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Estimating Gross Domestic Product, Informal Economy and Remittances of Mexico using Nighttime Satellite Imagery Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

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Estimating Gross Domestic Product, Informal Economy and Remittances of Mexico using Nighttime Satellite Imagery. Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge. Radiance-calibrated Nighttime Image of the World, 2000-2001. Source: Earth Observation Group, NGDC, NOAA. - PowerPoint PPT Presentation

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Page 1: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Estimating Gross Domestic Product, Informal Economy and Remittances of Mexico using

Nighttime Satellite Imagery

Tilottama Ghosh

Dr. Paul C. Sutton

Dr. Christopher Elvidge

Page 2: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Radiance-calibrated Nighttime Image of the World, 2000-2001

Source: Earth Observation Group, NGDC, NOAA

Page 3: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Nighttime Image of the World• Images are derived from hundreds of orbits of the Defense

Meteorological Satellite Program’s Operational Linescan System

• Screened for clouds, fires and other ephemeral light sources

• In the radiance calibrated nighttime images, the “brightness values” of the lights are converted from digital numbers to radiance values

• Radiance – calibrated nighttime Images of 1995-1996, 2000-2001, and 2005-2006 are available

• Helps to obtain brightness variations within urban centers

• Detection of diffused lighting in sparsely populated rural areas

• 30 arc-second grids (1 km2 at the equator), WGS 1984 coordinates

• Processed at NGDC, NOAA – Boulder, Colorado

Page 4: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Radiance-calibrated nighttime lights of the United States

Source: Elvidge C. D., K. E. Baugh, J. B. Dietz, T. Bland, P. C. Sutton, and H. W. Kroehl. 1999a. Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Remote Sensing of Environment 68: 77-88.

Page 5: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Use of Nighttime Images

Page 6: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Objective of Study

• Develop an alternative method to estimate the economic parameters of Gross Domestic Product (GDP), Informal economy and Remittances of an upper middle income country, Mexico based on –

Reliable estimates of sub-national GDP of a developed country, the United States, and

Based on the close relationship between lit urban areas, urban population and GDP, as derived from radiance calibrated nighttime satellite image

Page 7: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

What caused the rise of informal economy in Mexico? • Neo-liberalism – Privatization, Deregulation and Trade

Liberalization

• Mexico – joining GATT in 1986, 1995 WTO substituted for GATT; signing NAFTA with the U.S. and Canada in 1994

• Consequences: Downsizing the role of the state and employment in the

traditional public sector Creation of more temporary, low wage and unprotected

employment Outsourcing and subcontracting Increased participation of women in the workforce

Page 8: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Informal Economy

Page 9: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Informal Economy

• Manifested in industrialized, transition and developing economies

• Includes enterprises that are not legally regulated

• Size of the unit is usually very small

• Also includes employment relationships that are not legally regulated or protected

• Includes informal employment both within and outside agriculture

• Includes self-employment in small unregistered enterprises and wage employment in unprotected jobs

• No secure work, worker’s benefits or social protection

Page 10: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Remittances

• Funds sent by international migrants to their countries of origin

Page 11: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Some other definitions • Gross Domestic Product (GDP): Total market value of all

final goods and services produced within a given country or region in a given period of time (usually a calendar year)

• Gross National Income (GNI): It is GDP plus net receipts of primary income (compensation

of employees and property income from abroad)

• GNI and GDP are usually expressed in PPP US dollars

• This provides a better comparison of average income or consumption between economies

Page 12: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Where does informal economy and remittances

fit in GDP and GNI?

• Informal economy contributes to a large portion of the employment and the GDP of the nations, especially for the developing countries

• By definition, remittances are included in the GNI of a country

• GNI thus includes both informal economy and remittances

Page 13: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Importance of Study

• Problems of estimating employment in the informal economy and its contribution towards GDP

Complexity of definition – Employment in informal enterprises and those employed informally (without any benefits and social protection) in formal enterprises

Data on informal sector employment has been collected in only five countries – Tunisia, South Africa, Kenya, Mexico and India

Indirect methods are still used to estimate informal employment outside informal enterprises and thus total informal employment

Page 14: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

•Problems associated with data on remittances

Formal remittances may go unrecorded, due to weaknesses in data collection – related to both definitions and coverage

Flows through informal channels – unregulated money transfers or friends and family who carry remittances

Remittances are misclassified as export revenue, tourism receipts, nonresident deposits, or even foreign direct investment

Page 15: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

International comparability of data is difficult

In implementing the International Statistical definition of the informal sector, countries apply different criteria of non-registration, enterprise size

Some countries include informal employment in the agricultural sector and some countries do not

Data on the informal sector (excluding agriculture) are often compared to data on the total workforce (including agriculture) resulting in underestimation of the informal sector

Page 16: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Importance of Study – Inconsistencies in the data

Country Estimate Year Source Method Value (in dollars)

United States GNI 2000

World Dev. Report 2002

Atlas method- using 3 year

average exchange rate 9,646 billion

United States GNI 2000

World Dev. Report 2002

Purchasing Power Parity 9,646 billion

United States GNI 2000

Population Reference

Bureau In US Dollars 8,059 billion

Country Estimate Year Source MethodValue (in dollars)

United States GDP 2000

World Dev.

Report 2002

Average official exchange

rate of that year 9,883 billion

United States GDP 2000

US Bureau of

Economic Analysis Current US$ 9,749 billion

Page 17: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Importance of Study

Country Estimate Year Source Method Value

Mexico GNI 2000 INEGI In Pesos 5,491 billion

Mexico GNI 2000

In terms of exchange rate

(1US$=9.57 Mex Peso) In U.S. Dollars $ 574 billion

Mexico GNI 2000 In terms of PPP In U.S. Dollars $ 886 billion

Mexico GNI 2000World Dev. Report

2002

Atlas Method - using three year average

exchange rate $ 498 billion

Mexico GNI 2000World Dev. Report

2002Purchasing Power

Parity $ 864 billion

Mexico GNI 2000

Population Reference Bureau In U.S. Dollars $ 382 billion

Page 18: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Importance of Study

Country Estimate Year Source Method Value

Mexico GDP 2000 INEGI In Pesos 4,984 billion

Mexico GDP 2000

In terms of exchange rate

(1US$=9.57 Mex Peso) In U.S. Dollars $ 521 billion

Mexico GDP 2000 In terms of PPP In U.S. Dollars $ 804 billion

Mexico GDP 2000World Dev. Report

2002

Average official exchange rate of

that year $ 575 billion

Mexico GDP 2000World Dev. Report

2002Purchasing Power

Parity $ 896 billion

The Economists’ blunder in overestimating the Chinese economy by 4 trillion dollars!!! (New York Times, December 9, 2007; Reported by Eduardo Porter)

Page 19: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

So, why nighttime imagery again?

Nighttime imagery serves as a proxy measure of population and correlates of population such as economic activity and energy consumption

Proxies of socio-economic data has been generated at regional, national, sub-national, and other irregular spatial units

Available in time series, allows for change detection

Page 20: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

The Land/Geographic Unit area grids (2000) obtained from Global Rural–Urban mapping project dataset produced by

the Center for International Earth Science Information Network

Urban area extent grid expressed in square kms

Derived from NOAA’s stable city lights dataset

ESRI- Digital Chart of the World’s populated places

Tactical pilotage charts – Africa and Latin America

30 arc-second grids

WGS 1984 coordinates

Page 21: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Landscan Population Database (2000)

Developed in the Oak Ridge National Laboratory (ORNL)

Estimating urban populations at risk

Apportioning census counts to each grid cell at sub-national levels on the basis of likelihood coefficients based on proximity to roads, slope, land cover, nighttime lights.

30 arc-second grids and WGS 1984 coordinates

Page 22: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Other Data Sources Informal economy data:

Mexico – INEGI - System of National Accounts, Accounts by Institutional Sectors, Satellite account of the informal sub-sector of homes, 1998-2003

Contribution of informal economy towards GDP in Mexico was 12.3 percent in 2000 (INEGI)

Contribution of informal economy towards GDP in the U.S. is approximately 10 % (Mattera 1985; Investor’s Business daily 1998; Losby et al 2002; Mctague 2005)

Data on remittances from Bank of Mexico’s website

In 2000, remittance inflow into Mexico was 6.6 billion dollars

Informal Economy (2000) In dollars

In Pesos 616.1 billion

In PPP US Dollars 99.4 billion

Page 23: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Google Earth for determining urban cluster threshold

ArcView GIS 9.3 for raster and vector analyses

JMP, Version 6 for statistical analyses

Analyses of Data

Page 24: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Selecting threshold for demarcating urban clusters

Nighttime satellite image

Google Earth

Threshold of 20*1.35*10-10 watts/cm2 /sr

Page 25: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Why 20*1.35*10-10 as the threshold value?

Kansas

• The same threshold is used for delineating the urban areas of Mexico

• A higher threshold would not capture the smaller urban areas of Mexico

Page 26: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Population and Area of the clusters are determined

Demarcated clusters placed on the

area grid

Demarcated clusters placed on the

population grid

‘Sum’ of the area of the clusters are obtained ‘Sum’ of the population of the clusters are obtained

Page 27: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

• Disaggregated (fine resolution) numbers for money are not available, so disaggregated population numbers serve as proximate measures of GDP

• Estimated urban population of the clusters is determined from the log-log relationship of the area and population of the urban clusters in the US

• Estimated urban population of the states of the US are determined

• Estimated urban population and official GDP figures of the US are used to estimate GDP of the states of the US

• All of US’s parameters are then used to estimate the urban population and GDP of the states of Mexico

Page 28: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Ln(Est Urb Pop) = 4.4234017 + 1.192936*Ln(Area)

R2 = 0.96

Estimated urban population = Exp (4.4234017 + 1.192936*Ln(Area))Of the urban clusters of US

Estimated urban population of the clusters :

Slope and intercept parameters are used to estimate the urban population of the states of Mexico

Log – log Relationship between Population and Area of the Urban Clusters

Equation weighed by population so that larger cities have a greater influence on the estimation of the regression parameters than the smaller cities

Page 29: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Population Density of the Clusters

• Population density of the clusters are found out (Population/area) or population per km2

• Population of the states are determined using the Spatial Analyst tool in ArcGIS

Page 30: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Sum of Light of the States of US

• Sum of lights of all the states of the US are determined using the Spatial Analyst tool in ArcGIS

• Lights = money. Includes all those lights (that is, money) below the selected threshold level

Page 31: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Estimating GDP of the states of US• Estimated urban population of the states of the US, Sum of lights of the states of US and Published, official figures of the GDP of US are used to estimate the estimated GDP of the states of US

• The published, official sub-national GDP values of the US are considered more reliable than that of any developing countries

• 10 percent contribution of informal economy towards GDP is added to GDP

Ln (Est Urban Pop) Sum of lights

Weighing by Actual GDP

R2 = 0.84

Ln (

Act

ual G

DP)

Ln(Est GDP) = (5.3297817 + 0.3941618 * Ln(Est Urb Pop) + 0.00000020098 * Sum of Lights)

Est GDP = Exp (5.3297817 + 0.3941618 * Ln(Est Urb Pop) + Of the 0.00000020098 * Sum of Lights)states of US

Slope and intercept parameters are used to estimate the GDP of the states of Mexico

Page 32: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Tables of Actual, Estimated GDP and Percentage Residual

Page 33: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Actual and Estimated GDP of the states of US

Page 34: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Actual versus Estimated GDP of US

P < .0001

Page 35: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Residual Map

Percentage Residual = ((Actual GDP – Estimated GDP) /Actual GDP) * 100

Page 36: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Assumptions of this Analysis

• Patterns of Light are proxy measures of patterns of Money (e.g. GDP)

• Income distributions within a country are uniform (people in Texas have the same average incomes and income distributions as people in Nebraska, California, and Florida)

• We use disaggregate population information as a proxy measure of money because disaggregate GDP info not available

Page 37: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Demarcating lit areas of Mexico

Same threshold of 20*1.35*10-10 watts/cm2 /sr is used

Nighttime satellite image

Google Earth

Page 38: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

At this threshold level many of the smaller urban settlements

are captured

Page 39: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Determining population and area of the clusters

Demarcated clusters placed on the

area grid

Estimated urban population of the clusters determined using US Parameters:

Estimated urban population of the urbanClusters of Mexico = Exp (4.4234017 + 1.192936*Ln(Area))

‘Sum’ of the area of the clusters are obtained

Page 40: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Population Density of the Clusters

• Population density of the clusters are found out (Population/area) or population per km2

• Population of the states are determined using the Spatial Analyst tool in ArcGIS

Page 41: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Sum of Light of the States of Mexico

• Sum of lights of all the states of the Mexico are determined using the Spatial Analyst tool in ArcGIS

• Lights = money. Includes all those lights (that is, money) below the selected threshold level

Page 42: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Estimating GDP of the states of Mexico

The GDP of the states of Mexico are determined using US’s parameters

Ln(Est GDP) = (5.3297817 + 0.3941618 * Ln (Est Urb Pop) + 0.00000020098 * Sum of Lights)

Est GDP = Exp (5.3297817 + 0.3941618 * Ln (Est Urb Pop of Mexican states) + Of the 0.00000020098 * Sum of Lights of the Mexican states)states of Mexico

Page 43: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Tables of Actual, Estimated GDP and Percentage Residual

Page 44: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Actual and Estimated GDP of the states of Mexico

Page 45: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Actual vs Estimated GDP of Mexico

Distrito Federal and Mexico state are outliers

Page 46: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Actual vs Estimated GDP of Mexico

Mexico

Excluding Distrito Federal improves the R2 and Significance level

Page 47: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Residual Map

Percentage Residual = ((Actual GDP – Estimated GDP) /Actual GDP) * 100

Page 48: Tilottama Ghosh Dr. Paul C. Sutton Dr. Christopher Elvidge

Preliminary Results (Using US’s parameters to estimate urban population and also GDP)

  In dollars

Estimated GDP of Mexico (formal+informal+remittances) 1202 bn

Official estimates of the GNI of Mexico (formal+informal+remittances) 886 bn

Underestimated remittances and informal economy estimates 316 bn

   

Official estimates of Informal economy in 2000 99 bn

Official estimates of remittances in 2000 7 bn

Total official estimates of informal economy and remittances 106 bn

   

Underestimated remittances and informal economy estimates 316 bn

Total official estimates of informal economy and remittances 1056 bn

Magnitude of underestimation 3 times