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close this bookEmerging World Cities in Pacific Asia (UNU, 1996, 528 pages)
close this folderPart 2. Changing Asia-Pacific world cities
close this folderGlobal influences on recent urbanization trends in the Philippines
View the document(introductory text...)
View the documentIntroduction
View the documentRecent urbanization trends
View the documentA simple model for examining global influences
View the documentGlobal influences on urbanization trends
View the documentConcluding remarks
View the documentAcknowledgement
View the documentReferences

(introductory text...)

Orville Solon

Introduction

It is often argued that urban primacy in a developing country is largely a product of its colonial past. The dominance of Metro Manila in the Philippine urban system today continues to feed upon the same forces initiated during the Spanish occupation: Manila still serves as the main link to the world economy and remains the seat of political authority. This colonial heritage may be one of the more formidable obstacles confronting efforts to promote broad-based and balanced regional development in the Philippines.

Although the momentum generated by old global factors continues to influence the landscape of the Philippine economy, new global influences have appeared and are becoming more dominant. The concern is that the strength of new global forces, which build upon a more pronounced international division of labour, greater reliance on international finance, and more emphasis on international trade as the main engine for growth, may only reinforce tendencies towards urban primacy. The argument is that new global forces backed by new communications and transport technology work on the world economy through a system of mega-cities and, in so doing, worsen the unevenness of growth within countries (see chap. 2). Evidence showing the tendency of foreign direct investment to locate in and around national capital regions lends some validity to the hypothesis (Fuchs and Pernia, 1989).

However, more recently in the Philippines, the limited success of regional centres like Cebu in Central Visayas, Cagayan de Oro in Northern Mindanao, and Davao in Southern Mindanao in attracting direct foreign investment and in promoting exports raises a number of questions. With global restructuring, can the regional comparative advantages represented by regional urban centres compete directly at the global level? Can the recent changes in transport and communications technology elevate regional centres above the limits imposed by the existing national hierarchy of cities? The argument being suggested concerns the possibility that global influences may be harnessed to promote balanced growth via intermediate regional centres.

A preliminary examination of the question of whether or not new global factors have an inherent influence towards urban primacy is the main purpose of this chapter. This is done by looking at elements of both global and local influences on recent trends in Philippine urbanization. In particular, this chapter will address three questions: (1) Do global and local factors have different effects on urbanization patterns? (2) How do global and local factors affect each other? and (3) Does the existing pattern of urbanization itself affect the way global and local influences are applied across regions?

In asking the first question, this chapter intends to determine the nature and relative influence of global factors. The second question is raised to qualify answers to the first by determining whether the two sets of influences crowd each other in or out. The third question considers the possibility that global factors may not have an inherent predilection for capital cities but are only observed to have such a tendency as they respond to an existing pattern of urbanization. An underlying interest here concerns the prospect of harnessing global forces to develop secondary or regional urban centres.

The analysis here focuses on recent urbanization trends in the Philippines covering the period between 1980 and 1990. An overview is presented in the next section. Subsequently, an attempt is made to determine how changes in the relative levels of urbanization of the 13 regions in the Philippines are affected by global factors, particularly foreign direct investment and exports, using a simple model developed on the basis of the hypotheses and results presented in previous studies of Philippine urbanization such as those by Pernia et al. (1982) and Herrin and Pernia (1987). The hypotheses underlying the model are tested using three-year, thirteen-region panel data. The empirical specification of the model as well as the results are discussed. Concluding remarks are made in the final section.

Table 8.1 Urbanzation trends in the Philippines, 1948-1990


1948

1960

1970

1980

1990

Population ('000)

Philippines

19,234

27,086

36,683

48,101

60,680

Urban areas

3,829

5,810

8,776

12,432

16,371

Metro Manila

1,569

2,462

3,967

5,926

7,929

Urbanization ratiosa

Urban/Philippines

0.199

0.215

0.239

0.258

0.270



(7.75)

(11.53)

(8.03)

(4 39)

Manila/Philippines

0.082

0.091

0.108

0.123

0.131



(11.43)

(18.97)

(13.92)

(6.06)

Manila/Urban

0.410

0.424

0.452

0.477

0.484



(3.41)

(6.67)

(5.45)

(1.61)

Source: National Statistics and Census Bureau, Philippine Statistical Yearbook 1991.
a. Numbers in parentheses are percentage rates of change.

Recent urbanization trends

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)

Global influences on urbanization trends

The two models represented by Equations 4-8 and 9-12 were estimated using three-year, thirteen-region panel data by three-stage least squares (3SLS). The results are summarized in tables 8.2 and 8.3 respectively.

The first column of table 8.2 shows that increases in a region's share of exports, local investments, and government spending increase the region's share of total urban population. By specifying the model in terms of shares, the results imply that the regional distribution of exports, local investments, and government spending tends to produce a more even distribution of urban population.

The regression coefficient for foreign direct investment shares on urban population shares suggests that the way foreign investment is distributed among the regions tends to produce more uneven distribution of urban population. What seems to be happening is that foreign investment tends to gravitate around one or two regions (consider the peaks in fig. 8.5) so that urban growth concentrates on these regions. The growth in these selected regions may be sufficiently strong that the share of the "average" region (which is what the regression captures) diminishes. If this interpretation of the results is correct, then one can say that direct foreign investment tends to promote urban primacy.

The estimated equation for foreign investment determination is shown in the second column of table 8.2. The results demonstrate that a region's share of total urban population positively determines its share of foreign investment.

In the same equation, local investment shares seem to have no statistically significant influence on foreign investment shares. This suggests that no apparent phenomenon of crowding-in or crowdingout occurs between local and foreign investment. The other two determinants of regional foreign investment shares, namely, electrical connections and telephone exchanges, are shown to have unexpectedly adverse effects on foreign investment shares. The results suggest that the greater the region's share in power and communications facilities, the lower the share of total foreign investment. A possible explanation for the results could be measurement problems: having electrical connections does not necessarily guarantee that power is available (a significant portion of firms have had to procure their own power generation facilities owing to frequent power out-age); and having telephone exchange facilities does not necessarily imply access to telephones.

Table 8.2 Three-stage least squares estimates of model 1 (Equations 4-8)


Dependent variables

Independent variables

URBSHRrt

FlNVSHRrt

EXPSHRrt

LlNVSHRrt

GSDPSHRrt

URBSHRrt


4.120500

0.090514

0.627620

0.271270



(11.209)*

(0.200)

(0.514)

(7.560)*

FlNVSHRrt-1

- 0.228050


0.197010

-0.077599



(-4.433)*


(4.312)*

(-0.694)


EXPSHRrt-1

0.499110






(7.671)*





LINVSHRrt-1

0.378970

- 0.254180





(3.602)*

(-1.066)




GSDPSHRrt-1

0.601820






(2.231)*





ELCSHRrt


- 0.790800


- 0.371560




(-5.124)*

(-1.336)



TELSHRrt


- 1.628300

- 0.968120





(-4.765)*

(-6.841)*



BNKSHRrt



1.908200

1.104800





(4.138)*

(0.747)


INFDSHRrt





0.000075






(-0.873)

Constant

- 0.019373

-0.034394

-0.017510

-0.021788

0.060266


(-0.944)

(-1.192)

(-1.458)

(-1.059)

(9.109)*

Adjusted R2

.793393

.775950

.930913

.625412

.684492

F-statistic

25.000730

22.645540

85.215490

11.434990

28.118580

Durbin-Watson

1.609393

2.694194

1.738095

2.304809

2.777634

Values in parentheses are t-statistics.
* Significant at the 1 per cent level.

The estimated exports share equation presented in the third column of table 8.2 suggests that share of urban population does not have any statistically significant influence over export shares. This may suggest that, unlike direct foreign investment, exports continue to rely upon resource-based comparative advantage. The proxy variables for power and communications facilities are also shown to have adverse effects on regional shares of total exports.

The estimated local investment equation reported in the fourth column of table 8.2 shows that shares of urban population, foreign investment, and power and banking facilities do not have any statistically significant influence on the regional distribution of local investment.

Finally, the estimated equation for government spending suggests that the regional allocation of government resources is positively affected by urban shares but not by the regional social status represented by shares in infant deaths.

Table 8.3 presents the 3SLS estimates of Equations 9-13. As mentioned earlier, this system of equations was specified to test for the influence of lagged dependent variables on the basic model.

The estimated urbanization equation implies that whatever effect foreign and local investment as well as exports have on the regional distribution of the urban population is absorbed and outweighed by the influence of previous period urban population shares. Only government spending seems to have transcended such influence, indicating that public expenditure may be an important accelerator of changing urban patterns.

After introducing lagged dependent variables for the foreign investment, local investment, exports, and government spending equations, the regional distribution of urban population is now found to be a significant determinant. What seems to be happening is that the introduction of lagged dependent variables in effect isolated the effect of urbanization on the above-mentioned variables.

The results also suggest some indirect influence of direct foreign investment on urbanization via exports and local investment. The previous period foreign investment share is shown to have a positive influence over the current period export share of a region. This may be capturing the effects of increasing foreign participation in the promotion of non-traditional export commodities. On the other hand, as shown by the local investment shares equation, foreign investment seems to be crowding out local investment. These indirect effects could partly explain why foreign investment, exports, and local investment are shown to have no significant final influence on urbanization.

Table 8.3 Three-stage least squares estimates of model 2 (Equations 9-13)


Dependent variables

Independent variables

URBSHRrt

FINVSHRrt

EXPSHRrt

LlNVSHRrt

GSDPDHRrt

URBSHRrt


3.180100

0.875820

1.351600

0.210900



(1.562)

(2.571)*

(5.189)*

(7.618)*

URBSHRrt-1

0.701730






(7.842)*





FlNVSHRrt-1

- 0.037992

- 0.018691

0.091795

- 0.222690



-(1.040)

(-0.106)

(2.655)*

(-1.934)*


EXPSHRrt-1

- 0.056181


1.037000




(-0.714)


(12.088)*



LINVSHRrt-1

0.072502



0.102080



(1.059)



(0.363)


GSDPSHRrt-1

0.780080




0.498070


(5.335)*




(6.243)*

ELCSHRrt


- 0.655730






(-1.419)




BNKSHRrt


- 0.066869

- 0.621110





(-0.027)

(-1.597)



INFDSHRrt





0.000047






(-0.678)

Constant

- 0.035395

0.110680

-0.029500

- 0.017765

0.025000


(-3.162)*

(-3.466)*

(-3.927)*

(-0.858)

(3.284)*

Adjusted R2

.934967

.679622

.968618

.588741

.824761

F-statistic

72.883420

14.258200

193.907700

12.929640

40.220870

Durbin-Watson

1.494077

2.277040

1.709994

2.342220

2.701551

Values in parentheses are t-statistics.
* Significant at the 1 per cent level.

Concluding remarks

Despite the data used in the regression analysis and other limitations underlying the model, a number of interesting results concerning global influences on recent urban trends are noteworthy.

First of all, results show that the distribution of direct foreign investment across regions contributes to the tendency of urbanization to be concentrated in one or two regions. On the other hand, exports, the other global factor tested here, are shown to be contributing to a more balanced pattern.

Local factors tested in the analysis were not shown to be contributing to urban primacy. The share of local investment and government spending in a region increases the region's share of total urban population.

The results also show that foreign investment tends to reinforce a region's exports share, especially in the area of non-traditional export commodities. However, there is some evidence that suggests that foreign and local investment may be crowding-out each other. This indirect effect may weaken the final effect of these factors on urbanization.

A region's share of total government spending tends to improve the level of urban development. In turn, the distribution of government spending is also highly sensitive to urban population shares, but unfortunately is insensitive to social needs (represented in the model by the regional share of total infant deaths).

On the whole, the above results suggest that global factors do not necessarily lead to urban primacy. The recent trend where the dominance of Metro Manila relative to other regions seems to be declining may in fact be influenced by some global factors, especially exports. But, despite the declining trend, the momentum generated by the size of the National Capital Region continues to influence the distribution of both global and local factors. Furthermore, it is unfortunate that government spending has served to reinforce this phenomenon. But, as shown above, a reallocation of public resources could potentially effect balanced regional development.

Acknowledgement

The author gratefully acknowledges the research assistance provided by Ma. Peregrina Makabenta.

References

Fuchs, Roland J. and E. M. Pernia (1989), "The Influence of Foreign Direct Investment on Spatial Concentration." In Frank J. Costa et al. (eds.), Urbanization in Asia, Spatial Dimensions and Policy Issues. Honolulu: University of Hawaii Press, pp. 387-410.

Herrin, A. N. and E. M. Pernia (1987), "Factors Influencing the Choice of Location: Local and Foreign Firms in the Philippines," Regional Studies 21(6): 531-541.

Lamberte, Mario B., Rosario G. Manasan, Gilberto M. Llanto, Winfred M. Villamil, Elizabeth S. Tan, Fernando C. Fajardo, and Maren Kramer (1990), Balanced Regional Development Study. A study commissioned by the Asian Development Bank. Makati: Philippine Institute for Development Studies, May.

Pernia, E. M., G. B. Reyes, and E. M. Soliman (1982). The Spatial and Urban Dimensions of Development in the Philippines. Manila: Philippine Institute for Development Studies.