mobile money and monetary policy - international growth centre€¦ · anand and prasad (2010):...
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Mobile Money and Monetary Policy
Christopher Adam and Sébastien Walker
University of Oxford
12 February 2015
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Outline
Motivation: Mobile Money and Monetary Policy
An alternative framework: Anand and Prasad (2010)
Mobile money in the Anand and Prasad model
Policy implications
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Punchlines
I Financial frictions ⇒ supply shocks favour targeting headlinevs. core ination, especially with mostly rural population
I Mobile money ⇒ reduced volatility of all important variables
I Gains mostly to rural households
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Is mobile money a threat to the conduct of monetary policy?
The Implications of Innovations in the Financial Sector on the
Conduct of Monetary Policy in East Africa
David Weil, Isaac Mbiti, Francis Mwega (IGC WP, 2013).
By 2011:
I 70% of households use regularly use mobile money
I 18m registered users (compared to 1.4m with ATM cards)
...we conclude that the monetary implications of
mobile money are currently minimal in Kenya.
However...the developments and innovations in this space
could fuel the growth of mobile money such that it
reaches levels where it could have implications for
monetary policy
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The demise of money-based frameworks?
I Eective reserve-money targeting as practiced in East Africarelies on:
I predictability in the velocity of circulation of broad money(private sector demand behaviour)
I predictability in the money multiplier (the policy control /transmission mechanism)
I Instability ⇒ simple relationship between money and inationdicult to predict.
I Financial liberalization and innovation ⇒ end of moneytargeting in OECD... Is it doing the same in East Africa?
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Mobile money a threat to the conduct of monetary policy?I Central banks have expressed concern that these new
technologies undermine the ecacy of monetary policy asconventionally conducted.
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Kenya Monetary Ratios
Currency/Deposit Ratio [Left Scale]
M2 money multiplier [Right Scale]
M-Pesa Launched July 2007
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An East African transition towards IT frameworks
I East African Countries moving rapidly to IT-based frameworks
I Uganda, formal committment to `IT-Lite' in July 2011 withtarget of 5% for core ination
I Kenya, formal committment to an ination target of 5% (+/-2.5%) for headline ination
I In other EAC countries, committment to keep headline
ination in stable single digits
I All central banks actively moving towards frameworks that cansteer short-term market rates into a closer relationship withthe policy interest rates (`bank rate')
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The conventional wisdom
I Demand shock: ⇑ ination, ⇑ output (or vice versa)'
I Supply shock: ⇑ ination, ⇓ output (or vice versa).
I If supply shocks dominate strict IT exacerbates outputvolatility
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Implications for LICs
I Conventional IT solution: target core ination; accomodatenon-core price shocks (but not second-round eects)
I But if these are frequent and large an IT regime targeting coreination allows high volatility in headline ination.
I General result: the broader the measure of ination you seekto stabilize, the greater will be output volatility. . . Other (i.e.scal) policy instruments to stabilize output?
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Why supply shocks are likely to dominate in LICsThe Food Engel curve
Christopher Adam. 1
TZA
0.0
20.0
40.0
60.0
80.0
perc
ent
0 20000 40000 60000 800002008 GDP per capita at constant 2001 international prices
Source: USDA Economic Research Service
Share of food in total consumption
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Supply shocks dominate in LICsCorrelation between output gap and ination: demand shocks⇒ ρ > 0, supply shocks: ⇒ ρ < 0
Christopher Adam. 1
TZA
-1-.
50
.51
Corr
ela
tion c
oeffic
ient
0 20000 40000 60000Real GDP per capita, PPP
HP-filtered variables. All countries with 17 or more yrs of data starting in 1990.
Source: Adam et al. (2010).
Correlation between real GDP and inflation
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Anand and Prasad (2010): countering the conventionalwisdom for emerging market economies
I Optimal Price Indices for Targeting Ination Under Incomplete
Markets Rahul Anand and Eswar Prasad (IMF WP 20/200)
I Adapt conventional New-Keynesian model to dualistic setting:
I `Food-producing'/rural households: hand-to-mouth/Keynesianconsumers
I `Non-food-producing'/urban households can borrow/save
I Monetary policy transmission through consumption Eulerequation for urban households.
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Anand and Prasad(2010): key results and intuition
I With incomplete markets, targeting core ination may not beoptimal. Flexible headline ination targeting generallydominates.
I Absence of nancial markets means that relative price shocksin the food (ex-price) sector have direct income eects forcredit constrained households
I => aggregate demand eect which does not respond toconventional demand-side policy responses.
I Our research question: how robust are these ndings when weallow for mobile money technology in this class of model?
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Adding mobile money transfers to Anand and Prasad (2010)
LABOUR
FOOD
NON-FOOD
WAGES
WAGES
LABOUR
CREDIT/SAVINGS INTEREST RATE REMITTANCES/
MOBILE MONEY
TRANSFER
FOOD HHs NON-FOOD HHs
NON-FOOD FIRMS FOOD FIRMS
BOND MARKET
CENTRAL BANK
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Urban households
Urban HHs' demand for composite consumption good (C st ):
(C st )−σ = βEt
[(C s
t+1
)−σ Rt
Πt
](1)
β: discount factorRt : Gross nominal interest rateΠt : Gross ination rateσ: inverse of elasticity of intertemporal substitution
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Rural households
Rural HHs' demand for composite consumption good (C ft ):
C ft = xf ,tyf ,t − xf ,tC
∗ +mt
1 + µ(2)
xf ,t : relative price of foodyf ,t : food productionC ∗: subsistence consumption levelmt : remittancesµ: remittance `melt' rate
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Remittances
Remittance payment:
mt = me
−κ(
Ωt
Ω−1
)(3)
mt : remittancesm: steady-state mt
Ωt : rural HHs' pre-remittance income net of subsistenceconsumptionΩ: steady-state Ωt
κ: elasticity of mt w.r.t. Ωt
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Interest rate rule
log
(Rt
R
)= ρi log
(Rt−1
R
)+ ρπ log
(Πt
Π
)+ ρy log
(Yt
Y
)(4)
Rt : Gross nominal interest rateΠt : Gross headline or core ination rateYt : GDPρi = 0.7, ρπ = 2, ρy = 1
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Experiments
1. Targeting headline ination vs. core ination
2. Three remittance set-ups:I `No remittances': remittances xed at steady-state levelI `Constrained remittances': 1
2elasticity w.r.t. rural incomes,
20% `melt'I `Mobile money': unit elasticity w.r.t. rural incomes, no `melt'
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Proportional change in std. deviations: headline- overcore-ination-targeting, food shock
NR = no remittances, CR = constrained remittances,MM = mobile money
Remittance set-up NR CR MM
Headline ination -29% -20% -13%Core ination -32% -14% 2%GDP -32% -8% 84%Nominal int. rate 48% 93% 218%
Rural cons. -9% -5% -3%Urban cons. 4% 8% 16%
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Proportional change in std. deviations: CR/MM over NR,food shock
NR = no remittances, CR = constrained remittances,MM = mobile money
Ination target Headline Core
Remittance set-up CR MM CR MM
Headline ination -16% -32% -25% -44%Core ination -10% -21% -29% -47%GDP -30% -51% -48% -82%Nominal int. rate -25% -48% -43% -76%
Rural cons. -37% -73% -39% -74%Urban cons. -34% -64% -37% -68%
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Std. deviations by regime × 10, food shock
NR = no remittances, CR = constrained remittances,MM = mobile money
Ination target Headline Core
Remittance set-up NR CR MM NR CR MM
Headline ination 0.4229 0.3558 0.2874 0.5944 0.4453 0.3313Core ination 0.0160 0.0144 0.0126 0.0234 0.0167 0.0124GDP 0.2336 0.1638 0.1148 0.3446 0.1787 0.0624Nominal int. rate 1.0926 0.8148 0.5718 0.7405 0.4230 0.1797
Rural cons. 2.0488 1.2908 0.5619 2.2502 1.3632 0.5784Urban cons. 2.1881 1.4333 0.7857 2.1079 1.3331 0.6794
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Std. deviations by regime × 100, non-food shock
NR = no remittances, CR = constrained remittances,MM = mobile money
Ination target Headline Core
Remittance set-up NR CR MM NR CR MM
Headline ination 0.3127 0.3129 0.3129 0.3140 0.3136 0.3131Core ination 0.3219 0.3204 0.3185 0.3209 0.3197 0.3182GDP 0.5249 0.5184 0.5093 0.5326 0.5234 0.5121Nominal int. rate 0.3133 0.3317 0.3580 0.3286 0.3488 0.3739
Rural cons. 0.7200 0.4731 0.2132 0.7306 0.4777 0.2143Urban cons. 0.2824 0.5747 0.9256 0.2866 0.5803 0.9305
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Policy implications
I No threat to eective conduct of monetary policy with movefrom reserve-money targeting to IT
I Mobile money use ⇒ macroeconomic stability, encouragefurther spread
I A possible case for targeting core ination
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Punchlines
I Financial frictions ⇒ supply shocks favour targeting headlinevs. core ination, especially with mostly rural population
I Mobile money ⇒ reduced volatility of all important variables
I Gains mostly to rural households