Inflation Dynamics in Post-Secession Sudan Suwareh Darbo and Amandine Nakumuryango

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Inflation Dynamics In Post-Secession Sudan Suwareh Darbo and Amandine Nakumuryango

aper Series

n°305 orking P January 2019 W

African Development Bank Group

Working Paper No 305

Abstract parallel rate, credit to the private sector as a The objective of this working paper is to investigate percentage of GDP, and crude oil prices. The results the factors contributing to inflation in Sudan in the indicate that, in the long run, oil prices have a wake of South Sudan’s secession, which resulted in negative effect on inflation while money supply, the loss of 75% of the country’s oil exports. The credit to private sector, and nominal effective paper uses a single equation model in a Vector Error exchange rate have positive effects. This Correction Model (VECM) to investigate the underscores the need to manage money supply, the determinants of inflation. The independent variables exchange rate, and credit to the private sector, all of included in the model are money supply, the which can be influenced by the monetary nominal effective exchange rate based on the authorities—that is, the Central Bank of Sudan.

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Correct citation: Darbo S. and A. Nakumuryango, A (2019), Inflation Dynamics in Post-Secession Sudan, Working Paper Series N° 305, African Development Bank, Abidjan, Côte d’Ivoire.

Inflation Dynamics in Post-Secession Sudan

Suwareh Darbo1 and Amandine Nakumuryango 2

JEL classification: E310, E520, E580

Keywords: Inflation, money supply, exchange rate, credit, oil prices Sudan.

1 Principal Country Economist, African Development Bank (AfDB)

2 Consultant, African Development Bank (AfDB).

1. Introduction

Sudan faced daunting challenges in the conduct of monetary policy following the secession of South Sudan in 2011. Its economic conditions deteriorated rapidly, with GDP growth rates plummeting from an average of 7.5% in the five years preceding secession3 to 0.9% in 2011, with a slight improvement to 1.4% in 2012. Though GDP growth rates started to slowly pick up and reached 4.9% in 2015, the basic fundamentals of the economy remain very weak, with only modest growth of about 3% and 3.5% projected in 2016 and 2017, respectively (Figure 1), driven by the non-oil sectors, notably agriculture, gold, and services.

Figure 1: GDP growth and inflation

60 50 40 30 20 10 0 2010 2011 2012 2013 2014 2015 *2016 *2017

GDP Growth Rate Inflation

Source: AfDB Statistics database Note: *=Projections

The economy also slipped into fiscal and current account deficits in 2011 and 2012, from which it has not fully recovered. The current account deficit almost hit double digits (- 10.3% of GDP) in 2012 and -8.1% in 2013, up from -1.9% in 2011 due to the loss of oil revenue, while the fiscal deficit deteriorated from a surplus of 0.1% in 2011 to -3.1% and -2.2% of GDP in 2012 and 2013 respectively. The current account has not fully recovered either and remains stubbornly high at -5.3% of GDP in 2016 and projected at -4.9% in 2017 (Figure 2).

3 The year 2007 recorded a GDP growth rate of 10.5%, which made Sudan one of the fastest growing economies in Africa. 2

Figure 2: Current account and fiscal balance as a% of GDP

0 2010 2011 2012 2013 2014 2015 2016 *2017 -5

-10

-15 Current account as a % of GDP Fiscal Defiict as a % of GDP

Source: AfDB Statistics database Note: *=Projections

With very limited concessional resources due to the country’s staggering debt— estimated at USD 53.6 million as at December 2016—coupled with limited fiscal scape, the government of Sudan resorted to monetizing the fiscal deficit, fuelling high inflation—36.9% in 2014. Monetization of the fiscal deficit continues to increase, reflecting limited access to foreign borrowing and non-inflationary financing. Figure 3 compares Sudan’s average inflation rates during the period 2010-2016 with those of some of its regional neighbours, including Kenya, Ethiopia, Uganda, and Tanzania. Kenya, Uganda, and Tanzania have registered the lowest average rates (5.8%, 7.1%, and 8.6% respectively) while Ethiopia and Sudan (14% and 24.3%, respectively) registered the highest average rates.

However, fiscal consolidation policy4 implemented since 2012 curbed the fiscal deficit to about -1.6% of GDP in 2015, and estimated at -1.8% of GDP in 2016 (Figure 2), thus

4 Fiscal reform measures in 2012/2013 on the expenditure side included: (i) reducing the size of the government by about 50%; (ii) removing 50% subsidies on oil products; (iii) reducing government-procured goods and services; and (iv) eliminating 3 reducing inflation from 36.9% in 2014 to 13.5% in 2016. However, higher central bank purchases of gold, which accounted for 39% of exports in 2017, coupled with lending to agriculture caused reserve money to grow from 27.5% of GDP at end-2016 to 52% in June 2017. Consequently, inflation soared to 35.1% in September 2017 and reached 52% in January 2018. Hence the need the need to investigate the root causes of inflation.

Macroeconomic stability in Sudan is undermined by several other factors, including a narrow export base, quasi fiscal operations of the government5, an unconducive investment climate, failure to deepen reforms by agreeing with the IMF on the 14th Staff Monitored Program6 (SMP) in 2015, a multiple exchange rate system7, sanctions, and ill-targeted subsidies.

While the IMF advised the Central Bank of Sudan to tighten its control of credit to the government so as to reduce inflation and also avoid crowding out private investment, it also advised it to use other monetary instruments to mop up the bank’s reserves. However, because the central bank is operating under the Sharia law, it cannot use interest-bearing debt instruments or discount these instruments in the secondary markets. The central bank is thus left with three alternative instruments: equity-based instruments, quantitative ceilings on credit, or a reserve ratio. Each of these has shortcomings. The equity-based instrument cannot be priced efficiently. And quantitative ceilings and reserve ratios cannot guarantee full control of the supply of money. The central bank, therefore, has less effective monetary control instruments than other countries operating under the traditional non-Sharia system and must resort to monetization of the deficit (accommodative monetary policy). This is how it lost control over credit to government and public enterprises.

So far, the country has implemented 13 SMPs. The fourteenth one was not implemented due to policy slippages (disagreement on the macroeconomic framework), and the fact that it

exchange rate distortions. Revenue side measures included tax reform, tightening loopholes for corruption, and increasing oil and gold production. For details, please refer to the three-year Salvation Program, 2011-2013.

5 The central bank’s policy of purchasing gold at the parallel market exchange rate to finance strategic imports (fuel, wheat, and pharmaceuticals) at the official rate, which translates into money supply growth.

4 The Government of Sudan implemented 13 Staff Monitored Programs since 1997. The 14th one was not fully implemented due policy differences in the macroeconomic framework.

7 There are four exchange rates in Sudan: official rate, incentive rate, rate for public transactions and parallel market exchange rate. 4 has not resulted in highly indebted poor country (HIPC) debt relief. The authorities have realized that HIPC debt relief hinges more on political considerations.

The multiple exchange rate system subdues exports and therefore erodes the competitiveness of the external sector. This, coupled with U.S. sanctions8, has resulted in the rapid depreciation of the exchange rate and increased inflationary pressures.

In addition, the ill-targeted wheat subsidies9 and quasi-fiscal operations of the Central Bank of Sudan—including in particular buying gold at the incentive exchange rate and selling it overseas at the official exchange rate—have contributed to distortions in the foreign exchange market, thus adding to inflationary pressures.

The objective of this paper is to investigate the factors contributing to the country’s inflation in the wake of the secession of South Sudan, which resulted in the loss of 75% of the country’s oil exports. The introduction is followed by Section 2, the literature review. Section 3 explains the model used in the study, while Section 4 presents the empirical results. Section 5 concludes and makes recommendations.

2. Literature Review

Inflation is perceived as bad news. In addition to increasing prices, it erodes savings, discourages investments, and stimulates capital flight (into foreign assets, precious metals, or unproductive real estate). It may inhibit growth, makes economic planning a unpredictable, and in its extreme form evokes social and political unrest by exacerbating poverty. Consequently, governments regard inflation as a plague and try to squelch it by adopting conservative and sustainable monetary and fiscal policies.

Sahadudheen (2012) used an error correction model to study the determinants of inflation in India. The study concluded that an increase in GDP or broad money has a positive effect on inflation in the long run. On the other hand, an increase in the interest rate or exchange rate has a negative effect. The study found the income coefficient to be 0.37 and significant, implying that in India, an increase in income leads to a 0.37% increase in inflation. Similarly,

8 While it is still early to assess the impact of the sanctions, it is expected that the post-sanctions era will witness a surge in foreign direct investment (FDI) and facilitate the country’s reintegration into the global economy while building momentum for HIPC debt relief.

9 The wheat subsidies are not well targeted: the urban rich, who eat mainly wheat, take advantage of these subsidies, while the poor eat sorghum, which is not subsidized. 5 the study found the money coefficient to be 0.047 and significant, implying that in India, a 1% increase in money supply leads to a 5% increase in price.

Baciu (2015) estimated an error correction model to quantify the long-term relationship between the inflation rate and the main determinants in Romania, including the real interest rate, the money supply, the exchange rate, and the average nominal wage. The study concluded that the real interest rate had a significant influence on the inflation rate in the long term during the period analysed in Romania.

Moriyama (2008) investigated inflation dynamics in Sudan using three different approaches: the single equation model, the structural vector-auto regression (VAR) model, and a vector error correction model with data spanning 1995Q1 to 2007Q2. The estimated results concluded that money supply and nominal exchange rate changes affect inflation with an 18- 24-month lag.

Lim and Papi (1997) sought to explore the determinants of inflation in Turkey by analysing price determination within the framework of a multi-sector macroeconomic model during 1970-1995. The study found that monetary variables (money supply and exchange rate) and public sector deficits play a central role in the inflationary process and that inertial factors are quantitatively important. They therefore grouped inflation into three:

(i) that which focuses on a purely monetary approach and point to the clear relationship between money and price; (ii) that which employs a public finance approach and indicates the monetary expansion occurs in response to fiscal imbalances; and (iii) that which analyses structural and cost-push factors.

This categorization highlights the importance of oligopolistic pricing and cost pressures stemming from wage increases and devaluations, while recognizing that, generally, for structural and cost-push factors to operate, monetary policy must also be accommodative.

2.1 Monetary approach

The monetary approach assumes a stable demand for real money balances determined by real income and returns on alternative assets. Given aggregate supply constraints in the short run, an expansionary monetary policy will result in higher prices. OECD (1995) found that in the long run wholesale price inflation is exclusively determined by the money supply, while Togan

(1987) and De Santis (1993), using inverted money demand functions, produce estimates of inflation that track the trend of actual inflation rather well. Ozatay (1992) showed that monetary aggregates can help predict movements of nominal income and the GNP deflator.

2.2 Public finance approach

The public finance approach emphasizes that, given the limits on domestic and foreign borrowing dictated by financial market conditions and solvency requirements, monetization is the residual form of deficit financing.

Rodrik (1991) found a one-to-one relationship, at the margin, between public sector deficits and inflation, with inflation inertia not significant over the estimation period. He also noted that during the 1980s, the inflationary consequences of a given deficit were exacerbated by a decline in the demand for base money due to the relaxation of foreign exchange regulations, which induced a portfolio reallocation toward foreign currency–denominated assets and a significant erosion of the base of the inflation tax. Anand and Van Wijnbergen (1988), and Van Wijnbergen (1989), formulated their analysis in a framework that can be used to derive inflation for a given deficit and money demands. However, multiple equilibrium inflation rates can result because the inflation rate is also a determinant of money demand. Finally, Batavia and Lash (1983) found evidence of a vicious circle between inflation and public sector deficits during 1950-75; they maintained that inflation increases the public sector deficit because it raises expenditure faster than revenues.

2.3 Structural and cost-push explanations

Finally, a number of studies explore the role of structural and cost-push factors in inflation. These factors include the link between the exchange rate and prices; the mark up on final product prices due to the oligopolistic industrial structure; wage pressures stemming from indexation rules; and entrenched inflationary expectations and private credit.

Rising inflation and continued depreciation of a currency may give rise to the hypothesis of a devaluation-inflation spiral. This is especially so when an economy is relatively highly dependent on imports of capital and intermediate goods and there is a predominantly oligopolistic industrial structure that allows a markup over costs by manufacturing firms. This is exactly the case of Sudan, where the main import items are manufactured goods, machinery and equipment, food stuffs, transport equipment, wheat, petroleum products, and medicine.

Under these circumstances, increases in the price of foreign currency or the dollar price of imported inputs translate into higher prices of domestic products.

Moser (1995) studied the major determinants of inflation in Nigeria using an error correction model. The study concluded that the main determinants of inflation in Nigeria include monetary expansion arising from expansionary fiscal policies, the devaluation of the naira, and agro-climatic conditions. The study associated the devaluation of the naira to fiscal and monetary policies.

Maryam (2014) analysed the determinants of inflation in Malaysia during 1980-2012 using multiple regression analysis to identify the determinants of inflation between independent variables and a dependent variable. His study revealed that gross domestic product, interest rate, and government expenditure are negatively correlated with inflation whereas money supply is positively correlated.

Ruzima and Veerachamy (2015) investigated the influence of government spending, imports of goods and services, population growth, agriculture output, and foreign direct investment on inflation during 1970-2013. The ordinary least squares (OLS) method was employed to estimate the regression model. They found that agriculture output and import of goods and services are the main drivers of inflation in Rwanda. Population growth is statistically significant and negatively correlated with inflation. Government spending and foreign direct investment have, respectively, an insignificant negative and positive impact on inflation. The study found that inflation is mainly accelerated by the supply side (high cost of inputs) and external factors (import of goods and services). In this regard, the results show that inflation in Rwanda is not a monetary phenomenon. Therefore, in Rwanda, inflation can be efficiently controlled by fiscal policy through government spending, improvement in the terms of trade (lower imports), and a reduction in the agricultural sector’s production cost.

Laryea and Sumaila (2001) employed various econometric techniques to explain the main determinants of inflation in Tanzania in both the long run and the short run. In the short run, output and monetary factors are the main determinants of inflation. However, in the long run, the parallel exchange rate also plays a key role. The positive coefficients on the exchange rate variable reflect the effect on inflation via trade in goods, mainly through imports in the informal sector. The elasticities of price with respect to both money and output reveal that inflation in Tanzania is influenced more by monetary factors than by real factors, both in the

8 long run and the short run. Their study’s key policy implication is that inflation in Tanzania is a monetary phenomenon. Thus, to control inflation, the government will have to pursue a contractionary monetary and fiscal policy. The significance of the output variables in their analysis, especially in the long run, also suggests that the government can reduce inflation by increasing output, especially agricultural output. This is because food accounts for about 65% of the weight used in the consumer price index.

Onis and Ozmucur (1990) found evidence of a two-way causal link between exchange rates and prices, Rittenberg (1993) and Metin (1995), however, provided counter evidence. Using Granger causality tests, Rittenberg showed that causality runs from price level changes to exchange rate changes, but not vice versa.

Few studies have tested the relative significance of oligopolistic pricing behaviour on inflation. In general, these studies indicate the markup pricing alone cannot explain the causes of persistent inflation. Uygur (1992) found that firms’ mark ups, expressed as a function of changes in excess demand, statistically determine private manufacturing wholescale price inflation, but with only a relatively small impact. Instead, inflationary expectations are found to be the more relevant factor, accounting for about 75% of the magnitude of the price changes. In turn, these expectations are shown to be determined by (i) inertia—higher current inflation leads to higher expected inflation; (ii) public sector policy (e. g., energy prices); and (iii) uncertainty.

Some authors, including Yeldan (1993), argued that the underlying sources of inertial inflation usually originate from the prevailing income inequality and conflicting social claims on national output, which act to propagate cost inflation. Attempts by each sector to set its price so as to maximize its share of given output seldom lead to changes in income distribution, but instead result in inflation.

The macroeconomic implication of faster bank credit growth is not straightforward because, unlike demand for money, literature on demand for credit is scarce. If demand is rising faster than supply, then the economy may be overheating, which may lead to inflation or other macroeconomic instability. Korkmaz (2015) investigated the impact of private credit on inflation in 10 European countries and found that it did not affect inflation but did affect economic growth.

De Boissieu (1990) contends that the credit channel actually has two interlinked components: the channel that passes through the impact of changes in interest rates on the situation and the behaviour of borrowers, and those interested in the impact of changes in interest rates on the behavior of lenders, particularly banks. He thus, stressed that the channel of credit rate is therefore generally not independent of the interest rate channel, and both play in the same direction to enhance the impact of monetary policy.

Mohanty and John (2015) investigated the determinants of inflation in India using a structural vector-auto regression (SVAR) model for 1996-2014. The explanatory variables used included crude oil prices, output gap, fiscal policy, and monetary policy. The study revealed that monetary policy had a steady impact on inflation. Crude oil prices and the fiscal deficit were found to be major drivers of inflation during 2009-2011 and 2011-2012, respectively, while the output gap had less impact on inflation.

The Central Bank of Sudan (2010) undertook a study to identify the determinants of inflation in Sudan during 1970-2008. The model estimation results show that all the variables carry correct signs and are significant at least at the 5% level except the coefficient of the nominal exchange rate. The coefficient of foreign inflation is the largest, followed by that of real output, implying that these are the most influential determinants of domestic inflation in the Sudan in the long run. The results of the Granger causality test indicate a bi-directional causal effect between the nominal exchange rate and the money supply in addition to unidirectional causal effects running from domestic inflation to the nominal exchange rate and the real money supply, from real output to domestic inflation and nominal exchange rate, and from foreign inflation to domestic inflation, the nominal exchange rate, the real money supply, and real output.

The Central Bank of Sudan (2010) investigated inflation dynamics in Sudan using the SVAR, error correction and fiscal dominance models. The study revealed that the exchange rate is the major cause behind inflation; deficit financing by printing money (seignorage), which caused monetary expansion, was also found to be a main determinant of inflation in Sudan during 1970- 2008. The results also confirmed the validity of the argument that short-run changes in inflation are explained by currency fluctuations while the long-term behaviour of inflation is mainly explained by deficit financing and growth in money supply.

The Central Bank of Sudan examined the response of inflation to changes in nominal exchange rate in Sudan over two different exchange regimes (fixed and managed floating regime) by focusing on the currency devaluation in 2012 and 2013 (Talwa 2016). The study uses the interaction term as a technique to analyse the structural break dates on the given time series (inflation and nominal exchange rate). This includes the interaction between exchange rates; a binary variable represents a structural breaks. The study concluded that the devaluation is not feasible for Sudan, where there is a shortage of foreign currencies. This result is in line with empirical evidence from the 1997 Asian financial crisis 1997, showing that devaluation under such circumstances is not feasible.

Kurozumi and Van Zandweghe (2018b) of the Federal Reserve investigated the reason for the decline in inflation persistence in the United States in the 1980s and found that monetary policy makers responded more aggressively to inflation during this period. The study concluded that an aggressive policy response lessens the influence of past inflation on inflation dynamics and shifts the weight of shocks that drive inflation from more persistent shocks to less persistent ones.

Kurozumi and Van Zandweghe (2018a) in another study introduced variable elasticity demand curves in a staggered price model with trend inflation to investigate the behaviour of inflation. The study revealed that demand curves induce strategic complementarity in price setting and thus generate inflation persistence under positive trend inflation. They, therefore, concluded that credible disinflation leads to a gradual decline in inflation and a fall in output and that lower trend inflation reduces inflation persistence.

In the specific case of Sudan, there are several empirical studies focusing on the monetary transmission mechanisms. Moriyama (2008) investigated inflation dynamics in Sudan using three different approaches: the single equation model, the structural vector-auto regression model and a vector error correction model with data spanning 1995Q1-2007Q2. The estimated results concluded that money supply and nominal exchange rate changes affect inflation with 18-24-month lag. Jabrallah and Mohamed (2008) used impulse response analysis and the GARCH model with monthly data from July 1996 to December 2007. Their study suggested that the change in money supply will be reflected in inflation after 7-10 months. Abdoun (2012) used a model consisting of three equations: (i) one explaining price developments for tradables; (ii) one explaining price developments for nontradables; and (iii) one deriving inflation as a function of tradable and non-tradable inflation. The main model was

11 estimated over 1998Q1-2011Q4 and two sub-models were estimated relative to the high and low inflation periods, respectively. The estimation results found that the exchange rate, reserve money, and fiscal monetization and wages are key determinants of inflation.

3. Model

As secession was a major structural change in the economy, it is appropriate to study the monetary transmission mechanism against this background. To investigate the impact of factors affecting inflation in Sudan, the study builds on the following expression:

퐼푛푓푙 = 푓 (푟푦, 푚푠, 푃푟퐶푟푒푑푖푡, 푛푒푒푟, 표푖푙푝), where Infl is the inflation rate, ry is output growth, ms is money growth, neer is the nominal effective exchange rate based on the parallel rate, PrCredit is credit to the private sector as a percentage of GDP, and oilp is the crude oil price. The estimation uses lagged values of the explanatory variables.

Monetary expansion resulting from financing a deficit may trigger an inflationary spiral. A deprecation of the Sudanese pound will also result in an increase in inflation, especially if foreign trade is dominated by imports10. Similarly, an expansion in private credit may result in inflation. We used monthly time-series data from June 2011 to June 2017 from the Central Bank of Sudan. We follow VECM methodology. The VECM is an extension of VAR that assumes a co-integration relation between the dependent variables and the independent variables. The model allows us to estimate the long-term effects and to analyse the short-term adjustment process within one model. To explore the long-run relationship between variables, we use the Johansen co-integration test.

The conventional ECM for co-integrated series is specified as follows:

푛 푛 ∆푦푡 = 훽0 + ∑푖=1 훼∆푦푡−푖 + ∑푖=0 휃∆푋푡−푖 + 훿푍푡−1 + ℇ푡, where 푍푡−1 is the error correction term.

The co-integration regression only considers the long-run linkages between the level series of variables, while the error correction model (ECM) is developed to measure any

10 Sudan is an import-dependent country, as evidenced by the deterioration of the trade balance as a percentage of GDP from 2.5% in 2010 to -2.8% in 2016 due to the loss of oil. Its imports are dominated by food, manufactured goods, transport equipment, medicines, and chemicals.

12 dynamic adjustments between the first differences of the variables. An error correction term is defined by:

푍푡−1 = 푦푡−1 − 훽0 − 휃1푋푡−1.

In the VECM, all variables are endogenous and, provided there is co-integration, they correct in the long-run from short-term deviations. For example, if inflation and money supply are co-integrated, they do not deviate continually from their long-run relationship.

4. Empirical Results

Many macroeconomic time series contain unit roots dominated by stochastic trends as developed by Nelson and Plosser (1982). Unit roots are important in examining the stationarity of a time series because a non-stationary regressor invalidates many standard empirical results. The presence of a stochastic trend is determined by testing the presence of unit roots in time series data. A unit root test will be performed using the Augmented Dickey-Fuller (ADF) test. We first examined the trends in the determinants of inflation used in the study to obtain a basis for our empirical analysis.

4.1 Graph 1: money supply growth and inflation

According to Friedman (1963), “inflation is always and everywhere a monetary phenomenon”. Theoretically, an increase in money supply has a positive and direct impact on inflation. The Sudanese economy deteriorated after the secession of South Sudan in 2011, which led to loss of 75% of Sudan’s oil revenue. Hence, the monetization of the fiscal deficit has resulted in high inflationary pressures, with record-high inflation reaching 47.9% in March 2013. An examination of the growth trends of inflation and the money supply in Graph 1 reveals that both are increasing from 2011 to 2012. This phenomenon is explained by the central bank’s quantitative control of the money supply, which led to excessively high reserve requirements from the commercial banks and banks and failed to maintain an appropriate credit administration with no control of excess liquidity in the economy. During 2013-2014, inflation increased while money supply decreased. This is due to the adoption of prudent fiscal policies and reduction in money supply but the economic policies failed to contain inflation in 2014 because of infrastructure deficits which led to an increase in the cost of transportation and hence increased prices of final goods. During 2014-2016, Sudan suffered from low oil revenues because of low export prices. In 2015, inflation declined while the money supply increased because of reforms in tax

13 collection and public financial management, which led to a substantial reduction in inflation as shown in Graph 1. From mid-2016 to mid-2017, there was an increase in the money supply because of expansionary monetary policy coupled with inefficient liquidity management. Also, inflation rose due to an increase in non-food prices following a cut in subsidies. 50 50 45 45

40 40 35 35 30 30

25 25 20 20 15 15

10 10 2011 2012 2013 2014 2015 2016 2017

INFLATION Growth in money Supply( M2 %)

4.2 Graph 2: exchange rate and inflation

An appreciation of the Sudanese pound against the US dollar will lower the price of imported goods and thereby pushes down domestic inflation. On the other hand, a depreciation of the Sudanese pound against the US dollar is expected to have the opposite effect by increasing inflation. Graph 2 shows that, following the secession in 2011, the Sudanese pound (SDG/USD) depreciated considerably ever since, fuelling inflation. As a result of sanctions on Sudan, the shortage of foreign exchange contributed to the depreciation of the exchange rate on the parallel market, which led to an increase in inflation. However, the government implemented tight monetary policy reforms in 2012 and 2013 to temper exchange rate distortions and curb inflation. The parallel market exchange rate remained stable during 2013-2014 irrespective of the large difference between the official rate and the parallel market rate. The reforms also led to a drastic drop in inflation, from 44.4% in December 2012 to 12.6% in December 2015. During 2016–mid-2017, insufficient external financing coupled with rising non-food prices and the depreciation of the Sudanese pound against the dollar—from 11.2% in December 2015 to 18.5% in July 2016—led to an increase in inflation, which reached 32.6% in June 2016.

50 32

45 28

40 24

35 20

30 16

25 12

20 8

15 4

10 0 2011 2012 2013 2014 2015 2016 2017

INFLATION Parallel Market Exchange Rate (SDG/USD)

4.3 Graph 3: credit to private sector as a percentage of GDP and inflation

Graph 3 shows that credit to private sector as a percentage of GDP and inflation move in the same direction as inflation. Domestic credit to private sector reached its peak in December 2012 at 12.6% of GDP, thanks to the central bank’s monetary reforms in 2012 to provide more credit. Owing to the central bank’s inability to control reserve money, the increase in domestic credit to the private sector led to high inflation in 2014. In 2015, inflation dropped considerably thanks to a reduction in domestic credit to private sector, and it rose again in 2016 because of the expansionary monetary policy that was driven by the government’s financial needs, which led once again to a slight increase in credit to the private sector in 2017.

50 14

45 13

40 12

35 11

30 10

25 9

20 8

15 7

10 6 2011 2012 2013 2014 2015 2016 2017

INFLATION Credit to private sector / GDP %

4.4 Graph 4: crude oil prices and inflation

Changes in oil prices and fluctuations in inflation are generally seen as related. Choi et al. (2017) found that a 10% increase in global oil prices causes an average 0.4 percentage point increase in domestic inflation. Sudan produces oil and oil derivatives but its domestic production is insufficient for local consumption, and it has been a net importer of oil since South Sudan’s secession in 2011. Inflation in developing countries is always directly with correlated to international oil process and domestic demand pressures. Graph 4 shows that, following the drop in government revenues in Sudan due to the secession, inflation rose sharply in 2011,

15 reaching 47.49% in March 2013 despite the July 2012 policy reforms. Conversely, the decline in oil prices from 2014 to 2016 led to a reduction in production costs, which in turn led to lower inflation. This may have been thanks to better monetary policy—for example, the use of government investment certificates (GICs) as a government instrument to absorb liquidity.

50 160

45 140

40 120

35 100

30 80

25 60

20 40

15 20

10 0 2011 2012 2013 2014 2015 2016 2017 INFL OILP

After exploring the trends in determinants of inflation in Sudan, we investigated the stationarity properties of each variable. As explained earlier, unit roots are important in examining the stationarity of a time series because a non-stationary regressor invalidates many standard empirical results.

First, we established the order of integration of each of the time-series variables via co- integration analysis. We tested for the presence of a unit root in each variable using the Augmented Dickey-Fuller (ADF) test. As shown in Table 1, our model finds that inflation (Infl), output growth (ry), nominal effective exchange rate (neer), and credit to private sector (PrCredit) are integrated of order zero while money supply (ms) and oil prices (oilp) are integrated of order one.

Table 1: ADF Test for Unit Root

1st difference Level

Variables No constant No constant

Infl -0.038 -4.765***

ry 0.200 -5.930***

ms 0.143 -9.078**

PrCredit -1.854* -2.777***

neer 4.463*** -2.777***

oilp -1.478 -5.722*** Test significant at *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ calculations.

After having established the order of integration of each variable, a regression based on non-stationary time series explains the relationship during the study period only, implying that it is impossible to infer about the long run relationship of the variables. However, according to Johansen and Juselius (1995), it is possible to perform a co-integration test as long as we have two variables in the model that are I(1). We determined the appropriate lag length by using Akaike’s Information Criterion (AIC) and other tests to determine the optimal lag length. The result shows a lag selection of 2 lags (Table 2).

Table 2: Lag Order Selection

Lag LogL LR FPE AIC SC HQ

0 - 1144.186 NA 7535937. 32.86244 33.05517 32.93900

1 -500.8930 1157.927 0.220467 15.51123 16.86033* 16.04711*

2 -451.2996 80.76643* 0.152857* 15.12285* 17.62831 16.11805

3 -422.8676 41.42954 0.200997 15.33907 19.00091 16.79360

Source: Authors’ calculations.

We therefore employed the multivariate co-integration technique proposed by Johansen (1988) and Johansen and Juselius (1990) to check for long-run correlations. The results suggest at least 1 co-integrating vector (Tables 3a and 3b).

Table 3a: Unrestricted Co-integration Rank Test (Trace)

Hypothesized Trace 0.05

no. of CE(s) Eigenvalue statistic critical value Prob. **

None * 0.504699 124.7491 95.75366 0.0001

At most 1 * 0.339455 75.56792 69.81889 0.0162

At most 2 0.316030 46.53964 47.85613 0.0661

At most 3 0.162987 19.95076 29.79707 0.4262

At most 4 0.100953 7.496663 15.49471 0.5207

At most 5 0.000675 0.047256 3.841466 0.8279

Table 3b: Unrestricted Co-integration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue Statistic Critical value Prob.**

None * 0.504699 49.18121 40.07757 0.0036

At most 1 0.339455 29.02827 33.87687 0.1700

At most 2 0.316030 26.58888 27.58434 0.0666

At most 3 0.162987 12.45410 21.13162 0.5035

At most 4 0.100953 7.449407 14.26460 0.4374

At most 5 0.000675 0.047256 3.841466 0.8279

Max-eigenvalue test indicates 1 co -integrating equation(s) at the 0.05 level. Source: Authors’ calculations.

Co-integration in the data allows for VECM analysis that suggests long-run correlations. We then estimate the VECM (Table 4). Table 4: VECM Estimates

Vector Error-Correction Estimates Standard errors in ( ) & t-statistics in [ ]

Co-integrating eq: CointEq1

INFL(-1) 1.000000

RY(-1) 4.282846

(0.88329)

[ 4.84873]

MS(-1) -0.179589

(0.15146)

[-1.18572]

PRCREDIT(- 1) -12.30316

(2.16258)

[-5.68911]

NEER(-1) -2.396065

(0.40360)

[-5.93670]

OILP(-1) 0.492805

(0.13733)

[ 3.58852]

C 70.61428

(11.9787)

[ 5.89500]

D(INFLAT D(PRCREDI Error Correction: ION) D(RY) D(MS) T) D(NEER) D(OILP)

CointEq1 - 0.203535 - 0.011606 - 0.023599 - 0.006206 - 0.016289 - 0.146672

(0.07639) (0.00897) (0.08026) (0.00141) (0.01155) (0.12355)

[-2.66426] [-1.29333] [-0.29403] [-4.41633] [-1.41007] [-1.18719]

D(INFL(-1)) 0.532744 -0.014473 -0.033307 0.002700 0.037193 0.213385

(0.13424) (0.01577) (0.14103) (0.00247) (0.02030) (0.21709)

[ 3.96858] [-0.91781] [-0.23616] [ 1.09335] [ 1.83226] [ 0.98292]

D(INFL(-2)) 0.049619 0.007359 0.199457 0.006849 -0.023481 0.092385

(0.15905) (0.01868) (0.16710) (0.00293) (0.02405) (0.25721)

[ 0.31197] [ 0.39389] [ 1.19365] [ 2.34086] [-0.97631] [ 0.35917]

D(RY(-1)) 1.461647 0.240025 0.476386 0.022490 0.066070 1.192286

(1.08614) (0.12759) (1.14110) (0.01998) (0.16424) (1.75650)

[ 1.34572] [ 1.88128] [ 0.41748] [ 1.12561] [ 0.40228] [ 0.67878]

D(RY(-2)) -0.003304 0.270840 -0.146086 0.008315 0.034314 -2.080445

(1.10330) (0.12960) (1.15913) (0.02030) (0.16683) (1.78425)

[-0.00299] [ 2.08979] [-0.12603] [ 0.40969] [ 0.20568] [-1.16601]

D(MS(-1)) 0.043964 0.011737 -0.106054 0.004678 -0.023402 -0.012659

(0.12565) (0.01476) (0.13200) (0.00231) (0.01900) (0.20319)

[ 0.34990] [ 0.79523] [-0.80341] [ 2.02410] [-1.23171] [-0.06230]

D(MS(-2)) 0.076655 0.014717 0.235654 0.003318 0.027503 -0.108353

(0.13115) (0.01541) (0.13778) (0.00241) (0.01983) (0.21209)

[ 0.58449] [ 0.95529] [ 1.71032] [ 1.37535] [ 1.38684] [-0.51088]

D(PRCREDIT(- 1)) -15.15979 -0.883019 -3.379890 0.031947 -0.859834 8.044727

(7.82154) (0.91877) (8.21730) (0.14388) (1.18272) (12.6489)

[-1.93821] [-0.96109] [-0.41131] [ 0.22203] [-0.72700] [ 0.63600]

D(PRCREDIT(- 2)) -3.825421 0.075704 3.725487 0.150131 -0.746541 -12.90708

(6.37491) (0.74884) (6.69747) (0.11727) (0.96397) (10.3094)

[-0.60007] [ 0.10110] [ 0.55625] [ 1.28020] [-0.77444] [-1.25197]

D(NEER(-1)) -0.209568 0.016874 0.326891 -0.033389 0.262905 -0.911126

(0.91129) (0.10705) (0.95740) (0.01676) (0.13780) (1.47373)

[-0.22997] [ 0.15764] [ 0.34144] [-1.99172] [ 1.90789] [-0.61824]

D(NEER(-2)) -0.557070 -0.016056 0.676051 -0.031766 -0.064822 0.475333

(0.91203) (0.10713) (0.95817) (0.01678) (0.13791) (1.47492)

[-0.61080] [-0.14987] [ 0.70556] [-1.89336] [-0.47003] [ 0.32228]

D(OILP(-1)) 0.124656 -0.018541 -0.051870 0.001342 -0.013395 0.422887

(0.08069) (0.00948) (0.08478) (0.00148) (0.01220) (0.13050)

[ 1.54482] [-1.95609] [-0.61184] [ 0.90416] [-1.09782] [ 3.24060]

D(OILP(-2)) 0.095820 0.011031 0.000695 0.000884 0.015555 -0.136047

(0.08765) (0.01030) (0.09209) (0.00161) (0.01325) (0.14175)

[ 1.09316] [ 1.07132] [ 0.00755] [ 0.54840] [ 1.17354] [-0.95974]

Source: Authors’ calculations.

The error correction term (-0.235) is negative and significant, which suggests the existence of long-run relationships in the variables of interest. That is, last period deviations from long-run equilibrium influences the short-run dynamics of inflation. Thus the coefficient of ECT is the speed of adjustment as it measures the speed at which inflation returns to equilibrium after a change in one of the other variables.

From Table 4, the Johansen long-run co-integration equation can be derived as:

퐼푛푓푙 = −70.614 − 4.283 푟푦 + 0.18 푚푠 + 12.30316 푃푟퐶푟푒푑푖푡 + 2.396 푛푒푒푟 − 0.492 표푖푙푝 (0.88329) (0.15146) (2.16258) (0.40360) (0.13733)

The values in brackets are standard errors. All the coefficients are significant at the 5% level (Table 3a). From the long-run equation, output growth and oil prices have a negative effect on inflation: if they increase, inflation will fall. Conversely, money supply, credit to private sector, and nominal effective exchange rate have a positive effect on inflation: if any of them increase, inflation will too. Credit to private sector has the most impact on increasing the inflation, while output growth plays the biggest role in decreasing inflation.

From the short-run equation we find that only credit to private sector has a negative and significant impact on inflation: as credit to private sector increases, inflation falls. All other variables are not significant in the short run.

4.5 The impulse responses of Inflation

Figure 4 shows the impulse responses of inflation to the shocks using VECM analysis. A shock to oil prices increases inflation in the short run but decreases it in the long run. A one percentage point increase in oil prices reduces inflation by 0.49%. Money supply shocks lasting more than one year increase inflation after one year of such shocks. A one percentage point increase in money supply increases inflation by 0.17%.

Innovations in credit to private sector lead to an increase in inflation 8 months later, with a 1% increase in credit to private sector causing a 12.3% rise in inflation in the long run. Shocks to the nominal effective exchange rate (NEER) have a relatively weak impact on inflation: a 1% increase in the NEER increases inflation by 2.3%. Innovations in output growth have an immediate effect on inflation: a 1% increase in output growth results in a 4.28% reduction in inflation.

Figure 4: Results of impulse response functions

Response to Cholesky One S.D. Innovations Response of INFL to INFL Response of INFL to RY

4 4

2 2

0 0

-2 -2

-4 -4 10 20 30 40 50 60 70 10 20 30 40 50 60 70

Response of INFL to MS Response of INFL to PRCREDIT

4 4

2 2

0 0

-2 -2

-4 -4 10 20 30 40 50 60 70 10 20 30 40 50 60 70

Response of INFL to NEER Response of INFL to OILP

4 4

2 2

0 0

-2 -2

-4 -4 10 20 30 40 50 60 70 10 20 30 40 50 60 70 The variance decomposition of inflation (Table 5 and Figure 6) using the VECM shows that in both the short run and the long run much of the variation in inflation is explained by its own shocks. In the long run, the effect of inflation dynamics (35.5%) is followed by that of credit to private sector (29.2%), oil prices (17.3%), and output growth (13%), respectively. However, in the short run (6 months), variations in inflation are explained by its own dynamics (85.4%), followed by output growth (6.7%), nominal effective exchange rate (2.4%), oil prices (1.8%), and credit to private sector (1.7%). Table 5: Variance Decomposition

Period S.E. INFL RY MS PRCREDIT NEER OILP

1 2.828912 100.0000 0.000000 0.000000 0.000000 0.000000 0.000000

4 6.508364 88.49274 5.825015 1.899769 1.766085 0.258730 1.757658

6 6.778929 85.47396 6.722953 1.863874 1.729544 2.368951 1.840716

12 8.806197 64.58505 9.726800 1.416523 12.19853 4.455298 7.617799

18 11.99529 51.64307 11.84805 0.985822 20.47365 3.876718 11.17270

24 15.51218 45.08986 12.75451 1.218924 24.36097 3.132290 13.44344

30 18.95643 41.60867 13.10568 1.627847 26.28876 2.616512 14.75253

36 22.22888 39.54192 13.25295 2.015320 27.35174 2.266514 15.57154

42 25.29930 38.21179 13.31783 2.338065 27.99316 2.023776 16.11538

48 28.17088 37.30077 13.34695 2.597385 28.41003 1.849675 16.49519

54 30.85889 36.64611 13.35978 2.804257 28.69742 1.720757 16.77167

60 33.38218 36.15749 13.36492 2.969982 28.90519 1.622575 16.97984

66 35.75955 35.78141 13.36640 3.103920 29.06129 1.545950 17.14104

72 38.00828 35.48450 13.36614 3.213332 29.18232 1.484869 17.26884 Source: Authors’ calculations.

Figure 4 Variance Decomposition of INFL

100

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

INFL RY MS PRCREDIT NEER OILP

5. Recommendations

The above results indicate that, in the long run, an increase in oil prices has a negative effect on inflation while increases in money supply, credit to private sector, and nominal effective exchange rate have a positive effect on inflation. This underscores the need for managing the money supply, the exchange rate, and credit to the private sector.

Money supply growth, nominal effective exchange rate, and credit to the private sector as a percentage of GDP can be directly influenced by the monetary authorities—the Central Bank of Sudan. In this context, we make the following recommendations.

Unification of exchange rates: In terms of macroeconomic policy, there is an overriding need to unify the exchange rate. The IMF has estimated that the unification of the exchange rates could raise inflation by 12%, but if accompanied by monetary and fiscal tightening, including structural reforms, inflation could decline significantly in the medium

24 term. While unifying the exchange rate may be costly in the short run, it will improve the trade balance, increase reserves, and eventually restore both internal and external imbalances in the long run.

Avoid monetizing the deficit: The government should avoid monetizing the deficit in the face of limited fiscal space. Instead, it should use Islamic certificates to finance the deficit. Monetization of the deficit is as bad as seniorage because both are inflationary.

Avoid quasi-fiscal operations: The central bank’s quasi-fiscal operations of buying gold at the incentive rate and selling it at the official rate not only leaves a gap in the balance of payments but also increases money supply and hence inflation. This quasi-fiscal operation should be avoided as much as possible.

Address supply-side constraints to boost output: Our results also underscore the need to boost output to neutralize any inflationary tendencies. In this context, it behooves the government to address supply-side constraints such as infrastructure and access to productive resources such as land, credit, and fertilizers to boost production and productivity. This will have multiple advantages, including boosting output, containing the deficit, controlling inflation, expanding exports, and reducing the current account deficit while contributing towards employment creation and poverty reduction.

Neutralize inflationary tendencies from expansion of credit by boosting growth. Since credit to the private sector and inflation move in the same direction, the growth of the former must be consistent with or supported by growth in output, which would neutralize any inflationary tendencies. It will be important in this context to implement growth-enhancing policies such as improving infrastructure, financial sector, the investment climate, and tax and customs administration.

Set up an oil stabilization fund: Since Sudan is now a net importer of oil and oil products, it may consider setting up an oil stabilization fund that would cushion the adverse impact of future oil shocks.

Take advantage of the lifting of sanctions: It is an opportune time for Sudan to take advantage of the lifting of sanctions in October 2017 by improving the investment climate with a view to attracting foreign direct investment and reintegrating the country into the global

25 economy. This will go a long way toward improving the current account balance. The government has already improved the operational environment by formulating a series of sectoral policies and programs, including the Economic Reform Program (ERP) 2015-2019, which places particular emphasis on the role of the private sector as the engine of economic growth. Additionally, the government’s recent initiative to adopt the Public Private Partnership modality will go a long way toward revitalizing the private sector.

Continue improving the political environment to restore macroeconomic stability: The preceding measures were complemented by the signing of the “Addis Ababa Road Map to Peace” in 2016 with armed groups in Darfur and the “Two Areas” and by the conclusion of a national dialogue of political reconciliation in November 2016 followed by a Government of National Unity in May 2017 which includes opposition parties. These developments augur well for macroeconomic stability. Looking forward, the government must continue improving the political environment with a view to removing Sudan from the list of State Sponsors of Terrorism and garner support for debt relief. Debt relief is needed not only for enlarging the fiscal space but also for accessing concessional resources.

References

Abdoun, R., (2012), “Sudan’s Inflation Problem: Some Lessons from the Past 30 Years,” in “Sudan: Selected Issues Paper,” IMF Country Report 12/299. Washington DC: International Monetary Fund.

Anand, R., and S. Van Wijnbergen (1988), “Inflation, External Debt and Financial Sector Reform: A Quantitative Approach to Consistent Fiscal Policy with an Application to Turkey,” NBER Research Working Paper No 2731. Cambridge MA: National Bureau for Economic Research.

Baciu, I. (2015), “Determinants of Inflation in Romania: Empirical Analysis Using the Vector Error Correction Model,” The Macrotheme Review, Vol. 4, No. 3, pp. 16-30.

Batavia, B., and N.A. Lash (1983), “Self-Perpetuating Inflation: The Case of Turkey,” Journal of Economic Development, Vol. 8, No. 2, pp. 149-66.

Central Bank of Sudan (2010), Determinants of Inflation in Sudan: An Empirical Analysis. Central Bank of Sudan Research Publication Series No. 16. Khartoum, Sudan: Central Bank of Sudan.

Choi, S., D. Furceri, P. Loungani, S. Mishra, and M. Poplawski-Ribeiro (2017), ‘’Oil Prices and Inflation Dynamics: Evidence from Advanced and Developing Economies,” IMF Working Paper 17/196. Washington DC: International Monetary Fund.

De Boissieu, C. (1990), “Monetary Policy”, Chapter 46, Economic Encyclopaedia (1990), pp. 1667- 1705.

De Santis, R. (1993), “An Error Correction Monetary Model Explaining the Inflationary Process in Turkey,” The Warwick Economics Research Paper Series, No. 418. Warwick, United Kingdom: University of Warwick.

Friedman, M. (1963), Inflation Causes and Consequences. New York: Asia Publishing House.

Jabrallah, B.H., and M. Hasan Mohamed (2008), “Determining the Lag in the General Price Level’s Response to a Change in the Money Supply in Sudan, 1995-2007,” Al-Masrafi (The Banker Magazine). Khartoum, Sudan: Central Bank of Sudan.

Johansen, S. (1998), “Statistical Analysis of Co-integrating Vectors,” Journal of Economic Dynamics and Control, Vol. 12, No. 2-3, pp. 231-254.

Johansen, S., and K. Juselius (1988), “Hypothesis Testing for Cointegration Vectors: With Application to the Demand for Money in Denmark and Finland,” Discussion Paper No. 88-05, Department of Economics, University of Copenhagen. Copenhagen, Denmark: University of Copenhagen.

Korkmaz, S. (2015), “Impact of Bank Credits on Economic Growth and Inflation,” Journal of Applied Finance & Banking, Vol. 5, No. 1, pp. 57-69.

Kurozumi, T., and W. Van Zandweghe (2018a), “Variable Elasticity Demand and Inflation Persistence,” KCFed Research Working Paper RWP 16/09. Kansas City: Federal Reserve Bank of Kansas City.

Kurozumi, T., and W. Van Zandweghe (2018b), “Why Has Inflation Persistence Declined? The Macro Bulletin, Federal Reserve Bank of Kansas City, April 11.

Laryea, S., and U. Sumaila (2001), “Determinants of Inflation in Tanzania”, Chr. Michelsen Institute, Development Studies and Human Rights WP 2001:12. Bergen, Norway: Crh. Michelsen Institute.

Lim, G.C., and L. Papi (1997), “An Econometric Analysis of the Determinants of Inflation in Turkey,” IMF Working Paper WP/97/170. Washington DC: International Monetary Fund.

Maryam, H., I. Osman, and N. Elias (2014), “The Determinants of Inflation in Malaysia,” paper presented at the Third International Conference on Accounting, Business and Economics (ICABEC2014). Metin, K. (1995), “The Analysis of Inflation: The Case of Turkey (1948-1988),” Capital Markets Board Publication No. 20. Ankara: Nurol Publishing Co.

Mohanty, D., and J. John (2015), “Determinants of Inflation in India”, Journal of Asian Economics, Vol. 36, No. 1, pp. 86-96.

Moriyama, K. (2008), “Investigating Inflation Dynamics in Sudan,” IMF Working Paper WP/08/189. Washington DC: International Monetary Fund.

Moser, G. (1995), The Main Determinants of Inflation in Nigeria”, IMF Staff Papers, Vol. 42, No. 2, pp. 270-289.

Nelson, Charles R. & Plosser, Charles I., (1982), “Trends and random walks in macroeconomic time series : Some evidence and implications”

Onis, Z, and S. Ozmucur (1990), “Exchange Rates, Inflation and Money Supply in Turkey: Testing the Vicious circle Hypothesis,” Journal of Development Economics, Vol. 32, No. 1, pp. 133-54.

Organization for Economic Cooperation and Development (1995), OECD Economic Surveys: Turkey 1995 (Paris: OECD).

Ozatay, F. (1992), “Monetary Policy and Inflation Stabilization in Turkey,” Discussion Paper 9209, Research and Monetary Policy Department, Central Bank of the Republic of Turkey Ankara: Central Bank of the Republic of Turkey.

Rittenberg, L. (1993), “Exchange Rate Policy and Price Level Changes: Causality Tests for Turkey in the Post-Liberalization period,” Journal of Development Studies, Vol. 29, No. 2, pp. 245-59.

Rodrik, D. (1991), “ Premature Liberalization, Incomplete Stabilization: The Özal Decade in Turkey,” in Lessons of Economic Stabilization and Its Aftermath, ed. by M. Bruno, S. Fischer, E. Helpman, and N. Liviatan with L. Meridor (Cambridge MA: MIT Press).

Sahadudheen, I. (2012), A Cointegration and Error Correction Approach to the Determinants of Inflation in India, International Journal, Economic Research, Vol. 3, No. 1, pp. 105- 112.

Salvation Program 2011-2013, Government of Sudan.

Ruzima, M., and P. Veerachamy (2015), “A Study on Determinants of Inflation in Rwanda from 1970-2013” International Journal of Management and Development Studies, Vol. 4, No. 4, pp. 390-401.

Talha, E. (2016), Currency Devaluation and Inflation Dynamics in Sudan (Jan. 1993 - Sep. 2014). EPRA International Journal of Economic and Business Review, Vol. 4, No. 6, pp. 12-22.

Togan, S. (1987), “The Influence of Money and the Rate of Interest on the Rate of Inflation in a Financially Repressed Economy: The Case of Turkey,” Applied Economics, Vol. 19, No. 12, pp. 1585-1601.

Uygur, E. (1992), “Price, Output and Investment Decisions of Firms: An Explanation of Inflation and Growth in Turkish Industry,” in Price Dynamics, ed. by H. Ersel (Ankara: Central Bank of the Republic of Turkey).

Van Wijnbergen, S. (1989), “External Debt, Inflation, and the Public Sector: Toward Fiscal Policy for Sustainable Growth,” World Bank Economic Review, Vol. 3, No. 3, pp. 297- 320.

Yeldan, A.E. (1993), “Conflicting Interests and Structural Inflation: Turkey, 1980-1990,” Pakistan Development Review, Vol. 32, No. 3, pp. 303-27.