United States: Recent Economic Developments

This paper reviews economic developments in the United States during 1992–96. The paper briefly describes improvements in the national income and product accounts (NIPA) and some of their implications for the analysis of long-term trends in U.S. investment and saving. The paper highlights that the effect of the 1990–92 recession on employment was considerably less severe than the effect of the 1981–82 recession. During the 1990–92 recession, employment fell by 1½ percent, compared with a drop of 3 percent during the 1981–82 recession.

Abstract

This paper reviews economic developments in the United States during 1992–96. The paper briefly describes improvements in the national income and product accounts (NIPA) and some of their implications for the analysis of long-term trends in U.S. investment and saving. The paper highlights that the effect of the 1990–92 recession on employment was considerably less severe than the effect of the 1981–82 recession. During the 1990–92 recession, employment fell by 1½ percent, compared with a drop of 3 percent during the 1981–82 recession.

IV. Income Distribution and Macroeconomic Performance in the United States

1. Introduction

A number of indicators point to a widening in U.S. income distribution since the mid-1970s, while the proportion of households below the poverty line has increased. These developments have attracted a great deal of attention in the United States and have contributed to a critical reappraisal of government social welfare programs and tax policies. 1/

This paper examines trends in income distribution and considers the macroeconomic and other factors that have affected the distribution of income in recent decades. It finds that cyclical developments have an important effect on the income distribution, but that the increased skewness in the distribution since the mid-1970s is hard to explain on the basis of cyclical and macroeconomic factors alone.

Other factors that seem to help explain the widening of the distribution include the decline in the minimum wage relative to the average wage and the growth of information-technology investment, which has increased the wage premium paid to relatively skilled labor and possibly contributed to a steepening of the age-earnings profile. Other factors that also may have played a role include the aging of the baby-boom generation, the rise in single-female headed households, and the decline in the child-dependency rate. Moreover, the paper cautions that the income-based measures of skewness used in this study may exaggerate the extent of the increase in inequality and poverty, since they do not factor in changes in consumption expenditures, living standards, and economic mobility.

The paper is organized as follows. Section 2 illustrates the behavior of the most often cited measures of U.S. income equality and briefly compares them to data for other industrial countries. Sections 3 highlights findings from alternative measures of income distribution and discusses the factors that may have influenced income trends since the 1960s. Section 4 presents an empirical analysis of family income shares and tests the extent to which these shares have been affected by macroeconomic and other developments. Section 5 contains a summary of the principal conclusions.

2. Recent trends in the income distribution 1/

Real median income per family in the United States increased by over 200 percent between 1950 and 1979, before falling 1 percent between 1979 and 1994. 2/ Over the same 1979 to 1994 period, the income distribution appears to have become more skewed. The Gini ratio, which measures the extent to which the income distribution deviates from perfect equality, fluctuated narrowly around an average of 0.36 between 1947 and 1976. 3/ Subsequently, the Gini coefficient has increased, rising by 17 percent to reach 0.43 in 1993 (Chart 1).

Chart 1
Chart 1

UNITED STATES: INCOME DISTRIBUTION TRENDS

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Source: Bureau of the Census, U.S. Department of Commerce.

The upward trend in the Gini ratio since the mid-1970s appears to have mainly been the result of a rise in the average real incomes of the top quintile and a decline in the average real income of the bottom quintile. Between 1976 and 1993, the real mean income of households in the top quintile rose by 35 percent, while the real mean income of households in the bottom quintile fell by 12 percent. At the same time, the share of income accruing to the top quintile has increased sharply, while the income share of the bottom quintile fell. The top quintile’s income share reached 47 percent in 1993 compared to 43 percent in 1947, while the lowest quintile’s share fell to 4 percent in 1993 compared to 5 percent in 1947. The poverty rate—the share of households below the poverty line—increased from a historical low of 9 percent in the 1970s to 12 percent in 1993. 4/

Other major industrial countries generally have not experienced a similar widening of the income distribution. Atkinson (1996) compares trends in the income distribution for the United States and Europe. He finds that, unlike in the United States, income inequality tended to decline in many European countries during the 1970s. However, this downward trend ended during the 1980s, and income inequality has risen in a number of these countries, particularly in the United Kingdom. 1/ In contrast, data compiled by Deininger and Squire (1996) suggests that on average the income distribution has been relatively stable among other OECD and more developed countries since the 1960s.

3. Factors affecting the income distribution

A number of authors have focused on evidence of widening wage differentials for unskilled and skilled labor in explaining trends in the income distribution. For example, Buckberg and Thomas (1996) note that the differential between wages paid to college graduates and those paid to high school graduates rose sharply from 38 percent in 1980 to 53 percent in 1990. Competing explanations for this shift include the decline in the size of the U.S. manufacturing sector relative to the service sector, the effect of technological changes on the types of skills demanded in the labor market, and the increased penetration of imports from low-wage countries into the United States. 2/

Buckberg and Thomas find that the increase in the wage gap was mainly the result of the decline in the demand for relatively high-wage, low-skilled labor in the durable goods manufacturing sector and the effect of increased technological change (as proxied by business investment in computers). These factors were only partially offset by the rise in the supply of skilled workers (as proxied by college-educated workers) relative to less-skilled workers (as proxied by high school-educated workers). The authors reject the hypothesis that import penetration had a direct effect on wage differentials. However, they do find that the wage gap tends to be positively correlated with an increase in the real effective exchange rate, suggesting that a deterioration in U.S. competitiveness tends to adversely affect the wages of less-skilled workers. They also find that increases in the female participation rate had the effect of widening wage differentials across industries, possibly by increasing the relative supply of less-skilled workers.

Another factor that may help to explain income distribution trends is changes in lifetime earnings profiles. 1/ Census data suggest that the age-income profile has steepened sharply since the 1960s (Chart 2). The median income of householders aged 45 to 54 was only 18 percent higher than the median income of those aged 25 to 34 years in 1964; by 1994, however, the difference had increased to 60 percent. This development offers a possibly benign explanation of income distribution trends. The change in the lifetime earnings profile may reflect increasing returns paid to skills acquired on the job. In that case, the increased skewness of the income distribution may simply reflect the aging of the population and not necessarily be associated with a skewness in lifetime earnings.

Chart 2
Chart 2

UNITED STATES: INCOME AND AGE DEMOGRAPHICS

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Source: Bureau of the Census, U.S. Deportment of Commerce.

Changes in the distribution of wealth may have affected the income distribution by affecting the distribution of nonwage income. 2/ However, Weicher (1995) notes that between 1983 and 1989 the Gini ratio for the wealth distribution increased markedly, but by less than the income-based index. He suggests that, while household wealth is highly correlated with household income, the relationship seems to have weakened between 1983 and 1989. Wolff (1994) examines household survey data in 1962, 1983, and 1989 and suggests that the wealth distribution was relatively unchanged between 1962 and 1983, but confirms the increase in inequality between 1983 and 1989. He concludes that the increase over the latter period was the result of a rise in income inequality, an increase in stock prices relative to the price of housing, and low inflation. 3/

Concerns regarding income distribution trends have been mitigated by evidence suggesting that distributional mobility has been high. Cox and Alm (1996) analyzed mobility using University of Michigan survey data for the period from the mid-1970s to the early 1990s. The data suggest that a substantial proportion of those individuals in the lowest income quintile in 1975 had moved to the 4th or 5th quintile by 1991 (see tabulation below). The data also indicate that average income gains over the period were considerably greater for those individuals who were in the lower end of the income distribution in 1975. The authors also show that, while the most rapid rise in incomes was correlated with educational attainment, even those with high school diplomas or less education achieved real income gains during the period. 4/

Distributional Mobility 1/

(Percentage of households)

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A number of authors have examined the distribution of consumption as an alternative proxy for the relative well being of the U.S. population. The results show the consumption distribution to be considerably less skewed than the income distribution. For example, Cox and Alm (1995) note that average household income in the top quintile was 13 times higher than the average income in the lowest quintile, but that the ratio of consumption per person between the two quintiles was only 2 to 1. This was partly due to the fact that the size of households was greater at the top end of the income distribution and the redistributive effects of the tax system, as well as the fact that lower income households benefitted from government noncash transfer programs, which are not included in the Census data on income distribution.

Chart 3 suggests that trends in the distribution of consumption are less pronounced but similar to those for the income distribution. In 1972-73, the top quintile consumed 1.7 times as much as the average family; by 1993, the ratio had increased to 1.9. The data suggest that this trend was principally due to a relative worsening of the positions of the second and third quintiles. By contrast, consumption of the bottom quintile actually increased slightly relative to the average family between 1972-73 and 1993.

Chart 3
Chart 3

UNITED STATES: DISTRIBUTION OF INCOME AND CONSUMPTION

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Sources: Bureau of the Census, U.S. Department of Commerce; and Bureau of Labor Statistics, U.S. Department of Labor.

Slesnick (1993 and 1994) notes that the official Census data, which show a rise in poverty rates since the late 1970s and an increase in inequality since the 1970s, are distorted because they are income- and household-based. As a result, they do not account for demographic changes that have tended to reduce the size of households or for the effect of government tax and transfer programs. 1/ Slesnick shows that a consumption-based poverty rate, which takes into account the service flows from consumer durables, does not show a decline during the 1980s and 1990s. He also shows that consumption inequality has been relatively stable since the 1960s. Also, Cox and Alm (1996) show that the poor’s access to consumer durables has increased (see tabulation below), while their discretionary income has reached all-time highs. For households in the bottom quintile, spending on food, clothing and shelter was 45 percent of consumption in 1993, compared with 52 percent in 1973, 57 percent in 1950, and 75 percent in 1920. By contrast, Cutler and Katz (1991) argue that the distribution of consumption has been less skewed than the distribution of income, but that a trend toward greater inequality is evident.

Poverty and the Consumption of Durables 2/

(Percentage of households)

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Sociological factors also could be important in explaining the widening income distribution during the 1980s and 1990s. Only dual-income families have experienced relative income gains since the 1950s. In 1993, the median income for dual-income families was almost 1.4 times greater than the overall median household income, compared to a ratio of 1.2 in 1950. At the same time, the share of households with dual incomes rose from less than 20 percent in 1950 to almost 50 percent in 1993. By contrast, the median income of one-income married couples and single-parent households reached a historical low in 1993 as a share of the overall median household income (Chart 4). The effect of these trends on the income distribution was exaggerated by the fact that the share of households headed by a single female rose from less than 10 percent in 1950 to almost 20 percent in 1993.

Chart 4
Chart 4

UNITED STATES: INCOME AND FAMILY STRUCTURE

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Source: Bureau of the Census, U.S. Department of Commerce.

Cyclical economic conditions are also thought to play a role in shaping the income distribution. Cutler and Katz (1991) examine the effect of the expansion during the latter half of the 1980s and find that the decline in poverty rates was less than would have been expected on the basis of historical relationships. They conclude that this was not the result of demographic changes or the effect of generally weak wage growth. Instead, they attribute it to the secular decline in the relative size of the U.S. manufacturing sector, which reduced opportunities for the less-skilled.

4. Empirical analysis of factors affecting income distribution

The study by Cutler and Katz provides a convenient framework for analyzing the effects of various factors on income distribution. Their work was extended by re-estimating their equations for the period to 1993 in order to see whether their conclusions continue to hold when the effects of the 1989-90 economic downturn and subsequent recovery are included. In addition, their analysis was extended to consider the extent to which demographic and other variables might have explained the widening of the income distribution during the past ten years.

Table 1 replicates Cutler and Katz’s regressions for the period 1948 to 1993. Equations were estimated using household income shares for each quintile and the Gini ratio as dependent variables. The independent variables included the log of GDP per capita, the inflation rate, and the unemployment rate. 1/ In addition, a lagged dependent variable and a time trend were also included. The time trend was assumed to begin in 1976 on the basis of a Chow test, which suggested a structural break in that year. 2/

Table 1.

Macroeconomic Factors and the Income Distribution 1/

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Estimated using ordinary least squares (OLS); t-statistics are in parentheses.

The X2 statistic is the Lagrange multiplier test for first-order serial correlation.

The results are similar to Cutler and Katz’s findings. The unemployment rate appears to be a dominant cyclical predictor of changes in the overall index of the income distribution. Increases in the unemployment rate tend to widen the income distribution, lowering the income shares of the bottom three quintiles and increasing the share of the top two quintiles. Increases in the average per capita level of real GDP tended to narrow income distribution. However, the effect on the quintile shares was less even. An increase in GDP per capita tended to improve the position of the bottom and fourth quintiles at the expense, mainly, of the top and second quintile. The inflation rate was not found to be a significant determinant of the Gini coefficient or the quintile shares.

The size and significance of the coefficients on the lagged dependent variable suggests that macroeconomic shocks have a persistent effect on the income distribution. 1/ The trend variable, which began in 1976, also was found to be highly significant, suggesting that other non-cyclical factors contributed to the widening of income differentials since the mid-1970s. Chart 5 illustrates the importance of the trend variable. In particular, it shows that the out-of-sample forecasts of an equation that excludes the trend considerably underpredicted the Gini ratio after 1976.

Chart 5
Chart 5

UNITED STATES: ACTUAL AND PREDICTED VALUES USING HISTORICAL MACROECONOMIC RELATIONSHIPS

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Sources: Bureau of the Census, U.S. Department of Commerce; and Fund staff estimates.

In order to examine the factors that might have contributed to the widening of the income distribution, additional variables were added to proxy for demographic, sociological, and other developments. These variables were: the minimum wage as a ratio to average hourly earnings in the manufacturing sector, the share of families headed by single-mothers, the proportion of families headed by people over the age of 35, the child-dependency ratio, and the S&P 500 stock price index deflated by the Consumer Price Index. The results are shown in Table 2. 2/

Table 2.

Macroeconomic and Other Factors Affecting the Income Distribution 1/

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Gini ratio equation estimated using OLS; income share equations estimated using Seemingly Unrelated Regression Estimates (SURE); t-statistics are in parentheses.

The X2 statistic is the Lagrange multiplier test for first-order serial correlation. The statistics for the income-share equations were calculated from OLS regressions.

Wald test of the joint significance of the Independent variables, which is distributed X2.

In these new equations, the trend variable was no longer a significant determinant of the income distribution, indicating that the additional variables explained the increased skewness of the income distribution since the mid-1970s. 1/ In particular, the minimum wage ratio was negatively correlated with the Gini ratio and the top quintile’s income share and positively correlated with the income share of the lowest two quintiles. Chart 6 illustrates the correlation between the real minimum wage and the income distribution. In addition, the increased proportion of the population aged 35 and above also appeared to have contributed to the widening of the income distribution, by raising the top quintile’s share of income and reducing the share of the lower four quintiles.

Chart 6
Chart 6

UNITED STATES: INCOME DISTRIBUTION AND THE MINIMUM WAGE

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Sources: Bureau of the Census, U.S. Department of Commerce; and Bureau of Labor Statistics, U.S. department of Labor.

The growing proportion of families headed by single females was not a significant explanatory variable for the overall Gini ratio, but seemed to help explain the worsened position of the second quintile. 2/ The stock price index and the child-dependency ratio also were not significant determinants of the Gini ratio. However, the rise in stock prices and a decline in the child-dependency ratio had a limited negative effect on the income share of the fourth quintile. The inclusion of these other variables reduced the role of cyclical factors in explaining shifts in the income distribution. In particular, while the unemployment rate remained a significant determinant of the Gini coefficient, the level of real per capita GDP was no longer significant and was dropped from the regressions.

The discussion in the previous section suggests that technological changes may have increased the premium paid to skilled labor and contributed to the skewing of the income distribution. This observation is supported by the apparent correlation between the Gini ratio and a proxy for technological change—the share of business fixed investment devoted to information technology (Chart 7). 3/ Estimation results indicate a significant role for technology in explaining U.S. income distribution developments (Table 3). In particular, the rise in the share of business investment devoted to information technology is positively correlated with the Gini coefficient. Indeed, the estimates suggest that the 24 percent increase in the share of business investment devoted to information technology since 1976 explains just over 60 percent of the overall increase in the Gini ratio. The coefficient estimates also indicate that the effect of the increase in technology investment was to raise the income share of the top (fifth) quintile and to lower the share of the bottom four quintiles.

Chart 7
Chart 7

UNITED STATES: INCOME DISTRIBUTION AND TECHNOLOGICAL INVESTMENT

Citation: IMF Staff Country Reports 1996, 093; 10.5089/9781451839487.002.A004

Sources: Bureau of the Census, U.S. Department of Commerce; and Bureau of Economic Analysis, U.S. Department of Commerce (supplied by Hover).
Table 3.

Technology and the Income Distribution 1/

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Gini ratio equation estimated using OLS; income share equations estimated using SURE; t-statistics are in parentheses.

The X2 statistic is the Lagrange multiplier test for first-order serial correlation. The statistics for the income-share equations were calculated from OLS regressions.

Wald test of the joint significance of the independent variables, which is distributed X2.

The unemployment rate remained a significant determinant of the Gini ratio; a rise in the unemployment rate tended to improve the relative position of the top quintile, mainly at the expense of the middle three quintiles. The minimum wage also remained negatively correlated with the Gini ratio. However, somewhat surprisingly its effect on the bottom quintile was barely significant. The number of families headed by a single female and by someone aged over 35 years also were not significant determinants of the Gini ratio, but did appear to be positively correlated with the fourth quintile’s income share. The increase in the child-dependency rate was positively correlated with the lowest quintile’s share.

The significance of the explanatory variables in these regressions should be viewed with caution. For example, the possibility that a causal relationship exists between the explanatory variables was not considered. A case could be made that technological changes may have contributed to the trend in some of the sociological variables considered (e.g., number of dual income households, the number of single-female headed households, etc.). Similarly, the gap between the minimum wage and average hourly earnings would possibly be correlated with the cyclical position of the economy. The analysis also did not include a number of other variables that might also be significant determinants of the income distribution, including the distribution of the supply of skilled labor, wealth, etc.

5. Summary and concluding remarks

While there is substantial evidence that the income distribution in the United States has become more skewed since the mid-1970s, the significance of this trend is subject to considerable debate. In particular, income-based measures, such as those used in the analysis above, suffer from a number of drawbacks that may limit their usefulness in gauging trends in inequality. For example, the Census Bureau data excludes noncash income, does not take into account the effect of taxes on the distribution of income, and does not consider the effect of changes in family size. As a result, the data provide an imperfect measure of the distribution of consumption. The studies that have attempted to address these issues are not conclusive, but seem to suggest that the increase in inequality has been less than suggested by the income-based measures.

In addition, as is noted in Section 3, a number of other trends also suggest that the distribution of lifetime income and wealth may be less skewed than suggested by the income-based data. These include the aging of the baby boom generation, a steepening of the age-earnings profile, and increases in economic mobility, which may have contributed to an increase in annual measures of income inequality but may have had a lesser effect on the distribution of lifetime earnings. However, a comprehensive study of these issues would require examination of more disaggregated, consumption-based data, which is beyond the scope of the present exercise.

Subject to these caveats, the results presented here indicate that the trend increase in income inequality in the United States began around 1976. They confirm that the income distribution is sensitive to the business cycle and tends to widen during economic downturns. In particular, the income shares of the top two quintiles tend to rise with the unemployment rate, while the share of the bottom three quintiles tend to fall. Other macroeconomic variables, including real GDP per capita, the inflation rate, short-term interest rates, and imports as a share of GDP do not appear to affect significantly the income distribution.

The decline in the minimum wage relative to average hourly earnings appears to have contributed to the rise in income inequality, chiefly through its adverse effects on the lower quintiles. Evidence also suggests that technological factors have been a major source of the widening of the income distribution. In particular, the share of investment in information technology explained well over half the rise in the Gini coefficient during the past two decades. It is possible that technological changes have contributed to the steepened age-earnings profile discussed above.

A number of demographic and sociological variables also seem to be significant in explaining income distribution trends. In particular, the share of single-female headed households, the proportion of households headed by someone over the age of 35, and the child-dependency ratio help explain movements in income shares.

APPENDIX: Date Appendix

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References

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1/

While a number of studies have addressed the issue of whether the income distribution affects economic performance, this question is beyond the scope of the present study. Recent papers on the issue include Persson and Tabellini (1994), who identify a negative correlation between growth and the income distribution. They suggest that a skewed income distribution creates a demand for redistributive policies, which reduce economic efficiency. Galor and Zeira (1993) suggest that a skewed income distribution can stem from market inefficiencies (e.g., limits on access to credit markets), which also may limit growth.

1/

The data on income distribution described in this section are derived from the Current Population Survey (CPS), which is published by the Bureau of the Census. The data are based on a survey of the civilian noninstitutional population of the United States, i.e., excluding members of the armed forces living on military bases, and provide estimates of income before taxes received by each quintile of families ranked by income. The discussion focuses on data up to 1993, since changes in data collection methods are said to affect the comparability of the 1994 data to those of previous years. See also Barrionuevo (1993) for a description of recent trends in income distribution.

2/

The Census data are deflated using the Consumer Price Index, which has an upward bias. Nevertheless, using alternate price indices, such as the national accounts deflator for consumption expenditures, would still show a marked decline in the growth of real median income since the 1970s.

3/

A Gini ratio of 1 indicates perfect inequality, i.e., one family has all the income and the rest have none. A measure of 0 indicates perfect equality, i.e., all families have equal shares of income. For a more detailed description, see the Appendix.

4/

The poverty rate hit a cyclical peak of 12.3 percent in 1983.

1/

For example, the Gini ratio rose from about 0.23 in 1978 to about 0.32 in 1991, an increase of roughly 40 percent.

2/

For example, Katz and Murphy (1992) examine the effect of the decline in importance of the manufacturing sector in the United States on wage differentials and Krugman and Lawrence (1993) test whether trade has led to a reduction in U.S. wages relative to its low-wage trading partners. Richardson (1995) reviews the literature and concludes that import penetration caused a small but significant part of the increased income inequality in recent decades. However, he notes that the effect on economic well-being may have been offset by the effect of increased trade on economic growth. Blanchard (1995) notes that if the income distribution has been affected by an increase in the demand for skilled versus unskilled workers, the effect would be to increase overall unemployment.

1/

For example, see Cox and Alm (1996).

2/

Moreover, wealth may be a more appropriate measure of economic well-being since it may better proxy households’ permanent income, as well as their access to educational and other opportunities.

3/

Wolff also reports that the wealth gap between the races also widened considerably during the latter period. However, income distribution trends for the black and white populations seem to have been broadly similar.

4/

Cox and Alm also note that in 1994, 80 percent of the 400 richest Americans were self-made, i.e., they did not inherit their fortunes, while in 1984 only 63 percent of the richest individuals had created their own fortunes.

1/

Source: Cox and Alm (1996). The tabulation shows the movement of households from 1975 quintiles to 1991 quintiles. For example, 0.9 percent of households that were in the fifth quintile in 1975 moved to the first quintile in 1991.

1/

Slesnick also shows that poverty rates and measures of the change in income inequality have been biased by the use of the CPI index, whose consumption bundle is not representative of the consumption pattern of the poor.

2/

Source: Cox and Alm (1996). The poverty line is defined by the Census Bureau.

1/

The equations were estimated using ordinary least squares. In some cases, augmented Dickey-Fuller tests rejected the hypothesis that the dependent variables were stationary. However, since the dependent variables are bounded by zero and one, it was assumed that the evidence of nonstationarity was spurious. The fixed-weight GDP data was used as the proxy for real GDP, since chain-linked series are unavailable prior to 1959. For a detailed description of the data, see the Appendix.

2/

Chow tests were performed using sample break points between 1973 and 1983. The absence of a structural break in the relationship could only be rejected in the years 1973 through 1979, with a sample break point in 1976 providing the largest F-statistic (equal to 4.21). The regressions reported by Cutler and Katz used a time trend that began in 1983.

1/

In some cases, the x2 test did not reject the hypothesis of first-order autocorrelation of the residuals. In subsequent regressions, however, which included additional explanatory variables, the hypothesis of autocorrelation was rejected.

2/

The equation for the Gini coefficient was estimated using ordinary least squares, and the income share equations were estimated using the Seemingly Unrelated Regression Estimator (SURE) in order to test for the joint significance of the independent variables. For a detailed description of the variables used, see the Appendix. A number of other variables were also examined but found not to be significant. These included the female participation rate, the proportion of two-income households, the real 3-month treasury bill rate, imports as a percent of GDP, the percentage of household income earned from wages and salaries, and government transfer payments as a percent of GDP. Variables were excluded from the analysis on the basis of a Wald test of their joint significance in the income-share equations.

1/

The regressions were run over a shorter time period (1953 to 1993) than the previous regressions because observations on a number of the independent variables were not available prior to 1953.

2/

The significance of the variables in the income-share equations and their lack of significance in the equation for the Gini ratio likely reflects the disadvantage of using a summary index of the income distribution.

3/

This proxy is comparable to the one used by Buckberg and Thomas (1995). The ratio was constructed from estimates of real magnitudes from the fixed-weight national accounts series, due to the unavailability of the chain-linked data prior to 1959.

4/

The Lorenz curve graphs the cumulative percentage of families against the cumulative percentage of income. A 45-degree line represents perfect income equality, i.e., each family earns the some income.

United States: Recent Economic Developments
Author: International Monetary Fund