Another Look at Whether a Rising Tide Lifts All Boats



James R. Hines Jr.

University of Michigan and NBER



Hilary Hoynes

University of California, Davis and NBER



Alan B. Krueger

Princeton University and NBER







July 2001









This paper was prepared for the Russell Sage-Century Foundation project on Sustainable Employment Growth. The authors thank David Ellwood, Jonathan Parker and Robert Solow for helpful suggestions, and Melissa Clark, Christian Jaramillo, and Justin McCrary for helpful research assistance.

Another Look at Whether a Rising Tide Lifts All Boats

















ABSTRACT







Periods of rapid U.S. economic growth during the 1960s and 1970s coincided with improved living standards for many segments of the population, including the disadvantaged as well as the affluent, suggesting to some that a rising economic tide lifts all demographic boats. This paper investigates the impact of U.S. business cycle conditions on population well-being since the 1970s. Aggregate employment and hours worked in this period are strongly procyclical, particularly for low-skilled workers, while aggregate real wages are only mildly procyclical. Similar patterns appear in a balanced panel of PSID respondents that removes the effects of changing workforce composition, though the magnitude of the responsiveness of real wages to unemployment appears to have declined in the last 20 years. Economic upturns increase the likelihood that workers acquire jobs in sectors with positively sloped career ladders. Spending by state and local governments in all categories rises during economic expansions, including welfare spending, for which needs vary countercyclically. Since the disadvantaged are likely to benefit disproportionately from such government spending, it follows that the public finances also contribute to conveying the benefits of a strong economy to diverse population groups.







JEL Classification: E24, J23, J31, J28, H72.







James R. Hines Jr. Hilary W. Hoynes Alan B. Krueger

Office of Tax Policy Research Department of Economics Industrial Relations Section

University of Michigan University of California, Davis Firestone Library

701 Tappan Street One Shields Ave. Princeton University

Ann Arbor, MI 48109-1234 Davis, CA 94616-8578 Princeton, NJ 08544

jrhines@umich.edu hoynes@ssds.ucdavis.edu akrueger@princeton.edu



1. Introduction

President John F. Kennedy made famous the saying, "A rising tide lifts all boats." The American experience of the 1960s and 1970s, in which periods of rapid economic growth were accompanied by improved living standards for the disadvantaged, appeared to provide ample support for this view. Subsequent events have not always been supportive, however, as the steadily declining real earnings of low-wage workers during the economic expansions of the 1980s and early 1990s led many to question the ability of economic growth to ameliorate economic and social ills for the disadvantaged, and perhaps even for the median worker. This paper assembles evidence on the cyclicality of a number of important economic and social indicators since the early 1970s. A bottom line finding of our paper is that President Kennedy's shibboleth continues to hold water: the benefits of strong economic growth for the disadvantaged are at least as great as they are for the more advantaged, and the costs of a downturn are borne disproportionately by the disadvantaged.

Table 1 provides a brief summary of economic and social outcomes during the business cycle peak of 1989, the labor market trough of 1992, and the peak of 2000.(1) Although a number of factors contribute to the patterns in Table 1, the results provide a rough indication of the effect of the business cycle on economic and social outcomes for various groups. The table clearly indicates that good things tend to happen in good times. For example, the unemployment rate of African-Americans fell to its lowest level ever recorded in the economic expansion that culminated in 2000. In addition, between 1992-2000, the average real income of the bottom 20 percent of households grew more rapidly (15 percent) than did that of the middle 20 percent (12 percent), while the income of the wealthiest 20 percent of households grew the fastest (25 percent). In the 1989-1992 downturn, families at the bottom experienced the greatest relative decline in income (8 percent). The poverty rate also rose in that recession and fell in the subsequent growth period. Extreme poverty -- defined as having income less than half the poverty line for one's household size -- also moves with the business cycle, although it is less sensitive to business conditions because individuals in extreme poverty are less connected to the labor market. Undesirable social outcomes, such as criminal victimization and welfare participation, also appear to improve during expansions.

On the other hand, the high school dropout rate moves mildly countercyclically, perhaps in response to greater labor market opportunities (see, e.g., Card and Lemieux, 2000 ), and the single-parent rate has been growing secularly. Nevertheless, the picture that emerges from Table 1 is that a rising tide continues to lift all boats, the dinghies at least as much as the yachts, while a falling tide submerges many who are just barely staying afloat.

The behavior of employment, earnings, income and real wages over the business cycle are the outcomes that have been most thoroughly studied in the literature. Section 2 reviews and extends the evidence on the cyclical pattern of employment, earnings and real wages. The results point to a strong procyclical pattern of employment and work hours -- lower skilled individuals are particularly likely to find employment and work longer hours when the labor market tightens. In addition, real wages are mildly procyclical. We also find that changes in unemployment have a larger effect on family earnings and other outcomes at later stages of a recovery or recession, and we find some evidence of asymmetry over the cycle: the harmful effect of a one percentage point increase in unemployment during a downturn exceeds the helpful effect of a decline of equal magnitude during an upturn. Consequently, a less volatile economy (i.e., one with fewer downturns) is predicted to lead to better long-run outcomes than a more volatile economy with the same average growth rate.

The cyclical pattern of wage data is difficult to interpret because the composition of employment changes over the course of a business cycle. In Section 3 we use the Panel Study of Income Dynamics to examine the cyclical pattern of real wages for a balanced sample of individuals, following earlier work by Solon, Barsky and Parker (1994). Like theirs, our findings suggest that real wage gains accrue during a tight labor market even for a fixed set of workers; changes in the composition of the workforce tend to attenuate only slightly (if at all) the cyclical wage effect found in unbalanced samples. However, the responsiveness of wages to unemployment may have declined in the last two decades.

A more difficult question is whether the benefits of a high-pressure economy are lasting. Do they extend beyond the boundaries of a particular business cycle? If the benefits prove to be persistent -- e.g., by changing the long-run mix of jobs -- then a high-pressure economy has even more going for it than is commonly appreciated. Okun (1973), for example, presented evidence suggestive of "an upgrading of workers into more productive jobs in a high-pressure economy." The discussant of Okun's paper was none other than Alan Greenspan, who was skeptical of the long-term benefits of a high pressure-economy. "It is by no means clear to me," Greenspan remarked, "that class A employment [jobs with career ladders] can be promoted sustainably through high-pressure economic expansions." Our results in Section 3C provide suggestive evidence that a high-pressure economy makes it somewhat more likely that workers will move from dead end jobs to jobs with upwardly sloping seniority profiles.

Section 4 broadens our examination of the importance of cycles by looking at the impacts on crime, welfare participation, health, and education. Interestingly, work injuries, which are typically procyclical, declined considerably in the economic upturn of 1992-2000.

Lastly, the effect of strong economic growth on government finances has received little attention in the literature. Table 1 suggests that the federal government's financial position is particularly buoyant when the economy grows. The federal government budget deficit swelled to a seemingly intractable 4.7 percent of GDP at the depth of the 1992 downturn, while a surplus equal to 1.7 percent of GDP is estimated for 2000. Although cause and effect are difficult to distinguish, these figures suggest that the very strong procyclicality of federal government revenues may carry important implications for the economy. Indeed, the Treasury seems to be a major beneficiary of a strong economy.

In Section 5 we consider the effect of the business cycle on the level and distribution of government expenditures across spending categories. We focus on the state and local government level to exploit regional variability in economic conditions. The evidence indicates that all components of state and local government spending are procyclical, with capital spending (e.g., highways and parks and recreation) generally more procyclical than current spending (e.g., health and education). An important, and striking, exception is welfare spending, levels of which are not only procyclical but more strongly so than any other category of government spending. Since average individual needs for public assistance are countercyclical, the procyclicality of total welfare spending indicates that public generosity per welfare recipient is powerfully procyclical. Hence the cyclical nature of public finances reinforces the notion that the affluence associated with good economic times expands society's resources and thereby provides benefits to all income groups.



2. The Impact of the Business Cycle on Employment, Hours and Earnings

A. Previous Literature

The behavior of employment, earnings and wages are the outcomes that have received the most attention in the literature. In particular, prior research has examined the effect of business cycles and local labor market conditions on employment outcomes (Bartik 1991, 1993a, 1993b, and 1996, Blanchard and Katz 1992, Holzer 1991, Hoynes 2000a), real wages (Bils 1985, Blank 1990, Keane et al 1988, Solon et al 1994), racial differences in labor market outcomes (Bound and Holzer 1993, 1995), labor market outcomes of disadvantaged youths (Acs and Wissoker 1991, Bound and Freeman 1992, Cain and Finnie 1990, Freeman 1982, 1991a, 1991b, Freeman and Rodgers 2000), and family income, poverty and income inequality (Bartik 1994, Blank 1989, Blank 1993, Blank and Blinder 1986, Blank and Card 1993, Cutler and Katz 1991, Freeman 2001). These studies almost universally find that labor market outcomes are procyclical, with greater sensitivity among lower skilled groups.

The studies of disadvantaged youths relate labor market outcomes to local (typically Metropolitan Statistical Area or MSA) unemployment rates. That literature has consistently found that higher local unemployment rates lead to reductions in employment and earnings (Acs and Wissoker 1991, Bound and Freeman 1992, Cain and Finnie 1990, Freeman 1982, 1991a, 1991b, Freeman and Rodgers 2000), with larger effects for blacks, younger workers, and less educated workers (Acs and Wissoker 1991, and Freeman 1991b). Using microdata, Ellwood (1982) finds that extended spells of nonemployment among teenagers have a small effect on future employment prospects, but a large, adverse effect on future wages.

Other studies have examined how MSAs labor market conditions impact employment and wages in the population (Bartik 1991, 1993a, 1993b, 1994 and 1996, and Bound and Holzer 1993 and 1995). These studies estimate the impact of the growth and changing composition of MSA employment on area employment and earnings. The results differ somewhat across the studies, but they generally show that changes in labor demand lead to larger changes for blacks, younger persons, and those with lower education levels. The patterns seem to hold for men and women. Wilson (1996) carefully documents the decline in employment among low skill males since the late 1960s which he attributes to the fall in availability of jobs in central cities.

Hoynes (2000a) examines the effect of business cycles on the employment, earnings, and income of persons in different demographic groups defined by sex, education, and race. The business cycle impacts are identified using variation across MSAs in the timing and severity of shocks. The results consistently show that individuals with lower education levels, nonwhites, and low skill women experience greater cyclical fluctuation than high skill men. The results are the most striking when examining comprehensive measures of labor force activity such as the likelihood of full-time year around work. Government transfers and the earnings of other family members decrease the differences between groups, as business cycles have more skill-group neutral effects on family income than individual earnings. The evidence further suggests that the 1992 recession led to more uniform effects across skill groups than earlier cycles.

The studies of family income and poverty have typically used either national (Blank 1989, Blank 1993, and Bland and Blinder 1986, Cutler and Katz 1991) or regional (Blank and Card 1993) variation in unemployment rates or GNP as cyclical indicators. The studies find a consistent negative relationship between unemployment rates and inequality and poverty. In particular, Blank (1989) dissaggregates household income into many components and finds earnings and capital income to be pro-cyclical and some transfer income to be counter-cyclical. Overall, she finds greater variation in income over the cycle for those who are young, male, and nonwhite. More recently, Freeman (2001) used a pooled cross-state-time-series model to examine the impact of earnings, unemployment and inequality on poverty. He finds that decreases in unemployment or increases in real wages lead to declines in poverty.

Distinct from the above literature on labor market outcomes are empirical studies, dating back at least to Dunlop (1938), that examine the cyclicality of real wages. More recently, panel data have been brought to bear on this issue. This literature primarily uses aggregate measures of business cycles (national unemployment rates or GNP growth) and examines the degree to which real wages fluctuate with the business cycle, and whether changes in the composition of the work force over the cycle (e.g., more low-paid new entrants during upturns) confound procyclical movements in wages. Abraham and Haltiwanger (1995) provide a thorough review of this literature. A growing body of evidence uses panel data to hold the composition of the work force constant over the cycle by focusing on a fixed sample of workers. Bils (1985) uses the National Longitudinal Survey, and concludes that compositional changes have only a small effect on the cyclicality of real wages, while Solon, Barsky and Parker (1994) use the Panel Study of Income Dynamics and conclude that "the apparent weakness of real wage cyclicality in the United States has been substantially exaggerated by a statistical illusion," namely, changes in the composition of the work force. Solon, Barsky and Parker attribute their different conclusion from Bils's to his focus on young men, which misses changes in the age composition of the work force. Our reading of the evidence is that real wages have moved slightly procyclically since 1970, although we agree with Abraham and Haltiwanger (1995) that "the cyclicality of real wages is not likely to be stable over time."

An important issue that arises throughout the literature is whether one should use national, regional, or metropolitan area controls for business cycles. The main appeal of using the national cycle is that it is measured relatively precisely, and reflects movements in the aggregate economy. However, there are two principal weaknesses of using an aggregate cycle measure: first, it may pick up the influences of unmeasured aggregate variables; and second, it suffers from low power because the number of aggregate cycles is small. Furthermore, the use of an aggregate measure of the cycle does not exploit regional differences in the business cycle. In contrast, using regional or metropolitan area variation in labor market conditions leads to a substantial increase in the size of the estimation sample. This will, in general, lead to more precise estimates and allows for the estimation of models with unrestricted time effects. The time effects control for the unmeasured aggregate variables that are a concern in the aggregate models. Furthermore, some argue that labor market outcomes are more influenced by local variables than national variables (Blanchflower and Oswald 1994, Bartik 1994). However, using state or metropolitan areas introduces measurement error in the unemployment rate. In fact the Current Population Survey, the main data set used in this area, is not designed for reliable estimates of smaller MSAs. Another issue with using metropolitan samples is that the boundaries of these areas change (perhaps endogenously) over time and metropolitan areas do not cover the entire U.S.

Another estimation issue that arises is whether the data should be specified as a Phillips curve or a wage curve relationship. The Phillips curve relates the change in the dependent variable (e.g., log wages) to the level of the unemployment rate. The wage curve relates the level of the dependent variable to the level of the unemployment rate. In first differences, the wage curve relates the growth of wages to the change in the unemployment rate over the corresponding time period. The wage curve specification assumes that wages are higher when (or where) unemployment is low. The Phillips curve specification assumes that wages are growing when unemployment is low.

Blanchflower and Oswald (1994) promote the wage curve relationship. Card (1995) and Blanchard and Katz (1997) test whether wage data are more consistent with the Phillips curve or wage curve, and conclude that a Phillips curve provides a better description of wage data. Interestingly, Blanchard and Katz interpret the Phillips curve as a wage curve, in which the wage in year t-1 is proxying for the reservation wage. Because a wage curve specification has a more natural theoretical interpretation and fits the data (hours as well as wages) we use better than the Phillips curve specification, in the analysis below we estimate wage curve specifications. Our main qualitative conclusions are likely to be similar if a Phillips curve specification were used instead.

Our findings for the labor market, presented in sections 2 and 3, extend the literature by providing estimates through 1999 -- allowing us to analyze the impact of the sustained recovery and to examine whether this cycle is different from earlier cycles. We also examine in more detail the impact of cycles by exploring whether changes in unemployment have differential impacts in booms and busts or whether the length of the current boom or bust has an effect independent of the unemployment rate.



B. Estimating Impacts of Cycles Using Time Series Data

A natural starting point is to estimate the simplest model using annual time series data on average annual hours worked and unemployment rates. We use a sample of persons age 20-55 from the March Current Population Survey (CPS) covering the years 1975-1999 to calculate average annual hours worked in each year and combine it with BLS data on annual unemployment rates. The March CPS is an annual demographic file that includes labor market and income information for the previous year, at the individual and family level. The sample size is approximately 150,000 persons per year.(2)

Annual hours worked is averaged over workers and nonworkers and thus reflects changes in the employment rate as well as in the intensity of work. We use this "prime age" sample to minimize the impacts of early retirement and early schooling decisions. We estimate a specification that regresses the year-to-year change in the log of average annual hours worked (LNHRS) on the year-to-year change in the unemployment rate (UR). We chose this specification after exploring several different ones. The first-differenced specification consistently provided a better fit of the data than a Phillips curve specification (i.e., change on level). We use the first-differenced specification throughout the paper. The coefficient estimates and standard errors for the time series model are:



LNHRS = 0.010 - 0.015 ( UR ) - 0.0003 (Year) R2 =0 .87.

(0.002) (0.001) (0.0001)





A 3 percentage point decrease in the unemployment rate -- about the size of the reduction experienced in the recovery since the 1992 trough -- is associated with a 4.5 percent increase in average hours worked. To put this magnitude in perspective, note that annual work hours averaged 1,538 over this time period, and ranged from a low of 1,378 to a high of 1,675. At the overall average, hours would increase by almost 70 hours, or two weeks of full-time work a year.

Using the NBER national business cycle dating, we can allow the impact of the unemployment rate to differ in expansions (EXP) and contractions (REC). The coefficient estimates and standard errors are:



LNHRS = 0.011 - 0.014 (UR * EXP) - 0.017 (UR* REC) - 0.0004 (Year) R2 =0.87.

(0.003) (0.002) (0.002) (0.0002)





This suggests that a given change in unemployment rate has a bigger impact in a recession than in an expansion, although the differences are not statistically significant.(3)

To examine how stable the relationship is over time, we add a dummy for POST89 and interact it with the change in the unemployment rate. The estimates for that model are:



LNHRS = 0.005 - 0.009 POST89 - 0.015 UR + 0.004 UR* POST89 - 0.0003 (Year)

(0.003) (0.004) (0.001) (0.003) (0.0003) R2=0.90.





Although not precisely estimated, the results show that the impact of a change in unemployment rate has decreased in the last cycle.

An alternative cyclical indicator to the unemployment rate is the Federal Reserve Board's capacity utilization rate (CU). The capacity utilization rate captures the concept of sustainable practical capacity, and is equal to an output index divided by a capacity index. We were motivated to look at capacity utilization because Stock and Watson (1999) and others have highlighted that the price Phillips curve is much more stable if one uses the capacity utilization rate in place of the unemployment rate. The basic time-series first-difference model using capacity utilization yields the estimates:



LNHRS = 0.006 + 0.004 ( CU ) + 0.0001 (Year) R2 = .69.

(0.004) (0.001) (0.0003)





A 3.5 percentage point increase in the capacity utilization rate -- the increase in the last recovery -- is associated with a 1.4 percent increase in average annual hours. (We have divided the capacity utilization rate by 100 in the regression so a 3.5 percentage point increase is equal to 0.035.) This suggests a weaker effect compared to the unemployment rate. Like unemployment rates, the marginal impact of a change in the capacity utilization rate is larger in recessions:



LNHRS = 0.012 + 0.003 (CU* EXP) + 0.006 (CU*REC) - 0.0002 (Year) R2 = .73.

(0.005) (0.001) (0.001) (0.0003)





Here the coefficients are significantly different at the 10 percent level. Unlike the Phillips curve, however, the first-difference log of annual hours shows essentially the same degree of stability with either capacity utilization or the unemployment rate as the cyclical indicator. Adding POST89 dummy and interaction to the model generates the following estimates:



LNHRS = -0.001 - 0.017 POST89 + 0.005 CU - 0.002 CU* POST89 +0.001 Year R2 = .80.

(0.004) (0.006) (0.001) (0.002) (0.0004)





Like the unemployment rate results, we find that the impact of a change in the capacity utilization rate has decreased in the last decade, although this change is insignificant.

While these results provide a simple summary of the data, the use of aggregate data is somewhat limiting. In particular, the cyclical indicators (unemployment rates and capacity utilization rates) may, to some degree, pick up other unmeasured aggregate variables. In the next section we extend our analysis by presenting models that take advantage of regional variation in the timing and severity of cycles. This increases the power of the empirical analysis and, by including year dummies, controls for the effect of unmeasured aggregate variables that cut across regions.



C. Employment, Earnings and Wages and MSA-Specific Cycles

The aggregate regression will yield biased estimates if there are omitted factors that are correlated with the unemployment rate and that affect labor market outcomes (e.g., nationwide government policy changes). We follow other recent papers in the literature (for example Freeman and Rodgers 2000, Hoynes 2000a) by using Metropolitan Statistical Area (MSA) level data to take advantage of the substantial variation in business cycles across regions in the United States and account for time effects. As above, we start with a sample of persons age 20-55 from the March CPS. The analysis uses data from the 1977 to 2000 CPS surveys, which covers the years 1976 to 1999. In each year we calculate various labor market outcomes for each MSA identified in the CPS sample. In particular, we calculate the fraction employed at some time during the year (called the annual EPOP, or employment-to-population, rate), and mean values for hours worked, earnings, and hourly wages. Our measure of average wages is confined to workers, while the other outcome variables do not condition on work status. We also examine family outcomes including mean family earnings, income and poverty rates.(4)

Because we are ultimately interested in examining whether the responsiveness to cycles varies across groups, we form demographic groups defined by education (<12, 12, 13-15, 16+) , race (white, nonwhite), and sex.(5) The regressions are based on cell-level data where the cells are defined by MSA, year and demographic group. All regressions are estimated by Weighted Least Squares, using as weights the number of observations in each cell.

We will rely on the unemployment rate as our main measure of the cycle. The MSA level unemployment rates are available on an annual basis beginning in 1976 from the BLS Local Area Unemployment Statistics division.(6) Instead of using the national NBER dates of business cycles, we use the timing of the cycles at the Census division level. Specifically, we assigned cycle peaks and troughs for each of the nine regions by examining the local minimum and maximums in the division-level unemployment rates. Each MSA was assigned the cycle dates corresponding to the Census Division in which it is located.

These data allows us to estimate equations of the following form:

where yjmt is the mean labor market outcome such as mean real hourly wages for demographic group j in MSA m in time t and URmt is the unemployment rate in MSA m in period t. The regression also includes unrestricted effects for demographic group ( j ), MSA ( m ) and time (t). The identification of the key parameter, , comes from differences in the timing and severity of cycles within MSAs.

For comparability to the aggregate analysis above, we first relate the change in the log of average annual hours at the MSA-year-group level to the change in the aggregate unemployment rate. These results, shown in column (1) of Table 2, utilize nationwide time-series variability in the cycle. The MSA cell-level analysis generates essentially the same estimates that we find for the country as a whole. Column (2) adds fixed effects for demographic group and MSA, which do not substantively change the estimates. Column (3) replaces the national unemployment rate with the MSA-level unemployment rate. The results show that the coefficient on the change in the unemployment rate is about one-quarter lower at the MSA level: a three percentage point reduction in the mean unemployment rate is associated with a 3.6 percent increase in average annual hours in an MSA. This smaller effect may be due to measurement error in the MSA unemployment rate, or to different responses to local versus national labor market shocks. Column (4) adds fixed effects for demographic groups and MSAs, which does not alter the results. Adding time effects in column (5), however, reduces the effect of the unemployment rate by another third. This suggests that there are factors not being controlled for that are associated with higher unemployment rates and lower average annual hours worked.(7) All of the remaining estimates in this section are from models that control for year, MSA and demographic group.

Going beyond the use of annual hours as the labor market measure, Table 3 presents estimates for the full set of individual labor market and family outcome variables. The estimates in the top panel of the table are of the same specification as that used in Column (5) of Table 2. These estimates show that labor market outcomes are strongly procyclical. The results indicate that annual earnings are more procyclical that annual hours, while real hourly wages are less procyclical than wages or earnings. Average wages are particularly difficult to interpret when using pooled cross-section data since the mean is taken over a changing population if the composition of the work force changes. We will address this issue in the next section using panel data from the PSID.(8)

The last three columns in Table 3 examine the cyclicality of family outcomes. An analysis of families may differ from one of individuals in that families contain varying numbers of potential workers with differences in propensities for intra-family substitution of labor market activity. Furthermore, family income and poverty status depend on government transfers, which are strongly procyclical. The basic estimates show that, as expected, family earnings and income are strongly procyclical and poverty rates are countercyclical. Family income is less cyclical compared to family earnings (presumably due to countercyclical transfers). The results suggest that the 3 percentage point reduction in unemployment rates in the current recovery has led to a 0.6 percentage point reduction in the poverty rate. The actual decline in the family-level poverty rate was 2.6 points, so either other factors were at work, or the relationship has become stronger over time.

The bottom panel of Table 3 allows the impact of the unemployment rate to differ in recessions and expansions. As explained above, the cycles are dated using the nine Census Divisions. There seems to be an asymmetric impact of unemployment in recessions and expansions. For employment, hours and earnings, the impact of a change in unemployment rates is larger in recessions. The only exception is real hourly wages, which have a larger (but not statistically different) impact in expansions. These results imply that recessions tend to inflict a sharp amount of pain in a short period, while upswings lead to gradual improvements.

The models in Table 4 explore how the cyclicality varies across education groups and over time. The top panel in Table 4 adds to the base model interactions of the four education groups with the change in the unemployment rate. The results show that lower education groups (especially those without a high school diploma) are much more responsive to cycles than higher education groups. For example, the results for annual earnings show that a one percentage point increase in unemployment leads to a 2.5 percent reduction in annual earnings for those with less than a high school education, compared to a 1 percent decline for those with a high school education, a 0.5 percent decline for those with some college, and a 0.1 percent decline for those with a college degree or more. In further results not shown here, nonwhites have greater cyclicality than whites, while women experience less cyclicality compared with men, which seems to due in part to them behaving as added workers.

Referring back to Table 3, in the sample as a whole, family income exhibits slightly less cyclical variation compared to family earnings (-0.012 vs -0.009). This tendency is present for all education groups, but is much more pronounced for those with lower education levels. For example, among those with less than a high school education, a one percentage point increase in unemployment is associated with a 2.6 percent decline in family earnings but a 1.6 percent decline in family income, while among those with a college degree a one percentage point increase in unemployment leads to a 0.2 percent decline in family earnings and a 0.1 percent decline in family income. Thus, adding non-labor income to family earnings significantly reduces the differences in cyclical responses across demographic groups. This pattern was also found by Blank (1989), Blank and Card (1993), and Hoynes (2000a), and seems to be due to the impacts of countercyclical income transfer programs such as public assistance and unemployment compensation.

The bottom panel of Table 4 tests for a structural break in the effect of unemployment rates. In particular, we examine whether the most recent cycle (captured by the dummy POST89) differs from the earlier period 1976-1989. Like the aggregate regression above, the point estimates here generally show that sensitivity to the cycle decreased slightly in the 1990s. However, these differences are not statistically significant. It is possible, however, that our simple structural break in 1989 does not capture what is a more complicated time structure to the cyclicality. Our results are also consistent with Freeman (2001), who finds little change in the effect of the unemployment rate on poverty over time, conditional on changes in inequality and wage growth. These results suggest that the decline in the poverty rate in the 1990s is only partially a result of the tight labor market. Freeman's analysis suggests that factors such as declining inequality and the rising median wage in the latter part of the 1990s, apart from low unemployment, also played a role.



D. Does the Duration of the Recession/Expansion Have an Impact?

The specifications used above assume that a percentage point change in the unemployment rate has a uniform impact on labor market outcomes independent of the tightness of the market or point in the expansion or recession. Table 5 extends the analysis by including two additional variables: the duration of the recession and the duration of the expansion. The duration of the recession is measured as the number of years since the most recent peak (if in a recession, 0 otherwise). The duration of the expansion is measured similarly as the number of years since the most recent trough (if in a expansion, 0 otherwise). These duration variables are constructed using business cycle dates specific to each of the nine Census Divisions. This specification is a simple way to incorporate dynamic effects of the business cycle on labor market outcomes.

Panel A in Table 5 repeats the estimates of the base case specification in Table 3 for comparison. Panel B adds the duration variables to the base case specification. Adding these variables does not significantly change the importance of the unemployment rate. The point estimates on the duration variables show that, holding the change in unemployment rates constant, increasing the length of the recession by a year leads to a worsening of labor market outcomes, and increasing the length of the expansion leads to an improvement in labor market outcomes. The recession impacts are much larger than the expansion impacts, probably reflecting the fact that recessions are typically shorter and more intense than expansions. The test statistics reported in the table, however, indicate that the duration variables are jointly and individually insignificant.

Panel C in Table 5 presents estimates where we include interactions between the change in the unemployment rate and the duration of the expansion and contraction (as well as including the main effect of the change in the unemployment rate). This specification allows the impact of a given change in the unemployment rate to differ with years into the expansion or recession. These results show important and statistically significant impacts. For example, consider annual earnings. A one percentage point reduction in the unemployment rate in the second year into an expansion leads to a 0.6 percent increase in mean real earnings (0.002+2*0.0019) while the same reduction in the eighth year into an expansion leads to a 1.8 percent increase in mean real earnings (0.002+8*0.0019). These results could explain why such large improvements in earnings and family income were experienced toward the end of the 1990s.



3. Composition of Workforce and Jobs over the Cycle



A. Balanced and Unbalanced Samples of Workers from Panel Study of Income Dynamics



Employment and hours worked of less-skilled workers in particular tend to rise during an upturn in the economy, as indicated by Table 4. Even within narrowly defined demographic groups, the composition of the workforce could change over the business cycle. If lower paid workers are induced to join the labor force during an upswing, then the cyclical wage effects estimated previously will be understated -- that is, the average wage will be pulled down by lower paid new entrants. To explore the effect of a change in the composition of the workforce on the cyclical behavior of real wages, we extend the analysis of Solon, Barsky and Parker (1994) in Tables 6 and 7. These researchers examined the cyclicality of real wages for a balanced set of workers to prevent composition changes from affecting their results.

Table 6 uses the Panel Study of Income Dynamics (PSID) to explore the cyclicality of real wages for a balanced sample of individuals. Annual earnings data are currently available for 1967-96, collected in the 1968-97 waves of the survey. Following Solon, Barsky and Parker, we initially restricted the sample to male household heads age 25 to 59 who were continuously employed at least 100 hours each year from 1967 to 1987. Using this sample we calculated mean log real hourly earnings each year, denoted ln(Wt)(9) We regressed the year-over-year change in ln(Wt) on the change in the national unemployment rate and a linear time trend.(10) Results are reported in column 1 of Table 6. Column 2 reports the same estimated regression model, but uses the change in log real GNP as a cyclical indicator instead of the unemployment rate. Because the sample of individuals underlying these regressions is fixed, any effect of composition changes over the business cycle is removed. Columns 3-4 extend this analysis for a similarly defined sample of men who were continuously employed from 1976 to 1996.

For comparison, Table 7 presents analogous estimates for an unbalanced sample. In column 1 the dependent variable is the mean log real hourly wage. A varying, and less restrictive, sample of men was used to calculate the dependent variable each year; to be included in the sample in year t, the individual needed to work 100 or more hours in year t, and be older than age 16 in year t. Column 2 also uses an unbalanced sample, but first differences and regression adjusts the micro wage data. Specifically, we estimate the following model by weighted least squares:

ln (Wit) = 68 + ... + 88 + Xit + it

where ln (Wit) is the change in the log real wage from year t-1 to t, 68 ... 88 are coefficients on year dummies, and Xit is potential work experience (age minus education minus 6). In the second step regression reported in column 2 the coefficients on the year dummies are regressed on the unemployment rate in year t. Notice that because the regression model uses wage growth as the dependent variable, any wage gains from entering the labor market (which may be due to changes in the composition of jobs or employees) is missed in this specification, although these effects would be reflected in the Column 1 results. Columns 3 and 4 report analogous results for the 1977-96 period.

Preliminarily, it is reassuring to note that our point estimates for the 1968-88 period are very close to those found by Solon, Barsky and Parker (SBP), even though we made a few changes in the way we handled the data (e.g., applying sample weights, trimming outliers).(11) A 3 percentage point decline in the unemployment rate -- about the magnitude observed so far in the current expansion -- is associated with a 4 percent increase in real wages. The results for the balanced sample indicate a slightly stronger wage response to unemployment than do the results for the raw means in the unbalanced sample, but the differences among all three estimates (balanced sample, unbalanced means, and regression adjusted) are trivial. These results suggest that the mildly procyclical pattern of real wages displayed in Tables 3 and 4 are unlikely to be severely biased by a changing sample composition, especially in light of the fact that those results condition on demographic groups and education.(12)

Solon, Barsky and Parker, however, concluded that the balanced sample provides stronger support for procyclical wage behavior than the unbalanced, unadjusted data. The reason for this difference is that they weighted the wage data by hours worked in the regression corresponding to the one in column 1 of Table 7, because it is common to use total payroll divided by total hours worked as a measure of the hourly wage in macro models. We suspect that the hours weighting matters because, as shown previously, hours move with the business cycle, especially for less-skilled workers. Thus, changes in the composition of the workforce appear to be less important for the cyclicality of real wages than shifts in the share of hours worked by existing workers in different wage categories. To be comparable to the types of models estimated in Tables 3 and 4, however, we did not weight by hours. Moreover, the balanced data in Table 6 are not weighted by hours worked. If we do weight the hourly wage by hours worked in the unbalanced sample, however, we find that wage movements are about 50 percent more procyclical in the balanced sample than unbalanced one, similar to SBP.

A more important difference between their results and ours is suggested by the regressions for 1977-96. In the balanced panel, we find that the procyclical pattern of wages is statistically insignificant when the unemployment rate is used as the cyclical indicator, and about half as large as that found for the 1968-88 period. When real GNP growth is used as the cyclical indicator, however, the responsiveness of wages to economic growth in the latter period is quite close to that found in the earlier period. If the change in capacity utilization is used as the cyclical variable (not shown here), the results are in between: the coefficient on capacity utilization falls by a quarter in the latter period.

The coefficient on the unemployment rate is on the margin of statistical significance in the unbalanced samples. Interestingly, in contrast to SBP's results, in this period unemployment has a smaller magnitude in the balanced sample than in the unbalanced one. These results suggest that unemployment is becoming a less effective measure of labor market tightness, and that composition effects might even move in the opposite direction. Since real wage growth was particularly strong in the 1997-2000 period, it would be interesting to see if the results for the unemployment rate continue to hold when new wage data are available in the PSID. These results also highlight the added power obtained from identifying cyclical effects using regional differences in unemployment changes; the coefficient on unemployment in column 3 of Table 6, for example, is about equal in magnitude to that found with the SMSA level data, but here it is statistically insignificant, whereas it was significant when the disaggregated data were used.

We have not reported the corresponding regressions for women because the number of continuously employed female household heads and wives over a 20 year period in the PSID is fairly small (144 per year 1968-88 and 193 per year 1977-96). Nonetheless, when we estimated the analogous models for women, the results provided even less evidence of procyclical wage movement. For the balanced sample, for example, the coefficient on the unemployment rate was statistically insignificant, small, and positive in the 1968-88 period, and statistically insignificant and small, though negative, in the 1977-96 period.(13) Likewise, if we estimate separate models by sex using the CPS data in the previous section, we find that wages move procyclically for men, and neither pro- nor countercyclically for women.

Over the entire 1968-96 period, wages move procyclically in the PSID for men, and acyclically (but not statistically significantly) for women. If we estimate the model in Column 2 of Table 7 for the pooled 29 year period, for example, the coefficient on unemployment rate for men is -.011 (se = .003), and for women is -.002 (se = .004).

We should also note that we found relatively minor differences in the cyclicality of wages for different education groups using the PSID data, similar to Solon, Barsky and Parker (1994) and Swanson (2000). In the 1968-88 period, male high school dropouts exhibited more cyclically sensitive wages than did those with a high school or college degree. In the 1977-96 period, however, change in unemployment were not significantly related to wage growth for any of the education groups we examined.



B. Composition of Jobs

To control for shifts in the composition of jobs, we estimated a wage curve using data from the BLS's Employment Cost Index (ECI). Because the ECI is calculated for a fixed set of jobs, these results are unaffected by changes in the composition of jobs over the business cycle. Specifically, we regressed the proportionate change in the ECI (December to December) less the proportionate change in the CPI-UX1 on the change in the unemployment rate and a linear trend. We used data on the ECI for total compensation in the private sector, in the years 1980-99. The coefficient estimates (and standard errors) were as follows:

Real ECI Growth = -0.75 - 0.002 (Unemployment Rate) + .0004 (Year) R2 = .16.

(0.64) (0.002) (.0003)



Changes in unemployment are notably uncorrelated with changes in the ECI less inflation. Abraham and Haltiwanger (1995) reach the same conclusion using quarterly ECI data from 1976-93 and employment growth as a cyclical indicator.

On the one hand, these results suggest that whatever cyclicality in wages does exist is a result in changes in the mix of jobs. On the other hand, the PSID, CPS and the ECI suggest that real wages were not very sensitive to the unemployment rate in the 1980s and 1990s.



C. Do Workers Move to Jobs with Steeper Seniority Profiles in Good Times?

Okun (1973) argued that in a high-pressure economy employers are more likely to provide jobs that offer "a schedule of wage increases at regular intervals, fringe benefits and seniority privileges" and paid vacation to improve worker attachment. His empirical support was based primarily on the fact that high-wage industries such as manufacturing and construction tend to have relatively strong procyclical employment swings. We use the PSID to more directly test the hypothesis that workers tend to gravitate toward jobs with rising seniority profiles in a tight labor market.

Specifically, for each of 12 major industries we estimated a tenure slope by estimating a separate log wage equation that included years of tenure, potential experience and its square, sex, race, and years of schooling as explanatory variables, using the 1976 cross-sectional wave of the PSID.(14) The estimated tenure slopes ranged from virtually zero in Entertainment and Recreational Services, Mining, and Personal Services, to 2.8 percent higher pay per year of tenure in Finance, Insurance and Real Estate. We computed separate wage regressions for seven major occupations as well. The resulting returns to tenure ranged from nil for farmers to 2.2 percent per year for professional and technical workers. The estimated tenure slopes provide an indication of the extent to which jobs in particular industries and occupations offer upward sloping seniority profiles.

We then assigned each individual in the PSID the tenure slope corresponding to his or her industry in year t, and computed the change in each individual's industry-tenure slope between year t and t-1. We regressed this variable on the annual change in the national unemployment rate, sex, race, and potential experience and its square. The estimated coefficient on the change in the unemployment rate for the full sample is reported in column 1 of Table 8, while columns 2 and 3 report separate estimates for workers broken down by whether they earned more or less than median wage in year t-1. Columns 4-6 provide the corresponding estimates using the change in the occupation-based returns to tenure as the dependent variable.

The results provide some suggestive evidence that workers tend to gravitate towards jobs in sectors with steeper tenure-earnings profiles when the labor market becomes tighter. The coefficient on the unemployment rate change for the full sample is negative for both industry and occupation slopes, although only the former is statistically significant. Moreover, the significant shift in employment toward industries with steeper tenure profiles in good times appears to be driven primarily by the behavior of lower paid workers. The following calculation puts the magnitude of this effect in context, and suggests it is quite small in the aggregate. If the unemployment rate falls by 3 percentage points, the tenure profile is predicted to rise by .005 percent per year based on column 1. The average industry-based tenure slope is 1.9 percent per year across the whole sample, so a tighter labor market would only increase the average slope by a trivial fraction. Because only a minority of workers change industrial sectors in a year, perhaps it is unreasonable to expect a very large impact in the aggregate. In any event, the results do suggest that workers tend to gravitate toward jobs in sectors with steeper earnings profile when the labor market tightens.



4. Other Outcomes

A. Crime

Crime rates have dropped throughout the current economic expansion. Between 1991 and 1997 the total crime index dropped 17 percent. As shown in Table 1, violent crimes dropped by 27 percent in families with income less than $7500 and by 18 percent in families with income over $75000. Studies have found that crime rates respond to labor market opportunities, both unemployment rates and wage levels. This literature, summarized recently by Freeman (1999), shows that unemployment rates have a modest impact on crime, but that wages may be more closely correlated with criminal activity. Freeman and Rodgers (2000) use the Uniform Crime Reports to create a state level crime rate series. They explore the relationship between crime rates and unemployment at the state level. Freeman and Rodgers find that crime rates fell most rapidly in the states where unemployment fell the most. In particular, they find that a one percentage point decrease in unemployment is association with a reduction of 1.5 percent in the number of crimes per youth. Thus, the 3 percentage point reduction in the unemployment rate in this expansion would have reduced crime per youth by about 5 percent. Mocan and Rees (1999), studying criminal activity among juveniles, find that a one percentage point reduction in unemployment rates leads to a 0.4 percent reduction in the probability of selling drugs and a 0.3 percent reduction in the probability of committing robbery.



B. Welfare Participation

The 1990s have seen unprecedented reductions in welfare participation in the United States. Looking back at Table 1, the percent of the population receiving welfare fell from 5.2 percent in 1992 to 2.9 percent in 2000, the lowest rate in over 30 years. Overall, the national welfare caseload has fallen by more than half since its peak in 1993. Welfare caseloads tend to be countercyclical, with increases in periods of higher unemployment rates. However, the correlation between the business cycle and welfare participation appears to be stronger in the current cycle. As discussed in the Economic Report of the President (1999), significant changes in welfare programs and family structure may have masked the impact of cycles in the 1970s and 1980s.

A recent literature has explored the role that the strong labor market has played in the declining welfare caseload in the 1990s (see Blank, 2000b for a survey). Several studies use pooled cross-state data on welfare caseloads and economic conditions and estimate models that control for state fixed effects, time effects and state-specific time trends. The estimates in these studies vary somewhat, but overall they find that labor market conditions are important determinants of the welfare caseload (Blank 2000a, Council of Economic Advisors [CEA] 1997, Ziliak et al 2000). The CEA study estimates the relative contribution of the unemployment rate and welfare reform to the per-capita welfare caseload and finds that a one percentage point decline in each of two successive years leads to a 4 percent decline in the caseload in the second year. Blank and Zilliak, et al. find statistically significant but somewhat smaller effects of labor market conditions than the CEA study. Hoynes (2000b) finds that improvements in local labor market conditions lead to lower caseloads through increases in exits from welfare and reductions in recidivism.

We agree with Blank (2000b), who suggests that one should use caution in making conclusions based on the current research. She points out that many factors changed in the mid 1990s that could affect welfare caseloads, including large expansions in the Earned Income Tax Credit, minimum wage increases, welfare reform, and the very strong labor market. Blank argues that our ability to determine the relative importance of these factors is limited by the fact that the changes were coincident.





C. Health and Work Injuries

A surge in work-related injuries is usually an undesired side effect of higher productivity growth in an expanding economy (Smith, 1972). Injuries are expected to rise when unemployment falls because work intensity increases and because many inexperienced workers are hired. The anticipated rise in work injuries has not occurred in the latest business cycle, however. Since 1992, the number of work-related injuries and illnesses fell by an impressive 25 percent, from 8.9 to 6.7 per 100 full-time-equivalent workers. All major types of injuries have declined. Since 1992, missed-work-day cases are down by 36 percent for sprains and strains, 31 percent for broken bones, and 30 percent for carpal tunnel syndrome. Occupational fatalities are down by 13 percent, which suggests the trend toward safer working conditions is not a mere reporting phenomenon.

A regression of the change in the overall injury and illness rate per 100 workers on the change in the unemployment rate using data from 1973-91 yields:



(Injury Rate) = -0.11 - 0.35 ( UR ) R2 = 0.83,

(0.05 ) (0.04)

which is remarkably similar to Smith's (1972) estimate of the cyclicality of injury rates based on historical data. But if the equation is estimated with the small number of post-1991 years, the correlation is positive and has a p-value of 0.12.

Figure 1 displays the injury and illness rate each year and a prediction of the rate based on its pre-1992 relationship with the unemployment rate. In 1998, the latest year with available data, there were 5.9 million cases of work-related injuries and illnesses in the private sector, the vast majority of which were injuries. If the pre-1992 relationship between injuries and unemployment had held, one would have expected 3 million more injuries and illnesses in 1998 than the number actually recorded.

Interestingly, when Boden and Ruser (1999) find an inverse relationship between the state unemployment rate and work injuries across states in the 1990s, controlling for year and state fixed effects. This results suggests that injuries still move procyclically, but that this effect is masked by an even stronger, nationwide downward trend in injuries in the 1990s.

The cause of the trend toward fewer work injuries is something of a mystery. The decline in injuries is not due to shifts in employment toward jobs in safer industries. Instead, injuries and illnesses declined within most industries. For example, the incidence rate fell by 22 percent in manufacturing, 33 percent in construction, and 27 percent in services. The decline also does not correspond well to the timing of changes in federal OSHA policy, and is just as strong in states that run their own state OSHA programs as in those that are under federal OSHA.

Whatever the reason for the decline in injuries, the economic implications are sizable.

Viscusi's (1993) survey of compensating differentials finds that workers are willing to forego at least $33,000 in earnings (in current dollars) to take a job that entails no risk of injury as compared with one that carries a certain risk of injury, all else equal. If this figure is correct, the 3 million fewer injuries and illnesses than predicted in 1998 would be implicitly valued by workers at around $100 billion - a bounty that is not included in the Gross National Product. This amounts to a $1,000 raise per private-sector worker.



D. Education and Training

As indicated in Table 1, the likelihood that youth drop out of high school rises during the expansionary portion of the business cycle and falls during the contractionary portion. A countercyclical pattern of school enrollment has been documented carefully in studies by Gustman and Steinmeir (1981), Light (1995) and Card and Lemieux (2000), for example. This pattern likely arises because the opportunity cost of attending school is lower when the economy is depressed and good jobs are scarce. Likewise, the opportunity cost of attending school rises when jobs are plentiful. It is unclear, however, whether those who are induced to leave school early during good times return to school later on.

The cyclical pattern of job training is less well documented, and mixed. For non-college graduates, Lynch (1992) finds that higher local unemployment increased the probability of participation in an apprenticeship program but decreased the probability of receiving on-the-job training from an employer in the early 1980s. For a larger sample that included college graduates as well (again in the early 1980s) higher local unemployment increased the probability that young people who had completed school would take some off-the-job training.



5. Public Finances, the Business Cycle, and the Disadvantaged

The many beneficiaries of a strong economy include all levels of government. Rising incomes generate tax revenues that strengthen government finances, thereby facilitating expanded spending programs, tax cuts, debt reduction, or some combination of these three. Since the government may reasonably be expected to act as the agent of the citizenry, the benefits of government fiscal strength are received by citizens, though not necessarily in equal measure by all.

To the extent that economic expansion is associated with greater government spending, it follows that the disadvantaged are likely to receive many of the associated benefits. Government-funded activities provide services from which the disadvantaged benefit quite out of proportion to the tax obligations they incur to finance them. There are two reasons why economic fluctuations over the business cycle may have particularly strong implications for the disadvantaged: governments with greater revenue sources tend to spend money on programs that particularly benefit the disadvantaged, and rising incomes generally trigger tax obligations that increase most rapidly among higher-bracket taxpayers. To put the same matter differently, economic downturns entail spending cuts that negatively impact the disadvantaged without providing commensurate tax relief.

This section considers evidence on the pattern of government activity over the business cycle, and its likely impact on disadvantaged citizens. For this purpose, it is particularly useful to analyze data on American "states" (understood to include the District of Columbia), since states differ in the timing and magnitude of their economic cycles, and thereby offer 51 separate glimpses into the impact of the business cycle on public finances. Moreover, state and local fiscal activity is important in its own terms, representing by now a significant fraction of the economy. Finally, there are interesting questions concerning the impact of economic cycles on the ability of subnational governments to pursue countercyclical policies, particularly insofar as these policies affect the disadvantaged.



A. The Scope of Government

It is useful to review the scope of government activity in order to identify the potential impact of cycle-induced fiscal policy swings. Table 9 presents information on state and local government expenditures in the United States over the period 1952-1996. In 1996, state and local governments spent a total of $1.398 trillion, which represented 25.9 percent of personal disposable income in that year.(15) The largest single category of state and local government spending was public education, representing $399 billion, or 29 percent of the total. The next largest was welfare spending, at $193 billion, or 14 percent of the total, followed by health and hospitals, at $111 billion, or 8 percent of the total. Insurance trust expenditures also were 8 percent of total spending in 1996, followed by highways at 6 percent and police and fire protection at 4 percent.

The U.S. federal government has somewhat different spending priorities that reflect its own political situation. Its on-budget 1996 spending of $1.260 trillion included $266 billion for national defense, $226 billion for income security (much of it transferred to the states and included in their totals), $174 billion for Medicare, $119 billion for health, $52 billion for education, training, employment, and social services, $40 billion for transportation, $22 billion for natural resources and the environment, $18 billion for justice, and of course numerous other categories of expenditure.

The effects of American business cycles are evident from the simple patterns apparent in Table 9. Against a backdrop of steadily growing government, cyclical expansions are associated with growing state and local expenditures, while recessions are associated with spending slowdowns at the state and local level. For example, the recession of the early 1980s is reflected in the very modest growth ($36 billion, nominal) of total state and local expenditures between fiscal years 1981 and 1982, a figure that is small compared to nominal growth in other years and striking in light of the high rate of inflation during that time period. In order to identify more carefully the impact of business cycles on state and local fiscal activity, it is useful to examine the information available by comparing the experiences of American states over the cycle.



This section analyzes information on the spending activities of American state and local governments over the period 1977-1997. The following specification is useful in evaluating the impact of economic conditions on government fiscal activity:

in which Sijt is spending in category i, by state j, in year t. Yjt is personal income in state j in year t, Njt is the population of state j in year t, and 1i and 2i are parameters to be estimated. The dummy variable 1ij captures time-invariant state-specific effects on levels of spending in category i, while 2it captures time effects, and 3ij captures state-specific growth rates in spending on i.

Taking first differences of logs, the prior equation becomes:

in which ijt is the residual, equal to uijt - uijt-1.

The first two rows of Table 10 present estimates of the variant of this equation in which the Sijt variable equals total spending by state and local governments. The data analyzed in this regression are drawn from the U.S. Census of Governments, consisting of annual observations for the 50 states plus the District of Columbia over the period 1977-1997. The Census of Governments makes some efforts to verify that intergovernmental transactions (such as spending by state governments that take the form of transferring money to local governments for final spending) are counted just once in this tabulation. All of the regressions include time dummy variables (not reported) to capture the term. The regression reported in the first row of Table 10 omits state dummy variables, thereby implicitly imposing that the underlying expenditure growth rate is the same for all states, while the regression reported in the second row of Table 10 includes state dummy variables and therefore does not impose this equality.

The results indicate that total spending responds positively to higher income and higher population levels. The estimated 0.18 coefficient in the first row of Table 10 implies that a doubling of state income is associated (at a one-year frequency) with 18 percent higher state spending. The 0.67 coefficient likewise indicates that a doubling of state population is associated with 67 percent greater spending. Inclusion of state dummy variables in the regression reported in the second row of Table 10 changes these estimates only modestly.

It is hardly surprising that state and local government spending responds positively to personal income growth, since greater affluence typically is associated with demand for greater government services.(16) Furthermore, there is the widely documented "flypaper" effect that governmental receipt of cash windfalls or other revenue sources tend to be accompanied by greater spending (Ladd 1993, Hines and Thaler 1995, Strumpf 1998). It is, however, instructive to compare the spending results in the first two rows of Table 10 with those in rows three and four, in which the dependent variable is the capital component of state and local government spending. In these regressions income and population growth are again associated with greater spending, the difference being that the coefficients on income are now much larger. In the specification without state dummy variables, the estimated coefficient of 0.81 implies that a doubling of state income is associated with 81 percent greater capital expenditures. These results are consistent with the interpretation that governments fail to undertake worthwhile capital projects unless their finances are particularly strong, and in particular, that they cut back on capital projects in bad economic times.(17) Comparing the estimated income coefficients from the capital spending regressions reported in rows three and four of Table 10 with the estimated income coefficients from the non-capital ("direct") spending equations reported in rows five and six make this particularly apparent.

The results reported in Table 10 are robust to a variety of alternative specifications of the estimating equations. In particular, replacing the change in personal income with the change in state unemployment rate yields results with opposite signs - i.e., signs that again imply that state spending increases during strong economic times. Changing the dependent variable to the first difference of the log of per capita spending (and omitting the population variable on the right side) has only a small effect on the estimated coefficient on the change in log income. Inclusion of state dummy variables but omission of year dummy variables changes the results very little. Replacing OLS with a minimum-absolute-distance regression method, which is more robust to outliers, generates similar findings. And weighting the regressions by state population likewise had little impact on the results.

Further evidence of the impact of the business cycle on spending patterns is presented in Table 11, in which five separate spending categories - health and hospitals, education, highways, police, and parks and recreation - are distinguished. All categories respond positively to personal income growth, with the estimated short-run income elasticity highest for parks and recreation (0.95 in the regression without dummy variables capturing state-specific growth trends) and lowest for education (0.24). The result that these spending categories respond positively to economic expansions is robust to replacing the change in income with the change in the state unemployment rate, as well as replacing the dependent variables with per capita measures, weighting the regressions, using the minimum-distance regression method, and other straightforward specification checks.

Appendix Table 1 reports the results of reestimating the impact of the business cycle on major spending categories, using changes in state unemployment rates (in place of changes in log income) as indicators of business cycle movements. As the results in the table indicate, higher unemployment rates are associated with reduced spending, the strongest effect appearing for welfare spending.(18) The standard errors for these regressions are rather large, in part reflecting the imprecision of using state unemployment figures in place of personal income as measures of desired demand for public spending. Nevertheless, the patterns are consistent with the procyclicality of major government spending programs evidenced elsewhere. Other specification checks produced results that are similar to those reported in Tables 10-11.(19)

The disadvantaged benefit (along with the rest of the population) from greater government expenditures in these major categories, though the extent to which various groups benefit from the services provided by marginal expenditures is difficult to assess. The evidence on welfare spending reported in Table 12 suggests, however, that there may be a tendency to direct marginal resources in ways that benefit the disadvantaged.

Of perhaps the most consequence for the disadvantaged are government income maintenance programs, and it is for this spending category that the results are the most striking and at first glance perhaps the most paradoxical. Table 12 presents these regressions. The results reported in the first two rows of Table 12 indicate that total welfare spending responds positively to changes in local income, with a coefficient implying that a doubling of state personal income is associated with a 134 percent rise in total welfare spending. The relatively large standard errors make it impossible to reject the hypothesis that welfare spending is unaffected by changes in personal income, but the coefficient point estimates are positive. Similar results appear in other specifications, specifically, replacing changes in personal income with changes in state unemployment rates, defining dependent variables in per capita terms, removal of year dummy variables, and others.

What makes these results striking is the countercyclical nature of caseloads in major welfare programs such as AFDC, since economic downturns are responsible for greater numbers of individuals eligible for, and receiving, welfare payments.(20) In order for total welfare spending to respond positively to aggregate income growth, benefit levels per recipient of major programs must be strongly affected by the condition of local finances, in combination with the creation of new welfare programs at times when budgets make such initiatives feasible. Is it possible for benefit levels to exhibit such a strong dependency on local economic prosperity?

Were these purely cross-sectional regressions, performed at an international level, one might not find the results so puzzling. The United States has a much smaller fraction of its population in poverty than does India, yet per capita welfare spending in the United States vastly exceeds per capita welfare spending in India. The difference is obviously due to the much greater affluence of the United States, and its ability therefore to afford generous transfer programs. Even within the United States there are persistent differences between states in the ability and willingness to pay for income maintenance programs; these differences tend to be of the form that high income states with smaller recipient populations nevertheless spend more per capita on income maintenance than do low income states. The cross-sectional relationship for 51 states in 1997 is:



ln (Total welfare spending) = 9.924 + 1.420 (ln Income) - 1.313 (ln Population), R2 = 0.06.

(3.329) (0.998) (1.020)





Further evidence is available by examining categories of welfare expenditures, the determinants of which are estimated in the equations reported in lines 3-10 of Table 12. Cash assistance, welfare institutions, and categorical assistance all appear to respond strongly to changes in state income.(21) In all of these cases the estimated coefficients on changes in log income exceed unity, though in none of these cases is the estimated coefficient significantly different from zero. Nevertheless, the positive sign pattern is striking. Other types of welfare assistance show very little association with state income.

One possibility is that the one-year nature of the first differences of the spending equations imparts some kind of bias that obscures what might otherwise be the expected negative relationship between welfare spending and state income. A long first difference, covering the years 1977-1997 for 51 states, produces:



ln (Total welfare spending) = 0.232 + 1.332 (ln Income) - 0.422 (ln Population), R2 = 0.01,

(2.869) (2.667) (2.602)



which is reassuringly close to the panel results (though the standard errors are quite large).

One is left, therefore, with the impression that strong local economies contribute in a very important way to government spending and therefore economic outcomes for the disadvantaged. This analysis is necessarily somewhat imprecise about the magnitude of the associated benefits by income group, and it omits consideration of tax rate cuts during economic expansions and other ways in which the disadvantaged benefit from strong government finances. But the central fact is that greater affluence is shared through government operations. This implies that the costs of economic downturns are likewise shared, which makes such downturns particularly burdensome for those who stand to lose the most from reduced government activity.





6. Conclusion

The results in this paper highlight the value of maintaining strong, steady macroeconomic growth. Although growth does not cure all social problems, it serves to alleviate many of them. Hours worked and family income, in particular, tend to rise when the business conditions improve. The real wage rate, however, is only weakly procyclical, though there is no evidence that wages tend to fall during periods of economic growth. Nevertheless, workers tend to gravitate toward jobs in sectors with steeper seniority-wage profiles when times are good, and tend to gravitate toward dead-end jobs when times are bad. The main labor market lift from a rising economic tide comes about by expanding opportunities to work more hours in better jobs, particularly for lower-paid individuals.

Why does a rising tide provide so many more marginal workers the opportunity to work? The most obvious interpretation is that many of those who are unemployed, out of the labor force, or underemployed are constrained: they would like to work (or work more hours) at the going wage, but are unable to find employment. A cyclical upturn increases aggregate demand for labor. It also appears that wage rates are relatively sticky, both during a downturn and during an upturn. Consequently, for the most part, adjustments to employment and hours worked end up clearing the market, rather than wages. This simple model of the economy is consistent with the "wage curve" relationships documented in this paper.

Freeman (2001) notes that, despite its benefits, a rising tide has important limits when it comes to reducing poverty. He estimates that at the end of the 1990s, "close to 60% of adults in poor families were unlikely to be able to benefit much from the labor market" because they were disabled, retired, or had family obligations that prevented them from working enough to lift their families out of poverty. For this group, it is important to emphasize that that the public provision of goods and services, including income transfers, also tends to rise with a rising tide. Thus, a rising tide has the potential to raise the boats of individuals who do not have oars in the job market.

Two dark clouds associated with a sunny economy were noted. First, work injuries typically move procyclically - although the expansion of the 1990s is an exception to that pattern. Second, an upturn in the business cycle historically coincides with a slight increase in the high school dropout rate. The latter tendency suggests that focusing public policy on dropout prevention and skill development -- especially among those who are at a high risk of dropping out of high school -- during an upturn could make a good deal of sense.



References



Abraham, Katherine and John Haltiwanger. 1995. "Real Wages and the Business Cycle." Journal of Economic Literature 33, no. 3, pp. 1215-64.



Acs, Gregory and Douglas Wissoker. 1991. "The Impact of Local Labor Markets on the Employment Patterns of Young Inner-City Males." Mimeo, Urban Institute.



Bartik, Timothy J. 1991. Who Benefits from State and Local Economic Development Policies? W.E. Upjohn Institute for Employment Research. Kalamazoo Michigan.



Bartik, Timothy J. 1993a "The Effects of Local Labor Markets on Individual Labor Market Outcomes for Different Demographic Groups and the Poor." W.E. Upjohn Institute Working Paper 93-23.



Bartik, Timothy J. 1993b "Economic Development and Black Economic Success." Upjohn Institute Technical Report No. 93-001.



Bartik, Timothy J. 1994 "The Effects of Metropolitan Job Growth on the Size Distribution of Family Income." Journal of Regional Science 34(4):483-501.



Bartik, Timothy J. 1996. "The Distributional Effects of Local Labor Demand and Industrial Mix: Estimates Using Individual Panel Data." Journal of Urban Economics 40(2):150-178.



Bils, Mark. 1985. "Real Wages over the Business Cycle: Evidence from Panel Data." Journal of Political Economy 93(4) pp. 666-689.



Blanchard, Olivier J. and Lawrence F. Katz. 1992. "Regional Evolutions." Brookings Papers on Economic Activity 1:1992.



Blanchard, Olivier, and Lawrence F. Katz. 1997. "What We Know and Do Not Know about the Natural Rate of Unemployment," Journal of Economic Perspectives 11 (Winter): 51-72.



Blanchflower, David G and Andrew J Oswald. 1994. The Wage Curve. Cambridge, MA: The MIT Press.



Blank, Rebecca. 1989. "Disaggregating the Effect of the Business Cycle on the Distribution of Income." Economica 56:141-163.



Blank, Rebecca. 1990 "Why are Wages Cyclical in the 1970s?" Journal of Labor Economics 8(1):16-47.



Blank, Rebecca. 1993. "Why Were Poverty Rates So High in the 1980s?" In Poverty and Prosperity in the Late Twentieth Century, ed. Dimitri Papadimitriou and Edward Wolff. New York: St. Martin's Press.



Blank, Rebecca. 2000a. "What Causes Public Assistance Caseloads to Grow?" Journal of Human Resources.



Blank, Rebecca. 2000b. "Declining Caseloads / Increased Work: What Can We Conclude about the Effects of Welfare Reform," prepared for conference Welfare Reform Four Years Later, Federal Reserve Bank of New York.



Blank, Rebecca and Alan Blinder. 1986. "Macroeconomics, Income Distribution, and Poverty." In Fighting Poverty: What Works and What Doesn't, ed. Sheldon Danziger and Daniel Weinberg. Cambridge, MA: Harvard University Press.



Blank, Rebecca and David Card. 1993. "Poverty, Income Distribution and Growth: Are They Still Related?" Brookings Papers on Economic Activity, 2:1993.



Blanchard, Olivier and Lawrence Katz. 1997. "What We Know and Do Not Know About the Natural Rate of Unemployment", Journal of Economic Perspectives 11(1):51-72.



Bound, John and Richard Freeman. 1992. "What Went Wrong? The Erosion of Relative Earnings and Employment Among Young Black men in the 1980s" The Quarterly Journal of Economics, 107:201-232.



Bound, John and Harry Holzer. 1993. "Industrial Shifts, Skill Levels, and the Labor Market for White and Black Males". The Review of Economics and Statistics, August.



Bound, John and Harry Holzer. 1995. "Structural Changes, Employment Outcomes, and Population Adjustments Among Whites and Blacks: 1980-1990." Institute for Research on Poverty Discussion Paper #1057-95.



Boden, Leslie and John Ruser. 1999. "Choice of Medical Care Provider and Workplace Injuries," Mimeo., Washington, D.C., BLS.



Burke, Vee. 1996. "Cash and Noncash Benefits for Persons with Limited Income: Eligibility Rules, Recipient and Expenditure Data, Fiscal Years 1992-1994." Congressional Research Service Report 96-159 EPW (Washington, DC: Congressional Research Service).



Cain, Glen and Ross Finnie. 1990. "The Black-White Difference in Youth Employment: Evidence from Demand Side Factors." Journal of Labor Economics 8:S364-95.



Card, David. 1995. "The Wage Curve: A Review." Journal of Economic Literature 33:785-799.



Card, David and Thomas Lemieux. "Dropout and Enrollment Trends in the Post-War Period: What Went Wrong in the 1970s?" NBER Working Paper No. 7658, April 2000.



Case, Anne C., James R. Hines Jr., and Harvey S. Rosen. 1993. "Budget Spillovers and Fiscal Policy Interdependence: Evidence from the States." Journal of Public Economics 52: 285-307.



Council of Economic Advisors, "Technical Report: Explaining the Decline in Welfare Receipt, 1993-1996" Washington D.C. (1997).



Council of Economic Advisors, Economic Report of the President. 1999. "Benefits of a Strong Labor Market", Chapter 3.



Cutler, David M. and Lawrence F. Katz. 1991. "Macroeconomic Performance and the Disadvantaged." Brookings Papers on Economic Activity, 1:1991, 1-74.



Dunlop, John. 1938. "The Movement of Real and Money Wages," Economic Journal, 191, pp. 413-34.



Ellwood, David T. 1982. "Teenage Unemployment: Permanent Scars or Temporary Blemishes." In The Youth Labor Market Problem: Its Nature, Causes, and Consequences, ed. R Freeman and D. Wise. Chicago: University of Chicago Press.



Freeman, Richard. 1982. "Economic Determinants of Geographic and Individual Variation in the Labor Market Position of Young Persons." In The Youth Labor Market Problem: Its Nature, Causes, and Consequences, ed. R Freeman and D. Wise. Chicago: University of Chicago Press.



Freeman, Richard. 1991a. "Labour Market Tightness and the Mismatch Between Demand and Supply of Less-Educated Young Men in the United States in the 1980s." In Mismatch and Labour Mobility, ed. P. Schioppa. Cambridge: Cambridge University Press.



Freeman, Richard. 1991b. "Employment and Earnings of Disadvantaged Young Men in a Labor Shortage Economy." In The Urban Underclass, ed. C. Jencks and Peterson. Washington DC: Brookings Institution Press.



Freeman, Richard. 2001. "The Rising Tide Lifts...?" NBER Working Paper 8155.



Freeman, Richard and William Rodgers III. 2000. "Area Economic Conditions and the Labor Market Outcomes of Young Men in the 1990s Expansion", in Robert Cherry and William Rodgers III, eds., Prosperity for All, New York: Russell Sage Foundation, pp. 50-87.



Greenspan, Alan. 1973. Comment on Okun. Brookings Papers on Economic Activity, 1:1973, 256-57.





Gustman, Alan and Thomas Steinmeier. 1981. The Impact of Wages and Unemployment on Youth Enrollment and Labor Supply." The Review of Economics and Statistics 63, pp. 377-407.

Hines, James R., Jr., and Richard H. Thaler. 1995. "The Flypaper Effect." Journal of Economic Perspectives 9: 217-226.



Holzer, Harry J. 1991. "Employment, Unemployment, and Demand Shifts in Local Labor Markets." The Review of Economics and Statistics.



Hoynes, Hilary W. 2000a. "The Employment and Earnings of Less Skilled Workers Over the Business Cycle", in Finding Jobs: Work and Welfare Reform, edited by Rebecca Blank and David Card. Russell Sage Foundation: New York, pp. 23-71.



Hoynes, Hilary W. 2000b "Local Labor Markets and Welfare Spells: Do Demand Conditions Matter?", Review of Economics and Statistics, August.



Katz, Lawrence and Kevin Murphy. 1992. "Changes in Relative Wages, 1963-1987: Supply and Demand Factors." The Quarterly Journal of Economics, 107:35-78.



Keane, Michael, Robert Moffitt and David Runkle. 1988. "Real Wages over the Business cycles: Estimating the Impact of Heterogeneity with Micro Data." Journal of Political Economy 96:1232-1266.



Ladd, Helen F. 1993. "State Responses to the TRA86 Revenue Windfalls: A New Test of the Flypaper Effect." Journal of Policy Analysis and Management 12: 82-103.



Levy, Frank and Murnane, Richard J. 1992. "U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations." Journal of Economic Literature, 30(3), September, 1333-81.



Lynch, Lisa. 1992. "Private Sector Training and Earnings" American Economic Review.



Light, Audrey. 1995. "Hazard Model Estimates of the Decision to Re-enroll in School." Labour Economics 2, pp. 381-406.



Mocan, H. Naci and Daniel Rees. 1999. "Economic Conditions, Deterrence and Juvenile Crime: Evidence from Micro Data." NBER Working Paper 7405.



Okun, Arthur M. 1973. "Upward Mobility in a High-pressure Economy." Brookings Papers on Economic Activity, 1:1973, 207-52.



Poterba, James M. 1994. "State Responses to Fiscal Crises: The Effects of Budgetary Institutions and Politics." Journal of Political Economy 102: 799-821.



Poterba, James M. 1995. "Capital Budgets, Borrowing Rules, and State Capital Spending." Journal of Public Economics 56: 165-187.



Rubinfeld, Daniel L. 1987. "The Economics of the Local Public Sector." In: Alan J. Auerbach and Martin Feldstein eds., Handbook of Public Economics, Volume 2 (Amsterdam: North-Holland), pp. 571-645.



Smith, Robert S. 1972. Indusrial Relations Research Association, Proceedings.



Solon, Gary, Robert Barsky, and Jonathan Parker. 1994. "Measuring the Cyclicality of Real Wages: How Important Is Composition Bias?" Quarterly Journal of Economics 109(1), February, pp. 1-26.



Stock, J.H. and M.W. Watson (1999), "Forecasting Inflation," Journal of Monetary Economics 44, pp. 293-335.



Strumpf, Koleman S. 1998. "A Predictive Index for the Flypaper Effect." Journal of Public Economics 69: 389-412.



Viscusi, W. Kip. 1993. "The Value of Risks to Life and Health," Journal of Economic Literature, Vol. 31(4), Dec., pp. 1912-1946.



Wilson, William J. 1996. When Work Disappears: The World of the New Urban Poor, New York: Knopf.



Ziliak, James, David Figlio, Elizabeth Davis, and Laura Connelly. 2000. "Accounting for the Decline in AFDC Caseloads: Welfare Reform or Economic Growth?" Journal of Human Resources.





























Table 1: Economic and social outcomes over recent business cycles
Peak Trough Peak
1989 1992 2000
Unemployment Rate1
Overall 5.3% 7.5% 4.0%
White 4.5% 6.6% 3.5%
Black 11.4% 14.2% 7.6%
Hispanic 8.0% 11.6% 5.7%
Poverty Rate*2
Overall Poverty Rate 12.8% 14.8% 11.8%
Extreme Poverty (<.5 poverty line) 4.9% 6.1% 4.6%
Average Household Income*3
Bottom 20 Percent $9,433 $8,654 $9,940
Middle 20 Percent $38,862 $36,373 $40,879
Top 20 Percent $114,912 $108,189 $135,401
Federal Surplus (+) or Deficit (-)
as a Percent of GDP4 -2.8% -4.7% 1.7%
Social Indicators
Violent Crimes per 1,000 people5
Household income < $7,500 --- 84.7 57.5
Household income >= $75,000 --- 41.3 22.9
Welfare Utilization Rate6 4.4% 5.3% 2.1%
HS Dropout Rate**7 12.6% 11.0% 11.8%
Single Parent Rate**8 24.4% 26.7% 27.7%


Notes:

*Income and Poverty data for 2000 are not yet available, so 1999 data are used. Income is in 1999 dollars, deflated by CPI-U.

**1998 data used; more recent data are not currently available



1 Source: U.S. Bureau of the Census, CD-ROM "Income and Poverty: 1993" http://www.census.gov/hhes/income/incpov93/povtab2.html.

2 Source: U.S. Bureau of the Census, Current Population Survey. Poverty and Health Statistics Branch/HHES Division. http://www.census.gov/hhes/poverty/histpov/hstpov22.html.

3 Source: U.S. Bureau of the Census, Current Population Survey. Income Statistics Branch/HHES Division. http://www.census.gov/hhes/income/histinc/h03.html.

4 Source: Budget of the United States Government, Fiscal Year 2001, Tables 1.1 and 10.1. 2000

Surplus-to-GDP ratio is an estimate.

5 Source: Criminal victimization data are for 1993 and 1999 and pertain to people age 12 and older.
Source: Criminal Victimization 1999: Changes 1998-99 with Trends 1993-99.

6 Source: Welfare utilization figures for 2000 are for June. Source: Green Book and US Bureau of the

Census.

7 Source: Digest of Education Statistics, 1999, Table 108. HS Dropout rate is for 16-24 year olds.

8 Source: U. S. Bureau of the Census, Current Population Reports, Series P20-514, "Marital Status and Living Arrangements: March 1998 Update" and earlier reports, http://www.census.gov/population/socdemo/ms-la/tabch-1.txt

Table 2

Determinants of Change in MSA Log Average Annual Hours Worked

March Current Population Survey, 1976-1999

(1) (2) (3) (4) (5)
UR [National] -0.016

(0.001)

-0.016

(0.001)

UR [MSA] -0.012

(0.001)

-0.012

(0.001)

-0.008

(0.001)

Additional Controls

(Fixed Effects)



None Demographic Group,

MSA

None Demographic Group,

MSA

Demographic Group,

MSA, Year

Observations 44733 44733 44733 44733 44733



Notes:

Author's tabulations of the 1977-2000 March Current Population Survey. The sample consists of persons age 20-55 with positive CPS weights. The observations are MSA-demographic group-year cells. The demographic groups are defined by RACE (nonblack, black), GENDER (male, female), and EDUC (<12,12,13-15,16+). The dependent variable is the first difference of log average annual hours worked in the MSA-demographic group-year cell. It is a weighted regression with the number of CPS observations in the cells as the weight. The unemployment rate, UR, is measured as a percent of the labor force.



Table 3

Estimating the Impact of Cycles on Labor Market Outcomes Across MSAs Using CPS, 1976-1999

Basic Specifications





Key Explanatory Variable:


Change in

Annual

EPOP

(level)



Change in

Log Annual Hours



Change in

Log Real Annual Earnings



Change in

Log Real Hourly Wage (workers)



Change in Log Real Family Earnings


Change in Log Real Family Income


Change in Family Poverty Rate

(level)

Panel A: Base Case

UR -0.0030

(0.0006)

-0.0077

(0.0014)

-0.0097

(0.0020)

-0.0043

(0.0017)

-0.0121

(0.0024)

-0.0090

(0.0019)

0.0022

(0.0009)

Panel B: Asymmetries in Impact of Unemployment Rate

UR*Recession -0.0043

(0.0011)

-0.0136

(0.0023)

-0.0144

(0.0033)

-0.0020

(0.0028)

-0.0173

(0.0040)

-0.0128

(0.0032)

0.0045

(0.0015)

UR*Expansion -0.0022

(0.0008)

-0.0040

(0.0018)

-0.0067

(0.0026)

-0.0057

(0.0022)

-0.0087

(0.0031)

-0.0066

(0.0025)

0.0008

(0.0012)

P-value for test of equal coefficients



0.132


0.001


0.075


0.310


0.097


0.141


0.062

Notes:



Author's tabulations of the 1977-2000 March Current Population Survey. The sample consists of persons age 20-55 with positive CPS weights. The observations are MSA-demographic group-year cells. The demographic groups are defined by RACE (nonblack, black), GENDER (male, female), and EDUC (<12,12,13-15,16+). The dependent variable is the change in the (log or level) of the mean of the variable within the MSA-year-demographic cell. MSA unemployment rates are used. The model also includes fixed effects for demographic group, MSA, and year. It is a weighted regression with the number of CPS observations in the cells as the weight. The change in the unemployment rate, denoted UR, is measured as a percent of the labor force. Annual EPOP is the proportion of the population that worked during the year. Poverty rate is measured as a proportion of families.

Table 4

Estimating the Impact of Cycles on Labor Market Outcomes in MSAs using CPS, 1976-1999

Exploring Differences Across Education Group and Time



Change in

Annual

EPOP

(level)

Change in

Log Annual Hours

Change in

Log Real Annual Earnings

Change in

Log Real Hourly Wage (workers)

Change in Log Real Family Earnings Change in Log Real Family Income Change in Family Poverty Rate

(level)

Panel A: Differences Across Education Groups

UR * Less than HS -0.005

(0.001)

-0.020

(0.003)

-0.025

(0.004)

-0.005

(0.003)

-0.026

(0.005)

-0.016

(0.004)

0.006

(0.002)

UR * HS Grad. -0.004

(0.001)

-0.008

(0.002)

-0.010

(0.003)

-0.005

(0.002)

-0.014

(0.004)

-0.011

(0.003)

0.003

(0.001)

UR * Some College -0.002

(0.001)

-0.005

(0.002)

-0.005

(0.003)

-0.002

(0.003)

-0.009

(0.004)

-0.009

(0.004)

0.003

(0.002)

UR * College Grad. -0.0002

(0.001)

0.0004

(0.002)

-0.001

(0.003)

-0.005

(0.003)

-0.002

(0.004)

-0.001

(0.004)

-0.001

(0.002)

Panel B: Structural Break in Effect of Unemployment Rate

UR -0.003

(0.001)

-0.008

(0.002)

-0.012

(0.003)

-0.005

(0.002)

-0.013

(0.003)

-0.010

(0.003)

0.002

(0.001)

UR * Post89 -0.001

(0.001)

0.001

(0.003)

0.006

(0.004)

0.002

(0.003)

0.003

(0.005)

0.002

(0.004)

0.001

(0.002)

Notes: Author's tabulations of the 1977-2000 March Current Population Survey. Sample consists of persons age 20-55 with positive CPS weights. The observations are MSA-demographic group-year cells. Demographic groups are defined by RACE (nonblack, black), GENDER (male, female), and EDUC (<12,12,13-15,16+). The dependent variable is the change in the (log or level) the mean of the variable within the MSA-year-demographic cell. MSA unemployment rates are used. The model also includes fixed effects for demographic group, MSA, and year. It is a weighted regression with the number of CPS observations in the cells as the weight. The change in the unemployment rate, denoted UR, is measured as a percent of the labor force. Annual EPOP is the proportion of the population that worked during the year. Poverty rate is measured as a proportion of families.

Table 5

Estimating the Impact of Cycles on Labor Market Outcomes Across MSAs using CPS

Exploring the Role of Length of Recession/Expansion

Change in

Annual

EPOP

(level)

Change in

Log Annual Hours

Change in

Log Real Annual Earnings

Change in

Log Real Hourly Wage (workers)

Change in Log Real Family Earnings Change in Log Real Family Income Change in Family Poverty Rate

(level)

Panel A: Base Case

UR -0.0030

(0.0006)

-0.0077

(0.0014)

-0.0097

(0.0020)

-0.0043

(0.0017)

-0.0121

(0.0024)

-0.0090

(0.0019)

0.0022

(0.0009)

Panel B: Adding Duration of Recession/Expansion

UR -0.0028

(0.0007)

-0.0071

(0.0014)

-0.0094

(0.0021)

-0.0048

(0.0017)

-0.0108

(0.0024)

-0.0083

(0.0020)

0.0019

(0.0009)

Duration of Recession(Years) -0.0012

(0.0009)

-0.0025

(0.0019)

-0.0006

(0.0028)

0.0031

(0.0023)

-0.0039

(0.0033)

-0.0023

(0.0027)

0.0016

(0.0013)

Duration of Expansion(Years) -0.0002

(0.0005)

0.0003

(0.0011)

0.0008

(0.0016)

0.0007

(0.0013)

0.0017

(0.0019)

0.0009

(0.0015)

0.0000

(0.0007)

P-value for test of equal and opposite coefficients



0.236


0.380


0.949


0.207


0.620


0.313


0.313
P-value for joint significance

of duration variables



0.43


0.31


0.22


0.91


0.20


0.43


0.38
Panel C: Also Interacting Duration of Recession/Expansion with Change in Unemployment Rate
UR -0.0024

(0.0014)

-0.0039

(0.0029)

-0.0022

(0.0042)

-0.0033

(0.0035)

-0.0030

(0.0050)

-0.0000

(0.0041)

0.0022

(0.0019)

UR * Duration of Recession (Years) -0.0006

(0.0006)

-0.0034

(0.0013)

-0.0043

(0.0019)

0.0007

(0.0015)

-0.0051

(0.0023)

-0.0047

(0.0018)

0.0006

(0.0009)

UR * Duration of Expansion (Years) 0.0000

(0.0004)

-0.0004

(0.0009)

-0.0019

(0.0013)

-0.0009

(0.0011)

-0.0024

(0.0016)

-0.0025

(0.0013)

-0.0003

(0.0006)

P-value for joint significance of duration interactions



0.39


0.02


0.08


0.35


0.08


0.03


0.45




Notes:

Author's tabulations of the 1977-2000 March Current Population Survey. The sample consists of persons age 20-55 with positive CPS weights. The observations are MSA-demographic group-year cells. The demographic groups are defined by RACE (nonblack, black), GENDER (male, female), and EDUC (<12,12,13-15,16+). The dependent variable is the change in the (log or level) the mean of the variable within the MSA-year-demographic cell. MSA unemployment rates are used. Duration of recession and expansion are correspond to the cycles in the Census Division that the MSA resides in. The model also includes fixed effects for demographic group, MSA, and year. It is a weighted regression with the number of CPS observations in the cells as the weight. The change in the unemployment rate, denoted UR, is measured as a percent of the labor force. Annual EPOP is the proportion of the population that worked during the year. Poverty rate is measured as a proportion of families.



Table 6

Aggregate First-Differenced Wage Curve Estimates for Balanced Sample of Men, PSID,

1968-87 and 1977-96



Dependent Variable: Annual Change Mean Log Real Wage





Sample:

1967-68 - 1986-87 1976-77 - 1995-96

(1) (2) (3) (4)



Intercept .241 .180 .155 .120

(.038) (.041) (.080) (.074)



Change Annual -.013 --- -.006 ---

Unemployment Rate (.002) (.006)



Change Log Real --- .572 --- .564

GNP (.128) (.249)



Year -.003 -.002 -.002 -.001

(.0005) (.0005) (.0009) (.0008)



Durbin-Watson 1.34 1.93 2.65 2.59

Statistic



R2 .75 .70 .19 .33





Notes: Sample size is 20 years. Observations on 363 continuously employed male household heads born 1928-42 were used to calculate mean log earnings each year from 1967 to 1987, and observations on 335 continuously employed male household heads born 1937-51 were used each year from 1976 to 1996. Sample was limited to individuals who worked at least 100 hours each year, and did not have major assigned data for hours or labor income. The unemployment rate is measured as a percent of the labor force.





Table 7

Aggregate First-Differenced Wage Curve Estimates for an Unbalanced Sample of Men, PSID, 1968-87 and 1977-96



Dependent Variable: Annual Change Mean Log Real Wage





Sample:

1967-68 - 1986-87 1976-77 - 1995-96

(1) (2) (3) (4)



Intercept .178 .120 -.011 -.028

(.036) (.029) (.061) (.054)



Change Annual -.012 -.014 -.008 -.008

Unemployment Rate (.002) (.002) (.005) (.004)



Year -.002 -.002 .0002 .0002

(.0004) (.0004) (.0008) (.0006)



Regression Adjusted No Yes No Yes



Durbin-Watson 2.43 2.35 1.93 1.97

Statistic



R2 .71 .81 .14 .21





Notes: Sample size is 20 years. Sample each year includes all individuals age 16 or older who worked at least 100 hours that year, and did not have major assigned data for hours or labor income. In columns 1 and 3, the dependent variable is the change in the mean log hourly wage for the sample available those years. Micro data sample size is 50,461 in column 1 and 47,914 in column 3. In columns 2 and 4 a two step procedure was used. In the first step, the year-over-year change in each individual's log wage was regressed on year dummies and potential experience. In the second step regression (reported here), the year dummies were regressed on the change in the unemployment rate and a time trend. The sample restrictions used in the first step regression are identical to those used to calculate the dependent variable in columns 1 and 3, except that individuals were required to have worked in adjacent years. The unemployment rate is measured as a percent of the labor force.







Table 8

The Cyclicality of Seniority Profiles



Dependent Variable: Change in Tenure Slope Associated with Change in Industry or Occupation





Industry-based Seniority Returns Occupation-based Seniority Returns

High Low High Low

All Wage Wage All Wage Wage

(1) (2) (3) (4) (5) (6)



Change -.016 -.005 -.031 -.012 -.014 -.007

Unemployment (.009) (.011) (.017) (.008) (.008) (.016)

Rate (/1000)



Notes: Based on PSID. Returns to seniority for each major industry and occupation were estimated from a standard human capital log wage equation using the 1976 wave of the PSID. The year-over-year change in returns for each individual (based on their industry or occupation) was then regressed on the change in unemployment rate, sex, race, and experience and its square (see text). The coefficient on the change in the unemployment rate (times 1,000) is reported here. High wage is the subsample above the median wage in year t-1, low wage is the subsample below the median wage in year t-1. Occupation and industry refer to the current job, while the wage (and median wage) refer to the preceding calendar year. Sample size for regressions using industry-level tenure returns is 115,751; sample size for regressions using occupation-level returns is 125,411. The industry analysis includes the years 1973-97, and the occupation analysis includes the years 1970-97.



Table 9

State and Local Expenditures by Function

Fiscal Years 1952-1996

($Millions)



Police & Utility &
Public Health & Fire Admini- Insurance Liquor
Year Total Education Highways Welfare Hospitals Protection stration (a) Trust (b) Store (c) Other (d)
1952 $30,863 $8,318 $4,650 $2,788 $2,185 $1,525 $1,193 $1,698 $3,067 $5,439
1953 32,937 9,390 4,987 2,914 2,290 1,636 1,263 1,711 3,316 5,430
1954 36,607 10,557 5,527 3,060 2,409 1,783 1,375 2,423 3,482 5,991
1955 $40,375 $11,907 $6,452 $3,168 $2,524 $1,923 $1,452 $2,764 $3,886 $6,299
1956 43,152 13,220 6,953 3,139 2,772 2,067 1,560 2,376 4,065 7,000
1957 47,553 14,134 7,816 3,485 3,119 2,278 1,725 2,749 4,429 7,818
1958 53,712 15,919 8,567 3,818 3,462 2,483 1,843 4,168 4,693 8,759
1959 58,572 17,283 9,592 4,136 3,724 2,624 2,003 4,784 4,901 9,525
1960 $60,999 $18,719 $9,428 $4,404 $3,794 $2,852 $2,113 $4,031 $5,088 $10,570
1961 67,023 20,574 9,844 4,720 4,086 3,104 2,237 5,299 5,523 11,636
1962 70,547 22,216 10,357 5,084 4,342 3,254 2,338 4,888 5,453 12,615
1963 74,698 23,729 11,150 5,420 4,638 3,398 2,439 4,987 5,736 13,201
1964 80,579 26,286 11,664 5,766 4,910 3,588 2,567 5,094 6,184 14,520
1965 $86,686 $28,563 $12,221 $6,315 $5,361 $3,855 $2,773 $4,950 $7,058 $15,590
1966 94,906 33,287 12,770 6,757 5,910 4,152 2,974 4,782 7,282 16,992
1967 105,978 37,919 13,932 8,218 6,640 4,548 3,313 5,278 7,350 18,780
1968 116,234 41,158 14,481 9,857 7,546 5,033 3,647 5,653 8,170 20,689
1969 131,600 47,238 15,417 12,110 8,520 5,694 4,105 6,053 8,820 23,643
1970 $148,052 $52,718 $16,427 $14,679 $9,669 $6,518 $5,451 $7,263 $9,447 $25,880
1971 170,766 59,413 18,095 18,226 11,205 7,531 6,243 9,793 10,300 29,960
1972 190,496 65,814 19,021 21,117 13,023 8,584 7,056 10,548 11,398 33,935
1973 205,466 69,714 18,615 23,582 13,844 9,584 7,934 11,074 13,035 38,084
1974 226,032 75,833 19,946 25,085 15,945 10,326 8,844 12,667 14,406 42,980
1975 $269,215 $87,858 $22,528 $28,155 $18,846 $12,048 $10,154 $21,209 $17,285 $51,132
1976 304,228 97,216 23,907 32,604 20,686 13,429 11,247 27,954 19,542 57,643
1977 324,554 102,780 23,058 35,905 23,039 14,857 12,453 26,149 24,190 62,123
1978 346,786 110,758 24,609 39,140 24,951 16,108 16,618 23,526 26,277 64,799
1979 381,867 119,448 28,440 41,898 28,218 17,354 18,448 23,504 30,845 73,712
1980 $434,073 $133,211 $33,311 $47,288 $32,174 $19,212 $20,443 $28,796 $36,190 $83,448
1981 487,048 145,784 34,603 54,121 36,101 21,283 20,001 36,583 43,016 95,556
1982 522,760 154,573 34,545 58,050 40,259 23,387 22,224 39,466 47,971 102,285
1983 566,567 163,876 36,655 60,484 44,118 25,516 24,508 47,335 52,811 111,264
1984 600,222 176,108 39,419 66,414 46,330 27,464 26,355 40,153 55,062 122,917
1985 $657,888 $192,686 $44,989 $71,479 $49,581 $29,873 $28,890 $44,191 $59,798 $136,401
1986 717,430 210,819 49,368 75,868 53,508 32,272 31,803 46,538 65,297 151,957
1987 775,318 226,658 52,199 82,520 56,972 35,594 34,896 50,815 68,440 167,224
1988 826,849 242,683 55,621 89,090 61,940 38,030 37,419 51,879 70,048 180,139
1989 890,863 263,898 58,105 97,879 67,757 39,703 40,923 54,994 73,510 194,094
1990 $975,940 $288,148 $61,057 $110,518 $74,635 $43,763 $44,836 $63,321 $77,801 $211,861
1991 1,063,270 309,302 64,937 130,402 81,110 46,568 48,461 74,159 81,004 227,327
1992 1,146,853 326,275 66,689 154,642 88,112 48,903 50,334 90,276 84,361 237,261
1993 1,207,125 342,595 68,134 167,046 94,651 51,943 52,402 98,908 84,361 247,085
1994 1,260,642 353,287 72,067 179,829 100,430 54,768 55,715 95,462 91,163 257,921
1995 $1,347,763 $378,273 $77,109 $193,110 $105,946 $58,064 $60,018 $107,340 $94,235 $273,668
1996 1,397,634 398,859 79,092 193,480 110,813 62,392 62,145 108,751 95,608 286,495




Notes:

Duplicative intergovernmental transactions are excluded.

(a) Includes financial, judicial and legal, general public building and other governmental administration.

(b) Includes employee retirement, unemployment compensation, workers compensation and other social programs.

(c) Includes utility capital outlay, water supply, electric power, gas supply, transit, and liquor store.

(d) Includes interest, sewerage, utilities and other categories.

Source: Department of Commerce, Bureau of the Census.

Table 10

State and local expenditures and the business cycle



Dependent Variable ln Income ln Population State dums R2



ln Total spending 0.180 0.670 N .91

(0.045) (0.078)



0.171 0.670 Y .91

(0.047) (0.098)





ln Capital spending 0.807 0.870 N .29

(0.166) (0.286)



0.758 1.314 Y .30

(0.173) (0.363)



ln Direct spending 0.154 0.675 N .90

(0.047) (0.081)



0.141 0.680 Y .91

(0.048) (0.102)







Notes: Data consist of annual observations of spending by a panel of U.S. states (plus the District of Columbia) over the period 1977-1997. All regressions have 1020 observations. Dependent variables are the log first differences of indicated spending categories. All regressions include year dummy variables (not reported), and those with "Y" in the "States dums" column also include state dummy variables (likewise not reported). " ln Income" is the first difference of the log of state personal income, and " ln Population" is the first difference of the log of state population.





Table 11

Business cycle effects on major spending categories



Dependent Variable ln Income ln Population State dums R2



ln Spending on health 0.260 1.296 N .72

and hospitals (0.107) (0.184)



0.207 1.732 Y .74

(0.109) (0.229)



ln Education spending 0.243 0.865 N .84

(0.053) (0.092)



0.217 1.053 Y .85

(0.054) (0.114)

ln Highway spending 0.415 0.879 N .23

(0.138) (0.238)



0.383 1.144 Y .24

(0.144) (0.302)



ln Spending on police 0.353 0.889 N .83

(0.073) (0.126)



0.320 1.081 Y .83

(0.076) (0.159)



ln Spending on parks 0.949 1.582 N .67

and recreation (0.223) (0.386)



0.823 2.712 Y .68

(0.230) (0.484)





Notes: Data consist of annual observations of spending by a panel of U.S. states (plus the District of Columbia) over the period 1977-1997. All regressions have 1020 observations. Dependent variables are the log first differences of indicated spending categories. All regressions include year dummy variables (not reported), and those with "Y" in the "States dums" column also include state dummy variables (likewise not reported). " ln Income" is the first difference of the log of state personal income, and " ln Population" is the first difference of the log of state population.

Table 12

Welfare spending and the business cycle



Dependent Variable ln Income ln Population State dums R2



ln Total welfare 1.344 2.667 N .12

spending (1.404) (2.417)



1.135 4.661 Y .12

(1.468) (3.075)





ln Spending on 2.157 2.139 N .05

cash assistance (1.662) (2.861)



1.975 4.104 Y .06

(1.739) (3.643)



ln Spending on 3.732 1.144 N .02

welfare institutions (2.140) (4.258)



3.656 3.039 Y .04

(2.295) (6.361)



ln Spending on 1.246 2.338 N .11

categorical (1.592) (0.078)

assistance

1.113 3.724 Y .11

(1.665) (3.489)



ln Spending on 0.005 9.775 N .07

other public (3.881) (6.170)

assistance

-0.767 13.414 Y .08

(4.055) (7.839)

Notes: Data consist of annual observations of spending by a panel of U.S. states (plus the District of Columbia) over the period 1977-1997. All regressions have 1020 observations. Dependent variables are the log first differences of indicated spending categories. All regressions include year dummy variables (not reported), and those with "Y" in the "States dums" column also include state dummy variables (likewise not reported). " ln Income" is the first difference of the log of state personal income, and " ln Population" is the first difference of the log of state population.

Appendix Table 1

Effect of the unemployment rate on major spending categories



Dependent Variable Unemp. ln Population State dums R2



ln Spending on health -0.692 1.280 N .15

and hospitals (0.309) (0.192)



-0.795 2.169 Y .20

(0.309) (0.291)



ln Education spending -0.149 1.220 N .27

(0.160) (0.092)



-0.206 1.771 Y .30

(0.161) (0.152)

ln Highway spending -0.747 1.452 N .17

(0.430) (0.266)



-0.769 2.119 Y .19

(0.438) (0.413)



ln Spending on police -0.439 1.418 N .21

(0.222) (0.138)



-0.487 1.933 Y .23

(0.226) (0.213)



ln Total welfare -2.106 0.476 N .09

spending (4.507) (2.803)



-1.853 0.565 Y .10

(4.644) (4.378)



Notes: Data consist of annual observations of spending by a panel of U.S. states (plus the District of Columbia) over the period 1977-1997. All regressions have 1,020 observations. Dependent variables are the first differences of logarithms of indicated spending categories. All regressions include year dummy variables (not reported), and those with "Y" in the "States dums" column also include state dummy variables (likewise not reported). " Unemp." is the first difference of the state unemployment rate (where the unemployment rate is measured as a proportion rather than a percent), and " ln Population" is the first difference of the log of state population.

1. Although the recession officially ended in March 1991 according to the National Bureau of Economic Research, the unemployment rate did not peak until 1992. Consequently, we use 1992 as the trough year.

2. Specifically we use the March CPS for survey years 1976 to 2000, which provides annual labor market information for the following calender year.

3. If cycles were symmetric, in that the increase in unemployment rates in the recession equaled the decrease in unemployment rates in the expansion, then this estimated asymmetric effect of unemployment rates would suggest that after repeated cycles average annual hours would be lower than at the beginning of the period. However, we are not observing such a steady state economy, instead we are looking at a finite slice of time, and during this time period unemployment rates have been trending downward.

4. Some standard adjustments to the data are implemented. The earnings data are topcoded at $50,000 through 1981, $75,000 from 1982-1984, $100,000 from 1985-1988, and about $200,000 from 1989 on. Following Katz and Murphy (1992), the earnings of topcoded individuals are adjusted to be 1.45 times the topcoded value. Beginning in 1996, instead of giving each topcoded observation the value of the topcode, the CPS assigns the mean among the sample of topcodes (by demographic group). The earnings figures can be as high as $600,000 in this period. We make no adjustment for topcoding in these years. There is no apparent topcoding of family earnings or family income. Real earnings and income are constructed using the CPI-U-X1 deflator.

5. The nonwhite group includes both blacks and white Hispanics.

6. The Federal Reserve only provides an aggregate measure of the capacity utilization rate, so we cannot use this variable in the MSA level analysis..

7. We also estimated models that included MSA linear time trends, and unrestricted demographic group times year effects. Although including these variables improved the model fit considerably, they consistently had no significant impact on the estimated unemployment rate effects.

8. Note that for wages, earnings and income we use the change in the log of the mean outcome. It would be more consistent with an underlying individual model to take the mean of the log of the measure. However, since we are not, in general, conditioning on working we can not take the mean of log income or earnings due to the prevalence of zeros.

9. For comparison to Solon, Barsky and Parker, earnings were deflated by the GNP deflator. Hourly earnings were derived as the ratio of annual labor income to annual hours worked. Individuals with assigned earnings or hours data were eliminated from the sample. Unlike Solon, Barsky and Parker, we windsorized the hourly wage data (i.e., rolled extreme values back) at $2.13 and $100 per hour in 1996 dollars, and used sample weights to adjust for the low-income over sample; our results were not very sensitive to these changes.

10. When we tested the wage curve specification versus a Phillips curve specification, the PSID data preferred the wage curve; that is, if we include the current unemployment rate in the equations in Table 6 it is statistically insignificant, while the change remains significant (in column 1).

11. For example, for column 1 of Table 6 SBP find a coefficient of -.0135 and we find

-.0129, and for column 2 of Table 7 we both find -.0140. The standard errors are also close.

12. If we use the log of the mean hourly wage (as was done in the previous section) in the model in column 1 of Table 7, instead of the mean of the log hourly wage, the results are quite similar.

13. The coefficient (standard error) in the earlier period is .0046 (.0054) and in the later period is -.0033 (.0063). The estimates for the unbalanced sample are similarly insignificant, as are estimates that use GNP growth as the cycle variable.

14. To check the robustness of our results, we also used the 1985 wave of the PSID and the May 1979 CPS Pension Supplement to estimate the tenure profiles. Our findings were qualitatively similar when the 1985 PSID sample was used, but much less systematic when the CPS data were used.

15. Data reported in Table 8 represent fiscal years, which for almost all states run from July 1 of the previous calendar year to June 30 of the contemporaneous year. U.S. personal disposable income in the last two quarters of 1995 plus the first two quarters of 1996 was $5.398 trillion.

16. See, for example, the estimated income coefficients in Case, Hines and Rosen (1993), Poterba (1994), and those surveyed by Rubinfeld (1987). It is for this reason that we use annual income growth, rather than unemployment changes, as a measure of business cycle conditions.

17. See the evidence provided in Poterba (1994, 1995).

18. Unlike the preceding tables, the unemployment rate in these regressions is measured as a proportion, so a value of 0.01 corresponds to a 1 percentage point rise in state unemployment. Consequently, the estimates in the first column of Appendix Table 1 imply that a 1 percentage point rise in state unemployment (for example, the unemployment rate rising from 2.5% to 3.5%) corresponds to declines of 0.0069 percent in health and hospital spending, 0.0075 percent in highway spending, and 0.02 percent in welfare spending.

19. Other specification checks included adding additional lags of personal income changes to the main regressions and (separately) distinguishing large from small personal income changes to test for asymmetric reactions over the business cycle. No consistently new patterns emerged from adding additional lags of personal income changes to the main regressions, though estimated coefficients on lagged income terms were often significant. When the sample is divided in half according to the size of income changes, and the regressions run separately on each subsample, the results are similar to those reported for the whole sample. The estimated income coefficients tend to be somewhat larger for the sample with observations with slow income growth, suggesting that business cycle downturns have a greater impact on the estimates than do periods of rapid economic growth, but the differences are not significant in a statistical sense.

20. In this context, it is noteworthy that AFDC and TANF represent relatively small fractions of total welfare spending by state and local governments. In fiscal year 1994, state and local governments spent $11.8 billion on AFDC, representing 12% of their total cash and noncash payments (of $98.6 billion) to persons with limited income. AFDC spending was roughly equal to the sum of total state and local spending on foster care, general assistance, and Supplemental Security Income (Burke, 1996). Hence business cycle influences on AFDC expenditures might have modest influence on total welfare spending by state and local governments.

21. Welfare institutions consist of provision, construction, and maintenance of nursing homes and welfare institutions owned and operated by state and local governments for the benefit of needy persons; examples include public nursing homes, orphanages, homes for the elderly, and indigent care institutions. Categorical assistance programs include direct payments to beneficiaries under the SSI program, AFDC or TANF, and Medicaid.