Predicting Marriage Rate Using Machine Learning and Panel Data Analysis: A Case Study of China

After China's accession to the WTO and 20 years of rapid development, the marriage rate has shown a downward trend. This paper aims at fitting machine learning models with the marriage-related data, understanding which attributes affect the marriage rate and predicting the marriage rate. The data collection scope includes seven independent variables related to marriage rate such as GDP, house prices, birth rate, education level etc. over 31 regions in China during 2003–2022. Then the study applied three regression models - Pooled OLS, Random Effects, and Fixed Effects - in predicting China's crude marriage rate. The Random Effects model outperformed both the Pooled OLS and Fixed Effects models, as evidenced by its highest R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value (0.2910). However, based on Hausman Test, p-value of 6.458e-16.the Fixed Effects model is preferrable. All models suggested that the average year of education has the most positive effect to the marriage rate while the house price greatly negates the marriage rate. Results show the Random Effects model, with an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.2910, as the best fit. Key predictors include GDP, house prices, and gross dependency ratio (negative effects), and sex ratio and education (positive effects). The Random Effects model excels in prediction, with the lowest MSE (1.6610), RMSE (1.2888) and MAE (1.0888).