Measuring Services Complexity:A Novel Machine Learning Approach Using U.S. Input–Output Data

A stylized factin modern economies is that the more developed a country is,the greater the weight of the service sector.The economics of complexity has provided a new perspective that explains this growth in modern economies.However,thestudy of economic complexity through the standard measure of thecomplexity index presents an increasingly relevant omission in understanding the economic process and its growth.Ingeneral,the data used to measure the EconomicComplexity Index(ECI) are based on information about goods;however,there is a lack of informationon services.This paper proposes an ew methodology to retrieve information on the economic complexity in services.Forthis purpose,the US input-output matrix is used.This work is novel because,thanks to the structure of the data as a network,it is possible to infer them is sing information on the complexity of services. Using a machinelearning method,it ispossible to impute the complexity index for 146services,a level of disaggregation,that is strikingly higher than in other works.The index recovered by this method is consistent with previous results that found service sectors to be more complex than goods.The second result shows that the more restricted the core is in the center of the network,the greater the centrality of services and their complexity.Finally,the results confirm the relevance of the economic complexity index. However,the ECI forservices is better than the ECI for goods for predicting growth;aone-unit increase in the ECI of services increases GDP growth by more than 1 percentage point.