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|Tanzania, Republic of||9.2||8.1|
Source: R. van der Hoeven, W. van der Geest (1999)
Employment experiences in Asia have differed substantially in East and South Asian countries on the one hand and Southern Asian countries on the other. In the former countries there has been sustained high formal sector employment growth in most countries, resulting in increases in the real manufacturing employment. In South Asia on the contrary there are strong indications that employment in the informal sector has expanded (see ILO 1996 for more details). Also in Latin America, transitional costs of liberalization policies have been high. As Lee (1996) points out "The experience of Chile in the early 1980s illustrates the severe effects of overshooting in terms of stabilisation policy. Output contracted by 23 per cent in 1982-93 and unemployment remained above 23 per cent for 5 years. Similarly the Mexican crisis of 1994-95 illustrated the devastating effect of wrong monetary and exchange rate policies" (p.489).
Table 2: Informal employment as % of labour force (non-agricultural) Selected countries in Latin America
|Uruguay (Montevideo only)||36.3||36.7||36.6||37.0||37.9||37.7||37.9||37.1|
Source: ILO (1998).
Strong recovery took place in Latin America in the 1990s, with almost all countries having a positive GDP growth rate but as the Regional Office of the ILO (ILO 1995) indicates, unless the GDP growth rate is robust at levels well above the labour force growth and sustainable (see also Fanelli and Frenkel 1995 and Amadeo 1996), growth in formal sector jobs remains limited. In effect, also in most countries in Latin America one detects an increase in the number of workers in the informal sector (Table 2) which makes many workers understandably fearful of further liberalization measures. Investigations by the Regional Office of the ILO confirm that growth in formal sector jobs is correlated with high economic growth, irrespective of the type of labour market regulations followed (ILO, 1995).
Another phenomenon which is observed in many countries is an increase in wage and income inequality. For those countries where reliable data are available in the 1980s, income inequality increased in Asia in 6 out of 12 countries: Bangladesh, Indonesia, Thailand, China, Singapore and Sri Lanka; in Africa in 4 out of 6 countries: Nigeria, Tanzania, Kenya and Ethiopia; and in Latin America in 9 out of 14 countries: Bolivia, Mexico, Argentina, Brazil, Panama, Venezuela, Guatemala, Honduras, Peru. (See World Bank, 1996).
Changes in income inequality are in themselves often a sign of worry although these changes must be seen in wider perspectives. Firstly, some countries start from a low base. Income inequality is low in many Asian countries and slight increases in inequality, especially when accompanied by strong growth, will not result in increased concern by workers. And even in countries with high income inequality strong growth may diffuse concern by lower income classes. Secondly, income inequality figures do give only a limited indication of inequalities in society. A rich person paying for an expensive medical treatment, which is paid for in other countries by the state may be in fact not better off than a sick person in that other country. However, in general, changes in income inequality reflects changes in inequities in society, which can have important effects on the social climate and willingness to change.
Theory on income inequality and adjustment and trade liberalization points often to declining inequality, as adjustment and trade liberalization will favour the production of goods by the production factor in which a country has comparative advantage (for most developing countries unskilled labour) (Berry et.al. 1997). However, evidence is often not supporting these theoretical outcomes. ILO (1996) indicates for example that in most countries in the 1980s which underwent structural adjustment programmes, wage dispersion increased with falling real wages (Table 3). Also World Bank (1997) argues that "information on wage inequality in developing countries is sparse and mixed". "Evidence from East Asia supports the view that greater openness in countries with an abundance of unskilled labour benefits this type of labour" but "even for these countries however, the picture of relative wages is more complex, reflecting the interplay of the increase in relative demand for unskilled labour and the supply of skilled labour". For Africa "greater openness and policy changes in the 1980s are associated with recovery in growth and some reduction in poverty, but with an increase in equality in some cases". It continues that "The generally favourable verdict on East Asia in the 1960s and 1970s has been brought into question by analysis of experience in Latin America in the 1980s. In some countries increased openness has been associated with widening wage differentials" (p.61).
Table 3: Wage dispersion and real wage changes in manufacturing (US$) (1975-1979 to 1987-91)
|Wage Dispersion||Real Wage|
|Korea, Republic of||-8.2||116.9|
Source: ILO (1996).
The facts thus seem to be clear. The increasing inequality may lead to different conclusions however. One conclusion is that liberalization has not been advanced sufficiently and that domestic labour market constraints have inhibited the markets to profit from liberalization (as in World Bank, 1997). One might also conclude that the liberalization process is influenced by other mechanisms which are not explained by the traditional Hecksher-Ohlin theory which lie at the heart of the theories of comparative advantages. Alternative explanations for increased inequality introduce more than two categories of labour (namely no education, basic education and higher education) and argue that for successful export production at least basic education is necessary (Berry et.al. 1997, p.14, also Owens and Wood, 1997). Other explanations are that manufacturing tends to be dominated by large companies in the formal sector where wages are higher which have weak linkages to the small scale sector ("globalization accentuates the disadvantage of small scale producers"), or that liberalization makes it easier to import capital goods (especially if exchange rates are overvalued) which increases productivity and raises the demand for skilled labour (UNDP, 1997).
Furthermore Amsden and van der Hoeven (1996) observe that the distribution between incomes from labour and capital in industry has shifted in the direction of capital in the 1980s which has led to changes in consumption patterns and lifestyles adding to inequity (see also Pieper, 1997, and ILO, 1996). Also liberalization has resulted in the decline of trade union membership which has weakened the bargaining power of workers (as we will further discuss in section 2.4).
Liberalization and adjustment programmes in developing countries have put social expenditure under strong pressure. However in some countries downward pressure on expenses on education, health and social welfare, started already during the economic crisis before adjustment programmes were applied. Adjustment programmes are therefore not necessarily the principal cause of decline in social expenditure, although they failed in most cases to reverse the decline. A recent evaluation of adjustment programmes by the World Bank has pointed out (World Bank, 1996) that especially in Latin America and Africa, adjustment programmes were accompanied with a decline in the percentage of social expenditure in total government expenditures (Table 4) Given the fact that total government expenditure often declined in absolute terms, this resulted in declining per capita expenditure figures. Declining government expenditure will not necessarily be detrimental to poorer classes. Alesina (1998) points out that often middle classes and more vocal political groups profit most from government expenditure and that therefore a decline in government expenditure might hurt them more than the poor. However looking at educational and health indicators measuring primary and secondary school enrolment and infant mortality which are relevant to the poor one notices a deterioration in education standards and a slowdown in the decline in infant mortality rates during adjustment and less than full recovery after adjustment (Table 5). This is strongest felt in Africa, where actually in a number of countries primary school enrolment rates declined (a phenomenon unprecedented in history) affecting large parts of the population especially in poorer areas (van der Hoeven and van der Geest, 1999), as well as in Latin America where especially the middle class suffered large setbacks in providing their children with accessible quality education.
Table 4: Composition of social sector expenditures (percentage of GDP)
|Asia||Latin Americaa||Sub-Saharan Africaa|
|Total social spending||2.7||3.3||3.4||7.1||7.3||7.8||5.9||5.6||5.3|
|Percentage of total expenditures|
|Total social spending||17.9||19.6||19.6||23.7||23.4||19.3||26.1||22.4||19.9|
Note:a = Only countries with data for the post-adjustment period.
Source: World Bank (1996).
Limited or absence in progress in education has not only serious implications for efforts by countries to increase productivity for production for domestic markets and export markets but also for income inequality. Londono (1996) argues for example that the growing uneven distribution of human capital in Latin America has increased income inequality and provides figures that the dispersion in human capital increased the Gini concentration coefficient by 5 points. Furthermore a strong correlation between the growing number of households in poverty and the growing number of households headed by illiterate household heads is suggested (p.16).
Table 5: Trends in the selected social indicators
|% change in gross enrolment ratio||1.3||0.5||0.3||1.4||-0.4||1.0||4.7||-0.5||-0.4|
|% change in infant mortality rate||-2.5||-3.1||-3.6||-5.6||-2.5||-2.4||-1.8||-1.7||-1.4|
Note:a = only countries with data for the post-adjustment period.
Source: World Bank (1996).
UNCTAD (1997) reports on educational attainment and the skill intensity of exports in a number of countries. The analysis "lends support to the hypothesis that educational attainment is a necessary but not a sufficient condition for skill-intensive production". "All countries with a high share of skill-intensive exports also have a relatively high educational attainment while evidence from countries such as Argentina, Chile, Peru and Uruguay suggests that relatively high educational attainment does not automatically translate into skill-intensive exports". "Almost all countries where high educational attainment has translated into skill-intensive exports are those that have sustained a rapid pace of capital accumulation, technological upgrading and productivity growth over many decades" (p.158).
The relation between adjustment, education, skills and productivity increases are thus complex, but data are sufficiently robust to argue that a slowdown or reversal in primary and basic secondary and vocational education contributes to greater inequalities in societies and that this hampers countries possibilities to take full advantage of increased production for exports.
We have reviewed briefly the relation between stabilization, macroeconomic policy and adjustment policy on growth and equality. This chapter discusses in more detail the interlinkage between growth, inequality and poverty.
A trade-off between growth and income equality is often based upon the argument of accumulation, i.e. lower income inequality would lower national savings rates and hence, hamper future growth. Evidence of research in the 1970s has shown rather convincingly that the savings arguments for a trade-off between income equality and growth is often not valid, and were it valid, it is only a weak explanatory variable. Country studies have provided examples of countries which combined high income equality with high growth rates.
The discussion on inequality and growth has received recently impetus from authors who combined new growth theory - which endoginizes technical progress - with political economic models - which endoginize political decisions. These authors argue that inequality is harmful to growth. Alesina and Perotti (1994) discuss several causal links which underlie this notion. Links on a more traditional economic footing, include the effect of income inequality on the composition of the demand and the effect of inequality on factor endowment effecting the supply of human capital. A more equal income distribution leads to an increased demand for industrial goods which triggers off innovation and growth. Growth is further enhanced by increased investment in education by low income groups, as a consequence of increased equality in income and capital, allowing them to build up stocks of human capital more rapidly. Among the political explanations, two explanations seem to figure prominently. The first one postulates that inequality leads to voting behaviour which sanctions higher taxes and larger budget deficits with consequent negative influence on growth rates forces the government and application of redistributive policies which are growth destructive (Person and Tabellini 1994). The second explanation is that inequality causes political instability and prevents governments from effective management (a point also made by Stern in discussing the relevance of growth policies, Stern 1991). However, Alesina and Perotti (1994) show rather convincingly that the argument that inequality causes higher taxes which harm growth is not finding much support in the data. They explain this mainly on the basis of a weak link between inequality and taxation levels. The argument of policy uncertainty in explaining the positive relation between inequality and growth has found some support and is most recently confirmed by the unwillingness of private investors to continue to finance capital flows to countries with looming conflicts on land redistribution and poverty programmes.
The experiences of the 1980s and some findings of the new growth theory allow therefore to reconsider the growth and equity debate. On the one hand higher taxes and some deficit financing can affect decisions on savings negatively and through neoclassical reasoning distort growth, while on the other hand a higher level of government expenditure (as a result of higher taxes or deficit financing) can increase investment in human resources, support the development of markets and improve infrastructure which, following the new growth theories, contribute to higher levels of growth. The new growth theory thus offers, especially through the link between tertiary income distribution and the generation of future primary incomes, a more dynamic element than traditionally was the case of the relationship between income equality and economic growth.
As argued earlier macroeconomic policies can have a potentially positive redistributive slant, especially when emphasis is simultaneously placed on tax policies and on expenditure policies (Pyatt, 1993), with monetary policies playing only a lesser role of bringing stability into the economy. Macroeconomic policies can especially be more poverty-focussed if "sound" macroeconomic policies are carried out in tandem with a set of incomes policies including minimum wage policies and mesopolicies which underline the redistributive aspect of the macroeconomic policies. Incomes policy, when based upon consultation with employers and workers, can contribute to a better social climate and can therefore reduce inflationary pressure. Countries which reduced income inequality and had a reasonable growth record relied amongst others on a set of incomes policies which included an active minimum wage policy. Mesopolicies deal with the distribution of the fiscal burden of targeted public expenditure, of microeconomic policies (functioning of the labour market integration in the product market) and distribution of ownership assets. However, despite the potentially redistributive role of fiscal policies, fiscal policies are often not explored for that. Tax policies are often less redistributive than originally designed and budget deficits are often dealt with through reducing expenditure rather than to increase taxes. The distributive aspect of government expenditure is often less than it is claimed, since many public service programmes benefit the rich more than the poor and application of priority ratios to favour expenditure items affecting the poor is often not well developed. Yet some countries combined high priority ratios with high growth rates (UNDP, 1991).
However a pro-poor design of macroeconomic policies depends not on the macroeconomic policies themselves but on the social situation in the country and especially on the fact whether a society is willing to give priority to distributional issues in times of economic crisis (Khan 1992). Politically it is often more difficult to develop a distributional strategy in times of economic difficulty than in times of economic growth. The paradox is therefore that macroeconomic policies can have elements favouring the poor, especially in those countries which had already a more egalitarian society, but that applying more poverty-oriented macro policies in a less egalitarian society is probably doomed to fail. Changes in distribution of assets and human capital should become a necessary complement of macroeconomic policies to reduce inequality and stimulate growth. This issue is discussed in Stiglitz (1998) and will not be further pursued here. This conclusion thus weakens the case often made that policies of income distribution are less relevant than stimulating overall growth in a poverty alleviation strategy as a steady state growth will lift gradually all people above the poverty threshold. In the next section we will argue that through redistributive measures important gains can be made in poverty reduction.
One elegant way to consider the relation between equity and growth in relation to poverty levels is to construct a poverty measure which takes into account simultaneously growth and distribution. Ravallion (1997) has investigated this by running combined time series country data analysis and estimating for certain groups of countries a poverty reduction elasticity of growth, confirming that countries with lower inequality have a higher poverty reduction elasticity of growth. Also McKay (1997) has argued that a dynamic poverty count, which expresses a poverty index over time, should be split up between a growth component and a equity component, but is not able to present an integrated measure.
In this section we show that by using a perhaps somewhat restrictive assumption regarding the shape of the distribution function of inequality a composite index can be developed which can deal with tradeoffs and complementarity between distribution and growth.
The restrictive assumption is that the distribution of inequality follows a pattern of a log normal distribution. Various authors have argued (e.g. Aitchison and Brown 1973) that this assumption can be made for inequality in developed countries. The proposition has not been tested for developing countries but, pending further research we assume this to be the case.
If we assume that incomes are lognormally distributed, we can calculate the head count index of poverty, expressed as a fraction of the population below the poverty line, as follows:
(1) P ( X < 1/s log f + ˝ s ) X is N (0, 1)
This is the probability of a standard normally distributed variate X where f is the poverty line expressed as a fraction of per capita income and is the variance of the lognormal distribution and a measure of inequality. (There is a one to one relation between the and the Gini ratio, one of the most frequently used measures of inequality.)
The proof of relation (1) can be found in Annex I.
Table 6 indicates for various values of the poverty line (f) and various values of inequality ( and G) the percentage of the population below the poverty line. (1)
What is striking is that inequality does matter. For example in case of countries with high inequality (a Gini ratio of 0.6 like Brazil, see Table 8) a poverty line of 15 per cent of per capita income will result in 23 per cent of the population in poverty, but if that country would reduce its inequality to that of a low inequality country (a Gini ratio of 0.28), its percentage of population in poverty would be less than 1 per cent.
Another example on the tremendous effects of inequality reduction on poverty from Table 6 is that with a poverty line equal to 50 per cent of per capita income, a high inequality country (with a Gini ration of above 0.6) has 50 per cent of its population in poverty while a country with a low inequality (a Gini ratio of 0.3) has only 25 per cent of its population in poverty, a difference in magnitude of 100 per cent.
It is obvious that in formula (1), f (the poverty line as a fraction of average per capita income) will decrease as the average per capita income (y) increases.
Thus we have
(2) f = a / y
with a = the absolute poverty line expressed in money terms
and y = average per capita income in money terms
(3) y = yo e gt
y is growing per year with a growth rate g and yo is the per capita income in the base year
by substituting (2) and (3) in (1) it can be easily deducted that the fraction of the population below the poverty line is
(4) P ( X < 1/s ( log a/yo - g t) + ˝ s )
Table 6: Percentage of population below the poverty line as a function of inequality (G, ) and the poverty line (f) (expressed as a fraction of per capita income)
|f / s||0.5||0.55||0.60||0.65||0.70||0.75||0.80||0.85||0.90||0.95||1.00||1.05||1.10||1.15||1.20||1.25||1.30||1.35||1.40|
Formula (4) thus represents a poverty measure which depends on the absolute poverty line (a) and per capita income (yo) in the base year, the degree of inequality () and the growth of per capita income (g) as well as the time period (t) over which the growth is considered.
That inequality matters in the growth process is confirmed by a glance at Table 7 where we have indicated for an initial poverty line of 0.75 per cent of per capita income (a quite normal assumption, see Ravallion and Chen 1997), and a range of different inequality figures, the percentage of population below the poverty line in the initial year and after 5 years following various hypothetical per capita growth rates (g) ranging from 0.25 per cent to 3.5 per cent.
With a per capita growth rate of 2.0, a quite acceptable figure in the 1990s, a country with high inequality (Gini of 0.60) reduces its part of the population living below poverty from 64 per cent to 60 per cent. However a country with low inequality (a Gini ratio of 0.3) reduces the number of poor from 40 per cent to 33 per cent of the population. Thus when inequality is low (and the income distribution curve flatter) growth will reduce poverty faster than when inequality is high.
These are powerful instruments to emphasize in all policy measures the reduction in inequality, even if this will reduce growth somewhat. For example, in the case of an initial poverty line of 75 per cent of per capita income, reducing inequality from a Gini ratio of 0.60 to 0.40 with a 1.0 per cent per capita growth rate over 5 years reduces poverty more as compared to a per capita growth rate of 4 per cent and keeping inequality unchanged (the percent of people in poverty in the first case is 47 per cent and in the second case only to 57 per cent).
It is often argued that social and cultural factors prevent improvements, in income inequality, except at very high costs. A glance at Table 8 learns that changes in income distribution take place over time (some countries improving, others deteriorating) nullifying the argument that income distribution is a more or less given parameter.
The second objection to active policies reducing income inequalities is that there are costs to reducing inequality, but as argued earlier there are strong indications that income equality will contribute to faster growth and hence some of the costs of achieving higher equality will be gained back over time, although more research might be needed to substantiate this. It is nevertheless clear that increased attention to inequality and policies to reduce inequality should be a primary objective of development policy.
Table 7: Percentage of population below the poverty line as a function of inequality (G, ) and a 5 year annual growth of per capita income (g) from an initial poverty line of 75% of per capita income (f = 0.75) at year t =o
Table 8: Gini ratios and per capita growth for selected countries 1970s-1990s
|GINI RATIOS||PER CAPITA GROWTH|
Trinidad and Tobago
Source: Gini ratios: Bruno, Ravallion, Squire (1998); WIDER data base (*); GDP per capita: World Bank: World Development Indicators 1997; World Development Report 1998/89. Thanks are due to Mr. Kiiski for providing the data from UNU/WIDER.
Income inequality is assumed to follow a log normal distribution pattern, with average m and variance s
(A1) y = L (y; m , s2 )
(A2) = L ( ; m - log , s2 )
as y is log normally distributed we have the following relationship:
(A3) = log - ˝ s2
and (A2) can be written as:
(A4) = L ( ; m - - ˝ s2, s2 )
But is a maximum likelihood estimator for and the log normal distribution (A4) may be approached by:
(A5) = L ( ; - ˝ s2, s2 )
expressing this lognormal distribution as a standard normal distribution gives:
Thus if f is the poverty line income expressed as a fraction of average (or per capita) income, according the normal distribution function, the fraction of the population below the poverty line is given by:
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1. I would like to thank Fahrad Mehran for help in calculating the poverty counts in Tables 6 and 7.
|Table 1||Sub-Saharan Africa: Evolution of employment in the formal sector during the adjustment phase (as % of the active population)|
|Table 2||Informal employment as % of labour force (non-agricultural) selected countries in Latin America|
|Table 3||Wage dispersion and real wage changes in manufacturing (US$) (1975-1979 to 1987-91)|
|Table 4||Composition of social sector expenditures (percentage of GDP)|
|Table 5||Trends in the selected social indicators|
|Table 6||Percentage of population below the poverty line as a function of inequality and the poverty line|
|Table 7||Percentage of population below the poverty line as a function of inequality and a 5 year annual growth of per capita income from an initial poverty line of 75% of per capita income|
|Table 8||Gini ratios and per capita growth for selected countries 1970s-1990s|
Updated by JB. Approved by PA. Last update: 29 March 2000.
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