Abstract
This chapter measures the effects of various types of public expenditure on welfare at the disaggregate state level, in terms of agricultural productivity and rural poverty.
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Notes
- 1.
A condensed version of this chapter is found as: Bathla, S., P. Joshi, and A. Kumar, 2019. “Targeting Agricultural Investments and Input Subsidies in Low-Income Lagging Regions of India”. European Journal of Development Research 31 (5): 1197–1226. https://doi.org/10.1057/s41287-019-00207-5.
- 2.
Two likelihood tests compare the model with a saturated model (the model fits the covariance perfectly), and both statistics reject the null hypothesis that the model fits as well as the saturated model. The root mean squared error of approximation (RMSEA) measures the error of approximation based on a residual matrix that looks at discrepancies between observed and predicted covariances. The model is found to be a close approximate fit because the lower bound of the 90% confidence interval is below 0.05. The root mean squared residual is a measure of the mean absolute correlation residual or the overall difference between the observed and predicted correlations. The model is considered to be a favourable fit because the standardised root mean squared residual (SRMSR) is less than the recommended threshold of 0.08. The coefficient of determination is viewed as R-square for the model, suggesting that the model is adequately fit. Both the comparative fix index and Tucker–Lewis index compare the model performance to a baseline model (unconstrained estimates of variance); however, it was pointed out that this baseline model could be improper in many cases and should be treated carefully (Widaman and Thompson 2003). It is recommended to check any non-recursive models for stability, because the calculation of the indirect effect might fail to converge to finite results.
- 3.
The test results indicate that the analysis is stable and has higher goodness of fit based on the combined rule of low root mean squared error of approximation, low standardised root mean squared residual, high coefficient of determination, and high stability values. Besides fitness tests, we also experimented with different model specifications to check for possible misspecification. A Sargan test was performed for overidentification of the equations, and the test results show equations to be identified. A Hausman test indicated that some of the public expenditures and subsidy variables were endogenous. Variables lagged for one year were used, as they can be considered predetermined and weakly exogenous.
- 4.
The beneficiary households receiving seeds, manual- and power-operated sprayers, and other incentives and subsidies were able to realise higher productivity and income in most crops grown compared with the non-beneficiary households.
- 5.
MGNREGS is one of the important components of rural development expenditure. Studies provide evidence of the programme’s increasing impact on employment and on several other aspects of the rural economy such as wages, agricultural productivity, and gender empowerment. In a span of 10 years, the programme is reported to have generated 19.86 billion person-days of employment, benefiting 276 million workers (Himanshu 2016; Desai, Vashishtha, and Joshi 2015).
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Bathla, S., Joshi, P., Kumar, A. (2020). Welfare Effects of Investments and Input Subsidies. In: Agricultural Growth and Rural Poverty Reduction in India. India Studies in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3584-0_5
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