Encyclopedia of SociologyVol._1

BEHAVIORISM

Matching implies that an increase of 10 percent

(e.g., from 50 to 60 percent) in relative rate of reinforcement for one alternative results in a similar increase in relative rate of response. In many cases, the increase in relative rate of response is less than expected (e.g., only 5 percent). This failure to discriminate changes in relative rate of reinforcement is incorporated within the theory of generalized matching. To illustrate, low sensitivity to changes in rate of reinforcement may occur when an air-traffic controller rapidly switches between two (or more) radar screens. As relative rate of targets increases on one screen, relative rate of detection may be slow to change. Generalized matching theory allows behaviorists to measure the degree of sensitivity and suggests procedures to modify it (e.g., setting a minimal amount of time on a screen before targets can be detected).

Matching theory is an important contribution of modern behaviorism. In contrast to theories of rational choice proposed by economists and other social scientists, matching theory implies that humans may not try to maximize utility (or reinforcement). People (and animals) do not search for the strategy that yields the greatest overall returns; they equalize their behavior to the obtained rates of reinforcement from alternatives. Research suggests that matching (rather than maximizing) occurs because humans focus on the immediate effectiveness of their behavior. A person may receive a per-hour average of $10 and $5 respectively from the left and right handles of a slot machine.

Although the left side generally pays twice as much, there are local periods when the left option actually pays less than the right. People respond to these changes in local rate of reinforcement by switching to the lean alternative (i.e., the right handle), even though they lose money overall. The general implication is that human impulsiveness ensures that choice is not a rational process of getting the most in the long run but a behavioral process of doing the best at the moment (Herrnstein 1990).

MATHEMATICS AND BEHAVIOR

MODIFICATION

The matching law suggests that operant behavior is determined by the rate of reinforcement for one alternative relative to all other sources of reinforcement. Even in situations that involve a single

response on a schedule of reinforcement, the behavior of organisms is regulated by alternative sources of reinforcement. A rat that is pressing a lever for food may gain additional reinforcement from exploring the operant chamber, scratching itself, and so on. In a similar fashion, rather than work for teacher attention a pupil may look out the window, talk to a friend, or even daydream. Thus in a single-operant setting, multiple sources of reinforcement are functioning. Herrnstein (1970) argued this point and suggested an equation for the single operant that is now called the quantitative law of effect.

Carr and McDowell (1980) applied Herrnstein’s equation to a clinically relevant problem. The case involved the treatment of a 10-year-old boy who repeatedly and severely scratched himself. Before treatment the boy had a large number of open sores on his scalp, face, back, arms, and legs. In addition, the boy’s body was covered with scabs, scars, and skin discoloration. In their review of this case, Carr and McDowell demonstrated that the boy’s scratching was operant behavior. Careful observation showed that the scratching occurred more often when he and other family members were in the living room watching television. This suggested that a specific situation set the occasion for the self-injurious behavior. Further observation showed that family members repeatedly and reliably reprimanded the boy when he engaged in self-injury. Reprimands are seemingly negative events, but adult attention (whether negative or positive) can serve as reinforcement for children’s behavior.

In fact, McDowell (1981) showed that the boy’s scratching was in accord with Herrnstein’s equation (i.e., the quantitative law of effect). He plotted the reprimands per hour on the x-axis and scatches per hour on the y-axis. When applied to this data, the equation provided an excellent description of the boy’s behavior. The quantitative law of effect also suggested how to modify the problem behavior. In order to reduce scratching (or any other problem behavior), one strategy is to increase reinforcement for alternative behavior. As reinforcement is added for alternative behavior, problem behavior must decrease; this is because the reinforcement for problem behavior is decreasing

(relative to total reinforcement) as reinforcement is added to other (acceptable) behavior.

214

BEHAVIORISM

APPLIED BEHAVIOR ANALYSIS AND

EDUCATION

The application of behavior principles to improve performance and solve social problems is called applied behavior analysis (Baer, Wolf, and Risley 1968). Principles of behavior change have been used to improve the performance of university students, increase academic skills in public and high school students, teach self-care to developmentally delayed children, reduce phobic reactions, get people to wear seat belts, prevent industrial accidents, and help individuals stop cocaine abuse, among other things. Behavioral interventions have had an impact on such things as clinical psychology, medicine, education, business, counseling, job effectiveness, sports training, the care and treatment of animals, environmental protection, and so on. Applied behavioral experiments have ranged from investigating the behavior of psychotic individuals to designing contingencies of entire institutions (see Catania and Brigham 1978; Kazdin 1994).

One example of applied behavior analysis in higher education is the method of personalized instruction. Personalized instruction is a self-paced learning system that contrasts with traditional lecture methods that often are used to instruct college students. In a university lecture, a professor stands in front of a number of students and talks about his or her area of expertise. There are variations on this theme (e.g., students are encouraged to be active rather than passive learners), basically the lecture method of teaching is the same as it has been for thousands of years.

Dr. Fred Keller (1968) recognized that the lecture method of teaching was inefficient and in many cases a failure. He reasoned that anyone who had acquired the skills needed to attend college was capable of successfully mastering most or all college courses. Some students might take longer than others to reach expertise in a course, but the overwhelming majority of students would be able to do so. If behavior principles were to be taken seriously, there were no bad students, only ineffective teaching methods.

In a seminal article, titled ‘‘Good-bye, teacher. . . ,’’ Keller outlined a college teaching method based on principles of operant conditioning (Keller

1968). Keller’s personalized system of instruction (PSI) involves arranging the course material in a sequence of graduated steps (units or modules).

Each student moves through the course material at his or her own pace and the modules are set up to ensure that most students have a high rate of success learning the course content. Some students may finish the course in a few weeks, others require a semester or longer.

Course material is broken down into many small units of reading and (if required) laboratory assignments. Students earn points (conditioned reinforcement) for completing unit tests and lab assignments. Mastery of lab assignments and unit tests is required. If test scores are not close to perfect, the test (in different format) must be taken again after a review of the material for that unit. The assignments and tests build on one another so they must be completed in a specified order.

Comparison studies have evaluated student performance on PSI courses against the performance of students given computer-based instruction, audio-tutorial methods, traditional lectures, visual-based instruction, and other programmed instruction methods. College students instructed by PSI outperform students taught by these other methods when given a common final examination

(see Lloyd and Lloyd 1992 for a review). Despite this positive outcome, logistical problems in organizing PSI courses such as teaching to mastery level (most students get an A for the course), and allowing students more time than the allotted semester to complete the course, have worked against widespread adoption of PSI in universities and colleges.

SUMMARY

Modern behaviorism emphasizes the context of behavior and reinforcement. The biological history of an organism favors or constrains specific environment-behavior interactions. This interplay of biology and behavior is a central focus of behavioral research. Another aspect of context concerns alternative sources of reinforcement. An individual selects a specific option based on the relative rate of reinforcement. This means that behavior is regulated not only by its consequences but also by the consequences arranged for alternative actions.

As we have seen, the matching law and the quantitative law of effect are major areas of basic research that suggest new intervention strategies for behavior modification. Finally, applied behavior

215

BEHAVIORISM

analysis, as a technology of behavior change, is having a wide impact on socially important problems of human behavior. A personalized system of instruction is an example of applied behavior analysis in higher education. Research shows that mas- tery-based learning is more effective than alternative methods of instruction, but colleges and universities its implementation.

REFERENCES

Baer, D.M., M.M. Wolf, and T. R. Risley 1968 ‘‘Some Current Dimensions of Applied Behavioral Analysis.’’ Journal of Applied Behavior Analysis 1:91–97.

Bandura, A. 1986 Social Foundations of Thought and Action. Englewood Cliffs, N.J.: Prentice Hall.

Baum, W. M. 1974 ‘‘On Two Types of Deviation from the Matching Law: Bias and Undermatching.’’ Journal of the Experimental Analysis of Behavior 22:231–242.

Bem, D. J. 1965 ‘‘An Experimental Analysis of SelfPersuasion.’’ Journal of Experimental Social Psychology

1:199–218.

——— 1972 ‘‘Self-Perception Theory.’’ In L. Berkowitz, ed., Advances in Experimental Social Psychology, Vol. 6. New York: Academic Press.

Bradshaw, C. M., and E. Szabadi 1988 ‘‘Quantitative Analysis of Human Operant Behavior.’’ In G. Davey and C. Cullen, eds. Human Operant Conditioning and Behavior Modification. New York: Wiley.

Carr, E. G., and J. J. McDowell 1980 ‘‘Social Control of Self-Injurious Behavior of Organic Etiology.’’ Behavior Therapy 11:402–409.

Catania, C. A., and T. A. Brigham, eds. 1978 Handbook of Applied Behavior Analysis: Social and Instructional Processes. New York: Irvington Publishers.

Conger, R., and P. Killeen 1974 ‘‘Use of Concurrent Operants in Small Group Research.’’ Pacific Sociological Review 17:399–416.

De Villiers, P. A. 1977 ‘‘Choice in Concurrent Schedules and a Quantitative Formulation of the Law of Effect.’’ In W. K. Honig and J. E. R. Staddon, eds.,

Handbook of Operant Behavior. Englewood Cliffs, N.J.: Prentice-Hall.

Emerson, R. M. 1972 ‘‘Exchange Theory Part 1: A Psychological Basis for Social Exchange.’’ In J. Berger, M. Zelditch, Jr., and B. Anderson, eds. Sociological Theories in Progress 38–57. Boston: Houghton Mifflin Co.

Epling, W. F., and W. D. Pierce 1988 ‘‘Applied Behavior Analysis: New Directions from the Laboratory.’’ In

G. Davey and C. Cullen, eds., Human Operant Conditioning and Behavior Modification. New York: Wiley.

Fantino, E., and C.A. Logan 1979 The Experimental Analysis of Behavior: A Biological Perspective. San Francisco: W. H. Freeman.

Herrnstein, R. J. 1961 ‘‘Relative and Absolute Response Strength as a Function of Frequency of Reinforcement.’’ Journal of the Experimental Analysis of Behavior

4:267–272.

———1970 ‘‘On the Law of Effect.’’ Journal of the Experimental Analysis of Behavior 13:243–266.

———1990 ‘‘Rational Choice Theory: Necessary but Not Sufficient.’’ American Psychologist 45:356–367.

Homans, G. C. 1974 Social Behavior: Its Elementary Forms, rev. ed. New York: Harcourt Brace Jovanovich.

Kazdin, A. E. 1994 Behavior Modification in Applied Settings. New York: Brooks/Cole Publishing.

Keller, F. S. 1968 ‘‘Good-bye teacher. . .’’ Journal of Applied Behavior Analysis 1:79–89.

Lloyd, K. E., and M. E. Lloyd 1992 ‘‘Behavior Analysis and Technology of Higher Education.’’ In R. P. West and L. A. Hamerlynck, eds., Designs for Excellence in Education: The Legacy of B. F. Skinner 147–160. Longmont, Colo.: Spores West, Inc.

McDowell, J. J. 1981 ‘‘On the Validity and Utility of Herrnstein’s Hyperbola in Applied Behavior Analysis.’’ In C. M. Bradshaw, E. Szabadi and C. F. Lowe, eds., Quantification of Steady-State Operant Behaviour

311–324. Amsterdam: Elsevier/North-Holland.

——— 1988 ‘‘Matching Theory in Natural Human Environments.’’ The Behavior Analyst 11:95–109.

McLaughlin, B. 1971 Learning and Social Behavior. New

York: Free Press.

Pierce, W. D., and W. F. Epling 1983 ‘‘Choice, Matching, and Human Behavior: A Review of the Literature.’’

The Behavior Analyst 6:57–76.

———1984 ‘‘On the Persistence of Cognitive Explanation: Implications for Behavior Analysis.’’ Behaviorism 12:15–27.

———1999 Behavior Analysis and Learning. Upper Saddle River, N.J.: Prentice-Hall.

———, W.F. Epling, and D. Boer 1986 ‘‘Deprivation and Satiation: The Interrelations Between Food and Wheel Running.’’ Journal of the Experimental Analysis of Behavior 46:199–210.

Skinner, B.F. 1953 Science and Human Behavior. New York: Free Press.

——— 1957 Verbal Behavior. New York: Appleton Century Crofts.

216

BIRTH AND DEATH RATES

———1969 Contingencies of Reinforcement: A Theoretical Analysis. New York: Appleton Century Crofts.

———1974 About Behaviorism. New York: Knopf.

———1987 ‘‘Selection by Consequences.’’ In B.F. Skinner, Upon Further Reflection. Englewood Cliffs, N.J.: Prentice-Hall.

Sunahara, D., and W. D. Pierce 1982 ‘‘The Matching Law and Bias in a Social Exchange Involving Choice Between Alternatives.’’ Canadian Journal Of Sociology

7:145–165.

Zeiler, M. 1977 ‘‘Schedules of Reinforcement: The Controlling Variables.’’ In W.K. Honig and J.E.R. Staddon, eds., Handbook of Operant Behavior. Englewood Cliffs, N.J.: Prentice-Hall.

Zuriff, G.E. 1985 Behaviorism: A Conceptual Reconstruction. New York: Columbia University Press.

W. DAVID PIERCE

BIRTH AND DEATH RATES

Much of the birth and death information published by governments is in absolute numbers. These raw data are difficult to interpret. For example, a comparison of the 42,087 births in Utah with the 189,392 in Florida in 1996 reveals nothing about the relative levels of fertility because Florida has a larger population (Ventura et al. 1998, p. 42).

To control for the effect of population size, analyses of fertility and mortality usually use rates. A rate measures the number of times an event such as birth occurs in a given period of time divided by the population at risk to that event. The period is usually a year, and the rate is usually expressed per 1,000 people in the population to eliminate the decimal point. Dividing Florida’s births by the state’s population and multiplying by 1,000 yields a birth rate of 13 per 1,000. A similar calculation for Utah yields 21 per 1,000, evidence that fertility makes a greater contribution to population growth in the state with the large Mormon population.

BIRTH RATES

The crude birth rate calculated in the preceding example,

Crude birth rate per 1,000=

(1)

Live births in year x

Midyear population in year x

X 1,000

is the most common measure of fertility because it requires the least amount of data and measures the impact of fertility on population growth. Crude birth rates at the end of the twentieth century range from over 40 per 1,000 in many African countries and a few Asian countries such as Yemen and Afghanistan to less than 12 per 1,000 in the slow-growing or declining countries of Europe and Japan (Population Reference Bureau 1998).

The crude birth rate is aptly named when used to compare childbearing levels between populations. Its estimate of the population at risk to giving birth includes men, children, and postmenopausal women. If women of childbearing age compose different proportions in the populations under consideration or within the same population in a longitudinal analysis, the crude birth rate is an unreliable indicator of the relative level of childbearing. A portion of Utah’s 61 percent higher crude birth rate is due to the state’s higher proportion of childbearing-age women, 23 percent, versus 20 percent in Florida (U.S. Bureau of the Census 1994). The proportion of childbear- ing-age women varies more widely between nations, making the crude birth rate a poor choice for international comparisons.

Other rates that more precisely specify the population at risk are better comparative measures of childbearing, although only the crude birth rate measures the impact of fertility on population growth. If the number of women in childbearing ages is known, general fertility rates can be calculated:

General fertility rate per 1,000=

(2)

Live births in year x

Women 15–44 in year x X 1,000

This measure reveals that a thousand women in Utah of childbearing age produce more births in a year than the same number in Florida, 89 versus 64 births per 1,000 women between ages fifteen and forty-four (Ventura et al. 1998, p. 42). The 39 percent difference in the two states’ general fertility rates is substantially less than that indicated by

217

BIRTH AND DEATH RATES

	250										Zimbabwe
		218	206
	200										Japan
					180
											United States
	150					145					Ireland
					127
			114 114
		111		106
	100				93
	82				81			80
1,000	60		51					61
	50	40
						26	34				31
		15								13
Per	4							3	6		0.1 0.3 0.7
	0
	<20	20–24	25–29		30–34	35–39		40–44			45+

Figure 1. Age-Specific Fertility Rates: Selected Countries, Mid-1990s.

SOURCE: United Nations, Demographic Yearbook, 1996.

their crude birth rates which are confounded by age-composition differences.

Although the general fertility rate is a more accurate measure of the relative levels of fertility between populations, it remains sensitive to the distribution of population across women of childbearing ages. When women are heavily concentrated in the younger, more fecund ages, such as in developing countries today and in the United States in 1980, rather than the less fecund older ages, such as in the United States and other developed countries today, the general fertility rate is not the best choice for fertility analysis. It inflates the relative level of fertility in the former populations and deflates the estimates in the latter populations.

Age-specific fertility rates eliminate potential distortions from age compositions. These rates are calculated for five-year age groups beginning with ages fifteen to nineteen and ending with ages forty-five to forty-nine:

Age-specific fertility rate per 1,000=

(3)

Live births in year x to women age a

X 1,000

Women age a in year x

Age-specific fertility rates also provide a rudimentary measure of the tempo of childbearing.

The four countries in Figure 1 have distinct patterns. Zimbabwe, a less-developed country with high fertility, has higher rates at all ages. At the

other extreme, Japan’s low fertility is highly concentrated between the ages of twenty-five and thirty-four, even though the Japanese rates are well below those in Zimbabwe. In contrast, teenage women in the United States continue to have much higher fertility than teenage women in Japan and other industrialized countries, despite declines in the 1990s. The younger pattern of American fertility also is evident in the moderately high rates for women in their early twenties. At the other extreme, Irish women have an older pattern of fertility.

More detailed analyses of the tempo of childbearing require extensive information about live birth order to make fertility rates for each age group specific for first births, second births, third births, and so forth (Shryock and Siegel 1976, p.

280). A comparison of these age-order-specific rates between 1975 and 1996 reveals an on-going shift toward later childbearing in the United States (Ventura et al. 1998, p. 6). The first birth rate increased for women over thirty while it decreased for women ages twenty to twenty-four. As a result, 22 percent of all first births in 1996 occurred to women age thirty and over, compared to only 5 percent in 1975.

When the tempo of fertility is not of interest, the advantages of age-specific fertility rates are outweighed by the cumbersome task comparing many rates between populations. As an alternative, each population’s age-specific rates can be

218

BIRTH AND DEATH RATES

condensed in an age–sex adjusted birth rate (Shryock and Siegel 1976, pp. 284–288). The most frequently used age–sex adjusted rate is calculated:

Total fertility rate=

(4)

Sum (age-specific fertility rates per 1,000 X 5)

1,000

if the age-specific fertility rates are for five-year age groups. Single-year age-specific rates are summed without the five-year adjustment. When expressed per single woman, as in equation (4), the total fertility rate can be interpreted as the average number of births that a hypothetical group would have at the end of their reproduction if they experienced the age-specific fertility observed in a particular year over the course of their childbearing years.

Age-specific rates in real populations that consciously control fertility can be volatile. For example, the fertility rate of American women ages thirty to thirty-four fell to 71 per 1,000 in the middle of the Great Depression and climbed back to 119 during the postwar Baby Boom, only to fall again to 53 in 1975 and rebound to 84 in 1996

(U.S. Bureau of the Census 1975, p. 50; Ventura et al. 1998, p. 32). Consequently, a total fertility rate calculated from one year’s observed age-specific rates is not a good estimate of the eventual completed fertility of childbearing-age women. It is, however, an excellent index of the level of fertility observed in a year that is unaffected by age composition.

The total fertility rate also can be interpreted as an estimate of the reproductivity of a population. Reproductivity is the extent to which a generation exactly replaces its eventual deaths. While the total fertility measures the replacement of both sexes, other measures of reproductivity focus only on the replacement of females in the population (Shryock and Siegel 1976, pp. 314–316). The gross reproduction rate is similar to the total fertility rate except that only female births are included in the calculation of the age-specific rates.

It is often approximated by multiplying the total fertility rate by the ratio of female births to all births.

Theoretically, women need to average two births, one female and one male, and female newborns need to live long enough to have their own

female births at the same ages that their mothers gave birth to them to maintain a constant population size. In real populations some female newborns die before their mothers’ ages at their births and the tempo of fertility fluctuates. As a result, both the total fertility rate and the gross reproduction rate overestimate reproductivity. The net reproduction rate adjusts for mortality, although it remains sensitive to shifts in the tempo of childbearing. This measure of reproductivity is calculated by multiplying the age-specific fertility rates for female births by the corresponding life-table survival rates that measure the probability of female children surviving from birth to the age of their mothers, and summing across childbearing ages.

If the tempo of fertility is constant, a net reproduction rate of greater than one indicates population growth; less than one indicates decline; and one indicates a stationary population.

Because of the impact of female mortality, the total fertility rate must exceed two for a generation to replace all of its deaths. In industrialized countries with a low risk of dying before age fifty, the total fertility rate needs to be about 2.1 for replacement. Developing countries with higher mortality need a higher total fertility rate for replacement.

Malawi, for example, with an infant mortality rate of 140 per 1,000 live births and a life expectancy of only thirty-six needs a rate of about three births per woman for replacement.

The fertility of most industrialized countries has fallen below replacement (Population Reference Bureau 1998). Some, including the United

States (Figure 2), Ireland, Iceland, and New Zealand, are barely below replacement. Others have declined to unprecedented low rates. Spain (Figure 2), Portugal, Italy, Greece, Germany, most Eastern European countries, and Japan have total fertility rates of 1.1 to 1.4 births per woman. Some of these populations are already declining. Without constant, substantial net in-migration, all will decline unless their fertility rates rebound to replacement levels or above.

In contrast, the total fertility rates of most developing countries whose economies are still dependent primarily on agriculture exceed replacement (Population Reference Bureau 1998). The highest rates are found in Africa where most countries have rates greater than five births per

219

BIRTH AND DEATH RATES

	9									United States
	8	6.9								Spain
	7		7.0	7.1	6.8					China
			7.0	6.8		6.5				Egypt
	6	6.6			6.6
										Mexico
		6.2			6.1	5.5	5.3
			5.6	5.7				5.1
	5					4.9
							5.3	4.2	4.6
	4									3.8
woman		3.4	3.7						3.6
				3.3			3.3			3.1
	3				2.9	2.9
							2.6	2.5	2.4
				2.9
		2.6	2.7							2.0
per	2				2.5			1.8	1.9
						2.0	1.8			1.9
								1.9
Births	1								1.5
										1.3
	0
		1950-55	1955-60	1960-65	1965-70	1970-75	1975-80	1980-85	1985-90	1990-95

Figure 2. Total Fertility Rates: Selected Countries, 1950–1995.

SOURCE: United Nations, World Population Prospects, 1996.

woman. A few, including Ethiopia, Somalia, Niger, and Angola, equal or exceed seven births per woman. As a result, sub-Saharan Africa is growing at about 2.6 percent per year. If unchanged, Africa’s population would double in twenty-seven years. Change, however, appears underway in most African countries. The largest fertility declines since 1980 have occurred in North African countries like Egypt (Figure 2) and in Kenya, Zimbabwe, and South Africa. Nevertheless, fertility in all African countries remains well above replacement. The continent’s 1998 total fertility rate was 5.6 births per woman.

Fertility has declined to lower levels in other developing regions. Led by three decades of decline in Mexico (Figure 2), Brazil, Ecuador, Peru, and Venezuela, fertility rates for Central and South America have declined to 3.4 and 2.8 births per woman, respectively. Most Caribbean countries have near replacement-level fertility. Haiti, the

Dominican Republic, and Jamaica are the major exceptions.

Rapid fertility declines also have occurred throughout much of Asia. China’s aggressive birth control policy and nascent economic growth reduced its fertility to 1.8 births per woman (Figure 2), about the same as its Korean and Taiwanese neighbors. A number of other Asian countries, including the large populations of Bangladesh, Iran, Thailand, Vietnam, and Turkey, experienced

more than a 50 percent decline in the last two decades of the twentieth century. The even larger populations of India and Indonesia continued their previous slow downward trend, yielding 1998 rates of 3.4 and 2.7 births per woman, respectively.

Only a few Asian countries, other than traditional Moslem societies such as Afghanistan, Iraq, and

Pakistan, continue to have more than five births per woman.

The fertility measures discussed up to this point are period rates. They are based on data for a particular year and represent the behavior of a cross-section of age groups in the population in that year. Fertility also can be measured over the lifetime of birth cohorts. Cumulative fertility rates can be calculated for each birth cohort of women by summing the age-specific fertility rates that prevailed as they passed through each age (Shryock and Siegel 1976, p. 289). This calculation yields a completed fertility rate for birth cohorts that have reached the end of their reproductive years. It is the cohort equivalent of the period total fertility rate.

DEATH RATES

The measurement of mortality raises many of the same issues discussed with fertility. Rates are more informative than absolute numbers, and those rates that more precisely define the population at

220

BIRTH AND DEATH RATES

risk to dying are more accurate. Unlike fertility, however, the entire population is at risk to dying, and this universal experience happens only once to an individual.

The impact of mortality on population growth can be calculated with a crude death rate:

Crude death rate per 1,000=

(5)

Deaths in year x

X 1,000

Midyear population in year x

Crude death rates vary from over 20 per 1,000 in some African countries to as low as 2 per 1,000 (Population Reference Bureau 1998). The lowest crude death rates are not in the developed countries of Europe, North America, and Oceania, which have rates between 7 and 14 per 1,000. Instead, the lowest crude death rates are found in oil-rich Kuwait, Qatar, and United Arab Emirates, where guest workers inflate the proportion of young adults, and in the young populations of developing countries experiencing declining mortality. Developed countries that underwent industrialization and mortality decline before 1950 have old-age compositions. The proportion of people age sixty-five and over ranges between 11 and 17 percent in these countries compared to less than 5 percent in most African, Asian, and Latin American populations. Although there is a risk of dying at every age, the risk rises with age after childhood. Consequently, older populations have higher crude death rates.

To control for the strong influence of age on mortality, age-specific rates can be calculated for five-year age groups:

Age-specific mortality rate per 1,000=

(6)

Deaths in year x to the population age a

X

Population age a in year x

1,000

Before age five, the age-specific mortality rate usually is subdivided to capture the higher risk of dying immediately after birth. The rate for oneto four-year olds, like other age-specific rates, is based on the midyear estimate of this population. The conventional infant mortality rate, however, is based on the number of live births:

Infant mortality rate per 1,000=

(7)

Deaths under age 1 in year x

X 1,000

Live births in year x

The infant mortality rate is often disaggregated into the neonatal mortality rate for the first month of life and the postneonatal rate for the rest of the year.

Neonatal mortality rate per 1,000=

(8)

Deaths under 29 days of life in year x

X 1,000

Live births in year x

Postneonatal mortality rate per 1,000=

(9)

Deaths from 29 days to age 1 in year x

X 1,000

Live births in year x

Infant mortality varies widely throughout the world. Iraq, Afghanistan, Cambodia, and many

African countries have 1998 rates that still exceed

100 per 1,000 live births, although most have declined (Population Reference Bureau 1998). Infant mortality rates in most other African, Asian, and all other Latin American countries have declined as well. They now range in developing countries from 7 in Cuba to 195 in Sierra Leone.

In contrast, developed countries have rates at or below 10, led by Japan with under 4 infant deaths per 1,000 live births.

The declining American infant mortality rate,

7 per 1,000 live births in 1998, continues to lag behind Japan, Australia, New Zealand, Canada, and most Northern and Western European countries due to a higher prevalence of prematurity and low-birth-weight babies which are major causes of infant death (Peters et al. 1998, pp. 12–13). The prematurity and low-birth-weight rates for the country’s largest minority, African Americans, are double those of non-Hispanic whites (Ventura 1998, pp. 57–58). Not all minority mothers have higher rates than non-Hispanic whites. Low-birth-weight rates are lower for Americans of Chinese, Mexican, Central and South American origin, about the same for Native Americans and those of Cuban origin, and higher for Puerto Rican, Filipino, and Japanese Americans.

221

BIRTH AND DEATH RATES

Death Rates per 100,000 for the Fourteen Leading Causes of Death in the Population 45-64 Years of Age, by Sex and Selected Race and Ethnic Categories: United States, 1997

		Males			Females
	Non-Hispanic			Non-Hispanic
	Whites	Blacks	Hispanic	Whites	Blacks	Hispanic
Heart diseases	254	469	160	93	231	69
Cancers	254	431	147	219	285	129
Strokes	25	91	32	19	58	20
Respiratory diseases	26	31		24	22	7
Accidents
Motor vehicle	19	30	21	9	11	9
Other	23	49	26	8	14	6
Pneumonia and flu	11	32	11	8	15	6
Diabetes	20	60	33	16	54	29
HIV	6	72	25		18	5
Suicide	25		12	8
Liver disease and cirrhosis	26	44	53	10	15	14
Kidney disease						4
Septicemia				4	14
Homicide and legal intervention		28	12

Table 1. Death Rates per 100,000 for the Leading Causes of Death in the Population 45–64 Years of Age, by Sex and Selected Race and Ethnic Categories: United States, 1997.

SOURCE:Hoyert, D. Deaths: Final Data for 1997.

Infant mortality also varies by sex. The rate for male infants born to white mothers in the United

States is 6.7 per 1,000 live births, compared to 5.4 for female infants (Peters 1998, p. 80). Similarly, the rate for male infants born to African-American mothers is 16 per 1,000 live births, compared to 13 for female infants.

Like race and ethnic differences, the sex difference in mortality is evident at all ages. The greatest gap is among young adults; American males are two and one-half to three times more

likely to die between the ages of fifteen and twentynine than females due largely to behavioral causes.

These include motor vehicle and other accident fatalities, homicides, and suicides. Higher male death rates from congenital anomalies at younger ages and from heart disease in middle age (Table

1) suggest that biological factors also contribute to the sex difference in mortality. Behavioral causes continue to play a significant role in the sex difference in mortality in middle age with chronic liver disease and cirrhosis and human immunodeficiency virus (HIV) adding premature male mortality.

	90		80
	80	74					1900
			74
	70	67	63				1930
	60	60
							1960
	50	47	49
	40						1997
age	30				20		20	23
	20
of			14	15	16	15	16
Years	10
	0

Males at birth

Females at birth

Males at age 60

Females at age 60

Figure 4. Life Expectancy for Whites by Sex: United States, 1900–1996.

SOURCE:U.S. Bureau of the Census, Historical Statistics of the United States, Colonial Times to 1970, and D. Hoyert, Deaths: Final Data for 1997.

222

BIRTH AND DEATH RATES

	85
	80								78		80
								77
							76
	75					73			75		76
					71			74
			70	70			73		70		71
		69
	70					71
					70			68
				69							68.5
							65		67
	65		67					66.6
						63
		64					65
					60	63					63
	60
				58	59.6				60
								56
			55
	55						54	56
									56
		51				51	53
	50			49	49	49			United States		51
			45						Japan
				46
age	45		44		46				Mexico
		41		42					Zimbabwe
of	40	41							Indonesia
			40
Years									China
		37
	35
		1950-55	1955-60	1960-65	1965-70	1970-75	1975-80	1980-85	1985-90	1990-95

Figure 3. Life Expectancy at Birth: Selected Countries, 1950–1995.

SOURCE:United Nations, World Population Prospects, 1996.

Note that cause-specific death rates are calculated per 100,000 population in a specified group, rather than per 1,000 to avoid working with decimals.

Age-specific mortality rates usually are specific for sex and race or ethnicity because of the large differences evident in Table 1. Using five-year age categories results in thirty-eight rates for each racial or ethnic group. Analyses of such data can be unwieldy. When the age pattern of mortality is not of interest, an age-adjusted composite measure is preferable. The most readily available of these for international comparisons is life expectancy.

Life expectancy is the average number of years that members of an age group would live if they were to experience the age-specific death rates prevailing in a given year. It is calculated for each age group in a life table (Shryock and Siegel 1976, pp. 249–268). Life expectancy has increased since the nineteenth century in developed countries like the United States in tandem with economic growth and public health reforms. Japan has the longest

life expectancy at birth, eighty years, due to extremely low infant mortality. With the exception of Russia and some of the other former Soviet-bloc countries of Eastern Europe that have had declines in life expectancy, all other developed countries have life expectancies at birth that equal or exceed seventy years (Population Reference Bureau 1998).

Figure 3 presents trends in life expectancy since World War II in selected countries. Some of the most rapid increases have been in Asia and Latin America. China, Sri Lanka, Malaysia, Mexico, and Venezuela, for example, have 1998 life expectancies at birth of over seventy years. Increases in life expectancy in African countries, in contrast, where there is a relatively high prevalence of poor nutrition, unsanitary conditions, and infectious diseases, generally have lagged behind Asia and Latin America. Most have 1998 life expectancies in the forties or fifties, similar to those of developed countries at the end of the nineteenth century. The future trend in life expectancy in many African countries is uncertain.

223