WORLD DATA LAB. MarketPro datasets COVERAGE AND METHODOLOGY

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W O R L D D A T A L A B

MarketPro datasets

COVERAGE AND METHODOLOGY

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What exactly do our datasets include?

W E M E R G E R O B U S T , R E P U T A B LE D A T A S E T S S O Y O U C A N T A R G E T M A R K E T S W I T H P R E C I S I O N:

• Spending data: purchasing power parity normalized in 2011 dollars

• Demographic data: forecasts for 99+% of the world's population

• Subnational data: state-, province-, and city-level income/demographic forecasts

• Custom datasets: unique data-powered answers tailored to your requirements

M A R K E T P R O B Y W O R L D D A T A L A B

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• Exclusively gathered from accredited sources: World Bank, UN, IMF, OECD, IIASA, EUROSTAT, etc.

• Highest possible granularity: geography, age, gender, spending segments

• Consistent across the globe: 180+ countries and growing

• Forward-looking: through 2032 (historical data also available)

• Always current and accurate: updated at least twice a year

W E M A K E W O R L D - C LA S S D A T A S E T S T O I N F O R M M A R K E T I N G, B U S I N E S S S T R A T E GY A N D S I T E - S E L E C T I O N D E C I S I O N - M A K I NG:

What makes our datasets unique?

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D A T A P A C K A G E S

30+ year time series

(

from 2000 - 2032)

All regions

All countries

(except Syria, North Korea and 15 micro-states)

20 countries with subnational estimates

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Core elements of Market Pro’s methodology have been peer reviewed by the academic community and published online by the scientific journals “Palgrave Macmillan” and “Regional Studies”.

The econometric model draws upon publicly available sources (the World Bank, IMF, PovcalNet, the International Institute for Applied Systems Analysis) to calculate income distributions (Beta-Lorenz curves) for every country and to then project these distributions forward through 2032.

Methodology

H O W W E D E V E L O P E D M A R K E T P R O

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Methodology

S T E P B Y S T E P

Collect Data - World Data Lab collects microdata and household expenditure data from the World Bank and various National Statistical Offices throughout the world along with quintile data and survey means from

PovCal, GDP growth projections and household expenditure forecasts from the IMF, OECD, and IIASA, and population growth projections from the UN and IIASA.

Model Data - World Data Lab produces estimates from all collected data for different demographic groups (i.e.

age and gender) for each country and region. These estimates are used to construct different spending groups (in 2011 PPP $) and create future projections according to the overall economic trajectory of the country. This methodology has been peer-reviewed and published in Palgrave Communications.

Update Data - World Data Lab uses the latest data available and Market Pro is updated biannually in the spring

and fall of the year as new data is released from international organizations.

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F R E Q U E N T L Y A S K E D Q U E S T I O N S

How do we develop our forecasts?

1. Apply growth rates from the Shared Socioeconomic Pathways (SSP) to World Economic Outlook GDP series.

2. Apply the same growth rates to Household Survey Expenditures.

3. Separate the shape & scale (aka the “survey means”) of consumption distributions.

4. Forecast the survey means.

5. Slice the spending distribution into spending brackets.

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Afghanistan Angola

Albania

United Arab Emirates Argentina

Armenia Australia Austria

Azerbaijan Burundi Belgium Benin

Burkina Faso Bangladesh Bulgaria

Bahrain Bahamas

Bosnia & Herzegovina Belarus

Belize Bolivia Brazil

Barbados Brunei Bhutan

Spending forecasts with breakdown by age and gender

D A T A C O V E R A G E

Botswana

Central African Republic Canada

Switzerland Chile

China

Côte d’Ivoire Cameroon

Congo - Kinshasa Congo - Brazzaville Colombia

Comoros Cape Verde Costa Rica Cuba

Cyprus Czechia Germany Djibouti Denmark

Dominican Republic Algeria

Ecuador Egypt Eritrea

Spain Estonia Ethiopia Finland Fiji

France Gabon

United Kingdom Georgia

Ghana Guinea Gambia

Guinea-Bissau

Equatorial Guinea Greece

Guatemala Guyana

Hong Kong SAR China Honduras

Croatia Haiti

Hungary Indonesia India

Ireland

Iran Iraq Iceland Israel Italy

Jamaica Jordan Japan

Kazakhstan Kenya

Kyrgyzstan Cambodia South Korea Kosovo

Kuwait Laos

Lebanon Liberia Libya St. Lucia Sri Lanka Lesotho Lithuania Luxembourg Latvia

Macau SAR China Morocco

Moldova Madagascar Maldives Mexico

Macedonia Mali

Malta

Myanmar (Burma) Montenegro

Mongolia

Mozambique Mauritania Mauritius Malawi Malaysia Namibia Niger Nigeria Nicaragua Netherlands Norway

Nepal

New Zealand

Oman Pakistan Panama Peru

Philippines

Papua New Guinea Poland

Puerto Rico North Korea Portugal

Paraguay

Palestinian Territories Qatar

Romania Russia Rwanda

Saudi Arabia Sudan

Senegal Singapore

Solomon Islands Sierra Leone

El Salvador Somalia Serbia

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Spending forecasts with breakdown by age and gender

D A T A C O V E R A G E

South Sudan

São Tomé & Príncipe Suriname

Slovakia Slovenia Sweden Swaziland Chad

Togo

Thailand Tajikistan

Turkmenistan Timor-Leste Tonga

Trinidad & Tobago Tunisia

Turkey Taiwan Tanzania Uganda Ukraine Uruguay

United States Uzbekistan

St. Vincent & Grenadines Venezuela

Vietnam Vanuatu Samoa Yemen

South Africa Zambia

Zimbabwe

Subnational income forecasts

Austria Belgium

Czech Republic Denmark

Finland France Germany Greece Hungary Ireland Italy Kenya

Luxembourg Mexico

Netherlands Pakistan

Poland Slovakia Spain Sweden UK

Brazil India Nigeria Tanzania USA

All subnational forecasts for European countries are at the NUTS2 level

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Microdata Source: NSOs,

World Bank

Quintile Data

Source: PovCal; Poverty Equity (WB); UNU-

WIDER

Survey Means Source: PovcalNet

Distributional Data (Beta-Lorenz)

Survey Means (1980-2030)

Household Expenditure (1980-2030)

GDP Growth (1980-2030)

Spending Share

Spending Headcount

IIASA (SSP) UN (Pop)

MarketPro’s Global Spending Architecture

Population

Distributions

Household Expenditure Source: World

Bank

Survey means and forecasts Population projections Aggregate results

GPD Growth

Source: IMF (2023);

OECD, IIASA (2023-2030)

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What is the original source of your data?

Main input sources include:

• World Development indicators (World Bank)

• World Economic Outlook (IMF)

• Shared Socioeconomic Pathways (IIASA, OECD)

• United Nations population projections

• PovcalNet (World Bank)

• Poverty Equity Database (World Bank)

• UNU-WIDER (UN)

• National Statistic offices

How often do you update your forecasts?

Twice per year we do a major update to all our input data. These updates are aligned with the biannual release of the World Economic Outlook (WEO) by the International Monetary Fund (IMF). New GDP forecasts from WEO replace long-term forecasts based on SSP (more specifically, SSP2). Throughout the year we update parts of the data as

updates come available.

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What surveys do we use?

We get information from the World Banks I2D2 dataset on the cross dependency of inequality and demographics.

What data do we use from the surveys?

The main variables used are total household spending and household characteristics (size, demographic information of household members; all by region).

How do we get the numbers for the different brackets?

From the surveys we can interpolate spending distributions for each subgroup (e.g. 30- to 40-year-

old women in Mexico). From this we can get of the number of people below any given threshold.

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Why do we use spending instead of income?

Consumer spending in the most relevant metric for most private sector enterprises.

Total household spending is also contained in the System of National Accounts (SNA) and is sourced from the IMFs World Economic Outlook. This way we can create a

consistent dataset of household spending across all countries.

Our Global Spending Dataset is expressed in 2011 PPP US dollars.

How does Market Pro close data gaps?

For countries with limited data, Market Pro uses an approximation technique. Any informational gaps in distributional data (sources from PovcalNet, UNU-WIDER, and Poverty Equity) is imputed by finding countries that are similar along the dimensions of GDP, age structure, sectoral

employment and education structure (see “Twinning”, slide 14).

How are the spending data expressed?

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Our merge of spending and demographic information is based on survey data, WDL national spending distribution forecasts, and population forecasts from the International Institute for Applied Systems Analysis (IIASA). From the survey data we compute the mean spending per capita and the corresponding spending distribution for different demographic groups. These numbers are then matched with our national spending data as well as population data from IIASA.

To obtain forecasts of the group-specific means, we apply a combination of group and national spending growth rates. This is how we consider both the individual current development of different groups as well as national spending growth rates in our

projections. As a result, we obtain estimates of mean spending numbers as well as spending distributions by country, age, and gender.

Combining spending estimates with demographic variables

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To close data gaps in data scarce environment we create “economic twins” in combining data from other countries which show similar

pattern. Twinning allows us to obtain reasonable estimates of spending distributions by age and gender for countries where no information on the spending patterns of different demographic groups is available. The main idea of this approach is to match the survey information we have to countries we do not have sufficient survey data for. Matching is based on 4 sets of variables which summarize aspects related to

demography and socioeconomic characteristics: (i) the shares of five-year age groups in the total population by gender, (ii) the shares of

population employed in primary, secondary and tertiary sector, (iii) the shares of population by educational attainment (primary, secondary, and tertiary), and (iv) GDP per capita.

Based on this set of variables we try to find the countries that are most similar, the idea being that countries that are similar in that respect also have similar spending distributions. We then impose their spending distributions, anchored at the national expenditure of the missing countries.

Twinning

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We employ a similar methodology as in case of the national models. We use surveys that are representative on the subnational level to draw Beta-Lorenz curves and get mean spending levels.

To project mean spending on a subnational level we use conditional convergence model with spatial interactions and incorporate model uncertainty (see Crespo Cuaresma et al 2014). Covariates include initial GRP, initial tertiary education and sectoral

employment shares.

Subnational data sources

For European countries main data sources are Eurostat (subnational population projections by age and gender, GRP, various

covariates for the convergence model) and OECDs regional database (spending quantiles and mean spending per capita). For the United States main data sources are the American Community Survey and the Consumer Expenditure Survey.

Subnational Methodology

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• Jesús Crespo Cuaresma, Gernot Doppelhofer & Martin Feldkircher (2014). The Determinants of Economic Growth in European Regions, Regional Studies, 48:1, 44-67.

• Jesús Crespo Cuaresma, Wolfgang Fengler, Homi Kharas, Karim Bekhtiar, Michael Brottrager & Martin Hofer (2018). Will the Sustainable Development Goals be Fulfilled? Assessing present and future global poverty, Palgrave Communications, 4, 29.