Working Papers

Land, Collateral, and Public Investment

I develop a dynamic general equilibrium model in which land is both a productive input and the primary collateral asset for private firms and local governments. The framework is tailored to China’s land-finance system, where local governments issue land-backed debt and fund infrastructure through land sales and land collateral. The government behaves as a Stackelberg planner, choosing public investment, the quantity of land released to the market, and borrowing, internalizing how these choices affect endogenously determined land prices via private land demand. When private demand for land is inelastic and the government’s borrowing constraint binds, optimal policy forces land to grow more slowly than output: the government restricts land supply to raise the collateral value of its own land holdings and expand land-finance borrowing capacity. This crowding-out of land from private production reduces firms’ ability to generate output, and the planner responds by increasing infrastructure investment to restore productive capacity and sustain output growth. The model therefore rationalizes the Chinese pattern of seemingly excessive infrastructure investment, restrictive land supply, and high land prices as the outcome of a welfare-maximizing but collateral-constrained government that must use land values to expand its balance sheet.

Conglomerates, Liquidity Shocks, and Innovation-Led Growth

The interaction between financial intermediation and economic recovery is crucial to understanding the trade-offs faced by East Asian economies that rely on conglomerate systems. This paper presents a model combining financial intermediation and endogenous growth to explore how conglomerates manage credit shocks and allocate resources. Initially, conglomerates internalize R\&D externalities, enabling rapid productivity growth. However, during crises, these systems face a dilemma: coercing firms into short-term investments versus liquidating poorly performing firms. The analysis reveals that in normal times or under small shocks, saving firms is optimal for maintaining R&D and growth. However, as shocks increase or the economy advances, liquidation reallocates resources more efficiently. This dynamic highlights the delicate balance between maintaining firm boundaries and enabling economic recovery, with implications for policy responses to financial crises.

Policy Learning After Covid: Why Forward-Looking Rules Outperform AIT Under Phillips-Curve Misspecification

I study how average inflation targeting (AIT) compares to forward-looking Taylor rules when the central bank misperceives the Phillips curve and updates beliefs using forecast errors. In a New Keynesian model calibrated to post-Covid U.S. conditions, I show that forward-looking rules consistently outperform AIT, despite model misspecification. The advantage lies in timing: forward-looking policy responds earlier to shifting inflation dynamics, while AIT lags behind, anchored to outdated averages. This mechanism helps explain recent inflation overshooting and underscores a broader risk: backward-looking frameworks like AIT may be ill-suited for periods of rapid structural change. I challenge the common view that AIT is more robust under uncertainty and offer a theoretical explanation for why AIT may have contributed to post-Covid inflation overshooting.

Chinese Land-Price Dynamics, State Intervention, and Macroeconomic Fluctuations

I develop a model of the Chinese economy that emphasizes the roles of property markets, collateralized borrowing, and local government policies, arguing that local government land management is a key driver of Chinese business cycles. Land management policy involves pro-cyclical land production and counter-cyclical land purchases by state-owned LGFVs, which use the land as collateral to finance infrastructure projects. While these land policies help support land prices, they also encourage manufacturing firms to invest in land not used for production, distorting land allocation across sectors. The model successfully replicates key features of the Chinese business cycle, including the positive co-movement between land prices and business investment and steady increases in real estate prices. The model also reveals a tradeoff: although local government policies can mitigate certain shocks by exploiting the financial accelerator to smooth out the business cycle, other shocks, including a permanent negative shock to housing demand, would necessitate sustained land purchases and infrastructure spending by LGFVs. This could potentially lead to explosive government debt and a long period of deleveraging and lower output.

Forward Guidance When Inequality Matters (with Bo Li and Xiao Wang)  (Revise and Resubmit at Macroeconomic Dynamics)

Utilizing a heterogeneous-agent New Keynesian (HANK) model with realistic dividend allocation and fiscal environment, we analyze the quantitative implications of forward guidance. Our study emphasizes the importance of accurately modeling wealth distribution and incorporating entrepreneur households to understand the impact of forward guidance on output. We find that increased worker heterogeneity diminishes the effectiveness of forward guidance, as does concentrating dividend income. Through various experiments, we demonstrate the important role of fiscal policy. Using government transfers to balance the budget instead of tax changes helps mitigate negative effects but dampens policy effectiveness initially. When considering fiscal policy, expanded government spending boosts output but worsens wealth inequality, leading to more dramatic swings before and after the policy. Tax relief for lower income earners amplifies output initially far beyond government spending or forward guidance alone, but it leads to a much sharper decline in output afterward because of even more extreme redistributional effects when the policy is enacted and wound down. Additionally, we observe that the combined effects of forward guidance for monetary and fiscal policies together surpass their individual effects on impact though not at th。e time of announcement, but the downturn after the policies are reversed is also greater. Our study underscores the necessity for policymakers to consider both the quantitative and distributional ramifications of forward guidance and to anticipate the interactions of implemented policies as well the effects when they are concluded.

Globally Optimal Monetary Policy

Optimal monetary policy is important on a practical level for central banks. Computational barriers, however, have limited research into important optimal monetary policy questions. With some important exceptions using computational techniques tailored to specific cases, most of the applications addressed have been simplified to make them amenable to solving analytically or to easy computation. This paper brings into the optimal monetary policy literature recent machine learning techniques that apply to a wide range of practical applications for which computational barriers have previously been a problem. I illustrate these techniques as applied to the question of how firm expectations and price distortions should jointly influence optimal monetary policy. In a fully non-linear New Keynesian Model with price and labor distortions, I find that price level stabilization around a long-run value is best when distortions are small. However, when we start from the long-run value, the policy response should be nonlinear and more aggressive as pricing distortions increase. I show that interest rate policy should take into consideration both price dispersion and firm expectations on future costs, the latter directly relating to distortions from monopolistic competition.