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Labor and environment in global value chains: an evolutionary policy study with a three-sector and two-region agent-based macroeconomic model

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The world economy crucially depends on multi-layered value chains with high degrees of sector-related specialization. Its final products are of international character and serve the needs and wants of the global citizen. However, many production processes are causing severe damage to the environment and moreover create health hazard for workers and local populations. This research article focuses on the increasing global unequal economic- and ecological exchange, fundamentally embedded in international trade. Resource extraction and labor conditions in the Global South as well as the implications for climate change originating from industry emissions in the North are investigated with an agent-based model. The model serves as a testbed for simulation experiments with evolutionary political economic policies. An international institution is introduced sanctioning the polluting extractivist sector in the Global South as well as the emitting industrial capital good producers in the North with the aim of subsidizing innovation reducing environmental and social impacts. Both regions are modelled as macroeconomic complex adaptive systems where international trade is restricted to a three-sector value chain, originating from mining resources in the South that are traded to capital good producers in the North crafting machinery which is eventually traded to consumer good firms, both in the North and South. The main outcome of the study is that sanctions alone are not effective in countering unequal exchange. They only make a difference in combination with subsidies for innovation activities, which are protecting labor and reducing local pollution in mines as well as reducing carbon-emissions in capital good production.
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https://doi.org/10.1007/s00191-021-00750-7
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REGULAR ARTICLE
Labor andenvironment inglobal value chains:
anevolutionary policy study withathree-sector
andtwo-region agent-based macroeconomic model
LenaGerdes1 · BernhardRengs2 · ManuelScholz‑Wäckerle3
Accepted: 13 October 2021 /
© The Author(s) 2022
Abstract
The world economy crucially depends on multi-layered value chains with high degrees of
sector-related specialization. Its final products are of international character and serve the
needs and wants of the global citizen. However, many production processes are causing
severe damage to the environment and moreover create health hazard for workers and local
populations. This research article focuses on the increasing global unequal economic- and
ecological exchange, fundamentally embedded in international trade. Resource extraction and
labor conditions in the Global South as well as the implications for climate change originat-
ing from industry emissions in the North are investigated with an agent-based model. The
model serves as a testbed for simulation experiments with evolutionary political economic
policies. An international institution is introduced sanctioning the polluting extractivist sec-
tor in the Global South as well as the emitting industrial capital good producers in the North
with the aim of subsidizing innovation reducing environmental and social impacts. Both
regions are modelled as macroeconomic complex adaptive systems where international trade
is restricted to a three-sector value chain, originating from mining resources in the South that
are traded to capital good producers in the North crafting machinery which is eventually
Lena Gerdes, Bernhard Rengs and Manuel Scholz-Wäckerle contributed equally to this work.
* Manuel Scholz-Wäckerle
manuel.scholz-waeckerle@wu.ac.at
Lena Gerdes
lena.gerdes@wu.ac.at
Bernhard Rengs
bernhard.rengs@oeaw.ac.at
1 Institute forEconomic Geography andGIScience, Department ofSocioeconomics, Vienna
University ofEconomics andBusiness, Vienna, Austria
2 Wittgenstein Centre forDemography andGlobal Human Capital (IIASA, OEAW, University
ofVienna), Vienna Institute ofDemography/Austria Academy ofSciences, Vienna, Austria
3 Department ofSocioeconomics, Vienna University ofEconomics andBusiness, Vienna, Austria
Published online: 14 January 2022
Journal of Evolutionary Economics (2022) 32:123–173
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1 3
traded to consumer good firms, both in the North and South. The main outcome of the study
is that sanctions alone are not effective in countering unequal exchange. They only make a
difference in combination with subsidies for innovation activities, which are protecting labor
and reducing local pollution in mines as well as reducing carbon-emissions in capital good
production.
Keywords Evolutionary political economy· Agent-based modelling· Unequal
exchange· Resource extraction· Global value chains· Labor and environmental
protection· Climate change
JEL classification B52· C63· F12· F18· F64· F66· F68· O13
1 Introduction
The world economy is a highly interlinked, interdependent and fragmented produc-
tion system. The evolutionary process of political economic globalization leads to
complex value chains regarding natural resource extraction and capital good pro-
duction in particular. Final goods are largely of international character, serving the
needs and wants of global citizens. However, each step in such complex value chains
potentially causes severe damage to the environment or violates labor and human
rights (European Parliament 2021). Most of these negative effects are happening far
away from the place where the final good is eventually consumed. The historical
and geographical traces of multi-sectoral value chains become invisible once the
final product finds its place in the shelves of local and international retailers. The
impacts of production on labor and the environment often have to be carried by the
producing countries, majorly the Global South, while profits are accumulated in the
Global North. This causes economic, social and ecological tensions and inequalities
within and between political economic regions, often studied under the term unequal
exchange (Muradian and Martinez-Alier 2001; Ricci 2019; Dorninger etal. 2021).
To tackle social-ecological impacts emerging from global value chains, several
global institutions are in place, which structure international trade, like the WTO or
the universal declaration of human rights more generally. However, they are not very
effective in preventing damage to the environment and local populations. Moreo-
ver, they strongly rely on voluntary commitment to due diligence from multinational
organizations, since it is expected that companies would want to avoid damage to
their reputation (European Parliament 2021). But, as numerous recent studies show,
among others from the European Commission (Faracik 2017; Smit etal. 2020), the
voluntary approach is not sufficient to reduce negative impacts along value chains.
The European Parliament recently adopted a non-binding resolution, arguing the EU
“should urgently adopt binding requirements for undertakings to identify, assess,
prevent […] and remediate potential and/or actual adverse impacts on human rights,
the environment and good governance in their value chain” (European Parliament
2021).
124 L. Gerdes et al.
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1 3
Evolutionary economists were always interested in investigating international
trade and its effects on structural and industrial change, inequalities, development
and growth. Stimulating examples are given by Maggi (1993), Mainwaring (1993),
Daly (1995), Amable (2000), Kingston (2000), Herrmann-Pillath (2006), Fagiolo
etal. (2010), Gaffard and Saraceno (2012), Hanappi (2019) and Landesmann and
Stöllinger (2019), following a variety of different perspectives and approaches.
This research article aims to contribute to this stream of evolutionary political
economic research by presenting an agent-based model of unequal exchange. The
macroeconomic nature of the model we present is in similar spirit as given by Dosi
et al. (2010), Ciarli et al. (2010), Cincotti etal. (2010), Delli Gatti et al. (2011),
Seppecher (2012), Lengnick (2013), Ricetti etal. (2013), Chen etal. (2014), Dawid
etal. (2014), Lorentz etal. (2016), Caiani et al. (2016), and Safarzyńska and van
den Bergh (2016). In particular, it is inspired by Rengs and Scholz-Wäckerle (2014,
2017, 2019) and Rengs etal. (2020).
Agent-based macroeconomic models with multiple regions and trade relations are
seldom. Existing agent-based economic models with trade are presented e.g. via the
Euroace@Unibi model, which forms the basis for several studies, with a focus on
the European labor market and the stability of financial markets (Dawid and Harting
2012; Dawid etal. 2014, 2018; Petrovic etal. 2017, 2020). Financial markets itself
form the basis for another family of models (see Chiarella and Di Guilmi 2011;
Grauwe and Gerba 2017; Caiani etal. 2018) as well as the development of trade
markets, and trade agreements, including tests of different strategies and the impact
of free trade. For example, Lee etal. (2010) are using a game-theoretical approach,
Caiani et al. (2019) are investigating the interdependence of European trade and
wage regimes and Dosi etal. (2019, 2020) are studying growth patterns of interde-
pendent economies in a multi-country agent-based model.
However, none of these models have an environmental component with feedback
on the fragmented multi-regional production structure (e.g. health of workers or
damaging the means of production), where global value chains clearly have a weak
spot. Still, the presented agent-based model does not aim to mimic agent-based inte-
grated assessment models, as recently published by Lamperti etal. (2018). There are
some economic models with multiple regions specialized on climate policies, like
the Lagom model family (Wolf etal. 2013). Nevertheless, all of these models still
lack a conception of value chains. Our agent-based modelling approach integrates
two very crucial aspects of unequal exchange, involving a decline in labor and envi-
ronmental conditions in the Global South, i.e. (1) worsening labor conditions caused
by local pollution and (2) increasing risk for natural disasters caused by endogenous
global climate change.
Following this urgency to institutionalize mechanisms to reduce harm caused along
value chains, we address the increasing global drift in unequal economic- and ecologi-
cal exchange, fundamentally embedded in international trade in this research article.
We pay attention to the role of manufacturing firms (capital good producers) from the
secondary sector of the Global North as trading hubs in the fragmented world economy.
They usually depend on purchasing resources (such as raw materials) from the primary
sector of the Global South in order to manufacture machinery for the final goods sector
(consumption good firms as well as wholesalers and retailers) of both, the Global South
125Labor and environment in global value chains: an evolutionary…
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1 3
and North. The latter firms use this machinery to assemble, transport, distribute and
eventually sell final products to final consumers in their domestic economy. This three-
sector global value chain schema represents a large part of the fragmented production
of the world economy, including its social and ecological consequences. We investigate
historically and spatially evolved dependencies of the world economy with an empha-
sis on unequal exchange (Ricci 2019), in a three-sector, two-region agent-based model
of an artificial world economy. Evolutionary economic policies protecting labor and
the environment are tested, in order to reduce unequal exchange. For this very purpose
we refer to the concept of civilized markets introduced by Kapeller etal. (2016) who
are highlighting the significant role of international institutions in making global value
chains more just and less carbon intensive.
The article is organized as follows. In Section2, we elaborate on the motivation
of this framework with an emphasis on the fragmentation of the international pro-
duction system, unequal exchange and climate change. Furthermore, we discuss the
role of global institutions in operating policies aiming for more just and less carbon
intensive global value chains. Section3 will introduce the two-region, three-sector
agent-based model and Section 4 discusses the results of evolutionary economic
policy experiments. Section5 concludes.
2 The evolutionary political economy ofeconomic fragmentation
andunequal exchange
According to Ricci (2019) “Unequal exchange arises when spatial production of value is
disjointed from its geographical distribution” (p. 225) and can increasingly be observed
in the current times of economic globalization (see also Emmanuel 1972, Amin 1974).
The result is uneven development in the world system (Harvey 2006), originating from
the transfer of wealth from poorer countries to richer ones (Foster and Holleman 2014),
which is deeply rooted in historical developments of global value chains and complex
power relations (see Suwandi 2019; Fischer etal. 2021). Next to a political economic
perspective, there is an equally important ecological one, which is explored by an extend-
ing body of literature (see Muradian and Martinez-Alier 2001; Hornborg 2006; Jorgen-
son 2006; Jorgenson and Rice 2007; Lawrence 2009; Ricci 2019; Dorninger etal. 2021).
Many countries of the Global South are facing ecological disadvantages, often, but not
exclusively, imposed on them by countries of the Global North. In comparison, the eco-
logical intensity of exports from countries of the Global South is often much higher than
from countries of the Global North (Moran etal. 2013), mainly caused by the extraction
of large amounts of resources which are needed for the production of machinery and
final goods. The following section addresses these impacts of resource extraction on the
economy, the environment and local population.
2.1
Resource extraction, unequal exchange andclimate change
In the past decades, the demand for raw materials, in particular nonrenewable
resources, increased strongly especially by industrialized, but also by emerging
126 L. Gerdes et al.
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1 3
countries (Svampa 2013; Krausmann etal. 2017). The resources are an essential part
of the economic success, prosperity and industrial development, specifically in the
Global North. However, the process of extraction creates significant social and eco-
logical challenges at the place of extraction (Acosta 2013).1 The concept of extrac-
tivism coins this linkage, referring to “mining, oil, monocultural agriculture, as
well as other sectors that provide materials, usually for export” (Lang and Mokrani
2013). Generally, extractivism describes a division of labor on a global level. The
raw materials are extracted or produced by some countries, often from the Global
South, and then exported to other countries, often from the Global North, where
they are used to produce capital goods such as machinery and equipment (Lang and
Mokrani 2013). Many resource-rich countries of the Global South, especially in
South America, experienced a so-called re-primarization, which describes a stronger
focus on the exploitation and trade of natural resources. In South America, for
example, the export of primary products constitutes more than half, and up to over
90% of the total exports (Gudynas 2009). The reasons for this development are man-
ifold. Many resources are distributed very heterogeneously across the planet and the
extraction depends on existing deposits (Schaffartzik etal. 2016). Moreover, incen-
tives are given by rising industrial demand, rising commodity prices, and large-scale
foreign direct investment (FDI) (Veltmeyer and Petras 2014). Additionally, there is
a strong tendency of countries from the Global North to actively shift extractive and
high polluting industries to countries of the Global South, due to lower production
standards, lower labor costs and the attempt to outsource the high ecological and
social costs linked to extractive industries. This process if often referred to as “bur-
den-shifting” (Krausmann etal. 2017) or “externalization” (Lessenich 2019).
While the extraction and export of natural resources is an important source of
income (Giarracca and Teubal 2014), it leads to an increasing dependency on the
global market (Svampa 2013) and the profits are not equally distributed. Mines
are often owned or operated by foreign corporations and registered in tax havens
(Acosta 2013). Consequently, large parts of the revenue leave the country. Even in
countries with governments officially declaring to limit the power and the profit of
multinational corporations, like in Bolivia, the greatest beneficiaries are still large
multinational corporations, and the government only takes a small share of the total
profits (Veltmeyer and Petras 2014). Moreover, the raw materials are almost exclu-
sively exported and processed abroad, where other countries profit from higher val-
ues of capital and final goods (Gudynas 2009). This development leads to strong
global inequalities in terms of trade and costs caused by resource intensive indus-
tries. Looking at the European trade with Latin- and South America, this inequality
becomes clearly visible. Europe mainly exports machinery and manufactured goods,
hence goods with a high profit and high value added (Gudynas 2009), whereas it
imports mainly primary products like food and animals, as well as crude materials
and fuels (García-Herrero and Chiacchio 2017).
Next to economic unequal exchange, most of the environmental and social cost
have to be carried by the countries exporting natural resources, especially the local
1 See Kapp (1950, 1970) for an original evolutionary economic concept of social costs incurred by eco-
logical crisis.
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1 3
communities, while they only receive a very small part of the revenue (Acosta
2013). Social and ecological consequences of the extraction of resources are numer-
ous. The social structures are challenged and democratic mechanisms are under-
mined, since local communities often have no possibility to take part in decisions.
The release of a large variety of chemicals, sewage waters, and solid wastes pollute
the direct surrounding of the places of extraction, as well as places further away,
which are connected through natural water systems (Dudka and Adriano 1997). The
pollution can cause chronic diseases and other health issues, and can have negative
impacts on the quality of the soil (Giarracca and Teubal 2014). Through the mas-
sive extraction of resources, “serious and irreversible destruction of the ecosystem”
(Giarracca and Teubal 2014) is caused. Moreover, the effects are not limited to local
destruction of the environment, but also contribute to global challenges like climate
change. However, as Muradian and Martinez-Alier (2001) describe: “[under] neo-
classical thought it will be perfectly logical to displace environmental load to poorer
countries even if the rich countries have to financially compensate the environmen-
tal degradation of cheap land and diseases and deaths of poor people” (p. 293), espe-
cially because the costs depend on the large income inequalities.
2.2
A global evolutionary political economy perspective
Extractivism and unequal exchange in economic, social and ecological terms are crucial contem-
porary challenges, which are fundamentally rooted in the historical development of the world eco-
nomic system (e.g. see Wallerstein 1983; Harvey 2006; Hornborg etal. 2007). In order to steer com-
mon practices away from unsustainable and unjust trajectories, new international mechanisms are
needed. Here, the role of institutions can be highlighted. Currently, there are several global institu-
tions in place, which guide international trade, like the WTO or the universal declaration of human
rights. These are, however, not enough to effectively meet the ecological and social problems caused
by global value chains (Lang and Mokrani 2013; Svampa 2013; Kapeller etal. 2016), equally inef-
fective as relying on voluntary commitment to due diligence by corporations (European Parlia-
ment 2021). In order to protect labor and the environment, institutions observing and evaluating all
instances of international trade are necessary. However, to be effective, these institutions need to have
the ability to control and implement international standards, e.g. via taxes, penalty-tariffs and finan-
cial incentives to invest into labor and environment protection.
A proposal in this direction was made by Kapeller etal. (2016), introducing the con-
cept of civilized markets: a “civilized market tries to ensure that free entrepreneurship and
open markets are eventually compatible with those basic and universal values that also serve
as moral cornerstones of the European project” (p. 320). The authors propose an institu-
tion, which has the mandate to “set and enforce minimum standards for goods sold on the
European market” (p. 320), since the competitive market system currently in place under-
mines the environmental, social and moral foundations of our society and fosters unethical
behavior such as child labor, corruption, and the manipulation of earnings (Shleifer 2004).
In order to counter these developments, Kapeller etal. (2016) suggest – in Polanyian tradi-
tion – “to modify and improve the social embedding of market activities in international
trade by introducing appropriate institutions and politics in order to achieve a better align-
ment between social standards and market competition” (p. 323).
128 L. Gerdes et al.
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Taking these arguments into account, this paper follows an evolutionary political
economy perspective (Hanappi and Scholz-Wäckerle 2017), addressing the systemic
complexity of a policy framework for multi-regional redistribution. We take particu-
lar care for the feedback loops between micro and macro in each region and highlight
the importance of power, knowledge and learning in evolutionary policy making (see
Kapeller and Scholz-Wäckerle 2016). In particular, an institutional policy is tested with
emphasis on labor and environment protection. Gains from this policy are not merely
redistributed across households, but used to invest in labor and environmental protec-
tion. Emphasis is given to directly steering firms to new social-ecological paths, while
only assuming learning-by-imitation from other firms in their networks. Details of
the policy are explained in section4, after a thorough description of the model in the
following.
3 The structure ofthethree‑sector two‑region agent‑based model
In the model, heterogeneous agents interact in a self-organizing and endogenously devel-
oping economy. The economy contains two distinct regions – an abstract Global South
and Global North. There are three interlinked sectors, the consumption good–, capital
good–, and resource production sector. Each region contains an independent consumption
good sector, with domestic demand for final goods. They produce a fictitious consump-
tion good basket (see Rengs and Scholz-Wäckerle 2019), and sell it to the households in
the respective region. The production within each consumption good firm is based on the
same production function, which uses labor and physical capital (machine capacities) in a
constant relation as inputs. However, the prices, wages, number of workers and physical
capital of the firms develop independently throughout the simulation. The other sectors
are only present in one region. The capital good sector is only found in the Global North,
meaning capital goods (i.e. machines) are exclusively produced there, but are traded
to the foreign as well as the domestic market as an intermediary. For the production of
machines, capital firms need labor, machines themselves and resources. Prices and wages
are, like in the consumption good sector, developing independently in each capital firm.
The resource production sector, on the other hand, is only located in the Global South.
Mines extract resources and export them to the capital firms in the North. For the extrac-
tion of resources, the mines need labor and machines.
The households form the labor force in their respective region, there is no movement
of labor. The governments in both regions collect taxes from all firms and pay unem-
ployment benefits. Firms and banks form a simplified credit market, where firms can
borrow from banks to finance all payments and investments if they are illiquid.
In addition to economic interactions, the model also includes ecological as well as
social factors. Capital firms of the North produce carbon emissions, which are func-
tioning as a proxy for global emissions. The emissions are causing climate change,
leading to natural disasters damaging firms (machines) of all sectors eventually. Mines
of the South produce local pollution, affecting the health of mineworkers and thereby
decreasing their labor productivity. The basic flow structure of the model is presented
in Fig.1. A more detailed description of economic agents and structures is given in fol-
lowing sub-sections as well as in the appendices.
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1 3
3.1 Households
Households are boundedly rational agents based on satisficing rather than optimiz-
ing behavior, they form the workforce in both regions and buy consumption goods.
Similar to Lengnick (2013) and Rengs and Scholz-Wäckerle (2019), each household
keeps a shortlist of preferred consumption good firms that they are willing to buy
from at a given point in time. At the start of the simulation, the lists contain n ran-
domly selected consumption good firms, where n = α1. Throughout the simulation,
the households update this list, based on their experiences with the firms as well as
the prices. Each month, with a 25% chance, each household adapts its list in a two-
step process. First, they pick one of the vendors on their list and compare its prices
to a randomly chosen consumption good firm. If the price of the randomly chosen
one is lower than that from the firm on the list, the household replaces the old firm
with the new one with a 25% chance. In the second step, households may replace
Fig. 1 Basic flow-chart structure of the model
130 L. Gerdes et al.
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1 3
firms that did not satisfy their needs, due to limited inventories. With a probability
of 25%, the household replaces the firm with a randomly chosen new one.
Each round, households plan their consumption based on their available budget
(mh, t, where h denotes the household and t the time), which consists of the monthly
income (inh, t) as well as a share of the household’s savings (sh, t) (see Appendix 4 for
equations). The desired consumption (
cw
h,t
) in units of goods is based on the budget,
as well as on the mean price of the consumption good firms on the shortlist of pre-
ferred vendors (
ppref
i,t
)
:
The purchase of goods is organized in two rounds, to ensure that it is possible to
fulfil the minimum consumption need of goods for all households, in case of limited
stocks of the firms. In the first round, they only buy a part of the desired amount, and
in the second round the remainder, as far as possible. If during any step of the pur-
chase-process the consumption good firm cannot provide the desired goods to the
household, the household makes a note regarding the failure of the firm to satisfy its
demand and moves to another firm on the list in order to ask for the missing amount
of goods. In both rounds, the households repeat the process with each firm on their
list, or until they are satisfied.
3.2 Firms
There are three different types of firms: consumption good–, capital good–, and
resource production firms (mines). All firms follow similar behavioral rules, how-
ever, there are significant sector-related specificities, discussed in detail in the
following.
3.2.1
Consumption good firms
Consumption good firms (called “consumption firms” in the following) form the
largest part of the economy and produce the goods which the households are buying.
At the start of the simulation, each consumption firm in the same region is identical:
they have the same number of workers, pay the same wage and sell their goods for
the same price. At that time, the only difference between the consumption firms in
the Global North and the Global South is the wage. In the Global South, wages are
25% lower than in the North, loosely following existing wage differences (OECD
2020). All consumption firms are assigned a physical capital stock (machine capaci-
ties
(
xc
i,t
)
, where i denotes the firm and t the time), matching the initial labor (
x
l
i,t
,
i.e. the number of workers). The consumption firms adapt production, pricing and
wages following a short-run strategy, based on changes of the individual demand for
their goods. Nevertheless, they only adapt their plans slowly. They plan the
(1)
c
w
h,t=
m
h,t
ppref
i,t
131Labor and environment in global value chains: an evolutionary…
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1 3
production
(
qp
i,t
)
based on the production from the last period
(
qp
i,t1
)
and the
amount of unsold goods left
(
qps
i,t1
)
, which are reduced each month by a sector spe-
cific depreciation rate
(
𝛿
cons
1)
, and on excess demand
qex
i,t
which could not be satis-
fied, to meet changes in demand and adapt the production accordingly.
This leads to the following production plan:
Firms define excess demand each round anew. As applied by Rengs and
Scholz-Wäckerle (2019), following ideas from Godley and Lavoie (2006), con-
sumption firms want the inventory after sales
(
qps
i,t1
)
, i.e. the unsold goods, to
stay at an optimal level
(
qopt
i,t
)
. The optimal level is proportional to the previously
produced goods
(
qp
i,t1
)
by the factor α2:
If the current level of unsold goods is too low, meaning sales were higher than
expected, firms plan to increase production. Vice versa, they plan to decrease pro-
duction if the level is too high. To adjust the production plan, they define
qex
i,t
pro-
portional to the optimal reserve by the parameter α4. Moreover, they adapt the
price of the good (pi, t) by a fraction (α3) of region (R) specific maximum amount
(
pmch
R
) (for details see Appendix 4, Eqs.3349).
Production Firms produce using a transformative production function in which the output
(qi, t) depends on the machine capacities
(
xc
i,t
)
and labor
(
xl
i,t
)
available, as well as a capital
intensity coefficient (
𝛼
cons
5
)
and a constant production-technology coefficient
(
𝛼
cons
6)
:
In advance, the firms can change the production inputs (
xc
i,t,xl
i,t
)
to meet the
planned production (
q
p
i,t
)
. Consumption firms in the South may adapt labor every
month, in the North, due to better labor protection, only every two months. Machine
capacities are adapted every month in both regions.
If the available labor is not sufficient for the intended production plan, the con-
sumption firm searches for possible workers (unemployed households) in its region
and, if available, hires as many as needed. However, a firm will not increase its labor
to more than a7 times its previous labor force in one month.
If its production plan would require hiring more than available or the previously
mentioned limit allows, then the highest possible amount is hired and the planned
production is corrected downwards to the new capacities. If a firm has too much
(2)
q
p
i
,
t
=qp
i
,
t
1+qex
i
,
t
qps
i
,
t1
(3)
q
opt
i,t=
q
p
i,t1𝛼2
1+𝛼
2
(4)
q
i,t=𝛼cons
6min
(
xc
i,t,𝛼cons
5xl
i,t
)
132 L. Gerdes et al.
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1 3
labor available, it fires the surplus. Each round, however, only a limited number of
workers can be fired (see Appendix 4, Eqs.5056).
After adjusting labor capacity, machine capacity is addressed. Monthly depre-
ciation reduces machine capacity as well as the value of all firms’ capital. If, after
depreciation, a firm does not have enough machine capacity available to produce the
planned amount, it plans to acquire more, a reduction however, is not possible.
Each consumption firm has a shortlist of α10 capital firms, from which it can
buy machine capacities. This list is updated every three months, following the
same updating rules as the households’ shortlist of consumption good firms (see
sub-section3.1).
In order to buy machines, a consumption firm calculates the required funds and
seeks to acquire a loan if it does not have the necessary means available. The bank in
the respective region of the consumption firm grants the loan if the expected profit
rate (
re
i,t
) of the respective firm is larger than the interest rate of the bank (ir), factor-
ing in a credit-lenience (ν1) parameter, which is the same for every bank (see Appen-
dix 4, Eqs.5760).
If the loan is granted, the firm continues with the purchase of the additional
machines. If the loan is not granted, the firm reduces the number of machines it
wants to buy to the maximum amount possible given its remaining funds. Then, the
consumption firm requests a fraction (xc/α10) of the amount determined in the previ-
ous step from each capital firm on its list, which in sum matches the total number
of aspired machines. If the capital firms have the requested machines in stock, the
purchase is completed. If a capital firm does not have sufficient machines in stock,
the maximum amount available is purchased, the costs are adjusted accordingly and
the capital firm is marked as unsatisfying in the consumer firm’s short list. If it was
not possible to buy all requested machinery, the buying process is repeated with
the missing amount, only considering capital firms from the list which still have
machines in stock, until at least 95% of the needed machines are purchased, or no
capital firm on the list can provide any further machines.
After the number of machines as well as labor was adapted, if necessary, the con-
sumption firms produce consumption goods
(
qp
i,t
)
according to their capabilities (see
production function, Eq. (4) above) and add the produced amount to their inventory
(
qps
i,t
)
:
If a consumption firm did not produce anything for twelve consecutive months,
it is considered bankrupt and is dissolved, thus releasing any potentially remaining
workers.
3.2.2 Capital firms
Capital firms are only located in the Global North and produce machines, which are
sold to all other firms in both regions (consumption good firms, other capital firms
(5)
qps
i
,
t
=q
ps
i,t1
+q
p
i
,
t
133Labor and environment in global value chains: an evolutionary…
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1 3
and mines). Wages paid by this firm type have the same starting value as consump-
tion firms in the Global North. The production procedures are very similar to the
ones of the consumption good sector. Setting the production plans and prices, as
well as machine purchase and labor adaptation, follow the same rules, albeit using
sector specific variables (see Appendix 4, Eqs.6188). Labor adaptation is carried
out every second month. In addition to labor and machines, capital firms need
resources as an input, which are consumed during the production. These are bought
from mines in the Global South, in a process very similar to the purchase of
machines (see Appendix 4, Eq.89). The capital firms thus apply the following pro-
duction function
(
qcp
i,t
)
, adding the produced goods to their inventory
(
qcps
i,t
)
, while
reducing the stock of resources (
xr
i,t
):
3.3 Mines
In the resource production sector, mines extract resources in the Global South and
export them to the capital firms in the Global North. They follow the same logic
as the consumption firms regarding the production planning process as well as
in machine purchases (see Appendix 4 for equations), but pay much lower wages.
Labor, however, is a special case. Mines cause local pollution, which has a negative
effect on the workers. Depending of the level of the pollution in a specific mine, the
productivity of the workers employed there deteriorates each period that the worker is
employed there. Local pollution is described in more detail in sub-section3.5. Thus,
labor input is defined by the productivity of mines’ workers (
x
l_prod
i,t
), which is the
sum of the individual productivity level of each worker of the mine (prodh, i, t with h
indicating the worker,i the mine it is employed at and tthe respective point in time):
For a detailed description of the hiring process, which takes the productivity into
account, see Appendix 4, Eqs. 107113. After adapting machine capacities and
labor, mines produce, which means they extract the resources and add them to their
inventory (
qmps
i,t
).
(6)
qc
p
i,t
=𝛼cap
6min
(
xc
i,t,𝛼cap
5xl
i,t,𝛼12xr
i,t
)
(7)
qcps
i,t
=qc
ps
i,t1
+qc
p
i,t
(8)
xl_prod
i
,
t
=
prodh,i,t
(9)
qm
p
i,t
=𝛼mine
6min
(
xc
i,t,𝛼mine
5xl_prod
i,t
)
(10)
qmps
i,t
=qm
ps
i,t1
+qm
p
i,t
134 L. Gerdes et al.
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1 3
3.4 Wages, transfers, loans & taxes
At the end of each month, after all households consumed and all firms produced goods,
wages are paid by all firms (consumption firms, capital firms and mines) to their work-
ers. Within each region’s sector, wages start at the same level at the start of the simula-
tion. They are adjusted once a year by each firm, though not by all firms in the same
month, but rather distributed over the year to avoid the introduction of an artificial sea-
sonal effect. Wages are downward rigid, but if a firm has positive profits, it increases
wages, by a factor that depends on the relative increase of the firm’s good price over the
last 12months and a sector-specific maximum adaptation rate (see Appendix 4). Wages
are firm-specific, but all workers of the same firm receive the same wage, except in
mines. Mines make the wage dependent on the productivity of the individual workers,
who receive a share of the base-level equivalent to their productivity level (see Appen-
dix 4, Eq.118). Unemployed households receive unemployment benefits from the gov-
ernment of their region. The transfers amount to 80% of the mean wage in the region.
After paying wages, all firms have to pay back part of their loans, the amount depends
on the repayment rate of their bank (rr). At the end of the year, all firms calculate their
profit and pay corporate taxes to their government, based on the tax rate (trg).
Half of the profits of all firms are distributed to the households of the region the
firm is situated in, as a proxy for households holding shares of the firms. Households
receive a share of the distributed profits relative to their own wealth (savings), which
is added to their savings. The R&D-funds’ (introduced in sub-section3.7.) gains are
also distributed in the same manner among households of the respective region.
3.5 Local pollution atmines
During the production process, mines cause local pollution (poli, t), which affects
the health of the local workers, depicted as an effect on their productivity. At the
start of each simulation, each agent’s productivity is initialized as prodh, t = 1, which
decreases if an agent is employed at a mine. The scope of the pollution depends on
the production volume (
qmp
i,t
) and a mine specific pollution rate (γi, t):
The negative effect on the productivity of the workers (prodh, i, t) depends on the
mine specific pollution rate as well as a health-impact factor (α13), but the productiv-
ity can never fall below
1
3
:
Agents in the Global South are replaced after 30years by a new agent with equal
properties (such as employer and savings) but with unreduced productivity. This
assumption, serving as a crude proxy for retirement, allows exploring experiments
where the population’s productivity might partially or fully recover.
(11)
pol
i
,
t
=qm
p
i,t
𝛾
i
,
t
(12)
prodh,i,t=max
(1
3
,prodh,i,t1𝛾i,t𝛼13
)
135Labor and environment in global value chains: an evolutionary…
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1 3
3.6 Global emissions
Global emissions are caused only by the capital firms in the North as a byproduct
of the production process. The amount emitted per capital firm (ei, t) depends on the
amount of goods produced (
qcp
i,t
), the firm’s emission rate (βi, t) and an emission fac-
tor (α14):
The total global emission stock (
eglobal
t
) is the sum of all previous global emis-
sions and is increased by the monthly global emissions (∑ei, t). We assume that the
stock decreases monthly by a fixed amount, which is a fraction (α24) of the emis-
sion base level at the start of the simulation (
eglobal
t=0
). This is a crude proxy for natu-
ral carbon binding capabilities of the planet, which we assume to be constant. This
also allows us to explore rather utopian experiments with outcomes where the total
global emission stock may even start to gradually reduce in the medium run. The
reduction however is set to a small level in order not to be overestimated.
Increasing levels of global emissions increase the frequency of natural disas-
ters, which may damage machines each month. The damage is randomly distrib-
uted between all firms in both regions, caused by small individual disasters (“hits”)
affecting firms, based on the following procedure:
1. As a crude proxy of the damage caused by each hit (dt) we calculate the floating
average of total machine capacity in the whole system during the past 12months
multiplied by a damage-impact factor (α15):
2. The number of hits per month (ht) increases non-linearly with an increasing global
emission stock and thus depends on the current stock of global emissions
(
e
global
t )
in relation to the base level (
eglobal
t=0
) as well as a scenario-specific acceleration rate
(α16). In the policy experiments (see section4), three different acceleration rates
are tested, to account for the huge uncertainties in climate change acceleration.
3. These individual disasters are randomly hit firms (consumption firms, capital
firms and mines). Firms may be hit more than once per month, causing damages
to accumulate (dt). The hit firms lose machine capacity equivalent to the damage,
and their capital value (
𝜔c
i,t
) decreases accordingly:
(13)
e
i,t=qc
p
i,t
𝛽i,t𝛼
14
(14)
e
global
t=
(
eglobal
t1
(
eglobal
t=0𝛼24
))
+
ei,
t
(15)
d
t=
(
1
12
t
t=(t12)
xc
t
)
𝛼
15
(16)
h
t=
(
eglobal
t
eglobal
t=0)𝛼16
(17)
xc
i,t=xc
i,t1d
t
(18)
𝜔
c
i,t=𝜔c
i,t
(
1
(
dt
xc
i
,
t
1
))
136 L. Gerdes et al.
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1 3
Firms in the South are indirectly affected more, since they have to replace the lost
machine-capacities at the same price as firms in the North. However, price levels are
much lower in the South, which leads to higher relative costs. This reflects tenden-
cies that countries of the Global South tend to be affected stronger by climate change
induced natural disasters, taking into account economic, social and human damage
(Dinar etal. 2006; Eckstein etal. 2019), even without assuming that the South experi-
ences more disasters.
3.7 Innovation process
To address emissions and local pollution, mines and capital firms can invest in inno-
vation. Mines may purchase social-ecological and technical means reducing the local
mine specific pollution rate (γi, t). Capital firms may purchase socio-technical upgrades
reducing the firm specific emission rate (βi, t). In the following, the innovation process
of capital firms is elaborated, and the difference to the situation of mines is discussed
towards the end.
At the start of the simulation, each capital firm is initialized with a planned innovation
strategy (
ip
i,t
), which is randomly drawn from list of options. Each strategy on that list (l1 =
{0; 0.2; 0.4; 0.6; 0.8; 1}) represents a differently ambitious plan to upgrade a fraction of the
machines, where 0 represents no upgrade and 1 a complete upgrade. We assume that the
firms have no intrinsic motivation to reduce emissions, which represents at least a worst-
case scenario of the economy. Thus, we assume that capital firms observe the competition
to assess the consequences of different innovation strategies, by maintaining an initially ran-
domly filled short list of n (n = α17) other capital firms. Firms evaluate the consequences of
competitor’s strategies based on the profit rate of those firms. Each year, before investing
into innovation, the firm with the highest profit rate on that list is selected. In a second step,
the realized strategy of this firm is compared with the own strategy realized last year. If the
strategy of the competitor was to upgrade a higher share of the machines than the firm itself
did, then the firm will try to upgrade a higher share than the year before. Strategies are only
shifted by one share level, following the innovation strategy list (e.g., if the own strategy was
0.4, and the strategy from the best firm was 0.8, the own strategy would be increased by one
step to 0.6). Respectively, the own strategy is reduced, if the strategy of the best firm was to
upgrade a smaller share than the own. In case the firm itself had the highest profit rate on the
list, it randomly adapts the strategy with a one-in-three chance.
After deciding which innovation strategy the firm would like to choose for the next
year, it evaluates whether it has the required funds for the intended upgrades. The basic
cost of the upgrade (
cubase
t
) depend on the current mean machine capacity price (
pct
)
and the cost upgrade parameter
𝛼cap
18
:
The firm first evaluates the total cost (
cueval
i,t
) for the firm’s planned level of upgrade,
which are based on
cubase
t
, multiplied by the number of machines owned, times the
share of the machines to be upgraded, i.e., the planned innovation strategy (
ip
i,t
).
(19)
cubase
t
=pc
t
𝛼
cap
18
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1 3
During the policy experiments, capital firms may receive subsidies
(subi, t), the details of this process are described in the next section. In the case a capi-
tal firm received subsidies, it spends as much of them as possible. This means that if
cueval
i,t
<subi,
t
, the firm checks if it would be able to finance the costs of the next higher
strategy from the list with the subsidy alone. It then repeats this process until the high-
est possible strategy, which can be financed by the subsidy alone, is found and sets its
effective strategy (
ie
i,t
), which is the one that is actually implemented, to this value.
If on the other hand a firm does not have enough funds (own funds plus eventual
subsidies) to finance the cost of the originally planned upgrade (
cueval
i,t
), it reduces
the effective strategy by one step and calculates the cost of the adjusted plan. This
process is repeated until the cost of the reduced plan are affordable, or the effective
strategy is reduced to doing no upgrades at all (
ie
i,t
=0
).
The firm then calculates cui, t analogous to
cueval
i,t
, substituting the planned strat-
egy (
ip
i,t
) with the finally chosen strategy (
ie
i,t
). If an upgrade is purchased, the firm’s
current emission coefficient is reduced by the innovation improvement parameter
α19, weighted with the effective strategy, implying future upgrades will be less effec-
tive and that production will never be completely emission free.
As a modeling simplification, the funds required for the upgrades, including
eventual subsidies, are transferred to a regional R&D-fund, which itself redistributes
these gains to the regional population analogous to the distribution of firms’ profits.
Mines invest in innovation based on the same rules and thus keep a list of other
mines which are monitored and eventually imitated. The upgrades that mines pur-
chase improve the pollution rate (γi, t), which affects local pollution instead of emis-
sions (see Appendix 4, Eqs.119121). We assume the cost of reducing local pol-
lution, but especially in partially reducing the negative ramifications for the mine
workers, to be less expensive for mines, thus for mines the cost upgrade parameter
𝛼mine
18
replaces
𝛼cap
18
in Eq.20. Furthermore, mines can receive subsidies, just like cap-
ital firms. The price for the upgrades is added to an R&D fund in the Global South.
Both mines and capital firms may update the list of monitored firms that is used during
the innovation process every three months in two steps. First, one firm that is not active any
more, i.e., that has no workers left, can be replaced by a randomly selected other firm of
the same type. Second, with a 25% chance, one randomly selected firm from the list can be
replaced with a randomly selected one of the same type that is currently not part of the list,
but only if this new firm has a higher profit rate than the old one.
3.8 Policy experiments
We test different policy scenarios aimed at mitigating the social and ecological
costs of resource extraction and global production chains. The policies are inspired
by the proposal made by Kapeller etal. (2016). A global institution, the civilized
(20)
cueval
i,t
=cu
base
t
x
c
i,t
i
p
i,t
(21)
𝛽i
,
t=
𝛽
i
,
t
1
𝛽
i
,
t
1i
e
i,t
𝛼
19
138 L. Gerdes et al.
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1 3
market institution (CMI), is implemented, which sanctions capital firms for produc-
ing global emissions and mines for polluting the local environment and risking the
health of their workers. Two main mechanisms are evaluated, the first is the sanc-
tioning mechanism, and the second one is a subsidy scheme, where the collected
sanctions are redistributed to the capital firms and mines as subsidies tied to the pur-
pose of investing into innovation. Both mechanisms are outlined in the following.
3.8.1 Sanction mechanism
The CMI sets a base fine for each region (
f
base
R,t
), based on the price of the firms’
products. Thus, in the North, the base fine equals the current mean capital price
(
pct
) times the sanction-level parameter α20, and in the South it equals the current
mean resource price (
pmt
) times α20:
All capital firms and mines respectively are fined individually (fi, t) depending on
the emissions or pollution they caused in the last period:
The CMI collects all fines in separate accounts for each region (fundR, t).
3.8.2 Subsidy scheme
We test four subsidy schemes, which differ in the amount of money that is redistributed
to the capital firms and mines. Nevertheless, subsidies are always linked to the condition
to exclusively use them for innovation upgrades. As elaborated in section3.7, if a capital
firm or mine receives a subsidy, it will use it for innovation only and will spend as much
of it as possible. This means that it chooses the most effective innovation strategy that
can be financed with the subsidies, even if the originally intended innovation strategy
would have been less effective in reducing emissions or pollution respectively.
The four schemes:
1. No subsidies: The CMI does not pay subsidies. The collected fines are distributed
to the households of the respective regions, in the same manner as firms redis-
tribute profits.
2. Subsidies: The fines collected in the Global North (fundR = N, t) are distributed
among the capital firms, and respectively the fundR = S, t is distributed among all
mines. They receive a share of the subsidies (subi, t) in relation to the firm’s rela-
(22)
f
base
R=N,t
=pc
t
𝛼
20
(23)
f
base
R=S
,
t
=pmt𝛼
20
(24)
Capital f ir ms
fi,t=f
base
R=N,t
ei,
t
(25)
Mines
f
i
,
t
=f
base
R=S,t
pol
i
,
t
139Labor and environment in global value chains: an evolutionary…
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1 3
tive size, i.e. their machine capacity in relation to the total machine capacity of
the whole sector:
3. North-South transfer: A share (α21) of the money collected in the Global North is
transferred to the Southern fund, with α21 = 0.25. The scheme aims to mitigate the
effects of unequal exchange by making the Global North responsible and sensitive
to protecting labor and the environment in the South.
These adjusted funds are then distributed to capital firms and mines as subsidies,
following Eqs. (26) and (27).
4. State grant: The funds of the CMI are strongly increased by the regional govern-
ments by the grant-parameter α22. This scheme aims to explore effects of a huge
increase of previously set subsidies through public investment, though unlikely
politically achievable. Again, the adjusted funds are distributed to capital firms
and mines following Eqs.26 and 27.
4 Computational simulation: Policies, settings, results
anddiscussion
The following section presents and discusses results of simulation experiments con-
ducted with the following numbers of agents per region: 10000 households, 250
consumption good firms, one bank and one government. Furthermore, the Global
North features 50 capital firms and the Global South 50 mines. Please see Appendix
2 for other simulation settings and parameters and Appendix 3 for a separate simu-
lation experiment that is varying different state grant parameter settings (α22). The
simulated time horizon extends over 480 artificial months, i.e. 40years.
Following Maechler and Graz (2020), there are significant limits for the substi-
tution of risk for uncertainty, especially with regard to climate change and natural
disasters. There is a crucial quest of introducing new institutions capable to “absorb
(26)
Capital f ir ms
subi,t=
xc
i,t
xc_cap
i,t
fundR=N,
t
(27)
Mines
subi,t=
xc
i,t
xc_mine
i,t
fundR=S,
t
(28)
fund
R=N,t
=fundR
=
N,t
(
1𝛼21
)
(29)
fund
R=S,t
=fund
R=S
,
t
+fund
R=S
,
t
𝛼
21
(30)
fund
��
R=N,t
=fund
R=N
,
t(
1+𝛼22
)
(31)
fund
��
R=S,t
=fund
R=S
,
t(
1+𝛼22
)
140 L. Gerdes et al.
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1 3
uncertainty into manageable risk” (ibid.). This quest is addressed along the global
civilized market institution (CMI) with the direct aim to protect labor and environ-
ment in value chains. We test the evolutionary policy proposal previously discussed
via a combination of different sanction and subsidy schemes, resulting in combined
policy packages as given in Table1 (see section3.8. for details). Different accelera-
tion rates in the increase and impact of natural disasters are assumed and we pre-
sent three disaster scenarios on behalf of this rate (via variation of α16, see 3.6 and
Appendix 2) in order to express different ordinal levels of climate change’s endog-
enously changing impact. Scenario (A) represents a relatively slow accelerating rate
of natural disasters, (B) a faster rate and (C) an extreme case with a very high accel-
eration of disasters.
We will thus present the results of some focal measures of combining the 9 pol-
icy packages with the three disaster scenarios yielding 27 experiments, with each
experiment being repeated 100 times to account for the stochasticity of the model.
As shown e.g. in Rengs etal. (2020), models of this scope inevitably lead to initial
adaption processes of the self-organizing markets due to random initial conditions,
which do not reflect the later systemic behavior. To be able to more clearly discern
these from the results of the experiments proper, scenarios and policy packages are
effective only after ten years of simulation. In the following, we will thus exclude
this burn-in phase of the first ten years and only show the thirty years of the experi-
ment proper. Time series show annual aggregates of the respective average values
across the 100 repetitions, with the shaded area around these indicating their dis-
tribution from the 10th to the 90th percentile (extreme outliers are cut out thereby).
Discussion of simulation results follows an evolutionary comparative approach with
a focus on ordinal categories, different levels of change and complex path-dependent
dynamics.
4.1 Technical andsocial‑ecological innovation mechanisms
Capital firms’ innovation activities, addressed here, involve technical improvements
in production facilities, equipment and machinery in order to reduce carbon emis-
sions directly during production. Technological advances are not to be considered
as radical innovations somehow resolving carbon output out of the blue, but rather
piecemeal changes of recombining existing instrumental means to pragmatically
reduce emissions wherever possible. Capital firms innovate within a spectrum of
potential upgrade levels along the mechanisms explained in 3.7.
Table 1 Policy packages
No subsidies Subsidies North-South transfer State-grant
No sanction none
Low sanction low low+sub low+transfer_s low+gov_sub
High sanction high high+sub high+transfer high+gov_sub
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1 3
For capital firms, we may identify three qualitatively distinct innovation trajec-
tories dependent on combinations of sanctions and subsidies, see Fig.2. The low-
est trajectory represents the cases “none”, “low” and “high” which converge to 16%
after 30years for scenarios A and B, while scenario C first shows a similar behav-
ior that changes after 20years.2 This slight (eventual) upswing of effective innova-
tion strategies is a direct result of capital destruction induced by natural disasters,
strongly reducing upgrade costs sequentially. As we show in sub-section4.3, it is in
fact the result of the economy in both regions breaking down, due to lost machine
capacities after climate-induced disasters. Thereby, firms may realize their innova-
tion intentions to a higher degree, although without any effect anymore. In general,
the figure shows that policy packages without subsidies do not perform any better
than the baseline case (“none”) in our model. The reason is that even very high fines
do not have a huge effect on the overall profitability of the firms as all firms are sub-
ject to at least some degree of fines. Such policies would thus fail in light of simi-
larly structured firms that follow the most profitable competitor where innovation is
incremental instead of disruptive.
The second trajectory we are observing encompasses a greater set of mechanisms:
“low+sub”, “low+gov_sub”, “low+transfer_s”, “high+sub” and “high+transfer_s”.
Capital firms converge here on an innovation path around 33% in all scenarios.
Eventually, only the additional state grant (on top of the high-sanction financed one
– “high+gov_sub”) may shift capital firms onto a higher innovation path above 50%
of the upgrade level spectrum. The state grant policy package represents the highest
upgrade potential in comparison to all other policy mixes. The higher the subsidies
are, the more will be invested in upgrades in the first response. More upgrades mean
less emissions, which will reduce future sanctions. Fewer sanctions will result in
less subsidies. The higher these initially are, the faster the reduction of emissions,
sanctions and subsidies will take place. However, in total the different disaster sce-
narios (A,B,C) do not alter the shape of the innovation trajectories significantly.
Fig. 2 Mean innovation strategies of capital firms (unweighted)
2 The thick line in the Figures represents aggregate averages and the shaded area around it,the 10th and
the 90th percentile.
142 L. Gerdes et al.
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1 3
The situation in mines is different, since the innovation activities target pro-
tection of labor and local environment instead of reducing emissions. Figure3
shows innovation trajectories for mines, which are less differentiated than those
of capital firms. We observe four different innovation pathways, the lowest again
containing “none”, “low” and “high” converging around 20% for scenarios A and
B; and 25% for C after 10years. At about 35%, policies “low+sub”, “low+gov_
sub”, “low+transfer_s” and “high+sub” can be identified.
High subsidies in conjunction with the transfer mechanism from capital firms
to mines (high+transfer_s) enables the latter to upgrade 50% of their machines
after 30years. The best performing policy is again given by the additional state
grant. The upgrade potential of mines is generally higher than that of capital
firms, although as mentioned before, the associated costs and meaning of the
upgrades are different. Policies are not significantly affected by the different dis-
aster scenarios (A,B,C) as in the previous case.
Capital firms constantly improve their technology (β), which consequently devel-
ops along the three distinct paths identified before. As there is a technological limit
to the reduction of emissions, the trajectory would eventually flatten but never reach
zero. As can be seen in Fig.4, this not only leads to less carbon-intensive means of
Fig. 3 Mean innovation strategies of mines (unweighted)
Fig. 4 Mean emission rate (βi, t) of capital firms (unweighted)
143Labor and environment in global value chains: an evolutionary…
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1 3
production after 30years, but moreover, especially in the case of high subsidies that
are increased by state grants (high+gov_sub), to reaching a better technology much
earlier. As we will show in section4.2, this may lead to qualitatively different trajec-
tories with regard to global emission stock levels and thus disasters.
Similarly, regarding mines, we may observe the evolutionary policy effects by
plotting mean γ, which indicates the degree of technical and social-ecological means
to protect labor and the local environment (see Fig.5). Again, these follow four dis-
tinct pathways, where subsidies increased by state grants thus lead to the most fitted
means of social-ecological protection, followed by transfers from capital firms on
top of high subsidies. While only providing subsidies leads to a less strong, but still
significantly better effect than the baseline case and policies not providing subsidies.
4.2 Social‑ecological implications
In the previous section we discussed the implications of the policy packages on
firms’ innovative behavior, whereas we turn to their effects on labor and the environ-
ment now. Figure6 focuses on the mitigation of local pollution produced by mines.
Fig. 5 Mean local pollution rate (γi, t) of mines (unweighted)
Fig. 6 Annual local pollution of all mines, in % from the starting value at t = 0
144 L. Gerdes et al.
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1 3
Among the four distinct innovation trajectories for mines, “high+gov_sub”
performs best in terms of mitigation, indicating that a high sanction combined
with the state grant would be most effective, if politically achievable and via-
ble. Nevertheless, additional transfers from capital firms to mines represent most
probably the most convincing policy option (second-best outcome), since trans-
fers are completely recycled from funds gained from raised sanctions. Disas-
ter scenario C with the highest acceleration of natural disasters shows a drastic
reduction of annual local pollution in the low sanction mechanisms. As already
indicated, this is not a positive result of the policies, but the consequence of a
total breakdown of the economy. When mines reduce or stop production due to a
drop in demand, pollution will of course stop, too.
Eventually, local pollution affects workers’ health in the Global South, thereby
also reducing individual productivity of workers employed by mines as shown
in Fig. 7. The combination of the improvement of mines’ labor and environ-
ment protection and the eventual retirement, i.e. replacement of workers in the
South, in the long run leads to improvement of overall health. Again, this happens
much earlier and the reaction is stronger with the aforementioned policy pack-
ages, as the initially worst behavior of the lowest trajectory causes the sharpest
turnaround in scenario C, though it comes at the cost of an economic breakdown,
which would very likely affect health even more, but is not accounted for in this
model.
Figure8 shows annual emission flows induced by capital firms, where again
the three distinct trajectories are observable. The highest subsidies accompanied
by state grants reduce annual emissions to half of the initial level over 30years.
Again, the sharp drop of emissions in the otherwise worst policies in scenario C
is a result of the economies’ breakdown.
As emissions accumulate over the years, reducing emissions obviously only
reduces the inflow, which is only partially effective when assuming a fixed outflow
(e.g. natural binding of emissions). As indicated by Fig.9, for the policy packages
without subsidies, the inflow hardly reduces, with the exception of scenario C,
where the global emission stock even reaches critical levels, which eventually results
in a sharp turn-around due to the breakdown. The other policy packages lead to an
Fig. 7 Mean agent productivity of the whole agent population of the Global South
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1 3
eventual flattening of the curve, which happens at a later point in time for the more
severe disaster scenarios. The only severely different trajectory is the policy which
includes additional state grants, which performs significantly better than all other
policies in all disaster scenarios, because the technological improvement happens
much earlier, before the acceleration of disasters really kicks in.
This added value becomes even more visible when looking into the develop-
ment of destroyed machine capacities on behalf of climate-induced disasters. In
our worst disaster scenario (C) the damage reaches 5–7% of total machine capac-
ity destroyed each year (aggregated for both regions) for all policies except the
one with high subsidies and state grants. Only the latter policy seems to be effec-
tive enough to avoid emissions early enough and thus mitigating the total dam-
age on machine capacities in the long run. This is also the case for the average
disaster scenario (B), but much less expressive. Otherwise, in the most optimistic
scenario A, there is not much difference in terms of the damage caused.
We can now see the reason for the breakdown (respectively resulting into
political economic crisis) in scenario C more clearly. The increasing levels of
emissions cause a non-linear increase in natural disasters, which accelerates even
more as these cause the destruction of machines. This requires the production
Fig. 8 Global emission flow caused by capital-firms, in % of the starting value at t = 1
Fig. 9 Stock of accumulated global emissions, in % of the starting value at t = 1
146 L. Gerdes et al.
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1 3
of even more machines to replace them, which is a self-reinforcing process
(vicious loop), where the additional emissions caused by the increase in produc-
tion exceed the effect of reducing emissions by innovation.3 In the case of those
policies without subsidies, at some point so much production capacity of capital
firms or mines is destroyed, that they cannot replace machines quickly enough.
This at some points leads to a shortage of production capacity at mines and capi-
tal firms, which self-reinforces and quickly leads to a complete breakdown of the
economy. But this development will not reduce disaster frequency very quickly,
even if the inflow of emissions would reduce to zero in the case of a full stop of
the economy, because the stock of accumulated emissions has reached very high
levels. Further simulations with higher acceleration of natural disasters than sce-
nario C have shown, that even the medium trajectory policies, which do provide
subsidies, would get into such a vicious cycle in the same timeframe too, while
the policies with the worst trajectories would end up in a breakdown even sooner.
The much broader stochastic distribution bands around the badly performing tra-
jectories in scenarios C, in Fig.10 as well as the other figures, do not result from
qualitatively different outcomes for those experiments, but simply from a differ-
ent timing of the onset of the total breakdown. Thus, in the long run, all policies
that do not succeed in establishing a turn-around of the stock of aggregated emis-
sions soon enough, will end up in a breakdown.
4.3 Economic implications
As shown in the following, the presented policy packages themselves have hardly
any significant negative economic implications while still protecting labor and the
environment in global value chains. We start by looking a bit closer into some meso
level industrial sector variables.
The global value chain collapses only in scenario (C) if countermeasures are
not taken at sufficient magnitude. After 20–25years, the loss in machine capacities
Fig. 10 Relative destruction of machine capacity, in relation to total machine capacity
3 Compare Rengs et al. (2020) indicating the emergence of similar dynamics, though in a different
model.
147Labor and environment in global value chains: an evolutionary…
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1 3
(Fig.10) leads to a freefall of production in both sectors as shown in the previous
section. Figure11 shows sector exports from mines (South) to capital firms (North)
as well as from capital firms (North) to mines and consumption good firms (South).
If there are no policy measures taken at all, or just low measures (without addi-
tional transfers or state grants) the value chain cannot be stabilized. Capital firms
react to the disrupted value chain and the crisis-prone situation with an increase in
prices, as a direct response to damaged machine capacities (see Fig.12).
Overall, the capital and resource sectors have very different price levels,
reflecting the situation of unequal exchange in global value chains. Capital firms
have more market power and are able to extract higher surpluses from import-
ing resources at very low prices, which are only possible due to the lower wage
regime in the South in the first place.
The response of increased capital prices due to increased demand and at the
same time less available machines in scenario (C) is seconded by firing workers
in both intermediary sectors. Figure13 shows the development of their employ-
ment. Employment in the labor-intensive mining sector is higher when the cli-
mate-induced disasters destroy machines, as more resources are needed to build
more machines. Furthermore, policies which neglect to alleviate the negative
Fig. 11 Exports of capital-firms and mines, in % of capital-firm exports at t = 0
Fig. 12 Prices of goods sold by capital-firms and mines
148 L. Gerdes et al.
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1 3
health effects of working in a mine also result in higher employment in the min-
ing sector due to the loss of productivity. Of course, serious problems arise once
climate-induced disasters cause a breakdown of the economy, if not compen-
sated by high subsidies and state grants. With the exception of the loss of worker
Fig. 13 Employment in the capital-firms sector and mine sector, in % of the total population per region
Fig. 14 Unemployment in the Global North and Global South, in % of total population in each region
Fig. 15 Real GDP in the Global North and Global South, in % of total population in each region
149Labor and environment in global value chains: an evolutionary…
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1 3
productivity the same is true for capital firms, but as the capital good production
is much less labor intensive, it is much smaller in absolute terms.
But the effects are not only problematic for the intermediary sectors. Macroe-
conomic implications are given via the aggregated unemployment for both regions
(Fig.14) and the development of real GDP (Fig.15).
In general, unemployment is higher in the Global South, but stable. The evolu-
tionary policy mechanisms introduced obviously do not affect employment neg-
atively, even over a long simulation time horizon in the presented model. This is
crucial, since institutionalized policies are often regarded as too disruptive for the
dynamics of the circular flow. Even in scenario (C), with a high acceleration rate of
climate-induced disasters and heavy damage on machine capacities, the tested poli-
cies may keep employment stable in both regions, if high subsidies are operational-
ized, at best in combination with transfers from capital firms to mines or even with a
state grant of the institutionalized labor and environment protection fund.
Real GDP develops in a very stable way (Fig.15) and is thereby mirroring the
development of unemployment. GDP is far smaller in the South, but both regions’
real GDP is not affected by the tested policy packages. This notion implies that the
proposed policies may lead to a successful evolutionary adaptation of the whole sys-
tem in mitigating the uncertain damage done by local pollution on the one hand and
carbon-intensive production by capital firms on the other hand. Still, however robust
complex adaptive systems may be, they may still break apart abruptly in case of
maladaptation, in our case given by the non-recycled sanction policies in scenario
(C).
5 Concluding remarks
We present a multi-sector, multi-regional agent-based economic model contain-
ing a global value chain in this research article. The approach is novel and unique
since it addresses characteristics and adaptation problems of the fragmented world
economy via an artificial global value chain crossing two political economic
regions and containing three sectors: (1) mining in the Global South, (2) capital
good production in the Global North and (3) consumption good production and
retailing in both regions. The flow of the value chain starts with resource extrac-
tion of raw materials and primary input in mines of the Global South, which is
only bought by capital firms in the Global North. Mines adapt their production on
behalf of changes in the individual demand for resources by capital firms, which
themselves manufacture machinery for competitors as well as mines and consump-
tion good firms in both regions. The latter firms adapt their machine capacities and
labor input dependent on individual demand of domestic consumers in the North
and South. All three sectors are completely disaggregated and adapt in two macro-
economic systems.
The stated problem is of global evolutionary political economic relevance. Labor in the
mines of the South is exploited with bad working conditions and low wages, and environ-
mental damage is caused at the place of extraction, which permanently affects the produc-
tivity of workers. Cheap resources are exported to the North where profits can be made
150 L. Gerdes et al.
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1 3
with highly demanded complex machinery, manufactured with the low-cost primary
input. This aspect is referred to as unequal exchange in the literature and creates a double
burden for regions of the Global South. On the one hand, it makes those regions eco-
nomically dependent on the rules of free international trade, resulting into lower wages
and exploited labor, on the other hand, the local environment is heavily damaged due to
large amounts of solid waste and the use of toxic chemicals. This second burden threatens
not only the ecosystems in the local areas of the mines but also the health of the work-
force and the local population. Moreover, the production processes in capital firms are
emission-intense and therefore fuel anthropogenic climate change. Among other causes, it
contributes to higher frequencies of natural disasters on a worldwide scale, which destroy
physical capital worldwide and threaten human lives.
In our agent-based economic model, we test two different evolutionary political
economic policy mechanisms as a response to (i) the problem of local pollution of
mines and resulting impacts on workers’ health and (ii) the damage to firms’ produc-
tion capital induced by natural disasters. The policies are both operated by a global
“civilized market institution” that is charging fines and redistributing funds for inno-
vation activities addressing the raised problems in the North and South. A series
of policy packages is investigated in simulation experiments. The main outcome of
the study is that sanctions, regardless of their level, alone are not effective, neither
in countering unequal exchange, nor in avoiding damage caused by natural disas-
ters. Only in combination with subsidies for innovation activities, which are labor
protection and reduction of local pollution in mines as well as reduction of carbon-
emissions in capital good production, the value chain can become more sustainable
and just over the long run.
We investigate three policies where first, subsidies are financed only from raised
sanctions, second, subsidies are increased in the South via additional transfers from
Northern capital-firms to mines and third, subsidies are significantly increased with
a state grant. With the exception of the state grant scenario, all cases are cost-neutral
for public finances, since the subsidies are funded via the sanctions only. The first
evolutionary policy package already allows mitigating pollution and emissions at
such extent that even in an extreme disaster scenario, the value chain can be stabilized
and thus also both economies within the medium and long-run. The best performing
policy in terms of social-ecological implications is given by the additional state grant,
put on top of sanction-financed subsidies. Climate change induced damage as well as
local pollution can be decreased substantially. In the presented experiment, we chose
the state grant to considerably increase the funds available to the CMI to highlight
the theoretical potential. Nevertheless, the model yields comparable results for other
values of α22, as can be seen in the experiment presented in Appendix 3. Still, it is
important to highlight that this policy package may face problems with regard to
political achievability, since not all governments will be able to acquire a mandate
for such a significant fiscal expansion in that direction, or may intentionally refrain
from that option. Moreover, other unintended impacts of using public finance cannot
be analyzed with this model. In terms of effectiveness as well as political economic
implications, the policy which partially transfers subsidies to mines delivers the
most promising results. Results have shown that capital firms still invest in reducing
carbon emissions on the same level even with a somewhat lower subsidy. The reason
151Labor and environment in global value chains: an evolutionary…
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1 3
is that the magnitude of the transfers is low enough that the policy is not shifting
capital firms on a higher damage trajectory, but otherwise high enough to boost
labor protection and mitigation of local pollution in mines of the South, due to the
difference in the cost structure via the lower wage regime. Eventually this policy
delivers very good results in terms of health recovery, reduction of local pollution
flows and mitigation of climate change. However, the models’ firms feature simple
strategic behavior, driven by profit motives only, realized in the form of incremental
adaptations. As such, they have no intrinsic motivation to reduce emissions, local
pollution flows and to protect labor. The presented results thus do not rely on
optimistic assumptions of firms’ strategies. Thereby, the model represents a worst-
case mitigation mechanism of economies to cope with climate change.
We can highlight that the evolutionary policy packages – at best supported by addi-
tional transfers – don’t have any negative impact in terms of economic implications,
neither on micro, meso or macro level. We show that even with a rather minimalized
effort – concerning fiscal budget neutrality – an institutional approach to the problem
of unequal exchange may deliver very promising results in protecting labor and the
environment. It is indeed possible to stabilize the global value chain substantially on
behalf of sanction-financed subsidies for innovation activities in this direction. By
charging firms in the Global North, we recognize that many of the current global chal-
lenges are predominantly impacting countries of the Global South, while the Global
North profits from outsourcing pollutant industries and investing in complex and
highly profitable production processes in the North. Holding multinational corpora-
tions responsible for the damage they cause is an important contribution to sustainabil-
ity and equality in ecological as well as social terms. Binding due diligence rules, as
proposed by the European Parliament (2021) are promising developments.
Appendix1 – Timing ofEvents
One period in the simulation symbolizes one month, some events only take place
once per simulated year.
Monthly events:
Household consumption
Households decide about consumption expenditure
Households update shortlist of vendors (consumption good firms)
Households buy consumption goods
Consumption good production
Consumption good firms update the production plan and adjust prices
Consumption good firms hire or fire workers; every 2 months in the Global
North
Consumption good firms update shortlist of vendors (Capital firms), every
3months
Consumption good firms buy machine capacities
152 L. Gerdes et al.
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1 3
Consumption good firms produce consumption goods
Resource production
Mines update the production plan and adjust prices
Mines hire or fire workers
Mines update shortlist of vendors (Capital firms), every 3months
Mines update shortlist of tech-imitations (Mines), every 3months
Mines buy machine capacities
Mines extract resources
Local pollution caused by the production is calculated
Workers are affected by local pollution
Capital good production
Capital firms update the production plan and adjust prices
Capital firms hire or fire workers; every 2months
Capital firms update shortlist of vendors (Capital firms & Mines), every
3months
Capital firms update shortlist of tech-imitations (Capital firms), every 3months
Capital firms buy resources
Capital firms buy machine capacities
Capital firms produce capital goods
Emissions caused by the production are calculated
Wage payment
Consumption good firms, capital firms and mines pay wages
Governments adjust unemployment benefits
Governments pay unemployment benefits
Loan payment
Consumption good firms, capital firms and mines pay back loans
Biophysical events
Natural disasters: Global emissions randomly cause damage to the machine
capacities of some firms (consumption good firms, capital firms or mines)
Natural reduction of emissions
Annual events
Bankruptcy of consumption good firms
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1 3
Consumption good firms, capital firms and mines pay taxes to the governments
Policy experiments step I – CMI sanctions firms and pays subsidies
Policy experiments step II – capital firms and mines can buy upgrades
Consumption good firms, capital firms and mines adjust wages
Consumption good firms, capital firms and mines calculate profits
Appendix2 – Simulation parameters
The agent-based model is implemented in version 6.2 of NetLogo (Wilensky 1999),
a free and open source, widely used software development kit for agent-based mod-
elling.4 The simulation experiments were set up using NetLogo’s built-in Behavi-
orSpace experiment management engine to repeat experiments with different ran-
dom seeds. Aggregate time series data was generated directly by BehaviorSpace.
Data analysis and visualization are realized using the R language (with the ggplot2
package).
Simulation experiments, as described in Section4, are a combination of a pol-
icy package (none, low, low+sub, low+transfer_s, low+gov_sub, high, high+sub,
high+transfers_s, high+gov_sub) and a natural disaster scenario (A,B,C). Each
experiment was simulated 100 times with different seeds to account for different
realizations of random factors and thus render the obtained results more robust. Re-
runs of identical policy/disaster combinations generally varied only slightly due to
stochasticity, which underpins the high robustness of the results. Each of these runs
was simulated for 480 time steps, representing months, resulting in a simulated time
horizon of 40years.5
Appendix3 – Sensitivity ofthemodel tovariations ofthestate‑grant
parameter α22
To examine the impact of different levels of the state-grant parameter α22, we
conducted another simulation experiment. The parameters were the same as
those of the main experiment as shown in Appendix 2, with the one exception of
variating α22 while only taking the policy package with high sanctions and state
grants (high+gov_sub) into account. The results show that variation of the level
of the grant leads to comparable results as the presented experiment, where the
model reacts similar to the policy packages without state grants for low α22. In
the following, we present the results for the most important aggregate measures
shown in the main text, with dashed lines indicating the level of α22 that was
chosen in the main experiments.
4 The NetLogo source and documentation of the simulation is available under creative commons license
here:https:// www. comses. net/ codeb ases/ 8cd6f 0b3- acd2- 4bb6- 8b15- 3677e 752ef 4c/ relea ses/1. 0.0/
5 See Appendix 1 for a list showing the timing of monthly and annual simulation events.
154 L. Gerdes et al.
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1 3
Table 2 Parameter settings
Households
Length shortlist of households (α1) 7
Share of savings that are used for consumption each month (σ1) 0.0005
Firms (Cons cap mine)
Unsold stock depreciation rate for consumption good firms (
𝛿cons
1
) 0.3
Unsold stock depreciation rate for capital firms (
𝛿cap
1
). 0.06
Unsold stock depreciation rate for mines (
𝛿mine
1
)0.16
New price adoption probability (θ1) 1/3
Capital firm specific emission intensity, starting value (βi, t) 1
Mine specific local pollution rate, starting value (γi, t) 2
Production reserve stock rate (α2) 0.1
Maximum price adjustment (α3) 0.1
Excess demand rate (α4) 0.5
Capital intensity coefficient consumption good firms (
𝛼
cons
5
)
0.5
Capital intensity coefficient capital firms (
𝛼
cap
5
)
0.5
Capital intensity coefficient mines (
𝛼
mine
5
)
0.15
Production-technology coefficient (
𝛼
cons
6
)
1
Production-technology coefficient (
𝛼
cap
6
)
1
Production-technology coefficient (
𝛼
mine
6
)
1
Maximum labor increase (α7) 0.05
Stock adjustment indifference rate (α8) 0.25
Monthly Machine capacity & value depreciation rate (α9) 0.1 / 12
Number of capital firms on shortlist (α10) 3
Number of capital firms on shortlist (α11) 5
Resource intensity coefficient capital firms (α12) 1
Health-impact factor (α13) 0.005
Emission factor (α14) 1.5
Damage-impact factor (α15) 0.00001
Simulation-specific acceleration (α16) (for scenarios A,B,C) 1; 2.75; 5.5
Innovation-imitation network (α17) 5
Cost-upgrade parameter (
𝛼cap
18
)4
Cost-upgrade parameter (
𝛼mine
18
)0.4
Innovation improvement parameter (α19) 0.06
Sanction-level parameter (α20) (for scenarios no, low, high) 0; 0.1; 0.4
Transfer share (α21) 0.25
State grant parameter (α22) 4
Price adjustment parameter (
𝛼cons_north
23
)1.28
Price adjustment parameter (
𝛼cons_south
23
)1.26
Price adjustment parameter (
𝛼cap
23
)1.3
Price adjustment parameter (
𝛼mine
23
)1.1
Emission stock reduction parameter (α24) 0.02 / 12
155Labor and environment in global value chains: an evolutionary…
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1 3
Table 2 (continued)
Households
Government
Tax rate (trg) 0.15
Bank
Interest rate (ir) 0.07
Credit-lenience (ν1) -5
Repayment rate (rr) 0.05
Fig. 16 Mean agent productivity of the whole agent population of the Global South
Fig. 17 Stock of accumulated global emissions, in % of the starting value at t = 1
156 L. Gerdes et al.
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1 3
Fig. 18 Relative destruction of machine capacity, in relation to total machine capacity
Fig. 19 Employment in the capital-firms sector and mine sector, in % of the total population per region
Fig. 20 Real GDP in the Global North and Global South, in % of total population in each region
157Labor and environment in global value chains: an evolutionary…
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1 3
Even low levels of the state grant would enable a reversal of the negative health
effects on mine workers in the Global South, in every scenario (Fig.16). Figure17
shows that very high levels of the state grant might have the potential to even reach
the starting level of the accumulated global emissions within the simulated period in
most scenarios. Low levels of the state grant on the other hand would again perform
similar or slightly better than those in the medium level trajectories of the policy
packages shown in the main experiment (compare Fig.11). As shown in Fig. 18,
only a very low level of the state grant does not lead to a turnaround in the destruc-
tion of machine capacity, which might even lead to a collapse of the production sec-
tor in the most extreme scenario as described in the main body of the article (see
Fig.12).
As a result of the lower health and the consecutive lower productivity of mine
workers in the South, lower state grants result in higher employment in the min-
ing sector (Fig.19). As expected, the real GDP does not differ significantly for
different levels of the state grant (Fig.20).
Appendix4 – Formulas
Household budget calculation
Consumption good firms
Price setting andproduction plan
Consumption firm depreciation rate:
𝛿cons.
1
Inventory update:
Production plan:
Optimal reserve level proportional to the previously produced goods
(
qp
i,t1
)
by the
factor a2:
Sector specific maximum price change, based on the average price of all consump-
tion good firms within one region (R = {N; S}) after one year of the simulation:
(32)
m
h,t=inh,t+sh,t𝜎1+
((
1
sh,t
smax
h,t
)
sh,t𝜎1
)
(33)
qps
i,t1
=q
ps
i,t1(
1𝛿
cons
1)
(34)
q
p
i,t
=qp
i,t1
+qex
i,t
qps
i,t1
(35)
q
opt
i,t=
q
p
i,t1𝛼2
1
+𝛼2
158 L. Gerdes et al.
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1 3
Price andexcess demand adaptation
Adaptation process of the price of the good (pi, t) by a fraction (α2), depending on the
sales in the last period:
Lower sales than expected
(
qps
i,t1>2qopt
i,t
)
, price and production are decreased