The World Bank Economic Review, 37(1), 2023, 93–111 https://doi.org10.1093/wber/lhac029 Article Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 The Distribution of Effort: Physical Activity, Gender Roles, and Bargaining Power in an Agrarian Setting Jed Friedman , Isis Gaddis , Talip Kilic , Antonio Martuscelli , Amparo Palacios-Lopez, and Alberto Zezza Abstract Physical effort is a primary component in models of economic behavior. However, applications that measure effort are historically scarce. This paper assesses the differences in physical activity between men and women through wearable accelerometers and uses these activity measures as a proxy for physical effort. Crucially, the accelerometer-generated data measures the level of physical activity associated with each activity or task recorded in the data. In this rural setting, women exert marginally higher levels of physical effort. However, differences in effort between men and women among married partners are strongly associated with differences in bargaining power, with larger husband-wife effort gaps alongside differences in age, individual land ownership, and an overall empowerment index. Physical activity can exhibit an unequal distribution between men and women suggesting that gender disadvantage, at least within couples, extends to the domain of physical effort. JEL classification: D13, J16, J22 Keywords: physical activity, accelerometers, effort, time use, gender, intrahousehold bargaining 1. Introduction Human effort, both its determinants and consequences, has long been a subject of study in controlled settings in medicine and psychology. In economics, effort plays an integral role in theories of worker de- cision making (Chayanov 1966; Becker 1977; Becker 1985), and the marginal cost of human effort is a Jed Friedman (corresponding author) is a Lead Economist in the Development Research Group at the World Bank, Washing- ton, DC, USA; his email address is jfriedman@worldbank.org. Isis Gaddis (corresponding author) is a Senior Economist with the World Bank’s South Asia Gender Innovation Lab Gender Group and IZA Research Fellow, Washington, DC, USA; her email is igaddis@worldbank.org. Talip Kilic is a Senior Economist at the World Bank’s Development Data Group, Washing- ton, DC, USA; his email is tkilic@worldbank.org. Antonio Martuscelli is Assistant Professor at the LUMSA University, Rome, Italy; his email is a.martuscelli@lumsa.it. Amparo Palacios-Lopez is a Senior Economist at the World Bank’s Development Data Group, Washington, DC, USA; her email is apalacioslopez@worldbank.org. Alberto Zezza is a Senior Economist at the World Bank’s Development Data Group, Washington, DC, USA; his email is a.zezza@worldbank.org. The authors gratefully acknowledge funding from UK Aid, the William and Flora Hewlett Foundation, and the World Bank Strategic Research Pro- gram. They would like to thank Caren Grown for valuable comments, Wadonda Consult for excellent research support, and Akuffo Amankwah, Innocent Pangapanga, Michael Pratt, James Sallis, Kelli Cain, Terry Conway, and Chad Spoon for their expertise and advice. The authors dedicate this paper to the memory of Professor Ephraim Chirwa. The findings, interpreta- tions, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. © 2023 International Bank for Reconstruction and Development / The World Bank. Published by Oxford University Press. 94 Friedman et al. key factor in allocative decisions of limited resources such as time (e.g., Lazear 2000). However empirical studies are scarce as human effort has long been treated as a primary yet unobservable component of Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 behavioral theory. Now, the recent development of wearable technologies makes available the standard- ized measurement of physical activity and expands the empiricist’s ability to assess certain dimensions of human effort given the assumption of a direct relation between physical activity and physical effort. The present study applies this technology to assess the distribution of physical activity between men and women in a low-income rural setting. Previously, the closest empirical proxies for effort were the reported duration of activities as recorded in time-use studies. These studies document the allocation of scarce time resources across different tasks thereby implicitly comparing effort decisions made on both the extensive margin, as work and leisure activities likely require different levels of effort, as well as the intensive margin, typically defined as the quantity of time allocated to each activity. While the conceptual distinction between the extensive and intensive dimensions of effort extends at least as far back as Jevons (1879), few empirical applications observe both margins. The noted exception to this is time-use studies, however such studies only allow for effort to vary with respect to the time allocated to each activity type and cannot account for the varying intensity of effort expended per unit of time across subjects or within-subject across time. Recently, the development of wearable technologies to track human physical activity allows for im- proved measures of physical effort and allocative-effort decisions on both the extensive and the intensive margin, including estimates of the intensity of physical activity at the level of subject and unit of time. With these new measures, researchers can revisit long-standing topics such as the allocation of work or effort between men and women or within the firm. Using a unique dataset from Malawi, this study measures physical activity of adult household members who wore research-grade accelerometers, whose time-stamped records can be matched to 24-hour recall time-use diaries administered to the same sub- jects. These data provide an objective and standardized measure of the physical intensity of activity not observed in the more traditional time recall data. Because accelerometers capture physical intensity within each type of activity and for each minute of record, they enable the analysis of intrahousehold dynamics to go beyond the “typically male” and “typically female” activities framework used previously. It is now possible to observe the degree of physical activity expended within activities performed by each individual. The results show that differences in physical effort are mainly due to women exerting more intense physical activity in all tasks rather than through specialization in more strenuous tasks relative to men—a finding that traditional time-use analysis would not have been able to observe.1 The study explores the association between physical activity, the proxy for physical effort, and gender by documenting the distribution of physical effort across adult members of the same household. The results show that men accumulate significantly more sedentary time than women, with an average daily gender gap of 38 minutes in the full sample and of 51 minutes in the subsample of married partners.2 This is consistent with the stylized fact that women work more hours than men, when paid and unpaid activities are considered together. The physical activity data also reveal a pattern that is not observable from traditional time-use data. The study also finds that men spend more time than women on moderate- to-vigorous activities, which comes at a greater energy cost and hence requires more physical effort than light activities that are disproportionately undertaken by women. Using standardized energy expenditure 1 Effort is a complex concept widely seen to encompass various forms, including physical and mental effort. This study only observes physical activity, and it posits a direct link between physical activity and physical effort. Physical exertion is a predominant form of occupational effort in low-income agricultural settings and, therefore, plays a key role in the productivity and well-being of the population studied here. However, it is possible that the intrahousehold distribution of total effort is yet more unequal if tasks commonly conducted by women also require greater mental effort than those conducted by men. 2 Gender gap or gender difference refers to the difference between men and women. The World Bank Economic Review 95 as a summary measure of physical effort (i.e., total energy expenditure expressed as a multiple of the individual-specific basal metabolic rate), the results show that, all told, women exert marginally higher Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 levels of effort than men. To analyze the proximate causes of this gender pattern in effort the study combines physical activity data with more traditional time-use diaries. Gender differences in time use are among the most pertinent factors that distinguish the lives of men and women across the globe. Over a range of country contexts, men typically allocate more time to paid activities, while women spend a disproportionate amount of their time on care and other unpaid work (Rubiano-Matulevich and Viollaz 2019; Ilahi 2000). In addi- tion, women typically work more hours than men when all work activities (paid and unpaid) are con- sidered together and hence spend less time on leisure, particularly in lower-income countries (Blackden and Wodon 2006; Burda, Hamermesh, and Weil 2013). Gender differences in time use also vary over the life cycle, as parenthood may reinforce a traditional division of labor among couples (Anxo et al. 2011; Fengdan et al. 2016; Kongar and Memis 2017). To explain such gender differences in time use, one theory, formalized by Becker (1981), posits that husband and wife may gain from a specialization in either market work or household activities, as this specialization allows them to take advantage of economies of scale. In this setting, even small gender differences, for example those related to market discrimination or women’s biological advantage in child rearing during lactation, could cause large intrahousehold differences in time use. Theories of the distribu- tion of effort expenditure within the household are less developed, but if effort costs are observable within husband-wife pairs with identical preferences then households may choose an equitable distribution of effort across spouses, despite each partner’s specialization in either market or household work. Becker’s model assumes unitary household decision making, where the household members act as a single entity. This assumption rarely finds empirical support (Alderman et al. 1995; Lundberg, Pollak, and Wales 1997). Non-unitary models of household decision making emphasize power and bargaining dif- ferentials between household members as a potential explanation for intrahousehold differences in effort and time use (Chiappori and Lewbel 2015). However, the few studies that have looked at the relationship between bargaining power and time use have yielded mixed results. Friedberg and Webb (2005) show that an increase in wives’ relative wages in dual-earner households in the United States is related to an increase in the amount of time they spend on leisure. Fengdan et al. (2016), based on time-use data from China, demonstrate that husbands with higher bargaining power (proxied by the education gap between spouses) spend less time on unpaid work. Walther (2018) finds that men in patrilineal communities in Malawi, where men traditionally retain land ownership rights upon divorce, spend more time on agricul- tural labor and less time on wage labor compared to men in matrilineal communities. Fafchamps, Kebede, and Quisumbing (2009) find, however, that while spouses who brought more land to the marriage devote more time to social activities, they do not spend more time on leisure activities as a whole. Similar to many other contexts, men in the sample spend more time on market-related activities (which here include agricultural work), while women spend more time on non–market-related activities (e.g., care work, water and firewood collection, and other unpaid work). Since nonmarket activities in this setting are, on average, somewhat less strenuous than market activities, gender differences in time allocation across market and nonmarket activities tend to lower the relative effort levels of women compared to men. However, men also spend more time than women on social activities, which are associated with even lower levels of effort than either market or nonmarket activities. Further, with the ability to measure the intensity of time use it can be seen that women exert more physical effort within each activity category— though these within-category gender differences are not always statistically significant. Together, these findings explain the slightly higher levels of effort for women than for men. The study then relates the observed inequalities in expended effort across men and women (among those co-residing in dyadic pairs) to more traditional measures of intrahousehold bargaining power. Even though direct measures of decision making are not observed, measures are available that proxy 96 Friedman et al. for differences in advantage within household. These measures concern differences in human capital and assets such as levels of education, age, and the ownership and management of land.3 The study finds Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 gender differences in physical activity within dyadic couples to be exacerbated by larger intrahousehold differences in bargaining power, including husband-wife gaps in age, land ownership, and whether the couple lives in a union where women are at higher disadvantage. This suggests that greater gender differences in expended effort may emerge in situations with an unequal intrahousehold distribution of power, and that disadvantage within the household can extend to the dimension of physical exertion. As far as can be seen, no prior study has explored the relationship between intrahousehold bargaining and physical activity. The novelty of this approach allows for a more careful consideration of the intensive margin of effort than what is afforded in time-use studies, and identifies a new channel through which intrahousehold differences in advantage can translate into differences in well-being. 2. Activity Measures and Study Setting This work is part of a growing literature drawn from various academic disciplines that use accelerom- eters to measure physical activity and, consequently, the expenditure of energy. Validation studies have shown that inference from Actigraph accelerometers, such as the ones used in this study, acceptably cor- relate with energy expenditure derived using the doubly labeled water method, which is the gold standard for measuring total energy expenditure in free living conditions (Plasqui and Westerterp 2007; Plasqui, Bonomi, and Westerterp 2013; Chomistek et al. 2017). The vast majority of previous accelerometry studies focus on high-income countries, typically to doc- ument activity levels for specific population groups (Troiano et al. 2008; Colley et al. 2011) or to relate physical activity to environmental characteristics (van Dyck et al. 2010). However, some studies have measured physical activity levels of rural populations in developing countries and find that overall levels of physical activity are higher than in high-income countries (Benefice, Garnier, and Ndiaye 2001; Assah et al. 2011; Christensen et al. 2012). Closely related to this study is the work by Zanello, Srinivasan, and Nkegbe (2017), Picchioni et al. (2020), and Srinivasan et al. (2020), who pilot the use of accelerometers in rural communities in Ghana, India, and Nepal. These studies, however, relied on smaller sample sizes of approximately 40 individuals in each of the 3 countries (compared with over 400 individuals in this study), which limits analytic possibilities. This paper is also related to the work by Akogun et al. (2021), who use accelerometer data to proxy for worker productivity in physical occupations and validate the approach in a piece-rate wage setting. The data used in this paper were collected in the Ntcheu and Zomba districts of Malawi in 2017. A random subsample of 240 households, drawn from a larger representative household sample dedicated to an agricultural study, was interviewed weekly throughout the 2016–2017 agricultural season. In addition, the research team collected data on physical activity over a three-month period of March–May 2017, with each enumeration area (and all sampled households within) assigned in a random temporal order to a two- week data-collection cycle. The target was to deploy Actigraph GT3X accelerometers to 2 working-age (15 years and above) individuals in each study household. The accelerometers were left with the individual members for 17 days, and study participants were instructed to always wear them during the day, except for sleep time and during washing/bathing. To ensure technical standards, the investigators partnered with the Active Living Research Team of the University of California at San Diego, who participated in the enumerator training and provided quality control (Pratt et al. 2020). The enumerators visited the households 3 times during the 17 days when the accelerometers were with the participants, typically days 1 (deployment), 9 (check in), and 17 (pick up). Time-use data were 3 These are standard measures widely applied in many studies on intrahousehold bargaining power. See, for instance, Thomas (1994); Handa (1996); Quisumbing, Estudillo and Otsuka (2004); Panda and Agarwal (2005). The World Bank Economic Review 97 collected on days 9 and 17 using a 24-hour recall diary modeled after the time-use module of the Women’s Empowerment in Agriculture Index (WEIA) and that captured self-reported main and secondary activities Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 in 15-minute intervals during the reference day.4 During the visits on days 9 and 17 the team also measured the participants’ bodyweight, while information on height was collected during the endline survey. The time-use module distinguished between 26 different types of activities. For analytic purposes and ease of exposition, these activities are grouped into six broad activity categories based on the main re- ported activity—market work, nonmarket work, personal activities, social activities, sleeping and resting, and other activities (see appendix table A1). The subdivision of work into “market work” and “nonmarket work” is informed by the classification in Seymour, Malapit, Quisumbing (2020), who use a very simi- lar WEIA-type time-use module. However, this distinction should be regarded as a broad approximation since it is often difficult to determine with certainty whether an activity is destined for the market or for nonmarket (i.e., family consumption) purposes (Gaddis et al. 2020). Due to these ambiguities—and the broader conceptual and empirical questions involved in separating market from nonmarket work—this study also reports on a combined category of “total work.” The accelerometers provide time-stamped measures of an individual’s physical activity per minute.5 Starting from raw unit-record accelerometer files, all the data have been processed and validated accord- ing to accepted international standards and protocols (see Pratt et al. 2020 for further discussion of the training, data collection, wearing and scoring protocols). Each minute of the day (when the accelerometer was worn by the participant) was classified according to the intensity of physical activity (i.e., seden- tary, light, and moderate to vigorous physical activity (MVPA)) based on the cutoff points developed by Freedson et al. (1998), which are the most widely used reference values for measuring adult physical ac- tivity in the fields of public health and physical activity research. While the analysis assumes light activity levels during non-sleep non-wear time (i.e., moments the participants were likely awake but for some rea- sons not wearing the devices—mostly in the early mornings or evenings), the study assumes no physical activity during sleep time. Therefore, the analysis disregards any activity that occurred between 10:00 pm and 4:25 am, as these cutoffs are the 95th and 5th percentiles, respectively, of self-reported sleep and wake times. Without this time censoring, accidental movements, such as when devices are unintentionally bumped while under a pillow, would be scored as activity (usually in the “sedentary time” category). Days are coded as valid if the total wear time on that day exceeded 10 hours, and as non-valid otherwise. The compliance with the physical activity tracking protocol was exceptionally high in the sample, thanks to intensive supervision and attention to data quality control during the fieldwork. The average number of valid days across the study participants was 13.3 (out of 14), and the average daily hours of wear time was 14.2. Akogun et al. (2021) review numerous accelerometry studies and find a typical compliance far lower than that achieved here. This study uses the minute-by-minute individual-level physical activity counts together with anthro- pometric data to estimate an individual’s active energy expenditure, basal metabolic rate (BMR), and total energy expenditure. The latter is defined as the sum of the person’s active energy expenditure and BMR. The daily BMR is computed using the Harris-Benedict equation, which models energy require- ments while resting as a function of weight, height, age, and sex (Harris and Benedict 1918; De Lorenzo et al. 2001; Wejis and Vansant 2010; Al-Domi and Al-Shorman 2018).6 Active energy expenditure for 4 See “Guide and Instruments,” WEAI, https://weai.ifpri.info/weai-resource-center/guides-and-instruments/. 5 The waist-mounted accelerometers measure movements in three orthogonal directions but cannot detect isolated upper body movements that do not shift the center of gravity. They may also miss the full physical effort related to lifting or carrying of loads. 6 The Harris-Benedict equation is among the best predictive equations of BMR for healthy, normal-weight adults, though it may perform poorly among specific population groups, such as hospitalized patients or individuals with acute or chronic diseases (Bendavid et al. 2020). Moreover, it may underestimate the BMR of individuals with a high share of lean muscle mass (Garrel, Jobin, and De Jonge1996; Flack et al. 2016). The equation has been validated in many 98 Friedman et al. the time the participants were wearing the accelerometers is computed using the work-energy theorem using the accelerometers’ activity counts per minute and bodyweight as input factors (Actigraph Software Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Department 2011, p. 69).7 The analysis translates different activity categories into the single metric of total energy expenditure, which combines both resting energy expenditure (i.e., the BMR) and the energy cost of activity. Since energy expenditure is partly a function of a person’s weight and height, total en- ergy expenditure is normed as a multiple of the BMR and expressed as normalized energy expenditure. This is the proposed proxy for effort—total energy expenditure relative to the individual’s BMR, a com- monly used index of physical activity typically denoted as the physical activity level or PAL (e.g., Zanello, Srinivasan, and Nkegbe 2017).8 Appendix table A2 depicts the characteristics of study individuals and their households, both for the full sample of all participants and the sample restricted to dyadic pairs.9 Here the analysis briefly high- lights gender differences in bargaining-power measures. The average study participant is 35 years old, with no significant difference between men and women in the full sample. In the subsample of dyadic pairs, husbands are, on average, almost 5 years older than wives (39.3 versus 34.5 years). Men also have significantly higher levels of education than women, with a difference of 1.3 years of schooling in the full sample, and of 1.7 years among dyadic pairs. Men in the full sample manage 2.7 plots compared with 1.5 plots among women, and the difference is statistically significant. In the sample of dyadic pairs, this gender gap is even larger, with husbands managing 3.7 plots, compared with only 0.8 plots among wives. Similar gender gaps are present for the number of acres managed, the number of plots owned, and the number of acres owned. These differences are also statistically significant and to the disadvantage of women. The descriptive statistics suggest a possibility of differential bargaining power between men and women living in the same households, and especially between husband and wives. The analysis relates these differences to observed physical activity patterns in the analysis below. 3. Gender Differences in Physical Activity and Effort Table 1 summarizes the physical activity counts (upper panel) and the estimated caloric expenditure (bot- tom panel) by sex—both for the full sample and dyadic pairs. Physical activity is expressed in minutes per day and is reported for three mutually exclusive categories of physical activity—sedentary, light, and MVPA. Light activity is further subdivided into low light and high light, while MVPA consists of moderate and vigorous activity. In the full sample, men spend significantly more time sedentary (414 minutes, or 6 hours and 54 min) than women (377 minutes, or 6 hours and 17 minutes), a difference of 38 minutes. This gender gap is even larger within the dyadic pair (51 minutes). Conversely, women spend significantly more time doing light activities (mostly driven by the high light category)—with a gap of 50 minutes in the full sample countries, including nonwestern countries (Schoeller 1998; Shaneshin et al. 2011; Song et al. 2014) but there does not seem to have been any attempts to validate it in African contexts. 7 The analysis also computed active calories using the method outlined in Freedson, Melanson, and Sirard (1998). How- ever, the Freedson algorithm was calibrated for participants engaged in moderate to vigorous activities and assigns relatively high energy expenditure to light and sedentary activities. Since most of the activity in the sample is light, the study prefers using the more conservative estimates of the work-energy theorem. The correlations reported in this paper are not affected by this choice (see section 4). 8 Total energy expenditure is not a meaningful indicator for energy requirements of children (esp. below the age of two), who need additional energy to grow, or of pregnant/lactating women (FAO 2001). 9 When the analysis restricts to dyadic pairs, observations are lost as some married individuals do not have the spouse in the sample, either because the spouse did not take part in the study or has missing data on energy expenditure or time use. Of the 410 individuals in the sample 64 percent are married (262 individuals). For 46 out of the 262 married individuals, spousal information is not fully available thus leaving the analysis with 216 individuals and 108 dyadic pairs. The World Bank Economic Review 99 Table 1. Physical Activity and Energy Expenditure by Sex, Full Sample and Dyadic Pairs Full sample Dyadic pairs Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Total Male Female ࢞ (M-F) p-value Male Female ࢞ (M-F) p-value Activity (min/day) A. Sedentary 393.1 414.4 376.6 37.9 0.00 417.1 365.8 51.3 0.00 B. Light 383.8 355.6 405.6 −50.0 0.00 358.4 416.9 −58.5 0.00 Low light 232.7 234.9 231.0 4.0 0.42 237.7 235.3 2.4 0.72 High light 151.1 120.7 174.7 −54.0 0.00 120.7 181.6 −60.9 0.00 C. MVPA 84.4 93.2 77.6 15.6 0.00 89.5 75.3 14.2 0.02 Moderate 82.9 90.2 77.2 13.0 0.00 87.1 75.1 12.0 0.05 Vigorous 1.5 3.0 0.4 2.6 0.00 2.4 0.2 2.2 0.00 Total nonsedentary ( = B + C) 468.2 448.8 483.3 −34.5 0.00 447.9 492.2 −44.3 0.00 Nonwear time 578.7 576.8 580.2 −3.4 0.61 575.1 582.0 −7.0 0.44 Caloric exp. 2,027 2,120 1,955 166 0.00 2,112 1,973 139 0.00 BMR 1,340 1,412 1,284 129 0.00 1,406 1,293 113 0.00 Caloric exp./BMR 1.51 1.50 1.52 −0.02 0.09 1.50 1.52 −0.02 0.22 N (individuals) 410 179 231 108 108 N (individual-days) 5,639 2,452 3,187 1,479 1,485 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: Active energy expenditure calculated using the work-energy theorem; t-tests on differences between males and females. Standard errors are clustered at the household level. and 59 minutes among the dyadic pairs. On the other end of the spectrum, men are more engaged in MVPA—with a gap of about 15 minutes. Gender differences in non-wear time (which, as mentioned in section 2, includes sleep time) are not statistically significant. Overall, the data show that the sample of agricultural households in Malawi is a highly active population—with a total of 449 minutes (i.e., 7 hours and 29 minutes) of daily nonsedentary activity for males and 483 minutes (8 hours and 3 minutes) for females in the full sample. Although many of the participants are farmers and data were collected during the latter half of an agricultural season, it is not surprising that most of the activity is light or moderate. The defined vigorous physical activity category encompasses intense aerobic exercise of at least 6 metabolic equivalents (METs), comparable to running at a minimum speed of 8 kilometers per hour. The differences in physical activity do not immediately reveal whether women or men, on average, exert more physical effort. While women spend less time sedentary, they also spend less time engaging in MVPA. The bottom panel of table 1 shows that men in the full sample expend on average 2,120 calories per day, compared with 1,955 calories for women (with similar numbers for the dyadic pairs). This difference, however, partly reflects that the average man is taller and heavier than the average woman. If the analysis normalizes for basal metabolism, by expressing total energy expenditure as multiples of the BMR,it can be seen that women exert, on average, slightly higher levels of effort than men (1.52 vs. 1.50). However, this gender gap is only marginally statistically significant in the full sample and insignificant in the smaller sample of dyadic pairs. While the physical activity data provide measures of effort at high temporal resolution, they do not reveal the exact types of activities men and women engage in. To explore this, each participant’s time- stamped physical activity data is aggregated into 15-minute intervals and match the activity data to the recall-based time-use information elicited from the study participants. In doing so, the study gains con- textual understanding of physical activity patterns and can link the intensive margin of physical activity with the reports of time use on the extensive margin. Table 2 shows the average number of hours women and men spend per day by self-reported time-use category. The data show the traditional intrahousehold division of labor, with women in the full sample 100 Friedman et al. Table 2. Average Hours per Day and Normalized Energy Expenditure by Self-Reported Activity, Full Sample And Dyadic Pairs Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Full sample Dyadic pairs Total Male Female ࢞ (M-F) p-value Male Female ࢞ (M-F) p-value Panel A. Average hours per day by self-reported activity Work (total) 5,8 5,4 6,0 −0,6 0,03 6,0 6,4 −0,4 0,25 Market 3,8 4,6 3,3 1,3 0,00 5,1 3,2 1,9 0,00 Nonmarket 1,9 0,9 2,8 −1,9 0,00 0,9 3,2 −2,2 0,00 Personal 1,5 1,4 1,5 −0,1 0,27 1,3 1,5 −0,1 0,26 Resting & sleeping 9,6 9,5 9,6 −0,2 0,30 9,5 9,6 −0,1 0,62 Social 2,5 3,0 2,1 0,9 0,00 2,7 1,8 0,9 0,00 Other 1,3 1,6 1,2 0,4 0,01 1,4 0,9 0,6 0,00 N/A 3,4 3,1 3,6 −0,5 0,00 3,0 3,8 −0,8 0,00 Panel B. Normalized energy expenditure by self-reported activity Work (total) 1,80 1,81 1,78 0,03 0,34 1,84 1,78 0,06 0,23 Market 1,86 1,84 1,88 −0,03 0,44 1,87 1,86 0,01 0,87 Nonmarket 1,70 1,67 1,72 −0,05 0,29 1,66 1,73 −0,06 0,32 Personal 1,64 1,60 1,67 −0,07 0,10 1,56 1,68 −0,12 0,04 Resting & sleeping 1,31 1,30 1,32 −0,03 0,00 1,30 1,33 −0,03 0,00 Social 1,62 1,64 1,60 0,03 0,44 1,61 1,59 0,02 0,78 Other 1,91 1,81 2,01 −0,20 0,00 1,87 2,09 −0,22 0,04 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: Normalized energy expenditure is measured as individual total active energy expenditure/BMR. Active energy expenditure is calculated using the work-energy theorem’ t-tests on differences between males and females. N/A represents hours with unclassified or missing activities. Standard errors are clustered at the household- level. spending significantly more time than men on nonmarket work activities (2.8 hours versus 0.9 hour) and significantly less time on market activities (3.3 hours versus 4.6 hours). In addition, women spend significantly less time than men on social activities (2.1 hours versus 3.0 hours). There are no significant gender differences in the number of hours spent on personal activities or resting/sleeping. These broad patterns of time use carry over to the sample of dyadic pairs, where the gender differences in time spent on nonmarket vs. market activities are even larger. To relate differences in time allocation to gender differences in effort, Panel B of table 2 reports total energy expenditure relative to BMR by sex and self-reported activity. Market activities are associated with a somewhat higher level of effort than nonmarket work activities (1.86 vs. 1.70 PALs). In other words, gender differences in the allocation of time across market and nonmarket activities (as highlighted in table 2) should lower women’s total effort compared to that of men. On the other hand, women exert greater levels of effort within each activity category. These within-activity gender gaps, though typically not statistically significant, tend to increase women’s effort levels. The self-reported categories with sig- nificantly higher effort expenditure for women are, interestingly “resting and sleeping” and the residual “other” category (as well as the “personal” category among married pairs). Moreover, as reported in table 2, men spend more time than women on social activities, which are associated with lower levels of effort than either market or nonmarket activities. This combination of extensive and intensive margin differences in activity explains the marginally greater average levels of effort among women than men. This analysis also quantifies the importance of (a) the activity choice and (b) the intensity of physical activity within the activity category in contributing to the gender gap in effort using a simple decomposition. The difference in effort between males and females in dyadic pairs can be decomposed into the sum of (a) the difference in effort measured in activities common across gender and (b) the difference in effort in predominantly male-only or female-only activities The World Bank Economic Review 101 Figure 1. Physical Activity Level (PAL) by Sex and Age Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Source: Authors’ analysis based on data from the Malawi agricultural labor experiment survey 2016–2017 and Actigraph GT3X accelerometers. Note: The non-parametric relationship between energy expenditure and age, separate by gender, is estimated by nonparametric kernel regression. 95 percent confidence interval such as cooking, cleaning, and fetching water for females and farming and wage work for males. This decomposition shows that efforts spent in common activities explains about 152 percent of the overall effort gap that is only partially compensated by the effort differences in gender-exclusive activities where males expend more effort in total (−52 percent). 4. Sociodemographic Predictors of Effort, Including Bargaining Power The analysis next turns to the question of whether physical effort is related, differentially by male and female respondents, to individual- or household-level characteristics. Appendix table A3 summarizes de- scriptive regressions where the dependent variable is the effort measure and the independent variables are (i) a binary indicator if the participant is female, (ii) an individual or household-level characteris- tic, such as age, and (iii) the interaction between these two variables. With respect to characteristics like education, household size, marital status, or household dependency ratio, the study does not observe dif- ferential associations by gender, as indicated by insignificant interaction terms under columns (2) through (5). However, it can be seen that effort declines with age among men, as indicated by the positive and significant interaction effect, while effort expended by women is relatively constant over the age range. The gender differentiated age-effort profiles are also summarized in fig. 1, which shows declining activity levels for men but not women. While women exert higher levels of effort than men, especially at older ages, the gender gap in effort is relatively modest. However, it is possible that substantial gender differences in effort still emerge in situations with unequal distributions of bargaining power within the household. To explore this hypothe- sis, the analysis focuses on dyadic pairs and constructs seven different proxy measures of intrahousehold 102 Friedman et al. Table 3. Individual Effort (exp/BMR) and Within-Household Power Differentials, Dyadic Pairs (1) (2) (3) (4) (5) (6) (7) Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Age ࢞ Education ࢞ Managed plots ࢞ Managed area ࢞ Plots owned ࢞ Area owned ࢞ Power index ࢞ Female −0.00880 0.0173 0.0142 0.0192 0.00163 0.00664 −0.00597 (−0.41) (1.12) (0.77) (1.03) (0.09) (0.37) (−0.29) [Category] −0.00652** 0.000262 0.00524 0.0105* −0.00393 −0.00366 −0.00136 (−2.21) (0.07) (1.31) (1.89) (−0.97) (−0.66) (−0.49) Interaction 0.00640* 0.00290 0.00285 0.00134 0.00881** 0.00851* 0.00491** (1.92) (0.73) (0.74) (0.28) (2.52) (1.78) (2.13) Constant 1.532*** 1.575*** 1.561*** 1.556*** 1.585*** 1.583*** 1.508*** (71.88) (52.13) (52.60) (53.52) (53.95) (54.82) (67.92) Observations 216 216 216 216 216 216 216 R-squared 0.027 0.059 0.085 0.104 0.070 0.063 0.020 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: The t statistics are in parentheses. *p < 0.10; **p < 0.05; ***p < 0.01. Dependent variable is individual total energy expenditure/BMR. Active energy expenditure calculated using the work-energy theorem. Except for column (1), regressions also control for husband’s age and husband-wife age differential. Standard errors are clustered at the household-level. differences in bargaining power. Six measures are centered on the differences between the husband and wife in (1) age, (2) years of schooling, (3) the number of plots managed, (4) the number of acres (of agricultural land) managed, (5) the number of plots owned, and (6) the number of acres owned; in each case, a positive gap indicates a higher value for the husband. These measures are commonly used proxy indicators for intrahousehold differences in bargaining power—based on the notion that demographic and economic characteristics that improve an individual’s outside options (i.e., the utility level attained after leaving the marriage) translate into greater intrahousehold bargaining power.10 The seventh measure of bargaining power is a summary index of status difference within the household created using the six individual gender difference measures with the relative weights determined through principal component analysis. The study estimates a series of regressions where the dependent variable is physical activity and the independent variables are (i) a binary indicator if the participant is female, (ii) a specific measure of intrahousehold differences in bargaining power, and (iii) the interaction between these two variables. The last term makes it possible to understand whether the difference in bargaining power between husbands and wives is associated with a more unequal distribution of activity, which is the effort proxy. As age may have an independent effect on any gender difference in activity, the analysis also controls for both the age of the husband and the age differential between the pair. Results presented in table 3 indicate that imbalances within the households, at least by certain measures, are associated with differential levels of effort.11 In column (1), the interaction between the husband-wife gap in age and the female indicator variable is positive and marginally significant, which indicates that the gender gap in activity is exacerbated among couples where the husband is significantly older than his wife. Differences in activity tied to intrahousehold differences in bargaining power are even more pronounced under columns (5) through (7). In column (5), the interaction between the intrahousehold gender gap in the number of plots owned and the female 10 Other studies use a similar approach. For gaps in age and education see Oduro, Deere and Catanzarite 2015; Doepke and Tertilt 2018; and Afoakwah, Deng, and Onur 2020. For gaps in the ownership of land/assets see Doss 2013; Behrman 2017; and Schaner 2017. Intrahousehold differences in land management are included as a proxy of de facto use rights, though it is arguably a weaker measure of bargaining power than differences in land ownership. 11 The results in table 3 are qualitatively unchanged when the analysis uses the raw accelerometer output (raw activity counts) as dependent variables, suggesting that the transformation of physical activity counts into the PAL measure does not determine the results. The World Bank Economic Review 103 indicator variable is positive, statistically significant, and large in magnitude. Specifically, a one standard deviation increase in the gender gap in plot ownership (by 3.7 plots) is associated with an increase in the Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 activity gender gap by 0.033. In other words, in a couple in which the husband owns 3.7 more plots than his wife, the wife expends approximately 42 calories per day more than a wife in a couple with gender equality in plot ownership. While this increase in energy expenditure may not appear significant when viewed within a narrow time frame, it grows salient over longer time horizons. All else equal, expending 42 additional daily calories daily may translate into a loss of over four pounds of body weight over a year without compensating intake.12 Given that 17.5 percent of the Malawian population is estimated to be undernourished (FAO 2019)—an effort differential of this magnitude can translate into longer- run health consequences. While the present study did not collect data on individual-level food intake or consumption expenditure, other lines of research suggest that intrahousehold consumption inequalities in Malawi disadvantage women compared to men (Dunbar, Lewbel, and Pendakur 2013; Bose-Duker et al. 2021).13 Under column (6) of table 3, the interaction between the intrahousehold gender gap in the area of land owned and the female indicator variable is also positive, albeit marginally significant. Conversely, the interaction terms under columns (3) and (4) are not statistically significant, suggesting that spousal differences in land ownership, as opposed to land management, are more meaningful proxies for intra- household differences in bargaining power. Finally, column (7) documents that the gender gap in activity is exacerbated among spouses in unions where women are at higher disadvantage as summarized by the bargaining power difference index.14,15 A one standard deviation increase in the gender gap in the power index is associated with an increase in the activity gender gap of roughly the same magnitude as an increase in the age difference between spouses of one year. Given that effort differentials are observed for certain household types, it is natural to ask whether these effort differences are expressed in differential time use and, if so, which activity categories are affected. Table 4 explores this question by adopting similar specifications as in table 3, but now with time devoted to various activity categories as the dependent variable. The study reports results with the four most common activity categories: nonmarket work, market work, rest and sleep, and social activity. Interestingly, the results show that any relation between household inequality and effort does not translate into greater levels of nonmarket work. Reported nonmarket work times stay constant across all inequality measures (with women spending 2.5 hours more per day on these activities). For households where there is more inequality in plots owned or managed, women spend significantly more time working (about 20 minutes more per plot of difference) and less time in social activities. It is noteworthy that these 12 This assumes that an energy deficit of 3,500 kcal is associated with a one-pound decline in body weight and does not account for metabolic adaptations (Thomas et al. 2013; Hall 2008, 2018). 13 See also Brown, Calvi, and Penglase (2021) for evidence on within-household consumption disparities. 14 The study also looked at whether the couple is in a polygamous marriage. Being in a polygamous marriage has been linked to reduced bargaining power for wives, especially lower-ranking co-wives (Agadjanian and Ezeh 2000; Nanama and Frongillo 2012; Anderson et al. 2016). This study, however, does not report the result due to the low number of observations in polygamous marriages in the sample; however, it is found that the gender gap in activity is significantly exacerbated among spouses in polygamous unions. 15 The results are robust to a wide range of variations from the baseline specification. All results are similar if the analysis uses the algorithm described in Freedson et al. (1998) results are shown in appendix table A4. The only difference is that activity differentials related to the husband-wife age gap loses significance. Additionally, results are substantially unchanged if the analysis conditions on time spent in different activities. This suggests that the main mechanism works through the intensive within-activity margin related to effort expenditure per unit of time rather than through differences of extensive time allocation or total time devoted to each activity. Finally, results are robust to the inclusion of controls for the number of children and enumeration fixed effects that control for local area characteristics such as degree of remoteness. 104 Friedman et al. Table 4. Self-Reported Activity (Hours/Day) and Within-Household Power Differentials, Dyadic Pairs (1) (2) (3) (4) (5) (6) (7) Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Age ࢞ Education ࢞ Managed plots ࢞ Managed area ࢞ Plots owned ࢞ Area owned ࢞ Power index ࢞ Market work Female −1.748*** −1.587*** −2.348*** −2.216*** −2.200*** −2.074*** −2.468*** (−3.53) (−4.56) (−6.25) (−5.76) (−5.71) (−5.43) (−5.75) [Category] −0.00740 0.0929 −0.181** −0.111 −0.108 −0.0517 −0.0891* (−0.12) (1.51) (−2.47) (−1.11) (−1.66) (−0.61) (−1.91) Interaction −0.0143 −0.134* 0.186*** 0.175* 0.163** 0.140 0.113*** (−0.19) (−1.82) (2.95) (1.84) (2.42) (1.48) (2.66) Nonmarket work Female 2.027*** 2.059*** 2.203*** 2.215*** 2.258*** 2.234*** 2.261*** (5.69) (7.94) (8.25) (8.69) (7.85) (7.96) (6.84) [Category] −0.0312 −0.0603 −0.0108 0.00992 −0.0399 −0.0350 −0.0193 (−1.02) (−1.54) (−0.29) (0.24) (−0.85) (−0.56) (−0.67) Interaction 0.0297 0.0651 −0.0111 −0.0192 −0.0372 −0.0343 −0.0157 (0.68) (1.17) (−0.23) (−0.33) (−0.67) (−0.50) (−0.46) Resting and sleeping Female −0.124 −0.0136 0.0745 0.200 0.0547 0.0659 0.0992 (−0.36) (−0.05) (0.20) (0.55) (0.18) (0.22) (0.23) [Category] −0.00874 −0.0952* 0.0504 0.0328 0.0466 0.0289 0.0374 (−0.23) (−1.85) (0.62) (0.37) (0.67) (0.41) (0.71) Interaction 0.0249 0.00610 −0.0273 −0.0892 −0.0247 −0.0376 −0.0178 (0.55) (0.12) (−0.29) (−0.92) (−0.31) (−0.48) (−0.32) Social activity Female −0.866*** −0.826*** −0.463* −0.554** −0.518** −0.613** −0.393 (−2.75) (−3.56) (−1.71) (−2.00) (−2.05) (−2.52) (−1.29) [Category] 0.0203 0.0470 0.0451 0.0338 0.0752 0.0736 0.0334 (0.45) (0.96) (0.85) (0.52) (1.53) (1.22) (0.94) Interaction 0.0156 0.0210 −0.115** −0.104 −0.116** −0.0966 −0.0691* (0.32) (0.42) (−2.18) (−1.44) (−2.22) (−1.43) (−1.89) Personal Female 0.0527 0.0671 −0.00490 0.0394 −0.0809 −0.0819 −0.152 (0.26) (0.50) (−0.03) (0.25) (−0.56) (−0.57) (−0.88) [Category] −0.0200 −0.0331 −0.0338 −0.0274 −0.0615** −0.0715** −0.0332** (−1.01) (−1.57) (−1.39) (−0.87) (−2.58) (−2.23) (−2.03) Interaction 0.0171 0.0399 0.0495* 0.0424 0.0925*** 0.119*** 0.0502*** (0.65) (1.39) (1.67) (0.99) (3.11) (3.06) (2.70) Observations 216 216 216 216 216 216 216 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: The t statistics are in parentheses. *p < 0.10; **p < 0.05; ***p < 0.01. Dependent variable is number of hours reported for each activity type. Except for column (1), regressions also control for husband’s age and husband-wife age differential. Standard errors are clustered at the household level. forms of inequality (differential plot ownership/management) result specifically in more female work. Even though greater differential plot ownership/management is related to greater total land holdings, men in these households do not report working more than other men. Finally, even though table 3 did not find an effort difference related to spousal education differentials, where there is an education difference between spouses, men engage in market work a bit more (13 minutes per schooling year of difference) and women less (17 minutes per schooling year of difference). Most likely this difference reflects an income or specialization effect of the education difference. These women then reallocate time to nonmarket and social activities, but the effects are not precisely estimated. The World Bank Economic Review 105 5. Conclusion This study applies wearable technologies to measure the distribution of physical activity between men and Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 women in a low-income agricultural setting. Compared to other regions of Sub-Saharan Africa, Malawi has relatively small gender gaps in property ownership (Gaddis, Lahoti, and Li 2018) and son-preferred fertility-stopping behavior (Filmer, Friedman, and Shady 2009), suggesting that the communities observed in this study reside in a relatively gender-equitable region. Perhaps reflecting this relative gender equity, women in this study experience only marginally higher rates of physical activity than men experience. While women spend significantly less time sedentary than men, they also spend somewhat less time in moderate and physical activity. On balance, the normalized energy expenditure for women is almost 1.3 percent greater than for men, a slight yet precisely estimated difference. Women in the most gender disadvantaged households, however, also exert the highest levels of differ- ential physical effort. Conditioning on differences in traditional measures of household bargaining power, clear differentials in physical activity are observed for husband-wife pairs in marriages with a large age difference, with a difference in the number of plots owned or total land area owned, as well as a summary index of bargaining power.16 This suggests that disadvantage in the household can extend to expended effort—an often unobserved primal of behavioral theory that plays a key role in conceptions of well-being. Various questions emerge with this study’s analysis of the economic treatment of effort. These questions need to be addressed in future work. The analysis assumes a direct correspondence between measures of physical activity and experienced physical effort, which may appear to be uncontroversial when broadly considered. However economic and psychological theories ultimately posit effort as a subjective phe- nomenon. Inzlicht, Shenhay, and Olivola (2018) define effort as “the subjective intensification of mental and/or physical activity in the service of meeting some goal.” Field-based researchers currently do not ob- serve the process by which physical activity is experienced as effort and, further, how effort is translated into disutility—a process that presumably involves subjective assessment of the intensity or the degree of (un)pleasantness of the activity. This perceptual aspect of effort can also play a role in decision making. For example, Dillon, Friedman, and Serneels (2021) find that information regarding a worker’s health sta- tus may increase productivity if a good health diagnosis is unexpected. The authors posit that the surprise good health news may lower the perceived effort cost, at least in the short run. Future extensions of the methods used in this paper can perhaps match time-stamped physical activity measures with subjective assessments of activity intensity or quality or with complementary biometric measurement. For now, this approach simply posits a direct relation between physical activity and physical effort. Additionally, effort is widely seen to encompass at least two dimensions—mental and physical. This study only observes the physical dimension. It is possible that the intrahousehold distribution of total effort is yet more unequal if the sedentary and light-activity tasks commonly conducted by women in this study setting, such as child-care or cooking, also require greater mental effort than those conducted by men (e.g., attending social and religious events). Gender gaps in physical effort could also be underesti- mated given that accelerometers may not detect the full effort related to carrying loads. If women are more likely than men to carry children or heavy objects, as may well be the case considering women’s dispropor- tionate engagement in childcare and the collection of water and firewood, their physical effort and energy expenditure would be underestimated relative to those of men. Empirical evidence generally supports the notion that women and children in Sub-Saharan Africa often serve as pedestrian load-transporters, 16 In contrast to these findings, analysis based on nutritional status finds no evidence of gender disadvantage. The women in the study sample do not have an underweight disadvantage in relation to men and female BMI is not correlated with this study’s measures of intrahousehold gender disadvantage. This underscores the relative gender equality among the population in the study area and stands in contrast to nutrition-based measures of gender disadvantage studied elsewhere (e.g., Brown, Calvi, and Penglase 2021). For this population, an anthropometric based analysis would suggest less gender disadvantage than the PAL based analysis. 106 Friedman et al. especially of water and firewood—activities culturally regarded as more appropriate for females (Porter et al. 2013). Yet there is also evidence that some heavy-lifting tasks in the agricultural production cycle Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 are predominately undertaken by men (Maliro 2015). Finally, experienced disutility from physical activity can vary widely by setting. In advanced economies, populations exhibit generally low levels of physical activity. A moderate level of physical activity is an important contributor to good health, with a reduced risk of cardiovascular disease, cancer, diabetes, and other noncommunicable diseases (Vogel et al. 2009). However, in the setting studied here, like many rural low-income settings, physical occupations predominate, and few individuals can select a less physical lifestyle. Data Availability Statement Data may be obtained from a third party and are not publicly available. The requests for the anonymised, unit-record data that would be required to replicate the analyses presented in this paper should be directed to Talip Kilic (Program Manager, World Bank, tkilic@worldbank.org, ORCID: https://orcid.org/0000- 0002-3642-3123). Appendix Tables Table A1. Activity Groups Activity groups Disaggregated activity (as self-reported) Market work Working for a wage, salary, commission, or in-kind payment Working for other households free of charge as exchange laborer Managing, working, or helping in a nonagricultural or nonfishing household business Farming Livestock tending Fishing Hunting or gathering foodstuffs Fetching water from natural or public sources Cleaning the house, washing or ironing Making goods (furniture, pottery, baskets, clothing) Nonmarket work Buying food or other items or obtain services Cooking or preparing food or drinks to preserve them Collecting firewood or other natural products Household maintenance or own construction work Providing care or assistance to adults (18 + years) Looking after children (17 years or younger) Planning the household’s finances or bills Social activities Social or religious activities and hobbies Personal activities Eating and drinking Personal care Watching TV / listening to radio/reading Exercising Sleeping and resting Sleeping and resting Other activities School (incl. homework) Travelling and commuting Other The World Bank Economic Review 107 Table A2. Summary Sample Characteristics Full sample Dyadic pairs Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Total Male Female ࢞ (M-F) p-value Male Female ࢞ (M-F) p-value Individual characteristics Height (m.) 1.59 1.64 1.56 0.08 0.00 1.65 1.56 0.09 0.00 Weight (kg.) 53.83 55.24 52.74 2.50 0.00 56.77 53.33 3.44 0.00 Age (years) 35.00 34.31 35.54 −1.23 0.44 39.31 34.45 4.85 0.01 Education (years) 5.37 6.09 4.80 1.29 0.00 6.12 4.40 1.72 0.00 Marital status Married 0.64 0.69 0.60 0.10 0.05 1.00 1.00 0.00 . Married (polyg. Union) 0.04 0.03 0.04 −0.00 0.77 0.04 0.04 0.00 1.00 Widowed 0.06 0.01 0.11 −0.10 0.00 0.00 0.00 0.00 . Separated 0.09 0.03 0.14 −0.11 0.00 0.00 0.00 0.00 . Unmarried 0.21 0.27 0.16 0.12 0.00 0.00 0.00 0.00 . Area managed (acres) 1.50 2.08 1.05 1.02 0.00 2.79 0.52 2.27 0.00 Plots managed (number) 2.03 2.72 1.50 1.22 0.00 3.69 0.84 2.84 0.00 Area owned (acres) 1.45 1.87 1.13 0.74 0.00 2.52 0.68 1.84 0.00 Plots owned (number) 1.93 2.44 1.53 0.90 0.00 3.35 1.01 2.34 0.00 Household characteristics Dependency ratio 0.63 0.57 0.67 −0.09 0.05 0.71 0.71 . . Household size 5.35 5.35 5.34 0.01 0.96 5.22 5.22 . . N (individuals) 410 179 231 108 108 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: The dependency ratio is defined as children aged 0–10 years divided by members aged over 10. T-tests on differences between males and females. Standard errors are clustered at the household-level. Table A3. Sociodemographic Correlates of Effort—Age, Education, Household Size, Marital Status and Dependency Ratio (1) (2) (3) (4) (5) Age Education Household size Currently married Dependency ratio Female −0.021 0.045* 0.006 0.014 0.010 (0.028) (0.025) (0.036) (0.021) (0.020) [Category] −0.002*** 0.002 −0.000 0.004 −0.007 (0.001) (0.003) (0.005) (0.022) (0.024) Interaction 0.001* −0.005 0.003 0.013 0.018 (0.001) (0.004) (0.006) (0.024) (0.025) Constant 1.552*** 1.489*** 1.501*** 1.496*** 1.503*** (0.022) (0.022) (0.031) (0.018) (0.018) Observations 406 406 406 406 406 R-squared 0.025 0.011 0.008 0.009 0.008 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. Note: The t statistics are in parentheses. *p < 0.10; **p < 0.05; ***p < 0.01. Dependent variable is individual total energy expenditure/BMR. Active energy expenditure calculated using the work-energy theorem. Standard errors are clustered at the household level. Except for column (1), regressions also control for husband’s age and husband-wife age differential. Age and education are measured for the individual, while household size and dependency ratio are measured for the household level. 108 Friedman et al. Table A4. Individual Effort (exp/BMR) and Within-Household Power Differentials Using Freedson-Algorithm, Dyadic Pairs (1) (2) (3) (4) (5) (6) (7) Downloaded from https://academic.oup.com/wber/article/37/1/93/6967278 by International Monetary Fund / World Bank - IMF user on 11 September 2023 Age ࢞ Education ࢞ Managed plots ࢞ Managed area ࢞ Plots owned ࢞ Area owned ࢞ Power index ࢞ Female 0.019 0.0291** 0.0288 0.0335* 0.0171 0.0211 0.0124 (0.96) (2.02) (1.58) (1.86) (0.99) (1.23) (0.60) [Category] −0.00517* −0.00137 0.00504 0.00893* −0.00348 −0.00372 −0.001142 (−1.96) (−0.37) (1.27) (1.74) (−0.89) (−0.75) (−0.44) Interaction 0.00384 0.00498 0.00310 0.00185 0.00879** 0.00901* 0.00441* (1.30) (1.36) (0.74) (0.38) (2.38) (1.91) (1.76) Constant 1.478*** 1.510*** 1.492*** 1.491*** 1.516*** 1.514*** 1.459*** (75.87) (52.55) (53.95) (54.23) (54.65) (54.40) (69.23) Observations 216 216 216 216 216 216 216 R-squared 0.04 0.059 0.087 0.097 0.069 0.062 0.036 Source: Authors’ analysis based on data from the Malawi Agricultural Labor Experiment Survey 2016–2017 and Actigraph GT3X accelerometers. 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