Shocks and Coping Findings from the sixth round of the Myanmar Household Welfare Survey (June – November 2023) STRATEGY SUPPORT PROGRAM | WORKING PAPER XX MONTH YEAR MYANMAR STRATEGY SUPPORT PROGRAM | WORKING PAPER 52 MARCH 2024 2 CONTENTS Abstract .................................................................................................................................... 4 1. Introduction .......................................................................................................................... 5 2. Data and Methodology ........................................................................................................ 6 3. Shocks ................................................................................................................................. 7 3.1 Security Shocks ........................................................................................................... 7 3.2 Climatic Shocks ......................................................................................................... 13 3.3 Service Sector Shocks .............................................................................................. 14 3.4 Economic Shocks ...................................................................................................... 19 4. Coping Strategies .............................................................................................................. 25 5. Vulnerability Assessment ................................................................................................... 28 6. Conclusion ......................................................................................................................... 30 References ............................................................................................................................. 31 Appendix Tables .................................................................................................................... 32 TABLES Table 1. Percentage of households experiencing security shocks in their community over the past three months, July–December 2022 and June–November 2023 .................. 7 Table 2. Percentage of households experiencing security shocks against their household over the past three months, June–November 2023 ............................................ 12 Table 3. Percentage of households negatively impacted by economic shocks, July– December 2022 and June–November 2023 ....................................................... 23 Table 4. Coping mechanisms used to deal with lack of food or money in the past 30 days, September–December 2022 and August–November 2023 ................................. 25 Table 5. Estimates from logit fixed effects models of shocks on coping mechanisms ........... 29 Table A.1 Percentage of households experiencing community and household insecurity in the past three months, by state/region ................................................................ 32 Table A.2 Percentage of urban households experiencing community and household insecurity in the past three months, by state/region ............................................ 32 Table A.3 Percentage of rural households experiencing of community and household insecurity in the past three months, by state/region ............................................ 33 Table A.4 Percentage of households experiencing of lawlessness in the past three months, by state/region ..................................................................................................... 33 Table A.5 Percentage of farm households experiencing climatic shocks in the past three months, by state/region ....................................................................................... 34 Table A.6 Percentage of households experiencing negative economic shocks in the past three months, by state/region .............................................................................. 34 Table A.7 Percentage of urban households experiencing negative economic shocks in the past three months, by state/region ...................................................................... 35 Table A.8 Percentage of rural households experiencing negative economic shocks in the past three months, by state/region ...................................................................... 35 Table A.9 Most important challenges for farm/non-farm wage or salary incomes ................. 36 Table A.10 Most important challenges for crop production .................................................... 36 3 Table A.11 Most important challenges for crop sale .............................................................. 37 Table A.12 Most important challenges for farm or non-farm enterprises ............................... 37 Table A.13 Reduced food expenditure as a coping strategy, by food group ......................... 38 Table A.14 Use of each coping strategy by the number of coping strategies ........................ 39 Table A.15 Summary of coping strategies employed, by State/Region in percentage of households .......................................................................................................... 40 FIGURES Figure 1. Percentage of households reporting different risks in their community over the past three months for urban and rural households, June–November 2023 .................. 8 Figure 2. Percentage of households who experience violence in their community in July– December 2022 (left) and June–November 2023 (right) ..................................... 10 Figure 3. Percentage of households who experience drug use (top left), gambling (top right), petty crime (bottom left), or limited mobility (bottom right) in their community, June–November 2023 ......................................................................................... 11 Figure 4. Percentage of households experiencing security shocks against their household over the past three months, June–November 2023, by state/region ................... 13 Figure 5. Percentage of farming households experiencing climatic shocks over the past three months, July–December 2022 and June–November 2023 ................................. 14 Figure 6. Number of days with one hour power cuts, August–November 2023 ..................... 15 Figure 7. Number of hours in the past 24 hours without electricity, September–November 2023 .................................................................................................................... 15 Figure 8. Percentage of households accessing the internet (top) and barriers to internet access (bottom), August–November 2023 .......................................................... 17 Figure 9. Access to medical services in the past month, August–November 2023 ............... 18 Figure 10. Percentage of households with all children aged 5–14 enrolled in school, by state/region, July–December 2022 and June–November 2023 .......................... 19 Figure 11. Cost of the food inflation basket (nominal kyat) and food inflation (percent), by round ................................................................................................................... 20 Figure 12. Contribution of surveyed foods to food inflation .................................................... 21 Figure 13. Petrol prices (nominal kyat/gallon) and inflation (percentage), by round .............. 22 Figure 14. Housing rent (nominal kyat/adult equivalent/month) and inflation (percentage), by round ................................................................................................................... 22 Figure 15. Coping strategy by asset class, August–November 2023 .................................... 27 4 ABSTRACT The sixth round of the Myanmar Household Welfare Survey (MHWS), a nationally and regionally representative phone survey, was implemented between August and November 2023. It follows five rounds that were carried out since the beginning of December 2021. This report discusses the findings from the sixth round related to shocks and coping strategies. The security situation in Myanmar continued to deteriorate during the sixth-round recall period, which spanned from June to November 2023. Households felt insecure in their communities, as reported by 23 percent of households and had a low level of trust, as reported by 25 percent of households. This is because crime and violence continued to increase, affecting 20 and 10 percent of communities, respectively. Further, eight percent of households were directly affected by violence, either through violence against a household member, robbery, or appropriation and/or destruction of their assets. Lawlessness continues to rise in Myanmar. In June–November 2023, 21 percent of households reported a lot or some gambling in their community, 20 percent reported a high risk of burglary, theft, or robbery in their community, and 16 percent reported drug use. These issues were more prominent in urban areas, compared to rural areas. Another crucial challenge is that 15 percent of respondents felt that it was dangerous for them to move around and do everyday tasks in June–November 2023. Finally, three percent of respondents revealed that there was a risk of kidnapping in their community. The two states/regions where households felt the most insecure between June and November 2023 were Kayah and Chin. At the same time, the security situation in Rakhine, Tanintharyi, and Sagaing decreased the most compared to the same time last year. These areas witnessed the highest increases in lawlessness. While the lowest levels of reported insecurity continued to be in Ayeyarwady, Bago, and Nay Pyi Taw, these regions are still confronting much of the same risks as experienced across the country. Households faced multiple shocks besides insecurity. In June–November 2023, 19 percent of farm households reported being negatively impacted by at least one climatic shock. Intense wind was a major issue in Rakhine with 28 percent of households negatively impacted. Disruptions to the internet and electricity also negatively affected household wellbeing and livelihoods. For residents that accessed electricity from the national power grid, 55 percent of households had a power cut of at least one hour from 8:00 am to 8:00 pm all seven days of the week prior to the interview. Further, between June and November of 2023, almost half of the households (48 percent) did not have access to the internet regularly. Access to medical services and school enrolment improved at the end of 2023 compared to the end of 2022. The percentage of households who could never access medical services dropped from eight percent in July–December 2022 to two percent in June–November 2023. School enrollment improved from 79 percent of children aged 5 to 14 enrolled in July– December 2022 to 86 percent of children enrolled in June–November 2023. The rate of food inflation rose to 24 percent between March–June 2023 and September– November 2023, on average 5 percent per month. The prices of most foods in our survey increased considerably in the one-year period between October–December 2022 and September–November 2023, with median rice prices increasing by 75 percent. Further, the contribution of rice prices to the rising cost of the food inflation basket has become more 5 important over time. On the other hand, vegetable prices exhibited substantial volatility with large increases in prices between the third and fourth quarters of both 2022 and 2023. Seventy-five percent of households used at least one coping strategy to meet daily needs during the month prior to the sixth-round survey (June–November 2023). The three most common coping strategies used were spending savings, reducing non-food expenditure, and reducing food expenditure. This has been consistent across rounds. Further, some households exhausted some or all of their coping strategies. Thirty-five percent of households reported that they no longer have any savings to reduce. Finally, the number of households who borrowed money (30 percent) decreased significantly from the previous year (38 percent) but was still slightly higher than in the beginning of 2023. At the same time, 49 percent of households continued to be in debt. 1. INTRODUCTION In June through November of 2023, households continued to be affected by security, climatic, and economic shocks. During the recall period for the survey, major fighting was ongoing in the states/regions of Kayah, Chin, Sagaing, Magway, Mandalay, Kachin, Kayin, Mon, eastern Bago, and Tanintharyi (OCHA 2023a, OCHA 2023b, OCHA 2023c). Further, on October 26th, fighting intensified in a new front of conflict in Northern Shan between allied Ethnic Armed Organizations (EAOs) and the Myanmar Armed Forces. Myanmar currently has the highest level of organized criminality in the world, according to the Global Organized Crime Index (Global Organized Crime Index 2023). Along with the conflict and crime, in August and October 2023, heavy monsoon rains caused severe flooding in Rakhine, Bago, Kayin, Mon, Tanintharyi, Kayah, southern Shan, northern Shan, and Sagaing resulting in extensive damage to infrastructure and agriculture. About 100,000 acres of paddy were destroyed due to the flooding in Bago (OCHA 2023d). Households’ agricultural production in some areas was also affected by other climatic shocks such as irregular temperatures, drought, and strong winds. Further, despite inflation cooling in early 2023, in late 2023, inflation rose considerably making high food prices an important concern for households. Disruptions to the internet and electricity remained poor over the survey period, with most households having regular blackouts. All these factors continued to negatively impact household welfare leading households to employ numerous coping mechanisms. This paper provides an overview of the shocks faced and coping strategies used by households across Myanmar for the sixth round of the Myanmar Household Welfare Survey (MHWS). MHWS is a representative phone survey at the national, urban/rural, and state/region levels. The sixth round of the MHWS was carried out between the end of August and November 2023. For most indicators, there was a recall period of three months, so therefore most indicators report on the period spanning June through November of 2023. This recall period includes the monsoon season, which stretches from mid-May to late-October in the largest part of Myanmar as well as the beginning of the post-/pre-monsoon season. In this paper, we provide an update of the security, climatic, health, service, and economic shocks that Myanmar households face. We then explore the coping strategies households employ to meet their daily needs. Finally, we explore the association of shocks and coping strategies employed. The paper is organized as follows: Section two describes the data and methodology. Section three is a description of shocks that have negatively affected Myanmar’s people 6 including security, climatic, service, and economic shocks. Section four provides an overview of the coping strategies that households employ. Section five explores shocks associated with coping. Section six concludes. 2. DATA AND METHODOLOGY The analysis presented in this paper relies on data from the sixth round of the MHWS. The sixth round was carried out from August 29, 2023, to November 11, 2023.1 The indicators from the sixth round are compared to the fourth round of MHWS, which was conducted almost a year prior in October–December 2022 with indicators reported for July–December 2022. The indicators are also compared to the beginning of 2022, or the fifth round of MHWS which was conducted in March–June 2023; with indicators reported for December 2022–June 2023. The MHWS survey intends to monitor household and individual welfare through a range of different indicators including wealth, livelihoods, food insecurity, diet quality, health shocks, and coping strategies. A novel sampling strategy in combination with the development of household and population weights allows for estimates that are nationally, regionally, and urban/rural representative (Lambrecht et al. 2023). The analysis is mainly descriptive and employs straightforward indicators, although the construction of indicators related to shocks and poverty requires more detail. The shock indicators include only self-reported shocks. In the MHWS, respondents were asked about different shocks that their households or their communities experienced in the past three months. Depending on the date the household was interviewed, the past three months includes June–August 2023, July–September 2023, August–October 2023, or September– November 2023. Because of the difficulty in surveying conflict affected areas, it is likely that estimates of shocks underrepresent the extent of insecurity in the country. We compare our different indicators of vulnerability and welfare by the households’ main source of income and asset class. We divide households into five groups by their main source of income: non-farm business, non-farm salary, non-farm wage, farm wage/salary, and own farming. Households were categorized into three asset-class groups based on the number of assets they own: asset-poor (0–3 assets), asset-low (4–6 assets) and asset-rich (7–10 assets). This categorization is based on a count of 10 assets including: improved housing (semi-pucca, bungalow/brick, apartment/condominium), flush toilet, improved water source (piped into house or bottled water), grid-based electricity (not solar), rice cooker, fridge, TV, wardrobe, car/motorcycle/tuk-tuk, and a working computer/laptop/iPad.2 Households that operate food vendor businesses report current prices of rice, potatoes, pulses, chicken, fish, leafy green vegetables, onions, bananas, and cooking oil. We use these food prices to measure food inflation using the changing cost of a fixed food basket, where the food basket weights are calculated using household consumption patterns observed in the 2015 Myanmar Poverty and Living Conditions Survey. The MHWS also collects data on the cost of some non-food items. Food vendors report petrol prices in every survey round, and prices of soap, toothpaste, and paracetamol beginning in the fourth survey round. Households 1 The fighting in Northern Shan which began on October 26th and is ongoing in 9 out of 22 townships there is largely not captured in our survey, since the fighting began at the end of the survey period, and those townships lost electricity and phone service due to the conflict. 2 For household who newly joined the survey, asset information was collected in every round. For households that participated in previous survey rounds, asset information was preloaded from the previous round, and only asked again in the first and fifth rounds. 7 that rent their dwellings (26.4 percent of urban households and 3.2 percent of rural households) report monthly rental values beginning in the second survey round. Though the MHWS collects prices of a limited number of non-food items, there are barriers in a household phone survey to collecting the prices of a sufficient range of non-food items needed to estimate non-food inflation. Therefore, we do not estimate non-food inflation. Finally, we employ regression analysis to identify factors associated with the use of coping strategies. We use a logit model with household and round fixed effects regressions to estimate the association between specific types of shock and the likelihood of reducing food expenditure, reducing non-food expenditures, borrowing money, selling non-productive assets, and selling productive assets. We include three types of shocks in our analysis: security, climatic, and economic shocks. For security shocks, we include three quantiles of the number of violent acts towards civilians in the township in the three months prior to the interview, from ACLED data (ACLED 2022). Climatic and economic shocks are self-reported measures pertaining to the three months prior to the survey round. The climatic shock indicator measures whether the household was negatively impacted by natural or climatic shocks. The economic shocks include households negatively affected by high food prices, high fuel prices, job loss, and electricity shortages. We control for income from employment in agriculture, wage, salary or non-farm business, other sources of income and other household and respondent characteristics. We also control for household and community characteristics, and state/region. It is important to note that the recall period for each coping mechanism is one month, while the recall period for each shock is three months. Further, our estimates are only associations between each of the five coping strategies and the shock and control variables. 3. SHOCKS 3.1 Security Shocks In June–November 2023, 23.3 percent of households in Myanmar felt that their community was very or somewhat insecure (Table 1). The number of households who felt insecure remained the same compared to the end of 2022. Households’ trust in their community continued to erode, with 24.9 percent of households having no or low trust in their community which is significantly higher than in the previous year. Further, violence also increased since the same time last year. In June–November 2023, 10.2 percent of households reported that there was violence in their community (Table 1). This is an increase from 8.9 percent in July–December 2022. Table 1. Percentage of households experiencing security shocks in their community over the past three months, July–December 2022 and June–November 2023 National Rural Urban Community Jul–Dec 22 Jun–Nov 23 Jul–Dec 22 Jun–Nov 23 Jul–Dec 22 Jun–Nov 23 Feels insecure 23.1 23.3 21.6 22.0 26.9 26.8 Low levels of social trust 22.2 24.9*** 20.5 22.6*** 26.8 31.1*** Violence 8.9 10.2** 7.2 9.7*** 13.2 11.6 Observations 12,924 12,898 9,225 9,053 3,699 3,845 Note: Asterisks denote significant differences between MHWS July–December 2022 and June–November 2023 at the national, rural, and urban levels. Asterisks show significance at p-values * p < 0.10, ** p < 0.05, *** p < 0.01. Source: Author’s calculations based on MHWS data. 8 More urban households felt a low level of trust in their communities compared to rural households and compared to the same time last year, as well as the previous quarter. Further, the number of households reporting violence in rural communities increased compared with the previous year. At the same time, urban communities still report higher levels of violence than rural communities. Lawlessness continues to rise in Myanmar. In June–November 2023, 21.2 percent of households reported a lot or some gambling in their community, 20.2 percent reported a high risk of burglary, theft, or robbery in their community, and 16.3 percent reported drug use.3 These issues were more prominent in urban areas, compared to rural areas (Figure 1). Petty crime was particularly widespread in urban areas in the beginning and end of 2023, with 29.0 percent and 31.2 percent of urban dwellers reporting a risk of being robbed in the beginning and end of 2023, respectively. Another crucial challenge is that 14.6 percent of respondents felt that it was dangerous for them to move around and do everyday tasks in June–November 2023. Again, this impacted a greater number of urban households than rural households. Further, 10.5 percent of urban households and 7.4 percent of rural households reported that it was common for them to pay bribes to authorities. Finally, 2.9 percent of respondents revealed that there was a risk of kidnapping in their community. Figure 1. Percentage of households reporting different risks in their community over the past three months for urban and rural households, June–November 2023 Source: Author’s calculations based on MHWS data. 3 Households were asked “how do you feel about the situation of gambling in your community. In your community is there 1. Yes, a lot of gambling; 2. Yes, some gambling; 3. No, not much gambling; 4. No, no gambling at all; 8. prefer not to answer; 9. Do not know.” The question format is then repeated for other indicators including violence, petty crime, drug use, limited mobility, risk of kidnapping, and bribes. 14 19 3 16 14 7 21 26 3 31 17 10 16 21 3 20 15 8 0 5 10 15 20 25 30 35 Drug use Gambling Risk of kidnapping Petty crime Limited mobility Bribes Pe rc en ta ge o f h ou se ho ld s Rural Urban National 9 The three states/regions where households felt the most insecure between June and November 2023 were Kayah (52.1 percent of households), Chin (51.4 percent), and Sagaing (43.6 percent). Tables A.1, A.2, A.3 present state/region results at the national, urban, and rural levels, respectively. The number of households feeling insecure increased in Kayah, Nay Pyi Taw and Sagaing between July–December 2022 and June–November 2023. Yangon was the only region where fewer households reported feeling insecure at the end of 2023 compared to the end of 2022. Households in Chin (52.8 percent) and Kayah (37.1 percent) had the lowest levels of trust in their community (Table A.1). In July–December 2022 trust was already lower in Chin than in the other states/regions. In June–November 2023 there was a huge decline in the level of trust in Chin to 52.8 percent of households having low trust in their community. Trust significantly declined between July–December 2022 and June–November 2023 in Nay Pyi Taw, Rakhine, and Sagaing as well. In the remaining states/regions, levels of social trust remained low but did not decline. Households in Sagaing (24.3 percent of households) and Tanintharyi (21.8 percent) reported the most violence and reported significantly more violence compared to the end of 2022 (Figure 2). At the same time, the largest increase over the period occurred in Kayin, from 7.5 percent in July–December 2022 to 17.6 percent in June–November 2023. In Yangon, on the other hand, there was a significant reduction in reported violence from 13.0 percent in the latter part of 2022 to 9.1 percent in the latter part of 2023. In Ayeyarwady, the smallest share of households reported violence in their community in June–November 2023 (2.3 percent). Ayeyarwady also had the lowest percentage of households reporting violence in the second half of 2022 and the beginning of 2023 (Figure 2). 10 Figure 2. Percentage of households who experience violence in their community in July–December 2022 (left) and June–November 2023 (right) Source: Author’s calculations based on MHWS data. Along with the high incidence of violence, Kachin reported consistently high levels of lawlessness: 29.8 percent of households in Kachin reported not being able to move around to complete everyday tasks, 41.3 percent of households reported a risk of petty crime, 6.8 percent reported a risk of being kidnapped, and 61.7 percent reported high drug use in their community (Table A.1, Table A.4, and Figure 3). In Sagaing, where self- reported violence was the highest in June–November 2023, 27.4 percent households reported not being able to move around to complete everyday tasks, and a concerning 5.2 percent of households reported there was a risk of kidnapping. In Kayin, while mobility was comparatively easier (14.5 percent of households reported difficulty moving around), more respondents reported some or a lot of gambling (30.4 percent) and many respondents reported high drug use (35.9 percent). A similar percentage of respondents reported drug use in their community in Tanintharyi (35.8 percent). In Rakhine, about one-fifth of respondents feared having to pay a bribe while 11.7 percent of respondents reported a risk of being kidnaped—the highest risk of kidnapping reported among all states/regions. Many households in Kayah reported limited mobility (27.0 percent) and high petty crime (25.5 percent). In Kayin, Sagaing, Mon, and Rakhine there was a significant increase in reported community lawlessness from the beginning of 2023 to the end of 2023. 11 Figure 3. Percentage of households who experience drug use (top left), gambling (top right), petty crime (bottom left), or limited mobility (bottom right) in their community, June–November 2023 Source: Author’s calculations based on MHWS data. 12 Eight percent of households were directly negatively impacted by violence and/or crime against their household. This includes 1.1 percent of households who had a member assaulted or detained, 1.5 percent of households who suffered the destruction or appropriation of an asset, 3.8 percent of households who were impacted by theft or robbery, and 1.1 percent of households who were forced to give bribes or payments (Table 2). The incidence of households or household members being victims of theft/burglary was much higher in urban areas, 5.3 percent versus rural areas 3.2 percent. Theft/burglary of interviewed households decreased compared with the same period last year, although there was a small increase compared with the first half of 2023. This is because reported crime rates dropped significantly in Kayah, Yangon, Bago and Nay Pyi Taw (Table A.1). Table 2. Percentage of households experiencing security shocks against their household over the past three months, June–November 2023 Jul–Dec 22 Jun–Nov 23 Jul–Dec 22 vs Jun–Nov 23 Rural Jun–Nov 23 Urban Jun–Nov 23 Rural vs Urban Jun–Nov 23 Assault/detention 1.1 1.1 1.2 0.7 ** Destruction/appropriation of assets 1.3 1.5 1.4 1.8 Theft/robbery 4.9 3.8 *** 3.2 5.3 **** Bribery/forced payments 0.9 1.1 1.1 1.3 Observations 12,924 12,898 9,053 3,845 Note: Asterisks denote significant differences between July–December 2022 and June–November 2023, as well as the difference between rural and urban locals in June–November 2023. Asterisks show significance at p-values * p < 0.10, ** p < 0.05, *** p < 0.01. Source: Author’s calculations based on MHWS data. In June–November 2023, households in Kayah state continued to suffer from high levels of violence and crime. In Kayah, 3.4 percent of households suffered damage to an asset or had an asset appropriated and 10.1 percent of households endured theft/robbery (Figure 3 and Table A.1). This was much lower, though, than in July–December 2022. While there was a small drop in violence and crime against households in most states/regions compared to the second half of 2002, in Tanintharyi and Rakhine assault/detention, destruction/appropriation of assets, and theft all increased. 13 Figure 4. Percentage of households experiencing security shocks against their household over the past three months, June–November 2023, by state/region Source: Author’s calculations based on MHWS data. While the lowest levels of reported insecurity continued to be in Ayeyarwady (10.7 percent), Bago (14.6 percent), and Nay Pyi Taw (16.4 percent), these regions are still confronting much of the same risks as experienced across the country (Table A.1). Although Nay Pyi Taw had the lowest percentage of respondents feeling insure in their communities from September 2021 to June 2023 (R1–R5), it rose to the third lowest with a significant increase at the end of 2023 compared to the same time in the previous year. In Bago and Ayeyarwady, more than 20 percent of households reported high gambling in their communities and in Nay Pyi Taw and Ayeyarwady around 15 percent or respondents reported high petty crime in their neighborhoods. 3.2 Climatic Shocks In June–November 2023, 18.9 percent of farm households reported being negatively impacted by at least one climatic shock. The monsoon rains begin in mid-May and end in late-October, so the recall period for the survey round is mainly during the monsoon season and the beginning of the pre-/post-monsoon season. The main harvest period for monsoon rice is November, so most households were interviewed prior to the harvest. The number of households experiencing climatic shocks was identical to that one-year prior, in July– December 2022. At the same time, the climatic shocks reported were different. The two largest climatic shocks in the second half of 2023 were flooding (12.6 percent of households) and strong winds (4.9 percent of households) (Figure 5). On the other hand, the incidence of drought was more prevalent in the same period last year. At the regional level, flooding was a big issue for households in Mon and Kayin, negatively impacting 31.5 and 24.4 percent of households in those states, respectively (Table A.5). Drought was the most prevalent climatic shock in Magway, with 7.5 percent of households negatively impacted. Intense wind was a major issue in Rakhine with 28.4 percent of households negatively impacted. Further, in Mon and Chin, 10.7 and 9.1 percent of households were negatively affected by strong wind, respectively. Finally, irregular temperatures or rainfall was an important issue in Sagaing and Kachin. 3 4 2 4 2 1 2 1 0 2 2 0 1 1 1 10 6 6 3 4 5 4 4 4 2 2 3 2 3 3 1 3 1 1 2 1 1 2 2 1 1 0 2 0 0 0 2 4 6 8 10 12 14 16 Pe rc en ta ge o f h ou se ho ld s Destruction/appropriation of assets Theft/robbery Bribery/forced payments 14 Figure 5. Percentage of farming households experiencing climatic shocks over the past three months, July–December 2022 and June–November 2023 Source: Author’s calculations based on MHWS data. 3.3 Service Sector Shocks In the second half of 2023, 63.7 percent of households accessed power from the national grid. For residents who accessed electricity from the national power grid, 55.1 percent of households had a power cut off at least one hour from 8:00 am to 8:00 pm for all seven days of the week prior to the interview (Figure 6). In Ayeyarwady, Kayin and Mon, more than 80 percent of respondents reported having at least a one-hour of power cuts per day for seven straight days. In Nay Pyi Taw, on the other hand, 42.9 percent of respondents reported no daily power cuts and only 7.5 percent of households reported having at least a one-hour power cut per day in the past seven days. In Tanintharyi and Kayah, respondents recorded the second and third fewest power cuts during the day, with 26.0 and 24.8 percent of households in the two states experiencing no power cuts during the day, respectively. At the same time, however, most of these townships, instead, faced power cuts at night. 19 6 8 3 3 0 19 2 13 1 5 0 0 2 4 6 8 10 12 14 16 18 20 Climatic shock Drought Flood Irregular rain/ temperature Strong wind Climatic shock other Pe rc en ta ge o f f ar m in g ho us eh ol ds Jul-Dec 22 Jun-Nov 23 15 Figure 6. Number of days with one hour power cuts, August–November 2023 Source: Author’s calculations based on MHWS data. When we consider the past 24 hours only, in Kayah, the average duration without electricity was the longest (11.4 hours), followed by Mon (10.3 hours) and Chin (10.2 hours) (Figure 7). Twenty-six percent of households reported that they were negatively affected by this loss of electricity. The loss of electricity was particularly detrimental to urban residents with 37.2 percent of urban households reporting that they were negatively impacted by this loss (Table 8). In Yangon, Mon and Mandalay, the greatest number of households reported that they were negatively affected by the loss of electricity (Table A.6, Table A.7, Table A.8). Figure 7. Number of hours in the past 24 hours without electricity, September– November 2023 Source: Author’s calculations based on MHWS data. 0 10 20 30 40 50 60 70 80 90 100 Pe rc en ta ge o f h ou se ho ld s 7 6 5 4 3 2 1 0 11 10 10 9 8 8 7 7 7 6 6 5 5 5 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 H ou rs w ith ou t e le cr ity in th e pa st 2 4 h 16 Between June and November of 2023, almost half of the households (48.0 percent) did not have access to the internet regularly. During the same period in the year prior, 44.6 percent of households could not access the internet or could only access it a few times per month (Figure 8). In June–November 2023, 18.3 percent of households could not access the internet at all in the month prior to the survey, compared to 23.1 percent in July–December 2022, which shows that there has been a small improvement in access to the internet over the course of late-2022 to late-2023 among the households we were able to reach by phone. But there were huge regional differences in assessing the internet. Internet access was especially difficult in Chin and Sagaing where 73.9 and 57.7 percent of households could not access the internet at all in the month prior to the survey. In Chin, only 5.3 percent of respondents could access the internet anytime they wanted to. The lack of internet access was primarily a result of internet service disruptions, as reported by 45.6 percent of households. Households also reported not being able to afford to pay for the internet both because of high fees (8.2 percent), and a limited budget (25.9 percent). While the importance of disruptions of internet services increased by 2.6 percentage points between of the end of 2022 and the end of 2023, that of high fees and limited budget issues declined by 1.4 and 5.7 percentage points, respectively. On the other hand, 12.9 percent of households reported that they could not access the internet because they had no electricity, compared to 7.4 percent in the previous year. Internet service disruptions were the primary reason for the lack of internet in Chin (89.5 percent of households reported that this is why they had no internet access), Sagaing (84.3 percent), and Kachin (61.2 percent). In Yangon, Mon, Tanintharyi and Shan, electricity access was the most cited obstacle to accessing the internet. 17 Figure 8. Percentage of households accessing the internet (top) and barriers to internet access (bottom), August–November 2023 Source: Author’s calculations based on MHWS data Among twenty-six percent of the households who needed medical services, 2.2 percent of households in the month prior to the survey could not access medical services and 14.4 percent of households could only access medical services once or twice. Among households that needed medical services, overall access to medical services has increased since July–December 2022. But in some states/regions medical access continued to be limited. In Sagaing, 5.6 percent of households could not access medical services in the last month. This is in addition to the 18.9 percent of households who could only access medical services once or twice. In Chin and Rakhine, 51.4 and 28.2 percent of households, respectively, either could not access medical services or could only access them once or twice in the last month (Figure 9). 32 26 43 46 7 13 10 8 8 7 0 10 20 30 40 50 60 70 80 90 100 Jul-Dec 22 Jun-Nov 23 Pe rc en ta ge o f h ou se ho ld s Other High fees for internet Electricity access problems Internet services disruptions in the area No money for data 45 48 8 9 24 24 23 18 0 10 20 30 40 50 60 70 80 90 100 Jul-Dec 22 Jun-Nov 23 Pe rc en ta ge o f h ou se ho ld s Access never Access rarely Acces sometimes Access always 18 Figure 9. Access to medical services in the past month, August–November 2023 Source: Author’s calculations based on MHWS data. School enrollment increased from July–December 2022 to June–November 2023, from 79.3 percent of children aged 5 to 14 enrolled to 86.2 percent of children. There were increases in school enrollment in both rural and urban areas between July–December 2022 and June–November 2023, from 80.7 to 86.0 percent of rural children, and from 75.1 to 87.0 percent of urban children. Compared to the second half of 2022, in the second half of 2023 there were significant increases in school enrollment in Chin, Kayah, and Sagaing (Figure 10). The enrollment jumped from 55.9 percent to 82.9 in Chin, from 69.1 percent to 92.2 percent in Kayah, and from 44.8 percent to 61.1 in Sagaing. But Sagaing still had the lowest enrollment of all states/regions, at 61.0 percent followed by Tanintharyi, at 68.3 percent. Some children were not in school because they were working. Nearly six percent of households had a child under fifteen who worked at least 14 hours in a week in the three months prior to the interview. Rural children were more likely to be engaged in paid work compared to urban ones—6.7 percent of rural households had a child work for wages compared to 4.6 percent of urban households. In Chin and Shan, 10.2 and 9.3 percent of children worked for wages for at least 14 hours in a week in the three months leading up to the survey round. 0 20 40 60 80 100 Kayin Tanintharyi Kachin Mon Ayeyawady Mandalay Yangon Bago Magway Shan Nay Pyi Taw Sagaing Kayah Rakhine Chin Percentage of households who needed medical services Access most or all the time Access limited or never 19 Figure 10. Percentage of households with all children aged 5–14 enrolled in school, by state/region, July–December 2022 and June–November 2023 Source: Author’s calculations based on MHWS data. 3.4 Economic Shocks 3.4.1 Price Shocks Food inflation reached 34.7 percent between Q4 of 2022 and Q4 of 2023 (on average 3.4 percent per month), and 24.4 percent between Q2 and Q4 of 2023 (on average 5.0 percent per month) (Figure 11). Because the periods between survey rounds vary in length, we present both total and average monthly changes in food costs. In 2022, similar to 2023, there was also a large increase in food costs between Q2 and Q4 (42.3 percent or 6.8 percent per month). This repeated pattern is likely due to seasonality, particularly in vegetable prices, which is explored in (Figure 12). In the first survey period (Q1 2022), the nominal cost of the urban food basket was 9.5 percent higher than the rural basket. Over the course of 2022 and 2023, this gap narrowed to only 2.8 percent, with most of this change occurring in 2022. Between Q2 and Q4 2023, food inflation was similar in rural and urban areas (24.1 versus 25.0 percent). 0 10 20 30 40 50 60 70 80 90 100 Jun-Nov 23 Jul-Dec 22 20 Figure 11. Cost of the food inflation basket (nominal kyat) and food inflation (percent), by round Note: Percentage change noted between survey rounds refers to change in the nominal value of the food inflation basket at the national level. Quarterly survey periods are as follows: Q1 2022 refers to December 2021–February 2022; Q2 2022 refers to April 2022–June 2022; Q3 2022 refers to July 2022–August 2022; Q4 2022 refers to October 2022–December 2022; Q2 2023 refers to March 2023–June 2023; and, Q4 2023 refers to September 2023–November 2022. Source: Author’s calculations based on MHWS data. The prices of most foods in our survey increased considerably over the one-year period between the Q4 of 2022 and 2023, with median rice prices increasing by 75.0 percent. Over the same period, banana prices increased by 50.0 percent, pulses by 30.8 percent, cooking oil by 25.0 percent, animal source foods by 23.5 percent, and potatoes by 14.3 percent, while vegetable prices declined by 3.3 percent. The contribution of rice prices to the rising cost of the food inflation basket has become more important over time (Figure 12). In the first half of 2022, rising rice prices accounted for only two percentage points of the overall food inflation rate. In contrast, during the same period chicken/fish and cooking oil prices were significant factors, accounting for six and five percentage points, respectively. However, between Q2 and Q4 of 2023, rice contributed 11 percentage points, whereas chicken/fish and cooking oil contributed 2 and 3 percentage points, respectively. Vegetable prices exhibit substantial volatility with larger price increases between the Q2 and Q4 of both 2022 and 2023. +14.2 +3.6/month +5.2 +2.2/month +25.1 +6.4/month +8.2 +1.5/month +24.4 +5.0/month 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 Q1 22 Q2 22 Q3 22 Q4 22 Q2 23 Q4 24 no m in al k ya t Urban Rural National 21 Figure 12. Contribution of surveyed foods to food inflation Note: Percentage change refers to the total increase in the cost of the food inflation basket. Quarterly survey periods are as follows: Q1 2022 refers to December 2021–February 2022; Q2 2022 refers to April 2022–June 2022; Q3 2022 refers to July 2022–August 2022; Q4 2022 refers to October 2022–December 2022; Q2 2023 refers to March 2023–June 2023; and Q4 2023 refers to September 2023–November 2022. Source: Author’s calculations based on MHWS data. Petrol prices surged by 53.1 percent in the first half of 2022 followed by a net increase of only 5.2 percent between the Q2 2022 and Q4 2023 (Figure 13). At the beginning of 2022 urban and rural petrol prices were similar. However, rural prices were more heavily impacted by rising prices in the first half of 2022. At the same time, urban petrol prices increased at a higher rate compared to rural areas between Q2 2022 and Q4 2023 (7.5 versus 4.4 percent). By Q4 2023, petrol prices in rural areas remained 4.5 percent higher than in urban areas. 2% 8% 9% 11%5% 3% 6% 5% 4% 2% 4% 10% -6% 5% 2% 14% 5% 25% 8% 24% -10% 0% 10% 20% 30% Q1 22– Q2 22 Q2 22– Q3 22 Q3 22– Q4 22 Q4 22– Q2 23 Q2 23– Q4 23 Pulses Potatoes Fruit Vegetables ASFs Oils Rice Total inflation 22 Figure 13. Petrol prices (nominal kyat/gallon) and inflation (percentage), by round Note: Percentage change noted between survey rounds refers to change in petrol prices at the national level. Quarterly survey periods are as follows: Q1 2022 refers to December 2021–February 2022; Q2 2022 refers to April 2022–June 2022; Q3 2022 refers to July 2022–August 2022; Q4 2022 refers to October 2022–December 2022; Q2 2023 refers to March 2023–June 2023; and, Q4 2023 refers to September 2023–November 2022. Source: Author’s calculations based on MHWS data. The average monthly cost of renting housing increased by 38.1 percent between Q2 2022 and Q4 2023 and increased by 19.5 percent between Q4 of 2022 and 2023 (Figure 14). Rent is a more important issue in urban areas where 26.4 percent of households rent their dwelling compared to rural areas (3.2 percent of households rent). Large increases in rents are a considerable burden to urban renters where Q4 2023 rents are 27.6 percent of average income in urban households and 40.1 percent of average income in urban households dependent on non-farm wage work. Moreover, increases in rents are likely underestimated, as rents may not be frequently renegotiated. Thus, in a given period, rents may only increase for a fraction of renters. Figure 14. Housing rent (nominal kyat/adult equivalent/month) and inflation (percent- age), by round Note: Percentage change noted between survey rounds refers to change in housing rents at the national level. Quarterly survey periods are as follows: Q1 2022 refers to December 2021–February 2022; Q2 2022 refers to April 2022–June 2022; Q3 2022 refers to July 2022–August 2022; Q4 2022 refers to October 2022–December 2022; Q2 2023 refers to March 2023–June 2023; and, Q4 2023 refers to September 2023–November 2022. Source: Author’s calculations based on MHWS data. 53.1% -8.8% 6.7% 1.6% 6.3% 0 2,000 4,000 6,000 8,000 10,000 Q1 22 Q2 22 Q3 22 Q4 22 Q2 23 Q4 23 ky at /g al lo n Urban Rural National 12.9% 0.6% +13.9% +6.7% 0 10,000 20,000 30,000 40,000 Q2 22 Q3 22 Q4 22 Q2 23 Q4 23 ky at /a du lt eq ui va le nt /m on th Urban Rural National 23 In June through November of 2023, 35.8 percent of households were negatively impacted by higher food prices (Table 3). This is much lower compared to the last quarter of 2022, where 61.6 percent of households were negatively impacted by higher food prices. Interestingly, prices were much higher at the end of 2023 compared to the end of 2022, including the price of the main dietary staple, rice. Further, real incomes declined over the same period. Therefore, it is hard to understand why fewer households reported being negatively affected by high food prices. Since the recall period is during monsoon production and the beginning of harvesting, it may be that some households were able to supplement from their own-production, and or, anticipate benefiting from higher prices when they begin to sell their crops. In June through November of 2023, the number of households impacted by high fuel prices decreased considerably to 31.9 percent of households from 57.6 percent of households in the previous quarter. While fuel prices jumped considerably between Q1 and Q2 of 2022, they leveled off at a higher price until November 2023 (Figure 13). In Tanintharyi, households were still overwhelmingly negatively impacted by price shocks; 48.6 percent of households were negatively impacted by high food prices, while 50.3 percent of households were negatively impacted by higher fuel prices (Table A.13). Table 3. Percentage of households negatively impacted by economic shocks, July– December 2022 and June–November 2023 Jul–Dec 22 Jun–Nov 23 Jul–Dec 22 vs Jun–Nov 23 Rural Jun–Nov 23 Urban Jun–Nov 23 Rural vs Urban Jun–Nov 23 Higher food prices 61.6 35.8 *** 34.4 39.5 *** Higher fuel prices 57.6 31.9 *** 31.0 34.3 *** Loss of employment 37.1 21.8 *** 21.6 22.2 Exchange rate fluctuation 20.3 14.8 *** 13.2 19.0 *** Loss of electricity 33.2 26.1 *** 21.7 37.2 *** Unable to assess money in a bank account 5.2 2.5 *** 1.9 3.8 *** Observations 12,924 12,898 9,053 3,845 Source: Author’s calculations based on MHWS data. 3.4.2 Income shocks Twenty-two percent of households were negatively impacted by a loss of employment in June–November 2023, which is an improvement from 37.1 percent in July–December 2022 (Table 3). But in Kayah, 49.1 percent of households reported a loss of employment in June–November 2023, while in Tanintharyi 38.8 percent of households reported a loss of employment. This was particularly an issue in urban areas of Kayah and rural areas of Tanintharyi. At the same time, there was a statistically significant decline in the number of income sources between July–December 2022 and June–November 2023 in rural areas (MAPSA 2024). In addition to losing income streams, households continued to face numerous challenges with earning income including reduced working hours and higher prices of farm and non-farm business inputs. 24 Eighteen percent of salaried/wage farm and non-farm workers reported reduced working hours or less work as their main challenge in June through November of 2023, compared to 20.7 percent a year earlier (Table A.9). In MHWS households reported the main challenge they faced in the last three months, based on their principal source of income. Reduced working hours was the largest challenge faced by salaried/wage workers. This was a bigger issue in rural areas, 20.4 percent of wage/salaried workers versus 13.0 percent of wage/salaried workers in urban areas. Further, 7.2 percent of wage/salaried workers reported low/reduced wages as their principal challenge. While nationally, 4.2 percent of wage/salary workers reported it was unsafe to travel to their work location; in Kayah, 20.8 percent of wage/salary workers reported this issue, and in Sagaing 12.0 percent. Further, in Magway, Mon, and Kayah, 31.5 percent, 29.6 percent and 29.6 percent of wage-earning households reported less work and reduced working hours as their most important challenge. The main challenges that farmers faced between June and November of 2023 were weather (20.5 percent) and high prices of inputs or mechanization (12.3 percent). Compared to the end of 2022, fewer households reported high input prices and high fuel prices as the most important issue they faced (Table A.10). But high prices of inputs were still a considerable issue in Kayah, faced by 27.5 percent of farmers, Shan, 23.8 percent of farmers, and Rakhine, 20.7 percent of farmers. The high price of fuel was mainly an issue in Tanintharyi, where it was the main challenge faced by 6.0 percent of farmers. Issues with pests/diseases (6.7 percent) increased slightly as well and were quite high among farmers in Kayah, 16.1 percent. It is important to note that while nationally, 5.8 percent of farmers faced issues hiring workers, in Nay Pyi Taw, 11.7 percent of farmers faced this issue. Finally, weather conditions negatively impacted crop production the most in Magway, 29.4 percent of farmers, and Mandalay, 29.2 percent of farmers. The main issues farmers faced in terms of selling their crops were low prices for crops (15.1) and difficulty reaching traders (6.0 percent) (Table A.11). Low prices for crops continued to be a significant issue in Tanintharyi and Rakhine, where 42.6 percent and 28.1 percent of farmers reported this issue, respectively. In Kachin and Rakhine, about eleven percent of farmers reported that there were not many traders with whom to sell their crops. In Chin and Sagaing, 18.5 percent and 14.0 percent of farmers stated that buyers or traders could not reach their farm because of conflict. All of these above-mentioned issues increased significantly at the end of 2023 compared to the end of 2022. For non-farm enterprises, 16.6 percent reported high prices of raw materials as their main challenge in June–November 2023 which was almost the same in July–December 2022 (Table A.12). The number of businesses which reported having fuel/transportation issues declined slightly in June–November 2023, with 5.6 percent compared to 6.9 in the previous year. Thirteen percent of non-farm business owners reported that their greatest challenge was that no customers bought their products. This was particularly an issue in Kayah and Tanintharyi. This is likely due to the low purchasing power of inflation adjusted household income across the country. A growing issue that non-farm enterprises are facing is that people are not paying off their debts (3.2 percent), and more people are buying on credit, although it decreased slightly compared to the last quarter of 2023 (4.2 percent). In Rakhine and Tanintharyi, 8.8 percent and 8.1 percent respectively of non-farm enterprises faced this issue. Difficulties hiring workers and electricity supply problems were mentioned less in the second-half of 2023 compared to the previous year. Finally, 8.3 percent of non-farm 25 businesses stated that customers could not reach their business, which has not declined since the same time last year. This was an important challenge in Kachin, Sagaing and Chin. 4. COPING STRATEGIES Overall, 74.4 percent of households used at least one coping mechanism to deal with lack of food or money in the past 30 days, 75.0 percent of rural residents and 73.0 percent of urban residents (Table 10). Shocks can be particularly damaging to household well-being, when either the household cannot deploy a coping mechanism to ensure the same living standard or, the household is forced to use a coping mechanism that results in permanent loss of assets, income, or safety. In the MHWS, households identified all the coping strategies they used in the past 30 days to cope with lack of food or money. On average, households reported using 2.2 different coping mechanisms over the 30 days prior to the interview in September–November 2023. This marks a significant decline in both the percentage of households using a coping strategy and the average number of coping strategies used in August–November of 2023 compared to the last quarter of 2022. Table 4. Coping mechanisms used to deal with lack of food or money in the past 30 days, September–December 2022 and August–November 2023 National 22 vs 23 Rural Urban Rural vs Urban Sep–Dec 22 Aug–Nov 23 Aug–Nov 23 # coping mechanisms used 3.2 2.2 *** 2.2 2.1 Uses min. 1 coping mechanism 83.7 74.4 *** 75.0 73.0 Spent saving 70.0 59.9 *** 61.9 55.3 *** Reduced non-food expense 56.7 47.5 *** 46.5 50.2 *** Reduced food expense 56.7 47.5 *** 46.3 50.6 *** Borrowed money 37.5 30.1 *** 31.9 25.5 *** Reduced expense on health 42.0 35.3 *** 35.9 34.0 Mortgaged household assets/goods 20.7 16.2 *** 17.6 12.8 *** Sold household assets/goods 15.5 10.4 *** 10.3 10.7 Mortgaged non-ag productive assets/vehicles 0.9 0.6 0.4 1.0 ** Sold non-ag productive assets/vehicles 4.0 1.7 *** 1.4 2.5 *** Mortgaged/sold house 1.2 1.4 1.5 1.2 Mortgaged/sold land 0.5 0.4 0.6 0.1 *** Engaged in high-risk activities 5.4 3.3 *** 3.8 2.1 *** Migrate entire HH 1.1 1.2 1.0 1.7 Mortgaged/sold ag productive assets (ag HH only) 1.8 1.9 2.5 0.3 *** Number of observations 12,924 12,898 9,053 3,845 Notes: Household assets include radio, furniture, television, jewelry, etc. Non-agricultural productive assets include sewing machine, wheelbarrow, bicycle, car, etc. Asterisks show significance differences between rounds; * p < 0.10, ** p < 0.05, *** p < 0.01. Source: Author’s calculations based on MHWS data. 26 Overall, the most common coping strategies were spending savings (59.9 percent), reducing non-food expenditure (47.5 percent), and reducing food expenditure (47.5 percent). Overall, fewer households reported using these coping strategies in June through November of 2023 compared to the same period last year when 69.6 percent of households spent savings and 56.8 reduced their non-food expenditure and their food expenditure.4 This may be because households are beginning to exhaust their coping mechanisms. Thirty-five percent of households reported that they no longer have any savings to reduce. Finally, households who reduced their food expenditure did so mainly by decreasing their spending on meat (87.4 percent), oils, fats, and butter (84.1 percent), and fish (80.1 percent) (Table A.13). Rural households decreased their expenditures on those food groups more than urban households. To meet daily needs, 16.2 percent of households mortgaged household assets and 10.4 percent sold household assets. Nearly eleven percent of households reported that they could no longer mortgage or sell assets because they already used these strategies in the past. Mortgaging assets was more common in rural areas, but selling assets was nearly the same in both areas. Household assets include gold, jewelry, furniture, technology, and appliances. The most common asset sold and/or mortgaged was gold and/or jewelry. Two percent of households sold non-agricultural productive assets including sewing machines, wheelbarrows, bicycles, cars, and other means of transportation, and less than one percent mortgaged these assets. Some households also mortgaged or sold critical assets such as their dwelling (1.4 percent) or agricultural land (0.4 percent). Further, 1.9 percent of agricultural households mortgaged or sold agricultural productive assets, which is significantly higher than the previous year. Given the recall period of 30 days, the number of households that have mortgaged and/or sold assets continues to be concerning. The number of households who borrowed money (30.1 percent) decreased significantly from the previous year (37.5) but was still slightly higher than in the beginning of 2023. Some households could not borrow any more money (4.8 percent), while other households could not access a loan even though they wanted to borrow money (2.4 percent). At the same time, 48.7 percent of households continued to be in debt. Households also pursued risky activities to meet their daily needs. This includes 3.3 percent of households that engaged in income-generating activities that they themselves considered risky. Finally, 1.9 percent of families migrated with their entire household to deal with the dire economic situation in the month before the survey round. The use of these three coping strategies remained almost the same over the course of the year. Among households who used only one coping strategy, the most common coping strategy was spending savings (65.0 percent) (Table A.14). When households used two coping strategies, most households spent their savings (72.2 percent), and additionally, households also began to reduce their food and non-food expenses, 52.3 percent and 50.4 percent, respectively. Among households that used three coping strategies, nearly all of them spent savings and reduced food and non-food expenditure. Further, around 38.4 percent borrowed money and 45.2 percent reduced their expenditure on health. When households used four coping strategies, they began to increasingly mortgage and sell households assets. Finally, households that used six or more coping strategies, began to sell non-agricultural 4 Households were asked if they used the coping mechanism in the past 30 days. They could answer yes, no, not applicable, or no because they already exhausted the coping mechanisms. A reduction in expenditure is relative to how much they would like to spend, not relative to the previous period. 27 productive assets (10.3 percent), mortgage or sell their dwelling (8.0 percent), engage in high- risk activities (20.9 percent), and migrate with their household (8.8 percent). The situation of households is dire in Kayah as shown by the number of coping strategies used—92.6 percent of households used at least one coping mechanism in the past 30 days, and households used on average 4.0 out of 23 different coping mechanisms (Table A.15). Further, compared to other states/regions, more households in Kayah spent their savings (86.4 percent), reduced their non-food and food expenditure (73.2 and 74.5 percent), sold household assets (22.8 percent), borrowed money (47.1 percent), reduced health expenditure (58.6 percent), and sold non-ag productive assets (7.1 percent). At the same time, no state/region has been spared from the conflict and economic downturn, and in Nay Pyi Taw and Yangon, where coping strategy use is lowest, still 72.5 percent and 77.0 of households used at least one coping strategy. A high number of households mortgaged household assets in Bago, while many households sold assets in Rakhine. In Kachin State, 85.6 percent of households applied at least one coping mechanism, while using 3.2 mechanisms on average. Tanintharyi had the greatest number of households mortgage or sell dwellings. Also alarming is the percent of households who engaged in high- risk activities to meet daily needs, including 11.0 percent in Kayah, 9.7 percent in Chin, and 9.5 percent in Kachin. Further, approximately 8.1 percent of households in Kayah and 5.8 percent in Chin migrated from these states and highlighted their migration as a coping mechanism. Asset poor households were more likely to use coping strategies than asset low and asset rich households. Figure 13 shows different coping strategies used by asset class for August through November of 2023. During that period, 51.7 percent of asset poor households reduced their non-food expenditure, 54.0 percent reduced their food expenditure, and 66.5 percent spent their savings. Particularly striking is the difference between asset poor and asset rich households in terms of borrowing money. Thirty-nine percent of asset poor households borrowed money compared to 15.2 percent of asset rich households. Finally, asset poor households were most likely to sell and mortgage assets. Figure 15. Coping strategy by asset class, August–November 2023 Source: Author’s calculations based on MHWS data. 52 54 42 66 39 20 12 47 47 35 61 31 16 11 41 37 25 52 15 12 8 0 10 20 30 40 50 60 70 Reduced non- food expense Reduced food expense Reduced expense on health Spent saving Borrowed money Mortgaged household assets/goods Sold household assets/goods Pe rc en ta ge o f t he h ou se ho ld s Asset poor (0-3 assets) Asset low (4-6 assets) Asset rich (7-10 assets) 28 5. VULNERABILITY ASSESSMENT We use regression analysis to examine the relationships between three types of shocks (climatic, security, and economic shocks) and five coping strategies commonly observed as being employed following shocks. Table 5 presents the results from logit fixed effects models between experiencing climatic, security, and economic shocks and using each of the following coping strategies: reducing nonfood expenditure (column 1), reducing food expenditure (column 2), borrowing money (column 3), selling household assets (column 4) and selling productive assets (column 5). Climatic shocks are associated with a large probability of reducing nonfood expenditure, reducing food expenditure, borrowing money, selling household assets, and selling productive assets. Households that experienced significantly more violence than in a previous period (severe violence against civilians compared low violence) are significantly more likely to reduce their nonfood and food expenditure and to borrow money. Economic shocks, including households who were negatively affected by higher fuel prices, higher food prices, and a loss of employment, are associated with a large probability of reducing nonfood and food expenditure, borrowing money, selling household assets, and selling productive assets. Households that added income from farm wages are more likely to reduce their nonfood and food expenditures, borrow money, sell household assets, and sell productive assets. Households with remittances are less likely to sell household assets. Households that migrated in the past two years were less likely to reduce their nonfood expenditure and borrow money. Finally, households that had already borrowed money in the past 12 months or sold household or productive assets were less likely to again employ these coping mechanisms. 29 Table 5. Estimates from logit fixed effects models of shocks on coping mechanisms (1) (2) (3) (4) (5) Reduced nonfood expense Reduced food expense Borrowed money Sold household assets Sold productive assets Climatic shock 0.165** 0.294*** 0.329*** 0.221** 0.355*** (0.067) (0.070) (0.068) (0.096) (0.093) Moderate violence vs low violence 0.121** 0.012 0.017 -0.018 0.089 (0.055) (0.057) (0.057) (0.086) (0.090) Severe violence vs low violence 0.199*** 0.166** 0.180** 0.148 0.115 (0.075) (0.076) (0.077) (0.110) (0.113) HH affected by higher food prices 0.518*** 0.493*** 0.559*** 0.516*** 0.536*** (0.054) (0.056) (0.059) (0.088) (0.090) HH affected by higher fuel prices 0.461*** 0.447*** 0.488*** 0.081 0.626*** (0.057) (0.059) (0.060) (0.089) (0.094) HH affected by loss of employment 0.695*** 0.626*** 0.693*** 0.514*** 0.590*** (0.061) (0.060) (0.060) (0.081) (0.088) HH affected by loss of electricity 0.135** 0.193*** -0.028 0.143* 0.016 (0.054) (0.056) (0.057) (0.081) (0.084) Farm wage 0.310*** 0.150** 0.446*** 0.327*** 0.381*** (0.073) (0.074) (0.072) (0.108) (0.101) Non-farm wage 0.079 0.153** 0.243*** 0.016 0.477*** (0.071) (0.072) (0.071) (0.101) (0.114) Salary 0.105 0.019 0.022 -0.003 0.073 (0.086) (0.088) (0.094) (0.126) (0.150) Non-farm business 0.031 -0.063 0.049 -0.120 0.088 (0.068) (0.071) (0.072) (0.102) (0.107) Farming 0.112 0.106 0.256*** -0.154 0.361*** (0.076) (0.078) (0.077) (0.111) (0.123) Remittances -0.051 -0.002 0.052 -0.265** 0.065 (0.074) (0.075) (0.077) (0.113) (0.115) Assistance from family/friends 0.104 0.033 0.152* -0.137 0.067 (0.087) (0.089) (0.090) (0.130) (0.142) Migrated <2 years ago -0.301** 0.149 -0.614*** -0.289 -0.339 (0.126) (0.130) (0.136) (0.183) (0.212) Depleted coping mechanism -18.703 -19.085 -1.700*** -1.024*** -1.334*** (434.901) (594.203) (0.086) (0.064) (0.095) Observations 37,348 34,028 32,449 16,422 17,272 Number of households 9,284 8,481 8,042 4,085 4,256 Note: Climatic, security, and economic shocks pertain to the 3 months prior to the interview date. Coping strategies pertain to the month before the interview date. The model also controls for state/region, the sex of the respondent, remoteness, dependency ratio, and household size. Asterisks show coefficients significant at p-values * p < 0.10, ** p < 0.05, *** p < 0.01. Standard error in parentheses. Source: Author’s calculations based on MHWS data. 30 6. CONCLUSION Vulnerability is increasing in Myanmar. The MHWS survey data for round 6, which spans the period of June–November 2023, reveals an increasing frequency of shocks encountered by households, and associated negative consequences for household welfare. The security situation continued to deteriorate, with 23 percent of households feeling insecure in their communities and 25 percent of households having low or no trust in their community. This is because crime and violence continued to increase, affecting 20 and 10 percent of communities, respectively. Lawlessness is also on the rise, especially in Rakhine, Tanintharyi, and Sagaing where conditions deteriorated significantly between June–November 2023. Further, eight percent of households were directly affected by violence, either through violence against a household member, robbery, or appropriation and/or destruction of their assets. In June–November 2023, climatic shocks were equally prevalent compared to the same time last year, with flooding and strong winds posing significant difficulties for households in the southwest and southeast. Disruptions to the internet and electricity also negatively affected household wellbeing and livelihoods. Apart from Nay Pyi Taw, households in all states/regions experienced at least a five-hour blackout in the 24 hours prior to the interview. On the other hand, access to healthcare improved along with school enrollment. Compared to the second half of 2022, in the second half of 2023 there were significant increases in school enrollment in Chin, Kayah, and Sagaing. Households relied on coping strategies to meet their daily needs. Seventy-one percent of households employed at least one coping strategy to meet their daily needs during the month prior to the interview date. The three most common coping strategies used were spending savings, reducing non-food expenditure, and reducing food expenditure. This has been consistent across rounds. Further, some households exhausted some or all of their coping strategies. Myanmar’s households may be more vulnerable than described in this report. Our survey struggled to capture some of the most conflict-affected areas, especially in Sagaing, Rakhine, and Northern Shan. Finally, since internally displaced persons or other households in particularly precarious situations have limited access to phones, they are under sampled. Further analyses exploring the association of different factors with the use of coping mechanisms shows that shocks—and to the largest extent economic and climatic shocks— are detrimental to household welfare and significantly increase vulnerability. Conflict shocks and climatic shocks significantly increase the likelihood a household will reduce their nonfood expenditure, reduce their food expenditure, borrow money, sell household assets, and sell productive assets. Agricultural/non-farm casual wage-earning households are among the most vulnerable and added income from these sources increased the use of coping mechanisms. 31 REFERENCES ACLED (2022). ACLED data. Data Export Tool - ACLED (acleddata.com). Accessed February 1, 2023. Global Organized Crime Index (2023). Myanmar Profile. Criminality in Myanmar - The Organized Crime Index (ocindex.net). Accessed March 5, 2023. Lambrecht I, van Asselt J, Headey D, Minten B, Meza P, Sabai M, et al. (2023) Can phone surveys be representative in low- and middle-income countries? An application to Myanmar. PLoS ONE 18(12): e0296292. https://doi.org/10.1371/journal.pone.0296292 MAPSA (2024). Livelihoods and Welfare. Washington, DC. International Food Policy Research Institute (IFPRI). OCHA (2023a). Myanmar Humanitarian Update No. 31, 15 July 2023 OCHA (2023b). Myanmar Humanitarian Update No. 32, 8 September 2023 OCHA (2023c). Myanmar Humanitarian Update No. 33, 2 October 2023 OCHA (2023d). Myanmar Humanitarian Update No. 34, 10 November 2023 32 APPENDIX TABLES Table A.1 Percentage of households experiencing community and household insecurity in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Community Feels insecure 41.5 52.1 33.8 51.4 43.6 28.5 14.6 17.6 21.7 24.5 23.0 22.1 25.6 10.7 16.4 Low levels of social trust 30.1 37.1 30.6 52.8 31.3 32.7 17.7 21.6 21.4 27.0 27.0 30.3 27.0 15.6 26.6 Violence 17.2 9.4 17.6 12.2 24.3 21.8 6.4 8.7 8.5 9.9 13.7 9.1 8.1 2.3 5.5 Household Assault/detention 4.5 0.7 0.9 0.5 2.9 2.0 0.6 0.6 0.6 1.1 0.8 0.7 0.9 0.7 0.5 Destruction/appropriation of assets 1.8 3.4 1.5 0.7 4.1 2.5 0.7 0.8 1.7 0.5 3.7 1.3 0.9 0.4 1.5 Theft/robbery 5.6 10.1 2.4 1.7 3.1 3.7 2.5 2.6 4.3 4.0 5.5 5.4 3.5 3.3 2.0 Bribery/forced payments 1.3 0.6 0.8 1.6 1.3 2.1 0.4 0.1 1.0 1.6 3.5 1.1 2.1 0.5 1.2 Source: Author’s calculations based on MHWS data. Table A.2 Percentage of urban households experiencing community and household insecurity in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Community Feels insecure 40.7 53.0 35.1 61.5 41.8 34.8 15.3 24.8 24.2 21.7 11.1 26.1 36.2 16.5 17.8 Low levels of social trust 31.7 48.3 28.5 47.5 39.5 53.0 19.1 20.8 27.9 25.7 30.9 33.9 35.5 18.5 29.3 Violence 18.8 11.9 17.9 21.7 24.0 29.8 7.2 9.2 10.7 8.6 10.3 11.1 10.0 5.7 3.4 Household Assault/detention 1.3 3.3 0.0 0.0 1.2 0.2 0.3 0.0 0.5 3.3 0.0 0.9 0.1 0.9 0.0 Destruction/appropriation of assets 2.6 11.3 5.5 0.0 4.6 0.9 0.1 2.3 2.4 0.8 0.1 1.6 1.5 0.3 1.0 Theft/robbery 3.2 8.4 1.3 1.9 4.1 3.7 4.7 2.2 6.5 5.9 3.1 6.0 6.6 5.1 1.7 Bribery/forced payments 1.1 2.2 0.7 0.0 1.0 6.2 0.8 0.5 1.9 3.3 0.1 1.1 1.5 0.2 3.3 Source: Author’s calculations based on MHWS data. 33 Table A.3 Percentage of rural households experiencing of community and household insecurity in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Community Feels insecure 41.8 51.9 33.5 48.8 44.0 26.5 14.5 16.5 20.7 25.5 25.3 13.6 21.2 9.8 15.9 Low levels of social trust 29.4 33.9 31.1 54.2 29.7 26.4 17.4 21.7 18.5 27.5 26.3 22.8 23.4 15.2 25.5 Violence 16.4 8.7 17.5 9.7 24.4 19.3 6.2 8.6 7.5 10.3 14.4 4.9 7.4 1.8 6.3 Household Assault/detention 5.9 0.0 1.2 0.7 3.2 2.5 0.7 0.7 0.6 0.4 1.0 0.4 1.3 0.7 0.7 Destruction/appropriation of assets 1.4 1.1 0.7 0.8 3.9 3.0 0.9 0.6 1.4 0.3 4.4 0.7 0.7 0.4 1.8 Theft/robbery 6.7 10.6 2.7 1.7 2.9 3.6 2.1 2.7 3.3 3.3 6.0 4.2 2.2 3.0 2.0 Bribery/forced payments 1.4 0.1 0.8 2.1 1.4 0.8 0.3 0.0 0.6 1.0 4.1 1.1 2.3 0.5 0.3 Source: Author’s calculations based on MHWS data. Table A.4 Percentage of households experiencing of lawlessness in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Drug use 61.7 21.8 35.9 21.9 20.6 35.8 6.8 5.4 8.8 18.7 17.7 13.3 28.1 11.5 10.9 Gambling 29.0 14.6 34.6 10.9 18.5 18.6 20.4 19.1 19.0 19.6 30.4 19.7 20.4 22.1 23.2 Risk of kidnapping 6.8 1.7 4.4 8.6 5.2 5.8 1.7 1.5 0.9 2.9 11.7 1.6 3.7 0.6 0.2 Petty crime 41.3 25.5 19.0 19.4 15.7 22.6 13.9 12.6 22.7 25.1 23.4 27.2 21.2 15.6 15.0 Limited mobility 29.8 27.0 14.5 15.9 27.4 20.2 11.1 9.6 14.7 15.0 20.5 14.5 11.8 5.8 12.1 Bribes 14.9 6.7 11.9 13.5 8.9 11.6 7.5 6.3 4.9 7.6 19.8 9.0 4.4 6.7 7.3 Source: Author’s calculations based on MHWS data. 34 Table A.5 Percentage of farm households experiencing climatic shocks in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Negatively affected by any natural or climatic shock 17.6 27.0 29.8 20.6 21.7 17.3 20.2 16.0 18.3 36.5 39.5 18.8 14.8 8.7 8.7 Drought 4.5 6.5 0.4 1.4 1.9 0.0 1.4 7.5 4.1 0.7 0.3 0.4 2.3 1.2 6.5 Flood 7.7 17.9 24.4 10.9 15.4 15.7 16.1 8.1 11.7 31.5 15.0 17.2 10.9 5.0 1.4 Irregular rainfall or temperature 3.5 1.2 0.6 2.2 3.9 0.2 1.2 0.2 2.0 0.6 0.9 0.6 1.0 0.3 0.2 Strong wind 4.7 3.9 3.0 9.1 4.5 5.5 4.3 1.9 2.7 10.7 28.4 3.9 1.5 2.8 0.7 Source: Author’s calculations based on MHWS data. Table A.6 Percentage of households experiencing negative economic shocks in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Higher food prices 44.8 46.0 38.2 37.6 40.8 48.6 31.8 31.8 36.9 37.4 39.3 38.0 29.4 32.0 33.0 Higher fuel prices 42.9 46.6 33.4 37.7 40.5 50.3 24.5 27.2 37.6 34.6 30.8 27.8 31.2 26.8 30.5 Loss of employment 30.8 49.1 22.9 24.2 27.3 38.8 17.7 17.0 21.6 27.4 24.6 20.3 19.5 18.3 18.3 Exchange rate fluctuation 23.8 25.7 16.3 12.2 19.2 30.1 10.0 10.6 16.7 18.4 16.7 15.8 14.6 8.1 12.7 Loss of electricity 32.1 31.5 25.6 10.7 28.2 23.0 21.5 21.2 35.5 37.7 16.7 38.8 23.4 11.7 17.6 Unable to assess money in bank account 5.1 3.3 1.2 4.6 3.7 4.3 1.5 1.5 2.1 1.3 2.8 4.0 2.6 1.2 1.5 Number of observations 408 244 395 243 1,325 369 1,217 975 1,539 538 521 1,836 1,456 1,542 290 Source: Author’s calculations based on MHWS data. 35 Table A.7 Percentage of urban households experiencing negative economic shocks in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Higher food prices 42.0 52.2 49.2 48.9 45.8 43.5 35.2 38.3 43.6 34.8 41.2 39.0 37.5 35.4 28.0 Higher fuel prices 45.9 57.7 50.4 43.9 48.7 47.1 29.6 35.5 39.3 36.3 29.3 28.7 36.6 27.5 33.5 Loss of employment 25.3 54.9 17.8 29.3 29.4 30.2 22.8 18.5 22.2 18.9 23.8 20.6 25.4 18.0 16.2 Exchange rate fluctuation 24.0 31.7 24.4 24.0 27.1 29.2 13.7 18.5 19.9 19.1 17.6 17.9 20.4 11.6 17.8 Loss of electricity 35.7 36.8 32.8 31.9 42.0 18.6 33.0 27.8 38.7 41.9 32.2 43.1 32.5 31.0 11.4 Unable to assess money in bank account 4.6 6.9 4.5 17.1 4.8 8.0 3.4 1.6 2.9 1.8 1.3 4.8 2.7 2.8 1.0 Number of observations 158 107 86 66 233 71 286 134 523 153 75 1,300 348 215 90 Source: Author’s calculations based on MHWS data. Table A.8 Percentage of rural households experiencing negative economic shocks in the past three months, by state/region Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Higher food prices 46.0 44.3 35.7 34.6 39.8 50.2 31.2 30.8 34.0 38.2 38.9 35.9 26.0 31.4 34.9 Higher fuel prices 41.5 43.5 29.6 36.0 38.8 51.3 23.4 26.0 36.9 34.0 31.0 26.0 28.9 26.7 29.4 Loss of employment 33.3 47.4 24.1 22.9 26.9 41.5 16.7 16.8 21.3 30.3 24.7 19.6 17.0 18.4 19.1 Exchange rate fluctuation 23.7 24.0 14.5 9.0 17.6 30.4 9.3 9.4 15.2 18.2 16.6 11.5 12.2 7.6 10.8 Loss of electricity 30.5 29.9 24.0 5.1 25.4 24.4 19.2 20.3 34.1 36.2 13.8 29.8 19.6 8.7 19.9 Unable to assess money in bank account 5.3 2.2 0.4 1.4 3.5 3.2 1.1 1.4 1.7 1.1 3.0 2.2 2.5 0.9 1.7 Number of observations 250 137 309 177 1,092 298 931 841 1016 385 446 536 1,108 1,327 200 Source: Author’s calculations based on MHWS data. 36 Table A.9 Most important challenges for farm/non-farm wage or salary incomes Jul–Dec 2022 Jun–Nov 2023 Jun–Nov 2023 Rural Jun–Nov 2023 Urban No difficulty 57.8 61.4 59.1 65.3 Reduced working hours / less work 20.7 18.0 20.4 13.0 Low/reduced wages 6.8 7.2 6.8 7.8 Not safe to travel to work location 4.7 4.2 3.6 5.2 Unable to work due to health problems of worker or other household members 2.3 2.2 2.4 1.9 Not safe at work location 2.1 1.6 1.8 1.3 Not able to reach work location 1.1 2.0 2.6 0.9 Late payment/ Wages are not paid 2.4 1.4 1.2 1.9 High transportation costs 2.0 2.0 1.9 2.2 Number of observations 4,959 4,692 2,737 1,955 Source: Author’s calculations based on MHWS data. Table A.10 Most important challenges for crop production Jul–Dec 2022 Jun–Nov 2023 Jun–Nov 2023 Rural Jun–Nov 2023 Urban No difficulties 28.9 40.9 41.0 38.1 High prices of inputs or mechanization 25.5 12.3 12.2 15.4 Weather problems 21.8 20.5 20.4 21.4 Water/irrigation supply problems 2.8 2.9 3.0 2.6 Pest and disease problems 7.5 7.9 8.0 5.3 Disruption to banking services access 3.5 3.7 3.7 2.3 Difficulties hiring workers 3.0 5.8 5.9 5.3 Unable to acquire enough inputs or mech 2.9 2.8 2.8 1.4 I cannot reach my own farm 1.3 1.4 1.2 5.4 Number of observations 3,456 3,736 3,523 213 Source: Author’s calculations based on MHWS data. 37 Table A.11 Most important challenges for crop sale Jul–Dec 2022 Jun–Nov 2023 Jun–Nov 2023 Rural Jun–Nov 2023 Urban No difficulties 79.0 75.4 75.4 74.0 Low prices for crops 12.0 15.1 15.2 12.9 Buyers or traders cannot reach the farm or I cannot reach them 3.5 6.0 6.0 6.0 Not many traders 3.1 2.0 1.9 4.7 High price of fuel / high transportation cost 1.7 1.1 1.1 2.4 Payment problems 0.8 0.3 0.3 0.0 Markets are closed 0.0 0.0 0.0 0.0 Number of observations 3,456 3,736 3,076 190 Source: Author’s calculations based on MHWS data. Table A.12 Most important challenges for farm or non-farm enterprises Jul–Dec 2022 Jun–Nov 2023 Jun–Nov 2023 Rural Jun–Nov 2023 Urban No difficulties 40.3 43.8 46.0 40.8 Fewer/no customers interested in buying products 16.7 16.6 14.5 19.4 High prices of raw materials or supplies 14.9 13.1 11.6 15.2 Customers cannot reach my business, or I cannot reach customers 6.9 8.3 9.5 6.5 Unable to acquire enough raw materials / supplies (availability) 6.9 5.6 5.1 6.3 High prices of fuel / high transport costs 4.9 5.2 5.2 5.2 Consumer debt 4.2 3.2 4.1 2.0 Electricity/energy supply problems 1.9 2.2 2.4 2.0 Disruption to banking services, access to cash or loans 2.3 1.3 1.1 1.6 Difficulties hiring workers 1.0 0.7 0.5 1.0 Number of observations 3,054 2,921 1,567 1,354 Source: Author’s calculations based on MHWS data. 38 Table A.13 Reduced food expenditure as a coping strategy, by food group Sep–Dec 22 Aug–Nov 23 Aug–Nov 23 Rural Aug–Nov 23 Urban Staple grains, roots and tubers (%) 39.1 47.5 45.7 51.9 Beans and nuts (%) 37.0 42.8 42.2 44.1 Vegetables (%) 28.4 26.9 26.1 28.8 Fruits (%) 32.9 36.7 34.2 42.6 Meats (%) 85.8 87.4 89.5 82.5 Eggs (%) 52.9 59.5 61.9 53.9 Fish (%) 77.9 80.1 82.2 75.2 Dairy (%) 45.5 50.8 49.1 54.6 Sugary products (%) 56.6 60.8 59.6 63.4 Oils, fats and butter (%) 84.4 84.1 85.7 80.5 Condiments (%) 63.4 66.6 66.3 67.0 Restaurant meals, takeaway meals (%) 57.9 64.6 60.4 74.4 Number of observations 6,326 5,268 3,597 1,671 Source: Author’s calculations based on MHWS data. 39 Table A.14 Use of each coping strategy by the number of coping strategies 1 2 3 4 5 6 Spent saving 65.0 72.2 86.0 93.1 96.4 99.6 Reduced non-food expense 14.7 50.4 82.1 93.7 97.2 98.2 Reduced food expense 12.8 52.3 87.3 97.5 99.2 99.7 Borrowed money 13.6 27.3 38.4 53.2 76.9 93.2 Reduced expense on health 7.4 24.0 45.2 74.3 84.9 94.8 Mortgaged household assets/goods 7.3 16.0 20.5 27.7 41.5 63.4 Sold household assets/goods 1.7 5.1 11.4 16.8 31.8 57.9 Mortgaged non-ag productive assets or means of transport 0.1 0.2 0.4 0.8 1.9 4.5 Sold non-ag productive assets or means of transport 0.4 0.7 1.7 2.5 4.6 10.3 Mortgaged/sold house 0.4 0.6 1.1 1.8 5.0 8.0 Mortgaged/sold land 0.2 0.4 0.4 0.8 1.1 1.8 Engaged in high-risk activities 0.6 2.1 2.9 4.9 9.3 20.9 Migrate entire HH 0.9 0.6 0.7 1.2 3.0 8.8 Mortgaged/sold ag productive assets (ag HH only) 1.2 1.7 1.5 3.5 4.5 6.9 Source: Author’s calculations based on MHWS data. 40 Table A.15 Summary of coping strategies employed, by State/Region in percentage of households Kachin Kayah Kayin Chin Sagaing Tanintharyi Bago Magway Mandalay Mon Rakhine Yangon Shan Ayeyarwady Nay Pyi Taw Number of coping mechanisms used 3.2 4.0 2.9 3.4 3.0 3.3 2.9 2.8 2.6 2.8 3.5 2.6 2.8 3.0 2.4 Uses at least one coping mechanism (%) 85.6 92.6 83.3 83.5 82.6 84.7 82.1 81.0 77.8 80.4 87.0 77.0 80.4 81.8 72.5 Spent saving (%) 71.6 86.4 72.8 68.0 70.5 72.2 66.1 67.1 64.1 65.2 74.6 60.8 66.2 65.5 57.0 Reduced non-food expenditures (%) 62.7 73.2 54.0 59.3 54.6 59.6 52.8 53.2 48.0 54.1 60.5 54.7 53.7 53.6 44.5 Reduced food expenditures (%) 62.4 74.5 56.0 63.5 55.3 62.9 52.1 52.3 47.0 53.1 63.1 53.5 51.3 55.4 41.7 Borrowed money (%) 38.4 47.1 35.3 46.2 32.6 41.4 38.0 37.1 31.6 33.0 45.8 29.1 34.1 40.5 26.0 Reduced expenditures on health (%) 44.0 58.6 37.7 54.2 37.6 48.1 36.0 35.6 28.4 35.7 46.6 34.1 38.5 35.6 30.9 Mortgaged household assets (%) 13.7 12.3 10.7 3.2 10.6 12.8 27.5 21.2 16.2 16.8 32.5 17.4 7.3 28.2 26.8 Sold household assets (%) 13.0 22.8 13.9 9.6 13.2 19.3 12.8 13.2 14.3 17.8 22.8 15.0 11.1 11.9 13.7 Mortgaged non-ag productive assets/transport (%) 0.7 1.5 0.6 0.6 0.7 0.7 1.1 1.0 1.1 0.8 0.3 0.4 0.9 0.9 0.9 Sold non-ag productive assets/transport (%) 4.9 7.1 4.4 3.2 3.1 3.0 3.1 3.0 3.7 3.8 1.6 2.7 2.5 2.9 2.8 Mortgaged/sold house (%) 1.3 3.5 2.5 2.8 1.4 3.8 1.6 1.5 1.1 1.6 1.5 1.2 1.2 1.3 1.2 Mortgaged/sold land (%) 0.5 0.2 0.9 0.8 0.5 0.6 0.4 0.7 0.4 0.4 0.7 0.1 0.6 0.4 0.2 Engaged in high-risk activities (%) 9.5 11.0 4.4 9.7 5.3 6.5 3.6 3.0 3.9 4.0 7.1 2.6 3.5 4.1 2.3 Migrate entire HH (%) 1.0 8.1 1.4 5.8 1.6 1.3 1.0 1.0 1.0 1.0 1.9 1.7 1.3 0.8 0.7 Mortgaged/sold ag productive assets (ag HH only) (%) 3.5 3.2 2.4 3.3 2.9 0.6 2.3 2.2 2.3 1.4 1.3 0.4 1.5 1.8 0.8 Number of observations 408 244 395 243 1,325 369 1,217 975 1,539 538 521 1,836 1,456 1,542 290 Source: Author’s calculations based on MHWS data. 41 ACKNOWLEDGMENTS This work was undertaken as part of the Feed the Future Myanmar Agricultural Policy Support Activity (MAPSA) led by the International Food Policy Research Institute (IFPRI) in partnership with Michigan State University (MSU). This study was made possible by the support of the American people through the United States Agency of International Development (USAID), under the terms of Award No. AID-482-IO-21-000x. This publication has not gone through IFPRI’s standard peer-review procedure. The opinions expressed here belong to the authors, and do not necessarily reflect the views of USAID, IFPRI, MSU, or the United States Government. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE 1201 Eye St, NW | Washington, DC 20005 USA T. +1-202-862-5600 | F. +1-202-862-5606 ifpri@cgiar.org www.ifpri.org | www.ifpri.info IFPRI-MYANMAR IFPRI-Myanmar@cgiar.org www.myanmar.ifpri.info The Myanmar Strategy Support Program (Myanmar SSP) is led by the International Food Policy Research Institute (IFPRI) in partnership with Michigan State University (MSU). Funding support for Myanmar SSP is provided by the CGIAR Research Program on Policies, Institutions, and Markets; the Livelihoods and Food Security Fund (LIFT); and the United States Agency for International Development (USAID). This publication has been prepared as an output of Myanmar SSP. It has not been independently peer reviewed. Any opinions expressed here belong to the author(s) and do not necessarily reflect those of IFPRI, MSU, LIFT, USAID, or CGIAR. © 2024, Copyright remains with the author(s). This publication is licensed for use under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view this license, visit https://creativecommons.org/licenses/by/4.0. IFPRI is a CGIAR Research Center | A world free of hunger and malnutrition