2016 World Development Indicators (WDI), the country had a GNI per capita of US$350 in 2015. It has a relatively young and growing population (17.2 million people in 2015); and is one of the most densely populated countries in the world, with 177 people per sq. km of land area. Similar to most poor countries, the Malawian economy relies primarily on the agricultural sector which contributes over 30 percent of its GDP and employs 85 percent of its workforce (World Bank, 2016). Given the low return from agriculture and the persistent poverty facing the farm households, policy makers in Malawi would find it useful to know whether facilitating households’ movement into new, more productive, and more lucrative activities outside agriculture is a potential way for reducing poverty in Malawi.

# **Data sources**

This study uses the panel component of the Malawi Third Integrated Households Survey (IHS3) and the Integrated Household Panel Survey (IHPS) implemented through a joint effort of the Government of Malawi through the National Statistical Office (NSO; www.nso.malawi.net), and the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. These are multi-topic surveys with detailed information about households’ characteristics, activities and livelihood, agricultural practices and community-level information. The full balanced panel sample includes 3,104 households successfully tracked and interviewed in both IHS3 in 2010 and IHPS in 2013. The sample frame includes all three geographical regions of Malawi: North, Centre and South. The survey stratified the country into rural and urban strata. The urban stratum includes the four major urban areas: Lilongwe, Blantyre, Mzuzu, and the Municipality of Zomba. All other areas including Bomas are considered as rural areas.

In this study, we are interested in livelihood diversification strategies among rural population. Therefore, our analysis uses only the rural subsample, which represents about 70 percent of the sample (2,766 households). The IHPS data are representative at the national, urban/rural and regional levels.

We also use auxiliary data such as the consumption aggregates developed by the World Bank poverty and LSMS teams for poverty analysis in Malawi.<sup>3</sup> The use of these pre-generated variables allows us to compare and link our analyses to other reports and studies based on the publicly available LSMS data, thereby contributing coherently to the large discussion about livelihoods in rural Malawi, and Africa more generally.

# **Definition of main concepts**

As suggested by Barrett et al. (2001b), it is important to distinguish between various terminologies such as “off-farm”, “non-agricultural” , and “non-farm” employment, often used synonymously in the literature to describe the RNFE. Using an adaptation of the sectoral classification in Barrett et al. (2011),<sup>4</sup> we define non-farm employment in this paper to mean, all

> 3 The consumption aggregates data as well as a detailed discussion on how each component is calculated are in the dissemination documentation available for download along with the consumption aggregate data from the LSMS website.

> 4 Barrett et al. (2011) exclude completely the primary sector from non-farm employments.

4