Agglomeration Forces in the Philippines:Evidence From Census Data

By Manuel Leonard F. Albis

School of Statistics

University of the Philippines, Diliman

2017 BSP-UP Professorial Chair Lectures

21 November 2017

AVR-EFLC, BSP Complex, Manila

1

Agglomeration

• Agglomeration refers to the clustering of consumers and firms across space

• Sustained clustering of economic agents may lead to the formation of cities

• Firms and consumer receive benefits from agglomeration, e.g.,– Reduced production costs– Reduced transportation

costs

2

Agglomeration Forces – Sharing Hypothesis

• The consumers and firms benefit from shared access to available public amenities in cities

Firms and consumers

Access

Public Amenities

3

Agglomeration Forces – Sharing Hypothesis

• Shared access to available goods and labor

Producers of intermediate good

Firms that need the intermediate good

and labor

4

Available Labor in Cities

Agglomeration Forces – Sharing Hypothesis

• Sharing of risks due to the availability of capital markets in cities

Firms Capital Markets Households

5

Agglomeration Forces – Matching Hypothesis

• High success probability of searching raw materials or intermediate goods on spec

Producers of intermediate goods

Firm that needs the intermediate good

Specialized Good

Specifications

6

Agglomeration Forces – Matching Hypothesis

• Presence of labor in cities increases the firms’ chance of finding an employee who has the required skillset (quality of job matching)

Firm that needs the specialized labor

Specialized Labor

Job Requirement

Labor Market

7

Agglomeration Forces in the Philippines(Reyes-Macasaquit, 2008)

Trade Liberalization

• Specific policies improved industrialization by stimulating the drivers of agglomeration economies

Investment Incentives Export Processing Zones8

Objectives of the Study

• Exploratory analysis of agglomeration in the Philippines using the 2000 and 2010 Census of Population and Housing Barangay-level data collected by the PSA

• Identify agglomeration forces in terms of the growth in the variety of business establishments

• Gain insights whether these patterns are uniformly observable in all areas in the Philippines, or if several patterns exist

9

II. DATA

10

Census of Population and Housing

• The Census of Population and Housing (CPH) is a survey that counts the population and all housing units in the Philippines

• The CPH also gathers the Barangay-Level data, which captures urbanity characteristics, and presence of business establishments and other public infrastructures in each barangay

• The barangay data provide vital information about the resident firms, which can identify industry agglomeration if spatially mapped

11

CPH Barangay-Level Data

• Number of firms in the barangay following the broad classifications:

1. commercial establishments (e.g. wholesale and retail stores);

2. manufacturing;

3. auto repair vulcanizing, and other repair shops;

4. restaurants, cafeteria, barbershop, and other personal services establishments;

5. hotel, dormitory and other lodging places;

6. recreational establishments; and

7. banking and other financial institutions

12

• One limitation of the dataset is that the number of establishments in the 2000 CPH is capped at 10– i.e. even if the barangay had 25 commercial

establishments, for example, the value on record is only 10

• Data in the 2010 CPH record the actual number of establishments in the barangay

• Because of data limitations, this paper operationalizes the definition of a dominant industry in the barangay as an industry with at least 10 establishments

13

CPH Barangay-Level Data

Industry Variety Index

• Industry Variety Index

– Number of dominant industries in the barangay

𝑉𝑡,𝑖 =

∀𝑖

𝐼𝑡,𝑖𝑘; 𝐼𝑡,𝑖

(𝑘)=

1 𝑖𝑓 𝑁𝑡,𝑖(𝑘)

≥ 10

0 𝑖𝑓 𝑁𝑡,𝑖(𝑘)

< 10

• where 𝑁𝑡,𝑖(𝑘)

is the recorded number of establishments in the 𝑘𝑡ℎ broad industry classification, for the 𝑖𝑡ℎ

barangay, at time 𝑡 ∈ 0 𝑖𝑓 2000; 1 𝑖𝑓 2010

• A 𝑉𝑡,𝑖 that is close to 7 indicates that the barangay has a heterogeneous mix of dominant industries

14

• Industry Variety Growth Index– difference between the industry variety index in

2010 and 2000

𝐺𝑖 = 𝑉1,𝑖 − 𝑉0,𝑖

• A positive 𝐺𝑖 implies that dominant industries were added in the barangay within the decade, and industry heterogeneity increased

15

Industry Variety Growth Index

• Other Variables– (i) urbanity indicators (e.g. brangay part of the

city/town proper or a former poblacion of the municipality, street patterns);

– (ii) public amenities (e.g. existence of church, plaza, cemetery, market place, school);

– (iii) infrastructure (e.g. telephone lines, postal services, community waterworks)

16

CPH Barangay-Level Data

III. METHODOLOGY

17

Hotspot Analysis

• This paper uses the spatial association statistic developed by Getisand Ord (1992) to determine concentration or clustering of barangays with high index values

• The resulting Getis-Ord statistic is a spatially-weighted z-score of the variety growth index

𝐺𝑖∗ =

𝑗=1𝑛 𝑤𝑖𝑗𝑥𝑗 − 𝑥 𝑗=1

𝑛 𝑤𝑖𝑗

𝑠𝑛 𝑗

𝑛𝑤𝑖𝑗2 − 𝑗

𝑛𝑤𝑖𝑗2

𝑛 − 1

12

where 𝑥 = 𝑛−1 𝑗=1𝑛 𝑥𝑗, and 𝑠 = 𝑛−1 𝑗

𝑛 𝑥𝑗 − 𝑥21/2

, and the 𝐺𝑖∗

statistic has a standard normal distribution

18

Factor Analysis and Regression

• Factor analysis and regression are used as descriptive tools to identify potential drivers of industry variety growth index in 2010 using the initial conditions in 2000 as regressors

• Factor Analysis

19

High Income

Financial

Recreational

Hotel

Low Income

Auto Repair

Personal Services

Commercial

Industry Factors

Infra & Comm

Postal

Telephone

Waterworks

Others

City Hall

Market Place

Public Library

Public Amenities Factors

Factor Analysis and Regression

• The second step is to individually regress the extracted factors from the two sets of variables to the industry variety growth index, acting as the response variable, controlling for initial conditions

• The regression model is given by:𝐼𝑉𝐺𝑖 = 𝛽0 + 𝛽1𝐹𝑖 + "𝑖𝑛𝑖𝑡𝑖𝑎𝑙 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑠" + 𝜖𝑖

20

Industry Variety Growth

(2000 to 2010)

Industry Factors

(2000)

Public Amenities Factors (2000)

Initial Conditions

(2000)

IV. RESULTS

21

CPH Barangay Level Data

• The 2000 barangay dataset were first updated to reflect any changes in the administrative areas until 2010

• 41,043 barangays in 1,648 municipalities

• GIS Shapefiles are from PhilGIS

22

Number and Percentage of Barangays by Industry Variety Index and Year

Industry

Variety

Index

2000 2010

No. of

Barangays%

No. of

Barangays%

0 24,849 60.54 26,036 63.44

1 12,401 30.21 10,506 25.6

2 1,783 4.34 1,994 4.86

3 833 2.03 988 2.41

4 586 1.43 654 1.59

5 283 0.69 443 1.08

6 180 0.44 291 0.71

7 128 0.31 131 0.32

Total 41,043 100 41,043 100

Decrease in the number of barangays with an industry variety index of 1, while an increase in the number of barangays for other values of the index

– Small Market Hypothesis

– Productivity Threshold

23

Hotspot Analysis on Industry Variety Index

2000 201024

25

Hotspot Analysis on Industry Variety IndexLuzon Island Group

Region 2000 2010

National Capital Region (NCR)

Wide hotspot area in Metro Manila (spill-over to Central Luzon and CALABARZON Regions)

Reduction in land area of Metro Manila hotspot

Cordillera Administrative Region & Cagayan Valley

Large Luzon coldspot Remained a coldspot

Ilocos Region Laoag hotspot Vigan hotspot

Central Luzon The hotspot broke apart from the larger 2010 Metro Manila hotspot

CALABARZON Batangas City Hotspot Shrank in land size

MIMAROPA Puerto Prinsesa hotspot in the Palawan Island emerged

26

Hotspot Analysis on Industry Variety IndexVisayas and Mindanao Island Group

Region 2000 2010Western Visayas Large area of Negros

Occidental was a hotspotNorthern part of Negros Oriental remained

Eastern Visayas Samar and Leyte were coldspots

Remained coldspots

Northern Mindanao

Cagayan de Oro City formed a hotspot with Iligan and El Salvador cities

Increased in size

Davao Large Davao City hotspot Remained a hotspot

SOCCSKSARGEN General Santos City hotspot

Zamboanga &ARMM

Small hotspots around Cotabato City and Zamboanga City

Zamboanga hotspot grew in size

Hotspot Analysis Industry Variety Growth

27

• Areas in red are significant industry variety growth areas

Focus• Metro Manila• Cebu City• Davao City

Hotspot Analysis Industry Variety Growth

28

Cebu CityMetro Manila Davao City

Metro Manila is an industry variety growth hotspot, Cebu and Davao City are coldspots

Factor Analysis - LoadingsIndustries (2000)

Variable

Factor1:

Low

Income

Factor2:

High

Income

Commercial 0.3927

Recreational 0.6839

Manufacturing 0.6855

Hotel 0.3976

Finance 0.6252

Auto Repair 0.6181

Personal Services 0.7218

Overall

Loadings with abs(loading)<.3 are suppressed

Varimax Rotated Factor Loadings of Industry Dummies

Overall KMO: 0.842729

Factor Analysis - LoadingsPublic Amenities (2000)

Variable

Factor1:

Infra and

Comm

Factor2:

Others

Town/City Hall 0.4241

Market Place 0.4220

High School 0.5275

College/University 0.3904

Public Library 0.3505

Telephone 0.7177

Postal Services 0.5334

Community Waterworks 0.3496

Overall

Loadings with abs(loading) < .3 are suppressed

Varimax Rotated Factor Loadings of Public Amenities

Overall KMO: 0.7618 30

Regression ModelIndustry Factors; Dependent: IVG

31

Variable Stat Model 1 Model 2

Factor 1: Low Income

Coeff. -0.226 --

SE 0.011 --

Factor 2: High Income

Coeff. -- 0.304

SE -- 0.009

Initial: Public Goods

Coeff. 0.126 0.137

SE 0.003 0.003

Initial: IndustriesCoeff. -0.162 -0.356

SE 0.005 0.004

ConstantCoeff. 0.022 0.022

SE 0.005 0.004

N 41,043 41,043

R-Square 0.149 0.1625

Evidence for the Matching Hypothesis

• agglomeration is partly driven by the establishment of related industries

Regression ModelPublic Amenities Factors; Dependent: IVG

32

Variable Stat Model 3 Model 4

Factor 1: Infra & Comm

Coeff. 0.090 --

SE 0.009 --

Factor 2: Others

Coeff. -- -0.083

SE -- 0.011

Initial: Public Goods

Coeff. 0.087 0.153

SE 0.005 0.005

Initial: IndustriesCoeff. -0.247 -0.247

SE 0.003 0.003

ConstantCoeff. 0.022 0.022

SE 0.005 0.005

N 41,043 41,043

R-Square 0.142 0.1412

Evidence for the Sharing Hypothesis

• agglomeration is driven by the access of industries to common resources in the barangay

Conclusions

• No single type of agglomeration force is present in all areas in the Philippines; different patterns exist

• Evidence suggests that the matching of firms and sharing of public amenities are positively correlated with industry variety growth

• However, not all types of industries and public goods can stimulate agglomeration

33

Recommendations

• Role of terrain

• Demand side of agglomeration

• Further investigation is required to identify causal links to agglomeration and growth

34

The author would like to thank the Bangko Sentral ng Pilipinas and the University of the Philippines for the support toward the completion of this paper.

THANK YOU.

35