On Friday the 29^{th} of June I attended a thesis meeting with Dr. Poon and two of his other thesis students, Gary and Leslie. Dr. Poon had informed me that R was going to be used instead of E-views as the statistical software to be used for this project and the other two projects. After the conclusion of the meeting I downloaded and installed the latest version of R. Since I have no experience with this statistical software package, my additional goal for this week is to become familiar and competent with this tool. I have already found numerous tutorials on R and plan to complete a few of them to gain familiarity with R.

Project Plan

The meeting was extremely useful as we collectively came up with a broad project plan where we defined the particular thesis deliverables and goals we need to complete by the given date. The project plan can be seen below.

Over the coming weeks I will post up a more detailed project plan which will list the weekly milestones and deliverables I wish to achieve.

Additionally the meeting provided each of with greater clarity regarding the description and requirements of our project. In the sections below I have briefly outline the background to the problem and the specific sections which my project will be focusing on.

Problem Overview: Information Technology and Productivity

An extensive amount of literature has been written on the impact that information technology has on productivity. A measure of this impact can be represented by the following general mathematical representation:

where:

y is the output measured

IT are the input variables used to measure IT by

*f* is a specific productivity function applied to our input variables

Using this representation all the literature on information technology and its impact on productivity examines the following:

- Is the relationship between IT and its impact on productivity significant
- What are the specific factors that enhance this relationship

However a lot of the research into this area in particular during the 1980’s and early 90’s lead rise to a phenomenon called the productivity paradox. What the productivity paradox is that as information technology is introduces the productivity of a worker may actually decline as opposed to increasing.

The papers that I listed on my post on June the 7^{th} provided numerous explanations and potential hypothesis as an explanation for this phenomenon. I will give an overview of these papers later on in the week.

The main problems from the examination of the relationship between IT and productivity stems from three main sources which are:

- Finding a good quality measure of IT
- Measurement issues regarding both output and input variables in terms of adjustments for factors such as inflation etc.
- Issues regarding the measurement of complementary factors of IT

*Finding a good quality measure of IT*

IT can enhance the productivity of firm by two means. Firstly IT can be used as tool by acting as a substitute to manual labour and introducing automation. For instance, Brynjolfsson & Yang (1996) highlighted that, this particular use of IT is wide spread in the manufacturing sector where IT has automated a lot of processes such as ordering and has lead labour to become redundant in some instances. Secondly it can act as an enabler by adding value to a firms’ business process. For instance in the financial services industry (where I have experience in), I have observed that IT can add value to a business processes in various ways such as allowing profitable clients to be identified form their trading activities and thereby allowing more tailored client services to directed towards their needs.

The difficulty in finding a good quality of measure of IT is that whilst it is fairly easy to find data on IT acting as a tool it is more difficult to find data on IT as an enabler due the intangibility of it. Consequently if we do not capture this effect, our measurements of IT will be biased and this will flow through to our statistical tests and hypothesis interpretation.

*Measurement issues regarding both output and input variables*

* *Following on from the first problem is measurement issues in both output and input variable predominantly for the services industry. Brynjolfsson & Hitt (1993) listed the results of a survey which they conducted with managers that listed the five key rationales for investing into IT which are:

- Labour savings
- Better customer service
- Faster response time
- Greater product variety
- Improved quality

The majority of this are intangible items are very difficult measure in terms of the impact it would have on overall output of a firm and as these cannot be measured and we would essentially be omitting them from our data set once again we would be faced with biased estimators and incorrect inferences being drawn from the hypothesis tests.

Furthermore the data used needs be adjusted for factors such as inflation etc. The majority of deflators that are available do not account for the improved quality, improved customer service etc. and by using this incorrect adjustment our data would once again be faced with the statistical problems that measurement error causes in analysis.

*Issues regarding the measurement of complementary factors of IT*

The complementary factors of IT (which are used in answering the question as to what are the factors that can enhance this relationship), pose significant problems in the analysis of the impact of IT on productivity. This is because these factors which add value to the organisation are intangible and are poorly measured if it all. Consequently the data that is aggregated excludes these items and we are potentially faced with the issue of an omitted variable bias. This is a serious statistical issue which violates on of the fundamental classical linear regression model assumptions, which is that the disturbances terms are independent of the regressors. If the omitted variable is correlated (in most cases it would be) with the input variables we have included, we once again will have biases estimates and incorrect conclusions drawn form our hypothesis tests.

**Link between my thesis and the problem**

My thesis will be employing the Organisation of Economic Cooperation and Development (OECD) definition of information technology. The OECD categorises IT into four distinct categories which are:

- Hardware
- Software
- Communication
- Services

Using data from the Australian Bureau of Statistics (ABS), measures are available for the first two that is hardware and software but not for the last two. Essentially what my thesis will aim to show whether an aggregation error exists and if hardware and software essentially add to the notation of a productivity paradox. Hence it will focus on showing how two of the four explanations provided by Brynjolfsson & Yang (1996): the existence of measurement error and lags due to learning and adjustment that It causes, contribute to the productivity paradox. To illustrate that software and hardware are not substitutes, that is not interchangeable for each other but complements, I will need to employ the Allen partial elasticity of Substitution (AES).

**What needs to be done by the 9th of July**

My next meeting held with Dr. Poon and my fellow colleagues will be a week from now, July 9^{th}. Dr. Poon has entrusted us with finding more research papers that will give us further clarity in determining out thesis objectives and definitions. In addition to this I need to collect the data for my thesis from the ABS. The data will be take from the Economic Activity Survey, where I need to obtain the data for particular items of the survey. I will also aim to gain an understanding of AES and further my comprehension of the impact of input substitution on the productivity paradox.

Aims for next week:

- Obtain further reading material
- Start to learn and develop skills using R
- Investigate AES