|Keywords:||Economics, General; Computer Science|
|Full text PDF:||http://nrs.harvard.edu/urn-3:HUL.InstRepos:14226069|
In Chapter 1, I propose a growth theory-based framework for analyzing experimental research in the social sciences. I apply a number of variations of the two standard growth models – Solow-Swan model, and the Schumpeterial growth model – in the context of experimental research, and derive policy recommendations aimed at improving the technological state of the field. In Chapter 2, I lay out a functional specification of the Popper Framework – a suite of software tools designed to implement policy recommendations from Chapter 1. The specification provides a high-level description of four main functional components of the framework: Development Environment, Subject Pool Module, Popper Cloud, and Analysis Environment. The system enables multiple competing implementations for each for the functional components, which makes it extensible and robust to incremental changes of the technological landscape. In Chapter 3, we employ unique administrative data from Moscow to obtain a direct estimate of hidden incomes. Our approach is based on comparing employer-reported earnings to market values of cars owned by the corresponding individuals and their households. We detect few hidden earnings in most foreign-owned firms and larger firms, especially state-owned enterprises in heavily regulated industries. The same empirical strategy indicates that up to 80 percent of earnings of car owners in the private sector are hidden, especially in smaller companies and industries such as trade and services, where cash flows are easier to manipulate. We also find considerable hidden earnings in government services.