The World Bank Economic Review, 36(2), 2022, 305–328 https://doi.org10.1093/wber/lhab020 Article The Pass-Through of International Commodity Price Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Shocks to Producers’ Welfare: Evidence from Ethiopian Coffee Farmers Hundanol A. Kebede Abstract International commodity price shocks may have large impacts on producers in developing countries. In this paper, a unique household panel data from Ethiopia is utilized to show that a decrease in international coffee price has strong pass-through to the consumption of households that rely on coffee production as a main source of livelihood. It also results in decreases in on-farm labor supply (particularly male labor supply) and induces reallocation of labor towards non-coffee fields, but has negligible effect on off-farm labor supply. The decline in consumption has significant consequences on child malnutrition: children born in coffee-producing households during low coffee price periods have lower weight-for-age and weight-for-height z-scores than their peers born in non-coffee households. JEL classification: O12, O15, I15, I38 Keywords: child health, commodity price shocks, consumption smoothing, Ethiopia, income shocks 1. Introduction What is the effect of frequent booms and busts in international commodity prices on smallholder farmers in developing countries who rely on production of these commodities for their livelihood? This is a par- ticularly relevant question because conventional consumption smoothing options such as bank loans or personal savings are likely to be meagre in these parts of the world. A number of studies show that infor- mal risk-sharing arrangements among members of relatives or communities insure (perhaps imperfectly) household consumption against income shocks (see, for instance, Townsend 1995; Jacoby and Skoufias 1997; Gertler and Gruber 2002; and Fafchamps and Lund 2003), so that temporary income shocks may not necessarily have severe consequences on household consumption. However, some of the shocks that are discussed in these studies are idiosyncratic income shocks, such health or unemployment shocks to Hundanol A. Kebede is Assistant Professor at the Department of Economics, Southern Illinois University, Carbondale, IL, USA. Email: hundanol.kebede@siu.edu. The author would like to thank the editor and three anonymous referees for their helpful comments. The author would also like to thank Kerem Cosar, James Harrigan, John McLaren, Caglar Ozden, Sheetal Sekhri, and Sandip Sukhtankar for their comments and encouragements. This paper has also benefited from comments received from participants in UVA’s Development workshops. The data underlying this article are available in the article and its online supplementary material. A supplementary online appendix for this article can be found at The World Bank Economic Review website. © The Author(s) 2021. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 306 Kebede the household head. The effectiveness of informal risk-sharing against commodity price shocks that are correlated across households in a village is not clear, and is an empirical question. In this paper, unique household level panel data are used to estimate the pass-through of interna- tional coffee price shocks on household consumption, labor supply, and welfare in Ethiopia. A number of studies have looked at the effect of commodity price shocks on different outcomes such as child labor and schooling (Kruger 2007; Cogneau and Jedwab 2012; Bladimir 2020), child mortality (Miller and Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Urdinola 2010; Haaker 2018), and long-term adult mental health (Achyuta, James, and Anant 2019). A novel feature of this paper is its use of the panel data setting and household level measure of exposure to coffee price change (i.e., the fraction of household farmland allocated to coffee). Existing literature mostly relies on repeated cross-sections and/or a measure of exposure that is based on geographic loca- tions, so that all households located in a given geographic region are assumed to be equally exposed to price shocks. However, this approach masks massive variation in households’ choice of crop portfolio within a narrowly defined geographic unit. In Ethiopia’s top coffee-producing district, for instance, the fraction of farmland allocated to coffee ranges from less than 10 percent for some households to 100 percent for others, averaging about 60 percent.1 The approach in the current paper captures variations in exposure to coffee price shocks among households within a narrowly defined geographic unit. Moreover, Ethiopia provides an ideal setting to study this problem compared to most of the settings studied in the literature. About 30 percent of the households live below the poverty line (World Bank Group 2015), cof- fee production is the main source of livelihood for about 20 percent of the population (Central Statistical Agency 2016), and there is considerable variation in the share of coffee production in household income due to large geographic variation in agroclimatic suitability to grow coffee. The paper uses the Ethiopian Socioeconomic Survey (ESS) data collected by the joint effort of the Ethiopian Central Statistical Agency (CSA) and the World Bank.2 ESS offers nationally representative panel data (2011, 2013, and 2015 rounds) on about 4,000 households and provides information on household production, consumption, labor supply, and child anthropometric measures. These data have a number of unique features compared to similar datasets used in the literature. First, they include direct measure of household consumption expenditure disaggregated into food and nonfood items. Second, the dataset includes on- and off-farm labor supply decisions disaggregated by gender and age groups. Third, they include plot-level information on input usage and crop production. Finally, they include information on household’s access to social and economic services such as access to credit (formal and informal), transfers, and productive safety net programs. The results from empirical exercises show that changes in international coffee prices have significant pass-through to household food and nonfood consumption. A 1 percent decrease in international coffee price results in about a 0.8 percent decrease in adult-equivalent consumption expenditure in a household that allocates all its farmland to coffee. Between 2011 and 2013, coffee prices dropped by 50 percent, implying about a 40 percent decrease in consumption in a household that allocates all its farmland to coffee. Considering an average household among households that allocate at least a quarter of their farm- land to coffee, the 50 percent drop in coffee price results in a 23 percent decrease in adult-equivalent consumption. Another important result is that household on-farm labor supply responds positively to changes in coffee price. A decrease in coffee price decreases household labor supply on coffee fields, resulting in reallocation of household labor towards non-coffee fields, particularly for male members of the household. While there is no evidence of change in off-farm labor supply and labor income, there is limited evidence that households that already worked in the Productive Safety Net Program (PSNP) decrease the number of days of PSNP work and earn lower PSNP income during low coffee price periods. This is perhaps because 1 A similar pattern exists in other major coffee districts of the country, and looking at more narrowly defined geography such as a kebele, which is the lowest administrative unit, gives a similar conclusion. 2 See https://microdata.worldbank.org/index.php/catalog/2053/download/40408 for details on the sampling procedure and coverage of this data. The World Bank Economic Review 307 coffee farmers increase their PSNP labor supply by cutting on-farm labor supply during low coffee price periods, which diminishes non-coffee farmers’ opportunity to work more days in PSNP. While borrowing increases during low coffee price periods, and modestly helps households smooth their consumption, there is no evidence that households’ income from aid/transfers changes in response to coffee price changes. The latter could be attributed to the fact that most residents of coffee-producing areas are more or less exposed to coffee price decreases, implying less supply of inter-family/friend transfers. Hence, no significant role Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 of informal risk-sharing is discerned in household consumption smoothing during coffee price shocks. The decrease in household consumption during low coffee price periods has significant consequences on child health. Using child anthropometric measures, the paper shows that cohorts of children who were in utero during low coffee price periods and born in coffee-producing households have significantly lower weight-for-age z-scores and weight-for-height z-scores. This paper is closely related to studies that estimate the effects of commodity price fluctuations on household consumption and investment in child human capital. Miller and Urdinola (2010) show that periods of high coffee prices are associated with higher child mortality in Colombia because higher coffee prices increase the opportunity cost of mothers’ time, consequently decreasing mothers’ time spent on childcare. On the contrary, in the current data, decreases in coffee prices lead to significant decrease in household consumption, but have no effect on labor supply by female household members, consequently leading to poor child health. Another closely related paper is the study by Cogneau and Jedwab (2012), who study the effect of a cut in government-administered producer price for cocoa on child schooling and health in Côte d’Ivoire. They measure exposure to the price cut using a dummy variable indicating whether a household reported a positive cocoa production. However, they do not have household-level panel data that cover pre- and postshock periods to tightly identify the effect of the shock on household consumption. To the best of my knowledge, the current paper is the first to use panel data and household- level measure of exposure to commodity price shocks to estimate the effect of such shocks on household consumption. Some studies find that child labor increases and school enrollment decreases during periods of com- modity price booms, implying that temporary income shocks might have lasting effect on long-term eco- nomic growth by thwarting human capital accumulation. For instance, Kruger (2007) finds that periods of coffee boom are associated with lower school enrollment of poor children in Brazil, while Bladimir (2020) finds that cohorts that experienced coffee boom during their childhood completed lower years of schooling and have lower adulthood earnings in Colombia. This paper finds that school enrollment for young girls significantly decreases while enrollment for boys is not significantly affected during high coffee price periods. This suggests that the magnitudes of the substitution and income effects from changes in commodity prices might vary across genders. A number of influential papers have explored the link between early childhood/in utero exposure to adverse situations and later human capital development using various sources of variation such as pandemic (Almond 2006), Ramadan fasting (Almond, Mazumder, and van Ewijk 2015), rainfall shock (Tiwari, Jacoby, and Skoufias 2013; Mendiratta 2015; Shah and Steinberg 2017), and commodity price (Achyuta, James, and Anant 2019). For instance, Achyuta, James, and Anant (2019) use fluctuations in cocoa prices to study the effect of income shocks during childhood on adult mental health. The current paper uses a similar source of variation to income, but focuses on the short-term consequences on current household consumption and child malnutrition. A related strand of literature studies the effect on child health of income volatility due to macroe- conomic business cycles or commodity price movements, typically in high- or middle-income settings, including Dehejia and Lleras-Muney (2004), Neumayer (2004), Paxson and Schady (2005), Ferreira and Schady (2008), and Page, Schaller, and Simon (2017). These studies mainly seek to address the theoretical ambiguity about the effect of aggregate income shocks on child health. On the one hand, a positive aggre- gate income shock, e.g., macro economic boom, implies higher consumption for families, which improves 308 Kebede child outcomes through investments in food and health services. On the other hand, periods of booms also imply higher opportunity costs of mothers’ time, which would adversely affect child outcome if mothers spend less time on taking care of the children. These studies find that the opportunity cost channel dom- inates the consumption channel, i.e., child health measures are countercyclical in high-/middle-income countries. The current paper finds the exact opposite in a low-income country setting. This is attributed to the fact that most households in the current data live on a subsistence income, so that shocks to income Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 have a first-order effect on spending on child nutrition and health.3 On the other hand, no significant effect of coffee price change on female labor supply, and hence time investment on children is found. This paper is also tangentially related to the literature on resource curse in the context of developing countries (see, for instance, Samuel and Christopher 2014, Nicolas et al. 2017, and Eoin and Marshall 2020). This studies explore how international commodity price shocks affect the onset and duration of conflict in developing countries. For instance, Nicolas et al. (2017) show that an increase in food crop prices increases the incidence and onset of conflicts, while similar change in prices of cash crops has no significant effect on conflict. Eoin and Marshall (2020) find a contrasting result in Sub-Saharan Africa using international shocks to mineral prices as a source of variation. This suggests that commodity price shocks affect household welfare in a number of different channels. In this paper, the focus is on exploring the effect on household consumption and investment in child human capital. While there was no significant conflict in the study areas and period, the inclusion of zone-year fixed effects would absorb any effect of conflict due to commodity price movement. The rest of the paper is organized as follows. The Data section briefly describes the dataset and some key summary statistics. This is followed by a section that gives an overview of coffee production and marketing in Ethiopia. The Empirical Strategy and Results sections present the empirical strategy and discussion of th empirical results. The last section concludes the paper. 2. Data 2.1. Data Sources The main dataset used in this study is the Ethiopian Socioeconomic Survey (ESS) data collected by the Central Statistical Agency (CSA) in collaboration with the World Bank (see Central Statistical Agency 2012 for details about these data). This is exceptionally rich nationally representative panel data of about 4,000 (rural sample) households in 290 villages (enumeration areas). There are three rounds of this sur- vey: 2011, 2013, and 2015. In the rural sample, the attrition rate is 5 percent between 2011 and 2013, and 2 percent between 2013 and 2015.4 This attrition is not correlated with the households’ 2011 char- acteristics, such as food and nonfood consumption, share of land allocated to coffee, and many other variables, but is slightly negatively correlated with household size and land holding. See table S1.1 in the supplementary online appendix, which reports correlation of attrition between 2011 and 2013 on selected household 2011 characteristics. This dataset includes information on household production and consumption, disaggregated by crops. In particular, the dataset enables me to calculate the fraction of land and labor a household allocates across crops (or plots), as well as each household member’s on-farm and off-farm labor supply. Importantly, the sample locations cover both coffee-producing and non-coffee-producing districts (see fig. 1). A key variable that enables me to construct a measure of households’ exposure to coffee price shocks is the share of farmland allocated to coffee production—households’ exposure to coffee price shocks is proportional to the fraction of their farmland allocated to coffee production. The dataset also includes information on household demographic characteristics and anthropometric measures for children under five years of age. 3 According to UNICEF (2015), almost half of under-5 mortality is attributable to undernutrition in poor countries. See http://www.countdown2015mnch.org/documents/2015Report/Countdown_to__2015-A_Decade_of_Tracking_ Progress_for_Maternal_Newborn_and_Child_Survival-The2015Report-Conference_Draft.pdf. 4 For more information, see https://microdata.worldbank.org/index.php/catalog/2783/download/48264. The World Bank Economic Review 309 Figure 1. ESS Sample Locations and GAEZ Yield (kg/hectare) Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on data from Food and Agricultural Organization/Global Agro-Ecological Zones (FAO/GAEZ) and the Ethiopian Socioeconomic Survey (ESS). Note: This figure shows GAEZ estimated yield for coffee based on rain-fed and intermediate input usage farming techniques. There are some shortcomings of the dataset worth mentioning. Its main drawback is its relatively small sample size and short panel. The second main drawback is that the child anthropometric measures (height and weight) are poorly measured—height and weight measures reported are biologically implausible for about 3 percent of the children in the dataset,5 which is excluded from analysis. The international coffee price data comes from the International Coffee Organization, which maintains historical statistics on international coffee prices and trade. This paper uses a monthly coffee price index for the variety of coffee known as Brazilian Naturals, which is an arabica species and one of the most widely traded coffee types, with Ethiopia as one of the major suppliers. Figure 2 plots significant swings in the international coffee prices over the study period 2006–2016.6 To shed some light on how domestic prices move in response to international prices, fig. 3 plots international prices (adjusted by nominal exchange rate), together with domestic prices at major coffee production centers across Ethiopia, obtained from the Retail Price Survey (RPS). The correlations between monthly international price and domestic prices at each of the different coffee-producing centers are above 0.9. 2.2. Descriptive Statistics This subsection provides descriptive statistics of key variables from the dataset. Table 1 provides descrip- tive statistics of most of the variables used in the analysis. The descriptive statistics are provided for the 5 While this problem is also prevalent in the Demographic and Health Survey (DHS) dataset, the large sample size of DHS data means that one can still have good variation in the data after dropping these outliers. 6 The period 2006–2016 is the study period because some of under-5 children in ESS 2011 round data are in utero in 2006, and some of the under-5 children in ESS 2015/16 data are in utero in 2016. 310 Kebede Figure 2. Monthly International Coffee Price Index Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on data from the International Coffee Organization. Note: This figure plots the coffee price index for the variety of coffee known as Brazilian Naturals, which is an arabica species that is dried inside the fruit rather than after the fruit has been removed. It is one of the most widely traded coffee types, with Ethiopia one of the major suppliers. In addition to the international price index, I also plot the index adjusted for nominal exchange rate of birr/USD to facilitate comparison with domestic coffee prices below. The gap between the international price index and the index adjusted for a nominal exchange rate is expanding after 2012 due to the depreciation of birr against USD. The vertical red lines indicate the survey windows. whole sample and for two different groups of households: households that allocate at least 10 percent of their farmland to coffee and those that allocate less than 10 percent of their farmland to coffee. While the 10 percent threshold is arbitrary, it can be argued that farmers who allocate at least 10 percent of their farmland to coffee are likely to be significantly affected by coffee price fluctuation, and can be considered coffee producers. These households account for 11 percent of the sample size, and they allocate about 40 percent of their farmland to coffee, on average (see table 2). There are some notable differences in the summary statistics across the two groups. First, coffee-producing households have lower total adult- equivalent consumption than non-coffee farmers, even though this is statistically significant only at 10 percent. However, looking into the components, food and nonfood consumption are not significantly different across the two groups. There are statistically significant differences across the two groups of households in the means of the following variables: household size, female on-farm labor supply, spouse on-farm labor supply, probability of receiving assistance/transfer and the size of assistance/transfer, and rainfall. Besides these differences, the means of the variables across the two groups are not statistically significantly different. Panel B of table 1 provides descriptive statistics of child anthropometric measures. It shows an average Ethiopian child has poorer anthropometric measures compared to 2006 WHO child growth standards, as indicated by negative values of the weight-for-age z-score (WAZ), weight-for-height z-score (WHZ), and height-for-age z-score (HAZ).7,8 Comparing across the two groups of households, we see that children 7 WAZ, WHZ, and HAZ values are computed using the Stata program, which is based on 2006 WHO child growth standards. 8 Ethiopia performs worse than Sub-Saharan African (SSA) countries’ average in the proportion of under-5 children stunted, underweight, and wasted. Ethiopia has the highest percentage (76 percent) of children who have not received The World Bank Economic Review 311 Figure 3. Comovement of International and Domestic Coffee Prices Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on data from the International Coffee Organization (for the international coffee price index) and Retail Price Survey (RPS) (for domestic coffee prices). Note: This figure plots the international coffee price index (adjusted for nominal exchange rate depreciation) along with the domestic coffee price at major coffee production centers. Our retail price data cover only until 2014. in coffee-producing households have slightly better anthropometric outcomes than those in non-coffee- producing households. It is worth discussing the distribution of some key variables in detail. First, table 2 provides summary statistics of the share of farmland allocated to coffee for different subsamples of households. The first row presents the distribution of the share of land allocated to coffee across all farmers in the ESS data (rural sample). An average farmer allocates about 5 percent of their farmland to coffee. However, because the majority of farmers are not coffee producers (the 75th percentile farmer allocates a zero fraction of their farmland to coffee), the distribution of the share of land allocated to coffee is highly positively skewed. The second row provides the summary statistics of the share of land allocated to coffee among farmers who allocate at least 5 percent of their farmland to coffee. This sample accounts for nearly 15 percent of the total sample. Among these farmers, the average farmer allocates about one-third of their farmland to coffee, while the median and the 75th percentile farmers, respectively, allocate a quarter and a half of their farmland to coffee. The third row reports similar summary statistics for a subsample of farmers who allocate at least 10 percent of their farmland to coffee. These farmers account for slightly above 10 percent of the total sample, and they allocate about 40 percent of their farmland to coffee, on average. Rows 4–6 report similar summary statistics for subsamples of farmers who allocate at least a quarter, a third, and a half of their farmland to coffee production, respectively. These summary statistics for subsamples of the households are useful later when we interpret the magnitude of the estimated regression coefficients. any of the eight EPI immunizations in SSA countries. See Kanamori and Pullum (2013) for a detailed comparison of child health outcomes across 30 SSA countries using DHS data. 312 Kebede Table 1. Summary Statistics Households with land Households with land All households share of coffee ≥ 10% share of coffee < 10% Variable Mean SD Mean SD Mean SD Panel A: Household outcomes Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Number of observations 9,967 1,147 8,820 Household size 5.01 2.37 5.34 2.37 4.96 2.36 Total consumption AEq (birr) 5,459.87 5,023.29 5,206.51 4,820.59 5,491.74 5,047.58 Food consumption AEq (birr) 4,413.71 4,117.02 4,262.43 4,523.97 4,432.74 4,062.84 Nonfood consumption AEq (birr) 994.31 2,279.72 889.05 1,096.29 1,007.55 2,387.11 Received credit 0.25 0.43 0.24 0.43 0.25 0.43 Total credit (birr) 747.72 8,644.08 427.02 1,457.95 789.03 9,168.24 Received assistance 0.20 0.40 0.08 0.27 0.21 0.41 Total assistance (birr) 245.65 1,072.01 54.75 322.47 270.29 1,130.87 Land holding (hectare) 0.87 4.84 0.84 2.96 0.88 5.03 Rainfall (mm/year) 1,132.77 428.64 1,486.65 374.86 1,087.18 413.65 Household on-farm labor (hr) 1,133.93 2,072.87 1,205.97 1,595.08 1,124.56 2,127.02 Male on-farm labor (hr) 769.63 1,730.16 777.66 1,146.99 768.59 1,792.16 Female on-farm labor (hr) 309.25 578.76 374.72 619.19 300.73 572.78 Spouse on-farm labor (hr) 225.42 430.22 280.09 513.76 218.31 417.64 Child on-farm labor (hr) 55.05 271.10 53.58 166.45 55.24 281.88 Number of PSNP days worked 10.05 41.82 6.21 36.09 10.56 42.48 PSNP income 231.39 3,228.04 83.52 410.75 250.62 3,427.88 Number of days worked as laborer 12.69 44.34 14.00 47.95 12.52 43.85 Labor income 405.96 4,347.76 330.66 1,465.98 415.75 4,591.45 Panel B: Child outcomes Number of children 3,994 3,512 482 Weight-for-age z-score −1.08 1.30 −0.93 1.37 −1.10 1.29 Weight-for-height z-score −0.39 1.48 −0.30 1.47 −0.40 1.48 Height-for-age z-score −1.43 1.89 −1.24 1.93 −1.46 1.88 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: These statistics are calculated from ESS data by restricting to rural samples only. For child outcomes, biologically implausible height and weight records that lead to implausible values of any of WAZ, WHZ, or HAZ (i.e., above 5 or below −5) are dropped in the calculation of these statistics and in all the regressions below. Consumption spending is in adult-equivalent (AEq) terms. The second important variable is household consumption expenditure. The average adult-equivalent consumption in 2011 per day is 14.5 birr (about 2.75 in current PPP $). The median adult-equivalent consumption per day is about 11 birr (about 2 in current PPP $). The national poverty line for Ethiopia during the same year is 10.35 birr (World Bank Group 2015). The average (median) adult-equivalent consumption decreases slightly to 1.94 in current PPP $ (1.68 in current PPP $) in 2015. Figure 4 presents the distribution of log total adult-equivalent consumption spending.9 The left panel in this figure shows the distribution of nominal consumption for each of the survey rounds 2011, 2013, and 2015. It shows a clear rightward shift in the distribution of consumption over time. However, this measure does not account for food price inflation over years. To address this, the zone-year fixed effects are purged out to account for changes in prices over years in each zone, and the results are plotted in the bottom panel. The result shows that there is no clear rightward shift in the distribution of consumption over 9 For details about construction of the consumption variable from survey data, see https://microdata.worldbank.org/ index.php/catalog/2053/download/40407. The World Bank Economic Review 313 Table 2. Distribution of the Share of Land Allocated to Coffee in 2011 Sample Mean Median 75% 90% 95% SD N All farmers 0.05 0.00 0.00 0.13 0.37 0.16 3,466 Land share of coffee ≥ 5% 0.34 0.25 0.50 0.79 0.93 0.27 492 Land share of coffee ≥ 10% 0.41 0.32 0.59 0.85 0.94 0.27 389 Land share of coffee ≥ 25% 0.56 0.51 0.75 0.93 1.00 0.24 244 Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Land share of coffee ≥ 33% 0.63 0.60 0.79 0.95 1.00 0.21 190 Land share of coffee ≥ 50% 0.75 0.75 0.89 1.00 1.00 0.16 125 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table provides the distributive statistics of the share of household land allocated to coffee production in the year 2011 for different subsamples of farmers defined based on the farmers’ intensity of coffee farm. time. Household total consumption expenditure is composed of food and nonfood expenditures (which mainly include spending on household goods, clothing, and education). On average, nonfood expendi- tures account for only 18.5 percent of household total expenditures, with significant variation across households. 3. Coffee Production and Marketing in Ethiopia Ethiopia is a top coffee producer in Africa and ranked fifth in the world after Brazil, Vietnam, Colombia, and Indonesia in the year 2015/16. Ethiopia produced about seven million 60 kg bags of coffee in 2015/16, which is about 9 percent of the world coffee production. About 95 percent of Ethiopian cof- fee is produced by smallholder farmers with about a median land size of one hectare, and average coffee yield of 6.34 quintal per hectare. The remaining 5 percent is produced by government-owned farms and large-scale private farms. Over 5 million farm households engage in some level of coffee production in Ethiopia (Central Statistical Agency 2016), implying that international coffee price changes might have important implications for poverty. Coffee production in Ethiopia is largely concentrated in the southern and southwestern parts of the country, where there is high rainfall and forest cover, the two essential ingredients of arabica coffee production in Ethiopia. There are four coffee production systems in Ethiopia: namely, forest, semiforest, garden, and plantation coffee farms (Tefera and Tefera 2013). Forest coffee grows under the shade of natural forests with some loose (communal) ownership. Semiforest coffee is also grown under tree shade maintained by private owners. Garden coffee is grown in the farmers’ gardens and plots of land near their homes, whereas plantation coffee is grown by large-scale commercial farms by applying modern production techniques such as irrigation, and modern inputs. About 60 percent of coffee production is exported, and coffee export accounts for about a quarter of the country’s export revenue. Because of the importance of coffee as a source foreign exchange, the Ethiopian government tightly monitors local and export trade through licensing and regulations. The Derg military regime (1975–1991) required farmers to supply a quota of coffee production at a specified price to the government. Since the downfall of Derg, there has been a gradual liberalization of the coffee trade including the removal of entry barriers following Proclamation No. 70/1993, consolidation of taxes related to coffee trade into a single tax family in Proclamation No. 99/1998, and later complete removal of export taxes on coffee in 2002 (Bart et al. 2014). Coffee is exported by unions of cooperatives (which are essentially parastatals) or by licensed private exporters. Cooperative unions source their export coffee from (licensed) intermediate suppliers or hulling firms, while private exporters source it from coffee auc- tion markets in Addis Ababa or Dire Dawa. Despite the above liberalization steps, the government still has a heavy hand in the coffee trade. First, following the creation of the Ethiopian Commodity Exchange (ECX) in 2008, private traders are required to sell coffee through ECX (Gabre-Madhin 2012). Other 314 Kebede Figure 4. Distribution of Log Total Adult-Equivalent (AE) Consumption across Years Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This figure shows the distribution of log total AE consumption. The top panel shows the distribution of nominal values of log total AE consumption for each round of the survey. The bottom panel removes zone-specific trends to account for inflation at the local level. forms of government intervention include allocating licenses. For instance, the government frequently revokes export licenses from traders who hoard excessive amounts of coffee (over 500 metric tons) (Tefera and Tefera 2013), and closely monitors the domestic market to ensure that export-quality coffee is not being sold in local markets. Despite these government interventions, domestic coffee prices are highly correlated with international prices, as shown in fig. 3. The World Bank Economic Review 315 4. Empirical Strategy 4.1. The Pass-Through of Coffee Price Shocks to Consumption The ESS data are the main data used to estimate the pass-through of international coffee price changes to household consumption. These data allow us to measure household-level exposure to coffee price shocks using the share of household farmland allocated to coffee. The estimation equation is Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Log Cit = β0 + β1 (LandShareCoffeei × LogPricet ) + γi + γzt + εit , (1) where i indexes households, γ i is household fixed effects, and γ zt is zone-year fixed effects included to ac- count for changes in food prices in response to demand changes following movement in coffee prices (note that zone is a higher-level administrative unit than a kebele/village).10 An alternative way of capturing this is using zone-specific time trends, which give very similar estimates. The variable C denotes consumption. ESS data report household-level adult-equivalent per capita consumption expenditures disaggregated into food and nonfood. The variable LandShareCoffee is the share of household land allocated to coffee. The variable Price is the average international coffee price in the four quarters before the consumption survey month.11 Identification comes from large variations in the fraction of farmland allocated to coffee across house- holds within a zone and massive swings in the international coffee prices over time. Table S1.2 shows that about 82 percent of variation in adult-equivalent consumption and about 61 percent of variation in the fraction of land allocated to coffee are within-zone variation. The identification assumptions are (a) households cannot endogenously adjust the fraction of their farmland allocated to coffee in response to coffee price (at least in the short run), (b) international coffee price change is exogenous to Ethiopia, and (c) coffee price movement does not have any significant effect on non-coffee producers. To rule out any concern about households endogenously adjusting the fraction of land allocated to coffee in response to coffee price change, the share of land allocated to coffee at the beginning of the panel, i.e., the year 2011, is used. The lumpy nature of coffee production by itself rules out any endoge- nous adjustment of coffee production in the short run. Coffee plants take about five years to grow into trees and bear fruit. Hence, it is unlikely that farmers would adjust the share of their land allocated to coffee following fluctuations in coffee prices. Moreover, variation in the fraction of farmland allocated to coffee is largely attributed to geographic variation in agroclimatic suitability to grow coffee (see fig. 1 and table S1.2). Table S1.2 decomposes the variation in consumption, landholding, and the share of land allocated to coffee. It shows that about 72 percent of the variation in landholding is within a village, but only 32 percent of variation in the share of land allocated to coffee is within a village.12 The remaining 68 percent of variation in the share of land allocated to coffee is across villages, implying the importance of agroclimatic conditions to growing coffee. The international movement in coffee price is exogenous to Ethiopia, which has a low (below 10 percent) international market share. For instance, the coffee price hike in 2011 (which is crucial in my 10 Ethiopia is administratively divided into 10 regions, which are divided into 70 zones, which are further divided into 683 woredas/districts, and the woredas are divided into over 15,000 kebeles/villages, about 10,000 of which are rural villages. Hence, even after including zone-year fixed effects, there is sufficient variation in the data to precisely estimate the parameters of interest. See table S1.2. 11 Using average prices over one, two, or three quarters before the consumption survey month has no significant effect on the estimates. 12 While the 32 percent within-village variation is surprisingly high (given that there is little agroclimatic variation within a village—the average village size is about 25 km square), there are a number of potential factors that might explain this variation, including risk attitude (risk-averse farmers might like to diversify their crop portfolio), variation in forest coverage, family labor supply constraints, and size of farmland. 316 Kebede identification) is attributed to poor harvests in Colombia, Indonesia, Mexico, and Vietnam, mainly, and to growing demands in emerging economies such as Brazil, China, and India.13 Movement in coffee price might have local spillovers to non-coffee producers in the form of general equilibrium effects. Booms in coffee prices might increase local demand and lead to local food price inflation, which would affect non-coffee producers living in largely coffee-growing regions. Similarly, higher coffee prices may also affect local labor demand and wages. While it is impossible to completely Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 address these potential spillover effects, one can minimize the bias due to these spillovers by including time-varying location-specific fixed effects. Zone-year fixed effects are included in equation (3) to address this issue. 4.2. On-Farm and Off-Farm Labor Supply Responses On-Farm Labor Supply: To explore the effect of coffee price shocks on household on-farm labor supply, the following regression is estimated: Lonf it = β0 + β1 (LandShareCoffeei × LogPricet ) + γi + γzt + εit , (2) where i indexes households, γ i is household fixed effects, and γ zt is zone-year fixed effects included to account for changes in local labor demand in response to movement in coffee prices. The variable Lonf is hours of on-farm labor supply (which is obtained by adding on-farm labor supply across all crop fields cultivated by the household). This equation is estimated for men, women, spouses, and children separately to investigate potential heterogeneity in response across gender and age groups. Also, similar regression is coffee estimated by replacing Lonf by Lcoffee (hours of labor supply on coffee fields) and LLonf (the share of labor allocated to coffee fields). Off-farm labor supply: Households might also look for off-farm employment opportunities to smooth consumption during low coffee price periods. Two off-farm employment options are considered: daily labor and employment in the PSNP. We estimate the following equation: Loff it = β0 + β1 (LandShareCoffeei × LogPricet ) + γi + γzt + εit , (3) where i indexes households, γ i is household fixed effects, and γ zt is zone-year fixed effects included to account for changes in local labor demand in response to movement in coffee prices. The variable Loff is hours of off-farm labor supply per season. This equation is estimated separately for employment as a daily laborer (market labor) and employment in the PSNP program. 4.3. Credit and Transfers as Consumption Smoothing Options Households might use credits and transfers (from government, NGOs, and relatives) to smooth con- sumption during crises. To see whether borrowing or receipt of aid responds to coffee price shocks, the following regression is estimated: Yit = β0 + β1 (LandShareCoffeei × LogPricet ) + γi + γzt + εit , (4) where Yit is the amount of credit or transfers received by the household. Credit includes loans from formal financial sectors and informal moneylenders. Likewise, transfer includes assistance received from relatives, NGOs, and the government. 4.4. Coffee Price Shocks and Child Malnutrition The pass-through of coffee prices to household consumption may have consequences on child malnutri- tion and health. To investigate this, anthropometric outcomes of cohorts of children born during high and low coffee price periods in households that allocate different fractions of their farmland to coffee are 13 See https://www.theguardian.com/business/2011/apr/21/commodities-coffee-shortage-price-rise-expected. The World Bank Economic Review 317 compared. The regression equation estimated is Hcit = β0 + β1 (LandShareCoffeei × LogPriceUteroc ) + X δ + γi , + γz t + γm + εcit , (5) where Hcit is a measure of health outcome for a child c in household i born in year t. The variable PriceUteroc is the average coffee price in the months when the child was in utero. Exposure to coffee Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 price shock after birth is included as a control to see the sensitivity of the result. The variable X includes a vector of child and household characteristics including child’s age and gender, and mother’s education and land size. Also, time-varying village characteristics such as rainfall are included as controls. The term γ z t is zone-specific trends (included to account for zone-specific trends in access to health facilities or improvement in health infrastructure.)14 and γ m is month of birth fixed effects (included to account for the possibility that children who are in utero during harvest seasons might be better off compared to those in utero during slack seasons), and ε cit is the error term. The baseline specification includes household fixed effects γ i , so that identification comes from between-sibling comparison. However, because most of the variation in exposure to coffee price comes from between-household variation in the share of farmland allocated to coffee, between-sibling comparison is too restrictive. Also, due to the short panel, most households have only one child under five years of age. So results for alternative specifications where household fixed effects are replaced by village fixed effects are reported. Three standard child anthropometric measures are considered: WAZ, WHZ, and HAZ. These mea- sures are recommended by WHO as reliable measures of malnutrition for children less than 60 months old (World Food Program 2013). WAZ is considered a reliable measure of child acute and chronic mal- nutrition, whereas WHZ is considered a reliable measure of acute malnutrition and ill health. Low HAZ is an indicator of chronic malnutrition. 5. Results 5.1. The Pass-Through of Coffee Price Shocks to Consumption Figure 5 presents a visualization of how the correlation between household consumption and the share of farmland allocated to coffee changes over time following booms and busts in international coffee prices. The figure shows that during the 2011 coffee price boom, households with a higher fraction of their farmland allocated to coffee have significantly higher adult-equivalent consumption. This fact is completely reversed in the years 2013 and 2015, when coffee prices were 50 percent lower than their 2011 values—in 2013 and 2015, household consumption significantly decreases with the fraction of their farmland allocated to coffee. Table 3 presents formal estimations of the relationship depicted in fig. 5. The standard errors are clus- tered at zone level. Panel A reports the estimation results, while panel B presents the implied changes in consumption following a 50 percent decrease in coffee prices between 2011 and 2013. The first col- umn presents the effect on total household adult-equivalent consumption, whereas the second and third columns report the effect on food and nonfood consumption.15 Across all three columns, we see that a change in coffee price has significant effects on total consumption and its components (food and nonfood consumption). However, the effect on food expenditure is stronger than the effect on nonfood expenditure. The coefficient estimates in panel A can be interpreted as percentage change in consumption following a 1 percent change in coffee price for a household that allocates all its farmland to coffee (for the most exposed household). In equation (3), the effect of a percentage change in coffee price is given by β 1 × LandShareCoffeei , which varies across households depending on the fraction of farmland allocated to coffee. For a household that allocates all its farmland to coffee, LandShareCoffeei = 1, the effect of a 14 An alternative is to include zone-cohort fixed effects. Results from both approaches are very similar. 15 Nonfood expenditure mainly includes expenditure on health and education. Nonfood expenditure accounts for only 18.5 percent of total consumption expenditure, on average. 318 Kebede Figure 5. Correlation between Household Consumption and the Share of Land Allocated to Coffee Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This figure shows the correlation between log household adult-equivalent consumption and the share of land allocated to coffee in 2011. The effect of rainfall and price inflation (year fixed effects) are removed before plotting. The figure shows that in 2011, when the coffee price boomed, there is a significant positive correlation between household consumption and the fraction of household land allocated to coffee. This fact is totally reversed in the years 2013 and 2015, when the international coffee price is about 50 percent less than its 2011 level (see fig. 2). percentage change in coffee price is given by β 1 . For a household that does not produce any coffee, the effect of the price change is zero. Accordingly, for a household that allocates all its farmland to coffee, a 50 percent decrease in coffee price results in about a 40 percent decrease in household adult-equivalent food consumption and about a 30 percent decrease in nonfood consumption. The World Bank Economic Review 319 Table 3. Pass-Through of Coffee Price Shock to Household Consumption Total AEC Food AEC Nonfood AEC Panel A: Regression results Land share of coffee × LogPrice 0.822*** 0.834*** 0.580* (0.205) (0.207) (0.300) Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Panel B: Implied effects of 50% price decrease between 2011 and 2013 for average farmer All sample −2.06% −2.09% −1.45% ≥ 5% land share of coffee −13.49% −14.18% −9.84% ≥ 10% land share of coffee −16.85% −17.1% −11.89% ≥ 25% land share of coffee −23.02% −23.35% −16.24% ≥ 33% land share of coffee −25.89% −26.27% −18.27% ≥ 50% land share of coffee −30.83% −31.28% −21.75% N 9,525 9,525 9,525 R2 0.650 0.608 0.710 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table is based on three rounds (2011, 2013, and 2015) of the ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. AEC stands for adult-equivalent consumption. All dependent variables are in log units of monetary value adjusted for variation in living expense across regions. “Land share of coffee” is the fraction of household land allocated to coffee in 2011, and LogPrice is the log of average coffee price over the four quarters before the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 Panel B reports the implied changes in household total, food, and nonfood expenditures following a 50 percent price drop between 2011 and 2013 for households at different points in the distribution of the share of land allocated to coffee. The first row in panel B reports the implied decrease in consumption of an average farmer in ESS data (rural sample). An average farmer in the ESS data allocates only 5 percent of their farmland to coffee production (see table 2), and as a result, consumption decreases by only 2.5 percent following a 50 percent decrease in coffee price. However, because the share of land allocated to coffee is highly positively skewed (slightly more than three-quarters of households do not produce coffee at all), it is important to quantify the loss in consumption among farmers who produce coffee. The second row of panel B of table 3 shows that among households that allocate at least 5 percent of their farmland to coffee, the consumption of an average farmer decreases by about 13 percent following a 50 percent decrease in coffee prices. Rows 3–6 report estimates of consumption loss for an average household among households that allocate at least 10 percent, 25 percent, 33 percent, and 50 percent of their farmland to coffee, respectively. Clearly, the consumption losses are significantly higher for households that allocate a significant fraction of their farmland to coffee. For households that allocate over one-third of their farmland to coffee, coffee production can be considered as their main source of livelihood, and an average household among this group of households experiences about a 25 percent decrease in consumption following a 50 percent decrease in coffee prices. This level of consumption loss is likely to have a significant effect on the poverty level of the households, given the fact that the average household is very close to the national poverty line (see the section on Data). 5.2. On-Farm and Off-Farm Labor Supply Responses In this subsection, results on how the labor allocation of household members responds to changes in cof- fee prices is presented. Figure 6 gives a visualization of how household on-farm labor supply changes in response to change in coffee prices. It shows that household on-farm labor supply (on all fields) increases with the share of land allocated to coffee in 2011, when coffee price is high, but this correlation becomes almost zero when coffee price is low in 2013. Table 4 reports the formal estimation result. Panel A reports 320 Kebede Figure 6. Correlation between Household On-Farm Labor Supply and the Share of Land Allocated to Coffee Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: The top panel plots log household on-farm labor supply (after removing the effects of rainfall and year fixed effects) against the share of land allocated to coffee in 2011. The figure shows that in 2011, when the coffee price boomed, there is a significant positive correlation between household on-farm labor supply and the fraction of household land allocated to coffee. This positive correlation is significantly weaker in the year 2013, when the international coffee price is about 50 percent less than its 2011 level (see fig. 2). The bottom panel shows the correlation between the share of household labor allocated to coffee fields (after removing the effects of rainfall and year fixed effects) and the share of land allocated to coffee fields for the years 2011 and 2013. It shows that the fraction of labor allocated to coffee decreases between 2011 and 2013, given the fraction of land allocated to coffee. This implies reallocation of labor towards other crops following the decrease in coffee price. The World Bank Economic Review 321 Table 4. On-Farm Labor Supply Response to Coffee Price Shocks Household Male Female Spouse Child Panel A: On-farm labor supply on all fields Land share of coffee × LogPrice 2.119*** 1.876*** −0.804 −0.532 −0.124 (0.556) (0.647) (0.794) (0.812) (0.785) Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 N 9,870 9,870 9,870 9,870 9,870 R2 0.754 0.770 0.691 0.688 0.544 Panel B: On-farm labor supply on coffee field Land share of coffee × LogPrice 2.508*** 1.902*** 0.967 1.335* −0.066 (0.657) (0.627) (0.702) (0.681) (0.637) N 9,870 9,870 9,870 9,870 9,870 R2 0.892 0.869 0.803 0.778 0.557 Panel C: Share of labor allocated to coffee Land share of coffee × LogPrice 0.183*** 0.144* 0.084 0.112 0.083 (0.069) (0.079) (0.087) (0.083) (0.098) N 9,870 9,870 9,870 9,870 9,870 R2 0.818 0.803 0.679 0.657 0.529 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table is based on three rounds (2011, 2013, and 2015) of the ESS survey. Robust standard errors in parentheses. All regressions include household and zone-year fixed effects, and log rainfall. All dependent variables are measured as the inverse hyperbolic sine (IHS) transformation of hours per year to deal with zero values. The share of labor allocated to coffee is defined as the ratio of labor supply on coffee fields to 1 plus labor supply on all fields, to account for zero values. Male labor supply includes labor supply by all male members above 13 years of age (including the husband if any). Female labor supply includes labor supply by all female household members above 13 years of age (including the spouse if any). Children are defined as household members who are ≤ 13 years of age. The labor supply measure includes the sum of planting and harvesting labor applied on a field.“Land share of coffee” is the fraction of household land allocated to coffee in 2011, and LogPrice is the log of average coffee price over the four quarters before the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 how household labor supply on all fields changes in response to coffee price changes. The first column shows that household labor supply on all fields significantly decreases following decreases in coffee prices, and column (2) shows that this is driven by labor supply by male members of the household. The magni- tude of this effect is large. For an average household among those that allocate at least 5 percent of their farmland to coffee, a 10 percent decrease in coffee price leads to a 6.5 percent decrease in on-farm labor supply. Columns (3)–(5) show that female, spouse, and child labor supplies on all fields do not respond to changes in coffee prices. Panel B reports the effect of coffee price change on labor supply on coffee fields. We see that household labor supply on coffee fields significantly decreases in response to decreases in coffee prices. Moreover, the decrease in household labor supply on coffee fields is driven by decreases in labor supply by male members and the spouse (see columns (2) and (4) of panel B). To interpret the estimated coefficients, an average household among those that allocate at least 5 percent of their farmland to coffee experiences a 7.2 percent decrease in labor supply on coffee fields following a 10 percent decrease in coffee prices. One can infer that the decrease in labor supply on all fields reported in panel A is driven by the de- crease in labor supply on coffee fields reported in panel B. This is formally estimated in panel C, which demonstrates how the share of labor allocated to coffee farms changes in response to coffee price change. Column (1) shows that the fraction of household on-farm labor supply allocated to coffee fields signif- icantly decreases following decreases in coffee prices. Using the estimated coefficient and the summary statistics in the land share of coffee in table 2, one can conclude that, among households that allocate at least 5 percent of their farmland to coffee, a 1 percent decrease in coffee price leads to decrease in the share of labor allocated to coffee by 0.06 on average. This is significant compared to the average share of labor 322 Kebede Table 5. Off-Farm Labor Supply and Income Worked Number of PSNP Worked as Number of Laborer in PSNP PSNP days income laborer laborer days income Land share of coffee × 0.037 21.640** 372.586** 0.106 6.459 −263.042 LogPrice (0.078) (8.994) (184.920) (0.153) (16.145) (413.961) N 9,750 9,750 9,757 9,779 9,779 9,765 Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 R2 0.668 0.608 0.597 0.572 0.518 0.458 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table is based on three rounds (2011, 2013, and 2015) of the ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. Worked in PSNP and Worked as laborer are dummy variables indicating whether any member of the household worked in PSNP or as a daily laborer. “Land share of coffee” is the fraction of household land allocated to coffee in 2011, and LogPrice is the log of average coffee price over the four quarters before the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 allocated to coffee of 0.27 among households that allocate at least 5 percent of their farmland to coffee. Column (2) shows that the fraction of male on-farm labor allocated to coffee decreases significantly, while columns (3)–(5) show no significant change in the share of labor allocated to coffee for female, spouse, and child labor. Table 4 clearly shows that child on-farm and off-farm labor does not significantly change in response to coffee price movement.16 However, table S1.4 shows that coffee price shocks negatively affect school enrollment for young girls aged 5–20. The effect on school enrollment of boys is not significant. Since, child on-farm and off-farm labor is not changing significantly, the decrease in enrollment of girls suggests that girls are probably helping in domestic work when their parents increase on-farm and off-farm labor during coffee boom periods. The literature (see, for instance, Kruger 2007) suggests that an increase in agricultural wage (due to, say, coffee price increase) would have a substitution and income effect on a household’s decision to send children to school. What we learn from table S1.4 is that the magnitude of the substitution and income effects are probably different across gender: for girls, the substitution effect dominates the income effect so that enrollment decreases during a coffee boom period, while for boys the substitution and income effects exactly cancel out. Table 5 reports how households’ off-farm labor supply responds to coffee price changes. Two off-farm employment options are considered: employment in the PSNP and employment in the labor market at market wage. The results in table 5 show that while employment in PSNP does not respond on the exten- sive margin, there is significant change on the intensive margin, i.e., those who already worked in PSNP work fewer days and earn less income in PSNP following decreases in coffee prices. Given that households that supply labor to the PSNP are likely to be those with limited on-farm employment opportunities, as the coffee price decreases and on-farm labor supply on coffee fields decreases for some households, com- petition increases for households that work in the PSNP, and they work fewer days and earn less income in the program. Table 5 also shows that off-farm employment in the labor market does not respond to coffee price changes, either in the extensive or intensive margin. 5.3. Credit and Transfers as Consumption Smoothing Options The measure of credit includes loans received from formal and informal moneylenders. Aid includes trans- fers from families, friends, and government and nongovernment sources. Table 6 presents results on how changes in coffee price affect households’ borrowings and aid receipts. The first column shows that house- holds are less likely to receive credit when coffee prices are higher, and the second column shows that the 16 Note that on-farm labor is calculated from hours of labor spent on each farm field, whereas off-farm labor measures market labor. Both these measures miss hours of labor spent on domestic activities such as cooking, cleaning, fetch- ing water, collecting firewood, and taking care of children. Given the fact that domestic activities are predominantly performed by female members of the household, the on- and off-farm labor variables are likely to account for only a fraction of actual female labor supply. The World Bank Economic Review 323 Table 6. Coffee Price Shock, Borrowing, and Assistance Received credit Total credit Received assist Total assist Land share of coffee −0.240 −982.719** 0.068 −162.086 ×LogPrice (0.179) (459.695) (0.079) (242.118) N 9,968 9,968 9,968 9,933 R2 0.530 0.369 0.617 0.512 Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table is based on three rounds (2011, 2013, and 2015) of the ESS survey. Standard errors are clustered at zone level. All regressions include household and zone-year fixed effects, and log rainfall. Received credit and Received assistance are dummy variables indicating whether the household has received loan or transfers from formal or informal sources. “Land share of coffee” is the fraction of household land allocated to coffee in 2011. The variable LogPrice is the log of average coffee price over the four quarters before the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 Table 7. Credit, Assistance, and Consumption Smoothing Credit Assistance Total AEC Food AEC Total AEC Food AEC Land share of coffee × LogPrice 0.817*** 0.827*** 0.821*** 0.822*** (0.197) (0.202) (0.212) (0.215) Land share of coffee × LogPrice × credit −0.042* −0.056* — — (0.023) (0.033) Land share of coffee × LogPrice × assistance — — 0.003 0.024 (0.029) (0.030) N 9,525 9,525 9,525 9,525 R2 0.651 0.609 0.650 0.608 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This table is based on three rounds (2011, 2013, and 2015) of the ESS survey. Standard errors are clustered at zone level. AEC stands for Adult Equivalent Consumption. All regressions include household and zone-year fixed effects, and log rainfall. “Land share of coffee” is the fraction of household land allocated to coffee in 2011, and LogPrice is the log of average coffee price over the four quarters before the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 amount of credit received is lower when coffee prices are higher. Among households that allocate at least 5 percent of their farmland to coffee, an average household is more likely to receive credit by 0.8 following a 10 percent decrease in coffee prices. Columns (3)–(4) show that household’s aid receipt is not responsive to change in coffee prices, both in the extensive and intensive margins. Table 7 explores whether credit and aid help households to smooth consumption during low coffee price periods. The results in the first two columns suggest that borrowing seems to help households smooth food consumption. However, the estimated coefficients are economically small, implying that consumption smoothing is minimal even for households that receive credit.17 The last two columns show that aid has no meaningful role in consumption smoothing during low coffee price periods. Finally, whether households use migration of some household members to smooth consumption in re- sponse to coffee price drops is explored. Table S1.3 reports the correlation between reasons for a member of a household leaving the household between 2011 and 2013, and the share of household land allocated to coffee in 2011. The first column regresses migration for work (to any place, including other villages) on the share of household land allocated to coffee. The estimate coefficient is positive, but borderline statistically insignificant. Columns (2)–(5) explore whether other most commonly cited reasons for leav- ing households, including migration for education, mortality, divorce, and marriage, are correlated with the share of land allocated to coffee. None of these variables are significantly correlated with the coffee intensity of the households. 17 A potential explanation is that households may not have the flexibility to use loans for consumption smoothing and loans for direct consumption are rare. 324 Kebede Figure 7 Correlation between Child Anthropometric Measure and Land Share of Coffee across Years Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: This figure shows the correlation between child anthropometric measures and the share of land allocated to coffee in 2011, for cohorts born during low and high coffee price periods. The effects of rainfall, cohort-fixed effects, month-of-birth fixed effects, gender, and age were purged out before plotting. The top panel shows the correlation between weight-for-age z-score (WAZ) and the share of household land allocated to coffee for cohorts born between 2008 and 2010 (who are exposed to low coffee price in utero) and cohorts born between 2011 and 2013 (who are exposed to high coffee price in utero). It shows that for cohorts born during the low coffee price period, WAZ is negatively correlated with the share of land allocated to coffee. On the contrary, for cohorts born during the high coffee price period, WAZ is positively correlated with the fraction of household land allocated to coffee. The bottom panel shows a similar pattern using the weight-for-height z-score (WHZ). 5.4. Income Shocks and Child Malnutrition The previous subsection has shown that coffee price shocks have significant effects on household con- sumption. What is the consequence of this on child health? Figure 7 shows the visualization of the data. The World Bank Economic Review 325 Table 8. Coffee Price Shocks and Child Malnutrition Weight-for-age z-score Weight-for-height z-score Height-for-age z-score (WAZ) (WHZ) (HAZ) (1) (2) (3) (4) (5) (6) Panel A: Household fixed effects Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 Land share of coffee × Log price 0.492 0.352 1.150* 1.179* −0.565 −0.863 in utero (0.808) (0.867) (0.591) (0.649) (1.077) (1.047) Land share of coffee × Log price — −0.514 — 0.059 — −1.033 post birth (0.935) (0.975) (1.205) N 3,153 3,153 3,153 3,153 3,153 3,153 R2 0.629 0.629 0.582 0.582 0.555 0.555 Panel B: Village/kebele fixed effects Land share of coffee × Log price 0.972* 1.091* 1.084* 1.331** 0.485 0.375 in utero (0.506) (0.604) (0.563) (0.623) (0.652) (0.719) Land share of coffee × Log price — 0.365 — 0.755 — −0.336 post birth (0.711) (0.657) (0.727) N 3,994 3,994 3,994 3,994 3,994 3,994 R2 0.230 0.230 0.205 0.205 0.204 0.204 Source: Author calculation based on Ethiopian Socioeconomic Survey (ESS) data. Note: Standard errors are clustered at zone level. This table is based on children in three rounds (2011, 2013, and 2015) of the ESS survey and anthropometric measures of children under five years of age, after removing biologically implausible extreme values (WAZ, WHZ, and HAZ values of >5 or <−5). All regressions include whether the mother is educated, gender and age of the child, month of birth and year of birth fixed effects, and log village rainfall. “Land share of coffee” is the fraction of household land allocated to coffee in 2011, “Log price in utero” is log of the average coffee price when the child was in utero, and “Log price post birth” is log of the average coffee price over the period since the child’s birth to the survey month. * p < 0.10, ** p < 0.05, *** p < 0.01 It shows that, for children who are in utero during the coffee boom period (2010–2012), the anthropo- metric measures of the children are positively related to the share of household land allocated to coffee. For children who are in utero during the low coffee price period (2007–2009), child anthropometric mea- sure is negatively related to the share of household land allocated to coffee. Table 8 reports the results for the effect of coffee price shocks on anthropometric measures of children. Different specifications are estimated to explore the sensitivity of the estimates. Panel A estimates the most conservative specification with household fixed effects. That is, this panel utilizes variation in exposure to coffee price shock among siblings in a household. This is a conservative specification because it does not utilize between-household variation in the fraction of land allocated to coffee. The result shows that shocks to coffee price have a statistically significant effect on WHZ. More specifically, children who are in utero during higher coffee price periods have significantly higher WHZ measures than their siblings who are in utero during low coffee price periods. This result holds with and without controlling for exposure to coffee price shock after birth. The effect on WAZ is positive, but not statistically significant. Panel B estimates a specification with village fixed effects, i.e., this specification utilizes between-household variation in the fraction of land allocated to coffee within a village/kebele. The result shows that the effect of coffee price shock on WAZ and WHZ is statistically significant. Children who are in utero during high coffee price periods have higher WAZ and WHZ measures than children who are in utero during low coffee price periods the higher is the fraction of household land allocated to coffee. Again, these results hold with and without controlling for exposure to coffee price shock after birth. It is worth commenting on the size of these estimates, focusing on the specification with village fixed effects (preferred specification) that does not include the effect of exposure after birth. This specification is preferred because it utilizes variation in exposure among cohorts of children born in a village, and 326 Kebede households within a village are reasonably comparable. The estimates indicate that a 10 percent higher coffee price during the period when a child is in utero would increase a child’s WAZ by about 3.3 units (and WHZ by about 3.7 units) for a child who is born in an average household that allocates at least 5 percent of its farmland to coffee. Overall, these estimates are large, but not implausible, given that (a) households that are reliant on coffee have no meaningful mechanism to smooth their consumption during low price periods, (b) these households already live close to the poverty line, and significant consumption Downloaded from https://academic.oup.com/wber/article/36/2/305/6371000 by LEGVP Law Library user on 08 December 2023 loss is likely to push them into deep poverty, and (c) children are the most vulnerable members of a household. The pro-cyclical nature of child health observed in this paper is in contrast to several studies that report the countercyclicality of child health in other contexts, including those by Dehejia and Lleras-Muney (2004), Neumayer (2004), Paxson and Schady (2005), Ferreira and Schady (2008), Miller and Urdinola (2010), and Page, Schaller, and Simon (2017). In particular, it is worth comparing the current result with Miller and Urdinola (2010), who utilize a similar source of variation to income (coffee price movements) in a developing country context (Colombia). Miller and Urdinola (2010) find a stark countercyclicality of cohort size in Colombia, mainly due to procyclical under-5 mortality. They attribute this to increases in the opportunity cost of mothers’ time during high coffee price periods, which leads to reductions in the amount of time spent on childcare, such as time spent on fetching clean water, taking children to distant health stations for preventive and primary health care, and practicing good hygiene. Unfortunately, their paper does not separately identify the effect of price change on consumption (the consumption channel), which operates in the opposite direction to the above-mentioned opportunity cost channel. However, the fact that they find a positive effect of coffee price change on mortality implies that the opportunity cost channel dominates the consumption channel in their context. The results in this paper show the exact opposite—the consumption channel dominates the opportunity cost channel, so that child health is procyclical. As documented above, coffee price shocks have strong pass-through on household consumption. However, the results also show that female labor supply, particularly spouse’s labor supply, does not respond to coffee price changes, implying the opportunity cost channel is insignificant. 6. Conclusions In this paper, household-level panel data from Ethiopia are used to show that exogenous shocks to inter- national coffee prices have a significant effect on household consumption and investment in child human capital. For a household that produces only coffee, a 1 percent change in international coffee price would lead to a 0.8 percent change in household consumption. This strong pass-through of coffee price shocks to household consumption implies that households do not have meaningful consumption smoothing op- tions during periods of price shocks. The paper finds that borrowing increases slightly during periods of low coffee price, but it has a limited effect on household consumption smoothing, perhaps because the sizes of the loans are too small or because direct loans for consumption are limited. Moreover, the paper shows that households (particularly male members) adjust their on-farm labor supply in response to coffee price changes. Households reallocate a higher fraction of their on-farm labor supply to non-coffee fields when coffee prices drop. While coffee price movement has no significant effect on child labor (on-farm or off-farm labor), periods of high coffee prices are associated with a decrease in enrollment of girls but not boys. A plausible explanation for this is that young girls might have to engage in more domestic work to allow their parents work more on-farm and off-farm. The paper also shows that children who are in utero in coffee-producing households during low coffee price periods have lower measures of WAZ and WHZ. These results on the effect of coffee price change on school enrollment of girls and child malnutrition imply that commodity price shocks not only affect current household welfare but may also hamper future economic growth through their effect on human capital accumulation. The World Bank Economic Review 327 A major policy implication is that governments and NGOs should design effective means to shield producers from erratic change in commodity prices. For instance, innovative insurance schemes, such as commodity price indexed insurances (similar to weather indexed insurance schemes), may have a positive effect on welfare. Future studies that seek experimental evidence of the feasibility and effectiveness of these insurance schemes would be a step forward towards a solution to this problem. 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