the welfare state as transnational event evidence from sequences of policy adoption

Social Science History Association

The Welfare State as Transnational Event: Evidence from Sequences of Policy AdoptionAuthor(s): Andrew Abbott and Stanley DeVineySource: Social Science History, Vol. 16, No. 2 (Summer, 1992), pp. 245-274Published by: Duke University Press on behalf of the Social Science History AssociationStable URL: http://www.jstor.org/stable/1171289 .

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The Welfare State as Transnational Event: Evidence from Sequences of Policy Adoption ANDREW ABBOTT & STANLEY DEVINEY

STUDENTS OF THE WELFARE STATE have long speculated about the sequence in which welfare programs were adopted. Worker's compensation, for example, has generally been adopted early by the various countries, while family allowances have come later. The order of these programs is important both for its inherent interest and for-its bearing on theories of the welfare state.

Given this empirical importance, the relative absence of studies of the order of program adoption may seem surprising. There are a few such studies (Cutright 1965; Collier and Messick 1975; Schneider 1982). But some derive the sequence from cross- sectional data (Cutright 1965), while others treat types of programs as interchangeable (Collier and Messick 1975). To our knowledge, there are no studies directly comparing sequences of adoption.

In this article we perform a direct analysis of differences in welfare adoption sequences. Our opening discussion of conceptual and theoretical issues outlines three levels of analysis for welfare adoption. We then consider data sources and a method for se-

Andrew Abbott is professor of sociology at the University of Chicago. His substantive research concerns occupations and work. His methodological research concerns sequence and temporal modeling. Stanley DeViney is assistant professor of sociology at the University of Kansas. His research has focused on the determinants of welfare policies in advanced industrial countries. Social Science History 16:2 (Summer 1992). Copyright I1992 by the Social Science History Association. ccc oI45-5532/92/$I.50.



246 SOCIAL SCIENCE HISTORY

quence comparison, optimal matching. After this come sections separately examining each level of analysis. The first takes the traditional approach, in which individual countries are cases and adoption sequences reflect internal differences between countries. The second considers the possibility that diffusion affects adoption sequences. The third examines the theory that adoption sequences within countries are in fact random expressions of higher-level, worldwide events. The conclusion draws major themes together and indicates directions for future research.

Since direct analysis of sequence data is unfamiliar to social scientists, it will be necessary to introduce a certain amount of technical material. Indeed, the whole conceptual approach- taking sequences as units and comparing them-may seem unfamiliar. Nonetheless, we feel that the empirical results justify our thinking in sequence terms and developing methodologies to turn those thoughts into empirical practice.'

CONCEPTUAL ISSUES

In standard methods, the first task of an analysis is the establish- ment of a conceptual model. Methods analyzing sequence data are no different. Following Abbott (1984), we begin with a discussion establishing the proper level of analysis, then consider the substantively important events and their temporal character, and finally specify the theoretical sequence expected.

To whom do the events-welfare program adoptions-really occur? There are three possibilities. First, events may occur to individual countries. On this argument, our data consist of individual, independent histories of welfare state development. Our causal problem is to discover the country characteristics that determine the order of adoption. But this individual level of analysis is obviously problematic (Collier and Messick I975). Since programs can diffuse, we cannot assume that internal heterogeneity, rather than intercase contagion, is generating the pattern. There may be both an individual level of analysis (local variables may still be important) and a group level (for diffusion). And there is the further possibility that only the group level is real. On this argument, internal characteristics make little difference, and diffusion, if it occurs, is idiosyncratic rather than systematic. Among certain countries, there may have been so much exchange and



The Welfare State as Transnational Event 247

modeling with respect to welfare programs that programs diffused essentially at random. The major empirical issue of this article proves to be which of these three levels is in fact the proper one. Level of analysis is not merely a methodological issue but also a substantive one (cf. Watkins 1987).

Abbott's (1984) second task for sequence conceptualization is the specification of the events themselves. The events of interest here are familiar from welfare state studies. The literature has traditionally considered five basic welfare programs: worker's compensation; sickness and maternity benefits; old-age, invalidity, and death supports; family allowances; and unemployment insurance. These five program areas provide the major guarantees expected of a welfare state. They account for the vast majority of public social expenditures within the current sample. And their presence and age has previously been used as an indicator of welfare state development (Cutright 1965; Wilensky 1975). The limitations of this specification should, however, be noted. We do not include current education, housing, and indirect tax subsidies (Wilensky 1975; cf. Pampel and Williamson 1988: n. 7), a point to which we return in closing. For the purposes of this article, we assume that each program is "indicated" by the first law embodying it, since this is the only readily accessible date for ordering the events .2

Given a level of analysis and a list of events, we must finally consider the order of the sequence itself. In a classic article, Cut- right (1965) inspected cross-sectional data for 76 countries in 1961 and found them to lie in a Guttman scale: (I) workmen's compensation, (2) sickness and maternity benefits (health insurance), (3) old-age, invalidity, and death supports (pensions), (4) family allowances, and (5) unemployment insurance. Cutright argued that this showed that the policies were normally adopted in that order. The actual dates, however, prove otherwise. The question remains open as to what the order was, and why.

Theoretical perspectives on the welfare state produce a variety of expectations. Under the modernization theory, development is a general structural change arising from the industrial production of the means, wealth, and needs for a state organization (Cutright 1965; Wilensky 1975; Flora and Alber I98I). Modernization variables bearing on welfare programs include economic development (indicating both need and capacity), age of population (indicating




mainly need), and degree of overall bureaucratization (indicating capacity). Modernization assumes a relatively universal process and hence predicts similar sequences of adoption in various countries. Although late starters may run through the stages faster, all are expected to follow a universal set of stages.

By contrast, the demand approach sees the welfare state as a response to working-class demands (Myles 1983; Shalev 1983; Hicks and Swank 1984). The programs redistribute income among social classes and will be stronger insofar as class differences are consolidated and articulated. Therefore, the important variables are the elective representation of the working class and its degree of formal organization, although nonelectoral participation (through riots, for example) may also be important. Although the demand approach predicts that working-class participation would hasten those programs most directly benefiting the working class, it is not necessarily clear which these programs are likely to be. One might tentatively predict that immediate benefits like worker's compensation and unemployment insurance would precede long- term ones like pensions.

A final, state-centered theory emphasizes political organization and history on the grounds that developmental and class variables exercise their effect only through the state itself (DeViney 1983; Skocpol and Amenta 1986; Amenta and Carruthers 1988). The central variables in this approach are the development, organization, and authority of the state. Although there are no particular predictions about sequence, the presumption is that countries with comparable states will have comparable sequences in the evolution of their welfare states.

While these three theories assume independent, individual units of analysis, our second level of analysis mixes the individual and group levels. First, a diffusion theory assumes that contagion plays a part; countries "close" to others adopt in comparable sequence. Closeness may mean actual geographic proximity or general resemblance or closeness in interaction, through trade, cultural interchange, or capital flows. While such an approach believes that diffusion modifies an individual-level process, one might also theorize that individual parameters might modify a group-level process. Perhaps individual characteristics determine where on the list of all adopters of particular programs particular countries tend to fall; some may be leaders, others followers. We shall call this the "reaction" model.3




Our third level of analysis treats the welfare state as something that happened not to individual countries but to some larger group as a whole. (We are here using the nonsocialist developed world as that larger unit.)4 Perhaps the developed world made up a cultural community in which images of the state in general and of its welfare programs in particular were basically common property, an argument made by John Meyer and his students (see, e.g., Boli- Bennett and Meyer 1978; Thomas et al. 1987; Ramirez and Boli 1987; on welfare programs, see Thomas and Lauderdale 1987). World-level processes might also stem from the more economic forces of the world-system theorists. On these arguments, a given program would first be set forth in an abstract rather than a practi- cal form. Then a few early countries might adopt it, while others watched; then others would eventually take it up. The program sequences within individual countries would then basically reflect random sampling on the temporal "curve" of each program. Note that the three substantive theories considered earlier-modernization, demand, and state strength-also produce "shadow" hypotheses on this level. Modernization, for example, may en- tail rivalries and emulation that produce a group-level process. And certainly the international connections of socialism before the First World War provide a sound basis for hypothesizing a similar world-level process under the demand theory.

DATA

The data for this study come from Social Security Programs throughout the World, 1981, a standard source published by the U.S. Social Security Administration (1982). They consist of the date of the first law embodying each of the five programs, in each of I8 developed countries.5

Figure I shows the data visually. The countries are arranged from top to bottom by the date of their first program; the right-left dimension is time. For each program in each country the date of adoption is marked by a point; these points are then connected (by program) by the jagged verticals. The picture seems complex, but certain underlying patterns are at once clear. In all but two cases, worker's compensation, unemployment insurance, and family allowances fall in that order. In New Zealand and Australia, the last two are reversed by a small amount (four and three years, respectively). Compensation is generally the first program adopted




GERMANY 0....

23145

AUSTRIA :: 32145

DENMARK :

.

12345 SWEDEN 23145

BELGIUM -_,.- - 23415 FINLAND D --

---- 34152

NORWAY '.......---34215 UNITED KINGDOM * 312/45

FRANCE 34125 ITALY 321/45

NEW ZEALAND o:: 13542

LUXEMBOURG

f-.

23145

NETHERLANDS 31/245 AUSTRALIA 03152/4 CANADA : 31452

ICELAND 132/45

JAPAN . .." -...... ....o -

32145

SWITZERLAND er e- D 3/2415

I , I, Il, I I I l I I 1880 1900 1920 1940 1960 1980

--- HEALTH(2) -- COMP(3) ...... AGE(1) --- UNEMP(4) FAMILY(5)

Figure I Dates of first adoption of five welfare programs

and family allowances generally the last. By contrast with this order, health insurance and old-age pensions show much disorder. Health insurance was a central pillar of the early welfare states but seems particularly late in the former English colonies. Pensions simply wander without any apparent pattern.

As we noted earlier, variations in program adoption may reflect a variety of local variables. An important preliminary problem concerns when these variables ought to be measured. We are considering the development of welfare states as whole histories, taking from 70 to Ioo years. Since variables fluctuate in such a period, we must decide which of their values should be taken as determining the welfare adoption sequence.

The standard position is that variable fluctuations determine the history repeatedly, say year by year. The variables enter an iterative "generating model" that produces annual results, which, taken in retrospect, constitute the welfare adoption sequence. One could, however, argue that these countries have an unchanging "basic character"; the variables' fluctuations are random drift either around a fundamental value or around a fundamental ratio




to the values of other countries. Alternatively, one might believe that the basic parameters of the welfare state are set by initial conditions when the first programs are considered and adopted. Here again, subsequent fluctuations would be unimportant. These three positions (the generative, basic character, and initial conditions models) involve a complex debate in the philosophy of history, ex- tending well beyond the bounds of the present article (see Abbott 1990ob for a discussion). In practice, the first position involves great methodological difficulties both for the methods used here and for standard methods. We thus consider only the latter two: the basic character model, which permits the use of recent and hence very complete data, and the initial conditions model, which requires the use of quite old and hence less complete data.6

Our current data, drawn from a variety of sources (see Table I), include standard variables representing all three substantive theories: (I) modernization (percentage of labor force in agriculture, overall development index); (2) Left political strength (degree of labor organization, labor organization centralization, percentage of socialist seats in parliament, percentage of leftist members of cabinet, corporativism [Schmitter], structural index); and (3) government strength (government expenditures as a percentage of GDP, government revenues as a percentage of GDP).

Data pertaining to the era of the first welfare program are very scarce, and we could not find effective state development measures. For modernization theories, we drew labor force figures- for industrial, agricultural, and service (residual)-from the historical figures in the League of Nations' International Statistical Yearbook of 1929 and from B. R. Mitchell's (1978) European His- torical Statistics. Left demand is measured by data on socialist votes and seats from Mackie and Rose 1983, and direct demand (for age-related programs) by statistics on the proportion of the population over 65 and under 14 from the Statistical Yearbook.

SEQUENCES AS DEPENDENT VARIABLES

Having considered the independent variables that may affect welfare adoption sequences, we now turn to making information about adoption sequences into viable dependent variables. We employ optimal matching, a method that uses dynamic programming algorithms to minimize a distance measure between two sequences.




Table I Correlations of variables with scaling dimensions

Variables Source Mean S.D. x y

Adoption era Votes 11.37 10.56 .084 -.289 Seats 9.49 II.I8 .033 -.397 % < 14 33.21 2.51 .205 .260

% > 65 6.37 1.61 .187 .274 % < 14 or > 65 .319 .428 % industrial labor force 28.31 12.73 -.002 .133 % agricultural labor force 42.I4 17.18 .164 .233 % service (other) labor

force 29.52 9.02 -.309 -.633 Current

% of labor force in agriculture TJ 17-35 8.37 .299 -.197

Overall development index B 1.48 0.22 -.076 .20I

Degree of labor organization TJ 3.o8 0.98 - .025 .216

Labor organization centralization DL 5.64 2.44 .003 .197

% of socialist seats in parliament MR 32.I 13.87 -.022 .406

% of leftist members in cabinet MR 44-52 36.98 - .092 .378

Corporativism (Schmitter) SL 2.o6 0.73 .483 .356

Structural index LG 6.28 1.47 -.029 .221 Government expenditures

% of GDP TJ 24.24 4.67 -.416 .I27 Government revenues

% of GDP TJ 29.84 4.74 -.544 .050

Sources: B = Banks I98I; DL = U.S. Department of Labor 1965; LG = Lange and Garrett 1983; MR = Mackie and Rose 1983; SL = Schmitter and Lehmbruch 1982; TJ = Taylor and Jodice 1983. Note: Positive x = later pensions; positive y = earlier health insurance.




The matrix of distances between all the adoption sequences is then used for further analysis.

As a first step, we formalize the sequence information itself. There are five policies to be adopted. At any time, a given country has an "adoption profile" describing which of the five programs it has adopted. There are 32 possible profiles (2 to the fifth power), but since no country is observed before its first program, we use only 3 1. We can list the profile for each country at regular intervals since its first adoption of a welfare program. This list is its welfare adoption sequence.

For example, France acquired its first old-age benefits in 19Io, its maternity and sickness program in 1928, workmen's compensation in 1898, unemployment insurance in 1905, and family allowances in 1932. Its profile was thus "compensation alone" for most of the first decade after 1898, then "compensation + unemployment + old age" for most of the second and all of the third decades (1908-28), then "all programs" for most of the fourth and all subsequent decades to the 1980s. Unlike the simple order sequences listed on the right-hand side of Figure I, such sequences exist in real time.7

Two examples follow. Each W indicates a decade of welfare state experience. It is followed by an arbitrary digit labeling the dominant policy profile in that decade. W31 means that all policies are present. Australia W4 W5 W5 W5 W31 W31 W31 W31 Finland W4 W4 W12 W12 W13 W29 W29 W31 W31

The distance (or dissimilarity) between such sequences consists, roughly speaking, of the number of replacements, insertions, and deletions required to get from one sequence to another.8 Thus, one turns the Australia sequence into the Finland one by adding W4 at the beginning, turning three W5s into two W12s and a W13, and turning two W31s into W29s. Not all of these changes are equally "costly." The profile "workmen's compensation + old- age supports" (W5) shares one of its elements with the profile "workmen's compensation + unemployment insurance" (W12), and so we might say the two profiles, mismatched in two out of a total (in both) of three properties, are o.66 different. Alterna- tively, we might notice that the profiles both lack the two other programs of health insurance and family allowances. Including




these would make the mismatches two out of five (instead of two out of three), for a distance of 0.4. We have here used this "matching" measure, rather than the first (Jaccard) measure, because we feel that absence of programs should weigh as heavily in determining resemblance as presence does. Optimal matching also requires that we set costs of insertion and deletion. Customarily, these are set to a value slightly higher than the largest substitution cost, a procedure emphasizing durational differences. Since we consider duration less important relative to actual pattern, we have lowered the insertion/deletion cost to one-half the maximum substitution cost.

Given these costs of insertion, deletion, and substitution, we proceed to the optimal-matching step itself. Standard algorithms have been used to produce a matrix of distances between the welfare policy sequences. It is this matrix of resemblance that we try to explain with independent variables. (For ease of reference, this matrix will be called the "adoption sequence matrix" hereafter.)

THE HYPOTHESIS OF INDIVIDUAL-LEVEL EFFECTS

In the opening discussion we suggested three possible levels of analysis of these data. The first was internal differences in countries. There are several ways to investigate the effects of independent variables on resemblance data such as we have in our adoption sequence matrix. We have chosen to scale the resemblance data and treat the resulting coordinates of individual cases as variables for regression analysis (Kruskal and Wish 1983). The scaling of the adoption sequence matrix in two dimensions (Kruskal stress= . 18) is shown in Figure 2. It is clear by inspection that one dimension (lower left to upper right) concerns the place of health insurance in the sequence, while an orthogonal one (lower right to upper left) concerns that of old-age assistance. The two-dimensional pattern was so clear that we simply rotated the scalings with a sine/ cosine transformation so that the orthogonal health and pension dimensions coincided with the main axes. We then treated cases' coordinates as dependent variables. Table I presents the correlations of the independent variables-both the current and the initial-adoption-era ones-with the two dimensions of this space.9

Among the adoption-era measures, there is surprisingly little relation between the variables and the scaling dimensions. The




various dependency measures (young people, old people, both) correlate weakly with the pension dimension and, moreover, in the unexpected direction. (The reason for this is evident in the standard deviations: there is too little variance in the dependency variables to account for the sequence differences observed.)10 As for the health dimension, the service variable has a powerful negative relation with it (as service proportion rises, health insurance comes relatively late). And measures of Left strength are also somewhat negatively related to this dimension, while the overall dependency ratio pushes health insurance earlier. A regression model predicting the health dimension from service proportion, socialist seats in parliament, and a constant has an adjusted R2 of .51 and coefficients (p < .05) of -3.64 for service and - 1.87 for socialist seats. Interaction effects and other variables add little to this explained variance. In particular, the dependency ratio, despite its fairly strong correlation, lowers the explained variation considerably. On the x dimension, regression analysis shows no substantial effects among the adoption-era variables.

Like the adoption-era variables, few of the current-era variables are strongly related to the scaling dimensions. The correlations between the variables and the raw dates of welfare programs (not shown) tell why this is so; although these correlations are often strong, they are usually in the same direction for all programs. The variables shift the whole pattern but do not affect the order and duration of succession. (This pattern of overall shift gives some support to modernization theory, with its predictions of uniform sequence, and has been discussed by state-centered theorists as well [Orloff and Skocpol 1984].) The correlations presented show that the strongest relationship involves pensions: government revenues shift them earlier; corporativism makes them later. A regression model predicting the pension dimension from (current) government revenues and corporativism has an adjusted R 2 of .38, with coefficients of -5.2 and 28.5, respectively.

This individual-level analysis leaves us with perplexing conclu- sions. Optimal-matching techniques underscore the fact (evident from Figure I) that most variation in the sequences of adoption is in the ordering and duration of health insurance and old-age pensions. (It should be noted that the very existence of this variation seriously damages the modernization theory's prediction of a uniform sequence.) Neither correlation nor regression produces



DEN ICE

DEN GFR

NEZ SWE

LUX AUS

UKI FRA

NET BEL

ITA AUL

CAN NOR

SWI JAP

FIN

Figure 2 Scaling of distances between welfare sequences




strong support for any of the standard explanations for the development of welfare states. With the contemporary variables, this reflects in part a tendency for variables to push all adoption dates in a particular direction, as modernization theory predicts. But more generally, the lack of support simply reflects weak or surprising relationships. That rising government revenues should push pensions earlier might seem to support the state-centered theory (cf. ibid.), but there is no account of why the money should move that program in the sequence rather than others. Nor does the demand theory explain why rising numbers of socialist seats should move health insurance later. No theory predicts that nations with larger service sectors will more likely be late adopters of health insurance. Indeed, if we take the development of a service sector to be an indicator of economic development, this result reverses modernization theory. But neither the state-centered nor the Left- demand model predicts a link between service sector and program adaption, either. It should be noted, too, vis-a-vis demand models, that the obvious prediction that high dependency ratios and aged populations would produce earlier pensions fails.II

Perhaps the problem lies in the assumption that countries are independent adopters. In fact, countries make decisions partly with reference to the examples-good and bad-provided by other countries. This means that we must consider diffusion models, the second general class of models discussed above.

DIFFUSION OF WELFARE PROGRAMS

Diffusion models assume that countries that are close to one another ought to have similar adoption sequences. For example, national health insurance comes late in the former British colonies (Canada, New Zealand, and Australia, as in fact in the United States, to which it has never arrived). The explanation probably lies in the similarity of the medical professions involved, which are effective and autonomous bodies, unlike the quasi-civil-servant medical professions of the Continent, and which were willing and able to oppose national health insurance.12 This similarity in professions reflects direct "closeness," that of colonialism.

There are two kinds of measures of such closeness. The first are direct measures of exchange, either material (transactions of capital or goods) or cultural (migration of people, books, ideas).




The second are indirect measures of those resemblances facilitat- ing direct exchange. As with the individual-level data discussed earlier, there are difficult questions as to when to measure these data, and availability constraints are severe.

Our first measure is a direct one, trade flows. These are current data (1986) taken from the World Almanac (1988). The connection between two countries is the absolute sum of trade flows in each direction between them. Our second measure is indirect, the presence of common language and religion. This composite index includes I.o if the principal languages of the two countries are identical, 0.3 if a secondary language of one is the principal language of another, and 0.2 if two secondary languages are identical. Its religious component is based on a three-way typology: Catholic, Protestant, and half-and-half. The religious contribution to the index is I.o if two countries have similar religious types and 0.3 from any monoreligion country to any split-religion country. Japan is of course considered "other" with unit distance to all others.

Our third measure of diffusion is again indirect, a measure of similar labor policies. It is based on reaction to 33 ILO recommendations, as reported in Shotwell 1934. There are five ordinally scaled responses to specific ILO recommendations and four ordinally scaled legal actions related to those recommendations. These data were turned into three separate distance measures. The first uses all the information (66 scales), the second uses only the legal action data (33 scales), and the third simplifies the first (avoiding some possibly dangerous coding assumptions; 66 scales).

The ILO measure is midway between an indirect measure of diffusion and a simple resemblance measure. On the one hand, common ILO practice may reflect specific interaction at ILO meetings. On the other hand, the measure may simply be tapping a propensity to think similarly about labor questions. Our last closeness measure is a pure resemblance measure, born of our need to develop some estimate of adoption-era diffusion. We took the seven variables of the initial-adoption-era data and replaced them with standardized scores. We then transposed this matrix (making the variables into cases and the countries into "variables") and created correlations among the countries. We then used a simple linear transformation to turn the resulting correlation matrix into a distance matrix of the same form and scale as the adoption sequence matrix.13




Our basic procedure for each of these measures is similar. After developing the measures of closeness, we then compare each closeness matrix to the adoption sequence matrix. If the two matrices are themselves alike, then contagion is likely. This approach to diffusion has been extensively investigated in the spatial autocorrelation literature (e.g., Hubert and Golledge 1982; Hubert et al. 1981), which has developed both Monte Carlo and direct methods for such matrix comparison. In both, the sum of cross products of equivalent matrix elements is calculated; its mean and variance are then estimated, either by Monte Carlo permutation of the matrix or by direct calculation. If the z value of the unpermuted cross-product sum is significantly different from o, then the matrices are shown to be similar. Hubert and Golledge (1982) suggest normalizing both matrices prior to calculations. We report here both Monte Carlo (I,ooo permutations) and direct (gamma) methods, with both nonnormalized and normalized versions of each, a total of four test statistics on each possible measure of diffusion (Table 2). Since the adoption sequence matrix is a dissimilarity matrix while all the diffusion measures but the last are similarity matrices, resemblance between the two, in all but the last case, would produce negative rather than positive z scores.

Little evidence for diffusion is present. The large z scores under the nonnormalized gamma procedure for the ILO scales are in the wrong direction, indicating that labor policy resemblance may be inversely related to welfare policy resemblance. The only significant scores in the right direction are the language/religion scale under the normalized gamma procedure and the adoption-era general resemblance measure under the nonnormalized gamma. The former of these may be substantively meaningful, as Forrest and Abbott (1990) have argued that the normalized gamma is probably the most stable of the four procedures. Common cultural background may influence exchange related to welfare policies. By contrast, the adoption-era result is probably artifactual, since the method involved produces different results from the other three methods across all the data sets used here. Insofar as we are able to measure it, then, there is rather weak support for the hypothesis of diffusion.



Table 2 Z-scores measuring diffusion Method Normalization Trade Religion ILO I IL02 IL03 General

Permutation Nonnormalized -.679 -1I.ol .539 . 111 .0547 -.135 Permutation Normalized -.667 -.962 .550 .066 .0875 -.020 Gamma Nonnormalized .776 1.40 7.23 6.07 8.78 5.65 Gamma Normalized -i.57 -2.57 .172 -.167 -.0806 -.510




Table 3 Descriptive data for program adoption rates Pensions Health Compensation Unemployment Family

Mean year 1916 1916 1901 1924 1945 S.D. 16.4 23.8 9-3 12.8 I0.2

THE HYPOTHESIS OF A WORLD-HISTORICAL PROCESS

There remains the third possibility that adoption of welfare programs is essentially a world-historical process. On this view, each welfare program derives from an idea that has emerged throughout the developed world. First, a few countries try it out, then perhaps a mass of countries adopt it, then stragglers finally take it up. Differences between countries arise either randomly (the world-polity view) or through some as yet unmeasured parameter deciding a country's relation to this world-historical process (the reaction view).

At first glance, the data seem quite auspicious for these approaches (Table 3). The sequence of means is clear at all but one point: the tie of pensions and health. There are two possible rea- sons for this strong order. Either every country has the same order, although starting times and overall duration vary, or all countries follow the same order at the same time. The varying dates of first program (from 1883 in Germany to 1911 in Japan and Switzerland) might indicate the first, but the standard deviations here show that three of these events-workmen's compensation, unemployment insurance, and family allowances-are indeed period events. Each occurs to most countries within a 2o-year real-time epoch, irre- spective of when the country adopted its first program. And it is noteworthy that the 2o-year bounds (about two standard deviations wide in each case) do not overlap at all between compensation and unemployment and overlap but slightly between unemployment and family allowances.

We may further examine these patterns by listing the countries in order of adoption within the program, a listing given in Table 4. Each of the five events has a "spike," a short period within which a large number of countries adopt the program. Moreover, certain countries-the Netherlands, the United Kingdom, and Luxem- bourg-are nearly always found in the spike (four of five times, narrowly missing the fifth in each case). Other countries, by con-




Table 4 Orders of adoption of five major welfare programs

Age Health Compensation Unemployment Family

GFR 1889 GFR 1883 GFR 1884 FRA I905 NEZ 1926 DEN I89I AUS 1888 AUS 1887 NOR I906 BEL 1930 NEZ I898 SWE I89I NOR I895 DEN I907 FRA 1932 AUS I906 DEN 1892 FIN 1895 UKI 19II ITA 1937 UKI 1908 BEL 1894 UKI 1897 NET I916 NET 1939 AUL 1908 LUX 1901 ITA 1898 FIN 1917 AUL 1941 ICE 1909 NOR I909 FRA 1898 ITA I919 CAN 1944 FRA 1910 UKI 1911 DEN 1898 AUS 1920 UKI 1945 LUX 1911 SWI 1911 SWE 1901 BEL 1920 NOR 1946 SWE 1913 ITA 1912 NET 1901 LUX 1921 ICE 1946 NET 1913 NET 1913 AUL 1902 SWI 1924 SWE 1947 ITA I919 JAP 1922 LUX 1902 GFR 1927 LUX 1947 BEL 1924 FRA 1928 BEL 1903 NEZ 1930 AUS 1948 CAN 1927 ICE 1936 CAN I908 SWE 1934 FIN 1948 NOR 1936 NEZ 1938 NEZ I908 ICE 1936 SWI 1952 FIN 1937 AUL 1944 JAP 1911 CAN 1940 DEN 1952 JAP 1941 CAN 1957 SWI 1911 AUL 1944 GFR 1954 SWI 1946 FIN 1963 ICE 1925 JAP 1947 JAP 1971

Note: AUL = Australia; AUS = Austria; BEL = Belgium; CAN = Canada; DEN Denmark; FIN = Finland; FRA = France; GFR = Germany; ICE = Iceland; ITA =

Italy; JAP = Japan; LUX = Luxembourg; NET = Netherlands; NEZ = New Zealand; NOR = Norway; SWE = Sweden; swI = Switzerland; UKI = United Kingdom.

trast, are never in the spike; Germany, the original welfare state, and Japan, one of the last. (The United States, like Japan but not shown here, is always after the spike.) Here is evidence for the reaction view-some sort of unmeasured policy parameter characterizing each country as an adopter within the context of a world-historical process.

Abbott (1984) shows how to establish a null hypothesis distribution for a set of sequences consisting of unique events. (By sequence, here, we mean the simple order of events, not the formalized sequences used in optimal matching; the duration information enters this analysis in a different way.) He argues that overall events like "the development of family allowances" are realized in many specific laws; he therefore distinguishes these many specific "occurrences" from the single overall "event." By any given time, an event will have produced a certain percentage




of all the occurrences that will ultimately fulfill it; the function graphing this cumulative distribution is like a probability distribution and has a derivative that is analogous to a probability density function. Roughly speaking, this "event intensity function" (EIF) graphs the rate at which the event produces particular occurrences. For any set of time intervals, the EIF gives the likelihood of an occurrence of that "event type" in each interval. Note that in the present conceptualization, the event is worldwide "family allowances" and the occurrences are the individual country programs. The individual country welfare adoption sequences are simply samples in which each of the five EIFs has been sampled only once. Hence the probability of a given order in such samples follows directly by a simple, if laborious, multiplication of the interval probabilities.

We have performed these calculations in the following manner. We took the original data for each program/event and treated it as a time series, associating with each year the number of times that program was initiated in that year. Since there were only 18 events in each Ioo-year series, we smoothed the five time series with a standard three-pass filter.14 This yielded the EIFs shown in Figure 3. Although the fine detail in these curves cannot be taken seriously, given the sparsity of the data, the gross shapes are strikingly similar. In every case there is an early peak, a main peak (coinciding with the spike noted in the lists), and one or more subsequent peaks. This shape fits remarkably well with the model discussed above in which welfare state programs diffuse in a regular pattern, with early adopters, a main body of followers, and stragglers, and most countries randomly distributed in these peaks.

We have sectioned these EIFs each decade from 188o for decadal estimates of the probability of various programs and have then calculated the exact probability of each of the 120 possible sequences of the five programs.'5 Table 5 lists the 25 most probable sequences in descending order, with their individual probabilities, their cumulative probabilities, and the countries following them. Ties (countries where two events occurred in one year) are listed at the more probable sequence and starred. A few sequences seem uncommon; New Zealand's sequence (24135) is 32d from the top, and Australia's (31542 or 31524-there is a tie) is either 2oth or 38th. For any given sequence in this table, it is straightforward to calculate the binomial likelihood of getting the observed number




'I (I

.

i3 1 I1' S ,iIr

I ~1,....: ,'".

. / " -1

( '\

.. ,i .

-. 1 I I' 1880 1900 1920 1940 1960 1980

- - - HEALTH(2) -- COMP(3) ...... AGE(1) --- UNEMP(4) - FAMILY(5)

Figure 3 Event intensity functions for various welfare programs

of cases between this sequence and the top of the list. The probability of 3 or more cases in the top sequence is .1756; of 5 or more cases in the top 2 sequences, .1320; of IO or more cases in the top 6 sequences, .1729; of 15 or more cases in the top ii sequences, .o0930; and of all 18 cases in the top 32 sequences, .2032. There is a slight tendency for the cases to be grouped a little more than we would expect towards the top of the list, but not a significant one. There is thus no strong support for a fixed sequence of programs, as would be predicted by modernization theory, although of course, as we noted before, three of the programs do come in a nearly universal order. The variety of sequences we see is quite compatible with the hypothesis that for each country we simply take one occurrence from each event in accordance with the EIFS. This seems striking evidence against the ideas that internal conditions in the countries dictate the sequence of adoption and that there is a uniform sequence.

There is a further issue, however. One can get the most common order, 23145, by having 2 in the second decade, 3 in the third,




Table 5 Probabilities of particular sequences Sequence Probability 2 3 I 4 5 .07686 GFR, SWE, LUX

3 2 I 4 5 .06894 AUS, JAP, NETa, ITAa

3 I 2 4 5 .06356 UKIa 3 I 4 5 2 .06244 CAN 2 3 4 I 5 .06148 BEL, SWIa

3 2 4 I 5 .06121 3 I 4 2 5 .05955 3 4 I 5 2 .04558 FIN

3 4 I 2 5 .03991 FRA

I 3 2 4 5 .03980 ICEa 3 4 2 I 5 .03847 NOR

I 3 4 2 5 .03357 2 I 3 4 5 .03351 I 3 4 5 2 .03090 I 2 3 4 5 .02245 DEN

3 2 I 5 4 .o1596 2 3 I 5 4 .01588 3 2 4 5 I .01527 3 I 2 5 4 .oi499 3 I 5 4 2 .01466 AUSa 2 3 4 5 I .01409 2 4 3 I 5 .01277 3 4 5 I 2 .01230 3 4 2 5 I .00997 I 3 2 5 4 .00844 aTie: countries reported at the higher probability sequence.

I in the fourth, 4 in the fifth, and 5 in the sixth, or by getting them all in the third decade, or in many other ways. (Here is where the durational information enters.) Perhaps the actual ways these sequences were observed were unusual. Although the de- tails require too much space to be reported, we have shown that a substantial number of these sequences arose in rather unlikely ways. This suggests some sort of modifying parameter, making the process less than totally random: perhaps a unique parameter defining a country's relation to the main world-polity process, as in the reaction model, or perhaps some modifying local effects, as seen in the section on individual-level effects.




CONCLUSION

In this concluding section we develop provisional interpretations for the results. We begin by considering similarities and differences among the adoption sequences. We then turn to the curiously regular patterns of the curves in adoption patterns for each event. We close with some discussion of related variables and possibilities for future research.

We have found considerable evidence for a world-level process of policy adoption, modified in some cases by individual effects, conscious policy, and possibly by one kind of diffusion. Certain aspects of this world-level process are clearly more regular than others. In particular, policies for worker's compensation, unemployment, and family allowances are nearly always adopted in that order. Moreover, these three policies are period events, adopted at relatively uniform times across all these countries. By contrast, there is considerable variation among countries in the adoption of health insurance and old-age pensions. The only possible local variable effects we have found are the positive de- pendence of health insurance timing on the size of the service sector and on socialist representation, and of pension timing on corporativism (positive) and government expenditures (negative). For none of these have prior theories generated a serious rationale. There is only weak evidence for diffusion, with cultural similarity providing the only possible medium. Finally, there is exemplary (but not systematic) evidence for the reaction model; that some countries always adopt "with the pack" suggests conscious local policy response to a general process. Our strong results, then, are the period pattern for worker's compensation, unemployment insurance, and family allowances, and the general pattern of a world-historical process perhaps modified slightly by individual factors and policies and by diffusion.

A little reflection suggests theoretical rationales for this pattern. All five events involve work relationships to a greater or lesser extent. What distinguishes health insurance and pensions from the three clearly ordered events is the disagreement about the extent of that involvement across various countries. Health insurance can be conceived as directly related to the ability to work or as a larger entitlement program covering nonworkers as well. Some programs define the covered group via employment; some do not.




Some programs include specific unemployment benefits when ill- ness interferes with work, but others do so little or not at all. A still more distant relation obtains between work and pensions, which are usually a future reward for past and present work but which in some cases are universal entitlement programs unrelated to past work.

Presumably, the clear ordering of three of the programs reflects consensus on their salience to work itself. Among these three, programs are adopted earlier as they are perceived closer to the immediate wage relation. Thus, worker's compensation, which involves work-induced chronic physical inability to work, comes first. Unemployment insurance, which involves temporary non- physical inability to work that is not necessarily work-induced, comes later. Family allowances, which support those legally prevented from wage work and which free others to enter wage work, come a distant last. The variety in health insurance and pensions, on this theory, reflects variety in perceptions of how these relate to work. Where health was construed within a framework of enabling work, it should have come early. Where it was seen as a general entitlement matter, it should have come later. With pensions, one might make the same speculation, but there the connection is looser. In neither case are we simply predicting that programs with work-related coverage definitions will come earlier, although that is one possible interpretation. Rather, the issue is whether the programs are seen as immediate supports for people temporarily or permanently unable to work (because of health or age) or as global entitlements or rewards deserved on the basis of past behav- ior. Obviously, these two conceptions will coexist in any society at a given time, but our view is that early adoption of the programs reflects the narrow view.

Thus a theory based on work and its perception seems to make sense of the patterns of welfare adoption sequences. Very possibly this whole pattern reflects a world-level demand; perhaps early socialism had a greater effect in the West than is sometimes thought. Certainly, there is no obvious modernization-based or state-based rationale for this particular order.

While a theory based on images of work may account for temporal patterns of adoption across events, it does not account for the other striking regularity in these data. As we have seen, all of the events follow a "humped" adoption pattern; a distinct early




peak is followed by a larger peak and then one or more lesser ones. This regularity is particularly unusual because most "noisy" stochastic processes do not produce multipeak distributions. If there were simply generalized heterogeneity among the cases, we would expect unimodal and roughly symmetrical curves, while simple contagion would produce exponential curves on the leading temporal edges with precipitous declines on the trailing ones.

Several possibilities come to mind to explain the humped patterns we do observe. One of them is a "wait and see" model. On this model, implicitly suggested earlier, countries have varying thresholds for adoption. A group of low-threshold countries adopt a policy, and others wait to see if it works. After a sufficient time has passed, a group with higher thresholds decides to adopt. Within each "wave," we would expect symmetrical or exponential curves, depending on our theory of spread (heterogeneity or contagion). This model hinges on the assumption of thresholds, however, which simply pushes the question of discrete humps back into the perceptual arena.

How might such thresholds arise? Assume that countries are watching others as a wave of contagious adoption takes place. At a certain point, some likely country conspicuously fails to adopt. (The "conspicuous" character arises in local political processes; welfare policies are actively debated, and decisions about them are generally quite public.) Such a conspicuous nonadoption could generate its own wave of nonadoptions; nonadoption, that is, might be contagious as well. As a wave of nonadoption brings adoption to a halt, the situation is defined as one of "wait and see." Then eventually a country breaks rank and starts the next wave of (contagious) adoptions. Thus, a set of simple cognitive processes might effectually "discretize" the process.

An alternative model for these humps is much simpler. Perhaps they reflect actual international policy connections-at meetings of the ILO, for example. Plenary meetings are discrete affairs and could give rise to discrete adoption. However, our earlier evidence suggests that ILO policy profiles are not very closely related to adoption. Moreover, these countries were all strong participants in that particular organization. But the possibility remains of some relatively simple explanation for the discrete humps via actual events. It is curious, in this regard, that three of the five events have substantial peaks during the First World War (health insurance,




pensions, workmen's compensation) and that family allowances are clearly tied to the Second World War. It is also noticeable that unemployment insurance has its major adoption peak right after the First World War, although in fact unemployment in Europe was generally lower during 1918-20 than after 1920.

There are a variety of processes not directly considered here that seem important for further analysis. A number of commentators have suggested that countries may have adopted policies during bursts of state-building activity. Of course, in some sense, policy adoption is the same thing as state building, so we should not view these phenomena as completely separate. Nonetheless, it would be interesting to develop separate measures of state building and to relate them to policy adoption.

Another important issue, throughout these countries, is the development of education as an alternative to "welfare." On this view (see, e.g., Heidenheimer 1981), educational development may in some cases have been a surrogate for welfare state expansion. If this is so, then our adoption data need to be completed with information on education. However, there are cases where the two proceeded together (Sweden, for example), while in other countries (Germany) the two were considerably separated. Thus, while education may be an important complement to welfare, we feel that the preliminary viability of the present results is not questioned by the "educational complement" theory.

In short, a close study of welfare policy adoption sequences indicates that the welfare state arose in a process broadly common to the nonsocialist developed countries. A small degree of posi- tioning on the various policies may be attributable to individual countries' tendencies to lead or follow. If local variation and direct diffusion have any effects, they are surprisingly small. These results partly question and partly complement existing work on the welfare state. On the one hand, local differences have seemed significant in past studies combining all welfare programs under a single heading (e.g., via expenditure measures, as in Pampel and Williamson 1989, or via theoretical grouping, as in Orloff and Skocpol 1984) as well as in studies working with single programs (e.g., Pampel and Williamson 1985 and, comparing various U.S. states, Pavalko 1989). Our results suggest that simultaneous analysis of several differentiated programs may qualify these results considerably. Our results also strongly suggest that students of the




welfare state should take our second and third levels-those of diffusion and the world-polity-more seriously than they have in the past. Multilevel models are probably a necessity in this area, and local effects may seem considerably weaker once they are applied. At the same time, the exclusively local-level literature utilizes, particularly in Pampel and Williamson's (1989) extensive formulation, time series information on independent variables not yet used (because of methodological restrictions) in our multilevel approach. One could reconcile these findings by arguing that the kind of world-level process we describe dictates initial program adoption, but that local variables take over once that has occurred. It seems clear that work combining both approaches is necessary to test such a hypothesis.

NOTES

I For general background, see Abbott 199oa, 199ob. 2 Although problematic, this assumption is the only viable one. Using

Abbott's (1984) terms, we may call interest in workmen's compensation an "event" and the various laws embodying its various extents the "occurrences" spawned by that event. Our assumption is that comparable occurrences happen at comparable rates across events. It is necessitated by the difficulty of getting data on, for example, the full set of compensation laws across all countries.

3 Since the reaction view presumes a group-level process to modify, we consider it in the group-level section rather than in the diffusion one.

4 There are three possible group levels: the world, the developed world, and the nonsocialist developed world. We focus on the last because the first mixes diffusion arising from colonial policy with that arising from direct modeling, and the second involves the socialist countries' categorical rejection of one type of program (unemployment insurance).

5 We have omitted the United States from these analyses because it lacks one of the five major programs. However, we have run them including the United States and find few differences.

6 Whether these assumptions are justified depends on one's philosophy, on the empirical facts, and on the results the assumptions produce. In our methodological framework, the problems with the generative position lie in its use of continuous variables, which are only now beginning to be addressed by these methods. In standard methods, the generative position would require five-way competing-event event history models. In commenting on this article, Samuel Preston has suggested as another option using individual event history models with the other program states as predictors (thus using the information five separate times but losing the composite sequences). Ordinary event history models, which examine particular transitions or even




transitions by origin or destination alone, are prevented by the large number of states involved (31). For an illustrative example of those methods on related data, see Hannan and Carroll I98I. For an example of event history analysis (with U.S. states as units of analysis) of adoption of a single welfare policy-a procedure that ignores the entire problem of competing risks-see Pavalko 1989. For an excellent general study following the generative approach but reducing the dependent variable to an overall spending measure, see Pampel and Williamson 1989.

7 The choice of a Io-year interval is arbitrary. To check the effects of this interval, we have run all results standardizing all sequences to a 50-unit length; the results differed very little from the unstandardized results.

8 Optimal-matching techniques are reviewed in Sankoff and Kruskal 1983. For recent developments, see Miura 1986 and Doolittle 1990. For applica- tions, see Abbott and Forrest 1986 and Abbott and Hrycak 1990. Forrest and Abbott (I990) examine the empirical impact of coding variation on optimal matching. Optimal-matching calculations are done here with the Beldings program series of David Bradley of California State University, Long Beach (these programs may be obtained from Abbott).

9 The normal scaling practice of regressing independent variables as dependent variables on the scaled coordinates (Kruskal and Wish 1983) has the disadvantage of not allowing multivariate investigation. In our case, the pattern was so interpretable that we chose the procedure given in text.

Io In commenting on this article, Jane Menken has pointed out that lack of variance in these figures may very well imply constancy in an overall process, as modernization predicts. It is worth noting that (continuous) information on amount of coverage shows a stronger effect for age, at least once the programs are first adopted. See Pampel and Williamson 1985, 1988, 1989.

ii Reverse causality may be at issue here. Bismarck installed the welfare state partly to prevent socialist representation in parliament. The peculiar findings with respect to the demand theory may reflect the success of his (and others') policy judgment in this regard. We thank an unidentified seminar participant at the University of Pennsylvania for this point. For a similar reverse causation argument making welfare policies an instrument rather than an outcome of class conflict, see Stryker 1990.

12 That leaves, of course, the anomaly of England itself. Probably the reason for England's earlier adoption lies in the extreme intensity of socialist agitation immediately preceding adoption in 1911.

13 This last closeness measure may seem to repeat tests done in the prior section. But regression methods isolate each variable's effect, while here we consider them jointly. This procedure tests whether countries that are alike adopt alike but does not distinguish whether adoption similarity arises from internal similarity or contagion. There is a much weaker presumption of implicit diffusion here than in the religious and ILO measures.

14 The filter used was (I) a moving average pass with a five-year window, (2) a moving average pass with a two-year window, and (3) a weighted moving average with a three-year window and weights of 1:2:1. Note that these EIFs are not hazard rates, which would have a denominator defined by those




nations at risk, that is, those nations still lacking programs. Our conceptualization here sees the question not as one of "how many countries that did not have programs got them in this decade" but one of "how likely was a program of this type to fall in this decade." That is, the event is the unit of analysis, and adoptions are seen as instances of it, distributed over various possible years, rather than the countries being seen as units of analysis "at risk" of adopting some property. To compute curves based on the at-risk conception, one develops annual estimates of the probability of adoption among those at risk and then smoothes them with the same filters. If one considers a rectangular distribution of adoption-say, one adoption every five years-it becomes at once clear that such curves will generally ascend toward the right because of the shrinkage of the denominator. However, we have estimated hazard-type curves for these data and find that they in fact retain the character of the EIFs shown here; each one has a number of distinct humps.

15 Ties were handled by apportioning them equiprobably. Thus the sequence 12345 could arise, among many other ways, by all five events occurring in one decade. Since there are 120 arrangements of the five events, given that they all occur in one decade, we gave V20 of the total probability of this joint event (the product of the five independent probabilities that each event was observed in that decade) to each of those 120 arrangements. We handled the numerous parallel situations in the same way.

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