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Abstract
This study investigates the role of intersectoral networks in the transmission of aggregate technology shocks to sectors’ growth. First, we develop a theoretical model to obtain insights into the propagation of shocks through input-output linkages, which suggests that the network effect arises via sectoral downstream linkages. We then quantitatively assess this theoretical implication with US manufacturing industries, where the aggregate technology shocks are derived from a dynamic factor model. We find that aggregate technology shocks lead to an increase in the output growth of the sector, both directly and indirectly via its intersectoral linkages. More interestingly, we document a crucial role of the intersectoral network channel, which contributes about 50 percent of the total effect. In addition, the network-based effect comes mostly from downstream linkages of sectors, which is broadly consistent with theory.
JEL Codes: E32, C67, C33, L16, D24.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
Intersectoral network-based channel of aggregate TFP shocks
Does monetary policy affect income inequality in the euro area?
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Abstract
This paper examines how monetary policy affects income inequality in 10 euro area countries over the period 1999–2014. We distinguish macroeconomic and financial channels through which monetary policy may have distributional effects. The macroeconomic channel is captured by wages and employment, while the financial channel by asset prices and returns. We find that expansionary monetary policy in the euro area reduces income inequality, especially in the periphery countries. The macroeconomic channel leads to these equalizing effects: monetary easing reduces income inequality by raising wages and employment. However, there is some indication that the financial channel may weaken the equalizing effect of expansionary monetary policy.
JEL Codes: D63, E50, E52.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
A century of gaps
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Abstract
This paper considers the role of financial information in the estimation and dynamics of the US output gap over more than a century. To this end, we extend the parsimonious approach of Borio, Disyatat, and Juselius (2016, 2014) to allow for time-varying effects of financial factors. This novel feature significantly improves real-time estimates of the output gap. It signals the peak and trough in economic activity related to both the Great Recession and the Great Depression. Two major insights follow. Credit dynamics are the primary drivers of the observed financial crisis, albeit with different conduits over the century: the stock market in 1929 and the housing market in 2008. Accounting for credit growth, the US potential growth has been stable at 2% since the beginning of 1980.JEL Codes: C11, C32, E32, O47.The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.
Unemployment or credit: Who holds the potential? Results from a small-open economy
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Abstract
This paper investigates the importance of unemployment and credit in determining the potential level of real activity for a small-open economy with a low degree of financialization. We estimate a multivariate unobserved component model (MUC) to derive the potential output and its associated output gap for the Lithuanian economy. The model is estimated via Bayesian methods and the time-paths of unobserved variables are extracted via the Kalman filter. We find that the inclusion of unemployment into the MUC model substantially improves the estimates of output gap in real-time. Once information about unemployment is accounted for, adding information about credit does not substantially alter either the estimates of output gap or its performance in real time. We uncover a strong negative correlation between the model-implied unemployment gap (without credit) and real credit growth. This explains the relatively muted impact of the financial variable on the level and dynamics of the output gap. Data revisions appear not to be the primary source of revisions on output gaps estimates.
JEL Codes: C11, C32, E24, E32.
The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.