Shifting Demand: Online Music Piracy,Physical Music Sales,and Digital Music Sales

Traditionally, music has been sold to consumers by recording several individual songs/tracks in physical media such as CDs and cassettes. Sales of such physical music have been declining for the past several years. Many academic studies have attributed the decline of physical music sales to online music piracy, yet some other studies have not found evidence to support a negative relationship between online music piracy and physical music sales. Interestingly and importantly, we have observed that while many of the studies that found the negative relationship between online music piracy and physical music sales used data before 2003, other studies that do not find similar results have used post-2003 data. In fact, there was a significant structural change in the music market in 2003. Legal (iTunes-like) online channels for digital music that allow consumers to buy individual tracks became available during that year. This article complements the existing literature by analyzing the impact of online music piracy and physical music sales in the presence of iTunes-like legal channels for digital music using bivariate Granger testing. Our results show that the availability of legal channels for digital music has weakened the negative effect of online music piracy on physical music sales. Moreover, in the presence of legal channels for music distribution, digital music, not online music piracy, substitutes for physical music.     

An Approximate Test for Homogeneity of Correlated Correlation Coefficients

This paper develops and evaluates an approximate procedure for testing homogeneity of an arbitrary subset of correlation coefficients among variables measured on the same set of individuals. The sample may have some missing data. The simple test statistic is a multiple of the variance of Fisher r-to-z transformed correlation coefficients relevant to the null hypothesis being tested and is referred to a chi-square distribution. The use of this test is illustrated through several examples. Given the approximate nature of the test statistics, the procedure was evaluated using a simulation study. The accuracy in terms of the nominal and the actual significance levels of this test for several null hypotheses of interest were evaluated. This revised version was published online in June 2006 with corrections to the Cover Date.     

Imputation of Missing Values in the Fundamental Data: Using MICE Framework

Revolutionary developments in the field of big data analytics and machine learning algorithms have transformed the business strategies of industries such as banking, financial services, asset management, and e-commerce. The most common problems these firms face while utilizing data is the presence of missing values in the dataset. The objective of this study is to impute fundamental data that is missing in financial statements. The study uses ‘Multiple Imputation by Chained Equations’ (MICE) framework by utilizing the interdependency among the variables that wholly comply with accounting rules. The proposed framework has two stages. The initial imputation is based on predictive mean matching in the first stage and resolving financial constraints in the second stage. The MICE framework allows us to incorporate accounting constraints in the imputation process. The performance tests conducted on the imputed dataset indicate that the imputed values for the 177 line items are good and in line with the expectations of subject matter experts.

     

Can women's self‐help groups improve access to information,decision‐making,and agricultural practices? The Indian case

Effective agricultural extension is key to improving productivity, increasing farmers’ access to information, and promoting more diverse sets of crops and improved methods of cultivation. In India, however, the coverage of agricultural extension workers and the relevance of extension advice is poor. We investigate whether a women's self‐help group (SHG) platform could be an effective way of improving access to information, women's empowerment in agriculture, agricultural practices, and production diversity. We use cross‐sectional data on close to 1,000 women from five states in India and employ nearest‐neighbor matching models to match SHG and non‐SHG women along a range of observed characteristics. We find that participation in an SHG increases women's access to information and their participation in some agricultural decisions, but has limited impact on agricultural practices or outcomes, possibly due to financial constraints, social norms, and women's domestic responsibilities. SHGs need to go beyond provision of information to changing the dynamics around women's participation in agriculture to effectively translate knowledge into practice.     

The effect of agriculture on repayment efficiency: a look at MFI borrowing groups

There is a widely held industry assumption from microfinance institutions that agricultural loans have poorer repayment rates, which has resulted in many loans being provided for small businesses as opposed to agricultural purposes. Using data from a sample of 100 borrowing groups from a south Indian Microfinance Institutions (MFI), this study challenges this belief by analyzing the repayment efficiency of borrowing groups and reflects on the implications for agricultural microfinance loans. The analysis is run using Bayesian stochastic frontier estimation with an exponential hierarchical prior on the efficiency term. Our results indicate that the average efficiency of the borrowing groups analyzed was approximately 75% and that having a higher percentage of agricultural loans increased borrowing group efficiency while gains in efficiency also rose as the size of the borrowing group increases.