Abstract
In this study, we introduce an experimental approach to study the causal impact of trust on economic performance. We ask if trust can serve as a coordination device to help poor economies escape a poverty trap and, if so, whether such an impact is universal regardless of their initial levels of development. We follow Lei and Noussair (2002, 2007) and design a decentralized market economy that has the structure of an optimal growth model where output is allocated between consumption and saving over a sequence of periods. As in Lei and Noussair (2007), a threshold externality is introduced to generate two equilibria where the Pareto-inferior equilibrium is considered as a poverty trap. We find that trust matters in that it is more likely for high-trust economies, generated with an endogenous matching procedure, to escape the poverty trap. But we also find that the likelihood to escape depends partially on the initial endowment condition. Trust has a much weaker impact on the economies whose initial capital and output are below the Pareto-inferior equilibrium, suggesting that formal institutions and/or policy measures may be needed to engineer a “big push” for these least developed economies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The experimental data collected in this study are available upon request.
Notes
See Algan and Cahuc (2013) for a review of the recent attempts to address this issue in the literature.
See Amano et al. (2014) for a discussion of the pros and cons of using experimental methodology to study macroeconomic issues.
We do not exclude reverse causality in that trust can be further enhanced or mitigated after an economy succeeds or fails to escape the poverty trap. As a result, we will focus our data analysis mostly on the beginning of the growth game in Sect. 5.
As noted in Lei and Noussair (2002), the main drawback of setting \(\delta =1\) is that it risks the possibility of having zero capital stock in any given period if the economy consumes all its output. Without being able to produce output and re-accumulate capital, the economy would cease to exist. Therefore, having \(\delta =1\) may make it more difficult for our experimental economies to reach a steady-state equilibrium. Yet, technically speaking, this parameter choice also renders a more straightforward decision environment for our human subjects. Therefore, we continue the practice from Lei and Noussair (2002, 2007) and Capra et al. (2009) by choosing \(\delta =1\).
How we divided the production and utility schedules among five individuals will be detailed in Sect. 3.1.2 below.
See Lei and Noussair (2007) for more details about the competitive equilibria noted here.
The minimum wage in 2011 was HK$28 or US$ 3.60 per hour, which was paid by most of the fast food restaurant chains in Hong Kong.
The fact that the experiment consisted of two parts was written in the instructions and thus common knowledge to all participants. Nevertheless, other than knowing that the grouping determined in period 10 of Part I would be used to determine their group affiliation in Part II, subjects did not have any information regarding the exact contents of Part II until after Part I was completed. See Sect. 3 for the grouping procedure.
The corresponding initial capital stocks were 6 and 19, respectively. Both levels were below the threshold capital level of 31 that would permit economies to use a more efficient technology to produce. We departed from Lei and Noussair (2007) by equally dividing the initial aggregate output, rather than capital, among five group members. Since different agents were endowed with different production capabilities, having the same initial capital stock would immediately result in income inequality that might hinder economic growth in our experiment. We therefore decided to start with a more homogenous income distribution than in Lei and Noussair (2007).
This decentralized market setting was first introduced, along with a treatment called Social Planner, in Lei and Noussair (2002) to study the Ramsey-Cass-Koopmans optimal growth model. In the Social Planner treatment where each subject took on the role of a social planner, the experiment was designed to be as close as possible to the literal formulation of the model. In the Market treatment, however, the design departed from the literal formulation with a double auction mechanism that is known in the experimental literature to be extremely effective in enhancing the efficient allocation of resources and, as a consequence, directing a decentralized market toward equilibrium. Of course, to promote the use of the market, gains from trade must exist. In their experiment, this was done through endowing agents with different production and utility functions. We adopt the same procedure to ensure heterogeneity among individual agents. This will be explained in the next subsection entitled Individual Production and Utility Function.
Overall, only three substitutes were recruited in this experiment.
This was similar to the voluntary association mechanism used in Page et al. (2005), although subjects in Page et al. only needed to make a regrouping decision every three periods.
If a participant received 0 from his trustor in stage 1, a N/A was shown on the screen as his “% Retuned.”.
For each possible group of 5 subjects, the computer summed up the members’ mutual ranking decisions. It performed this procedure for all 3,003 possible grouping combinations to find the smallest sum in order to form Group A.
After excluding Group A’s five members from the remaining ten subjects’ ranking lists, the computer had to first rescale the ranks down to 1–9 for each individual. Then, it repeated the same procedure as described in footnote 14 to find the smallest sum among 252 possible grouping combinations to form Group B.
The grouping procedure was explained in the instructions to the subjects.
Since some subjects moved from one group to another, we cannot treat each group as an independent observation. As a result, Wilcoxon matched-pairs signed-ranks tests are more appropriate for the pair-wise comparisons here.
We use the percentage amount sent rather than the absolute amount sent in order to directly compare the relative importance between trust and trustworthiness.
Note that subjects ranked the other 14 participants from 1 (the highest and most favorable rank) to 14 (the lowest and least favorable rank).
As shown in Figs. 4 and 5, the amount sent or percentage returned in the endogenous matching groups suffers from a considerable end-game effect in period 10. Therefore, we chose to use the average percentage sent or returned between periods 9 and 10 to mitigate the impact of the end-game effect. Note that the group composition in period 9 was not exactly the same as that in period 10 (the number of new group members is, on average, 1.27 in period 10). Nonetheless, we made such a decision based on the assumption that the norm would remain stable even with a slightly different group composition between the last two periods.
We take each group as an independent observation unit.
A likelihood ratio test rejects the null hypothesis that the data are temporal independence (p-value < 0.0100).
As discussed in Sect. 2.1, our economies, if guided by a benevolent social planner, should conserve most, if not all, of its output in the first three periods in order to escape the poverty trap. Therefore, our primary interest in the following analysis is on economies’ behavior in duration 1.
By grouping three or more periods into one time interval, we are assuming that the probability that an economy will escape the poverty trap remains constant within periods 1–3, 4–6, etc. See Jenkins (2004) for more details on the characterization of duration dependence in discrete time hazard models.
A GLS random-effects model allows us to take advantage of the cross-sectional and time-series variation in the data. Furthermore, since the group trust generated in Part I of the experiment might have a path-dependent impact, depending on how the growth game evolved over time and whether or not it was repeated more than once, we believe a random-effects model is more appropriate than a fixed-effects model for our data.
References
Algan Y, Cahuc P (2010) Inherited trust and growth. Amer Eco Rev 100(5):2060–2092
Algan Y, Cahuc P (2013) Trust, growth and well-being: new evidence and policy implications. In: Aghion P, Durlauf S (eds) The handbook of economic growth, vol 2. North Holland, pp 49–120
Amano R, Kryvtsov O, Petersen L (2014) Recent developments in experimental macroeconomics. Bank of Canada Review. 11:1–11
Ananyev M, Guriev S (2015) Effect of income on trust: evidence from the 2009 crisis in Russia. SSRN J. https://doi.org/10.2139/ssrn.2542001
Arrow KJ (1972) Gifts and exchanges. Philos Public Aff 1(4):343–362
Banerjee R (2016) Corruption, norm violation and decay in social capital. J Public Econ 137:14–27
Beck N, Katz JN, Tucker R (1998) Taking time seriously: time-series-cross-section analysis with a binary dependent variable. Amer J Polit Sci 42(4):1260–1288
Bednar J, Chen Y, Liu TX, Page S (2012) Behavioral spillovers and cognitive load in multiple games: an experimental study. Games Econom Behav 74(1):12–31
Berg J, Dickhaut J, McCabe K (1995) Trust, reciprocity, and social history. Games Econom Behav 10:122–142
Berggren N, Elinder M, Jordahl H (2008) Trust and growth: A shaky relationship. Emp Eco 35(2):251–274
Beugelsdijk S (2006) A note on the theory and measurement of trust in explaining differences in economic growth. Camb J Econ 30:371–387
Beugelsdijk S, van Schaik T (2005) Social capital ad growth in european regions: an empirical test. Eur J Polit Econ 21:301–324
Beugelsdijk S, de Groot HLF, van Anton BTM, Schaik. (2004) Trust and economic growth: a robustness analysis. Oxf Econ Pap 56:118–134
Capra CM, Tanaka T, Camerer CF, Feiler L, Sovero V, Noussair CN (2009) The impact of simple institutions in experimental economies with poverty traps. Economic Journal 119:977–1009
Cason NT, Gangadharan L (2013) Cooperation spillovers and price competition in experimental markets. Econ Inq 51:1715–1730
Cason NT, Savikhin AC, Sheremeta RM (2012) Behavioral spillovers in coordination games. Eur Econ Rev 56:233–245
Cox DR (1975) Partial Likelihood. Biometrika 62:269–276
Dasgupta P, Sergaldin I (2000) Social capital: a multifaceted perspective. The World Bank, Washington, DC
Dearmon J, Grier K (2009) Trust and development. J Econ Behav Organ 71:210–220
Doh S, McNeely CL (2012) A Multi-dimensional perspective on social capital and economic development: an exploratory analysis. Annals Reg Sci 49:821–843
Fischbacher U (2007) z-Tree: zurich toolbox for ready-made economic experiments. Exp Econ 10:171–178
Hardin R (1982) Collective action. Johns Hopkins University Press, Baltimore
Helliwell, John F. 1996. Economic Growth and Social Capital in Asia. NBER Working Paper. 5470.
Herz H, Taubinsky D (2018) What makes a price fair? an experimental study of transaction experience and endogenous fairness views. J Eur Econ Assoc 16:316–352
Horváth R (2012) Does trust promote growth? J Comp Econ 41(3):777–788
Jenkins, Stephen P. 2004. Survival Analysis. Unpublished Manuscript, Institute for Social and Economic Research, University of Essex, Colchester, UK.
Keck S, Karelaia N (2012) Does competition foster trust? The role of tournament incentives. Exp Econ 15(1):204–228
Knack S (1997) Does social capital have an economic payoff? a cross-country investigation. Quarterly Eco J 112:1251–1288
Knorringa P, van Staveren I (2006) social capital for industrial development: organizing the concept. UNIDO, Vienna
Lei V, Noussair CN (2002) An experimental test of an optimal growth model. Amer Eco Rev 92(3):549–570
Lei V, Noussair CN (2007) Equilibrium selection in an experimental macroeconomy. South Econ J 74(2):448–482
Miller A, Mitamura T (2003) Are surveys on trust trustworthy? Social Psychol Quart 66(1):62–70
Müller D, Torgler B, Uslaner EM (2012) A comment on ‘inherited trust and growth.’ Eco Bullet 32(2):1481–1488
Naef, Michael, and Jűrgen Schupp. 2009. Measuring Trust: Experiments and Surveys in Contrast and Combination. IZA Discussion Paper. 4087.
Nooteboom B (2007) Social capital, institutions and trust. Rev Soc Econ 65(1):29–53
North DC (1990) Institutions, institutional change, and economic performance. Cambridge University Press, New York
Ostrom E (1990) Governing the commons: the evolution of institutions for collective action. Cambridge University Press, Cambridge, UK
Page T, Putterman L, Unel B (2005) Voluntary association in public goods experiments: reciprocity, mimicry and efficiency. Economic Journal 115:1032–1053
Paul Z, Knack S (2001) Trust and growth. Eco J 111:295–321
Peiró-Palomino J, Tortosa-Ausina E (2013) Can trust effects on development be generalized? a response by quantile. Eur J Polit Econ 32:377–390
Porta La, Rafael F-d-S, Shleifer A, Vishny RW (1997) Trust in large organizations. Amer Eco Review 87:333–338
Putnam RD, Leonardi R, Nanetti RY (1993) Making democracy work. Princeton University Press, Princeton, NJ
Reeskens T, Hooghe M (2008) Cross-cultural measurement equivalence of generalized trust: evidence from the european social survey (2002 and 2004). Soc Indic Res 85(3):515–532
Roth F (2009) Does too much trust hamper economic growth? Kyklos 62(1):103–128
Savikhin A, Sheremeta RM (2012) simultaneous decision-making in competitive and cooperative environments. Econ Inq 51(2):1311–1323
Staveren V, Irene PK (2007) Unpacking social capital in economic development: how social relations matter. Rev Soc Eco 65(1):107–135
Whiteley PF (2000) Economic growth and social capital. Political Studies 48:443–466
Yamagishi T, Kikuchi M, Kosugi M (1999) Trust, gullibility, and social intelligence. Asian J Soc Psychol 2(1):145–161
Acknowledgements
We are grateful to the Department of Economics and Finance at the City University of Hong Kong for financial and laboratory support. We thank David Cooper, Rachel Croson, Roberto Ricciuti, Stephane Robin, Bradley Ruffle, Marie-Claire Villeval, Marc Willinger, Chun-Lei Yang, seminar participants at Groupe d’Analyse et de Théorie Economique, Université de Montpellier, Università degli Studi di Verona, Academia Sinica, Wilfrid Laurier University, participants at the 2015 Workshop on Social and Moral Norms and the 2016 International Conference of the French Association of Experimental Economics
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chan, K.S., Lei, V. & Vesely, F. Escape poverty trap with trust? An experimental study. Soc Choice Welf 62, 37–66 (2024). https://doi.org/10.1007/s00355-023-01480-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00355-023-01480-4