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Purpose This study aims to examine the asymmetric effects of energy efficiency on employment in India. Instead of relying on partial factor energy efficiency measures, this study uses a total factor energy efficiency (TFE) measure to estimate sector-specific energy efficiency for empirical investigation. Design/methodology/approach Multi-sectoral panel data for India from 2000 to 2014 are considered for empirical estimation. The sector-specific energy efficiency estimates (using the TFE measure) are estimated in the initial stage using the stochastic frontier approach (SFA). Then the asymmetric effect of energy efficiency on employment is investigated by using a non-linear panel autoregressive distributed lag model. Findings The estimates of energy efficiency display that there is not much significant change in the trend of average energy efficiency over the period. The negative and statistically significant value of the error-correction term confirms the existence of asymmetric cointegrating relationship between energy efficiency and employment in India. Moreover, in the empirical findings, the positive and negative shocks in energy efficiency provide a long-run asymmetric and short-run symmetric effect on employment in India. Originality/value Rather than depending on the absolute measure of energy efficiency (energy to output ratio), this study estimates the sector-specific energy efficiency for India using panel SFA, which provides a relative measure of energy efficiency. Moreover, to the best of the authors’ knowledge, it is the first empirical study investigating the asymmetric impact of energy efficiency on employment at an aggregate level in developing countries like India. By contrast, previous studies have either concentrated on the symmetric effect of energy efficiency on employment or primarily restricted to developed countries.

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Realizing the significance of agricultural credit and considering millets and rice are the major crops in Odisha, this study proposes to examine the impact of agricultural credit disbursement (crop loan and term loan) on the yield of total cereals, millets and rice for the period 2000–01 to 2019–20. To examine the cointegration relationship among the variables, an autoregressive distributed lag (ARDL) bound F‐test, and to investigate the impact of agricultural credit on yield of total cereals, millets and rice, an ARDL regression modelling framework is employed. The empirical result for the bounding F‐test provides a statistically significant relationship between agricultural credit and yield of total cereals, millets and rice at 1% level, confirming the long‐run equilibrium relationship in the models. The long‐run impact of crop loans positively affects the yield of total cereals and rice, whereas it has no statistically significant effect on the yield of millets. Moreover, the change in term loan negatively affects the yield of total cereals, millets and rice in the long‐run, where the magnitude is higher in the rice yield compared to the yield of millets. However, in the short‐run, both crop and term loans negatively affect the yield of total cereals, millets and rice. Based on the findings of this study, few policy implications have been suggested to reduce the negative consequences of agricultural credit on cereal yields in Odisha.