This chapter will cover recent research in historical economics that uses ethnographic data and data from surveys and lab experiments. The study of historical economics, particularly outside of non-Western countries, has been constrained by availability of historical data. However, recent work...

Primary historical sources are often by-passed for secondary sources due to high human costs of accessing and extracting primary information-especially in lower-resource settings. We propose a supervised machine-learning approach to the natural language processing of Chinese historical data. An...

This paper develops linear estimators for structural and causal parameters in nonparametric,nonseparable models using panel data. These models incorporate unobserved, time-varying, individual heterogeneity, which may be correlated with the regressors. Estimation is based on an approximation of...

We propose a conformant likelihood estimator with exogeneity restrictions (CLEER) for random coefficients discrete choice demand models that is applicable in a broad range of data settings. It combines the likelihoods of two mixed logit estimators--one for consumer level data, and one for...

Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms. Using data from a large...

New, "big" data sources allow measurement of city characteristics and outcome variables higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it...

In this paper, we highlight the potential for linked employer-employee data to be used in entrepreneurship research, describing new data on business start-ups, their founders and early employees, and providing examples of how they can be used in entrepreneurship research. Linked...

We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components and provide the asymptotic distribution of these estimators. Empirically, we study the high frequency covariance...

In recent years, the increased use of "big data" and statistical techniques to score potential transactions has transformed the operation of insurance and credit markets. In this paper, we observe that these widely-used scores are statistical objects that constitute a one-dimensional summary of...

We present and describe a new dataset of capital control restrictions on both inflows and outflows of ten categories of assets for 100 countries over the period 1995 to 2013. Building on the data first presented in Martin Schindler (2009), and other datasets based on the analysis of the IMF's...