Consumption Indicators - Mozambique

Importance of Consumption Indicators: Consumption indicators reveal crucial insights into economic health and purchasing behavior. Consumer spending, driven by disposable income, is a key measure of economic activity and growth. These indicators span sectors like food, housing, healthcare, and education, reflecting how individuals allocate their spending. Understanding these patterns helps assess economic vitality and guides public and private sector decisions.

Sectoral Contributions to Economic Activity: Each sector contributes uniquely to consumption indicators, offering a broad view of economic activity. Spending on essentials like food and housing, along with discretionary items such as healthcare and education, is influenced by disposable income. Analyzing these sectors helps stakeholders understand the interaction between different parts of the economy and the impact of income changes on overall performance.

Monitoring Trends and Market Conditions: Tracking consumption indicators is essential for monitoring trends and assessing market conditions. These indicators guide decisions on resource allocation, market strategies, and economic policies. Trends in disposable income, in particular, provide insights into consumer purchasing power, allowing stakeholders to adapt their strategies to current and future market dynamics.

Challenges in the Consumption Indicators Domain: The consumption indicators domain faces challenges from regulatory factors and evolving trends. The rise of e-commerce, sustainable consumption, demand for personalized experiences, and technological advancements are reshaping consumer behavior. Addressing these challenges is key for stakeholders to stay competitive and meet the changing needs of consumers, especially as disposable income varies in a dynamic market.

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.