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Record W6907533061 · doi:10.22103/jdc.2022.18687.1183

Investigating the effect of trust on economic growth in developed and developing countries (Generalized Method of Moments (GMM))

2022· article· en· W6907533061 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsDeveloping countryIndex (typography)Social capitalGeneralized method of momentsValue (mathematics)EstimationHomogeneousPanel data

Abstract

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Objective: According to studies, one of the main determinants of economic growth and development is social capital, which has different components. One of the main components of social capital is "trust", which is an essential aspect of economic and social relations. Trust means as a positive expectation that the other party will not act opportunistically in their speech, actions and decisions. The result of some researches shows the difference between countries in terms of their industrial structure depends more on the level of their social capital than on the level of their developmental level, i.e., the degree of trust of individuals in one society to another and their participation in the formation of civic groups and associations. Emphasizing the importance of the role of trust in economic growth and to answer the question of whether the trust index in developing countries affects economic growth in a similar way to developed countries, the main purpose of this study is to show the trust index on economic growth in the two-selected groups of developed and developing countries in the period 2009-2019. Methods: To achieve this goal in this study, the trust index was first extracted from the World Value Survey. Then, to investigate the relationship between trust and innovation with economic growth in two selected groups of developed and developing countries, the two-stage Generalized Method of Moments (GMM) model has been used for dynamic panel data. Applying the GMM method has some advantages such as considering individual non homogeneous and more information, eliminating the biases in cross-sectional regressions. For a more detailed study of these indicators in addition to other effective control variables that are considered as factors affecting the economic growth and development of countries, are also added to the regression equation. Delayed variables of GDP at real price, fixed capital formation, human development index, consumer inflation rate, innovation index, number of labor force, economic freedom index and trade openness index along with confidence index have been added to the model. The statistical population of the present study includes 26 developing countries including: Islamic Republic of Iran, Belarus, Brazil, Colombia, Ecuador, Egypt, Guatemala, Indonesia, Iraq, Jordan, Kazakhstan, Kyrgyzstan, Lebanon, Malaysia, Mexico, Nigeria, Pakistan, Peru, Philippines, Russia, Serbia, Thailand, Tunisia, Turkey, Ukraine and Vietnam and 25 developed countries including: Argentina, Australia, Canada, Chile, Cyprus, Estonia, Finland, France, Germany, Hong Kong, China, Hungary, Italy, Japan , Korea, Netherlands, New Zealand, Norway, Poland, Romania, Singapore, Slovenia, Spain, Sweden, Switzerland and the United States from 2009 to 2019 (statistics are available by year). These countries were grouped based on the Human Development Index so that countries with a human development index higher than 0/8 in the group of developed countries and less than 0/8 in the group of developing countries. Results: In both group of selected countries, the significance level of Sargan statistics is more than 0.05. At the 95% confidence level, the validity of the tools used in the estimation cannot be denied. So, the null hypothesis that the instruments of the disturbance are not correlated cannot be rejected. Therefore, it can be concluded that the instrumental variables used for estimation have the necessary validity. Also, the results show that all explanatory variables have unit root and the Kau test indicates a long-term relationship between variables and economic growth. According to the results, in countries with low levels of development, the variables related to the physical relations of production mainly affect economic growth. Also, the effect of the trust index on the economic growth of these countries is negative. In developed countries, as expected, the impact of the trust index on economic growth is positive. And for one percent increase in trust index, economic growth increases by 0.013 percent. The highest impact of the model variables on economic growth is related to the human development index, which will increase by 2.45% for one percent growth of this economic growth index. The positive impact of the lag of economic growth in both groups of developed and developing countries indicates that economic growth in these countries is subject to stable and long-term macroeconomic policies and requires forward-looking planning. According to theoretical expectations, by increasing the rate of fixed capital formation, labor force and economic freedom will lead to more economic growth in developed countries. Conclusion: According to the results, the trust index in the selected developed countries has a positive and significant effect on economic growth, but in the selected developing countries at a significant level of 90% has a negative effect on economic growth. This result shows that the developed countries have advantages due to the high level of trust in these countries: first, in result of trust, the communication and the transfer of information is done easily. Secondly, facilitating the transfer of information takes place in technological environments, which is one of the effective ways of trust category to solve the problem of information deficiency category of organizational learning. Finally, the problem of free riding is improved through group activities. But the inverse relationship between trust and economic growth in the selected developing countries can have two main reasons. First, the level of trust in economic policies in these countries is very low. Second, the issue of data quality is trust in these countries. Distrust, which is a form of formal and informal institutions in the economy that has always caused fear of partnership and cooperation between people, and people prefer distrust to avoid losses and limit their economic activities to the circle of friends and Their acquaintances do. One of policies that could develop trust level in developing countries is to increase institutional trust by improving the transparency and integrity of institutions. Another and even more important policy is related to educational programs in such a way that the main emphasis is shifted to the team working of students and strengthens cooperation between new generations. These policies are able to increase social capital and consequently public trust.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.191
GPT teacher head0.538
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes1
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