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Economists' wariness of data mining may be misplaced, even in cases where economic theory provides a well-specified model for estimation. We discuss how new data mining/ensemble modeling software, for example the program TreeNet, can be used to create predictive models. We then show how for a standard labor economics problem, the estimation of wage equations, TreeNet outperforms standard OLS regression in terms of lower prediction error. Ensemble modeling resists the tendency to overfit data. We conclude by considering additional types of economic problems that are well-suited to use of data mining techniques.

E-commerce represents a rapidly growing share of consumer spending in the United States. We use transactions-level data on credit and debit cards from Visa, Inc. between 2007 and 2017 to quantify the resulting consumer surplus. We estimate e-commerce reached 8 percent of consumption by 2017, yielding the equivalent of a 1 percent boost to their consumption, or over $1,000 per household per year. While some of the gains arose from avoiding travel costs to local merchants, most of the gains stemmed from substituting to merchants available online but not locally. Higher income consumers gained more, as did consumers in more densely populated counties.

Daily city-level expenditures and prices are used to estimate the price responsiveness of gasoline demand in the United States. Using a frequency of purchase model that explicitly acknowledges the distinction between gasoline demand and gasoline expenditures, the price elasticity of demand is consistently found to be an order of magnitude larger than estimates from recent studies using more aggregated data. Estimating demand using higher levels of spatial and temporal aggregation is shown to produce increasingly inelastic estimates. A decomposition is then developed and implemented to understand the relative importance of several different factors in explaining this result.

Objective. We calculate and compare the monetary returns to interstate mobility during the mid to late 1980s by sex and marital status, both overall and for college graduates. Methods. We analyze data from the Surveys of Income and Program Participation. Results. Returns exhibit high variance, with a negative mean for couples and single men, while single women and single college graduates receive positive returns from moving. Married women experience a large reduction in personal income upon moving because of their reduced employment rates, which reflects their status as \"tied\" movers. The finding of lower returns to migration compared with studies using data from the 1960s and the 1970s reflects fundamental changes in economic motivations for migrating.

Women who leave the labor market for family reasons often return to wages lower than those of women who did not. They lose seniority and are less likely to receive on-the-job training, their jobs may depreciate, and employers may believe they will again take a leave. (Author)

Working Paper No. 25610 E-Commerce represents a rapidly growing share of consumer spending in the U.S. We use transactions-level data on credit and debit cards from Visa, Inc. between 2007 and 2017 to quantify the resulting consumer surplus. We estimate that E-Commerce spending reached 8% of consumption by 2017, yielding consumers the equivalent of a 1% permanent boost to their consumption, or over $1,000 per household. While some of the gains arose from saving travel costs of buying from local merchants, most of the gains stemmed from substituting to online merchants. Higher income cardholders gained more, as did consumers in more densely populated counties.

E-Commerce represents a rapidly growing share of consumer spending in the U.S. We use transactions-level data on credit and debit cards from Visa, Inc. between 2007 and 2017 to quantify the resulting consumer surplus. We estimate that E-Commerce spending reached 8% of consumption by 2017, yielding consumers the equivalent of a 1% permanent boost to their consumption, or over $1,000 per household. While some of the gains arose from saving travel costs of buying from local merchants, most of the gains stemmed from substituting to online merchants. Higher income cardholders gained more, as did consumers in more densely populated counties.

Daily city-level expenditures and prices are used to estimate the price responsiveness of gasoline demand in the US. Using a frequency of purchase model that explicitly acknowledges the distinction between gasoline demand and gasoline expenditures, we consistently find the price elasticity of demand to be an order of magnitude larger than estimates from recent studies using more aggregated data. We demonstrate directly that higher levels of spatial and temporal aggregation generate increasingly inelastic demand estimates, and then perform a decomposition to examine the relative importance of several different sources of bias likely to arise in more aggregated studies.