The Financialization of Food: An Inquiry into the 2008-2011 Food Price Spikes

This dissertation investigates the question: has financial speculation contributed to global food price volatility since the mid 2000s? I problematize the mainstream academic literature on the 2008-2011 food price spikes as being dominated by neoclassical economic perspectives and offer new conceptual and empirical insights into the relationship between financial speculation and food. Presented in three journal style manuscripts, manuscript one uses circuits of capital to conceptualize the link between financial speculators in the global north and populations in the global south. Manuscript two argues that what makes commodity index speculation (aka ‘index funds’ or index swaps) novel is that it provides institutional investors with what Clapp (2014) calls “financial distance” from the biopolitical implications of food speculation. Finally, manuscript three combines Gramsci’s concepts of hegemony and ‘the intellectual’ with the concept of performativity to investigate the ideological role that public intellectuals and the rhetorical actor the market play in the proliferation and governance of commodity index speculation. The first two manuscripts take an empirically mixed method approach by combining regression analysis with discourse analysis, while the third relies on interview data and discourse analysis. The findings show that financial speculation by index swap dealers and hedge funds did indeed significantly contribute to the price volatility of food commodities between June 2006 and December 2014. The results from the interview data affirm these findings. The discourse analysis of the interview data shows that public intellectuals and rhetorical characters such as ‘the market’ play powerful roles in shaping how food speculation is promoted, regulated and normalized. The significance of the findings is three-fold. First, the empirical findings show that a link does exist between financial speculation and food price volatility. Second, the findings indicate that the post-2008 CFTC and the Dodd-Frank reforms are unlikely to reduce financial speculation or the price volatility that it causes. Third, the findings suggest that institutional investors (such as pension funds) should think critically about how they use commodity index speculation as a way of generating financial earnings.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.