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Dresses & Skirts - Hungary

Hungary
  • In Hungary, the Dresses & Skirts market generated a revenue of US$14.03m in 2024.
  • It is projected to have an annual growth rate of 4.02% (CAGR 2024-2029).
  • When compared globally, China leads the market with a revenue of US$3bn in 2024.
  • Looking at the per capita figures, each person in Hungary is estimated to generate a revenue of US$1.40 in 2024.
  • By 2029, the volume in the Dresses & Skirts market is expected to reach 3.3m pieces.
  • There will be a volume growth of 4.9% in 2025.0.
  • On average, each person in Hungary is projected to contribute to a volume of 0.3pieces in the Dresses & Skirts market in 2024.
  • The demand for traditional Hungarian embroidered dresses and skirts is experiencing a revival among younger generations.

Definition:

The Dresses & Skirts market contains all forms of dresses and skirts for children.

Additional Information:

The market comprises revenue and average revenue per capita, volume and average volume per capita, price per unit, sustainable apparel share, and sales channels. Online channel distribution refers to the purchase of physical goods in online retail. In other words, the purchase is concluded via the internet – on a desktop PC, tablet, or smartphone. The offline distribution channel covers all purchases in stationary stores made via telesales or mail orders (e.g., print catalogs).

In-Scope

  • Dresses
  • Skirts

Out-Of-Scope

  • Trousers
  • Jeans
  • Shorts

Revenue

Notes: Data was converted from local currencies using average exchange rates of the respective year.

Most recent update: Sep 2024

Source: Statista Market Insights

Most recent update: Sep 2024

Source: Statista Market Insights

Most recent update: Mar 2024

Sources: Statista Market Insights, Annual reports of key players, Desk research, Statista Consumer Insights Global

Volume

Most recent update: Sep 2024

Source: Statista Market Insights

Most recent update: Sep 2024

Source: Statista Market Insights

Price

Most recent update: Sep 2024

Source: Statista Market Insights

Key Players

Most recent update: Mar 2024

Source: Statista Market Insights

Sales Channels

Most recent update: Mar 2024

Source: Statista Market Insights

Global Comparison

Most recent update: Sep 2024

Source: Statista Market Insights

Methodology

Data coverage:

Data encompasses B2C enterprises. Figures are based on the consumer spending on clothing which comprises women, men, and children segments that are produced for private end customers for both offline retail (department stores, traditional specialist shops) and online retail (e-commerce, ordering by catalog).

Modeling approach / Market size:

Market sizes are determined by a combined Top-Down and Bottom-Up approach, based on a specific rationale for each market segment. As a basis for evaluating markets, we use resources from the Statista platform, national statistics, industry research, market data from independent databases and third-party sources, historical developments, current trends, reported performance indicators from the key market players, and Statista interviews with market experts. Next we use relevant key market indicators and data from country-specific associations such as GDP, consumer spending, consumer price index and population. This data helps us to estimate the market size for each country individually.

Forecasts:

In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the particular market. For example, the exponential trend smoothing illustrates suited forecasting for the Apparel market with projected steady growth. The main drivers are GDP per capita and consumer spending per capita.

Additional Notes :

The market is updated twice per year in case market dynamics change. The impact of the COVID-19 pandemic and the Russia-Ukraine war are considered at a country-specific level.

Key Market Indicators

Notes: Based on data from IMF, World Bank, UN and Eurostat

Most recent update: Sep 2024

Source: Statista Market Insights

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