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Economies of scale, bulk discounts, and liquidity constraints: comparing unit value and transaction level evidence in a poor country


We examine bulk discounts, which are claimed to explain the Deaton and Paxson puzzle about household size and food demand, and which may matter to household behavior studied in other literatures. Most previous studies use unit values, which are subject to several biases and reflect economizing choices made by households, so may not reliably estimate the bulk discount schedule. Instead, individual transaction records in household expenditure diaries are used, which report expenditure, quantity, brand, unit size and number purchased per transaction. The bulk discount schedule is estimated for four foods (rice, canned meat, canned fish and chicken) that make up one-third of the total food budget in a survey in urban Papua New Guinea. For each food we use the dominant brand(s) so there is no quality variation and the estimated price schedule only reflects discounts due to variations in purchase quantity. All foods have precisely measured but small elasticities of unit price with respect to quantity purchased.

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  1. Other proposed solutions are: the two-good model generating the predictions is too restrictive (Horowitz 2002) but a multi-good model of Deaton and Paxson (2003) does not resolve the puzzle; scale economies in food preparation (Gan and Vernon 2003) but these deepen the puzzle because a reduction in per capita preparation costs should allow increased food expenditures per head; and, consumption measurement errors correlated with household size (Gibson and Kim 2007) but evidence from a comprehensive survey experiment with eight different survey designs randomly assigned to poor households finds no support for this hypothesis (Gibson et al. 2015).

  2. This is also known as “shipping the good apples out” (Borcherding and Silberberg 1978). Empirical confirmation that higher quality foods are relatively cheaper in markets where fixed costs have been added to the net cost is given by Gibson and Kim (2015) in the context of a poor country.

  3. The use of branded food products should not limit the applicability of the results to other developing countries. The share of packaged foods is already over one-third in countries like Indonesia, Malaysia and Thailand and is being driven by the rapid rise in supermarkets. For example, over 60 percent of retail food sales in Latin America are now through supermarkets, versus only 10–20% prior to 1980 (Reardon et al. 2003). Growth of the supermarket sector is even more rapid in East and Southeast Asia and is not limited to major cities and to the rich and middle class, but instead is penetrating deeply into the food markets of the poor.

  4. An example of the analyses that are possible in a middle-income country (Mexico) with a consumer panel that uses scanners to study weekly purchases of almost 60,000 barcoded products is provided by Aguilar et al. (2016).

  5. Frankel and Gould (2001) argue that search gives a U-shaped relationship between food prices and neighborhood income levels in the United States. The rich spend less time searching because of the opportunity cost of their time, while the poor lack the means to search. Local inequality is used to proxy for this effect in results reported below.

  6. This assumption is unlikely to be true, with varying qualities of these foods available. In another low-income setting, Gibson and Kim (2015) show a 40% price difference between low quality and high quality rice.

  7. These elasticities are largely the same across five of the specifications in Table 2 and only differ when Census Division (equivalent to a census tract) fixed effects are used, which increase the magnitude of the elasticities for rice and corned beef and reduce them for canned fish and frozen chicken.

  8. Gibson and Rozelle (2002) report that the poorest quartile of the urban population consumes only 70 percent of energy requirements. Calories for the second quartile are also below requirement. Gibson and Rozelle also show one form of food sharing—from poorer people visiting their richer kin at meal times—because the survey kept a roster of the number of diners at the main meal each day. This guest effect added ten percent to the calorie demand of the richest quartile of households. In the capital city, where rice is the main staple, the average household has seven people, so with the guest effect there would be eight or more people at the main meal.

  9. For example, Gibson and Fatai (2006) show that women in urban PNG have 2 years less schooling than men, and the wage workforce is 80 percent male. One could think of bride price as a capitalized value of what Grossbard (2015) calls work-in-household (WiHo), so that someone more experienced in cooking and household management attracts a higher price. This is especially because the main bride price payment is made several years after a woman has moved into her husband’s household, so she has had time to reveal her productivity in WiHo activities. Moreover, studies in other settings, that are like PNG in having both matrilineal and patrilineal descent traditions, suggest that when descent traditions may limit outside marriage market options, married women may use domestic labor as a tool to incentivize husbands (Walther 2017). While there are no similar time-use data for PNG to examine the same outcomes, variation between patrilineal and matrilineal customs in PNG are associated with differences in gender bias against girls (Gibson and Rozelle 2004), according to the ‘adult goods’ method of Deaton (1989), which is consistent with the general argument that inheritance traditions can affect intra-household bargaining. However, the interpretation of bride price values in PNG is also complicated by the role of social competition between the families of grooms, and by the fact that the bride price payments are redistributed throughout the community according to various reciprocal obligations, and so the amount paid for a particular bride may say more about the family of the groom than about the productivity-related characteristics of the bride.

  10. Many urban dwellings are in close proximity to others, including being built over the sea on stilts so that people have to walk along jetties past their neighbors carrying their shopping. Thus it is difficult to disguise having food.


  • Abdulai, A. (2003). Economies of scale and the demand for food in Switzerland: Parametric and non‐parametric analysis. Journal of Agricultural Economics, 54(2), 247–267.

    Article  Google Scholar 

  • Aguiar, M., & Hurst, E. (2007). Lifecycle prices and production. American Economic Review, 97(5), 1533–1559.

    Article  Google Scholar 

  • Aguilar, A. Gutierrez E., & Seira, E. Taxing to reduce obesity. 2016. Mimeo downloaded from on 14 July 2017.

  • Alchian, A., & Allen, W. (1967). University Economics (2nd ed.). Belmont, California: Wadsworth Publishing Company.

    Google Scholar 

  • Attanasio, O., & Frayne, C. (2006) Do the poor pay more? Paper presented at the Eighth BREAD Conference, Cornell University, May 2006.

  • Borcherding, T. E., & Silberberg, E. (1978). Shipping the goods apples out: The Alchian and Allen Theorem reconsidered. Journal of Political Economy, 86(1), 131–138.

    Article  Google Scholar 

  • Beatty, T. (2010). Do the poor pay more for food? Evidence from the United Kingdom. American Journal of Agricultural Economics, 92(3), 608–621.

    Article  Google Scholar 

  • Deaton, A. (1988). Quality, quantity, and spatial variation of price. American Economic Review, 78(3), 418–430.

    Google Scholar 

  • Deaton, A. (1989). Looking for boy-girl discrimination in household expenditure data. The World Bank Economic Review, 3(1), 1–15.

    Article  Google Scholar 

  • Deaton, A., & Paxson, C. (1998). Economies of scale, household size, and the demand for food. Journal of Political Economy, 106(5), 897–930.

    Article  Google Scholar 

  • Deaton, A., & Paxson, C. (2003). Engel’s what? A response to Gan and Vernon. Journal of Political Economy, 111(6), 1378–1381.

    Article  Google Scholar 

  • Dillon, B., De Weerdt, J., & O’Donoghue, T. (2016) Paying more for less: Why don’t households in Tanzania take advantage of bulk discounts? Mimeo, Evans School of Public Policy, Seattle: University of Washington.

  • Frankel, D., & Gould, E. (2001). The retail price of inequality. Journal of Urban Economics, 49(2), 219–239.

    Article  Google Scholar 

  • Gan, L., & Vernon, V. (2003). Testing the Barten model of economies of scale in household consumption: Toward resolving a paradox of Deaton and Paxson. Journal of Political Economy, 111(6), 1361–1377.

    Article  Google Scholar 

  • Gibson, J., & Fatai, O. (2006). Subsidies, selectivity and the returns to education in urban Papua New Guinea. Economics of Education Review, 25(2), 133–146.

    Article  Google Scholar 

  • Gibson, J., & Kim, B. (2007). Measurement error in recall surveys and the relationship between household size and food demand. American Journal of Agricultural Economics, 89(2), 473–489.

    Article  Google Scholar 

  • Gibson, J., & Kim, B. (2013). Do the urban poor face higher food prices? Evidence from Vietnam. Food Policy, 41(1), 193–203.

    Article  Google Scholar 

  • Gibson, J., & Kim, B. (2015). Hicksian separability does not hold over space: Implications for the design of household surveys and price questionnaires. Journal of Development Economics, 114(1), 34–40.

    Article  Google Scholar 

  • Gibson, J., Beegle, K., De Weerdt, J., & Friedman, J. (2015). What does variation in survey design reveal about the nature of measurement errors in household consumption? Oxford Bulletin of Economics and Statistics, 77(3), 466–474.

    Article  Google Scholar 

  • Gibson, J., & Rozelle, S. (2002). How elastic is calorie demand? Parametric, nonparametric and semiparametric results for urban Papua New Guinea. Journal of Development Studies, 38(6), 23–46.

    Article  Google Scholar 

  • Gibson, J., & Rozelle, S. (2004). Is it better to be a boy? A disaggregated outlay equivalent analysis of gender bias in Papua New Guinea. Journal of Development Studies, 40(4), 115–136.

    Article  Google Scholar 

  • Griffith, R., Leibtag, E., Leicester, A., & Nevo, A. (2009). Consumer shopping behavior: How much do consumers save? Journal of Economic Perspectives, 23(2), 99–120.

    Article  Google Scholar 

  • Grossbard, S. (2015). The Marriage Motive: A Price Theory of Marriage: How Marriage Markets Affect Employment, Consumption, and Savings. New York, NY: Springer.

    Book  Google Scholar 

  • Hall, B. (1983). Neighborhood differences in retail food stores: Income versus race and age of population. Economic Geography, 59(3), 282–295.

    Article  Google Scholar 

  • Horowitz, A. (2002) Household size and the demand for food: A puzzle resolved? Paper presented at the Northeast Universities Development Consortium Conference, Williams College, October, 2002.

  • Kaufman, P., MacDonald, J., Lutz, S., & Smallwood, D. (1997) Do the poor pay more for food? Item selection and price differences affect low-income household food costs Agricultural Economic Report No. 759, Economic Research Service, United States Department of Agriculture.

  • Kunreuther, H. (1973). Why the poor may pay more for food: Theoretical and empirical evidence. Journal of Business, 46(4), 368–383.

    Article  Google Scholar 

  • MacDonald, J., & Nelson, P. (1991). Do the poor still pay more? Food price variations in large metropolitan areas. Journal of Urban Economics, 30(3), 344–359.

    Article  Google Scholar 

  • McKenzie, D., & Schargrodsky, E. (2011). Buying less but shopping more: Changes in consumption patterns during a crisis. Economia, 11(2), 1–43.

    Google Scholar 

  • Musgrove, P., & Galindo, O. (1988). Do the poor pay more? Retail food prices in northeast Brazil. Economic Development and Cultural Change, 37(1), 91–109.

    Article  Google Scholar 

  • Prais, S., & Houthakker, H. (1955). The Analysis of Family Budgets. New York, NY: Cambridge University Press.

    Google Scholar 

  • Rao, V. (2000). Price heterogeneity and ‘real’ inequality: A case-study of prices and poverty in rural South India. Review of Income and Wealth, 46(2), 201–211.

    Article  Google Scholar 

  • Reardon, T., Timmer, P., Barrett, C., & Berdegué, J. (2003). The rise of supermarkets in Africa, Asia, and Latin America. American Journal of Agricultural Economics, 85(5), 1140–1146.

    Article  Google Scholar 

  • Walther, S. (2017). Moral hazard in marriage: The use of domestic labor as an incentive device. Review of Economics of the Household, 15(2), 357–382.

    Article  Google Scholar 

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We are grateful to the guest editor and editor and two anonymous reviewers for helpful comments. All remaining errors are the responsibility of the authors. This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A3A2044637).

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Correspondence to John Gibson.

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Gibson, J., Kim, B. Economies of scale, bulk discounts, and liquidity constraints: comparing unit value and transaction level evidence in a poor country. Rev Econ Household 16, 21–39 (2018).

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  • Bulk discounts
  • Consumer demand
  • Economies of size
  • Prices
  • Unit values

JEL Classification

  • D12
  • I32
  • O15