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/lp/emerald-publishing/impact-of-food-price-increases-on-poverty-in-indonesia-empirical-qVUkMY62k0
- Publisher
- Emerald Publishing
- Copyright
- © Faharuddin Faharuddin, M. Yamin, Andy Mulyana and Y. Yunita
- ISSN
- 2515-964X
- DOI
- 10.1108/jabes-06-2021-0066
- Publisher site
- See Article on Publisher Site
Abstract
Purpose – Using cross-sectional household survey data, this paper aims to determine the impact of food price increases on poverty in Indonesia. Design/methodology/approach – This paper uses the quadratic almost ideal demand system applied to the 2013 Indonesian household survey data. The impact of food price increase on household welfare is calculated using a welfare measure, compensating variation. Findings – Three food groups with the most outstanding price impact on poverty, rice, vegetables and fish, were studied. The 20% increase in the price of each food group causes an increase in the headcount ratio by 1.360 points (rice), 0.737 points (vegetables) and 0.636 points (fish). Maintaining food price stability for these food groups is very important because the more the price increases, the more the impact on poverty. Food price policies in rural areas are also more critical than in urban areas because the impact of food price increases in rural areas is higher. Research limitations/implications – This paper does not consider the positive impact of rising food prices on food-producing households. Practical implications – Implementing appropriate poverty alleviation policies through food policies for main food groups and social protection. Social implications – Promoting rural development policies and agricultural growth. Originality/value – This paper contributes to the existing literature by providing empirical results regarding the impact of domestic food prices increase on poverty in Indonesia. Keywords Food price increase, Poverty, Indonesia, QUAIDS, Compensating variation Paper type Research paper 1. Introduction Poverty alleviation is the commitment of the Indonesian government as mandated by the National Medium-Term Development Plan (Rencana Pembangunan Jangka Menengah – RPJMN) and as an indicator of global development in the Sustainable Development Goals (SDGs). Therefore, through the National Statistical Office (BPS – Statistics Indonesia), the announcement of the poverty rate always draws public attention. Efforts to alleviate poverty need to pay attention to the root causes of poverty, including providing basic needs. One of the scourges for increasing poverty is the price increase, especially food prices. Rising food prices increase income for food producers, in this case, farmers, but reduce the purchasing power of food consumers, on the other hand. For consumers, the food prices increase directly rise household spending on food. If household income remains unchanged, a JEL Classification — Q12, Q17, Q18, I32 © Faharuddin Faharuddin, M. Yamin, Andy Mulyana and Y. Yunita. Published in Journal of Asian Business and Economic Studies. Published by Emerald Publishing Limited. This article is published Journal of Asian Business and Economic Studies under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), pp. 126-142 Emerald Publishing Limited subject to full attribution to the original publication and authors. The full terms of this licence may be 2515-964X DOI 10.1108/JABES-06-2021-0066 seen at http://creativecommons.org/licences/by/4.0/legalcode rise in food prices decreases purchasing power and increases poverty. During the 1997–1999 Impact of food crisis period, for example, a high price increase contributed to the high poverty incidence in price increases Indonesia in the short run. However, the foodstuffs inflation rate in Indonesia in recent years was relatively lower. Therefore, in March 2019, the Indonesian poverty rate was 9.41% and had consistently declined since 2006 (BPS, 2019). Despite the decline, the poverty rate in Indonesia is still much higher than in neighboring countries such as Malaysia and Thailand (World Bank, 2020). With the above empirical fact between the increase in food inflation and poverty, it is necessary to examine the negative impact of food price increases on poverty. It will be helpful for government policy responses, such as price policy, food aid policy or food export and import policy. Recent studies on household response to food prices increase in Indonesia at the micro- level have been carried out by Pangaribowo and Tsegai (2011), Widarjono (2012), Faharuddin et al. (2017, 2019), Allo et al. (2018), Devi and Purnomosidi (2019) and Khoiriyah et al. (2020). Unfortunately, these studies only calculate the expenditure/income elasticity and price elasticity to identify the characteristics of each food group. Besides calculating price and income elasticity, some of them also calculated the price impact on household welfare using a compensating variation (CV). They had proposed policy recommendations, but they did not specifically address the rising food price’s impact on poverty. There has been little recent research into the relationship between rising food prices and poverty in Indonesia. From international literature, we found McCulloch (2008), Ivanic et al. (2012), Warr and Yusuf (2013) and Misdawita et al. (2019). McCulloch (2008) and Ivanic et al. (2012) studied increasing rice price impact on poverty in Indonesia, while Warr and Yusuf (2013) use international prices for six food commodities to calculate the poverty impact. Misdawita et al. (2019) used the social accounting matrix to find the effect of four food commodities prices (rice, maize, soybean and sugar) on poverty. Most households in Indonesia, including rural areas, are negatively affected by the increase in rice prices because of decreasing household welfare and increasing poverty. Although these studies were necessary for the government policy responses, it is better to expand them to the impact of other food group prices rather than primary food commodities, rice, maize, soybean or sugar. This paper aims to examine the impact of food prices’ increase on poverty in Indonesia using cross-sectional household survey data. We calculate the food price impact using a welfare measure (CV) based on simulations of food prices increase. Applying the Foster– Greer–Thorbeck (FGT) poverty index on household CV-reduced expenditure, we will get the poverty rate after the price’s increase. The poverty impact is the changes in the FGT poverty index before and after the price increase. Our main contribution to the literature is to provide the extent impacts of domestic food prices increases of many food groups on poverty, not just the primary food commodities. Information on the effect of food price increase on poverty gives better policymaking on food security and poverty alleviation. Although, in this paper, we do not consider the positive impact of rising food prices on food-producing households. 2. Literature review Poverty is defined as “a pronounced deprivation in well-being” (Haughton and Khandker, 2009). The poor are the people who cannot meet their minimum living standard due to their low income and lack of control over economic resources. In terms of capability deprivation, poverty arises because of the inability to function in society due to a lack of, for example, education, poor health, insecurity and self-confidence. Although poverty is multidimensional, the most widely used poverty measure is income (or expenditure) poverty. The poverty measure is determined based on the threshold of income called the poverty line. Poor people are those who have income (or expenditure) below the poverty line. The World Bank currently uses a threshold of US$1.25 and US$2 per capita per day in purchasing power parity as a poverty line. However, poverty lines between JABES countries around the world vary much between US$1 and US$40 in 2005 purchasing power 30,2 parity (Ravallion, 2012). In Indonesia, poverty is measured using the cost of basic needs approach, which is based on income poverty. The poverty line is the amount of expenditure (in rupiahs) required to meet the minimum basic needs, food and non-food. The poverty line is the sum of the food poverty line and the non-food poverty line. The food poverty line is per capita expenditure (in rupiahs) equivalent to food consumption of 2,100 kcal per capita per day, while the non-food poverty line is per capita expenditure (in rupiahs) to meet the minimum needs such as housing, clothing, education and health. Commodity baskets of basic food needs are represented by 52 food commodities, while non-food basic needs consist of 51 non-food items for urban areas and 47 for rural areas (BPS, 2010). Empirically, the relationship between rising food prices and household welfare focused on much of the international literature. Some recent studies, for example, conducted by Ivanic and Martin (2008), McCulloch (2008), Valero-Gill and Valero (2008), Robles and Torero (2010), Vu and Glewwe (2011), De Hoyos and Medvedev (2011), Ivanic et al. (2012), Ferreira et al. (2013), Attanasio et al. (2013), Warr and Yusuf (2013), Fujii (2013), Caracciolo et al. (2013), Ivanic and Martin (2014), Headey (2018), Misdawita et al. (2019), Adekunle et al. (2020) and Hovhannisyan and Shanoyan (2020). These studies report the negative impact of rising food prices on welfare or poverty, although they vary depending on the share of food consumption and household characteristics. The link between food prices and poverty is summarized as follows. Initially, if household income does not change, an increase in food prices directly increases household budget spend on food and then reduces the purchasing power. Households then make food consumption adjustments by reducing their demand for food or replacing it with other food. Thus, rising food prices impact changes in household food consumption patterns both in quantity and quality. Furthermore, rising food prices will increase poverty rates, but prices are not linear (Ivanic and Martin, 2014). Declining purchasing power makes some households are unable to meet their minimum food needs, especially for the near-poor and vulnerable households. Moreover, the near-poor and vulnerable populations are relatively high in Indonesia (Suryahadi and Sumarto, 2003). The impact of rising food prices for households that have linkages with the agricultural commodity market may be positive on increasing incomes (de Hoyos and Medvedv, 2011). However, as mentioned by McCulloch (2008), in Indonesia, most are net food consumers so that this positive impact is relatively small, even according to Warr and Yusuf (2013), this positive impact is only received by owners of agricultural land and capital, not by poor farmers. It is also in line with Adekunle et al. (2020) that most farmers in Nigeria are net food buyers, and rising food prices affect welfare losses. The Hicksian CV is used in the analytical framework of the relationship between food price changes and welfare. CV is the amount of money needed to keep the household at the same level of utility as before the price increase. If prices increase, then CV value means the amount of money must be given as compensation for price increases so that the household 0 1 welfare level remains the same as before. If p and p denote the price vectors before and after the price increase and u0 is the initial utility, then CV is defined as: 1 0 0 0 CV ¼ E p ; u E p ; u (1) 0 0 where Eðp; u Þ is the minimum expenditure at price p to reach the utility level u . A welfare loss is indicated by a positive CV value, while a negative value means a welfare gain (Hovhannisyan and Shanoyan, 2020). 3. Data and methodology Impact of food The principal analytical method used in this study is quadratic almost ideal demand systems price increases (QUAIDS), proposed by Banks et al. (1997). Besides QUAIDS, this study also uses CV and poverty indicators to calculate the impact of rising food prices on household welfare and poverty. The scope of this study is the Indonesian territory using data from household surveys, the first quarter of 2013 National Socioeconomic Survey (Survei Sosial Ekonomi Nasional-Susenas) obtained from the National Statistical Office (BPS – Statistics Indonesia). The first quarter of Susenas 2013 collects data on household consumption of more than 200 food commodities in March, with a sample of about 75,000 households throughout Indonesia. This paper uses 2013 Susenas because it covers more complete food commodities than the recent Susenas. The price of food or food groups calculated using the unit value approach, as well as the ratio of food expenditure to food consumption. The median of the unit value is used to justify the price of the unit value approach. It overcomes the problem of differences in the food quality consumed by households (Deaton, 1987). Following Hoang (2009), we used equations: v ¼ v þ w x þ θD þ ε and p ¼ v þ εb (2) i i i i i i i hi x x 1 ik ik where v ¼ v and v ¼ are, respectively, the unit value of the ith food group i ik ik n k x q i i ik and the kth food commodity; x is expenditures (in rupiahs) for the kth food commodity on the ik ith food group; x is subtotal food expenditure for the ith food group; x is the total food expenditure; q is the quantity consumed for the kth food commodity on the ith food group; n ik i is the number of food commodities of the ith food group; D is a vector of demographic variables; w and θ are unknown parameters; ε is residual; v and v þ εb denote mean of unit i i i i value in community level (census block); and p is adjusted prices will be used in the QUAIDS model. The QUAIDS model used in this study is formulated as follows (Poi, 2012): " # ( " #) x λ x w ¼ α þ γ ln p þ β þ η D ln þ ln þ u (3) i i j i ij i i m ðDÞaðpÞ bðpÞcðp; DÞ m ðDÞaðpÞ 0 0 where w is the share of expenditure from the ith food group, p is the jth food group price, x is i j the total household expenditure for food, D is the vector of the demographic variable, p is the 0D Q P P j n n price vector, m ðDÞ¼ 1 þ ρ D, cðp; DÞ¼ p ,ln aðpÞ¼ α þ α ln p þ 0 0 i i j j i¼1 i¼1 P Q n n γ ln p ln p is the Stone price index, bðpÞ¼ p is a Cobb–Douglas price ij i j j¼1 i¼1 i aggregator, and α , γ , β , λ , η , ρ are unknown parameters. i i ij i i Food consumption patterns often differ according to the demographic characteristics, so we add demographic variables in the above model following Poi (2012).Weuse the following demographic variables in our QUAIDS model, household size, the number of children under five years old, urban–rural classification, education of the head of household (graduated high school or not), the primary industry of head of the household (agriculture or not) and income groups (the lowest 40%, 40% middle income and 20% the highest income). We also use 14 groups of food commodities: rice, non-rice cereals, tubers, fish, meat, eggs, milk, vegetables, pulses, fruits, oils and fats, beverage ingredients, spices and other foods. The restrictions imposed on the QUAIDS model above to conform to the demand theory P P P P n n n n are adding up ð α ¼ 1; γ ¼ 0; β ¼ 0Þ, homogenous ð γ ¼ 0Þ and i ij i ij i¼1 i¼1 i¼1 j¼1 symmetry ðγ ¼ γ Þ. Nonlinearity in parameters and restrictions on demand equations is ij ji overcome by using an iterative feasible generalized nonlinear least squares method (IFGNLS) JABES to obtain more efficient parameter estimates. The last equation of the demand system must be 30,2 discarded in the estimation process to avoid the singularity of u Covariance matrix due to adding-up restriction. The estimation process is carried out using the Stata code developed by Poi (2012) in Stata software. Following Friedman and Levinson (2002) and Robles and Torero (2010), the impact of changes in food prices on household welfare is calculated using welfare measures, CV, as follows: n n n X X X CV Δp 1 Δp Δp i i j ≅ w þ e w (4) i i ij 0 0 0 0 x p 2 p p i i j i¼1 i¼1 j¼1 0 0 where x is the initial household food expenditure before the price increase, p is the initial food price before the price increase, e ¼ e þ e w is the compensated cross-price ij ix j ij ij μ elasticity; e ¼ − δ is the uncompensated cross-price elasticity; e ¼ þ1is the ij ij ix w w i i λ ðβ þη DÞ vw i i i i expenditure or income elasticity; μ ≡ ¼ γ − μ α þ γ ln p − i k ij ij i k jk vln p bðpÞcðp;DÞ no hi hi vw λ x i 0 i x ln ; μ ≡ ¼ðβ þ η DÞþ ln ; δ is the Kronecker delta (δ ¼ 1 ij ij i i i aðpÞ vln x bðpÞcðp;DÞ m ðDÞaðpÞ Δp Δp for i5 j and δ ¼ 0for i ≠ j); and and are the magnitude of food price increase used in ij 0 0 p p i j the simulation process. Using a different method with Yu (2014), the CV value obtained in equation (4) reduces the value of household expenditure after food price increases. The poverty indicator is then calculated using the original value and the reduced value of the household expenditure. The measure of poverty used is the FGT index of Foster et al. (1984): H * 1 z x * * P ¼ ψ I z≥ x (5) α h N z h¼1 where P is the poverty measure (α5 0, 1, 2) after food price increase; N is the number of populations; H is the number of households; ψ is the hth household size; z is the poverty line; x ¼ðx − CVÞ=ψ is the reduced value of per capita household expenditure after h h food price increase; x is the initial value of hth household expenditure (both food and non- * * food) before price increase; and Iðz ≥ x Þ is an indicator function, which is 1 if z ≥ x ,and h h 0 if vice versa. The measure of poverty before the food prices increase P is obtained by * * 0 0 replacing x and x with x and x , respectively, in the above FGT equation where h h h h 0 0 x ¼ x =ψ . P is the headcount ratio, P is the poverty gap index and P is the poverty h 0 1 2 h h severity index. The magnitude of the impact of the increase in food prices on poverty is the difference in the value of the poverty measure between the original value and the new value after the increase in food prices. A similar method was carried out by Fujii (2013) and Caracciolo et al. (2013). Fujii (2013) also reduced the value of household expenditure with CV to calculate the new poverty indicator. By contrast, Caracciolo et al. (2013) added CV values to the poverty line. To assess the impact of rising food prices on poverty in Indonesia, four simulations of rising food prices are used, a low-price increase of 5% (Sim 1), a moderate price increase of 10% (Sim 2), a high price increase of 15% (Sim 3) and very high increase at 20% (Sim 4). We apply the 2013 national poverty line, both urban and rural, on household per capita Impact of food expenditure (before and after simulated price increase) to get the impact of food price increase price increases on poverty. 4. Results and discussion 4.1 Food inflation Dan poverty trend Table 1 presents food inflation and poverty trend in Indonesia collected from various publications of BPS – Statistics Indonesia. In 1996–2019, annual general inflation in Indonesia varied from the lowest 2.78% in 2009 to the highest 77.54% in 1998 during the economic crisis. General inflation is almost always below two digits (lower than 10%), except for six times in 1997, 1998, 2001, 2002, 2005 and 2008. From 2009 to 2014, annual inflation has always been consistently lower than 10%. Foodstuffs inflation tends to be higher than general inflation, as the annual foodstuffs inflation was above 10% more often than the general inflation. Double-digit foodstuffs inflation occurred ten times during the 1996–2019 period, in 1997, 1998, 2001, 2005, 2006, 2007, 2008, 2010, 2013 and 2014. Food inflation was also more varied than general inflation, where the lowest inflation was 5.25% in 1999, and the highest was 118.35% in 1998. The variation in annual foodstuffs inflation is even greater when viewed by the food group. The highest inflation variation was spices, nuts, oils and fat, while the lower variation was eggs General Foodstuffs Processed food/beverage/ Poverty Food expenditure Year inflation inflation cigarettes/tobacco inflation headcount ratio* share** (1) (2) (3) (4) (5) (6) 1996 6.47 6.32 4.33 19.78 50.90 1997 10.31 19.92 7.82 –– 1998 77.54 118.32 94.35 25.72 55.95 1999 2.01 5.25 3.60 26.03 57.61 2000 9.34 4.00 11.09 19.14 58.92 2001 12.55 12.03 14.48 18.41 55.54 2002 10.03 9.13 9.18 18.20 51.67 2003 5.06 1.72 6.24 17.42 49.33 2004 6.40 6.38 4.85 16.66 47.69 2005 17.11 13.91 13.71 15.97 47.50 2006 6.60 12.94 6.36 17.75 47.04 2007 6.59 11.26 6.41 16.58 44.27 2008 11.06 16.35 12.53 15.42 45.08 2009 2.78 3.88 7.81 14.15 45.37 2010 6.96 15.64 6.96 13.33 46.18 2011 3.79 3.64 4.51 12.49 44.29 2012 4.30 5.68 6.11 11.96 44.76 2013 8.38 11.35 7.45 11.37 44.42 Table 1. 2014 8.36 10.57 8.11 11.25 43.72 General inflation, 2015 3.35 4.93 6.42 11.22 41.53 foodstuffs inflation, 2016 3.02 5.69 5.38 10.86 41.96 processed food/ 2017 3.61 1.26 4.10 10.64 44.61 beverage/cigarettes/ 2018 3.13 3.41 3.91 9.82 43.70 tobacco inflation, 2019 2.72 4.28 3.97 9.41 43.08 poverty headcount Note(s): *2011–2019 used data in March ratio and food **Not including tobacco/cigarettes expenditure expenditure share in Source(s): BPS – Statistics Indonesia, various publications Indonesia, 1996–2019 and milk, meat and fruits. By contrast, inflation of processed foods, beverages, cigarettes and JABES tobacco has a slight variation and similar pattern to general inflation. 30,2 In 1998, when the economic crisis hit Indonesia from mid-1997, the percentage of the poor population increased dramatically, while in the next period, the poor population had a declining trend over time. The same percentage of poor people was reached again in 2003 as before the economic crisis. The poverty rate also increased slightly in 2006, but then consistently declined to 9.41% in 2019. The poverty rate increase in 1998 would be closely related to the high inflation rate (Table 1). Likewise, high annual inflation in 2005 was related to the rise in the poverty rate in 2006. 4.2 Food expenditure share More than 40% of the household budget in Indonesia is spent on fulfilling food needs, although the share of food expenditure has a declining trend in recent years (Table 1). By Engel’s law, this decrease indicates an increase in household welfare. The higher the income, the smaller the proportion of household expenditure allocated to food, so the share of food expenditure in urban areas is lower than in rural areas. The food expenditure share decreases with increasing income, in contrast to the increase in total food expenditure (in rupiahs), because the income elasticity of food demand is lower than those of non-food. Changes in food expenditure share are also affected by changes in food prices. In 1998– 1999, the food expenditure share increased dramatically because of the high foodstuffs inflation during the economic crisis. It strongly suggests the importance of maintaining affordable food prices to avoid household welfare reduction and poverty rate increase. The highest share of food group expenditure is rice, vegetables and fish. Rice consumption is a top priority for household expenditure, with 22.03% of the household budget for food is allocated for rice purchases. Rice consumption is the highest in rural compared to urban (in line with BPS, 2021) because rural people eat more carbohydrate-rich food than urban. Rice expenditure share decreases with increasing income (see also Misdawita et al., 2019). The vegetable and fish expenditure share in rural areas is also higher than in urban areas (Table 2). Expenditure for other foods (w14) is also quite large, mainly because of the large consumption of processed food, especially in urban and high-income groups. On the other hand, the expenditure share for each of the ten food groups is 5% or less to the total food expenditure. The consumption patterns of these food groups have not changed much in the short term (Faharuddin et al., 2019). 4.3 Expenditure and price elasticity Our discussion begins with expenditure and price elasticity because the estimates of the QUAIDS model using IFGNLS on STATA software are not discussed here but presented in Table A1. Expenditure elasticity is the ratio of increased food expenditure (percent) to the total expenditure increase (percent). The price elasticity represents the change in food expenditure concerning the price change (in percent). The resulting expenditure elasticities and the price elasticities are present in Table 3. In general, the food expenditure elasticity in urban areas is lower than in rural areas, as Devi and Purnomosidi (2019) in Java. It indicates that food consumption is still a welfare symbol of the rural residents so that the rural people will consume more food if their income increases. Most food groups have expenditure elasticity lower than 1, except for four higher prices: milk, meat, fruits and other foods. Meanwhile, rice, pulses, oils and fats, non-rice staples, beverages ingredients and vegetables are the smallest expenditure elasticities. All the expenditure elasticities are positive, which means that an increase in income would also Impact of food Areas Income groups Overall price increases Variable Urban (%) Rural (%) Low (%) Middle (%) High (%) (%) (1) (2) (3) (4) (5) (6) (7) w15 rice 17.41 25.51 28.57 20.45 12.09 22.03 w25 non-rice staples 0.45 1.01 1.19 0.54 0.39 0.77 w35 rubers 0.76 2.70 2.70 1.48 0.96 1.86 w45 fishes 10.01 11.91 10.55 11.98 10.40 11.09 w55 meat 3.83 2.81 1.91 3.66 5.10 3.25 w65 eggs 2.89 2.58 2.49 2.97 2.66 2.71 w75 milk 3.55 1.80 1.38 2.75 4.51 2.55 w85 vegetables 10.24 12.72 12.97 11.74 8.85 11.65 w95 pulses 3.07 2.69 3.19 2.86 2.17 2.85 w105 fruits 5.42 4.83 3.90 5.30 7.02 5.08 w115 oil and grease 3.85 4.90 5.17 4.39 3.12 4.45 w125 beverage ingredients 4.29 5.86 5.77 5.24 3.90 5.18 w135 spices 2.24 2.68 2.72 2.51 1.97 2.49 w145 processed foods and others 31.99 18.00 17.49 24.14 36.84 24.02 Table 2. Total 100 100 100 100 100 100 Food group Household food expenditure share to 40.90 51.44 58.76 50.64 34.71 45.43 expenditure share by total expenditure urban–rural and Source(s): Author’s calculation income groups Uncompensated own-price Compensated own-price Expenditure elasticity elasticity elasticity Variables Urban Rural Overall Urban Rural Overall Urban Rural Overall (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) w1 0.305 0.462 0.390 0.420 0.583 0.508 0.371 0.479 0.434 w2 0.769 0.767 0.768 1.897 1.510 1.667 1.892 1.502 1.661 w3 0.728 0.710 0.718 1.022 1.015 1.018 1.016 1.007 1.011 w4 0.970 0.966 0.968 1.062 1.056 1.059 0.971 0.955 0.964 w5 1.445 1.564 1.494 1.180 1.220 1.197 1.120 1.166 1.139 w6 0.931 0.963 0.945 0.934 0.934 0.934 0.906 0.906 0.906 w7 1.650 1.906 1.740 0.778 0.656 0.734 0.710 0.605 0.675 w8 0.791 0.791 0.791 1.141 1.112 1.126 1.060 1.014 1.038 w9 0.899 0.913 0.906 1.376 1.359 1.368 1.346 1.327 1.337 w10 1.421 1.482 1.447 1.254 1.281 1.266 1.173 1.203 1.186 w11 0.720 0.740 0.730 1.170 1.136 1.153 1.143 1.102 1.122 w12 0.777 0.784 0.780 1.022 1.014 1.018 0.989 0.969 0.979 Table 3. w13 0.926 0.925 0.926 0.879 0.898 0.889 0.858 0.873 0.866 Expenditure and price w14 1.301 1.548 1.390 1.194 1.326 1.242 0.772 0.992 0.860 elasticity by Source(s): Author’s calculation urban–rural increase food expenditure on all food groups. However, only four commodity groups have relatively higher differences in expenditure elasticity, namely, rice, milk, meat and other foods. Milk and meat are luxury goods, with the elasticity of milk and meat being higher than 1.5. Regarding the own-price elasticity, all the coefficients of price elasticity are negative, consistent with demand theory, where the higher the price of food, the lower the demand for the food. Almost all commodity groups are elastic in urban and rural areas, and only four commodity groups have elasticity less than 1, namely, rice, milk, spices and eggs. However, only two groups show a relative difference between urban and rural areas, namely, rice and JABES non-rice staples. Rice is price inelastic (Aftab et al., 2017), although the price elasticity of rice in 30,2 rural areas is higher than in urban areas. On the contrary, the price elasticity of the non-rice staple food in rural areas is lower. The cross-price elasticity in urban and rural areas is relatively small, less than 0.5, and not much different between (Table A2). However, the price increase of rice and other food received the most response by household consumption. We also find that compensation can change both the absolute value of the cross-price elasticity and the sign of some of the price elasticity from negative to positive (Table A3). 4.4 Impact of food prices increase on poverty The upcoming discussions will center on how rising food prices affect household welfare and poverty. First, we will explain the food price impact using CV, and then we will move on to the poverty impact using the FGT poverty index. The rising food price’s impact on household welfare varies according to the magnitude of the increase in food group prices and the expenditure share of food groups. The higher the price increase and the food group expenditure share, the higher the impact on the decline in household welfare. Thus, the highest impact on household welfare is the price increase of other foods, rice, vegetables and fish. In addition to higher food shares, these four food groups have lower income elasticities. The increase in prices of other food groups, rice, vegetables and fish by 5% causes a decrease in welfare (in terms of CV) by 1.345, 0.941, 0.546 and 0.480%, respectively. The higher the price increase, the percentage of CV decline is also higher. At a very high price increase (20%), the decline in household welfare is 5.025, 3.638, 2.011 and 1.777%, respectively (Table 4). We also find that the impact of rising prices of rice, fish, non-rice cereals and vegetables on household welfare in rural areas is higher than in urban areas. On the other hand, the impact of rising prices for other food, meat and fruits is higher in urban household welfare. Table 5 shows the impact of increasing food group prices on changes in poverty headcount ratio (P ). Similar to the above CV analysis, the price increases of the four food groups that have the highest poverty impact are other food groups, rice, vegetables and fish. It could be because these four food groups have the highest food expenditure share. In total, Urban Rural Overall Food Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim groups 1 2 3 4 1 2 3 4 1 2 3 4 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) w1 0.797 1.578 2.345 3.097 1.113 2.199 3.259 4.291 0.941 1.860 2.760 3.638 w2 0.030 0.056 0.080 0.101 0.052 0.101 0.145 0.185 0.040 0.076 0.109 0.139 w3 0.042 0.081 0.118 0.154 0.058 0.113 0.166 0.215 0.049 0.096 0.140 0.181 w4 0.458 0.893 1.306 1.695 0.506 0.987 1.443 1.874 0.480 0.936 1.368 1.777 w5 0.203 0.395 0.574 0.742 0.168 0.325 0.472 0.610 0.187 0.363 0.528 0.682 w6 0.146 0.285 0.418 0.543 0.144 0.282 0.413 0.538 0.145 0.284 0.416 0.541 w7 0.200 0.392 0.578 0.756 0.131 0.258 0.381 0.501 0.169 0.331 0.488 0.640 w8 0.495 0.964 1.405 1.820 0.608 1.184 1.728 2.241 0.546 1.064 1.552 2.011 w9 0.157 0.303 0.438 0.562 0.166 0.320 0.464 0.595 0.161 0.311 0.449 0.577 w10 0.277 0.537 0.781 1.008 0.254 0.492 0.715 0.922 0.267 0.517 0.751 0.969 w11 0.181 0.351 0.511 0.659 0.225 0.437 0.636 0.822 0.201 0.390 0.567 0.733 w12 0.209 0.407 0.594 0.771 0.280 0.546 0.799 1.037 0.241 0.470 0.687 0.892 w13 0.111 0.216 0.317 0.413 0.131 0.256 0.376 0.489 0.120 0.234 0.344 0.448 w14 1.590 3.117 4.582 5.984 1.052 2.051 2.996 3.887 1.345 2.631 3.857 5.025 Table 4. CV (%) by urban–rural Source(s): Author’s calculation Impact of food Urban Rural Overall price increases Food Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim Sim groups 1 2 3 4 1 2 3 4 1 2 3 4 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) Impact on poverty headcount ratio (P ) w1 0.241 0.414 0.591 0.812 0.429 0.927 1.349 1.774 0.348 0.706 1.023 1.360 w2 0.014 0.017 0.025 0.036 0.025 0.044 0.065 0.088 0.020 0.032 0.048 0.066 w3 0.014 0.025 0.038 0.056 0.030 0.047 0.072 0.093 0.023 0.038 0.058 0.077 w4 0.180 0.275 0.369 0.437 0.185 0.372 0.567 0.787 0.183 0.330 0.482 0.636 w5 0.075 0.166 0.201 0.166 0.075 0.121 0.171 0.121 0.075 0.140 0.184 0.140 w6 0.009 0.014 0.014 0.017 0.004 0.014 0.022 0.025 0.007 0.014 0.019 0.022 w7 0.075 0.157 0.201 0.224 0.056 0.099 0.140 0.185 0.065 0.124 0.166 0.201 w8 0.194 0.290 0.389 0.465 0.213 0.451 0.718 0.944 0.205 0.382 0.576 0.737 w9 0.056 0.139 0.175 0.197 0.072 0.121 0.171 0.209 0.065 0.128 0.173 0.204 w10 0.121 0.194 0.236 0.304 0.099 0.181 0.249 0.356 0.108 0.186 0.243 0.334 w11 0.063 0.148 0.194 0.208 0.095 0.162 0.228 0.317 0.081 0.156 0.213 0.270 w12 0.075 0.166 0.201 0.236 0.101 0.194 0.295 0.401 0.090 0.182 0.255 0.330 w13 0.036 0.081 0.139 0.169 0.056 0.099 0.138 0.179 0.047 0.091 0.138 0.174 w14 0.417 0.814 1.194 1.569 0.417 0.860 1.224 1.607 0.417 0.840 1.211 1.591 Impact on poverty gap index (P ) w1 0.078 0.156 0.234 0.313 0.039 0.078 0.118 0.157 0.107 0.214 0.323 0.432 w2 0.003 0.007 0.009 0.012 0.001 0.003 0.004 0.005 0.005 0.010 0.014 0.018 w3 0.004 0.008 0.011 0.015 0.002 0.004 0.006 0.007 0.005 0.011 0.016 0.020 w4 0.037 0.073 0.107 0.140 0.022 0.044 0.065 0.084 0.048 0.094 0.139 0.182 w5 0.013 0.026 0.038 0.049 0.010 0.019 0.028 0.036 0.016 0.031 0.045 0.058 w6 0.001 0.002 0.003 0.004 0.001 0.001 0.002 0.003 0.001 0.003 0.004 0.005 w7 0.011 0.022 0.033 0.043 0.010 0.019 0.028 0.037 0.012 0.024 0.036 0.048 w8 0.043 0.085 0.125 0.163 0.024 0.047 0.070 0.091 0.058 0.114 0.167 0.219 w9 0.012 0.024 0.034 0.044 0.008 0.015 0.021 0.027 0.016 0.030 0.044 0.057 w10 0.019 0.038 0.055 0.072 0.013 0.026 0.038 0.050 0.024 0.047 0.068 0.088 w11 0.016 0.031 0.045 0.059 0.009 0.017 0.025 0.032 0.021 0.042 0.061 0.078 w12 0.019 0.038 0.056 0.073 0.010 0.020 0.029 0.038 0.027 0.052 0.076 0.099 w13 0.009 0.018 0.027 0.035 0.005 0.010 0.015 0.020 0.012 0.024 0.036 0.046 w14 0.091 0.182 0.271 0.358 0.079 0.158 0.238 0.317 0.101 0.200 0.296 0.389 Impact on poverty severity index (P ) w1 0.024 0.048 0.073 0.098 0.012 0.023 0.035 0.047 0.033 0.067 0.102 0.137 w2 0.001 0.002 0.003 0.004 0.000 0.001 0.001 0.001 0.002 0.003 0.004 0.005 w3 0.001 0.002 0.004 0.005 0.001 0.001 0.002 0.002 0.002 0.003 0.005 0.006 w4 0.011 0.022 0.033 0.043 0.007 0.013 0.019 0.025 0.015 0.029 0.043 0.057 w5 0.004 0.008 0.012 0.015 0.003 0.006 0.008 0.011 0.005 0.010 0.014 0.018 w6 0.000 0.001 0.001 0.001 0.000 0.000 0.001 0.001 0.000 0.001 0.001 0.002 w7 0.003 0.007 0.010 0.013 0.003 0.006 0.008 0.011 0.004 0.008 0.011 0.015 w8 0.013 0.026 0.039 0.051 0.007 0.014 0.021 0.027 0.018 0.036 0.052 0.069 w9 0.004 0.007 0.011 0.014 0.002 0.004 0.006 0.008 0.005 0.009 0.014 0.018 w10 0.006 0.012 0.017 0.022 0.004 0.008 0.011 0.015 0.007 0.015 0.021 0.028 w11 0.005 0.010 0.014 0.018 0.003 0.005 0.007 0.010 0.007 0.013 0.019 0.024 w12 0.006 0.012 0.017 0.022 0.003 0.006 0.009 0.011 0.008 0.016 0.024 0.031 Table 5. w13 0.003 0.006 0.008 0.011 0.002 0.003 0.005 0.006 0.004 0.008 0.011 0.014 Impact of food price w14 0.028 0.056 0.084 0.111 0.023 0.047 0.071 0.096 0.031 0.063 0.093 0.123 increase on poverty Source(s): Author’s calculation rate by rural–urban 5% of the price increase of other foods, rice, vegetables and fish causes an increase in the percentage of poor people respectively by 0.417, 0.348, 0.205 and 0.183 points. The higher the price increases, the higher the impact on poverty, even though not linear. The 20% increase in the price of each food group causes an increase in poverty headcount ratio 1.591 points (other JABES food), 1.360 points (rice), 0.737 points (vegetables) and 0.636 points (fish). 30,2 Using a poverty line of US$1.25 per capita per day, Ivanic et al. (2012) found that a 20% increase in world rice prices caused an increase in the poverty headcount ratio in Indonesia by 0.57 points from the original 7.5% in 2010. This finding is higher than Ivanic et al. (2012) and Warr and Yusuf (2013). The difference could be because of domestic food prices used here, and we do not consider the positive impact of rising food prices on food-producing households. The increase in food prices in rural areas reduces the negative impact on rural poverty because the income of food-producing households is rising (Friedman and Levinsohn, 2002). However, the effect is relatively small (Warr and Yusuf, 2013). The effect of rising food prices on poverty in rural areas is higher than in urban areas, in line with Warr and Yusuf (2013) and Misdawita et al. (2019), especially for the four food groups. An increase of 20% in the price of these food groups increases the percentage of poor people by 1.774 points in rural areas and 0.812 points in urban areas (rice); 1.607 points in rural areas and 1.569 points in urban areas (other foods); 0.944 points in rural areas and 0.465 points in urban areas (vegetables); 0.787 points in rural areas and 0.437 points in urban areas (fish). Even though farmers mostly live in rural, Warr and Yusuf (2013) argue that the beneficiaries of rising food prices in rural areas are not rural poor people but landowners, many of whom live in urban areas. This study reveals that price increases in foods that are frequently monitored by the government (other than rice), such as meat, granulated sugar (beverage ingredients), chilies (spices) and onions (spices), have a relatively lower impact on poverty. It is due to these food groups having a relatively small share of expenditure (Table 2). A 20% of price increase caused an increase in the poverty rate by 0.140 points (meat), 0.330 points (beverage ingredients) and 0.174 points (spices). However, the rise of these foods prices usually politically draws public attention because some are imported foodstuffs. However, the government needs to be aware of the high increases in food commodity prices simultaneously. If the prices of all food groups increase simultaneously by 20%, the percentage of the poor will increase by 6.142 points. For example, if all food prices increased by 20% in 2020, the poverty headcount ratio would be increased from 9.41% in 2019 to 15.55% in 2020. This increase in food prices for all food commodities by 20% also impacts rural poverty more than urban areas (7.086 points increase in rural poverty headcount ratio and 4.896 points increase in urban poverty). The effect of rising food prices on other poverty indicators, namely, the poverty gap index and poverty severity index, has a similar pattern to the impact of food prices on the headcount ratio as described above. The higher effect also comes from the four food groups, namely, other foods, rice, vegetables and fish (Table 4). 5. Conclusion and policy implications Using household survey data, we use demand analysis to calculate the impact of rising food group prices on household welfare and poverty in Indonesia. The welfare measure used is CV to calculate the impact of price increases on changes in household consumption. The 2013 national poverty line then applied to obtain the magnitude of rising prices’ impact on increasing poverty. Although we did not take into account the relatively minor positive impact of rising food prices on the increasing income of food-producing households, the findings of this study will be extremely useful for government policies aimed at controlling food prices and combating poverty. The findings of this study support previous studies regarding the negative impact of rising food prices on reducing household welfare and increasing poverty. This negative impact is higher in rural households than in urban areas. The highest impact comes from the price increase of the highest expenditure share food groups; rice, vegetables and fish. A very high price increase (20%) causes an increase in Impact of food poverty incidence 1.591 points (other food), 1.360 points (rice), 0.737 points (vegetables) and price increases 0.360 points (fish). Conversely, the increase in prices of other foodstuffs, meat, sugar (beverage ingredients), chilies (spices) and onions (spices) have a relatively small impact. Our findings suggest some policies recommendation to reduce the negative impacts of this high food price increase. To begin with, people need appropriate food price policies to avoid uncontrolled price increases in the short run. However, as mentioned above, high food prices are harmful to consumers, even though some food producers benefit from increased income. The pricing policy should be predominantly for food commodities with a large share of expenditure and higher poverty impact. If price stability sustains during the current COVID-19 pandemic, a dramatic increase in poverty incidence in Indonesia might not occur. In addition, the price policies must execute in conjunction with other consumer protection policies such as food aid or cash transfer for the lowest income population. Based on the findings above, this policy will reduce the expenditure burden of vulnerable groups due to rising food prices. The protection policies for the poor were an effective anti-poverty program during the crisis in 1997–1999 (Dhanani and Islam, 2002). Finally, this study also recommends rural development policies and promotes agricultural growth. In addition to the higher poverty impact on the rural population, the rural areas are generally identical with agriculture as food producers. In the long term, investment in agriculture, especially food crops, will increase food security through domestic food sufficiency. Agricultural economic growth is also crucial in poverty alleviation in Indonesia (Suryahadi et al., 2009). Increasing domestic food production will protect the country against international food price increases (Headey, 2018). This paper does not consider the positive impact of rising food prices on food-producing households. Although the effect is relatively small, we recommend that further research should take this into account. 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Parameter estimates of the QUAIDS model Dependent variables Independent variables w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Alpha-i (constants) 0.320*** 0.019*** 0.030*** 0.156*** 0.150*** 0.015*** 0.230*** 0.010 0.030*** 0.180*** 0.002 0.025*** 0.017*** 0.518*** Beta-i (ln x/p) 0.012** 0.004*** 0.006*** 0.004 0.011*** 0.008*** 0.049*** 0.005 0.017*** 0.004 0.003* 0.001 0.005*** 0.031*** Gamma-ij ln p1 0.110*** 0.002*** 0.003*** 0.021*** 0.005*** 0.001* 0.022*** 0.006*** 0.008*** 0.021*** 0.006*** 0.001** 0.006*** 0.057*** ln p2 0.006*** 0.000 0.000* 0.001** 0.001*** 0.000** 0.003*** 0.001*** 0.004*** 0.003*** 0.000 0.000 0.001*** ln p3 0.000 0.001*** 0.000 0.001*** 0.000 0.003*** 0.001*** 0.002*** 0.000 0.000* 0.000 0.002*** ln p4 0.006*** 0.000 0.001* 0.004*** 0.006*** 0.002*** 0.003*** 0.001*** 0.002*** 0.000* 0.024*** ln p5 0.006*** 0.004*** 0.001* 0.001 0.000 0.002*** 0.001*** 0.001** 0.000 0.012*** ln p6 0.002*** 0.003*** 0.001*** 0.002*** 0.002*** 0.001*** 0.001*** 0.003*** 0.002*** ln p7 0.018*** 0.002*** 0.002*** 0.003*** 0.001*** 0.002*** 0.002*** 0.014*** ln p8 0.015*** 0.004*** 0.004*** 0.003*** 0.000 0.001*** 0.004*** ln p9 0.012*** 0.003*** 0.001 0.003*** 0.001*** 0.004*** ln p10 0.012*** 0.002*** 0.000 0.002*** 0.017*** ln p11 0.006*** 0.000 0.001*** 0.001** ln p12 0.001*** 0.001*** 0.001 ln p13 0.003*** 0.000* ln p14 0.008*** Lambda-i ((ln x/p)^2) 0.008*** 0.001*** 0.001*** 0.000 0.001** 0.001*** 0.003*** 0.002*** 0.001*** 0.002*** 0.001*** 0.001*** 0.000* 0.011*** Eta-i D1 0.001** 0.000*** 0.000 0.001*** 0.001*** 0.000*** 0.001*** 0.001*** 0.000 0.001*** 0.000 0.001*** 0.000*** 0.005*** D2 0.008*** 0.000*** 0.000*** 0.001*** 0.000** 0.000*** 0.002*** 0.001*** 0.000 0.001*** 0.000*** 0.000*** 0.000** 0.008*** D3 0.005*** 0.000 0.000*** 0.001*** 0.000** 0.000*** 0.008*** 0.001*** 0.000*** 0.000 0.001*** 0.001*** 0.000*** 0.001** D4 0.001** 0.000 0.000 0.000** 0.001*** 0.000*** 0.002*** 0.001*** 0.000*** 0.001*** 0.000** 0.001*** 0.000*** 0.001** D5 0.004*** 0.000*** 0.000*** 0.002*** 0.000 0.000 0.001*** 0.002*** 0.000*** 0.000 0.001*** 0.001*** 0.000*** 0.011*** D6 0.003*** 0.000*** 0.000 0.004*** 0.002*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.000** 0.000*** 0.002*** D7 0.008*** 0.000*** 0.000** 0.004*** 0.003*** 0.000*** 0.002*** 0.005*** 0.003*** 0.003*** 0.002*** 0.002*** 0.001*** 0.009*** Rho Rho 15 0.186*** Rho 25 0.048*** Rho 35 0.001 Rho 45 0.055*** Rho 55 0.027* Rho 65 0.049*** Rho 75 0.195*** Note(s): *** significant at 1% ** significant at 5% * significant at 10% The empty cells are not stated because of the symmetry constraint Source(s): Author’s calculations Impact of food price increases Table A2. Uncompensated cross- price elasticities (Marshallian) Food groups w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) w1 0.508 0.012 0.009 0.059 0.034 0.018 0.026 0.059 0.052 0.034 0.039 0.021 0.021 0.032 w2 0.208 1.667 0.015 0.025 0.101 0.099 0.029 0.374 0.164 0.404 0.312 0.038 0.001 0.290 w3 0.226 0.012 1.018 0.119 0.023 0.057 0.043 0.319 0.028 0.189 0.020 0.042 0.000 0.378 w4 0.224 0.004 0.010 1.059 0.009 0.005 0.038 0.058 0.021 0.038 0.015 0.015 0.003 0.260 w5 0.045 0.016 0.014 0.030 1.197 0.122 0.053 0.042 0.005 0.016 0.033 0.012 0.013 0.039 w6 0.005 0.029 0.022 0.015 0.137 0.934 0.049 0.035 0.066 0.081 0.038 0.020 0.090 0.053 w7 0.401 0.001 0.023 0.185 0.070 0.067 0.734 0.089 0.005 0.017 0.031 0.071 0.064 0.018 w8 0.025 0.028 0.028 0.068 0.013 0.014 0.005 1.126 0.033 0.062 0.022 0.005 0.003 0.146 w9 0.197 0.040 0.010 0.070 0.029 0.060 0.023 0.124 1.368 0.121 0.013 0.094 0.034 0.084 w10 0.318 0.067 0.042 0.020 0.010 0.029 0.000 0.053 0.055 1.266 0.031 0.017 0.028 0.057 w11 0.118 0.063 0.005 0.012 0.001 0.034 0.008 0.052 0.017 0.081 1.153 0.005 0.027 0.132 w12 0.009 0.006 0.008 0.011 0.037 0.007 0.017 0.013 0.068 0.017 0.002 1.018 0.016 0.110 w13 0.265 0.001 0.002 0.010 0.002 0.109 0.062 0.029 0.045 0.091 0.037 0.026 0.889 0.022 w14 0.168 0.004 0.007 0.051 0.009 0.019 0.015 0.007 0.026 0.014 0.007 0.010 0.009 1.242 Note(s): The cells in italic represent own-price elasticities Source(s): Author’s calculation JABES 30,2 Table A3. Compensated cross- price elasticities (Hicksian) Food groups w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 w11 w12 w13 w14 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) w1 0.434 0.015 0.005 0.020 0.049 0.029 0.013 0.103 0.065 0.012 0.056 0.041 0.012 0.140 w2 0.354 1.661 0.008 0.050 0.131 0.076 0.056 0.461 0.189 0.362 0.344 0.000 0.020 0.501 w3 0.089 0.006 1.011 0.190 0.005 0.035 0.019 0.400 0.004 0.149 0.050 0.077 0.017 0.575 w4 0.040 0.004 0.019 0.964 0.046 0.024 0.005 0.167 0.054 0.091 0.025 0.033 0.020 0.526 w5 0.239 0.028 0.001 0.117 1.139 0.078 0.002 0.126 0.055 0.066 0.029 0.086 0.024 0.449 w6 0.185 0.021 0.012 0.078 0.101 0.906 0.017 0.141 0.097 0.133 0.077 0.026 0.113 0.207 w7 0.070 0.014 0.005 0.014 0.003 0.015 0.675 0.106 0.053 0.079 0.041 0.015 0.022 0.496 w8 0.175 0.034 0.036 0.146 0.043 0.037 0.032 1.038 0.007 0.105 0.011 0.044 0.016 0.363 w9 0.369 0.047 0.001 0.159 0.064 0.087 0.054 0.023 1.337 0.170 0.051 0.139 0.056 0.165 w10 0.043 0.055 0.027 0.162 0.046 0.072 0.050 0.215 0.103 1.186 0.091 0.055 0.063 0.454 w11 0.257 0.069 0.012 0.060 0.027 0.055 0.033 0.029 0.041 0.121 1.122 0.041 0.045 0.332 w12 0.158 0.000 0.016 0.066 0.067 0.016 0.010 0.100 0.094 0.060 0.034 0.979 0.035 0.324 w13 0.089 0.007 0.007 0.081 0.038 0.137 0.031 0.075 0.076 0.142 0.075 0.071 0.866 0.277 w14 0.096 0.015 0.021 0.188 0.063 0.022 0.062 0.149 0.020 0.091 0.050 0.059 0.025 0.860 Note(s): The cells in italic represent own-price elasticities Source(s): Author’s calculation
Journal
Journal of Asian Business and Economic Studies – Emerald Publishing
Published: May 26, 2023
Keywords: Food price increase; Poverty; Indonesia; QUAIDS; Compensating variation; Q12; Q17; Q18; I32