2026 BOK/ERI-BOJ/IMES Joint Research Workshop

The standard New Keynesian models typically assume that the probability of price adjustment remains constant over time. However, rapid shifts in corporate price-setting behavior observed during the recent high-inflation period have prompted a re-evaluation of the validity of this fundamental premise.

In this context, Matthew Fink (RBA) analyzed corporate price-setting behavior before and after the COVID-19 pandemic, using an extensive Australian dataset that includes over 500 million price observations across 10 million items from 61 retail firms.(2) He reported that during the early stages of the pandemic, heightened uncertainty had led firms to delay price adjustments, resulting in a temporary increase in price rigidity. In contrast, as inflation approached its peak through 2023, the frequency of price adjustments rose, leading to more flexible price-setting behavior than in the pre-pandemic period. He pointed out that corporate pricing strategies had changed significantly.

Furthermore, a counterfactual analysis using the standard dynamic stochastic general equilibrium (DSGE) model employed by the RBA revealed that assuming a constant frequency of price adjustment could lead to an underestimation of inflation in post-pandemic forecasts by approximately 0.4 to 1.3 percentage points. In addition, it was shown that the Philips curve steepened as price-setting became more flexible. Fink argued that in this environment a more aggressive interest rate hike might be optimal, rather than that suggested by standard models.

These results provide empirical evidence that corporate price-setting behavior can change during large inflationary shocks, offering a valuable basis for reconsidering the underlying assumptions of standard macroeconomic models.

In Korea, soaring house prices have pushed household debt to high levels, with approximately 50% of private-sector credit concentrated in real estate, including housing loans. This credit concentration has gained attention in policy discussions for its potential to dampen economic growth, making the impact of private-sector credit composition on economic performance a critical issue for investigation.

Indo Hwang (BOK-ERI) addressed this issue by conducting an empirical analysis using panel data from 43 countries spanning from 1975 to 2024.(3) He reported that a higher share of corporate debt within total private debt was associated with stronger subsequent economic growth. He added that upon closer examination, the results revealed an "inverted U-shaped" relationship between private debt and economic growth, where excessive debt levels eventually hinder growth. Furthermore, he showed that for any given level of total debt, a higher proportion of corporate debt was consistently associated with greater economic growth.

He then conducted a simulation to assess the impact of shifting credit from households to corporations by applying the cross-country results to the Korean economy. He reported that reducing the household debt-to-GDP ratio by 10 percentage points and reallocating that same amount to corporate debt would increase the economic growth rate by 0.2 percentage points. He explained that this transmission channel involved the expansion of corporate credit, which boosts the investment-to-GDP ratio and leads to enhanced productivity. Notably, the simulation showed that this positive effect was particularly pronounced with credit reallocated to sectors with high external finance dependence, small and medium-sized enterprise (SME)-intensive sectors, and high-productivity sectors, whereas no such effect was observed with credit directed toward the real estate sector.

Hwang proposed specific policy measures to channel credit toward high-productivity sectors, including adjusting risk weights for real estate loans, improving credit evaluation infrastructure for SMEs and startups, and expanding equity financing. These recommendations offer practical implications for shaping Korea's growth strategy.

As major central banks have transitioned away from the era of ultra-low interest rates, a key challenge in measuring the monetary policy stance is, in addition to interest rates, how to account for the scale of central bank balance sheets, which expanded under quantitative easing (QE).

To address the challenge, Dora Xia (BIS-HK) proposed the Monetary Policy Conditions Index (MCI) as a new metric that incorporates both interest rate levels and balance sheet size.(4) Specifically, the MCI is defined as a weighted average of the two-year Treasury yield and the ratio of the central bank's balance sheet to the potential GDP. The weights for this index are derived using a Bayesian Vector Autoregression (BVAR) model, incorporating the Financial Conditions Index (FCI),(5) inflation rates, and the output gap. The estimation on the U.S. data yielded a weight of 0.8 for the interest rate variable and 0.2 for the balance sheet variable, suggesting that a one-percentage-point increase in the policy rate is equivalent to a four-percentage-point reduction in the balance-sheet-to-potential-GDP ratio.

Xia reported that, according to the estimated MCI, the substantial expansion of the Federal Reserve's balance sheet since 2015 resulted in a monetary policy stance significantly more accommodative than what short-term interest rates or shadow rates would suggest.(6) She added that while the aggressive easing measures following the pandemic supported economic recovery, they also contributed to the persistent inflation observed since 2022. Regarding the current environment, she argued that despite recent interest rate hikes and quantitative tightening (QT), the MCI remained at an exceptionally low and accommodative level by historical standards due to the still-large absolute scale of the balance sheet.

These findings suggest that the proposed empirical framework enables a more comprehensive evaluation of the monetary policy stance, which can be applied widely to the policy analysis of central banks around the world.

What types of firms should receive subsidies to improve the efficiency of resource allocation across the entire economy? While many countries implement size-dependent subsidy policies targeting SMEs, the fact that a firm is "small" does not necessarily mean it faces financial constraints. Firms may remain small not only because their growth is hindered by financial constraints but also simply because their productivity is low.

Jaeyoung Seo (BOK-ERI) addressed this issue by analyzing data on Korean firms, showing that while there is no clear correlation between firm size and capital productivity, there is a distinct negative correlation between firm age and capital productivity, with younger firms exhibiting significantly higher levels of capital productivity.(7) Based on the results, he argued that younger firms were more likely to be constrained by insufficient self-finance and thus more strongly influenced by financial frictions.

Seo then conducted simulations using a heterogeneous firm model and reported the following results. In an economy with relatively weak financial frictions, such as the current Korean economy, size-dependent subsidies may contrarily worsen allocative efficiency. In contrast, while size-dependent subsidies can improve efficiency in economies with strong financial frictions, age-dependent subsidy policies—which target younger firms—outperform size-dependent ones in either case. He emphasized that firm age served as a superior indicator for identifying companies truly facing financial constraints. He argued that reallocating resources to these younger firms improved the efficiency of resource allocation, which in turn boosts GDP.

These results underscore the importance of indicators that capture the intensity of financial frictions and the actual status of firms in designing subsidy policies, providing a valuable empirical basis for reconsidering existing SME support policies in Korea that rely on simple size-based criteria.