To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

Immigrants Admitted: Philippines data was reported at 53,287.000 Person in 2016. This records a decrease from the previous number of 56,478.000 Person for 2015. Immigrants Admitted: Philippines data is updated yearly, averaging 54,446.000 Person from Sep 1986 (Median) to 2016, with 31 observations. The data reached an all-time high of 74,606.000 Person in 2006 and a record low of 30,943.000 Person in 1999. Immigrants Admitted: Philippines data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s USA – Table US.G086: Immigration.

United States Imports from Philippines was US$14.59 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Philippines - data, historical chart and statistics - was last updated on July of 2025.

United States Trade Balance: Philippines data was reported at -271.000 USD mn in May 2018. This records a decrease from the previous number of -252.000 USD mn for Apr 2018. United States Trade Balance: Philippines data is updated monthly, averaging -139.700 USD mn from Jan 1985 (Median) to May 2018, with 401 observations. The data reached an all-time high of 152.500 USD mn in Nov 2012 and a record low of -671.700 USD mn in Sep 2000. United States Trade Balance: Philippines data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA010: Trade Statistics: Census Basis: By Country: Trade Balance.

Philippines Imports from United States was US$8.85 Billion during 2024, according to the United Nations COMTRADE database on international trade. Philippines Imports from United States - data, historical chart and statistics - was last updated on June of 2025.

Philippines Exports to United States was US$12.12 Billion during 2024, according to the United Nations COMTRADE database on international trade. Philippines Exports to United States - data, historical chart and statistics - was last updated on July of 2025.

United States Exports of live animals to Philippines was US$20.28 Million during 2024, according to the United Nations COMTRADE database on international trade. United States Exports of live animals to Philippines - data, historical chart and statistics - was last updated on July of 2025.

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Philippines were selected based on the following criterion: (a) smallholder rice growers Location: Luzon - Mindoro (Southern Luzon) mid-tier (sub-optimal CP/SE use): mid-tier growers use generic CP, cheaper CP, non hybrid (conventional) seeds
Smallholder farms with average to high levels of mechanization
Should be Integrated Pest Management advocates
less accessible to technology: poor farmers, don't have the money to buy quality seeds, fertilizers,... Don't use machinery yet
simple knowledge on agronomy and pests
influenced by fellow farmers and retailers
not strong financial status: don't have extra money on bank account and so need longer credit to pay (as a consequence: interest increases) may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab.

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

Philippines Imports from United States of Tantalum was US$540 during 2024, according to the United Nations COMTRADE database on international trade. Philippines Imports from United States of Tantalum - data, historical chart and statistics - was last updated on July of 2025.

Philippines Exports of cinematographic cameras and projectors to United States was US$9.77 Thousand during 2022, according to the United Nations COMTRADE database on international trade. Philippines Exports of cinematographic cameras and projectors to United States - data, historical chart and statistics - was last updated on July of 2025.

United States Imports from Philippines of Office machines not specified elsewhere was US$98.09 Million during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Philippines of Office machines not specified elsewhere - data, historical chart and statistics - was last updated on July of 2025.

Philippines Visitor Arrivals: North America: USA data was reported at 75,202.000 Person in Apr 2025. This records a decrease from the previous number of 78,749.000 Person for Mar 2025. Philippines Visitor Arrivals: North America: USA data is updated monthly, averaging 52,864.000 Person from Jan 2001 (Median) to Apr 2025, with 291 observations. The data reached an all-time high of 110,188.000 Person in Jan 2025 and a record low of 56.000 Person in May 2020. Philippines Visitor Arrivals: North America: USA data remains active status in CEIC and is reported by Department of Tourism. The data is categorized under Global Database’s Philippines – Table PH.Q001: Visitor Arrivals. [COVID-19-IMPACT]

United States Export: FAS: PK: Office Machinery and Automatic Data Processing data was reported at 3.833 USD mn in May 2018. This records a decrease from the previous number of 4.069 USD mn for Apr 2018. United States Export: FAS: PK: Office Machinery and Automatic Data Processing data is updated monthly, averaging 1.769 USD mn from Jan 1990 (Median) to May 2018, with 341 observations. The data reached an all-time high of 9.511 USD mn in Feb 2014 and a record low of 0.133 USD mn in Sep 1996. United States Export: FAS: PK: Office Machinery and Automatic Data Processing data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

Key information about Philippines Total Exports to USA

• Philippines Total Exports to USA recorded 1.128 USD bn in Jan 2025, compared with 947.767 USD mn in the previous Dec 2024
• Philippines Total Exports to USA data is updated monthly, available from Jan 1978 to Jan 2025, with an averaged value of 615.258 USD mn
• The data reached an all-time high of 1.224 USD bn in Aug 2024 and a record low of 66.490 USD mn in Jan 1978

United States Import: Customs: PH: Commodities and Transactions, nes data was reported at 20.974 USD mn in May 2018. This records an increase from the previous number of 18.252 USD mn for Apr 2018. United States Import: Customs: PH: Commodities and Transactions, nes data is updated monthly, averaging 20.731 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 52.813 USD mn in Aug 2014 and a record low of 6.328 USD mn in May 1996. United States Import: Customs: PH: Commodities and Transactions, nes data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

United States Export: FAS: PK: Manufactured Goods data was reported at 7.379 USD mn in May 2018. This records an increase from the previous number of 7.247 USD mn for Apr 2018. United States Export: FAS: PK: Manufactured Goods data is updated monthly, averaging 5.730 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 13.413 USD mn in Nov 2011 and a record low of 1.082 USD mn in Jul 1999. United States Export: FAS: PK: Manufactured Goods data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

United States Export: FAS: PK: Food and Live Animals data was reported at 10.994 USD mn in May 2018. This records a decrease from the previous number of 13.692 USD mn for Apr 2018. United States Export: FAS: PK: Food and Live Animals data is updated monthly, averaging 7.744 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 60.651 USD mn in Jan 1996 and a record low of 0.077 USD mn in Apr 1998. United States Export: FAS: PK: Food and Live Animals data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

United States Import: Customs: PH: Miscellaneous Manufactured Articles data was reported at 179.468 USD mn in May 2018. This records an increase from the previous number of 175.476 USD mn for Apr 2018. United States Import: Customs: PH: Miscellaneous Manufactured Articles data is updated monthly, averaging 202.179 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 328.397 USD mn in Aug 2000 and a record low of 119.088 USD mn in Apr 2009. United States Import: Customs: PH: Miscellaneous Manufactured Articles data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

United States Import: Customs: PH: Food and Live Animals data was reported at 67.549 USD mn in May 2018. This records an increase from the previous number of 49.859 USD mn for Apr 2018. United States Import: Customs: PH: Food and Live Animals data is updated monthly, averaging 47.641 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 125.254 USD mn in Nov 2011 and a record low of 19.837 USD mn in Apr 1996. United States Import: Customs: PH: Food and Live Animals data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.

United States Export: FAS: PK: Beverages and Tobacco data was reported at 0.003 USD mn in May 2018. This records a decrease from the previous number of 0.049 USD mn for Apr 2018. United States Export: FAS: PK: Beverages and Tobacco data is updated monthly, averaging 0.019 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 1.901 USD mn in Mar 1997 and a record low of 0.000 USD mn in Jan 2018. United States Export: FAS: PK: Beverages and Tobacco data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA062: Trade Statistics: Pakistan and Philippines: SITC.