Policy Research Working Paper 10604 Economic Sentiments and Expectations in Sub-Saharan Africa in a Time of Multiple Shocks Philip Wollburg Thomas Bentze Talip Kilic Development Economics A verified reproducibility package for this paper is Development Data Group available at http://reproducibility.worldbank.org, November 2023 click here for direct access. Policy Research Working Paper 10604 Abstract Against the background of high inflation, climate shocks, 12 months. Close to 54 percent of households—home to and concerns about rising food insecurity, this study docu- 206 million individuals—further expect that climate shocks ments the state of economic sentiments and expectations of will have adverse impacts on their finances in the next year. households in five African countries—Burkina Faso, Ethi- Economic sentiments are closely related to livelihood out- opia, Malawi, Nigeria, and Uganda—that are home to 36 comes such as food insecurity, lack of access to staple foods, percent of the Sub-Saharan African population. Leverag- income loss, and unemployment, and sentiments about the ing nationally representative phone survey data, 57 percent household financial situation, country economic situation, of households across the five countries report that their price increases, and climate shocks are also interdependent. financial situation and their country’s economic situation Households whose financial situation has worsened in the have worsened significantly in the past 12 months. While past year are consistently more pessimistic about their finan- expectations for the future are more positive, there are cial future. Food insecure households, in particular, are not marked differences across countries that suggest uneven only more likely to report a worsening financial situation recovery prospects and nonnegligible uncertainty about the in the recent past and pessimism about the future, but also future. Households overwhelmingly report prices to have more likely to expect to be adversely impacted by climate increased considerably over the past 12 months and expect shocks. prices to increase faster, or at the same rate, over the next This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pwollburg@worldbank.org, tbentze@worldbank.org, and tkilic@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click here for direct access. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Economic Sentiments and Expectations in Sub- Saharan Africa in a Time of Multiple Shocks Philip Wollburg*†, Thomas Bentze† and Talip Kilic†1 JEL Codes: C83, D84, I31. Keywords: Economic sentiments, expectations, shocks, phone surveys, Sub-Saharan Africa. 1 † Living Standards Measurement Study, Development Economics Data Group, World Bank.* Corresponding author, pwollburg@worldbank.org. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Introduction Over the past three years, the lives and livelihoods of households and individuals in Sub-Saharan Africa have been affected by the COVID-19 pandemic, severe climate shocks, tightening fiscal constraints, high inflation, and concerns about rising food insecurity (World Bank, 2022). We document the state of economic sentiments and expectations of households in five African countries in this difficult environment. Our study countries are home to 421 million individuals in total, representing 36 percent of the population of Sub-Saharan Africa in 2021 (World Bank, 2023). Economic sentiments and expectations are widely studied in North America, Europe, and other high-income contexts, are frequently reported, and inform a sizable academic literature (Ludvigson, 2004). However, there is a scarcity of evidence on the economic sentiments and expectations of African households and of households in low- and lower-middle-income countries more generally. Existing evidence is limited to expectations in other domains such as education (Jensen, 2010), migration (Baseler, 2020), and agricultural production (Vargas Hill, 2009). We use data from national high-frequency phone surveys (HFPS) in Burkina Faso, Ethiopia, Malawi, Nigeria and Uganda, implemented since April 2020 by the respective national statistical offices with support from the World Bank Living Standards Measurement Study (LSMS). The specific HFPS rounds that inform our analyses were implemented between May 2022 and January 2023. The HFPS questionnaires capture current perceptions and future expectations regarding the financial situation of households and the economic direction of each country, as well as perceived current and expected future price changes and impacts of climate shocks. The questions are asked of the household member that is deemed most knowledgeable about the household economy, as such the respondents are assumed to provide a representative outlook of households but not of the general adult population (Brubaker et al., 2021). The HFPS in each country use as a sampling frame a recent round of the national face-to-face longitudinal household survey and the nationally representative face- to-face survey data are used to recalibrate the HFPS sampling weights to adjust for coverage and non-response biases that are inherent in phone surveys (Ambel et al., 2021.) Leveraging the longitudinal and multi-topic nature of the HFPS rounds, we link economic sentiments and expectations to household livelihood outcomes, such as food insecurity or income loss, which have affected households in the recent past. Results Current economic situation and future outlook African households consistently assess their financial situation to have deteriorated over the past 12 months. While their expectations regarding the future are more positive, there are marked differences across countries that suggest uneven recovery prospects and non-negligible uncertainty regarding the future country economic situation. A majority of survey respondents report being worse off financially at the time of the survey compared to 12 months earlier, and few report being better off now, with marked cross-country differences. Across countries, a (weighted) average of 57 percent of respondents report being worse off, representing 39 million households in total. The most negative assessments of the household’s financial situation relative to 12 months ago (as seen in Table A1) were reported in Malawi, followed by Uganda, Burkina Faso, Ethiopia and Nigeria - 75 percent, 69 percent, 60 percent, 59 percent and 48 percent, respectively, of respondents answered that their household’s financial situation had gotten worse over the past year. Sentiments relating to the economic situation of the country are even more dire. Although Ethiopian households are the most positive about their country’s economic situation during the last 12 months vis-à-vis their counterparts in other countries, still around 57 percent report that the country’s economic situation got a little worse or got a lot worse. The share 2 stands at approximately 67 percent of households in Nigeria, 85 percent in Malawi, 88 percent in Burkina Faso, and 89 percent in Uganda (Table A3). Expectations for the future are more optimistic across the board. The cross-country weighted share of survey respondents expecting to be better off in the next 12 months is 49 percent. Almost 7 in 10 Nigerian survey respondents expect to be better off financially in a year’s time than today, and almost 6 in 10 respondents in Burkina Faso. Compared to Nigeria and Burkina Faso, respondents in Ethiopia, Uganda and Malawi are less optimistic about the future, with only 2 to 4 and out of 10 respondents expecting to be better off in a year’s time (Table A2). While low, this is still a higher rate of optimism vis-a-vis the current financial situation. We observe a similar trend when it comes to sentiments about the development of the country’s economic situation in the next five years. In Burkina Faso, Nigeria and Ethiopia, close to 73 percent, 65 percent and 58 percent of households, respectively, expect their country’s economic situation to get “a little” or “a lot” better. The corresponding rates are 28 percent and 18 percent for Uganda and Malawi, respectively (Table A4). It is important to note that a substantial portion of respondents answer that they “don’t know” if the country situation will get better, especially in Uganda and Nigeria (25 percent and 19 percent of respondents, respectively). Figure 1: Responses to the sentiments module, pt1 A B Would you say that you and your household are financially … than you How do you think the economic situation in the country has changed were a year ago? during the previous 12 months? Burkina Faso Burkina Faso Ethiopia Ethiopia Malawi Malawi Nigeria Nigeria Uganda Uganda 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Proportion of responses (%) Proportion of responses (%) Got a lot better Got a little better Stayed about the same Better Now Same Worse Don't Know Got a little worse Got a lot worse Don't know C D Do you think that a year from now you and your household will be In the next 5 years, how do you expect the general economic situation better off financially? in the country to develop? Burkina Faso Burkina Faso Ethiopia Ethiopia Malawi Malawi Nigeria Nigeria Uganda Uganda 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Proportion of responses (%) Proportion of responses (%) Get a lot better Get a little better Stay about the same Will be better off Same Will be worse off Don't know Get a little worse Get a lot worse Don't know Expected price increases and extreme weather 3 Across study countries, survey respondents overwhelmingly report that prices have increased ‘a lot’ over the past year. The cross-country weighted share of households reporting considerable price increases over the past year stands at 86 percent. There is limited country variation: a total of 79 percent in Ethiopia, 85 percent of respondents in Burkina Faso, 87 percent in Uganda, 90 percent in Nigeria, and 97 percent in Malawi have opted for this answer (Table A5). On the other hand, price expectations for the next 12 month differ significantly between countries. In Malawi, more than 82 percent of respondents expect prices to increase faster, or at the same rate, in the next 12 months than they have in the past year, while 76 percent of respondents in Ethiopia, 57 percent in Uganda, 46 percent in Nigeria and 23 percent of respondents in Burkina Faso respond the same. Again, respondents in Uganda, and especially in Malawi are pessimistic about the future (Table A6). Many survey respondents expect an extreme weather event to adversely affect the household financially. The share of respondents who deem it “likely” or “extremely likely” that extreme weather events will negatively impact them stands at 54 percent overall, with some variation across countries: 78 percent in Malawi, 77 percent in Burkina Faso, 58 percent in Uganda, 55 percent in Ethiopia and 45 percent in Nigeria (Table A7). In all countries, “delayed rains” is the most commonly cited source of weather shocks, except in Uganda, where “drought” is the most cited (Table A8). Figure 2: Responses to the sentiments module, pt2 A B How have prices evolved over the past year? How do you expect that prices in general will develop during the next 12 months? Burkina Faso Burkina Faso Ethiopia Ethiopia Malawi Malawi Nigeria Nigeria Uganda Uganda 0 10 20 30 40 50 60 70 80 90 100 Proportion of responses (%) 0 10 20 30 40 50 60 70 80 90 100 Proportion of responses (%) Gone up a lot Gone up somewhat Stayed the same Gone down Don't know Go up more than last 12 mths Go up the same than last 12 mths Go up less than last 12 mths Stay about the same Go down Don't know C D How likely is it that extreme weather events will negatively affect your Which shocks are likely to affect your household financially? household financially? Burkina Faso Ethiopia Malawi Nigeria Uganda 0 10 20 30 40 50 60 70 80 90 100 Proportion of responses (%) Extremely likely Likely Neither likely nor unlikely Unlikely Extremely unlikely Dont know 4 We assess if economics sentiments varied according to several household characteristics: gender of the household head, urbanicity, and household dependency ratio. We find few consistent correlations, suggesting that these characteristics do not shape economic sentiments and expectations. Economic sentiments and livelihood outcomes How are economic sentiments and expectations related to the state of household livelihoods? Here, we explore the following livelihood outcomes: food security, access to staple foods, income, and employment. Households that have experienced food insecurity are consistently more likely to report a worsening financial situation in the past 12 months and more pessimistic expectations regarding their financial future. Respondents from households that experienced moderate or severe food insecurity in the past year are 20.4 percentage points likelier to also report a worsening financial situation in the past 12 months in Burkina Faso, 18.6 percentage points likelier in Nigeria, 10.2 percentage points likelier in Uganda and 6.5 percentage points in Malawi, while in Ethiopia the correlation is positive but not significant (Table A9). However, this does not translate consistently to future expectations (Table A11) nor to sentiments about the country’s economic situation or future (Table A13, Table A15). However, food insecure households are more likely to expect being severely affected by extreme weather events in Ethiopia (26.3 percentage points), Malawi (6.8 percentage points) and Uganda (17 percentage points), as seen in Table A21. Relatedly, there is a systematic relationship between lack of access to staple food and economic sentiments and expectations. Across all study countries (Table A9), survey respondents that report lacking access to staple foods are more likely to report a worsening financial situation in the past 12 months (Burkina Faso: 22.5 percentage points; Ethiopia: 21.6 percentage points; Malawi: 8.4 percentage points; Nigeria: 13.0 percentage points; Uganda: 14.8 percentage points). In Burkina Faso and Uganda (Table A11), those households are also more likely to expect a worsening financial situation for the household in the coming year (13.5 percentage points and 19.1 percentage points, respectively). There is little systematic association between lack of access to staple foods and sentiments and expectations for the country’s economic situation or prices. Experiences of income loss correlate with a negative assessment of the household’s financial situation (Table A9). Households that experienced any recent income loss in Ethiopia, Burkina Faso, and Nigeria are respectively 20, 15.1, and 10.7 percentage points percentage points likelier to report a worsening financial situation. Similarly, income loss is correlated with a negative assessment of the country situation in Malawi (8.5 percentage points), Nigeria (10.8 percentage points), and Uganda (7.9 percentage points), as seen in Table A13. Income loss does not consistently affect future expectations about the household’s financial situation or the country’s general economic direction (very few coefficients are distinguishable from 0), with inconsistent results for price expectations. Unemployed respondents in Uganda are also more likely to report a worsening financial situation over the last 12 months (11.6 percentage points, Table A9), to be worse off in a year from now (9.6 percentage points, Table A11), and price increases to accelerate (13.8 percentage points, Table A19) – the latter is also true of respondents in Burkina Faso (9.5 percentage points). Unemployed respondents in Ethiopia are 31.4 percentage points more likely to report a worsening financial situation and 21.7 percent more likely to expect the country’s economic situation to deteriorate, though they are less likely to report prices going up by a lot in the past 12 months (-33 percentage points), as seen in Table A9, Table A13 and Table A17. 5 Figure 3. Bivariate regressions A B Percent of respondents reporting a worsening household situation (Q1) Percent of respondents reporting a worsening household situation (Q1) Percent of respondents reporting a worsening household situation Burkina Faso Burkina Faso Ethiopia Ethiopia Malawi Malawi Nigeria Nigeria Uganda Uganda 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Percent (%) Percent (%) All households Household that lack access to staple foods All households Household has lost farm income Food insecure households Households with unemployed respondent Household has lost business income C D Percent of respondents reporting a worsening country situation (Q3) Percent of respondents expecting an acceleration of price increases (Q6) Burkina Faso Burkina Faso Ethiopia Ethiopia Malawi Malawi Nigeria Nigeria Uganda Uganda 0 10 20 30 40 50 60 70 80 90 100 Percent (%) 0 10 20 30 40 50 60 70 80 90 100 Percent (%) All households Household has lost farm income Household has lost business income All households Household that lack access to staple foods Food insecure households Households with unemployed respondent Interdependence between economic sentiments and expectations How persistent are economic sentiments over time and across domains? We find that households which experienced a worsening financial situation in the past 12 months are much more likely to hold pessimistic views about their financial future. This is especially true in Malawi and Uganda, where households are respectively 47.1 and 48 percentage points more likely to expect a worsening financial situation in the upcoming 12 months if they experienced a worsening situation in the past 12 months, but it also holds in Ethiopia (29 percentage points more likely), Burkina Faso (14 percentage points more likely) and in Nigeria (18.8 percentage points more likely), as seen in Table A23. We find similar patterns for questions on the country economic situation: households in Ethiopia, Malawi, Uganda and Nigeria are, respectively, 31, 27, 26.5 and 18.8 percentage points more likely to hold pessimistic views on the future of the country economic situation if they report worsening country conditions (Table A24). Additionally, households in Nigeria, Uganda, and Malawi are, respectively, 36.9, 36.3 and 17,6 percentage points more likely to expect an acceleration in price increases over the next year if they report a significant increase in prices over the past year (Table A26). 6 Households’ financial situations and their sentiments about the country’s economic direction are closely related. Households that experienced a worsening financial situation in the past year are also more likely to state that the country’s economic situation has deteriorated in Nigeria (25.4 percentage points more likely), Uganda (17.6 percentage points more likely) and Malawi (13.6 percentage points more likely), as seen in Table A25. Likewise, there is an even stronger correlation between expecting a worsening financial situation for the household and a worsening economic situation for the country (Table A23). This is observed in all countries: Uganda (40.8 percentage points more likely), Ethiopia (34.2 percentage points more likely) Malawi (26.2 percentage points more likely), Burkina Faso (17.9 percentage points more likely) and Nigeria (13.8 percentage points more likely). Finally, we find that price expectations are associated with sentiments about the household’s financial future and about the country’s economic direction. Households which expect prices in the next 12 months to increase more than in the previous 12 months are significantly more likely to expect a worsening financial situation for their household (27.2 percentage points more likely in Uganda, 19.3 percentage points in Malawi and 13 percentage points in Burkina Faso), as shown in Table A23. We also observe in Table A24 an effect of these price expectations on the likelihood of reporting that the country’s economic situation will deteriorate (35.6 percentage points in Uganda, 22.5 percentage points in Malawi, 18.1 percentage points in Nigeria and 15.9 percentage points in Burkina Faso). Figure 4. Coefficient plots for a selection of bivariate regressions A B Increase in prob. of negative future household opinions Increase in prob. of negative future household opinions if household holds negative opinions on the past if household holds negative opinions on the country future 0 10 20 30 40 50 0 10 20 30 40 50 Estimated % increase in prob. (with 90% CI) Estimated % increase in prob. (with 90% CI) C D Increase in prob. of negative future household opinions Increase in prob. of negative future country opinions if household believes that price increases will accelerate if household believes that price increases will accelerate 0 10 20 30 40 50 0 10 20 30 40 50 Estimated % increase in prob. (with 90% CI) Estimated % increase in prob. (with 90% CI) Burkina Faso Ethiopia Malawi Nigeria Uganda 7 Note: the graphs above plot the point estimates and 90% CIs of marginal effects from bivariate, country-level regressions. Discussion This study sheds light on the state of economic sentiments and expectations among African households at a time of multiple crises. Our results reflect the current moment’s challenging environment, marked by shocks to food prices and food access, as well as shocks related to climate: African households perceive their household’s financial situation and their country’s economic situation to have worsened considerably in the last year. While there is more optimism regarding the future, there are marked differences across countries that reveal uneven recovery prospects and non-negligible uncertainty. Households overwhelmingly report prices to have increased a lot in the past year, which they broadly expect to continue into the next year. Many expect that climate shocks will affect them financially in the next year. Economic sentiments and expectations are linked to livelihood outcomes, as food insecure households, those that lack access to food, recently experienced income loss or unemployment are more pessimistic about their current economic situation and prospects for the future. Economic sentiments are persistent over time, and expectations on the household financial situation, country economic situation, and price increases are interdependent. Households’ perceptions and expectations are not only related to household characteristics and informed by past experiences, they have also been shown to shape behavior and decisions in a wide range of domains, including labor market and education decisions (Attanasio and Kaufmann, 2017), health (Delavande and Kohler, 2012; Shapira, 2017), migration (McKenzie et al., 2013), and farming input choices (Delavande, 2022). Economic and price expectations, in particular, affect consumption, investment, and savings decisions (Ludvigson, 2004) and predict overall economic activity (D’Acunto et al., 2023; Golinelli and Parigi, 2004). In light of this, our results on future expectations are of particular relevance. African households are generally optimistic, rating their future outlook more highly than their current economic situation. This is striking in particular in Burkina Faso and Nigeria where 59 percent and 68 percent, respectively, expect their financial situation to improve in the next 12 months, while only 7 percent and 22 percent, respectively, say their situation has improved in the past 12 months. We also document that expectations of greater inflation are associated with a more pessimistic outlook on the household’s financial future. At the same time, we show that expectations for the future are to some extent informed by perceptions of the present and the past. While the data availability on economic sentiments is still limited to one or few time points per country, the future phone survey rounds will allow for analyses of the dynamics of economic sentiments and expectations over time. Methods Data and variables We use data from High Frequency Phone Surveys (HFPS) in five Sub-Saharan African countries: Burkina Faso, Ethiopia, Malawi, Nigeria and Uganda. Drawing on the Michigan Survey of Consumers and the European Commission Consumer Survey (Curtin and Dechaux, 2015), the HFPS questionnaires capture respondents’ current perceptions and future expectations on the financial situation of their households and the economic direction of the country, as well as perceived current and expected future price changes and impacts of climate shocks. Data for Malawi were collected in May 2022, data for Burkina Faso and Nigeria were collected in June 2022, data for Uganda were collected between June and July 2022, and data for Ethiopia were collected between December 2022 and January 2023. The module on economic sentiments was fielded for all respondents in Malawi and Uganda, and for a random sub-sample of HFPS respondents in Burkina Faso, Ethiopia, and Nigeria. There are 8 questions analyzed in this study: 8 1. We are interested in how people are getting along financially these days. Would you say that you and your household are financially better off, about the same, or worse off than you were a year ago? The response options are: BETTER NOW; SAME; WORSE; DON’T KNOW. 2. Now looking ahead--do you think that a year from now you and your household will be better off financially, or worse off, or just about the same as now? The response options are: WILL BE BETTER OFF; SAME; WILL BE WORSE OFF; DON’T KNOW. 3. Now turning to the economic situation in the country as a whole. How do you think the general economic situation in the country has changed during the past 12 months? It has … The response options are: GOT A LOT BETTER; GOT A LITTLE BETTER; STAYED ABOUT THE SAME; GOT A LITTLE WORSE; GOT A LOT WORSE; DON’T KNOW 4. And during the next 5 years, how do you expect the general economic situation in this country to develop? It will … The response options are: GET A LOT BETTER; GET A LITTLE BETTER; STAY ABOUT THE SAME; GET A LITTLE WORSE; GET A LOT WORSE; DON’T KNOW 5. Now turning to prices in general: During the last 12 months, do you think prices in general have gone up a lot, gone up somewhat, stayed the same, or gone down? The response options are: GONE UP A LOT; GONE UP SOMEWHAT; STAYED THE SAME; GONE DOWN; DON’T KNOW 6. By comparison with the past 12 months, how do you expect that prices in general will develop during the next 12 months? The response options are: GO UP MORE THAN IN THE LAST 12 MONTH; GO UP AT THE SAME RATE AS IN THE LAST 12 MONTHS; GO UP LESS THAN IN THE LAST 12 MOTHS; STAY ABOUT THE SAME; GO DOWN; DON’T KNOW 7. We would now like to ask you about extreme weather events, such as drought conditions, delayed rains, floods, and heatwaves, how likely is it that extreme weather events will negatively affect you and your household financially during the next 12 months? The response options are: EXTREMELY LIKELY; LIKELY; NEITHER LIKELY NOR UNLIKELY; UNLIKELY; EXTREMELY UNLIKELY; DON’T KNOW 8. (If EXTREMELY LIKELY or LIKELY) Which events, do you expect will negatively affect you and your household financially during the next 12 months? The response options are: DROUGHT CONDITIONS; DELAYED RAINS; FLOODS; HEATWAVES Along with the data collected on economic sentiments, respondents were asked to provide information on a range of topics, including employment status, food availability, and socio-demographic characteristics, which we make use of in this analysis. Our food insecurity indicator was compiled from Food Insecurity Scale (FIES) modules and is equal to one if a household is at least moderately food insecure. Following methodology from Adjognon et al. (2021), we define as “moderately food insecure” households with a raw FIES score above three. We rely on employment modules across surveys to create an indicator equal to one if the respondent declares having been unemployed in the past week. 9 Furthermore, we collected data on food access and income shocks from prior phones survey rounds (dating from up to a month prior to the sentiments data collection), as the corresponding modules were not always fielded in the same year as the economics sentiments survey module. In addition, HFPS surveys allow us to identify “household heads” (usually consisting of members who make key decisions in the household (National Bureau of Statistics (NBS), n.d.). With this information, we have computed an indicator equal to one if the household head is female. We have also collected data from past rounds to create a dependency ratio, calculated as the ratio of household individuals that are between 15 and 65 years of age, to those that are under 15 and over 65 years old. Furthermore, using the GPS coordinates of households in our sample, we classify households into “urban” and “semi-urban” areas, according to the classifications from the Global Human Settlement Layer (GHSL) project (CEU. JRC., 2019). This data uses a “degree of urbanization” model to create a refined classification of areas by degree of settlement. Sampling and representativeness Households in the high frequency phone surveys were sampled from nationally representative in-person household surveys, which in turn used two-stage clustered sampling. The baseline surveys are the following: the 2018/2019 round of the Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM) for Burkina Faso, the 2018/2019 Ethiopia Statistics Survey (ESS) for Ethiopia, the 2019 Integrated Household Panel Survey in Malawi, the 2018/2019 General Household Survey Panel for Nigeria, and the 2019/2020 Uganda National Panel Survey. In-person households were included in the phone survey if they had provided a phone contact number (either of their own or of a friend or relative inside or outside of the household). However, not all households in the in-person survey samples have access to a phone, such that the phone survey sample suffers from a degree of under-coverage. In addition, as is common in phone surveys, there was some non-response, when households did not pick up the phone or their numbers were disconnected. To mitigate sample selection biases from under- coverage and non-response, the survey weights from the face-to-face surveys were recalibrated with propensity score matching techniques using information on the characteristics of selected and non-selected households (Brubaker et al., 2021; Himelein, 2014). In Burkina Faso, Ethiopia and Nigeria, weights were additionally re- adjusted at the stratum level to maintain national representativity, as only a random sub-sample of HFPS respondents filled out the economic sentiments module. The HFPS respondent was purposively selected to be an adult knowledgeable of the household’s economy and affairs. Estimation In section 1, we present weighted means of the responses to the categorical economic sentiments questions. Logistic regressions were used to estimate parameters in Sections 2 and 3, and marginal effects were computed for interpretability (Greene, 2000). Results presented in this analysis take the complex survey structure of the data into account. Sample weights are included to ensure that all results are representative of populations in the countries included in the study. 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Journal of Human Resources 52, 680–718. Vargas Hill, R., 2009. Using Stated Preferences and Beliefs to Identify the Impact of Risk on Poor Households. The Journal of Development Studies 45, 151–171. https://doi.org/10.1080/00220380802553065 World Bank, 2023. “Population, total” World Bank Development Indicators. World Bank, 2022. World Development Report 2022: Finance for an Equitable Recovery. The World Bank. https://doi.org/10.1596/978-1-4648-1730-4 11 Appendix Part 1: Proportions by question Q1: Would you say that you and your household are financially … than you were a year ago? Table A1 Better Now Same Worse Don't Know Burkina Faso 6.870 % 29.466 % 59.909 % 3.755 % Proportion of responses (1.072) (2.519) (2.662) (1.124) [5.300 % ; 8.862 % ] [25.490 % ; 33.780 % ] [55.453 % ; 64.207 % ] [2.284 % ; 6.116 % ] Aggregate number of households 210,155 901,363 1,832,617 114,873 Aggregate number of individuals 1,391,714 6,079,060 12,621,347 1,119,053 Ethiopia 17.217 % 23.495 % 59.288 % 0 Proportion of responses (3.840) (2.848) (4.683) [11.768 % ; 24.490 % ] [19.125 % ; 28.511 % ] [51.396 % ; 66.728 % ] Aggregate number of households 4,489,795 6,168,403 15,584,618 Aggregate number of individuals 18,246,683 28,199,652 72,392,646 Malawi 10.556 % 13.288 % 75.478 % 0.679 % Proportion of responses (1.547) (1.580) (2.364) (0.486) [8.248 % ; 13.415 % ] [10.875 % ; 16.138 % ] [71.343 % ; 79.190 % ] [0.206 % ; 2.213 % ] Aggregate number of households 377,952 475,768 2,702,509 24,312 Aggregate number of individuals 2,032,822 2,392,163 14,037,814 48,624 Nigeria 21.617 % 29.639 % 48.532 % 0.212 % Proportion of responses (1.682) (2.224) (2.312) (0.159) [18.973 % ; 24.518 % ] [26.109 % ; 33.432 % ] [44.737 % ; 52.344 % ] [0.061 % ; 0.728 % ] Aggregate number of households 5,827,395 7,989,907 13,082,714 57,035 Aggregate number of individuals 40,612,843 58,521,269 86,744,806 318,286 Uganda 13.100 % 17.194 % 68.845 % 0.861 % Proportion of responses (1.169) (1.299) (1.568) (0.364) [11.293 % ; 15.148 % ] [15.158 % ; 19.440 % ] [66.206 % ; 71.367 % ] [0.429 % ; 1.722 % ] Aggregate number of households 1,113,855 1,461,899 5,853,636 73,204 Aggregate number of individuals 5,749,493 7,501,214 31,327,521 314,198 Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 12 Q2: Do you think that a year from now you and your household will be better off financially? Table A2 Will be better off Same Will be worse off Don't know Burkina Faso 58.568 % 15.600 % 12.212 % 13.620 % Proportion of (2.733) (1.993) (1.867) (1.576) responses [54.005 % ; 62.988 % ] [12.589 % ; 19.173 % ] [9.453 % ; 15.637 % ] [11.225 % ; 16.432 % ] Aggregate number of 1,791,590 477,208 373,562 416,647 households Aggregate number of 12,847,614 2,867,154 2,770,014 2,726,393 individuals Ethiopia 38.927 % 35.031 % 23.529 % 2.512 % Proportion of (4.480) (5.762) (4.162) (0.722) responses [31.840 % ; 46.516 % ] [26.208 % ; 45.013 % ] [17.362 % ; 31.064 % ] [1.559 % ; 4.022 % ] Aggregate number of 10,259,498 9,115,462 6,207,897 659,960 households Aggregate number of 46,893,521 43,156,075 26,060,292 2,729,092 individuals Malawi 24.975 % 15.493 % 48.149 % 11.383 % Proportion of (1.937) (1.330) (2.323) (1.266) responses [21.898 % ; 28.328 % ] [13.410 % ; 17.833 % ] [44.309 % ; 52.010 % ] [9.445 % ; 13.660 % ] Aggregate number of 894,232 554,744 1,723,978 407,586 households Aggregate number of 4,908,298 2,682,294 8,753,285 2,167,546 individuals Nigeria 67.943 % 9.960 % 9.388 % 12.708 % Proportion of (1.893) (1.323) (1.226) (1.288) responses [64.746 % ; 70.981 % ] [7.981 % ; 12.364 % ] [7.553 % ; 11.613 % ] [10.733 % ; 14.987 % ] Aggregate number of 18,315,524 2,684,977 2,530,760 3,425,792 households Aggregate number of 126,580,378 19,804,329 17,808,229 22,004,269 individuals Uganda 30.487 % 21.344 % 32.653 % 15.516 % Proportion of (1.606) (1.416) (1.554) (1.199) responses [27.908 % ; 33.195 % ] [19.106 % ; 23.768 % ] [30.147 % ; 35.262 % ] [13.642 % ; 17.594 % ] Aggregate number of 2,592,204 1,814,820 2,776,333 1,319,236 households Aggregate number of 13,594,443 9,583,032 14,584,859 7,130,092 individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 13 Q3: How do you think the general economic situation in the country has changed during the previous 12 months? Got a lot Got a little Stayed about Got a little Got a lot Table A3 better better the same worse worse Don't know Burkina Faso 1.228 % 4.909 % 2.349 % 25.095 % 63.317 % 3.102 % Proportion of (0.436) (0.959) (0.735) (2.410) (2.552) (0.783) responses [0.683 % ; [3.549 % ; [1.399 % ; [21.335 % ; [59.019 % ; [2.041 % ; 2.198 % ] 6.754 % ] 3.918 % ] 29.271 % ] 67.413 % ] 4.688 % ] Aggregate number 37,554 150,173 71,852 767,657 1,936,871 94,902 of households Aggregate number 218,640 1,123,865 294,172 5,314,581 13,595,599 664,317 of individuals Ethiopia 7.768 % 23.797 % 10.396 % 24.515 % 32.923 % 0.601 % Proportion of (3.678) (5.807) (1.406) (4.150) (4.596) (0.295) responses [3.486 % ; [15.552 % ; [8.295 % ; [18.325 % ; [25.828 % ; [0.268 % ; 16.415 % ] 34.621 % ] 12.953 % ] 31.978 % ] 40.892 % ] 1.346 % ] Aggregate number 2,021,607 6,186,835 2,778,962 6,398,879 8,700,993 155,541 of households Aggregate number 7,760,797 33,535,268 12,457,159 26,411,258 38,057,588 616,910 of individuals Malawi 2.272 % 5.940 % 5.232 % 17.741 % 67.601 % 1.213 % Proportion of (0.528) (1.187) (1.451) (1.827) (2.799) (0.522) responses [1.542 % ; [4.250 % ; [3.285 % ; [14.907 % ; [62.790 % ; [0.592 % ; 3.336 % ] 8.246 % ] 8.236 % ] 20.981 % ] 72.067 % ] 2.469 % ] Aggregate number 81,359 212,699 187,343 635,219 2,420,486 43,435 of households Aggregate number 406,801 1,175,383 1,028,207 3,267,540 12,453,118 180,374 of individuals Nigeria 9.238 % 14.901 % 5.753 % 14.394 % 52.632 % 3.082 % Proportion of (1.280) (1.506) (1.006) (1.437) (2.014) (0.757) responses [7.334 % ; [12.585 % ; [4.302 % ; [12.183 % ; [49.306 % ; [2.051 % ; 11.575 % ] 17.558 % ] 7.654 % ] 16.929 % ] 55.935 % ] 4.606 % ] Aggregate number 2,490,322 4,016,909 1,550,811 3,880,235 14,188,038 830,736 of households Aggregate number 20,815,610 28,709,398 10,758,943 25,661,596 95,010,700 5,240,957 of individuals Uganda 1.159 % 4.215 % 3.055 % 28.249 % 61.369 % 1.953 % Proportion of (0.401) (0.691) (0.608) (1.534) (1.668) (0.396) responses [0.654 % ; [3.214 % ; [2.198 % ; [25.793 % ; [58.589 % ; [1.397 % ; 2.043 % ] 5.511 % ] 4.231 % ] 30.841 % ] 64.077 % ] 2.725 % ] Aggregate number 98,519 358,389 259,733 2,401,887 5,217,991 166,074 of households Aggregate number 533,672 1,938,551 1,569,259 12,207,931 27,748,878 894,134 of individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. Q4: In the next 5 years, how do you expect the general economic situation in the country to develop? 14 Get a lot Get a little Stay about Get a little Get a lot Table A4 Don't know better better the same worse worse Burkina Faso 30.129 % 43.327 % 2.934 % 8.299 % 4.452 % 10.859 % Proportion of (2.434) (2.797) (0.938) (1.229) (1.045) (1.559) responses [26.274 % ; [38.788 % ; [1.726 % ; [6.485 % ; [3.014 % ; [8.544 % ; 34.286 % ] 47.982 % ] 4.944 % ] 10.562 % ] 6.529 % ] 13.708 % ] Aggregate number 921,646 1,325,389 89,738 253,854 136,191 332,190 of households Aggregate number 6,658,294 9,332,217 747,406 1,571,107 801,184 2,100,966 of individuals Ethiopia 21.617 % 36.998 % 18.492 % 11.264 % 8.367 % 3.262 % Proportion of (3.869) (3.916) (6.379) (3.729) (1.421) (1.190) responses [15.912 % ; [30.802 % ; [10.143 % ; [6.420 % ; [6.302 % ; [1.779 % ; 28.669 % ] 43.654 % ] 31.318 % ] 19.020 % ] 11.029 % ] 5.909 % ] Aggregate number 5,704,582 9,691,773 4,783,362 3,041,951 2,177,243 843,905 of households Aggregate number 25,900,325 44,192,987 23,426,485 10,655,206 10,106,861 4,557,117 of individuals Malawi 7.495 % 10.242 % 7.478 % 12.574 % 47.993 % 14.218 % Proportion of (1.237) (1.129) (0.947) (1.349) (2.033) (1.842) responses [5.682 % ; [8.513 % ; [6.050 % ; [10.499 % ; [44.631 % ; [11.424 % ; 9.826 % ] 12.275 % ] 9.212 % ] 14.990 % ] 51.373 % ] 17.561 % ] Aggregate number 268,358 366,713 267,770 450,213 1,718,397 509,090 of households Aggregate number 1,570,275 1,898,638 1,303,823 2,435,114 8,982,574 2,320,998 of individuals Nigeria 42.023 % 23.662 % 1.459 % 3.645 % 9.786 % 19.426 % Proportion of (2.218) (1.989) (0.414) (0.684) (1.126) (1.768) responses [38.417 % ; [20.541 % ; [0.912 % ; [2.670 % ; [8.081 % ; [16.676 % ; 45.716 % ] 27.095 % ] 2.325 % ] 4.957 % ] 11.806 % ] 22.506 % ] Aggregate number 11,328,111 6,378,459 393,228 982,592 2,638,086 5,236,575 of households Aggregate number 87,518,885 37,032,616 3,364,208 6,711,256 18,012,855 33,557,385 of individuals Uganda 5.991 % 22.061 % 12.527 % 9.313 % 25.050 % 25.059 % Proportion of (0.861) (1.390) (1.151) (0.924) (1.463) (1.464) responses [4.720 % ; [19.857 % ; [10.750 % ; [7.900 % ; [22.719 % ; [22.727 % ; 7.576 % ] 24.434 % ] 14.548 % ] 10.950 % ] 27.535 % ] 27.545 % ] Aggregate number 509,354 1,875,735 1,065,081 791,867 2,129,901 2,130,655 of households Aggregate number 2,868,130 9,813,804 5,171,028 4,375,447 11,030,699 11,633,317 of individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 15 Q5: During the last 12 months, do you think prices in general have gone …? Gone up Stayed the Table A5 Gone up a lot Gone down Don't know somewhat same Burkina Faso 84.610 % 13.819 % 0.107 % 1.207 % 0.257 % Proportion of (1.862) (1.825) (0.047) (0.511) (0.162) responses [81.286 % ; 87.435 [11.076 % ; 17.111 [0.052 % ; 0.222 [0.599 % ; 2.417 [0.091 % ; 0.724 %] %] %] %] %] Aggregate number of households 2,588,220 422,728 3,283 36,920 7,856 Aggregate number 17,369,559 3,416,141 22,864 333,113 69,498 of individuals Ethiopia 78.882 % 5.653 % 9.420 % 5.963 % 0.082 % Proportion of (6.146) (1.629) (6.157) (3.621) (0.082) responses [67.024 % ; 87.285 [3.494 % ; 9.020 [3.067 % ; 25.477 [2.140 % ; 15.536 [0.016 % ; 0.428 %] %] %] %] %] Aggregate number 20,760,635 1,481,644 2,436,723 1,542,551 21,263 of households Aggregate number 93,119,506 6,195,159 14,261,055 5,170,833 92,429 of individuals Malawi 96.542 % 2.133 % 0.822 % 0.503 % 0 Proportion of (0.685) (0.534) (0.368) (0.264) responses [95.204 % ; 97.516 [1.405 % ; 3.225 [0.390 % ; 1.724 [0.210 % ; 1.198 %] %] %] %] Aggregate number 3,456,716 76,374 29,442 18,008 of households Aggregate number 17,842,883 427,625 191,173 49,742 of individuals Nigeria 90.386 % 6.439 % 0.750 % 2.296 % 0.130 % Proportion of (1.206) (1.089) (0.251) (0.612) (0.129) responses [88.207 % ; 92.198 [4.861 % ; 8.483 [0.432 % ; 1.299 [1.477 % ; 3.552 [0.025 % ; 0.666 %] %] %] %] %] Aggregate number 24,365,483 1,735,690 202,127 618,815 34,938 of households Aggregate number 167,652,596 12,638,807 1,384,523 4,382,236 139,043 of individuals Uganda 86.872 % 8.277 % 0.903 % 1.848 % 2.099 % Proportion of (1.115) (0.899) (0.317) (0.446) (0.476) responses [84.926 % ; 88.601 [6.912 % ; 9.882 [0.507 % ; 1.606 [1.241 % ; 2.745 [1.443 % ; 3.045 %] %] %] %] %] Aggregate number 7,386,382 703,719 76,820 157,167 178,506 of households Aggregate number 39,056,093 3,632,552 426,212 918,989 858,580 of individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 16 Q6: How do you expect that prices in general will develop during the next 12 months? Go up more Go up the Go up less Stay about Table A6 than last 12 same than last than last 12 Go down Don't know the same months 12 months months Burkina Faso 9.874 % 13.823 % 12.160 % 8.866 % 45.208 % 10.069 % Proportion of (1.463) (1.824) (1.735) (1.426) (2.822) (1.471) responses [7.711 % ; [11.082 % ; 17.111 [9.578 % ; [6.780 % ; [40.612 % ; [7.891 % ; 12.561 % ] %] 15.321 % ] 11.514 % ] 49.888 % ] 12.765 % ] Aggregate number 302,041 422,833 371,976 271,208 1,382,930 308,020 of households Aggregate number 1,721,158 2,825,707 2,647,283 2,015,498 10,126,153 1,875,375 of individuals Ethiopia 54.091 % 22.267 % 3.857 % 6.045 % 13.648 % 0.093 % Proportion of (5.244) (3.063) (0.915) (3.567) (4.652) (0.058) responses [45.405 % ; [17.623 % ; 27.722 [2.600 % ; [2.233 % ; [7.614 % ; [0.033 % ; 62.534 % ] %] 5.685 % ] 15.345 % ] 23.258 % ] 0.260 % ] Aggregate number 14,217,108 5,853,092 997,706 1,605,232 3,535,406 34,273 of households Aggregate number 68,657,692 26,225,878 4,811,472 5,310,737 13,728,905 104,296 of individuals Malawi 72.734 % 9.031 % 4.448 % 2.648 % 3.469 % 7.670 % Proportion of (2.210) (1.304) (0.741) (0.676) (0.706) (1.257) responses [68.914 % ; [7.087 % ; 11.443 [3.368 % ; [1.729 % ; [2.470 % ; [5.826 % ; 76.247 % ] %] 5.853 % ] 4.035 % ] 4.853 % ] 10.035 % ] Aggregate number 2,604,288 323,363 159,258 94,803 124,214 274,617 of households Aggregate number 13,618,996 1,678,604 818,730 419,950 663,199 1,311,943 of individuals Nigeria 42.433 % 4.105 % 7.440 % 4.132 % 26.099 % 15.791 % Proportion of (2.010) (0.777) (1.007) (0.790) (1.885) (1.521) responses [39.158 % ; [2.999 % ; 5.594 [5.941 % ; [3.010 % ; [23.114 % ; [13.443 % ; 45.776 % ] %] 9.280 % ] 5.649 % ] 29.322 % ] 18.462 % ] Aggregate number 11,438,680 1,106,494 2,005,642 1,113,879 7,035,481 4,256,875 of households Aggregate number 79,992,687 6,994,283 12,755,657 8,085,880 52,379,635 25,989,062 of individuals Uganda 35.306 % 22.016 % 5.493 % 15.391 % 8.614 % 13.181 % Proportion of (1.611) (1.388) (0.788) (1.222) (1.003) (1.112) responses [32.700 % ; [19.816 % ; 24.386 [4.330 % ; [13.485 % ; [7.100 % ; [11.455 % ; 38.002 % ] %] 6.945 % ] 17.512 % ] 10.415 % ] 15.123 % ] Aggregate number 3,001,888 1,871,925 467,020 1,308,620 732,404 1,120,737 of households Aggregate number 15,776,551 10,252,690 2,324,059 6,679,798 4,005,686 5,853,641 of individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 17 Q7: How likely is it that extreme weather events will negatively affect your household financially? Extremely Neither likely Extremely Don’t know Table A7 Likely Unlikely likely nor unlikely unlikely Burkina Faso 23.027 % 54.340 % 10.124 % 6.652 % 0.064 % 5.792 % Proportion of (2.229) (2.629) (1.618) (1.392) (0.053) (1.060) responses [18.941 % ; [49.145 % ; [7.358 % ; [4.386 % ; [0.013 % ; [4.028 % ; 27.694 % ] 59.443 % ] 13.774 % ] 9.968 % ] 0.325 % ] 8.262 % ] Aggregate number of 704,402 1,662,277 309,686 203,499 1,967 177,176 households Aggregate number of 5,078,488 11,199,896 2,063,243 1,569,619 12,861 1,287,068 individuals Ethiopia 6.322 % 49.800 % 18.454 % 22.877 % 1.119 % 1.428 % Proportion of (1.255) (4.238) (1.996) (2.458) (0.357) (0.487) responses [4.542 % ; [42.859 % ; [15.387 % ; [19.076 % ; [0.660 % ; [0.812 % ; 8.737 % ] 56.748 % ] 21.974 % ] 27.181 % ] 1.889 % ] 2.501 % ] Aggregate number of 1,647,617 12,992,212 4,887,653 6,020,041 325,860 369,435 households Aggregate number of 8,289,931 58,735,568 21,584,635 26,649,135 1,806,845 1,772,868 individuals Malawi 55.535 % 22.674 % 8.606 % 6.010 % 4.981 % 2.194 % Proportion of (2.085) (2.012) (1.097) (1.042) (0.734) (0.644) responses [51.368 % ; [18.929 % ; [6.663 % ; [4.246 % ; [3.711 % ; [1.221 % ; 59.625 % ] 26.913 % ] 11.048 % ] 8.443 % ] 6.656 % ] 3.910 % ] Aggregate number of 1,988,457 811,836 308,134 215,208 178,360 78,545 households Aggregate number of 10,243,082 4,270,605 1,752,189 1,055,364 820,246 369,936 individuals Nigeria 8.343 % 36.757 % 15.812 % 19.771 % 10.418 % 8.899 % Proportion of (0.984) (2.143) (1.925) (1.563) (1.176) (1.372) responses [6.602 % ; [32.656 % ; [12.384 % ; [16.878 % ; [8.323 % ; [6.546 % ; 10.491 % ] 41.060 % ] 19.972 % ] 23.022 % ] 12.967 % ] 11.989 % ] Aggregate number of 2,248,971 9,908,738 4,262,343 5,329,584 2,808,518 2,398,897 households Aggregate number of 15,093,780 65,772,762 37,122,741 33,531,297 18,777,403 15,899,221 individuals Uganda Proportion of 24.145 % 33.428 % 10.973 % 15.050 % 5.269 % 11.135 % responses (1.469) (1.595) (1.043) (1.228) (0.749) (1.038) 18 [21.380 % ; [30.374 % ; [9.086 % ; [12.796 % ; [3.979 % ; [9.256 % ; 27.144 % ] 36.626 % ] 13.194 % ] 17.621 % ] 6.947 % ] 13.340 % ] Aggregate number of 2,052,963 2,842,214 932,956 1,279,659 448,020 946,782 households Aggregate number of 11,435,372 15,440,832 5,099,843 5,925,655 2,255,264 4,735,460 individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals. 19 Q8: Which events, do you expect will negatively affect you and your household financially during the next 12 months? This question was only answered if the respondent answered “likely” or “extremely likely” to Q7. In addition, the options given below were not mutually exclusive. Table A8 Heatwaves are likely Floods are likely Droughts are likely Delayed rains are likely Burkina Faso 8.115 % 30.155 % 61.284 % 69.090 % Proportion of (1.353) (2.731) (2.854) (2.599) responses [6.146 % ; 10.643 % ] [25.853 % ; 34.838 % ] [56.488 % ; 65.871 % ] [64.651 % ; 73.202 % ] Aggregate number of 233856 869028 1766114 1991049 households Aggregate number of 1530630 6089897 12411014 14003808 individuals Ethiopia 2.601 % 5.612 % 8.968 % 11.866 % Proportion of (0.619) (1.252) (1.798) (1.787) responses [1.753 % ; 3.843 % ] [3.870 % ; 8.072 % ] [6.410 % ; 12.410 % ] [9.219 % ; 15.146 % ] Aggregate number of 455480 974806 1492439 1968238 households Aggregate number of 1969286 5248747 7736486 8267645 individuals Malawi 5.714 % 26.706 % 41.770 % 43.842 % Proportion of (1.059) (2.400) (2.231) (2.556) responses [4.189 % ; 7.749 % ] [22.913 % ; 30.875 % ] [38.117 % ; 45.515 % ] [39.652 % ; 48.122 % ] Aggregate number of 200095 935228 1462780 1535355 households Aggregate number of 987766 4822379 7718623 7831608 individuals Nigeria 16.076 % 26.505 % 21.905 % 29.283 % Proportion of (1.525) (1.831) (1.965) (2.273) responses [13.719 % ; 18.751 % ] [23.600 % ; 29.630 % ] [18.839 % ; 25.315 % ] [25.682 % ; 33.164 % ] Aggregate number of 3948039 6509255 5379585 7191413 households Aggregate number of 26428781 40988766 37169052 51833088 individuals Uganda 18.667 % 4.389 % 43.865 % 43.099 % Proportion of (1.350) (0.740) (1.772) (1.787) responses [16.545 % ; 20.992 % ] [3.319 % ; 5.782 % ] [40.972 % ; 46.801 % ] [40.185 % ; 46.063 % ] Aggregate number of 1410417 331621 3314375 3256504 households Aggregate number of 7959952 1911700 18638905 17392923 individuals Note: standard errors (in percentage points) and 90% confidence intervals are reported for response rates. Sample weights are used in the computation of response rates, as well as aggregate counts of households and individuals, 20 Part 2: Regression results (full tables) Regression results for Q1: Would you say that you and your household are financially … than you were a year ago? Table A9. Heterogeneity of responses for Q1 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household reports household reports household reports household reports household reports Dependent a worsening a worsening a worsening a worsening a worsening variable (Y) financial situation financial situation financial situation financial situation financial situation over last year over last year over last year over last year over last year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or 0.204 *** 0.169 0.065 * 0.186 *** 0.102 *** severely food (0.002) (0.104) (0.072) (0.001) (0.001) insecure household (vs. [0.096 ; 0.312] [-0.002 ; 0.341] [0.006 ; 0.123] [0.095 ; 0.277] [0.049 ; 0.155] less food n = 782 n = 810 n = 1420 n = 697 n = 1857 insecure) Household 0.225 *** 0.216 ** 0.084 * 0.130 ** 0.148 *** reports lack of (0.009) (0.025) (0.065) (0.026) (0.000) access to staple foods (vs. access [0.084 ; 0.366] [0.058 ; 0.374] [0.009 ; 0.159] [0.034 ; 0.226] [0.080 ; 0.216] to staple foods) n = 795 n = 812 n = 1136 n = 698 n = 1797 Respondent 0.119 0.319 *** -0.023 0.044 0.116 *** unemployed at (0.193) (0.002) (0.740) (0.477) (0.004) time of survey (vs. employed) [-0.031 ; 0.268] [0.151 ; 0.488] [-0.138 ; 0.092] [-0.058 ; 0.147] [0.050 ; 0.181] n = 847 n = 1214 n = 1420 n = 1273 n = 1868 Household has 0.151 *** 0.200 *** -0.002 0.107 * 0.005 experienced a (0.008) (0.004) (0.964) (0.076) (0.874) loss in income [0.058 ; 0.244] [0.086 ; 0.314] [-0.078 ; 0.074] [0.008 ; 0.207] [-0.051 ; 0.062] from any source (vs. no loss in n = 847 n = 811 n = 1420 n = 710 n = 1868 income) Household farm 0.340 *** 0.073 0.034 0.196 *** 0.036 income has (0.001) (0.645) (0.425) (0.000) (0.349) decreased (vs. [0.169 ; 0.510] [-0.190 ; 0.336] [-0.036 ; 0.105] [0.112 ; 0.281] [-0.027 ; 0.098] farm income has not decreased) n = 169 n = 238 n = 1263 n = 596 n = 1384 Household 0.224 *** 0.028 0.071 0.187 *** 0.011 business income (0.003) (0.787) (0.115) (0.001) (0.849) has decreased [0.102 ; 0.345] [-0.145 ; 0.202] [-0.003 ; 0.145] [0.095 ; 0.280] [-0.082 ; 0.103] (vs. business income has not n = 394 n = 313 n = 1077 n = 600 n = 674 decreased) Household wage 0.531 *** 0.098 -0.003 0.069 -0.143 * income has (0.000) (0.622) (0.940) (0.345) (0.073) decreased (vs. [0.348 ; 0.714] [-0.229 ; 0.425] [-0.077 ; 0.070] [-0.052 ; 0.190] [-0.274 ; -0.012] wage income has not decreased) n = 140 n = 541 n = 977 n = 345 n = 346 Proportion of households for 62.246% 59.288% 75.994% 48.635% 69.443% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 21 Table A10. Heterogeneity of responses for Q1 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household reports household reports household reports household reports household reports Dependent a worsening a worsening a worsening a worsening a worsening variable (Y) financial situation financial situation financial situation financial situation financial situation over last year over last year over last year over last year over last year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an -0.038 -0.002 0.180 *** -0.179 *** urban area (vs. (0.566) (0.964) (0.000) (0.000) rural area) [-0.146 ; 0.071] [-0.068 ; 0.064] [0.103 ; 0.258] [-0.250 ; -0.109] n = 1194 n = 1418 n = 1267 n = 1845 Household in a -0.08 -0.103 0.087 -0.033 semi-urban area (0.674) (0.196) (0.122) (0.516) (vs. rural area) [-0.393 ; 0.234] [-0.234 ; 0.028] [-0.005 ; 0.180] [-0.115 ; 0.050] n = 1194 n = 1418 n = 1267 n = 1845 0.027 0.072 0 -0.003 -0.070 ** Female (0.700) (0.382) (0.995) (0.965) (0.040) household head (vs. male) [-0.087 ; 0.140] [-0.064 ; 0.209] [-0.088 ; 0.087] [-0.096 ; 0.091] [-0.127 ; -0.014] n = 847 n = 1214 n = 1401 n = 1271 n = 1835 0.163 * -0.128 0.059 -0.150 ** 0.011 Household dependency ratio (0.073) (0.426) (0.347) (0.027) (0.837) is above 2 (vs. [0.013 ; 0.312] [-0.394 ; 0.138] [-0.045 ; 0.163] [-0.262 ; -0.038] [-0.078 ; 0.101] below 2) n = 847 n = 814 n = 1420 n = 1273 n = 1847 Proportion of households for 62.246% 59.288% 75.994% 48.635% 69.443% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 22 Regression results for Q2: Do you think that a year from now you and your household will be better off financially? Table A11. Heterogeneity of responses for Q2 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household Dependent expects to be expects to be expects to be expects to be expects to be variable (Y) worse off in the worse off in the worse off in the worse off in the worse off in the next year next year next year next year next year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or -0.038 -0.184 * 0.089 * 0.067 * 0.05 severely food (0.540) (0.058) (0.062) (0.090) (0.167) insecure household (vs. [-0.141 ; 0.064] [-0.344 ; -0.025] [0.011 ; 0.167] [0.002 ; 0.132] [-0.009 ; 0.109] less food n = 676 n = 785 n = 1261 n = 623 n = 1555 insecure) Household 0.135 *** -0.149 0.049 0.054 0.191 *** reports lack of (0.006) (0.144) (0.365) (0.197) (0.000) access to staple foods (vs. access [0.055 ; 0.216] [-0.317 ; 0.019] [-0.040 ; 0.138] [-0.015 ; 0.124] [0.128 ; 0.253] to staple foods) n = 686 n = 787 n = 1005 n = 625 n = 1508 Respondent 0.106 0.171 -0.066 0.022 0.096 ** unemployed at (0.103) (0.155) (0.400) (0.577) (0.021) time of survey (vs. employed) [-0.001 ; 0.213] [-0.027 ; 0.368] [-0.195 ; 0.063] [-0.043 ; 0.088] [0.027 ; 0.164] n = 726 n = 1180 n = 1261 n = 1132 n = 1564 Household has 0.063 * 0.008 0.057 0.015 0 experienced a (0.080) (0.944) (0.371) (0.752) (1) loss in income [0.004 ; 0.123] [-0.176 ; 0.192] [-0.049 ; 0.164] [-0.062 ; 0.091] [-0.062 ; 0.062] from any source (vs. no loss in n = 726 n = 786 n = 1261 n = 634 n = 1564 income) Household farm 0.072 -0.034 0.049 0.114 *** 0.036 income has (0.462) (0.669) (0.347) (0.004) (0.398) decreased (vs. [-0.090 ; 0.234] [-0.163 ; 0.096] [-0.037 ; 0.136] [0.050 ; 0.178] [-0.034 ; 0.106] farm income has not decreased) n = 147 n = 229 n = 1130 n = 534 n = 1155 Household 0.126 *** 0.114 0.072 0.046 0.044 business income (0.008) (0.226) (0.180) (0.248) (0.453) has decreased [0.048 ; 0.204] [-0.041 ; 0.268] [-0.017 ; 0.162] [-0.020 ; 0.112] [-0.053 ; 0.141] (vs. business income has not n = 343 n = 303 n = 954 n = 537 n = 569 decreased) Household wage -0.029 -0.186 *** 0.022 -0.032 -0.193 ** income has (0.653) (0.003) (0.699) (0.552) (0.019) decreased (vs. [-0.135 ; 0.077] [-0.287 ; -0.084] [-0.073 ; 0.117] [-0.120 ; 0.056] [-0.327 ; -0.058] wage income has not decreased) n = 117 n = 522 n = 868 n = 313 n = 297 Proportion of households for 14.137% 24.136% 54.334% 10.755% 38.65% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 23 Table A12. Heterogeneity of responses for Q2 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household Dependent expects to be expects to be expects to be expects to be expects to be variable (Y) worse off in the worse off in the worse off in the worse off in the worse off in the next year next year next year next year next year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an 0.012 0.071 0.02 -0.064 urban area (vs. (0.839) (0.162) (0.500) (0.255) rural area) [-0.086 ; 0.110] [-0.013 ; 0.154] [-0.029 ; 0.070] [-0.157 ; 0.029] n = 1160 n = 1259 n = 1126 n = 1544 Household in a 0.046 -0.071 -0.056 0.018 semi-urban area (0.744) (0.474) (0.141) (0.755) (vs. rural area) [-0.186 ; 0.278] [-0.236 ; 0.093] [-0.118 ; 0.007] [-0.077 ; 0.113] n = 1160 n = 1259 n = 1126 n = 1544 -0.031 -0.021 -0.012 0.037 0.027 Female (0.550) (0.767) (0.864) (0.266) (0.507) household head (vs. male) [-0.116 ; 0.054] [-0.139 ; 0.097] [-0.130 ; 0.106] [-0.018 ; 0.092] [-0.039 ; 0.093] n = 726 n = 1180 n = 1244 n = 1131 n = 1534 0.022 -0.237 -0.017 -0.056 -0.04 Household dependency ratio (0.707) (0.129) (0.838) (0.222) (0.485) is above 2 (vs. [-0.074 ; 0.118] [-0.494 ; 0.020] [-0.158 ; 0.123] [-0.132 ; 0.020] [-0.134 ; 0.054] below 2) n = 726 n = 789 n = 1261 n = 1132 n = 1547 Proportion of households for 14.137% 24.136% 54.334% 10.755% 38.65% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 24 Regression results for Q3: How do you think the general economic situation in the country has changed during the previous 12 months? Table A13. Heterogeneity of responses for Q3 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household reports household reports household reports household reports household reports Dependent that the country that the country that the country that the country that the country variable (Y) economic situation economic situation economic situation economic situation economic situation has gotten worse has gotten worse has gotten worse has gotten worse has gotten worse over the past year over the past year over the past year over the past year over the past year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or -0.037 -0.217 ** 0.04 -0.036 0.014 severely food (0.324) (0.039) (0.161) (0.503) (0.504) insecure household (vs. [-0.099 ; 0.025] [-0.390 ; -0.044] [-0.007 ; 0.088] [-0.124 ; 0.052] [-0.020 ; 0.047] less food n = 783 n = 801 n = 1411 n = 685 n = 1821 insecure) Household 0.021 -0.149 0.004 0.065 -0.042 * reports lack of (0.608) (0.245) (0.915) (0.241) (0.054) access to staple foods (vs. access [-0.047 ; 0.090] [-0.360 ; 0.062] [-0.056 ; 0.064] [-0.026 ; 0.156] [-0.077 ; -0.006] to staple foods) n = 796 n = 803 n = 1129 n = 685 n = 1764 Respondent 0.051 -0.033 -0.014 0.061 -0.001 unemployed at (0.272) (0.845) (0.836) (0.278) (0.971) time of survey (vs. employed) [-0.026 ; 0.128] [-0.315 ; 0.248] [-0.127 ; 0.098] [-0.032 ; 0.154] [-0.041 ; 0.039] n = 847 n = 1200 n = 1411 n = 1242 n = 1832 Household has -0.005 -0.057 0.085 ** 0.108 ** 0.079 *** experienced a (0.852) (0.632) (0.013) (0.041) (0.000) loss in income [-0.050 ; 0.040] [-0.255 ; 0.140] [0.029 ; 0.140] [0.021 ; 0.195] [0.045 ; 0.113] from any source (vs. no loss in n = 847 n = 802 n = 1411 n = 697 n = 1832 income) Household farm 0.057 -0.072 0.070 * 0.100 * 0.098 *** income has (0.245) (0.704) (0.067) (0.060) (0.000) decreased (vs. [-0.024 ; 0.137] [-0.385 ; 0.241] [0.007 ; 0.132] [0.013 ; 0.188] [0.055 ; 0.140] farm income has not decreased) n = 166 n = 236 n = 1254 n = 584 n = 1362 Household -0.012 0.065 0.057 0.138 ** 0.111 *** business income (0.710) (0.492) (0.203) (0.017) (0.004) has decreased [-0.067 ; 0.042] [-0.091 ; 0.222] [-0.017 ; 0.130] [0.043 ; 0.234] [0.047 ; 0.174] (vs. business income has not n = 391 n = 306 n = 1071 n = 590 n = 664 decreased) Household wage 0 -0.335 *** 0.003 0.053 0.094 * income has (0.000) (0.945) (0.493) (0.080) decreased (vs. [-0.491 ; -0.180] [-0.071 ; 0.077] [-0.075 ; 0.180] [0.006 ; 0.182] wage income has not decreased) n = 538 n = 971 n = 344 n = 341 Proportion of households for 91.243% 57.786% 86.39% 69.157% 91.404% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 25 Table A14. Heterogeneity of responses for Q3 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household reports household reports household reports household reports household reports Dependent that the country that the country that the country that the country that the country variable (Y) economic situation economic situation economic situation economic situation economic situation has gotten worse has gotten worse has gotten worse has gotten worse has gotten worse over the past year over the past year over the past year over the past year over the past year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an 0.119 0.046 0.082 * 0.024 urban area (vs. (0.138) (0.349) (0.054) (0.416) rural area) [-0.013 ; 0.251] [-0.035 ; 0.126] [0.012 ; 0.152] [-0.024 ; 0.071] n = 1180 n = 1409 n = 1236 n = 1809 Household in a 0.118 -0.006 0.051 -0.032 semi-urban area (0.498) (0.919) (0.318) (0.324) (vs. rural area) [-0.169 ; 0.406] [-0.106 ; 0.094] [-0.033 ; 0.134] [-0.085 ; 0.021] n = 1180 n = 1409 n = 1236 n = 1809 -0.035 -0.119 0.039 0.059 0.025 Female (0.157) (0.309) (0.379) (0.274) (0.267) household head (vs. male) [-0.092 ; 0.022] [-0.257 ; 0.019] [-0.034 ; 0.113] [-0.030 ; 0.147] [-0.012 ; 0.063] n = 847 n = 1200 n = 1393 n = 1240 n = 1799 0.027 -0.074 -0.057 -0.032 0.084 *** Household dependency ratio (0.566) (0.672) (0.282) (0.611) (0.006) is above 2 (vs. [-0.051 ; 0.105] [-0.360 ; 0.213] [-0.145 ; 0.031] [-0.136 ; 0.072] [0.034 ; 0.134] below 2) n = 847 n = 805 n = 1411 n = 1242 n = 1811 Proportion of households for 91.243% 57.786% 86.39% 69.157% 91.404% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 26 Regression results for Q4: In the next 5 years, how do you expect the general economic situation in the country to develop? Table A15. Heterogeneity of responses for Q4 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household reports household reports household reports household reports household reports Dependent that the country that the country that the country that the country that the country variable (Y) economic situation economic situation economic situation economic situation economic situation will get worse over will get worse over will get worse over will get worse over will get worse over the next 5 years the next 5 years the next 5 years the next 5 years the next 5 years Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or -0.072 -0.165 * 0.081 ** 0.014 0.008 severely food (0.290) (0.096) (0.037) (0.769) (0.831) insecure household (vs. [-0.183 ; 0.040] [-0.328 ; -0.002] [0.017 ; 0.145] [-0.062 ; 0.089] [-0.057 ; 0.073] less food n = 719 n = 789 n = 1248 n = 579 n = 1399 insecure) Household 0.068 -0.145 0.037 -0.034 0.173 *** reports lack of (0.239) (0.164) (0.506) (0.481) (0.000) access to staple foods (vs. access [-0.027 ; 0.164] [-0.316 ; 0.026] [-0.055 ; 0.129] [-0.114 ; 0.046] [0.102 ; 0.245] to staple foods) n = 728 n = 791 n = 998 n = 578 n = 1367 Respondent 0.056 0.217 ** 0.058 0.048 0.03 unemployed at (0.467) (0.039) (0.251) (0.302) (0.519) time of survey (vs. employed) [-0.071 ; 0.183] [0.044 ; 0.390] [-0.025 ; 0.141] [-0.029 ; 0.125] [-0.046 ; 0.106] n = 770 n = 1181 n = 1248 n = 1052 n = 1410 Household has 0.046 0.06 0.066 0.057 -0.037 experienced a (0.194) (0.593) (0.236) (0.263) (0.372) loss in income [-0.012 ; 0.103] [-0.125 ; 0.245] [-0.026 ; 0.158] [-0.027 ; 0.141] [-0.105 ; 0.031] from any source (vs. no loss in n = 770 n = 790 n = 1248 n = 589 n = 1410 income) Household farm 0.026 0.044 0.036 0.075 0.045 income has (0.658) (0.556) (0.409) (0.133) (0.351) decreased (vs. [-0.070 ; 0.121] [-0.079 ; 0.166] [-0.036 ; 0.108] [-0.007 ; 0.158] [-0.034 ; 0.124] farm income has not decreased) n = 154 n = 228 n = 1115 n = 491 n = 1035 Household 0.094 * 0.122 0.048 0.024 -0.074 business income (0.067) (0.123) (0.360) (0.605) (0.234) has decreased [0.010 ; 0.179] [-0.008 ; 0.252] [-0.039 ; 0.135] [-0.052 ; 0.099] [-0.176 ; 0.028] (vs. business income has not n = 354 n = 302 n = 960 n = 499 n = 529 decreased) Household wage -0.229 -0.11 0.148 *** 0.015 -0.153 * income has (0.215) (0.184) (0.001) (0.837) (0.080) decreased (vs. [-0.534 ; 0.076] [-0.246 ; 0.026] [0.079 ; 0.217] [-0.103 ; 0.133] [-0.298 ; -0.009] wage income has not decreased) n = 127 n = 530 n = 868 n = 297 n = 286 Proportion of households for 14.304% 20.293% 70.605% 16.669% 45.854% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 27 Table A16. Heterogeneity of responses for Q4 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household reports that the reports that the reports that the reports that the reports that the Dependent country country country country country variable (Y) economic economic economic economic economic situation will situation will situation will situation will situation will get worse over get worse over get worse over get worse over get worse over the next 5 years the next 5 years the next 5 years the next 5 years the next 5 years Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an 0.066 -0.043 -0.056 -0.087 urban area (vs. (0.205) (0.382) (0.132) (0.131) rural area) [-0.020 ; 0.151] [-0.126 ; 0.039] [-0.116 ; 0.005] [-0.182 ; 0.008] n = 1162 n = 1246 n = 1047 n = 1391 Household in a -0.016 -0.106 -0.033 -0.035 semi-urban area (0.880) (0.177) (0.455) (0.586) (vs. rural area) [-0.195 ; 0.162] [-0.235 ; 0.023] [-0.105 ; 0.039] [-0.142 ; 0.071] n = 1162 n = 1246 n = 1047 n = 1391 -0.021 -0.043 0.053 0.043 -0.038 Female (0.693) (0.530) (0.276) (0.302) (0.377) household head (vs. male) [-0.106 ; 0.065] [-0.156 ; 0.070] [-0.027 ; 0.132] [-0.026 ; 0.113] [-0.109 ; 0.033] n = 770 n = 1181 n = 1234 n = 1050 n = 1386 -0.218 *** -0.171 0.05 0.036 0.064 Household dependency ratio (0.009) (0.237) (0.468) (0.452) (0.312) is above 2 (vs. [-0.354 ; -0.082] [-0.409 ; 0.067] [-0.064 ; 0.165] [-0.042 ; 0.114] [-0.040 ; 0.169] below 2) n = 770 n = 793 n = 1248 n = 1052 n = 1392 Proportion of households for 14.304% 20.293% 70.605% 16.669% 45.854% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 28 Regression results for Q5: During the last 12 months, do you think prices in general have gone …? Table A17. Heterogeneity of responses for Q5 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household reports that reports that reports that reports that reports that Dependent prices have prices have prices have prices have prices have variable (Y) “gone up a lot” “gone up a lot” “gone up a lot” “gone up a lot” “gone up a lot” over the last over the last over the last over the last over the last year year year year year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or 0.082 -0.029 -0.004 0.064 0.051 ** severely food (0.214) (0.861) (0.766) (0.123) (0.022) insecure household (vs. [-0.027 ; 0.191] [-0.307 ; 0.248] [-0.029 ; 0.020] [-0.004 ; 0.132] [0.014 ; 0.087] less food n = 800 n = 809 n = 1422 n = 699 n = 1826 insecure) Household 0.002 0.055 -0.011 0.092 ** -0.017 reports lack of (0.968) (0.759) (0.584) (0.038) (0.465) access to staple foods (vs. access [-0.078 ; 0.082] [-0.242 ; 0.352] [-0.046 ; 0.023] [0.019 ; 0.164] [-0.057 ; 0.022] to staple foods) n = 815 n = 811 n = 1138 n = 700 n = 1769 Respondent 0.091 -0.333 *** 0.011 0.067 0.047 * unemployed at (0.199) (0.000) (0.638) (0.114) (0.080) time of survey (vs. employed) [-0.026 ; 0.208] [-0.393 ; -0.272] [-0.027 ; 0.048] [-0.003 ; 0.136] [0.003 ; 0.091] n = 869 n = 1213 n = 1422 n = 1276 n = 1837 Household has 0.018 -0.288 ** -0.025 0.017 0.01 experienced a (0.631) (0.025) (0.171) (0.686) (0.661) loss in income [-0.043 ; 0.079] [-0.498 ; -0.078] [-0.055 ; 0.005] [-0.052 ; 0.086] [-0.027 ; 0.047] from any source (vs. no loss in n = 869 n = 810 n = 1422 n = 712 n = 1837 income) Household farm 0.158 * 0.052 -0.003 0.069 0.026 income has (0.069) (0.777) (0.873) (0.159) (0.309) decreased (vs. [0.015 ; 0.301] [-0.251 ; 0.355] [-0.030 ; 0.024] [-0.012 ; 0.151] [-0.016 ; 0.067] farm income has not decreased) n = 169 n = 238 n = 1265 n = 598 n = 1362 Household 0.044 -0.028 -0.028 0.045 -0.006 business income (0.429) (0.634) (0.170) (0.346) (0.874) has decreased [-0.048 ; 0.136] [-0.124 ; 0.068] [-0.061 ; 0.006] [-0.034 ; 0.124] [-0.068 ; 0.056] (vs. business income has not n = 397 n = 312 n = 1077 n = 602 n = 663 decreased) Household wage 0.032 -0.329 *** -0.004 0.119 ** 0.025 income has (0.644) (0.005) (0.842) (0.013) (0.684) decreased (vs. [-0.083 ; 0.148] [-0.521 ; -0.136] [-0.034 ; 0.027] [0.041 ; 0.198] [-0.076 ; 0.127] wage income has not decreased) n = 144 n = 541 n = 977 n = 348 n = 345 Proportion of households for 84.828% 78.947% 96.542% 90.504% 88.735% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 29 Table A18. Heterogeneity of responses for Q5 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household reports that reports that reports that reports that reports that Dependent prices have prices have prices have prices have prices have variable (Y) “gone up a lot” “gone up a lot” “gone up a lot” “gone up a lot” “gone up a lot” over the last over the last over the last over the last over the last year year year year year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an 0.067 -0.029 * -0.002 -0.011 urban area (vs. (0.462) (0.094) (0.947) (0.720) rural area) [-0.083 ; 0.218] [-0.057 ; -0.001] [-0.048 ; 0.044] [-0.062 ; 0.040] n = 1193 n = 1420 n = 1270 n = 1814 Household in a 0.196 0.002 0.001 0.067 semi-urban area (0.223) (0.931) (0.965) (0.119) (vs. rural area) [-0.069 ; 0.460] [-0.029 ; 0.033] [-0.050 ; 0.052] [-0.004 ; 0.137] n = 1193 n = 1420 n = 1270 n = 1814 -0.003 -0.05 0.005 0.054 -0.006 Female (0.944) (0.645) (0.759) (0.111) (0.802) household head (vs. male) [-0.079 ; 0.072] [-0.230 ; 0.129] [-0.022 ; 0.032] [-0.002 ; 0.109] [-0.044 ; 0.032] n = 869 n = 1213 n = 1403 n = 1274 n = 1805 0.027 0.314 * 0.034 0.017 0.023 Household dependency ratio (0.617) (0.091) (0.241) (0.717) (0.454) is above 2 (vs. [-0.063 ; 0.118] [0.008 ; 0.619] [-0.014 ; 0.081] [-0.061 ; 0.095] [-0.028 ; 0.074] below 2) n = 869 n = 813 n = 1422 n = 1276 n = 1816 Proportion of households for 84.828% 78.947% 96.542% 90.504% 88.735% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 30 Regression results for Q6: How do you expect that prices in general will develop during the next 12 months? Table A19. Heterogeneity of responses for Q6 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household expects household expects household expects household expects household expects prices to increase prices to increase prices to increase prices to increase prices to increase Dependent more next year more next year more next year more next year more next year variable (Y) than they already than they already than they already than they already than they already have in previous have in previous have in previous have in previous have in previous year year year year year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or -0.09 0.129 0.070 ** 0.081 -0.064 * severely food (0.109) (0.335) (0.044) (0.177) (0.075) insecure household (vs. [-0.183 ; 0.002] [-0.091 ; 0.349] [0.013 ; 0.126] [-0.018 ; 0.180] [-0.123 ; -0.005] less food n = 712 n = 806 n = 1327 n = 611 n = 1612 insecure) Household 0.035 0.022 -0.031 0.099 0.065 reports lack of (0.404) (0.884) (0.532) (0.119) (0.115) access to staple foods (vs. access [-0.034 ; 0.103] [-0.224 ; 0.268] [-0.114 ; 0.052] [-0.005 ; 0.203] [-0.003 ; 0.134] to staple foods) n = 721 n = 808 n = 1066 n = 611 n = 1563 Respondent 0.095 ** 0.148 0.008 0.004 0.138 *** unemployed at (0.026) (0.329) (0.878) (0.955) (0.001) time of survey (vs. employed) [0.025 ; 0.165] [-0.102 ; 0.398] [-0.077 ; 0.093] [-0.103 ; 0.110] [0.073 ; 0.203] n = 763 n = 1208 n = 1327 n = 1093 n = 1622 Household has -0.016 -0.104 0.058 0.065 -0.113 *** experienced a (0.625) (0.362) (0.204) (0.338) (0.002) loss in income [-0.070 ; 0.038] [-0.292 ; 0.084] [-0.017 ; 0.133] [-0.047 ; 0.177] [-0.174 ; -0.053] from any source (vs. no loss in n = 763 n = 807 n = 1327 n = 618 n = 1622 income) Household farm -0.067 -0.193 0.046 0.032 -0.081 * income has (0.284) (0.187) (0.240) (0.652) (0.057) decreased (vs. [-0.171 ; 0.036] [-0.433 ; 0.048] [-0.018 ; 0.110] [-0.086 ; 0.150] [-0.150 ; -0.011] farm income has not decreased) n = 151 n = 237 n = 1180 n = 519 n = 1194 Household -0.005 0.056 0.101 *** 0.008 -0.205 *** business income (0.920) (0.621) (0.004) (0.911) (0.000) has decreased [-0.091 ; 0.081] [-0.131 ; 0.244] [0.044 ; 0.158] [-0.110 ; 0.126] [-0.291 ; -0.119] (vs. business income has not n = 366 n = 310 n = 1008 n = 523 n = 596 decreased) Household wage -0.291 0.302 *** 0.080 * 0.105 0.076 income has (0.126) (0.001) (0.057) (0.185) (0.375) decreased (vs. [-0.605 ; 0.022] [0.149 ; 0.456] [0.011 ; 0.150] [-0.025 ; 0.235] [-0.065 ; 0.217] wage income has not decreased) n = 124 n = 539 n = 913 n = 312 n = 316 Proportion of households for 10.979% 54.141% 78.776% 50.39% 40.666% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 31 Table A20. Heterogeneity of responses for Q6 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household expects prices expects prices expects prices expects prices expects prices Dependent to increase to increase to increase to increase to increase variable (Y) more next year more next year more next year more next year more next year than they than they than they than they than they already have in already have in already have in already have in already have in previous year previous year previous year previous year previous year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an 0.055 -0.037 0.011 -0.061 urban area (vs. (0.470) (0.368) (0.823) (0.265) rural area) [-0.071 ; 0.182] [-0.106 ; 0.031] [-0.071 ; 0.093] [-0.151 ; 0.029] n = 1188 n = 1326 n = 1088 n = 1600 Household in a 0.14 0.025 0.103 * 0.016 semi-urban area (0.144) (0.594) (0.057) (0.788) (vs. rural area) [-0.018 ; 0.298] [-0.053 ; 0.104] [0.014 ; 0.192] [-0.082 ; 0.114] n = 1188 n = 1326 n = 1088 n = 1600 0.018 0.184 ** 0.123 *** -0.015 -0.006 Female (0.657) (0.014) (0.004) (0.783) (0.873) household head (vs. male) [-0.050 ; 0.087] [0.061 ; 0.307] [0.054 ; 0.191] [-0.104 ; 0.074] [-0.072 ; 0.059] n = 763 n = 1208 n = 1308 n = 1091 n = 1592 -0.003 0.153 -0.005 -0.039 -0.033 Household dependency ratio (0.956) (0.350) (0.941) (0.545) (0.570) is above 2 (vs. [-0.095 ; 0.088] [-0.117 ; 0.423] [-0.106 ; 0.097] [-0.145 ; 0.067] [-0.128 ; 0.062] below 2) n = 763 n = 810 n = 1327 n = 1093 n = 1603 Proportion of households for 10.979% 54.141% 78.776% 50.39% 40.666% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 32 Regression results for Q7: How likely is it that extreme weather events will negatively affect your household financially? Table A21. Heterogeneity of responses for Q7 along dimensions of livelihood outcomes and shocks Dummy: Dummy: Dummy: Dummy: Dummy: household expects household expects household expects household expects household expects Dependent to be negatively to be negatively to be negatively to be negatively to be negatively variable (Y) impacted by impacted by impacted by impacted by impacted by weather in the weather in the weather in the weather in the weather in the future future future future future Country Burkina Faso Ethiopia Malawi Nigeria Uganda Moderately or -0.054 0.263 ** 0.068 * -0.059 0.170 *** severely food (0.282) (0.011) (0.061) (0.320) (0.000) insecure household (vs. [-0.137 ; 0.029] [0.093 ; 0.432] [0.008 ; 0.128] [-0.157 ; 0.039] [0.115 ; 0.225] less food n = 758 n = 797 n = 1396 n = 658 n = 1657 insecure) Household 0.043 0.151 0.054 0.071 0.049 reports lack of (0.509) (0.219) (0.212) (0.297) (0.229) access to staple foods (vs. access [-0.065 ; 0.151] [-0.051 ; 0.354] [-0.017 ; 0.126] [-0.041 ; 0.182] [-0.018 ; 0.117] to staple foods) n = 769 n = 799 n = 1116 n = 658 n = 1601 Respondent -0.092 0.252 ** -0.058 -0.042 0.018 unemployed at (0.128) (0.013) (0.142) (0.516) (0.663) time of survey (vs. employed) [-0.192 ; 0.008] [0.086 ; 0.418] [-0.123 ; 0.007] [-0.148 ; 0.064] [-0.050 ; 0.086] n = 819 n = 1194 n = 1396 n = 1188 n = 1667 Household has 0.019 0.263 *** 0.077 ** 0.104 * 0.022 experienced a (0.666) (0.000) (0.049) (0.099) (0.551) loss in income [-0.054 ; 0.092] [0.155 ; 0.371] [0.013 ; 0.141] [0 ; 0.208] [-0.039 ; 0.083] from any source (vs. no loss in n = 819 n = 798 n = 1396 n = 670 n = 1667 income) Household farm -0.089 0.164 0.062 0.116 * 0.021 income has (0.294) (0.289) (0.102) (0.065) (0.596) decreased (vs. [-0.228 ; 0.051] [-0.091 ; 0.419] [0 ; 0.124] [0.013 ; 0.219] [-0.043 ; 0.085] farm income has not decreased) n = 162 n = 237 n = 1246 n = 560 n = 1260 Household -0.024 -0.147 0.103 *** 0.075 -0.073 business income (0.662) (0.141) (0.002) (0.238) (0.219) has decreased [-0.116 ; 0.067] [-0.311 ; 0.017] [0.051 ; 0.156] [-0.030 ; 0.179] [-0.170 ; 0.025] (vs. business income has not n = 381 n = 305 n = 1057 n = 564 n = 607 decreased) Household wage 0.11 0.234 * 0.084 * 0.04 -0.268 *** income has (0.508) (0.054) (0.084) (0.626) (0.000) decreased (vs. [-0.165 ; 0.386] [0.034 ; 0.433] [0.004 ; 0.165] [-0.095 ; 0.176] [-0.383 ; -0.154] wage income has not decreased) n = 136 n = 534 n = 960 n = 331 n = 312 Proportion of households for 82.124% 56.935% 79.963% 49.506% 64.787% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 33 Table A22. Heterogeneity of responses for Q7 along household and respondent characteristics Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household expects to be expects to be expects to be expects to be expects to be Dependent negatively negatively negatively negatively negatively variable (Y) impacted by impacted by impacted by impacted by impacted by weather in the weather in the weather in the weather in the weather in the future future future future future Country Burkina Faso Ethiopia Malawi Nigeria Uganda Household in an -0.308 *** -0.02 -0.028 -0.101 ** urban area (vs. (0.000) (0.612) (0.614) (0.047) rural area) [-0.376 ; -0.240] [-0.086 ; 0.046] [-0.120 ; 0.064] [-0.185 ; -0.018] n = 1174 n = 1394 n = 1182 n = 1647 Household in a -0.465 *** -0.035 -0.007 -0.098 * semi-urban area (0.000) (0.543) (0.899) (0.070) (vs. rural area) [-0.667 ; -0.262] [-0.129 ; 0.060] [-0.097 ; 0.083] [-0.187 ; -0.009] n = 1174 n = 1394 n = 1182 n = 1647 -0.029 -0.08 -0.047 0.098 * -0.056 Female (0.636) (0.404) (0.258) (0.072) (0.133) household head (vs. male) [-0.131 ; 0.072] [-0.238 ; 0.078] [-0.115 ; 0.021] [0.009 ; 0.188] [-0.118 ; 0.005] n = 819 n = 1194 n = 1377 n = 1186 n = 1639 -0.068 0.082 0.082 -0.091 -0.042 Household dependency ratio (0.267) (0.606) (0.194) (0.129) (0.452) is above 2 (vs. [-0.169 ; 0.033] [-0.179 ; 0.343] [-0.022 ; 0.186] [-0.190 ; 0.008] [-0.133 ; 0.049] below 2) n = 819 n = 801 n = 1396 n = 1188 n = 1650 Proportion of households for 82.124% 56.935% 79.963% 49.506% 64.787% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 34 Regression results, section 3 Table A23. Interactions between Q3 and other economic sentiments Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household Dependent expects to be expects to be expects to be expects to be expects to be variable (Y) worse off in the worse off in the worse off in the worse off in the worse off in the next year next year next year next year next year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Dummy: 0.140 *** 0.287 *** 0.471 *** 0.188 *** 0.480 *** household reports (0.001) (0.000) (0.000) (0.000) (0.000) a worsening financial situation [0.070 ; 0.210] [0.155 ; 0.419] [0.415 ; 0.527] [0.129 ; 0.246] [0.423 ; 0.538] over last year n = 710 n = 1180 n = 1259 n = 1129 n = 1560 Dummy: 0.179 *** 0.342 *** 0.262 *** 0.138 *** 0.408 *** household reports (0.000) (0.000) (0.000) (0.000) (0.000) that the country economic situation [0.117 ; 0.240] [0.274 ; 0.411] [0.190 ; 0.335] [0.093 ; 0.183] [0.381 ; 0.436] will get worse over n = 680 n = 1160 n = 1138 n = 969 n = 1270 the next 5 years Dummy: 0.130 *** 0.126 0.193 *** 0.045 0.272 *** household expects (0.006) (0.156) (0.001) (0.104) (0.000) prices to increase [0.053 ; 0.207] [-0.020 ; 0.273] [0.101 ; 0.285] [-0.001 ; 0.090] [0.222 ; 0.322] more next year than they already have in previous n = 669 n = 1178 n = 1195 n = 991 n = 1413 year Proportion of households for 14.137% 24.136% 54.334% 10.755% 38.65% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 35 Table A24. Interactions between Q4 and other economic sentiments Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household reports that reports that reports that reports that reports that the country the country the country the country the country Dependent variable (Y) economic economic economic economic economic situation will situation will situation will situation will situation will get worse over get worse over get worse over get worse over get worse over the next 5 the next 5 the next 5 the next 5 the next 5 years years years years years Country Burkina Faso Ethiopia Malawi Nigeria Uganda Dummy: household -0.025 0.309 *** 0.270 *** 0.180 *** 0.265 *** reports that the country (0.652) (0.000) (0.000) (0.000) (0.000) economic situation has [-0.116 ; gotten worse over the [0.183 ; 0.435] [0.198 ; 0.342] [0.111 ; 0.249] [0.141 ; 0.390] 0.066] past year n = 761 n = 1171 n = 1243 n = 1036 n = 1395 Dummy: household 0.159 *** 0.065 0.225 *** 0.181 *** 0.356 *** expects prices to increase (0.000) (0.464) (0.000) (0.000) (0.000) more next year than they already have in previous [-0.081 ; [0.088 ; 0.230] [0.153 ; 0.298] [0.122 ; 0.240] [0.313 ; 0.399] year 0.212] n = 707 n = 1179 n = 1191 n = 951 n = 1307 Proportion of households 14.304% 20.293% 70.605% 16.669% 45.854% for which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 36 Table A25. Interactions between Q2 and other economic sentiments Dummy: Dummy: Dummy: Dummy: Dummy: household household household household household reports a reports a reports a reports a reports a Dependent variable worsening worsening worsening worsening worsening (Y) financial financial financial financial financial situation over situation over situation over situation over situation over last year last year last year last year last year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Dummy: household 0.075 0.036 0.136 ** 0.254 *** 0.176 *** reports that the (0.352) (0.731) (0.017) (0.000) (0.001) country economic situation has gotten [-0.058 ; 0.209] [-0.136 ; 0.208] [0.043 ; 0.229] [0.189 ; 0.320] [0.090 ; 0.262] worse over the past n = 826 n = 1200 n = 1409 n = 1238 n = 1824 year Proportion of households for which 62.246% 59.288% 75.994% 48.635% 69.443% Y=1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 37 Table A26. Interactions between Q6 and other economic sentiments household household household household household expects prices expects prices expects prices expects prices expects prices to increase to increase to increase to increase to increase Dependent more next year more next year more next year more next year more next year variable (Y) than they than they than they than they than they already have in already have in already have in already have in already have in previous year previous year previous year previous year previous year Country Burkina Faso Ethiopia Malawi Nigeria Uganda Dummy: 0.021 -0.017 0.176 ** 0.369 *** 0.363 *** household (0.589) (0.925) (0.023) (0.000) (0.000) reports that prices have [-0.043 ; 0.085] [-0.322 ; 0.287] [0.050 ; 0.302] [0.254 ; 0.484] [0.256 ; 0.470] “gone up a lot” over the last year n = 762 n = 1207.000 n = 1327 n = 1092 n = 1602 Proportion of households for 10.979% 54.141% 78.776% 50.39% 40.666% which Y = 1 Note: each cell reports the results of a weighted bivariate logistic regression between the dependent variable and the indicator in the first column. Marginal effects, p-values and 90% confidence intervals are reported in each cell, for each model. Significant coefficients are reported in bold. 38