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.

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;

The Gross Domestic Product per capita in Sri Lanka was last recorded at 4186.50 US dollars in 2024. The GDP per Capita in Sri Lanka is equivalent to 33 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: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.500 % in 2019. This records a decrease from the previous number of 9.700 % for 2016. Sri Lanka LK: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 9.200 % from Dec 1985 (Median) to 2019, with 9 observations. The data reached an all-time high of 9.700 % in 2016 and a record low of 5.900 % in 1990. Sri Lanka LK: Proportion of People Living Below 50 Percent Of Median Income: % 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: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. 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.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

The gross domestic product (GDP) per capita in Sri Lanka stood at 4,324.87 U.S. dollars in 2024. Between 1980 and 2024, the GDP per capita rose by 3,990.95 U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend.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 rates and then divided by the total population. The gross domestic product 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).

Monthly and long-term Sri Lanka GDP Per Capita data: historical series and analyst forecasts curated by FocusEconomics.

Actual value and historical data chart for Sri Lanka GDP Per Capita Us Dollar

Adjusted net national income per capita (annual % growth) in Sri Lanka was reported at 0.65731 % 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 November of 2025.

Actual value and historical data chart for Sri Lanka Adjusted Net National Income Per Capita Current Us$

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.

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 - excluding the Nothern Province and the Trincomalee District

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

A two stage stratified random sample design was used in the survey. Urban, rural and estate sectors of the District are the domains for stratification. The sample frame is the list of buildings that were prepared for the Census of Population and Housing 2001.

Selection of Primary Sampling Units (PSU's): Primary Sampling Units are the census blocks prepared for the Census of Population and Housing - 2001. The sample frame, which is a collection of all census blocks in the domain, was used for the selection of primary sampling units. A sample of 2500 primary sampling units was selected from the sampling frame for the survey. Each selected block was updated to include newly built housing units and excluded demolished housing units, which are no longer in existence. This updating operation was also carried out over a period of 12 months, starting from June 2006 to May 2007. For each PSU, updating was done about one month prior to the scheduled interviewing.

Selection of Secondary Sampling Units (SSU's): Secondary Sampling Units are the housing units in the selected 2500 primary sampling units (census blocks). From each primary sampling unit 10 housing units (SSU) were selected for the survey. The total sample size of 25000 housing units was selected and distributed among Districts in Sri Lanka.

Sample Allocation: Allocation of the number of Primary Sampling Units for the districts and sectors were done proportionately to the number of housing units and the standard deviation of the expenditure values reported in the respective domains in the Household Income and Expenditure survey 2002 (Neymann Allocation). The district sample was equally distributed among the 12 monthly rounds.

Note: Details of sample design information is presented in the survey final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of nine parts. i. Demographic Characteristics ii. School education (Aged 5 to 20 years) iii. Information related to health vi. Household expenditure v. Household income vi. part a : Inventory of durable goods vi. part b : Debts of the household vii. Access to facilities in the area viii. Information about housing ix. Agriculture holdings and livestock

Response rate

The all Island response rate was 91%.

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 survey provides comprehensive information on household income, individual income, household expenditure, per capita expenditure etc. which are required to study the living conditions and nutritional status of the different segments of population. 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 differnt income levels

Geographic coverage

National

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design A multi-stage stratified random sample design was used in this survey. Sectors of the district are the domains for stratification. Master sample frame prepared for Demographic Survey 1994 was used in this survey. There were about 4000 Primary Sampling Units (PSUs) in this frame. From thus frames, sample of 1061 PSU's were drawn for Income and Expenditure Survey 1995/96. From each sampled PSU, 20 numbers of housing units (final sampling units) were drawn to reach a sample of 21,220 housing units. Therefore the weighting factors calculated for Income and Expenditure Survey 1995/96 were based on the corresponding factors of the Demographic Survey 1994.

Sample Allocation The district and sector allocation of the number of housing units to be surveyed was done proportionate to the square root of the total number of housing units in the district and sector. However selected areas under the urban sector of the Colombo district and the Gampaha district were over sampled to obtain a better representative sample and to obtain weighting factors for computation of Consumer Price Indices.

(Refer Section 1.3 of the Final Report)

Mode of data collection

Face-to-face [f2f]

Research instrument

The data was collected by direct interviews based on a special schedule which had three sections:-
(1) Demographic characteristics (2) Household expenditure (Food and Non-food items) (3) Household income (Monetary and Non-monetary 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.

Sampling error estimates

Refer Section 1.4 of the Final Report regarding Estimation Procedures

GNI per capita based on PPP of Sri Lanka leapt by 8.70% from 14,020 international dollars in 2023 to 15,240 international dollars in 2024. Since the 1.14% downward trend in 2022, GNI per capita based on PPP jumped by 10.20% in 2024. GNI per capita based on purchasing power parity (PPP). PPP GNI is gross national income (GNI) converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as a U.S. dollar has in the United States. 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.

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 Average Daily Wage: Rubber Tapping data was reported at 815.000 LKR in Aug 2018. This records an increase from the previous number of 811.000 LKR for Jul 2018. Sri Lanka Average Daily Wage: Rubber Tapping data is updated monthly, averaging 143.470 LKR from Jan 1984 (Median) to Aug 2018, with 416 observations. The data reached an all-time high of 860.000 LKR in Apr 2018 and a record low of 26.380 LKR in Mar 1984. Sri Lanka Average Daily Wage: Rubber Tapping data remains active status in CEIC and is reported by Central Bank of Sri Lanka. The data is categorized under Global Database’s Sri Lanka – Table LK.G026: Average Daily Wage.

Sri Lanka Average Daily Wage: Manufacturing data was reported at 413.950 LKR in 2016. This records an increase from the previous number of 411.070 LKR for 2015. Sri Lanka Average Daily Wage: Manufacturing data is updated yearly, averaging 105.060 LKR from Dec 1986 (Median) to 2016, with 31 observations. The data reached an all-time high of 413.950 LKR in 2016 and a record low of 25.320 LKR in 1986. Sri Lanka Average Daily Wage: Manufacturing data remains active status in CEIC and is reported by Department of Labour. The data is categorized under Global Database’s Sri Lanka – Table LK.G026: Average Daily Wage.

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 all establishments (those with 10 or more 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 Long Form) to which this data set belongs was administered to all establishments having 10 or more 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 stratum h, qlh is the proportion of