Research

INTRODUCTION

Economic uncertainty makes it difficult for actors throughout social-ecological systems, such as producers, processors, logistics providers, wholesalers, and retailers, to plan their activities and earn stable livelihoods. Price volatility, a key measure of economic uncertainty, is defined as variation in prices (Piot-Lepetit and M’Barek 2011, Bellemare et al. 2013, Estruch and Grandelis 2014). In food systems, price volatility can contribute to adverse food security and nutrition outcomes for consumers, particularly for the rural poor (Piot-Lepetit and M’Barek 2011, Bellemare et al. 2013, Estruch and Grandelis 2014). On the macroeconomic scale, food price volatility can erode long-run economic growth because of reductions in productive capital and limitations on productivity gains linked to a population’s health and education (Strauss and Thomas 1998, Timmer 2000, Dawe and Timmer 2012, Darpeix 2019). While some critics have argued that the rhetoric surrounding food prices conflates volatility with high price levels (Barrett and Bellemare 2011), price volatility has nonetheless received significant attention in the literature and earned a prominent place on policy-making agendas around the world, given its broad economic, food security, and nutrition consequences (IMF and UNCTAD 2011, Piot-Lepetit and M’Barek 2011, Kornher and Kalkuhl 2013).

Ecological uncertainty and variability often contribute to economic uncertainty such as price volatility. For instance, changes in landings and associated price volatility in fish food systems can be driven by a number of factors, including ecological dynamics such as size selection, which can induce recruitment variability in fishes (Crowder et al. 2018), and boom and bust cycles that are driven by fishers’ responses to catch and market prices in open-access governance systems, as well as the intrinsic biological growth rates of fish stocks (Smith 1969, Li and Smith 2021). Ecological disturbances such as land-use change, habitat degradation, and climate change can also interact with these mechanisms to drive landings and price volatility. For example, climate change has been linked to price volatility for small pelagics (Pincinato et al. 2020). Further, although market integration and commercialization tend to dampen price responsiveness to landings changes (Asche et al. 2012, Deb et al. 2022), hypoxia (low dissolved oxygen levels, which can cause fish mortality events) drives short-run price variation, even in highly integrated global fish markets (Smith et al. 2017).

Economists have studied price volatility in financial markets since the mid-20th century, but research on its implications for food security and livelihoods remains limited. Most studies to date have explored the causes of price volatility, rather than the impacts; where impacts of price volatility are considered, research has focused on actors at each end of the value chain, i.e., producers (e.g., Bellemare et al. 2013) and consumers (e.g., Gilbert and Morgan 2010, Dawe and Timmer 2012, Minot 2014, Wossen et al. 2018, Darpeix 2019). Therefore, a critical gap remains in the literature on the effect of food price volatility on the behavior and livelihood outcomes of midstream and downstream value chain actors such as retailers. In fact, the impact of food price volatility on livelihoods in the developing world has been identified as an important but under-investigated topic in the price volatility literature (Darpeix 2019).

Similarly, most social-ecological resilience literature on small-scale fisheries to date has focused on fishing (production), fisheries management, and the livelihoods of fisherfolk, whereas little attention has been paid to the post-harvest sector and the resilience of fish processors, wholesalers, and retailers’ livelihoods (e.g., Allison and Ellis 2001, Allison et al. 2009, Badjeck et al. 2010, Hanich et al. 2018, Macusi et al. 2020, 2021, Gianelli et al. 2021, Partelow et al. 2021, Renck et al. 2023). Social-ecological systems include primary producers such as fishers, and pre- and post-harvest sector actors, all of whom are affected by ecological disturbances. However, little focus has been placed on how pre- and post-harvest actors experience and adapt to ecological and economic uncertainties.

We address these gaps by evaluating the resilience of small-scale usipa (Engraulicypris sardella; a small pelagic fish native to Lake Malawi) retailers’ livelihoods to price volatility. By concentrating on fish retailers in the downstream area of the value chain, we provide new insight into the resilience of small-scale fisheries actors in the broader fish food system. Using a mixed-methods approach, we sought to answer two questions: (1) What are the impacts of price volatility on retailers’ livelihood outcomes? (2) What are the impacts of adaptation on retailers’ livelihood outcomes?

In addition to focusing on retailers, this study contributes to the literature in several ways. First, it provides insight into capture fisheries, which have been far less investigated than agricultural commodities in the context of price volatility (Belton and Thilsted 2014, Dahl and Oglend 2014, Asche et al. 2015, Dahl and Jonsson 2018). Capture fisheries are particularly important because they remain the most affordable source of animal protein and micronutrients to many people in low- and middle-income countries (Bennett et al. 2022, Robinson et al. 2022). Second, this study uses a mixed-methods approach to investigate the resilience of usipa retailers’ livelihoods. Although some studies have similarly employed mixed methods in social-ecological contexts (e.g., Partelow et al. 2021), a recent review found that most social-ecological systems research uses quantitative methodologies (Nagel and Partelow 2022). Third, this study applies social-ecological resilience theory to the social domain of the usipa social-ecological system. Social-ecological resilience literature suggests that the social domain and ecological domain of social-ecological systems can be addressed by the same resilience theory, concepts, and framework (Walker et al. 2006, MathisonSlee et al. 2022). However, the majority of resilience literature to date focuses on the ecological domain, given that resilience originated as an ecological concept (Cretney 2014). Fourth, we bring together concepts from the economic and resilience literature. Although some social-ecological resilience literature integrates economic concepts with resilience theory and acknowledges that social conditions of poverty undermine the resilience of social-ecological systems such as fisheries (e.g., Allison et al. 2009, Brown 2015, Hodbod and Eakin 2015), the economic concept of spatial arbitrage has yet to be operationalized in the resilience literature.

METHODS

Definitions and hypotheses

In this study, we operationalize price volatility as a shock to small-scale usipa retailers with implications for resilience. Resilience can be defined as the capacity of a system to experience shocks while maintaining the same function, structure, feedback, and identity (Walker et al. 2006). Vulnerability, or susceptibility to harm, accounts for a system’s level of exposure and sensitivity to shocks (Smit and Wandel 2006). The bridge between the concepts of resilience and vulnerability is known as adaptive capacity (Engle 2011). Adaptive capacity is the ability of a system to prepare for shocks and change in advance or to adjust and respond to shocks after the fact (Smit et al. 2001, Engle 2011). Although it is only a portion of total resilience, analyzing adaptive capacity is critical to understanding where interventions can be most effective to improve the resilience of the system. Finally, adaptation, or a deliberate change in anticipation of or in reaction to external stressors, is the process of using adaptive capacity (Nelson et al. 2007).

Price volatility creates uncertainty and makes it difficult for actors to plan their activities around price shocks or fluctuations. In this case study, resilient traders can adapt in response to price volatility to remain profitable. In contrast, traders who are unable to adapt, i.e., have low adaptive capacity, are vulnerable to price fluctuations and are often forced to sell at a loss.

In considering the ability of different groups of small-scale usipa retailers to adapt to price volatility, we understand spatial arbitrage as an adaptive strategy and operationalize spatial arbitrage as an indicator of adaptive capacity^[1]. Spatial arbitrage is defined as a process in which a product is purchased and resold in different geographic locations to exploit a price discrepancy (Overby and Clarke 2012). Assessment of spatial arbitrage provides insight into trade flows and contributes to understanding a retailer’s response to price volatility. When price volatility causes short-run prices at a given market to plumet or spike, a trader can use spatial arbitrage as a tool to respond to price volatility and travel to another market to buy or sell at better prices. The literature has shown that traders with greater access to capital, a component of adaptive capacity, tend to take advantage of spatial arbitrage more often, travel further distances, and obtain better marketing margins (Minten and Kyle 1999). In this way, adaptive capacity divides retailers into two categories: arbitrageurs and sellers.

Arbitrageurs are defined as retailers who travel from the market where they purchase their product to a distinct selling market to find better prices and take advantage of spatial price variation (Overby and Clarke 2012). Sellers are defined as retailers who buy and sell their product in the same market, which limits their ability to adapt to price volatility (Overby and Clarke 2012). Because of their use of spatial arbitrage as an adaptive response to price volatility, we hypothesize that arbitrageurs earn greater net daily income than sellers, on average.

When considering access to capital in the Malawian context, it is important to note that women tend to have less access to capital than men, both broadly and in the context of small-scale fisheries (Torell et al. 2021, Rice et al. 2023). Price volatility has been found to affect women disproportionately because coping strategies that consist of changes in time allocation are most felt by women, who are more time constrained due to reproductive activities and domestic work (Estruch and Grandelis 2014). Because of this factor, we hypothesize that more men act as arbitrageurs, whereas more women act as sellers.

Study area

Malawi is a landlocked country in sub-Saharan Africa bordered by Zambia, Tanzania, and Mozambique, and is home to > 18 million people (Fig. 1; https://www.worldbank.org/en/country/malawi/overview). Lake Malawi, one of the African Great Lakes, spans > 560 km from north to south along the country’s eastern border and is > 80 km wide (https://www.worldbank.org/en/country/malawi/overview). In total, more than one-fifth of the country’s surface area comprises surface waters (McCracken 1987). Small-scale fisheries in Lake Malawi and several other smaller inland lakes provide livelihoods throughout the value chain to > 200,000 Malawians and contribute USD $454 million annually, equivalent to 7.2% of Malawi’s gross domestic product (Torell et al. 2020, Simmance et al. 2021).

In addition to livelihood contributions, fish resources meaningfully contribute to food security and nutrition in Malawi. Fish are landed at the lakeside, then are processed and transported throughout the country to wholesale and retail markets, reaching consumers near the lake and inland, urban and rural (Bennett et al. 2022). Fish accounts for > 20% of animal protein consumed by Malawi’s population (Donda and Njaya 2007). Small pelagic fish such as the lake sardine usipa are considered to play a direct role in food security and nutrition, given that they are accessible and affordable to the majority of the population in Malawi and are rich in micronutrients, i.e., essential lipids, minerals, and vitamins (Isaacs 2016, Nölle et al. 2020, Bennett et al. 2022). Furthermore, usipa makes up the largest proportion of national catch, accounting for > 70% of total annual landings (Makwinja et al. 2018). Considering the magnitude of usipa catch and its notable contributions to food security, nutrition, and livelihoods in Malawi, usipa, and specifically usipa retailers, were selected for the focus of this study.

The usipa market in Malawi is an ideal case with which to study price volatility for several reasons. First, it is expected that price volatility in capture fisheries will increase as fisheries resources become more limited and production becomes more variable due to ecological disturbances such as land-use change, habitat degradation, and climate change. In the setting of Malawi, it is important note that the effects of climate change on global fisheries are expected to be most severe in the Global South, increasing vulnerability and reducing income and food security for African households (Lam et al. 2012, Wossen et al. 2018, Cojocaru et al. 2022). Such predictions are particularly concerning for usipa, given its role in food security among low-income and rural populations in Malawi (Bennett et al. 2022). Although climate change is predicted to play a key role in altering the social-ecological system, it is important to note that Malawian fishers perceive overfishing to be a greater threat than climate change to fish stocks (Limuwa et al. 2018). Second, despite global progress with the integration of mobile phones over the past three decades, access to market information via mobile phones or other sources has remained a complex challenge in much of sub-Saharan Africa (Munyua et al. 2009, Manyati and Mutsau 2021). This is an important point because traders’ inability to access timely and reliable market information has been found to exacerbate price volatility (Clapp 2009, Munyua et al. 2009). A lack of market information creates market inefficiencies, including excess price dispersion, allocation inefficiencies, and suboptimal arbitrage (Jenson 2007). Third, according to reported statistics, usipa is sold almost exclusively on the domestic market within Malawi (Bennett et al. 2022). This factor is critical because internationally traded food products tend to have more stable prices (Minot 2014). Fourth, usipa is only produced by capture fisheries, not aquaculture. Fish prices in capture fisheries are more volatile than in aquaculture (Dahl and Oglend 2014). Further, prices for small pelagic fish species such as usipa are the most volatile of all fish species groups (Dahl and Oglend 2014, Pincinato et al. 2020). Ecological disturbances, market information challenges, the lack of a formal international market for usipa, poor infrastructure, and daily fluctuations in supply inherent to a small pelagic capture fishery have all enabled extreme price volatility to persist in the market for usipa in Malawi. The prevalence of fish price volatility in Malawi is reflected in a common Chichewa phrase “nsomba ilibe mtengo,” which translates to “fish don’t have a price”.

Surveys

Market survey data used in our analysis were collected in Malawi over a four-month period, from October 2019 through January 2020. Using a value chain mapping approach, the three largest beach landing sites in the country (Ngara, Nkhata Bay, Msaka) were selected as points of origin from which enumerators progressed through processing and wholesale nodes until they reached retail markets where fish were sold directly to end consumers. A total of 72 retail markets were identified, where 604 total usipa retailers (474 arbitrageurs, 130 sellers) were surveyed using a convenience sampling approach. Convenience samples are often used in research contexts in which surveys occur in locations where informants are moving in and out of public spaces, such as the open-air fish markets in Malawi; they are often used in the absence of a complete sampling frame (Peek and Fothergill 2009). Given that there is no formal record of fish retailers in Malawi from which to obtain a random sample from the entire population, we used a convenience sampling approach for feasibility, despite its limitations. From the 72 retail markets where traders were surveyed selling usipa to consumers (selling markets), an additional 31 markets were identified as markets where the same surveyed traders reported to have bought their fish (buying markets), for a total of 103 markets (Fig. 2). The 103 markets represent a sample of the formal usipa markets in Malawi but likely did not capture the extent of informal markets throughout the country.

The surveys were composed of three parts. First, the surveys collected socioeconomic information about the traders, such as gender, age, and meals consumed per day. Second, they collected information about the market, such as urban or rural, and latitude and longitude. Third, they collected economic information about the traders’ transactions, such as market fees, transport costs, and buying and selling prices and quantities. The survey data collected transaction information on usipa in four different processing forms: fresh, sundried, parboiled, and smoked.

Focus group discussions

Focus group discussions were conducted in January 2022 by research partners based in Malawi from Lilongwe University of Agriculture and Natural Resources. The focus group discussions sought to provide qualitative insight into usipa traders’ decision-making. A total of 36 focus group discussions were conducted that involved 212 participants (106 women and 106 men), ranging from 18 to 66 years old. The composition of each focus group was homogenous by gender, i.e., all-women discussions and all-men discussions. Each discussion was conducted in the local languages and lasted 1–2 h.

Focus groups were conducted at a total of 12 markets throughout Malawi, 4 in each region: north, central, and south (Fig. 3). First, nine focus group markets were selected based on descriptive analysis of the previously described market survey data set. The variable that indicated the total number of traders at each market was used as an indicator of the overall size of each market. The minimum, median, and maximum numbers of traders (size of market) were selected for each region to account for variation in the types of traders at different markets in all three regions of Malawi; at least one urban market and one rural market was included in each region. After the initial systematic selection of the nine focus group markets, three additional markets were chosen after being deemed critical regional markets by local experts in Malawi. Within the 12 focus group markets, a convenience sample was used to recruit individual traders to participate in the discussions.

Quantitative analysis

Market survey data were analyzed using StataIC version 16.1 to address both research questions 1 and 2 (StataCorp 2019). We operationalized price volatility as the coefficient of variation (CV) in usipa prices.

(1)

Where σ is the standard deviation and μ is the mean. The CV in selling price was calculated by district to measure usipa price volatility at retail markets. The CV in buying price was calculated by district to measure usipa price volatility at beach landing sites or wholesale markets for fish retailers in Malawi. The CV shows the extent of variability within a district relative to the mean of the population (Eq. 1). The smaller the CV, the less dispersed the data; the larger the CV, the more dispersed the data.

The CV has been used in the literature as a measure of food price volatility and is particularly useful for comparing variation between groups (e.g., Bellemare 2015). To investigate whether the adaptive strategy of spatial arbitrage helps retailers mitigate price volatility, we calculated the CV in buying and selling price for both arbitrageurs and sellers.

We used net daily income (NI), calculated in the local currency, Malawian Kwacha (MK), to measure retailers’ livelihood outcomes.

(2)

The team weighed nonstandard buying units (5-L pails) and nonstandard selling units (mulu; translates as mound or handful; N = 52) at random throughout different markets to develop a conversion factor to weight in grams based on the median weight in grams per nonstandard selling units. The quantity of usipa that a retailer purchased, excluding the value of any fish lost, spoiled, or kept for their own consumption, was used to calculate the value sold in Eq. 2. While we acknowledge that retailers may not always sell the entirety of their purchased usipa in one day at market, we used this approach because most markets in Malawi do not have storage for fish that are not sold by the end of the day. We observed in the field that traders tend to sell all their fish in one day, even if it means lowering their prices later in the day, to avoid having their fish stolen overnight. Further, many usipa traders do not live close to the markets where they sell their fish; therefore, they prefer to sell in one day so they can travel home and avoid additional lodging costs.

To address research question 2, a nonparametric Mann-Whitney U-test was run on net daily income between arbitrageurs and sellers, distinguished by adaptive capacity. A nonparametric Mann Whitney U-test is used instead of a t-test because it is not based on the assumption that the sample came from a normally distributed population. Given the convenience sampling approach used in the collection of the survey data, the Mann Whitney U-test is more appropriate. The null hypothesis for the Mann-Whitney U-test is that the two groups are equal.

To determine the significance of the effect of selling price volatility and spatial arbitrage on livelihood outcomes while controlling for other relevant independent variables, we ran an ordinary least squares (OLS) regression on followed by a two-stage least squares (2SLS) regression on NI.

(3)

Where β are coefficients to be estimated, and ε_i is the error term.

The 2SLS approach was used to address issues of endogeneity, given that selling price is in both sides of the equation: on the left-hand side as a part of the NI calculation, and on the right-hand side as a part of the CV in selling price calculation, i.e., the price volatility independent variable. We created an instrumental variable (IV) that satisfies the conditions for a good instrument for the CV in selling price. The IV is an integer rank variable that positions the CV in selling price by district from lowest to highest, then assigns an integer rank to each individual trader in the sample based on their retail district.

Qualitative analysis

We used a mixed-methods approach in which qualitative methods are used subsequent to quantitative analysis to help explain quantitative findings (Steckler et al. 1992). Focus group data were analyzed using Nvivo 12 (Lumivero 2017) qualitative software to provide further insight into research question 2. Understanding spatial arbitrage as an indicator of adaptive capacity, focus group discussions sought to understand differences in decision-making between arbitrageurs and sellers to discern underlying factors that influence livelihood outcomes. To gain insight into usipa traders’ decision-making, we asked the following questions: Do you sell fish at the same market where you buy fish? Explain your answer. How do you choose where (which market) to sell the fish?

The focus group data were analyzed using the constant comparison analysis method in which key themes (e.g., supply and demand, costs, access to capital, spatial constraints, social capital) were identified from coded information. Constant comparison consists of three stages of analysis: open coding, axial coding, and selective coding (Onwuegbuzie et al. 2009). During open coding, transcripts and notes from the focus group discussions were sectioned into smaller units and a code was assigned to each unit. In axial coding, the codes were grouped into categories. During selective coding, a theme was identified to express the content of each group to interpret and summarize the overall qualitative findings.

RESULTS

Quantitative findings

CV analysis reveals that sellers experience greater price volatility in both sales prices and purchase prices than do arbitrageurs (Table 1, Fig. 4). Arbitrageurs and sellers differing buying price CVs may indicate that arbitrageurs have greater bargaining power than do sellers. For instance, arbitrageurs were found to pay less money than sellers when buying usipa, purchasing a 5-L pail for an average of MK3165 and MK3931, respectively. Arbitrageurs and sellers differing selling price CVs indicate that sellers are less able to adapt to price volatility at retail markets. Arbitrageurs can reduce their exposure to price volatility by traveling to other markets to obtain better prices, whereas sellers do not have the ability to adapt to volatile prices at market. Although the selling price ranges for sellers and arbitrageurs are similar, more arbitrageurs are selling at prices near the mean, reducing variation within the group (Fig. 4).

A Mann-Whitney U-test on NI reveals that arbitrageurs earn significantly greater NI on average than sellers (P = 0.0314), with a mean NI of MK17,305 and MK10,446, respectively (Table 2). However, the median NI is greater for sellers than for arbitrageurs. This result highlights the fact that there are several high values that pull up the mean NI for arbitrageurs. After investigating these high values, we determined that there are too many of them (14% of the arbitrageur data) to be true outliers. Rather, they appear to be an important part of the spatial arbitrage story. The high values are driven, in part, by the fact that arbitrageurs, on average, operate in larger volumes than sellers, with mean quantities of 16.03 and 10.59 5-L pails of usipa for arbitragers and sellers, respectively. When controlling for volume, a Mann-Whitney U-test on per-unit marketing margins indicates that the difference in mean margins between arbitrageurs and sellers is statistically significant (P = 0.0005; Table 2).

Descriptive statistics of the data reveal that although there are more male than female arbitrageurs (258 male, 216 female), this discrepancy is because there are more male retailers than female retailers in the profession overall. Proportionally, a similar percentage of female retailers are arbitrageurs (79.7%) compared to male retailers (77.5%; Table 3). Despite nearly equal proportions of female and male retailers participating as arbitrageurs, we find that female retailers compared to male retailers overall earn significantly lower NI (Mann-Whitney U-test, P = 0.005; Table 2). Within the category of arbitrageurs, we also find that female arbitrageurs earn significantly lower NI than male arbitrageurs (P = 0.0013; Table 2). This result reveals that although women are arbitraging as much as men, women are not benefitting from spatial arbitrage as much as men are. This is an important finding because it reveals that differences in participation in spatial arbitrage between female and male retailers is not what explains the difference in NI between female and male traders overall.

This finding illuminates the role of more complex gender norms in influencing the success of women and men retailers, whether or not they engage in spatial arbitrage, such as differing childcare responsibilities. Investigation into the characteristics of female fish retailers who act as arbitrageurs compared to female retailers who act as sellers reveals that arbitrageurs are often younger or older than sellers. For instance, the minimum age of a female arbitrageur in our survey data is 18 years, whereas the minimum age for a female seller is 19 years old. The maximum age of a female arbitrageur is 65 years, compared to 55 years for female sellers in our data set. This dispersion of female traders by age may be explained by the fact that female retailers with children are more likely to act as sellers to remain close to home, whereas young female retailers without children, and older female traders with grown children, have more flexibility and are able to engage in arbitrage and travel farther from home to sell their fish.

We ran an OLS regression to explore how variables are associated with NI (Table 4). We find that spatial arbitrage is positively associated with NI and is statistically significant (P = 0.011); price volatility is negatively associated with NI but is not significant (P = 0.214). Robust standard errors are used in the regression model to account for heteroscedasticity, given that the null hypothesis for the Cook-Weisberg test, constant variance in the error term, was rejected (P < 0.0001). Although an important explanatory factor, the female dummy variable was removed from the model because of multicollinearity concerns; a correlation matrix revealed that female is highly correlated with other independent variables such as experience and education. Mann-Whitney U-tests indicate that male retailers have more years of experience than female retailers (P = 0.0518), male retailers are more educated than female retailers (P = 0.0577), male retailers are more dependent on the fish trade as their primary livelihood activity compared to female retailers (P < 0.0001), male retailers sell at urban markets more than do female retailers (P = 0.0326), and male retailers sell at markets located in the south more than do female traders (P < 0.0001; Table 2).

We also conducted 2SLS with robust standard errors on NI using an IV for price volatility (Table 4). Wooldridge’s score test of endogeneity, which was selected because it can tolerate heteroscedastic error terms, indicates that the price volatility variable, the CV in selling price, is endogenous; the null hypothesis, that the instrumented variable is exogenous, was rejected (P = 0.023). Therefore, the 2SLS model is preferred over the OLS model, given that the 2SLS model accounts for the endogeneity problem. A weak identification test was conducted to determine the strength of the integer rank instrumental variable, where the null hypothesis is that the IV is a weak instrument. The calculated Cragg-Donald Wald F-statistic (F = 7836) is greater than the Stock-Yogo critical value (10% critical value = 16.38) and is statistically significant (P < 0.0001). Therefore, we can reject the null hypothesis that the IV is a weak instrument and satisfy the relevance condition of the instrument. The exclusion restriction condition is met given that the effect of the IV on NI is not direct as an integer rank variable, but only works through the endogenous CV conditional on the list of explanatory variables used in the regressions.

The 2SLS results are quite robust, as the OLS and 2SLS models give similar results with the sign on the coefficients (positive or negative) not changing for any variables between models, and the level of significance only changing for the spatial arbitrage and price volatility variables. The 2SLS regression reveals that price volatility has a significant negative effect on NI, with a negative coefficient on the IV for price volatility (P = 0.051). The negative relationship causally indicates that price volatility reduces retailers’ NI. The 2SLS regression finds spatial arbitrage to have a significant positive impact on NI, with a positive coefficient on the arbitrageur dummy variable (P = 0.009). This result reveals that spatial arbitrage is an important adaptive strategy for fish traders, playing a significant role in shaping their earnings, even when controlling for several other influential factors, including price volatility, scale of operation, experience, education, primary livelihood source, processing method, urban or rural marketplace, and market location. This result supports our descriptive findings that arbitrageurs earn significantly more NI than do sellers.

In addition to the significant effects of the primary independent variables of interest, i.e., price volatility and spatial arbitrage, the 2SLS regression model reveals several other relevant relationships between trader demographics and NI. The dummy variable for experience, which indicates whether a trader has at least the median number of years of experience in the usipa business, has a significant positive effect on NI (P = 0.003). This result indicates that the longer a trader has worked in the usipa business, the better their returns tend to be. The dummy variable for primary livelihood, which indicates whether the usipa trade is a retailer’s top income source, has a significant positive impact on NI (P = 0.021). This result reveals that the relative importance of the usipa trade to each retailer, an indicator of resources invested, significantly influences their earnings from their usipa business. The model reveals that a trader’s highest level of educational attainment has a positive but insignificant impact on NI (P = 0.135). This finding indicates that the factors required to succeed in the usipa trade may be obtained outside of formal education (e.g., social capital).

The 2SLS model also reveals relationships between the characteristics of a transaction or marketplace and NI. The dummy variable for large scale, which indicates whether a retailer trades at least the median volume of usipa (scale of operation), has a significant positive effect on NI (P < 0.0001). Using fresh usipa as a base, the processing method dummy variables for sundried, parboiled, and smoked reveal that the processing form usipa is sold in has a significant impact on NI (P < 0.0001). Usipa being sold at an urban market, rather than a rural market, was found not to have a significant impact on NI. Finally, controlling for spatial variation, the easting and northing variables, which represent longitude and latitude, respectively, have a significant influence on NI (P < 0.0001 and P = 0.003, respectively). In the geographic context of Malawi, the further west a market is located, the further away it is from Lake Malawi, which runs along the eastern boarder of the country. The necessary transportation of fish from the lake to markets located in the western part of the country drives prices up at these more distant markets, explaining the negative coefficient on easting. Similarly, market prices in the northern region in Malawi tend to be lower than in the central and southern regions, which are farther from Lake Malawi and more populated.

This analysis reveals that price volatility has a significant negative effect on NI and that the ability to adapt to price volatility through spatial arbitrage has a significant positive effect on NI for usipa retailers in Malawi. We find that arbitrageurs have a smaller selling price CV and that arbitrageurs earn more NI on average than sellers; arbitrageurs are less vulnerable to price volatility than are sellers. To generalize these findings, using spatial arbitrage as an indicator of adaptive capacity, we find that (1) price volatility and livelihood outcomes are significantly negatively related, (2) adaptive capacity and livelihood outcomes are significantly positively related, and (3) groups of traders with more adaptive capacity are less vulnerable to price volatility (Fig. 5).

Qualitative findings

In analyzing the focus group discussions, it was clear that usipa traders understood price volatility to be a major challenge to the success of their business. For instance, a woman at Kasungu Market said that the biggest problem hindering her progress in the usipa trade is “unstable prices that change all the time.” Another focus group participant echoed this sentiment, saying that their biggest challenges are “the lack of capital and price fluctuations.” Similarly, other traders stated that their biggest issue is “not having a real price for the fish” and “that prices change every day.”

Using the same two groups as in the quantitative analysis, we characterized each focus group participant as either a seller or an arbitrageur by analyzing their response to the question, “Do you sell fish at the same market where you buy fish? Explain your answer.” This approach allowed for a complementary qualitative analysis in which mixed-methods results build upon and strengthen one another. After coding and categorizing the translated and transcribed focus group data using the constant comparison analysis method, several key themes were identified.

When arbitrageurs were asked to explain why they do not buy and sell fish at the same market, two themes emerged from their responses: (1) lack of consumer demand where they buy fish, and (2) lack of supply where they sell their fish. Arbitrageurs overall discussed a spatial mismatch of supply and demand in the market for usipa in Malawi. Arbitrageurs explained that when there are no wholesalers physically present at their selling market, they must go where fish is available, closer to Lake Malawi, to purchase usipa. However, they then lament that markets near the beach do not have the same level of demand. Therefore, they choose to travel further from the lake where they feel prices are better and they have a greater opportunity to profit. This qualitative finding supports the quantitative finding in the regression models that retailers’ NI increases as they move further west and south and sell at markets further away from Lake Malawi.

When sellers conversed regarding their reasoning for buying and selling fish at the same market, three themes materialized: (1) presence of wholesalers, (2) reduced costs, and (3) lack of capital. First, sellers discussed the fact that the presence of wholesalers at certain selling markets enables them to buy and sell at the same market without having to travel. The markets where wholesalers are present tend to be the largest urban markets in each region (e.g., Mzuzu in the North, Lilongwe in the Central Region, and Blantyre in the South). Another key reason that sellers choose to buy and sell usipa within the same market is because it reduces costs, specifically transport costs. Third, sellers acknowledged that their own lack of capital is a factor hindering their ability to sell at other markets. Due to a lack of capital, sellers operate in smaller quantities than arbitrageurs, which contributes to differences in NI. This qualitative theme supports the quantitative finding that arbitrageurs on average purchase larger quantities of usipa to trade than do sellers. An usipa retailer explained in a focus group discussion that, “if you have fish in good quantity, you explore other markets for better prices, but if you have a small quantity, you can’t risk it because the money might just go to transport.”

Although women tend to have less access to capital than do men, our quantitative findings revealed that similar proportions of women and men fish retailers engage in spatial arbitrage. Nonetheless, we found statistically significant differences in NI between women and men retailers overall and women and men arbitrageurs specifically, with women earning less income than men on average. This result highlights the fact that women and men do not benefit from spatial arbitrage equally, for a varied set of reasons. For instance, several focus groups discussed the gendered dimensions of spatial arbitrage and mentioned that men often travel further distances than women; therefore, men have access to more markets and potentially better prices. One woman explained that distance is a hindering factor for women because “men easily ride a bicycle to order elsewhere unlike us [women], we return home and have no other options because of the distance.” In addition, women bear a disproportionate burden of domestic responsibilities such as childcare, which may limit their ability to engage in spatial arbitrage. Domestic responsibilities may limit women’s years of experience as arbitrageurs; our quantitative data set revealed that middle-aged women are typically sellers, whereas younger women without children and older women with grown children are more able to arbitrage. Further, domestic responsibilities may limit the physical distance that women are able to arbitrage. One woman explained, in regard to markets where women sell their fish, that the “nearer to our homes the better, so that we are able to look after our families.” The compounding gendered dynamics of access to capital and differing abilities to travel to distant markets contribute to differences in overall NI for women and men fish retailers.

Further discussion was prompted in the focus groups by the question, “How do you choose where (which market) to sell the fish?” Responses from arbitrageurs resulted in the following key themes: prices and number of customers. Overall, arbitrageurs talked about choosing to sell at markets with the highest prices and the greatest number of customers possible. It was generally understood by the traders that these two factors tend to go hand-in-hand, as increased demand drives up the equilibrium price. One trader explained that their decision-making “depends on demand mostly; we follow where the fish is being sold expensively.” Similarly, an arbitrageur at Nchisi Market said, “We change locations if the preferred destination is offering better retail prices.”

While sellers also consider prices and number of customers when choosing their market, a few distinct themes appeared, including: location and market membership. One consideration expressed by sellers regarding at which market to buy and sell usipa was the market location. At the decision-making level for retailers, spatial dimensions were considered in the context of a market’s proximity to a trader’s residential place. The farther away a market is from a trader’s home, the less desirable it is for them to sell at that market, illustrating the presence of distance decay. Sellers also noted that if they hold a membership for a specific marketplace, then they consistently sell at that same market regardless of prices at other markets.

Finally, there is generally a lack of access to market information for all usipa traders. Therefore, both arbitrageurs and sellers noted that their social relations influence at which markets they sell. For instance, traders tend to choose to sell at markets where their friends and kin are also selling because they have access to reliable market information, i.e., retail prices, at these select places through their social networks.

DISCUSSION

Our results provide insight into how small-scale fisheries actors in the broader fish food system experience and adapt to uncertainty, filling a critical gap in both the social-ecological systems literature and the price volatility literature. We find that price volatility has a significant and negative impact on NI for fish retailers, and that spatial arbitrage is an important adaptive strategy that has a significant positive impact on NI. Supporting our first hypothesis, we find that arbitrageurs earn greater NI than do sellers. Arbitrageurs have greater adaptive capacity than do sellers and are more resilient to price volatility.

Although we find spatial arbitrage to be an effective adaptive strategy for fish retailers, we also find that not all retailers are able to employ this strategy. Access to capital drives retailers’ ability to engage in spatial arbitrage. Further, women record lower levels of NI than do men, whether they engage in spatial arbitrage or not. Despite disproving our second hypothesis in finding that women and men retailers participate in spatial arbitrage at similar levels, the benefits of participation in spatial arbitrage are not equally realized between genders.

Based on our findings, we recommend that potential interventions focus on building adaptive capacity to support spatial arbitrage, rather than directly addressing price volatility. Although above-market price floors are common food-price stabilization policies in Eastern Africa for staple crops such as maize, they are not common for perishable food products such as fish (Jayne 2012). Although focus group participants indicated a desire for price stabilization interventions, it is important to note that there is evidence that price stabilization policies may unintentionally exacerbate price volatility (Jayne 2012, Minot 2014). Research has shown that maize price volatility is higher in sub-Sahara African countries where most price stabilization interventions have been implemented (Minot 2014). It is possible that government interventions create further uncertainty in a system and disincentivize temporal arbitrage, reducing the price-smoothing effects associated with temporal arbitrage (Minot 2014). Further, price stabilization policies call for increased market integration. In a study of an inland fish market in Namibia, increased market integration of fish prices was found to increase the prices that fish traders received over time (Bronnmann et al. 2020). Although increased fish prices improve the livelihoods of fish traders, it is important to consider that higher prices for fish traders also mean higher prices for consumers, potentially making fish less accessible to low-income rural populations (Cojocaru et al. 2022). This concern is particularly problematic for usipa, given its food security contributions in Malawi (Bennett et al. 2022).

Because of the potential unintended consequences of price stabilization policies, including exacerbated price volatility and decreased consumer access to fish, we recommend that interventions focus on building fish traders’ adaptive capacity. Interventions designed to increase access to capital for fish traders should be considered as a key avenue to build adaptive capacity, specifically buffer capacity. Buffer capacity, or the ability to buffer shocks through access to capital, has been found to increase livelihood resilience in small-scale fisheries (Wintergalen et al. 2022). Interventions aimed at increasing access to capital, such as access to credit through microfinance institutions, enable retailers to operate in larger quantities, take on additional transport costs, and explore other markets for better prices; increasing access to capital enables retailers to engage in spatial arbitrage and to adapt to price volatility. The recommendation of increasing access to capital for fish traders is supported by literature in the agricultural sector, which has found that timely access to credit enables farmers to exploit arbitrage opportunities (Burke et al. 2019).

Adaptive capacity in the system could also be improved through interventions targeted at developing market infrastructure. For example, building storage facilities would increase adaptive capacity by enabling temporal arbitrage, i.e., selling dried fish in the same location at a different time. Temporal arbitrage reduces price volatility without directly implementing an above-market price floor (Minot 2014).

Further, aquaculture has the potential to emerge as a strategy to cope with price volatility in the future. Coupling variable capture fishery supply with steady aquaculture supply can stabilize prices in the macro fish market (Asche et al. 2015). However, it is important to note that aquaculture is only viable for a subset of species in Malawi, such as chambo (Oreochromis lidole, Oreochromis squamipinnis, and Oreochromis karongae; Macuiane et al. 2015). Small pelagic species such as usipa are not viable for aquaculture. Rather, there are concerns that small pelagic stocks can be depleted by aquaculture because wild small pelagic species are frequently used as feed in aquaculture operations globally, but evidence for this problem is inconclusive (Naylor et al. 2000, Natale et al. 2013, Asche et al. 2022).

CONCLUSION

We present an analysis of the impact of price volatility on retailers’ livelihood outcomes. By concentrating on fish retailers in the post-harvest sector, we provide new insight into how small-scale fisheries actors in the broader fish food system experience and adapt to uncertainty. Our study fills an important gap in the literature, given that most work to date has focused on the adaptation of actors at each end of the value chain, i.e., producers and consumers, and has not considered processors, wholesalers, retailers, and logistics providers within the social-ecological system.

To address the limitations of our study, future work should collect time-series data. Time-series market data will enable analysis of temporal arbitrage, in addition to spatial arbitrage, to develop a more complete understanding of how actors respond to price volatility. Temporal data will also provide an understanding of temporal trends in price volatility and livelihood outcomes in line with environmental changes over time. Within increasing environmental changes that drive price volatility (e.g., land-use change, habitat degradation, and perhaps most prominently, climate change), it is critical to understand the impacts of such uncertainty on livelihoods and how people adapt across the whole value chain. The approach used here, bringing together concepts from the economic literature and the resilience literature, can be applied in future work to develop strategies to aid actors in social-ecological systems that face growing ecological and economic uncertainty.

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^[1] Our study focuses only on spatial arbitrage rather than traditional arbitrage over time. Although we acknowledge that temporal arbitrage may be a relevant factor influencing livelihood outcomes, especially due to the well-known challenge of storage in the system, data limitations prevent us from exploring temporal arbitrage.

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