TOPOGRAPHY AND SPATIAL POPULATION DUALISM IN THE PEOPLE'S REPUBLIC OF CHINA (1933–1990)

Review of Urban and Regional Development Studies 6 (1994)

TOPOGRAPHY AND SPATIAL POPULATION DUALISM IN THE PEOPLE'S REPUBLIC OF CHINA (1933-1990)"

Dimitrios S . Dendrinos The Universily of Kansas, Lawrence, KS, 66045, USA

Jiang B a n g The University of Pennsylvania, Philadelphia, PA, 19104, USA

Zhen-Chao Qian The University of Pennsylvania, Philadelphia, PA, 19104, USA

Received September 1993

Looking at half a century of available data, a sharp spatial dualism is observed in the regional population distribution of the People's Republic of China. The vast share of the PRC's population is located in the Eastern and South-Eastern regions. Two topographical variables related to accessibility, distance from the eastern seaboard and elevation, seem to be at the core of this dualism. Their isolated and combined effects upon the spatial distribution of the PRC's population are examined for the period 1933-1990, It is documented that the population concentration in certain regions of the PRC is not so much because of their proximity to the seaboard, but because of their low elevation, especially in the eastern part of the nation. Also documented is a result indicating that in time the combined effects of these impedances on the regional population distribution in the PRC is linearly declining. Although policy issues are touched upon only slightly, the quantified linkages can be insightful to the PRC's efforts to more evenly distribute population over space (a demographic based policy), while still favoring the Eastern Seaboard (an economics based policy).

1. Introduction

Were one to draw a straight line from the city of Aihui in Heilongjiang Province in the Northeast part of the People's Republic of China (PRC), to the city of Tengchong in Yunnan Province in the south-central part of the PRC, a pronounced spatial dualism would be obtained (Figure 1). To the right of the line, about 90 percent of the PRC's population (as of 1983) would be found. Whereas to its left, and within an area of approximately 60 percent of the PRC's land mass, resides only ten percent of the population (Hu). This sharp dualism in the spatial concentration of the PRC's regional population is one of the most striking examples of a broader spatio-temporal phenomenon of dualism (Dendrinos). The fact that the country contains about a fifth of the worlds population is also further evidence of dualism in population concentrations on a worldwide scale.

Going beyond spatial population distribution, and in its broader manifestation, dualism is observed over income (and particularly wealth) distributions among various socio-economic classes in nations. It is also found in the very high concentrations of ethnic groups

*A first draft of this paper was prepared in July 1988.

D. Dendrinos, J . Zhang and Z. Qian, Topography and Spatial Population Dualism 79

Fig. 1 PRC's 29 Provinces

Notes: 1. Dots are the locations of Provincial Capitals. 2. B: Beijing; T: Tianjin 3. Taiwan is not shown.

(mostly minorities) in particular regions within national boundaries. Dualism can further be recorded in the significant agglomeration of industrial activity (in terms of employment, capital stock, output) in particular locations within national economies. It is also detected in the wide disparities in the rates of economic growth/decline among regions/sectors of a national economy, and in a variety of other contexts. In Dendrinos the hypothesis was advanced that most of these dualisms are to a degree interdependent.

In Dendrinos the hypothesis was advanced that most of these dualisms are to a degree interdependent. The initial impetus triggering the onset of such spatial dualisms, it was argued, is differentials in locational comparative advantages. In turn, these differentials are due to spatial heterogeneities largely attributable to topographical, geological, climatological and other physical differences among locations.

In the case of the PRC, a severely skewed spatial population distribution has forced the central government into a planned population relocation campaign over the past twenty years or so. This, demographics instigated, campaign is guided by the government's

80 D . Dendrinos, J . Zhang and 2. Qian, Topography and Spatial Population Dualism

principles on population distribution, including: (i) attaining a comparatively even population distribution through reasonable relocation; (ii) gradually converting rural population into urban population; and (iii) restraining the size of large cities, reasonably developing medium-sized cities, and promoting the growth of small cities (Zhou).

Counter to these goals has been, however, the establishment of numerous special economic zones along the southern and south-eastem part of the PRC over the past fifteen years or so. This is a purely economics-based policy.

A number of goals are behind the large scale relocation campaign. Two concerns seem to be central in them. One is the exploitation of the many and vast natural resource reserves of the Chinese hinterland, although the economics of this movement are limited. Far more important from the government's standpoint is the easing of an apparent dualism in the concentration of various ethnic population stocks in regions in the hinterland, particularly the non-Han ethnic minorities found in the north-central, western and south- central parts of the country. As of the early 1980s these minorities constituted about 6.7 percent of the PRC's population (Hook).

Certain impacts from this campaign toward a more uniform spatial population distribution are evident from data presented here. A moderate diffusion of population toward the hinterlands is detected. However, aggregate (in terms of ethnic groups) spatial population dualism is the focus of this paper; dualism in its spatio-ethnic manifestation is not explored in any detail. Nonetheless, some inferences can be drawn regarding the latter from evidence supplied here for the case of the PRC. A full exposition of the subject is left to future research.

We attempt to explore here the end and combined effect of these two (seemingly inconsistent) policies, along with the end effect of numerous other governmental policies and individual actions which directly impact upon the spatial distribution of the PRC's population.

Our analysis of the spatial (regional) distribution of the PRC's population seems to indicate that two variables directly related to accessibility from/to the country's coastal line, elevation and proximity to the eastern seaboard, play a major role in the country's spatial population dualism. The main objective of this paper is to analytically document the role of these two topographical features in the spatial dualism phenomenon during the period 1933-1990. In doing so, inferences can be drawn upon the resistance (or inertia) present at any point in time-space to: first, overcome these two spatial impedances in the process of population diffusion (deconcentration, dispersal) toward the hinterland of the PRC; and second, to succumb to forces of agglomeration along the south-eastem seaboard. Our analysis is specific to the PRC, although similar forces at work in other nations may be subject to equivalent findings.

2. Spatial Population Dualism in a Comparative Context Before presenting the conceptual problem and the analytical results from the PRC's

experience, a few examples of population distribution dualism in other countries will be presented . They place this broadly observed phenomenon and its association with socio- economic and topographical factors in a larger comparative context.

D . Dendrinos, J . Zhang and 2. Qian, Topography and Spatial Population Dualism 8 1

Country Regions

In 1993 the PRC accounted for about 1.18 billion people, or approximately 21.4 percent of the world's population (Population Reference Bureau). Thus, what takes place there constitutes a large part of global population behavior.

Not only in the PRC but in many nations in all continents, human population is found in lowlands and in close proximity to major waterways (rivers) and coastal areas. These areas contain high peaks in national contour maps of accessibility. Hinterlands and/or highlands have been and are slowly colonized, as population expands and spatially relo- cates, gradually moving from regions with such peaks in accessibility towards regions with troughs.

Qualitatively similar, although not always identically pronounced or commonly motivated, spatial dualism phenomena are observed in a variety of national landscapes. Table 1 displays the cases of six developing nations. The first three, India, Brazil, and Indonesia,

%Land %Pop. Year Ref.

Table 1 Spatial Dualism of Population Distribution in Some Developing Countries

6.9 63.8 1971

Countries of large land area size

Five coastal regions: Western, Central, Accra Metropolitan, Eastem, and Upper Volta.

Non-desert areas: The Nile Valley, the Delta, and the Seaboard along the Red Sea.

India

Brazil

Indonesia

=25 =57

=4 >95

Four coastal states: Gujarat, Maharashtra, Tamil Nadu, and West Bengal

~~

Eight states in three regions: Ceara, Pemambuco, Bahia in Northeast; Minas Gerais, Rio de Janeiro, Sao Paulo in Southeast; and Parana, Rio Grande do Sul in South.

The Island of Java

I I Countries of smaller land area size

Nigeria

Ghana

Egypt

Four coastal states: Lagos Metropolitan Region, West, Rivers, and Gross River.

13.6 1 29.3

India, 1984: A Reference

Manual

Anuario Estatistico do Brasil, 1984

Cho/ Nitisas.

1 1977 Bienen and

Diejomaoh

1987 DicksonKay

1987 Marbo

I

82 D. Dendrinos, J . Zhang and Z . Qian, Topography and Spatial Population Dualism

represent examples of spatial dualism in medium to large land area size nations. As in China, lowland and coastal regions in these nations contain large shares of their populations. Population concentration is relatively mild in India; it is more evident in Brazil, and extremely pronounced Indonesia. The next three nations, Nigeria, Ghana, and Egypt, are developing nations with smaller land areas, yet they also exhibit various (but high nonetheless) degrees of spatial population dualism.

Among developed nations, on the other hand, spatial dualism is also visible, especially in those with a large land mass. Examples include Canada, Australia, and the U.S. It is noted that in these three countries, spatial population dualism is accompanied by spatial segregation of various ethnic minorities, particularly the indigenous population stocks such as Indian reservations in the U.S. (Waldman). However, developed nations with medium to small land area, for example those of Western Europe, do not seemingly exhibit this spatial dualism phenomenon - at least not to the degree that it appears in developed nations with a large land mass or in developing nations.

From the above comparison, it would appear that population size, land area, or level of economic development in isolation are not as important determinants of spatial population dualism as topographical features. The stability of general spatial dualism of population distribution over time horizons - usually much greater than those of political, social, and economical landscapes (eg., their vicissitude in the PRC in the past half century) - suggests the importance of the relationship between population distribution and topography. It underlies a very close linkage between population density and accessibility.

Fig. 2 Population Density vs. Elevation

1 I I I I I I I I

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Elevation (kilometer) 0 China + Asia 0 World

Sources: Data on the World and Asia are from Zhou; Data on the PRC are derived from the regression line of Model 3 in this paper.

D. Dendrinos, J . Zhang and 2. Qian, Topography and Spatial Population Dualism 83

Fig. 2 shows population density as a function of elevation in the PRC, Asia and the World. Spatial dualism is seen in all three cases. However, the position and the slope of the PRC's curve indicate not only that the PRC's population density is high above the continental as well as the global averages, but also that its spatial population dualism stands out of the background.

3. The Conceptual Framework: Topography and Spatial Population Distri- butions At the outset, one must link the effects of topographical features, such as elevation

from sea level and distance from some point of interest found in the national landscape, to the applicable spatial scale.

Elevation at the microspatial scale, for example within the political boundaries of a city, may vary considerably with significant implications to the extent that intra-urban spatial population distribution is concerned. The presence of hilly areas within cities is an important determinant of inter-urban spatial population density distributions, as they effect commuting patterns as well as costs for providing public services (sewers and water, for example). Clearly, intra-urban accessibility and elevation at the microspatial scale of a city are very closely linked; it makes little sense to use elevation averages when there is considerable variation in elevation of various sections of a city.

At the macrospatial scale (the scale of a nation), however, the use of a city's average elevation for purposes of linking it with inter-urban population density distributions is acceptable. In such case, interregional differentials in elevation are more closely linked to geological and climatological conditions.

Distance of various sections of a city (the microspatial scale) from a point of interest in the national landscape may not be as important as distance of various cities (the macrospatial scale) from that same point. At the macrospatial scale, distance from a point of interest within a city may be much more important in determining inua-urban spatial population distributions.

In Fielding it is suggested that three major factors attract human inhabitation, thus explaining to a large extent the variations in spatial population distribution: attractive physical environment; low elevation; and the borders of continents and countries. All these factors are closely related to topography, and are included into what regional analysts refer to as "locational comparative advantages" (Dendrinos).

Favorable environmental conditions, such as mild climate, do not only afford comfort to the inhabitants and contribute to their good health; they also provide vital resources - fertile land and adequate rainfall for agriculture, for instance - to support human activities. Devoid of these favorable conditions, additional infrastructure, with extra costs, must be set up as compensation if residence on such locations is deemed desirable.

Flatness of lowlands facilitates transportation and infrastructure construction (easiness of access), while mountainousness of highlands does not. Among other things, great height could impose a biological threat to lives with deficient oxygen and low air tempera- ture. In Tibet for example, all crops only grow below an elevation of 4730 meters (Zhou). Borders of continents and countries, which often coincide with boundaries of topographic

84 D . Dendrinos, J . Zhang and Z . Qian, Topography and Spatial Population Dualism

features such as coastal lines, major rivers, etc., are usually dotted with transportation hubs. Easy access to interregional or international trade is a great attraction for population agglomeration (Dendrinos).

It is common to find in the geographical literature the notion of "uninhabitable" space, for example in Marbo (p. 40). In fact, all spaces can be inhabitable at some cost, as argued earlier. A major component of these costs are the necessary infrastructure (housing, road, utilities, etc.) and transportation costs (including passenger and freight movements) to be incurred locally over a time horizon pertinent for socio-economic activity to occur.

Other broader social, economic, political, cultural and environmental costs are also involved, accompanied with similar benefits. That a particular point in space-time is not inhabited must mean that the per capita current net composite benefits of locating there do not match those at other inhabited locations. In other words, the opportunity cost for population to locate there is simply too high. According to the standard economic theory of land use, land prices to a large extent discount for ease in access.

A locational principle thus emerges. At any point in space, at a given point in time, the currently perceived per capita stream of composite net benefits (i.e.. benefits minus costs) over a time horizon at all human settlements must be at least as high as to match a national or even worldwide prevailing rural average. This is a dynamic locational equilibrium condition found in Dendrinos. Interregional population movements occur among various points in space-time to equalize potential differences in these per capita net benefits among sites in a kind of spatial arbitrage process.

This is the concept of a worldwide prevailing "relative parity" elaborated by Dendrinos. According to this dynamic theory, fast movements (as those by populations in the migration process) move the system from a disequilibrium state toward a worldwide relative parity dynamic equilibrium, whereas slow movements or sudden shocks, like technological innovation, discoveries of natural resources, natural catastrophes, or changes in the worldwide social, economic, political, cultural, and/or environmental (climatic) conditions shock these inter-locational dynamic equilibria in time.

The principle applies not only globally, but within national boundaries as well. It contains the end effect of social (individual and collective) actions which may or may not be congruent or consistent. Thus, one may find the apparently contradictory policies of the Chinese government (one favoring the hinterland, one favoring the coastal areas) included in forces working toward the end effect of the above-mentioned relative parity principle.

Only a modest part of this larger theoretical construct is tested here. The effort focuses on testing specific hypotheses associated with the effects of specific accessibility related elements within the bundle of forces acting upon spatial population distributions. In particular, an attempt is made to identify the extent to which access to coastal lines and impedance of elevation in the topographical landscape of nations influence population densities.

Several questions are asked. First, is the hypothesis acceptable that these two topographical elements - distance from the coast, and elevation from sea level - affect population densities. Second, what is the specific form of the model linking regional population density and distance/elevation. Third, what is the relative influencing strength of the two topographical elements, And fourth, has the influence of topography diminished in time.

D . Dendrinos, J . Zhang and Z . Qian, Topography and Spatial Population Dualism 85

Drawing from the PRC's experience over the period 1933-1990, we next proceed to test these hypotheses. We document these occurrences and interpret them so that inferences can be made for other nations. To the extent that access to waterways and high elevation are concerned, these associations may potentially shed more light on the factors underlying the world's population distribution.

4. Model Testing In Table 2, the PRC's regional population densities are shown for 29 provinces and for

the years 1933, 1953, 1964, 1980, 1985, and 1990. Topographic& features for each of the provinces are found in Table 3. Capitals of the provinces are considered to be centroids. These capitals' average elevation and airline distance from the eastern seaboard (Yellow, East, or South China Sea, whichever is closest) are used as the topographical determinants of their population densities.

Consideration of centroids is consistent with the way land use and transportation models account for origin and destination of trips from and to zones. A refining of the results reported here could involve more precise estimation of each province's centroid location by considering the intra-provincial spatial distribution of population densities. The analysis could be further refined by measuring distance in travel time between the centroids and the major ports (Tianjin, Shanghai, etc.), or from the special economic zones (Shengzhen, Zhuhai, Shantou, Xiameng, etc.).

The relationship between population density and topographical features is assumed to be:

Y(t) = Y(t)[X] = A(t) + Xi Bi(t)Xi (1)

where Y(t) is an indicator of provincial population density at time period t. In our tests, either population density (linear model) or its natural logarithm (log-linear model) are used for Y. X is topographical variables, including elevation from sea level (L), and/or distance from the seaboard (D). Parameters A and B may vary from minus to plus infinity, and they are expected to be negative. The intercept A identifies the weight that the average value of the population density indicator commands, at each time period, upon the effect of the spatial impedance (L and/or D) on regional population distribution. Parameter Bi specifies the per unit effect (in meter or kilometer) of the i-th spatial impedance variable (L or D), at each time period, upon the regional population density. Their relative increase/decrease in time would indicate whether the inertia to population dispersal toward the highlands (when L is in X) and/or hinterlands (when D is in X) is risinglfalling.

Alternative specifications of the original model (Eq. 1) were tested, differing in the representation of Y and in the inclusion of variables in X. Linear models, in which provincial population density P is used as Y, all gave poor fit, with adjusted R2 values below 0.2 for any time period, and for any combination of the topographic variables. In fact, the non-linearity of the relationship between P and L is clearly seen in Fig. 2. The analysis, therefore, is focused on log-linear models, in which the logarithmic transforma- tion of provincial population density, PL, is used as Y .


Table 2 PRC's Regional Population Densities (personshq. km): 1933-1990

PROVINCE

1 Beijing 2 Tianjin 3 Hebei 4 Shanxi 5 Innermongolia

6 Liaoning 7 Jilin 8 Heilongjian

9 Shanghai 10 Jiangsu 11 Zhejiang 12 Anhui 13 Fujian 14 Jiangxi 15 Shandong

16 Henan 17 Hubei 18 Hunan 19 Guangdong 20 Guangxi

21 Sichuan 22 Guizhou 23 Yunnan 24 Xizang

25 Shaanxi 26 Gansu 27 Qinghai 28 Ningxa 29 Xingjiang

China

1933(*)

122.5 132.8 133.0 74.1 3.7

112.9 42.6 9.8

806.5 292.6 201.8 160.8 117.6 105.3 245.1

195.8 141.6 143.8 159.1 51.1

92.7 64.1 30.1 0.7

51.9 12.3 1.7 7.4 1.6

48.3

1953(b)

246.3 238.2 192.3 91.7

6.1

141.0 60.1 25.1

1371.0 374.5 224.6 219.8 107.9 100.6 319.3

265.0 148.3 158.1 173.5 76.3

115.9 85.3 44.6

1.1

77.5 25.5 2.2

29.4 3 .O

62.1

1964(')

452.1 553.3 209.7 115.4 10.2

184.9 83.4 42.5

1745.3 434.2 278.2 223.9 137.7 126.3 362.5

301.6 179.8 176.9 191.0 100.7

120.1 97.3 52.2

1.1

101.3 27.8 2.8

31.9 4.5

73.7

1980(d)

523.4 663.1 275.0 158.6 15.6

239.2 117.6 67.0

1848.5 579.1 375.9 350.6 206.8 196.1 476.8

431.5 249.9 25 1.3 272.9 153.5

173.3 157.6 80.9

1.6

138.1 42.1 4.8

56.6 7.9

104.6

1985(')

57 1 .O 715.3 295.2 168.3 16.7

252.8 122.2 70.0

1963.0 605.9 395.9 369.6 222.9 207.5 502.6

462.2 263.1 267.5 295.3 168.0

179.8 168.4 86.9

1.7

146.5 44.9 5.2

62.8 8.3

111.0

1990(O

646.0 782.6 327.7 185.7 18.0

272.1 132.1 74.9

2156.6 659.9 409.4 406.7 249.5 228.4 554.7

518.3 290.2 291.6 331.0 184.9

190.7 185.4 95.1

1.9

161.8 49.6 5.7

71.1 9.4

121.9

Note: Hainan, a new province established in the late 1980s, is considered part of Guangdong Province. Taiwan, Hongkong and Macau are not included in the analysis.

Sources: (a) Hu, p.22. (b) ibid, p.23. (c) China Oficial Yearbook, p.414. (d) Kaplan and Sobin, pp.35-36; Clarke, p.VII1. (e) China, A Statistics Survey in 1985, p.20; The China Official Yearbook 198516; Statis-

(f) Staristical Yearbook of China, 1990, p.81. tical Yearbook of China, 1986, p.73.

D . Dendrinos, J . Zhang and Z. Qian, Topography and Spatial Population Dualism 87

Table 3 Topographical Features of Provinces: Closest Distance From

the Eastern Seaboard, and Elevation

PROVINCE

(North Region) 1 Beijing 2 Tianjin 3 Hebei 4 Shanxi 5 Innermongolia

(Northeast Region) 6 Liaoning 7 Jilin 8 Heilongjiang

9 Shanghai 10 Jiangsu 11 Zhejiang 12 Anhui 13 Fujian 14 Jiangxi 15 Shandong

(East Region)

CAPITAL

Beijing Tianjing S hijazhuangC Taiyuan Huhehaote

S henyang Changchun Haerbin

Shanghai Nanjing HangzhouC Hefei Fuzhou Nanchang Jinan

(South Region, containing the Middle) 16 Henan Zhen gzhouC 17 Hubei Wuhan 18 Hunan Changsha 19 Guangdong Guangzhou 20 Guangxi Naming

21 Sichuan C h e n g d u 22 Guizhou Guiyang 23 Yunnan Kunming 24 Xizang Lasa

25 Shaanxi Xian 26 Gansu LanzhouC 27 Qinghai XiningC 28 Ningxa YinchuanC 29 Xingjiang Wulumuqi

(Southwest Region)

(Northwest Region)

DISTANCEa

150 50

270 430 550

150 420 480

10 200

80 350

20 450 180

500 630 600 50

120

980 570 670

1920

920 1230 1380 960

2530

ELEVATION^

31.2 3.3

81.8 777.9

1250.0

41.6 236.8 171.7

4.5 8.9 7.2

23.6 84.0 46.7 51.6

110.4 23.3 60.0 6.3

120.0

363.1 1071.2 1891.4 3658.0

396.9 1517.5 2261.2 1111.5 1000.0

Sources: (a) Distance of Capital City from the closest seaboard was taken from the map China,

(b) Chinese Central Bureau of Climate for all capitals except those indicated under foot-

(c) For these captials elevation was estimated from altitude contours shown in Note a). (d) For Sichuan Province, the topographical determinants are not the ones from Chenpdu,

but the location of the geometric mean between Chengdu and Sichuan’s largest city: Chongqing.

Mongolia, and Korea, John Bartholomew and Son, Ltd., 1977.

note (c), and in China City Briefs.


Model Indep. Intercept Slope Slope Var. A Bi BZ R2

1. L 5.835 -0.001530 0.7245 (32.20)* (-8.640)*

2. I) 6.233 -0.002176 0.6692 (26.56)* (-7.593)*

3. L, D 6.194 -0.000977 -0.001158 0.8181 (35.55)* (-4.805)* (-3.858)*

Table 4 shows the results of the log-linear models for the 1985 data, using the SAS package, with the step-wise option chosen for Model 3. All the coefficients have the expected signs. For each time period, the highest significance is seen in Model 3. Con- taining the combined effects of both spatial impedances, the model can be expressed as:

F

74.65*

57.65*

63.96*

PL(t) = A(t) + Bi(t)L + Bz(t)D

P(t) = A'(t) exp[ Bl(t)L + B2(t)D 1.

(2)

(3) or


Table 4 shows the results of the log-linear models for the 1985 data, using the SAS package, with the step-wise option chosen for Model 3. All the coefficients have the expected signs. For each time period, the highest significance is seen in Model 3. Con- taining the combined effects of both spatial impedances, the model can be expressed as:

where A' = exp(A). Running the model, both L and D entered in the regression without forced entry, with L entering first. The pattern repeats itself in the runs for all the other time periods. The bulk of the discussions, hence, are hereon based on this model.

The nonlinear (logarithmic) connectivity between topographical features and population density means that the high population densities near the core (i.e., the coastal regions, especially the Shanghai and Beijing-Tianjin metropolitan regions) drop very fast at a relative small distance or higher altitude away from it, becoming almost flat thereafter in the periphery (i.e., regions further from the coastal regions and/or at higher elevations).

It can be inferred that infrastructure and transportation costs, both of which are increasing functions of elevation and distance from the seaboard, must deter in general and exponentially fast the regional density and possibly spatial spreading of population.

Another possible explanation for this mild non-linearity (exponential formulation) is the multi-stage connection between topography, population density, and socio-economic conditions. Topography originally, and through accessibility, affects population density. In turn, population density influences a bundle of socio-economic conditions (land prices and land uses, for instance) within a complex interplay. A by-product of this interdepen- dence might be the mild non-linearity (exponential in form) in the relationship between topography and population density.

Notes: 1 . R2 is the adjusted values; 2. t-values are in parentheses; 3. * significant at the five percent level.

D. Dendrinos, J . Zhang and Z. Qian, Topography and Spatial Population Dualism 89

1980

1985

1990

6.131 -0.000987 4.001148 0.8187 64.21* 4.01781 (35.16)* (-4.86)* (-3.82)* [ 0.7 1671

6.194 -0.000977 -0.001158 0.8181 63.96* -0.02003 (35.55)* (4.81)* (-3.86)* [0.7139]

6.284 -0.000976 -0.001151 0.8151 62.73* -0.02389 (35.83)* ( 4 . 7 7 ) * (-3.81)* [0.7120]

Notes: 1. RZ is the adjusted values; 2. t-values are in round parentheses in columns 2-4; 3. Adjusted R2 values after elevation enters regression are in square parentheses in column

4. * significant at the five percent level. 5. df = 27.

5;

Table 5 shows that for Model 3, the adjusted R-squared, the F-statistics and their level of significance, the t-test for the regression coefficients and its level of significance are all well within satisfactory ranges.

Questions one and two of the previous section are thus answered. The hypothesis that the two topographical features significantly effect the spatial population distribution in the PRC during the study period cannot be rejected, and their relationships are log-linear in nature. The long time horizon involved in the analysis (1933-1990), covering highly heterogeneous snapshots of Chinese history, and the strength of the findings attest to a remarkable robustness in the descriptive power of such a simple topography-based model of spatially distributed population densities.

The Pearson correlation coefficient between elevation and distance from the seaboard is 0.706. This moderate value and their entry into the regression without forcing suggest the nonexistence of severe multicollinearity. In all the six time periods, elevation entered the regression first, followed by distance from the seaboard. The percentage of variation in population density explained by elevation alone ranges from 84.8% in 1933 to 87.3 in 1990. It suggests an answer to Question 3: ttlcviitivn is a stronger determinant of population density than distance from the seaboard. This is an interesting finding, were one to


seek relative strength of the various factors involved in the bundle referred to as "accessibility" due to topography.

That elevation and distance from a major center for transportation (a port) are critical in the distribution of population densities within an urban area is a well expected result, according to standard urban economic theory. That one finds similar results at the regional scale is notable: apparently certain spatio-temporal processes are independent of scale, as what occurs at a smaller spatial scale is repeated at a larger spatial scale as well.

As is evident from Table 5, more recent statistics provide a better fit than earlier ones (although the improvement in fitness levels off in the most recent years). This must be the outcome of socio-economic changes. One might detect here the differential bias of the two governmental policies alluded to earlier (section 1); the initial policy to redistribute population to the hinterland may not have been as effective as the later policy to strengthen the economic base of the special economic zones of the southeast.

Given the relative constancy of topographical features in such a short time horizon (both in terms of geological time, as well as in governmental intervention time affecting the nation's transportation infrastructure), reduction in the unexplained (residual) variation by the model must be attributed to an interregional adjustment of spatial population distribution to better fit the national topography. This issue is discussed at some length in the concluding section of the paper.

In 1933 the constant (over space) rate of decline in provincial population densities per meter of elevation B1 = dP/P was 0.000986 DL, or, a decrease of 0.0986 percent in density per meter increase in elevation from sea level (for simplicity, the time subscripts are dropped). The equivalent rate per kilometer of distance B2 = @/P was 0.001363 dD, i.e., a decrease of 0.0001363 percent in density per meter increase in distance from the seaboard. Thus in 1933, the effect of elevation on population density was approximately 723 times stronger than that of distance, on a per meter basis. The corresponding figures for the following study years were 768, 798, 859, 843, and 847. It seems that over time the effect of elevation had been gaining in importance over that of distance, at least until the end of the 1970's.

As densities are directly related to land values (or prices). the estimates of marginal distance and elevation effects upon population density could be linked to marginal changes in land prices at the provincial level in the PRC. This open research topic is left to future work, when and if data on average land prices become available.

The changes in the coefficients of the model over time are depicted in Figures 3 and 4. The intercept, A, is obviously a linearly increasing function of time, reflecting a stable population growth over the entire time span. The slope for elevation, B1, does not seem to be a function of time; but the slope for distance from the seaboard, B2, appears to be linearly increasing over the years. Linear regression tests on these two parameters proved the above observation: while the null hypothesis cannot be rejected for B1, the regression on B2 yields a highly significant result, that being

Bz(t) = -0.001346 + 0.00000379 t, O(1933) 5 t I57(1990) Adj. R2 = 0.907 (4)


4.00105

-0.00110

-0.00115

-0.00120

-0.00125

-0.00130

4.00135

-0.00140

Fig. 3 Changes in the Intercepts

China: 1933-1990

- - - - - - - - I 1 1 1 1 1 1

6.4

6.3

6.2

6.1

6

5.9

5.8

5.7

5.6

5.5

5.4

5.3

5.2 1930 1940 1950 1960 1970 1980 1990

Year

Fig. 4 Changes in Slopes

China: 1933-1990

-0.00090

-0.00100 4.m5L . Year

0 Distance + Elevation


To answer Question 4, it is seen that over the years changes in socio-economic variables, like increases in transportation expenditures and infrastructure capital outlays in the hinterlands, have significantly reduced the influence of distance, although not much that of elevation. The initial highly skewed population distribution clustering around the core diffused in space as public infrastructure projects were carried out in the periphery, accompanied by population relocation movements.

It is of interest to note the 1953-64 behavior in both slopes, which indicates a short- term trend counter to the 1933-90 trend (Fig. 4). This event points to a special socio- economic activity in the PRC during the time period: Mao Zetung's "Great Leap Forward" movement. This "rush to advancement" campaign resulted in wide-spread social costs, especially in the northern, north-westem and south-western provinces, and in an outpour of refugees from some hinterland provinces like Sichuan and Hunan (Zhou). The population growth there was consequently curbed during that period. On the other hand, the emphasis by the central government on exploiting the north-eastern regions, particularly Heilongjiang and Jilin, also contributed to the counter trend.

With the exception of the intercepts, the leveling off of all the statistics for the most recent decade (1980-90) is also of interest. As has been remarked, since the establishment of the PRC in 1949 until the end of the Cultural Revolution in the late 1970s, the overwhelming majority of interregional population migration took the form of government-organized population relocation from the more developed Coastal regions to undevel- oped hinterland areas. In the case of Shanghai, during the three decades after 1949 the net outflow of skilled workers, technical and managerial personnel, and school graduates from the metropolis amounted to over one million (Zhou). In the 1980s, as the government's development philosophy shifted from "self-sufficiency" to an "open" policy, this in- and up-ward migration paled before a surging migration flow from hinterland provinces to coastal regions, especially to the Special Economic Zones.

5. Interregional Variation Analysis

The analysis hitherto acquires an even sharper focus on the topography of the PRC. Earlier, variations in the effects of elevation and distance from the eastern seaboard over time were explored. These effects were not, however, analyzed over space, Spatial variations are bound to exist given the vastness of the nation and the great variety in the physical, social, and economical conditions of different regions. An interregional analysis, therefore, will yield meaningful region-specific information, possibly affording more policy implications. Two more questions may be of interest: first, what is the difference in the regional population densities after the topographical effects have been controlled; and second, is there any interregional variation in the relative influencing strength of the two topographical features on the population distribution, and, if there is, how much is the variation.

For the definition of the regions, the notion of the six Administrative Regions desig- nated by the central government of the PRC is adopted; these include the Northwest (NW), the North (N), the Northeast (NE), the East (E), the South (S), and the Southwest (SW). The composition of the Administrative Regions (hereinafter "Regions") in terms of

D. Dendrinos, J . Zhang and Z . Qian, Topography and Spatial Population Dualism 93

provinces is seen in Table 3. There are other schemes for the division of PRC into regions, such as the eight-region scheme in Hu. Tests show, however, that regressions based on the specific six-region scheme supply the best fit.

The small size of the sample (N = 29) poses an obstacle to the interregional variation analysis, as division into the six Regions will reduce the sample size further, To overcome this, the six small samples, each for a study year, were clustered into one large sample. To catch the interregional differences in population density, five dummy (0-1 valued) variables, each representing a region, were introduced into the model. The SW Region was chosen as the base, with no corresponding dummy variable, and its effect is captured by the intercept. A region dummy of an observation equals one only when the province of the observation belongs to the region represented by the dummy.

A source of disturbance to the regression results is the population growth over the years, since the clustered sample now contains observations from different time periods. To control the effects of population increase, five additional dummy variables corresponding to each of the time periods, except for the base year (1933), are added into the model. The rule for assigning 0-1 values to the time dummy variables is similar to that for the region dummies.

The model thus is expanded to

Y = Y [X, R, TI = A + xi BiXi + xj CjRj + z k DkTk (5 )

where R are region dummy variables and T are time dummy variables. Y, X, A, and B are defined as before, saving the time subscript. C indicates the differences in population density due to deviation from the base regions, and D captures the demographic differences due to deviation from the base year.

The regression results from the clustered data are reported in Table 6 . All the independent variables entered the model with high significance. The coefficients for the time dummy variables are seen to increase steadily over the years, as expected. A linear regression of D over t (for t see Eq. 4) resulted in a coefficient of 0.0207, with R2 = 0.997, reflecting a realistic two percent annual population growth rate in PRC over the past half century.

To ensure the validity of the findings, tests for heteroskedasticity and autocorrelation of the error terms were conducted using the provided SAS options and additional calcula- tions (Odland). While heteroskedasticity was not detected in the original model (Eq. 3), it was found in the expanded model (Eq. 5). Consequently, the results of hypothesis testing displayed in Tables 6 through 8 were based on White's robust error approach. The calcula- tion of the Moran's I statistic, according to Hornby, shows that spatial autocorrelation is nonexistent in both the models.

More interesting are the variations among the parameters for the region dummy variables. The parameters are all negative, ranging from about -2.0 for the NE Region to about -0.8 for the NW Region. This negativity indicates that for its topographical conditions, the SW Region is the most "crowded" area, followed by the NW Region. On the other hand, the NE Region, the parameter of which is on the lower end, is topographically the most sparsely populated region. These two stand apart from the general picture of an exponen-


Ind. Variable

intercept

elevation distance

(region dummies)

North

East South

(time dummies) 1953 1964 1980 1985 1990

Northwest

Northeast

Parameter Estimates t-value

A 6.4225 39.178*

Bi -0.001284 -17.948* €32 -0.0013 15 -13.7 13*

c1 -0.7669 -5.8 15* c2 -1.265 1 -6.448*

c4 -0.9769 -6.106* c5 -1.203 1 -7.126*

c3 -1.9982 -1 1.851*

Di 0.3749 2.343* D2 0.6105 3.899* D3 0.9734 6.539* D4 1.0376 6.975* D5 1.1316 7.578*

tially declining population density governed by topographical factors. These indications are well supported by empirical evidence (Fan). As of 1990, Sichuan

Province, a major province in the SW Region inhabited by about one tenth of the country's total population, was the most populous province nationally. For the past several decades it has been, and still is, a major origin of migration. On the contrary, the NE Region, especially Heilongjiang Province, which takes up over half of the region's area, has seen a constant influx of new settlers since early this century (Zhou). This striking contrast, not readily explainable topographically, must stem from non-topographical reasons.

The results of the analysis suggest a reasonable population relocation policy for the PRC government to follow: further development of the North-East Region through new settlement, as well as foster development of new resources there; and at the same time, relocate population away, or at least not severely restrain the outgoing migration from the SW Region, if minimization of settlement costs (infrastructure, transportation, etc.) without a decline in the settlers' living standards is desired. As the continuous improvement of the model fit over time demonstrates the sustained power of the topographical features in determining the population distribution of the PRC, and as the modeling results are closely reflected by numerous facts in the nation's history, the above policy suggestions can be regarded as well-grounded.

Notes: 1. * significant at the five percent level. 2. t-values were calculated using White's heteroskedastisity-consistent covariance matrices. 3. Adj. R2 = 0.888, F = 106.14*, df = 161, Moran's I = -0.01633.


Ind. Variable

intercept

elevation distance

(region dummies)

East Northeast

south

(time dummies) 1953 1964 1980 1985 1990

Parameter Estimates t-value

A 5.5147 34.552*

Bi -0.002434 -12.137* B2 -0.000657 -2.143*

c3 -1.0933 -7.524* c4 -0.098 1 -0.758 c s -0.4169 -2.910*

Di 0.3058 1.564 DZ 0.5779 3.022* D3 0.8991 4.951* D4 0.9613 5.312* Ds 1.0530 5.812*

Table 8 Regression Results on High-West Region: 1933-90

I Ind. Variable Parameter

Dependent Variable: LP

Estimates t-value

-0.6389

I intercept I A I 6.102 -T 38.205* I

I -5.239*

elevation distance

Bt €32

-0.001 106 -21.228* I -0.00 1426 -20.829*

(region dummies) Northeast c1

(time dummies) 1953 1964 1980 1985 1990

0.5283 0.6830 1.1386 1.2073 1.3064

2.830* 3.803* 6.564* 6.955* 7.466*

Notes: 1. * significant at the five percent level. 2. t-values were calculated using White's heteroskedastisity-consistent covariance matrices. 3. Adj. R2 = 0.959, F = 133.04*, df = 45, Moran's I = -0.2510.


It should be pointed out that the interregional variations in population density captured by the region dummy variables are obtained with only the effects of topographical features, as well as the effect of time, being statistically controlled. The introduction of other social, economical, and political factors not influenced by topography could alter the map. One of such major factors is obviously the presence of natural resources, which could partly justify the government's long-standing efforts to develop the NW Region. Other possible reasons include national defense and ethnic balancing. All this, despite the unfa- vorable topographical conditions of the Region, compared with the national average. Nev- ertheless, since topographical features are relatively the most stable, any long-term population policy should take them into consideration.

With one combined sample, it is difficult to explore the spatial variation of the relative importance of the two topographical features on the population dismbution. In order to see the variation, the combined sample was spatially split into two larger "Super Regions": the Low-East Region, containing N, NE, E, and SE, and the High-West Region, made up of NW and SW (Figure 5). The definition is derived from the notion of the "Three Plat- forms", a typical division of the PRC's territory by province elevation (Hu), although direct

High-West Region

Fig. 5 PRC's Six Administrative Regions and Two Super-Regions

Note: Taiwan is not shown.

D . Dendrinos, J . Zhang and Z. Qian, Topography and Spatial Population Dualism 97

use of the "Three Platforms" notion is inappropriate due to its conflict with the division into the six Administrative Regions. Roughly speaking, the Low-East Region corresponds to the First Platform, while the High-West Region is equivalent to a combination of the Second and the Third Platforms. From Table 3 it is seen with the exception of the Inner Mongolia, all provinces above 1,000 meters from sea level fall into the High-West Region.

Tables 7 and 8 summarize the results of running the expanded model on the tempo- rally-clustered samples of the two Regions. Except for two, all the coefficients in the two models show high statistical significance. Some differences are observed between the two tables. First, time dummy variables in the High-West Region model have higher parameter values than those in the Low-East Region model, which demonstrates a higher population growth rate in the hinterland. Second, the High-West Region data give a better fit to the model than those of the Low-East Region data, suggesting a greater variety or a more active role of non-topographical factors in the eastern low-elevation areas.

The most conspicuous difference between Tables 7 and 8 is seen in the relative strength of the two topographical factors. In the Low-East Region, the parameter for elevation doubles from its value for the nation-wide sample, yet the parameter for distance from the seaboard halves itself. On the other hand, in the High-West region the two parameters approximate their national average values, although elevation looses some of its effects while distance from the seaboard sees a marginal gain.

The above phenomenon points to the fact that in the eastern areas of the PRC, which is largely lowland, elevation is a much more important determinant of population distribution than distance from the seaboard (which is already small). Compared to the west, the eastern provinces enjoy a better transportation network.

Hence, the impedance to population spread comes not so much from horizontal distance, which is well compensated for by good transportation; rather it draws from vertical distance, which deteriorates farming and living conditions, as discussed earlier, and cannot be easily compensated for by the current economic and technological conditions in the PRC. The existence of an extensive and economic waterway transportation system in the lower areas of the eastern part of the PRC may also contribute to the attractiveness of these areas for human inhabitance.

Elevation of most provinces in the High-West Region, on the other hand, is such that weather conditions, in particular inadequate precipitation, are severe enough to render the region hardly suitable for extensive habitation. Its influence on population distribution in the region, therefore, declines relative to that of distance from the seaboard; elevation gains strength due to the vast expansion of the Region, enough so to overcome its poor transportation conditions.

Priorities in economic development in the eastern areas, the findings suggests, must consider topographical factors. These priorities should be based more on conditions ema- nating from low elevation than those associated with vicinity to the seaboard. On this basis, for instance, development ought to favor Heilongjiang over Jilin. For the western provinces, nevertheless, distance to the coastline should still be included in the priority scheme. Again, these policy implications are made solely in light of topography related factors. In real policy formulation, factors independent of topographical features obviously cannot be ignored.

98 D. Dendrinos, J . Zhang and Z. Qian, Topography and Spatial Population Dualism

6. Conclusions and Research Suggestions A negative exponential model of regional population densities distribution as a func-

tion of elevation from sea level and distance from the eastern seaboard was unveiled for the People's Republic of China during the period 1933-1990. The model may carry some broader implications for geographical analysis.

Both topographical factors were found to be very significant in explaining spatial distribution of population within the PRC. Sharp differences in elevation seem to account mainly for the stark spatial dualism in the population distribution of the country. The effects of elevation become even more evident in the outer parts of the nation.

Results further indicate that for the past half century the PRC has seemingly been undergoing the traditional process of decentralization. This process, however, seems to have slowed down during the most recent decade, possibly due to the adoption of an economics-based "open-door" policy by the government. Such a policy has favored the southern coastal regions. A counter-trend to this south-eastern seaboard-biased economic policy has been a western regions-oriented favoritism in population resettlement. Clearly, more data are needed to pinpoint the end effects of these seemingly incongruent and possibly dichotomous demographic-economic policies.

It was acknowledged that non-topographical (i.e., socio-economic) factors also must determine regional population density distributions. The inter-temporal and inter-spatial variations found in the parameters of the models employed in this paper must be due to changes in these non-topographical variables, as topographical features of regions do not change rapidly. These factors could also account for the remainder of the unexplained variations in the models supplied. Examples of such factors include the distribution of various natural resources in space, climatic conditions, soil and terrain type, other land uses (particularly the spatial distribution of various animal and plant species), and socio- logical, economic, cultural and ethnic, historical, and other factors.

Precise measurement of these variables, however, is difficult to obtain at present, therefore impeding the analysis. These variables' effects could, however, to a large extent be thought of as being discounted in the values (and the manner in which they change in

time) of the parameters estimated by the models tested. The effects are also incorporated in the particular form of the models used, which were based merely upon topographical features of a national landscape. The relatively high values of the statistics obtained (particularly the R2s) attest to this conclusion,

Motivated by the finding that processes expected to work at the spatial micro-scale are found to function at the spatial macro-scale as well, one could extend the analysis further by constructing two worldwide curves: one identifying the cumulative percent of human population located at increasing altitude, the other depicting the cumulative percent of human population located at increasing segments of (some minimum in length measuring) distance from coastal lines. These two distributions would generalize the findings of this work on the scale of worldwide population densities distribution. Within this framework, some of the inherent difficulties of land-locked nations could possibly be identified in the development process.

Finally, another possible extension of this work is the derivation of a dynamic model of spatial population distribution and diffusion. A non-linear dynamic (continuous or

D. Dendrinos, J . Zhang and Z . Qian, Topography and Spatial Popularion Dualism 99

discrete in time) model of spatial population densities in a non-homogeneous (discrete), non isotropic, three-dimensional space could be constructed, through which one could look at the physics of population concentrations or de-concentrations.

A Cellular Automata-based spatio-temporal diffusion model might provide a good qualitative descriptor of the population distribution and its underlying forces depicted here. Notions associated with speed of motion, acceleration or deceleration, attraction or repul- sion, inertia etc., could be invoked, and energy and entropy present in each population distribution scheme could be computed. With the findings established here from the (static, exponential) model tested, including the combined or isolated effects of topographical impedances, parameter values and their changes in time, the stage for such a construct is set.

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TOPOGRAPHY AND SPATIAL POPULATION DUALISM IN THE PEOPLE'S REPUBLIC OF CHINA (1933–1990)

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