Carlos Gradín
email: cgradin@uvigo.es

Poverty among minorities in the United States: Explaining the racial poverty gap for Blacks and Latinos

Keywords: D31 -Personal Income, Wealth, and Their Distributions < D3 -Distribution poverty differential, minorities, United States, decomposition JEL Classification: D31, D63, I32, J15, J82 poverty, gap, race, decomposition, Oaxaca-Blinder, United States, CPS, labor market, participation, education, family characteristics

The two largest minorities in the United States, African Americans and people of Hispanic origin, show official poverty rates at least twice as high as those of non-Hispanic Whites. These similarly high poverty rates between the two minorities are, however, the result of different combinations of factors related to the specific characteristics of these two groups. In this paper, we analyze the role of demographic and labor-related variables in the current differential of poverty rates among racial and ethnic groups in the United States and its recent evolution. Our results show, first, that these differentials are largely explained by differing family characteristics of the ethnic groups. Furthermore, we show that while labor market activity of family members and a preponderance of single mothers play a more significant role in the higher poverty rates of Blacks, a larger number of dependent children is closely associated with higher poverty among Latinos, who also suffer from a larger educational attainment gap and higher immigration rates.

Introduction

Most ethnic minorities in the United States have historically shown low performance in a number of socioeconomic indicators when compared with majority non-Hispanic Whites. Their share of the population is increasing, so that nowadays more than one in four Americans belong to one of the two largest minorities, Blacks and Latinos. But the median per capita family income for these two groups is below 60 percent of levels for non-Hispanic Whites, and their poverty rate is at least twice as high. These minorities, which constitute half of all poor Americans, share similar socioeconomic and demographic patterns of deprivation in several other dimensions: they have less education, less health insurance coverage, and a larger risk of being prison inmates, unemployed, or low-paid workers. 1 Furthermore, they share several demographic characteristics typically associated with disadvantaged groups, including more dependent children in their families and a larger share of female-headed families, especially single mothers; however, both minorities also differ in a number of relevant aspects.

Non-Hispanic Blacks account for a steady 13 percent of the overall population. Only 8 percent of them are foreign born, compared with 13 percent of the overall population. Blacks appear to have a larger incarceration rate than any other group, as well as a larger proportion of female-headed families and of young males out of work. It is a well-known fact that almost 70 percent of all Black children are born to unmarried mothers (US DHHS, 2004) and, consequently, about half of all Black children live with a single mother. Additionally, more than 11 percent of Black males aged 25 to 34 were incarcerated as of June 2006 (US DJOJP, 2007). Indeed, it comes as no surprise that these factors appear among the most common explanations for their higher poverty rates.

On the other side, most Hispanics are immigrants who arrived in the United States after the 1980s from Latin America, especially Mexico, settling in the southern and eastern portions of the country. As a consequence of rapid population growth, the number of Latinos grew from 10 million to 45 million between 1970 and 2007 (from 5 to 15 percent of the total population), recently becoming the largest minority group in the country, with a population still growing 1 As a matter of fact, 20 percent of Blacks and 34 percent of Hispanics had no health insurance in 2007, compared with 11 percent of non-Hispanic Whites (US CB, 2007). According to our own estimations, unemployment rates in the same year were respectively 8.4 and 5.7 percent of the active population, compared with 4 percent of Whites, and 19 and 13 percent respectively of Black and Latino adults older than 25 went to college compared with 32 percent of Whites. An estimated 4.8 percent of Black men were in prison or jail in midyear 2006, compared with 1.9 percent of Hispanic men and 0.7 percent of White men (US DJOJP 2007). faster than that of any other ethnic group. 2 The result of this recent migration process is that two in five Latinos are foreign born, and they have a higher fertility rate and a larger educational gap than any other group.

In this paper, we aim to identify the nature of higher poverty rates among African Americans and Latinos in the United States, and to assess to what extent they are associated with the poor endowments of these groups in terms of demographic characteristics, educational attainment, and labor market performance. Alternatively, higher poverty rates may be the result of these characteristics being less effective for pushing their families above the poverty line. Furthermore, we want to identify how the distinct features of Blacks and Latinos affect their poverty risks in different ways. To answer these questions, we estimated logit poverty regressions and used an Oaxaca-Blinder approach adapted to this specific framework to decompose the racial difference in poverty rates into characteristics and coefficient effects. This decomposition was conducted at two different levels: at the aggregate level, we estimated the joint contribution of all characteristics and all coefficients respectively, whereas at the detailed level we identified the individual contributions of each set of characteristics.

The structure of the paper is as follows. We first describe the data and main definitions, then review and compare the main socioeconomic patterns of racial groups in the United States.

After this, we introduce the decomposition technique and present our empirical results. The final section summarizes our main conclusions.

Data and some definitions

The data used in the main analysis were taken from the Current Population Survey, 2007 Annual Social and Economic (ASEC) March Supplement, conducted by the U.S. Census Bureau. 3 The 1994 and 2002 surveys will also be used for the analysis of time trends. This data source provides comprehensive monthly labor force data for persons 15 years old and over, as well as the main characteristics of families. While demographic data refer to the time of the survey, data on employment and income refer to the preceding year. In this survey, people are asked to answer questions about their race and Hispanic origin. Since 2003, respondents have been allowed to report more than one race, selecting from six distinct race groups. Further, this survey inquires whether the origin of each person is Spanish, Hispanic, or Latino. On the basis of these questions, we distinguish three nonoverlapping groups in the population: non-Hispanic Whites (those who declared only this race), non-Hispanic Blacks or African Americans (identifying themselves as non-Hispanic and Black, either alone or in combination with other races), Hispanics or Latinos (of any race). 5 The 2007 sample includes 190,818 observations. 6

The definition of poverty used in this paper corresponds to the official poverty definition employed by the U.S. Census Bureau following Statistical Policy Directive 14 of the Office of Management and Budget (OMB) (May 1978). It comprises a set of income thresholds that vary by family size and composition to determine who is in poverty. If a family's total income is less than that family's threshold, then that family and every individual in it is considered to live in poverty. The official poverty definition uses money income before taxes, and does not include capital gains or noncash benefits. 7 These thresholds are updated annually for inflation using the Consumer Price Index (CPI-U).

Racial poverty profiles in the United States

Persistence of high poverty rates in the United States, with levels higher than those of the majority of the developed world, has been so far well documented, as well as the fact that the distribution of income has become more unequal and polarized. Additionally, there is a large differential in well-being across U.S. ethnic and racial groups, as well as increasing racial polarization resulting from the growing proportion of American minorities at the bottom of U.S. income distribution. On average, Black and Latino minorities are poorer than Whites in the 5 For the sake of simplicity in what follows, we will refer to Blacks or Whites while omitting their "non-Hispanic" origin. 6 Of which 132,853 are Whites, 34,125 Hispanics, and 23,840 Blacks. The total sample size for 1994 was 144,010, and for 2002 203,233. 7 These absolute poverty thresholds were formulated in the mid 1960's, with only minor adjustments since then, and have been widely criticized not only for the limited notion of income used, but also because they have not reflected any changes in lifestyle or in consumption standards since they were established. However, they still constitute the main reference for all poverty analysis in the United States. For 2006, these limits were $10,488 for one person under 65 years old and $20,444 for a family of two adults with two related children under 18. United States: 24.3 and 20.8 percent of their respective populations, 2.5 to 3 times more than among Whites (8.3 percent) in 2006. 8 What are the reasons for these differences in poverty rates among racial and ethnic groups?

Obviously, they may be the result of specific characteristics of the families in which they live.

Clearly, these differentials can be explained to some extent by the fact that minorities are more likely to live in the poorest areas, have more children, live in single-mother families, work fewer hours, and be employed in low-paid occupations. Let us briefly review some of these differences in the relevant attributes.

First, ethnic minorities are unevenly distributed across the U.S. geography and are overrepresented in regions with the highest poverty levels, even if this is not expected to substantially increase their poverty risk due to the low range of variation of poverty across regions in the United States.9 Furthermore, these minorities are more likely to live in the largest metropolitan areas (more than 2.5 million inhabitants) than in non-metropolitan areas, which actually had the highest poverty rates. 10 Blacks and Latinos are also characterized by different living arrangements from those of Whites, and this is expected to affect their higher poverty rates. Both populations were less likely to live in married-couple families in 2007: 62 percent of Hispanics and only 40 percent of Blacks, compared with 68 percent of Whites; only 6 percent of those living in this type of family were poor in 2006. On the contrary, Blacks were more likely to live in a female-headed family without a spouse present (45 percent) than the other main groups (20 percent among Whites and 24 for Latinos). The single-female type of family faced a poverty risk 10 percentage points higher than that of male-headed families without a spouse present. More specifically, minorities also had a larger share of people living in single-mother families: 15.6 percent among Hispanics and 26.5 among Blacks, compared with only 6.6 among Whites.11 These families' reported poverty rate was higher than that for any other type (38 percent). Furthermore, Latinos, and to a lesser extent Blacks, are more likely to have larger families: their families had 3.5 members on average in 2007 (compared with 3.0 in the case of Blacks and 2.9 of Whites), with more dependents of all ages among them, but especially children (1.25 compared with 1 and 0.76, respectively). As a consequence, 43 percent of Hispanic family members were economically dependent, compared with 38 percent among Blacks and only 25 percent among Whites.

Poverty risk among people in families headed by a non-American citizen was 22 percent, twice as high as in the other cases, including those headed by either naturalized foreign-born or second-generation immigrants. This appears to be particularly relevant for the Hispanic population, given that 40 percent lived in a family with a foreign head, compared with only 2 percent of Whites and 5 percent of Blacks. Family heads, and the general population, tend to be younger among Hispanics and Blacks than among Whites, and this is another factor that might increase the poverty levels of minorities, considering that poverty risk declines with the age of the family head, and was especially high for young-headed families (32 percent were poor when the head was younger than 24); this group comprised 10 percent of Hispanics, 8 percent of Blacks, and only 5 percent of Whites. Another characteristic that is clearly distinctive of minorities is their level of attained education. Only 11 percent of Hispanic family reference persons went to college, compared with 18 percent of Blacks and 33 percent of Whites. A similar gap was found among the population as a whole.

Labor Market performance

Labor market performance varies greatly across ethnic and racial subpopulations. Latinos and Whites participate in the labor market at a higher rate than Blacks. The employment rate of Hispanic and White adults (15 years or older) was 63 and 62 percent, respectively, in 2006, compared with 57 percent of Blacks; however, these aggregate figures conceal several specific features of each group. Hispanic males had the largest employment rates: 74 percent compared with 68 percent of Whites and 57 percent of Blacks; whereas Hispanic females, on the contrary, had the lowest employment rate, 52 percent compared with about 56 percent of Black and White females. 12 Further, there was substantial occupational segregation by ethnic group in the labor market, and Whites were more likely than any other group to work in 12 The high employment rate of Hispanic males was intimately related to their large immigration rate. Indeed, the employment rate of Hispanic males born in a foreign country was 88 percent, in contrast with 71 percent of those born in the United States, similar to the 73 percent of non-Hispanic White males in the same situation. managerial and professional jobs in the private sector or to be self-employed. 13 Despite a lack of significant difference in the average number of hours and weeks worked by employed adults, the earnings of Black and Hispanic workers were, respectively, 70 and 65 percent of those of Whites. 14 During the economic expansion of the 1990s, the employment rate of those not enrolled in higher education increased in the case of Blacks and Latinos (but not of Whites) for most subgroups according to gender, age, and education; however, the increase was especially marked for females below the age of 55 with no college studies, amounting to between 7 and 12 percentage points, depending on age and minority. The 2000s did not significantly reverse these improvements. The fact that the employment rates of low-educated young Black males did not significantly advance during the last economic booms has been a major academic issue to date. Among the possible explanations for this phenomenon, direct and indirect consequences of large and increasing incarceration rates (even in a context of decreasing criminality) and the migration of jobs from inner cities to suburbs have been mentioned. 15 Another remarkable feature of labor force participation expected to have an impact on poverty is the dramatic increase in employment rates for single mothers during the economic boom of the 1990s. This was especially important for Blacks and Hispanics, as they showed, respectively, 14 and 22 percentage-point increases. This unprecedented shift to work was the result of drastic changes in economic incentives faced after the mid-1990s by welfare beneficiaries, mainly single mothers, combined with a strong economic boom that allowed welfare reform to be implemented thanks to low unemployment and readily available jobs (Blank, 2002). 16 13 Note also that Blacks were more likely than Hispanics to work in skilled jobs or in the public sector, as well as to be unemployed, but less likely to be self-employed. 14 Several reasons, including wage discrimination, have been given for these racial and ethnic gaps in earnings. For example, Antecol and Bedard (2004) emphasized the role of labor market attachment differences among young males, while Carneiro, Heckman, and Masterov (2005) argued that the major source of economic disparity by race and ethnicity in U.S. labor markets was to be found in pre-labor factors. 15 For example Holzer, Raphael, and Stoll (2006) argued that the high rates of crime and incarceration among young Black males limited employment opportunities not only of those directly engaged in such behavior, but also of those not engaged in crime as a result of statistical discrimination by employers. Further, Foster-Bey (2006) found evidence that spatial mismatch in the blue-collar sector affected labor participation of young males residing in the urban core of metropolitan areas. 16 In particular, as the result of reforms of social assistance programs through the 1996 Personal Responsibility and Work Opportunity Act and increases in the Earned Income Tax Credit and minimum wage, "cash assistance became far less available, welfare recipients were pushed much harder to employment and leave the rolls, the returns to low-wage work rose, and the availability of work supports (child care and health insurance) increased to low-income families" (Blank, 2002, page 1108). We examined the contribution of a number of family characteristics to the differential in poverty rates among racial groups in the United States by applying an extension of the wellknown regression-based Oaxaca-Blinder decomposition approach to the probability of being poor. 17 In our framework, the ith person in group g is considered poor when his or her family income falls below poverty line z. Then, in a logit probability model, the likelihood of this person being poor ( ) is given by

g i y g i P       g g i g g i g g i g i g i X X X F z y P    êxp 1 êxp ) ( Pr      , ( 1
)
where F represents the logistic probabilistic cumulative distribution, is a vector of characteristics describing i's family, and is the associated vector of coefficient estimates.

We estimated regressions separately for Whites, Blacks, and Hispanics. We also included the type of family, as this can also affect the ability of the family head to get a job. 19 Given that family income can also be provided by other family members and that the presence of dependents can increase family needs but not income, we included the number of other family members as well. 20 Other variables included were the geographic region of 17 See Oaxaca (1973) and Blinder (1973). 18 These variables included several types of occupation according to the sector (private or public), contract (part-or full-time), required skills (managerial/professional occupation or not), employment status (self-employed or employee), and the number of weeks and average weekly hours worked. 19 We distinguished among families composed of a married couple, with additional distinction according to the sex of the head, and those composed by a male or a female without a spouse present. In the case of female heads, we additionally distinguished whether children were present in order to identify single mothers, with an interaction variable indicating their employment status. 20 We distinguished the number of dependents (of different ages) from the number of labor and nonlabor income receivers (by education attained and gender, and, in the case of employed, also by age and characteristics of the job). residence and the size of the metropolitan area, to take into account potential differences in economic opportunities.

We estimated the probability of a person being poor with all explanatory variables collected at the family level. 21 As a consequence of this specification, the standard i.i.d. assumption is violated due to correlation within families (clusters). For this reason, following Cappellari and Jenkins (2004), we computed robust variance estimators, allowing for arbitrary correlation between observations within the same sample cluster, while assuming independence across clusters.

This simple econometric specification allowed us to identify the statistical association between the probability of being poor and each family attribute when the other characteristics are controlled for; however, we should be cautious in interpreting the results, as no control for possible endogeneity sources was made, and no causal relationship can be assessed. 22 An interesting property of the logit specification in our framework is that the head-count ratio of poverty in group g, g H , is equal to the average predicted probability for this group (with population ):

g N      g N i g g i g g g i g g X F N X F P H 1 ___ __________ ____ ) ( 1 ) (   . (2)
This property allows us to use the average estimated probabilities to break the observed differential in poverty rates among two given groups 0 (Whites) and 1 (Blacks or Hispanics),

___ __________ 0 0 ___ __________ 1 1 ___ 0 ___ 1 0 1 ) ( ) (   X F X F P P H H      , (3)
into two distinct terms, when the characteristics of group 0 are taken as reference: 23

21 In other words, each family is replicated m times in the regression, where m is the family size. The reason for this is that poverty is usually measured as the proportion of individuals (not families) below the poverty line, while the poverty status of any person is determined according to his or her family income. 22 For example, consider the potential double causality in the case of the number of dependent children in the family. A large number of children directly affects the probability of being poor by increasing family needs (through the official poverty line) while not providing additional income. However, this characteristic can also be interpreted as a consequence of living in a poor, less-educated family. 23 This way, the same reference group is considered in the analysis of Blacks' and Latinos' differentials in poverty. From a theoretical point of view, the alternative reference group (the minority) might also be considered, providing similar results to those presented in the empirical sections. Note, however, that improving the characteristics of poor minorities so they converge with the affluent majoritarian Whites seems a more plausible political aim than the alternative of equalizing groups by worsening Whites' endowments.

                         ___ __________ 0 0 ___ __________ 1 0 ___ __________ 1 0 ___ __________ 1 1 0 1 ) ( ) ( ) ( ) (     X F X F X F X F H H , (4)
The first term is the aggregate characteristics effect, and the second one is the aggregate coefficients effect.

To evaluate the individual contribution of each variable (or set of variables) to the total explained difference, which is usually referred to as the detailed decomposition, we followed the linearized method proposed by Even and Macpherson (1990, 1993) and Yun (2004). The detailed decomposition of the characteristics effect for any nonlinear function F is given by

                ___ __________ 1 0 ___ __________ 1 1 1 ___ __________ 1 0 ___ __________ 1 1 ) ( ) ( ) ( ) (     X F X F W X F X F K k k X , ( 5
)
where is the individual relative contributions of characteristic k (k=1,…, K) to the overall characteristics effect such that:

k X W      1 0 1 1 0 1   X X X X W k k k k X     , . (6)
1 1     K k k X W
This technique has a few advantages over other proposed methods that appear in the literature. First, the weights are quite transparent and simple to compute, because only estimates of the coefficients and sample means for the characteristics are required. Second, this procedure overrides the problem of path dependency common to all sequential approaches to nonlinear models, in which values of characteristics and/or coefficients of one group need to be switched with those of the other group. Third, unlike these sequential approaches, the detailed characteristics effect can be obtained without matching individuals of one group with the characteristics of another. Additionally, the original Oaxaca-Blinder approach is shown to be a particular case of this decomposition when F is a linear function.

Finally, standard errors will be provided following the Delta method sketched in Yun (2005).

Explaining the racial poverty gap

The analysis of decomposition of the racial poverty gap is based on the estimation of multivariate logit regressions explaining the likelihood of a person being poor conditioned by family characteristics. 24 Poverty rates among Blacks were 15.9 percentage points higher than among Whites in 2006, and the decomposition analysis of this differential, presented in Table 2, shows that this can largely be explained by the characteristics effect. That is, 12.2 percentage points (76.6 percent) of this raw racial poverty gap for Blacks can be attributed to differences in the observed family characteristics of both groups. Despite the fact that observed characteristics explain a large share of the racial poverty gap for Blacks and Latinos, our results also suggest that the impact of certain attributes on poverty risk might be different for these groups compared with Whites. The aggregate coefficient effect is in all cases significant25 . As a result, the conditional racial poverty gap-that prevailing if Blacks shared the characteristics of Whites-was 3.7 percentage points (the remaining 23.4 percent of the gap).

Now we discuss in more detail the main factors explaining the poverty gap (detailed characteristics effect). Other nonlabor-related factors were crucial in explaining the higher poverty rates of Blacks compared with Whites. In fact, 35.3 percent of the raw gap was explained by differences in demographic characteristics, namely the larger number of dependent children in Black families (13.5 percent), the lower proportion of married couples and higher proportion of femaleheaded families (jointly explaining 11.2 percent), and the generally younger age of family heads (7 percent). The geographical area of residence appears to be irrelevant because the effect of the higher concentration of Blacks in the poorest states was statistically insignificant at 95 percent of confidence; further, it was overcompensated for by the fact that Blacks were more likely to live in the largest metropolitan areas (which, in fact, made a negative contribution to the gap).26

Among our other target group, the difference in poverty rates between Hispanics and Whites in 2006 was 12.5 percentage points, and observed family attributes explained a proportion of the raw gap, which was globally similar to the case of Blacks. Indeed, the characteristics effect accounted for 9.5 percentage points (76 percent of the total difference between Hispanics and Whites), resulting in a conditional poverty gap of 3.0 percent (24 percent). Nevertheless, the underlying reasons for higher poverty among Latinos are substantially different from those explaining the higher incidence among Blacks. For example, the sociodemographic characteristics of Hispanics were even more important and explained more than half the overall racial poverty gap (51.9 percent), due mainly to a larger number of dependent children in Hispanic families, accounting for 25 percent, and to their immigration profile, which explained another 15.1 percent. The contribution of young age of family head to explaining the racial poverty gap was similar for Latinos and Blacks (7.1 percent), but not the type of family, which barely contributed 3.2 percent.

Furthermore, achieved education and labor activity among all family members jointly explained about 20 percent of the raw poverty gap, way below the overall contribution of these variables for Blacks. Additionally, the driving factors were also different for the two groups. The main contribution for Latinos in this case came from the lower level of education attained by Hispanic family heads, explaining almost 18 percent of the total gap (compared with only 5 percent for Blacks). An additional 5.1 percent was attributable to the labor participation of family heads resulting from segregation into low-paid occupations; unlike Blacks, the larger number of hours these heads worked partially compensated for this segregation (with a negative contribution of 3.2 percent). The participation of other family members in the labor market did not explain much of the gap in the case of Hispanics because of the high employment rates of males, but nonlabor incomes did explain a non-negligible 7 percent. Finally, the geographical area where Latinos were living played no significant role, just as in the case of Blacks.

A brief analysis of the evolution of the poverty gap, reported in

Conclusions

In this paper, we have analyzed the gap in poverty rates for ethnic and racial groups in the United States and found that it can largely be explained by family characteristics. We have also

  4

  poor means that a family lacks sufficient income to make ends meet, we included among the explanatory variables a number of characteristics of the family reference person that might influence his or her ability to earn income: demographic variables such as gender, age, attained education (primary, secondary, or college), citizenship (native with/without foreign-born parents, foreign-born naturalized, or not naturalized), and mover status (change of residence in the previous year), as well as a set of variables describing labor force participation of the head and, in the case of those employed, characteristics of the job.18

  About 37.6 percent of the raw poverty gap was explained by differences in education attainment and the labor-market-related variables of all family members. As a matter of fact, the level of education and labor activity of the family head explained 14.4 percent of the gap, due mainly to fewer weeks and hours worked (6 percent) and to an education gap (5.5 percent). The gap in education and the labor activity of other family members explained an . In this last case, fewer hours worked (15 percent) appears to be especially important, together with low participation and segregation of those actually employed in low-paid occupations (8.2 percent). An additional 4.2 percent was explained by nonlabor income received by other members of the family (taking into account their education).

  main reasons diverge when explaining why Blacks and Latinos are more likely to be poor than Whites. Almost half of what explains the higher poverty of Blacks (37 percent of the overall gap) can be attributed to demographic characteristics, especially a large number of dependent children, family type, and age of the family head. Of similar relevance are education and performance in the labor market, especially low labor market participation by family members other than the family head. On the contrary, in the case of people of Hispanic origin, at least two-thirds of what can be explained (more than half the raw gap) is attributable to the demographic characteristics of families, with even more relevance of the number of children than in the case of Blacks, with a special (even if decreasing) role played by their predominant immigration status, and with the younger age of their family heads being similar in relevance to the case of Blacks. As a consequence of the high employment rates of Hispanic males, labor-market-related characteristics play a less fundamental role than in the case of Blacks, one almost fully accounted for by their larger educational gap. The state or region where minorities live play no role in explaining their higher poverty rates. Further, certain demographic characteristics, including type of family and age of the family head, and other labor variables, e.g., number of hours worked, were shown to have a different impact on the risk of being poor among minorities compared with Whites.Finally, we have shown that reduction of the racial poverty gap during the 1990s can be fully accounted for by the less important role played by differences in characteristics. Nevertheless, it is important to note that the fact that characteristics accounting for the largest part of the racial poverty gap are vanishing over time does not mean that discrimination is no longer relevant in explaining poverty among minorities in the United States. The poorer characteristics of Blacks and Latinos could be partly the result of different opportunities for access to education and well-paid jobs, and additionally there remain some significant and persistent coefficients effects.

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Table 1 Decomposition of the racial poverty gap, 2006

 1

	Blacks	% Hispanics	%

  Table3, shows a remarkable reduction in the racial poverty gaps during the 1990s (from 23 percentage points to 15 for Blacks and from 21 to 14 for Hispanics).27 This reduction can be fully accounted for by the lower role played by differences in sociodemographic and labor-related characteristics. The

conditional racial poverty gap, however, has remained at a constant level among Blacks (3.5-3.7 percentage points) and is increasing across time among Latinos (from 1.7 in 1991 to 3 in 2006).

Table 2 Decomposition of the racial poverty gap, 1993, 2001, 2006

 2 Standard error in parentheses: Linearized standard errors (Raw Gap), and Delta standard errors (aggregate and detailed effects).

					Blacks					Hispanics
		1993	% 2001	% 2006	% 1993	% 2001	% 2006	%
	RAW GAP	23.0	100	14.6	100	15.9	100	20.7	100	13.6	100	12.5	100
		(0.45)		(0.31)		(0.23)		(0.39)		(0.29)		(0.37)
	AGGREGATE COEFFICIENTS EFFECT	3.5 15.4	3.8 25.9	3.7 23.4	1.7	8.2	2.5 18.5	3.0 24.0
		(0.58)		(0.48)		(0.40)		(0.74)		(0.53)		(0.51)
	AGGREGATE CHARACTERISTICS EFFECT	19.5 84.6	10.8 74.1	12.2 76.6	19.0 91.8	11.1 81.5	9.5 76.0
	DETAILED:	(0.53)		(0.45)		(0.39)		(0.81)		(0.59)		(0.54)
	1. Geographic	0.3	1.2	-0.2	-1.5	-0.1	-0.6	-0.2	-0.8	-0.1	-0.6	-0.4	-2.9
		(0.51)		(0.36)		(0.31)		(0.81)		(0.36)		(0.34)
	2. Sociodemographic	8.5 36.8	5.2 35.3	5.6 35.3	11.9 57.7	8.1 59.8	6.5 51.9
		(0.93)		(0.54)		(0.50)		(0.91)		(0.66)		(0.58)
	3. Education and labor activity	9.4 41.1	5.4 37.1	6.0 37.6	6.8 32.8	2.5 18.7	2.5 19.9
		(0.76)		(0.41)		(0.38)		(0.59)		(0.49)		(0.40)
	4. Nonlabor incomes	1.3	5.5	0.5	3.2	0.7	4.2	0.4	2.2	0.5	3.6	0.9	7.0
		(0.31)		(0.13)		(0.11)		(0.21)		(0.16)		(0.16)

The U.S. Bureau of Census estimated a growth of 24 percent for the Hispanic population between 2000 and 2006 (compared with 6 percent for the overall population), with a projection of 102.6 million Latinos by 2050, nearly a quarter of the entire population (US CB, 2008).

The universe of this survey was the civilian noninstitutional population of the United States living in housing units, and members of the armed forces living in civilian housing units on a military base or in a household not on a military base.

To be consistent with Census Bureau publications, our income and poverty estimates refer to the year in which income was obtained, with characteristics as of March of the following year (the survey's year).

Examples of recent detailed analysis of poverty in the United States are[START_REF] Iceland | Poverty in America[END_REF] and[START_REF] Rodgers | Income, Poverty, and Health Insurance Coverage in the United States: 2006[END_REF].

Indeed, both minorities were more likely to live in the south-central eastern region, where the poverty rate was 16 percent in 2006, and Hispanics were also more likely to live in the south-central western region, where the poverty rate was 17 percent. However, most Hispanics and Blacks actually lived in regions with poverty rates between

and 12 percent. 10 Note, however, that we cannot here consider the effect on poverty caused by residential segregation within metropolitan areas, with minorities living in poor inner cities and Whites in rich suburbs.

There is no consensus about the causes of change in marriage, divorce, and nonmarital childbearing that occurred during past decades leading to this situation. Changes in social norms, declining wages among low-skilled men, and the unintended incentives of the welfare system have been pointed out as possible explanations[START_REF] Maclanahan | Single mothers, fragile families[END_REF].

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

The results for all racial and ethnic groups using the 2007 surveys (with results referring to poverty in 2006) are presented in the Appendix. In general, these results suggest that the coefficients are similar in sign for all groups, although they may differ in magnitude and statistical significance.

However, most of the detailed effects of grouped variables, not discussed here, are not. One important exception is the number of hours worked by Hispanic family heads, which was less effective than those worked by Whites in protecting their respective families from being poor, as it explained about 1.6 percentage points of the raw gap in poverty rates (13 percent of the gap). See[START_REF] Gradín | Why is poverty so high among Afro-Brazilians? A decomposition analysis of the racial poverty gap[END_REF] for details.

This result strongly contrasts with the case of African Americans in Brazil, where almost 17 percent of their higher poverty rate compared with Brazilian Whites can be explained by the concentration of Blacks in the poorest states in the north and northeast of the country[START_REF] Gradín | Why is poverty so high among Afro-Brazilians? A decomposition analysis of the racial poverty gap[END_REF].

Regression estimates for poverty in 1993 and 2001 and more detailed results of the evolution can be found in[START_REF] Gradín | Why is poverty so high among Afro-Brazilians? A decomposition analysis of the racial poverty gap[END_REF].

Page 11 of 17

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Submitted Manuscript

Logit coefficients of the probability of being poor in 2006 Whites Blacks Hispanics

New England -0.082 (.10) -0.729 (.30) 0.205 (.22) East North Central -0.236 (.09) 0.053 (.17) -0.407 (.18) West North Central -0.103 (.10) -0.177 (.22) 0.027 (.21) South Atlantic -0.262 (.09) -0.219 (.16) -0.128 (.15) East South Central 0.140 (.11) 0.001 (.20) 0.160 (.29) West South Central -0.135 (.10) 0.226 (.18) 0.259 (.14) Mountain -0.394 (.10) -0.150 (.25) -0.088 (.16) Pacific -0.372 (.10) -0.489 (.21) -0.304 (.13) non metropolitan area 0.468 (.09) 0.376 (.17) 0.196 (.14) 0.1-0.25 million inhabitants 0.205 (.11) -0.037 (.23) 0.103 (.15) 0.25-0.5 million inhabitants 0.224 (.10) 0.184 (.18) -0.066 (.15) 0.5-1 million inhabitants 0.000 (.11) 0.116 (.18) 0.089 (.13) 1-2.5 million inhabitants+ 0.071 (.10) 0.044 (.15) -0.060 (.12) 2.5-5 million inhabitants+ 0.093 (.10) -0.096 (.16) -0.273 (.12) Family: female-headed married couple -0.102 (.16) 0.239 (.25) 0.027 (.18) Family: Male (no spouse) 0.705 (.10) 0.490 (.19) 0.431 (.14) Family: Female (no spouse, no children)

1.036 (.10) 0.975 (.18) 0.668 (.15) Family: Female (no spouse, with children 1.179 (.14) 0.687 (.22) 0.764 (.17) Head: Native, foreign parents -0.160 (.10) -0.099 (.27) -0.005 (.11) Head: Naturalized American -0.121 (.14) 0.107 (.23) -0.003 (.12) Head: Foreigner 0.191 (.16) 0.454 (.20) 0.514 (.10) Head: moved within same county 0.459 (.08) 0.317 (.13) 0.293 (.12) Head: moved from different county 0.367 (.10) 0.404 (.20) 0.176 (.16) Head: employed lone-mother -0.179 (.15) 0.103 (.22) 0.115 (.19) Head: 25-55 years old -0.825 (.07) -0.273 (.14) -0.069 (.11) Head: 56+ years old -2.350 (.09) -1.515 (.17) -0.653 (.15) Head: secondary education -0.323 (.07) -0.362 (.11) -0.407 (.09) Head: College -0.627 (.07) -0.822 (.12) -0.826 (.10) Head: inactive -0.285 (.14) -0.170 (.23) 0.008 (.17) Head: unemployed 0.495 (.15) 0.730 (.24) 0.622 (.20) Head: pv, mp -0.392 (.14) -0.472 (.20) -0.280 (.18) Head: pv, p-t, non mp 0.764 (.12) 0.615 (.18) 0.757 (.14) Head: pb, mp -0.256 (.18) -0.438 (.36) -1.180 (.35) Head: pb, non mp 0.364 (.20) -0.326 (.21) 0.057 (.24) Head: self-employed 0.494 (.13) 0.186 (.30) 0.221 (.18) Head: No. of weeks worked -0.048 (.00) -0.046 (.00) -0.037 (.00) Head: No. of hours worked -0.017 (.00) -0.019 (.00) -0.016 (.00) No. children below 10 years old 0.463 (.04) 0.585 (.07) 0.418 (.05) No. children 10-15 years old 0.288 (.05) 0.477 (.07) 0.491 (.06) No. dependents 16-45 years old 0.566 (.05) 0.536 (.08) 0.469 (.06) No. dependents 46-64 years old 1.366 (.10) 0.907 (.16) 0.613 (.14) No. dependents 65+ years old 1.904 (.16) 1.224 (.24) 0.230 (.21) No. other employed, 15-45, primary -0.234 (.20) -0.252 (.33) -0.332 (.18) No. other employed, 15-45, secondary -0.378 (.18) -0.408 (.30) -0.387 (.19) No. other employed, 15-45, College -0.730 (.18) -1.506 (.37) -0.832 (.23) No. other employed, 46+, primary -0.779 (.55) 0.108 (.46) -0.492 (.27) No. other employed, 46+, sec-College -0.849 (.14) -1.062 (.31) -1.043 (.22) No. other employed, pv, mp -0.468 (.15) -0.387 (.28) -0.416 (.26) No. other employed, pv, f-t, non mp -0.161 (.11) -0.283 (.18) -0.247 (.14) No. other employed, pv, p-t, non mp 0.138 (.14) 0.215 (.22) 0.248 (.17) No. other employed, pb, mp -0.500 (.21) -2.029 (.51) -0.593 (.40) No. other employed, pb, non mp -0.912 (.25) -0.492 (.29) 0.070 (.33) No. other self-employed, 0.472 (.15) 0.039 (.38) 0.608 (.23) No. of other female employed 0.266 (.16) 0.473 (.23) 0.388 (.16) Weeks worked by others -0.035 (.01) -0.020 (.01) -0.031 (.01) Hours worked by others -0.008 (.01) -0.020 (.01) -0.008 (.01) No. other nonlabor income, primary -0.364 (.16) -0.259 (.19) -0.583 (.16) No. other nonlabor income, secondary -0.787 (.17) -1.058 (.23) -1.131 (.28) No. other nonlabor income, College -0.805 (.19) -0.948 (.24) -0.683 (.23) N. other income female receivers -0.135 (.17) -0.330 (.23) -0.391 (.20) Constant 0.669 (.21) 0.890 (.36) Poverty regressions for 1993 and 2001 omitted for the sake of presentation (see Gradín, 2008).


Ekaterini Panopoulou

Theologos

Theologos Pantelidis
email: pantelidis@econ.soc.uoc.gr.

Convergence in Per Capita Health Expenditures and Health Outcomes in the OECD Countries

Keywords: Convergence clubs, health expenditure, health determinants, health outcomes, OECD countries JEL Classification: C23, I10 Convergence clubs, health expenditure, health determinants, health outcomes, OECD countries Convergence in per capita Health Expenditures and Health Outcomes

Convergence in Per Capita Health Expenditures and

Health Outcomes in the OECD Countries

Introduction

Health care expenditure has been rising rapidly in the developed countries in the recent years. This rise constitutes a major concern for health policy makers. In this respect, research has focused on the determinants of health care expenditure with income growth being the undebatable determinant (see, inter alia, [START_REF] Hitiris | The determinants and effects of health expenditure in developed countries[END_REF] Despite its potential importance and usefulness, the analysis of cross-country disparities in this context has not received much attention as very few studies focus on the issue of health care convergence for a set of countries. [START_REF] Hitiris | Health care expenditure and integration in the countries of the European Union[END_REF] examines whether health expenditures converge for a group of European Community countries and finds no evidence of convergence. He argues that economic convergence of the EU members will eventually lead to convergence of health expenditure. In a more recent study, [START_REF] Hitiris | Convergence of health care expenditure in the EU countries[END_REF] find that EU economic integration leads to income convergence and to convergence in various sectors and the health sector in particular.

However, the authors conclude that there is no clear evidence in favour of the existence of a single health function for all the EU member states. Such a case can become even more complex if the analysis is extended to cover the OECD countries. Employing an empirical growth theoretical framework in order to examine the determinants of growth of per capita health expenditures for 24 OECD countries, [START_REF] Barros | The black box of health care expenditure growth determinants[END_REF] finds that the higher the health care expenditures per capita are, the lower the growth rate is. The author suggests that this is an indication of convergence of health care expenditures.

The rapid growth in US health care expenditure and the 'catch-up' hypothesis in the OECD countries is the focus of [START_REF] Narayan | Do health expenditures 'catch-up' ? Evidence from OECD countries[END_REF] study. Specifically the author examines whether per capita health expenditures of Canada, Japan, Spain, Switzerland and the UK converge to the US per capita health expenditures and finds strong evidence in favour of convergence. 1Taking a different route, Alcande-Unzu et al. (2009) address the issue of cross country disparities in health care expenditure by employing a factor decomposition model. The authors extend the decomposition of [START_REF] Cheng | Income inequality and efficiency: a decomposition approach and applications to China[END_REF] to include per capita health care expenditure and show that it can be expressed as the product of the ratio of health care expenditures to GDP, labour productivity, employment rate, activity rate and the ratio of working age population to total population. Their findings suggest that the main determinants of cross-country dispersion in per capita health care expenditure are the ratio of health care expenditures to GDP and labour productivity. This factor decomposition is the starting point of our analysis. Specifically, the first objective of our analysis is to examine convergence in per capita health expenditures for a group of 19 OECD countries over the period 1972-2006 and use the aforementioned decomposition to identify the reasons for the observed divergent behaviour of some countries. It is obvious that the examination of the economic characteristics that lead to health expenditure convergence/divergence is critical for policymakers since the majority of the OECD countries face budget deficits. Health care spending has put a pressure on public budgets during the past two decades. Moreover, evidence on convergence would point to increased expenditure on health and thus increased deficits, unless some other sectors, such as education and other public services have to be sacrificed. This can potentially be a contentious issue in some countries. Our results suggest convergence in per capita health expenditures among 17 OECD countries, while Norway and especially the US follow a different path with substantially higher per capita health expenditures. It turns out that the divergence of the US comes from the divergence in the 'ratio of health care expenditures to GDP' component, while Norway's divergence is mainly caused by the 'labour productivity' component.

We also move one step forward and investigate whether convergence in per capita health expenditures implies convergence in health outcomes as well. Interestingly, our results show that convergence in per capita health expenditures does not, in general, lead to convergence in health outcomes. Finally, we extend our analysis to provide some insights into the cross-country disparities in major determinants of health expenditures.

We should note that our analysis is based on a new methodology introduced by Phillips and Sul (2007a, PS henceforth). This methodology has two important advantages compared to alternative approaches available in the literature. First, it is based on a nonlinear time-varying factor model that incorporates the possibility of transitional heterogeneity or even transitional divergence. Moreover, the methodology is robust to the stationarity properties of the series under scrutiny. Second, and more importantly, this methodology provides a simple empirical algorithm that can be used to identify groups of countries that converge to different equilibria when the full panel of the countries under scrutiny diverge. Moreover the algorithm also detects individual countries that diverge from the panel. This enables us to identify the reasons for the divergent behaviour of some countries.

The rest of the paper is organised as follows. Section 2 describes the methodology we use to examine health convergence among the OECD countries. Sections 3-5 report our empirical findings and Section 6 concludes the paper.

Econometric Methodology

In this section, we outline the econometric methodology we employ to examine the existence of convergence in per capita health care expenditure, health outcomes and various determinants of health expenditures. The methodology was introduced by PS in order to test for conditional sigma convergence in a panel of countries. We also briefly discuss the clustering algorithm, put forward by PS and employed in Phillips

The log t Test

Suppose that we have panel data for a variable X it , i = 1, ...N and t = 1, ...T where N and T are the number of countries and the sample size respectively. PS decompose X it into the systematic, g it , and the transitory, a it , components. In other words,

X it = g it + a it (1)
We can discriminate between the common and idiosyncratic components of the panel by using the following transformation of (1):

X it = µ g it + a it μ t ¶ μ t = δ it μ t , for all i, t (2)
In this way, the variable of interest, X it , is decomposed in two components, one common, μ t , and one idiosyncratic, δ it , both of which are time-varying. The idiosyncratic component, δ it , is a measure of the distance between X it and the common component, μ t . This formulation allows us to test for convergence by testing whether the factor loadings δ it converge to a constant, δ, by taking ratios instead of differences and thus eliminating the common component. To do so, PS define the relative transition parameter, h it , as

h it = X it 1 N N P i=1 X it = δ it 1 N N P i=1 δ it (3)
which measures the loading coefficient δ it in relation to the panel average and as such the transition path for the variable of interest of country i relative to the panel average.

H t = 1 N N X i=1 (h it -1) 2 → 0, as t → ∞
Note that the cross-sectional mean of the relative transition paths, h it , is unity by construction. PS implement the following semiparametric model for δ it :

δ it = δ i + σ i ξ it L(t)t α
where ξ it ∼ iid(0, 1) across i, σ i are idiosyncratic scale parameters, L(t) is a slowly varying function, such as log(t) for which L(t) → ∞ as t → ∞, and α denotes the speed of convergence, i.e. the rate at which this cross-sectional variation decays to zero. This representation ensures that δ it converges to δ i whenever α º 0. The null hypothesis of convergence is as follows:

H 0 : δ i = δ and α º 0 while the alternative is

H A : δ i 6 = δ for all i or α ≺ 0
The null hypothesis implies convergence for all countries, while the alternative hypothesis implies no convergence for some countries. The alternative hypothesis can accommodate both overall divergence and club convergence, i.e. the possibility that some of the countries under scrutiny form convergent groups at different factor loadings, say δ 1 and δ 2 . The following subsection describes an empirical algorithm that can be implemented to identify such convergent subgroups. PS show that under convergence, H t has the following limiting form

H t v A L(t) 2 t 2α , as t → ∞ ( 4
)
where A is a strictly positive constant.

log(H 1 /H t ) -2 log L(t) = b c + b b log t + u t , t = [rT ], ..., T
where L(t) = log(t) and r > 0. Specifically, the regression is run after a fraction (r) of the sample is removed. PS recommend setting r equal to 0.3, since extensive Monte Carlo simulations show that this choice of r is satisfactory in terms of both size and power properties of the test.

The fitted coefficient b b of logt converges to the speed of convergence parameter 2α

under the null hypothesis of convergence where b α is the estimate of α in H 0 (see also equation ( 4)). We focus on the sign of the estimated coefficient, b b, since convergence of the full panel requires that b is either positive or equal to zero. This is tested by means of the conventional t-statistic, t b , that is, convergence is supported if t b  -1.65. The standard error of our estimates is calculated using a HAC estimator for the long-run variance of the residuals. In this study, we employ the Quadratic spectral kernel and determine the bandwidth by means of the Andrews (1991) data-dependent procedure.

Finally, we should note that the size of b indicates the speed of convergence (for positive values). That is, high values of b suggest a fast rate of convergence.

The aforementioned convergence test has numerous advantages over existing ones.

First, the methodology does not rely on any particular assumption concerning trend stationarity or stochastic nonstationarity. Second, it is based on a model that incorporates the possibility of transitional heterogeneity or even transitional divergence. This is crucial since it is not proper to test for convergence by means of standard unit root and cointegration tests in the presence of heterogeneity. For example, PS show that two series can converge even if they are not cointegrated (this is the case of asymptotic cointegration). Thus, the methodology of PS can be considered as an asymptotic cointegration test that does not suffer from the small sample problems of standard unit root and cointegration tests. Third, when the full panel of the countries under scrutiny diverges, the methodology provides a simple empirical algorithm (described in the following section) that can be used to identify groups of countries that converge to different equilibria and at the same time allow individual countries to diverge.

Club Convergence Algorithm

Whenever a researcher examines convergence within a panel of countries, it is often the case that the full panel diverges. In such cases, PS propose an empirical algorithm that can be used to determine subgroups of countries that converge to different steady states. We now briefly describe the four steps of this empirical algorithm.

• Step 1 (Ordering): We order the members of the panel according to the last observation, since evidence of convergence will, in general, be most apparent in the recent years. Alternatively, the ordering of the series can be done based on a time series average of the final observations.

• Step 2 (Core Group Formation): We try to identify a core group of countries that provide strong evidence of convergence. Specifically, we estimate a sequence of logt regressions using the k highest members (Step 1) for all different values of k (i.e. 2 ≤ k < N). We choose the regression that generates the maximum convergence t-statistic t b,k (where t b,k > -1.65 so that convergence is ensured for the corresponding group). The corresponding group forms the core convergence group.

• 3 Factor decomposition of per capita health care expenditure

In this section we describe the methodology employed to decompose per capita health care expenditures in factors and then apply the econometric methodology described in the previous section to reveal cross-country disparities among the OECD countries.

The decomposition methodology we employ comes from the literature of per capita income convergence and was proposed by [START_REF] Cheng | Income inequality and efficiency: a decomposition approach and applications to China[END_REF]

x it ≡ s it • y it • e it • a it • w it (5)
We are interested in examining convergence in per capita health care expenditures.

The Specifically, the first convergence club contains Norway and the US with substantially higher per capita health expenditures compared to the remaining 17 countries that form the second club. It is now of great interest to identify the reasons of divergence for Norway and the US from the rest of OECD countries. We can investigate this issue based on the decomposition of per capita health expenditures given in equation [START_REF] Baumol | Containing the medical cost: why price controls wouldn't work[END_REF]. Specifically, we examine convergence among the 19 OECD countries included in our analysis for each one of the five factors reported in equation ( 5). The results are reported in Table 2 and can be summarised as follows.

1. There is full convergence among the 19 countries for three out of five factors, namely the employment rate (e it ), the activity rate (a it ) and the proportion of working age population in total population (w it ). Convergence is faster for e it and w it (the estimated values of b are statistically greater than zero) and slower for the activity rate (the estimated b is not statistically different from zero). As a conclusion, none of these three factors can account for the divergent behaviour of Norway and the US in per capita health expenditures.

2. There is no full convergence in the ratio of health care expenditures to GDP (s it ) as suggested by the negative and statistically significant estimated value of b ( b b = -0.85). The implementation of the club convergence algorithm reveals that Ireland and the US diverge from the remaining 17 countries following a different path each. To be more specific, Ireland moves below but close to the 17 countries, while the US is well above the panel average. Interestingly, the divergence of the US from the other OECD countries increases over time. This is illustrated at the left-hand side plot of Figure 2

Convergence in Health Outcomes

The analysis so far has shown that OECD countries broadly converge as far as per capita health expenditure is considered (with the exception of the US and, to a lesser degree, Norway). Does this convergence imply convergence in health outcomes as well?

The link between additional health expenditure and patient benefits, i.e. improved health outcomes is one of the fundamental and yet unresolved issues in health policy.

Employing a cross-section of 18 developed countries, [START_REF] Cochrane | Health service 'input' and mortality 'output' in developed countries[END_REF] examine the relationship between mortality rates, GNP and consumption of inputs such as health care provision. They find that health care inputs are not in general associated with health outcomes, such as mortality rates. To avoid international heterogeneity, In their own study, they employ data for 15 EU countries over the period 1980-1995 and three health outcomes measures -life expectancy at birth for males and females, and the infant mortality rate -and conclude that although health expenditure and the number of physicians have made a significant contribution to improvements in infant mortality, '... health care expenditure has made a relatively marginal contribution to the improvements in life expectancy in the EU countries over the period of the analysis'.

More recently, Martin et al. (2008) take advantage of a major new dataset developed in

English health care, in the form of programme budgets, which enables them to address some of the difficulties associated with estimating the impact of health care expenditure on health outcomes in two of the largest programmes of health care: circulatory disease and cancer. Their study shows that health care expenditure has a strong positive effect on outcomes in the two programmes of care investigated.

Following the extant literature, we proxy health outcomes with the following variables: life expectancy (females), life expectancy (males), life expectancy (total population), crude mortality rate, infant mortality and potential years of life lost. 3 For each one of the aforementioned health outcomes we implement the log t test to examine convergence among the countries under scrutiny. When testing for convergence of the full panel of 19 countries, the results, reported in Panel A of Table 3, suggest that convergence occurs only for infant mortality. For all the other variables the null hypothesis of convergence is rejected. In general, the main conclusion drawn from Panel A of Table 3 is that the existence of convergence among 17 of the 19 countries under examination in per capita health expenditures does not lead to convergence in health outcomes.

Moreover, the implementation of the club convergence algorithm leads to the formulation of various convergence clubs that differ for each one of the variables considered 3 The variable definitions are given in 3, may be summarised as follows:

1. There is a core group of six countries that converge for all the variables considered in this section. This group contains Austria, Canada, Germany, Norway, Spain and the UK. Furthermore, Finland and Ireland deviate from this core group only in the case of life expectancy of males. Similarly, New Zealand does not belong to this core group only for the total life expectancy variable.

2. In most cases, Japan behaves in a totally different way compared to the other OECD countries. A typical example is the mortality rate where 17 of the 19 countries form two convergence clubs while Japan (and Sweden) diverges following its own different path. On the whole, Japan's health outcomes are better off compared to the panel average. Specifically, Japan belongs to above average groups for all life expectancy measures, below average for the mortality rate and the middle group for the potential years of life lost variable.

3. Contrary to Japan and despite the high per capita health expenditure level, US health outcomes are worse off compared to the other OECD countries. More in detail, the US are below the panel average with respect to all life expectancy measures and above the panel average when mortality rate and potential years of life lost are considered.

Convergence in Health Determinants

'What determines the quantity of resources a country devotes to medical care?' Professor Newhouse (1977) asked some 30 years ago. Employing a cross-section regression of per capita health expenditure on per capita GDP in 13 OECD countries, he inferred that per capita GDP accounted for 92% of the sample variation in health expenditure.

Moreover, the estimated elasticity (greater than one) implied that health care is a "luxury" good, rather than a necessity. Since this seminal paper, attention has been paid on 14 In this section, we attempt to reveal cross-country disparities among the OECD countries with respect to the basic determinants of health expenditure. Our choice of variables is limited by data availability. For example, health stock variables are not included in our analysis as we could not get a sufficient number of cross-section and time-series observations. With respect to the economic variables, the variables we employ are (i) per capita GDP, (ii) income equality measured by the share of nominal wages in nominal GDP, (iii) the inflation rate measured by the growth rate of the GDP deflator as wages in the health sector will rise proportionally and (iv) the 'Baumol variable' measured by the difference between wage increases and labour productivity. 4 Health care expenditure also depends on the nature of the health system. We expect public reimbursement systems to raise health spending. A proxy for the nature of the health system of each country is the proportion of public health expenditure in total health expenditure. The results of the log t test, presented in Table 3 (Panel B), suggest convergence among the OECD countries for all economic determinants except for income equality. In the latter case, the countries form three convergence clubs, while Ireland diverges. 5 Furthermore, the demographic structure of the population affects health expen- 4 Baumol (1988) argues that the health sector is a service sector and as such, productivity gains may be lower than the rest of the economy. For a detailed analysis, see [START_REF] Hartwig | What drives health care expenditure?-Baumol's model of 'unbalanced growth' revisited[END_REF]. 5 To reserve space we do not report club convergence results for health determinants. These are available from the authors upon request. Lastly, countries differ with respect to lifestyle and consumption behaviour and in this respect they are exposed to different types of diseases. This means that the probability for all types of illnesses will differ from country to country leading countries to target specific types of diseases. Altering the lifestyle and consumption behaviour of people is feasible through spending more on preventive health care. For the purpose of our analysis we employ the following variables: (i) sugar consumption, (ii) fruits & vegetables consumption, (iii) tobacco consumption and (iv) alcohol consumption. The results, reported in Table 3 From an empirical point of view, the main advantage of this methodology (in addition to its robustness to the stationarity properties of the series under scrutiny) is the provision of a simple algorithm that can be used to identify groups of countries that converge to different equilibria when the full panel of countries under examination diverges.

Our results support the existence of convergence in per capita health care expenditures for 17 countries, while the US and, to a lesser degree, Norway follow a different path with substantially higher per capita health expenditures. We then decompose per capita health expenditures into five components and examine convergence among the OECD countries for each one of the components. In this way, we are able to identify what causes the divergent behaviour of Norway and the US. It turns out that divergence of the US comes from the divergence in the 'ratio of health care expenditures to GDP' component, while Norway's divergence is probably caused by the 'labour productivity' component.

Moreover, we investigate whether convergence in per capita health expenditures among the majority of the OECD countries under scrutiny is accompanied by convergence in health outcomes. Specifically, we use six different health outcomes. With the exception of infant mortality, there is divergence of the full panel of OECD countries for all the health outcomes considered in this study, although we can still identify groups of countries that converge to different steady states. We, therefore, conclude that convergence in per capita health expenditures does not lead to convergence in

  ; Hansen and King, 1996; Di Matteo and Di Matteo, 1998; Karatzas, 2000; Herwartz and Theilen, 2003; Wang, 2009a; Cantarero and Lago-Penas, 2010). The long run elasticity of health expenditure on growth and the question of whether health is a luxury good has attracted attention by another line of the literature that focuses on the integration and cointegration properties of the variables of interest (see, inter alia, Hansen and King, 1996; Hitiris, 1997; McCoskey and Selden, 1998; Gerdtham and Lothgren, 2000; MacDonald and Hopkins, 2002; Jewell et al., 2003; Clemente et al., 2004; Ang, 2010).

4

 4

  2007b, 2009), which allows us to classify countries into convergence subgroups in cases where the full panel diverges.

Figure 1 10 Page

 110 Figure1presents the relative transition paths, h it , calculated based on equation[START_REF] Ang | The determinants of health care expenditure in Australia[END_REF] for Norway, the US and the remaining countries (calculated as the cross-country average of their relative transition paths). Each transition path shows the position of the country or club relative to the panel average. The 19-country average corresponds to the unity level. A transition path below unity indicates that the level of the country or club is below the panel average, while a transition path above unity indicates that2 Note that the period and countries under scrutiny are slightly different for some of the variables examined in the following sections. The relevant information is reported in Panel B of Table1.

  that presents the position of Ireland and the US relative to the other OECD countries (for s it ). It is therefore clear that the divergence of the US per capita health care expenditures from the other countries 11 from its divergence in s it .3. Similarly, there is no full convergence in labour productivity ( b b = -0.562). The countries form two convergence clubs. The first club includes Iceland, Japan, New Zealand, Portugal, Spain and Switzerland, while the remaining countries form the second club. The relative position of the two clubs is revealed in the right-hand side plot of Figure2that also shows the position of Norway in a separate line. We observe that over the recent years Norway displays an upward tendency moving away from the panel average. We therefore suspect that the divergence of Norway from the other countries in per capita health care expenditures is mainly caused by the labour productivity component.

Cremieux 12 Page

 12 et al. (1999) examine the relationship between expenditure and outcomes across 10 Canadian provinces over the 15-year period 1978-1992. Their results point to a significant link between lower health-care spending and a significant increase in infant mortality along with a decrease in life expectancy. Or's (2001) study of the determinants of variations in mortality rates across 21 OECD countries between 1970and 1995 reveals that health expenditure is significant on health for women proxied by life lost, but not for men. In a detailed review, Nixon and Ulmann (2006) refer to 16 studies that have examined the issue providing a variety of results.

  cointegration properties of health expenditure and GDP (see, inter alia, Hansen and King, 1996; Hitiris, 1997; McCoskey and Selden, 1998; Gerdtham and Lothgren, 2000; MacDonald and Hopkins, 2002; Jewell et al., 2003; Clemente et al., 2004). Another strand of the literature focuses on revealing the determinants of health expenditures (see, inter alia, Hitiris and Posnett, 1992; Hansen and King, 1996; Di Matteo and Di Matteo, 1998; Karatzas, 2000; Herwartz and Theilen, 2003). The key variables employed in the literature as determinants of health expenditure can be broadly categorised as economic, health stock, institutional, demographic and life-style variables.

15 Page

 15 categories of the population tend to consume more health related products. Among the demographic variables employed in the literature are the following: (i) the dependency ratio measured by the ratio of the population under 14 and over 65 over the population aged 15-64, (ii) the ratio of the population aged under 14 to total population, (iii) the ratio of the population aged over 65 to total population, and (iv) the female labour force participation rate. Table3 (Panel C) reports the results for the convergence tests for the aforementioned demographic variables. The results suggest full convergence for the dependency ratio and the female labour force participation rate. In both cases, convergence is fast since the estimated value of b is positive (b equals 0.915 and 1.156 for the dependency ratio and the female labour force participation rate respectively) and statistically different from zero. On the other hand, there is divergence among the OECD countries for the other two demographic variables considered in this study. In both cases, the countries form two clubs that converge to different steady states.

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 16 (Panel D), indicate convergence in alcohol consumption among the OECD countries under scrutiny (at a slow rate as suggested by the estimate of b that is not statistically different from zero). On the other hand, the OECD countries diverge for the other three variables considered in our analysis. Specifically, in the case of tobacco consumption the countries form three convergence clubs. Finally, the results for the nutrition-related variables (i.e. sugar consumption and fruits & vegetables consumption) suggest that the OECD countries are, in general, divided into two groups The issue of health care expenditure has attracted the interest of many researchers over the last 30 years. This study contributes to the existing literature by examining convergence in per capita health care expenditure among 19 countries for the period 1972-2006 by means of a new methodology introduced by Phillips and Sul (2007a).

17

 17

  outcomes. Finally, we examine convergence in various determinants of health expenditures. Our findings support (i) convergence in all economic determinants examined in this study with the exception of income equality and (ii) club convergence in most of the demographic and life-style variables under investigation.

Figure 1 .

 1 Figure 1. Relative transition paths for per capita health expenditure

Figure 2 .Figure 3 .

 23 Figure 2. Relative transition paths for 'health care expenditure as a share of GDP' (lefthand side) and 'labour productivity' (right-hand side)

  Step 4 (Recursion and Stopping): We run the logt regression for all the countries not included in the club formed in the previous step. If the corresponding tstatistic indicates convergence, these countries form a second convergence club.

	the criterion for convergence.
	• Otherwise, Steps 1 to 3 are repeated in order to reveal additional subconvergence
	clusters. If no core group can be found (Step 2), then these countries display a
	divergent behaviour.
	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	the core convergence group. Finally, we check whether the whole group (i.e. the
	members of the initial core group and the additional selected members) satisfies
	8

Step 3 (Club Membership): We now evaluate each individual country not included in the core convergence group (Step 2) for membership in this group. More in detail, we add one country at a time and calculate the convergence t-statistic from the logt regression. The new country (member) satisfies the membership condition if the associated t-statistic is greater than a chosen critical value c * (i.e. t b > c * ). All countries that satisfy the membership condition are added to

  In the same manner, we letY it, E it, A it and W it (s it = H it /Y it ), labour productivity (y it = Y it /E it ), employment rate (e it = E it /A it ),activity rate (a it = A it /W it ) and the proportion of working age population in total population (w it = W it /N it ). As a result, the variable of interest, x it , can be written as follows:

	Submitted Manuscript	Page 10 of 25
	F o r
	P
	e
	e r
	R
	e
	v i e
	w
	be country's i GDP, total employment, active population and working-age population,
	respectively. It is easy to see that per capita health care expenditure can be written
	as the product of the following factors: the ratio of health care expenditures to GDP
	9

. The authors show that per capita GDP can be decomposed into the product of labour productivity, employment rate, activity rate and the ratio of working age population to total population.

Alcalde-Unzu et al. (2009) extend this decomposition to include per capita health care

expenditure and show that it can be expressed as the product of the ratio of health care expenditures to GDP, labour productivity, employment rate, activity rate and the ratio of working age population to total population. More in detail, let x it denote the per capita health care expenditure in country i in period t. It can be calculated by dividing health care expenditure (H it ) and the total population in country i (N it ).

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  data we employ are taken from the OECD Health Database (OECD, 2008). Since our methodology requires a balanced panel, we end up with 19 OECD countries examined over the period 1972-2006. The countries under scrutiny are listed in Table 1, Panel A. The same table (Panel B) contains a list of variables' definitions and conversions employed.

2

We first employ the log t test for the panel of per capita health care expenditures,

x it , for the 19 countries under scrutiny. The point estimate of b, reported in Table

2

, is statistically less than zero ( b b = -0.471, t b = -5.024) suggesting divergence of the 19 OECD countries. Afterwards, we implement the algorithm described in Section 2.2 to identify subgroups of countries that converge to different equilibria. Our results indicate that the countries under examination are divided into two convergence clubs.

Table 1 (

 1 Panel B). in this study. Our findings of the club convergence algorithm, reported in Figure

	13

Table 1 . List of countries and variables

 1

	Panel A: List of countries
	Australia	Austria	Belgium	Canada
	Denmark	Finland	Germany	Iceland
	Ireland	Japan	Netherlands	New Zealand
	Norway	Portugal	Spain	Sweden
	Switzerland	UK	US
	Panel B: List of variables / definitions
	Variable		Definition -Expressed in

Table 2 . Per capita health-care expenditure decomposition -Convergence tests

 2 indicates rejection of the null hypothesis of convergence at the 5% level.

	b ˆ	t b -stat

Table 3 . Health outcomes, economic variables, demographic variables, life-style and behavior -Convergence Tests

 3 indicates rejection of the null hypothesis of convergence at the 5% level.

	Panel A: Health outcomes
		b ˆ	t b -stat
	Life expectancy (females)	-0.772*	-17.752
	Life expectancy (males)	-0.476*	-6.613
	Life expectancy (total)	-0.554*	-8.555
	Mortality rate	-0.619*	-12.776
	Infant mortality	-0.201	-0.743
	Potential years of life lost (<70)	-0.597*	-8.869
	Panel B: Economic variables
		b ˆ	t b -stat
	GDP per capita	-0.034	-0.489
	Income equality	-1.524*	-13.213
	Inflation	-0.008	-0.030
	Baumol variable	-1.678	-1.174
	Public health expenditure	-0.037	-0.369
	Panel C: Demographic variables
		b ˆ	t b -stat
	Dependency ratio	0.915	4.451
	Ratio of under-14 population to total population	-0.377*	-6.538
	Ratio of over-65 population to total population	-0.337*	-6.091
	Participation in labour force of females	1.156	17.270
	Panel D: Life-style and behavior
		b ˆ	t b -stat
	Sugar consumption	-1.004*	-17.770
	Fruits & vegetables	-0.368*	-2.107
	Tobacco consumption	-1.573*	-18.823
	Alcohol consumption	-0.042	-0.580
	Notes: *

26 of 25 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Wang (2009b) also focuses on the degree of convergence of US health care expenditures and its major components using state level data. His findings point to moderate convergence in total health care costs accompanied by divergence in the components.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Acknowledgements: We would like to thank Donggyu Sul for making the Gauss code available to us. We are also grateful to an anonymous referee for useful comments and suggestions. Finally, the authors would like to thank the University of Crete for financial support under the KA 2877 research grant of the "Program of Financing Basic Research". 18

Availability Per capita health expenditure Total expenditure on health /capita -US$ PPP 1972-2006