Examining the effect of economic development,region,and time period on the fisheries footprints of nations (1961

Abstract

Anthropogenic activities are impacting marine systems, and the future sustainability of many global fisheries are in serious question. Our analysis draws on prior research in environmental sociology and food systems to better understand the association between economic development and the ecological footprint of fisheries. We provide a series of models to make comparisons across all nations, distinguishing between less-affluent nations and affluent nations over a 50-year period. We focus our analysis on the fisheries footprint of less-affluent nations to further explore how the effect of economic development varies across levels of national economic prosperity, region, and time period. The results of the study indicate that, over time, economic development is increasingly driving the fisheries footprint in less-affluent nations. Because this effect does not occur in affluent nations, we posit that less-affluent nations suffer the ecologically deleterious consequences of economic development more acutely. Furthermore, by utilizing post-estimation techniques for easier comparisons, our findings suggest that the magnitude of economic development’s effect on fisheries is strongest in more recent decades. Our findings also reveal that the effect of economic development is modified by region, as it has a stronger effect on fisheries footprint for less-affluent nations in Central and South America, but weaker in the Middle East and Africa. We conclude with a discussion of the implications for marine sustainability and the challenges posed by an environmentally intensive world capitalist food system.

Keywords

Aquaculture environmental sociology fishing food system human ecology world systems theory

Introduction

Marine and other aquatic systems provide an essential source of sustenance and economic livelihood for populations throughout the world. Seafood is a valuable source of protein and nutrients, expected to help meet the growing global demand for food over the next several decades (Kharas, 2015; United Nations, 2015b). Indeed, aquaculture production already outpaces beef production, and the United Nations positions fisheries as especially important for the economic security of poor and, particularly, coastal populations (Food and Agriculture Organization of the United Nations (FAO), 2014a; Jennings et al., 2016). Accordingly, development organizations consider sustaining coastal and marine environments as essential for the future of economic and food security (United Nations 2015a; World Bank, 2017).

Over the last several decades, anthropogenic activities pressured marine systems to the point that future sustainability of many fisheries is in serious question. Overfishing and pollution, for example, reduced predator biomass in the ocean, contributed to drastic declines in sustainable fishing yields, and generally curtailed the ecological resilience of marine systems (Pauly et al., 1998; Smith et al., 2011; Stramma et al., 2012). There exists a tension at the center of marine sustainability, seafood consumption, and economic production in the marine realm. This tension stems from social organization, social action, and the material demands of human society. As such, marine problems are social problems which require continued sociological examination (Longo and Clark, 2016).

This study draws on prior research in environmental sociology and food systems to further examine the effects of economic development on the ecological impact of fisheries production and consumption in the modern world food system. We build upon previous studies of fishery footprint and seafood consumption (e.g. Clark et al., 2018; Jorgenson et al., 2005; Clausen and York, 2008) to explore how the effect of economic development varies across levels of national economic affluence, region, and time period. In this study, we emphasize these effects in relation to the fisheries footprint of less-affluent nations, as this is a critical area of both social and ecological concern.

We begin with a brief background on contemporary issues in modern fisheries. We follow that with a review of relevant theory and literature. Subsequently, we detail our methods and data, present our findings, and conclude with a substantive discussion of key findings and suggestions for future research. For further clarification, we often use the shorthand term seafood to refer to all aquatic food, including marine- or freshwater-based organisms consumed by humans. We also use the terms fish and fisheries to include fish and non-fish aquatic species, such as crustacean and mollusks, and their capture.

Background

With more than 55 percent of ocean territory currently experiencing industrial fishing, the world ocean has been broadly subjected to intensive fishing practices (Kroodsma et al., 2018). As a result, roughly 31 percent of all fish stocks suffer from overfishing and about 58 percent of fish stocks are fully exploited, meaning that increased fishing yields would result in the transgression of maximum sustainable yields (FAO, 2016). However, many estimates lack completeness, and international agencies such as the Food and Agriculture Organization of the United Nations (FAO) likely understate the effects of fishing on aquatic organisms and marine environments (Pauly and Zeller, 2016; Victorero et al., 2018; Watson and Pauly, 2001). When including the reverberating effects of overfishing and pollution, human activity leaves no area of the ocean system unaffected (Halpern et al., 2008).

Despite these concerns, the demand for seafood increased substantially over recent decades (FAO, 2010). Accordingly, global institutions began to promote programs aimed at incentivizing growth and productivity in seafood production while reducing ecological impact largely through increasing the efficiency of production (FAO, 2014a). This strategy, commonly dubbed “blue growth,” generally coalesces with broader assumptions about the benefits of economic growth on human well-being (Barbesgaard, 2018; World Bank, 2014a). Put differently, leading developmental organizations consider growth in fisheries production and consumption as key components to increasing economic opportunities, providing nutrition, and, thus, advancing human welfare in less-affluent nations (World Bank, 2017). The normative policy prescription for improving access to “blue resources” in a sustainable fashion is to advance policies, programs, and techniques that are compatible with the broader aims of economic expansion, including increasing commodification and novel technologies, and intensifying trade of certain food commodities (Barbesgaard, 2018; McMichael, 2009a).

This study seeks to further examine the effect of economic growth on the ecological footprint of fisheries across different socio-structural factors, as little is known about how political economic indicators interact with geography, time period, and level of development (Jorgenson et al., 2005). Recent research in sociology indicates that population, economic development, and the structure of food consumption within nations correspond with increasing fisheries footprints (Clark et al., 2018). As economic growth often marks a central goal in the development of fisheries policies, our study aims to shed further light on how policies and programs that promote growth-centered production and consumption may affect the conditions of marine and aquatic ecosystems. The following section explores how past literature theoretically situates and empirically investigates questions and concerns pertinent to this study.

Environmental sociology, food systems, and fisheries

Like other species, humans exist within a greater ecological complex, characterized by reciprocal interactions between human institutions and ecosystems (Catton, 1994; Duncan, 1961). In recent years, environmental sociologists accomplished much toward explicating the primary, socio-structural drivers of ecological impacts by making use of diverse measures, such as deforestation, threatened species, and emissions (Ergas and York, 2012; Jorgenson, 2006, 2008; Shandra et al., 2009). Generally speaking, research utilizing a structural human ecology perspective demonstrates that factors related to economic affluence, technological advancement, and population dynamics contribute to ecological impacts (Rosa et al., 2004; Dietz and Jorgenson, 2013). Researchers often break down these metrics to explore the effect of gross domestic product (GDP) per capita in relation to, for example, carbon emissions, or how household size relates to air quality (e.g. Cramer, 1998; Jorgenson and Clark, 2012). Also, scholars commonly examine the specific effects of per capita GDP on the measure of environmental impact, such as whether it increases, plateaus, or declines at higher levels affluence (Dietz et al., 2007; Dinda, 2004). Overall, research in structural human ecology repeatedly demonstrates that a society’s population dynamics, level of material affluence, and technical development drive ecological change (Carolan, 2011; Dietz et al., 1996; Dietz and Jorgenson, 2013; Stern et al., 1997; York et al., 2003).

Scholars of food systems also utilize a human ecology framework to account for changes in consumption and production dynamics. For example, York and Gossard (2004) demonstrated that geographic factors such as climate and latitude, along with factors such as economic growth, are associated with nations’ levels of meat consumption. Regarding aquatic food systems, past research that draws from the human ecology framework indicates positive associations between economic growth and declines in aquatic biodiversity and increases in fishery footprint (Clark et al., 2018; Clausen and York, 2008). Similarly, Longo et al. (2013) demonstrate that economic growth and incorporation into the global food system are positively associated with increases in ecologically intensive aquaculture.

Researchers in the human ecology tradition try to incorporate ecological indicators into their models. For example, extending upon studies that utilize more strict measures of human food consumption, Clark et al. (2018) employed the fisheries footprint metric from the Global Footprint Network in order to more effectively include the ecological impacts associated with seafood consumption at the national level. The Global Footprint Network provides ecological footprints for five forms of consumption, as well as the aggregate ecological footprint score for nations, annually and over a period of several decades (Global Footprint Network, 2017). Conceptually, an ecological footprint represents the amount of productive, ecological territory needed to provide for the consumption of a particular environmental resource. Extending upon Clark et al.’s (2018) analysis, we apply the fisheries footprint measure as our dependent variable in this study. Prior sociological research employed similar frameworks to model the socio-structural drivers of other footprint indicators (Jorgenson and Clark 2009, 2011; York et al., 2003). We discuss this measure in detail in the methods and data section.

The tools of structural human ecology prove useful for gaining a better understanding of changing environmental impacts within food systems. In addition, though, we wish to emphasize that nations’ food systems exist within a larger political-economic context associated with the dynamics of the capitalist world-system. The world-systems approach conceptualizes the global economic order as characterized by a division of labor and power, resulting in a tripartite order classified as core, periphery, and semi-periphery nations (Wallerstein, 1974). In the modern world-system, wealth and power tends to concentrate in the core nations, or the global North. The implications of this global economic order are vast. In relation to the issues we examine here, world-systems theory provides a lens for considering the dynamics of global systems of commodity production in relation to ecological impacts and food production (Hornborg et al., 2007; Longo, 2011; McMichael, 2008). Scholars of environmental resource consumption and the world-system emphasize that, over the long history of capitalist development, poorer nations often export environmental resources to wealthy, or core, nations (Bunker, 1984). The body of extant literature on unequal ecological exchange in the world-system supports the notion that as global capital proliferates and strengthens capitalist relations between nations, environmental degradation expands in a way that uniquely, and often adversely, affects poorer nations (Jorgenson and Kick, 2003).

The inequitable distribution of environmental goods and external costs exists in relation to inequitable power relations between nations within the capitalist world-system (Hornborg, 2009). Richer nations tend to increase their aggregate consumption of environmental space by, for example, procuring favorable trade relations with less powerful, export-oriented nations (Jorgenson, 2009). Periphery regions and nations also, oftentimes, become sinks for various forms of pollution (Frey, 2003; Jorgenson and Rice, 2012).

Thus, when considering the effect of economic development, we posit that level of affluence of nations likely matters greatly in order to grasp the effect of economic development and to interpret this effect. McMichael (2012b) emphasizes that, along with the globalization of industry and textiles, capitalist globalization expanded into the agri-food systems of less-affluent nations over the last several decades. In regard to food systems operating within the structural parameters of capital accumulation, what we call the capitalist world food system, capitalist development is often ecologically intensive (Longo et al., 2013; York and Gossard, 2004). As highlighted by treadmill of production scholars, capitalist development requires ever-increasing environmental withdrawals in order to keep up with the on-going, structural requirement for profit maximization (Gould et al., 2004; Schnaiberg, 1980). Social metabolic scholars extend upon this to argue that social developments associated with capitalist production tend to disrupt ecological systems and cycles, with serious consequences for terrestrial and aquatic food systems (Foster, 1999; Foster et al., 2010; Holleman, 2018; Longo et al., 2015).

This incongruence likely stems from the contradictory demands that the food system places on less-affluent nations. For example, less-affluent nations within the capitalist world food system often experienced concomitant increases in internal demand for animal protein consumption and external pressures to increase their export-oriented production of agri-food commodities. As European super market chains targeted poorer nations for export-oriented livestock production, two-thirds of recent increases in global meat consumption occurred in the global South (McMichael, 2009b). Incorporation into the world capitalist food system may therefore result in dual increases in domestic consumption and export-oriented production of particular types of food commodities, such as animal products. Broadly speaking, the expansion of the capitalist world food system resulted in nations in the global South becoming more dependent upon imports for feeding their own population (McMichael, 2009a). Yet, at the same time, the structural imperatives of the world capitalist food system pressured many poorer nations to modernize and increasingly commodify their food production systems in order to better integrate into the global food system that large multi-national firms dictate (Holt-Giménez, 2017). Research suggests that the resulting dynamics indicate that poorer nations typically import basic food provisions, while core nations increasingly import higher value or luxury food items (Otero et al., 2013). Overall, within the dynamics of this global food order, increases in protein production, consumption, and trade with core nations characterizes less-affluent nation incorporation into the world capitalist food system (McMichael, 2012a).

Seafood is among the highest traded of all food commodities. Combining insights from human ecology and world-systems theory, we can better analyze the processes of the global economic order in relation to fisheries footprints. Like many food commodities, biophysical conditions shape and limit fish production. For example, ocean up-welling—a process where cold nutrient rich water rises and displaces surface waters—characterizes some of the most productive fisheries in the world, such as those off the coasts of Chile and Peru. Biophysical conditions and political-economic processes such as economic production and consumption dynamics, national-level fisheries policies, and national access agreements, resulted in significant fishing efforts by fleets in productive waters of the global South, including by fleets from the North (Havice and Campling, 2010; McCauley et al., 2018). For example, depletion of fisheries, changes in quotas, and limits on access have been shown to shift fishing to areas of the global South, like coastal regions of Africa (Berkes et al., 2006; Worm et al. 2009) Fisheries management regulations, agreements, and associated capture quotas sometimes drive captures by ships flying flags of convenience, that is registering ships to particular nations in a way to skirt capture quotas. These and other efforts that result in increased levels of so-called illegal, unregulated, and unrecorded captures, which tend to increase exploitation of fisheries in the global South. These practices, which can obfuscate the social sources of ecological degradation, conceal that the “successes” of fishery management in the global North coincide with ecologically intensive fishing practices in the global South (Hilborn et al., 2005).

The seafood economy of the modern world-system, including patterns of production and consumption outlined above, trade dynamics, and associated economic and political power, interact in marine spaces. Like other natural resources that are largely place-based, fishing fleets from wealthy and less-affluent nations develop fishing grounds in the global South. Wealthy nations of the global North possess more autonomy over their associated fishing grounds through greater management, regulation, and monitoring, which in recent years led to limits on fishing, while those in the global South often continued to suffer exploitation in both legal and illegal manners (Worm et al. 2009). Wealthy nations dominate fishing all over the world through heavily subsidized distant water fleets. Recent research tracking industrial fishing practices identified that 97 percent “of all industrial fishing effort detected” originate from wealthy nations, and “Eighty-four percent of the industrial fishing effort in lower-income EEZs [exclusive economic zones] was conducted by foreign countries” (McCauley et al., 2018: 3).

With this in mind, it is worthwhile to examine fisheries footprints of nations over time, along with the regional patterns of change. Furthermore, we suggest it useful to analyze this with a focus on the fisheries footprint in the global South. As discussed, the dynamics of the modern world-system are an important force in shaping the ways in which fisheries exploit marine systems. Thus, it is sensible that our analysis emphasizes whether and to what degree fisheries footprint is associated with economic development in less-affluent countries in contrast to affluent ones.

Data and methods

In our analysis of the dynamic relationships between economic growth and fisheries footprints, we employ a modeling approach commonly used in cross-national environmental sociological research. We analyze national-level data using a multi-variate regression approach consistent with the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) analysis (York et al., 2003). We drew the explanatory and control variables for this study from the World Bank, and Food and Agriculture Organization of the United Nations (FAO, 2018a; World Bank, 2018). We obtained the dependent variable, fisheries footprint, from the Global Footprint Network. We constructed several time-series models, five of which we present in this study.

Our first three models include no interaction terms, but enable comparisons across nations in the sample. Our first model groups all nations together, to explore the main effects of our explanatory variables across the whole world-system. We then delineated nations according to their level of income, according to World Bank measures. Following Jorgenson and Clark (2012) wealthy nations fall into the World Bank’s high-income category, which we determined by considering a nation’s per capita gross national income. In order to maintain a consistent and conservative approach, we only grouped nations into this category if the nation maintained this designation over the entire course of time (i.e. each year) that the World Bank utilized this classification. We grouped all other nations together as less-affluent nations, signifying countries that could be designated as periphery and semi-periphery nations. In total, we analyzed 132 less-affluent nations and are listed in Table 1. Table 2 contains affluent nations utilized in this sample.

Table 1.

Less-affluent nations included in the study grouped by region.

Asia	Central and South America	Middle East	Africa	Europe and North America
Armenia Azerbaijan Bangladesh Cambodia China Fiji Georgia India Indonesia Kazakhstan Kenya Kiribati Korea Rep Kyrgyz Republic Lao PDR Malaysia Maldives Myanmar Nepal Pakistan Philippines Russian Federation Samoa Solomon Islands Sri Lanka Tajikistan Thailand Timor-Leste Turkmenistan Ukraine Uzbekistan Vanuatu Vietnam	Argentina Belize Bolivia Brazil Chile Colombia Costa Rica Cuba Ecuador El Salvador Guatemala Guyana Honduras Nicaragua Panama Paraguay Peru Suriname Trinidad and Tobago Uruguay Venezuela RB	Afghanistan Algeria Cyprus Egypt Arab Rep Iran Islamic Rep Iraq Jordan Lebanon Morocco Oman Saudi Arabia Tunisia Turkey Yemen Rep	Angola Benin Botswana Burkina Faso Cameroon Central African Republic Chad Congo Dem Rep Congo Rep Ethiopia Gabon Gambia The Ghana Guinea Guinea-Bissau Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Sierra Leone South Africa Sudan Swaziland Tanzania Togo Zambia Zimbabwe	Albania Antigua and Barbuda Barbados Belarus Bosnia and Herzegovina Bulgaria Cabo Verde Croatia Dominican Republic Estonia Greece Grenada Haiti Hungary Jamaica Latvia Lithuania Macedonia FYR Malta Mexico Montenegro Poland Portugal Serbia St Lucia St Vincent and the Grenadines

Table 2.

Affluent nations included in the study.

Affluent Nations
Japan Australia Austria The Bahamas Belgium Bermuda Canada Czech Republic Denmark Finland France Germany Iceland Ireland Italy Luxembourg Netherlands New Zealand Norway Spain Sweden Switzerland United Kingdom United States Israel Kuwait United Arab Emirates

Delineating nations by level of income is logical on both theoretical and empirical grounds. Other studies demonstrate that environmentally intensive consumption varies across level of national income, or affluence (Jorgenson, 2009; York and Gossard, 2004). In spite of gains in efficiency, environmental sociologists generally accept that affluent nations consume more environmental space and more luxurious food commodities than less-affluent nations (Otero et al., 2013; York et al., 2003). Furthermore, world-system theory suggests that production and consumption processes occur differently in nations according to world-system position.

Regarding less-affluent nations, major development institutions argue that further marketization and modernization of poorer nations will increase seafood consumption in a way that improves food security, economic development, and ecological sustainability (FAO, 2014b; World Bank, 2017). Furthermore, substantial increases in fishery footprint occurred in less-affluent nations—especially when compared with wealthy nations—over the five-decade time period that our sample covers (Global Footprint Network, 2018). Thus, focusing on less-affluent nations allows us to explore these claims and the drivers of ecological change in a way that grouping all nations together, regardless of economic position in the world-system, would not. We organized data into a panel data set, with observations grouped by nation and occurring annually between 1961 and 2010.

Dependent variable: fisheries footprint of nations

The dependent variable, fisheries footprint (Table 3), represents the total marine territory needed to sustain levels of consumption of various seafood products within a nation (Ewing et al., 2010; Folke et al., 1998). The calculation of fisheries footprint also considers the ecological conditions associated with particular species, essentially providing a weight for species that require more ecological resources/space. In this measure, for example, higher trophic-level species (i.e. higher on the food chain) require more ocean territory (Borucke et al., 2013; Galli et al., 2012). The footprint also includes aquaculture production and aquafeeds (Borucke et al., 2013; Ewing et al., 2010). Finally, the fisheries footprint (EF_C) is calculated as the sum total of fisheries production (EF_P) and imports (EF_I), with fisheries exports (EF_E) subtracted from that total (Global Footprint Network, 2017). Thus, via the structure of its calculation, the fisheries footprint considers trade flows between nations. We present the equation for the fishery footprint, developed by the Global Footprint Network, as follows

E F_{C} = E F_{P} + E F_{I} - E F_{E}

Table 3.

Descriptive statistics for dependent variable, log of fisheries footprint.

Decade	Mean (all nations)	Mean (less-affluent nations)	Mean (affluent nations)	Median (all nations)	Median (less-affluent nations)	Median (affluent nations)	Total missing observations	Total N
1960s	11.78	11.24	13.99	11.55	11.04	13.98	373	1377
1970s	12.29	11.84	14.12	12.27	11.69	14.20	310	1430
1980s	12.56	12.15	14.26	12.66	11.99	14.21	292	1448
1990s	12.63	12.29	14.27	12.61	12.19	14.34	68	1672
2000s	12.97	12.72	14.13	12.84	12.51	14.19	22	1718

The Global Footprint Network (2017) determines the EF_P from the specific trophic level of each harvested or farmed, marine fish species within a nation. Put differently, the amount of marine territory a specific species requires to reproduce itself matters greatly for determining a nation’s footprint. Thus, the harvesting or farming of a certain amount of a high trophic species, like salmon or tuna, counts more toward EF_P than would the same amount of a lower trophic species, like oysters or herring. The EF_I and EF_E measures calculate the fishery product trade balance of a nation. Similarly, these measures rely upon trophic indicators to determine their value. These trophic considerations matter greatly for the calculation of the ecological footprint of fisheries, and thus make them a distinct indicator from seafood consumption (Clark et al., 2018).

Yet, because the FAO aggregates seafood commodity trade metrics, the Global Footprint Network (2017) relies upon more general, non-species-specific trophic indicators to determine EF_I and EF_E. The calculation utilizes a fairly conservative approach to determine the trophic load, or footprint intensity, of nations’ fishery trade balances. Specifically, it assumes that a nation’s exported fish products possess the same trophic load as the nation’s harvested and imported fish. Similarly, the Global Footprint Network’s calculation assumes that the trophic load of imports of a particular nation is equal to the average global harvest (Lin et al., 2018). Historical increases in a nation’s fishery footprint, especially in more export oriented nations, suggest elevated levels of high trophic fishery production as this trophic level is, effectively, more directly weighted in the calculation of the metric.

Independent variables

We control for de facto population size, urban population rate, non-dependent population (ages 15–64, percent of total population), and terrestrial meat consumption (measured in tons), as prior literature shows that these factors drive consumption of ecological resources and fisheries footprint (Clark et al., 2018; Dietz et al., 2007). Following Clark et al., 2018, we also control for livestock production, as prior studies demonstrate links between protein production and consumption (Gossard and York, 2003; Jorgenson and Birkholz, 2010). These variables were collected from the World Bank (2018), except for meat consumption and livestock production, which we drew from the FAO’s collection of food and agricultural data (FAO, 2018a). Our main variable of interest, GDP per capita, operationalizes economic growth of nations and is taken from the World Bank (2018). We utilize a constant GDP per capita, equivalent to 2010 US dollars, to account for inflation over time. In the results section and onward, we report coefficients pertaining to per capita GDP as economic development. Economic development, in this analysis, signifies capitalist economic development, which is fundamentally driven by a growth imperative, including the continuous expansion of GDP.

In order to consider the effects of economic development during specific periods and major regions in the world, we construct interactions between time period (decade) and per capita GDP, and region and per capita GDP. We incorporate these interactions into our models and present them along with the main effects. We delineate nations into five different regions: Asia, Africa, Middle East, Europe and North America, and Central and South America. For our regional categories, we borrowed from extant research in environmental sociology, which constructed regional categories to examine differences in seafood consumption and the carbon intensity of well-being (Jorgenson, 2009; York and Gossard, 2004). Extant literature also examines the effects of socio-structural drivers as they vary over periods of time (Jorgenson and Clark, 2010; Jorgenson and Givens, 2015). We constructed periods as decades, and interacted them with our economic development measure. In constructing these interactions, we could then assess if the effect of economic development varied across decades. In addition, by utilizing the average marginal effects, we are able to provide the magnitude of the effect of economic development over each decade and across different regions in our models, allowing for relatively easy comparisons. Our models thus detail not only if economic development varies with time and region, but they also demonstrate the nature of these differences. Overall, these interactions allow us to investigate the relationships between economic growth and fisheries in further detail than prior studies and, accordingly, better elaborate on economic development policies and their potential effects on fisheries footprint.

Models

In order to account for common issues with distribution and skew with cross-national studies, we log transformed all variables to construct a log-log model. This approach is consistent with the STIRPAT modeling method used to test the association between various forms of environmental impact and variables related to social development, economic affluence, and population dynamics (York et al., 2003). When interpreting the results of the models, coefficients indicate the percentage change in the dependent variable for a 1 percent change in the corresponding independent variable. For example, a coefficient of .5 would indicate a .5 percent increase of the dependent variable for a 1 percent change in that independent variable.

We conducted several pre-model tests to help determine the appropriate modeling approach for our analysis. Specifically, we ran several diagnostic tests to explore common issues with panel data. These issues include heteroscedasticity, cross-sectional dependency, and serial correlation (Torres-Reyna, 2007). After running a modified Wald test (heteroscedasticity), Woolridge test (serial correlation), and a Pesaran’s test (cross-sectional dependency), we found statistically significant results for all three tests p < .05. We, therefore, needed to rely upon a modeling approach that could account for heteroscedasticity and contemporaneously correlated error terms. We thus utilized ordinary least-squares (OLS) models with panel corrected standard errors (xtpcse command in Stata) (Beck and Katz, 1995). To deal with serial correlation, we included an autoregressive (ar1) option to account for previous responses potential lag effect on subsequent responses (Rabe-Hesketh and Skrondal, 2012).

Because of the unbalanced nature of our dataset, which contain some missing observations especially in earlier periods, we also utilized a pairwise deletion (rather than casewise deletion) option to include as many observations as possible. Fixed effects regression techniques prove useful for controlling for unobserved, time invariant factors that may influence a nation’s fisheries footprint, such as coastline, land territory, and climate. However, panel corrected standard errors do not allow for a fixed effects option. We thus included country-specific effects within most of our models to better account for unobserved heterogeneity across nations, effectively allowing for interpretation as a fixed effects model (Tables 4 and 5).

Table 4.

List of variables included in the study.

Variable	Type of variable	Transformation	Source	Description
Fisheries footprint	Dependent variable	Logged	Global footprint network	Global hectares
Total population	Control	Logged	World Bank	De facto, total residents
Meat consumption, tons	Control	Logged	UN Food and Agriculture Organization	Total tons of beef, poultry, veal, pork, and sheep consumed
Urban population, percent of total population	Control	Logged	World Bank	Percent of total, that is, number of persons in “urban” zone per 100
Per capita GDP, constant US dollars	Explanatory	Logged	World Bank	GDP divided by midyear population
Livestock production	Control	Logged	UN Food and Agriculture Organization	Dressed carcass weight, commercial and farm slaughter
Non-dependent age population	Control	Logged	World Bank	Percent of total population between ages 15 and 64
Period decade × per capita GDP	Explanatory	Interactive effect between periods (decades) and log of per capita GDP	World Bank
Region × per capita GDP	Explanatory	Interactive effects between regions and log of per capita GDP	World Bank

GDP: gross domestic product.

Table 5.

Initial model description.

Models	Description	Time frame	Observations	Nations	Average observations per nation	R ²
Model 1	Affluent and non-affluent nations	1961–2010	5806	159	36.5	.906
Model 2	Less-affluent nations	1961–2010	4680	132	35.4	.888
Model 3	affluent nations	1961–2010	1126	27	41.7	.973

We also ran a post-estimation test for multi-collinearity after our first model, which included our variables of interest across all nations in the sample. Aside from the obvious correlation between the quadratic term of economic development (GDP²) and per capita economic development (GDP), the model presents little evidence for concern in regards to multi-collinearity, as only one relationship barely exceeded a .6 correlation. In our final model which includes country-specific effects and region, we did take some steps to address potential confounding effects when controlling for regional and country-specific effects. We briefly describe these measures in the results section. In addition, we ran several unpresented models that explored the potential confounding effects of outliers. We estimate models that excluded China (e.g. FAO, 2018b) as well as high percentile observations for our dependent variable across the sample (e.g. Longo et al., 2019). These models yielded similar results, with no changes in statistical significance or direction of the coefficients, and very little—if any—change in their magnitude. We thus concluded that outliers or unusual observations did not pose significant reason for concern.

We ran a series of preliminary models to explore the nature of our main effects without the interaction terms on all nations in the sample (Model 1), affluent nations (Model 2), and less-affluent nations (Model 3). Below, in Table 5, we report the total number of observations, groups (i.e. nations) in each model, and average observations per nation for the unbalanced, panel data set.

Results

We present the results of Models 1, 2, and 3 in Table 6. These models do not include any interaction terms. Our discussion of the coefficients is in the context of the models, and we, therefore, report them net of all other effects.

Table 6.

Results for Models 1, 2, and 3.

Variables	All nations, Model 1	Less-affluent nations, Model 2	Affluent nations, Model 3
Per capita GDP	1.14*** SE = .19	.405*** SE = .059	3.842*** SE = 1.45
GDP quadratic	−.052*** SE = .011	–	−.196*** SE = .070
Total population	1.48*** SE = .115	1.81*** SE = .134	.372** SE = .165
Non-dependent population	.847** SE = .38	.70* SE = .407	.654SE = .721
Urban population	−.242** SE = .113	−.507* SE = .125	.753SE = .472
Meat consumption	.062SE = .046	.03SE = .050	.096SE = .085
Livestock production	.012SE = .007	.019* SE = .010	−.003SE = .011

GDP: gross domestic product; SE: standard error.

***

p < .01.

p < .05.

p < .1.

For Models 1, 2, and 3, we considered the potential effect of a quadratic term of economic development to explore whether the relationship to the fishery footprint takes a curvilinear form and, importantly, if the magnitude of the effect potentially declines at higher levels of economic development. The quadratic coefficient of economic development was negative for Model 1, which suggests that the effect of economic development shows declines at higher levels of affluence. Based on the results for the more specified Models 2 and 3, this effect of high values of economic development only occurs in nations that are historically the most affluent. The results from Model 1 indicate that, when including all nations, fisheries footprint reaches a ceiling and levels off with no decline. Due to the lack of statistical significance and high collinearity between the quadratic of economic development and the log of economic development, we report Model 2’s results sans the insignificant quadratic coefficient.

In Model 1, all controls except livestock production and meat consumption are statistically significant (p < .05). The effects of total population and non-dependent age population are positive. These results conform to our expectations and with prior literature. More people in a nation typically require more food resources, and a higher percentage of working-age adults suggests greater spending power. A higher percentage of urban residents corresponds with lower levels of fishery footprint.

Our second model, Model 2, considers less-affluent nations. Similar to Model 1, meat consumption did not reach statistical significance. For less-affluent nations, the GDP quadratic did not reach statistical significance, and we thus removed it from this model to avoid confounding results or confusing explanations of the effect of per capita GDP. The positive coefficients for total population and non-dependent population do reach statistical significance and, thus, increases in total population and the percentage of the population who are of working age both positively affect fishery footprint over time, in less-affluent nations. The moderate results for livestock production in less-affluent nations suggest that there is some evidence that increases in animal commodity production correspond with increases in nations’ fishery footprints. The magnitude of our main coefficient of interest, per capita GDP, reaches statistical significance. The coefficient of .405 suggests an approximate .4 percent increase in a less-affluent nation’s fishery footprint for every 1 percent increase in per capita GDP. Thus, we interpret economic development in less-affluent nations as an important driver of increases in these nations’ fishery footprints.

For affluent nations, Model 3 suggests that for every 1 percent increase in a nation’s population, fishery footprint will increase by a corresponding .372 percent. This was the only statistically significant control variable. In this model, total population within nations is the most consistent predictor of fishery footprint increases outside of per capita GDP, our main variables of interest. Per capita GDP and its quadratic both reach statistical significance. The GDP quadratic indicates that in affluent nations, higher levels of economic development appear to correspond with decreasing levels of fishery footprint. The estimated turning point is at about US$19,000. Thus, the marine environmental impact—measured in fishery footprint—of economic development is most consistent and deleterious for non-affluent nations over time. In order to analyze this in more detail, Models 4 and 5, therefore, focus on how the effect of economic development varies across region and period of time for exclusively less-affluent nations.

We develop these models based on the results of the previous models, which indicate statistically significant relationships between the dependent and independent variables. In Table 7, we present the results for Models 4 and 5. These include the main effects and the interaction terms for economic development and period (Model 4) and economic development and region (Model 5), net of all other controls in the models.

Table 7.

Interaction models.

Variables	Period × GDP for non-affluent nations, Model 4	Region × GDP for non-affluent nations, Model 5
Per capita GDP	.328*** SE = .064	.497*** SE = .097
GDP quadratic	–	–
Total population	1.81*** SE = .142	1.77*** SE = .142
Non-dependent population	.498SE = .398	.112SE = .469
Urban population	−.451*** SE = .127	−.447*** SE = .141
Meat consumption	.038SE = .051	.052SE = .058
Livestock production	.017* SE = .010	.012SE = .010
Period 1 (1960s)	Baseline	–
Period 2 (1970s)	−.129SE = .178	–
Period 3 (1980s)	−.257SE = .239	–
Period 4 (1990s)	−.704*** SE = .269	–
Period 5 (2000s)	−.609** SE = .294	–
Period 1 × per capita GDP	Baseline	–
Period 2 × per capita GDP	.025SE = .025	–
Period 3 × per capita GDP	.039SE = .032	–
Period 4 × per capita GDP	.091** SE = .036	–
Period 5 × per capita GDP	.086** SE = .038	–
Asia	–	Baseline
Central/South America	–	−1.28SE = 1.87
Middle East	–	−2.97** SE = 1.13
Africa	–	9.41*** SE = 1.12
North America/Europe	–	–
Asia × per capita GDP	–	Baseline
Central/South America × per capita GDP	–	.349* SE = .191
Middle East × per capita GDP	–	−.275** SE = .139
Africa × per capita GDP	–	−.283*** SE = .107
North America/Europe × GDP	–	–

GDP: gross domestic product; SE: standard error.

North America/Europe omitted due to collinearity with country-specific effects.

***

p < .01.

p < .05.

p < .1.

Model 4 considers the nature of the relationship between economic development and fishery footprint in relation to different periods. Specifically, this model examines whether the effect of economic development changes with time and, if so, the character of this change. In doing so, this model explores, in greater detail, discussions and debates about the nature of economic growth and development and its effect on consumption and environmental impact of food systems (Spaargaren et al., 2012). With the exception of meat consumption and non-dependent population, all controls, as well as the main effect of economic development reach statistical significance. The statistically significant interaction terms for periods 4 (1990s) and 5 (2000s) indicate that the effect of economic development on fisheries footprint, for less-affluent nations, does vary by time period. These results signify that the effect of economic development increased relative to the baseline category of the 1960s.

Model 5 examines how the effect of economic development varies by region. This allows us to consider economic development in a more nuanced, non-monolithic way that places nations within categories that can broadly consider some degree of cultural and/or geographic differences. All controls except nondependent population, livestock production, and meat consumption reach statistical significance when accounting for the interactions of GDP and region. In Model 5, the effect of economic development in Central/South America, the Middle East, and Africa differs from the baseline category (p < .05), Asia. Thus, the results suggest that the effects of economic development on fisheries footprint vary by region. We omitted less-affluent nations from North America due to issues with collinearity stemming from the inclusion of country-specific effects. Finally, it should be noted that Asia (even when excluding China) has the highest levels of fisheries and aquaculture production in the world (FAO, 2018b).

Finally, Table 8 presents the average marginal effects of economic development at each period and region for less-affluent nations. Run after each interaction model in STATA as a post-estimation technique, the margins command allows for more direct comparisons of magnitude across period of time and region, respectively, for the effect of economic development.

Table 8.

Average marginal effects of economic development for time periods and regions.

Average marginal effect of GDP	Model 4		Model 5
	Coefficient	Standard error	Coefficient	Standard error
1960s	.328***	.064
1970s	.353***	.063
1980s	.367***	.063
1990s	.420***	.061
2000s	.415***	.061
Asia			.497***	.097
Central and South America			.846***	.193
Middle East			.221***	.128
Africa			.214**	.079
North America/Europe			–	–

GDP: gross domestic product.

***

p < .01.

p < .05.

p < .1.

Average marginal effects demonstrate a clear trend in the effect of economic development over time on nations’ fishery footprints in less-affluent nations. In addition to the average marginal effect of economic development reaching statistical significance (p > .01) across each period of time, the effect of economic development increases in less-affluent nations over most of the periods. In the latest decade available, the effect of economic development decreases slightly relative to the 1990s, but remained at a comparatively high level. Figure 1 displays this trend graphically.

Figure 1.

Average effect of economic development across decade.

The average marginal effects also confirm, with greater clarity, that the presence and magnitude of the effect of economic development does vary by region. For less-affluent nations in Africa, economic development demonstrates the smallest effect on fishery footprint in comparison to other regions. Conversely, the strongest effect occurs for less-affluent nations in Central/South America followed by Asia, and then the Middle East. For example, the model predicts that, for less-affluent Central/South American nations, for every 1 percent increase in economic development, there is a predicted .846 percent increase in these nations’ fishery footprints. Conversely, for less-affluent nations in Africa, fishery footprint increases only .214 percent for every 1 percent increase in economic development. For less-affluent nations in the Middle East, the effect is slightly higher at .221. Overall, these results suggest that region strongly modifies the effect of economic development on fisheries footprint in less-affluent nations.

Discussion

Ecological impact or food security?

Modern seafood production practices are a central aspect of marine ecological change. The ecological impact associated with fishing is well-evidenced in numerous examples of overfishing, including the overall decline in the mean trophic-level of species, where fishers rely on species at lower levels of marine food webs (Pauly et al., 1998). Not only does this phenomenon imply overfishing but also increased fishing at these levels can have wider reverberating impacts on marine biodiversity and ecology (Smith et al., 2011). In relation to other food commodities, seafood and fisheries products are thus unique in that they rely upon wild stocks for a significant portion of the total, that is, beyond aquaculture. Thus, for fisheries, metabolic cycles must continue, relatively unabated, in order to prevent the collapse of future populations (Clausen and Clark, 2005). In this context, the driving influence of economic development on increasing fisheries footprints in less-affluent nations is a critical socio-ecological concern.

With this in mind, we argue that the ecological footprint of fisheries is a better indicator of a nation’s marine ecosystem impact than it is the quality of dietary consumption. The footprint indicator says nothing about the distribution of fisheries products within a nation. Furthermore, as a broad measure, it reveals little about the specifics of how fisheries products are utilized within a nation. For example, a nation’s fishery footprint may increase due to a small portion of a nation’s population becoming wealthy and expanding their consumption of protein commodities. As suggested above, another potential source of fisheries footprint expansion is the use of fisheries products as an input commodity for agriculture, such as animal feed or fertilizer, or aquaculture, in the form of fish feed (Clark, Forthcoming; Longo et al., 2019). Neither of these uses suggest a widespread improvement of diet.

However, the footprint indicator does account relatively well for aggregate bioproductive area needed to support total consumption of fisheries products. As such, while it is uncertain how the increase of a fishery footprint impacts dietary quality, we are far more confident that a larger fishery footprint poses challenges for sustainability in marine systems. Thus, our findings for less-affluent nations, whose waters comprise the vast majority of the world’s fishing territory, reveal that economic development within the dynamics of a capitalist world food system tends to result in increasingly deleterious effects on marine sustainability over time. The potential for metabolic disruption is especially troublesome when one considers future food security of less-affluent nations, whose position in the world-system is associated with food insecurity (Kick et al., 2011) and who rely on seafood as an important source of protein and employment (FAO, 2018b).

Comparing economic development across levels of affluence

Model 1, which examined the drivers of fishery footprint for all nations, shows some consistency with similar work on fishery footprint and seafood consumption (Clark et al., 2018). The effects of economic development and total population remain positive; however, meat consumption and livestock production both fail to reach statistical significance. The inclusion of non-dependent age population, whose effect is positive, conforms to previous research on this metric as a potential driver of ecological impact (MacKellar et al., 1995). The negative effect of the GDP quadratic in Model 1 indicates that, at the most affluent levels of economic development, the effect of economic development on fishery footprint reaches a maximum. Therefore, no significant reduction in fisheries footprint associated with economic development is indicated in this model, only a leveling effect once a ceiling is reached.

There exists substantial variation in the nature of these effects when we examine them more closely according to nations’ level of affluence. In regard to our main coefficients of interest, those that examine the effect of economic development, level of affluence matters greatly. For affluent nations only (Model 3), the presence of a quadratic effect for economic requires some elaboration. The world’s wealthiest nations are increasingly importing their consumed seafood from the global South, whose waters they depend upon for continued expansions of fisheries based consumption and production. For example, wealthy nations such as the United States, Japan, and those within the European Union comprise around 12 percent of the world’s population, but consume 30 percent of the world’s seafood (Swartz et al., 2010). Indeed, the vast majority of seafood consumed in affluent nations, such as the United States, is imported—by some estimates more than 90 percent by value and 80 percent by volume for the United States alone (National Oceanic and Atmospheric Association (NOAA), 2017, 2019).

Indeed, according to the World Bank’s Development Indicators, seafood consumption within affluent nations increased substantially across our time sample. Yet, this increase does not correspond perfectly with the fishery footprint indicator, which remained comparatively stagnant for affluent nations. A likely explanation for this discrepancy lies in the methodology for the fishery footprint indicator. As discussed, the trophic level of produced (captured, farmed) species matters greatly for determining nations’ footprint indicators; however, the Global Footprint Network assumes that the trophic load of imports of a particular nation is equal to the average global harvest. This assumption may obscure inequitable trade dynamics because affluent nations are known to import high trophic, high value species harvested in the waters of nations in the global South (Deutsch et al., 2011; Van Mulekom, 2006). We argue that if this concern were corrected (i.e. estimated more appropriately), there would be important differences in the results. Specifically, it is likely that the relatively stagnant fishery footprint in affluent nations corresponds with a rise in higher trophic catches in the waters of the global South, which the current measurement approach does not include. We, therefore, advocate for the revision of the footprint indicator in future development of this measure.

Thus, when relying upon the fishery footprint metric, we find it difficult to determine if an unequal ecological exchange relationship exists within global fisheries. However, our study does suggest that less-affluent nations generally suffer the ecologically deleterious effects of economic development worse than their affluent counterparts (often their trading partners). For example, our models present no evidence that a quadratic effect occurs within the fisheries of less-affluent nations. We posit that the global political economic conditions and legal arrangements of the modern world food system, which allow easier access and extraction to the resources in less-affluent nations, along with the biophysical conditions that form the ecological foundation of production, likely account for this disparate effect of economic development.

Economic development, less-affluent nations, and fisheries footprint

Our results suggest that the effects of economic development on fisheries footprint differ according to level of affluence. Thus, after we examined our initial models, we sought to further explore the effect of economic development within less-affluent nations, which the models indicate experienced greater marine impacts associated with seafood production and consumption. We developed more refined models to examine period and region effects, along with their interactions with economic development, focusing solely on less-affluent nations. Our findings comparing the effects of different periods (decades) over the time of our study may suggest some slowing of the effect of economic growth on fisheries footprint in the most recent decade (2001–2010). We interpret this moderate decline as likely due to the nature of fisheries, and the character of food production and consumption. Global fisheries captures stagnated over the last several decades, as fish populations are finite and thus bounded by natural limits. Also, food consumption tends toward a ceiling due to biological and economic factors. Thus, we expect that fisheries footprint cannot expand indefinitely. However, as aquaculture added significant quantities of fish, not offsetting captures, we still expect some continued expansion (Longo et al., 2019). It is thus likely that the positive effect of economic development on fishery footprint will continue into the future, due to aquaculture and intensified fishing efforts.

The results of our study indicate that region also moderates the effect of economic development for less-affluent nations. This regional variation suggests that economic development contributes to differences in these nations’ use of marine ecological space associated with consumption (including imports and local production) of seafood products. Economic development demonstrated the strongest effect for less-affluent nations in Central/South America. This finding makes sense given that nations within this region, in recent decades, increased their share of world aquaculture production, as well as per capita aquaculture production (FAO, 2016). On the contrary, economic development demonstrated relatively small impacts for less-affluent nations in Africa, despite the fact that fisheries production increased substantially (by more than 10%) for Africa (World Bank, 2014b). The comparatively small effect of economic development for less-affluent nations in Africa suggests that Africa’s fisheries resources may witness major economic investment in the coming years, leading to increased fishery footprints across African waters. More negative fishery trade balances in African nations in comparison to less-affluent nations in other regions may also account for the relatively small effect. Previous research in fisheries science suggests that this is an important dynamic in Africa in particular (Worm et al. 2009). Thus, this study signals that future research in fisheries and the world food system should expand upon the limitations of this study to explore inequitable ecological exchange relations across levels of the world system. Such work may require other metrics beyond the fisheries footprint.

The increase of the ecological footprint of fisheries in less-affluent nations, largely driven by capitalist economic development, suggests that both the quantity of the catch and, importantly, the trophic load of these catches are unsustainable. We, therefore, interpret the ecological impact of economic development—which has increased with time—as a serious concern for marine sustainability, and future food and economic security for less-affluent nations. The undercutting of ecological processes can result in long-term strains on social conditions which, especially for poorer nations, is incongruent with broader aims for social and economic development (Amin, 1974; Rice, 2009). The prioritization of ecological processes requires special diligence for future seafood security, as fisheries production necessitates the long-term protection of species in precarious marine ecosystems.

Conclusion

The key takeaways from this study concern the impact of economic development on fishery footprint and, by close proxy, marine sustainability over the last five decades. First, only affluent nations seem to demonstrate the potential for an environmental Kuznets curve effect. Second, the more ecologically intensive effects of economic development thus appear to be occurring in the fishing grounds of poorer nations. Our models that specifically examine less-affluent nations make clear this uneven impact of ecologically intensive development. Third, the nature of economic development’s impact on fishery footprint in less-affluent nations generally intensified over time, with some moderate flagging in the 2000s. We interpret this slowing as the result of global overfishing and the reduced potential of wild fish stocks to sustain their ecological metabolism. Finally, the nature of economic development’s effect varies substantially by region, and this variation raises questions about export-oriented development and future potential for fishery expansion (or lack thereof) in the global South.

The results of this study thus add more detail on the nature of economic development’s effect on fishery footprint. Overall, our study provides evidence to suggest that the expansion of the world capitalist food system is fraught with contradictions for fisheries and food security. Relatedly, if these trends and dynamics persist, they risk the potential for marine sustainability.

Leading developmental organizations correctly emphasize that “for billions around the world—especially the world’s poorest—healthy oceans mean jobs, food, and protection,” (United Nations, 2017: 1). Yet, our study suggests that the paradigmatic, developmental assumptions of the capitalist world food system, which emphasize commodification, profit maximization, and expansion, may have serious consequences for marine sustainability. In the coming years, policies and programs must offer new prescriptions to ensure the sustainable governance of fisheries resources in such ways that fairly distribute impacts throughout nations, in different regions, and across varying levels of affluence.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Timothy P Clark

References

Amin

(1974) Accumulation on a World Scale. New York: Monthly Review Press.

Barbesgaard

(2018) Blue growth: Savior or ocean grabbing? The Journal of Peasant Studies 45(1): 130–149.

Beck

Katz

(1995) What to do (and not to do) with time-series cross-section data. American Political Science Review 89(3): 634–647.

Berkes

Feeny

McCay

et al. (2006) Globalization, roving bandits, and marine resources. Science 311: 1557–1558.

Borucke

Moore

Cranston

et al. (2013) Accounting for demand and supply of the biosphere’s regenerative capacity: The national footprint accounts’ underlying methodology and framework. Ecological Indicators 24: 513–533.

Bunker

(1984) Modes of extraction, unequal exchange, and the progressive underdevelopment of an extreme periphery. American Journal of Sociology 89: 1017–1064.

Carolan

(2011) The Real Cost of Cheap Food. New York: Earthscan.

Catton

(1994) Foundations of human ecology. Sociological Perspectives 37(1): 75–95.

Clark

(Forthcoming). Mining the sea: A within-case comparative analysis of the Atlantic menhaden fishery in the age of capital. Sociology of Development.

10.

Clark

Longo

Clark

et al. (2018) Socio-structural drivers, fisheries footprints, and seafood consumption: A comparative international study, 1961–2012. Journal of Rural Studies 57: 140–146.

11.

Clausen

Clark

(2005) The metabolic rift and marine ecology: An analysis of the ocean crisis within capitalist production. Organization & Environment 18(4): 422–444.

12.

Clausen

York

(2008) Global biodiversity decline of marine and freshwater fish: A cross-national analysis of economic, demographic, and ecological influences. Social Science Research 37(4): 1310–1320.

13.

Cramer

(1998) Population growth and air quality in California. Demography 35: 45–56.

14.

Deutsch

Troell

Limburg

et al. (2011) Global trade of fisheries products: Implications for marine ecosystems and their services. In: Köllner

(ed.) Ecosystem Services and Global Trade of Natural Resources. London: Routledge, pp. 120–147.

15.

Dietz

Jorgenson

(eds) (2013) Structural Human Ecology: New Essays in Risk, Energy, and Sustainability. Pullman, WA: Washington University Press.

16.

Dietz

Kalof

Frisch

(1996) The human ecology of the vegetarian diet: A bibliography. Human Ecology Review 2(2): 181–186.

17.

Dietz

Rosa

York

(2007) Driving the human ecological footprint. Frontiers in Ecology and the Environment 5(1): 13–18.

18.

Dinda

(2004) Environmental Kuznets curve hypothesis: A survey. Ecological Economics 49(4): 431–455.

19.

Duncan

(1961) From social system to ecosystem. Sociological Inquiry 31: 140–149.

20.

Ergas

York

(2012) Women’s status and carbon dioxide emissions: A quantitative cross-national analysis. Social Science Research 41: 965–976.

21.

Ewing

Reed

Galli

et al. (2010) Calculation Methodology for the National Footprint Accounts, 2010 Edition. Oakland, CA: Global Footprint Network.

22.

Folke

Kautsky

Berg

et al. (1998) The ecological footprint concept for sustainable seafood production: A review. Ecological Applications 8(1): S63–S71.

23.

Food and Agriculture Organization of the United Nations (FAO) (2010) Recent trends and possible consequences for fisheries and aquaculture. Available at: http://www.fao.org/docrep/003/x8002e/x8002e07.htm (accessed March 2018).

24.

Food and Agriculture Organization of the United Nations (FAO) (2014a) Sustainable fisheries and aquaculture for food security and nutrition. Available at: http://www.fao.org/3/a-i3844e.pdf (accessed March 2018).

25.

Food and Agriculture Organization of the United Nations (FAO) (2014b) The State of World Fisheries and Aquaculture. Rome: Food and Agriculture Organization of the United Nations. Available at: http://www.fao.org/3/a-i3720e.pdf (accessed March 2018).

26.

Food and Agriculture Organization of the United Nations (FAO) (2016) State of the world fisheries and aquaculture Rome: Food and agriculture program of the United Nations. Available at: http://www.fao.org/3/a-i5555e.pdf (accessed March 2018).

27.

Food and Agriculture Organization of the United Nations (FAO) (2018a) FAOSTAT. Food and agriculture data. Available at: http://www.fao.org/faostat/en/#home (accessed March 2018).

28.

Food and Agriculture Organization of the United Nations (FAO) (2018b) The state of world fisheries and aquaculture 2018—Meeting the sustainable Development Goals.” Rome. Licence: CC BY-NC-SA 3.0 IGO. Available at: http://www.fao.org/3/I9540EN/i9540en.pdf (accessed March 2018).

29.

Foster

(1999) Marx’s theory of metabolic rift: Classical foundations for environmental sociology. American Journal of Sociology 105(2): 366–405.

30.

Foster

Clark

York

(2010) The Ecological Rift: Capitalism’s War on the Earth. New York: Monthly Review Press.

31.

Frey

(2003) The transfer of core-based hazardous production processes to the export processing zones of the periphery: The maquiladora centers of northern Mexico. Journal of World-systems Research 9: 317–354.

32.

Galli

Moore

Brooks

et al. (2012) Mediterranean ecological footprint trends. Global Footprint Network. Available at: http://www.footprintnetwork.org/content/images/article_uploads/Mediterranean_report_FINAL.pdf

33.

Global Footprint Network (2017) Data and methodology. Available at: http://www.footprintnetwork.org/resources/data/ (accessed August 2017).

34.

Gossard

York

(2003) Social structural influences on meat consumption. Human Ecology Review 10(1): 1–9.

35.

Gould

Pellow

Schnaiberg

(2004) Interrogating the treadmill of production. Everything you wanted to know about the treadmill but were afraid to ask. Organization and Environment 17(3): 1–9.

36.

Halpern

Walbridge

Selkoe

et al. (2008) A global map of human impact on marine ecosystems. Science 319: 948–952.

37.

Havice

Campling

(2010) Shifting tides in the western and central pacific ocean tuna fishery: The political economy of regulation and industry responses. Global Environmental Politics 10: 89–114.

38.

Hilborn

Orensanz

Parma

et al. (2005) Institutions, incentives and the future of fisheries. Philosophical Transactions of the Royal Society B 360: 47–57.

39.

Holleman

(2018) Dust Bowls of Empire. Imperialism, Environmental Politics, and the Injustice of Green Capitalism. New Haven, CT: Yale University Press.

40.

Holt-Giménez

(2017) A Foodie’s Guide to Capitalism: Understanding the Political Economy of What Ee Eat. New York: New York University Press.

41.

Hornborg

(2009) Zero-sum world challenges in conceptualizing environmental load displacement and ecologically unequal exchange in the world-system. International Journal of Comparative Sociology 50: 237–262.

42.

Hornborg

McNeill

JJR

Alier

(2007) Rethinking Environmental History: World-system History and Global Environmental Change. Lanham, MD: AltaMira Press.

43.

Jennings

Stentiford

Leocadio

et al. (2016) Aquatic food security: Insights into challenges and solutions from an analysis of interactions between fisheries, aquaculture, food safety, human health, fish and human welfare, economy and environment. Fish and Fisheries 17: 893–938.

44.

Jorgenson

(2006) Global warming and the neglected greenhouse gas: A cross-national study of the social causes of methane emissions intensity, 1995. Social Forces 84: 1779–1798.

45.

Jorgenson

(2008) Structural integration and the trees: An analysis of deforestation in less-developed countries, 1990–2005. The Sociological Quarterly 49(3): 503–527.

46.

Jorgenson

(2009) The sociology of unequal exchange in ecological context: A panel study of lower-income countries, 1975–2000. Sociological Forum 24(1): 22–46.

47.

Jorgenson

Birkholz

(2010) Assessing the causes of anthropogenic methane emissions in comparative perspective. Ecological Economics 69(12): 2634–2643.

48.

Jorgenson

Clark

(2009) The economy, military, and ecologically unequal relationships in comparative perspective: A panel study of the ecological footprints of nations, 1975-2000. Social Problems 56(4): 621–646.

49.

Jorgenson

Clark

(2010) Assessing the temporal stability of the population/environment relationship: A cross-national panel study of carbon dioxide emissions, 1960-2005. Population & Environment 32(1): 27–41.

50.

Jorgenson

Clark

(2011) Societies consuming nature: A panel study of the ecological footprints of nations, 1960-2003. Social Science Research 40(1): 226–244.

51.

Jorgenson

Clark

(2012) Are the economy and the environment decoupling? A comparative international study, 1960-2005. American Journal of Sociology 118(1): 1–44.

52.

Jorgenson

Givens

(2015) The changing effect of economic development on the consumption-based carbon intensity of well-being, 1990-2008. PLoS ONE 10: e0123920.

53.

Jorgenson

Kick

(2003) Introduction: Globalization and the environment. Journal of World-systems Research 9(2): 195–203.

54.

Jorgenson

Rice

(2012) The Sociology of Ecologically Unequal exchange in comparative perspective. In: Babones

Chase-Dunn

(eds) Routledge Handbook of World-systems Analysis. London: Routledge, pp. 431–439.

55.

Jorgenson

Rice

Crowe

(2005) Unpacking the ecological footprint of nations. International Journal of Comparative Sociology 46(3): 241–260.

56.

Kharas

(2015) The unprecedented expansion of the global middle class. Brookings Institute Report. Available at: https://www.brookings.edu/research/the-unprecedented-expansion-of-the-global-middle-class-2/ (accessed December 2017).

57.

Kick

McKinney

Thompson

(2011) Intensity of food deprivation: The integrative impacts of the world system, modernization, conflict, militarization and the environment. International Journal of Comparative Sociology 52(6): 478–502.

58.

Kroodsma

Mayorga

Hochberg

et al. (2018) Tracking the global footprint of fisheries. Science 359(6378): 904–908.

59.

Lin

Hanscom

Murty

et al. (2018) Ecological footprint accounting for countries: Updates and results of the national footprint accounts, 2012—2018. Resources 7(3): 58.

60.

Longo

(2011) Global sushi: The political economy of the Mediterranean Bluefin Tuna fishery in the modern era. Journal of World Systems Research, XVII:: 403–427.

61.

Longo

Clark

(2016) An ocean of troubles: Advancing marine sociology. Social Problems 63(4): 463–479.

62.

Longo

Clark

York

(2013) The globalization of ecologically intensive aquaculture (1984-2010). Journal of Environmental Studies and Science 3(3): 297-305.

63.

Longo

Clark

York

et al. (2019) Aquaculture and the displacement of fisheries captures. Conservation Biology 33: 832–841.

64.

Longo

Clausen

Clark

(2015) The Tragedy of the Commodity. New Brunswick, NJ: Rutgers Press.

65.

McCauley

Jablonicky

Allison

et al. (2018) Wealthy countries dominate industrial fishing. Science Advances 4: eaau2161.

66.

MacKellar

Lutz

Prinz

et al. (1995) Population, households, and CO2 emissions. Population and Development Review 21(4): 849–865.

67.

McMichael

(2008) Peasants make their own history, but not just as they please. Journal of Agrarian Change 8(2-3): 205–228.

68.

McMichael

(2009a) A food regime analysis of the “world food crisis.” Agriculture and Human Values 26(4): 281.

69.

McMichael

(2009b) A food regime genealogy. The Journal of Peasant Studies 36(1): 139–169.

70.

McMichael

(2012a) Development and Social Change: A Global Perspective. Los Angeles, CA: SAGE.

71.

McMichael

(2012b) Food regime crisis and revaluing the agrarian question. In: McMichael

(ed.) Rethinking Agricultural Policy Regimes: Food Security, Climate Change and the Future Resilience of Global Agriculture. Bingley: Emerald Group Publishing, pp. 99–122.

72.

National Oceanic and Atmospheric Association (NOAA) (2017) Imports and exports of fishery products annual summary. Available at: https://www.st.nmfs.noaa.gov/Assets/commercial/trade/Trade2017.pdf (accessed March 2019).

73.

National Oceanic and Atmospheric Association (NOAA) (2019) U.S. Aquaculture. Available at: https://www.fisheries.noaa.gov/national/aquaculture/us-aquaculture (accessed March 2019).

74.

Otero

Pechlaner

Gürcan

(2013) The political economy of “food security” and trade: Uneven and combined dependency. Rural Sociology 78(3): 263–289.

75.

Pauly

Zeller

(2016) Global Atlas of Marine Fisheries: A Critical Appraisal of Catches and Ecosystem Impacts. Washington, DC: Island Press.

76.

Pauly

Christensen

Dalsgaard

et al. (1998) Fishing down marine food webs. Science 279(5352): 860–863.

77.

Rabe-Hesketh

Skrondal

(2012) Multilevel and Longitudinal Modeling Using Stata, 3rd edn. College Station, TX: Stata Press.

78.

Rice

(2009) The transnational organization of production and uneven environmental degradation and change in the world economy. International Journal of Comparative Sociology 50(3–4): 215–236.

79.

Rosa

York

Dietz

(2004) Reflections on the STIRPAT research program. Environment, Technology and Society 9: 1–42.

80.

Schnaiberg

(1980) The Environment: From Surplus to Scarcity. New York: Oxford University Press.

81.

Shandra

Leckband

McKinney

LLA

et al. (2009) Ecologically unequal exchange, world polity, and biodiversity loss a cross-national analysis of threatened mammals. International Journal of Comparative Sociology 50: 285–310.

82.

Smith

ADM

Brown

Bulman

et al. (2011) Impacts of fishing low–trophic level species on marine ecosystems. Science 333(6046): 1147–1150.

83.

Spaargaren

Oosterveer

Loeber

(2012) Sustainability transitions in food consumption, retail and production. In: Spaargaren

Oosterveer

Loeber

(eds) Food Practices in Transition; Changing Food Consumption, Retail and Production in the Age of Reflexive Modernity. Abingdon: Routledge, pp. 1–34.

84.

Stern

Dietz

Ruttan

et al. (1997) Environmentally Significant Consumption: Research Directions. Washington, DC: National Academy Press.

85.

Stramma

Prince

Schmidtko

et al. (2012) Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes. Nature Climate Change 2(1): 33–37.

86.

Swartz

Sala

Tracy

et al. (2010) The spatial expansion and ecological footprint of fisheries (1950 to Present). PLoS ONE 5(12): e15143.

87.

Torres-Reyna

(2007) Panel data analysis fixed and random effects using STATA. Princeton University. Available at: https://www.princeton.edu/~otorres/Panel101.pdf

88.

United Nations (2015a) The 2030 agenda for sustainable development. Available at: https://sustainabledevelopment.un.org/post2015/transformingourworld (accessed December 2017).

89.

United Nations (2015b) World population projected to reach 9.7 billion by 2050. Available at: http://www.un.org/en/development/desa/news/population/2015-report.html (accessed December 2017).

90.

United Nations (2017) 6th Economic and social council youth form concept note. Ecosoc Youth Forum. Available at: https://www.un.org/ecosoc/sites/www.un.org.ecosoc/files/files/en/2017doc/Conserve%20and%20sustainably%20use%20the%20oceans%2C%20seas%20and%20marine%20resources%20for%20sustainable%20development.pdf

91.

Van Mulekom

Axelsson

Batungbacal

et al. (2006) Trade and export orientation of fisheries in Southeast Asia: Under-priced export at the expense of domestic food security and local economies. Ocean & Coastal Management 49: 546–561.

92.

Victorero

Watling

Deng Palomares

et al. (2018) Out of sight, but within reach: A global history of bottom-trawled deep-sea fisheries from >400 m depth. Frontiers in Marine Science 5.

93.

Wallerstein

(1974) The Modern World-System I: Capitalist Agriculture and the Origins of the European World-Economy in the Sixteenth Century. Oakland: University of California Press.

94.

Watson

Pauly

(2001) Systematic distortions in world fisheries catch trends. Nature 414: 534–536.

95.

World Bank (2014a) Raising more fish to meet rising demand. Available at: http://www.worldbank.org/en/news/feature/2014/02/05/raising-more-fish-to-meet-rising-demand (accessed March 2018).

96.

World Bank (2014b) Africa Program for Fisheries: Improving Livelihoods and Creating Wealth Through Sustainable Fisheries. Washington, DC: World Bank Group.

97.

World Bank (2017) Oceans, fisheries, and coastal economies. Available at: http://www.worldbank.org/en/topic/environment/brief/oceans (accessed March 2018).

98.

World Bank (2018) World Development Indicators. Available at: http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators (accessed March 2018).

99.

Worm

Hilborn

Baum

et al. (2009) Rebuilding global fisheries. Science 325: 578–585.

100.

York

Gossard

(2004) Cross-national meat and fish consumption: Exploring the effects of modernization and ecological context. Ecological Economics 48(3): 293–302.

101.

York

Rosa

Dietz

(2003) Footprints on the earth: The environmental consequences of modernity. American Sociological Review 68: 279–300.

Examining the effect of economic development,region,and time period on the fisheries footprints of nations (1961–2010)

Abstract

Keywords

Introduction

Background

Environmental sociology, food systems, and fisheries

Data and methods

Dependent variable: fisheries footprint of nations

Independent variables

Models

Results

Discussion

Ecological impact or food security?

Comparing economic development across levels of affluence

Economic development, less-affluent nations, and fisheries footprint

Conclusion

Footnotes

Funding

ORCID iD

References