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3721 items in total found

Working Papers | 2017

Turning Over a Golden Leaf?
Global Liquidity and Emerging Market Central Banks'
Demand for Gold after the Financial Crisis

Balagopal Gopalakrishnan and Sanket Mohapatra

The quantity of gold reserves held by central banks in emerging markets and developing economies (EMDEs) has risen sharply following the global financial crisis in 2008. This paper examines factors driving holding of gold by central banks in 50 EMDEs using a dynamic panel generalized method of moments model. We find that monetary expansion in advanced economies is robustly related to the post-crisis increase in EMDE gold reserves, after controlling for domestic factors and changes in the global risk environment. This effect holds across different measures of global liquidity, and is robust to alternate model specifications, inclusion of additional covariates, and alternate estimation methods. We argue that the unprecedented monetary expansion in advanced economies has resulted in a shift in EMDE reserve asset holding strategy, resulting in continued accumulation of gold reserves even after the peak of the financial crisis.

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Working Papers | 2017

Intra-Industry Trade and Labour Market Adjustment: Indian Manufacturing Sector

Poornima Varma and Issar Akash

The study investigates the role of trade, labor market regulations and institutions on labour adjustment costs. The study develops a linear dynamic panel model using quasi-maximum likelihood fixed effects estimator. Using a panel data of 40 Indian manufacturing sectors we find that the better labour market regulations and institutions reduce the labour market adjustment costs. This result using both the set of proxies for labour adjustment costs -job re-allocation rates as well as absolute employment change- supported this view. We find the same to be true when examining the male and female labour adjustment costs individually. Nonetheless, the study did not find any evidence to support the impact of trade expansion as well as the structure of trade expansion on labour market adjustment costs. The results are robust to static and dynamic panel methods.

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Working Papers | 2017

Shiny Alternative for Finance in the Classroom

Jayanth R. Varma and Vineet Virmani

Despite the popularity of open-source languages like R and Python in modern empirical research and the data-science industry, spreadsheet programs like Microsoft Excel remain the data analysis software of choice in much of the business-school curriculum, including at IIMA. Even if instructors are comfortable with modern programming languages, they have to pitch their courses at the level of computer literacy prevalent among students. Excel then appears to be a natural choice given its popularity, but this choice constrains the depth of analysis that is possible and requires a certain amount of dumbing-down of the subject by the instructor. Recent software advances however make the ubiquitous web browser a worthy challenger to the spreadsheet.
This article introduces one such browser-based tool called Shiny for bringing finance applications to the classroom and smart phones. Fueled by the availability of high-quality R packages in finance and statistics, Shiny brings together the power of
HTML with the R programming language. It naturally creates an environment for the instructor to focus on the role of parameters and assumptions in analysis without the clutter of data, and allows the instructor to go beyond the toy problems that are necessitated by the nature of spreadsheets. The learning curve is short for an interested instructor with even a rudimentary exposure to programming in any language. The
article ends with the discussion of a fully-worked out example of Shiny for teaching the mean variance efficient frontier in a basic investments course.

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Working Papers | 2017

Travel Time Prediction for Taxi-GPS Data Streams

A. K. Laha and Sayan Putatunda

The analysis of data streams offers a great opportunity for development of new methodologies and applications in the area of Intelligent Transportation Systems. In this paper, we propose a new incremental learning approach for the travel time prediction problem for taxi GPS data streams in different scenarios and compare the same with four other existing methods. An extensive performance evaluation using
four real life datasets indicate that when the drop-off location is known and the training data sizes are small to moderate the Support Vector Regression method is the best choice considering both prediction accuracy and total computation time. However when the training data size becomes large the Randomized K-Nearest Neighbor Regression with Spherical Distance becomes the method of choice. Even when
the drop-off location is unknown then the Support Vector Regression method is the best choice when the training data size is small to moderate while for large training data size the Linear Regression method is a good choice. Finally, when continuous prediction of remaining travel time and continuous updating of total travel time along the trajectory of a trip are considered we find that the Support Vector
Regression method has the best predictive accuracy. We also propose a new hybrid method which improves the prediction accuracy of the
SVR method in the later part of a trip.

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Working Papers | 2017

Real Time Location Prediction with Taxi-GPS Data Streams

A. K. Laha and Sayan Putatunda

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a Damped window model namely, Multivariate multiple regression, spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. The Multivariate multiple regression method and the Ensemble of the three methods are found to be the two best performers. The next pickup location problem is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here
also we find that the Multivariate multiple regression method and the
Ensemble of the three methods gives better performance than the rest.

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Working Papers | 2017

Enabling a Mobile Cloud Service: Data-Sharing in Ad-hoc Device-to-Device Mobile Networks

Kavitha Ranganathan

The objective of this work is to build a data-sharing application for an ad-hoc network of mobile devices, where users can exchange data/files among themselves without relying on traditional communication channels like telecom or network operators. In other words, we aim to build a mobile cloud service for data sharing. This paper examines the resource discovery and selection (also called replica selection) issue in such a mobile cloud. We propose a novel decentralized algorithm where nodes can first discover and then choose the best replica to request for, from among the different alternatives identified. Additionally, our paper comes up with a new metric to evaluate different replicas, that is, what could be a desirable definition of the 'best' replica in such a network.

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Working Papers | 2017

India's Horticulture Sector - A Port- Level Analysis of Onion Export Pricing

Poornima Varma and Issar Akash

Extending the traditional model of agricultural pricing behaviour and market structure with the constructed commodity specific export weighted exchange rates, this paper analyses the exchange rate induced market power, asymmetric effects of exchange rate, country specific discrimination as well as the impact of government's minimum export price policy on the export prices of Indian onion exporters using port-level data. Onion price escalation has been seen to cause tears not only in the kitchen but tumble governments. Although this study observes a competitive market structure in majority of the destination market, however, the pricing-to-market behaviour was prevalent in three destination markets where the exporters were following local currency stabilization. Furthermore, minimum export price policy variable showed that even when the minimum export price requirement was in place, exporters were able to adjust their price downward and sell in those markets.

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Working Papers | 2017

Adoption of Natural Resource Management Technologies under Information Constraints: The Case of System of Rice Intensification (SRI) in India

Poornima Varma

This study examines the role of information constraints in the adoption of System of Rice Intensification (SRI) in India by explicitly incorporating information in the adoption model. The results showed that effective information along with other factors such as membership in farmer organisation, availability of labourers, irrigation facility etc were important in determining the SRI adoption. The results also revealed the importance of scaling up of activities under the Government of India's National Food Security Mission programme for promoting greater dissemination and adoption of SRI.

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Working Papers | 2017

Adoption of System of Rice Intensification and its Impact on Rice Yields and Household Income: An Analysis for India

Poornima Varma

Natural resource management (NRM) technologies, such as the system of rice intensification (SRI) are recognized as a promising systemic approach to enhance rice production at affordable costs without harming the environment. Yet there is no consensus in the literature with respect to the factors influencing the adoption as well as the welfare outcomes of adoption. This paper identifies the factors that affect farmers' decisions to adopt SRI in major rice producing States of India and its impact on rice yield and household income. The multinomial endogenous treatment effects model adopted in the present study analyses the factors influencing the adoption and the impact of adoption in a joint framework. Results suggest that household assets, irrigation, access to information etc. increased the likelihood of household adopting SRI whereas the size of landholding, the number of years household stayed in rice cultivation, fear of poor yield, etc. decreased the likelihood of adopting SRI. The welfare impacts of SRI adoption revealed that all combinations of SRI individually and as a group (plant management, water management and soil management) had an impact on yield. However, the impact of SRI adoption on household income was quite mixed.

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Working Papers | 2017

Institutional Quality and International Differences in Firm Productivity

Issar Akash, Jamus Jerome Lim, and Sanket Mohapatra

In this paper, we examine how firm-level productivity growth is dependent on a broad range of institutional quality measures at the country level. Using a sample of 3,446 firms in 58 advanced and emerging economics, we show that such institutions exert a statistically and economically significant effect on changes in firm TFP. We utilize data envelopment analysis to construct firm-level measures of Malmquist productivity, which we then condition on a range of country-level institutions, using both a full set of fixed effects and system generalized method of moments to address potential endogeneity concerns. The baseline effect is robust to alternative measures of institutions, variations in model specification, alternative temporal aggregations, and the inclusion of external instruments. Additional decompositions further reveal that the institutional effect operates via improved productive efficiency (rather than technological progress), and that the key institutions are those associated with rule of law and regulatory quality

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