What can we learn about monetary policy transmission using international industry-panel data?

Sangyup Choi, Tim Willems and Seung Yong Yoo

How does monetary policy really affect the real economy? What kinds of firms or industries are more sensitive to changes in the stance of monetary policy, and through which exact channels? Despite advances in our understanding of the monetary transmission mechanism, existing studies have not reached a consensus regarding the exact mechanics of transmission. In a recently published Staff Working Paper, we aim to contribute to this understanding by analysing the impact of monetary policy on industry-level outcomes across a broad international industry-panel data set, exploiting the notion that different transmission channels are of varying degrees of importance to different industries.

Covering 105 countries and 22 industries from 1973 to 2019, our study combines estimates of monetary policy surprises with industry-level data to identify the industries which are particularly sensitive to changes in monetary policy. Industry-level data are especially informative on the monetary transmission mechanism since factors determining the sensitivity to monetary policy typically vary more across industries within a country than across countries.

New measures of international monetary policy shocks

We compile a comprehensive international data set on monetary policy shocks, which covers not only advanced economies but also numerous emerging market and developing economies. In order to identify the causal impact of monetary policy, it is necessary to disentangle unexpected changes in the stance of monetary policy (also known as monetary policy ‘shocks’ or ‘surprises’) from policy rate movements that occur systematically in response to changes in variables like inflation or growth. Otherwise, it is not clear whether the observed subsequent movement in (say) inflation caused monetary policy to respond, or whether the causality actually runs in the opposite direction (from monetary policy to inflation, which is the direction of causality monetary policy makers are most interested in). Understanding the direction of causality is crucial when it comes to conducting policy counterfactuals (‘what would happen if the central bank increased the interest rate by 50 instead of 25 basis points?’), which is why we are interested in creating a broad database of such shocks.

In many emerging/developing economies, it is difficult to identify unexpected monetary surprises due to data limitations. Consequently, we utilised a hierarchical approach that prioritised surprise measures generated by methods that are deemed superior, according to the following hierarchy: (i) shocks identified by others via high-frequency methods (such as Cesa-Bianchi et al (2020) for the UK) which is seen as the gold standard, (ii) changes in the short-term yield around dates involving monetary policy decisions (the idea being that these changes capture the ‘surprise’ component associated with each monetary policy decision), (iii) the surprise-component implied by interest rate forecasts from Bloomberg’s survey of financial market participants, (iv) deviations from an estimated Taylor rule (a way for the central bank to set its policy rate as a function of inflation and growth), and (v) in case of countries that peg their exchange rate: the estimated monetary policy shock in the anchor country (often the US, for which we conveniently have high-quality shock estimates generated through high-frequency methods).

Figure 1 shows that using the resulting monetary policy shocks in a panel Vector Auto Regression model produces conventional ‘contractionary’ responses in the cyclical components of real GDP and the GDP deflator, giving credence to the underlying shock series that sits at the core of our analysis. These impulse-responses were estimated on data from the 105 countries included in our analysis and can be seen as cross-country averages.

Figure 1: Impulse responses following a positive monetary policy shock

Note: Dashed lines represent the 95% confidence interval.

Test of various theoretical channels of monetary policy transmission

To investigate the transmission of monetary policy, we use our newly constructed monetary surprise data and adopt a ‘difference-in-differences’ approach that interacts monetary surprises with industry-level characteristics – essentially asking whether industries that score higher along a particular dimension are more sensitive to monetary surprises. In particular, we implement this strategy by estimating regressions of the following type (which follows the approach underlying Rajan and Zingales (1998) who used it to estimate the impact of financial development on growth):

Yi,c,t+1 = αi,c + αi,t + αc,t + β (Xi × MPSc,t) + εi,c,t+1 (1)

In equation (1) the subscript i denotes industries, c countries, and t years. Yi,c,t is a measure of output growth in industry i, in country c in year t. The variable Xi characterises industry i along eight dimensions (such as external financial dependence, asset tangibility, and durability of output; see Table B); MPSc,t is our measure of the monetary policy shock for each country c during year t (with positive values indicating monetary contractions). Regression (1) also contains industry-country, industry-time, and country-time fixed effects (αi,c, αi,t, and αc,t, respectively). This constitutes a powerful set of controls (with αc,t for example controlling for the aggregate state of the economy), reducing any lingering concerns about omitted variables, model misspecification, or reverse causality; the fact that we use monetary policy shocks further helps on this front.

The main object of interest in equation (1) is β, the coefficient on the interaction term (Xi × MPSc,t). The interpretation of β is akin to a difference in differences approach, which measures the differential impact of monetary contractions in industries with characteristics as proxied by Xi. This coefficient is informative about what type of industries are particularly affected by the monetary policy shock, which is, in turn, informative about the importance of the various transmission channels. When the estimate of β < 0, this means that a monetary contraction (MPS > 0) ends up having a larger negative effect on output growth in industries that score higher along characteristic X.    

This approach enables us to examine four prominent transmission channels that have been identified in the literature, namely: the interest rate channel, the credit channel, the exchange rate channel, and the cost channel (see Table A for a brief description of each channel). The dimensions we investigate are summarised in Table B, along with their predicted effect according to the various transmission channels.

Table A: Description of transmission mechanisms included in our analysis

Table B: Industry-level characteristics and associated theoretical channels

Our results reveal that industries with assets that are more difficult to collateralise (ie, industries with lower asset tangibility, lower investment intensity, greater labour intensity, and higher depreciation) experience a more substantial decline in output in response to an unanticipated monetary contraction, followed by industries that produce durable goods. The latter finding lends support to the interest rate channel (predicting that consumption of durables falls after a monetary tightening), while the former finding highlights the crucial role of financial frictions and the associated credit channel. In particular, our results point to the importance of unsecured financing in the monetary transmission mechanism, with a ‘flight to quality’ (ie, towards secured financing) in downturns. This poses a challenge for models in the spirit of Kiyotaki and Moore (1997), where the liquidation value of secured debt drives/amplifies the business cycle, but provides support for models featuring both secured and unsecured lending (see, eg, Luk and Zheng (2022)).

Conversely, we do not find consistent evidence to support the hypothesis that exporting industries are more vulnerable to monetary tightening. Instead, our findings are more in line with the theory of ‘dominant currency pricing’ where widespread US dollar-invoicing (meaning that trades between countries tend to be denominated in US dollars, even when neither country that is party to the trade uses the dollar as legal tender) implies that exports are relatively insensitive to fluctuations in the exchange rate (with most of the action happening on the side of imports). Additionally, we fail to find evidence to support the cost channel (predicting that prices go up after a rate increase, due to borrowing costs being part of the production process): if anything, we find that relative prices of products produced by industries that are more likely to borrow to satisfy their working capital needs tend to decrease following monetary contractions, thus going against the prediction of the cost channel. This supports the conventional view among policymakers that rate hikes work to reduce inflation, not fuel it (as critics occasionally claim).

Conclusions and policy implications

Our results suggest that the effects of changes in the stance of monetary policy are likely to be heterogeneous, with output in industries producing durables and industries that have lower access to collateral being more responsive. This points to transmission taking place via the interest rate channel and the credit channel. Estimates in our paper also indicate that the credit channel becomes less important as a country’s level of financial development increases. That suggests that, of the channels considered by our paper, it is the interest rate channel (running via durable purchases) that may be most important to a financially developed country like the UK. 

With respect to our results on prices (in particular, the absence of evidence for the cost channel of monetary policy), our results provide support to the conventional view that interest rate hikes work to lower inflation.

Finally, we hope that our database (containing monetary policy shock estimates in over 170 countries, available for downloading) will prove useful to other researchers in answering related or different questions.

Sangyup Choi works at Yonsei University, Tim Willems works in the Bank’s Structural Economics Division and Seung Yong Yoo is a PhD candidate at Yale University.

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