Abstract

This dataset corresponds to the baseline survey for an emergency livelihoods and food security support project to strengthen the resilience of crisis-affected populations. Data was collected from 1376 households, including 675 beneficiaries and 701 non-beneficiaries. The objective is to know the situation of households before the project in terms of various indicators of food consumption, sources of income, access to basic services, ownership of assets (productive and non-productive), agricultural and fisheries production, adoption of innovative techniques, social safety nets, exposure to shocks and coping strategies. The questionnaire is based on the Short RIMA questionnaire, which collects the minimum amount of information necessary to calculate the resilience capacity index using the RIMA methodology developed by FAO.

Geographic coverage

Prefectures of Bangui, Ombella M'poko, Lobaye, Ouham, Haute Kotto, Sangha Mbaéré, Mambéré Kadéi, Nana Mambéré, Nana Gribizi, Kémo and Ouaka

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline survey targets the 12 prefectures with project villages. For the treatment group, the target population of the study is the project beneficiary households. The sample was drawn from the selected beneficiaries. To provide a comparison group, non-beneficiary villages in the same intervention prefectures were targeted. Households in the comparison group were selected from outside the beneficiary villages to minimize contamination effects. The sample was distributed to reflect the weight of beneficiaries per prefecture. A total of 1376 households were interviewed, including 675 beneficiary households and 701 non-beneficiary households. The population surveyed among the beneficiaries was almost equally divided between the three types of intervention (cash transfers, direct distribution and vouchers).

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

This dataset corresponds to the baseline survey for the ProActing project (Global Network for Food Crisis Prevention) in the Grand Sud of Madagascar. The objective is to find out the situation of households before the project in terms of various indicators of food consumption, sources of income, access to basic services, ownership of assets (productive and non-productive), agricultural and fisheries production, adoption of innovative techniques, social safety nets, exposure to shocks and survival strategies. The questionnaire is based on the Short RIMA questionnaire, which collects the minimum amount of information necessary to calculate the resilience capacity index using the RIMA methodology.

Geographic coverage

Great South of Madagascar, Androy, Anosy and Atsimo-Atsinanana regions.

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

As the intervention area consists of 21 communes from 7 districts in the Atsimo-Atsinanana, Androy and Anosy regions, the study was conducted on a representative sample at district level. The control group was drawn from the same communes but in fokontany not covered by the project. A two-stage sampling plan was adopted for a sample of 1,260 households, divided into 840 beneficiary households and 420 control group households. At the primary level, the sample is made up of 84 fokontany drawn at random with probability proportional to their size. In each fokontany sample, the secondary sample consists of 15 households drawn with equal probability. This sample allows for an accuracy of results of around 10% at the district level.

Mode of data collection

Computer Assisted Personal Interview [capi]

Abstract

The Uganda 2018 Resilience Index Measurement and Analysis (RIMA) measures the food security and resilience of refugees and host communities in South-West Uganda. The survey was administered in March 2018 in two districts of Southwest Uganda. The data were collected through surveys of both refugee and host community households, coordinated by the Resilience Measurement Unit (RMU) of the Office of the Prime Minister (OPM). The survey covered a total of 705 households from refugee and host communities. It was conducted in the settlements of Kyaka II and Rwamwanja (housing refugees from Burundi and the DRC, among other countries), as well as nearby host communities in the districts of Kyegegwa (for Kyaka II) and Kamwenge (for Rwamwanja).

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

Refugees and host community households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample includes 705 households in total, of which 395 are refugee households (202 in the Kyegegwa District and 193 in the Kamwenge District) and 310 are part of the host communities (158 in the Kyegegwa District and 152 in the Kamwenge District). The sample size is determined based on power of design, which, in addition to taking into account the size of the population, uses the estimated impact we expect to see based on previous exercises, the standard deviations on the main indicator of interest (FAO-RCI), as well as measures to reduce possible estimation errors. The sample size has been calculated in order to be able to detect a minimum impact of 10 percent, with a 95 percent level of confidence. Other assumptions include (i) a null expectation in differences between the two populations (refugee/host) at the beginning, but possibly seeing one over time; (ii) a correlation between the resilience capacity between the two groups of 0.4 and 0.5; (iii) differences in the standard deviations between the two groups, i.e. more homogeneous characteristics amongst the host communities than the refugees; and (iv) intra-cluster correlation. This estimation was done at both settlement and district level clusters. The 20 percent oversampling was added at settlement level, to account for the expected fluid nature of refugee status. The sample is representative at district and settlement levels.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data were collected through computer-assisted personal interviewing (CAPI) with digital tablets. The use of electronic devices reduces the duration of interviews, limits errors during the interview and data entry phases, and enables the collection of geographic information system (GIS) information at the household level. The data were transmitted daily through Kobo Toolbox, a suite of software tools for data collection in challenging environments, allowing for the use of remote data control protocols.

Abstract

The Uganda 2016 Resilience Index Measurement and Analysis (RIMA) measures the food security and resilience in Karamoja, North-East, Uganda. In 2015, three United Nations (UN) agencies – the United Nations Children’s Fund (UNICEF), the Food and Agriculture Organization of the United Nations (FAO), and the World Food Programme (WFP) – developed a resilience strategy for Karamoja. This Joint Resilience Strategy (JRS) represents a commitment and collaborative focus for UNICEF, FAO, and WFP’s efforts to build resilience in the Karamoja region. The overall goal of the JRS is to improve the food security and nutrition status of the region during the period from 2016 to 2020. This JRS identifies the need for the three agencies to develop a common approach to measuring resilience in the context of Karamoja, which have thus adopted FAO’s Resilience Index Measurement and Analysis-II (RIMA II) approach to measure resilience to food insecurity there.

Geographic coverage

Regional Coverage

Analysis unit

Households

Universe

Households in Karamoja region.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the household survey is composed in total of 2 380 households. The sampling strategy is stratified according to the following five strata: (1) target households, which are those reached by the JRS in 12 parishes of the Moroto and Napak districts; (2) direct spillover households, which are those located in the remaining parishes of the Moroto and Napak districts and are not involved in the JRS; (3) indirect spillover households, which are those located in the two districts where the JRS is not actually operating (Kotido and Nakapiripirit) but where other UN projects are ongoing; (4) the ‘different ethnicity’ group, which includes those households located in two districts (Abim and Amudat) populated with ethnic groups that are different from the Karamojong;21 (5) and the pure control group, comprised of households located in the Kaabong district, which have the same ethnic group and socioeconomic conditions, mostly pastoralism, as the target group, but which are not involved in the JRS.

Mode of data collection

Computer Assisted Personal Interview [capi]

Ankle-Brachial Index Measurement Market size was valued at USD 273.88 Million in 2023 and is projected to reach USD 414.91 Million by 2031, growing at a CAGR of 5.29% from 2024 to 2031.

Global Ankle-Brachial Index Measurement Market Outlook

The prevalence of peripheral artery disease (PAD) is rising globally, driven by an aging population, increasing rates of diabetes, and other cardiovascular risk factors. According to the American Heart Association, PAD affects over 200 million people worldwide. In the United States alone, PAD impacts approximately 8.5 million people aged 40 and older. This condition is associated with a significant risk of cardiovascular events such as heart attacks and strokes, making early diagnosis and management crucial. The rising incidence of PAD is further evidenced by global statistics. In Europe, an estimated 40 million individuals suffer from PAD, with prevalence rates continuing to climb. The growing burden of PAD is also notable in emerging economies.

Abstract

Mali is a Sahelian country, landlocked and structurally vulnerable to food insecurity and malnutrition. The economy is heavily dependent on the primary sector: agriculture, livestock, fishing and forestry account for 68.0% of the active population1 . This sector is itself dependent on exogenous factors, mainly climatic, such as recurrent droughts. In 2018, the prevalence of food insecurity at the national level was 19.1%, of which 2.6% was severely food insecure. The most affected regions were Kidal, Gao, Timbuktu, Mopti and Kayes. The Global Food Crisis Network Partnership Programme baseline studies are designed to feed into the overall monitoring, evaluation, accountability and learning programme of each project. In this regard, the baseline study has short, medium and long-term objectives.

Geographic coverage

Regional coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The EAC-I 2014 has been designed to have national coverage, including both urban and rural areas in all the regions of the country except Kidal. The domains were defined as the entire country, district of Bamako, other urban areas, and rural areas; and in the rural areas: agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 51 explicit sample strata were selected. The target population was drawn from households in all regions of Mali except Kidal which was not accessible for security reasons. Kidal also has very low population density.The sample was chosen through a random two stage process: - In the first stage, 1070 enumeration areas (EAs) were selected with Probability Proportional to Size (PPS) using the 2009 Census of Population as the base for the sample, and the number of households as a measure of size. - In the second stage o 3 households were selected with equal probability in each of the rural EAs o 9 households were selected with equal probability in each of the urban EAs The total estimated size of the sample for the survey was 4,218.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Please refer to the Questionnaires for the value labels of the variables.

Cleaning operations

Data Entry & Data cleaning Data entry was performed at the CPS/SDR using a CSPro application designed by an international consultant recruited by the LSMS team. The data entry program allows three types of data checks: (1) range checks; (2) intra-record check to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies pertinent between the different modules of the questionnaire. Data entry for the first visit was done from August 11th, 2014 to November 30th, 2014 and, from February 9th 2015 to March 27th, 2015 for the second visit. Data cleaning was done from May 2015 to July 2015. Data cleaning was done in a CSPro application. The data cleaning focused on more intra-record and inter-record checks.

Market Analysis of Refractive Index Measurement Services The global refractive index measurement services market is projected to expand at a CAGR of XX% from 2025 to 2033, reaching a value of XXX million by 2033. The increasing demand for precise and accurate measurements in various industries, including food & beverage, pharmaceutical, and chemical, fuels market growth. The rapidly evolving landscape of industries that rely on refractive index measurements, such as quantum computing, nanotechnology, and biotechnology, creates opportunities for service providers. Key drivers of market growth include the increasing adoption of advanced analytical techniques, rising demand for product quality control, and the growing adoption of refractive index measurement services for forensic analysis. Furthermore, the emergence of new technologies such as hyperspectral imaging and spectroscopy is expected to fuel the demand for advanced refractive index measurement capabilities. However, factors such as the high cost of equipment and the need for skilled professionals may restrain market growth. Key market players include AMS Technologies, OMT Solutions, Filmetrics, RI Instruments, and Ohara Corporation. North America holds a significant market share, while Asia-Pacific is expected to experience notable growth due to the increasing demand from emerging pharmaceutical and chemical industries.

The refractive index measurement services market is experiencing robust growth, driven by increasing demand across diverse sectors like pharmaceuticals, semiconductors, and food & beverage. Technological advancements, particularly in sensor technology and automation, are enhancing the accuracy and speed of measurements, leading to wider adoption. The market is characterized by a diverse range of applications, with precise refractive index determination crucial for quality control, process optimization, and research & development. While the precise market size is not provided, based on industry trends and the presence of established players like AMS Technologies and Mettler Toledo, a reasonable estimate for the 2025 market size might be in the range of $500 million to $700 million. A Compound Annual Growth Rate (CAGR) in the range of 6-8% over the forecast period (2025-2033) is plausible, driven by ongoing technological innovation and increasing application areas. This growth will be facilitated by the continued expansion of established companies and the emergence of new players, leading to competition and innovation within the industry. The market's segmentation reflects the varied application areas. Specific segments likely include handheld and benchtop instruments, with each offering varying levels of precision and automation. The geographical distribution is likely to be skewed towards developed economies with advanced manufacturing and research infrastructure. However, increasing industrialization in developing regions presents a significant opportunity for future growth. While regulatory requirements and the relatively high cost of advanced instruments pose some restraints, the overall market outlook remains positive, driven by the essential role of accurate refractive index measurements in ensuring product quality and process efficiency across multiple industries. The companies mentioned represent a mix of established players and specialized providers, indicating a market with potential for both consolidation and innovation.

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

Abstract

Mauritania, like many countries in the Sahel, regularly face recurrent plagues such as droughts, floods, bird invasions, off-season rains, as well as, regional security issues. Drought, for example, is a common phenomenon in the south of Mauritania, which favors food insecurity and malnutrition, and significantly reduces household resilience while increasing their vulnerability to future shocks. Apart from the fact that only 0.5 percent of the land is suitable for agriculture, Mauritania consists of reliefs and very large, fragile agroecological complexes which are also faced with the effects of climate change.

In recent years, food crises and nutritional factors have been regularly observed due to structural causes which has poverty as its common denominator. These crises, as well as, climatic factors have a negative consequence on natural resources and reduce the resilience of livelihoods, thereby generating a loss in productivity and poor governance of natural resources. The concept of resilience generally defines the capacity of individuals, households, communities and countries to absorb shocks and adapt to a changing environment, while transforming the institutional environment in the long term. Thus, it is necessary to set up interventions that will have an impact on adaptability and risk management over time, in order to strengthen the resilience of vulnerable households.

For more than 10 years, FAO has measured the household resilience index in different countries, using a tool developed for this purpose; Resilience Index Measure and Analysis (RIMA). RIMA analysis requires household data, covering the different aspects of livelihood; activities (productive and non-productive), social safety nets, income, access to basic services (such as schools, markets, transportation etc.) and adaptive capacity. Following the two RIMA surveys carried out in 2015 and 2016 during the lean season and the post-harvest period, this survey was carried out in 2017 to determine the resilience index in all regions of the country.

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling size used in the household survey was determined by the FAO - ESA statistical team based on the results of the General Census of Population and Housing (RGPH) 2013, Permanent Survey on Household Living Conditions (EPCV) 2014 and the results of Resilience Index Measurement and Analysis (RIMA) surveys conducted in 2015 and 2016. A total sample of 3,560 households was selected. A 2 stage, simple random sampling method was employed to select the sampled households, distributed among the different rural and urban areas of the country.

The first stage sampling frame consists of an exhaustive list of Census Districts (CD) from the mapping of the RGPH carried out in 2013. An average CD has a population of about 1,000 people (approximately 200 households). The frame has been reorganized into 25 strata, corresponding to the total number of districts in the country, each subdivided into two environments, except Nouakchott which constitutes the 25th stratum. Drawing units called primary units are made up of census districts in the sampling frame at the level of each stratum.

The second stage sampling frame consists of the list of households in each CD sampled. This database was updated after a preliminary count which takes place shortly before the actual data collection in order to reduce the risks linked to the mobility of households. A total of 20 households were drawn from each CD counted.

Out of the 3,560 sampled households, 2,826 were interviewed.

Sampling deviation

Some teams encountered several difficulties related mainly to access, due to the collection period (winter). Also, the methodology used i.e carrying out census of districts before drawing sample households caused a delay in data collection and therefore the time provided was not sufficient to ensure collection at all level of the sampled census districts.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data collection operation was performed using tablets. The program entered, designed by the statistical office has been tested and all constraints/controls necessary to ensure data quality have been integrated into the program.This program has been shared and tested before training. Also, consistency procedures have been incorporated into the program to minimize collection errors and ensure harmonization and consistency between different sections of the questionnaire.

In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Response rate

The response rate was 79.4%.

Data appraisal

A 5-day training was provided by the FAO team in collaboration with the team from the national statistical office on the RIMA-national questionnaire. This training was done to examine the questionnaire and explain to the different participants the meaning of all the questions asked. During this training, a practical session on the tablets was provided by the statistical team in order to allow the data collection agents understand the handling and testing of the questionnaire. At the end of this training, a pilot survey was organized in some districts of Nouakchott. This survey revealed errors in the collection program which were corrected before field teams were deployed for data collection.

The data collection in the field lasted 1 month and 10 days. In addition to regular checks carried out by supervisors, a mission to supervise progress and quality of data collected as part of the RIMA-National project was organized during the period from 11 to 22 August 2017. This 10-day mission allowed to visit all the deployed teams in the field. It was organized just after the departure of the teams, on August 8, 2017, in order to better supervise the start of the data collection phase in the field. This mission had several objectives: 1. Identify problems and provide solutions 2. Examine the quality of work by verifying the data collected 3. Recover all the data already collected and corrected in the field to serve as a backup.

Discover the latest insights from Market Research Intellect's Glass Refractive Index Measurement System Market Report, valued at USD 450 million in 2024, with significant growth projected to USD 750 million by 2033 at a CAGR of 7.5% (2026-2033).

Graph and download economic data for Producer Price Index by Industry: Other Measuring and Controlling Device Manufacturing: Physical Properties Testing and Inspection Equipment and Kinematic Testing and Measuring Equipment (PCU3345193345194) from Jun 1985 to May 2025 about equipment, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

Abstract

The baseline survey of beneficiaries targeted by project GCP/MYA/028/EC is designed to feed into the overall monitoring, evaluation, accountability and learning agenda of the Global Network against Food Crises Partnership Programme. In this regard, the baseline has short, medium and long-term objectives. In the short-term, the baseline will provide feedback on the project's theory of change – whether it is well-conceived in terms of project entry points or if some adjustments/complementary actions are to be considered. From the analysis, the programming team can ascertain if the strategy of the project will address the critical factors for resilience to food insecurity. It also serves to support the targeting strategy of the project to ensure that the criteria for selection are aligned with local profiles and realities of the implementation context.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

GNAFC project beneficiaries

Kind of data

Sample survey data [ssd]

Sampling procedure

In total, 300 beneficiaries or 6% of the total 4,500 project beneficiaries were randomly selected from the beneficiary list made available by the implementing partner. The sample can be considered representative of the overall beneficiary population with a ±5% margin of error at a 95% confidence level.

Sampling deviation

Due to the delay in receiving travel authorizations to the project areas, FAO M&E officers and field assistants could not travel to the sampled villages but had to administer the questionnaires from offices located in Buthidaung and Maungdaw township (the respective District headquarters) from the 13th to 18 August 2019. The selected beneficiaries were, therefore, requested to reach the interview locality with FAO covering the cost of the travel. Moreover, and due to heightened insecurity at the time of the survey, Rathedaung township was not accessible, thus reducing the possibility to interview beneficiaries from this township and further reducing the sample size.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

The data were collected through computer-assisted personal interviewing (CAPI) with digital tablets. The use of electronic devices reduces the duration of interviews, limits errors during the interview and data entry phases, and enables the collection of geographic information system (GIS) information at the household level. The data were transmitted daily through Kobo Toolbox, a suite of software tools for data collection in challenging environments, allowing for the use of remote data control protocols.

United States - Producer Price Index by Commodity: Machinery and Equipment: Measuring Instruments and Lenses was 180.98100 Index Dec 1985=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Machinery and Equipment: Measuring Instruments and Lenses reached a record high of 180.98100 in April of 2025 and a record low of 108.20000 in February of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Machinery and Equipment: Measuring Instruments and Lenses - last updated from the United States Federal Reserve on June of 2025.

This data package includes the underlying data to replicate the charts, tables, and calculations presented in Modernizing price measurement and evaluating recent critiques of the consumer price index, PIIE Working Paper 25-3.

If you use the data, please cite as:

Sichel, Daniel E., and Christopher Mackie. 2025. Modernizing price measurement and evaluating recent critiques of the consumer price index. PIIE Working Paper 25-3. Washington: Peterson Institute for International Economics.

This paper describes a weekly economic index (WEI) developed to track the rapid economic developments associated with the onset of and policy response to the novel coronavirus in the United States. The WEI is a weekly composite index of real economic activity, with eight of 10 series available the Thursday after the end of the reference week. In addition to being a weekly real activity index, the WEI has strong predictive power for output measures and provided an accurate nowcast of current-quarter GDP growth in the first half of 2020, with weaker performance in the second half. We document how the WEI responded to key events and data releases during the first 10 months of the pandemic.

Abstract

In order to make the progressive Uganda refugees policy successful in the medium- and long-term, the refugees’ response needs to facilitate their inclusion in the country’s development agenda. No longer focusing exclusively on short-term, life-saving interventions, the response should act as a vector for refugees’ integration in the economy; improving the management of land, water, and natural resources; exploiting the socio-economic opportunities associated with the refugees’ presence, skills, and development; and strengthening the hosting districts’ capacity to absorb and manage these resources. The positive impact would affect refugees, host communities, and hosting districts alike, thus moving towards social and economic integration. In August 2017, FAO was asked by the Commissioner for Refugees (Office of the Prime Minister of Uganda, OPM) to support the implementation of a socio-economic analysis within the refugees’ settlements and host communities, with the aim of providing a comprehensive assessment of the current state of the refugees’ food security, well-being and resilience. Although refugees in Uganda are given land and mobility rights, their food security remains low, with a high dependency on food aid. The assumption was that by better understanding refugees’ preferences and livelihoods strategies which determine their resilience, it would be possible to unlock the development potential of the land, increase productivity and help them achieve independence and self-reliance. The Uganda 2020 Resilience Index Measurement and Analysis (RIMA) measures the food security and resilience of refugees and host communities in Northern Uganda. The survey was administered between November and December, 2020.

Geographic coverage

Regional coverage

Analysis unit

Households

Universe

Refugees and host community households (Households living near settlements)

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy of the third wave was to re-interview all the refugee and host households interviewed in the first wave (between 2017 and 2019) plus the households in the Koboko district visited for the first time during December 2019. The field teams targeted the households interviewed in the first wave, interviewed or not during the second one. The total number of received questionnaires is 4,180. The final panel sample excluding Koboko is composed of 4,047 households. With the first-wave target of 6,236 households, we have an attrition of 35 percent.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Section A: Household details Section B1: Household Demographics Section B2: Household Demographics at the time of the first arrival to Uganda Section C: Household Assets Assessment Section Da: Household access to services Section Db: Energy and cooking fuels Section E: Household expenditure and loans Section F(1): Household Crop production details Section F(2): Household vegetable production details Section F(3): Tree-based production for the last year (January 2019 – December 2019) Section N: Livestock and fishing Section G(1): Food consumption patterns Section G(2): Coping Strategies Section H(1): Household enterprise Section H(2): Participation in Social Networks, Trainings and Enterprises Section S: Social cohesion Section J: Shocks and hazards Section W: Household Member Employment Section X: Assistance and transfers Section K: COVID-19

Cleaning operations

The data were collected through computer-assisted personal interviewing (CAPI) with digital tablets. The use of electronic devices reduces the duration of interviews, limits errors during the interview and data entry phases, and enables the collection of geographic information system (GIS) information at the household level. The data were transmitted daily through Kobo Toolbox, a suite of software tools for data collection in challenging environments, allowing for the use of remote data control protocols.

Outline of measurement concepts for the publication of Consumer Price Index data.

Macromeasurement: Northeast: Rio Grande do Norte data was reported at 74.680 % in 2022. This records an increase from the previous number of 69.090 % for 2021. Macromeasurement: Northeast: Rio Grande do Norte data is updated yearly, averaging 65.970 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 76.920 % in 2018 and a record low of 50.390 % in 2013. Macromeasurement: Northeast: Rio Grande do Norte data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB004: Operational Indicators: Water Measurement Index.

Macromeasurement: Brazil data was reported at 72.330 % in 2022. This records a decrease from the previous number of 80.040 % for 2021. Macromeasurement: Brazil data is updated yearly, averaging 76.610 % from Dec 2012 (Median) to 2022, with 11 observations. The data reached an all-time high of 81.750 % in 2019 and a record low of 72.330 % in 2022. Macromeasurement: Brazil data remains active status in CEIC and is reported by Ministry of Cities. The data is categorized under Brazil Premium Database’s Environmental, Social and Governance Sector – Table BR.EVB004: Operational Indicators: Water Measurement Index.