Household formation by scenario, local authority and year, for the 4 scenarios described in the project methodology for the years 2017-2040 https://www.esri.ie/publications/regional-demographics-and-structural-housing-demand-at-a-county-level The 4 scenarios are: Baseline/Business as usual - based on medium term projections for the economy with an underlying assumption that net inwards migration would converge to 15,000 p.a. by 2024 and remain at that level throughout the projection horizon. 50:50 City - based on a similar outlook in terms of net inwards migration but whereby population growth is distributed in line with the objectives of the National Planning Framework (See National Policy Objectives 1a and 2a of https://npf.ie/wp-content/uploads/Project-Ireland-2040-NPF.pdf) High Migration - assumes that net inwards migration stays at an elevated level throughout the projection horizon (net inwards migration of 30,000 p.a) Low Migration - assumes that net inwards migration falls to net inwards migration of 5,000 by 2022 before converging back to the business as usual levels (i.e. net inwards migration of 15,000 p.a.) by 2027 and remaining at that level thereafter.

As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.0 Population and Employment Forecasts (2050 Version 2.0, v2.0) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates future population for five year age-sex cohorts using the 2020 Census base population, and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data.The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request. DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020–2050. 2019 land use model results are also available. A forthcoming Analytical Data Report will document the forecasting process and methodologies.

Ghana GH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -0.200 % in 2016. Ghana GH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -0.200 % from Dec 2016 (Median) to 2016, with 1 observations. Ghana GH: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Income, consumption and wealth (ICW) statistics are experimental statistics computed by Eurostat through the statistical matching of three data sources: the EU Statistics on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These statistics enable us to observe at the same time the income that households receive, their expenditures and their accumulated wealth.

The annual collection of EU-SILC was launched in 2003 and is governed by Regulation 1700/2019 (previously: Regulation 1177/2003) of the European Parliament and of the Council. The EU-SILC collects cross-sectional and longitudinal information on income. HBS is a survey conducted every 5 years on the basis of an agreement between Eurostat, the Member States and EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. HFCS collects information on assets, liabilities, and to a limited extent income and consumption, of households. The survey is run by National Central Banks and coordinated by the European Central Bank.

This page focuses on the main issues of importance for the use and interpretation of ICW statistics. Information on the primary data sources can be found on the respective EU-SILC and HBS metadata pages and following the links provided in the sections 'related metadata' and 'annexes' below.

Experimental ICW statistics cover six topics: household economic resources, affordability of essential services, saving rates, poverty, household characteristics and taxation. Each topic contains several indicators with a number of different breakdowns, mainly by income quantile, by the age group of the household reference person, by household type, by the educational attainment level of the reference person, by the activity status of the reference person and by the degree of urbanization of the household. The indicators provide information on the joint distribution of income, consumption and wealth and the links between these three economic dimensions. They help to describe households' economic vulnerability and material well-being. They also help to explain the dynamics of wealth inequalities.

All indicators are to be understood to describe households, not persons. Breakdowns by age group, educational attainment level and activity status refer to the household reference person, which is the person with the highest income. The only exception are the tables icw_pov_01, icw_pov_10, icw_pov_11 and icw_pov_12 for which the income, consumption and wealth of households have been equivalised such that equal shares were attributed to each household member. Values in tables icw_aff are calculated for households reporting non-zero values only.

Note on table icw _res_01 and icw_res_02: The indicator “Households” [HH] in icw_res_01 shows the share of households in the selection, which hold the corresponding shares of total disposable income [INC_DISP], consumption expenditure [EXPN_CONS] and net wealth [WLTH_NET] of the entire population. In theory, turning two of the three dimensions [quant_inc, quant_expn, quant_wlth] to TOTAL and the third one to any quintile, should result into a share of 20% of households. Nevertheless, this share is often below or above 20% of the total population of households in the country. The reason for this is that our figures are based on sample surveys. This means that the share of households corresponds indeed to 20% of households in the sample, however when we multiply each household of the sample with its sampling weight, the resulting shares of households in the total population differ from the 20%. If, for example, we disregard the income and wealth of households in our sample, the first consumption quintile contains the 20% of households with lowest consumption in the sample. However, multiplying this selection of households with their corresponding sampling weights may result into a different share of the total population. The “Households” [HH] indicator indicates the real share of households in the population that make up the theoretical quintile.

We analyze past and anticipated future trends in crop yields, per capita consumption, and population to estimate agricultural land requirements globally by 2050 and 2100. Assuming “business as usual,†high-income countries are expected to show little or no net growth in cropland by the end of the century whereas land requirements will nearly double in low-income countries. We consider two possible strategies that might reduce cropland expansion: decreasing per capita caloric consumption in the high-income countries and accelerating the economic development of the low-income countries. Our analysis suggests that accelerating economic development in low-income countries would have a greater impact on reducing global cropland expansion than lowering consumption in high-income countries. Economic development would reduce population growth and improve crop yields to an extent that could more than offset increased per capita consumption in these countries. Combining the two strate..., , All of the data files are analyzed using R., , # Reversing the great degradation of nature through economic development

This README file was generated on 2025-03-07 by Erik Nelson.

GENERAL INFORMATION

1. Title of Dataset: Reversing the Great Degradation of Nature through Economic Development.
2. Author Information Name: Erik Nelson Institution: Bowdoin College Address: Brunswick, ME USA Email: enelson2@bowdoin.edu

Some datasets in this project give country-level statistics on land use, agricultural production, crop yield, kilocalorie consumption, agricultural trade volumes, and population for the years 1961 through 2018.

Other datasets in this project give statistics on land use, agricultural production, crop yield, kilocalorie consumption, and population in the United States from the mid-19th century through 2018.

Other datasets in this project give future projections of country-level crop yields, kilocalorie consumption, kilocalorie trade, population, and gross domestic product p...

The real gross domestic product (GDP) of Malaysia grew by 5.11 percent in 2024 compared to the previous year and was forecast to remain at around four percent for the medium term. What affects GDP? GDP is the sum of spending in a country by consumers, investors, and the government, plus net exports. High GDP growth is associated with low unemployment, because a growing economy demands a growing labor force. There are also inflationary pressures, but responsible monetary and fiscal policy can keep the inflation rate low. GDP and development Developmental economists focus more on GDP per capita than GDP. Looking at how much each member of the economy generates gives a general idea of the level of development, with strong correlations between this and other development indicators. If population growth is faster than GDP growth, residents in the country will be worse off, in spite of a growing economy.

Abstract

Household consumer expenditure (HCE) during a specified period, called the reference period, may be defined as the total of the following: (a) expenditure incurred by households on consumption goods and services during the reference period (b) imputed value of goods and services produced as outputs of household (proprietary or partnership) enterprises owned by households and used by their members themselves during the reference period (c) imputed value of goods and services received by households as remuneration in kind during the reference period (d) imputed value of goods and services received by households through social transfers in kind received from government units or non-profit institutions serving households (NPISHs) and used by households during the reference period.

Reference period and schedule type: The reference period is the period of time to which the information collected relates. In NSS surveys, the reference period often varies from item to item. Data collected with different reference periods are known to exhibit certain systematic differences. In this round, two schedule types have been drawn up to study these differences in detail. Sample households will be divided into two sets - Schedule Type 1 will be canvassed in one set and Schedule Type 2 in the other. The reference periods to be used for different groups of consumption items are given below, separately for each schedule type.

Geographic coverage

The survey covers the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

For Leh (Ladakh) and Kargil districts of Jammu & Kashmir there is no separate sample first-stage units (FSUs) for "central sample". For these two districts, sample FSUs drawn as "state sample" will also be treated as central sample. The state directorate of economics and statistics (DES) will provide a copy of the filled-in schedules to Data Processing Division of NSSO for processing.

Analysis unit

Household, Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

Outline of sample design: A stratified multi-stage design has been adopted for the 66th round survey. The first stage units (FSU) are the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. In addition, two non-UFS towns of Leh and Kargil of Jammu & Kashmir are also treated as FSUs in the urban sector. The ultimate stage units (USU) are households in both the sectors. In case of large FSUs, one intermediate stage of sampling is the selection of two hamlet-groups (hgs)/ sub-blocks (sbs) from each rural/ urban FSU.

Sampling Frame for First Stage Units: For the rural sector, the list of 2001 census villages (henceforth the term "village" would mean Panchayat wards for Kerala) constitutes the sampling frame. For the urban sector, the list of latest available UFS blocks is considered as the sampling frame. For non-UFS towns, frame consists of the individual towns (only two towns, viz., Leh & Kargil constitute this frame).

Stratification: Within each district of a State/ UT, generally speaking, two basic strata have been formed: i) rural stratum comprising of all rural areas of the district and (ii) urban stratum comprising of all the urban areas of the district. However, within the urban areas of a district, wherever there are one or more towns with population 10 lakhs or more as per population census 2001 in a district, each of them forms a separate basic stratum and the remaining urban areas of the district are considered as another basic stratum.

Sub-stratification: There is no sub-stratification in the urban sector. However, to net adequate number of child workers, for all rural strata, each stratum has been divided into 2 sub-strata as follows: sub-stratum 1: all villages with proportion of child workers (p) >2P (where P is the average proportion of child workers for the sate/ UT as per Census 2001) sub-stratum 2: remaining villages

Total sample size (FSUs): 12784 FSUs for central sample and 15132 FSUs for state sample have been allocated at all-India level. Further, data of 24 state sample FSUs of Leh and Kargil districts of J & K surveyed by DES, J & K will be included in the central sample

Allocation of total sample to States and UTs: The total number of sample FSUs is allocated to the States and UTs in proportion to population as per census 2001 subject to a minimum sample allocation to each State/ UT. While doing so, the resource availability in terms of number of field investigators has been kept in view.

Allocation of State/ UT level sample to rural and urban sectors: State/ UT level sample size is allocated between two sectors in proportion to population as per census 2001 with double weightage to urban sector subject to the restriction that urban sample size for bigger states like Maharashtra, Tamil Nadu etc. should not exceed the rural sample size. A minimum of 16 FSUs (to the extent possible) is allocated to each state/ UT separately for rural and urban areas. Further the State level allocations for both rural and urban have been adjusted marginally in a few cases to ensure that each stratum/ sub-stratum gets a minimum allocation of 4 FSUs.

Allocation to strata/ sub-strata: Within each sector of a State/ UT, the respective sample size is allocated to the different strata/ sub-strata in proportion to the population as per census 2001. Allocations at stratum/ sub-stratum level are adjusted to multiples of 4 with a minimum sample size of 4 and equal number of samples has been allocated among the four sub rounds.

Selection of FSUs: For the rural sector, from each stratum/ sub-stratum, required number of sample villages has been selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001. For urban sector, from each stratum FSUs have been selected by using Simple Random Sampling Without Replacement (SRSWOR). Both rural and urban samples have been drawn in the form of two independent sub-samples.

More information on sampling and estimation procedure is available in the document " Note on Sample Design and Estimation Procedure of NSS 66th Round". including information on: - Formation and selection of hamlet-groups/ sub-blocks - Listing of households - Formation of second stage strata and allocation of households - Selection of households - Estimation Procedure

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 1.0 consists of several blocks to obtain detailed information on the consumption expenditure and other particulars of the sample household.

It has been decided that two types of Schedule 1.0 viz. Schedule Type 1 and Schedule Type 2 will be canvassed in this round. Schedule Type 1 is similar to Schedule 1.0 of NSS 61st round. Schedule Type 2 has different reference period (7 days) for some items of food, pan, tobacco and intoxicants as compared to 30 days' reference period for these items in Schedule Type 1.

Schedule Type 1 uses the same reference period system as used in the 61st and 50th round consumer expenditure surveys (where there was only one schedule type). Schedule Type 1 requires that for certain items (Group I items), the same household should report data for two reference periods - "Last 30 days" and "Last 365 days".

Schedule Type 2 has the same reference periods as Schedule Type 2 (Sch.1.0) of NSS 60th round. For Group I items, the reference period used in Schedule Type 2 is "Last 365 days".

As in the 60th round, items of food, pan, tobacco and intoxicants (Food-plus category) are split into 2 blocks instead of being placed in a single block. - The first block (Block 5.1) consists of the item groups cereals, pulses, milk and milk products, sugar and salt (the "F1" category). This block has a reference period of 30 days in both Schedule Type 1 and Schedule Type 2. - Block 5.2 consists of the other items of food, along with pan, tobacco and intoxicants (the item category "F2+"). This block is assigned a reference period of "Last 30 days" in Schedule Type 1 and a reference period of "Last 7 days" in Schedule Type 2.

Thus Schedule Type 1, like Schedule 1.0 of NSS 61st round, uses the "Last 30 days" reference period for all items of food, and for pan, tobacco and intoxicants.

Schedule 1.0 consists of several blocks to obtain detailed information on the consumption expenditure and other particulars of the sample household.

WHAT IS NEW IN THE SCHEDULE (compared to the 61st/64th round)

• There are two schedule types. Schedule Type 1 is similar to the 61st round schedule. It uses, for some blocks, a double reference period - "last 30 days" and "last 365 days". Schedule Type 2 uses different reference periods of 7, 30 and 365 days for different items. For any particular block, it uses only one reference period.
• Unlike the 61st round schedule (Block 3), there is no question on possession of ration card or on ration card type.
• Unlike the 61st round (Block 3), there is no question on food assistance schemes of the Government from which the household has benefited.
• Block 3 will have a question on use of the internet by household members during the last 30 days.
• In both schedule types, the food-plus item category (food, pan, tobacco & intoxicants) has been split into 2 blocks: Block 5.1 and Block 5.2. For Block 5.1 (cereals, pulses, milk & milk products, sugar and salt), both schedule types use a 30-day reference period. For the remaining food groups, and for pan, tobacco and