Romanian labour market efficiency analysis

Transcription

1 Romanian labour market efficiency analysis MIHAI DANIEL ROMAN The Bucharest Academy of Economic Studies Calea Dorobantilor Str., District 1, Bucharest ROMANIA MARIA DENISA VASILESCU The Bucharest Academy of Economic Studies Calea Dorobantilor Str., District 1, Bucharest National Scientific Research Institute for Labour and Social Protection 6-8 Povernei Str., District 1, Bucharest ROMANIA Abstract: - The purpose of the paper is to investigate the labour market efficiency of the Romanian counties. We use the Data Envelopment Analysis (DEA) method as a multi-input multi-output optimization model to measure labour market relative efficiency of the best practice counties. We complete this non-parametric approach with bootstrapping, in order to obtain more efficient estimates. The empirical results show that only 8 out of the 40 considered counties are technically efficient when transforming inputs into labour market specific outputs. Key-Words: - Data Envelopment Analysis, technical efficiency, labour market 1 Introduction The labour market performance is a key topic on the agenda of any policy maker. In general, two indicators of performance can be distinguished: labour productivity and labour participation. Labour productivity gives a great competitiveness advantage to an economy (a country or a region) and high employment rates raise the standard of living. The main factors influencing labour participation are the employment opportunities and, of course, the share of active population. Romania, as well as most of the European countries, manifests a demographic ageing trend. This phenomenon becomes a challenge since the active population and labour force depend on the dimension and the structure of the population. The proportion of older persons (60 years or over) has risen from 8.2 per cent in 1950 to 10 per cent in 2000, to 10.7 per cent in 2007 and it is expected to reach 21.1 per cent in 2050 [13]. The ageing population endangers the economic activity; therefore the efficient use of the existing human resources is very important. A great potential of labour is immobilized in the unemployed persons [3]. The creation of new jobs is the healthiest way to lower unemployment. The birth of new firms and the expansion of existing ones are prerequisites for employment growth and the reduction in unemployment. National and foreign investments play a major role in establishing more firms or in developing the existing ones, process through which jobs can be created. Although high employment rates indicate a solid background for the functioning of labour market, an even stronger indicator of performance is the labour productivity. When productivity is growing, living standards tend to rise [10]. Labour productivity can be increased by increasing the skills of the workforce. Better skills make workers more efficient [8]. Indeed, research has suggested that human capital is one of the major drivers behind explaining differences in productivity. Crafts and O Mahoney (2001) suggest that one reason why UK productivity has been lower than other countries is a lack of skills [6]. A study by Lynch and Black (1996) finds that there is a positive relationship between workers year of schooling and productivity. An increase in the level of productivity reflects an improvement in inputs efficiency. Therefore, the same level of inputs is able to produce a higher output level and the cost of production will be reduced. In other ISBN:

2 words, it reflects an improvement in the quality of inputs [7]. A study by Mason and Finegold (1997) in the United States and Britain support the positive relationship between human capital and firm s performance. They find that education and training are more important than physical capital in determining productivity [9]. The evaluation of labour market performance is essentially comparative, assessed over space (countries, regions) or time. Performance is also multi-dimensional, thus, a single indicator can only provide a limited, one-sided view of labour market efficiency. Anxo and Storrie (1997) developed an efficiency frontier, where labour market efficiency is assessed using both outputs and inputs to the production process [11]. Storrie and Bjurek (2000) construct a performance frontier on the basis of employment and unemployment output indicators in order to compare labour markets over countries in the EU [12]. We use a Data Envelopment Analysis to assess the efficiency of the Romanian counties in terms of labour market performance. 2 Data description In evaluating labour market efficiency in Romania at county level we use four input variables and two outputs. The analysis refers to the year It should be noted that we excluded from the analysis the capital of Romania, Bucharest, as well as the nearest county, Ilfov, because these two show a completely different pattern compared to the rest of the country and the registered variables act as outliers in this context. The considered inputs are the following: the activity rate, the number of active firms, the number of tertiary education graduates and the value of new investments. The activity rate represents the ratio, expressed as percentage, between the civil economically active population and the total population of the county. The highest activity rate, 73.2%, is registered in Bihor and the minimum value is in Bacau, 47%. The average activity rate is 60.75%. The number of active firms was included in this analysis as an indicator of each county s power of absorption regarding labour resources. In figure 1 we can see the position of the counties with respect to the activity rate and the number of active firms. The figure is divided in four by the lines indicating each variable s mean. The upper right quadrant groups the counties with the highest potential: a large number of active firms as well as high activity rates. The lower right quadrant shows a possible risky situation: high activity rates and few active firms can lead to unemployment, this, of course, in correlation with the size of the firms, i.e. the actual number of jobs. Fig. 1 Scatter plot of activity rate and number of active firms, 2011 The number of tertiary education graduates was used with the values for 2010, leaving one year gap for these young people to enter labour market. We make use of this variable as a proxy for high skilled human capital, keeping in mind that there is a strong correlation between increased levels of qualification and labour productivity. The fourth input taken into consideration in this study is the value of new investments. This variable includes both foreign investment as well as Romanian investment. It is well known that investment is a powerful engine for economic growth. This is no less true for labour market. New investment leads to the creation of new jobs or/and fixed capital endowment, consisting in modern technologies, which increases productivity. As for the outputs, we considered that the employment rate and the labour productivity are good indicators of labour market performance. The labour productivity was calculated as the ratio between gross domestic product and employed population. In figure 2 we have the same design as earlier. Four groups of counties are formed based on the relation between the employment rate and labour productivity. We can observe a higher density in the area with low productivity and average employment rates. In terms of labour market performance we see that 2 counties stand out: Timis, with 70.3% employment rate and lei/person employed labour productivity and Cluj, with 70.3% ISBN:

3 employment rate and lei/person employed labour productivity (lei is the Romanian currency, 1 euro = 4.24 lei in 2011) Fig. 2 Scatter plot of employment rate and labour productivity, 2011 We will see later in this study if the counties with high employment rates and increased labour productivity are also technically efficient. The correlation matrix of input variables shows a low intensity of the relation between them, meaning that we don t have redundant inputs. The main source of the data was the National Institute of Statistics. We used estimated values for the gross domestic product (the source of the estimates was the Romanian Prognosis Commission). 3 Methodology When performance is evaluated, the production units or the decision making units (DMUs) can be described as more or less "efficient" or more or less "productive". Productivity of a DMU is determined using the relationship between outputs and inputs. The efficiency of a production unit means a comparison between the observed values of outputs and inputs and their optimal values, the optimum being defined in terms of production possibilities and tehnical efficiency. The notion of comparing production plans lead to the need for a "standard of excellence" to serve as a reference point. This standard must be that level of technical efficiency that is achieved with: a) the least amount of inputs and constant outputs (for input orientation) and b) the maximum of outputs with constant inputs (for output orientation). The literature reports three approaches to measure technical efficiency: the index numbers approach, the econometric approach, and the mathematical programming approach. The mathematical programming approach does not require the specification of a functional form for production data. This nonparametric method of performance evaluation was initiated by Charnes, Cooper and Rhodes (1978) under the name DEA - Data Envelopment Analysis. DEA approach uses linear programming techniques to analyze the inputs consumed and outputs produced by the decision making units and builds an efficient production frontier based on best practices. The efficiency of each decision making unit is then measured relative to this frontier. This relative efficiency is calculated based on the ratio of the weighted sum of all outputs and the weighted sum of all inputs. DEA identifies the inefficient decision-making units and the sources and amounts of inefficiency. A DEA model can be input or output oriented as well as with constant or variable returns to scale The CRS model The CRS model was originally proposed by Charnes, Cooper, and Rhodes (1978), also being known as CCR model [4]. It assumes constant returns to scale: if all inputs are increased with a certain amount, then the outputs will increase proportionally with the same amount. The model requires complete information about inputs and outputs for a set of homogeneous decision-making units. The CRS model is a linear program that compares the efficiency of each DMU with all linear combinations of other units, including the one under consideration. Let N be the inputs and M the outputs for each of I DMUs, with X the N x I input matrix and Q the M x I output matrix. The CRS input oriented model is: min, q i Q 0 x i X 0 0 where θ is a scalar and λ is a Ix1 vector of constants [5] The VRS model Banker, Charnes and Cooper (1984) noted that the assumption of constant returns to scale distorts the results when comparing decision making units which significantly differ in size [2]. In such cases it is relevant to know how the scale of operation of a decision unit influence its efficiency. Therefore, Banker, Charnes and Cooper developed a new formulation of data envelopment analysis known in ISBN:

4 the literature as the VRS model (by assuming variable returns to scale). The VRS model allows the use of other production function and is used to calculate efficiency under the assumption of variable returns to scale: an increase of inputs does not necessarily lead to a proportional increase in outputs. The VRS model is focused on the maximal movement towards the efficient frontier by proportional reduction of inputs (for the input orientation) or by proportional augmentation of outputs (for the output orientation). The VRS output oriented model is: max, x i y i Y 0 X 0 N where 1, N 1 1 is the convexity condition and 1 / defines the TE score that varies between zero and one [5]. A TE score of 1 means that the DMU is technically efficient. The lower the efficiency score, the more inefficient the DMU is. A downside of DEA analysis is that it offers no information on estimates uncertainty, therefore we cannot determine if the estimates are statistically significant. A possible solution to this problem is to apply a bootstrap method to resample the estimates and to use the empirical distribution of resampled estimates to calculate bootstrap confidence intervals that establish the statistical inference [11]. 4 Results We used Data Envelopment Analysis as a multiinput multi-output optimization model to measure labour market relative efficiency of the best practice counties. We chose an output orientated model because the counties objective is to maximize output resulting from input values. We calculated the efficiency scores under the assumption of constant returns to scale (CRS model) as well as of variable returns to scale (VRS model), the latter being also used to study the operating scale of the counties. Next, we applied a bootstrap technique to obtain confidence intervals for the efficiency scores. The bootstrap mean has the same value as the initial efficiency score only for the efficient counties; all other counties obtained lower efficiency scores after bootstrap. The comments on the results are made based on the bootstrap mean for the efficiency scores obtained by running a CRS output oriented model in PIM-DEA Software. In figure 3 we can see the efficiency frontier and the position of each county relative to the frontier. Eight counties are on the frontier meaning that they are technically efficient (Botosani, Covasna, Giurgiu, Gorj, Ialomita, Mehedinti, Salaj, Teleorman). A medium efficiency was measured for 20 counties (TE scores smaller than 1 but higher than 0.5). The rest of 12 counties have a low technical efficiency (TE scores under 0.5). Fig. 3 The efficiency frontier Let s examine the counties that we found to be technically efficient. They are not in top 10 regarding the value of outputs, nor abounding in resources. Seven of these eight efficient counties have labour productivity below average and the same pattern is also observed for the employment rate. Regarding inputs, we notice that all eight counties have below average number of firms and tertiary education graduates and only 2 of them have above average activity rates and new investments. However, these eight counties have a high capacity to successfully transform inputs into outputs, and therefore to be technically efficient. Counties with high employment rates and high labour productivity per person employed (Cluj and Timis) are considered good performers on the labour market and are known in Romania as wealthy counties. But in our analysis they obtained very low TE scores, therefore being categorized as technically inefficient. This apparently antagonistic situation can be easily explained. In order to achieve convergence, the counties with little resources need to grow faster ISBN:

5 than rich ones to reduce disparities among regions. Having at their disposal smaller amounts of resources they compensate by being technically efficient. 5 Conclusion The Data Envelopment Analysis models allowed us to rank the Romanian counties according to their technical efficiency in transforming given inputs into labour market specific outputs. We found that only eight counties are efficient and 20 counties have TE scores above 0.5. An interesting remark is that the efficient counties are not the rich ones. The amount of available resources in these counties is below average and still they are capable of obtaining good levels of outputs. This result could be of use to the policy makers to better promote these counties in order to attract more investors, since an efficient labour market is a prerequisite of a successful business. References: [1] Anxo D., Storrie D, Measuring Labour Market Efficiency and Performance: A Conceptual Analysis, in Tronti, L. Benchmarking Employment Performance and Policies. European Employment Observatory, 1997 [2] Banker, R.D., Charnes, A., Cooper, W.W. Some Methods for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 1984, pp , [3] Candidatu C, Zaharia R. M., Population Ageing The Case of Romania, Proceedings of the International Conference on Applied Economics, 2008 pp , ntent/uploads/articles/2011/10/ pdf [4] Charnes, A., Cooper W. W., Rhodes. E. Measuring the efficiency of decision making units, European Journal of Operational Research, 1978, pp ; [5] Coelli T., Prasada Rao D. S., O Donnell C. J., Battese G. E, An Introduction to Efficiency and Productivity Analysis - Second Edition, Springer, 2005 [6] Crafts N., O Mahoney M., A Perspective on UK Productivity Performance, Fiscal Studies, vol 22, no. 3, Institute for Fiscal Studies, 2001 [7] Lynch L., Black S., Beyond the Incidence of Training: Evidence from a National Employers Survey, National Bureau of Economic Research, Working Paper No. 5231, 1995 [8] Lindsay C., Labour productivity, Office for National Statistics, Labour Market Trends, 2004 [9] Mason, G., Finegold D., Productivity, Machinery and Skills in the United States and Western Europe, National Institute Economic Review, 162, 1997, pp 85-98, [10] Nasar S., Productivity, The Concise Encyclopaedia of Economics, The Library of Economics and Liberty, [11] Roman, M., Roman, M. Data Envelopment Analysis Method in Labour Efficiency Analysis, Economic Computation and Economic Cybernetics Studies and Research, no. 1-4, 2000, pp [12] Roman M. D., Jaba E., Roman M. - Economic development and political cycles in Romania, in Recent advances in environment, ecosystems and development - Proceedings of the 7th WSEAS International Conference on Environment, Ecosystems and Development (EED '09), Puerto de la Cruz, Tenerife, Canary Islands, Spain, 2009, pp , [13] Simar L, Wilson P. W., Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Management Science, 1998, 44(1) [14] Storrie D., Bjurek H., Benchmarking European Labour Market Performance with Efficiency Frontier techniques, Centre for European Labour Market Studies, Discussion Paper, 2000 [15] United Nations, Department of Economic and Social Affairs, Population Division, World population ageing: , 2002, worldageing ISBN: