Special Issue || Region, Migration and the Earnings of White and Black Men

Region, Migration and the Earnings of White and Black MenAuthor(s): Phillips CutrightSource: Social Forces, Vol. 53, No. 2, Special Issue (Dec., 1974), pp. 297-305Published by: Oxford University PressStable URL: http://www.jstor.org/stable/2576023 .

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Region, MiIigration and the Earnings of White and Black Men* PHILLIPS CUTRIGHT, Indiana University

ABSTRACT

Some consequences of net regional mobility for the male labor force aged 30-37 in 1964 are reported. The relationship of academic achievement (measured by the Armed Forces Qualification Test) and the probability of regional or intraregional migration, by race and region of origin, is measured. The form of this relationship varies by race and region of origin. The likely effect of military service on postservice geographic mobility also varies by race, region of origin and ability group. Comparisons of mean achievement and years of schooling for migrants and nonmigrants test the "selection" hypothesis. The effect of geographic mobility on earnings is estimated from earnings differences by mobility status, after controlling for age, sex, race, region of employment, education and academic achievement.

This paper, based on Selective Service System (SSS) and Social Security Administration (SSA) records, first examines the relationship of academic achievement (AFQT) and military service to geographic mobility. The data also provide measures of the degree to which migrants differ from men who do not migrate. Recent research (e.g., Bacon, 1973; Duncan et al., 1972) has examined the impact of mobility on occupational status or economic posi- tion. Our analysis of the effect of mobility on earnings controls region of employment, as well as individual characteristics related to earnings levels, before attempting to assess the contribution of geographic mobility to the earnings of white and black men.

THE DATA AND THE VARIABLES'

Data

One percent of men registered with SSS in April 1953 were sampled by SSS. In 1959 military service and some other items were updated. From the 1959 records SSA researchers pulled a systematic sample of whites and blacks from 44 strata defined by race, region, military service, and AFQT-years of school groups. Only men born from 1927-34 who had taken a preinduction examination were included in the SSA sample. Sampling

fractions varied in an effort to have exactly 200 white and 200 black cases within each stratum. One group with unknown characteristics was also drawn, and the records of these men as well as other records with ambiguous or missing data were returned to local draft boards and corrected.

The following analysis includes only men with reported civilian earnings (men paid from military sources are excluded) in 1964 within the fifty states. Men without reported earnings in 1964 were necessarily excluded, because location in 1964 was taken from SSA earnings records. Of the 6,054 whites in the SSS sample, 98.6 percent were linked to an SSA account and over 93 percent of men with an account had reported earnings in 1964. After also deleting whites with military pay and men with earnings outside the fifty states, sample size was 5,398. Of the 1,722 blacks in the sample, 95.6 percent were linked to an SSA account, and 87.4 percent of these had reported earnings. Deletions as noted for whites reduced the sample size for blacks to 1,361. The characteristics of both samples, before and after attrition, are quite similar (Cutright, 1973). The sample is representa- tive of American men in the specified year of birth cohort who were given a preinduction examination by April 1953.

When SSS files are linked to SSA records, two problems inherent in census or other survey data are, to some extent, resolved. One is underenumeration, particularly of black males. Coale and Rives (1973) estimate that the de- cennial census misses around 20 percent of nonwhite males aged 25-34. Furthermore,

* Research was supported by the Social Security Administration. Interpretations of data are the author's alone. My thanks to John Goering for his helpful comments on an early draft.

'See Cutright (1973:Appendices A-E) for de- tailed discussion of the sampling design and measures introduced in this section.

t297]



298 / SOCIAL FORCES / vol. 53:2, dec. 1974

available data (National Academy of Sciences, 1972) indicate that these missing men are different from those enumerated. By contrast, SSS registration may provide more complete coverage of the black population. The second problem is the comparatively low reliability of income reported to census (Siegel and Hodge, 1968). Use of SSA records minimizes this measurement problem because earnings reports are usually sent directly to SSA by employers.

Variables

Region. Regions were defined as South or non- South according to the census definition of 16 states and the District of Columbia as "southern."

1950-64 Migration Status. The state in which a man registered with SSS in 1950 was defined as his 1950 place of residence. Location in 1964 was taken from SSA earnings records. Three migration status codes are used: (1) stable men-those for whom 1950 and 1964 state of residence are the same; (2) intra- region migrants-those living in a different state but within the same region in 1964 as in 1950; (3) regional migrants-those living outside the region in which they had registered in 1950.

Social Security Earnings in 1964. Employers and self-employed persons in occupations required to report earnings to SSA provide quarterly information each year. Income from sources unrelated to employment is excluded. Men with no reported earnings (whether resulting from employment in occupations not required to report to SSA or from long-term unemployment) are omitted. When compared to the mean earnings of men of the same age from the SSA national 0.1 percent files, the mean 1964 earnings of whites sampled in this research were $15 below the national mean. The black sample mean was $190 above the national statistic.

Race. Men were classified as either black or other than black. About 1.3 percent of men termed "white" are nonwhites other than blacks.

Armed Forces Qualification Test. The AFQT is used by the military to screen out men who are ''very slow learners." The content of the test taken by most of the sample was equally divided into vocabulary, arithmetic, and spa-

tial relations sections. Test items are similar to those used by schools to measure academic achievement, and the test score may be considered a measure of academic achievement around age 20-the time of the preinduction examination. AFQT scores were converted into percentiles, based on national norms. Men were placed into various AFQT deciles prior to cross-tabular analysis.2 Mean AFQT of both the white and the black samples are very close to that reported by other studies that include enlistees, as well as Selective Service preinduction men (Karpinos, 1966). Likewise, mean black AFQT of 14.1 is about 1.25 standard deviations below the white mean-a difference one would expect with military test data (Fulk and Harrell, 1952; Karpinos, 1966).

Years of Education. Years of schooling were collected at the time of registration (between age 18 and 18 and one-half years) but may be updated at the preinduction examination- about age 20. Underreporting of completed years of school depressed the mean years of school for this sample.3

Military Service. Men known to have entered service were termed "draftees," even though a minority may have enlisted. Other men are nonveterans.

SOME CONSEQUENCES AND CORRELATES

OF MIGRATION

Eflects of Net Regional Migration

Net regional migration had both large and small effects on the general characteristics of

2 AFQT scores for a group of men are not likely to have changed between 1950 and 1964; the correlation of similar tests among adults over time is very high (Jencks et al., 1972). AFQT scores are converted to percentiles, based on national norms. Because a major goal of the research from which the present analysis is drawn was the assessment of the impact of military service on postservice earnings (Cutright, 1974a), low AFQT men were grouped into the 0-9 and 10-29 AFQT deciles. Above this level men were placed into the 30-49, 50-79 and 80-99 intervals to maintain sample size and simplify analysis. The last three groupings were arbitrarily selected.

3 In the present study schooling data were used only to adjust earnings within AFQT intervals, and for this purpose underreporting of schooling should not threaten the resulting conclusions if the difference between reported and true years of schooling is similar for men in different migration status codes. Given controls on AFQT and race this assumption seems plausible.



Region, Migration & Earnings / 299

men in the two regions. For example, in this sample 27 percent of whites lived in the South in 1950; by 1964, only 22.6. Among blacks, 76 percent were in the South in 1950; in 1964, just 47.5. Among whites net regional migration increased mean years of schooling in the southern region by just 0.1 year while decreas- ing by 0.2 years the mean for the non-South. Among blacks net regional migration decreased mean years of schooling in the non-South by 1.0 year but had virtually no impact on southern schooling level. In the South, net regional mobility had only negligible effects on mean AFQT (a change of less than I percentile) of both whites and blacks. In the non-South, though, mobility dropped mean AFQT by 3 and 8 percentiles among whites and blacks, respectively.

Regional migration also changed the number and racial composition of the total sample for each region. Within each region the ratio of the number of men in 1964 divided by the number in 1950 indicates the relative effect due to net regional migration. Among whites and blacks in the South, the ratios are 0.83 and 0.62, respectively, indicating a larger relative black decline. For the non-South the ratio is 1.06 for whites and 2.23 for blacks, indicating a much smaller relative white than black increase. The data on racial composition show that between 1950 and 1964, the percentage white in the South increased from 68.8 to 74.7; in the non-South it declined from 95.1 to 90.2.

A different measure of the impact of regional migration is found in the 1964 percentage of men within each AFQT interval who were migrants from another region (see Table 1). This discussion notes only the significant trends: (1) The contribution of southern whites to the nonsouthern white labor force declines rapidly as AFQT increases. (2) The

reverse trend holds for nonsouthern whites' contribution to the southern labor force. (3) The contribution of black regional migrants to the two regions, by AFQT, follows the same pattern observed among whites. Other consequences of regional migration patterns are dis- cussed elsewhere (e.g. U.S. Commission on Population Growth and the American Future, 1972).

Correlates of Migration

Lee (1966:56) summarized previous work relating education to mobility as follows: "If we plot the characteristics of total migrants along a continum ranging from poor to ex- cellent, we often get a J-shaped or U-shaped curve." Given the positive relationship between education and achievement, Lee's summary suggests that mobility should be either (1) highest at either extreme of the AFQT distribution, or (2) low and stable at the lower intervals and accelerating with further gains in AFQT. A second factor that may affect mobility has been noted but not studied. The "forced mobility" imposed on some but not others by military service may stimulate mobility among veterans (Eldridge, 1965).

AFQT and Migration. Table 2 displays migration status by region of registration in 1950, race, and AFQT. The number of cases is inflated to represent the population (i.e., the 1953 SSS sample) from which the present sample, in turn, was drawn. Space limitations require that this discussion note only the broad patterns found in the data. Obviously region has more effect on the probability of mobility among whites and blacks than does variation in AFQT. Whites in both regions at either end of the AFQT distribution are somewhat more likely than other men to move. No similar pattern is evident for blacks. The mobility of whites and blacks differs for only those men registered in the South.

Military Service and Migraton. For draftees military service was an event intervening between the time at which residence was observed (around 1950) and 1964. Table 3 shows the mobility difference between draftees and nonveterans. The data in Table 3 show that only in the lowest AFQT decile are draftees always more mobile than nonveterans. Since draftees in this decile also reported an average of two more years schooling than nonveterans, further analysis (not shown here)

Table 1. Percentage of Sample Who Were Regional In-Migrants; by 1964 Region, AFQT, and Race*

Race and 1964 Region

White Nonwhite AFQT

Interval Non-South South Non-South South

0-9 25.7% 6.3% 73.0% 1.5% 10-29 15.0 6.8 54.5 3.3 30-49 8.0 8.5 - 1 -1 50-79 5.1 10.0 20.9 9t 10.3f t 80-99 3.8 20.4 -J -J Total 8.5 9.5 57.4 2.6

* Tables 5-8 show actual number of men in the base used to calculate these percentages.

t Men in AFQT intervals 30-99 are combined.




Table 2. Migration Status 1950-64, by 1950 Region, AFQT, and Race

1950-64 AFQT Interval M igration

1950 Region Race Status 0-9 10-29 30-49 50-79 80-99 Total

Nonsouthern White Stable 74.6% 77.4% 76.6% 76.2% 70.6% 74.7% Intrareg. 19.5 19.0 20.6 21.6 26.6 22.4 Regional 5.8 3.6 2.9 2.3 2.8 3.9

100%= 1055 3003 3149 5018 5391 17616

Black Stable 74.2% 77.4% 80.6% 67.4% 82.1% 76.2% Intrareg. 19.3 17.8 16.9 23.6 17.9 18.6 Regional 6.6 4.9 2.5 9.0 0.0 5.2

100%= 275 349 160 89 39 912

South White Stable 58.4% 62.5% 64.8% 62.0% 51.0% 60.0% Intrareg. 14.2 11.6 13.7 17.2 22.5 14.9 Regional 27.4 26.0 21.6 20.8 26.5 24.5

100%= 1256 1967 1237 1276 790 6526

Black Stable 49.8% 46.0% 40.7% 37.1% t 48.1% Intrareg. 13.2 9.4 18.6 21.0 t 12.4 Regional 37.0 44.5 39.8 41.9 t 39.5

100% = 1,882 892 112 62 3 2,951

* Percentages may not add to 100.0 due to roundingerror. Sample size is inflated to represent the population from which the sample was drawn. See Tables 4-8 for actual numbers of men.

t Groups with fewer than 25 actual cases are not percentaged.

Table 3. Percentage Point Difference Between Draftee and Nonveteran Mobility, by 1950 Region, AFQT, and Race*

AFQT Interval 1950

Region Race 0-9 10-29 30-49 50-79 80-99 Tota I

Percent migrating within or outside region

White 9.6 3.8 -0.9 1.1 -7.1 -0.4 Non-South Black 5.7 -10.8 - -5.0t - -3.7

Percent migrating to non-South

White 17.1 3.8 0.4 9.9 6.7 5.0 South Black 9.0 -12.6 - 0.5t - 4.8

* Positive difference indicates that draftees were more likely than nonveterans to migrate. t Blacks in 30-99 AFQT intervals are combined. Actual sample size for nonsouthern blacks in this AFQT range

was 129 draftees and 60 nonveterans. Comparison of southern blacks in the 30-99 AFQT range involved 97 draftees and 21 nonveterans.

controlled for years of education. Even with that control, low AFQT draftees were still more likely to migrate than matched nonveterans. We may conclude, then, that in the lowest AFQT decile, the draftee's experience of forced mobility probably stimulates subse- quent geographic mobility. For southern whites above the 0-9 AFQT decile, military experience may increase regional mobility. Military service has no consistent positive effect on the mobility of nonsouthern men of either race or on southern blacks above the 0-9 AFQT decile.

Mean AFQT and Education. Concern that selection for one or another characteristic may distort analysis of the effects of mobility has

been a major preoccupation of previous research relating mobility to occupational status (Blau and Duncan, 1967; Duncan et al., 1972). Table 4 shows mean AFQT and years of schooling by race, 1950 region, and 1950-1964 migration status. In general the data show that when region of origin and race are controlled, differences among men in the three migration statuses appear small, although intraregional migrants in all four groups show somewhat higher mean AFQT levels and years of schooling than stable men or regional migrants. The data do not support the notion that regional migrants differ sharply from men who remain in their region of origin. Within racial groups the differences in mean AFQT or schooling related to region of origin are much greater




than those related to migration status. While white and black regional migrants resemble other men in their region of origin, their academic and schooling levels differ from those of men in the region to which they migrate.

REGION, RACE, MIGRATION STATUS,

AND EARNINGS

This analysis makes the following assumption: earnings differences among men with different migration statuses that remain after the men have been matched on region of employment, race, AFQT, and education are a result of migration status rather than of unmeasured variables. Previous analyses of earnings that related

geographic mobility to SSA earnings records have been unable to control variables such as education. In addition, these studies have looked for mobility effects on earnings only a year or two after migration occurred (e.g., Cox, 1971; Gallaway, 1969). By contrast, most of the migrants studied below probably moved several years before their earnings were measured in 1964.

Migration Status and Earnings. Table 5 shows mean 1964 earnings of whites employed in the non-South. Among whites of nonsouthern origin in all AFQT intervals, stable men gen- erally earn less than intraregional migrants.

Table 4. Mean AFQT and Years of Schooling, by 1950 Region, Race, and 1950-64 Migration Status

1950-64 Migration Status

1950 Achievement I ntra- Regional Region Race Measure Stable region Migrant Total

Non-South White Mean AFQT 56.1 60.5 51.4 56.9 Mean sch. 11.0 11.5 10.8 11.1

Actual N 3,036 856 134 4,026

Black Mean AFQT 24.7 25.4 22.1 24.8 Mean sch. 9.9 9.7 9.9 9.9

Actual N 439 105 33 577

South White Mean AFQT 36.6 43.3 36.0 37.5 Mean sch. 9.3 10.1 9.3 9.5

Actual N 857 195 317 1,369

Black Mean AFQT 10.3 11.0 11.1 10.7 Mean sch. 7.7 8.2 8.1 7.9

Actual N 360 99 325 784

Table 5. Deviations from Mean 1964 Earnings of Whites Employed in Non-South, before and after Adjustment for Years of Schooling, by Migration Status and AFQT


AFQT Deviation Intra- Regional Mean 1964 Interval from the Mean Stable region Migrants Earnings

0-9 Unadjusted $-129 $410 $48 $4,304 Adjusted -147 305 153

10-29 Unadjusted 70 -114 -191 5,202 Adjusted 14 -170 34

30-49 Unadjusted 41 -203 120 6,005 Adjusted 41 -313 433

50-79 Unadjusted -55 294 -400 6,494 Adjusted -9 156 -216

80-99 Unadjusted -472 1245 49 8,018 Adjusted -374 1050 -146

Tota I Unadjusted -113 713 -823 6,459 Adjusted -164 407 -7

Actual N 3,036* 856t 317t

* Numbers of cases within AFQT intervals 0-9: 80-99 are 332, 731, 641, 763, and 569, respectively. Numbers of cases within AFQT intervals 0-9: 80-99 are 74, 187, 169, 198, and 228, respectively. Numbers of cases within AFQT intervals 0-9: 80-99 are 63, 89, 67, 54, and 44, respectively.




Table 6. Deviations from Mean 1964 Earnings of Blacks Employed in the Non-South, before and after Adjustment for Years of Schooling, by Migration Status and AFQT



0-9 Unadjusted $-360 $-521 $145 $4,030 Adjusted -375 -521 149

10-29 Unadjusted -106 -407 136 4,053 Adjusted -281 -432 261

30-99 Unadjusted -265 442 467 4,430 Adjusted -265 387 533

Tota I Unadjusted -172 -107 118 4,107 Adjusted -293 -210 198

Actual N 439* 105t 325t

* Numbers of actual cases within AFQT intervals 0-9: 30-99 are 137, 158, and 144, respectively. t Numbers of actual cases within AFQT intervals 0-9: 30-99 are 33, 36, and 36, respectively. t Numbers of actual cases within AFQT intervals 0-9: 30-99 are 170, 105, and 50, respectively.

However, the overall earnings advantage of intraregional migrants over stable men is largely caused by the very large earnings advantage of intraregional migrants over stable men in the 80-99 interval. The very large earnings advantage of intraregional migrants in the 80-99 interval may indicate that characteristics other than those measured by migration status alone are at work. Although white in-migrants to the non-South earn less than nonsouthern whites, this disadvantage is removed when the impact of lower southern education levels is controlled.

Table 6 shows 1964 earnings for blacks employed in the non-South.4 When blacks in all AFQT intervals are compared, the earnings of both stable men and intraregional migrants are about the same, suggesting no earnings benefit from intraregional mobility. When the data are adjusted for schooling, the net advantage of all southern black in-migrants compared to all nonsouthern blacks is $504. Analysis of 1960 Census data (Masters, 1972) supports this conclusion as does a study using the 1967 Survey of Economic Opportunity data for black males aged 20-64 (Weiss and William- son, 1972) and Long's (1974) examination of 1970 Census data. These consistent findings may suggest an unmeasured factor favoring black southern in-migrants over black residents of the non-South or may represent a negative impact of nonsouthern socialization on blacks reared in the non-South.

Table 7 shows 1964 earnings for whites employed in the South. In brief, white in-migrants from the non-South have earnings above the

mean before and after adjustment for schooling. This overall earnings advantage is largely determined by the very high earnings of in- migrants in the 80-99 AFQT interval. Below the 80th percentile, in-migrants have earnings similar to those of stable men. After adjustment for schooling the earnings of stable men are virtually equal to those of intraregional migrants in the South, indicating no benefit of intraregional mobility on white earnings in the South.

Finally, for blacks employed in the South, Table 8 reveals little difference in earnings between stable men and intraregional migrants, thus indicating no benefit from intraregional mobility. Black in-migrants show earnings about equal to the mean earnings of all southern blacks but, when adjusted for schooling, their earnings are well below the mean. Small sample size restricts comparisons within AFQT intervals.

The Effect of Region on Earnings

Measures of the effect of regional mobility on earnings that neglect regional earnings differences are prone to error. These differences (derived from Tables 5-8) are shown in Table 9. The differences favor men working in the non-South and are very large. Among whites the regional earnings difference is rather stable across AFQT intervals. When education is controlled, 38 percent of the total regional earnings difference among whites is removed leaving an adjusted difference of $1,001.

Among blacks regional earnings differences (again favoring non-South workers) are larger than those among whites. Regional differences tend to increase with rising AFQT, although small sample size in the 30-99 AFQT inter-

4See Cutright (1 974b) for analysis of the earnings difference between white and black men.




Table 7. Deviations from Mean 1964 Earnings of Whites Employed in South, before and after Adjustment for Years of Schooling, by Migration Status and AFQT



0-9 Unadjusted $-110 $333 $346 $3,212 Adjusted -110 376 172

10-29 Unadjusted -34 79 220 4,298 Adjusted 5 -7 -6

30-49 Unadjusted 90 -109 -592 5,009 Adjusted 90 -109 -592

50-79 Unadjusted -29 154 -91 5,260 Adjusted 7 132 -91

80-99 Unadjusted -298 -675 1613 7,275 Adjusted -232 -786 1613

Total Unadjusted -152 116 941 4,839 Adjusted -56 -44 589

Actual N 857* 195t 1341

* Numbers of cases within AFQT intervals 0-9: 80-99 are 152, 214, 212, 186, and 93, respectively. t Numbers of actual cases within AFQT intervals 0-9: 80-99 are 36, 37, 40, 46, and 36, respectively. t Numbers of actual cases within AFQT intervals 0-9: 80-99 are 25, 28, 26, 25, and 30, respectively.

Table 8. Deviations from Mean 1964 Earnings of Blacks Employed in South, before and after Adjustment for Years of Schooling, by Migration Status and AFQT



0-9 Unadjusted $-45 $164 * $2,750 Adjusted -17 25

10-29 Unadjusted 49 * * 2,995 Adjusted 62

30-99 Unadjusted 55 * * 2,717 Adjusted 70

Tota I Unadjusted -7 33 -39 2,825 Adjusted 21 -9 -319

Actual N 360t 99: 33

* Fewer than 25 actual cases not shown. t Numbers of actual cases within AFQT intervals 0-9: 30-99 are 209, 105, and 46, respectively. $ Number of actual cases in the 0-9 AFQT interval is 56.

Table 9. Dollar Difference between Regions before and after Adjustment for Schooling Differences, by Race and AFQT

Race

White Black

AFQT Before After Before After Interval Adjustment Adjustment* Adjustment Adjustment*

0-9 $1,092 $901 $1,280 $1,203 10-29 904 770 1,058 1,058 30-49 996 949 - - 50-79 1,234 1,200 1,713 t 1,680 t 80-99 743 805 - - Tota l 1,620 1,001$ 1,282 1,166$

The regression coefficients giving the dollar value of one year of schooling within and across AFQT intervals by race, when multiplied by the regional difference in years of schooling within or across AFQT intervals by race, yield the dollar adjustment. Positive differences indicate higher earnings in the non-South.

t Men in the 30-99 AFQT intervals combined. t If regional differences on AFQT rather than schooling are applied, the adjusted difference is $1,016 for whites

and $1,169 for blacks.




vals may preclude a firm judgment. The effect of the regional difference on black earnings can be appreciated by considering that our data show that the value of an added year of school for blacks in the nation as a whole was $129 in 1964. Hence, the difference between the expected earnings of blacks with 5 compared to 12 years of school would be $1,032-a difference smaller than the adjusted regional earnings differences for blacks. The value of an added year of school for whites was $476 in 1964-hence the adjusted white regional earnings difference ($1,001) was equal to a little more than 2 years of added schooling.

Summary of Effects of Mobility on Earnings

Regional Migrants. White and black regional migrants from the South at every AFQT interval earn more than comparable whites and blacks who remain in the South. These differences are about equal to the regional earning differences in Table 9. It is unlikely (contrary to Bacon, 1973) that selection on unmeasured characteristics (e.g., prior work skills or work habits) accounts for this effect, especially when the age of departure from the South (the early twenties for most men) is considered. White and black southern migrants to the non-South have earnings equal to or greater than those of comparable nonsouthern men working in the non-South. High AFQT white regional migrants moving to the South earn much more than comparable southern whites-perhaps due to a selection factor. The few black regional migrants to the South earn less than expected, given their level of AFQT or education.

Intraregional Migrants vs. Stable Men. Among nonsouthern whites, intraregional migrants earned more than stable men, but this overall effect came primarily from the strong showing of migrants in the lowest and highest AFQT intervals. No benefit from intraregional migration was evident among nonsouthern blacks. Among southern whites the earnings differences between stable men and intraregional migrants varied irregularly by AFQT level. Overall there was no earning benefit for southern whites or blacks as a result of intraregional mobility.

DISCUSSION

Although a J-shaped relationship between academic achievement and mobility was observed among whites and southern blacks, no clear

pattern was evident among nonsouthern blacks. Among whites, mobility differentials may be slightly larger when years of schooling rather than academic achievement scores are used as an independent variable (Long, 1973). Be- cause AFQT and years of schooling correlate .68 and .48 among whites and blacks, respectively, a pattern of differences in mean years of schooling across mobility status codes also reflects a similar pattern of differences on academic achievement.

Regional migrants are quite similar on years of schooling and AFQT to men who remain in their region of origin-indicating little selection. Regional migrants differ from men already working in the region to which they move. Intraregional migrants tend to have somewhat higher AFQT and schooling than stable men within each region.

Military service may stimulate regional mobility among southern whites in all AFQT intervals, but among nonsouthern whites and blacks from both regions service had consistent positive effects on mobility only among men in the bottom AFQT decile.

In general, intraregional migrants had 1964 earnings little different from the earnings of similar men in the same region. Both black and white regional migrants to the non-South had 1964 earnings equal to or greater than those of comparable nonsouthern men. Re- gional migrants to the South (except for high AFQT whites) had earnings similar to those of southern workers. The impact of regional mobility on earnings is larger than one might expect from previous studies which took occupational status as the dependent variable (Duncan et al., 1972). This difference is probably due to regional differences in earnings levels within occupational groups. Regional earning differences also seem more likely than unmeasured attributes of regional migrants to account for the effects of regional mobility on poverty (Bacon, 1973). Further work assessing the effect of mobility on poverty status or earnings levels should consider differences in earnings levels between the place of origin and destination before attributing to migrants selective characteristics that explain differences in the earnings of migrants and stable men.

REFERENCES

Bacon, L. 1973. "Migration, Poverty, and the Rural South." Social Forces 51 (March):358-55.

Blau, Peter, and Otis D. Duncan. 1967. The Ameri- can Occupational Structuire. New York: Wiley.




Coale, A. J., and N. W. Rives, Jr. 1973. "A Statistical Reconstruction of the Black Popula- tion of the United States 1880-1970: Estimates of True Numbers by Age and Sex, Birth Rates, and Total Fertility." Population Index 39(Janu- ary): 3-36.

Cox, D. 1971. "The Effect of Geographic Mobility and Industry Mobility on Income: A Further Comment." Journal of Human Resources 6- (Fall) :525-7.

Cutright, Phillips. 1973. Achievement, Mobility and the Draft: Their Impact on the Earnings of Men. Social Security Administration, Office of Research and Statistics. Washington: Gov- ernment Printing Office.

. 1974a. "The Civilian Earnings of White and Black Draftees and Nonveterans." Ameri- can Sociological Review 39(June):317-27.

1974b. "Academic Achievement, School- ing and the Earnings of White and Black Men." Journal of Social and Behavioral Sciences 20- (Summer).

Duncan, Otis D., D. L. Featherman, and B. Duncan. 1972. Socioeconomic Background and Achievement. New York: Seminar Press.

Eldridge, H. 1965. "Primary, Secondary, and Re- turn Migration in the United States, 1955-60." Demography 2:444-55.

Fulk, B., and T. Harrell. 1952. "Negro-White Army Test Scores and Last School Grade." Journal of Applied Psychology 36(February): 34-5.

Gallaway, L. 1969. "The Effect of Geographic Labor Mobility on Income: A Brief Com- ment." Journal of Human Resources 4(Winter): 103-9.

Jencks, Christopher, M. Smith, A. Acland, M. Bane, D. Cohen, H. Gintis, B. Heyns, and S. Michelson. 1972. Inequality: A Reassessment

of the Effect of the Family and Schooling in America. New York: Basic Books.

Karpinos, B. 1966. "The Mental Test Qualifica- tion of American Youths for Military Service and its Relationship to Educational Attainment." Proceedings of the American Statistical As- sociation, Social Statistics Section. Washington, D.C.: American Statistical Association.

Lee, E. 1966. "A Theory of Migration." Demog- raphy 3:47-57.

Long, L. H. 1973. "Migration Differentials by Education and Occupation: Trends and Varia- tions." Demography 10:243-58.

1974. "Poverty Status and Receipt of Welfare among Migrants and Nonmigrants in Large Cities." American Sociological Review 39- (February):46-56.

Masters, S. 1972. "Are Black Migrants from the South to the Northern Cities Worse Off than Blacks Already There?" Journal of Human Resources 7(Fall) :411-23.

National Academy of Sciences. 1972. America's Uncounted People. Washington, D.C.: National Academy of Sciences.

Siegel, P. and R. W. Hodge. 1968. "A Causal Approach to the Study of Measurement Error." In H. M. Blalock, Jr. and A. B. Blalock (eds.) Methodology in Social Research, New York: McGraw Hill.

U. S. Commission on Population Growth and the American Future. 1972. Population Distribution and Policy. S. M. Mazie (ed.), Vol. V. of Commission research reports. Washington, D.C.: Government Printing Office.

Weiss, L. and J. Williamson. 1972. "Black Edu- cation, Earnings and Interregional Migration: Some New Evidence." American Economic Review 62(June)372-83.



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Special Issue || Region, Migration and the Earnings of White and Black Men