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PurposeDoubling farming households' income through occupational diversification to the non-farm sector has been advocated to be of paramount importance in an agrarian economy such as India. The purpose of this paper is to analyse the effects of non-farm activities on rural household incomes in four different Indian states by using a propensity score matching technique and developing an endogenous switching model.Design/methodology/approachThe research is based on secondary data taken from four quinquennial rounds of employment and unemployment surveys conducted by the National Sample Survey Organization.FindingsThe matching results indicate a maximum monthly rise in per capita income of Rs. 60 in Gujarat and a minimum increase of Rs. 18 in Rajasthan among rural households employed in the non-farm sector as compared to the farm sector. The findings confirm that rural non-farm structural diversification cannot be viewed as a blueprint for increasing rural household incomes in different states. Further, it suggests the need to segmenting the different states on the basis of agricultural development for increasing rural incomes.Research limitations/implicationsThe study argues that Indian states with a strongly developed farm sector i.e. Gujarat and Punjab are not ideally suited to undergo structural changes in their economic pursuit. The estimates suggest that the transition of rural households from farm to non-farm-sector activities is a very weak strategy in agriculturally developed states of Gujarat and Punjab, whereas non-farm diversification becomes a pivotal strategy for increasing rural household incomes in less agriculturally developed states such as Rajasthan and Uttar Pradesh. A contrasting point that arises from these evidence is that although diversification to the non-farm sector leads to higher income, but the resultant figures are very scanty.Originality/valueThe present study contributes to the existing literature by providing evidence and policy implications on rural non-farm diversification in India and its impact on the rural household income. The study can help the policymakers in framing policies aiming at increasing the income of the rural household through the structural transition of the rural economy.

AbstractMental rotation, the cognitive process of moving an object in mind to predict how it looks in a new orientation, is coupled to intelligence, learning, and educational achievement. On average, adolescent and adult males solve mental rotation tasks slightly better (i.e., faster and/or more accurate) than females. When such behavioral differences emerge during development, however, remains poorly understood. Here we analyzed effect sizes derived from 62 experiments conducted in 1705 infants aged 3–16 months. We found that male infants recognized rotated objects slightly more reliably than female infants. This difference survives correction for small degrees of publication bias. These findings indicate that gender differences in mental rotation are small and not robustly detectable in the first months of postnatal life.Research Highlights
We analyzed effect sizes of 62 mental rotation experiments including 1705 infants.
Looking time reveals that 3–16‐months‐old infants are able to perform mental rotation.
Mental rotation is slightly more reliable in male infants compared to female infants.
Gender difference in mental rotation is robust to small degrees of publication bias.

Technology adoption has been advocated as an important way to improve agricultural productivity and welfare of farmers in the semi-arid regions across the globe. The Government of Karnataka implemented the Bhoosamrudhi program in four districts of the state (Bidar, Chikballapur, Dharwad, and Udupi) as a pilot project to increase the crop yield and income of smallholder farmers. This program was launched on the theme of technology adoption along with convergence among different departments of agriculture. Farmers have been classified into five categories based on their levels of technology intensification to assess the impact of different levels of technology intensification on their welfare. The research is built on a primary survey conducted in pilot districts of the state in 2018 over a sample of 1,465 farmer households. The results generated using econometric methods of propensity score matching (PSM) and inverse probability weighted with regression adjustment (IPWRA) highlight that the higher the intensification, the higher the net returns to the farmers. The results state that non-adopters would receive a benefit of an additional Rs.3200 per month if they adopt at least one level of technology intensification. Hence, this program turned out to be a successful model for smallholder farmers in semi-arid regions of India. Steps should be taken to maintain and expand the momentum of adoption to ensure food and livelihood security in the economy. ; Non-PR ; IFPRI1; DCA; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and Governance ; SAR

Laser land leveling (LLL) is a climate-smart technology that improves water use efficiency and reduces risk in crop cultivation due to weather variability. Hence, this technology is useful for cultivating water-intensive crops in a sustainable way. Given this background, the state government of Karnataka initiated to promote LLL in drought-prone districts and selected Raichur district for implementation. Moreover, farmers in this district had observed drought situation during monsoon paddy growing season in 2018. Therefore, this study attempts to investigate the importance of LLL technology for paddy cultivation under drought conditions.