The table captures district-wise GDP (₹ crore) and annual growth rates in Mizoram from 1999–2006.
| Name | Type | Nullable | Description |
|---|---|---|---|
| District | categorical | No | Name of the district within the state of Mizoram. |
| Year | string | No | Financial or reporting year for which the GDP and growth rate values are recorded. |
| District GDP (Current Prices) | float | No | Gross Domestic Product of the district measured at current market prices, typically expressed in rupees crore. |
| GDP Growth Rate (%) | float | No | Annual percentage growth rate of district GDP compared to the previous reporting period. |
Aizawl shows the highest GDP consistently, followed by Lunglei, while smaller districts like Saiha and Serchhip remain at lower levels. Growth is positive across most districts, with some fluctuations, especially Saiha showing a sharp surge in 2001–02. Overall, steady economic expansion is visible, though with inter-district disparities in pace and scale.
The dataset maintains consistent formatting for GDP values and growth rate percentages. Numerical values are structured in a standardized format across rows. Some rows may include calculated growth values derived from GDP changes across years. Missing values may occur where growth rates cannot be calculated due to absence of previous year data.
Regional economic development analysis, district-level economic performance comparison, economic growth tracking within Mizoram, policy planning, academic research in regional economics, and visualization of district economic trends.
The dataset focuses only on GDP values and growth rates and does not include sector-wise economic breakdowns, demographic indicators, employment data, or price-adjusted real GDP figures. It also only covers a limited geographic scope restricted to Mizoram.
The dataset was likely compiled from administrative economic records and statistical reports prepared by state economic departments. GDP estimates at the district level are generally calculated using sectoral production estimates, income data, and aggregated administrative statistics.
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