other professional, scientific and technical activities
Administrative and support service activities
Public administration and defence
Education
Human health and social work activities
Other service activities
Activities of households as employers

0.46
0.39
0.26
0.35
0.50
0.34
0.42
0.34
0.35
0.10
0.25
0.31
0.46
0.43
0.21
0.44
0.36
0.40
0.38
0.39
0.28
0.38
0.25
0.16
0.29
0.28
0.48
0.57
0.56
0.66
0.32
0.20
0.11
0.37
0.35
0.56
0.24
0.35
0.41
0.38
0.51
0.39
0.27
0.39
0.35
0.28
0.18
0.11
0.25
0.45
0.58
0.64
0.60
0.47
0.09

0.16
0.09
0.06
0.08
0.08
0.05
0.11
0.05
0.08
0.02
0.07
0.06
0.03
0.06
0.03
0.06
0.06
0.09
0.07
0.10
0.04
0.10
0.06
0.02
0.04
0.04
0.08
0.18
0.12
0.15
0.07
0.06
0.01
0.12
0.12
0.07
0.05
0.03
0.06
0.06
0.12
0.13
0.10
0.06
0.07
0.04
0.03
0.01
0.07
0.14
0.09
0.10
0.11
0.04
0.01

0.00
0.02
0.04
0.03
0.02
0.06
0.01
0.01
0.00
0.01
0.01
0.02
0.02
0.06
0.02
0.06
0.05
0.03
0.09
0.01
0.02
0.09
0.07
0.01
0.02
0.06
0.08
0.03
0.06
0.04
0.04
0.01
0.01
0.02
0.03
0.13
0.00
0.06
0.00
0.03
0.06
0.01
0.01
0.01
0.03
0.02
0.01
0.03
0.01
0.01
0.15
0.17
0.11
0.09
0.02

0.11
0.05
0.05
0.08
0.13
0.07
0.10
0.12
0.12
0.01
0.02
0.06
0.14
0.13
0.05
0.12
0.05
0.04
0.05
0.09
0.05
0.05
0.01
0.02
0.09
0.04
0.13
0.11
0.13
0.09
0.05
0.05
0.03
0.09
0.03
0.13
0.04
0.05
0.15
0.11
0.10
0.08
0.05
0.11
0.08
0.07
0.04
0.02
0.06
0.11
0.13
0.12
0.14
0.11
0.00

0.05
0.10
0.02
0.03
0.11
0.02
0.04
0.03
0.00
0.02
0.04
0.07
0.12
0.03
0.01
0.05
0.05
0.11
0.03
0.03
0.07
0.04
0.01
0.04
0.01
0.05
0.05
0.13
0.11
0.19
0.09
0.01
0.02
0.01
0.05
0.08
0.04
0.10
0.02
0.02
0.08
0.04
0.01
0.07
0.05
0.00
0.03
0.00
0.03
0.03
0.05
0.05
0.11
0.11
0.01

Weighted MeanMedian

0.48
0.36

0.09
0.07

0.07
0.02

0.11
0.08

0.07
0.04

i,t )

2
SSL(π̄

i,t )Note: Weights for the weighted mean have been computed as the size of each sector in terms of direct (idiosyncratic
productivity) and the sum of direct and indirect labor (value-chain productivity), respectively.

19

is not only statistically significant and, therefore, non-negligible in empirical studies on market
selection. It also implies that its influence is at least as strong as the direct effect pertaining
to the productive performance of the focal producer. This suggests that effective supply-chain
management, e.g. careful selection of new suppliers or joint efforts with existing partners to
improve productivity, has a crucial influence on the market success of individual producers.
Moreover, since the value-chain perspective increases the explanatory power of productivity on
growth, our results imply that the effect of competition on market outcomes is stronger thansuggested in the existing empirical literature. This does not imply, however, that competitive
selection based on productive performance is the only factor that explains the growth of individual
producers, and we are aware that our global analysis based on aggregated input-output tables
neglects many factors influencing individual growth dynamics. Our main contribution here isthat the consideration of GVC linkages, ceteris paribus, improves the explanatory power of theselection mechanism relative to the situation in which value-chain linkages are ignored.
We conclude by describing avenues for further research. A front for immediate extension of
our empirical framework is to apply it to firm-level micro data. While we find it remarkable that
selection effects can be identified even at the more aggregated country-sector level, assessing the
explanatory power of our approach based on more granular data would certainly enhance ourunderstanding of competition in value chains, and would also allow to study the mediating effectsof various firm specificities such as firm size, diversification and internationalization patterns on
the influence of value-chain productivity on individual growth. A second promising trajectorywould be to evaluate the relative contributions of suppliers’ and customers’ fitness on individual
growth. This paper approaches value-chains from the last production unit that sells output
to final consumers, focusing on the network effect pertaining to the upstream suppliers. To
generalize this perspective, one should focus on arbitrary stages of the value chain and assess therelative influence of supply- and demand-driven network effects, potentially as a function of the
producer’s position in the value chain. We hope that our study will stimulate more empirical
work in this direction.

Acknowledgements
IS acknowledges financial support from the Russian Science Foundation (RSF grant number
19-18-00262).

20References
Acemoglu, D., Carvalho, V., Ozdaglar, A., and Tahbaz-Salehi, A. (2012). The network origins
of aggregate fluctuations. Econometrica, 80(5):1977–2016.
Alcacer, J. and Delgado, M. (2016). Spatial organization of firms and location choices throughthe value chain. Management Science, 62(11):3213–3234.
Amador, J. and Cabral, S. (2016). Global value chains: A survey of drivers and measures.
Journal of Economic Surveys, 30(2):278–301.
Amoroso, S. and Martino, R. (2020). Regulations and technology gap in Europe: the role of firm
dynamics. European Economic Review, 129(103551).
Antràs, P. and Chor, D. (2013). Organizing the global value chain. Econometrica, 81(6):2127–
2204.
Antràs, P. and Chor, D. (2018). On the measurement of upstreamness and downstreamness inglobal value chains. World Trade Evolution: Growth, Productivity and Employment, page 126.
Antràs, P., Chor, D., Fally, T., and Hillberry, R. (2012). Measuring the upstreamness of production and trade flows. American Economic Review, 102(3):412–16.
Antràs, P. and De Gortari, A. (2020). On the geography of global value chains. Econometrica,
88(4):1553–1598.
Antràs, P. (2020). Conceptual Aspects of Global Value Chains. The World Bank.
Bartelsman, E. J. (2010). Searching for the sources of productivity from macro to micro andback. Industrial and Corporate Change, 19(6):1891–1917.
Bottazzi, G., Dosi, G., Jacoby, N., Secchi, A., and Tamagni, F. (2010). Corporate performances and market selection: some comparative evidence. Industrial and Corporate Change,
19(6):1953–1996.
Cantner, U., Krüger, J. J., and Söllner, R. (2012). Product quality, product price, and share
dynamics in the German compact car market. Industrial and Corporate Change, 21(5):1085–
1115.
Cantner, U., Savin, I., and Vannuccini, S. (2019). Replicator dynamics in value chains: explainingsome puzzles of market selection. Industrial and Corporate Change, forthcoming.
Carvalho, V. M. and Grassi, B. (2019). Large firm dynamics and the business cycle. American
Economic Review, 109(4):1375–1425.
Carvalho, V. M. and Tahbaz-Salehi, A. (2019). Production networks: A primer. Annual Review
of Economics, 11:635–663.
Chor, D. (2019). Modeling global value chains: approaches and insights from economics. In
Handbook on Global Value Chains. Edward Elgar Publishing.
Costinot, A., Vogel, J., and Wang, S. (2013). An elementary theory of global supply chains.
Review of Economic Studies, 80(1):109–144.
Di Giovanni, J., Levchenko, A., and Mejean, I. (2014). Firms, destinations, and aggregate
fluctuations. Econometrica, 82(4):1303–1340.
21

Doraszelski, U. and Satterthwaite, M. (2010). Computable Markov-perfect industry dynamics.
The RAND Journal of Economics, 41(2):215–243.
Dosi, G. and Grazzi, M. (2006). Technologies as problem-solving procedures and technologies
as input-output relations: some perspectives on the theory of production. Industrial andCorporate Change, 15(1):173–202.
Dosi, G., Moschella, D., Pugliese, E., and Tamagni, F. (2015). Productivity, market selection andcorporate growth: comparative evidence across US and Europe. Small Business Economics,
45(3):643–672.
Ericson, R. and Pakes, A. (1995). Markov-perfect industry dynamics: A framework for empiricalwork. The Review of Economic Studies, 62(1):53–82.
Foster, L., Haltiwanger, J. C., and Krizan, C. J. (2001). Aggregate productivity growth. Lessons
from microeconomic evidence. In New Developments in Productivity Analysis, pages 303–372.
University of Chicago Press.
Galeotti, A., Goyal, S., Jackson, M. O., Vega-Redondo, F., and Yariv, L. (2010). Network games.
The Review of Economic Studies, 77(1):218–244.
Gereffi, G., Humphrey, J., and Sturgeon, T. (2005). The governance of global value chains.
Review of International Political Economy, 12(1):78–104.
Griliches, Z. and Regev, H. (1995). Firm productivity in Israeli industry: 1979-1988. Journal of
Econometrics, 65(1), 175-203.
Hopenhayn, H. A. (1992). Entry, exit, and firm dynamics in long run equilibrium. Econometrica:
Journal of the Econometric Society, pages 1127–1150.
Johnson, R. C. (2018). Measuring global value chains. Annual Review of Economics, 10:207–236.
Jovanovic, B. (1982). Selection and the evolution of industry. Econometrica: Journal of the
Econometric Society, pages 649–670.
King, M., Tarbush, B., and Teytelboym, A. (2019). Targeted carbon tax reforms. European
Economic Review, 119:526–547.
Korzinov, V. and Savin, I. (2018). General purpose technologies as an emergent property. Technological Forecasting and Social Change, 129:88–104.
Laursen, K. and Meliciani, V. (2000). The importance of technology-based intersectoral linkages
for market share dynamics. Review of World Economics, 136(4):702–723.
Leontief, W. (1936). Quantitative input-output relations in the economic system of the UnitedStates. Review of Economics and Statistics, 18(3):105–125.
Liu, E. (2019). Industrial policies in production networks. The Quarterly Journal of Economics,
134(4):1883–1948.
López González, J., Meliciani, V., and Savona, M. (2019). When Linder meets Hirschman:inter-industry linkages and global value chains in business services. Industrial and Corporate
Change, 28(6):1555–1586.
Maliranta, M. and Määttänen, N. (2015). An augmented static Olley-Pakes productivity decomposition with entry and exit: Measurement and interpretation. Economica, 82:1372–1416.
22

Mazzucato, M. (1998). A computational model of economies of scale and market share instability.
Structural Change and Economic Dynamics, 9(1):55–83.
Metcalfe, J. S. (1994). Competition, Fisher’s principle and increasing returns in the selection
process. Journal of Evolutionary Economics, 4(4):327–346.
Metcalfe, J. S. (2008). Accounting for economic evolution: Fitness and the population method.
Journal of Bioeconomics, 10(1):23.
Pasinetti, L. (1973). The notion of vertical integration in economic analysis. Metroeconomica,
25(1):1–29.
Savin, I. and Egbetokun, A. (2016). Emergence of innovation networks from R&D cooperation
with endogenous absorptive capacity. Journal of Economic Dynamics and Control, 64:82–103.
Savin, I., Mariev, O., and Pushkarev, A. (2019). Survival of the fittest? Measuring the strengthof market selection on the example of the Urals Federal District. HSE Economic Journal,
23(1):90–117. in Russian.
Savin, I., Mariev, O., and Pushkarev, A. (2020). Measuring the strength of market selection in
Russia: When the (firm) size matters. Voprosy Ekonomiki, 2:101–124. in Russian.
Taylor, P. and Jonker, L. (1978). Evolutionary stable strategies and game dynamics. Mathematical Biosciences, 40:145–156.
Timmer, M. and Ye, X. (2017). The Oxford Handbook of Productivity Analysis, chapter Productivity and substitution patterns in global value chains. Oxford University Press.
Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., and De Vries, G. J. (2015). An illustrated
user guide to the world input–output database: the case of global automotive production.
Review of International Economics, 23(3):575–605.
Timmer, M. P., Erumban, A. A., Los, B., Stehrer, R., and de Vries, G. J. (2014). Slicing up
global value chains. The Journal of Economic Perspectives, pages 99–118.
van den Bergh, J., Savin, I., and Drews, S. (2019). Evolution of opinions in the growth-vsenvironment debate. Futures, 109:84–100.
Wan, Z. and Wu, B. (2017). When suppliers climb the value chain: A theory of value distribution
in vertical relationships. Management Science, 63(2):477–496.
Weintraub, G. Y., Benkard, C. L., and Van Roy, B. (2008). Markov perfect industry dynamics
with many firms. Econometrica, 76(6):1375–1411.
Winter, S. G., Kaniovski, Y. M., and Dosi, G. (2003). A baseline model of industry evolution.
Journal of Evolutionary Economics, 13(4):355–383.

23

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142 Carolin Martin, Noemi Schmitt and Frank Westerhoff, Housing markets, expectation
formation and interest rates, January 2019
143 Marc P. Saur, Markus G. Schlatterer and Stefanie Y. Schmitt, Horizontal product differentiation with limited attentive consumers, January 2019
144 Tim Hagenhoff and Joep Lustenhouwer, The Rationality Bias, February 2019145 Philipp Mundt and Ilfan Oh, Asymmetric competition, risk, and return distribution, February 2019
146 Ilfan Oh, Autonomy of Profit Rate Distribution and Its Dynamics from Firm Size
Measures: A Statistical Equilibrium Approach, February 2019147 Philipp Mundt, Simone Alfarano and Mishael Milakovic, Exploiting ergodicity in forecasts of corporate profitability, March 2019
148 Christian R. Proaño and Benjamin Lojak, Animal Spirits, Risk Premia and Monetary
Policy at the Zero Lower Bound, March 2019
149 Christian R. Proaño, Juan Carlos Peña and Thomas Saalfeld, Inequality, Macroeconomic Performance and Political Polarization: An Empirical Analysis, March 2019
150 Maria Daniela Araujo P., Measuring the Effect of Competitive Teacher Recruitment on
Student Achievement: Evidence from Ecuador, April 2019151 Noemi Schmitt and Frank Westerhoff, Trend followers, contrarians and fundamentalists: explaining the dynamics of financial markets, May 2019152 Yoshiyuki Arata and Philipp Mundt, Topology and formation of production input interlinkages: evidence from Japanese microdata, June 2019
153 Benjamin Lojak, Tomasz Makarewicz and Christian R. Proaño, Low Interest Rates,
Bank’s Search-for-Yield Behavior and Financial Portfolio Management, October 2019
154 Christoph March, The Behavioral Economics of Artificial Intelligence: Lessons from
Experiments with Computer Players, November 2019155 Christoph March and Marco Sahm, The Perks of Being in the Smaller Team: Incentives
in Overlapping Contests, December 2019
156 Carolin Martin, Noemi Schmitt and Frank Westerhoff, Heterogeneous expectations,
housing bubbles and tax policy, February 2020
157 Christian R. Proaño, Juan Carlos Peña and Thomas Saalfeld, Inequality, Macroeconomic Performance and Political Polarization: A Panel Analysis of 20 Advanced Democracies, June 2020
158 Naira Kotb and Christian R. Proaño, Capital-Constrained Loan Creation, Stock Markets
and Monetary Policy in a Behavioral New Keynesian Model, July 2020
159 Stefanie Y. Schmitt and Markus G. Schlatterer, Poverty and Limited Attention, July
2020
160 Noemi Schmitt, Ivonne Schwartz and Frank Westerhoff, Heterogeneous speculatorsand stock market dynamics: a simple agent-based computational model, July 2020
161 Christian R. Proaño and Benjamin Lojak, Monetary Policy with a State-Dependent Inflation Target in a Behavioral Two-Country Monetary Union Model, August 2020
162 Philipp Mundt, Simone Alfarano and Mishael Milakovic, Survival and the ergodicity ofcorporate profitability, October 2020
163 Tim Hagenhoff and Joep Lustenhouwer, The role of stickiness, extrapolation and pastconsensus forecasts in macroeconomic expectations, October 2020
164 Andrea Gurgone and Giulia Iori, Macroprudential capital buffers in heterogeneousbanking networks: Insights from an ABM with liquidity crises, October 2020
165 María Daniela Araujo P., Guido Heineck and Yyannú Cruz-Aguayo, Does Test-Based
Teacher Recruitment Work in the Developing World? Experimental Evidence from Ecuador, November 2020
166 Jan Schulz and Mishael Milaković, How Wealthy Are the Rich?, December 2020
167 Nadja Bömmel and Guido Heineck, Revisiting the Causal Effect of Education on Political Participation and Interest, December 2020
168 Joep Lustenhouwer, Tomasz Makarewicz, Juan Carlos Peña and Christian R. Proaño,
Are Some People More Equal than Others? Experimental Evidence on Group Identity
and Income Inequality, February 2021
169 Sarah Mignot, Fabio Tramontana and Frank Westerhoff, Speculative asset price dynamics and wealth taxes, April 2021
170 Philipp Mundt, Uwe Cantner, Hiroyasu Inoue, Ivan Savin and Simone Vannuccini,Market Selection in Global Value Chains, April 2021