The efficiency of the construction industry is heavily reliant on its level of productivity. As noted by Adnan et al (2007), until the productivity level of construction activities is improved, the nation’s economy will continue to suffer setbacks. Hence, improving productivity has become a major concern of every profit-oriented organisation (Adnan et al, 2007). An important component of the construction industry productivity is labour. Labour costs represent a considerable proportion of the final cost of a building, usually accounting for between 40 to 60% of the building cost (Butchan et al, 1993). In addition, labour is known to be the most important factor of production since it is the only factor that creates value and sets the general level of productivity (Ameh and Odusami, 2002). According to Yates and Guhathakurta (1993), labour productivity is the value of gross output per worker, referred to as man-hour or work-hour. It also could be referred to as the careful attempts to measure the physical output of labour taking into account the other factors that affect construction productivity.

Recognising its importance, several studies have focused on the factors that affect labour productivity (Oloko, 1983; Lema, 1995; Yagba and Ayandele, 1999; Ameh and Odusami, 2002; Adnan et al, 2007; Kane et al, 2007). However, none of these studies provides an empirical evaluation of the level and extent of influence of these factors on the productivity of the Nigerian construction labour. Given the importance of such an issue, this research seeks to examine the influence of some productivity factors on labour outputs that were empirically established by the authors for some selected trades, and published elsewhere.

According to Ashworth (2002), the common method of estimating the costs of construction works involves the multiplication of unit rates and the measured quantities in the Bills of Quantities (BOQ). The calculation of the unit rates for the individual measured items in a BOQ requires the collation of current cost information for labour, plant and materials, as well as overhead and profit (Ayeni, 1999). The estimation of the cost figures of materials, plant and overhead and profit has never been a point of discourse and contention. This is because they involve the quantitative estimation of the cost values for plants and overheads while

market survey research for materials prices are the basis for the material cost estimation (Ashwort, 1999). The aspect of labour pricing is usually done on the basis of the output constants collected on each trade (Ayeni, 1999).

Establishing a unit rate of construction works involves collating current information with regards to the cost of materials, labour, plants and overheads and profit. The materials, plant, overhead and profits components can be quantitatively estimated. The labour component however, cannot be precisely estimated due to variety of factors that affect the determination of the labour output. The accuracy of the labour constants commonly used in the Nigerian Construction Industry is unclear and uncertain because BESMM3 and geographical location were not considered.

A previous study conducted by Sani (2011), on determination of labour output for doors and window fixing did not take precedence of the work items in BESMM3. It has therefore become necessary to empirically determine the labour output constant for the Nigerian Construction Industry to comply with the Building and Engineering Standard Method of Measurement (BESMM3).

The high degree of inaccuracy found in our BOQ estimates is mostly attributed to the uncertainty of the accuracy of the labour constants used in pricing labour costs. In Nigeria for example, most of the output constants used by estimators are either derived from experience or are remnants of British colonial heritage Abdulrazaq et al, 2010. Hence, non-uniform outputs are widely used across the construction industry. These outputs do not take in to account, Nigeria’s BESMM3 and geographical locations.

Furthermore, the study will afford Construction Managers the opportunity to (Gilleard, 1992):

a.        Determine how effectively his or her projects are being managed;

b.        Detect adverse trends quickly so that corrective action may be taken quickly;

c.        Determine the effects of changed methods and conditions;

d.        Identify both high and low areas of productivity and reasons for these differences;

e.        Compare performance between sites.

The dangers associated with inaccurate estimate cannot be overemphasized. According to Garret (2006), the sustainability and success of the construction industry depends greatly on the level of accuracy of the project cost estimates. This is because financial commitments required for a typical construction project are very high and inaccurate cost estimates can have detrimental effects on all parties involved (Onukwube, 2002). Garret (2006) noted that if the cost estimate for a project is wrong, then financial pressures, hardship, supply chain conflict and quality problems will result. In addition, the client’s vulnerability as the ultimate risk holder of the finished building can be horribly exposed and no one may possibly gain (Morledge, 2006). The accuracy of project cost estimates is an important issue to all stakeholders.

The aim of this research is to determine the labour outputs for fixing selected windows and doors in Kano and Jigawa states Nigeria.

1.       To examine the influence of labour factors on productivity.

2.       To establish the labour output of some selected windows and doors fixing.

3.      To investigate through literature, the sources and origin of some of the labour outputs currently in use in Nigeria.

In order to conduct inferential test to determine the influence of the labour productivity factors on the labour outputs established, both null and alternative hypothesis were found deemly necessary (Abdullahi, 2009).

These hypotheses are:

·         Hypothesis (H1): The ages of workers have a significant difference over their outputs on site.

·         Null hypothesis (H1o): There is no output difference between the various age groups observed.

·         Hypothesis (H2): The outputs of workers vary with changes in the period of work.

·         Null hypothesis (H2o): there is no output difference in all the different periods of observations.

·         Hypothesis (H3): The level of outputs of workers depends on the type and level of payment made to them.

·         Null hypothesis (H3o): there is no output difference in respect of the type and level of Payment to workers under observation.

·         Hypothesis (H4): The group of highly experienced workers has higher outputs than those with low work experience.

This material content is developed to serve as a GUIDE for students to conduct academic research