FOOD SECURITY POLICY PROJECT RESEARCH REPORT #7 

 
 

 

Research Report 
August 2016 

 

 

 

 

 

 

Rural Livelihoods in Mon State: 

Evidence from a Representative Household Survey 

 

 

 

 

 

 

  

  



 
 

 
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Authors 

Myanmar Centre for Economic and Social Development: 

Aung Hein, Kyan Htoo, L. Seng Kham, Myat Thida Win, Aye Mya Thinzar, Zaw Min Naing, Mi Win Thida, Ni 

Lei, Lu Min, Naw Eh Mwee, Zaw Oo 

International Food Policy Research Institute: 

Mateusz Filipski, Ulrike Nischan, Joanna Van Asselt, Brian Holtemeyer, Emily Schmidt, Mekamu Kedir, Adam 

Kennedy, Xiaobo Zhang, Paul Dorosh 

Michigan State University:  

Ellen Payongayong, Ben Belton, Duncan Boughton 

 

Acknowledgments: This study was made possible by the generous support of the American people through the United 

States Agency for International Development (USAID). This document is also supported with financial assistance from 

the Livelihoods and Food Security Trust (LIFT) Fund, supported by Australia, Denmark, the European Union, France, 

Ireland, Italy, Luxembourg, the Netherlands, New Zealand, Sweden, Switzerland, the United Kingdom, the United States 

of America, and the Mitsubishi Corporation. We thank these donors for their kind contributions to improving the 

livelihoods and food security of rural people in Myanmar. The views expressed herein should in no way be taken to 

reflect the official opinion of USAID, the United States government, or any of the LIFT donors. The contents are the sole 

responsibility of the authors, who are affiliated with the Centre for Economic and Social Development of Myanmar 

(CESD), Michigan State University, and the International Food Policy Research Institute. 

The authors of this report also thank the following people for their invaluable help toward the completion of this 

report: Tim Dobermann from the International Growth Center; Kyaing Kyaing Sein, Thawtar Khaing, Aung Lwin, Nwe Ni, 

Swe Mar Hlaing, Win Hsu Marlar, Nyan Lin Oo, and Kyaw Htay from the CESD administrative team; all officials from the 

General Administration Department, from the regional to the village levels; all members of the survey advance team; 

and all enumerators. Most importantly we thank all interviewees of the Mon State Rural Household Survey for their 

generous contributions of time and hospitality. We sincerely hope this report will contribute to the well-being of their 

families.  

 

 

  



 
 

 
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Executive Summary 10 

1. Introduction 13 

2. Data and methodology 13 

2.1 Sample design and implementation 14 

2.2 Community questionnaire 15 

2.3 Household questionnaire 16 

2.4 Data cleaning and analysis 16 

3. Characteristics of Mon State 16 

3.1 Geography and Administrative Divisions 16 

3.2 Climate, Topography, and Agroecology 19 

3.3 Infrastructure and Public Services 20 

3.4 Economy 22 

4. Household Characteristics 23 

4.1 Household Characteristics and Demographics 23 

4.2 Educational Attainment and Employment 25 

4.3 Health 26 

4.4 Housing Conditions 26 

5. Household Activities, Incomes, and Assets 29 

5.1 Overview 29 

5.2 Location and Household Incomes 30 

5.3 Income Sources and Livelihood Strategies 32 

5.4 Landownership and Tenure Arrangements 37 

5.5 Other Assets 42 

6. Agricultural Income-Generating Activities 46 

6.1 Rice Production in Mon State 50 

6.2 Rubber Production in Mon State 61 

6.3 Other Crops 69 

6.4 Livestock 78 

6.5 Agricultural Extension 81 

7. Off-Farm Income-Generating Activities 82 

7.1 Casual Wage Employment 82 

7.2 Salaried Employment 85 

7.3 Nonfarm Enterprises 87 

7.4 Natural Resource Extraction (Excluding Fisheries) 94 



 
 

 
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7.5 Fishing 97 

7.6 Other Income Sources 107 

8. Migration and remittances 109 

8.1 Migration Scale and Scope 109 

8.2 Migrant Characteristics 111 

8.3 Migrant Destinations 112 

8.4 Working Abroad 113 

8.5 Migrant Remittances 115 

8.6 Conclusion: Migration in Rural Mon State 118 

9. Household Consumption and Vulnerability 119 

9.1 Consumption 119 

9.2 Perceptions of Well-Being 122 

9.3 Shocks 123 

9.4 Access to Credit and Savings 125 

10. General Conclusions 132 

Appendix A. Sampling Frame 135 

 
TABLES 

Table 2.1 Allocation of sample enumeration areas and households for Mon State Rural Household Survey by 

activity stratum 15 

Table 3.1 Number of sampled households per township (ordered from north to south) 17 

Table 3.2 Village access 21 

Table 3.3 Access to services (percentage of villages surveyed) 21 

Table 4.1 Household characteristics in Mon State, by agroecological zone 24 

Table 4.2 Educational attainment by gender, for population older than 16 26 

Table 5.1 Share of households in income quintiles, by location characteristics 31 

Table 5.2 Household characteristics, by per capita income quintile 31 

Table 5.3 Difference of quintile average from sample average (percentage) 34 

Table 5.4 Agricultural landholdings by township (north to south) 37 

Table 5.5 Agricultural landholdings by agroecological zone 38 

Table 5.6 Number and area (acres) of holdings by land use type 39 

Table 5.7 Means of parcel acquisition by parcel type 40 

Table 5.8 Share of households possessing documents conferring user rights, by parcel type and document 

type (percentage) 41 



 
 

 
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Table 5.9 Parcel use change from original to current 41 

Table 5.10 Prevalence of asset ownership by asset type and household characteristics (percentages) 42 

Table 5.11 Contribution of remittances to the purchase of large assets in households with migrants 

(percentage of households reporting) 43 

Table 5.12 Change in large asset ownership during the last five years, by expenditure group 44 

Table 5.13 Prevalence of asset ownership by small asset type and household characteristics (percentage) 44 

Table 5.14 Share of households owning small assets, percentages, by consumption expenditure quintile 45 

Table 6.1 Mean income and costs for agriculture sectors 48 

Table 6.2 Cost breakdown by agriculture sector 48 

Table 6.3 Mon State rice farming characteristics 50 

Table 6.4 Mon State rice varieties planted 52 

Table 6.5 Mon State fertilizer use 53 

Table 6.6 Mon State machine and animal use 54 

Table 6.7 Mon State permanent workers in rice farming 55 

Table 6.8 Permanent workers in rice farming by township (north to south) 55 

Table 6.9 Temporary workers by activity 57 

Table 6.10 Rice income and production by rice income quintile 58 

Table 6.11 Income and costs (in Myanmar kyats) 59 

Table 6.12 Costs 59 

Table 6.13 Percentage of rubber farms by original use 62 

Table 6.14 Stage of rubber production by township (north to south) 63 

Table 6.15 Average fertilizer used (kg/acre) 64 

Table 6.16 Per-acre rubber farm workers 64 

Table 6.17 Average rubber prices per pound, May–June 2015 67 

Table 6.18 Mon rubber income and costs (Myanmar kyats) 68 

Table 6.19 Quintiles of rubber income (per acre) 68 

Table 6.20 Households producing different crop types in Mon State, by crop and agroecological zone 

(percentage) 70 

Table 6.21 Annual crop parcels cultivated by crop type (percentage) and mean parcel size (acres) 70 

Table 6.22 Reasons for choosing to produce annual crops (share of households responding, percentage) 72 

Table 6.23: Per-acre other annual crop costs 72 

Table 6.24 Share of households using inputs by crop type (percentage) 73 

Table 6.25 Mean value of crops sold and consumed 73 

Table 6.26 Annual crop production costs and income (in Myanmar kyats) 74 

Table 6.27 Characteristics of orchard crop production 77 



 
 

 
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Table 6.28 Average input costs and income per household for orchard crops (in Myanmar kyats) 77 

Table 6.29 Distribution of households with livestock activities 78 

Table 6.30 Average number of animals owned 78 

Table 6.31 Commercial and subsistence livestock operations 79 

Table 6.32 Distribution of livestock points of sale and buyers 79 

Table 6.33 Distribution of live and slaughtered sales 79 

Table 6.34 Average sale price per animal (in Myanmar kyats) 80 

Table 6.35 Average total livestock revenue and profit (in Myanmar kyats, households with livestock) 80 

Table 6.36 Mean total value of livestock sold or consumed, subsistence and commercial households 81 

Table 7.1 Share of rural households participating in casual wage labor (percentage) 83 

Table 7.2 Participation in casual labor by gender 83 

Table 7.3 Location where casual labor is performed (percentage of households) 84 

Table 7.4 Average wage by type of work (in Myanmar kyats) 85 

Table 7.5 Gender breakdown of salaried employees, percentages 86 

Table 7.6 Nonfarm enterprise activities in the Mon State sample 87 

Table 7.7 Distribution of business activities by agroecological zone 89 

Table 7.8 Nonfarm businesses and landownership, percentages 90 

Table 7.9 Net monthly earnings by type of business, Myanmar kyats 94 

Table 7.10 Distribution of resource extraction activities, by type and gender 95 

Table 7.11 Average monthly earnings from resource extraction 97 

Table 7.12 Types of net used, by fishing environment (percentages) 102 

Table 7.13 Most important fish by quantity, by fishing environment (percentage of respondents) 103 

Table 7.14 Most important fish by value, by fishing environment (percentage of respondents) 104 

Table 7.15 Median catch per boat and estimated total catch for Mon State, by fishing area 105 

Table 7.16 Share of households receiving income and annual average, by income source 108 

Table 8.1 Distribution of households with and without migrants (percentage of respondents) 111 

Table 8.2 Relationship of (nonseasonal) migrants to household head (percentage of households with 

nonseasonal migrants) 111 

Table 8.3 Characteristics of current long-term migrants, by gender 112 

Table 8.4 Average cost of migration 113 

Table 8.5 Summary statistics on employment of migrants returned from abroad at destination, percentages

 115 

Table 8.6 Remittances received from current migrants 116 

Table 8.7 Largest expenses met using remittances (percentage of responses) 118 

Table 9.1 Average household dietary diversity score 121 



 
 

 
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Table 9.2 Household perception of adequacy of basic needs (percentages) 123 

Table 9.3 Share of households with one or more loans 125 

Table 9.4 Average amount of loans received per loan and per year (in Myanmar kyats) 129 

Table A.1 Distribution of Mon rural enumeration areas in the sampling frame by crop/fishing activity and 

level 136 

Table A.2 Distribution of Mon rural enumeration areas in the sampling frame by combination of crop and 

marine fishing activities 137 

Table A.3 Hierarchical criteria used for defining strata for predominant activities 137 

Table A.4 Final distribution of sampling frame of rural enumeration areas in Mon State by predominant 

activity stratum and level substratum 138 

Table A.5 Allocation of sample enumeration areas and households by activity stratum and level substratum

 139 

 

FIGURES 

 

Figure 3.1 Map of Mon State and townships 18 

Figure 3.2 Survey locations by agroecological zone 20 

Figure 4.1 Origin of household members who were not born in the village they were surveyed in 24 

Figure 4.2 Population pyramid of rural Mon State, 2015 25 

Figure 4.3 Distribution of household dwellings by number of rooms, excluding kitchens and bathrooms 27 

Figure 4.5 Type of roofing used in dwelling 28 

Figure 4.6 Access to electrical connection in the household, by district and income level 28 

Figure 4.7 Main source of lighting 29 

Figure 4.8 Main source of drinking water in the household 29 

Figure 5.1 Median income, by per capita income quintiles (participating households only) 30 

Figure 5.2 Distribution of number of income sources 32 

Figure 5.3 Average number of income sources and household size, by income quintile 33 

Figure 5.4 Share of households participating in income-generating activity, by income quintile 34 

Figure 5.5 Percentage of households with majority income from various sources, by income quintile 35 

Figure 5.6 Composition of total income by income quintile 36 

Figure 5.7 Composition of agricultural income by income quintile 36 

Figure 5.8 Lorenz curves for landownership 38 

Figure 6.1 Percentage of households with agriculture income, by township (north to south) 46 

Figure 6.2 Percentage of households with agriculture production income 47 

Figure 6.3 Percentage of agricultural households per income quintile 49 



 
 

 
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Figure 6.4 Agricultural income as percentage of total household income 49 

Figure 6.5 Percentage of farmers using machines, animals, or manual labor for rice farming, by township 

(north to south) 53 

Figure 6.6 Average wage paid to unskilled rice workers by township (north to south) 56 

Figure 6.7 Rice yields country comparison (kg/acre) 58 

Figure 6.8 Percentages of rice-farming households that experience preharvest loss 60 

Figure 6.9 Final uses of harvested rice 60 

Figure 6.10 Fertilizer use by township (north to south) 63 

Figure 6.11 Rubber yields by township (north to south), in pounds per acre 65 

Figure 6.12 Rubber yields, Mon State and Myanmar, Ministry of Agriculture and Irrigation estimates (pounds 

per acre) 66 

Figure 6.13 Rubber yields of largest rubber producers (pounds per acre) 66 

Figure 6.14 Rubber prices by township (north to south), May–June 2015 67 

Figure 6.15 Cost breakdown by parcel size group (acres) 69 

Figure 6.16 Share of producing households selling annual crops (percentage) 71 

Figure 6.17 Income and costs per acre in production of annual crops, by township (north to south) 74 

Figure 6.18 Distribution of crop-growing households by income quintile 75 

Figure 6.19 Percentage of orchard crops grown by agroecological zone 76 

Figure 7.1 Share of casual laborers employed in selected farm and nonfarm activities, by month (percentage)

 84 

Figure 7.2 Share of salaried workers by employment type (percentage) 86 

Figure 7.3 Percentage of households engaged in nonfarm business activities, by household subgroup 89 

Figure 7.4 Percentage of households who run a nonfarm enterprise, by income quintile 90 

Figure 7.5 Income distribution of business owners, by type of business 91 

Figure 7.6 Location of business, percentage of households 91 

Figure 7.7 Average number of household members engaged in the business activity 92 

Figure 7.8 Percentage of businesses operating year-round 93 

Figure 7.9 Percentage of businesses operating each month 93 

Figure 7.10 Share of households engaging in resource extraction activity, by household type 95 

Figure 7.11 Share of households selling all or part of the resources they extracted, by activity (percentage) 96 

Figure 7.12 Share of households engaging in extraction of bamboo from forest, by month (percentage) 96 

Figure 7.13 Share of all households reporting fishing income (percentage) 97 

Figure 7.14 Types of fishing trips 98 

Figure 7.15 Fishing seasonality, inland freshwater (with boat) and estuary 99 

Figure 7.16 Fishing seasonality, inshore sea 100 



 
 

 
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Figure 7.17 Fishing seasonality, offshore sea 100 

Figure 7.18 Most important fish, by quantity (percentage of respondents) 102 

Figure 7.19 Most important fish, by value (percentage of respondents) 103 

Figure 7.20 Most important buyer of fresh fish (percentage of respondents) 104 

Figure 7.21 Perception of change in catch now compared with five years ago, by fishing type 105 

Figure 7.22 Share of capture fishing households that also process fish 106 

Figure 7.23 Share of total processing revenue (percentage), by product 107 

Figure 8.1 Daily minimum wages in Myanmar, Malaysia, and Thailand, 2015 US dollars 109 

Figure 8.2 Histogram: Year of departure for all current and past migrants 110 

Figure 8.3 Share of households with migrants by income quintile (percentage of households) 111 

Figure 8.4 Distribution of migration destinations for nonseasonal migrants (percentage of nonseasonal 

migrants) 113 

Figure 8.5 Share of total household income from remittances 117 

Figure 9.1 Average food expenditure per capita, per week, by income quintile (in Myanmar kyats) 119 

Figure 9.2 Shares of food groups in total food budget (percentage), by income quintile 120 

Figure 9.3 Share of households consuming food groups within the preceding 24 hours 121 

Figure 9.4 Perception of state of the households, five years ago and today 122 

Figure 9.5 Perception of wealth compared with neighbors, currently and five years ago 123 

Figure 9.6 Shocks experienced, percentage of households 124 

Figure 9.7 Most severe shock experienced, percentage of households 124 

Figure 9.8 Primary coping strategies 125 

Figure 9.9 Loan use 126 

Figure 9.10 Sources of loans 127 

Figure 9.11 Share of formal and informal loans 128 

Figure 9.12 Location of lender by source of loan 128 

Figure 9.13 Loan procurement 129 

Figure 9.14 Mean and median annualized interest rate paid 130 

Figure 9.15 Opinion of best way to hold savings 131 

 

  



 
 

 
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Executive Summary 
The purpose of this report is to provide information and analysis to government, civil society, and donors 

interested in improving the well-being of the rural population of Mon State. Specifically, the report analyzes 

the different sources of income for rural households, as well as their socioeconomic characteristics, with a 

view to identifying potential pathways to improving incomes, especially for poor households, and stimulating 

inclusive rural growth. The overall picture that emerges is one of an economy heavily dependent on services 

for local employment and on international migration for income. Like a two-legged stool, such an economy is 

potentially unstable in the face of external shocks. Diversification of the Mon State economy, including 

diversification and increased productivity within the agricultural sector, will lessen the relative dependence 

on external migration remittances and result in more resilient growth in the future. 

The analysis presented in this report is based on a sample of 1,632 rural households. The sample households 

were selected from village communities identified by rural enumeration areas (EAs) in the 2014 population 

census. All potential EAs were first stratified according to the primary agricultural activity (rice, rubber, 

orchard, or marine fishing). A total of 140 EAs (a little more than 6 percent of the sampling frame of rural 

EAs) were randomly selected, 35 from each of the four activity strata. For each selected EA, 12 households 

were randomly selected based on a household listing. The sample is designed to be representative of rural 

households in Mon State as a whole, as well as the major agricultural activities that rural households engage 

in. 

The household questionnaire collected demographic information on all household members, farm and 

nonfarm income-generating activities, migration, assets (including land), credit, consumption, and shocks. A 

community survey was also administered in public areas to a group of up to six prominent village figures, 

such as village leaders, religious leaders, youth group or women’s group representatives, and so on. The 

community questionnaire focused principally on villagewide characteristics such as infrastructure (roads, 

electricity, waterways, and the like), the availability of services (banking, schooling, and so on), natural 

disasters, conflict, and so forth.  

In terms of livelihood strategies for rural households, agriculture, remittances from migrant family 

members, nonfarm enterprises, and wage labor are the largest sources of income. Wealthier households 

have more diversified and more remunerative income sources, emphasizing remittances, agricultural 

production, nonfarm enterprises, and fishing. Although nonfarm enterprises are an important source of 

earnings at all income levels, poorer households are more likely to depend primarily on income from wage 

labor. 

Almost half of households in the sample had a member in Thailand, where wages are almost three times as 

high as in Mon State. Offering ample opportunities for unskilled laborers, migration is a common choice for 

working-age household members of both genders. Remittances sent by family members abroad generate 

almost a quarter of all income in our sample, at all levels of the income distribution. The earnings of 

migrants contribute significantly to consumption and asset accumulation, in particular land purchases and 

house construction. While migration helps bolster the Mon State economy, the absence of workers is being 

felt acutely in the state, where rising costs of labor are jeopardizing profitability in labor-intensive sectors 

such as rice and rubber. 

Small-scale capture fisheries support the livelihoods of 34 percent of residents in Mon State’s coastal zone. 

Many of these people are asset poor and landless, with few other livelihood alternatives. The contribution of 

small-scale coastal fisheries to the Mon State economy is similar to that of rice or rubber, but the fisheries 

receive little recognition or attention. However, the capacity of coastal fisheries to support fisher livelihoods 



 
 

 
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and make a significant contribution to the state economy is under threat from extremely limited 

management of fisheries for sustainable utilization of fish stocks. 

 

Agriculture is an important component of rural livelihoods, but agriculture is not fulfilling its potential. Half 

of all households engage in agriculture, and one in five earns wages from agriculture. Households engaging 

in agriculture earn about half their income from farming and half from nonfarm income sources. Rice and 

rubber are the most common agricultural enterprises (with 39 percent and 36 percent of households 

participating, respectively), followed by betel leaf, roselle, and green gram (mung beans). Livestock rearing is 

practiced by 40 percent of households, usually on a small scale with just one type of animal. Labor scarcity 

and cost is a major constraint to profitability, given low productivity. 

Access to land is a major constraint to livelihood strategies. Three out of every five households have no 

access to agricultural land, and hence are much more dependent on wage labor for their income. Even 

among those who do have access to land, the distribution is very unequal. The top 20 percent of households 

own 56 percent of the agricultural land, compared with just 2 percent owned by the bottom 20 percent of 

households. Only slightly more than one-third of households owning agricultural land have an official land 

title document. One result of unequal land distribution is that a high proportion of farmers, 43 percent in the 

case of rice, hire permanent workers (or sharecroppers in the case of rubber). Most permanent workers are 

of local origin. 

The area planted in rubber has increased rapidly in recent years, and the majority of trees have yet to reach 

productive age. Mature trees are harvested with average yields of 900 pounds per acre, compared with 

more than 1,400 pounds per acre in Thailand and more than 1,500 pounds in Vietnam. Limited fertilizer use, 

unimproved varieties, and inadequately skilled labor contribute to low yields. The profitability of rubber is 

further undermined due to low prices associated with poor quality (a high level of impurities and moisture) 

and inefficient marketing channels (multiple handlers). The potential for improvement is demonstrated by 

the top 20 percent of rubber income earners, who achieve average yields of almost 1,700 pounds per acre 

and three times the profit per acre of the average rubber farmer. 

The primary reasons for the low performance of rice and annual crops are (1) the small percentage of area 

cultivated in the winter season under irrigation (only one acre out of eight is cultivated in the winter season, 

and only 3 percent of rice farmers practice double cropping), (2) limited use of improved technologies, and 

(3) preharvest losses due to flooding and pests.  

Low use of improved technology is a constraint to the performance of agriculture. Lack of access to 

irrigation for winter-season production limits agricultural activity largely to the monsoon season. Median rice 

yields are only 50 baskets (a little more than a ton1) per acre. Despite labor shortages, only one in four rice-

growing households owns a power tiller or a tractor. Even though rental markets allow almost 60 percent of 

rice farmers to use mechanized land preparation, there is considerable scope to increase access to 

mechanization for timely operations. Reflecting the predominance of monsoon rice cultivation, the most 

popular rice varieties are traditional long-stemmed varieties that are resistant to flooding and fetch a high 

market price. Fertilizer use is low and chemical-based weed and pest management negligible. Improvements 

in crop management could greatly increase productivity and profitability. The top 20 percent of rice growers 

in terms of profitability have yields double those of the average rice farmer but with similar costs per acre. 

                                                           
1 Throughout the text, tons are metric tons. 



 
 

 
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Limited diversification of agricultural production also constrains the contribution of agriculture to 

household incomes. Mon State is suitable for a wide variety of horticultural production (vegetables and fruit 

trees), yet only one in five agricultural households engages in it. For those that do, incomes per acre are 

much higher than for rice or rubber. 

Limited commercialization of agricultural products is both a reflection of and a contributing factor to low 

productivity at the farm level. Only half of rice farmers achieve a marketable surplus, and those who do have 

a surplus sell it shortly after harvest. A much higher proportion of other annual crops are sold. Most rubber 

is destined for low-quality use with multiple handling between farm and processor rather than coordinated 

supply chain management for high-quality manufacturing. 

In conclusion, the agriculture and nonfarm sectors could make much larger contributions to rural incomes 

in Mon State in the future than they do today. Realizing this potential would diversify the sources of income 

for the state economy, providing expanded income sources for families without migrants as well as resident 

members of migrants’ families. Diversification of Mon State’s agriculture requires expanded access to 

irrigation for more diversified, high-value production, as well as increases in the productivity and quality of 

its traditional food staple and cash crops (rice and rubber). Improved access to and quality of market-

oriented farm advisory services, initially publicly financed, is a necessary investment to support this 

transformation.  

But diversification into high-value activities needs to occur in the nonfarm sector as well as in agriculture. 

Besides improved energy and road infrastructure, for Mon State to create higher-wage employment in the 

off-farm sector, the current low levels of educational attainment need to improve dramatically. Among five 

dimensions of well-being (food consumption, housing, clothing, healthcare, and education), households are 

least satisfied with the adequacy of education. Because improvements in education take time and will come 

too late for many school leavers over the coming decade, attention should also be given to literacy and 

vocational skills training opportunities, such as rubber tapping, construction, carpentry, and mechanical and 

electrical repair.  

International migration, especially to Thailand, will continue to be an important source of income (directly 

and through consumption linkages) for many years, quite possibly decades, to come. Efforts should be made 

to improve migrant safety and welfare through insurance, language training, and education on Thai law and 

worker rights. 

  



 
 

 
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1. Introduction  
The present document reports on work undertaken as part of the Myanmar component of the Feed the 

Future Innovation Lab for Food Security Policy (FSP) program. It presents a comprehensive analysis of the 

economic livelihoods of households in rural Mon State, based on data from the Mon State Rural Household 

Survey (MSRHS).  

The overall goal of the FSP program is to promote inclusive agricultural productivity growth, improved 

nutritional outcomes, and enhanced livelihood resilience for men and women through improved policy 

environments. The specific objectives are twofold: (1) to address evidence gaps for informed policy debate 

and formulation, and (2) to foster credible, inclusive, transparent, and sustainable policy processes. Both of 

these objectives entail integrating knowledge and actions at the regional and national levels for 

comprehensive and effective evidence-based policies.  

Within this framework, FSP-Myanmar conducted the MSRHS in Mon State to deliver microlevel evidence in 

support of both objectives (1) and (2). This survey was conducted in May–June 2016 from a rural-

representative sample of households and complemented by a community survey at the village or ward level. 

It collected detailed information on all income-generating activities of households and individual household 

members, household consumption, household assets and living conditions, and the services household 

members have access to.  

Following the twofold objectives of the FSP, this dataset forms the basis for a two-pronged study leading to 

two documents. First, the statistical analysis of the MSRHS dataset provides a comprehensive picture of the 

economic opportunities and constraints that Mon rural residents are facing, based on numerical evidence 

generated from representative data. This serves to directly address objective (1) of the FSP to fill policy-

relevant evidence gaps. The results of this statistical analysis are reported in the present document.  

In addition to the present document, which is focused on the results of statistical analysis, a sister document 

was drafted to bridge the gap between the hard evidence generated from the MSRHS analysis and the 

requirements of the policy process. This sister document, titled “Revitalized Agriculture for Balanced Growth 

and Resilient Livelihoods: Toward a Rural Development Strategy for Mon State,” was designed to serve as 

the basis for informed policy discussions in the context of a transparent policy process.  

The remainder of the present report is organized as follows: Section 2 presents the data and methodology, 

Section 3 background information on Mon State, Sections 4 and 5 general information on households and 

their economic activities. Section 6 provides detailed information on all agricultural activities, including rice 

and rubber farming but also other crops, as well as livestock. Section 7 details off-farm or nonagricultural 

livelihoods such as wage work or salaried work, as well as self-employed activities in all sectors from fishing 

to commerce. Section 8 details the role of migration and remittances in Mon incomes. Finally, Section 9 

provides information on household consumption, perceptions of well-being, and risk-coping strategies such 

as saving behavior. Section 10 concludes.  

 

2. Data and methodology  
This report is based primarily on analysis of data collected through a survey of rural livelihoods. The survey 

comprised a community questionnaire, which collected general information about a sample of 143 

communities (each usually a single village), and a household questionnaire, which collected detailed 

socioeconomic information about a sample of 1,627 households within those communities. The data were 



 
 

 
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collected over nearly eight weeks in the months of May and June 2015 and analyzed in the fall of that same 

year. Throughout the report, we complement household survey results with information culled from 

secondary sources. This section describes in detail the survey methodology as well as the methodologies 

used for analysis. 

 

2.1 Sample design and implementation 

The sampling relied on a stratified two-stage design. The sampling frame for the Mon State Rural Household 

Survey (MSRHS) was based on preliminary data and maps from the 2014 Population and Housing Census of 

Myanmar. Given the survey objectives, the sampling frame was limited to the rural households in Mon State. 

The primary sampling units selected at the first sampling stage were the census enumeration areas (EAs), 

which are segments defined within the village tracts and wards for the purposes of data collection for the 

2014 census. The original frame from the 2014 Myanmar census included 2,256 rural EAs for Mon State, 

with an average of 132 households per EA. Auxiliary information was used to classify the village tracts in 

Mon and help select the appropriate sample for a study focusing on household income-generating activities 

and livelihood strategies.  

The stratification design was based on the predominant activities in each EA. We focus on four primary 

activities of interest: rice farming, rubber farming, orchards, and marine fishing. Each EA was classified into 

one of four strata according to which of these four activities was predominant. A sample of 35 EAs per 

stratum was selected, to ensure a reasonable dispersion of the sample within each stratum. Within each 

activity stratum, substrata for high and low activity levels were defined. EAs were oversampled from areas 

with high levels of our primary activities of interest to ensure large enough sample sizes of households 

performing those activities (the oversampling is corrected for with sample weights to avoid bias). This led to 

a tentative sample size of 140 EAs and 1,680 households, with 420 sample households per predominant 

activity stratum (12 per EA). This sample size provides a reasonable level of precision for the indicators by 

activity, especially because many sample households will be involved in more than one activity. For example, 

rice farming is found in all the sampling strata.  

During fieldwork, small modifications to the tentative sampling framework had to be made to account for 

unforeseen circumstances. Seven EAs were replaced or dropped from the sample for security reasons 

(presence of armed groups or banditry). In addition, three EAs turned out not to be marine fishing areas as 

originally expected, and were resampled. Further, small numbers of fisher households prompted us to add 

five EAs in marine fishing areas during fieldwork, so as to increase the likelihood of obtaining significant 

estimates in the analysis. The final sample included 143 EAs (Table 2.1). All of these modifications were 

accounted for in the weighting scheme.  

 

  



 
 

 
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Table 2.1 Allocation of sample enumeration areas and households for Mon State Rural Household Survey by 

activity stratum 

Stratum Predominant activity 

Total 

Sample enumeration areas Sample households 

1 Marine fishing 41 469 

2 Orchards 32 361 

3 Rubber 35 395 

4 Rice 35 402 

Total  143 1,627  

Represented population (number of 

rural Mon residents) 1,195,321  

Source: All data in this table, and in the other tables and figures in this paper, come from the 2015 Mon 

State Rural Household Survey, unless otherwise specified. 

 

We selected 12 households in each selected EA. Households were selected at random, excluding only those 

who did not participate in any way in any activity of interest for the purpose of our survey. Finally, a number 

of households could not be interviewed or provided incomplete responses and thus had to be dropped 

entirely from the dataset. This led to a final sample size of 1,627 households. The final distribution of the 

sample EAs and households is shown in Table 2.1. A more detailed explanation of the sample design and 

weighting procedures for the MSRHS is presented in Appendix A. This final dataset is representative of the 

1.2 million people living in rural Mon State.  

 

2.2 Community questionnaire 

In each of the sampled EAs, we collected general information using a community questionnaire. The 

questionnaire was administered in public areas to a selected group of up to six official respondents, usually 

including prominent village figures, such as village leaders, religious leaders, youth group or women’s group 

representatives, and so on. Gender-diverse groups were selected where possible.  

The community questionnaire focused principally on villagewide infrastructure (roads, electricity, 

waterways, and so on) and the availability of services (banking, schooling, and the like). It also collected 

information on local projects and programs, both public and private. Additional sections of the questionnaire 

included questions about the environment and natural disasters, conflict, land, and prices. In total, the 

survey took three to five hours to administer.  

In addition, the community survey collected the Global Positioning System (GPS) location of all medical, 

religious, and educational facilities, as well as marketplaces and major water points.  

The questions in the community questionnaire referred to the village where the interview took place, 

regardless of EA boundaries. EAs are not administrative units, and their boundaries do not correspond to 

administrative boundaries. The community questionnaire is meant to capture the living environment of the 

interviewed households; therefore, it would not be appropriate to restrict responses to the geographic 

boundaries of EAs, which are meaningless to local inhabitants.  

 



 
 

 
16 

 
 

2.3 Household questionnaire 

The household questionnaire was administered to 12 households in each of the 140 sample EAs. In each 

household we interviewed one primary respondent (usually the head of the household, but another member 

could answer in the head’s place if necessary).  

The household questionnaire collected complete demographic information on household members, as well 

as information on their education, health, and occupation. In addition to current household members, it also 

recorded details about migrants, both short- and long-term.  

Major sections of the survey were devoted to agricultural and nonagricultural income-generating activities. 

Separate sections recorded information on rubber, rice, other permanent crops, and other annual crops. 

Livestock rearing was given a separate section as well. Among nonagricultural activities, we separated 

salaried labor, wage work, nonagricultural business, and resource extraction activities into separate sections. 

Information on fish capture activities was also separated from other resource extraction activities, so as to 

collect information with greater detail. For each of the activities above, we took care to gather detailed 

information about labor use and input costs. We also recorded other sources of income, such as 

remittances, transfers, gifts and donations, and so on. All together, these sections allow us to determine 

household incomes with great precision.  

The survey also recorded detailed information on expenditures of households and the assets they own 

(including land). Finally, the survey gathered information on housing, living conditions, and shocks.  

 

2.4 Data cleaning and analysis  

Data were collected on paper forms and then digitized using Census and Survey Processing System (CSPro) 

software. All questionnaires were entered twice independently, to ensure that digital files reflected the true 

answers collected on paper. Files were then compiled into .dta format to facilitate analysis with Stata 

software.  

The survey included a number of open-ended questions that did not restrict respondents to any particular 

set of answers, such as household member occupation, grain varieties used in production, and so on. After 

the data were collected, these answers were compiled, classified into categories, and recoded according to 

the new classification. In addition, many categorical questions allowed for an open-ended response in case 

the prespecified category choices were not sufficient (“Other, specify:”). These answers were also compiled 

and assigned new codes in the clean version of the data.  

In addition to recoding work, data cleaning also made sure to correct aberrations, impossible values, 

inconsistencies between sections, and so on. Certain households had to be dropped because of missing 

information. At the end of this process, the final sample size was 1,627 households (out of the original 

1,680), with 7,262 members.  

3. Characteristics of Mon State  

3.1 Geography and Administrative Divisions 

Mon State is located in the south of Myanmar, bordered by Bago region to the north, Kayin (also spelled 

Karen) to the east, and Tanintharyi to the south (Figure 3.1). It also shares a short southeastern border with 



 
 

 
17 

 
 

Thailand. Mon is a coastal state, flanked to the west by the Andaman Sea. At 12,000 square kilometers, it is 

among the smaller states of Myanmar, but with about 2 million inhabitants, it is relatively densely 

populated. Its proximity to Yangon and Thailand contribute to its economic importance.  

Mawlamyine is the economic capital and the largest city in Mon State. With 300,000 people, it is also the 

fourth largest city in Myanmar. Administratively, Mon State is divided into two districts: Thaton in the north 

and Mawlamyine in the south. A larger portion of rural households are located in the district of Mawlamyine 

(55.2 percent) than in Thaton (44.5 percent). The next administrative level is the township, of which there 

are 10. In some cases we present statistics at the township level.  

Table 3.1 shows the number of households we interviewed in each of the townships. The number of 

households per township varies substantially, reflecting the size of rural population (Mawlamyine township 

is primarily urban and thus mostly not covered by our sample).  

Table 3.1 Number of sampled households per township (ordered from north to south) 

Township Number of households Percentage 

Kyaikto 230 14.14 

Bilin 160 9.83 

Thaton 175 10.76 

Paung 183 11.25 

Mawlamyine 25 1.54 

Chaungzon 183 11.25 

Kyaikmaraw 188 11.56 

Mudon 130 7.99 

Thanbyuzayat 134 8.24 

La Mine* 89 5.47 

Ye 130 7.99 

Total 1,627 100 

Note: * La Mine is a subtownship of Ye but was its own unit in our survey’s sampling frame.  

 

 

 

 



 
 

 
18 

 
 

Figure 3.1 Map of Mon State and townships 

.  

 

 



 
 

 
19 

 
 

3.2 Climate, Topography, and Agroecology 

The climate of Mon State is tropical monsoon (type Am in the Köppen classification). Temperatures average 

26.3°C and vary little throughout the year: the lowest monthly average is 24.2°C (January) and the highest is 

29.0°C (April).2 The seasons are defined by the monsoon cycles: the state gets about 4,000 mm (4 meters) of 

rainfall per year, a quarter of which falls in August. In contrast, average rainfall in January is 5.1 mm. Most of 

the central and western parts of the state are part of a coastal floodplain. Elevation rises as one moves east 

toward the mountainous Kayin State.  

Based on this topography, we defined three agroecological zones: coastal areas, lowlands, and uplands 

(Figure 3.2). Coastal areas were defined as those within 4 kilometers of the coastline. The rest of the state 

was split between lowlands (elevation of less than 14 meters) and uplands (elevation of more than 14 

meters). Each enumeration area in the sample was assigned geospatial information collected during 

fieldwork using GPS units. More households live in the lowland agroecological zone (45.2 percent) than in 

coastal or upland zones (16.9 percent and 37.9 percent, respectively). We use these categories throughout 

the report to inform the remainder of the analysis.  

                                                           
2 Climate data from Canty and Associates. 2016. Weatherbase database. Accessed April 24, 2016. 
www.weatherbase.com. Rainfall and temperature data refer to Mawlamyine.  

http://www.weatherbase.com/


 
 

 
20 

 
 

Figure 3.2 Survey locations by agroecological zone 

 

Note: HH = household. 

3.3 Infrastructure and Public Services  

The community questionnaire asked about access to the village and to key infrastructure and services such 

as paved roads, electricity, and so on. This section will provide a brief overview of the state of the most 

relevant key services and infrastructure items in rural Mon State.  



 
 

 
21 

 
 

By and large, rural Mon residents live in villages that are easily accessible by road (Table 3.2). The majority 

(95 percent) of households in the survey live in a village accessible by a paved road, though only 87 percent 

of villages are accessible by car in the monsoon season. Monsoon season rains not only decrease 

accessibility but also increase travel time. This effect, however, is small on average: travel time to the 

nearest urban center increases on average by 7 minutes in monsoon season. But for a small percentage (6 

percent) of rural Mon residents, the increase is more than 30 minutes.  

Table 3.2 Village access 

Characteristic Value 

Percentage with paved road 95% 

Percentage accessible by car in dry season 94% 

Percentage accessible by car in monsoon 87% 

Average travel time to closest urban center—dry season 33 mins 

Average travel time to closest urban center—monsoon season  40 mins 

Percentage with increased commute during the monsoon season > 30 minutes  6% 

 

Table 3.3 shows the prevalence of the availability of certain services in rural Mon State. Public transportation 

is limited: only one-fifth of villages (21 percent) have public transportation to reach the closest urban area. 

Only 61 percent of villages have a medical facility of any kind (rural health center, public hospital, private 

hospital, or private clinic). Of the 39 percent without local access to a medical facility, 91 percent have to 

travel to the township capital for medical services, some even farther. The far distances rural residents of 

Mon State have to travel to receive medical care put an onerous burden on households with sick family 

members, who may need to take time off of work to accompany their sick or injured relative to a treatment 

facility. The lack of publicly provided transportation options adds to the hardship by indirectly increasing the 

cost of medical care. 

Table 3.3 Access to services (percentage of villages surveyed) 

Service 

Percentage 

with 

access 

Public transportation 21 

Medical facility 61 

Primary school 80 

Secondary school 37 

Public electricity  51 

Private electricity  56 

At least one cell phone provider 97 

The majority of households (80 percent) live in a village that has a primary school, but only 37 percent live in 

a village with a secondary school. Educational attainment in Mon State is therefore greater than 50 percent 

for primary school completion but drops off significantly for secondary school completion (see Section 4.2).  

Electricity is available to 51 percent of households through public provision and 56 percent through private 

provision. Taken together, 81 percent of households live in a village with access to either public or private 

provision of electricity, or both. Rural households have electricity connections at much lower rates (see 

Section 4.4). 



 
 

 
22 

 
 

The recent liberalization of the telecom sector has expanded cellular access in Mon State, with 97 percent of 

households living in a village with functioning access to one or more carriers.  

 

3.4 Economy  

No official gross domestic product (GDP) estimates are available at the state level. Through estimation, we 

computed that the economy of Mon State is dominated by services, with agriculture and industry 

contributing smaller shares to GDP. Using national data, we obtained a crude estimate as follows: First, we 

computed per-worker contribution to GDP for each sector (agriculture, industry, services) at the national 

level. Applying those figures to the Mon State sectoral employment figures (46 percent in agriculture, 13 

percent in industry, 36 percent in services), we can estimate the contribution of each sector to total GDP in 

the state. This procedure gives estimates of about 70 percent of GDP from services, 16 percent from 

industry, and 14 percent from agriculture. The accuracy of such figures depends on whether national-level 

per-worker GDP contribution is a good approximation of Mon State per-worker GDP contribution.3  

For agriculture, the two main crops are rice and rubber. Besides those two, notable agricultural activities 

include orchards, horticulture, and some production of pulses. While less common, these activities have 

become relatively more lucrative in the recent past. Chapter 6 of this report provides a detailed analysis of 

agricultural activities. Industry in the state is mostly based on the processing of agricultural output, primarily 

rubber but also paper and sugar. Some mining exists in the state as well, with production of tungsten and 

antimony. The coastal areas, Ye in particular, support a sizable fishing industry, with associated processing of 

fish products.  

  

                                                           
3 Calculations courtesy of Tim Dobermann from the International Growth Center.  



 
 

 
23 

 
 

4. Household Characteristics 
This section describes the general characteristics of households in rural Mon State, including demographics, 

ethnicity, education, and health. The end of the section also provides statistics on the living standards of 

households in our sample. Throughout the section, we present statistics for different household types, 

disaggregated by administrative area, geographic location, gender, landownership, or income (among other 

criteria).  

4.1 Household Characteristics and Demographics 

Rural Mon State is ethnically diverse, with three dominant ethnic groups: Mon (35.3 percent), ethnic 

Burmese (or Bamar) (36.3 percent), and Kayin (16.3 percent) (Table 4.1). The remaining 12 percent includes 

Pa-o, Tamil, and others. These proportions vary geographically: the Mon are a majority in the coastal areas 

(55 percent), and the Kayin represent more than one-third of the population in the uplands, closer to Kayin 

State (36.4 percent). These ethnicities correlate with the language spoken at home, though our survey also 

shows that a majority of household heads can speak Burmese (81.5 percent). 

While the state is ethnically diverse, it is very homogenous in terms of religion. Buddhism is the dominant 

religion in rural Mon State, with more than 90 percent of household heads identifying as Buddhist, 

consistently across all agroecological zones.  

Basic household characteristics vary little across the different zones (Table 4.1). The average household size 

is 4.38 people, and household heads are on average a little more than 50 years old. A high percentage of 

households are headed by females (23.2 percent), likely reflecting the high propensity of male heads to 

migrate. Landlessness is high—almost 60 percent of households do not own any agricultural land—and it is 

highest in the lowlands (62.4 percent landless).  

The population in Mon is relatively mobile. Within our sample, approximately 16.9 percent of household 

members were born outside of the village where their household is located. Among them, 34.4 percent were 

household heads, 21.9 percent were spouses (indicating they may have migrated for marriage), and the 

remainder are mainly children or grandchildren (20.8 percent and 9.8 percent, respectively). Some children 

belong to families that moved together, while others are children of migrant parents, usually cared for by 

grandparents.  

  



 
 

 
24 

 
 

Table 4.1 Household characteristics in Mon State, by agroecological zone 

Characteristic 
All 

households Coastal Lowland Upland 

Average household size 4.38 4.65 4.31 4.33 

Percentage female-headed  23.2% 21.1% 25.4% 21.6% 

Average age of household head 52.38 52.06 53.11 51.64 

Average age (all members) 29.6 29.0 30.1 29.4 

Mean dependency ratio* 0.84 0.95 0.88 0.75 

Percentage with Buddhist household head 94.3% 98.3% 91.1% 96.4% 

Percentage without agricultural land 59.0% 61.0% 62.4% 54.0% 

Ethnic group of household head     

 Mon 35.3% 55.9% 34.6% 27.0% 

 Kayin 16.3% 4.8% 9.7% 29.3% 

 Bamar (Burmese) 36.3% 33.8% 37.0% 36.4% 

 Other 12.2% 5.6% 18.7% 7.4% 

Number of households in sample (unweighted) 1,627 346 587 633 

Note: * The dependency ratio is the ratio of number of members aged 0–14 or 65 and older to the number of those 

aged 15–64, within each household. 

 
The majority of those members who have moved have done so from within Mon State (Figure 4.1). Most 

commonly they came from villages/wards in the same township (35.4 percent) and other townships in Mon 

State (21.9 percent). The second most common pattern involves moving from neighboring states, 

predominantly the Bago (12.4 percent) and Ayeyarwady (10.3 percent) regions. As we will see in the rest of 

the report, a large fraction of households have members (or former members) that have migrated away, 

most of whom left in search of job opportunities.  

Figure 4.1 Origin of household members who were not born in the village they were surveyed in 

 

Our sample shows that there is a steep drop-off in population between the 10–14 age bracket and the 15–19 

age bracket, particularly among male household members (Figure 4.2).4 The sample counts more females 

                                                           
4 Starting from the 5–9 age group, the ratio of males to females is less than 1. While this disparity can easily be 
explained by migration for older age groups, migration is a less likely explanation for children 5–14. It may be the case 

0
5

10
15
20
25
30
35
40

Ya
n

go
n

 R
e

gi
o

n

B
ag

o
 R

eg
io

n

A
ye

ya
rw

ad
y 

R
e

gi
o

n

K
ay

in
 S

ta
te

O
th

er
 S

ta
te

 in
M

ya
n

m
ar

Th
ai

la
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d

A
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o
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er
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/w
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d
 in

 t
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A
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er
 t

o
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ip
 in

M
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 S

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O
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P
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Where person lived before moving to village (%)



 
 

 
25 

 
 

(54.6 percent) than males, most likely due to migration. Almost half of households have one or more former 

household members that are currently (nonseasonal) migrants, most of whom are males (55 percent). This 

phenomenon contributes to the high share of female-headed households reported in Table 4.1 (23.2 

percent). The propensity of young residents to migrate may also explain the relatively high dependency ratio 

(0.84 on average, and as high as 0.95 in the coastal zone).  

Figure 4.2 Population pyramid of rural Mon State, 2015 

 

Source: Mon State Rural Household Survey, May–June 2015. 

 

4.2 Educational Attainment and Employment 

Educational attainment is an important predictor of potential earnings, while the level of school enrollment 

among school-age children is an important indicator of the future human capital of a country. In rural Mon 

State, almost 23 percent of children between 5 and 16 years old were not enrolled in school in 2015, and 

almost 1 in 10 (9.3 percent) has never attended a formal school (Table 4.2). While this level of exclusion is an 

improvement over that of previous generations (among whom 1 in 4 adults never attended school), it is still 

a matter of serious concern. Males are slightly more likely to have never attended school (10 percent of 

males versus 8.6 percent of females among current school-age children). Among the current population 

older than 16, a little more than half have completed primary school, and about 1 in 20 has completed high 

school.  

  

                                                           
that there is a preference to migrate with male children due to their future income-earning potential. It may also be 
possible that this discrepancy is the result of a sample size insufficient to get a clean distribution.  

Under 5
5 to 9
10 to 14
15 to 19
20 to 24
25 to 29
30 to 34
35 to 39
40 to 44
45 to 49
50 to 54
55 to 59
60 to 64
65 to 69
70 to 74
75 to 79
80 to 84
85 to 89
90 to 94
95 to 100

A
g

e
 G

ro
u
p

s

80 70 60 50 40 30 20 10 10 20 30 40 50 60 70 80

Number of People in Thousands

 Males  Females

Source: Mon State Rural Household Survey, May-June 2015

Mon State Rural Male and Female Population by Age, 2015



 
 

 
26 

 
 

Table 4.2 Educational attainment by gender, for population older than 16 

Educational attainment 
 By gender (percentage) 

All (percentage) Male Female 

No formal schooling 23.8 26.2 21.9 

Less than primary completion 23.3 20.9 25.2 

Completed primary 20.3 19.1 21.2 

Some secondary school 21.7 23.9 20.1 

Completed high school 6.6 6.9 6.3 

Completed tertiary degree 4.4 3.0 5.4 

 

 
Educational achievement varies geographically. In Thaton district, the adult literacy rate (among those older 

than 16) is 84.4 percent, slightly higher than in Mawlamyine district (76.1 percent). Female-led households 

have a lower literacy rate (65.3 percent) than male-headed (80.4 percent). The coastal area has a slightly 

lower literacy rate than other zones (74.7 versus 77–79 percent). The pattern of lower literacy in the coastal 

zone will likely continue in future generations because the zone also has a below-average rate of school-age 

educational enrollment (73.1 percent). 

 

4.3 Health 

According to the 2014 census, average life expectancy in Mon State is 71.7 years, around the global average 

but lower than the regional developing-country average. Based on survey data, about 78 percent of children 

younger than five in rural Mon State have received a vaccination, leaving 22 percent of children younger 

than five unvaccinated.  

At the time of the survey, about 16.5 percent of rural households reported that one member had been ill 

within the last 30 days. Chronic illness is common among 10.9 percent of family members and much higher 

among those older than 65 (37.6 percent). Female-headed households have slightly higher rates of illness 

(20.8 percent) and reported a higher rate of chronic illness for all members and for those older than 65 (14.8 

percent and 39.6 percent, respectively). Chronic illness is more common in Mawlamyine district (12.7 

percent) than in Thaton (8.6 percent). The disparity is greater for those 65 and older, who are almost twice 

as likely to be suffering from a chronic illness in Mawlamyine than in Thaton (46.4 percent and 23.7 percent, 

respectively). Landless households have higher rates of chronic illness for those older than 65 (41.3 percent), 

compared with landed households (33.8 percent).  

 

4.4 Housing Conditions 

More than 90 percent of households in Mon State report owning the dwelling they live in (90.4 percent). 

Only 84.8 percent of households own a plot of land (agricultural or residential), which means that upwards 

of 5 percent do not own the land on which their dwelling is located, perhaps because the dwelling is located 

on a family member’s plot. The rental market is seemingly limited in Mon State, with only 1.2 percent of 

households living in a rented dwelling. The remainder of dwellings are borrowed or subsidized.  



 
 

 
27 

 
 

The average number of rooms in a dwelling is 1.2 and the majority of households (77 percent) live in a 

dwelling that has between 1 and 2 rooms, excluding the bathroom and kitchen (Figure 4.3). Since the 

average number of household members is 4.4, this indicates cramped living conditions.  

Figure 4.3 Distribution of household dwellings by number of rooms, excluding kitchens and bathrooms 

 

More than half of the dwellings in rural Mon State have roofs made of improved materials (57.3 percent), 

defined as corrugated sheet metal, tile, or concrete (Figure 4.4). The rate is slightly higher in the southern 

district of Mawlamyine (63.5 percent) than in northern Thaton (49.7 percent). We also computed these 

figures separately for the bottom three consumption quintiles (49.5 percent) and the top two (65.9 percent). 

The percentage of households with improved toilet facilities, defined here as a covered pit with a water seal 

or better, is quite low (39.3 percent). Once again, households in Mawlamyine and those in the top two 

consumption quintiles have higher rates of improved housing characteristics, with approximately 44 percent 

of Mawlamyine households and 44 percent of upper-quintile households using improved toilet facilities, 

versus 33.4 percent of Thaton households and 35.8 percent in the lowest three quintiles of consumption. 

Figure 4.4 Households with improved roofing and sanitation facilities, by district and income 

  

Rural Mon State residents have made improvements to their dwellings over the last five years by switching 

from inferior to longer-lasting and better-protecting materials. The use of thatch, leaves, or palm as the 

primary material for roofs has decreased from 46.4 percent in 2010 to 36.5 percent in 2015, while the use of 

35%

42%

16%

5%

2%

1
2
3
4
5+

0

10

20

30

40

50

60

70

Mawlamyine Thaton Bottom 60% Top 40% All

P
er

ce
n

ta
ge

 o
f 

h
o

u
se

h
o

ld
s

Roof Toilet



 
 

 
28 

 
 

corrugated sheet metal roofs has increased from 46.3 percent to 58.9 percent (Figure 4.5). The use of wood 

and “other” materials has also decreased slightly. 

Figure 4.5 Type of roofing used in dwelling  

 

Access to electricity is much higher in rural Mon State (55.5 percent) than the national rural average (18 

percent).5 Overall, electrification has increased by 85 percent compared with five years prior to the survey, 

and it has done so across all subgroups (Figure 4.6). This rate is expected to continue to increase given the 

Ministry of Electric Power’s National Electrification Plan, which calls for attaining 100 percent electrification 

by 2030. Donor funding has also been secured for a project that upgrades a gas-fired power plant in Thaton.  

However, some disparities exists with respect to access to an electrical connection. Approximately 48.3 

percent of households in Thaton, where the main power plant is located, have an electrical connection, 

whereas the rate in Mawlamyine is 61.3 percent (Figure 4.6). Similar disparities exist between households in 

the top two expenditure quintiles versus those in the bottom three.  

Figure 4.6 Access to electrical connection in the household, by district and income level 

  

                                                           
5 International Energy Agency. 2015. World Energy Outlook Electricity Access database. Accessed April 25, 2016. 
http://www.worldenergyoutlook.org/resources/energydevelopment/energyaccessdatabase/. 

0

10

20

30

40

50

60

70

Thatch/leaves/palm Wood Corrugated sheet Other

P
e

rc
e

n
ta

ge
 o

f 
h

o
u

se
h

o
ld

s

5 years ago Now

0%

10%

20%

30%

40%

50%

60%

70%

Mawlamyine Thaton Bottom 60% Top 40% AllP
e

rc
e

n
ta

ge
 o

f 
h

o
u

se
h

o
ld

s

Five years ago Now



 
 

 
29 

 
 

Figure 4.7 Main source of lighting 

 

The expansion of rural electrification has had implications for improved lighting (Figure 4.7). The majority of 

households reported candles as their main source of lighting in 2010 (58.4 percent), which were overtaken 

by electricity in 2015. Solar power also grew as a source of lighting, from 1.3 percent five years ago to 10.8 

percent in 2015.  

Figure 4.8 Main source of drinking water in the household 

 

In contrast to dramatic improvements in access to electricity, there has been only limited change in water 

access over the last five years (Figure 4.8). Of all households, 7.3 percent had indoor or outdoor piped water 

in 2010, versus 9.9 percent in 2015. The majority of households report sourcing their drinking water from 

private wells (65.2 percent) and public wells (12.5 percent). We are unable to determine the health 

implications of the latter two water sources.  

 

5. Household Activities, Incomes, and Assets  

5.1 Overview 

In order to compare the income profiles of households in Mon State, the entire sample was first ranked from 

top to bottom by income level per household member, and then divided into five groups of equal size 

0 20 40 60 80

Electricity

Solar power

Candles

Other

Percentage of households

5 years ago Now

0 20 40 60 80

Piped water (inside)

Piped water (outside)

Private well

Public well

Other

Percentage of households

5 years ago Now



 
 

 
30 

 
 

(quintiles). The quintile of sample households with the highest per capita income is quintile 5, and the lowest 

quintile 1.  

The median annual income in Mon State for the year prior to the survey was 1,612,000 Myanmar kyats 

(MMK), or about US$1,375. The range of incomes across rural households varies widely. Households in the 

highest income quintile, for example, earned a median income 3.2 times greater than the median for the 

whole sample, and even more on a per capita income basis (Figure 5.1). Households in the lowest income 

quintile reported a median income equivalent to just 17 percent of the sample median.  

Figure 5.1 Median income, by per capita income quintiles (participating households only) 

  

Note: Excludes households with reported negative income. 

 

5.2 Location and Household Incomes 

Income profiles also vary by geographic location (Table 5.1). Mawlamyine district has a higher proportion of 

households in the top two income quintiles than does Thaton district (22–24 percent, compared with 15–21 

percent). Thaton district has a higher proportion of households in the lowest quintile. Perhaps due to access 

to the fishing and shipping industries, the coastal agroecological zone has a lower rate of households in the 

lowest income quintile and a higher rate in the top quintile (15 percent and 24 percent, respectively). The 

opposite pattern is found in the upland agroecological zone, where only 16 percent of households are in the 

top quintile of income and 23 percent are classified in the bottom quintile.  

  

71
240

400
650

1,425

413270

1,095

1,700

2,598

5,196

1,612

0

1,000

2,000

3,000

4,000

5,000

6,000

Q1 Q2 Q3 Q4 Q5 All

Th
o

u
sa

n
d

s 
o

f 
M

ya
n

m
ar

 k
ya

ts

Per capita

Total



 
 

 
31 

 
 

Table 5.1 Share of households in income quintiles, by location characteristics 

Location Share of households in quintile (percentage) 

 Q1 Q2 Q3 Q4 Q5 All 

District             

 Mawlamyine 18 19 17 22 24 100 

 Thaton 25 17 22 21 15 100 

Agroecological zone             

 Coastal 15 21 19 21 24 100 

 Lowland 21 17 18 22 22 100 

 Upland 23 19 20 21 16 100 

 

Households in the lowest income quintile are more likely to be female-headed (29 percent) or headed by a 

member with no formal education (42 percent, compared with the sample average of 32 percent) (Table 

5.2). Households in the top income quintile are less likely to be headed by family members with no formal 

education (24 percent). Having a tertiary degree–holding family member further reinforces the relationship 

between educational attainment and household income. Households in the top income quintile are four 

times as likely to have a family member with a tertiary degree as are households in the bottom quintile (19 

percent versus 5 percent).  

Average household size does not vary greatly across income quintiles, although it is slightly smaller for the 

top income quintile. 

It is not surprising that households in the two upper income quintiles have higher-than-average rates of 

improved toilet facilities, 42 percent and 57 percent, respectively, compared with the average of 39 percent. 

The upper two income quintiles also have access to electricity at higher-than-average rates, 60–72 percent, 

compared with the Mon average of 56 percent.  

Table 5.2 Household characteristics, by per capita income quintile 

Characteristic Share of households by quintile (percentage) 

 Q1 Q2 Q3 Q4 Q5 All 

Head characteristics             

 Median age 53 51 51 53 52 52 

 Female 29 20 21 23 23 23 

 No formal schooling 42 36 29 29 24 32 

Highest level of education in 

household is tertiary 5.0 4.4 9.5 12.8 19.2 10.2 

Average household size 4.4 5.0 4.5 4.3 3.8 4.4 

Housing characteristics             

 Improved toilet  32 33 33 42 57 39 

 Electricity 46 46 53 60 72 56 

Own agricultural land 40 39 32 44 55 42 

 

In rural Mon State, agriculture is an important source of livelihoods, with nearly 50 percent of households 

earning income from farming. Households in the middle income quintile own agricultural land at lower-than-



 
 

 
32 

 
 

average rates, 32 percent compared with the sample average of 42 percent, and those in the top quintile are 

more likely to own agricultural land (55 percent) than the average.  

 

5.3 Income Sources and Livelihood Strategies 

Households in rural Mon State derive livelihoods from a variety of income sources including agriculture (rice, 

rubber, orchards, and other annual crop and livestock farming), remittances, nonfarm enterprises, wage 

labor, fishing, salaried earnings, resource extraction, and other sources.6 In this section we describe the 

contribution of these different income sources and how households combine them into livelihood strategies.  

Households in rural Mon State average a little more than two income-generating activities (Figure 5.2). One-

third of households participate in only one form of income generation, 36 percent participate in two 

activities, and 29 percent participate in three or more activities.  

Figure 5.2 Distribution of number of income sources 

 

Households in the bottom quintile participate in fewer activities than the average, 1.7, whereas households 

in the top two quintiles are more diversified, participating in 2.2–2.4 activities on average (Figure 5.3).  

                                                           
6 Other sources are a mixture of pensions, lottery winnings, donations, and gifts received. These sources were lumped 
together to emphasize their impermanence. 

2%

33%

36%

20%

9%
0

1

2

3

4+



 
 

 
33 

 
 

Figure 5.3 Average number of income sources and household size, by income quintile 

 

The five most common income sources are wage labor, remittances, nonfarm business, rice farming, and 

other sources (Figure 5.4). While 49 percent of households in the top income quintile receive money from 

remittances, only 15 percent in the bottom income quintile do so, indicating the importance of migration as 

a strategy to improve household income. Nonfarm enterprises also increase in prominence as we move up 

the income quintiles, increasing from 17 percent in quintile 1 to 37 percent in quintile 5. Wage labor is the 

most frequent income source for households that fall in the middle income quintiles (53–55 percent for 

households in quintiles 2 and 3).  

Due to constraints on land use that emphasize rice farming, it is not surprising that this is a fairly consistent 

activity across all income quintiles, though there is slightly greater participation among the highest-income 

households (29 percent) and the lowest participation is among middle-income households (12 percent). 

Credit, donations, and gifts are also a fairly consistent source of income across quintiles, with an average of 

16 percent of households. The other income sources have participation rates of 12 percent or less. 

Participation rates across quintiles are fairly consistent for livestock farming, other crops, and rubber 

farming, with an average of 12 percent, 10 percent, and 9 percent, respectively. Orchard farming is more 

prominent among both the lowest income quintile and the highest, 12 percent and 15 percent, respectively. 

It is possible that the bottom-quintile households have invested in orchards and will realize higher incomes 

once their trees reach maturity.  

Households in the top quintile fish at a rate 80 percent greater than average (18 percent), whereas 

households in the bottom quintile have fishing rates 53 percent lower than average (5 percent) (Table 5.3). 

Salaries are a rare income source among the bottom two quintiles but are 30–60 percent greater than 

average among the top three quintiles, ranging from 10 percent to 12 percent of households. Resource 

extraction is not a popular income source among all household quintiles, but it employs a larger share of 

households in the lower income brackets. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0

1

2

3

4

5

6

Q1 Q2 Q3 Q4 Q5

In
co

m
e 

so
u

rc
es

 p
er

 m
em

b
er

 

H
o

u
se

h
o

ld
 s

iz
e

Livelihood strategies Household size Strategies/Household size



 
 

 
34 

 
 

Figure 5.4 Share of households participating in income-generating activity, by income quintile 

 

Note: Households with negative or zero income were not included (approximately 3 percent of households). 

Table 5.3 Difference of quintile average from sample average (percentage) 

Income source Q1 Q2 Q3 Q4 Q5 

Wage labor -10 31 25 0 -43 

Remittances -57 -9 5 20 44 

Nonfarm enterprises -42 1 -6 19 29 

Rice farming  -12 -5 -40 9 46 

Credit and other sources 4 -5 -23 2 19 

Livestock -7 4 -2 3 4 

Orchard farming 14 -44 -5 -7 39 

Other annual crops -3 -16 -22 24 14 

Fishing -53 -37 12 0 80 

Rubber farming -22 -26 10 16 21 

Salaried labor -90 -35 37 30 60 

Resource extraction 23 64 29 -52 -56 

 

While it is important to know the prominence of the different livelihood strategies, more context is needed 

to understand how much these strategies contribute to household income. Assuming a dominant livelihood 

strategy to be one from which a household derives more than 50 percent of its income, we present the 

0

10

20

30

40

50

60

1 2 3 4 5 6

P
er

ce
n

ta
ge

Income quintile per capita

Wage labor

Remittances

Nonfarm enterprise

Rice farming

Credit and other sources

Livestock

Orchard farming

Other annual crops

Fish

Rubber farmers

Salaried earnings

Resource extraction

Q1 Q2               Q3              Q4               Q5              All



 
 

 
35 

 
 

share of households with a majority of their income from different strategies by income quintile in Figure 

5.5. It is important to note that livestock, salaried labor, annual crops, rubber farming, orchard farming, 

resource extraction, and other sources (credit, donations, and gifts) each constitute the dominant livelihood 

strategy for less than 5 percent of rural Mon households. Because the share of households that participate in 

these activities is higher than the share of those that derive a large portion of income from them, we can 

infer that these activities make up supplementary income for many households.  

Figure 5.5 Percentage of households with majority income from various sources, by income quintile 

 

Wage labor, remittances, and nonfarm enterprises are prominent, with 59 percent of households reporting 

receiving the majority of their income from one of these sources. Approximately 24 percent of all 

households receive 50 percent or more of their income from wage labor, but this source is more prominent 

among households in the lower income quintiles (31–37 percent). While households in the top two quintiles 

are more likely to receive income from remittances (41–49 percent), it is a dominant source of income for 

only half of those who receive it (20–21 percent), serving as supplementary income for the remainder. 

Fishing and rice farming are more likely to be dominant sources of income for households in the top quintiles 

(17 percent and 19 percent, respectively) than for other quintiles. Nonfarm enterprises are the dominant 

income source for 17 percent of households, and this share stays relatively constant across quintiles.  

For the whole sample, the largest share of income comes from agricultural (crop) production (24 percent), 

followed by remittances (22 percent), nonfarm businesses (18 percent), wage labor (14 percent), and fishing 

(11 percent) (Figure 5.6). However, the composition varies greatly by income quintile. Wage labor makes up 

almost half of total income (46 percent) for the lowest income quintile, but drops to just 4 percent for the 

top quintile. Fishing income has the opposite pattern, accounting for just 4 percent of total income for 

quintile 1 but 14 percent in quintile 5. Remittances make up about 11–17 percent of total income for the 

bottom three quintiles but increase to 23–25 percent for the top two. Nonfarm businesses consistently make 

up 15–19 percent of income across the quintiles.  

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6

P
er

ce
n

ta
ge

Income quintile per capita

Wage labor

Remittances

Nonfarm enterprise

Rice farming

Fishing

Credit and other sources

Salaried labor

Annual crops

Rubber farming

Orchard farming

Resource extraction

LivestockQ1 Q2              Q3              Q4              Q5             All



 
 

 
36 

 
 

Figure 5.6 Composition of total income by income quintile  

 

Note: For ease of interpretation, negative income was removed from the calculations in this figure. 

As a share of agricultural production income, rice farming fluctuates from one quintile to the next (Figure 

5.7). Households in the bottom income quintile have higher landownership rates and receive a greater 

percentage of their income from rice farming than do those in quintiles 2 and 3. The number increases again 

for quintile 5, which also has a higher-than-average rate of landownership and percentage of income 

received from rice farming. All other income sources from agricultural production make up a low and fairly 

constant share of income across quintiles, ranging from 3 to 4 percent, though, like rice production, their 

share is also slightly higher among households in quintiles 1 and 5.  

Figure 5.7 Composition of agricultural income by income quintile  

 

Note: For ease of interpretation, negative income was removed from the calculations in this figure. 

0

20

40

60

80

100

1 2 3 4 5 All

P
er

ce
n

ta
ge

 o
f 

to
ta

l i
n

co
m

e

Income quintile, per capita

Agricultural production

Remittances

Nonfarm enterprise

Wage labor

Fishing

Salaried labor

Credit and other

Livestock

Resource extraction

0

5

10

15

20

25

30

35

1 2 3 4 5 All

P
er

ce
n

ta
ge

 o
f 

to
ta

l i
n

co
m

e

Income quintile, per capita

Rice

Annual crops

Rubber

Fruit



 
 

 
37 

 
 

In summary, households in Mawlamyine district are more likely to be classified in the upper quintiles than 

households in Thaton district, as are those in the coastal agroecological zone compared with the other 

zones. Households in the upper income quintiles have higher educational attainment and greater access to 

improved housing characteristics, such as electricity, plumbing, and roofing. Four distinct patterns emerged 

from the livelihood strategies and incomes. First, agricultural production, remittances, nonfarm enterprises, 

and wage labor are the largest sources of income for households and have the highest rates of participation. 

Second, households in the upper two income quintiles have slightly more diversified livelihood strategies 

than the average household. They are heavily reliant on remittances and slightly more reliant on income 

from agricultural production and fishing. Third, the bottom three quintiles participate in and receive the 

majority of their income from wage labor. Last, nonfarm enterprises are a steady source of income across all 

income quintiles. 

 

5.4 Landownership and Tenure Arrangements  

Agricultural landownership varies greatly among the different regions of Mon State (Table 5.4). Nearly 59.5 

percent of households in Mon have no agricultural land, but this ranges from 73.2 percent in Chaungzon 

township to 38.5 percent in Mudon township. Of those households with agricultural land, the average area 

of holdings is 7.3 acres (median 5.0 acres), but again, this varies greatly across townships, ranging from 

almost 10 acres in Ye and Thanbyuzayat in the south to a little more than 2 acres in Mawlamyine township. 

There is little difference between the area of agricultural holdings owned and the area cultivated, an average 

difference of just 0.1 acres.  

Table 5.4 Agricultural landholdings by township (north to south) 

Township 

Households owning 

agricultural land 

(percentage) 

 

Average area of 

agricultural land 

owned (acres) 

Median area of 

agricultural land 

owned (acres) 

Average area of 

agricultural land 

operated (acres) 

Kyaikto 34.6 6.7 4.0 6.1 

Bilin 38.1 5.6 3.5 5.6 

Thaton 34.5 7.1 4.0 7.4 

Paung 36.6 6.6 5.0 6.9 

Mawlamyine 28.0 2.3 1.5 2.3 

Chaungzon 26.8 5.3 4.0 5.2 

Kyaikmaraw 51.1 7.3 5.0 7.2 

Mudon 61.5 8.3 6.0 8.0 

Thanbyuzayat 51.2 9.9 6.0 9.4 

La Mine 52.2 8.6 6.0 8.8 

Ye 34.6 9.3 6.4 8.7 

Mon State overall 40.5 7.3 5.0 7.2 

Note: Includes land rented in and omits land rented out. 

 

  



 
 

 
38 

 
 

Table 5.5 Agricultural landholdings by agroecological zone 

Zone Households owning 
agricultural land 
(percentage) 

Average area of 
agricultural land 
owned (acres) 

Median area of 
agricultural land 
owned (acres) 

Average area of 
agricultural land 
operated (acres) 

Coastal 38.6 10.3 6.0 9.9 

Lowland 37.3 6.6 5.0 6.6 

Upland 45.5 6.9 4.0 6.8 

 

Mean and median landholdings are higher in coastal areas (Table 5.5). Median landholdings are lowest in the 

upland zone, where landownership rates are slightly higher than the state average but still less than half of 

all households.  

We compute the Gini coefficient, one of the most widely used summary measures of inequality, for 

landholdings. The Gini coefficient can vary between the extremes of 0 (everyone has the same amount of 

land), and 1 (one person has all the land). The estimated Gini coefficient of landownership in Mon State is 

0.76, reflecting the highly unequal distribution presented in Table 5.4. The coefficient increases marginally to 

0.77 when operated holdings (those borrowed or rented in) are included, indicating that rather than 

offsetting the unequal distribution of land, borrowing and leasing have a slightly negative redistributive 

effect. 

The Gini coefficient for the distribution of agricultural land (excluding landless households) is less extreme 

but still fairly high, at 0.53. To help interpret this coefficient, Figure 5.8 displays the Lorenz curves for 

landownership. It shows that the top 20 percent of households own 56 percent of all agricultural land, 

compared with just 2 percent of land owned by the bottom 20 percent of households. The top 40 percent of 

households own more than 75 percent of all agricultural land while the bottom 40 percent own less than 10 

percent. This coefficient would likely be larger still if large plantations were included in our survey. 

Figure 5.8 Lorenz curves for landownership  

 

  



 
 

 
39 

 
 

 

Table 5.6 Number and area (acres) of holdings by land use type 

Item 

Parcel type 

Residence 

Paddy 

field 

Rubber 

plantation Orchard 

Other 

crops 

Virgin/vacant

/forest land All land 

Mean area of land owned per 

household 0.2 6.0 5.5 3.5 3.6 5.2 2.5 

Median area of land owned 

per household 0.1 5.0 3.0 2.0 1.5 1.5 0.3 

Mean area of land 

rented/borrowed/leased in 

per household 0.1 4.8 3.9 1.2 1.0 1.2 1.2 

Median area of land 

rented/borrowed/leased in 

per household 0.1 4.0 1.0 1.0 0.5 0.5 0.1 

Mean area of parcels 

operated by operating 

households 0.2 5.8 5.4 3.3 3.1 4.8 2.3 

Median area of parcels 

operated by operating 

households 0.1 5.0 3.0 1.5 1.5 1.5 0.2 

Share of parcel type in total 

number of parcels 

(percentage) 59.2 16.3 14.5 4.0 3.7 2.0 100.0 

Share of parcel type in total 

land area (percentage) 4.9 39.4 36.4 5.9 4.3 4.1 100.0 

Share of parcel type in total 

parcel area owned 

(percentage) 82.7 84.3 96.6 87.0 81.5  89.4 85.3 

Maximum area of parcels 

owned per household 5.0 67.0 90.0 60.0 30.0 70.0 90.0 

 

Respondents reported six major land use types: residence, paddy field, rubber plantation, orchard, other 

crops, and virgin/vacant/forest land. The average area of land owned, accessed via other tenure 

arrangements, and operated, per household, by parcel type, is reported in Table 5.6. In each case, the mean 

is calculated using the number of households owning or operating each type of land. There is little difference 

in the average area of parcels owned for rubber and rice (about 6 acres each) although, interestingly, the 

median area of rubber plantations is considerably smaller than that of paddy fields (5 versus 3 acres). Parcels 

owned are generally larger on average than parcels accessed through other tenure arrangements. 

Residences account for the majority of parcels (59 percent), followed by paddy fields and rubber plantations 

(39 percent and 36 percent of total area, respectively).  

The majority of land (85.3 percent) is owned outright. Among agricultural land use types, the share of land 

accessed through tenure arrangements other than ownership (that is, rented, borrowed, or shared in) 

ranges from 3.4 percent to 18.5 percent, and is highest for farmers of “other annual crops.” The maximum 



 
 

 
40 

 
 

area of parcels owned by landowning households is quite low, reaching an upper limit of 90 acres for rubber 

(Table 5.6).  

Somewhat surprisingly, purchase is the most important form of land acquisition, accounting for between 40 

percent and 64 percent of land acquisitions across all land use categories, followed by inheritance (Table 

5.7). Seventeen percent of households access residences or land for rice cultivation by renting in or 

borrowing, but only 3 percent access land for rubber cultivation in this way. Very little sharecropping occurs, 

even for rice, and parcel acquisition by state grant is relatively insignificant.  

Table 5.7 Means of parcel acquisition by parcel type 

Means of 

acquisition 

Parcel type  

Residence 

Paddy 

field 

Rubber 

plantation Orchard 

Other 

crops 

Virgin/vacant/forest 

land 

 

 

Mon as 

a 

whole 

Given by local 

state 3.9 1.8 3.7 2.8 4.3 3.7 

3.7 

Inherited 29.5 30.5 25.7 33.4 34.0 24.0 28.6 

Purchased 48.4 49.6 62.2 50.5 40.4 64.3 50.9 

Received as gift 0.7 0.2 1.7 1.1 0.0 0.0 0.7 

Occupied 0.9 0.4 4.1 2.0 1.6 0.0 1.7 

Rented 

in/borrowed 16.6 17.3 2.7 9.2 19.7 8.1 

14.3 

Sharecropped in 0.1 0.2 0.2 1.1 0.0 0.0 0.2 

 

Land tenure security is an important issue in Myanmar. Two-thirds of land parcels are reported to have some 

form of documentation that confers land use rights or indicates a history of land use. This figure is highest 

for agricultural parcels, at 76 percent. However, among agricultural land parcels, only 25 percent are 

covered by Form 7, a document introduced in 2012 that confers relatively secure, transferrable use rights, 

and 11 percent by Form 105, the land use right document that preceded Form 7. The primary form of 

documentation to show land use rights is either tax receipts (45 percent) or contracts (29 percent). Only 10 

percent of residential parcels are covered by a house grant (ain grant), the most secure form of tenure for 

homestead land (Table 5.8). Of households with agricultural land who do not yet possess Form 7, 87 percent 

had yet to begin the application process to obtain one. Of these households, 47 percent reported that they 

did not know it was required, 22 percent that there was no need, and 18 percent that they did not know 

how to do so.  

  



 
 

 
41 

 
 

Table 5.8 Share of households possessing documents conferring user rights, by parcel type and document 

type (percentage) 

Parcel type Form 7 Form 105 Contract 

Tax 

receipt 

House 

grant  Other 

Does not 

know Total 

Residence  1.3 1.9 36.4 48.2 10.9 0.6 0.3 100 

Agriculture 24.9 10.7 19.9 41.5 0.9 1.2 0.9 100 

All parcels 13.0 6.3 28.5 44.9 6.0 0.9 0.5 100 

 

The total number of parcels owned by surveyed households has changed little between the time of the 

parcel’s original acquisition and the time of the survey (Table 5.9). The total number of residences increased 

from 1,276 to 1,505, up 11 percent. The number of plots devoted to rubber grew from 180 to 365 (an 

increase of 85 percent). There was little change in the number of plots devoted to orchards. The number of 

plots used as paddy fields fell by 10 percent. The biggest decline in plot numbers was for vacant/virgin/forest 

land, which contracted by 91 percent. The largest number of plot conversions were from 

vacant/virgin/forest land to residences (43 percent of all plot use changes), followed by vacant/virgin/forest 

land to agriculture (31 percent of all plot use changes). Expansion of the area under rubber cultivation was 

thus implicated in a substantial reduction in vacant/virgin/forest landholdings, while construction of new 

residences reduced the area of agricultural land and, to a lesser extent, vacant/virgin/forest land.  

Table 5.9 Parcel use change from original to current 

Parcel use 

Original use Use five years ago Current use 

Number 

of 

parcels 

Share of 

parcels 

(%) 

Mean 

parcel 

size 

(acres) 

Number 

of 

parcels 

Share of 

parcels 

(%) 

Mean 

parcel 

size 

(acres) 

Number 

of 

parcels 

Share of 

parcels 

(%) 

Mean 

parcel 

size 

(acres) 

Residence 1,276 50.7 0.4 1,501 59.6 0.4 1,505 59.8 0.2 

Paddy field 446 17.7 5.6 417 16.6 6.1 409 16.3 6.3 

Rubber 

plantation 180 7.2 4.9 275 10.9 5.5 365 14.5 5.6 

Orchard 109 4.3 3.2 110 4.4 3.6 97 3.9 3.6 

Other crops 72 2.9 3.1 76 3.1 3.1 90 3.6 2.9 

Vacant/virgin

/forest land 404 16.1 2.9 115 4.6 2.9 41 1.6 4.1 

Total 2,519 100 2.3 2,519 100 2.5 2,519 100 2.5 

 

Average parcel size declined from 2.5 to 2.3 acres, driven primarily by the growth in the number of 

residential parcels of smaller average size (from 0.7 to 0.4 acres) (Table 5.9). Interestingly, the average size of 

paddy parcels increased, from 5.5 to 6.4 acres, as numbers of individual paddy parcels fell, suggesting a 

degree of nascent consolidation taking place. The average size of rubber plantations grew slightly (from 5.0 

to 5.4 acres), even as parcel numbers increased.  

Conflicts were reported to have occurred over usage rights for only 3 percent of parcels. Of these, 91 

percent had been resolved at the time of the survey. Conflicts were slightly more prevalent in Chaungzon 

and Kyaikmaraw townships (6 percent and 5 percent of parcels, respectively) than elsewhere, and conflicts 



 
 

 
42 

 
 

over residential plots were marginally more common than over other types of land. There was little 

difference in prevalence of land use conflicts by ethnic group.  

 

5.5 Other Assets  

5.5.1 Large Assets 
We define large assets as motor vehicles, boats, mills, and land and buildings used for nonagricultural 

business, excluding housing. Of the households surveyed, 90 percent own two or fewer large assets, with a 

little more than half of households (52 percent) owning no large assets at all. Motorbikes are by far the most 

common type of large asset owned, with 36 percent of rural Mon households possessing at least one (Table 

5.10). Households owning agricultural land are almost twice as likely as households without agricultural land 

to own a motorbike (51 percent versus 26 percent). Households with agricultural land also have higher levels 

of ownership of large agricultural assets, such as tractors and power tillers, than those without agricultural 

land. As would be expected, coastal households and those engaging in fishing have higher levels of boat 

ownership than nonfishing households and those located further inland. Cars are the most expensive large 

asset owned, costing approximately MMK 6,766,000 (US$5,775) on average. Motorbikes and small 

motorized boats are relatively more affordable, averaging MMK 924,000 and MMK 705,000, respectively.  

Table 5.10 Prevalence of asset ownership by asset type and household characteristics (percentages)  

Type of asset owned Coastal Lowland  Upland  

Households 

with 

agricultural 

land  

Households 

without 

agricultural 

land 

Fishing 

households 

All 

households  

Motorbike/scooter 41.3 33.1 37.5 50.7 25.9 32.2 36.3 

Land used for 

nonagricultural business 12.4 7.4 14.5 24.3 0.7 0.4 10.1 

Small boat, motorized 14.6 2.4 3.1 5.0 4.3 32.8 4.6 

Tractor 3.3 3.2 1.6 4.9 0.8 2.8 2.5 

Car 5.0 1.6 2.3 3.9 1.3 3.1 2.4 

Building used for 

nonagricultural business 1.5 3.5 0.9 4.4 0.6 0.5 2.2 

Power tiller 1.5 2.8 0.7 4.4 0.2 0.0 1.9 

Small boat, not motorized 4.8 2.0 0.5 1.9 1.9 13.2 1.9 

Trawlarjee 2.0 1.4 1.4 2.8 0.4 0.0 1.4 

Three-wheeled motorized 

vehicle  1.6 1.3 1.2 1.8 1.0 0.0 1.3 

Large boat, motorized 3.5 0.9 0.3 1.6 0.8 6.8 1.1 

Other motorized 

transportation  1.2 0.9 0.8 1.1 0.8 1.5 1.0 

Mill 0.0 0.3 0.2 0.7 0.0 0.0 0.3 

Truck 0.4 0.1 0.2 0.3 0.1 0.0 0.2 

Note: A trawlarjee is a rudimentary motorized vehicle consisting usually of a tractor engine mounted onto a 

cart or trolley. 



 
 

 
43 

 
 

Remittances play an important part in facilitating the purchase of large assets, particularly for those 

households who reported having a migrant member at the time of the survey (Table 5.11). Contributions 

from remittances to the purchase of large assets are greatest for motor vehicles, agricultural machinery, and 

investments in nonagricultural business. For example, more than one-third of households with a migrant 

member reported that purchase of cars and motorbikes had been funded mainly by remittances, and more 

than half of households with a migrant who had purchased a tractor reported that remittances accounted 

for part of the purchase costs. This indicates that remitted incomes are spent on both productive and 

nonproductive assets. Boats, however, are rarely purchased using remitted incomes, perhaps suggesting that 

fishing is not considered to be a high-potential investment opportunity. In addition, remittances play a large 

role in financing home construction, which we return to in Section 8.5. We devote an entire section of this 

report, Section 8, to the role of migration and remittances in the Mon economy.  

Table 5.11 Contribution of remittances to the purchase of large assets in households with migrants 

(percentage of households reporting) 

Type of asset owned 

Extent of contribution  

None at all A small part The majority 

Car 54.5 9.1 36.4 

Motorbike/scooter 54.2 12.5 33.3 

Building used in nonagricultural business 56.3 18.8 31.3 

Tractor 47.6 23.8 28.6 

Other machinery for nonagricultural business 33.3 33.3 22.2 

Power tiller 72.2 11.1 22.2 

Land used for nonagricultural business 74.4 9.0 15.4 

Other motorized transportation  87.5 0.0 12.5 

Trawlarjee 66.7 22.2 11.1 

Small boat, motorized 70.8 20.8 8.3 

Three-wheeled motorized vehicle 58.3 41.7 0.0 

Small boat, not motorized 80.0 20.0 0.0 

Large boat, motorized 100.0 0.0 0.0 

Mill 100.0 0.0 0.0 

Total 60.2 14.0 26.0 

Note: A trawlarjee is a rudimentary motorized vehicle consisting usually of a tractor engine mounted onto a 

cart or trolley. 

Ownership of large assets increased substantially over the preceding five years, up from an average of 1.1 to 

1.9 large assets per household (Table 5.12). Increases in asset ownership occurred at roughly equal rates for 

the bottom 60 percent and the top 40 percent of households by income, though households in the top 40 

percent own 0.4 more assets than those in the bottom 60 percent. When broken into expenditure quintiles, 

the bottom quintile owned an average of 1.7 large assets in 2015, compared with 2.1 for the top quintile.  

  



 
 

 
44 

 
 

Table 5.12 Change in large asset ownership during the last five years, by expenditure group 

 Expenditure group Mean number of large 
assets owned per 
household in 2010 

Mean number of large 
assets owned per 
household in 2015 

Increase in mean number 
of large assets owned per 
household (percentage) 

Lowest 3 quintiles 1.0 1.7 70.0 

Highest 2 quintiles 1.3 2.1 60.0 

All 1.1 1.9 68.3 

 

5.5.2 Small Assets 
The most common small assets owned in Mon State are lamps (84 percent), followed by locks (63 percent), 

televisions (61 percent), and mobile phones (58 percent) (Table 5.13). Overall, nonelectronic durable goods 

such as household furniture and cooking equipment are the most common type of small asset owned, with 

95 percent of households owning at least one type of durable good. Coastal households own on average 

more electronic and nonelectronic durable goods, electrical and entertainment products, and fishing gear 

than upland and lowland households. Households with agricultural land are more likely to own small assets 

than households without agricultural plots, especially manual and mechanical agriculture implements, 

vehicles, durable electronic goods, and entertainment products.  

Table 5.13 Prevalence of asset ownership by small asset type and household characteristics (percentage) 

Type of asset owned 

Percentage of households owning, by household group  

All 

households Coastal Lowland Upland 

Owns 

agricultural 

land 

Does not 

own 

agricultural 

land 

Durable goods 

(nonelectronic) 98.4 94.0 95.0 95.5 94.8 95.0 

Electrical communication 

and entertainment products 87.7 80.5 79.6 86.2 76.7 80.6 

Durable goods (electronic) 46.0 42.2 35.9 46.0 35.3 39.6 

Agricultural implements 

(manual) 31.3 37.2 35.4 57.3 20.9 36.1 

Gold 33.9 30.9 33.6 35.9 29.0 31.8 

Fishing and forestry gear 48.9 26.7 27.5 32.9 28.7 30.5 

Agricultural implements 

(mechanical) 9.4 15.7 14.8 25.7 6.2 14.3 

Vehicle/transportation 7.3 10.1 9.5 17.1 4.6 9.9 

  

There is a significant difference in small asset ownership across consumption expenditure quintiles (Table 

5.14). Durable nonelectronic goods are the most widely owned small asset, with 91 percent ownership in the 

first quintile and 99 percent in the fifth quintile. In the fifth quintile, 95 percent of households own electronic 

communication and entertainment products, compared with only 60 percent in the first quintile. This is 

mainly driven by television and cell phone ownership. Agricultural implements, both mechanical and 

manual, are more widely owned in the top quintiles. Vehicles, on the other hand, which include bicycles, 

carts, and trishaws (three-wheeled vehicles), have even ownership over the top four quintiles. 



 
 

 
45 

 
 

Table 5.14 Share of households owning small assets, percentages, by consumption expenditure quintile 

Type of asset owned Q1 Q2 Q3 Q4 Q5 All 
quintiles 

Durable goods (nonelectronic) 91.1 92.5 95.4 96.6 99.2 95.0 

Electrical communication and 
entertainment products 

59.4 72.9 85.9 89.1 94.9 80.6 

Durable goods (electro