Data Envelopment Analysis (DEA) is becoming an increasingly popular tool for assessing the relative performance of industries and companies. By applying DEA theory to the non-financial sector, the relative efficiency of 27 listed corporations in the United Arab Emirates (UAE) has been analyzed in this paper. The focus of the study has been on the impact of the financial crisis and the recovery thereafter. Further, the productivity change was decomposed into technical efficiency change and technological change by using the non-parametric Malmquist Productivity Index (MPI) over the period from 2007 to 2014. Based on Malmquist analysis, we find that the most efficient industries during the post-crisis period were food and beverages, telecommunication and pharmaceuticals. In contrast, the sectors that were adversely affected by the crisis were services, real estate, construction and cements. The break-up of the TFP indicated that the efficiency indices in the top performing industries were driven by technological improvements or frontier effects. The top-performing companies in the UAE during the 2007-14 period demonstrated innovation-led growth, aided by the use of better technology, investments in capital equipment, and adoption of new production processes.
The global financial crisis during 2008-2009 has been considered as the most severe economic setback since the Great Depression of 1929-1933. Global GDP growth rate fell from 5.2% in 2007 to 3.2% in 2008 and to −2.2% in 2009 along with dramatic falls in industrial production and global trade [
The UAE was adversely affected not only by the global recession and fall in the price of oil in 2009 but also due to the exodus of its expatriate population and the significant decline in tourism. The growth in real GDP in the UAE came down from 13% in 2006 to 1.3% in 2009 [
In the face of the economic slowdown, the UAE government began to pursue its diversification strategy into non-oil high-value manufacturing and service sectors. They adopted various industrial policies for sustainable development in the long-run and perhaps the most significant labor policy implemented in 2009 was the Wage Protection System (WPS) which was considered as a breakthrough in monitoring payments to unskilled construction workers. Another major effort to boost the manufacturing sector came through the trade agreements to increase domestic productivity and prepare the new, high-value industries to compete internationally. As a result, UAE’s non-oil exports rose from AED 65.4 bn in 2009 to AED 148.2 bn in 2013 [
The present study looks into the impact of the global recession on the non- financial sectors in the UAE. We apply data envelopment analysis (DEA) techniques to analyze productivity growth for the publicly listed companies in the UAE. The study adopts the efficient frontier approach, by using Malmquist’s [
The remainder of this paper is organized as follows. Section 2 introduces DEA and its extensions to the TFP growth through computation of the Malmquist Index. Section 3 describes the data and empirical findings. Section 4 discusses the sector-wise analysis of the empirical results. Section 5 provides conclusions and possible future directions.
In this paper, we use performance measures of decision-making units (DMUs) using data envelopment analysis (DEA) techniques. Estimation of frontier production and measures of efficiency began with Farrell [
The econometric approach was subsequently taken over by the mathematical programming approach using Data Envelopment Analysis (DEA). DEA is an optimization method that uses linear programming for assessing the efficiency and productivity of DMUs in terms of a proportional change in inputs or outputs [
Suppose a decision making unit (DMU) generates the outputs y i , ( i = 1 , 2 , ⋯ , t ) from the inputs x k , ( k = 1 , 2 , ⋯ , m ) , according to the weights ( v i = 1 , 2 , ⋯ , t ; w k = 1 , 2 , ⋯ , m ) on the variables. To measure the efficiency of DMU “p”, Charnes, Cooper, and Rhodes [
∑ i = 1 t v i y i ∑ k = 1 m w k x k (1)
The Data Envelopment Analysis program utilizes this rate of total factor productivity and maximizes the performance of the DMU “p” relative to the performance of the other units. We can transform the fractional programming model into a linear programming model [
Max v i w k ( ∑ i = 1 t v i y i p ∑ k = 1 m w k x k p ) (2)
Subject to
0 ≤ ( ∑ i = 1 t v i y i c ∑ k = 1 m w k x k c ) ≤ 1 and v i , w k > 0
where c = 1 , 2 , ⋯ , p , ⋯ , z
v i = 1 , 2 , ⋯ , t = weights of the inputs,
w k = 1 , 2 , ⋯ , m = weights of the outputs.
When the constraint reaches a value of 1, the DMU under measurement is said to be technically efficient and lies on the efficiency frontier that is composed of the set of efficient units. The observed data of inefficient units are said to be enveloped by the frontier. So, DEA measures the efficiency of each observation relative to the frontier that envelops all the observations. Solution of the linear programming model gives us an efficiency value of “p” DMU and the weights to reach this efficiency level. The concept of frontier is especially important for the analysis of efficiency, because we measure efficiency as the relative distance to the frontier. Firms that are technically inefficient operate at points in the interior of the frontier, while those that are technically efficient operate along the frontier.
Charnes, Cooper, and Rhodes [
The Malmquist Productivity Index (MPI) consists of distance functions representing multi-output and multi-input technologies based on the input and output quantities. The output distance function is used to consider a maximum proportional expansion of the output, given the inputs and the MPI measures the Total Factor productivity (TFP) growth change between two data points by calculating the ratio of the distances of each data point relative to a common technology.
The MPI represents Total Factor Productivity (TFP) growth of a DMU and reflects the increase or decrease in efficiency with progress or regress of the frontier technology over time under multiple inputs and multiple outputs framework. The TFP index is used to estimate the productivity change, which is decomposed into technical efficiency change and technological change.
The framework employed in the current study can be illustrated by
Malmquist [
In order to calculate these indices it is necessary to solve several sets of linear programming problems. If there are N companies and each uses varying amounts of K different inputs to produce M different outputs, then we would have ( K × N ) input matrix and ( M × N ) output matrix. This would help us construct a nonparametric envelopment frontier over the data points such that all observed points lie on or below the production frontier. The input distance functions that have been used to construct the Malmquist indices in this paper are the reciprocals of Farrell’s [
The DEA measure of efficiency is relative in that it is calculated in relation to all other DMUs in the sample. The efficiency score for each company is compared with technologies from the same time period and then compared to technologies from different time periods. The Malmquist Productivity Index constructs an efficient frontier based on the data and each company is then compared to that frontier. The closeness of a company to the frontier is “catching up” while the shifting of the frontier itself is the “innovation”. The product of these two components yields a frontier version of productivity change, which is the Malmquist TFP Index.
The Malmquist productivity index measures the total factor productivity change between two data points over time, by calculating the ratio of distances of each data points relative to a common technology. Färe et al. [
M t + 1 t ( y t + 1 , y t , x t ) = [ d t ( y t + 1 , x t + 1 ) d t ( y t , x t ) * d t + 1 ( y t + 1 , x t + 1 ) d t + 1 ( y t , x t ) ] 1 / 2 (3)
where M is the Malmquist productivity index in time t + 1 using the “t + 1” technology relative to the production in time t using the “t” technology. This equation represents the Malmquist productivity or TFP growth between the time period t and t + 1 as a geometric mean of the efficiency measures of each period. Moreover, the efficiency measure in a particular time period is given by the distance function relative to the frontier technology. A value greater than one will indicate a positive TFP growth from period t to period t + 1 while a value lesser than one will indicate a decrease in TFP growth relative to the previous year. We now write the Malmquist Productivity Index as
M t + 1 t ( y t + 1 , y t , x t ) = d t + 1 ( y t + 1 , x t + 1 ) d t ( y t , x t ) * [ d t ( y t + 1 , x t + 1 ) d t + 1 ( y t + 1 , x t + 1 ) * d t ( y t , x t ) d t + 1 ( y t , x t ) ] 1 / 2 (4)
where the first term defines changes in technical efficiency (E) from period t and t + 1 and the second indicates changes in technology (P), i.e., a shift in the frontier from period t to period t + 1. So, we now have our equations as follows:
Malmquist ( TFP ) Index = TechnicalEfficiencyChange * TechnologicalChange ( catchingupeffect ) ( innovationeffect ) M = E * P (5)
If an organization fails to achieve an output combination on its production possibility frontier, and fails beneath this frontier, it can be said to be “technologically inefficient”. Over time the level of output an organization is capable of producing will increase due to technological changes that affect the ability to optimally combine inputs and outputs. These technological changes cause the production possibility frontier to shift upward, as more outputs are obtainable from the same level of inputs. Thus, for any organization in an industry, productivity improvements over time may be either technical efficiency improvements (catching up with their own frontier) or technological improvements (the frontier is shifting up over time) or both.
Further, the variable returns to scale (VRS) are incorporated by introducing convexity constraints into the linear program. Using these models, and the Fare et al. [
1) technical efficiency change (E) (relative to a CRS technology);
2) technological change (P);
3) pure technical efficiency change (PT) (relative to a VRS technology);
4) scale efficiency change (S); and
5) total factor productivity (M).
We can then calculate scale efficiency from the technical efficiency measures for VRS technologies in the following way:
Technical Efficiency IndexVRS = Technical Efficiency IndexVRS × Scale Efficiency Index
Fare et al. [
TechnicalEfficiency = PureTechnicalEfficiency × Scale E f f i c i e n c y ChangeIndexChangeIndex C h a n g e I n d e x E = P T * S (6)
If M > 1, then productivity gains occur, and if M < 1, productivity losses occur. Technical efficiency increases (decreases) if and only if E is greater (less) than one and technological progress (regress) has occurred if P is greater (less) than one. An assessment can also be made of the major sources of productivity gains/losses by comparing the values of E and P. If E > P then productivity gains are largely the result of improvements in efficiency, whereas if E < P productivity gains are primarily the result of technological progress. Moreover, if PT > S then the major source of efficiency change is improvement in pure technical efficiency, whereas if PT < S the major source of efficiency is an improvement in scale efficiency. Further details on the interpretation of these indices may be found in Charnes et al. [
The first empirical application of the decomposition of productivity changes into technical and technological changes was used to measure hospital productivity in Sweden by Nishimizu and Page [
Some of the countries that have been included in the research for performance evaluation of non-financial sector in the Middle Eastern region include Israel by Friedman and Stern [
Company level studies have been found for the automobile industry and a comparison between Toyota, Nissan, and Ford by Cusumano [
DEA approach has also been applied for various other industries across the globe. Performance evaluation of the pharmaceutical sector using the non-pa- rametric DEA approach has been carried out in India and output efficiency of firms has been studied by Majumder [
The performance evaluation of small and medium enterprises has also been of particular interest, especially in the Far Eastern countries. Batra and Tan [
Therefore, we find that the non-parametric productivity analyses have been applied to various sectors but most have concentrated on one particular segment of the country. Firm-level trend analysis has been few and sparse. More importantly, all research relating to the UAE studied the financial companies within the banking and the insurance sectors. Our study makes an important contribution to the existing literature by looking into the firm level trend analysis of non-manufacturing units in the UAE.
We evaluated the overall efficiency of 27 firms in the United Arab Emirates by using firm-level information for the years 2007 to 2014 for companies that were listed on the Abu Dhabi Securities Exchange and the Dubai Financial Market. We have taken a balanced panel and the data was collected from individual audited annual reports from companies for each year.
Our study included two outputs and three inputs. Ideally, the analysis of efficiency should include the physical volume of outputs and inputs. However, in the absence of data following standard practice [
In this study there are 27 DMUs with three inputs: Labour (measured in terms of the wages and salaries of workers); Capital (measured in terms of the net book
Industry | Number of Firms |
---|---|
Real Estate& Construction | 7 |
Cements | 5 |
Manufacturing | 4 |
Services | 4 |
Food and Beverages | 3 |
Telecommunications | 2 |
Pharmaceuticals | 2 |
Total Number of Firms | 27 |
value of the property, plant, equipment under the non-current assets of the Balance Sheet); and Materials (measure in terms of the cost of goods sold in the income Statement). The two outputs were revenue (measured in terms of sales) and earnings per share (EPS). The time duration for the analysis is eight years, from 2007 to 2014.
The Malmquist productivity approach based on data envelopment analysis (DEA) was used to identify the major source of productivity growth where the DEA allows for the estimation of Total Factor Productivity (TFP) as a Malmquist Productivity Index (MPI). The MPI was decomposed to the “frontier” and “catching up” effects which was further decomposed to pure technical efficiency change and scale efficiency change.
Inputs | Outputs | ||||
---|---|---|---|---|---|
Industry | Labor (AED) | Capital (AED) | Materials (AED) | Revenue (AED) | EPS |
Cements | 191,059 | 5,710,086 | 3,564,858 | 4,217,946 | 0.14 |
Food & Beverages | 692,192 | 2,282,533 | 4,403,920 | 5,949,558 | 0.48 |
Manufacturing | 856,358 | 7,288,552 | 7,948,016 | 10,508,025 | 0.17 |
Pharmaceuticals | 1,138,959 | 7,542,615 | 2,764,029 | 5,843,692 | 0.19 |
Real Estate | 2,383,352 | 19,241,503 | 31,278,697 | 43,634,411 | 0.39 |
Services | 727,671 | 26,146,715 | 4,874,580 | 7,115,163 | 0.54 |
Telecom | 22,187,961 | 128,293,411 | 88,278,060 | 174,919,808 | 0.66 |
Inputs | Outputs | ||||
---|---|---|---|---|---|
Labor (AED) | Capital (AED) | Materials (AED) | Revenue (AED) | EPS | |
Mean | 2,692,788 | 21,314,877 | 17,902,277 | 28,755,520 | 0.35 |
Median | 738,418 | 6,772,662 | 5,511,522 | 8,553,126 | 0.24 |
Maximum | 50,943,429 | 380,442,006 | 263,427,532 | 403,565,665 | 2.97 |
Minimum | 22,982 | 62,010 | 117,991 | 104,062 | (4.39) |
StdDev | 7,187,276 | 43,985,495 | 33,876,106 | 57,941,466 | 0.55 |
Skewness | 4.702 | 4.563 | 4.184 | 3.893 | (1.616) |
Kurtosis | 22.75 | 27.11 | 21.18 | 16.77 | 27.64 |
Industry | Malmquist Index |
---|---|
Food and Beverages | 1.02 |
Telecommunication | 1.01 |
Pharmaceuticals | 0.99 |
Manufacturing | 0.97 |
Services | 0.94 |
Real Estate & Construction | 0.94 |
Cements | 0.92 |
nological change, improvements through innovation and adoption of new technologies (
Companies Above Average | Companies Below Average | ||
---|---|---|---|
Emirates Integrated Telecom | 1.094 | Gulf Medical Projects | 0.965 |
Dubai Refreshments | 1.039 | RAK Cements | 0.960 |
Emaar | 1.023 | Arkan Building Materials | 0.953 |
Foodco | 1.015 | Aldar Properties | 0.950 |
Gulf Pharmaceuticals | 1.014 | Fujairah Building Industries | 0.949 |
Abu Dhabi Shipbuilding Co | 0.997 | Sharjah Cements | 0.943 |
Agthia Group | 0.996 | Abu Dhabi National Hotels | 0.940 |
Tabreed | 0.991 | Etisalat | 0.931 |
RAK Ceramics | 0.985 | National Corp for Tourism | 0.924 |
Union Properties | 0.981 | Emirates Driving Company | 0.909 |
Drake and Scull International | 0.977 | Arabtec | 0.906 |
Gulf Cement Company | 0.972 | National Marine Dredging | 0.898 |
Fujairah Cement Company | 0.850 | ||
Umm-al Quwain Cements | 0.847 | ||
MEAN | 0.970 | GGICO | 0.846 |
Companies | Malmquist Index | Technological Efficiency | Technical Efficiency | Pure Tech Efficiency | Scale Efficiency |
---|---|---|---|---|---|
M | P | E | PT | S | |
Emirates Integrated Telecom | 1.094 | 1.065 | 1.027 | 1.025 | 1.002 |
Dubai Refreshments | 1.039 | 1.044 | 0.995 | 1.017 | 0.979 |
Emaar | 1.023 | 1.030 | 0.993 | 1 | 0.993 |
Foodco | 1.015 | 1.015 | 1 | 1 | 1 |
Gulf Pharmaceuticals | 1.014 | 1.045 | 0.970 | 0.971 | 0.999 |
a stable demand. After the crisis, since 2011, the UAE Government began to encourage domestic food production to reduce reliance on imports and to drive expansion of local providers. The support from the Government was coupled with a renewed influx of expatriate population, rising affluence, hectic lifestyles, and renewed growth in tourism. During 2012-13, the Food Security Centre of Abu Dhabi undertook initiatives to encourage new investments and set up special zones such as the Khalifa Industrial Zone Abu Dhabi (KIZAD) and Dubai Investments Park (DIP) with modern infrastructure, advanced warehousing facilities and excellent transportation network. So, demand for food products enjoyed an unprecedented growth both from the domestic as well as tourist population along with the support from the Government to step up the indigenous supplies. This resulted in a soaring Malmquist Productivity Index for the Foods and Beverages industry from 2012, as can be seen in
There are three listed companies in the Foods and Beverages sector in the
Improvements in both Technological and Technical Efficiency | Emirates Integrated Telecom Foodco |
---|---|
Improvements in Technological change but decline in Technical Efficiency | Abu Dhabi Shipbuilding Co Agthia Group Aldar Properties Arkan Building Materials Drake and Scull International Dubai Refreshments Emaar Etisalat Fujairah Building Industries Gulf Cement Company Gulf Medical Projects Gulf Pharmaceuticals RAK Ceramics Sharjah Cements Tabreed |
Improvements in Technical Efficiency but decline in Technological change | Emirates Driving Company Fujairah Cement Company Union Properties |
Decline in both Technological and Technical Efficiency | Abu Dhabi National Hotels Arabtec GGICO National Corp for Tourism National Marine Dredging RAK Cements Umm-al Quwain Cements |
UAE and two of them were amongst the highest TFP scorers during the 2007- 2014 period.
Although the food and beverages sector showed the highest level of efficiency, it was driven by technological advances without any commensurate efficiency improvements in the internal management of the firms. Foodco was the only company that showed a combination of technological and technical efficiency. Foodco launched several incentives and training programs; introduced effective quality check system along with proper inventory controls. Foodco also established a new Commodities Division in 2012 with a dedicated sales team, followed by expansion of their facilities in 2013 at the new premises in Mafraq.
However, such human resource incentives of Foodco to improve internal technical expertise of the company were not evident across the industry. Despite the technological improvements and capital expansions, both Agthia and Dubai Refreshments showed decline in technical efficiency, implying positive investments without a balance of the new inputs versus outputs.
The UAE telecommunications sector is among the strongest and most advanced in the world and was served by a monopolist provider, Emirates Telecommunications Corporation (Etisalat). Competition in the sector started in 2007 when Emirates Integrated Telecom (Du) launched its mobile network services ending 30 years of monopoly by Etisalat. Soon after the entry of Du, the financial crisis hit the sector resulting in 20% decrease in fixed lines during 2009-10, leading to the downturn in 2008-2010 (
Emirates Integrated Telecom also showed a positive technical efficiency change (catching up effect) as the company successfully promoted various programmes among employees which included Employee Wellness scheme, Personal Action Plans, graduate training programmes and a strong Emiratisation drive. However, the technical efficiency of Etisalat began to suffer as the company lost its monopoly in the sector and also lost its coverage of the lucrative areas in Dubai such as Dubai Marina and TECOM.
Healthcare has been a top priority for the government and the sector received strong incentives to encourage local manufacturer through favorable pricing structures of generics; reduction of imports; policies relating to mandatory health insurance. The pharmaceutical industry remained strong and stable during the recession years owing to mandatory health insurance and the growth of regional medical tourism including huge investments in the Dubai Healthcare City as well as the Dubai Biotechnology & Research Park. The UAE developed into one of the most advanced countries in terms of its healthcare services through its strategies of increasing longevity, eradication of all types of diseases, and an effective mechanism of early detection system of chronic diseases. The Ministry of Health continued to develop primary healthcare services through the establishment of an extended network of world class clinics all over the country.
Gulf Pharmaceuticals mainly drove the growth of the industry through its entry into the high-technology field of biotech as it began production of bulk human insulin crystals. Despite the recessionary pressures, the company continued to launch new products to diversify its market presence and successfully began off-shore investments in Saudi Arabia, Ethiopia and Algeria. However, the major hurdle for local manufacturers was the capital intensive nature of operations and the shortage of knowledge and skilled manpower which led to the decline in the technical efficiency of the sector. Further, the companies are highly dependent on imports of equipment, ingredients, and medicines for end use which makes the industry vulnerable to foreign exchange fluctuations, leading to a fall in the Malmquist index since 2011.
Significant decline in tourism, widespread closures and exodus of expatriates from the UAE led to the downtrend in these sectors. The services industry recovered the quickest owing to international conferences/exhibitions by multinational companies; leisure tourism from the emerging Asian economies; niche markets like spa tourism, halal tourism, cultural tourism and budget tourism.
The sectors that were adversely affected by the 2008-09 crisis were real estate, construction and cements. The slowdown in the Dubai property market during the crisis years was caused by a phenomenon known as Dutch Disease-a situation in which capital outflows caused a dramatic fall in the profitability of the booming real estate sector and thus causing resources (labor and capital) to move out of this sector. This led to the decline in both technical efficiency measures for almost all (6 out of 7) real estate companies.
With regards to changes in scale efficiency, the values for 15 out of 27 companies were approximately 1 which showed that these companies were operating at optimum scale and experiencing constant returns to scale.
Performance measurement is crucial for any firm in any sector, not only for determining its own efficiency and achievement but also by benchmarking itself in comparison with its peers. This research paper uses the Malmquist productivity index which has been widely used across the globe, to evaluate the productivity trend over time, for multi-inputs and multi-outputs production units. The Total Factor Productivity growth has been measured over the period 2007-2014 for the non-manufacturing production units in the UAE and the efficiency trends have been studied with respect to the frontier technology. The TFP index has been further decomposed into technical efficiency change and technological change. Technical efficiency (catching up effect) shows a movement towards the efficient frontier of production while technological change (frontier shirt) implies innovation and investment. Based on the findings above, several useful managerial insights and implications have been discussed in the context of the non-financial sector of the UAE.
Our results are extremely encouraging for the period of the financial crisis and the recovery thereafter (2007-2014), as we found 17 out of 27 companies experienced technological improvement through investments in infrastructure, expansion of facilities, adoption of new technologies and/or their on research and development. However it is a matter of concern that only 5 out of 27 companies show a positive technical efficiency change―this implies that although there have been positive investments and government support in all the sectors to encourage expansions, the industries shows lack of proper balance of the new inputs versus outputs.
Pharmaceuticals and Food sectors in the UAE showed a high degree of resilience during the crisis years and did not suffer with the deterioration in the economic conditions of the country. Foods and services sectors were quick to display high growth from 2011 with inflow of expatriates and tourists into the UAE. The Government also enhanced the recovery by encouraging huge investments in facilities and infrastructure in Abu Dhabi, Sharjah and Dubai. Although technological efficiency was high, there was an overall decline in technical efficiency since the improvements in technology were not supported by efficiencies in internal management of the companies. As a result, we do not find “catching up” effect in any of the industries during this period except for Food and Beverages.
This paper identifies the efficiency indices-total factor productivity along with technical and technological efficient change indices-the application of which has been very sparse not only in the UAE but also in the overall application of DEA models with Malmquist index. Further, most of the studies on non-parametric efficiency measures have been applied to the financial sector and there are a host of studies pertaining to banks, insurance companies, non-banking financial institutions, financial cooperative societies etc. The application to the non-finan- cial firms is scant. More importantly, there have been no studies that have looked into the recovery period in the last five years. This is particularly important for the UAE, since the country was one of the worst hit during the financial crisis of 2008 and has made a remarkable comeback. It was particularly interesting to note the efficiency measure of the various industries in order to trace the recovery and identify the factors that helped to improve the efficiency measures. Therefore, this study makes a major contribution to the existing literature of efficiency measures using non-parametric approach.
There are some limitations of this research since the DEA approach determines only relative efficiency and also does not identify the factors that give rise to inefficiency. So, we could only highlight those units in which inefficiency exists and those that require attention and the inefficiency index is only relative to the most efficient frontier. More importantly, we would be able to gain more insight by incorporating a model of structural change within this framework-so that we can identify the difference in trends during the crisis years (2007-2010) and the recovery period (2010-2014).
Majumdar, S. and Asgari, B. (2017) Performance Analysis of Listed Companies in the UAE-Using DEA Malmquist Index Approach. American Jour- nal of Operations Research, 7, 133-151. https://doi.org/10.4236/ajor.2017.72010