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• 1. [Article] Eco-labels on the range and in the forests of the interior Northwest

  2 pages

  Citation × Citation Citation Abstract Copy Title: Eco-labels on the range and in the forests of the interior Northwest Author: Gwin, Lauren, Ellison, Autumn, Moseley, Cassandra Year: 2015, 2013 2 pages Title: Eco-labels on the range and in the forests of the interior Northwest Author: Gwin, Lauren, Ellison, Autumn, Moseley, Cassandra Year: 2015, 2013 2 pages Close

• 2. [Article] Superstar CEOs and Innovation

  I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial ...

  Citation × Citation Citation Abstract Copy Title: Superstar CEOs and Innovation Author: Park, Keun Jae Year: 2015 I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial entrenchment and reduce managers’ efforts to create valuable innovations because they will either pursue value-decreasing pet projects or simply enjoy the quiet life (agency hypothesis). Conversely, the managerial myopia theory predicts that these awards may increase managerial job security and allow the CEO to focus on long-term innovative projects rather than on boosting the stock price through current earnings. Finally, the overconfidence theory predicts that these awards increase managerial incentives to innovate if they make CEOs overconfident. In my first set of tests, I find evidence that is consistent with the managerial myopia and overconfidence theories: award-winning CEOs innovate more than otherwise similar CEOs who do not win awards, both in terms of the number of patents and the number of citations per patent during the period 1992–2002. After further examination, the evidence continues to be consistent with the managerial myopia theory: there is a clear increase in CEO power and job security after winning a prestigious award and no such increase in overconfidence. I also document that the positive effect of CEO awards on innovation is weaker for firms with high institutional ownership. Overall, the evidence suggests that winning a prestigious award relieves managerial myopia and brings positive long-term benefits for the firm. Title: Superstar CEOs and Innovation Author: Park, Keun Jae Year: 2015 I empirically evaluate three theoretical views of whether and how winning a high-profile CEO award affects innovation decisions. The agency theory predicts that receiving an award will increase managerial entrenchment and reduce managers’ efforts to create valuable innovations because they will either pursue value-decreasing pet projects or simply enjoy the quiet life (agency hypothesis). Conversely, the managerial myopia theory predicts that these awards may increase managerial job security and allow the CEO to focus on long-term innovative projects rather than on boosting the stock price through current earnings. Finally, the overconfidence theory predicts that these awards increase managerial incentives to innovate if they make CEOs overconfident. In my first set of tests, I find evidence that is consistent with the managerial myopia and overconfidence theories: award-winning CEOs innovate more than otherwise similar CEOs who do not win awards, both in terms of the number of patents and the number of citations per patent during the period 1992–2002. After further examination, the evidence continues to be consistent with the managerial myopia theory: there is a clear increase in CEO power and job security after winning a prestigious award and no such increase in overconfidence. I also document that the positive effect of CEO awards on innovation is weaker for firms with high institutional ownership. Overall, the evidence suggests that winning a prestigious award relieves managerial myopia and brings positive long-term benefits for the firm. Close

• 3. [Article] Robust Large Margin Approaches for Machine Learning in Adversarial Settings

  Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform ...

  Citation × Citation Citation Abstract Copy Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Title: Robust Large Margin Approaches for Machine Learning in Adversarial Settings Author: Torkamani, MohamadAli Year: 2016 Machine learning algorithms are invented to learn from data and to use data to perform predictions and analyses. Many agencies are now using machine learning algorithms to present services and to perform tasks that used to be done by humans. These services and tasks include making high-stake decisions. Determining the right decision strongly relies on the correctness of the input data. This fact provides a tempting incentive for criminals to try to deceive machine learning algorithms by manipulating the data that is fed to the algorithms. And yet, traditional machine learning algorithms are not designed to be safe when confronting unexpected inputs. In this dissertation, we address the problem of adversarial machine learning; i.e., our goal is to build safe machine learning algorithms that are robust in the presence of noisy or adversarially manipulated data. Many complex questions -- to which a machine learning system must respond -- have complex answers. Such outputs of the machine learning algorithm can have some internal structure, with exponentially many possible values. Adversarial machine learning will be more challenging when the output that we want to predict has a complex structure itself. In this dissertation, a significant focus is on adversarial machine learning for predicting structured outputs. In this thesis, first, we develop a new algorithm that reliably performs collective classification: It jointly assigns labels to the nodes of graphed data. It is robust to malicious changes that an adversary can make in the properties of the different nodes of the graph. The learning method is highly efficient and is formulated as a convex quadratic program. Empirical evaluations confirm that this technique not only secures the prediction algorithm in the presence of an adversary, but it also generalizes to future inputs better, even if there is no adversary. While our robust collective classification method is efficient, it is not applicable to generic structured prediction problems. Next, we investigate the problem of parameter learning for robust, structured prediction models. This method constructs regularization functions based on the limitations of the adversary in altering the feature space of the structured prediction algorithm. The proposed regularization techniques secure the algorithm against adversarial data changes, with little additional computational cost. In this dissertation, we prove that robustness to adversarial manipulation of data is equivalent to some regularization for large-margin structured prediction, and vice versa. This confirms some of the previous results for simpler problems. As a matter of fact, an ordinary adversary regularly either does not have enough computational power to design the ultimate optimal attack, or it does not have sufficient information about the learner's model to do so. Therefore, it often tries to apply many random changes to the input in a hope of making a breakthrough. This fact implies that if we minimize the expected loss function under adversarial noise, we will obtain robustness against mediocre adversaries. Dropout training resembles such a noise injection scenario. Dropout training was initially proposed as a regularization technique for neural networks. The procedure is simple: At each iteration of training, randomly selected features are set to zero. We derive a regularization method for large-margin parameter learning based on dropout. Our method calculates the expected loss function under all possible dropout values. This method results in a simple objective function that is efficient to optimize. We extend dropout regularization to non-linear kernels in several different directions. We define the concept of dropout for input space, feature space, and input dimensions, and we introduce methods for approximate marginalization over feature space, even if the feature space is infinite-dimensional. Empirical evaluations show that our techniques consistently outperform the baselines on different datasets. Close

• 4. [Article] EXAMINING THE RELATION BETWEEN CONTEXTUAL FIT AND IMPLEMENTATION FIDELITY ON BEHAVIOR SUPPORT PLANS AND STUDENT OUTCOMES

  An extensive body of empirical evidence indicates that function-based behavior support plans are likely to be more effective and efficient in school settings than plans that are not function-based. Designing ...

  Citation × Citation Citation Abstract Copy Title: EXAMINING THE RELATION BETWEEN CONTEXTUAL FIT AND IMPLEMENTATION FIDELITY ON BEHAVIOR SUPPORT PLANS AND STUDENT OUTCOMES Author: Monzalve, Manuel Year: 2016 An extensive body of empirical evidence indicates that function-based behavior support plans are likely to be more effective and efficient in school settings than plans that are not function-based. Designing technically adequate behavior support plans, however, is not sufficient to ensure that these plans will be implemented with fidelity by school staff. The contextual “fit” of support plan procedures with the values, skills, resources and administrative support of implementing personnel also affects the likelihood of implementation. In this dissertation a single-subject concurrent multiple baseline design across participants was used to examine the efficacy of the Contextual Fit Enhancement Protocol (CF Intervention). The CF Intervention was designed to improve the contextual fit of support plans for four elementary school students with problem behaviors. This CF Intervention was designed based on the assumption that when procedures included in a plan match the values, skills, administrative support and resources of the people responsible for implementation the plans will be implemented with higher fidelity and will be more likely to produce desired student outcomes. Results from the study indicate that after implementation of the CF Intervention, support plans that were already technically adequate improved in contextual fit: instructional staff (a) had a better understanding of behavioral procedures being used, (b) received specific, systematic feedback about its implementation, (c) perceived high levels of administrative support, and (d) perceived a collective commitment to improve current plans. Following implementation of the CF Intervention, substantial increases in implementation fidelity and decreases in student problem behavior were observed. In addition, teacher participants rated the CF Intervention process as effective and efficient. Limitations and implications for future research, practice, and training are discussed. Title: EXAMINING THE RELATION BETWEEN CONTEXTUAL FIT AND IMPLEMENTATION FIDELITY ON BEHAVIOR SUPPORT PLANS AND STUDENT OUTCOMES Author: Monzalve, Manuel Year: 2016 An extensive body of empirical evidence indicates that function-based behavior support plans are likely to be more effective and efficient in school settings than plans that are not function-based. Designing technically adequate behavior support plans, however, is not sufficient to ensure that these plans will be implemented with fidelity by school staff. The contextual “fit” of support plan procedures with the values, skills, resources and administrative support of implementing personnel also affects the likelihood of implementation. In this dissertation a single-subject concurrent multiple baseline design across participants was used to examine the efficacy of the Contextual Fit Enhancement Protocol (CF Intervention). The CF Intervention was designed to improve the contextual fit of support plans for four elementary school students with problem behaviors. This CF Intervention was designed based on the assumption that when procedures included in a plan match the values, skills, administrative support and resources of the people responsible for implementation the plans will be implemented with higher fidelity and will be more likely to produce desired student outcomes. Results from the study indicate that after implementation of the CF Intervention, support plans that were already technically adequate improved in contextual fit: instructional staff (a) had a better understanding of behavioral procedures being used, (b) received specific, systematic feedback about its implementation, (c) perceived high levels of administrative support, and (d) perceived a collective commitment to improve current plans. Following implementation of the CF Intervention, substantial increases in implementation fidelity and decreases in student problem behavior were observed. In addition, teacher participants rated the CF Intervention process as effective and efficient. Limitations and implications for future research, practice, and training are discussed. Close

• 5. [Article] A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change

  Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from ...

  Citation × Citation Citation Abstract Copy Title: A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change Author: Praskievicz, Sarah Year: 2014 Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from climate change. Climate-driven hydrological changes are potentially significant for the fluvial geomorphology of river systems. In unchanging climatic and tectonic conditions, a river's morphology will develop in equilibrium with inputs of water and sediment, but climate change represents a potential forcing on these variables that may push the system into disequilibrium and cause significant changes in river morphology. Geomorphic factors, such as channel geometry, planform, and sediment transport, are major determinants of the value of river systems, including their suitability for threatened and endangered species and for human uses of water. This dissertation research uses a hierarchical modeling approach to investigate potential impacts of anthropogenic climate change on river morphology in the interior Pacific Northwest. The research will address the following theoretical and methodological objectives: 1) Develop downscaled climate change scenarios, based on regional climate-model output, including changes in daily minimum and maximum temperature and precipitation. 2) Estimate how climate change scenarios affect river discharge and suspended-sediment load, using a basin-scale hydrologic model. 3) Examine potential impacts of climate-driven hydrologic changes on stream power and shear stress, bedload sediment transport, and river morphology, including channel geometry and planform. The downscaling approach, based on empirically-estimated local topographic lapse rates, produces high-resolution climate grids with positive forecast skill. The hydrologic modeling results indicate that projected climate change in the study rivers will change the annual cycle of hydrology, with increased winter discharge, a decrease in the magnitude of the spring snowmelt peak, and decreased summer discharge. Geomorphic modeling results suggest that changes in reach-averaged bedload transport are highly sensitive to likely changes in the recurrence interval of the critical discharge needed to mobilize bed sediments. This dissertation research makes an original contribution to the climate-change impacts literature by linking Earth processes across a wide range of spatial scales to project changes in river systems that may be significant for management of these systems for societal and ecological benefits. This dissertation includes unpublished co-authored material. Title: A Hierarchical Modeling Approach to Simulating the Geomorphic Response of River Systems to Climate Change Author: Praskievicz, Sarah Year: 2014 Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from climate change. Climate-driven hydrological changes are potentially significant for the fluvial geomorphology of river systems. In unchanging climatic and tectonic conditions, a river's morphology will develop in equilibrium with inputs of water and sediment, but climate change represents a potential forcing on these variables that may push the system into disequilibrium and cause significant changes in river morphology. Geomorphic factors, such as channel geometry, planform, and sediment transport, are major determinants of the value of river systems, including their suitability for threatened and endangered species and for human uses of water. This dissertation research uses a hierarchical modeling approach to investigate potential impacts of anthropogenic climate change on river morphology in the interior Pacific Northwest. The research will address the following theoretical and methodological objectives: 1) Develop downscaled climate change scenarios, based on regional climate-model output, including changes in daily minimum and maximum temperature and precipitation. 2) Estimate how climate change scenarios affect river discharge and suspended-sediment load, using a basin-scale hydrologic model. 3) Examine potential impacts of climate-driven hydrologic changes on stream power and shear stress, bedload sediment transport, and river morphology, including channel geometry and planform. The downscaling approach, based on empirically-estimated local topographic lapse rates, produces high-resolution climate grids with positive forecast skill. The hydrologic modeling results indicate that projected climate change in the study rivers will change the annual cycle of hydrology, with increased winter discharge, a decrease in the magnitude of the spring snowmelt peak, and decreased summer discharge. Geomorphic modeling results suggest that changes in reach-averaged bedload transport are highly sensitive to likely changes in the recurrence interval of the critical discharge needed to mobilize bed sediments. This dissertation research makes an original contribution to the climate-change impacts literature by linking Earth processes across a wide range of spatial scales to project changes in river systems that may be significant for management of these systems for societal and ecological benefits. This dissertation includes unpublished co-authored material. Close

• 6. [Article] PARKS, PEOPLE, & PLANNING: Open Spaces in Small Places

  52 pages

  Citation × Citation Citation Abstract Copy Title: PARKS, PEOPLE, & PLANNING: Open Spaces in Small Places Author: Nichols, Dana Year: 2016 52 pages Title: PARKS, PEOPLE, & PLANNING: Open Spaces in Small Places Author: Nichols, Dana Year: 2016 52 pages Close

• 7. [Article] Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene

  8 pages

  Citation × Citation Citation Abstract Copy Title: Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene Author: Houle, W. A., Brown, H. M., Griffith, O. H. Year: 2016, 1979 8 pages Title: Photoelectric properties and detection of the aromatic carcinogens benzo(a)pyrene and dimethylbenzanthracene Author: Houle, W. A., Brown, H. M., Griffith, O. H. Year: 2016, 1979 8 pages Close

• 8. [Article] SURPRISING PROPERTIES OF STATIC AND FLOWING GRANULAR MATTER

  The Janssen effect is a unique property of confined granular materials experiencing gravitational compaction in which the pressure at the bottom saturates with increasing filling height due to frictional ...

  Citation × Citation Citation Abstract Copy Title: SURPRISING PROPERTIES OF STATIC AND FLOWING GRANULAR MATTER Author: Karim, Mohammad Year: 2017 The Janssen effect is a unique property of confined granular materials experiencing gravitational compaction in which the pressure at the bottom saturates with increasing filling height due to frictional interactions with side walls. In this paper we replace gravitational compaction with frictional compaction. We study friction-compacted 2D granular materials confined within fixed boundaries on a horizontal conveyor belt. We find that even with high friction side walls the Janssen effect completely vanishes. Our results demonstrate that gravity-compacted granular systems are inherently different from friction-compacted systems in at least one important way: vibrations induced by sliding friction with the driving surface relax away tangential forces on the walls. Remarkably, we find that the Janssen effect can be recovered by replacing the straight side walls with a sawtooth pattern. The mechanical force introduced by varying the sawtooth angle $\theta$ can be viewed as equivalent to a tunable friction force. By construction, this mechanical friction force cannot be relaxed away by vibrations in the system. This work is described in Chapter 2 and has been published in \textit{Physical Review Letters}. We experimentally study quasi-2d dilute granular flow around intruders whose shape, size and relative impact speed are systematically varied. Direct measurement of the flow field reveals that three in-principle independent measurements of the non-uniformity of the flow field are in fact all linearly related: 1) granular temperature, 2) flow field divergence and 3) shear-strain rate. The shock front is defined as the local maxima in each of these measurements. The shape of the shock front is well described by an inverted catenary and is driven by the formation of a dynamic arch during steady flow. We find universality in the functional form of the shock front within the range of experimental values probed. Changing the intruder size, concavity and impact speed only results in a scaling and shifting of the shock front. We independently measure the horizontal lift force on the intruder and find that it can be understood as a result of the interplay between the shock profile and the intruder shape. This work is described in Chapter 3. This dissertation includes previously published and unpublished co-authored material. Title: SURPRISING PROPERTIES OF STATIC AND FLOWING GRANULAR MATTER Author: Karim, Mohammad Year: 2017 The Janssen effect is a unique property of confined granular materials experiencing gravitational compaction in which the pressure at the bottom saturates with increasing filling height due to frictional interactions with side walls. In this paper we replace gravitational compaction with frictional compaction. We study friction-compacted 2D granular materials confined within fixed boundaries on a horizontal conveyor belt. We find that even with high friction side walls the Janssen effect completely vanishes. Our results demonstrate that gravity-compacted granular systems are inherently different from friction-compacted systems in at least one important way: vibrations induced by sliding friction with the driving surface relax away tangential forces on the walls. Remarkably, we find that the Janssen effect can be recovered by replacing the straight side walls with a sawtooth pattern. The mechanical force introduced by varying the sawtooth angle $\theta$ can be viewed as equivalent to a tunable friction force. By construction, this mechanical friction force cannot be relaxed away by vibrations in the system. This work is described in Chapter 2 and has been published in \textit{Physical Review Letters}. We experimentally study quasi-2d dilute granular flow around intruders whose shape, size and relative impact speed are systematically varied. Direct measurement of the flow field reveals that three in-principle independent measurements of the non-uniformity of the flow field are in fact all linearly related: 1) granular temperature, 2) flow field divergence and 3) shear-strain rate. The shock front is defined as the local maxima in each of these measurements. The shape of the shock front is well described by an inverted catenary and is driven by the formation of a dynamic arch during steady flow. We find universality in the functional form of the shock front within the range of experimental values probed. Changing the intruder size, concavity and impact speed only results in a scaling and shifting of the shock front. We independently measure the horizontal lift force on the intruder and find that it can be understood as a result of the interplay between the shock profile and the intruder shape. This work is described in Chapter 3. This dissertation includes previously published and unpublished co-authored material. Close

• 9. [Article] Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon

  144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree ...

  Citation × Citation Citation Abstract Copy Title: Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon Author: Greenblatt, Dana Rebecca Year: 2017, 2016 144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016 Title: Citizen Access to Complex Environmental Decison Making: A Case Study of the Proposed LNG Projects in Oregon Author: Greenblatt, Dana Rebecca Year: 2017, 2016 144 pages. A thesis presented to the Department of Planning, Public Policy and Management and the Clark Honors College of the University of Oregon in partial fulfillment of the requirements for degree of Bachelor of Arts, Fall 2016 Close

• 10. [Article] Back to Production: Labor-Value Commodity Chains and the Imperialist World Economy

  Despite the complexities and decentralization that characterize global supply chains in today’s world economy, imperialist relations of exchange continue to prevail, due to the fact that the differences ...

  Citation × Citation Citation Abstract Copy Title: Back to Production: Labor-Value Commodity Chains and the Imperialist World Economy Author: Suwandi, Intan Year: 2017 Despite the complexities and decentralization that characterize global supply chains in today’s world economy, imperialist relations of exchange continue to prevail, due to the fact that the differences between wages of North and South is greater than the difference of their productivities. This dissertation examines the global exploitation of labor that mostly occurs in the global South, as a form of such imperialist relations, particularly under the domination of multinational firms emanating primarily from the core of the system. I start by laying out the theoretical and empirical groundwork for the labor-value commodity chains framework that puts labor, along with the question of control and class, at the center of its formulation. By incorporating a calculation of cross-national variation in unit labor costs in manufacturing—a measurement that combines labor productivity with wage costs in a manner closely related to Marx’s theory of exploitation—the labor-value chains framework is a means to operationalize exploitation within the framework of the labor theory of value. Findings show that the global organization of labor-value chains is a means to extract surplus value through the exploitation of workers in the global South, where not only are wages low, but productivity is also high. I then show the concrete processes of how global North capital, personified in multinational corporations, captures value from the global South by applying systemic rationalization and flexible systems as mechanisms to exert control over their dependent suppliers in labor-value commodity chains. The burden of such mechanisms is borne by the workers —the direct producers of commodities—employed by these dependent suppliers. Case studies of two Indonesian companies that supply to multinationals are presented to illustrate these phenomena at the point of production. This observation further suggests that labor-value commodity chains are a form of unequal exchange and thus reveal the imperialistic characteristics of the world economy. This dissertation includes both previously published and coauthored materials. Title: Back to Production: Labor-Value Commodity Chains and the Imperialist World Economy Author: Suwandi, Intan Year: 2017 Despite the complexities and decentralization that characterize global supply chains in today’s world economy, imperialist relations of exchange continue to prevail, due to the fact that the differences between wages of North and South is greater than the difference of their productivities. This dissertation examines the global exploitation of labor that mostly occurs in the global South, as a form of such imperialist relations, particularly under the domination of multinational firms emanating primarily from the core of the system. I start by laying out the theoretical and empirical groundwork for the labor-value commodity chains framework that puts labor, along with the question of control and class, at the center of its formulation. By incorporating a calculation of cross-national variation in unit labor costs in manufacturing—a measurement that combines labor productivity with wage costs in a manner closely related to Marx’s theory of exploitation—the labor-value chains framework is a means to operationalize exploitation within the framework of the labor theory of value. Findings show that the global organization of labor-value chains is a means to extract surplus value through the exploitation of workers in the global South, where not only are wages low, but productivity is also high. I then show the concrete processes of how global North capital, personified in multinational corporations, captures value from the global South by applying systemic rationalization and flexible systems as mechanisms to exert control over their dependent suppliers in labor-value commodity chains. The burden of such mechanisms is borne by the workers —the direct producers of commodities—employed by these dependent suppliers. Case studies of two Indonesian companies that supply to multinationals are presented to illustrate these phenomena at the point of production. This observation further suggests that labor-value commodity chains are a form of unequal exchange and thus reveal the imperialistic characteristics of the world economy. This dissertation includes both previously published and coauthored materials. Close