Sabuj Kumar Mandal

The objective of the present study is to identify the major motivating factors and thereby tracing out the existence of any entry barrier for several categories of rural non-farm sector (RNFS) employment in India. We conduct our analysis using household-level data from semi-arid tropics (SAT) and Eastern regions of India for the period 2010–2014. We disaggregate the RNFS activities into various categories—wage employment, self-employment, and others—and use a multinomial logit model as the baseline model to determine the factors driving participation in the various types of non-farm employment. Furthermore, Heckman Selection Model to account for selection bias in our sample and a multinomial fractional logit model to account for the intensity of RNFS income are used. The empirical results, based on a multinomial logit model, reveal that education in general and technical education, in particular, access to credit and endowment of social capital, are the major determinants of RNFS employment in India. However, these determinants are not same across the various RNFS sub-sectors. It is found that while education affects participation in wage employment and self-employment, technical education affects participation in wage employment and others only. Also, social capital determines employment in self-employment and wage employment, but does not determine employment under the ‘others’ category. Other factors that determine RNFS diversification are land and non-land assets, age, and gender of the household head, household size and distance from market. Policy implications of our empirical results are also discussed.

Amidst the laggardness of the farm sector, a major shift away from the farm sector to the rural non-farm sector (RNFS) has been observed in India, in the recent decades. However, the diversification into the RNFS, especially for the small and the marginal farmers, may be restricted due to the presence of various entry barriers like, lack of education in general and technical education in particular, credit constraint and a lower endowment of social capital. In this context, the study develops a simple theoretical model to incorporate the labour allocation decisions of the rural farm households, focussing on the potential entry barriers in the RNFS. The theoretically determined entry barriers along with other covariates are then used to empirically estimate the intensity of RNFS participation. We employ household level panel data on Indian states belonging to Semi-arid tropics (SAT) and Eastern regions, for the years 2010–14. A fractional response model is used to empirically analyse the determinants underlying RNFS diversification. The empirical results of the study confirm the presence of entry barriers in the form of lack of education and technical education, and access to credit and social capital. Other variables that are found to have a significant impact on diversification are land asset, family size, gender of the household head, age of the household head and farm income. Since, access to education, skill, credit and social capital are vital factors determining RNFS diversification, specific policies are required to be implemented for increasing access to these assets so as to increase RNFS employment in India.

The objective of this study is to estimate environmental energy efficiency (EEE)—defined as energy efficiency measures incorporating undesirable output in the production process—for high-income, middle-income and low-income economies as well as at the global level for the period 1993–2013. Under the broad framework of Data Envelopment Analysis, the traditional input-oriented measures of efficiency, the joint production approach and the latest by-production approach have been used to assess EEE. The empirical results confirm a gradual improvement in EEE level except in the years 1998–1999 and 2009. The high-income economies spearheaded this improvement in EEE followed by the middle-income and low-income economies. This might be explained by the use of a relatively greater share of renewable energy in high-income economies. Moreover, the middle-income economies appear to converge to global EEE levels while low-income economies have lagged behind. Policy implications of our results are also discussed.

The objective of the present study is two-fold. Firstly, to estimate environmental efficiency of selected developing economies in terms of carbon-dioxide (CO2) and sulfar-dioxide (SO2) emission. Secondly, to examine the validity of Kuznets type – U-shaped- relationship between economic growth and environmental efficiency for the period 1992–2011. We apply Directional Distance Function to estimate environmental efficiency and adopt Generalized Method of Moments (GMM) to estimate the relationship between growth and environmental efficiency. Our estimates do not confirm any Kuznets type relationship between economic growth and environmental efficiency in terms of both the pollutants. While environmental efficiency in terms of CO2 per capita exhibits an “inverted N-shaped” relationship with economic growth, an insignificant impact of growth on efficiency is observed in case of SO2. Our results suggest while economic growth, to some extent, could be a remedy for environmental degradation in terms of CO2 emission, it is not a remedy for SO2 emission.

The Indian stock market witnessed a sharp recovery in the post-COVID-19 period owing to the sweeping investor enthusiasm. The event studies show that the 2020 lockdown and announcement of the first fiscal package had a significant impact on the stock returns. The impact of other events like the first COVID-19 case, second fiscal package, beginning of the vaccination drive, and the second wave was insignificant. Our estimates also show a significant impact of investor sentiment on stock returns, except during periods of extreme volatility. Further, the stock returns are positively related to oil price and negatively related to the exchange rate.

In the face of various agro-climatic shocks when agricultural income becomes highly volatile, farmers often undertake multiple jobholding and engage in non-farm activities for income smoothing. The earnings from these activities are often used to purchase productivity enhancing agricultural inputs. In this context, the impact of non-farm income on intensification of agricultural inputs and the consequent impact on over-all farm efficiency is well documented in the literature. However, with a rapid rise in usage of agricultural inputs with environmentally detrimental impact, very little is known about whether non-farm income has any impact on farmers’ environmental efficiency-ability to reduce the amount of polluting inputs to the largest extent possible without reducing the amount of agricultural production. Our study fills the gap in the literature by analysing the impact of non-farm income on environmental efficiency of the farmers. We first estimate the environmental efficiency scores adopting the non-parametric data envelopment analysis (DEA) method and using a household level panel data from Village Dynamics of South Asia project on Indian states for a span of five years (2010–2014). We then estimate the impact of non-farm income on environmental efficiency using Instrumental Variable Tobit Model. Our results show that average environmental efficiency of the Indian farmers was 46 % during the study period indicating the fact that a reduction in polluting agricultural inputs by 54 % was possible without compromising the level of farm production. We also find that for 1 % increase in non-farm income, environmental efficiency of farmers rises by around 4 %. This reflects the environment friendly behaviour of farmers as a channel through which non-farm activities affect usage of environmentally linked inputs. These results provide vital policy insights in terms of how non-farm activities could be integrated with policies related to farming, in order to ensure sustainable agricultural practices.