Using the User Cost of Monetary Assets to Explain the Investment Portion of Gross Domestic Product

 

By Adam Bourgoin-Stone

Over the summer, I have been co-writing a paper with Dr. Biyan Tang on the merits of utilizing the user cost of monetary assets rather than the interest rate in economic research, specifically research into the investment part of Gross Domestic Product. The paper itself is not complete yet, and the current results may change if errors in the regression are detected.  The paper has been submitted to present in a conference hosted by the Midwest Economics Association. This project has been and continues to be an incredible opportunity for me to not only learn more about economics, but also expand my critical reading and research skills.

Gross Domestic Product is often used in economics as an indicator of general economic growth. GDP is equal to Consumption + Investment + Government Spending + Net Exports. According to the Bureau of Economic Analysis, investment has remained at between 16% and 26% since 1947. Since investment is a significant portion of GDP, it is important to understand which factors correlate with it, and which factors can promote its growth. Based on traditional economic theory, the interest rate is negatively related to the level of investment that firms are willing to make. In other words, with a higher interest rate, investment will fall assuming other economic or political conditions remain the same. However, the relationship between the interest rate and investment can be unclear. Some research in the past has found a negative relationship; some has found a positive relationship; one paper found that the correlation changes significantly based on the interest rate level at the time of adjustment. A more intuitive tool for measuring investment as a part of GDP may be the user cost of monetary assets. The user cost is measured by the opportunity cost, or the forgone interest, of holding certain liquid monetary assets (like currency or checking account balance) versus holding pure investment assets. For example, if you held $1000 in cash, the user cost of your monetary asset would be the federal interest rate that you could have earned on the money had you kept it entirely in the form of investment assets such as treasury bonds. The user cost of monetary assets can be separated into the user costs for the various monetary aggregates, such as M1, M2, M3, and M4. A general overview of the monetary aggregates is that M1 contains the most liquid assets, and that each subsequent monetary aggregate contains the previous aggregate plus less liquid assets. For example, M1 contains physical currency, demand deposits, traveler’s checks, and other checkable deposits. M2 contains everything in M1, and also savings deposits, money market securities, mutual funds, and other time deposits.

Barnett (1978) derives a user cost formula for monetary assets:

where p_it is the current period user cost of the per capita real balances of monetary asset i during period t, p^*_t is an aggregate index of the prices of good/services and of the prices of durable goods rental during period t, R_t is the yield on per capital bond holdings during period t, and r_it is the nominal yield on monetary asset i during period t. The formula demonstrates that as the benchmark interest rate rises, the forgone interest rate increases with it.

The relationships of different economic factors with investment can be described using the Ordinary Least Squares regression

PriInv = b0 + b1(IntRate) + b2(rGDP) + b3(PubInv) +b4(PriCredit) + b5(CorpTax) + b6(Income) + b7(Inflat) + b8(TreasBond) + b9(Savings)

where the dependent variable is private investment in billions of dollars, and the independent variables are the interest rate, real GDP, public investment, credit available to the private sector, the effective corporate tax rate, aggregate income, the inflation rate, the treasury bond yield rate, and the savings rate. The user cost data was organized by the user costs for the different monetary aggregates (e.g. M1, M2, M3, M4). Two regressions were used, one using the interest rate, and the other replacing the interest rate with the user cost of monetary assets for M1. The adjusted R², which is the percent of the variance of the dependent variable that is explained by the independent variables, was compared to determine whether the user cost is a better determinant of private investment than the interest rate. Regressions were run with the user cost for each monetary aggregate, with the regression that yielded the highest adjusted R², the user cost for M1, being compared with the interest rate regression. The t-values of the interest rate and the user cost for each monetary aggregate were used to determine the statistical significance of each variable’s correlation with gross private investment. The statistical significance of each monetary aggregate’s user cost and the interest rate is visualized in Figure 1. Clearly, the interest rate is shown to have the most statistically significant correlation with the investment, and the user cost for M1 is shown to have the most significant relationship out of all of the user costs, though it is still statistically insignificant. Figures 2 and 3 show the interest rate and the user cost of M1 over time with gross private investment, respectively. The user cost and the interest rate are shown to be almost identical in proportion.

Figure 1, t-values of the interest rate and the monetary aggregate user costs

 

Figure 2, gross private investment and the interest rate over time

 

Figure 3, gross private investment and the user cost of M1 over time

 

The coefficients of each of the variables of the regression using the interest rate are shown in Figure 4. In this regression the interest rate is found to have a positive correlation with private investment. While at first glance, this seems contrary to traditional economic theory, this is likely because we did not add a lag to the regression. The relationship is shown to be positive because when investments fall, the federal reserve lowers the interest rate to stimulate investment, and when investment rises again, the federal reserve slowly returns interest rates to their previous values. The effective corporate tax rate was also shown to have a positive correlation, when a higher tax rate would be expected to lower the funds firms have at their disposal to invest, thereby reducing private investment. Figure 5 shows the regression using the M1 user cost (as that is the user cost that had the most significant correlation with investment, and the regression using the M1 user cost yielded the highest adjusted R²). The effective corporate tax rate is shown to have a positive relationship in this regression as well. In fact, most of the variables have similar values to the regression using the interest rate, except for the treasury bond yield rate and the inflation rate.

Figure 4, regression results for the interest rate. Standard errors are below coefficient values. *** – significant at 1% level. ** – 5% level. * – 10% level

 

Figure 5, regression results for the user cost. Standard errors are below coefficient values. *** – significant at 1% level. ** – 5% level. * – 10% level

 

The adjusted R² of the regression using the interest rate was .9905, and the adjusted R² of the regression with the user cost was .9881. This result is not unexpected, given the fact that the user cost was found to be statistically insignificant, while the interest rate was found to be significant at the 0.1% significance level, meaning that there is only a 0.1% risk of concluding that it is significant when it isn’t.

According to these results, the interest rate is probably a better variable than the user cost for explaining the variance of investment in the US. Secondary results include the statistical insignificance of the inflation rate when explaining investment, and also the inconsistency of the treasury bond yield rate’s significance. When using the interest rate, it was found to be mostly insignificant, but when using the user cost for M1, it was found to be significant at or above the 1% level. Based on these current results, I cannot conclude that the user cost of monetary assets is a more efficient factor than the interest rate for analysis of the investment part of GDP.

I want to thank the Dean of the UMass Dartmouth College of Arts and Sciences, Dr. Pauline Entin, for the generous stipend that I was granted for my summer project.

Works Cited in Paper

Acha, Anthony, and Kelechi Acha. “Interest Rates in Nigeria: an Analytical Perspective.” Research Journal of Finance and Accounting, vol. 2, no. 3, 14 Apr. 2016, pp. 71–81.

Agu, Osmond Chigozie. “DETERMINANTS OF PRIVATE INVESTMENT IN NIGERIA: AN ECONOMETRIC ANALYSIS.” International Journal of Economics, Commerce and Management, vol. 3, no. 4, Apr. 2015.

Ahmed, Afaq, and Muhammad Mubarik. “Impact of Interest Rate and Inflation on Stock Market Index: A Case of Pakistan .” Jan. 2012.

Ajide, Kazeem, and Olukemi Lawanson. “Modelling the Long Run Determinants of Domestic Private Investment in Nigeria.” Asian Social Science, vol. 8, no. 13, 2012.

Akhtar, M. A. “Effects of Interest Rates and Inflation on Aggregate Inventory Investment in the United States.” The American Economic Review, vol. 73, no. 3, June 1983, pp. 319–328.

Albu, Lucian Liviu. “TRENDS IN THE INTEREST RATE–INVESTMENT– GDP GROWTH RELATIONSHIP .” Romanian Journal of Economic Forecasting, vol. 3, Jan. 2006, pp. 5–13.

Appienti, William, et al. “KEY DETERMINANTS OF INVESTMENTIN GHANA: COINTEGRATION AND CAUSALITY ANALYSIS.” May 2016.

Awan, Rehmat, et al. “Rate of Interest, Financial Liberalization & Domestic Savings Behavior in Pakistan.” International Journal of Economics and Finance, vol. 2, no. 4, 2010.

Bagci, Erdem, and Emre Erguven. “Relations between Interest Rate, Inflation, Growth AndInvestment in Turkey, 2002-2015 .” 2016.

Bitros, Georgios, and M. Ishaq Nadiri. “Elasticities of Business Investment in the U.S. and Their Policy Implications : A Disaggregate Approach to Modeling and Estimation .” July 2017.

Chen, K. C., and Daniel Tzang. “Interest-Rate Sensitivity of Real Estate Investment Trusts.” The Journal of Real Estate Research, vol. 3, no. 3, 1988, pp. 13–22.

Heim, John. “THE INVESTMENT FUNCTION: DETERMINANTS OF DEMAND FOR INVESTMENT GOODS.” Jan. 2008.

Kosma, Olga. “Determinants of Investment Activity: the Case of Greece.” 2015.

Maccini, Louis, et al. “The Interest Rate, Learning, and Inventory Investments.” American Economic Review, vol. 94, no. 5, Dec. 2004, pp. 1303–1327.

Magableh, Sohail, and Sameh Ajlouni. “Determinants of Private Investment in Jordan: An ARDL Bounds Testing Approach.” Dirasat, vol. 43, no. 1, Jan. 2016.

Mueller, Glenn, and Keith Pauley. “The Effect of Interest-Rate Movements on Real Estate Investment Trusts.” Journal of Real Estate Research, vol. 10, no. 3, Feb. 1995, pp. 319–326.

Munir, Rahila, et al. “Investment, Savings, Interest Rate and Bank Credit to the Private Sector Nexus in Pakistan.” International Journal of Marketing Studies, vol. 2, no. 1, 2010.

Noman, Muhammad. “Rate of Interest and Its Impact on Investment to the Extent of Pakistan .” Sept. 2018.

Obamuyi, Tomola, and Sola Olorunfemi. “Financial Reforms, Interest Rate Behaviour and Economic Growth in Nigeria.” Journal of Applied Finance & Banking, vol. 1, no. 4, 2011, pp. 39–55.

Olweny, Tobias. “The Effect of Monetary Policy on Private Sector Investment in Kenya.” Apr. 2012.

Opreana, Alin. “THE LONG-TERM DETERMINANTS OF INVESTMENT: A DYNAMIC APPROACH FOR THE FUTURE ECONOMIC POLICIES.” Studies in Business and Economics, vol. 5, no. 3, Jan. 2010, pp. 227–237.

Oriavwote, Victor, and Dickson Oyovwi. “Interest Rate and Investment Decision in Nigeria: A Cointegration Approach.” American Journal of Business and Management, Mar. 2014.

Osei-Assibey, Eric, and William Baah-Boateng. “Interest Rate Deregulation and Private Investment: Revisiting the McKinnon-Shaw Hypothesis in Ghana.” The IUP Journal of Applied Economics, vol. 11, no. 2, Apr. 2012, pp. 12–30.

Pelgrin, Florian, and Sebastian Schich. “PANEL COINTEGRATION ANALYSIS OF THE FINANCE-INVESTMENT LINK IN OECD COUNTRIES.” Document De Travail De L’OFCE, Jan. 2002.

Peng, Liang, and Thomas Thibodeau. “Interest Rates and Investment: Evidence from Commercial Real Estate.” Journal of Real Estate Finance and Economics, vol. 60, no. 4, 1 May 2020.

Peter, Audu, and Oluwoyo Temidayo. “Testing the Validity of McKinnon-Shaw Hypothesis: Empirical Evidence from Nigeria.” Asian Journal of Economics, Business and Accounting, vol. 2, no. 2, 2017, pp. 1–24.

Sharpe, Steve, and Gustavo Suarez. “Why Isn’t Investment More Sensitive to Interest Rates: Evidence from Surveys.” 1 Oct. 2015.

Tan, Willie. “GNP, Interest Rate and Construction Investment (Tan 1987).” Construction Management and Economics, vol. 6, no. 3, 28 July 1988, pp. 185–193.

Udah, Enang. “MACROECONOMIC REFORMS, GOVERNMENT SIZE, AND INVESTMENT BEHAVIOR IN NIGERIA: AN EMPIRICAL INVESTIGATION.” European Journal of Social Sciences, vol. 33, no. 2, Aug. 2012.

Waheed, Abdul. “Determinants of Domestic Private Investment: Test of Alternative Hypotheses for Pakistan.” International Journal of Research in Business and Social Science, 2015.

Behavioral Expression of Anger in Preschoolers

 

by Sophia Baxendale

Over the summer and into the fall, I worked with Dr. Locke-Arkerson to learn to code facial, bodily, and vocal expressions of context-appropriate and inappropriate anger in preschoolers. I was awarded a summer research award for 2020 by the Office of Undergraduate Research, funded by the College of Arts and Sciences. The opportunity to work one-on-one with Dr. Locke-Arkerson has provided me with an excellent opportunity to learn more about behavioral data and research with children. Learning how to code and working with Dr. Arkerson has fostered confidence in my ability to learn different aspects of research, and I look forward to continuing to code this sample to learn more about emotional expression in different contexts.

Background

Anger regulation involves the modification of emotional responses to situational changes (Thompson, 1994). Until now, research on anger expression has been limited to identifying children who show more anger than others in appropriate contexts (e.g., delay of gratification, blocked goals; Brooker et al., 2014; Cole et al., 2011; Gilliom et al., 2002). However, Dr. Locke-Arkerson has also measured anger during threatening and positive situations (context-inappropriate “CI anger”) (Locke et al., 2009; Locke et al., 2015). CI anger has been found to increase the risk of externalizing behavior and peer rejection (Locke & Lang, 2016; Locke et al., 2017; Locke et al., 2015). CI anger uniquely predicts these outcomes above and beyond CA anger. It also differs from the way anger has historically been studied, as its correlation with CA anger is only moderate. Dr. Locke-Arkerson’s research utilizes a novel approach to understanding emotional responses by studying them in distinct contexts to determine which children are unable to regulate anger responses in adaptive and acceptable ways.

The collection of behavioral data in research conducted with children has use beyond what is possible to collect from questionnaires. In order to translate collected behavioral data into ratings, a standardized and objective system of observation must be in place. The individuals rating of behaviors (“coding”) must reach a level of agreement, or interrater reliability. Once reliable, the individual coders can rate behaviors independently and these ratings can be used quantitatively for statistical analysis.

The aim of this project was to learn and become reliable in coding facial, bodily, and vocal expressions of context-appropriate and inappropriate anger in preschoolers. This project met the goal of obtaining acceptable interrater reliability for assessing the level of facial, bodily, and vocal expressions of anger during emotion-eliciting episodes in preschool-age children. CI anger in prior studies focused on facial expressions of anger in response to affective pictures, slides, and videos (Locke et al., 2015), making the data coded in this project unique.

Method

Participants

Participants included 69 four-to-five-year-old children from preschools local to UMass Dartmouth.

Procedures

Data used for this project was collected for a larger study exploring emotional and biological measures associated with behavior problems in children. For this project, I was trained on how to code expressions of anger during an anger-eliciting episode. Training on the coding of behavioral data required regular remote meetings and practice sessions with the master coder, Dr. Locke-Arkerson. Prior to training, Dr. Locke-Arkerson developed our training manual for the anger episode. I also filled out the coding sheets with the begin and end times of the episode for each individual subject. Dr. Locke-Arkerson reviewed the episode-specific manual and the System for Identifying Affect Expressions by Holistic Judgments coding system (AFFEX; Izard, Dougherty, & Hembree, 1989) manual with a graduate student, Samantha Clark, and myself. We were then assigned practice cases to code independently, and to discuss together. Samantha and I met several times to compare our codes, and then brought our discrepancies to Dr. Locke-

Arkerson. We reviewed the discrepancies as a group and worked together to determine our sensitivity level to minute expressions of anger. This training process took approximately 127 hours, which was followed by reliability coding, in which I was assigned cases to code independently for all four variables. My codes were compared to the Master Codes created by Dr. Locke-Arkerson and were reviewed to understand where we disagreed. Through the training and reliability process, Dr. Locke-Arkerson and I were able to completely code 24 participants and partially code 17 more for the anger-eliciting episode.

Measures

Context-Appropriate and Inappropriate Anger. Children were videotaped during episodes of the Laboratory Assessment Battery (LabTAB; Goldsmith et al., 1999), which were used to assess appropriate and inappropriate anger in distinct contexts (positive, threatening, frustrating).

Anger episode. The anger episode that was used for reliability training was the I’m Not Sharing game. During the I’m Not Sharing game, the experimenter shares candy with the child and will give themselves more candy than the child. In the end, the experimenter gives the child two pieces of candy.

Behavioral scoring. Coders rated anger facial, bodily, and vocal expressions during the episode using the LabTAB and Affex coding systems.

Pictured: A child playing I’m Not Sharing game.

Results

Dr. Locke-Arkerson assigned me reliability coding for the different variables after an intensive training period. Agreement between my codes and the master codes were determined through a calculation of hits and misses for each variable: presence for vocalizations and bodily resistance, intensity for bodily and facial expressions of anger. The minimum Cohen’s Kappa score to be reliable was .7, or approximately 90% agreement with the Master codes. I was assigned 10 cases for each variable until I was reliable according to the calculated Cohen’s Kappa. I required 10 cases to reach a Cohen’s Kappa of .7 for bodily resistance, or instances where the child attempted to stop the episode. I required 21 cases to reach a Cohen’s Kappa of .7 for vocalizations of anger. I required 20 cases to reach a Cohen’s Kappa of .74 for bodily expressions of anger. I required 20 cases to reach a Cohen’s Kappa of .73 for facial expressions of anger. This means that I will be able to code reliably for the rest of the episode and other episodes moving forward to produce qualitative data that can be used quantitatively for data analysis to address study hypotheses. The coding completed through training and reliability amounted to around 60% of the total data set being at least partially coded for the I’m Not Sharing episode.

Discussion

Over the course of this project, I accomplished my goal of becoming a reliable coder and leading to the near completion of coding all participants for the I’m Not Sharing anger-eliciting episode. With Dr. Locke-Arkerson’s commitment to this project and my training, I was able to learn how to code and become reliable. Given that we were unable to meet in person, my training required modifications to be the most effective for a remote modality. This meant meeting with Dr. Locke-Arkerson several times a week for up to three hours at a time to make sure that we were identifying the same behaviors as anger. The process of learning such a skill was exciting, as it enhanced my working relationship with Dr. Locke-Arkerson and allowed me to see a side of research that I had misunderstood in an academic setting previously. I did not understand the value of behavioral data, as I believed it to be too subjective. After going through such an intense and rigorous training process, I now have a better understanding of how behavioral data can be quantified. It was also exciting to be able to identify slight tells of anger in children, something I was not able to pick up explicitly when I helped administer the episodes prior to the pandemic. While I may have been able to sense frustration, I was not able to identify specific incidences of anger prior to watching the episode as a trained coder with an understanding of slight expressions of anger, especially in the body and face.

I anticipate being able to complete coding of our sample for I’m Not Sharing soon, to be followed by positive and non-social fear episodes. The positive episodes that may be coded include the Surprise! and Popping Bubbles episodes. The non-social fear episode that will be coded is the Scary Mask episode. I am excited to see how our sample expresses anger in contexts that would not be considered appropriate, allowing for more questions to be asked. The completion of coding will allow Dr. Locke-Arkerson and her lab to use the data to enhance the literature on child behavioral and emotional expression, as well as assisting findings in Dr. Locke-Arkerson’s ongoing study. This data will become a resource for myself and future research assistants to use for poster presentations and manuscripts. Specific analyses that this data set can be used for include assessment of the association between behavioral measures and a parent-report measure of CI anger (Locke & Lang, 2016). This data set may also be used to supplement an ongoing manuscript that Dr. Locke-Arkerson, Samantha Clark, and I are working on regarding children who withdraw.

Learning how to code and becoming reliable is a skill has been invaluable to my understanding of hands-on data processing and I am incredibly grateful for the opportunity to build on my research experience. It has been rewarding to gain perspective from both data collection and coding that I have had the opportunity to be a part of in Dr. Locke-Arkerson’s Child Emotion Lab. Thank you to the Office of Undergraduate Research and the College of Arts and Sciences, especially Dean Entin for expressing interest in this project and awarding me the funds to pursue it. The time invested in this project has and will continue to propel progress in our lab and the research program. Thank you to Dr. Locke-Arkerson for committing to this project and taking the time to strengthen my confidence in attention to detail.

References

Arsenio, W., Cooperman, S., & Lover, Anthony. (2000). Affective predictors of preschoolers’ aggression and peer acceptance: Direct and indirect effects. Developmental Psychology. 36. 438-48. doi: 10.1037//0012-1649.36.4.438.

Brooker, R. J., Buss, K. A., Lemery-Chalfant, K., Aksan, N., Davidson, R. J., & Goldsmith, H. H. (2014). Profiles of observed infant anger predict preschool behavior problems: Moderation by life stress. Developmental Psychology, 50(10), 2343-2352.

Cole, P. M., Tan, P. Z., Hall, S. E., Zhang, Y., Crnic, K. A., Blair, C. B., & Li, R. (2011). Developmental changes in anger expression and attention focus: Learning to wait. Developmental Psychology, 47(4), 1078–1089. doi: 10.1037/a0023813.

Cole, P.M., Martin, S.E. and Dennis, T.A. (2004), Emotion regulation as a scientific construct: Methodological challenges and directions for child development research. Child Development, 75: 317-333. doi:10.1111/j.1467-8624.2004.00673.x.

Cole, P. M., Michel, M. K., & Teti, L. O. (1994). The development of emotion regulation and dysregulation: A clinical perspective. Monographs of the Society for Research in Child Development, 59(2-3), 73–100, 250–283. doi: 10.2307/1166139.

Gilliom, M., Shaw, D. S., Beck, J. E., Schonberg, M. A., & Lukon, J. L. (2002). Anger regulation in disadvantaged preschool boys: Strategies, antecedents, and the development of self-control. Developmental Psychology, 38(2), 222–235. doi: 10.1037/0012-1649.38.2.222.

Goldsmith, H. H., Reilly, J., Lemery, K. S., Longley, S., & Prescott, A. (1999). The Laboratory Temperament Assessment Battery (Lab-TAB): Preschool Version 1.0. Technical manual. Madison: University of Wisconsin, Department of Psychology.

Izard, C. E., Dougherty, L. M., & Hembree, E. A. (1989). A system for identifying affect expressions by holistic judgments (Affex) (rev. ed.). Newark: University of Delaware, University Media Services.

Locke, R. L., Davidson, R. J., Kalin, N. H., & Goldsmith, H. H. (2009). Children’s context inappropriate anger and salivary cortisol, Developmental Psychology, 45(5), 1284-1297. doi: 10.1037/a0015975.

Locke, R. L., & Lang, N. J. (2016). Emotion knowledge and attentional differences in preschoolers showing context-inappropriate anger. Perceptual and Motor Skills, 123(1), 46-63. doi: 10.1177/0031512516658473.

Locke, R. L., Lemery-Chalfant, K., Brooker, R., Davidson, R. J., & Goldsmith, H. H. (2017, April). Physiological and behavioral outcomes associated with anger dysregulation. Paper presented at the Biennial Meeting of the Society for Research in Child Development, Austin, TX.

Locke, R. L., Miller, A. L., Seifer, R., & Heinze, J. E. (2015). Context-inappropriate anger, emotion knowledge deficits, and negative social experiences in preschool. Developmental Psychology, 51(10), 1450-1463. doi: 10.1037/a0039528.

Thompson, R.A. (1994). Emotion regulation: A theme in search of definition. Monographs of Society for Research of Child Development. 59. 25-52. doi: 10.1111/j.1540- 5834.1994.tb01276.x.

Anthropogenic Road Noise Effects on Small Mammals

 

by Alyssa Giordano

Over the summer I worked on an experiment that investigated the effects of chronic road (anthropogenic) noise on free-living small mammals. I was a recipient of the 2020 summer research award from the Office of Undergraduate Research (OUR), which helped me greatly to pursue this research under the supervision of Dr. Michael Sheriff. I have always felt a strong connection to ecology and conservation biology, and this project has captivated me and rooted me further to this field, propelling me to pursue this kind of research in my career. This opportunity has helped me in deciding to continue my education to earn a master’s degree. I hope to pursue further projects related to anthropogenic effects, with implications to ecology and wildlife management.

Background:

Predators can alter prey populations through direct killing and consumption, but also through non-consumptive, risk effects (Peacor et al. 2020; Sheriff et al. 2020). Such effects include changes to behavior, physiology, fecundity, and survival. Small mammals for example are known to respond to predation risk by changing their habitat use, activity patterns, and foraging behavior (Lima 1998).

Chronic traffic (anthropogenic) noise has shown dramatic increases over the last few decades with the expansion of resources and transportation (Shannon et al. 2016). For example, the United States’ population increased by about one third and traffic nearly tripled between 1970 and 2007 (Barber et. al. 2010). Road noise can affect prey’s ability to perceive predation risk cues and, thus, alter their risk responses which can be critical to survival (Francis and Barber 2013; Shannon et al. 2016). Road noise is hypothesized to alter prey responses to predation risk in three distinct ways, i) mask auditory cues of predation resulting in greater antipredator responses, as prey have more difficulty detecting their predators and find the area riskier (Barber et. al. 2010), ii) mask and distract prey due to the excess of auditory signals resulting in a reduction in antipredator responses, as prey do not perceive the area as risky given they cannot detect their predators (Blumstein 2014, Chan et. al. 2010), or iii) be perceived as a threat itself, with increased antipredator responses above that with which prey respond to risk alone (Shannon et. al. 2016, Tyack et. al. 2011). Within this research I tested each of these hypotheses by examining the food intake and foraging behavior of free-living deer mice concurrently exposed to both road noise and predation risk. I predicted that if road noise resulted in an increase in antipredator behavior, small mammals would find the area to be riskier and forage less. If road noise resulted in a decrease in antipredator behavior, small mammals would be distracted by the excess sound and forage more. If road noise resulted in an increase in antipredator behavior when not concurrently treated with predation risk, small mammals would find the road noise itself risky and forage less.

Methods:

To conduct my experiment, I used audio playbacks to manipulate the acoustic environment of free-living deer mice and other small mammals. My audio treatments consisted of non-predatory control, avian predators, road noise, and road noise + avian predators. Each treatment was played for three days, with a two day buffer between treatments to avoid contamination of effects from one treatment to the next. To measure foraging activity, I set up giving-up density (GUD) trays. GUDs are based on the marginal value theorem (Charnov 1976), such that the return of a foraging patch diminishes and the cost increases the more an animal forages, ultimately the animals ‘give up’ and move on (Brown et. al. 1999). The point at which an animal gives up has been shown to be impacted by predation risk (Brown 1988). Plastic foraging trays were filled with 2.5g of millet seed and 2 cups of sand (figure 1). Six trays were placed into the field for 2 consecutive 24 periods beginning at 0700h on days 2 and 3 of a treatment. Motion detecting cameras (purchased as part of the OUR grant) were deployed at 3 of the GUDs to measure foraging behavior. Each treatment was replicated three times and the total duration of the experiment occurred from July 1st to September 13th, 2020.

I analyzed the foraging data using 2-way ANOVAs and a tukey test with treatment and night as fixed effects (RStudio Desktop 1.3.1093).

Results:

I found that there was a significant effect of treatment and of night (Table 1) on the amount of food eaten by small mammals (Fig. 2). When exposed to predation risk small mammals decreased the amount of food eaten, when exposed to predation risk and road noise small mammals ate a similar amount, and when exposed to road noise alone small mammals slightly increased the amount that they ate compared to the control treatment.

I also found that small mammals ate more on night 2 compared with night 1 (Fig. 2).

Discussion:

This data supports the hypothesis that road noise will cause prey to reduce their antipredator responses to predation risk. (Chan et al. 2010) found something similar, that during noise exposure Caribbean hermit crabs allowed simulated predators to get closer, suggesting they had an impaired ability to respond. This may occur because prey have a harder time perceiving risk cues when also exposed to noise, creating a clouded soundscape. This will have consequences to prey by exposing them to predators more, as the prey will not engage in antipredator responses as much or as fast.

I still need to analyze the behavioral data from the motion detecting cameras with footage taken over 2.5 months. The video footage will be analyzed for the number of visits to the GUDs, the time spent during each visit, and the time spent being vigilant. This portion of the project will delve deeper into the specific effects on animal risk response when exposed to chronic road noise and predation. I have determined the general effect of chronic road noise, but it will be very interesting to see their true behavior, and if it shows anything that the preliminary foraging data cannot.

A special thanks to the Office of Undergraduate research and the College of Arts and Sciences for providing me with funding for my project. Though the analysis is not yet fully complete, the funding has given me an invaluable opportunity to explore an important wildlife conservation topic in depth. Finally, I’d like to acknowledge and thank Dr. Michael Sheriff for the support and guidance throughout my project.

References:

Barber, J. R., Crooks, K. R., & Fristrup, K. M. (2010). The costs of chronic noise exposure for terrestrial organisms. Trends in ecology & evolution, 25(3), 180-189.

Blumstein, D. T. (2014). Attention, habituation, and antipredator behaviour: implications for urban birds. Avian urban ecology, 41-53.

Brown, J.S., Laundre, J.W., & Gurung, M. 1999. The ecology of fear: optimal foraging, game theory, and trophic interactions. Journal of Mammalogy 80: 385-399.

Brown, J.S. 1988. Patch use as an indicator of habitat preference, predation risk, and competition. Behavioral Ecology and Sociobiology 22: 37-47.

Chan, A. A. Y. H., Giraldo-Perez, P., Smith, S., & Blumstein, D. T. (2010). Anthropogenic noise affects risk assessment and attention: the distracted prey hypothesis. Biology letters, 6(4), 458-461.

Charnov, E. L. (1976). Optimal foraging, the marginal value theorem.

Francis, C. D., & Barber, J. R. (2013). A framework for understanding noise impacts on wildlife: an urgent conservation priority. Frontiers in Ecology and the Environment,11(6), 305-313.

Lima, S.L. 1998. Nonlethal effects in the ecology of predator-prey interactions. What are the ecological effects of anti-predator decision-making? BioScience 48: 25-34.

Peacor, S. D., Barton, B. T., Kimbro, D. L., Sih, A., & Sheriff, M. J. (2020). A framework and standardized terminology to facilitate the study of predation‐risk effects. Ecology, e03152.

Shannon, G., Crooks, K. R., Wittemyer, G., Fristrup, K. M., & Angeloni, L. M. (2016). Road noise causes earlier predator detection and flight response in a free-ranging mammal. Behavioral Ecology, 27(5), 1370-1375.

Sheriff, M. J., Peacor, S. D., Hawlena, D., & Thaker, M. (2020). Non‐consumptive predator effects on prey population size: A dearth of evidence. Journal of Animal Ecology.

Tyack, P. L., Zimmer, W. M., Moretti, D., Southall, B. L., Claridge, D. E., Durban, J. W., … & McCarthy, E. (2011). Beaked whales respond to simulated and actual navy sonar. PloS one, 6(3).

Neuronal Protection Effects of Blueberries through Inhibition of Key Enzymes involved in the Neurogenerative Diseases

 

By Chelsea Spitz

This past summer, I was awarded a grant from the Office of Undergraduate Research (OUR) to conduct research in the Chemistry Department under the guidance of Dr. Shuowei Cai. The purpose of this research is to study the neuronal protection effects of blueberries against neurodegenerative diseases and develop the extraction method for blueberries. I also planned on identifying active compounds in blueberries and develop a LC-MS based method to fingerprint the blueberry extract from other extraction methods. Unfortunately, due to the ongoing pandemic, I could not get access to LC-MS system, therefore, my research is mainly focusing on development of extraction method and study the neuronal protection effects of blueberries through inhibition of key enzymes involved in the neurodegenerative disease, including the inhibition kinetics, to understand the mechanism of the neuronal protections of blueberries.

Alzheimer’s disease (AD), the most common form of dementia, is a neurodegenerative disease affecting the structural integrity of the brain. Individuals suffering from AD undergo both steady memory loss and significant cognitive decline as a result of the progressive neuronal damages leading to the death of neurons in brain. AD accounts for 60 to 80 percent of dementia cases, while vascular dementia, due to microscopic bleeding and blood vessel blockage in the brain, is the second most common cause of dementia (Alzheimer’s Association, Alz.org). It is estimated that one in 10 Americans over 65 years of age is currently living with symptomatic AD, and worldwide, 50 million people live with symptomatic AD. AD puts a huge burden on both caregivers and the health system. In 2018, the direct costs to American society for caring of those with AD totaled $277 billion, and it is projected to over $1 trillion by 2050 (Alz.org). Yet, there is no cure for AD and only a handful of drugs have been approved by FDA to manage the symptoms that includes cholinesterase inhibitors and N-methyl-D-aspartate receptor (NMDA) receptor antagonist (i.e. memantine). There is even no treatment that can slow down the progresses of the disease. This may be partly due to the lack of knowledge of the mechanism of AD.

Based on the differences seen in AD’s brains, several potential causes have been hypothesized: deficits in the cholinergic transmission; beta-amyloid plagues (Aβ); tau tangles; oxidative damage and mitochondrial dysfunction; neuronal inflammation; synapse loss; vascular changes; endosomal abnormalities, among others. Several of those hypotheses have been found to be connected to each other, and collectively, they lead to the neuron death. For example, acetylcholinesterase (AChE) is a critical enzyme to regulate the level of the neurotransmitter, acetylcholine. Both Aβ and abnormally hyperphosphorylated tau (p-tau) can increase AChE expression. The increased AChE further influences PS1 and tau-protein kinase GSK-3β. GSk-3β induces hyperphosphorylated tau (P-tau), while PS1 affects the APP processing and Aβ production. Inhibition of AChE not only can rescue the deficit of cholinergic transmissions, but also can potentially reduce Aβ and P-tau.

Tyrosinase is a key enzyme in the biosynthesis of melanin. It catalyzes two reactions: the hydroxylation of tyrosine to L-DOPA and the subsequent oxidation of L-DOPA to dopaquinone. This enzyme may also oxidize dopamine to form melanin pigments through the formation of dopamine quinone, a reaction results in the formation of highly reactive oxygen or nitrogen species (ROS) capable of inducing neuronal cell death. Oxidation stress links to both inflammation and endosomal abnormalities, which hold key for neurodegenerative diseases, including AD.

Our research, therefore, is focusing on examination of neuronal protection effects of blueberries through their inhibition of AChE and tyrosinase. Most phytochemicals are extracted from plants using methanol-based solvent. Residual methanol is highly toxic for human consumption. To explore a safer solvent for extraction of phytochemicals from blueberries, we investigate using ethanol as the extraction solvent. Over the course of the summer, we extract the polar components from blueberries using three different types of solvent system: ethanol alone; methanol alone, and methanol/acetone/water/formic acid (40/40/19/1). Our lab has been used the methanol/acetone/water/formic acid extraction system for extraction of blueberries, and our aim is to compare the biological activities of the blueberry extract using ethanol with those with methanol-based extraction. The activity of AChE was examined using the Ellman method, the tyrosinase activity is determined using L-DOPA as the substrate and monitored the enzymatic product at 490 nm. Both assays were carried out using 96-well microplate, and each sample was run triplicate. To further study the mechanism of inhibition on tyrosinase, we carried out the inhibition kinetics with the blueberry extract from ethanol. The kinetics of tyrosinase was measured every 20 s in 3 min to obtain the initial velocity rate. The contraction of blueberry extract used for inhibition kinetic measurement was 0.25 mg/ml and 0.15 mg/ml.

As shown in Figures 1,2 and 3, the extracts from all three solvent systems showed strong inhibition on AChE and tyrosinase. The extract from methanol/acetone/water/formic acid solvent showed the strongest inhibition on both enzymes. The USDA solvent mixture inhibited the enzymes approximately double that of the other two solvents while the USDA MDS and USDA EDS extracts inhibited relatively close to one another. The methanol-based extract however was still slightly stronger than the ethanol-based extract but overall, they were relatively the same. The IC₅₀s show that the USDA Solv mixture is a much stronger inhibitor than the other two solvents because it requires a lower concentration of the extract to inhibit 50% of the enzyme.

Table 1: IC₅₀ Values For Tyrosinase and AChE

	Solvent Mix	MDS	EDS
Tyrosinase	0.17 mg/mL	1.66 mg/mL	1.14 mg/mL
AChE	0.72 mg/mL	3.48 mg/mL	6.89 mg/mL

 

While blueberry extract from ethanol showed less potent as that from methanol-based solvent, it still showed strong inhibition on two key enzymes that related to neurodegenerative diseases. Here, we demonstrated that ethanol can be a safe alternative to extract the bioactive phytochemicals. We further examined the inhibition kinetics of blueberry extract from ethanol to understand the inhibition mechanism on tyrosinase. As shown in Figure 4, the blueberry extract inhibits tyrosinase in a non-competitive manner (mixed mode inhibition). The inhibition constant Ki and Ki’ are 0.056 mg/ml and 0.82 mg/ml, respectively (Table 2). This suggested that the compounds in blueberry both directly interact with the active site of tyrosinase the substrate-enzyme complex.

Table 2: Inhibition Constant of Blueberry Extract

	KI (mg/ml)	KI’ (mg/ml)
0.25 mg/ml	0.060	0.079
0.15 mg/ml	0.051	0.085
Average	0.056 (0.006)*	0.082 (0.005)

*: the figure in parenthesis is the standard deviation from the two concentrations of blueberry extract

 

Future Plan:

The plan to continue this project consists of continuing to perform more kinetics assays using USDA MDS extract and to try and see if the data is reproducible. We also plan to work on modeling and studying the structure of the enzyme more closely as well as the compounds found in the extracts from the LC-MS data. Once we gain the access to LC-MS instrument, we will identify the compounds in the ethanol extract, and compare with those from methanol-based extracts. We will further be using NMR to confirm the compounds identified from LC-MS.

The research grant I received from the Office of Undergraduate Research allowed me to learn new skills in the lab such as the enzyme kinetics assays as well as help me find my footing for my research goals. I would like to thank my research advisor Dr. Shuowei Cai for guiding me along the way. As well as the Dean to the College of Arts and Sciences, Dean Entin, and the Office of Undergraduate Research for funding my research this summer.

Harbor seal vibrissa morphology inspires comprehensive computational simulations and experiments studying footprints left behind moving hydrodynamic objects for online database

 

By Sarah Dulac

 

Portrait of Sarah Dulac

 

My OUR project was rewarded with a grant this summer 2020, from the Office of Undergraduate Research (OUR) to conduct research via Remote desktop due to the recent pandemic under supervision of Dr. Banafsheh Seyed-Aghazadeh. My OUR research project was entitled ‘Harbor seal vibrissa morphology inspires comprehensive computational simulations and experiments studying footprints left behind moving hydrodynamic objects for online database’.

The objective of the research is to experimentally and computationally quantify the left behind footprint from different hydrodynamic objects at varying velocities, to help better understand how the harbor seal’s whisker reacts to these stimuli like a sensor. Using both the experimental and computational data collected, another goal was to create an online platform that would provide valuable data to numerous other research projects and educational purposes to anyone looking to learn. This was a fully remote project and some of my tasks are yet to be completed due to my limited access to the needed campus facilities. This project depended heavily on self-reliance, which allowed me to gain new skills including literature review to Computational Fluid Dynamics.

In order to understand how the harbor seal whisker detects the hydrodynamic pattern from a fast-starting fish, investigating the flow response of stationary cylinders with different cross-sections is principal. The information about possible flow characteristics of a left behind footprint is not available in literature, which is a crucial step to better understand the mechanism behind this “sensing”. The first step towards better understanding of these footprints left behind, is to investigate the flow response of the cylinders with different cross-sectional geometries using Computational Fluid Dynamics (CFD). Flow past a circular cylinder is the foundational start to a path for studying more complex shaped bodies and their footprint’s produced. The case of flow past a circular cylinder has a great deal of attention in research due to its simplicity of flow results and it is a very common phenomenon in engineering applications.

Starting with building and running computational simulations on COMSOL Multiphysics simulation software was a challenge considering my first step was to learn how to use the fluid flow module before successfully modeling my application. I found numerous resources online that allowed to me build my knowledge on how to correctly model my simulation. In order to verify my simulation is running correctly, data including RMS lift C_L, mean drag coefficient C_D, and Strouhal number St were gathered and analyzed to verify quantitatively.

Where L, D, ρ, U, A, ƒ, and d are lift force, drag force, density, inflow velocity, area, frequency of vortex shedding, and diameter, respectively. A computational model was constructed using the laminar flow module under fluid flow on COMSOL Multiphysics. The governing equations are the incompressible Navier-Stokes equation(1) which represents the conservation of momentum and the continuity equation(2) which represents the conservation of mass.

The domain was computed using laminar flow model in COMSOL Multiphysics because of the flow being in the range of Reynolds number equal to 100, characterizing the flow as laminar. Reynolds number is a dimensionless quantity that predicts whether the flow of a fluid on a surface is laminar or turbulent.

Where, ρ, U, d and μ are density, inflow velocity, diameter and dynamic viscosity, respectively. The domain and the constructed mesh is shown in Figure 1. Experiment[4] and simulations[1,2,3] of flow past a circular cylinder were used to compare my results. Simulations[5,6,7] of flow past a square cylinder were used to compare my results. Experiment[9] and simulation[8] of flow past a triangular cylinder were used to compare my results. Results of mean drag coefficient for circular, square and triangular cylinders are all plotted in Figure 2. Results of RMS lift for circular and square cylinders are all plotted in Figure 3. Results for the triangular cylinder were not plotted in this figure due to the reference literature using mean lift coefficient alternatively, which resulted in a value of zero because of the symmetry of the geometry. Strouhal numbers were also plotted for all cylinders as shown in Figure 4. From comparison of my resulting values and what is found in literature, I was able to confirm my simulation was running correctly.

Along with gathering and analyzing RMS lift, mean drag coefficient and Strouhal number qualitative methods of visualizations were used as well. The post processing tools offered on COMSOL Multiphysics have allowed me to plot the vorticity of the flow past the cylinders as shown in Figure 5. A tutorial of how to set up the CFD simulations and post process important data will be created to serve as an educational tool for MNE 332 – Fluid Mechanics and for an online library with open access.

There was one other task I was able to complete due to the needs of the task being accessed remotely. This task consisted of designing and constructing a three dimensional (3D) model of the whisker geometry using solid modeling computer-aided design program (SOLIDWORKS), which was available through UMassD remote access. The final design of the whisker has been completely modeled using SOLIDWORKS (Figure 6) and it ready to be 3D printed. For my future with this project, it consists of experimentally investigating the flow response of stationary cylinders with different cross-sections including the whisker geometry designed. The data gathered from all CFD simulations and experiments will be organized in a way, so it is easy to navigate and access via the online library.

Sources

[1] Park, J., Kwon, K., & Choi, H. (1998). Numerical solutions of flow past a circular cylinder at Reynolds numbers up to 160. KSME International Journal, 12(6), 1200-1205. doi:10.1007/bf02942594

[2] Singha, S., & Sinhamahapatra, K. (2010). Flow past a circular cylinder between parallel walls at low Reynolds numbers. Ocean Engineering, 37(8-9), 757-769. doi:10.1016/j.oceaneng.2010.02.012

[3] Tezduyar, T., Mittal, S., Ray, S., & Shih, R. (1992). Incompressible flow computations with stabilized bilinear and linear equal-order-interpolation velocity-pressure elements. Computer Methods in Applied Mechanics and Engineering, 95(2), 221-242. doi:10.1016/0045-7825(92)90141-6

[4] Tritton, D. J. (1959). Experiments on the flow past a circular cylinder at low Reynolds numbers. Journal of Fluid Mechanics, 6(4), 547-567. doi:10.1017/s0022112059000829

[5] Sohankar A, Norberg C, Davidson L. Low-Reynolds-number flow around a square cylinder at incidence: study of blockage, onset of vortex shedding and outlet boundary condition. International Journal for Numerical Methods in Fluids 1998; 26:39–56.

[6] Sahu AK, Chhabra RP, Eswaran V. Two-dimensional unsteady laminar flow of a power law fluid across a square cylinder. Journal of Non-Newtonian Fluid Mechanics 2009; 160:157–167.

[7] Singh AP, De AK, Carpenter VK, Eswaran V, Muralidhar K. Flow past a transversely oscillating square cylinder in free stream at low Reynolds numbers. International Journal for Numerical Methods in Fluids 2009; 61:658–682.

[8] Bao, Y., Zhou, D., & Zhao, Y. (2009). A two-step Taylor-characteristic-based Galerkin method for incompressible flows and its application to flow over triangular cylinder with different incidence angles. International Journal for Numerical Methods in Fluids. doi:10.1002/fld.2054

[9] Seyed-Aghazadeh, B., Carlson, D. W., & Modarres-Sadeghi, Y. (2017). Vortex-induced vibration and galloping of prisms with triangular cross-sections. Journal of Fluid Mechanics, 817, 590-618. doi:10.1017/jfm.2017.119

 

Southeastern New England Marine Science and Technology Workforce Gap Analysis

 

By Salvador Balkus

 

 

Portrait of Salvador Balkus

 

Recent research by the UMass Dartmouth Public Policy Center has demonstrated that Southeastern Massachusetts, also known as SENE, has been largely excluded from the thriving Greater Boston innovation economy. Meanwhile, the traditional maritime-related economic drivers of the region have encountered many economic challenges in recent years. As a result, UMass Dartmouth has risen to the task of developing a Southcoast Blue Economy Corridor in order to strengthen the region’s maritime sector. New technologies in blue economy-related industries will be imperative to the success and revitalization of the maritime sector in the region, and as such, an initial goal of this project is to conduct a comprehensive assessment of the Marine Science and Technology (MST) sector in and around the region. This portion of the project has been taken up by the Public Policy Center.

Working at the Public Policy Center with support from the Office of Undergraduate Research, I spent this summer completing an important component of this research project: conducting a gap analysis for the SENE Marine Science and Technology regional workforce. My job was to analyze the occupations most relevant to the sector, create a profile of the “high priority occupations” which are critical to the operations of MST firms and pose a great challenge to attract and hire, and determine the gaps between the educational programs currently offered in SENE and the education necessary for MST workers. To analyze the Marine Science and Technology sector workforce, I relied on an inventory of Marine Science and Technology firms, survey data, and key informant interview notes from the Public Policy Center, as well as economic data from Emsi, an economic modeling software. This workforce assessment will help the university inform policy decisions and succeed in their endeavor of successfully creating a Blue Economy Corridor.

NAICS, which stands for North American Industrial Classification System, is a standardized, code-based classification for industries. My first task was to determine an appropriate NAICS-based definition of the Marine Science and Technology sector that would include all of the private MST companies in the region. To do this, I obtained two different lists of NAICS codes. The first was compiled by the UMass Donahue Institute, while the second came from the NAICS codes of all businesses contained in the PPC inventory of Marine Science and Technology firms. I performed an analysis of the MST sector using both of these codes to obtain as accurate a picture of the sector as possible.

Using the Emsi Staffing Patterns tool, I compiled a list of all of the occupations employed by businesses in the MST sector. However, even if an occupation is employed within one of the industries that makes up the sector, it is not necessarily an important occupation to the sector as a whole. In order to identify the high priority occupations – those which are both critical to the operation of MST firms and also pose a challenge to attract and hire – I used data science techniques to rank the importance of each occupation to the sector. A high-priority occupation is defined by one or more of the following metrics:

• High number of jobs in Marine Science and Technology industries, indicating that many workers of this occupation are needed in the sector.
• High ratio of Marine Science and Technology jobs to total jobs for the occupation in the region, indicating that this job is relatively unique to the sector.
• Large difference between growth in Marine Science and Technology industries and growth overall, indicating that the occupation’s employment is growing faster than normal.
• Low Location Quotient (LQ), indicating that the region has a low concentration of workers in this occupation compared to the rest of the country.

For presentation, I decided to select the top 25 occupations as the high-priority occupations; these are shown below. These occupations fell into three neat categories: engineering, production, and natural science, each of which requires different types of education and preparation. Further research was also performed using Emsi to get a sense of the tasks that these occupations perform, as well as analyze commuting patterns within these occupations.

     Figure 1: Priority Occupations for SENE Marine Science and Technology sector, 2018

Next, I analyzed PPC survey data. This data allowed me to examine the importance of various worker qualifications to MST employers, see what type of degree one would need to work in Marine Science and Technology, and explore the relationship between MST businesses and universities within Southeastern New England. This informed me which educational areas I should focus on researching. I also used text-entry responses from the survey, which asked which skills and types of worker were most difficult to find for MST firms, as well as notes from key informant interviews conducted by the Public Policy Center to inform further research.

From the survey, I found that an education in engineering is the most important qualification for workers in MST, along with related skills such as lab experience, qualification in advanced manufacturing or precision machining, and quality control experience. Companies typically require 4-year or graduate degrees. The biggest workforce-related challenge is finding workers with the right technical skills for the job. Despite the importance of engineering education, less than half (40%) of respondents considered universities in the region a source of skilled labor for the business. Free response survey questions and interview data also indicated that the most difficult workers to find were software engineers and skilled manufacturing workers.

An example of the survey data was shown below.

     Figure 2: Sample chart created from the PPC survey data

After going over the survey and interviews, I researched available programs in engineering and production, the two groups of occupations found to be of the greatest priority to the sector. These were compiled into list graphics for the final report. A chart showing the available engineering degrees is shown in Figure 3. The region offers a wide selection of traditional engineering degrees, including computer science, the field with the most bachelor’s degree programs. The engineering field with the lowest number of degree programs is software engineering. Furthermore, the region offers five programs that teach advanced manufacturing, machining, or welding skills, though the Massachusetts side of SENE includes an abundance of high-school programs offered through vocational schools across the region.

     Figure 3: Engineering programs available in the SENE region

One of the scarcest and most highly sought-after occupations in MST is that of the software engineer – specifically, the occupation of “systems software developer” as defined by the Bureau of Labor Statistics. Despite the abundance of computer science degrees, my research found that most do not teach the hardware and software engineering skills necessary to work as a software engineer in the sector. These skills are mostly found in computer engineering and software engineering programs, two of the least abundant engineering programs in SENE. An adequate education for a systems software developer to fix this educational gap would include aspects of both computer engineering and software engineering.

In addition to software engineering, the other occupation type facing a significant workforce gap is that of the skilled production worker. In other words, the region needs to train more welders and machinists. As well as receiving an education in the necessary skills for the job, these workers also must go through an apprenticeship in order to be ply their trade. My research found that apprenticeships in the region are scarce, and access to those that do exist is controlled by unions, which may be hard to enter if one did not have the foresight in eighth grade to attend a vocational school. Furthermore, interest in production jobs among middle and high school students has been waning over the past eight years. If unions developed a new strategy to attract workers, or if new businesses arose that allowed welders and machinists to gain experience as apprentices and go on to work at an MST business, this educational gap in production may be eliminated.

—–

Through this summer research project, I was able to practice my data analysis skills while also improving my writing ability and collaborating with other researchers. In the future, when I go on to work in the data science field, these skills will make me a more well-rounded employee and increase my employment prospects and work quality, for which I am very glad. Likewise, I am also prepared to do further research during my undergraduate career. I would like to thank the Public Policy Center for giving me the opportunity to work on this project, and specifically research associate Michael McCarthy for providing valuable report-writing advice. Thank you, Office of Undergraduate Research, for allowing me to work on such a valuable project!