Previous Literature on Labor Productivity and Internet Connectivity 

Get Complete Project Material File(s) Now! »

Adam Smith’s theory on labour productivity

The idea behind this theory is that laborers will be more productive when there is a division of labor so that each worker is doing what they are technically good at. The division of labor using the same manpower creates a scenario where the skill of each worker is put in the right place. In other words, productivity is improved if the skill of the worker completing the specific task is the best out of all the available manpower. Furthermore, this theory emphasizes that when there is a division of labor and not just a few people doing everything then the amount of time wasted from switching from one individual task to another will be reduced. The third element of the theory of Labor by Smith that explains improved productivity is the use of machines that facilitate the individual working steps. This part utters that technological progress shortens working hours and enables workers to complete the work of many workers alone (Brem, 2013).
Smith’s work in developing a theory that explains labor productivity was important at the time and for some years to come because his theory had been the first fully systematic treatment of the subject of Labor economics (McNulty, 1973). The implication of Smith’s theory is ultimately that specialization is required in the workforce because this would result in interpersonal differences between men and not the other way around (McNulty, 1973).

Karl Marx’s Theory on Labour Productivity

Another theory on labor productivity was established by Karl Marx (Marx, 1887). In his theory, Marx asserts that productivity depends on the real appropriation of the means of production. In other words, there has to be a right combination between the subjective element of the labor process; that is the work itself, and the objective element of the labor process; that is the tools and objects used for the work. Therefore, the more ability and skill direct producers have, the more they can really appropriate the means of production, thus increasing productivity (Gartman, 1978).

Previous literature on Labour Productivity and Internet Connectivity

Both Smith and Marx’s theory on labor productivity provides the first general framework of what it is one needs to consider when pondering labor productivity. However, to pinpoint the relationship between internet connectivity and productivity we need to consider other measurable specific factors that could influence productivity. The ability of other economists after Smith and Marx to comprehend specific variables that could affect productivity and then investigate it was made possible through the theoretical framework their body of work provided. In the twentieth century after Smith and Marx, Charles Cobb and Paul Douglas developed a production function that measures the productive potential of a country (Hajkova et al., 2007). This past research is linked to the theory of productivity by Smith and is thus useful for this paper as the developed production function indirectly shows that the level of the average product of labor depends on specific variables such as capital and technical change. The Cobb Douglas production function is as follows: = where X, A, L, and K represent output at either firm/country level, technical change, labor, and capital respectively (Zellner et al., 1966). Rearranged as ∂ ∂ = −1 = , the Cobb Douglas function predicts a positive impact of technical change and capital on labor productivity. It is important to note that this production function makes certain assumptions that one could consider restrictive. For instance, the share of labor and capital in output is assumed to be constant at 75% and 25% respectively (Hajkova et al., 2007). In addition, it presumes constant returns to scale which restricts the elasticity of output concerning labor and capital to values between zero and one and their sum to being equal to one (Hajkova et al., 2007).
Another study that will help guide the process of answering the research question is on the effect of human capital on labor productivity. There have been several past studies on human capital theory including what the most important components are to consider when measuring human capital. Schooling is an element that many researchers on the topic find integral to increasing human capital (Delsen et al., 1999). However, it is up to debate which stage of schooling has the most effect on labor productivity. In the view of Nelson et al. (1966), higher initial education which is a component of human capital is a source of productivity growth. The idea is that if you improve the higher education of laborers, you will increase the human capital they possess, thus their labor productivity. However, after applying their new model using data on 10 out of the 14 UK industries from 1995 to 1999, Delsen et al. (1999) suggest that investments in lower and intermediate education may be more profitable to obtain a high productivity level. The reason is that higher qualifications in education are useful for more flexible work as well as a role where one takes care of the efficient allocation of inputs. This fosters productivity but according to them, intermediate education contributes more to the static worker effect which is a more significant component of labor productivity (Delsen et al., 1999). All in all, the conclusion drawn from both studies is that human capital influences productivity in a positive way. A further study by Corvers (1997) on the impact of human capital on labor productivity in about 15 manufacturing sectors in each of the 7 European Union countries studied comes to the same conclusion. The years studied in this research are from 1988-to 1991. In this paper, human capital is measured by the employment shares of intermediate and highly -skilled workers, and labor productivity reflect the worker and the allocative effect of their labor (Corvers, 1997). The main finding of this paper is that the effect of intermediate and highly-skilled labor on sectoral labor productivity is positive. However, it is only the worker and allocative effect of highly-skilled labor that is found to be significantly positive in the low-skilled sector.

READ  Akaike's Information Criterion (AIC)

Percentage of the Population aged 65 and above

This variable is another independent variable included in the regression that is used to explain labor productivity. It is a percentage so it is not logged in the regression. It is included in the regression to account for the effect the dynamics or make-up of a distinct demographic has on the labor productivity of that economy. We expect that the more a population ages, that is, the higher this variable is in a country the less labor productivity the country will have. We anticipate this based on the simulation study carried out by Rangelova et al. (2011) as well as the empirical research executed by Hellerstein, (1999) and Skirbekk, (2003). In the regression, this variable is measured by dividing the number of residents aged 65 and above in an economy regardless of marital status or citizenship, by the total population in the same economy and then multiplying by 100 (Data World Bank, 2022). The aggregation method is a weighted average.

Investment/Gross Fixed Capital Formation

The acquisition and utilization of capital in a country may affect the growth of labor productivity. This is why we have included this variable in the regression to control for this effect. The idea behind this variable as an indicator for capital is that the more investment in produced assets including the production of assets by producers for their use, the more capital an economy possesses. Therefore, we expect this to have a positive effect on the growth of productivity, the same way Cobb and Douglas proposed in their production function (Hajkova et al., 2007). Again this variable will not be logged in the regression as it measures the quarterly percentage change over the course of each year. For the regression, we took data on the rate of investment at quarter one of each year for every country.

Global Innovation Index

We included this variable because technical change, that is, innovation is assumed to play a significant role in productivity growth. This variable is supposed to enable us to observe how innovative an economy is. The higher this score is for a country the more innovation is apparent in that country. This variable is measured as an index therefore it is not logged in the regression. Moreover, it is the average of two other indices. Namely, the innovation input sub-index and innovation output sub-index. What this variable entails more so as an index compared to any other form of measurement is that it delineates how inventive an economy is as it pertains to its infrastructure, institutions, technology output, creative output, business sophistication, etc (Gii, n.d.). What is expected is that countries with a higher GII score will experience productivity growth. This outlook coincides with the Cobb Douglas production function which enforces the positive impact of technical change on productivity.

Table of contents :

1 Introduction 
2 Background
3 Theoretical Framework/Literature Review 
3.1 Adam Smith’s Theory on Labor Productivity
3.2 Karl Marx’s Theory on Labor Productivity
3.3 Previous Literature on Labor Productivity and Internet Connectivity
4 Hypothesis 
5 Method of Data Collection and Analysis 
5.1 Data
5.2 Method of Data Analysis
5.3 Variables included in the Regression
6 Result and Analysis 
6.1 Report on Data
6.2 Result
6.3 Analysis
7 Conclusion 
8 Reference List 
9 Appendix


Related Posts