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Introduction
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Objective
This report analyses the relationship between economic growth, employment and poverty in post-war Mozambique. It summarises recent macro-economic trends and provides a detailed and novel micro-economic assessment of the welfare effects of
employment. Based on these findings, the study derives policy recommendations for enhancing growth, creating employment and alleviating poverty in post-war Mozambique.
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Approach
The approach of the study is to combine macro-economic analysis with a detailed assessment of the only two nationally representative household surveys undertaken in Mozambique to date. The macro-economic analysis provides a framework for the
micro-economic investigation and supports the interpretation of the detailed microeconomic findings.
Methodologically, the use of two household surveys from 1996-97 and 2002-03 allows us to establish the nationally representative determinants of household welfare and household employment and their interdependence. Estimating the effect of
employment outcomes on household welfare addresses an important linkage between growth, employment and poverty.
Conceptually, it is possible to think of a number of variables, which could influence the probability of a household being poor in terms of inadequate income. The variables could be asset-related (e.g. the possession of income-generating assets),
human capital related (e.g. education and skill levels of the working members of a household) or employment related (e.g. the sector and quantity of employment of the workers, wages, productivity, etc.). The analysis will also include regional variables
to capture district endowments important for pro-poor growth.
Other papers on Mozambique using the same data sources focus in more detail on issues of pro-poor growth (James, Arndt et al. 2005), consumption inequality (Fox, Bardasi et al. 2005), poverty determinants (Maximiano, Arndt et al. 2005),
demography (Klasen and Woltermann 2004), and the methodology of the surveys (Instituto Nacional de Estatнstica 2004). This paper instead focuses on the employment-poverty nexus and its linkages to the macro-economic developments in Mozambique.
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Data Sources
The data used for the analysis are the IAF data (Inquйrito aos Agregados Familiares), which represent the only nationally representative data on employment and consumption available in Mozambique. The first IAF dataset was collected in the
period from February 1996 to April 1997 (Government of Mozambique 1998). The second survey ran from July 2002 to June 2003 (Government of Mozambique 2004).
In both cases the survey was designed and organised by the Instituto Nacional de Estatнstica (INE).1 In 1996-97 the emphasis was on the households’ living conditions, whereas in the later survey it was not as much on living conditions as on expenditures. More details on sampling method and data collection can be fo und in the two reports that resulted from the surveys (Government of Mozambique 1998; Government of Mozambique 2004).
Both surveys cover both rural and urban areas and are nationally representative. All 10 provinces were included and Maputo City was considered separately, as an eleventh province. Within each province all districts are included. The household
sampling of the 1996-97 survey was based on the latest census available i.e. the 1980 census while the 2002-03 sampling was based on the more recent census of 1997. For each of the primary sampling units the survey teams used simple random selection techniques for inclusion of households in the sample. In 1996-97 nearly 8300 households were interviewed and 8700 in 2002-03. The IAF data do not have a panel character but have to be used as two cross-section datasets.
Information was collected both at household and individual level. At the individual level, there is information on age, gender, health, education and employment status. The latter topic is more broadly tackled in the first survey. At the household level there is information on land- and tree holdings, livestock ownership, dwelling characteristics, asset ownership and agricultural production. In the second survey not all of these topics are as extensively treated as in the first one and some are even left out such as land and livestock ownership. Both surveys have sections on household expenditure, recorded in much more detail in the second survey. This slightly different focus entails some constraints for our empirical analysis since we decided
for comparability reasons to use only data that were collected in both surveys. However, many interesting changes can be observed using only the variables that overlap.
The analysis has a double focus. On the one hand we aim to understand welfare, measured by household consumption, and the role employment patterns in the household can play. On the other hand, we also aim at identifying personal or household characteristics that actually allow for a certain pattern of employment. Throughout the report we basically use a two-way classification of employments (which provide earned income). On the one hand, we distinguish by sector, i.e. agricultural versus non-agricultural employment2, where the distinction only refers to the main activity of the site where the work is performed and not to the location, which could be rural or urban. On the other hand, we distinguish income earners by function, i.e. self-employment versus wage employment. Using this framework, all income earners are sorted by their main activity into one of these four categories. A fifth category consists of persons who are working but who do not get a monetary income. In most cases these will be helpers in the activities of other members of their household or family. Another definition we may use is off-farm employment which broadens the non-agricultural category to include wage work in the agricultural sector. So off- farm includes all employment that is held outside of the own farm.
Additionally, we may make a distinction between rural and urban areas. The determination of where exactly lies the border between a rural and urban area may be subject to the survey designers’ views. Between both surveys the definition of rural and urban even changed, including some of the former rural areas in the urban category in the 2002-03 survey. Ten percent of the sample population living in urban areas in 2002 would have been living in rural areas under the 1996 rural-urban definitions. Obviously, the boundaries should change in the course of the urbanization process. For comparative reasons we applied the 2002-03 definition also to the 1996-97 sample. Other than rural urban differences, regional differences may also exist. It was shown that poverty differences and changes thereof were strikingly more prominent between regions than between rural-urban areas. Hence we opt to focus both on regional differences as well as on the rural-urban divide. Whenever we make the rural-urban division, we use the 2002-03 definition for both surveys. By this definition, the North and Central regions are equally “rural” as the percentage of the sample living in rural areas was 65 and 64 percent respectively in 1996 and 60 and 61 percent respectively in 2002. The South is the “urban” region with 53 and 65 percent of its sample population living in urban areas in 1996 and 2002 respectively. In all regions we notice an increase in the urban population. In what follows all statistics are weighed to correct for sampling probabilities.3
In the empirical literature on welfare or employment choice the rural- urban distinction is widely used as a tool to divide the population (Heltberg and Tarp 2002; Justino and Litchfield 2002; Gibson and Rozelle 2003). Also the distinction between agricultural and non-agricultural sectors or the farm and non-farm sector is broadly applied (Barrett, Reardon et al. 2001). Some authors focus on one intersection of both which is usually the rural non-farm group (Reardon 2000; Mecharla 2002; Isgut 2004).
We would have liked to assess the structure of real wages and earnings of wage-paid workers and real earnings of the self-employed in order to analyse another important element in the channel of transmission of benefits of growth to the poor. However, for two reasons this was not possible. First, the available data focuses on the analysis of household consumption levels but neither on household income data nor on wage rates. Second, the smallholder farm sector in Mozambique is characterised by a large share of auto-consumption and it accounts for the majority of employment in the country. Therefore data on wage rates are neither available nor would they be easy to calculate in principle. The methods of this section have therefore been adjusted to the needs of a dataset containing the consumption data of many rural, self-employed farm households.
Footnotes:
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Summary statistics on most of the variables collected in both surveys have been published in Mozambique (Instituto Nacional de Estatнstica 1999).
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Non-agricultural employment refers to employment in a sector other than agriculture, forestry or fishery. It does not only refer to employment off the household’s farm. In the 2002-03 IAF survey activities related to either one of the three sectors (agriculture, fisheries, forestry) were grouped as one sector hence our definition of agriculture includes all three.
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The weights are the inverse of the probability with which a particular household in the primary sampling unit could be selected for being interviewed.
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