Jonathan D.A. Jewell j.d.a.jewell@open.ac.uk

Cliometrics.jl :toc: preamble :icons: font

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A Julia library for quantitative economic history analysis.

Overview

Cliometrics applies economic theory and quantitative methods to the study of historical economic phenomena. This package provides tools for:

• Historical Data Analysis: Load, clean, and analyze historical economic datasets

• Growth Accounting: Decompose economic growth into capital, labor, and TFP contributions

• Convergence Analysis: Test for economic convergence across regions and time periods

• Institutional Analysis: Quantify and analyze the role of institutions in economic development

• Counterfactual Modeling: Estimate treatment effects and alternative historical scenarios

Installation

[source,julia]

using Pkg Pkg.add(“Cliometrics”) —-

Quick Start

[source,julia]

using Cliometrics using DataFrames

data = load_historical_data(“maddison_historical_gdp.csv”) growth_rates = calculate_growth_rates(data, :real_gdp_per_capita)

decomposition = decompose_growth( data, output=:gdp, capital=:capital_stock, labor=:labor_force, alpha=0.35 )

convergence = convergence_analysis( country_data, :initial_gdp_1950, :growth_rate_1950_2000 )

quality_index = institutional_quality_index( institutions_data, [:rule_of_law, :property_rights, :contract_enforcement], weights=[0.4, 0.3, 0.3] ) —-

Features

Growth Analysis ✅

• Geometric and arithmetic growth rate calculations

• Solow residual (TFP) estimation

• Growth accounting decomposition

• Long-run growth trend analysis (planned for v0.2.0)

Convergence Testing ✅

• Beta-convergence analysis

• Sigma-convergence testing (planned for v0.2.0)

• Conditional convergence estimation (planned for v0.2.0)

• Half-life calculations

Institutional Analysis ✅

• Composite institutional quality indices

• Institutional change measurement

• Relationship between institutions and growth

Data Tools ✅

• Historical time series cleaning

• Missing value interpolation

• Outlier detection and handling (planned for v0.2.0)

• Cross-country data alignment (planned for v0.2.0)

Causal Inference ✅

• Counterfactual scenario modeling

• Difference-in-differences estimation (DiD)

• Treatment effect analysis

Examples

Example 1: Industrial Revolution Growth Analysis

[source,julia]

using Cliometrics uk_data = load_historical_data(“broadberry_uk_gdp.csv”) pre_industrial = filter(row → 1700 ⇐ row.year < 1780, uk_data) industrial = filter(row → 1780 ⇐ row.year ⇐ 1870, uk_data) pre_growth = mean(calculate_growth_rates(pre_industrial, :gdp_per_capita)) post_growth = mean(calculate_growth_rates(industrial, :gdp_per_capita)) println(“Pre-Industrial: $(round(pre_growth100, digits=2))% per year”) println(”Industrial Revolution: $(round(post_growth100, digits=2))% per year”) —-

Example 2: Great Divergence Analysis

[source,julia]

divergence_data = DataFrame( year = 1500:50:1800, western_europe_gdp = [1200, 1300, 1450, 1650, 1900, 2200, 2600], china_gdp = [1100, 1150, 1200, 1250, 1280, 1300, 1320] ) comparison = compare_historical_trajectories( divergence_data, [“Western Europe”, “China”], variable=:gdp_per_capita ) —-

Example 3: Institutions and Growth

[source,julia]

institutions = DataFrame( country = [“USA”, “Haiti”, “South Korea”, “North Korea”], inclusive_institutions = [0.9, 0.3, 0.8, 0.1], gdp_per_capita_1960 = [15000, 2000, 1200, 1100], gdp_per_capita_2020 = [65000, 1800, 42000, 1300] ) institutions.growth_rate = (institutions.gdp_per_capita_2020 ./ institutions.gdp_per_capita_1960) .^ (1/60) .- 1 using GLM model = lm(@formula(growth_rate ~ inclusive_institutions), institutions) —-

Methodology

• Growth Accounting: Following Solow (1957) and subsequent literature

• Convergence Tests: Based on Barro & Sala-i-Martin (1992)

• Institutional Indices: Inspired by Acemoglu et al. (2001)

• Historical National Accounts: Compatible with Maddison Project format

Data Sources

Compatible with major historical datasets:

• Maddison Project Database

• Penn World Table (historical extensions)

• Broadberry et al. historical national accounts

• Polity IV (institutional data)

• V-Dem (institutional indicators)

Citation

[source,bibtex]

@software\{cliometrics_jl, author = \{Jewell, Jonathan D.A.}, title = \{Cliometrics.jl: Quantitative Economic History in Julia}, year = {2026}, url = \{https://github.com/hyperpolymath/Cliometrics.jl} } —-

Contributing

See CONTRIBUTING.md for guidelines.

References

• Solow, R. M. (1957). “Technical Change and the Aggregate Production Function.” Review of Economics and Statistics, 39(3), 312–320.

• Barro, R. J., & Sala-i-Martin, X. (1992). “Convergence.” Journal of Political Economy, 100(2), 223–251.

• Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). “The Colonial Origins of Comparative Development.” American Economic Review, 91(5), 1369–1401.

• Crafts, N., & Toniolo, G. (Eds.). (1996). Economic Growth in Europe Since 1945. Cambridge University Press.

• Maddison, A. (2007). Contours of the World Economy 1–2030 AD. Oxford University Press.

Wondering how this works? See EXPLAINME.adoc.

License

SPDX-License-Identifier: CC-BY-SA-4.0 + See LICENSE.