Exchange
Rate Pass-Through Effect and Inflation Dynamics in Nigeria
Christopher N. Ekong1,
Paul A. Orebiyi2 & Michael A. Udofia3
1,2,3Department Of Economics, University of Uyo, Uyo
ABSTRACT
This
paper examined the exchange rate pass-through effect on inflation in Nigeria
using quarterly data from 1995Q1 to 2023Q4. The data was analyzed using the
fully modified ordinary least squares (FMOLS) and the vector autoregression
(VAR) with accompanying impulse response function. The VAR model incorporated
two lags with exchange rate, import prices, crude oil price, and real output
growth being the variables within the system. Findings from the FMOLS technique
of estimation indicated that both exchange rate and import prices exerted
positive and significant effect on the consumer price index in Nigeria. This
implies that both exchange rate and import prices are key drivers of
inflationary pressure within the Nigerian economy. However, crude oil prices
exerted a negative and significant effect on the consumer price index in
Nigeria. The VAR model revealed that the exchange rate passes through the import
prices to affect domestic prices in Nigeria. The impulse response functions
indicated that the domestic prices responded positively to shocks in the import
prices but negatively to shocks in crude oil prices. The domestic prices
responded positively to shocks in exchange rate up to the sixth period after
which the response became negative afterwards. The paper recommended that the
Federal Government of Nigeria in conjunction with the National Planning Commission
should prioritize structural reforms through the strategic implementation of
import substitution policies.
Keywords:
exchange rate, inflation, producer price
index, consumer price index, VAR.
1. INTRODUCTION
The impact of exchange rate
fluctuations on inflation and economic activity has been one of the major challenges
to managing economic policy globally, and especially in emerging and developing
nations. Exchange rate fluctuations are thought to impact a nation's economic
competitiveness and cause internal economic distortions. The detrimental
impacts of exchange rate fluctuation are widely known in the literature, and
policymakers frequently hesitate to modify exchange rates because they believe
it would have a negative impact on the economy, mostly through pass-through
effects. The significance of the exchange rate as a practical instrument for
attaining overall economic advancement has also been emphasized in the
literature. This is predicated on the relationship between the exchange rate
and other economic factors as well as the exchange rate's pivotal role in the
formulation of monetary policy, as it is a vital component of the signalling
channel via which policy choices are sent in order to accomplish the intended
macroeconomic goals (Bada et al., 2016). Because central banks are tasked with
controlling exchange rates and maintaining price stability, a though
understanding of exchange rate pass-through is crucial for formulating
policies.
Exchange rate pass-through (ERPT) is
the percentage change in local currency import prices which occurs when the
exchange rate between the exporting and importing economies changes by 1% (Goldberg
and Knetter, 1997). The ERPT is typically used to describe how changes in
exchange rates affect consumer pricing, import and export prices, trade
volumes, and investments (Frimpong and Adam, 2010). The impact of exchange rate
fluctuations on domestic prices and the rate of its transition defines their
importance in macroeconomic adjustment. A high degree of pass-through means
that changes in the exchange rate will alter the relative pricing of
commodities, which will cause trade balances to quickly adjust. A high ERPT
degree, for instance, makes imported goods more expensive, reduces demand for
imports, and causes consumers to switch to domestically produced goods. Conversely,
a low ERPT degree has little effect on domestic prices and trade balances.
Exchange rate regimes are crucial to
ERPT; in a fixed exchange rate regime, economic agents quickly adjust prices
because they believe that any change in the exchange rate is permanent, whereas
in a flexible exchange rate regime, they do not quickly adjust their prices
because they believe that changes are temporary. Economic agents in high-income
nations do not quickly alter prices in reaction to fluctuations in the exchange
rate because they limit enterprises' pricing power by fostering greater levels
of domestic market competition. However, in low-income nations, the opposite is
true (Razafimahefa, 2012). While expansionary monetary policy is linked to high
ERPT because economic agents see it as unstable and tend to quickly adjust
prices, contractionary monetary policy reduces the degree of ERPT.
Similarly, expansionary fiscal policy
will increase the degree of pass-through because economic agents believe that
the government will increase taxes or cut spending to address the accumulated
fiscal deficit, which will inevitably lower firm profitability or contract the
market. Conversely, contractionary fiscal policy will lessen the amount of
pass-through due to the reduced level of fiscal deficit that is associated with.
Overall, minimal pass-through is linked to sound macroeconomic policies which
is an indication that policy effectiveness aids the effective management of the
exchange rate as not to trigger inflationary tendencies within an economy.
Prices of imported consumption items,
locally produced goods which inputs are priced in foreign currency, and prices
of imported intermediate goods are the three main ways that changes in exchange
rates affect consumer prices. In the first two channels (which are prices of
imported consumption items and prices of locally produced goods which inputs
prices are in foreign currency), changes in the exchange rate have a direct
impact. This is because exchange rate depreciation will cause the prices of
such goods to increase which therefore trigger a domestic rise in the prices of
such goods in the economy. There are two steps in the pass-through procedure.
Changes in the exchange rate are passed on to import prices at the first step.
Changes in import prices are passed on to consumer prices in the second stage.
For the last channel, changes in exchange rates have an indirect impact on
local pricing by altering manufacturing costs (Sahminan, 2002). According to
Lafleche (1996) and later affirmed by Hyde and Shah (2004), there are two main
ways that changes in the exchange rate might impact domestic prices: directly
and indirectly.
According to this line of reasoning,
changes in the prices of imported inputs (such as raw materials and capital
goods) and finished commodities can have a direct impact on local pricing. The
domestic price of imported finished goods decreases when the value of the
domestic currency increases. Similarly, consumers are more inclined to pay
higher import costs when the value of the home currency declines. Additionally,
currency depreciation raises the cost of imported inputs, which might raise
local enterprises' marginal cost of production. As a result, domestically made
items become more expensive. However, it is stated that the indirect route
happens when the native country's currency rate declines (Abdulahi, 2023).
Because local items are less expensive than those made abroad, they are more
affordable for overseas consumers. This will lead to an increase in aggregate
demand and the amount of the demand for exports, which would raise domestic
prices.
Evidence of incomplete ERPT and
significant country-specific variations have been identified in empirical
literature for both developed and developing economies, which naturally raises
the question of what the fundamental factors influencing pass-through are (Ca'
Zorzi et al., 2007). Taylor (2000) proposes that prices' response to changes in
the exchange rate is positively correlated with inflation. Evidence from many
investigations seems to support the Taylor theory. It seems that developing
markets have a stronger positive correlation between inflation and the degree
of pass-through (Choudhri and Hakura, 2006). Prior research on ERPT in Nigeria
used a variety of approaches, including unconstrained error correction method
(UECM), vector autoregression (VAR), Granger causality tests, and the
correlation and vector error correction framework. By incorporating crude oil
price to the modelling framework and extending the research period with
quarterly data to incorporate higher frequencies, this study uses the vector
autoregression (VAR) to investigate exchange rate pass-through to inflation in
Nigeria from the first quarter of 1995 to the fourth quarter of 2023.
The paper which is presented in five
sections has section 1 being the introduction followed by literature review in
section 2. In section three, the methodology of the research is presented while
section 4 captures the empirical findings. Lastly, section 5 presents the
conclusion and recommendations from the study.
2.
LITERATURE REVIEW
2.1 Conceptual Literature
2.1.1. Exchange Rate Pass-Through (ERPT)
The exchange rate pass-through (ERPT) describes how
changes in exchange rates affect consumer pricing, import and export prices,
trade volumes, and investments (Frimpong and Adam, 2010). It is measured as the
percentage change in local currency import prices which occurs when the
exchange rate between the exporting and importing economies changes by a unit
percentage point (Goldberg and Knetter, 1997). Precisely, exchange rate
pass-through the elasticity of local currency import prices with respect to the
local currency price of foreign currency. Thus, it is given as the ratio of
import prices to changes exchange rate of importing countries. According to
Goldberg and Knetter (1997), it is sometimes expressed as the percentage change
in import prices in the local currency that results from a one percent change
in the exchange rate between the importing and exporting nations. Retail and
consumer pricing are impacted when import costs fluctuate. Exchange rate
pass-through (ERPT) can also be referred to the extent to which changes in the
exchange rate affect domestic prices. It measures how fluctuations in a
country’s currency value influence the prices of imported and domestically
produced goods. ERPT can be classified into two types: Complete Pass-Through is
when a 1% change in the exchange rate leads to a 1% change in domestic prices.
While, an Incomplete Pass-Through is when a 1% currency depreciation or change
in the exchange rate leads to less than a 1% rise or changes in domestic
prices.
The degree
of ERPT depends on various factors, including the openness of an economy,
monetary policy credibility, inflation expectations, and market structures.
Studies suggest that ERPT is generally higher in developing economies due to
their reliance on imports and weak monetary policy frameworks, whereas in
advanced economies, strong central bank credibility and inflation-targeting
regimes tend to reduce ERPT.
A high
degree of pass-through means that changes in the exchange rate will alter the
relative pricing of commodities, which will cause trade balances to quickly
adjust. A high ERPT degree, for instance, makes imported goods more expensive,
reduces demand for imports, and causes consumers to switch to domestically
produced goods. Conversely, a low ERPT degree has little effect on domestic
prices and trade balances. There is a larger transmission of inflation between
nations when exchange-rate pass-through is higher (Campa and Goldberg, 2005).
Therefore, the law of one price and purchasing power parity are linked to
exchange-rate pass-through.
2.1.2.
Producer Price Index (PPI)
The
Producer Price Index (PPI) measures the average change over time in the prices paid
to United State producers of goods and services. It serves as an indicator of
inflationary pressures at the production level before they are passed on to
consumers. The Producer Price Index is crucial in analyzing cost-push
inflation, as increases in producer prices may lead to higher consumer prices
if firms pass higher costs onto consumers. However, the extent of this
transmission depends on market conditions, pricing power, and demand elasticity.
Producers Price Index is also linked to exchange rate movements, as currency
depreciation can increase the cost of imported raw materials, leading to higher
producer prices and potential inflationary pressures.
2.1.3.
Consumer Price Index (CPI)
The
Consumer Price Index (CPI) measures the average change in prices paid by
consumers for a basket of goods and services over time. It is the most widely
used measure of inflation and serves as a key economic indicator for
policymakers, businesses, and consumers.
CPI is categorized into:
(a)
Headline
CPI: Includes all goods and services, including volatile components like food
and energy.
(b)
Core
CPI: Excludes food and energy to provide a clearer picture of underlying
inflation trends.
Exchange rate fluctuations
affect CPI through the import price channel, where depreciation increases the
cost of imported goods, directly raising consumer prices. However, the extent
of CPI changes depends on firms’ pricing strategies, competition, and monetary
policy responses.
2.1.4.
Inflation and Its Linkages
Inflation
refers to the persistent increase in the general price level of goods and
services in an economy over time (Phillips, 1958). Inflation can erode the
purchasing power of consumers, distort price signals, and reduce the standard
of living.
It can be classified into:
(a) Demand-Pull Inflation: Caused by rising
demand outpacing supply.
(b) Cost-Push Inflation: Resulting from
increased production costs, including higher wages and import prices.
(c) Imported Inflation: Stemming from
currency depreciation leading to higher prices for imported goods.
The
relationship between exchange rates, Producers Price Index, Consumer Price Index,
and Inflation is dynamic. A depreciation of the currency raises import costs,
increasing Producers Price Index, which may then pass through to Consumers Price
Index, leading to higher inflation. However, central bank policies, wage
dynamics, and market competition play critical roles in determining the extent
of this transmission.
Understanding
the interactions between exchange rate pass-through, PPI, CPI, and inflation is
crucial for economic policy formulation. Policymakers must consider these
linkages when designing exchange rate policies, inflation-targeting strategies,
and price stabilization mechanisms to ensure economic stability.
2.2
Theoretical Literature
The theoretical underpinnings for
this study hinges on the law of one price (LOOP), the purchasing power parity
(PPP), and the monetary approach to exchange rate determination.
2.2.1
Law of One Price and Purchasing Power Parity
With the assumptions that there are
no trade restrictions or transportation cost, the theory of purchasing power
parity (PPP), further developed from the law of one price (LOOP), provides the
theoretical basis for the link between prices and exchange rates. Nevertheless,
trade barriers occur in real-world scenarios, which skew the fundamental assumptions
of PPP. The law of one price is still helpful in comprehending how prices and
exchange rates are related, despite the weak assumption of absence of trade
barriers. This relationship between the domestic price and exchange rate is
derived from the LOOP, which stipulates that identical goods sold in different countries
must sell for the same price when prices are expressed in a common currency,
provided that trade barriers are removed and there is free competition and
price flexibility. As a result, when two markets are in equilibrium, the prices
of tradable items should not fluctuate when represented in the same currency,
ensuring a full pass-through. Therefore, even if the marketplaces are in two
different nations, a change in the native currency in one market would result
in an identical change in the pricing in the other market (Bada et al., 2016). The
PPP without any transportation costs or tariffs can be expressed algebraically
as follows:
Equation (1) states that the domestic price at time t (
However, in some cases, the law of
one price might or might not hold due to trade restrictions. This is predicated
on the reality that a variety of factors impact domestic import prices,
including manufacturing costs, producer mark-ups, and fluctuations in exchange
rates. The principle of PPP is the macroeconomic counterpart to the law of one
price. While the law of one price links exchange rates to the relative prices
of an individual commodity, the purchasing power parity relates exchange rates
to the relative prices of a basket of goods. Depending on whether the focus is
on the macroeconomic or company level, both theories are utilized as the
theoretical foundation for exchange rate pass-through. However, transaction
costs, non-traded items, price stickiness, imperfect competition, and some
regulatory barriers make purchasing power parity unsustainable in the short
term (Feenstra and Taylor, 2008).
2.2.2
Monetary Approach to Exchange Rate Determination
The monetary theory explains how
changes in exchange rates will directly impact price levels by combining
Krugman's (1986) monetary exchange rate model with the law of one price and the
purchasing power parity. According to the monetary approach to prices and
exchange rates, an increase in the money supply growth rate should, under all
other circumstances, be equivalent to an increase in the rates of inflation and
exchange rate depreciation. The method demonstrates the long-term
interdependence of all nominal variables, including the money supply, interest
rate, price level, and exchange rate. Therefore, decisions on monetary policy
have the power to significantly impact several significant economic outcomes,
most notably inflation and pricing.
2.3
Empirical Literature
Some studies have been conducted to
ascertain the exchange rate pass-through (ERPT) effect across different
economies. McCarthy (2000) discovered that in some developed economies, the
pass-through of exchange rates to consumer prices was quite low. According to
the study, the rate of pass-through has a negative link with exchange rate
volatility and a positive link with trade openness. However, Gagnon and Ihrig
(2001) were unable to identify a consistent link between ERPT and the monetary
behaviour of the developed nations under study. The effect of exchange rate
swings on import and consumer prices in Japan, Singapore, Korea, and Thailand
was assessed by Kang and Wang (2003). The authors discovered that during the
post-crisis era (1998–2001), exchange rate fluctuations had a greater impact on
import and consumer prices than they did during the pre-crisis period
(1991–1996).
For each of the 18 goods they
examined, Hoque and Razzaque (2004) demonstrated different pass-through impacts
on Bangladeshi export prices. With the exception of the nation's main export,
ready-made clothing, which was determined to be insensitive to fluctuations in
the exchange rate, it likewise demonstrated full pass-through effects for all
of its main export items. The study also showed that, depending on the
commodity's demand pattern in different export markets, export prices exhibited
unique pass-through behaviour. Evidence of short-term partial pass-through in
23 OECD nations was shown by Campa and Goldberg (2005). According to the study,
import prices in local currencies represented around 46% of short-term and 65%
of long-term changes in exchange rates. For several of the selected nations,
total pass-through was refuted even if individual pass-through elasticities
were determined to be closer to 1. Since the pass-through into import prices
was lower in nations with low average inflation and little exchange rate fluctuation,
the authors also discovered that macroeconomic factors have a substantial but
limited role in explaining cross-country variations in pass-through
elasticities.
McCarthy (2007) shows that ERPT has
decreased in sub-Saharan African (SSA) economies during the mid-1990s.
Additionally, the discovered that the mid-1990s saw the most noticeable and
statistically significant change in the nominal effective exchange rate
variable's coefficient. All model specifications showed a significant drop in
elasticities. It was predicted that pass-through elasticities were reduced by
around 50%. When Ghosh and Rajan (2007) calculated the ERPT's impact on
consumer prices in India, they discovered a long-term pass-through elasticity
of 40% and a short-term one of 10%. Additionally, they demonstrated evidence of
a larger pass-through in the post-liberalization era, which they ascribed to
increased economic openness.
Aliyu et al. (2008) used quarterly
data from 1986 to 2007 to investigate the extent of ERPT to import and consumer
prices in Nigeria. Despite the fact that import costs were greater than
consumer prices during that time, the study demonstrated that ERPT was
considerable in Nigeria. According to their findings, a one percent exchange
rate shock had pass-through effects on import and consumer prices of 14.3% and
10.5%, respectively, four quarters in advance. The conservative view in the
literature that ERPT is always significantly greater in developing nations than
in industrialized economies is somewhat refuted by their suggestion that ERPT
in Nigeria decreases down the pricing chain. According to Shintani et al.
(2009), decreased inflation was linked to the US ERPT's reductions in the 1980s
and 1990s. In a related study, Ghosh and Rajan (2009) discovered that in every
situation, Thailand's ERPT to inflation was greater than Korea's. Additionally,
they demonstrated that ERPT was higher for import costs than for consumer
prices in both nations, which further suggests that pass-through decreases
occur along the pricing chain.
In a similar vein, Sahaa and Zhanga
(2011) calculated the pass-through to CPI and verified that the ERPT to import
prices was complete. Using a structural VAR model, their results showed that
exchange rates had less of an impact on China's and India's growing domestic
prices. Adelowokan (2012) used yearly data from 1970 to 2010, and the ordinary
least squares estimate approach to examine the interest and inflation rate
channels of ERPT in Nigeria. Since neither the Naira's lagged value nor its
exchange rate with the US dollar could affect consumer prices, the study was
unable to identify any evidence of ERPT to inflation in Nigeria at that time.
Nonetheless, it discovered proof of the interest rate pass-through impact.
Razafimahefa (2012) investigated the
exchange rate pass-through to domestic pricing and its drivers in sub-Saharan
African nations. The investigation discovered an incomplete pass-through. The
pass-through is greater after depreciation than after appreciation of the local
currency. The average elasticity is predicted to be around 0.4. It is lower in
nations with more flexible exchange rates and greater income levels. A low inflation
environment, smart monetary policy, and a sustainable fiscal policy are also
connected with reduced pass-through rates.
External Sector Division, Research
Department of Central Bank (2012) used yearly data for Nigeria from 1980 to
2010 and concluded that the exchange rate pass through was not full in Nigeria
since the long- and short-term elasticities were 76.0 % and 31.0 %,
respectively. They point out that fluctuations in the exchange rate have raised
domestic prices for two straight periods and emphasize the necessity of
maintaining exchange rate stability given its significant influence on domestic
pricing. However, they discovered that while oil price pass-through was more
severe than the ERPT, import price pass-through was quite modest. Adeyemi and
Samuel (2013) used the VECM technique and data from 1970 to 2008 to examine the
ERPT to consumer prices in Nigeria. The findings of their examination of
impulse response functions (IRFs) showed that there was a significant ERPT to
consumer prices in Nigeria, with a long-term percentage of almost 83%.
According to the study, the money supply had less of an impact on Nigeria's growing
inflation than the exchange rate.
Adeniji (2013) used Granger-causality
and the Vector Error Correction Model (VECM) to examine exchange rate
volatility and inflation upturn in Nigeria using yearly time series data from
1986 to 2012. The findings of VECM indicate that whereas real GDP has a
negative and negligible link with inflation, exchange rate volatility, the
money supply, and the budget deficit have a positive and substantial
relationship with inflation. Additionally, a bidirectional link between the
variables is demonstrated by the Granger causality result. Additionally, Bobai,
Ubangida, and Umar (2013) evaluated Nigeria's inflation and exchange rate
volatility using yearly time series data from 1986 to 2010. According to the
VECM finding, there is a negative shock between inflation and the exchange
rate, meaning that as inflation rises, the exchange rate falls.
Jiang and Kim (2013) investigated the
effects of exchange rate fluctuations on Chinese producer and retail pricing
using the structural VAR technique. According to the study, the pass-through to
producer prices was greater than that to retail prices, however ERPT to
producer and retail prices were not complete. Alim and Lahiani (2014) examined
whether three East Asian and two Latin American nations' exchange rate pass-through
is decreased by a credible monetary policy intended to manage inflation.
According to their findings, a credible monetary policy regime that tried to
manage inflation was linked to reduced ERPT. Additionally, they discovered that
Latin American economies had greater ERPT than East Asian ones.
Inam (2015) also conducted an
empirical study on Nigerian inflation and exchange rate volatility using yearly
data from 1970 to 2011. According to the results of the Ordinary Least Squares
(OLS) regression, the exchange rate has a negligible negative impact on the
rate of inflation. This suggests that the inflation rate falls when the
currency rate declines and vice versa. The Granger-causality test result showed
that there was no causal relationship between the two variables in Nigeria. Okoli,
Mbah, and Agu (2016) investigated the connection between Nigerian inflation and
currency rate volatility. A unidirectional causal relationship between
inflation and real exchange rate volatility is demonstrated using the
Granger-causality test. This suggests that the economy won't see further
inflationary tendencies if the value of the Naira declines.
Bada et al. (2016) studied the
aggregate impact of exchange rate pass-through on Nigerian import and consumer
prices from 1995Q1 to 2015Q1. The study concluded that the exchange rate
pass-through into Nigeria's CPI inflation was insufficient by applying the
Johansen technique to cointegration and a vector error correction methodology.
The baseline and alternative models' long-term pass-through elasticities were
determined to be 0.24 and 0.30, respectively. It was shown that import prices
had a greater influence than consumer prices, suggesting that the pass-through
effect diminishes as one moves up the pricing chain.
Obiekwe and Osubuohien (2016) used
monthly time series data from 2006:01 to 2015:12 to examine the link between
exchange rate volatility and inflation in Nigeria, as well as the extent to
which inflation is passed through to the official and parallel currency rates.
In the short term, exchange rate volatility and inflation have a negative and
significant relationship, according to the Generalized Autoregressive
Conditional Heteroscedasticity (GARCH) and VECM results; in the long term,
however, the co-integration result demonstrates a positive and significant
relationship between the variables. Additionally, the analysis shows that the
official exchange rate only passes through to inflation over the long term, but
the parallel exchange rate only passes through to inflation in the near term.
Monfared and Akın (2017) used the
Vector Autoregression (VAR) model and the Hendry General to Specific Modeling
approach to examine the link between inflation and the exchange rate based on
time series data for the years 1976–2012. In order to estimate the VAR model,
the study additionally examined quarterly data from 1997Q3 to 2011Q4. The
Hendry model produced the conclusion that the exchange rate and inflation are
directly related. Inflation increases as foreign currency rates rise. Both the
money supply and the exchange rate have a positive impact on inflation when the
money supply variable is included in the VAR model.
Gidigbi, Babarinde, and Lawan (2018)
looked into how Nigerian price inflation was affected by exchange rate
volatility pass-through. Time-series data with annualization from 1981 to 2015.
The link between the stated key variables was estimated using the Vector Error
Correction Model (VECM). All of the factors included in the model are
significant in Granger producing inflation over the long term, according to
VECM estimate. There was no short-term correlation between exchange rate
volatility and inflation, according to the study. In the short term, however,
there was a positive correlation between the money supply and inflation. It is
clear from variance decomposition that other important variables or factors in
the model have a greater impact on changes in inflation than exchange rate
volatility.
Nuhu (2021) studied the impact of exchange
rate volatility on inflation in Nigeria using yearly time series data from 1986
to 2019. Using the VECM, his findings revealed that the money supply and
nominal exchange rate had a positive and considerable impact on the consumer
price index.
Ighoroje and Orife (2022)
investigated how Nigeria's inflation rate was impacted by fluctuations in the
currency rate. The 1987–2019 deregulated economy period was the focus of the
study. Nominal exchange rates were used to depict exchange rate fluctuations,
while control variables such as interest rates, money supply, imports, and GDP
growth were used to support these claims. Data analysis was done using the OLS
and GLS. According to the findings, Nigerian inflation is unaffected by the
exchange rate or other macroeconomic factors such as the money supply, interest
rates, imports, and GDP. This suggests that Nigeria's inflation rate is not
primarily caused by macroeconomic factors. Inflation can be caused by social
and political factors including political upheaval, consumer confidence, and so
on.
Abdullahi
(2023) used structural vector auto regression to
investigate the influence of macroeconomic shocks on the transmission of
exchange rate changes into consumer price inflation. The findings indicate that
exchange rate pass-through to consumer price inflation in Nigeria is minimal
and partial. Furthermore, the pace of adjustment to structural shocks arising
from exchange rate, production, monetary policy rate, and money supply is
rapid.
Musandiwa and Ngwakwe (2023) assessed
how South Africa's consumer price index was impacted by currency exchange
rates. The OLS regression was used to analyze monthly data on exchange rates
and the CPI from 2020 to 2021 that were taken from the Fusion Media investment
database. Within the parameters of the data, the research demonstrated that
exchange rate fluctuations had a large and beneficial impact on CPI. The
results have both academic and practical ramifications for comprehending the
theoretical short-term period impact of the exchange rate on the consumer price
index in the context of South Africa and for the application of advanced
economic policies in practice to mitigate potential adverse effects on
investment and savings.
Lastly, Jakpa, Ezi, and Egbon (2024)
investigated how Nigerian inflation is affected by exchnage rate pass-through.
The Autoregressive Distributed Lag (ARDL) model was employed as the analytical
approach for the 1990–2022 research period. The results show that whereas
import prices have a significant and negative impact on consumer prices,
exchange rates have a positive and considerable impact on consumer prices.
Based on this finding, the study recommended that monetary authorities use
caution when devaluing the home currency in an attempt to spur economic
expansion, as this would likely raise the ERPT in addition to aggravating
domestic inflation. Other recent empirical works are Effiong et al. (2022), and
Effiong et al. (2025).
2.4 Summary of Empirical Literature Reviewed
The empirical studies have portrayed
divergent findings on the exchange rate pass-through effect with majority of
the studies reporting partial and incomplete pass-through effect on inflation
(see Aliyu et al., 2008; Bada et al., 2016; Monfared and Akın, 2017; Abdullahi,
2023). Most of the studies have focused on using the VAR and VECM in the
analysis to obtain both the short run and long run elasticities for the
exchange rate pass-through coefficient. This study therefore applies both the
fully modified ordinary least squares (FMOLS) and the VAR model to estimate the
exchange rate pass-through elasticities for Nigeria using recent data with
higher frequencies. The VAR model aid in obtaining the impulse response
function to check how consumer prices respond to innovations in the exchange
rate and other key variables in the model such as import prices and crude oil
price.
3.
METHODOLOGY
3.1
Model Specification
The model for this study was
formulated drawing insights from the work of Ghosh and Rajan (2009) in
estimating the exchange rate pass-through for Korea and Thailand, where the
consumer price index was expressed as a function of exchange rate, United
States producer price index, and output growth. The US Producer Price Index
(USPPI) was incorporated in the model to capture how import prices could affect
domestic prices within the domestic economy. Thus, exchange rate can pass
through the import prices to affect the domestic price level. Hence, such
interaction is crucial to be incorporated in the model especially in the case
where the VAR model will be incorporated.
The original model is specified as follows:
where i denotes Korea or Thailand, j connotes United
States or Japan,
Where CPI is the consumer price index, EXCH is the exchange rate
of the naira per dollar, USPPI is the United States producer price index (a
proxy for world import prices), OILP is the global price of Brent Crude (U.S.
Dollars per Barrel, Quarterly, Not Seasonally Adjusted), and RGDP is the growth
rate of gross domestic product.
3.2
Nature and sources of Data
This study utilized quarterly data
from the first quarter of 1995 (1995Q1) to the last quarter of 2023 (2023Q4).
The data was obtained for key variables in the model which were consumer price
index, exchange rate, United States producer price index, crude oil price, and
real GDP growth. These data were obtained strictly form secondary sources.
While data on real GDP and exchange rate were obtained from the Central Bank of
Nigeria (2023) statistical bulletin, data on crude oil price was obtained from
the International Monetary Fund (retrieved from the Federal Reserve Bank of St.
Louis). Also, data on consumer price index and United States producer price
index were obtained from Ha et al. (2023) on “global database of inflation” and
Organization for Economic Co-operation and Development (2025) respectively.
These databases are reliable and officially recognized to generate the desired
data for this study.
3.3
Technique of Data Analysis
The empirical analysis begins by
checking the stationarity property of the time series variables for the study.
The test was conducted based on the Augmented Dickey-Fuller unit root test with
drift and trend assumption to ascertain the order of integration of the time
series variables. This was later followed by checking for cointegration using
the Hansen parameter instability test for cointegrating relationships. The long
run estimates of the model were estimated using the fully modified ordinary
least squares (FMOLS) technique of analysis due to the prevailing higher order
of integration recorded from the series.
The study further utilized the five-variable VAR model
with exchange rates and the two variables of import and consumer prices being the
key variables in the analysis, and oil price and real GDP growth being the
control variables. The general VAR model is specified as follows:
Where
The two main factors in the analysis are
import and consumer prices, as well as exchange rates. As oil prices rise, more
money is received, which might result in the accumulation of reserves and a
future appreciation in the value of the naira, which would lower inflation.
Increased inflation and exchange rate depreciation are caused by rising import
and consumer prices (Bada et al., 2016). As a result, real output and import
prices will be positive, while the vector coefficients of the exchange rate
variable and crude oil prices will be negative.
4. EMPIRICAL FINDINGS
4.1 Stylized Facts
The
trends in the consumer price index (CPI) and the United States’ producer price
index (USPPI) are presented in Figure 1.
Figure 1: Trends in consumer price index and United
States’ producer price index
The
trend presented in Figure 1 connotes the fact that both the CPI of Nigeria and
the USPPI have been moving in the same direction over the years. In the 1980s
and early 2000s, the CPI for Nigeria was far below the USPPI. The CPI of
Nigeria stood at 21.27 in the first quarter of 1995 and rose steadily to 91.20
in the first quarter of 2008. Within the same period, the USPPI rose from 66.31
to 91.67 respectively. With the CPI rising continuously over the years from
104.37 in 2010Q1 to 228.40 in 2018Q1 with a further increase to 497.59 in
2023Q4, the USPPI was below the CPI as it declined from 108.21 in 2014Q1 to 51.18
in 2020Q. However, the USPPI maintained an upward trend thereafter with some
periods of fluctuations to 73.25 in 2021Q1 with a further increase to 82.89 in
2023Q4. This pattern of movements in these two variables portrays some sort of
relationship between them over the years.
The trends of CPI and exchange rate
(EXCR) is also captured in Figure 2 where the two variables tend to exhibit
similar pattern of movements over the years. The two variables maintained an
upward trend over the years with exchange rate depicting some sort of
fluctuations in some periods therefore exhibiting periods of appreciation and
depreciation of the naira relative to the US dollar.
It can be observed that the exchange
rate of the naira relative to the dollar maintained a stable value of N21.89/$1 from the first quarter of 1995
to the last quarter of 1998. This was followed by a substantial depreciation of
the domestic currency to N86.97/$1 in
the first quarter of 1999. This depreciation continued consistently up to N 132.87/$1
in 2005Q2 before appreciation set in the third quarter of 2005 where the $1
exchanged for N130.81. This period of
appreciation of the naira could not stand the test of time as in only lasted up
to 2008Q3 where the exchange rate was N117.73/$1.
Figure 2: Trends in consumer price index and Exchange
rate
Thereafter,
the exchange rate depreciated massively to N149.83/$1
in 2010Q1 with no significant appreciation (observable between 2012Q4 and
2014Q3) before the economy was plunged back to period of depreciation to the
tune of N169.68/$1 in 2014Q4. This depreciation continued consistently till
2023 as the exchange rate depreciated from N306.40/$1
in 2017Q1 to N381.00/$1 in 2020Q4 with
a further depreciation to N899.07/$1 in
2023Q. As could be observed from Figure 2, both CPI and exchange rate moved in
the same direction which portrays the existence of some sort of relationship
between them.
4.2
Descriptive Statistics
The result presented in Table 1
captures the descriptive properties of the variables utilized for this study.
Table 1: Descriptive properties of the variables
|
|
CPI |
EXCR |
USPPI |
OILP |
RGDP |
|
Mean |
138.27 |
188.68 |
91.91 |
58.00 |
6.27 |
|
Median |
96.35 |
148.95 |
92.58 |
56.12 |
3.02 |
|
Maximum |
497.59 |
899.07 |
142.19 |
122.22 |
28.64 |
|
Minimum |
21.27 |
21.89 |
66.31 |
11.50 |
-24.09 |
|
Std. Dev. |
113.66 |
147.20 |
19.55 |
32.04 |
47.96 |
|
Skewness |
1.23 |
1.90 |
0.62 |
0.33 |
10.02 |
|
Kurtosis |
3.80 |
8.27 |
2.96 |
1.97 |
15.60 |
|
Jarque-Bera |
32.57 |
204.16 |
7.48 |
7.29 |
52.02 |
|
Probability |
0.00 |
0.00 |
0.02 |
0.03 |
0.00 |
|
Observations |
116 |
116 |
116 |
116 |
116 |
Source: Researcher Computation
The
CPI recorded a mean value of 138.27 having a standard deviation of 113.66. The
variable has a minimum value and maximum value of 21.27 and 497.59
respectively, and it is positively skewed and leptokurtic in nature. Exchange
rate averaged N188.68/$1 during the
period under consideration with a standard deviation of 147.20. While the
minimum and maximum values were respectively N21.89
and N899.07/$1, the variable exhibits a
positively skewed and leptokurtic distribution. The USPPI averaged 91.91 with a
maximum and minimum value of 142.19 and 66.31 respectively. With the standard
deviation of 19.55, the variable is positively skewed and platykurtic in its
distribution. The global oil price during the study period averaged $58 per
barrel (pb) with a maximum and minimum value of $122.22 pb and $11.50 pb
respectively. While the standard deviation is 32.04, the variable is positively
skewed and platykurtic in its distribution. The Nigerian economy recorded an
average RGDP growth rate of 6.27% with a standard deviation of 47.96. The
variable positively skewed and leptokurtic in nature.
4.3
Correlation Analysis
To check the association between
variables in the model, the correlation analysis is conducted based on the
Pearson correlation analysis. The result is presented in Table 2 where negative
coefficient signifies negative association, and positive sign denotes positive
association between the variables concerned.
Table
2: Correlation matrix
|
|
CPI |
EXCR |
USPPI |
OILP |
RGDP |
|
CPI |
1 |
||||
|
EXCR |
0.9545 |
1 |
|||
|
USPPI |
0.9326 |
0.8625 |
1 |
||
|
OILP |
0.4740 |
0.3905 |
0.7430 |
1 |
|
|
RGDP |
-0.0288 |
-0.0168 |
0.0091 |
0.0665 |
1 |
Source: Researcher Computation
From
the result in Table 2, the CPI exhibited strong positive correlation with both
exchange rate and USPPI given their correlation coefficient of +0.9545 and
+0.9326 respectively. Also, CPI and oil price are positively correlation, but
the degree of association is weak given the correlation coefficient of +0.474.
However, CPI and RGDP exhibited a weak negative correlation given the
coefficient of -0.0288. The regressors do not exhibit any form of perfect
linear relationship with each other hence, the possibility of multicollinearity
is ruled out. Since correlation does not imply any cause-effect relationship,
there is need to further establish such which will be captured using the
regression analysis.
4.4
Stationarity Test
The stationarity test was conducted
in order to ascertain the order of integration of the time series variables
utilized for the study. The test was conducted based on the Augmented
Dickey-Fuller unit root test with constant and trend assumption. It is required
that the t-statistic must be negative and statistically significant at the 5%
level for the null hypothesis to be rejected. Table 3 presents the result of
the test where I(0), I(1) and I(2) denotes that the variable is stationary at
level, first difference, and second difference respectively.
Table 3: Unit root test
result
|
Variables |
ADF Statistic |
Order of Integration |
|||||
|
Level |
p-value |
First Difference |
p-value |
Second Difference |
p-value |
||
|
CPI |
5.2315 |
1.0000 |
3.1180 |
1.0000 |
-10.456 |
0.0000 |
I(2) |
|
EXCR |
2.1321 |
1.0000 |
-3.2079 |
0.0881 |
-10.1374 |
0.0000 |
I(2) |
|
USPPI |
-2.1334 |
0.5214 |
-4.4552 |
0.0027 |
----- |
------ |
I(1) |
|
OILP |
-2.8312 |
0.1894 |
-8.1642 |
0.0000 |
----- |
----- |
I(1) |
|
RGDP |
-10.564 |
0.0000 |
------ |
---- |
------ |
----- |
I(0) |
Source: Researcher Computation
The unit root test result presented
in Table 3 indicates that both CPI and EXCR were stationary at second
difference given that their respective ADF statistic became significant upon
the variables being differenced twice. Therefore, CPI and EXCR are I(2) time
series variables. Also, the USPPI and OILP were stationary at first difference
given that their respective ADF statistic became stationary upon first
differencing. Thus, both USPPI and OILP are I(1) time series variables. On the
contrary, only RGDP was stationary at level hence, it is an I(0) time series
variable. Since the variables reported higher order of integration the fully
modified ordinary least squares (FMOLS) technique is utilized to derive the
parameter estimates.
4.5
Fully Modified Ordinary Least Squares (FMOLS) Model Estimation
The FMOLS estimation technique was
deployed to obtain the parameter estimates of the model since some of the
variables were stationary at second difference. Therefore, the cointegration
test is first conducted and the result is presented in Table 4.
Table 4: Cointegration Test - Hansen Parameter
Instability
|
Series: CPI EXCR USPPI OILP
RGDP |
||||
|
Null hypothesis: Series are
cointegrated |
||||
|
Cointegrating equation
deterministics: C |
||||
|
|
Stochastic |
Deterministic |
Excluded |
|
|
Lc statistic |
Trends (m) |
Trends (k) |
Trends (p2) |
Probability |
|
0.788481 |
4 |
0 |
0 |
0.097 |
Source: Researcher Computation
The cointegration test result
presented in Table 4 based on the Hansen parameter instability test generated
Lc statistic of 0.788481 with a p-value of 0.097 which is statistically
insignificant at the 5% level of significance. Since the Lc statistic is insignificant,
the null hypothesis that the series are cointegrated is therefore accepted.
Consequently, there is cointegration in the model and the cointegration
regression model is estimated based on the FMOLS which Table 5 presents the
result.
Table
5: FMOLS estimates
|
Dependent Variable: CPI |
||||
|
Method: Fully Modified Least
Squares (FMOLS) |
||||
|
Sample (adjusted): 1995Q2
2023Q4 |
||||
|
Included observations: 115
after adjustments |
||||
|
Cointegrating equation
deterministics: C |
||||
|
Long-run covariance estimate
(Bartlett kernel, Newey-West fixed bandwidth = 5.0000 |
||||
|
Variable |
Coefficient |
Std. Error |
t-Statistic |
Probability |
|
EXCR |
0.1849 |
0.0497 |
3.7174 |
0.0003 |
|
USPPI |
5.9876 |
0.5152 |
11.6219 |
0.0000 |
|
OILP |
-1.4086 |
0.1734 |
-8.1213 |
0.0000 |
|
RGDP |
-0.0455 |
0.0522 |
-0.8723 |
0.3850 |
|
C |
-365.5960 |
30.3748 |
-12.0362 |
0.0000 |
|
R-squared |
0.9822 |
Mean
dependent var |
139.2915 |
|
|
Adjusted R-squared |
0.9816 |
S.D.
dependent var |
113.6218 |
|
|
S.E. of regression |
15.4291 |
Sum
squared resid |
26186.4000 |
|
|
Long-run variance |
714.9219 |
|
|
|
Source: Researcher Computation
The
regression result presented in Table 5 indicated that exchange rate exerted a
positive and significant effect on the consumer price index. This therefore
implies that exchange rate depreciation increases inflationary tendencies
within the Nigerian economy. Hence, the long run exchange rate pass-through
coefficient of 0.1849 is an indication that a 1% increase in exchange rate will
lead to a 0.1849% increase in the rate of inflation in the long run. The
findings indicate that exchange rate pass-through to consumer price inflation
in Nigeria is minimal and partial. This aligns with the earlier findings such
as Aliyu et al. (2008), Sahaa and Zhanga (2011), Razafimahefa (2012), Bada et
al. (2016), Monfared and Akın (2017) and Abdullahi (2023).
Also, the USPPI exerted a positive
and significant long run effect on inflation in Nigeria. Therefore, an increase
in the US producer price index will affect Nigeria’s domestic prices since
Nigeria is an import dependent economy with some of her importation coming from
the United States of America. Consequently, a 1% increase in the US producer
price index will lead to a 5.9876% increase in Nigeria’s domestic prices. This
clearly portrays that import prices can influence domestic prices through the
transfer of such prices to the consumers. Further, oil price exerted a negative
and significant effect on inflation in Nigeria. Rising oil prices will lead to
a greater increase in the revenue of the government since Nigeria is an oil
dependent economy. This will reduce the fiscal deficit which has been pointed
out in the literature as a key driver of inflationary pressure. Therefore, a 1%
increase in crude oil prices will lead to a 1.4086% decrease in domestic
prices. Lastly, real GDP growth exerted a negative but insignificant effect on
domestic prices during the period of analysis.
4.6
Vector Autoregression
The vector autoregression was
utilized to estimate the exchange rate pass-through effect on inflation in
Nigeria. This approach aids in ascertaining the key variable(s) through which
exchange rate influences domestic prices. The process begins with the determination
of the optimal lag followed by the estimation of the VAR model.
4.6.1
VAR Lag Order Selection Criteria
The VAR model must be estimated using
an optimal lag length to be determined using lag selection criteria. Such
include the Akaike information criterion (AIC), the Schwarz information
criterion (SIC), and Hannan-Quinn (HQ) criterion. The lag length with the
lowest AIC, SIC and HQ value yields the optimal lag to be included in the VAR
model estimation. The result in Table 6 presents the optimal lag length selection.
Table 6: Lag order selection result
|
Lag |
LogL |
LR |
FPE |
AIC |
SIC |
HQ |
|
0 |
-2644.81 |
NA |
2.44e+14 |
47.31809 |
47.43946 |
47.36733 |
|
1 |
-1878.64 |
1450.254 |
4.36e+08 |
34.08288 |
34.81105 |
34.37832 |
|
2 |
-1817.07 |
111.051 |
2.28e+08* |
33.42980* |
34.76477* |
33.97144* |
|
3 |
-1799.40 |
30.28357 |
2.61e+08 |
33.56077 |
35.50256 |
34.34861 |
|
4 |
-1773.19 |
42.60363 |
2.59e+08 |
33.53903 |
36.08762 |
34.57307 |
|
* indicates lag order
selected by the criterion |
||||||
|
LR: sequential modified
LR test statistic (each test at 5% level) |
||||||
|
FPE: Final prediction
error |
||||||
|
AIC: Akaike information
criterion |
||||||
|
SC: Schwarz information
criterion |
||||||
|
HQ: Hannan-Quinn
information criterion |
|
|
|
|||
Source: Researcher Computation
The result presented in Table 6
indicated that at lag 2, we have the lowest AIC, SIC and HQ values. Therefore,
the optimal lag to be incorporated in the VAR model estimation is 2 lags.
4.6.2
Vector Autoregression Model Estimation
To ascertain the exchange rate
pass-through effect on inflation in Nigeria, the VAR model is estimated, and
the result is presented in Table 7.
Table 7: Vector
Autoregression Estimates
|
|
CPI |
EXCR |
USPPI |
OILP |
RGDP |
|
CPI(-1) |
1.554153 |
2.804129 |
0.087704 |
-0.481144 |
1.004886 |
|
[17.3203] |
[3.23755] |
[0.92646] |
[-0.95397] |
[0.36659] |
|
|
CPI(-2) |
-0.559978 |
-3.000405 |
-0.07397 |
0.424224 |
-1.417209 |
|
[-6.16852] |
[-3.42410] |
[-0.77234] |
[0.83138] |
[-0.51104] |
|
|
EXCR(-1) |
0.018875 |
1.497869 |
-0.016108 |
0.000746 |
-0.067034 |
|
[1.95601] |
[16.0814] |
[-1.58229] |
[0.01375] |
[-0.22740] |
|
|
EXCR(-2) |
-0.018119 |
-0.584935 |
0.019046 |
0.036641 |
0.134039 |
|
[-1.68887] |
[-5.64828] |
[1.68271] |
[0.60760] |
[0.40897] |
|
|
USPPI(-1) |
0.380891 |
4.599286 |
0.665874 |
-1.372063 |
0.005475 |
|
[ 2.51602] |
[ 3.14746] |
[ 4.16917] |
[-1.61244] |
[ 0.00118] |
|
|
USPPI(-2) |
-0.195037 |
-2.726799 |
0.244713 |
1.755317 |
2.070734 |
|
[-1.38762] |
[-2.00985] |
[ 1.65027] |
[ 2.22181] |
[ 0.48226] |
|
|
OILP(-1) |
-0.043132 |
-0.825692 |
0.131753 |
1.424210 |
0.635495 |
|
[-1.52461] |
[-3.02367] |
[ 4.41434] |
[ 8.95635] |
[ 0.73533] |
|
|
OILP(-2) |
-0.009159 |
0.319037 |
-0.118323 |
-0.583463 |
-0.984452 |
|
[-0.31338] |
[ 1.13093] |
[-3.83752] |
[-3.55180] |
[-1.10265] |
|
|
RGDP(-1) |
-0.002873 |
-0.020917 |
-0.000155 |
-0.003763 |
-0.014428 |
|
[-0.89658] |
[-0.67631] |
[-0.04583] |
[-0.20896] |
[-0.14740] |
|
|
RGDP(-2) |
0.002455 |
0.000687 |
0.000629 |
-0.000918 |
0.008245 |
|
[ 0.76547] |
[ 0.02220] |
[ 0.18602] |
[-0.05091] |
[ 0.08416] |
|
|
C |
-11.65403 |
-108.9493 |
5.585087 |
-21.69384 |
-123.8375 |
|
[-2.39002] |
[-2.31476] |
[ 1.08568] |
[-0.79152] |
[-0.83135] |
|
|
R-squared |
0.999812 |
0.989504 |
0.992839 |
0.924458 |
0.031910 |
|
Adj. R-squared |
0.999794 |
0.988485 |
0.992144 |
0.917124 |
-0.06208 |
|
Sum sq. resids |
274.3105 |
25558.19 |
305.3190 |
8666.590 |
255992.4 |
|
S.E. equation |
1.631934 |
15.75239 |
1.721703 |
9.172876 |
49.85342 |
|
F-statistic |
54745.87 |
971.0475 |
1428.044 |
126.0477 |
0.339503 |
|
Log likelihood |
-211.8085 |
-470.2723 |
-217.913 |
-408.6278 |
-601.6112 |
|
Akaike AIC |
3.908922 |
8.443374 |
4.016018 |
7.361892 |
10.74756 |
|
Schwarz SC |
4.172941 |
8.707393 |
4.280037 |
7.625911 |
11.01158 |
|
Mean dependent |
140.3048 |
191.6027 |
92.35465 |
58.70893 |
6.361288 |
|
S.D. dependent |
113.6004 |
146.7975 |
19.42453 |
31.86326 |
48.37449 |
Note: t-statistics are enclosed in square brackets [ ].
Source: Researcher Computation.
The
result presented in Table 7 indicates that both the first period lag and the
second lag of CPI exerted a significant effect on the current period CPI. This
is an indication that CPI is strongly endogenous in predicting itself. Thus,
the first period lag increased the current CPI by 1.5542% on the average while
the second period lag of CPI reduces the current CPI by 0.60% on the average. Consequently,
it can be adduced that expectations drives the consumer price index within the
Nigerian economy. Further, the one period lag of CPI increased exchange rate by
about 2.8041% on the average while the second period lag of CPI reduces
exchange rate by 3.0% on the average. Thus, CPI is strongly exogenous in
predicting exchange rate variations during the study period. However, CPI is
weakly exogenous in predicting the changes in USPPI, OILP, and RGDP during the
study period.
The effect of exchange rate on CPI
indicates that the first period lag of exchange rate exerted a positive and
significant effect on CPI while the second period lag exerted a negative
effect. Therefore, the exchange rate pass-through effect on inflation varies
over time. Thus, the previous year’s exchange rate increases the current year’s
CPI by 0.0189% on the average while the past two year’s exchange rate reduces
the current CPI by about 0.0181% on the average. This portrays that exchange
rate is strongly exogenous in predicting the variations in CPI in Nigeria.
Also, exchange rate is strongly endogenous in predicting itself given that the
first period and the second period lags of exchange rate exerted a significant
effect on the current period’s exchange rate. From the estimated coefficient,
the previous year’s exchange rate increased the current period’s exchange rate
by 1.4979% on the average while the second period lag reduces the current
period’s exchange rate by 0.5849% on the average. Exchange rate was also noted
to be strongly exogenous in predicting USPPI. In the first period lag, exchange
rate exerted a negative and significant effect on USPPI by reducing it by
0.016% on the average. However, the second period lag of exchange rate reduced
the current USPPI by about 0.019% on the average. It was further noted that
exchange rate was weakly exogenous in predicting OILP and RGDP during the study
period.
The effect of USPPI on CPI in the
first period was positive and significant while the effect became negative but
insignificant in the second period. Thus, the USPPI increased the CPI by about
0.38% on the average. Hence, the USPPI is strongly exogenous on predicting CPI
within the Nigerian economy. Given this notable transmission, it can be adduced
that the exchange rate pass through the USPPI to affect the domestic prices
within the Nigerian economy. The estimated model further portrayed that the
USPPI exerted a significant effect on the exchange rate which presents a
feedback mechanism within the system. From the estimated coefficient, the
previous year’s USPPI increased the current period exchange rate by about
4.5992% on the average while its second period lag reduced exchange rate by
about 2.7268% on the average. As a result, USPPI is strongly exogenous in
predicting exchange rate in Nigeria. The USPPI was strongly endogenous in
predicting itself since its lags exerted a significant effect. Thus, the first
period and second period lags of USPPI increased the current USPPI by 0.6659%
and 0.24471% respectively. The USPPI was also strongly exogenous in predicting
crude oil prices during the study period. The estimated coefficient indicated
that the one period lag of USPPI reduced the crude oil price by 1.372% on the
average while the second period lag of USPPI increased the current crude oil
prices by 1.7553% on the average. However, USPPI was weakly exogenous in
predicting RGDP during the study period.
The result further portrayed that oil
price only exerted a significant effect on CPI at the one period lag while the
effect was insignificant with the second lag. Thus, the first period lag of
crude oil price reduced the CPI by about 0.0431% on the average hence, oil
price is strongly exogenous in predicting consumer price index in Nigeria. The
oil price was also strongly exogenous in predicting exchange rate and the
USPPI. The first period lag of crude oil price caused the exchange rate to
reduce by 0.8257% on the average. On the contrary, the first period lag of
crude oil price increased the USPPI by 0.1318% on the average while its second
period lag reduced the USPPI by 0.1183% on the average. This result therefore
establishes a feedback effect between USPPI and crude oil price during the
period of analysis. The crude oil price was strongly exogenous in predicting
itself during the study period. The first period lag of crude oil price
increased the current period crude oil price by 1.4242% while its second period
lag reduced the current crude oil price by 0.5835% on the average. Crude oil
price was further observed to be weakly exogenous in predicting RGDP during the
study period.
For RGDP, the VAR estimates portrayed
that it was weakly endogenous in predicting itself as well as being weakly
exogenous in predicting CPI, exchange rate, and USPPI. The estimated VAR model
further portrayed that CPI would assume a negative value of -11.65 if all the
regressors are held constant. Also, exchange rate will assume a negative value
of -108.95 if all the explanatory variables were held constant. The value of
USPPI, OILP, and RGDP would assume a value of zero since their intercepts were
statistically insignificant. The R-squared indicated that the explanatory
variables in model explained 99.98% of the total variations in CPI; 98.95% of
the total variation in exchange rate; 99.28% of the total variation in USPPI;
92.45% of the total variations in crude oil price; and just 3.19% of the total
variations in output growth (RGDP). The overall models were statistically
significant given the significance of their respective F-statistics at the 5%
level.
4.7
Impulse Response Function
The impulse response was generated
from the VAR framework to depict how shocks in the exogenous variables could
affect the consumer price index. Such responses to shocks within the system is
presented in Figure 3. It was observed that the CPI responded positively to
shocks in exchange rate up to the sixth period after which the response became
negative. This portrays the fact that short run spikes in exchange rate could
drive price gyrations within the Nigerian economy in the short term. After due
adjustments, the positive response is decomposed such that the effect becomes
negative over time. The CPI was also observed to respond positively to shocks
in the USPPI up to the 10th period. This is an indication that
increased USPPI will increase the import cost which translates to higher prices
of imported goods within the economy. In an attempt for businesspersons to be
able to afford foreign goods, the prices of domestic goods are also inflated
thereby leading to a concurrent increase in the CPI within the Nigerian
economy. The CPI was also observed to respond negatively to shocks in crude oil
prices up to the 10th period. Thus, positive innovations/shocks in
crude oil prices will cause the CPI to decline substantially over time.
Figure 3: Response to Cholesky one SD (d.f. adjusted)
innovations
The
impulse response function captured in Figure 3 further indicated that exchange
rate responded positively to shocks in USPPI. This implies that any positive
innovations in the import prices will cause the exchange rate to rise
substantially over time. However, exchange rate responded negatively to shocks
in crude oil prices. Thus, any positive innovations in the crude oil prices
will lead to an appreciation of the naira relative to the dollar. Meanwhile,
the import prices responded negatively to shocks in exchange rate up to the
fifth period after which such responses became positive. Finally, the import
prices responded positively to shocks in crude oil prices implying that
positive innovations in the crude oil market will lead to an increase in the import
prices up to the 9th period before the impact is being decomposed. The
impulse response function therefore portrays a sort of interaction among
consumer price index, exchange rate, and United States producer price index
with shocks in the real GDP not exerting any significant response from the
variables within the system.
4.8 Discussion of Major Findings
The exchange rate pass-through effect
in this study have been established to be incomplete given the low coefficient of
0.1849 being estimated. The pass-through has been established to be from
exchange rate to import prices to influence the domestic prices. High import
prices in an import dependent economy like Nigeria will certainly diffuse to
affect domestic prices in terms of cost of core inputs and consumables. The
incomplete exchange rate pass-through effect is an indication that exchange
rate does not directly affect the domestic prices, but passes through the
import prices to affect domestic prices.
5.
CONCLUSION AND RECOMMENDATIONS
The exchange rate pass-through effect
on inflation in Nigeria have been examined in this study with the use of
quarterly data from 1995Q1 to 2023Q4 making a total of 116 observations. The
data for the study was obtained from secondary sources including the Central
Bank of Nigeria, Organization for Economic Co-operation and Development, the
Global database on inflation, and the Federal Reserve Bank of St Louis. The
methodology of the study follows the VAR framework which facilitated the
detection of the exchange rate pass-through effect as well as the impulse
response function to ascertain how consumer prices responded to shocks in
exchange rate, crude oil price, United States producer price index, and the
real GDP. The fully modified ordinary least squares (FMOLS) technique was also
utilized to estimate the long run parameter estimates for the model given the
higher order of integration established by the unit root test.
The result from the FMOLS estimation
indicated that exchange rate exerted a positive and significant effect on
domestic prices. This therefore is an indication that depreciation of the
currency could put forward an upward trend in the domestic price level
especially in an import dependent economy like Nigeria. The long run exchange
rate pass through coefficient which was 0.1849 is an indication that the
domestic consumer price level will increase by 0.1849% is the exchange
depreciates by 1% on the average. Further, the United States producer price
index also exerted a positive and significant effect on domestic price level
indicating that higher production price index in America which is a core
trading partner with Nigeria will definitely diffuses into the domestic economy
to affect the price level due to trade relationship (a clear case of imported
inflation). A 1% increase in the USPPI will therefore cause the domestic
consumer prices to increase by about 5.9876% on the average. This established
relationship portrays the interdependence of the domestic consumer prices with
trading partner’s production price index. The crude oil price was also observed
to exert a negative and significant effect on domestic prices. This therefore
implies that an increase in crude oil prices will reduce domestic prices in the
domestic economy.
The VAR estimation result has
indicated evidence of the exchange rate pass-through effect on inflation in
Nigeria. It was observed that exchange rate there is a feedback mechanism
between exchange rate and the domestic price level as the lags of both variables
significantly affected each other. In both cases, first period lag of CPI
positively affected exchange rate and the first period lag of exchange rate
also positively affected the CPI significantly. The exchange rate pass-through
coefficient being 0.0189 is an indication that exchange rate depreciation will
cause the domestic prices to increase by about 0.0189% on the average. The
findings indicate that exchange rate pass-through to consumer price inflation
in Nigeria is minimal and partial. However, an increase in CPI will lead to a
2.8041% increase in exchange rate (exchange rate depreciation). The VAR
estimates also indicated that exchange rate positively affected the US producer
price index and the producer price index in turn positively affected the
consumer prices. This therefore implies that exchange rate passes through the
US producer price index to affect Nigeria’s consumer prices since Nigeria is an
export dependent economy. The impulse response function portrayed that the CPI
responded positively to innovations in US producer price index and negatively
to shocks in crude oil prices. Further, the CPI responded positively to shocks
in exchange rate up to the 6th period after which such shocks are
decomposed.
This paper therefore concluded that
the exchange rate pass-through effect on inflation in Nigeria occurs through
the impact of import prices on the domestic price level due to import
dependency. It is therefore recommendation that over reliance on import has
made the Nigerian economy susceptible to the exchange rate pass-through effect
which has been noted to be significant in terms of import prices (as observed
from the US producer price index). The Federal Government of Nigeria in
conjunction with the National Planning Commission should prioritize structural
reforms through the strategic implementation of import substitution policies. By
increasing domestic production capacity, Nigeria can reduce dependency on
imports, curbing imported inflation.
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