Firm capabilities are crucial to economic development in poor nations, and therefore understanding how firms differ and how they evolve is a crucial component for any robust theory of growth. For instance, why do firms in developing countries struggle to grow and increase productivity, even though the hard and soft technologies are available? Cross country information on the distribution of organizations and their change over time is sparse. There are two particular data deficiencies; how the distribution of firm performance differs in poor countries compared to rich ones, and the internal organization and management practices in all nations. Methods for testing and developing theories of growth are inadequate due to these shortcomings. This project began by auditing the existing usable datasets - appraising what data is currently “out there” and what needs to be done. After surveying 100 datasets, we found only one with information on developing countries. Most surveys suffered from small sample size and a high degree of non-response, leading to risk of selection biases and inaccurate aggregate indicators. Surveys are usually only conducted once or twice over a short period of time, meaning a weak time series dimension. Whilst many surveys ask about changes over time, fewer ask about levels of organizational structure. This makes macro time series comparisons problematic as the questions are conditional on survival and we do not know the initial conditions. Finally, there is a lack of comparability between surveys, either across or even within countries. Although some of the existing datasets could be useful for testing particular hypotheses, it is not possible to use them to draw broad conclusions about generalized changes over time. We seek to draw from administrative and commercial databases, which are preferable in terms of reliability and coverage, and to seek stylised facts from this data.