Compare real economic growth indicators

Audit economy/year records and separate constant-price levels, rebased growth and a common-year comparison.

Economics & finance · Lesson 01🔋 Offline core analysis⏱ 50 minEmbedded real data📦 numpy · pandas · matplotlib · IPython

Goal and how to run

This lesson assumes basic Python knowledge. Read each method and caption before changing parameters. AI exercises are optional; no AI account is needed for the core workflow. Data lives in notebook metadata, so there is no companion data download. Keep the notebook saved in the active working folder.

Your goal is to understand the research question, execute transparent calculations, check the results, and state the limits of the conclusion.

Setup and parameters

In [1] · Python

from pathlib import Path
import base64, gzip, hashlib, json, platform
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import display, HTML
from IPython.display import display as _display
def display(value):
    if isinstance(value,pd.DataFrame):
        table=value.to_html(max_rows=12,escape=True)
        style="<style>.vibeit-readable-table{max-width:100%;overflow-x:auto}.vibeit-readable-table table{width:max-content;max-width:none;border-collapse:collapse}.vibeit-readable-table td,.vibeit-readable-table th{white-space:nowrap!important;word-break:normal!important;overflow-wrap:normal!important}</style>"
        return _display(HTML(style+'<div class="vibeit-readable-table">'+table+'</div>'))
    return _display(value)
RECIPE_ID='ec01-real-growth'
OUTPUT_DIR=Path.cwd()/"results"/RECIPE_ID
plt.rcParams.update({"figure.dpi":120,"font.size":10,"axes.spines.top":False,"axes.spines.right":False,
                      "axes.prop_cycle":plt.cycler(color=["#18785f","#416aa6","#bc6541","#9974a5"])})
print("Python:",platform.python_version(),"; NumPy:",np.__version__,"; Pandas:",pd.__version__)
print("Working folder:",Path.cwd())
print("Core data mode: embedded snapshot; no network requests")

Python: 3.13.13 ; NumPy: 2.5.3 ; Pandas: 3.0.6
Working folder: /Users/zhiluo/Developer/vibeit-cookbook/cookbook/downloads
Core data mode: embedded snapshot; no network requests

Load the embedded snapshot

In [2] · Python

def load_snapshot(recipe_id, expected_sha256, snapshot_key):
    """Read embedded data from this notebook, including a renamed copy."""
    for path in sorted(Path.cwd().glob("*.ipynb")):
        try:
            notebook = json.loads(path.read_text(encoding="utf-8"))
            metadata = notebook.get("metadata", {}).get("vibeit_cookbook", {})
            blob = metadata.get("snapshots", {}).get(snapshot_key)
            if metadata.get("recipe_id") != recipe_id or not blob:
                continue
            if blob.get("sha256") != expected_sha256:
                continue
            raw = gzip.decompress(base64.b64decode(blob["gzip_base64"], validate=True))
            if hashlib.sha256(raw).hexdigest() != expected_sha256:
                raise ValueError("Embedded snapshot checksum mismatch: " + snapshot_key)
            return json.loads(raw)
        except (OSError, json.JSONDecodeError):
            continue
    raise FileNotFoundError(
        "Save/open the complete .ipynb in the active working folder. "
        "The embedded snapshot could not be located for " + recipe_id)

SNAPSHOT_HASHES={'worldbank': 'd45779250297fb9371748b85b81321b2ae88f93ecf70cfb88256647a68e54376'}
def snapshot(key):
    return load_snapshot(RECIPE_ID,SNAPSHOT_HASHES[key],key)
print("Embedded snapshots:",list(SNAPSHOT_HASHES))

Embedded snapshots: ['worldbank']

Read the method functions

In [3] · Python

def worldbank_panel(payload):
    """Preserve missing observations; official economy metadata excludes aggregates."""
    economies=pd.DataFrame(payload['economies']).set_index('iso3')
    observations=pd.DataFrame(payload['observations'])
    observations=observations[observations.iso3.isin(economies.index)].copy()
    if observations.duplicated(['iso3','year','indicator']).any():raise ValueError('Duplicate economy/year/indicator observations')
    observations['value']=pd.to_numeric(observations.value,errors='raise')
    wide=observations.pivot(index=['iso3','year'],columns='indicator',values='value').sort_index()
    return wide,economies,observations

print("Method ready: worldbank_panel")

Method ready: worldbank_panel

Data and model boundary

World Bank records retain definitions, missing observations and reference years. The six economies are declared examples, not a random global sample. Country averages, price-basket conversions and Gini indices describe different quantities; survey concepts and coverage can differ.

Steps

1. Audit economy and indicator definitions

NY.GDP.PCAP.KD is GDP per capita in constant 2015 US dollars. Use official economy metadata to exclude aggregates. The selection is a worked example, not a population survey.

In [4] · Python

wb=snapshot("worldbank");panel,economies,observations=worldbank_panel(wb)
countries=["CHN","USA","DEU","BRA","IND","ZAF"]
assert set(countries)<=set(economies.index)
print("Economy/year records:",len(panel),"; official aggregates excluded; missing values preserved")
display(economies.loc[countries,["name","region","income_group"]])
indicator="NY.GDP.PCAP.KD";levels=panel[indicator].unstack("iso3").reindex(columns=countries)
print("Indicator:",wb["indicators"][indicator]["name"])
assert levels.dropna().gt(0).all().all()

Economy/year records: 3255 ; official aggregates excluded; missing values preserved

	name	region	income_group
iso3
CHN	China	East Asia & Pacific	Upper middle income
USA	United States	North America	High income
DEU	Germany	Europe & Central Asia	High income
BRA	Brazil	Latin America & Caribbean	Upper middle income
IND	India	South Asia	Lower middle income
ZAF	South Africa	Sub-Saharan Africa	Upper middle income

Indicator: GDP per capita (constant 2015 US$)

2. Compare observed constant-price levels

Missing years remain gaps. A higher absolute level does not imply a higher growth rate. Source national-accounting and conversion conventions matter for cross-economy interpretation.

In [5] · Python

fig,ax=plt.subplots(figsize=(9,5))
for code in countries:ax.plot(levels.index,levels[code],marker="o",ms=3,label=economies.loc[code,"name"])
ax.set(xlabel="Indicator year",ylabel="Real GDP per capita (constant 2015 USD)",title="Frozen source levels; missing observations remain missing");ax.legend(fontsize=8);plt.tight_layout();plt.show()

3. Separate rebased growth from size

Set each observed 2010 level to 100. The chart compares relative change, not economy size or household wealth. Missing or zero baseline values require explicit handling.

In [6] · Python

base=levels.loc[2010];assert base.notna().all() and base.gt(0).all();indexed=100*levels/base
fig,ax=plt.subplots(figsize=(9,5))
for code in countries:ax.plot(indexed.index,indexed[code],label=economies.loc[code,"name"])
ax.set(xlabel="Indicator year",ylabel="Real GDP-per-capita index (2010=100)",title="Relative growth under a common baseline");ax.legend(fontsize=8);plt.tight_layout();plt.show()

4. Use a common dated cross-section

Use 2023 for every declared economy. Mixing the latest available years changes the comparison. Economy-level averages do not give the complete distribution of individual incomes.

In [7] · Python

cross=levels.loc[2023];display(cross.rename("constant_2015_USD").to_frame())
fig,ax=plt.subplots(figsize=(8,4));ax.barh([economies.loc[c,"name"] for c in countries],cross)
ax.set(xlabel="Real GDP per capita (constant 2015 USD)",title="Common reference year: 2023");plt.tight_layout();plt.show()

	constant_2015_USD
iso3
CHN	12483.979110
USA	65667.281580
DEU	44368.992007
BRA	9288.027015
IND	2229.714912
ZAF	5756.550495

5. Reconcile and export levels and changes

Use fill_method=None in percent changes, avoiding invented zero changes at missing observations. Export original levels separately from rebased indices and calculated rates.

In [8] · Python

growth=100*levels.pct_change(fill_method=None)
assert np.allclose(indexed.loc[2010],100) and growth.loc[2010].isna().all()
OUTPUT_DIR.mkdir(parents=True,exist_ok=True);levels.to_csv(OUTPUT_DIR/"real-GDP-per-capita.csv")
indexed.to_csv(OUTPUT_DIR/"2010-base-index.csv");growth.to_csv(OUTPUT_DIR/"annual-percent-change.csv")
print("Baseline and missing-year checks passed; exports:",OUTPUT_DIR)

Baseline and missing-year checks passed; exports: /Users/zhiluo/Developer/vibeit-cookbook/cookbook/downloads/results/ec01-real-growth

Exercises and reference explanation

Distinguish constant-price level and indexed growth.
Why use a common dated cross-section?

Reference explanation. Rebasing removes original level differences. Common dates avoid mixed vintages; economy averages do not determine individual income distributions.

Keep a copy before changing parameters. Compare old and new figures and record which inputs, calculations and conclusions changed.

Optional AI coding exercises

Prompt 1

Using NumPy/Pandas/Matplotlib, add annual changes with missing years preserved.

Prompt 2

With the same packages, change to another observed positive common base year while retaining original levels.

Human acceptance. Base indices equal 100, units remain constant 2015 USD and aggregates are excluded.

Review the edited source, then manually run affected cells and subsequent checks. Ask the assistant to explain the purpose, packages and data provenance of its changes, and reconcile that explanation with actual output.

Optional online extension

In [9] · Python

RUN_ONLINE_UPDATE=False
if RUN_ONLINE_UPDATE:
    import requests
    try:
        response=requests.get('https://api.worldbank.org/v2/indicator/NY.GDP.PCAP.KD?format=json',timeout=20)
        response.raise_for_status()
        print("Source:",response.url)
        print(response.text[:700])
    except requests.RequestException as error:
        print("Online extension unavailable:",type(error).__name__,str(error)[:160])
else:
    print("Online extension skipped; embedded core data unchanged")

Online extension skipped; embedded core data unchanged

Sources, snapshots and next steps

World Bank economy metadata — World Bank CC BY 4.0 with additional terms; retain original indicator attribution; retrieved 2026-10-09. SHA-256 c9acf3932785191a649cbc04c3056af4cf64a236e5c3b49d49be25ddb0440bce. Preserve economy IDs, region and income-group metadata; exclude aggregates in student analyses.
World Bank NY.GDP.PCAP.KD — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 59a9efaf20ab359c7832f24da45dda2282511c0eaf927d044583f47d08e02cda. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata NY.GDP.PCAP.KD — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 46689488cd435920647264ede1bfb2d4b51b09800fb36f3d3b1422a0b11ea49d. Preserve indicator definition, source organization and source note.
World Bank NY.GDP.MKTP.KD — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 466f4e3176b509613f3df99acffce89b5f1ea14472c376dd10d8b8f67f6172b0. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata NY.GDP.MKTP.KD — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 bc4bc0ffec1755410f52b1091c6b1f46917c7417977102350d3d1102b4659216. Preserve indicator definition, source organization and source note.
World Bank SP.POP.TOTL — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 7ecf8d749dbddd87a269f2019e6d42ed32875c57565cf5c5af097c6e6c9af528. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata SP.POP.TOTL — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 2f8491efc00bf2c0995112ff8e87aba69e5e9d22d0880b22f7e3a4ec757ba05c. Preserve indicator definition, source organization and source note.
World Bank SP.DYN.LE00.IN — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 d8b5c6bd7fecaf8cee0e422444719ba1f42ec70996308bc74afd14ee48799055. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata SP.DYN.LE00.IN — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 a39cb6fddfbbb953053690366243dffe77a266c4deae79c46ce5a06da9f5fc31. Preserve indicator definition, source organization and source note.
World Bank FP.CPI.TOTL — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 55c3b71ad4dd8c92c15fa7dc2b502a3d60f26cf47ae9a24e384bc2ff6108ff56. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata FP.CPI.TOTL — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 13b1e3c4b2b72f7fb3a630b4dde37f0e68e6d5e78dc06b2437dfea0c0b93af46. Preserve indicator definition, source organization and source note.
World Bank FP.CPI.TOTL.ZG — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 849b6a88a84a7efdeb92d89e8db1db36f86543e28ff8eb645e0feaa8c634def7. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata FP.CPI.TOTL.ZG — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 df9371a3b6c01e55a3f28991e62e1b1ebbe0cd2a7da94456be9938c7a2962233. Preserve indicator definition, source organization and source note.
World Bank SI.POV.GINI — World Bank CC BY 4.0 with additional terms; original sources retained in indicator metadata; retrieved 2026-10-09. SHA-256 4919413d3b9ef06b7445c3fd42d47b8a09e9836bbab7be84e4993099ffdc7ab2. Retain official observations including null values and footnotes, 2010–2024; no interpolation; no aggregate-country mixing.
World Bank indicator metadata SI.POV.GINI — World Bank CC BY 4.0 with additional terms; retrieved 2026-10-09. SHA-256 e377bf82b38024748153880ff90c0106a0ef0fd8cb04761146cc60a2609cab3d. Preserve indicator definition, source organization and source note.

Next steps. Transfer these methods to your own public or authorized data while retaining input checks, recorded parameters and result validation. Document the new experimental design and method limits; this example does not validate a new experiment. Full data licenses and generation instructions accompany the cookbook.