Goodman's Law: Economic Implications and Applications

This essay examines Goodman's Law, which states that when a measure becomes a target, it ceases to be a good measure. We explore its implications in economics and finance through historical case studies and analysis of major economic events. The research demonstrates how Goodman's Law has influenced financial markets, economic policy, and business strategy. Furthermore, we propose methods for mitigating its effects in investment strategies and startup development. Our findings suggest that awareness and strategic application of Goodman's Law can lead to more robust economic decision-making and policy formulation.

1. Introduction

Goodman's Law, often attributed to British statistician Charles Goodhart but also known as Campbell's Law in some contexts, states that "When a measure becomes a target, it ceases to be a good measure" (Goodhart, 1975). This principle has profound implications for economics, finance, and policy-making, as it highlights the unintended consequences of setting specific targets or indicators as goals.

The law suggests that once a particular indicator is chosen to drive policy or strategy, its very nature as a reliable measure is compromised. This occurs because individuals and organizations, aware of the importance placed on the measure, will alter their behavior to optimize it, often at the expense of the underlying objective the measure was intended to represent.

In this essay, we will explore the theoretical underpinnings of Goodman's Law, examine its manifestations in financial history and major economic events, and discuss its applications in contemporary economics. We will also provide insights into how investors and entrepreneurs can navigate the challenges posed by this principle.

2. Theoretical Framework

Origins and Development

Goodman's Law was formally articulated by Charles Goodhart in a 1975 paper presented to the Reserve Bank of Australia. Goodhart, a financial markets expert, observed that statistical regularities tend to collapse once pressure is placed upon them for control purposes (Goodhart, 1975). This observation was made in the context of monetary policy, where he noted that any observed statistical regularity would tend to break down once used for policy purposes.

The concept, however, has roots that predate Goodhart's formulation. Anthropologist Donald T. Campbell had described a similar phenomenon in 1976, which became known as Campbell's Law. Campbell stated, "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor" (Campbell, 1976).

Related Economic Theories

Goodman's Law is closely related to several other economic concepts:

1. Lucas Critique: Developed by economist Robert Lucas Jr., this principle argues that it is naive to try to predict the effects of a change in economic policy entirely on the basis of relationships observed in historical data (Lucas, 1976). This aligns with Goodman's Law in highlighting how behavior changes in response to policy.

2. Principal-Agent Problem: This theory deals with the difficulties that arise under conditions of incomplete and asymmetric information when a principal hires an agent. Goodman's Law often manifests in principal-agent scenarios, where agents may optimize for the measured target rather than the principal's true objectives.

3. Cobra Effect: Named after an anecdote from colonial India, this term describes the unintended consequences of a policy or intervention. It relates to Goodman's Law in that both concepts deal with the unexpected outcomes of setting specific targets or incentives.

4. McNamara Fallacy: Named after Robert McNamara, U.S. Secretary of Defense during the Vietnam War, this fallacy involves making decisions based solely on quantitative observations (metrics) and ignoring all others. It's a direct consequence of overreliance on specific measures, as cautioned by Goodman's Law.

Understanding these related theories provides a broader context for Goodman's Law and its implications in economic thought and practice.

3. Real-life Examples in Financial History

Goodman's Law has manifested in numerous financial events throughout history. We will examine two significant cases that illustrate its impact.

Case Study 1: The Subprime Mortgage Crisis (2007-2008)

The subprime mortgage crisis that led to the 2008 global financial crisis provides a stark illustration of Goodman's Law in action.

Background: In the early 2000s, the U.S. government and financial institutions placed a strong emphasis on homeownership as a measure of economic health and social progress. The homeownership rate became a key target, rising from 64% in 1994 to a peak of 69.2% in 2004 (U.S. Census Bureau, 2021).

Manifestation of Goodman's Law: As homeownership became a target, financial institutions developed new products and relaxed lending standards to meet this goal. Subprime mortgages, adjustable-rate mortgages, and other high-risk products proliferated. The focus on increasing homeownership led to a deterioration in the quality of mortgages and the overall stability of the financial system.

Quantitative Evidence:

• Subprime mortgage originations increased from $190 billion in 2001 to $600 billion in 2006 (Financial Crisis Inquiry Commission, 2011).
• The percentage of subprime mortgages relative to total originations increased from 8% in 2003 to 20% in 2006 (Inside Mortgage Finance, 2008).
• Mortgage-backed securities (MBS) issuance grew from $1.1 trillion in 2000 to $3.1 trillion in 2006 (Securities Industry and Financial Markets Association, 2017).

Consequences: The focus on homeownership as a target led to a housing bubble, excessive risk-taking, and ultimately, the financial crisis of 2008. The very measure that was supposed to indicate economic health became the catalyst for economic instability.

Case Study 2: The LIBOR Scandal (2012)

The London Interbank Offered Rate (LIBOR) scandal provides another example of Goodman's Law in the financial sector.

Background: LIBOR was a benchmark interest rate based on the rates at which banks lent unsecured funds to each other. It was used globally as a reference rate for financial instruments worth trillions of dollars.

Manifestation of Goodman's Law: As LIBOR became a crucial measure of bank health and a determinant of profits on countless financial products, it became a target for manipulation. Banks had an incentive to report rates that would benefit their trading positions rather than reflect true market conditions.

Quantitative Evidence:

• Estimates suggest that the total value of financial products tied to LIBOR was around $300 trillion in 2012 (The Economist, 2012).
• Barclays Bank was fined $450 million in 2012 for manipulating LIBOR (U.S. Commodity Futures Trading Commission, 2012).
• A study by Youle (2014) estimated that the average magnitude of LIBOR suppression during the financial crisis was approximately 30-40 basis points.

Consequences: The LIBOR scandal eroded trust in financial markets, led to billions in fines for major banks, and ultimately resulted in the phasing out of LIBOR as a benchmark rate.

These case studies demonstrate how Goodman's Law can manifest in financial markets, leading to distorted incentives, manipulated metrics, and ultimately, systemic instability.

4. Impact on Major Economic Events

Goodman's Law has played a significant role in shaping major economic events beyond the financial sector. We will examine two such events to illustrate its broader economic impact.

Event 1: The Great Leap Forward in China (1958-1962)

The Great Leap Forward, a campaign by the Communist Party of China to rapidly transform the country from an agrarian economy to an industrialized society, provides a striking example of Goodman's Law on a national scale.

Background: The Chinese government set ambitious targets for industrial and agricultural production, aiming to surpass the economic output of Western countries rapidly.

Manifestation of Goodman's Law: As production targets became the primary measure of success, local officials began to falsify reports to meet or exceed these targets. This led to a disconnect between reported production and actual output.

Quantitative Evidence:

• Official statistics claimed grain production increased from 195 million tons in 1957 to 375 million tons in 1958, a highly implausible 92% increase (Dikötter, 2010).
• Actual grain production likely decreased from 195 million tons in 1957 to 170 million tons in 1959 (Ashton et al., 1984).
• The campaign resulted in an estimated 15-55 million deaths due to famine and economic disruption (Dikötter, 2010).

Consequences: The focus on meeting unrealistic production targets led to widespread misallocation of resources, environmental damage, and ultimately, a severe famine. The very measures intended to indicate economic progress became detached from reality, leading to catastrophic outcomes.

Event 2: The European Sovereign Debt Crisis (2009-2019)

The European Sovereign Debt Crisis, particularly its manifestation in Greece, illustrates how Goodman's Law can operate at the level of national economic statistics.

Background: The Maastricht Treaty established criteria for European Union member states to join the Eurozone, including limits on government deficit (3% of GDP) and debt (60% of GDP).

Manifestation of Goodman's Law: As these fiscal targets became crucial for Eurozone membership and ongoing compliance, some countries, notably Greece, engaged in creative accounting and data manipulation to appear to meet the criteria.

Quantitative Evidence:

• In 2004, Eurostat revealed that Greece's budget deficit for 2003 was 4.6% of GDP, not the 1.7% originally reported (Eurostat, 2004).
• After a 2009 audit, Greece's 2008 budget deficit was revised from 5% to 7.7% of GDP, and further to 9.8% in April 2010 (European Commission, 2010).
• Greece's public debt was revised from 99.6% of GDP to 115.1% for 2009 (Eurostat, 2010).

Consequences: The manipulation of economic statistics to meet Eurozone criteria masked underlying fiscal problems in Greece and other countries. When the true state of these economies was revealed, it triggered a severe debt crisis that threatened the stability of the entire Eurozone.

These examples demonstrate how Goodman's Law can operate at a macroeconomic level, influencing national policies and international economic relations. In both cases, the emphasis on specific targets led to distorted reporting and decision-making, ultimately resulting in severe economic consequences.

5. Leveraging Goodman's Law in Economics

Understanding Goodman's Law can provide valuable insights for economic policy-making and forecasting. By recognizing the potential for measures to be distorted when they become targets, economists and policymakers can design more robust systems and make more accurate predictions.

Applications in Economic Policy

1. Multiple Indicators: Instead of relying on a single measure, policymakers can use a basket of indicators to assess economic performance. For example, rather than focusing solely on GDP growth, a more comprehensive approach might include measures of income inequality, environmental sustainability, and quality of life.

2. Adaptive Policies: Policies can be designed to adapt as behaviors change in response to targets. This might involve regularly reviewing and adjusting the metrics used to assess policy effectiveness.

3. Qualitative Assessments: Incorporating qualitative assessments alongside quantitative targets can provide a more holistic view of economic conditions. For instance, surveys of consumer sentiment or business confidence can complement hard economic data.

4. Transparency and Accountability: Implementing robust systems for data collection and verification can help mitigate the risk of manipulation. This might include independent audits of economic statistics and clear consequences for misreporting.

Implications for Economic Forecasting

1. Behavioral Considerations: Economic models should account for how actors might change their behavior in response to specific targets or measures. This aligns with the Lucas Critique and can lead to more accurate forecasts.

2. Scenario Analysis: Instead of relying on point forecasts, economists can use scenario analysis to consider a range of possible outcomes, including those where measures have been distorted due to Goodman's Law.

3. Historical Pattern Recognition: By studying past instances where Goodman's Law has manifested, forecasters can better identify similar patterns in current economic conditions.

4. Cross-Validation: Using multiple data sources and methods to cross-validate economic forecasts can help identify potential distortions in specific measures.

6. Solving Goodman's Law Challenges

While Goodman's Law presents significant challenges, there are strategies that can be employed to mitigate its effects, both in investing and in building startups.

In Investing

1. Diversification of Metrics: Investors should consider a wide range of metrics when evaluating investments, rather than focusing on a single measure like earnings per share or revenue growth.

2. Look Beyond the Numbers: Qualitative factors such as company culture, management quality, and competitive positioning should be considered alongside quantitative metrics.

3. Understand Incentive Structures: Investors should analyze how company executives and employees are incentivized, as this can reveal potential areas where metrics might be manipulated.

4. Long-Term Perspective: Focusing on long-term performance rather than short-term targets can help avoid the pitfalls of metric manipulation.

5. Independent Research: Relying on independent research rather than company-provided figures can provide a more objective view of a company's performance.

In Building Startups

1. Holistic Goal Setting: Instead of focusing on a single metric (e.g., user growth), startups should set goals that encompass various aspects of the business, including customer satisfaction, product quality, and sustainable growth.

2. Regular Metric Review: Regularly review and adjust the metrics used to measure success. This can help prevent employees from optimizing for outdated or irrelevant targets.

3. Align Metrics with Long-Term Vision: Ensure that the metrics being targeted align with the long-term vision and values of the company, not just short-term growth or profitability.

4. Encourage Ethical Culture: Foster a culture that values ethical behavior and long-term sustainability over short-term gains or metric manipulation.

5. Transparency: Maintain transparency about how metrics are calculated and used, both internally and with investors. This can help prevent unintended consequences of metric-driven behavior.

7. Conclusion

Goodman's Law provides a crucial lens through which to view economic measures, policies, and strategies. Its manifestations in financial history and major economic events demonstrate the potential for significant negative consequences when measures become targets without proper safeguards.

However, awareness of Goodman's Law does not render quantitative measures useless. Rather, it calls for a more nuanced, multi-faceted approach to economic policy-making, forecasting, investing, and business strategy. By employing diverse metrics, maintaining flexibility, and always considering the potential for unintended consequences, economic actors can harness the insights provided by quantitative measures while mitigating the risks highlighted by Goodman's Law.

As our economic systems grow increasingly complex, the wisdom encapsulated in Goodman's Law becomes ever more relevant. It reminds us that in economics, as in many fields, the map is not the territory. Measures are tools for understanding economic reality, not goals in themselves. By keeping this principle in mind, we can work towards more robust, sustainable economic systems that truly serve their intended purposes.

References

Ashton, B., Hill, K., Piazza, A., & Zeitz, R. (1984). Famine in China, 1958-61. Population and Development Review, 10(4), 613-645.

Campbell, D. T. (1976). Assessing the impact of planned social change. Occasional paper series, 8.

Dikötter, F. (2010). Mao's Great Famine: The History of China's Most Devastating Catastrophe, 1958-1962. Walker & Company.

European Commission. (2010). Report on Greek Government Deficit and Debt Statistics. COM(2010) 1 final.

Eurostat. (2004). Report by Eurostat on the Revision of the Greek Government Deficit and Debt Figures. STAT/04/18.

Eurostat. (2010). Provision of deficit and debt data for 2009 - second notification. STAT/10/170.

Financial Crisis Inquiry Commission. (2011). The Financial Crisis Inquiry Report. U.S. Government Printing Office.

Goodhart, C. A. E. (1975). Problems of Monetary Management: The U.K. Experience. Papers in Monetary Economics. Reserve Bank of Australia.

Inside Mortgage Finance. (2008). The 2008 Mortgage Market Statistical Annual. Inside Mortgage Finance Publications.

Lucas, R. E. (1976). Econometric Policy Evaluation: A Critique. Carnegie-Rochester Conference Series on Public Policy, 1, 19-46.

Securities Industry and Financial Markets Association. (2017). US Mortgage-Related Issuance and Outstanding.

The Economist. (2012). The rotten heart of finance. July 7, 2012.

U.S. Census Bureau. (2021). Quarterly Residential Vacancies and Homeownership, Fourth Quarter 2020. CB21-15.

U.S. Commodity Futures Trading Commission. (2012). CFTC Orders Barclays to pay $200 Million Penalty for Attempted Manipulation of and False Reporting concerning LIBOR and Euribor Benchmark Interest Rates. Release 6289-12.

Youle, T. (2014). How Much Did Manipulation Distort the Libor? University of Minnesota Working Paper.

Additional References:

Chrystal, K. A., & Mizen, P. D. (2003). Goodhart's Law: Its Origins, Meaning and Implications for Monetary Policy. Central Banking, Monetary Theory and Practice: Essays in Honour of Charles Goodhart, 1, 221-243.

Haldane, A. G., & Madouros, V. (2012). The dog and the frisbee. Revista de Economía Institucional, 14(27), 13-56.

Manheim, D., & Garrabrant, S. (2018). Categorizing Variants of Goodhart's Law. arXiv preprint arXiv:1803.04585.

Ordóñez, L. D., Schweitzer, M. E., Galinsky, A. D., & Bazerman, M. H. (2009). Goals Gone Wild: The Systematic Side Effects of Overprescribing Goal Setting. Academy of Management Perspectives, 23(1), 6-16.

Osterloh, M., & Frey, B. S. (2013). Motivation Governance. In A. Grandori (Ed.), Handbook of Economic Organization: Integrating Economic and Organization Theory (pp. 26-40). Edward Elgar Publishing.

Rodrik, D. (2015). Economics Rules: The Rights and Wrongs of the Dismal Science. W. W. Norton & Company.

Strathern, M. (1997). 'Improving ratings': audit in the British University system. European Review, 5(3), 305-321.