This study examines the Total Factor Productivity (TFP) growth of the preexisting units on a balanced sample for ten years (1998-1999 to 2007-2008) following the Levinsohn and Petrin (2003) technique. This study uses data from the Annual Survey of Industries at factory level. The results of the study indicate that most of the industries achieved positive TFP growth except a few; and thus within plant efficiency exists in Indian manufacturing sector. A further analysis of determinants of energy intensity using panel data model shows that productive plants in terms of TFP, are energy efficient. It is also observed that medium low-tech and high-tech industries on the basis of OECD classifications are energy efficient compared to the low-tech and the medium high-tech industries. The study also validates the "productivity dilemma hypothesis" for the sample firms indicating TFP and plant output are the major determinants of energy intensity.

Background Progress towards universal health coverage requires strengthening the country's health system. In developing countries, the increasing disease burden puts a lot of stress on scarce household finances. However, this burden is not the same for everyone. The economic burden varies across the disease groups and care levels. Government intervention is vital in formulating policies in addressing financial distress at the household level. In India, even when outpatient care forms a significant proportion of out-of-pocket expenditure, government schemes focus on reducing household expenditure on inpatient care alone. Thus, people resort to hardship financing practices like informal borrowing or selling of assets in the event of health shocks. In this context, the present study aims to identify the disease(s) that correlates with maximum hardship financing for outpatients and inpatients and to understand the change in hardship financing over time. Methods We used two waves of National Sample Survey Organisation’s data on social consumption on health- the 71st and the 75th rounds. Descriptive statistics are reported, and logistic regression is carried out to explain the adjusted impact of illness on hardship financing. Pooled logistic regression of the two rounds is estimated for inpatients and outpatients. Marginal effects are reported to study the changes in hardship financing over time. Results The results suggest that cancer had the maximum likelihood of causing hardship financing in India for both inpatients (Odds ratio 2.41; 95% Confidence Interval (CI): 2.03 - 2.86 (71st round), 2.54; 95% CI: 2.21 - 2.93 (75th round)) and outpatients (Odds ratio 6.11; 95% CI: 2.95 - 12.64 (71st round), 3.07; 95% CI: 2.14 - 4.40 (75th round)). In 2018, for outpatients, the hardship financing for health care needs was higher at public health facilities, compared to private health facilities (Odds ratio 0.72; 95% CI: 0.62 - 0.83 (75th round). The marginal effects model of pooled cross-section analysis reveals that from 2014 to 2018, the hardship financing had decreased for inpatients (Odds ratio 0.747; 95% CI:0.80 - -0.70), whereas it had increased for outpatients (Odds ratio 0.0126; 95% CI: 0.01 - 0.02). Our results also show that the likelihood of resorting to hardship financing for illness among women was lesser than that of men. Conclusion Government intervention is quintessential to decrease the hardship financing caused by cancer. The intra-household inequalities play an important role in explaining their hardship financing strategies. We suggest the need for more financial risk protection for outpatient care to address hardship financing.

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