Weather Shocks, Food Prices and Food Security: Evidence from South Africa

Weather shocks, food prices and food security: Evidence from South Africa

Kubik Z¹, May J²

¹Center For Development Research (ZEF), ²DST-NRF Centre of Excellence in Food Security Corresponding author email: [email protected]

Abstract: In this paper, we analyze the food access dimension of food security, and we model the link between weather shocks and food security that acts specifically through food prices. We focus on dietary diversity as a measure of food security, and we employ an instrumental variable model where household dietary diversity is determined by food prices instrumented with standardized precipitation evapotranspiration index (SPEI), a measure of weather shock. Our findings suggest that food prices have a significant negative impact on household food security, i.e. a one per cent increase in local food prices induced by a weather shock decreases the number of food items consumed by household by around 2.5 per cent, and the number of food groups by almost one per cent. The low-income households are particularly vulnerable to weather and price shocks; however, their response to shocks seem to depend on the level of poverty. The moderately poor households from the second wealth quartile show the greatest response to shocks, but the extremely poor household from the first wealth quartile have little scope to decrease their dietary diversity which is already very low. While own food production might alleviate food insecurity of the poorest, it does not insulate them form the weather and food price shocks. Our findings are robust to employing self-reported measures of food security.

#33

Weather shocks, food prices and food security:

Evidence from South Africa

Abstract In this paper, we analyze the food access dimension of food security, and we model the link between weather shocks and food security that acts specifically through food prices. We focus on dietary diversity as a measure of food security, and we employ an instrumental variable model where household dietary diversity is determined by food prices instrumented with standardized precipitation evapotranspiration index (SPEI), a measure of weather shock. Our findings suggest that food prices have a significant negative impact on household food security, i.e. a one per cent increase in local food prices induced by a weather shock decreases the number of food items consumed by household by around 2.5 per cent, and the number of food groups by almost one per cent. The low-income households are particularly vulnerable to weather and price shocks; however, their response to shocks seem to depend on the level of poverty. The moderately poor households from the second wealth quartile show the greatest response to shocks, but the extremely poor household from the first wealth quartile have little scope to decrease their dietary diversity which is already very low. While own food production might alleviate food insecurity of the poorest, it does not insulate them form the weather and food price shocks. Our findings are robust to employing self-reported measures of food security.

1 Introduction

Over the last year, the state of food security has deteriorated in many parts of sub-Saharan Africa and South-Eastern and Western Asia, according to a recent FAO report. After a prolonged decline over a decade, the global hunger is once again on the rise, and climate-related shocks are considered to be amongst the factors that have contributed to this reversal of trends (FAO, IFAD,

UNICEF, WFP & WHO, 2017). Climate change, expected to entail greater rainfall and temperature variability and increased incidence of extreme events (IPCC, 2007), constitutes an important challenge to food security particularly in developing countries which are already vulnerable to hunger and undernutrition (Wheeler and von Braun, 2013; Parry et al., 1999). Not only will these areas face the most adverse effects of climate change in the near future (Mora et al., 2013), but also, their exposure to weather risk is substantial as a result of dependence on rain- fed agriculture, while, at the same time, their adaptive capacity remains low due to financial constraints (Ringler et al., 2010).

Starting with the seminal paper by Rosenzweig and Parry (1994), the potential impact of climate change on agricultural productivity in sub-Saharan Africa has been thoroughly analyzed. Despite uncertainty observed in different projections and the underlying climate models, a consistent pattern has emerged in the literature. The lowest-income tropical countries are expected to incur the sharpest losses (Parry et al., 1999; WB, 2010; Deryng, Sacks, Barford & Ramankutty, 2011), especially given that crops such as maize, cassava, yam, plantain sorghum, wheat and millet, the top sources of calories on the continent, seem to be particularly sensitive to weather extremes

(Schlenker & Lobell, 2010; Lobell et al., 2008). Several meta-analyses provided a robust evidence of the projected decreases in yields (Müller et al., 2011; Roudier et al., 2011; Knox et

2 al., 2012); for example, yields across Africa are expected to decline by 17 per cent for wheat, 15 per cent for sorghum, 10 per cent for millet and 5 per cent for maize (Knox et al., 2012), with important regional differences (Lobell et al., 2008). The assessment of the adverse impact of climate change on agricultural productivity would potentially be even more distressing if changes in livestock, fisheries, and other crops, such as vegetables or pulses, which play a minor role globally but might be relevant at a local level, were accounted for (Wheeler & von Braun,

2013; Campbell et al., 2016).

Accordingly, even though food security is an inherently complex concept, as reflected in the multidimensional FAO definition encompassing food availability, access, utilization and stability

(FAO, 1996), the debate on climate change and food security has focused on one aspect, that is food supply or availability (Wheeler & von Braun, 2013; Campbell et al., 2016). However, since the remaining components of food security are expected to be affected by climate change as well, it is necessary to include them in the analysis by adopting the food system approach rather than focusing on food production alone (Porter et al., 2014; Vermeulen, Campbell, & Ingram, 2012).

One of the main indirect channels through which climate-related shocks might undermine food security is the change in levels and volatility of food prices that, in turn, is a key determinant of food access (Porter et al., 2014). In the climate change context, the IFPRI ‘business-as-usual projections’ anticipate a rise in prices of most cereals and meats, reversing long-established downward trends. For example, the price of maize is projected to increase by 104 per cent between 2005 and 2050 if no technology adoption is taken into account (Rosegrant et al., 2014).

Declines in yields and production due to climate change are expected to significantly contribute

3 to imbalances in supply and demand, together with the population and income growth on the demand side (Nelson et al., 2010; Godfray et al., 2010).

Weather shocks are only one of the interlinking domestic and external factors affecting food prices (Headey & Fan, 2010). Literature on domestic food prices in developing countries has also focused on aspects such as market integration and price transmission (see Abdulai, 2000;

Rashid, 2004; Minot, 2011; Burke & Myers, 2014). Even though the systematic analysis of the relationship between weather shocks and domestic food prices is rather limited (Mirzabaev &

Tsegai, 2012), the available evidence clearly suggests that in the context of many local markets in developing countries, domestic weather shocks are a major source of short-term food price variability. As a result of a limited scope of market integration, weather shocks have been shown exert a greater influence than external shocks (Baffes, Kshirsagar, & Mitchell, 2015; Brown &

Kshirsagar, 2015).

Since they are endogenous outcomes of underlying market forces, prices cannot be considered as a fundamental cause of changes in food security conditions (Kalkuhl, von Braun, & Torero,

2016); nevertheless, they are likely to signal food security risks (Ringler et al., 2010). Since the share of food in the total budget of the poor amounts to two-thirds (Cranfield, Eales, Hertel, &

Preckel, 2003), a change in food prices implies a change in real income, but whether this change is positive or negative depends on a household’s net food position, with higher food prices expected to harm the net buyers but benefit net sellers of food (Deaton, 1989). Taking into account that in many low- and middle-income countries, the poor are typically net food buyers, albeit often only marginally so (Aksoy & Isik-Dikmelik, 2008), it is generally expected that

4 increases in real food prices entail negative consequences in terms of welfare (de Hoyos &

Medvedev, 2011; Ivanic & Martin, 2014). The long-term secondary effects on labour market and wages introduce further complexity (Ravallion, 1989; Hertel, 2016; Headey & Martin,

2016).

In the short run, the evidence suggests that food price shocks adversely affect food security, and particularly so in case of the poorest households (Alem & Söderbom, 2012; Akter & Basher,

2014). In response to food price shocks, households adjust their consumption patterns in a number of ways: by decreasing caloric intake (Robles & Torero, 2010; Tiwari & Zaman, 2010;

Anríquez, Daidone, & Mane, 2013), decreasing number of meals per day (Matz, Kalkuhl, &

Abegaz, 2015), decreasing food diversity (Hertel, 2016), or substituting with less preferred foods

(Matz et al., 2015). More importantly, even temporary disruptions in food access resulting from food inflation can entail long-term, often irreversible nutritional damage, especially amongst infants and young children in the period critical for growth and development (Robles & Torero,

2010; de Brauw, 2011; Arndt et al., 2016).

Therefore, if weather shocks translate into food price shocks which, in turn, adversely affect food security, then the ongoing climate change should be considered as an important risk. This extends beyond subsistence households relying on own food production, as commonly assumed in the literature, but also, to net food buyers. This question has been addressed mainly in the macroeconomic setting; for example, Badolo and Kinda (2012) find evidence that rainfall volatility is a factor of food insecurity particularly in sub-Saharan Africa, and that this adverse effect of weather shocks is exacerbated for countries that are vulnerable to food price shocks.

5 Ringler et al. (2010) predict that as a result of climate change and consecutive price increases, caloric intake in Africa will decrease while the number of malnourished children will increase.

Similar results are obtained for South Asia (Bandara & Cai, 2014). Several microeconomic studies, even though they do not address the weather shocks – prices – food security link in detail, find evidence of a non-negligible impact of adverse weather conditions on food security outcomes. Weather shocks have been associated with lower child growth (Hoddinott & Kinsey,

2001), malnutrition (Jensen, 2000) and nutrient deficiencies in pregnant women (Gitau et al.,

2005).

In this paper, we analyze the food access dimension of food security, and we model the link between weather shocks and food security that acts specifically through food prices. First, we assume that negative weather shocks affect agricultural productivity and lead to a decrease in local food supply. This reduction in food supply is then likely to entail an increase in domestic food prices. Second, we expect the increase in food prices to negatively affect household food security, or, more specifically, its access dimension. This study is a departure from existing approaches focusing only on the direct impact of weather shocks on food availability in sub-

Saharan Africa to consider the situation in which a large share of households are net food buyers.

This is so the case of South Africa and will increasingly be relevant in other middle income countries in an urbanizing Africa (May, 2017). We focus on dietary diversity as a measure of food security, and we employ an instrumental variable model where household dietary diversity is determined by food prices instrumented with standardized precipitation evapotranspiration index (SPEI), our measure of weather shock. Our findings suggest that food prices have a significant negative impact on household food security, i.e. a one per cent increase in local food

6 prices induced by a weather shock decreases the number of food items consumed by household by around 2.5 per cent, and the number of food groups by almost one per cent. In line with expectations, the low-income households are particularly vulnerable to weather and price shocks; however, their response to shocks seem to depend on the level of poverty.

Background

For several reasons, South Africa is a distinctive case study in terms of food security analysis, and in particular, its access dimension, in comparison to other countries in sub-Saharan Africa.

With its overall surplus agricultural production and the net food exporter status (Rakotoariosa,

Iafrate, & Paschali, 2011), South Africa is considered to be food-secure at the national level

(BFAP, 2012). Nevertheless, the country is confronted with substantial food insecurity at the household level (StatsSA, 2012). Hendriks (2005) estimates that more than half of South African households may experience some form of food insecurity. The 2011 General Household Survey

(GHS) indicates that 11.5 per cent of the sampled households reported experiencing hunger

(StatsSA, 2012), and even though this proportion has been falling over years, it represents close to ten million people (Hendriks, 2013). However, the biggest challenge for South African food security remains persistent undernutrition or “hidden hunger” resulting from diets lacking the quality and variety of foods necessary to meet nutritional requirements (Altman, Hart, & Jacobs,

2009).

In this context, food insecurity in South Africa should be seen not as food availability failure, but rather as a livelihood failure (Devereux & Maxwell, 2001) that substantially undermine

7 households’ food access (Altman et al., 2009). Indeed, this dimension of food security seems to be particularly important in case of South Africa where most households, both urban and rural, are net food buyers (Hendriks & Maunder, 2006) and where food expenditures account for up to

80 per cent in case of the low-income households (Baiphethi & Jacobs, 2009). The role of own food production is very limited as a result of a highly dualistic nature of the agricultural sector, with around 35,000 large-scale farms (Aliber & Cousins, 2013) producing almost all marketed output (Vink & van Rooyen, 2009); and around 2.5 million black households involved in small- scale farming (Aliber & Hall, 2010), of which only around 200,000 derive any monetary income from these activities (Aliber & Cousins, 2013). Additionally, a considerable movement in and out of agriculture over years suggests that farming is considered as rather residual activity by most households (Aliber & Hart, 2009). More importantly, rural households, including subsistence farmers, are increasingly reliant on food purchases, i.e. more than 90 per cent of rural households purchase most of the grain, meat and dairy products they consume from formal retailers, and this number is only lower in case of vegetables (Baiphethi & Jacobs, 2009); consequently, agricultural production does not significantly increase household food security unless home production exceeds subsistence (Hendriks, 2003; Kirsten et al., 1998).

Therefore, household purchasing power, determined by household livelihood strategy and income on the one hand, and market prices on the other, is the key to understand food security

(Altman et al., 2009; Webb et al., 2006). Several studies pointed to the rising food prices as a food security risk in South Africa (Hendriks, 2005; Altman et al., 2013; Hart, 2009), with particularly dire consequences for the poorest households who experience greater inflation rates than richer ones (FPMC, 2004), and disproportionately so in rural areas where retail prices are

8 typically much higher than in urban areas (Watkinson & Kakgetla, 2002, cited by Hendriks,

2005; Altman et al., 2009). Additionally, weather shocks, and droughts especially, are identified as a compounding factor of food insecurity (Hendriks, 2013; Webb et al., 2006; Fraser, Monde, van Averbeker, 2003; Mekuria & Moletsane, 1996). Indeed, extreme climate events such as droughts, floods, cyclones, and veld fires are already seriously affecting the country, putting a strain on agriculture and water resources; for example, the country has recently experienced the worst drought disaster in more than a century (Archer et al., 2017). Over the past five decades, mean annual temperatures have increased by 50 per cent more than the observed global average of 0.65℃ while rainfall has become more disparate (Ziervogel et al., 2014); and this trend is expected to continue under the climate change scenarios, i.e. a warming of over 2.5º, and in some areas well over 3º, is projected by 2050 (Johnston et al., 2013). This will have serious implications for agricultural yields, especially in case of staples such as maize (Lobell et al.,

2011; Schulze, Kiker, & Kunz, 1993), and water demand for irrigation (Ziervogel et al., 2014), and might translate into local food prices (Vink & Kirsten, 2002).

Data

The analysis is based in the Income and Expenditure Survey (IES) and Living Conditions Survey

(LCS) administered by Statistics South Africa (StatsSA), South Africa’s national statistics organization.. Both the IES and LCS use the diary method for recording food expenditures, but additionally, they also provide a data on household characteristics. We use 2005/06 and 2010/11 waves of the IES and 2008/09 and 2014/15 waves of the LCS, each of them being nationally representative and covering between 21,000 and 25,000 households, which produces a pooled

9 cross-section of almost 95,000 households for which we have all the required information. Of course, ideally, we would have a panel dataset to observe changes in consumption patterns over years within the same households (see, for example, Matz et al., 2015); nevertheless, we believe that the variation in weather and prices between different survey years will enable us to capture the impact of these shocks on food security. Importantly, earlier waves of the IES has previously been used in other studies on food security (Rose, Bourne, & Bradshaw, 2002; Rose & Charlton,

2002), producing estimates comparable with the literature (Hendriks, 2005).

We employ dietary diversity as our main measure of food security, and this choice is dictated by several reasons. First of all, dietary diversity is a well-recognized and attractive indicator of food access (Hoddinott & Yohannes, 2002; Swindale & Bilinsky, 2006; Kennedy, Ballard, & Dop,

2011), and precisely this dimension of food security is the focus of the present paper. Second, since the typical South African diet is energy dense but micronutrient poor (Shisana et al., 2013, cited by Hendriks, 2014), dietary diversity, a proxy of nutrient adequacy (Swindale & Bilinsky,

2006), is more relevant than other measures, such as caloric intake. Besides, it is well established that dietary diversity is correlated with different indictors of food security, such as energy availability, birth weight or child anthropometric status (Hoddinott, 1999; Hoddinott &

Yohannes, 2002; Swindale & Bilinsky, 2006), also in the case of South Africa (Charlton & Rose,

2002). Last but not least, the availability of detailed expenditure records in our faciliaties computing dietary diversity rather than other measures. We employ two indicators of dietary diversity, i.e. a simple count of various food items consumed by the household over a reference

10 period1 as well as Household Dietary Diversity Score (HDDS), i.e. the number of different food groups consumed (see Kennedy, Ballar, & Dop, 2011 for details), which is expected to better reflect the quality of a diet (Swindale & Bilinsky, 2006). Additionally, we also conduct the analysis of food composition, i.e. the share of different food groups in total consumption; and we use self-reported measures of food insecurity as a robustness check. The monthly data on retail prices were obtained from StatsSA. These series are incomplete for some years in case of maize, which is our proxy of food prices, and the missing observations were imputed using the CPI for cereals, also obtained from StatsSA. We compute the mean price per kilogram, and we employ this measure at the country level, and, alternatively, at the province level. The province level data are available only starting from 2008; similarly, the missing observations were imputed using the province-level CPI for cereals. Additionally, we also check the results for commodity price, i.e.

SAFEX white maize spot price obtained from Johannesburg Stock Exchange (JSE).

Our principal measure of weather shock is the Standardized Precipitation Evapotranspiration

Index (SPEI)2 obtained from the high-resolution (0.5x0.5 degree) gridded dataset by Vicente-

Serrano et al. (2010). SPEI is a monthly index of deviations from the average water balance, i.e. precipitations minus potential evapotranspiration; with negative values indicating dry and positive values wet conditions; it is therefore suitable to monitor onset, intensity, and duration of drought (Masih et al., 2014). Contrary to other widely used drought indices such as Standardized

Precipitation Index (SPI) or Palmer Drought Severity Index (PDSI), it incorporates the role of temperature in drought severity through its impact on the atmospheric evaporation demand

1 The reference period is four weeks in the IES 2005/06 and LCS 2008/09, and two weeks in the IES 2010 and LCS 2014/15. 2 Following Masih et al., 2014, we employ SPEI at the 12 months scale. However, our results are robust to change in scale of the index.

11 (Vicente-Serrano et al., 2010). More specifically, we apply this measure to the last growing season. That is, we compute the mean index value for months from October to April at the province level. Additionally, we also use the country average weighted by the share of each province in the total area grown to white maize in order to capture the effect of local weather shocks in the three provinces that provide the bulk of maize production, i.e. Free State, North

West and, to a lesser extent, Mpumalanga (Figure 1), on the total country supply. The data on area grown and output per province come from GRAIN SA. Since each wave of the IES and

LCS surveys was conducted over 12 months, households interviewed in different months were exposed to shocks in different growing seasons; in total, we can capture the variation in weather conditions from eight different growing seasons. More importantly, during this period South

Africa experienced severe droughts, for example in the growing season 2007/08 and 2014/15. As a robustness check, we also employ data from Agriculture Stress Index System (ASIS) by

FAO’s Global Information and Early Warning System (GIEWS).

Descriptive Statistics

The descriptive statistics presented in Table 1 seem to support our arguments with respect to weather shocks, food prices and food security in South Africa. On average, South African households consumed 19 food items from 8 food groups over a reference period. Note, however, that the reference period is different across survey waves, i.e. it is four weeks in the IES 2005/06 and LCS 2008/09, and two weeks in the IES 2010 and LCS 2014/15. The length of the reference

13 period affects the dietary diversity measure (Ruel, 2003), and we expect that the longer the period over which households are reporting their consumption, the greater the diversity of food consumed. Indeed, the figures in Table 1 indicate that dietary diversity is definitely higher in waves with four-week reference period. We control for this issue in all the regressions that follow. Here, when we compare the mean dietary diversity between waves with the same reference period, we can observe that both the number of food items and the number of food groups consumed are lower in the LCS 2008/09 than in the IES 2005; and in the LCS 2014/15 than in the IES 2010/11.

Not surprisingly, the dietary diversity decreases in years of weather stress and price spikes.

Indeed, the LCS 2008/09 and LCS 2014/15 households experienced much drier conditions, with the SPEI index of -0.5 on average pointing to a moderate drought; however, the observed drought intensity was much higher in certain provinces. The figures relative to ASI present a similar picture, even though the increase in the agricultural stress in 2014/15 is not as spectacular as in 2008/09. This, in turn, may result from the fact that while SPEI typically indicates meteorological drought (Masih et al., 2014), ASI seeks to detect agricultural drought which takes longer to develop, and indeed, ASI reached extremely high values only in 2016. Similarly, the drought years coincide with the maize price spikes (Figure 2). This is especially visible in case of the commodity price, which is not surprising taking into account asymmetry in the adjustment process between commodity and retail prices (Vink & Kirsten, 2002). Interestingly, it is not only the dietary diversity that decreases in the years of drought; food composition changes as well, with, in particular, the increase in the share of cereals in the total consumption value. With respect to the self-reported food security indicators, the lack of data in the IES 2005/06 and

IES2010 doesn’t allow for comparisons between years; however, the figures point to an overall relatively high level of food insecurity in South Africa. More than 25 per cent of households consider themselves to be food insecure, and around 20 per cent experienced any form of hunger within past 12 months preceding the survey.

15 Table 1. Descriptive statistics All waves IES 2005/06 LCS 2008/09 IES 2010/11 LCS 2014/15 Variable Mean St. dev. Mean St. dev. Mean St. dev. Mean St. dev. Mean St. dev. Food security Dietary diversity Number of food items consumed 19.125 12.116 24.221 13.413 22.272 12.541 16.886 10.554 13.565 8.574 Household Dietary Diversity Score 8.257 2.906 9.455 2.689 9.099 2.586 7.578 2.819 7.007 2.800 Reference period 4 weeks 4 weeks 2 weeks 2 weeks Food composition Share of cereals in total cons. 0.294 0.177 0.261 0.153 0.334 0.168 0.280 0.177 0.297 0.197 Share of meat in total cons. 0.233 0.171 0.240 0.159 0.221 0.147 0.239 0.178 0.233 0.194 Share of diary in total cons. 0.094 0.094 0.089 0.083 0.092 0.080 0.097 0.100 0.100 0.110 Share of fruits and vegetables in total cons. 0.127 0.105 0.129 0.091 0.120 0.089 0.130 0.111 0.130 0.125 Self-reported food insecurity Food insecure household 0.264 0.441 na. 0.273 0.445 na. 0.255 0.436 Hunger (adults) 0.209 0.406 na. 0.214 0.410 na. 0.203 0.402 Hunger (children) 0.183 0.387 na. 0.197 0.398 na. 0.168 0.374 Food variety lower than preferable 0.320 0.467 na. na. na. 0.320 0.467 Maize prices Retail price, total country average (kg) 6.442 1.114 4.576 0.067 6.612 0.039 6.688 0.166 7.682 0.467 Commodity price (t) 1,711.762 590.143 1,090.076 180.102 1,657.200 154.153 1,505.351 229.179 2,556.119 436.457 Weather shocks SPEI, total country weighted average -0.091 0.573 -0.256 0.030 -0.476 0.049 0.822 0.231 -0.517 0.075 ASI, total country weighted average 11.322 18.095 5.439 3.191 33.721 22.821 0.112 0.037 4.763 3.629 Household characteristics Rural 0.381 0.486 0.439 0.496 0.382 0.486 0.357 0.479 0.354 0.478 Female head 0.442 0.497 0.441 0.496 0.444 0.497 0.436 0.496 0.447 0.497 Household size 3.860 2.508 4.019 2.683 3.888 2.496 3.752 2.351 3.803 2.515 African / Black 79.590 76.270 81.150 79.290 81.240 Coloured 10.980 12.740 10.050 10.750 10.650 Indian / Asian 1.750 1.650 1.600 1.850 1.890 White 7.680 9.350 7.200 8.110 6.210 Income, nominal 85,085.560 157,707.900 57,897.550 129,752.400 68,817.710 128,240.600 97,301.320 162,570.300 113,887.000 193,775.200 Expenditure, nominal 67,206.940 108,927.600 45,298.830 80,379.750 57,088.210 85,496.380 77,434.990 118,977.900 86,791.900 134,602.700 Food expenditure, nominal 11,469.090 11,923.650 7,492.015 8,175.455 12,952.610 12,399.060 11,837.260 12,316.920 13,075.880 12,969.140 Social welfare grant (exc. old age pension) 0.340 0.474 0.075 0.264 0.349 0.477 0.463 0.499 0.438 0.496 Own food production 0.168 0.374 na. 0.140 0.347 0.190 0.392 0.173 0.378 Income derived from own food production 0.011 0.106 na. 0.015 0.121 0.003 0.058 0.017 0.128 Number of observations 94,927 21,144 25,328 25,075 23,380

16 Empirical Strategy

As noted earlier, typically, and particularly so in case of sub-Saharan Africa, the literature analyzed the direct impact of weather shocks on food availability in case of own food production by farmers. Here, we depart from this traditional approach and we model the link between weather shocks and food security that acts specifically through food prices; therefore, this model will be particularly relevant in contexts where a large share of households are net food buyers.

First, we assume that negative weather shocks affect agricultural productivity and lead to a decrease in local food supply; this has previously been well established in the large literature and does not require further attention in this paper. This reduction in food supply is then likely to entail an increase in domestic food prices, and this effect might be especially pronounced in the case of South Africa which is a net food exporter and where the international price transmission mechanism is very limited (see below). In principle, the impact of weather shocks on prices might be attenuated or exacerbated by the level of food stocks or grain reserves (Mirzabaev &

Tsegai, 2012), but because of data limitations, we are not able to control for this in our analysis.

Second, we expect the increase in food prices to negatively affect household food security, or, more specifically, its access dimension. Of course, the impact of food prices on food security might depend on the household food buyer / food seller position; however, in our dataset, around

17 per cent of households are involved in own food production activities, but only one per cent derive any income from selling food produced on own plot, we can therefore safely assume that households in our dataset are net food buyers. Note that in this model, we do not account for any long-term secondary effects of weather shocks and food price increases on labour market

17 conditions, and these might, indeed, be non-negligible3; however, we are not able to capture such effects with the household survey data. In light of the assumptions discussed above, we employ an instrumental variable model as in eq. (1):

퐹푆푖푗푡 = 푓 (퐹푃푗푡, 푋푖푗푡) + 휖푖푗푡 (1)

퐹푃푗푡: 𝑖푛푠푡푟푢푚푒푛푡푒푑 푤𝑖푡ℎ 푊푆푗푡

where household’s i food security 퐹푆푖푗푡, measured as dietary diversity, depends on the level of food prices 퐹푃푗푡 in province j in year t, and where weather shocks 푊푆푗푡 do not affect food security directly, but only through food prices. As a means of comparison, we also employ the model with food prices and weather shocks at the country level; in this case the subscript j for province should be removed. We also control for a set of household characteristics 푋푖푗푡, including type of settlement (rural or urban), gender of household head, household size, population group, own food production, and social grants. In order to represent the main mechanism in our model, but also to take into account the endogeneity of food prices, we instrument them with weather shocks. Technically, therefore, we capture the marginal contribution of weather shocks to food prices, and use this variable to explain household food security. Of course, weather shocks are only one of many factors influencing prices, and in particular, we don’t account here for the long-term factors such as income levels, changes in tastes and preferences or population growth.

3 For example, the South African Agricultural Business Chamber reports on the commercial agriculture sector clearly indicate that the employment is very responsive to seasonality, weather and overall market conditions (AGBIZ, 2017).

More importantly, we focus in this analysis specifically on the price of maize for several reasons.

First of all, white maize is a staple food in South Africa and therefore plays a central role in the diets of the low-income households (Abidoye & Labuschagne, 2014; Altman et al., 2013).

Second, yellow maize is the single most important input in the dairy, pig, beef, and poultry industries (Vink & Kirsten, 2002), and therefore, an increase in the price of maize implies that the price of dairy and meat, major sources of protein in local diets, will increase accordingly.

Indeed, increase in the price of maize has been identified as the main driving force behind food price inflation events (Vink & Kirsten, 2002; NAMC, 2003). Additionally, from the agricultural production perspective, maize constitutes approximately 70 per cent of grain production and covers 60 per cent of the country’s cropping area (Akpalu, Hassan, & Ringler, 2009), and remains highly vulnerable to climate variability and climate change (Lobel et al., 2011).

What is more, since Southern Africa is the largest producer and consumer of white maize in the world, the world price of white maize is largely determined by conditions in the South African market (Vink & Kirsten, 2002). Further the international price transmission mechanism is considered to be weak, with several studies suggesting that world maize prices have no significant effect on South African maize prices (Minot, 2011; Kirsten, 2012), while others found the threshold effect, where only large long-run deviations, such as oil price spike, are transmitted to domestic market, but not the smaller changes (Abidoye and Labuschagne, 2014). The rand exchange rate is, however, found to have an impact on the domestic food prices (Vink & Kirsten,

2002). In this context, we can expect that South African price of maize, but also prices of dairy

19 and meat products, are shaped, to a large extent, by domestic conditions, and in particular, local weather outcomes.4

Results

The main results of the 2SLS analysis are presented in Table 2. For sake of brevity, we do not report the first-stage regressions output. The exogeneity of our instrument, i.e. weather shocks, seems to be irrefutable, both intuitively and statistically; and its validity is confirmed in standard tests (F-test, Anderson-Rubin Wald test5). Additionally, we verify these findings with the conditional likelihood ratio (CLR) approach developed by Moreira (2003)6. Since we suspect heteroscedasticity in our data, and indeed, the Breusch-Pagan test in the simple OLS regression7 supports this argument, we account for this issue in the following analyses. We present the results for both measures of dietary diversity, i.e. the number of food items consumed and the

Household Dietary Diversity Score (HDDS). For ease of interpretation, we employ both measures in logarithms; however, note that both are count variables, which might be particularly meaningful in case of HDDS, and therefore, we also present the results for the Poisson

4 The remaining food groups, i.e. fresh products such as fruits and vegetables, are sourced locally, and therefore their prices depend solely on local market conditions (Vink & Kirsten, 2002). 5 Note that our model is exactly identified, and therefore we cannot use other standard tests such as Sargan-Hansen test. 6 Not reported here. 7 Not reported here.

20 regression8 in Table A1 in the Annex. The main explanatory variable, i.e. price of maize, is also used in logarithm.

In columns (1) – (2), we employ the total country average price of maize, and the country mean seasonal SPEI averaged by the share of each province in the total area grown to white maize as its instrument. The underlying assumption here is that local markets are spatially integrated and therefore weather shocks in the last growing season, and in particular the shocks occurring in the three provinces that are major producers of white maize, will affect prices in the whole country.

This might be plausible if we recall that South African households are net food buyers, and that the share of big retail chains in total food sales is dominant and growing (see, for example,

Peyton, Moseley, & Battersby-Lennard, 2015).

An important limitation of such model, from a technical point of view, is that because of multicollinearity, we cannot include dummy years and therefore control for possible structural changes overs years. Keeping that in mind, and noting that the period under investigation follows the sharp rise in commodity prices in 2006 and that beyond continued intensification of production, does not appear to have had any other major change, we can, nevertheless, conclude that, according to our expectations, food price shocks, entailed by weather shocks, do indeed affect household food security, i.e. an increase in food prices by one per cent leads to a decrease in the number of food items consumed by almost two per cent, or a decrease in the number of food groups consumed, as reflected in the HDDS, by 0.8 per cent.

8 More specifically, we employ ivpois method, which is a GMM estimator of Poisson regression allowing for instrumental variables. Since the dependent variable is not over-dispersed and does not have an excessive number of zeros, Poisson regression model seems appropriate here.

On the other hand, if local markets are not well integrated, and if the small-scale or informal trade plays an important role, then it is more appropriate to use the price and the weather data at the province level9 as in columns (3) – (4)10. Johann Kirsten (personal communication, 2 October

2017) suggests that local food shortages occur even if the total country production registers a surplus, and this, of course, have implications for local food prices; and Figure 2 shows that while, globally, prices in South African provinces follow the same trend, they differ in the extent of reaction to shocks, especially in recent years of severe drought. The results with respect to the effect of price of maize on dietary diversity are overall confirmed, but the magnitude of the coefficient increased substantially, especially in column (3) suggesting that indeed, this model is more suitable. Importantly, in this model, year dummies are included which helps to isolate the impact of weather shock and food prices on food security from structural factors. The results are similar in terms of economic and statistical significance in the Poisson regression in Table A1.

The results with respect to control variables are generally in line with the literature, i.e. dietary diversity is lower for rural households, while female-headed households demonstrate higher food security. Important differences can be observed between different population groups; of course, this might be as well related to the differences in wealth between these groups, and indeed, wealthier households typically consume more diversified diets. Importantly, especially from the policy perspective, social welfare grants, which cover a large population share in South Africa, seem to improve household food security, even though, since they are targeted at low-income,

9 More disaggregated data would be an optimal solution, but unfortunately, is not available. 10 Note that price data at the province level is not available only for the IES 2005/06; therefore, the number of observations decreases.

22 and, potentially, food insecure, recipients, this variable might be flawed with endogeneity.

Finally, own food production, even though it might contribute only marginally to the total food consumption, has a positive impact on household dietary diversity.

Additionally, we test if weather and price shocks, apart from affecting the number of food items or food groups consumed, also influence household diets by changing the composition of food consumption. We expect that as a result of food price increases, households, and especially the poorest ones, will spend a greater share of their food expenditure on calorie-dense staples, i.e. cereals, decreasing, at the same time, consumption of protein-rich but more expensive food groups, such as meat or dairy. The figures in columns (5) – (8) confirm our hypothesis, with the strongest effect observed for cereals; while the coefficient is insignificant in case of meat. On the other hand, the magnitude of this effect is rather low; for example, an increase in food price by one per cent entails an increase in the share of cereals in total consumption expenditure by 0.5 percentage point. This might be related to the fact that rather than substituting entire food groups, household resort to brand switching, i.e. choosing lower quality products than usually. Such effect was, for example, observed in a small-scale study presented by NAMC (2003); in our study, we are not able to detect this kind of substitution due to the fact that our data on food consumption is aggregated at the COICOP level.

23 Table 2. Weather shocks, food prices and food security Dietary diversity Food composition

Number of food Household Number of food Household Fruits and items consumed Dietary Diveristy items consumed Dietary Diveristy Cereals Dairy Meat vegetables (log) Score (log) (log) Score (log)

2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS (1) (2) (3) (4) (5) (6) (7) (8)

Maize price, total country (log) -1.867*** -0.831*** -0.0423 (0.0294) Maize price, province (log) -2.506*** -0.881** 0.496*** -0.316*** 0.0766 0.228** (0.585) (0.382) (0.158) (0.0961) (0.168) (0.107) Rural -0.0844*** -0.0622*** -0.0917*** -0.0643*** 0.0290*** -0.0104*** -0.0226*** 0.000495 (0.00632) (0.00439) (0.00688) (0.00469) (0.00200) (0.00111) (0.00196) (0.00130) Housheold size 0.0473*** 0.0246*** 0.0476*** 0.0247*** 0.00713*** -0.00203*** -0.00270*** -0.00191*** (0.00101) (0.000669) (0.00103) (0.000672) (0.000302) (0.000153) (0.000282) (0.000183) Black (reference group)

Coloured 0.0636*** 0.0301*** 0.0634*** 0.0304*** -0.0223*** 0.00127 0.0218*** -0.00555*** (0.00894) (0.00561) (0.00905) (0.00564) (0.00227) (0.00140) (0.00266) (0.00146) Indian / Asian -0.0201 -0.00717 -0.0155 -0.00546 -0.0261*** 0.0317*** -0.0332*** 0.0153*** (0.0180) (0.0118) (0.0180) (0.0118) (0.00468) (0.00312) (0.00543) (0.00324) White 0.207*** 0.0905*** 0.204*** 0.0894*** -0.0695*** 0.0346*** -0.0288*** 0.0341*** (0.0104) (0.00640) (0.0105) (0.00644) (0.00230) (0.00176) (0.00288) (0.00181) Female household head 0.0224*** 0.0177*** 0.0223*** 0.0176*** 0.00638*** 0.000189 -0.0171*** 0.0125*** (0.00451) (0.00305) (0.00456) (0.00306) (0.00131) (0.000731) (0.00130) (0.000853) Wealth index 0.0884*** 0.0394*** 0.0869*** 0.0389*** -0.0162*** 0.00658*** 0.0127*** -0.00189*** (0.00163) (0.00109) (0.00169) (0.00113) (0.000489) (0.000264) (0.000471) (0.000315) Social welfare grant 0.0302*** 0.0260*** 0.0313*** 0.0265*** 0.00335** -0.00295*** -0.00203 0.00176* (0.00514) (0.00345) (0.00521) (0.00347) (0.00152) (0.000822) (0.00148) (0.000959) Own food production 0.0244*** 0.0110** 0.0240*** 0.0112** 0.00676*** -0.00370*** -0.0105*** 0.00634*** (0.00643) (0.00444) (0.00652) (0.00446) (0.00203) (0.00103) (0.00184) (0.00129) Constant 5.984*** 3.389*** 7.538*** 3.553*** -0.924** 0.847*** 0.104 -0.371 (0.0869) (0.0599) (1.331) (0.869) (0.359) (0.219) (0.382) (0.244) Province dummy Yes Yes Yes Yes Yes Yes Yes Yes Year dummy No No Yes Yes Yes Yes Yes Yes

Observations 72,927 72,980 72,927 72,980 72,927 72,927 72,927 72,927 R-squared 0.272 0.190 0.256 0.186 0.149 0.057 0.060 0.012

Notes: Instrumented: logarithm of maize price. Instrument: SPEI12. First-stage regressions omitted. All regressions control for the survey reference period. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

24 Table 3. Dietary diversity by wealth quartile 1st quartile 2nd quartile 3rd quartile 4th quartile Mean St. dev. Mean St. dev. Mean St. dev. Mean St. dev.

Number of food items consumed 14.57 8.53 16.30 9.21 19.48 10.56 26.16 15.49 Household Dietary Diveristy Score 7.37 2.79 7.77 2.74 8.45 2.73 9.43 2.93

Number of observations 23732 23779 23702 23714

The figures in Table 2 suggest that wealth affect household food security, which corresponds to our expectations and to the findings in the literature. Indeed, simple tabulations show that the dietary diversity differs substantially between wealth groups (Table 3), i.e. the first quartile reports 15 food items and 7 food groups on average, whereas the fourth 26 food items and 9 food groups respectively. More importantly, the standard deviation, especially in case of the number of food items consumed, increases with wealth, too; we might therefore expect that different wealth groups will respond differently to a price shock. Table 4 presents the results of regressions by wealth quartile, and these seem to be compatible with our expectations. In terms of the number of food items consumed, the food price shock turns out significant only for the two middle quartiles of wealth distribution, but is insignificant for the poorest and for the richest households. This is not surprising; the poorest households, almost by definition, are the most food insecure and consume the least diversified diets limited to the necessary minimum, and, as result, they have little scope to decrease their dietary diversity even further as a response to food price shocks. The richest households, on the other hand, are not financially constrained and therefore, food price shocks do not affect their food consumption patterns. More importantly, with respect to HDDS, only households from the second quartile adjust their food consumption to food price shocks; and this result is probably even more meaningful taking into account that contrary to the simple count of food items, HDDS reflects better the distribution of food groups,

25 and consequently, macro and micronutrient, in the food basket. These findings are in line with the literature pointing to the fact that the poor are at a higher risk of food insecurity.

Table 4. Weather shocks, food prices and food security by wealth quartiles Number of food items consumed (log) Household Dietary Diveristy Score (log) 1st quartile 2nd quartile 3rd quartile 4th quartile 1st quartile 2nd quartile 3rd quartile 4th quartile 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS (1) (2) (3) (4) (5) (6) (7) (8)

Maize price, province (log) -1.372 -2.552*** -3.696** 31.24 -0.482 -1.321*** -0.983 22.13 (0.854) (0.581) (1.538) (64.84) (0.602) (0.395) (0.988) (42.44) Rural -0.113*** -0.0751*** -0.0867*** 0.206 -0.0794*** -0.0512*** -0.0574*** 0.150 (0.0121) (0.0119) (0.0154) (0.621) (0.00874) (0.00821) (0.0101) (0.407) Housheold size 0.0511*** 0.0410*** 0.0428*** 0.0475*** 0.0291*** 0.0216*** 0.0211*** 0.0209*** (0.00190) (0.00200) (0.00205) (0.00689) (0.00129) (0.00136) (0.00133) (0.00500) Black (reference group)

Coloured 0.0245 0.0224 0.0523*** 0.107 0.0136 0.00163 0.0192* 0.0664 (0.0279) (0.0220) (0.0165) (0.0672) (0.0193) (0.0143) (0.0103) (0.0454) Indian / Asian 0.302*** 0.188*** -0.0116 -0.0972 0.129 0.113*** 0.00421 -0.0596 (0.0968) (0.0626) (0.0380) (0.144) (0.110) (0.0409) (0.0253) (0.0954) White 0.127 0.176*** 0.0754** 0.258** -0.0139 0.0624 0.0201 0.126* (0.125) (0.0656) (0.0373) (0.104) (0.0851) (0.0411) (0.0233) (0.0686) Female household head 0.0345*** 0.0320*** 0.0234*** -0.0206 0.0185*** 0.0179*** 0.0218*** -0.00197 (0.00926) (0.00902) (0.00876) (0.0311) (0.00666) (0.00623) (0.00572) (0.0208) Social welfare grant 0.0819*** 0.0736*** 0.0357*** -0.0784*** 0.0523*** 0.0492*** 0.0275*** -0.0305 (0.0107) (0.0104) (0.00967) (0.0304) (0.00762) (0.00714) (0.00620) (0.0211) Own food production 0.0415*** 0.00455 0.0109 0.0589 0.0227*** -0.00298 0.00423 0.0373 (0.0109) (0.0118) (0.0142) (0.0733) (0.00776) (0.00821) (0.00939) (0.0515)

Constant 4.668** 7.588*** 10.34*** -69.87 2.459* 4.536*** 3.835* -49.24 (1.925) (1.314) (3.515) (149.3) (1.357) (0.894) (2.257) (97.72) Province dummy Yes Yes Yes Yes Yes Yes Yes Yes Year dummy Yes Yes Yes Yes Yes Yes Yes Yes

Observations 18,064 18,289 18,283 18,291 18,104 18,297 18,287 18,292 R-squared 0.209 0.158 0.140 -3.336 0.160 0.125 0.143 -4.971 Notes: Instrumented: logarithm of maize price. Instrument: SPEI12. First-stage regressions omitted. All regressions control for the survey reference period. Wealth quartiles based on asset-based index based on the polychoric principal component analysis (Kolenikov & Angeles, 2004). Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Interestingly, own food production increases dietary diversity only in case of the poorest households, suggesting that diversification into subsistence farming might be a rational livelihood strategy. We follow this path more in detail and verify if own food production has a potential to insulate households from the harmful effects of weather and food price shocks in

26 Table 5. The results point, however, to the contrary and show that households involved in farming activities are actually more vulnerable to weather and food price shocks, with the magnitude of the coefficient more than three times higher. This does not come as a surprise when we take into account the complexity of livelihoods strategies of the poorest households. Indeed, typically only the poorest households resort to subsistence farming, i.e. the incidence of subsistence farming activities is 25 per cent for the first wealth quartile and less than 7 per cent for the fourth quartile. Whereas this strategy proves overall helpful and, by decreasing the dependence on food purchases, albeit marginally, it increases household food security, as shown in Table 4, it can exacerbate household vulnerability to weather shocks. We can hypothesize that a weather shock brings about crop failure; as a consequence, households will have to substitute food from own production with food purchases; however, they will face higher food prices resulting from the aggregate food shortage. Indeed, the extent of this effect is much higher when we employ the country level measures of food price and weather shock.11 Additionally, with reference to Hendriks (2003), we control for the income derived from own food production in columns (2) and (5); however, we do not find any significant effect, which is very likely taking into account the low number of households reporting such income.

11 Results not reported here.

27 Table 5. Weather shocks, food prices and food security: role of own food production Number of food items consumed (log) Household Dietary Diveristy Score (log) No food No food Own food production Own food production production production 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS (1) (2) (3) (4) (5) (6)

Maize price, province (log) -5.356*** -5.374*** -1.737** -2.225*** -2.227*** -0.432 (0.999) (1.000) (0.734) (0.656) (0.657) (0.477) Rural -0.0799*** -0.0801*** -0.0912*** -0.0527*** -0.0527*** -0.0642*** (0.0164) (0.0165) (0.00791) (0.0108) (0.0108) (0.00540) Housheold size 0.0446*** 0.0446*** 0.0482*** 0.0232*** 0.0232*** 0.0250*** (0.00215) (0.00215) (0.00119) (0.00140) (0.00140) (0.000773) Black (reference group)

Coloured -0.0171 -0.0170 0.0627*** -0.00123 -0.00122 0.0294*** (0.0481) (0.0481) (0.00919) (0.0287) (0.0287) (0.00576) Indian / Asian 0.150 0.150 -0.0184 0.0745 0.0745 -0.00604 (0.150) (0.151) (0.0182) (0.0862) (0.0863) (0.0120) White 0.265*** 0.263*** 0.197*** 0.127*** 0.127*** 0.0857*** (0.0409) (0.0410) (0.0110) (0.0240) (0.0240) (0.00675) Female household head 0.0304*** 0.0307*** 0.0203*** 0.0144* 0.0144* 0.0181*** (0.0114) (0.0114) (0.00501) (0.00753) (0.00753) (0.00337) Wealth index 0.0726*** 0.0725*** 0.0896*** 0.0324*** 0.0324*** 0.0402*** (0.00417) (0.00417) (0.00189) (0.00272) (0.00272) (0.00127) Social welfare grant 0.0557*** 0.0559*** 0.0263*** 0.0346*** 0.0346*** 0.0247*** (0.0131) (0.0131) (0.00570) (0.00873) (0.00874) (0.00378) Income from own food production 0.0204 0.00256 (0.0238) (0.0159)

Constant 13.75*** 13.78*** 5.812*** 6.474*** 6.479*** 2.540** (2.224) (2.226) (1.676) (1.460) (1.461) (1.089) Province dummy Yes Yes Yes Yes Yes Yes Year dummy Yes Yes Yes Yes Yes Yes

Observations 12,307 12,307 60,620 12,311 12,311 60,669 R-squared 0.105 0.104 0.275 0.110 0.110 0.196 Notes: Instrumented: logarithm of maize price. Instrument: SPEI12. First-stage regressions omitted. All regressions control for the survey reference period. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Robustness

As a way to check the robustness of our results, we take advantage of the additional questions on self-reported food security status available in the LCS surveys. In both the LCS 2008/09 and

LCS 2014/15, households were asked to assess their food consumption (less than adequate, just

28 adequate, or more than adequate), the experience of hunger in case of children as well as adult household members (never, seldom, often, or always). We use these questions to construct dummies for food insecurity (equal to one if a household reports less than adequate food consumption) as well as child and adult hunger (equal to one if children or adults, respectively, within a household were reported to seldom, often, or always go hungry). Additionally, the LCS

2014/15 provides data on household coping strategies in case of insufficient availability of food, including reduction in food variety. We apply an instrumental variable probit model to estimate if the food price, instrumented with weather shock, affect the probability of these measure of food insecurity measures being reported. Keeping in mind that the sample size is now reduced by half due to the lack of the data in the IES surveys, this is, nevertheless, a valuable way to compare the official indicators of food security with individual perceptions.

The figures in Table 6 confirm our main findings. In particular, weather shocks and food prices significantly affect household food security status; i.e. a one per cent increase in food prices increases the probability of a household being food insecure by around 0.7 percentage point12, and similar effect is found for the remaining measures of food insecurity, even though the magnitude of the effect is much lower in case of child hunger. Most notably, a price increase induced by weather shock increases the probability of a household reporting lower food diversity, and this result offers relatively best comparability with our main findings. Regarding the control variables, previous results are confirmed with respect to the impact of wealth or population group, but they are less precise in case of own food production or rural dummy.

These discrepancies might, however, result from the contextual factors affecting individual

12 Remember that food prices are employed in logarithm, therefore, the marginal effect reported in Table 6 refers to a change in food prices by one log.

29 perceptions and reference groups. Additionally, we checked the robustness of our main findings by employing the commodity price of maize as the principal explanatory variable and second, and by using the Agricultural Stress Index (ASI) and, alternatively, the drought dummy from

EM-DAT dataset, as the instrument for food prices13. The results for commodity price are similar to the ones for the retail price at the country level, even though the magnitude is smaller which is not surprising taking into account the asymmetric adjustment between commodity and retail prices (Vink & Kirsten, 2002). On the other hand, the results for ASI produce higher estimates of the effect of food price on dietary diversity, which might be due to the fact that ASI captures longer-term changes in weather patterns.

13 Results not reported here.

30 Table 6. Weather shocks, food prices and self-reported measures of food insecurity Food insecure Hunger Lower food Hunger (adults) household (children) variety IV probit IV probit IV probit IV probit (1) (2) (3) (4)

Maize price, province (log) 1.257*** 1.086*** 0.793*** 1.593*** (0.234) (0.226) (0.221) (0.212) Rural -0.0251*** -0.0623*** -0.0584*** -0.0487*** (0.00499) (0.00458) (0.00532) (0.00930) Housheold size 0.0119*** 0.0115*** 0.0139*** 0.0205*** (0.000760) (0.000697) (0.000797) (0.00116) Black (reference group)

Coloured -0.0504*** -0.0289*** -0.0404*** -0.0352*** (0.00668) (0.00637) (0.00686) (0.0103) Indian / Asian -0.0941*** -0.1000*** -0.116*** -0.100*** (0.0159) (0.0142) (0.0161) (0.0233) White -0.156*** -0.143*** -0.120*** -0.149*** (0.00831) (0.00747) (0.0102) (0.0129) Female household head 0.0214*** 0.0151*** 0.0389*** 0.0296*** (0.00386) (0.00362) (0.00412) (0.00574) Wealth index -0.0657*** -0.0619*** -0.0517*** -0.0751*** (0.00139) (0.00121) (0.00134) (0.00238) Income from own food production -0.0146*** 0.0189*** 0.0268*** 0.0626*** (0.00546) (0.00531) (0.00576) (0.00818)

Observations 47,786 46,833 33,118 23,074 Notes: Instrumented: logarithm of maize price. Instrument: SPEI12. Results presented in terms of the average marginal effects. First-stage regressions omitted. All regressions control for the survey reference period. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Conclusion

31 food prices. Second, we expect the increase in food prices to negatively affect household food security, or, more specifically, its access dimension. This model is particularly relevant in contexts where a large share of households are net food buyers, such as in the case of South

Africa. We focus on dietary diversity as our measure of food security, and we employ an instrumental variable model where household dietary diversity is determined by food prices instrumented with standardized precipitation evapotranspiration index (SPEI), our measure of weather shock. The limitation of our study is that we do not analyze the temporal dimension of the adjustment between weather shocks and retail food prices more in detail.

Our findings suggest that food prices have a significant negative impact on household food security, i.e. a one per cent increase in local food prices induced by a weather shock decreases the number of food items consumed by household by around 2.5 per cent, and the number of food groups by almost one per cent. In line with expectations, the low-income households are particularly vulnerable to weather and price shocks; however, their response to shocks seem to depend on the level of poverty. The moderately poor households from the second wealth quartile show the greatest response to shocks, but the extremely poor household from the first wealth quartile, since their dietary diversity is already very low by definition, have little scope to decrease their dietary diversity even further. On the other hand, own food production might alleviate food insecurity of the poorest; however, it does not insulate them form the weather and food price shocks. Our findings are robust to employing self-reported measures of food security.

This study is a departure from previous approaches that focus only on the direct impact of weather shocks on food availability in sub-Saharan Africa. More importantly, it shows that the

32 link between weather shocks and food security is relatively complex and affects a large share of populations, both food producers and food buyers. Not surprisingly, the low-income households are particularly vulnerable, especially in the context of increasing reliance on food purchases.

Since the predicted climate change is expected to bring about an increase in the incidence and intensity of weather shocks, exposing the vulnerable populations to even more food security risks, policymakers should integrate all food security dimensions into their climate action plans, including the dimension of food utilization which is often neglected.

33 REFERENCES

Abdulai, A. (2000). Spatial price transmission and asymmetry in the Ghanaian maize market. Journal of Development Economics, 63, 327-349.

Abidoye, B. O., & Labuschagne, M. (2014). The transmission of world maize price to South African maize market: a threshold cointegration approach. Agricultural Economics, 45, 501-512.

AGBIZ (Agricultural Business Chamber) (2017). 40 000 job cuts recorded in SA agriculture in Q2, 2017. Agribusiness Research 07 August 2017. Pretoria: AGBIZ.

Akpalu, W., Hassan, R. M., & Ringler, C. (2009). Climate Variability and Maize Yield in South Africa: Results from GME and MELE Methods. IFPRI Discussion Paper No. 843, Washington, DC: IFPRI.

Aksoy, M. A., & Isik-Dikmelik, A. (2008). Are Low Food Prices Pro-Poor? Net Food Buyers and Sellers in Low-Income Countries. WB Policy Research Working Paper 4642. Washington, DC: WB.

Akter, S., & Basher, S. A. (2014). The impacts of food price and income shocks on household food security and economic well-being: Evidence from rural Bangladesh. Global Environmental Change, 25, 150-162.

Alem, Y., & Söderbom, M. (2012). Household-level consumption in urban Ethiopia: the effects of a large food price shock. World Development, 40, 146-162.

Aliber, M., & Hart, T. G. B. (2009). Should subsistence agriculture be supported as a strategy to address rural food insecurity? Agrekon, 48, 434-458.

Altman, M., Hart, T., & Jacobs, P. (2009). Household food security status in South Africa. Agrekon, 48, 345-361.

Anríquez, G., Daidone, S., & Mane, E. (2013). Rising food prices and undernourishment: a cross-country inquiry. Food Policy, 38, 190–202.

Arndt, C., Hussain, M. A., Salvucci, V., & Osterdal, L. P. (2016). Effects of food price shocks on child malnutrition: The Mozambican experience 2008/2009. Economics and Human Biology, 22, 1-13.

Badolo, F., & Kinda, S. R. (2012). Rainfall Shocks, Food Prices Vulnerability and Food Security: Evidence for Sub-Saharan African Countries. Mimeo.

Baffes, J., Kshirsagar, V., & Mitchell, D. (2015). What Drives Local Food Prices? Evidence from the Tanzanian Maize Market. WB Policy Research Working Paper 7338. Washington, DC: WB.

34 Baiphethi, M. N., & Jacobs, P. T. (2009). The contribution of subsistence farming to food security in South Africa. Agrekon, 48, 459-482.

Bandara, J. S., & Cai, Y. (2014). The impact of climate change on food crop productivity, food prices and food security in South Asia. Economic Analysis and Policy, 44, 451-465.

BFAP (Bureau of Food and Agricultural Policy) (2012). BFAP baseline: agricultural outlook 2012-2021, Pretoria: BFAP.

Brown, M. E., & Ksirsagar, V. (2015). Weather and international price shocks on food prices in the developing world. Global Environmental Change, 35, 31-40.

Burke, W. J., & Myers, R. J. (2014). Spatial equilibrium and price transmission between Southern African maize markets connected by informal trade. Food Policy, 49, 59-70.

Campbell, B. M., Vermeulen, S. J., Aggarwal, P. K., Corner-Dolloff, C., Girvetz, E., Loboguerrer, A. M., … Wollenberg, E. (2016). Reducing risks to food security from climate change. Global Food Security, 11, 34-43.

Cranfield, J. A. L., Eales, J. S., Hertel, T. W., & Preckel, P. V. (2003). Model selection when estimating and predicting consumer demands using international cross section data. Empirical Economics, 28, 353–364. doi:10.1007/s001810200135 de Brauw, A. (2011). Migration and child development during the food price crisis in El Salvador. Food Policy, 36, 28-40. de Hoyos, D., Medvedev, D. (2011). Poverty effects of higher food prices: a global perspective. Review of Development Economics, 15, 387–402.

Deryng, D., Sacks, W. J., Barford, C. C., & Ramankutty, N. (2011). Simulating the effects of climate and agricultural practices on global crop yield. Global Biochemical Cycles, 25. doi: 10.1029/2009GB003765

Devereux, S., & Maxwell, S. (2001). Introduction. In: Food security in sub-Saharan Africa [Devereux, S., & Maxwell, S. (eds.)], Pietermaritzburg: University of Natal Press, 1-12.

Devereux, S., & Waidler, J. (2017). Why does malnutrition persist in South Africa despite social grants? Food Security SA Working Paper Series No.001, Cape Town: DST-NRF Centre of Excellence in Food Security.

Dukhi, N., Sartorius, B., & Taylor, M. (2017). Stunting in children (0–59 months): what is the current trend in South Africa? Early Child Development and Care, 187, 1874-86.

FAO, IFAD, UNICEF, WFP & WHO (2017). The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Rome: FAO.

35 FAO, Rome declaration on world food security and World Food Summit plan of action, World Food Summit, Rome, 13 – 17 November 1996.

Fraser, G., Monde, N, & van Averbeker, W. (2003). Food security in South Africa: a case study of rural livelihoods in the Eastern Cape. In: The challenge of change: agriculture, land and the South African economy [Nieuwoudt, L., & Groenewald, J. (eds.)], Pietermaritzburg: University of Natal Press, 171-84.

Gitau, R., Makas, M., Kasonka, L., Sinkala, M., Chintu, C., Tomkins, A., Filteau, S. (2005). Maternal micronutrient status and decreased growth of Zambian infants born during and after the maize price increases resulting from the southern African drought of 2001–2002. Public Health Nutrition, 8, 837–843.

Godfray, H. C., Beddington, J. R., Crute, I. R., Haddad, L., Muir, J. F.,… Toulmin, C. (2010). Food Security: The Challenge of Feeding 9 Billion People. Science, 327, 812-818. doi: 10.1126/science.1185383

Harvey, A. C. (1990). Forecasting, structural time series models, and the Kalman filter. Cambridge: Cambridge University Press.

Headey, D., & Fan, S. (2010). Reflections on the Global Food Crisis: How Did It Happen? How Has It Hurt? And How Can We Prevent the Next One? Research Monograph 165. Washington, DC: IFPRI.

Headey, D., & Martin, W. J. (2016). The Impact of Food Prices on Poverty and Food Security. Annual Review of Resource Economics,2016., 8, 329-351.

Hendriks, S. L. (2003). The potential for nutritional benefits from increased agricultural production in KwaZulu-Natal. South African Journal of Agricultural Extension, 32, 28-44.

Hendriks, S. L., & Maunder, E. M. W. (2006). Reflecting on the FIVIMS.ZA pilot and food insecurity and vulnerability: Modelling approaches to advise on future directions. Paper prepared for the World Food Programme. Pietermaritzburg: African Centre for Food Security.

Hendriks, S. L. (2013). South Africa’s National Development Plan and New Growth Path: reflections on policy contradictions and implications for food security. Agrekon, 52, 1-17.

Hertel, T. W. (2016). Commentary: Food security under climate change. Nature Climate Change, 6, 10-12.

Hoddinott, J., & Kinsey, B. (2001). Child growth in the time of drought. Oxford Bulletin of Economics and Statistics, 63, 409-436.

IPCC (2007). Climate change 2007: The physical science basis. Cambridge: Cambridge University Press.

36 Ivanic, M., Martin, W. (2014). Short- and long-run impacts of food price changes on poverty. Policy Research Working Paper 7011, Washington, DC: WB.

Jensen, R. (2000). Agricultural volatility and investments in children. American Economic Review, 90, 399–404.

Kalkuhl, M., von Braun, J., & Torero, M. (2016). Volatile and Extreme Food Prices, Food Security, and Policy: An Overview. In: Food Price Volatility and Its Implications for Food Security and Policy [Kalkuhl, M., von Braun, J., & Torero, M (eds.)], 3-31.

Kirsten, J. F., Townsend, R., & Gibson, C. (1998). Determination of agricultural production to household nutritional status in KwaZulu-Natal, South Africa. Development Southern Africa, 15, 573-587.

Kirsten, J. F. (2012). The political economy of food price policy in South Africa. WIDER Working Paper 2012/102, Helsinki: UNU-WIDER.

Knox, J., Hess, T., Daccache, A., & Wheeler, T. (2012). Climate change impacts on crop productivity in Africa and South Asia. Environmental Research Letters, 7, 041001.

Johnston, P., Thomas, T. S., Hachigonta, S., & Sibanda, L. M. (2013). South Africa. In: Southern African agriculture and climate change: A comprehensive analysis [Hachigonta, S., Nelson, G. C., Thomas, T. S., & Sibanda, L. M. (eds.)], Washington, DC: IFPRI, 175-212.

Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon, W. P., & Naylor, R. L. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030, Science, 319, 607-610. doi: 10.1126/science.1152339

Lobell, D. B., Bazinger, M., Magorokosho, C., & Vivek, B. (2011). Nonlinear heat effects on African maize as evidenced by historical yields trials. Nature Climate Change, 1, 42-45.

Masih, I., Maskey, S., Mussa, F. E. F., & Trambauer, P. (2014). A review of droughts on the African continent: a geospatial and long-term perspective. Hydrology and Earth System Sciences, 18, 3635–3649.

Matz, J. A., Kalkuhl, M., Abegaz, G. A. (2015). The short-term impact of price shocks on food security-Evidence from urban and rural Ethiopia. Food Security, 7, 657-679.

Mekuria, M., & Moletsane, N. P. (1996). Initial findings of rural housheold food security in selected districts of the Northern Province. Agrekon, 33, 309-13.

Minot, N. (2011). Transmission of World Food Price Changes to Markets in Sub-Saharan Africa. IFPRI Discussion Paper 01059, Washington, DC: IFPRI.

37 Mirzabaev, A., & Tsegai, D. (2012). Effects of weather shocks on agricultural commodity prices in Central Asia. ZEF – Discussion Papers on Development Policy No. 171, November 2012, Bonn: ZEF.

Mora, C., Frazier, A. G., Longman, R. J., Dacks, R. S., Walton, M. M., Tong, E. J., … Giambelucca, T. W. (2013). The projected timing of climate departure from recent variability. Nature, 502, 183-187 (10 October 2013). doi:10.1038/nature12540

Moreira, M. (2003). A conditional likelihood ratio test for structural models. Econometrica, 71, 1027-1048.

Müller, C., Cramer, W., Hare, W. L., & Lotze-Campen, H. (2010). Climate change risks for African agriculture. Proceedings of the National Academy of Sciences of the United States of America, 108, 4313–4315, doi: 10.1073/pnas.1015078108

NAMC (National Agricultural Marketing Council) (2003). Food Pricing Monitoring Committee. Final Report. December 2003, Pretoria, NAMC.

Nelson, G. C., Rosegrant, M. W., Palazzo, A., Gray, I., Ingersoll, C., Robertson, R., … You, L. (2010). Food Security, Farming, and Climate Change to 2050: Scenarios, Results, and Policy Options. Washington, DC: IFPRI.

Parry, M., Rosenzweig, C., Iglesias, A., Fischer, G., & Livermore, M. (1999). Climate change and wolrd food security: a new assessment. Global Environmental Change, 9, 51-67.

Porter, J. R., Xie, L., Challinor, A. J., Cochrane, K., Howden, S. M., Iqbal, M. M., Lobell, D. B., & Travasso, M. I. (2014). Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., & White, L. L. (eds.)]. Cambridge: Cambridge University Press, 485-533.

Peyton, S., Mosely, W., & Battersby-Lennard, J. (2015). Implications of supermarket expansion on urban food security in Cape Town, South Africa. African Geographical Review, 34, 36-54.

Rakatoarisoa, M. A., Iafrate, M., & Paschali, M. (2011). Why has Africa become a net food exporter? Explaining Africa agricultural and food trade deficits. Rome: FAO.

Rashid, S. (2004). Spatial integration of maize markets in post-liberalized Uganda. MTID Discussion Paper No. 71, Washington, DC: IFPRI.

Ringler, C., Zhu, T., Cai, J. K., & Wang, D. (2010). Climate Change Impacts on Food Security in Sub-Saharan Africa: Insights from Comprehensive Climate Change Scenarios. IFPRI Discussion Paper No. 1042, Washington, DC: IFPRI.

Robles, M., Torero, M. (2010). Understanding the impact of high food prices in Latin America. Economia, 10, 117–164.

Rosegrant, M. W., Koo, J., Cenacchi, N., Ringler, C., Robertson, R., Fisher, M., Cox, C., Garrett, K., Perez, N. D., & Sabbagh, P. (2014). Food Security in a World of Natural Resource Scarcity. The Role of Agricultural Technologies. Washington, DC: IFPRI.

Rosenzweig, C., & Parry, M. L. (1994). Potential impact of climate change on world food supply. Nature, 367, 133 - 138 (13 January 1994); doi:10.1038/367133a0

Ruel, M. T. (2003). Operationalizing Dietary Diversity: A Review of Measurement Issues and Research Priorities. The Journal of Nutrition, 133, 3911-3926.

Said-Mohamed, R., Micklesfield, L. K., Pettifor, J. M., & Norris, S. A. (2015). Has the prevalence of stunting in South African children changed in 40 years? A systematic review. BMC Public Health, 15, 534-544. doi: 10.1186/s12889-015-1844-9

Schlenker, W., & Lobell, D. B. (2010). Robust negative impact of climate change on African agriculture. Environmental Research Letters, 5, 014010.

Schulze, R. E., Kiker, G. A., & Kunz, R. P. (1993). Global climate change and agricultural productivity in southern Africa. Global Environmental Change, 3, 330-349.

StatsSA (2012). GHS Series Volume IV: Food security and agriculture 2002-2011. In-depth analysis of the General Household Survey data. Report no. 03-18-03, Pretoria: StatsSA.

Tiwari, S., & Zaman, H. (2010). The impact of economic shocks on global undernourishment. World Bank Policy Research Working Paper No. 5215, Washington, DC: WB.

Vermeulen, S. J., Campbell, B. M., & Ingram, J. S. I. (2012). Climate Change and Food Systems. Annual Review of Environment and Resources, 37, 195-222.

Vicente-Serran, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalara drought index sensitive to global warming: The standardized precipitation evapotranspiration index. Journal of Climate, 23, 1696-1718.

Vink, N., & Kirsten, J. (2002). Pricing behavior in the South African food and agricultural sector. A report to the National Treasury. Pretoria: University of Pretoria.

Webb, P., Coates, J., Frongillo, E., Lorge Rogers, B., Swindale, A., & Bilinsky, P. (2006). Measuring household food insecurity: why it’s so important and yet so difficult to do. Journal of Nutrition, 136, 1404-1408.

Wheeler, T., & von Braun, J. (2013). Climate Change Impacts on global Food Security. Science, 341, 508-513. doi: 10.1126/science.1239402

WB (2010). World development report 2010: Development and climate change. Washington: WB.

Ziervogel, G., New, M., Archer van Garderen, E., Midgley, G., Taylor, A., … Warburton, M. (2014). Climate change impacts and adaptation in South Africa. WIREs Clim Change 2014, 5, 605–620. doi: 10.1002/wcc.295

ANNEX

40 Table A1. Weather shocks, food prices, and food security: IV Poisson regression Number of food items consumed (log) Household Dietary Diveristy Score (log) Own food No food Own food No food Total sample Total sample production production production production IV Poisson IV Poisson IV Poisson IV Poisson IV Poisson IV Poisson (1) (2) (3) (4) (5) (6)

Maize price, province (log) -3.849*** -10.91*** -2.613** -1.164*** -2.411*** -0.843* (1.295) (0.0439) (1.240) (0.416) (0.785) (0.504) Rural -0.0882*** -0.0861*** -0.0870*** -0.0556*** -0.0442*** -0.0567*** (0.00862) (0.0190) (0.00920) (0.00423) (0.00966) (0.00494) Housheold size 0.0469*** 0.0421*** 0.0481*** 0.0235*** 0.0210*** 0.0241*** (0.000963) (0.00249) (0.00108) (0.000580) (0.00122) (0.000666) Black (reference group)

Coloured 0.0751*** -0.0282 0.0759*** 0.0294*** -0.00754 0.0290*** (0.00850) (0.0532) (0.00833) (0.00485) (0.0243) (0.00494) Indian / Asian -0.0120 0.223 -0.0157 -0.0112 0.0815 -0.0122 (0.0160) (0.144) (0.0160) (0.0102) (0.0650) (0.0103) White 0.225*** 0.303*** 0.222*** 0.0855*** 0.110*** 0.0831*** (0.00979) (0.0485) (0.00991) (0.00552) (0.0216) (0.00577) Female household head 0.0200*** 0.0253* 0.0174*** 0.0149*** 0.0146** 0.0146*** (0.00424) (0.0131) (0.00457) (0.00263) (0.00651) (0.00290) Wealth index 0.0877*** 0.0719*** 0.0902*** 0.0386*** 0.0320*** 0.0398*** (0.00174) (0.00469) (0.00187) (0.000992) (0.00239) (0.00111) Social welfare grant 0.0237*** 0.0501*** 0.0178*** 0.0242*** 0.0327*** 0.0222*** (0.00498) (0.0150) (0.00531) (0.00299) (0.00749) (0.00327) Own food production 0.0286*** 0.0158*** (0.00612) (0.00380) Constant 9.363*** 24.86 6.975*** 3.828*** 6.706*** 3.192*** (2.357) (0) (2.260) (0.756) (1.611) (0.915) Province dummy Yes Yes Yes Yes Yes Yes Year dummy Yes Yes Yes Yes Yes Yes

Observations 73,783 12,366 61,417 73,783 12,366 61,417 Notes: Instrumented: logarithm of maize price. Instrument: SPEI12. First-stage regressions omitted. All regressions control for the survey reference period. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1