Lebanon: real per capita consumer spending on food and non-alcoholic beverages 2014-2029

The real per capita consumer spending on food and non-alcoholic beverages in Lebanon was forecast to continuously decrease between 2024 and 2029 by in total 131.6 U.S. dollars. After the eleventh consecutive decreasing year, the real food-related per capita spending is estimated to reach 131.54 U.S. dollars and therefore a new minimum in 2029.
Consumer spending, in this case food-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 01. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.

Find more key insights for the real per capita consumer spending on food and non-alcoholic beverages in countries like Qatar and Bahrain.

Real per capita consumer spending on food and non-alcoholic beverages in Lebanon from 2014 to 2029 (in U.S. dollars)

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Special properties

all values are estimates

Supplementary notes

The shown forecasts represent a blend of multiple input datasets from both internal (primary) and external (secondary) sources. Whereas primary data are generated via Statista's own surveys like the Global Consumer Survey, secondary input datasets are mostly sourced from international institutions (such as the IMF, the World Bank or the United Nations), national statistical offices, trade associations and from the trade press. These datasets are often incomplete as there are gaps between survey years or no or no reliable information might be available for a specific indicator in a specific country or region. Data for missing years are interpolated by various statistical means, such as linear or exponential interpolation or cubic splines. Data for missing countries or regions are imputed by considering known information from other countries or regions that are found to be similar by cluster analyses like k-means or similar procedures.
Most indicators are composites of multiple input sources with slightly varying methodologies that have been processed by our analysts to be aligned and consistent with each other and with all other indicators in the KMI database. As new data becomes available or methodologies are adapted to suit changing requirements it can be possible that data is not comparable any longer with previously published data or is changed retroactively according to the new definitions.
Because of the high degree of processing no specific external source can be named for each data point and all data for historical years (usually until the last finished year before the current one) have to be considered Statista estimates. Future years are mostly Statista projections These projections or forecasts are conducted by regression analyses, exponential trend smoothing (ETS) or similar techniques and extrapolate the found historical trend.
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