Innovation, Growth, and Trade
Saturday, Jan. 8, 2022 3:45 PM - 5:45 PM (EST)
- Chair: Susan Fleck, U.S. Bureau of Labor Statistics
Assessing Factors that Influence Women’s Participation in the Invention Ecosystem
AbstractTo explore factors that may be contributing to the underrepresentation of women in patenting, we adopt a model that explores: (i) the relationship between local economic and inventive environments and increasing women inventor participation, and (ii) how higher education influences a county’s probability of hosting its first woman inventor. To this end, we combine patent grant and inventor gender and location information from PatentsView (1990-2019) with U.S. Census and Bureau of Economic Analysis county-level higher education and economic information. Our findings indicate that a county’s per-capita income and labor force size had small but positive effects on increasing the number of women inventors. The evidence favors an environment of other inventors as being relatively more influential to expanding the number of women inventors. Counties with higher patenting activity in chemistry technologies had the highest and most consistent impact on increasing women inventor counts. We further find that team size has a non-linear relationship with women inventorship. While average team size is 2.7, larger teams (4.475) have a higher propensity to have women inventors, but then decreases as team size increases thereafter. The effect of team size also varies regionally. For example, larger inventor teams in the USPTO Silicon Valley region had the highest likelihood of adding women inventors relative to the other USPTO regions. Interestingly, although the size of all male inventor teams was weakly complementary to women inventor counts in all areas of the country, its effect was largest on the East Coast.
For What It’s Worth: Measuring Land Value in the Era of Big Data and Machine Learning
AbstractWe develop methods and provide new estimates for land valuation in the continental United States using a novel machine learning approach paired with “big data” from Zillow. Because this data includes detailed information from hundreds of millions of property transactions covering much of the US, the heterogeneous nature of this data serves as fertile ground for highlighting some of the practical limitations of linear hedonic regression techniques in the context of “big data.” We first construct hedonic estimates of land value at the parcel-level for most of the US in order to build up from small geographies to aggregate values of land, a key asset and historically important input into the economy. For comparison, we then modify the hedonic method using a machine learning approach, generating new land value estimates using a random forest and gradient boosting procedure. We show how a machine learning approach can systematically address issues of spatial controls and thin cells at smaller levels of geography and population density (with fewer corresponding market transactions), along with addressing other shortcomings associated with linear hedonic regression approaches to valuation of heterogeneous assets. Our initial estimates using the traditional hedonic approach show land values fell about $10 trillion (or 28%) from the boom to bust periods in the 2000s and experienced a nearly full recovery by the middle of the second decade. These proof-of-concept estimates show that private land in the contiguous 48 states to be worth approximately $35.5 trillion in 2016.
Are Tariffs Biased? The Effects of the 2018 U.S. Tariffs on The Gender Wage Gap
AbstractIn 2018, the United States imposed tariffs on all imported steel and aluminum. Although the U.S. has a long history of using unilateral tariffs, the breadth of and justification for these tariffs renewed global attention on the effects of trade policies. In this paper, we examine how these tariffs changed the gender wage gap in the years leading up to the COVID-19 pandemic. Specifically, pooling waves 1–3 of the 2018 panel of the Survey of Income and Program Participation (SIPP) and using an Ordinary Least Squares (OLS) model, we explore the effects of the 2018 U.S. tariffs on steel and aluminum on industry-specific gender wage gaps. Ex ante, the effects of tariffs on the gender wage gap is ambiguous. Tariffs on steel and aluminum as final goods may lower competition by increasing the prices of foreign goods relative to domestic goods, increase demand for domestic products, and therefore increase employment. Given the higher concentration of male-to-female workers at steel and aluminum industries, the increase in employment is likely concentrated among men, leading to an increase in the male-to-female wage ratio. Alternatively, tariffs on steel and aluminum as intermediate goods may increase the cost of production at downstream domestic industries, and lead to a price hike. This price hike would lower demand and increase employment layoffs in industries producing final goods. Depending on the composition of these downstream industries, more men may be laid off, relative to women, which would lower the gender wage gap.
- O0 - General
- F1 - Trade