Abstract:  

This paper studies the effects of infrastructure investment on the labor market. Between 1920 and 1950, the US government began constructing its first interstate highways, the Numbered Highway System. Regions gaining access to the Numbered Highway System experienced significant population growth alongside a shift from self-employment to salaried jobs. To identify the causal effect of this highway network on local labor markets, I use a novel instrumental variable approach exploiting a hypothetical set of highways proposed by the US Army for national defense. I interpret my causal estimates through a spatial equilibrium model in which highway construction induces households to select out of self-employment as local agglomeration forces increase the cost of doing business near highways. I find that investment in the Numbered Highway System can account for one fifth of the decline in the aggregate self-employment rate over this period, underscoring how infrastructure investment fosters regional integration and structural change.

Abstract:  

Growing access to item-level transaction data has led to widespread use of average sales-price metrics for both narrow and broad product classes. Such Unit Value Indices (UVI) conflate pure price changes with shifts in product mix so mismeasure inflation for heterogeneous goods. This paper derives an exact decomposition of the UVI into (i) a within component reflecting the arithmetic matched-model Laspeyres index and (ii) a product-mix component that captures changing mix among continuing, entering and exiting products.  The mix term reflects price dispersion driven in part by quality variation. A quality-adjusted UVI (QUVI) can correct or at least reduce this bias, but estimating quality adjustment is challenging. Using hedonic procedures for adjustment factors, we derive the conditions when QUVI measures coincide with hedonic indices that take into account product turnover and quality.  The framework also considers the identification challenges of disentangling quality effects from market frictions. 

Abstract: 

Businesses, individuals, and government policymakers rely on accurate and timely measurement of nominal sales, inflation, and real output, but current official statistics face challenges on a number of dimensions. First, these key indicators are derived from surveys conducted by multiple agencies with different time frames, yielding a complex integration process. Second, some of the source data needed for the statistics (e.g., expenditure weights) are only available with a considerable lag.  Third, response rates are declining, especially for high-frequency surveys. Focusing on retail trade statistics, we document important discrepancies between official statistics and measures computed directly from item-level transactions data. The long lags in key components of the source data delay recognition of economic turning points and lead to out-of-date information on the composition of output. We provide external data sources to validate the transactions data when their nominal sales trends differ importantly from official statistics. We then conduct counterfactual exercises that replicate the methodology that official statistical agencies use with the transactions data in the construction of nominal sales indices. These counterfactual exercises produce similar results to the official statistics even when the official nominal sales and item-level transactions data exhibit different trends.