Research Statement
Carly Dollin, November 2023
School of Economic Sciences
Washington State University
My major research area is Development Economics with a particular interest in wage inequality, technology, Skilled-Biased Technological Change (SBTC), and human capital. Additional topics such as the knowledge stock, societal welfare, and reverse brain drain are in the scope of my future research. In my job market paper, I collect and analyze data on patents and their citations to determine rates of decay and diffusion of knowledge stock to examine the effect of technology on real average wages. I write a second paper which focuses on a Principal Component analysis (PCA) on several countries to build a global societal welfare index through a data driven approach. My approach to research involves applying economic theory and econometric models to address practical problems. I aim to introduce creative solutions to real-world issues, pushing the boundaries of knowledge in the field of applied economics while highlighting potential policy implications.
Ongoing research
My main research explores the impact of computer technology on the average real wages in the U.S. manufacturing industry. I used different sources of secondary data related to wages, technology, patents and patent citations at subsector level. I created a knowledge stock index to consistently measure the effect on the real average wages for the manufacturing sector. This paper covers 455 subsectors categorized at a 6-digit level by the North American Industry Classification System (NAICS) and spans the period from 2007 to 2012. Specifically, I analyze the wage disparities among subsectors that are capital-intensive, labor-intensive, and those that require professional and technical services.
To investigate this causal effect of technology (proxied by computer hardware, and software) on compensations received by different categories of workers (High and Low skilled), I adopted a Two-Stage Least-Squares (2-SLS) approach within a two-way fixed effects model. In this model, I utilize a knowledge stock index built from Caballero and Jaffe’s double exponential model as an instrument for technology. My findings reveal a positive elasticity of computer technology on the average wages ranging from 11.6% to 12.9% for hardware, and 15.7% to 18.3% for software across the high-tech subsectors. This positive linkage suggests that more adoption of computers and related products at the workplace is associated with higher average wages. Further, the results display greater marginal effects of technology on wages for labor-productive, capital-intensive, and technology-intensive subsectors. Overall, the paper provides evidence for the skilled-biased technological change (SBTC) argument, which indicates that technological advancements create opportunities that benefit the high skilled while disadvantaging the low skilled workers.
The second paper of my dissertation aims to reevaluate the societal well-being using a global index that adjusts for inequality. This index is derived from a principal component analysis (PCA) based on data from 178 countries. I aggregate a wide range of social and economic indicators into 13 dimensions using a methodology similar to Tauhidur et al. (2005) who applied a one-year PCA method to 43 countries. Additionally, my paper draws inspiration from Estes (2019) who used 40 variables and factor analysis to create the Weighted Index of Social Progress (WISP) with four principal factors. The paper also builds upon Chaaban et al. (2016) who developed a societal index by combining basic Human Development Index (HDI) dimensions with aspects related to safety, security, housing, civic engagement, community, and social life.
My findings reveal that countries ranking high in health also tend to perform well in social lifestyle, and other social and economic composite indicators. However, the Security and Industrialization indicators exhibit different patterns compared to the others. When compared to classic development indices like HDI, IHDI, and PHDI, the Inequality-Adjusted Global Wellbeing Index (IAGWI) shows moderate deviations from the traditional UNDP measures. Notably, there is a substantial variation in how countries are classified across specific dimensions. In conclusion, the concept of societal welfare is a complex and multifaceted index which requires a thorough examination from multiple dimensions.
My third paper examines the decay and diffusion rates of technological knowledge in manufacturing, focusing on four subsectors: aerospace, computer, food, and motor vehicle. It uses a sampling method to collect patents and citations in 25 industries, spanning 1995 to 2012. In addition, the research investigates the link between patent claims, citations, and knowledge stock across the industries. Results reveal that patent claims impact citations, especially backward citations which is a key determinant of knowledge stock. Applying the double exponential specification coined by Caballero and Jaffe (1993), the study finds decay and diffusion rates of 0.0741 and 0.00022 for the entire sample. The aerospace subsector exhibits the highest decay (0.167), while the computer industry shows largest diffusion parameter (0.00063). Despite its low decay parameter (0.041) and relatively high diffusion rate (0.0005), due to fewer annual patents granted the food subsector displays the lowest technological knowledge. The research pinpoints the importance of both the quantity, and notably the quality of patents in determining the level of technological knowledge. The findings highlight how obsolescence and diffusion are sensitive to technological knowledge across subsectors. The computer ranks highest in technological knowledge, and the aerospace lowest despite its relatively substantial number of patents. To establish these rates, I utilize a utility cohort of patents and their citations which serve as a valuable source for determining the parameters governing the obsolescence and diffusion of knowledge stock within the manufacturing industry. The insights gained from this study have the potential to be applied in further research related to technological knowledge within the manufacturing sector. Furthermore, this paper contributes by highlighting the varying degrees of technological characteristics and absorptive capacity across different subsectors within the manufacturing industry.
Future research
In addition to my doctoral dissertation, there are further research I am planning to investigate. For example, I am interested in investigating the effect of reverse brain drains on research and development activities. By the last three decades, policy pertaining to repatriate skilled professionals from abroad to enhance human capital in the home country has proved its efficiency in China, Taiwan and South Korea. From the Balik program, high-skilled Filipinos trained in U.S. and other developed countries have been offered the opportunity to share their knowledge and promote innovative, scientific, agro-industrial, and economic development of their home country, on a short-term or long-term basis. I hypothesize this dynamic of “brain drain” to “brain gain” may consistently contribute to the development of several sectors. In this paper, I aim to measure the variable of interest by the number of qualified returnees to home country. I may also consider the impact of returnees on the graduation rates, or on the ratio of the number of scientific publications.
Is the choice for outsourcing services a better option in the development of an innovative product? In this paper, I aim to extend the model developed by Aghion, Detewatripont, and Stein (2008) “Academic freedom, private-sector focus, and the process of innovation”. Similar to their approach, I am analyzing the optimal stage, and pros and cons to externalize or internalize an innovation research project while adding the credit-labeling as a potential constraint to innovate at an outsourcing country. My analytical framework argues the imposition of sharing the labeling as an additional bottleneck to the creation of the innovation product.