Abstract

We analyze how regional economic structures affect the impact of monetary policy on rates of inflation across 34 Indonesian provinces. The paper first applies structural factor augmented vector autoregressive model (SFAVAR) to all the 34 provinces based on monthly provincial data in order to measure the length and magnitude of responses of regional inflation to monetary policy shock, derived from the consequential impulse response functions of 34 provinces. In the second step, we analyze the impact of economic structures on the length and magnitude of regional inflationary responses of 34 provinces. We find that the impacts of monetary policy across regions are significantly influenced by economic structural variables such as manufacturing sector share to GDP, mining sector share to GDP, bank lending share to GDP and export share to GDP. In addition, we found the spatial lag, rate of inflation of neighboring provinces, is also statistically significant. In a similar fashion, economic structural variables such as manufacturing sector share to GDP, construction sector share to GDP and investment share to GDP are found statistically significant in explaining regional differences of monetary policy efficiency. Our findings imply economic structures of provinces have to be incorporated to designing monetary policy in Indonesia.

Highlights

  • The effects of monetary policy actions are evaluated at the national level

  • 5.1 Heterogeneous effects of a monetary policy shock on regional inflation The estimated coefficients and factors in the structural factor augmented vector autoregressive model (SFAVAR) model are shown in Table 5 in the Appendix

  • 5.2 Regional economic structure and monetary transmission Up to this point, we have shown our findings from the SFAVAR analysis of differences in regional responses following monetary policy actions

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Summary

Introduction

The effects of monetary policy actions are evaluated at the national level. Using CPI data across 82 cities in Indonesia, Jangam and Akram (2019) and Aginta (2020a) find that regional prices do not converge to a common path. They show that instead, city prices evolve in four convergence clubs. Aginta (2020b) uses CPI data of the 34 Indonesian provinces and finds four convergence clubs of price dynamics across provinces.

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