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The Journal of Chinese Sociology

Table 2 Income effects of occupational interaction based on the benchmark difference model N = 6263

From: Occupational interactions and income level: a social capital study using the first-order difference method

Variables

Least square method model

First-order difference model

M1

M2

M3

Explanatory variable

Monthly income of first job

Monthly income of present job

Monthly income difference

Internal interaction

−.005

.010

.012*

(.004)

(.008)

(.004)

Outward interaction

.051***

.057**

.031**

(.008)

(.013)

(.007)

Seeking job by pulling strings

.234***

.012

.053

(.041)

(.034)

(.032)

Male

−.084**

−.139**

–

(.023)

(.032)

–

Party member

.431***

.152**

.345***

(.049)

(.034)

(.044)

Frequency of job changes

–

.018

.023

–

(.009)

(.013)

Intercept

.304***

.874***

.664***

(.050)

(.083)

(.050)

R square

.094

.092

.033

Sample size

6263

6263

6263

  1. Note: (1) *p < 0.05, **p < 0.01, ***p < 0.001. (2) Values inside the parentheses are standard errors obtained by taking heteroskedasticity robustness and urban cluster robustness into account. (3) In a difference mode, what an explanatory variable corresponds to is the change value. For the conciseness of the table, different numerical values of dummy variables such as unit nature, way of gaining entry to work and occupation, etc. are processed as continuous variables. In a logical sense, their establishments do not signify specific meanings. For example, variables for the way of gaining entry to work are −1, 0, and 1, standing for variation from pulling strings to not pulling strings, being invariant, and from not pulling strings to pulling strings, separately. Therefore, it represents the degree of “changing as pulling strings” in practice. In robustness analysis, original dummy variables are adopted to perform tests and highly consistent results are achieved
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