reseacharticle quantifying urban sprawl and its driving...

Research ArticleQuantifying Urban Sprawl and Its Driving Forces in China

JintaoWang Shiyou Qu Ke Peng and Yanchao Feng

School of Economics and Management Harbin Institute of Technology Harbin 150001 China

Correspondence should be addressed to Yanchao Feng m15002182995163com

Received 21 January 2019 Accepted 21 April 2019 Published 6 May 2019

Academic Editor Maria Alessandra Ragusa

Copyright copy 2019 Jintao Wang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Against the background that urbanization has proceeded quickly in China over the last two decades a limited number of empiricalresearches have been performed for analyzing the measurement and driving forces of urban sprawl at the national and regionallevel The article aims at using remote sensing derived data and administrative data (for statistical purposes) to investigate thedevelopment status of urban sprawl together with its driving forces Comparedwith existing studies NPPVIIRS data andLandScandata were used here to examine urban sprawl from two different perspectives urban population sprawl and urban land sprawlFurthermorewe usedpopulationdensity as a counter-indicator of urban sprawl and the regression results also prove the superiorityof the urban sprawl designed by us The main results show that the intensity of urban population sprawl and urban land sprawlhas been enhanced However the upside-down between the inflow of migrants and the supply of urban construction land amongdifferent regions aggravates the intensity of urban sprawl According to the regression analyses the driving mechanism of urbansprawl in the eastern region relying on land finance and financial development has lost momentum for the limitation of urbanconstruction land supply The continuous outflow of population and loosely land supply have accelerated the intensity of urbanland sprawl in the central and western regions The findings of the article may help people to realize that urban sprawl has becomea staggering reality among Chinese cities thereby urban planners as well as policymakers should make some actions to hinder theurban sprawl

1 Introduction

More and more scholars as well as urban planners beginto pay attention to urban sprawl considering its economicsocial and environmental costs and effects [1ndash8] In fact theurban sprawl and land degradation have consequences onbiodiversity too which is highly vulnerable in areas whichhave cropped up without formal planning [9ndash11] Urbansprawl as a famous American phenomenon originally ismainly characterized by low-density development leapfrogand scattered development as well as poor accessibility incomparison to compact development [12] However sinceurban sprawl becomes serious resulting fromquick urbaniza-tion as well as urban expansion in city fringes and the edge ofmetropolitan areas the study of the measurement of urbansprawl together with its driving forces in Chinese cities hasemerged as a hot topic in recent years [13ndash22]

Due to the fact that focusing on one obvious dimensionis more effective than focusing on multiple indexes during

measurement themeasurement of urban sprawl in early timemostly takes into account population density or urban areagrowth [1 23] However there is a big deviation betweenregistered population and permanent population since a largeamount of people in inland migrates to coastal regions andit lacks long-term panel data of permanent population ingovernment collected data The problem of data distortionin the population density calculated by the registered pop-ulation increases our attention [24] Furthermore cities inChina greatly suffer illegal or unauthorized developmentissue which however do not receive enough attention in thedata collected by the government [2] In recent studies abouturban sprawl scholars have adopted a multidimensionalmeasurement method [25] But no consensus has beenreached about which dimension shall be included into themultidimensional measurement about urban sprawl alsothere is no uniformity about using individual dimension orcombining dimensions [23] Fortunately in recent dozens ofyears regarding the spatial analysis on urban sprawl it has

HindawiDiscrete Dynamics in Nature and SocietyVolume 2019 Article ID 2606950 14 pageshttpsdoiorg10115520192606950

2 Discrete Dynamics in Nature and Society

been a common method by applying remote sensing deriveddata to the estimation of urban sprawl because it can accu-rately reflect the spatial distribution of peoplersquos economicsocial and environmental activities compared to governmentcollected data [26ndash32] Based on the existing research resultswe use remote sensing derived data to calculate the extentof urban population sprawl and urban land sprawl and thensquare the product of the two The final result is the indexof urban sprawl used in this paper The temporal and spatialdevelopment mode of urban sprawl in single cities have beenstudied in most researches based on the measurement resultof urban sprawl particularly these metropolises in developedeastern coastal areas [13 33ndash39] However little is knownabout urban sprawl among different cities throughout Chinautilizing national datasets therefore it is especially necessaryto use nationally representative datasets to deeply study urbansprawl in China

New research suggests that dramatic urban sprawl inChina on the one hand has been promoted by the marketand economic development just like the US and othercountries in the west and on the other hand is attributableto Chinarsquos land financing as well as the land-centeredurbanization strategies [40ndash42] As the rural-to-urban landcirculation system is loose in China a large portion ofmunicipal revenues of local government are obtained viaurban sprawl [43] However on the background of nar-rowing the imbalance among different regions the centralgovernment of China keeps requiring local governments totighten up rural-to-urban land conversion process as wellas preserve farmland which makes the land financing andland-centered urbanization policies lose the advantage [44]Through the leverage of bank credit land finance with thecore of land mortgage loan has obtained more disposablefunds from urban sprawl in China ldquogrowing wealth byland and supporting land by wealthrdquo is a vivid reflection ofthe driving forces of urban sprawl [45] Therefore financialdevelopment can also significantly promote the intensity ofurban sprawl However due to the serious imbalance ofregional development the mechanism of land finance andfinancial development on urban sprawl has significant spatialheterogeneity among different regions in-depth studies of thedriving forces of urban sprawl using regionally representativedatasets are much needed

Based on the discussion above previous related literatureis insufficient The paper aims to compare different regionsin terms of their sprawl degree analyze the effects of landfinance financial development and their interaction onurban sprawl in different regions and reveal the spatialheterogeneity of land finance financial development andtheir interaction on urban sprawl at the national and regionallevel Specifically using the 2012-2017 remote sensing deriveddata urban sprawl was quantified by virtue of two metricsextracted fromNPPVIIRS data and LandScan data followedby the comparison of different cities in terms of the sprawldegree difference Furthermore we compared the spatialheterogeneity of land finance financial development andtheir interaction on urban sprawl based on spatial Durbinmodel A qualitative analysis was performed at last on thedriving forces of above findings

This paper falls into five sections Section 2 involvesdata and variables like data source data extraction processmethod tomeasure urban sprawl and independent variablesSection 3 presents the spatial Durbin model as well as spatialweight matrix Section 4 presents the results including theeffects of land finance and financial development on urbansprawl at the national and regional level Section 5 draws ourconclusions and offers some policy implications

2 Data and Variables

There are two levels of Chinese cities based on the administra-tive level namely the prefecture-level cities and the county-level cities As regulated by administrative division systemprefecture-level cities include municipal districts county-level cities counties towns and other units In terms ofthe identification of city in China a prefecture-level cityusually refers to amunicipal district similar to city in westerncountries [23] However there are no clear central urbanareas in a county-level city which to a large extent hasmany nonurbanized areas On that account prefecture-levelcities were selected as samples in the study Due to dataincompleteness (some regions are excluded provisionallyconsidering the lack of data such as the Taiwan Area Macauand Hong Kong of China many cities have underwentadjustment about administrative divisions in the past tenyears and some other cities have lost data of certain years)a panel data set of 285 prefecture-level cities in the time rangeof 2011-2017 has been used as our samples Chinarsquos currentadministrative division criteria divide the samples into threeregions namely the eastern region the central region andthe western region (Figure 1)

The study mainly uses four data types NPPVIIRSdata LandScan data administrative boundary data andadministrative statistical data Our data sources are listed inTable 1 in terms of the format and the source The EarthObservations Group (EOG) at NOAANCEI is producing aversion 1 suite of average radiance composite images by virtueof the nighttime data obtained from the Visible InfraredImaging Radiometer Suite (VIIRS) DayNight Band (DNB)since April 2012 The version 1 VIIRS DNB Nighttime Lightswere available at the official website of NGDC (httpsngdcnoaagoveogviirsdownload dnb compositeshtml) Priorto the averaging the DNB data affected by lighting straylight cloud cover and lunar illumination have been filteredout As for the generation of the data the spatial resolutionis 15 arc seconds spanning -1800 to 1800 in longitude and -650 to 750 in latitude The temporal averaging is calculatedby month and year However the version 1 series of annualcomposites has not been announced to the public at presentFurthermore as version 1 series of monthly compositesdid not receive filtration treatment for screening out lightsfrom fires aurora and boats as well as other temporallights it is necessary to perform further extraction in ourresearch LandScan of ORNL acts as a community crite-rion for population distribution data worldwide (availableat httpslandscanornlgovlandscan-datasets) With spatialresolution of 1 km (3010158401015840 X 3010158401015840) or so an ambient populationdistribution (average over 24 hours) can be displayed in

Discrete Dynamics in Nature and Society 3

N0 450 900 1800 Miles

Studying areas

Eastern region

Central region

Western region

Figure 1 Studying areas

ORNL Being refreshed each year it can be released tobroader user community at nearly October Data of admin-istrative boundary were collected from the National Geo-matics Center of China (available at httpwwwngcccn)Administrative statistical datawere collected fromChinaCityStatistical Yearbook China Urban Construction StatisticalYearbook and China Land and Resources Almanac all of thecore explanatory variables and control variables used in thispaper are selected from these datasets

21 Dependent Variables The projection of NPPVIIRS datawas carried out through Lambert Azimuthal Equal Areaprojection and resampling was performed when the spatialresolution was 1 km By removing noise and averagingthe monthly nighttime light data annual nighttime lightdata were obtained during 2012-2017 Furthermore usingthe annual nighttime light image with an average value ofabove 10 as a mask we extracted the area with a populationdensity greater than 1000 person per square kilometer fromLandScan data as our research sample

Population density used to be a counter-indicator ofurban sprawl to characterize the degree of population agglo-meration Although this method roughly reflects the generalsituation of urban sprawl it is difficult to truly reflect thespatial pattern of a city [23] We propose the characteristicsof urban sprawl from two aspects urban population sprawland urban land sprawl [46]

119880119875119878119894119905 = 05 lowast (119871119875119894119905 minus 119867119875119894119905) + 05 (1)

0320

0340

0360

0380

0400

0420

0440

2012 2013 2014 2015 2016 2017

Urb

an p

opul

atio

n sp

raw

l

Year

ChinaEastern

CentralWestern

Figure 2 Urban population sprawl in China during 2012-2017

0650066006700680069007000710072007300740

2012 2013 2014 2015 2016 2017

Urb

an la

nd sp

raw

l

Year

ChinaEastern

CentralWestern

Figure 3 Urban land sprawl in China during 2012-2017

119880119871119878119894119905 = 05 lowast (119871119871 119894119905 minus 119867119871 119894119905) + 05 (2)

where 119880119875119878119894119905 is the value of urban population sprawl in city119894 at year 119905 119871119875119894119905 is the proportion of the population withpopulation density below the national average value accountsfor total population in city 119894 at year 119905 119867119875119894119905 is the proportionof the population with population density above the nationalaverage value accounts for total population in city 119894 at year119905 Correspondingly 119880119871119878119894119905 is the value of urban land sprawlin city 119894 at year 119905 119871119871 119894119905 is the proportion of the land area withpopulation density below the national average value accountsfor the total land areas in city 119894 at year 119905119867119871 119894119905 is the proportionof the land area with population density above the nationalaverage value accounts for the total land areas in city 119894 at year119905 These two indicators are ranging from zero to one and thelarger value means the higher sprawl and vice versa

To illustrate the spatial correlation of urban sprawl in anintuitive way the urban population sprawl and urban landsprawl of 285 prefecture-level cities are investigated from 2012to 2017 presented in Figures 2 and 3 There are three mainobservations First there is an imbalance between urban pop-ulation sprawl and urban land sprawl regarding their growthrate the growth rate of urban land sprawl has exceeded urbanpopulation sprawl at the national and regional level during2012-2017 Second we have investigated that the easternregion exhibits a stronger urban population sprawl compared


Table 1 The datasets used in the study (by format and source)

Data Type Year Format Data SourceNPPVIIRS data 2012-2017 Geo Tiff httpsngdcnoaagoveogviirsdownload dnb compositeshtmlLandScan data 2012-2017 Geo Tiff httpslandscanornlgovlandscan-datasetsAdministrative boundary data 2012-2017 Shp httpswwwngcccn

Administrative statistical data 2011-2016 ExcelChina City Statistical Yearbook

China Urban Construction Statistical YearbookChina Land and Resources Almanac

with the central and western regions as a larger number ofpeople in the inland migrate to the coastal regions Thirdwe investigated urban land sprawl in the central and westernregion during 2014-2016 surpasses that in the eastern regionimplying that Chinarsquos national governmentrsquos inclination isto supply more urban construction land in the central andwestern regions compared with the eastern region whichcontrasts with the strict control of the first-tier citiesrsquo landsupply in the eastern regions

Furthermore we propose a comprehensive index to testthe extent of urban sprawl based on the above two equations

119880119878119894119905 = radic119880119875119878119894119905 lowast 119880119871119878119894119905 (3)

where 119880119878119894119905 is the value of urban sprawl in city 119894 at year 119905Correspondingly the value of urban sprawl is ranging fromzero to one the larger valuemeans the higher sprawl and viceversa

22 Core Explanatory Variables The tax-sharing reformcaused the situation of ldquorelocation of financial powerrdquo andldquoretention of administrative powerrdquo in China since 1994[40] Moreover considering Chinarsquos national governmentrsquosemphasis on peoplersquos livelihood expenditure the fund sup-porting mechanism and the large-scale implementation ofthe project system it was difficult for general public budgetexpenditure to cover large-scale urban infrastructure con-struction subsidize industrial land and investment expen-ditures like tax reduction which caused a huge gap betweenlocal fiscal revenue and expenditure [41] In the face of thehuge demand for urbanization and the restrictions imposedby the Budget Law on local borrowing land finance hasbecome the ldquosecondary financerdquo for local governments [42]Financial development is one of the important forces fordriving urban sprawl by reducing transaction costs improv-ing allocation efficiency and optimizing industrial structure[43] Under the combined effect of limited land supply andrigid housing purchases house prices have been pushed up[43] High profits attracted more funds to participate inthe competition in the real estate market which intensifiedthe competition in the commercial and residential landmarket thus forming the coexistence of high housing pricesand urban sprawl [41] Also land finance obtained moredisposable funds for local governments from land transferthrough the leverage effect of bank credit which played a rolein fueling the formation of land finance [43] Therefore wechoose land finance and financial development as the coreexplanatory variables of this paper

Specifically this paper chooses the shares of land leasingrevenue in GDP as a substitute for land finance becauseland leasing revenue belongs to extra-budgetary income orgovernment fund income local government has more powerto control the application of it Besides this paper chooses theshares of both deposits and loans in GDP as a substitute forfinancial development because the impact of direct financingis more important than securities financing on urban sprawlin China

23 Control Variables In China the urban sprawl is alsoaffected by some economic and institutional factors [8ndash16]As a result the econometric estimation includes six controlvariables (1) human capital (HC) ie the number of collegestudents per 10000 people (2) gross domestic product(GDP) ie per capita GDP (3)fiscal expenditure (FE)ie per capita fiscal expenditure (4) education expenditure(EDU) ie per capita education expenditure (5) hospitalcondition (HOS) ie number of beds in hospital per 10000people (6) green degree (GD) ie green area coverage inbuilt-up areas

Taking the year of 2011 as the base period we process theeconomic variables at a constant price aiming at eliminatingthe influence of price fluctuations while all variables havereceived logarithmic treatment for eliminating the influencebrought by heteroscedasticity Specifically considering thetime lag of impacts all independent variables are processedin a one-stage lag Table 2 reports the descriptive statistics ofrelevant variables which were used in the paper

3 Methodology

31 Spatial Durbin Model The spatial econometrics theorystates that a regional space unit in a certain economicgeography phenomenon or certain attribute values is sig-nificantly related to a neighborhood space unit [47] Theestimated result of the OLS estimation which makes anassumption that observations are not spatially correlatedwill be a biased and nonconsistent estimation of parameter[48] A spatial econometric model shall be built for gettingaccurate estimation results Therefore we construct a spatialDurbinmodel (SDM) to consider the impacts of land financefinancial development and their interaction on urban sprawlin China The common SDM can be expressed as

119910 = 120588119882119910 + 119883120573 + 119882119883120579 + 120572 + 120583 (4)

where 119882 denotes the nonnegative 119873 times 119873 spatial weightmatrix which reflects the interdependent space relation


Table2Descriptiv

estatistic

s

Varib

ales

Definitio

nObs

Unit

SDMean

Min

Firstq

uartile

Medianqu

artile

Third

quartile

Max

Kurtosis

Skew

ness

ln119880119878

itUrban

sprawl

1710

-0328

-0712

-6908

-0907

-0696

-0506

000

073994

-4083

ln119875119863

itPo

pulatio

ndensity

1710

Person

km2

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119871119865

it-1

Thes

hareso

fland

leasingrevenu

ein

GDP

1710

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119865119863

it-1

Thes

hareso

fboth

depo

sitsa

ndloansin

GDP

1710

0500

3826

-053

03637

3916

4150

4567

7607

-2001

ln119867119862

it-1

Then

umbero

fcollege

studentsp

er10000

peop

le1710

Person

0410

5322

4074

5030

5260

5568

7240

0748

064

6

ln119866119863

119875 it-1

Perc

apita

GDP

1710

RMB

1047

4785

0637

4073

4816

5481

7179

-0078

-012

9

ln119865119864

it-1

Perc

apita

fiscalexp

enditure

1710

RMB

0573

10858

8327

10483

10861

11253

13056

0212

-0020

ln119864119863

119880 it-1

Perc

apita

education

expend

iture

1710

RMB

0603

9083

6536

8715

9117

9443

11723

1162

0059

ln119867119874

119878 it-1

Num

bero

fbedsin

hospita

lper

10000

peop

le1710

Bunk

0550

7263

4218

6948

7272

7557

9826

2291

-0053

ln119866119863

it-1

Green

area

coverage

inbu

ilt-upareas

1710

0432

4198

-1202

3958

4251

4474

5554

14860

-1716


between different cross-sections 119882119910 and 119882119883 are the spatiallag terms of the dependent variables and independent vari-ables respectively Relying on such kind of spatial lag termsthe spillover effects of neighboring cities on certain city canbe analyzed

SDM takes into accounts the impacts of both the spatiallag dependent variable and the spatial lag independent vari-able Based on certain assumption SDM can be reduced totwo modes spatial lag model (SLM) and spatial error model(SEM) From (4) two assumptions were considered (i) 11986710 120579 = 0 and (ii) 11986720 120579 + 120573120588 = 0 If 11986710 holds the SDM can bereduced to a SLM while if11986720 holds SDMcan be reduced to aSEM when both conditions hold it can equal to a nonspatialpanel model [48 49] Therefore compared to other spatialmodels the SDM is a more generalized form However formaking sure the applicability of SDM to certain regressionanalyses it is necessary to perform relevant statistical testsand the Wald and likelihood ratio (LR) test shall be carriedout for confirming if the SDM can be reduced to a SLM orSEM [50] The Hausman test helps the study to confirm thatwhich effect is adopted by the spatial econometric modelfixed effect or random effect [51]

It is impossible for the independent variable coefficientsin the regression model to make an accurate reflection aboutthe margin effect as the spatial panel model exhibits spatialcorrelation There are two types of marginal effect namelydirect effect and indirect effectThe two types of margin effectcan be employed to explain the model about its informationThe SDM can be transferred as follows

119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)

where 119868 is an N times 1 unit matrix and N is the quantity ofcities The spatial Leontief inverse matrix can be expandedinto following formula

(119868 minus 120588119882)minus1 = 119868 + 120588119882 + 12058821198822 + sdot sdot sdot (6)

The 1st term of the right equation (5) refers to the directeffect and the remaining part stands for the indirect effect[52] The 1st partial derivative of dependent variables toindependent variables is expressed as

120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903

= 119878119903 (119882)119894119895 for all 119894 = 119895 and for all 119903 (8)

119878119903 (119882) = (119868119873 minus 120588119882)minus1 (119868119873120573119903 minus 119908119903119882) (9)

where 120573119903 is the coefficient of the rth independent variableand 119908119903 is the coefficient of the spatial lag term of the rthindependent variable 119878119903(119882)119894119894 stands for the element in thediagonal line which indicates how the independent variableaffects the dependent variable in the ith city ie the directeffect That is to say simply averaging the elements in thediagonal line can get the average direct effect The off-diagonal elements reflect how the independent variable of

the jth city affects the dependent variable of the ith city iethe indirect effect or spillover effect That is to say simplyaveraging all the off-diagonal elements can get the averageindirect effect Summing up average direct effect and indirecteffect can obtain the average total effect and also the averageof all the elements

From above analyses the following SDM is applied tostudying land finance and financial development as well asthe spillover effects on urban sprawl

ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1

+ 1205732 ln119865119863119894119905minus1 + 1205733 ln119871119865119894119905minus1 lowast ln119865119863119894119905minus1

+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1

119882119894119895 ln119871119865119894119905minus1 lowast ln119865119863119894119905minus1 + 119883119894119905minus1120574

+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)

In order to contrast with urban sprawl we also usepopulation density (PD) greater than 1000 extracted fromLandScan data as a counter-indicator

ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)

Themost likelihood estimation (MLE) method is appliedto the estimation of (10) and (11)

32 Spatial Weight Matrix Different from the OLS estima-tion spatial econometric method introduces spatial weightmatrix [49] which can be constructed following two stan-dards namely the neighboring standard and the distancestandard The paper mainly considers nonbordering regionswhich approach to the concerned cities in geography and areeasily affected by nonbordering regions in a mutual mannerTherefore simple binary geographic unit matrix is not usedas the spatial weight matrix in the paper Besides we take the


reciprocal of distances between different cities as the elementin distance weight matrix expressed as

119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)

where 119889119894119895 is the greater-circle distance obtained on the basisof the latitude and longitude between city 119894 and city 119895119882119894119895 considers the relation of all cities and it allows theexamination of all interactions in whole territory

4 Analysis and Discussion

41 Estimation Results for the Whole Sample In the applica-tion of SDM we firstly investigate spatial dependence Fromthe results the global Moranrsquos I index of ln119880119878it is 0202inconsistent with the original hypothesis at 1 significancelevel indicating that it is suggested to apply the maximumlikelihoodmethod to selecting the spatial econometric modelfor statistical verification The LR test and the Wald test showthat the SDM cannot degenerate into the SLM or the SEMThe Hausman test result shows that under 1 significancelevel it is suggested to select the fixed effect model ofSDM After comprehensively analyzing the R squared thenatural log-likelihood function value log L and the jointsignificance of LR test (space fixed and time fixed) SDM ismore reasonable under the fixed effect of space-time Similarto the above steps for selecting a proper econometric modelwe investigate that the SDM is more reasonable under therandom effect when the dependent variable is populationdensity Hence we choose the results of the above twomodelsfor analysis and Table 3 lists various model test results

As can be seen in Table 3 the coefficients of land financeand financial development on urban sprawl are positiveand significant indicating that land finance and financialdevelopment accelerated urban sprawl during 2012-2017 Byobserving the results of two different dependent variableswe find that the signs of most coefficients are oppositeindicating that population density can be used as a counter-indicator of urban sprawl to some extent However thecoefficients of land finance and financial development are notsignificantly associated with population density indicatingthat it is not satisfactory to use population density as atraditional counter-indicator of urban sprawl at the nationallevel Moreover the coefficient of the interaction betweenland finance and financial development on urban sprawl isnegative and significant indicating that land finance andfinancial development had a substitution effect on influencingurban sprawl in China Furthermore the coefficients ofcontrol variables are not significantly associated with urbansprawl implying the core role of land finance and financialdevelopment influence urban sprawl when compared withother driving forces Besides the spatial coefficients (120588)also exhibit an obvious significance strongly proving urbansprawlrsquos spatial dependence at the national level

Considering spatial autocorrelation it is impossible forthe regression coefficients of independent variables to reflect

the marginal effects or for the coefficients of the spatial lagsof independent variables to reflect the spatial spillover effectin an accurate manner However the impacts of land financeand financial development and their spatial spillover effect onurban sprawl at the national level are quantified by virtue ofdirect effect and indirect effect as well as total effect which areobtained from regression coefficients of SDM

Table 4 shows the decomposition estimates of the directeffect indirect effect and total effect calculated accordingto (7)-(9) as well as the regression coefficients of SDM inTable 3 The respective direct effect of land finance financialdevelopment and their interaction on urban sprawl is 03540261 and -0061 with a significant level of 5 while theindirect effects of land finance financial development andtheir interaction on urban sprawl are 0237 0258 and -0044 without passing the significant test respectively Theseresults show that land finance financial development andtheir interaction have significant direct effects on the urbansprawl of local cities but the effect on the urban sprawl ofsurrounding cities is not significant Comparing the totaleffects we investigate that the coefficients of land financefinancial development and their interaction on urban sprawland population density are opposite It indicates that pop-ulation density can be used as a counter-indicator of urbansprawl to some extent once again Land finance and financialdevelopment accelerated urban sprawl during 2012-2017while they had a substitution effect on influencing urbansprawl at the national level

42 Estimation Results for the Subregional Sample China isa big country with vast territory and land area Thereforethe impact of land finance and financial development onurban sprawl in different regions varies greatly In order totake full account of the differences in urban sprawl acrossregions the regression is reestimated using the subsamplesof three geographical regions (namely the eastern regioncentral region and western region) proposed by the NationalBureau of Statistics (NBS) The results for regression in thesethree regions are reported in Table 5

Generally the results of three different regions are not allconsistent with the results of the whole sample which meansthe spatial heterogeneity of different regions is significantThe estimation results of land finance financial developmentand their interaction in the central region have similarity andmore significant estimation results using the whole sampleHowever the estimation results of land finance financialdevelopment and their interaction in the western regionhave similar estimation results using the whole sample butnot significant statistically One possible reason is that theamount of land finance and financial development in thewestern region was relatively low compared to the centralregion Furthermore the estimation results of land financefinancial development and their interaction in the easternregion have opposite estimation results using the wholesample but not significant statistically One possible reason isthat Chinarsquos national governmentrsquos control over the indicatorsof urban construction land compared to the other tworegions restricted the urban sprawl in the eastern regionIn addition the spatial coefficients (120588) are also exhibit an


Table 3 The results for the whole sample

Variables Dependent VariableUrban Sprawl Population Density

Constant-4362lowastlowastlowast 8983lowastlowastlowast(-3399) (7703)

ln119871119865it-10419lowastlowastlowast 0318lowastlowast 0471lowastlowast 0342lowastlowast -0075 0063 -0113 0033(2209) (2075) (2502) (2234) (-0444) (0848) (-0679) (0462)

ln119865119863it-10114 0281lowastlowast 0137 0254lowastlowast 0084 0008 0070 0038(0859) (2372) (1041) (2138) (0711) (0137) (0601) (0685)

ln119871119865it-1lowast -0061lowast -0054lowast -0070lowastlowast -0059lowastlowast 0002 -0011 0009 -0005ln119865119863it-1 (-1723) (-1884) (-1997) (-2050) (0060) (-0779) (0283) (-0401)

ln119867119862it-1-0042lowastlowastlowast -0002 -0044lowastlowastlowast -0006 0056lowastlowastlowast -0001 0055lowastlowastlowast 0001(-4565) (-0266) (-4580) (-0628) (6893) (-0158) (6531) (0135)

ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)

ln119865119864it-10003 0012 -0004 -0002 0057 -0001 0064lowastlowastlowast 0014(0110) (0525) (-0156) (-0072) (2322) (-0087) (2625) (1321)

ln119864119863119880it-10042 0009 0043 0015 -0104lowastlowastlowast -0015 -0105lowastlowastlowast -0021(1428) (0412) (1488) (0684) (-4034) (-1402) (-4079) (-1942)

ln119867119874119878it-1-0130lowastlowastlowast -0004 -0139lowastlowastlowast -0009 0139lowastlowastlowast 0022lowastlowast 0147lowastlowastlowast 0025lowastlowast(-6327) (-0203) (-6801) (-0427) (7612) (2184) (8093) (2536)

ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)

Wlowast ln119871119865it-10387 0119 0492lowast 0180 -0360 0040 -0436lowast -0036(1433) (0585) (1836) (0878) (-1501) (0408) (-1831) (-0377)

Wlowast ln119865119863it-10444lowastlowast 0265lowast 0485lowastlowast 0208 -0452lowastlowastlowast -0144lowastlowast -0476lowastlowastlowast -0081(2352) (1766) (2591) (1375) (-2700) (-1998) (-2863) (-1142)

Wlowast ln119871119865it-1lowast -0082 -0022 -0100lowastlowast -0034 0081lowast -0007 0095lowastlowast 0007ln119865119863it-1 (-1631) (-0565) (-2001) (-0882) (1819) (-0406) (2134) (0394)

Wlowast ln119867119862it-10009 0018 0005 0006 -0027lowastlowast -0002 -0028lowastlowast 0002(0700) (1624) (0332) (0449) (-2414) (-0357) (-2205) (0280)

Wlowast ln119866119863119875it-10042 0120lowastlowastlowast 0044 0039 -0022 -0070lowastlowastlowast -0020 0003(1437) (3358) (1502) (0924) (-0833) (-4042) (-0782) (0173)

Wlowast ln119865119864it-10078lowast 0065lowast 0062 0026 -0059 -0045lowastlowastlowast -0038 -0004(1929) (1821) (1508) (0703) (-1626) (-2645) (-1035) (-0224)

Wlowast ln119864119863119880it-1-0040 -0006 -0033 0021 0075 0019 0064lowast -0004(-0975) (-0170) (-0810) (0557) (2084) (1080) (1767) (-0247)

Wlowast ln119867119874119878it-10082lowastlowastlowast -0022 0054lowast -0048 -0135lowastlowastlowast -0034lowast -0109lowastlowastlowast -0008(2635) (-0591) (1709) (-1260) (-4874) (-1885) (-3898) (-0471)

Wlowast ln119866119863it-10015 0022 0014 0020 0004 -0023lowastlowast 0007 -0020lowast(0474) (0972) (0453) (0880) (0142) (-2077) (0251) (-1845)

120588 0167lowastlowastlowast 0108lowastlowastlowast 0135lowastlowastlowast 0101lowastlowastlowast 0223lowastlowastlowast 0250lowastlowastlowast 0198lowastlowastlowast 0170lowastlowastlowast(6347) (3983) (5044) (3697) (8762) (9928) (7640) (6394)

Space-fixed No Yes No Yes No Yes No YesTime-fixed No No Yes Yes No No Yes Yes


Table 3 Continued


R-squared 0176 0788 0194 0790 0229 0942 0246 0945Log-likeli-hood

-360299 790660 -338309 815560 -164947 2025206 -142850 2093934

Moranrsquos I 0162lowastlowastlowast 0210lowastlowastlowastLR jointspace fixed 2372376lowastlowastlowast 4577916lowastlowastlowastLR jointtime fixed 82005lowastlowastlowast 367134lowastlowastlowastWaldspatial lag 12065 11662

LR spatiallag 12036 11612

Waldspatial error 12903 10763

LR spatialerror

12860 10687

Hauman test 272140lowastlowastlowast 11315Obs 1710 1710 1710 1710 1710 1710 1710 1710Notes the t-statistical data is provided in the parentheses lowastlowastlowast lowastlowast and lowast refer to the statistical significance level (1 5 and 10 respectively)

Table 4 The direct indirect and total effects of the whole sample

Variables Urban Sprawl Population DensityDirect Effect Indirect Effect Total Effect Direct Effect Indirect Effect Total Effect

ln119871119865it-10354lowastlowast 0237 0591lowastlowast -0104 -0451 -0555(2305) (1077) (2165) (-0606) (-1522) (-1472)

ln119865119863it-10261lowastlowast 0258 0519lowastlowast 0049 -0521lowastlowast -0472lowast(2222) (1589) (2675) (0410) (-2514) (-1789)

ln119871119865it-1lowast -0061lowastlowast -0044 -0106lowastlowast 0008 0098lowast 0106ln119865119863it-1 (-2125) (-1066) (-2051) (0260) (1771) (1508)

ln119867119862it-1-0006 0006 0001 0055lowastlowastlowast -0018 0036lowastlowast(-0599) (0410) (0035) (7232) (-1369) (2395)

ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)

ln119865119864it-1-0001 0027 0026 0054lowastlowast -0056 -0002(-0044) (0671) (0555) (2258) (-1316) (-0046)

ln119864119863119880it-10017 0025 0042 -0101lowastlowastlowast 0065 -0037(0771) (0627) (0932) (-3947) (1581) (-0869)

ln119867119874119878it-1 -0011 -0054 -0065 0130lowastlowastlowast -0126lowastlowastlowast 0005(-0512) (-1327) (-1387) (7246) (-3807) (0120)

ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)

Notes the t-statistical data are provided in the parentheses lowastlowastlowast lowastlowast and lowast refer to the statistical significance level (1 5 and 10 respectively)

obvious significance strongly proving the spatial dependenceof urban sprawl among different regions

The decomposition estimates of the direct effect indirecteffect and total effect of the eastern region are listed inTable 6 As shown in Table 6 all the coefficients of landfinance financial development and their interaction are notsignificant statistically implying the driving mechanism of

urban sprawl relying on land finance and financial develop-ment has lost momentum for the limitation of urban con-struction land supply and using compact urban developmentto replace urban sprawl may become the future direction ofthe eastern region in the long run

The decomposition estimates of the direct effect indirecteffect and total effect of the central region are listed in


Table 5 The results of the subregional sample

Variables Eastern Central WesternUrban Sprawl Population Density Urban Sprawl Population Density Urban Sprawl Population Density

ln119871119865it-1-0116 0079 1273lowastlowastlowast -0101 0125 -0097(-0917) (0772) (3283) (-0754) (0959) (-0857)


ln119871119865it-1 lowast ln119865119863it-10022 -0017 -0223lowastlowastlowast 0020 -0029 0024(0929) (-0884) (-3006) (0795) (-1187) (1096)

ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)

ln119867119874119878it-1 -0024 0000 -0081 0071lowastlowastlowast 0003 0026lowast(-1307) (-0017) (-1322) (3367) (0182) (1909)

ln119866119863it-10033lowast -0019 -0025 0004 -0012 0014lowast(1777) (-1273) (-0489) (0227) (-1347) (1842)

Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)

Wlowast ln119871119865it-1lowast -0025 0032 -0071 -0016 -0038 0005ln119865119863it-1 (-0698) (1096) (-0711) (-0471) (-1255) (0178)

Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)

Wlowast ln119865119864it-10009 -0019 0012 0053 0066lowastlowast -0032(0295) (-0771) (0121) (1504) (2097) (-1157)

Wlowast ln119864119863119880it-1-0023 0039 0260lowastlowastlowast -0081lowastlowast -0053lowast 0021(-0763) (1605) (2709) (-2449) (-1763) (0787)

Wlowast ln119867119874119878it-1 -0024 0038 -0359lowastlowastlowast -0015 0028 0005(-0784) (1503) (-3119) (-0379) (0958) (0206)

Wlowast ln119866119863it-10007 -0043 0058 -0090lowastlowast 0012 -0002(0181) (-1391) (0537) (-2436) (0907) (-0203)

120588 0008 0108lowastlowast 0065 0110lowastlowast 0189lowastlowastlowast 0135lowastlowastlowast(0167) (2445) (1431) (2458) (4218) (2941)

Space-fixed Yes Yes Yes Yes Yes YesTime-fixed Yes Yes Yes Yes Yes YesR-squared 0934 0955 0685 0948 0922 0941Log-likelihood 761164 884216 51525 689940 530713 601290Moranrsquos I 0195lowastlowastlowast 0221lowastlowastlowast 0057lowast 0032 0212lowastlowastlowast 0221lowastlowastlowastLR joint space fixed 1502513lowastlowastlowast 1729845lowastlowastlowast 566985lowastlowastlowast 1604641lowastlowastlowast 1044349lowastlowastlowast 1194864lowastlowastlowastLR joint time fixed 84622lowastlowastlowast 159327lowastlowastlowast 11915lowast 94979lowastlowastlowast 81177lowastlowastlowast 106811lowastlowastlowastWald spatial lag 12395 12931 19640lowastlowast 15045lowast 19951lowastlowast 18072lowastlowastLR spatial lag 12277 12801 19498lowastlowast 14919lowast 19544lowastlowast 17722lowastlowastWald spatial error 12424 12544 20434lowastlowast 15505lowast 18564lowastlowast 17472lowastlowastLR spatial error 12381 12451 20157lowastlowast 15340lowast 18161lowastlowast 17116lowastlowastHauman test 145872lowastlowastlowast 153106lowastlowastlowast 53154lowastlowastlowast 144955lowastlowastlowast 39194lowastlowastlowast 135500lowastlowastlowastObs 606 606 600 600 504 504Notes the t-statistical information is provided in the parentheses lowastlowastlowast lowastlowast and lowast stand for the statistical significance level (1 5 and 10 respectively)


Table 6 The direct indirect and total effects of eastern regions


ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)

ln119866119863it-10033lowast 0008 0042 -0021 -0050 -0071lowast(1757) (0209) (0911) (-1393) (-1483) (-1795)

Notes the t-statistical information is provided in the parentheses lowastlowastlowast lowastlowast and lowast stand for the statistical significance level (1 5 and 10 respectively)

Table 7 The direct indirect and total effects of the central region


ln119871119865it-11281lowastlowastlowast 0493 1774lowastlowastlowast -0097 0045 -0052(3305) (0899) (2661) (-0722) (0232) (-0221)

ln119865119863it-11073lowastlowastlowast 0523 1596lowastlowastlowast -0119 -0009 -0127(3442) (1220) (3240) (-1117) (-0056) (-0713)

ln119871119865it-1lowast -0225lowastlowastlowast -0088 -0313lowastlowast 0019 -0014 0006ln119865119863it-1 (-3027) (-0836) (-2452) (0757) (-0369) (0126)

ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)

ln119864119863119880it-10041 0278lowastlowastlowast 0318lowastlowastlowast -0032lowastlowast -0091lowastlowast -0124lowastlowastlowast(0903) (2767) (2926) (-2088) (-2399) (-2945)

ln119867119874119878it-1 -0087 -0383lowastlowastlowast -0469lowastlowastlowast 0070lowastlowastlowast -0007 0063(-1400) (-3065) (-3221) (3316) (-0157) (1201)

ln119866119863it-1-0024 0066 0042 0001 -0098lowastlowast -0097lowastlowast(-0447) (0580) (0324) (0048) (-2387) (-2111)

Notes the t-statistical information is provided in the parentheses lowastlowastlowast lowastlowast and lowast stand for the denote statistical significance degree (1 5 and 10respectively)

Table 7 As is shown in Table 7 the coefficients of the directand total effects of land finance financial development andtheir interaction have a significant correlation with urbansprawl similar to the regression coefficients of SDM inTable 5 However the coefficients of the indirect effect ofland finance financial development and their interaction are

not significant statistically implying land finance and finan-cial development have significant promoted urban sprawlin the central region and there is a substitute effect onthe increase of urban sprawl in the central region Thespillover effect is relatively weak compared to the directeffect


Table 8 The direct indirect and total effects of the western region


ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)

ln119865119863it-10069 0335lowastlowast 0404lowastlowast -0056 -0126 -0183(0728) (2499) (2326) (-0660) (-1200) (-1300)

ln119871119865it-1lowast -0033 -0053 -0086lowast 0023 0008 0031ln119865119863it-1 (-1355) (-1521) (-1903) (1066) (0283) (0844)

ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)

Notes the t-statistical information is provided in the parentheses lowastlowastlowast lowastlowast and lowast represent the statistical significance degree (1 5 and 10 respectively)

The decomposition estimates of the direct effect indirecteffect and total effect of the western region are listed inTable 8 As is shown in Table 8 the coefficients of thetotal effect of land finance financial development and theirinteraction have significant correlations with urban sprawlwhich are similar to the coefficients of central regions inTable 5 However the coefficients of the direct effect of landfinance financial development and their interaction are notsignificant statistically The coefficients of the indirect effectof land finance and the interaction between land finance andfinancial development are also not statistically significantwhile the coefficients of the indirect effect of financial devel-opment have a positive and significant correlation with urbansprawl implying that land finance and financial developmenthave significantly promoted urban sprawl in the westernregion and they have substitute effects on urban sprawl inthe western region on the whole the direct effect is weakcompared to the central region

5 Conclusions and Policy Implications

With the panel data of 285 prefecture-level cities in Chinafrom 2011 to 2017 an index of urban sprawl is constructedand calculated in this paper by using two metrics (urbanpopulation sprawl and urban land sprawl) extracted from theNPPVIIRS data and LandScan dataThrough the applicationof SDMandunified analysis themechanisms aswell as effectsof land finance financial development and their interactionon the impact of urban sprawl are investigated Three mainconclusions can be drawn from the above analysis Firstduring the investigation the intensity of urban populationsprawl and urban land sprawl has been enhanced however

the upside-down between the inflow of migrants and thesupply of urban construction land aggravates the intensityof urban sprawl Second the impact of land finance finan-cial development and their interaction on urban sprawlvaries from region to region In the eastern region all ofthe coefficients of land finance financial development andtheir interaction are not significant statistically implyingthe driving mechanism of urban sprawl relying on landfinance and financial development has lost momentum forthe limitation of urban construction land supply In thecentral and the western regions land finance and financialdevelopment have significantly promoted urban sprawlTheyhave substitutes effect on the increase of urban sprawlHowever the direct indirect and total effects of land financefinancial development and their interaction on urban sprawlin the western region are weak compared to the centralregion Third the spatial coefficients (120588) are also highlysignificant at the national and regional level which is strongevidence of spatial dependence of urban sprawl

The findings in the paper contribute to three importantpolicy implications First urban population sprawl in theeastern region deserves more attention Although the con-traction of urban construction land had effectively reducedthe speed of urban land sprawl it also pushed up houseprices significantly forcing a large number of inflows togather in the city fringes and the edge of metropolitanareas and eroding urban sustainable development ability inthe long run Limited to the supply of urban constructionland it should further improve the use efficiency of landto achieve a compact form Second it is required to paymuch attention to preventing urban land sprawl in thecentral and western regions In order to promote coordinated


development among different regions Chinarsquos national gov-ernment has relaxed the constraints on urban constructionland in central regions and western regions however thecontinuous outflow of population and loosely land supplyhave accelerated the intensity of urban land sprawl As aresult it is necessary for Chinarsquos national government tomakea further control about the total urban construction landamount as well as focus more on assessing urban planningso as to improve the binding force on these cities What ismore local government shall reform the fiscal system so as topromote the urban development more rationally Third theimbalance of urban development policies in different regionsshall be rethought Policymakers usually take advantage ofthe surging city diseases in eastern regions to control thesupply of urban construction land However urban landsprawl in central regions and western regions have not gainedenough attention Thus the advantages and disadvantages ofthe imbalanced urban development policies shall be takeninto a remarkable consideration to achieve a more balanceddevelopment policy

Despite above-mentioned valuable insights the paperalso suffers three limitations which should be studied infurther research The first is that the study only covers sevenyears due to data limitation To confirm our findings it issuggested to lengthen the time span to a longer period and usemore information and data for comprehensive and thoroughanalysis Second in our study urban sprawl is dividedinto two types based on the difference between populationand land and each type of urban sprawl is measured bythe standard of population density In further research anexpansion of the indicator system may be considered toobtain more guiding conclusions Third the SDM is adoptedto do the empirical analysis in this paper but spatiotemporaleffect is ignored so the results may have some deviationscompared to the actual situation To expand the researchdynamic SDM should be applied to an empirical studyon the impact of land finance financial development andtheir interaction on urban sprawl in China as well as otherdeveloping countries which experience similar processes ofurbanization and modernization

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The author declares that they have no conflicts of interest

Acknowledgments

This research is supported by the National Natural ScienceFoundation of China (no 71473057 and no 71874042) Par-ticularly we would like to thank the experts who participatedin the improvement of this paper Any remaining errors arethe responsibility of the authors

References

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[2] C Zhang C Miao W Zhang and X Chen ldquoSpatiotemporalpatterns of urban sprawl and its relationship with economicdevelopment in China during 1990ndash2010rdquo Habitat Interna-tional vol 79 pp 51ndash60 2018

[3] S Hamidi R Ewing Z Tatalovich J B Grace and D BerriganldquoAssociations between urban sprawl and life expectancy in theUnited Statesrdquo International Journal of Environmental Researchand Public Health vol 15 no 5 p 861 2018

[4] B Wilson and A Chakraborty ldquoThe environmental impactsof sprawl emergent themes from the past decade of planningresearchrdquo Sustainability vol 5 no 8 pp 3302ndash3327 2013

[5] XDeng J Huang S Rozelle andE Uchida ldquoEconomic growthand the expansion of urban land in Chinardquo Urban Studies vol47 no 4 pp 813ndash843 2010

[6] X Y Li L M Yang Y X Ren H Y Li and Z M WangldquoImpacts of urban sprawl on soil resources in the Changchun-Jilin economic zone China 2000-2015rdquo International Journal ofEnvironmental Research and Public Health vol 15 no 6 p 11862018

[7] P Monforte and M A Ragusa ldquoEvaluation of the air pollutionin a Mediterranean region by the air quality indexrdquo Environ-mental Modeling amp Assessment vol 190 no 11 p 625 2018

[8] F Famoso J Wilson P Monforte R Lanzafame S Bruscaand V Lulla ldquoMeasurement and modeling of ground-levelozone concentration in Catania Italy using biophysical remotesensing and GISrdquo International Journal of Applied EngineeringResearch vol 12 no 21 pp 10551ndash10562 2017

[9] R M S Costa and P Pavone ldquoDiachronic biodiversity analysisof a metropolitan area in the Mediterranean regionrdquo ActaHorticulturae vol 1215 pp 49ndash52 2018

[10] R Costa andP Pavone ldquoInvasive plants andnatural habitats therole of alien species in the urban vegetationrdquoActaHorticulturaeno 1215 pp 57ndash60 2018

[11] G Ferrauto R M S Costa P Pavone and G L CantarellaldquoHuman impact assessment on the sicilian agroecosystemsthrough the evaluation ofMelliferous areasrdquoAnnali di Botanicavol 3 pp 237ndash244 2013

[12] C Barrington-Leigh and A Millard-Ball ldquoA century of sprawlin the United Statesrdquo Proceedings of the National Acadamy ofSciences of theUnited States of America vol 112 no 27 pp 8244ndash8249 2015

[13] W Yue Y Liu and P Fan ldquoMeasuring urban sprawl and itsdrivers in large Chinese citiesThe case of Hangzhourdquo Land UsePolicy vol 31 pp 358ndash370 2013

[14] J Y Liu J Y Zhan and X Z Deng ldquoSpatio-temporal patternsand driving forces of urban land expansion in china duringthe economic reform erardquo Ambio A Journal of the HumanEnvironment vol 34 no 6 pp 450ndash455 2005

[15] G Zhou and Y He ldquoThe influencing factors of urban landexpansion in Changshardquo Journal of Geographical Sciences vol17 no 4 pp 487ndash499 2007

[16] Q Ma C He and J Wu ldquoBehind the rapid expansion ofurban impervious surfaces in China Major influencing factorsrevealed by a hierarchical multiscale analysisrdquo Land Use Policyvol 59 pp 434ndash445 2016

[17] W Kuang J Liu J Dong W Chi and C Zhang ldquoThe rapid andmassive urban and industrial land expansions inChina between


1990 and 2010 A CLUD-based analysis of their trajectoriespatterns and driversrdquo Landscape and Urban Planning vol 145pp 21ndash33 2016

[18] W Kuang W Chi D Lu and Y Dou ldquoA comparative analysisof megacity expansions in China and the US Patterns ratesand driving forcesrdquo Landscape and Urban Planning vol 132 pp121ndash135 2014

[19] Y Fang and A Pal ldquoDrivers of urban sprawl in urbanizingChina ndash a political ecology analysisrdquo Environment and Urban-ization vol 28 no 2 pp 599ndash616 2016

[20] T Zhang ldquoLandmarket forces and governmentrsquos role in sprawlThe case of Chinardquo Cities vol 17 no 2 pp 123ndash135 2000

[21] C Kowalczyk J Kil and K Kurowska ldquoDynamics of develop-ment of the largest cities - Evidence from PolandrdquoCities vol 89pp 26ndash34 2019

[22] W Sun W Chen and Z Jin ldquoSpatial function regionalizationbased on an ecological-economic analysis inWuxi City ChinardquoChinese Geographical Science vol 29 no 2 pp 352ndash362 2019

[23] Z Liu S Liu W Qi and H Jin ldquoUrban sprawl among Chinesecities of different population sizesrdquo Habitat International vol79 pp 89ndash98 2018

[24] W Ma G Jiang W Li and T Zhou ldquoHow do populationdecline urban sprawl and industrial transformation impactland use change in rural residential areas A comparativeregional analysis at the peri-urban interfacerdquo Journal of CleanerProduction vol 205 pp 76ndash85 2018

[25] W Yue L Zhang and Y Liu ldquoMeasuring sprawl in largeChinese cities along the Yangtze River via combined single andmultidimensional metricsrdquo Habitat International vol 57 pp43ndash52 2016

[26] R M Ryznar and T W Wagner ldquoUsing remotely sensedimagery to detect urban change Viewing detroit from spacerdquoJournal of the American Planning Association vol 67 no 3 pp327ndash336 2001

[27] J Luo D Yu and M Xin ldquoModeling urban growth using GISand remote sensingrdquoGIScience amp Remote Sensing vol 45 no 4pp 426ndash442 2008

[28] B Bhatta S Saraswati andD Bandyopadhyay ldquoQuantifying thedegree-of-freedom degree-of-sprawl and degree-of-goodnessof urban growth from remote sensing datardquo Applied Geographyvol 30 no 1 pp 96ndash111 2010

[29] L Wang C Li Q Ying et al ldquoChinarsquos urban expansion from1990 to 2010 determined with satellite remote sensingrdquo ChineseScience Bulletin vol 57 no 22 pp 2802ndash2812 2012

[30] Q Weng ldquoRemote sensing of impervious surfaces in the urbanareas requirements methods and trendsrdquo Remote Sensing ofEnvironment vol 117 pp 34ndash49 2012

[31] B Gao Q Huang C He Z Sun and D Zhang ldquoHow doessprawl differ across cities in China A multi-scale investigationusing nighttime light and census datardquo Landscape and UrbanPlanning vol 148 pp 89ndash98 2016

[32] Z Zhang F Liu X Zhao et al ldquoUrban expansion in Chinabased on remote sensing technology a reviewrdquo Chinese Geo-graphical Science vol 28 no 5 pp 727ndash743 2018

[33] L Wang H Han and S Lai ldquoDo plans contain urban sprawlA comparison of Beijing and TaipeirdquoHabitat International vol42 pp 121ndash130 2014

[34] C Zeng Y Liub A Steind and L Jiao ldquoCharacterization andspatial modeling of urban sprawl in the Wuhan MetropolitanArea Chinardquo International Journal of Applied EarthObservationand Geoinformation vol 34 no 1 pp 10ndash24 2015

[35] J Qian Y Peng C Luo C Wu and Q Du ldquoUrban landexpansion and sustainable land use policy in Shenzhen A casestudy of Chinarsquos rapid urbanizationrdquo Sustainability vol 8 no 1pp 1ndash16 2016

[36] G Jiang W Ma Y Qu R Zhang and D Zhou ldquoHow doessprawl differ across urban built-up land types in China Aspatial-temporal analysis of the Beijing metropolitan area usinggranted land parcel datardquo Cities vol 58 pp 1ndash9 2016

[37] L Tian B Ge and Y Li ldquoImpacts of state-led and bottom-up urbanization on land use change in the peri-urban areas ofShanghai Planned growth or uncontrolled sprawlrdquo Cities vol60 pp 476ndash486 2017

[38] S Q Zhao D C Zhou C Zhu et al ldquoRates and patterns ofurban expansion in Chinarsquos 32 major cities over the past threedecadesrdquo Landscape Ecology vol 30 no 8 pp 1541ndash1559 2015

[39] Q Zhang and S Su ldquoDeterminants of urban expansion andtheir relative importance A comparative analysis of 30 majormetropolitans in Chinardquo Habitat International vol 58 pp 89ndash107 2016

[40] C Ding and X Zhao ldquoLand market land development andurban spatial structure in Beijingrdquo Land Use Policy vol 40 pp83ndash90 2014

[41] L Ye and A M Wu ldquoUrbanization land development andland financing Evidence from chinese citiesrdquo Journal of UrbanAffairs vol 36 no 1 pp 354ndash368 2014

[42] Y Liu P Fan W Yue and Y Song ldquoImpacts of land finance onurban sprawl inChinaThe case ofChongqingrdquoLandUse Policyvol 72 pp 420ndash432 2018

[43] G Lin and F Yi ldquoUrbanization of capital or capitalization onurban land Land development and local public finance inurbanizing Chinardquo Urban Geography vol 32 no 1 pp 50ndash792011

[44] Y D Wei H Li and W Yue ldquoUrban land expansion andregional inequality in transitional Chinardquo Landscape andUrbanPlanning vol 163 pp 17ndash31 2017

[45] A Schneider C Chang and K Paulsen ldquoThe changing spatialform of cities in Western Chinardquo Landscape and Urban Plan-ning vol 135 pp 40ndash61 2015

[46] B N Fallah M D Partridge and M R Olfert ldquoUrban sprawlandproductivity Evidence fromUSmetropolitan areasrdquoPapersin Regional Science vol 90 no 3 pp 451ndash472 2011

[47] J P Elhorst ldquoSpecification and estimation of spatial panel datamodelsrdquo International Regional Science Review vol 26 no 3pp 244ndash268 2003

[48] L F Lee and J H Yu ldquoIntroduction to spatial econometricsrdquoGeographical Analysis vol 42 no 3 pp 356ndash359 2010

[49] J P LeSage and Y Sheng ldquoA spatial econometric panel dataexamination of endogenous versus exogenous interaction inChinese province-level patentingrdquo Journal of Geographical Sys-tems vol 16 no 3 pp 233ndash262 2014

[50] L-F Lee and J Yu ldquoIdentification of spatial Durbin panelmodelsrdquo Journal of Applied Econometrics vol 31 no 1 pp 133ndash162 2016

[51] J P Elhorst ldquoApplied spatial econometrics Raising the barrdquoSpatial Economic Analysis vol 5 no 1 pp 9ndash28 2010

[52] J P Elhorst ldquoDynamic spatial panels Models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1 pp5ndash28 2012

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been a common method by applying remote sensing deriveddata to the estimation of urban sprawl because it can accu-rately reflect the spatial distribution of peoplersquos economicsocial and environmental activities compared to governmentcollected data [26ndash32] Based on the existing research resultswe use remote sensing derived data to calculate the extentof urban population sprawl and urban land sprawl and thensquare the product of the two The final result is the indexof urban sprawl used in this paper The temporal and spatialdevelopment mode of urban sprawl in single cities have beenstudied in most researches based on the measurement resultof urban sprawl particularly these metropolises in developedeastern coastal areas [13 33ndash39] However little is knownabout urban sprawl among different cities throughout Chinautilizing national datasets therefore it is especially necessaryto use nationally representative datasets to deeply study urbansprawl in China

New research suggests that dramatic urban sprawl inChina on the one hand has been promoted by the marketand economic development just like the US and othercountries in the west and on the other hand is attributableto Chinarsquos land financing as well as the land-centeredurbanization strategies [40ndash42] As the rural-to-urban landcirculation system is loose in China a large portion ofmunicipal revenues of local government are obtained viaurban sprawl [43] However on the background of nar-rowing the imbalance among different regions the centralgovernment of China keeps requiring local governments totighten up rural-to-urban land conversion process as wellas preserve farmland which makes the land financing andland-centered urbanization policies lose the advantage [44]Through the leverage of bank credit land finance with thecore of land mortgage loan has obtained more disposablefunds from urban sprawl in China ldquogrowing wealth byland and supporting land by wealthrdquo is a vivid reflection ofthe driving forces of urban sprawl [45] Therefore financialdevelopment can also significantly promote the intensity ofurban sprawl However due to the serious imbalance ofregional development the mechanism of land finance andfinancial development on urban sprawl has significant spatialheterogeneity among different regions in-depth studies of thedriving forces of urban sprawl using regionally representativedatasets are much needed

Based on the discussion above previous related literatureis insufficient The paper aims to compare different regionsin terms of their sprawl degree analyze the effects of landfinance financial development and their interaction onurban sprawl in different regions and reveal the spatialheterogeneity of land finance financial development andtheir interaction on urban sprawl at the national and regionallevel Specifically using the 2012-2017 remote sensing deriveddata urban sprawl was quantified by virtue of two metricsextracted fromNPPVIIRS data and LandScan data followedby the comparison of different cities in terms of the sprawldegree difference Furthermore we compared the spatialheterogeneity of land finance financial development andtheir interaction on urban sprawl based on spatial Durbinmodel A qualitative analysis was performed at last on thedriving forces of above findings

This paper falls into five sections Section 2 involvesdata and variables like data source data extraction processmethod tomeasure urban sprawl and independent variablesSection 3 presents the spatial Durbin model as well as spatialweight matrix Section 4 presents the results including theeffects of land finance and financial development on urbansprawl at the national and regional level Section 5 draws ourconclusions and offers some policy implications

2 Data and Variables

There are two levels of Chinese cities based on the administra-tive level namely the prefecture-level cities and the county-level cities As regulated by administrative division systemprefecture-level cities include municipal districts county-level cities counties towns and other units In terms ofthe identification of city in China a prefecture-level cityusually refers to amunicipal district similar to city in westerncountries [23] However there are no clear central urbanareas in a county-level city which to a large extent hasmany nonurbanized areas On that account prefecture-levelcities were selected as samples in the study Due to dataincompleteness (some regions are excluded provisionallyconsidering the lack of data such as the Taiwan Area Macauand Hong Kong of China many cities have underwentadjustment about administrative divisions in the past tenyears and some other cities have lost data of certain years)a panel data set of 285 prefecture-level cities in the time rangeof 2011-2017 has been used as our samples Chinarsquos currentadministrative division criteria divide the samples into threeregions namely the eastern region the central region andthe western region (Figure 1)

The study mainly uses four data types NPPVIIRSdata LandScan data administrative boundary data andadministrative statistical data Our data sources are listed inTable 1 in terms of the format and the source The EarthObservations Group (EOG) at NOAANCEI is producing aversion 1 suite of average radiance composite images by virtueof the nighttime data obtained from the Visible InfraredImaging Radiometer Suite (VIIRS) DayNight Band (DNB)since April 2012 The version 1 VIIRS DNB Nighttime Lightswere available at the official website of NGDC (httpsngdcnoaagoveogviirsdownload dnb compositeshtml) Priorto the averaging the DNB data affected by lighting straylight cloud cover and lunar illumination have been filteredout As for the generation of the data the spatial resolutionis 15 arc seconds spanning -1800 to 1800 in longitude and -650 to 750 in latitude The temporal averaging is calculatedby month and year However the version 1 series of annualcomposites has not been announced to the public at presentFurthermore as version 1 series of monthly compositesdid not receive filtration treatment for screening out lightsfrom fires aurora and boats as well as other temporallights it is necessary to perform further extraction in ourresearch LandScan of ORNL acts as a community crite-rion for population distribution data worldwide (availableat httpslandscanornlgovlandscan-datasets) With spatialresolution of 1 km (3010158401015840 X 3010158401015840) or so an ambient populationdistribution (average over 24 hours) can be displayed in


N0 450 900 1800 Miles

Studying areas

Eastern region

Central region

Western region





119880119875119878119894119905 = 05 lowast (119871119875119894119905 minus 119867119875119894119905) + 05 (1)

0320

0340

0360

0380

0400

0420

0440

2012 2013 2014 2015 2016 2017

Urb

an p

opul

atio

n sp

raw

l

Year

ChinaEastern

CentralWestern


0650066006700680069007000710072007300740

2012 2013 2014 2015 2016 2017

Urb

an la

nd sp

raw

l

Year

ChinaEastern

CentralWestern


119880119871119878119894119905 = 05 lowast (119871119871 119894119905 minus 119867119871 119894119905) + 05 (2)










119880119878119894119905 = radic119880119875119878119894119905 lowast 119880119871119878119894119905 (3)






3 Methodology


119910 = 120588119882119910 + 119883120573 + 119882119883120579 + 120572 + 120583 (4)



Table2Descriptiv

estatistic

s

Varib

ales

Definitio

nObs

Unit

SDMean

Min

Firstq

uartile

Medianqu

artile

Third

quartile

Max

Kurtosis

Skew

ness

ln119880119878

itUrban

sprawl

1710

-0328

-0712

-6908

-0907

-0696

-0506

000

073994

-4083

ln119875119863

itPo

pulatio

ndensity

1710

Person

km2

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119871119865

it-1

Thes

hareso

fland

leasingrevenu

ein

GDP

1710

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119865119863

it-1

Thes

hareso

fboth

depo

sitsa

ndloansin

GDP

1710

0500

3826

-053

03637

3916

4150

4567

7607

-2001

ln119867119862

it-1

Then

umbero

fcollege

studentsp

er10000

peop

le1710

Person

0410

5322

4074

5030

5260

5568

7240

0748

064

6

ln119866119863

119875 it-1

Perc

apita

GDP

1710

RMB

1047

4785

0637

4073

4816

5481

7179

-0078

-012

9

ln119865119864

it-1

Perc

apita

fiscalexp

enditure

1710

RMB

0573

10858

8327

10483

10861

11253

13056

0212

-0020

ln119864119863

119880 it-1

Perc

apita

education

expend

iture

1710

RMB

0603

9083

6536

8715

9117

9443

11723

1162

0059

ln119867119874

119878 it-1

Num

bero

fbedsin

hospita

lper

10000

peop

le1710

Bunk

0550

7263

4218

6948

7272

7557

9826

2291

-0053

ln119866119863

it-1

Green

area

coverage

inbu

ilt-upareas

1710

0432

4198

-1202

3958

4251

4474

5554

14860

-1716





119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)




120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903






ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)


ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)





119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018









N0 450 900 1800 Miles

Studying areas

Eastern region

Central region

Western region





119880119875119878119894119905 = 05 lowast (119871119875119894119905 minus 119867119875119894119905) + 05 (1)

0320

0340

0360

0380

0400

0420

0440

2012 2013 2014 2015 2016 2017

Urb

an p

opul

atio

n sp

raw

l

Year

ChinaEastern

CentralWestern


0650066006700680069007000710072007300740

2012 2013 2014 2015 2016 2017

Urb

an la

nd sp

raw

l

Year

ChinaEastern

CentralWestern


119880119871119878119894119905 = 05 lowast (119871119871 119894119905 minus 119867119871 119894119905) + 05 (2)










119880119878119894119905 = radic119880119875119878119894119905 lowast 119880119871119878119894119905 (3)






3 Methodology


119910 = 120588119882119910 + 119883120573 + 119882119883120579 + 120572 + 120583 (4)



Table2Descriptiv

estatistic

s

Varib

ales

Definitio

nObs

Unit

SDMean

Min

Firstq

uartile

Medianqu

artile

Third

quartile

Max

Kurtosis

Skew

ness

ln119880119878

itUrban

sprawl

1710

-0328

-0712

-6908

-0907

-0696

-0506

000

073994

-4083

ln119875119863

itPo

pulatio

ndensity

1710

Person

km2

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119871119865

it-1

Thes

hareso

fland

leasingrevenu

ein

GDP

1710

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119865119863

it-1

Thes

hareso

fboth

depo

sitsa

ndloansin

GDP

1710

0500

3826

-053

03637

3916

4150

4567

7607

-2001

ln119867119862

it-1

Then

umbero

fcollege

studentsp

er10000

peop

le1710

Person

0410

5322

4074

5030

5260

5568

7240

0748

064

6

ln119866119863

119875 it-1

Perc

apita

GDP

1710

RMB

1047

4785

0637

4073

4816

5481

7179

-0078

-012

9

ln119865119864

it-1

Perc

apita

fiscalexp

enditure

1710

RMB

0573

10858

8327

10483

10861

11253

13056

0212

-0020

ln119864119863

119880 it-1

Perc

apita

education

expend

iture

1710

RMB

0603

9083

6536

8715

9117

9443

11723

1162

0059

ln119867119874

119878 it-1

Num

bero

fbedsin

hospita

lper

10000

peop

le1710

Bunk

0550

7263

4218

6948

7272

7557

9826

2291

-0053

ln119866119863

it-1

Green

area

coverage

inbu

ilt-upareas

1710

0432

4198

-1202

3958

4251

4474

5554

14860

-1716





119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)




120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903






ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)


ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)





119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018















119880119878119894119905 = radic119880119875119878119894119905 lowast 119880119871119878119894119905 (3)






3 Methodology


119910 = 120588119882119910 + 119883120573 + 119882119883120579 + 120572 + 120583 (4)



Table2Descriptiv

estatistic

s

Varib

ales

Definitio

nObs

Unit

SDMean

Min

Firstq

uartile

Medianqu

artile

Third

quartile

Max

Kurtosis

Skew

ness

ln119880119878

itUrban

sprawl

1710

-0328

-0712

-6908

-0907

-0696

-0506

000

073994

-4083

ln119875119863

itPo

pulatio

ndensity

1710

Person

km2

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119871119865

it-1

Thes

hareso

fland

leasingrevenu

ein

GDP

1710

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119865119863

it-1

Thes

hareso

fboth

depo

sitsa

ndloansin

GDP

1710

0500

3826

-053

03637

3916

4150

4567

7607

-2001

ln119867119862

it-1

Then

umbero

fcollege

studentsp

er10000

peop

le1710

Person

0410

5322

4074

5030

5260

5568

7240

0748

064

6

ln119866119863

119875 it-1

Perc

apita

GDP

1710

RMB

1047

4785

0637

4073

4816

5481

7179

-0078

-012

9

ln119865119864

it-1

Perc

apita

fiscalexp

enditure

1710

RMB

0573

10858

8327

10483

10861

11253

13056

0212

-0020

ln119864119863

119880 it-1

Perc

apita

education

expend

iture

1710

RMB

0603

9083

6536

8715

9117

9443

11723

1162

0059

ln119867119874

119878 it-1

Num

bero

fbedsin

hospita

lper

10000

peop

le1710

Bunk

0550

7263

4218

6948

7272

7557

9826

2291

-0053

ln119866119863

it-1

Green

area

coverage

inbu

ilt-upareas

1710

0432

4198

-1202

3958

4251

4474

5554

14860

-1716





119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)




120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903






ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)


ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)





119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018









Table2Descriptiv

estatistic

s

Varib

ales

Definitio

nObs

Unit

SDMean

Min

Firstq

uartile

Medianqu

artile

Third

quartile

Max

Kurtosis

Skew

ness

ln119880119878

itUrban

sprawl

1710

-0328

-0712

-6908

-0907

-0696

-0506

000

073994

-4083

ln119875119863

itPo

pulatio

ndensity

1710

Person

km2

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119871119865

it-1

Thes

hareso

fland

leasingrevenu

ein

GDP

1710

0301

8742

7252

8549

8724

8962

9796

0760

-019

9

ln119865119863

it-1

Thes

hareso

fboth

depo

sitsa

ndloansin

GDP

1710

0500

3826

-053

03637

3916

4150

4567

7607

-2001

ln119867119862

it-1

Then

umbero

fcollege

studentsp

er10000

peop

le1710

Person

0410

5322

4074

5030

5260

5568

7240

0748

064

6

ln119866119863

119875 it-1

Perc

apita

GDP

1710

RMB

1047

4785

0637

4073

4816

5481

7179

-0078

-012

9

ln119865119864

it-1

Perc

apita

fiscalexp

enditure

1710

RMB

0573

10858

8327

10483

10861

11253

13056

0212

-0020

ln119864119863

119880 it-1

Perc

apita

education

expend

iture

1710

RMB

0603

9083

6536

8715

9117

9443

11723

1162

0059

ln119867119874

119878 it-1

Num

bero

fbedsin

hospita

lper

10000

peop

le1710

Bunk

0550

7263

4218

6948

7272

7557

9826

2291

-0053

ln119866119863

it-1

Green

area

coverage

inbu

ilt-upareas

1710

0432

4198

-1202

3958

4251

4474

5554

14860

-1716





119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)




120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903






ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)


ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)





119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018












119910 = (119868 minus 120588119882)minus1 (119883120573 + 119882119883120579 + 120572 + 120583) (5)




120597119910119894120597119909119894119903

= 119878119903 (119882)119894119894 for all 119894 and 119903 (7)

120597119910119894120597119909119895119903






ln119880119878119894119905 = 120588119873

sum119895=1

119882119894119895 ln119880119878119895119905 + 1205731 ln 119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(10)


ln119875119863119894119905 = 120588119873

sum119895=1

119882119894119895 ln119875119863119895119905 + 1205731 ln119871119865119894119905minus1


+ 1205791119873

sum119895=1

119882119894119895119871119865119894119905minus1 + 1205792119873

sum119895=1

119882119894119895 ln119865119863119894119905minus1

+ 1205793119873

sum119895=1


+ 120593119873

sum119895=1

119882119894119895119883119895119905minus1 + 120572119894 + 120583119894119905minus1

(11)





119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018










119882119894119895 =

0 119894 = 1198951

(119889119894119895)2 119894 = 119895 (12)


















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018
















ln119866119863119875it-1-0017 -0016 -0016 -0034 0002 -0023 0002 -0007(-0839) (-0672) (-0793) (-1357) (0092) (-1943) (0137) (-0570)




ln119866119863it-1-0028 -0011 -0025 -0011 0007 0007 0006 0007(-1421) (-0767) (-1290) (-0740) (0433) (1060) (0360) (1018)













Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018









Table 3 Continued



-360299 790660 -338309 815560 -164947 2025206 -142850 2093934




LR spatialerror

12860 10687








ln119866119863119875it-1-0034 0038 0004 0001 -0027 -0026(-133) (0844) (0089) (0045) (-0856) (-0732)




ln119866119863it-1-0010 0021 0011 0007 0006 0013(-0724) (0877) (0376) (0376) (0188) (0321)












ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018














ln119867119862it-1-0008 0001 -0022 0004 0013 0001(-1076) (0155) (-1055) (0581) (1619) (0109)

ln119866119863119875it-1-0008 0013 -0060 -0006 0001 -0044(-048) (0956) (-1154) (-0360) (0032) (-1359)

ln119865119864it-10016 0010 -0016 0020 -0032 0020(0816) (0621) (-0270) (0999) (-1436) (1041)

ln119864119863119880it-10013 -0026 0034 -0029lowast 0000 -0004(0642) (-1499) (0747) (-1826) (-0004) (-0223)



Wlowast ln119871119865it-10128 -0151 0395 0058 0195 -0019(0673) (-0978) (0760) (0325) (1216) (-0136)

Wlowast ln119865119863it-1-0054 -0099 0437 0010 0276 -0109(-0368) (-0834) (1052) (0074) (2424) (-1101)


Wlowast ln119867119862it-1-0006 0007 0035 0003 -0009 0019(-0499) (0735) (1129) (0245) (-0664) (1727)

Wlowast ln119866119863119875it-10028 -0037 0056 0024 0006 0077(1026) (-1641) (0538) (0657) (0132) (1811)










ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018











ln119871119865it-1-0112 0124 0012 0073 -0150 -0077(-0901) (0636) (0053) (0713) (-0893) (-0373)

ln119865119863it-1-0020 -0059 -0078 0073 -0095 -0022(-0198) (-0396) (-0481) (0890) (-0746) (-0148)

ln119871119865it-1lowast 0021 -0024 -0003 -0016 0031 0015ln119865119863it-1 (0915) (-0663) (-0069) (-0826) (1001) (0403)

ln119867119862it-1-0008 -0006 -0015 0001 0009 0010(-1117) (-0549) (-1215) (0219) (0814) (0855)

ln119866119863119875it-1-0008 0029 0021 0013 -0038 -0025(-0460) (1075) (0742) (0955) (-1534) (-0914)

ln119865119864it-10017 0009 0026 0010 -0019 -0010(0833) (0296) (0768) (0579) (-072) (-033)

ln119864119863119880it-10014 -0024 -0010 -0025 0039 0014(065) (-0802) (-0292) (-1459) (1456) (0447)

ln119867119874119878it-1 -0024 -0025 -0049 0001 0040 0041(-1366) (-0821) (-1479) (007) (1561) (1405)








ln119867119862it-1-0021 0037 0016 0004 0003 0008(-0965) (1176) (0424) (0594) (0299) (0548)

ln119866119863119875it-1-0059 0055 -0003 -0006 0024 0018(-1099) (0499) (-0027) (-0319) (0614) (0405)

ln119865119864it-1-0017 0012 -0005 0022 0057 0080lowast(-0291) (0113) (-0044) (1128) (1517) (1776)










ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018











ln119871119865it-10145 0265 0409lowast -0093 -0031 -0124(1117) (1455) (1736) (-0827) (-0210) (-0652)



ln119867119862it-10012 -0007 0005 0002 0021 0023(1553) (-0475) (0277) (0265) (1600) (1435)

ln119866119863119875it-10000 0010 0010 -0041 0081lowast 0039(0008) (0174) (0147) (-1254) (1736) (0735)

ln119865119864it-1-0027 0069lowast 0042 0018 -0032 -0014(-1172) (1809) (0853) (0886) (-1056) (-0365)

ln119864119863119880it-1-0004 -0061lowast -0065 -0003 0022 0019(-0193) (-1737) (-1490) (-0146) (0739) (0531)

ln119867119874119878it-1 0004 0033 0037 0026 0011 0037(0248) (0899) (0836) (1935) (0387) (1095)

ln119866119863it-1-0010 0011 0001 0014 -0001 0013(-1167) (0735) (0049) (1804) (-0084) (0793)










Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018











Data Availability




Acknowledgments


References






























































Journal of












Journal of







Volume 2018






Volume 2018




















































Journal of












Journal of







Volume 2018






Volume 2018








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