Regional inflation persistence in Turkey

Abstract

The purpose of the current study is to investigate the degree of inflation persistence, its geographical variation, sources of cross‐regional variation, and presence of geographical/sectoral aggregation bias in national monetary policy. Our data set covers 26 NUTS‐2 level Turkish regions and monthly CPI inflation over the period 2003–2019. We first estimate the degree of regional inflation persistence by autoregressive regressions, check its robustness against the presence of structural breaks (by Bai–Perron's algorithm) and nonlinearities (by Markovian Regime Switching regressions). Second, we examine the possibility of geographical and sectoral aggregation bias. Third, we investigate the cross‐regional determinants of inflation persistence by panel data analysis, employing hybrid‐effects spatial panel regressions. We analyze the direct and indirect effects of the determinants and test for regional spillover effects. Three main results are obtained. First, estimated persistence degrees are heterogeneous across regions. The geographical pattern is empirically robust against structural breaks and nonlinearities. We find that inflation persistence is distributed in a spatially correlated manner. Second, when sectoral and regional aggregation bias is tested, only sectoral aggregation indicates a considerable level of bias. Third, we find that the presence of large firms in the region and a higher share of agricultural output in GDP leads to lower persistence, while an increased share of industrial output, and increased trade volume leads to higher inflation persistence. Moreover, we find spatial spillovers of price variability evident in regression analysis. From a policy standpoint, it is required that structural policy programs are targeted to maintain flexibility in the regions where persistence is high (i.e., providing market entry/exit, institutional quality, policy credibility, stimulation of SMEs). Moreover, sectors that have high persistence, such as Hotels and Restaurants (persistence degree 0.55) and Health Services (0.39) should be weighted more in CPI calculations.

Inflation and Spatial Spillovers in a Large Archipelago: Evidence from Indonesia*

“…Our study falls in the body of literature that measure spatial spillovers of inflation within country (see, e.g. Yesilyurt & Elhorst, 2014; Nagayasu, 2017; Yu & Huang, 2016; Yang et al ., 2021; Duran & Dindaroğlu, 2021; Çakır, 2022). Applying spatial Durbin model (SDM) of the hybrid Phillips curve using data from sixty‐seven provinces in Turkey from 1987 to 2001, Yesilyurt and Elhorst (2014) discover that backward‐looking expectations—both the province's lagged inflation and the lagged inflation in neighbouring regions—have a significant impact on inflation.…”

“…The author finds significant effects of lagged inflation and productivity (both in the province and neighbouring provinces), consistent with the BS effect. Duran and Dindaroğlu (2021) present another application of SDM by investigating inflation persistence across twenty‐six Turkish regions. They show that economic characteristics from neighbouring regions, such as industrial and agriculture share, trade openness and the proportion of large firms, have a considerable impact on inflation persistence in a focal region.…”

Autoregressive Vector (VAR) Method in Analyzing Inflation Persistence in Indonesia

“…Previous research has examined a lot of issues related to the persistence of inflation in a region or country. Where the results of the study some relate it to economic variables that are variables in measuring the level of persistence of inflation, such as exchange rate shocks (Bems, 2021;Dua, 2021), the way monetary policy is conducted (Bilici, 2020;Granville, 2019;Hakim et al, 2017;City & Lazaretou, 2011;Van Doorslaer &;Vermeiren, 2021), differences in regional layout (Gajewski, 2018), due to exchange rate regimes ( Caporale &;Gil-Alana, 2020;Wu, 2018), goods and services inflation (Boaretto, 2019), oil price shocks (Oloko, 2021 ;Salunkhe &;Patnaik, 2019), agricultural output in GDP (Duran, 2021), increased taxation progressivity (Geronikolaou et al, 2020), agricultural commodity prices (Tule et al, 2020), and unemployment rate (Albulescu &;Tiwari , 2018;Alogoskoufis, 2018;Ashley, 2020). Meanwhile, in this study, researchers will later use the Consumer Price Index (CPI) as a variable to see the level of persistence of inflation that occurs in Indonesia.…”