Paris was Western Europe's largest city in 1650, with an estimated 400 thousand inhabitants, which is almost double it's population 150 years previously. In second place is London, with 350 thousand inhabitants, however it has grown by a substantially higher rate than Paris during this time, now seven times larger than it was in the year 1500. Naples remains in the top three largest cities, growing from 125 to 300 thousand inhabitants during this time. In the previous list, the Italian cities of Milan and Venice were the only other cities with more than one hundred thousand inhabitants, however in this list they have been joined by the trading centers of Lisbon and Amsterdam, the capital cities of the emerging Portuguese and Dutch maritime empires.

Between 1500 and 1800, London grew to be the largest city in Western Europe, with its population growing almost 22 times larger in this period. London would eventually overtake Constantinople as Europe's largest in the 1700s, before becoming the largest city in the world (ahead of Beijing) in the early-1800s.

The most populous cities in this period were the capitals of European empires, with Paris, Amsterdam, and Vienna growing to become the largest cities, alongside the likes of Lisbon and Madrid in Iberia, and Naples or Venice in Italy. Many of northwestern Europe's largest cities in 1500 would eventually be overtaken by others not shown here, such as the port cities of Hamburg, Marseilles or Rotterdam, or more industrial cities such as Berlin, Birmingham, and Munich.

Throughout the early modern period, the largest city in Italy was Naples. The middle ages saw many metropolitan areas along the Mediterranean grow to become the largest in Europe, as they developed into meeting ports for merchants travelling between the three continents. Italy, throughout this time, was not a unified country, but rather a collection of smaller states that had many cultural similarities, and political control of these cities regularly shifted over the given period. Across this time, the population of each city generally grew between each century, but a series of plague outbreaks in the 1600s devastated the populations of Italy's metropolitan areas, which can be observed here. Naples At the beginning of the 1500s, the Kingdom of Naples was taken under the control of the Spanish crown, where its capital grew to become the largest city in the newly-expanding Spanish Empire. Prosperity then grew in the 16th and 17th centuries, before the city's international importance declined in the 18th century. There is also a noticeable dip in Naples' population size between 1600 and 1700, due to an outbreak of plague in 1656 that almost halved the population. Today, Naples is just the third largest city in Italy, behind Rome and Milan. Rome Over 2,000 years ago, Rome became the first city in the world to have a population of more than one million people, and in 2021, it was Italy's largest city with a population of 2.8 million; however it did go through a period of great decline in the middle ages. After the Fall of the Western Roman Empire in 476CE, Rome's population dropped rapidly, below 100,000 inhabitants in 500CE. 1,000 years later, Rome was an important city in Europe as it was the seat of the Catholic Church, and it had a powerful banking sector, but its population was just 55,000 people as it did not have the same appeal for merchants or migrants held by the other port cities. A series of reforms by the Papacy in the late-1500s then saw significant improvements to infrastructure, housing, and sanitation, and living standards rose greatly. Over the following centuries, the Papacy consolidated its power in the center of the Italian peninsula, which brought stability to the region, and the city of Rome became a cultural center. Across this period, Rome's population grew almost three times larger, which was the highest level of growth of these cities.

In 1500, the largest city was Paris, with an estimated 225 thousand inhabitants, almost double the population of the second-largest city, Naples. As in 1330, Venice and Milan remain the third and fourth largest cities in Western Europe, however Genoa's population almost halved from 1330 until 1500, as it was struck heavily by the bubonic plague in the mid-1300s. In lists prior to this, the largest cities were generally in Spain and Italy, however, as time progressed, the largest populations could be found more often in Italy and France. The year 1500 is around the beginning of what we now consider modern history, a time that saw the birth of many European empires and inter-continental globalization.

This data set contains points for 1600 populated places, cities and towns, in New Mexico. The points were generated from latitude and longitude coordinates contained in the GNIS file, and therefore, do not have a known scale.

About this dataset

Context

The dataset tabulates the Huachuca City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Huachuca City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Huachuca City was 1,600, a 0.81% decrease year-by-year from 2022. Previously, in 2022, Huachuca City population was 1,613, a decline of 0.68% compared to a population of 1,624 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Huachuca City decreased by 166. In this period, the peak population was 1,963 in the year 2007. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Huachuca City is shown in this column.
• Year on Year Change: This column displays the change in Huachuca City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Huachuca City Population by Year. You can refer the same here

The Italian city of Venice was one of the largest cities in medieval and Renaissance era Europe. It was the center of the Republic of Venice, a maritime empire in the Mediterranean, and had one of Europe's largest ports for exotic goods (particularly from Asia), or luxury goods such as glassware. Impact of plague While its population was relatively small by modern standards, it is believed that Venice was among the five most populous cities in Western Europe in the given years between 1050 and 1650. The city's population did fluctuate over time due to devastating pandemics, and it is believed that Venice was one of the main points of entry for the Black Death in Europe. Venice was one of the hardest-hit cities during the Black Death; estimates fluctuate greatly across sources, but it is believed that the city lost around 40 percent of its population during the initial outbreak in the 1340s. Decline Furthermore, Venice lost roughly a third of its population during further plague pandemics (both introduced via war) in the 1570s and 1630s. Because of this, the population was kept fairly consistent across the given years between 1600 and 1800. The 18th century also saw the decline of the Venetian Empire, as other states gained power and influence in the Mediterranean. Venice also lost its importance as the entry point of exotic goods into Europe, as other European powers had already established their own maritime empires and trade routes across the globe. Eventually, the crumbling Venetian Empire fell to Napoleon in 1796, and its overseas territories were gradually taken by or split among various other powers. While the empire fell, the city itself continued to be a center for art and culture in Europe, and it has maintained this status until today. In 2021, Venice had a population of more than 250,000 people.

In the year 1500, the share of Western Europe's population living in urban areas was just six percent, but this rose to 31 percent by the end of the 19th century. Despite this drastic change, development was quite slow between 1500 and 1800, and it was not until the industrial revolution when there was a spike in urbanization. As Britain was the first region to undergo the industrial revolution, from around the 1760s until the 1840s, these areas were the most urbanized in Europe by 1890. The Low Countries Prior to the 19th century, Belgium and the Netherlands had been the most urbanized regions due to the legacy of their proto-industrial areas in the medieval period, and then the growth of their port cities during the Netherlands' empirical expansion (Belgium was a part of the Netherlands until the 1830s). Belgium was also quick to industrialize in the 1800s, and saw faster development than its larger, more economically powerful neighbors, France and Germany. Least-urban areas Ireland was the only Western European region with virtually no urbanization in the 16th and 17th century, but the industrial growth of Belfast and Dublin (then major port cities of the British Empire) saw this change by the late-1800s. The region of Scandinavia was the least-urbanized area in Western Europe by 1890, but it saw rapid economic growth in Europe during the first half of the following century.

At over four kilometers above sea level, Potosí, Bolivia is one of the highest cities in the world. According to census data from 2012, the population of Potosí was almost 176,000 people; a figure that is barely 16 thousand more than its estimated population four centuries before. It is estimated that the population of Potosí grew from 14 thousand in 1547, to 150 thousand by the turn of the 17th century (and some estimates suggest that it exceeded 200,000 in subsequent decades). With this explosion in population growth, Potosí quickly became the most populous city in the Americas, and was even larger than most European cities in the 17th century. This growth came following the discovery of silver deposits in 1545 in a nearby mountain, later named Cerro Rico ("Rich Hill"); this silver would provide a significant share of the Spanish crown's wealth during the 16th century, helping Spain grow to become the most powerful nation on earth at this time. Forced labor Following the defeat of the Incas in 1536, Spanish colonizers then subjugated the native populations of the Andes and put them to work. As it was impractical and expensive to transport African slaves to this region of the Andes, and the New Laws of 1542 prohibited the enslavement of indigenous Americans, the Spanish simply used violence and intimidation to force local populations to mine the silver at Potosí. The Incan tradition of Mit'a; where adult males were drafted to provide mandatory labor for the betterment of local infrastructure and facilities; was eventually appropriated by Spanish authorities as the legal basis of their demand for labor from local areas. It is estimated that one in every seven indigenous adult males was drafted from nearby communities to work in the mines of Potosí. Some reports suggest that the locals viewed this work as a death sentence, as the survival rate among drafted workers was fewer than 15 percent in some periods. There are further reports that forced laborers were expected to do the most strenuous tasks, which included carrying 25 or 45 kilogram sacks of silver along 300 meter shafts, as often as 25 times per day. The high death rate was not only due to over-intensive labor, accidents and injuries (cave-ins were common), but also malnutrition, disease and extreme temperatures and altitudes, as well as respiratory illnesses caused by the inhalation of dust, mercury and arsenic, among others.

"Valer un Potosí" Around the turn of the 17th century, the Spanish Americas produced almost all of the silver mined in the world. The Spanish crown claimed a significant share of this silver, and in some years, Potosí silver was responsible for a quarter of all Spanish revenues. This silver also played a significant part in the emergence of inter-continental trade, as a large portion of it was eventually used as currency for trade with China; some historians define this as the birth of the global economy. Eventually, the legend of Potosí grew, attracting thousands of voluntary workers from all over the Americas, as well as large numbers of Europeans in search of fortune. The silver deposits began to dry up in the mid-1600s, and the population dropped to just 60,000 by the end of the century, when silver output was just one third of its peak level. As time passed, the silver all but disappeared, and miners turned to other materials such as tin, zinc and copper (which continue to be procured today); however the legacy of Potosí's wealth continues and is used in the Spanish language when describing something of considerable value as being; "valer un Potosí" (worth a Potosí).

Abstract

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National coverage. Domains: Urban/rural; Regions (East Forest, West Forest, East Savannah, West Savannah)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample selection process for the CILSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households.

A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time.

It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

Sampling Procedures for Block 1 Data

The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.

The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type.

Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census.

Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.)

Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Bias in the Selection of Households within PSUs, Block 1 Data

Analysis of the four years of the CILSS data revealed that household size (unweighted), dropped by 24 percent between 1985 and 1988. Three possible explanations were considered: (1) area l demographic change; (2) non-sampling measurement errors were involved; or (3) some sort of sampling bias. Investigation ruled out the first two possibilities. The third possibility clearly was an issue because the sampling frame and listing procedures had indeed changed in midstream and this was likely to have had an effect. In fact, the investigation found that the substantial part of the drop in household size over the years occurred between the first and second panel data sets in 1987, i.e. the tail end of Block 1 data and the start of Block 2 data. From this, it is reasonable to assume that differences in the sampling frame and sampling procedures between the two blocks were indeed responsible.

The listing procedures for Block 1 data indicate d that the selection of households within PSUs was likely to have been biased toward the selection of larger dwellings. Based on a discussion with Christopher Scott, statistical consultant, Demery and Grootaert explain as follows: "In the selected primary sampling units, where the listing of households was to occur, enumerators were instructed to start the listing process at a random location in the primary sampling unit and from this point to select every nth household, that is, with a given fixed "step" until sixty-four households were listed. There are two sources of potential bias in this listing procedure. First, the selection of the starting point might not have been random, but subject to motivated bias on the part of the enumerator (such as the selection of a point where there are numerous dwellings or that is easily accessible). Second, in practice, enumerators counted doors to achieve the "step", rather than counting actual households. This method leads to

Abstract

The Côte d'Ivoire Living Standards Survey (LSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey. The Côte d'Ivoire Living Standards Survey (LSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique. The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLE DESIGN The principal objective of the sample selection process for the LSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households. A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time. It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

(b) SAMPLE FRAME 1. Sampling Procedures for Block 1 Data The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type. Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

1. Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census. Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.) Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The Household Questionnaire was almost entirely pre-coded, thus reducing errors involved in the coding process. Also, the decentralized data entry system allowed for immediate follow-up on inconsistencies that were detected by the data entry program. Household and personal identification codes were recorded in each section, facilitating merging data across sections

Sampling error estimates

(a) ACCURACY The general consensus is that the quality of the LSS household data is very good. An informal review of data quality conducted by Ainsworth and Mehra (1988) assessed the 1985 and 1986 LSS data in terms of their accuracy, completeness, and internal consistency. The LSS household data were found to score high marks on each of these three counts. One measure of data quality is the extent to which individuals in question respond for themselves during the interview, rather than having proxy responses provided for them by other household members. The investigation of CILSS household survey data for 1985 and 1986 showed that 93 percent of women responded for themselves to the fertility section and that 79 to 80 percent of all adult household members responded for themselves to the employment module. The percent of children responding for themselves to the employment module was far less, 43 to 45 percent. Nevertheless, these rates were found to be higher than for the Peru Living Standards Survey (29 percent).

(b) COMPLETENESS

Investigation of several variables and modules in the LSS (sex, age, parental characteristics, schooling, health, employment, migration, fertility, farming and family business), found that missing data in the household survey are rare. Rates for missing data were found to be close to 0 (0.01 to 0.05 percent) in many cases, but in any case, no higher than 0.76 percent.

Abstract

The Côte d'Ivoire Living Standards Survey (CILSS) was the first LSMS Survey to have field tested the methodology and questionnaire developed by LSMS. It consists of three complementary surveys: the household survey, the community survey and the price survey. The household survey collected detailed information on expenditures, income, employment, assets, basic needs and other socio-economic characteristics of the households. The Community Survey collected information on economic and demographic characteristics of the rural communities to which each cluster of households belonged. This was designed to enable the linkage of community level with household level data. The price survey component of the CILSS collected data on prices at the nearest market to each cluster of households, so that regional price indices could be constructed for the household survey.

The Côte d'Ivoire Living Standards Survey (CILSS) was undertaken over a period of four years, 1985-88, by the Direction de la Statistique in Côte d'Ivoire, with financial and technical support from the World Bank during the first two years of the survey. It was the first year-round household survey to have been undertaken by the Ivorian Direction de la Statistique.

The sample size each year was 1600 households and the sample design was a rotating panel. That is, half of the households were revisited the following year, while the other half were replaced with new households. The survey thus produced four cross-sectional data sets as well as three overlapping panels of 800 households each (1985-86, 1986-87, 1987-88).

Geographic coverage

National coverage. Domains: Urban/rural; Regions (East Forest, West Forest, East Savannah, West Savannah)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The principal objective of the sample selection process for the CILSS Household Survey was to obtain a nationally representative cross-section of African households, some of which could be interviewed in successive years as panel households.

A two-stage sampling procedure was used. In the first stage, 100 Primary Sampling Units (PSUs) were selected across the country from a list of all PSUs available in the sampling frame. At the second stage, a cluster of 16 households was selected within each PSU. This led to a sample size of 1600 households a year, in 100 cluster s of 16 households each. Half of the households were replaced each year while the other half (the panel households in 1986, 1987 and 1988) were interviewed a second time.

It is important to note that there was a change in the sampling procedures (the sampling frame, PSU selection process and listing procedures), used to select half of the clusters/households interviewed in 1987 (the other half were panel households retained from 1986), and all of the clusters/households interviewed in 1988. Households selected on the basis of the first set of sampling procedures will henceforth be referred to as Block 1 data while households based on the second set of sampling procedures will be referred to as Block 2 data.

Sampling Procedures for Block 1 Data

The Sampling Frame. The sampling frame for the 1985, 1986, and half of the 1987 samples (except for Abidjan and Bouaké) was a list of localities constructed on the basis of the 1975 Census, updated to 1983 by the demographers of the Direction de la Statistique and based on a total population estimated at 9.4 million in 1983.

The Block 1 frame for Abidjan and Bouaké was based on data from a 1979-80 electoral census of these two cities. The electoral census had produced detailed maps of the two cities that divided each sector of the city into smaller sub-sectors (îlots). Sub-sectors with similar types of housing were grouped together by statisticians in the Direction de la Statistique to form PSUs. From a list of all PSUs in each city, along with each PSU's population size, the required number of PSUs were selected using a systematic sampling procedure. The step size was equal to the city's population divided by the number of PSUs required in each city. One problem identified in the selection process for Abidjan arose from the fact that one sector of the city (Yopougon) which had been relatively small in 1980 at the time of the electoral census, had since become the largest agglomeration in Côte d'Ivoire. This problem was presumably unavoidable since accurate population data for Yopougon was not available at the time of the PSU selection process.

Selection of PSUs. Geographic stratification was not explicitly needed because the systematic sampling procedure that was used to select the PSUs ensured that the sample was balanced with respect to region and by site type, within each region. The main geographical regions defined were: East Forest, West Forest, and Savannah. Site types varied as follows: large cities, towns, large and small villages, surrounding towns, village centers, and villages attached to them. The 100 PSUs were selected, with probabilities proportional to the size of their population, from a list of PSUs sorted by region and within each region, by site type.

Selection of households within each PSU. A pre-survey was conducted in June-July of 1984, to establish the second-stage sampling frame, i.e. a list of households for each PSU from which 16 households could be selected. The same listing exercise was to be used for both the 1985 and 1986 surveys, in order to avoid having to conduct another costly pre-survey in the second year. Thus, the 1984 pre-survey had to provide enough households so as to be able to select two clusters of households in each PSU and to allow for replacement households in the event that some in the sample could not be contacted or refused to participate. A listing of 64 households in each PSU met this requirement. In PSUs with 64 households or fewer, every household was listed. In selecting the households, the "step" used was equal to the estimated number of households in the PSU divided by 64. For example, if the PSU had an estimated 640 households, then every tenth household was included in the listing, counted from a random starting point in the PSU. For operational reasons, the maximum step allowable was a step of 30. In practice, it appears that enumerators used doors, instead of housing structures, in counting the step. Al though enumerators were supposed to start the listing process from a random point in the PSU, in rural areas and small towns, reportedly, the lister started from the center of the PSU.

Sampling Procedures for Block 2 Data

The Sampling Frame. The sampling frame for Block 2 data was established from a list of places from the results of the Census of inhabited sites (RSH) performed in preparation for the 1988 Population Census.

Selection of PSUs. The PSUs were selected with probability proportional to size. However, in order to save what might have been exorbitant costs of listing every household in each selected PSU in a pre-survey, the Direction de la Statistique made a decision to enumerate a smaller unit within each PSU. The area within each PSU was divided into smaller blocks called `îlots'. Households were then selected from a randomly chosen îlot within each PSU. The sample îlot was selected with equal probability within each PSU, not on the basis of probability proportional to size. (These îlots are reportedly relatively small compared with the size of PSUs selected for the Block 1 frame, but no further information is available about their geographical position within the PSUs.)

Selection of households within each PSU. All households in each îlot selected for the Block 2 sample were listed. Sixteen households were then randomly chosen from the list of households for each îlot.

Bias in the Selection of Households within PSUs, Block 1 Data

Analysis of the four years of the CILSS data revealed that household size (unweighted), dropped by 24 percent between 1985 and 1988. Three possible explanations were considered: (1) area l demographic change; (2) non-sampling measurement errors were involved; or (3) some sort of sampling bias. Investigation ruled out the first two possibilities. The third possibility clearly was an issue because the sampling frame and listing procedures had indeed changed in midstream and this was likely to have had an effect. In fact, the investigation found that the substantial part of the drop in household size over the years occurred between the first and second panel data sets in 1987, i.e. the tail end of Block 1 data and the start of Block 2 data. From this, it is reasonable to assume that differences in the sampling frame and sampling procedures between the two blocks were indeed responsible.

The listing procedures for Block 1 data indicate d that the selection of households within PSUs was likely to have been biased toward the selection of larger dwellings. Based on a discussion with Christopher Scott, statistical consultant, Demery and Grootaert explain as follows: "In the selected primary sampling units, where the listing of households was to occur, enumerators were instructed to start the listing process at a random location in the primary sampling unit and from this point to select every nth household, that is, with a given fixed "step" until sixty-four households were listed. There are two sources of potential bias in this listing procedure. First, the selection of the starting point might not have been random, but subject to motivated bias on the part of the enumerator (such as the selection of a point where there are numerous dwellings or that is easily accessible). Second, in practice, enumerators counted doors to achieve the "step", rather than counting actual households. This method leads to sample

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