This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is IHS (IHS) Poised for Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This data is a CSV file containeing ship technical details from IHS Sea-web. This dataset is not publicly accessible because: EPA cannot release CBI, or data protected by copyright, patent, or otherwise subject to trade secret restrictions. Request for access to CBI data may be directed to the dataset owner by an authorized person by contacting the party listed. It can be accessed through the following means: The vessel details in this dataset can be accessed via a subscription to IHS Sea-web: https://maritime.ihs.com/EntitlementPortal/Home/Index. Format: This data set was compiled as a CSV file. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IHS's (IHS) Shares: Forecast Sees Potential Upside

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IHS (IHS) Stock Forecast: Positive Outlook

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Producer Price Index by Industry: Aerospace Product and Parts Manufacturing (PCU3364133641) from Jun 1985 to Jun 2025 about aerospace, parts, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

The Dashboard contains the following information:CPI (Consumer Price Index) Inflation Rate (Region, Year, Month, Topic (product))PPI (Production Price Index) Inflation Rate (Region, Year, Month, Topic (product))CPI & PPI Monthly Rate (Region, Year, Topic (product))Data from DEDAT Annual Report using IHS data.Dynamic dashboard reflecting the Outcome Indicator Release - Outcome Indicator: Real regional GDP growth rate per provinceThe total GDP of the Western Cape in RandsThe percentage contribution of provincial GDP to the country's GDPPercentage contribution of each industry to total GDPR of the Western CapePublication Date10 October 2022LineageData is sourced from DEDAT reports using IHS data. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:1. Real regional GDP growth rate per province2. The total GDP of the Western Cape in Rands3. The percentage contribution of provincial GDP to the country's GDP4. Percentage contribution of each industry to total GDPR of the Western CapeData Sources:DEDAT Annual Report (using IHS Data)Lineage:Data is sourced from DEDAT reports using IHS data. Data is transformed into a BI format and quality assured. Data is consumed by a dashboard created in Power BI. Four reports exist for this dashboard:Real regional GDP growth rate per provinceThe total GDP of the Western Cape in RandsThe percentage contribution of provincial GDP to the country's GDPPercentage contribution of each industry to total GDPR of the Western Cap

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IHS IHS Holding Limited Ordinary Shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract

The Integrated Household Survey (IHS) 2015 provided data for the measurement of the economic well-being of the population. The data has a valuable input in the CPI and national accounts and remains valuable in the proper construct CPI. Data from the survey constituted one of the two basic types of data needed to update the weighting pattern of the Consumer Price Index (CPI) to ensure it adequately reflected the spending habits of the Gambian population which is reflective of the seasonal nature of household expenditure. The IHS is the ideal large-sample Household Expenditure survey, which is more appropriate to provide regional breakdown compared to the weaker outlet-type breakdowns

This survey was important because it provided The Gambia Government with comprehensive information on the socioeconomic status of the population and to enable government to monitor the determinants of poverty and its dynamics. Information from the IHS can be used to assess the current levels of differences among population and to evaluate basic household needs in key sectors such as drinking water, energy, schooling, health facilities, sanitation, employment and other sectors. The specific objectives of the survey are:

1. To provide a database that allows for end time analysis of national level government policies embedded within the PAGE.
2. To understand the poverty dynamics across the country and factors influencing them.
3. To obtain in depth understanding on the living standards of households to livelihood strategies and measures of income diversification.
4. To get information on household expenditure patterns to update the National Accounts.
5. To obtain a new set of weights for the basket of goods and services that allow for upgrading the Consumer Price Index (CPI).
6. To build capacity and development of sustainable systems to produce accurate and timely information on households in The Gambia.
7. Some training activities are envisaged under this project such as a STATA workshop, household survey design and management, and study tours for poverty analysis.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Integrated Household Survey 2015 was divided into four sub-samples representing seasonal/ quarterly variation.

A two-stage probability proportional to size (PPS) stratified random sampling (size being number of households per EA) without replacement was adopted. At each stage, sub-samples of equal size were independently drawn without replacement. Sampling units were selected for each subsample with simple random sampling without replacement. Each survey period (a quarter - 3 months) was allocated one sub-sample. Local Government Area (LGA) and District corresponds to the survey Stratum. Enumeration Areas (EAs) were taken as the first stage units whilst 20 households within EAs were selected as the second stage units.

First Stage Stratification: Except for Kanifing LGA which does not have district connotation per se, EAs were stratified per districts for the other seven LGAs. The actual sample size was 600 EAs (12,000 households) but due to rounding up the sample increase to 605 EAs.10 districts have less than 4 EAs and to be able to capture the sessional variation in them they are adjusted to 4 EAs. A total of 22 EAs were added to the sample. The final sample is about 622 EAS (12,440 households). A total of 44 (district) first stage strata including Kanifing were determined.

First Stage Sample: Taking into consideration the available resources and manpower, 622 EAs consisting of four subsamples of 155.5 EAs each was covered during the entire survey period of twelve months. Thus, each phase (a quarter - 3 months) of the survey was allocated 155.5 EAs.

Second Stage Sample: Again, the available resources dictated a sample size of 12,440 households. It required twelve teams constituting twelve (12) supervisors and seventy (70) enumerators each were assigned to different geographical locations, taking into account social and cultural considerations amongst others. Each enumerator covered a total of 259. Seventeen (17) households in each phase of a three-month period corresponding to 12.96EAs. Each team will be allocated about 4.32 EAs or 86.38 households per month. Twenty (20) households per EA were selected with simple random sampling without replacement - all of which part one and part two questionnaires were administered.

Mode of data collection

Face-to-face [f2f]

Graph and download economic data for Producer Price Index by Industry: Aircraft Manufacturing (PCU336411336411) from Dec 1985 to Jun 2025 about aircraft, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

IHS Forecasts: Holding's Share Price Seen with Potential for Gains

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Why did the Caustic Soda Price Change in July 2025? Caustic Soda Price Index in North America showed mixed trends over Q2 2025, with FOB USA prices largely holding steady, while CFR USA prices fluctuated due to variable import logistics and trade policy developments.

The Integrated Household Survey (IHS) 2015 provided data for the measurement of the economic well-being of the population. The data has a valuable input in the CPI and national accounts and remains valuable in the proper construct CPI. Data from the survey constituted one of the two basic types of data needed to update the weighting pattern of the Consumer Price Index (CPI) to ensure it adequately reflected the spending habits of the Gambian population which is reflective of the seasonal nature of household expenditure. The IHS is the ideal large-sample Household Expenditure survey, which is more appropriate to provide regional breakdown compared to the weaker outlet-type breakdowns This survey was important because it provided The Gambia Government with comprehensive information on the socioeconomic status of the population and to enable government to monitor the determinants of poverty and its dynamics. Information from the IHS can be used to assess the current levels of differences among population and to evaluate basic household needs in key sectors such as drinking water, energy, schooling, health facilities, sanitation, employment and other sectors. The specific objectives of the survey are: 1. To provide a database that allows for end time analysis of national level government policies embedded within the PAGE. 2. To understand the poverty dynamics across the country and factors influencing them. 3. To obtain in depth understanding on the living standards of households to livelihood strategies and measures of income diversification. 4. To get information on household expenditure patterns to update the National Accounts. 5. To obtain a new set of weights for the basket of goods and services that allow for upgrading the Consumer Price Index (CPI). 6. To build capacity and development of sustainable systems to produce accurate and timely information on households in The Gambia. 7. Some training activities are envisaged under this project such as a STATA workshop, household survey design and management, and study tours for poverty analysis.

Markit Services PMI is an indicator of business conditions in India, calculated by IHS Markit based on monthly surveys of purchasing managers. The index provides operational information covering

Abstract

The Integrated Household Survey is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO) roughly every 3-5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs), the goals listed as part of the Malawi Growth and Development Strategy (MGDS) and now the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption expenditure commodities/items
• Communities
• Agricultural household/ Holder/ Crop
• Market

Universe

Members of the following households are not eligible for inclusion in the survey: • All people who live outside the selected EAs, whether in urban or rural areas. • All residents of dwellings other than private dwellings, such as prisons, hospitals and army barracks. • Members of the Malawian armed forces who reside within a military base. (If such individuals reside in private dwellings off the base, however, they should be included among the households eligible for random selection for the survey.) • Non-Malawian diplomats, diplomatic staff, and members of their households. (However, note that non-Malawian residents who are not diplomats or diplomatic staff and are resident in private dwellings are eligible for inclusion in the survey. The survey is not restricted to Malawian citizens alone.) • Non-Malawian tourists and others on vacation in Malawi.

Kind of data

Sample survey data [ssd]

Sampling procedure

The IHS5 sampling frame is based on the listing information and cartography from the 2018 Malawi Population and Housing Census (PHC); includes the three major regions of Malawi, namely North, Center and South; and is stratified into rural and urban strata. The urban strata include the four major urban areas: Lilongwe City, Blantyre City, Mzuzu City, and the Municipality of Zomba. All other areas are considered as rural areas, and each of the 27 districts were considered as a separate sub-stratum as part of the main rural stratum. The sampling frame further excludes the population living in institutions, such as hospitals, prisons and military barracks. Hence, the IHS5 strata are composed of 32 districts in Malawi.

A stratified two-stage sample design was used for the IHS5.

Note: Detailed sample design information is presented in the "Fifth Integrated Household Survey 2019-2020, Basic Information Document" document.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

HOUSEHOLD QUESTIONNAIRE The Household Questionnaire is a multi-topic survey instrument and is near-identical to the content and organization of the IHS3 and IHS4 questionnaires. It encompasses economic activities, demographics, welfare and other sectoral information of households. It covers a wide range of topics, dealing with the dynamics of poverty (consumption, cash and non-cash income, savings, assets, food security, health and education, vulnerability and social protection). Although the IHS5 household questionnaire covers a wide variety of topics in detail it intentionally excludes in-depth information on topics covered in other surveys that are part of the NSO’s statistical plan (such as maternal and child health issues covered at length in the Malawi Demographic and Health Survey).

AGRICULTURE QUESTIONNAIRE All IHS5 households that are identified as being involved in agricultural or livestock activities were administered the agriculture questionnaire, which is primarily modelled after the IHS3 counterpart. The modules are expanding on the agricultural content of the IHS4, IHS3, IHS2, AISS, and other regional agricultural surveys, while remaining consistent with the NACAL topical coverage and methodology. The development of the agriculture questionnaire was done with input from the aforementioned stakeholders who provided input on the household questionnaire as well as outside researchers involved in research and policy discussions pertaining to the Malawian agriculture. The agriculture questionnaire allows, among other things, for extensive agricultural productivity analysis through the diligent estimation of land areas, both owned and cultivated, labor and non-labor input use and expenditures, and production figures for main crops, and livestock. Although one of the major foci of the agriculture data collection effort was to produce smallholder production estimates for major crops, it is also possible to disaggregate the data by gender and main geographical regions. The IHS5 cross-sectional households supply information on the last completed rainy season (2017/2018 or 2018/2019) and the last completed dry season (2018 or 2019) depending on the timing of their interview.

FISHERIES QUESTIONNAIRE The design of the IHS5 fishery questionnaire is identical to the questionnaire designed for IHS3. The IHS3 fisheries questionnaire was informed by the design and piloting of a fishery questionnaire by the World Fish Center (WFC), which was supported by the LSMS-ISA project for the purpose of assembling a fishery questionnaire that could be integrated into multi-topic household-surveys. The WFC piloted the draft instrument in November 2009 in the Lower Shire region, and the NSO team considered the revised draft in designing the IHS5 fishery questionnaire.

COMMUNITY QUESTIONNAIRE The content of the IHS5 Community Questionnaire follows the content of the IHS3 & IHS4 Community Questionnaires. A “community” is defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognize as being their community. The IHS5 community questionnaire was administered to each community associated with the cross-sectional EAs interviewed. Identical to the IHS3 and IHS4 approach, to a group of several knowledgeable residents such as the village headman, the headmaster of the local school, the agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. The instrument gathers information on a range of community characteristics, including religious and ethnic background, physical infrastructure, access to public services, economic activities, communal resource management, organization and governance, investment projects, and local retail price information for essential goods and services.

MARKET QUESTIONNAIRE The Market Survey consisted of one questionnaire which is composed of four modules. Module A: Market Identification, Module B: Seasonal Main Crops, Module C: Permanents Crops, and Module D: Food Consumption.

Cleaning operations

DATA ENTRY PLATFORM To ensure data quality and timely availability of data, the IHS5 was implemented using the World Bank’s Survey Solutions CAPI software. To carry out IHS5, 1 laptop computer and a wireless internet router were assigned to each team supervisor, and each enumerator had an 8–inch GPS-enabled Lenovo tablet computer. The use of Survey Solutions allowed for the real-time availability of data as the completed data was completed, approved by the Supervisor and synced to the Headquarters server as frequently as possible. While administering the first module of the questionnaire the enumerator(s) also used their tablets to record the GPS coordinates of the dwelling units. In Survey Solutions, Headquarters can then see the location of the dwellings plotted on a map of Malawi to better enable supervision from afar – checking both the number of interviews performed and the fact that the sample households lie within EA boundaries. Geo-referenced household locations from that tablet complemented the GPS measurements taken by the Garmin eTrex 30 handheld devices and these were linked with publically available geospatial databases to enable the inclusion of a number of geospatial variables - extensive measures of distance (i.e. distance to the nearest market), climatology, soil and terrain, and other environmental factors - in the analysis.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data entry application allowed for a wide variety of range and consistency checks to be conducted and reported and potential issues investigated and corrected before closing the assigned enumeration area. Headquarters (NSO management) assigned work to supervisors based on their regions of coverage. Supervisors then made assignments to the enumerators linked to their Supervisor account. The work assignments and syncing of completed interviews took place through a Wi-Fi connection to the IHS5 server. Because the data was available in real time it was monitored closely throughout the entire data collection period and upon receipt of the data at headquarters, data was exported to STATA for other consistency checks, data cleaning, and analysis.

DATA MANAGEMENT The IHS5 Survey Solutions CAPI based data entry application was designed to stream-line the data collection process from the field. IHS5 Interviews were collected in “sample” mode (assignments generated from headquarters) as opposed to “census” mode (new interviews created by interviewers from a template) for the NSO to have more control over the sample.

The range and consistency checks built into the application was informed by the LSMS-ISA experience in previous IHS waves. Prior programming of the data

p = p-value for association to BMI in the Sorbs; iHS = integrated Haplotype score; CEU = Central Europeans; CHB = Han Chinese from Beijing; JPT = Japanese from Tokyo; YRI = Yoruba from Ibadan; n. a. = not availablePopulation genetic measures on unstandardized iHS and Fst.

Markit Manufacturing PMI is an indicator of business conditions in India, calculated by IHS Markit based on monthly surveys of purchasing managers. The index provides operational information covering

Abstract

The principal focus of the survey is the welfare level of Malawian individuals and households. The survey data analyses will assist in determining what proportion of Malawians are unable to meet their basic needs to enjoy an adequate standard of living and are living in poverty. These studies will also consider what accounts for some households being able to attain and sustain such a standard of living and what might be done to assist those households and individuals now living in poverty to escape poverty. The information collected in the IHS will also be used in a range of other studies, including examining employment, health, nutritional status, agriculture, as well as better understanding how households respond to changes in the macroeconomic environment. The data collected using the IHS is particularly rich because it integrates a wide range of aspects of household and individual characteristics.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the IHS-2 is different from the sample design in IHS-1. In both surveys, the sample was designed to provide district estimates of welfare indicators. Because a census had been done in 1998 after the IHS-1, it was possible to have an updated sample frame for the sample design used in the IHS-2. The sample for IHS-2 was drawn using a two-stage stratified sampling procedure from a sample frame using the 1998 Population Census Enumeration Areas (EAs). The population covered by the IHS-2 was all individuals living in selected households.

The sample frame includes all three regions of Malawi: north, centre and south. The IHS-2 stratified the country into rural and urban strata. The urban strata include the four major urban areas: Lilongwe, Blantyre, Mzuzu, and the Municipality of Zomba. All other areas including Bomas2 are considered as rural areas. The total sample was 11,280 households (564 EAs x 20 households). Information on sampling errors for consumption from the IHS1 (October 1997 - September 1998) was used to help determine the minimum sample size in each domain. These domains were further divided into a number of smaller strata based on the administrative system in the country. Each of the twenty-seven districts was considered as a separate sub-stratum of the main rural stratum (for IHS-2, Likoma District was excluded because of difficulty in travel to the island, so only twenty-six administrative districts were considered). Thus the total number of strata in the survey was thirty: twenty-six districts and four urban centers.

Additional information on sampling is provided under technical documents in external resources.

Mode of data collection

Face-to-face [f2f]

Research instrument

The IHS-2 survey used two questionnaires to collect the information: a household questionnaire and a community questionnaire, as was the case for the 1997-98 ISH-1. Both the household and community questionnaires were significantly revised for the IHS-2. The IHS-2 household questionnaire maintained comparisons with the earlier IHS-1 household questionnaire wherever possible. However, the IHS-2 questionnaire is longer and more detailed. In addition, new modules were added.

There were five modules included in the 2004-05 questionnaire that did not appear in the 1997-98 questionnaire. These included Security and Safety, Social Safety Nets, Credit, Subjective Assessment of Well-being, and Recent Shocks to the Household. In addition there were seven agricultural modules that collected more detailed information on the agricultural situation in households than was collected in IHS-1.

Unlike in the 1997-98 survey, the monthly diary of expenditure was not used in the IHS-2 because of the problems encountered in the proper filling out of this module in 1997-98. The diary was replaced with 5 modules that were administered by the Enumerator: Module H Consumption of Selected Food over the Past Three Days, Module I on Food Expenditure with a recall period of the past week, Module J Non-Food Expenditure with a recall period of the past week and one month, Module K Non-Food Expenditures with a recall period of the past three months, and Module L Non-Food Expenditures with a recall period of the past 12 months. Different items are included in each module depending on the frequency of purchase. Module H Consumption of Selected Food over the Past Three Days, was created to provide comparability to the data collected in the in 1997-98. Anthropometric information was collected from every child aged between 6-59 months in both surveys. The information collected in IHS-2 included a measure of the presence of OEDEMA in addition to weight in kilograms, and height (or length) in centimeters.

The IHS-2 Community Questionnaire was designed to collect information that is common to all households in a given area. During the survey a “community” was defined as the village or urban location surrounding the enumeration area selected for inclusion in the sample and which most residents recognise as being their community. The questionnaire was administered to a group of several knowledgeable residents such as the village headman, headmaster of the local school, agricultural field assistant, religious leaders, local merchants, health workers and long-term knowledgeable residents. Information collected included basic physical and demographic characteristics of the community; access to basic services; economic activities; agriculture; how conditions have changed over the last five years; and prices for 47 common food items, non-food items, and ganyu labor.

China's Caixin Composite Purchasing Managers Index (PMI) is an indicator of nationwide manufacturing activity, which reflects private sector business development trends. The study conducted by IHS

Abstract

The Integrated Household Survey (IHS) is one of the primary instruments implemented by the Government of Malawi through the National Statistical Office (NSO; www.nso.malawi.net) roughly every 5 years to monitor and evaluate the changing conditions of Malawian households. The IHS data have, among other insights, provided benchmark poverty and vulnerability indicators to foster evidence-based policy formulation and monitor the progress of meeting the Millennium Development Goals (MDGs) as well as the goals listed as part of the Malawi Growth and Development Strategy (MGDS).

The First Integrated Household Survey (IHS1) was implemented with technical assistance from the International Food Policy Research Institute (IFPRI) and the World Bank (WB). The IHS1 was conducted in Malawi from November 1997 through October 1998 and provided for a broad set of applications on policy issues regarding households’ behavior and welfare, distribution of income, employment, health and education. The Second Integrated Household Survey (IHS2; http://go.worldbank.org/JABABM36V0) was implemented with technical assistance from the World Bank in order to compare the current situation with the situation in 1997-98, and to collect more detailed information in specific areas. The IHS2 fieldwork took placed from March 2004 through February 2005.

The Third Integrated Household Survey (IHS3) expanded on the agricultural content of the IHS2 and was implemented from March 2010 to March 2011 under the umbrella of the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative, whose primary objective is to provide financial and technical support to governments in sub-Saharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys with a strong focus on agriculture.

A sub-sample of IHS3 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection. The LSMS-ISA initiative provided technical and financial assistance to the design and implementation of the IHPS, alongside DFID, Norway and Government of Malawi funding for the exercise. The IHPS main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

The IHPS attempted to track all baseline households (from the IHS3) as well as individuals that moved away from the baseline dwellings between 2010 and 2013 as long as they were neither servants nor guests at the time of the IHS3; were projected to be at least 12 years of age and were known to be residing in mainland Malawi but excluding those in Likoma Island and in institutions, including prisons, police compounds, and army barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

A sub-sample of IHS3 sample enumeration areas (EAs) (i.e. 204 EAs out of 768 EAs) was selected prior to the start of the IHS3 field work with the intention to (i) to track and resurvey these households in 2013 in accordance with the IHS3 fieldwork timeline and as part of the Integrated Household Panel Survey (IHPS) and (ii) visit a total of 3,246 households in these EAs twice to reduce recall associated with different aspects of agricultural data collection.. The LSMS-ISA initiative provided technical and financial assistance to the design and implementation of the IHPS, alongside DFID, Norway and Government of Malawi funding for the exercise. The IHPS main fieldwork took place during the period of April-October 2013, with residual tracking operations in November-December 2013.

At baseline, the IHPS sample was selected to be representative at the national, regional, urban/rural levels and for each of the following 6 strata: (i) Northern Region - Rural, (ii) Northern Region - Urban, (iii) Central Region - Rural, (iv) Central Region - Urban, (v) Southern Region - Rural, and (vi) Southern Region - Urban.

Mode of data collection

Face-to-face [f2f]

Research instrument

The IHPS was comprised of the following questionnaires : 1. Agriculture Questionnaire 2. Community Questionnaire 3. Fishery Questionnaire 4. Household Questionnaire 5. Market Questionnaire

Cleaning operations

The IHPS CSPro based data entry application was designed to stream-line the data collection process from the field. Completed data capture for enumerations areas was packaged automatically by the data entry application into a compressed zip file specific to each enumeration area code. These files contained all household level interviews for that enumeration area and were then submitted back to the NSO central office. These files were to be transmitted back on a rolling basis. For IHPS the field teams were each provided an internet dongle and airtime for timely submission of the data files as limited access to internet cafes and file corruption was a notable issue in the IHS3 project.

Once data files were received by the NSO central office, enumeration area files were downloaded and cataloged by date received. Data was compiled and exported into Stata files on a regular basis and weekly reports were generated with assistance from the IHPS World Bank Resident Advisor on the status of data completion. Over-all data collection status reports were relayed to NSO IHPS Managers on a weekly basis. Overdue or incomplete data files were flagged for immediately follow-up.

The IHPS data files received from the field were also downloaded by the IHPS Data Manager and uploaded to the data verification server to await second data entry. To perform second data entry, individual computers would retrieve and load the field data for the specific enumeration area. Once data verification was complete, verified enumeration data files were zipped and uploaded automatically to the server. Daily back-up of the server to a local network computer was conducted at the end of every day and back-ups to remote location weekly.

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