In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

Real household disposable income per person in the United Kingdom is expected to grow by 2.6 percent in 2024/25, with disposable income growth slowing from that point onwards. In 2022/23, disposable income fell by two percent, after falling by 0.1 percent in 2021/22, and 0.3 percent in 2020/21.

A May 2022 survey analyzed the impact of the rising cost of living on day trips by Britons. According to the study, ** percent of respondents aged 35 to 44 stopped spending on same-day journeys altogether due to higher living costs in the UK. On the other hand, just **** percent of interviewed people aged 18 to 24 did the same. Meanwhile, ** percent of surveyed Britons aged 25 to 34 stated to have not made any cutbacks on day trips.

This dataset contains information about the cost of living in almost 5000 cities across the world. The data were gathered by scraping Numbeo's website (https://www.numbeo.com).

Data dictionary

Column	Description
city	Name of the city
country	Name of the country
x1	Meal, Inexpensive Restaurant (USD)
x2	Meal for 2 People, Mid-range Restaurant, Three-course (USD)
x3	McMeal at McDonalds (or Equivalent Combo Meal) (USD)
x4	Domestic Beer (0.5 liter draught, in restaurants) (USD)
x5	Imported Beer (0.33 liter bottle, in restaurants) (USD)
x6	Cappuccino (regular, in restaurants) (USD)
x7	Coke/Pepsi (0.33 liter bottle, in restaurants) (USD)
x8	Water (0.33 liter bottle, in restaurants) (USD)
x9	Milk (regular), (1 liter) (USD)
x10	Loaf of Fresh White Bread (500g) (USD)
x11	Rice (white), (1kg) (USD)
x12	Eggs (regular) (12) (USD)
x13	Local Cheese (1kg) (USD)
x14	Chicken Fillets (1kg) (USD)
x15	Beef Round (1kg) (or Equivalent Back Leg Red Meat) (USD)
x16	Apples (1kg) (USD)
x17	Banana (1kg) (USD)
x18	Oranges (1kg) (USD)
x19	Tomato (1kg) (USD)
x20	Potato (1kg) (USD)
x21	Onion (1kg) (USD)
x22	Lettuce (1 head) (USD)
x23	Water (1.5 liter bottle, at the market) (USD)
x24	Bottle of Wine (Mid-Range, at the market) (USD)
x25	Domestic Beer (0.5 liter bottle, at the market) (USD)
x26	Imported Beer (0.33 liter bottle, at the market) (USD)
x27	Cigarettes 20 Pack (Marlboro) (USD)
x28	One-way Ticket (Local Transport) (USD)
x29	Monthly Pass (Regular Price) (USD)
x30	Taxi Start (Normal Tariff) (USD)
x31	Taxi 1km (Normal Tariff) (USD)
x32	Taxi 1hour Waiting (Normal Tariff) (USD)
x33	Gasoline (1 liter) (USD)
x34	Volkswagen Golf 1.4 90 KW Trendline (Or Equivalent New Car) (USD)
x35	Toyota Corolla Sedan 1.6l 97kW Comfort (Or Equivalent New Car) (USD)
x36	Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment (USD)
x37	1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans) (USD)
x38	Internet (60 Mbps or More, Unlimited Data, Cable/ADSL) (USD)
x39	Fitness Club, Monthly Fee for 1 Adult (USD)
x40	Tennis Court Rent (1 Hour on Weekend) (USD)
x41	Cinema, International Release, 1 Seat (USD)
x42	Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child (USD)
x43	International Primary School, Yearly for 1 Child (USD)
x44	1 Pair of Jeans (Levis 501 Or Similar) (USD)
x45	1 Summer Dress in a Chain Store (Zara, H&M, ...) (USD)
x46	1 Pair of Nike Running Shoes (Mid-Range) (USD)
x47	1 Pair of Men Leather Business Shoes (USD)
x48	Apartment (1 bedroom) in City Centre (USD)
x49	Apartment (1 bedroom) Outside of Centre (USD)
x50	Apartment (3 bedrooms) in City Centre (USD)
x51	Apartment (3 bedrooms) Outside of Centre (USD)
x52	Price per Square Meter to Buy Apartment in City Centre (USD)
x53	Price per Square Meter to Buy Apartment Outside of Centre (USD)
x54	Average Monthly Net Salary (After Tax) (USD)
x55	Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate
data_quality	0 if Numbeo considers that more contributors are needed to increase data quality, else 1

The COVID-19 pandemic triggered social and economic stagnation worldwide, significantly impacting people’s lives. In addition, the Russia-Ukraine war that began in 2022 resulted in rising food prices globally, severely affecting low- and middle-income countries. This study aimed to examine the impact of these unprecedented crises on individual values, focusing on Senegal’s urban population. This study is the first to quantitatively assess changes in the values of urban Senegalese during this global crisis. Surveys were conducted in Saint-Louis, Senegal, in August-September 2018 and June-July 2022. The timing of these studies coincides with the onset of the COVID-19 pandemic in early 2020 and the outbreak of the Russia-Ukraine war in February 2022. The findings revealed a 19.9% decrease in the average monthly cost of living per capita between 2018 and 2022, attributed to the combined effects of rising food prices and unemployment. Furthermore, the proportion of households spending less than $3.50 per person per day—below the lower-middle-income class poverty line—increased by 11.05%. Our analysis indicates a decline in values such as benevolence, universalism, hedonism, and self-direction. In contrast, values related to power and achievement significantly increased following the pandemic. These results suggest that individual values are flexible and may change in response to external factors such as global crises.

View monthly updates and historical trends for US Inflation Rate. from United States. Source: Bureau of Labor Statistics. Track economic data with YCharts…

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

Consumer Price Index CPI in the United States increased to 324.80 points in September from 323.98 points in August of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

Snapshot img

US Senior Living Market Size 2025-2029

The senior living market in US size is forecast to increase by USD 30.58 billion at a CAGR of 5.9% between 2024 and 2029.

The senior living market is experiencing significant growth due to various driving factors. One of the primary factors is the aging population, as the number of seniors continues to increase, the demand for services is also rising. Another key trend is the integration of technology into senior living facilities, which enhances the quality of care and improves the overall living experience for seniors. Innovations in artificial intelligence, data analytics, predictive modeling, and personalized care plans are disrupting traditional care models and improving overall financial sustainability through cost containment and value-based care. However, affordability remains a challenge for many seniors and their families, as the cost of services can be prohibitive. This report provides a comprehensive analysis of these factors and more, offering insights into the current state and future direction of the market.

What will be the Size of the Market During the Forecast Period?

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The market encompasses a range of services designed to address the unique needs of an aging population, including long-term care, end-of-life care, palliative care, hospice care, respite care, adult day care, home health services, geriatric care, and various forms of cognitive and behavioral health support. This market is driven by demographic trends, with the global population of individuals aged 65 and above projected to reach 1.5 billion by 2050. 


Key challenges in this market include addressing cognitive decline, social isolation, fall prevention, medication management, nutritional support, mobility assistance, personal care assistance, continence management, and other aspects of daily living. Additionally, there is a growing focus on quality of life, resident satisfaction, staffing ratios, caregiver training, technology adoption, and regulatory compliance. The aging services network is evolving to provide a continuum of care, from independent living to palliative care, with a focus on evidence-based practices, industry best practices, and regulatory compliance.

How is this market segmented, and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Service TypeAssisted livingIndependent livingCCRCAge GroupAge 85 and olderAge 66-84Age 65 and underBy TypeMedical ServicesNon-Medical ServicesDistribution ChannelDirect SalesAgency ReferralsOnline PlatformsEnd-UserBaby BoomersSilent GenerationGen XGeographyUS

By Service Type Insights

The assisted living segment is estimated to witness significant growth during the forecast period. Assisted living communities cater to seniors who require assistance with daily activities but do not necessitate full-time nursing care. These residences offer a combination of personalized care, social engagement, and medical support in a secure and comfortable setting. The market is experiencing growth due to the expanding aging population, rising life expectancy, and a preference for home-like environments over traditional nursing homes. Personalized care services are a defining feature of assisted living. Residents receive aid with activities of daily living, such as bathing, dressing, grooming, medication management, and mobility assistance, based on their individual needs.
Trained staff members are available 24/7 to ensure the safety and well-being of residents. Memory care communities are a specialized segment within assisted living, designed for seniors with Alzheimer's disease and other forms of dementia. These facilities provide secure environments and specialized care techniques to address the unique needs of these residents. Independent living communities offer seniors the opportunity to live in a social, active environment while maintaining their independence. These communities provide housing solutions with minimal support services, such as meal preparation and housekeeping. Nursing care homes and skilled nursing facilities offer comprehensive care for seniors with chronic health conditions and complex care needs.

Get a glance at the market report of share of various segments Request Free Sample

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in adoption of US Senior Living Market?

An aging population is the key driver of the market. The market in the US is experiencing significant growth due

Poverty ratio at $1.9 a day of Tajikistan increased by 2.50% from 4.0 % in 2009 to 4.1 % in 2015. Since the 30.83% drop in 2007, poverty ratio at $1.9 a day sank by 55.43% in 2015. Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Inflation Rate in India decreased to 0.25 percent in October from 1.44 percent in September of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Serbia emerged as a small independent nation-state in the economic periphery of nineteenth-century Europe. This article leverages uniquely abundant town-level data to examine spatial inequality in prices and wages within this late-developing economy. I first build a new dataset on prices of traded and household goods, and wages of skilled and unskilled workers for a panel of 42 urban settlements in Serbia, in the period from 1863 to 1910. I apply the welfare ratio approach to calculate real wages of day labourers and masons. Second, I find strong spatial convergence in grain prices and costs of living, but divergence in wages, both nominal and real. Lastly, I investigate the determinants of price convergence and wage divergence with panel-data models. The results suggest that falling transport costs decreased price gaps between locations, whereas rising population differences increased inter-urban wage gaps.

Consumers’ growing awareness of fast food’s nutritional content and shift towards healthier eating habits have challenged demand for fast food and takeaway food services. In response, fast food brands have expanded their menus to include more nutritious, premium options with reduced fat, sugar and salt. Major companies have adapted to this trend, with McDonald's expanding its premium burger range and KFC focusing on fresh, locally sourced ingredients. The number of chicken-based fast food, which is considered healthier than traditional fast food, is also increasing. The recent cost-of-living crisis has had a mixed impact on the industry as consumers ‘trade down.’ Although people are refraining from overspending on eating out, they’re preferring to spend on fast food meals instead of paying for full meals at restaurants. Industry revenue is expected to have grown at an annualised 2.6% over the five years through 2024-25 to $29.6 billion. This trend includes an anticipated 2.9% jump in 2024-25. Consumers’ surging reliance on online delivery platforms during the pandemic boosted industry revenue but also pressured profitability, since online delivery platforms charge commissions per order. Rising food inflation has led businesses to increase menu prices to offset higher purchasing costs, with most major franchises able to pass on costs downstream to consumers, which has driven profitability growth over the five years through 2024-25. Shifting consumer preferences and evolving business models will drive industry growth over the coming years. Companies will increasingly focus on offering plant-based alternatives, reshaping their menus, with major brands set to expand their vegetarian and vegan options to capture rising demand for sustainable, health-conscious meals. Refranchising will also improve industrywide profitability, as fast food giants will reduce their operational costs by shifting company-owned stores to franchisees. This model allows brands to focus on marketing and innovation while franchisees manage day-to-day operations. These strategies, alongside international expansion, will boost competition and industry growth. Revenue is forecast to rise at an annualised 4.3% over the five years through 2029-30 to reach $36.6 billion.

Abstract

In 1997, Slovenia Household Budget Survey (HBS), which had been conducted in the country previously, was converted into a continuous study and redesigned according to EUROSTAT recommendations.

The Household Budget Survey provides information about living standards and social situation of private households, especially information on development and structure of their expenditures and incomes. Classification of Individual Consumption According to Purpose (COICOP) is applied, as required for HBS by EUROSTAT.

Random probability sample is used to select households. Households are surveyed throughout the year, and each household cooperates in the survey for 14 days. Data is collected through interviews and expenditure and consumption diaries filled by household members.

Geographic coverage

National

Analysis unit

• Individuals,
• Households.

Household is a community of persons who live together and share their income to cover the basic cost of living (food, accommodation, etc.). A member of a household can however temporarily live apart because of a work, school or other reasons. A household is also a person who lives alone and does not have his/her own household elsewhere. She/he can live in the same dwelling with other persons but does not share income for covering the cost of living.

Universe

All private households. The survey does not cover collective households, foreigners temporarily living in Slovenia, and the homeless.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample stratification was made based on 12 statistical regions and six types of settlements. In bigger settlements (with over 10,000 inhabitants) simple random sampling was used. In smaller settlements sampling of clusters with four people, who define the household, was applied. First, enumeration areas were selected (taking into account their size) for the whole year and then for each quarter four persons in each enumeration area were selected. In bigger settlements only persons were selected with simple random sampling for each quarter. The method of substitution (selecting substitute households that would replace the ones that did not cooperate) was not used; instead researchers increased the sample according to the response rate from previous years.

The Central Population Register was used as the sampling frame.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire and diaries are used to collect data.

Two types of diaries were designed: 1) Diary for the main purchaser (Diary A); 2) Diary for other members 14 or more years old (Diary B).

Respondents fill in the diaries for 14 days, starting one day after the first visit of the interviewer.

Diary B is voluntary; it is not kept by each household member. It is designed for household members who usually make their own purchases. Diary B records the same information as Diary A; its structure is the same as the structure of Diary A, but it is a little shorter. If a main purchaser fills information for purchases made by other household member in Diary A, the same expense should not be recorded in Diary B.

The questionnaire is divided into two parts. The first part is filled in during the first visit before the recording period. The interviewer hands out the diaries and starts with the first part of the interview which covers information on household members (gender, marital status, educational level, work), housing conditions and housing costs, purchases of a dwelling or house and availability of durables. The second part of the interview takes place after 14 days, at the second visit. It includes information on expenditures not covered by the diary (purchase of a car, motorcycle, boat, major durables, furniture, clothing and footwear, domestic help, health and education expenditure, insurance, financial transfers and financial situation, some taxes and other expenditure), holidays, income and consumption of own production.

California Grid Hourly Prices

2009 Electricity Prices and Net Load Data

By [source]

About this dataset

This dataset offers a comprehensive examination of hourly energy prices and net load for California during 2009. Accessed via HiGRID, this dataset contains detailed information such as the day, hour, net load ([MW]), and electricity price ([$/MWh]) to provide users with an insightful view of the energy consumption in the region throughout the year. By understanding these prominent figures of electricity use, users can develop economically savvy solutions to reduce their energy costs while living sustainably

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How to use the dataset

This dataset contains hourly electricity prices and net load data for California in 2009. It is intended to be used as input for modeling energy-efficiency in buildings.

Here’s how you can use this dataset to model the energy efficiency of a building: - Gain an understanding of the current net load in your area (Net Load [MW]). Net load refers to the total amount of electricity used by all customers minus the total amount generated from power plants and other sources. It’s important to understand current conditions since they will affect your building’s power consumption and future bills. 2 Examine day-of-week trends in energy usage (Day). Studying these trends will help you predict when peak demand occurs, as well as when pricing may increase or decrease due to changes in consumer behavior.
3 Analyze hourly levels of electricity price (Electricity Price [$/MWh]). Knowing what time each day is more expensive than others allows you to adjust building behaviors accordingly, such as using more efficient equipment during peak hours or implementing strategies like storage or load shifting that take advantage of any price arbitrage opportunities between different times blocks during certain days of the week . 4 Review overall average costs over a long period of time (Hour). Comparing month-to-month values for both net load and prices helps ensure that planned improvements are creating real cost savings results over time, especially when benchmarked against previous normal operating conditions observed over a long period giving reliable normalized baseline accuracy with less variability analysis than any individual data set could provide from within its respective domain's sample space alone

Research Ideas

• Analyzing the correlation between electricity prices and net load in order to identify optimal times for businesses to purchase and use electricity.
• Assessing the impact of different external factors (e.g., weather) on energy prices and net load in order to inform decision making on energy strategy and investment opportunities.
• Utilizing time-series data analytics to study patterns in net load across days of the week, as well as within specified time frames (e.g., peak hours) over larger periods of time, such as months or years

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Historical_Net_Load_and_Electricity_Price.csv | Column name | Description | |:------------------------------|:-----------------------------------------------------------| | Day | The day of the week. (String) | | Hour | The hour of the day. (Integer) | | Net Load [MW] | The amount of electricity being used in megawatts. (Float) | | Electricity Price [$/MWh] | The cost of electricity per megawatt hour. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Abstract

In 1997, Slovenia Household Budget Survey (HBS), which had been conducted in the country previously, was converted into a continuous study and redesigned according to EUROSTAT recommendations.

The Household Budget Survey provides information about living standards and social situation of private households, especially information on development and structure of their expenditures and incomes. Classification of Individual Consumption According to Purpose (COICOP) is applied, as required for HBS by EUROSTAT.

Random probability sample is used to select households. Households are surveyed throughout the year, and each household cooperates in the survey for 14 days. Data is collected through interviews and expenditure and consumption diaries filled by household members.

Geographic coverage

National

Analysis unit

• Individuals,
• Households.

Household is a community of persons who live together and share their income to cover the basic cost of living (food, accommodation, etc.). A member of a household can however temporarily live apart because of a work, school or other reasons. A household is also a person who lives alone and does not have his/her own household elsewhere. She/he can live in the same dwelling with other persons but does not share income for covering the cost of living.

Universe

All private households. The survey does not cover collective households, foreigners temporarily living in Slovenia, and the homeless.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample stratification was made based on 12 statistical regions and six types of settlements. In bigger settlements (with over 10,000 inhabitants) simple random sampling was used. In smaller settlements sampling of clusters with four people, who define the household, was applied. First, enumeration areas were selected (taking into account their size) for the whole year and then for each quarter four persons in each enumeration area were selected. In bigger settlements only persons were selected with simple random sampling for each quarter. The method of substitution (selecting substitute households that would replace the ones that did not cooperate) was not used; instead researchers increased the sample according to the response rate from previous years.

The Central Population Register was used as the sampling frame.

Mode of data collection

Face-to-face [f2f]

Research instrument

A household questionnaire and diaries are used to collect data.

Two types of diaries were designed: 1) Diary for the main purchaser (Diary A); 2) Diary for other members 14 or more years old (Diary B).

Respondents fill in the diaries for 14 days, starting one day after the first visit of the interviewer.

Diary B is voluntary; it is not kept by each household member. It is designed for household members who usually make their own purchases. Diary B records the same information as Diary A; its structure is the same as the structure of Diary A, but it is a little shorter. If a main purchaser fills information for purchases made by other household member in Diary A, the same expense should not be recorded in Diary B.

The questionnaire is divided into two parts. The first part is filled in during the first visit before the recording period. The interviewer hands out the diaries and starts with the first part of the interview which covers information on household members (gender, marital status, educational level, work), housing conditions and housing costs, purchases of a dwelling or house and availability of durables. The second part of the interview takes place after 14 days, at the second visit. It includes information on expenditures not covered by the diary (purchase of a car, motorcycle, boat, major durables, furniture, clothing and footwear, domestic help, health and education expenditure, insurance, financial transfers and financial situation, some taxes and other expenditure), holidays, income and consumption of own production.

Abstract

The Central Statistics Office conducted the fifth Household Income and Expenditure Survey (HIES) from November 1993 to January 1995. As the title suggests, the main focus of the exercise was on household income and expenditure. Notwithstanding that, as a by-product, data on other socio-economic characteristics have been made available through the exercise.

Answers to economic questions such as: " Are households now spending more money on transport than on housing?", "Are the poor households getting poorer?" etc. - hinges on the results of the HIES. In the context of planning for economic development, studies of household income and expenditure are invaluable. These studies are helpful in evaluating the changes which occur, as a result of economic development, in household consumption patterns, levels of income, income distribution and the extent of the inequality, and trends in the preference of the different segments of the society. Viewed from another angle, the levels of these variables may be useful in determining the speed of socio-economic development in the foreseeable future.

Objectives and uses It is probably appropriate to start by defining a household income and expenditure survey. A household income and expenditure survey is a survey designed to collect information on various sources of income (money or in kind) received by the households and details as to how they dispose of this income (on expenditure, remittances etc.). In essence, all the details of receipts by the households and those relating to the acquisition of goods and services for own consumption are recorded within the given reference period. The reference period, like in the previous survey, was one month. The key words in this definition, i.e. Household, Income, and Expenditure are defined in details under the section dealing with concepts and definitions.

The main objectives of the 1993/94 Household Income and Expenditure Survey were: - i.) to determine household consumption expenditure patterns for urban towns, urban villages and rural areas so as to revise the weights used in the cost-of-living index. Information collected on itemized expenditure is useful for checking the existing basket-of-goods to ensure that the basket remains representative of national expenditure patterns. ii) To determine the sources of household income, estimate income levels and distribution hence ascertain the extent of the inequality. Combined with details of the household structure and other socio-economic variables, such data are invaluable to planners and policy makers. iii) to provide an independent source of information to estimate and improve the figures on "private final consumption" for National Accounts. iv) to provide consumption data that enable the construction of a "Poverty Datum Line". v) to provide business investors with information on consumption of specific products so as to determine potential consumer demand. vi) to provide a range of baseline data for researchers.

In brief, the objective of the survey was the provision of comprehensive data on household income and consumption patterns for socio-economic analysis and planning.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all households living in private dwellings, apart from households of foreign diplomats and their families. Also excluded from the survey are hotels, army camps, nurses hostels and other institutional accommodation. It should be mentioned, however, that Botswana Defence Force families living in ordinary private dwellings were included. The Ngamiland delta was not covered because of the difficult terrain.

Kind of data

Sample survey data [ssd]

Sampling procedure

Survey Design Of the many factors that influence the sampling design of a particular survey, the nature of the subject is the most paramount. It is well known that the distribution of income among the households is uneven, with a few households accounting for a relatively large proportion of income. Botswana is no exception in this regard and indeed the 1985/86 survey revealed a high rate of inequality. There is a significant difference between income levels of urban towns, urban villages and the rural areas. Consumption expenditure depends, to a large extent, on income hence the arguments about income levels equally apply to consumption. The reason for going at length to elicit these problems is to give a background against which the 1993/94 Household Income and Expenditure Survey was conducted.

As in the 1985/86 survey, a two-stage stratified sample design was adopted. All surveys conducted under "The Household Survey Programme" have been two-stage stratified design. The multiple stages have been for ease of sample selection as well as for the fact that up-to-date sampling frame of the elementary units (households) are not available. On the other hand stratification is employed to provide separate estimates for the stratification factors as well as for gain in precision. Precision is gained when there is a reduction in the variance of the estimates.

Stratification Factors In the 1985/86 Household Income and Expenditure Survey, the sampling frame which comprised the 1981 census was sorted into five strata. - Urban - Lands - Villages - Cattleposts - Freehold farms.

Following the 1991 census, nineteen of Botswana's villages are now classified as "urban", i.e. fewer than 25 percent of their workforce are working in traditional agriculture. Nonetheless, other characteristics of these villages may still be markedly different from the more established urban areas such as Gaborone, Lobatse, Francistown etc. Consequently, it was proposed that, for the 1993/94 HIES, an "urban-village" stratum, comprising these villages be created. The remaining villages were combined with lands areas, cattleposts and freehold farms into a "rural" stratum. For most practical purposes the difference in income levels between the areas constituting the rural stratum does not justify splitting the group into separate strata. Therefore, for the 1993/94 HIES the strata are: - Urban - Urban villages - Rural

Unlike before, these stratification groups allow for presentation of separate results for each stratum.

Note: See detailed sampling procedure which is presented in the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Although the main theme of the survey was income and expenditure a whole range of topics were covered, as a by-product. Information collected in the HIES falls naturally into two categories: 1. that which could be collected from single interviews (Book 1), and 2. that collected on a day-to-day basis over a period of one month (Book 2).

The questionnaires and other forms used in collecting auxiliary data are presented in the appendix.

The single interview questionnaires (Book 1) comprised: 1. i) Demographic data, and ii) Economic activities and employment (Section A) 2. Sources of household income (Section B) 3. Housing data (Section C) 4. Household enterprises (Section D) 5. Crops and livestock (Section E) 6. Employment earnings and deduction (Section F) 7. Major expenditure during past 12 months (Section G) 8. Regular monthly and annually payment (Section H) 9. Miscellaneous (Section I)

The day-to-day questionnaires were combined into the daily notebook (Book 2) which is subdivided into the following schedules: 1. Daily expenditure and other disbursements (Schedule D-1) 2. Cash receipts (Schedule D-2) 3. Goods and services received (Schedule D-3a)/given (Schedule D-3b) 4. Business receipts (Schedule D-2) 5. Business expenditure (Schedule D-5) 6. Own produce consumed (Schedule D-6) Schedules D-1 to D-5 covered the full survey round of 30 days. On the other hand schedule D-6 covered a period of seven consecutive days within the survey month.

Cleaning operations

Data entry The in-house Data Processing Unit was responsible for data entry, maintenance of data entry and validations systems, and the production of tables. As for data entry, questionnaires were entered by one data entry operator and verified by another.

Manual editing The fact that the HIES is a very difficult exercise to undertake cannot be overemphasized. A number of the questionnaires contained inconsistent data and this was not wholly attributable to the field staff. The complexity of the survey also had a bearing on that. Besides inconsistencies and cases of item non-response which of course were expected, records for items dealing with income were often not true. Editing teams had the enormous task of sorting out these problems. In addition, they had to contend with hundreds of records - up to a maximum of 500 records for one household in some cases.

Editing was done in two stages. The first stage involved checks for consistencies and completeness. Questionnaires whose data were not comprehensible were referred to the field supervisors for correction. The second stage needed more care as it involved some calculations and transcribing of records from one section to another since not all sections were entered directly. Care had to be exercised in transcribing data moreover that some items (e.g. rent) may have been recorded in three sections. Editors undertook limited imputations particularly in the case of missing price quotes for small items. Transcribing records from the daily notebook was rather cumbersome in that records were first grouped and then summed by item type. At the validation stage checks were made to ensure that information transferred was not duplicated.

Manual editing and coding began four months after the start of field work. The delay

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