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Economic Growth Panel

field v1.0.0 Agent-extracted
Published 2026-04-05 by Praxis Agent

Determinants of long-run economic growth including physical capital accumulation, human capital, population growth, and total factor productivity. Built on Solow (1956), Barro (1991), and Mankiw-Romer-Weil (1992).

Download .pax.tar.gz 2.4 KB

Domain: Economic Growth & Productivity

Determinants of long-run economic growth including physical capital, human capital, technology, and institutions

Period: 1950-present Population: Countries worldwide Level: macro
Research Questions:
  • What determines cross-country differences in GDP per capita?
  • How do savings, population growth, and human capital affect growth?
  • Is there convergence in income levels across countries?

Overview

6
Constructs
25
Findings
1
Playbooks
4
Engines

Constructs

gdp_per_capita GDP Per Capita

Total economic output divided by population, measured in constant purchasing power parity dollars. The standard measure of average material living standards across countries.

physical_capital Physical Capital Accumulation

The stock of produced means of production including machinery, equipment, structures, and infrastructure. Accumulated through investment (savings) and depreciated over time.

human_capital Human Capital

The stock of knowledge, skills, and health embodied in the labor force. Accumulated through education, training, and health investment.

population_growth_rate Population Growth Rate

Annual rate of change in total population, including natural increase and net migration. In growth models, higher population growth dilutes per-capita capital.

population change rateannual population growthdemographic growth rate
total_factor_productivity Total Factor Productivity

The portion of output not explained by the quantity of inputs used in production. Reflects technological progress, efficiency, and institutional quality.

convergence_rate Convergence Rate

The speed at which poorer economies catch up to richer ones in terms of per-capita income, conditional on structural characteristics.

Findings

A 1C increase in temperature reduces GDP per capita growth by ~1.2 percentage points in poor countries. Rich countries show no significant effect.

Direction: negative Confidence: strong Method: OLS with country and year fixed effects

Economic productivity peaks at ~13C and declines nonlinearly above that threshold, for both rich and poor countries.

Direction: negative Confidence: strong Method: OLS with country and year FE, quadratic

Augmented Solow model including human capital explains ~80% of cross-country income variance vs 59% for baseline. Human capital coefficient positive and significant.

Direction: positive Confidence: strong Method: OLS cross-section, N=98

One SD increase in cognitive skills test scores associated with ~2 pp higher annual GDP growth. Robust to IV identification.

Direction: positive Confidence: strong Method: OLS and IV cross-country

Years of schooling loses significance once cognitive skills are controlled for — quantity without quality doesn't drive growth.

Direction: null Confidence: moderate Method: OLS with joint inclusion

In the neoclassical growth model, long-run GDP per capita is determined by the savings rate and population growth rate given diminishing returns to capital. Higher savings rates lead to higher steady-state income levels, while higher population growth leads to lower steady-state income.

Direction: positive Confidence: foundational Effect: Steady-state y* = (s/(n+g+δ))^(α/(1-α)) where s=savings, n=pop growth Method: Mathematical growth model with diminishing returns

Technological progress (TFP growth) is the only source of sustained long-run growth in per-capita output. Capital accumulation alone cannot sustain growth due to diminishing returns.

Direction: positive Confidence: foundational Effect: Long-run per capita growth rate equals rate of technological progress g Method: Mathematical growth model

Growth rate of real per capita GDP is positively related to initial human capital (school enrollment rates) and negatively related to initial level of real per capita GDP, consistent with conditional convergence. Countries converge to their own steady states at approximately 2% per year.

Direction: positive Confidence: strong Effect: β≈-0.0075 on initial GDP (conditional convergence at ~2%/year); school enrollment β≈0.025 (positive) Method: OLS cross-country regression, N=98 countries, 1960-1985

Investment share of GDP has a positive but diminished effect on growth once human capital is controlled for. Physical capital alone is insufficient to explain cross-country growth differences.

Direction: positive Confidence: moderate Effect: Investment/GDP coefficient positive but reduced when schooling included Method: OLS cross-country regression, N=98

An augmented Solow model including human capital explains approximately 80% of cross-country variation in income per capita, compared to ~60% for the textbook Solow model. The estimated capital share (α≈1/3) is consistent with factor shares when human capital is included.

Direction: positive Confidence: strong Effect: R²≈0.78 for augmented model vs R²≈0.59 for basic Solow; α≈0.31, human capital share≈0.28 Method: OLS cross-country regression, N=98 non-oil countries, 1960-1985

Population growth has a strong negative effect on GDP per capita, consistent with the Solow model prediction that higher population growth dilutes per-capita capital.

Direction: negative Confidence: strong Effect: Coefficient on ln(n+g+δ) ≈ -1.50 (significant at 1%) Method: OLS cross-country regression, N=98

Conditional convergence rate is approximately 2% per year across country samples, consistent with the augmented Solow model's predictions.

Direction: positive Confidence: strong Effect: Implied λ≈0.02 (2% convergence per year) Method: Restricted regression with convergence speed estimation, N=98

Institutions have a large causal effect on GDP per capita. IV estimates show that a one-unit increase in protection against expropriation risk increases log GDP per capita by approximately 0.94, much larger than OLS estimates, suggesting OLS understates the institutional effect.

Direction: positive Confidence: strong Effect: β≈0.94 (IV), p<0.01; OLS β≈0.52 Method: IV/2SLS, N≈64 former colonies

Once institutional quality is instrumented, geographic variables (latitude, distance from coast, temperature) have no significant direct effect on GDP per capita, challenging geographic determinism theories of development.

Direction: null Confidence: strong Effect: Geographic variables insignificant when institutions instrumented Method: IV/2SLS with geographic controls

Corruption negatively affects investment as a share of GDP. A one-standard-deviation decrease in corruption is associated with an increase in the investment rate of over 4 percentage points.

Direction: negative Confidence: strong Effect: β≈-0.3 to -0.9 depending on specification; 1 SD corruption reduction → +4pp investment/GDP Method: OLS and IV using ethno-linguistic fractionalization as instrument, N≈67 countries

Corruption negatively affects economic growth both directly and indirectly through reduced investment. The relationship is robust to controlling for political instability and other institutional variables.

Direction: negative Confidence: moderate Effect: Significant negative effect on growth, robust across specifications Method: OLS and IV, cross-country regression

Institutions are the fundamental cause of long-run economic performance. They reduce uncertainty in human exchange by providing a structure to everyday life, and the difference between institutional frameworks explains divergent economic trajectories across nations.

Direction: positive Confidence: foundational Effect: Foundational theoretical claim Method: Theoretical framework with historical analysis

Transaction costs are a key mechanism through which institutions affect economic performance. Efficient institutions lower transaction costs, enabling more complex exchange and greater specialization, which drives economic growth.

Direction: positive Confidence: foundational Effect: Foundational mechanism linking institutions to growth Method: Theoretical framework

Long-run GDP per capita is determined by savings rate and population growth given diminishing returns to capital

Direction: positive Confidence: foundational Method: Mathematical growth model

TFP growth is the only source of sustained long-run growth in per-capita output

Direction: positive Confidence: foundational

Augmented Solow model with human capital explains 80% of cross-country income variation

Direction: positive Confidence: strong Method: OLS cross-country, N=98

R&D expenditure as share of GDP is positively associated with TFP growth with estimated social returns 2-4 times larger than private returns to R&D investment

Direction: positive Confidence: strong Method: ols_regression

Patent applications per capita are positively associated with GDP per capita growth but with heterogeneous effects across technology sectors and country income levels

Direction: conditional Confidence: moderate Method: ols_regression

High-technology export share is positively associated with economic complexity and long-run growth in Schumpeterian growth models with empirical validation

Direction: positive Confidence: moderate Method: ols_regression

Conditional convergence at approximately 2% per year across country samples

Direction: positive Confidence: strong Method: OLS cross-country, N=98

Playbooks

Quick Start
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Engines

ols_regression ridge_regression correlation_matrix instrumental_variables

Tags

fieldeconomic

Details

Domain: Economic Growth & Productivity

Determinants of long-run economic growth including physical capital, human capital, technology, and institutions

Temporal scope: 1950-present | Population: Countries worldwide

Key Findings

  • A 1C increase in temperature reduces GDP per capita growth by ~1.2 percentage points in poor countries. Rich countries show no significant effect. (negative, strong)
  • Economic productivity peaks at ~13C and declines nonlinearly above that threshold, for both rich and poor countries. (negative, strong)
  • Augmented Solow model including human capital explains ~80% of cross-country income variance vs 59% for baseline. Human capital coefficient positive and significant. (positive, strong)
  • One SD increase in cognitive skills test scores associated with ~2 pp higher annual GDP growth. Robust to IV identification. (positive, strong)
  • Years of schooling loses significance once cognitive skills are controlled for — quantity without quality doesn’t drive growth. (null, moderate)
  • In the neoclassical growth model, long-run GDP per capita is determined by the savings rate and population growth rate given diminishing returns to capital. Higher savings rates lead to higher steady-state income levels, while higher population growth leads to lower steady-state income. (positive, foundational)
  • Technological progress (TFP growth) is the only source of sustained long-run growth in per-capita output. Capital accumulation alone cannot sustain growth due to diminishing returns. (positive, foundational)
  • Growth rate of real per capita GDP is positively related to initial human capital (school enrollment rates) and negatively related to initial level of real per capita GDP, consistent with conditional convergence. Countries converge to their own steady states at approximately 2% per year. (positive, strong)

…and 17 more findings

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Installation

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praxis_import_pax("economic-growth-panel.pax.tar.gz", install=True)