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Table 4 Income effects of occupational interaction based on the precise difference model N = 2536

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

Variable

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

−.001

−.012

.008*

(.004)

(.013)

(.003)

Outward interaction

.018**

.033*

.016*

(.004)

(.011)

(.005)

Seeking job by pulling strings

.098***

.014

.034

(.017)

(.051)

(.036)

Male

−.078**

−.120**

(.017)

(.032)

Age

.003

.003

.029**

(.011)

(.009)

(.007)

Square of age/100

.020

−.017

.029*

(.026)

(.014)

(.010)

Education level

.044***

.059***

.08***

(.004)

(.008)

(.01)

Party member

089

.133*

.269**

(.058)

(.046)

(.061)

Frequency of job changes

.006

.011

(.011)

(.013)

State-owned unit

−.453***

−.207***

−.178***

(.043)

(.037)

(.01)

Unit size/100

−.001

.005

.001

(.001)

(.005)

(.009)

Administrative staff

.083*

.187**

.179***

(.029)

(.036)

(.032)

Category of employment (10 categories)

Yes

Yes

No

Intercept

.091

.789*

.884**

(.164)

(.247)

(.197)

R square

.398

.224

.107

Sample size

2536

2536

2536

  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) “Yes” means this variable is controlled for in the model. (4) Similar to Table 3, in a different 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, they do signify specific meanings. For example, variables for unit nature are −1, 0, and 1, and these represent variation between state-owned and privately owned sectors, which are invariant, and between privately owned and state-owned, respectively. Therefore, if seen as a continuous variable, the difference in the nature of the unit represents the degree of “becoming state-owned” in practice. In robustness analysis, original dummy variables are adopted to perform tests and highly consistent results are achieved