SAFE Working Paper No. 382

Mamma Mia! Revealing Hidden Heterogeneity by PCA-Biplot - MPC Puzzle for Italy’s Elderly Poor

I investigate consumption patterns in Italy and use a PCA-biplot to discover a consumption puzzle for the elderly poor. Data from the third wave (2017) of the Eurosystem’s Household Finance and Consumption Survey (HFCS) indicate that Italian poor old-aged households boast lower levels of the marginal propensity to consume (MPC) than sug-gested by the dominant consumption models. A customized regression analysis exhibits group differences with richer peers to be only half as large as prescribed by a traditional linear regression model. This analy-sis has benefited from a visualization technique for high-dimensional matrices related to the unsupervised machine learning literature. I demonstrate that PCA-biplots are a useful tool to reveal hidden rela-tions and to help researchers to formulate simple research questions. The method is presented in detail and suggestions on incorporating it in the econometric modeling pipeline are given.