Students & academic research
A thesis on Pakistan's economy usually starts with weeks of copying numbers out of PDFs. With PakDataHub it starts with a CSV. The free plan's 500 calls a month cover a typical research project's downloads.
Long-run series for time-series work
| Topic | Series | From |
|---|---|---|
| Exchange rate | fx.rate.avg.usd |
1947 |
| FX reserves | reserves.total_sbp_reserves |
1948 |
| GDP (constant factor cost) | gdp.gross_domestic_product_total_gross_value_add_3 |
1950 (to 2000) |
| GDP, current base | gdp.gross_domestic_product_total_gross_value_add_2 |
2000 |
| CPI inflation | industry.general_cpi_inflation_measure_year_year_2 |
1964 (to 2020) |
| SBP reverse-repo rate | rates.reverse_repo |
1956 |
| Remittances | remittances.total_inflow_workers_remittances |
1972 |
| Health facilities | health.total_number_hospitals |
1947 |
| Population by age (census) | population.b_w_10_39_years |
1951 |
History depth depends on your plan: the free plan returns the last year. For decades of history, Pro unlocks the full series. Ask your department about a shared key.
Load it in your tool
R
x <- read.csv("https://api.pakdatahub.com/v1/series/fx.rate.avg.usd?format=csv&sort=asc&limit=10000&api_key=pk_live_xxx")
Stata
import delimited "https://api.pakdatahub.com/v1/series/inflation.cpi.national.yoy?format=csv&sort=asc&api_key=pk_live_xxx", clear
Python
import pandas as pd
df = pd.read_csv("https://api.pakdatahub.com/v1/series/remittances.total?format=csv&sort=asc&api_key=pk_live_xxx",
parse_dates=["date"])
Cite it properly
Cite the official publisher (e.g. "State Bank of Pakistan, Average Exchange Rate of Pak Rupees per U.S. Dollar"), accessed via PakDataHub (pakdatahub.com) on the date you downloaded it. meta.source and the catalog's source_url tell you the publisher and the exact official dataset.