CONCLUSION AND FUTURE RESEARCH

Putting the literature on the distribution of (individual) wages in the context of the (household) income distribution we are struck by the disconnect between the two. There is an extensive literature with a long tradition on each of them but very little on both, despite the fact that wage earnings are by far the most important source of income in modern society.

Subsequently turning to the distribution of wages alone we have gone back to the origins of the debate in the early 1980s and sketched developments toward what is now a large and complex literature. We find that the unanticipated rise in earnings inequality in the United States over the 1970s put LMIs, such as (declining) unionization, as one possible explanation among others, such as demography or deindustrialization, on the research agenda. During the 1990s, the debate gave rise to the thesis of skill-biased technological change but also to international comparisons. The former approach has focused on market forces of supply and demand, the latter deemed those insufficient because of the growing international divergence in wage-inequality trends and has put the limelight on the role of national LMIs. After some leapfrogging of the two approaches from one consensus explanation to another during the 1990s, the two seem to be increasingly growing apart during the 2000s when important new contributions were made to the disadvantage of an integrated approach that could give each its proper place. Both sides may be at risk of creating “Ptolemaic epicycles” aimed to incorporating new observations. The supply-and-demand approach is challenged by the need for find­ing better empirical measures of technological change aimed at dispelling doubts that the relative demand of skills may be a tautology. It has added “tasks,” “offshoring,” and even “consumer preferences,” which risk being ad hoc additions,¹ without realizing their

150

Compare Autor’s (2013, 25) remark “that there are almost as many distinct task classifications as there are papers in the task literature.”

institutional preconditions.

In light of this, the double aim of future work on both sides should be to foster itself and to integrate the other side into its own framework at the same time. Pursuing this may seem more a problem of empirical method for the institutional side, although by contrast on the demand and supply side the problem may be more one of theoretical method as long as institutions keep being viewed as bodies alien to the market and to theorizing. For both sides there is a perspective of work to do at the firm level. Matched employer— employee data can help enlighten the role of both institutions as well as labor supply and demand. Such data are increasingly becoming available. This brings us back to the availability and quality of the data in addition to the earnings/incomes fact-finding already mentioned. Data and analysis shall move beyond the commonly used earnings data for full-time workers only, which are less and less representative especially at the margins of earnings and incomes. Therefore, more needs to be done regarding individ­uals’ and households’ work efforts and earning outcomes on an annual basis. In addition, though much attention has been paid to data quality, a better grasp of the customary use of inequality measures—currently, each of the two approaches has its own rather exclu­sive preferences—seems desirable.

Finally, we have set up a simple model accounting for the correlation of the different components of inequality (between and within) with LMIs. We find indeed that union­ized labor markets are ceteris paribus less unequal in terms of annual earnings, because both hourly wages and worked hours are more evenly distributed.

ACKNOWLEDGMENTS

We are grateful to Christina Haas, John Schmitt, Thomas Lemieux, Nicola Fuchs-Schiindeln for their help with the data and Anna Salomons for some useful suggestions. We thank in particular our editors, Tony Atkinson and Francois Bourguignon, for their very helpful comments and suggestions.

APPENDIX A. COUNTRY CODES

APPENDIX B.

We obtain data from the EUSILC survey conducted in 2010 (ver.1 dated 01/03/12), to which we added populations from Cyprus and Ireland extracted from the 2009 survey (ver.2 dated 01/08/11). Overall among European countries we consider 476,265 observations (of which 9902 Irish and 7557 Cypriots). For these individuals we know relevant demographics (age, gender, education,^[239] marital status, birthplace), occupa­tional characteristics (whether employed or self-employed, full-time or part-time, permanent or temporary contract, ISCO occupational code, workplace size, work experience).

We obtain data on the United States from participants to PSID survey conducted in 2011. Basic demographics (age, gender, education,^[240] marital status, labor market status) are obtained from individual file (file ind2011er.zip downloaded on 22/07/13), which includes 24,661 observations. Information on labor earnings are collected from one respondent for each of8907 households (typically a male household head), who responds about wage and hours for himself and his spouse (file fam2011er.zip downloaded on 22/07/13).

We adopt two selection rules:

(a) population in relevant working age, which we define as being between 20 and 64 years old. This is justified to allow for secondary school completion, and to take into account different early retirement rules in different countries. This leads to the exclusion of employed youngsters aged 15—19, a fraction of which is employed with an average hourly wage that is on average half of the average wage in the adult pop­ulation. Because countries differ in the duration of compulsory education as well as in institutional design, we have preferred to leave the youngster component of the labor force out of our analysis.^[241]

(b) population in the labor market, who self-define as either employed (employee or self-employed) or unemployed. This takes as exogenously given the significant cross-country differences in participation/employment rates (see Table 18.A1).

The labor earnings variable is defined as either “gross yearly earnings from dependent employment—cash or near cash”^[242] or “gross yearly cash benefits or losses from self­employment” (see Table 18.A2 for means and standard deviations).^[243] Unemployed subsidies received by (temporarily) unemployed workers are also considered in the computation of earnings inequality.^[244]

In order to distinguish between annual earnings and hourly wages, we need infor­mation about the number of hours worked. In both survey hours worked are recon­structed thanks combining answers to two questions (weekly hours usually worked in recent months—thus referred to the period of interview—and number of months worked in the previous year).^[245] The gross hourly wage rate is then computed dividing the yearly earnings by the hours worked.^[246] Descriptive statistics on hours worked and hourly wages are reported in Table 18.A3. Notice that there is a significant loss of infor­mation when moving from yearly data (259,500 observations with nonnegative annual earnings) to hourly wage (228,153 observations with nonmissing hourly wages), due to missing information about weekly hours worked. A probit estimate indicates that young uneducated women holding a temporary contract are more likely not to report hours.

Table 18.A1 Descriptive statistics computed from microdata—SILC 2010 and PSID 2011—labor market attachment (sample weights)

Female

Table 18.A2 Descriptive statistics computed from microdata—SILC 2010 and PSID 2011-labor earnings (sample weights)

Gross

Note: Data in 2010 Euros except US where data are in 2011 US dollars.

Table 18.A3 Descriptive statistics computed from microdata—SILC 2010 and PSID 2011—hours and wages (sample weights)

Note: Data in 2010 Euros except US where data are in 2011 US dollars.

Table 18.A4 Estimates of model relevant parameters—employed or unemployed individuals—SILC 2010 and PSID 2011

APPENDIX C. DATA SOURCES AND DESCRIPTIVE STATISTICS ON LMIs

Data on institutional measures were collected over a time interval spanning half a century, from 1960 to 2010.

Union density

It measures the fraction of wage and salary earners who are members of trade unions. It excludes unemployed and retired workers (net version). Source: ICTWSS database version 2 (Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts in 34 countries between 1960 and 2007—see Visser, 2009—variable Ud—downloaded on 04/04/13).^[247]

Coverage

It measures the fraction of employees covered by wage-bargaining agreements over all wage and salary earners in employment with the right to bargaining. Source: ICTWSS database version 2 (variable AdjCov).

Wage centralization

It represents a summary measure (ranging between 0 and 1) of centralization and coordination of union wage bargaining, taking into account both union authority and union concentration at multiple levels—source: ICTWSS database version 2 (variable CENT).

Strike activity

It measures the days not worked for strikes and lockouts divided by participant worker—total economy. Source is ILO (downloaded on 04/04/13).

Minimum wage

It takes the ratio of the statutory minimum wage relative to mean wage of full-time workers (sometimes known as “Kaitz index”—see Dolado et al., 1996). However this measure does not consider the possibility of differentiation across workers types. For this reason, Aghion et al. (2011) have combined the ratio of the minimum wage to the GDP per capita with an index of stringency derived from ILO.^[248] Forthis reason, the variable is set to zero when minimum wage provision is absent. Data are downloaded from the OECD Stats website (except than in the case of Iceland, whose values are taken from Table 5.5 of Danish Technological Institute. Assessment of the Labour Mar­ket in Iceland. Contract no. VC/2010/038 Final report—Policy and Business Analysis—April 2011).

Employment protection legislation

The measure we use is provided by OECD, which recently have partially revised their country assessment (OECD, 2012).^[249] It measures the stringency of firing regulation and is based on eighteen dimensions of the firing procedure.^[250] There is a second series provided by World Bank, which has been used among others such as Botero et al. (2004).^[251]

Unemployment benefit

Unemployment insurance and unemployment assistance benefits—gross replacement rate (ratio to the average wage) for a full-time adult worker. The source is OECD historical series, which is available in odd years and imputed using intermediate means in even years. It is the average between single worker and one-earner married couple with two children.^[252]

Tax wedge

Average tax wedge (sum of social contributions and income taxes as ratio to the aver­age wage). It considers the average between single worker with no child and one- earner married couple with two children. The source are the estimates from the OECD microsimulation model.

Social expenditure

It measures the expenditure for cash benefits and benefit in kind for social assistance, as percentage of GDP. The source is OECD historical series, which are available on 5-year base, and then interpolated.

Child care

It measures the enrollment rate in early child care and preprimary education (aver­age between age 3, 4, and 5—full- and part-time students) and proxies the availabil­ity of child care facilities. Available values for years 2005 and 2010, while intermediate values are interpolated. The source is OECD, Education at a Glance 2012, Table C2.1.

Parental leave

It captures the possibility of reconciling work and fertility, by measuring weeks of paid leave for childbirth. The series is available since 1970. The source is Thevenon and Solaz (2013). Further documentation can be found at http://www.oecd.org/social/ soc/oecdfamilydatabase.htm

Tax treatment of household incomes

This variable aims to capture the potential favorable tax treatment of working couples vis-a-vis individual taxation. It is constructed as the ratio between the average tax rate of single earner family (earning 170% of average wage) and the average tax rate of a two earners family (main earner at average wage and second earner making 67% of average wage). The reported variable consists of a further averaging between two household situation with respect to children (zero children and two children families). A higher value would indicate a favorable treatment of labor market participation of a second earner. Data available since 2001. The underlying data is obtained from the OECD microsimulation model, available at http://stats.oecd.org/Index.aspx? DataSetCode=FIXINCLSA.

Active and passive labor market policies

It considers the public expenditure on active or passive labor market policies as per­centage of GDP. It combines two data sources: when available we have been using OECD statistics for homogeneity with other series; otherwise we have resorted to Eurostat, which classifies as actives expenditure categories from 2 to 7 (2. Training—3. Job rotation and job sharing—4. Employment incentives—5. Sup­ported employment and rehabilitation—6. Directjob creation—7. Start-up incen­tives) and passive expenditure categories 8 and 9 (8. Out-of-work income maintenance and support—9. Early retirement).

Overall means and standard deviations for these variables are reported in Table 18.A5. Country means are reported in table with reference to the most recent decade.

Table 18.A5 Descriotive statistics for institutional measures, sample period 1960-2010, 30 countries

Table 18.A6 Sample means of institutional measures—recent years (average 2000-2010)

APPENDIX D. LITERATURE SUMMARY TABLES: HOUSEHOLD INCOMES AND EARNINGS AND WAGE DISPERSION AND INSTITUTIONS¹⁶⁵

165

Tables A7 and A8 make generous use of summaries, abstracts, introduction and conclusions of the underlying papers.

Table 18.A7 Household incomes and Earnings (see Section 18.2)—cont'd

Data sets* and

id="Picutre 398" class="lazyload" data-src="/files/uch_group77/uch_pgroup315/uch_uch7350/image/image398.jpg">

Table 18.A7 Household incomes and Earninas (see Section 18.2)—cont'd

Table 18.A8 Wage dispersion and the recent polarization and offshorability approaches to supply and demand (see Section 18.4.5)—cont'd

Continued

Table 18.A8 Wage dispersion and the recent polarization and offshorability approaches to supply and demand (see Section 18.4.5)—cont'd

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)

Continued

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Continued

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Data sets* and

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Data sets* and

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Continued

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Continued

Continued

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

Table 18.A9 Wage dispersion and institutions (see Section 18.4.6)—cont'd

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