Does Economic Growth Reduce Child Malnutrition?

Transcription

1 Does Economic Growth Reduce Child Malnutrition? By Rasmus Heltberg Institute of Economics University of Copenhagen Studiestr 6, 1455 København K, Denmark Contact details after 9 September, 2002: Rasmus Heltberg The World Bank, 1818 H Street, N.W., Washington, D.C Rheltberg@worldbank.org Abstract: This paper analyses the impact of economic growth on the incidence of chronic child malnutrition using data on spells of change. Growth is found to have a significant but small effect on chronic child malnutrition except in Sub-Saharan Africa, where it does not seem to have any impact. The estimates imply that outside of Africa, a doubling of income will reduce the incidence of chronic child malnutrition by 5-10 percentage points. JEL-codes: I1, I3, O1 1

2 1. Introduction Improved nutrition and health is a priority in a lot of development projects, programs, and research. Better health and nutrition is both an end in itself and a means to escape income poverty. Child malnutrition and ill health is of utmost concern since deprivation in early childhood often causes irreversible damage to physical and mental health, reduces learning at school, and leads to lower incomes as adult (ACC/SCN and IFPRI, 2002). Several of the Millennium Development Goals of the United Nations reflect on child malnutrition, including the goal to halve by 2015 the number of people living in hunger, and the goal to reduce child mortality by two-thirds. An important question faced in development policy and research is the strength of the link between income and child health and nutrition. This has important implications for whether economic growth can be relied upon to substantially reduce the number of malnourished children in the developing countries. If not, urgent priority needs to be assigned to direct health and nutrition interventions, for example in the areas of behavioral change, maternal health, supplementary feeding, improved water and sanitation etc. In the context of income/consumption poverty, research has established that growth, on average, has a strong impact on poverty (cross-country studies find the elasticity of $1/day headcount poverty with respect to growth to be around ) The aim of this paper is to investigate whether economic growth has a similar effect on chronic child malnutrition. Moreover, it is also of interest to assess whether growth impacts differently in different regions of the world, given that progress towards reaching the Millennium Development Goals is known to vary markedly between 2

3 continents, with Africa clearly not on target to meet the goals (Devarajan, Miller, and Swanson, 2002). 2. Data The height of a child (relative to its age) is a commonly used indicator of its long-run health (de Onis, 2000; Pelletier, 1994; Eveleth and Tanner, 1990). The height-for-age z-score is found by comparing the height of an individual child to the height distribution of a well-nourished reference population of the same age and sex. Children falling below two standard deviations of the reference population s median are said to suffer from chronic (or long term) malnutrition, also called stunting. 2 Stunting reflects the cumulative impact of prolonged and/or repeated episodes of inadequate nutrition, illness, or both. Stunted children experience elevated mortality and morbidity risk, and stunting accounts for around half of all child deaths (Pelletier, 1994). Stunted children also suffer slower mental development, begin school later, and perform less well in school (de Onis, Frongillo and Blössner, 2000). Stunting in infancy and early childhood can only partially be reversed later in life, and translates into smaller adult stature, inferior capacity for physical work, reduced work productivity, lower wages, less resilience to social, economic, and natural shocks, and increased reproductive and maternal health risks (ACC/SCN and IFPRI, 2002). For those that survive it, chronic malnutrition in childhood is therefore a powerful cause of poverty later in life. Substantial evidence suggests that well-nourished children around the world have roughly similar growth potential. 3 The rapid increase in height for each successive generation of immigrant populations, and the historical gains in height in all of the developed countries over the 3

4 last few centuries are also suggestive that the average height of a population is largely determined by nutrition and health (Fogel, 1994; Steckel 1995). The WHO has systematized and standardized anthropometric data collection based on a single global reference population (WHO, 1995), and has made available a sizeable database of comparable stunting data 4, making it possible to consistently track chronic child malnutrition over time and across countries. A few previous studies have carried out cross-country regressions of nutrition indicators on per capita income (Smith and Haddad, 2000; Alderman et al, 2001; Steckel, 1995). 5 Yet unobserved and omitted country fixed effects correlated with both the level of income and the nutrition indicator may affect the results of cross-country analyses and lead to biased estimates. This problem can be overcome by differencing the data, which removes all country fixed effects. The idea is to look at spells of change in chronic malnutrition. A spell is the change between any two years for which a given country has observations on both malnutrition and GNI. Denoting by M i,t the incidence of stunting in country i in year t, a spell of malnutrition change is defined as (M i,t+1 M i,t ), i.e. the percentage point change in malnutrition between the end year and the start year. This may then be regressed on the percentage change in GNI per capita (defined as (Y i,t+1 Y i,t )/Y i,t ). A similar procedure has been used to analyze the impact of economic growth on income/consumption poverty incidence (see Ravallion, 1995 and 1997; Ravallion and Chen, 1997; Squire, 1993; and the survey by Heltberg, 2002b). 6 **Please insert Table 1 around here** Employing the World Bank s World Development Indicators (2001), a total of 166 spells of change in chronic malnutrition could be constructed. The distribution of spells by sign is shown in Table 1 for the entire sample (as well as for Sub-Saharan Africa alone in parenthesis). It can be seen that 45 spells are positive (i.e. increasing malnutrition), 118 are negative and 3 are zero. The 4

5 average spell is a 2.6 percentage point fall in malnutrition and the average accompanying GNI growth is 27 percent Results Figure 1 shows a scatter plot of the data with a non-parametric regression line included. 8 A negative relationship between growth and change in chronic malnutrition incidence can be seen in the graph, yet the curve does not seem to have a very steep slope. Interestingly, the effect differs when the data is sorted by continent (see Figures 2-5): Stunting incidence appears to respond fairly well to growth in Asia and in Latin America (including the Caribbean), while in Africa there is virtually zero relationship. The picture appears somewhat unclear in the Middle East & North Africa. **Please insert Figures 1-5 around here*** **Please insert Table 2 around here** In order to better judge the magnitude and the significance of the growth elasticity of stunting, a number of OLS regressions were run using the spells data set. Column 1 of Table 2 presents the baseline model, in which malnutrition change is regressed on GNI growth. Here, outliers have been excluded (based on a DFBETA criteria) and robust t-statistics are given in parentheses. 9 5

6 Growth has a highly significant effect on change in stunting incidence. This also holds when stunting incidence at the beginning of the spell is controlled for (in column 2), and when the entire sample (including outliers) is used for the regression (column 3). The estimates imply that a doubling of income on average will reduce the incidence of child malnutrition by 5-7 percentage points. At the mean of the data (stunting incidence = 30 percent), this implies an elasticity of around 0.2 (i.e. 10 percent economic growth will reduce stunting incidence by 2 percent, from 30 to 29.4 percent of all children). Although significant, this can hardly be called a large effect. The negative and significant parameter for pre-spell level of stunting indicates that stunting is easier improved from an initial high level. This makes sense if relatively easy and/or cheap health technologies are available to improve health in high-malnutrition countries. Countries that have already achieved relatively low stunting levels may need to invest in more costly prevention and treatment interventions if they wish to improve child health even further. In order to assess whether the impact of economic growth on stunting differs between regions, a set of four continent dummies was interacted with the growth rate of GNI. Column 4-6 of Table 2 shows that in Sub-Saharan Africa, economic growth does not affect stunting in any significant way. This holds whether or not the initial level of stunting incidence is included, and also when the seven outlying observations are included in the regression sample. In the three other regions, economic growth has a highly significant effect on stunting in all cases. The estimates imply that outside of Africa, a doubling of income will reduce malnutrition incidence by 5-10 percentage points depending on continent and model specification. 6

7 4. Discussion The answer to the question raised by the title of this paper is Yes: economic growth significantly reduces child malnutrition, but not by very much, and unfortunately not in Africa South of the Sahara. Although there is no doubt (outside of Africa) that growth affects child malnutrition, the elasticity of stunting incidence with respect to growth is modest, at the most onetenth of the growth elasticity of income/consumption poverty. To reduce stunting by half through economic growth alone would require approximately 250 percent increase in GNI, corresponding to 3.7 real growth per capita per year for 25 years. In Africa, even this much growth appears unlikely to have the required impact on child malnutrition. I do not have any good explanation for the absence of any growth effect on stunting in Africa. It is not merely caused by high initial levels of malnutrition in Africa, since this variable is controlled for in some of the regressions. Moreover, in South Asia where malnutrition incidence is larger than in Africa the impact of economic growth is not significantly different from the rest of the sample. It seems that the mechanisms linking higher incomes to better child health enhanced spending by households and governments on food, health care, medicine, sanitation, and clean water for some reason work differently in Africa than elsewhere. The conclusion is that supplementary interventions to directly improve child health are called for with special urgency in Africa, but also elsewhere. 7

8 References ACN/SCN and IFPRI (2002) Nutrition: A Foundation for Development, Geneva. Alderman, H, S Appleton, L Haddad, L Song, and Y Yohannes (2001) Reducing child malnutrition: How far does income growth take us?, CREDIT Research Paper No 01/05, University of Nottingham. Behrman, Jere R., Foster, Andrew D., and Rosenzweig, Mark R. (1997) The Dynamics of Agricultural Production and the Calorie-Income Relationship: Evidence from Pakistan, Journal of Econometrics 77(1), March 1997, pages Bouis, Howarth E. and Haddad, Lawrence-J (1992) Are Estimates of Calorie-Income Elasticities Too High? A Recalibration of the Plausible Range, Journal of Development Economics, 39(2), October 1992, pages Devarajan, S, M.J. Miller, and E.V. Swanson (2002) Goals for development: History, prospects, and costs. World Bank, processed. Eveleth, P.B. and J.M. Tanner (1990) Worldwide Variations in Human Growth, 2 nd edition, Cambridge University Press. Fogel, Robert W. (1994) Economic Growth, Population Theory, and Physiology: The Bearing of Long- Term Processes on the Making of Economic Policy, American Economic Review; 84(3): Heltberg, Rasmus (2002a) Counting the World s Deprived: Can Indicators of Child Malnutrition Resolve the Problems?. Institute of Economics University of Copenhagen. Available at Heltberg, Rasmus (2002b) The Growth Elasticity of Poverty. WIDER Discussion Paper, Helsinki (also available at Onis, Mercedes de Measuring nutritional status in relation to health Bulletin of the World Health Organization, 78(10), p Onis, Mercedes de, Edward A. Frongillo, and Monika Blössner (2000) Is malnutrition declining? An analysis of changes in levels of child malnutrition since Bulletin of the World Health Organization, 78(10), p Pelletier, David L (1994) The relationship between child anthropometry and mortality in developing countries: Implications for policy, programs, and future research, The Journal of Nutrition 124(10), 2047S- 2081S. Ravallion, Martin (1995) Growth and Poverty: Evidence for Developing Countries in the 1980s, Economics Letters 48: Ravallion, Martin (1997) Can high-inequality developing countries escape absolute poverty?, Economics Letters 56: Ravallion, M and S Chen (1997) What can new survey data tell us about recent changes in distribution and poverty?,world Bank Economic Review 11(2):

9 Smith, Lisa C. and Lawrence Haddad (2000) Overcoming child malnutrition in developing countries: Past achievements and future choices. Food, Agriculture, and the Environment Discussion Paper 30, IFPRI, Washington, DC. Squire, Lyn (1993) Fighting Poverty American Economic Review, 83(2), May: Steckel, Richard H. (1995) Stature and the Standard of Living, Journal of Economic Literature, 33(4): Svedberg, Peter (2000) Poverty and Undernutrition: Theory, Measurement, and Policy, Oxford University Press for WIDER. WHO (1995) Physical status: The use and interpretation of anthropometry. WHO Technical Report Series No. 854, WHO, Geneva. World Bank World Development Indicators (2001 electronic edition on CD-ROM), Washington, DC. 9

10 Table 1: Matrix of spells Change in stunting incidence Change in GNI Increased No change Reduced per capita Positive 35 (14) 3 (1) 107 (22) No change 4 (2) 2 (3) Negative 6 (1) 9 (2) In total 45 (17) 3 (1) 118 (27) Data refer to number of spells in the full sample (figures for Sub-Saharan Africa in parenthesis). Table 2: Regression of spells of change in malnutrition Independent variable: percentage point change in stunting incidence (1) (2) (3) (4) (5) (6) Growth rate of GNI (6.79)** (5.39)** (3.78)** Initial incidence of stunting (4.16)** (3.53)** (3.78)** (3.77)** Growth rate of GNI X Africa dummy (1.19) (0.95) (0.26) Growth rate of GNI X Asia dummy (4.36)** (2.95)** (2.79)** Growth rate X Americas dummy (3.83)** (4.98)** (7.20)** Growth rate X North Africa & Middle East dummy (3.28)** (2.64)** (3.19)** Constant (1.98)* (2.31)* (1.84) (2.01)* (2.29)* (2.47)* Observations Outliers included? No No Yes No No Yes R-squared Cook-Weisberg heteroskedasticity test ** Robust t-statistics in parentheses * significant at 5% level; ** significant at 1% level 10

11 Running mean smoother, bandwidth =.4 20 Change in malnutrition Proportional change in GNI per capita Figure 1: Growth-malnutrition relationship at the global level Running mean smoother, bandwidth =.4 Running mean smoother, bandwidth = Change in malnutrition 0 Change in malnutrition Proportional change in GNI per capita Figure 2: Growth-malnutrition relationship in Africa Proportional change in GNI per capita Figure 3: Growth-malnutrition relationship in Asia Running mean smoother, bandwidth =.4 Running mean smoother, bandwidth = Change in malnutrition 0-10 Change in malnutrition Proportional change in GNI per capita Figure 4: Growth-malnutrition relationship in the Americas Proportional change in GNI per capita Figure 5: Growth-malnutrition in Middle East & North Africa 11

12 1 See Ravallion (1997). Note that the growth elasticity of poverty is smaller in countries that are relatively more unequal and/or more poor. 2 Low weight, also refererd to as wasting, is an indicator of a short-term nutritional shortfall, and is reversible. I prefer to work with stunting because it better captures permanent deprivation in nutrition and health (Heltberg, 2002a). 3 See Eveleth and Tanner (1990) and the recent survey by Svedberg (2000: Ch 12). Note however that since the present study is based on spells of change over time within individual countries, the ongoing discussions among human biologists, geneticists, and nutritionists of whether or not a single global reference population is valid for all human populations are impertinent to the results presented here. 4 See de Onis, Frongillo and Blössner (2000) and The data is also included in the World Bank s World Development Indicators. All these data are based on nationally representative surveys, where height relative to age of children below 5 years is compared to a single, US reference population, with stunting defined as the proportion falling below two standard deviations of the reference. Due to the reliance on unevenly spaced national surveys, the database is a highly unbalanced panel with individual countries having from 0-11 stunting observations. 5 A substantial body of literature already exists on the income elasticity of household demand for calories (see for example Behrman, Foster, and Rosenzweig, 1997 and the survey by Bouis and Haddad, 1992). Yet calories is merely one input in the child health production function, and by assessing directly the impact of income and changes therein on final outcomes (stunting), this paper is immune to some of the methodological problems of the calorie-demand literature. 6 A major difference, however, is that poverty headcounts can be matched with mean household consumption from the survey that generated the headcount, whereas in the stunting context one has to rely on an income measure from the national accounts (i.e. GNI per capita). 7 Data has purposively not been annualized, but refer to entire spells (lasting from 1 to 20 years each). 8 STATA s ksm command with unweighted smoothing was used to produce Figures The R 2 of the regressions appear satisfactory. The Cook and Weisberg test for heteroskedasticity and the Ramsey RESET test for omitted variables are both insignificant at the 5 percent level in all cases except column 3. 12