7
The Individual Economic Well-Being of Native American Men and Women During the 1980s: A Decade of Moving Backwards

Robert G. Gregory, Annie C. Abello, and Jamie Johnson

Introduction

The decade of the 1980s was one of the best ever for U.S. employment growth. Between 1979 and 1989, the employment-population ratio increased from 59.9 to 63.0 percent to reach the highest level since World War II. A commonly noted characteristic of the U.S. and other wealthy economies is that when job opportunities grow quickly, the least skilled and those who are disadvantaged in labor markets are able to do better (Okun, 1973). There is a strong up-draft effect. Low hourly earnings tend to increase relative to the median, and unemployment falls. Native Americans have always been disadvantaged in the labor market, and on the basis of aggregate job growth over the 1980s, their economic position should have improved.

The 1980s, however, were unusual. Despite strong job growth, the labor market conditions for low-skilled, low-paid men deteriorated. Real hourly earnings and employment fell (Katz and Murphy, 1992; Freeman and Katz, 1994). In such an environment, it might be thought that Native American men would fare badly. They tend to be over represented among the unskilled and have always found it difficult to find employment. In addition, the U.S. government reduced income support for Native American people during most of the period, and this, too, must have affected Native American incomes (Levitan and Miller, 1991). It is an interesting question whether, on balance, the income opportunities of Native American




Thanks to E. Klug for providing excellent research support.



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--> 7 The Individual Economic Well-Being of Native American Men and Women During the 1980s: A Decade of Moving Backwards Robert G. Gregory, Annie C. Abello, and Jamie Johnson Introduction The decade of the 1980s was one of the best ever for U.S. employment growth. Between 1979 and 1989, the employment-population ratio increased from 59.9 to 63.0 percent to reach the highest level since World War II. A commonly noted characteristic of the U.S. and other wealthy economies is that when job opportunities grow quickly, the least skilled and those who are disadvantaged in labor markets are able to do better (Okun, 1973). There is a strong up-draft effect. Low hourly earnings tend to increase relative to the median, and unemployment falls. Native Americans have always been disadvantaged in the labor market, and on the basis of aggregate job growth over the 1980s, their economic position should have improved. The 1980s, however, were unusual. Despite strong job growth, the labor market conditions for low-skilled, low-paid men deteriorated. Real hourly earnings and employment fell (Katz and Murphy, 1992; Freeman and Katz, 1994). In such an environment, it might be thought that Native American men would fare badly. They tend to be over represented among the unskilled and have always found it difficult to find employment. In addition, the U.S. government reduced income support for Native American people during most of the period, and this, too, must have affected Native American incomes (Levitan and Miller, 1991). It is an interesting question whether, on balance, the income opportunities of Native American Thanks to E. Klug for providing excellent research support.

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--> men improved in response to stronger aggregate job growth or deteriorated in response to the declining income opportunities of the less skilled and reduced government support. The employment growth of the 1980s particularly favored women; their share of total employment increased from 41.7 to 45.2 percent. They, too, were subject to a widening hourly earnings and income gap between those with labor market skills and those with less education and labor market experience. However, by 1989, white women from all parts of the income distribution had moved up the income ladder relative to men. Another issue, therefore, is whether Native American women shared in these gains. This paper is organized as follows. The next section reviews in broad terms the change in the ratio of the means of individual Native American and white incomes. The story is clear, stark, and generally depressing. At the beginning of the 1980s, the average income of a Native American male was just 62.5 percent of the average white male income. By the end of the decade, the income ratio had fallen to 54.4 percent. There is a similar story for women. At the beginning of the 1980s, Native American women reported incomes that were on average 77.0 percent of white female incomes. By 1989, the ratio had fallen to 69.8 percent. In economic terms, one of the most disadvantaged groups in the United States moved backwards. The third section of the paper examines the components of the income ratio change in terms of changes in annual earnings, annual hours worked, and earnings per hour. Native Americans have lost ground on all dimensions, but the greatest losses have been in terms of earnings per hour, followed by annual hours worked. The fourth section presents estimates-of-income equations for Native Americans and whites, which are the counterparts to human capital hourly earning equations. These equations summarize quite well the different outcomes for Native Americans and whites and enable us to focus on the effects of education, experience, marital status, and location on the shifting income relationship between the two groups. The fifth section then applies the same model to explain the income shifts in terms of changes in annual hours worked and earnings per hour. The sixth section draws some of the threads of the discussion together, while the last offers concluding comments. A Simple Method Of Analysis And Data Presentation The data for this analysis are drawn from the 5 percent public-use sample from the 1980 and 1990 U.S. censuses and include all respondents

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--> who identified as white1 or Indian (American) in response to the race question. Native American people are a small proportion of the U.S. population—approximately 0.5 percent—but their population is increasing quickly as a result of high fertility levels, declining death rates, reduced under enumeration in census collections, and increased self-identification.2 Note that in the present discussion, we do not address the issues that arise from increased self-identification and from the economic, social, regional, and tribal diversity of different groups of Native Americans. We begin with the income data, which represent aggregate annual income from all sources as reported by men and women aged 16-64 in the calendar years of 1979 and 1989. The data are often presented as the ratio of the means of the individual incomes of Native Americans and whites. The income ratio is a summary measure incorporating Native American-white differences in employment rates, hours of work, hourly earnings, welfare payments, and other income. We prefer to work with an income ratio in the first instance, even though hourly earnings is the usual focus of economic analysis. The income ratio is a better measure of well-being as it includes income from all sources and the effects of different employment rates. To help in understanding why average income is so different for Native Americans and whites and why the income ratio changed over the decade, the first method of analysis proceeds in two steps. The method is rather mechanical, but it provides a useful technique for focusing on aggregate changes, describing the data, and assessing the effects of changes in income dispersion on the mean income ratio. The changing economic circumstances of Native Americans have not been extensively analyzed, and it is useful to spend some time on basic summary statistics. The method is supplemented by a more detailed analysis in the next section. The method is described in the context of the male income ratio. The first step is to determine the position of Native American men on the white male income distribution ladder. That position will depend on individual 1   White includes white Hispanics. 2   Changing levels of self-identification and under enumeration in the census illustrate that population definition is a complex issue (Snipp, 1989:Chapter 3). The population of Native Americans, as measured by the U.S. census, increased 79 percent over the decade 1969-1979. About 60 percent of this growth is attributable to either increased self-identification on the census form or inadequate correction for under registration of Native American births (Passel and Berman, 1986). Over the decade 1979-1989, the Native American population increased a further 38 percent. Very little is known as to whether the increased self-identification imparts a bias to income and employment statistics. Our best guess is that for our purposes, the bias is small, but such a guess is based on little information.

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--> human capital characteristics, such as education and labor market experience, and the rate of return to those characteristics. Thus, on average, Native American men may be lower down the white income ladder because their average human capital characteristics are lower relative to whites or because their human capital delivers a lower rate of return. The second step is to determine the change in income compression, that is, the extent to which the white male income distribution ladder changes over the period. Other things being equal, the average income of those at the bottom of the income ladder will fall relative to white mean income if the income distribution among whites becomes less compressed. The change in the white income ladder can be thought of as a summary measure of economy-wide influences on income compression. More formally, we proceed as follows. White males at each date are ranked by reported income levels and the population divided into deciles. Native American men are then placed in each of these deciles according to their income. Then the income ratio, I/w is written as the sum of ten terms, each the product of three components, where superscripts I and W represent indigenous (Native American) and white, respectively. The first two components are used to provide measures of the position of indigenous men on the white income ladder; πi is the proportion of indigenous men whose income falls in the ith white income decile, and φi is the ratio of the indigenous to the white male income mean within each white income decile iI / iW. The third component, iW/ iW, measures white male mean income in each decile as a ratio of the overall white male mean income and is used to calculate income compression. Applying the above method, we find that in 1979, 19.1 percent of Native American men aged 16-64 received income that placed them in the same income range as the bottom decile of the white male income distribution (column 1 of Table 7-1). At the other end of the income ladder, 3.5 percent of Native Americans were in the same income bracket as the top 10 percent of white males. Native American men are disproportionately concentrated at the bottom of the income ladder and underrepresented at the top. By 1989, the ladder position of Native American men had further deteriorated (column 2 of Table 7-1). The proportion of Native American men in the top decile of the white distribution had fallen 25 percent, while the proportion in the bottom decile had increased 13 percent. Native American men lost income because they slipped down the income ladder.

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--> TABLE 7-1 Percentage of Native American Men and White and Native American Women Classified by White Male Income Deciles, 1979 and 1989 (percent) White Male Income Deciles Male Native Americans Female White Americans Female Native Americans   1979 1989 1979 1989 1979 1989 1st 19.1 21.6 32.8 24.6 36.2 30.0 2nd 16.0 17.2 18.4 18.0 21.7 23.7 3rd 14.9 14.8 16.0 14.9 17.9 17.3 4th 13.7 11.5 14.0 12.3 12.1 11.1 5th 8.7 9.4 7.9 9.8 5.6 7.0 6th 7.3 7.1 4.8 7.3 2.9 4.4 7th 6.3 6.2 2.7 5.3 1.7 3.0 8th 5.6 5.2 1.7 3.8 1.0 1.9 9th 4.9 4.3 1.0 2.4 0.6 1.1 10th 3.5 2.6 0.7 1.5 0.3 0.4   SOURCE: Census of Population and Housing 1980 and 1990. Public Use Microdata Sample (5 percent). In 1979, the Native American male with median income was positioned opposite a white male ranked at the 30th percentile. By 1989, the Native American male with median income had shifted to the 27th percentile of the white distribution. The combined effect of position on the income ladder and compression determines the income ratio, which is presented in Table 7-2. The entries along each diagonal are the actual income ratios in 1979 and 1989. Thus the row 1, column 1 entry is the 1979 actual male income ratio, 62.5. The off-diagonal term is a hypothetical income ratio that allows us to determine the effects of ladder and compression changes. Row 2, column 1 places Native American and white males in their 1989 ladder position and calculates the income ratio this would produce at the 1979 level of income compression. Thus, if income compression had not changed, the income ratio would have fallen from 62.5 to 57.4 percent. Native Americans lost 5.1 percentage points of relative income because they slipped down the white ladder. The remaining change from 57.4 to 54.4 percent is the result of the changed compression of the white income ladder. Native Americans lost 3.0 percentage points because the income distribution of whites widened. The slip down the ladder accounts for two-thirds of the decline in the income ratio.3 This suggests that influences particular to Native Americans 3   This assumes that ladder positions and compression are independent.

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--> TABLE 7-2 Native American and White Income Ratios, 1979 and 1989   Income Compression   Native American Men/White Men White Women/ White Men Native American Women/White Men Native American Women/White Women Native American Women/ Native American Men Ladder Position 1979 1989 1979 1989 1979 1989 1979 1989 1979 1989 1. 1979 62.5   35.8   28.3   77.0   45.2   2. 1989 57.4 54.4 49.0 45.4 34.2 31.7 69.7 69.8 59.5 58.2

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--> are more important than the general influences that were changing the degree of compression of the white income distribution. Even after accounting for growing inequality in the United States, there is a large Native American relative income decline that needs to be accounted for. Columns 3 and 4 of Table 7-1 document the large move up the white male income ladder that occurred among white females over the 1979-1989 decade. The proportion of white women who received income in the top decile of the white male income distribution increased from 0.7 to 1.5 percent, while the proportion in the bottom decile decreased from 32.8 to 24.6 percent. In 1979, the white woman who received median income for her group was placed at the 19th percentile of the white male distribution; by 1989, her ranking had moved up to the 25th percentile. Native American women also made gains. Columns 5 and 6 indicate that although they occupied the lowest ladder positions, they, too, unlike Native American men, moved up the ladder. The proportion of Native American women who received income in the top decile of the white male income distribution increased from 0.3 to 0.4 percent, while the proportion in the bottom decile fell from 36.2 to 30.0 percent. Columns 3 to 6 of Table 7-2 apply our simple technique to white and Native American women. For both groups, the move up the white male ladder increased their income relative to white males by a significant amount: from 35.8 to 49.0 for white women (row 1, column 3 to row 2, column 3) and from 28.3 to 34.2 for Native American women (row 1, column 5 to row 2, column 5). As remarked earlier, Native American women have made significant strides up the white male income ladder, especially when compared with Native American men, who slipped in ladder position. Both white and Native American women are still disproportionately positioned in the bottom half of the white male income ladder. They gained income from moving up the ladder relative to white males during the period, but the widening income distribution of the white male income ladder took some of those gains away. The size of these losses is quite significant: 49.0 to 45.4 for white women and to 31.7 for Native American women. The change in the income ratio therefore understates quite considerably the gains women made relative to men in similar circumstances to themselves at the beginning of the decade. Women have been making economic progress, but this has been hampered because they have been ''swimming upstream" against the increased inequality of the 1980s (Blau and Kahn, 1994). Columns 7 and 8 of Table 7-2 describe the position of Native American women relative to their white counterparts. The Native American-white female income ratio was 77.0 in 1979, but had fallen to 69.8 by 1989. Although Native American women made significant gains relative to

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--> white men, they did not keep pace with white women. Columns 9 and 10 of Table 7-2 show the change for Native American women relative to Native American men. Native American women made income gains of 28.8 percent relative to Native American men, a gain similar to that made by white women relative to white men. The economic balance is shifting between the genders as women of both groups are increasing their income share. Annual Income, Annual Earnings, Annual Hours Worked, And Hourly Earnings The decline in the annual income ratio could come from many sources. A comparison of rows 1 and 2 of Table 7-3 allows us to apportion the income ratio change to employment and nonemployment income, and a comparison of rows 3 and 4 then enables us to apportion the change in employment earnings into the change in annual hours worked and average earnings per hour employed.4 Each of the variables for Native Americans and whites—annual income, annual earnings, annual hours worked, and average earnings per hour—is divided by its respective working-age population, aged 16-64; the individual means of both groups are then expressed as a ratio. We also include the employment-population ratio at the time of the census (row 5) and the proportion of the population employed some time during the year (row 6). The lower annual income of Native American men arises from all three sources: lower income from nonemployment, fewer hours worked per year, and lower average earnings per hour. Native American and white men receive the same proportion of their income (8 percent) from nonemployment sources, and consequently the income ratio (row 1) and the earnings ratio (row 2) are approximately the same. Within the employment income category, the lower income for Native men is accounted for in roughly equal proportions between fewer hours worked per year and lower average earnings per hour. During 1979, Native American men worked 23 percent fewer hours during the year and were paid, on average, 19 percent less per hour. The proportions of earned and nonearned income are slightly different for women. Native American women received 16 percent of their 1979 income from nonemployment sources; for white women, the ratio is 4   It is noticeable that the total income ratio is lower than the ratios in each education category. This occurs because Native Americans are disproportionately represented in the low-income, low-education groups; see Appendix Tables 1 and 3 for the original data.

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--> a little less, at 14 percent. The difference between the income and earnings ratio therefore is quite small, 2 to 3 percentage points. The largest differences in income between white and Native American women also arise within the employment income category. Relative to their white counterparts, Native American women were employed in the labor market for 16 percent fewer hours per year and in 1979 received hourly earnings that were 11 percent less. As with men, the largest difference between Native American and white women is annual hours worked, rather than earnings per hour. We now turn to our primary concern, the change in the income ratio over the decade. There has been no change in the relationship between male income and earnings ratios and only a marginal shift for women. Therefore, the income ratio change between 1979 and 1989 arises almost completely from changes in annual hours worked and hourly earnings, rather than changes in nonemployment income. For both groups, the decline in relative earnings is more important. The data in Table 7-3 suggest that to explain changes in the income ratio, we should focus our attention on annual hours worked and earnings per hour and not on income from nonemployment. Since annual earnings, annual hours, and earnings per hour are linked by an identity, we could estimate equations for two variables and combine them to explain changes in the third. Alternatively, we could fit equations to explain all three variables and ignore the relationship between them. It seemed best to estimate an income equation consistent with the data of Tables 7-1 and 7-2; this we do in the next section. Then in the following section, we estimate equations for individual earnings per hour and annual hours worked. The advantage of this approach is that parameter estimates and their significance levels can be directly observed for all variables. There remains the question of model choice. We could have emphasized locational aspects, industry, and occupation of employment, but chose a simple methodology that seemed particularly appropriate given the paucity of economic research on the determinants of Native American income, employment, and earnings. We adopted a human capital model that stresses the role of education and labor market experience and, in the interest of parsimony, puts aside industry and occupation of employment.5 The models also use the same explanatory variables in each equation. We see this as an advantage as it consistently maps simple relationships that may point to relevant directions for the future. 5   This is obviously a simplification. It is not difficult to show there are industry and occupational effects on wages over and above the effects of human capital characteristics (see Dickens and Katz, 1987).

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--> TABLE 7-3 Income, Earnings, Annual Hours, Hourly Earnings, and Employment/Population Native American-White Ratios, 1979 and 1989   1979 1989 Percent Change 1989-1989   No High School High School Some College College Total No High School High School Some College College Total No High School High School Some College College Total Men Income 0.67 0.70 0.77 0.75 0.62 0.67 0.64 0.70 0.68 0.55 1 -8 -9 -9 -12 Earnings 0.67 0.71 0.77 0.74 0.62 0.67 0.65 0.70 0.68 0.55 0 -8 -10 -9 -12 Annual Hrs 0.75 0.81 0.88 0.91 0.77 0.75 0.77 0.86 0.90 0.74 0 -5 -3 -1 -3 Hrly Earnings 0.89 0.88 0.87 0.82 0.81 0.89 0.85 0.81 0.76 0.73 0 -4 -7 -8 -9 Emp/pop 1 0.75 0.83 0.88 0.93 0.78 0.73 0.81 0.87 0.94 0.77 -2 -2 -1 1 -1 Emp/pop 2 0.89 0.93 0.95 0.97 0.89 0.86 0.92 0.93 0.96 0.88 -4 -1 -2 -1 -1 Women Income 0.82 0.89 0.94 0.97 0.77 0.84 0.80 0.85 0.90 0.70 4 -10 -10 -8 -9 Earnings 0.75 0.90 0.95 1.02 0.75 0.74 0.77 0.84 0.91 0.67 -1 -14 -11 -11 -10 Annual Hours 0.80 0.94 1.03 1.08 0.84 0.78 0.86 0.94 1.03 0.81 -3 -8 -9 -4 -4 Hourly Earnings 0.93 0.96 0.92 0.95 0.89 0.95 0.90 0.90 0.89 0.83 2 -6 -3 -7 -7 Emp/pop 1 0.79 0.92 0.96 1.00 0.82 0.75 0.86 0.92 1.00 0.81 -5 -6 -5 1 -1 Emp/pop 2 0.91 0.99 1.03 1.04 0.90 0.85 0.94 0.94 1.00 0.88 -6 -5 -6 -4 -3 NOTES: All ratios computed based on mean values for working-age population (aged 16-64). See Appendix Table 1 for base data. Income = earnings + unearned income Hourly Earnings = annual earnings / annual hours Earnings = hourly earnings × annual hours Emp/pop 1 = no. of employed/population Annual Hours = weeks worked in the year × usual hours worked per week Emp/pop 2 = no. employed anytime within the year/population   The Income Equation A fuller development of the human capital model is found in Mincer (1974). But very briefly, the model explains individual hourly earnings in terms of formal education, labor force experience, and family attributes. When undertaking formal education, the student forgoes contemporaneous earnings in the labor market, which are thought of as an investment that subsequently receives a rate of return. It is the return to this investment that leads to higher income for workers with more education. With respect to the relationship between earnings and labor force experience, workers are thought of as investing in on-the-job training, for which they receive lower earnings when they are young; the gap between the lower earnings per hour during on-the-job training and the alternative market wage is further investment in human capital. More-experienced

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--> workers receive higher wages than those less experienced, part of which is a return to earlier investment. On-the-job training leads to a positive slope of the experience-earnings profile until the depreciation of human capital (represented by a quadratic or nonlinear component) begins to dominate the returns to investment, and the experience-earnings profile peaks and then declines. Finally, family variables, such as marital status, are included in the model. The link between these variables and human capital is not usually developed in any detail. Family variables can be thought of as reflecting motivation in the labor market and willingness to invest in on-the-job training (which is typically not measured in these data sets) and serving as proxies for interrupted labor force experience (which is also not measured in these data sets). A similar human capital analysis can be applied to decisions to seek

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--> With regard to the large decline in relative annual hours worked, most of the change is specific to Native Americans. Our research has provided no indication of how to interpret this Native American-specific effect. It could be because Native Americans are last hired and first fired, or it could be an indication of a supply response to large wage declines. The changing economic circumstances for women have been better. Native American women lost significant income relative to white women during the period—a 9 percent loss in the income ratio, a 7 percent loss in average hourly earnings, and a 4 percent loss in annual hours worked—but they gained income relative to Native American and white men. With the exception of those who had not completed high school, they experienced real income gains. Among Native American women with college degrees, for example, the increase in real income over the decade was 29 percent. Most of this increase is attributable to an increase in annual hours worked. The change in the economic circumstances of women is also largely attributable to changes in the labor market valuation of human capital characteristics. Thus, most of the changes in hourly earnings can be explained by economy-wide effects, while the opposite is true of hours worked. An important finding of this research is the role of economy-wide relative to Native-specific effects on the economic outcomes for Native Americans. The pattern is similar for both genders. Approximately half of the decline in the income ratio is attributable to changes in economy-wide coefficients and half to Native American-specific effects. The economy-wide effects dominate the change in hourly earnings, while the Native American-specific effects dominate the change in annual hours worked. With the exception of the effect on annual hours worked by women, changes in the education levels or labor market experience of Native Americans have exerted little influence on relative incomes. This result suggests that closing the income gap for Native Americans, or reversing the decline of the last decade, is not going to be easy. The need for Native Americans to increase their education, skill, and labor market experience if they wish to increase income levels seems even greater than in the past. If returns to the low-skilled continue to fall, Native Americans will need to improve their human capital characteristics substantially relative to whites just to maintain their relative income level. The large economic changes that occurred over the decade seem to suggest a new range of pressures on Native Americans. For example, what are the implications for the structure of Native American families when for women, income and hours worked are increasing considerably, while for men, income and hours worked are declining by very large

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--> amounts? What are the implications for the geographic dispersion of Native Americans as the income premium from living in metropolitan areas increases?16 The economy-wide effects on the distribution of American wages over the decade have been well documented, although the exact importance of different sources of these changes is not known. Some authors suggest that reduced trade union power, increased international trade, increased levels of low-skilled immigrants, and technological changes biased against the low-skilled have all made a contribution to reducing the income of the low-skilled (Freeman and Katz, 1994). It is not possible to forecast future changes, but it is not clear what will reverse these trends. If these trends continue, the economic fortune of Native Americans relative to their white counterparts is likely to continue to deteriorate. Snipp (1989) concludes his study of the Native American data from the 1980 census with the following comment: Despite these hardships, the future of the American Indian population is in some ways brighter today than it has been for a long time. Whether this will continue in the future is impossible to predict but the 1990 census will provide some very important clues. Those clues, at least with regard to the economic circumstances of Native Americans as a group, are rather depressing for men, but much brighter for women. On the basis of the 1990 census, we cannot say for Native Americans as a group that ''the (economic) future of the American Indian population is brighter today than it has been for a long time." One lesson is that we need to comment differently for men and women. Another is that judgments cannot be made on the basis of looking at Native Americans alone. To a considerable degree, the economic future of Native Americans is being determined by economy-wide changes and not just by changes that are specific to them, particularly with respect to the changes in hourly earnings. Whether some of the changes in annual hours are indeed Native American-specific effects or evidence of employment discrimination (the effect of which has increased with changes in the 16   As noted frequently throughout this volume, there is considerable economic variation among Native American peoples. This chapter treats Native Americans as a group. There should be considerable gains in understanding the large changes that are occurring once we begin to disaggregate the data. On average, those who live on reservations receive lower incomes than those employed in cities, mainly because job opportunities for the latter are limited (Snipp, 1989). Those who speak only a native language typically receive 40 percent less income than those who speak only English. Among families in which one spouse is Native American, median family income is 23 percent higher than when both family members are Native American. This chapter makes none of these or many other interesting distinctions. There would be considerable value in disaggregating the data further, but doing so would lead to a much larger study.

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--> economy) has yet to be determined. Of course, in the future there will be economic gains for Native Americans from gambling casinos and particular development projects, but the general changes that are currently occurring in the U.S. economy seem adverse for the majority of Native American men. References Blau, F.D., and L.M. Kahn 1994 Rising wage inequality and the U.S. gender gap. The American Economic Review 84(2):22-28. Dickens, W., and L. Katz 1987 Inter-industry wage differences and industry characteristics. Pp. 48-89 in K. Lang and J. Leonard, eds., Unemployment and the Structure of Labor Markets. New York: Basil Blackwell. Freeman, R.B., and L.F. Katz 1994 Rising wage inequality: The United States versus other advanced countries. Pp. 29-62 in R.B. Freeman, ed., Working Under Different Rules. New York: Russell Sage Foundation. Gregory, R.G., and A.E. Daly 1994 Welfare and economic progress of indigenous men of Australia and the U.S., 1980-1990. Centre for Economic Policy Research Discussion Paper 318. Juhn, C., K.M. Murphy, and B. Pierce 1993 Wage inequality and the rise in returns to skill. Journal of Political Economy 101(3):410-442. Katz, L.F., and K.M. Murphy 1992 Changes in relative wages 1963-87: Supply and demand factors. Quarterly Journal of Economics 107(Feb):35-78. Levitan, S.A., and E.I. Miller 1991 The Equivocal Prospects for Indian Reservations. Washington, D.C.: Center for Social Policy Studies, The George Washington University. Mincer, J. 1974 Schooling, Experience and Earnings. New York: National Bureau of Economic Research. Murphy, K., and F. Welch 1992 The structure of wages. Quarterly Journal of Economics 107(1):215-326. Oaxaca, J.R. 1973 Male-female wage differentials in urban labor markets. International Economic Review 14:693-709. Okun, A.M. 1973 Upward mobility in a high-pressure economy. Pp. 207-261 in A.M. Okun and G.L. Perry, eds., Economic Activity Volume 1. Washington, D.C.: The Brookings Institution. Passel, J.S., and P.A. Berman 1986 Quality of 1980 census data for American Indians. Social Biology 33:163-182. Sandefur, G.D., and W.J. Scott 1983 Minority group status and the wages of Indian and black males. Social Science Research 12:44-68. Snipp, C.M. 1989 American Indians: The First of This Land. New York: Russell Sage Foundation.

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--> APPENDIX TABLE 1A Income, Earnings, Annual Hours, Hourly Earnings and Employment/Population (1982-1984 = $100)   1979 1989 NA/White Percent change   White Native Am. White Native Am. 1979 1989       1979-1989 Men, N 251,862 16,346 559,977 34,592       Total income 20,817 12,911 22,480 12,330 0.62 0.55 -12 No high school 13,365 8,903 10,312 6,956 0.67 0.67 1 High school 19,346 13,499 18,400 11,857 0.70 0.64 -8 Some college 20,700 15,865 21,455 14,985 0.77 0.70 -9 College degree 31,719 23,646 38,948 26,465 0.75 0.68 -9 Earnings 19,220 11,910 20,650 11,278 0.62 0.55 -12 No high school 11,749 7,837 8,907 5,969 0.67 0.67 0 High school 17,908 12,707 16,960 11,039 0.71 0.65 -8 Some degree 19,200 14,825 19,872 13,846 0.77 0.70 -10 College degree 29,795 22,184 35,942 24,420 0.74 0.68 -9 Annual hours 1,704 1,307 1,765 1,314 0.77 0.74 -3 No high school 1,315 982 1,135 852 0.75 0.75 0 High school 1,788 1,440 1,815 1,394 0.81 0.77 -5 Some college 1,762 1,559 1,848 1,580 0.88 0.86 -3 College degree 1,954 1,777 2,089 1,880 0.91 0.90 -1 Hourly Earnings 11.28 9.11 11.70 8.59 0.81 0.73 -9 No high school 8.94 7.98 7.85 7.01 0.89 0.89 0 High school 10.01 8.82 9.35 7.92 0.88 0.85 -4 Some college 10.90 9.51 10.75 8.76 0.87 0.81 -7 College degree 15.25 12.48 17.20 12.99 0.82 0.76 -8 Emp/pop 1a 0.80 0.62 0.81 0.63 0.78 0.77 -1 No high school 0.65 0.48 0.59 0.43 0.75 0.73 -2 High school 0.83 0.69 0.82 0.67 0.83 0.81 -2 Some college 0.82 0.72 0.84 0.73 0.88 0.87 -1 College degree 0.90 0.84 0.92 0.86 0.93 0.94 1 Emp/pop 2a 0.88 0.79 0.89 0.78 0.89 0.88 -1 No high school 0.75 0.67 0.73 0.62 0.89 0.86 -4 High school 0.91 0.84 0.90 0.83 0.93 0.92 -1 Some college 0.93 0.89 0.94 0.87 0.95 0.93 -2 College degree 0.95 0.92 0.96 0.93 0.97 0.96 -1 NOTE: Sample includes working-age population (16-64 years). a See Appendix Table 2 for definitions.

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--> APPENDIX TABLE 1B Income, Earnings, Annual Hours, Hourly Earnings and Employment/Population (1982-1984 = $100)   1979 1989 NA/White Percent change   White Native Am. White Native Am. 1979 1989 1979-1989 Women, N 266,954 16,823 578,086 36,029       Total income 7,709 5,936 10,192 7,154 0.77 0.70 -9 No high school 4,475 3,652 4,130 3,489 0.82 0.84 4 High school 7,191 6,418 8,228 6,581 0.89 0.80 -10 Some college 8,599 8,058 10,825 9,180 0.94 0,85 -10 College degree 12,906 12,559 18,123 16,234 0.97 0.90 -8 Earnings 6,671 5,016 9,158 6,173 0.75 0.67 -10 No high school 3,450 2,584 3,199 2,382 0.75 0.74 -1 High school 6,330 5,667 7,338 5,659 0.90 0.77 -14 Some degree 7,593 7,191 9,872 8,287 0.95 0.84 -11 College degree 11,315 11,529 16,602 15,136 1.02 0.91 -11 Annual hours 944 796 1,164 942 0.84 0.81 -4 No high school 618 496 616 480 0.80 0.78 -3 High school 983 920 1,139 980 0.94 0.86 -8 Some college 1,064 1,091 1,298 1,217 1.03 0.94 -9 College degree 1,173 1,261 1,472 1,516 1.08 1.03 -4 Hourly Earnings 7.07 6.30 7.87 6.55 0.89 0.83 -7 No high school 5.59 5.21 5.19 4.96 0.93 0.95 2 High school 6.44 6.16 6.44 5.77 0.96 0.90 -6 Some college 7.14 6.59 7.60 6.81 0.92 0.90 -3 College degree 9.65 9.14 11.28 9.98 0.95 0.89 -7 Emp/pop 1a 0.56 0.45 0.65 0.52 0.82 0.81 -1 No high school 0.38 0.30 0.40 0.30 0.79 0.75 -5 High school 0.57 0.52 0.63 0.54 0.92 0.86 -6 Some college 0.62 0.60 0.72 0.66 0.96 0.92 -5 College degree 0.71 0.70 0.80 0.80 1.00 1.00 1 Emp/pop 2a 0.66 0.59 0.75 0.66 0.90 0.88 -3 No high school 0.47 0.43 0.52 0.44 0.91 0.85 -6 High school 0.66 0.66 0.72 0.68 0.99 0.94 -5 Some college 0.75 0.77 0.83 0.80 1.03 0.97 -6 College degree 0.79 0.83 0.86 0.87 1.04 1.00 -4 NOTE: Sample includes working age population (16-64). a See Appendix Table 2 for definitions.

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--> APPENDIX TABLE 2 Definitions of Variables Used in the Regressions   Dependent Variables Income Total income from all sources, 1979 and 1989, in real terms,a for those with real annual income greater than or equal to $500. Hourly Earnings Annual earnings divided by annual hours, in real terms, for those with positive annual hours and positive hourly earnings in said years. Annual hours Weeks worked in 1979 and 1989, multiplied by usual hours worked per week in 1979 and 1989, for those with positive annual hours and positive hourly earnings in said years.   Independent Variables Education   No high school DV: One if in or finished 11th grade or lower. High school DV: One if in or finished 12th grade. Some college DV: One if in or finished 1-3 years in college/beyond high school. College degree DV: One if in or finished 4th year of college or higher. Experience Age minus years of schooling minus 6. Marital Status   Single DV: One if never married. Married DV: One if now married. Other married DV: One if widowed, separated, or divorced. Location: MSA DV: One if county groups located within standard metropolitan statistical areas (SMSAs) or mixed SMSA/non-SMSA areas. NOTE: DV = (1,0) Dummy variable. a Deflated using CPI base year 1982-1984 (U.S. President, 1995. Economic Report of the President. Washington, D.C.: U.S. Government Printing Office, p. 341).

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--> APPENDIX TABLE 3 Variable Means for Income, Hourly Earnings and Annual Hours Regressions, 1979 and 1989   Men Women   1979 1989 1979 1989 Variables* White NA White NA White NA White NA N Income eq. 229,971 13,863 509,191 29,047 183,713 11,481 433,746 27,296 Hourly earnings eq. 213,709 12,439 481,847 26,425 160,926 9,548 395,520 22,859 Annual hours eq. 213,709 12,439 481,847 26,425 160,926 9,548 395,520 22,859 Ln Income 9.65 9.20 9.68 9.13 8.90 8.68 9.05 8.73 Education No high school 0.22 0.36 0.15 0.26 0.19 0.35 0.13 0.24 High school 0.34 0.34 0.32 0.37 0.39 0.35 0.34 0.35 Some college 0.22 0.21 0.29 0.28 0.24 0.22 0.32 0.32 College degree 0.22 0.09 0.24 0.09 0.17 0.07 0.21 0.09 Experience Experience 18.34 16.62 18.94 17.71 18.38 16.44 18.76 17.79 Experience sqd 538.74 455.18 526.83 467.20 552.04 451.80 527.42 472.52 Marital S. Married 0.66 0.59 0.63 0.53 0.58 0.49 0.58 0.46 Single 0.26 0.29 0.27 0.32 0.23 0.26 0.24 0.27 Other married 0.08 0.12 0.10 0.15 0.19 0.26 0.19 0.27 Location MSA 0.78 0.61 0.83 0.66 0.79 0.61 0.83 0.83

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--> APPENDIX TABLE 3 Variable Means for Income, Hourly Earnings and Annual Hours Regressions, 1979 and 1989   Men Women   1979 1989 1979 1989 Variablesa White NA White NA White NA White NA Ln Hrly Earnings 2.22 2.02 2.19 1.91 1.82 1.71 1.85 1.85 Education No high school 0.20 0.33 0.14 0.24 0.16 0.29 0.11 0.19 High school 0.34 0.36 0.32 0.37 0.40 0.38 0.34 0.35 Some college 0.23 0.22 0.29 0.29 0.26 0.25 0.33 0.35 College degree 0.23 0.09 0.25 0.10 0.18 0.08 0.22 0.10 Experience Experience 17.21 15.28 17.89 16.44 16.36 14.30 17.35 16.34 Experience sqd 482.25 389.70 474.08 406.83 453.05 353.24 455.92 401.55 Marital S. Married 0.66 0.59 0.62 0.53 0.59 0.52 0.58 0.50 Single 0.26 0.30 0.28 0.33 0.25 0.27 0.25 0.27 Other married 0.08 0.12 0.10 0.15 0.16 0.21 0.17 0.23 Location MSA 0.79 0.61 0.83 0.67 0.80 0.61 0.84 0.66 Ln Hours 7.41 7.17 7.43 7.16 6.99 6.87 7.11 6.96 Education No high school 0.20 0.33 0.14 0.24 0.16 0.29 0.11 0.19 High school 0.34 0.36 0.32 0.37 0.40 0.38 0.34 0.35 Some college 0.23 0.22 0.29 0.29 0.26 0.25 0.33 0.35 College degree 0.23 0.09 0.25 0.10 0.18 0.08 0.22 0.10 Experience Experience 17.21 15.28 17.89 16.44 16.36 14.30 17.35 16.34 Experience sqd 482.25 389.70 474.08 406.83 453.05 353.24 455.92 401.55 Marital S. Married 0.66 0.59 0.62 0.53 0.59 0.52 0.58 0.50 Single 0.26 0.30 0.28 0.33 0.25 0.27 0.25 0.27 Other married 0.08 0.12 0.10 0.15 0.16 0.21 0.17 0.23 Location MSA 0.79 0.61 0.83 0.67 0.80 0.61 0.84 0.66 a Income, hourly earnings, and annual hours data are in logs. Education, marital status, and location variables present the proportion of the sample in that category. Experience and experience squared are in years.

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