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Socioeconomic Indicators - Papua New Guinea

Papua New Guinea
  • The gini coefficient in Papua New Guinea is forecast to amount to 0.41 in 2024.
  • The unemployment rate in Papua New Guinea is forecast to 2.60% in 2024.
  • The unemployed people in Papua New Guinea is forecast to 85.13k in 2024.
  • The employment rate in Papua New Guinea is forecasted to 41.21% in 2024.
  • The total labor force in Papua New Guinea is forecasted to 3.27m in 2024.
  • The labor productivity in Papua New Guinea is forecasted to US$6.53 in 2024.
  • The total population in Papua New Guinea is forecast to amount to 10.52m inhabitants in 2024.
  • The number of households in Papua New Guinea is forecast to amount to 1.95m in 2024.
  • The share of population in Papua New Guinea who earns less than $2.15 per day is forecast to amount to 34.36% in 2024.

The Socioeconomic Indicators provide a comprehensive look at past, current, and anticipated situations, as well as the performance of social progress in a global comparison. These indicators help understand social developments and evaluate policy decisions.

Structure:

The Socioeconomic Indicators cover five focus areas.

  • The Demography focus area shows the households and population-related data.
  • The Economic Inequality focus area encompasses diverse indicators used to gauge economic disparity. This section pays attention to GINI coefficients, poverty share, and poverty headcount across different geographical regions.
  • The Social Progress focus area includes various indicators to assess different aspects of social progress including the World Press Freedom Index.
  • The Education focus area provides government expenditure on education as a percentage of the country's GDP. It also covers the cost of education as the consumer price index for education and schooling year as the average number of years in school.
  • The Employment focus area shows the general characteristics of the labor force, such as unemployment and employment rates, labor force participation, labor productivity, average weekly working hours, and average hourly earnings.

In-Scope

  • Demography
  • Economic Inequality
  • Social progress
  • Education
  • Employment

Out-Of-Scope

  • Fertility
  • Migration
  • Environment
  • Gender pay gap
  • Social mobility
  • Housing
  • Food security

Demography

Most recent update: Aug 2024

Sources: Statista Market Insights, International statistical offices, National statistical offices

Most recent update: Aug 2024

Sources: Statista Market Insights, UN DESA - United Nations Department of Economic and Social Affairs, National statistical offices

Economic Inequality

Most recent update: Aug 2024

Sources: Statista Market Insights, ILO

Most recent update: Aug 2024

Sources: Statista Market Insights, World Bank

Most recent update: Aug 2024

Sources: Statista Market Insights, World Bank

Most recent update: Aug 2024

Source: Statista Market Insights

Social Progress

Most recent update: Aug 2024

Sources: Statista Market Insights, UN

Most recent update: Aug 2024

Source: Statista Market Insights

Most recent update: Aug 2024

Source: Statista Market Insights

Most recent update: Aug 2024

Sources: Statista Market Insights, WIPO - World Intellectual Property Organization

Education

Notes: Data shown is using current exchange rates.

Most recent update: Aug 2024

Sources: Statista Market Insights, IMF

Most recent update: Aug 2024

Sources: Statista Market Insights, World Bank

Employment

Most recent update: Aug 2024

Sources: Statista Market Insights, World Bank, ILO, IMF WEOD - International Monetary Fund World Economic Outlook Database

Most recent update: Aug 2024

Sources: Statista Market Insights, ILO

Most recent update: Aug 2024

Sources: Statista Market Insights, ILO

Analyst Opinion

Income Inequality: Income inequality is a major issue in the United States and Europe, especially in large cities and between urban and rural areas. The widening gap between the wealthy and the less affluent threatens social cohesion and economic stability. Addressing this requires policies that promote more equitable economic opportunities across diverse communities.

Social Welfare Systems: Social welfare systems vary widely, particularly in Europe, where some countries have robust structures while others rely on societal self-organization. This disparity highlights the challenge of creating more uniform social protection across regions. Ensuring access to basic social services is crucial as economic inequalities persist.

Employment, Labor Productivity, and Demographic Shifts: Youth unemployment remains a pressing issue, especially in Europe, leading to long-term socioeconomic problems. Additionally, Europe faces stagnation in labor productivity, with recent increases mostly driven by inflation rather than genuine productivity improvements. Aging populations in Europe and parts of Asia further strain pension systems and healthcare, requiring policies that adapt to these demographic shifts and better integrate younger workers.

Poverty Reduction and Sustainable Development: While progress in reducing poverty has been made in Asia, challenges remain in ensuring sustainable and inclusive growth. Rural areas‘ structural weakness creates disparities of opportunity that hinder equitable development. Achieving sustainable growth that benefits all is essential for long-term stability.

Methodology

Data coverage:

The dataset encompasses data from 152 countries. The charts depict the situation of each country in six different domains. These domains are socioeconomic indicators, macroeconomic indicators, health indicators, digital and connectivity indicators, consumption indicators, as well as logistics and transport indicators. Within these domains, various segments are covered, including demography, economic measures, economic inequality, employment, consumption, health determinants, and much more.

Modeling approach:

The composition of each domain follows a comprehensive approach that combines both top-down and bottom-up methodologies, with each domain and segment being guided by a specific rationale. To evaluate the situation of these six domains within each country, we rely on pertinent indicators and data from reputable international institutions, local national statistical offices, industry associations, and leading private institutions. Additionally, we undertake data processing procedures to address issues such as missing timelines, outliers, and data inconsistency. Our data processing incorporates advanced statistical techniques, including interpolation, exponential moving weighted average, and the Savitzky-Golay filter. These methods contribute to the refinement and enhancement of data quality.

Forecasts:

In our forecasting process, a wide range of statistical techniques is utilized based on the characteristics of the markets. For example, the S-curve function is employed to forecast the adoption of new technology, products, and services, aligning the forecast model with the theory of innovation adoption. Additionally, the data is forecasted using ARIMA with and without seasonality considerations, exponential trend smoothing, and the Compound Annual Growth Rate (CAGR), with the option to incorporate adjustment factors when necessary. These techniques enable accurate and reliable forecast methods tailored to the unique characteristics of the data in each market and country.

Additional notes:

The data is updated twice per year or every time there is a significant change in their dynamics. The impacts of the COVID-19 pandemic and of the Russia/Ukraine war are considered at a country-specific level.

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Papua New Guinea - Statistics & Facts

Papua New Guinea is an Oceanian country located in the eastern half of the island of New Guinea. The regions of Papua and West Papua, both part of Indonesia nowadays, constitute the island’s other half. Situated along the “Ring of Fire”, an area where a significant number of earthquakes and volcanic eruptions occur, Papua New Guinea is frequently exposed to these natural hazards.
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