During the tabulation of votes in the 2000 presidential election, the world was shocked at the technological inadequacy of electoral equipment in many parts of the US. In reaction to public dismay over "hanging chads", Congress quickly enacted the Help America Vote Act (HAVA), legislation to fund the acquisition of advanced vote-counting technology. However, the intention was to enable, rather than mandate, choices of new electoral equipment. This paper takes advantage of a unique historical opportunity to test whether electoral equipment follows the pattern predicted by well-established models of innovation diffusion, merging electoral data with census data on socioeconomic characteristics. We infer that fiscal constraints to acquisition are strong but are not the only limitations to technology adoption, particularly within certain types of easily identifiable populations.
This paper evaluates the contribution of patent-related events to changes in stock prices, proposing that economics has traditionally failed to find much effect for two reasons which we identify and correct. First, patents vary widely in quality so we use quantile analysis and alternative measures of patent quality to identify effects. Second, we permit the possibility that information leaks out into investor sentiment during the long and uncertain time until patent grant, so evaluate the stock price effect at four different dates in the life of each patent. As a case study to test this approach, track all patents over a 27-year period for Apple Inc., permitting design patents to have different effects that traditional utility-model patents, and isolate the effect that Steve Jobs’s name on a patent has at each stage of a patent’s life.
We model the diffusion of economic knowledge using an epidemiological model of susceptible, exposed, infected, and recovered populations (SEIR). Treating bibliographic citations as evidence of contagion, we estimate the coefficients of a four-equation system simultaneously for each of 759 subfields of economics. Results show that some subfields grow endogenously much faster than others, and just over half have basic reproduction properties sufficient to ensure survival without the annual addition of new protégé scholars.
In 2006, philanthropic giving to higher education institutions totaled $28 billion, with the top school receiving just under a billion dollars. Roughly fifteen percent of those funds came from alumni donations. This paper builds upon existing economic models to create an econometric model predicting the ever-more important pattern of alumni giving. We test the model using data from over 22,000 alumni at a private liberal arts college, and report on the probable profiles for annual fund donors and alumni willing and able to give major gifts.
Although previous empirical studies have found relationships between patent characteristics and value, none have determined how specific attributes relate to auction value or even the probability of a successful auction sale. Using a Heckman two-step model, we regress thirteen independent variables against unique patent auction data, finding that publicly-owned and frequently referenced patents are more valuable, and that other things equal, there is an optimal time to offer a patent up for auction.
Agriculture, like many primary and service sectors, is a frequent recipient of innovation intended for its use, even if those innovations originate in industrial sectors. The challenge has been identifying them from patent data, which are recorded for administrative purposes using the International Patent Classification (IPC) system. We reprogram a well-tested tool, the OECD Technology Concordance (OTC), to identify 16 million patents granted between 1975 and 2006 worldwide which have potential application in agriculture. This paper presents the methodology of that dataset’s construction, introduces the data via summaries by nation and industrial sector over time, and suggests some potential avenues for future exploration of empirical issues using these data.
In an effort to explore the potential for financing environmental innovation, this paper examines different forms of financing and attempts to evaluate their effectiveness. The study considers both public and private forms of funding as well as providing policy suggestions for the support of appropriate financing for eco-innovation.
This paper aims to summarize the state of academic knowledge surrounding the economics of environmental innovation. Following a definition of environmental technology, the paper enumerates and describes the obstacles or constraints to the development of eco-innovation.
This study shows how social capital affects the outreach and operational self-sufficiency of microfinance institutions (MFIs) around the world. Defining social capital as social networks, social norms, and trustworthiness, this research merges quantitative data from the Microfinance Information Exchange and World Values Survey to empirically test a which aspects of social capital have the greatest influence on MFI performance in the presence of an endogenous peer effect between MFIs. Regression results show that aspects of social capital have a direct influence on MFI performance, suggesting a tradeoff between outreach and sustainability, and display a strong endogenous peer effect.
While there is anecdotal evidence that home values decline when a big-box store (such as Wal-Mart) decides to locate in the area, there is a paucity of evidence on that effect. This paper uses a repeat sales model to compare residential property values, and the speed of sale of the property, to compare the impact that an arrival has. Results conclude that there is a "news effect" surrounding the arrival, and that the total effect is small at most. For most specifications tested, the number of stores nearby, the arrival of new stores, and the distance to the nearest store all have insignificant impacts on both property resale value and the number of days that a property spends on the market prior to sale. In the worst-case scenario, the arrival of a Wal-Mart is associated with a decline equivalent to roughly one percent of the home's square footage and is not absorbed by those closest to the new retailer but by rather more distant neighbors.
This paper considers the challenges to the dissemination of environmental innovation. Following a brief exploration of the legal and regulatory regimes surrounding environmental technologies, the paper examines diffusion mechanisms, market factors, social characteristics and political elements that facilitate and complicate dissemination. Given the importance of innovation to economic development and growth, the diffusion of innovation is of great interest to economists and policymakers alike.
On the popular game show “Who Wants To Be A Millionaire”, men appear to average higher winnings than women. This paper investigates potential reasons, including different uses of information sources (lifelines) and different perceptions of risk. We include gender-based tests of Kahneman and Tversky’s prospect theory, but offer instead the counterintuitive conclusion that men are rewarded for acting slightly more cautiously than women do.
This paper examines the location of innovations within pharmaceutical technology, using U.S. patent citation data to trace the knowledge flows over time. It is clear that knowledge clustering is certainly present. Our study utilizes multivariate left-censored Tobit regression analysis to control for identifiable factors, to examine whether over time the distance between successive innovators has changed. We find the distance to be increasing significantly over time, both when considering all citations and only inter-city transfers.
This paper examines the evidence on the clustering of innovators within the telecommunications sector, using U.S. patent citation data to trace their locations over time. While clustering is clearly evident, we use multivariate left-censored Tobit regression analysis to control for identifiable factors, showing that the distance between successive innovators has been rising over time, perhaps even exponentially.
This paper analyzes a database of over 18,000 women micro-finance clients of the Negros Women for Tomorrow Foundation (NWTF), a database using the Progress Out of Poverty (PPI) Scorecard as a measure of poverty. Analysis using both OLS and quantile regression models shows how observable characteristics of borrowers affect the ability of clients to reduce their measured poverty. Loan size, duration, and the economic activity supported all have strongly identifiable effects. Moreover, estimates suggest which among the poor are receiving the greatest effective help by the program. Results offer advice to the NWTF and offer insight useful to policymakers and other micro-lenders.
Colorado College uses an economic system to allocate scarce course seats: annually during a sealed-bid auction, each student receives nontransferrable, nonbankable currency with which to bid on courses. We estimate an instrumental variables probit model to determine whether particular student populations are a) implicitly wealthier, having the ability to afford more expensive electives, or b) more risk-averse, choosing to avoid ambiguity by bidding more strongly and/or remaining in a class rather than selecting another after pre-registration. Beyond the anticipated department-specific and instructor-specific effects attributable to popular majors or charismatic instructors, we find strong evidence that students bid more strongly for courses that have perceived scarcity of seats, courses that offer a higher expected grade, courses taught by an instructor similar to themselves, or courses with special attributes like limited enrollment or field trip components. We also find evidence of some populations being more willing to “shop around” for new class experiences after the pre-registration period.
This paper examines the location of innovations within solar technology, using U.S. patent citation data to trace their diffusion over time. Knowledge clustering is clearly present. We employ multivariate left-censored Tobit regression analysis to control for identifiable factors, to examine whether the distance between successive innovators has changed over time. We find the distance to be increasing slightly over time, both when considering all citations and only inter-city transfers.
Using over 200,000 U.S. patent citations, we test whether knowledge transfers in the transportation sector are sensitive to distance, and whether that sensitivity has changed over time. Controlling for self-citation by inventor, assignee and examiner, multivariate regression analysis shows that physical distance is becoming less important for spillovers with time, albeit in a nonlinear fashion.
This paper examines the location of innovations within green technology, using U.S. patent citation data to trace their inter-generational knowledge flows over time. Clustering is clearly evident, and we use multivariate left-censored Tobit regression analysis to control for identifiable factors, to show that the distance between successive innovators has not been rising over time. The interesting exception is nuclear energy in which distance appears to be decreasing over time. If we consider only inter-city transfers, the waste management also becomes more concentrated over time, while transportation declusters.
Using over 250,000 U.S. patent citations, we test whether knowledge transfers in the energy sector are sensitive to distance, and whether that sensitivity has changed over time. Controlling for self-citation by inventor, assignee and examiner, multivariate regression analysis shows that physical distance is becoming less important for spillovers with time.
This paper investigates whether the curricular structure of an Economics course (semester, trimester, or compressed block schedule) has an effect on an undergraduate's subsequent retention of course material. We test separately for theoretical/process comprehension and for graphical construction/interpretation, while separating micro from macro content as well. We use an instrument to address the no stakes testing problem, and our Heckman two-stage estimations present some interesting results for educators and institutional policymakers alike.
Closely following the notion of innovative geographic clusters, this paper examines knowledge flows in the US agriculture industry for evidence of innovative agglomeration. The data indicate that a closer distance between any two agricultural patent origins increases the probability that one cites the other as prior art. Further, subtle interregional variations characterize the degree to which proximity advances agricultural innovation. Finally, the results show that older innovations in agriculture proliferate more readily than recently created knowledge.
This paper considers the challenges associated with conducting research with undergraduates – limited time and resources, limited skills, the tedious nature of data gathering, etc.. We discuss four models of effective research approaches. One is Aju Fenn’s which is to identify a topic and a workable approach, such as competitive balance in sports, and apply it in different contexts – football, basketball, soccer, etc. with different students working on different sports. This model is also successful because much data on both inputs and performance is collected in sports and is readily available from non-propriety sources. The Dan Johnson Model is to develop a huge data set, in this case patents, and then set students to work on problems involving some aspect of the data set while asking them develop one part of the data set through their research. The Smith Model which is to divide a related problem into distinct parts and have students work on each part. Smith discusses this approach on research on recreation values for the Arkansas River a quantitative problem while Stimpert shows its application to a qualitative problem, the role of corporate boards.
The effect of spatial factors on competition and the price of gasoline have been sparsely explored by previous studies. Existing work examines how gasoline prices differ based on distance from the distribution site as well as how cost factors influence gasoline prices. Using market data from six midsized U.S. metro areas with similar isolation from neighboring retail markets, this paper examines the effects of location on retail price, while controlling for brand effects. Spatial regression analysis accommodates the potential of spatially correlated errors, and sensitivity analysis tests for several measures of retail location concentration. Results point to reproducible brand premiums and some location-based price differences, but also show the counterintuitive finding that areas with more market competition do not show significantly lower retail gas prices.
Recent Wal-Mart openings have been accompanied by public demonstrations against the company’s presence in the community, asserting (among other things) that their presence is deleterious to residential property values. This study empirically evaluates that claim, analyzing the spatial correlation between Wal-Mart locations and residential property values, while comparing Wal-Mart with other big-box retailers for a frame of reference and controlling for other important aspects of a home’s market value. We recognize that market value may represent a trade-off between price and patience, so perform a similar analysis using a property’s days on the market to evaluate any big-box effect. Finally, we interpret the resulting effects in two ways, from both the resident’s and retailer’s point of view, casting new light on the NWIMBY effect.