Dynamics of structural transformation and business cycle synchronization: a panel quantile regression approach for India

ABSTRACT

Recent contributions to the business cycle literature point towards the presence of potential nonlinearities in the relationship between similarities in sectoral composition of countries and their bilateral business cycle synchronization. By using a dynamic indicator of structural change (as opposed to the traditional static indicators), this article attempts to identify whether the similarities in the evolution of structural change across countries influence their bilateral business cycle synchronization heterogeneously across quantiles. With focus on India and its advanced and developing trade partners, this article employs the quantile regression model with non-additive fixed effects to assess this relationship. While India and its advanced countries' trade partners display a homogeneous and positive relationship between the similarity in evolution of agriculture and industry and their bilateral business cycle synchronization, there exists a homogeneously negative relationship with respect to the service sector. Similarities in the evolution of the industry and the service sector between India and its developing trade partners, on the other hand, have a heterogeneous impact on their bilateral business cycle synchronization.

KEYWORDS:

• Structural transformation
• business cycle
• synchronization
• dynamic
• non-linear

JEL CODES:

• F15
• F43
• F44
• C21
• C23

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ For relevant literature, please see Frankel and Rose (1998); Imbs (1999), Imbs (2004); Baxter and Kouparitsas (2005); Otto, Voss, and Willard (2001); De Grauwe (1998), amongst others..

² The World Bank groups its member countries into 4 income groups: low income, lower-middle income, upper-middle income and high income based on their gross national income per capita..

³ For further details, please see Duval et al. (2014).

⁴ As suggested by the reviewer, to ensure the robustness of our findings (given the shortcomings of the quasi-correlation variable), we also employed 10-year rolling window correlations (by transforming it using the Fisher’s Z transformation (Fisher 1921) as the dependent variable, to ensure an approximately normal distribution). The results (presented in Appendix C) were, by and large, similar to our baseline results..

⁵ For further robustness check, the article also employed one-period lagged values of Trade Openness ($TI{I}_{ij,t-1})$as an IV for bilateral trade. The results for the coefficients estimates were broadly similar to those obtained by using product of the (logarithm) GDPs of the two countries as an IV for bilateral trade..

⁶ We also conducted the joint tests for skewness and kurtosis for both the sub-groups (D’Agostino, Belanger, and D’Agostino 1990; Royston 1992). The results provided significant evidence of rejection of the null hypothesis of normality..

⁷ We repeat the analysis by employing the instrumental variable quantile regression model (Chernozhukov and Hansen 2008) for robustness purposes. See Tables E1 and E2 in Appendix E.

⁸ Please see Harding and Lamarche (2009) and Liu (2014) for details.

Additional information

Funding

The authors declare that they have no conflict of interests or funding sources to report.