DRAFT. Inclusive Growth in Africa: Measurement, Causes, and Consequences

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1 DRAFT This paper is a draft submission to the Inclusive Growth in Africa: Measurement, Causes, and Consequences September 2013 Helsinki, Finland This is a draft version of a conference paper submitted for presentation at UNU-WIDER s conference, held in Helsinki on September This is not a formal publication of UNU-WIDER and may reflect work-in-progress. THIS DRAFT IS NOT TO BE CITED, QUOTED OR ATTRIBUTED WITHOUT PERMISSION FROM AUTHOR(S).

2 Household Nonfarm Enterprises and Structural Transformation: Evidence from Uganda Louise Fox Obert Pimhidzai KEYWORDS: Sub-Saharan Africa, Uganda, Structural transformation, Poverty reduction, Employment, Non-farm household enterprises, Livelihood transformation JEL Classification: O12, O17, O55, J21, I31 Corresponding author: This version: December, 2012 This paper was prepared for the World Bank Africa Regional Project on Improving the Productivity and Reducing Risk of Household Enterprises. Preparation was supported by the World Bank, the Belgian Partnership for Poverty Reduction, and the donors to the TFESSD Trust Fund. The findings, interpretations, and conclusions expressed in this volume do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent. 1

3 Abstract Recent empirical literature on growth and development in lower income Sub-Saharan Africa (SSA) using macro data has concluded that not much structural transformation - the movement of the labor force from low productivity activities to higher productivity ones has taken place. The macro analysis misses the fact that most employment in low income SSA countries today still takes place as non- employment within households; either in family farming or in household non-farm enterprises (NFEs). What does this imply for the analysis of structural transformation? We analyze this question through a case study of Uganda, a country which has had high and broad-based income growth with shifts in both output and employment out of agriculture, and substantial poverty reduction. Rather than rely on macro aggregates, we use multiple crosssections of survey data on the economic activities of households to track the movement of labor out of low productivity into higher productivity activity. We show that macro aggregates, which rely on national employment data, understate the extent of the transformation in the structure of employment, because most of the employment shift over the last two decades was within the non- sector, not from non- to employment. A large share of the labor force actually works in both the agriculture and the non-agriculture sectors, because they have created their own household NFEs while maintaining their family farm. Although we cannot directly estimate labor productivity in household non-farm enterprises relative to agriculture from the household survey data, higher incomes after controlling for human capital suggests that this change does indeed represent movement into a higher productivity activity. We argue that structural transformation at the household level represents the first stage in structural transformation in low income SSA, and this transformation has been an important contributor to poverty reduction and equitable growth in Uganda. These results suggest that it is important to complement country-specific aggregate growth analysis with analysis of household livelihood transformation. It also suggest that a policy for structural transformation which only focuses on employment will miss important potential productivity gains, arguing for incorporation of the household nonagricultural sector into growth and development policy. 2

4 1. Introduction Structural transformation - the movement of the labor force from low productivity activities to higher productivity ones is widely agreed to be a key feature of sustained growth and economic development. This labor reallocation necessarily involves a shift towards nonfarm employment hence the focus of debates on how to push structural transformation forward has mostly focused on creating more private sector enterprises offering higher productivity employment (McMillan,and Rodrik, 2011). This focus however overlooks the fact that most employment in low income Sub Sahara African countries (henceforth SSA) today still takes place as non- employment within households, either in family farming or in household non-farm enterprises. Private non-farm employment accounts for a low share of employment in general, with its share of total employment above 10 percent in only a few countries. Owing to high growth in the labor force, this is likely to persist for some time (Fox and Sohnesen, 2012). As a result, initial reallocation of labor in SSA could be dominated by a movement from non farm employment to non- non-farm employment a shift that is less anticipated but as we show in this paper, still raises aggregate productivity and is welfare enhancing. This paper analyzes the nature of structural transformation in Uganda, a country where more than three quarters of households still report agriculture as a major source of income and non-farm and salary jobs account for a very small share of employment, despite a sustained high GDP growth rate over the past two decades (averaging 8% per annual between 1992 and 2009). How much reallocation of labor to higher productivity activities occurred during this time period? To assess this question, our analysis combines both macro data and multiple cross-sections of survey data on the economic activities of households. This is a departure from recent empirical literature on growth and development in lower income Sub- Saharan Africa (SSA) that only uses macro aggregates to analyze structural transformation (McMillan,and Rodrik, 2011, Michaels, Rauch & Redding, 2012). Using micro data has an advantage of identifying more subtle forms of structural transformation of employment such as a shift to non-agricultural activities through a reallocation of time by people who remain primarily engaged in agriculture. Such a transformation is missed or understated when the sectoral shares of primary employment are used as a measure of transformation as the case of Uganda attests. Our analysis shows that during this growth period, the shift in the structure of primary employment in Uganda was mainly within the non- sector, from family farming to selfemployment in non-farm household enterprises rather than from family farming to employment. About 55.6 percent of all non-agriculture primary employment growth between 1992/93 and 2009/10 was in non- employment. While the share of private nonagricultural employment actually doubled, it only reached 11% of total primary employment, as this growth took place from a low base. We also show that data on primary employment actually understates the movement of labor out of agriculture, because a large share of the labor force actually works in both the agriculture and the non-agriculture sector. As a result, national 3

5 employment data - which only consider primary employment - underestimate the extent of transformation as they do not show how household livelihood patterns have changed through the development of rural non-farm enterprise sector. Although we cannot directly estimate labor productivity in these household enterprises relative to agriculture from the household survey data, higher incomes after controlling for human capital suggests that this change does indeed represent movement into a higher productivity activity. It also increases incomes by reducing underemployment. While they do not infer causality, our consumption regressions show that households with a non- nonagricultural job (non-farm household enterprise) in their portfolio had significantly higher levels of welfare. We argue that even though dominated by movements within non- employment, structural transformation observed in Uganda raised average productivity and has been an important contributor to poverty reduction and equitable growth in Uganda. The paper is organized as follows. Section 2 briefly reviews the standard SSA structural transformation argument based on macro data and then proposes another way of looking at structural transformation using country level micro data. Section 3 is the country level analysis. It starts with an examination of the transformation in the structure of output by looking at trends in sectoral output shares in the country. This is followed by an analysis of employment transformation and the evolution of versus non- employment in the non-agriculture sector and then analyzes the evolution of livelihoods and explores the role of the livelihood transformation in raising household income and welfare. The paper concludes with a discussion on what this analysis means for the quest for structural transformation in SSA. 2. Analyzing structural transformation the question and a methodology Structural transformation is widely defined as the reallocation of economic activity across three broad sectors - agriculture, manufacturing [meaning industry] and services in the process of modern economic growth (Herrendorf et al, 2011). Economic activity in this case can be either measured in either labor or output terms. The process of structural transformation naturally implies an increase in aggregate productivity in the economy, either through improvements in value added in each sector or through reallocation of labor to the higher productivity sector (McMillan and Rodrik, 2011). Productivity gains within sector and reallocations among sectors are empirically observed in economic history, with the output share in the low productivity sector (agriculture) declining faster than the labor share (Timmer, 1988). Lewis (1954) further described the transformation as a movement family farming (the traditional sector) to employment in legally registered enterprises (the modern sector). An analysis of structural transformation would thus necessarily look for long term shifts in economic activity towards more productivity sectors, typically the non-agriculture sectors. Such a shift is manifested in changes in sectoral output shares as an increasing output contribution of the non-agriculture sectors is observed. It is also observed in a higher rate of employment growth in the non-agriculture sectors than in the agriculture sector. Given the share of agriculture in employment starts out very high and that agriculture employment will also necessarily grow if the labor force is also growing in the early stages, a structural change in employment shares is observed only when the growth rate in non-agriculture employment 4

6 shares is much higher than agriculture employment growth. The transformation in employment is thus observed with a lag (Timmer, 1988). Following Lewis, analysis would also look for the share of the labor force earning a by working in larger private non-farm enterprises (what he termed the modern sector ) to rise. Sectoral employment shares based on the number of people reporting primary employment in a particular sector in household surveys may not accurately identify employment transformation. If people engage in multiple economic activities in two (or more) sectors, the reallocation of labor can take the form of an increase share of time being spent on nonagriculture activities by people primarily engaged in agriculture. Even as they start to get the majority of their income from this source, they may still report agriculture as their primary activity, especially in rural areas. This transformation would not necessarily show up in sectoral employment shares. To see this sort of transformation, an analysis of household production and livelihood portfolios is needed. The aggregate data suggest that SSA s recent period of growth has brought slow, if any structural transformation in most countries (see Fengler and Devarajan, 2012 for the regional analysis which reaches this conclusion). Although the share of agriculture in value fell between 1998 and 2008 and now averages around percent, the share of value added in manufacturing in many of the same countries did not grow or grew very slowly over the same period. Value added in the service sector has grown rapidly, but and salary employment in the non-farm private sector (e.g. Lewis modern sector) remains stubbornly low below 10% of the labor force, and the share of labor in agriculture remained persistently high. Compared with countries with similar per capita incomes in East and South Asia, on all measures of structural transformation, SSA indicators are low (Fengler and Devarajan, 2012). This has raised questions about the growth process and why structural transformation has not taken place (Badiane, 2011, McMillan and Rodrik, 2011, Michaels, Rauch & Redding, 2012). But these analyses only use macro data. Going beyond sectoral share analysis may shed more light on questions about the nature of structural transformation in SSA. It is possible that household level transformation, via reallocation of labor from household agriculture to household non-agriculture activities is occurring as a first step toward structural transformation in SSA but macro level analysis is not able to pick this up. A country level microeconomic analysis is thus needed. Focusing on Uganda, this paper makes such a country level micro analysis. Our analysis of structural transformation in this paper goes beyond analysis of sectoral output and employment shares to analyze the types of economic activities within sector (i.e. non- vs activities within the non-agriculture sector) and structure of economic activities at the household level, identifying the portfolio of activities undertaken by households and how this has changed over time. We look for a transformation at the household level, where the labor force is able to enter the higher productivity non-farm sector through the creation of their own non-farm enterprises. The majority of these are own-account (self-employed) enterprises that may employ contributing family workers. Some do employ at least one non-family worker on a continuous basis. All are unincorporated and owned by households and are often called the 5

7 informal sector or the informal economy 1. Nevertheless, a re-allocation of time to these nonfarm household activities by agricultural households could raise aggregate productivity. 3. Structural transformation in Uganda Over the past 2 decades, Uganda experienced a sustained rate of growth averaging 8 percent per annum since 1992, leading to the doubling of GDP per capita between 1992 and This growth was broad based and resulted in the halving of poverty in the country over the same period. Notwithstanding this, about 85% of the population still lives in areas classified as rural and close to 86% of all households in Uganda have an agriculture income. These factors have led to questions on the nature of economic growth in Uganda and the characteristics of its structural transformation, if it is happening at all. The most recent policy focus has thus shifted from simply poverty reduction to faster structural transformation from a peasant to a modern prosperous country within 30 years (Government of Uganda National Development Agency, 2010). This section addresses the questions on the characteristics of structural transformation in Uganda, a country not dissimilar to other low income countries in SSA, where agriculture employment appears to have remained dominant and the share of the labor force in private employment remains low (World Bank, 2012). Data The analysis of Uganda combines both macro data on GDP and output sectoral shares obtained from the World Development Indicators and the micro survey data on household economic activities from multiple cross sectional Uganda National Household Surveys (UNHS). These nationally representative surveys with data collected across the span of 12 months in each survey, have been undertaken since 1992/93 and the most recent survey was done in 2009/10. Measurement of employment status and economic participation was not performed consistently across surveys. In this analysis, we built the employment variables carefully, only using surveys where these variables can be constructed in a comparable way, e.g. by building from main employment status questions in the labor, household enterprise, and farm modules using similar recall periods. The 1992/93, 2005/06 and 2009/10 are therefore mainly used. The livelihood transformation analysis primarily uses the 1992/93 and 2005/6 because the 2009/10 survey did not capture participation in secondary employment in the 12 month recall making it less useful for a livelihood analysis. The careful selection of surveys and use of additional information from farm and household enterprise modules mitigates against comparability challenges posed by various changes made to the survey over time (see Fox and Pimhidzai, The term informal economy is an extensive term used in many contexts, often with different meanings. However, the ILO (2011) has developed specific definitions. The informal economy refers to a (i) condition of employment and a (ii) condition of firm behavior. Informal employment refers to workers who do not have a contract that is in accordance with national labor regulations, or are not included in a nation social security system. Informal firms are unincorporated and are not registered with a government authority. The problem with applying the second part of the definition in SSA is that (a) registration is a continuum, ranging from getting a local permit to operate to registering with national tax authorities, and (b) some household businesses are not required to register (see Fox and Sohnesen, 2012 for a discussion). For this reason we do not use the term informal in this paper, even though most of the activities we analyze would meet the ILO definition. We prefer to focus on the household nature of the production. 6

8 Share of GDP Real GDP per capita (UG Shillings) and World Bank, 2012). All other variables are the same ones as published in World Development Indicators and in Ugandan national statistics. Changes in output composition Since the early 1990s, Uganda experienced sustained economic growth. Growth in value added in agriculture has been modest, while annual growth in industry and services has been double digit. The share of industry in GDP has more than doubled, and is now a respectable 26% while services, including government, account for 50% of GDP (see Figure 1). Within industry, growth came from the construction sector, the development of a modest manufacturing sector, and the development of non-traditional exports (World Bank, 2007). While growth by sector has followed the expected path of transformation (e.g agriculture as a share of GDP shrinking, industry increasing), most of this transformation occurred by the early 2000s, meaning in the first decade and a half after the end of the civil war. Since then, each sector has essentially maintained its share, increasing value added through adding labor and capital and through productivity increases. Figure 1: Trends in sectoral share of GDP in Uganda, , , , , , , , Agriculture (% of GDP) Services (% of GDP) Industry (% of GDP) GDP per capita (on right axis) Source: World Development Indicators, Some of the growth in GDP was generated by the very high growth of the labor force about 3 percent per annum. But investment in the non-agricultural sectors over the years contributed as well. Economic activity shifted towards more productive sectors, raising aggregate productivity per worker from an estimated USD$ 438 per capita in 1992 to $667 in While sectoral aggregate labor productivity data are less reliable owing to the difficulty 2 Authors calculations based on WDI data on GDP and our own estimates of the size of the labor force from the household survey data. For a discussion of Uganda s growth performance, see World Bank, (2007). 7

9 in capturing output and employment in the household production sector, it is widely agreed that labor productivity in the agricultural sector is low, and that growth in labor productivity in this sector has lagged well behind the rest of the economy. Growth in the agricultural sector has been primarily based on expansion of area, although there has been some introduction of better technology such as higher value crops (World Bank, 2007, 2012). This means that aggregate growth in labor productivity must have been driven primarily by activity in the non-agricultural sectors. As we show below, employment shifts played an important role in this outcome. Changes in employment composition As expected, the share of employment in agricultural declined more slowly than output. While the share of agriculture in GDP has more than halved between 1992 and 2010, its share of primary employment declined by less than a fifth in the same period (see Table 1). The overwhelming majority of the workforce in the most recent household survey (UNHS 2009/10) still reported agriculture as their primary economic activity. Given the high share of agriculture employment in 1992, its decline by 12 percentage points however translates to a substantial increase in non-agriculture employment. Starting from a low base of 16.4% in 1992/03, the share of primary employment outside agriculture increased to 30% in 2009/10 an increase of 83%. This translates to a substantial reallocation of labor to the non-agriculture sector. Table 1: Sectoral composition of primary employment, 1992/ /10 Year 1992/ / /10 Labor force Agriculture Industry Services Source: Authors calculations based on Uganda national household surveys. Dissecting the employment transformation The reallocation of labor in Uganda from agriculture between 1992/93 and 2009/10 happened while total employment doubled and more than 2.7 million workers were added to the non-agriculture of sector. Unexpectedly, the majority of the new non-agricultural jobs were non activities, meaning household non-farm enterprises (NFEs). The change in the structure of reported primary employment in the non-agricultural sector by type of employment is shown in Figure 2, which shows that more than 1.5 million net non- primary jobs in the nonagriculture sector were created between 1992/93 and 2009/10 while 1.2 million net nonagriculture jobs were created over the same period. Thus non- employment contributed 25 percent more non-agriculture jobs than employment. The faster pace of growth in non- non-agriculture jobs resulted in non- employment constituting 56 percent of the new jobs while employment constituted 44 percent of these jobs (see Table 2). Surprisingly, the share of non- employment to total net job creation was particularly high in the manufacturing sector relative to that of employment. During this period, newly created jobs in manufacturing added less than 1 percent of total new nonagricultural jobs, while new non- jobs accounted for 17 percent. Thus even though the share of private non-agricultural jobs in total employment increased by 5.8 percentage points (in fact doubling), 8

10 it was overshadowed by an even higher jump (8.3 percentage points) in the share of non- non agriculture jobs. This was not the result a Lewis-type model expected. Figure 2: Net non-agriculture jobs created in the Uganda labor market, 1992/ /10 1,800,000 1,600,000 1,400,000 1,200,000 1,000, , ,000 Wage Non 400, ,000 - Industry Services Non-Agriculture Source: Authors calculations based on the IHS 1992/93, and UNHS 2009/10 Table 2: Sectoral net change in primary non-farm employment by employment type, 1992/ /10 Sector Share of labor force Percentage net change in number of jobs Share of net change in net non-agriculture jobs growth 1992/ /10 Wage Non- Wage Non Non- Non- Wage Wage All Wage All Industry of which manufacturing Services All non-agriculture Source: Authors calculations based on the IHS 1992/93, and UNHS 2009/10 The data above only include primary employment, but in 2005/6, over 40 percent of Ugandans reported having a second job, many of them in household enterprises. A comparison of household sources of income in 1992/93 and 2005/06 in Figure 3 shows that the proportion of households with an income from non-farm sources increased dramatically. The proportion of households with a private non-agriculture income increased by 10 percentage points while that of households with non-farm household enterprise increased by almost 14 percentage points (i.e. by nearly 50 percent). By 2009/10, about 40 percent of rural households operated a non-farm household enterprise (HE) compared to 24 percent in 1992/93. It is also clear from Figure 3 that a significant share of households added non-agriculture income to their agriculture 9

11 income given that the share of households with income from agriculture only marginally dropped. These factors imply that the reallocation of labor to the non-agriculture sector also included more time spent on non-agriculture economic activities by people primarily engaged in agriculture as well. The above analysis, which focused on shifts in primary employment only, therefore significantly understates the growth of non-agricultural employment. Figure 3: Incomes sources of households in Uganda, 1992/ /06, showing that share of non-agricultural income sources grew Uganda Rural areas Agriculture Agriculture Public non-agriculture Public non-agriculture Private non agriculture Private non agriculture Non farm enterprise Non farm enterprise Farm Farm / / / /93 Source: Authors calculations based on the IHS 1992/93 and UNHS 2005/06 The above data show that the nature of structural transformation in a country like Uganda, where multiple activities are prevalent, is captured more robustly by using survey data on the economic activities of households in a livelihood analysis. Livelihood analysis captures the reallocation of time across multiple activities by the same unit, as opposed to sectoral shares of employment which cannot accommodate multiple activities (the combined share would exceed one) 3. The results from the total livelihood analysis shown in Figure 4 below indicate an even more dramatic shift in livelihoods that implied by analysis of primary employment sectoral shares. The share of rural households solely relying on subsistence agriculture declined by 43 percent between 1992/93 and 2005/06 (see Figure 4). At a national level (not shown), the proportion of households solely relying on agriculture income declined by a third, from 54 percent in 1992/93 to 36 percent in 2005/06. While agriculture remains an important source of income for 77 percent of households in Uganda, by 2005/6 many households were 3 The concept of household livelihood analysis, as opposed to the analysis of individual earnings, has a long history in both agricultural economics and in social analysis. See Fox et al (2008) chapter 2 for a discussion and application of the approach to an analysis of inclusive growth in Mozambique. 10

12 complementing it with income from other activities which are often more productive meaning that they added activities over the period, rather than switching sectors entirely. In urban areas (not shown), the expansion of employment was also important, but in rural areas it clearly was non agriculture household enterprises. 4 Figure 4: Comparison of rural household livelihood portfolios in Uganda, 1992/ /06 HE 2% Family Farm, HE & ag 2% Family Farm & non ag 9% HE & non ag 0% Family Farm & HE 18% Non ag 2% Other 5% Ag only 2% Family Farm 53% Ag & Family farm 7% Rural areas, 1992/93 Non ag 3% HE & non ag 1% HE 3% Family farm, HE & ag 5% Other 14% Family farm & HE 21% Ag only 2% Rural areas, 2005/06 Family farm 30% Ag and family farm 13% Family farm & non ag 8% Source: Authors calculations based on the IHS 1992/93 and UNHS 2005/06 The shift appears more dramatic in the household livelihood analysis because the household enterprises that drove this transformation were mainly reported as a secondary activity by most households (especially in rural areas). Their enterprise was an addition to the household livelihood, not a sectoral switch. The high growth in non-farm enterprises is therefore not reflected as dramatically in the aggregate employment data (which shows only primary employment). This leads to the perception of a continued dominance of agriculture in the structure of the labor force when structural change is measured in the traditional way. If multiple jobs were included in the analysis of the employment structure and an analysis of household livelihood portfolios was conducted, the reallocation of labor to non-agriculture economic activities would be more obvious. Implications of Uganda s structural transformation The evidence so far presented shows that structural transformation in Uganda was characterized by a shift from household farm production to household non-farm production in 4 Note that as we have four types of activities, we actually have combinations, even though we only show the top nine in the figure. The expansion in the other category refers to these omitted combination, including the households with four types of income. 11

13 rural areas, as well as the growth of both and non- jobs in urban areas. Given the inherent assumption that productivity is low in the NFE sector (see Ihrig and Moe, 2004 for example) and the generally negative views of this sector in the literature owing to its size and lack of formalization, this shift to non- non-agriculture employment might seem less desirable. 5 We therefore turn attention to the question on whether this shift increased productivity and household incomes, thus helping to the sustain economic growth. Impact on earnings/ productivity Adding an NFE to the household portfolio could raise total labor productivity in the household through a shift in hours worked from high productivity to low productivity activities, or it could simply increase total hours worked without adding much to labor productivity, but reducing underemployment. 6 Analysis of the UNHS 2005/06 data suggests both types of changes occurred. Figure 5: Distribution of daily earnings by primary employment Log of daily earnings Non agriculture Household enterprise Family Agriculture Agriculture Source: Authors calculations based on the UNHS 2005/06. These are based on earnings in primary activities only excluding s in public employment. Lack of information on all factors of production in each household enterprise forces us to infer productivity from earnings. When we consider only daily earnings, the median earnings 5 See Fox and Sohnesen, (2012) for a discussion of the formalization literature. 6 In the strictest sense, if all assets of the household are included, working more hours with the same fixed capital raises labor productivity. Our data do not allow us to measure the joint household production function this carefully. If an individual increases hours worked, but at the same average earnings, we label this a reduction in underemployment. This raises GDP, but not necessarily aggregate productivity. 12

14 are quite close (Figure 5) but earnings in the household NFE sector exhibit a wide range- much wider than agriculture. In the lower end, earnings are lower than in agriculture but at the upper end, they are higher than employment. In this case, agriculture appears to be a safe activity with lower risk, while for some, NFE activity is much higher productivity than agriculture. However, analysis of hours worked data indicates that underemployment is much more likely in agriculture, which would lower monthly or yearly earnings owing to underemployment. Those agricultural households who added an enterprise worked more hours. implying a shift to the sector reduces underutilization of labor and raises monthly earnings (which is an increasing function of both the daily earnings and number of days worked in a month). In 2005/6, owners of household enterprises as a primary activity reported more than twice as many hours per month as those whose main activity is self-employment in agriculture. Men who reported having a primary activity in family farming, but a secondary non-agricultural activity also reported more hours than those specialized in agriculture, but not more than men who specialized in the NFE sector (Table 3). Table 3: Average Hours Worked in Past 1 Month by Main Occupation in Past Month, 2005 Have more than one activity Have only one activity All Men Self-employed agriculture Self-employed non-agriculture Women Self-employed agriculture Self-employed non-agriculture Source: Authors calculations from UNHS 2005/06 Earnings are substantially higher for households combining both family agriculture and a household enterprise (see Figure 6). While this can also be attributable to higher utilization of labor, it can also point to complementarities between the two activities. Thus rather than happening independently, the expansion into non-farm enterprises could help to raise agricultural productivity and vice versa. The diversification into non-farm income increases household incomes, and contributes to the structural transformation within the agricultural sector by providing extra liquidity, thus compensating for the failure of rural credit markets. Evidence from the UNHS 2005/06 shows agricultural households with other sources of income report higher income from agriculture on average. They are also more likely to buy other fertilizers, seeds and other marketed inputs. This implies that instead of substituting for agricultural income, households with a diversified livelihood portfolio use their various income sources to compliment farm incomes by providing working capital for their farms. Other studies have found similar relationships between nonfarm enterprises and modernization of farming practices in Asia (Haggblade et al, 2010) while qualitative evidence shows that increases in farm cash incomes support the growth of the non-farm enterprise sector by increasing demand for these products (Bakeine, 2010). Figure 6: Distribution of household monthly earnings, farming vs both farming and having an HE 13

15 Log of monthly earnings Family agriculture only Both family agriculture and Household enterprise Source: Authors calculations from the UNHS 2005/06 Impact on household consumption and poverty reduction Analysis of the role of Uganda s livelihood transformations in household welfare and poverty reduction suffers from inherent problems of endogeneity. Nonetheless, the evidence strongly suggests a relationship. Despite the enormous problems of comparing earnings in and non- sectors, the evidence above points to the non-farm sector offering higher earnings opportunities than family farming alone. This suggests that the diversification of household livelihood portfolios into non-farm sources of income increases income and household welfare. When household incomes arise from multiple sources, multivariate analysis of households according incomes is problematic (Deaton, 1997). Each type of income (, NFE, farm) shows different types of measurement errors. These errors are likely to be correlated with the key independent variables. Consumption is therefore much more widely used as a measurement of long term income and monetary welfare. We follow this approach to estimate the importance of structural transformation at the household level in improving household welfare. We run the standard OLS regression with the natural log of consumption per adult equivalent as our dependent variable, but in additional to measures of human capital, we include type of income source and their interactions as explanatory variables controlling for other confounders. The results from the consumption regressions are presented in Table 5 and summary statistics of explanatory variables used in the regression are presented in Table 6 in the appendix. Regression results show that controlling for household composition and human capital, households with non-farm income do better overall. The presence of farm income lowers consumption relative to the average, especially for those with farm income. Consumption 14

16 of households with income from a household enterprise is on average higher by at least 14 percent in rural areas and 23 percent in urban areas, while those with non-agriculture income have higher consumption by at least 12 percent in rural areas and 13 percent in urban areas (columns 1(a) and 2(a)). The specification controls for level of education so we can see the role of the ability of at least one member of the household to access nonfarm income in raising income and consumption. Note that even in 2005/6, human capital was low in Uganda - threequarters of the rural working age population and at almost 40 percent of the urban had not completed primary education. In rural areas, there was still a substantial share of the labor force with no education. This means that pathways for improving labor productivity and transforming livelihoods were limited for these households. Moving into a Lewis type and salary job was not an option. The question for households whose opportunities were limited by their human capital was whether they stayed in the farm sector, or started a non-farm enterprise. Those who did not diversify did worse. Columns 1(b) and 2(b) present results with specifications that use household livelihood portfolios. These results clearly show that households with a non-farm enterprise or income have higher levels of welfare than households with family farm income only. Compared to similar households with only farm income, those earning an income from household enterprises only have a higher welfare by nearly 27 percent and 40 percent in rural and urban areas respectively, while those with a nonfarm income only respectively have a higher by 36 percent and 32 percent. Households in rural areas with both family farm and non-farm income do better as well, and the coefficients on the combinations are about as high as in column 1(a). But in urban areas, the combinations which include enterprise income clearly stand out as the best, even controlling for education. Thus the increase of the proportion of households with income from nonfarm enterprises since 1992 was a strong driver of increased income and poverty reduction in Uganda. These regression results are consistent with other studies that estimated the the relationship between sector and type of economic activity among household members, and household welfare in Uganda is widely documented. Consumption regressions based on data from previous UNHS also show that type of income is a significant independent predictor of household welfare, with income from non-farm sectors having a greater effect on welfare (World Bank, 2006). But the strength of the results by portfolio type in this analysis makes the association appear even stronger. Correlations between the growth of non-farm income opportunities and lower poverty, either over time in Uganda or at the household level in repeated cross sections do necessarily imply causality, but they do suggest that the expansion of household non-farm enterprises was indeed good structural transformation for Uganda as it seeks to alleviate poverty. Despite the importance of non-farm household enterprise incomes in increasing household welfare, it has to be recognized that most economic activities in non-farm enterprises use low capital and basic technology. Although Figure 5 suggests that labor productivity in some enterprises may be higher than labor productivity of unskilled earning workers, it also shows that in some NFE may be lower than in farming. These low productivity enterprises are usually retail trade, or the production of low quality goods (e.g. home brew). Therefore labor productivity growth in this sector has limits and the risk of failure is also high. Although some 15

17 NFEs hire external labor, most depend only on family labor input or are pure self-employment. Nearly 80 percent of Ugandan NFEs did not have a hired worker in 2005/06 while only 6 percent had three or more workers. 7 This means that output and employment growth in this sector occurs through the creation of new enterprises, not by the growth of existing ones. Even with these caveats, the analysis above still suggests that supporting the creation of new enterprises can be a successful development strategy for individuals and for households, in both towns and in urban areas. A recent study showed that the proliferation of trading in mobile phone credits by hawkers for MTN, Vodafone and other telecommunication giants in Ghana is providing above-average earnings to these vendors while benefitting the companies (Kottoh, 2008). This is more evidence that this type of economic development in SSA can bring both growth and poverty reduction. 4. Conclusion Uganda s experience shows that a growth strategy which - intentionally or not - encouraged livelihood transformation through the creation of NFEs by households allowed strong growth to be shared widely. We don t know how much total economic growth and the growth in aggregate productivity came from household NFEs as the national accounts are not disaggregated in this way, but the sector clearly contributed, otherwise household incomes would not have grown. And this household livelihood transformation allowed those household with limited human capital to access non-farm incomes, which may have been the only way they could increase total income per capita since major improvements in labor productivity and reduction of underemployment in agriculture have proved elusive, not just in Uganda but throughout SSA. Similar to other countries in Africa, Uganda is experiencing very high labor supply growth (nearly 3 per cent per year) owing to past high fertility. An average of one hundred thousand non-agriculture jobs were created per year between 2002/03 and 2005/06, but an average of four hundred thousand people entered the labor market each year outnumbering the net private jobs created by a factor of four. Absorbing these in nonagriculture jobs would have required more than doubling non-agriculture jobs in three years (i.e. requiring an astronomical non-agriculture employment growth rate of 31 per annum). And still the majority of the labor force (i.e. those already in the labor force) would have been outside the and salary sector. Thus given Uganda s population dynamics, a structural transformation to employment dominated by private non-farm sector will take several decades at least. Many labor market entrants will continue to be absorbed in selfemployment in agriculture and household enterprise in the medium to long term even with continued high growth in non-agriculture employment. At this point, it is better for these entrants to work in households with a mixed livelihood strategy than to be trapped in a nontransforming agricultural livelihood. Would growth and poverty reduction have been higher if more high productivity jobs in the non-agricultural sector had been created, through the creation of even more large firms? Of course, and this is an argument of McMillan and Rodrik, (2011), and others who argue 7 Author s calculations from UNHS 2005/6. See Fox and Sohnesen for a regional comparison. 16

18 for public policies and programs which will encourage the private investments needed. As a number of economic historians have pointed out, the firm is one of the most efficient economic institutions in the world, and the path to sustainable middle income status in the last 100 years, especially for small countries, has involved the growth of large, efficient, export-oriented firms. Low income SSA countries have, for the most part, not been able to grow achieve enough output and employment growth in this sector. The argument offered here is not against a strategy of structural transformation through export manufacturing output and employment, but simply to state that it will not be enough. Countries at Uganda s stage of development, with a rate of labor force growth more than twice as high as those in low income South or East Asian countries, should not expect a rapid transformation of employment into Lewis modern sector. 8 Reallocation of labor away from lower productivity activities will have to occur in both the and non- segments of the economy for incomes to continue to grow. Progress in transformation of the non- sector requires a strategy which accepts this segment of employment, and recognizes it role in income generation and inclusive growth. This means first undertaking the micro-level analysis to understand the sector, its role in job creation, the drivers of productivity, and the policies which could support it to play its role in country structural transformation. This is a different empirical approach to analyzing structural transformation than has been employed to date in most regional and country studies. 8 In this paper, we have not discussed the role played by the green revolution that is increasing agricultural productivity - in supporting structural transformation in Asia. Asian experience does suggest that supporting the household NFE sector as a strategy for rural households to increase productivity and income is not enough; improvements in agricultural productivity will need to occur as well. 17

19 Table 4: Regression Results: Determinants of the log of consumption per adult equivalent in Uganda, 2005/6 a Rural Areas Urban Areas 1(a) 1(b) 2(a) 2(b) Variable Description Coeff S.E Coeff S.E Coeff S.E Coeff S.E Household demographic characteristics Household size *** *** *** *** Household size squared 0.004*** *** *** *** Male headed household Age of household head 0.007** ** *** *** Age of household head squared ** ** *** *** Household education characteristics b Prop. 15+ with some primary education 0.132*** *** *** *** 0.07 Prop. 15+ with complete. primary education 0.387*** *** *** *** Prop. 15+ with some secondary education 0.557*** *** *** *** Prop. 15+ with complete. secondary or degree 1.088*** *** *** *** Household sources of income Family farming (non ) income *** Farm income *** *** Non-farm enterprise (non-) income 0.134*** *** Non-farm income 0.113*** *** Receives remittances 0.041** ** Livelihood category (Base category Family Farm only) Farm income only Non-farm enterprise income only 0.236*** *** Nonfarm income only 0.308*** *** Family farm & farm income only *** Family farm & non-farm enterprise income only 0.119*** *** Family farm & non-farm income only 0.054* ** Family farm, non-farm & non-farm enterprise income only 0.276*** *** Non-farm & non-farm enterprise income only 0.322*** *** Other livelihood category ** Household location Resides in an Internally Displaces People's Camp *** *** * * Constant *** *** *** *** Observations Adjusted R -Squared Notes: (a) ; * p<.1; ** p<.05; *** p<.001, S.E robust standard errors. Results based on unweighted regressions and includes regional fixed effects (not shown).(b) Education variables are based on household members aged 15 and above who are not currently in school 18

20 References Badiane, Ousmane (2011). Agriculture and Structural Transformation in Africa. Processed. Bakeine, Amos (2010). Uganda Country Study Report: Raising Productivity and Reducing the Risk of Household Enterprises. World Bank, Washington DC Deaton, Angus The Analysis of Household Surveys A microeconometric approach to development policy, Baltimore, Md., U.S.A.: Johns Hopkins University Press for the World Bank. Devarajan, Shanta and Wolfgang Fengler Is Africa's Recent Growth sustainable?, Institut francais des relations International, Paris, October 2012 Fox, Louise Sharing the Growth in Uganda: Recent Labor Market Outcomes and Poverty Reduction Summary Draft Policy Note April 2009, The World Bank, Washington DC. Fox, Louise, et al Beating the Odds: Sustaining Inclusion in Mozambique s Growing Economy, The World Bank, Washington DC. Fox, Louise and Obert Pimhizai Different Dreams Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub-Saharan Africa - The Case of Uganda. Washington D.C. Processed. Fox, Louise and Thomas Sohnesen, Household Enterprise in Sub-Saharan Africa, - Why they matter for growth, jobs and livelihoods. Policy Research Working Paper no. WPS 6184, Washington D.C.: World Bank. Government of Uganda National Planning Authority, National Development Plan 2010/ /15, Uganda. Government Printers, Kampala. Haggblade, Steven, Peter Hazell and Thomas Reardon 2010 The Rural Non-Farm Economy: Prospects for Growth and Poverty Reduction, World Development, 38(11): Herrendorf, Berthold, Richard Rogerson, and Akos Valentinyi Structural Transformation and Economic Growth,". Chapter prepared for the Handbook of Economic Growth. Ihrig, J & Moe, KS, Lurking in the shadows: the informal sector and government policy. Journal of Development Economics 73,