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The Global Entrepreneurship Monitor publishes annual datasets on startup activity, entrepreneurial attitudes, innovation rates, and business ecosystem quality across 50+ economies. Used in data engineering for economic research pipelines, startup ecosystem benchmarking, and building entrepreneurship indicator dashboards in Python.
GEM data is downloadable as Excel/CSV from gemconsortium.org. Engineers use `pandas.read_excel()` to load country-level entrepreneurship indicators, then merge with World Bank and OECD data for multi-source startup ecosystem analysis.
GEM entrepreneurship data enables AI tools for startup ecosystem analysis and policy research. Build RAG systems indexed on GEM Global Reports so LLMs can answer 'Which countries have the highest rates of early-stage entrepreneurial activity?' AI models trained on GEM data predict startup success conditions.
# pip install pandas openpyxl
import pandas as pd
# Download GEM Adult Population Survey data from gemconsortium.org
df = pd.read_excel("GEM 2023-2024 APS Global Individual Level Data.xlsx",
engine="openpyxl")
# Early-stage entrepreneurial activity (TEA) by country
tea = df.groupby("economy")["tea"].mean().sort_values(ascending=False)
print("Top countries by early-stage entrepreneurial activity:")
print(tea.head(10))Official dataset source
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