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According to the Eastern Economic Journal (Al-Yousif and Darrat, 1999), the relationship between a country’s economy and its population size is the subject of much debate.  Many researchers contend that population increases diminish economic growth.  Others argue that if a population expands, an economy will also; while some suggest that the two variables are uncorrelated (Darrat & Al-Yousif, 1999).  To developing nations like Kenya, testing and determining the relationship between macroeconomic factors like population and the economy is especially critical.

For the past four decades, Kenya has grappled with a series of major socioeconomic issues.  Current research indicates that 55% of the Kenyan populace lives at or below the poverty level, 6.1% has HIV/AIDS, and the median age is eighteen years (Yin and Kent, 2008).  Additionally, the country has 37.9 million people, making it the 39th largest in the world in terms of population (U.S. Census Bureau, 2008).  In comparison, Kenya’s 2007 gross domestic product (GDP) in United States’ dollars is approximately 29.29 billion, making it the 79th largest economy in the world (CIA World Factbook, 2007).

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To better understand and address the challenges that developing countries like Kenya face, the researcher asks several questions, “What is the relationship between population and the economy?” “Is there a cause-and-effect relationship between the two variables?”  The researcher has analyzed time-series information from the International Monetary Fund, World Economic Outlook Report, 2008 to answer those questions, and has developed the following hypotheses:

H0: Population growth has no effect on economic growth.  µ1 = µ2

H1: Population growth has an effect on economic growth.  µ1 ? µ2

In this study, the data covers a twenty-nine year period, from 1980 – 2008, to give the researcher a fairly large sample size and any indication of possible trends that may have occurred over time.  The researcher also uses regression analysis in Microsoft Excel to determine how population (the independent variable) affects the size of the economy (the dependent variable), and sets a 95% confidence interval for the test.

Prior to performing any regressions, the researcher has reviewed the data and observes some significant findings.  One observation is that Kenya’s population grew every year between 1980 and 2008, with an average growth rate of 2.6%.  A second observation is that a positive change in Kenya’s population seemed to correspond with a positive change in the country’s aggregate GDP, indicating that a larger populace produces more goods and services.  This, however, did not occur each year under study.  For example, the graph indicates a sharp decline in aggregate GDP during 1992, despite a population increase.  Kenya has an economy that is more than 50% agricultural.  In 1991-92, a severe drought reduced food production and caused high levels of unemployment (U.S. Census, 2008).

 The researcher adds a second independent variable, per capita GDP, to determine if the average income per household increased when population increased; in other words, “Did the standard of living increase?”  Overall, the graph below indicates that per capita GDP usually increased when aggregate GDP increased and/or the population expanded.  In some instances, per capita GDP actually declined while the other two variables increased.  In other words, the average household earned less money, despite a larger population and overall economy.  The causes of that phenomenon could be a number of things – higher inflation, market or government instability, currency devaluation, unfavorable trade balances, etc.  In 1995-96, for example, despite an increase in both population and aggregate GDP, per capita GDP declined.

In the first regression, the researcher tests the effect of population on aggregate GDP, using year (to account for time-series data) and population as the two independent variables.  The results are as follows:

Using a 95% confidence interval, a 1% change in Kenya’s population over time accounts for more than 70% of the change that occurs in the country’s aggregate GDP.  The “t” test and p-values can be used to substantiate these results.  The “t” is high and the “p” value is <.05 for both independent variables, indicating the results are statistically significant.  The researcher can, thus, reject the null hypothesis that population has no impact on the economy (aggregate GDP).

In the second regression, the researcher tests the effect of population on per capita GDP, using year (to account for time-series data) and population as the two independent variables.  The results are as follows:

Using a 95% confidence interval, a 1% change in Kenya’s population over time accounts for more than 40% of the change that occurs in the country’s per capita GDP.  The percentage unaccounted for (with r-square) could exist because of numerous other factors including inflation, the age and health of the population, infrastructure, currency and government stability, etc. – all of which have an impact on a population’s standard of living. The “t” test and p-values can be used to substantiate these results.  The “t” is high and the “p” value is <.05 for both independent variables, indicating the results are statistically significant.  The researcher can, thus, reject the null hypothesis that population has no impact on the economy (per capita GDP).

            In conclusion, an increase in population seems to indicate an increase in the size of the economy.  There are, however, innumerable other variables that impact economic conditions.  Despite vast natural resources, a nation like Kenya is especially vulnerable because its population is impoverished, young, has an illiteracy rate of 46%, and is weakened by diseases such as the HIV/AIDS epidemic (Yin and Kent, 2008).

References

Ashford, L. (2007, June). Africa’s youthful population: risk or opportunity? Population

Reference Bureau. Retrieved April 18, 2008 from www.prb.org/pdf07/AfricaYouth.pdf

Central Intelligence Agency. (2007). Kenya. The World Factbook.  Retrieved April 19, 2008

from https://www.cia.gov/library/publications/the-world-factbook/geos/ke.html#Econ

Darrat, A.F. ; Al-Yousif, Y.K. (1999, Summer). On the long-run relationship between

population and economic growth: some time series evidence for developing countries. Eastern Economic Journal.  Retrieved April 17, 2008 from http://findarticles.com/p /articles/ mi_qa3620 /is_199907/ai_n8830319

International Monetary Fund. (2008, April). World economic and financial surveys: by countries

(country-level data).  World Economic Outlook Database. Retrieved April 18, 2008 from

 http://www.imf.org/external/pubs/ft/weo/2008/01/weodata/weoselser.aspx?c=664;t=1

U.S. Census Bureau. (2008). Country summary: Kenya. International Data Base. Retrieved

April 18, 2008 from http://www.census.gov/ipc/www/idb/country/keportal.html

Yin, S. ; Kent, M. (2008, January). Kenya: the demographics of a country in turmoil.

Population Reference Bureau. Retrieved  April 19, 2008 from http://www.prb.org/                                 articles/2008/kenya.aspx

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