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COVID-19 market disruptions and food security: Evidence from households in rural Liberia and Malawi

We use data collected from panel phone surveys to document the changes in food security of households in rural Liberia and Malawi during the market disruptions associated with the COVID-19 lockdowns in 2020. We use two distinct empirical approaches in our analysis: (a) an event study around the date of the lockdowns (March to July 2020), and (b) a difference-in-differences analysis comparing the lockdown period in 2020 to the same months in 2021, in order to attempt to control for seasonality. In both countries, market activity was severely disrupted and we observe declines in expenditures. However, we find no evidence of declines in food security.

Rural Africa has largely been an afterthought during the COVID-19 pandemic, due in 2 part to substantially lower (reported) disease prevalence. Yet while case counts have 3 stayed relatively modest, economic disruptions were nearly as intense as those in 4 developed countries, especially near the beginning of the pandemic [1]. What was the 5 impact of these lockdowns on the livelihoods of rural households? 6 This study is set in rural Liberia and Malawi. Both countries implemented versions 7 of lockdowns from roughly March 2020 to July 2020, though restrictions were more 8 severe in Liberia (which ordered a full shelter-in-place for 3 months) than in Malawi 9 (which did not impose shelter-in-place but still closed schools and placed restrictions on 10 transportation and gatherings). Both countries restricted cross-border movement. In respondents per village) were selected to take part in monthly phone surveys that began 22 in 2019, well before the global onset of COVID-19. The survey was conducted every 2 23 months for each household (with half of the sample interviewed each month). The 24 surveys continued during the lockdowns, and for more than a year after the lockdowns 25 were first implemented. The respondents in this sample are representative of the 26 approximately 32,000 households (150,000 people) in the study region. 27 The main outcome that we study in this paper is food security, which we measured 28 both before and after the pandemic in a consistent fashion, i.e., using an identical set of 29 survey questions and with an unchanged survey modality. We use 3 measures which are 30 recommended for use by organizations such as the FAO and USAID, and have been 31 validated in several settings to meaningfully correlate with food security [2]. These are 32 (1) the household dietary diversity score (HDDS); (2) the food consumption score 33 (FCS); and (3) the household hunger scale (HHS). In addition, the surveys included 34 questions on income, labor supply, expenditures, transfers and other related outcomes, 35 as well as a module on attitudes towards COVID, and resultant behavior changes, 36 which was added in May 2020. 37 We use this data in two distinct empirical strategies to document how food security 38 changed in these areas during the lockdowns. In the first, we use a similar methodology 39 as several other studies of COVID, and use the time series from our survey data to 40 measure the changes in food security immediately after the lockdowns were 41 implemented relative to the period just before, i.e., an event study design. 42 In the second strategy, we use data collected in 2021 to implement a 43 difference-in-differences design. Specifically, we measure changes in food security in 44 April-August of 2020 (relative to January-February 2020) and compare them against the 45 difference between the same months in 2021. The advantage of this second design is 46 that it can attempt to net out seasonal differences, which are likely important in this 47 setting in which seasonality (due especially to the harvest cycle) is an important 48 determinant of prices and food availability. Our identifying assumption is that the 49 effects of the lockdown on food security and prices had dissipated by 2021, such that 50 2021 is a valid counterfactual. We acknowledge, however, that it is possible that 2021 51 was also affected by COVID and other local and global macroeconomic conditions, and 52 we view this evidence as fundamentally descriptive. Nevertheless, we argue that given 53 the ubiquitous nature of the shock, this approach is the limit of what can be 54 accomplished in this setting, and we argue that this topic is of sufficient importance for 55 such evidence to be valuable. 56 Our main result, using either empirical design, is that we observe no evidence of a 57 decline in food security during the lockdown. We want to emphasize that these 58 households are food insecure to begin with: for example, the probability at baseline that 59 at least one household member went to be hungry in the past month was 40% in Liberia 60 and 48% in Malawi. However, these meager food security levels were preserved in the 61 post-pandemic period. We believe that these results stem in large part from these being 62 mostly subsistence households -while we observe a decline in food expenditures and 63 total expenditures, we do not find a decline in food consumption itself. This is also in 64 line with the evidence reviewed in [3], who identify two main channels through which and 2021 [4]. The design was nearly identical in both countries, with minor 72 context-specific differences. In each country, we evaluated the effect of UCTs given out 73 by the NGO GiveDirectly (henceforth, GD). The cash transfers averaged $500 at 74 current exchange rate (not PPP), roughly equivalent to annual household expenditures 75 of our study households. The treatment was randomized at the village level: in 76 treatment villages, all households received cash, while control villages received nothing. 77 Transfers were made via mobile money; since pre-existing mobile money usage was low, 78 beneficiaries were given the option to buy cell phones.

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The study areas were chosen by GD and USAID, the funding partner, based on 80 poverty levels, cell phone coverage, and proximity to roads. In Liberia, the project took 81 place in 6 districts in Bong and Nimba counties; in Malawi, it took place in Chiradzulu 82 and Machinga districts in the Southern Region. In Liberia, the project was phased in 83 over 2 years, and we utilize only the second wave for this project (because data collection 84 for the first wave does not coincide with the lockdown period). This wave, covering 210 85 villages in Bong and Nimba counties, began enrolling in early 2020. However, due to 86 COVID-related disruptions, many villages were not enrolled until late August. In

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Malawi, all villages were enrolled in 2019. S1 Fig shows a timeline of project activities. 88 We drew a sample using information provided by GD. To select villages, GD visited 89 each village considered for study inclusion, where GD field staff marked each habitation 90 structure with a GPS pin. We randomly selected 10 households from this list of GPS 91 pins, and we selected the female head of household for surveys, i.e., the spouse or 92 partner of the male head of household was interviewed. The fact that a preponderance 93 of our respondents is made up of women is potentially advantageous in this context as 94 the main outcome of interest is household food consumption, a subject about which 95 women are likely to have the most accurate information [5].

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In total, 600 villages were sampled (300 in each country -the relevant second wave 97 in Liberia included 210 of these 300), and we attempted to enroll 10 households per 98 village in the data collection study. For this particular paper, the relevant sample is 99 made up of 255 villages (150 villages in Malawi and 105 villages in Liberia) as we 100 exclude villages who received cash transfers to be able to document the effects of the 101 lockdowns for the average household in these areas.

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As described below, our main outcome is food security. In both countries, food  Our evaluation was designed to measure the time-varying effects of cash transfers. We 111 randomly selected 2 households per village to receive cell phones (worth $10-15), and In addition to food security, the phone survey also included questions on income, 118 expenditures, transfers, savings, and related outcomes. We have 3 measures of food 119 security: (1) the household dietary diversity score (HDDS), for which foods are grouped 120 into 12 categories, and the enumerator queries the respondent about each individual 121 category, recording whether at least one food item in each category was consumed in 122 the past 24 hours, summarized into an index ranging from 0-12; (2) the food 123 consumption score (FCS), which is similar to HDDS but measures frequency of 124 consumption rather than just indicators for 9 food groups (over the past 7 days), and 125 ranges from 0-112; and (3) the household hunger scale (HHS), which is based on a series 126 of 6 questions such as "In the past 4 weeks (30 days), was there ever no food to eat of 127 any kind in your house because of lack of resources to get food?" and "In the past 4 128 weeks (30 days), did you or any household member go to sleep at night hungry because 129 there was not enough food?" and ranges from 0-6.

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Shortly after the lockdowns began, we expanded the scope of phone surveys to 131 include modules geared towards measuring the impact of the unfolding crisis. These 132 surveys started in May 2020 after IRB approvals. First, we asked a series of questions 133 about knowledge, attitudes and behavior changes around COVID. Second, we added 134 modules to retrospectively measure outcomes that had not been measured previously.

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Specifically, we added questions on spousal labor income as well as business outcomes. 136 To construct a comparison month, we measured these month-by-month from February 137 2020 to May 2020 in the May/June 2020 round of the surveys (recall that half the 138 phone survey sample was interviewed every other month) and for the month preceding 139 the survey during survey rounds thereafter. While we administered the redesigned 140 surveys to everyone in the phone survey sample, this paper reports results only for the 141 GD control group in order to be able to abstract away from any protective benefits 142 afforded by the cash transfers, which would make the results less generalizable. 143 S1 Table shows attrition from the household phone surveys. Completion rates 144 ranged from 49%-82% in Liberia and were higher, 77%-100%, in Malawi. This is likely 145 because the cellular network is stronger in rural Malawi. In general, completion rates 146 trended downwards over time, as respondents changed phone numbers or decided to opt 147 out of the surveys. There is no break in completion around the COVID lockdown period 148 (March -July 2020), so sample composition changes around the lockdown are unlikely 149 to drive our results.

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Nevertheless, attrition does affect the external validity of our results. In both 151 countries, some households never participated in the phone surveys. In Liberia, of the 152 206 control households, only 150 ever participated in the phone surveys; in Malawi, of 153 the 297 control households, 285 ever participated. It is these 150 households in Liberia 154 and the 285 in Malawi that form our analysis sample for our first empirical specification 155 -the event study design. In S2 Table we show regressions where the dependent variable 156 is an indicator for appearing in the event study analysis sample. We regress this indicator in bivariate regressions on 16 household characteristics as shown in the table. 158 In Liberia, 2 characteristics are significant at 5% (access to a mobile phone, and the net 159 value of financial assets), and 3 more at 10% (age and 2 indicators for food insecurity). 160 In Malawi, 3 are significant at 5% (gender, an indicator for having a thatch roof and the 161 value of land and housing), and 1 is significant at 10% (monthly expenditure). We 162 conclude that we therefore see some deviation from the representative sample we 163 constructed at baseline. In particular, those in the sample are somewhat better off, 164 especially in Malawi (although the effect of land and housing assets, while significant, is 165 minuscule), and more likely to own a phone in Liberia (where the network is poor -166 recall that we gave every respondent a cell phone, so this effect is not mechanical).

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Our second empirical design utilizes difference-in-differences regressions in which we 168 include only those households who are present in the data for that calendar month in 169 both 2020 and 2021. We show correlates of inclusion into this analysis in S3 Table. In 170 this table, the dependent variable is the percentage of survey rounds in which the 171 household participated (which requires having data for that month in both 2020 and 172 2021). In Liberia, we see 3 coefficients significant at 5%: gender, household monthly 173 expenditure, and access to a cell phone. In Malawi, we also see 3 coefficients significant 174 at 5%: monthly food expenditure, home ownership, and value of physical assets. The 175 size of these coefficients is modest, however: for example, in Malawi a 1 standard 176 deviation increase in food expenditure is correlated with an increase in the proportion of 177 completed survey rounds by 2 percentage points on a base of 85 percent.

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While the pattern of coefficients in S2 Table and S3 Table suggests non-random   179 attrition, it is not clear in which direction this would bias results, and we find no 180 qualitative difference in results between the event study and difference-in-differences 181 specifications, suggesting that attrition is not the driving force between our findings (see 182 below). That said, it is possible that results would differ slightly in a fully 183 representative sample.

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Phone surveys with food vendors 185 In parallel to the household phone surveys, we also collected data on prices in markets 186 near the cash transfer evaluation, and in comparison markets. There were a total of 80 187 markets in Liberia and 95 in Malawi. We enrolled a set of vendors in each market in a 188 price data-collection exercise in which vendors were called once a month. In Liberia, the 189 items were salt, imported rice, cassava, cassava flour, chicken, fresh fish, dried fish, palm 190 oil, okra, and onions. In Malawi, the items were salt, sugar, sweet potatoes, rice, maize, 191 maize flour, chicken, dried fish, beans, groundnuts, tomatoes, eggs, and onions. We use 192 this data to construct market prices in order to show the impact of the pandemic on 193 food prices as this could be a key channel through which food security would be   Table 1 presents summary statistics from our baseline survey. Because we want this 207 paper to be able to speak to the average lockdown experience, we focus our analysis 208 only on the cash transfer control group and present statistics for that group only.

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From Panel A, the vast majority of the sample was female (since we purposefully 210 selected female heads of household for the interviews), and the average respondent was 211 39-43 years old. Most respondents were partnered and the average household had 4-5   Table 2 documents disruptions in overall economic activity after lockdown measures 260 were implemented by the national governments during March-July 2020. Panel A shows 261 that in Liberia, all activities were almost universally restricted. The extent of 262 disruptions was much smaller in Malawi, but nevertheless schools, religious centers and 263 public transportation were restricted or closed.

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Panel B summarizes self-reported behavior changes around the start of the pandemic. 265 Almost everyone in both countries reported that they stopped shaking hands, started 266 washing hands more frequently, and followed social distancing norms. A significant 267 fraction of people reported limiting travel and wearing masks. S5 Table shows a few   268 other selected indicators. Respondents were universally aware of the virus, and levels of 269 concern about it were quite high (this is true even in Malawi where public health 270 measures were more muted). Respondents overwhelmingly trusted information coming 271 from the government, and took the virus as a serious threat. However, from Panel C, no 272 households in Malawi reported any assistance to cope with the crisis (we did not collect 273 this data for Liberia).  a This is calculated from the reported cost of procuring a fixed bundle of items February 2020 versus when the survey was conducted, which ranges from May-July 2020. data. We find large reductions in profits in Liberia, declining more than 50% in April 281 relative to the February levels, and smaller but still substantial losses in Malawi of 282 about 40% in April and 20% in June.

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Food security 284 We start by presenting a time series of the food security index for both countries, 285 starting well before the beginning of the lockdowns (November 2019) and continuing 286 until the end of our data collection in August 2021. This series is plotted in Fig 1 and 287 provides visual evidence that neither country experienced a worsening of food security 288 during or after the lockdowns. In a simple pre-post comparison, food security actually 289 improved in Malawi and was mostly unchanged in Liberia. Fig 1. Trends in Household Food Security Index (z-score) Note: Food Security Index is a standardized z-score of HDDS, FCS, and HHS (negatively weighted), using inverse covariance weighting [6] with the mean and standard deviation for January/February 2020.

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To analyze this data rigorously, we utilize our phone survey data in two distinct 291 empirical approaches: (1) an event study around the date of the lockdown in March 292 2020; and (2) a difference-in-differences analysis comparing the same calendar months in 293 2020 and 2021. For the event study, we restrict analysis to data collected between 294 January and August 2020 (except March 2020, which was only partially affected). We 295 run the following specification using January-February 2020 as a reference group: where D mt is a dummy variable for month m in year t and µ i is a household fixed effect. 297 The outcome variable y imt is a composite food security index ("FSI") of household i in 298 month m in year t of three different indices of food security -HDDS, FCS, and HHS -299 and higher values indicate greater food security. Please note that since lower values of 300 HHS means higher food security, the FSI includes the inverted value of the HHS. β mt 301 represents the difference in food security between pre-and post-COVID lockdowns.

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For the difference-in-differences specification, we add data from those same calendar 303 months in 2021 (January-August, but again excluding March), and run the following, 304 with standard errors clustered at the village level: where D 2020 is a dummy variable, taking a value of 1 for year 2020 and 0 for year 2021. 306 γ m captures seasonality for a particular month as an average across the two years. The 307 coefficient on the interaction term, β m , is a vector of difference-in-differences estimators 308 that compare the change in food security between January/February and month m in 309 2020 to the change over the same period in 2021. The idea is to use the year 2021, when 310 there were no market disruptions due to COVID restrictions, as a comparison group of 311 the year 2020. We acknowledge, however, that it is possible that 2021 was also affected 312 by COVID and other local and global macroeconomic conditions, and may therefore 313 have limited validity as a counterfactual. The evidence from the difference-in-differences 314 specifications should therefore be viewed in conjunction with the event study to get a 315 fuller picture of food security during this period.

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Finally, in order to account for endogenous attrition, we run a third specification, 317 similar to Eq 2, but with the addition of a household-calendar month fixed effect. Doing 318 so ensures that the estimated coefficient for each month is based on the support of only 319 that sample which is present in the data in that calendar month for both the years -320 June 30, 2022 9/17 2020 as well as 2021. Therefore, the regression we estimate is the following: We present regression results separately for Liberia and Malawi graphically in Fig 2. 322 For each country, the left-hand panel shows the event study estimates for each year and 323 the right-hand panel shows the difference-in-differences estimates. The 324 difference-in-differences estimates are based on the specification given in Eq 3. 325 Fig 2. Household Food Security Index (z-score) Note: Food Security Index is a standardized z-score of HDDS, FCS, and HHS (negatively weighted), using inverse covariance weighting [6] with the mean and standard deviation for January/February 2020. Data collected in March are excluded because March 2020 was the start of the pandemic. The regression results for this figure are reported in S7 Table. Subfigures on the left plot the change in levels across months, while those on the right report coefficients (with 95% confidence intervals) from the difference-in-differences specification in Eq 3. Regressions include household-by-calendar fixed effects. Standard errors are clustered at the village level.
Food security in 2020 is denoted in the blue dotted lines, and shows that food 326 security actually improved during COVID lockdown months (i.e. months starting from 327 April 2020) relative to the pre-COVID period (i.e. January and February 2020). The

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To difference this seasonal trend out, we implement the difference-in-differences in 335 which we compare changes in 2020 to 2021. The difference-in-differences estimates on 336 the right show that controlling for trends mitigates observed changes, but does not turn 337 estimates negative. In fact, if anything, food security improved significantly in Malawi 338 (0.5-0.8 standard deviations). 339 We present corresponding regression results for both the event study and the 340 diff-in-diff design in S7 Table. Panel A shows Liberia and Panel B shows Malawi.

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Columns 1 and 4 show the event study for each country, and are followed by 342 difference-in-differences specifications with and without household-month fixed effects. 343 From the event study, we see an increase in food security of 0.10-0.39 standard 344 deviations during the COVID lock-down period (except for July 2020) in Liberia, and 345 an even bigger rise of more than 1 standard deviation in Malawi. From the 346 difference-in-differences estimates, we see that this increase is partially seasonal -effects 347 are attenuated in both countries -in Liberia, all months are indistinguishable from zero, 348 and some coefficients are even negative. Similarly, the estimates are smaller in Malawi 349 but still substantial. This latter result could be attributed to a stronger harvest in 350 Malawi in 2020 -the maize harvest in 2020 was 11% larger than that in 2019, and 28% 351 larger than the 5-year average for the country [7]. primarily measure dietary diversity, while the third (HHS) is a measure of the quantity 355 of food consumed. However, we find similar results for each of these three measures. 356 We complement our main food security outcome, FSI, with food and household 357 expenditure in S8 Fig and S9 Fig. While the levels of food expenditure did not decline 358 in either country, we do see some evidence of declines when accounting for seasonality in 359 the difference-in-differences (especially in Liberia). In Liberia, food expenditure declines 360 by 5-10 USD per month until August. In Malawi, point estimates are negative but not 361 significant, and average less than 5 USD. Total expenditures, by contrast, in S9 Fig, did 362 not decline in either country. One possible explanation for the difference between the 363 expenditure and food security results is that households switched to lower quality foods, 364 or to consumption from home production. While we cannot definitively determine the 365 mechanism here, this result does speak to how food expenditures may not always be a 366 good proxy for food consumption.  The figure shows that prices increased modestly in Liberia and actually declined in 380 Malawi. This latter effect is due in part to the seasonal maize harvest, which occurs 381 around this time (as can be seen in the 2021 data). We show these results in formal 382 regressions in S8 Table. In a difference-in-differences specification comparing 2020 and 383 2021, we observe price increases of 9-19% in Liberia for all crops (somewhat higher for 384 staples, though lower for rice alone), and modest declines in Malawi.

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We examine how income was affected in S9 Table. The odd-numbered Columns show 387 the event study and the even-numbered Columns show the difference-in-differences. The 388 tables show business profits (Columns 1-2), labor income (Columns 3-4), and total 389 non-agricultural income (Columns 5-6). Our data shows that people report earning very 390 little income, and so there is little to lose -the bottom of the Table shows that monthly 391 mean pre-lockdown income was only about $7 in Liberia and $8 in Malawi. While it is 392 possible that income is not comprehensively measured (especially spousal income), we 393 nevertheless do not see any evidence of large declines. It is also worth noting that small 394 declines in income -even for these poor households -may not necessarily translate into 395 consumption declines for bare necessities such as food, as households will likely 396 reallocate their budget away from discretionary expenditures to preserve food 397 consumption.

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Relationship to existing literature and discussion 399 Because countries like Malawi and Liberia are so poor and lack formal safety nets, many 400 commentators initially expected devastating effects of the lockdowns, warning of 401 millions being pushed into poverty and even of imminent starvation [8][9][10]. Given the 402 contrast with popular discourse, the preservation of food security levels during the 403 pandemic was not expected by the research team. However, emerging evidence from 404 academic studies suggests that perhaps the worst fears from the initial days of the 405 pandemic were unfounded -while many studies show significant declines in food security 406 in the aftermath of the lockdowns, there are many others who find more modest effects. 407 In this section, we review some of these findings and discuss how our paper relates to 408 the broader literature.

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A number of prominent papers on this topic find negative effects of the pandemic on 410 food security. [11] construct a sample of 16 surveys in 9 countries in Africa, Asia, and 411 Latin America, and find substantial declines in employment, income, and food security 412 in all settings; [12] find evidence of a decline in food security using panel data in Nigeria, 413 and [13] provide similar evidence for Mali. A number of other studies also show declines 414 in food security in many developing countries (e.g. [14][15][16][17][18][19]). [20] provide a review of the 415 evidence generated in the immediate aftermath of the pandemic).

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The rural setting of our study might have also been protective. Rural areas were less 427 likely to be affected by the virus, because of low population density, remoteness from 428 population centers, and reliance on farming (which can be practiced at social distance), 429 and so COVID itself did not spread widely in rural Africa. Perhaps some of these 430 factors also insulated households from lockdowns -while economic activity in urban 431 population centers clearly declined, this may not have spilled over into more rural areas. 432 In our samples, people earn very little income to start with (less than $10 per month), 433 and we find no evidence of a decline in income in Liberia. While we find a modest 434 decline in Malawi, this appears to be mostly seasonal. Similarly, while markets were 435 restricted, they were nevertheless open, and the price of food changed only modestly.

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On the other hand, we also note that our study is not entirely unique in finding that 437 food security levels did not dramatically change during the COVID-19 market 438 restrictions. [21] analyze longitudinal data from five African countries, and find evidence 439 supporting a worsening of food security in only one (Nigeria), leading them to conclude 440 that a number of contextual factors, such as a terms of trade improvement in the case of 441 Mali, and a bumper harvest in the case of Malawi, may have afforded protection from 442 the pandemic, a finding that is mirrored in our work. [22] find that impacts of 443 lockdowns were linked with market integration levels -farmer incomes declined in 444 remote areas where market integration was poorer, but consumption improved because 445 more stocks were available locally. Availability of food at the local level is also identified 446 as a major determinant of pandemic-induced food insecurity by [3] in their review of 447 more than 300 documents on the impacts of the lockdowns. Finally, we point to the 448 evidence provided in [13] who show that while food security worsened nationally due to 449 the pandemic in the country of Mali, only urban households identified the pandemic as 450 the reason behind the lower levels of food security, suggesting that seasonal changes in 451 food availability are an important consideration in rural areas, and that all changes in 452 rural food security are not necessarily attributable to the pandemic.

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Our findings are also similar to [23] and to [24], who find no worsening of food 454 security in urban Addis Ababa and Delhi, respectively, despite a decrease in incomes; in 455 the case of Delhi, however, this is driven by government-provided assistance. In our 456 context, however, direct government support was non-existent: no households in our 457 samples reported receiving any cash or food support from the government or an NGO 458 during the lockdown. Also, unlike these papers, we do not observe a decline in income 459 in our data, since our sample is made up largely of subsistence farmers. It is also worth 460 noting that income declines will not necessarily lead to declines in food security as in the 461 face of an income shock, households will likely reallocate expenditures from discretionary 462 consumption towards essentials, such as food. For example, in the multi-country studies 463 described in [11], even though there are declines in food security, they are, by and large, 464 much smaller in magnitude than the declines in income. [25] also make this nuanced 465 point by showing that in the wake of the pandemic, poor households in rural Kenya 466 were able to preserve their level of food expenditure by reallocating spending away from 467 schooling and transportation, as well as by cutting back on informal risk-sharing and 468 delaying repayment of loans. Similarly, [26] show that while households in India had to 469 reduce their consumption of vegetables and animal-source foods, they were able to 470 largely maintain their consumption of staples, suggesting that the lockdowns largely 471 impacted access to perishables. Therefore, the take-away from this set of papers should 472 not be that poor households in developing countries have not been impacted by the 473 pandemic, but simply that they have managed to preserve their low levels of food 474 security during the pandemic, likely by cutting back on other expenditures. 475 Finally, we want to caveat our findings by noting that the results of this paper may 476 be impacted by the fact that due to attrition, our phone survey sample is positively 477 selected to be richer. As such, this is not a problem that is unique to our study as other 478 papers that use panel surveys dating back to the pre-pandemic period face similar 479 attrition issues [12,13]. Nevertheless, our findings should be interpreted with caution.

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We document how households in rural Liberia and Malawi fared in the aftermath of the 482 COVID-19 market disruptions using panel phone surveys of households and food 483 vendors. We find high levels of awareness and behavior change and large declines in 484 market activity. However, we find no evidence of increased food insecurity.

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While our prior was that we would find large declines in food security, a plausible ex 486 post reason for these results is that that rural areas like the ones we study are poorly 487 connected to economic centers of activity; and while this may be a core reason for their 488 poverty, paradoxically the isolation of these areas may make them more immune to 489 economic declines in urban centers. We see evidence in favor of this both from the 490 modest changes in prices, and in income (in large part because households earn so little 491 income in the first place).

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All in all, it appears that the worst fears about lockdowns were not realized, at least 493 for these contexts. Our results suggest that lockdowns can be implemented in rural 494 areas if necessary, without causing large increases in food insecurity (at least for some 495 amount of time), even in very poor settings. The disease itself has not yet spread widely 496 in much of rural Africa, and activities like subsistence farming have apparently 497 continued with modest disruption. Other sources of income were disrupted for some 498 time, but most households earned very little from such activities. Similarly, in other 499 contexts, people have worried about the loss of services such as school meals -yet in 500 this setting, kids were not getting meals in the first place anyway, so there was little to 501 lose. (see S10 Table). In this context, market disruptions -which limit but do not 502 eliminate economic activity, and not accompanied by a direct loss of assets -might be 503 easier to cope with than natural disasters, even for very poor households.