Factors Affecting Financial Statements of Indian Banks

							     Labor Economics
            Stepan Jurajda
Office #2 (2nd floor) CERGE-EI building
          (Politickych veznu 7)
      stepan.jurajda@cerge-ei.cz
   Office Hour: Tuesdays after class
             Introduction
• Consider the distribution of wages:
  What can explain why some people earn
  more than others?
  (based on exposition by Alan Manning)
Overall Distribution of Hourly Wages in the
  UK – trimmed (£1 to £100 per hour)
            .8
            .6
  Density



            .4
            .2
             0




                 0   1   2             3   4   5
                             lnwages
Models of Distribution of Wages
• Start with perfectly competitive model
• Assumes labour market is frictionless so a single
  market wage for a given type of labour – the ‘law
  of one wage’ (note: this assumes no non-
  pecuniary aspects to work so no compensating
  differentials)
• ‘law of one wage’ sustained by arbitrage – if a
  worker earns CZK100 per hour and an identical
  worker for a second firm earns CZK90 per hour,
  the first employer could offer the second worker
  CZK95 making both of them better-off
        The Employer Decision
       (the Demand for Labour)
• Given exogenous market wage, W,
  employers choose employment, N to
  maximize:
              F ( N , Z )  WN
• Where F(N,Z) is revenue function and Z
  are other factors affecting revenue
  (possibly including other sorts of labour)
• This leads to familiar first-order condition:
                F ( N , Z )
                             W
                   N

• i.e. MRPL=W
• From the decisions of individual employers
  one can derive an aggregate labour
  demand curve:

              N  N (W , Z )
                d      d
          The Worker Decision
         (the Supply of Labour)
• Assume the only decision is whether to work or
  not (the extensive margin) – no decision about
  hours of work (the intensive margin)
• Assume a fraction n(W,X) of individuals want to
  work given market wage W; there are L workers.
  X is other factors influencing labour supply.
• The labour supply curve will be given by:
•
              N  n(W , X ) L
                  s
                Equilibrium
• Equilibrium is at wage where demand equals
  supply. This also determines employment.
• What influences equilibrium wages/employment
  in this model:
  – Demand factors, Z
  – Supply Factors, X
• How these affect wages and employment
  depends on elasticity of demand and supply
  curves
     What determines wages?
• Exogenous variables are demand factors, Z, and
  supply factors, X.
• Statements like ‘wages are determined by
  marginal products’ are a bit loose
• True that W=MRPL but MRPL is potentially
  endogenous as depends on level of employment
• Can use a model to explain both absolute level
  of wages and relative wages. Go through a
  simple example:
    A Simple Two-Skill Model
• Two types of labour, denoted 0 and 1.
  Assume revenue function is given by:
                                       (1/  )
         Y  A  N0  (1   ) N1 
               
                                
                                   

• You should recognise this as a CES
  production function with CRS
• Marginal product of labour of type 0 is:
                                                                            1 
         Y                                            (1/  ) 1       Y 
                N 0 1 A  N 0  (1   ) N1 
                                                                       
         N 0                                                           N0 

• Marginal product of labour of type 1 is:
                                                                                   1 
       Y                                               (1/  ) 1            Y 
            (1   ) N1 1 A  N 0  (1   ) N1 
                                                                  (1   )  
       N1                                                                     N1 
• As W=MPL we must have:
                                  1 
                W1 (1   )  N 0 
                                
                W0      N1 
• Write this in logs:

           n1  n0  d   (w1  w0 )
• Where ζ=1/(1-ρ) is the elasticity of substitution
• This gives relationship between relative wages
  and relative employment
A Simple Model of Relative Supply
• We will use the following form:
        n1  n0  s   ( w1  w0 )
• Where ε is elasticity of supply curve. This
  might be larger in long- than short-run
• Combining demand and supply curves we
  have that:                 d s
               w1  w0 
                            
• Which shows role of demand and supply
  factors and elasticities.
Data from the US
   What about unemployment?
• As defined in labor market statistics (those
  who want a job but have not got one) does
  not exist in the frictionless model.
• Anyone who wants a job at the market
  wage can get one (so observed
  unemployment must be voluntary).
• Failure of this model to have a sensible
  concept of unemployment is one reason to
  prefer models with frictions.
Before we go there, a reminder
• Unemployment has different definitions
  (ILO, registered)
• US-EU unemployment gap used to be
  different
• An unemployment rate does not mean
  much without an employment rate
    The Distribution of Wages in
     Imperfect Labour Markets
• Discuss a simple variant of a model of
  labour market with frictions – the Burdett-
  Mortensen 1998 IER model. Here, MPL=p
  with perfect competition but with frictions
  other factors are important.
• Frictions are important: people are happy
  (sad) when they get (lose) a job. This
  would not be the case in the competitive
  model.
Labour Markets with frictions, cont.
• Assume that employers set wages before
  meeting workers (Pissarides assumes that
  there is bargaining after they meet. Hall &
  Krueger: 1/3 wage posting 1/3 bargained.)
• L identical workers, get w (if work) or b.
• M identical CRS firms, profits= (p-w)n(w).
  There is a firm distribution of wages F(w).
• Matching: job offers drawn at random
  arrive to both unemployed and employed
  at rate λ; exog. job destruction rate is δ.
Labour Markets with frictions, cont.
• Unemployed use a reservation wage
  strategy to decide whether to accept the
  job offer or wait for a better one (r=b).
• 1. steady state unempl.: Inflow = Outflow:
  δ(1-u) = λ[1-F(r)]u + 2. In equilibrium
  F(r)=0 (why offer a wage below r? – you’ll
  make 0 profits) => equilibrium u= δ / (δ+λ).
• Employed workers quit: q(w)= λ[1-F(w)]
Labour Markets with frictions, cont.
• In steady state, a firm recruits and loses
  the same number of workers:
  [δ+q(w)]n(w)=R(w)= λL/M[u+(1-u)N(w)]
  where N(w) is the fraction of employed
  workers who are paid w or less.
• Derive n(w): firm employment and profit.
  Next, get equilibrium wage distribution
  F(w) & average wage E(w).
• EQ: all wages offered give the same profit
  (π=(p-w)n(w) higher w means higher
  n(w).) + no other w gives higher profit.
• Average wage is given by:

• So the important factors are
                                                 p b
  –   Productivity, p                  E  w 
                                                  
  –   Reservation wage, b
  –   Rate of job-finding, λ and rate of job-loss, δ
  –   i.e. a richer menu of possible explanations
• But, also equilibrium wage dispersion (even
  when workers are all identical; a failure of the
  ‘law of one wage’) so luck also important.
• Perfect competition if λ/δ=∞. Frictions disappear.
  Competition for workers drives w to p (MP).
    Institutions also important
• Even in a perfectly competitive labour
  market institutions affect wages/emplmnt
• Possible factors are:
  – Trade unions
  – Minimum wages
  – Welfare state (affects incentives, inequality)
    Example: higher unempl. benefit increases
    the wage share and reduces inequality, but it
    also increases the unempl. rate thus making
    the distribution of income more unequal.
        Stylized Facts About the
         Distribution of Wages
• There is a lot of dispersion in the distribution of
  ‘wages’
• Most commonly used measure of wages is
  hourly wage excluding payroll taxes and income
  taxes/social security contributions
• This is neither reward to an hour of work for
  worker nor costs of an hour of work to an
  employer so not clear it has economic meaning
• But it is the way wage information in US CPS,
  EU LFS is collected.
          Overall Distribution of Hourly
          Wages in the UK - Untrimmed
           .8
           .6
Density



           .4
           .2
            0




                -2   0       2     4   6
                         lnwages
Overall Distribution of Hourly Wages in the
  UK – trimmed (£1 to £100 per hour)
            .8
            .6
  Density



            .4
            .2
             0




                 0   1   2             3   4   5
                             lnwages
          Overall Distribution of CZ Hourly Wages
   1Q2006: median: 105CZK, 5th percentile: 55CZK, 95th: 253
            1
           .8
           .6
Density



           .4
           .2
            0




                4        6                      8   10
                        Log of hourly wage rate
              Comments
• Sizeable dispersion (there is also much
  dispersion in firm-level productivity)
• Distribution of log hourly wages
  reasonably well-approximated by a
  normal distribution (the blue line)
• Can reject normality with large samples
• More interested in how earnings are
  influenced by characteristics
      The Earnings Function
• Main tool for looking at wage inequality is
  the earnings function (first used by Mincer)
  – a regression of log hourly wages on
  some characteristics:
               ln  w   x  
• Earnings functions contain information
  about both absolute and relative wages
  but we will focus on latter
Interpreting Earnings Functions
• Literature often unclear about what an
  earnings function meant to be:
  – A reduced-form?
  – A labour demand curve (W=MRPL)?
  – A labour supply curve?
• Much of the time it is not obvious –
  perhaps best to think of it as an estimate
  of the expectation of log wages conditional
  on x
An example of an earnings function
           – UK LFS
• This earnings function includes the following variables:
   –   Gender
   –   Race
   –   Education
   –   Family characteristics (married, kids)
   –   (potential) experience (=age –age left FT education)
   –   Job tenure
   –   employer characteristics (union, public sector, employer size)
   –   Industry
   –   Region
   –   Occupation (column 1 only)
         An example of an earnings function – UK LFS
                             all     all     men      women
female                     -0.175   -0.202     0         0
                           -0.008   -0.008     0         0
black                       -0.04   -0.052   -0.136    -0.032
                           -0.032   -0.034   -0.056    -0.042
indian                     -0.057   -0.072   -0.046    -0.115
                            -0.03   -0.032   -0.043    -0.047
pakistan                   -0.127   -0.098   -0.086    -0.144
                           -0.052   -0.055   -0.073    -0.084
bengali                     -0.26   -0.178   -0.206    -0.104
                           -0.089   -0.095   -0.116    -0.172
chinese                    -0.093   -0.053   -0.025    -0.033
                           -0.091   -0.097   -0.162    -0.116
           Education variables

              all     all     men      women
degree      0.286    0.507    0.484    0.489

            -0.011   -0.01    -0.015   -0.012
A' level    0.082    0.113     0.098    0.094

            -0.009    -0.01   -0.014   -0.013
no quals    -0.059   -0.105   -0.127   -0.087

             -0.01   -0.011   -0.017   -0.014
         Family Characteristics

                    all      all     men     women
married + kids    0.111    0.121    0.201    0.015

                  -0.011   -0.012   -0.018   -0.017
married+no kids   0.107    0.128    0.159    0.079

                  -0.011   -0.012   -0.018   -0.016
single+kids        -0.02   -0.022   -0.103   -0.045

                  -0.016   -0.017   -0.029   -0.02
       Experience/Job Tenure
                     all      all     men      women
experience/10       0.231    0.264    0.31     0.213

                    -0.011   -0.012   -0.018   -0.016
experience/10
  squared           -0.046   -0.054   -0.058   -0.051

                    -0.002   -0.002   -0.003   -0.003
tenure/10           0.145    0.191    0.161    0.225

                    -0.011   -0.012   -0.017   -0.018
tenure/10 squared   -0.02    -0.026   -0.02    -0.036

                    -0.004   -0.004   -0.005   -0.006
        Employer Characteristics

                           all      all     men women
union                    -0.014   -0.043   -0.091 0.018

                         -0.008   -0.008   -0.012   -0.011
whether work in public
  sector                 0.031    0.021    -0.054   0.063

                         -0.012   -0.013   -0.02    -0.016
ln employer size         0.051    0.051     0.07    0.033

                         -0.003   -0.003   -0.005   -0.004
        Industry (selected relative to manufacturing)
                                                        wome
                                all     all    men        n
g:wholesale, retail trade     -0.158 -0.123 -0.071 -0.142
                              -0.014 -0.013 -0.019 -0.019
h:hotels & restaurants        -0.209 -0.232 -0.21 -0.237
                            -0.022 -0.023 -0.04 -0.028
i:transport & communication 0.001 -0.016 -0.017 0.038
                              -0.014 -0.015 -0.018 -0.027
j:financial intermediation    0.192 0.271 0.342 0.217
                              -0.017 -0.018 -0.026 -0.024
k:real estate, renting         0.048 0.107 0.12     0.12
                              -0.014 -0.015 -0.02 -0.022
       Region (selected relative to
              Merseyside)
                       all      all     men     women
inner london         0.277    0.309    0.312    0.369

                     -0.028   -0.03    -0.047   -0.043
outer london         0.222    0.249    0.253     0.317

                     -0.025   -0.027   -0.042   -0.038
rest of south east   0.149    0.175    0.234    0.185

                     -0.022   -0.024   -0.038   -0.035
south west           0.034    0.03     0.069    0.068

                     -0.024   -0.026   -0.04    -0.037
          Occupation (relative to craft
          workers) – only 1st column
                              0.4                               0.002

                                       6 personal, protective
1 managers and administrators -0.015      occupations           -0.017
                              0.447                             0.025


2 professional occupations    -0.017   7 sales occupations      -0.019
                              0.263                             -0.04
3 associate prof & tech                8 plant and machine
   occupations                -0.016       operatives           -0.015
                              0.041                             -0.129

4 clerical,secretarial
    occupations               -0.015   9 other occupations      -0.017
    Stylized facts to be deduced from
           this earnings function
•   women earn less than men
•   ethnic minorities earn less than whites
•   education is associated with higher earnings
•   wages are a concave function of experience,
    first increasing and then decreasing slightly
•   wages are a concave function of job tenure
•   wages are related to ‘family’ characteristics
•   wages are related to employer characteristics
    e.g. industry, size
•   union workers tend to earn more (?)
         The same stylized facts for CZ
                              (1)     (2)                            (1)       (2)
Female                       -0.24   -0.26   Industry relat. to Agriculture
Educ. Relat. to Primary                                  Mining     0.26      0.32
            Apprenticeship   0.08    0.07        Manufacturing      0.21      0.21
      Secondary w/ GCE       0.34    0.32               Utilities   0.39      0.36
 College and University      0.82    0.82          Construction     0.22      0.21
            Post-graduate    1.04    1.04                 Retail    0.10      0.08
Age                          0.04    0.04                Hotels     0.07      0.15
Age squared                  -0.04   -0.04            Transport     0.25      0.25
Part-time                    -0.05   -0.05                Banks     0.54      0.63
Firm size (employment)       0.06    0.07    RealEstate+R&D.        -0.02     -0.03
Firm size squared            -0.02   0.04        Other Services     0.12      0.11
                                             _const                 3.49      3.48
                                             Trade unions                     0.004
                                             N                          1m    0.5m
The variables included here are common but
 can find many others sometimes included
• Labour market conditions – e.g. unemployment
  rate, ‘cohort’ size
• Other employer characteristics e.g. profitability
• Computer use- e.g. Krueger, QJE 1993
• Pencil use – e.g. diNardo and Pischke, QJE 97
• Beauty – Hamermesh and Biddle, AER 94
• Height – Persico, Postlewaite, Silverman, JPE
  04
• Sexual orientation – Arabshebaini et al,
  Economica 05
Raises question of what should be
 included in an earnings function
• Depends on question you want to answer
• E.g. what is effect of education on earnings –
  should occupation be included or excluded?
• Note that return to education lower if include
  occupation
• Tells us part of return of education is access to
  better occupations – so perhaps should exclude
  occupation
• But tells us about way in which education affects
  earnings – there is a return within occupations
     Other things to remember
• May be interactions between variables e.g. look
  at separate earnings functions for men and
  women. Return to experience lower for women
  but returns to education very similar.
• R2 is not very high – rarely above 0.5 and often
  about 0.3. So, there is a lot of unexplained
  wage variation: unobserved characteristics,
  ‘true’ wage dispersion, measurement error.
       Problems with Interpreting
          Earnings Functions
• Earnings functions are regressions so potentially
  have all usual problems:
  – endogeneity e.g. correlation between job tenure and
    wages
  – omitted variable e.g. ‘ability’
  – selection – not everyone works e.g. the earnings of
    women with very young children
• Tell us about correlation but we are interested in
  causal effects and ‘correlation is not causation’