Kashish Arora

Problem Definition: Large cities around the globe are facing an alarming growth in traffic congestion and greenhouse gas emissions, to which a significant contributor in recent years are on-demand cabs operated by ride-hailing platforms. Pooled transportation options, such as shuttle services, are cheaper and greener alternatives. However, those alternatives are still new to many customers and policy makers. The design and effectiveness of their promotion policies demand careful investigation. This paper studies how we can reduce the number of on-demand cabs on the road and, therefore, their GHG emissions, by promoting the usage of pooled transportation such as shuttle services. Practical Relevance: Reducing the number of ride-hailing vehicles on the road has become an important goal in many cities' green transport policy design. For example, cities like New York implemented congestion surcharge policies targeting ride-hailing vehicles in recent years. Methodology: In this work, we use detailed usage data and build a structural model to study customers' preferences on price and service features when choosing between private cabs and a scheduled shuttle service. Results: Using the estimated model, we identify and evaluate the efficacy of improving service features like reducing the walking distance to shuttle stops on customers' choices of transport and, therefore, the number of ride-hailing vehicles on the road. We find that a 20\% decrease in walking distance can reduce more than one million dollars worth of CO2 emissions, and achieve 40\% of the benefits attained by commonly adopted congestion surcharge policies. In addition, we demonstrate the implementability of walking distance reduction policies by addition of stops on existing shuttle routes. Managerial Implications: Our findings suggest that, by changing operations levers such as service features of pooled transport, cities can achieve a substantial amount of the benefits from reducing congestion compared with congestion surcharge policies with essentially zero cost, leading to much more efficient green transport policies.

Effective management of a firm's operating cash flow is essential for supporting growth, servicing debt, and maintaining overall financial health. Mismanagement of cash flows can result in severe liquidity challenges and even business failure. However, managing operating cash flow is complex due to its intricate endogenous relationships with operational variables like sales, operating costs, inventory, payables, and the impact of exogenous macroeconomic factors on a firm. In this paper, we present a structural model of operating cash flow that untangles this endogeneity, allows us to estimate causal relationships among these variables, and provides a valuable tool for evaluating cash flow management policies. Applying our model to quarterly financial data from S\&P's Compustat database spanning from 1990 to 2020, along with macroeconomic indicators, we provide empirical evidence of the endogenous nature of cash flow with other operational variables. We then showcase the practical value of our model by (i) identifying the characteristics of structural shocks and the new equilibria they induce within the system, (ii) offering a tool for evaluating alternative managerial actions or policy decisions to counteract these shocks, (iii) predicting the impacts of macroeconomic events, such as global recessions and fluctuations in economic sentiment, on firm performance, and (iv) demonstrating superior forecasting performance compared to traditional univariate models. In summary, our structural model of operating cash flow enhances our understanding of its dynamics, enabling better-informed decision-making and more effective cash flow management in firms.

Despite being one of the most cost-effective and sustainable modes for transporting freight, railways globally have been rapidly losing market share in the in-land freight transportation sector. One of the salient reasons for this is the extremely slow speed of freight trains in many parts of the world. For example, in Indian Railways, the world's fourth-largest in size, the average freight train speed is only around 25 kmph and has remained constant for the past few decades. The slow pace of freight trains is because passenger trains, which share the same infrastructure, get prioritized in dispatch by railway traffic managers (also known as section controllers). In this paper, we empirically study freight delays in the Indian railway setting by analyzing how section controllers make freight train stop and hold decisions while managing the movement of freight trains. Subsequently, we propose policies to reduce freight delays and, thus, increase trains' speed through the network. We use detailed high-frequency network congestion data and estimate a structural model to estimate the key parameters underlying the controllers' decisions. The estimated parameters provide empirical evidence for (i) the priority accorded to passenger trains over freight trains, (ii) the push effects in the freight train queue, and (iii) the strategic behavior of section controllers in holding trains at larger stations. Using the estimated model, we conduct a set of counterfactual analyses to address the problem of slow freight train speeds. First, we evaluate the impact of constructing Freight Only Corridors (FOCs), high-capacity corridors reserved for freight transport. We find that the FOCs lead to about a 29% reduction in freight train delays and a 12% improvement in train speeds. Then, we also evaluate non-capacity-investment-based alternatives to FOCs, like (i) threshold-based releases for freight trains dwelling longer than a specified time limit and (ii) freight capacity consolidation by using vertically stacked trains. Interestingly, we find that our non-capacity interventions can provide benefits similar to those of FOCs while being considerably cheaper. Specifically, a 45-minute threshold release policy leads to around 31% reduction in dwell times and 9% increase in speeds. Similarly, vertically consolidating freight capacity by about 25% leads to around a 10% increase in speed, comparable to the improvement achievable with the FOC. Our policy recommendations for improving freight speeds could enhance the overall efficiency of India's transportation infrastructure, benefiting the country's economic and social development.

Vertical integration, a strategic approach where firms control multiple stages of production and distribution, offers potential operational efficiencies and market power. While operations management literature emphasizes its benefits for supply chain coordination, reduced transaction costs, and risk mitigation from informational asymmetries, antitrust studies concern how vertical mergers may enable dominant firms to limit competition by creating entry barriers or causing input or customer foreclosure. However, empirical evidence on vertical integration's anti-competitive effects remains scarce. This paper contributes to closing this gap by examining vertical mergers' impact on firms' markups, thus assessing the potential anti-competitive effects due to increased market power. Our research investigates post-integration markups to determine if firms gain anti-competitive market power, diverging from competitive market pricing. Since firms' markups are not observed in the data, we estimate markups using production function estimations, following recent advances in empirical industrial organization. We compile a novel dataset on vertical integration cases using the FactSet and SDC platinum databases. Specifically, we identify vertical mergers involving 326 firms with existing buyer-supplier relationships from 2003 to 2022. We employ a staggered difference-in-difference approach in conjunction with instrumental variables (constructed using mutual fund stock outflows) that cause an exogenous variation in firms' decisions to integrate vertically. Our analysis reveals that vertical integration increases acquiring firms' market power by 13% and their rivals' market power by 11%. We also report a steady increase in post-integration markups since 2003, which suggests growing market power and industry concentration and implies a need for stricter antitrust enforcement. Interestingly, we find that the increase in firms' market power post-integration does not come from improved operational or cost efficiencies but rather from higher than competitive prices due to vertical foreclosure. Our contributions are fourfold: (i) we carry out one of the first empirical investigations of vertical integration in the operations management literature; (ii) we utilize recent advancements in production function estimation to establish empirical markups; (iii) we offer causal evidence of vertical integration's potential anti-competitive outcomes; and (iv) highlight the role of network structures for vertical integration, adding to the traditionally linear supply chain perspective. These insights contribute to ongoing operations and antitrust discussions and are relevant for high-profile vertical merger cases.

With the help of more than 700 reviewers we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hard- and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles at least part of the dataset was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared to the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, out of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in dataset accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, but also soft- and hardware requirements and code complexity. Our findings highlight the importance of journal code and data disclosure policies, and suggest potential avenues for enhancing their effectiveness.