This data collection supplies standard monthly labor force information for the week prior to the survey. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. Supplemental items pertain to immigrant women. Information provided includes date of birth, country of birth, citizenship status, year entered the United States, number of children born, date of birth of the most recent child, total number of children born in countries outside American jurisdiction, and number of children born in countries outside American jurisdiction currently living in the household. Information on demographic characteristics such as, age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin, is available for each person in the household enumerated. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08265.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

The Sudan Demographic and Health Survey (SDHS) was conducted in two phases between November 15, 1989 and May 21, 1990 by the Department of Statistics of the Ministry of Economic and National Planning. The survey collected information on fertility levels, marriage patterns, reproductive intentions, knowledge and use of contraception, maternal and child health, maternal mortality, and female circumcision. The survey findings provide the National Population Committee and the Ministry of Health with valuable information for use in evaluating population policy and planning public health programmes. A total of 5860 ever-married women age 15-49 were interviewed in six regions in northern Sudan; three regions in southern Sudan could not be included in the survey because of civil unrest in that part of the country. The SDHS provides data on fertility and mortality comparable to the 1978-79 Sudan Fertility Survey (SFS) and complements the information collected in the 1983 census. The primary objective of the SDHS was to provide data on fertility, nuptiality, family planning, fertility preferences, childhood mortality, indicators of maternal health care, and utilization of child health services. Additional information was coUected on educational level, literacy, source of household water, and other housing conditions. The SDHS is intended to serve as a source of demographic data for comparison with the 1983 census and the Sudan Fertility Survey (SFS) 1978-79, and to provide population and health data for policymakers and researchers. The objectives of the survey are to: assess the overall demographic situation in Sudan, assist in the evaluation of population and health programmes, assist the Department of Statistics in strengthening and improving its technical skills for conducting demographic and health surveys, enable the National Population Committee (NPC) to develop a population policy for the country, and measure changes in fertility and contraceptive prevalence, and study the factors which affect these changes, and examine the basic indicators of maternal and child health in Sudan. MAIN RESULTS Fertility levels and trends Fertility has declined sharply in Sudan, from an average of six children per women in the Sudan Fertility Survey (TFR 6.0) to five children in the Sudan DHS survey flTR 5.0). Women living in urban areas have lower fertility (TFR 4.1) than those in rural areas (5.6), and fertility is lower in the Khartoum and Northern regions than in other regions. The difference in fertility by education is particularly striking; at current rates, women who have attained secondary school education will have an average of 3.3 children compared with 5.9 children for women with no education, a difference of almost three children. Although fertility in Sudan is low compared with most sub-Saharan countries, the desire for children is strong. One in three currently married women wants to have another child within two years and the same proportion want another child in two or more years; only one in four married women wants to stop childbearing. The proportion of women who want no more children increases with family size and age. The average ideal family size, 5.9 children, exceeds the total fertility rate (5.0) by approximately one child. Older women are more likely to want large families than younger women, and women just beginning their families say they want to have about five children. Marriage Almost all Sudanese women marry during their lifetime. At the time of the survey, 55 percent of women 15-49 were currently married and 5 percent were widowed or divorced. Nearly one in five currently married women lives in a polygynous union (i.e., is married to a man who has more than one wife). The prevalence of polygyny is about the same in the SDHS as it was in the Sudan Fertility Survey. Marriage occurs at a fairly young age, although there is a trend toward later marriage among younger women (especially those with junior secondary or higher level of schooling). The proportion of women 15-49 who have never married is 12 percentage points higher in the SDHS than in the Sudan Fertiliy Survey. There has been a substantial increase in the average age at first marriage in Sudan. Among SDHS. Since age at first marriage is closely associated with fertility, it is likely that fertility will decrease in the future. With marriages occurring later, women am having their first birth at a later age. While one in three women age 45-49 had her first birth before age 18, only one in six women age 20-24 began childbearing prior to age 18. The women most likely to postpone marriage and childbearing are those who live in urban areas ur in the Khartoum and Northern regions, and women with pest-primary education. Breastfeeding and postpartum abstinence Breastfeeding and postpartum abstinence provide substantial protection from pregnancy after the birth uf a child. In addition to the health benefits to the child, breastfeeding prolongs the length of postpartum amenorrhea. In Sudan, almost all women breastfeed their children; 93 percent of children are still being breastfed 10-11 months after birth, and 41 percent continue breastfeeding for 20-21 months. Postpartum abstinence is traditional in Sudan and in the first two months following the birth of a child 90 percent of women were abstaining; this decreases to 32 percent after two months, and to 5 percent at~er one year. The survey results indicate that the combined effects of breastfeeding and postpartum abstinence protect women from pregnancy for an average of 15 months after the birth of a child. Knowledge and use of contraception Most currently married women (71 percent) know at least one method of family planning, and 59 percent know a source for a method. The pill (70 percent) is the most widely known method, followed by injection, female sterilisation, and the IUD. Only 39 percent of women knew a traditional method of family planning. Despite widespread knowledge of family planning, only about one-fourth of ever-married women have ever used a contraceptive method, and among currently married women, only 9 percent were using a method at the time of the survey (6 percent modem methods and 3 percent traditional methods). The level of contraceptive use while still low, has increased from less than 5 percent reported in the Sudan Fertility Survey. Use of family planning varies by age, residence, and level of education. Current use is less than 4 percent among women 15-19, increases to 10 percent for women 30-44, then decreases to 6 percent for women 45-49. Seventeen percent of urban women practice family planning compared with only 4 percent of rural women; and women with senior secondary education are more likely to practice family planning (26 percent) than women with no education (3 percent). There is widespread approval of family planning in Sudan. Almost two-thirds of currently married women who know a family planning method approve of the use of contraception. Husbands generally share their wives's views on family planning. Three-fourths of married women who were not using a contraceptive method at the time of the survey said they did not intend to use a method in the future. Communication between husbands and wives is important for successful family planning. Less than half of currently married women who know a contraceptive method said they had talked about family planning with their husbands in the year before the survey; one in four women discussed it once or twice; and one in five discussed it more than twice. Younger women and older women were less likely to discuss family planning than those age 20 to 39. Mortality among children The neonatal mortality rate in Sudan remained virtually unchanged in the decade between the SDHS and the SFS (44 deaths per 1000 births), but under-five mortality decreased by 14 percent (from 143 deaths per 1000 births to 123 per thousand). Under-five mortality is 19 percent lower in urban areas (117 per 1000 births) than in rural areas (144 per 10(30 births). The level of mother's education and the length of the preceding birth interval play important roles in child survival. Children of mothers with no education experience nearly twice the level of under-five mortality as children whose mother had attained senior secondary or nigher education. Mortality among children under five is 2.7 times higher among children born after an interval of less than 24 months than among children born after interval of 48 months or more. Maternal mortality The maternal mortality rate (maternal deaths per 1000 women years of exposure) has remained nearly constant over the twenty years preceding the survey, while the maternal mortality ratio (number of maternal deaths per 100,000 births), has increased (despite declining fertility). Using the direct method of estimation, the maternal mortality ratio is 352 maternal deaths per 100,000 births for the period 1976-82, and 552 per 100,000 births for the period 1983-89. The indirect estimate for the maternal mortality ratio is 537. The latter estimate is an average of women's experience over an extended period before the survey centred on 1977. Maternal health care The health care mothers receive during pregnancy and delivery is important to the survival and well-being of both children and mothers. The SDHS results indicate that most women in Sudan made at least one antenatal visit to a doctor or trained health worker/midwife. Eighty-seven percent of births benefitted from professional antenatal care in urban areas compared with 62 percent in rural areas. Although the proportion of pregnant mothers seen by trained health workers/midwives are similar in urban and rural areas, doctors provided antenatal care for 42 percent and 19 percent of births in urban and rural areas, respectively. Neonatal tetanus, a major cause of infant deaths in developing countries, can be prevented if mothers receive tetanus toxoid vaccinations.

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

The total population of Germany was estimated at over 84.4 million inhabitants in 2025, although it is projected to drop in the coming years and fall below 80 million in 2043. Germany is the most populous country located entirely in Europe, and is third largest when Russia and Turkey are included. Germany's prosperous economy makes it a popular destination for immigrants of all backgrounds, which has kept its population above 80 million for several decades. Population growth and stability has depended on immigration In every year since 1972, Germany has had a higher death rate than its birth rate, meaning its population is in natural decline. However, Germany's population has rarely dropped below its 1972 figure of 78.6 million, and, in fact, peaked at 84.7 million in 2024, all due to its high net immigration rate. Over the past 75 years, the periods that saw the highest population growth rates were; the 1960s, due to the second wave of the post-WWII baby boom; the 1990s, due to post-reunification immigration; and since the 2010s, due to high arrivals of refugees from conflict zones in Afghanistan, Syria, and Ukraine. Does falling population = economic decline? Current projections predict that Germany's population will fall to almost 70 million by the next century. Germany's fertility rate currently sits around 1.5 births per woman, which is well below the repacement rate of 2.1 births per woman. Population aging and decline present a major challenge economies, as more resources must be invested in elderly care, while the workforce shrinks and there are fewer taxpayers contributing to social security. Countries such as Germany have introduced more generous child benefits and family friendly policies, although these are yet to prove effective in creating a cultural shift. Instead, labor shortages are being combatted via automation and immigration, however, both these solutions are met with resistance among large sections of the population and have become defining political issues of our time.

According to a survey of global internet audiences in the third quarter of 2024, 83.6 percent of internet users played video games on any device. The Philippines had the highest video gaming usage reach, ranking first with a gaming penetration of 96.6 percent. Indonesia ranked second, with 96.4 percent of responding internet users stating that they played video games. The United Kingdom and Japan ranked last, with 71.1 and 64.4 percent of internet users, respectively, reporting their participation in video gaming. The impact of mobile gaming The global gaming penetration among online users was over 83 percent, highlighting how much of a mainstream hobby gaming has become. Additionally, as shown by the many mobile-first digital markets with strong gaming reach, the impact of readily available mobile gaming devices cannot be overstated. A survey from the third quarter of 2024 found that smartphones were the most popular way to play video games worldwide, with more than 67 percent of respondents stating to play video games in such a way. This usage rate was miles ahead of second-ranked laptop or desktop PCs, which only 34 percent of global gamers stated to use. Who are the global gaming audiences? A survey conducted in the third quarter of 2023 found that 89.6 percent of female internet users aged 16 to 24 years worldwide played video games on any kind of device. During the survey period, 92.6 percent of male respondents in the same age group stated that they played video games. Gaming is a more popular activity among younger age groups, but even seven in ten respondents between 55 and 64 years old stated that they were gamers. Gaming genre preferences vary by age group, but overall, shooters and action-adventure games rank first in terms of popularity among users. Other popular gaming genres were simulation and sports games.

Abstract

The survey MICS, carried out by the National Statistics Office (INE), with technical and financial support from the United Nations Children's Fund (UNICEF) provides new data which serves to establish a representative information base on health, nutrition, water, hygiene and sanitation, education, and demography, amongst others. In the first place we intend these statistics to enable us to up-date the statistical base line on the conditions of the Angolan population. In the future it will be used by the many sectors already mentioned in the regular tasks of planning, programming, monitoring and evaluation. This survey is the first operation and most extensive of its kind to collect and up-date data to be carried out in the country as a whole since Independence, and as such we believe that it will fulfil this objective. Secondly, the results presented here can be used as a point of departure for more detailed studies, which could contribute to a better understanding of the causes which determine the living conditions of the population, and for the better definition of programmes and policies which favour the child, other vulnerable groups and the most disadvantaged. To further these objectives, INE will be able to make the database available to interested parties in order to facilitate more specific research.

Geographic coverage

The survey was national, including all the country's provinces, urban and rural areas, areas administered, and at that time, controlled by the Government or by UNITA.

Kind of data

Sample survey data [ssd]

Sampling procedure

Interviews were carried out in 4,337 households during the fieldwork stage, which lasted from August-December 1996.

The sampling plan for MICS was intended to obtain a multiple purpose sample to be applied in 6 extended regions, defined as the research areas, on the basis of, on the one hand a UNICEF interventionist plan in Angola, and on the other hand, taking into consideration their geographical features.

From this sample it was possible to obtain estimates at the national level and at the level of the six geographically defined regions. Estimates at a more disaggregated level were not advisable, otherwise running the risk of losing the representative nature of the results.

Due to the war situation the country was facing factors, such as a displaced population and difficult access to certain localities. The last population census dates from 1983-84. Apart from the fact that these data are out of date, they only refer to a part of the country. For this reason, all the information available was used, and from various sources, to construct the Sampling Frame in order to select Primary Sampling Units (P.S.U.). Sources such as the Electoral Register/Census of 1992, information from the Ministry of Territorial Administration (MAT) the Provincial Governments and the social and economic provincial profiles prepared for the donors' round table in Brussels in 1995 for UNDP were used.

With the exception of the first stratum of the region "Capital C" (constituted by the Province of Luanda) the sampling of selected families for the research was probabilistic with 3 selection stages. In each region a potentially self-weighted sample of households was selected, though this characteristic self-weighting could be lost due to various factors, especially variations in population estimates.

The unit used in the first stage (PSU) was the "comuna" (the smallest administrative area in Angola) whose selection within each region was made independently, systematically and with probability proportional to the estimated size of the population.

The village in the rural areas or the neighbourhood in the urban areas constitutes the unit used in the second stage (SSU) and its selection was made without replacement and from a list of villages, which were accessible and based on information collected by regional co-ordinators. Thus, the selection was generally proportional to the number of inhabitants in the villages. In some cases (absence of population information) the treatment was: 1. When no information was available concerning the people of the village, the selection was made on a simple random basis (enumeration method); 2. When a list of villages did not exist or any information concerning its or their population (inhabitants) selection was made randomly from a point on the map, after it had been divided into 20 parts. (Map method) Finally, the family constituted the Third Stage Unit (TSU) and its selection was without replacement and with equal probability within each selected village. The method used by PAV (Extended Vaccination Programme) was applied to barrios or neighbourhoods outside Luanda. This method consists in spinning a bottle to select a random direction. Following this the first family surveyed is randomly selected in this direction. The other families are those closest to the first.

In the case of Luanda, the sample was probabilistic with two selection stages. The unit used in the first stage was the census section in the Demographic Census of 1983/84, updated when the Priority Survey on Household Living Conditions was carried out in 1995 (IPCVD) by INE. The selection of primary sampling units was made independently and systematically with probability proportional to the number of dwellings. The secondary sampling unit was the family, whose selection was without replacement and with equal probability within each selection made. The selection of families in Luanda was made using a complete list of families taken from the selected census section.

The final probability of selection for each household is obtained from the product of the probabilities at each selection stage. The analysis of the weighted results was used to facilitate national and regional estimates and in order to correct the information used in the selection of PSUs and SSUs in the next selection stage.

The sample size was defined with a level of confidence of 95% to estimate the proportion of variable keys for the research based on information available to UNICEF. The level of precision was 5%, with the exception of some variables linked to breast-feeding in which more limited age-groups were used. In these cases the level of precision used was 8%.

The estimation of the necessary sample size was made separately for each of these key variables. Quite different sizes were obtained for the sample from each of the variables, having in the end to opt for the largest size. This confers a higher level of accuracy on the other variables than that originally expected. Or, that is to say, estimates can be obtained from the survey data with a maximum error of plus or minus 5%, with the exception of those variables related to breast-feeding where the maximum error was plus or minus 8%.

The "Design Effect" (Deff) is a factor used to adjust the variance obtained from a complex sampling design using clusters with the variance of a simple random sample.

In the definition of the sample, size 2 was assumed as the lowest value and 10 as the highest, using the highest value only in the case of Water and Sanitation.

In the analysis of data the confidence intervals of 95% were calculated for the main indicators using Program Epi Info 6, which calculates the value of DEFF directly from the data, . The sample size was fixed at 4,410 families distributed equally among the six regions, resulting in a sample of 735 families, 21 primary units (PSUs) and 21 secondary units (SSUs) for each of the six regions. In this way in each secondary unit selected, 35 families were chosen.

In summary, the size of the national sample was defined in: (21 clusters per region) X (35 families per cluster) X (6 regions) = 4,410 families.

Sampling deviation

MICS is the first survey since the country's independence to be carried out on a national scale.During its implementation it was necessary to call on the co-operation and help of a large number of organisations in order to overcome a whole series of political and logistic difficulties.

In spite of this help it was not always possible to reach the selected “comunas” and in some cases it was not possible to have access to all the villages which constitute the “comuna”. This lack of access was generally due to mines, collapsed bridges or lack of security.

Initially a total of 28 “comunas” were selected, however, these were in fact inaccessible. They were replaced respectively by those that were nearest and accessible, the term “nearest” having been defined as the distance between the main towns and villages of the “comuna”. The replacement of inaccessible “comunas” served to maintain the size of the sample for each region, where the nearest “comuna” was used to try and represent what had been rejected or replaced.

Obviously the situation of these replaced “comunas” will be different or probably worse than the situation of those used to replace them.

This leads us to say that the estimates arrived at as a result of the survey cannot represent the whole Angolan population and the regions, but only the population that was accessible. In the results analysis it was possible to use "weightings" in order to try and make adjustments to represent approximate numbers, as part of the population was inaccessible, but it was never possible to get exact information about this same population. All the data should be seen in this light.

However, if it is considered that the population of these “comunas” might have been overestimated on the basis of the survey, and that some of them were practically under-populated, then we can estimate the proportion of the initial sample that was lost as between 10-20%. We found that the regions with greatest access problems were those to the East and

In 1800, the population of Ethiopia was 2.95 million. Like most other Sub-Saharan countries, Ethiopia experienced slow but steady growth for much of the 18th century, and growth which would increase exponentially as the country entered the 20th century. Ethiopia’s population grew more rapidly as the 20th century progressed, however, this growth was offset in the late 1970s, with the beginning of the Ethiopian Civil War and the coinciding Qey Shibir (Red Terror) campaign. However, despite experiencing a significant famine from 1983 to 1985, which would result in approximately one million deaths, Ethiopia’s population would begin to grow rapidly once more, from 35 million in 1980 to 66 million by the beginning of the 21st century. By 2020, Ethiopia is estimated to have a population of almost 115 million, with some experts predicting that Ethiopia may become one of the most populous countries in the world by 2100.

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

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