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Approval of the Graduate School of Social Sciences
Prof.Dr.Sencer Ayata Director I certify that this thesis satisfies all the requirements as a thesis for the degree of Doctor of Philosophy.
Prof.Dr. Haluk Erlat Head of Department
This is to certify that we have read this thesis and that in our opinion it is fully adequate, in scope and quality, as a thesis for the degree of Doctor of Philosophy.
Assoc.Prof.Dr. Hakan Ercan Supervisor Examining Committee Members Assoc.Prof.Dr. Hakan Ercan Prof.Dr.Aysıt Tansel Assoc.Prof.Dr. Burak Günalp Assoc.Prof.Dr. Ramazan Sarı Dr. Murat Kırdar (METU,ECON) (METU,ECON) (HU,ECON) (AIBU,ECON) (METU,ECON)
I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.
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However. Prof. Dr. wage decomposition. Murphy and Pierce (1993) and quantile regression to March Current Population Survey data of the US Bureau of Labor Statistics data and Luxembourg Income Study data for a few selected European countries. Keywords: Wage inequality. The European countries do not show a common trend in the direction of wage inequality during the 90s. We analyze the US wage inequality in the more recent years to see if this trend continues. US. In addition. caused by increased dispersion both between and within groups of people with similar personal characteristics and skills. 128 pages There was a well documented surge of wage inequality in the US that started from mid-70s and continued in 80s. We apply the decomposition technique of Juhn. Hakan Ercan August 2007. Department of Economics Supervisor : Assoc.D. especially in the second half. We find that the increase in wage inequality continues during the 90s. the focus of their wage inequality seems to be shifting towards the lower half of the wage distribution as opposed to that of US. slowing down by mid-90s.ABSTRACT WAGE INEQUALITY TRENDS IN EUROPE AND THE USA Yağanoğlu. Nazmi Yükselen Ph.. quantile regression iv . the focus of wage inequality shifts into the upper half of the wage distribution after mid80s.
quantile regresyon v . Nazmi Yükselen Doktora. ktisat Bölümü Tez Yöneticisi : Doç.ÖZ AVRUPA VE ABD'DE ÜCRET EŞ TS ZL Ğ TRENDLER Yağanoğlu. oradaki ücret eşitsizliğinin odağının ABD'dekinin tersine ücret dağılımının alt yarısına kaydığı gözlemlenmektedir. hem benzer hem de birbirinden farklı kişisel özellikler ve becerilere sahip insanlar arasındaki ücret farklılıklarının artmasıyla ilgili olarak ortaya çıkmış bir ücret eşitsizliği artışının varlığı. Bu çalışmada. ücret ayrıştırması. Dr. Bununla birlikte. Bu amaçla Juhn. Ek olarak. daha önceki çalışmalarda belgelenmişti. Avrupa ülkeleri 90'larda ücret eşitsizliğinin yönü ile ilgili ortak bir eğilim göstermemektedirler. Anahtar Kelimeler: Ücret eşitsizliği. yakın zamanlarda da bu trendin sürüp sürmediğini görmek için ABD'deki ücret eşitsizliğini inceliyoruz. ücret eşitsizliğinin odağı özellikle 80'lerin ortasından sonra ücret dağılımının üst yarısına kaymış bulunmaktadır. Murphy and Pierce (1993) tarafından önerilen ayrıştırma tekniğini ve quantile regresyonu US Bureau of Labor Statistics'in Mart ayı Current Population Survey ve Luxembourg Income Study'nin birkaç Avrupa ülkesi için verilerine uyguluyoruz. Hakan Ercan Ağustos 2007. özellikle de ikinci yarısında devam ettiği görülmektedir. 128 sayfa ABD'de 70'li yılların ortalarında başlayıp 80'lerde devam eden ve 90'ların ortalarında yavaşlayan. ABD'de ücret eşitsizliğindeki artışın 90'larda.
To my family. for their unconditional support and faith vi .
Several people have made life much easier for me while dealing with the technical difficulties.Prof. Burak Günalp. Emilia Niskanen of Luxembourg Income Study for her quick solutions to problems related to the LISSY system. Aysıt Tansel. Ramazan Sarı and Dr. which was at least as instrumental as the my own work in the completion of this thesis.Prof. Engin Akyürek of METU. Hakan Ercan for his encouragement. Murat Kırdar have been very much appreciated. and to Mr.Prof.Dr.Dr. Department of Economics. to Prof. The constructive criticism and comments of Prof. Assoc. Ben Jann of ETH Zurich for his help in overcoming some problems with the applications in software. guidance and support. This study was supported by BAP-2006-07-03-00-12 project.Dr. for his assistance with computer hardware.ACKNOWLEDGMENTS I would like to express my deepest gratitude to my supervisor Assoc. I am grateful to Ms.Dr. vii . Assoc.
.....................................................................................................................1 2 US WAGE INEQUALITY: EMPIRICAL OBSERVATIONS ..................................................................................................................................................................................3 Concluding Remarks..................2 Application to US Data ...............................................................................iii ABSTRACT........9 2.................................................................................................................................................vi ACKNOWLEDGMENTS.........................xiii CHAPTERS 1 INTRODUCTION .....................................................................iv ÖZ..............4 Concluding Remarks....................................TABLE OF CONTENTS PLAGIARISM........................3 Evidence on Wage Inequality..............................44 4 QUANTILE REGRESSION ...............................................9 2.................................viii LIST OF TABLES............................................................................................................................................................................................................................................................................................x LIST OF FIGURES...............................................................1 The Method ...........................................................................46 viii ..................................................................................................................................................................19 2.....................................................................34 3...............v DEDICATION........vii TABLE OF CONTENTS..........................13 2..................1 Data ...2 An Overview of the Data..................................33 3 JUHN-MURPHY-PIERCE DECOMPOSITION.................................34 3...................................................................................38 3.............................
....................................95 6............... 129 ix .............. 107 6................50 4................................................................................4 JMP Decomposition and Quantile Regression .............................................................................................3 Conditional and Counterfactual Distributions................................................95 6...............................................93 6 QUANTILE REGRESSION ANALYSIS OF THE EUROPEAN DATA................................1 Data Set .......................................................................79 5...4 JMP Decomposition........................................................3 Conditional Wage Distribution................................................................2 Education................................................................................ 120 7 CONCLUSION ........................................................................4 Concluding Remarks.................2 Application to the US Data.................................................................................4................................................................ 101 6...............................................65 4.......................... 124 CURRICULUM VITAE ......................................5 Concluding Remarks.................................5 Concluding Remarks..............................................69 5......................................1 Experience ............ LUXEMBOURG INCOME STUDY ......................................................................46 4..............................................................................................89 5.....................................70 5..........................59 4........................................................................................ 121 REFERENCES ............3 Education and Experience ...............................................2 Wage Inequality................82 5........67 5 A CROSS-COUNTRY ANALYSIS OF WAGE INEQUALITY USING ..............................1 The Method ........
......................2 Impact Upon Dispersion-Education ............................9 Counterfactual Conditional Distributions-1968 Covariates .......................1 Marginal Effects-Education.....................5 Impact upon Dispersion-Experience ...........................................................................................................................1 JMP Decomposition for 5-year Intervals.8 Comparison of Conditional and Empirical Wage Distributions ............................................63 Table 4...................................................66 Table 5......................LIST OF TABLES Table 2.....................................................................................2 Measures of Inequality .................3 Effect of Education..............................................................................10 Counterfactual Conditional Distributions-1968 Coefficients ......2 Variance Decomposition of Wages For Industry Composition Effect ...................11 Comparison of JMP and Quantile Regression ...................................60 Table 4.....................................3 Residual Inequality...............................................52 Table 4..........................44 Table 4..5.......41 Table 3...........58 Table 4.....59 Table 4...........................................................3 College Premium..................54 Table 4............... Education of the Wage-Earner Sample ........62 Table 4.........51 Table 4................................................90 x ................2................................a JMP Decomposition-Germany............. Waves and Sample Size...4 Effect of Experience...........................32 Table 3...............................4 Marginal Effects-Experience ..88 Table 5............1 Descriptive Statistics ..............77 Table 5...................20 Table 2........................................57 Table 4.....87 Table 5...55 Table 4...........................1............................................................................74 Table 5...................6 Industries.14 Table 2........7 Conditional Wage Distribution ...........................................
......d JMP Decomposition-Spain ..............................13 Marginal Effect of Education-Netherlands..............5...................................................105 Table 6................................99 Table 6......91 Table 5.....Table 5......14 Impact upon Dispersion-Education-Netherlands ............102 Table 6....7 Marginal Effect of Experience............8 Impact upon Dispersion-Experience-Spain ........................................................................................................2 Impact upon Dispersion-Experience-Germany..................................................................................9 Marginal Effect of Experience.................20 Impact upon Dispersion-Education-Hungary .......100 Table 6.106 Table 6...........104 Table 6.............11 Marginal Effect of Education-Germany ................107 Table 6..5......99 Table 6..100 Table 6....................................107 Table 6.......105 Table 6................96 Table 6........6 Impact upon Dispersion-Experience-Sweden....103 Table 6.....................................98 Table 6...................Sweden ......................92 Table 5.........5 Marginal Effect of Experience..93 Table 6.........Spain..b JMP Decomposition-Netherlands .....4 Impact upon Dispersion-Experience-Netherlands ...................17 Marginal Effect of Education-Spain....................................................16 Impact upon Dispersion-Education-Sweden ...................................................12 Impact upon Dispersion-Education-Germany ........................................................................e JMP Decomposition-Hungary..97 Table 6.....................10 Impact upon Dispersion-Experience-Hungary ...............................Germany ..........................104 Table 6...1 Marginal Effect of Experience...........96 Table 6.........................98 Table 6...5..............Netherlands ..............................Hungary .3 Marginal Effect of Experience...19 Marginal Effect of Education-Hungary....c JMP Decomposition-Sweden ................92 Table 5..102 Table 6....................18 Impact upon Dispersion-Education-Spain..................15 Marginal Effect of Education-Sweden .............................................21 Conditional Wage Distribution-Germany...................5.....108 xi .............................................101 Table 6.........................................................
........118 Table 6.............111 Table 6..............115 Table 6..................................23 Conditional Wage Distribution-Netherlands ..........................................................28 Counterfactual Distributions-Spain....Table 6.....119 xii .........24 Counterfactual Distributions-Netherlands .................113 Table 6....27........................................29 Conditional Wage Distribution-Hungary .....26 Counterfactual Distributions-Sweden ...........................................110 Table 6............ Conditional Wage Distribution-Spain ...........30 Counterfactual Wage Distributions-Hungary ...........................................117 Table 6.......................................25 Conditional Wage Distribution-Sweden...........116 Table 6...................................................22 Counterfactual Distributions-Germany ..112 Table 6....
.....109 Figure 6.........2 Conditional Wage Distributions-Netherlands ...........17 Figure 2..............................1 Decomposition of 90th-10th Log Wage Differential over the Years ....................3...................7 Changes in Percentiles of Wage Distribution over Time........... Theil Index and Variance of Logs...................................26 Figure 2.... .............................19 Figure 2............86 Figure 6.3 Industries (Percentage of workers employed in the industry) .......................................................................112 Figure 6..........39 Figure 4....................... Change in Log Wages by Education.....................................................LIST OF FIGURES Figure 2....6 Differences of Log Wage Percentiles . Counterfactual Wage Distributions ......................3 Conditional vs...8 Changes in Wage Percentiles.....2 Shares of Experience Groups in the Sample ..27 Figure 2............. 1988-2005.............................1 Shares of Education Groups in Sample...........................................15 Figure 2...................1 Conditional Wage Distributions-Germany.......................114 Figure 6........9 Within Inequality .......56 Figure 4....................4 Gini Coefficient.............2. Change in Log Wages by Experience ....................117 Figure 6....64 Figure 5...........................................1 Wage Inequality..........................5 Indexed Log Weekly Wages by Percentile .....23 Figure 2.....................4 Conditional Wage Distributions-Spain .........................5 Conditional Wage Distributions-Hungary...........................................28 Figure 3.......................24 Figure 2..........................79 Figure 5........119 xiii .........1 Marginal Effect of Experience..59 Figure 4.....................................................3 Conditional Wage Distributions-Sweden..........2 Conditional Wage Distributions .....................................................................................83 Figure 5........................................21 Figure 2....
the male wage inequality1 began to rise in the US in the second half of the 1970s. Even though some showed wage inequality increases. especially in the first half. whenever we mention wage inequality. Then. We could talk about two types of wage inequality depending on the source of it. The "between" inequality is simply the wage inequality casued by differences 1 From this point on. Most of this increase has been attributed to an increase in demand for people with skills to use of high-technology tools and personal computers that have become common in many industries since the beginning of 80s. it means male wage inequality.CHAPTER 1 INTRODUCTION Following a long period of relative stability in the post-war era. Spain and Hungary to explore how wage inequality changed in the recent years. while the European labor market could not do this because of their highly regulated labor markets and typically strong unions. Wage inequality can be described as the differences in wages due to some measureable and unmeasurable (or observable and unobservable) characteristics of earners. This difference was mostly attributed to the institutional difference between US and most European countries that let the US labor market adapt to changes in supply and demand via changes in wages. Sweden. beginning to increase again in the second half of the decade. Netherlands. it slowed down by mid90s. they were of a smaller magnitude than that of the US. with the exception of United Kingdom. This trend continued during the 80s. 1 . The European countries did not respond to this increase during the 80s. The aim of this work is to analyze and determine the components and dynamics of the changes in wage inequality in the USA and a selection of European countries that includes Germany.
workers with lower experience. even "narrowly defined groups" might not be as similar as we expect. The literature on overall male wage inequality in the US generally agrees that it recorded an increase starting from mid-1970s and gained pace during the 1980s. Even though these groups of people have seemingly similar observable skills (such as education and experience). we cannot argue that the data sets available to us gives all the details about a person. Levy and Murnane (1992) give supply and demand shifts for worker characteristics that are not observable from the standard data sets available to us as well as industry and plant specific characteristics as examples of unobservables that lead to within inequality. This measure is also known as "residual" inequality. One could also look for gender. due to their representation of unobserved or unmeasurable skills and characteristics. For one thing. Murphy and Pierce (1993). On the other hand. Thus. A generally accepted measure of within inequality is the inequality observed among OLS residuals obtained from a Mincerian wage regression (Mincer (1974)). These changes were accompanied by three important facts (See Juhn. college graduates or workers with higher experience vs. a change in demand for the things the former worker can do but the latter cannot will result in higher wages for the former. location or industry for such comparisons. if one of them can do certain things that the other cannot (unmeasured skills). If we consider two workers with similar years of experience and education. Murphy and Welch (1992). usually they still display wage inequality that cannot be explained by their observed skills. Katz and Autor (1999)): 2 . "within" inequality represents the wage inequality within narrowly defined groups of similar characteristics. race.in observable skills and characteristics. for example high school graduates vs. slowing down toward the mid-90s. Katz and Murphy (1992). It can be seen easily by comparing the wage distributions of people with different characteristics. Obviously it is not an easy task to measure the effect of something that is unobservable or unmeasurable.
The returns to education expressed as years of schooling showed a changing
pattern from 1960s to the end of 1980s. It increased during the 60s, then fell during the 70s, and rapidly increased during the 80s. There was also an increase in the gap between the returns to high school and college degrees. 2) levels. 3) There was also an increase in the wage inequality among the workers with The returns to experience also increased, especially during the 80s, with more
experienced workers gaining on the younger workers especially on the low education
similar characteristics such as experience and education. The first two of the above are related to "between" inequality, while the last one is an indicator of "within" or "residual" inequality. Within inequality carries considerable weight in the debates over reasons for these changes because of the difficulty of measuring it. It was found by Juhn, Murphy And Pierce (JMP here after) (1993) that the change in residual inequality explained a much higher portion of the changes in wage inequality than that of education, experience or other observable demographic characteristics. A number of studies pointed towards the changes in workplace during the 80s that resulted in increased use of skill-intensive technology. Katz and Murphy(1992) mention the rapid growth in demand for more educated and "more skilled" workers among the reasons for changing wage inequality. Berman, Bound and Griliches(1994) point towards increased employment of skilled workers and relate this to investment in computers and research and development facilities. Autor, Katz and Krueger(1998) also confirm the effect of computers and reveal that during 19401996 period, the relative demand for college graduates grew strongly and persistently. It was generally argued that such changes favored high-skilled workers over the low-skilled ones. During the 80s, it was observed that the increase in wage inequality was mostly in the shape of higher demand for the more skilled workers and somewhat lower demand for the less skilled ones, the former having a more 3
dominant effect (JMP(1993)). The increase in the wage inequality favoring college graduates over high school graduates and new college graduates over older ones confirm this view. The increase in within inequality, as explained above, showed that the workers with newly needed skills were also favored over the ones that do not have those skills. All in all, the argument that the increased demand for high-skilled workers caused an increase in wage inequality is generally known as "Skill-biased Technological Change Hypothesis" (SBTC). Acemoglu(2002) concludes that most of the technological change in the 20th century is skill-biased, this kind of technological change gained pace during the 80s with the increased use of computers and demand for people with higher education and required skills increased, and the increase in wage inequality is closely related to an acceleration in this trend. Another attempt at explaining the increases in wage inequality, yet did not find as many followers as the first one, was the effect of increased trade with the developing countries which shifted the focus of production towards exports and high-skill oriented products, while increased imports of cheaper low-skill products hit the demand for less skilled workers Wood, (1995), Leamer(1992) and Berman et al, (1994) all focus on the importance of increased international trade and its such effects. Some institutional factors were also mentioned among the causes of this increase in wage inequality. Dinardo, Fortin and Lemieux (1994) found that ,as well as some supply and demand shocks and de-unionization, declining real value of the minimum wage appeared to explain a serious amount of the rising inequality, especially at the lower end. Lee(1999), Card(2001) and Teulings(2003) are among the researchers who found the importance of labor market institutions such as unions and minimum wage to be important. Demographic factors like immigration were also seemed to have some explanatory power (Morris and Western, 1997) While labor economists were focused on explaining the reasons for this change in wage inequality, studies on 1990s data showed a relative slowdown, even a stabilization in the wage inequality around mid-90s (Card and Dinardo (2002)-CPS 4
data through 2001, Acemoglu (2003a)-LIS data through 1997, among many others)) in the US. This new trend, which appeared despite continuing improvement in technology, brought out some doubts over the validity of SBTC thesis (Card and Dinardo (2002). The main criticism was that despite the skill-biased technological change continued, the wage inequality did not keep pace with it. Revisiting the historical change in wage inequality, Autor, Katz and Kearney (2005) conclude that none of the explanations that have been listed above can be sufficient to explain the changes in wage inequality in the last few decades, since different measures and components of it have changed in different times themselves, making one dominant explanation unrealistic. The European experience during the same period proved different in more ways than one. In major European countries, there was little increase in wage inequality during the 1980s, with the exception of Italy and Germany (Gottschalk and Smeeding (1997)). Also the wage inequality, calculated as the log difference of 90th-10th percentile shares of the wage distribution, was clearly less than the US in countries like France, Germany, Sweden etc. This measure of wage inequality actually fell even further in the mid-1990s for a number of countries in Europe (Acemoglu (2003)). Here, The United Kingdom stands apart from the rest of Europe, in that it followed a path more similar to the US than to the rest of Europe, with considerable increase in inequality, rates of return for skill and education. Another point of comparison between the US and Europe is unemployment. The unemployment levels in the European part of the OECD surpassed those in the US in the mid-80s and the gap kept opening throught the 90s. Siebert(1997) and Nickel and Bell(1996) attributed this trend to the inflexibility of labor markets in most European countries, which in turn prevented them from acting to counter the effects of changes in demand for skills. A number of other studies challenged this theory by arguing that the unemployment was not only a matter of the lower part of the distribution, but a more widespread phenomenon (see, Gregg and Manning (1997) among others). Acemoglu (2003) attributed the differences in inequality to a combination of 5
unemployment. and more importantly. Rojas and Vergara(1999) conclude that opening to international trade increased wage inequality in Chile. During his discussion of the concept of "flexicurity". Skoufias and Suryahadi(2002) reveal that the wage inequality increased in Indonesia between 1986 and 1998. Kijima(2005) found that the wage inequality increase in India between 1983 and 1999 was mainly due to increases in returns to skills which was caused by higher demand for skilled labor. They concluded that the reason for increasing wage inequality in Brazil is the relatively small share of college graduates in the work force. Attanasio. Ebbinghaus & Kittel(2005) challenge the general assumption that European labor markets are too rigid by showing that countries have different experiences under different structures. wage inequality studies have been limited in Turkey. Goldberg and Pavcnik(2004) found increasing within-group inequality in Colombia over 1984-1998. Dickerson and Arbache (2001) did not find a strong relationship between trade and wage inequality for Brazil. (Tsou (2002). Green. When we look at the Latin American countries. Feliciano (2001) found that the lowering of import license coverage increased the wage inequality in Mexico. Recently there have been calls to change the things in the European Union. Due to the lack of available data. Wilthagen (2004) points that there is a strong need to make European Labor markets more flexible while providing security to vulnerable employee groups. The trend seems to have continued in Taiwan during the 80s and 90s. we see widening wage inequality. differences in demand for skills. Some of the East Asian economies saw an improvement in wage inequality during 60s and 70s. One notable study by Tansel and Bircan (2006) compares the effect of 6 . Lin and Orazem (2004)). Fan and Cheung (2004) found an increasing wage inequality in Hong-Kong in 1982-1994 and claimed that this can be largely attributed to increasing trade with China. Beyer. Wage inequality in Argentina increased sharply during the 90s (Galiani and Sanguinetti (2003)). On the other hand. differences in the rate of increase in supply of skills.
This study takes stock by applying both methods to the data of the US and selected European countries. Margo (1995) followed the technique to look into racial wage inequality in the US during 40s. 1994 and 1998. as well as some international comparisons on overall wage inequality. Although the JMP method also compares the 7 . for example. Machado and Mata. In recent years. The decomposition method that was suggested and used by JMP (1993) has been used extensively since its introduction. 1996 and some later studies) used it mainly to analyze the dynamics of gender wage gap in the United States. namely Germany. The method has been applied to analyze the wage inequality in different areas and countries. Some later studies used quantile regression techniques (see. Netherlands. and Kijima (2005) for India are examples). This contribution is important in the sense that it gives an account of the contribution of "within" inequality (represented by unobservables) to overall wage inequality separately from the contribution of "between" inequality (represented by observables). JMP method's advantage is that it decomposes the change in wage inequality into observable prices. Martins and Pereira. 2001.education on the wage inequality picture for the years 1994 and 2002. JMP's decomposition method and its versions (see Yun (2006) for a recent version) have been used in several studies to analyze wage inequality in the US and different countries. revealing the existence of between and within inequality. quantile regression methods were started to be used to analyze the trends in wage inequality. Sweden. Running a number of quantile regressions. Labor economists used the method in analyzing the wage inequality picture in a number of other countries (Orazem & Vodopivec (2000) for Estonia and Slovenia. Spain and Hungary. After their two initial papers (1991 and 1993). observable quantities and unobservable prices and quantities. Blau and Kahn (1994. Buchinsky. one can obtain a very useful tool of observing the relative importance of covariates at different parts of the wage distribution. This technique is useful since it shows the effects of covariates on the distribution of dependent variable at different quantiles. 2004).
we will also make a comparison of the results obtained by JMP and quantile regression methods Using March Current Population Survey (CPS) data of the US Bureau of Labor Statistics (BLS). we find that the wage inequality in the US. Although most of this increase came from the lower half of the wage distribution until the mid-80s. and thus the conditional mean. In Chapter 4 we go over the wage inequality picture using a quantile regression approach and make a comparison of JMP and quantile regression results. Our analysis concerning the European countries shows that the wage inequality experience was not uniform in Europe. we describe the European data that comes from Luxembourg Income Study (LIS from this point onwards) and apply the JMP method to a selection of European countries.conditional quantiles of wage distribution. In Chapter 5. The role of within inequality in these changes are reflected in our JMP decompositions and it looks still strong. 8 . We find that they might report similar values if the conditional wage distribution does not change much at different quantiles. In this study. We go into details about the wage inequality dynamics in each country. they assign the same coefficients for the covariates for any point on the distribution of wages and sometimes have problems with identifying the changes in the tails of the distribution. We also generate the conditional quantile comparisons of JMP method by using quantile regression and compare the results. In Chapter 3 we give wage inequality measures and use the decomposition method proposed by JMP (1993). the change in the later years came almost exclusively from the dispersion in the upper half of the wage distribution. which increased remarkably during the 80s. their technique is based upon ordinary least squares estimation. In Chapter 6 we apply the quantile regression techniques to go into more details of European wage inequality. has been still increasing at a slower rate. In Chapter 7 we conclude. The rest of this study will be as follows: In Chapter 2 we give technical details about the US data and describe the historical progress of some key variables. Naturally.
it is a good idea to look closely into the data and understand the main characteristics of our wage-earner sample. then interviewed again next year during the same four months.CHAPTER 2 US WAGE INEQUALITY: EMPIRICAL OBSERVATIONS The detail. Although some data is available from earlier years for the US from the same survey. our decision to start with 1967 is directly related to the change in formation of data in 1967. Before going into a detailed analysis of US wage inequality. 2.000 households for 2005). Bateman & Margo (2004) using different sources of data. which especially makes comparisons with earlier years a bit tricky. The CPS data is collected monthly by interviewing a large number of households (it was around 57. As we will see in the later chapters. Then we introduce some observations and measures of wage inequality to establish the basis for our later analysis. availability and reliability of wage and demographic data for the US have been one of the main reasons for abundance of studies on wage inequality. a sizable part of wage inequality is related to personal characteristics.1 Data In this study we have used the Current Population Survey (CPS) March Annual Social and Economics Supplement (ASEC) for years 1967 to 2005. Each household is interviewed once a month for four months in a row every year. 9 . It is possible to do such analysis for periods as early as 1940s (Goldin & Margo (1991)) or even nineteenth century (Atack.
org/data/cps.nber. When we add the information for families and households that these people belonged to. The related raw data files concerning 1967 to 2005 can be downloaded from National Bureau of Economic Research (NBER) web site. The data in the site is named in a somewhat misleading way. For example. whenever we mention years. age is given in 2-digits. area of living etc. a person's age in 2005 data is reported by the number that starts at the 15th column and ends at the 16th one. For 1967. ASEC is released for the month of March every year. This number increased to 396 in 1988.000 for 1967. a given year's data is published during the next year. this number increases to 396. Because of the way the survey is conducted. number of children. That is. the length of a row is. non-institutional population in every state of the United States of America and the District of Columbia. it also contains data on demographic characteristics of the population such as age. families or households (of course there are "fillers". we mean the actual year that the data belongs. income and migration. family and person. 10 . Each row consists of a household. The data is organized to give three perspectives: household. To give an idea about the size of the data. Throughout our analysis. Thus. The latest data that is available. including everything the other months' data have as well as additional information on work experience. and labeled as the year of publication. or blanks in some places). we can say that a total of around 210. race. The structure of CPS yearly files is hierarchical. Thus. when our data begins. or a person record. the 2005 data. CPS is conducted among the civilian.html. One can think of this structure as a huge matrix. the length of a row in the giant matrix was 360 columns. in a way. This number was around 200.The main purpose of the survey is to collect employment information within the United States of America. http://www. So. Each column represents one digit. However.000.000 separate persons (rows) were covered in the data for the year 2005. a family. the data labeled as 2006 in the above link actually belongs to year 2005. There are indicators showing which type of record the row holds. a person's record is reported in his row by the columns that make up numbers of different length depending on the detail of the corresponding statistic. an indication of the amount of detail the data includes about persons.
unrelated subfamilies and non-relatives follow as family records. not living in group quarters. We have limited our analysis to the males who are between the ages of 16-64. each followed by person records categorized under them. a new household record comes up. In our analysis we only concentrate on person records. the persons are still categorized under the first family record as a family). We concentrate on log values of weekly wage and salary income throughout our studies.33 times the reported maximum value of the variable for that year.has 976 columns. the data structure was changed significantly in 1975 and 1987. www. but in many cases the detail level of a variable has been increased as well. earn at least $67 in 1982 dollars per week (half of the real minimum wage based on 40-hour week in 1982 dollars). The annual wage and salary income entries in ASEC are given in top coded form. This increase has been achieved not only by increasing the number of variables. using household and family data attributed to them whenever necessary. For example. as well as some minor changes almost every year. The natural logarithm of this value divided by the number of weeks worked during the reference 11 . During the period we are interested in. have at least 1 year of potential labor market experience (defined as: age . Then we deflate the annual wage and salary income to 1982 values. using the personal consumption expenditure deflator from National Income and Product Accounts (NIPA) which can be downloaded from the website of the Bureau of Economic Analysis (BEA.htm). information on the type of living quarters and region come from the household record which includes the person under consideration. Once every family under the same household is covered.bea. Industry data is a good example of this. not self-employed or working without pay. work full-time and full year (defined as working at least 40 hours a week and 35 weeks a year).years of education . The data is organized as follows: the household record is followed by a family record. which in turn is followed by the record of each person in that family (even if it is a non-family household. with changes of smaller scale in 1981 and 2002.gov/bea/dn1. Then the records of related subfamilies. We impute the top coded values as 1.6).
year gives us average log weekly wage and salary income. In none of the years top coded (and imputed) values are numerous enough to affect the percentiles we used to analyze wage inequality seriously. Wage and salary income was reported as a single entry in CPS March data before 1987. However, primary and secondary job earnings started to be reported separately after this year. We imputed the top coded values with 1.33 of the top coding value separately before adding them up to find the total wage and salary income. One obvious problem for these years is that top coded values show big fluctuations between years, especially for the secondary job earnings. Wage and salary income is available in six digits for the entire range of years. However, we have some trouble with the weekly wages. Number of weeks worked during the reference year began to be reported as the exact number of weeks only from the 1975 data. It was a recoded variable before that, giving a value 1 to 7 representing a number of weeks in each group. We used an average of the number of weeks falling into each of these groups for 1975, 1976 and 1977, weighted by March Supplement Weights, to recode the previous years' data into the actual number of weeks worked. The educational attainment variable sees a few changes in its coding within the range of years we use. However, there is one major point of change: 1990. Before this year, the data was gathered from two separate variables, one that gives the highest grade of the school attended and another that states whether it is completed or not. If a person did not complete his/her last year of education, we simply assumed the years of education without that year. However, starting with 1991, the reporting of the years of education changed significantly. The variable became a recoded one that focuses not on the years of education, but on the degree received. Some values of the new variable represent several years of education grouped together. Some others report only a degree received (for example high school graduate). It became quite hard to follow the exact years of education. Following the general approach by other researchers in this area, we decided not to use the years of education in our 12
regression due to this problem. Instead, we divided our sample into four groups of educational attainment: Less than high school (lowedu-less than 12 years of education), high school graduate (HS-12 years of education), some college (scedubetween 12 and 16 years of education) and college graduate (colledu-16 years or higher). We used four dummies in our regression indicating whether a person belonged to a group or not. Of course there is still the question of calculating the potential labor market experience variable for each person. We made this calculation by using the average years of schooling for each group from 1988, 1980 and 1990. Park (1994) indicates this method as one of the plausible alternatives of dealing with the change in structure of this variable. 2.2 An Overview of the Data Table 2.1 gives us some information concerning the demographic details of the sample we are working with, as well as the education and industry information, for the years 1967, 1990 and 2004. We see first a decrease, then an increase in the mean age of our sample. This is not a surprise to anyone who is familiar with the baby-boom, a term which marks the sudden increase in the population of the US following the Second World War which saw its peak around 1960. One can easily guess that babies born during this era will have some effect on the average and median age of the working population of the country starting from late 60s. The tide turns around in 80s and 90s, as this group grows towards middle age. The implications of this trend for the future, although not part of our analysis, are a line of work that is gaining interest (Baker, 2001). We also see a decrease in the share of white people in our sample. This is no doubt due to migration from South and Central American countries, as well as the increased number of people who left their ex-communist homes to live in the US within the last decade and half. 13
The share of married males also decreases from about 90% in 1967 to 66% in 2005, marking the results of a great social transformation in the country in this time period, especially in the 70s and 80s.
Table 2.1 Descriptive Statistics 1968 n = 67,852 Mean Std. 408.55 234.08 10.40 0.29 0.22 0.46 0.46 0.49 0.34 0.37 0.44 0.44 0.44 0.40 0.12 0.11 0.28 0.49 0.30 0.36 0.38 0.27 1990 n = 77,797 Mean Std. 456.47 298.75 36.86 0.87 0.66 0.65 0.11 0.39 0.22 0.28 0.28 0.36 0.24 0.13 0.02 0.01 0.09 0.27 0.11 0.17 0.25 0.09 9.86 0.34 0.47 0.48 0.32 0.49 0.42 0.45 0.45 0.48 0.42 0.34 0.13 0.11 0.28 0.44 0.31 0.38 0.43 0.28 2005 n = 106,302 Mean Std. 552.06 546.39 39.18 0.83 0.62 0.71 0.11 0.32 0.26 0.31 0.23 0.29 0.30 0.19 0.01 0.01 0.12 0.18 0.11 0.15 0.35 0.06 10.43 0.37 0.49 0.46 0.31 0.47 0.44 0.46 0.42 0.45 0.46 0.39 0.11 0.09 0.32 0.39 0.31 0.36 0.48 0.24
Weekly Pay Demographics Age 37.67 White 0.91 Married 0.95 SMSA St. 0.70 Education and Experience Less than HS 0.31 High School 0.39 Some Coll. 0.14 College Grad. 0.16 E<=10 0.26 10<E<=20 0.27 20<E<=30 0.27 30<E<=40 0.20 Industry Agriculture 0.02 Mining 0.01 Constr. 0.08 Manufacturing 0.38 Tr.Com&P.U. 0.10 Trade 0.15 Finance&Serv. 0.18 Government 0.08
3-year averages are given, centered on the specified year
from 39% to 32%. The drop in the share of people who have lesser than high school degree is much sharper. It nearly doubled both for people with college degree or for people with some college education but not a 4-year college degree.The share of high school and lower degree holders seems to have dropped between 1967 and 2005. HS Grad. Coll.1 gives the shares of all 4 categories of education we use for each year in percentages. enabling us to see a much more detailed picture of educational attainment in the US over this period. saw a relatively milder decrease. The share of high school graduates. The former falls before 1990 and remains level after that while the latter is the same level as it was in 1990 but decreases after that. Some Coll. although with different timing. 1967-2005 Less than HS 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 Years Shares of four education groups in the wage-earner sample. 15 Share in Sample .1 Shares of Education Groups in Sample Since we will be talking about the educational attainment quite a bit. Figure 2. Shares of Education Groups. it is worth spending some more time on this subject. from 31% to 11%. on the other hand.Grad Figure 2. The share of people going to college (either 4 year or lower) increased from 1968 to 1990 and from 1990 to 2005.
It is clear from the figure that the share of college graduates has been increasing since the beginning of our data. we notice that it remains level until 1990(40%) and then it drops in a matching move with the some college group in 1991 (37%). The first period is when the share of college graduates increased the fastest. However it is a point that should be kept in mind while analyzing the relationship between wage inequality and education. They lose about 2/3 of their share and become the smallest of all categories by far. However one has to identify 3 different periods: 1967-1982. In just 14 years of time. The rate is slower (4% in about 10 years) but still it is noteworthy. Share of the people with some college education but not a 4-year degree keeps increasing until 1990 (21%). the share of this group remains more or less the same. In the last period we see that the share of college graduates in the whole sample is increasing again. From the way high school and some college lines match. one is led to consider the possibility that some people that formerly had been in the high school category were transferred to some college category in 1991. The low education group (less than high school graduate) decreases from 1967 (35%) until 1992 (10%) and stays there. 1982-1996. Unfortunately the new data structure does not give us a way to prove this. their share increased from 16% to 28% of the sample. and 1996-2005. since the group under consideration includes every kind of education between high school diploma and a 4-year college diploma. This jump is very likely to be a result of the change in educational attainment coding. The stabilizing of 16 . The fast increase in the supply of college graduates during the 70s attracted attention as an indicator of an increase in supply of skills. All of these changes tell us that the employees of 2005 are much better educated than those of 1967. After 1991. and then it suddenly jumps in 1991 (25%). Then during the next 15 years it increased just by 1%. When we look at the high school graduates line. After that point. it remains level again with one or two minor fluctuations.
college graduate supply at the beginning of 80s is seen as one of the reasons for the increase in wage inequality during the 80s, combined with the increase in demand.
Experience, 1967-2005 E<=10 40% 35% 30% 25% 20% 15% 10% 5% 0% 10<E<=20 20<E<=30 30<E<=40
Share in Sample
19 88
19 94
19 79
19 76
19 82
19 85
19 91
19 97
20 00
Shares of three experience groups in the wage-earner sample.
Figure 2.2 Shares of Experience Groups in the Sample
The composition of potential labor market experience was also reported in Table 2.1. We see a more detailed picture in Figure 2.2. The figure shows the percentages of 4 groups of workers in the wage-earner sample: less than 10, between 10 and 20(not including 10), between 20 and 30(not including 30), and between 30 and 40 (not including 40). Of course the shares of these groups are related in a much closer way than those of the education groups. Since all persons in our group are full time and full year employees, and their experience increases as years pass, obviously a new entrant in 1967 will pass from the lowest experience group to the second lowest one in 1977. Even though some people might drop out of the labor force before they reach the highest experience group, there is still a strong sense of continuity here. 17
20 03
19 67
19 70
19 73
Although this looks quite trivial, there is still something to be learned from here. The group with the lowest experience, which is the persons with 10 years or less potential labor market experience, grow compared to other groups until 1980(35%). Then their share starts falling and never recovers again. The share of the 10-20 group begins to increase mid-70s (25%) and this trend goes on until 1990(36%). Then it enters its own free fall which still continues. The third experience group (20-30 years of experience) recovers from a fall in 1985 (21.1%) and rises until 2002 (30%). Then it begins to fall too. And the last group, people with 30-40 years experience, lose their share in the work force until 1990 (13%), then their share begins t rise in a move that still continues (19.3% in 2005). These transitions are obviously affected by the demographic development of babyboomer population. Someone who was born in 1955 would be in the work force by 1975, adding to the numbers of the lowest experience group. These people would have ten years of experience during around mid-80s and 20 years of experience around mid-90s. This obviously fits with our peaks. However, our main focus is not the baby boomer population. We are more interested in the dates of these transitions. For our later discussion in wage inequality, it might be a good idea to remember that the lowest experience group had its highest share at the beginning of 80s then started decreasing. And also the third group, that is the people with 20 to 30 years o experience, started increasing their share after 1985. We will refer to these two groups as "low experience" and "high experience" groups in our discussions. Figure 2.3 gives us details about the distribution of our sample over major industry groups. The numbers are given in percentage of people employed in each industry in a given year. Looking at Figure 2.3, we see the transformation of US economy in the last three decades. Manufacturing industry had 38% of all employees that met our criteria of selection in 1967. In 2005, only 18% of our sample was working in this industry. Of course there was also productivity increase, however it only partly explains the change here. This big drop is no surprise to anyone who remembers how some 18
manufacturing sectors like automobile industry have moved their production plants outside the US. With all of the other industries keeping more or less the same share as they had in 1967, it appears that the loss of jobs in manufacturing sector was balanced by more job openings in finance and services sector which doubles its share after three decades of steady increase. The only other noteworthy change is the increasing trend in construction share during the nineties and into the new century.
Industrial Distribution, 1967-2005
Agric. TCOPU
45 40 35 30 25 20 15 10 5 0
Mining Trade
Constr. Fin.&Serv.
Manuf. Gov.
Share in Sample
1967 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year
Shares of major industry groups in the wage-earner sample. Agric: Agriculture. Constr: Construction. Manuf: Manufacturing. TCOPU: Transportation, Communication and Public Utilities. Fin. &Serv: Finance and Services. Gov: Government.
Figure 2.3 Industries (Percentage of workers employed in the industry)
2.3 Evidence on Wage Inequality Now it is time to see how these characteristics of the wage-earner sample are related to wage inequality. Three of the fairly standard measures of inequality are the Gini coefficient, the Theil Index and variance of logs. They do not give a detailed analysis of the nature of wage inequality beyond stating whether it improved or worsened. 19
300 (0.003) 0.003) 0.002) 0.001) 0. The Theil Index is sensitive to the size of the sample.However we feel it will be a good starting point to see just what they say about the wage distribution of US between 1967 and 2005.343 (0.432 (0.261 (0. there is higher inequality as their values increase.321 (0.of Logs 0.372 (0. Gini coefficient is more sensitive to the changes around the middle of the distribution (the mode).304 (0.125 0.155 (0.003) 0.004) 0.433 (0.004) 0.002) 0.002) 0.283 (0.1.003) 0.003) 0.002) 0.232 (0.381 (0.2 are the values of these measures corresponding to the real weekly wages of our sample for each year in 1982 dollars.001) 0. Reported in table 2. In general.261 0.002) 0.003) 0.003) (0.228 (0.126 (0. Then.151 (0.002) 0.383 (0.273 (0.215 (0. We will not go into a deep analysis of these values since we present them only as an introduction to wage inequality picture.005) 0. Theil Index Var.002) 0.289 (0.254 (0. as one can see from Table 1. our samples are of quite different sizes each year.120 (0.005) 0.001) 0. 20 .004) Bootstrapped standard errors are given in parentheses. one can say that each of these measures represent "no inequality" when they take 0 value.002) (0.311 (0.2 Measures of Inequality Years 1967 1975 1980 1985 1990 1995 2000 2005 Measures of Inequality Gini Coeff. so it should be used with caution since. Table 2.001) 0.
30 0. Theil Var.All three measures show that wage inequality is much more serious in the US in 2005 than it was in 1967.20 0.35 0. the 90s. there is a serious increase in the Gini coefficient. the tide is reversed and there is a drop in inequality for the Gini coefficient and the Theil index.05 0. The Theil Index and variance of logs more than double their measures. if not as big as the others.25 0.4 Gini Coefficient.4. pass with remarkable increases.345 0.50 0.164 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 Figure 2. while the variance of logs shows only minimal increase. quite surprisingly if we remember what we read about the literature. After the turn of the century.10 0. as we see in Figure 2. The first big jump in inequality seems to be during the first half of 80s. it might be a bit misleading 21 .45 0.40 0.of Logs 0.15 0.2. The values given on the figure are for 1994. Theil Index and Variance of Logs. Looking at 5-year intervals is no doubt helpful for following how things develop over a period of time. Then.00 Year Values of three inequality measures. and should be compared to the values for 1995 in Table 2.317 0. In the second half of the same decade we see a slowdown. Inequality Measures Gini 0. However.
confirm that there was another tide of increase in wage inequality during the second half of nineties. We see in the figure that the 10th percentile starts losing ground in real terms at the beginning of 80s and actually goes below 1967 level after 1982. After staying at this level for about a decade with minor fluctuations. we see a different situation in the first half of the nineties. After an intial jump from 1967 to 1975. One can learn a great deal by looking at the percentiles of a wage distribution. It only catches up with the 1967 level in 2002. Although the jump that was seen in between 1980 and 1985 was a gradual process that was distributed over the years within the interval. Year-by-year values. Based on the log weekly wages. however. ending up at 8 indexed points gain compared to its 1967 level. it jumps up during the mid. Log weekly wage of the ninetieth percentile tells a different story overall. it remains steady until the 80s. A huge chunk of the big increase that we saw between 1990 and 1995 happened actually between 1994 and 1995. Then it grows until 1987. 50th (median) and 10th percentiles over the years. It rebounds back around mid-nineties. no doubt thanks to the live economy of the nineties which saw an increase in demand for all sorts of workers. if not completely missed. the 90th percentile seems to increase while 10th 22 . however one has to look at different points of a distribution to see what makes it change. the values are indexed to 1967=100. There are often movements in the tails that are not easily captured by measures like Gini coefficient. Cumulative measures of inequality have their uses.as to the timing of changes. since the wage inequality in the US was supposed to have been stabilized by mid-90s.5 shows the change in 90th. Wage inequality stabilizes at a higher level by the turn of the new century. We will return to this point later. staying below that level for a long time. The interesting story here is that while the median keeps a steady course especially after early 70s. To make a clearer comparison possible. Figure 2. This is a bit of a surprise.and late-nineties. The recovery of the lower tail of the distribution in the second half of the nineties is noteworthy and points to an important change in the wage inequality pattern of the US.
Especially during the second half of 90s there is an obvious divergence.percentile decreases in real terms. We notice that all three measures remain a level at 2002 from which they do not increase any more. starts expanding afterwards. we notice two trends here. especially during the nineties. probably due to the 23 . the gap between 90th and 50th percentile. Firstly. Secondly. Figure 2. not big until the mid-80s. then stabilizes. One also has to note that the indexed values of 90th percentile and the median go close to each other until the mid-80s. then they start spreading from each other. from mid-seventies until the 90s the gap between 50th and 10th percentiles increases.5 Indexed Log Weekly Wages by Percentile Overall. while both keep their distance with the 10th percentile throughout the 90s. 1967-2005 10th percentile 110 median 90th percentile Indexed Value 105 100 95 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 Years Three percentiles of log weekly wages indexed to 1967=100.
In Figure 2.40 90-10 50-10 Min.40 1.60 Log Wage Difference 1. 1967-2005 90-50 1.Wage 19 69 19 71 19 73 Differences of 90th. Wage Inequality.60 0.5.00 0. which can be compared to the three general inequality measures that we observed before.6 Differences of Log Wage Percentiles 90th-10th percentile difference can be thought of as a measure of overall wage inequality. It has been increasing since the mid-70s with a few fluctuations.slowdown in the US economy and uncertainty related to the unfortunate events in 2001. Figure 2. 50th-10th and 90th-50th percentiles of log weekly wages. We get a better picture of their relative growth in Figure 2.80 0. making comparisons between their relative positions when there is a clear divergence and convergence.20 1. 50th and 10th percentiles of log wages for each year. 24 19 75 19 77 19 79 19 81 19 83 19 85 19 87 19 89 19 91 19 93 19 95 19 97 19 99 20 01 20 03 20 05 Year 19 67 .80 1. we saw the individual paths of these three measures. The minimum wage is reported in log 1982 values. which shows the difference between 90th-10th.6.
and it starts increasing around mid-90s at a slower rate than 80s. one can think of 90-50 difference as a measure of wage inequality in the upper half of the wage distribution. Also it grows the fastest during the second half of the nineties.The period of the most dramatic increase seems to be the 80s. which is the 90-10 difference. We also see from the graph that the log real minimum wage values remain relatively stable over the same period in which the 50-10 difference remains steady. the 90-50 difference follows a different path. This indicates that one of the reasons for the stability of dispersion in the lower half of the wage distribution in the recent years could be the increases in minimum wage during the nineties which kept the real value relatively stable. On the other hand. 90-10 and 90-50 follow very parallel moves of increase. and then reaches at a level that has been kept more or less the same until today. The 50-10 difference. the 90-50 difference become bigger than the 50-10 difference in 2005 for the only time in our range of years. more or less the whole decade. One can think of 90-50 and 50-10 measures as two pieces that add up to create a new one. We see from the figure that the increase in 90-10 difference between mid-70s and 80s was fueled by the 50-10 difference. The remarkable increase in 90-10 during the 80s was a result of increased dispersion in both halves of the distribution. with 50-10 remaining at the same level. It stays steady until 80s. while the jump during the mid-80s coincides with a long drop in the real value of minimum wage. Actually. in other words the inequality at the lower half of the distribution. then starts an increasing trend which still continues in 2005. it is closely related to the upper-half inequality. Then it stabilizes for a few years. Then it looks to have stabilized since 2003. although we say that 90-10 difference has kept growing for the better part of our range of years. while in the nineties and to the day. Similar to the 50-10 difference. 25 . this growth is a result of 2 different trends: Until the mid-80s it is mostly a product of lower-half inequality increase. grows parallel to the 90-10 difference until mid-80s. Thus. For the rest of our time range.
Two-year pooled data is used to avoid measurement error.5 that the 90th percentile of log real weekly wages increased faster than the 10th percentile over the period 1967-2005. of course in real terms.7.00 1. Obviously there is a striking similarity between the two figures that could trick someone into thinking that nothing much changed between 1988 and 2005.a we have the same picture for the whole time period of analysis.We observed from Figure 2.00 2. 1967-68 to 1987-88 15 25 35 45 55 65 75 85 Percentile Percentile Change in percentiles of wage distribution between two years. 1967-68 to 2004-2005 3. pointed out by JMP(1993) as well.50 2.50 0.00 Change 1. This is a general trend. as we will see in our next figure. In fact the two figures represent quite different stories. The change is calculated as the difference of corresponding percentile between two points in time.00 -0. pointing towards an increase in overall wage inequality.00 2.7. They showed that there is a clearly positive relationship between the percentiles and the change in real wages for the period 1964-88.00 Change 1.00 0. In both figures.7 Changes in Percentiles of Wage Distribution over Time 26 95 5 15 25 45 65 85 75 95 35 55 .50 -1. earners over the 20th percentile recorded increases in their wages.50 2. a. JMP's figure of percentiles and wage changes was similar to Figure 2. Obviously this implies increased wage inequality over the whole distribution of wages. Figure 2.00 0. while the ones below this level earned less than what they earned in 1967-68. with the change in the real wage increasing as one goes higher in percentiles.50 -1.00 5 b.50 1.50 0.00 -0.b except that their data started from 1964.50 3. From our previous figures and tables we have a feeling that this might not be the case. In Figure 2.
50 0. although very small in 15th and 25th percentiles.7.50 1.8 Changes in Wage Percentiles. the relationship betwen percentiles and change in wages is not as clear as it was in the previous figure.50 3. there is a gain at every percentile. there is no such clear trend.00 2. Two-year pooled data is used to avoid measurement error. both in the upper half and overall. Although there is some positive relationship between the percentile and the change here as well.50 2. 1988-2005 27 Change . This should mean more inequality. Figure 2. and stronger after the 60th percentile.50 -1.00 0. In Graph. Log Weekly Wage Change 1988-2005 88-89 to 04-05 3. The change is calculated as the difference of corresponding percentile between two points in time. it is mostly in the upper half of the distribution. we break it into smaller sub-periods in Figure 2. For 1988-2005 period. For the lower levels.8: 1988-89 to 1993-94 and 1993-94 to 20042005. we saw that the lowest 20% of the distribution actually lost in real terms for 1967-1988. However we can say that the highest 25% of the distribution gained more than the rest. Also.00 -0.00 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 Percentile 88-89 to 93-94 93-94 to 04-05 Change in percentiles of wage distribution between two years.2.00 1.To show that 1988-2005 period was different from the 1967-1988 period.
10 0.15 Change 0.05 Exp20 1980-1990 Exp30 1980-1990 0.00 -0.20 0.30 1.20 Change 0.00 -0.05 0.00 5 15 25 35 45 55 65 75 85 95 Percentile Change Exp10 1980-1990 0.20 5 15 25 35 45 55 65 75 85 95 Percentile Change 0.25 0.15 0.15 0.15 0.20 0.80 0.25 0.10 0.10 0.25 0.35 Change 0.15 -0.05 -0.10 -0.10 0.40 5 15 25 35 45 55 65 75 85 95 Percentile 0.9 Within Inequality 28 .20 0.20 -0.20 -0. Figure 2.Exp10 1967-2005 <HS College 0.40 0.60 Change Exp20 1967-2005 Exp30 1967-2005 HS 0.15 -0.10 -0.00 -0. The change is calculated as the difference of corresponding percentile between two points in time.10 -0.15 Change 0.30 0.10 5 15 25 35 45 55 65 75 85 95 Percentile 0.10 Change 0.20 -0.25 0.20 -0.15 0. Due to space limitations.20 0.60 0.05 0.20 0.40 5 15 25 35 45 55 65 75 85 95 Percentile Exp10: 1967-1980 Exp20 1967-1980 Exp30 1967-80 0.05 5 15 25 35 45 55 65 75 85 95 Percentile Change 0.05 0.00 -0.15 0.30 0.20 -0.05 -0.00 -0.05 0.00 -0.40 Change 0.60 0.40 0.40 0.50 0.05 -0.30 -0.00 -0.00 -0.15 0.25 5 15 25 35 45 55 65 75 85 95 Percentile Exp10 1995-2005 Exp20 1995-2005 Exp30 1995-2005 0.00 -0.45 0.20 0. Two-year pooled data is used to avoid measurement error.10 0.20 0.05 0.05 Change 5 15 25 35 45 55 65 75 85 95 Percentile 0.00 -0.30 0.50 0.10 -0.10 0.10 0.60 Change 0.00 -0.05 0. the legend is given only for the first figure.35 0.05 5 15 25 35 45 55 65 75 85 95 Percentile 0.10 0.00 0.30 0.20 0.10 5 15 25 35 45 55 65 75 85 95 Percentile Change in percentiles of wage distribution between two years.05 -0.25 -0.40 0.40 5 15 25 35 45 55 65 75 85 95 Percentile 0.10 0.05 5 15 25 35 45 55 65 75 85 95 Percentile 0.15 0.20 0.
The wage inequality among the people with similar observable qualities such as education and work experience has also shown some interesting tendencies over the years. Our analysis so far has focused on wage inequality in general sense. Each row of figures 29 . 1980-1990 and 1995-2005. meaning that all earners recorded real gains during the this period. except for the highest few. We have created 3 groups of workers based on their years of potential labor market experience: Exp10 represents those with 10 years or less experience. However. Exp20 represents those with between 10 and 20 years of experience (not including 20) and Exp30 represents those with between 20 and 30 years of experience (not including 30). benefiting 75th and higher percentiles more than the others. Figure 2.4 and 2. and in the whole range as well.8 should be interesting. that is. the size of the gap between high earners and low earners.5) within these groups for the periods 1967-2005. Another aspect of wage inequality growth in the United States that has been drawing attention is the so-called within inequality. comparing the sub-periods in Figure 2. This is more severe in the later period. HS stands for HS graduate and no more education. These all confirm our findings earlier that especially from the mid-nineties it is mostly a matter of upper-half inequality growth. and College stands for 4-year college graduates and higher levels than that. there is increased inequality in the upper half of the wage distribution. 1988-89 to 1993-94 period sees a loss in most percentiles. We have also created 3 subgroups of workers according to their educational attainment: <HS stands for education less than high school degree. On the other hand. We calculated the change in wages by percentile (same as in figures 2. In this respect. we also observe that in both sub-periods. The first thing that draws attention is that the differences between 93-94 and 2004-2005 are all positive. There is more to this story than just that. The increased demand for labor in 90s obviously meant gains for all percentiles as we can see from the the line corresponding to 93-94 to 2004-2005.We had observed a considerable jump in overall inequality after 1994. Then we organized each experience group according to their education levels. It is no longer possible to observe clearly positive relationship overall betwen the percentile and the change in either case. 1967-1980.9 gives us an interesting picture of this phenomenon.
shows the same time period for 3 different experience levels. This is an evidence of within inequality among the earners with similar education and experience profiles. For example. Naturally. We notice the same trend here as we saw in 1967-2005 picture. 1967-80 pictures do not give a clear picture about between inequality. higher percentiles gaining more than lower percentiles. There is an increase in inequality generated by college graduates gaining more than both of the other two groups. It is also noteworthy that the ones on the edges of wage distribution for this group are better off than the ones nearer to the median. When calculating the percentiles. we took averages of the last 2 years on each boundary of the time period. The figures for 1967-2005 period show that people with college degree not only earn better wages in 1967 than in 2005. So we can say that there is increased wage inequality due to educational differences. There is also evidence of within inequality here. but we notice that there is an increase in lower-half inequality for the high school graduates in Exp20 and Exp30 groups. which are the more experienced earners. especially for Exp20 and Exp30 groups. Also high school graduates gain more than the lower education group especially in upper percentile. We also observe that there is an increase in wage inequality among the college graduates and among the high school graduates themselves. those with less than high school degree. The same cannot be said for the third education group. 30 . 1967-2005 change is the difference of the average 2004 and 2005 from that of 1967 and 1968. For one thing its seems like they earn less than 1967 except for the highest 5th or 10th percentiles. their gain is also better than high school graduates on all percentiles (except for the Exp10 group where they gain more than high school graduates after the 20th percentile). we are interested in the 1980-1990 period which showed increased wage inequality in our earlier analysis as well as in the literature.
The low education group with 10 years or less experience makes small gains for this time period throughout its wage distribution. Panel C shows how the "residual inequality" compares to the inequality measures from empirical data. noticeably in the upper percentiles. We also see what we pointed out before. and quite popular. but there is no clear sign of any sort of within inequality there.9 we see that this period is when the college graduates obtained the biggest gains over the other groups. the college graduates' earnings increased more than those of the high school graduates in each experience group. which partly represent the effects of unobservable characteristics on wages. Panel B reports the same values from the residual distribution from a wage regression. In other words. Another. There is also a growing within inequality which shows itself especially in the upper percentiles. the inequality in the distribution of regression residuals (residual inequality) for a given year can be seen as an indicator of within inequality. One can see the existence of strong within inequality from the table. It is also informative to compare this figure with Graphs 2. Following this logic. way of getting an idea about the evolution of within inequality is by observing regression residuals. This is especially so for the workers with higher experience. upper-half wage inequality growing within both college and high school categories especially for Exp10 and Exp20 groups. in percentage terms. The 31 . We see that the 1980-1990 period falls into the time period when the share of college graduates in the wage-earner sample was more or less stable. The residual distribution seems to explain about ¾ of the inequality in the empirical data.3 we see the 90-10. 90-50 and 50-10 percentile differences for 5-year intervals. Panel A gives these values for log weekly wages that is the empirical data. although the between inequality picture is not as clear for 19952005 period as it was for 1908-1990 period. In panel C we see how much of the values in Panel A are explained by the values in Panel B. In Figure 2. So.2.The 1995-2005 figures give a different picture.1 and 2. On Table 2. probably due to increased demand and stable supply. we can see that it is still there. For one thing.
32 .40 1980 1.LOG WEEKLY WAGES Percentile 90-10 90-50 50-10 std 1968 1. white.50 1975 1.57 0.66 B.47 0.82 0.18 0.57 0. However.51 1980 1.51 0. residual distribution has highest match in 1980.64 0.66 0.23 0.57 0.52 1985 1.11 0.44 1990 1.62 0.18 0.24 0.83 0.61 0.3 Residual Inequality A.62 0. which is a 2-year pooled regression of 2004 and 2005.45 0.overall inequality (90-10 difference) and upper half inequality (90-50 difference) values.68 0. There seems to be evidence of an increase in within inequality in the lower half.81 0. as the residuals represent a number of other things than unobserved variables.61 0.53 2005 1.49 0.WEEKLY WAGE RESIDUALS Percentile 90-10 90-50 50-10 std 1968 0. some college and college graduate. All regressions are 3year pooled regressions centered on the indicated year except 2005.80 0.65 2005 1. It is also interesting to note that the portion of 90-10 difference explained by the residuals remained mostly the same since 1980. while it decreases in the upper half.54 0. high school graduate.28 0. Of course one needs to approach these values with a little bit of care.50 2000 1. For the lower half inequality. Table 2.61 0.75 0. industry dummies and demographic dummies like married.64 0. metropolitan area and living in the south.46 1995 1. education dummies for less than high school. this is a result of two opposite trends: The portion of 90-50 inequality explained by residuals has decreased.26 0.82 0.57 1990 1.50 0.44 0.% OF DIFFERENCE EXPLAINED BY RESIDUALS Percentile 90-10 90-50 50-10 std 1968 78% 81% 76% 80% 1975 76% 80% 73% 79% 1980 78% 84% 73% 79% 1985 74% 82% 69% 78% 1990 75% 77% 72% 78% 1995 75% 75% 75% 80% 2000 76% 76% 76% 81% 2005 76% 72% 79% 80% The residuals are obtained from a regression of log weekly wages on a quadratic of experience.41 1985 1.59 1995 1.68 0.58 0.85 0.81 0.55 0.95 0.44 0.49 0. the best match is 2005.40 1975 0.00 0.52 0.72 0.08 0.53 C.62 0.53 0.62 2000 1.93 0.56 0. while that of 50-10 increased.
especially of those with less than high education. 33 . There is also evidence of strong within inequality across the education and experience groups.4 Concluding Remarks There have been considerable changes in the structure of the wage-earner sample in nearly four decades. the composition of wage inequality seems to have changed. the percentage of people who attended college increased. Notably. while the percentage of people with lower degrees. A part of employment seems to have shifted from manufacturing to finance and services which usually demand people with computing skills.2. fueling the increase in wage inequality. The dispersion in the lower half stabilized while the dispersion in the upper half has been increasing ever since. After mid-80s. Although the increase slowed down a bit after mid-80s. it continued until 2003. decreased in dramatic rates. The increase in wage inequality from mid-70s can easily be observed from the data.
There is also some inequality that cannot be explained by observable skill differences.1 The Method It starts with a wage equation such as: Yit = X it β t + u it (1) X it where Y it is the log weekly wage for individual i in year t. is a vector of individual characteristics and u it is the part of wages accounted for by the unobservable characteristics. experience and industry of employment that reflect ownership of certain skills. this method decomposes the changes in wages into the effects of changes in observable individual characteristics (education. the effects of changing prices of these observable skills. experience etc. Developed by JMP (1993). Some of this increase can be explained by the changes in some observable characteristics such as education. they told us little in terms of the relative magnitudes of their contribution. and changes in the distribution of residuals using a wage equation and comparing for different percentiles. defined by an individual's percentile in the residual 34 . that is the inequality among people with similar characteristics. 3.). Although the figures and tables in the previous chapter gave us an indication about how these factors work toward inequality.CHAPTER 3 JUHN-MURPHY-P ERCE DECOMPOS T ON We have seen so far that the wage inequality increased for the better part of the last three decades.
(3) Equation (3) attributes the change in wage distribution from one year to the other only to the changes in X's. On the other hand. by adding and subtracting X itβ and F (θ it ) on the right hand side. observable characteristics.). we have Yit1 = X it β + F −1 (θ it ) . changes in β 's and changes in Ft( .distribution. Then. These residuals can be expressed as: u it = Ft −1 ( θ it ) The wage equation can be manipulated so as to capture three sources of inequality: changes in the distribution of X's. the wage distribution can be generated by 35 . Suppose that −1 β symbolizes the average coefficients vector and F (θ it ) symbolizes average inverse distribution function of the wage equation residuals over the two periods in −1 question. and the last one captures the effect of changes in the distribution of wage residuals. if we allow the quantities and prices of the observable characteristics change over time. the second one captures the effect of changing skill prices of these observable characteristics. with average observable skill prices and average residual distribution. For example. θ it .). we have the following: Yit = X it β + X it( β t − β ) + F −1 (θ it ) + Ft (θ it ) − F −1 (θ it ) [ −1 ] (2) The first term on the right-hand side captures the effect of changes in observable characteristics. Then. one can restrict (2) to find different wage distributions. and the distribution function of the wage equation residuals Ft( .
1 Hypothetical distribution (3) is constructed to calculate Yit for each year. The estimated coefficients from this regression are used as β . quantities and the distribution of residuals to change in time. one generates these hypothetical distributions as follows: a) A pooled OLS regression of wages on individual characteristics over the whole sample is run. The residuals from the same regression are ranked by −1 percentiles to construct F (θ it ) b) Separate OLS regressions are run for each year. Each individual is assigned the residual from F (θ it ) that is of the same percentile as her rank in the residual distribution from the regression run for year t.Yit2 = X it β t + F −1 (θ it ) (4) Finally. allowing observable prices. the average coefficients vector. 2 Hypothetical distribution (4) is constructed to calculate Yit for each year. coefficient vector for each year. we have Yit3 = X it β t + Ft (θ it ) = X it β t +u it −1 (5) Obviously (5) is the same as (1). d) This time. 36 . Coefficients are recorded −1 as β t . Residual distributions Ft (θ it ) are constructed for each year as in (a). Residuals are assigned in the same way as (c). In practice. β t are used as the coefficient vector for the individual characteristics vector. The c) average coefficients vector is used as coefficients for individual characteristics −1 vector β . X it .
After this point. one proceeds as follows to decompose the change in wage distributions: 1 The change in inequality measures of Yit from one year to the other is attributed to the change in observable characteristics. f) Then. The main advantage of the method is that it decomposes the change in wage inequality into observable prices. This contribution is important in the sense that it actually gives an account of the share of "residual" inequality in the overall wage inequality. the inequality measure that needs to be decomposed (90th-10th percentile difference. As a result. enabling us to compare it to the effects of changing observable skill composition and prices of these skills. 2 The change in inequality measures of Yit over time is calculated and compared to the change in Yit . 3 The change in inequality measures of Yit from year to year is calculated and 1 compared to the change in Yit . The difference of this change from the change in (4) is attributed to the change in the coefficients of observable characteristics (skill prices). Any difference is attributed to the change in the coefficients of observable characteristics (skill prices). or unmeasured skills and their prices.e) Hypothetical distribution (5) is simply constructed from the estimated coefficients and residuals from the separate regressions run for each year. observable quantities and unobservable prices and quantities. and the difference between the change in (5) and the change in (4) is attributed to changes in unobservable characteristics. we get a picture of the change in "within" inequality that has made 2 37 . for example) is calculated for each of these six distributions. Any further increase or decrease is attributed to changes in unobservable characteristics.
and unobservable skills and prices to overall wage inequality2.1 to see what kind of information this decomposition could give us. education dummies for less than high school. 90th-10th percentile gap grows starting from about mid-70s. which is a 2-year pooled regression of 2004 and 2005. 38 .many people busy since the first signs of increase in wage inequality in the US during 80s. which sometimes makes the total of decomposed components to be slightly different from the observed total change (Lemieux.1 says to us.. (1999)). it will be a good idea to have a look at Figure 3. white. Panels B to D show the decomposition of change in 90th-10th percentile difference for each year compared to the values obtained from a pooled regression. industry dummies and demographic dummies like married. they are reported as difference from their long-term means. dropped industry is agriculture. we need to make a few points about its structure. Panels B to D give us an idea about the contribution of observable quantities (skills and characteristics). (2002)). Dropped education category is less than high school.. Panel A shows the observed values of 90th-10th percentile difference of log wages.2 Application to US Data At this point. After that. All regressions are 3-year pooled regressions centered on the indicated year except 2005. at first remarkably fast during 80s. Another criticism brought against the application of the method was about the way the changes in the distribution of residuals were modeled. some college and college graduate. It is used here to be able to see the status of wage inequality at any certain point of time. Thus. It was actually reported earlier. 3. it keeps growing. high school graduate. metropolitan area and living in the south. JMP's decomposition technique was criticized for the sensitivity of its results to the order of decomposition (Autor and Katz. as Graph. This structure lets 2 These results are obtained from application of JMP on the regression of log weekly wages on a quadratic of experience.. then slower recently. Before going into what the Figure 3. observable prices.
This corresponds to the changes in composition of education and experience categories.05 0.15 0.10 -0.60 1.00 -0.Observable Quantities 0.05 0.15 0.00 -0. A.15 -0.10 0.00 -0.05 0. We see that after mid-70s the contribution of the change in observable characteristics to the total change is very limited.50 1. either.10 1.10 0.40 1.30 1. When we look at Panel B.15 1.10 1.90-10 Log Wage Differential 0. the more contribution that component makes to the overall wage inequality for the given period of time.15 -0.20 1967 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year Figure 3.05 -0.00 1967 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year 0.05 -0. it is always close to zero with no clear trend.05 -0.20 1.10 -0.1 Decomposition of 90th-10th Log Wage Differential over the Years 39 .70 B.Observable Prices 0. it is quite clear that the contribution of observed skills and characteristics to the sharp increase in wage inequality during the 80s are very limited.us see if a component contributes to wage inequality significantly or not.20 1967 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year C. The farther are the values from 0. Unobservables 0. They do not seem to affect wage inequality much after the 80s.10 -0.15 -0.20 1967 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 Year D.
one can see from Panel C that changes in observable skill prices. we can see it jumping in the first half of 80s. Panel D shows that effect of the change in the composition and prices of unobserved skills looks to be both more noticeable and older than that of the prices of observable skills. Here. It is important to note here that while the impact of observable prices is not very large prior to 80s. While not very remarkable during the 70s.1 lets us see the story that lies under Figure 3. have a clearer effect on the overall change. we do not decompose the difference from overall means. the increase in the earnings of people with higher education and more experience result in higher wage inequality. This point was made by JMP (1993) as well. the unobservables constitute a large part of inequality from the very beginning of the time period we analyze. the second shows a picture of upper half of the wage distribution and the third lets us see the situation in the lower half. or returns to skills. Due to both supply and demand factors. 90th –50th and 50th-10th percentile differences of log wages. However.1. It is also important to remind here that all references to a specific year actually mean 3-year data centered around the mentioned year. The table reports JMP decomposition for three measures of inequality: 90th-10th. this table is different from Figure 1 in its setting. Table 3. The first interval is actually seven years since it would not mean much to create a 2-year interval to decompose the change in wage inequality. While the first one of these three measures gives us an idea about the overall wage inequality. This is in accordance with our earlier remarks about changing returns to education and experience during the 80s.On the other hand. It is evident that there has always been some sort of increase in wage inequality since 1967. This time we decompose the change in inequality between two points in time for five-year intervals. We also notice that the overall inequality has been growing since the 90s. The coefficients and distributions are all taken from the same regressions that were used to construct Figure 1. 40 . We have already looked into the change in 90th-10th percentile difference a bit. beginning in 80s.
13 0.01 0. For all the periods after that.04 0.01 0.03 0.01 0.04 0.but at a lower rate than it did during the first half of 80s.02 -0.08 0.17 0.08 -0.01 0.03 -0.02 0.00 0.05 0.02 -0.01 0.01 0.03 0.04 0.01 0.02 0.02 0. The effect of unobservables is always positive.06 0.Quant.01 0.03 0.03 0.1985-1990 E.06 0.00 -0.01 41 .03 0.04 0.03 0.03 0.03 0.07 0.1967-1975 Percentiles Total Change 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 0.00 B.1990-1995 F.01 0.06 -0.02 0.1980-1985 D.03 0.04 0.01 0.1995-2000 G.01 0.06 0.11 0.09 0.03 0.00 0.04 0.01 0.04 0.2000-2005 Components Obs. They account for about 47% of the total change between 1980 and 1985.01 0. we sees that the latter is a deciding factor in the composition of total change.01 0.06 -0.05 -0. Table 3.Price Unobserved 0. meaning that they tend to increase the inequality.04 0.03 0.02 0.02 0.03 0.02 0.06 -0.01 0.07 0.06 0.01 0. Obs. they account for more than half of the total change.00 0.02 0.01 0.1975-1980 C.00 0.01 -0.01 0. with the exception of 2000-2005.05 0.00 0.02 0.04 -0.02 -0.05 0.03 -0. When we compare the total effect of observable prices and quantities (they even cancel each other out in some cases) to that of unobservable prices and quantities.01 0.1 JMP Decomposition for 5-year Intervals Year A.01 0.09 0.
The story in the lower half of the wage distribution is a bit different from that of the upper half. On the other hand.The dispersion in the upper half of the wage distribution (90th-50th percentile difference) is quite limited until 80s. and the effect of the change in the composition of observed characteristics was very small after mid-80s. they work towards increasing the wage inequality. It is also interesting to note in the table that while observable and unobservable quantities have both increasing effect on the wage inequality for the first two periods. In the upper half. It is remarkable that 1990-95 period is where it grows the fastest. then it picks up speed for the rest of the decade. and continues to grow during the nineties as well. starting from 198042 . Katz and Kearney(2005) confirm the second argument. We had noted above that most of the change during the period of analysis comes from the changes in unobserved characteristics. The effect of unobservable skills and their prices is quite strong here. Different directions of upper and lower half wage inequality is actually a point gaining interest recently. We notice that about two thirds of the increase in overall inequality comes from the lower half of the wage distribution for the period 1980-85. they work towards stretching it in the lower half. as well. and that the skillbiased technological change did not affect the lower end of the wage distribution (Autor. observable prices work in the other direction. especially since the mid-90s. Levy and Murnane (2003)). We see this trend here as well: Although the change in composition of observed characteristics constitutes a sizable part of the modest overall inequality increase overall until 1980. the change in the dispersion of the lower half of wage distribution seems to be quite limited. After 1980. After this point. Autor. with two explanations offered: high demand and tight labor markets which benefited workers with low skills during the 90s. observable characteristics affect inequality in upper and lower half of the wage distribution in opposite ways.
Observed skill prices constitute most of the change for 1985-90 period. Here. there are differences among the timing of the change in inequality. The variance of all wages in an industry in year t can be decomposed as follows: σ t2 = ∑ s jt σ 2 + ∑ s jt ( w jt − w t ) 2 jt j j where s jt denotes the share of industry j in the employment of year t. wt is the average wage in the economy at year t. and the within effect stands for the effect of wage variance changes within industries. σ t is the overall variance of wages at year t. One has to point out that all 3 of these components have some time periods when they become more important than the others. With only one exception (50th-10th percentile difference for 1985-90). Thus. w jt σ2 jt is the variance of wages in industry j at year t. since it would indicate that the composition of workforce between industries has a significant effect on wage inequality. we showed that there has been a shift in the composition of work force between the industries. A similar observation can be made for the upper and lower half inequality as well. To test if these shifts had a considerable effect on the wage inequality. 43 . The first term on the right hand side shows the "within" effect. and generally constitute a big part of the changes. What we want to see is if there is a strong between effect. the between effect symbolizes the effect of wage changes between industries. In Previous Reference. is the average wage in industry j at year 2 t. we use a simple variance decomposition that has been used often in the literature.85 period it is less important than the other two components except the last 5 years. they always work towards increasing inequality. while the second term shows the "between" effect. It actually works to decrease the lower level inequality.
002 0.2 Variance Decomposition of Wages For Industry Composition Effect Within Industry Total Chng.039 0.000 0.019 0.003 -0. This is in harmony with our earlier findings. in Comp.001 0.2.006 0.009 One can compare the variances of wages in two separate years using this decomposition to see which part affects the change more. in Wages 0. Effect 0.004 0.004 0. in Variance Variance Effect 0. it still keeps increasing at a lower rate.001 0. 3.003 0.004 0.042 0. We also notice that the composition effect.002 0. Manipulating the difference equations a little bit.019 0.016 0.009 0.011 0.003 0. Chng. Although some of the increasing wage inequality can be explained by changes in the supply of observable skills (high school vs.002 Period 1967-75 1975-80 1980-85 1985-90 1990-95 1995-00 2000-05 Total 0.037 0.002 0.003 0.001 -0.001 0.041 0.024 Total 0. measured by 90-10 percentile difference of log wages.007 Comp.001 0.046 0.001 0. slowed down in the next decade.Table 3. However.001 0.008 0.005 Between Industry Chng.008 0.002 0.059 0.002 0.003 0.052 0.018 0.002 -0.039 0. is small in both within and between industry changes. college graduates and higher experienced versus 44 .020 0.002 0.002 0.3 Concluding Remarks Data indicates that the relatively fast increase during the 80s in overall wage inequality.054 0. We report these values in Table 3.001 -0. we can decompose within and between effects into the effects of changing wages and effects of changing industry composition. We see that the in each case the change in the variance of wages comes mostly from within industry changes. that is the effect of change in the industry composition.
45 . However. They increase inequality in the upper half and decrease in the lower half. a large portion of it comes from unobservable skills. we find that within industry wage variance effect surpasses between industry wage changes and composition changes in explaining the change in variance of wages in the economy Our estimates using the JMP decomposition confirm that the increase in overall inequality was driven mostly by lower-end inequality until mid-80s. education and unobserved skills (as computed by residuals) follow very different trends. This could be interpreted as evidence that between inequality plays an important role in the surge of the upper half wage inequality in the recent years. although the skill premia increased since the 1960s. The total effect of changes in observable characteristics have been limited since the 80s. it is mostly upper-end inequality. the effect of unobservables is always large. Since then. When we look at the change in wage inequality among the industries. sometimes going well beyond. thus making it very hard to attribute the overall change exclusively to one of them. in general we can say that their combined effect has matched that of the unobservables in the upper half since 1980s. As it was reported in JMP (1993). the changes in returns to experience. Although the effect of observable prices does not follow quite the same trend. However this seems to be a result of opposite movements in the two halves of the wage distribution. in other words inequality within same observable skill groups.lower experienced) and the skill premia for these skills.
Then. see Martins and Pereira (2004) and Machado and Mata (2001) for applications). This means that θ % of the cumulative density of Y falls below qθ . one needs to take these unusual characteristics into account when doing analysis.5 . For example when the distribution has thick tails. focusing only on OLS estimates might be misleading in certain situations when the "mean" loses some of its "meaning". it can be used to examine the dynamics of wage inequality under a new light. Quantile regression estimates can be used to see the effect of a covariate on within-group wage inequality.1] . that is when the distribution is less like a normal distribution. qθ = inf(Y : F (Y ) ≥ θ ). 46 . the θth quantile of a random variable Y is a number qθ such that Pr (Z< qθ ) ≤ θ ≤ Pr (Z ≤ qθ ). Although the method is quite informative as we saw in the previous chapter. or not symmetric. In a more standard expression. For example. 4.CHAPTER 4 QUANTILE REGRESSION The JMP decomposition technique provides its results based on Ordinary Least Squares (OLS) regression. Estimating the model for each quantile using the quantile regression techniques has the benefit of giving a parsimonious description of the entire conditional wage distribution. the median can be symbolized as q0.1 The Method Given θ ∈ [0. whatever the shape of it. as well as seeing the effects of different skill attributes in each quantile (Buchinsky (1994). that is the conditional mean of the data.
if we want to find the 25th quantile. This is how we move the weights around to find different quantiles.75 Yi − k k i:Yi p k i:Yi ≥ k It is obvious that we assign more weight to the values below the θth quantile (positive errors) than the ones above it (negative errors). this approach can easily be modified to obtain other quantiles as well: n n min ∑ θ Yi − k + ∑ (1 − θ ) Yi − k . we have the following: n n min ∑ 0. This minimization obviously leads us to the median. In a wage equation model. For example. The standard quantile regression model that is in use today was introduced by Koenker and Bassett (1978).One can also think of the quantiles as the solution of a minimization problem: min ∑ Yi − m . Following a k i =1 n "pinball logic" as it was called in Koenker and Hallock (2000). we can define the quantile regression setup as: w i = xi' β θ +u θi with Q θ ( w i | x i ) = xi' β θ . This idea is more or less what the quantile regression is centered around. where xi is a vector of exogenous variables.25 Yi − k + ∑ 0. βθ is a vector of parameters and Q θ ( w i | x i ) denotes the θth conditional quantile of w given x. Any given conditional quantile θ can be derived by solving the following problem: 47 . k i:Yi p k i:Yi ≥ k What we do here is to minimize the weighted sum of absolute deviations to find the θth quantile of the sample distribution.
1986) for the large sample properties of quantile regression estimators. it is not possible to use standard optimization methods. increasing θ from 0 to 1. It can be solved as a linear programming model. One can see Koenker and Bassett (1978) and Powell (1984. ε ≥ 0 (θ − 1)ε . Since the objective function is not differentiable. A number of software packages have quantile regression options. . we get a chance look beyond the conditional mean and see how the effects of covariates change in the tails and other quantiles of interest. which can be written as β n ln wi p x i β ln wi ≥ xi β min β 1 1 ∑ ρ θ ( wi − xi' β θ ) = min n ∑ ρ θ (uθi ) . ε < 0 ρθ (ε ) = An alternative expression that can be derived from this setup is: min β 1 ∑ θ − 12 − 12 sgn wi − xi' βθ n i [ ( )](w i − xi' β θ ) In either case. In this study we used Stata which is one of the two standard econometric software packages 48 . thus giving the user a means to compare distributions. 1 min ∑ θ |w i − xi' β θ | + ∑ (1 − θ ) |w i − xi' β θ | . Tracing the whole distribution of w this way. Buchinsky (1998) shows that Generalized Method of Moments can also be applied for estimation. β n i i where ρθ (ε ) is the check function defined as θε . one can trace the whole distribution of w conditional on x The coefficient estimates of quantile regression denote the effects of covariates on the distribution of the regressor at the corresponding quantile.
we get a change to see the effects of covariates on the creation of these statistics. even though the estimator β θ lacks a bit in efficiency compared to the least squares estimator in case of a Gaussian distribution. The method of quantile regression can be seen both as an alternative and a complement to the usual methods of linear regression.mentioned by Koenker and Hallock(2001) as having functionality for inference. lower and upper halves of the wage distribution. One does not need to go further than our very first figure to see how closely quantile regression is linked with the analysis of wage inequality. it is much more efficient and robust for a large array of non-Gaussian situations. Especially for the cases when the conditional distribution of the dependent variable (conditional on covariates) in question has thick tails. As we will see later. The quantile regression estimator is robust to outliers. In our discussion so far. that is acceptable standard errors. β θ should be interpreted in a different way than the linear regression estimator. This nice feature enables us to draw different regression lines for different quantiles and observe their shape changes as well as their scale and location as one goes along the conditional distribution of the regressor. ˆ Although they are derived by two somewhat analogous methods. we also compare the JMP estimates with the quantile regression ones in analyzing the wage inequality picture. or unimodal. 49 . the meaning attributed to the linear regression estimator can be made much stronger with the help of quantile regression estimators which provide better information about the distribution of the variable in question. As it was forcefully proven by ˆ Koenker and Bassett (1978). The benefits of this method in our analysis are obvious. is asymmetric. With the application of quantile regression. we often referred to the notions like 90-10 percentile difference. the former is the effect of covariates on the specified quantile of the distribution of regressor. While the latter simply shows the effect of the covariates on the regressor at the conditional mean.
1. State standard errors are given in parentheses. our remarks about the uses of quantile regression in the previous section become clearer.4. The dropped category of education is less than high school. as we just mentioned. It looks like we can split the high school table into two. In this part we will look at the picture from a different angle. the 10th quantile workers did not see much of this increase. keeping the distribution of observed qualities or estimated skill prices between two years. Although the OLS estimates give a good idea about what the data "on average" shows. to see the effect of the other (Machado and Mata (2001)). 25th. while the OLS suggests that returns to having a high school degree have kept increasing from 1975 until 2005.9). some college. and the high school diploma actually meant less in 2005 than what it meant in 1980 (a drop from 33. and college degrees in Table 4. the returns to having a high school degree increase as years pass. Our aim here is first to analyze the quantile regression estimates then to generate a conditional wage distribution from quantile regression estimates and compare it to the empirical one. On the other hand. We also report the OLS estimates for corresponding years to be able to compare the results. One can also create counter-factual distributions. For the median and higher quantiles. 50 . there are some other stories not told by them. We used the same covariates as in JMP analysis in our quantile regressions. 50th 75th and 90th quantiles. it goes down for the 10th percentile since 1985.We evaluate marginal effects of having high school. Just looking at a few coefficients. Since this trend is offset by the changes in other quantiles. we do not see it in the OLS estimates. For example. It also peaked for the 25th percentile in the same year. All quantile regressions were run on 3-year pooled data centered on the indicated year. but it remained more or less the same level after.5 to 27. We ran regressions for 10th.2 Application to the US Data We have given details and facts about the US data concerning wage inequality.
3 (0.8) 33.1 (0.3 (0.7 (0.7) 33.Table 4.9) 51.2) 30.5 61.7) (0.0) 48.5 (0.3) 79.8 (0.5 46.4 (1.3 21.7) (0.7) 10 24.4 62.7 (1.3 (1.5) (0.9) (0.4) 90.0) (0.9) 20.8) (1.1 60.2 (0.5 (0.4) 79.2 (0.5 (1.7) (0.4) 48.1) 28.3 84.9 (0.9 (0.1 (0.7) 41.6 19.9) (0.6 (0.7 (1.7 (0.2) 27.5 (0.5) 63.1) 32.7 (0.2 36.9) (0.3 33.0 (1.8) (0.6 (0.1) (1.0 (1.1) 32.8) (1.6) 50.5) (0.3) 31.3 (0.3 97.3) 42.0) (0.9) 53.6 (1.1) 42.5) 30.8 (0.5 (0.2 (0.7) (0.4 (1.3 (0.4 (1.6 70.2 101.8) (0.0 (0.4 (1.2 (0.6) 31.4 (0.3 (0.5) 33.4 (0.0) 25.4 (1.1) (0.7) 22.4) 47.2) 45.0) 65.7) 31.7) (0.5 93.7) (0.6) 82.1 (0.5 (1.4) 25.8) 47.7) 50.2) 47.4 105.7) (0.5) 62.6) (0.1) 83.7) 32.7) 50.6) 26.8 65.8 (0.9 (1.8) 32.6 (0.7) 22.8 (0.8) 39.7 (0.9) 75 18.1 (1.3) 50.8 89.9 (0.9 (0.1 (1.3) 88.6 (0.6 (0.0) 36.2 (0.4) 29.9) (0.7) (0.1) 86.4) 22.7) (0.4 (0.8) 80.6) All coefficients are multiplied by 100.8) 32.3) 29.7 (0.2 94.6 89.3 (0.1 82.0) 90 20.9 (0.4 (1.4) 30.0) 77.5) 37.9 (1.9) (0.9) 33.6 (1.8) 32.6 (0.9) (1.9) (0.8) 46.2 (1.4 (0.7) 32.0) (0.9) (0.0 60.9 60.7) 41.9) (1.8 53.5 76.7 (0.6) OLS 34.5) 34.7) 93.5) 34.3 74.2 (1.3) 50.9) 75 33.4 (0.9) (1.6 (0.6) 88.6) 29.6 (0.1 (0.5 63.6) 32.8) (0.7) 10 33. 51 .7) 10 58.9 (1.8 32.9 (1.0 (0.8 (1.5) 77.6) 19.6) College 25 50 75 90 60.7 (0.7 (1.1) 90 37.0 (1.3 49.8) 25.9 34.6 (0.1 (0.7 (1.3 (1.6 34.3 83.8) 34.8 (0.3 25.6) 48.5 (0.5) Some College 25 50 33.2 (0.8) 60.4) 23.4 (0.9 (0.9) (0.7 64.1 (1.2 54.0 31.8) (0.0 (0.9 31.1 Marginal Effects-Education Year 1968 1975 1980 1985 1990 1995 2000 2005 OLS 21.7) 52.5) High School 25 50 22.0 101.1 (1.4) 51.7) 51.6 (1.1) 60.7) (0.4 96.5 (1.0 (0.1 (1.7) 96.5) 45.0 (1.3) 80.9 (1.4 (1.3 (1.6) (0.3) 64.1 (0.5 (1.5) 82.6) 48.7 30.8) 53.5 (0.0 (1.9 77.8) (1.4 (1.7) 29.7 (0.7) 31.7) OLS 62.1 (0.6) (0.0) 30.9) 51.5) 19.1) (0.0) (0.9) 32.5 50.2 (1.
Buchinsky (1994) showed that one can have an idea about within inequality by looking at the differences between relevant conditional quantiles of the same group.2 All values are differences of indicated quantiles from Table 4. Table 4.0 1.7 2.3 -4.3 2. the within inequality.9 -0. Even the 10th percentile see some improvement over the years.8 -4.8 -5.1 11.4 -1.6 -6.0 -5.4 8.1 -6.1 -2.7 4.8 0.0 -2.2 1.2.5 -7.8 -10.8 -9.5 -6.5 8.3 -0.2 16.3 24.9 -3.2 4.2 -4.4 3.6 -4.0 0.9 -4.The "some college" table shows very similar trends to the high school one except the fact that this time the 10th percentile peaks at 1990.2 -1.3 -9.5 College 90-10 90-50 50-10 12.7 3.1 -0.6 -1.6 7.6 -7.5 3.1 3. We see the "impact upon dispersion" values which simply show that in Table 4.4 19. Machado and Mata (2001) suggested that one can see an indication of the effect of each group on the overall within inequality by observing the differences of marginal effects of the same group from relevant quantiles.1 4.7 6.4 -2.7 4.8 -1.4 -3. For the people with college degrees the life seems to have been much better.0 -5.9 -0.5 -1.2 9.6 6.9 -4.5 -0.4 4.8 -8. We follow the changes in these values over time to follow within inequality. 90-50 and 50-10 differences.9 -4.2 4.5 4. We have 90-10.2 15.5 -1. One can look at these numbers as an indication of the contribution of a group to the overall wage inequality due to the inequality it generates within itself. not 1985.2 -1.4 3.8 10.2 52 .8 -9.6 -6.6 -4.2 Impact Upon Dispersion-Education 1968 1975 1980 1985 1990 1995 2000 2005 Impact Upon Dispersion High School Some College 90-10 90-50 50-10 90-10 90-50 50-10 -3.3 0. Following this logic.
High school graduates at the 90th percentiles are relatively worse off compared to the other quantiles.1. The within inequality of college group has been increasing recently.1. Apparently the college graduates group is the one that is contributing most to the overall inequality via within inequality. once again. The reason for such a picture is probably the ground lost by the high school graduates to the college ones in the upper quantiles. On the other hand. On the other hand . there is a trend of increasing within inequality in the lower half of the high school distribution especially after 1990. the ratio of people with some sort of college education to those with high school diploma has been increasing. We also see that the within inequality of the college group is the lowest in the eighties when wage inequality was increasing rapidly. It becomes more obvious that this unusual picture has much more to do with the 90th percentile than the 10th percentile when we look at the 90-50 and 50-10 differences and table 4. both in the upper and lower half of the distribution. the share of high school graduates among people with no college has been increasing throughout our data set. These lead one to consider the possibility that the higher returns to high school diploma in the lower quantiles than in the upper quantiles might be related to the fact that high school graduates have become a majority in the lower quantile while they have been losing their share in the upper ones. However one has to recognise another trend that might be a strong factor in shaping the values of Tables 4.2. 53 . as it can be expected.We notice that being a high school graduate is a better thing in the 10th quantile than what it is in the 90th quantile. As we observed in Figure 2. the group with less than high school education is disappearing. within inequality keeps growing in this group. In other words. The some college category follows more or less the same trends as the high school one. The only exception to this is in 2005. This might have something to do with the high demand for and the stable supply of college graduates during the eighties. In a way we can say that the high school group might have seen a decrease in within inequality over the years. and this trend weakens especially in the nineties.1 and 4. Also.
the quantile regression estimates for experience data generally follow the direction of OLS estimates.7 44.7 33.9 51.7 51.6 34.9 61.Table 4.3 48.9 57. However.5 37.4 47. Since experience enters the regressions as a quadratic.7 63.2 69. This remarks the contribution of the change in inequality caused by education to the slowing down of wage inequality in the last few years.1 41.8 63.3 College Premium OLS 40.5 52.8 58.9 38. Between 2000 and 2005.2 46. the marginal effect has been calculated by evaluating the first derivative of it given for 3 values.0 39.1 37.7 58. Table 4.1 72.1 52.6 36.7 54.3 37.2 49.6 39. Of course one has to keep in mind that the two figures have different scales. we have a different story here. the difference between the returns to college grade and those to high-school diploma.3 44.6 53. Unlike the education case. the change in all quantiles is modest.0 66.2 can give us a better feeling of this.5 56.1 53.4 40.6 44. However we observe that the changes become smaller recently.6 College-High School Difference 10 25 50 75 33.4 reports the marginal effects of experience at 5.2 90 49. 15 and 25 years.5 53.1 74.3 55.1 64.0 57.7 39. Both OLS and quantile regression results suggest that this value kept increasing steadily from 1980 on.0 1968 1975 1980 1985 1990 1995 2000 2005 The difference between the coefficients of college and high school graduate dummies One interesting point here is the so-called college premium.2 46. It is obvious that having 25 years of work experience was best rewarded during the 80s. 54 .4 45. Figure 4.6 48. to different degrees depending on the quantile. We can say this for all quantiles and for all three experience categories.5 53. We cannot say the same thing for 5 years of experience.
14) 0.87 (0.08) 0.11) 0.71 (0.97 (0.33 (0.81 (0.09) 0.09) 0.05) 0.93 (0.20) 5.45 (0.16) 5.99 (0.83 (0.49 (0.12) 4.42 (0.08) 1.84 (0.41 (0.19 (0.39 (0.74 (0.69 (0.76 (0.08) 0.22) 25 8.48 (0.41 (0.62 (0.08) 5.47 (0.15) 5.06) 1.67 (0.73 (0.09) 0.Table 4.07) 1.20) 5.96 (0.07) 0.05) 10 0.08) 0.34) 4.08) 1.71 (0.20 (0.95 (0.30 (0.06) 1.20) 1.07) 1.06) 1.11) 4.12) 0.14) 4.06) 0.07) 1.46 (0.53 (0.78 (0.46 (0.05) 1.15) 5.07) 1.88 (0.46 (0. All coefficients are multiplied by 100.06) 0.15) 5.45 (0.44 (0.12) 25 Years 75 1.12) 1.76 (0.10) 0.45 (0.08) 0.06) 10 9.08) 0.32 (0.11) 90 5.06) 1.96 (0.52 (0.74 (0.15) 4.16) OLS 0.09 (0.20) 4.15) 0.07) 50 0.41 (0.05 (0.72 (0.46 (0. the marginal effect has been calculated by evaluating the first derivative of it given for 3 values.81 (0.09) 1.15) 50 6.22) 5.11) 4.27) 4.14) 0.30 (0.16) 5.83 (0.28 (0.46 (0.50 (0.22 (0.13 (0.06) 0.19) 4.13) 5.40 (0.15) 1.67 (0.07) 1.08) 1.17) 4.89 (0.13) 4.63 (0.53 (0.72 (0.78 (0.05 (0.18) 5.13) 0.05) 10 0.68 (0.51 (0.11) 1.11) 1.16 (0.07 (0.09) 4.05) 75 0.06) 0.05) 0.34 (0.11) 4.30) 4.28) 1.11) 1.85 (0.31 (0.39 (0.87 (0.07) 50 0.37 (0.10) 5.15 (0.05) 0.14) 5.47 (0.54 (0.18) 5.07 (0.09) 25 0.36) 1.94 (0.10) 4.31 (0.09) 4.64 (0.95 (0.07) 1.14) Since experience enters the regressions as a quadratic.08) 0.28 (0.33 (0.28 (0.07) 0.08) 1.12) 4.24 (0.24 (0.08) 0.22 (0.66 (0.26) 4.08) 0.11) 0.12) 0.05) 1.51 (0.08) 1.08) 1.13) 4.08) 1.06) 0.09) 1.04) 1.14 (0.07) 0.07) 1.20) 4.19 (0.39 (0.74 (0.81 (0.07) 0.06) 0.33 (0.11 (0.07 (0.34 (0.10 (0.09) 0. Standard errors are in parentheses.11) 1.10) 15 Years 75 6.07) 0.07) 0.59 (0.58 (0.08) 6.10 (0.09) 0.72 (0.38 (0.09) 0.56 (0.38 (0.07) OLS 0.24) 5.84 (0.05) 90 1. 55 .46 (0.37 (0.80 (0.56 (0.10) 25 0.06) 1.12) 4.72 (0.19) 1.64 (0.89 (0.06) 0.38 (0.23) 4.56 (0.89 (0.14) 1.85 (0.10) 1.22 (0.63 (0.86 (0.11) 5.64 (0.4 Marginal Effects-Experience Marginal Effects of Experience 5 Years OLS 1968 1975 1980 1985 1990 1995 2000 2005 7.09) 0.47 (0.75 (0.76 (0.15 (0.57 (0.88 (0.45 (0.80 (0.06) 1.24) 4.07) 0.09 (0.79 (0.49 (0.33 (0.90 (0.84 (0.28) 1.25 (0.10) 0.31 (0.17 (0.05) 1.12) 0.59 (0.06) 90 0.18) 5.56 (0.23) 1.06) 1.14) 5.49 (0.50 (0.
At 2000 and 2005. So.1 and Table 4.Experience-25 years OLS 50 10 75 25 90 Marginal Effect Marginal Effect 1975 1980 1985 1990 1995 2000 2005 Year Since experience enters the regressions as a quadratic.4 that the marginal effect of years of experience on wage distribution increases as one goes higher in the quantiles.It is also noteworthy from Figure 4.6 0.8 0. This is in accordance with the findings concerning increased wage inequality starting from 70s and going until mid-nineties. A. the 10th quantile gains on 25th quantile and the median.5 reports the impact upon dispersion for three fictional years of experience. one can assume higher returns to experience in the higher quantiles than the lower ones. Figure 4.0 1968 B.0 0.1 Marginal Effect of Experience Table 4.Experience-5years OLS 50 12 10 8 6 4 2 0 1968 1975 1980 1985 1990 1995 2000 2005 Year 10 75 25 90 1. thus confirming our earlier findings about slowing or decreasing lower end inequality.2 1. the marginal effect has been calculated by evaluating the first derivative of it given for 5 and 25 years.4 0. At each experience level. additional years of experience gain better returns for workers in higher quantiles than they do for the ones in lower quantiles.2 0. Except for 1985 when the within inequality 56 . We notice that the low experience group contributed overall inequality the most during the stretch from 1980 to 1995.
communication and public utilities always give higher returns than average (with the exception of 2004).24 0.23 1995 0. Table 4.54 2005 0.24 0.50 0. not dispersed.92 -3.49 0.70 -0.04 0.36 0.46 0. manufacturing and transportation. one could probably say that industry differences contribute to wage inequality as well.48 0.45 1980 0.22 0.85 0.58 0.29 0.50 1975 0.13 0.18 0. On the other hand.62 0. All values in this figure are given in the form of deviation from the mean.01 0.06 0.47 0.5 Impact upon Dispersion-Experience Impact Upon Dispersion 5 Years 15 Years 90-10 90-50 50-10 90-10 90-50 50-10 1968 -3.28 0.08 50-10 0.50 0.16 The effect of being in an industry on wage distributions is given in Table 4. both tails of the distribution contributed to this effect in a balanced way.57 0.87 0.47 0.45 0.33 0.05 1.10 0. Looking at this picture.96 -0.49 0. However.15 0.41 2000 -0.64 0.65 0. We note that trade and finance and services always give less than average returns.66 0. 57 .70 0.6 for certain quantiles.21 0.07 90-10 0.24 -0.14 1.11 1.01 -0.30 1990 1.74 25 Years 90-50 0. we see that the upper half inequality gains much more importance in 2000 and 2005 against the lower half which actually stretched.37 0.01 0.01 0. We can also see the differences of return within the sectors here.42 0.71 0.24 0.20 0.07 0. depending on the quantile.32 0.19 0.41 1985 0.10 -0. Before 1990.00 0. it is the upper half inequality that dominates the picture of within inequality in the 25-year experience group.17 -0. we see that lower half inequality is more important than the upper half inequality from 1990 to 2000.25 0.61 0.17 -0.was caused predominantly by upper-level inequality.18 0.76 0.18 0.20 0. while mining.
4 -2.8 6.4 -4.2 -12. -11.5 Mining 17.6 Fin.7 -12.&Serv.7 27.5 3.2 -20.9 Manuf.2 Construction -4.2 24.3 -13.3 -2.8 28.7 Trade -16.9 15.9 -13.5 7.4 2.U.4 4.9 -1.1 Fin.4 -3. -5.0 -8.8 2.6 4.U.1 -6.7 -10.8 -7. 4.9 -11.U.7 6.4 0.4 4.4 0.7 -15.U.7 -16.1 -0.2 -3. 6. -0.8 4.1 -11.0 -22.U.2 -2.8 3.7 -4.3 -5.4 Fin. -5.2 -13.5 -1.0 3.Com&P. 58 . 2.2 Fin.5 -1.0 6. 3.6 -5.9 0.0 Construction 3.4 -10.2 1.Com&P.7 Gov.0 8.2 -3.1 Construction -2.6 0.7 -6.5 2. -0. 7.5 8.5 -13.&Serv.7 Trade -14.4 -10.5 10.2 -4.3 -4. 3.0 -20.5 -6.3 26.7 -13.7 -19.6 4.Com&P.4 7. -5.9 2.1 -13.8 -10.4 -2.6 4.4 9.4 5.4 -15.6 3.8 -7.6 7.1 -11.9 0.0 -7.Com&P.3 -9. 2.7 24.8 -0.4 Gov.0 -15.4 19. -13.5 -16.1 Gov.5 -15.2 -9.2 -6.8 Construction -1.3 2.0 -21.5 -5.3 Manuf.4 0.9 25.1 Construction -1.4 Gov.5 -0.5 -15.4 26.3 -7.5 -0.5 -9.9 -9.4 -8.6 24.8 -12. -15.1 -7.7 5.8 Gov.7 -1.2 18.6 -18.5 18.&Serv.9 -17.6 Industries 1968 OLS 10 25 50 75 90 1975 OLS 10 25 50 75 90 1980 OLS 10 25 50 75 90 1985 OLS 10 25 50 75 90 1990 OLS 10 25 50 75 90 1995 OLS 10 25 50 75 90 2000 OLS 10 25 50 75 90 2005 OLS 10 25 50 75 90 Mining 8.5 -12.1 5.1 29.1 -2.0 17.4 10.4 4.1 Manuf.8 -8.0 9.5 -15.3 8.4 -16.5 1.7 5.1 Manuf.1 23.Table 4.3 Construction -3.9 8.2 -14.7 -3.2 19.0 2. 6.1 1.9 3.3 4.6 1.6 3.7 -6.8 9.8 2. -14.9 -8.2 -4.9 10.6 2. 1. -10. 0.Com&P.5 -12.9 -0.Com&P.6 4.6 2.9 23.3 Mining 24.1 -3. -0.5 Fin.&Serv.2 3.8 -5.4 -17.3 -13.U.0 1.9 Tr.0 5.9 2.2 Trade -10.1 -9.4 -2.6 -5.7 -14.8 -6. 3.9 Mining 24.5 20.0 6.3 -3.1 2.3 -19.2 1.7 Tr.8 28.9 1.6 -17.8 -15.7 2.5 Manuf.0 -3.2 7.1 Manuf.0 5.2 4. 5.7 Gov.3 -15.4 Mining 25.3 Trade -7.2 16.9 -6.0 7.7 24.4 4. -5. 3.Com&P.2 15.5 -16.&Serv.6 -18.5 2.8 22.4 3.5 -2.3 18.9 Tr.7 -2.3 -11.3 -2.7 -10.5 -17.2 2.8 Mining 17.3 -0.8 6. -0.7 -8.7 Fin.1 Mining 26.8 8.3 -9.8 9.9 Manuf.7 7.7 -17.2 2.3 Industry effects are calculated as the difference of regression coefficients from the average industry effect for that quantile for that year.4 -1.9 3.3 2.4 Gov.9 2.9 19.5 -14.5 -0.Com&P.5 -12.7 Tr. 2.4 -0.8 9.4 8.0 26.0 -2.4 4.U.4 -8.0 4.2 -1.3 Tr.2 25. -3.5 -10.0 Trade -12.7 1.3 23.1 18.4 -10.&Serv.1 -6.6 17.4 -12.U.9 6.5 Trade -15. -10.9 -16.7 2.8 -1. 7.4 -8.2 25.3 Fin.8 -5.9 -8.9 1.7 0.9 Gov.1 -8.7 Trade -9.3 10.3 4.1 -15.6 Tr.4 Fin.6 -3.&Serv.9 4.3 -4.8 -0.1 -2.3 4.8 Tr.1 -2. -10.&Serv.0 -7.5 26.7 -3.3 7.7 -10.2 8.3 22.6 3.1 -7.1 Trade -13.7 2.2 -0.7 -14.0 22.9 1.5 Construction -2.1 Manuf.6 -11.4 0.8 7.5 5.3 -0.4 Construction 3. -3.1 0.3 -6.4 Tr.6 -6.3 -8.7 4.5 Mining 23.5 7.1 5.
4 Log Wage 6.346 5.724 5.7 Conditional Wage Distribution Percentile 10 25 50 75 90 1968 5.903 6.461 1985 5.7.653 5.416 5. Conditional Wage Distribution 1968 6.410 5.254 6.218 6.230 6. Table 4.251 6.2 6.660 5.8 6. Each value in this table has been created by predicting the wage for the corresponding quantile by means of quantile regression estimates for that quantile and mean values of the covariates for the whole sample.993 6.6 5.986 6.6 6.4.517 5.701 5.964 6.346 1975 5.367 6.414 5.536 2000 5.0 5.960 6.985 6.740 6.678 All values are predicted wages for each quantile using quantile regression estimates and average values of covariates.498 1990 5.057 6.460 5.8 5.421 1980 5.056 6.729 6.369 6.670 5.432 5.2 Conditional Wage Distributions 59 . We see such distributions for our data in Table 4.2 10 25 50 Percentile 75 90 1985 1995 2005 Figure 4.494 1995 5.756 5.241 6.379 5.129 6.676 2005 5.3 Conditional Wage Distribution Analysis of quantile regression estimates naturally leads us to conditional wage distributions.4 5.
66 0.90 1.77 0.07 0.8 using the dispersion measures we have become quite familiar with now.64 2005 1. 90-50 and 50-10 percentile differences from conditional and empirical log wage distributions.01 0. confirming the slowdown in wage inequality increase.62 0.82 0.05 0.53 0.01 C.02 1968 0.94 0.76 0.83 0.Conditional/Empirical Ratio 1975 1980 1985 1990 1995 0.75 0.56 0.62 0. thus it ignores the dispersion created by the differences between individual workers.2 shows how the conditional wage distribution changed over time.04 0.55 0.47 0.75 2000 1.43 0.81 90-10 90-50 50-10 Perc.26 0.73 0.Diff.59 0.03 -0.82 05-00 0.00 0.05 0.49 1968 1. Panel C shows the ratio of the dispersion from conditional 60 .81 0.75 0. that the slope change is smaller between 1995 and 2005.76 0.Diff.76 2005 0. Table 4.62 0.62 0.69 0. 90-10 90-50 50-10 1968 0.02 2000 0.72 0.77 A.51 0.05 0.77 0.Such an approach indirectly means that we create the conditional distribution for an "average" individual.85 0.19 0. It is quite obvious from panels A and B that the dispersion from the conditional distribution is smaller than the one directly from the data.80 0.62 2000 1. Panel C shows the percentage of empirical distribution measure that is explained by the conditional distribution one.00 1.02 0. 90-10 90-50 50-10 9 0-10.61 B. Figure 4.68 0. We notice. 90-10 90-50 50-10 Perc.64 75-68 -0.24 1.24 0.04 0.73 0.73 0.Dispersion from the Data 1975 1980 1985 1990 1995 1.53 0.03 0.Diff. Of course the main reason for this difference is the usage of average values of covariates in the calculation of conditional distribution.83 0.82 Changes in Dispersion(Conditional) 80-75 85-80 90-85 95-90 00-95 0.79 0.28 1.85 0.58 0.48 0.Dispersion from Conditional Distribution 1975 1980 1985 1990 1995 0.05 0.44 0.57 0.78 0.73 0.69 0.57 0.49 1. however.68 0.04 0.78 0. We see a comparison of conditional and empirical wage distributions in Table 4.08 0.78 0.02 0. The changing slope that marks increasing wage inequality is unmistakable.08 1.45 1.18 0.11 1.75 0.03 0.01 -0.77 0.10 0.79 2005 1.8 Comparison of Conditional and Empirical Wage Distributions Perc.54 0.
77 range. the story between 1975 and 1985 is told a bit differently here. From Table 4. This trend is captured in both conditional and empirical distributions.distribution to the dispersion from empirical data. and still keeps increasing at a low rate.72-0. Table 4. However. This was also mentioned in our analysis with JMP results. 90-10 measure change between 1975 and 1980 for conditional distribution is more than twice as much as the empirical one. The counterfactual distribution approach is certainly not limited to quantile regression analysis. this difference might be attributable to the changes in observable personal characteristics. It has 61 . More than half of the values are within the 0. The rest are attributable to personal characteristics. However. This kind of approach leads us to think that the effect of changes in personal characteristics were more important for the wage inequality increase between 1980 and 1985 than between 1975 and 1980. continues to rise in the 80s and 90s.7 we also see the overall wage inequality measure of 90th-10th percentile difference confirms that the wage inequality begins to increase from the second half of the seventies. The conditional distribution that we have just seen can be used to create another very nice tool to see the different dimensions of wage inequality.6 shows us that the conditional wage distribution keeps shifting over time. we notice that the inequality in the upper half of the distribution begins to increase faster than the dispersion in the lower half of the distribution. For the following years. conditional and empirical distributions more or less match each other in 90-10 measure. the same statistic for conditional distribution increase less than half as much as the empirical one from 1980 to 1985. It looks like we can assume that our conditional distribution explains about three fourths of the wage inequality. The balance between the lower and upper half wage inequality is more or less maintained until 1990. starting from 1995 data. On the other hand. If we remember how we set up the conditional distribution.
02 0.177 6.52 0.421 6. Now we compare the conditional distributions for different years assuming that personal characteristic and skill composition is what it was in 1968.546 2000 1.9 we see the counterfactual distributions created using 1968 personal characteristics of the workers and quantile regression coefficients for each year. Table 4.129 6.59 Conditional wage distributions are created from 1968 averages of covariates and corresponding year coefficient estimates.239 6.08 0.56 0.59 05-00 0.380 5. 62 .453 Dispersion 1975 1980 1985 1990 1995 0.59 Changes in Dispersion 80-75 85-80 90-85 95-90 00-95 0.56 0.549 2005 1.55 0.463 6.966 6. This way we take what we did in creating conditional distributions one step further.09 0.742 5. However. In other words we isolate the changes in prices of skills and personal characteristics (represented by the regression estimates) from the changes in demographic characteristics.346 1968 0.02 0.00 2000 5.00 0.415 5.58 0.98 1.94 0.895 6.58 0.9 Counterfactual Conditional Distributions-1968 Covariates Percentile 10 25 50 75 90 Percentile 90-10 90-50 50-10 Percentile 90-10 90-50 50-10 1968 5.04 0.14 0.368 5.01 2005 5.50 0.02 -0.17 0.05 1.767 5.001 6.259 6.05 0. Fortin and Lemieux (1997).08 1.972 6.669 5.05 0.05 -0.04 0.51 0.443 6.649 5. without any assumptions about the shape and properties of it.469 6.07 0.01 0.234 6. this is a tool that is best suited for use with quantile regression estimates since they represent the entire distribution.377 5.02 0.531 5.01 0.been used in some very influential studies of wage inequality (Dinardo.260 6.02 0.01 0.931 5.485 5.410 5. In Table 4.660 5.689 5.603 6.903 6.49 75-68 -0.198 6.04 0.47 0.17 0.89 0.44 0.222 6.46 0.02 Counterfactual Wage Distribution(1968 averages) 1975 1980 1985 1990 1995 5.42 0.969 5.309 5.001 5.669 5.
154 6. To see how the changes in skill prices (quantile regression coefficients) affect the wage inequality picture.010 50-10 0. This shows that using the 1968 characteristics does not really change the conditional wage distributions very much until 1985. the counterfactual distribution that uses 1968 quantile regression coefficients and corresponding year's average values of covariates is given in Table 4.005 0.443 0.We notice that while the change in dispersion generated by the counterfactual distribution (1968 averages) explains the change in dispersion in the original conditional wage distribution quite reliably until 1985.355 6.010 0.184 90 6.006 0.007 2005 5.898 5.383 6.678 5.903 5.10.018 0.544 Finally.405 5.991 0.469 0.356 6.012 0.651 5.399 5.519 0.914 5.523 0.033 0.191 6.012 0.493 0.660 5.008 2000 5.462 0.033 0.131 6.131 6.686 50 5.942 6.439 2005 1.001 0.009 0.935 6.504 0.194 6. Comparing the numbers here.895 5.474 0. 63 .005 0.007 0. let the covariates change and create counterfactual distributions for each year.936 75 6.529 Changes in Dispersion 80-75 85-80 90-85 95-90 00-95 75-68 90-10 0.997 1.019 0.496 0.392 5.129 6. its explanatory power decreases afterwards.936 0.391 5.537 05-00 0.018 90-50 0.164 6.981 0. this time we hold the skill prices constant.675 5.407 25 5.688 5.422 Dispersion 1975 1980 1985 1990 1995 1968 90-10 0.410 5.522 0.10 Counterfactual Conditional Distributions-1968 Coefficients Conditional Wage Distribution(1968 coefficients) Percentile 1968 1975 1980 1985 1990 1995 10 5.667 5.648 5. one can come to the conclusion that observable characteristics contribute to the change in wage distribution more after 1985.922 5.375 5.396 6.003 0.007 0.005 0.015 90-50 0.485 50-10 0.386 5.512 0.438 2000 1.016 0.346 6.457 0.969 0. Table 4.014 0.049 0.
6 5. Counterfactual Wage Distributions One can see a possible reason for these trends in Figure 4. where we have all three distributions for years 1985 and 2005.0 5.6 1968 Covariates Conditional 1968 1968 Coefficients Covariates 1968 Coefficients 6.4 5.4 5.6 5.8 6. The 1985 figure shows that the conditional 64 Log Wages .8 6.8 5.2 6.0 5.4 6.6 5.2 6.2 6.4 6.8 6.2 Log Wage Log Wage 10 10 25 25 Percentile 50 75 50 90 75 90 Percentile Conditional Wage Distributions-2005 Conditional 6. After that the difference starts falling.This time we notice that the last counterfactual distribution does a poor job in matching the changes in dispersion from the conditional log wage distribution.2 10 25 50 Percentile 75 90 1968 Covariates 1968 Coefficients Figure 4.8 5.2 5.4 6.6 6.6 6. However we see that the difference is most serious during the period 1975 and 1990.8 5.4 5. Conditional Distributions-1985 Conditional Distributions-1985 Conditional 6.3.3 Conditional vs.0 6.
4. even though their effect is still less than the skill prices. On the other hand. 50-10 in our case).4 JMP Decomposition and Quantile Regression The JMP decomposition is a very powerful and practical method which enables us to identify the magnitude and shape of the effect of unobserved skills on the increase or decrease of wage inequality. indicating that between 1968 and 1985. This is why it has been in use for the last 15 years despite the facts that many other methods of decomposition have been proposed during the same time period. we see that the change in all percentiles are in the direction of the change in OLS estimates 65 . since most of the data is centered on the mean and the tails are not strong.1 lets us make a comparison of JMP and quantile regression. However. when we come to 2005 we see that changing skill compositions now contribute to the changes in wage distribution as well. Table 7. When the distribution of wages is normal. we see the changes in 90-10 percentile difference that was drawn from conditional distributions in five-year periods. When we check the marginal effects of education and experience from Tables 4. The method itself depends on OLS estimates which represent the conditional mean. one can comfortably make use of JMP results. if the shape is different there might be problems. changes in skill prices were more serious than the change in skill composition. one has to be careful about the shape of the distribution of wage when commenting on JMP results. However. In Panel A. However. Since JMP decomposition is done for the changes in the difference of quantiles (90-10.distribution and the counterfactual distribution using 1968 covariates are more or less the same. Generally the 90s. as our analysis with the quantile regression estimates proved. In Panel B.2 for these periods. especially 1995-2000 period is when the two tables match the most in both magnitude and the weight of changes. the distribution using 1968 coefficients stands alone. 90-50. such a distribution could limit the meaning of decomposition.1 and 4. we have the total change value calculated by JMP decomposition for the same time periods.
11 Comparison of JMP and Quantile Regression A. However. Koenker & Bassett (1978) proved that quantile regression estimates perform better than the OLS ones for most non-normal distributions.JMP(90-10) 85-80 90-85 0.057 0.015 80-75 0.Quantile Regression (90-10) 80-75 85-80 90-85 95-90 0.018 0.011 05-00 0.067 0. 66 .031 -0. Marginal effects of having a high school degree .015 0.001 Changes in the values of 90th-10th percentiles o As we mentioned before. for the 1985-1990 difference.010 05-00 0.036 95-90 0.042 0. follow OLS in the upper half.096 0.075 0. they lack the practicality and functionality of the JMP method when looking at the shape and magnitude of within inequality in the big picture.016 0. For this reason.060 0.006 0.172 0. It is possible to get some within inequality measure for a narrowly defined group from quantile regression (our impact upon dispersion tables.050 0.022 0. However.047 0.083 0.031 0.044 0.054 0. but goes in the opposite direction in the lower.110 -0.029 0.027 00-95 0.038 90-10 90-50 50-10 75-68 -0.014 0.014 -0.039 0. It is certainly a very handy tool to see how the effects of different covariates change in different parts of the distribution.062 0.002 0.042 0.017 00-95 0. and of similar magnitude. Table 4.045 0.044 0. for example). we feel it is always a good idea to support any analysis of wage inequality with quantile regression estimates. we typically see contradicting patterns in the upper and lower halves.094 0.047 0. for example.016 90th-10th 90th-50th 50th-10th 75-68 0.042 0.with an exception or two. We have seen time and again in our tables that the OLS estimates sometimes miss important shifts in the tails.028 B.
We have also seen that the increase in difference between college and high school marginal effects slowed down after 2000. While the JMP decomposition gives us the advantage of comparing the relative magnitudes of between and within inequalities as well as an average picture of inequality representing the whole distribution. quantile regression enables us to see what happens at the tails and different parts of the wage distribution as well as created conditional distributions to compare different alternatives. 67 . Conditional wage distributions that have been created from the estimates of quantile regressions and average characteristics of workers in a given year are useful for comparison of effects of the changes in composition of observable characteristics and the effects of changes in the prices of observable characteristics. 50th. 75th and 90th quantiles reveal a more detailed picture of the inequality in the US. probably due to the fact that there are more high school graduates in the lower quantiles and less in the upper quantiles compared to earlier years. Also the earlier findings from JMP decomposition about skill prices affecting wage inequality more than the observable skills has been confirmed by our counterfactual distributions. 25th. they let us see the effects of experience and education on wages in a more informative way. the closer the estimates of both methods. One such distribution showed that observable characteristics affected wage distribution more after 1985 than it did before 1985.4.5 Concluding Remarks Quantile regressions on our Mincerian wage equation for 10th. The more normal the wage distribution. Quantile regression and the JMP decomposition have their own advantages compared to the other. It appears that college degree is the main educational component that gives way to increased wage inequality. Since the quantile regressions give a better picture of how the effects of covariates change on different parts of the distribution. The returns to high school seem to be higher in the lower quantiles than upper quantiles.
then one has to support JMP decomposition with quantile regression estimates to avoid serious mistakes. 68 .However if the wage distribution is not normal.
Continental countries have non-employment benefits and strong unions. high dispersion of wages and relatively higher rate of low-pay employment. Anglo-Saxon countries are characterized by weak labor unions. In this chapter we examine the data for a number of European countries to see how their pathc compares to that of the US in more recent years. Netherlands from the second. The choice of each country among the groups they belong has been made according to the availability of data we need to apply our analysis. In addition to these four social models. from which we have Hungary. The Mediterranean countries display a highly compressed wage structure due to collective bargaining in the formal sector. 69 . and Spain from the last group. We give the details below.CHAPTER 5 A CROSS-COUNTRY ANALYSIS OF WAGE INEQUALITY USING LUXEMBOURG INCOME STUDY The experience of European countries with wage inequality has been different from that of the US with different degrees. Sapir (2006) states that European countries can be classified into four social models: Nordic Countries represent the high levels of government intervention and strong labor unions that compress wages. one could possibly mention the East European countries. Germany from the third. Our group of countries in this study includes Sweden from the first. It is generally agreed that none of the European countries with the exception of the United Kingdom had the sharp increase in wage inequality that appeared in the US during the 80s. Some even recorded decreases (Gottschalk & Smeeding (1997)).
Participation into the project requires submission of microdata from the participating organization's country that is applicable to LIS system. Although LIS database reports a single wage and salary income value in the domestic currency for each country. Held in Luxembourg. A German person's gross wage and salary income value 3 I had the chance to participate LIS Summer Workshop 2006. Calculation of wage and salary income at person level is a good example of this procedure. 70 .5. Mexico. however the process might be a bit slow sometimes due to the nature of the remote access relationship. it is mainly funded by research and statistics organizations of 30 member countries which are spread over four continents. Canada. Except for some extreme cases. receiving the output via e-mail from this system in turn. Standardization corresponds to simply recoding the country information into some standard file structure. During my time in Luxembourg I had the opportunity to learn about the applications of LIS data as well as how to access it. The users send short programs written for a selection of software packages like Stata. Many income variables are this way. Poverty and Policy Studies (CEPS). these values come from different questions in each country's domestic household or similar survey. Taiwan.3 The LIS database has been prepared with a certain objective in mind: Creating a data structure that will enable researchers to make international comparisons on household. the member roster also includes countries like the USA. not even during the Workshop. Although mainly European. except for those that are not able to supply such funds (non-contributing countries). the organization does not let the users do hands-on work with microdata. It is a well organized and functioning system.1 Data Set LIS is a research project that started in 1983 under the joint sponsorship of the government of Luxembourg and by the government of Luxembourg and the Centre for Population. The data is collected from household surveys of the member countries and "lissified" to fit into a certain standard data structure which includes "standardized" or "harmonized variables". as well as yearly contribution is made by most members. SAS and SPSS and send them to a system called LISSY via e-mail. person(adult) and child level. A non-profit project. this is a a yearly gathering of academicians to work on the LIS data and exchange ideas on their own projects. Australia and Israel.
the LIS data sets have their own problems and shortcomings. keep the country-specific data without recoding it. One has to make decisions to create comparable data sets. which have to be taken into account for an analysis based on them to be meaningful: a) First of all.is calculated by adding up eight different items for 1989 data. an Italian person's wage and salary income is reported in a single variable from the original data set. and in some cases the industry data. the eduational level variable (PEDUC) exists in every person data set. LIS collects its data in "waves" of five-year intervals. The wave VI. and it changes by time and by country. Each wave centers around a certain year and the member countries submit their data from a survey done in a year as close to the center year as possible. is obviously not correct. it is impossible to perfectly fit the data of every country in a pre-set data structure. 1995 and 2000. which corresponds to the years centered around 2005. or does not involve some industries. On the other hand. standardized. is currently under "lissification". provided that the member country supplied any information on educational level. on the other hand. Even though it is a very impressive attempt at creating reliable international microdata. the structure of this variable is totally country-specific. So far there have been 5 waves: years centered around 1980. 1985. 1990. and each decision made as such increases the number of footnotes. or simply not available. For example. Some countries report more than one year around (or including) the reference year. Although we have been very careful in our choice of the countries to reflect as much reliable and 71 . However. Inevitably the data coming from some countries is much more detailed and better classified than the others (US is the obvious example) and it is very hard to avoid overstretching of data to create something that compares to it from a country which simply does not report much. In some cases wage and salary income values are not reported. Harmonized variables.
sometimes it is obvious that the industry data is not reliable. simply because some questions were not asked in the original survey. number of weeks worked and industry. While some countries use standard 2. While some countries have data only for the last two waves. this is mostly a problem with education data. while it is quite hard to find reliable data older than 1990 for ex-communist countries of Eastern Europe.822 out of 18. d) The data structure of one country sometimes changes over time. e) Reliability of data in the harmonized variables is a problem that is also hard to deal with. one still has to keep in mind that this is second-hand. Among the variables that are of interest to us. others do their own classification. there are holes in most data sets. The most noteworthy example of this problem is the variable representing a person's industry. or it is not possible to generate the needed information from other survey questions. 72 . due to re-design or different survey. making it hard to work with them. f) Some of the critical labor market variables are not well documented in some countries' data sets. recoded data. The German data goes as far back as 1973. For example 1995 data for Spain can link only 1. c) Especially for the earlier waves.and 3-digit coding. b) Although there are 5 waves.643 persons to an industry in a standard 3-digit set. In some cases the numbers reported just do not make much sense. the availability of data changes from country to country. making it impossible to convert it into the standard system. Also. making it hard to do within-country comparisons. The most notable examples are data on educational attainment. some others have older information than the first wave.comparable information as possible. grouping industries in different ways.
1994. No person weights given for 1991. gives all the necessary information one needs to know before using the data and gives links to each country's own survey agency. The data on Sweden is drawn from Income Distribution Survey (HINK). 5 For 1983(Wave 2) it is not possible to identify full-year workers. Sweden6. The Dutch data comes from Additional Enquiry on the Use of (Public) Services (AVO1983). 1999) and Netherlands European Community Household Panel (NL ECHP) (1994. one should not fail to see the enormous amount of data and research possibilities that comes with such a data set. the best being waves IV and V. LIS has a remarkable amount of information for every country's data sets which warn the user against obvious problems and let one see the details of the variable in question.org. not gross.lisproject. Family Expenditure Survey(1990) and Spanish 4 The German data is well documented and very detailed for waves 2 to 5. This choice is as much by availability of data as by representation. so we did not use it. 8 Net wages are reported. it is a remarkable opportunity to compare coutries and test theories on a wide range of variables and official data sets from 30 separate countries. Full-year status cannot be identified for 1990. In our study. www. Full-year status cannot be identified for any year. 6 Industry data is not reliable. 73 . Spain7 and Hungary8. Socio-Economic Panel (SEP-1991. As we can see. 1994 and 1999 data is a combination of two surveys. Wave I data does not give any information on full-time or full-year status. Also government employees are not identified for Waves 2 and 3. it would be unfair not to note that the usability of data sets improve remarkably over time.Although it is important to be careful about the problems that we have listed above. German data comes from German Social Economic Panel Study (GSOEP). Netherlands5. The web site. Industry data is either unavailable or unusable for all years we use. we use the person level data files for five countries: Germany4. Also. Once we look beyond the footnotes. 7 Wages are reported as net. 1999).
Numbers in parenthesis are sizes of our wage-earner samples for that country and year. However. Table 5.1. aged 16-64 years. Germany actually has longer historical data than indicated here. full-time employed wage and salary workers for whom there is enough data on education to create LIS' standard low.European Community Household Panel (ES ECHP-1995.1 shows the years in which data as we want is available for our selected countries and their respective waves. We have limited our sample to male. The sample is limited further to include only full-year workers whenever it is available for all years of a country. we were unable to use them because they failed to supply some of the essential criteria for 74 . 2000) are sources for Spanish data. Table 5. Waves and Sample Size I(1980) Germany Netherlands Sweden Spain Hungary Waves II(1985) III(1990) IV(1995) V(2000) 1984 1989 1994 2000 (2790) (2417) (2811) (8936) 1983 (2044) 1991 (1876) 1994 (2266) 1999 (4552) 1992 (4929) 1995 (4840) 2000 (14054) 1990 (11214) 1995 (2460) 2000 (4458) 1991 (794) 1994 (651) 1999 (1080) Reported years are each country's contribution to the related Wave. The Hungarian data is from Household Monitor Survey. medium and high education dummies about which we give information later. The German data goes as far back as 1973.
one has to keep in mind that PPPs are actually designed to compare GDPs. all monetary variables are reported in domestic currency unit.constructing our sample. as we explained above. We notice that the sample size grows for all countries as we go higher on waves. The numbers in parentheses are the sizes of wage-earner samples. all wages are converted into the year 2000 value of their local currency. Thus. We used the GDP deflators from the IMF website for this conversion. Similarly. On the other hand. using the Purchasing Power Parity (PPP) conversion rates. Then these values are converted into US dollars. making the comparison a bit biased. It is obviously not an ideal situation when you want to make comparisons between countries. First of all. PPPs also reflect some information that is not related to it. it is an average concept. probably an indication of improvement of domestic survey. Out of the five 75 . PPP conversions are useful in the sense that they let us compare income values across the countries in a way that does not overlook the differences between countries in terms of the level of prices. Also. PPP is calculated for an aggregate bundle of goods consumed in the country. The choice of this threshold is because of the need to make it comparable to the American data ($67 per week in year 1982 values). using the GDP deflator. the Dutch sample for 1983 is from a different survey from the rest. Thus it is important to convert the monetary values into a unit of exchange that can be compared. Thus. In LIS data sets. the actual consumption bundles might change depending on where you are at the income distribution. The considerable drop in the size of Spanish sample from 1990 to 1995 is due to the fact that they are from different surveys. A number of issues involving differences in purchasing power and inflation make such comparisons unreliable. although they certainly indicate a good deal about personal income. not personal income. Although it is very convenient to have as a tool. It is generally believed that in most countries personal income is about 2/3 of the GDP. Our sample includes individuals who earn a gross amount of $110 per week in PPPconverted year 2000 values.
The first important diference between educational systems is that the timing of their primary. Netherlands and Sweden) or net ( for Spain and Hungary). calculated by dividing yearly wages by 52. Our unit of analysis is the weekly wage. since it is an harmonized variable. Netherlands and Sweden. While some countries tend toward a more general approach in their pre-college education. we chose not to use it since we do not have this information on regular basis for other countries. This is well reflected in the educational attainment variable of LIS data set. for example Germany has a very detailed mixture of general and vocational training. Since we will be looking into changes of wage inequality. whether gross (for Germany. the educational path shows big differences among different countries. we used the person weights (except in a case or two where no weights are available). The usual way of doing this in the literature is by dividing the yearly wages by the actual weeks worked during the year. LIS supplies a Stata "do" file which breaks the sample into three educational categories to be used for every country in our list except Hungary. secondary and tertiary education do not usually match. Yearly wages. We 76 .33 times the topcoded value. However these values have only theoretical meaning. Netherlands. and net wage and salary income for two (Spain and Hungary). since no one in our wage-earner sample seems to be below $110 in either country. even though we had information on weeks worked in a year for all years from Germany. reporting of educational attainment is very important. Sweden and Spain have this info for some years and they do not have it for others. Thankfully. we have gross wage and salary income for three Germany. However.5% taxation in 1995)) and over instead of $110. Hungary simply does not supply it for any year. were imputed at their reported maximum by 1. Using data from Eurostat on implicit tax rates in countries. Using the German data on weeks worked would simply overvalue their weekly wage and salary earnings compared to others. This is one of the more problematic areas since countries display huge differences in their educational systems. Also. For each sample statistic and calculation we report here.countries that we include here. we decided to limit the Spanish wages to $78 (29% taxation in 1995) and the Hungarian wages to $63 (42.
8 51.0 53.3 22. taking into account the advice given by the LIS staff in their web page.6 28.0 67.5 51. The numbers are given in percentage values. So.0 22.7 49. In Germany we see that the low education group gets smaller with each wave.3 26.5 16. Education of the Wage-Earner Sample Waves Low Germany Medium High Low Netherlands Medium High Low Sweden Medium High Low Spain Medium High Low Hungary Medium High II 13. we characterize the educational attainment with three groups for each of the five countries: the low education group includes primary and lower secondary level of education as well as initial vocational training. The medium level education group includes high-school and pre-tertiary level general education and vocational training.5 15.1 16.5 30.8 III 12.5 27. we report the distribution of our samples in each country into these three categories.0 18.had to create our own dummies for the Hungarian data.5 17.6 56.1 56.6 65.9 68.0 47.9 23.4 30.0 60.9 34.6 V 8. High education groupincludes university level and beyond general and specialized vocational education. While the medium education level group grows in waves III and IV compared to wave II.8 18.8 58.6 13.1 15.8 40.6 13. Table 5.1 27.4 22.3 30.7 21.0 29.3 32.0 All values reported here are percentages of the wage-earner sample. somewhat indicating the supply of different skill levels.0 60.4 25.4 29.2. In Table 5.3 7.0 65.4 16.4 IV 10.2. it returns to more or less the same level as 77 Countries .8 47.5 47.0 17.7 58.
in return. This. Medium level education also increases at a more modest rate. or it is not desirable to go to college. which demonstrates a sharp decline in low level education and a matching sharp increase in the high level category. brings up questions of returns to high level of education which is of very much interest to us. One has to be very careful making comments on such variables in a country which recently witnessed drastic changes to its social and economic structure. college education has become rather popular in Netherlands. Sweden and Spain show similar patterns to Germany in the disappearance of low education category. The different story here is Netherlands. We will see if there is an element of wage inequality anywhere in this story. The German educational system gives a large array of occupational training opportunities at pre-tertiary level. It decreases by a third in Sweden and by a fifth in Spain between waves III and V. who uses a similar educational system) in the weight of medium level of education. Germany stands out (together with Hungary.wave II. there are no major changes except the increase in high level education between the 3rd and 4th waves. In Hungary. It is very likely that part of the increase in medium education category is related to the increase in high level education. However. This is probably the main reason behind the high weight of medium level of education in the German sample. As a matter of fact. with high level of education either remaining the same or decreasing slightly. It is the high level education group that grows up. leading to a career. which is obviously the final target. either the system does not create enough opportunities to go to college for these newly educated people. Evidently. from 31 percent in wave II to 35 percent in wave V. In both cases it is the medium level of education category that grows noticeably. The share of low level education actually seems to increase in the last wave. This probably means that although the overall level of education gets better over the years. 78 . we have to keep in mind that we are talking about a span of 16 years here.
6 0.1.2 0.0 1.2 Wage Inequality 1. 90th-50th and 50-10th percentiles of log wage distribution for five countries.3 0.6 0.0 II III Wave IV V 1.5 Difference 1.6 0.3 0.2 0. Time periods are given in waves as it was explained in Table 5. Figure 5.9 0.6 0.3 0.8 1.0 II III Wave IV V 1.8 1.d Wage Inequality-Spain 90-10 90-50 50-10 1.b Wage Inequality-Netherlands 90-10 90-50 50-10 1.a Wage Inequality-Germany 90-10 90-50 50-10 1.5 Difference 1.5 Difference Difference 1.9 0.8 1.8 1.9 0.8 1.0 III IV Wave V III IV Wave V 1.5 Difference 1.9 0.3 0.0 III IV Wave V Differences between 90th-10th.2 0.9 0.3 0.e Wage Inequality-Hungary 90-10 90-50 50-10 1.6 0.1 Wage Inequality 79 .c Wage Inequality-Sweden 90-10 90-50 50-10 1.5 1.5.2 0.2 0.
00) in wave V. However. its share reduces to 51% (0. we see that the tide turns again in wave V and upper half wage inequality picks up pace when lower half wage inequality 80 . that is the in the first half of the 90s. one of the more popular methods of inspecting changes in wage inequality is by looking at the difference between different quantiles of the log wage distribution.89). We notice serious changes in its composition as well.48). The change comes mainly from the lower half. The overall wage inequality keeps increasing in Netherlands at a very slow pace throughout the years from wave II to V (0. while the slight increase in overall wage inequality in Netherlands between waves II and V is mainly fueled by a relatively faster increase in lower of the wage distribution (and a decrease in upper-half wage inequality). while the upper level inequality remained level (it actually decreased a tiny bit). but the upper level inequality seems to increase slightly as well.36 to 0.54/0. and 50th-10th percentile difference to see the change in the lower half. Then it changes direction between the third and the fourth waves. it remains more or less the same between waves IV and V. While the upper level wage inequality (90-50) makes about 60% of the overall change in wave II (0. 90th-50th percentile difference to see the change in the upper half of the wage distribution. The 90th-50th percentile gap is wider than the 50th-10th percentile gap.a we see that Germany records a decline in overall wage inequality in the second half of the 80s. we notice that this time the change is mainly fueled by an increase in inequality at the lower half of the log wage distribution. we see that the 50-10 difference increased by 20 percent compared to a 10 percent in 90-50 difference. Also it kept increasing after wave III.00). On the other hand. In Figure 5. In short. wage inequality in the lower half of the wage distribution seems to increase between wages II and IV (50-10 difference increase from 0. Looking at the general picture.89 to 1.As we mentioned earlier.49/0. then picks up a bit to 53% (0.97) by wave IV.1. As the German overall inequality keeps increasing slightly in the second half of the nineties. We limit our analysis here to three such measures: 90th-10th percentile difference to see the overall change.53/1.
we see an increase in wage overall wage inequality from wave III to IV in all countries except Sweden. Compared to this. it jumps up to 0. It is also remarkable that while the increase in overall inequality from wave III to wave IV came from upper half inequality. However we note that lower half inequality. However.37 in wave IV. Sweden shows sharp increases in overall and lower level inequality. less than the upper half inequality in wave III. kept increasing and caught it in wave V. While the 50-10 difference is 0. the 16% increase in upper half wage inequality seems quite modest. This is another example of how much one misses just looking at the overall wage inequality values. In short. a 90% increase. Although upper half wage inequality also increases. there is a slight change in the composition of it. then decreases back to its level at the beginning. its composition changes quite a bit. the dispersion of wages remained more or less the same in the second half of 90s. lower half inequality in Wave V being less than its value in wave III and upper half inequality being higher. Although Hungary also saw an increase in overall wage inequality between waves III and IV (first half of 90s). Swedish data has a different story to tell. This is actually why the composition changed. although it is still higher than what we called "increase in wage inequality" in Netherlands. upper half and lower half inequality. In all countries except Spain. The internal dynamics of wage inequality seem to have worked differently for Hungary than they did for Spain. Their wage inequality figures remain stable during the first half of the90s. lower half 81 . Between waves III and V. and then another increase from wave IV to wave V in Germany.70 in wave V. In the second half of the 90s.remains the same. the decrease in overall inequality that brought it back to its level in wave III was fueled mostly by a decrease in lower half inequality. it is the remarkable change in the lower half wage inequality that catches our attention. overall wage inequality in Spain first increases. And this is true for overall. Netherlands and Sweden.
Figure 5. Second. this indicates an increase in wage inequality due to education. Finally. this signifies a reduction in wage inequality by education. Of course these are the change in log real wages. if the high education group gains ground. although to a lesser extent. The high education group always earns more than the medium education group in absolute terms. we see two important points.2 pictures the change in log wages between the beginning and end of the time period we analyze (waves II to V for Germany and Netherlands and waves III to V for other countries) for different percentiles of the wage distribution. Similarly. if the medium education group gains ground compared to high education group. we give a picture of how observable characteristics like education and experience affected wage inequality in the countries included in our study. as we explained before.3 Education and Experience In figures 2 and 3. When we compare this picture with the US data. marking increasing dispersion at the lower half of the wage distribution. 82 . all countries in our analysis with the exception of Spain report that there is considerable increase in the lower half of the wage distribution. while the American wage inequality increase came mostly from the upper half during the nineties. We will try to see if there is any information in these pictures to connect to an increase or decrease of wage inequality in these countries. Thus. most of these countries saw increased wage inequality just like the US during 80s and 90s. 5. in other words.inequality becomes more important relative to upper half inequality by wave V. First. an increase in within group inequality. a positive slope in these figures means that higher-percentile earners within the same educational group earn less than the lower-percentile ones.
15 0.02 0 -0.Netherlands 1983-2000 Medium High Log Wage Change Log Wage Change 10 25 50 75 90 10 25 50 75 90 Percentile Percentile C. Two separate line for medium and high education groups.1 -0.4 -0.Germany 1984-2000 Medium 0.1 -0.15 High 0.2.2 0.A.3 0.05 0 -0.Spain 1990-2000 Medium High 10 25 50 75 90 25 50 Percentile 75 90 E.Hungary 1991-1999 Medium Log Wage Change 0 -0.Sweden 1992-2000 Medium 0.04 0.2 0.1 0 10 Percentile Log Wage Change D.05 -0. Change in Log Wages by Education 83 .1 0 -0.2 -0.4 -0.3 -0.08 B.06 -0.4 0.02 -0.2 -0.5 Percentile 10 25 50 75 90 High Changes are calculated as the difference between the same percentile of two years.1 0. Figure 5.1 -0.5 Lowg Wage Change High 0.3 -0.5 0.04 -0.
where they are very close.It appears that everyone below median of wage distribution earn less in wave V (year 2000) than they did in wave II (1984) in real terms. In Hungary everyone lost in real terms. Both the medium and high level groups of education lose in real terms from wave II(1983) to wave V(2000) in Netherlands with a few exceptions. Probably we could even go one step further and say that probably the increase in within inequality is more serious in the high education group. In Spain we notice that both groups got better in real terms between waves III and V. The medium education group gained ground compared to the high education group after 25th percentile. and more than the high experience group did. since high education group gained more than low education group in every point except the 25th percentile. Sweden is similar to Germany in the sense that lower half of the wage distribution lost in real terms (in the medium education group. as well. In Germany the low experience group earned more wages for all percentiles. There is increased within inequality in the medium education group. losing less than them. Lower 84 . However. more than the lower half). especially stronger in the tails. and there is evidence of increase in within inequality. We see the same picture for low experience (10 years or less potential labor market experience) and high experience (20 to 30 years of potential labor market experience) groups in Figure 5. it is different in the sense that there is also an indication of higher between inequality. There is also strong evidence of increased between inequality. There is no clear indication of change in within inequality here. there is obviously an increase in within inequality. Perhaps we could conclude that the higher education group is better in the tails and medium education group is better towards the center of the distribution compared to each other. However.3. We do not get a clear indication of change in "between" inequality. One could probably go ahead and say that there is some evidence of more within inequality in the high education group.
Sweden gives more or less the same story. For no-certificate recodes. we attained highest possible number of years of completed education without getting the next certificate. which is only significant at 89% confidence. a dummy if the person is married. since the years of education that we use to create the experience value might not reflect fully the diversity of German education system. It looks like the lower tail of the low experience group lost ground considerably against the upper tail.percentiles of the higher experience group actually lost in real terms. It is not surprising to get this kind of result for Germany. since their reliability is questionable in several cases. The coefficients are soundly significant for all years for all variables except experience for Germany in 1989 (wave III). 9 Calculated as: Age . as we explained earlier. depending on the educational system. but have very different levels of work experience depending on the level of occupational training. High experience group recorded higher wage increases in all percentiles. and educational dummies we mentioned before. We see a similar picture in Hungary in that lower experience group gaining ground compared to higher experience one. but in Hungary only the upper half of the wage distribution of the lower experience group gained higher real wages. we have a different picture than the others. which could create two individuals with the same number of years in training. It has to be noted that the years of education is generally a recoded value in LIS data. It looks like there should be less within inequality for the low experience group in wave V compared to wave II. with the exception that everyone lost in real wages. 85 . We have the opposite picture with Netherlands. To be able to see how education and experience affect the wages. for medium and high level of education (with low level excluded).6. We imputed values to different levels of educational attainment for each country. The regression did not include industrial dummies. we ran a Mincerian wage regression of log weekly wages on a quadratic of potential labor market experience9. Everyone else get lower wages in wave V compared to what they earned in wave III. There is also an evidence of within inequality in the lower experience group.years of education. For Spain.
Log Wage Change 1 0.2 0 10 D. Change in Log Wages by Experience 86 .6 -0. High Exp.8 0.Netherlands 1983-2000 Low Exp.Germany 1984-2000 Low Exp.2 0 -0.Sweden 1992-2000 Low Exp.2 0 -0.2 -0.6 Percentile 10 25 50 75 90 High Exp. 0.6 0.A. 0. High Exp.2 1 2 3 Percentile 4 5 High Exp.4 0.4 Log Wage Change 0.8 Percentile 10 25 50 75 90 High Exp.2 Log Wage Change 0 -0. 25 50 Percentile 75 90 E. 10 25 50 75 90 Percentile C.6 Log Wage Change Log Wage Change 0.Hungary 1991-1999 Low Exp.3.4 -0.2 -0. 0.4 0.4 -0. Figure 5.4 -0.2 -0.4 0.6 -0.Spain 1990-2000 Low Exp.8 B. 0.2 0 -0.
3 38.2 60.5 73. we see the regression estimates for education dummies.7 12.2 50. while the movement ends at the fourth wave in Netherlands.2 72. On 87 .5 107. All values are multiplied by 100.3 Effect of Education Wave II III IV V II III IV V III IV V III IV V III IV V Medium Level High Level 21. it drops drastically in Spain.1 41.4 73. Spain and Sweden do not observe much change in the first half of the nineties.3 20.7 40.1 32.0 Germany Netherlands Sweden Spain Hungary In Germany and Netherlands.4 12.3 10.6 64. Table 5. except the big fall in Netherlands from wave II to wave III.2 14.. There is not much change in the college premium in Germany.In Table 5.6 34.4 59. While medium level (high school) education becomes a bit more favorable in Sweden.7 12.1 37. but they go into different directions in the second half.0 24. Hungary sees a sharp increase in the first half of nineties and a following sharp decline in the second half.8 73.3 52.5 24.4 21.0 34.1 74.7 20. we see that the effect of having medium level of education decreases during the second half of the 80s and the first half of the nineties. We can observe the effect of having medium and high levels of education on wages here.5 19. It keeps falling towards the last wave in Germany.3.6 87.5 39.4 48. the Netherlands and Sweden for any year.7 53.
The other countries do not display considerable changes in this context.In the case of Germany we see that college education becomes more favorable fast.32 2.70 1. Spain. albeit slower.67 0.64 -0.49 2. namely 5. 15 and 25 years of experience.37 0.56 9.24 4.4. means an advantage over having a high school equivalent degree in all countries here.03 0.61 1. Also.44 0.55 1.69 4. we observe that having a college degree.50 -0. We will make this point clearer in out discussion about quantile regression. All values have been multiplied by 100.40 1.86 2.79 3.02 -0.40 0.19 0.47 6.96 10.84 12.73 1.39 0.43 0.12 -0.08 0. Table 5.27 2.13 6.the other hand Spain and Hungary see sharp increases in the first half of nineties followed by sharp decreases to original levels in the second half.62 0.22 2.71 5. Since these coefficients are from an ordinary least squares regression. Since experience enters the regression as a quadratic. we evaluate its marginal effect for a few values of it. the moves in high school premium are matched by parallel moves in college premium in all countries except Germany.65 6.00 3.25 0.02 5. one has to keep in mind that they do not say anything about the distribution of wages. For one thing.4 Effect of Experience Wave II III IV V II III IV V III IV V III IV V III IV V 5 years 10. as it should be expected.97 3. The marginal effects at these points are reported in Table 5.45 0.90 2.70 25 years -0.17 Germany Netherlands Sweden Spain Hungary 88 .53 7.84 0. shows a similar pattern.67 0.15 2.75 8.46 15 years 1.89 0.37 1.75 0.
Sweden and Hungary all see falling values for 15 years of experience.a-e report the results we got from applying this method on our selected countries. we notice that Germany. it is only for 5 years of experience we can identify increasing returns to one additional year of experience (Germany and Sweden). changes due to the change in prices of the known characteristics(premia of experience and education) and thirdly. T stands for total change. For the 25 years of experience. the values for Sweden and Spain keep decreasing. 5. In Netherlands. For Spain and Hungary we cannot identify a clear trend. What catches out attention in the experience picture is that while we see different trends for different countries. For higher years of experience (15 and 25 years) we either find that either it is less favorable or more or less the same. Tables 5. the method decomposes the differences between two wage distributions into 3 parts: changes due to the change in quantities of known characteristics (in our case education and experience mostly).In Germany we see that. after an initial fall. Q for changes in 89 .5. 5 years experience group gets better until the last wave when they see a fall. This could be considered as an indication of higher returns to "unobserved" skills such as the ones related to computer usage.3. In Sweden they see improvement from wave III to wave V. As we explained before. that we talked about before. As we explained in Chapter 3.4 JMP Decomposition One good way of analyzing these changes in wage inequality is the decomposition method proposed by JMP (1993). The values have been calculated from the same regression we mentioned in section 5. while the others are not clear about the direction. the changes in unobserved prices and quantities. For Netherlands and Spain there is no big change in this group. low experience groups begin to recover again. When we look into the returns to higher experience.
04 Q 0.00 P 0.observed skills and characteristics.01 0.5.01 0.00 0.03 Q 0.05 0.01 0.06 U 0. It seems that in Germany.01 0.08 - - - - Q 0. changes in unexplained characteristics constitute a very large portion of wage inequality for almost all layers. In Germany. upper half and lower half).02 P 0.02 Q 0.02 0.04 0.a JMP Decomposition-Germany Percentile Difference 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th - Wave III-II IV-III V-IV - V-II T 0.02 0.01 0.03 0. This might be due to the compressed nature of the wages in Germany 90 .02 0.01 0.07 T 0.04 0.02 0. we see some sizable changes in overall wage inequality (wave IVIII for example).16 0. the between inequality is fueled by changing skill composition in the lower half of the wage distribution.05 0.01 P 0.02 0.03 - U 0.06 0. Then we se increases in 90-10 difference for the following waves.00 0.02 0. but its main source is the changing skill prices in the upper half. P for changes in the prices of observed skills and characteristics.12 0.10 T 0.00 0.01 U 0.04 - - P 0.11 0.04 0.07 Our model estimates a fall in wage inequality from wave II to wave III in all 3 comparisons (overall.05 0.a. and U stands for the changes in the prices and characteristics.13 0.07 0.05 T 0. As it was in the US. it seems to be the increase in the dispersion in lower half that makes up for most of the increase or decrease in wage inequality. revealing within inequality.03 0. However. Table 5.03 0. There is also an increase in wage inequality when we compare wave V with wave II. These estimates are all in harmony with Figure 1.02 U 0.02 0.02 0.
00 U 0.00 0.00 0.07 0.02 0.01 0.04 0.10 T 0. does not show big changes in overall wage inequality from wave to wave.01 Q 0. Table 5.00 T 0.01 P 0. one cannot help but be shocked about the remarkable change in Sweden in a five-year span (wave IV to 91 .02 0.01 0.01 0.5.00 0.03 0.01 0. As we noticed in Figure 1.02 0.00 U 0. however there is continuing increase which leads to a higher value between waves II and V.02 T 0.04 0.b JMP Decomposition-Netherlands Percentile Difference 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th - Wave III-II IV-III - V-IV - V-II - T 0. However. it will be good to be careful about making overrreaching remarks about them.a.01 0.04 0.04 0.07 - - - P 0. The bulk of wage inequality increase switches from lower half to upper half with each new wave.12 0. There is a remarkable change in composition here.00 Q 0.01 - U 0.01 0.02 0.06 U 0.06 Q 0.resulting from institutional structure (Prasad (2004).02 0.02 0.01 P 0.02 0.08 0.01 0.04 0.12 - - - - Q 0.03 P 0.01 0. Since we have only 3 waves for the remaining countries. In this sense we can say that the Dutch experience is a bit similar to that of the US.02 0.01 0. as we saw in Figure 2. The unexplained part is important in Netherlands as well.01 0.08 0. even if it is not as strong as Germany.04 The Dutch data . the upper half wage inequality ( represented by 90-50 percentile difference) shows either no change or little increase.02 0.
09 0.02 Q 0.03 0.5.00 0.12 - U 0. Unexplained skills and their prices constitute about half of the change.01 0.01 0.24 0.02 0.08 0.04 92 .33 T 0.00 P 0.03 0.09 P 0.c JMP Decomposition-Sweden Percentile Wave Difference 90th-10th IV90th-50th III 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th V-IV V-III T 0.41 0.33 - - Q 0.00 0.01 T 0.01 - P 0.01 0.08 0.02 0.05 0.10 - - Q 0.41 0.00 0.14 0.5.12 0.07 0.00 0. or 1995-2000).06 0. comes mainly from a jump in the increased dispersion of lower half of the log wage distribution.11 T 0.00 0.03 U 0.04 P 0.d JMP Decomposition-Spain Percentile Difference 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th - Wave IV-III V-IV V-III T 0.14 Q 0.06 0. The change.00 T 0.18 U 0.06 Q 0.00 0.00 0.00 0.02 0.wave V.15 0.03 0.06 0.02 U 0.18 Table 5.14 0.15 0.27 0.03 0.11 0.04 0.02 0. as we observed before.02 P 0.01 0.08 0.05 0.01 0.01 0.03 U 0. Table 5.09 0.04 Q 0.12 0.12 - P 0.05 U 0.03 0.10 0.07 0.
19 - - - Q 0.01 0. always pulling the overall inequality value up. perhaps also revealing the side effects of fast economic transition.01 P 0.12 0.01 0.09 0.09 T 0. We notice a remarkable increase in wage inequality in the lower half of wage distribution.03 P 0.05 0.02 Q 0.04 0.02 - - P 0.00 0.11 T 0. while the upper half fluctuates.e JMP Decomposition-Hungary Percentile Difference 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th 90th-10th 90th-50th 50th-10th - Wave IV-III V-IV V-III T 0.02 0.d indicates that the unexplained part is important for Spain as well.5.21 0.Table 5. Table 5.09 0.15 0.01 0. making it look like the overall wage inequality did not change between waves III and V. and these two moves balance each other.02 0.08 U 0.5 Concluding Remarks Our analysis of the distribution of log wages in five countries of Europe for the last couple decades with the help of JMP decomposition revealed that even though there 93 .13 5. Table 5. The effect of the unexplained part is very strong in Hungary.5.21 0.04 Q 0. In Spain.03 0.03 0.13 0.05 0.5.19 0.e shows the Hungarian JMP values.13 0.00 0.09 0. the upper half of the wage distribution witnesses an increase in wage inequality.01 0.11 0. while the lower half sees a decrease. We see that the unexplained portion explains a bit less than half of the change in overall inequality between waves III and V.06 U 0.04 U 0.
There are also different directions of movement in education and experience comparisons. One thing that catches attention is the increase in lower end inequality. but not in other countries. Although the returns to education and experience explain some part of the change in wage inequality. Similarly. People with high education gained more than the ones with medium education in Sweden and Spain. which contradicts with the increase in higher end inequality in the US during the 90s. 94 . the gap between high experience and low experience people opened up clearly in Netherlands and Sweden only. We observe this change in all five countries when we compare the beginning wave and the end wave. The main suspect seems to be the institutional differences between the European countries and the US such as the strength of unions and labor market regulations. This points to the existence of strong within inequality just like it was in the US.are some increases in wage inequality overall in all countries. in other words by unobserved characteristics. that is the first half of the 90s. the largest portion is explained by the residuals. This difference is important in the sense that it means the reasons for change in wage inequality between US and the five countries we have here might be completely different. there is no clear trend except that all five countries showed either an increase or stability between the third and fourth waves.
we can say that labor market experience has an increasing effect on wage inequality from Wave II to Wave III. 5 and 15 years of experience groups both see a drop in their marginal effects on wages from Wave II to Wave III. On the other hand. 15 and 25 years of experience. we calculate marginal effects for 5. although it never catches up again with the original levels of Wave II. As we said before. Combined together. we see decreases for 25 years of experience in all quantiles except for the last two.1 reports the marginal effects of experience for German data. the high-experience group passes from negative to positive marginal effect between these two waves. The 15 years of experience group does not go in a clear direction. this points to an increase in wage inequality based on experience. The fact that the drop for 15 years of experience is much smaller compared to that of 5 years of experience supports this theory. This is the second half of the 80s. Then we see the tide turns. In general. 6. As in our earlier analysis with the US data. Marginal effect of 5 years of experience increases in all quantiles excepth the 90th until wave V. 95 .CHAPTER 6 QUANTILE REGRESSION ANALYSIS OF THE EUROPEAN DATA We have run the quantile regressions with the same covariates that were used to fun the OLS regressions that supplied the estimates to apply the JMP method. quantile regression estimates are different in nature from the OLS estimates and give us a better understanding of how the effects of covariates might change depending on where we are at the wage distribution.1 Experience Table 6. then it works toward decreasing wage inequality. On the other hand.
07 0.34) (0.19) (0.61 -0. and has decreasing within inequality.64 0.96 1.06 -4.23 90-10 0.01 0.31 1.30 0.93 0.37) (0.94) (1.32 0.13) (0.05) (0.64 1.92) (1.50 0.44) (0.54 0.28) (0.69 -0.70 1.40) 6.24 0.13) (0.25) (0.06) (1.47 10. The other two experience groups have positive impacts upon dispersion on overall wage inequality (90-10 difference) and increasing within inequality after the second wave.72 (1.24) (0.66 -6.51 2.65 7.94 1.75 1.61 1.19) (0.1 96 .25) (0.41 2.2 Impact upon Dispersion-Experience-Germany Wave II III IV V 5 Years 90-10 90-50 -15. Since it looks like the contribution to wage inequality increases as one goes higher in experience years.09 4.63 0.81 Differences of indicated quantiles from Table 6.57 1.64) (0.25) (1.55 1.52 0.77) (1.12 0.26 1.45 1.64) (0.71 1.04 -0.15 0.34) (0.18 -8.25 -0.45 0.58 (0.09 -6.54 0.31 15 Years 90-50 0.30) (0.10 0.24) (0.46 3.75 9.14) (0.93) (1. We see that this is mostly fueled by upper half of the distribution for 15 years and the lower half of the distribution for 25 years.11) (0.27 4.32 1.79 4.26) (0.18) (0.14) (0.33 -0.07) (1.55 0.34) (0.25) (0.Standard errors are given in parentheses.23) IV V 3.24) (0.20) (0.23 1.43 -0.55 0.85) (1.40 0.93 -0.34 0.18) (1. It looks like the five years group has negative impact upon dispersion.31) (1.40 0.28 0.91 -6.34 0.18) (0.42 (0.39 0.22 0.89 0.49 18.85 -1.11 25 Years 90-50 0. the marginal effect has been calculated by evaluating the first derivative of it given for 3 values.09) (0.20) (0.31 0.61 3.11) (0.76 1.19) (0. we go to our impact upon dispersion table.01 2.67) (1.21) Since experience enters the regressions as a quadratic. as we can see from the 90-10 column.50) (2.25) (0.83) (1.69 7.04) (3.22 0.76 4.65 1.18) (0.08 1.31 90-10 1.37 1.96 0.66) (0.97) (1.39 0.71 -0.37) (0.37) (0. we could say that experience contributes to wage inequality.04) (0.15) (0.Germany Wave 5 Years 15 Years 25 Years OLS 10 25 50 75 90 OLS 10 25 50 75 90 OLS 10 25 50 75 90 II 10.19 0.23) III 2.Table 6.1 Marginal Effect of Experience.67 12.02 -1.28 0.08 -0.28) (0.30 2.20) (0.36 -6.34) (0.15) (0.30 50-10 0.93 1.24 0.33) (0.54 2.01 50-10 -9. Table 6.56 -0.20) (0.14) (0.40 1.40 2.16) (0.05 0.50) (1.13) (0.21) (0. To see how the distributions of each group changed.91 2.49 1.30 (0.00 50-10 -0.30) (0.19) (0.35 2.26 1.05) (1.16) (0.71 -0. All coefficients and errors are multiplied by 100.17 0.
35) ( 0.69 0.13 1.37 3.40 -0.15) (0.17) (0.53 0. with the effect of it always higher in the 10th percentile than in the 90th.06 ( 0.Netherlands Wave II 5 Years 15 Years 25 Years OLS 10 25 50 75 90 OLS 10 25 50 75 90 OLS 10 25 50 75 90 6. Between Wave IV and Wave V.17) ( 0.34) ( 0.32) IV 12.24) ( 0.21) (0. However we are not in a position to claim this as strongly as we did in the case of Germany.39 -0.20) ( 0.10) ( 0.35) ( 0.08 14.73 7.88 7.75 1.18) ( 0.Table 6. The 5-year group shows quite a uniform trend in terms of the direction of changes. as the marginal effect of the 5 year experience group is increasing while others do not show a clear move.90) (1.37 6. In general.75 0.13) ( 0.46) ( 0.28) III 10.18) ( 0.50 8.41) ( 0.12) We see the marginal effects of experience for Netherlands in Table 6.12) ( 0.27 2.18) ( 0.44 0.80) ( 1.87 1.38) ( 0.54) ( 0.3.13 2.62) (0.00) ( 0.41) ( 0.35) ( 0.26 2.46 0. there is a decrease.28 0. We notice that this increase is especially related to an increased dispersion in the lower half of the distribution.16) (0. We could perhaps say that from Wave II to Wave IV. although the upper half contributes as well.34 6.80 2.91 6.34) ( 0.22 0.14) ( 0. We see increases in marginal effects for all quantiles.09) (0.22 2.72) (1.3 Marginal Effect of Experience.15) (0.05 6.23) (0. Low experience group seems to have a negative impact upon dispersion for Netherlands.68 12.96 6.85 1.19) ( 0.92) ( 1.25) ( 0.58 0. The other two groups do not show clear trends.86 1.15) (0. experience works towards less inequality.26) ( 0.03 1.68 4.84 17. and the OLS.51 0.27) ( 0.73) ( 1. since the other groups show fluctuations on different points of the distribution.62 0.50 2.96 1.43) ( 0.16) ( 0. However we notice that as one goes higher in experience.17) (0.81 6.12) ( 0.36 1.22) ( 0.64 2.20) ( 0.94 0.16) ( 0.91 2.30 0.30 ( 0.99 2.70 1.53 2.23 0.75) (1.18) ( 0.55 7. 97 .53 20.16 2.51 1. and increase.80 0.86) (0.24) (0.06 0.13) (0.74 2.15) ( 0.40 1.12) ( 0.17) ( 0.14) ( 0.25) ( 0.13 6.97 12. until Wave IV.08 2.45) ( 0.48) (0.31 ( 0.22) ( 0.08 7.10 2.84) (1. one can say that the within inequality generated by experience groups contributes to increased wage inequality in Netherlands.49 1.66) ( 0.25 9.13 0.69) ( 0.24) V 7.67 1. the impact upon dispersion values for 90-10 dispersion turn positive.11) ( 0.83) ( 0.19) ( 0.15 ( 0.10 0.31 6.18) ( 0.66) (0.15) (0.93 0.
64 0.14) (0.55 90-10 1.80 0. that of 25 years of experience decreases in all quantiles as well as the OLS regression.23 10.09) (0. While the marginal effect of 5 years of experience increases from Wave III to Wave IV to Wave V. it looks like higher experience does not always mean higher within inequality.52) (1.Table 6.44 0.25 1.25 1. So.95 0.24) (0.68 0.03 1. although the inequality within experience groups seems to 98 .35 -5.98 4.08) (0.19) (0.19 (0.22) (0.01 -1.41 0.58 0.12) Sweden displays a somewhat different picture than Netherlands and Germany about the within inequality generated by experience.69 4.25 1.16 (0.72) (0.5 Marginal Effect of Experience.80 -13.5.46 2.11) (0.17 1.68 1.46 0.11) (0.04 -0.29 -10.40 0.16) (0.01 2.43 15 Years 90-50 0.92) (0.13) (0.08) (0.12 50-10 0. in fact it works in the opposite direction.30 3.44 0.75 7. It is no doubt clear that 15 years of experience creates more dispersion in itself than 5 years of experience.13) (0.65) (0.24 (0.48 -0. however it is also cause of more dispersion than 25 years of experience.25) (0.19) (0. Most of the within inequality we observe in the 15 years of education group comes from the upper half of distribution.25 0.73 1.13) (0.24) (0.63 1.Sweden 5 Years 15 Years 25 Years Wave OLS 10 25 50 75 90 OLS 10 25 50 75 90 OLS 10 25 50 75 90 III 3.61 0.90 1.07 0.03 0.43 0.85) (0.21) (0.06) (0.32) (0.96 1.45) (0.86 -6.23) (0.19 0.18 1.48 0.23) (0.64 5.01 1.74 3.12) (0.09) (0.13) (0.56 -1.54 0.06) (0. The 15-year experience group displays a similar picture to 25-year group in the upper half of the distribution.85 0.09) (0.44 -0.08 2.38) (0.61 0.38 4.05 3.98 1.16 4.15) (0. All in all.09) (0.43 3. we can say that experience does not contribute to increasing wage inequality.56 0.98 1.18) (0.32 0.17 25 Years 90-50 0.47) (0.23 -0.18 0.17) 8.28 1.59 -1. but it fluctuates in the lower tail.17 5 Years 90-50 -0.86 1. Table 6.51) (0.50 50-10 0.02 8.84) (0.67 Marginal effect of experience for Sweden is reported in Table 6.90) (0.85 6.43) (1.71) (0.04 0.60 50-10 0.36 0.15) (0.67 0.13) (0.09) (0.21 0.99 -11.11) (0.57 90-10 0.87 1. Here.4 Impact upon Dispersion-Experience-Netherlands Wave II III IV V 90-10 -0.68) (0.26 -9.76 0.29 7.30 1. 5-year and 25year experience groups show opposite movements.29) (0.27) (0.18) IV V 4.51 0.10 0.11) (0.19) (0.
01 1.95 -3.32 The Spanish marginal effects of experience do not give us very clear messages about the wage inequality created by experience in Spain.15) (0.92 4.51) (0.02 25 Years 90-50 0.20) (0.13) (0.52) (0.75 2.26) 4.07) (0.91 2.52 1.33 15 Years 90-50 0.11) (0. In short.97) (0.72 0.71 0.24) (0.39) (0.27 0. Then from Wave IV to Wave V we see an increase in the marginal overall.71 5.7 Marginal Effect of Experience.06 1.95 0.26 0.20) (0.54 -0.15) (0.53) (0.23) (0.65 5 Years 90-50 -0.23) (0.13) (0. Table 6.contribute to the overall inequality to some degree.22 90-10 0.83) (0.98 4.64 0.07 50-10 -0.27) (0.89 0.09) (0.26 -0. with a couple exceptions.23 2.68 1.48 -1.52) (0.67 2. namely the lower tails of 15 and 25 years of experience groups.05 0.12) (0.19) (0.04) (0.66) (0.29) (0.25) (0.16) (0. as we see in Table 6.49) (0.77) (0.98 0.24 12.14) (0.31 50-10 0.09 1.16 -0.04 (0.33) (0.67 -0.05 1.15 3. one cannot safely say that this contribution increases as one goes higher in experience groups. Table 6.51 -3.56 -0.70 1. it is not possible to observer a clear direction that the experience takes in contributing to wage inequality.14 6.78 0.77) (1.16) (0.53 3.85 0.60 0.63 1.21) (0.16) (0. it looks like all three groups see their effects on the distribution wages increase.61 1.59 0.47 1.85 (0.03 2.22 0.63 0.75 -0.31 3.71) (0.36) (0.55 4.49) (0.32 3.85 1.12) IV V 6.93 1.19 -0.32 (0.55 2. This time the upper tails of 15 and 25 years of experience groups do not follow the trend and see their effects increase.11 2.29 -1.6 Impact upon Dispersion-Experience-Sweden Wave III IV V 90-10 -1.19) (0.10) (0.71 0.18 1.89 50-10 0.43) (0.38) (0.07 5.13 0.03 -1.07 2.13 10.26) (0.58 90-10 1.17 3.46) (1.17) (0.43 3.94 2.61 0.39) (0.19 2.15) (0.4.21) (0. From Wave III to Wave IV.32 3.69 1.50) (0.29) (0.Spain Wave 5 Years 15 Years 25 Years OLS 10 25 50 75 90 OLS 10 25 50 75 90 OLS 10 25 50 75 90 III 5.95) (0.15) 99 .
01) (2.70) (2.74) 3.86 15 Years 90-50 50-10 0.99 0.02 0.56 0.66) (0.67 -0.35 0.34 -0.11 0.84) (0.49) (0.59) Table 6.Hungary Wave 5 Years 15 Years 25 Years OLS 10 25 50 75 90 OLS 10 25 50 75 90 OLS 10 25 50 75 90 III 5. In the lower half.61) (0.44 5. The upper tails of 15 and 25 years of experience also show increases all along.56 3.06) (1.70 -1. they increase all the way.09) (3.50) (0. we see the marginal effect of experience for Hungary in Table 6.35 1.63) (0.70) (0.9.34) (1.44 0.39 -4.36) (0.81) (0.24 50-10 -6.26) (0.52) (0.56 5.32 5 Years 90-50 -0.84 0.68) (0.96 -8.02 -0.85 8.57) (0.49) (0.47 0.02 6.35 12.37) (0.64 -0.8 shows that the within inequality among similar experience groups increases as one goes higher in experience in Spain.41 5.72) (0.66) (1.97) (0.10 -1.40) (0.54 (2. For one thing.89) (0.37 -0.54 -7.53 90-10 0.37 0.40) (0.07 90-10 -1.44 1.77 0.00 13.92 1.12 0.62 0.75) (2.59 -2.16) (1. Table 6.71) (0.50) (0.57) (0.42 -0.80 -2.34 0.67) (0.39 7.35) (1.33) (0.39 0. It is quite different from what we have seen in other countries.90) (2.57 (1.93) (0.65) (3.21 -0.66 0.83 3.77) (2.00 0.70) (0.55) (0.62) (1. then decrease as one goes from Wave III to Wave V. The other percentiles do not show clear tendencies.70) (2.17 2.40) (0.95 9.58) IV V 9.11 -0.9 Marginal Effect of Experience. In the upper half.36) (2.39 -0.41 (1.98 0.Table 6.16 -0.8 Impact upon Dispersion-Experience-Spain Wave II III IV 90-10 -6.51) (0.29 0.70 -0.28) (0.72) (0.54 1.10) (0.31) (2.24 2.88 0.51 -1.37 Lastly.79 -0. and this contribution is mostly generated by the upper tail.39 10. Table 6.9 reports the impact upon dispersion values for Hungary.99 1.23 0.44 -0. 5 years of experience shows different moves in the two halves of the distribution.99 0.26 -0.85) (0.03 -0. the marginal effects first increase.12 -0.33 2. Thus we could say that experience contributes to wage inequality in Hungary.97 -0. within inequality in the 100 .58 25 Years 90-50 50-10 0.27 -0.22 0.46 3.03 -1.82) (0.33 -1.
94 6.1.06 -0. we see a drop in the third and fourth waves compared to the first and second ones.75 1.88 2.32 0. We notice that there is a dramatic increase in high education premium in all percentiles except the 90th from wave III to wave IV. especially in the upper half of the distribution. We do not see this sort of drop in the high education level group.95 1.38 -4.11. we could probably say that this difference between the medium and high education group is a reason for the increase in wage inequality in Germany between waves III and IV that we observed in Figure 5.02 -1.62 -0.30 0.38 -0. 101 .83 -1.56 -0.02 0. For the medium education group. In Panel B of Table 6.83 4.10 Impact upon Dispersion-Experience-Hungary Wave III IV V 5 Years 15 Years 25 Years 90-10 90-50 50-10 90-10 90-50 50-10 90-10 90-50 50-10 1.33 -2.lower experience group contributes to overall wage inequality. but none as strong as the low experience one.08 4.15 -2.43 -0.13 8.75 -8. There is also some contribution of within inequality from other experience groups.2 Education The marginal effects of having medium (high school equivalent) and high (college and higher) education on wages are reported in Panel A Table 6.00 -4. Table 6.87 0. Thus. A clearer picture of medium versus high level of education is the simple comparison of their marginal effects.59 0.11 we give this measure.95 -1. This confirms our observation from the previous table that educational differences contributed to the increase in wage inequality between the third and the fourth waves.68 -0.68 6.
it is always positive as well.23 (2.17) 14.26 (2.37 III 31.93 37.77 35.70 75 37.16) 36.11 Marginal Effect of Education-Germany A.72) 75 57.05 90 35.Table 6.64 (5.59) 34.06 (3.88 43.52 (2.99) 37.08) 13.87 IV 40.81 24. Table 6.77 (3.71) 25.70 19.40 (2. except for the first wave.77) III IV V 19.12) 56.02 (3.60 (3.07 (4.50) OLS 53. sometimes increasing and sometimes decreasing.00) 69.13) 14.14 1.63) 42.16 (3.30 31.24 -4.52) 60.55 42.58 5. and this contribution has been consistently supported by an increase in upper half wage inequality.55 (1.09 28.52) 4.85 16.25 (2.49) 9.99 49.42 (2.82 11.78 20.77 12.34) (2.84) 50.18 40.31 (6.33 102 .59 (2.17 12.41) 16.99 53.64) 12.12) (3.08 (2.87 10.86 8.05 50 34.19 28.95 34.93 26.59) 40. High Education Premium Wave OLS 10 25 II 31.18 (6.70 (2.43) -6.39) 10 43.12) shows that both medium and high level of education contribute to wage inequality due to the inequality within themselves.23) 57.76 11.14 (3.25 (4. The lower half wage inequality within both groups is somewhat shaky. we can in general say that education contributes to wage inequality in Germany.38 10.67) 10.43) 14.33 (2.78) 71.65) 17.29 (3.73 (3.05) 48.37 (2.14 44.67 18.73 The impact upon dispersion table for Germany (Table 6.75) 22.21) (3.40 (2.12 14.97 38.18 (6.83 1.23 45.86 18.97 (1.41 (1.89) 21.62) 52.16) 2.53) 6.58 (3.12 Impact upon Dispersion-Education-Germany Wave II III IV V Medium Level High Level 90-10 90-50 50-10 90-10 90-50 50-10 4.72 (2.93 V 38.91 (2.33 8.15) 50.28) 26.04) (2.82 (3.82 (3.15 29.47 (2.76 10. Thus.47) (2.81 (2.43 (4.12 8.80 35.34) High Level 25 50 44.89 18.Marginal Effect of Education Medium Level Wave OLS 10 25 50 75 II 21.01 30. However.24) B.87) 10.75 35.10 28.18) 63.24 42.37 (2.47) 53.05 (2.53 (4.61) 24.70 (4.81) 90 58.96 (2.62 14.11 (3.60) 90 23.77 24.
27 50 44.02 (2. This is one of the reasons for the mild inequality increases in Netherlands between these two waves. the within inequality in the high education group is both larger and increasing since the third wave.36 (1.38) III IV V 24.91) 26.13 that after an initial drop from wave II to Wave III.04 (2.50 (2.38 (1. Marginal Effect of Education Medium Level Wave OLS 10 25 50 75 II 34.73) 49.97) 20.70 (1.21 31.62) 25.25) 61.23 (1.30) 67.65) 55.02) 19.41 (3.21 32.74) 17.11 (3.24 (1.73 (2.09 (1.51) 54.45) (3. However.22) 19.91) 25. 103 . Both groups contribute to dispersion in Dutch wages.24 34.79 (3.86 35.74 30.09) 50.00) 59.22) 59.97 (1.49 41.76 (3.04 (1.34) 19.96 75 41.70) 55. the high education premium does not fall between waves II and III at the upper tail.07) OLS 73.76 (2.90 41.24) 18.55) High Level 25 50 73.66 90 40.79) 20.28 The Dutch data shows in Table 6.76 35.60) (1.14) 19.14.45) 21.39 (1.34 39.45) (1.24 38.82 26.59 (1.42 (2.37) 21.09 (2.99 (3. while it falls in the lower one.97 (1.32) (1.74 (1.73) 57. We also see this from the high education premium table as well.43 (2.35) 56.51 (3.86) 20.97) (1.Table 6.57) 18.84 III 39.41 V 39.11 (2.66 40.13) 64.28 (2.86 38.26 39.88) 67.66) 60.58) 10 59.58) 17.69) 64.08 (1. we do not see the evidence of a strong effect of educational differences to increase the wage inequality in Netherlands.87) B.76 (2. Still.47) 58.65 (1.17 (1.95 (2.92 42.00 (1.65) 53.95 (3.41 (2.62) 90 37. After Wave III.97 IV 39.46 (1.15 44.76 (1.13 Marginal Effect of Education-Netherlands A.02 32. the marginal effects of both medium and high level of education do not change much.78 (3.66 (1.44 32.44 (5.02) 75 81.37) 66.10) 23. The impact upon dispersion table for Netherlands is given in table 6.36 (3.33 (2. High Education Premium Wave OLS 10 25 II 38.31 35.23 34. The within inequality of medium education group is positive except for Wave III.92) 90 78.65 42.60 79.
38 V 27.00 6.74 50 23.21) (2.91 (3.38) 10 31. This could be considered a minor move towards lower inequality.69 35.24) 53.95) 16.38 0.82 (2.42 17.77) 14.14 (2.00 -7.15 shows that while there are some fluctuations in the tails of the distribution.07 (1.13 17.37) 8.01 3.06 8.05 (3.05 (2.05 6.67) 10.39) 75 46.28 10.39 1.30 35.71 (1.34) 31.15 Marginal Effect of Education-Sweden A.70 50-10 20.85 18.80 (4.20 (2.52 (1. Table 6.62) 26.43 14.75) III IV 12.06 (1.18 (1.29 6.09 7.23) 75 16.17 (1.23 75 30.65 (4.55) 24.33 9.14 IV 25.17) B.31) 9.89 (2.93 (1.53) 28.05 90 38.57 -1.54 (1.05) 65. while it remains the same in the upper one.32 High Level 90-50 -1.02) 7.60) 37.50 (2.58 5.70 (1.08) 18. their within inequalities increase over time.20 (1.16 39.31 (0.90) 25.54 (1.82 (1.10 19.97 20. there are no big changes towards increased or decreased inequality.67 (2.52) OLS 38. High Education Premium Wave OLS 10 25 III 26.41 14.98 90-10 19.65 (0.06) 90 54.55 (1.82 It is clear from Table 6.00 10.00 8.89 11.84) 32.52 9.08 30.53) 36.63) 12.16 that both medium and high level of education groups contribute to wage inequality via their within inequality.03 34.43 (1.36 Medium Level 90-50 50-10 2. Table 6.62 We do not see big changes in marginal effects of medium or high level of education in Sweden.88) 41.15 (1.58 7. This is more emphasized in the high level of 104 .11 (1.39) (1. The high education premium decreases in the lower tail.11) 51.46 (1.69) High Level 25 50 28. Besides.78) 42.52) (1.84) 90 16. Marginal Effect of Education Medium Level Wave OLS 10 25 50 II 12.14) 16.72) (1.70 1.63 27.58 18.20 (2.30) 7.44 20.40 21.75 (2.14 Impact upon Dispersion-Education-Netherlands Wave II III IV V 90-10 11.Table 6.
01) IV V 40.48 (1.92) OLS 74. while the marginal effects of the higher education group increase throughout the distribution between waves III and IV.25) 72.06 (4.44 (2.49 90-10 23.77 4.68) 67.67 (1.95 (6.86 (2.75 8.70 (2.63 -7.68 29.49) 79.84) 32.82) 65.13 16.11 4. Then the marginal effects of both groups decrease between waves IV and V.92) 45.89 51.23) 24.19) 26.71) B.81 41.54) 10 74. High Education Premium Wave OLS 10 25 III 35.51 (4. Table 6.52 39.90) (1.44) 88.48 (2.04) 87. We see the same 105 .98 (2.87 90 39.92 75 36.92) (1.84 25.18) 36.13) 75 66.64) 28.03) 90 74.46 47.53) 33.94 41.15 1.15) 30.42 (2.17 shows the marginal effects and high education premium for Spain.55) (3.77 1.95) 29.69 33.23 64.63 (4.54 IV 46.79 (2.01 III (1.65 37.17 Marginal Effect of Education-Spain A.07 21.10 4.07 (3. contributing to the decrease in overall wage inequality.33) 99.39 47.26 (2.01) 76.34) 78.98) 80.59) 82.99) 90 35.07 40.06 (1.84) (1.51 (2. except for the lowe tail.93) 33.96 Table 6.52 (2.52) High Level 25 50 63.12 (4.43 41.34 9. Marginal Effects of Education Medium Level Wave OLS 10 25 50 75 39.47 25.61 33.82 (2.78 34. The upper half is also more active in the medium level of education group as well.education group and its upper half.49 V 40.60 9.16 Impact upon Dispersion-Education-Sweden Wave III IV V Medium Level 90-10 90-50 50-10 6.89 Table 6.57 (2.45 63.73 45.89 (7.07 (1. The medium level education does not see much change in its marginal effects.1 earlier seems to have something to do with educational differences.50 42.76 30.85) 35.98 (2.11) (1.42) 69.21 50-10 3.18 (1. The increase in wage inequality that we observed in Figure 5.10 High Level 90-50 20.94 (3.62 (4.55 (2.00 (3.57 (2.16 50 30.
40 10.97 -3. The high education group sees the first increase as well. From Panel B of Table 6. Especially the changes in the upper tail are noteworthy. The medium education group contributes only in the last wave. The impact upon dispersion values for Spain are given in Table 6.33 1. The medium education group sees an increase in marginal effects between waves III and IV in all percentiles as well as the OLS. Table 6.18 Impact upon Dispersion-Education-Spain Wave III IV V 90-10 -5. the marginal effects drop back throughout the distribution between waves IV and V. However.76 High Level 90-50 10.84 50-10 -9. we see that the high level premium first increases then decreases for all percentiles as well as the OLS.08 For the analysis of Hungarian data. The most serious fall between waves IV and V is in the upper tail.62 13.18.23 Medium Level 90-50 1. in both groups we see contribution to dispersion from the upper half of the distribution. 106 .10 -1. we have Table 6.57 90-10 0. The high education group contributes to overall inequality in all waves.72 14. However. The upper tail does not see this drop.64 4. as expected.moves in high education premium values.58 -10.19. This effect is offset in the medium education group by the stretching of lower half.89 -11.31 1.04 19.64 -9. Then the lower half of the distribution and the median lose some of this increase back between waves IV and V.19.80 50-10 -6. This is the picture of overall within inequality.
they are expected to show less inequality than the empirical distribution.09 -1.52 77.84) (4.93) (8.Table 6.43 -3.98) 90 97. 107 .56) (3.01) 22.27) 35.86 (5. Since we use the average values of covarites for a given wave.64 60.10 (5.99 -0.91 (3.20. High Education Premium Wave OLS 10 25 III 52.20) 28.26) High Level 25 50 72.08 69.45) 23.98 15.34) (6.09 (12.65) 75.13 73.71 90 81.73 -12.53 53.30 101.54 113.17) 87.79 IV 73.34 (4.75) IV V 34.04 62.92 113.47 Table 6.20) 26.83) 80.37 (10.95) 28.64 50.93 97.27 107.39) B.84 63.28 (9.62 (4.70) (7.97 75 64.77) OLS 72.73 -0.14) (10.14 (8.20 6.57 38.45) 24.92 50-10 13.87 -5.20 Impact upon Dispersion-Education-Hungary Wave III IV V 90-10 -13.64) 27.62 -4.85 -22. Although there is strong within inequality among the high education group in wave III.10) (12.99 (6.10 91.24 High Level 90-50 14.19 Marginal Effect of Education-Hungary A.31 17.23 (8.10 82.92) (11.63 -16.01 (7.73 105. These distributions have been prepared following the same logic as we used in Chapter 4.3 Conditional and Counterfactual Distributions We have the conditional wage distributions for Germany in Table 6. 6.82) 90 15.68 Lastly.33 (8.77) 75 79.86) (5.86 (7.72 50 64.29 (6.90 (7.57 (14.65 90-10 28.89 (9.57 56.99 12.85 19.56) 89.52 52.84 22.16 V 48.11 -7. Marginal Effect of Education Medium Level Wave OLS 10 25 50 75 III 20.42 29.94 (11.61) (8.48 (6.21.81) 79.99 (9.46 (6.85) 10 68.51 (6.77) 73.23) (10.98 -11. we see the values that show the impact of education on dispersion in Table 6.81) (10. neither group contributes to inequality in the following two waves.88) 24.08 Medium Level 90-50 50-10 -1.46) 44.25 (8.
Diff.697 10 6.45 0. 90-50 and 50-10 percentile differences quite successfully when compared to the empirical distribution.75 0.37 0.21 Conditional Wage Distribution-Germany Conditional Wage Distribution Waves Percentile II III IV V 6.Diff.75 90-50 0.80 0.94 0.Diff.170 50 7.469 7.70 0.1. II III IV V 0.59 90-50 0.96 50-10 Changes in Dispersion(Conditional) Perc.231 7.33 0.708 6.604 6. It is not very straightforward from the table how the conditional distribution evolved over time.399 75 7.017 0.60 0.045 7.061 0.033 50-10 We notice in Table 6.004 90-50 -0.47 50-10 Dispersion from Empirical Distribution Perc.044 0. II III IV V 0.079 7. Similarly Waves IV and V are alike.881 6.823 6.09 90-10 0.076 -0. Wave III is a shift up from wave II.942 25 7. As expected.825 6.35 0.99 0. It shows that the conditional distributions for waves II and III have similar slopes. the conditional distribution correctl marks the direction of changes in dispersion over time for Germany.335 7.89 0.152 7.49 50-10 Portion of Empirical Dispersion Explained by Conditional Perc. we have Figure 6. For this.45 90-50 0. V is a shift up from IV.114 0. III-II IV-III V-IV -0.21 that the conditional distribution captures the dispersion measures of 90-10.89 0. It is especially on target with the lower half wage inequality measure.72 0.273 7.04 1.96 0.Table 6.41 0.85 0.39 0.494 7.44 0.190 0.84 90-10 0.525 7.70 0.76 0.44 0.53 0.54 0.Diff.92 90-10 0. The interesting point here is that the shape of conditional wage distribution changes between the third and the fourth 108 .79 0. II III IV V 0.970 6.030 90-10 -0.37 0.616 90 Dispersion from Conditional Distribution Perc.88 1.
22 shows these two distributions.waves. It appears that this counterfactual distribution explains the changes in dispersion until wave V quite nicely. Table 6. However it fails to explain the change in dispersion between waves IV 109 . the counterfactual distribution that uses the average values of covariates from wave II helps us isolate the effect of changes in the distribution of covariates. The degree at which this distribution can explain the changes in dispersion from the conditional distribution is an indication of how important are the changes we isolate.50 6. and using the Wave II coefficients from our quantile regressions and each wave's averages of covariates. As well as what we said about education and experience effects. Figure 6.00 10 25 50 Percentile 75 90 III IV V Roman numbers represent waves of LIS data.1 Conditional Wage Distributions-Germany We also have two sets of counterfactual wage distributions for Germany: using the Wave III averages of covariates and each wave's coefficients. this change helps explaining the nature of increase in wage inequality in Germany in that period.50 7.00 Log Wage 7. This is a change that favored higher quantiles over lower ones. As we explained in Chapter 4.00 6. Conditional Wage Distributions-Germany II 8.
903 6.38 0.469 7.463 7.10 0. This is an indication that changes in the distribution of covariates became more important at this point. II III IV V 90-10 0.47 Changes in Dispersion Perc.04 50-10 -0.270 7.36 Changes in Dispersion Perc.and V.70 0. III-II IV-III V-IV 90-10 -0.33 0.615 6.708 6.75 0.44 0.340 7.886 50 7.37 0. We notice from the comparison of conditional and empirical distributions that the conditional distribution explains the change in dispersion from the data quite accurately.75 90-50 0. With wave V the effect of the composition of covariates increases.716 6.06 0.273 7.36 0.01 50-10 -0.959 50 7.02 90-50 -0.02 0.081 7.00 90-50 -0.44 0.351 90 7.23.469 7.646 25 6.119 75 7.881 6.231 7.708 6.04 0.37 0.281 7.76 0.88 0.22 Counterfactual Distributions-Germany Counterfactual Wage Distribution(Wave III Averages) Waves Percentile II III IV V 10 6.07 -0.39 0.975 6.273 7.827 6.736 6.828 6.548 Dispersion Perc.793 25 6.530 7.079 7.40 50-10 0.475 7.Diff. This time we isolate the effects of changes in the prices of personal characteristics and attempt to see how this affects the distribution.521 Dispersion Perc.39 0. Combining this with our findings from the first counterfactual distribution. II III IV V 90-10 0.76 0.Diff.37 0.01 0.158 7.Diff.01 0.Diff.051 7.04 Counterfactual Wage Distribution(Wave III Coefficient) Waves Percentile II III IV V 10 6. The second counterfactual distribution does just the opposite.39 50-10 0. It turns out that this distribution explains the changes in overall wage inequality between Waves IV and V better than any other time period.37 0.02 -0. we could probably assume that until wave V changes it is the prices of covariates that affects the wage distribution.886 6.74 0.155 75 7. Table 6.88 90-50 0.18 0.01 -0.00 0.079 7.38 0.01 The conditional wage distribution of Netherlands is given in Table 6.496 7.323 90 7.881 6. III-II IV-III V-IV 90-10 -0.091 7.01 0. if not as 110 .
01 0.080 7.263 7.62 0.35 50-10 0.77 0.037 0.96 0.89 0.48 Portion of Empirical Dispersion Explained by Conditional Perc.226 7.Diff.63 0.383 7. II III IV 90-10 0. we do not observe the radical slope changes that we saw in German data.36 0.066 0. For the Dutch data.324 7.53 0.48 V 0.988 7.79 0.54 0.874 7.043 90-50 -0.158 7.018 0.Diff.796 6.246 7.425 7.97 90-50 0.Diff.091 7.006 50-10 Figure 6.66 0.489 7.070 0. The wage inequality trends of the Dutch data we saw in Figure 5.628 Dispersion from Conditional Distribution Perc.571 Percentile 10 25 50 75 90 V 6. III-II IV-III V-IV 90-10 0.37 Dispersion from Empirical Distribution Perc.2 are well captured in the conditional distribution.2 shows how the conditional distributions for different waves compare.82 0. 111 .004 0.78 Changes in Dispersion (Conditional) Perc.37 V 1.46 0.23 Conditional Wage Distribution-Netherlands Conditional Wage Distribution Waves II III IV 6.72 0. The Dutch distribution also shifts.69 0.50 0.004 0.Diff.well as the case of Germany.75 0.70 0.29 0.423 7. Table 6.75 90-50 0.74 0.014 0.33 0.798 6.38 0.945 6. but towards higher wages for all quantiles. II III IV 0.36 0.72 50-10 0. II III IV V 90-10 0.859 6.49 50-10 0.70 0.044 7.33 0. The shifts favor higher percentiles a bit more than the lower ones.71 90-10 90-50 0.
30 0. II III IV V 90-10 0.34 0. III-II IV-III V-IV 90-10 0.781 6.30 50-10 0.29 0.63 0.509 7.128 7.263 7.488 7.66 0.369 7.Diff.33 0.Conditional Wage Distribution-Netherlands II 8.170 7.33 0.65 90-50 0.798 6.68 0.02 0.03 -0.101 25 6.01 0.62 0.206 7.006 25 6.573 90 7.36 Changes in Dispersion Perc.30 0.494 90 7.945 50 7.03 0.00 0.50 6.35 0.004 7.01 0.798 6.830 6.35 50-10 0.091 7.00 -0.66 90-50 0.32 0.465 7.160 7.03 -0.437 7.945 50 7.63 0.Diff.00 6.315 7.63 0.270 75 7.33 0.425 Dispersion Perc.00 Log Wage 7.144 75 7.425 Dispersion Perc.286 7.05 -0.00 90-50 50-10 0.03 112 .263 7. II III IV V 90-10 0.03 0.32 0.01 0.910 7.34 0.962 6.50 7.874 6.323 7.00 0.24 Counterfactual Distributions-Netherlands Counterfactual Wage Distribution(Wave II Averages) Waves Percentile II III IV V 10 6.844 6.00 10 25 50 Percentile 75 90 III IV V Figure 6.03 90-50 50-10 0.Diff.01 -0.091 7.06 -0.2 Conditional Wage Distributions-Netherlands Table 6.30 Changes in Dispersion Perc.838 6.461 7.Diff.999 7. III-II IV-III V-IV 90-10 0.046 7.29 0.307 7.04 Counterfactual Wage Distribution(Wave II Coefficient) Waves Percentile II III IV V 10 6.
42 0.243 113 .011 0. IV-III V-IV 90-10 0.34 0.41 0.86 0.195 6.75 0.87 1.025 0.734 5.060 90-50 50-10 0. Table 6.085 6. III IV V 90-10 0.014 0. III IV V 90-10 0.603 6.84 Changes in Dispersion Perc.855 5.50 0.024 5.Diff.87 0.37 0. explaining such a big portion of wave II-wave III difference is something that can be expected. the second counterfactual distribution does a better job of explaining changes in overall dispersion from the conditional distribution from the third wave to the last.50 0.380 90 6.Diff. We can say that in the later years changes in the composition of personal characteristics become more important.85 0.897 50 6.82 0.94 0.623 Dispersion from Conditional Distribution Perc. Interestingly.84 90-50 0.547 25 6.89 0.77 1.83 50-10 0.304 0.Diff.24.70 Portion of Empirical Dispersion Explained by Conditional Perc. The first counterfactual distribution explains about 85% of change in dispersion from Wave II to Wave III but loses its explanatory power after then.141 75 6.The counterfactual distributions for Netherlands are given in Table 6.48 50-10 0.920 5.08 90-50 0.58 50-10 0.37 0.59 Dispersion from Empirical Distribution Perc.35 0.507 6.398 6. III IV V 90-10 0. since holding them constant changes the distribution totally.Diff. Since we use the Wave II averages in the calculation of this distribution.91 0.291 6.25 Conditional Wage Distribution-Sweden Conditional Wage Distribution Waves Percentile III IV V 10 5.28 90-50 0.
However. Conditional Wage Distribution-Sweden III 7. wages for all percentiles decreasing. Spain and Hungary due 114 .26. This is something we have not seen in Germany and Netherlands. From wave III to wave IV. On the other hand. changing the shape of wage distribution so that the upper tail of the wage distribution recovers what it lost in the previous wave.50 5. Wave V is yet another story.00 5.3.3 Conditional Wage Distributions-Sweden The counterfactual distributions for Sweden are given in Table 6. We also notice that the changes in dispersion values represent the increased wage inequality from wave IV to wave V accurately.00 Log Wage 6. Although the comparison of the effects of changes in the composition of covariates with that of the prices of them is a bit less meaningful in the case of Sweden.50 6. as we see in Figure 6. we see a downward shift. wages of the lower tail drop even further.00 10 25 50 Percentile 75 90 IV V Figure 6.The portion of the change in empirical dispersion that is explained by the conditional distribution is quite high for Sweden for all waves. the conditional distributions of Sweden shift in a different way from Germany and Netherlands.
75 0.72 90-50 0. although less so than the Swedish conditional distribution.906 5. It appears that the second counterfactual distribution does not have very much explanatory power while the first one explains quite a bit of the change in dispersion. Like the earlier countries.Diff.717 5.01 The conditional distribution for Spain is reported in Table 6.484 6.41 0.34 0.871 5. we will still cover that.01 0.069 6.627 25 6.00 -0.603 6.to the shortness of time period.77 1.75 0.26 Counterfactual Distributions-Sweden Counterfactual Wage Distribution(Wave II Averages) Waves Percentile III IV V 10 5. Wages for the Spanish sample seem to be higher than those for other countries.27.195 6.038 5.624 6.00 -0.603 6. Surprisingly.34 0.03 90-50 0.41 0. IV-III V-IV 90-10 0.35 0. Changes in dispersion generated from this conditional distribution describes the changes in inequality in Spain adequately.354 90 6.195 6.398 6.41 0.00 -0.01 0. 115 .823 25 6.30 90-50 0.444 90 6.024 5.962 50 6.33 Changes in Dispersion Perc. III IV V 90-10 0.22 Counterfactual Wage Distribution(Wave II Coefficients) Waves Percentile III IV V 10 5. III IV V 90-10 0.07 90-50 0. IV-III V-IV 90-10 0.024 6.153 75 6.Diff.57 Changes in Dispersion Perc.985 50 6.855 5.75 0.201 75 6.855 5.42 0. Table 6. the first counterfactual distribution does a better job of explaining the change in dispersion from wave IV to wave V than it does with the previous period.211 6.02 0.543 Dispersion Perc.Diff. the Spanish conditional distribution explains a good deal of wage dispersion from the empirical data.Diff.39 50-10 0.08 50-10 0.34 0.416 6.693 Dispersion Perc.398 6.49 50-10 0. This is probably due to some limitations of the original survey that was given to LIS.02 50-10 0.272 6.
708 50 10.68 50-10 0.028 90-50 0. III IV V 90-10 0.46 1.162 11.011 50-10 0.200 90 11.Diff. There is no evidence of serious shifts in different parts of the distribution.78 0. It looks from the table that both composition of covariates and their prices are important in explaining the changes in dispersion in Spain.Diff.360 25 10.87 0.936 10.964 75 10.50 90-50 50-10 0.496 10.4.31 90-50 0.14 1.78 0.68 0. with another and very small shift up.036 0. III IV V 90-10 1. the conditional distribution is almost the same.28.Table 6.935 11.97 Changes in Dispersion(Conditional) Perc. Conditional Wage Distribution-Spain Conditional Wage Distribution Waves Percentile III IV V 10 10.57 0. 116 .46 0.076 -0.467 Dispersion from Conditional Distribution Perc.659 10.64 0.60 Dispersion from Empirical Distribution Perc.74 50-10 0.62 Portion of Empirical Dispersion Explained by Conditional Perc.85 90-50 0.705 10. The counterfactual distributions for Spain can be seen in Table 6. IV-III V-IV 90-10 0.78 0.27.78 0. III IV V 90-10 1.138 10.31 1. From wave IV to wave V.72 0.72 0.429 11.Diff.49 0. we look at Figure 6.74 0.294 10.11 0.Diff.185 11.02 1.58 0. It appears that the conditional distribution jumped up between waves III and IV with minimal change in slope.112 -0.038 To see how the conditional distribution evolved in Spain.
01 50-10 0.Conditional Wage Distribution-Spain III 12.496 10.950 10.06 90-50 0.Diff.44 0.741 50 10.50 9.Diff.04 90-50 -0.907 90 11.02 1. IV-III V-IV 90-10 0.347 25 10. III IV V 90-10 1.162 11.50 11.01 0. IV-III V-IV 90-10 -0.49 0.4 Conditional Wage Distributions-Spain Table 6.02 1.714 10.705 10.50 50-10 0.508 10.312 10.935 10.28 Counterfactual Distributions-Spain Counterfactual Wage Distribution(Wave III Averages) Waves Percentile III IV V 10 10.61 Changes in Dispersion Perc.11 0.994 75 10.671 10.01 Counterfactual Wage Distribution(Wave III Coefficients) Waves Percentile III IV V 10 10.496 10.224 90 11.04 Log Wage 117 .07 0.03 0.65 Changes in Dispersion Perc.705 10.00 9.50 10.138 10.46 0.01 0.Diff.930 10.00 10 25 50 Percentile 75 90 IV V Figure 6.935 11.57 0.138 10.00 11.02 90-50 0.57 0.686 75 10.471 50 10.498 Dispersion Perc.134 Dispersion Perc.00 10.15 90-50 0.45 50-10 0. III IV V 90-10 1.074 25 10.13 1.58 0.156 11.199 11.138 10.64 0.02 1.Diff.442 11.162 11.46 0.01 0.01 50-10 0.
29.Hungarian conditional wage distribution is given in Table 6.703 90 10.752 8. 118 .49 1. the wage inequality increase in Hungary is not reflected by the wages of higher percentiles increasing more than the lower ones.29 Conditional Wage Distribution-Hungary Conditional Wage Distribution Waves Percentile III IV V 10 8.92 Changes in Dispersion Perc.96 0.28 1. III IV V 90-10 1.60 0.49 90-50 0.198 9.63 0.745 9.024 90-50 Figure 6.64 0. They also capture the increase in wage inequality from wave III to wave IV as well as anothe slight increase from wave IV to wave V.Diff.75 0.74 Portion of Empirical Dispersion Explained by Conditional Perc. The conditional distributions give very close dispersion measures to those of the empirical distribution.376 50 75 9.Diff.68 50-10 Dispersion from Empirical Distribution Perc.12 1.90 90-50 0.Diff.056 9.Diff.88 50-10 0.99 0.65 0.85 0.27 1.034 0.111 9.5 shows us the Hungarian conditional distributions as a group.025 10.52 0.946 8.75 50-10 0.468 9. III IV V 90-10 1. It is marked by a decrease in the wages of lower percentiles while the wages of the upper tail remained more or less the same.062 0. Interestingly enough.691 9. III IV V 90-10 0.153 0.73 0.393 9.82 0.067 10.66 0.698 25 9.54 0.33 90-50 0. IV-III V-IV 90-10 0.84 0.033 Dispersion from Conditional Distribution Perc. Table 6.88 0.
12 1.539 25 9.968 25 9.198 9.09 0.217 9. III IV V 90-10 1.Diff.226 50 9.745 9.01 90-50 0.118 Dispersion Perc.198 9.14 0.52 0.62 50-10 0.074 8.319 75 9.991 50 9.067 10.19 90-50 0.946 8.637 9.67 50-10 0.989 9.00 119 .957 8.468 9.60 0.491 9. IV-III V-IV 90-10 0.990 Dispersion Perc. IV-III V-IV 90-10 0.783 90 10.5 Conditional Wage Distributions-Hungary Table 6.26 1.468 9.5 8 10 25 50 Percentile 75 90 IV V Figure 6.343 9.01 0.773 9.01 0.17 Counterfactual Wage Distribution(Wave III Coefficient) Waves Percentile III IV V 10 8. III IV V 90-10 1.01 50-10 0.65 0.5 Log Wage 10 9.61 0.45 90-50 0.05 0.5 9 8.15 90-50 0.745 9.Diff.60 0.067 9.02 50-10 0.52 0.Conditional Wage Distribution-Hungary III 10.635 90 10.14 1.500 75 9.78 Changes in Dispersion Perc.53 0.946 8.61 0.02 0.096 10.30 Counterfactual Wage Distributions-Hungary Counterfactual Wage Distribution(Wave III Averages) Waves Percentile III IV V 10 8.730 8.Diff.Diff.53 Changes in Dispersion Perc.12 1.
In many cases it is possible to group a few of them under a group. there are generalizations one can make out of these different results. while for Spain and Hungary. 6. how diverse the wage inequality experience can be among a few countries on the same continent. Netherlands and Sweden it is mostly the changes in prices of covariates that increase the wage inequality. 120 . Secondly.30. Wage inequality experience within a country seem to be able to change direction from wave to wave as well. Due to the nature of quantile regression analysis. we have been able to look into different aspects of wage inequality throughout the wage distribution for these countries. The fact that this is not obvious from our earlier analysis with JMP might be a good example of the usefulness of quantile regression.We can see the counterfactual distributions for Hungary in Table 6. both the changes in prices and composition of covariates are effective in creating wage inequality. and part of it is created by wage dispersion within the same education and experience groups.4 Concluding Remarks Quantile regression analysis of five European countries reveals. if not anything else. Still. Thus we say that changes in both the composition of personal characteristics and their prices contributed to inequality in Hungary. For one thing. our anaysis with counterfactual distributions lets us divide the countries into two groups in terms of the relative effects of changes in the composition of observable characteristics (covariates) and their prices (quantile regression estimates. within inequality seems to be strong to a degree in all countries. Both distributions do a poor job of explaining changes in dispersion from the empirical data. however a general grouping does not seem to be possible since the composition of groups keep changing. In Germany.
While the wage inequality in the lower half of the wage distribution was more important in explaining the increase in wage inequality from 70s to early nineties. Some part of this increase in the wage inequality can be explained by educational differences. We also found that the effect of the changes in the composition and prices of unobservable characteristics and skills dominates these two effects combined in causing greater inequality. the effect of the prices of these skills (represented by regression estimates) on the year-by year increase of wage inequality has been increasing in the same time period. the gains of college graduates are more underlined as one goes higher in experience. On the other hand. For example the college graduates recorded huge gains compared to other groups between 1980 and 1990 throughout the wage distribution. Inter-industry wage dispersion does not seem to be much of a factor compared to the within-industry wage 121 .CHAPTER 7 CONCLUSION The US data shows that wage inequality has been increasing with pauses and jumps in the last three decades. The fast increase during the 80s has been documented in the literature. The application of JMP method to the US data revealed that the effect of changes in observable skills on wage inequality has been quite limited since the beginning of 80s. There are signs of stability after the turn of the century. The biggest portion of wage inequality is explained by inequality within narrowly defined groups. but it is still too early to tell if this will be persistent. but we observe a sharp increase in wage inequality in the second half of the nineties as well. the upper half of the wage distribution has been gaining more weight since then. We observe higher wage inequality within groups of people with similar education and experience levels. Also.
While the between inequality has been slowing down in the recent years. or at least not experience a decrease (Sweden). The quantile regression results improved our understanding of the behavior of wage inequality in the US by proving that in many cases the marginal effects of covariates differ wildly in the tails from those of OLS. as well as among the college graduates. Countries have very different experiences of wage inequality. The details of wage inequality are all country-specific with no clear direction that these countries take. which is actually one of the time periods that the wage inequality in the US did not increase as fast as it did in other periods. but to a lesser degree than education. Sweden.inequality. some see a decrease (Spain) and some stay more or less the same (Hungary and Netherlands). Experience seems to contribute to both within and between inequalities. However. for example. the within inequality of the college group has been increasing. which is not surprising if one thinks about the differences of labor market institutions. One thing that contradicts in most European countries that we analyzed is that the weight of lower half wage inequality has been increasing since the beginning of 90s in all countries with different rates. some record increases in wage inequality (Germany and Sweden. at least for some countries. This is roughly the first half of the nineties. displays a remarkable increase in the overall wage inequality that bears a heavy within inequality effect in it (also fueled by educational and experience differences to a lesser degree) which concentrated on the lower half of the wage distribution. The European experience gives us hints on the impossibility of talking about a single European labor market. most notably). We also see milder increases in wage inequality in Germany for the whole decade of nineties. one could probably say that the theories of European countries not experiencing wage inequality due to their highly regulated labor markets and strong labor unions might not be as strong as they used to be. Then. The increase is more pronounced in the second 122 . One thing that is common in all five countries is that they experience some sort of increase in wage inequality between the third and the fourth waves. in the second half of the nineties. There seems to be sizable wage inequality between college graduates and others.
although it had been closing the gap throughout the nineties. On the other hand. One has to recognize the importance of this difference since it shows that the reasons for change in wage inequality during the 90s might be different between the two sides of Atlantic Ocean. the upper half wage inequality of the US. The change in the American side is probably related to the fact that the skill differences caused by SBTC have reached such a point that the employers are even differentiating between people with higher skills. We have clues that the experience of these countries before the 90s might also be different from the US. the European side has not been able to respond to SBTC effectively due to its institutional rigidities. did not catch up with the lower half until the turn of the century. stretching the wage gap there. On the other hand.half of the decade. since they all start with higher levels of upper half wage inequality than the lower half one. This is another difference from the US data in which we saw that the effect of the upper half of wage distribution has been growing in the same period. 123 .
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tr EDUCATION Degree Institution Year of Graduation MS BS High School WORK EXPERIENCE Boston University Economics METU Economics Kayseri Lisesi. Kayseri 1998 1992 1987 Year November 2000-present June 2000November 2000 1992-1996 Place METU Department of Economics Enrollment Research Assistant Çanakkale 18 Mart University Department of Economics T.Ziraat Bankası Research Assistant Expert (Corporate Banking) FOREIGN LANGUAGES Advanced English 129 . Sivas Marital Status: Single Phone: +90 312 447 42 84 – 0535 639 3051(Mobile) email: email@example.com. Nazmi Yükselen Nationality: Turkish (TC) Date and Place of Birth: 9 October 1970 .CURRICULUM VITAE PERSONAL INFORMATION Surname. Name: Yağanoğlu.
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