Graph and download economic data for State and Local government current expenditures: Coverage differences: Net investment (NIPA vs. Census) (L319301A027NBEA) from 1959 to 2022 about residual, state & local, investment, expenditures, Net, government, GDP, and USA.

Graph and download economic data for State and Local government current expenditures: Coverage differences: Unemployment insurance fund benefits paid (NIPA vs. Census) (L319291A027NBEA) from 1959 to 2022 about residual, paid, state & local, insurance, benefits, expenditures, government, unemployment, GDP, and USA.

The study on the modern state was conducted by the Forschungsgruppe Wahlen Telefonfeld on behalf of the Press and Information Office of the German Federal Government. During the survey period 23.05.2022 to 08.06.2022, the German-speaking population aged 18 and over was surveyed in telephone interviews (CATI) on the following topics: citizens´ expectations of a modern, digital and effective state: satisfaction with democracy and state responsibility, official matters online, information on applicable laws and regulations and the importance of various Internet-based applications, the census in Germany and digitization in healthcare. Respondents were selected using a multistage random sample according to the RDD method, including fixed-network and mobile phone numbers (dual-frame sample).
Satisfaction with democracy and state responsibility: satisfaction with democracy; state responsibility or ownership in various areas (protection against crime, protection against the spread of epidemics and diseases, environmental and climate protection, protection against impoverishment, protection in the event of illness, financial security in old age); preference for a country with high taxes and extensive social services vs. country with low taxes and low social benefits; in the economy, too many vs. too few things are regulated by the state; better results by the state vs. private companies in the provision of services in various areas (health care, nursing care, local public transport, road construction, waste disposal).

State responsibility: preference with regard to decision-making processes in the administration (always the same rules for everyone vs. deviating from these rules in individual cases, implementing state projects as quickly as possible vs. extensive citizen participation, examining each individual case as closely as possible vs. keep administrative burden low); agreement with various statements (the state interferes too much in our lives, the opinion of the population is not taken into account enough in important political decisions, citizens are well informed in advance of important political decisions, people should not rely so much on the state but tackle their problems more themselves); opinion on the extent of government spending on various tasks (police, education, health care, defense, social affairs and climate protection); satisfaction with last contact with a government agency or administration; last contact with a government agency or administration online, in writing, by telephone or in person; preferred method of contact; evaluation of the effort spent on government agency matters; change in the effort spent on government agency matters in recent years.

Public authority matters online: Importance of the possibility of handling official matters online or on the Internet; expected problems for citizens if more official matters are handled online in the future; type of expected problems (data not safe from hacker attacks, no longer able to control one´s own data, insufficient help in operating the system, technical malfunctions delay matters, other problems); have already handled matters with authorities and offices via e-mail or the Internet (e.g., child benefit application); rather good or rather bad experiences; possession of an electronic ID card; use of electronic ID card to identify oneself on the Internet.

Information on applicable laws and regulations and importance of various Internet-based applications: Being informed about applicable laws and regulations; importance of public authorities and public institutions offering information about applicable laws and regulations via the Internet; having already searched for information about applicable laws and regulations on the Internet at public authorities and public institutions; having had rather good or rather bad experiences in this regard; importance of the Internet for the economy and society in various areas (expansion of fast Internet for all, greater promotion of the use of the Internet and computers in schools and further education institutions, greater promotion and development in the area of the Internet and information technology, greater promotion of business start-ups and combating cybercrime).

Census in Germany and digitalization in healthcare: Knowledge that the population census (Zensus) is taking place in Germany this year; evaluation of the Zensus; reasons for a negative evaluation of the Zensus (state uses data for purposes other than it claims, transparent citizen, misuse of data, compulsion to participate, waste of money, other); rather advantages or rather disadvantages of digitization in healthcare; evaluation of the electronic patient record; willingness to make own health data available for research purposes; party preference.

Demography: sex; age; education: school-leaving qualification or intended school-leaving qualification; university degree; occupation; professional position; simple, higher or...

Graph and download economic data for State and Local government current expenditures: Coverage differences: State and local employee retirement plan transactions (NIPA vs. Census) (L319251A027NBEA) from 1959 to 2022 about residual, retirement, state & local, expenditures, government, employment, GDP, and USA.

The local authority interactive tool (LAIT) is an app that presents information in interactive tables and charts, along with local authorities’ rank positions in England and against statistical neighbours.

It includes local authority, regional and national data on:

• children looked after and adoption
• child protection
• the children’s social care workforce
• special educational needs and disability
• pupil attainment and attendance
• children’s health
• youth justice
• post-16
• finance

The ‘Children’s services statistical neighbour benchmarking tool’ allows you to select a local authority and display its ‘closest statistical neighbours’ (local authorities with similar characteristics). The tool has been reviewed and rebuilt to include updated socio-economic variables from the 2021 census. More information is available in the associated update note and technical report.

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

Graph and download economic data for Expenses for Local Messengers and Local Delivery, All Establishments, Employer Firms (LMALDEAEEF34922) from 2004 to 2022 about deliveries, employer firms, establishments, expenditures, and USA.

Graph and download economic data for Expenses for Local Messengers and Local Delivery, Establishments Subject To Federal Income Tax, Employer Firms (LMALDEESTFI34922) from 2004 to 2022 about deliveries, employer firms, establishments, tax, expenditures, federal, income, and USA.

Graph and download economic data for Expenses for Specialized Freight (Except Used Goods) Trucking, Local, All Establishments, Employer Firms (SFUGTLEAEE3148422) from 2004 to 2022 about used, freight, employer firms, trucks, establishments, expenditures, goods, and USA.

Graph and download economic data for Expenses for General Freight Trucking, Local, All Establishments, Employer Firms (GFTLEAEEF348411) from 2004 to 2022 about freight, employer firms, trucks, establishments, expenditures, and USA.