Abstract

Smallholder rice farming is central to poverty reduction, food security, and rural development in the Philippines. One key issue is that around 41 percent of the country's irrigable land is not irrigated. Moreover, many irrigation systems are suggested to be poorly managed with unequal water distribution.

The Irrigated Rice Production Enhancement Project (IRPEP) was implemented in three regions (VI, VII and X) of the Philippines, between 2010-2015. It was designed to improve rice productivity and smallholder livelihoods by strengthening canal irrigation infrastructure of Communal Irrigation Systems (CIS), improving the capacity of the Irrigators' Associations (IAs) that manage the CIS, and offering complementary marketing support, Farmer Field Schools, and emergency seed buffer stocks.

The data collected are used to test the effectiveness of the 5-year Irrigated Rice Production Enhancement Project to improve the livelihoods of smallholder rice farmers in the Philippines.

For more information, please, click on the following link https://www.ifad.org/en/web/knowledge/-/publication/impact-assessment-irrigated-rice-production-enhancement-project.

Geographic coverage

Rural coverage. Sample covers six provinces of the Philippines across three regions (Region VI, VIII, X).

Analysis unit

Households

Universe

Smallholder farmer households

Kind of data

Sample survey data [ssd]

Sampling procedure

The analysis is based on quantitative data from 2,104 households and 113 IAs covering beneficiary and non-beneficiary groups, along with qualitative data from project and IA staff. The IRPEP's impact is estimated by comparing beneficiary and nonbeneficiary households and IAs using statistical matching techniques to ensure a clean and unbiased comparison. This process resulted in a household dataset used for analysis that covers 1,015 treatment and 664 control households, and an IA dataset used to assess impact on IA level indicators from 58 treatment and 55 control IAs.

To identify a well-matched set of treatment and control CISs and households, the sample selection for the impact assessment sought to mirror IRPEP's beneficiary selection process by initially conducting the identification at the CIS level. At the start of the process there were a number of non- beneficiary CIS in the project provinces, allowing for control CIS to be selected from within the same provinces. Using these IRPEP and non-IRPEP CIS, a two-stage process was used to select the final set of treatment and control CIS. This involved both data analysis and the knowledge of local staff.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The household and IA questionnaires collected a wide range of information, which was then used to create the impact indicators and other variables to be used in the data analysis. The household questionnaire included detailed questions on agricultural production and marketing collected by season, parcel and crop for the previous 12 months, as well as socio-demographic characteristics, other income generating activities, asset ownership, experience of shocks, access to credit, and receipt of external support from various sources. The IA questionnaire gathered information on their structure and facilities, irrigation water coverage, gender differentiated membership, and income and expenditures over the past 12 months, including irrigation fee collection and operation and maintenance spending.

Note: some variables have missing labels. Please, refer to the questionnaire for more details.

Just a few income and employment variables of various Philippines regions which can be used in KIVA analysis for Philippines loans.

Following are the variables

• 1) agr_wage_farm_workers_allgender_2015 - Agriculture Wage Rates of Farm Workers (All Gender) in 2015 - UNIT: pesos

• 2) agr_wage_farm_workers_male_2015 - Similar to above for males - UNIT: pesos

• 3) agr_wage_farm_workers_female_2015 - Similar to above for females - UNIT: pesos

• 4) avg_annual_total_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos

• 5) avg_annual_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos

• 6) avg_annual_off_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos

• 7) avg_annual_non_farm_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos

• 8) avg_annual_other_sources_incm_farm_households_02_03 - Average annual total income for farm households Year 2002-03 - UNIT: pesos

• 9) avg_rural_income_2000 - Average rural income - UNIT: pesos

• 10) total_emply_2016 - Total employment 2016 - UNIT: thousand persons

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

As of 2023, about 4.82 million hectares of land were dedicated to cultivating palay in the Philippines. The total land area used for growing palay in the country fluctuated within the given period of time, with 2023 recording the highest values.  How much does it cost to produce palay in the Philippines? The Philippines ranks high alongside countries such as China and India when it comes to rice consumption globally. Rice is a main staple for Filipinos, making this crop among the most important agricultural products produced by farmers in the country. On average, palay production costs in the Philippines amounted to about 54 Philippine pesos per hectare in 2022, with Cagayan Valley recording the highest production costs nationwide. Meanwhile, the cost of palay production per kilogram amounted to an average of 15 Philippine pesos in the same year. The cost of producing palay is attributed to factors such as the cost of planting materials, labor and transport costs, irrigation fees, as well as rental fees for land used.  Average wage rate on palay farms in the Philippines In 2019, the average wage rate on palay farms in the Philippines was highest in CALABARZON, amounting to around 357 Philippine pesos per day. The lowest average was recorded in the BARMM region with 213 Philippine pesos. Although no recent reports have been published regarding this, the poverty incidence of farmers in the country has gradually declined since 2015.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Climate Change, External Debt, Trade.