Homeownership and the American Dream - An Analysis of Intergenerational Mobility Effects

The benefits of homeownership feature prominently in the academic literature and policy discussion alike. Increasing homeownership has been a major policy goal in the US for decades, especially in low-income areas. We show that the positive relationship between homeownership and intergenerational mobility is highly place-dependent. First, we link commuting zone-level homeownership rates to intergenerational mobility, and find a strong positive relationship. The relationship persists after instrumenting for ownership using housing supply and price shocks. Second, we show that the positive relation between of homeownership and upward mobility is significantly diminished, or disappears, in areas with high sprawl or segregation, whether we use income segregation, racial segregation, or a new measure of homeowner segregation. These results, as well as additional findings on the formation of social capital and on school quality, suggest that homeownership may not benefit, or even disadvantage children in segregated, poor areas, possibly through reduced residential mobility.

  • Dr. Nirupama Kulkarni, Research Director at CAFRAL
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  • 2016-09-30

High-Cost Debt and Borrower Reputation: Evidence from the U.K

When taking up high-cost debt signals poor credit risk to lenders, consumers must trade off alleviating credit constraints today with exacerbating them in the future. We document this trade-off by exploiting the random assignment of applicants to loan officers with different propensities to approve otherwise identical loans by a high cost lender in the U.K. For the average applicant, taking up a high-cost loan has a large, immediate, and permanent impact on the credit score. Take-up also leads to more default and credit rationing by standard lenders. In contrast, borrowers whose credit score is not affected by take-up — because they already have low credit scores at the time of application — are no more likely to default and experience no further credit rationing. Thus, high cost credit has a negative impact on future financial health when it affects borrower reputation, but not otherwise. The evidence suggests that high-cost borrowing may leave a self-reinforcing stigma of poor credit risk.

  • Dr. Vikram Pathania, University of Sussex
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  • 2016-09-14

A mixed integer linear programming model for optimal sovereign debt issuance

Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modelled as a mixed integer linear programming problem. The stochastic model for the interest rates is calibrated using a Kalman filter and the possible future yield curves are represented using a recombining trinomial lattice. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving binary variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data.

  • Dr. Paresh Date, Department of Mathematics, Brunel University
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  • 2016-09-02

Batching Decisions for E-Commerce Order Fulfilment: Technology, Models, and Data Insights

Due to today’s customer expectations of fast delivery of a wide-range of products, companies are under pressure to reduce the order fulfilment time, i.e., the time from receiving an order to the time it takes to get an order to a customer’s home or to its stores. In this research, we particularly focus on reducing item picking process times for E-Commerce orders by investigating how to design and operate dynamic order batching policies in parts-to-picker systems.

E-commerce orders require piece picking and are special because most of the order sizes are one and there could be item commonalities between orders that can be leveraged for improving throughput times in the picking process. In dynamic batching, multiple orders are processed in parallel but an individual order is released as soon as it is complete. A dynamic batching policy has the advantages of both sequential processing (by meeting individual order deadlines) and static order batching (by fulfilling several orders concurrently thereby reducing the completion time per order). The execution of a dynamic batching policy in a distribution center necessitates investment in a complex and expensive material handling system that enables the release of order totes, which violate the first come first serve queue discipline.

The goal of this research is to understand how best to design and operate a dynamic batching system in a parts-to-picker system. Specifically, we are interested in how the order batch size, item commonalities among orders, wait for items, item pick time and tote-interchange time can affect the performance of static and dynamic batching decisions, and how incoming order profile data can be used to improve performance through sequencing. Specifically, we will answer two research questions:

• What is the difference in throughput of a pick station that uses a static order batching policy versus one that uses a dynamic batching policy? We will explore various environmental factors (such as item commonality between orders, batch size, and order delivery deadline schedules).

• How should orders and items be sequenced to improve throughput performance in dynamic batching systems? Order sequencing algorithms will be developed and the effectiveness of algorithms for dynamic batching will be evaluated using order profile data.

  • Prof. Debjit Roy, Indian Institute of Management, Ahmedabad
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  • 2016-09-01