Challenges in Implementing Fiscal Policy

The government has two tools to implement its fiscal policy, namely, taxes and government spending. If the economy is in recession, the government may decide to increase aggregate demand, or decrease taxes to stimulate the economy and increase aggregate demand. Similarly, if the economy is facing inflationary economic boom, it may decrease spending or increase taxes.

When the government takes specific actions to influence aggregate demand, it’s called the discretionary fiscal policy.

The discretionary fiscal policy does not always work as intended by the government. There are many reasons as to why the fiscal policy may not be as effective as desired, or sometimes even be counterproductive. Some of these reasons are discussed below:

  1. If the government relies on inaccurate statistics, then it’s likely to make wrong policy decisions in the first place.
  2. There could be a lag in implementing a policy decision, and/or the impact of a policy decision. For example, by the time the policymakers recognize the problem and take decision to do something, it may already be too late (Recognition lag and action lag). Once the government implements a policy, there may be a time lag till the policy has an impact on the economy (impact lag).
  3.  An expansionary fiscal policy may end up decreasing aggregate demand because of crowding-out effect.  Increased government borrowing leads to an increase in interest rates, which leads to a decrease in aggregate demand.
  4. The economy may be slow because of shortage of resources rather than lower demand. In this case, fiscal policy will not help (it may actually increase inflation).
  5. Since expansionary fiscal policy increases fiscal deficit, there is constraint over how much deficit the government can tolerate.
  6. While fiscal policy solves one problem, it may aggravate another problem.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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