Political Ambiguity and Economic Growth: The MENA Countries by Juliane Brach and Willem Spanjers

Political Ambiguity and Economic Growth:The MENA Countries

by

Juliane Brach andWillem Spanjers

(Kingston University andRimini Centre for Economic Analysis)

14th May 2013University of Bologna at Rimini

Rimini, Italy

Contents:1. Introduction2. Ambiguity3. An Example of Modelling Ambiguity4. Decision Making under Ambiguity5. Basic Growth Strategies6. The Model7. The Data8. Empirical Results9. Concluding Remarks

Political Ambiguity and Growth: the MENA Countries 2

1. IntroductionStylized facts:• Some MENA countries consistently grow below their

potential.Basic question:• May this be due to the impact of ambiguity and fear?

Answer: • Possibly yes.• Ambiguity with respect to:- the internal political situation (e.g. succession)- the external political situation (e.g. conflict).


2. Ambiguity

• Keynes (1937) gives a description of ambiguity:“By ‘uncertain’ knowledge, let me explain, I do not mean

merely to distinguish what is known for certain from what is only probable. The game of roulette is not subject, in this sense, to uncertainty [...]. The sense in which I am using the term is that [...] there is no scientific basis on which to form any calculable probability whatever. We simply do not know.” [pp. 113-114]


To Keynes, this is not without consequences for economic theory:“[T]he fact that our knowledge of the future is fluctuating,vague and uncertain, renders wealth a peculiarly unsuitablesubject for the methods of the classical economic theory. This theory might work very well in a world in which economic goods are necessarily consumed within a short interval of their being produced. But it requires, I suggest, considerable amendment if it is to be applied to a world in which the accumulation of wealth for an indefinitely postponed future is an important factor; and the greater the proportionate part played by such wealth accumulation the more essential does such amendment become.” [p. 113]


He than continues to discuss its implications:“Now a practical theory of the future [...] has certain markedcharacteristics. In particular, being based on so flimsy afoundation, it is subject to sudden and violent changes. Thepractise of calmness and immobility, of certainty andsecurity, suddenly breaks down. New fears and hopes will,without warning, take charge of human conduct. The forcesof disillusion may suddenly impose a new conventional basisof valuation. All these pretty, polite techniques, made for awell-panelled board room and a nicely regulated market areliable to collapse. At all times vague panic fears and equallyvague and unreasoned hopes are not really lulled, and lie buta little way below the surface.” [pp. 114-115]


The Modelling of Ambiguity• Belief functions- Dempster (1968) and Shafer (1976)

- Example by Mukerjee (ET 1997)- E(llsberg)-capacities as in Eichberger and Kelsey (1999).

• Multiple Prior Approach - in the tradition of Gilboa and Schmeidler (1989).

• Choquet Expected Utility - in the tradition of Schmeidler (1982/1989) - Neo-additive capacities as in Chateauneuf et al. (2007).


3. An Example of Modelling Ambiguity

• Consider a US investor holding Greek assets. • There are four relevant potential situations θ є Θ

relevant for his payouts:


Market State Asset Prices Exchange Rate

θ1 stable medium

θ2 stable weak

θ3 low medium

θ4 low weak

• The asset price and the exchange rate are determined by the state of the economy, ω є Ω, where:

State of the economy Crisis Deficit

ω1 mild medium

ω2 mild high

ω3 severe medium

ω4 severe high

ω5 Greece leaves Eurozone


• The investors know the probabilities with which the various states of the economy will occur, but can not always predict their effects on the asset market and the foreign exchange market:

Probability Market States Γ(ω)

ω1 0.2 {θ1}

ω2 0.4 {θ3}

ω3 0.1 {θ2,θ3}

ω4 0.2 {θ4}

ω5 0.1 {θ1,θ2,θ3,θ4}


• The minimum probability of an event T⊆Θ is

v(T) := Σ ω: Γ(ω) ⊆T P({ω}) • The function v is not additive because:

v({θ2}) = P(∅) = 0

v({θ3}) = P({ω2}) = 0.4

but v ({θ2,θ3}) = P(∅) + P({ω2}) + P({ω3})

= 0 + 0.4 + 0.1 = 0.5.


• Now suppose the portfolio generates the following payouts for the situation θ є Θ in the asset and foreign exchange markets:

• A cautious “pessimistic” decision investor assigns to state ω the worst outcome that may occur in this state.

• An exuberant “optimist” decision maker assigns to the state ω the best outcome that may occur in this state.

Markets θ1 θ2 θ3 θ4

Payout f(θ1) f(θ2) f(θ3) f(θ4)


• For a pessimistic decision maker, the outcomes assigned to the states of nature ω є Ω are:

Prob. Market States Outcome

ω1 0.2 {θ1} f(θ1)

ω2 0.4 {θ3} f(θ3)

ω3 0.1 {θ2,θ3} min{f(θ2), f(θ3)}

ω4 0.2 {θ4} f(θ4)

ω5 0.1 {θ1,θ2,θ3,θ4} minθєΘ f(θ)


The expected payout for this pessimistic investor isnow obtained as:

P({ω1}) f(θ1) + P({ω2}) f(θ3) + P({ω4}) f(θ4) +

P({ω3}) min{f(θ2), f(θ3)} +

P({ω5}) minθєΘ f(θ)

=

0.2 f(θ1) + 0.4 f(θ3) + 0.2 f(θ4) +

0.1 min{f(θ2), f(θ3)} + 0.1 minθєΘ f(θ).


• Consider the payout profiles g:= (5, 10, 3, 0) and h := (5, 0, 10, 3).

• Now we have

and implied probabilities over Θ

g h

min{f(θ2),f(θ3)} 3 (for θ3) 0 (for θ2)

minθєΘ f(θ) 0 (for θ4) 0 (for θ2)θ1 θ2 θ3 θ4

g 0.2 0.0 0.5 0.3

h 0.2 0.2 0.4 0.2


• So we find that the implied probability weighting over the market states Θ depends on the ranking of the payouts over the market states.

• A situation where the probabilities assigned to the different outcome would normally be referred to as “superstition”.

• It would also run counter to separating the beliefs over which outcomes are obtained from the evaluation of the specific outcomes.

• But the “minimum probability function” (belief function) v is independent of the outcomes!


Simple Capacity• Consider the state space S:={1,...,n}, a probability

distribution π and a coefficient γ є [0,1]. • Now the capacity v is a simple capacity if

v(E) := γπ(E) if E ≠ S 1 if E = S.

• Let (t1,...,tn) be a permutation of S such that f(t1) ≤ ... ≤ f(tn), so minsєS f(s) = f(t1).

• Applying the above procedure now givesγ Eπ{f(s)} + (1 - γ) minsєS f(s).


4. Decision Making under AmbigiutyLet (π1,..., πS) denote the probability estimate

γ [0,1] denote the level of ∊ confidence in the probability estimateβ [0,1] denote the level of ∊ optimism/

pessimism of the decision maker.Now the utility of the decision maker is:

U(x1,...,xS ; π1,..., πS ; γ; β) := γ 𝔼{u(x)}+ (1 – γ)∙β∙max s∊{1,...,S} u(xs)+ (1 – γ)∙(1-β)∙min s∊{1,...,S} u(xs).


5. Basic Growth StrategiesCountries face a trade-off between:• a medium technology development strategy

characterized by agglomeration effects and overall increasing returns scale (“urban sectors”)

and • a low technology development strategy

characterized by overall decreasing returns to scale and a tendency to geographical dispersion of economic activity (“rural sectors”).


Low technology development strategy:- decreasing returns to scale- low requirements on:

- infrastructure- the education and judicial systems- regional and redistributive policies- openness to international trade

- low vulnerability to financial and political shocks. - low contribution f(x) to rate of growth where x denotes the available amount of resources for private and public investment.


Medium technology development strategy:- increasing overall returns to scale- high requirements on:

- infrastructure- the education and judicial systems- regional and redistributive policies- openness to international trade

- high vulnerability to financial and political shocks- high contribution g(y) to the rate of growth where y denotes the available amount resources for private and public investment.


6. The Model• Each country has fixed resources, to invest in:

- low technology sectors- medium technology sectors- the oil sector.

• The growth created by further resources invested:- in low technology decreases due to decreasing returns to scale.- in medium technology decreases due to restrictions on the absorbing capacity of sectors.- in the oil sector is constant until the maximum absorbing capacity is reached.


• Low technology sectors:- low expected growth: f(x), f ’(x) > 0, f ’’(x) < 0 - worst case f min(x) not too bad.

• Medium technology sectors:- high expected growth: g(y), g’(y) > 0, g’’(y) < 0 - worst case gmin(y) very bad; for all attainable x and y: f min(x) > gmin(y) and f min ’(x) > gmin ’(y) .

• Oil sector:- expected growth: h(z), h’(z) = hconst for z ≤ zmax

- worst case hmin ’(z) = hconst.


•

g’(y)f ’(x)

gmin’ (y)

y*

f min’ (x)

x*

y = 0x = 0

growthCase: x + y = c

c


••

Case: x + y = cgrowth

f min ’(x)

gmin ’(y)x = 0 y = 0c

γ∙f ’(x) + (1-γ)∙f min ’(x)

γ∙g’(y) + (1-γ)∙g min ’(y)

x*(γ) y*(γ)


• •

Case: x + y + z = cgrowth

f ’(x)g’(y)

f min ’(x)

gmin ’(y)c

x* y*z*

h’(z) = hmin ’(z)


• •• •

Case: x + y + z = cgrowth

γ∙f ’(x) + (1-γ)∙f min ’(x)

γ∙g’(y) + (1-γ)∙g min ’(y)h’(z) = γ∙h’(z) +

(1-γ)∙h min ’(z)

x*(γ) y*(γ)z*(γ)c


7. The DataThe data sources we use are:• GIGA/IMES Database on political succession in

the MENA region.Created by Juliane Brach at GIGA, available on request.

• UCD/PRIG Armed Conflict Dataset Codebook, p. 7.

• Worldbank.• The analysis focuses on the period 1980 – 2008.


The data we consider fall into five groups:• Growth: average growth of GDP per capita.• Strategy: industry, agriculture, fuel export.• Ambiguity: polity form (democratic to autocratic),

government form, formal duration of legislature, changes of government, years in power, family ties, type of conflict the country was involved in.

• Control variables: population, ln GDP.• Interaction: polity form / duration, industry /

govt. form, fuel / govt. form, agriculture / govt. form.


8. Empirical ResultsPreliminary results seem to indicate that inexplaining GDP per capita growth over 1980 - 2008:• of the ambiguity variables

govt. form, duration and family tiesare significant with the expected signs, but NOT

polity form.• all the control variables

type of conflict, population and ln GDP, are significant, as is the strategy variable – industry.


In explaining GDP per capita level of income over 1980 - 2008:•of the ambiguity variables

govt. form, duration and family ties are significant and with the correct signs, but again the is no significance for the variable

– polity •the control variable

ln GDP, is significant, as are all the strategy variables – industry, agriculture, and fuel exports.


•The difference of the results in the two specification is not in line with what one would expect in standard growth regressions.•Two reasons for this come to mind:- the drivers of growth may be different from the factors that initially caused the high levels of income in the region, e.g. initial windfall profits relating to fuel extraction. - misspecification and endogeneity problems.


9. Concluding Remarks• Our preliminary findings seem to be in line with

our initial hypothesis that ambiguity may distort policy decisions toward low growth strategies.

• The result that the degree of democracy / autocracy is not significant is surprising.

• The preliminary results seem to provide a good starting point for a more detailed analysis of the available data.


Documents

Political Ambiguity and Economic Growth: The MENA Countries by Juliane Brach and Willem Spanjers