Notes on the postulate of the monotonicity in distance in inequality

In this paper, we strengthen Cowell and Flachaire's observation regarding the failure to satisfy Cowell's “monotonicity in distance” postulate by several well-known inequality indices. We also identify a general analytical reason for this failure to arise.     

Globally distributed talent communities: A typology of innovation problems and talent characteristics

As the world moves toward the “New Normal” with borderless innovation and remote work, Multinational Enterprises (MNEs) are increasingly involved in tapping talent that is external to organizational boundaries. This study distills learnings from the use of globally distributed external talent that has been facilitated by innovation intermediaries, a development that holds significant managerial implications for the post-COVID industrial era. Moving beyond the conventional and recognized need for global talent management practices, we provide a perspective on talent management outside organizational boundaries, a topic that that has received limited attention so far. Through the lenses of open innovation and talent management, we define a typology of innovation problems on the basis of latent talent needs. We take a step further, and for each problem type, we identify the competencies that are relevant, the reward mechanisms of the intermediaries, and the extent of collaboration required with internal talent. This typology provides a basis for researchers in the talent management community to study talent acquisition and management strategies of MNEs across various contexts and various innovation needs.     

Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)

Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007–08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015–16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a Dynamic Factor Model (DFM) model and a univariate Autoregressive Integrated Moving Average (ARIMA) model in terms of both in-sample and out-of-sample Root Mean Square Error (RMSE). Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model outperforms DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVCR model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid–19 shock of 2020–21.

     

Rural Bangladeshi consumers’ (un)willingness to pay for low-milled rice: Implications for zinc biofortification

Zinc deficiency is a severe public health problem in Bangladesh. We examine the effects of nutritional information on rural consumers’ willingness-to-pay (WTP) for two ways to increase zinc intake through rice, the main staple crop–low-milling that gives rice grains a distinctive light brown color (a visible trait) and sets it apart from the culturally preferred high-milled white rice grain and biofortification of rice with increased zinc content (an invisible trait), which is also low-milled to retain maximum zinc content. Results of our economic experiments suggest that with nutritional information, consumers are willing to pay a premium of 4.6% for zinc biofortified rice compared to non-biofortified rice, when milled at the same level. However, results confirm the strong preference for high-milled rice by Bangladeshi consumers who discounted low-milled rice by 8%–10% even after receiving information on the nutritional benefits of biofortified or low-milled rice. We find that consumers’ WTP for the two high-zinc-low-milled rice types (biofortified and non-biofortified) is positively correlated with being a female, more educated, belonging to households engaged in non-farm activities and with children under 5 years of age. Results point to the importance of nutritional awareness campaigns for increasing zinc biofortified and low-milled rice consumption and guiding the targeting strategy for such campaigns. Given the consumer preference for high-milled rice, this study also points to the need for exploring the rice fortification strategy to address the challenge of malnutrition.