They are also known to lead to increasing returns on human and physical capital by increasing educational attainment, public health outcomes and total factor productivity Knack and Keefer, ; Gilson, ; Rocco et al, Trust and reciprocity have been positively related with international trade and investment, and innovation Narayan and Pritchett, ; Zak and Knack, ; Guiso et al, The functioning of these preferences is such that, if all transactions and markets are forms of contracts then with them come the issues of enforceability, imperfect knowledge and market failure.
The preferences in questions are known to decrease the likelihood of market failures and also reduce transaction costs.
It can be said that recognising social preferences through modelling and also framing policies accordingly can pave the new road towards development. If trust and reciprocity are to be focused on, then societies can take measures to increase the degree of these preferences by increasing the flow of information, transparency and accountability. Societies can also formulate policies which exploit these preferences such as, introducing and expanding micro-finance programs.
These programs are specifically very beneficial for the less developed economies. According to Besley and Coate , micro-financial setups, along with reducing the asymmetric information gap impose a sense of interdependence upon its beneficiaries.
Ripple effects include greater financial coverage and inclusion, increased access to credit, increased investment in education and health care, and important welfare effects such as reduced consumption of alcohol and tobacco Karlan, ; Bachas et al, As mentioned previously, social preferences also vary across gender. However, this variance is a function of the societal structures and norms according to some it is because of the gender difference in genetic make up.
The very fact that a gender difference exists is enough to change the direction of policy formulation. Ranehill and Weber , show that a gender difference in preferences translate into a gender difference in policy preferences. That is, women tend to prefer a different set of policies than men. Therefore, societies can take advantage of the gender gap in social and policy preferences and increase the decision-making power of women through quotas and reservations to change the flow of resources towards areas which women prefer.
This can help bring about-the much required and sought after-gender equity. This could directly lead to women empowerment which is helpful for economic development. In fact, women empowerment and economic development share a bi-directional causal relationship World Bank, ; Duflo, Andreoni, James. Journal of Political Economy 97 6 : University of Chicago Press. Bachas, Pierre et al. Games and Economic Behavior 10 1 : Elsevier BV.
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Home Discover A fresh perspective to economic theory: Soci…. A fresh perspective to economic theory: Social preferences and their impact on gender and policy. Related content. Sheral Shah Exploring Economics, Level: advanced. Shagata Mukherjee Introduction The Neoclassical and New Keynesian models have been traditionally used in economic theory and practice. Types of social preferences As mentioned previously, over the years various forms of social preferences have been studied by experimental and behavioral scientists.
Experimetal designs to study social preferences The above explained social preferences have been studied by using various experimental designs. Determinants of social preferences: Nature vs. Nurture The existing knowledge about how social preferences develop is attributable to two streams of thought i. Importance of social preferences As opposed to the conventional over-simplified assumption of self-interested individuals, strong evidence points towards the presence of heterogeneous other-regarding preferences in agents.
References Andreoni, James. Title photo by Hans-Peter Gauster on Unsplash. Tags: behavior change behavioral economics behavioral macroeconomics experiments game theory student essay trust.
Level: advanced Actors, Behaviours and Decision Processes. Sigrid Stagl and Roman Hausmann. Level: beginner Behavioural vs Complexity Economics: Approaches to Development. Erika Sloan.
Politics as supermarket? Or how current policy design changes the relationship between the state and its citizens. Alexander Feldmann. The Evolution of Trust. Nicky Case. Behavioural and Complexity Macroeconomics. Michael Roos. Further, populations are heterogeneous, suggesting that the composition of social preference types within a group may impact the ability to sustain voluntary public goods contributions.
We conduct agent-based simulations of contributions in a public goods game, varying group composition and the weight individuals place on their beliefs versus their underlying social preference type. We then examine the effect of each of these factors on contributions. We find that social preference heterogeneity negatively impacts provision over a wide range of the parameter space, even controlling for the share of types in a group.
Examples of these welfare-enhancing public goods include health services e. Alesina et al. Ensuring an adequate level of these and other public goods, through either government or voluntary provision, is important for the overall well-being of the citizenry.
Central to this conclusion is the hypothesis that all individuals are selfish. Incorporating the welfare of others into one's own utility function is referred to as social preferences: Significant heterogeneity in the types of social preferences held by members of society has been robustly documented see e. Agent-based simulations are particularly useful in this area since the composition of populations cannot be exogenously varied and simulations runs can be systematically analysed.
This heterogeneity can take many forms, including but not limited to race or ethnicity e. We complement the existing literature by examining social preference heterogeneity. We examine voluntary public goods provision in these groups under several rules for determining contributions.
We then examine the role of group composition in provision, paying particular attention to the impact of social preference heterogeneity. Note that while we are using the standard fractionalization index we are considering a new type of diversity not typically discussed in the literature: the diversity of social preferences. We then examine the robustness of our results to a variety of parameter specifications. For the full set of social preference types, a significantly negative relationship only exists for a small portion of the parameter space.
These results complement and extend the prior literature, which has examined fractionalization based on observable characteristics. Our agent-based approach allows for a flexible and complete testing of behaviour heterogeneity in groups. We now turn to a discussion of previous research before detailing our agent-based simulation and the results from our experiment. Previous Research 2. While we focus on social preference heterogeneity, our study intersects several existing lines of research.
We will now discuss them in turn. Diversity, Public Goods, and Welfare 2. Rising incomes directly improve welfare, but this is not the only mechanism through which ethnic diversity may impact welfare. Importantly, racial fractionalization has been suggested to be negatively related with infrastructure development Alesina et al. Additionally, ethnic fractionalization is correlated with governance quality Alesina et al.
As populations become more ethnically diverse these effects may be exacerbated. The negative relationship holds for a broad spectrum of goods, including: goods provided by the government e.
It is important to note that heterogeneity may occur along many dimensions, including race or ethnicity as well as language e. Doing so will help increase the well-being of the poor, as documented through the link between public goods provision and welfare e. Further, since diversity could theoretically be positively related to growth by spurring new ideas and innovation, if the negative spiral can be broken, then the positive effects may dominate.
One way to break the spiral may be through appropriately designed institutions Easterly We find that the negative impact of fractionalization extends to this domain. We now turn to a discussion of the literature on social preference and group composition. One common approach is to rely on demographics like ethnicity to examine heterogeneity and then argue that this heterogeneity impairs the ability of social preferences--like trust or conditional cooperation--to function.
This is due, at least in part, to the inability to either measure or vary social preference diversity on a large scale. Further, a set of distinct social preference types and been identified and measured experimentally Ahn et al. In fact, a strict or perfect conditional cooperator will give exactly what he expects others to contribute.
Threshold players are individuals who refuse to contribute until others are giving what they deem as 'enough' and then contribute fully. Fischbacher et al. We allow all types in the estimation in the interest of completeness.
Further details about the preference types and their implementation in the simulation are discussed in section 3. While a more thorough study is warranted, since some of the social preference types comprise only a small portion of the population, it would be difficult to systematically varying all types using human subjects in a laboratory.
We therefore conduct an agent-based simulation to examine the issue. Agent-Based Simulation 3. We use the structure of the public goods game, which has been studied widely experimentally see Chaudhuri ; Ledyard for reviews. In the typical public goods game, agents receive an endowment which they can allocate between an individual account and a group account.
Tokens deposited in the individual account are kept by the agent while contributions sent to the group account are multiplied by some factor and divided evenly between all agents. The process through which utility functions and interact to produce actions is an important avenue for future behavioural research. For purposes of our simulation, agents are programmed as adhering to a strategy, with a different strategy for each social preference type.
For purposes of the simulation, we will refer to these as the agents' 'contribution preferences' or 'social preference types. We are able to separate payoff-maximization in this manner because the payoff-maximizing strategy is always the same in the game with finite rounds: to contribute zero. Specifically, the action chosen in a particular period can be a completely determined by preference type, b completely determined by beliefs or c determined by a combination of type and beliefs. The agent's programmed strategy is then executed over ten rounds with the same group members.
Specifically, agent i calculates her contribution to the public good C i,t at time t using the following equation: 1 where P i,t is agent i 's underlying preference at period t and B i,t is agent i 's subjective belief at period t. Agent i 's preference P i,t may further be a function of her beliefs B i,t see eq. After the initial round, beliefs are computed using the following formula: 2 Thus, B i,t is simply the weighted average of others' contributions at period t-1 , x t —1 and agent i 's own beliefs in period t-1 , B i,t —1.
Since we focus on the role of group composition, rather than information about the composition, we do not allow the first-round beliefs to vary by group composition. Preference types can be broadly classified as either unconditional or conditional. Within each of these are further distinctions leading to a total of 9 different preference types: 3. Once drawn, this value is constant for all periods. In every round, the contribution preference is calculated by a random draw from a uniform distribution within the full range of possible contributions.
The implication is thus that individuals want others to be cared for, but own-provision and other-provision are substitutes. Thus, as other group members give more to the public good the pure altruist will actually give less: The giving of others crowds out own-giving as set out in Croson ; see also Andreoni , as well as Becker This means that they prefer to contribute less as their beliefs about other group members increase. The threshold level has been set to half the maximum possible contribution for all agents.
Similar to threshold players, the threshold level has been set to half the maximum possible contribution for all agents. Groups can be created with an arbitrary number of participants and each participant will have a stable social preference type associated throughout the simulation runs. Each simulation run is asynchronous i. Each simulated period will compute the following: If beliefs are included, a random initial belief value, B i1 , ranging from zero to the maximum possible contribution for every type of agent in the simulated public goods group.
Unconditional Low and High cooperators randomly generate a contribution preference value, P i , within the lower and upper half of the contribution range, respectively. Noisy cooperators randomly generate a contribution preference value, P it , within the contribution space.
Every type of agent computes C it based on equation 1. If beliefs are included , every individual agent updates beliefs based on equation 2.
If the final period has been reached, halt the simulation, otherwise repeat from step 3. Each group is simulated independently, so that the simplified pseudo-algorithm described above is executed in every run, including B it and P it. These include the aforementioned initial random setting of values for certain agent types and a new value for each period for the noisy agent.
Due to these non-deterministic features, the model is not fully driven by the initial conditions. Further, notice that, with the exception of the noisy agent, P it , B it and C it are not calculated with errors. Specifically, we include the noisy player as one of the possible contribution preference type, not to add noise to the data. We thus test their role along with the other possible social preference types found in the public goods game setting.
Finally, the model allows changes in the relative weights of beliefs and preferences as they contribute to contribution decisions. The advantage of using the agent-based approach to this research problem is to fully account for the heterogeneous interaction occurring at the individual level, which ultimately leads to insights at the group level. The model has been implemented in NetLogo 3. Should the reader wish to read the simulation source, please contact the corresponding author.
Description of Data 4. The dependent variable in the analysis is the average number of tokens contributed to the group account in period t , ranging from 0 the Nash equilibrium in the traditional game to 20 the social optimum. Groups are allowed to vary between completely homogeneous and completely heterogeneous, with all possible combinations in between, making use of all nine contribution preference types.
This results in distinct group compositions. For each group composition the simulation runs separate times with 10 rounds for each run. This results in a data set of 49, groups and , data points for the Fully Preference Driven and Belief Model 3 models.
With 10 rounds executed a independent times, this process generates a sample of 15, data points. Since we utilize their parameters, we restrict our sample as well. Results: Social Preference Heterogeneity 5. Recall that the unit of observation is the average number of tokens contributed to the group account.
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