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Journal Articles | 2021

Impact of gendered participation in market-linked value-chains on economic outcomes: Evidence from India

Vivek Pandey, Hari K. Nagarajan, and Deepak Kumar

Food Policy

We combine the results of a laboratory experiment and survey of agricultural households to estimate the welfare impacts of a market-based intervention with links to value-chain. We investigate whether increased participation by women in such value-chains improves their relative bargaining power and therefore their ability to contribute to household welfare. We utilize the National Dairy Plan-I as an example to estimate pathways through which such interventions may affect household decision-making. We find that the program design significantly increased women’s relative bargaining power within the household, which acts as an important channel for enhancing women’s ability to contribute to household welfare through decision-making processes related to food, nutrition, branded food items, and child education. The instrumental variable estimates show that if value-chains are gender-neutral then direct program effects are significant but small. Participation in National Dairy Plan-I, on the other hand, improved women’s relative bargaining power, allowing them to make substantial contribution to welfare. We show that when women’s bargaining power mediates participation in value-chains, the nutrition elasticity rises from 0.26 to 0.94. While the impact on analytical ability (i.e., mathematics Z-score) is negligible in the absence of female agency, performance improves by 0.35σ when gendered element(s) of the program are allowed to act as a channel.

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Journal Articles | 2021

Elite vs. mass politics of sustainability transitions

Nicolas Schmid, Christopher Beaton, Florian Kern, Neil McCulloch, Anish Sugathan, and Johannes Urpelainen

Environmental Innovation and Societal Transitions

While the past decade of transitions scholarship has increasingly acknowledged the centrality of politics, key questions on transition politics deserve further research. Here, we develop a heuristic framework from the discipline of political science that separates transition politics into the classic categories of interests, ideas, institutions, as well as elite and mass politics. Based on this framework, we conduct a review of existing transitions literature on politics. We find that some areas of our framework are better covered than others. For instance, while the institutional foundations of elite politics are relatively well researched, there are only few studies on interests and ideas in mass politics. In geographical and sectoral terms, research is biased toward energy transitions in Europe and North America. Based on our review, we map areas for future research we believe to be indispensable to better understand varieties of transition politics.

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Journal Articles | 2021

Gravity and depth of social media networks

Pritha Guha, Avijit Bansal, Apratim Guha, and Anindya S. Chakrabarti

Journal of Complex Networks

Structures of social media networks provide a composite view of dyadic connectivity across social actors, which reveals the spread of local and global influences of those actors in the network. Although social media network is a construct inferred from online activities, an underlying feature is that the actors also possess physical locational characteristics. Using a unique dataset from Facebook that provides a snapshot of the complete enumeration of county-to-county connectivity in the USA (in April 2016), we exploit these two dimensions viz. online connectivity and geographic distance between the counties, to establish a mapping between the two. We document two major results. First, social connectivity wanes as physical distance increases between county-pairs, signifying gravity-like behaviour found in economic activities like trade and migration. Two, a geometric projection of the network on a lower-dimensional space allows us to quantify depth of the nodes in the network with a well-defined metric. Clustering of this projected network reveals that the counties belonging to the same cluster tend to exhibit geographic proximity, a finding we quantify with regression-based analysis as well. Thus, our analysis of the social media networks demonstrates a unique relationship between physical spatial clustering and node connectivity-based clustering. Our work provides a novel characterization of geometric distance in the study of social network analysis, linking abstract network topology with its statistical properties.

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Journal Articles | 2021

Competitive hub location problem: Model and solution approaches

Richa Tiwari, Sachin Jayaswal, and Ankur Sinha

Transportation Research Part B: Methodological

In this paper, we study the hub location problem of an airline that wants to set up its hub and spoke network, in order to maximize its market share in a competitive market. The market share is maximized under the assumption that customers choose amongst competing airlines on the basis of utility provided by the respective airlines. We provide model formulations for the airline’s problem for two alternate network settings: one in the multiple allocation setting and another in the single allocation setting. Both these formulations are non-linear integer programs, which are intractable for most of the off-the-shelf commercial solvers. We propose two alternate approaches for each of the formulations to solve them optimally. The first among them is based on a mixed integer second order conic program reformulation, and the second uses Kelley’s cutting plane method within Lagrangian relaxation. On the basis of extensive numerical tests on well-known data-sets (CAB and AP), we conclude that the Kelley’s cutting plane within Lagrangian relaxation is computationally the best for both the single and multiple allocation settings, especially for large instances. We are able to solve instances upto 50 nodes from AP data-set within 120 and 10 minutes of CPU time for single and multiple allocation settings, respectively, which were unsolved by mixed integer second order cone based reformulation or Kelley’s cutting plane algorithm in the maximum allowed CPU time (3 hours for single allocation and 1 hour for multiple allocation).

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Journal Articles | 2021

Solving bilevel optimization problems using Kriging Approximations

Ankur Sinha and Vaseem Shaikh

IEEE Transactions on Cybernetics

Bilevel optimization involves two levels of optimization, where one optimization problem is nested within the other. The structure of the problem often requires solving a large number of inner optimization problems that make these kinds of optimization problems expensive to solve. The reaction set mapping and the lower level optimal value function mapping are often used to reduce bilevel optimization problems to a single level; however, the mappings are not known a priori , and the need is to be estimated. Though there exist a few studies that rely on the estimation of these mappings, they are often applied to problems where one of these mappings has a known form, that is, piecewise linear, convex, etc. In this article, we utilize both these mappings together to solve general bilevel optimization problems without any assumptions on the structure of these mappings. Kriging approximations are created during the generations of an evolutionary algorithm, where the population members serve as the samples for creating the approximations. One of the important features of the proposed algorithm is the creation of an auxiliary optimization problem using the Kriging-based metamodel of the lower level optimal value function that solves an approximate relaxation of the bilevel optimization problem. The auxiliary problem when used for local search is able to accelerate the evolutionary algorithm toward the bilevel optimal solution. We perform experiments on two sets of test problems and a problem from the domain of control theory. Our experiments suggest that the approach is quite promising and can lead to substantial savings when solving bilevel optimization problems. The approach is able to outperform state-of-the-art methods that are available for solving bilevel problems, in particular, the savings in function evaluations for the lower level problem are substantial with the proposed approach.

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Journal Articles | 2021

Seasonal time trade-offs and nutrition outcomes for women in agriculture: Evidence from rural India

Vidya Vemireddy and Prabhu L Pingali

Food Policy

Women in agriculture are involved in agricultural activities and are solely responsible for household-level unpaid work. They face severe time trade-offs between agricultural and household activities across crop seasons. Recent literature suggests that these time trade-offs may negatively impact their nutrition. However, there is no quantitative evidence exploring this relationship within an agricultural context. This paper addresses this research gap by analyzing the relationship between women’s time trade-offs and their nutritional outcomes. Using a unique ten-month primary panel data of 960 women from India, our findings show that women are severely time-constrained, as they contribute significantly to agricultural as well as domestic work. Our results show that during peak seasons relative to lean seasons, women’s time trade-offs (rising opportunity cost of time) are negatively associated with the intake of calories, proteins, iron,zinc and Vitamin A. We show that this negative relationship is manifested severely among women who are landless and cultivate paddy alone (food crop) or paddy and cotton (mixed crop). This study highlights the gendered role of agricultural activities in rural households and the need to recognize time as a scarce resource when implementing policies and programs involving women in agriculture. We contribute to the literature of agriculture-nutrition linkages by examining the the time use pathway in detail. Besides providing novel metrics, we discuss several policy implications to reduce women’s time constraints and enhance their nutrition.

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Journal Articles | 2021

Space between products on display: The impact of interspace on consumer estimation of product size

Yuli Zhang, Hyokjin Kwak, Marina Puzakova, and Charles R. Taylor

Journal of the Academy of Marketing Science

This research examines the effect that leaving space between products has on consumers’ estimation of product size. We theorize and empirically confirm that when space is left between products (i.e., the display is interspaced), consumers are better able to distinguish the product from the environment, which results in more attention being devoted to the product, and, in turn, larger estimation of the product’s size. Furthermore, we demonstrate downstream outcomes (i.e., consumer choices, purchase intentions) of the effect of interspatial product display on product size estimates; that is consumers react more favorably to products that are displayed in an interspatial product display when their product usage goals require large-sized products. Meanwhile, non-interspatial product displays are preferred when consumers holding a consumption goal geared to a small product size. Finally, we validate and solidify these novel interspace effects in both advertising and retailing contexts via a series of six studies including five different product types (e.g., shampoo, food, water bottle).

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Journal Articles | 2021

How does the adoption of digital payment technologies influence unorganized retailers’ performance? An investigation in an emerging market

Anirban Adhikary, Krishna Sundar Diatha, Sourav Bikash Borah, and Amalesh Sharma

Journal of the Academy of Marketing Science

Unorganized retail dominates the retail landscape across emerging markets (EMs) and is undergoing rapid digitalization. However, the extant literature has not explored the impact of digital payment system adoption on unorganized retailer (UR) performance. By conducting three related studies and relying on the tenets of the resource-based view of firms, we show that digital payment technologies’ adoption increases economic performance (i.e., revenue) for a sample of 403 EM URs. This effect is enhanced by such retailers’ prioritization of technological investments and attenuated by their credit facilities. We find that card-based and app-based technologies positively impact UR performance. URs can maximize their performance by adopting two technologies, and there is a synergistic effect between card-based and account-based technologies. On average, adoption increases a UR’s economic performance by 9.6%. We present a nuanced understanding of whether, how much, and which digital payment technologies should be adopted by EM URs.

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Journal Articles | 2021

Asymmetric political attention across foreign and domestic private equity real estate investors

Ashish Gupta and Prashant Das

Journal of Property Research

Private equity real estate (PERE) markets suffer from information inefficiency. In this study, we examine if Google Trends could help in partially mitigating the inefficiency issues. Using monthly PERE investment activities in India between 2005 and 2017, and controlling for macroeconomic variables, we show that relevant search trends are significantly associated with future investment activities. Compared to domestic investors, foreign investors are subject to information asymmetry and their investment activity is particularly sensitive to political search trends in the target country. We detect a mutually causal association between investment activity and political searches. Although significant, the effect of political Google Trends on investment activity is short-lived and fades within two months.

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Journal Articles | 2021

Alternate second order conic program reformulations for hub location under stochastic demand and congestion

Sneha Dhyani Bhatt, Sachin Jayaswal, Ankur Sinha, and Navneet Vidyarthi

Annals of Operations Research

In this paper, we study the single allocation hub location problem with capacity selection in the presence of congestion at hubs. Accounting for congestion at hubs leads to a non-linear mixed integer program, for which we propose 18 alternate mixed integer second order conic program (MISOCP) based reformulations. Based on our computational studies, we identify the best MISOCP-based reformulation, which turns out to be 20–60 times faster than the state-of-the-art. Using the best MISOCP-based reformulation, we are able to exactly solve instances up to 50 nodes in less than half-an-hour. We also theoretically examine the dimensionality of the second order cones associated with different formulations, based on which their computational performances can be predicted. Our computational results corroborate our theoretical findings. Such insights can be helpful in the generation of efficient MISOCPs for similar classes of problems.

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