Spatial Microsimulation of Residential and Employment Location Choices in Metro Manila: Towards a...

Spatial Microsimulation of Residential and Employment Location

Choices in Metro Manila:Towards a Spatial Planning and

Decision Support Tool

Noriel Christopher C. TIGLAO

CUPUM 2005University College London (UCL)

29 June - 1 July 2005

National College of Public Administration and Governance

University of the Philippines

Morito TSUTSUMI

Department of Planning and Policy Science

University of Tsukuba

2

Presentation Outline

• Background

• Imperatives for Urban Modeling

• Development of Spatial Microsimulation Model for Manila (InformalSim)

• Location Choice Extensions

• Implications for Spatial Planning and Policy Analysis

• Insights into the Future

3

Background

• Development of urban models arises from the need for…– Informed policy recommendations from planners– Informed decisions by policymakers

• Continuous development and application in advanced countries

• Little work in developing countries due to…Lack of reliable dataComplex inter-relationships in the urban system

4

Urban Modeling Centers in the World

Source: Wegener (1994)

5

Imperatives for Urban Models in Developing Countries

• Needs for ability to forecast future urban growth, land use changes

• Needs for ability to forecast the possible impacts of urban and environmental (including social) policies

• However, developing countries exhibit…– Complex social organizations and individual

behavior– Presence of many market imperfections

6

Urban Realities

7

Urban Realities

8

Modeling Issues in Developing Countries

• Explosive population growth– Complex and dynamic relationships

• Presence of severe economic inequality among individuals and households– Rethinking representative households

• Large informal sector

Serious problems in data availability and the range of analysis that can be done

9

Research Objectives

• Develop household microdata for the City of Manila in 1990– Spatial microsimulation approach– Address problems in lack of data– Tackle estimation and validation issues

• Explore residential and employment location choice behavior of households– Develop insights on spatial choice behavior

10

Spatial Microsimulation Approach

• Directly concerned with microunits such as persons, households, or firms

• Models lifecycle by the use of conditional probabilities

• Spatial microsimulation is increasingly applied in the quantitative analysis of economic and social policy problems

• One major objective in spatial microsimulation is the estimation of microdata

11

Example of Spatial Microsimulation Process (Clarke et. al)

Steps

1. Age,sex, and marital status (M) of hh head

2. Probability of hh head of given age, sex, and M being an owner-occupier

3. Random number (computer generated)

4. Tenure assigned to hh on the basis of random sampling

5. Next hh (keep repeating until a tenure type has been allocated to every hh)

Head of household (hh)

1st

Age: 27Sex: maleM: married

2nd 3rd

0.7

0.542

owner-occupied


0.7

0.823

rented

Age: 87Sex: femaleM: divorced

0.54

0.794

rented

Steps

1. Age,sex, and marital status (M) of hh head

2. Probability of hh head of given age, sex, and M being an owner-occupier

3. Random number (computer generated)

4. Tenure assigned to hh on the basis of random sampling

5. Next hh (keep repeating until a tenure type has been allocated to every hh)

Head of household (hh)

1st


2nd 3rd

0.7

0.542

owner-occupied


0.7

0.823

rented

Age: 87Sex: femaleM: divorced

0.54

0.794

rented

12

Advantages of Spatial Microsimulation Modeling

• Provides a powerful framework in overcoming data and complex modeling problems– Data augmentation– Data modeling

• Capable of building reliable disaggregate data sets at finer geographic and attribute details– Integration with GIS– Visualization

13

Application of Microsimulation to Urban and Regional Modeling

• Wegener (1985) used Monte Carlo simulation approach to model Dortmund housing market

• Clarke (1996) applies spatial microsimulation in social policy simulation and analysis– Tax Benefit Incidence– Housing

• Waddell, et al.(1998) applies microsimulation to landuse-transport modeling in UrbanSim

14

Spatial Microsimulation of Informal Households in Metro Manila

• InformalSim was developed for Manila City– 54 traffic zones

– 900 barangays

– 1.59 million pop. in 1990 (308,874 households)

Cities/ Municipalities

ManilaTraffic Zones

BarangaysCities/ Municipalities

ManilaTraffic Zones

Barangays

15

Existing Data Sets Used by InformalSim

Zone

System

Data Set Description/ Coverage

City 1997 Family Income and Expenditure Survey (FIES)

• Household demographics, some housing variables

• Detailed household incomes and expenditures

• 4,030 samples for Metro Manila

Traffic Zone 1996 Metro Manila Urban Transportation Integration Study (MMUTIS)

• Selected household demographics• Member/ household income• 50,000 samples for Metro Manila

Barangay 1990 Census of Population and Housing (CPH)

• Detailed household and housing characteristics

• No income/employment variable• Non-response on housing variables• All households in 1990 (1,567,665

households)

GIS 1996 MMUTIS Land Use GIS

1997 Building Footprint Data

• Urban land use zoning map for entire Metro Manila

• Building footprints for most cities

16

Object Representation of Household Microdata

BaselineCharacteristics

UnobservedCharacteristics

Target of Microsimulation

ComputationalObjects/ Models

MEMBERVariablesProvince-IDDistrict-IDBarangay-IDHousehold-IDMember-IDRelation to headAgeSexMarital statusEducation(Occupation)(Employment sector)(Income)MethodsGetEconomicActivityGetOccupationGetEmploymentSectorGetIncome…

HOUSEHOLDVariablesProvince-IDDistrict-IDBarangay-IDHousehold-IDHousehold sizeAge of headSex of headMarital status of headEducation of head(Economic Activity of head)(Occupation of head)(Employment sector of head)Members [Vector]Building typeRoof typeWall typeState of repairYear builtHousehold incomeHouse ownership

Land ownershipMethodsGetEconomicActivityofHeadGetOccupationofHeadGetEmploymentSectorofHeadGetIncomeofHead…

17

Estimate household income based on characteristics of

household head

Assign employment sector of household head by Monte

Carlo sampling

Assign occupation of household head by Monte

Carlo sampling

InformalSim StructureInitialize base household using

1990 CPH data (age of head, sex of head,

marital status of head, education of head and household size)

Compute occupation probabilities from

Occupation Choice Model

Compute employmentchoice probabilities from

Employment Choice Model

Bias-adjusted household income function based on

employment status

Bias-adjusted housing value function based on

tenure statusEstimate housing tenure and

housing value

Compute economic activity rate of household head

from MMUTIS

18

InformalSim Modules

1) Economic Activity2) Occupational Choice3) Employment Sector Choice4) Employment Status5) Household Income6) Permanent Income7) Housing Tenure8) Housing Value9) Inequality Measure10) Mapping and Visualization

19

Simulated Mean Household

• Clustering of zones according to mean household income

Low

Low

High

MiddleHigh

Low

20

Simulated Housing Values and Housing Tenure

• Housing values correspond to higher incomes, and consequently lower percentage of informal housing

21

Simulated Informal Employment

• Both low income and high income areas have high percentage of informal employment

22

Inequality Measures

• Low income areas tend to be more equal as characterized by lower Gini coefficient (more cohesive)

23

Validation of Microsimulation Results

Compilation of AvailableSecondary Data

Estimation of Mean Household Incomes

at the Traffic Zone Level

Estimation of Household Incomes at the Barangay

Level

Reliable Estimates of Mean Household Incomes

at Traffic Zones

Aggregate Household Incomes at Traffic Zones

Estimation of Household Characteristics

VALIDATION

STATISTICAL SMALL AREA ESTIMATION

SPATIAL MICROSIMULATION

24

Statistical Small Area Estimation• Sample surveys

– Intended to provide reliable estimators of totals or means for the population under a pre-specified domain

– Direct survey estimators, y=xi/n, may be appropriate when the number of samples is large

– Model-based estimators may provide more reliable estimates with few samples by using auxiliary data

• Model estimators: – yi correspond to samples and ei are sampling errors

• Use auxiliary data in a generalized regression framework ˆ T

i i i i iy z e x

ˆi i iy y e

25

Types of Small Area Estimation Models

i ~ IID N(0, b2)

~ known positive constants

~ IID N(0, e2)

i ~ IID N(0, b2)

• Unit-level Model (Battese et al., 1988)

i i i iy x z

i i i ijy x e

• Area-level Model (Fay and Herriot, 1979)

iz

ije

26

Small Area Estimation of Mean Household Income in Metro Manila

• Nested Error Regression Model

iju : random errors associated with household income

ijy : mean household income for zone j in city i

ijyi : i-th city effect

: random effects in zone j in city i ije

i : 1 to m cities

j : 1 to ni traffic zones

ijx : covariate for income in zone j in city i

,ij ij ij ij i ijy x u u e 2IID N (0, )i

2IID N (0, )ije

27

Selection of Spatial Covariates

Dependent variable: Log Mean Household Income

Independent variables: CAROWN - % of car-owning households (from MMUTIS) WORKER - % of individual engaged in gainful employment (from

CPH) DENSITY – persons per unit of area (from CPH) DWELLING – average area of individual dwelling units (from GIS)

INCOME DENSITY CAROWN WORKER DWELLING

INCOME 1.000 -0.244 0.809 0.447 0.715

DENSITY 1.000 -0.360 -0.383 -0.373

CAROWN 1.000 0.389 0.647

WORKER 1.000 0.586

DWELLING 1.000

Correlation matrix:

28

Calculation of Dwelling Unit Sizes using GIS

Urban land use zoning map Building footprint data

•Dwelling units may not be located on residential zones (eg. squatters on government or industrial lands)

•Judgment and knowledge of the area was used in selecting polygons into the database of the zone

• Automatic calculation under specified zone system

29

Validation of Microsimulation Results

Validation of Microsimulation ResultsEBLUP-CAROWN Benchmark

0

5000

10000

15000

20000

25000

30000

0 5000 10000 15000 20000 25000 30000

Estimated True Values

Sim

ula

ted

Va

lue

s

Validation of Microsimulation ResultsEBLUP-DWELLING Benchmark

0

5000

10000

15000

20000

25000

30000

0 5000 10000 15000 20000 25000 30000

Estimated True Values

Sim

ula

ted

Va

lue

s

Binondo

Ermita

Ermita

Benchmark values are computed Empirical Best Linear Unbiased Predictor (EBLUP) model using the following auxiliary variables:• car-ownership levels • mean dwelling unit sizes

30

Ground Truths

Smokey Mountain

Port Area, Tondo

Pandacan

Punta

31

Modeling Household Location Choices

• Bid-choice theory (Martinez 1992)

• Based on consumer surplus, defined as the willingness to pay for an alternative less the market price of that alternative

• Similar to UrbanSim

32

Modeling Household Location Choices (contd)

• The probability that a consumer h will choose lot i:

where

is the willingness of consumer h to pay for lot

is the market price of lot i

( )

| ( )

hi i

hj j

p

i h p

j

eP

e

hi hi iCS p

hi

ip

Consumer surplus

33


• Waddell (1998) provides an approach to deal with aggregation of alternatives to the zone since the model does not explicitly deal with elemental housing or lots as the level of choice

( ln )

| ( ln )

hi i

hj j

CS S

i h CS S

j

eP

e

34


Bid Price Functions

is the bid price of household h on dwelling i

are the dwelling attributes

are zone or neighborhood attributes

are parameters to be estimated

hi0hi j j k kBP X Z

jX

kZ

35

Estimation of Bid Functions

Income Household Size Tenure Under P 9,000 Less than 5 Formal P 9,000 – P 14,999 5 or more Informal P 15,000 – P 29,999 P 30,000 or more

16 household types

36

Household Price VariablesVariable Definitions

Occpd1, Occpd2, Occpd3, Occpd4, Occpd5, Occpd6

Dummy variable for occupation type of the household head: Professional (Occpd1=0), Administrative (Occpd1=1), Clerical (Occpd2=1), Sales (Occpd3=1), Services (Occpd4=0), Agriculture (Occpd5=0), Production (Occpd6=1),

Flrarea29, Flrarea30, Flrarea50

Percent of dwelling units with area less than or equal to 29 sq. m, 30 sq. m to less than 50 sq. m and 50 sq. m or more, respectively

Yrbuilt80, Yrbuilt81, Yrbuilt86

Percent of dwelling units that are built in 1980 and earlier, between 1981 and 1985, and after 1986, respectively

Rooftype Percent of dwelling units with durable roof quality

Walltype Percent of dwelling units with durable wall quality

Repair Percent of dwelling units not needing repair

Lowinc, Midinc, Highinc Percent of households with low income (less than P9,000), middle income (between P9,000 and 14,999), and high income (more than P15,000)

Formal Percent of households with formal tenure

Single, Duplex, Multi Percent of dwelling units under single, duplex and multi-unit types, respectively

Landval Average land value Access, Distmkti,

Timemkti Accessibility measure, Distance and travel time to the Makati CBD area

Density Population density of the zone

Res, Educ, Ind, Comm Percent of land classified as residential, educational, industrial, and commercial, respectively

37

Estimation of Residential Location Choice Model

Variable Formal

Households Informal

Household

Consumer Surplus 0.31685 (1.690)

0.56290 (2.024)

Nunits -0.31719 (-1.383)

-0.30316 (-1.343)

Log-Likelihood -8956.3508 -11510.0356

Estimation results:

Procedure:•5,000 samples were randomly selected from the household microdata (formal and informal sector)•Bid functions were used to generate estimates of the consumer surplus for each alternative•10 alternative residential zones, including the observed choice, were sampled.

38

Estimation of Residential Location Choice Model (contd.)

Preliminary model estimates:•Yielded significant parameter estimates and expected signs for the consumer surplus terms•Size terms are not significant

39

Modeling Employment Location Choices

• Multinomial Logit that includes the ff. variables:– Accessibility measures (e.e. access to population

areas, distance or travel time to CBD)– Agglomeration variables– Land use characteristics

hi

ip

40

Modeling Employment Location Choices (contd)

Possible microsimulation process:1) Generate conditional probability of a household head

having a particular workplace zone socio-economic characteristics (age, sex, marital status, educational level, economic activity rate, etc.)

2) Assign the workplace zone for each household head using monte carlo sampling

3) Stratify the household heads according to employment status (formal or informal)

4) Generate alternative workplace zones by random sampling

5) Estimate the employment location choice model for each household head

41

Implications for Spatial Planning and Policy Analysis

• Estimation of household incomes and housing tenure characteristics distinguishes households ‘on the ground’

• Spatially-disaggregated view of urban location choice behavior of households

• More practical use of existing census-based information

42

Further Insights

• Spatial microsimulation provides a very flexible and powerful platform for modeling cities in developing countries

• Additional modules can be efficiently added into the simulation system (e.g. residential location choice)

• It is envisioned that spatial microsimulation will become an indispensable tool in policy analysis and urban modeling in developing countries

Thank you!

44

Occupational and Industry Choice ModelUsing Multinomial Logit (MNL) formulation

log 2,3, , ; 1,2,3, ,jte t j

it

PX j N t T

P

K K

1

2

1

1 t i

t Nx

j

Pe

2

1

t i

t i

x

it Nx

j

eP

e

where i, j : are choices t : observation indexXt : vector of characteristics of individual t : parameters to be estimated

Back

45

Occupational Choice Model

Dependent Variable

Constant Age Education

loge (P2/P1) 2.19964

(2.80)

0.03299

(3.55)

-0.56933

(-5.21)

loge (P3/P1) 5.73023

(7.10)

-0.01963

(-1.90)

-0.80484

(-7.19)

loge (P4/P1) 6.74246

(9.38)

0.01746

(2.02)

-1.13837

(-11.35)

loge (P5/P1) 9.27353

(12.93)

-0.01619

(-1.85)

-1.31615

(-13.17)

loge (P6/P1) 8.83468

(8.98)

0.00114

(0.08)

-1.82601

(-13.45)

loge (P7/P1) 11.70120

(16.99)

-0.02722

(-3.36)

-1.49805

(-15.53)

Number of samples = 2782

Log likelihood = -3910.89Occupation groups: 1-Professional, 2-Administrative , 3-Clerical, 4-Sales, 5-Services, 6-Agriculture, 7-Production

Dependent Variable


loge (P2/P1)

Admin.

-3.28201

(-1.54)

0.04599

(2.29)

0.09137

(0.32)

loge (P3/P1)

Clerical

1.91185

(1.07)

-0.03428

(-1.78)

-0.08785

(-0.36)

loge (P4/P1)

Sales

8.15741

(5.60)

0.01268

(0.84)

-1.3016

(-6.51)

loge (P5/P1)

Services

9.40614

(6.04)

0.00086

(0.05)

-1.58440

(-7.50)

loge (P6/P1)

Agriculture

2.30222

(0.48)

0.06049

(0.96)

-1.57401

(-2.88)

loge (P7/P1)

Production

9.51814

(5.99)

-0.03491

(-1.89)

-1.32354

(-6.25)


Log likelihood = -638.35Occupation groups: 1-Professional, 2-Administrative , 3-Clerical, 4-Sales, 5-Services, 6-Agriculture, 7-Production

• Higher education invariably makes it more likely to be in a higher-order group (professional)• Age (a proxy for experience) makes it more likely to be in production and administrative-type work• Females with higher education tend to be in sales and services

Male samples Female samples

Back

46

Industry Choice Model

Dependent Variable


loge (P2/P1) 2.68336

(4.31)

-0.03011

(-3.00)

0.19582

(2.43)

loge (P3/P1) 0.24130

(0.37)

0.00053

(0.05)

0.29745

(3.55)

loge (P4/P1) 1.52469

(2.38)

-0.01635

(-1.58)

0.20948

(2.52)

loge (P5/P1) -2.4167

(-3.28)

-8.38E-03

(-0.73)

0.71486

(7.37)

loge (P6/P1) -.03647

(-0.06)

-0.00210

(-0.21)

0.45899

(5.59)


Log likelihood = -4419.81Industry groups: 1-Agriculture, 2-Manufacturing, 3-Wholesale/Retail, 4-Transportation, 5-Financing, 6-Community Services

Dependent Variable


loge (P2/P1) 6.86490

(1.51)

-0.09258

(-1.54)

0.29546

(0.60)

loge (P3/P1) 6.24433

(1.39)

-0.04996

(-0.84)

0.20742

(0.43)

loge (P4/P1) 0.12557

(0.03)

-0.054891

(-0.88)

0.94319

(1.78)

loge (P5/P1) -0.42337

(-0.09)

-0.05421

(-0.89)

1.14472

(2.22)

loge (P6/P1) 4.94025

(1.10)

-0.04064

(-0.68)

0.36853

(0.76)


Log likelihood = -595.07Industry groups: 1-Agriculture, 2-Manufacturing, 3-Wholesale/Retail, 4-Transportation, 5-Financing, 6-Community Services

Male samples Female samples

• Workers with higher education tend to work in service industries• Younger workers tend to join the manufacturing industry• Males tend to work in financing and community services• Females tend to work in financing industry

Back

47

Household Income Models with Selectivity on Employment Status

• 3-Stage Estimation (Lee, 1978)

0 1 2

0 1 2

*0 1 2 3

ln

ln

(ln ln )

SEi SE SEi E SEi E SEi

Ei E Ei E Ei E Ei

i SEi Ei i i i

I X Z

I X Z

I I I X Z

*

*

1if 0

0 if 0

i i

i i

I I

I I

where ln I are log of incomes, X are socioeconomic characteristics of the individual and Z are industry attributes, and I* is the latent variable for employment choice

• Probit formulation

Self-employed

Wage-earner

SEi ~ N(0, SE2)

Ei ~ N(0, E2)

Back

48

* * * * *, ( | 0) ( | )i i i i i i i iI E I E

*1( )

( )i

i

f

F

* * *( | 0) ( | )i i i i iE I E

*2( )

1 ( )i

i

f

F

• Reduced-form Probit of Employment Status

• Bias-corrected Income Functions

*

*

0 1 2 1

0 1 2 2

( )ln

( )

( )ln

1 ( )

iSEi SE SEi E SEi E

i

iEi E Ei E Ei E

i

fI X Z

F

fI X Z

F

Given the assumption of the model, the individual enjoys comparative advantage in the group if 1*<0 and 2*>0.

Back

49

Probit Model for Employment StatusDependent variable: EMPSTATUS

Number of observations: 2522

Log likelihood = -893.81

Fraction of Correct Predictions = 0.868

Estimated Coefficient

Standard Error

t-statistic P-value

C 2.68835 0.48844 5.50 0.000

HHSEX -0.52009 0.0989 -5.26 0.000

HHAGE 0.05500 0.01827 -3.01 0.003

HHAGE2 0.00033 0.00019 1.74 0.081

HHSTAT -0.12583 0.07232 -1.17 0.082

HHEDUC 0.10401 0.02436 4.27 0.000

MEMBERS -0.04319 0.01406 -3.07 0.002

HHOCC 0.08043 0.01874 4.29 0.000

HHIND 0.09008 0.02172 4.15 0.000

• Higher education increase probability to be self-employed• Workers with more professional-type job has greater tendency to be self-employed• Being male increases probability to be self-employed

1 – Self-employed0 – Wage-earner

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50

Income Functions

Self-employed Wage-earnersDependent variable: LWAGEINC (SELF-EMPLOYED)


R-squared = 0.107 Adjusted R-squared = 0.082


Standard Error

t-statistic P-value

C 7.20424 0.76581 9.41 0.000

HHSEX -1.02470 0.31477 -3.26 0.001

HHAGE -0.00858 0.02975 -0.29 0.773

HHAGE2 -0.00003 0.00025 -0.14 0.892

HHSTAT -0.04287 0.12344 -0.47 0.729

HHEDUC 0.24917 0.06492 3.84 0.000

MEMBERS 0.00141 0.02778 0.05 0.960

HHOCC 0.08912 0.04702 2.89 0.059

HHIND 0.11326 0.05549 2.04 0.042

SEL -3.26608 1.19332 -2.74 0.007

Dependent variable: LWAGEINC (WAGE-EARNER)




Standard Error

t-statistic P-value

C 7.99318 0.27178 29.41 0.000

HHSEX -0.16622 0.05854 -2.84 0.005

HHAGE 0.02353 0.01217 1.93 0.053

HHAGE2 -0.00013 0.00012 -1.11 0.269

HHSTAT -0.07688 0.03266 -2.35 0.019

HHEDUC 0.17533 0.01389 12.62 0.000

MEMBERS 0.07398 0.00737 10.04 0.000

HHOCC -0.08084 0.01106 -7.31 0.000

HHIND -0.01791 0.01261 -1.42 0.156

SEL 0.06198 0.85883 0.07 0.942

• Workers who are either self-employed or wage-earners experience comparative advantage in their respective group• Correction for selectivity bias is significant for self-employed

Back

51

Permanent Income Model

• Explanatory variables for permanent income are human and non-human wealth• Permanent income increases with age• Additional education increases permanent income

Dependent variable: LOG(TOTINC)




Standard Error

t-statistic P-value

C 4.58910 0.17408 26.36 0.000

HHAGE 0.02026 0.00487 4.16 0.000

HHAGE2 -0.00008 0.00005 -1.12 0.129

HHEDUC -0.24477 0.03722 -6.58 0.000

HHEDUC2 0.03879 0.00385 10.08 0.000

HHLDTYPE 0.18657 0.02041 9.14 0.000

LHHINC 0.46505 0.01253 37.10 0.000

Back

52

Probit Model for Housing Tenure Status

• Formal tenure consists of owners, renters, and those who own land; Informal tenure refers to those who may own house but does not own the land• The higher the income the higher the probability to be in formal housing• Higher education increases probability to be in formal housing• Larger household size increases probability towards informal housing

Dependent variable: TENURE


Log likelihood = -1399.48

Fraction of Correct Predictions = 0.738


Standard Error

t-statistic P-value

C -2.23073 0.40986 -5.44 0.000

HHEDUC 0.09740 0.02150 4.53 0.000

HHLDSIZE -0.0422 0.01258 -3.35 0.001

HHTOTINC 0.27398 0.04804 5.70 0.000

0 – Informal tenure1 – Formal tenure

Back

53

Housing Value Model Housing value for formal households

Housing value for informalhouseholds

Dependent variable: HVALUE (FORMAL TENURE)




Standard Error

t-statistic P-value

C -5.80280 1.38139 -4.20 0.000

HHEDUC 0.15966 0.04046 3.95 0.000

HHLDSIZE -0.08032 0.01705 -4.72 0.000

HHTOTINC 1.24664 0.09529 13.08 0.156

SEL -2.10730 0.83606 -2.52 0.012

Dependent variable: HVALUE (INFORMAL TENURE)




Standard Error

t-statistic P-value

C 0.72809 1.33387 0.54 0.585

HHEDUC 0.01062 0.01871 0.57 0.571

HHLDSIZE -0.06616 0.01053 -6.28 0.000

HHTOTINC 0.99019 0.05176 19.13 0.156

SEL -5.01102 2.03424 -2.46 0.014

• Selectivity correction term is significant for both formal and informal tenure status Back

54

Inequality Index Calculations

• Gini Coefficient

• Theil Index

• Coefficient of Variation

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