You already have a Masters in Statistics, which is great! In general, I'd suggest to people to take as much statistics as they can, especially Bayesian Data Analysis.
Depending on what you want to do with your PhD, you would benefit from foundational courses in the discipline(s) in your application area. You already have Economics but if you want to do Data Science on social behavior, then courses in Sociology would be valuable. If you want to work in fraud prevention, then a courses in banking and financial transactions would be good. If you want to work in information security, then taking a few security courses would be good.
There are people who argue that it's not valuable for Data Scientists to spend time on courses in sociology or other disciplines. But consider the recent case of the Google Flu Trends project. In this article their methods were strongly criticized for making avoidable mistakes. The critics call it "Big Data hubris".
There's another reason for building strength in social science disciplines: personal competitive advantage. With the rush of academic degree programs, certificate programs, and MOOCs, there is a mad rush of students into the Data Science field. Most will come out with capabilities for core Machine Learning methods and tools. PhD graduates will have more depth and more theoretical knowledge, but they are all competing for the same sorts of jobs, delivering the same sorts of value. With this flood of graduates, I expect that they won't be able to command premium salaries.
But if you can differentiate yourself with a combination of formal education and practical experience in a particular domain and application area, then you should be able to set yourself apart from the crowd.
(Context: I'm in a PhD program in Computational Social Science, which has a heavy focus on modeling, evolutionary computation, and social science disciplines, and less emphasis on ML and other empirical data analysis topics).