國
立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
The result of equation (38) suggests that with higher social trust level, the accumulation of physical capital and more importantly, the economy grow faster, i.e. that trust has growth effect. As the output of the economy is a linear function of the level of physical capital under the specification of AK model, the growth effect of trust will also promise the income effect. Notice the implication of equation (38) is dramatically different from the result of our modified RBC model. The mathematical results of our modified AK model suggest that trust has both output and growth effect, while the modified RBC model indicates that trust should only have income effect.
To sum up, the two models in which households face risky investment market provide several important insights about the economic effect of trust that needs to be tested in our later empirical analysis:
a. Both model suggest that a higher trust level will encourage households to increase private investment. In the long run, such decision will benefit the households with better welfare level.
b. Both model suggest that an economy with higher trust level, benefit from higher private investment, will produce a higher overall output, i.e.
that trust should have income effect.
c. The growth effect of trust is ambiguous. The modified RBC model suggests that growth rate of the output of certain economy is not
affected by the trust level in that society, while the modified AK model, on the contrary, indicates that trust has growth effect.
III. Data
A. Measuring Trust
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
The most important step in testing the prediction of the model is to identify a reliable measure of trust. For cross-country data, we use the measure based on the data from the sixth wave of World Value Survey (WVS) conducted from year 2010 to year 2014. The advantage to use the latest wave of the WVS data is that we can includes as many as the number of the countries in our
estimation and it helps to see the general trust effect on economy in different countries, the drawback is obviously that it may mess with the causality in the equation with the time lag between trust indicator and countries’ economic performance. Nevertheless, literature in the past cautiously consider trust is less sensitive to time, or in other words, generalized trust is stable over time (Bjørnskov, 2007); in Keefer and Knack’s estimation (1997, p. 1267), they find “ trust value for 1980 and 1990 are correlated at .91” and “changes in trust over the decade are uncorrelated with growth rates”. Thus these findings imply that trust however can be viewed as a slowly-changed characteristic of certain society in a quite large time scale.
The question used to access the level of trust for a certain country is:”
Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people”. The trust indicator we use in the paper is the percentage the respondents in each country replying “Most people can be trusted”. This trust indicator we use follows Zak and Knack’s framework when testing the trust’s growth effect in their 2001’s work, and since then many other researches concerning the subject of general trust also pick the same trust indicator. Furthermore, Knack and Keefer (1997) provide empirical support for the validity of these data and find that values for trust is consistent with lab experiment results and case study across countries.
For Chinese data, we use the measure based on the data form the Chinese
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
General Social Survey (CGSS) conducted in year 2003, 2005, 2010 and 2013.
The question used in the survey to measure trust level is that “Generally speaking, how do you trust the strangers” in the year 2003 and 2005; and for year 2010 and 2013, the question is “Generally speaking, would you say that most people can be trusted”. Different from the World Value Survey, the Chinese General Social Survey measures the intensity of individual’s trust beliefs on a scale from 1 to 5, in which “1” means that strongly distrust and
“5” means strongly trust. With this trust intensity data, we are able to estimate the relationship between trust and individual income. Furthermore, we use the mean value of individuals’ trust in each province as the trust indicator on province-level and then test the trust effect on macro economy performance as our theoretical models predict.
One problem with using survey data to represent and compute society trust level is that the survey can only capture the subjective trust level of
individuals. Though using mean value can predict the average trust level for all individuals in the society by eliminating the impact of certain individual’s subjective deviation from the society trust, it is still necessary to verify that if the mean of subjective individual trust is a good proxy for the overall society’s objective trust level. Aiming this, we provide a Monte Carlo simulation based on a simplified trust model to test the relationship between the individuals’
subjective mean trust value and the society’s objective trust level.
In order to realistically imitate the mechanism of how real social trust works, the simulation model includes the randomness of the individual’s trust, the way subjective social trust is established, and the mutually adjustment of trust levels between society and individuals. In the trust model for the purpose of simulation, we use a uniform distribution between 0 and 1 to randomly
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
generate the initial trust level of individuals. The subjective trust level, is calculated using the individuals’ average. And the key adjustment mechanism is represented with a one-time Bernoulli trial as an approximation for the individual investment.
The simulation starts with only one individual. The system first generates a random number between 0 and 1 for the individual to represent the born trust level. Thus at the start, the subjective social trust equals to the individual trust, as it is calculated as the individuals’ average in the whole society. Later the first individual do the one-time Bernoulli trial with the success probability equals to the objective social trust given in advance. Recall the models in Section II, as all households face a non-discriminative investment market, the probability for successive investment equals to the objective trust level, or more specifically 1 − 𝜇. If the investment trial succeeds, the individual will increase his trust but no more than the ceiling limit of the uniform distribution.
The process of the increase will be generated by the system randomly within the uniform distribution. If the investment trial fails, the individual will decrease his trust level randomly, in the same way with a result of success investment. With the adjustment of the individual’s trust, the subjective social trust will in turn be calculated again. Following the same process, the system includes more individual for one person each time and repeat the loop
continuously. Under this framework, we are able to investigate the dynamic of subjective social trust and compare it with the exogenous objective trust level.
Figure 1 shows the logic of a single loop for the process,
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
Figure 1 the Simulation Process
Figure 2 shows the simulation results with the success probability value, i.e.
the objective social trust, set at 0.2, 0.6 and 0.9. The line from bottom to top are the results for success values set at 0.2, 0.6 and 0.9, showing the dynamic values of subjective social trust level during the simulation. The results show that the system equilibrium for average subjective trust level is critically determined by the success level. With higher success level or social trust level, the mean value of individuals’ subjective trust tends to be higher than it in a low social trust level society. The confusion of the result is that the average subjective though accurately reflect the rank of the objective trust, the value of the average subjective trust is not exactly the same as the objective trust given.
This problem lies in the fact that for the simplicity of the simulation, the born trust level of individuals is randomly generated using the uniform distribution and not affected by the objective social trust; that is to say, only the mutual relationship between individuals and social trust is included in the simulation, but the mutual relationship between individuals is neglected. Anyway, the focus of the simulation is to test whether average subjective trust can be a proxy for the objective trust, and as long as the rank of trust is not affected, it is reasonable to use mean value of individuals’ subjective trust survey data as
I. Individual Trust
II. Subjecitve Social Trust
III. Bernoulli trial IV. Individual
Adjustment V. Subjective
Social Trust Adjustment
‧
國立 政 治 大 學
‧
N a tio na
l C h engchi U ni ve rs it y
an approximation for the objective social trust level. Therefore, the trust data from both WVS and CGSS can be confidently used in our estimation.
Figure 2 Simulation Result for Different Social Trust Level Notes: From bottom to top, the values of social trust are set at 0.2, 0.6 and 0.9.