Unemployment data - India with projections for 2026
Description
The code analyzes unemployment rates across Indian states and union territories, providing both current data and future projections6. Here's a breakdown: Data Source: The primary data, sourced from the Periodic Labour Force Survey (PLFS) conducted by the Ministry of Statistics and Programme Implementation (MoSPI), reflects unemployment rates for individuals aged 15 years and above16. The survey period spans from June 2022 to July 20236. 2022-23 Unemployment Rates: The unemployment rate in India was 3.2%6. The data reveals variations across regions, with some states exhibiting significantly higher rates than others56. For example, in December 2023, Haryana had the highest unemployment rate, while Odisha reported one of the lowest5. Recent Trends: Several reports indicate a decreasing trend in India's unemployment rate67. The unemployment rate for individuals aged 15 years and above has decreased to 3.1% in 20233. This decline is observed in both males and females, as well as in urban and rural areas1. Pandemic Recovery: The improvement in the employment situation is attributed to the recovery from the COVID-19 pandemic, with increased economic activity following the easing of lockdown restrictions13. Future Projections: The code estimates unemployment rates for 2026, projecting a rise based on an assumed annual growth rate4. However, these projections should be interpreted cautiously, considering other forecasts suggest varying trends4. Data Interpretation Considerations: Data from the Centre for Monitoring Indian Economy (CMIE) indicates different unemployment rates and trends compared to the PLFS data45. It's important to consider the source and methodology when interpreting unemployment statistics6. State-wise Analysis: State-specific data provides valuable insights into regional disparities in employment56. Factors such as economic policies, industrial development, and demographic characteristics can influence unemployment rates in different states5.
Files
Steps to reproduce
# Re-import necessary libraries as execution state was reset import pandas as pd import numpy as np # Given data for 2022-23 unemployment rates data = { "State/UT": [ "Andhra Pradesh", "Arunachal Pradesh", "Assam", "Bihar", "Chhattisgarh", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jharkhand", "Karnataka", "Kerala", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Odisha", "Punjab", "Rajasthan", "Sikkim", "Tamil Nadu", "Telangana", "Tripura", "Uttarakhand", "Uttar Pradesh", "West Bengal", "Andaman & N. Island", "Chandigarh", "Dadra & Nagar Haveli & Daman & Diu", "Jammu & Kashmir", "Ladakh", "Lakshadweep", "Puducherry", "All India" ], "Unemployment Rate (2022-23)": [ 4.1, 4.8, 1.7, 3.9, 2.4, 1.9, 9.7, 1.7, 6.1, 4.3, 1.7, 2.4, 7.0, 1.6, 3.1, 4.7, 6.0, 2.2, 4.3, 3.9, 6.1, 4.4, 2.2, 4.3, 4.4, 1.4, 4.5, 2.4, 2.2, 9.7, 4.0, 2.5, 4.4, 6.1, 11.1, 5.6, 3.2 ] } # Convert data to DataFrame df = pd.DataFrame(data) # Estimating unemployment rate for 2026 assuming a 2% annual increase in unemployment due to economic fluctuations annual_growth_rate = 0.05 # 5% increase per year # Predict for 2026 (3 years from 2023) df["Unemployment Rate (2026)"] = df["Unemployment Rate (2022-23)"] * ((1 + annual_growth_rate) ** 3) # Display the updated table df