Ilaria Francesca Rizzuti

Pietro Mascheroni

Silvia Arcucci

Zacchari Ben-Mériem

Audrey Prunet

Catherine Barentin

Charlotte Rivière

Hélène Delanoë-Ayari

Haralampos Hatzikirou

Julie Guillermet-Guibert

Morgan Delarue
email: morgan.delarue@laas.fr

Mechanical-control of cell proliferation increases resistance to chemotherapeutic agents

While many cellular mechanisms leading to chemotherapeutic resistance have been identified, there is an increasing realization that tumor-stroma interactions also play an important role. In particular, mechanical alterations are inherent to solid cancer progression and profoundly impact cell physiology. Here, we explore the influence of compressive stress on the efficacy of chemotherapeutics in pancreatic cancer spheroids. We find that increased compressive stress leads to decreased drug efficacy. Theoretical modeling and experiments suggest that mechanical stress decreases cell proliferation which in turn reduces the efficacy of chemotherapeutics that target proliferating cells. Our work highlights a mechanical-form of drug resistance, and suggests new strategies for therapy.

Mechanical alterations of solid tumors are a hallmark of cancer progression. Among the many occurring modifications, the most representative forms of mechanical alterations in tumors are changes in extracellular matrix rigidity [1] and build-up of compressive stress [2]. Compressive stress accumulation can be found in many cancers such as glioblastoma multiforme [3] or pancreatic ductal adenocarcinoma (PDAC) [2].

PDAC is one of the deadliest cancers with extremely poor prognosis and no efficient treatment available besides surgery. PDAC development is characterized by excessive deposition of extracellular material and strong modifications of the mechanical environment. In particular, the accumulation of negatively-charged hyaluronic acid leads to electroswelling of extracellular matrix and subsequent compressive stress experienced by tumor cells [4]. The local growth of cancer cells in an elastic environment also leads to build-up of compressive stress through a process known as growth-induced pressure [5,6]. PDAC tumors are extremely compressed, within the kPa range [7].

In vitro, compressive stress can alter cell physiology in multiple ways, from proliferation [8,9] to migration [10,11]. In vivo, it has recently been shown that compressive stress in PDAC tumors can exceed blood pressure and participate in vessel collapse [12]. Most of large vessels (diameter above 10µm) are clamped, leading to poor perfusion. Vessel collapse is associated with drug resistance: classical first-line chemotherapeutics such as gemcitabine are thought to be unable to reach the tumor, decreasing or even preventing the effect of the drug. Intravenous injection of a pegylated-form of hyaluronidase, an enzyme digesting hyaluronic acid, renormalizes blood vessels and, in combination with gemcitabine, increases chemotherapeutic efficacy [12].

The proposed mechanism overcoming this form of resistance is better tumor perfusion through decrease compressive stress. However, it remains unclear how the combination of hyaluronidase and gemcitabine really works. Indeed, if the vessels are so collapsed that gemcitabine does not penetrate the tumor, hyaluronidase should not have a better chance to reach it. Another potential explanation which does not depend on perfusion is that compressive stress could directly act on tumor cells and decrease the efficacy of gemcitabine. Experimentally, scarce instances of drug resistance stemming from mechanical stress have been observed for cells growing on substrata of different rigidities [13] or under shear stress [14], with no clear mechanism. In this letter, we wished to explore the paradigm of compression-modulation of drug resistance. We investigated the impact of mechanical stress on chemotherapeutic resistance, considering the case of a well-perfused genetically homogeneous tumor, e.g. no drug perfusion issues and no mutation-based resistance.

Given the complexity of a tumor, uncoupling the effect of biochemical and mechanical interactions is a daunting challenge. A good in vitro system is the multicellular spheroid as a mesoscopic tumor model: threedimensional cellular aggregates which remarkably mimic the relevant physiological gradients of mitogens, oxy- gen, or glucose. They have been extensively used as tumor model for the study of drug delivery [15]. Although their mechanical properties might differ from those of tumors, for many purposes, spheroids can be viewed as a tumor subunit. Because they do not have any biochemical crosstalk with their environment, spheroids are ideal to evaluate the impact of mechanical stress on tumor growth. We formed spheroids from a pancreatic Kras G12D cell line, representative of pancreatic cancer mutations [16], using a classical agarose cushion protocol [8] (Fig. 1a). Under normal growth conditions, spheroids grew over time in the hundreds of micrometer range (Fig. 1b). We restricted ourselves to the use of small spheroids (diameter below 400µm) to avoid chemical gradients. We detail in the Supplementary Information and Fig. S1 the statistics associated to the data.

We treated freely growing spheroids with gemcitabine to investigate the effect of this chemotherapeutic without mechanical stress. Gemcitabine is a cytidine-analog which activates within the cell into a deoxycytidinetriphosphate (dCTP) [17]. dCTP lead to cell death by creating an irreversible error upon DNA incorporation. Not surprisingly, we found that spheroids subjected to 10µM gemcitabine decreased in size (Fig. 1b). The decrease was apparent after an average of 1 day postdrug addition (Fig. 1c), consistent with the fact that only S-phase cells are sensitive to the drug. We performed capillary western blots to investigate the changes in proliferating and dying cells under gemcitabine treatment (Fig. 1d, see also Methods for protocol). We measured the ratio between a phosphorylated-form of ERK (pERK) over total ERK and the amount of cleavedcaspase 3 as a proxy for cells undergoing programmed cell death [18] (Fig. 1e). Even though gemcitabine can activate ERK [19], the latter is also a proxy for cell proliferation on long timescales [20]. We observed that the amount of pERK/ERK was halved after 5 days of treatment, and the amount of dying cells increased by roughly a factor 2, consistent with gemcitabine preferentially killing proliferating cells.

We next sought to investigate the impact of growthinduced pressure on the efficacy of gemcitabine. Several strategies have been developed to study cells under growth-induced pressure, such as microfluidic confining chambers [21], embedding single cells in agarose or PEGheparin hydrogels [5,9] or in alginate shells [6], or directly embedding spheroids into alginate hydrogels [22]. In order to easily follow single spheroids, we opted for a strategy where spheroids were directly embedded into lowmelting 1% agarose. Briefly, 200µL of a 2% low-melting agarose solution kept at 37 o C was mixed with a 200µL solution containing a spheroid, and polymerized on ice to limit the rapid sedimentation of the spheroid. Gelling on ice was fast and did not affect the initial growth of the spheroids.

Measurement of growth-induced pressure requires careful characterization of material property, a point that is often disregarded, as reminded in [9]. Rheological measurements of agarose showed a plateau for low strain followed by an apparent softening of the material (Fig. 2a). Softening has also been observed and characterized in a recent study by Kalli et al. [23]. One cannot exclude the fact that slippage at the plate/sample interface in the rheometer could lead to this apparent softening -we used rough sandpaper during rheological measurement to avoid this effect. We wish to point out that in the case of lower agarose concentration (0.5%), one could reach a critical deformation of about 70% above which macroscopic rupture is observed, leading to an effective unconfinement of the spheroid (Fig. S2). This rupture could explain the softening observed at high strains. In our study, we used 1% agarose samples, kept the deformation below 70% strain and discarded the rare instances of "rugby-shaped" spheroids [24].

Incorporating softening into the Neo-Hookean material properties of a finite-element simulation of a spheroid growing in agarose [23] gave a rough linear increase of normal stress applied onto the spheroid during growth (Fig. 2b). This calibration can be used to extract growth-induced pressure curves from the growth of embedded spheroids (Fig. 2c). While other models like viscoelastic ones have been used to derive growth-induced pressure [25], we do not believe the qualitative variation and order of magnitude of the obtained data would strongly depend on the model chosen for small deformations. Expansion of agarose-embedded spheroids resulted in slower growth (Fig. 2d), similar to what has been described for other embedding solutions [5]. We found that growth-induced pressure rose to the kPa range over a period of a few days (Fig. 2e).

We added 10µM of gemcitabine after 2 days of confined growth and recorded size evolution. Interestingly, we observed that compressed spheroids were less sensitive to the chemotherapeutic than freely growing ones (Fig. 2f and Fig. S3): While unconfined spheroids decreased in size by roughly 30-40%, compressed spheroids decreased by less than 10% (inset of Fig. 2f).

Gemcitabine is a small molecule, slightly smaller than Hoescht 33342 DNA intercalent. We did not find any difference in Hoescht penetration in between a control and a compressed spheroid (Fig. S4), strongly suggesting that the observed effect of gemcitabine was not due to altered penetration of the drug under compression. Two non-mutually exclusive hypotheses can explain the effect of mechanics. The first one entails that compressive stress triggers mechanosensitive pathways directly acting on the effect of the chemotherapeutic, for instance on gemcitabine activation within the cell or import/export rates [17]. The second hypothesis is that compressive stress would trigger mechanosensors specifically decreasing cell proliferation [9,22,26], indirectly impacting chemotherapeutic efficacy. While capillary western blots could not be performed on agarose-embedded spheroids without biological perturbation, we performed immunostaining of the proliferation indicator Ki67 [8] of paraffin-embedded samples and observed that compressive stress decreased cell proliferation (Fig. S5).

To the best of our knowledge, there is no study mentioning mechanosensitive pathways acting on the activity of the drug. Although we cannot rule out their existence, we developed a generic mathematical framework in order to get insight into the potential mechanism limiting drug efficacy under mechanical compression (see Method). We assumed that the total number of cells in the spheroid varied according to pressure-dependent cell proliferation and drug-induced death. Cell death without drug was considered negligible (see Note on cell death). Cell proliferation was exponentially distributed over the spheroid radius, with a pressure-independent characteristic length (Figs. S5 andS6). We observed that cell death induced by the drug is not instantaneous. Indeed, cells must be in a proliferative state for the drug to be effective. The waiting time for a cell to enter the proliferative phase could be assumed to be exponentially distributed, giving a delay for the drug to be effective of about 24 hours. We wrote the first-order temporal variation of the spheroid radius R as:

Ṙ = (1 -d) γ β = (1 -d)g
where γ was the pressure-dependent cell proliferation rate and β a quantity accounting for spheroid compressibility (see Methods). Then, we denoted by g the effective growth velocity which accounted for potential changes in cell density, and by d the death term induced by the drug which included time delay (see Methods for full derivation). By construction, d linearly coupled the druginduced death rate to the proliferation rate: if no cells proliferated, the death rate was zero. The dependence on pressure was only considered for the growth velocity g which took value g 0 in control conditions (i.e. no compression and no drug). We used a single growth curve to fit each parameter individually. In particular, g = g 0 was fitted through the control experiment, g = g c through the compressed spheroid, and the drug-induced death term d = d 0 through unconfined and drug-treated spheroids. Under the assumption that death and proliferation rates are similarly affected by mechanical stress (d is not a function of pressure), we predicted that the combined effect of mechanical stress and drug should lead to a growth velocity proportional to (1 -d 0 ) g c on long timescales. The precision of the prediction was scored by a square difference of the measurement with the expected value. Our model remarkably predicted the experimental data (Fig. 3a), with a score χ = 0.94, suggesting that, indeed, d did not depend on pressure. This independence indicated that mechanical stress mainly signaled to pathways implicated in cell proliferation rather than in the action of the drug.

Our model predicted that a decrease in compressive stress could lead to an increase in cell proliferation and a higher efficacy of the chemotherapeutic. Although we could not instantaneously relax mechanical stress without any biological perturbation, we took advantage of the fact that treated spheroids decreased in size over time. After 6-7 days of drug treatment, some spheroids saw their radius R(t ≥ 7) reducing below the initial inclusion size R(t = -2) such that they were not anymore confined and did not experience compressive stress (pictures inset of Fig. 3b). Note that the spheroids presented in Fig. 3a were always confined. Within our framework, quiescent cells would re-enter the cell cycle, proliferate faster, and consequently die faster. Our model perfectly captured the experimental data (Fig. 3b, χ = 0.86): a slow initial death velocity during compression, followed by a faster one in the unconfined phase.

The excellent agreement of the model with the experimental data was consistent with a mechanism where mechanics would decrease chemotherapeutic efficacy through a modulation of cell proliferation. This mechanism made two key predictions: If the efficacy of a proliferation-based chemotherapeutic decreased because of a modulation of cell proliferation, then the observed modulation of efficacy (i) should not depend on the type of drug used, but rather on the fact that the chemotherapeutic targeted proliferating cells, and, similarly, (ii) should not depend on the type of mechanical stress applied, but rather on the fact that mechanical stress could decrease cell proliferation. We investigated these two predictions, by treating with a different chemotherapeutic, docetaxel, and applying a different kind of mechanical stress, an osmotic compression with dextran [8].

Docetaxel is a taxol-based drug which stabilizes microtubules, leading to cell death during M-phase [27]. We confined spheroids in 1% low-melting agarose gel as previously described, and treated the spheroid after 2 days of compression with 10µM docetaxel. Our model predicted the experimental data, the efficacy being reduced for compressed cells, in a predictive manner (χ = 0.94) (Fig. 4a).

The addition of high-molecular weight dextran to the culture medium reduced cell proliferation in a similar way as growth -induced pressure [8,26,28]: We observed that osmotically compressed spheroids restricted cell proliferation to the outermost layers (Fig. S5) with the same proliferation profile as the control (Fig. S6). While ERK could potentially be activated by mechanical stress [22], capillary western blots showed a decrease in pERK/ERK over long timescales, consistent with immunofluoresence staining of Ki67 (Fig. S7). Moreover, similar to what was previously found [8], increasing compression decreased growth rate (Fig. S8). Treatment of osmotically compressed spheroids with gemcitabine showed a qualitatively comparable modulation of efficacy of the drug with growth-induced pressure (Fig. 4b). Once again, our model remarkably predicted the effect of the drug combined to osmotic treatment (χ = 0.92).

Many past studies have identified key features which can eventually lead to drug resistance. Most studies have focused on the inactivation of the drug by the host, the alteration of the drug target or DNA mutations that could create de novo resistance. While all of these mechanisms are cell-centered, there is an increasing realization that stromal components could also participate in drug resis-tance.

Our experimental data suggest a novel mechanicalform of drug resistance which could arise from tumorstroma mechanical interaction. Triggering of signaling cascade reducing the activity of a chemotherapeutic under mechanical stress seems not to be needed for resisting the drug. Rather, its efficacy can directly be altered by a mechanical-control of cell proliferation which can occur through dedicated sensors. The noteworthy theoretical prediction of the experimental data, with the underlying assumption that growth and drug-induced death rates are similarly affected by mechanical stress, strongly supports this mechanism [29]. We observed that growthinduced pressure, a highly common type of mechanical stress present in most solid tumors, can modulate the efficacy of chemotherapeutics acting on different parts of the cell cycle. Moreover, an osmotic compression, stress of very different origin and sensing which could arise due to accumulation of oncotic pressure [30] also led to similar modulation of chemotherapeutic. There are now evidences that quiescence is one major mechanism leading to drug resistance [31] and that mechanics can turn cells towards quiescence [8,32]. However, to our best knowledge, no quantitative models linking mechanics to drug resistance through a direct modulation of proliferation have been proposed so far.

In this study, we only considered the case of an inert and homogeneous stroma mechanically interacting with a tumor spheroid. The complexity of a real microenvironment with extracellular matrix signaling on cell proliferation could as well play a role in the efficacy of chemotherapeutics [33][34][35]. Similarly, larger levels of mechanical stress could lead to unforeseen mechanochemical crosstalk. We notably observed that our prediction became worse for higher concentration of dextran (Fig. S9). While we cannot exclude unknown effects of dextran itself, this observation suggested a non-linear coupling between death and growth rates and potential signaling to pathways implicated in drug activation.

Most if not all solid tumors experience compressive stress. This stress can be heterogeneous in vivo creating pockets of drug-resisting compressed cells. Within the framework of a mechanical-form of drug resistance, it clearly appears that the mechanical modulation of the microenvironment could be an interesting therapeutic option. For instance, hyaluronidase, which is currently under clinical trial, may have a very different effect than only modulating tumor perfusion: by reducing matrix swelling and subsequent compressive stress experienced by cells, it could also modulate cell proliferation. A direct mechanical-modulation of drug efficacy through cell proliferation could appear particularly deleterious, notably because it does not rely on any specific gene alteration targeting the mode of action of the drug.

In conclusion, a mechanical-form of drug resistance calls for a better understanding of the mechanosensors at play leading to proliferation reduction: A therapy targeting these sensors to enforce cell proliferation under mechanical stress coupled with a proliferation-driven chemotherapeutic could represent an appealing strategy to battle compressed tumors.

FIG. 1 .

 1 FIG. 1. Effect of gemcitabine on free growth. a. Schematic of free growing spheroid. b. Representative pictures of growing spheroids and one treated with gemcitabine after two days. c. Growth quantification over time. Median values normalized to time 0 ± SD over N ≥ 30 spheroids. d. Example of capillary western blot after 5 days under drug. e. Analysis of capillary wester blots. The data under gemcitabine treatment are normalized to the control. Mean ± SEM over N≥ 3 replicates. Data were pooled together. The quantity was renormalized by total volume of spheroids.

2 FIG. 2 .

 22 FIG.2. Effect of gemcitabine on confined growth. a. Measurements of elastic (G', •) and shear (G", ) moduli of 1% low-melt agarose. Different colors correspond to different forces used to hold the sample. b. Comsol simulation of a spheroid growing in a Neo-Hookean material modeled with parameters extracted from (a). Inset: Heat map of normal stress onto a spheroid (grey, S). c. Schematic of agaroseconfined spheroid. d. Representative pictures of a confined spheroid and one treated with gemcitabine. e. Growthinduced pressure of spheroids in 1% low-melt agarose as a function of time. Median values normalized to time 0 ± SD over N ≥ 20 spheroids. f. Growth quantification of confined spheroids and treated ones with drug. Inset: Median curves for free (black) and confined (blue) spheroids, dashed lines correspond to gemcitabine treatment. Median ± SD over N ≥ 20 spheroids. Time 0 correspond to agarose confinement.

FIG. 3 .

 3 FIG. 3. Prediction of the model on confined data. a. Model plotted on confined spheroids exposed to gemcitabine (large dashed purple). Prediction score χ = 0.94. b. At drug addition, R(t = 0) ≥ R(t = -2), while at about day 7, the radius decreases due to drug treatment to a value R(t = 7) ∼ R(t = -2) and become unconfined: subsequent decrease happens without mechanical stress. The model (large dashed green) captures this effect, switching from rate of confined cells (large dashed purple), to rate of unconfined cells. χ = 0.86. In both cases, median values normalized to the time of drug addition ± SD over N ≥ 20 spheroids. Time 0 correspond to drug treatment.

2 FIG. 4 .

 24 FIG. 4. Prediction of the model for (a) confined spheroids exposed to docetaxel (χ = 0.94) and (b) osmotically compressed spheroids exposed to gemcitabine (χ = 0.92). In both cases, median values normalized to the time of drug addition ± SD over N ≥ 10 spheroids for docetaxel and ≥ 30 spheroids for dextran. Drug was added at time 0 for (a) and at time 2 for (b).

Acknowledgement

The authors would like to thank Davide Ambrosi for useful discussions. IR, SA, JGG and MD performed biological experiments; AP, CB, CR and HDA performed rheological experiments and simulations; PM, HH and MD did the theoretical modeling; and IR, PM, HDA, HH, JGG and MD analyzed the data and wrote the article. H.H. and P.M. acknowledge the funding support of MicMode-I2T (01ZX1710B) by the Federal Ministry of Education and Research (BMBF). JGG and MD thank the Fondation Toulouse Cancer Santé, Plan Cancer Inserm, and Cancéropole Grand Sud Ouest for financial support. We thank the research engineers of LAAS-CNRS, CRCT and I2MC Inserm.


Bruno Carballa Smichowski

Cédric Durand
email: cedric.durand@unige.ch

Steven Knauss

Participation in global value chains and varieties of development patterns

Keywords: Global value chains, Development, PCA JEL classification: F63

published or not. The documents may come

Introduction

Since the mid-2000s, international institutions, development agencies and governments have embraced the global value chains (GVCs) framework to refine their development policies [START_REF] Werner | Linking up to development? Global value chains and the making of a post-Washington consensus[END_REF], often providing cross-country measurements of GVC involvement to nurture their analyses and recommendations. While some have perceived this popularisation of the GVC framework as a contribution to the emergence of enlightened post-Washington consensus development policies [START_REF] Gereffi | Global value chains in a post-Washington consensus world[END_REF], others point to the disbanding of the critical content of the global chains perspective and its cooptation in service of the neoliberal agenda [START_REF] Neilson | Value chains, neoliberalism and development practice: the Indonesian experience[END_REF]. Both sides, however, agree that the framework's journey from the academic universe of critical scholarship to the world of policy making has been accompanied by significant alterations in the conceptualisation of GVCs and GVC-related dynamics.

The mainstream story of GVC development patterns used to point to a baseline rosy scenario whereby any country that increases its participation in GVCs should experience an improvement in its economic and social situation (OECD, WTO and UNCTAD, 2013;UNCTAD, 2013;[START_REF] Kowalski | Participation of Developing Countries: Implications for Trade and Trade-Related Policies[END_REF]. The theoretical mechanisms underpinning this rosy scenario were generally not made explicit but nonetheless derived from comparative advantage arguments about the benefits of specialisation and the opportunities for knowledge spillovers [START_REF] Romer | Idea gaps and object gaps in economic development[END_REF]. They could be summarised as follows: a country increasingly engaged in GVCs benefits from a productivity spillover thanks to learning processes and cheaper inputs; this translates into greater domestic value-added and trickles down to the whole economy through higher profits and investment, higher wages and higher tax collection, which altogether contribute to improved socio-economic outcomes. This idea has been challenged both theoretically and empirically [START_REF] Milberg | Outsourcing Economics: Global Value Chains in Capitalist Development[END_REF][START_REF] Campling | Global inequality chains: integrating mechanisms of value distribution into analyses of global production[END_REF] and nuanced by the World Bank itself (Taglioni and Winkler, 2016, p. 4).

One major problem arising from the adoption of the GVC perspective by policy institutions concerns the diffusion of GVC-related macroeconomic indicators. These measurements are mobilised to build stylised facts and carry out econometric analyses assessing that GVCs provide potential mechanisms for countries to improve income, employment and/or productivity (OECD, WTO and UNCTAD, 2013;UNCTAD, 2013;[START_REF] Kummritz | Do Global Value Chains Cause Industrial Development?[END_REF][START_REF] Kummritz | Economic Upgrading Through Global Value Chain Participation: Which Policies Increase the Value Added Gains?' Policy Research Working Paper 8007[END_REF]World Bank, 2017). Even when it is acknowledged that free market policies are insufficient in and of themselves to automatically bring the benefits of GVC participation, the concepts and indicators used by the international institutions are purely descriptive and disregard the relational understanding of GVCs elaborated by the scholarly work. This is a major issue as the emphasis of the interdependence of development dynamics along GVCs is arguably one of the single most important specificities of chain analysis. 1 Due to this mis-conceptualisation, cross-country macroeconomic analyses obscure the variety of socio-economic outcomes and the compossibility2 of uneven development patterns along the chains.

The main contribution of this article is to account for the diversity and interdependence of development through a cross-country analysis for 51 countries between 1995 and 2008. We identify three patterns of socio-economic development related to various degrees and modes of GVC participation: a social upgrading mirage, the reproduction of the core and unequal growth.

This result is achieved thanks to the introduction of two new elements to the literature: first, we elaborate new macroeconomic indicators of GVC participation and economic gains that are explicitly based in a theoretically consistent definition of GVCs; second, we identify a variety of compossible development patterns related to GVC participation through the use of principal component analysis (PCA) and cluster analysis that relate these new GVC indicators to socio-economic development variables. To the best of our knowledge, these techniques have not been used previously in the GVC literature Section 2 begins by clarifying our conceptualisation of GVCs. In our view, GVCs represent a specific form of the division of labour: a GVC delineates a geographically-and often also legally-fragmented economic space where incomplete commodities are functionally integrated and valorised through a unified labour process. One achievement of this definition is that it allows for a precise delimitation of the frontiers of GVCs. Using the OECD's trade in value-added (TiVA) database, we are then able to propose a more appropriate methodology to measure GVC participation and value capture than those currently employed in the literature (Section 3). Section 4 introduces common indicators of economic and social upgrading (investment rate, median income, labour share, Gini index, employment rate) and conducts a PCA and a cluster analysis for 51 countries between 1995 and 2008. Our results, discussed in Section 5, indicate interdependent yet asymmetrical relationships between countries, suggesting that development patterns in GVCs need to be understood as constitutive parts of a global process of uneven development, in sharp contrast to the rosy scenario offered by policy institutions.

Clarifying GVC boundaries

Over the past few years, research inspired by the value chains perspective has attained a new dimension. While the GVC literature used to be limited to an accumulation of case studies, with a degree of bias toward success stories (Bair, 2009), a growing strand of research mobilises the framework to build cross-country analyses at the macro or industry level [START_REF] Milberg | Outsourcing Economics: Global Value Chains in Capitalist Development[END_REF]UNCTAD, 2013;[START_REF] Miroudot | Mapping Global Value Chains[END_REF]Timmer et al., 2014;[START_REF] Durand | Is labour the fall guy of a financial-led globalisation? A cross-country inquiry on globalisation, financialisation and employment at the industry level[END_REF][START_REF] Gangnes | Global value chains and the trade-income relationship: implications for the recent trade slowdown[END_REF][START_REF] Taglioni | Making Global Value Chains Work for Development[END_REF][START_REF] Kummritz | Economic Upgrading Through Global Value Chain Participation: Which Policies Increase the Value Added Gains?' Policy Research Working Paper 8007[END_REF]. This represents a significant improvement in the way knowledge on GVCs could inform policies, and is a welcome development insofar as the GVC literature has suffered from a long acknowledged micro-macro aggregation problem (Dallas, 2014).

Unfortunately, however, the attempts of GVC measurement are built on shaky theoretical ground as we will elaborate further (Section 3.2). To be fair, this difficulty in policy-related measurement reflects, in part at least, the theoretical limitations of the GVC framework itself [START_REF] Yeung | Toward a dynamic theory of global production networks[END_REF]. GVCs are most of the time defined in the literature in a descriptive manner, for example as 'the full range of activities that firms and workers perform to bring a product from its conception to end use and beyond' (Gereffi and Fernandez-Stark, 2018, p. 306). 3 This kind of definition is symptomatic of the difficulty to move beyond 'a typological description of the immediate outer manifestations of the determinations at stake' and 'to provide an explanation of the very specific phenomenon that it sets to investigate' (Starosta, 2010, p. 435). As far as the issue of macro-level indicators is concerned, the consequences of these theoretical limitations is that GVC approaches do not provide a clear-cut conceptual understanding of the boundaries of GVCs, which is a prerequisite to building any consistent measurement.

Another popular framework used to analyse GVCs is the transaction costs approach where the frontiers of the chains are drawn by the distinctiveness of the economic relations involved in GVCs vis-à-vis other economic relations (Antras, 2014, p. 119). However, the assumption that transaction arrangements are efficient considering the characteristics of production processes and asset specificities is highly problematic (Pitelis, 1994).

A more promising venue is to consider GVCs as a 'form of industrial organization' (Milberg and Winkler, 2013, p. 19). This is the direction taken by Taglioni and Winkler when they write that GVCs consist of: complex networks of production, in which participating firms are specialists in one activity and external international sourcing arrangements imbue inter-firm trade with characteristics similar to intra-group trade: better control from the center, higher levels of bilateral information flow, tolerance of asset specificity, and harmonization and immediate integration of business processes that increase the potential for foreign activities to integrate seamlessly with activities performed at home. (Taglioni and Winkler, 2016, p. 12) This describes a hierarchised network of firms with complementary assets and skills that coordinate through various cooperation mechanisms. The emphasis on the control from the centre points to asymmetric economic relations related to uneven control over the production process itself, within the legal frontier of the firm (within dispersed affiliates of transnational corporations [TNCs]) and beyond the legal frontier of the firm (with sub-contracting and retailing networks).

This emphasis on the production process takes us beyond the organisational approach and its classical triptych of markets, hierarchies and networks. In other words, although the legal organisational boundaries between production units may shift from hierarchical to market arrangements, one can also looks at GVCs as a form of the division of labour where the relations of production mediate the question of the size of individual units of production and their modes of coordination. In this transnational economic space-whether or not directly internal to a TNC-economic powers are unevenly distributed and geographically dispersed between productive entities that contribute to the making of a commodity. GVCs then design a transnational economic space where the process of valorisation occurs. 4Within value chains, 'incomplete commodities' are functionally integrated in order to make complete commodities, which will be sold and used beyond the chain. Integration is the key issue here [START_REF] Nathan | A note on profits, rents and wages in global production networks[END_REF]. The criterion to consider a product as an incomplete commodity is that its potential value realisation outside the chain would be lower than within it. To put it differently, GVCs exhibit network externalities: because of their complementarity, the diverse products circulating within a value chain have a higher value when they are combined than if they were sold separately, which welds the dispersed entities together. This complementarity manifests a profound unity at a deeper level: the integration of the fragmented components in the chain is supported by a variegated set of command mechanisms through which lead firm(s) shape the labour process (technology, labour standards, etc.). This degree of involvement in the integration process, that is the ability to shape the labour process, is an economic form of power whose manifestation is that transfer prices along the chain gives lead firm(s) the ability to capture part of the profits generated by the dispersed entities. The frontier of a given value chain is reached where this economic power of integration terminates, that is when price mechanisms become disconnected from the command over production parameters. Arriving at the boundary of a GVC, a product becomes a (full) commodity whose conditions of exchange are governed by institutionally shaped market mechanisms.

In sum, a GVC organises an institutional and economic production and valorisation space where one (or a small number of) lead actor(s) exert(s) economic power to (partially) centralise profits and control(s) to some degree the labour process over geographically and often legally dispersed productive units. Defining GVCs as a form of the division of labour delineating a transnational space of production and valorisation allows us to establish a theoretical distinction between trade within and outside of GVCs that is not arbitrary (contrary to the two borders rule) and that goes beyond legal formalism (it encompasses both intra-firm international trade and trade between firms). This clear conceptualisation of the frontiers of GVCs paves the way for the elaboration of theoretically grounded GVC indicators.

Measuring the gains from GVC participation: a reappraisal

The 'rosy scenario' posits a uniform and positive relationship between a country's increasing GVC participation and seeing increased economic gains. However, the indicators routinely used in the literature to assess such a relationship have often been elaborated by scholars affiliated with major policy institutions, whose interests are rather in immediate policy challenges than in the subtleties of GVC scholars' theoretical refinements. The result is that usual measurements poorly reflect the analytical breakthroughs of the GVC academic research. This section will lay the groundwork for a more theoretically informed use of indicators than that found in the current literature. After a brief discussion of the strengths and limitations of micro approaches (Section 3.1), we move to propose new macro indicators of GVC participation (Section 3.2) and the resulting economic gains in terms of value capture (Section 3.3), drawing on our understanding of GVCs as a specific form of the division of labour. We present the key differences between the standard measurements and our own indicators, along with cross-country descriptive statistics highlighting the advantages arising from measurement based on our conceptual and empirical proposals vis-à-vis standard measurements.

Micro approaches to development in GVCs

While a major strength of the case study method is its ability to zoom in on the relevant object of study and to provide more rounded insights that may be overlooked by macro indicators, a corresponding limitation is the difficulty in drawing any general conclusions from an accumulation of individual cases. In the case of GVCs, there are concerns that this limitation has in the past been aggravated by a 'selection bias problem' toward studying examples of successful upgrading (Bernhardt and Milberg, 2013, p. 490). However, this potential bias has been partially corrected by research that addresses the relationship between GVC participation and upgrading at the micro-level.

Of particular relevance is the paper by [START_REF] Phillips | Unfree labour and adverse incorporation in the global economy: comparative perspectives from Brazil and India[END_REF], whose study of forced labour in Brazilian agriculture demonstrates that a marked deterioration in social standards may result, not from exclusion from GVC networks, but rather from the incorporation on unfavourable terms into GVC networks, what she calls 'adverse incorporation'. This possibility is precisely what is missing from the rosy scenario of the policy institutions, whose indirect measurements of trade data overlook the novel forms of labour relations-a form of contractual 'neo-bondage'-easily picked up by Phillips' focus on the incorporation of Brazilian agriculture into GVCs.

Other notable cases illustrate further advantages of the academic literature's micro-orientation. In constructing their object of study in different ways, for example, [START_REF] Kraemer | The distribution of value in the mobile phone supply chain[END_REF] and [START_REF] Ding | The Role of a Technological Platform in Facilitating Innovation in the Global Value Chain: A Case Study of China's Mobile Phone Industry[END_REF], both studying the global smartphone industry, are able to reveal very different facets of the prospects for upgrading involved. Kraemer et al. start with a flagship final consumer product-various high-end smartphones-and trace the value-added accruing across different firms and countries for all stages of its production, uncovering the extremely skewed distribution of value-added toward the lead firm and the difficulty of upgrading for suppliers. Ding and Hioki, by including all smartphones-the high-end segment as well as the medium-range market-in their case study, are able to show how the continued dominance of global lead firms in the high-end segment has coexisted with significant upgrading in the emergence of a new wave of vertically integrated Chinese firms serving the domestic medium-range market.

Yet for all the additional richness provided by the case study methodology, the glaring problem remains of the inability to draw general conclusions. As the example of the smartphone GVC case studies demonstrates, even in a single industry case studies may paint contrasting pictures, and it can be difficult to say which tendency-toward adverse incorporation or upgrading-generally prevails.

Here, we have chosen to concentrate on the macro-level, on the same ground where the measurements of the policy institutions are to be found. This decision is what will best allow us to develop indicators, based on our conceptualisation of GVCs that is more in tune with the insights of the academic literature, that can be directly contrasted with those found in the mainstream and can therefore best judge the applicability of the rosy scenario of GVC participation and economic development.

GVC participation measurement

The standard way of measuring GVC participation in the macro literature derives from the two borders rule: GVC trade covers the portion of a given product that crosses at least two frontiers (Hummels et al., 2001, p. 76). Country participation in GVCs is then measured in terms of vertical specialisation with a backward and a forward dimension: the backward component (VS)-foreign value-added content of total exports-assesses how dependent a country's export sector is on foreign inputs; the forward component (VS1)-domestic value-added in third countries' exports-shows how domestic exports rely on other countries' exports. VS and VS1 are sometimes taken separately as independent measures in order to see whether participating as a buyer or as a seller can have a different impact [START_REF] Taglioni | Making Global Value Chains Work for Development[END_REF][START_REF] Kummritz | Economic Upgrading Through Global Value Chain Participation: Which Policies Increase the Value Added Gains?' Policy Research Working Paper 8007[END_REF]. More commonly, VS plus VS1 are taken together as a share of exports to measure a country's total (backward and forward) participation in GVCs.

The combination of VS and VS1 as a proxy for GVC trade leaves considerable room for further precision5 in light of the conceptualisation of the frontiers of GVC activity offered in this article. Table 1 highlights the differences between the products included in our proposed measurement of participation in GVCs as a form of the division of labour and the standard VS + VS1 measure of vertical specialisation.

Deriving from the accounting strategy delineated in equation ( 1), the full measure that we use for the rate of GVC participation is as follows: Equation 1: GVC participation rate as a form of the division of labour

(XDVA) * (1 -ppX) + ipM * (1 -ppM) GDP (1)
Where 'XDVA' is domestic value added in gross exports, 'ppX' is the share of primary products in total exports, 'ipM' is gross imports of intermediate products and 'ppM' the share of primary products in total imports The key differences between our measurement of GVC participation and the standard measure concern the exclusion of primary products and the relaxation of the two borders rule.

It is the norm in GVC inspired macro studies to include primary products trade in GVC measurement. However, some recent studies have begun to exclude certain groups of primary commodities (Taglioni and Winkler, 2016, p. 88;[START_REF] Kummritz | Economic Upgrading Through Global Value Chain Participation: Which Policies Increase the Value Added Gains?' Policy Research Working Paper 8007[END_REF]. Revealingly, the reason for this exclusion is that natural resource-intensive countries introduce a bias in aggregate measurements and international comparisons, but this point is not theoretically articulated. There is a problem of consistency here: either what is important is the two borders rule, in which case the composition of trade is not relevant to measure GVC participation; or, if it is relevant to differentiate among the products traded, then the specificities of trade within GVCs must be explained and, in this case, there is no reason to maintain the two borders rule.

The reason we exclude primary products is straightforward: our understanding of GVCs as a specific form of the division of labour and its emphasis on a degree of transnational command over production do not concern most of primary products trade. Primary products are relatively homogenous in terms of quality and their prices are highly volatile due to the low elasticity on both the demand and the supply side and the resulting strong sensibility to geopolitical shocks and/or climactic variations (Dicken, 2011, pp. 253-71). Primary products are also material inputs that have a generic character; with a wide array of potential uses and buyers, they are often traded on the open market and, thanks to their liquidity, they constitute an asset class on financial markets (Newman, 2009, pp. 550-56). The quality settings, the production process and the pricing of this kind of products are thus generally not dependent upon inter-firm negotiations and repeated interactions. This implies that primary products trade occurs beyond GVCs as a form of the division of labour; they are in most cases full commodities exchanged in an economic space where market coordination dominates. Correspondingly, our understanding of GVC trade leaves no reason to maintain the two borders rule. We consequently include all imports and exports of non-primary products as GVC trade, with the exception of the direct import of a finished product for domestic use. This exception allows us to exclude the imports of finished goods when calculating the GVC participation of the importer-a Volkswagen car produced in Mexico imported by the USA, an I-phone imported from China to Italy, machinery bought by Korean SMEs from Siemens in Germany-although they are taken into account when calculating the GVC participation of the exporting country. These transactions should not be considered GVC trade for the importing country, as there is no transnational command over production exercised by the importer.

Clearly, some significant distortions arise when one moves from the theoretical realm to the realm of empirical complexity. For example, when a Nike shoe is imported from Bangladesh to be sold by a Foot Locker store in New York (this applies to any case where the importer country is at the same time the location base of the lead firm), it will not be counted as GVC trade in spite of the fact that this import relies to a considerable degree on transnational command on the part of Nike based in the USA over production in Bangladesh. This is one drawback of the choice to exclude imports of finished goods in the GVC participation of the importer. As concerns the exclusion of primary products, some agricultural production processes are extensively framed by interactions with buyers, as shown for fresh vegetables exported from Kenya to European markets [START_REF] Humphrey | The impact of European market changes on employment in the Kenyan horticulture sector[END_REF], grapes and other fruits in Brazil's São Francisco Valley [START_REF] Selwyn | Labour flexibility in export horticulture: a case study of Northeast Brazilian grape production[END_REF] or Thai cassava's exports to China [START_REF] Kaplinsky | China as a final market: the Gabon timber and Thai Cassava value chains[END_REF]. It is also true that some intermediate manufactured products such as iron and steel bars or standard memory chips are inputs so widely used that they are standardised in generic terms and traded in commodity-like conditions.

However, to the best of our knowledge, there is no simple way to overcome this limitation when using country-level trade statistics. The various broad choices involved in operationalising our conception of GVC trade should thus be considered not as a perfect reflection of our theoretical understanding, but rather as theoretically grounded proxies allowing one to work with country-level aggregated data to delineate the extent of GVC participation. This marks an advance over the status quo in measurement.

One last difference between our measure of GVC participation and the standard in the literature is that we opt for a ratio that divides by GDP rather than following the usual practice of dividing by gross exports. This is because our purpose is not seeing how much of world trade has become GVC trade, but rather looking at the developmental effects of the GVC division of labour. From this point of view, it is more relevant to gauge the level of GVC openness in relation to the economy itself. In other words, our indicator can be interpreted as a measure of the value involved in GVC trade relative to the value created in a country, that is relative to the size of a country's economy.

Figure 1 illustrates the differences between our indicator and the standard VS+VS1 measure. It displays the value of a given country's GVC participation according to the different indicators, for the longest possible time span and greatest amount of countries that the existing TiVA data permit. That is, for 59 countries for the period that goes from 1995 to 2011. The countries are ranked in order of their 2011 participation values. We do see some similarities between the two indicators, for example the 10 leading countries according to both indicators share seven countries in common. The differences between the two graphs, however, demonstrate the greater precision of our indicator in capturing GVC dynamics.

First, as expected, our indicator offsets the effects of the 2000s commodity boom. This is reflected in the slower growth, on average, of GVC participation seen throughout the countries in our indicator in comparison with the standard indicator. The difference is particularly noticeable for major commodity exporters such as Australia, Brazil, Russia, Indonesia, South Africa and, in a particularly extreme case, Brunei. Beyond the commodity issue, our measure more easily identifies a limited number of countries that dramatically increased their involvement in GVCs, namely Germany, China and a handful of mostly East Asian and Eastern European countries.

GVC value capture measurement

Although the measurement of economic upgrading in GVCs is not as standardised in the literature as it is in the case of GVC participation, the most commonly used indicator is based on the domestic value-added share of exports [START_REF] Milberg | Outsourcing Economics: Global Value Chains in Capitalist Development[END_REF]UNCTAD, 2013;[START_REF] Taglioni | Making Global Value Chains Work for Development[END_REF]. The logic of this type of indicator sees economic upgrading as something that is done relative to others, in keeping with the academic literature's emphasis on climbing the value-added ladder. It thus also implies the possibility of downgrading, as the measure highlights the amount of value-added in trade being retained by one country as opposed to being lost to others. For this reason we call it a 'value capture' measure, as opposed to other approaches to upgrading (including some seen in Table 2 below) that place the emphasis on upgrading as an absolute rather than a relative endeavour.

Table 2 summarises the most recent approaches to measuring value capture in GVCs.

Our understanding of economic upgrading agrees with the most common approach of utilising a value capture measure, but innovates in taking domestic value-added in exports as a share of total GVC trade, which for us includes imported intermediates and excludes primary products (see Section 3.2 above), rather than merely as a share of exports.

The formula that emerges to calculate our measure of economic upgrading from GVC participation, therefore, is as follows: Equation 2: XDVA in GVC trade

(XDVA) * (1 -ppX) (XDVA) * (1 -ppX) + ipM * (1 -ppM) (2)
Where 'XDVA' is domestic value added in gross exports, 'ppX' is the share of primary products in total exports, 'ipM' is gross imports of intermediate products and 'ppM' the share of primary products in total imports As the reader will notice, the numerator of equation ( 2) corresponds to the total value captured by a country when exporting non-primary products (i.e. the total value Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020 captured by a country through GVC-related trade) and the denominator to the total value of GVC-related trade as defined above (the numerator of equation 1).

The reason for including domestically absorbed intermediate imports in the denominator stems directly from our above conceptualisation that sees all secondary goods and services trade (excluding the import of finished products) as GVC trade. This type of GVC import represents a real cost that could offset some of a country's gains in terms of capturing value through GVC exports. In the case of some countries where the cost of such imports is particularly high due to an underdeveloped domestic input sector, omitting domestically absorbed intermediate imports from the denominator of the value capture measure would therefore give an unrealistically high GVC gain rate. Including such real costs of GVC participation as well as the gains from domestic value-added in exports provides a more precise measure of the real gains from participation.

Figure 2 illustrates the differences between our value capture measure, taking XDVA as a share of total GVC trade, and the most commonly used value capture indicator that only takes XDVA as a share of gross exports. As with Figure 1 in the previous section, the same 59 countries are ordered by their 2011 values while also providing their 1995 values.

Here, the most striking thing to notice is again the greater precision of our indicator in identifying the value captured in GVCs by netting out the overall non-GVC dynamics of the overlapping commodity boom period. Notice, for example, that 8 out of the top 10 countries in terms of 2011's value capture by the more standard measure are major commodity exporters, with primary products ranging from 65% of total exports (Brazil) to 97% (Brunei), far above the sample average of 37% for 2011. Their high levels of value capture are therefore misleading, since this is value captured overwhelmingly through the commodity boom and not through participation in GVCs. In contrast, with our indicator, these countries are concentrated among the bottom ten countries in 2011, reflecting the weak involvement of their domestic production in GVC trade. Our indicator shows that the countries that gained the most relative to their involvement in GVC trade are high-income industrialised countries plus China while, among countries ranking in the middle, one finds developing countries and peripheral European countries. Aside from the primary products issue, the other significant advantage arises from the fact that the denominator of our indicator includes both intermediate imports and exports, in sharp contrast to other measurements of value capture relating domestic value-added to exports alone. Consequently, our indicator is able to weigh countries' gain against their reliance on intermediate imports. This difference shows up in Figure 2, as countries with a notorious trade deficit, such as the United States and Greece, score much lower in value capture while major trade surplus countries such as Japan and Germany shoot toward the very top of the list.

A final indication of the greater precision of our indicators for value capture, and also for GVC participation, can be seen by looking at the standard deviation for the countries' rate of change between 1995 and 2011, which is considerably larger by our measure both for GVC participation (49.9% versus 19.7% for the standard measure) and value capture (14% versus 9.9%). In other words, we are better able to capture the heterogeneity of participation and country gain trajectories throughout the period, allowing us to more accurately contrast the divergent fates of countries in the GVC era while at the same time reducing the theory-measurement gap.

Overall, our novel measurement of GVC participation and value capture does a better job of identifying variety among country performances and avoids confounding non-GVC phenomena. This provides a stronger foundation for the econometric analysis of GVC development patterns.

Data analytics

As the aim of this contribution was to show the diversity of GVC integration patterns, we cannot rely on cross-national macro regressions that mask the heterogeneity of relationships among the variables by sub-groups of countries [START_REF] Rodríguez | Trade policy and economic growth: a skeptic's guide to the cross-National evidence[END_REF]. We therefore perform a PCA, a methodology that is perfectly suited to capture the heterogeneity of relationships between economic and social variables among groups of countries. In addition to our GVC-related measures, the variables included are: the investment rate, which captures another facet of economic development beyond that of value capture, and four social outcome variables that speak to the multi-dimensional nature of 'social upgrading' (Milberg and Winkler, 2013, p. 251). The social variables are the rate of employment, median income, the Gini index and labour's share of income. 6We combine these various data to run our PCA for 51 countries for the period from 1995 to 2008 (Section 4.1). We use the results to perform a cluster analysis that leads to the identification of three groups of countries that represent three GVC-related development patterns (Section 4.2).

Principal component analysis

Outline of the evolution of the variables

Before performing the PCA, we take a look at the direction in which the analyzed variables evolved in order to provide a first glance of the general trends for the 51 countries in our dataset.

Table 3 shows some general trends in the variations of the raw variables analyzed. As expected, participation in GVCs increased for the vast majority (84%) of countries, on average by 29%. On the other hand, value capture decreased for 73% of them, although the mean decrease is negligible (-4%). The investment rate, the employment rate and especially median income increased for most countries and on average. The two variables chosen to measure inequality, the labour share and the Gini index, tell different stories. Measured in terms of labour share, inequality increased in 78% of countries and rose by 6% on average, whereas using the Gini coefficient, inequality rose in roughly half of the countries and did not evolve on average. This reinforces our decision to include both variables to measure inequality.

Treatment of data in order to perform the analysis

Taking percentage increases of variables alone throughout the period under consideration would have made comparisons between countries misleading since the starting values of variables vary significantly between countries. For this reason, we decided to create an index of each variable that weighs the percentage increases between 1995 and 2008 by the value of each variable at the beginning of the period under analysis, that is, its value in 1995. Additionally, in order not to privilege social upgrading variables in our analysis, we created a composite index of the four social variables, with all four social variables (employment rate, median income, Gini index and labour share) weighted equally. We are therefore left with four index variables to conduct the PCA, that is, GVC participation ('PART_INDEX'), value capture ('VALCAPT_ INDEX'), investment ('INVESTMENT_INDEX') and the social upgrading composite ('SOCIAL_INDEX'). The results are robust with respect to these choices of treatment, as is further discussed along with more details of the treatment method in the online appendix.

Results

Following the Kaiser criterion, we retain three axes (F1, F2 and F3) in the PCA. The information contained in these axes concentrates 83.14% of the variables' information. Table 4 shows the coordinates of the variables for each axis and Table 5 the contributions of each variable to each axis. Particularly important coordinates and contributions are highlighted in bold. Figure 3 shows the correlation circle on axes F1 and F2 that resulted from the PCA.

Table 4 shows that the right side of axis F2 is strongly characterised by the variable VALCAPT_INDEX and that the variable SOCIAL_INDEX is highly represented on one side of axis F3. Both PART_INDEX and INVESTMENT_INDEX are associated with the right side of axis F1. Although these two variables have coordinates of 0.36 and 0.37, respectively, in axis F2, they should not be interpreted as being associated with axis F2, since, as shown in Table 5, the share of their information contained in axis F2 is small. Finally, as shown in the lower-right cell of Table 5, the variable SOCIAL_INDEX is highly correlated with axis F3. The variable factors map being two-dimensional, this correlation cannot be visualised in Figure 3. Some preliminary conclusions can be made. First, the fact that PART_INDEX VALCAPT_INDEX and SOCIAL_INDEX are represented along different orthogonal axes indicates that these three variables are independent of each other. This result confirms that there is no direct correlation between GVC participation, value capture and social outcomes. Second, INVESTMENT_INDEX and PART_INDEX being both associated with the right side of axis F1 indicates that, in general terms, countries that have increased their participation indexes the most are also the ones that have increased their investment indexes the most. Bearing in mind that the indexes are comprised of the percentage change of the variables in the 1995-2008 period and their starting 1995 values in equal parts, this can be interpreted in two non-mutually exclusive ways: countries that have most increased their participation in GVCs are countries that have also seen the largest increases in their investment rates in percentage terms and/or they are countries that already had large investment rates in 1995.

Cluster analysis

Methodology

We now turn to a cluster analysis in order to identify groups of countries (classes) for which the four indexes (GVC participation, value capture, investment and social outcomes) evolved in the same direction.

Using both the agglomerative hierarchical clustering (AHC) method and the k-mean clustering method we find three classes translating three distinct GVC development patterns for the 1995-2008 period.

Results

Table 6 shows the country composition of each class along with the number of countries in each.

When we performed the same analysis using the traditional GVC participation and value capture indicators instead of the authors' indicators introduced in Section 3, the country composition of the classes was profoundly altered. Moreover, the three classes presented close-to-average values for all variables except for value capture in class 2, which shows that, if traditional GVC and value capture indicators are used, the cluster analysis is inconclusive.

To understand the specific features of these three country groupings, we now turn to their intrinsic characteristics. We proceed by calculating the mean value of the four variables used in the PCA for each class and we compare them to the sample mean. The rationale for this method is simple: when the mean of one of the variables for a class is significantly higher/lower than the mean of all countries in the sample we can say that a high/low value of that variable is characteristic of the class. Given that the raw variables were standardised in order to build the indexes, the mean of the sample is equal to 0 for each index. Figure 4 shows the result of these calculations in a radial graph. Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Bera normality tests run at a significance level of 0.05 for each variable and class conclude that, for each class, all the variables follow a normal distribution; it is therefore safe to interpret the mean of each variable for each class shown in Figure 4 as representative of its corresponding class. The individual countries included in each class are listed in Table 6.

Interpretation of the results

As Figure 4 shows, class 1 is characterised by a very small increase in GVC participation and value capture, an average increase in investment and a high increase in social variables. Taking into account the country composition of the class, two different trajectories that converge into the same GVC development pattern can be construed.

The first one corresponds to a 'GVC resource curse' and applies to countries like Argentina, Australia, Canada, Chile, Indonesia, Iceland, New Zealand, Norway and Russia. Here, the countries are net primary commodity exporters that benefited from the historically exceptional increase in the international prices of commodities. Given the definition of our indicators, this implies a disengagement from GVCs and a loss in value capture coming from GVCs. Investment did not particularly evolve due to this dynamic but, on the contrary, the policies implemented and social dynamics that took place between 1995 and 2008 contributed to the use of these commodity-related income gains to obtain social improvements in terms of equality, median income and employment.

The second trajectory found in this first cluster is that of peripheral European countries that benefited up to the 2008 crisis from foreign financial inflows which allowed for temporary social improvements [START_REF] Stockhammer | European growth models and working class restructuring: an international post-Keynesian political economy perspective[END_REF]. These countries lagged behind in GVC participation and economic upgrading, which led to underperformance in terms of GVC-related value capture. Yet, this same process brought about a flow of financial-related income that was distributed in a way that led to an increased median income and decreased inequality. In the case of Greece, Spain and Portugal, they benefited from capital inflows with their integration in the Eurozone and, by that means, were able to undergo a process of social upgrading. Nonetheless, this process adversely affected their competitiveness and resulted in a lag in GVC participation and economic upgrading. The severe economic crises these countries are undergoing since 2008 illustrate the mirage-like quality of the non-GVC led social upgrading path of class 1.

Class 2 is characterised by medium to low scores in GVC participation and investment rates coupled with an average score in the social variable and a very positive evolution in terms of value capture. The countries that constitute this class are mainly developed countries. 7 This suggests a trajectory characterised by a slow increase in GVC participation but in which participation was increasingly concentrated in the tiers of value chains that are able to capture more value. Given that these countries have been developed for decades if not centuries, they already had developed productive structures that allowed them to achieve highly profitable positions in GVCs without a sharp increase in investment. That this class of countries' social index score is at the sample mean suggests, looking back at Table 3, that their populations benefited from an increase in median income compatible with their sharp increase in GVCrelated value capture but that employment did not significantly increase and inequality did not change or even increased, depending on the specific case.

In this sense, we can think of this dynamic as a reproduction of the core trajectory: the most developed countries in 1995 did not increase their GVC participation as much as others during the globalisation boom, yet they were able to capture more value than the others. They were thus able to reproduce their dominant position in the global economy through GVC trade without producing much in terms of social upgrading for their populations.

Class 3 is characterised by very high scores in GVC participation and investment rates, a slight decrease in value capture and low scores in social terms. This can be characterised as a pattern of 'unequal growth'. The countries that compose this class are mainly developing countries in Asia and Eastern Europe that in many cases were starting to undergo a process of integration into global capitalism after decades of socialist or developmental regimes. Their economies opened sharply and they joined the globalisation boom by participating strongly in GVCs, which, consequently, implied sharp increases in their investment rates that bolstered their productive structures. Value capture, however, slightly decreased in contrast to countries of class 2 ('reproduction of the core'). Moreover, in terms of social upgrading this GVC development pattern was not beneficial overall: inequalities increased more than in any other group of countries and growth in the employment rate was virtually null (less than 1% on average), clearly the slowest compared to the other two classes. However, median income saw an average increase, sometimes even a spectacular one in countries such as China where, as is known, the 1978-2015 period saw real average income per adult grow 38-fold, putting even the bottom 50% of the population's average income growth at around 4.5% per year despite their share of national income being roughly halved due to sharp increases in inequality [START_REF] Piketty | Capital Accumulation, Private Property and Rising Inequality in China, 1978-2015[END_REF].

To summarise, this analysis shows that there is no single story concerning the relationship between GVC participation and outcomes relating to value capture, investment or social standards at the country level. On the contrary, three distinct configurations of relationships among these variables were identified for sub-groups of countries in our dataset, indicating three development patterns that we have identified as a social upgrading mirage (that includes a GVC resource curse for some countries), a reproduction of the core and a pattern of unequal growth.

Discussion

Based on the conceptual and methodological achievements of Sections 2 and 3, the PCA and cluster analyses conducted in Section 4 question the idea that the internationalisation of production processes fosters development or provides policy space to address developmental challenges. It relates and contrasts three broad development patterns found in GVCs between 1995 and 2008: a social upgrading mirage, the reproduction of the core and unequal growth (Table 7). Our findings echo earlier work by Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020 spectacularly as in the case of China-growing personal and functional income inequality and/or poor employment performances point to a pattern of partially skewed development associated with growing involvement in GVCs.

It is important here to stress that even when value capture improves, social upgrading is far from evident, depending on the internal unfolding of class relations. Indeed, if economic gains are captured by capital, they could fuel higher inequality and limit the spillover effect that should increase the population's income. Moreover, higher profits do not necessarily translate into higher investment-they could result in higher financial payments, which means that the overall impact on employment is not straightforward. Thus, as a result of greater inequality or unproductive uses of profits, greater value capture resulting from GVC participation can be associated with social downgrading. Conversely, positive social outcomes necessitate that labour, which represents the bulk of the population, manages to capture part of the gains, either directly through higher wages or, indirectly, through tax-funded public welfare. For such an outcome to occur, the key mechanisms are a higher labour demand resulting from productive uses of profits and workers' ability to mobilise some structural and/or associational power [START_REF] Wright | Working-class power, capitalist-class interests, and class compromise[END_REF].

Interestingly, the best social outcomes revealed by our analysis occurred in countries that stayed relatively insulated from GVC dynamics. We nonetheless call this configuration a social upgrading mirage because it rests on external conditions of possibility which are, on the one hand, the commodity boom of the 2000s and, on the other hand, the massive financial inflows in countries from the peripheral European countries during the first decade of the Euro. These conditions favoured overall improvements in terms of median income, employment and sometimes inequality, but unfortunately they later proved unsustainable as these countries were among the most heavy hit by the reversal of the commodity boom and the 2008 financial crisis and its destabilising effect on the European monetary area.

With this overall picture in mind we can come back to the importance of our initial conception of GVCs as a specific form of the division of labour. One of the core elements of this conceptualisation is that within the boundaries of GVCs, the economic ability to capture the gains and the ability to frame the productive processes are unevenly but interdependently distributed. This allows for an original understanding of the diversity and complementarity of uneven development patterns along value chains. Uneven development patterns typically result from the fact that GVCs delineate transnationally fragmented labour processes, often dispersed among formally independent entities that are nonetheless to some degree economically unified under a dominant locus of valorisation. Positions of market power reflect some degree of control over labour processes that descends along the chains and allows value capture at considerable geographical removal from the countries where productive development takes place. This focus on fragmented-unified valorisation processes also sheds a new light on social outcomes. They cannot directly be deduced from GVC participation and can only be understood if one takes into account the distribution of capitals' powers along the chain in addition to other dimensions such as the institutional set of constraints and regulations or the position of labour at the point of production. In this perspective, the complementarity between the three development patterns described by our empirical investigation is a reflection, at the inter-country level, of a strongly hierarchised organisation of the world economy that is spreading at a more granular level among unequal power nodes within trade, financial and policy networks.

Conclusion

This article explored the variety of socio-economic outcomes associated with GVC participation at the country level and challenged the dominant baseline rosy scenario whereby any country that increases its participation in GVCs should experiment an improvement in its economic and social situation.

Focusing on GVC dynamics at the macro-level, our PCA and cluster analyses indicate three main patterns of development in GVCs between 1995 and 2008: a social upgrading mirage, the reproduction of the core and unequal growth. Contrary to the mainstream narrative about the expected positive effects of GVC participation, we show a more nuanced reality where gains from GVC participation are unevenly distributed between and within countries and point to the interdependence the diverse GVC development patterns reflecting the specificities of the global division of labour within value chains.

We developed this empirical argument along with two theoretical and methodological contributions. In order to overcome the disjuncture between, on the one hand, theoretical developments and micro empirical approaches in the GVC literature and, on the other hand, macro, multi-country measurements, we presented an original theoretical conceptualisation of GVCs as a form of the division of labour. On such a basis, we offered new indicators of GVC participation and value capture along with new stylised facts concerning their evolution that allowed a preliminary econometric inquiry into the different patterns of development taking place along GVCs.

This paper thus identifies economic mechanisms that are difficult to disentangle through case studies and does not suffer from the inability of common econometric analysis to account for heterogeneous trajectories. We hope that it will contribute to new avenues for theoretical discussion and empirical inquiry within the GVC community.

Figure 1 .

 1 Figure 1. Standard and authors' measurement of countries' participation in GVC trade in 1995 and 2011.

Figure 2 .

 2 Figure 2. Standard and authors' measurement of countries' value capture in GVC trade in 1995 and 2011.

Figure 3 .

 3 Figure 3. Correlation circle on axes F1 and F2.

Figure 4 .

 4 Figure 4. Mean value of each variable by class and for the sample.

  Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

Table 1 .

 1 Standard versus authors' measurement of traded products included in GVC trade

	Approach	Numerator

Table 2 .

 2 Standard versus authors' measurement of value capture in GVCs

	Indicator Domestic value-added share of exports Domestic value added in exports (per capita) Domestic value added by industry Import content of export expansion ratio Domestic value added in GVC trade	Formula XDVA/X XDVA/ population DVAi ICt -ICt-1 * (Xt/Xt -1) XDVA / (GVCX + GVCM)	Sources Taglioni and Winkler (2016); UNCTAD (2013) Kowalski et al. (2015) Kummritz et al. (2017) Jiang and Milberg (2013) Author	Strengths/limitations Considers DVA gains in relation to exports but not in relation to GVC trade Typically increases for all with increased GVC participation Very distant to GVC trade Considers DVA gains in relation to exports but not in relation to GVC trade Captures DVA gains in relation to total GVC-related trade	Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

Table 3 .

 3 Distribution of country evolution and mean percentage change between 1995 and 2008 for each indicator

		Participation Value	Investment	Labour	Employment	Gini	Median
			capture	rate	share	rate	(-1)	income
	Countries with	84%	27%	67%	22%	63%	53% 88%
	positive evolutions
	Countries with	16%	73%	33%	78%	37%	47% 12%
	negative evolutions
	Mean change	29%	-4%	10%	-6%	2%	0% 37%

Table 4 .

 4 Factor loadings of each variable for axesF1, F2 and F3

		F1	F2	F3
	PART_INDEX	0.76	0.36	0.20
	VALCAPT_INDEX	0.03	0.91	-0.15
	INVESTMEMT_INDEX	0.78	0.36
	SOCIAL_INDEX	-0.19	0.08	0.97
	Particularly important coordinates and contributions are highlighted in bold.

Table 5 .

 5 Contributions of the variables to axes F1, F2 and F3 in percentage points

		F1	F2	F3
	PART_INDEX	47.4	12.0	4.1
	VALCAPT_INDEX	0.1	75.2	2.3
	INVESTMEMT_INDEX	49.7	12.2	0.1
	SOCIAL_INDEX	2.8	0.6	93.5

Table 6 .

 6 Country composition of the classes (World Bank country abbreviations)

	Class Number of observations	1 Argentina Australia Belgium Canada Chile Denmark Spain Greece Indonesia Iceland Norway New Zealand Portugal Russian Federation 14	2 Austria Brazil Switzerland Colombia Germany France United Kingdom Israel Italy Japan Cambodia Netherlands Philippines Sweden Turkey United States 16	3 China Costa Rica Czech Republic Estonia Finland Croatia Hungary India Ireland South Korea Luxembourg Mexico Malaysia Poland Romania Slovakia Slovenia Thailand Tunisia Vietnam South Africa 21	Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

For example,[START_REF] Milberg | Shifting sources and uses of profits: sustaining US financialization with global value chains[END_REF] and[START_REF] Palpacuer | Bringing the social context back in: governance and wealth distribution in global commodity chains[END_REF] follow this relational perspective when they explicitly link GVC dynamics of uneven development to financialisation.Lee and Gereffi (2015) stress the uneven distribution of upgrading opportunities that benefits lead firms at the expense of the bulk of

suppliers.2 By compossibility, Bob Jessop refers to 'the importance of structural coupling, co-evolution, and mutual complementarities-exclusivities among', national Varieties of Capitalism (2014, p. 54).

This definition is very close to the canonical one given in the 2001 Handbook for Value Chain Research(Kaplinsky and Morris, 2001, p.

4), testifying to the resilience of this conception.

Elements of this analysis were previously traced in Aglietta's analysis of sub-contracting networks[START_REF] Aglietta | A Theory of Capitalist Regulation: The US Experience[END_REF]; on the related issues of possession and economic property relations, see[START_REF] Bettelheim | Calcul Économique et Formes de Propriété[END_REF][START_REF] Poulantzas | Les Classes Sociales Dans le Capitalisme Aujourd'hui[END_REF]. Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

[START_REF] Wang | Characterizing Global Value Chains: Production Length and Upstreamness[END_REF] recent move away from the two border rule for characterising GVC trade represents an important step among trade economists in recognising the limitations of the VS+VS1 definition of GVC participation.Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

The sources used, treatments chosen and the robustness of the results concerning different choices can be found in the Supplementary Appendix. Supplementary Appendix also provides a discussion of the relative impact of the various elements informing our choice of the novel indicators presented in Section 3. Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

Particularly important coordinates and contributions are highlighted in bold.Downloaded from https://academic.oup.com/cje/advance-article/doi/10.1093/cje/beaa046/5909137 by University de Geneve user on 21 September 2020

Some of the countries in this class (Brazil, Colombia, Cambodia, Philippines and Turkey) are developing countries with heterogeneous development patterns. Their belonging to class 2 illustrates the limitations of cluster analyses conducted based on observations' factor scores in a PCA. Nevertheless, the homogeneity of the development pattern among the developed countries of class 2 that is explained in this section accounts for two thirds of the observations of the class, which makes our characterisation of the 'reproduction of the core' development pattern reliable.

 Milberg and Winkler (2013, pp. 198-202)in showing that relations between GVC outcome variables differ significantly depending on the specific grouping of countries considered, with the difference being that Milberg and Winkler formed their country groupings through pre-selected institutional categories whereas ours emerge through the process of statistical analysis.

Strikingly, our analysis does not show any rosy scenario where GVC participation goes along with relative improvements in terms of value capture, productive development and socio-economic outcomes. Rather, it shows an apparent compossibility between three regimes revealed by the polarisation in each of the dimensions of our typology, which suggests that development patterns in GVCs need to be understood as constitutive parts of a global process of uneven development. This finding reconnects with one key insight of the original research on global commodity chains: the relational character of development patterns along the chains [START_REF] Hopkins | Patterns of development of the modern world-system[END_REF].

In the case of the reproduction of the core pattern, a category that comprises most of the biggest, high-income economies, value capture is disconnected from productive development measured in terms of investment. It echoes the possibility that GVC participation could lead to greater value capture thanks to the exercise of market power. Such market power could be related to economic barriers to entry, for example arising from the concentration of intangibles, and/or to an asymmetric political structure resulting in the protection of standards and intellectual property rights [START_REF] Durand | Intellectual monopoly in global value chains[END_REF]. In such cases, benefits from GVC participation do not result from higher productivity but from the ability to extract rent from foreign actors, an idea already raised by dependency theorists [START_REF] Palma | Dependency: a formal theory of underdevelopment or a methodology for the analysis of concrete situations of underdevelopment?[END_REF]. For example, the ability of global buyers to benefit from cheaper inputs could be completely disconnected from any productive improvement, in which case its overall impact will depend on the distribution and the uses of the gains [START_REF] Milberg | Shifting sources and uses of profits: sustaining US financialization with global value chains[END_REF].

The counterpart of this privilege of the core is a process of unequal growth where increases in the quality or quantity of output resulting from productivity gains are more than compensated by diminishing prices, resulting in lower value capture. In such cases greater productive efficiency does not translate into greater economic gains but rather leads to social downgrading as previous uses of resources have been disrupted by the involvement in GVCs [START_REF] Kaplinsky | The globalization of product markets and immiserizing growth: lessons from the South African furniture industry[END_REF][START_REF] Mohan | Institutional change in value chains: evidence from tea in Nepal[END_REF]. This second kind of pattern corresponds to the fate of mainly developing countries that experienced a rapid insertion in GVCs along with important productive development and, in the meantime, poor social outcomes. Even as median income improved-sometimes