Applying Mathematical Models to Study the Role of the Immune System in Chronic Myelogenous Leukemia

Although tyrosine kinase inhibitors (TKIs) such as imatinib have transformed chronic myelogenous leukemia (CML) into a chronic condition, these therapies are not curative in the majority of cases. Most patients must continue TKI therapy indefinitely, a requirement that is both expensive and that compromises a patient's quality of life. While TKIs are known to reduce leukemic cells' proliferative capacity and to induce apoptosis, their effects on leukemic stem cells, the immune system, and the microenvironment are not fully understood. A more complete understanding of their global therapeutic effects would help us to identify any limitations of TKI monotherapy and to address these issues through novel combination therapies. Mathematical models are a complementary tool to experimental and clinical data that can provide valuable insights into the underlying mechanisms of TKI therapy. Previous modeling efforts have focused on CML patients who show biphasic and triphasic exponential declines in BCR-ABL ratio during therapy. However, our patient data indicates that many patients treated with TKIs show fluctuations in BCR-ABL ratio yet are able to achieve durable remissions. To investigate these fluctuations, we construct a mathematical model that integrates CML with a patient's autologous immune response to the disease. In our model, we define an immune window, which is an intermediate range of leukemic concentrations that lead to an effective immune response against CML. While small leukemic concentrations provide insufficient stimulus, large leukemic concentrations actively suppress a patient's immune system, thus limiting it's ability to respond. Our patient data and modeling results suggest that at diagnosis, a patient's high leukemic concentration is able to suppress their immune system. TKI therapy drives the leukemic population into the immune window, allowing the patient's immune cells to expand and eventually mount an efficient response against the residual CML. This response drives the leukemic population below the immune window, causing the immune population to contract and allowing the leukemia to partially recover. The leukemia eventually reenters the immune window, thus stimulating a sequence of weaker immune responses as the two populations approach equilibrium. We hypothesize that a patient's autologous immune response to CML may explain the fluctuations in BCR-ABL ratio that are regularly seen during TKI therapy. These fluctuations may serve as a signature of a patient's individual immune response to CML. By applying our modeling framework to patient data, we are able to construct an immune profile that can then be used to propose patient-specific combination therapies aimed at further reducing a patient's leukemic burden. Our characterization of a patient's anti-leukemia immune response may be especially valuable in the study of drug resistance, treatment cessation, and combination therapy.

Studying the Causes and Consequences of Internal Labor Migration Using Survey and Administrative Data Sources

This dissertation comprises three chapters. The first chapter motivates the use of a novel data set combining survey and administrative sources for the study of internal labor migration. By following a sample of individuals from the American Community Survey (ACS) across their employment outcomes over time according to the Longitudinal Employer-Household Dynamics (LEHD) database, I construct a measure of geographic labor mobility that allows me to exploit information about individuals prior to their move. This enables me to explore aspects of the migration decision, such as homeownership and employment status, in ways that have not previously been possible. In the second chapter, I use this data set to test the theory that falling home prices affect a worker’s propensity to take a job in a different metropolitan area from where he is currently located. Employing a within-CBSA and time estimation that compares homeowners to renters in their propensities to relocate for jobs, I find that homeowners who have experienced declines in the nominal value of their homes are approximately 12% less likely than average to take a new job in a location outside of the metropolitan area where they currently reside. This evidence is consistent with the hypothesis that housing lock-in has contributed to the decline in labor mobility of homeowners during the recent housing bust. The third chapter focuses on a sample of unemployed workers in the same data set, in order to compare the unemployment durations of those who find subsequent employment by relocating to a new metropolitan area, versus those who find employment in their original location. Using an instrumental variables strategy to address the endogeneity of the migration decision, I find that out-migrating for a new job significantly reduces the time to re-employment. These results stand in contrast to OLS estimates, which suggest that those who move have longer unemployment durations. This implies that those who migrate for jobs in the data may be particularly disadvantaged in their ability to find employment, and thus have strong short-term incentives to relocate.

Community- and population-level effects of chytridiomycosis in a Neotropical amphibian community

As the number of fungal pathogen outbreaks become more frequent worldwide across taxa, so have the number of species extirpations and communities persisting with the pathogen. This phenomenon raises questions, such as: “what leads to host extinction during an outbreak?” and “how are hosts persisting once the pathogen establishes?.” But the data on host populations and communities across life stages before and after pathogen arrival rarely exist to answer these questions. Over the past three to four decades, the amphibian-killing fungus Batrachochytrim dendrobatidis (Bd) spread in a wave-like manner across Central America, leading to rapid species extirpations and population declines. I collected data on tadpole and adult amphibians in El Copé, Panama before, during, and after the Bd outbreak to answer these questions. I used Bayesian statistical approaches to account for imperfect host and pathogen detection of marked and unmarked individuals. In the tadpole community, within 11 months of Bds arrival, density and occupancy rapidly declined. Species losses were phylogenetically correlated, with glass frogs disappearing first, and tree frogs and poison-dart frogs remaining. I found that tadpole communities resembled one another more strongly after the outbreak than they did before Bd invasion. I found no tadpoles within 22 months of the outbreak and limited signs of recovery within 10 years. In contrast, at the same site, for a population of male glass frogs, Espadarana prosopleon, I found that 10 years post-outbreak, the population was consistently half its historic abundance, and that the lack of recruits to the population explained why the population had not rebounded, rather than high pathogen-induced mortality. And finally, examining the entire amphibian community, I found high pathogen prevalence, low infection intensities, and high survival rates of uninfected and infected hosts. Bd transmission risk, i.e., the probability a susceptible host becomes infected, did not relate to host density, pathogen prevalence, or infection intensity– Bd transmission risk was uniform across the study area. My results are especially relevant to conservation biologists aiming to predict the future impacts of Bd outbreaks, those trying to manage persisting populations, and those interested in reintroducing species back into wild amphibian communities.

Essays on Uncertainty, Imperfect Information, and Investment Dynamics

Understanding how imperfect information affects firms' investment decision helps answer important questions in economics, such as how we may better measure economic uncertainty; how firms' forecasts would affect their decision-making when their beliefs are not backed by economic fundamentals; and how important are the business cycle impacts of changes in firms' productivity uncertainty in an environment of incomplete information. This dissertation provides a synthetic answer to all these questions, both empirically and theoretically. The first chapter, provides empirical evidence to demonstrate that survey-based forecast dispersion identifies a distinctive type of second moment shocks different from the canonical volatility shocks to productivity, i.e. uncertainty shocks. Such forecast disagreement disturbances can affect the distribution of firm-level beliefs regardless of whether or not belief changes are backed by changes in economic fundamentals. At the aggregate level, innovations that increase the dispersion of firms' forecasts lead to persistent declines in aggregate investment and output, which are followed by a slow recovery. On the contrary, the larger dispersion of future firm-specific productivity innovations, the standard way to measure economic uncertainty, delivers the ``wait and see" effect, such that aggregate investment experiences a sharp decline, followed by a quick rebound, and then overshoots. At the firm level, data uncovers that more productive firms increase investments given rises in productivity dispersion for the future, whereas investments drop when firms disagree more about the well-being of their future business conditions. These findings challenge the view that the dispersion of the firms' heterogeneous beliefs captures the concept of economic uncertainty, defined by a model of uncertainty shocks. The second chapter presents a general equilibrium model of heterogeneous firms subject to the real productivity uncertainty shocks and informational disagreement shocks. As firms cannot perfectly disentangle aggregate from idiosyncratic productivity because of imperfect information, information quality thus drives the wedge of difference between the unobserved productivity fundamentals, and the firms' beliefs about how productive they are. Distribution of the firms' beliefs is no longer perfectly aligned with the distribution of firm-level productivity across firms. This model not only explains why, at the macro and micro level, disagreement shocks are different from uncertainty shocks, as documented in Chapter 1, but helps reconcile a key challenge faced by the standard framework to study economic uncertainty: a trade-off between sizable business cycle effects due to changes in uncertainty, and the right amount of pro-cyclicality of firm-level investment rate dispersion, as measured by its correlation with the output cycles.

ESSAYS ON DISPLACED WORKERS AND RESIDENTIAL MIGRATION

In this dissertation, I explore how workers’ human capital, local industry composition, and business cycles affect employment outcomes and residential migration for job losers and other workers. I first examine whether the poor employment outcomes of job losers are due to a lack of jobs that require their human capital within their local labor market. I answer this question by analyzing the extent to which the industry composition in the job loser’s local labor market affects employment outcomes when job loss occurs during expansions and during recessions. I find that if job losers reside in an area with a high employment concentration of their original industry of employment, they are 2.1-2.8 percent more likely to be re-employed at another job if job loss occurs during an expansion; I find an insignificant relationship in most specifications when job loss occurs during a recession, and in some specifications I even find a negative relationship between industry concentration and employment. I conclude that the industry composition within an area matters for job losers, since firms are more willing to hire workers from within their own industry, as these workers have more relevant accumulated human capital. However, firms are less likely to hire during a recession, making job losers’ human capital less important for job finding. Next, Erika McEntarfer, Henry Hyatt, and I examine whether the business cycle affects earnings changes for job losers, and the factors that explain these differences across time. We find that job losers who lost their job during the Great Recession have earnings changes that are 10 percent more negative relative to other job losers from other periods. This result is driven primarily by longer nonemployment lengths and worse subsequent job matches. Finally, Erika McEntarfer, Henry Hyatt, Alexandria Zhang, and I explore the extent to which residential migration is driven by job opportunities. We use four databases and find that changes in job moves explain some of the changes in residential migration, but the relationship is not as strong as previously documented. We find that migration patterns differ across databases, with some databases documenting steeper declines and more cyclicality.

Essays on Macroeconomic Trends and Cycles

This dissertation describes two studies on macroeconomic trends and cycles. The first chapter studies the impact of Information Technology (IT) on the U.S. labor market. Over the past 30 years, employment and income shares of routine-intensive occupations have declined significantly relative to nonroutine occupations, and the overall U.S. labor income share has declined relative to capital. Furthermore, the decline of routine employment has been largely concentrated during recessions and ensuing recoveries. I build a model of unbalanced growth to assess the role of computerization and IT in driving these labor market trends and cycles. I augment a neoclassical growth model with exogenous IT progress as a form of Routine-Biased Technological Change (RBTC). I show analytically that RBTC causes the overall labor income share to follow a U-shaped time path, as the monotonic decline of routine labor share is increasingly offset by the monotonic rise of nonroutine labor share and the elasticity of substitution between the overall labor and capital declines under IT progress. Quantitatively, the model explains nearly all the divergence between routine and nonroutine labor in the period 1986-2014, as well as the mild decline of the overall labor share between 1986 and the early 2000s. However, the model with IT progress alone cannot explain the accelerated decline of labor income share after the early 2000s, suggesting that other factors, such as globalization, may have played a larger role in this period. Lastly, when nonconvex labor adjustment costs are present, the model generates a stepwise decline in routine labor hours, qualitatively consistent with the data. The timing of these trend adjustments can be significantly affected by aggregate productivity shocks and concentrated in recessions. The second chapter studies the implications of loss aversion on the business cycle dynamics of aggregate consumption and labor hours. Loss aversion refers to the fact that people are distinctively more sensitive to losses than to gains. Loss averse agents are very risk averse around the reference point and exhibit asymmetric responses to positive and negative income shocks. In an otherwise standard Real Business Cycle (RBC) model, I study loss aversion in both consumption alone and consumption-and-leisure together. My results indicate that how loss aversion affects business cycle dynamics depends critically on the nature of the reference point. If, for example, the reference point is status quo, loss aversion dramatically lowers the effective inter-temporal rate of substitution and induces excessive consumption smoothing. In contrast, if the reference point is fixed at a constant level, loss aversion generates a flat region in the decision rules and asymmetric impulse responses to technology shocks. Under a reasonable parametrization, loss aversion has the potential to generate asymmetric business cycles with deeper and more prolonged recessions.

Behavioral Response to Environmental Taxation: Evidence from the Transportation Sector

This dissertation analyzes how individuals respond to the introduction of taxation aimed to reduce vehicle pollution, greenhouse gases and traffic. The first chapter analyzes a vehicle registration tax based on emissions of carbon dioxide (CO2), a major greenhouse gas, adopted in the UK in 2001 and subject to major changes in the following years. I identify the impact of the policy on new vehicle registrations and carbon emissions, compared to alternative measures. Results show that consumers respond to the tax by purchasing cleaner cars, but a carbon tax generating the same revenue would further reduce carbon emissions. The second chapter looks at a pollution charge (polluting vehicles pay to enter the city) and a congestion charge (all vehicles pay) adopted in 2008 and 2011 in Milan, Italy, and how they affected the concentration of nitrogen dioxides (NOx). I use data from pollution monitoring stations to measure the change between areas adopting the tax and other areas. Results show that in the first quarter of their introduction, both policies decreased NOx concentration in a range of -8% and -5%, but the effect declines over time, especially in the case of the pollution charge. The third chapter examines a trial conducted in 2005 in the Seattle, WA, area, in which vehicle trips by 276 volunteer households were recorded with a GPS device installed in their vehicles. Households received a monetary endowment which they used to pay a toll for each mile traveled: the toll varied with the time of the day, the day of the week and the type of road used. Using information on driving behavior, I show that in the first week a $0.10 toll per mile reduces the number of miles driven by around 7%, but the effect lasts only few weeks at most. The effect is mainly driven by a reduction in highway miles during trips from work to home, and it is strongly influenced by past driving behavior, income, the size of the initial endowment and the number of children in the household.