Difference in return between rich and poor countries in relation to schooling
Countries produce a different type of products. The type and quality of the products which they produce vary between rich and poor countries. However, this paper focuses on understanding how the patterns of returns differ between the United States and Kenya in relation to schooling. The schooling system in our countries especially the public schools should be the major equalizer in the provision of the same education to each and every student in the country (Hanushek, Schwerdt & Woessmann, 2015. The system should support equality in the opportunities for the young and more so their performance in the job market independent of the student background. There is no any instance the system has ever offered equality to the students and therefore, according to the researches that have been contacted, they point out that it is much less as an equalizer today than it used to be for the past few years. This has been depicted by the nature of the existing difference between the returns of workers from higher developed countries like the United States and the returns of workers from developing countries like Kenya. Hanushek et, al. (2015) reveals that the market share of industries in the United States is higher than the market share of industries in Kenya. This is because of the quality of production of workers from the United States is of high quality compared to the quality of production of Kenyan workers. We also learn that the industries in the United States tend to export certain types of machinery, for instance, hospital equipment which require a high level of knowledge when designing them something which is hard to find in developing countries like Kenya (Valdés, 2017). The topic of this paper is to outline the variation in return amid the developed and the undeveloped countries in relation to schooling. Therefore, focusing on this topic, we narrow down to the thesis of this paper which states that; there is a difference in returns between The United States and Kenya in terms of schooling. In this scenario, the United States of America represents the developed countries and Kenya represents the less developed countries. This paper covers six sections in its structure. The first one is the introduction. It is followed by a literature review where discussion about previous researches has been done. After the literature review, we have the theory and methods where explanations about the theoretical methods that have been used in analyzing this topic are discussed along with the predictions that have been applied in the topic (Manuelli, & Seshadri, 2014). We have data where a detailed discussion of the report including the exact units, variables and changes to the raw data are discussed. We also have the results where the paper findings along with the data analyzing methods are discussed. Finally, there is the conclusion where the whole report is summarized and restating the main points of the report and how the findings relate to the theory
Historically, random researches have been done trying to give explanations about how the quality of production amid the developed and the undeveloped countries vary as well as across different continents (Razin & Wahba, 2015). For instance, Berg et, al. (2015) in his article states that the education gradient socially is steep. The reason behind this is that people that have high social, economic status achieve higher outcomes than the people with low social, economic status. Referring to his country South Africa, Berg et, al. (2015) further explains that the children from the richest quintile of schools are the one-and-a-half difference in standards compared to them that are in the poorest quintile in the test of SACMEQ mathematics. In another interesting result, Berg et, al. (2015) helps us understand that the quality of education outcome difference in poor countries like Kenya contains many of the children living in poverty. In reality, many children in Kenya perform badly; thus the gradient is socially remarkably flat (Brian, 2015). He further adds that the education level that the children attain hardly helps them in producing quality and competitive products in the market. The wealthy also does not gain from quality public services in that country.
Theory and Model
This paper explains that there is a difference in return between The United States and Kenya in terms of schooling using the theory of comparative advantage. To begin with, comparative advantage refers to a condition where one country does the production of goods and services at a lower production cost compared with other countries (Costinot, Donaldson, & Smith, 2016). Opportunity cost itself is for measuring trade-off. A country that has comparative advantage makes the trade-off worth it. Costinot et, al. (2016) explains that the advantages of purchasing their goods are more important than the disadvantages. The nation could be the most excellent in the production of a commodity, but the commodity possesses a reduced cost opportunity cost for importation by other nation.
Taking an example of oil-producing companies in Kenya, they have a comparative advantage in chemicals. Their oil that is produced locally provides a cheap source of raw material for chemical production compared to companies in other countries that do not produce oil. The cost of their chemicals being low makes them have a low competitive advantage. Some years back, competitive advantage was more evidence in goods and rare in services. The reason behind it is that products were easier to export. Advancement in technology like the internet nowadays makes it easier to sell abroad. Such services comprise of call banking, entertainment, and centers.
Comparative advantage theory
Back in the eighteenth century, the theory of comparative advantage was created by an economist named David Ricardo. Ricardo explains that a country increases its growth economically by placing its focus on areas where it has the most comparative advantage. For instance, the United States has the ability of manufacturing cloths at a low price. A country like Kenya might have the conditions required for growing coffee (Rassekh, 2015). Predictions by Ricardo are that the United States of America would stop growing coffee and Kenya would stop manufacturing cloths, and that was correct. Therefore, The United States would make more money selling its products to Kenya for Coffee, and the opposite is true. For it would be more expensive for The United States to grow all coffee it requires because the climate condition is not conducive. This would be the same thing for Kenya. Therefore, both countries gained by exchanging what they lacked. The comparative advantage theory makes explanations why protectionism of trade does not perform in the long run. Leaders are always under pressure from their locals so that they can protect jobs by raising tariffs from the international competition (Witt & Jackson, 2016).
In writing this paper, I choose this theory because it presents my case in a clear way. This is because; comparing the difference in return between The United States and Kenya regarding schooling is a scenario of competitive advantage. The quality of competitive advantage is seen where both countries The United States and Kenya have a labor force, but the type of labor force in The United States is much more educated compared to that found in Kenya. Therefore, The United States is more advantageous compared to Kenya because through its production using highly educated personnel; it has the capability of delivering higher quality products than Kenya. Thus the theory of comparative advantage appeared to be the best for me.
In the previous part, we have realized that there exists a comparative advantage between the two countries, i.e., Then the United States and Kenya (Davis, & Dingel, 2014). This is because developed countries seem to have a comparative advantage in one set and the less developed countries in another. Previously, we got to learn that such kind of a pattern has relation to the intensity of the industry in using tertiary education. For what reason donations have a comparative advantage in education-intensive companies? There are several possibilities in this section.
Differences in factor endowment
To begin with, we are going to consider a story for factor endowment: developed countries are high on education-intensive companies for the reason that the number of workers with tertiary education is high. For this reason, we will find the relative mean productivities AiJ=AUS;j through the use of a function of production with three methods of labor (Burger, Van der Berg, & Von Fintel, 2015).
By measuring the cost relatively by w3i; the US only makes sense that the quality of labor is just similar across the two nations. But, the findings of the literature are that the education quality differs significantly between the two nations. Therefore, if the education quality is varying across the countries; then there is a need for wages adjustment because of this varying.
The educational quality measures that we use are from Duvivier et, al. (2017). These are the estimates that he makes concerning the education quality for the 130 nations with the use of data of immigrants for the United States (Duvivier, Burch, & Boulet, 2017). After making the adjustments on the wages using his measures of quality, we then do the calculation on the relative wage contribution by education by the pattern of mean productivities.
The findings found on this paper are that there is a disparity in return amid the developed and the less developed countries regarding schooling. Unlike other researches, the focus of this paper is on the workers with tertiary education in both countries as a separate factor of production and collecting data for their use in the two countries, i.e., the United States and Kenya. Different from other studies that have been performed earlier, this one has made the focus on the worker's education rather than their classification either as skilled or unskilled. This is because there is some little similarity between the skill of production and the education level (Brazendale, Beets, Weaver, Pate, Turner-McGrievy, Kaczynski & von Hippel, 2017).
In this research, I have considered four reasons that make high developed countries have a comparative advantage in education. To begin with, the labor that is highly educated is relatively cheap in highly developed countries when we choose to consider the quality of education. A lot of evidence exists that education quality differs significantly across many countries. Among this evidence are the international test scores and the immigrant’s earnings (Cavender-Bares, Balvanera King & Polasky, 2015).
The second reason why the United States schooling return rate is high compared to that of Kenya is that innovations take place at a faster rate in education-industries of countries that are developed. This is possible because of patents and computer usage. The third reason is that adoption of technology in less developed countries is low in the education-intensive industries (Patrinos, 2016). We also get to learn that foreign technology licensing is rampant in education-intensive of the developed countries compared to other industries and countries. Unavailability of educated labor is a contributing factor to slow technology adoption.
The other reason is that the industries that are education-intensive calls for highly experienced management methods. Therefore, through the use of international data management technology, we learn that education-intensive industries make use of intensive management technology (Mensah, & Alagidede, 2017). Similarly, the management of developed countries is of high quality. The relation of this paper is extensive to the literature that searches for the pattern of trade and specialization. The model of Ricardian tells us to check the comparative advantage that is driven by the production differences of labor.
Nevertheless, there is something that is not satisfying about the model of Ricardian. The pattern of trade is determined by the differences in the productivity of labor. But what determines the differences in labor productivity. Heckscher (1919) and Ohlin (1924) created a model that explained the difference in the productivity of labor by the differences of factor endowments across countries and differences of factor use across industries (Lecoeuvre, 2016). The existing weakness is that the empirical performance of the factor endowment is not good. Previous researches that have been contacted have indicated differences in factor endowment clarify a little part of the comparative advantage. Disparities in productivity are required to explain the rest.
The look for clarification of trade pattern largely parallels macroeconomic search for an explanation of differences in per capita income across countries, also known as accounting development. In development accounting, large disparities in full aspect production across countries are required to explain per capita income differences. Such kinds of differences in productivity are interpreted as technological differences (Barker III, & Schmitt, 2017).
There exists empirical literature that does the investigation of the effects of education on growth. Generally, education effects on growth are found to be weak. Several reasons for these findings have been found to have been said in the literature: (a) for instance we have attenuation because of mismeasured schooling data. (b) Differences in cross-country in educational quality. When the differences in the quality of education are accounted for, there is a significant increase in education effects (Tasrip, Husin, & Alrazi, 2017). This paper is focusing on the literature on inequality returns. Because the level of returns is related to education, there happen to be some changes in trade policy; the workers with different levels of education will be affected thus affecting income inequality as well.
The progress of this report is as follows; I began with the estimation of the country as well as the industry productivity measures that gave information regarding the Ricardian comparative advantage of the countries. I then took patterns across the industries in the two countries. The obtained results suggested that there were some missing factors from the analysis that contains large power of explaining productivity for cross-industry and cross country. I hypothesized the missing factor is human capital (Fowler, 2019). To learn about the effects of human capital, narrow down the three forms of according to education level: labor with more than the primary section, more than primary but less than tertiary education. I gathered data of employment of such kind of labor in 10 industries in the United States which is a developed country as well as Kenya which is undeveloped country (de Souza, Assis, & Pal, 2017). This is the first record of data collection. I also looked for earnings for data for these types of labor in both the countries of the dataset. It results that the production factor that has large power of production for the patters of trade is highly educated labor – with some tertiary education.
In conclusion, both developed, and undeveloped countries have specialized in product development from diverse companies (Chakraborty, Chatterjee, Dey, Ashour, & Hassanien, 2017). What characters defines these companies and what are they? By doing investigation concerning this issue and then doing estimations on the United States and Kenya about the exact measures of manufacture, the investigation finally gives information concerning the Ricardian theory of comparative advantage of the two countries. We then looked at how the production happens and the kind of people that are involved in the production process (O'connor, 2017). We finally found out that the quality of the products produced depends on the level of education of the workers in the industry and also the type of education that have impacted on the workers too. What differentiates the industries whose elasticity is high to those whose elasticity is low? Is it possible that some factors are in low supply in low developed countries and high in more developed countries?
Answering these questions applies the use of the singular factor decomposition technique (SVD). The obtained results indicate that there exists one big power of explaining the cross-country variation in their efficiency (Drucker, 2017). Statistical analyzing indicates that companies that have high intensity in any of these factors are the ones that can produce more in relations to per capita income. This means that their limiting factor is the same in less developed countries. It is suggested that the missing factor is capital which for a long period has been unavailable from the international trade empirical analysis (Storey, 2016). Breaking down effects of human capital, there were three types based on education level: labor without primary education, labor that is less tertiary but above primary leave, and finally labor that is tertiary and above. After examining the above three type of data, for about 15 companies in both countries, i.e., The United States and Kenya, it opens up to me that the largest production factor is propagated by highly educated labor and this is the labor with at least tertiary level of education. These are the type of industries found in The United States where the level of production is high, and they are the ones that have a comparative advantage over the poor countries.
Now the remaining question is why do these developed countries specialize more on education-intensive goods? The research contacted reveals that educated labor in the developed countries is less expensive than in undeveloped countries. In addition to this, this paper reveals that innovation occurs first in the developed countries and takes place faster in the industries that are education intensive compared to others. The licensing of new technologies is more in the education intensive companies. There are various reasons that hinder licensing in less developed countries (Drucker, 2014). Therefore, the adoption of technology in education-intensive industries takes place faster when compared to less developed countries. For one, developed countries have a large number of an educated workforce which can adopt technology faster; secondly, the licensing of technology is much spread in developed countries compared to less developed countries. The result is that the technological gap between the developed and less developed countries is big because of the difference in the education level of the labor force. Therefore, because of the above reasons, it is evident that there exist differences in returns between The United States and Kenya with reference to the level of education.
Barker III, V. L., & Schmitt, A. (2017). 17 Firm Turnarounds in Knowledge-Intensive Industries. Turnaround management and bankruptcy: A research companion, 69, 327.
Brian, K. (2015). OECD Insights Income Inequality The Gap between Rich and Poor: The Gap between Rich and Poor. OECD Publishing.
Brazendale, K., Beets, M. W., Weaver, R. G., Pate, R. R., Turner-McGrievy, G. M., Kaczynski, A. T., ... & von Hippel, P. T. (2017). Understanding differences between summer vs. school obesogenic behaviors of children: the structured day's hypothesis. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 100.Burger, R., Van der Berg, S., & Von Fintel, D. (2015). The unintended consequences of education policies on South African participation and unemployment. South African Journal of Economics, 83(1), 74-100.
Cavender-Bares, J., Balvanera, P., King, E., & Polasky, S. (2015). Ecosystem service trade-offs across global contexts and scales. Ecology and Society, 20(1).
Chakraborty, S., Chatterjee, S., Dey, N., Ashour, A. S., & Hassanien, A. E. (2017). Comparative approach between singular value decomposition and randomized singular value decomposition-based watermarking. In Intelligent techniques in signal processing for multimedia security (pp. 133-149). Springer, Cham.
Costinot, A., Donaldson, D., & Smith, C. (2016). Evolving comparative advantage and the impact of climate change in agricultural markets: Evidence from 1.7 million fields around the world. Journal of Political Economy, 124(1), 205-248.
Davis, D. R., & Dingel, J. I. (2014). The comparative advantage of cities (No. w20602). National Bureau of Economic Research.
de Souza, J. C. S., Assis, T. M. L., & Pal, B. C. (2017). Data compression in smart distribution systems via singular value decomposition. IEEE Transactions on Smart Grid, 8(1), 275- 284.
Drucker, P. (2014). Innovation and entrepreneurship. Routledge.
Drucker, P. (2017). The age of discontinuity: Guidelines to our changing society. Routledge.
Duvivier, R. J., Burch, V. C., & Boulet, J. R. (2017). A comparison of physician emigration from Africa to the United States of America between 2005 and 2015. Human resources for health, 15(1), 41.
Fowler, B. M. (2019). Clinical Education to Decrease Perceived Barriers to Delirium Screening in Adult Intensive Care Units. Critical Care Nursing Quarterly, 42(1), 41-43.
Hanushek, E. A., Schwerdt, G., Wiederhold, S., & Woessmann, L. (2015). Returns to skills around the world: Evidence from PIAAC. European Economic Review, 73, 103-130.
Laursen, K. (2015). Revealed comparative advantage and the alternatives as measures of international specialization. Eurasian Business Review, 5(1), 99-115.
Lecoeuvre, L. (Ed.). (2016). The Performance of Projects and Project Management: Sustainable Delivery in Project Intensive Companies. Routledge.
Manuelli, R. E., & Seshadri, A. (2014). Human capital and the wealth of nations. American Economic Review, 104(9), 2736-62.
Mensah, J. O., & Alagidede, P. (2017). How are Africa's emerging stock markets related to advanced markets? Evidence from copulas. Economic Modelling, 60, 1-10.
O'connor, J. (2017). The fiscal crisis of the state. Routledge.
Patrinos, H. A. (2016). Estimating the return to schooling using the Mincer equation. IZA World of Labor.
Razin, A., & Wahba, J. (2015). Welfare magnet hypothesis, fiscal burden, and immigration skill . The Scandinavian Journal of Economics, 117(2), 369-402.
Rassekh, F. (2015). Comparative Advantage in Smith's" Wealth of Nations" and Ricardo's" Principles": a Brief History of its Early Development. History of Economic Ideas, 23(1), 59-76.
Storey, D. J. (2016). Understanding the small business sector. Routledge.
Tasrip, N. E., Husin, N. M., & Alrazi, B. (2017). The Energy Disclosure Among Energy Intensive Companies in Malaysia: A Resource-Based Approach. In SHS Web of Conferences (Vol. 36, p. 00014). EDP Sciences.
Valdés, G. (2017). Con respeto: Bridging the distances between culturally diverse families and schools: An ethnographic portrait. Teachers College Press.
Witt, M. A., & Jackson, G. (2016). Varieties of Capitalism and institutional comparative advantage: A test and reinterpretation. Journal of International Business Studies, 47(7), 778-806.