Are there simple and observable aggregate statistics that might help determine and forecast the degree of wealth inequality? Do they explain the differences in wealth inequality across economies and over time? To answer these questions, we build a general equilibrium, neoclassical growth model, in which the stationary wealth distribution has heavy right tail with the Pareto tail index, Pt. In the simplest cases of the model, the tail index depends on interest rate (r), growth rate (g), aggregate labor income share (EY), and aggregate capital to output ratio, (KY), summarized in a formula Pt = Pt ( r,g,EY,KY) (rather than the simple gap r-g , put forth in Thomas Piketty's “Capital in the 21st century”). In addition, financial development, production technology, corporate and wealth taxes affect the tail index through their effects on these sufficient statistics. When we calibrate the model to the U.S. economy, we find that the model requires significant persistent heterogeneous returns to investment in order to generate the tail index of U.S. wealth distribution. In this calibration, earnings inequality and, to a lesser extent, initial wealth distribution have negligible effects on top end wealth inequality. The transition of the model economy after a uniform wealth destruction shock or changes in corporate tax (but not after a financial deregulation shock) produces the joint dynamics of EY, KY, and wealth inequality experienced in major developed economies after World War II. Lastly, we find empirical evidence for persistent heterogeneous returns in the PSID surveys.