Graph and download economic data for Nominal Gross Domestic Product for United States (NGDPSAXDCUSQ) from Q1 1950 to Q2 2025 about GDP and USA.

Key information about Jordan Nominal GDP Growth

• Jordan Nominal GDP Growth was reported at 4.600 % in Sep 2024.
• This records an increase from the previous number of 3.975 % for Jun 2024.
• Jordan Nominal GDP Growth data is updated quarterly, averaging 6.364 % from Mar 1993 to Sep 2024, with 127 observations.
• The data reached an all-time high of 35.849 % in Sep 2008 and a record low of -5.102 % in Jun 2020.
• Jordan Nominal GDP Growth data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Nominal GDP: Y-o-Y Growth: Quarterly.

Nominal Median and Nominal Mean Income Measures by National Income Definition, Year and Statistic

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Graph and download economic data for Nominal Statistical Discrepancy for Estonia (NSDGDPSAXDCESQ) from Q1 1995 to Q2 2023 about residual and Estonia.

Graph and download economic data for Nominal Households Final Consumption Expenditure for United States (NCPHISAXDCUSQ) from Q1 1959 to Q4 2023 about consumption expenditures, consumption, households, and USA.

Austria Services Turnover Index: Nominal data was reported at 124.000 2010=100 in Dec 2017. This records an increase from the previous number of 117.100 2010=100 for Sep 2017. Austria Services Turnover Index: Nominal data is updated quarterly, averaging 108.900 2010=100 from Mar 2011 (Median) to Dec 2017, with 28 observations. The data reached an all-time high of 124.000 2010=100 in Dec 2017 and a record low of 99.400 2010=100 in Jun 2011. Austria Services Turnover Index: Nominal data remains active status in CEIC and is reported by Statistics Austria. The data is categorized under Global Database’s Austria – Table AT.H013: Nominal Services Turnover Index: 2010=100. Rebased from 2010=100 to 2015=100 Replacement series ID: 403929797

Wage and Payroll Statistics - Table 220-19005 : Nominal Wage Indices for employees up to supervisory level by occupational group (September 1992 = 100)

Real interest rates describe the growth in the real value of the interest on a loan or deposit, adjusted for inflation. Nominal interest rates on the other hand show us the raw interest rate, which is unadjusted for inflation. If the inflation rate in a certain country were zero percent, the real and nominal interest rates would be the same number. As inflation reduces the real value of a loan, however, a positive inflation rate will mean that the nominal interest rate is more likely to be greater than the real interest rate. We can see this in the recent inflationary episode which has taken place in the wake of the Coronavirus pandemic, with nominal interest rates rising over the course of 2022, but still lagging far behind the rate of inflation, meaning these rate rises register as smaller increases in the real interest rate.

Wage and Payroll Statistics - Table 220-19025 : Quarter-to-quarter rates of change in the seasonally adjusted series of Nominal and Real Indices of Payroll per Person Engaged by industry section

Korea HS: OU: Income: Nominal data was reported at 3,669,884.000 KRW in Mar 2018. This records a decrease from the previous number of 3,762,108.000 KRW for Dec 2017. Korea HS: OU: Income: Nominal data is updated quarterly, averaging 2,449,037.000 KRW from Mar 1990 (Median) to Mar 2018, with 113 observations. The data reached an all-time high of 3,762,108.000 KRW in Dec 2017 and a record low of 860,860.000 KRW in Mar 1990. Korea HS: OU: Income: Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H056: Household Income and Expenditure Survey (HS): Other Urban Household: Nominal.

Graph and download economic data for Nominal Statistical Discrepancy for India (NSDGDPNSAXDCINQ) from Q2 2004 to Q1 2025 about residual and India.

Wage and Payroll Statistics - Table 220-19021 : Nominal Indices of Payroll per Person Engaged by industry section (Q1 1999 = 100)

Refer to the 'Current Geographic Boundaries Table' layer for a list of all current geographies and recent updates.

This dataset is the definitive version of the annually released statistical area 3 (SA3) boundaries as at 1 January 2025 as defined by Stats NZ. This version contains 929 SA3s, including 4 non-digitised SA3s.

The SA3 geography aims to meet three purposes:

1. approximate suburbs in major, large, and medium urban areas,
2. in predominantly rural areas, provide geographical areas that are larger in area and population size than SA2s but smaller than territorial authorities,
3. minimise data suppression.

SA3s in major, large, and medium urban areas were created by combining SA2s to approximate suburbs as delineated in the Fire and Emergency NZ (FENZ) Localities dataset. Some of the resulting SA3s have very large populations.

Outside of major, large, and medium urban areas, SA3s generally have populations of 5,000–10,000. These SA3s may represent either a single small urban area, a combination of small urban areas and their surrounding rural SA2s, or a combination of rural SA2s.

Zero or nominal population SA3s

To minimise the amount of unsuppressed data that can be provided in multivariate statistical tables, SA2s with fewer than 1,000 residents are combined with other SA2s wherever possible to reach the 1,000 SA3 population target. However, there are still a number of SA3s with zero or nominal populations.

Small population SA2s designed to maintain alignment between territorial authority and regional council geographies are merged with other SA2s to reach the 5,000–10,000 SA3 population target. These merges mean that some SA3s do not align with regional council boundaries but are aligned to territorial authority.

Small population island SA2s are included in their adjacent land-based SA3.

Island SA2s outside territorial authority or region are the same in the SA3 geography.

Inland water SA2s are aggregated and named by territorial authority, as in the urban rural classification.

Inlet SA2s are aggregated and named by territorial authority or regional council where the water area is outside the territorial authority.

Oceanic SA2s translate directly to SA3s as they are already aggregated to regional council.

The 16 non-digitised SA2s are aggregated to the following 4 non-digitised SA3s (SA3 code; SA3 name):

70001; Oceanic outside region, 70002; Oceanic oil rigs, 70003; Islands outside region, 70004; Ross Dependency outside region.

SA3 numbering and naming

Each SA3 is a single geographic entity with a name and a numeric code. The name refers to a suburb, recognised place name, or portion of a territorial authority. In some instances where place names are the same or very similar, the SA3s are differentiated by their territorial authority, for example, Hillcrest (Hamilton City) and Hillcrest (Rotorua District).

SA3 codes have five digits. North Island SA3 codes start with a 5, South Island SA3 codes start with a 6 and non-digitised SA3 codes start with a 7. They are numbered approximately north to south within their respective territorial authorities. When first created in 2025, the last digit of each code was 0. When SA3 boundaries change in future, only the last digit of the code will change to ensure the north-south pattern is maintained.

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High-definition version

This high definition (HD) version is the most detailed geometry, suitable for use in GIS for geometric analysis operations and for the computation of areas, centroids and other metrics. The HD version is aligned to the LINZ cadastre.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007

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Further information

To download geographic classifications in table formats such as CSV please use Ariā

For more information please refer to the Statistical standard for geographic areas 2023.

Contact: geography@stats.govt.nz

Salaries and Employee Benefits Statistics - Managerial and Professional Employees (Excluding Top Management) - Table 220-25003 : Nominal Salary Indices (B) for middle-level managerial and professional employees by industry section (June 1995 = 100)

Korea HS: OH: Income: Nominal data was reported at 3,599,960.000 KRW in Mar 2018. This records a decrease from the previous number of 3,728,516.000 KRW for Dec 2017. Korea HS: OH: Income: Nominal data is updated quarterly, averaging 3,115,133.000 KRW from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 3,728,516.000 KRW in Dec 2017 and a record low of 2,221,416.000 KRW in Mar 2003. Korea HS: OH: Income: Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H052: Household Income and Expenditure Survey (HS): Other Household: Nominal.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_64f98475cef1e94300362cb400a50012/view

Korea HS: UH: Income: Nominal data was reported at 4,854,856.000 KRW in Mar 2018. This records an increase from the previous number of 4,472,246.000 KRW for Dec 2017. Korea HS: UH: Income: Nominal data is updated quarterly, averaging 2,789,028.000 KRW from Mar 1990 (Median) to Mar 2018, with 113 observations. The data reached an all-time high of 4,854,856.000 KRW in Mar 2018 and a record low of 880,499.000 KRW in Mar 1990. Korea HS: UH: Income: Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H040: Household Income and Expenditure Survey (HS): Urban Household: Nominal.

Graph and download economic data for Nominal Statistical Discrepancy for United States (NSDGDPSAXDCUSQ) from Q1 1950 to Q1 2021 about residual and USA.

Korea HS: OU: Expenditure (Exp): Nominal data was reported at 2,777,012.000 KRW in Dec 2016. This records a decrease from the previous number of 2,952,003.000 KRW for Sep 2016. Korea HS: OU: Expenditure (Exp): Nominal data is updated quarterly, averaging 2,088,456.500 KRW from Mar 1990 (Median) to Dec 2016, with 108 observations. The data reached an all-time high of 3,015,263.000 KRW in Mar 2014 and a record low of 746,871.000 KRW in Mar 1990. Korea HS: OU: Expenditure (Exp): Nominal data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H056: Household Income and Expenditure Survey (HS): Other Urban Household: Nominal.

Graph and download economic data for Nominal Statistical Discrepancy for Germany (NSDGDPNSAXDCDEQ) from Q1 1991 to Q1 2022 about residual and Germany.