METHODOLOGY AND APPLICATIONS OF. Andrea Furková

Transcription

1 METHODOLOGY AND APPLICATIONS OF STOCHASTIC FRONTIER ANALYSIS Andrea Furková

2 STRUCTURE OF THE PRESENTATION Part 1 Theory: Illustration the basics of Stochastic Frontier Analysis (SFA) Concept of efficiency Estimation Identification of sources of inefficiency Part 2 An applications of SFA

3 Theory usually presents the producers as successful optimizers. They maximize production, minimize cost, and maximize profs. Conventional econometric techniques build on this Conventional econometric techniques build on this base to estimate production/cost/prof function parameters using regression techniques where deviations of observed choices from optimal ones are modeled as statistical noise.

4 Econometric estimation techniques should allow for the fact that deviations of observed choices from optimal ones are due to two factors: failure to optimize i.e., inefficiency due to random shocks. Stochastic Frontier Analysis is one such technique to model producer behavior.

5 Methods based on efficient frontier Based on benchmarking, that is, a un s performance is compared wh a reference performance (so-called efficient frontier). Un s inefficiency can results from technological Un s inefficiency can results from technological deficiencies (technical inefficiency) or nonoptimal allocation of resources into production (allocative inefficiency). Both technical and allocative inefficiencies are included in cost (economic) inefficiency.

6 Generally, there are two families of methods based on efficient frontier: Non-parametric methods, like Data Envelopment Analysis (DEA) or Free Disposal Hull (FDH). These methods originate from operations research and use linear programming to calculate an efficient deterministic frontier against which uns are compared. Parametric methods, like Stochastic Frontier Analysis (SFA), Thick Frontier Approach (TFA) and Distribution Free Approach (DFA). Econometric theory is used to estimate pre-specified functional form and inefficiency is modeled as an addional stochastic term.

7 Stochastic Frontier Analysis time invariant efficiency Based on econometric theory and pre-specified functional form is estimated and inefficiency is modeled as an addional stochastic term. The Stochastic frontier production function model (single Cobb-Douglas form for panel data): ln y = β + 0 β ln x + v u i =1,..., N t = 1,..., T ε y x u i v 0 n n = v ui u i 0 - observed output quanties of the i-th un in year t, - observed inputs quanties of the i-th un in year t, - non negative time-invariant random variables capturing time-invariant technical inefficiency, - random variables of i-th un in year t reflecting effect of statistical noise n i

8 Frontier cost function: identifies the minimum costs at a given output level, input prices and existing production technology Stochastic frontier cost function model (single Cobb- Douglas form for panel data): ln C = β + y y + 0 β ln β n ln wn + v + ui u i 0 ε n C y w u i v = v + u i i =1,..., N t = 1,..., T - observed total costs of the i-th un in year t, - a vector of outputs of the i-th un in year t, - an input price vector of the i-th un in year t, - time-invariant cost inefficiency, - random variables of i-th un in year t reflecting effect of statistical noise

9 SPECIFICATION OF THE MODEL 1. Deterministic kernel of the model Cobb-Douglas (in log form) Translog (a flexible functional form) 2. Estimation of the model Maximum likelihood estimation (ML) Generalised Least Squares method (GLS) Method of moments (MM) 3. Calculating un s specific efficiency

10 Maximum likelihood estimation (ML) Distribution assumptions: u i ~iidn (0, σ u ) or u i ~ iidn ( µ, σ u ), v ~ iidn(0, σ v ) Maximization of the log likelihood function The individual estimates of the technical (cost) inefficiency: JLMS decomposion - the condional distribution of u i, condional mean E ( u ) i ε or condional modus M ( u i ε ) of this distribution can be used as a point estimator for u i The individual estimates of the technical (cost) efficiency: TE ( ε ) i = exp E u i

11 Analyzing Efficiency Behavior Two questions: What is the behavior of efficiencies over time? Are they increasing, decreasing or constant? What explains the variations in inefficiencies among uns and across time?

12 Time behavior of inefficiencies Assumption: u = u i + β t Model: ln y = β + 0 β ln t n n x n + v u i =1,..., N t = 1,..., T Cornwell, Schmidt and Sickles, Lee and Schmidt, Kumbhakar, Battese and Coelli

13 Battese and Coelli proposed a simple model that can be used to estimate the time behavior of inefficiencies: u = exp η { ( t T )} u i + 2 where ui ~ iidn ( µ, σ u ) and is a parameter to be estimated. Inefficiencies in periods prior to T depend on the parameter η. Efficiency behavior is monotonic and ordering of uns in terms of inefficiencies time-invariant. Good for understanding aggregative behavior. η

14 Explaining efficiency Certain factors influence the environment in which production takes place e.g. degree of competiveness, input and output qualy, network characteristics, ownership form, regulation etc. Two ways to handle them Include them as variables in the production process as control variables. Using this interpretation, these variables influence the structure of the technology by which conventional inputs are converted into outputs, but not efficiency. Associate variation in estimated efficiency wh variation in the exogenous variables.

15 Battese a Coelli Inefficiencies are assumed to be a function of a set of explanatory variables associated wh inefficiency of uns over time : T u = z δ + w where z - vector of variables which may influence the efficiency of uns δ - vector of unknown parameters to be estimated 2 w ~ iid N (0, σ w ) random variables reflecting effect of statistical noise u ~ iid N + T ( δ,σ u2 ) z

16 EMPIRICAL APPLICATIONS 1. REGULATION OF DISTRIBUTION UTILITIES Benchmarking analysis can be used by regulator as an addional instrument to establish a larger informational basis for more effective price cap regulation Incentive price cap regulation ( X efficiency factor) Regulation of Slovak and Czech electricy distribution utilies Based on incentive price - cap regulation Main issue: How to set efficiency factor X, i.e. cost efficiency prediction

17 Data: Panel data set (55 observations) for 3 Slovak and 8 Czech electricy distribution utilies over the period Cost function specifications (Cobb-Douglas form) : Time invariant cost efficiency (estimation method ML, GLS): C P P K L ln Y Y K L CUD P = β0 + β ln + β ln + + P β ln P β ln CUD Time varying cost efficiency (estimation method ML): C P P K L ln Y Y K L CUD P = β0 + β ln + β ln + + P β ln P β + v lncud + v + u i =1,..., N t = 1,..., T + u i =1,..., N t = 1,..., T i

18 where C represents total costs, Y is the output, PK, PL, PP are the prices of capal, labor and input purchased energy respectively, CUD is customer densy Input prices labor price (PL) the average annual salary of utily employees capal price (PK) the ratio of capal expenses to the total installed capacy of the utily's transformers in MVA purchased energy price (PP) average price of purchased energy from generator Output variable: total output (Y) - measured as the total number of delivered electricy in MWh

19 2. COST EFFICIENCY ESTIMATION OF THE BANKING SECTOR The efficiency measuring and relative efficiency comparison of banks are crucial questions for analysts as well as for economic policy creators. Methodology for analyzing banking efficiency Financial ratios - the standard technique of banking efficiency measuring Advantage: simplicy of understanding. Disadvantage: they fail to consider the multidimensional input and output process, and are unable to identify the best performers in a group of uns. Methods based on efficient frontier

20 Model specification and Data Data: unbalanced panel data set (66 observations) for 9 Slovak banks over the period: Tatra banka, a.s., Všeobecná úverová banka, Tatra banka, a.s., Všeobecná úverová banka, a.s., Slovenská sporeľňa, a.s., Dexia banka Slovensko, a.s., OTP banka Slovensko, a.s., Istrobanka, a.s., Poštová banka, a.s., Unibanka, a.s., Ľudová banka, a.s.

21 Cost function specification (Cobb-Douglas form) Time invariant cost efficiency (estimation method ML, GLS): C P ln = + L P β 0 β ln F P L F P + βk ln P K F + β lnu U + β lnv i =1,..., N t = 1,..., T V + β lnz Z + β T+ v T + u i Time varying cost efficiency (estimation method ML): C ln = + L P β 0 β ln F where u δ 0 + δ Z + = 1 P P L F w + β K P ln P K F + β U lnu + β lnv V i =1,..., N t = 1,..., T + β T + v T + u

22 C represents total costs, PK, PL, PF are the prices of capal, labor and funds respectively. Three inputs (physical capal, labor, funds) are used to produce two outputs total loans U and total deposs V and both models include addional variables Z equy capal and T trend. total costs (C) - the sum of total expenses price of capal (PK) - the depreciation over fixed assets price of labor (PL) - the ratio of personnel expenses over total assets price of funds (PF) - the ratio of interest expenses over the sum of deposs equy capal (Z) - the amount of bank equy that reflects the size of banking operations

23 3. ANALYSIS OF REGIONAL COMPETITIVENESS Last few years economic policy making and research have shown increasing interest for regional competiveness evaluation (economic efficiency of regions represents the basis of economic success for micro-economic level and also the competiveness of the country) Regional competiveness evaluation methods: Non existence of unique methodology for competiveness evaluation Specific economic indicators of efficiency Indicator compares a concrete level of the value in the region wh respect to s total level in the country Methods based on efficient frontier

24 Model specification and Data The competiveness of regions is compared through the estimated levels of technical efficiency as the efficiency we perceived as the mirror of the competiveness. Data: balanced panel data set (280 observations) for 35 V4 (Visegrad four countries) NUTS2 (Nomenclature of Uns for Terrorial Statistics) regions observed over a period from 2001 to 2008 : Slovakia - 4 NUTS2 regions Czech Republic - 8 NUTS2 regions Hungary - 7 NUTS2 regions Poland -16 NUTS2 regions

25 Production function specifications (Cobb-Douglas form) Time invariant technical efficiency (estimation method ML, GLS): ln( GDP ) = β ( ) ( ) ( ) 0 + β1 ln ER + β2 lngfcf + β3 ln NI + v ui i =1,..., N t = 1,..., T Time varying technical efficiency (estimation method ML): ( GDP ) = β ( ) 0 + β1 ln ER + β 2 ln( GFCF ) + β ( ln NI ) + v u ln 3 where u = exp η { ( t T )} u i i =1,..., N t = 1,..., T

26 Inputs: Employment Rate - ER (annually in %) - overall performance of the regional economy affects the number of people employed in various sectors Gross Fixed Capal Formation GFCF (in % GDP) - efficiency in our model should demonstrate the abily of the regions to transform s capal for s further development. Net Disposable Income of Households - NI (per capa) - in terms of competiveness the disposable income plays an important role, directly reflects the purchasing power of the region. Output: Gross Domestic Product GDP (in purchasing power pary standards per capa) - the most important macroeconomic indicator.

27 4. MACROECONOMIC PERFORMANCE EVALUATION Economic growth analysis of transion countries Centrally planned economies are characterized by low economic efficiency and low productivy growth. Main issue: Evaluation the transion to market based economy. Foreign Direct Investment and economic growth relationship Conflicting predictions concerning the economic growth FDI relation. According to the theory and empirical lerature economic growth may induce FDI inflow, and FDI may also stimulate economic growth.

28 Model specification and Data Data: unbalanced panel data set (266 observations) for 33 OECD countries observed over a period from 2002 to 2010 Production function specification (translog form) Time varying technical efficiency (estimation method ML): ln Y + β 13 where = β + β lnc β ln L 2 ( lnc ) t + β ( ln L ) t + β t + v u β + (1/ 2) + β ( lnc ) + β22( ln L ) 12 + i =1,..., N t = 1,..., T ( lnc )( ln L ) u = δ ln Openness + 1 ln FDI + δ 2 ln Corruption + δ3 w

29 Factor Inputs: Capal (C) expressed as Gross Capal Formation (in constant 2000 USD millions) Labor (L) labor defined as civilian labor force (in thousands) Interaction terms of explanatory variables Linear and non-linear time trends Inefficiency variables: FDI (in USD millions) CPI - The Corruption Perceptions Index (Corruption) chosen to control for instutional inefficiency, index varies from 0 (highly corrupt) to 10 (highly clean) Openness - country s trade (sum of exports and imports in USD millions) Output variable: Real GDP (Y) (in of millions of USD)

30 USEFULNESS OF SFA SFA produces efficiency estimates or efficiency scores of individual uns. Thus one can identify those who need intervention and corrective measures. SFA provides a powerful tool for examining effects of intervention. For example, has efficiency of the banks changed after deregulation? Has this change varied across ownership groups? Since efficiency scores vary across uns, they can be related to un s characteristics like size, ownership, location, etc. Thus one can identify source of inefficiency.