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A country’s wage structure depends on multiple variables such as if their economy is going through an expansion period, the type of industries they specialize in, trade policies, policies based on gender discrimination, and the level of educational attainment throughout the country.  This chapter will analyze the factors that affect inter-industry wage differentials,  gender wage differentials, CEO compensation, the effects of immigration, education and skills across different countries.  Making these international comparisons will help explain why workers experience different degrees of wage dispersion and it also provides a reason as to why wages may be more polarized in certain countries. This paper will examine the existence and extent of wage inequality in different countries.

Review Of Literature
Inter-Industry Wage Differentials

Wage disparities still exist amongst workers with similar characteristics employed in firms that operate in different sectors of the economy.  A common belief is that industry affiliation, trade openness, and capital openness are the main factors that cause wage differentials in a country (Wang,Milner, and Scheffel, 404).  Other causes are different industry-level productivity performance, compensation for unmeasured working conditions, and matching highly competent workers to high wage firms (Papapetrou, 51). Wage structure is subject to change across any growing economy over time and wage inequality is bound to occur.  The cause of this dispersion is to be inspected  along with the degree of its effect on different industries.

Wang, Milner and Scheffel examined Chinese household survey data; the individual wages were regressed against worker-specific and job-related characteristics to yield an estimated industry wage premium.  This premium measures the part of wage variation explained by industry affiliation in China.  The second stage of their study was to use the estimated industry premium and pool it across previous years and have it be regressed on various globalization variables at the industry level. The theoretical predictions and results are shown below.

When a country is unskilled labor abundant, the increased specialization in their comparative advantage raises the relative demand and wages of unskilled labor in all industries (Wang,Milner, and Scheffel, 406).  An increase in exports means that more labor would be required by firms to reach their required output for the specialized good. A rising relative wage for unskilled workers caused by trade expansion forces these unskilled labor-intensive industries to raise their wages.  An impactful factor that explains wage inequality across industries is trade-induced productivity changes, along with technological changes (Wang,Milner, and Scheffel, 407).  This causes an increase in output capabilities which can raise wages for workers due to higher productivity. Trading turns out to be an impactful factor on wages in China.  It was found that wage levels are positively correlated with the import of intermediate goods or export of final goods (Wang,Milner, and Scheffel, 419).  The estimated wage premium regressed against trade-openness and capital-openness exhibits that it is mainly exposure to trade in final goods that drives industry wage differentials (Wang,Milner, and Scheffel, 427).  The effects of intermediate imports and exports were found to be insignificant.  The unskilled labor abundance in China, which exports many goods, increases the demand for labor in export industries and for relatively low-skill intensive activities (Wang,Milner, and Scheffel, 418).  On the other hand, the productivity-enhancement effects of exporting industries are biased towards skilled workers, which raises their wages but lowers the wage of unskilled workers.  The data ended up revealing that industry affiliation explains a relatively small proportion of actual wage variations (Wang,Milner, and Scheffel, 405). Men, white collar workers, workers in large firms, people satisfied with their jobs, and those with a higher social/economic status tend to earn more than other workers in China (Wang,Milner, and Scheffel, 427).  We can conclude that the wage premium level tends to be larger the more that the industry’s involved in the exporting of final goods.

Next, to see if wage dispersion exists amongst different sectors and the extent of those gaps, Belgium and Greece will be examined.  Previous studies have suggested that sectoral effects are greater in countries with little corporatism no matter what time period was studied (Barth and Zweimüller 1994) and Belgium ranks as intermediate or high on the level of corporatism while America is at the bottom of the scale.  The reasons behind inter-industry wage differentials include that there are specific characteristics of employers in different sectors and individual characteristics of employees are not distributed randomly among industries.

Different sectors in the economy have different levels of productivity and some require a higher skill/risk and some only require low-skill intensive work.  First, we must examine to see if there are actually differences in wages among sectors in Belgium.  The telecommunications sector is the best paid, earning fifty-eight percent more than the average worker in Belgium, followed by the electricity/gas/hot water supply sector, then the manufacturers of petroleum and nuclear fuel, financial intermediaries, and finally the hotel and restaurant sector which earns thirty percent less than the average worker in the economy (Rycx, 562). These results show that the inter-industry wage differentials do exist in Belgium and are the result of characteristics of employers in each sector, and that the determination of wages in each sector is influenced by technological and organizational characteristics of the firms making it up.  This data also agrees with the theory of the existence of a negative relationship between this wage gap and the degree of corporatism. Overall, the evidence showed that the size of firms, level of wage bargaining, and the skills of the labor force affects their wages; as the least skilled jobs earn between twenty-four and thirty percent less than the average worker in Belgium (Rycx, 562).  After examining Greece, there was also evidence of high wage dispersion across industries.  The best-paying industries dealt with manufacturing of things such as coke, refined petroleum, water, and tobacco products (Papapetrou and Tsalaporta, 58).  Other high paying industries included electricity, gas, steam and air supply, water collection, and telecommunications (Papapetrou and Tsalaporta, 58).  While the lowest paying industries were comprised of office administrative work, office support, security and investigation, cleaning, hairdressing, information service, and food service activities (Papapetrou and Tsalaporta, 58).  The wages earned in each of these industries differ because of how much revenue certain sectors make as compared to others, and the skill or risk involved in completing the demanded tasks from the employers. Inter-industry wage differentials will continue to exist as sectors require different levels of skill and people will continue to fill in any jobs that they can match their services with.

Taking a look at another country that has high wage differentials for different industries is urban China.In urban China, pay differs depending on industry. Service workers get 20.2% less pay than professionals and technicians for male migrants and for females the gap is 31%. Meanwhile, the greatest wage gap is in the finance, insurance, and real estate industry in urban China. Only 5.4% of males and 2.1% of females have professional or technical positions (Magnani and Zhu 782). It is difficult for people in urban China to find jobs in the public sector or foreign sector with low schooling levels. The returns for schooling are about 4% for both male and female migrants (Magnani and Zhu 784). Wholesale, retail and food services, and the social service sector are the major employers of migrants in China (Magnani and Zhu 782). People who immigrate to China are more likely to work in an industry of retail or food services. The industry people work in depends on their schooling. Those who work in the wholesale, retail, and food services do not need a very high level of schooling to get a job in these industries because it is not required. Certain industries require more schooling or skill, therefore, this contributes to wage differentials.

CEO Compensation

CEO compensation occurs due to an employee’s set of skills, schooling,  and prior work experience, but the structure of their wages can differ across countries.  For this section an in depth analysis of Japan and the United States is conducted. The structure of CEO pay in Japan and the United States of America is drastically different (Zhou and Pan, 2261).  These two systems both compensate CEOs with much higher pay than the average employee of the firm, but differ in how they hire their CEO and the way in which they receive their income.

The sample being looked at covered 424 companies in Japan from 2010 to 2015 and 274 pairs of one-for-one matched Japan and U.S firms. Overall the data proved CEO compensation to be substantially different from the United States (Zhou and Pan, 2261).  It was found that Japanese CEO’s base salaries accounted for seventy-one percent of their total compensation while in the United States their base salary represented only twenty-one percent of their total compensation (Zhou and Pan, 2263). Bonuses and stock options proved to account for much more when it comes to American CEOs, making up more than seventy-three percent of their earnings while in Japan it makes up merely twenty-three percent of their net compensation (Zhou and Pan, 2263).  This is one example of how Japan and America differ in the structure of how CEOs are paid.  Another between these two countries is that CEO pay in Japan is relatively insensitive to firm size and performance as compared to the United States.  The elasticity of CEO total compensation with respect to sales in Japan was only 0.13, but in the U.S the elasticity was much larger at 0.45 (Zhou and Pan, 2263).  Regarding performance, in Japan there is a five-cent change in CEO pay for every thousand-dollar change in shareholder value, while in the United States it is a fourteen-cent change per thousand dollar change (Zhou and Pan, 2263).  One final statistic, that might be the most crucial, is the total compensation paid to the average CEO in each country.  In Japan their average chief executive officer receives 1.1 million dollars a year and in the United States their average was 4.8 million dollars a year (Zhou and Pan, 2263).  That is 3.4 times higher than Japan and helps prove that international wage differentials have not decreased or vanished between all countries. CEO pay and firm size have a relationship that can vary significantly from country to country; this means that when examining larger sized firms, international wage differentials could be much greater as compared to average sized companies, which proved to be true in this case.  The reasoning behind such a large gap such as this could be accredited to unique Japanese culture and tradition which is difficult to quantify and model.  They have long-term employment relationships in Japan with an average CEO tenure of 20 years compared to 12 years in the U.S and focus on internal promotions and even post-CEO positions.  In America one-third of new CEOs come from outside the company as compared to just seven-percent in Japan (Zhou and Pan, 2264).  These reasons can explain that in Japan it could be more about the reputation and hard work that comes with earning the position of chief executive officer internally, while in America they must offer incentives of higher earnings.

The American CEO Education, Pay and Performance

Looking at the educational attainment of 6,305 CEO’s from top American universities compiled by Forbes over a ten-year period from 1997-2006 and comparing these with certain characteristics included with CEO compensation. A second data set has been used to compare the CEO’s compensation and their performance,using this information the study was able to determine whether the CEO’s pay is scaled to their investors return (Furumo, Jalbert and Terrance). Higher pay would be assumed to be tied with higher returns of assets, equity and investments.  Several studies have been done looking at the effects of the Stock returns on CEO compensation.  Pennathur and Shelor have found there to be a positive relationship between the stock returns in the real estate market and the CEO compensation within that market. Dechow (2006) concluded that the compensation of a CEO is more sensitive to a negative stock return than a positive return on stock. When looking at the wage differential among CEOs an important factor is to look at the negative relationship between the CEO’s age and risk as discovered by (Furumo, Jalbert and Terrance 5).

When looking at how bonuses are impacted by previously attending a top university, we can see that the bonuses are not simply impacted by obtaining an undergraduate or graduate degree. Salary overall is significantly impacted by the undergraduate degree but not a graduate degree.

Using these together we are able to see that graduate schooling is a heavy influencer on the overall salary of CEOs. However, bonuses and overall compensation are impacted by performance and not attributed to the schooling of CEO’s as much as the base salary. Harvard accounts for 11.5% of all CEO’s and counts for 12.5% of CEO’s that hold degrees. Harvard has grown to the number one provider of degree wielding CEOs. Despite this fact the study has found that Harvard graduates actually have a lower return for their investors when compared to the other top Universities (Furumo, Jalbert and Terrance 20). This study shows how even among CEOs there is a wage disparity, CEOs who have graduated from CUNY Queens and CUNY CITY earn the highest total compensation of all CEO’s.

Gender Wage Differentials

There are numerous factors that go into gender wage differentials. Some think that gender differentials can be explained by individual characteristics, education, effort, employment experience, skill difference, and industry (Schafer and Gottschall 468). It also has been found that women-friendly policies, labor market structure, wage-setting institutions, industry and sector employment and regulation all contribute to the gender earnings gap (Schafer and Gottschall 469).

Industry Gender Discrimination

There tends to be inter-industry segregation and intra-industry segregation for women. Women tend to concentrate in lower-paying jobs or have lower-paying occupations in firms (Schafer and Gottschall 468). Especially among the private sector industries, including the manufacturing and finance industries. These industries have less enforcement on equal pay policies (Schafer and Gottschall 474). Women in these fields will obtain less earnings than their male counterparts even if they have the same skills and education. There is a smaller gap in gender earnings in the public sector, because of wage policies. An example of a wage policy that lessens the gap is a more compressed overall wage structure (Schafer and Gottschall 470).

Looking at three different industries among 25 European countries, Schafer and Gottschall found that the largest gender gap was in the manufacturing industry, then finance, and the lowest gap in the health industry. This shows that there is a larger gender earning gap in private market sectors compared to the public non-market sector of employment. The extent of gender wage differentials does vary by country, but there is a trend throughout that the private market sectors have larger wage gaps. There could be less enforcement of equal pay policies in these industries. Some of the larger gaps in the manufacturing industry are found in Cyprus, Norway, Lithuania, Bulgaria, Poland, Malta, and Hungary (Schafer and Gottschall 479). In the health industry, Finland, Czech Republic, Latvia, Luxembourg, France and Spain have high gender wage gaps (Schafer and Gottschall 480). Countries where gender composition influences wages in industries show higher wage levels in male dominated industries compared to female-dominant industries. Women tend to be underrepresented in the industries that have higher gender gaps, and the women working in the industries they are underrepresented in are being underpaid compared to their male counterparts.

Gender wage differentials vary based on industry. Two of the  main influences on the gender wage gap is that industries with less emphasis on equal pay policies will pay women less and private market sectors will most likely underpay women compared to their male counterparts. Also, there are larger gender wage gaps in some countries because women are usually underrepresented in those industries.

Collective Bargaining, Wage Policies, and Centralization Effect on Gender Differentials

Countries that have high collective bargaining coverage rates and a high minimum wage relative to median earnings have a lower gender wage gap, especially among the manufacturing, finance, and health industries (Schafer and Gottschall 481).  If women can negotiate their wages, then they are more likely to receive a wage closer to their male counterpart. Moreover, labor force participation is strongly and positively related to predictors of earnings. The payoffs for participating in the labor force are significantly higher for men than women. More men tend to make up the labor force because they get more out of it then than their female counterparts.

Additionally, egalitarian earnings distributions can decrease the gender wage gap. Lower gender gaps are shown in countries with egalitarian earnings distributions when measured in nominal terms (Mandel and Semyonov 957). There is more emphasis on equal rights and equal pay among genders. The gender gap is considerably lower in liberal states, except Australia (Mandel and Semyonov 960).

Another factor that contributes to the gender wage gap is centralization. The higher the degree of centralization in a country, the less women will earn, leading to a higher gender wage gap (Schafer and Gottschall 483). Countries that have high degrees of centralization are Austria, Sweden, the Netherlands, Norway, Germany, Slovakia, and Latvia. Women tend to have lower odds at earning a living wage compared to their male colleagues. Across European countries women suffer a significant gender earnings gap.

In some countries, discrimination is prohibited based on gender, and it is state in their constitution’s. Some of these countries include Finland, France, Germany, Greece, Italy, the Netherlands, Spain and to some extent Belgium (Schafer and Gottschall 473). The gender earnings gap will be lower in these countries because there is an emphasis on equal rights and the government enforces it.

Gender Differentials Based on Family Policies

Family policies are expected to affect the earnings inequality between women and men in different countries. In a study among 20 countries, family policies were grouped into two indicators (Mandel and Semyonov 955). One indicator is the number of fully paid weeks on maternity leave and the other being the percentage of preschool children in publicly funded childcare facilities (Mandel and Semyonov 955). A third factor, but is not as prominent, is included which is the percentage of the total workforce employed in the public social service sector, such as Health, Education, and Welfare. This measures the availability of public services that are provided by the state and as the employer, the role of the welfare state.

Countries with well-developed family policies tend to have more equal wage systems, leading to a lower gender earnings gap (Mandel and Semyonov 959). When looking at maternity leave policies, it shows these policies have a considerable and positive effect on gender earning inequality. Family-supportive policies, such as long lasting maternity leave policies, can affect women’s opportunities negatively. Employers are discouraged from hiring women if the maternity leave will interrupt work significantly. This deters employers from hiring women for high-status and managerial positions, because they fear they will have children and stay home with them for long periods of time (Mandel and Semyonov 963). Women can barely compete with men for high paying jobs in these countries. Equal paying wage systems decrease the gender earnings inequality, but family-friendly policies do not lead to a reduction in gender gap.

Gender Differentials Based on Educational Attainment

Males and females tend to obtain degrees in different fields. Based on the degrees received, can contribute to the gap in gender wage differentials. A study conducted in the United Kingdom looked at degrees from elite schools versus non-elite schools and the degree obtained had created a gender wage differential (Sullivan 667). The degrees were grouped into three main categories: STEM (Science, Technology, Engineering, and Mathematics), LEM (Law, Economics, and Management), and OSSAH (Other Social Sciences, Arts and Humanities which includes languages). An OSSAH degree from a non-elite university was more common for women than men (Sullivan 669). Women tend to go into fields of social sciences, arts, and humanities, which also tend to be lower paying jobs.

From this study, men were twice as likely to obtain a STEM degree from a non-elite university than women were (6% vs 3%). There was a smaller gap for elite universities and STEM degrees (3% vs 2%). At an elite school, there was a smaller gap between men and women for obtaining a STEM degree (Sullivan 669). However, the gender differential in STEM was greater at non-elite universities. There was no substantial gender difference in LEM degrees from elite and non-elite universities.

Overall, more men tend to graduate with a STEM degree than women. Women tend to graduate with an OSSAH degree, and this contributes to the gender wage gap because higher paying jobs are often in the STEM field.

Studies

In a study of full-time workers among 25 European countries, the gender earnings gap was the smallest in Italy at 9.4% and the highest was Cyprus at 28.3% (Schafer and Gottschall 479). Countries like Finland, Norway, Sweden, and Germany have some of the highest gender earning gaps. Belgium, Poland, Hungary, Spain, Slovenia, Italy, and Ireland are some of the countries that had the lowest gender wage gap (Schafer and Gottschall 479).

In a study done among 20 countries, the average wages of males were higher by 26% than the average wages of females with the same characteristics (Mandel and Semyonov 956). Even if a female has the same characteristics as a male in the same field, the female is still subject to lower earnings. Furthermore, inequalities in gender earnings are the most prominent in the Netherlands (.48) and Germany (.46). They were the least prominent in Hungary (.12), Italy (.15), and Finland (.17). Hungary remains to be the most equal rights and opportunity country, followed by Israel and France. In these countries the percentile earnings gap between men and women is less than 10 percentiles (Mandel and Semyonov 956). In the Netherlands, Germany, Switzerland, Denmark, Norway, and Sweden these gaps are twice as large.

Additionally, in a sample that consisted of 51% males and 49% female (Sullivan 667). Of the males, 8% were in the top 5% of earners at age 42, while only 3% of women were in the top 5% of earners by this age. This means that 76% of the top earners were males. Women are much less likely to be top earners than men.

In every country there are gender wage gaps. There are numerous factors that lead to these gaps such as women being underrepresented in some industries, family policies being less developed, and the degrees received among males and females are different. Some countries do have larger gaps than others because these factors are more heightened. Overall, the general trend in each country is that males earn more than females.

Effect of Education and Skills

Education can have a significant effect on wages earned. In 2018, a study was conducted in the United Kingdom to determine if those with characteristics of degrees from elite schools versus non-elite schools and the degree obtained created a wage differential (Sullivan 667). The three main degrees looked at were STEM, LEM, and OSSAH.

People who attended private schools rather than public school were more likely to obtain a degree (66% vs 21%). People who went to private schools to obtain a degree in OSSAH and STEM are more likely to gain these degrees from elite universities rather than non-elite universities (Sullivan 670).

Those who did not obtain a degree were least likely to be in the top 5% of earners (2%), then people who obtained an OSSAH degree from a non-elite institution (5%), and also those who achieved a STEM degree from a non-elite institution (14%). From elite universities, those who obtained a degree in OSSAH could only account for 17% of the top 5% of earners. Most commonly, the degrees held by the top earners were LEM from elite institutions (32%), then STEM from elite institutions (22%), and LEM from a non-elite institution (21%). The majority of top earners are from STEM and LEM fields, and not OSSAH fields, even if the degree was received from an elite institution (Sullivan 672).

Moreover, socio-economic background is important as well. People with graduate parents made up 12% of the top earners, compared to those whose parents had no qualifications at 1%. 41% of the top earners came from a household of graduates. It is found that individuals who grow up in high-income families are more likely to make it to the top 5% than those who come from lower income families. Often, high-income families will send their children to elite universities, which often leads to higher earnings. The people who did not attain a degree were shown to be at a disadvantage due to their childhood social origins (Sullivan 677). Family background can contribute to getting an education, which then affects the wage gap for educational attainment.

Entry Level: Gender and Education Differences in Switzerland

In Switzerland the educational wage gap has been coming to a close. Despite Switzerland being one of the “later” countries to promote women’s rights for their nations women. Swedish women have been recently added to the voting scene as they  received voting rights in 1971 (Bertschy 10). When looking at the gender wage differential, occupational differences are estimated to make up 10-12% of the wage differential. Switzerland has an average as high as 19% gender wage differential when compared to the OECD (Organisation for Economic Cooperation and Development) countries average less than 16 % gender wage differential. (Liebig 159). Using data from the Swiss Youth Panel and TREE (Transitions from Education to Employment) is used to see the career entry wages, wage discrimination and salary increases within the time frame of the first few years with their job. The TREE study is a data set of 6,000 young people who entered into the Programme for International Student Assessment survey (PISA) during the year 2000 and left the required schooling by law that year. During the time frame ending in 2007 these young people were annually interviewed concerning their transition from their required schooling to the labor force.  This resulted in an average monthly wage for Men of 4,058 CHF and 3,753 CHF for Women with a total average of 3,908 CHF. The gender difference is 305 CHF with men making 150 above the total monthly average and women making 155 CHF below the monthly average (Liebig 165).

The data set also contains the human capital achieved by these young adults via their PISA Reading Literacy Score as well as their highest level of education received. (Bertschy 159). 75% of both male and females have completed an apprenticeship, 16% of both have completed vocational baccalaureate and 5% of all completing a University of Applied Sciences Degree. While the gender differences for completions are very similar, women have better formal qualifications and have better Reading Literacy than the males (Liebig 165).

This study does back up the findings of the previous article with the Canadian education system. It has been found that women at entry levels experience an estimated wage iscrimination of about 7.3% (Liebig 170). This discrimination is not based on risk, occupation or productivity. Rather based of gender discrimination as a whole (Liebig 170). When compared to Europe Switzerland shows great respect for gender equality and female labor force participation is marked as some of the highest in European countries. Switzerland holds an even household distribution of unpaid and paid labor when compared to more traditionalist countries (Bertschy 11). This study looks at entry level positions to try and eliminate the “Human capital factor” and given that the educational gender gap has been shortened with many young women obtaining an education compared to men, according to this there should not be a pay gap.

Looking at the segregational effects of the starting wages among industries that hold 70% of each respective gender, defined as “Male or Female Professions”. Women make 3,824 CHF per month, this is more than the average wage of the total entry level women. While men working in male dominated industries make less in these male dominated industries on average per month at 4,028 CHF (Bertschy 165). Males working in female dominated industries tend to make a higher salary, the same is not said for the reserved roles as women may occupy niche jobs in those industries. The data presents a wage gap that is found to be linked to women accepting and picking professions or training that are paid at a lesser rate in the labor market (Bertschy 11).

Immigrant Wage Differentials

Every country has people who immigrate from another country and try to find work in the country they reside in. There is a wage differential among migrants and natives. Migrants are defined as people who were born overseas and natives were born in that country (Islam and Parasnis 3). Migrants often choose to enter a labor market where the economy is booming, and their skills are in high demand.

Based on experience, the language, and minority status, wage differences can be determined among varying ethnicities. There is a high correlation between immigration, language, and minority status that needs to be accounted for to estimate ethnic wage differentials (Fang and Heywood 114). Also, migrants may not have the skills that the country’s labor market requires, and they will have to gain the skills over time. For example, more recent immigrant minorities who use a foreign language at home do earn less (Fang and Heywood 120).  They may not know how to fluently and effectively communicate with others because of a language barrier. Migrants who speak the language of the place they reside in will obtain higher wages than their migrant counterparts who do not speak the language at all or fluently.

It takes time for migrants to learn the work culture, language, and gain the skills needed to succeed in the country. More experienced immigrant workers can work their way up and potentially make significant earnings.

Wage Differentials Among Migrants and Natives: White Collar and Blue Collar Workers

When looking at two different categories of occupations, white collar and blue collar, there is a difference in wages for migrants and natives. White collar workers consist of managers and professionals while blue collar workers consisted of technicians, trade workers, community and personal service workers, clerical and administrative workers, sales workers, machinery operators and drives, and labors (Islam and Parasnis 3). In general, migrants tend to have higher rates of unemployment compared to natives. It may be harder for migrants that do not have skills to find a job in another country.

There is a greater proportion of migrants that have white collar occupations. Migrants tend to earn higher wages than natives in white collar jobs. Education is one factor that can explain the migrant wage advantage in these jobs. Migrants are more qualified than natives for white collar jobs, and have a higher education compared to natives (Islam and Parasnis 14). However, in blue collar occupations, migrants earn less than their native counterparts (3%). If a migrant and a native have the same educational attainment, the migrant earns less than the native in blue collar and white collar occupations. It is not statistically significant among white collar individuals though.

Conclusion

After drawing comparisons across multiple countries, using the analysis from above, these were the factors found across different countries that had the largest impact on the wages that workers experience:

• The size of firms, level of wage bargaining, and the skills of the labor force affect sectoral wage differences.
• Trade-openness and technological advancements have a positive impact on wages.
• Inter-industry wage differentials are the result of characteristics of employers in each sector.
• The CEO to worker pay ratio differs between countries, especially Japan and the United States.
• Women tend to be more concentrated in lower wage fields, creating a gender wage gap based on industry.
• Collective bargaining, egalitarian wage systems, high minimum wage, a lower degree of centralization, and more well-developed family policies lead to smaller gender wage gaps.
• The gender wage gap is generally smaller in more progresive decentralized countries. This implies that the governing policies of these progressive countries have a negative relationship with the gender wage gap.
• The international average from OECD Countries regarding the gender wage gap is less than 16%. Women are less likely to be in the top 5% of earners compared to men. Several studies have suggested gender discrimination to be a major component to the wage gap, we have collectively concluded this to be case.
• Those who obtain a STEM or LEM degree from an elite institution are more likely to earn more throughout industries and nations. Higher education may affect base salaries but not success or rates of performance.
• Socio-economic conditions surrounding education and before entering the labor force also have a large effect on the attainment of higher income. Migrants will tend to make less than natives because migrants do not have the skills yet to succeed in a different country.

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