Carlos Azzoni (p), Patrício Aroca

Anna Shchankina

We calculate the synchronization of the state’s economic cycles with the Brazilian economy and the synchronization of pairs of states between 1947 and 2021. Due to a lack of continuous data, we split the period in two: 1947-70 and 1985-2021. We observe more synchronization in the second period, indicating an overtime increase in the economic integration of the states. With more detailed data, we identified the factors explaining synchronization from 1989 through 2021, both between states and the national economy and between states. We found that states with larger shares of international exports and more specialized in some sectors are less integrated into the national economy. In contrast, states with a large economy and a large share of government in GDP are more integrated. The paper provides a long-run view of the regional economies in Brazil, showing increasing regional integration and highlighting the factors behind it.

Michiel Daams, Philip McCann (p), Paolo Veneri, Richard Barkham

Carlos Azzoni

This paper uses uniquely-detailed large-scale commercial real estate investment data in order to examine how financial markets perceived the attractiveness of investing in UK regions during the last two decades. We examine transactions over a 21 year period 2003-2023 and using various econometric and model-based techniques, we are able to calculate year-by-year regional and city risk premia for all UK cities and regions. Comparing this with the risk-free sovereign rates of the central bank, this allows us to identify how the ‘external finance premium’ of Ben Bernanke varies year-by-year for all UK cities and regions over these two decades. Our analysis demonstrates that prior to the 2008 global financial crisis, all regions of the UK were perceived in a similar manner in terms of risks and expected growth rates. However, the 2008 crisis engendered a ‘flight to safety’ of capital into London, largely at the expense of other UK regions. The London economy enjoyed a surge of capital inflows at very low prices, also enhancing the leveraging and collateral positions of local real estate owners. The recovery of investors’ confidence in London’s recovery was rapid and London responded to QE Quantitative Easing . In contrast, in the immediate aftermath of the 2008 crisis other UK regions shifted rapidly into junk bond territory, and have remained there ever since. The resulting core-periphery economic geography of capital pricing was then further exacerbated by the 2016 Brexit vote. This both widened the core-periphery risk premia gaps between the more and less prosperous regions, and our results demonstrate that it was the central business districts of the second-tier and third-tier cities which bore the brunt of the adverse capital shocks associated with both the 2008 global financial crisis and the 2006 Brexit vote. The Brexit vote also ended the extent to which London responded positively to QE, while having no beneficial effects on the rest of the UK’s cities and regions. These asymmetric capital shocks led to profound and adverse impacts on the subsequent productivity growth of the UK city and regional economies, with the regions and cities facing the highest rise in capital pricing facing the most severe falls in productivity and employment growth rates.

Olga Demidova, Anna Shchankina (p)

Philip Mccann

Monetary policy affects aggregate demand through transmission channels, the most important one is interest rate channel. The purpose of this research is to test the following hypotheses. Hypothesis 1. There is a long-term relationship between regional mortgage rates and the monetary policy rate. Hypothesis 2. The reaction of regional mortgage rates to changes in monetary policy rate significantly depends on the time interval under consideration. Hypothesis 3. These changes vary significantly across the Russian regions. To test these hypotheses, we used the Error correction model (ECM) with the dependent variables iwt (weighted average mortgage rate), icomt (mortgage rate without concessional loans) and MIACR (Moscow Interbank Actual Credit Rate) as independent variable. The time period was heterogeneous, it included both very low rates during the COVID-19 period and very high rates after the February 2022, so we estimated the models at three time periods: January 2016 – February 2020, January 2016 – February 2022, and at the entire time interval (January 2016 – August 2023). Let us briefly describe the results obtained. For the period from January 2016 to the beginning of the COVID-19 pandemic, a significant long–term relationship between commercial mortgage rate and MIACR was observed for 61 regions, and a relationship between weighted average mortgage rate and MIACR was observed for 76 regions. For the period from January 2016 to the February 2022 long-term relationship between commercial mortgage rate and MIACR was observed only for a few regions: for 14 regions in the case of commercial mortgage rates and for 4 regions in case of weighted average mortgage rates. Apparently, this is due to the fact that other factors, including non-market factors (government support for banks and the public), influenced household decisions regarding taking out loans. For the entire period from January 2016 to August 2023, a long-term relationship between commercial mortgage rate and MIACR was also observed for a small number of regions, for 30 regions in case of commercial mortgage rates and for 5 regions in case of weighted average mortgage rates. According to the results obtained, hypothesis 1 received partial empirical confirmation. For example, in the case of the period from January 2016 to August 2023 relationship between the change in mortgage rates and changes in MIACR was not identified for most of Russian regions. Hypotheses 2 and 3 were empirically confirmed.