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Indicators suggest that, although the tendency towards the primacy of Metro Manila remains positive, it may be slowing down. Table 8.1 presents three measures of this tendency: (1) the ratio of the urban population to the total population; (2) the ratio of Metro Manila's population to the total population; and (3) the ratio of Metro Manila's population to the total urban population. Rates of changes for these measures between the five census years show that urbanization and urban primacy accelerated until 1970 but declined towards 1990. The upward trend is explained by Pernia et al. (1982) as being the result of the import-substitution programme that was implemented in the 1960s. The decline may be a response to increasing congestion in the metropolis, to rising land values, and to incentives underlying the industrial dispersal programme initiated in the mid-1970s.
The relative decline of the primacy of Metro Manila after 1970 was anticipated by the work of Pernia et al. (1982) and was observed by a more recent study by Lamberte et al. (1990). An explanation for this trend is that Metro Manila's growth spilled over into its peripheral regions: Central Luzon (Region 3) and Southern Tagalog (Region 4).
Fig. 8.1 Regional population shares in the Philippines, 1948-1990 (NCR = National Capital Region; CAR = Cordillera Autonomous Region. Source: National Statistics and Census Bureau, Philippine Statistical Yearbook 1991)
As shown in figure 8.1, the decline has not actually led to more balanced regional growth.
The changing regional distribution of population over the five census years from 1948 to 1990 is shown in figure 8.1. The 13 regions of the Philippines are ranked in terms of the 1990 regional population shares. The almost equal number of marks above (and to the left of) and below (and to the right of) the curve shows how the regional distribution of population tilted over time in favour of Metro Manila (the National Capital Region) and its peripheral regions.
The distribution of the total urban population among the different regions may be more telling about other aspects of recent urbanization trends. Figure 8.2 shows that Metro Manila has the highest concentration of urban population with a 5 per cent share. Whereas it was shown earlier that in terms of total population the regions peripheral to Metro Manila (Regions 3 and 4) were among those with the largest shares, these regions actually rank low in terms of urban population shares. This indicates that the peripheral regions largely remain rural and that increased modern economic activities located in these regions continue to enjoy amenities. Although Central Visayas (Region 7) and Western Visayas (Region 6) are among the most highly urbanized regions next to the National Capital Region (NCR), their individual shares of the total urban population are less than a fifth of that of Manila.
Fig. 8.2 Regional population shares and growth in the Philippines, 19801990 (Source: National Statistics and Census Bureau, Philippine Statistical Yearbook 1991)
Another interesting pattern shown in figure 8.2 concerns the rate of growth of the urban population across regions. Between 1980 and 1990 Manila grew by 3.3 per cent while the next most urbanized regions grew at slower rates (Region 7 at 3.1 per cent and Region 6 at 2.2 per cent). What is surprising is that regions with less than half a per cent share of total urban population actually grew faster than Manila. These regions include Southern Mindanao, where Davao City is located, Central Mindanao, where Cotabato City is, and Cagayan Valley, where Baguio City used to belong before the Cordillera Autonomous Region was organized.
In terms of cities, regional centres outside Metro Manila are also shown to have grown faster. In figure 8.3, the top 20 cities in the Philippines are ranked in terms of population size. Ranked first is Metro Manila followed by Davao (Region 11), Cebu (Region 7), Zamboanga (Region 9), Bacolod (Region 6), Cagayan de Oro (Region 10), and so on. Although it is not surprising that Manila remains at least 10 times larger, there are a number of smaller cities that are growing at a noticeably faster rate relative to Manila. Among the fastest-growing cities are General Santos (ranked 8th in population size) in Region 10, which has recently been very active in food processing of tropical fruit and marine products for export, and Mandaue (ranked 15th in population size) in Metro Cebu, Region 7, where commercial, trading, and light manufacturing activities have likewise been growing.
Fig. 8.3 Population growth of the top 20 cities in the Philippines, 1980-1990
The questions raised by the patterns revealed by figures 8.2 and 8.3 are the following: Will the prevailing growth patterns (Manila vs. regional centres) be sustained? Moreover, will these trends eventually effect a change in the urban landscape? How are these changes affected by global factors, including foreign direct investment and exports?
To begin answering the above questions, let us consider some indicators on economic performance of the different regions. In figure 8.4, the regions are ranked according to their respective regional gross domestic products (GRDP) relative to that of the National Capital Region. The two other indicators shown are the GRDP growth rates between 1980 and 1990, and the change in each region's share of the gross national product for the same years, which is taken to indicate the region's performance relative to others.
The regional distribution of national income follows a pattern similar to that observed for the regional distribution of population. In figure 8.4, the line marked by squares shows the gross domestic product of a region relative to that of Manila. It indicates, for example, that the second most productive region (Region 4) is producing only as much as 45 per cent of Manila's output while the poorest region (Region 2) is producing only less than 1 per cent. The line indicates the degree of concentration of productive activity in the national capital and its peripheral regions.
Fig. 8.4 Growth and shares of gross regional domestic product (GRDP) en GNP, 1980-1990 (Source: National Statistics and Census Bureau, Philippine Statistical Yearbook 1991)
It is also interesting to note that only four regions were able to expand their share of total national income between 1980 and 1990. What is surprising is that the Central Visayas Region (Region 7) is shown to be one of the fastest-growing regions. The growth of the Central Visayas, particularly of Cebu, is reputedly driven by global factors including direct foreign investment, tourism, and exports.
The general influence of some global factors such as foreign direct investment and exports on urbanization is shown in figure 8.5. The observed pattern is that the level of urbanization of a region measured in terms of its share of the total urban population is positively correlated with the region's share of total exports and foreign direct investment.
There are a number of notable kinks in the correlations shown in figure 8.5. Regions 4 and 1 are shown to have received shares of total foreign direct investment disproportionately larger than their urbanization levels. Investments flowing into Region 1 may be responding to location-specific resources of the region, especially mineral resources. Foreign investments in Region 4 are still driven by economies of size provided by Metro Manila. The provinces of Cavite, Laguna, and Batangas are close enough to Manila for investors to enjoy banking facilities, communications networks, and other infrastructure offered by the metropolis.
Fig. 8.5 Global influences on urbanization in the Philippines (FOR NV = foreign investment. Source: National Statistics and Census Bureau, Philippine Statistical Yearbook 1991)
It is apparent from the discussion in this section that, although global factors tend to be positively correlated with the existing urbanization pattern, developments in the regions suggest the possibility that global influences need not necessarily lead to greater urban primacy. A number of questions are subsequently raised. Do global factors have any influence on the smaller and less perceptible changes at the regional level and across time? Do local factors behave any differently? What about the interaction between local and global influences? Does an existing urbanization pattern have any influence over the way global forces are applied initially?
A simple model for examining global influences
The analytical model
The basic analytical model presented below is constructed with three basic design elements in mind: (1) a simultaneous system of equations; (2) variables are expressed in terms of shares; and (3) time is explicitly introduced.
URBSHRrt = URBSHRrt (GLBLSHRrt-1, LCLSHRrt-1) (1)
GLBLSHRrt = GLBLSHRrt (URBSHRrt, LCLSHRrt, STRCSHRrt) (2)
LCLSHRrt = LCLSHRrt (URBSHRrt, GLBLSHRrt, STRCSHRrt) (3)
where
URBSHRrt - share of total urban population by region r in time t
GLBLSHRrt - share of total global factor influence present in region r at time t
LCLSHRrt - share of total local influence present in region r at time t
STRCSHRrt - share of total structural facilities in region r at time t
Equation 1 shows how urbanization may be influenced by both global and local factors. It will be through this equation that the question of whether or not global influences exacerbate urban primacy is discussed. Equation 2 shows how the application of global influence on the urban system may be influenced by existing urbanization patterns, local factors, and structural facilities. Equation 3 likewise shows how the distribution of local influences is affected by existing urban patterns, global factors, and structural facilities.
The model is constructed as a simultaneous system of equations in order to address three basic issues: (1) the nature of the influence of both global and local factors; (2) the influence, in turn, of existing urban patterns on the way these factors exert their influence; and (3) the interaction between global and local factors.
Since the focus of the analysis is on the influence of global factors on the pattern of urbanization in the Philippines rather than on the urban growth of individual provinces or regions, the model is cast to discern relative rather than absolute changes and influences. A more meaningful examination of the question concerning globalization and urban primacy, for example, can be made only in these terms.
The variables used in the model are, therefore, measured as shares. For example, urbanization is measured as the urban population in the region over the total urban population in the country. Hence, an increase in the share of urbanization of one region necessarily reduces that of others. The influences on urban primacy become more interesting to study under this formulation.
Take, for example, the relationship between foreign direct investment and urbanization. If the variables in the model were to be measured in absolute terms, an observed positive relationship would not be sufficient to show that foreign direct investment promotes urban primacy. Cast in relative terms, on the other hand, the model should be able to provide a sufficient test of this hypothesis.
To demonstrate this, consider the following simple linear regression model: URBSHRr = b0 + b1FINVSHRr + u, where FINVSHRr is the share of total foreign investment going to region r and u is the error term. If b1 were estimated to be negative, this would mean that, as the foreign investment share of the average region increases, the share of the total urban population of the average region declines. This happens only when a substantial share of foreign investment is applied to only one or two regions. As the urbanization levels in these favoured regions increase in response, the shares of the other regions decline and then the average region will experience a marginal decline. Hence, b1 < 0 will indicate a positive relationship between foreign investment and urban primacy.
The other element built into the simple model is time. This is deemed necessary considering that, whereas global and local factors have substantial short-run effects, urbanization patterns tend to change only in the long term. Simple dynamics are introduced in the model to reconcile the simultaneous examination of both short-run and long-run variables.
Main hypotheses
The design parameters of the simple model presented earlier should allow for the analysis of the following hypotheses:
(1) Global factors and urban primacy. As discussed earlier, the use of relative values would allow us to determine whether global factors such as direct foreign investment and exports help strengthen the dominance of the national capital city. If this is the case, we should expect the partial effect of GLBLSHRrt-1 on URBSHRrt in Equation 1 to be negative.
(2) Local factors and balanced growth. The introduction of local factors as an argument in Equation 1 should allow us to determine if local factors such as local investment and government spending are more responsive to regional development objectives. If this were so, we can expect the partial effect of LCLSHRrt-1 on URBSHRrt to be positive.
(3) Existing urban patterns and the distribution of global and local factors. In both Equations 2 and 3, the relative urbanization level of a region is presented as a determinant of the way global and local factors are geographically distributed. The interest here is to determine the ability of regional centres to attract these influences. If the regions, through their city centres, had such abilities, the partial effects of URBSHRrt on the right-hand-side variables of Equations 2 and 3 should be positive.
(4) Crowding between global and local factors. LCLSHRrt and GLBLSHRrt are introduced as arguments in Equations 2 and 3, respectively, to test whether global and local factors crowd-in or crowd-out one another. If both sets of factors crowd-in one another, the partial effects should be positive. Otherwise, we should expect negative partial effects.
Empirical specification and data
The model represented by Equations 1-3 is specified as a system of five linear equations using regional-level data for 1980, 1985, and 1990. Urbanization is presented in terms of the regional share of the total urban population (URBSHR) as mentioned earlier. The influence of global factors is to be examined using regional shares of total foreign direct investment (FINVSHR) and of total exports (EXPSHR). On the other hand, the influence of local factors will be examined in terms of the regional shares of total local investment (LINVSHR) and of government spending (GSPDSHR).
Other variables introduced in the model as exogenous determinants are regional shares of: total banking offices (BNKSHR), total electrical connections (ELCSHR), total telephone exchanges (TELSHR), and total infant deaths (INFDSHR). The first three are used as measures of structural facilities, while the fourth is introduced as a determinant of government spending representing broad social objectives.
The basic empirical model is presented in the following system of equations:
URBSHRrt = b1 + b2FINVSHRrt-1 + b3EXPSHRrt-1 + b4LINVSHRrt-1 + b5GSPDSHRrt-1 + u1 (4)
FINVSHRrt = b6 + b7URBSHRrt + b8LINVSHRrt-1 + b9ELCSHRrt + b10TELSHRrt + u2 (5)
EXPSHRrt = b11 + b12URBSHRrt + b13FINVSHRrt-1 + b14BNKSHRrt + b15TELSHRrt + u3 (6)
LINVSHRrt = b16 + b17URBSHRrt + b18FINVSHRrt-1 + b19BNKSHRrt + b20ELCSHRrt + u4 (7)
GSPDSHRrt = b21 + b22URBSHRrt + b23INFDSHRrt-1 + u5 (8)
The system that consists of Equations 4-8 is taken to be the basic empirical specification of the simple analytical model represented by Equations 1-3. It must be noted that the way the empirical model is specified was primarily driven by data restrictions rather than by theoretical soundness. First of all, data to complete a three-year, thirteen-region panel are available only for the variables specified here. Secondly, degrees of freedom limitations allow us to introduce truly exogenous variables as well as lagged endogenous variables only sparingly.
In order to get around the second limitation, an alternative specification of the basic empirical model is also presented. In the new system, more lagged variables are introduced to test dynamic effects underlying the model. In particular, previous period values of the dependent variables are introduced in each equation. The alternative specification is as follows:
URBSHRrt = b1 + b2URBSHRrt-1 + b3FINVSHRrt-1 + b4EXPSHRrt-1 + b5LINVSHRrt-1 + b6GSPDSHRrt-1 + u1 (9)
FINVSHRrt = b7 + b8URBSHRrt + b9FINVSHRrt-1 + b10ELCSHRrt + b11BNKSHRrt + u2 (10)
EXPSHRrt = b12 + b13URBSHRrt + b14FINVSHRrt-1 + b15BNKSHRrt + b16EXPSHRrt-1 + u3 (11)
LINVSHRrt = b17 + b18URBSHRrt + b19FINVSHRrt-1 + b20LINVSHRrt-1 + u4 (12)
GSPDSHRrt = b21 + b22URBSHRrt + b23INFDSHRrt-1 +b24GSPDSHRrt-1 + u5 (13)