Revisiting the issues of labor discrimination

It is generally accepted by economists that discrimination is an influential factor in affecting the functioning of a modern day economy. Theories regarding discrimination – its impacts on the global economy and possible solutions to the problem – have been debated and argued by influential economists over the decades. A key question that we ask ourselves is: will the market regulate itself and be able to eliminate discrimination, or does the government have to intervene? If so, what is the best approach to government intervention? This question can be seen as a part of the larger debate between the neoclassical economists and the Keynesian economists over the role of government in our economies and social lives.

This problem of whether or not to regulate the issue of discrimination has been debated by politicians and economists over the years, especially since discrimination is not only an economic issue, but also a social one. Historically, governments have taken a generally laissez-faire approach to economics in society, and regulations are few, especially in the US. This changed dramatically starting in the late 19th century, with the emergence of populist movements such as women’s suffrage, and accelerated dramatically during the two World Wars and the Depression era, when governments began to take a more active economic role in society and mandated fairness in hiring in order to receive federal funding. Finally, the Civil Rights movement of 1950s and 60s pushed the issue to the forefront and the government enacted broad legislations regarding labor employment practices. Two of the most notable legislations of the period are the Equal Pay Act of 1963 and Title VII of the Civil Rights Act of 1964. The Equal Pay Act have stated that firms should take into consideration a person’s gender in the determination of wages using the theory that that the same amount of work deserve the same amount of pay. Title VII of the Civil Rights Act made it illegal to discriminate based on a person’s “race, color, religion, sex or national origin”, and implemented a comprehensive list of anti-discriminatory methods.

However, in more recent years, the problem of discrimination takes on a new turn with the rise of “Deregulation” and the stepping away of government from some of its historic stances on promoting more equality in the market-place, and igniting the debates anew.

labor and management.jpgThe current consensus is, in a way, a reaction against the free-market advocates, which have become especially popular in the US since the 1980s. In fact, it has been argued by economists that discrimination has increased from the ‘80s onward, in large part due to the popularity of this line of argument. We can examine this opposite side of the argument by looking at the positions taken by two of the greatest economists of the latter twentieth century: Milton Friedman and Robert Lucas. Friedman had argued that the free market will resolve the problem of discrimination itself because discrimination is inefficient in the long-run (“Capitalism and Freedom”, 1962). In one of his most often quoted passages, he stated “It is a striking historical fact that the development of capitalism has been accompanied by a major reduction in the extent to which particular religious, racial, or social groups have operated under special handicaps in respect of their economic activities; have, as the saying goes, been discriminated against.” Friedman believed that the employer’s self-interest will cause them to overlook the other categorical attributes of an individual in favor of whoever can work the cheapest for the most amount of productivity.

On an interesting note, Friedman was himself the subject of discriminations during his times at the University of Wisconsin at Milwaukee, and one of the chief reasons he chose the University of Chicago for its PhD program was due to its open and more tolerant environment. In a sense, Friedman affirmed the idea that discrimination is detrimental to the employer (in this case the university) by “voting with his feet” to a location that was more tolerant.

Writing along a similar line, Robert Lucas stated that any irregularity in the “Market” introduces a distortion that will resolve itself over time. And in his view, government attempts in ending discrimination will simply introduce new inefficiencies in the marketplace that has to be resolved. What both of these economists suggested is that firms are very rational and they pursue the maximum amounts of profits possible. In order to do this, it only makes them to only care about costs and benefits, and since race/ethnicities/gender, etc. does not have a specific benefit or cost associated with them, firms will not discriminate. For those firms that do discriminate, in the long run they will become inefficient and the competition will eliminate them from the marketplace. The free market is the best left alone, according to Friedman and Lucas, since the mechanism of incentives in a rational society will help to eliminate discrimination and get rid of these inefficiencies.

 

Meanwhile, the mainstream have taken the view that in order for discrimination to be solved, the markets must be regulated through governmental legislations and acts. They are essentially arguing for a top-down, command-and-control method in regulation approaches to enforce those regulatory methods. Many noted that more regulation has been the historical trends, as more legislations have come on board over the years to prohibit certain behaviors from employers. They outlined two main approaches by governments to combat discrimination. The first is what is generally referred to as “Nondiscrimination” where employers are essentially blind to race, ethnicity, or sex, and to determine that those factors should not play any role in the selection of workers (This is the principle behind the Equal Pay Act). The other approach is termed “Affirmative Action”, where employers MUST take race, ethnicity, and gender into account to ensure fair representation, especially for historically disadvantaged groups. These two approaches have proven to be somewhat contradictory, i.e. how to ask ask employers to be blind to the differences between workers while at the same time be cognizant of the fact that certain groups should be considered more highly, holding other factors constant? This contradiction made it difficult to implement some of these methods in ending discrimination, and it is somewhat flawed as a result.

In addition, Title VII also distinguished between disparate treatment and disparate impact; where disparate treatment is defined as being proof that the workers are intentionally being discriminated against, while disparate impact are defined as result from actions, however unintentional, that results in some groups being disproportionately impacted. All of these are important considerations for firms that are trying to avoid discrimination.

In cases where it can be difficult to implement equal for equal work, they introduced the idea of comparable worth to help measure employee value. Often, many noted, it is impractical to “achieve equal pay for equal work”. Therefore, some have supported the goal of equal pay for jobs of “comparable worth”, and what determines the comparable worth is market forces. Comparable-worth policies have generally relied on job-rating schemes by employers to determine or justify pay differentials. However, this job-rating scheme is highly subjective and subject to great controversies.

As a case example, many pointed to the example of the Federal Contract Compliance Program, where governments monitor hiring and promotion practices of federal contractors. This program utilized affirmative action to ensure that groups that have been historically disadvantaged received preferences. In terms of absolute numbers, the federal contract compliance program increased opportunities for minority groups tremendously. The concerns with these programs is that when underrepresented groups are given preferences in hiring, this might result in less qualified workers being hired. And since the programs only covered the federal contractors, it is possible that while the program attracted talented minorities, there might be no overall gains in employment due to other sectors of the economies being neglected. As evidence of the effectiveness of the government programs, some have pointed out that government policies have distributed new employment opportunities among federal contractors towards blacks and Hispanics. The ratio of black to white incomes has risen since the 1960s, but we cannot effective draw causation relationships between this and the governmental legislations.

Finally, the mainstream believed that it is important to continuously monitor the economy to catch discriminators. One way to do this is to conduct an audit where blind experiments are conducted, telling auditors to look at firms and measure the effects of discrimination. However, these studies are very difficult to conduct since the auditors cannot know the purpose of the experiment (since that will introduce an element of bias), while at the same time, they are very difficult to conduct due to cost constraints. In another famous experiment, which has since been replicated worldwide, experimenters send out resumes to a number of different firms. It was found that white-sounding names needed 10 resumes to receive one call back, while black sounding names required 15 resumes to receive one call back, a 50% difference in employer response rate. However, even this experiment can be subject to bias, as the names may in themselves be a signal on the quality of the workers, and not necessarily having anything to do with race itself. For instance, it is possible to have a name of “Jared” being associated with a bad worker, but not necessarily to that person’s race.

 

I believe that while the the mainstream’s position is elegantly argued for, and we agree with the general premise that the markets need to be regulated. However, I believe that regulations may not work in all cases. The solutions many economists presented are excellent, but may not be adequate since it doesn’t allow a degree of freedom to the individual to decide in specific cases of discrimination. Governments can do a number of other things that can combat the effects of discrimination, besides direct, top-down regulation. I believe that the government should embrace a comprehensive, top-down approach in fighting discrimination, while at the same time, it might work with other players in the market so that anti-discriminatory laws can be used effectively and efficiently.

Firstly, I believe that free markets are efficient in the sense that it generally can allocate resources as needed to the market actors. Markets generally have a very remarkable ability to become efficient with the right incentives. However, in the case of discrimination, it may become inefficient due to the lack of those incentives. In many cases, discrimination can be good for businesses since they are able to charge different wages to different individuals, and they are able to get the same amount of work out of some workers while costing a fraction of the wage expense. This has historically been the case with what we call the “gender wage gap”, where men and women are paid different wages for essentially the same amount and quality of work. In addition, we often see firms hire workers whom they or their employee knows well (a network effect). This can be discriminatory because the results (disparate impacts) can be discriminatory in nature. The only way to solve these issues is by having firms being regulated directly by the government to change the historic legacy.

Secondly, I believe that governments should take a leading role, but not the only role in helping to end discrimination. A government’s approach should be based on both “carrot” and “sticks”. Governments can directly punish the worst discriminatory offenders, while at the same time, they offer incentives to encourage diversity in the workplace. Governments should consult the private sector to see why they may not want to hire women/minorities, and work with them to help design incentives to help end discrimination.

Thirdly, governments can also utilize other methods that are not direct regulations, for instance through education in non-discrimination. This in fact has been promoted in the schools’ educational curriculum in the past few decades and have been credited with helping new generations of workers and employers understand the value of diversity in the workplace. Educational changes can cause the deepest changes in the way workers interact with others and in a firm’s hiring practices. In many cases, the markets simply are not aware of the potential benefits a diverse workforce can bring along, and it takes some educational efforts, in part facilitated by the government, to change the firm’s hiring practices.

Lastly, I believe that the free movement of people has been extremely beneficial for firms and discriminatory practices would stop this free movement of people. Government should do all it can to make sure that worker mobility is not impacted, as historically, workforces that move around tend to reward firms that are the fairest and most efficient at utilizing labor. For instance, during mass construction projects that are undertaken by the government or large corporations in the past, people of different ethnicities often come and work together, albeit sometimes on different parts of the same project (i.e. the transcontinental railroad). This has been very beneficial for the employers as they are able to attract the best talents due to the mobile workforce.

To conclude, I believe that our solution is a compromise between the neoclassical, free-market advocates on the one hand, and the regulation-heavy advocates on the other. Businesses exist in an environment where discrimination exists and governments need to ensure that workers do not encounter discrimination through regulations, workplace incentives and education programs. At the same time, governments need to consult with private companies to see what works best to end discrimination. A collaborative environment between governments and businesses, we believe, is often the best one in ending discrimination. Behind all of these proposals in ending discrimination is our firm belief that markets, when given the right incentives, will come to the rational conclusion: Discrimination results in an inefficient utilization of resources, firms will lose out on some of the best talents, and in the long run, only firms that do not discriminate can survive in our global, interconnected world.

Fertility Rates: Why are they so different around the world

I wrote about this topic recently for a class of mine, and I thought I would share this topic here, since it’s an issue that have interested demographers and other social scientists for a long time.

Introduction and Significance of the Study:

It has been frequently observed that women around the world today have vastly different fertility rates. Last year, a news article from CBS news suggests that the dropping birthrates, especially in developed nations, is threatening global economic growth rate.(CBS) This is indeed a worrisome issue for policy-makers, from Germany to Japan. At the same time, we note that these developed nations are also among the most densely populated regions in the world, suggesting that these nations in the past have had high population growth rates, but subsequently slowed their birth rates. At the same time, many nations in Sub-Saharan Africa have relatively low density populations and abundant agriculturally productive land (Kenya, Tanzania), yet are economically underdeveloped. In class, we spoke about the “demographic transition”, i.e. each of these countries are in a different stage of this transition from high to low birth rates (Goldstein). However, given the observation that many nations that have low birthrate already have a high population concentration, we wondered if population density in fact affects the number of children a woman will have and if other underlying factors – such as governmental actions, social norms (especially for women), and levels of economic development – will affect the number of children a women have over the course of her lifetime.

Hypothesis: Regions with high population density would have lower fertility rates; this is due to economic development over time, the role of women in society and government policies.

In this study, we looked at 3 broad geographic regions: East Asia & Pacific, Middle East & North Africa, Sub-Saharan Africa and compared their developments over time. These 3 regions were used since the cultural practices, economic fortunes, and governmental influences were vastly different and provides a good cross-sectional study for analyzing the changes in global fertility rates. We will determine if the changes in fertility rates in these three regions are indeed negatively correlated with population density and other underlying factors such as economic development, women’s employment and other factors such as the availability of contraception.

2) Data Extraction and Methods:

All data for this study came from the World Bank Data, from 2012 and 2013 depending on its availability. We utilized all available data the following variables to complete the study:

  • Total fertility rates: the average number of children that a woman is expected to have over the course of her lifetime (for 1960-2013)
  • Overall population density: total population of the country divided by its total land, in people/km^2 (for 1960-2013)
  • GDP Per Capita. The Gross Domestic Product (GDP), a measure of total national economic output, divided by the country’s population for a given year (for 1960-2013)
  • Female labor force participation rate: percentage of women active in the labor force, aged 15 or older. (for 1990-2012)
  • Contraception prevalence: the percentage of women (or her partner) who were practicing any form of contraception; for women ages 15-49. Data available only for 1990, 2000, and 2010.

In this study, we used several different prospective factors that may affect the overall population density and were associated with changes in fertility rates for women: GDP per capita, female labor force participation and contraception usage. The three regions were chosen based on their differences in changes in Total Fertility Rates, such as timing and speed of decline, in order to study what could have contribute to this different variations in their respective patterns of decline. The observed period of time was selected as the maximum number of years for which data was available to ensure that whatever correlation we observed was not do to random variations within the data set. In addition, a separate study was done for China to measure a special case of the effect of government policies on the decline in birthrates.

For the sources of data, our date ranges are from 1960 to 2013 for fertility rates, overall population density, and GDP per capita; and ranged from 1990 to 2013 for female labor participation and contraception usage. We use the largest date range available for each variable in order to more accurately determine the long-term trends for each variable.

Methodology: We decided to analyze the data by presenting the relationship between the variables in a graphical format. For readability, we divided the variables into two sets of 3 graphs each, with each graph representing a separate region. The first set of graphs presented fertility, population density and GDP per capita in each of the graphs. For the next set of 3 graphs, we presented fertility rates with women’s labor force participation and access to contraceptives. Then we calculated the correlations between the fertility rates with each of the other variables to give a more definite, mathematical result. The final graph measured specifically China’s decline in birthrate and increasing per capita income.

3) Presentation of Results:

Graphs 1-3 records data for the three regions from 1963 -2013. It measured the changes through time of fertility rates, population density and economic output. Graph 1 depicted Middle East/North African fertility, GDP per capita and population density trends over time. There were several trends common to all. First, there was a very strong negative correlation between the fertility and population density/GDP per capita in all three regions measured (a correlation between -0.8 and -0.98). Second, population density had been steadily increasing for East Asia & Pacific and Middle East, while the Sub-Saharan African density had been increasing much more dramatically. Thirdly, the most rapid phase of GDP per capita increase occurred in all regions after 2000.

For Graph 1, we saw that the fertility rate for the Middle East steadily decreased from 1960 to 1985 (6.87 to 5.88) and then had a steeper decline from 1985 to 2000 (5.88 to 3.04), and finally the decline in fertility stabilized at around 2.75. Meanwhile, the population density increased dramatically from around 10 people per square kilometer to around 36 people per square kilometer, increasing roughly linearly. Therefore, there was a strong negative correlation between population density and fertility decline in this region. Meanwhile, per capita income in the region has also increased, most significantly from 1973 to 1980 and from 2000 onward. Graphs 2 and 3 told a similar story. East Asian &Pacific fertility declined drastically from late 1960s, from 5.5 children per woman in 1968 to 1.85 per woman in 1998; economically, the region’s per capita income steadily increased until 1995, and then stagnated from 1995 until around 2002, before starting to increase drastically once again. For Sub-Saharan Africa, the decline in fertility occurred much later, starting around 1987, and had been declining at a slower pace than for the other two regions discussed. Likewise, sustained per capita increases only occurred starting around 2001.

Graphs 4-6 records data for the three regions from 1990 -2010. It measured the changes through time of fertility rates, percentage of women in the labor force and the prevalence of contraceptives. All three regions witnessed the increased use of contraception: East Asia increased from 73% using contraception to 80% usage rates; Sub-Saharan Africa from 15 to 25%. Graph 4 depicted this increase in the East Asia/Pacific region and showed a roughly steady participation by women in the labor force. Aside from East Asia, there existed strong correlation between labor force participation by women and declining birth rates (-0.95 for both Middle East and Sub-Saharan Africa). Finally, a separate graph (Graph 7) was drawn for China by plotting its decline in fertility rates over time for the purposes of examining the effect of public policy on fertility. We see that fertility rates in China declined drastically from 6.3 in 1965 to 2.71 in 1980. Declining further until 1998, and it has held steady at 1.6 since then.

4). Conclusion

This paper studied the relationship between fertility rates and population density and prospective underlying causes for changing population densities. We found that there was a clear negative correlation between declining fertility rates and each of the individual factors measured: GDP per capita, female labor force participation and the prevalence of contraceptives. However, we cannot isolate any of these individual factors and point to it as a cause for declining fertility rates. Each of these factors are not mutually exclusive and acted to reinforce one another as well. For instance, increasing GDP per capita can increase contraceptive use since more women now could afford these new products; or along the lines of Boserup, increasing population could lead to greater density and more innovations and technological changes, which in turn increases income and decreasing the fertility rates. (Boserup) And it is possible that the variables examined are the result rather than the cause of fertility decline (i.e. a demographic “dividend” from having less child dependency) (Factsheet). The causes of fertility decline were complex and this paper only sought to examine a small amount of variables that can affect it.

The effect of family planning and government measures were more open to debate. For example, in China, we saw that fertility rates has already fallen to 3 by 1980, the year the so called “one-child policy” was implemented (Moore). Thereafter, the fertility rates steadily decreased, but based on comparisons with East Asia as a whole, it appeared that this fertility decline would have taken place even without the said policy. What appeared to be more significant in causing fertility decline remained the other factors discussed, such as increasing economic performances and contraception usages.

Limitations of the study:1. Exclusion of certain countries and regions from the study. There are incomplete information (missing fertility rates etc.) for certain country’s data. Therefore, these countries are not included in the regional averages. Some of the countries excluded have very high population density and relatively high birthrates (ex. some Pacific Island states) which are both factors we are attempting to draw conclusions from in this paper. This exclusion could result in errors that can affect our conclusions based on the graph and these data, once included, may result in slightly altered correlations and possible interpretations.

  1. Numerous other factors that may affect population density and fertility rates. There are other underlying factors that can cause a decline in fertility rates other than the economic development, women’s participation in the economy or government policy. Even though fertility rates negatively correlates between each of these factors, we cannot conclusively state that fertility rates rate is caused by these factors. Other factors that may be impactful include women’s educational attainment, and increasing quality and quantity of public health services. More studies need to be done how the effects of some of these other factors may directly impact fertility rates.
  2. The factors that contribute to fertility decline are not fully independent of one another. For example, the increased distribution of contraceptives may be the result of increasing economic output as measured by increases in GDP per capita, which enabled women to purchase contraceptives in the first place. The variables measured in this study can and do influence each other. Therefore, the conclusion drawn (that a negative correlation exists between fertility rates and all the other variables), may be an oversimplification.

Appendix:

Graph 1, Middle East GDP Graph 2, East Asian GDP Graph 3, Africa GDP Graph 4, East Asian labor force Graph 5, Middle East labor force Graph 6, African labor force Graph 7, China's GDP

Works Cited

Boserup, Ester. “Population and Technology in Preindustrial Europe.” Population and Development Review 13.4 (1987): 691-701. JSTOR. Web. 01 Apr. 2015.

“Contraceptive Prevalence (% of Women Ages 15-49).” World Bank, n.d. Web. 01 Apr. 2015. <http://data.worldbank.org/indicator/SP.DYN.CONU.ZS&gt;.

“Dropping Birth Rates Threaten Global Economic Growth.” CBSNews. CBS Interactive, 7 May 2014. Web. 01 Apr. 2015.

“Fact Sheet: Attaining the Demographic Dividend.” Fact Sheet: Attaining the Demographic Dividend. Population Reference Bureau, n.d. Web. 01 Apr. 2015.

“Fertility Rate, Total (births per Woman).” World Bank, n.d. Web. 03 Apr. 2015. <http://data.worldbank.org/indicator/SP.DYN.TFRT.IN&gt;.

“GDP per Capita (current US$).” World Bank, n.d. Web. 01 Apr. 2015. <http://data.worldbank.org/indicator/NY.GDP.PCAP.CD&gt;.

“Labor Force, Female (% of Total Labor Force).” World Bank, n.d. Web. 01 Apr. 2015. <http://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS&gt;.

Moore, Malcolm. “What Is China’s One-child Policy?” The Telegraph. Telegraph Media Group, 30 Oct. 2014. Web. 01 Apr. 2015.