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CSAE Working Paper WPS/2014 18

Employment Creation, Poverty and the Structure of the Job Market in Nigeria

Francis Teal Centre for the Study of African Economies

University of Oxford

Abstract Job creation is a central part of the policy of almost all African countries. The problems are particularly acute in Nigeria where over the period of the early 2000s there was a substantial decline in the number of private wage jobs. While policy discussion focuses on the extent of unemployment the unemployment rate, as measured in labour force surveys, is low in Nigeria. This is a common finding across a range of sub-Saharan African countries. To understand the nature of the employment problem it is argued in this paper that jobs need to be linked to the incomes those jobs generate. While wage jobs do, on average, produce more income than those in self-employment a critical issue is the extent of the distribution of incomes within occupational categories and the overlaps across these sectors. It is the very low incomes we observe in Nigeria at the bottom of the distribution, for both wage and the self-employed, that creates high exit rates from the labour market – the jobs simply pay too little. In this paper the evidence is reviewed as to how far the more rapid growth of recent years has translated into poverty reduction and how these poverty measures link to job creation. There is evidence that the headcount measure of poverty has fallen and has been associated with a rapid rise in rural employment over the period from 1999 to 2006. It is this sector which has seen the largest increases in income. This was not due to investment in human capital, the return for which has fallen over the period, but to a general increase in the returns to the labour and land owned by the poor.

Paper prepared for the 53rd Annual Conference of the Nigerian Economic Society August 27-30 2012

Abuja, Nigeria.

Revised April 2014

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Outline of Paper 1. Introduction .............................................................................................................................. 32. From macro growth to poverty reduction .............................................................................. 4

2.1 Macro growth .................................................................................................................................. 42.2 Poverty reduction ............................................................................................................................ 52.3 Mechanisms that can link GDP growth to poverty reduction ...................................................... 6

3. What jobs are being created in Nigeria .................................................................................. 73.1 Unemployment or Joblessness ....................................................................................................... 73.2 Types of Employment .................................................................................................................... 9

4. Incomes 124.1 Average incomes across sectors .................................................................................................. 124.2 The dispersion of wages in Nigeria ............................................................................................. 134.3 Mincerian earnings equations ...................................................................................................... 17

5 Jobs and the incomes from those jobs .................................................................................. 18References 19Appendix: Measuring Employment, Unemployment, and Joblessness ........................................... 20

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1. Introduction The question of how jobs can be created is a critical question facing governments in Africa, Fox and Gaal (2008). This question is seen as even more pressing for young people who are both a high proportion of Africa’s population and one for whom a lack of jobs is a particularly acute problem. The African Economic Output for 2012 has as its special theme: Promoting youth employment. It identifies the problem in part as “too many bad jobs in poor countries, too few jobs in middle income countries” (page 105) and, in part, that public and private formal sector hiring is insufficient (page 125). The obstacles that young people in African labour markets are identified as an “insufficient demand for their labour (page 133), lack of skills (pages 133 and 134) and that jobs are allocated to those with connections (page 134). The solution is in part that enterprises need to grow and that for small firms a two-pronged strategy is argued to be needed “1) removing barriers to small and microenterprise, enabling them to grow and fill the missing middle and 2) supporting young people to be entrepreneurs and create their own jobs” (page 137). While small enterprise needs to be encouraged it is also the case that governments should undertake efforts to increase formalisation (page 138) and ensure that education and skill mismatches are addressed (pages 141ff). Both diagnosis and prescription are familiar. What is strikingly absent from both is any attempt to link jobs to incomes. This is not a peculiarity of the analysis in the African Economic Outlook - it dominates discussion of the jobs agenda in poor countries. By implication informal sector jobs are poorly paid and formal sector ones are well paid, the latter are always preferred and the problem is how governments can create them. This account of the informal sector has been challenged by Maloney (2004) and Günther and Launov (2012) who both argue that, at lease for some, the informal sector is preferred. However, at least judging by the African Economic Outlook, this has led at best to a slight qualification to the general presumption that creating jobs must mean creating wage opportunities. In this paper the pattern of job creation in Nigeria over the period from 1999 to 2006 is examined. This is done in the context of the greatly improved macro growth rate of the Nigerian economy since the mid 1990s, which has not relied on the performance of the oil sector: “Since 2001, Nigeria has had the longest period of sustained expansion of its non-oil economy since independence. Growth has occurred across all sectors of the economy and has been accelerating. While non-oil growth averaged about 3 to 4 percent from 1995 to 2000, it more than doubled to more than 7 percent since 2003, and it rose to 8 to 9 percent in recent years.” Volker (2010, page 2) This improved macroeconomic performance has been associated with a fall, albeit modest, in the headcount poverty rate. The data used enables a link to be made from this improved macro performance to the rate and nature of job creation that is occurring in the economy both in terms of the incomes from those jobs and the sectors in which jobs are being created. It will be argued that to understand how the improvement in macro outcomes has been translated into poverty reduction it is essential to focus, not on jobs, but on the incomes from those jobs. It will be shown that incomes appear to have risen rapidly over the period from 1999 to 2006 and these increases have been highest among the least skilled. One consequence of this has been that the return to

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education, particularly among the secondary and higher educated, has declined substantially. The data on income is also used to show that a focus on differences in averages across wage and self-employment misses the substantial dispersion across of incomes within occupation categories and also the very substantial overlaps across those distributions. As the quotations above from the African Economic Outlook indicate the problem of job creations is seen as one of ensuring more formal sector wage jobs associated with higher levels of education. The data used in this paper shows the problem is rather different. It is explaining the extent of the dispersion of incomes and explaining what limits those at the bottom part of the distribution from moving up. As has been noted in the case of developed country labour markets this dispersion cannot be explained by observable human capital, Mortensen (2005). In the next section this concern for jobs is linked with the long-standing central focus of policy which is the need to reduce poverty in Africa. This section examines how far the more rapid macro growth in sub-Saharan Africa generally, and in Nigeria in particular, has translated into poverty reduction. Section 3 is concerned to document which types of jobs have been created in Nigeria. The link between jobs and incomes is investigated in Section 4 and a final section draws out the implications of the findings for how jobs link to incomes and poverty reduction. 2. From macro growth to poverty reduction 2.1 Macro growth The background to the current concern with jobs is the dramatic improvement in Africa’s macro performance. Figure 1, which is based on data from the World Bank Development Indicators, shows the sustained nature of the recovery in per capita incomes at a macro level that has occurred both in Nigeria and other sub-Saharan African (SSA) countries since the mid1990s.

Figure 1

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1960 1970 1980 1990 2000 2010Year

NigeriaSub-Saharan Africa (excluding Nigeria and South Africa)

Source: World Development Indicators

Nigeria and other sub-Saharan African countries GDP per capita in constant (2000) price US$

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Between the mid1990s and 2010 GDP per capita has increased by approximately 40 per cent for Nigeria and, at 33 per cent, slightly less for the rest of sub-Saharan Africa. By the standards of China’s growth this is a modest achievement, by the standards of Africa’s recent economic history it is clearly a major one. Nigeria’s performance is better than that for the average of other SSA countries. Nigeria has seen the longest sustained rise in per capita income in its post-independence history. If improvements in macro performance do lead to poverty reduction and more jobs we should be seeing much less poverty and many more jobs. In the next sub-section we ask if that is what we do observe. 2.2 Poverty reduction If macro data is used and one is willing to assume that the distribution of income has not changed very much then the implication is that there must have been a very dramatic decline in poverty, an argument advanced by Sala-i-Martin and Pinkovskiy (2010). They have the poverty headcount measure falling from nearly 45 per cent to approaching 30 per cent in the space of 15 years. Their argument is that if Africa is growing as fast as the data suggest and the distribution of income is not changing very much then poverty must be falling fast. This is an important argument and it underlies a belief that if only economies can be made to grow that will solve the problem of poverty at the same time. However does this inference from the use of macro result hold if we use micro data? In Figures 2 and 3 patterns of poverty reduction are shown both for sub-Saharan Africa as a whole (Figure 2) and then specifically for Nigeria (Figure 3). Figure 2: Poverty measures from micro data in sub-Saharan Africa Percentages of Population Below Poverty Lines $1.25 and $2.25 (Weighted by Population) Comparing 1993 and 2001 Comparing 2001 with 2009

A Note on the Countries in Figure 14.1 All four comparisons include Burundi, Cote d’Ivoire, Ghana, Kenya, Madagascar, Mali, Nigeria, Senegal, South Africa, Tanzania, Uganda and Zambia, with the total population of 348 million or 52% of the total population of Sub-Saharan Africa. The 2009 versus 2001 comparison also includes Burkina Faso, Cameroon, Ethiopia, Gambia, Malawi, Mozambique, Rwanda and Seychelles, which gives the total population of 481 million or 72% of the total population of Sub-Saharan Africa. The 2001 versus 1993 comparison also includes Mauritania, which gives the total population of 351 million or 52% of the total population of Sub-Saharan Africa.

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Figure 3

These data are taken from the World Bank Poverty data base created by Martin Ravallion and his collaborators used in Chen and Ravallion (2008, 2010). This data is based on micro surveys and is calculated quite independently of the macro statistics – except in so far as these surveys are used by macro statisticians. The figures report two poverty lines those traditionally termed the 1 US$ per day and the 2 US$ per day (although adjusted for price changes they are slightly above those numbers). The key difference for any comparison with Sala-i-Martin and Pinkovskiy (2010) is that this data is based on micro surveys which cover consumption and seek to ask how many of those surveyed have consumption above the poverty level. In summary the micro data looks from the ground up while the macro data looks from the top down. They give very different perspectives but ones it is necessary to reconcile. Figure 2 shows that over the period from 1992 to 2001 the headcount measure for the whole of sub-Saharan Africa increased modestly while over the period from 2001 to 2009 it decreased modestly leaving it very little changed from 1993. Given the rate of population growth for African countries over this period this implies a large increase in the numbers in poverty. In Figure 3 similar data is presented for Nigeria using the four survey points for which the World Bank reports data – 1985, 1992, 1996 and 2003. This data too shows a decline in the headcount poverty measure from 1996 to 2003 but these declines are too modest to prevent the poverty headcount measure being higher than in 1985 and much higher than in 1992. In fact for Nigeria the macro data and the micro data suggest over the period from 1985 to 2003 a strong positive relationship between GDP and the headcount measure of poverty, a point illustrated graphically in Figure 4. While after 1992 there is an inverse relationship between poverty and GDP the long run relationship is clearly positive, higher GDP per capita was associated with a rise in the headcount measure of poverty. Something is going radically wrong in translating GDP growth into poverty reduction if these numbers are remotely close to the truth. 2.3 Mechanisms that can link GDP growth to poverty reduction The question posed by the data shown in Figure 4 is when does GDP growth lead to poverty reduction? In an economy dependent on the income from natural resources, as has been the case for much of Nigeria’s

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The Headcount Poverty Measure in Nigeria: 1985 - 2003

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Figure 4

post-independence history, the connection between GDP growth and poverty reduction would appear to run through the mechanisms by which the rents are appropriated and distributed. As has been widely documented that mechanism has clearly not operated. What is of particular interest for the period after 2000 is that the oil sector has not dominated the growth of the economy and some poverty reduction has been achieved. In any economy where rents are not of a size to enable substantial distribution, or are not used for that purpose, then the link from GDP growth to poverty reduction must be through the price and assets of the relatively unskilled. In an economy such as Nigeria such a linkage implies rises in the prices of assets owned by the poor, namely their land and labour. If we are to understand how growth links to poverty reduction then it is through the incomes from the employment of the poor. To establish that link we need to know how the employment structure in Nigeria has been changing. 3. What jobs are being created in Nigeria Measuring jobs is a complex issue in an African economy and how it can be done depends on the type of data that is available. This section summarises work reported on in more detail in Haywood and Teal (2010). That paper used two complementary sources of data. One is the 2003/2004 Nigeria Living Standards Survey (NLSS) the data from which is presented and analysed in Haywood (2007). The second is the data from the Nigerian Bureau of Statistics General Household Survey (GHS). The existence of two sources for 2004 (one from the regular GHS and one from the NLSS, which was undertaken in 2003/04), enabled a direct comparison of certain key measures for that year. Further the GHS data enabled an investigation of changes over time. 3.1 Unemployment or Joblessness A common problem across both questionnaires — indeed, with any labor market survey of African economies — is how the labor force is to be defined (an Appendix to this paper shows the different

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methods employed by the NLSS and GHS). There are several problems in an African context with the standard procedures used for both the NLSS and the GHS. The first is that all those who do not seek work, because they know that there is no work available for them, are likely to be classed as economically inactive. The second is that women who work in the household, but not on the farm or in an enterprise, may be classified as economically inactive, even though their activities are, in fact, part of the work of the farm or non-farm enterprise. For all these reasons, it is important not to confine attention to the labor force as conventionally measured. It may well be that some defined as out of the labor force (i.e., economically inactive) are indeed that; but especially among the young, it seems far more likely that the definitions used are failing to capture those who seek jobs but, for reasons we need to investigate, cannot find any. The importance of this point is apparent from Table 1 which presents the breakdown of the population aged between 15 and 65 who are not in school from the NLSS 2003-04 data and from the GHS for 2004, using the definitions discussed in the Appendix. At this overall level, there is a broad agreement between the numbers from the NLSS and the GHS. The GHS for 2004 shows that the proportion of the population aged 15 to 65 which is not in school and is defined as out of the labor force is 23 percent; while the NLSS shows 26 percent. While the measured unemployment rate in the GHS data, at 3 percent, is much higher than that from the NLSS at 1 percent, it is clear that for these national averages, unemployment as conventionally measured is very low. Table 1: Breakdown for Population aged 15-65 not in schooling National Living Standards Survey 2003-2004

Labor force 73.9%

Not in labor force 26.1%

Employed 98.9%

Unemployed 1.1%

General Household Survey 2004 Labor force 77.0% Not in labor force

23.0% Employed

97.0% Unemployed 3.0%

Note: For both the NLSS and the GHS the numbers are weighted using household weights provided by the Nigerian Office of Statistics. Source: Haywood (2007) and authors’ calculations. As the data in Table 1 shows while unemployment is low this is in the context of a definition of the labour force which excludes about 25 per cent of the population. The most likely interpretation of these numbers is that by using a conventional measure to establish the extent of unemployment the questionnaires have missed the fact that those defined as outside the labour force would indeed seek jobs if there were perceived to be any available. If that argument is correct then interpreting the unemployment numbers requires an examination of the types of employment that are available. That is the subject of the next section.

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3.2 Types of Employment Where most jobs are not wage jobs, and this is the case in most African countries, including Nigeria, it is far from obvious how the notion of a “job” should be interpreted from the data, Sen (1975). It seems clear from much of the discussion about the lack of jobs for the newly educated secondary graduates that “jobs” are defined as wage jobs. This is one of four categories of employment that can be identified from the NLSS, the other three are family agriculture, non-agriculture self-employment and non-agriculture unpaid family work. The data for wage employment can be further sub-divided and that is discussed below. Haywood and Teal (2010) sought to make the jobs identified from the GHS as comparable as possible to those from the NLSS in a manner set out in the appendix. The GHS data can be used to extend the analysis by looking at changes over time. It has already been noted that the measures of these types of employment and non-employment may be sensitive to how the questionnaire is designed. The advantage of the GHS is that over the period 1999-2006, while it underwent an extension in terms of the questions that were asked, there were only minor changes to some of the categories used, and the overall structure of the questionnaire remained unchanged. Table 2 presents the data from the NLSS for both the overall structure of employment and the divide in wage employment between the private and public sectors as a prelude to the breakdown available from the GHS. Table 2: Types of Employment National Living Standards Survey 2003-04

Percent of the

labor force Percent of the

population Number in

sample

Family agriculture 51.3 37.8 19,505Non-agricultural self-employed 30.0 22.1 6,796Non-agricultural unpaid family work 3.4 2.5 1,213Wage employment 14.3 10.4 3292Unemployed 1.1 0.8 315Not in the labor force - 26.3 10,193Total 100 100 41,314 Percentage of the wage

labor force Number in sample

Private sector (including cooperatives) 21.6 652 Public sector 62.1 2,235 NGOs, international organizations 9.8 405 Total 100 3,292 Source: Haywood (2007). The figures presented are weighted. The cross-section picture of the labour market that emerges from the Haywood (2007) study of the NLSS data is a familiar one for African labour markets. Small scale agriculture continues to be by far the most important source of employment followed by non-agricultural self-employment. Within the small wage

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sector (10 per cent of the population aged 15 to 65) public sector employment dominates. As already noted it is the high percentage who have no employment that shows the problems posed by a lack of jobs, not the very low rate of measured unemployment, which is shown in Table 2 to be less than 1 per cent of the labour force. While the NLSS survey for 2004 provides us with a valuable cross-section, it is the data from the GHS that can be used to chart changes in the structure of the labour market. As household weights from the NBS are available for the data it is possible to provide a picture of changes in employment opportunities across sectors. As an important concern of policy is with the labour market for young adults, as well as its overall structure, the GHS data is used in Table 3 to show the changes in employment for the population aged 15 to 65 and in Table 4 a similar breakdown is given for young adults, those aged from15 to 25. For both tables those in full time education are excluded from the data. Thus the data is used to provide a view as to how all aged 15 to 65, who are not in school, are employed when indeed they do have a job. Table 3: Changes in Types of Employment and Non-Employment

(for the population aged 15 to 65 and not in full time education) General Household Survey 1999-2006 using weights

1999 2004 2006

Family agriculture 14,642,919 19,174,958 20,879,318 (31.1) (36.6) (38.1)

Self-employed non agriculture 11,459,221 13,530,848 12,646,622 (24.4) (25.8) (23.1)

Private wage job 3,648,687 2,469,547 2,416,849 (7.8) (4.7) (4.4)

Public sector wage job 2,921,613 2,900,464 2,662,995 (6.2) (5.5) (4.9)

Non-agricultural unpaid 756 37,759 33,335 (0) (0.10) (0.08)

Apprenticeship 983,805 570,224 1,034,794 (2.1) (1.1) (1.9)

Unemployed 804,128 1,262,148 1,074,311 (1.7) (2.4) (2.0)

Not in the labour force 12,555,196 12,422,276 14,094,686 (26.7) (23.7) (25.7)

Total 47,016,325 52,368,224 54,842,910 (100) (100) (100)

Note: The figure shown are estimates of the numbers in each category derived from the GHS survey using household weights. The figures in ( ) are column percentages. The figures for unpaid non-agricultural workers are based on a very small sample. Source: Authors’ calculations.

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The most important structural changes in the sense of changes in the proportions across sectors are the rise in employment in family agriculture and the decline in private wage employment. While the numbers working in family agriculture have increased by more than 40 per cent, the numbers in private wage employment have fallen by over 30 per cent. The numbers employed in non-agricultural self-employment have changed little. Public sector wage employment has also fallen albeit by much less than that for the private sector. In summary the absolute number of wage jobs fell by nearly a quarter. Table 4: Changes in Types of Employment

(for the population aged 15 to 25 and not in full time education) General Household Survey 1999-2006 using weights

1999 2004 2006

Family agriculture 1,409,202 3,141,123 3,098,297 (13.5) (26.9) (25.8)

Self-employed non agriculture 1,556,786 2,333,866 1,756,573 (14.9) (20.0) (14.6)

Private wage job 1,178,754 338,950 322,854 (11.3) (2.9) (2.7)

Public sector wage job 219,067 147,183 141,765 (2.1) (1.3) (1.2)

Non-agricultural unpaid 756 27,818 20,384 (0.01) (0.24) (0.17)

Apprenticeship 862,776 457,383 823,830 (8.3) (3.9) (6.9)

Unemployed 606,557 844,086 627,683

(5.8) (7.2) (5.2)

Not in the labour force 4,596,961 4,405,973 5,218,190 (44.1) (37.7) (43.5)

Total 10,430,859 11,696,382 12,009,576 (100) (100) (100)

Note: The figure shown are estimates of the numbers in each category derived from the GHS survey using household weights. The figures in ( ) are column percentages. The figures for unpaid non-agricultural workers are based on a very small sample. Source: Authors’ calculations. The data in Table 3 are based on the population aged from 15 to 65 who were not in full time education. If there is an increasing problem in finding jobs, this will be particularly apparent in the youth labour market. Table 4 repeats the breakdown of the data from Table 3 but now confines the sample to those aged 15 to 25, still excluding those in full time education. The pattern of declining earning opportunities in wage employment is now much more accentuated. Private wage employment as a share of the population fell from 11 to 3 per cent, a decline from 1.2 million jobs to just over 300,000. It is also the case that non-agricultural self-employment for this age group increased only very modestly between 1999

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and 2006. It is in family agriculture that employment increased, more than doubling over this period. It will also be noted from Table 4 that while for this age group the rates of measured unemployment are much higher than for the whole labour force they remain modest being 5.2 per cent of the population age group in 2006. In 2006, for young people between ages 15 and 25 who were not in full-time education, only 32 percent of those in the urban sector had a job in the form of wage or non-agricultural self-employment. In the rural sector, the comparable number was 12 percent. In summary, if jobs over this period meant non-farm employment, either in the form of wage jobs or non-agricultural self-employment, only 19 percent of those aged 15 to 25 had one. 4. Incomes 4.1 Average incomes across sectors In the last section it was shown that over the period from 1999 to 2006 the proportion of the population, aged 15-65, in wage jobs declined substantially while the number of non-agricultural self-employed increased modestly. It was noted in the introduction that, in policy discussions, jobs frequently meant wage jobs so has this absolute decline in the number of wage jobs led to a decline in the income opportunities for Nigerians? Income data is available both from the NLSS (2004) survey and from the GHS surveys. As with the data on the pattern of employment it is possible to compare these income numbers for 2004 as there was an overlap in the timing of the surveys. Haywood (2007) provides a figure for incomes of Naira 15,619 per month (Naira 9,373 in 2003 prices) from the NLSS data from a job, it is shown below that the number for 2004 from the GHS surveys was Naira 9,735 (in 2003 prices) per month, remarkably similar. The approach in the GHS to measuring incomes is very different from that in the NLSS. A single question was asked of all those who reported having a job: “What was the income in the last month from all jobs and including all allowances?” Table 5 reports the answers to this question for each of the main employment categories identified from the data. The top panel of Table 5 reports mean earnings in real Naira (using 2003 prices). The finding is that real average earnings have increased 66 percent over this period, from Naira 5,800 per month to 9,600 (2003 prices). Further, this rise has been highest for those in the poorest sector, namely family agriculture, where earnings have doubled. Those in wage jobs, while still having the highest earnings, have seen a rise of less than half that in family agriculture. As a result of this higher rate of growth of earnings for family agriculture, the gap between private wage employment and earnings from family agriculture declined from 1.6 to 1.2 over the period from 1999 to 2006, a quite remarkable fall. The second panel of Table 5 uses US dollars rather than Naira. As the Naira has appreciated over this period, the rise in US dollar-denominated prices is larger than that in real Naira – in fact, almost twice as large. This much larger increase in incomes in US dollar terms, rather than in domestic prices, implies that labor costs are becoming increasingly uncompetitive on the international market. In the context of labor-intensive manufacturing exports, labor costs are important. While systematic data comparing wage rates are scarce, wages of US$50 per month have been cited for China. As Table 5 shows, by 2006

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average wages in the private sector were US$125 per month. These appear to be very high wages for jobs in a very poor country. The bottom panel of Table 5 shows the average years of education across the three employment types. Over the period 1999 to 2006, the average years of education of those in these employment categories increased by 13 percent. This increase was highest for those in family agriculture so that the gap between the average education level of those in wage employment relative to those in family agriculture declined. However, it remained significant, with the average level of education of those in public sector wage jobs continuing to rise such that by 2006 it was 40 per cent higher than for those with private wage jobs and twice that for the urban self-employed.

Table 5: Mean Monthly Earnings and Education by Employment Type (for the population aged 15 to 65 and not in full time education)

General Household Survey 1999-2006 1999 2004 2006

Monthly incomes in Naira (2003 prices)

Family agriculture 4,574 8,219 9,086

Self-employed non-agriculture 6,066 9,175 8,947

Private wage job 7,518 13,759 10,955

Public sector wage job 11,358 18,646 15,243

Total 5,759 9,735 9,628

Monthly incomes in US$

Family agriculture 26.8 70.3 100.8

Self-employed non-agriculture 35.0 80.1 102.6

Private wage job 43.3 120.2 125.4

Public sector wage job 65.7 162.5 175.1

Total 33.5 84.2 108.5

Average education in years

Family agriculture 2.7 3.5 3.5 Self-employed non-agriculture 5.6 6.3 6.2

Private wage job 8.4 9.6 8.7 Public sector wage job 10.9 11.7 12.1

Total 4.6 5.4 5.1

4.2 The dispersion of wages in Nigeria In the last section data on average incomes in Nigeria were presented. These averages hide important aspects of the processes of income determination in Nigeria. The first is the problem of measurement which has several dimensions. One is that even among those classified as having an income earnings

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activity that is being a farmer, non-agricultural self-employed or a wage employee 14 per cent failed to record an answer. The answers that are recorded are, as already noted, a response to a single question. It is clearly open to doubt that this can be well answered for any form of self-employment particularly in the agricultural sector. A second issue with the presentation of means in the data above is that if the distribution is much closer to normality in logs, as is indeed the case, then the median can be substantially below the mean. These possible issues with the incomes data are investigated in two stages. First the distribution of the logs of the income measures are shown, then evidence on the ability of the Mincerian earnings function to predict earnings will be presented. In Figure 4 the distribution of wages and incomes from self-employment are shown. There is a substantial degree of overlap in the distributions although the higher level of wage incomes is clear from the figure. However as Figure 5 shows the overlap between private sector wages and the non-agricultural self-employed is much greater.

Figure 4

Figure 5

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The distribution of incomesfrom wages and urban self-employment in 2004

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The distribution of incomesfrom private wage jobs and urban self-employment in 2004

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Finally in Figure 6 we compare the distributions for the urban and the rural self-employed. It appears from Figure 6 that these two distributions for the two self-employed sectors more or less completely overlap. What appears to have happened for the rural sector is that the answers are bunched with the respondents (or more likely the enumerators) choosing certain rounded numbers.

Figure 6

The figures use US$ as these are more easily interpretable and, as is implied by the distributions being much closer to log normality, the medians are substantially below the mean. The median incomes in US$ for 2004 are rural self-employment 65, urban self-employment 72, private wage 87 and public wage 131. Thus there is a hierarchy of incomes across sectors consistent with the view that rural incomes opportunities are inferior, on average, to those in urban areas. However the distributions shown in figures 4 – 6 emphasise that these averages hide sizable overlaps across all these sectors and, in the case of the rural and urban self-employed, a more or less complete overlap. There appear to be many self-employed who are better off than many wage employees. The data in figures 4 to 6 is for measured incomes. It might be objected that the distribution would differ if there were controls for human capital. In the appendix the distribution is shown with controls taken from the Mincerian earnings function reported below and such controls make almost no difference to the extent of the overlap in the distribution for urban self-employment incomes and private sector wages. One qualification to these findings must, of course, be the issue of measurement. That self-employment incomes are more poorly measured than wage ones is suggested by the ranking of the coefficients of variation which are 1.1 for public wage employees, 1.6 for private wage employees, 2.3 for the urban self-employed and 2.8 for the rural self-employed. In the appendix we report Mincerian earnings functions for each of the four categories of employment identified from the survey data. While the ability to explain the farmer’s income is lower than that for the other categories, the regression for farmers is very similar in its point estimates to the other income sources. It does seem to be the case that the rather simple procedure used by the GHS survey has produced usable numbers of income differences across sectors. To assess the sources of the changes in income shown in Table 5 the next section presents pooled versions of the Mincerian earnings equations in Table 6.

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The distribution of incomesfrom urban and rural self-employment in 2004

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Table 6 A Mincerian Earnings Equation for Nigeria Dependent variable Ln(Monthly income in Naira at 2003 prices)Male 0.48*** 0.49*** 0.49*** (0.006) (0.006) (0.006)

Age 0.05*** 0.06*** 0.06*** (0.002) (0.002) (0.002)

Age_sq/100 -0.05*** -0.05*** -0.05*** (0.002) (0.002) (0.002)

Primary Incomplete 0.15*** 0.15*** 0.25** (0.033) (0.033) (0.115)

Primary Complete 0.22*** 0.22*** 0.26*** (0.007) (0.007) (0.009)

Secondary Reached 0.38*** 0.37*** 0.45*** (0.008) (0.008) (0.011)

Post Secondary 0.73*** 0.74*** 0.83*** (0.013) (0.013) (0.020)

Urban 0.18*** 0.17*** 0.17*** (0.007) (0.007) (0.007)

Dummy year 2004 0.48*** 0.46*** 0.51*** 0.53*** (0.007) (0.006) (0.009) (0.011)

Dummy year 2006 0.51*** 0.49*** 0.62*** 0.70*** (0.007) (0.006) (0.009) (0.010)

Self-employed non- 0.17*** 0.19*** 0.32*** 0.30***agriculture (0.007) (0.007) (0.009) (0.009)

Wage private 0.52*** 0.23*** 0.32*** 0.28*** (0.013) (0.012) (0.017) (0.018)

Wage public 0.92*** 0.47*** 0.54*** 0.49*** (0.009) (0.011) (0.015) (0.016)

Self-employed non- -0.16*** -0.14***agriculture*Year 2004 (0.014) (0.015)

Self-employed non- -0.31*** -0.25***agriculture*Year 2006 (0.014) (0.015)

Wage private*Year 2004 -0.03 0.01 (0.026) (0.028)

Wage private*Year 2006 -0.32*** -0.22*** (0.027) (0.029)

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Wage public*Year 2004 0.00 0.06** (0.020) (0.025)

Wage public*Year 2006 -0.33*** -0.19*** (0.021) (0.027)

Primary incomplete* -0.07 Year 2004 (0.134)

Primary incomplete* -0.20 Year 2006 (0.122)

Primary complete* -0.04**Year 2004 (0.016)

Primary complete* -0.15***Year 2006 (0.016)

Secondary reached* -0.07***Year 2004 (0.018)

Secondary reached* -0.23***Year 2006 (0.018)

Postsecondary*Year 2004 -0.09*** (0.030)

Postsecondary*Year 2004 -0.23*** (0.031)

Constant 8.10*** 6.31*** 6.25*** 6.23*** (0.005) (0.032) (0.032) (0.032)

Observations 83,017 83,017 83,017 83,017R-squared 0.16 0.30 0.31 0.31 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

4.3 Mincerian earnings equations The descriptive statistics presented so far appear to show very substantial rises in earnings across all types of employment. This section uses the Mincerian earnings function to ask whether these rises were the result of increases in the returns to education or reflected the underlying growth factors in the economy. Table 6 reports, in column (1), an equation which pools the sources of incomes and controls only for the sector and the year effects but not for the human capital attributes of the workers. Earnings rose by 67 percent from 1999 to 2006 and the occupational dummies reflect the ranking already seen above. The first question addressed, in Column (2), is whether the observable dimensions of human capital can explain these occupational differentials. Human capital does not change the ranking but it does alter, very substantially, the differential between the occupations. With controls for observable human capital the differential between urban self-employment and private wage employment is reduced to 4 percentage points. Further the differential between private and public wages is roughly halved.

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In Column (3) the occupational and time dummies are interacted. It is thus possible to see how the relativities across these occupational groups have changed over this seven year period. In Column (3) the time dummies have an interpretation as the rise in income for the rural self-employed. The data suggest that the income in this sector increased by 86 per cent - far higher than the other sectors. The relativities within the other occupational groups changed little implying a pattern by which rural incomes grew faster than those of all other sectors at an approximately constant rate. It is possible these results, which suggest substantial rises for the relatively poor, might be misleading if the returns to education are changing. In column (4) interacted dummies for education and time are added to the specification. The result is to increase the relative rise in the income of the rural self-employed. There are substantial and significant falls in the return to education at the secondary and higher levels. It is clear from these tables that the pattern of income growth over this period in Nigeria was one that favored those in low-paying sectors, in particular farmers and, within these sectors, those who were poorest, i.e., those with the lowest levels of education. 5 Jobs and the incomes from those jobs The evidence presented above suggests that the rate of job creation in Nigeria has not varied over the period from 1999 to 2006. The rate of non-employment is high at some 25 per cent of the population aged 15 to 65 who are not in full time education. While as conventionally measured unemployment rates are low these low rates hide low participation in the labour force. Those classified as outside the labour market in the tables above would almost certainly take jobs if you were on offer if - and this is a crucial qualification - those jobs offered a higher rate of pay than is currently the position. It is this failure to link a discussion of jobs with the incomes those jobs generate that renders much of the current policy discussion focused on jobs otiose. While jobs confer other benefits in addition to income (see Sen (1975)) it remains true that income is a crucial aspect of a job. It is particularly crucial for the young and among the urban young the rate of non-participation has been rising from 31 to 38 per cent of the population aged 15 to 25 (still excluding those in full time education). In understanding how the labour market in Nigeria operates it is necessary to distinguish, not between employment and unemployment, but between being in and outside the labour force as conventionally defined and, for the employed, the incomes generated by the job. Such an approach contrasts with a focus on unemployment and the divide between self and wage employment. As the data presented above has shown there is a hierarchy of incomes from jobs with public sector ones at the top and rural based ones at the bottom. However these differences in average income hide the sizable dispersion of income within each occupation and the overlaps across occupations. They also miss the changes in these differences over the period for which we have data -1999 to 2006. One of the most striking findings from the analysis is the rate at which incomes, for the employed, have been rising, Employment has been shown to have shifted to the rural sector in which incomes have been rising most rapidly. This pattern would be consistent with the poverty data showing a decline in the headcount measure from 1996 to 2003. The poorest have seen the most rapid rise in their incomes. While that result may surprise it is necessary to set it in context. The picture that emerges from the analysis of the data is in many ways a familiar one. Income earnings opportunities for young adults, those

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aged 15 to 25, are scarce. Some 60 per cent of these did not have a measured source of income in the survey. Wage employment is small and has been declining rapidly over this period. Such joblessness and a lack of wage opportunities are commonly cited features of Nigeria’s labour market and one amply confirmed by the data presented above. However the analysis of this paper shows that a focus on jobs is misleading. The problem is the incomes that the jobs that are available generate and the problem is to understand, not primarily differences in averages across occupations, but the dispersion of those incomes. The gap within private wage employment and urban self-employment shown in Figure 5 suggest a range from less than US$10 to US$400 per month. A similar range can be seen in comparing urban and rural self-employment in Figure 6. All forms of employment can generate relatively high incomes it is just that instead of being typical such high incomes are exceptional. Why that is the case should be the focus of policy debate not whether particular forms of jobs are being created. References African Economic Outlook (2012), Special theme: Promoting Youth Employment, African Development

Bank and OECD Development Centre for Economic Commission for Africa. Chen, S. and M. Ravallion (2008) The Developing World Is Poorer Than We Thought, But No Less

Successful in the Fight against Poverty, World Bank Policy Research Working Paper, WPS4703. [Updated August 2009]

Chen, S. and M. Ravallion. (2010). The Developing World is Poorer than We Thought, but No Less

Successful in the Fight against Poverty. Quarterly Journal of Economics 125: 1577-1625. Fox, L. and M. Gaal (2008), “Working out of Poverty: Job creation and the quality of growth in Africa”,

The World Bank, Washington. Günther, I. and A. Launov (2012) “Informal employment in developing countries: Opportunity or last

resort?” Journal of Development Economics 97 (2012) 88–98 Haywood, L. (2007) “The Performance of the Labor Market in Nigeria: A Report on the 2003–2004

Nigeria Living Standards Survey.” Centre for the Study of African Economies, University of Oxford, U.K.

Haywood, L. and F. Teal (2010) “Chapter 2 Employment, Unemployment, Joblessness and Incomes in

Nigeria, 1999-2006”, in Volker Treichel (editor) World Bank Report: NIGERIA: Employment and Growth Study Africa Region, Washington, D.C.

Kingdon, G., J. Sandefur, and F. Teal (2006): “Labour market flexibility, Wages and Incomes in Sub-

Saharan Africa in the 1990s,” African Development Review, Vol. 18, No. 3, December 2006. Maloney, W.F. (2004) “Informality Revisited”, World Development Vol. 32, No. 7, pp. 1159–1178. Mortensen, D.T. (2005) Wage Dispersion: Why are similar workers paid differently?, The MIT Press,

Cambridge, Massachusetts.

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Sala-i-Martin, X. and Maxim Pinkovskiy (2010) “African poverty is falling ... much faster than you think!” NBER Working Paper 15775.

Sen, A.K. (1975) Employment, Technology and Development, Oxford University Press. Treichel, V. (2010) World Bank Report: NIGERIA: Employment and Growth Study Africa Region,

Washington, D.C. Volker (2010) Appendix: Measuring Employment, Unemployment, and Joblessness The National Living Standards Survey (2003-04) and the General Household Surveys (1999, 2004 and 2006) used very different approaches to measure the labor force. The NLSS (2003-04) determines those who were available for work by asking the following four questions: During the past 12 months, have you done work for which you received a wage or any other payments? During the past 12 months, have you been paid money, including payment in kind through self-employment (for example, trading)? During the past 12 months, have you worked on a farm, in a field, or herding livestock? During the past 12 months, have you worked unpaid for an enterprise belonging to a member of your household? Individuals who answered “yes” to at least one of these questions were counted as labor market participants. While these four questions identified only past activities in the labor market, a further question allowed the inclusion of people who were available for work even if they may not have worked in the past: Were you available for work in the last 7 days? Individuals who were engaged in any of these activities plus those who were available for work during the last 7 days constitute the labor force. Attention, in the use made of the data in this paper, is confined to those in the age group 15-65. Further individuals currently in educational institutions are excluded from the labor force. In a standard analysis of labor market data, it would be assumed that all of those not in the labor force so defined were economically inactive. Further, a standard analysis would divide those in the labor force between the employed and unemployed. The latter are people who were not active during the last 12 months but who were available for work during the last 7 days. In contrast to the indirect approach used by the NLSS, the GHS adopted a direct approach by asking “What was your main job in the last week?” The following possible options were listed in the 2006 version of the questionnaire.1

1 There were minor changes in the design of the questionnaire over the period from 1999 to 2006. In 1999 and 2004,

an additional category, “Community/voluntary work,” was identified as a wage job if the respondent also gave employee or member of a producer cooperative as his or her employment status.

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Worked for pay Got job but did not work Worked for profit On attachment but did not work Apprenticeship Stayed home Went to school Did nothing. Using the GHS data, this study defines the labor force as a person who was classified under any of the first five categories given above for “What was your main job in the last week?” It also includes those who answered “Did nothing” and gave as a reason for this inactivity one of the following: Looked for job Sick Believed no job available Laid off 30 days or less Waiting to begin work. Those who answered “Stayed home” were classified as out of the labor force, as were those who answered “Did nothing” and gave as a reason one of the following: Retired Invalid Other. The measure of unemployment was obtained by counting those who answered “Did nothing” as their main job and who gave as a reason one of the following: Looked for job Believed no job available Laid off 30 days or less. Clearly this manner of identifying those in the labor force and, conditional on being in the labor force, being unemployed, is very different from the procedure used for the NLSS. The advantage of the GHS is that it gives comparable data for three years: 1999, 2004, and 2006. For 2004, a direct comparison can be made between the NLSS and the GHS, so issues of comparability can be addressed directly. There are several potential problems in comparing the NLSS approach with that of the GHS. The first is that for the GHS, the way of identifying whether the work is agricultural or non-agricultural self-employment is to use the industry classification. In contrast, the NLSS has a direct question: “During the past 12 months, have you worked on a farm, in a field, or herding livestock?” This study assumed, for the GHS data, that those who answered “Worked for profit” and who are classified as working in agriculture, fishing, or forestry are working as self-employed in agricultural households. The study also assumes that all those working in other industries and who report their main job as “Working for profit” are in non-farm self-employment. Thus, in seeking to establish the numbers in self-employment in and outside of

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agriculture, the NLSS and GHS data are not directly comparable. As is discussed in Haywood and Teal (2010) the two surveys show different shares of workers between agricultural and urban self-employment and these differences may reflect these alternative methods of asking the questions. A further problem, discussed in Haywood (2007), is that it is not possible to identify apprentices in the NLSS data. This was partly a matter of how the questionnaire was designed and partly due to how the questions were answered. As is clear from reviewing the questions, apprentices could possibly be classified as out of the labor force. Given the importance of this occupation for the young, this is an important lacuna in the data. In contrast, in the GHS questionnaire, apprenticeship is one of the job choices, so this occupational choice can be explicitly identified. Appendix Table 1 A Mincerian Earnings Equation for Nigeria Dependent variable Ln(Monthly income in Naira at 2003 prices) Family agriculture Non-agriculture

self-employment Privatewage Public sector wage

Male 0.45*** 0.60*** 0.42*** 0.13*** (0.009) (0.009) (0.026) (0.016)

Age 0.06*** 0.05*** 0.06*** 0.06*** (0.002) (0.003) (0.006) (0.007)

Age_sq/100 -0.06*** -0.04*** -0.06*** -0.06*** (0.002) (0.003) (0.008) (0.008)

Primary incomplete 0.11*** 0.22*** 0.17* 0.43* (0.041) (0.069) (0.091) (0.223)

Primary complete 0.19*** 0.28*** 0.23*** 0.20*** (0.009) (0.012) (0.037) (0.058)

Secondary reached 0.27*** 0.46*** 0.44*** 0.44*** (0.012) (0.012) (0.036) (0.057)

Post secondary 0.52*** 0.76*** 0.86*** 0.78*** (0.035) (0.025) (0.039) (0.056)

Urban 0.12*** 0.19*** 0.20*** 0.09*** (0.015) (0.009) (0.021) (0.015)

Dummy 2004 0.51*** 0.34*** 0.47*** 0.50*** (0.009) (0.011) (0.024) (0.018) Dummy 2006 0.63*** 0.31*** 0.30*** 0.29*** (0.009) (0.011) (0.026) (0.019)

Constant 6.26*** 6.60*** 6.41*** 6.91*** (0.044) (0.052) (0.127) (0.147) Observations 45,581 26,118 4,552 6,766R-squared 0.20 0.31 0.32 0.29Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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Appendix Figure 1

This figure shows the distribution of earnings for private wage jobs and the urban self-employed with controls for human capital taken from the Mincerian earnings function reported in Appendix Table 1. The data in the figure was obtained by taking the residuals from the earnings functions and adding to them the average log incomes for the self-employed and wage employees.

0

.2

.4

.6

Den

sity

4 6 8 10 12

Private Wage Job Urban Self-Employed

The distribution of incomesfrom private wage jobs and urban self-employment

conditioning on human capital