Neural representations of social and non-social uncertainty in human decision making

The social landscape is filled with an intricate web of species-specific desired objects and course of actions. Humans are highly social animals and, as they navigate this landscape, they need to produce adapted decision-making behaviour. Traditionally social and non-social neural mechanisms affecting choice have been investigated using different approaches. Recently, in an effort to unite these findings, two main theories have been proposed to explain how the brain might encode social and non-social motivational decision-making: the extended common currency and the social valuation specific schema (Ruff & Fehr 2014). One way to test these theories is to directly compare neural activity related to social and non-social decision outcomes within the same experimental setting. Here we address this issue by focusing on the neural substrates of social and non-social forms of uncertainty. Using functional magnetic resonance imaging (fMRI) we directly compared the neural representations of reward and risk prediction and errors (RePE and RiPE) in social and non- social situations using gambling games. We used a trust betting game to vary uncertainty along a social dimension (trustworthiness), and a card game (Preuschoff et al. 2006) to vary uncertainty along a non-social dimension (pure risk). The trust game was designed to maintain the same structure of the card game. In a first study, we exposed a divide between subcortical and cortical regions when comparing the way these regions process social and non-social forms of uncertainty during outcome anticipation. Activity in subcortical regions reflected social and non-social RePE, while activity in cortical regions correlated with social RePE and non-social RiPE. The second study focused on outcome delivery and integrated the concept of RiPE in non-social settings with that of fairness and monetary utility maximisation in social settings. In particular these results corroborate recent models of anterior insula function (Singer et al. 2009; Seth 2013), and expose a possible neural mechanism that weights fairness and uncertainty but not monetary utility. The third study focused on functionally defined regions of the early visual cortex (V1) showing how activity in these areas, traditionally considered only visual, might reflect motivational prediction errors in addition to known perceptual prediction mechanisms (den Ouden et al 2012). On the whole, while our results do not support unilaterally one or the other theory modeling the underlying neural dynamics of social and non-social forms of decision making, they provide a working framework where both general mechanisms might coexist.

Essays in corporate finance

China has been growing rapidly over the last decades. The private sector is the driving force of this growth. This thesis focuses on firm-level investment and cash holdings in China, and the chapters are structured around the following issues. 1. Why do private firms grow so fast when they are more financially constrained? In Chapter 3, we use a panel of over 600,000 firms of different ownership types from 1998 to 2007 to find the link between investment opportunities and financial constraints. The main finding indicates that private firms, which are more likely to be financially constrained, have high investment-investment opportunity sensitivity. Furthermore, this sensitivity is relatively lower for state-owned firms in China. This shows that constrained firms value investment opportunities more than unconstrained firms. To better measure investment opportunities, we attempt to improve the Q model by considering supply and demand sides simultaneously. When we capture q from the supply side and the demand side, we find that various types of firms respond differently towards different opportunity shocks. 2. In China, there are many firms whose cash flow is far greater than their fixed capital investment. Why is their investment still sensitive to cash flow? To explain this, in Chapter 4, we attempt to introduce a new channel to find how cash flow affects firm-level investment. We use a dynamic structural model and take uncertainty and ambiguity aversion into consideration. We find that uncertainty and ambiguity aversion will make investment less sensitive to investment opportunities. However, investment-cash flow sensitivity will increase when uncertainty is high. This suggests that investment cash flow sensitivities could still be high even when the firms are not financially constrained. 3. Why do firms in China hold so much cash? How can managers’ confidence affect corporate cash holdings? In Chapter 5, we analyse corporate cash holdings in China. Firms hold cash for precautionary reasons, to hedge frictions such as financing constraints and uncertainty. In addition, firms may act differently if they are confident or not. In order to determine how confidence shocks affect precautionary savings, we develop a dynamic model taking financing constraints, uncertainty, adjustment costs and confidence shocks into consideration. We find that without confidence shocks, firms will save money in bad times and invest in good times to maximise their value. However, if managers lose their confidence, they tend to save money in good times to use in bad times, to hedge risks and financing constraint problems. This can help explain why people find different results on the cash flow sensitivity of cash. Empirically, we use a panel of Chinese listed firms. The results show that firms in China save more money in good times, and the confidence shock channel can significantly affect firms’ cash holdings policy.

Forecasting exchange rates in the presence of instabilities

Many exchange rate papers articulate the view that instabilities constitute a major impediment to exchange rate predictability. In this thesis we implement Bayesian and other techniques to account for such instabilities, and examine some of the main obstacles to exchange rate models' predictive ability. We first consider in Chapter 2 a time-varying parameter model in which fluctuations in exchange rates are related to short-term nominal interest rates ensuing from monetary policy rules, such as Taylor rules. Unlike the existing exchange rate studies, the parameters of our Taylor rules are allowed to change over time, in light of the widespread evidence of shifts in fundamentals - for example in the aftermath of the Global Financial Crisis. Focusing on quarterly data frequency from the crisis, we detect forecast improvements upon a random walk (RW) benchmark for at least half, and for as many as seven out of 10, of the currencies considered. Results are stronger when we allow the time-varying parameters of the Taylor rules to differ between countries. In Chapter 3 we look closely at the role of time-variation in parameters and other sources of uncertainty in hindering exchange rate models' predictive power. We apply a Bayesian setup that incorporates the notion that the relevant set of exchange rate determinants and their corresponding coefficients, change over time. Using statistical and economic measures of performance, we first find that predictive models which allow for sudden, rather than smooth, changes in the coefficients yield significant forecast improvements and economic gains at horizons beyond 1-month. At shorter horizons, however, our methods fail to forecast better than the RW. And we identify uncertainty in coefficients' estimation and uncertainty about the precise degree of coefficients variability to incorporate in the models, as the main factors obstructing predictive ability. Chapter 4 focus on the problem of the time-varying predictive ability of economic fundamentals for exchange rates. It uses bootstrap-based methods to uncover the time-specific conditioning information for predicting fluctuations in exchange rates. Employing several metrics for statistical and economic evaluation of forecasting performance, we find that our approach based on pre-selecting and validating fundamentals across bootstrap replications generates more accurate forecasts than the RW. The approach, known as bumping, robustly reveals parsimonious models with out-of-sample predictive power at 1-month horizon; and outperforms alternative methods, including Bayesian, bagging, and standard forecast combinations. Chapter 5 exploits the predictive content of daily commodity prices for monthly commodity-currency exchange rates. It builds on the idea that the effect of daily commodity price fluctuations on commodity currencies is short-lived, and therefore harder to pin down at low frequencies. Using MIxed DAta Sampling (MIDAS) models, and Bayesian estimation methods to account for time-variation in predictive ability, the chapter demonstrates the usefulness of suitably exploiting such short-lived effects in improving exchange rate forecasts. It further shows that the usual low-frequency predictors, such as money supplies and interest rates differentials, typically receive little support from the data at monthly frequency, whereas MIDAS models featuring daily commodity prices are highly likely. The chapter also introduces the random walk Metropolis-Hastings technique as a new tool to estimate MIDAS regressions.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.

A study on the effect of functional distribution of income on aggregate demand

In this thesis, we study the causal relationship between functional distribution of income and economic growth. In particular, we focus on some of the aspects that might alter the effect of the profit share on growth. After a brief introduction and literature review, the empirical contributions will be presented in Chapters 3,4 and 5. Chapter 3 analyses the effect of a contemporaneous decrease in the wage share among countries that are major trade partners. Falling wage share and wage moderation are a global phenomenon which are hardly opposed by governments. This is because lower wages are associated with lower export prices and, therefore, have a positive effect on net-exports. There is, however, a fallacy of composition problem: not all countries can improve their balance of payments contemporaneously. Studying the country members of the North America Free Trade Agreement, we find that the effect on export of a contemporaneous decrease in the wage share in Mexico, Canada and the United States, is negative in all countries. In other words, the competitive advantage that each country gains because of a reduction in its wage share (to which is associated a decrease in export prices), is offset by a contemporaneous increase in competitiveness in the other two countries. Moreover, we find that NAFTA is overall wage-led: the profit share has a negative effect on aggregate demand. Chapter 4 tests whether it is possible that the effect of the profit share on growth is different in the long run and in the short run. Following Blecker (2014) our hypothesis is that in the short run the growth regime is less wage-led than it is in the long run. The results of our empirical investigation support this hypothesis, at least for the United States over the period 1950-2014. The effect of wages on consumption increases more than proportionally compared to the effect of profits on consumption from the short to the long run. Moreover, consumer debt seem to have only a short-run effect on consumption indicating that in the long run, when debt has to be repaid, consumption depends more on the level of income and on how it is distributed. Regarding investment, the effect of capacity utilization is always larger than the effect of the profit share and that the difference between the two effects is higher in the long run than in the short run. This confirms the hypothesis that in the long run, unless there is an increase in demand, it is likely that firms are not going to increase investments even in the presence of high profits. In addition, the rentier share of profits – that comprises dividends and interest payments – has a long-run negative effect on investment. In the long run rentiers divert firms’ profits from investment and, therefore, it weakens the effect of profits on investment. Finally, Chapter 5 studies the possibility of structural breaks in the relationship between functional distribution of income and growth. We argue that, from the 1980s, financialization and the European exchange rate agreements weakened the positive effect of the profit share on growth in Italy. The growth regime is therefore becoming less profit-led and more wage-led. Our results confirm this hypothesis and also shed light on the concept of cooperative and conflictual regimes as defined by Bhaduri and Marglin (1990).