LSMS+ Program in Cambodia Findings from individual‑level data collection on labor and asset ownership Section 1. The LSMS+ program in Cambodia III Copyright © 2021 The World Bank. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http://creativecommons.org/licenses/ by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following condition: Attribution—Please cite the work as follows: Hasanbasri, Ardina; Kilic, Talip; Koolwal, Gayatri; Moylan, Heather. 2021. LSMS+ Program in Cambodia: Findings from Individual‑Level Data Collection on Labor and Asset Ownership. Washington, D.C.: World Bank Group. Disclaimer The findings, interpretations, and conclusions expressed in this Guidebook are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Living Standards Measurement Study (LSMS) World Bank Development Data Group (DECDG) lsms@worldbank.org www.worldbank.org/lsms data.worldbank.org Cover images: © World Bank LSMS+ Program in Cambodia Findings from individual‑level data collection on labor and asset ownership Ardina Hasanbasri Lecturer, University of Michigan and Independent Consultant, Development Data Group (DECDG), World Bank ardinah@umich.edu Talip Kilic Senior Economist, DECDG, World Bank tkilic@worldbank.org Gayatri Koolwal Founder, Development | Science and Independent Consultant, DECDG, World Bank gkoolwal@worldbank.org Heather Moylan Survey Specialist, DECDG, World Bank hmoylan@worldbank.org i Contents Executive Summary............................................................................. 1 Key findings..................................................................................................... 4 Section 1 The LSMS+ program in Cambodia..........................7 1. Overview of the LSMS+..................................................................................... 8 2. Cambodia: survey and country context.............................................. 10 3. Implementation of LSMS+ modules in Cambodia....................... 12 4. Characteristics of respondents................................................................. 14 Section 2 Labor outcomes....................................................................................... 19 1. Introduction............................................................................................................. 20 1.1. Moving towards self‑reporting of labor........................................................... 20 1.2. Labor outcomes covered in LSMS+................................................................... 22 2. Labor module ....................................................................................................... 23 2.1. Overall statistics on labor outcomes................................................................ 23 2.2. Participation and hours by age......................................................................... 25 2.3. Work across multiple activities and intrahousehold trends................... 30 3. Findings on daily time use: paid and unpaid work burdens............................................................................................................... 35 ii Section 3 Asset ownership and rights............................................. 43 1. The importance of individual‑level interviews in measuring asset ownership and rights.............................................. 44 2. Asset survey questions in the LSMS+................................................. 46 3. Ownership and rights of land and housing..................................... 48 3.1. What patterns emerge across men and women? ........................................ 48 3.2. Bundles of land ownership and rights............................................................ 52 3.3. Reporting discrepancies among couples....................................................... 54 4. Ownership of livestock and consumer durables ....................... 56 5. Financial account and mobile phone ownership........................ 58 Annex 1............................................................................................................................ 63 Annex 2........................................................................................................................... 64 iii © Chor Sokunthea / World Bank Executive Summary Monitoring progress towards several targets of the Sustainable Development Goals (SDGs), across poverty reduction, agriculture, gender, employment, and inequality, requires sex‑disaggregated data on asset ownership, labor, time use, and roles in family enterprises. Expanding survey data collection on these topics is a priority for the World Bank, as expressed through the World Bank’s Household Survey Strategy, Gender Strategy, and the 18th replenishment of the International Development Association (IDA18), which committed to launch pilot data collection in at least six IDA countries to “gather direct respondent, intra‑household level information on employment and assets.”1 1 International Development Association (IDA), 2016. IDA18 Special Theme: Gender and Development. World Bank. 1 © World Bank Against this background, the World Bank Living In its current phase, the LSMS+ has built on Standards Measurement Study‑Plus (LSMS+) the multi‑topic survey questionnaire design program was established in 2016 to support survey that was pioneered by the LSMS and has data production and methodological research been supporting national statistical offices activities to improve the availability and quality in select IDA countries in operationalizing of individual‑disaggregated survey data in low‑ the latest international recommendations for and middle‑income countries on key dimensions individual‑disaggregated survey data collection of men’s and women’s economic opportunities on asset ownership and labor. The latter involves and welfare. The LSMS+ is part of the Living administration of individual‑level survey modules Standards Measurement Study (LSMS), which that are administered in private to household over the last four decades, has provided technical members age 18 and older and that focus on assistance to national statistical offices globally work and employment, non‑farm enterprises, on designing and implementing high‑quality, and ownership of and rights to selected physical multi‑topic household surveys. These surveys and financial assets, including, at a minimum, have been used extensively in policy making and dwelling and non‑dwelling land, financial assets research on a wide range of topics, including and mobile phones. The approach of surveying poverty, consumption and income inequality, multiple individuals per household captures employment, non-farm enterprises, agriculture, intra‑household dynamics in labor and economic education, and health, among others. decision‑making — the understanding of which is critical for designing policy around improving economic opportunities, particularly for women. Besides data production, the LSMS+ also supports methodological survey research activities to improve the foundations of individual‑disaggregated data collection in household surveys. 2 © World Bank The 2019‑20 round of the Cambodia The Cambodia LSMS+ Survey joins three other Socio‑Economic Survey (CSES 2019‑20) was country surveys that have been supported by the implemented by the National Institute of Statistics LSMS+ program since 2016: the Malawi Integrated of Cambodia. The LSMS+ supported data collection Household Panel Survey, or IHPS 2016‑17; Fourth was piggybacked on this operation and was Ethiopia Socioeconomic Survey, or ESS4 2018‑19; implemented in the 252 Enumeration Areas visited and the Tanzania Fifth National Panel Survey, or as part of the CSES from October – December NPS5 2019‑20, which are discussed in a companion 2019. In each of these EAs, the CSES interviewed report: LSMS+ Program in Sub‑Saharan Africa: 10 households, and an additional 6 households Findings from individual‑level data collection on were selected for the LSMS+ survey. The total labor and asset ownership. This report presents LSMS+ survey sample was then composed of 1,512 descriptive statistics and trends across men households across these 252 EAs. In each sampled and women in the Cambodia LSMS+ for labor household, all adult household members (18 years market outcomes and asset ownership and and above) were targeted for private interviews rights. Through these initial findings, this report on labor and ownership of and rights to selected aims to detail and underscore the importance of the physical and financial assets, among other topics as LSMS+ survey approach in understanding economic discussed above. The Cambodia LSMS+ also was constraints and opportunities for men and women. the first among other LSMS+ supported surveys The intended audience for this report includes conducted thus far to include an individual‑level national statistical offices (NSOs) that are interested time use module (24‑hour time diary) for men in improving individual‑level data collection to and women age 18 and older. Self‑reporting inform economic policies. The report’s findings are was very high — within the assets modules, all also aimed at data users, including researchers respondents reported for themselves, and in the and policymakers, interested in addressing gender labor module, 90 percent of men and 95 percent inequalities across these areas and the role of data for women self‑reported. in targeting these disparities. Relevant for NSOs and survey practitioners, a companion report to this one, LSMS+ Program Overview and recommendations for improving individual‑disaggregated data on asset ownership and labor outcomes, also discusses operational guidance and experience in implementing the LSMS+ survey modules across countries. 3 Key findings regarding labor and asset ownership and rights to assets include: Labor + + Most working men and women The LSMS+ time use module in the Cambodia LSMS+ are sheds greater light on total concentrated in wage employment (paid as well as unpaid) work and agriculture, with men burdens of men and women. significantly more likely to While men spend greater time participate in these activities, in income‑generating activities, particularly in wage work. About women’s total work burdens half of urban and rural men across paid and unpaid activities were in wage employment over are greater. Women in agriculture the past year, for example, and who are not working compared to 33 percent of spend about 4‑5 hours per day urban women and 29 percent on average in unpaid work, of rural women. The shares compared to about 1‑2 hours of men and women owning or for men. Among respondents in running non‑farm enterprises off‑farm work, hours in unpaid were roughly similar, on the other work does decline, but is still hand—a departure from findings about 2‑3 hours per day on among the LSMS+ Sub‑Saharan average for rural and urban Africa countries. Women were also women, compared to less significantly less likely to engage than an hour on average for in agricultural activities mainly men. Unpaid work burdens are for market or sale, and hence therefore relatively sticky, even to be counted as employed in for those who are economically agriculture, following a narrowed active. Rest and leisure tend to be definition of employment to less for women. The time use data work for pay or profit under the also reveal significant commuting 19th International Conference of time for men and women in wage Labor Statistics (ICLS). work, as well as rural men and women in agriculture and non-farm work (NFE). 4 Asset ownership and rights Land ownership Livestock Mobile phones and and rights financial accounts + Rural women were significantly + more likely to own large + Within the LSMS+ supported The share of mobile phone livestock. In urban areas, surveys, individual‑level ownership is quite high – about however, a slightly greater share modules on land span exclusive 75 and 90 percent for rural women of men (20 percent, compared to or joint ownership (reported, and men, respectively, as well 17 percent of women) reported economic, documented) and as about 85 and 95 percent of large livestock ownership. rights (sell and bequeath) over urban women and men. Financial dwelling and non‑dwelling account ownership, on the other land. Individual modules on hand, was much lower—less than financial accounts and mobile Consumer 10 percent in rural areas, and in phone ownership also ask about urban areas about 21 percent of exclusive versus joint roles. durables women and 30 percent of men owned a financial account. + + Compared to land and livestock, In Cambodia, shares of however, ownership of larger respondents who own land consumer durables — across and have rights over land are computers and vehicles, for quite high. Because of civil example — was substantially law designating most assets higher for men, and across a acquired during marriage as wider range of durables categories joint, a substantially higher share in rural areas (with the exception of reported and economic land of lower‑cost vehicles such as ownership (across dwelling and bicycles, where rural women non‑dwelling land) is joint as reported greater ownership). opposed to exclusive. + Overall, the LSMS+ approach of surveying multiple individuals Women who are not household heads tend to have significantly per household is aimed at capturing intra‑household dynamics higher joint reported and in labor and economic decision‑making — an understanding of economic ownership of land which is critical for designing policy around improving economic than men overall — as well as mobility, and notably for women who typically face poorer being “SDG owners” (documented economic opportunities. Surveying across different asset classes owner or with rights to sell also allows for a sharper focus on where women’s ownership or bequeath). Women’s land and/or rights are lower than for men. Further investigation ownership and rights do decline using the breadth of data across the different socioeconomic in older age, however, relative to men, due largely to a large decline and demographic modules in the LSMS+ can shed further in joint land ownership. light on important distributional aspects of asset ownership and rights among men and women. The following sections present specific findings on labor outcomes across paid and unpaid work (Section 2), as well as men’s and women’s asset ownership and rights (Section 3). 5 © World Bank Section 1 The LSMS+ program in Cambodia 1. Overview of the LSMS+ 2. Cambodia: survey and country context 3. Implementation of LSMS+ modules in Cambodia 4. Characteristics of respondents Section 1 / Summary 7 1. Overview of the LSMS+ The World Bank Living Standards Measurement sex‑disaggregated data across asset ownership, Study‑Plus (LSMS+) program has been established labor and time use, as well as roles in family in 2016 to enhance the availability and quality of enterprises (Annex 2). individual‑disaggregated survey data collected Standard survey approaches such as proxy in low‑ and middle‑income countries on key reporting (where one household member reports dimensions of men’s and women’s economic for others), and interviewing individuals in groups opportunities and welfare.2 In the first phase or non‑private settings as opposed to one‑on‑one, of the program (2016‑21), the LSMS+ partnered can cloud the accuracy of labor statistics as well with national statistical offices in Cambodia, as intra‑household allocations of time and assets. Ethiopia, Malawi, Nepal, Sudan, and Tanzania to This has particular implications for understanding operationalize international recommendations economic inequalities between men and women. for individual‑disaggregated survey data These measurement issues are likely further collection on asset ownership, employment, aggravated in developing‑country contexts, and entrepreneurship. In doing so, the program where large shares of men and women engage has worked towards fulfilling the gender data in seasonal or informal employment, women commitment as part of 18th replenishment of the engage in contributing family work that is not well International Development Association (IDA18).3 measured,4 as well as in areas with more restrictive LSMS+ also builds on growing international gender norms that may affect women’s reporting on momentum around the need for better their employment status, as well as ownership and individual‑disaggregated data to help refine rights to land, durables, and financial accounts. the targeting of economic policies, including towards women (Annex 1). Several targets of the Since 2016, nationally‑representative surveys Sustainable Development Goals (SDGs), across that have been conducted and directly supported poverty reduction, agriculture, gender, employment, by LSMS+ include the 2016 Malawi Integrated and inequality, also hinge on improved Household Panel Survey (IHPS); 2019‑20 Tanzania National Panel Survey (NPS 5); 2018‑19 Ethiopia Socio‑Economic Survey (ESS 4); and the 2019‑20 2 For more information on LSMS+, please visit: https://www.worldbank. org/lsmsplus. LSMS+ has been established with grants from the Cambodia Socio‑Economic Survey.5 Forthcoming Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust surveys will be supported in 2021 in Nepal and Fund for Statistical Capacity Building, and the International Fund for Agricultural Development, and is implemented by the World Bank Sudan. Descriptive findings from the Malawi, Living Standards Measurement Study (LSMS) Team, in collaboration Tanzania, and Ethiopia LSMS+ are discussed in a with the World Bank Gender Group and partner national statistical offices. The program leveraged existing World Bank partnerships parallel report, LSMS+ Program in Sub‑Saharan with (1) United Nations Evidence and Data for Gender Equality Africa: Findings from individual‑level data (EDGE) Project on methodological experimentation and international collection on labor and asset ownership. guidelines on measuring asset ownership and control from a gender perspective, and (2) the ILO, FAO, the Data2X Project and the Hewlett Foundation on methodological experimentation for operationalizing the 19th ICLS definitions of work and employment, with a focus on 4 Contributing family work is supporting work in the family farm or subsistence agriculture. business, and usually unpaid, although compensation might come 3 International Development Association (IDA), 2016. IDA18 Special indirectly in the form of family income. This type of work is considered Theme: Gender and Development. World Bank. The commitment was employment by ILO classifications. to launch pilot data collection in at least six IDA countries to gather 5 Questionnaires for the LSMS+ supported surveys can be found here: self‑reported intra‑household level information on labor and assets www.worldbank.org/lsmsplus. 8 LSMS+ Program in Cambodia © World Bank The individual‑level modules supported by the LSMS+ target adult household members age 18 years and older on work and employment, non‑farm enterprises, and ownership of and rights to selected physical and financial assets, including, LSMS+ modules at a minimum, land, dwelling, financial assets and mobile phones. Pre‑existing individual‑level include asset survey modules on other topics, such as education and health, are also integrated into the ownership and individual questionnaire. Along with emphasizing rights, employment, self‑reporting in the individual interview modules, LSMS+ surveys work to minimize bias in reporting and non-market work due to social norms, by ensuring, to the best extent possible, gender matching between interviewers as well as a time and respondents, as well as conducting individual interviews in private, and if possible simultaneously diary for Cambodia across different household members. In each country, the LSMS+ finances the implementation costs for the additional sample of households or individuals who receive the new modules. The program provides direct technical assistance to the NSOs by the World Bank LSMS program, in collaboration with the World Bank Gender Group, to ensure proper integration and successful implementation of the improved survey methods. Section 1. The LSMS+ program in Cambodia 9 2. Cambodia: survey and country context The 2019‑20 round of the Cambodia Socio‑Economic Cambodia has maintained high growth and Survey (CSES) was implemented by the National labor force participation, but more needs to Institute of Statistics of Cambodia and covers be understood about factors associated with a nationally representative sample of 10,080 underlying gender inequalities in economic households and 1,080 enumeration areas (EAs). With opportunities. Cambodia’s annual GDP growth support from LSMS+, the CSES 2019‑20 covers 6 rate is among the highest in the region, and additional households in each of 252 CSES EAs to has remained relatively stable around 7 percent be visited during the period of October‑December annually, from 2013‑2019. Labor force participation 2019, for a total of 1,512 households. In each sampled (LFP) rates were also among the highest in the household, all adult household members (18 years region in 2019, albeit with gender gaps—in 2019, and above) were targeted for private interviews employment to population ratio for men age 15 on labor and ownership of and rights to selected and older was about 88 percent, compared with physical and financial assets, among other topics 76 percent for women. About three‑quarters of the as discussed above. The Cambodia LSMS+ Survey population live in rural areas, but activities have also included an individual‑level time use module diversified rapidly away from agriculture. Since (24‑hour time diary) for men and women age 18 and 2013, the share of employed men in agriculture has older, discussed further in Section 2. fallen from about 48 to 30 percent (and the share in wage and salaried employment increased from 45 to 57 percent). Box 1.1 LSMS+ supported national survey in Cambodia Cambodia Survey 2019/20 Cambodia Socio‑Economic Survey (CSES) Implementing agency1 National Institute of Statistics of Cambodia Sample size for individual interviews supported by LSMS+ 1,512 households Number of enumeration areas 252 Fieldwork period October‑December 2019 Asset classes covered in LSMS+ individual questionnaire Dwelling and non‑dwelling land (ownership and rights); apartments; livestock; consumer durables; mobile phones and financial accounts (ownership) Additional modules in LSMS+ individual questionnaire Labor; time use (24‑hour time diary); education; health 1 Surveys also received technical and financial support from LSMS-ISA and LSMS+; and using the World Bank Survey Solutions Computer-Assisted Personal Interviewing (CAPI) platform. 2 All surveys, as well as the sample size for individual interviews supported by LSMS+, are nationally representative. 10 LSMS+ Program in Cambodia © Chor Sokunthea / World Bank For employed women, the share in agriculture has fallen from 51 to 34 percent between 2013‑19, and those in wage or salaried work from 39 to 50 percent.6 A more in‑depth, individual‑level examination of the types of activities men and women are engaged in is needed, as well as other sources of economic bargaining power such as access to land and non‑land assets. In 2019, 58 percent of employed women and 44 percent of employed men were in occupations the ILO has identified as vulnerable, including own‑account work (self‑employed with no employees) or contributing family work.7 Understanding patterns in land and other asset ownership can help shed light on additional sources of economic vulnerability across men and women, as well as intra‑household bargaining power. 6 All statistics from World Development Indicators, 2019 (modeled ILO estimates). 7 ILOSTAT Database, 2020. In 2019 58% employed women 44% employed men were in occupations the ILO has identified as vulnerable, including own‑account work (self‑employed with no employees) or contributing family work. Section 1. The LSMS+ program in Cambodia 11 3. Table 1.1a Share of eligible respondents interviewed in Cambodia LSMS+ land modules Implementation Total Men Women of LSMS+ (1) Among HH with non‑dwelling land Number of eligible individuals modules in Total 2,561 1,231 1,330 Rural 2,003 969 1,034 Urban 558 262 296 Cambodia Share responding: Total 0.91 0.89 0.94 Rural 0.90 0.88 0.93 Urban 0.96 0.94 0.97 The Cambodia LSMS+ fieldwork was conducted by 21 field teams, each made up of one male and (2) Among HH with dwelling land one female enumerator. This allowed teams to aim, Number of eligible individuals to the best extent possible, for gender matching Total 3,640 1,732 1,908 between the enumerators and respondents, Rural 2,518 1,207 1,311 and to conduct at least some of the interviews Urban 1,122 525 597 simultaneously. Respondents were considered eligible for the LSMS+ individual interviews if they Share responding: were 18 years and above, or the household head Total 0.92 0.89 0.94 or spouse. This constituted about 62 percent of Rural 0.91 0.88 0.93 the total sample of respondents. Most households Urban 0.94 0.92 0.96 (91 percent) had more than one eligible respondent, 1 Eligible respondents = Household head or spouse, as well as those 18 and in these cases, efforts were made to conduct and older. at least two of the private interviews for a given 2 Within the assets modules, all respondents self-reported. household at the same time. Share of eligible respondents interviewed: Individual‑level The individual‑level assets modules in the Cambodia LSMS+ covered ownership and rights of dwelling and non‑dwelling (primarily agricultural) reporting was around 86 percent for the mobile land; apartments; livestock; consumer durables; phones module, and 92 percent for the financial mobile phones; and financial accounts. All assets accounts module. Response rates in rural areas modules were self‑reported. There was generally were somewhat lower compared to urban areas, very low non‑response among eligible individuals but were still quite high overall (90 percent for the (Tables 1.1a‑1.1b).8 land modules, 83 percent for the mobile phone For households with non‑dwelling and dwelling module, and 91 percent for the financial accounts land, respectively, the share of eligible respondents modules as well as those on labor, education and reporting was around 91‑92 percent. Across health). Response rates among men also tended other assets, the share of eligible respondents to be lower than for women. Section 3 discusses the assets questions and outcomes in detail. Within the labor, health, and education modules, response Individual interview statistics for dwelling and non‑dwelling land are rates were similar to those in the financial accounts not reported in this table, since those questions were asked only within households that owned plots – with all other respondents being coded module, and the time use module was entirely as having no ownership. self‑reported. 12 LSMS+ Program in Cambodia Share at the Household‑level At the household-level, 91 percent of the 1,512 Table 1.1b Share of eligible respondents Cambodia LSMS+ households completed at least interviewed in additional Cambodia LSMS+ modules1 one individual interview in the mobile phone asset module. Table 1.2 shows that across all households, Total Men Women regardless of the number of adults, all eligible Number of eligible individuals adults were successfully interviewed 87 percent of the time. The remaining 13 percent of households Total 3,938 1,845 2,093 had more than one adult, but enumerators failed to Rural 2,710 1,272 1,438 interview at least one of them. These cases were Urban 1,228 573 655 mostly concentrated in two‑person households Share of eligible respondents interviewed where only one was successfully interviewed, or across modules: where 2 out of 3 eligible adults were interviewed. (a) Assets: mobile phone ownership: Total 0.86 0.84 0.88 Rural 0.83 0.81 0.85 Table 1.2 Distribution of households, Urban 0.92 0.91 0.94 by the number of adults interviewed in LSMS+ assets module1 (b) Assets: financial account ownership: Total % Total 0.92 0.89 0.94 Households Interviewed 1,380 Rural 0.91 0.88 0.94 All Eligible Adults Interviewed 1,202 87% Urban 0.94 0.92 0.96 4 or more adults 211 15% (c) Labor/health/education: 3 adults 243 18% Total 0.92 0.90 0.95 2 adults 646 47% Rural 0.91 0.88 0.94 Urban 0.95 0.93 0.97 1 adult 102 7% 1 Eligible respondents = Household head or spouse, as well as those 18 Subset of Eligible Adults 178 13% and older. Interviewed 2 Within the assets modules, all respondents self-reported. 3 out of 4 37 3% 3 Within the labor/health/education modules, there was some proxy reporting, and the estimates above reflect only the share of those self- 2 out of 4 30 2% reporting (which was quite high). 1 out of 4 7 1% 2 out of 3 48 3% © World Bank 1 out of 3 7 1% 1 out of 2 49 4% 1 For each country, the number of eligible adults from the roster (those 18 years and older, and the household head or spouse) was compared with the interviews conducted in the assets modules (specifically, the mobile phones module). 88-97 % Around respondent rates for individual land ownership modules Section 1. The LSMS+ program in Cambodia 13 4. Characteristics of respondents Table 1.4 shows that, among eligible respondents of six years of schooling. A higher share of men in the Cambodia LSMS+, about 27 percent of men in the sample were also married, while women and women lived in urban areas; while a very high respondents were significantly more likely to be share of respondents lived in households that widowed (15 percent, compared to three percent of had access to electricity (about 85‑86 percent), men) or divorced/separated (four percent, compared far fewer had access to other infrastructure such to one percent of men). On average, men spend a as piped water (about 26 percent). There are higher number of months away from the household; substantial gender differences as well—about 90 in the data, about 27 percent of men spent at least percent of eligible men had ever attended school some time away from the household in the last year, (with an average of about seven years of schooling), compared to just ten percent of women. compared to 77 percent of women with an average Table 1.4. Demographic and socioeconomic characteristics of individuals eligible for LSMS+ interviews: Cambodia LSMS+1 Men Women HH head 0.638*** 0.153*** Age: 18‑24 0.154* 0.133* Age: 25‑34 0.264** 0.239** Age: 45‑542 0.140 0.152 Age: 55+ 0.210*** 0.257*** Ever attended school 0.897*** 0.771*** Years of school, if attended 7.287*** 6.177*** Married 0.776*** 0.692*** Separated/divorced 0.014*** 0.040*** Widowed 0.027*** 0.147*** Number of months individual is away from HH 0.716*** 0.382*** HH size 4.796** 4.687** HH dependency ratio 3 1.499*** 1.405*** HH has electricity 0.851 0.860 HH has piped water 0.262 0.272 HH: walls made of concrete 0.262 0.258 Lives in urban area 0.271 0.266 Observations 1,845 2,093 1 All estimates are weighted. Statistically significant differences between men and women, within each survey, are indicated by asterisks (***p<0.01, **p<0.05, * p<0.10). 2 Excluded category is 35-44. 3 Indicates dependency ratio of children and elderly. 14 LSMS+ Program in Cambodia Figures 1.1 and 1.2 provide additional context on the Figure 1.1 Age Distribution of LSMS+ age and consumption distributions of the eligible Eligible Sample respondent sample. Looking at Figure 1.1, urban men and women tend to be younger than their Men Rural Men Urban rural counterparts, and women in both urban and Women Rural Women Urban rural areas also tend to be slightly older than men (a higher share of men than women in the 18‑35 age group, for example, with the trends reversing 0.03 for those age 50 and older). Annual household consumption per capita tends to be similar for men and women in rural and urban households, with a 0.02 density significant drop‑off for respondent age 50 and older. 73% 0.01 0.00 0 25 50 75 100 of men age and women 1 Estimates are weighted using the household sampling weight. live in rural areas Figure 1.2 Annual household consumption per capita, by men and women Men Women Rural Urban log annual HH consumption log annual HH consumption 15.5 15.5 15.0 15.0 14.5 14.5 14.0 14.0 20 40 60 80 20 40 60 80 age age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends of men and women. Section 1. The LSMS+ program in Cambodia 15 © World Bank Section 1 / Summary + + + + The Living Standards The 2019/20 round of the Individual-level modules Along with emphasizing Measurement Study‑Plus Cambodia Socio-Economic were administered on work self‑reporting in the (LSMS+) program has Survey (CSES) has been and employment, time individual interview been established in supported by the LSMS+ use (24-hour time diary), modules, LSMS+ surveys 2016 to enhance the and has been implemented non-farm enterprises, work to minimize bias in availability and quality of by the National Institute and ownership of and reporting due to social individual‑disaggregated of Statistics of Cambodia. rights to selected physical norms, by ensuring, to survey data collected in Respondents were and financial assets, the best extent possible, low- and middle‑income considered eligible for including dwelling land, gender matching countries on key the LSMS+ individual non‑dwelling land, between interviewers dimensions of men’s interviews if they were financial assets, and and respondents, as well and women’s economic 18 years and above. mobile phones. Pre‑existing as conducting individual opportunities and welfare. Non‑response was very low individual-level survey interviews in private, and across the assets (mobile modules on other topics, if possible simultaneously phones and financial such as education and across different household accounts modules), as well health, are also integrated members. as the education, health, into the individual and labor modules. The questionnaire. Cambodia LSMS+ also included a time use module, where all respondents 85% self‑reported. men and women but only one quarter have access to have access to electricity piped water in their home in their home 16 LSMS+ Program in Cambodia © World Bank Surveying multiple individuals per household captures intra-household dynamics in labor and economic decision-making 7.2 6.2 years of schooling completed on average men women Section 1. The LSMS+ program in Cambodia 17 © World Bank 18 LSMS+ Program in Cambodia Section 2 Labor outcomes 1. Introduction 1.1. Moving towards self‑reporting of labor 1.2. Labor outcomes covered in LSMS+ 2. Labor module 2.1. Overall statistics on labor outcomes 2.2. Participation and hours by age 2.3. Work across multiple activities and intrahousehold trends 3. Findings on daily time use: paid and unpaid work burdens Section 2 / Summary Section 2. Labor outcomes 19 1. Introduction 1.1. another national survey that allowed for proxy reporting and non‑private interviews and did not Moving towards require a gender match between respondents and enumerators. Kilic et al. (2020) compare the two self‑reporting of labor surveys to show how the LSMS+ survey approach raises reporting of employment among men and women, finding that proxy reporting is associated Employment and labor questionnaires, across with underreporting of labor in the concurrent multi‑topic household surveys as well as labor survey.9 force surveys, have typically relied heavily on proxy reporting as well as other standard survey methods While time and resource‑saving, commonly used (such as interviewing respondents in groups or survey interview approaches such as proxy non‑private settings). In the labor modules of the reporting can limit the accuracy of men’s and LSMS‑Integrated Surveys on Agriculture (LSMS‑ISA) women’s labor outcomes. Risks are particularly high in Sub‑Saharan Africa, for example, the share of in areas where work is more seasonal and where men and women age 18 and older who reported hours and earnings are more irregular — increasing themselves were 53 and 61 percent, respectively. the variability of proxy respondents’ information In comparison, the LSMS+ supported surveys in about household members’ time and earnings. Sub‑Saharan Africa (Ethiopia 2018‑19, Tanzania Prevailing cultural norms and other barriers that 2019‑20 and Malawi 2016), had self‑response rates limit women’s economic mobility can also lower the in the labor module ranging from 72‑79 percent for men, and 80‑89 percent of women. The Malawi 9 Kilic, Talip, Goedele Van den Broeck, Gayatri Koolwal, and Heather survey provides a unique perspective since the Moylan. 2020a. “Are You Being Asked? Impacts of Respondent LSMS+ survey was conducted concurrently with Selection on Measuring Employment.” World Bank Policy Research Working Paper 9152. © World Bank 20 LSMS+ Program in Cambodia Table 2.1. Share of self-reporting in Cambodia LSMS+ labor module across location, age, and sex Total Urban Rural Men Women Men Women Men Women (A) Number of eligible All age groups 1,845 2,093 573 655 1,272 1,438 Age: 18‑25 332 327 98 106 234 221 Age: 26‑35 478 518 145 166 333 352 Age: 36‑55 680 755 223 237 457 518 Age: 56+ 355 493 107 146 248 347 (B) Share self‑reporting All age groups 0.90 0.95 0.93 0.97 0.88 0.94 Age: 18‑25 0.80 0.88 0.87 0.94 0.76 0.86 Age: 26‑35 0.89 0.95 0.92 0.98 0.88 0.94 Age: 36‑55 0.93 0.97 0.95 0.98 0.92 0.97 Age: 56+ 0.94 0.94 0.96 0.95 0.93 0.94 1 Estimates are weighted using the household sampling weight. reliability of responses that arise from non‑private as opposed to one‑on‑one interviews. These While time and measurement issues can impair the precision of aggregate labor estimates across communities, resource‑saving, as well as an understanding of intra‑household outcomes across paid and unpaid work. commonly used Table 2.1 shows that self‑reporting was quite high in survey interview the labor module (90 percent for men overall, and 95 percent for women, with higher shares in urban approaches such as areas). All respondents in the time use module also self‑reported. Self‑reporting is highest among the proxy reporting can elderly, and lower among younger men in particular (among men age 18‑25, for example, the incidence limit the accuracy of of self‑reporting was 87 percent in urban areas, and 76 percent in rural areas). men’s and women’s labor outcomes 90 95 % % Self-reporting in the labor module men women Section 2. Labor outcomes 21 © World Bank 1.2. Labor outcomes covered in LSMS+ The LSMS+ labor modules include questions on individual activities over the last seven days and 12 months (Box 2.1). This section focuses on the sample of eligible individuals who self‑reported in the labor modules on three kinds of activities: (a) any agricultural activities (production for own consumption or sale); (b) running or working in a non‑farm enterprise; and (c) wage employment. Within wage employment, annual hours and earnings were also covered across the three surveys. The following discussion examines trends in the Cambodia LSMS+ on participation across different age groups, multiple employment activities, work in non‑market activities, and an examination of demographic and socioeconomic characteristics associated with participation in different activities. Box 2.1 Individual‑level data on labor and time use in the Cambodia LSMS+ Labor module Time use module + + The LSMS+ individual‑level labor module covered a range of Men and women 18 and older were also asked about their outcomes: time use in the last 24 hours, covering primary and secondary activities (those conducted simultaneously) in 15‑minute - Participation in the last 7 days and 12 months in intervals. The following general areas were covered, with agricultural and non‑agricultural (enterprise, wage and more specific detail in the questionnaire: salaried) activities, as well as hours in the last 7 days across these activities; - Unpaid work (across household chores; collecting firewood/water; care of children and adults) - Hours and earnings in the last 12 months in wage or salaried work, as well as additional details on the nature of - Economic activities and labor (wage and salary work; these occupations; non‑farm enterprises; agriculture; making goods at home, and free or exchange labor) - Questions on unemployment, job search and labor underutilization; - Leisure (reading; watching or listening to TV or radio; exercising; social activities and hobbies) - Intended use of agricultural production (whether for market, or the household’s own consumption), was asked - Traveling (also includes commuting) in line with recommendations by the 19th International - Schooling Conference of Labor Statisticians (ICLS 19) on measuring employment in agriculture — following a narrowed - Rest and sleep international definition of employment to work for pay or profit.1 1 See ILO, 2013. Resolution I concerning statistics of work, employment and labor underutilization. 19th International Conference of Labour Statisticians, Geneva. 22 LSMS+ Program in Cambodia 2. Labor module © World Bank 2.1. Overall statistics on and gender gaps widening in rural areas for wage work. About half of urban and rural men labor outcomes were in wage employment over the past year, for example, compared to 33 percent of urban women and 29 percent of rural women. Among Table 2.2 presents, for different economic those in wage work, however, neither weekly nor activities, summary statistics on (a) participation annual hours were significantly different across over the last 7 days, and past 12 months; (b) men and women — roughly 45 hours per week — average hours worked in the last seven days; with slightly higher weekly hours in urban areas. as well as (c) average hours and earnings in Women were significantly less likely to engage wage work in the last 12 months. Most men and in agricultural activities mainly for market or sale, women were concentrated in wage employment and hence be counted as employed in agriculture, and agriculture, with a significantly greater share following a narrowed definition of employment to of men than women engaged in both sectors, work for pay or profit under the 19th ICLS. Section 2. Labor outcomes 23 Table 2.2. Labor outcomes, Cambodia LSMS+1 Total Urban Rural Men Women Men Women Men Women Participation (Y=1, N=0) 2 Any agricultural activity (7d) 0.50*** 0.46*** 0.21* 0.17* 0.61*** 0.56*** Among those in agr: mainly for sale (7d) 0.24*** 0.20*** 0.08 0.06 0.30*** 0.26*** Any agricultural activity (12m) 0.60*** 0.55*** 0.27** 0.22** 0.73*** 0.67*** Among those in agr: mainly for sale (12m) 0.26*** 0.22*** 0.10 0.08 0.32** 0.28** Owner/manager of NFE (7d) 0.18 0.19 0.29 0.29 0.13 0.15 Owner/manager of NFE (12m) 0.20 0.21 0.32 0.32 0.15 0.17 Supporting work in NFE (7d) 0.08 0.07 0.11 0.10 0.07 0.05 Supporting work in NFE (12m) 0.09 0.08 0.14 0.12 0.08 0.07 Wage employment (7d) 3 0.41*** 0.24*** 0.44*** 0.27*** 0.41*** 0.22*** Wage employment (12m) 3 0.50*** 0.30*** 0.50*** 0.33*** 0.50*** 0.29*** Hours worked in last 7 days (among those working): Agriculture 20.5*** 15.4*** 19.5** 14.1** 20.6*** 15.6*** NFE owner 46.1** 50.2** 52.0 52.8 41.0*** 48.3*** NFE helper 27.2 29.3 34.0 30.5 22.8 28.5 Main wage employment 45.7 45.9 47.7 47.9 44.8 45.0 Annual hours and earnings in main wage employment (among those working): Annual hours 1,594 1,599 1,918 1,840 1,466 1,497 Total Observations 1,657 1,982 532 635 1,125 1,347 1 All estimates are weighted using the household sampling weight. Statistically significant differences between men and women are indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10). 2 Participation in multiple activities was possible. Agricultural activity = crops/livestock/fishing. 32 % About A substantial, and roughly similar, share of men and women owned or managed non‑farm enterprises (NFEs) in the past year — about 32 percent of urban men and women, and 15 and 17 percent of rural men and women, respectively. A smaller share of men and women also provided supporting work to a household NFE. Compared to other activities, of urban men working time spent in NFEs was also much higher for urban respondents — about 52 hours per week in and women urban areas, for both men and women. Rural women who owned NFEs also worked significantly more weekly hours (48 hours) than men (41 hours). work in NFE and about half that in rural areas. 24 LSMS+ Program in Cambodia 2.2. where activity begins to decline only after age 60; Participation and similar findings emerged from the Malawi LSMS+, hours by age where older women were significantly more likely to be engaged in NFEs. A comparatively lower share of men and women in Cambodia provide Accurate data on individual labor outcomes can supporting work to NFEs, and generally do not contribute substantially to targeting of employment exhibit an inverted‑U pattern (actually increasing policies, including through an improved slightly by age for urban women). On gender understanding on how labor participation across differences, Figure 2.2 also shows that women activities evolves over individuals’ life cycles. work significantly more hours in running or Figure 2.1 reports locally weighted regressions managing NFEs, and (to a smaller extent) higher of participation in different activities (agriculture, hours in supporting work. Women who own NFEs wage, and NFE work) over the last 12 months, by work roughly consistent hours—on average, about age and rural/urban residence. Figure 2.2 also 50 hours in the last week—between ages 30‑50, presents trends in hours worked in the last week whereas for men, hours drop off after age 40. across these activities, along with hours in the last For wage employment, Figure 2.1 shows that 12 months for respondents’ main wage activity. activity tends to decline linearly with age. As Within agriculture, participation increases for men seen in Table 2.1 as well, across activities, and women in both rural and urban areas until wage employment has the widest gender gaps about age 60, declining thereafter. Gender gaps in participation, and Figure 2.1 shows this is remain fairly consistent across age groups, except particularly the case for men and women around for a narrowing of the gap among rural men and 40‑60 years of age. Figure 2.2 shows that, among women around 35‑40 years of age. Among those those working, men’s and women’s hours in working in agriculture, hours worked (Figure 2.2) wage work are not very different from each other exhibit a similar pattern. among younger age groups but start to diverge substantially after around age 35‑40. Among those Compared to agriculture, the share of men and working, men’s and women’s hours in the last women running NFEs increases among younger week are quite similar for younger age groups, age groups but tends to decline after around but women’s hours decline substantially relative to 50 years of age. One exception is for rural women, men’s after age 35‑40 as well. © World Bank Section 2. Labor outcomes 25 Figure 2.1 Share of men and women participating in different activities over the last 12 months, Cambodia LSMS+ Men Rural Men Urban Women Rural Women Urban Agriculture Wage 0.75 0.75 Share participating Share participating 0.50 0.50 0.25 0.25 0.00 0.00 -0.25 20 40 60 80 20 40 60 80 age age NFE (run/manage) NFE (support) 0.6 0.3 Share participating Share participating 0.4 0.2 0.1 0.2 0.0 0.0 -0.1 -0.2 -0.2 20 40 60 80 20 40 60 80 age age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends for men and women. Tables 2.3 and 2.4 present additional OLS Similar to the age‑related trends in Figures 2.1 regressions that examine the profile (not causal and 2.2, Tables 2.3‑2.4 show that participation is relationships) between socioeconomic and higher around the middle of the age distribution demographic characteristics, and participation in (excluded category, age 35‑44) relative to other different labor activities in the last 12 months.10 ages, especially older age groups. For women, Table 2.3 examines correlates of participation over labor participation is more closely linked with the the last year for rural and urban women, across overall age distribution (for example, there are agriculture, NFEs, and wage work. Table 2.4 significant negative correlations from women being examines the same for men. Only self‑reporting in younger as well as older age groups compared respondents were included, which as discussed to the excluded category, whereas for men, most of in Table 2.1 was quite high (roughly 90 percent for the effects are focused among being in the oldest men overall, and 95 percent for women). age group). Urban married women are significantly less likely to be in wage work, and widowhood is 10 Results were similar to participation over the last 7 days. also negatively associated with labor participation in different areas. 26 LSMS+ Program in Cambodia Figure 2.2 Among those working: hours worked by age, Cambodia LSMS+ Men Women Agriculture (last 7 days) Wage (last 7 days) 20 50 Weekly Hours Weekly Hours 40 10 30 0 20 40 60 80 20 40 60 80 age age Wage (last 12 months) NFE (run/manage, last 7 days) 1000 60 Weekly Hours Annual Hours 50 500 40 0 30 20 40 60 80 20 40 60 80 age age NFE (support, last 7 days) 60 Granular data allow us to understand how Weekly Hours 40 20 labor participation 0 evolves over the -20 life cycle 20 40 60 80 age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends for men and women. Section 2. Labor outcomes 27 Table 2.3. Profile of women working in different activities over the last 12 months: Cambodia LSMS+ Participation in the last 12 months (Y=1 N=0): Agriculture NFE (run/manage) Wage Urban Rural Urban Rural Urban Rural Individual characteristics HH head 0.05 0.145*** 0.284*** 0.029 ‑0.185*** 0.081* [0.77] [3.15] [3.80] [0.67] [‑3.31] [1.69] Age: 18‑24 ‑0.044 ‑0.152*** ‑0.194** ‑0.135*** 0.150** 0.081 [‑0.77] [‑2.65] [‑2.53] [‑2.79] [2.20] [1.42] Age: 25‑34 ‑0.042 ‑0.091** ‑0.122* ‑0.069* 0.008 0.066 [‑1.01] [‑2.34] [‑1.86] [‑1.80] [0.13] [1.51] Age: 45‑54 0.031 ‑0.012 0.01 ‑0.018 ‑0.055 ‑0.158*** [0.57] [‑0.34] [0.13] [‑0.47] [‑0.81] [‑4.22] Age: 55+ 0.095* ‑0.094** ‑0.163** ‑0.066 ‑0.230*** ‑0.225*** [1.88] [‑2.21] [‑2.32] [‑1.59] [‑3.77] [‑5.55] Years of schooling ‑0.002 ‑0.007* 0.004 0.009** 0.00 0.008* [‑0.63] [‑1.70] [0.71] [2.15] [0.08] [1.80] Individual: Married/non‑formal union 0.078 0.087* 0.174** 0.029 ‑0.199*** ‑0.074 [1.51] [1.67] [2.51] [0.68] [‑3.09] [‑1.49] Individual: Separated/Divorced 0.012 ‑0.15 0.287** 0.131 0.011 0.00 [0.15] [‑1.18] [2.00] [1.29] [0.11] [0.00] Individual: Widowed ‑0.081 ‑0.173** ‑0.099 0.022 0.059 ‑0.135** [‑1.04] [‑2.36] [‑0.96] [0.36] [0.66] [‑2.37] Number of months away from household ‑0.011 ‑0.032* ‑0.017 ‑0.021*** 0.033** 0.055*** [‑0.81] [‑1.86] [‑0.74] [‑3.88] [2.08] [3.82] Individual ownership of assets (from LSMS+ module) Owns working mobile phone 0.035 0.025 0.061 0.109*** 0.033 0.01 [0.71] [0.89] [0.99] [4.38] [0.50] [0.32] Owns a financial account ‑0.024 ‑0.121*** ‑0.123** 0.062 0.269*** 0.131** [‑0.58] [‑2.80] [‑2.31] [1.17] [4.15] [2.46] Non‑dwelling land: reported owner 0.150*** 0.259*** ‑0.017 0.00 ‑0.012 ‑0.071** [4.53] [7.79] [‑0.33] [0.01] [‑0.29] [‑2.17] Dwelling land: reported owner 0.052* ‑0.097** 0.008 0.019 ‑0.072 0.013 [1.85] [‑2.04] [0.11] [0.43] [‑1.33] [0.31] Household characteristics Log household size 0.062** ‑0.056 ‑0.006 ‑0.018 ‑0.039 ‑0.024 [2.13] [‑1.48] [‑0.11] [‑0.52] [‑0.67] [‑0.67] Household dependency ratio (of children and elderly) ‑0.031 0.052** 0.003 ‑0.023 ‑0.055 ‑0.012 [‑1.17] [2.57] [0.08] [‑1.10] [‑1.53] [‑0.61] House: dwelling has electricity ‑0.113 ‑0.051 0.127 0.078*** ‑0.09 ‑0.083** [‑1.16] [‑1.46] [1.08] [2.68] [‑0.91] [‑2.04] House: drinking water from personal pipe in dwelling or ‑0.041 ‑0.093* 0.081 0.044 ‑0.041 0.007 compound [‑1.10] [‑1.66] [1.26] [0.93] [‑0.77] [0.15] House: walls made of concrete or bricks ‑0.031 ‑0.084** ‑0.009 0.171*** ‑0.085* ‑0.067* [‑0.62] [‑2.25] [‑0.18] [3.80] [‑1.91] [‑1.76] Observations 612 1,220 612 1,220 612 1,220 R‑squared 0.371 0.347 0.225 0.182 0.337 0.308 1 All regressions weighted by the household sampling weight. Only the self‑reporting sample included; as seen in Table 1, self‑reporting rates were about 97 percent for urban women and 94 percent for rural women. 2 District and interview month fixed effects included. 3 Excluded age category is 35‑44. 4 Results similar to those working in the last 7 days. 28 LSMS+ Program in Cambodia Table 2.4 Profile of men working in different activities over the last 12 months: Cambodia LSMS+ Participation in the last 12 months (Y=1 N=0): Agriculture NFE (run/manage) Wage Urban Rural Urban Rural Urban Rural Individual characteristics HH head 0.126*** 0.081* ‑0.025 0.00 0.056 0.029 [2.93] [1.80] [‑0.34] [0.01] [0.66] [0.65] Age: 18‑24 ‑0.015 ‑0.034 ‑0.163* ‑0.035 0.052 0.161** [‑0.21] [‑0.60] [‑1.84] [‑0.76] [0.50] [2.27] Age: 25‑34 0.027 0.03 ‑0.059 0.028 0.079 0.047 [0.54] [0.82] [‑0.81] [0.69] [1.01] [0.91] Age: 45‑54 ‑0.006 0.027 0.04 ‑0.01 ‑0.093 ‑0.174*** [‑0.10] [0.68] [0.53] [‑0.22] [‑1.16] [‑3.08] Age: 55+ 0.035 ‑0.009 ‑0.163*** ‑0.039 ‑0.198*** ‑0.359*** [0.61] [‑0.22] [‑2.71] [‑1.03] [‑2.80] [‑6.92] Years of schooling ‑0.006 ‑0.006 ‑0.008 0.005 0.007 ‑0.006 [‑1.26] [‑1.37] [‑1.36] [1.22] [0.99] [‑1.06] Individual: Married/non‑formal union ‑0.123* 0.045 0.134 0.077* ‑0.029 0.057 [‑1.84] [0.77] [1.40] [1.78] [‑0.30] [0.89] Individual: Separated/Divorced ‑0.125 0.05 ‑0.369** ‑0.139 0.421** 0.177 [‑1.27] [0.30] [‑2.42] [‑1.38] [2.10] [1.18] Individual: Widowed ‑0.034 ‑0.224** ‑0.108 0.025 0.235 ‑0.077 [‑0.31] [‑2.15] [‑0.72] [0.48] [1.58] [‑0.97] Number of months away from household ‑0.005 ‑0.034*** ‑0.020** ‑0.006 0.008 0.033*** [‑0.49] [‑3.27] [‑2.07] [‑0.88] [0.51] [3.86] Individual ownership of assets (from LSMS+ module) Owns working mobile phone ‑0.025 0.02 0.247*** 0.076** ‑0.01 ‑0.001 [‑0.37] [0.46] [2.76] [2.52] [‑0.11] [‑0.03] Owns a financial account 0.041 ‑0.074 ‑0.091 0.026 0.247*** 0.114* [0.90] [‑1.43] [‑1.56] [0.59] [3.53] [1.75] Non‑dwelling land: reported owner 0.126*** 0.277*** 0.055 ‑0.045 ‑0.100** ‑0.097** [3.26] [6.39] [1.24] [‑1.51] [‑2.17] [‑2.40] Dwelling land: reported owner 0.068 ‑0.039 ‑0.127* 0.074 ‑0.01 0.045 [1.53] [‑0.61] [‑1.86] [1.64] [‑0.14] [0.73] Household characteristics Log household size 0.033 ‑0.002 0.065 ‑0.002 0.078 0.024 [0.74] [‑0.06] [1.05] [‑0.07] [1.04] [0.54] Household dependency ratio (of children and elderly) ‑0.053 0.005 ‑0.037 0.011 ‑0.083 ‑0.067** [‑1.57] [0.22] [‑0.87] [0.45] [‑1.24] [‑2.44] House: dwelling has electricity ‑0.410*** ‑0.006 0.209*** 0.109*** 0.097 ‑0.105** [‑5.18] [‑0.14] [2.78] [3.56] [0.67] [‑2.40] House: drinking water from personal pipe in dwelling or ‑0.063* ‑0.104 ‑0.035 0.117* ‑0.056 ‑0.03 compound [‑1.96] [‑1.59] [‑0.54] [1.78] [‑0.90] [‑0.47] House: walls made of concrete or bricks ‑0.100* ‑0.102** 0.041 0.094* ‑0.058 ‑0.02 [‑1.93] [‑2.20] [0.76] [1.97] [‑0.97] [‑0.39] Observations 517 1,032 517 1,032 517 1,032 R‑squared 0.458 0.316 0.261 0.199 0.237 0.289 1 All regressions weighted by the household sampling weight. Only the self‑reporting sample included; as seen in Table 1, self‑reporting rates were about 93 percent for urban men and 88 percent for rural men. 2 District and interview month fixed effects included. 3 Excluded age category is 35‑44. 4 Results similar to those working in the last 7 days. Section 2. Labor outcomes 29 © World Bank 2.3. Work across multiple Some household socioeconomic characteristics (electricity, access to piped water, and solid house activities, and construction) are generally negatively associated intrahousehold trends with agricultural and rural wage work among men and women, and instead a positive association with NFE activity. Urban men, as well as all women, Self‑reported, individual‑level data can also shed in wage work are also significantly more likely to greater light on labor outcomes that are more be away from home for longer periods of time, complex to understand with proxy and non‑private which is consistent with the large share of migrant interviews. As discussed below, this includes a labor in Cambodia.11 better understanding of participation in multiple activities (as opposed to just respondents’ main The regressions reveal strong links between labor activity), as well as greater detail on intra‑household participation and individual ownership of assets labor allocation. such as land, financial accounts, and mobile phones, all of which were collected in the LSMS+ assets modules. The assets data is discussed Share of respondents in in more detail in Section 3, including how these multiple economic activities outcomes were measured in particular. Tables 2.3 and 2.4 show that owning a working mobile Engaging in multiple areas of work is common, phone is positively associated with own‑enterprise particularly in contexts where activities are activity for urban men and rural women. Owning a seasonal. Across the work categories discussed financial account also generally has a significant above (agriculture for own‑consumption or market; link with wage work for both men and women. running or managing an NFE; supporting work Ownership of non‑dwelling land is, as expected, in an NFE; wage employment), Table 2.5 shows significantly positively associated with men’s and substantial activity across different areas of women’s work in agriculture, whereas ownership work, especially among rural respondents. Even of dwelling land has more of a mixed pattern—for over the past seven days, for example, 18 and example, a positive association with rural men’s 31 percent of urban and rural men, respectively, NFE activity, but negative for urban men in this engaged in two different areas of work, as well area. These links can be explored through further as 13 percent of urban and 22 percent of rural analysis of the data. women. Additional details on the sectors of work are presented in Table 2.6. These shares 11 See, for example, OECD Development Centre, 2017. rose even further for those working in the last Interrelations between Public Policies, Migration and 12 months, including nearly half of rural men and a Development in Cambodia. https://www.oecd‑ilibrary.org/development/ interrelations‑between‑public‑policies‑migration‑and‑development‑in‑cambodia_9789264273634‑en. third of rural women. The CSES 2019/20 also has detailed modules covering internal/ domestic and international migration of household members. 30 LSMS+ Program in Cambodia A small share of respondents participated in Table 2.5 Among individuals working (last seven days or last 12 months): three or more activities over the last 12 months; share in multiple areas of work1 this mostly involves a combination of ownership and supporting work in different NFEs along with Urban Rural agriculture and/or wage work. Men Women Men Women Table 2.6 shows that among the sample of men and Last 7 days women engaged in up to two areas of activity over Two areas of 0.18** 0.13** 0.31** 0.22** the last 12 months, men were much more likely than work women to be engaged in multiple areas of work Three or more 0.02** 0.001** 0.04** 0.02** —and in particular, in agriculture as well as wage areas of work activity. This combination tended to be the most Number of 478 474 1,055 1,115 common among individuals in multiple areas of individuals work, particularly in rural areas, where 32 percent working (7 days) of men and 19 percent of women were in both Last 12 months agriculture and wage work. A closer look at the Two areas of 0.28*** 0.19*** 0.45*** 0.33*** data showed that most of these respondents were work in agriculture for own consumption and worked in a Three or more 0.05*** 0.01*** 0.08*** 0.05*** wage occupation separately. In rural areas, a similar areas of work share (eight to nine percent) of men and women were also involved in agriculture as well as running Number of 514 532 1,122 1,235 individuals their own non‑farm enterprise. working (last 12 months) 1 All estimates are weighted using the household sampling weight. Statistically significant differences between men and women are indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10). Table 2.6 Sector‑specific breakdown of individuals working in up to two activities in the last 12 months, Cambodia LSMS+ Urban Rural Agr. NFE NFE Wage Agr. NFE NFE Wage (own) (support) (own) (support) Men Agriculture 0.09 0.02 0.01 0.17 0.33 0.08 0.03 0.32 NFE (own) 0.25 0.04 0.00 0.04 0.01 0.01 NFE (support) 0.05 0.03 0.01 0.01 Wage (salaried) 0.34 0.17 Women Agriculture 0.14 0.05 0.02 0.06 0.46 0.09 0.02 0.19 NFE (own) 0.27 0.02 0.02 0.06 0.02 0.00 NFE (support) 0.09 0.01 0.03 0.00 Wage (salaried) 0.31 0.13 1 All estimates are weighted using the household sampling weight. 2 Shaded cells are those where respondents are working in multiple activities. Section 2. Labor outcomes 31 © World Bank Intra‑household distribution of work The high degree of self‑reporting allows for a more accurate understanding of labor allocation within households. Table 2.7 shows that within urban and 64 of urban % rural areas, a high share of households (around 75 percent) had both men and women working in farm or off‑farm work. For off‑farm work specifically households had both men and women working (work in NFEs or for a wage), 64 percent of urban in off-farm work, compared to households had men and women working in off‑farm 40 percent in rural areas. work, compared to 40 percent in rural areas. Table 2.7 Within‑household distribution of labor Table 2.8 presents a sector‑specific disaggregation across households: last 12 months1 of all households where both men and women were working in the last 12 months. As highlighted by the Urban Rural yellow shaded cells, about a third of households Share of HH with men/women overall had men and women working strictly within working the same activity (15 percent in agriculture, nine Any woman working 0.86 0.90 percent in NFEs, and 7 percent in wage work, with Any man working 0.86** 0.80** 14 percent in both agriculture and wage activity). The purple shaded cells indicate the share of Both men and women working 0.75 0.74 households (greater than 1 percent) where men and (any) women within the same households are working Both men and women working 0.64*** 0.40*** in different activities. Most of these households (any off‑farm: wage/NFE) involve women working only in agriculture, while Number of households 456 1,056 men are in a combination of agriculture and wage work — and a smaller share with these gender 1 All estimates are weighted using the household sampling weight. roles reversed. Far fewer households had men and Statistically significant differences between urban and rural areas are women in a combination of NFE and wage work. indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10). 32 LSMS+ Program in Cambodia Table 2.8 Within‑household distribution of activities across men and women, among households where men and women are both working: last 12 months Men only agr. agr. & NFE agr. & wage agr., NFE & only NFE NFE & only wage wage wage only agr. 0.15 0.01 0.15 0.005 0.002 NA 0.01 agr. & NFE 0.01 0.05 0.01 0.03 0.01 0.004 0.01 agr. & wage 0.04 0.01 0.14 0.01 NA 0.0001 0.02 Women agr., NFE & wage 0.001 0.01 0.01 0.01 0.00 NA 0.0002 only NFE 0.002 0.01 0.001 0.001 0.09 0.02 0.02 NFE, wage NA 0.004 0.001 0.001 0.01 0.01 0.01 only wage 0.01 0.001 0.02 0.001 0.01 0.01 0.07 1 All estimates are weighted using the household sampling weight. There were 1,134 households where both men and women working in the past 12 months. 2 Yellow shaded cells = all men and women in the household working in the same activity or combination of activities. Blue shaded cells = areas where 2 percent or more households had men and women working in different activities. 3 NA = equal to zero.   © World Bank About a third of households overall had men and women 15 in in % 9 7 % in wage % working strictly within the same activity agriculture NFEs work Section 2. Labor outcomes 33 Figure 2.3 presents a within‑household percentiles. For urban women in particular, distribution of men’s and women’s hours worked hours in wage work increases only among lower in different sectors (not conditional on working), expenditure percentiles and tends to fall thereafter. by percentile of rural and urban household per As seen earlier in Table 2.3, urban women in capita expenditure. Relative to the household wage work also tend to be in households with distribution of per capita expenditure, women’s and significantly poorer household construction as men’s hours tend to move together, particularly well. Households in the upper half of the per capita in agriculture as well as running or managing expenditure distribution also have greater hours in NFEs. Hours in agriculture and wage work tend NFE work. Urban women are again an interesting to increase with per capita expenditure, but then case — having the highest hours in running or decline or flatten out for higher expenditure managing NFEs compared to other groups. Figure 2.3 Within-household distribution of working hours in the last week by sector, by men and women Men Rural Men Urban Women Rural Women Urban Agriculture Wage 15 25 Sum of weekly hours Sum of weekly hours (for each group) (for each group) 20 10 15 5 10 0 5 0 20 40 60 80 100 0 20 40 60 80 100 Percentile of HH nonfood Percentile of HH nonfood per capita expenditure per capita expenditure NFE (run/own) NFE (help) 30 8 Sum of weekly hours Sum of weekly hours (for each group) (for each group) 6 20 4 10 2 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Percentile of HH nonfood Percentile of HH nonfood per capita expenditure per capita expenditure 1 Percentiles of HH per capita expenditure calculated separately for urban and rural areas. 34 LSMS+ Program in Cambodia © World Bank 3. Findings on daily time use: paid and unpaid work burdens The Cambodia LSMS+ included a time use module they were doing anything else at the same time. to shed greater light on men’s and women’s total They repeated these questions until the respondent work burdens, across paid and unpaid work. The indicated that they went to bed for the night. The module was structured as a 24‑hour time diary, time diary allowed for respondents to choose where men and women were asked to report their across 26 activity categories spanning economic activities over the last day. Reporting was set up activity, unpaid work (domestic work, collection of in 15‑minute increments across different activity resources for the household, care of children and categories and allowing for secondary activities adults), as well as leisure, traveling, education, and in time increments with more than one activity rest and sleep. All individuals who reported their conducted simultaneously. Enumerators first asked time use self‑reported; there was a relatively small the respondent what time they woke up and what share of eligible individuals (about 8 percent) that time they went to sleep. Then they began guiding did not respond to the module.12 respondents throughout their day asking for the first activity they did in the morning, an estimate on how long they engaged in the activity, and whether 12 Specifically, there were 313 out of the 3938 eligible individuals who did not respond to the time use module. Section 2. Labor outcomes 35 Table 2.9 Number of hours across main activities, by women/men and rural/urban Number Unpaid Economic Leisure Traveling Schooling Rest/sleep Other of individ‑ work activity/ uals (total) labor Urban men 529 0.9*** 7.2*** 4.4*** 0.9*** 0.2 10.2 0.3 Urban women 631 3.3*** 5.2*** 3.9*** 0.7*** 0.2 10.5 0.2 Rural men 1,119 0.9*** 6.4*** 4.0*** 1.0*** 0.04*** 11.3 0.4 Rural women 1,346 3.6*** 4.3*** 3.7*** 0.7*** 0.15*** 11.2 0.4 1 Time use data collected on the basis of a 24‑hour time diary, for men and women age 18+. 2 Statistically significant differences between men and women within each activity category indicated by asterisks; ***p<0.01 **p<0.05 Table 2.10 Number of hours with unpaid and economic activities (as a main activity), by women/men and rural/urban Unpaid activities Economic activity/labor Number House‑ Collect‑ Child Care of Wage/ NFE Agricul‑ Making Free/ ex‑ of indi‑ hold ing fire‑ care adult HH salary ture goods1 change viduals chores wood/ mem‑ labor2 water bers Urban men 529 0.4*** 0.1 0.5*** 0.01*** 3.4*** 3.0 0.7*** 0.02 0.04 Urban women 631 1.8*** 0.1 1.4*** 0.10*** 2.1*** 2.7 0.4*** 0.02 0.1 Rural men 1,119 0.5*** 0.2 0.3*** 0.01*** 2.8*** 0.9 2.6*** 0.001*** 0.1 Rural women 1,346 2.0*** 0.2 1.3*** 0.04*** 1.6*** 1.1 1.5*** 0.03*** 0.1 1 Time use data collected on the basis of a 24‑hour time diary, for men and women age 18+. All estimates are weighted using the household sampling weight. 2 Making items such as furniture, pottery, baskets, clothing 3 Work for other household free of charge as exchange laborer Table 2.9 presents the average number of hours economic activity. Most of this gender difference for men and women in rural and urban areas, is in wage and salary work in urban areas, and across categories aggregated from the 26 areas both wage and salary as well as agriculture in rural covered in the time diary, and Table 2.10 provides a areas; hours spent in non‑farm enterprise work were more specific breakdown of economic activity and not significantly different across men and women. labor market outcomes, as well as unpaid work. Women in both urban and rural areas spend nearly Significant differences between men and women three hours a day more, on average, in unpaid are also indicated in the tables. work than men, due to greater time spent by women in household chores, as well as childcare. The data show clear differences in men’s and Women also spend significantly more time in care women’s time across paid and unpaid work, of adult household members, although the average with strong similarities across urban and rural amount of time is not substantial compared to areas. While men spend greater time in other areas. Men also spend more time in leisure income‑generating activity, women’s total work activities than women (a statistically significant burdens across paid and unpaid activities are difference of about 20 minutes more per day, on greater, and with less leisure time. Tables 2.9 and average, which if extrapolated to the week overall 2.10 show that men spend about 2 hours a day would be about 2.3 hours more per week). more, on average, than women in labor market and 36 LSMS+ Program in Cambodia Figure 2.4 Men’s and women’s time allocation in the last 24 hours, by age (primary activity) Men Women Unpaid Work Paid Work Leisure 4 8 5.0 3 4.5 6 hours hours hours 2 4.0 1 4 3.5 0 3.0 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 age age age Traveling Schooling Sleeping and Rest 1.6 0.75 12 0.50 1.2 hours hours hours 0.25 11 0.8 0.00 0.4 10 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 age age age 3 Other 0.8 Nearly hours a day more spent by women in both urban and rural 0.6 areas, on average, in unpaid work than men. hours 2 0.4 0.2 About hours a day more spent by men on average, than women 20 30 40 50 60 in labor market/economic activity. age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends for men and women. Section 2. Labor outcomes 37 Figures 2.4‑2.5 present locally weighted These gender gaps, in particular, remain fixed and regressions of the distribution of men’s and do not narrow as men and women age. Gender women’s time use by age, with confidence intervals differences in leisure time are also statistically indicating significant differences. Differences in significant just for the 25‑35 age group. Traveling men’s and women’s unpaid and paid work are time, likely due to work‑related commutes, is also particularly stark in the 25‑35‑year age cohort, highest for men in the youngest group (around 20 likely due to shifting gender roles around marriage years of age), as well as around age 40 – with an and raising children (as seen in Figure 2.5 as well). average of about an hour per day in commuting. Figure 2.5 Men’s and women’s time allocation within unpaid work (as a primary activity) in the last 24 hours, by age Men Women Unpaid Work: Unpaid Work: Unpaid Work: House chores/improve Collect water/firewood Childcare 2.5 0.3 2.0 2.0 1.5 0.2 hours hours hours 1.5 1.0 0.1 1.0 0.5 0.5 0.0 0.0 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 age age age Unpaid Work: Elder care 0.3 There are stark differences 0.2 in men’s and women’s unpaid and paid work, hours 0.1 0.0 likely due to shifting gender -0.1 roles around marriage and 20 30 40 50 60 raising children age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends for men and women. 38 LSMS+ Program in Cambodia © World Bank Finally, Table 2.11 examines time use in the last day among men and women’s participation in off‑farm Ownership of assets, (wage or NFE) work and agriculture, as collected in the labor module. A key finding is that higher and in particular hours in unpaid work among rural respondents — and particularly, women — is the highest among financial accounts, those economically active in agriculture (nearly four hours per day, compared to about an hour motorized vehicles, for men), or not working in any economic activity (about five hours per day, compared to roughly and dwelling land, two hours for men). Among respondents in off‑farm is associated with work, hours in unpaid work does decline, but is still about three hours per day on average for rural less unpaid work for and urban women working in NFEs, compared to less than an hour on average for men; and about women, particularly two hours per day among women in wage work, compared to half an hour for men. For wage work among those also in in particular, this gender difference is particularly pronounced given that men and women work off-farm work similar hours per day in their jobs. Unpaid work burdens are therefore relatively sticky, even for those who are economically active. Rest and leisure Table 2.11 reveals additional constraints on men’s also tend to be less for women; among those in and women’s time, including that urban men and agriculture, women sleep nearly an hour less than women in wage work spend roughly an hour per men per day. Gender differences in leisure time day on average in traveling or commuting, as are also statistically significant across groups, with well as substantial daily travel for rural men and wider gender gaps among urban women and men women in agriculture and NFEs. More detailed data working in NFEs and in agriculture, among whom collection on time use also reveals some additional women spend about half an hour less per day in economic activity (albeit low) among rural men and leisure activities. women, who otherwise report in the labor module that they are not in agriculture or off‑farm work. Section 2. Labor outcomes 39 Table 2.11. Among those working in off‑farm work and/or agriculture: number of hours in main activity over the last day, by women/men and rural/urban Number Unpaid Economic Leisure Traveling Schooling Rest/sleep Other of individ‑ work activity/ uals (total) labor (A) Among those working: NFEs Urban men 191 0.9*** 8.6*** 4.0*** 0.6 0.1 9.6 0.1 Urban women 221 3.1*** 7.4*** 3.3*** 0.5 0.1 9.5 0.1 Rural men 203 0.7*** 7.1 3.8*** 1.1*** 0.0** 10.8*** 0.4 Rural women 236 2.7*** 6.8 3.1*** 0.6*** 0.1** 10.2*** 0.3 (B) Among those working: wage employment Urban men 219 0.5*** 8.2 3.9 1.1** 0.1 10.0 0.2 Urban women 166 1.8*** 7.6 3.6 0.9** 0.1 9.8 0.1 Rural men 446 0.6*** 7.9* 3.6** 1.1 0.0** 10.5** 0.2 Rural women 294 1.7*** 7.5* 3.3** 1.2 0.1** 10.1** 0.1 (C) Among those working: agriculture Urban men 133 0.9*** 6.7*** 4.4*** 0.9 0.0** 10.9 0.2 Urban women 124 3.8*** 4.3*** 3.9*** 0.7 0.1** 10.9 0.2 Rural men 707 0.9*** 6.8*** 3.9*** 0.9*** 0.0*** 11.2** 0.3 Rural women 777 3.7*** 4.6*** 3.6*** 0.7*** 0.2*** 10.9** 0.4 (D) Among those working: in both off‑farm and agriculture Urban men 69 0.8 7.6 4.1 0.9 0.0 10.4 0.2 Urban women 44 2.4 7.3 3.8 0.8 0.1 9.6 0.0 Rural men 318 0.6*** 8.0** 3.5** 0.9 0.0* 10.7*** 0.2 Rural women 228 2.3*** 7.4** 3.1** 0.8 0.1* 10.0*** 0.2 (E) Those not working in either (A) or (B) Urban men 74 2.1 1.3 6.4 0.8 1.0 11.4 0.9 Urban women 169 4.8 0.9 5.0 0.6 0.4 11.9 0.5 Rural men 115 1.7*** 2.0*** 5.2* 0.7** 0.1 13.2* 1.0** Rural women 283 4.7*** 1.0*** 4.6* 0.4** 0.2 12.6* 0.5** 1 Time use data collected on the basis of a 24‑hour time diary, for men and women age 18+. All estimates are weighted using the household sampling weight. 2 Statistically significant differences between men and women within each group indicated by asterisks; ***p<0.01 **p<0.05. 3 Estimates in italics have less than 100 observations and tests of statistical significance not conducted. 1 hour spent roughly on average by urban men and women in wage work in traveling or commuting, as well as substantial per day daily travel for rural men and women in agriculture and NFEs. 40 LSMS+ Program in Cambodia © World Bank Section 2 / Summary + + + To improve the accuracy The LSMS+ labor module revealed important trends The LSMS+ time use module, of labor statistics, including across men and women: structured as a 24‑hour intra‑household statistics, time diary, sheds greater the LSMS+ labor and time - Most working men and women were concentrated light on total (paid as well use modules emphasized in wage employment and agriculture, with men as unpaid) work burdens of self‑reporting as opposed significantly more likely to participate in these men and women. Women’s to proxy reporting, as well activities, particularly in wage work. Women were time spent in unpaid work as one‑on‑one interviews also significantly less likely to engage in agricultural tends to be at least double that aim for gender matching activities mainly for market or sale, and hence to that of men’s, with much between enumerators and be counted as employed in agriculture, following a wider gaps among those respondents. narrowed definition of employment to work for pay or working in agriculture profit under the 19th ICLS. or not working. Across + - For NFEs, on the other hand, a substantial, and urban and rural areas, these Self‑reporting was quite gender gaps also persist high in the LSMS+ labor roughly similar, share of men and women owned or regardless of whether they module (90 percent for men managed non‑farm enterprises (NFEs) in the past are economically active in overall, and 95 percent for year. Women worked greater hours in this activity than agriculture or in no work. women, with higher shares in men, and rural women, in particular, continue run or Given smaller differences urban areas). Self‑reporting manage NFEs well into older age. Owning or managing among economically active is highest among the one’s own enterprise also tended to be associated men and women in their time elderly, and lower among with better individual and household socioeconomic spent in income‑generating younger men in particular characteristics for men and women, compared to wage work, these trends reveal (among men age 18‑25, for and agricultural work. higher overall time spent example, the incidence of - The data also reveal insights on participation in across paid and unpaid work self‑reporting was 87 percent multiple economic activities and intra‑household for working women in both in urban areas, and 76 distribution of labor, as well as strong links between urban and rural areas. percent in rural areas). Within labor participation and individual ownership of assets the time use module, all such as land, financial accounts and mobile phones respondents self‑reported. — all of which were collected in the LSMS+ assets modules. Section 2. Labor outcomes 41 © World Bank 42 LSMS+ Program in Cambodia Section 3 Asset ownership and rights 1. The importance of individual‑level interviews in measuring asset ownership and rights 2. Asset survey questions in the LSMS+ 3. Overview of the LSMS+ 3.1. What patterns emerge across men and women? 3.2. Bundles of land ownership and rights 3.3. Reporting discrepancies among couples 4. Ownership of livestock and consumer durables 5. Financial account and mobile phone ownership Section 3 / Summary Section 3. Asset ownership and rights 43 1. The importance of individual‑level interviews in measuring asset ownership and rights International momentum behind improving the This work ultimately resulted in the United availability and quality of individual‑disaggregated Nations Guidelines for Producing Statistics on survey data on asset ownership and control has Asset Ownership from a Gender Perspective accelerated, in large part to the United Nations (UNSD, 2019),14 which emphasize (a) moving Evidence and Data for Gender Equality (EDGE) away from the common practice of relying on a initiative. Specifically, the UN EDGE initiative “most knowledgeable” household member who supported survey experiments and pilots across also reports for other household members; (b) countries between 2014‑16, including the emphasizing interviewing multiple adults per Methodological Experiment on Measuring Asset household; and (c) one‑on‑one (as opposed to Ownership from a Gender Perspective (MEXA) in non‑private interviews) regarding respondents’ Uganda,13 aimed at understanding how to better personal ownership of and rights to assets, either collect individual‑level data on asset ownership exclusively or jointly with someone else.15 Overall, and rights. individual interviews following recommendations under 2019 UN guidelines on self‑reported data over different types of ownership and rights, and exclusive or joint roles, (a) provide a clearer picture of ownership of and rights to assets within households, particularly among women; (b) minimize distortionary proxy respondent effects and intra‑household discrepancies in reporting; and (c) can reveal hidden assets. 14 The guidelines can be accessed at https://unstats.un.org/edge/ publications/docs/Guidelines_final.pdf. 15 Also see related work by Grown et al. (2005) and Doss et al. (2011): Grown, C., Rao Gupta, G., and Kes, A. (2005). Taking action: achieving gender equality and the millennium development goals. London: Earthscan Publications.; and Doss C., Deere, C. D., Oduro, A., Swaminathan, H., Suchitra, J., Lahoti, R., Baah‑Boateng, W., Boakye‑Yiadom, L., Twyman, J., Catanzarite, Z., 13 Additional country pilots supported by UN EDGE were implemented Grown, C., and Hillesland, M. (2011). “The gender asset and wealth by the national statistical offices across Georgia, Maldives, Mexico, gaps: evidence from Ecuador, Ghana, and Karnataka, India.” Bangalore: Mongolia, the Philippines and South Africa. Indian Institute of Management. @ photo credit 44 LSMS+ Program in Cambodia © World Bank During survey administration, the head of household The LSMS+ supported surveys are based on these and his or her spouse guidelines. During survey administration, the head of household and his or her spouse (if one exists) (if one exists) were were always among the individuals interviewed; within‑household interviews were also always always among administered in private and were attempted to be administered simultaneously and if possible with the individuals gender matching between the enumerator and respondent.16 Within Malawi, comparisons of the interviewed LSMS+ survey with a concurrent national survey have revealed some important initial findings on men’s and women’s land ownership and rights. Kilic et al. (2020b)17, specifically, compared the Malawi and rights — finding that the IHS4 resulted in LSMS+/IHPS with the concurrent IHS4 that asked higher rates of exclusive reported and economic only one “most knowledgeable” respondent about ownership of agricultural land among men, household members’ agricultural land ownership and lower rates of joint reported and economic ownership among women. Malawi was also a 16 For more information on the organization and implementation of unique case where this comparison across survey the individual‑disaggregated data collection as part of the IHPS, please consult the survey’s basic information document, which approaches could be made, since the IHS4 was can be accessed here: https://microdata.worldbank.org/index.php/ conducted at the same time, and with the same catalog/2939/download/47216. 17 Kilic, Talip, Heather Moylan, and Gayatri Koolwal. 2020b. “Getting the questionnaire format as the non‑dwelling land (Gender‑Disaggregated) Lay of the Land: Impact of Survey Respondent assets module in the LSMS+, but with a different Selection on Measuring Land Ownership and Rights.” World Bank Policy Research Working Paper 9151. interview approach. Section 3. Asset ownership and rights 45 2. © World Bank Asset survey questions in the LSMS+ The LSMS+ modules delve into intra‑household ownership across different types of assets — and in turn highlight important patterns of ownership and decision‑making that can inform policy efforts to expand access to financial services, land, and property rights in general. For all countries where the LSMS+ has been implemented, individual‑level modules on assets span (a) ownership and rights to land parcels18, as well as (b) respondents’ Respondents are also asked about perceived ownership (exclusive or joint) over other assets tenure security. The questions on rights were including financial accounts, and mobile phones.19 not asked of the respondent if he/she did not name himself/herself as a reported owner for a On land, both dwelling and non‑dwelling land given parcel.21 Respondents are also asked to are covered in the Cambodia survey. For identify, in the case of joint ownership or where non‑dwelling land, a roster of parcels is formed for permission is needed to exercise rights, up to each household, and then carried forward to the three household members and the numbers of individual interviews. For each parcel, respondents male and female non‑household members who are asked about different types of ownership share ownership or give permission. Questions on (reported, economic, and documented); rights (to the household dwelling follow the same structure sell, bequeath, use as collateral, rent out, and make as the parcel module (following household‑level improvements or invest); as well as decision‑making questions on the dwelling and then asking the in the case of agricultural parcels (Box 3.1).20 same individual‑level questions on different types of ownership and rights). 18 A parcel is defined as a continuous piece of land which can have more than one plot. Among other asset classes, including financial 19 The module on land, specifically, addresses the data needs for both SDG accounts and mobile phones, respondents were indicators 1.4.2 and 5.a.1 – covering all land owned or accessed via use asked about whether they owned these assets rights, and following recent recommendations in 2019 by the Food and Agricultural Organization (FAO), the World Bank, and UN Habitat. The exclusively or jointly with others (and, if jointly, with modules on mobile phones and financial accounts cover SDG indicators which other household members). 5.b.1 and 8.10.2. 20 Along with rights and ownership, respondents reported on how each parcel was acquired; identified the individuals from whom the asset was 21 The scope of rights included in the questionnaire was influenced by inherited or received as a gift, as applicable; and provided the current Schlager and Ostrom’s (1992) theoretical framework which focuses, hypothetical sales value for each asset (and the construction costs in the context of natural resources, on issues related to access, specifically for the dwelling) and limited information on their knowledge withdrawal, management, exclusion and alienation while defining a of asset transactions in their communities. bundle of rights. 46 LSMS+ Program in Cambodia Box 3.1 LSMS+ questions over parcel ownership and rights, as well as decision‑making Reported Economic Documented Rights1 Decision‑making owner owner owner (agricultural With regard to this [PARCEL], parcels) Do you own this If this [PARCEL] were Does your household are you among the [PARCEL], either to be sold today, have a document for individuals who have this Are you among the alone or jointly with would you be among this [PARCEL], such right, even if you need to decision‑maker(s) on someone else? the individuals to as an application obtain consent or permission this [PARCEL] regarding (If jointly, list others – decide how the receipt, land from someone else? the timing of crop up to three members money is used? investigation paper, activities, crop choice, If yes, do you need from HH roster, and (if others also certificate (title) from and input use? permission or consent from up to two non‑HH involved, list up the government, (if others also involved, anyone else (name those members) to three members paper from local list up to three members members; and up to two from HH roster, and authority, lease or from HH roster, and non‑HH members)? up to two non‑HH rental contract? up to two non‑HH members) With regard to this [PARCEL], members) Are you listed on the who else has this right, even title or ownership if they needed to obtain document as owner consent or permission from of this parcel? someone else? Does the (If others also listed, person need permission or name up to three consent? From whom does members from HH the person need permission roster, and up to two or consent? non‑HH members) 1 Questions on rights are asked separately for rights to sell, bequeath, use as collateral, rent out, and make improvements/invest in it. © World Bank Section 3. Asset ownership and rights 47 3. © World Bank Ownership and rights of land and housing 3.1. What patterns emerge across men and women? What do the LSMS+ supported surveys reveal on men’s and women’s land ownership and rights? Figure 3.1 presents data capturing exclusive versus joint ownership and rights of non‑dwelling and dwelling land across urban and rural areas, respectively. The figure also includes a dichotomous variable, SDG owner, based on the definition of the SDG indicator 5.a.1, namely if the individual is a documented owner, has the right to sell, or has the right to bequeath related to any parcel. Shares of ownership and rights are broken out by women and men overall, as well as for women non‑heads of household.22 Adjusted Wald tests for equality of means were conducted across these three groups, with significant differences (p<0.05) indicated by darker‑colored bars. In Cambodia, dwelling land ownership tends to be greater than non‑dwelling land ownership, and rural areas tend to have greater ownership of both types of land. Reported and economic ownership also tend to follow similar patterns. In urban areas, for example, about 67 percent of men and 72 percent of women had reported or economic ownership of dwelling land (adding exclusive and joint shares); in rural areas these shares were 77 percent for men and 79 percent for women. For non‑dwelling land, about 40 percent of urban men and women had reported or economic ownership, compared to 62 percent of rural men and women. 22 As compared to women where there was greater diversity in household status by head, spouse, or other household members, nearly all men reporting ownership were household heads. 48 LSMS+ Program in Cambodia Figure 3.1 Shares of men and women with different ownership and rights, non-dwelling and dwelling land (Darkened bars = significant gender differences at p<0.05)1 All men All women Non - HH head women Non-dwelling land - urban Non-dwelling land - rural 0.8 0.8 0.7 0.7 ** 0.6 0.6 ** 0.5 0.5 ** ** ** ** ** 0.4 ** 0.4 0.3 ** 0.3 ** 0.2 0.2 0.1 0.1 0 0 Exclusive Joint Exclusive Joint Exclusive Joint Exclusive Joint Reported Economic SDG Reported Economic SDG Owner Owner Dwelling land - urban Dwelling land - rural 0.8 0.8 ** ** ** 0.7 0.7 ** ** ** 0.6 0.6 ** ** 0.5 ** ** 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 Exclusive Joint Exclusive Joint Exclusive Joint Exclusive Joint Reported Economic SDG Reported Economic SDG Owner Owner 1 Dark blue bars with ** reflect cases where men’s outcomes are significantly different (p<0.05) from (a) the full sample of women (when significant, indicated by dark orange bars with**), or (b) from women who are not household heads (when significant, indicated by black bars with **). In all other cases (light colored bars) there were no significant differences across these two sets of groups. All estimates are weighted using household sampling weights. 2 “SDG owner” is based on the definition of the SDG indicator 5.a.1. (takes the value one if the individual is a documented owner, has the right to sell, or has the right to bequeath). Figure 3.1 also reveals a substantially higher share property acquired by one or both spouses during of joint as opposed to exclusive ownership, for marriage, but excludes property obtained by either both dwelling and non‑dwelling land, and women spouse before the marriage, as well as property who are not household heads in fact tend to have either spouse receives as a gift or inheritance during significantly higher joint reported and economic the marriage.23 ownership of land than men — as well as being SDG 23 van der Keur, Dorine, 2014. “Legal and Gender Issues of Marriage and owners. This is due in large part to the Cambodian Divorce in Cambodia.” The Cambodia Law and Policy Journal, vol 2. Civil Code, under which joint property includes all http://cambodialpj.org/article/ legal‑and‑gender‑issues‑of‑marriage‑and‑divorce‑in‑cambodia/ Section 3. Asset ownership and rights 49 Findings from Cambodia further underscore the These patterns can also vary by other demographic importance of local customs on understanding and socioeconomic characteristics; as an initial look, patterns in land ownership and rights. Joint ownership Figure 3.2 presents, by dwelling and non‑dwelling tended to be higher than exclusive ownership in land, how land ownership and rights vary by age.24 the LSMS+ Sub‑Saharan Africa countries, except in Up to about 40 years of age, women’s ownership Malawi, where matrilineal customs favor women’s land and rights over dwelling and non‑dwelling land is ownership and rights — and, as a result, the share of higher than men’s, but after about age 50 the trend women with exclusive ownership of non‑dwelling land reverses, and women’s ownership and rights decline. was much higher than that for men, and also higher in A closer look at the data (Figure 3.3) reveals that turn compared to the share of men and women with this gender difference among older men and women joint ownership. Gender inequalities in ownership is largely due to a greater decline in women’s joint and rights tended to be much greater in Ethiopia and ownership relative to men; the share of older women Tanzania. with exclusive ownership is relatively higher and increasing with age, although outmatched by the decline in older women’s joint ownership. 24 The age distribution was cut off at 65, since there were fewer respondents beyond this threshold. Figure 3.2 Land ownership and rights, by age of respondents Economic Reported SDG Economic Reported SDG Men Men Men Women Women Women Non−Dwelling Dwelling 0.6 0.8 Share of respondents Share of respondents 0.7 0.5 0.6 0.4 0.5 20 40 60 80 20 40 60 80 age age 1 Estimates are weighted using the household sampling weight. 40 50 Up to Over age age women’s ownership and rights Women’s ownership and rights over dwelling and non‑dwelling tend to decline as they are land is higher than men’s. older. 50 LSMS+ Program in Cambodia © World Bank Figure 3.3 Exclusive versus joint reported ownership of land, among older age groups Joint Men Exclusive Men Joint Women Exclusive Women Non−Dwelling Dwelling 0.6 0.75 Share of respondents Share of respondents 0.4 0.50 0.2 0.25 0.0 0.0 40 50 60 70 80 40 50 60 70 80 age age 1 Estimates are weighted using the household sampling weight. 95% confidence intervals are presented for trends for men and women. Ownership of land in Women who are not household heads also had significantly higher joint Cambodia is reported and economic ownership of land than men, as well as different mainly joint. rights over land. Section 3. Asset ownership and rights 51 © World Bank 3.2. Bundles of land ownership and rights 40 of respondents % claiming all ownership and rights Figure 3.4 presents the share of women and men with different bundles of ownership and rights, for dwelling as well as non‑dwelling land.25 In Cambodia, about 40 percent of men and women claim all ownership and rights to non‑dwelling land; for dwelling land, this share is about 50 percent. As seen in the LSMS+ supported surveys in over land for non-dwelling land Sub‑Saharan Africa as well, economic and reported ownership are linked closely together, as well as rights to sell or bequeath, with respondents either 50 % having both or neither. Among owners, about 72 percent of men and women had rights to sell and bequeath non‑dwelling land; for dwelling land, these shares were about 70 percent of women and 68 percent of men. Overall, gender differences in bundles of ownership and rights tend to be small, of respondents particularly for non‑dwelling land—for dwelling land, a slightly higher share of women than men have ownership as well as rights to sell and bequeath. claiming all ownership and rights over land for dwelling land 25 For both non‑dwelling and dwelling land, rural/urban patterns for Cambodia matched the patterns for the total sample fairly closely. 52 LSMS+ Program in Cambodia Figure 3.4 Share of women and men with different bundles of ownership and rights Men Women Non-dwelling land ALL OWNERSHIP RIGHTS * Reported, no economic OWNERSHIP Neither reported nor economic Reported and economic Among owners, RIGHTS Neither sell nor bequeath Both sell and bequeath Bequeath, no sell Sell, no bequeath 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 Dwelling land ALL OWNERSHIP RIGHTS * Reported, no economic OWNERSHIP Neither reported nor economic Reported and economic Among owners, RIGHTS Neither sell nor bequeath Both sell and bequeath Bequeath, no sell Sell, no bequeath 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 All estimates are weighted using household sampling weights. 2 Because of the order in which questions were administered, no respondents across countries had economic but not reported ownership. Section 3. Asset ownership and rights 53 3.3. Reporting discrepancies among couples The individual‑level data collection approach In Cambodia, most couples agree that both the in the LSMS+ allows greater insight into husband and wife do share ownership (reflecting intra‑household economic outcomes (as seen the overall trend of joint ownership in the survey with men’s and women’s labor participation as sample), or that neither have ownership. The well in Section 2), as well as on the extent of majority of couples report that both the husband agreement and disagreement over such issues and wife have rights to bequeath and sell land. The as asset ownership and rights. As seen in the small share of parcels where couples disagree tend LSMS+ supported surveys in Sub‑Saharan Africa, to be focused on disagreements over rights to sell as well as Figure 3.5, the share of parcels and bequeath (about 14‑15 percent of couples), and where couples agree on ownership and rights mainly that the husband reports he has exclusive over land is quite high —above 90 percent for rights to sell and bequeath, whereas the woman reported and economic ownership in Cambodia, reports that she has exclusive rights. as well as around 80 percent for rights to Within the LSMS+ supported surveys in sell and bequeath. The general consistency in Sub‑Saharan Africa, prevailing gender differences within‑couple reporting helps address potential in ownership and rights claimed by men and concerns over within‑household disagreement women also tend to be associated with the obscuring an assessment of individuals’ true direction of disagreement (whether the husband ownership and rights — although recent studies or wife claims a greater role, for example). In (Annan et al., 2019; Kilic et al., 2020) have also Cambodia, there is a much stronger direction found that intra‑household disagreement over towards agreement on joint or no ownership, and decision‑making or ownership and rights can yield disagreement, while minimal, is focused on rights important information on women’s empowerment. over land as opposed to ownership. © Chhor Sokunthea / World Bank 54 LSMS+ Program in Cambodia Figure 3.5 Share of parcels where spouses agree, and disagree, over land ownership and rights (dwelling land)1 Bequeath Sell Economic Reported Dwelling land Disagreement (most common scenarios) Total (disagreement) Husb: J, Wife: Doesn’t own Husb: H, Wife: W Husb: H, Wife: J Total (agreement) No ownership Agreement Wife Joint Husband 0.0 0.2 0.4 0.6 0.8 1.0 90 For about % of land 1 Results were the same for non-dwelling land. H = husband, W = wife, J = joint. 2 All estimates are weighted using household sampling weights. 3 In Ethiopia, right to sell was not asked for non-dwelling land. 4 In Malawi, only ownership and rights of non-dwelling land were asked, parcels and in particular only of agricultural households. couples agree on who owns the land, for reported as well as economic ownership. Section 3. Asset ownership and rights 55 4. Ownership of livestock and consumer durables The Cambodia LSMS+ included a module on Table 3.1 Share of men and women owning exclusive and joint ownership of large livestock large livestock, in rural and urban areas1 (which was also included in the Ethiopia LSMS+). Table 3.1 presents differences in ownership across Women Men men and women, as well as by rural and urban (1) (2) areas. The share of respondents owning large Rural livestock was, as might be expected, much higher in Overall 0.510*** 0.457*** rural areas. Looking at gender differences, women (0.500) (0.498) in rural areas were actually significantly more likely to own large livestock (51 percent of rural women, Exclusive 0.256*** 0.194*** compared to 46 percent of men; this was largely (0.437) (0.395) due to higher exclusive ownership among women). Joint 0.269 0.275 This is similar to the findings discussed above on (0.444) (0.447) land, where women were slightly more likely than Observations 1,438 1,272 men in Cambodia to have ownership and rights to non‑dwelling and dwelling land, at least among Urban younger age groups. While the findings from rural Overall 0.167** 0.203** Cambodia are in stark contrast to the LSMS+ survey (0.374) (0.403) conducted in Ethiopia — where rural men were significantly more likely than rural women to own Exclusive 0.066*** 0.081*** large livestock — the urban findings are roughly (0.249) (0.274) similar to Ethiopia. Specifically, about 17 percent Joint 0.108 0.126 of urban women, and 20 percent of urban men (0.310) (0.332) reported ownership of livestock in Cambodia, with Observations 655 573 the gender difference stemming mainly from a small increase in exclusive ownership among urban men. 1 All estimates are weighted using household sampling weights. Standard deviations in parentheses. Statistically significant differences between men and women indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10). 2 Livestock categories included the following: bulls, oxen, cows, steers, heifers, calves, goats, sheep, camels, horses, mules, and donkeys. 3 Because a household can own multiple livestock, a respondent could have exclusive as well as joint ownership of different animals. 51 of women owned % large livestock, © World Bank compared to 46 percent of men. 56 LSMS+ Program in Cambodia © World Bank Compared to land and livestock, however, ownership of larger consumer durables — across computers and vehicles, for example — was substantially higher for men, and across a wider range of durables categories in rural areas (Table 3.2). The individual‑level LSMS+ interviews covered ownership of computers, as well as vehicles across bicycles, motorcycles, cars, and more traditional forms of transport such as tuk‑tuks; ownership of boats and tractors was also asked among respondents. Looking at Table 3.2, ownership overall varied widely across categories — ownership of computers, and cars, for example, was relatively low, and much larger shares of respondents owned bicycles and motorcycles. A common Table 3.2 Share of men and women owning large livestock, theme is greater ownership for men, in rural and urban areas1 particularly in rural areas, with the exception of lower‑cost vehicles such Rural areas Urban areas as bicycles where rural women had Rural Rural Urban Urban higher shares of ownership. In urban women men women men areas, men were significantly more (1) (2) (3) (4) likely to own computers (11 percent Computer 0.011** 0.023** 0.055*** 0.109*** of men, compared to 6 percent of (0.11) (0.15) (0.23) (0.31) women), motorcycles (83 percent Bicycle 0.407*** 0.340*** 0.228 0.213 compared to 64 percent), and cars (0.49) (0.47) (0.42) (0.41) (20 percent compared to 15 percent). Motorcycle 0.587*** 0.766*** 0.635*** 0.825*** (0.49) (0.42) (0.48) (0.38) Car 0.035*** 0.053*** 0.147*** 0.198*** 11 % (0.18) (0.22) (0.35) (0.40) Tuk‑tuk2 0.021** 0.029** 0.045 0.053 (0.14) (0.17) (0.21) (0.22) Boat 0.037 0.046 0.014 0.024 (0.19) (0.21) (0.12) (0.15) of men Tractor 0.148*** (0.36) 0.191*** (0.39) 0.024* (0.15) 0.031* (0.17) in rural areas Observations 1,438 1,272 655 1 All estimates are weighted using household sampling weights. Standard deviations in 573 were significantly more likely parentheses. Statistically significant differences between men and women indicated by to own computer, compared to asterisks (***p<0.01, ***p<0.05, * p<0.10). 6 percent of women. 2 A Tuk‑tuk is a traditional form of transport similar to a rickshaw, i.e. carriage pulled by a motorcycle. Section 3. Asset ownership and rights 57 © World Bank 5. Financial Looking first at mobile phones, the share of ownership is quite high – about 75 and 90 account and percent for rural women and men, respectively, as well as about 85 and 95 percent of urban women mobile phone and men. Joint ownership of mobile phones was also substantial (and greater for rural areas — about 40 and 50 percent of rural women and ownership men, respectively, compared to about 30 percent of urban women and men). This is a considerable difference vis‑a‑vis the findings from the LSMS+ supported surveys in Sub‑Saharan Africa, where Figure 3.6 presents financial account and mobile almost all ownership of mobile phones was phone ownership among men and women from exclusive. For financial accounts, only about eight the LSMS+ modules, and also breaks this down percent of rural women and five percent of rural by urban and rural areas. Men were significantly men owned a financial account; in urban areas more likely than women to own mobile phones in these shares were about 21 and 30 percent, rural and urban areas, as well as financial accounts respectively. Financial account ownership in urban in urban areas, although the differences are not areas was primarily exclusive. as large as in other LSMS+ supported surveys in Overall, the evidence from Cambodia shows Ethiopia, Tanzania, and Malawi.26 substantial joint ownership of different types of assets, across dwelling and non‑dwelling land, 26 In Ethiopia, for example, 50 percent of men owned a mobile phone, as well as other assets such as mobile phones. compared to 27 percent of women; these shares were 78 and 58 Women also tend to have greater asset ownership percent, respectively, in Tanzania, and 56 and 36 percent, respectively, in and rights for dwelling and non‑dwelling land, Malawi. In Cambodia, differences were not statistically significant across men and women in for financial account ownership in rural areas. 58 LSMS+ Program in Cambodia Figure 3.6 Share of women and men owning a mobile phone and financial account Men Women Mobile phone ownership - rural Mobile phone ownership - urban 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 Overall Exclusive Joint Overall Exclusive Joint ownership ownership ownership ownership ownership ownership Financial account ownership - rural Financial account ownership - urban 0.5 0.5 0.45 0.45 0.4 0.4 0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 0 0 Overall Exclusive Joint Overall Exclusive Joint ownership ownership ownership ownership ownership ownership 1 All estimates are weighted using household sampling weights. 2 All differences between men and women were statistically significant, with the exception of financial account ownership in rural areas. ownership of as well as livestock (in rural areas) than in the financial accounts 8 5 % % LSMS+ supported surveys in Sub‑Saharan Africa — although as seen earlier, gender differences do emerge in Cambodia among older age groups in land ownership and rights. Additional individual‑level data on other asset classes, however, including consumer durables, mobile phones, and financial accounts, reveals gender women in rural areas men in rural areas 21 30 inequalities in the opposite direction. Further investigation using the breadth of data across the different socioeconomic and demographic modules in the LSMS+ can shed further light on important distributional aspects of asset ownership and rights % % for men and women. women in urban areas men in urban areas Section 3. Asset ownership and rights 59 © World Bank Section 3 / Summary + Within the LSMS+ supported surveys, individual-level modules on land span exclusive and joint ownership (reported, economic, and documented) and rights (sell and bequeath) over dwelling and non-dwelling land. Individual modules on financial accounts and mobile phone ownership also ask about exclusive versus joint roles. Findings on land ownership and rights: Findings on livestock ownership: - In Cambodia, dwelling land ownership tends to be greater - Rural women were also significantly more likely to own large than non-dwelling land ownership, and rural areas also tend livestock (51 percent of rural women, compared to 46 percent to have greater ownership of both types of land. Shares of men; this was largely due to higher exclusive ownership of respondents who own land and have rights over land among women). In urban areas, however, a slightly greater are quite high. For dwelling land, for example, roughly 70 share of men (20 percent, compared to 17 percent of women) percent of urban men and women had reported or economic reported large livestock ownership. ownership; in rural areas the share was around 78 percent. For non-dwelling land, about 40 percent of urban men and Findings on consumer durables: women had reported or economic ownership, compared to 62 percent of rural men and women. - Compared to land and livestock, however, ownership of larger consumer durables — across computers and vehicles, - Because of civil law designating most assets acquired during for example — was substantially higher for men, and across marriage as joint, a substantially higher share of reported and a wider range of durables categories in rural areas (with the economic land ownership (across dwelling and non-dwelling exception of lower-cost vehicles such as bicycles, where rural land) is joint as opposed to exclusive. women reported greater ownership). - Women who are not household heads tend to have significantly higher joint reported and economic ownership of Findings on financial account and mobile phone ownership: land than men — as well as being SDG owners (documented owner, or with rights to sell or bequeath). Women’s land - The share of mobile phone ownership is quite high – about ownership and rights do decline in older age, however, 75 and 90 percent for rural women and men, respectively, as relative to men, due largely to a large decline in joint land well as about 85 and 95 percent of urban women and men. ownership. Financial account ownership, on the other hand, was much lower—less than 10 percent in rural areas, and in urban areas - As with the LSMS+ supported surveys in Sub-Saharan Africa, about 21 percent of women and 30 percent of men owned economic and reported ownership are linked closely together a financial account. Joint ownership of mobile phones was in Cambodia, as well as rights to sell or bequeath; and a also high compared to other LSMS+ supported surveys in high share of married couples in Cambodia agree on land Sub‑Saharan Africa, and particularly in rural areas. ownership (more than 90 percent of parcels) and rights (more than 80 percent of parcels). 60 LSMS+ Program in Cambodia Annex 1. SDG targets and indicators requiring individual‑level data on economic outcomes Goal 1. End poverty in all its Goal 5. Achieve gender small‑ and medium‑sized enterprises, forms everywhere equality and empower all including through access to financial women and girls services. Target 1.2 / By 2030, reduce at least by 8.3.1 / Proportion of informal employment half the proportion of men, women and Target 5.4 / Recognize and value unpaid in non‑agriculture employment, by sex. children of all ages living in poverty in care and domestic work through the all its dimensions according to national provision of public services, infrastructure Target 8.5 / By 2030, achieve full and definitions (also Target 1.1 on eradicating and social protection policies and the productive employment and decent work extreme poverty for all). promotion of shared responsibility within for all women and men, including for young 1.2.1 / Proportion of population living below the household and the family as nationally people and persons with disabilities, and the national poverty line, by sex and age. appropriate. equal pay for work of equal value. 5.4.1 / Proportion of time spent on unpaid 8.5.1 / Average hourly earnings of female Target 1.4 / By 2030, ensure that all men and male employees, by occupation, age domestic and care work, by sex, age and and women, in particular the poor and the and persons with disabilities. location. vulnerable, have equal rights to economic 8.5.2 / Unemployment rate, by sex, age resources, as well as access to basic services, Target 5.a / Undertake reforms to and persons with disabilities. ownership and control over land and other give women equal rights to economic forms of property, inheritance, natural resources, as well as access to ownership Target 8.6 / By 2020, substantially reduce resources, appropriate new technology and and control over land and other forms of the proportion of youth not in employment, financial services, including microfinance. property, financial services, inheritance education or training. 1.4.2 / Proportion of total adult population and natural resources, in accordance with 8.6.1 / Proportion of youth (age 15–24 with secure tenure rights to land, (a) with national laws. years) not in education, employment or legally recognized documentation, and (b) 5.a.1 / (a) Proportion of total agricultural training. who perceive their rights to land as secure, population with ownership or secure rights Target 8.10 / Strengthen the capacity by sex and type of tenure. over agricultural land, by sex; and (b) share of domestic financial institutions to of women among owners or rights‑bearers encourage and expand access to banking, of agricultural land, by type of tenure. Goal 2. End hunger, achieve insurance and financial services for all. food security and improved Target 5.b / Enhance the use of enabling 8.10.2 / Proportion of adults (15 years nutrition and promote technology, in particular information and and older) with an account at a bank sustainable agriculture communications technology, to promote or other financial institution or with a the empowerment of women. mobile‑money‑service provider. Target 2.3 / By 2030, double the 5.b.1 Proportion of individuals who own a agricultural productivity and incomes of mobile telephone, by sex. Goal 10. Reduce inequality small‑scale food producers, in particular women, indigenous peoples, family within and among countries farmers, pastoralists and fishers, including Goal 8. Promote sustained, Target 10.2 / By 2030, empower and through secure and equal access to land, inclusive and sustainable promote the social, economic and political other productive resources and inputs, economic growth, full and inclusion of all, irrespective of age, sex, knowledge, financial services, markets productive employment and disability, race, ethnicity, origin, religion or and opportunities for value addition and decent work for all economic or other status. non‑farm employment. 10.2.1 / Proportion of people living below Target 8.3 / Promote 2.3.1 / Volume of production per labor 50 percent of median income, by sex, age development‑oriented policies that unit (day) by classes of farming/pastoral/ and persons with disabilities. support productive activities, decent job forestry enterprise size. creation, entrepreneurship, creativity 2.3.2 / Average income of small‑scale and innovation, and encourage the food producers, by sex and indigenous formalization and growth of micro‑, status. 61 Annex 2. International momentum on improving the measurement of individual asset ownership and labor outcomes Asset ownership and control Work, employment and enterprise activity + + The United Nations Evidence and Data for Under recent changes to the international definitions of work and Gender Equality (EDGE) Project supported the employment under the 19th Conference of International Statisticians (19th implementation and analysis of the Methodological ICLS) only paid work is counted as employment — but the new standards Experiment on Measuring Asset Ownership from in turn emphasize the need for surveys to measure individuals’ total work a Gender Perspective (MEXA) in Uganda (Kilic and burdens across paid and unpaid activities. Respondent selection will Moylan, 2016) testing five different approaches be an important issue when incorporating these changes in surveys, to to respondent selection. In particular, MEXA make sure that important areas of work – particularly for women, such as devised questionnaire modules to elicit different contributing work in the family farm or enterprise – are not undercounted. types of asset ownership and rights, as well as understand the importance of interviewing respondents individually as opposed to relying + ILO’s Recommendation No. 204 on transitioning from the informal to on a proxy or “most knowledgeable” household formal economy is particularly relevant for women, who according to the member. MEXA, in turn, informed the design of ILO’s 2018 global report on Women and men in the informal economy: the EDGE‑supported country pilots that were a statistical picture, are among the most heavily engaged in informal implemented by the national statistical offices employment, particularly in rural areas — casting them as a crucial across Georgia, Maldives, Mexico, Mongolia, demographic as countries look to develop strategies for targeting and Philippine and South Africa. collecting data on informal employment. + + Findings from the EDGE pilots ultimately The recently‑adopted International Classification of Status in Employment culminated in the 2019 United Nations Guidelines (ICSE‑18) during the 20th ICLS also has strong implications for women for Producing Statistics on Asset Ownership from a engaged in informal employment. ICSE‑18 creates a new employment Gender Perspective. category on dependent contractors that would capture homeworkers or workers in supply chains, as well as sub‑categories to allow identification + of employees with non‑standard employment arrangements (including A 2019 methodological note by the World Bank, seasonal, casual and short-term workers). ICSE‑18 also places priority FAO and UN Habitat, Measuring Individuals’ on a survey question on place of work, which will also improve data on Rights to Land: An Integrated Approach to Data home‑based work. Collection for SDG Indicators 1.4.2 and 5.a.1, also targets national statistical offices (NSOs) and other survey practitioners on implementing survey + The 2018 Technical Report on Measuring Entrepreneurship: Lessons modules to capture these indicators, with a focus Learned from the EDGE Project is a step towards developing guidance for on land owned. individual‑disaggregated data on entrepreneurship, based on data from the EDGE pilot countries. Guidance will also be based on the definition of entrepreneurship in the recently‑adopted Resolution I concerning statistics on work relationships of the 20th ICLS, as well as ICSE‑18 (also adopted under Resolution I), that can also help improve cross‑country comparability of statistics in this area. 62 © World Bank 63 Acknowledgments This work was made possible by funding from the Umbrella Facility for Gender Equality Trust Fund, the World Bank Trust Fund for Statistical Capacity Building, and the International Fund for Agricultural Development. The authors would like to thank Caren Grown, Kathleen Beegle, and Calogero Carletto for their valuable inputs into the design and implementation of Living Standards Measurement Study – Plus (LSMS+) program activities. We are grateful to Amparo Palacios-Lopez, Isis Gaddis, and Sydney Gourlay for their inputs into the design of the questionnaires for the surveys that have been supported by the LSMS+ program over the period 2016-2020. We would like to thank (i) Hiroki Uematsu, Helle Buchhave, and Dimitria Gavalyugova for their comments on the earlier version of this report, (ii) Pietro Bartoleschi and Cristina Vitelli for creating the visual identity of this report and transitioning the content into the publication template, (iii) Ilaria Lanzoni and Giulia Altomare for their support throughout the publication process, and (iv) Sile O’Broin for her editorial review of the earlier version of this report. Our heartfelt thanks and gratitude are due to (i) the National Institute of Statistics (NIS) Cambodia LSMS+ Survey management team members: H.E. Khin Song, Chinda Phan, Vanndy Nor, Kim Net, Mao Po, Tonnere So, Mao Chhem, Ly Sophanith, Khieu Khemarin, Tep Sakmakara, and Khoem Socheat, (ii) the NIS Cambodia LSMS+ Survey field staff, and (ii) the World Bank staff members: Kimsun Tong and Phay Sokcheng. Talip Kilic and Heather Moylan oversaw the provision of the World Bank technical assistance to the NIS on the design and implementation of the Cambodia LSMS+ Survey. Photo credits go to Nathan Dappen of Day’s Edge Productions. 64 Living Standards Measurement Study – Plus (LSMS+) www.worldbank.org/lsmsplus II LSMS+ Program in Cambodia