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Category Archives: C22

Understanding Financial Incentive Health Initiatives: The Impact of the Janani Suraksha Yojana Conditional Cash Transfer Program on Institutional Delivery Rates and Out-of- Pocket Health Expenditure

By Ritika Jain

Demand-side financing is a policy tool used by nations to incentivize utilization of public institutions, and India’s Janani Suraksha Yojana (JSY) is one of the largest such financial incentive programs in the world. The program pays eligible pregnant women to deliver their babies in health institutions partnered with the program. This paper studies the impact of the JSY on changes in mothers’ health-seeking behavior to deliver in-facility and on the out-of-pocket expenditure (OOPE) for delivery that they incur. Using data from the most recent wave of India’s District-Level Household Survey conducted in 2007-08, this paper finds that the overall introduction of the program in districts in India does not lead to significant changes in institutional delivery or out-ofpocket expenditure outcomes. Further analysis of subpopulations shows that marginalized populations are responsive to JSY introduction in their district with increased probability of delivering in-facility of 1.10 – 3.40 percentage points. Lastly, results show that receiving JSY payments leads to a 1.34 percentage point increase in the probability of incurring OOPE, but a 4.81 percent decrease in the amount of OOPE incurred. The JSY is helping to reduce overall out-of-pocket spending on deliveries. However, the majority of program benefits are not reaching poor pregnant women as the JSY aims, communicating the need for improvement in population targeting.

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Advisor: Alison Hagy, Kent Kimbrough, Manoj Mohanan | JEL Codes: C22, I12, I18 | Tagged: Conditional Cash Transfer, Demand-side Financing, Difference-in-difference-in-differences, Difference-in-differences, Healthcare Reform, Maternal Health

Simultaneous Occurrence of Price Jumps and Changes in Diffusive Price Volatility

By Shunting Wei

This paper uses high frequency financial data to study the changes in diffusive stock price volatility when price jumps are likely to have occurred. In particular, we study this effect on two levels. Firstly, we compare diffusive volatility on jump and non-jump days. Secondly, we study the change in diffusive volatility in local windows before and after 5-minute intervals on which price jumps are likely to have occurred. We find evidence that market price jumps occur simultaneously with a change in diffusive volatility with negative dependence in the direction of the jump and the volatility change. However, a similar relationship is not detectable in individual stock price data.

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Advisor: George Tauchen | JEL Codes: C22, G1, G19 | Tagged: Diffusive Volatility, Jump Tests, Realized Volatility, Stock Price Jumps

Time-Varying Beta: The Heterogeneous Autoregressive Beta Model

By Kunal Jain

Conventional models of volatility estimation do not capture the persistence in high-frequency market data and are not able to limit the impact of market micro-structure noise present at very finely sampled intervals. In an attempt to incorporate these two elements, we use the beta-metric as a proxy for equity-specific volatility and use finely sampled time-varying conditional forecasts estimated using the Heterogeneous Auto-regressive framework to form a predictive beta model. The findings suggest that this predictive beta is better able to capture persistence in financial data and limit the effect of micro-structure noise in high frequency data when compared to the existing benchmarks.

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Advisor: George Tauchen | JEL Codes: C01, C13, C22, C29, C58 | Tagged: Beta, Financial Markets, Heterogeneous Autoregressive, Persistence

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