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Is Uganda’s Growth Profile Jobless? Edward Bbaale School of Economics, Makerere University, Kampala-Uganda Abstract This paper set out to establish the relationship economic growth and employment in Uganda for the period 2006-2011. We obtained data from World Development Indicators, Uganda National Household Panel Survey (2011) and United Nations Statistical Data Base and we adopted the Job Generation and Decomposition (JoGGs) Tool of the World Bank for the analysis. Our findings reveal interesting insights into the relationship. The growth profile of Uganda for the period 2006-2011 was jobless as evidenced by 36% change in per capita value added emerging from a decrease in the employment rate. Agricultural sector contributed the most to the decline in the percentage change in per capita GDP and in the employment rate by 31% and 6.5%, respectively. Manufacturing sector contributed positively to the percentage change in per capita GDP by 8% but negatively to change in total employment rate by 0.2%. Positive contributions to the employment rate and the percent of total change in per capita GDP were observed in the services and industrial sectors. The services sector contributed 83% and 1.7% to the total change in per capita GDP and in the employment rate, respectively. The industrial sector contributed 35% and 0.5% to the percent of total change in per capita GDP and in the employment rate, respectively. Therefore, by sectors, the growth profile in agriculture and manufacturing was jobless while the growth profile in services and industry was job-creating. It is further noted that productivity or output per worker contributed over 100% to total change in per capita GDP growth. By sectors, output per worker was lowest in the agricultural sector and highest in the industry followed by the services sector. Hence the industrial and services sectors have higher prospects in an effort by the country to alleviate poverty via employment creation. The inter-sectoral shifts had a positive contribution to the output per worker meaning that, on average, labor moved from lower than average productivity sectors to above average productivity sectors. All sectors, apart from manufacturing, contributed positively to the intersectoral shift component. Given that the employment rate in agriculture was negative and that it is a low productivity sector implies that the relocation of workers from agriculture to other sectors had a positive effect on per capita growth via the inter-sectoral shift component. On the other hand, the employment rate in the services and industry was positive and that they are high productivity sectors means that the relocation of workers to these sectors had a positive effect on the per capita growth via the inter-sectoral shift component. The change in the employment rate 1 in manufacturing was zero and that this sector having the lowest productivity after agriculture, means that the relocation of workers to this sector had a negative but modest effect on per capita growth via the inter-sectoral shift component. Overall, the promising sectors for poverty reduction through productivity and employment generation in Uganda by order of importance; services sector, industrial sector and manufacturing. However, this is not to say that agriculture is not important, it’s still the major employer in the country (employing almost 70% of the labor force). This notwithstanding, it is a low productivity sector yielding very low returns to its participants and hence having limited ability to alleviate poverty. The demographic changes of the population are generating a window of opportunity for raising per capita incomes if well exploited. There are now fewer dependents per working age adults and if these engage in productive activities, this has clear dampening effect on poverty. 2 1 Introduction and study concern The role of employment in poverty reduction and welfare improvements cannot be underscored given that unemployment and underemployment are major causes and consequences of widespread poverty (Sodipe et al., 2011). Full, productive and decent employment is the most important source of income security and paves the way for broader social and economic advancement, strengthening individuals, their families and communities (ILO et al., 2012). In developing countries such as Uganda, most of the poor households earn a living by selling their labor either to themselves or to others; making employment an important channel through which economic growth impacts poverty (Fox and Gaal, 2008). Indeed, the impact of growth on poverty is seen as depending on the extent to which growth generates employment and good earning opportunities. Therefore, there is a great need for a strong employment component in the sustainable development strategies which aim at raising the productivity of the poorest workers, and ensuring that they get to keep most of their increased earning power by progressively strengthening labour market institutions (ILO et al., 2012). In line with these arguments, employment is an important conduit for the attainment poverty reduction, accelerated sustainable growth that is inclusive and equitable. According ILO et al. (2012) as work becomes decent in income terms, people’s concern over corruption and interest in democratic governance increases and are also more likely to invest in health and education of their children and themselves. There is little wonder then that many countries around the world are taking purposeful efforts of attaining economic growth that is employment enhancing particularly for the vulnerable groups; the youth and women. It is noteworthy that the Ugandan economy has exhibited a strong growth stance since the nineties. The GDP average yearly growth was 6.86% (1990-1999), 5.52% (2000-2005), and 7.7% (2006-2010) (UBOS, Statistical Abstract 2010). This may be attributed to a number of fundamental changes that the government has implemented over the period. These include among others; improving the investment climate and attracting foreign direct investment, improving the quality of labor force through universal primary and secondary education and completely eliminating the red tape in the business environment in order to reduce the cost of doing business. These reforms have generated major changes in the drivers of the economy with the services sector (over 52% of GDP) and the industrial sector (over 25% of GDP) playing a more dominant role than the agricultural sector (less than 14% of GDP). It is noteworthy that the economy has experienced sectoral shifts in GDP composition but with no sectoral shifts in employment. For example, the agricultural sector still employs approximately 70% of the labour force in the country. On the other hand, the services and manufacturing sectors employ 24% and 6%, respectively (UNHS 2009/10). This pattern implies that the participants in the agricultural sector get meager returns to their labour and hence suffer from biting poverty. On the other hand it might be the case that there are intra-sectoral shifts within the agricultural sector such that workers keep moving from less profitable activities to more profitable ones. Therefore, understanding the dynamics within (intra-sectoral shifts) and between (inter-sectoral) sectors 3 shall enable government to design appropriate policies that can uplift workers from poverty through productivity enhancement. The informal sector is by far the greatest employer in Uganda. For example 3.5 million persons were engaged in the informal sector including non-crop agriculture out of which 2.1 million persons were engaged in non-agricultural businesses (UNHS, 2009/10). Consequently, unemployment is seemingly no longer the problem in Uganda as evidenced by the low unemployment rates recorded recently; 3.5% (UNHS, 2002/03, 1.9% (UNHS, 2005/06) and 3.5% (UNHS, 2009/10). As per the ILO definition of unemployment, the total number of unemployed population increased from 0.3 million in 2002-03 to 0.5 million in 2009-10.1 In view of the existing realities, such low rates of open unemployment are expected in a country such as Uganda since the poor cannot afford to be unemployed, they are compelled to engage in some activity—even for few hours and at low wages in the informal sector— in order to make ends meet. Consequently, the informal sector has become more assertive, with self-employment, part-time employment, and unpaid employment in family enterprises being dominant components of the overall employment (UNHS, 2009/10). As a result, the ‘standard’ unemployment rate does not provide a real picture of the supply-demand balance of the labor market and inadequately reflects the degree of inefficiency that prevails in the labor market. In a situation like this, alternative indicators such as underemployment rates and work intensity are necessary to supplement the standard indicator of unemployment rate in order to reveal the labor market reality. A person is classified as time-underemployed if she or he has worked less than 40 hours a week and is willing and available to work more hours (UNHS, 2009/10). According to the UNHS Report (2009/10), underemployment has declined since 2002; 4 percent of workers were underemployed in 2009/10 compared to 12 percent in 2005/06 and 17% in 2002/203. Despite these enormous reforms and structural changes in the economy, employment enhancement has lagged behind the speed at which growth has been taking place indicating a phenomenon of jobless growth as indicated in the literature of labor economics (see Yeldan, 2006). It is not surprising therefore that Uganda’s impressive growth has reduced headcount poverty only by slightly more than a quarter of its level for period 2002-2010. Headcount poverty as a percentage of the total population decreased from 38.8% in 2002/03 to 31.1% in 2005/06 and to 24.5% (2009/10). In absolute terms, this translates to 9.8 million (2002/03), 8.44 million (2005/06) and 7.5 million (2009/10) living below the poverty line (UNHS, 2009/10). It is the slow pace at which people were being moved out of poverty that led to the realization by many poor countries and their development partners that employment creation is a crucial aspect of any growth agenda. In the same line of argument, it has become a critical question how 1 Following the ILO recommendation, in Uganda a person of age 14-64 years is considered as unemployed if he/she did not work at all during the preceding week of the survey (even an hour in the reference week) and was actively looking for work or was available for work but did not work due to temporary illness or because there was no work available. 4 employment growth is influenced by economic growth in developing countries such as Uganda. This is despite the fact that, hitherto, employment-economic growth nexus had not been lent an empirical regularity for the case of Uganda. This paper is therefore an attempt to fill this gap and answers the following pertinent questions. i) how is growth reflected in employment generation and in changes in output per worker, ii) how is growth reflected in the sectoral pattern of growth and employment generation, iii) What are the sources of changes in output per worker. Answers to these questions will help to understand whether the pattern or profile of growth observed is conducive to poverty reduction, by pinpointing the sector and factors that should be further analyzed. This paper is expected to trigger public policy dialogues and debates which may provide the government with alternative policy options that can lift the country from poverty emerging from unemployment and underemployment problems. This paper is organized as follows: section 2 gives a brief literature survey; section 3 presents the methodology and data issues; 4 gives the findings and section 5 concludes with implications for policy. 2 Literature review Quite extensive strand of literature exists on the relationship between employment and economic growth in both developed and developing countries. Melamed et al., (2011) undertakes a rigorous survey of the most recent literature highlighting the key insights the different authors have provided on the relationship between economic growth and employment. This blend of literature underscores the differential importance of the different sectors of the economy in generating gainful employment. A substantial strand of literature supports the view that the services sector has come to the forefront of generating gainful employment in many countries; an issue that is practically observable in Uganda’s economy. This view is supported by Kapsos (2005) who contends that the growth in the services sector generates more employment opportunities per percentage increase in their growth rates than growth taking place in either agriculture or manufacturing. Huong et al. (2006) documents that for the case of Vietnam employment opportunities in the services sector were more income generating than employment opportunities in manufacturing. Ravallion (2009) also documents that the services played a dominant role in poverty reduction in Brazil while it was growth in the agricultural sector that was most important in China. The author also contends that in contrast to China whose poverty reduction benefited more from agricultural growth, in India the services sector was more powerful. Demeke et al. (2006) showed that the growth of the overall economy was largely attributed to the growth of the services sector for the period 1960-2002 and hence absorbed much of the growing labour force in Ethiopia of the 1990s due to the slow growth of the agricultural sector. Fox and Gaal (2008) show that export-oriented growth in industrial and service output has the potential to greatly 5 expand formal employment Africa. The authors contend that the share of output has increased in the service sector and the labor force has followed hence it might generate more employment than agriculture and manufacturing. However, Melamed et al., (2011) notes that many jobs in the services sector have strong linkages to manufacturing or agriculture and hence a strategy that focuses entirely on any single sector would be unlikely to succeed over the long term. Loayza et al. (2010) show that agriculture is the most poverty reducing sector followed by construction and manufacturing; while mining, utilities and services do seem to dampen poverty. Khan (2008) documents the case of East Asian pioneers where rapid growth of industries and modern services increased the proportion of workers employed in activities with higher levels of productivity and remuneration and helped to increase the productivity of workers who were left behind in agriculture and other traditional activities. Another strand of literature shows that the phenomenon of jobless growth is persistent in many countries (Melamed et al., 2011). Aryeetey et al. (2007) contend that for the case of Ghana policies narrowly focused on achieving macroeconomic stability and accelerated growth without adequate employment consideration leading to a jobless growth. Jobless growth is also visible in the recent times of India (Mehta et al., 2011), and is also existent in Latin America (Jemio et al., 2006). Mehta et al. (2011) observes dependence on agriculture seems to have become a poverty trap for many, especially in the wake of the jobless growth since the mid1990s. The authors further noted, in rural India, households whose chief earners were landless wage earners constituted 30% of all poor households. In the same line of argument ILO estimates that around 40% of workers worldwide are still poor, precisely below the poverty line (ILO, 2011). Melamed et al., (2011) presents two channels through which growth can be poverty dampening via the labour market. According to these authors the changes in economic growth and the associated shifts in the structure of the economy have to generate increased demand for labour and an increase in the productivity of each worker. The authors further contend that an increase in the quantity of employment without simultaneous increase in productivity increases the number of working poor due to the fact that wages stagnate with low productivity. Gutierrez et al. (2009) supports this argument by showing that the sectoral productivity and employment pattern of growth may have important implications for poverty alleviation while Kapsos (2005) shows that in East Asia, rapid economic growth has resulted in large gains in labour productivity, contributing to rising living standards while also fostering robust mployment growth. Therefore, decent employment should be the key to labour market policy than generating employment per se (Fields, 2007). Recent literature also highlights the important inequalities in the labour market by age and gender of the players (Melamed et al., 2011). It has been documented by Kapsos (2005) that the youth cohort (aged 15-24) has experienced low and stagnant employment elasticities. This view is supported by Ernst (2008) who shows that the youth (15-24 years) comprise of 25% of the labour force but account for 44% of the unemployed and 20% of those working live below the 6 one dollar a day poverty line. Other authors document the fact that youth unemployment is many times higher than that of the adult counterparts (ILO, 2011). Zependa (2007) contends that unemployment is highest particularly among the youth (15-24 years) and mature educated workers (55-64 years). Messkoub (2008) shows that poverty and unemployment, especially among the young, are widespread in the Middle East and North Africa region There is need to exploit the opportunities presented by the demographic transition in order to avert the demographic disaster. Countries need to look at the youth as a strong force of men and women that are dynamic, energetic and able to work for long hours and hence leading to a quicker economic transformation. The inequality along the gender dimension also stands out in the literature. It has been documented by several authors that men are more likely than women to be employed in formal private and public sectors and to earn a higher wage (ILO, 2011; Rahman et al., 2006). Zependa (2007) also documents that women are more likely to be unemployed than their male counterparts in Kenya. Heintz, (2005) indicates substantial labor force segmentation in Ghana where women are disproportionately represented in more precarious forms of employment. According to Melamed et al. (2011), gender inequality in the labour market has been more researched than the inequality between the young and old workers. Against this backdrop therefore, it is much more important than ever before to critically examine the labor market dynamics and outcomes for the youth. The big number of the youth in many economies should be viewed more of a blessing than a curse for the future development of any one country provided ample employment opportunities for the youth are available. 7 3 Methodology and Data Requirements We adopted the Job Generation and Growth Decomposition Tool (JoGGs) of the World Bank to analyze the impact of growth on poverty through employment generation and productivity. The aim of this methodology is to understand how growth is linked to changes in employment, output per worker and population structure at the aggregate level and by sectors. Additionally, we also decompose the sources of output per worker growth into total factor productivity (TFP) growth, movements of employment from one sector to another, or changes in the capital-labor ratio. To perform the decomposition we needed data on output (aggregate and by sectors), employment by sectors, population by ages, capital stock and the share of capital stock in total income for two periods and in our case we considered 2006-2011. Data on output, capital stock and the share of capital stock in income was obtained from the World Development Indicators (WDI) online facility of the World Bank. Growth can be measured either by GDP or by Value Added. Value Added, is roughly the same as GDP but excludes subsidies and taxes. National Accounts disaggregated by sectors refers to Value Added rather than GDP, so using Value Added rather than GDP ensures that sectoral disaggregations are consistent with the aggregate ones. Data on employment by sectors was obtained from Uganda National Household Panel Survey 2009/10. Data on population by ages was obtained from United Nations online Statistical Data base. All our monetary data are in constant dollars of 2000. Additionally, in order to perform other descriptive analysis, we used data from earlier Uganda National Household Survey, especially that of 2002/03 and 2005/06. These data supported analysis on a wide range of issues including, but not limited to, the job creation and employment trends by sector, geographical location, sex and age groups of the population and factors influencing observed trends-determinants of job creation and employment. 4 Findings and their discussion 4.1 Understanding the aggregate employment and productivity profile of growth We sought to understand how economic growth generates increases in employment and productivity at the aggregate level and by sectors. As is conventionally known from standard economic theory, GDP per capita, Y/N=y can be expressed as Y Y E A N E AN Or: y *e*a (1) (2) Y is total value added, E is total employment, A is the total population of working age and N is total population. By extension it implies that Y/E=ω is a productivity measure (total output per 8 worker), E/A is a measure of the employment rate (i.e. share of working age population that is employed) and A/N is the share of population of the working age in the total population. Therefore, ω, e, and a in the equation above refer to labour productivity, employment rate and share of labor force in the total population. The summation of the growth in , e and a refers to the total change in GDP per capita. Thus if we let , e and a denote the fraction of growth linked to each component then the growth rate of an economy can be expressed as: y y y y e a y y y y (3) And total growth as: y * y e * y a * y (4) * y refers to the contribution of output per worker to the growth in output per capita while the employment rate e and the share of population of working age a remain fixed. There are in fact several ways in which these two components can stay unchanged. They can both remain in the level observed in the initial year, they can both stay at the level observed in the final year, or one of them can stay in the level observed in the initial year and the other stay in the level observed in the final year. Some decompositions only consider the case where both components stay in the level observed in the initial or final year, and thus end up with a residual. However, the Shapley decomposition considers all possible alternatives, and then makes a weighted average of each (with the weights reflecting the number of ways each component can remain unchanged), eliminating in this way the residual, which can be very large. Each component thus has the interpretation of a counterfactual scenario. Similarly, e * y refers to the contribution of the employment to the growth in output per capita while the productivity parameter , and the share of population of working age a remain fixed. Finally, a * y refers to the contribution of the demographic changes to the growth in output per capita while the first two components remain fixed. In Table 1 we present the main data used for the aggregate decomposition: Output, employment and populations, as well as employment shares, output per worker, and share of population of working age. Uganda registered a growth rate of 17% in per capital Value Added, for the whole period. This growth was accompanied by decreases in employment rate (-5.41%), increases in labor productivity (22.37%) and increases in the share of population of working age (0.41%). The results from this data description already points to a profile of growth that is jobless since its associated with a reduction in the share of the employed in the total population of working age. 9 Table 1: Employment, Output, Productivity and Population. Uganda 2006-2011 2006 GDP (value added) (in Ugandan Shillings) Total population 2011 9,395,979, 621 % change 12, 895,873,648 37.2 28,431,204 33,424,683 17.6 Total population of working age 13,438,875 15,935,601 18.6 Total number of employed 11,113,222 12,464,780 12.2 GDP (value added) per capita 330 386 16.74 Output per worker 845 1,035 22.37 Employment rate 82.69 78.22 -5.41 Share of population of working age 47.27 47.68 0.41 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.1.1 Results and interpretation Using equation (4) we decompose the aggregate per capita growth into its main components and results are reported in Table 2 and figure 1 below. The table shows the contribution in US dollars of 2000 to absolute observed growth in per capita GDP as well as the percent contribution. The figure illustrates the results in US dollars of 2000. Table 2: Decomposition of Growth in per capita Value Added, Uganda 2006-2011 2000 USD Percent of total change in per capita value added growth Total Growth in per capita GDP (value added) 55.34 100 Growth linked to output per worker 72.23 19.97 3.08 130.52 Growth linked to changes employment rate Growth linked to changes in the share of population of working Age -36.09 5.57 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. The results suggest that 6 percent of the change in per capita Value Added can be linked to changes in the structure of the population. In other words, had everything else stayed the same, the sole change in the age structure of the population would have generated a growth equivalent to 6 percent of the actual observed growth. In other words, there were fewer dependants (minors and elderly) per each working age adult. Changes in productivity or output per worker were the most important, accounting for some 131 percent of observed growth. Unfortunately, changes in employment acted in the opposite direction. This suggests that 36% of the change in per capita value added can be linked to a decrease in the employment rate. This implies that the growth 10 profile in Uganda for the period 2006-2011 was a job-less growth. Figure 1 below shows the contribution to growth in value added per capita, 2000 US dollars. It shows that the demographic change and productivity contributed USD 3.1 and 72.2 per capita, respectively. Yet, the employment rate contributed negatively USD 20 per capita. Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Having decomposed aggregate per capita growth we can go further and undertake a decomposition of its key components as follows: i) we decompose changes in the overall employment rate e , to understand the role played by different sectors and ii) decompose total output per worker , to understand the role of capital, total factor productivity and inter-sectoral employment shifts. This last step can be undertaken both at the aggregate level and by sectors. This amounts to doing a step-wise decomposition: first decomposing aggregate growth into employment and productivity changes and then decomposing employment and productivity changes, into other sub-components. Figures 2a and 2b illustrate employment by sectors and output per worker by sectors, respectively. 11 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Figure 2a shows that agriculture is the leading employer in Uganda employer well over 8 million Ugandans (translating to over 70% of the entire labor force) in both periods. This is followed by the services sector that is employing over 2 million Ugandans (19% in 2006 and 22% in 2011). Both manufacturing and industry (comprises other components apart from manufacturing e.g. construction, mining and utilities etc.) employ well below 1 million Ugandan but manufacturing is doing better than the other industry. As expected, in terms of productivity, we observe the opposite in figure 2b. Agriculture has the lowest output per worker and industry has the highest in both 2006 and 2011. The industrial sector is followed by the services sector and manufacturing. This puts a policy question on the productivity of jobs held in the agricultural sector in terms of income that workers can generate. Lower productivity implies lower earnings for workers involved in that type of economic activity and, by extension, it implies low prospects of overcoming the problem of poverty by those employed in that sector. Agriculture being the major employer in Uganda, it implies that the majority of workers in Uganda are holder low paying jobs an issue which has a great bearing on poverty alleviation. 12 Figure 2b: Output per Worker by Sectors, Uganda 2006-2011 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.2 Understanding the role of each sector in employment generation To understand the way in which sectors contributed to employment generation and to total per capita growth we can further decompose employment (rate) growth ( e ) by sectors. The easiest is of course to express the total growth in employment as the sum of employment generation in each sector. s e ei (5) i 1 13 Ei is just the change in employment in sector i as a share of total working age A population. This gives a simple measure of which sector contributed more to changes in the employment rate. Where ei Once we understand the sources of growth in the employment rate, we can understand the link of employment growth in sector i , to the observed change in per capita output by combining the results in steps 1 and 2. The total contribution of sector i will be its contribution to changes in total employment times the contribution of employment rate changes to total growth calculated in step 1. As before this can be interpreted as the per capita growth consistent with a counterfactual scenario, in which all else (productivity, demographics, and employment in the remaining sectors) had all remained unchanged, and the only change had been the observed employment growth in sector i . Table 3 presents the data on employment by sector. All sectors registered absolute growth in the number of employed, with the industrial sector leading followed by the services, manufacturing and agriculture came last. It is worth noting that only services and industry gained in the share of employment in the working age population. Figure 2a illustrates the same data. Total employment grew by 12%, but as a result of the simultaneous growth in the working age population, the employment rate declined by only 5.41%. Table 3: Employment by Sectors of Economic Activity, Uganda 2006-2011 Total employment Agriculture services Manufacturing Industry Total 2006 2011 8,389,075 2,095,951 444,565 183,631 11,113,222 8,913,741 2,760,421 498,418 292,200 12,464,780 Employment/pop. of working age % change 6.25 31.70 12.11 59.12 12.16 2006 62.42 15.60 3.31 1.37 82.69 2011 % change 55.94 17.32 3.13 1.83 78.22 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.2.1 Results and Interpretation Table 4a and Figure 3a show the results of the decomposition of the employment growth by sectors of the economy. This enables us to tell a sector whose growth was jobless or job-creating. The first column of the table shows how the -4.47 percentage points of decline in the employment rate were distributed among the different sectors. Agriculture is responsible for most of the decline and then the manufacturing sector. Agriculture and manufacturing sectors contributed to the decline in the employment rate by 6.5 and 0.2 percentage points respectively. 14 -10.39 11.07 -5.45 34.19 -5.41 Positive increments are noted in the services and industrial sector with 1.73 and 0.5 percentage points, respectively. Figure 3a illustrates how the decline of the employment rate of 4.47 percent points was distributed among the different sectors. These results imply that growth in the agricultural and manufacturing sectors was jobless while growth in the services and industrial sectors was job generating. These findings combined with high productivity in services and industrial sectors give a great prospect to these sectors in an effort to reduce poverty via employment creation. Table 4a: Contribution of employment changes to overall change in employment rate, Uganda 2006-2011 Agriculture services Manufacturing Industry Total employment rate Contribution to change in total employment rate (percent points) Percent contribution of the sector to total employment rate growth -6.49 1.73 -0.18 0.47 -4.47 145.0 -38.6 4.0 -10.4 100.0 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 15 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Tables 4b and Figure 3b, show the contribution of sectoral employment changes to growth in total per capita output. Of the -20 US dollars registered in per capita output, agriculture and manufacturing contributed negatively with -29 and -0.8 US dollars, respectively. Services and industry contributed positively 7.7 and 2.1 US dollars, respectively. Agriculture being the major sector in Uganda, a small contraction in employment led to a very big negative effect on growth such that the positive effect in services and industry could not offset it. Table 4b: Contribution of employment changes to overall change in per capita GDP (value added), Uganda 2006-2011 Agriculture services Manufacturing Industry Total contribution Contribution to change in per capita GDP (value added) Percent of total change in per capita GDP (value added) -29.0 7.7 -0.8 2.1 -20.0 -52.3 13.9 -1.5 3.8 -36.1 16 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.3 Decomposing output per worker growth in within sectoral changes and between sectoral changes We further decompose output per worker into sectoral employment shifts and changes in output per worker within sectors by noting that: Y E Y i i E S Ei E (6) Or equivalently: S i si (7) i 1 Where Yi is value added of sector i 1,...S ; E i is employment in sector i ; and E is total employment. This means that i Yi will correspond to output per worker in sector i , Ei 17 Ei is the share of sector i in total employment. This equation just states that total output E per worker is the weighted sum of output per worker in all sectors, where the weights are simply the employment share of each sector. si Using the Shapley approach, changes in aggregate output per worker can be decomposed into changes in output per worker within sectors (also known as within component), and movements of labor between sectors (also referred to as between component). Increases in output per worker within a sector will increase average output per worker. How big the effect is, will depend on the size of each sector (i.e. its share in total employment). On the other hand, relocation of workers across sectors of different productivity levels can increase average output per worker if the final relocation implies that a larger share of workers becomes employed in higher productivity sectors. Once we can trace the growth in output per worker back to its within and between components, we can then calculate the amount of growth in total output per capita that can be linked to changes in output per worker in sector i, and to inter-sectoral relocation of labor by combining the contribution of each of these components to changes in output per worker, with the contribution of changes in output per worker to total per capita growth (obtained in step 1). As before, the contribution of changes in output per worker within a sector can be interpreted as the total per capita growth consistent with a counterfactual scenario, in which all else (employment rate, demographics, and output per worker in the remaining sectors) had all remained unchanged, and the only change had been the observed change in output per worker in sector i. The contribution of the inter-sectoral shift component can be interpreted as a counterfactual scenario in which the employment rate, the demographic structure of the population and output per worker in each sector had remained unchanged, and labor had reallocated across sectors as observed. Table 5 shows output per worker for the two years under consideration as well as the percent growth for the period. The industrial sector saw a sharp decline in the output per worker (11%) while the agricultural sector saw a small positive growth of 2% compared to manufacturing (24%) and services sector (13%). Table 5: Changes in Output per Worker by Sectors. Uganda 2006-2011 2006 Agriculture services Manufacturing Industry 249 2,118 1,427 12,13 2011 254 2,392 1,764 10,77 18 % change 1.84 12.94 23.57 -11.23 Total output per worker 8 845 5 1,035 22.37 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Table 6a and Figure 4a below; show the results of applying the above formula to Ugandan data. The first column of the table shows the contribution of each sector as well as of inter-sectoral employment shifts to the observed growth in total output per worker. The $189.1 increase in output per worker for the period is accounted for by a $27 reduction from industry and an increase in Agriculture ($3.4), Services ($56.2), Manufacturing ($13.5) and inter-sectoral labor relocation of $143.3. The fact that inter-sectoral shifts had a positive contribution means that on average labor moved from lower than average productivity sectors to above average productivity sectors. As we saw in the previous section all sectors increased their employment shares, but agriculture had the lowest share of 6.3%. Thus we can conclude that an important share of growth in output per worker was due to movements of the labor force into other sectors and to a small extent into agriculture. The second column of Table 6a shows the results as percentage of total output per worker growth. Of the total increase in output per worker, agriculture accounted for 2%, services (30%), manufacturing (7%) and the inter-sectoral shift (76%). The industrial sector accounted for a decline of 14.4%. In summary, Agriculture, Manufacturing and Services, all saw increases in output per worker, and given their large share in total employment had important positive effects on aggregate output per worker. The industry exerted a negative effect on average output per worker. Inter-sectoral shifts, which capture movement of labor between sectors, exerted a positive effect on output per worker, which means that on average labor moved from low productivity sectors to high productivity sectors. Table 6a: Decomposition of Output per Worker into Within Sector Changes in Output per Worker and Inter-sectoral Shifts. Uganda 20062011 Contribution to Change in Total Output per Worker Agriculture services Manufacturing Industry Inter-sectoral shift Total change in output per worker 3.4 56.2 13.5 -27.2 143.3 189.1 19 Contribution to Change in Total Output per Worker (%) 1.8 29.7 7.1 -14.4 75.8 100.0 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Table 6b and Figure 4b show the contribution of changes in output per worker and inter-sectoral shifts to total growth in per capita value added. Table 6b: Contribution of within Sector Changes in Output per Worker and Inter-sectoral Shifts to Change in GDP (value added) per capita. Uganda 20062011 Contribution to change in GDP (value added) per capita Agriculture services Manufacturing Industry Inter-sectoral shift Total contribution to change in per capita GDP (value added) 20 Percent of total change in GDP (value added) per capita 1.3 21.5 5.1 -10.4 54.7 2.3 38.8 9.3 -18.8 98.9 72.2 130.5 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.4 Understanding the sources of changes in total output per worker (net of inter-sectoral shifts) at the aggregate level The previous section decomposed changes in output per worker into changes in output per worker within sectors and changes in output per worker due to movements of labor between sectors with different levels of productivity. Aggregate and sectoral changes in output per worker (Δω), will capture changes in output per worker, but its interpretation is not so straight forward. Increases in output per worker can come from three different sources: i) increases in capital labor ratio ii) increases in Total Factor Productivity (TFP) and iii) relocation of jobs between bad jobs sectors (low productivity) to good 21 jobs sector (high productivity), which we described above as the between component of growth in output per worker or the labor relocation effects. To see this, note that under constant returns to scale, if Yt t f ( Et , K t ) where K t is the capital stock t and a multiplicative shift parameter, then output per worker will be Yt Et t f (1, K t Et ). Therefore it will capture changes in capital labor ratio K t Et , and changes in the shift parameter, t . Changes in the shift parameter will be capturing all other sources of growth not due to changes in capital labor ratio, or in other words the Solow residual. It will mainly capture changes in technology and relocation of production between sectors with different productivity levels (inter-sectoral shifts), but it may also capture cyclical behavior of output: firms operating in economic downturns may have underutilized capital, when the demand rises again; it will be reflected as rise in output per worker. We can thus decompose within productivity changes (or productivity changes net of inter-sectoral shifts) into changes due to increases in the capital-labor ratio and the residual, which can be interpreted (with caution) as TFP growth. If data on capital stock is available then we can assume a particular functional form for the production function and separate the contribution of higher capital-labor ratios and the rest. For example if we are willing to assume that the production function is Cobb-Douglas then: 1 Y K E E (8) In competitive markets 1 is the share of payments to capital in total Value Added. Once we have an estimate of , we can calculate total factor productivity as a residual: In the first period it will be: (1 ) Y K TFPt 0 . / E t 0 E t 0 (9) In the second period we need to take into account that part of the change in output per worker due to relocation shifts so that: (1 ) Y K TFPt 1 B / E t 1 E t 1 (10) The term in square brackets is just output per worker in period two net of relocation effects. 22 In this way we are able to see whether changes in output per worker net of relocation effects, were due to increases in capital per worker (K/E) or in total factor productivity (net of relocation effects). Once we have decomposed changes in output per worker into its two components –TFP and the capital labor ratio- we can calculate the amount of total per capita growth that can be linked to each component. Table 7 illustrates the data used for this decomposition. Table 7: Data used for Decomposition of Output per Worker, Capital Stocks, Capital Labor Ratio and Share of Capital in Total Income. Uganda 2006-2011 2006 Share of Capital in Total Income (%) Capital Total output per worker Output per worker net of inter-sectoral shifts Capital Labor Ratio TFP residual net of inter-sectoral shifts 21% 2,250,759,969 845 845 203 278 2011 24% 3,073,422,546 1,035 891 247 232 % change 0.00 36.55 22.37 5.41 21.74 -16.51 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.4.1 Results and interpretation Figure 5 show the decomposition of changes in aggregate output per worker for Uganda between 2006 to 2011. It illustrates the role of capital and TFP using the above formulas, as well as the inter-sectoral shifts we calculated earlier on. Total output per worker increased by 22.37 percent (Table 7). Of this increase inter-sectoral employment shifts exerted a positive effect on output per worker contributing with $143.3 of total productivity growth. The capital labor ratio also increased, contributing an additional $204. But TFP suffered an important reduction of $158, however, did not offset the positive effects (Figure 5). From this data we can clearly say that TFP changes are responsible for the lower output per worker. A natural next step would be to try to understand what the forces behind TFP decreases were. 23 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 4.5 Understanding the role of inter-sectoral employment shifts It is possible to understand further how changes in the share of employment in the different sectors help explain the overall contribution of inter-sectoral shifts to per capita growth or output per worker. A large number of studies have found that structural change, which is movements of labor force shares from low productivity sectors to high productivity sectors, is an important factor behind growth. Increases in the share of employment in sectors with above average productivity will increase overall productivity and contribute positively to the inter-sectoral shift term. On the contrary, movements out of sectors with above average productivity will have the opposite effect. By the same token, increases in the share employment in sectors with below average productivity should reduce growth, while reductions in their share should contribute positively to growth. Thus if sector i has productivity below the average productivity, and decreases its share si its contribution will be positive, that is outflows from this low productivity sector have contributed to the increase in output per worker. If on the other hand, the sector sees an increase in its share, these inflows into this low productivity sector will decrease output per worker and thus have a 24 negative effect on the inter-sectoral shift term. The magnitude of the effect will be proportional to: i) the difference in the sector’s productivity with respect to the average and ii) the magnitude of the employment shift. Once we have calculated the contribution of each sector to changes in output per worker linked to employment relocation effects, we can proceed further and calculate the amount of total per capita growth that can be liked to relocation effects in each sector. 4.5.1 Results and interpretation Table 8 illustrates the results of this exercise. The first column shows average output per worker between 2006 and 2011, with the last row of the column showing average output per worker for the whole economy. The second column shows the change in employment shares in each sector. The final column shows the contribution of each sector to the $143.3 contributed by intersectoral employment shifts to total growth in output per worker. The change in the employment share in agriculture is negative and agriculture being a low productivity sector, contributed positively to the inter-sectoral shift component. The change in employment share in services and industry is positive and these sectors being high productivity sectors, they contributed positively to the inter-sectoral shift component. The change in the employment share in the manufacturing sector was neutral and hence had a minor negative contribution to the inter-sectoral shift component. Of the three positively contributing sectors, agriculture had the lowest contribution because it’s a low productivity sector while industry and services are high productivity sectors and hence contributed most to the inter-sectoral shift component. Hence, movements of labor to industry and services sectors are associated with increases in per capita growth or productivity growth. Table 8: Understanding the Role of Inter-Sectoral Employment Shifts. Uganda 2006-2011 Change in employment share (percent points) Average Output per Worker Agriculture services Manufacturing Industry Aggregate 252 2,255 1,595 11,457 940 -0.040 0.033 0.000 0.007 Sectoral contribution to inter-sectoral shift component 27.37 43.22 -0.01 72.76 143.33 Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Table 9 shows the percent contribution of each term to the inter-sectoral shift or between components of productivity changes. Whereas agricultural had a negative employment share 25 shift, it contributed positively (by 19%) to inter-sectoral shifts due to the fact that it’s a low productivity sector and hence any shifts of workers from this sector positively influences per capita growth. Services and industry had a positive employment share and being high productivity sectors, contributed positively (by 30% and 51%, respectively) to inter-sectoral shifts. Hence any shift of labor to these sectors positively influences per capita growth. Manufacturing had a negative employment shift share and being a high productivity sector, contributed negatively (by 0.01%) to inter-sectoral shifts. This implies that shifts of labour from the manufacturing sector somewhat hurts per capital growth. Table 9: Decomposition of Inter-sectoral Shifts. Uganda 2006-2011 Direction of Employment Share shift Sectoral contributions Agriculture services Manufacturing Industry Contribution to Inter-sectoral Shifts (%) + + 19.09 30.15 -0.01 50.76 Total Contribution of inter-sectoral shifts 100 4.6 Putting everything together Once the above steps are completed the percent contribution of each factor to total changes in GDP per capita can be obtained. A simple example will illustrate how this is done. Assume we want to know what the contribution of TFP changes to per capita GDP growth was. Using step 3 we calculate the percent contribution of TFP growth to total output per worker growth, denote this by TFP , and from step 2 we calculated the share of growth of GDP per capita that was linked to changes output per worker, denote this by . Thus the share of growth in per capita GDP that can be attributed to TFP changes will be TFP TFP * . And the amount of growth that can be linked to changes in TFP will be TFP * y. 4.6.1 Results and interpretation Table 10a and Table 10b below illustrate the results for Uganda, in percentage contribution and in US dollars of 2000, respectively. The demographic component accounts for 6% of all the change. The other 94% is explained by an increase in output per worker within sectors (32%), a decrease in the share of working age population employed (36%), and a positive effect of labor relocation (99%), which on average moved from low productivity sectors to high productivity sectors. When looking across sectors the biggest role was played by the Services sector (83%), 26 industrial sector (35%) and manufacturing sector (8%). Agriculture contributed negatively by 31%. Sectoral contributions are decomposed into: i) contribution of within changes in output per worker (first column), ii) contribution of changes in employment (second column), and iii) contributions of the sector to the inter-sectoral employment shifts. The final column is the total effect of the sector. Overall, services, industry and manufacturing had a positive contribution while the agricultural sector had a negative contribution. Table 10a: Growth Decomposition. Percent Contribution to Total Growth in GDP (value added) per capita, Uganda 2006-2011 Contribution of within sector changes in output per worker (%) Contribution of changes in Employment (%) Sectoral contributions Agriculture 2.32 services 38.79 Manufacturing 9.28 Industry -18.80 Subtotals 31.60 Demographic component Total Total % change in value added per capita 2006-2011 -52.33 13.92 -1.45 3.77 -36.09 - Contributions of Inter-sectoral Shifts (%) 18.89 29.83 -0.01 50.22 98.92 Total (%) -31.12 82.54 7.82 35.18 94.43 5.57 100.00 16.74 Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. Despite the positive output per worker contribution of the agricultural sector, the decrease in its employment rate was so large that it more than offset the output per worker growth. Even, the positive effect of shifts of labor away from agriculture to other sectors could not offset this effect, so that in the aggregate agriculture reduced total per capita growth by 31%. The services sector had all its contribution positive; contributed 39% to output per worker, 14% to the growth in employment, 30% to the inter-sectoral shifts and overall contributed 83% to total per capital growth. In all three sectors the effect was mostly due to increases in inter-sectoral shifts. The industrial sector on the other hand contributed negatively to output per worker by 19%, but contributed positively and strongly via inter-sectoral shifts by 50% and a positive but modest contribution to the employment rate by 4% leading to an overall contribution of per capita growth of 35%. The manufacturing sector contributed positively only to output per worker by 9% but contributed negatively by 1.5% and 0.1% to employment rate and inter-sectoral shifts respectively. However, the positive effects more than offset the negative contribution leading to an overall positive contribution of 8% to overall per capital growth. 27 Table 10b: Growth Decomposition. Contribution to Total Growth in GDP (value added) per capita, Uganda 2006-2011 Contribution of within sector changes in output per worker Sectoral contributions Agriculture services Manufacturing Industry Subtotals Demographic component Total change in value added per capita 1.29 21.47 5.14 -10.41 17.48 - Contribution of changes in Employment Contributions of Inter-sectoral Shifts -28.96 7.70 -0.80 2.09 -19.97 - 10.45 16.51 0.00 27.79 54.74 Total Monetary values are 2000 USD Source: Table obtained using JoGGs Decomposition tool with data from World Development Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base. 28 -17.22 45.68 4.33 19.47 52.25 3.08 55.34 Conclusions and implications This paper set out to establish the link economic growth and employment in Uganda for the period 2006-2011. We obtained data from World Development Indicators, Uganda National Household Panel Survey (2011) and United Nations Statistical Data Base and we adopted the Job Generation and Decomposition (JoGGs) Tool of the World Bank during the analysis. Our findings reveal interesting insights into the link. The growth profile of Uganda for the period 2006-2011 was jobless as evidenced by 36% change in per capita value added emerging from a decrease in the employment rate. Agricultural sector contributed the most to the decline in the percent of total change in capita GDP and in the employment rate by 31% and 6.5%, respectively. Manufacturing sector contributed positively to the percent of total change in per capita GDP by 8% but negatively to change in total employment rate by 0.2%. Positive contributions to the employment rate and the percent of total change in per capita GDP were observed in the services and industrial sectors. The services sector contributed 83% and 1.7% to the percent of total change in per capita GDP and in the employment rate, respectively. The industrial sector contributed 35% and 0.5% to the percent of total change in per capita GDP and in the employment rate, respectively. Therefore, by sectors, the growth profile in agriculture and manufacturing was jobless while the growth profile in services and industry was job-creating. It is further noted that productivity or output per worker contributed over 100% to total change in per capita GDP growth. By sectors, output per worker was lowest in the agricultural sector and highest in the industry followed by the services sector. Hence the industrial and services sectors have higher prospects in an effort by the country to alleviate poverty via employment creation. The inter-sectoral shifts had a positive contribution to the output per worker meaning that, on average, labor moved from lower than average productivity sectors to above average productivity sectors. All sectors, apart from manufacturing, contributed positively to the intersectoral shift component. Given that the employment rate in agriculture was negative and that it is a low productivity sector implies that the relocation of workers from agriculture to other sectors had a positive effect on per capita growth via the inter-sectoral shift component. On the other hand, the employment rate in the services and industry was positive and that they are high productivity sectors means that the relocation of workers to these sectors had a positive effect on the per capita growth via the inter-sectoral shift component. The change in the employment rate in manufacturing was zero and that this sector having the lowest productivity after agriculture, means that the relocation of workers to this sector had a negative but modest effect on per capita growth via the inter-sectoral shift component. The key messages from our analysis are: First, the results show the importance of the Services sector with a positive and strong effect on the output per worker, employment rate, inter-sectoral shifts and hence on the overall per capita value added growth. Second, despite the positive output per worker and inter-sectoral shifts contribution of the agricultural sector, the decrease in its employment rate was so large that it more than offset the positive effects so that in the aggregate 29 agriculture reduced total per capita growth. Third, the industrial sector contributed negatively to output per worker but contributed positively and strongly via inter-sectoral shifts and positively but modestly via the employment rate leading to an overall positive contribution to per capita growth. Fourth, the manufacturing sector contributed positively only to output per worker by 9% but contributed negatively to employment rate and inter-sectoral shifts respectively. However, the positive effects more than offset the negative contribution leading to an overall positive contribution to overall per capital growth. Fifth, the demographic changes of the population are generating a window of opportunity to raise per capita income and thus reduce poverty: there are now fewer dependents per working age adults and this trend is likely to continue. If adults manage to engage in productive activities and sectors, this demographic shift will have an important poverty reducing impact. 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