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Risk, Uncertainty and Exporting: Evidence from a Developing Economy

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Abstract

This study investigates the effects of economic policy uncertainty (EPU) and geopolitical risks (GPR) originating from various destination economies on the export demand facing a developing economy, i.e., India. We employ monthly panel data on exports from India to 29 economies spanning January 2015 to December 2019. For analysis, we use a range of estimators that address potential econometric issues such as non-stationarity, serial correlation, cross-sectional dependence, and different lag orders and breaks in error processes. Our results suggest adverse and sizeable effects of destinations’ EPU and GPR on the export revenues of India. We also find that income, relative inflation, and the exchange rate serve as channels via which uncertainty affects exports. Our evidence is novel and helps further the available body of knowledge on the impact of economic and geopolitical uncertainties on developing economies through an established channel, i.e., export.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Notes

  1. Sharma and Paramati (2021) examine uncertainty, measured by uncertainty indices, effects on Indian imports. Their results show a fairly large and negative effect on domestic and global uncertainty.

  2. Economic Survey (2019–20).

  3. Directorate General of Commercial Intelligence and Statistics (DGCI&S), Ministry of Commerce, Government of India, is the original data source. We retrieved it from CMIE as the data is presented there in easily usable format.

  4. https://www.policyuncertainty.com/.

  5. Westerlund cointegration test is conducted in this study using xtwest command of Stata (Persyn and Westerlund 2008).

  6. In a set of unreported results, we also estimate these models using fully-modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimators. Their results remain consistent with our broad findings. Further, while FMOLS regression allows for short-term cross-country heterogeneity, these estimators do not control for dependence structure in the data.

  7. Many scholars study the composite effect of prices and exchange rates by denominating goods prices in terms of a single currency in the price ratio (see Edwards and Alves 2006). As highlighted before, the present study measures the effect of prices by taking the ratio of CPI in the sample economies. Thus, while the question of currency denomination does not arise in our set up, our price index ratio may still capture the effect of exchange rates captured in the prices of imported inputs.

  8. Caggiano et al. (2018) suggest that the effects of one country’s uncertainties may impact others despite their low trade intensity with each other.

  9. For instrumentation to be effective, two conditions must hold. One, there must be sufficient correlation between the instrument and the simultaneously determined explanatory variable. Two, the lagged variables must not feature in the estimating equation.

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Acknowledgements

The authors thank the anonymous reviewers of this paper for their comments and helpful suggestions on earlier draft, which have helped in deeply enhancing the quality of this research.

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Authors and Affiliations

  1. Indian Institute of Management Lucknow, Lucknow, India

    Chandan Sharma

  2. Indian Institute of Management Rohtak, Rohtak, 124010, India

    Rupika Khanna

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Appendix

Appendix

See Tables 8, 9, 10, and 11.

Table 8 Sample countries
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Table 9 Variable description
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Table 10 Correlation matrix
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Table 11 Effects of policy uncertainty and geopolitical risk: excluding the United States
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Sharma, C., Khanna, R. Risk, Uncertainty and Exporting: Evidence from a Developing Economy. J. Quant. Econ. 22, 151–177 (2024). https://doi.org/10.1007/s40953-023-00377-4

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