The data used in Economics may be of three types; Time series, Cross-section and Panel.
3.1.1 Time series data
This is data collected at specific points in time. It is a set of observations on the values that a variable takes at different times. Such data may be collected at regular time intervals such as daily (like stock prices, whether reports etc.), weekly (like money supply figures), monthly (like consumer price index etc.) quarterly (like GDP), annually (like government budget etc.) or biannually etc. Financial data measures phenomena such as changes in the price of stocks. This type of data is collected more frequently than the above, for instance, daily or even hourly. In all of these examples, the data are ordered by time and are referred to as time series data.
In economics, we commonly use the notation Yt to indicate an observation on variable Y (e.g. real GDP) at time t. A series of data runs from period t = 1 to t = T. “T” is used to indicate the total number of time periods covered in a data set. To give an example, if we were to use annual real GDP data from 1980–2017 – a period of 38 years – then t = 1 would indicate 1980 up to t = 38 for 2017 and T = 38 the total number of years. Hence, Y1 would be real GDP in 1980, Y2 real GDP for 1981, etc. Time series data is typically presented in chronological order. With the advent of high-speed computers, data can now be collected over an extremely short interval of time, such as the data on stock prices, which can be obtained literally continuously (the so-called real-time quote).
Although time series data are used heavily in economics, they present special problems for econometricians. Most empirical work based on time series data assumes that the underlying time series is stationary and this is not the case with most of the time series data. Loosely speaking a time series is stationary if its mean and variance do not vary systematically over time.
3.1.2 Cross section data
Cross-section data are data on one or more variables collected at the same point of time.
A common example is census of population, data on the wage of all people in a certain
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company or industry. We use the notation Yi to indicate an observation on variable Y for individual i. Observations in a cross-sectional data set run from individual i = 1 to N. By convention, N indicates the number of cross-sectional units (e.g. the number of people surveyed). Microeconomist may ask N = 100 managers from manufacturing companies about their profit figures in the last month. In this case, Y1 will equal the profit reported by the first company, Y2 the profit reported by the second company, through to Y100, the profit reported by the 100th company. With cross-sectional data, the ordering of the data typically does not matter (unlike time series data). Just as time series data create their own special problems (because of the stationarity issue), cross-sectional data too have their own problems, specifically the problem of heterogeneity.
3.1.3 Panel data
Some data sets will have both a time series and a cross-sectional component. This data is referred to as panel data. Economists working on issues related to growth often make use of panel data. For instance, GDP for many countries from 1980 to the present is available.
A panel data set on Y = GDP for 12 African countries would contain the GDP value for each country in 1980 (N = 12 observations), followed by the GDP for each country in 1951 (another N = 12 observations), and so on. Over a period of T years, there would be T \ N observations on Y. We use the notation Yit to indicate an observation on variable Y for unit i at time t. In the economic growth example, Y11 will be GDP in country 1, year 1, Y12 GDP for country 1 in year 2, etc. Longitudinal or Micro Panel Data is a special type of pooled data in which the same cross-sectional (say a family or firm) is surveyed overtime.
3.1.4 Primary and secondary data
Research data is often classified as primary and secondary data or quantitative and qualitative data. Primary data is the original data that are collected for the specific research problem at hand. It is collected with the aim of being foundation for the analyses in your investigation. Researcher collects primary data using experiments and surveys. Secondary data on the other hand is data already collected or produced by others. Collection of secondary data is appropriate for laying the foundation for your own work; in attempt to
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know how, things are done by others or how others interpreted situations or occurrences;
and when it is impossible to get primary data.
3.1.5 Quantitative and qualitative data
Quantitative data involves expressing data statistically or numerically. Such data is usually expressed in one of three ways:
i. Numbers (sometimes called raw numbers). For example, the total number of people who live in poverty.
ii. Percentages: the number per 100 in a population. For example, 30% of Nigeria’s labour force is unemployed.
iii. Rates: the number per 100, 1000 etc in a population. For example, birth rate, inflation rate etc. Research data is often expressed as a rate or percentage because it allows accurate comparisons between and within groups and societies. For example, comparing unemployment between Britain and America as a raw number wouldn’t tell us very much, since the population of America is roughly 5 times larger. Expressing unemployment as a percentage or rate on the other hand allows us to compare "like with like”. Quantitative data lends itself to statistical comparisons and gives researcher the ability to express relationships numerically.
This is useful for things like hypothesis testing or cross-cultural comparisons.
Although the ability to quantify the social world can be a significant advantage for social researchers one of the major criticisms of quantification relates to the validity of the data collected. Quantitative methods only capture a relatively narrow range of data about people's behaviour. It tells us very little about the reasons for people’s behaviour. This is partly a problem of lack of depth: the more complex the behaviour, the more difficult it is to quantify.
Qualitative data tries to capture the quality of people’s behaviour and such data says something about how people experience the social world. It can be used to understand the meanings they give to behaviour. Where a research objective is to understand the meaning of people’s behaviour, they must be allowed the scope to talk freely. Qualitative data
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encourages this because a researcher doesn't impose their interpretation on a situation (by asking direct, quantifiable, questions for example). Qualitative data provides greater depth and detail about behaviour since they are likely to involve digging more deeply into people’s beliefs and behaviours.
Where qualitative research generally focuses on the intensive study of relatively small groups, opportunities to generalise research data may be limited. For similar reasons it’s difficult to compare qualitative research across time and space because researchers are unlikely to be comparing "like with like". Qualitative data also tends to be structured in ways that make the research difficult to replicate.
Although we’ve considered quantitative and qualitative data as separate entities, there are occasions when a researcher may want to combine quantitative and qualitative types of data. This mixed method is called methodological triangulation and is one that can also be used to improve both research validity - by creating a more-accurate measurement of something - and
reliability by using the strengths of one type of data (the ability to quantify behaviour, for example) to offset the weaknesses of the other.
SELF ASSESSMENT EXCERCISE
Discuss the types of data used in economic research. Differentiate between quantitative and qualitative data.