Abstract
Urban household food insecurity continues to be a major problem in many urban households of Sub-Saharan Africa. The ineffectiveness of policies addressing the problem has hinged in particular on the paucity of information about consumption patterns under changing economic conditions. Elasticities of food demand were estimated through the Linear Approximated Almost Ideal Demand System (LA/AIDS) and inferences about access to food were drawn. Shifts in consumption were evident when changes occurred in income, prices and household demography. As the urban poor are sensitive to variation in food prices and income, they should be cushioned against their negative effects in order for their access to food to be enhanced and hence their food security improved. Dairy and dairy products and wheat and wheat products were identified as subsidy carriers which would improve the nutrition of the urban poor. These results provide guidance for the design of food security and nutrition strategies and programs at the micro and macro-economic levels.
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Notes
In this study we also don’t test the separability of the categories. Sellen and Goddard 1997 cite studies in which the assumption of separability has been tested. The assumption, although used and theoretically plausible has been rejected in most studies (Pudney 1981 in Sellen and Goddard 1997). Further discussion on the issue of separability can be found in LaFrance (1991) and Edgerton (1993).
In the two step approach, the system of demand is estimated as conditional to purchases. A probit model is estimated for purchases and non-purchases in every equation, except the deleted equation and from the coefficients of the probit and Inverse Mills Ratio which corrects for the selection bias is used as an instrument in the system. The IMR (Φ) for a nonzero budget share household was derived as; \( {\Phi_{ih}} = \frac{{\theta (Z)}}{{\Theta (Z)}} \), While for the zero budget share households was derived as \( {\Phi_{ih}} = \frac{{\theta (Z)}}{{1 - \Theta (Z)}} \), Where θ and Θ are the standard normal density (pdf) and cumulative density (cdf) as defined by the probit independent variables. The inverse Mill’s ratio was then used in estimation of equation (4) as an instrument in the respective equations of the system.
Adding-up, \( \mathop \Sigma \limits_{i = 1}^n {\rho_{io}} = 1;\;\mathop \Sigma \limits_{i = 1}^n {\beta_{ij}} = 0;\;\mathop \Sigma \limits_{i = 1}^n {\gamma_i} = 0\;and\;\sum\limits_{k = 1}^s {{\rho_{ik}} = 0} \); Homogeneity \( \mathop \Sigma \limits_j {\beta_{ij}} = 0. \); and Symmetry \( {\beta_{ij}} = {\beta_{ji}} \).
Hayes et.al 1990 estimated LA/AIDS elasticities as (5) \( {\varepsilon_{ii}} = - 1 + \frac{{{\beta_{ii}}}}{{{w_i}}} - {\gamma_i} \) (6) \( {\varepsilon_{ij}} = \frac{{{\beta_{ij}}}}{{{w_i}}} - {\gamma_i}\frac{{{w_j}}}{{{w_i}}} \).(7) \( {\delta_{ii}} = - 1 + \frac{{{\beta_{ii}}}}{{{w_i}}} + {w_i} \) (8) \( {\delta_{ij}} = \frac{{{\beta_{ij}}}}{{{w_i}}} + {w_j} \) and expenditure elasticity as \( {\eta_i} = 1 + \frac{{{\gamma_i}}}{{{w_i}}} \).
\( \mathop \delta \nolimits_{ij}^h = \mathop \varepsilon \nolimits_{ij}^m + {w_j}{\eta_i} \) for cross elasticities and \( \delta_{ii}^h = \varepsilon_{ii}^m + {w_i}{\eta_i} \) for own elasticities
A detailed sampling procedure can be found in Musyoka et al. (2009).
Unit values are used when the actual market prices are not collected in the survey. Sometimes unit values may underestimate the actual value/price of the unit item especially when economies of scale exist due to bulk selling and buying
This is a mixture of boiled maize and beans or any other leguminous complement. Often in some tribes like the Kikuyu it is mashed with potatoes.
Disaggregated coefficients between the poor and non-poor categories are available on request.
Ugali is a local food made from mixture of maize flour and water
Empirical studies on demand in Africa are scant and the few published differ in methodological approach besides the categorization and aggregation of agricultural products. This makes it difficult to have meaningful comparisons of results
References
Abdulai A, Jain DK, Sharma AK (1999) Household food demand analysis in India. J Agric Econ 50:316–327
Alderman H (1986) The effect of food price and income changes on the acquisition of food by low-income households. International Food Policy Research Institute, Washington
ASARECA (2008) Responding to the food price crisis in Eastern and Southern Africa: policy options for national and regional action. ASARECA, Entebbe
CBS (2003) Statistical abstract. Central Bureau of Statistics, Ministry of Planning and National Development, Nairobi
Deaton A, Muellbauer J (1980a) Economics and consumer behaviour. Cambridge University Press, New York
Deaton A, Muellbauer J (1980b) An almost ideal demand system. Am Econ Rev 70:312–326
Edgerton LD (1993) On the estimation of separable demand models. J Agric Resour Econ 18(2):141–146
FAOSTAT (2009) FAOSTAT data base http://faostat.fao.org
Friedman LS (1984) Microeconomic policy analysis. New York, McGraw-Hill, Inc
Government of Kenya (GoK) (2007) The Kenya Vision 2030 Ministry of Planning and National Development and the National Economic and Social Council (NESC). Government Printers, Nairobi
Hayes DJ, Wahl TI, Williams GW (1990) Testing restrictions on a model of Japanese meat demand. Am J Agric Econ 72:556–566
Heien D, Wessells CR (1990) Demand system estimation with micro data: a censored regression approach. J Bus Econ Stat 8:365–371
IMF (2009) International monetary fund, world economic outlook database. October. www.imf.org
Jayne TS, Argwings KG (1997) Consumer response to maize market liberalization in urban Kenya. Food Policy 22(5):447–458
Kimuyu P (1993) Urbanization and consumption of petroleum products in Kenya. Energy Policy
LaFrance JT (1991) When is expenditure “exogenous” in separable demand models? West J Agric Econ 16(1):49–62
Maddala GS (1983) Limited dependent and qualitative variables in econometrics. Cambridge, Cambridge
Ministry of Agriculture (MoA) (2009) Ministry of agriculture strategic plan 2008–2012. Government Printers, Nairobi. ISBN 9966-776-04-4
Mukumbu M (1994) Consumer and milling industry responses to maize market reform in Kenya. ‘Proceedings of the conference on Market Reforms, Agricultural Production and Food Security’ June 1994, Nairobi (Policy Analysis Matrix, Egerton University)
Musyoka MP, Lagat JK, Ouma DE, Wambua T, Gamba O (2009) An analysis of urban household food demand in Nairobi, Kenya: application of translog demand function. International Journal of Applied Agricultural Economics and Agribusiness, (in press)
Obudho RA (1997) Nairobi: natural capital and regional hub. In: Rakodi C (Ed) The urban challenge in Africa growth and management of its large cities. Tokyo United Nations University
Odhiambo W, Kulundu MD (2003) Urban poverty and labour force participation in Kenya. Kenya Institute for Public Policy Research and Analysis (KIPPRA). Paper presented at World Bank Urban Research Symposium, Washington D.C. 15–17 December
Sadoulet E, De Janvry A (1995) Quantitative development policy analysis. John Hopkins University Press
Sellen D, Goddard E (1997) Weak separability in coffee demand systems. Eur Rev Agric Econ 24:133–144
Tambi EN (1998) Testing for habit formation in food commodity consumption patterns in Cameroon. J Int Food Agribus Mark 10(1):15–30
Teklu T (1996) Food demand studies in Sub-Saharan Africa: a survey of empirical evidence. Food Policy 21(6):479–496
Tiffin A, Tiffin R (1999) Estimates of food demand elasticities for Great Britain: 1972-1994. J Agric Econ Res 50(1):140–147
Timmer P (1981) Is there curvature in the Slutsky Matrix? Rev Econ Stat 63(3):395–402
Weliwita A, Nyange D, Tsujii H (2003) Food demand patterns in Tanzania: a censored regression analysis of micro data. Sri Lankan Journal of Agricultural Economics 5:10–23
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Appendix
Appendix
Appendix 1. Coefficients on maximum likelihood estimations of the share equations (demographic effects)
Expenditure category | Rel | Ic | hg | hl | hb | ha | hs | f2 | f3 | M2 | M3 | Mf4 | Mf1 | Mills ratio |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Sifted flour (sp) | −0.0074 | −0.0045 | −0.0050 | −0.0029 | −0.0082 | 0.0000 | 0.0970b | −0.0969b | −0.0913b | −0.0964b | −0.0964b | −0.1032b | −0.0997b | −0.0011 |
(0.451) | (0.429) | (0.459) | (0.715) | (0.286) | (0.992) | (0.022) | (0.023) | (0.031) | (0.024) | (0.025) | (0.015) | (0.019) | (0.709) | |
Posho (sm) | −0.0112c | 0.0010 | −0.0019 | −0.0019 | −0.0001 | 0.0002 | 0.0038 | −0.0032 | −0.0040 | −0.0042 | −0.0031 | −0.0044 | −0.0025 | −0.0024 |
(0.082) | (0.715) | (0.566) | (0.641) | (0.975) | (0.283) | (0.861) | (0.884) | (0.854) | (0.848) | (0.887) | (0.839) | (0.908) | (0.007) | |
Dairy & products (sd) | −0.1189a | −0.0160b | −0.0008 | 0.0159 | −0.0068 | 0.0002 | −0.1416b | 0.1333b | 0.1504a | 0.1312b | 0.1374b | 0.1511a | 0.1364b | 0.0040 |
(0.000) | (0.036) | (0.927) | (0.131) | (0.511) | (0.547) | (0.013) | (0.020) | (0.008) | (0.022) | (0.018) | (0.008) | (0.017) | (0.263) | |
Vegetables (sv) | 0.0278b | −0.0024 | −0.0060 | −0.0089 | −0.0042 | 0.0000 | 0.0081 | −0.0087 | −0.0135 | −0.0042 | −0.0060 | −0.0101 | −0.0085 | −0.0044 |
(0.020) | (0.728) | (0.473) | (0.348) | (0.655) | (0.958) | (0.876) | (0.868) | (0.794) | (0.936) | (0.909) | (0.846) | (0.870) | (0.495) | |
Fruits (sf) | 0.0183c | 0.0075 | 0.0060 | −0.0040 | 0.0113 | −0.0003 | −0.0113 | 0.0187 | 0.0085 | 0.0055 | 0.0069 | 0.0179 | 0.0065 | −0.0047 |
(0.091) | (0.228) | (0.427) | (0.642) | (0.183) | (0.368) | (0.808) | (0.690) | (0.856) | (0.908) | (0.885) | (0.702) | (0.889) | (0.380) | |
Sugar (ss) | 0.0002 | 0.0018 | 0.0035 | 0.0000 | 0.0007 | 0.0000 | 0.0537c | −0.0496c | −0.0572b | −0.0543c | −0.0521c | −0.0532c | −0.0500c | 0.0056 |
(0.973) | (0.629) | (0.438) | (0.998) | (0.889) | (0.898) | (0.057) | (0.081) | (0.042) | (0.056) | (0.069) | (0.061) | (0.078) | (0.102) | |
Maize (sz) | 0.0091 | 0.0071b | 0.0008 | −0.0097b | −0.0082c | −0.0001 | 0.0096 | −0.0075 | −0.0077 | −0.0013 | −0.0068 | −0.0063 | −0.0033 | 0.0061c |
(0.117) | (0.034) | (0.842) | (0.034) | (0.067) | (0.466) | (0.699) | (0.763) | (0.757) | (0.958) | (0.788) | (0.799) | (0.894) | (0.064) | |
Poultry (sc) | 0.0532a | 0.0003 | −0.0086 | 0.0021 | 0.0020 | 0.0002 | −0.0279 | 0.0261 | 0.0248 | 0.0268 | 0.0290 | 0.0240 | 0.0278 | 0.0097a |
(0.000) | (0.960) | (0.181) | (0.771) | (0.778) | (0.573) | (0.481) | (0.513) | (0.530) | (0.502) | (0.471) | (0.546) | (0.484) | (0.009) | |
Beef & Products(sb) | 0.0495a | 0.0126c | −0.0015 | 0.0078 | 0.0137 | 0.0000 | 0.0506 | −0.0541 | −0.0531 | −0.0411 | −0.0481 | −0.0643 | −0.0497 | −0.0042 |
(0.000) | (0.075) | (0.857) | (0.423) | (0.153) | (0.950) | (0.337) | (0.308) | (0.313) | (0.438) | (0.368) | (0.224) | (0.348) | (0.250) | |
Wheat & Products(sw) | −0.0205b | −0.0013 | 0.0066 | 0.0048 | 0.0062 | −0.0002 | 0.0017 | −0.0030 | −0.0004 | −0.0006 | −0.0034 | −0.0027 | 0.0007 | −0.0088c |
(0.013) | (0.783) | (0.251) | (0.462) | (0.334) | (0.501) | (0.962) | (0.934) | (0.992) | (0.988) | (0.925) | (0.941) | (0.985) | (0.056) | |
Rice (sr) | −0.0042 | −0.0022 | 0.0052 | −0.0026 | −0.0051 | 0.0002 | −0.0367 | 0.0382 | 0.0366 | 0.0319 | 0.0369 | 0.0438 | 0.0370 | 0.0040 |
(0.542) | (0.585) | (0.274) | (0.638) | (0.339) | (0.437) | (0.214) | (0.198) | (0.214) | (0.283) | (0.217) | (0.139) | (0.211) | (0.260) |
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Musyoka, M.P., Lagat, J.K., Ouma, D.E. et al. Structure and properties of urban household food demand in Nairobi, Kenya: implications for urban food security. Food Sec. 2, 179–193 (2010). https://doi.org/10.1007/s12571-010-0063-6
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DOI: https://doi.org/10.1007/s12571-010-0063-6