B. Other data and estimation model
IV. Empirical analysis
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because education, working experience help individual to accumulate higher human capital, the effect of both should enhance one’s ability to earn more income.
Table 2 provides summary statistics for the main variables for the Chinese individual data.
Table 2 Descriptive Statistics for Chinese Individual Data
Variables Mean SD Min Max
Income(Yuan) 19738.744 30652.840 500 800000
Individual Trust 2.474 1.174 1 5.000
Social Trust 2.498 0.730 1.430 3.960
Household Registration(1 stands for urban area)
0.645 0.478 0 1
Gender(0 stands for female) 0.549 0.498 0 1
Schooling Years 10.204 3.867 0 18.500
Average Working Hours per Week 51.454 14.268 20 98.000
Working Experience(Year) 20.799 12.697 0 50.000
Based on a sample of 18809 observations
IV. Empirical Analysis A. Trust and Nation’s Wealth
Table 3 shows the result for testing the growth effect of trust. Equation (1) and Equation (3) provide base regression result for cross-country data and Chinese province data.
For cross-country data, the basic regression model performs well in
predicting the dynamic of country level 5-year growth rate. The variable GDP per capita, the growth rate of population, investment rate, human capital indicator and technology indicator are all statistically significant.
Nevertheless, the total 𝑅2 for both the cross-country regression equations are not ideal. The possible reason for this result is that different countries may have quite different economic growth strategy. Consider only labor supply,
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Table 3 Estimation Result for Growth Effect of Trust
Cross-country Date Chinese Province Data
Equation (1) (2) (3) (4) (5) (6)
Dependent variable Growth Growth Growth Growth Growth Solow residual
GDP per capita(log) -0.0211345***
(0.002648)
Growth of Pop 0.2094946***
(0.069101)
Human Capital 0.0127934***
(0.0040128)
First Industry Share -0.24466**
(0.0920267)
a. As the result of Hausman test suggests, equation (1), (2), (6) are estimated using random effects model; equation (3), (4), (5) are estimated using fixed effects model.
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*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
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investment, human capital and technology may not be sufficient to account for all factors that contributes to particular country’s growth. Thus it may not be suitable to use one simple and same growth regression to portray the dynamic of growth in 57 different countries included in our dataset. However, as the growth effect of trust is the main concern for the paper, comparing the equation (1), (2) and we see that no evidence according to the regression suggest trust contributes to positive growth for certain economy.
For Chinese province data, the basic regression we used in the estimation performs as predicted with only two exceptions, the growth rate of population and the
investment rate. The level of GDP per capita, the human capital indicator and the share of first industry in GDP are statically significant. Economy tends to grow faster with lower GDP per capita and higher human capital level; in addition, agricultural provinces experience less growth compared with industrial province, which is commonly observed in China. Combining both equation (3) and (4), similar to the result for the cross-country data, the estimated coefficient for trust is not statistically significant thus implying trust has no growth effect on province level in China.
Therefore, both regression from cross-country data and Chinese province data are consistent with the prediction of the modified RBC model in Section II that the growth rate of the output of certain economy is not affected by the trust level in that society.
Recall that in the modified RBC model of Section II, the mathematical solution indicates that trust affect the macro economy through a constant saving rate under the assumption of no government purchase and fully depreciation. Controlling for the saving rate in the growth regression (equation 5), we see that both the trust coefficient and the saving rate coefficient remain statistically insignificant. This finding indicates that the economic growth in provinces of China is not affected by trust directly, or through saving rate indirectly.
To further justify our empirical finding that trust may have no growth effect, we
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𝑆𝑅 = 𝑌̂ − 𝑠𝑘𝐾̂ − 𝑠𝑙𝐿̂ = 𝑠𝑘𝑟̂ + 𝑠𝑙𝑤̂,
where 𝑠𝑘 ≡ 𝑟𝐾/𝑌, 𝑠𝑙 ≡ 𝑤𝐿/𝑌 are the factor income share. Variable 𝑟 and 𝑤 denote the payments to per unit capital and labor so that 𝑌 = 𝑟𝐾 + 𝑤𝐿. Variable 𝑌̂, 𝐾̂ and 𝐿̂ denote the growth rate of output, capita and labor. Therefore, Solow residual is the residual that the growth rate of output after subtracting the share-weighted growth in factor quantities. In other words, Solow residual is the residual growth rate of output that cannot be explained by direct factor inputs and if trust contributes to certain economy’s growth, then trust should be significantly related with the Solow residual. As the constraint of data availability, only the Solow residual of Chinese provinces are calculated. Equation (6) provides the empirical result for the link between Solow residual and trust. The result of Hausman test equals to 0.9399, thus the regression is estimated using random effects model. The finding of the estimation shows that no statistically significant relation exists between these two variable and thus furthermore verifies that no growth effect of trust.
Combing the empirical result both from the cross-country data and Chinese province data, we are confident with the estimation finding that trust has no growth effect.
Table 4 shows the result for testing the income effect of trust using cross-country data. Our base model specification performs well in predicting the income per capita level on cross-country data. Similar to economic theory and past literature suggests, both investment rate, human capital and the technology level are statistically
significant, positively related with the country’s income per capita level. Combing the estimation result of equation (7) and (8), it seems strangely that trust also has no income effect. However, inspired by Knack and Keefer (1997)’s estimation that their trust-growth relationship is sensitive to the choice of human capital measures, we then first test the income-trust relationship with the absence of human capital indicator in our regression. Eliminating the interference of human capital indicator (equation 9),
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Table 4 Estimation Result for Income Effect of Trust with Cross-Country Data
Equation (7) (8) (9) (10) (11)
Dependent variable(Log) GDP per capita GDP per capita GDP per capita Human Capital GDP per capita
I/Y 0.9401036***
Human Capital 1.272623***
(0.0151541)
a. GDP per capita as dependent variable is taken 5-year average for clear causality purpose.
b. Because only one wave trust data are used to construct cross-country dataset, all equations are estimated using random effects model.
Sources: Author calculations.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
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Table 5 Estimation Result for Income Effect of Trust with Chinese Province Data
Equation (12) (13) (14) (15) (16) (17)
Dependent variable(Log) GDP per capita GDP per capita GDP per capita GDP per capita GDP per capita GDP per capita
I/Y 0.5125063
Human Capital 76.9608***
(12.87152) First Industry Share -4.810724**
(1.827107)
Rural Population Share -.5221559
(0.927342)
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b. As the result of Hausman test suggests, all equations are estimated using fixed effects model.
Sources: Author calculations.
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
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The reason that the income-trust relationship is sensitive to human capital indicator is that human capital is strongly related with trust as equation (10) shows. Therefore, we include a new cross term 𝑇𝑟𝑢𝑠𝑡 ∗ 𝐻𝐶 which equals to the value of human capital indicator multiplied by the value of trust indicator, in the income-trust estimation.
Controlling the relation between human capital and trust, the result of equation (11) provides several important implications: (a) Controlling the trust and human capital level at the observations’ mean which equals to 0.261 and 2.294, 1 percent positive change of human capital will lead to a 0.0237(2.294*1 percent *1.034523) positive income change from human capital itself, and 0.00565 (0.261*2.294*1 percent
*0.94351113) positive income change from the mutual relationship between trust and human capital. Notice that the sum of 0.0237 and 0.00565 equals 0.02935 is larger than the result of 1 percent human capital change in equation (7) and (8) which equals to 0.02919(2.294*1 percent *1.272623) and 0.02918(2.294*1 percent *1.272192).
This finding suggests that trust increase the revenue for human capital. (b) Controlling the trust and human capita level at the observations’ mean which equals to 0.261 and 2.294, 1 percent positive change of trust change will result in a 0.00568(0.261*1 percent *2.17814) negative income change directly from trust itself, and 0.00565 (0.261*2.294*1 percent *0.94351113) positive income change from the mutual relationship between trust and human capital. The sum of these two effects equals to -0.00003, suggesting a very small negative overall income change with 1 percent positive trust change, when the value of trust and human capital are controlled at observations’ mean. (c) The finding described in implication (b) also indicates that for trust to improve the income, the value of certain country’s human capital indicator must meet a minimum value. The threshold condition for human capital indicator according to the equation (11) is 2.309(2.17814/0.9435113), which is slightly larger than the mean human capital indicator 2.294 of our observations. It indicates that trust has a positive income effect in those countries with human capital indicator exceeding the value of 2.309. As long as a country has a human capital indicator lager than the
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only cause economic loss. As Douglas North noticed, “the inability of societies to develop effective, low-cost enforcement of contracts is the most important source of both historical stagnation and contemporary underdevelopment in the Third World”
(North, 1990, p. 54). Considering the mutual relationship between human capital and trust, it is reasonable to conclude that the Northian poverty trap can be solved by increasing both the social trust level and human capital level. As is accepted that trust can generate economic revenues by reducing the transaction cost, it is important for individuals in the society to fully understand and appreciate the value of trust for the trust-income relationship to work. More importantly, the successful transactions based on trust require both parties to strictly dripline themselves. Therefore, to overcome the poverty trap and realize the benefit of trust, it is essential for a society to accumulate enough human capital and increase the civilized population base who respect the value of mutual trust. In the real world, it is common to observe less developed countries still stuck in poverty trap after repeated political reforms which are
supposed to have positive economic benefit. The key problem for these struggles may lies in the insufficient accumulation of human capital. Recall the economic success of eastern Asia, it is certain that after proper institution reforms, high accumulation of human capital plays an important base in their economic take-offs.
Table 5 shows the result for testing the income effect of trust using Chinese province data. Similar to the situation in cross-country data, the base model
specification as equation (12) shows performs well. Equation (13) demonstrates the result when adding the trust indicator into the base model. Because the coefficient of trust indicator is statistically significant, it is clear that income per capita level is strongly influenced by trust indicator.
Equation (14) and (15) provide the estimation when saving rate is added in the regression process. Recall in Section II, that the mathematical solution for our
modified RBC model implies that output level is determined by the saving rate of the
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strongly and positively related with each province’s saving rate. Notice that the coefficient for investment rate becomes statistically significant when controlling the saving rate for province, this change indicates that personal saving is an important part for investment to improve the income level per capita. When controlling both the saving rate and trust level, the equation (15) shows that only the coefficient of trust indicator is statistically significant and the income effect of saving rate is dominated by the income effect of trust. This estimation result is consistent with our modified RBC model’s prediction that representative household’s saving rate is determined by trust level of the society.
Inspired by the mutual relationship between human capital and trust, the same cross term is added in the Chinese provinces’ estimation. The equation (16) shows the result controlling the relationship between human capital and trust: (a) Controlling the trust and human capital level at the observations’ mean which equals to 2.706 and 0.017, 1 percent positive change of human capital will lead to a 0.0038 (22.49756*1 percent
*0.017) positive income change from itself, and 0.0093 (20.19377*2.706*1 percent
*0.017) positive income change from the mutual relationship. Notice the sum of the positive income change equals to 0.0131 which is larger than the result of 1 percent of solely human capital change in equation (12) which equals to 0.0130. This finding is consistent with the result of cross-country data that trust will increase the revenues of human capital. (b) Controlling the trust and human capital level at the observations’
mean which equals to 2.706 and 0.017, 1 percent positive change of trust will lead to 0.0093 (20.19377*0.017*1 percent *2.706) positive income change solely from the mutual relationship. Notice the positive income change from 1 percent trust change in equation (13) equals to only 0.0070 (0.258044*1 percent *2.706). Thus it is
reasonable to conclude that in China, human capital can also increase the revenues of trust. This result is slightly different from the cross-country data which require a minimum value of human capital indicator for increasing trust to have positive income change; the key difference lies in that the variable trust is not statistically
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however, when referring back to the human capital indicator in our cross-country dataset, we find that after year 2003, the human capital indicator of China ranges from 2.410701 to 2.579169. Thus the solely positive income change from trust is because after year 2003, the human capital indicator in China is larger than the threshold value we calculated in the cross-country data which equals to 2.309. Therefore, the result is in fact consistent with our analysis for the cross-country data.
Equation (17) provide a simple robust test for our trust indicator. As it is known that the difference between rural area and urban area is substantial and foreign investment plays an important role in some province’s economic performance, we include the rural population share, the proportion of rural individual consumption on urban individual consumption and the share of foreign direct investment in GDP in the estimation. Notice only the variable equals to the rural consumption level divided by the urban consumption level is statistically significant, thus it suggests that the smaller the gap between rural area and urban area, the better average income level in that province. Therefore, this result verifies the common agreement in China that to stimulate the economic development, it is essential for local government to pay their attention in the rural area. In addition, the estimation result also shows that the trust indicator is rather robust with all these new variables added. During our estimation, other variables are also included to test the robustness of the income-trust
relationship. All regression shows that the trust indicator in this paper is generally robust regardless which new variable is included in the regression model.
Both the estimation result of cross-country data and Chinese province data indicate that trust can only influence the economy’s income level but not the growth. An economy with higher trust level will produce higher overall output. However, the growth rate of the output of certain economy is not affected by the trust level in that society. That is, our empirical finding suggest that trust has only income effect but no growth effect, which is consistent with the predictions of the modified RBC model.
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Recall the representative household optimization problem in Section II, the two models both predict that households earn higher income with higher social trust level.
In this part, we test the income-trust relationship on individual level based on the CGSS survey data.
Table 6 shows the result for the estimation. The basic model specification as equation (18) shows, indicates that male residents living in urban area tend to have higher overall income level. The estimation suggests an income gap between rural and urban residents, male and female individuals, which is commonly observable in Chinese society. Furthermore, human capital is positively related with the individual income level. The Intuition of this result is rather straightforward, individuals get more wage paid with higher human capital as it enhances individuals to work more efficiently. The residents living in provinces located in eastern region, especially coastal provinces, have higher income level because the fact that the eastern provinces are more developed in China. Notice the sign of the coefficient of the working
experience is surprisingly negative, in addition, both the coefficients of working hours and square of working experience remain statistically insignificant. These findings somehow contradict with our prediction in Section III. There may be two possible reasons for this strange estimation result: (a) The wage gap in China is rather large and thus the income in the polarizing job market may not necessarily determined by individuals’ working hours and experience. (b) The reason may also lie in the nature of the dataset that it is constructed based on the survey data. All data are self-reported by respondents and errors shall be expected. Though it may take more work to specify a clear relationship between individual income and working hours, experience in China, it is not the main concern of this paper.
Equation (19) and (20) test the link between individual income and trust. In equation (19), only individual trust is included in the regression, and the estimation result demonstrate a strong positive income-trust relationship. The finding suggests that with higher individual trust level, people generally earn more than those distrust
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Table 6 Estimation Result using CGSS Individual Survey Data
Equation (18) (19) (20) (21) (22) (23)
Dependent variable Income Income Income Income Income Income
Hukou (1 as urban Gender (1 as male) 0.3937577***
(0.0136098) Schooling Years 0.1119743***
(0.002136) Working Experience -0.0149011***
(0.0019052)
East Region 0.465123***
(0.0136897)
Individual Trust 0.2271343***
(0.0056544)
0.0024275 (0.006289)
-0.0309399 (0.0209769)
Social Trust 0.6574203***
(0.0105712)
Trust Value=2 0.2156505***
(0.0171256)
-0.0725112***
(0.0164176)
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(0.0203408) (0.0192145)
Trust Value=4 0.7913898***
(0.0193436)
0.0139832 (0.0219779)
Trust Value=5 0.7259889***
(0.036756)
-0.0677873*
(0.036204)
Adjusted R-squared 0.3578 0.4079 0.5093 0.5094 0.4180 0.5111
Observations 18,809 18,716 18,716 18,716 18,809 18,809
Sources: Author calculations
*** Significant at the 1 percent level.
** Significant at the 5 percent level.
* Significant at the 10 percent level.
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belief, it is more likely for them to seize the profitable investment opportunity.
Secondly, people with higher trust belief, are more cooperative and their trust in other people, especially strangers, makes them better team player than those individuals who hold pessimistic attitudes. Therefore, people with high trust belief tends to work more efficiently.
Controlling the social trust level, however as equation (20) shows, the social trust level dominates the income effect of individual trust. The estimation suggests that as long as people live in a society with high social trust level, the income will be higher than those live in society with low trust level. The estimation also suggests that no matter what the individual’s trust belief is, it is the society’s trust level mostly matters for income rather than the individual’s belief. No matter what the subjective
individual trust belief is, all in all, the individual economic performance is constrained by the structure of the society. In a society with high trust level, individuals with pessimistic attitudes can still enjoy the benefits provided by the positive trust-income
individual trust belief is, all in all, the individual economic performance is constrained by the structure of the society. In a society with high trust level, individuals with pessimistic attitudes can still enjoy the benefits provided by the positive trust-income