College of Behavioral & Social Sciences
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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Item Affective Reactions to Uncertainty as Driven by Past Experiences, Personality, and Perceived Valence(2022) Ellenberg, Molly Deborah; Kruglanski, Arie; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The assumption that uncertainty is inherently threatening which underlies decades of research belies the fact that people rarely react negatively to uncertain situations about which they do not care, and that some are excited by uncertainty. I propose that affective reactions to uncertainty are driven not by uncertainty itself, but by people’s expectations of positive and negative outcomes to personally relevant uncertain situations. I find that positive past experiences predict higher optimism and higher resilience, both of which predict higher tolerance of uncertainty and more positive perceptions of uncertain events. I also find that negative past experiences predict higher pessimism and lower resilience, both of which predict higher intolerance of uncertainty and more negative perceptions of uncertain events. The second study suggests that optimistic people are more likely to approach, rather than avoid, uncertainty. The third study finds that mindfulness training, which emphasizes non-attachment to outcomes, results in more neutral reactions to uncertainty. Theoretical and practical implications are discussed.Item Need to know: the need for cognitive closure impacts the clinical practice of obstetrician/gynecologists(Springer Nature, 2014-12-24) Raglan, Greta B; Babush, Maxim; Farrow, Victoria A; Kruglanski, Arie W; Schulkin, JayNeed for cognitive closure (NFCC) has been shown to be a consistent and measurable trait. It has effects on decision making and has been associated with more rapid decision making, higher reliance on heuristics or biases for decision making, reduced tolerance for ambiguity, and reduced interest in searching for alternatives. In medical practice, these tendencies may lead to lower quality of decision making. This study measured NFCC in 312 obstetrician/gynecologists using a survey-style approach. Physicians were administered a short NFCC scale and asked questions about their clinical practice. Obstetrician/gynecologists with high NFCC were found to be less likely to address a number of clinical questions during well-woman exams, and were more likely to consult a greater number of sources when prescribing new medications. NFCC of physicians may have an important impact on practice. It is possible that increased training during residency or medical school could counteract the detrimental effects of NFCC, and steps can be taken through increased use of electronic reminder systems could orient physicians to the appropriate questions to ask patients.Item Essays on Uncertainty, Imperfect Information, and Investment Dynamics(2016) Jia, Dun; Aruoba, Boragan; Stevens, Luminita; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Essays in Trade and Uncertainty(2015) Carballo, Jeronimo Rafael; Limao, Nuno; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Firms face uncertainty on many different dimensions: demand level, productivity and input prices, taxes and regulations. Furthermore, some argue that uncertainty is higher in recessions (cf. Bloom et al. (2012)) and one of the causes of the slow recovery during the recent Great Recession (cf. Stock and Watson (2012) and Baker et al. (2012)). However, most trade models assume uncertainty away by considering a deterministic framework or introduce uncertainty in a very limited way. In this dissertation, I argue that uncertainty can be particularly important for two topics in international trade: (i) firms’ global sourcing decisions and (ii) firms’ exports decision when facing multiple sources of uncertainty. Firms’ decisions to enter new foreign markets, exit from foreign markets that they are currently serving and whether to vertically integrate or outsource with foreign firms (i.e. their global sourcing decisions). Not only do these decisions require high sunk costs (cf. Roberts and Tybout (1997) and Antras and Helpman (2004)) but they are also subject to an additional set of uncertain conditions, e.g. exchange rates, foreign market conditions, and foreign policies. In particular, these potential multiple sources of uncertainty can work as an amplification mechanism, specially during recessions. The first chapter discusses the key insights that motivates my dissertation. The second chapter develops a dynamic model of international trade with heterogeneous firms who endogenously decide when to start exporting to foreign markets, under which sourcing scheme, and when to exit foreign markets in a framework with foreign demand uncertainty. The third chapter focuses on empirically evaluating the theoretical model of the previous chapter using U.S. firm-level data. I find that integration reduces the probability that a firm exits by as much as 8%, while uncertainty increases this probability by 23%. The fourth chapter looks into the interaction between demand and policy uncertainty during the Great Trade Collapse and is joint work with Kyle Handley and Nuno Limao. We examine if the resulting change in policy uncertainty initially deepened the collapse and then helped reverse it, when the worst fears of protection were not realized.Item Essays on Firm Dynamics and Macroeconomics(2015) Decker, Ryan Allen; Haltiwanger, John C; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)I describe two studies in firm dynamics and macroeconomics. Chapter 1 reports on the large decline in entrepreneurial activity that preceded and accompanied the Great Recession and proposes a model relating this decline to the housing collapse. The collapse in entrepreneurial activity coincided with a historic decline in home values that preceded the onset of the broad recession by at least nine months. I describe a heterogeneous agent general equilibrium model with both housing and entrepreneurship. The model is characterized by financial frictions that affect both credit supply and credit demand. I consider the consequences of a “housing crisis” as compared to a “financial crisis.” The model produces a negative response of entrepreneurship to a housing crisis via a housing collateral channel; this mechanism can account for at least a quarter of the empirical decline in entrepreneurs’ share of activity. A financial crisis (which works through credit supply) has more nuanced effects, causing economic disruption that entices new low-productivity entrepreneurs into production. Chapter 2 describes a theory of endogenous firm-level risk over the business cycle based on endogenous firm market exposure. Firms that reach a larger number of markets diversify market-specific demand shocks at a cost. The model is driven only by total factor productivity shocks and captures the observed countercyclicality of firm-level risk. Consistent with the model, data from Compustat and the Longitudinal Business Database show that market reach is procyclical and that the countercyclicality of firm-level risk is driven mostly by those firms that adjust their market reach. This finding is explained by a negative elasticity between firm-level volatility and various measures of market exposure.Item Essays on Monetary Economics and International Finance(2012) Fendoglu, Salih; Aruoba, Boragan; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis consists of two chapters. In the first chapter, I study optimal monetary policy rules in a general equilibrium model with financial market imperfections and uncertain business cycles. Earlier consensus view --using models with financial amplification with disturbances that have no direct effect on credit market conditions-- suggests that financial variables should not be assigned an independent role in policy making. Introducing uncertainty, time-variation in cross-sectional dispersion of firms' productive performance, alters this policy prescription. The results show that (i) optimal policy is to dampen the strength of financial amplification by responding to uncertainty (at the expense of creating a mild degree of fluctuations in inflation). (ii) a higher uncertainty makes the planner more willing to relax `financial stress' on the economy. (iii) Credit spreads are a good proxy for uncertainty, and hence, within the class of simple monetary policy rules I consider, a non-negligible interest rate response to credit spreads (32 basis points in response to a 1% change in credit spreads) -together with a strong anti-inflationary stance- achieves the highest aggregate welfare possible. In the second chapter, I study global, regional and idiosyncratic factors in driving the sovereign credit risk premium (as measured by sovereign credit default swaps) for a set of 25 emerging market economies during the last decade. The results show that (i) On average, global and regional factors account for a substantial portion of the movements in sovereign risk premium (of 63% and 21%, respectively). (ii) there exists noticeable heterogeneity in the contribution of factors across the emerging markets. (iii) The (extracted) global factor is best reflected by the VIX (investors' risk sentiment) among the financial market indicators considered. (iv) There are regime changes in the relation between the global factor and the financial market indicators.Item QUANTIFICATION OF ERROR IN AVHRR NDVI DATA(2011) Nagol, Jyoteshwar Reddy; Prince, Stephen D; Vermote, Eric F; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Several influential Earth system science studies in the last three decades were based on Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) series of instruments. Although AVHRR NDVI data are known to have significant uncertainties resulting from incomplete atmospheric correction, orbital drift, sensor degradation, etc., none of these studies account for them. This is primarily because of unavailability of comprehensive and location-specific quantitative uncertainty estimates. The first part of this dissertation investigated the extent of uncertainty due to inadequate atmospheric correction in the widely used AVHRR NDVI datasets. This was accomplished by comparison with atmospherically corrected AVHRR data at AErosol RObotic NETwork (AERONET) sunphotometer sites in 1999. Of the datasets included in this study, Long Term Data Record (LTDR) was found to have least errors (precision=0.02 to 0.037 for clear and average atmospheric conditions) followed by Pathfinder AVHRR Land (PAL) (precision=0.0606 to 0.0418), and Top of Atmosphere (TOA) (precision=0.0613 to 0.0684). ` Although the use of field data is the most direct type of validation and is used extensively by the remote sensing community, it results in a single uncertainty estimate and does not account for spatial heterogeneity and the impact of spatial and temporal aggregation. These shortcomings were addressed by using Moderate Resolution Imaging Spectrometer (MODIS) data to estimate uncertainty in AVHRR NDVI data. However, before AVHRR data could be compared with MODIS data, the nonstationarity introduced by inter-annual variations in AVHRR NDVI data due to orbital drift had to be removed. This was accomplished by using a Bidirectional Reflectance Distribution Function (BRDF) correction technique originally developed for MODIS data. The results from the evaluation of AVHRR data using MODIS showed that in many regions minimal spatial aggregation will improve the precision of AVHRR NDVI data significantly. However temporal aggregation improved the precision of the data to a limited extent only. The research presented in this dissertation indicated that the NDVI change of ~0.03 to ~0.08 NDVI units in 10 to 20 years, frequently reported in recent literature, can be significant in some cases. However, unless spatially explicit uncertainty metrics are quantified for the specific spatiotemporal aggregation schemes used by these studies, the significance of observed differences between sites and temporal trends in NDVI will remain unknown.Item Tackling Uncertainties and Errors in the Satellite Monitoring of Forest Cover Change(2010) Song, Kuan; Townshend, John R. G.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study aims at improving the reliability of automatic forest change detection. Forest change detection is of vital importance for understanding global land cover as well as the carbon cycle. Remote sensing and machine learning have been widely adopted for such studies with increasing degrees of success. However, contemporary global studies still suffer from lower-than-satisfactory accuracies and robustness problems whose causes were largely unknown. Global geographical observations are complex, as a result of the hidden interweaving geographical processes. Is it possible that some geographical complexities were not expected in contemporary machine learning? Could they cause uncertainties and errors when contemporary machine learning theories are applied for remote sensing? This dissertation adopts the philosophy of error elimination. We start by explaining the mathematical origins of possible geographic uncertainties and errors in chapter two. Uncertainties are unavoidable but might be mitigated. Errors are hidden but might be found and corrected. Then in chapter three, experiments are specifically designed to assess whether or not the contemporary machine learning theories can handle these geographic uncertainties and errors. In chapter four, we identify an unreported systemic error source: the proportion distribution of classes in the training set. A subsequent Bayesian Optimal solution is designed to combine Support Vector Machine and Maximum Likelihood. Finally, in chapter five, we demonstrate how this type of error is widespread not just in classification algorithms, but also embedded in the conceptual definition of geographic classes before the classification. In chapter six, the sources of errors and uncertainties and their solutions are summarized, with theoretical implications for future studies. The most important finding is that, how we design a classification largely pre-determines what we eventually get out of it. This applies for many contemporary popular classifiers including various types of neural nets, decision tree, and support vector machine. This is a cause of the so-called overfitting problem in contemporary machine learning. Therefore, we propose that the emphasis of classification work be shifted to the planning stage before the actual classification. Geography should not just be the analysis of collected observations, but also about the planning of observation collection. This is where geography, machine learning, and survey statistics meet.