Lasting Legal Legacies: Early English Legal Ideas and Later Caselaw Development During the Industrial Revolution

Peter Grajzl; Peter Murrell

doi:10.1515/rle-2021-0070

Published by De Gruyter March 16, 2022

Lasting Legal Legacies: Early English Legal Ideas and Later Caselaw Development During the Industrial Revolution

Peter Grajzl and Peter Murrell

From the journal Review of Law & Economics

https://doi.org/10.1515/rle-2021-0070

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Abstract

We explore English legal evolution by empirically investigating the relevance of late-medieval and early-modern legal ideas for caselaw development during the Industrial Revolution, an era of unprecedented societal change. To ascertain the prevalence of specific legal ideas in pre-1765 case reports, we draw on existing topic model estimates. We measure the relevance of those ideas for subsequent caselaw development using post-1764 citations to the pre-1765 cases. We show that deliberations on court cases heard between 1765 and 1870 systematically invoked a broad range of preexisting legal ideas. Strikingly, the strongest effects are exhibited by Coke-style analysis and precedent-based thought. A key legacy of early English caselaw therefore lay in bestowing modes of reasoning. The reason why a subset of preexisting legal ideas does not exert a detectable effect is that those ideas were generally no longer key to post-1764 legal disputes. Our approach to investigating legal development could be applied in many other contexts.

Keywords: English caselaw; legal development; industrial revolution; legal history; machine learning

JEL Classification: K10; K30; N43; P10; C81

Corresponding author: Peter Grajzl, Department of Economics, Williams School of Commerce, Economics and Politics, Washington and Lee University, 204 West Washington St., Lexington, VA 24450, USA; and CESifo, Munich, Germany, E-mail: grajzlp@wlu.edu

Acknowledgments

We are grateful to Martin Schmidt for sharing his data. For helpful comments and suggestions, we thank participants at the annual meeting of the French Association of Law and Economics, conference of the Online Workshop on the Computational Analysis of Law (OWCAL), seminars at Tel Aviv University and Washington and Lee University, as well as Sofia Amaral-Garcia, Geoffrey Hodgson, Michael Livermore, Christopher Engel (our editor), and an anonymous reviewer.

Research Funding: None.
Competing interests: The authors declare that they have no conflict of interest.
Data availability statement: The data that support the findings of this study are available from the corresponding author upon request.

Appendix

Each pre-1765 report is a mixture of 100 topics. We begin with the following model for the conditional mean of citation counts, as implied by the negative binomial model:

(A1) E y T 1 , … , T 100 = exp α 0 + ∑ i = 1 100 α i T i .

In (A1), y is the number of citations to a particular report in a specific post-1764 period. For ease of exposition, we drop all subscripts denoting observations. Without loss of generality, we also suppress time-period fixed effects. T _i is the proportion of topic i in a given report, bounded from below by zero and from above by one. α _i’s are the corresponding parameters.

(A1) implies:

(A2) ln ⁡ E y T 1 , … , T 100 = α 0 + ∑ i = 1 100 α i T i .

For any report, the sum of topic proportions is one:

(A3) ∑ i = 1 100 T i = 1 .

Thus, model (A2) is not identified because of perfect collinearity. One topic proportion variable must be omitted. Suppose, for illustrative purposes only, that the omitted topic proportion variable is T ₁₀₀. Then, solving (A3) for T ₁₀₀ and substituting the resulting expression into (A2) gives:

(A4) ln ⁡ E y T 1 , … , T 100 = α 0 + α 100 + ∑ i = 1 99 α i − α 100 T i .

Averaging (A4) across all reports yields:

(A5) ln ⁡ E y T 1 , … , T 100 ̄ = α 0 + α 100 + ∑ i = 1 99 α i − α 100 T ̄ i ,

where X ̄ denotes the report-level average for the variable X. Subtracting (A5) from (A4) and rearranging terms:

(A6) ln ⁡ E y T 1 , … , T 100 = ln ⁡ E y T 1 , … , T 100 ̄ + ∑ i = 1 99 α i − α 100 T i − T ̄ i σ i σ i ,

where σ _i is the report-level standard deviation for T _i. Thus, (A6) can be expressed as

(A7) ln ⁡ E y Z 1 , … , Z 99 = β 0 + ∑ i = 1 99 β i Z i ,

where

(A8) β 0 ≡ ln ⁡ E y T 1 , … , T 100 ̄

and

(A9) β i ≡ α i − α 100 σ i

(A10) Z i ≡ T i − T ̄ i σ i

for i ∈ {1, 2, …, 99}. That is, Z _i in (A7) is the standardized version of topic proportion i with mean equal to zero and standard deviation equal to one.

(A7) is the empirical model (1) for the conditional mean of citation counts that we estimate. Note that expression (A7) omits one standardized topic proportion, Z ₁₀₀. The reason is that a model otherwise analogous to (A7) but with all Z ₁, …, Z ₁₀₀ included among the explanatory variables is not identified, even if the constant β ₀ is dropped. To see this, average (A3) across the reports to obtain:

(A11) ∑ i = 1 100 T ̄ i = 1 .

Then, using (A3) to express T ₁₀₀, (A11) to express T ̄ 100 , and inserting the resulting expressions into (A10) for i = 100 yields:

(A12) Z 100 = T 100 − T ̄ 100 σ 100 = 1 − ∑ i = 1 99 T i − 1 + ∑ i = 1 99 T ̄ i σ 100 = − ∑ i = 1 99 T i − T ̄ i σ 100 = − ∑ i = 1 99 T i − T ̄ i σ i σ i σ 100 .

Thus, (A12) simplifies to

(A13) Z 100 = − ∑ i = 1 99 Z i σ i σ 100 .

That is, a model with all of Z ₁, …, Z ₁₀₀ included among the explanatory variables, with or without the regression constant, is not identified.

How should one interpret the parameters β _i, i ∈ {1, 2, …, 99}, from expression (A7)? A one-unit change in the standardized proportion for topic k from the baseline value of Z _k to the value Z _k + 1 (that is, a one-standard deviation change in T _k) gives rise to the following change in the expected incidence of citations:

(A14) ln ⁡ E y Z 1 , … , Z k + 1 , … , Z 99 − ln ⁡ E y Z 1 , … , Z k , … , Z 99 = β k .

Substituting (A9) into the right-hand side of (A14) and exponentiating the resulting expression implies that the corresponding incidence rate ratio (IRR) equals:

(A15) E y Z 1 , … , Z k + 1 , … , Z 99 E y Z 1 , … , Z k , … , Z 99 = e β k = e α k − α 100 σ k .

This implies:

(A16) E y Z 1 , … , Z k + 1 , … , Z 99 E y Z 1 , … , Z k , … , Z 99 = e α k e α 100 σ k .

That is, the IRR associated with a one-standard deviation change in the prevalence of topic k equals the ratio of the IRR associated with a unit-change in the (non-standardized) proportion of topic k to the IRR associated with a unit-change in the (non-standardized) proportion of the omitted topic, adjusted by the extent of the variability of the prevalence of topic k.

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Received: 2021-09-14

Accepted: 2022-01-05

Published Online: 2022-03-16

Lasting Legal Legacies: Early English Legal Ideas and Later Caselaw Development During the Industrial Revolution

Abstract

Acknowledgments

References

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