Georgian Labor Market Overview
This report provides a study of the LABOR market in Georgia based on an analysis 1998-1999 data of the Georgia LABOR Force Survey, which has been carried out by the State Department for Statistics with the assistance of UNDP and the ILO.
It begins with a historical background, briefly describing the Soviet LABOR market and the early years of transition to a market economy. It reveals that by the end of the 1990s, almost 10 years after the disintegration of the Soviet Union, the massive collapse in output had not been matched by an increase in open unemployment, as had been widely predicted. This was initially due to LABOR hoarding within enterprises, and then to a reallocation of LABOR from paid-employment into self-employment in small-plot agriculture. Contrary to expectations, privatization and restructuring have not led to a growth in small and medium enterprises, which were to be the driving force of economic growth. Read more
Definition of Econometrics
Econometrics is based upon the development of statistical methods for estimating economic relationships, testing economic theories, and evaluating and implementing government and business policy. The most common application of econometrics is the forecasting of such important macroeconomic variables as interest rates, inflation rates, and gross domestic product. While forecasts of economic indicators are highly visible and are often widely published, econometric methods can be used in economic areas that have nothing to do with macroeconomic forecasting. For example, we will study the effects of political campaign expenditures on voting outcomes. We will consider the effect of school spending on student performance in the field of education. In addition, we will learn how to use econometric methods for forecasting economic time series. Econometrics has evolved as a separate discipline from mathematical statistics because the former focuses on the problems inherent in collecting and analyzing nonexperimental economic data. Read more
Panel or Longitudinal Data
A panel data (or longitudinal data) set consists of a time series for each cross-sectional ember in the data set. As an example, suppose we have wage, education, and employment history for a set of individuals followed over a ten-year period. Or we might collect information, such as investment and financial data, about the same set of firms over a five-year time period. Panel data can also be collected on geographical units. For example, we can collect data for the same set of counties in the United States on immigration flows, tax rates, wage rates, government expenditures, etc., for the years 1980, 1985, and 1990. The key feature of panel data that distinguishes it from a pooled cross section is the fact that the same cross-sectional units (individuals, firms, or counties in the above examples) are followed over a given time period. Most books at the undergraduate level do not contain a discussion of econometric methods for panel data. However, economists now recognize that some questions are difficult, if not impossible, to answer satisfactorily without panel data. As you will see, we can make considerable progress with simple panel data analysis, a method which is not much more difficult than dealing with a standard cross-sectional data set.
Pooled Cross Sections
Some data sets have both cross-sectional and time series features. For example, suppose that two cross-sectional household surveys are taken in the United States, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family size, and so on. In 1990, a new random sample of households is taken using the same survey questions. In order to increase our sample size, we can form a pooled cross section by combining the two years. Because random samples are taken in each year, it would be a fluke if the same household appeared in the sample during both years. (The size of the sample is usually very small compared with the number of households in the United States.) This important factor distinguishes a pooled cross section from a panel data set. Pooling cross sections from different years is often an effective way of analyzing the effects of a new government policy. The idea is to collect data from the years before and after a key policy change. As an example, consider the following data set on housing prices taken in 1993 and 1995, when there was a reduction in property taxes in 1994. Read more
