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Aggregate Fluctuations and Technological Shocks: The Indian Case

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Abstract

This study is one among the few attempts to link aggregate fluctuations with productivity and technical efficiency using the data of the Indian industrial sector. In doing so, this study uses firm-level data from the CMIE Prowess and macroeconomic indicators of Indian economy from various government databases. We estimate productivity and technical efficiency using the standard econometric approach. Further, a structural vector error correction (SVEC) model is employed to understand the importance of technological shocks in explaining the aggregate fluctuations. The result without ambiguity indicates that the percentage of variance explained by aggregate demand shocks is larger at lower lag and decreasing. However, the share of technology shock shows an increasing trend over the period of time. Therefore, the aggregate demand shock and the technology shock have conflicting impact as far as aggregate output fluctuations are concerned. The results are similar when we substitute TFP with TE. The findings in general indicate the transitory nature of aggregate demand shocks compared to technology shocks.

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Notes

  1. 1.

    Technological shocks are assumed to be exogenous in RBC, but it is not so in Schumpeterian models. Please see Akcigit and Kerr (2018), Acemoglu et al. (2012) and Aghion and Jaravel (2015), for modern interpretations of Schumpeterian growth models.

  2. 2.

    See for example Cooley and Prescott (1995), King and Rebelo (1999).

  3. 3.

    Similarly, Basu et al. (2006) constructed a measure of aggregate technology change and argued that sticky-price models fit the data well compared to RBC models. Some studies stressed other important shocks that affect aggregate fluctuations like “fundamental disturbance to the functioning of financial sector” (Justiniano et al. 2010), investment-specific technology shocks (Greenwood et al. 1997; Fisher 2006) and news shocks (Beaudry and Portier 2006).

  4. 4.

    Many studies have highlighted the importance of idiosyncratic firm-level shocks to aggregate fluctuations (Gabaix 2011)

  5. 5.

    For detail methodology, please see Ackerberg et al. (2015).

  6. 6.

    VECM was estimated with two lags as suggested by AIC information critera.

  7. 7.

    Only the results of variance decomposition of output due to output, TFP/TE and real money supply are represented in the tables. The results of other variables are available upon request.

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Acknowledgements

This is a modified and updated version of our earlier paper presented during the “Silver Jubilee Seminar of Madras School of Economics” jointly organised by Forum of Global Knowledge Sharing (Knowledge Forum) during August 11, 2018. We would like to thank the participants of the seminar for constructive comments and suggestions during the presentation. Usual disclaimer applies.

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Correspondence to Santosh Kumar Sahu .

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Paul, S., Sahu, S.K., Jacob, T.I. (2020). Aggregate Fluctuations and Technological Shocks: The Indian Case. In: Siddharthan, N., Narayanan, K. (eds) FDI, Technology and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-15-3611-3_11

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