Imports in the United States increased to 358.80 USD Billion in July from 338.80 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Imports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Statistics Netherlands collects monthly data on imports and exports of goods. In this table on imports and exports of goods the change of ownership of the goods is decisive, not whether they crossed the Dutch border. The table comprises index figures and changes in terms of percentage of total imports and exports of goods, broken down by value, price and volume. The indices are based on 2021=100. The changes in terms of percentage are compared with the same period in the previous year.

Data available from: 1995 January

Status of the figures: Annual data from 1995 up to and including 2023 are final. Monthly and quarterly data on 2023, 2024 and 2025 are provisional.

Correction as of July 23th 2025: During the changes on July 11th 2025, wrong data on the importvolume and importprices in 2022 have been made final. The final data have now been corrected.

Changes as of August 14th 2025: Data from June and the second quarter of 2025 have been added. The data from March, April and May have been revised.

Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. This table contains revised data. For further information see section 3.

Import and export figures may be adjusted as new or updated source information from the monthly international trade statistics and producer prices becomes available. In addition, the figures are adjusted retrospectively to fit those of imports and exports of goods in the quarterly National Accounts and the annual National Accounts. A complete revision of the National Accounts is carried out once every five years.

When will new figures be published? Six to seven weeks after the end of the month under review.

Dominican Republic DO: Imports: % of Total Goods Imports: The Arab World data was reported at 0.166 % in 2016. This records a decrease from the previous number of 0.183 % for 2015. Dominican Republic DO: Imports: % of Total Goods Imports: The Arab World data is updated yearly, averaging 0.069 % from Dec 1960 (Median) to 2016, with 35 observations. The data reached an all-time high of 0.573 % in 1960 and a record low of 0.001 % in 1983. Dominican Republic DO: Imports: % of Total Goods Imports: The Arab World data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: Imports. Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

This table contains information on Dutch imports, exports and net balance of services broken down by various service types and countries (groups) provided or purchased by companies and persons domiciled in the Netherlands.

An entirely renewed classification of services which are used by all EU countries from 2014 onwards is based on the “Balance of Payments Manual 6” (BPM6).

Data available from: 2020

Status of the figures: As of 2020, a redesign of the International trade in services has taken place. To make time series and mutual comparison possible, 2020 will be published in 2 ways. In the 2014-2020 time series table 2020 is model-based using response data and auxiliary data from the tax authorities. The first quarter of 2021 has temporarily been added to the old 2014-2020 table awaiting the publication of the new table. With the release of the new table starting in 2020, the first quarter 2021 is no longer published in the old time series.

As of 2017 is the publication and revision policy of international trade in services review. The figures on the Dutch international trade in services are available on quarterly and annual basis. Provisional quarterly figures are one quarter after the end of the reporting period available. With the publication of a new quarter, also the figures for the previous quarter and if applicable the other previous quarters of the year are adjusted on the basis of new source material (provisional figures). At the time of a year four quarters are available quarterly on the basis of these four figures a year calculation is made. This year calculation is available in the autumn of the following year. The year calculation is then adjusted again the following year, this time final. Also the quarterly figures of the year reviewed.

Changes as of June 17, 2025: The provisional figures of the fourth quarter 2024 have been adjusted and are revised provisional figures now, also the provisional figures of the first quarter 2025 have been added.

Changes as of November 19, 2024: In the previously published data for 2022, the quarterly figures were reported correctly. However, an error occurred when calculating the total for the year, as the June data point was mistakenly used instead of the correct final quarterly figure. As a result, the quarterly figures did not add up to the published annual total. This error has now been corrected, ensuring that the published total aligns with the sum of the individual quarters.

When will new figures be published? The first figures become available approximately 12 weeks after the reporting period.

Dominican Republic DO: Imports: % of Total Goods Imports: Residual data was reported at 0.784 % in 2016. This records a decrease from the previous number of 1.278 % for 2015. Dominican Republic DO: Imports: % of Total Goods Imports: Residual data is updated yearly, averaging 3.996 % from Dec 1960 (Median) to 2016, with 48 observations. The data reached an all-time high of 73.491 % in 1996 and a record low of 0.130 % in 1999. Dominican Republic DO: Imports: % of Total Goods Imports: Residual data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank.WDI: Imports. Merchandise imports by the reporting economy residuals are the total merchandise imports by the reporting economy from the rest of the world as reported in the IMF's Direction of trade database, less the sum of imports by the reporting economy from high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise imports by the economy.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Trade in goods by importer characteristics data available by country of export. Users have the option of selecting information related to the value of imports and the number of importing establishments in all provinces and territories in Canada.

ABOUT THE DATA

The dataset is powered by: Country Coverage: 80+ Update Frequency: Daily No. of Government Authorities Checked for Compliance: 440+ No. of Import & Export Compliance Data Entities: 880K+

Get access to insights like: Country-specific regulations: Access detailed information on trade controls enforced by individual countries, ensuring businesses stay informed about specific import and export regulations.

Export Licensing Requirements: Gain insights into export licensing requirements for various goods, enabling businesses to obtain the necessary permissions and ensure regulatory compliance.

Dual-Use Goods: Understand restrictions related to dual-use goods that have both civilian and military applications, ensuring businesses comply with regulations governing these sensitive items.

Customs and Regulatory Changes: Stay ahead of customs and regulatory changes across different jurisdictions, allowing businesses to adapt quickly to evolving trade control measures.

USE CASES

Compliance Management Businesses can utilize the datafeed to stay informed about trade restrictions, embargoes, and sanctions imposed by different countries, ensuring strict adherence to compliance regulations in international trade.

Risk Mitigation By regularly monitoring the datafeed, businesses can identify and assess potential risks associated with specific regions, entities, or goods, allowing them to implement proactive risk mitigation strategies.

Export Licensing The datafeed offers detailed information on export licensing requirements, enabling businesses to obtain the necessary permits for the lawful export of goods and ensuring compliance with regulatory frameworks.

Customs Compliance Businesses can enhance their customs compliance efforts by staying up-to-date with changes in customs and regulatory requirements across different jurisdictions, preventing delays, and ensuring the smooth flow of goods.

Due Diligence Performing due diligence is simplified with the Trade Controls Datafeed. Businesses can thoroughly vet potential partners, suppliers, and customers to ensure they align with trade control regulations and do not pose compliance risks.

ABOUT TRADEMO

Trademo compiles billions of data points using big data, machine learning, NLP, entity resolution, and graph databases to clean, enrich, and analyze unstructured data. This process provides detailed insights on over 50% of global trade by dollar value, establishing Trademo as a reliable source for global supply chain information.

Request the complete dataset at dm@trademo.com

This dataset provides figures for the number of animals imported (from third countries) or consigned (from the European Union (EU)) into Great Britain in 2016. These datasets cover a number of livestock species. The layout and structure of the dataset is the same for each species. The data have been gathered from intra-community trade animal health certificates (ITAHC) for trade from the EU, and Community Veterinary Entry Document - Animals (CVED-A) for imports from third countries outside the EU that must accompany a consignment. Structure: The datasets contain information for England, Wales and Scotland. The top row of the spreadsheet indicates which country the figures relate to. Please note: figures given in these datasets may vary to those produced by HMRC. This is because APHA's data is taken directly from certificates, while HMRC calculate statistical figures based on other sources. Data Fields in Imports Datasets: Certified Purpose: The second row of the spreadsheet lists the purpose for import as indicated on the certificate. Animals can be imported/consigned for a number of reasons:- Breeding: Have to comply with certain disease requirements and be resident in the place of origin for 30 days before export. They are consigned for purposes of breeding. Fattening: Have to comply with certain disease requirements and be resident in the place of origin for 30 days before export. They are consigned for purposes of food production. Slaughter: There are fewer animal health guarantees on animals consigned for slaughter as they must travel directly to slaughter. Approved Bodies: Animals imported under Balai regulations can be exempted from rabies quarantine if imported from an approved premises. Country: The left column indicates which country the animals were imported/consigned from. This may include an assembly centre where animals have been resident for no longer than 6 days. Number of Consignments: Under each certified purpose, figures are given for the number of consignments received from the country listed into England, Wales or Scotland. Number of Animals: Under each certified purpose, figures are given for the number of animals received from the country listed into England, Wales or Scotland. Total Animals: The total number of animals imported/consigned from the country listed into England, Wales and Scotland, across all consignments.

United States Imports from China was US$462.62 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from China - data, historical chart and statistics - was last updated on September of 2025.

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit 

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema 

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys 

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{
  _id: 

Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data was reported at 16.375 % in 2016. This records a decrease from the previous number of 17.542 % for 2015. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data is updated yearly, averaging 1.661 % from Dec 1960 (Median) to 2016, with 43 observations. The data reached an all-time high of 17.542 % in 2015 and a record low of 0.064 % in 1983. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Imports. Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Comprehensive dataset containing 3 verified Import export company businesses in Jeju-do, South Korea with complete contact information, ratings, reviews, and location data.

Exports in the United States increased to 280.46 USD Billion in July from 279.65 USD Billion in June of 2025. This dataset provides the latest reported value for - United States Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table provides an overview of the non-financial transactions of the institutional sectors of the Dutch economy, distinguishing between uses and resources. Non-financial transactions consist of current transactions and transactions from the capital account. Furthermore, this table provides the main balancing items of the (sub)sectors. Non-financial transactions are estimated for the main institutional sectors of the economy and the rest of the world. Sectors are presented both consolidated and non-consolidated.

Data available from: Annual figures from 1995. Quarterly figures from first quarter 1999.

Status of the figures: Annual figures from 1995 up to and including 2023 are final. Quarterly data from 2023 are provisional.

Changes as of September 23rd, 2025: Data of the second quarter 2025 have been added.

Adjustment as of April 10th 2025: Due to an error made while processing the data, the initial preliminary figures for government expenditure in 2024 were calculated incorrectly, which means that the figure published for the general government balance was also incorrect. We refer to the Government Finance Statistics for the current figures. Links to the Government Finance Statistics could be found in paragraph 3. Until the publication end of June the Sector accounts therefore diverge from the Government Finance Statistics.

Adjustment as of July 12th 2024: Total consolidated resources and uses are adjusted for most sectors, due to a calculation error. For the sector rest of the world, the non-consolidated total resources and uses have also been adjusted. Imports and exports of goods and services were wrongly not included in the total resources and uses. For the sectors non-financial corporations and financial corporations, capital taxes (uses) were wrongly shown as empty cell (figure not applicable).

When will new figures be published? Annual figures: The first annual data are published 85 day after the end of the reporting year as the sum of the four quarters of the year. Subsequently provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Furthermore the sector accounts are annually revised for all reporting periods. These data are published each year in June. Quarterly figures: The first quarterly estimate is available 85 days after the end of each reporting quarter. The first quarter may be revised in September, the second quarter in December. Should further quarterly information become available thereafter, the estimates for the first three quarters may be revised in March. If (new) annual figures become available in June, the quarterly figures will be revised again to bring them in line with the annual figures. Please note that there is a possibility that adjustments might take place at the end of March or September, in order to provide the European Commission with the latest figures. Revised yearly figures are published in June each year.

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data was reported at 0.611 % in 2016. This records a decrease from the previous number of 1.467 % for 2015. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data is updated yearly, averaging 0.274 % from Dec 1977 (Median) to 2016, with 32 observations. The data reached an all-time high of 1.467 % in 2015 and a record low of 0.001 % in 1980. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Europe & Central Asia data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Imports. Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data was reported at 0.102 % in 2016. This records a decrease from the previous number of 0.110 % for 2015. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data is updated yearly, averaging 0.110 % from Dec 1967 (Median) to 2016, with 39 observations. The data reached an all-time high of 1.281 % in 1967 and a record low of 0.001 % in 1992. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Sub-Saharan Africa data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Imports. Merchandise imports from low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Sub-Saharan Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

The Census data API provides access to the most comprehensive set of data on current month and cumulative year-to-date imports using the Harmonized System (HS). The Census data API also provides quantity, value, shipping weight, and method of transportation totals at the district level for all U.S. trading partners. The Census data API will help users research new markets for their products, establish pricing structures for potential export markets, and conduct economic planning. If you have any questions regarding U.S. international trade data, please call us at 1(800)549-0595 option #4 or email us at eid.international.trade.data@census.gov.

Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: East Asia & Pacific data was reported at 14.548 % in 2016. This records a decrease from the previous number of 14.818 % for 2015. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: East Asia & Pacific data is updated yearly, averaging 0.939 % from Dec 1960 (Median) to 2016, with 42 observations. The data reached an all-time high of 14.818 % in 2015 and a record low of 0.013 % in 1977. Dominican Republic DO: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: East Asia & Pacific data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Imports. Merchandise imports from low- and middle-income economies in East Asia and Pacific are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

China Imports from United States was US$164.59 Billion during 2024, according to the United Nations COMTRADE database on international trade. China Imports from United States - data, historical chart and statistics - was last updated on September of 2025.