This chapter explores the role of trust in facilitating economic transactions. We cover seminal and more recent research suggesting how the game-theoretic approach in economics relies on trust to explain market transactions between two parties – individuals and firms. We also study how the experimental results of the trust game (and assorted variations) introduced by economists helps better our understanding of how trust affects the underlying dynamic in dyadic transactions. Relationships between trust levels in society and macroeconomic growth are also explored.

Abstract

There has been a steep increase in empirical research in economics in the past 20–30 years. This chapter brings together several actors and stakeholders in these developments to discuss their drivers and implications. All types of data are considered: official data, data collected by researchers, lab experiments, randomized control trials, and proprietary data from private and public sources. When relevant, emphasis is placed on developments specific to Europe. The basic message of the chapter is that there is no single type of data that is superior to all others. We need to promote diversity of data sources for economic research and ensure that researchers are equipped to take advantage of them. All stakeholders – researchers, research institutions, funders, statistical agencies, central banks, journals, data firms, and policy-makers – have a role to play in this.

Introduction

The past 20–30 years have witnessed a steady rise in empirical research in economics. In fact, a majority of articles published by leading journals these days are empirical, in stark contrast with the situation 40 or 50 years ago (Hamermesh, 2013). This change in the distribution of methodologies used in economic research was made possible by improved computing power but, more importantly, thanks to an increase in the quantity, quality and variety of data used in economics.

This chapter brings together several actors and stakeholders in these changes to discuss their drivers and implications. All types of data are considered. When relevant, emphasis is placed on developments specific to Europe. Sections 13.2 and 13.3 deal with official microdata. Section 13.2 focuses on the level of access to microdata in Europe and its determinants. Section 13.3 focuses on cross-country data harmonization. Section 13.4 then switches gears entirely and discusses the benefits and costs of large-scale data collection efforts led by researchers, instead of statistical offices. Section 13.5 discusses data produced by researchers, either in the context of lab experiments or in the context of randomized control trials. Both types of data have led to major advances; for the first one in our understanding of human behaviour and the robustness of economic institutions; for the second in our understanding of the impact of policies and themechanisms underlying them.

Neuroeconomics seeks to ground microeconomic theory in details about how the brain works (Zak, 2004; Camerer et al., 2004; Chorvat and McCabe, 2005; Sanfey et al., 2006). Neuroeconomics is a subfield of behavioural economics-behavioural economics which uses empirical evidence of limits on computation, willpower and greed to inspire new theories; see Mullainathan and Thaler, (2000); Camerer, (2005). It is also a subfield of experimental economics because neuroeconomics requires mastery of difficult experimental tools which are new to economists (discussed in further detail in Section 1 below).

THE THEMES AND PHILOSOPHY OF BEHAVIORAL ECONOMICS

Behavioral economics applies models of systematic imperfections in human rationality, to the study and engineering of organizations, markets and policy. These imperfections include limits on rationality, willpower and self-interest (Rabin, 1998; Mullainathan and Thaler, 2000), and any other behavior resulting from an evolved brain with limited attention. The study of individual differences in rationality and learning is also important for understanding whether social interaction and economic aggregation minimize effects of rationality limits. In one sense, behavioral economics is the inevitable result of relaxing the assumption of perfect rationality.

INTRODUCTION

Theories of labor supply predict how the number of hours people work will change when their hourly wage or income changes. The standard economic prediction is that a temporary increase in wages should cause people to work longer hours. This prediction is based on the assumption that workers substitute labor and leisure intertemporally, working more when wages are high and consuming more leisure when its price - the forgone wage - is low (e.g., Lucas and Rapping 1969). This straightforward prediction has proven difficult to verify. Studies of many types often find little evidence of intertemporal substitution (e.g., Laisney, Pohlmeier, and Staat 1996). However, the studies are ambiguous because when wages change, the changes are usually not clearly temporary (as the theory requires). The studies also test intertemporal substitution jointly along with auxiliary assumptions about persistence of wage shocks, formation of wage expectations, separability of utility in different time periods, and so forth.

An ideal test of labor supply responses to temporary wage increases requires a setting in which wages are relatively constant within a day but uncorrelated across days, and hours vary every day. In such a situation, all dynamic optimization models predict a positive relationship between wages and hours (e.g., MaCurdy, 1981, p. 1074).

INTRODUCTION

Psychological studies demonstrate that most individuals are overconfident about their own abilities, compared with others, as well as unreasonably optimistic about their futures (e.g., Taylor and Brown 1988, Weinstein 1980). When assessing their position in a distribution of peers on almost any positive trait such as driving ability or income prospects, 90% of people say they are in the top half (see Svenson 1981). Few people say they are below average, although half must be.1

This paper explores one setting in which optimistic biases could plausibly and predictably influence economic behavior: entry into competitive games or markets. Many empirical studies show that most new businesses fail within a few years. For example, using plant level data from the U.S. Census of Manufacturers spanning 1963-1982, Dunne, Roberts, and Samuelson (1988) estimated that 61.5% of all entrants exited within 5 years and 79.6% exited within 10 years. Most of these exits are failures (see also Dunne, Roberts, and Samuelson 1988, 1989a, Shapiro and Khemani 1987).

There are many possible explanations for the high rate of business failure (reviewed below). In this paper we consider the hypothesis that business failure is a result of managers acting on the optimism about relative skill they exhibit in surveys.

The workhorses of economic analysis are simple formal models that can explain naturally occurring phenomena. Reflecting this taste, economists often say they will incorporate more psychological ideas into economics if those ideas can parsimoniously account for field data better than standard theories do. Taking this statement seriously, this article describes 10 regularities in naturally occurring data that are anomalies for expected utility theory but can all be explained by three simple elements of prospect theory: loss aversion, reflection effects, and nonlinear weighting of probability; moreover, the assumption is made that people isolate decisions (or edit them) from others they might be grouped with (Read, Loewenstein, and Rabin 1999; cf. Thaler, 1999). I hope to show how much success has already been had applying prospect theory to field data and to inspire economists and psychologists to spend more time in the wild.

The 10 patterns are summarized in Table 16.1. To keep the article brief, I sketch expected utility and prospect theory very quickly. (Readers who want to know more should look elsewhere in this volume or in Camerer 1995 or Rabin 1998a). In expected utility, gambles that yield risky outcomes xi with probabilities pi are valued according to Σpiu(xi), where u(x) is the utility of outcome x. In prospect theory they are valued by Σπ(pi)v(xi - r), where π(p) is a function that weights probabilities nonlinearly, overweighting probabilities below.

Introduction

Classic literature on organizations recognizes that the paramount function of an organization is the coordination of physical and human assets to produce a good or service (e.g., Barnard, 1938; Chisholm, 1989; Schein, 1985). Coordination in this early literature was defined broadly, as for example by Mooney (1947, p. 5): “Coordination therefore, is the orderly arrangement of group effort, to provide unity of action in the pursuit of a common purpose.” Mooney argues further that coordination is the first principle of organization and that any other organizational principles “are simply principles through which coordination operates and thus becomes effective” (p. 5). The landmark work of Thompson (1967) distinguished kinds of interdependence that give rise to coordination problems and ways in which coordination might occur – for example, by standardization, planning, or mutual adjustment. Coordination also plays a central role in recent thinking about the economics of internal organization (Becker & Murphy, 1992; Milgrom & Roberts, 1992), the history of business organization (Lamoreaux & Raff, 1995, esp. p. 5), core competencies in business strategy, mutualism and legitimation in organizational evolution, macroeconomics (Cooper & John, 1988), and other fields.

The need for coordination arises if the organization's success depends on the decisions made by each of a group of actors, and the decisions interact in determining success. In the traditional sense, a coordination problem exists if achievement of a particular organizational goal requires that each actor select the appropriate action, and the goal is not fully achieved if all members of the group do not select goal-fulfilling actions.

Economic theory assumes people strive for efficient agreements that benefit all consenting parties. The frequency of mutually destructive conflicts such as strikes, litigation, and military conflict, therefore, poses an important challenge to the field.

Among economists, game theorists have devoted the most attention to inefficiency and have proposed a number of theories to explain why inefficient agreements occur. Most of these revolve around a common theme: Inefficiencies arise because parties possess incomplete information. Bargainers often lack information about how much other parties value an agreement (Babcock, 1991; Hayes, 1984; Tracy, 1987), about the personal characteristics (e.g., impatience) of the other party (Rubinstein, 1985), or the likely consequence of nonsettlement as determined by the ruling of an arbitrator or the existence of other parties willing to make a deal (Priest & Klein, 1984).

Incomplete information impedes settlement for two reasons. First, when bargainers lack information about the other party, they may use the bargaining process to find out the missing information. For example, a union might call a strike to assess management's ability to withstand a strike. Second, when bargainers lack information about the other party's tastes or opportunities, they may overestimate the other party's willingness to make concessions. For example, the seller of a used car may hold out for an unrealistically high price in the erroneous belief that the buyer really likes the car or is impatient to buy it.