Managers’ career paths and interlocal collaboration: an agent network collaboration model

Figures & data

Figure 1. Direct agent network hypothesis.

Legend: Arrow: career transfer event Solid circle: dyad of cities with enhanced probability for interlocal collaboration where city manager(s) serves in City Y from year t to year t+n; Empty circle: city with baseline probability for interlocal collaboration

Figure 2. Indirect agent network hypothesis.

Legend: Solid Arrow: career transfer event Solid circle: dyad of cities with enhanced probability for interlocal collaboration where city manager(s) serves in City Z from year t+n+1 to year t+m; Empty circle: city with baseline probability for interlocal collaboration; Arc: indirect network connection

Table 1. Measurement and data sources.

Variables		Measures	Data sources
Dependent variable	ILAx,y/z,t	Annual number of environmental ILA between the dyad of cities	manually coded through media sources
Agent network variables	Direct Agent Networkx,y,t	= 1 for the existence of direct agent network connection of the dyad cities in Yeart	Career path data are collected from public administrators’ professional resumes
	Indirect Agent Networkx,z,t	= 1 for the existence of indirect agent network connection of the dyad cities in Yeart
Control variables
Environmental variables	Prior ILA x,y/z,t	= 1 if the dyad of cities had interlocal cooperation before yeart	Coded by authors
	Waste Waterx,y/z,t	Absolute difference in waste water discharge between Cityx and Cityy/z in Yeart	Guangdong Statistical Yearbook (Waste water discharge is measured in 100 million tons; Industrial waste gas emission in one trillion cubic feet; industrial solid waste in 1 million ton; Industrial soot (dust) is measured in 10 thousand tons.)
	Industrial Waste Gasx,y/z,t	Absolute difference in Industrial Waste Gas Emission between Cityx and Cityy/z in Yeart
	Industrial Solid Wastex,y/z,t	Absolute difference in industrial Solid Waste Produced between Cityx and Cityy/z in Yeart
	Industrial Dustx,y/z,t	Absolute difference in Industrial Soot(Dust) Emission between Cityx and Cityy/z in Yeart
Socioeconomic and demographic variables	Population_Growthx,y/z,t	Absolute difference in population growth between Cityx and Cityy/z in Yeart	Raw data including Permanent population at the year-end (10000 persons), gross domestic product (100 million yuan), and General budgetary revenue (100 million yuan) is from Guangdong Statistical Yearbook. The variables are calculated by authors based on the raw date
	Budget_Growthx,y/z,t	Absolute difference in budget growth between Cityx and Cityy/z in Yeart
	GDP Growthx,y/z,t	Absolute difference in GDP growth between Cityx and Cityy/z in Yeart
	City Sizex,y/z	Absolute difference in city size between Cityx and Cityy/z (km2)
	Proximityx,y/z	Binary variable: ‘1’ if the Cityx and Cityy/z share geographic borders, ‘0’ otherwise

Table 2. Descriptive statistics and correlation.

	Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
	Mean	.65	.149	.042	.359	5.043	3.707	1.946	1.243	1.118	7.091	3.18	6535.644	.283
	St.Dev	2.207	.357	.202	.48	4.633	2.913	1.534	.975	1.506	11.592	2.807	4607.46	.451
	Min	0	0	0	0	.02	.008	.004	0	.002	.004	.005	34.28	0
	Max	22	1	1	1	17.55	14.573	6.515	4.8	6.811	94.382	19.454	17465.17	1
(1)	ILAx,y/z,t	1.000
(2)	Direct Agent Networkx,y,t	.080	1.000
(3)	Indirect Agent Networkx,z,t	.115	−.066	1.000
(4)	Prior ILA x,y/z,t	.100	.026	.005	1.000
(5)	Waste Waterx,y/z,t	−.025	−.017	.013	.188	1.000
(6)	Industrial Waste Gasx,y/z,t	−.043	.027	−.014	.096	.604	1.000
(7)	Industrial Solid Wastex,y/z,t	.039	.112	.082	.111	.310	.469	1.000
(8)	Industrial Dustx,y/z,t	.017	−.036	−.053	.002	.010	−.061	−.053	1.000
(9)	Population_Growthx,y/z,t	.080	−.028	.034	−.072	.235	.145	.046	−.051	1.000
(10)	Budget_Growthx,y/z,t	−.057	.037	−.054	−.173	.022	.044	−.032	.013	.030	1.000
(11)	GDP Growthx,y/z,t	−.024	−.008	−.021	−.099	−.100	−.042	−.074	.005	.069	.017	1.000
(12)	City Sizex,y/z	−.052	−.000	−.062	−.167	−.053	−.128	−.159	.127	−.002	.025	.149	1.000
(13)	Proximityx,y/z	.068	.031	.024	−.136	−.031	.046	.001	.012	.199	−.053	−.037	−.033	1.000

Note: number of observations: 637.

Table 3. Zero-inflated negative binomial models for ILAs.

	Model 1 ZINB	Model 2 ZINB	Model 3 ZINB	Model 4 ZINB	Model 5 ZINB	Model 6 Hurdle NB	Model 7 NB	Model 8 AME
Negative Binomial Model
Direct Agent Networkx,y,t	.577**	.633**	.692**	.605**	.605*	1.041***	.996**	.698**
	(.286)	(.281)	(.271)	(.269)	(.346)	(.392)	(.411)	(.308)
Indirect Agent Networkx,z,t		.739*	.658**	.789**	.789**	1.346**	1.186***	1.784**
		(.400)	(.334)	(.309)	(.333)	(.523)	(.299)	(.773)
Prior ILA x,y/z,t			1.104**	.970**	.970**	19.389***	3.237***	2.219***
			(.441)	(.457)	(.416)	(1.315)	(.345)	(.828)
Waste Waterx,y/z,t			−.031	−.063*	−.063	−.020	.022	−.005
			(.031)	(.034)	(.048)	(.051)	(.056)	(.021)
Industrial Waste Gasx,y/z,t			−.086*	−.041	−.041	−.096	−.132*	−.069**
			(.047)	(.047)	(.046)	(.082)	(.070)	(.034)
Industrial Solid Wastex,y/z,t			.082	.074	.074	.093	.185*	.115**
			(.077)	(.076)	(.086)	(.125)	(.104)	(.054)
Industrial Dustx,y/z,t			−.048	−.009	−.009	−.173	−.161	−.056
			(.126)	(.132)	(.176)	(.181)	(.149)	(.077)
Population_Growthx,y/z,t				−.073	−.073	−.047	−.256***	−.124**
				(.079)	(.097)	(.119)	(.099)	(.056)
Budget_Growthx,y/z,t				.032	.032	.037	−.032	−.004
				(.027)	(.031)	(.041)	(.022)	(.016)
GDP Growthx,y/z,t				−.190***	−.190***	−.181**	−.054	−.075**
				(.054)	(.061)	(.079)	(.037)	(.034)
City Sizex,y/z				.000	.000	.000	.000	.000
				(.000)	(.000)	(.000)	(.000)	(.000)
Proximityx,y/z				−.214	−.214	−.375	.165	.029
				(.222)	(.254)	(.331)	(.250)	(.154)
_cons	−1.373***	−1.451***	−1.706***	−1.530***	−1.530***	−22.650***	−2.813***
	(.242)	(.246)	(.411)	(.475)	(.523)	(.726)	(.656)
Logit Model
Direct Agent Networx,y,t	−.427	−.510	−.475	−.569	−.569	−.683**
	(.484)	(.474)	(.487)	(.528)	(.798)	(.330)
Indirect Agent Networkx,z,t		−1.549*	−2.111**	−2.061**	−2.061**	−1.520***
		(.834)	(1.073)	(.942)	(.900)	(.546)
Prior ILA x,y/z,t			−4.890***	−5.068***	−5.068***	−3.349***
			(.878)	(.952)	(1.572)	(.402)
Waste Waterx,y/z,t			−.069	−.148**	−.148	−.024
			(.058)	(.074)	(.100)	(.039)
Industrial Waste Gasx,y/z,t			.108	.173	.173	.116*
			(.097)	(.109)	(.130)	(.062)
Industrial Solid Wastex,y/z,t			−.226	−.278*	−.278*	−.269***
			(.147)	(.156)	(.164)	(.104)
Industrial Dustx,y/z,t			.097	.208	.208	−.095
			(.200)	(.236)	(.323)	(.131)
Population_Growthx,y/z,t				.316*	.316*	.372***
				(.168)	(.187)	(.108)
Budget_Growthx,y/z,t				.102***	.102***	.045
				(.039)	(.036)	(.030)
GDP Growthx,y/z,t				−.198*	−.198*	.016
				(.101)	(.115)	(.054)
City Sizex,y/z				.000	.000	−.000
				(.000)	(.000)	(.000)
Proximityx,y/z				−.690	−.690	−.496*
				(.460)	(.468)	(.286)
_cons	−14.443	−14.199	−15.832	−3.294	−3.294***	2.836***
	(531.075)	(479.866)	(1733.078)	(2.367)	(1.253)	(.543)
Model Fit
N	637	637	637	637	637	637	637
AIC	1075.117	1068.022	945.151	937.105	937.105	925.189	979.807
BIC	1150.882	1152.700	1074.397	1110.919	1110.919	1099.003	1068.942
Vuong test	3.879***	4.094***	3.622***	4.063***
Alpha	1.405***	1.326***	.615***	.524***	.524***		2.261***
Log likelihood	−520.558	−515.011	−443.575	−429.552	−429.552	−423.595	−469.904
LR chi^2	46.857	49.754	87.087	100.195	321.510	107.529	815.584
N_zero	501.000	501.000	501.000	501.000	501.000

notes: Standard errors in parentheses; * p < .1, ** p < .05, *** p < .01. Reported estimates are coefficients.

Year fixed effects have been added to all above models in both count and logit models in ZINB and in NB models but are neglected here for succinctness.

Model 5 did not report Vuong test because of using cluster standard error.

Figure 3. Effects of direct and indirect ANC on ILAs.

Note: Based on model 5 in Table 3, the predictive margins (predicted number) of ILAs are generated on each value of Direct ANC and Indirect ANC.

Table A1. Zero-inflated negative binomial models for ILA formation.

	Model A1. ZINB	Model A2. ZINB	Model A3. AME	Model A4. ZINB	Model A5. AME	Model A6. ZINB	Model A7. ZINB
Negative Binomial Model
Direct Agent Networkx,y,t	.637***	.756***	.548***	0.656**	0.515***	.619**
	(.240)	(.262)	(.186)	(0.274)	(0.191)	(.268)
Indirect Agent Networkx,z,t	.732**	.821**	.952***	0.638*	0.859***	.834***
	(.297)	(.343)	(.298)	(0.333)	(0.282)	(.314)
Lagged ILA	.047**
	(.024)
Interaction of Direct and						−1.063
Indirect Agent Network						(1.469)
Lagged Direct Agent Networkx,y,t Lagged Indirect Agent Networkx,z,t							0.828***
							(0.316)
Prior ILA x,y/z,t		1.531***	1.883***	1.518**	1.524***	.969**	0.990**
		(.488)	(.361)	(0.771)	(0.317)	(.455)	(0.454)
Waste Water	−.034	−.308***	−.021	−0.007	0.022	−.067*	−0.065*
	(.030)	(.060)	(.060)	(0.036)	(0.023)	(.034)	(0.034)
Industrial Waste Gas	−.064	.000	−.000	0.000	0.000	−.040	−0.036
	(.043)	(.000)	(.000)	(0.000)	(0.000)	(.047)	(0.047)
Industrial Solid Waste	.067	.003***	−.000	0.000	0.001	.073	0.063
	(.069)	(.001)	(.001)	(0.001)	(0.001)	(.075)	(0.077)
Industrial Dust	−.035	−.099	−.069	−0.290*	0.069	−.014	−0.039
	(.113)	(.111)	(.068)	(0.152)	(0.103)	(.131)	(0.128)
Population_Growth	−.056	17.975*	16.036**	−2.867	15.905*	−.072	−0.082
	(.074)	(9.657)	(6.736)	(7.538)	(9.227)	(.079)	(0.078)
Budget_Growth	.023	1.989	.567	−1.261	0.151	.036	0.033
	(.024)	(1.762)	(1.884)	(3.121)	(1.752)	(.027)	(0.027)
GDP Growth	−.164***	2.326	−1.089	2.989	0.830	−.196***	−0.209***
	(.050)	(4.857)	(3.201)	(8.130)	(4.585)	(.054)	(0.055)
City Sizex,y/z	.000	−.000**	−.000	0.000	−0.000	.000	0.000
	(.000)	(.000)	(.000)	(0.000)	(0.000)	(.000)	(0.000)
Proximityx,y/z	−.219	−.279	−.085	−0.169	0.073	−.214	−0.220
	(.205)	(.221)	(.154)	(0.223)	(0.141)	(.221)	(0.223)
_cons	−.900***	−1.216**		−2.009***		−1.512***	−1.463***
	(.325)	(.559)		(0.673)		(.473)	(0.469)
Logit Model
Direct Agent Networx,y,t	−.027	−.392		−0.641		−.547
	(.588)	(.697)		(0.634)		(.525)
Indirect Agent Networkx,z,t	−1.578*	−2.468**		−2.991**		−1.900**
	(.942)	(1.221)		(1.398)		(.949)
Lagged ILA	−4.799***
	(.958)
Interaction of Direct and						−23.296
Indirect Agent Network						(2.0e + 05)
Lagged Direct Agent Networkx,y,t							−0.143
							(0.502)
Lagged Indirect Agent Networkx,z,t							−1.441*
							(0.804)
Prior ILA x,y/z,t		−5.218***		−3.672**		−5.041***	−4.920***
		(1.210)		(1.641)		(.952)	(0.953)
Waste Water	−.157**	−.991***		−0.173*		−.152**	−0.149**
	(.073)	(.348)		(0.105)		(.074)	(0.073)
Industrial Waste Gas	.098	.001		−0.000		.172	0.193*
	(.115)	(.001)		(0.001)		(.108)	(0.111)
Industrial Solid Waste	−.260	.011**		−0.003		−.277*	−0.324**
	(.164)	(.004)		(0.003)		(.155)	(0.157)
Industrial Dust	.011	.036		−1.678***		.202	0.142
	(.234)	(.237)		(0.629)		(.236)	(0.238)
Population_Growth	.429**	−26.554		−117.125		.314*	0.334**
	(.181)	(26.077)		(77.962)		(.168)	(0.169)
Budget_Growth	.113***	3.956		−6.307		.105***	0.105***
	(.043)	(9.425)		(5.832)		(.040)	(0.039)
GDP Growth	−.183*	14.640		7.101		−.207**	−0.236**
	(.096)	(11.180)		(11.019)		(.102)	(0.110)
City Sizex,y/z	.000	−.000		0.000*		.000	0.000
	(.000)	(.000)		(0.000)		(.000)	(0.000)
Proximityx,y/z	−.676	−.521		−1.192*		−.691	−0.707
	(.449)	(.649)		(0.631)		(.460)	(0.465)
_cons	−3.038	−16.857		0.175		−3.361	−3.473
	(2.648)	(377.759)		(2.999)		(2.486)	(2.531)
N	637	637	637	637	637	637	637

Notes.

1. Model A1 replace Prior ILA _xy/z,t with Lagged ILA, the number of ILA signed by dyad of cities last year. Other specifications stay the same as model 5 in Table 3 in the paper.

2. Model A2 use control variables of Waste Water, Industrial Waste Gas, Industrial Solid Waste, Industrial Dust, Population_Growth, Budget_Growth, GDP Growth at the City_x level. Other specifications stay the same as model 5 in Table 3 in the paper.

3. Model A3 presents variables’ average marginal effect based on model A2’s estimation.

4. Model A4 use control variables of Waste Water, Industrial Waste Gas, Industrial Solid Waste, Industrial Dust, Population_Growth, Budget_Growth, GDP Growth at the City_y level. Other specifications stay the same as model 5 in Table 3 in the paper.

5. Model A5 presents variables’ average marginal effect based on model A4’s estimation.

6. Model A7 use Lagged Direct Agent Network_xy,t and Lagged Indirect Agent Network_x,y,t to test whether their influences last over the next year.

Data availability statement

The data is available in https://doi.org/10.7910/DVN/RP09MJ.