Data and Code for: State Dependent Government Spending Multipliers: Downward Nominal Wage Rigidity and Sources of Business Cycle Fluctuations

Principal Investigator(s): View help for Principal Investigator(s) Yoon Joo Jo, Texas A&M University; Sarah Zubairy, Texas A&M University

Version: View help for Version V1

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Project Citation:

Jo, Yoon Joo , and Zubairy, Sarah. Data and Code for: State Dependent Government Spending Multipliers: Downward Nominal Wage Rigidity and Sources of Business Cycle Fluctuations. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-12-20. https://doi.org/10.3886/E197641V1

Project Description

Summary: In a New Keynesian model with downward nominal wage rigidity (DNWR), we show that government spending is more effective in stimulating output in a low-inflation recession relative to a high-inflation recession. The government spending multiplier is large when DNWR binds, but the nature of recession matters due to the opposing response of inflation, and consequently for real wages. Using U.S. historical time series data, we provide evidence of larger spending multipliers in low inflation recessions and the importance of the depth of recessions. We also employ cross-sectional data from U.S. states to document supporting evidence on multipliers and our proposed mechanism.

Scope of Project

Subject Terms: Government Spending Multipliers; Source of Fluctuation; Regional multipliers; Downward Nominal Wage Rigidity

JEL Classification:
      E24 Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
      E32 Business Fluctuations; Cycles
      E62 Fiscal Policy

Geographic Coverage: United States, US States

Time Period(s): 1/1989 – 12/2015; 1/1963 – 12/2019

Collection Date(s): 1/1989 – 12/2015; 1/1963 – 12/2019

Universe: United States
US States

Data Type(s): aggregate data; program source code

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Published Versions

V1 [2024-12-20]

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