McLaughlin’s “The Causal Effect of Regulations on Economic Growth”

---

Issue Areas

---

The case against excessive regulations continues to build. A recent paper from the Mercatus Center by Patrick McLaughlin and John Wong estimates that a 10 percent increase in regulatory restrictions causes real GDP to fall by 0.37 percentage points. Put differently, if three percent growth is our benchmark, losing 0.37 points amounts to a 12 percent drop. The hit is even bigger in states that already struggle with slower growth, ultimately translating into lower incomes, a reduction in investment, and fewer jobs.

The authors arrive at their results by using the state’s age as a natural experiment—older states tend to have had more time to accumulate regulations, allowing the researchers to measure the causal effect on growth. For the average state, this implies that annual growth is less than one-seventh of what it would have been otherwise for every 10 percent increase in restrictions. Their findings align with several federal-level studies showing that regulations slow economic growth, while regulatory rollbacks can boost it. Given ongoing efforts by the Department of Government Efficiency and Virginia’s Office of Regulatory Management (with a slew of other states following suit), McLaughlin and Wong’s recent article is especially timely and merits close attention.

The authors draw on several datasets to create an unbalanced panel spanning from 2021 to 2023. First, the Bureau of Economic Analysis supplies each state’s chained real GDP, capturing growth and output fluctuations over time. Next, QuantGov’s State RegData measures regulatory intensity by counting every instance of restrictive language (e.g., “shall,” “must,” “may not,” “required,” and “prohibited”) in each state’s statutory and administrative codes. Finally, the U.S. Census Bureau’s “Historical Statistics of the United States” provides each state’s formation year, which the study uses to measure how long an economy has had to accumulate regulations. Because not all states have complete data for every year in the sample, the resulting panel is unbalanced, but it still allows for meaningful comparisons across time and place.

Drawing on Mancur Olson’s “institutional sclerosis” hypothesis,[i] McLaughlin and Wong posit that regulatory accumulation over time increases complexity, enlarges both government and private-sector administrative burdens, and empowers bureaucrats to apply rules more freely. To test this empirically, they employ a two-stage least squares (2SLS) strategy. In the first stage, they regress the percent of regulatory restrictions on a state’s age while controlling for other relevant factors. In the second stage, they regress the one-period continuous compounding growth rate of GDP on the fitted values of regulatory restrictions derived from the first stage. This design helps isolate the causal effect of regulatory accumulation on economic growth, reducing concerns about endogeneity and reverse causation.

The key assumption of this approach is that a state’s age directly affects the burden of regulatory restrictions but has no other influence on the compounding growth rate of GDP. Stated another way, the instrument (state age) must be correlated with the treatment variable (regulatory restrictions) but uncorrelated with any unobservable factors that also affect economic growth. This requires the following to hold:

1. Relevance: Older states must accumulate more regulations over time so that age serves as a meaningful predictor of regulatory intensity in the first-stage regression.
2. Exclusion: State age should not affect GDP growth via any pathway other than its effect on regulations.
3. Monotonicity: The relationship between age and regulatory accumulation should be consistent. For instance, states should not get fewer regulations the older they become. What is more, regulations must continue to accumulate irrespective of short-term changes in the percentage of new rules.

This method allows the authors to isolate the effect of regulatory restrictions on growth, though it hinges on the credibility of the claim that age only influences economic performance through regulations.

Use of a state’s age as an instrument is intuitively appealing. However, there are several potential concerns. Older states may differ systematically in ways that directly affect growth, such as historic investments in infrastructure, stronger legal institutions, or well-established industrial bases apart from the accumulation of regulations. The stringency of restrictions for newer states may also outpace older states from the composition of industries, the complexity of its markets, or the powers assigned to its public agencies. In addition, political and cultural factors that evolve over a state’s history could independently shape both regulatory policy and economic outcomes. If these elements are not fully accounted for, the exclusion restriction is violated—the observed link between age and regulations might partly reflect alternative channels influencing growth. Moreover, variations in how aggressively a state updates or prunes its code—some may repeal outdated rules more systematically—could weaken the monotonic relationship presumed between age and regulatory burden. For example, certain legislatures adopt Sunset provisions or governors lead red-tape reduction efforts. Should these assumptions fail, the results may be biased, attributing a causal impact to regulations when, in fact, other historical or institutional dynamics are at play.

Future research would build on McLaughlin and Wong’s contribution by refining how states’ regulatory burdens are measured while broadening the scope of variables used to explain, regulatory accumulation. For example, instead of relying on a state’s age, researchers might track the number and age of government agencies within each state. This is important because agencies with longer tenures or broader remits have had more opportunities to issue complex regulations, adding to the overall burden as a result. Different types of agencies—environmental, labor, financial, or occupational licensing boards—may also exhibit distinct regulatory trajectories. Disaggregating data by agency function could uncover which sectors disproportionately contribute to overall regulatory costs, providing a more nuanced picture of how and where regulations constrain economic activity. By including these elements, future studies could more precisely isolate the mechanisms by which regulations accumulate and affect economic growth. That, in turn, would deepen our understanding of regulatory complexity and identify the particular agencies and policy environments most conducive to–or restrictive of–economic growth.          

We at the Cicero Institute advocate for regulatory reforms that increase transparency, accountability, and flexibility while aiming to reduce outdated or redundant regulations. McLaughlin and Wong’s findings reinforce the practical importance of our proposals in several ways:

1. Streamlining Regulatory Accumulation
   According to the Mercatus Center paper, the sheer volume of regulatory restrictions hinders long-term economic performance. This insight dovetails with Cicero’s emphasis on replacing rules that have lost relevance or are redundant. By setting up mechanisms such as mandatory review schedules or automatic rule expirations, policymakers can help prevent the build-up of regulations that are costly for growth.
2. Sunset Legislation and Periodic Review
   The Cicero Institute stresses the power of Sunset laws, which automatically retire regulations unless they are explicitly renewed. McLaughlin and Wong’s results suggest that regulatory burdens accumulate over time and these burdens reduce GDP growth. Regularly auditing and sunsetting rules directly combat this accumulation, potentially helping to preserve or increase the economic dynamism that McLaughlin and Wong measure.
3. Reducing Bureaucratic Complexity
   Both the Mercatus Center and the Cicero Institute emphasize that bureaucratic complexity can delay business decisions, stifle innovation, and deter new market entrants. By calling for clearer administrative procedures and fewer overlapping mandates, Cicero’s model bills align with empirical findings that show how a denser thicket of regulation can adversely affect growth.
4. Data-Driven Regulatory Reform
   The Mercatus Center’s work on measuring regulatory restrictions (e.g., via QuantGov’s State RegData) illustrates the value of empirical tracking and public transparency. Cicero’s approach similarly encourages rigorous measurement and reporting of a state’s regulatory load. Integrating these measurement tools can help policymakers monitor whether reforms effectively reduce regulatory stock over time.
5. Accountability for Policymakers and Agencies
   One recurring theme in the Cicero Institute’s recommendations is to clarify the roles and responsibilities of agencies. McLaughlin and Wong’s study underscores how the expansion of regulations over a state’s lifespan can empower public bureaucrats in ways that may not always align with economic priorities. Cicero’s prescriptions, such as enhanced oversight committees or robust stakeholder input mechanisms, can mitigate the risk of unchecked regulatory growth.

In short, the Mercatus Center findings show that effectively pruning and preventing excessive regulation has meaningful implications for economic well-being. The Cicero Institute’s policy proposals provide a concrete blueprint to achieve such reforms, ensuring that states take deliberate steps to minimize regulatory burdens while still protecting public interests.

---

[i] See Olson’s book, The Rise and Decline of Nations.