Measuring the pass-through effect of global food price volatility and South Africa’s CPI on the headline inflation of Zimbabwe

Abstract

Pronounced food price volatility has challenging effects on the macroeconomic performance of countries. Particularly, large food price swings can generate rippling effects on inflation and poverty. This article examines the inflation effect of global food price volatility and South Africa’s Consumer Price Index (CPI) on Zimbabwe using annual data spanning 1995 to 2019. Using the Generalized Autoregressive Conditional Heteroscedastic (GARCH) techniques for volatility modelling and a standard backward-looking Phillips curve framework that controls for an output gap, the results of this article indicate that volatility of global food prices and the variations in South Africa’s CPI are significantly transmitted to Zimbabwe’s headline inflation. For instance, every 10% increase in global food prices results in 33% of the variation being passed through into the headline inflation of Zimbabwe. For South Africa’s CPI, the results indicate that about 228% is passed through into Zimbabwe’s headline inflation for every 10% increase in prices. Policymakers in Zimbabwe, therefore, should be wary of global food prices and South Africa’s CPI and it is also fundamental that the country improves its food processing capacity in terms of both the revival and efficacy of existing manufacturing facilities within its food industry.

Keywords:

• CPI
• food price volatility
• GARCH
• headline inflation
• pass-through effect
• South Africa
• Zimbabwe

1. Introduction

Despite food prices having historically been subjected to insurmountable volatility, the global 2008 and 2011 food price hikes invigorated empirical efforts targeted at ascertaining the key drivers of the global food price volatility (Roache, 2010; Wang et al., 2018), the manner in which global price fluctuations pass-through to domestic retail prices (Ferrucci et al., 2010), and its eventual effects on macroeconomic performance (De Winne & Peersman, 2016). From a macroeconomic standpoint, food price volatility has been found to weaken the balance of payment position of countries, generate inflation, and increase poverty as well as food insecurity levels (Roache, 2010).

Accordingly, food price volatility is a major concern to policymakers in the world. For developing countries, where an enormous share of household income is commonly spent on food products, food price volatility poses a fundamental challenge as it threatens the food security of a large proportion of the population and creates import bill uncertainty (Capitanio et al., 2020). The situation is further exacerbated by the global phenomenon of climate change, which has led to severe yield shocks, triggering unpredictable food outcomes (Anderson et al., 2023). In fact, such uncertainty has the potential to generate surges in global food prices. Undoubtedly, a greater magnitude of the global food price surges emanating from climate-related shocks are likely to be felt in net-food importing countries, most of which are developing countries. Moreover, many developing countries are too small to influence world prices. This holds more especially for those developing countries in Africa, including Zimbabwe. As such, pronounced global food price fluctuations can spring into inflation and other macroeconomic and political challenges.

The magnitude of the pass-through effect depends on factors, such as inter alia, the economy’s level of global integration, and the provision of subsidies. It is said to be complete if the entire global price change transmits into the domestic headline inflation and incomplete if part of the change is absorbed along the supply chain. A number of empirical studies (Awokuse & Yang, 2003; Blomberg & Harris, 1995; Ferrucci et al., 2010; Jalil & Esteban, 2011; Jumah & Kunst, 2007; Zoli, 2009) have been conducted to examine the pass-through effect of global food prices on domestic inflation in developing countries and the majority of these studies confirm an incomplete pass through. However, in spite of such an overwhelming board of related literature, there is no country-specific study available for Zimbabwe in the scholarly domain.

In relation to other countries, Zimbabwe experienced severe macroeconomic instability in the past two decades. More significantly, Zimbabwean producers and consumers rely heavily on imports of both food products and food intermediate inputs. Particularly, given that Zimbabwe’s industrial capacity has become extremely low, consumers rely more on foreign produced products, which might make the pass-through effect considerable enough to warrant empirical examination. The decade commencing 2000 to 2010 saw Zimbabwe experiencing stagflation. The monetary authorities responded to the high levels of inflation through, among other measures, the persistent revaluation of the Zimbabwean dollar. In February 2009, the monetary authority de-jure dollarized the economy. Less effort, in any case, was made at that time to formally understand the role of global food prices on inflation and this, combined with the presence of a fragmented commodity market, motivates the necessity of a study of this nature.

Probing the link between suppressed inflation and money demand, Muñoz (2006) notes that prices in Zimbabwe appeared to have responded to other factors other than changes in monetary policy. Nonetheless, his limited scope and coverage with respect to the underlying sources of inflation left open the question of the pass-through effect of food price volatility to inflation dynamics. This article attempts to fill this gap by focusing on the role of global food price volatility on inflation in Zimbabwe. It is plausible to think of global food prices as a potential driver of inflation, given that the hyperinflationary environment in Zimbabwe developed at a time when the economy was under a dramatic transformation from being a breadbasket to a net food importer.

Conventional wisdom posits that countries that largely depend on the global market are vulnerable to external shocks that may translate into increased domestic inflation. In Zimbabwe, food occupied a weight of 29.2% in the Consumer Price Index (CPI) in 2001, while total food imports accounted for about 5.1% of merchandise imports and 1.3% of annual GDP in the same year. However, in 2020, food occupied a weight of 31.3% in the CPI, while total food imports accounted for about 19.5% of merchandise imports and 5.4% of annual GDP in Zimbabwe (WB World Bank, 2022a; ZIMSTATS Zimbabwe National Statistics Agency, 2019).

The surge in food imports as a percentage of aggregate merchandise imports, from 5.1% in 2001 to 19.5% in 2020, echoes the country’s reliance on the global food market. Consequently, this is a cause of concern when viewed in the context of global price shocks that are emerging to be a recurrent scenario. In fact, by the virtue of being a price-taker with respect to the global market, the pass-through effect triggered by variations in global commodity prices might compromise domestic macroeconomic performance. Evidence of this circumstance can be the tremendous inflation growth witnessed in Zimbabwe during the last two decades.

Zimbabwe also imports most of its processed food products requirement mainly from South Africa and other neighbouring countries. South Africa, in particular, has seen a surge in food price inflation over the period 1995 to 2019, a trend that has been prevalent even before the advent of democracy in 1994. As shown in Figure 1(a), the country’s food and non-alcoholic beverages CPI has increased by approximately 407%, from 17.4 to 88.3, over this period. On the other hand, Zimbabwe’s imports of food and non-alcoholic beverages from South Africa (see Figure 1(b)) have also increased from US$58.82 million in 1995 to a peak of US$479.01 million in 2011, before declining to US$134.40 million in 2019 and increasing again to US$324 million in 2019.

Figure 1. South Africa’s CPI and Zimbabwe imports of food and non-alcoholic beverages from South Africa (1995 to 2019).

Source: Authors compilation from data retrieved fromWB (2022b) and SARB (2022)

As a major importer of food and non-alcoholic beverages products originating from South Africa, it logically follows that variations in South Africa’s food price inflation are passed on to the headline inflation of Zimbabwe.

Within the contextual foundation posed above, the principal objective of this article is, therefore, to establish how volatility of global food prices and South Africa’s CPI explains the variations in headline inflation in Zimbabwe. Cachia (2014) provides a study of a comparable nature, but its focus is on regional-level pass-through, which suffers from an unrealistic assumption of regional homogeneity with respect to policy responses to global food price movements. Hence, it is necessary to perform a country-specific study that allows one to isolate the pass-through effect on a particular country, while at the same time acknowledging the heterogeneity in policy responses across individual countries. The present article additionally circumvents the generic approach of focusing on general price movements and focus primarily on the pass-through effect of global food price volatility and South Africa’s CPI.

According to the Food and Agricultural Organization (FAO, 2011), world agricultural prices have experienced an increase in the degree of volatility in the decade leading to 2010. In light of this, addressing the inflationary effects of such volatility is essential for designing appropriate monetary policy responses geared at local price stabilisation. The remainder of this article is organised as follows: Section 2 provides a brief overview of literature relating to the trends in global food price movements and the inflation in Zimbabwe, from 1995 to 2019, as well as a review of related theoretical and empirical literature; Section 3 discusses the data and estimation technique employed in this article; and Section 4 presents and discusses the empirical findings of this article, while a conclusion and policy implications are provided in Section 5.

2. Literature review

While the rise in the nominal prices of food in the global market must be viewed as an unsurprising event that is rather normal, food price volatility is a cause for concern for governments, policymakers, households, researchers, and other stakeholders with vested interest (Ahmed et al., 2014). As such, the resulting impact of food price volatility can be viewed at both the national and household levels. However, it largely depends on the nature of the economy (i.e., developed or developing) and the income status of the households (i.e., high-income or low-income) under examination.

In the context of the above, developing economies characteristically have a higher proportion of food in their overall consumer baskets, and ultimately in their CPI, relative to advanced economies. For instance, food and non-alcoholic beverages account for 31.3% of CPI in Zimbabwe comparative to merely 11.6% in the United Kingdom. Hence, households in developing countries such as Zimbabwe are more likely to encounter significant challenges emanating from food product inflation in the global markets. Specifically, the surge in food prices affects low-income households the most as they spent a higher proportion of their income on food products as compared to high-income households. Such price increases in the global food market can be passed through into the headline inflation of countries like Zimbabwe that relies heavily on the importation of food products.

2.1. Trends in global food price volatility (1995–2019) and the inflation in Zimbabwe (1995–2019)

Global food prices have been experiencing a rising trend since 2001, reversing the long-run trend of decline in comparative food prices experienced over the decades leading to the new millennium (Rayner et al., 2011). Since the onset of the 21^st Century, pronounced surges in global food prices were witnessed in 2008 and 2011, before following a declining trend thereafter (see Figure 2). The 2011 food price hike is the most outstanding since 1995 and the episodes of upward and downward swings are almost equal in terms of frequency during the 25-year period. Factors responsible for such excruciating global food price hikes vary in empirical literature, but most pragmatic studies link them to the rapid population growth especially in East Asia, a higher demand for oil as well as the limited supply of agricultural commodities emanating from climate changes (Shi & Arora, 2012).

Figure 2. Global food price index from 1995 to 2019.

Source: Authors compilation from data retrieved from the United Nations Conference on Trade and Development (UNCTAD, 2022)

The current invasion of Ukraine by Russia has also generated immediate ramifications for global food markets as both countries, directly involved in the war, are large global exporters of agricultural products, such as wheat, sunflower oil, corn, and barley. In fact, the prices of basic food products have surged, since the invasion of Ukraine by Russia in February 2022, as a direct effect of the war itself and indirectly through economic and other related sanctions imposed on Russia by Western countries. It must be acknowledged, however, that food prices were already following an upward trend since 2020 due to the economic and trade misfortunes ushered in by the emergence of the COVID-19 pandemic in 2019 and the consequential global production and supply disruptions. Irrespective of this, Russia’s invasion of Ukraine has essentially added to the wider post-pandemic global food price growth.

For Zimbabwe, the period from 1995 to 2019 saw a massive economic and political meltdown. In fact, economic growth and development was subdued starting from the late 1990s before worsening post the new millennium. In fact, the era prior to the adoption of full dollarisation in February 2009 is typified by persistent surges in Zimbabwe’s year-on-year inflation (see Figure 3(a)) as measured by the CPI. During this period, the Zimbabwean economy experienced high levels of growth in money supply, price distortions, and acute foreign exchange shortages propagated mainly by speculation in almost all the economic sectors.

Figure 3. Zimbabwe year-on-year inflation from 1980 to 2019 (annual %).

Source: Authors compilation from data retrieved from World Data (2022)

The abovementioned developments triggered higher inflationary levels, especially between 2005 and 2010, with the country reaching a hyperinflationary status by March 2007. This period has been removed from the inflation data used to plot the graphs in Figure 3 as the inflation figures are too high to the extent that they create gargantuan outliers that cannot be accommodated on a graphical plot. During the hyperinflation period, the Reserve Bank of Zimbabwe (RBZ) made regular attempts to defend the domestic currency to no avail as the domestic currency continued to depreciate in relation to the currencies of its trading partners.

Additional interventions aimed at cushioning the masses from skyrocketing prices, such as price controls and the basic commodities supply intervention scheme (better known as BACOSI), created a vibrant informal market. The shortage of basic goods in the formal market, coupled with excess printing of the Zimbabwean dollar in the form of bearer cheque series by the RBZ, gave rise to demand-pull inflation. Domestically produced goods became too expensive and local consumers substituted domestic goods for imported foreign products. Given that food occupied and still occupies a larger proportion in Zimbabwe's CPI basket, as shown in Table 1, the situation created further inflationary pressure, which bolstered the deterioration of the country’s trade and economic conditions.

Table 1. Composition of the CPI in Zimbabwe (1990 to 2019)

Category	Weight (Year = 100)
	1990	1996	2001	2012	2019
Food and Non-Alcoholic	29.2	33.6	31.9	33.6	31.3
Alcoholic beverages and tobacco	9.9	16	4.9	4.4	4.9
Clothing and Footwear	9.8	6.9	5.7	6.1	4.3
Rent, Fuel and Power	18.7	17.3	16.2	17.7	27.6
Furniture, Household Equipment and Maintenance	7.2	7.5	15.1	9.9	5.3
Health	2.8	1.7	1.3	2.2	1.4
Transport	8.4	6.6	9.8	9.8	8.4
Communication	-	-	1.0	3.4	2.7
Recreation and culture	2	1.2	5.8	2.1	2.3
Education	7.6	4.5	2.9	5.7	4.3
Restaurants and Hotels	-	-	1.5	1.4	11
Miscellaneous goods and services	4.4	4.7	3.9	3.9	6.5
Total	100	100	100	100	100

Notes: transport was combined with communication in the 1990 and 1996 CPI weights, while there was no restaurants and hotel category during the same periods.

Source: Adapted from the Zimbabwe National Statistics Agency (ZIMSTATS, 2019)

In order to manage the hyperinflationary and deteriorating economic environment, the 2008 Zimbabwean government of national unity adopted dollarisation in 2009, which ushered in economic stability until the reintroduction of the Real-Time Gross Settlement (RTGS) dollars in June 2019. Since 2012, the annual inflation of Zimbabwe has been on a downward trend, exhibiting signs of disinflation at first and deflation thereafter. Since the reintroduction of RTGS dollars as the only legal tender, inflation has risen to 255% in 2019 (see Figure 3(b)) and reaching over 500% by 2022. The deterioration in the purchasing power of the RTGS dollars is currently pointing to an imminent hyperinflationary environment in the near future.

As illustrated in Table 1, food in Zimbabwe have more weight in the country’s CPI basket relative to other categories in both the current (2019) and previous (1990, 1996, 2001, and 2012) revisions. This means that food contributed largely to the movements in headline inflation during the study period. However, there are also other factors that are important in defining the progressions in Zimbabwe’s headline inflation. Such factors include: the domestic fuel and power prices; the Rand/US$ exchange rate; and South Africa’s CPI, since the majority of imported food products consumed in Zimbabwe are imported from South Africa.

2.2. Theoretical literature

The role of global food price volatility as a precursor of domestic inflation dynamics has been studied expansively in literature (Blomberg & Harris, 1995; Garner, 1995; Ndou & Gumata, 2017; Timmer, 2022). Global food prices are argued to be leading indicators of inflation through two basic channels, namely the direct price mechanism and the indirect price mechanism (Khan & Ahmed, 2014). The focus here will be on the direct price transmission mechanism, which comprises the pass-through effect, the spending channel, and the liquidity channel.

The pass-through effect has both the direct and the indirect effects. In the direct effect, changes in global food prices affect the prices of final goods through their effects on imports. If the price of imported inputs increases, these will translate to an increase in the price of final goods (Lescaroux & Mignon, 2008). Headline inflation can also be influenced by movements in global food prices, and this can take place through the trade channel. An increase in global food prices implies that food imports become relatively expensive and, therefore, in the absence of trade restrictions, the price increase of the imported commodities from the world market is likely to be reflected in domestic headline consumer price indices (Kim, 2023).

In terms of indirect pass-through effects, consumer prices commonly respond swiftly to general economic shocks such as an increase in demand (Khan & Ahmed, 2014). Similarly, food prices fluctuate because of idiosyncratic shocks such as floods and droughts that decimate the supply of certain agricultural products. Such price fluctuations are subsequently passed through to overall prices (Singh et al., 2022). Depending on the type of shock, the observed link between commodity price and inflation would be expected to be different. In fact, the variation in the mix of shocks in the economy overtime could affect the stability of the bivariate link between commodity prices and inflation (Jongwanich & Park, 2011).

If the conditions in food markets reflect aggregate supply and demand in the whole economy, then an increase in aggregate demand, might eventually translate into higher price inflation (Amjad et al., 2011). Food prices may lead general price movements through their forward-looking element, which arises from the storability of food products (Borsellino et al., 2020). A traditional argument that explains the link between food price movements and headline inflation asserts that food prices enter the production process with a lag (Khan & Ahmed, 2014). This denotes that current food prices have a direct cost effect on the movements of future general consumer price indices.

The spending channel works through an increase in the private or government spending, reflecting strong Gross Domestic Product (GDP) growth and a rise in government revenues. When international commodity prices increase at the expense of domestic prices, this will induce demand for the locally produced food products that will transcend into excess demand causing prices to increase (Mahmoudinia, 2021). The liquidity channel works through its impact of capital inflows on foreign exchange reserve accumulation and excess liquidity in the banking sector (Zhang & Pang, 2008). In other words, it works by increasing money supply into the economy, which will cause an increase in the demand of the domestic products.

2.3. Empirical literature

There is a plethora of empirical literature (e.g. Blomberg & Harris, 1995; Ferrucci et al., 2010; Jalil & Esteban, 2011; Jumah & Kunst, 2007; Zoli, 2009; Awokuse & Yang, 2003;) that has examined the pass-through effects of global food prices on domestic inflation. However, despite such an overwhelming board of literature, the results of these studies are not directly comparable due to the differences in data, study areas, model specification, estimation techniques, and other methodological aspects employed by the distinct scholars.

Shortly after the 2008 global food price hike, Zoli (2009) assessed the role of international commodity prices, cyclical fluctuations, and convergence in driving inflation in 18 European emerging economies. The results indicated that global commodity price shocks fed through to domestic headline inflation and the effect was asymmetric. This was in line with similar conclusions that were reached in earlier studies by Awokuse and Yang (2003) and Jumah and Kunst (2007), despite the use of different methodological approaches. Contrary to the theoretical prediction and empirical observation by Zoli (2009), Blomberg and Harris (1995) as well as Furlong and Ingenito (1996) had earlier confirmed no relationship between commodity prices and inflation.

Although the earlier studies by Blomberg and Harris (1995) and Furlong and Ingenito (1996) could not explicitly show the reasons behind the nonexistence of a bivariate relationship between commodity prices and inflation, a study by Ferrucci et al. (2010) provides several reasons why commodity price shocks may not necessarily translate into increased headline inflation. Firstly, they argue that external price shocks may not be inflationary if they get absorbed in producers’ margins or neutralised by advances in domestic productivity. Secondly, global commodity price shocks may have no noticeable effect on inflation if local consumer prices are subject to other off-setting internal shocks at any given point in time. Other related studies such as Huh et al. (2012), conducted at regional level, found that local food prices respond to regional price shocks, while global price shocks do not have any significant effect on headline inflation. The results from such studies provide further evidence in favour of the conclusions of Blomberg and Harris (1995) and Furlong and Ingenito (1996).

One of the contentious issues surrounding the pass-through literature relates to the choice between focusing on headline or core inflation. Jalil and Esteban (2011) address this issue when they focused on the pass-through effect of global commodity prices, food inflation, and core inflation for a set of Latin American countries. Like Huh et al. (2012), the authors employed a Vector Error Correction Model (VECM), which essentially describes the long-run pass-through and the corresponding short-run dynamics. After controlling for other domestic monetary variables, such as the exchange rate and the interest rate, Jalil and Esteban (2011) found a limited pass-through effect of global food prices on domestic food prices, a result that is very much in line with that confirmed by Huh et al. (2012).

Despite the conflicting conclusions reached in the abovementioned studies, the present article is unique in the sense that Zimbabwe experienced hyper-inflation, in the decade leading to 2010, that has been argued to be a result of exchange rate misalignments and expansions in money supply, among other issues. However, the country is a net food importer and developments in the prices of food on the global market are hypothetically significant in defining Zimbabwe’s headline inflation, since food products account for a higher proportion in its CPI basket. Hence, in order to assist in explaining the inflation dynamics in Zimbabwe, it is necessary to perform a country-specific study that permits one to isolate the pass-through effect of food price volatility on the headline inflation of Zimbabwe.

3. Data and estimation technique

3.1. Data

The data employed in this article stretch from 1995 to 2019. This period permits us to capture Zimbabwe’s transition from being a breadbasket to a net food importer. The variables employed in this article are listed and described in Table 2. Annual data on inflation as measured by the CPI were obtained from UNCTAD (2022) consumer price indices, while that of global food prices were obtained from the WB (2022a) commodity price data (the pink sheet). For the real GDP, labour and capital stock, annual data were drawn from the WB (2022c) world development indicators. The output gap was calculated as the disequilibrium residuals obtained by regressing the real GDP on labour and capital stocks. All the data analysis and econometric computations in this article were performed using the analytical tools of the EViews 12 Enterprise Edition software package.

Table 2. Description of variables

Variables	Symbols	Description	Sources
Headline Inflation	${\pi }_t^{headline}$	The headline inflation of Zimbabwe measured by the CPI with base year 2010.	UNCTAD (2022)
Global food prices	Fpvol	Prices of food on the global market proxied by the global food price index.	WB (2022a)
Output gap	Output_gap	It is measured as the difference between actual and potential output.	Authors’ calculation
South Africa CPI	SAcpi	CPI of South Africa	UNCTAD (2022)
Real GDP	GDP	GDP at constant 2010 US$. Domestic values for GDP are converted from domestic currencies to the US$ using the 2010 official exchange rates.	WB (2022c)
Labour stock	L	Proxied by the total labour force.	WB (2022c)
Capital stock	K	Proxied by gross capital formation (current US$).	WB (2022c)

Note: CPI, Consumer Price Index; GDP, Gross Domestic Product; UNCTAD, United Nations Conference on Trade and Development; WB, World Bank; WDI, World Development Indicators

Source: Authors compilation

From the descriptive statistics provided in Table A.1, it can be seen that they were 25 observations per each variable in the dataset analysed in this article. The main variables of interest (i.e., the global food prices and the CPI of Zimbabwe) had means of 70.82 and 188.60. As confirmed by the Jarque-Bera normality test, these variables are normally distributed at the 5% level of significance. The presence of a relatively greater variability in the CPI of Zimbabwe and South Africa with standard deviations of 90.11 and 45.72, respectively, can also be observed. Exclusive of the CPI of Zimbabwe with a skewness of 0.24, suggesting a fairly symmetrical distribution as the skewness is between −0.5 and 0.5, all the other variables are moderately skewed comprising skewness between −1 and − 0.5 or between 0.5 and 1. Since the kurtosis of all the series is greater than one, this indicates that their corresponding distribution is too peaked or spiky.

The correlation matrix (see Table A.2 in Appendix) indicates the presence of statistically significant moderate correlation of less than 0.7 between the CPI of South Africa and two variables, namely the global food price volatility and the headline inflation of Zimbabwe. This is still within the acceptable limit of less than 0.8 or greater than −0.8. Except for the correlation coefficient of the output-gap and South Africa’s CPI as well as that of the output gap and Zimbabwe’s headline inflation, the Spearman correlation coefficients of all the other variables are statistically significant at 1% level. For all the output gap computation variables, the correlation coefficients are significant and less than 0.5.

Unit root tests were performed on all the variables to ensure that all the information regarding the data generating processes is considered. The unit root tests were performed using both the Augmented Dicky Fuller (ADF) and the Phillips-Perron (PP) test for robustness check (see Table A.3. in appendix). The test results show that all the variables are I(1).

3.2. Modelling food price volatility

The Generalized Autoregressive Conditional Heteroscedastic (GARCH) model, originally proposed by Bollerslev (1986) as a variation of the Autoregressive Conditional Heteroscedastic (ARCH) model of Engle (1982), is commonly applied as the conventional procedure for modelling volatility in food prices. The GARCH model has the advantage of permitting the variance of returns (and hence volatility) to vary over time as a function of lagged squared residuals (${\epsilon }_{t-i}^2$) and lagged variance (${\sigma }_{t-i}^2$). As such, the volatility of global food prices is modelled in this article using the first-order GARCH (1, 1) model. GARCH models essentially characterize the conditional distribution of the error term (${\epsilon }_t$) by imposing serial dependence on the conditional variance of the innovations. Specifically, the variance model imposed by GARCH(q, p) model, conditional on the past, is given by:

(1) ${var}_{t-1}\left(y_t\right)=E_{t-1}\left({\epsilon }_t^2\right)={\sigma }_t^2$(1)

(2) ${\sigma }_t^2=\omega +\sum _{i=1}^q{\alpha }_i{\epsilon }_{t-i}^2+\sum _{i=1}^p{\beta }_i{\sigma }_{t-i}^2$(2)

Where in both Equation 1(1) ${var}_{t-1}\left(y_t\right)=E_{t-1}\left({\epsilon }_t^2\right)={\sigma }_t^2$(1) and 2, ${\sigma }^2$ is the variance, E is the expected outcome, t is the current period, and i is the previous period. Equation 2(2) ${\sigma }_t^2=\omega +\sum _{i=1}^q{\alpha }_i{\epsilon }_{t-i}^2+\sum _{i=1}^p{\beta }_i{\sigma }_{t-i}^2$(2) is referred to as a GARCH (p, q) model, with p lagged terms of the squared error term and q terms of the lagged conditional variance, associated with Bollerslev (1986). In terms of the equation, the conditional variance of the error term $\epsilon$ at time t depends not only on the squared error term in the previous period ${\epsilon }_{t-i}^2$, but also on its conditional variance in the previous time period ${\sigma }_{t-i}^2$. If in Equation 2(2) ${\sigma }_t^2=\omega +\sum _{i=1}^q{\alpha }_i{\epsilon }_{t-i}^2+\sum _{i=1}^p{\beta }_i{\sigma }_{t-i}^2$(2) , $p=q=0$, then the variance process collapses to a white noise with variance $\omega$.

Volatility of global food prices will be confirmed in this article by the presence of ARCH effects following the estimation of a GARCH (1, 1) model. The three parameters, in Equation 2(2) ${\sigma }_t^2=\omega +\sum _{i=1}^q{\alpha }_i{\epsilon }_{t-i}^2+\sum _{i=1}^p{\beta }_i{\sigma }_{t-i}^2$(2) , are assumed to be non-negative (i.e., ${\alpha }_i>0$, ${\beta }_i>0$ and $\omega >0$). Furthermore, ${\alpha }_i+{\beta }_i<1$ to achieve stationarity of the GARCH process.

3.3. The pass-through effect on inflation

The generated volatility series of global food prices is then inserted in the backward-looking Phillips curve specification similar to the one used by Gelos and Ustyugova (2012) to appreciate its pass-through effect. The model takes the following form:

(3) ${\pi }_t^{Headline}=\delta +\sum _{i=1}^n{\theta }_i{\pi }_{t-1}^{Headline}+\sum _{j=0}^m{\lambda }_j^{Output\mathrm{\_}ga{p}_{t-j}}+\sum _{j=0}^p{\varphi }_{j}Fpvo{l}_{t-j}+\sum _{j=0}^p{\phi }_{j}SAcp{i}_{t-j}+{\epsilon }_t$(3)

Where ${\pi }_t^{headline}$ denotes headline inflation at time $t$, while $\delta ,\beta ,\lambda ,\phi$ and $\varphi$ represent unknown model parameters to be estimated. Since the model contains a lagged term of the dependent variable as one of the explanatory variables, the full pass-through effect will be determined by:

(4) $\frac{\sum _{j=0}^p{\varphi }_j}{1-\sum _{i=1}^n{\theta }_i}$(4)

The article considers an ARDL (1, 1, 1) model to establish whether volatility of local food prices could feed into the headline inflation. The output gap is defined as the difference between actual output ($y_t$) from potential output (${y_t}^T$). During the study period, Zimbabwe suffered related short-term price rigidities and other demand shocks that could have partially altered the level of domestic output resulting in an increased output gap and corresponding inflation.

Output gap has been generally measured using the Hodrick and Prescott Filter method, which essentially generates a smoothed series where deviations of actual output from this smoothed series are termed output gap. However, despite its simplicity, this approach suffers from the weakness of having virtually no economic logic regarding the sources of growth. This article, therefore, resorts to the use of a production function which, unlike the Hodrick and Prescott filter method, accounts for economic forces of growth, particularly factor accumulation and total factor productivity (Heytens & Zebregs, 2003). This is achieved by regressing the real GDP on labour and capital stock and defining the output gap as the disequilibrium residuals. The model is estimated using the Stock and Watson Dynamic Ordinary Least Squares (DOLS) technique, which controls for endogeneity through the inclusion of leads and lags of the first differenced endogenous variables. The present article utilises one lag and one lead based on the Schwarz Information Criterion.

4. Empirical findings

A first step of inquiry in this paper was the computation of food price volatility using a GARCH (1, 1) model. Based on information from the autocorrelation functions and the partial autocorrelation function, the residuals from the mean equation could be best described as an Auto Regressive Moving Average (ARMA 1, 1) process. The residual plot (see Figure A.1 in appendix) also confirmed the presence of volatility clustering. Due to failure of the normality assumption to hold, the GARCH model was estimated with a t-distribution to capture excess kurtosis.

The estimated GARCH (1, 1) model was diagnosed of ARCH effects and their presence confirmed that global food prices were volatile during the study period. On the other hand, the sum of the ARCH and GARCH term in the variance equation is close to one signalling an inefficient global food market where unexpected shocks could remain persistent for quite a long period of time. This observation could perhaps justify why the upward swings in global food prices have turned out to be more of a recurrent scenario.

With respect to output gap, the study shows that between 1995 and 2019, actual output was way below the potential output, signalling a negative output gap. A positive output gap was observed in the early and late 80s and in the late 90s, respectively. The potential output series did recognise the economic slowdown suffered by Zimbabwe post 2000. The generated output gap was then used as a covariate in the standard backward Phillips curve specification, along with the global food price volatility indicator and the CPI of South Africa, to understand how they relate to the headline inflation in Zimbabwe. The results are presented in Table 3.

Table 3. Regression results of the GARCH (1, 1) model

Global Food Price Volatility	ARCH Effects
F-statistic	89.5334
	(0.0000)
Obs*R-squared	71.9429
	(0.0000)
	GARCH (1, 1)
	Mean Equation	Variance Equation
	$\omega$	${\alpha }_i$	${\beta }_i$
Coefficient	−0.0061	0.1832	0.7875
Standard error	0.0845	0.0428	0.0449
z-Statistic	−0.0725	4.2802	17.5344
	(0.9422)	(0.0000)	(0.0000)
Log likelihood	−753.3193
Durbin-Watson stat	1.4008
Akaike info criterion	4.2191
Pass through	FPvol	SAcpi
Coefficient	−5.1361	−35.2252
Standard error	2.2728	13.8816
t-Statistic	−2.2599	−2.5375
	(0.0381)	(0.0219)
$\frac{\sum _{j=0}^p{\mathrm{\Phi }}_i}{\sum _{i=1}^n{\theta }_i}$	3.3247	22.8025
Adjusted R^2	0.7262
Durbin-Watson stat	2.1205
F-statistic	5.8855
	(0.0004)

Probabilities in parentheses.

The full pass-through of the global food price volatility is found to be 3.33 indicating that about 33% could be passed on to headline inflation in Zimbabwe following a 10% increase in global food price volatility during the study period. For the CPI of South Africa, the full pass-through is found to be 22.80 suggesting that approximately 228% could be passed on to headline inflation in Zimbabwe following a 10% increase in South Africa’s CPI during the study period. Supportively, the coefficients of both the food price volatility and South Africa CPI variables are significantly different from zero, suggesting that the volatility of global food prices and the variations in South Africa’s CPI are significantly transmitted to headline inflation in Zimbabwe.

The findings above are, however, not reflective of the massive government interventions, particularly in the form of price controls, that characterised the economy of Zimbabwe during the study period, especially post the new millennium. The government of Zimbabwe regularly intervened in the market by setting maximum permissible prices, a situation which led to an establishment of a black market in the informal economy. Such price controls by the government of Zimbabwe forced local producers to absorb all external price shocks, a situation which could explain why it was expected in this article that shocks in global food prices are not translated into the headline inflation in Zimbabwe. Again, the presence of disorganised and fragmented commodity markets insinuated that the increase in domestic headline inflation in the formal economy might cause consumers to divert their consumption towards goods offered in the fragmented markets, where prices are in most cases lower than those from the formal market.

5. Conclusion and recommendations

World agricultural prices have been experiencing an increase in the degree of volatility in the last two decades, a situation which has ignited interest among researchers regarding their effects on the performance of small open economies. This article focused on the pass-through effect of global food price volatility and South Africa’s CPI on the headline inflation of Zimbabwe using annual time series data stretching from 1995 to 2019. The results of the article indicate that movements in global food prices and the CPI of South Africa had noticeable effects on the country’s headline inflation. The same is true for the output gap.

In terms of the global food price volatility, the results obtained in this article are inconsistent with those obtained by Blomberg and Harris (1995) and Furlong and Ingenito (1996) who found that instabilities in global food prices do not significantly affect headline inflation. At regional level, the results also depart from Huh et al. (2012) who also found that global price shocks do not have any significant effect on headline inflation.

While there is always at least one more fact that we know nothing about in every country’s situation, the results of this article recommends that policymakers in Zimbabwe should be wary of global food prices and South Africa’s CPI as their variations are significantly passed through to the domestic headline inflation. In addition, it is of utmost importance that Zimbabwe improves its food processing capacity through, for instance, reviving and augmenting the capacity and efficacy of the existing manufacturing facilities within the country’s food industry. This, of course, will not happen promptly, but must be progressive and developed gradually over a span of time.

The current research can be extended by looking at asymmetric effects to establish whether upward or downward swings in food prices have uniform effects on headline inflation. Future research can also: firstly, investigate the structural break of the impact caused by COVID-19 and the war in Ukraine on food prices; secondly, assess the possible future impact of climate change on food price volatility and transmissions emanating from weather and production shocks worldwide. Suggested literature in this regard includes Liu et al. (2021), Hasegawa et al. (2022), and Anderson et al. (2023); and thirdly, while the GARCH model is conventionally utilised in modelling food price volatility, future research can make use of Copula-based functions, similar to those applied in Fabian et al. (2019), which captures well the dependence structure of food price volatility and inflation movements.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available online at https://doi.org/10.6084/m9.figshare.22793570

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23311932.2023.2212458.

Additional information

Notes on contributors

Gabriel Mhonyera

Gabriel Mhonyera is a Postdoctoral Research Fellow at the School of Public Management, Governance and Public Policy (SPMGPP) of the College of Business and Economics (CBE), University of Johannesburg, South Africa. Through the application of quantitative and qualitative research methods, his research interest focuses on: trade policy, export promotion, foreign direct investment, and regional trade and economic integration in Africa.

Stein Masunda

Stein Masunda is a Lecturer at the Department of Agricultural Economics and Development (DAED) of the Faculty of Business Sciences (FBS), Midlands State University, Zimbabwe. He is also a Doctoral Candidate in the Faculty of Economic and Management Sciences (FEMS) at the North West University, Potschefstroom Campus, South Africa. His research interest is on trade policy, export growth, trade dynamics and econometric modelling.

Daniel Francois Meyer

Daniel Francois Meyer is a Professor in the College of Business and Economics (CBE) at the University of Johannesburg, South Africa. He is a National Research Foundation (NRF) rated researcher. Daniel is a Development Economist and a specialist in regional and local economic development analysis and policy development. His research is multi-disciplinary through the combination of development economics, business, public management, and governance.