C HANGING P RODUCTIVITY OF O IL F IRMS IN N IGERIA By David Mautin Oke (Ph.D.) Department of Economics Lagos State University, Nigeria.

O UTLINE  Introduction  Measuring Changing Productivity of Oil Firms  Methodological Issues  Discussion of Findings  Concluding Remarks

1. I NTRODUCTION  Oil and gas industries across the globe have continued to face diverse set of political, environmental, human and technological challenges in the process of exploration and production.  Gathering and analyzing data quickly and effectively in a controlled laboratory environment is another hiccup faced by energy firms in Sub Saharan Africa (IBM, 2004).

I NTRODUCTION C TDS.  Aside management obstacles faced by some firms, many field workers operate independently in harsh and remote oil and gas fields. Centralized monitoring of wells requires oversight and procedural changes that may be difficult to introduce.  Productivity growth is fundamental in every industry. It is one benchmark of effective operational management strategies and adoption of best practices.

2. M EASURING C HANGING P RODUCTIVITY OF O IL F IRMS  Changing productivity of oil firms refers to movements in productivity performance of an oil firm over time.  There are several simple and intuitive methods for measuring productivity change. Four popular approaches are often adopted. 1) Using the rate of change in output relative to change in input

M EASURING P RODUCTIVITY C HANGES C TDS. 2) Using growth in profitability after making appropriate adjustments for movement in input and output prices over the base and the current periods 3) Comparing the observed outputs in the base period and current period with the maximum level of outputs (keeping the output mix constant) that can be produced using inputs in the base and current period operating under the reference technology.

P RODUCTIVITY C HANGES M EASUREMENT C TDS. 4) Component-based approach in which the TFP change is decomposed based on its sources. For instance, Balk (2001) decomposed productivity change into efficiency change, technical change and scale change. Coelli et al.(2005) decomposed TFP change further into four components namely technical efficiency change (relative to a CRS technology), technological change, pure technical efficiency change ( relative to a VRS technology) and scale efficiency change.

3.0 M ETHODOLOGICAL I SSUES Based on Coelli et al. (2005) Efficiency change = d t 0 ( q t, x t ) / d s 0 ( q s x s ) … (1) Technical change = [ d s 0 ( q t, x t ) / d t 0 ( q t x t ) × d s 0 ( q s, x s ) / d t 0 ( q s x s ) ] 0.5 … (2) Pure efficiency change= d t 0v ( q t, x t ) / d s 0v ( q s x s ) ………………………. (3) Scale efficiency change = d t 0c ( q t, x t ) / d s 0c ( q s x s )÷ d t 0v ( q t, x t ) / d s 0v ( q s, x s ) … (4)

M ETHODOLOGICAL I SSUES C TDS. The sum of capital and reserves are used as input so as to cover a broad range of inputs. This is always equal to total assets employed based on accounting principle. The output used is turnover. These data were extracted from the audited financial reports and accounts of five selected oil firms in Nigeria: Total Plc, Oando Plc, Mobil Plc, Niger Delta Exploration and Production Plc, and Forte Oil Plc- formerly African Petroleum Plc over the period This purposive sample is a combination of oil firms that engaged in production and marketing operations.

M ETHODOLOGICAL I SSUES C TDS.  Using the Malmquist DEA and adopting the work of Coelli et al. (2005), TFP change is decomposed into four items as shown in eqs. (5)- (10) {d o t (q t, x t )} -1 = Max ø,λ Ø, Subject to - Øq it + Q t λ ≥ 0, x it – X t λ ≥ 0, …………………. (5) λ ≥ 0,

M ETHODOLOGICAL I SSUES C TDS. {d o s (q s, x s )} -1 = Max ø,λ Ø, Subject to - Øq is + Q s λ ≥ 0, x is – X s λ ≥ 0, …………………. (6) λ ≥ 0, {d o t (q s, x t )} -1 = Max ø,λ Ø, Subject to - Øq is + Q t λ ≥ 0, x is – X t λ ≥ 0, ……………… (7) λ ≥ 0,

M ETHODOLOGICAL I SSUES C TDS. {d o s (q t, x t )} -1 = Max ø,λ Ø, Subject to - Øq it + Q s λ ≥ 0, x it – X s λ ≥ 0, …………… (8) λ ≥ 0, {d o t (q t, x t )} -2 = Max ø,λ Ø, Subject to - Øq it + Q t λ ≥ 0, x it – X t λ ≥ 0, …………… (9) I1 ' λ = 1 (where I1 is an I × 1 vector of ones) λ ≥ 0,

M ETHODOLOGICAL I SSUES C TDS. And {d o s (q s, x s )} -2 = Max ø,λ Ø, Subject to - Øq is + Q s λ ≥ 0, x is – X s λ ≥ 0, …………… (10) I1 ' λ = 1 λ ≥ 0,

4. F INDINGS FIRMEFFCHTECHCHPECHSECHTFPCH Total Plc Oando Plc Mobil Plc NDEP Plc Forte Oil Plc Average Source: Author ’ s Computation Table I: Firm-Level Malmquist Index in 2007

F INDINGS C TDS. FIRMEFFCHTECHCHPECHSECHTFPCH Total Plc Oando Plc Mobil Plc NDEP Plc Forte Oil Plc Average Table II: Firm-Level Malmquist Index in 2008 Source: Author’s Computation

F INDINGS C TDS. FIRMEFFCHTECHCHPECHSECHTFPCH Total Plc Oando Plc Mobil Plc NDEP Plc Forte Oil Plc Average Table III: Firm-Level Malmquist Index in 2009 Source: Author’s Computation

F INDINGS C TDS. YEAREFFCH TECHC H PECHSECHTFPCH Table IV: Malmquist Index Summary of Annual Means Source: Author’s Computation

F INDINGS C TDS. FIRMEFFCHTECHCHPECHSECHTFPCH Total Plc Oando Plc Mobil Plc NDEP Plc Forte Oil Plc Average Table V: Malmquist Index Summary of Firm Means Source: Author’s Computation

5. C ONCLUDING R EMARKS  Global practices needed in Nigerian oil firms  Need for continuous use of modern technologies e.g. digital oil field and collaboration technologies  Closing the labour and skills gap through training, re-training of indigenous oil workers and increase local and foreign scholarship opportunities for graduates and undergraduates in engineering and management fields