Is the world overpopulated? Are we heading toward a global population "crash"? In the late 1960s and early 1970s some environmentalists began making sensational claims that the world’s ever increasing population would soon outstrip the planet’s limited resources leading to an environmental cataclysm of horrific proportions. In these scenarios, a massive worldwide famine was just around the corner. The number of people would keep increasing while the amount of available food would stay the same or even decline. The inevitable result, the experts argued, was famine by the early 1980s at the latest. The only way to lessen the severity of the impending disaster was to adopt strict policies to control population. Today, many of those predictions could be right around the corner as world population has topped 6 billion and severe repercussions are inevitable if population growth continues at such rates.

LITERATURE REVIEW

Concern that population growth would strain resources is a worry that goes back literally thousands of years. The most prominent advocate of this view in more recent history was the Rev. Thomas Malthus who became famous for his 18^th century book, Essay on the Principle of Population. Malthus predicted that the growing European population would quickly outstrip available resources. Many of Malthus’ immediate concerns about population growth and the possibility of the world reaching its carrying capacity quickly were rendered obsolete with the coming of the industrial revolution. Agricultural advances that allowed the speed of food production to continue to stay in front of population growth is beginning to be a major concern to population experts worldwide. In many areas food production has fallen behind population growth and has resulted in dire consequences.

Biologist Paul Ehrlich and his wife are the most authoritative source on 20^th century population trends and their effects on agriculture, environment and health. Ehrlich (1969) picked up where Malthus left off with his book, The Population Bomb. Ehrlich combined the ideas of Malthus and others with sensationalistic imagery about the horrors of a world with too many people. The Population Bomb predicted that tens of millions of people would starve to death in the 1970s following an inevitable crash of the global food supply. Dwindling natural resources such as oil would soon be used up and the world ran a real risk of returning to a pre-industrial dark age. Also of concern to Ehrlich are the current problems with ecosystem health. He believes that population growth is highly correlated with greenhouse warming, acid rain, depletion of the ozone layer, and other environmental disasters. He believes this happens because humans interact with the world’s ecosystem, but as the number of humans’ increases this interaction begins to tax the ecosystem beyond its capacity.

Pierre R. Crosson and Kenneth D. Frederick (1977) express their fears about growing populations in their book, The World Food Situation. They attribute the entire world’s food production and distribution problems to two ominous trends—the addition of millions of already starving and malnourished people each year and their dependence upon imported food to support their inadequate amounts of food production. They say that the food consumption of the poorest 15-20 percent of the world population has remained at an inadequate level while their numbers increased by 200 million from 1952 to 1972. The problem is most acute in developing regions where two-thirds of the developing countries are unable to meet their food demands. They believe that this problem is only going to get worse because the lower-income countries, not including communist countries, account for 83 percent of the world population growth. This trend is only going to continue until these countries can produce more themselves or get more from other countries¾ both of which they have been unable to do in the past. This study was done in the late 1970s and the problem, we are sure, is much worse today. All the sources we were able to locate agreed that population growth is a serious problem and if allowed to go unchecked will only get worse. It seemed that the majority of our sources dealt primarily with agricultural problems but all at least touched on environment repercussions. After reading the literature we made some of our own assumptions based on our own intuitions about population growth.

AN INITITAL INVESTIGATION

Of course what failed were the apocalyptic predictions. Most indicators of human well being improved dramatically in the last quarter of the 20^th century. Regardless of the fact that much of the literature has proved to be quite dramatic, there is some truth in the author’s claims of a worldwide population problem. We have set out to explore many of our own assumptions and determine through statistical analysis where the actual problems lie. We will examine agriculture, ecosystem health, human health and national attributes by running various multivariate statistical analyses to determine the actual importance of each variable in relation to population growth. This process will allow us to either find confirmation of our predictions or show if our assumptions are disconfirmed. The confirmed variables directly corresponding to population growth and its concurrent problems will show areas where help is most needed and allow prescriptions for future policy initiatives to be directed toward the right places.

Our research group started with some basic assumptions about the findings regarding population growth. Generally speaking and based on the literature, we supposed that certain things would be greatly affected by population growth. We expected that poorer health conditions would occur in areas of high population growth. We also supposed that as population increases, the amount of arable land per capita would fall, but that the percentage of arable land in relation to the total amount of land in a particular country would increase. We predicted that CO₂ emissions would rise as populations increased. We predicted that there would be higher numbers of AIDS patients and tuberculosis cases in areas of high population growth. Our final assumption regarding the effects of population growth was that the general level of environmental and ecological conditions would degenerate, as populations in countries increased.

Data used in testing are taken from the Martin Peace Institute’s archives. Attribute indicators¹ are: GNP/ capita, population total, urban population percent, fertility, life expectancy, infant mortality, population per physician, passenger cars, urban population total, urban population percent of total, population growth rate annual percent, population growth rate urban annual percent, population density per square kilometer, crude birth rate, crude death rate, arms exports in millions, armed forces in thousands, armed forces per 1000 of population, arms imports in millions, civil rights, military expenditures in millions, political rights, and GNP (size). National attributes are represented by symbols D_V1 to D_V23, in the above order, with shifts between 1970 and 1989 indicated by VDIFF1 to VDIFF23. The symbol VP2 indicates the population total difference for Figure 1.

Cooperation indicators include: surrender, praise, promise, express regret, extend economic aid, make agreements, ask for information, offer proposals, and the Vincent Scale of Cooperation.² National cooperation behaviors are represented by D_COP1 to D_COP9 with shifts indicated by COPDIF1 to COPDIF9.

Conflict indicators include: reject, accuse, protest, deny, demand, warn, threaten, hold demonstrations, reduce diplomatic actions, expel from country, and seize possessions, use force and the Vincent Scale of Conflict.³ National conflict behaviors are represented by D_CON1 to D_CON13 with shifts indicated by CONDIF1 to CONDIF13.

Power is defined as a combination index (POWER89) created by adding the z-scores

of number of nuclear weapons, population, armed forces in thousands, armed forces per 1000 of population, military expenditures in millions and GNP/capita for the 1985-89 time frame. POWSH89 defines the shift in power between 1970 and 1989.

Eight additional variables are included in this study. They are (1) organic water pollution emissions, (2) access to safe water, (3) arable land per capita, (4) land cereal production, (5) arable land as a percent of total, (6) CO₂ emissions, (7) AIDS growth, and (8) tuberculosis. They are not provided in the Table 1 correlations, but are shown in Figure 1.

Factor analysis was performed on the attribute and behavior data consisting of twenty- three attributes scores, nine "cooperation" scores and the thirteen "conflict" scores. Each was measured both within time (1989) and across time (1970-1989). The resulting factor scores, via Kaiser Varimax rotation, were correlated (using Rho) with the growth indicators, that is, the 1970-1989 difference scores for population growth. The Rho correlation results were then used to test hypotheses derived from the literature about the dynamics of population growth with the additional eight variables.

Any variables involved in the definition of a population growth indicator are, of course, dropped in each factor analysis of the remaining variables to get the factors for the Rho correlations. For example, in the analysis of population growth, VP2 becomes the variable focused upon and all other variables are entered into the factor analysis. Table 1 shows the significant Rho relationships of VP2 with the Martin Archive factors and there associated loadings.

TABLE 1

Rho .451 Rho .337 Rho .520

Of the fifteen factors that were correlated against population growth, four factors (Factors 1, 2, 3 and 6) loaded high enough to demonstrate significant findings. We found that high growth states tended to score high on Factor 1 (Rho +.451). Factor 2 shows states with high growth rates tend to have high scores for passenger cars (.91), tend to have high population urban total (.50), tend to have high arms exports in millions (.69), tend to have high armed forces in thousands (.51), high military expenditures in millions (.85), and high GNP (.89). These same states tended to score highly on the attribute difference variables that correspond to the variable above.

States that have high growth rates tend to have very high levels of cooperation and conflict. They tend to have high rates of surrender, yield (.97), high praise and hail (.98), high promise own policy support (.98), and high rates of expressing regret (.94). To avoid replication, we will not list all of the factors with high correlations. Nearly all of the factors concerned with cooperation and conflict, including difference variables, have a correlation of .95 or higher as confirmed in Table 1.

States that have low population growth tend to be low on the factors listed above. They tend to have low numbers of passenger cars, low population urban total, low arms exports, low armed forces, and low military expenditures. Two different types of states seemed to emerge from Factor 2 . They are "high population-high conflict" and "low population-low conflict."

States with high growth rates loaded moderately on Factor 2 (Rho +.337). Factor 2 indicates that states with high population growth tend to have low GNP per capita (-.70), low population urban percent (-.80), high fertility (.92), low life expectancy (-.90), high infant mortality per 1000 deaths (.89), high population per physician (.49), low urban population percent of total (-.80), high population growth rate annual percent (.82), high population growth rate urban annual percent (.88), high birth rate crude per 1000 (.93), high death rate crude per 1000 (.60), high civil rights (.66), and high political rights (.61). The attribute difference variables that correspond to the variable above loaded highly as well.

States that have low population growth tend to have the opposite attributes as high population growth. They tend to have high GNP per capita, low fertility, high life expectancy, and low infant mortality. By examining this factor, two types of states seem to emerge. They are "high population-poor living conditions" and "low population-good living standards."

States with high growth rates scored moderately on Factor 3 (Rho +.520). They tend to have high population total (.84), high population urban total (.83), high armed forces in thousands (.80), and also tend to have high difference variable for high population urban total (.92), and high difference of armed forces in thousands (.50). These same states scored low on difference variables for praise and hail (-.70), low on total cooperation (-.50), and low accuse (-.60).

States with low population growth tend to have the opposite attributes as did the high population growth states. They tend to have low population total, low population urban total, and low armed forces in thousands. Because there are so few attributes that have high scores on Factor 3 it is hard to determine a trend for those states..

States with high population growth also scored high on Factor 6. Factor 6 shows that states with high population growth tend to have high death rate crude per 1000 (.69), a high difference score (or growth) for fertility (.86), low difference score for life expectancy (-.60), and also high difference score for death rate crude per 1000 (.84).

States with low population growth tend to have the opposite scores as states with high population growth. They tend to have low death rates crude per 1000, low difference scores for fertility, high difference scores for life expectancy, and low difference scores for death rate crude per 1000. This factor seems to reinforce the state types that emerged from Factor 1. States with high population growth experience poor living conditions as evidenced by low life expectancy and high death rates.

Since Martin Archive variables basically span the 1970s to 1990 and allow multivariate interpretations of shifts on a basically stable nation list, variables gathered after 1990, if placed with the Martin collection, can cause interpretive problems. This is because a nation is dropped if information is missing on any variable when using factor analysis. To maximize the N for correlation purposes, we thus treat all non-Martin Archive data (where the N varies considerably) using a simple bivariate Rho of the correlation between population growth and the eight additional variables mentioned above. These are found in Table 2.

Table 2 Factors Correlated with Population Growth Differences

Of the fourteen non-Martin archive variables, ten of them had significant correlations, or Rho’s, with population growth. First, as we predicted, emission of water pollution had a strong Rho correlation of +.903. This extremely high Rho means that when a state has high a population growth rate it tends to have high rates of water pollution. The variable, access to safe water, in 1982 shows a strong Rho correlation of +.923, which indicates that in 1982 states with high growth rates tended to have access to safe water. By 1995, this strong Rho correlation dropped to a weak correlation of +.194, which indicates that in 1995 states with high population growth had less access to safe water. Arable land per capita shows a modest Rho correlation of +.209 in 1981 with an increase to +.315 in 1996, which implies that states with high growth rates tend to have better stores of arable land per capita. This came as a bit of a surprise to us because we predicted that states with high growth rates would have less arable land per capita as their populations grew. Cereal production indicates a moderate Rho correlation of +.524 in 1981, but by 1997 the Rho correlation became nonexistent. Arable land as a percentage of total land shows a strong Rho correlation of +.710 in 1980 with an increase to +.781 in 1996, which suggests that as populations grow states are forced to use more of their total land for agriculture. AIDS growth shows a modest Rho correlation of +.484, which indicates that states with high population growth tend to have much higher rates of AIDS. Lastly, the tuberculosis variable shows a strong Rho correlation of +.940, which suggests that states with high population growth tend to have extremely high rates of tuberculosis.

DISCUSSION

General Predictions

Our research group started with some basic assumptions regarding population growth. Generally speaking, based on the literature, we supposed that certain things would be greatly affected by population growth. We predicted that poorer health conditions would occur in areas of high population growth. We also supposed that as population increases, the amount of arable land per capita would fall, but that the percentage of arable land in relation to the total amount of land in a particular country would increase. We predicted that CO₂ emissions would rise as populations increased. We predicted that there would be higher numbers of AIDS patients and tuberculosis cases in areas of high population growth. Our final assumption regarding the effects of population growth was that the general level of environmental and ecological conditions would degenerate as populations in the country increased.

The research yielded confirmations on the majority of our predictions. For example, general health conditions tended to get worse as population growth increased. Countries that have high population growth tended to have high incidences of infant mortality, AIDS cases, and tuberculosis cases. These findings confirmed our expectations on population growth and its adverse effects on health conditions in areas or countries with rapidly growing populations.

We correctly predicted the status of arable land in counties regarding population growth. We expected that as population grows the amount of arable land per capita would decrease as population increased. However, we also predicted that the total percent of arable land in a country with high population growth would increase to sustain the rising population. The research confirmed our predictions. The statistics provided the more definite correlations found in the data.

Some of our predictions were not confirmed. For example, CO₂ emissions tended not to rise as population increased. Water pollution was also not significantly effected by population growth. Later, we theorized that many of the countries experiencing population growth are still developing nations, thus are generally not industrialized and therefore do not create as much industrial pollution as first world countries that tend to have relatively low levels of population growth.

Clearly, population growth is a global concern. Environmental issues must be placed at the top of worldwide awareness as population continues to grow. As demonstrated in the findings of this study, pollutants, as well as depletion of natural resources and landscape, are all problems that are currently facing the world’s people. What to do next is the looming question. An increasing amount of suggestions on that subject have become apparent in the current literature. Unfortunately, most of the suggestions are so extreme that nobody really wants to, or can, implement any of the ideas.

One example was the idea of limiting the amount of children a family could have. Perhaps the biggest coup for the advocates of limiting population growth was China’s 1979 adoption of the controversial one-child policy. Having more than one child requires permission from the Chinese government. Not everyone in China was pleased with the implementation of this policy. Environmentalist Garrett Hardin (1993) complained that the Chinese government lacked the will to properly enforce the policy and unless it took more draconian measures to prevent couples from having more than one child the policy was doomed to fail. Nonetheless, this is an example of what the world’s nations must consider in order to limit population growth.

As we start a new millennium, it must be realized that there is a serious population problem. Although many of the current ideas for population control seem extreme too much of the world, the results from studies such as these may serve as a necessary instrument to show people that the pending crisis is also extreme.

NOTES