Key information about Sri Lanka Household Income per Capita

• Sri Lanka Annual Household Income per Capita reached 1,422.182 USD in Jun 2019, compared with the previous value of 1,385.427 USD in Jun 2016.
• Sri Lanka Annual Household Income per Capita data is updated yearly, available from Jun 1981 to Jun 2019, with an averaged value of 577.877 USD.
• The data reached an all-time high of 1,422.182 USD in Jun 2019 and a record low of 125.248 USD in Jun 1981.

CEIC calculates Annual Household Income per Capita from Monthly Average Household Income per Capita multiplied by 12 and converts it into USD. The Department of Census and Statistics provides Average Household Income per Capita in local currency. Federal Reserve Board average market exchange rate is used for currency conversions. Household Income per Capita is in annual frequency, ending in June of each year.

Sri Lanka HIES: Household Income per Month: Per Capita data was reported at 20,527.000 LKR in 2019. This records an increase from the previous number of 16,377.000 LKR for 2016. Sri Lanka HIES: Household Income per Month: Per Capita data is updated yearly, averaging 4,896.000 LKR from Jun 1981 (Median) to 2019, with 11 observations. The data reached an all-time high of 20,527.000 LKR in 2019 and a record low of 180.000 LKR in 1981. Sri Lanka HIES: Household Income per Month: Per Capita data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.H002: Household Income and Expenditure Survey: Household Income per Month.

Wages in Sri Lanka decreased to 1135 LKR/Day in February from 1147.20 LKR/Day in January of 2018. This dataset provides - Sri Lanka Wage Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Sri Lanka HIES: Household Income per Month data was reported at 76,414.000 LKR in 2019. This records an increase from the previous number of 62,237.000 LKR for 2016. Sri Lanka HIES: Household Income per Month data is updated yearly, averaging 20,048.000 LKR from Jun 1981 (Median) to 2019, with 11 observations. The data reached an all-time high of 76,414.000 LKR in 2019 and a record low of 881.000 LKR in 1981. Sri Lanka HIES: Household Income per Month data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.H002: Household Income and Expenditure Survey: Household Income per Month.

The Gross Domestic Product per capita in Sri Lanka was last recorded at 3968.68 US dollars in 2023. The GDP per Capita in Sri Lanka is equivalent to 31 percent of the world's average. This dataset provides - Sri Lanka GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Sri Lanka LK: GDP: Growth: Adjusted Net National Income per Capita data was reported at 4.770 % in 2016. This records a decrease from the previous number of 5.746 % for 2015. Sri Lanka LK: GDP: Growth: Adjusted Net National Income per Capita data is updated yearly, averaging 4.544 % from Dec 1972 (Median) to 2016, with 45 observations. The data reached an all-time high of 20.418 % in 1981 and a record low of -5.910 % in 1990. Sri Lanka LK: GDP: Growth: Adjusted Net National Income per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion.; ; World Bank staff estimates based on sources and methods in World Bank's 'The Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium' (2011).; Weighted average;

Adjusted net national income per capita (current US$) in Sri Lanka was reported at 3612 USD in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sri Lanka - Adjusted net national income per capita - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Sri Lanka LK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 4.800 % in 2016. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 4.800 % from Dec 2016 (Median) to 2016, with 1 observations. Sri Lanka LK: 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 Sri Lanka – Table LK.World Bank: 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.

Sri Lanka gni per capita for 2023 was $3,540, a 2.21% decline from 2022.

<ul style='margin-top:20px;'>

<li>Sri Lanka gni per capita for 2022 was <strong>$3,620</strong>, a <strong>9.95% decline</strong> from 2021.</li>
<li>Sri Lanka gni per capita for 2021 was <strong>$4,020</strong>, a <strong>3.61% increase</strong> from 2020.</li>
<li>Sri Lanka gni per capita for 2020 was <strong>$3,880</strong>, a <strong>8.06% decline</strong> from 2019.</li>
</ul>GNI per capita (formerly GNP per capita) is the gross national income, converted to U.S. dollars using the World Bank Atlas method, divided by the midyear population. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States.

Abstract

This survey provides information on household income and expenditure to be able to measure the levels and changes in the living condition of the people, to observe the consumption patterns and to compute various other indicators such as poverty, food ratio, gini co-efficient of income and expenditure etc. Key objectives of the survey - To identify the income patterns in Urban, Rural, Estate Sector and Districts. - To identify the income patterns by income levels. - Average consumption of food items and non food items. - Expenditure patterns by sector and by different income levels. - To identify the incidence of poverty by sector and income levels

Geographic coverage

National coverage. For this survey a sample of buildings and the occupants therein was drawn from the whole island.

Analysis unit

Household, Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design of the survey is two stage stratified and the Urban, Rural and the Estate sectors in each district of the country are the selection domains thus the district is the main domain used for the stratification. The sampling frame is the list of housing units prepared for the Census of Population and Housing (CPH) 2011.

Selection of Primary Sampling Units Primary sampling units (PSUs) are the census blocks selected for the survey. The sampling frame, which is the collection of all the census blocks prepared in CPH 2011 in Sri Lanka, is used for the selection of the PSUs at the first stage of the selection. The PSU selection is done within all the independent- selection domains that are assigned different sample size allocations to total the targeted sample size of 2,500 PSUs. The method of selection of the PSUs at the first stage is systematic with a selection probability given to each census block proportionate to the number of housing units available in the census blocks within the selection domains (PPS). The selected PSUs are updated to include newly built housing units and to exclude demolished or vacated housing units, which are no longer considered as housing units according to the survey definitions, to capture variation of natural growth and to make necessary adjustments for the same. The PSU updating operation in field is generally done less than one month prior to the survey and it was carried out for the 12 months starting from October 2015 to September 2016 to support the scheduled 12 survey months started from January to December in 2016 for the HIES 2016.

Selection of Secondary Sampling Units Secondary Sampling Units (SSUs) or Final sampling units (FSUs) are the housing units selected at the second stage from the 2,500 PSUs selected at the first stage. From each PSU, 10 SSUs (housing units) are systematically selected giving each housing unit in the PSU an equal probability to be selected for the survey. The total sample of size 25,000 housing units is resulted at the end of the sampling process and this sample represents the whole country in different probabilities depend on the different sample sizes allocated for the selection domains.

Mode of data collection

Face-to-face [f2f]

Research instrument

All the Questionnaires are included in the final report of HIES 2016

Cleaning operations

No content available

Response rate

See page number 2 for further details

Sri Lanka: Bank cost to income ratio, in percent: The latest value from 2021 is 43.49 percent, a decline from 48.27 percent in 2020. In comparison, the world average is 54.80 percent, based on data from 133 countries. Historically, the average for Sri Lanka from 2011 to 2021 is 50.69 percent. The minimum value, 43.49 percent, was reached in 2021 while the maximum of 55.6 percent was recorded in 2011.

Adjusted net national income per capita (annual % growth) in Sri Lanka was reported at 0.56023 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sri Lanka - Adjusted net national income per capita (annual % growth) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Explore the progression of average salaries for graduates in Information And Communication Technology 3Year Sri Lankan Degree from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Information And Communication Technology 3Year Sri Lankan Degree relative to other fields. This data is essential for students assessing the return on investment of their education in Information And Communication Technology 3Year Sri Lankan Degree, providing a clear picture of financial prospects post-graduation.

The gross domestic product (GDP) per capita in Sri Lanka was approximately 4.32 thousand U.S. dollars in 2024.Fluctuating rise between 1980 and 2024A comparison to the earliest shown observation from 1980 reveals a total increase by approximately 3.99 thousand U.S. dollars. The trajectory from 1980 to 2024 shows however that this increase did not happen continuously.This indicator describes the gross domestic product per capita at current prices. Thereby the gross domestic product was first converted from national currency to U.S. dollars at current exchange prices and then divided by the total population. The gross domestic products is a measure of a country's productivity. It refers to the total value of goods and service produced during a given time period (here a year).

Abstract

This survey provides information on household income and expenditure to be able to measure the levels and changes in the living condition of the people and to observe the consumption patterns .

Key objectives of the survey - To identify the income patterns in Urban, Rural and Estate Sectors & provinces. - To identify the income patterns by income levels. - Average consumption of food items and non food items - Expenditure patterns by sector and by different income levels.

Geographic coverage

National coverage.

Analysis unit

Household, Individuals

Universe

For this survey a sample of buildings and the occupants therein was drawn from the whole island

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design A two stage stratified random sample design was used in this survey. Bach domain for which separate estimates were required was made a separate stratum. As such each sector (Urban, Rural, Estate) within each district was considered as a separate stratum.

Sample Size In this design the first stage units (FSU) were the census blocks prepared at the 1981 Census of Population and the second stage units (SSU) were the housing units. It was decided to select 10 housing units from each selected census block. Thus, a first stage sample of about 2,500 census blocks have to be selected from the entire island.

Sample Allocation and Method of Selection The allocation of 2,500 census blocks to each district was made proportional. to the square root of the population· ( as at 1981 Census ) in that district. These values have been rounded to multiples of twelve. ( Refer page 5 of the final report attached in the external resources section) It was decided to over sample the urban sector in each district in comparison to the rural and estate sectors with the objective of allocating roughly by one-third of the total sample.

Allocation between the rural and estate sectors in each district was proportional to 1981 population. Within each stratum, the assigned number of FSU's were selected with probability proportionate to size (using the Census or adjusted housing unit counts ) with replacement.

The lists of census units prepared for the Census of Population and Housing 1981, of each selected block were updated to include new ·housing units and to exclude ones which are no longer in existence. This updating operation was also staggered over a period of twelve months starting from May 1990 to April 1991. For each FSU, updating was done about one month prior to the scheduled interviewing.

Sampling deviation

Non-Response Of a sample of 25080 housing units selected only 19,401 households were covered in the survey. Northern and Eastern provinces were excluded in the survey due to the prevailing conditions in the area.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires

The survey schedule was designed to collect data on household basis and separate schedules were used for each household (identified according to the definition of the household) within the housing units selected for the survey. The survey Schedule consists of three main sections .

       1. Demographic section 
       2. Expenditure
       3. Income

The demographic characteristics and usual activities of the inmates belonging to the household are reported in the Demographic section of the schedule and close relatives temporarily living away are also listed in the section. Expenditure section has two sub sections to report food and non-food consumption data separately. Expenditure incurred on their own decisions by boarders and servants are recorded in the sub section under the expenditure section. The income has seven sub sections categorized according to the main sources of income.

Response rate

Out of 19401 households 95 percent the households were fully completed while 0.2 percent have refused. Sector wise completion rates show that Urban sector had 93 percent, Rural sector 96 percent and the Estate sector 97 percent.

Sampling error estimates

Refer the pages 7 to 10 of the final report attached in the External Resources section.

Sri Lanka HIES: Household Expenditure per Month: Per Capita data was reported at 14,473.000 LKR in 2016. This records an increase from the previous number of 10,677.000 LKR for 2013. Sri Lanka HIES: Household Expenditure per Month: Per Capita data is updated yearly, averaging 9,251.000 LKR from Jun 2007 (Median) to 2016, with 4 observations. The data reached an all-time high of 14,473.000 LKR in 2016 and a record low of 5,639.000 LKR in 2007. Sri Lanka HIES: Household Expenditure per Month: Per Capita data remains active status in CEIC and is reported by Department of Census and Statistics. The data is categorized under Global Database’s Sri Lanka – Table LK.H003: Household Income and Expenditure Survey: Household Expenditure per Month.

Abstract

Main objectives of the Household Income and Expenditure Survey are: i. To measure levels and observe the changes of living conditions of individuals and households ii. To estimate household income and expenditure iii. To compute several important poverty indicators iv. To provide information to calculate price indices v. To analyze the impact of social protection transfers vi. To provide information on different living standard measurements.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Consumption good and services

Kind of data

Sample survey data [ssd]

Frequency of data collection

Every 3 years

Sampling procedure

Sample design of the survey is two stage stratified and the Urban, Rural and the Estate sectors in every district are the selection domains. Thus the district is the main domain used for the stratification. The sampling frame is the list of housing units prepared for the Census of Population and Housing (CPH) 2011.

Primary sampling units (PSUs) are the census blocks selected at the first stage selection and the sampling frame, which is the collection of all the census blocks prepared in CPH 2011is used for the selection.

Secondary Sampling Units (SSUs) or Final sampling units (FSUs) are the housing units selected at the second stage from the 2,500 PSUs selected at the first stage. From each PSU, 10 SSUs (housing units) are systematically selected giving each housing unit in the PSU an equal chance to be selected for the survey. The total sample of size 25,000 housing units results at the end of the sampling process and this sample represents the whole country in different probabilities depend on the different sample sizes allocated for the selection domains.

Allocation of the number of PSUs or determining the sample sizes for the districts is made proportionate to the number of housing units and the standard deviations of the mean household expenditure values reported in the respective districts in previous surveys (Neymann Allocation). Sector allocation of the district sample is made proportionate to the square roots of the sizes of the respective selection domains (Urban, Rural and Estate sectors in the district). The sample of PSUs within the selection domain is equally distributed among the 12 survey months and the monthly sample too is equally dispersed among all the weeks in the month assigning a specific week for each PSU for the survey activities.

Sample allocation and completion by district District == Housing units Selected == Housing units Responded == Households responded

Sri Lanka  6557    5407    5442
Colombo 770   575   577
Gampaha 638   501   504
Kalutara  410   325   330
Kandy  340   275   277
Matale 170   154   158
Nuwara Eliya  260   208   213
Galle  389   344   345
Matara 350   302   304
Hambantota 230   188   188
Jaffna 200   168   170
Mannar 90   72   72
Vavuniya  80   73   73
Mullaitivu 80   70   70
Kilinochchi 110   93   93
Batticaloa 200   170   170
Ampara 240   209   209
Trincomalee 130   107   107
Kurunegala 350   298   299
Puttalam  200   174   174
Anuradhapura  240   213   213
Polonnaruwa 180   153   153
Badulla 220   186   191
Moneragala 190   154   154
Ratnapura  260   222   224
Kegalle 230   173   174 

Sample allocation for sectors sector == Housing units Selected == Housing units Responded == Households responded

Sri Lanka  6557    5407    5442
Urban  1720    1386    1397
Rural  4149    3457    3475
Estate 688   564   570

Mode of data collection

Face-to-face [f2f]

Research instrument

The HIES questionnaire was revised in 2006/07 and now it con-sists of nine sections to collect household information covering the following areas. i. Demography ii. School education iii. Health iv. Food and non-food expenditure v. Income vi. Inventory of durable goods vii. Access to facilities in the area and debts of the households viii. Housing Information ix. Agriculture holdings and Livestock

GDP per capita (current US$) in Sri Lanka was reported at 3828 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sri Lanka - GDP per capita - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data was reported at 5.280 % in 2016. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total Population: Annualized Average Growth Rate data is updated yearly, averaging 5.280 % from Dec 2016 (Median) to 2016, with 1 observations. Sri Lanka LK: Survey Mean Consumption or Income per Capita: Total 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 Sri Lanka – Table LK.World Bank: Poverty. The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total 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.

Abstract

The Census of industry 2004 covered establishments engaged in the economic activities of

1. Mining & quarrying
2. Manufacturing
3. Generation and Distribution of Electricity, Gas and Water

Three questionnaires Long Form, Short Form and M&Q Form were used to canvess Large and Medium scale industrial establishments, Small scale establishments and Mining and Quarrying establisdhments respectively.

The final Census was conducted during October - November 2004 by posting the questionnaires to approximately 9000 large and medium scale industrial (person engaged 10 and more) establishments and by personally visiting approximately 21000 establishments which is a representative sample of small scale industries (persons engaged less than 10).

The Department of Census and Statistics (DCS) usually conducts Census of Industry once in ten years in order to have a full coverage of industrial establishments within the territorial boundary of Sri Lanka. The earliest attempt made at seeking information from the industrial sector was in the "Census of Agriculture and Industries", which was conducted in conjunction with the Population Census in 1946. With the steady increase in industrial activities in Sri Lanka and the growing recognition of the importance of industrial statistics for the purposes of planning, a systematic attempt was made to collect data on industrial production through the Census of Industry in 1952.

This covered Mining and Quarrying, Manufacturing, Electricity and Gas and also Construction. The Census of Industry, 1952 was confined only to the factory type of establishments, i.e. industrial establishments which had not less than 5 paid employees and which had employed a capital of not less than Rs. 3,000 and used mechanical power in any of its production processes. Among the major agro-based export industries, coconut and oil milling were covered in the 1952 census, while tea factories and rubber mills were excluded, and brought instead within the scope of the Census of Agriculture.

The next Census of Industry was conducted in 1964, the scope and coverage of which was similar to that of the 1952 census. The frame for this census was based on a list of buildings prepared for the Census of Population 1963. However, there was considerable difficulty in identifying the buildings in which industrial activities were carried out. As a result the list of industrial establishments compiled on this basis did not provide a satisfactory frame to determine the overall magnitude of "factory establishments" in the industrial sector. The results as analyzed from the limited number of census returns received, could thus prove to be inadequate for depicting a sufficiently realistic picture of the level and structure of industrial activity in the country.

The Census of Industry conducted by the Department of Census & Statistics in 1983 in accordance with the United Nations program was the last Census of Industry. The 1983 Census of Industry, consisted of two stages and in the first stage, information relating to industries included in the pre-listing schedule F1, in which all buildings were listed in the Census of Population and Housing in 1981, was copied into a separate form and updated depending on the nature of Industry and the number of employees engaged.

In 1983 Sri Lanka participated in the 1983 world programmed Industrial Statistics by carrying out a Census of Industry, on a nation - wide scale. The DCS was supposed to have undertaken the Census of Industry in 1993, but had to postpone until 2003 due to the prolonged unrest prevailed in certain areas of the country.

The Census of Industry held in 2004 is the sixth of its kind in a series of Industrial Censuses conducted by the Department of Census and Statistics for over nearly six decades. It covers establishments engaged in the activities of Mining and Quarrying, Manufacturing and the Generation and Distribution of Electricity, Gas and Water according to the International Standard Industrial Classification (ISIC) Revision - 3 of the United Nations (UN).

Geographic coverage

National Coverage.

The target population for this questionnaire was a sample of establishments (those with less than 10 persons engaged) in Sri Lanka that are engaged in the production of one class of homogeneous goods in the field of

(a) Mining and Quarrying (b)Manufacturing (c) The generation and distribution of electricity and water

Analysis unit

A questionnaire has to be completed for each establishment (plant, factory, mill, mine, workshop etc.) or jointly for a group of establishments on one site or several sites in the same Grama Niladhari division or ward under one accounting system.

A qualified establishment has its own manufacturing facility its own accounting and a distinct management and location

Ancillary units including administrative offices, warehouses. such as garages, repair shops(which primarily serve the production units) should be treated as part of the establishment.

Industrial establishments - Defined as the unit directed by a single owning or controlling entity that is engaged in the production of the most homogeneous group of goods and services, usually at one location but sometimes over a wider area, for which separate records are available(eg. plant, factory, mill, mine, workshop etc)

In cases where industrial enterprises were engaged in the production of more than one homogeneous group of goods and services in different locations, separate returns were generally obtained for each such product group and location. In cases where establishments operated by a single owner or enterprise was located within the area of one GS Division or Ward, these several units could furnish a single return and this would be reckoned as one establishment.

Ancillary units including warehouses, garages repair shops electric plants which primarily served the needs of a single establishment, if they were in the same site within the same GS division , or Ward were treated as part of the main establishment. Otherwise these were treated as separate establishments but classified to the same industry as the parent establishment.

Universe

The census covered establishments engaged primarily in the activities of Mining and Quarrying, Manufacturing and the production and distribution of Electricity, Gas and water which correspond to major divisions 2,3 and 4 respectively of the UN classification of ISIC and represented the industrial sector specified for census coverage.

The questionnaire (called Short Form) to which this data set belongs was administered to a sample selected from all establishments having less than 10 persons employed.

Kind of data

Sample survey data [ssd]

Sampling procedure

In October-November 2003, DCS conducted a listing operation of Census of Industry prior to the canvass of detailed information on establishments. The census registry was based mainly on notations made during door-to-door canvassing in mid 2000 for the Census of Population and Housing. List of Establishments by Grama Niladhari Divisions were sent in latter part of 2003 to each Grama Niladhari with a request to be updated for industrial establishments (mostly newer ones) that were lacking in 2001, the closures of older ones and for some changes on establishments. The updated list of all industrial establishments was employed as the sampling frame. The whole frame was divided into two groups as establishments with less than 10 persons engaged (Small establishments) and establishments with 10 and more persons engaged (Medium and Large establishments). The small establishments that had less than 10 persons engaged was further divided into two groups as establishments with less than 30 same type of industries (ISIC 4 digits level) and establishments with 30 and more same type of industries (ISIC 4 digits level) in each district.

A total of 30,913establishments were selected. Those 9,950 establishments that have 10 and more persons engaged were selected with certainty. The small establishments with less than 30 same kind of industries were selected with certainty totaling 9089 while others (i.e. establishment with 30 and more same kind of industries) were selected by using the stratified simple random sample design. In general, strata were defined by the kind of industries at ISIC 4 digits level and district groups In absence of any other auxiliary variables in the list frame that could be used in the sample allocation and selection, sample sizes across strata were determined using proportional allocation. That is, if Nh is the population size in stratum h and N IS the population size, the first iteration sample size nh in stratum h is derived by

Nh=Nh x11874/ N

Sampling deviation

The non-response weight is the ratio the sample size to the total respondents. The establishments that were considered as non-respondents are those who refused to participate in the Census. The following are considered with frame problems:
those establishments that cannot be located, those that were closed (they should not be included in the sampling frame), those that are out-of-scope (the ISIC classification was not specified correctly) and those that were duplicates and mergers.

Of the small establishments with 30 and more same kind of industries in the sampling frame, 10.9% should not have been included. This is rather a big percentage of the such small establishments and therefore, requires an adjustment factor to be incorporated in the weight. To illustrate, if Nh is the population size for stratum hand nh is the corresponding sample size, then the corresponding selection probability Ph is

               Ph = nh/Nh

If given the