# Factors Affecting Financial Statements of Indian Banks

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```							     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
• 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
• 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:
– 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
• 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
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’

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