metadata: authors: 0: author: Eli,Shari
1: author: Li,Nicholas
count: India
docna: 0: docna: Caloric intake and energy expenditures in India
docty: Policy Research Working Paper
subtopic: Energy and Environment,Energy Demand,Energy and Mining,Demographics,Educational Sciences,Labor Markets,Nutrition
teratopic: Social Protections and Labor,Health, Nutrition and Population,Education,Energy
abstracts: cdata!: Total energy expenditures for the Indian
            population between 1983 and 2012 are estimated to shed light
            on the debate concerning falling measured caloric intake
            during the period (Deaton and Dre ze 2009). Anthropometric,
            time-use, and detailed employment surveys are used to
            estimate the separate components of total energy expenditure
            related to metabolism and physical activity levels. Despite
            a significant drop in adult physical activity levels, total
            energy expenditures are flat overall between 1983 and 2012.
            Rising metabolic requirements due to increases in weight
            dampened the effect of falling activity levels on total
            energy expenditure. In addition, the 10 percent decline in
            the population share of children in the period raised
            average total energy expenditures considerably as children
            have much lower metabolic requirements and activity levels
            than adults.
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repnme: repnme: Caloric intake and energy expenditures in India
display_title: Caloric intake and energy
            expenditures in India
listing_relative_url: /research/2020/06/32201419/caloric-intake-energy-expenditures-india
topicv3: Energy and Extractives m1326595 507,Nutrition m1326779 644,Demographic Change m1327040 1448,Education m1327048 470,Jobs m1503931 2670
publishtoextweb_dt: 2020-06-30T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,Education,Energy
origu: DEC-Knowledge and Strategy (DECKS)
no_of_pages: 66
model_extractions: Policy Research Working Paper 9309 Caloric intake and energy expenditures in India _Shari Eli Nicholas Li_ Development Economics Knowledge and Strategy Team June 2020
Policy Research Working Paper 9309

# **Abstract**

Total energy expenditures for the Indian population between 1983 and 2012 are estimated to shed light on the debate concerning falling measured caloric intake during the period (Deaton and Drèze 2009). Anthropometric, time-use, and detailed employment surveys are used to estimate the separate components of total energy expenditure related to metabolism and physical activity levels. Despite a significant drop in adult physical activity levels, total energy expenditures are flat overall between 1983 and 2012. Rising metabolic requirements due to increases in weight dampened the effect of falling activity levels on total energy expenditure. In addition, the 10% decline in the population share of children in the period raised average total energy expenditures considerably as children have much lower metabolic requirements and activity levels than adults..

This paper is a product of the Knowledge and Strategy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nick.li@utoronto.ca.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, 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._ Produced by the Research Support Team
# Caloric intake and energy expenditures in India

### Shari Eli and Nicholas Li

Nicholas Li (corresponding author) is a professor at the University of Toronto, Toronto, Canada; his email address is nick.li@utoronto.ca. Shari Eli is a professor at the University of Toronto, Toronto, Canada; her email address is shari.eli@utoronto.ca. The authors thank Eric Edmonds, two anonymous referees, Pierre-Olivier Gourinchas, Chang-Tai Hsieh, Ronald Lee, Ted Miguel, Gustavo Bobonis, Stephan Litschig, Daniel Lafave, Pranab Bardhan, Tom Vogl, Anne Case, Jeff Hammer, as well as participants of seminars at Berkeley, Princeton, Toronto, NEUDC and PAA and Lucas Parker for outstanding research assistance.

JEL Codes: D12, I31, I32, O12, O15 Keywords: India, calories, energy, expenditure, activity, height, weight, metabolism

## **1 Introduction**

Caloric intake and food expenditures are often viewed as key indicators of an individual’s ability to satisfy the most basic of needs and as good predictors of net nutritional outcomes, which has made these indicators fundamental to the measurement of household welfare since the work of (Engel 1895). Applications include the construction of India’s national poverty measures, guided by household caloric “norms” of 2400 calories per day in rural areas and 2100 in urban areas, as well as equivalence scales (Barten 1964; Deaton and Muellbauer 1986). In this context, (Deaton and Dr`eze 2009) note the puzzling finding that based on India’s National Sample Survey (NSS), per capita caloric intake in India fell substantially between 1983 and 2005 despite rising per capita expenditures. They consider explanations ranging from measurement error in consumption data to consumer-driven changes (due to tastes, prices, or changes in the set of available goods) to changes in
physical activity levels and the disease environment that may have lowered the demand for calories, but reach no firm conclusion.<sup>1</sup> This article presents estimates of Total Energy Expenditures (TEE) for the Indian population over the 1983-2012 period to provide insight into this puzzling finding. Total energy expenditures are the total calories required for maintaining one’s weight level given the body’s metabolic processes and physical activity levels. Anthropometric data from three waves of India’s National Family Health Survey (1998, 2005, 2015) are used to estimate Resting Energy Expenditures (REE) that capture metabolic requirements related to height, weight, age and gender for individuals at rest. Detailed time-use data from India’s 1998-1999 Time-Use Survey, matched to activity factors in (FAO/WHO/UNU Expert Consultation 2001), are used to estimate activity levels (AL) that capture the additional energy used for physically intensive activities. The large set of variables in common between these surveys and the NSS employment surveys (Schedule 10) that were conducted between 1983 and 2012 are used to estimate individual TEE. This quantification provides insight into the evolution of population TEE and the contributions of changes in demographics, occupation and industry, work status, and laborintensive domestic tasks.

The main finding is that population level TEE in India is fairly flat between 1983 and 2012 and actually increased slightly over the 1983 to 2005 period before declining slightly between 2005 and 2012. The quantification highlights two reasons for flat TEE over time. First, individual and household characteristics that predict lower Activity Levels (AL) also tend to predict higher Resting Energy Expenditures (REE) for a given demographic composition. For example, higher levels of education, higher levels of expenditure, and less labor-intensive occupations are all associated with lower AL but higher REE. Because TEE is computed as the product of REE and AL (FAO (2001)), as discussed later, the effect of changes in education, expenditures, and occupation on TEE tend to be smaller than their effect on AL. Overall,

> 1Note that the “puzzle” documented by (Deaton and Dr`eze 2009) for India has also been documented in other contexts, e.g. (Du et al. 2002) for China and (Clark et al. 1995) for Great Britain during the Industrial Revolution. Literature on measurement error and the disease environment in the Indian context are discussed below. Other contributions include (Basole and Basu 2015), who focus on the role of prices and food budgets squeezed by rising expenditures on fuel, education and medical goods, (Patnaik 2010), who argues that higher relative prices for food lowered demand, and (Gupta 2013), who argues that conspicuous consumption (Veblen goods) could explain lower food intake.

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AL fell and REE increased over this period controling for demographic composition. The analysis also uncovers evidence of household scale economies in TEE. Households with more members, but similar demographic composition and per capita expenditures, have a lower AL that is only partly offset by higher REE. Although household scale economies contribute little to the main findings on population TEE, this supports the conjecture in (Deaton and Paxson 1998) that “caloric overheads” (fixed costs of common household activities in developing countries, such as travel for work, buying/selling goods, or collection of water or fuel) could explain the common empirical finding that larger households have lower per capita food expenditures holding constant demographic composition and per capita expenditures.

Second, the estimates imply that TEE fell by over 100 calories per day over this period for individuals aged 15 or older, which on its own would be enough to account for most of the decline in caloric intake in NSS data during this period. However, over the 1983-2012 period, India underwent substantial demographic change, with the share of the population aged 14 and under falling from 40% to 30%. Children have much (about 40%) lower TEE than adults, due to both lower REE (due to smaller size) and lower AL (particularly in rural areas where primary/middle-school attendance appears much less physically active than adult activities). Thus even though TEE is falling for both adults and (to a lesser extent) children throughout this period, demographic change at the population level is more than enough to offset this “within” effect, except after 2005 when activity levels fell particularly quickly.

The findings of flat population TEE and modest gains in nutritional status over time are discussed in light of the potential contribution of measurement error and changes in the disease environment to the (Deaton and Dr`eze 2009) puzzle of declining measured caloric intake . (Smith 2015) argues that the NSS estimates of caloric intake may be biased down, especially in recent years, due to rising consumption of food away from home.<sup>2</sup> Evidence on the magnitude of recall bias identified using different survey recall periods ((National Sample Survey Organization 2000; NSSO 2013)) and the sensitivity of estimated caloric intake to assumptions about the caloric value of food expenditures is reviewed. The findings are also discussed in the context of (Duh and Spears 2017), who test one of the potential mechanisms dis-

> 2See also (Fiedler and Yadav 2017) for evidence from the 2011-2012 NSS survey and (Smith et al. 2014) for a general review of the reliability of food data collected in consumption and expenditure surveys.

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cussed in (Deaton and Dr`eze 2009). (Duh and Spears 2017) show that infant mortality and other indicators of disease related to nutritional absorption (e.g. latrine ownership, diarrhea, and open defecation) are strongly associated with within-district changes in NSS caloric intake between 1988 and 2005 as well as in the cross-section. They find that this pattern holds even when controlling for household and individual variables that capture much of the variation in TEE explored in this paper, concluding that an improved disease environment can account for a substantial decline in caloric intake in India because households “lose” less calories to disease and therefore choose to consume less. Relative to their work, this article tests a different potential mechanism discussed in (Deaton and Dr`eze 2009), using the most detailed available data on physical activity and physical size to directly quantify TEE. Overall, the finding that TEE is fairly flat over the 1983-2012 period, combined with the evidence on modest gains in nutritional status, suggests that most of the decline in NSS caloric intake is a result of measurement error and lower caloric burden of disease rather than large decreases in population TEE.

The paper is organized as follows: Section 2 describes the data and methodology used for estimating total energy expenditure and discusses some important sources of variation across households, Section 3 presents results for changes in total energy expenditure over time and discusses the broader context of changes in caloric intake measured in the NSS, and Section 4 offers concluding comments and suggestions for future research.

# **2 Data and measurement of components of Total Energy Expenditure (TEE)**

This section briefly outlines the methodology and data used to measure TEE from household datasets. The Appendix contains a more detailed description including robustness to alternative assumptions. TEE measures the amount of energy used by the human body during a given period. When TEE equals caloric intake there is no weight gain or loss. A standard method to calculate TEE is the factorial method described in FAO (2001). (Indian Council of Medical Research 2009) uses this method to estimate prescriptive caloric requirements for Indian households, based on the 95th percentile height and weight observed in rural areas and providing recommended daily caloric in4
takes for sedentary, light, moderate, and heavy physical activity. The factorial method can be used to generate descriptive estimates of population-level total energy expenditure using the height, weight, demography, and activitylevels observed in the Indian population using various data sources. The factorial method computes TEE for individual _i_ using the following equation: where _i_ is an individual, _REEi_ is the Resting Energy Expenditure of individual _i_ and _ALi_ represents the Activity Level of individual _i_ . REE makes up the bulk of TEE (about two thirds for the average person) and reflects the energy required for essential metabolic functions. An individual in a complete state of rest has _AL_ = 1, _TEE_ = _REE_ , and still needs a lot of calories to avoid weight loss.<sup>3</sup> An individual that does more than rest will have an AL greater than one. The factorial method implies that a given reduction in AL generates a larger reduction in TEE for individuals with larger REE. Thus, when estimating levels or changes in TEE for a population, one cannot simply average _AL_ or _REE_ across individuals but must sum up the individual _TEE_ as constructed above. Measurement of these two components for the Indian population is discussed next.

# **2.1 Resting Energy Expenditures (REE)**

Although REE can only be directly measured in a laboratory setting, there are numerous published predictive equations that are determined by regressing measured REE on more commonly observed attributes such as age, sex, height and weight. While these equations miss an important idiosyncratic element of metabolism, they typically yield an _R_<sup>2</sup> over 0.7. The equations – provided in (Henry 2005) are used here these are based on regressing REE on height and weight separately by sex and age groups (1-3, 4-9, 10-17, 1830, 31-60, 60+) for a large population of individuals drawn from around the world including tropical and developing countries.

The anthropometric micro-data used come from India’s National Family Health Survey (NFHS), a household survey that measures height and weight

> 3Basal Energy Expenditure (BEE) is closely related to REE. Measuring BEE requires a minimum period of fasting before testing and is lower than REE, which incorporates the energy cost of metabolizing food. Most laboratory measures use REE. It is possible to directly quantifying the energy cost of metabolizing different macronutrients but the differences over time and across households appear minimal.

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for a subset of the Indian population. Eligibility for anthropometric measurement within sampled households varies by year – in 1998, children under 3 and ever-married women aged 15-49 were measured, while in 2005 and 2015 all children under 5, all women aged 15-49, and random subsample of men aged 15-54 were measured. The results of applying the (Henry 2005) equation are reported in the first panel of Table 1.<sup>4</sup> As discussed in Appendix A, data generated by the National Nutritional Monitoring Bureau (reported in (Indian Council of Medical Research 2009)) provides additional measurement of height and weight by age for rural areas of 16 Indian states in 2000-2002 to extrapolate outside of the age range in the NFHS. The NFHS estimates shown in Table 1 highlight three facts that will be important later: (1) adult men have higher REE than adult women who have higher REE than children, (2) REE is higher in urban than rural areas as urban individuals are slightly taller and substantially heavier for a given age/sex, and (3) REE has been increasing over time for a given age and sex, mostly due to increasing weight (see Appendix Table S.1 in the supplementary appendix).

# **2.2 Activity Levels (AL)**

Activity levels are estimated by aggregating up from the most detailed activity data available. With data on the share of time allocated across _J_ different activities, an estimate of individual _i_ ’s activity level (AL) is as follows: where _Shareij_ is the fraction of individual _i_ ’s time spent on activity _j_ . _AFj_ is an activity factor for activity _j_ that measures the intensity of activity _j_ relative to rest (equal to one). Individual _AL_ are thus weighted averages of activity factors where the weights vary across individuals based on their time-use.

The most detailed activity data for India comes from the Time-Use Survey (TUS) conducted in 1998-1999 by the National Sample Survey Organization for six states (Gujarat, Haryana, Madhya Pradesh, Meghalaya, Orissa, and

> 4Validation studies by (Shetty et al. 1986) and (Ferro-Luzzi et al. 1997) find that these types of prediction equations, which are typically estimated on European descended populations, have a reasonably good fit for subjects in India.

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Tamil Nadu) that make up 25% of India’s population and are close to representative in terms of regional variation. These data cover all individuals aged 6 or older from the sampled households. Individuals report their activities, classified into 154 activities (see Appendix Table S.2), in 20 minute increments based on a 24 hour recall period, with variant days capturing weekends/market days. FAO (2001) reports activity factors (derived from field measurement) for a long list of activities that are common in developing countries. Each activity in the TUS can be matched to an activity factor, with some adjustments to deal with codes that have no obvious match (e.g. “other” categories and travel) discussed in Appendix B.

Panel B of Table 1 reports estimates of activity levels separately by rural and urban residence, sex, and two age groups (6-14, 15 and over) for individuals in the TUS. The data indicate that rural activity levels are substantially higher than urban levels, with the average rural-urban gap equal to about 75% of the population standard deviation for adults. The rural-urban gap is considerably smaller for children, and children in general have much lower activity levels than adults, reflecting the relatively low activity levels associated with school attendance. A literature review identified only two studies that directly estimate activity levels in India, by directly measuring REE and TEE for tiny samples of “free-living” adults. The estimates here are in line with those reported in (Borgonha et al. 2000), who find an AL of 1.9 for rural men near Bangalore, and (Krishnaveni et al. 2009), who find an average AL of 1.45 for boys and girls aged 8 to 9 in Mysore.

# **2.3 Imputation of TEE to NSS data**

As REE and AL cannot be directly estimated for the same data set using the factorial method, and the coverage in terms of age, state, and time period of the NFHS and TUS is incomplete, the estimates of TEE over time use NSS data that is representative at the population level. This is done using the large number of common variables between the NFHS, TUS, and NSS Schedule 10 Employment Survey (hereafter NSS-E). Predictive equations for REE or AL are first estimated using the NFHS or TUS and the large set of individual and household variables in common withˆ the NSS-E,ˆ andˆthese predictive equations are then used to generate _TEEi_ = _REEi ∗ ALi_ for each individual in the NSS-E. The analysis here focuses on the “thick” NSSE survey rounds beginning with the oldest available (1983) through to the most recently available (2011-2012).

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A detailed discussion of this procedure can be found in Appendix B. Depending on the data set (NFHS or TUS) these variables include individual and household demographics, education levels, 2-digit occupation and industry codes at the household and/or individual level, work status (school, domestic work, casual versus salary versus self-employment), labor-intensive “domestic” tasks typically carried out by females not in the labor force (e.g. collecting firewood or water, husking paddy and grinding grains), laborintensive agricultural tasks, land possessed, and total expenditures deflated using an all-India CPI (see Appendix Tables S.3 and S.4). When restricting estimation to the six states in the TUS, state fixed effects are also included. The _R_<sup>2</sup> from these predictive equations ranges from 0.14 to 0.35 for the NFHS REE estimates and from 0.37 to 0.63 for the TUS AL estimates, suggesting the variables in common with the NSS-E are rich enough to capture a lot of the sample variation in REE and AL from the other data sets. The estimation procedure here is adequate for estimation population or conditional means, which is the goal here, but it would need to be amended if one were interested in the variance or other moments of the distribution (e.g. fraction of households with TEE below some threshold).

# **2.4 Analysis of household characteristics**

While Table 1 makes it clear that demographic composition and rural/urban location will generate large differences in TEE (and its REE and AL components) across households, there is also considerable cross-sectional variation related to other variables. Before turning to estimates of population TEE over time, some interesting cross-sectional patterns are discussed to demonstrate the richness of these data. For this analysis, only the 1998-1999 Time-Use Survey and REE imputation procedure are used.

Figure 1 plots household TEE (averaged across individuals within the household) against mean years of schooling for members aged 18 and up, monthly per capita expenditures, and household size. Panel A shows that TEE declines in years of schooling, a net effect that combines a sharper decline in AL and a partly offsetting rise in REE. Panel B shows a similar pattern for per capita expenditures, except that in this AL rises with per capita expenditure for the poorest households, likely reflecting a combination of unemployment/underemployment for the poorest households and a higher proportion of non-earning children. TEE is falling in household size, particularly in the range of 2 to 6 members, which is mostly driven by lower 8
AL, although the unconditional relationship in Figure 1 partly reflects demographic composition.

The finding that TEE is falling in household size is notable given the observation by (Deaton and Paxson 1998) that for several developing countries, measured food expenditure per capita declines in household size. A similar pattern holds for measured caloric intake. (Deaton and Paxson 1998) argue that this is a “puzzle” in the sense that larger households with a given demographic composition should benefit from economies of scale that increase their per capita consumption of relatively private/rival goods like food. They – suggest that “caloric overheads” energy expenditures that only need to be incurred by one household member on behalf of the entire household – could be one possible explanation. The TUS data is rich enough to capture differences in time spent on domestic chores, travel, etc. that would be hard to detect in most household surveys.

To examine this further and to also control for demographic composition, Table 2 estimates a log-log regression specification similar to the one used by (Deaton and Paxson 1998). The dependent variable is log household average TEE, REE or AL. Detailed demographic controls for age/sex ratios, log household size, log per capita expenditures, and mean years of adult education are included. When including all household sizes (1-14), there is close to a zero partial elasticity of TEE to household size, resulting from a perfect offset of REE and AL. Larger households economize on energy, but their members are also physically “larger” on average. When restricting to household sizes between 2 and 8, which make up 92% of the sample, there is a significant negative partial elasticity of about 0.017, due to a negative effect on AL that is much larger than the positive effect on REE. Both expenditure and education reduce _TEE_ holding constant demographics and appear to have independent effects.

Columns 7 and 8 of Table 2 estimate the same specification but replace TEE with caloric intake estimated for the 1993-94 NSS 50th round Schedule 1 (consumption survey) in the same states (see Appendix C). The coefficient is similar in magnitude (-0.029 using households sized 2 to 8) suggesting that scale economies in energy expenditures can potentially explain much of the apparent decline in caloric intake with household size.<sup>5</sup> Note that the

> 5The decline is “apparent” because (Gibson 2002) and (Gibson and Kim 2007) show that much of the puzzling decline first documented in (Deaton and Paxson 1998) is reduced when using survey instruments less subject to recall errors, implying that a substantial part of the measured decline in most household surveys is simply measurement error that

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coefficient on education has a similar negative coefficient for caloric intake as for TEE, but per capita expenditures only has a large positive coefficient for caloric intake.

The patterns documented here in the cross-section foreshadow one of the main findings in the time-series, which is that REE often moves in the opposite direction of AL in terms of covariance with observable household characteristics. This dampens the effect of changes in these characteristics on changes in TEE. Appendix Figure S.1 shows that this pattern also occurs across household main industry – households headed by agricultural or construction workers have the highest activity levels in India but also have the lowest REE.

# **3 Estimates of TEE over 1983-2012**

## **3.1 Population estimates**

Table 3 presents estimates of population average TEE for India between 1983 and 2012. Three separate panels present results for all India, rural India only, and for the six states in the TUS only. The first column reports the surveyweighted population average TEE expressed in calories per day. TEE is fairly flat over the entire 1983-2012 period, rising by only 0.8%. There is a small increase over the 1988-1994 and 2000-2005 periods, and a small decrease from 2005-2012, but these changes are small relative to plausible measurement and estimation errors. Columns 2 presents the population average REE which rose by 6% throughout the 1983-2012 period. Column 3 presents the population average AL which fell throughout the 1983-2012 period by about 4%, mostly after 2005.

Recall that the product of population average REE and AL is not population average TEE, and that changes in population AL and REE capture both changes within age/sex demographic groups and changes in the demographic composition of the population. Estimates are reported separately for population TEE and AL for males 15 and over (columns 4-5), females 15 and over (columns 6-7), and children aged 14 and below (columns 8-9). TEE is negatively correlated with household size. However, they still find some “real” decline in caloric intake with household size when using the alternative survey instruments. See also (Beegle et al. 2012) and (Brzozowski et al. 2017) for more evidence on food consumption survey measurement error correlated with household size.

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declined substantially for adult men (135 calories) and women (86) calories over the 1983-2012 period but by less for children (30). Activity levels for adult men and women fell over 6%, and these decreases were only partly offset by their rising REE. For the 1998-1999 TUS, the decrease in AL is equivalent to about one third of the urban-rural gap for adult men and one half for women.

Although the estimated decline in AL for adult men and women appears plausible and quite large, it is instructive to consider what variables in the NSS-E drive these results. Descriptive statistics for all years using the NSS employment survey are reported in Appendix Table S.5. Agriculture is one of the more physically active industries/occupations and declined considerably as a share of employment, from 60% to 43% as a share of main household industry; however, there was large and partly offsetting increase in construction work, from 3% to 12%, which is also very physically active. Within agriculture, the probability that individuals engage in specific manual labor intensive agricultural task in the previous week has only fallen slightly, e.g. 12% to 9% for harvesting activities, 2% to 1% for plowing. The share of women engaged mostly in domestic duties remains very high. Although some of these duties are very physically active, most have declined only slightly, with the largest effect coming from the decline in the probability of bringing water from outside the household premises from 11% to 5%, smaller effects coming from decreases in food processing and animal husbandry, and no change in the probability of collecting firewood or cooking/heating fuel (around 7% throughout the period). The predictive equations used adopt a more flexible specification, but the linear estimates from Table 2 are also instructive. The 3.3 year increase in years of schooling for adults and the over 50% increase in real per capita expenditure imply only a modest decrease in AL over this period (about 3%) for demographically similar households, with a small offset from the decrease in average household size from 6.3 to 5.3 individuals.

The fact that TEE is falling for adult men, adult women and children yet rising for the population reflects the important role played by populationlevel demographic change. The share of children aged 14 and under in the population fell from about 40% in 1983 to 30% by 2012. This implies considerably higher TEE, not just because children are physically smaller and have lower REE, but because children are also much less active than adults according to the TUS estimates, particularly in rural areas. In 1983 child TEE was about 60% of adult TEE, implying that the demographic shift over 11
the 1983-2012 period, holding constant average adult and average child TEE, would generate a 4.7% _increase_ in population TEE.

# **3.2 Comparison of TEE with caloric intake, measurement error, and estimates of disease effects**

Table 4 combines several data sources to provide an overall assessment of trends and levels for nutrition for India between 1983 and 2012. Columns 1 and 2 provide an illustration of the basic puzzle raised by (Deaton and Dr`eze 2009). Column 1 shows that real per capita expenditure (deflated to 1993-1994 rupees) has increased, particularly in the most recent years since 2005. Column 2 reports official estimates of caloric intake from the National Sample Survey Organization ((NSSO 2013)) that are almost identical to the numbers reported in (Deaton and Dr`eze 2009) for the 1983-2005 period but incorporate the latest available data (NSS Rounds 66 and 68 conducted in 2009-2010 and 2011-2012). The official numbers show that caloric intake declined further from 2005 to 2010, but then increased considerably from 2010 to 2012, which erased almost half of the decline between 1983 and 2005 measured in (Deaton and Dr`eze 2009).

Columns 3 and 4 of Table 4 provide some insight into measurement error related to recall bias. It is well known that longer recall periods can lead to downward biased estimates of consumption, and the 51st through 54th NSS rounds (1994-1999) randomly allocated a 7-day as well as traditional 30-day recall period for food across households. The results confirmed that consumption estimates are higher for the 7-day recall period, but also that the difference was much larger for food categories like fruits and vegetables, eggs, fish and meat, and beverages and processed foods (over 40%) compared to under 15% for categories like milk and cereals ((National Sample Survey Organization 2000)). These findings led the NSS to use two different recall periods (canvassed for different households) for the food categories with the highest discrepancy between 7-day and 30-day recall periods in 2010 and 2012. For these categories, the magnitude of 7-day/30-day bias was similar to the earlier 1994-1999 period at around 44% for caloric intake ((NSSO 2013) Tables 5A and 5B).<sup>6</sup> 6 Note also that the use of 30-day and 7-day recall period for the _same_ households in the 55th NSS round (1999-2000) is likely to account for some of the unusual pattern in Table 12
These facts about recall bias in the NSS have two implications for the analysis. First, assuming that the 7-day recall period is more accurate, the overall level of caloric intake in the NSS data using a 30-day recall period is substantially biased downward. The difference in levels of caloric between the two recall periods in 2010-2012 is as large as the entire measured decline between 1983-2005 or 1983-2012 using the consistent 30-day recall period, and this underestimates the overall bias since categories like cereals and milk (which in 2010 and 2012 are measured using a 30-day recall period for all households) also exhibit a downward bias for 30-day recall, albeit to a lesser degree ((National Sample Survey Organization 2000)). A higher estimate of caloric intake is more in line with the reported TEE estimates, particularly in recent years, when 30-day measures of intake are well below the population TEE estimates. Second, the change in caloric intake over time using the 30-day recall period likely overstates the decline in caloric intake that would have been measured using 7-day recall, because the share of expenditures on the goods with the highest degree of bias rose from 36% to 50% between 1983 and 2010. Column 3 provides an illustrative estimate of what caloric intake in earlier rounds would have looked like under the assumption that the degree of bias for the 7-day recall categories in 2010 and 2012 remains constant over time. This assumption seems reasonable going back to 1994 in light of the results in (National Sample Survey Organization 2000) but these estimates are intended to be illustrative and need to be treated with caution, particularly for the 1980s where there is no direct measurement of 7-day vs. 30-day recall bias. Column 3 reports the official results using the 7-day recall period for select foods in 2010 and 2012 (from (NSSO 2013)) and imputations for earlier years that adjust caloric intake upwards but by a smaller percentage for earlier years (reflecting the change in the expenditure share of these categories reported in column 4). The result of this exercise is that the decline in caloric intake is about 20% smaller over the 1983-2005 period considered in (Deaton and Dr`eze 2009) or the entire 1983-2012 period.

Beyond the potential effects of recall periods on measurement error, numerous imputations are required to estimate caloric intake from a given NSS survey and these imputations have become more important over time. Al4 whereby caloric intake rose from 1994-2000 and fell from 2000-2005. The discrepancy between 30-day and 7-day recall quantities appear smaller when the recall periods are implemented simultaneously for the same households, but the level of 30-day recall is likely to be higher than for households that are not subjected to an additional 7-day recall period (see (Deaton and Kozel 2005) for a discussion).

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ternative estimates of caloric intake using NSS data, reported in column 5, provide a sense of the magnitudes of this source of measurement error. The details of the procedure are discussed in Appendix C, but the main departure from the standard NSS practice is imputation of calories for most expenditures in the beverage and processed food categories (which includes purchased “meals”). These expenditures rise from about 5% to 9% of average household food expenditure, and as they lack quantities or report units like “number of cooked meals” or “number of cold beverages: bottled/canned,” untested assumptions about how to impute their caloric value have become more important. The procedure used here yields very similar levels of estimated intake for 1983 through 1994, but a substantially smaller decline between 1994 and 2005, such that the total decline between 1983-2005 is only 66% as large as estimated by (NSSO 2013) or (Deaton and Dr`eze 2009). – The estimates begin to diverge even more after 2005 while there is a similar pattern of decline from 2005-2010 and recovery from 2010-2012, overall the decline between 2005 and 2012 is about 40 calories per day as opposed to an increase of 47 per day according to official estimates. The alternative assumptions are not necessarily better than the official procedure, as there is no “ground truth” against which to assess either measure. Rather, the point is that measurement assumptions can have a large effect even using the _same data_ , particularly in recent years when the importance of cereals and other unprocessed agricultural staples has fallen. These imputation assumptions have the potential to generate bias of a similar magnitude as the recall errors that potentially bias down both the level and decline in caloric intake.<sup>7</sup> Returning to the other side of the calorie equation, columns 6 and 7 again report estimates of population TEE and average TEE for adults. Relative to the official estimates using the 30-day recall period, the estimated population – TEE is almost perfectly negatively correlated over different time periods it is rising from 1983-1994, stable over 1994-2000 when intake appears to increase, peaks in 2005 when caloric intake is near its minimum, and then declines from 2005-2012 when caloric intake rises. Furthermore, by 1994 intake using 30-day recall is below TEE and by 2005 it is _much_ lower. The levels of TEE estimated in later periods are more consistent with the esti-

> 7Note that per capita caloric intake levels under any methodology are somewhat sensitive to outliers, and the analysis here follows the standard practice of censoring the distribution around the 1% and 99% percentile cutoffs (corresponding to 960 and 4400 calories a day per capita). However the trend in population averages over time are not very sensitive to outliers and generally track the population median.

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mated caloric intake using the 7-day recall period. The alternative estimates of caloric intake in column 3 (using the 30-day recall) are also lower than TEE in the later period, but display a similar decline between 2005-2012, such that TEE could “explain” about half of the decline in caloric intake estimates during this period. In column 7, average TEE for adults is reported to once again underscore that, were it not for demographic change, changes in estimated TEE for adults would be enough to “explain” a decrease in caloric intake of 34 calories per day between 1983-2005 (about 20% of the 150 calorie decline in the official estimates) or 111 calorie per day between 1983-2012 (over 100% of the 102 calorie decline in the official estimates).

Further evidence that changes in TEE may have contributed to lower caloric intake between 2005-2012 but are unlikely to have done so for the 1983-2005 period comes from an analysis of state-level changes. Figure 2 plots the change in state caloric intake against the change in state TEE for the 1983-2005 period and the 2005-2012 period. In the earlier period, although there are some states that appear to line up along the 45 degree line, most lie well below, indicating that measured caloric intake relative to TEE declined. The association is also very weak and not statistically significant. In the later period, the relationship is considerably stronger, with most states lining up along the 45 degree line and a statistically significant slope of 0.666 (with associated _R_<sup>2</sup> of 0.08). Using the full state panel, TEE estimates are significantly correlated with changes in caloric intake within states over time.<sup>8</sup> Given that the finding that population TEE rose during the 1983-2005 period of falling caloric intake considered by (Deaton and Dr`eze 2009), it is worth considering the best estimates of the potential effect of the disease environment on caloric intake from (Duh and Spears 2017). They use a district panel to show that within-district decreases in infant mortality between 1988 and 2005 are associated with within-district decreases in NSS caloric intake, with a slope coefficient of about 1.7. They find smaller but still highly significant cross-sectional associations between village/block infant mortality and caloric intake using a different survey, with slope coefficients ranging from 0.426 (when including household and demographic controls) to 1. They also report results based on open defecation and latrine ownership, but infant mortality has data available for the entire period and plausibly capture changes in the disease environment related to diarrhea, parasites, and in-

> 8See Appendix Table S.6 for these results.

15
testinal distress that could impede nutrient absorption and potentially raise TEE. Column 9 of Table 4 reports the level of infant mortality (per 1,000 live births), which declined from 106 to 42 over this period. This can be interpreted as a mid-level estimate of the total caloric burden of disease using the slope coefficient of 1 and linear specification reported in (Duh and Spears 2017), and low (column 8) and high (column 10) end estimates are also reported based on coefficients reported in their paper. Quantitatively, the decline in infant mortality mirrors the fall in caloric intake between 1983 and 2005, although the timing of the decline does not fit as well with the between-round variation (e.g. the huge drop in caloric intake between 2000 and 2005). If these estimates capture the true caloric burden of disease, they also imply that caloric intake measures (official, or the independent estimates presented here) using the 30-day recall period must almost surely be too low, particularly in recent years, but the estimated caloric burden of disease is of the correct magnitude to reconcile the TEE estimates with the official 7-day recall period estimates.

A final piece of evidence on net nutrition comes from anthropometric data. (Deaton and Dr`eze 2009) recognize that there has been some increase in average height in India during the 1983-2005 period, a trend that has continued since although the level of height and the rate of increase remain low compared to other countries with similar GDP per capita and economic growth trajectories. (Smith 2015) cites additional evidence of improving nutritional outcomes for children. A rise in average body mass for individuals of a given age and sex is one of the factors contributing to the increase in population REE estimated in this paper.<sup>9</sup> Although changes in height undoubtedly reflect nutrition, the quantitative relationship between net caloric surplus (intake minus TEE and any caloric cost of disease) and height is not well known. Changes in population height may be more reflective of micro-nutrients, macro-nutrients composition, and the timing of life-cycle nutrition from conception to adolescence rather than changes in the average caloric surplus of the population. However, the NFHS allows for measurement of weight gain over time, which does have a well known quantitative relationship with caloric surplus. While a single cross-section

> 9In Appendix Table S.7, even larger increases in REE for adults and children and hence smaller decreases (or even small increases) in TEE over the 1983-2012 period are found when including a linear time trend in the REE prediction equation. This may be reasonable in later period but is likely to be a heroic assumption when extrapolating a linear trend observed between 2005 to 2015 back 22 years.

16
only allows comparisons of weight across cohorts, the availability of multiple NFHS rounds allows for estimation of average weight gain with age for adults _within cohort_ . In Appendix Table S.8, the average weight gain is estimated controlling for cohort (birth-year) fixed effects across survey rounds, with data availability allowing for measurement of average weight gain between 2005 and 2015 for men and women, and between 1998 and 2005 for ever married women. Estimates controlling for height are also reported, which may capture some non-random attrition within a cohort related to nutritional status. The data imply an average weight gain of between 0.5KG and 0.6KG per year within cohort, with a slightly higher rate between 2005-2015 than 1998-2005 for ever married women. A weight gain of 1 kilogram is associated with a caloric surplus of 7700 for adults ((St. Jeor and Stumbo 1999)), so the reported level of weight gain translates into between 10.5 (= 0 _._ 5 _∗_ 7700 _/_ 365) and 12.7 (= 0 _._ 6 _∗_ 7700 _/_ 365) surplus calories per day above TEE and the caloric cost of disease. This suggests that, on average, the Indian (adult) population has been in caloric surplus since at least 1998 and even more between 2005 and 2015, even as official NSS caloric intake estimates using 30-day recall plummeted during the 2000s to reach levels well below the TEE estimates. Given that the analysis above suggests a limited scope for such a large decrease in population TEE, and that most of the improvement in the disease environment occurred before this period, the overall evidence is consistent with the view espoused in (Smith 2015) that the steep decline in official caloric intake estimates beginning around 2000 is likely the result of substantial measurement error.

# **4 Conclusion**

In this paper, population level Total Energy Expenditures (TEE) for India over the 1983-2012 period are quantified using a combination of time-use data, anthropometric data, and the NSS Schedule 10 employment survey. The main finding is that population TEE was fairly flat over this period, rising slightly until 2005 and then declining slightly only in the 2005-2012 period. This finding is driven by two main features of the data. First, household characteristics that tend to predict lower activity levels, such as education, expenditures, and industry/occupation, also tend to be associated with greater height and weight that imply offsetting increases in metabolic requirements. Second, even though the TEE estimates for adults in India fell 17
by over 100 calories per day between 1983-2012, more than enough to account for the measured decline in caloric intake and equivalent to between a third and a half of the average rural-urban gap around 1998, there was substantial demographic change in the population that more than offset this effect. The share of children under age 15 fell from 40% to 30% during this period, and the analysis shows that they have both lower metabolic requirements and substantially lower activity levels, particularly in rural areas. Because the average TEE of adults is much higher than children, population-level demographic change of the magnitude that occurred in India during this period exerts very powerful upward pressure on population TEE, enough to offset reasonably large reductions in TEE for a given age/sex. Thus, although variation in TEE can help account for the caloric-intake household size relationship explored in (Deaton and Paxson 1998), and can explain some of the national and cross-state patterns of caloric intake in the 20052012 period, it appears unlikely that a large reduction in TEE could “explain” the decline in caloric intake measured in the NSS. To the extent that there was a real (as opposed to measured) decline in population caloric intake during the 1983-2005 period, an improving disease environment (as shown by (Duh and Spears 2017)) is a more likely explanation. The estimates of TEE reported here are also consistent with a large degree of measurement error in NSS caloric intake, which recent data suggests is large enough to plausibly explain much of the apparent decline.

Although TEE is not usually considered by economists, the analysis suggests that there are interesting insights from quantifying it. Better measures of TEE could be used for the formulation and revision of poverty lines and equivalence scales, although given the difficulty of accurately measuring caloric intake or TEE it seems clear that anthropometric outcomes (e.g. stunting, wasting, BMI and weight gain over time) are more reliable for the evaluation of adequate nutritional status. Estimates of TEE could also be useful inputs into quantitative models of structural and demographic change since “subsistence” food requirements may vary with mechanization and household composition. In addition, quantifying TEE is a useful contri– bution to debates about estimated caloric intake and nutritional outcomes given the scope for measurement error, measuring both calories in, calories out, and net caloric surplus can shed light on biases in measurement. Timeuse and anthropometric surveys are available for many developing countries, and the potential availability of multiple waves of time-use data in developing countries could be particularly useful for this type of exercise, as it may 18
allow for better quantification of changes in physical activity levels related to travel and the intensive (per hour) and extensive (number of hours) physical margin of domestic and market work that are harder to quantify with the methodology and data adopted here. Another promising alternative to costly household surveys is the use of technology for the purpose of capturing TEE. Most modern smartphones can track physical activity levels reasonably well and simple devices that monitor heart rates offer the potential to estimate variation in activity levels for larger samples, greatly increasing the scope for estimating activity levels for populations outside the laboratory relative to the prohibitively expensive chemical methods that prevail in the nutrition literature.

# **A Estimation of Resting Energy Expenditures (REE)**

The best anthropometric predictor of REE is fat-free mass, but as this is rarely measured in the field, prediction equations have been developed based on regressing laboratory measured REE on more commonly observed variables ((St. Jeor and Stumbo 1999)). A standard approach, adopted by FAO (2001) and (Indian Council of Medical Research 2009), is to use prediction equations that regress REE on gender, age, weight, and in some cases height. Estimating separate equations by age and sex, and incorporating height, helps account for systematic average differences in fat-free mass, e.g. women, older, and shorter individuals tend to have lower fat-free mass for a given weight. Equations provided in (Henry 2005) use height and weight and are estimated separately for several age/sex groups. Results are not very sensitive to the use of alternative prediction equations. In Appendix Table S.1 Panel A, alternative REE estimates are generated using the prediction equations used by (Indian Council of Medical Research 2009). Similar results can be obtained using the prediction equations from (St. Jeor and Stumbo 1999). There have been laboratory measurements of REE in India but none for a large or representative sample of individuals and none generating specific predictive equations. (Ferro-Luzzi et al. 1997) attempt a validation of the FAO equations on Indian data and find a reasonable fit.

The method used here for imputing REE to individuals in other data sets is to regress the REE predicted by the (Henry 2005) equations using the 19
age, sex, height and weight from the NFHS on a set of common variables available in the NSS Schedule 10 employment survey. Because age and sex are always observed, this procedure generates additional variation in TEE conditional on age and sex that captures average differences in height and weight associated with socioeconomic status, maternal and childhood nutrition. Appendix Table S.4 reports the variables and coefficients for these regressions, which include individual and household age, education, demographics, school attendance, land holdings, and two-digit primary occupation code (using NCO1968 classification and available concordances for the later NFHS rounds). Separate equations are estimated by sector and sex, pooling the available years of data (which includes 1998 for women and 2015 for both men and women) and including year fixed effects (the omitted category is 2005). In principal one could also use separate predictive equations for height and weight, and then apply the (Henry 2005) equation, but results are similar either way.

Note that these equations can only be estimated for the adult age ranges in the NFHS eligible for height and weight measurement (15-49 for women and 15-54 for men). For individuals outside of this range, the following procedure is adopted:

1. Assume that their age is equal to 15 or 49 (54 for men) and use the predictive equation for REE

2. Scale this REE by the ratio of average REE for that individual’s age and sex relative to the average REE for their “assumed age” used in the predictive equation.

REE is scaled using the age and gender specific mean height and weight reported by the National Nutritional Monitoring Bureau for rural areas of 16 Indian states between 2000-2002 (reported in (Indian Council of Medical Research 2009)), together with the (Henry 2005) equations. Note that (Indian Council of Medical Research 2009) uses the 95th percentile of this same distribution to generate their recommended daily caloric intake, highlighting an important difference between prescriptive estimates of recommended daily caloric intake and the descriptive estimates of population total energy expenditure reported here.

The estimates reported in the text make two additional adjustments. First, because children under 18 are still growing, calories consistent with 20
“normal” growth (the pattern of observed weight gain) are added. This adjustment uses the 2 kilocalories per gram of tissue synthesis suggested in FAO (2001) combined with the average annual weight gain for each age and gender in the National Nutrition Monitoring Bureau data. Adjustments for the additional energy required by pregnant or nursing mothers are difficult due to lack of data, but energy requirements of infants themselves can be included. REE for infants (children under age 1) is set such that male babies have TEE of 650 calories and female babies have TEE of 600 calories, consistent with the (Indian Council of Medical Research 2009) report. Second, values outside of the 1% of the tails of the distribution are replaced with the percentile cutoff values prior to estimation.

# **B Estimation of Activity Levels (AL)**

Appendix Table S.2 lists the time-use survey activity codes and headings, the average share of time spent on the activity by rural and urban individuals, and the matched activity factors from FAO (2001). Estimates are not very sensitive to using alternative sources of activity factors, such as those developed for richer countries that tend to contain more detailed classifications of sports and exercise activities. Appendix Table S.2 includes the matched activities and activity factors from (Ainsworth et al. 2000) (used by (Cutler et al. 2003)) for comparison, and Appendix Table S.2 Panel C shows that the difference in activity levels using the measure used in the text and this alternative set of activity factors is quite small.

The estimates reported in the text make two additional assumptions about activity factors to derive activity levels. First, since the time-use data reports time spent on “related” or “other” activities within certain headings, one must assign an activity factor in these cases. The activity factor used in this case is equal to the average for the activities matched under the same heading. Second, since mode of travel is unobserved, the estimates use a single activity factor equal to 3 for all travel. This seems reasonable given that car ownership is very low in India and many other forms of transport (including bicycles, motorcycles, animal-carts and public transit) are equally and sometimes more energy intensive than walking. For example, FAO (2001) reports activity factors for “sitting on a bus/train” (1.2), “driving a car/truck (2.0), “walking around/strolling” (2.1), and more intense activities like “carrying a 20-30kg load on head” (3.5), “walking quickly” (3.8), and “cycling” 21
(5.6). An activity factor for travel of 3 lies between these extremes, and it is in the same range as “walking slowly” and “driving a motorcycle.” Based on NSS data, the car ownership rate in rural areas as late as 2005 was only 1%, and in urban areas was only 3%. Motorcycle and bicycle ownership rates are higher in 2005 (8% and 48% respectively in rural areas, 25% and 44% in urban areas). The approach used here still allows better-off households (proxied by per capita expenditure) to economize on travel time by using faster modes of transport, which leads to lower activity levels since travel is fairly energy-intensive compared to most other activities, but does not allow for potentially higher or lower activity factors associated with different modes of transport for a given amount of travel time measured in the TUS.

To impute activity levels to individuals in the NSS-E, individual activity levels in the TUS are regressed on a set of common variables to generate predictive equations. Separate equations are used for each combination of rural/urban, male/female, under 15 and over. Appendix Table S.3 presents the set of variables and regression coefficients for these regressions, which include detailed age and household demographics, education levels, land, per capita expenditure, household and individual two-digit NIC (1987 National Industry Classification) and NCO (1968 National Classification of Occupations), and work status variables (e.g. self-employed, casual worker, salaried worker, student, domestic duties, etc.). Dummy variables are also constructed for the TUS to match the detailed agricultural task variables (based on the previous week) and domestic chores (for women with primary status as domestic), based on whether households report any time spent during the last week on activities like weeding, harvesting, caring for animals, collecting firewood or water, food collection and preparation, etc. As with REE, values outside of the 1% of the tails of the distribution are replaced with the cutoff values prior to estimation. For individuals younger than 6, activity levels are set equal to the sample mean for 6 year olds, which is 1.46.

# **C Estimation of caloric intake**

The alternative, independent caloric intake estimates reported here, the NSS consumption survey (schedule 1) is used for the same years as the NSS-E. For 2010and 2012 the main results use a comparable 30-day recall period. Household respondents are asked to recall the total quantity and expenditure for each food item from a detailed list over the previous 30-days. The standard 22
methodology of directly converting quantities of each food into calories using the calories per unit weight for each item reported in (Gopalan et al. 2004) is used, with additional supplemetal data from the MedIndia web-site.<sup>10</sup> This covers 90.8% to 95.4% of average household food expenditure between 1983 and 2012.

For goods that have missing units or that are classified as “other” within a major category heading (e.g. fruits, vegetables, meat, dairy), calories proportional to the average calorie per rupee calculated within that category and NSS region are assigned. This increases the share of food expenditures covered by an additional 0.5%. The rest of food expenditures fall under the processed food and beverages categories, where the lack of units and/or vague classification make the use of a caloric conversion difficult if not impossible, e.g. the count of cooked meals or “cold beverages bottled/canned” or unitless expenditures on “prepared sweets” or “salted refreshments.” Calories from reported tea, coffee, and alcohol consumption are straightforward to incorporate because their units are reported. For the other goods, the reported estimates assume that calories per rupee are equal to 50% of the calories per rupee that can be directly converted across all goods. Ingredient costs appear to make up 40% of the sale price at large Indian restaurants ((Federation of Hotel and Restaurant Associations of India 2004)) while the value for richer countries is typically in the 20-35% range. A value of 50% puts the calories/rupee of processed foods, beverages and cooked meals roughly equal to the dairy category, while a value of 66% puts it equal to pulses or pure sugar.

The other adjustment made is similar to the official NSS estimates, which incorporate survey information on the number of meals consumed by household members at home and away on payment (which are drawn from the calories measured in the survey), the number of free meals consumed away from home through employers, schools and other households (which are not included in the survey quantities), and meals given to non-household members (which are drawn from the survey quantities but do not contribute to household caloric intake). The simple adjustment factor is based on the formula _adjustment factor = (meals at home + meals away from home free)/(meals at home + meals to others)_ . This assumes that households that consume more calories per meal at home both give and receive free meals that are proportionately higher in calories, and that free meals given and received en-

> 10See http://www.medindia.net/calories-in-indian-food/index.asp.

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ter symmetrically. The reported estimates censor the estimated distribution at 960 (equivalent to the 1% cutoff in 1983) and 4400 (the 99% cutoff in 2012), which lowers the estimated level of intake for some years due to the presence of large right tail outliers but has a minimal effect on the trends.

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Figure 1: Household Total Energy Expenditure varies with education, expenditure and household size in the cross-section <!-- Start of picture text --> 0 5 10 15 20 4 5 6 7 8 0 2 4 6 8 10<br>A. Mean adult schooling B. Log per capita exp. C. Household size<br>TEE REE TEE REE TEE REE<br>AL (right axis) AL (right axis) AL (right axis)<br>2500 1.9 2500 1.9 2500 1.9<br>1.8 1.8 1.8<br>2000 2000 2000<br>1.7 1.7 1.7<br>1500 1500 1500<br>1.6 1.6 1.6<br>1000 1.5 1000 1.5 1000 1.5<br><!-- End of picture text --> _Source:_ Authors’ analysis based on data from 1998-1999 India Time-Use Survey and National Family Health Survey rounds 2, 3 and 4. _Note:_ Unconditional relationship between household Total Energy Expenditure (TEE) (daily per person) and mean years of education for members over 18, log per capita expenditure (dropping observations in the 1% left and right tails), and household size (up to size 10) in the 1998-1999 Time Use Survey (TUS) cross-section. Activity Levels (AL) is measured directly and Resting Energy Expenditure (REE) is imputed using the National Family Health Survey (see text for description). Each observation represents a household average. Note that 92% of households are between 2 and 8 members.

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Figure 2: Changes in per capita caloric intake and Total Energy Expenditure by state, 1983-2012 <!-- Start of picture text --> 1983-2005 2005-2012<br>Andhra Pradesh<br>Kerala<br>Punjab<br>RajasthanHaryana<br>West Bengal<br>Tamil Nadu Bihar<br>Kerala<br>OrissaAssam<br>Uttar Pradesh<br>MaharashtraOrissa<br>Tamil NaduWest Bengal<br>Uttar PradeshBihar Delhi Gujarat Karnataka DelhiAssamMadhya Pradesh<br>MaharashtraAndhra Pradesh<br>Karnataka<br>Haryana Gujarat<br>Madhya PradeshPunjab<br>Rajasthan<br>-100 0 100 200 -150 -100 -50 0 50<br>Change in mean TEE per capita Change in mean TEE per capita<br>State Linear fit State Linear fit<br>45 degree line 45 degree line<br>400<br>50<br>200<br>0<br>0 -50<br>-200 -100<br>Change in mean caloric intake per capita Change in mean caloric intake per capita<br>-400 -150<br><!-- End of picture text --> _Source:_ Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, 3 and 4, and National Sample Survey (NSS) rounds 38, 43, 50, 55, 61, 66, and 68. _Note:_ State-level average Total Energy Expenditure (TEE) is imputed using NSS Schedule 10 as described in the text and caloric intake per capita is the authors’ estimate using NSS Schedule 1. The scatter plot excludes states with population below 10 million for readability.

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Table 1: Daily Resting Energy Expenditure (REE) and Activity Level (AL) <u>estimates</u>

|Panel A: REE (calories/day) from National Family Health|Survey (NF|HS)|
|---|---|---|
|Ever married women 15-49<br>Women 15-49|Men 15-54|Children 0-3|
|1998 (NFHS-2)<br>1128<br>.|.|502|
|1998 Rural<br>1114<br>.|.|499|
|1998 Urban<br>1169<br>.|.|513|
|2005 (NFHS-3)<br>1145<br>1140|1496|492|
|2005 Rural<br>1126<br>1122|1471|485|
|2005 Urban<br>1189<br>1177|1542|517|
|2015 (NFHS-4)<br>1180<br>1169|1531|514|
|2015 Rural<br>1161<br>1151|1510|507|
|2015 Urban<br>1221<br>1205|1570|532|
|Panel B: AL (rest=1) from Time Use Survey (TUS)|1998-1999||
|Men 15+<br>Women 15+|Boys 6-14|Girls 6-14|
|1998-99 mean<br>1.93<br>1.89|1.49|1.51|
|1998-1999 SD<br>0.47<br>0.34|0.23|0.23|
|1998-99 Rural mean<br>2.03<br>1.96|1.50|1.53|
|1998-99 Urban mean<br>1.69<br>1.71|1.47|1.46|

_Source_ : Authors’ analysis based on 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Panel A is based on applying the equations in (Henry 2005) to heights and weights reported in NFHS. Panel B is based on using the matched activity factors from FAO (2001) and activities reported in the TUS (see Appendix Table S.2). Reported values are population statistics using survey sampling weights.

29
||Cal<br>2-8<br>(8)|-0.007***<br>(0.001)<br>0.432***<br>(0.016)<br>-0.029***<br>(0.009)<br>24,908<br>0.372|
|---|---|---|
||Cal<br>All<br>(7)|-0.008***<br>(0.001)<br>0.440***<br>(0.014)<br>-0.015**<br>(0.006)<br>28,248<br>0.394<br>ly Health<br>tes only).<br>Expendi-<br>clustered<br>male aged<br> by state|
|cteristics|AL<br>2-8<br>(6)|-0.009***<br>(0.001)<br>-0.014***<br>(0.004)<br>-0.033***<br>(0.006)<br>17,013<br>0.528<br>tional Fami<br>e Survey sta<br>ting Energy<br>parentheses<br>os (male/fe<br>, and sector|
|ehold chara|REE<br>2-8<br>(5)|0.006***<br>(0.000)<br>0.002**<br>(0.001)<br>0.016***<br>(0.001)<br>17,013<br>0.940<br> Survey, Na<br>ix Time-Us<br>TEE), Res<br>rd errors in<br>raphic rati<br>us, religion|
|) and hous|TEE<br>2-8<br>(4)|-0.004***<br>(0.001)<br>-0.013**<br>(0.006)<br>-0.017**<br>(0.006)<br>17,013<br>0.692<br> Time-Use <br>(1993-94, s<br>penditure (<br>ta. Standa<br>ude demog<br> Tribe stat|
|diture (TEE|AL<br>All<br>(3)|-0.009***<br><br>(0.001)<br>-0.011**<br>(0.004)<br>-0.012**<br>(0.005)<br>18,528<br>0.502<br>-1999 India <br>ey round 50<br>l Energy Ex<br>Cal) per capi<br>Controls incl<br>/Scheduled|
|ergy Expen|REE<br>All<br>(2)|0.006***<br>(0.000)<br>0.002<br>(0.001)<br>0.014***<br>(0.002)<br>18,528<br>0.922<br>a from 1998<br>Sample Surv<br>usehold Tota<br>ric Intake (<br>, * p_<_0.1. <br>duled Caste|
|Table 2: Household Total En|endent variable (logs)<br>TEE<br>sehold sizes<br>All<br>(1)|n school<br>-0.004***<br>(0.001)<br> exp. p.c.<br>-0.010*<br>(0.005)<br> hh size<br>0.003<br>(0.007)<br>ervations<br>18,528<br>quared<br>0.668<br>_ce_: Authors’ analysis based on dat<br>ey rounds 2, 3 and 4, and National<br>: Dependent variable (in logs) is ho<br>(REE), Activity Level (AL), or Calo<br>ate/sector. *** p_<_0.01, ** p_<_0.05<br>-5,6-9,10-14,15-17,18-59,60+), Sche<br>mies.|
||Dep<br>Hou|Mea<br>Log<br>Log<br>Obs<br>R-s<br>_Sour_<br>Surv<br>_Note_<br>ture<br>by st<br>0-2,3<br>dum|

30
Table 3: Estimates of daily Total Energy Expenditure (TEE) and its com- <u>ponents</u> over time

||Po|pulatio|n|Males|15+|Females|15+|Under|age 1|5||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Year|TEE|REE|AL|TEE|AL|TEE|AL|TEE|AL|Population|share|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)||(10)|
|||||||All India|mean|||||
|1983|2136|1179|1.77|2864|1.95|2244|1.93|1499|1.52||0.40|
|1988|2134|1186|1.77|2858|1.94|2234|1.92|1475|1.50||0.39|
|1994|2158|1200|1.77|2855|1.93|2241|1.91|1463|1.49||0.36|
|2000|2155|1210|1.75|2829|1.91|2225|1.89|1477|1.48||0.36|
|2005|2172|1222|1.75|2825|1.90|2215|1.88|1476|1.47||0.34|
|2009|2159|1243|1.72|2753|1.85|2182|1.83|1460|1.44||0.31|
|2012|2153|1250|1.70|2729|1.83|2158|1.81|1469|1.44||0.30|
||||||Ru|ral secto|r mean|||||
|1983|2168|1165|1.81|2959|2.04|2290|1.99|1505|1.53||0.41|
|1994|2190|1183|1.81|2951|2.02|2289|1.97|1458|1.49||0.37|
|2000|2184|1191|1.79|2930|2.00|2275|1.96|1472|1.48||0.37|
|2005|2199|1201|1.79|2930|2.00|2264|1.94|1469|1.47||0.35|
|2012|2179|1230|1.74|2828|1.92|2197|1.86|1455|1.43||0.31|
||||||T|US State|mean|||||
|1983|2152|1183|1.78|2864|1.95|2280|1.95|1490|1.51||0.39|
|1994|2184|1204|1.78|2857|1.94|2281|1.94|1454|1.48||0.35|
|2000|2190|1214|1.77|2847|1.93|2264|1.92|1462|1.47||0.34|
|2005|2208|1224|1.77|2847|1.93|2260|1.91|1468|1.47||0.32|
|2012|2199|1254|1.73|2776|1.87|2196|1.83|1452|1.42||0.28|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, 3 and 4, and National Sample Survey rounds 38, 43, 50, 55, 61, 66, and 68. _Note_ : TEE is Total Energy Expenditure, REE is Resting Energy Expenditure, and AL is activity level. Columns 1-3 report population means (using sampling weights). Columns 4-9 are population means for subsets of the population. Column 10 is the share of the children aged 14 and under in the population. The reported years correspond to the last year covered by National Sample Survey “thick” survey rounds (38, 43, 50, 55, 61, 66, 68).

31
|Surplus<br>NFHS<br>(11)<br>10.5-12.7<br>10.5-12.7<br>10.5-12.7<br>10.5-12.7|
|---|
|n of disease,<br>e<br>Mid(=IMR)<br>Low<br>(9)<br>(10)<br>106<br>45<br>95<br>40<br>81<br>35<br>68<br>29<br>57<br>24<br>46<br>20<br>42<br>18<br>-64<br>- 27<br>-49<br>-21<br>-15<br>-6<br>, National Sample<br> Columns 2 is the<br> is missing in the<br>dairy, and pulses.<br>ic intake between<br>7 are the authors’<br>the calorie-disease<br> India (per 1,000<br> 10 multiply IMR<br>et caloric surplus|
|burde<br>Diseas<br>High<br>(8)<br>184<br>165<br>141<br>118<br>99<br>80<br>73<br>-111<br>-86<br>-26<br> 3 and 4<br>edule 1. <br>8 as this<br>cereals, <br> in calor<br>s 6 and <br>ents for<br>MR) for<br>ns 8 and<br> is the n<br> S.8).|
|Table 4: Comparison of daily Total Energy Expenditure (TEE), caloric intake, caloric <br>and caloric surplus implied by weight gain<br>Consumption<br>TEE<br>Real mpce<br>NSS calories<br>NSS cal. 7-day<br>Share 7-day<br>Authors’ cal.<br>Pop TEE<br>Adult TEE<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>1983<br>302<br>2190<br>2307*<br>0.37<br>2186<br>2136<br>2557<br>1988<br>305<br>2202<br>2337*<br>0.42<br>2180<br>2134<br>2553<br>1994<br>321<br>2133<br>2269*<br>0.43<br>2139<br>2158<br>2553<br>2000<br>350<br>2151<br>2290*<br>0.44<br>2169<br>2155<br>2531<br>2005<br>355<br>2040<br>2184*<br>0.48<br>2083<br>2172<br>2524<br>2010<br>386<br>2000<br>2140<br>0.48<br>2017<br>2159<br>2471<br>2012<br>479<br>2087<br>2225<br>0.49<br>2044<br>2153<br>2446<br>∆1983-2012<br>-102<br>-82<br>-142<br>17<br>-111<br>∆1983-2005<br>-150<br>-123<br>-102<br>36<br>-34<br>∆2005-2012<br>47<br>41<br>-40<br>-19<br>-78<br>_Source_: Authors’ analysis based on data from 1998-1999 India Time-Use Survey, National Family Health Survey rounds 2, <br>Survey rounds 38, 43, 50, 55, 61, 66, and 68, with independent estimates by other authors cited below.<br>_Note_: Column 1 is the authors’ calculation of real mean per capita expenditure (mpce) using National Sample Survey sch<br>ofcial estimate of caloric intake from (NSSO 2013) using the 30-day recall period (with (Deaton and Dr`eze 2009) for 198<br>report). Column 3 is the ofcial estimate using the new schedule type 2, which uses a 7-day recall for all food except <br>* indicates our imputation, based on the share of expenditures on the 7-day recall items (column 4) and the diference <br>the 30-day and 7-day schedule. Column 5 is the authors’ independent estimate of caloric intake (see appendix). Column<br>population and adult Total Energy Expenditure (TEE) estimates. Columns 8 through 10 are based on the reported coefci<br>slope in (Duh and Spears 2017), where disease is proxied by infant mortality. Column 9 reports infant mortality rate (I<br>live births) from the World Development Indicators and the mid-level estimate based on a slope coefcient of one. Colum<br>by 1.74 sand 0.426, the high and low end estimates reported in (Duh and Spears 2017) Table 8. Column 11 “Surplus” <br>consistent with average weight gain using the National Family Health Survey (NFHS) data (see text and Appendix Table|

32
Supplemental Appendix for “Caloric intake and energy expenditures in India” by Shari Eli and Nicholas Li 1
2 Figure S. 1: Household Total Energy Expenditure (TEE), Rest Energy Expenditure (REE) and Activity Level (AL) by 1-digit National Industrial Classification (NIC) industry <!-- Start of picture text --> Agr. Mining Manuf. natural Manuf. other<br>Utility Construct. Distrib. Transport<br>FIRE/Bus. Oth. service Not working<br>TEE REE<br>AF<br>.05<br>0<br>-.05<br>-.1<br>-.15<br>.05<br>0<br>-.05<br>-.1<br>-.15<br>.05<br>% deviation from sample mean 0<br>-.05<br>-.1<br>-.15<br><!-- End of picture text --> Graphs by Household head 1-digit industry classification _Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Classification based on 1987 NIC 1-digit industry codes for head of household. Estimates based on TUS sample with REE imputed based on variables common to NFHS, reported as differences relative to the sample mean.
3 Table S. 1: Alternative Resting Energy Expenditure (REE) and Activity Level (AL) estimates

|Panel A: REE from National Fami|ly Health Surve|y (NFHS)||
|---|---|---|---|
|Ever mar. female 15-49|Female 15-49|Male 15-54|Child 0-2|
|1998 (NFHS-2)<br>1139|.|.|486|
|1998 Rural<br>1124|.|.|484|
|1998 Urban<br>1182|.|.|492|
|2005 (NFHS-3)<br>1153|1161|1452|461|
|2005 Rural<br>1133|1143|1430|453|
|2005 Urban<br>1199|1199|1491|487|
|2015 (NFHS-4)<br>1193|1194|1496|479|
|2015 Rural<br>1173|1177|1476|472|
|2015 Urban<br>1235|1229|1532|496|
|Panel B: Weight (kilograms) from Nation<br>|al Family Healt|h Survey (NF|HS)|
|1998 (NFHS-2)<br>46.5|.|.|8.8|
|1998 Rural<br>45.0|.|.|8.7|
|1998 Urban<br>50.9|.|.|8.9|
|2005 (NFHS-3)<br>48.0|47.3|55.1|8.3|
|2005 Rural<br>46.0|45.6|53.3|8.2|
|2005 Urban<br>52.6|51.1|58.4|8.8|
|2015 (NFHS-4)<br>51.9|50.5|58.7|8.6|
|2015 Rural<br>49.8|48.7|57.0|8.5|
|2015 Urban<br>56.1|54.1|61.7|8.9|
|Panel C: AL from Time Use Su<br>Male 15+|rvey (TUS) 199<br>Female 15+|8-1999<br>Male 6-14|Female|
|1998-99 mean<br>1.80|1.82|1.43|1.45|
|1998-1999 SD<br>0.45|0.34|0.24|0.24|
|1998-99 Rural mean<br>1.87|1.89|1.45|1.48|
|1998-99 Urban mean<br>1.62|1.66|1.39|1.39|

_Source_ : Authors’ analysis based on data from 1998-1999 India Time-Use Survey (TUS) and National Family Health Survey (NFHS) rounds 2, 3 and 4. _Note_ : Panel A uses the (Indian Council of Medical Research, 2009) REE equations, which are based on the (FAO/WHO/UNU Expert Consultation, 2001) equations with a 5% downward adjustment for children. Panel B reports weight in kilograms. Panel C uses the activity factors found in Ainsworth et al. (2000). All statistics use sample weights.
4 Table S. 2: Time-Use classification and activity factors

|TUS code<br>111|<br>TUS desc.<br>PLOUGHING PREPARING LAND CL|% Rur. tim<br>00153|e<br>% Urb. tim<br>00007|e<br>FAO desc..<br>louhin horse 48 buffalo 3|Factor<br>4|Compendium desc.<br>clearin land|Factor<br>5|
|---|---|---|---|---|---|---|---|
|112|, ,<br>SEWING, PLANTING, TRANSPLANTI|.<br>0.0066|.<br>0.0004|pgg .,<br>planting rice|3.7|g<br>planting seedlings, shrubs, tr|4.5|
|113<br>114|APPLICATION OF MANURE, FERTIL<br>WEEDING|0.0283<br>00103|0.0022<br>00007|<br>fertilizing general<br>di l|5.2<br>4|<br>walking, applying fertilizer o<br>di ltiti d|2.5<br>45|
|115|SUPERVISION OF WORK|.<br>0.0049|.<br>0.0006|weeng genera<br>standing|1.4|weeng, cuvang garen<br>standing miscellaneous|.<br>2|
|116<br>|KITCHEN GARDENING BACKYARD<br>|0.0008<br>|0.0002<br>|weeding garden<br>|3.3<br>|general gardening<br>|4<br>|
|117|STOCKING, TRANSPORTING TO HOM|0.0021|0.0003|bundling rice|3.7|picking fruit off trees, picki|3|
|118<br>119|SALE AND PURCHASE RELATED ACT<br>TRAVEL TO THE WORK|0.0003<br>00112|0.0000<br>00009|standing<br>deends on te|1.45|Sitting-light offce work, etc<br>deends on te|1.5|
|121|<br>GRAZING ANIMALS OUTSIDE|.<br>0.0072|.<br>0.0003|p yp<br>walking around/strolling|.<br>2.1|p yp<br>farming, chasing cattle (non-s|.<br>3.5|
|122|TENDING ANIMALS CLEANING, W|0.0093|0.0009|tending animals|4.6|farming, feeding cattle, horse|4.5|
|123|<br>CARING FOR ANIMALS : BREEDING|0.0009|0.0001|<br>tending animals|4.6|<br>farming, taking care of animal|6|
|124|MILKING AND PROCESSING OF MIL|0.0021|0.0003|milking by hand|3.6|milking by hand moderate effor|3|
|125<br>|MAKING DUNG CAKES<br>|0.0010<br>|0.0001<br>|making mud bricks (squatting)<br>|3<br>|FAO/WHO/UNU<br>|3<br>|
|126<br>127|POULTRY REARING FEEDING, CL<br>OTHER RELATED ACTIVITIES|0.0002<br>0.0010|0.0000<br>0.0001|tending animals|4.6<br>0|farming, feeding small animals|4<br>0|
|128|SALE AND PURCHASE RELATED ACT<br>|0.0003|0.0001|standing<br>|1.45|Sitting-light offce work<br>|1.5|
|129|TRAVEL TO THE WORK|0.0016|0.0001|depends on type|.|depends on type|.|
|131<br>132|NURSERY - SEEDLINGS<br>PLANTING TENDING PROCESSING|0.0001<br>00002|0.0000<br>00000|Forester-nursery work<br>li|3.6<br>41|planting seedlings, shrubs<br>f li b hd|4.5<br>6|
|133|, ,<br>COLLECTING, STORING AND STOCK|.<br>0.0004|.<br>0.0000|pantng trees<br>picking by hand|.<br>3.4|orestry, pantng y an<br>picking fruit of trees,|3|
|134<br>|WOOD CUTTING, CHOPPING AND<br>|0.0012<br>|0.0002<br>|4.2 chopping wood (for fuel).<br>|5<br>|forestry ax chopping slow<br>|5<br>|
|135|FISH FARMING, CLEANING SEA-BE|0.0008|0.0004|line fshing 1.9, spear fshin|2.3|fshing general|3|
|136<br>137|CARE OF HOUSE PLANTS, INDOOR<br>FLOWER GARDENING LANDSCAPIN|0.0006<br>00001|0.0002<br>00002|weeding garden<br>di d|3.3<br>33|general gardening<br>l di|4<br>4|
||<br>|.<br>|.<br>|weeng garen<br>|.<br>|genera garenng<br>||
|138|SALE AND PURCHASE RELATED ACT|0.0002|0.0001|standing|1.45|Sitting-light offce work|1.5|
|139|TRAVELLING TO THE WORK|0.0007|0.0001|depends on type|.|depends on type|.|
|140|<br>FETCHING OF WATER|0.0022|0.0012|<br>collecting water (from well)|4.5|<br>farming, hauling water for ani|4.5|
|141<br>142|COLLECTION OF FRUITS, VEGETAB<br>COLLECTION OF MINOR FOREST PR|0.0020<br>00008|0.0010<br>00002|picking fruit by hand<br>collectin wood (for fuel)|3.4<br>33|picking fruit of trees,<br>ickin fruit of trees|3<br>3|
|143|<br>COLLECTION OF FUEL/FUEL WOOD/|.<br>0.0013|.<br>0.0001|g<br>collecting wood (for fuel) is|.<br>3.8|pg ,<br>carrying, loading or stacking|5|
|144|COLLECTION OF RAW MATERIAL FO|0.0022|0.0003|collecting wood (for fuel)|3.3|picking fruit of trees,|3|
|145|COLLECTION OF BUILDING MATERI|00000|00000|<br>bild i d|66|<br>i ldi tki|5|
|146|<br>COLLECTION OF FODDER|.<br>0.0013|.<br>0.0001|uer carryng woo<br>carrying straw|.<br>3.1|carryng, oang or sacng<br>FAO/WHO/UNU|3.1|
|147|SALE AND PURCHASE RELATED ACT|0.0011|0.0001|standing|1.45|Sitting-light offce work|1.5|
|148|<br>COLLECTION OF OTHER ITEMS|0.0002|0.0001|collecting wood (for fuel)|3.3|<br>picking fruit of trees,|3|
|149|TRAVEL TO WORK|0.0009|0.0001|depends on type|.|depends on type|.|
|152|MILLING HUSKING POUNDING|00007|00003|<br>oundin rain|56|<br>FAO/WHO/UNU|56|
|153|, ,<br>PARBOILING|.<br>0.0002|.<br>0.0000|pg g<br>standing|.<br>1.45|FAO/WHO/UNU|.<br>1.4|
|154|SORTING, GRADING|0.0004|0.0002|sorting ground nuts|1.9|FAO/WHO/UNU|1.9|
|155|<br>GRINDING, CRUSHING|0.0003|0.0003|<br>grinding grain using millstone|4.6|FAO/WHO/UNU|4.6|
|156<br>157|ANY OTHER RELATED ACTIVITY<br>SALES AND PURCHASE RELATED AC|0.0002<br>00000|0.0002<br>00001|standing|0<br>145|Sitting-light offce work|0<br>15|
|159|<br>TRAVEL FOR THE WORK|.<br>0.0001|.<br>0.0000|depends on type|.<br>.|<br>depends on type|.<br>.|
|161|MINING/EXTRACTION OF SALT,|0.0000|0.0000|shovelling|4.6|coal mining/general|6|
|162|MINING/DIGGING/QUARRYING OF S|00009|00004|drillin with jackhammer|39|<br>coal minin/eneral|6|
|163|<br>DIGGING OUT CLAY, GRAVEL AND|.<br>0.0007|.<br>0.0003|g<br>shovelling|.<br>4.6|gg<br>coal mining/general|6|
|164|DIGGING OUT MINERALS MAJOR|0.0000|0.0000|shovelling|4.6|coal mining/general|6|
|165|<br>TRANSPORTING IN VEHICLES|0.0001|0.0001|driving a car/truck|2|<br>driving|2|
|166|STORING, STOCKING|0.0001|0.0002|loading operations|3.2|shovelling coal|7|
|167|ANY OTHER RELATED ACTIVITY|00002|00002||0||0|
|168|<br>SALE AND PURCHASE RELATED ACT|.<br>0.0000|.<br>0.0000|standing|1.45|Sitting-light offce work|1.5|
|169|TRAVEL FOR THE WORK|0.0003|0.0002|depends on type|.|depends on type|.|
|211|BUILDING AND CONSTRUCTION OF DW|00032|00034|<br>Chisellin=5 sawin/lanin a|5|<br>construction outside remodelli|55|
|212|<br>CONSTRUCTION AND REPAIR OF AN|.<br>0.0001|.<br>0.0001|g, gpg|5|<br>construction outside remodelli|.<br>5.5|
|213<br>|CONSTRUCTION OF WALL, STORAGE<br>|0.0004<br>|0.0001<br>||5<br>|construction outside remodelli<br>|5.5<br>|
|214|CONSTRUCTION OF PUBLIC WORKS/|0.0006|0.0004||5|building road|6|
|217|ANY OTHER ACTIVITY RELATED|0.0005|0.0009||0||0|
|218<br>|SALES AND PURCHASE RELATED AC<br>|0.0001<br>|0.0001<br>|standing<br>|1.45|Sitting-light offce work<br>|1.5|
|219|TRAVEL TO THE WORK|0.0006|0.0007|depends on type|.|depends on type|.|
|221|FOOD PROCESSING AND COOKING F|0.0010|0.0021|bakery work|2.5|bakery general moderate effort|4|
|222|BUTCHERING CURING PROCESSIN|00001|00001|<br>bk k|25|<br>bthi|6|
|223|, ,<br>MANUFACTURING OF TEXTILES S|.<br>0.0031|.<br>0.0074|aery wor<br>textile factory work|.<br>3.1|ucerng<br>tailoring general|2.5|
|224|MAKING HANDICRAFTS POTTERY|0.0024|0.0023|shoemaker|2.6|shoe repair general|2.5|
|225|, ,<br>FITTING, INSTALLING, TOOL SET|0.0007|0.0018|textile factory work|3.1|<br>machine tooling|3|
|226|ASSEMBLING MACHINES, EQUIPMEN|0.0002|0.0006|textile factory work|3.1|automobile repair|3|
|227|PRODUCTION RELATED WORK IN LA|00016|00030|<br>textile factor work|31|<br>automobile reair|3|
|228|<br>SALE AND PURCHASE RELATED ACT|.<br>0.0003|.<br>0.0006|y<br>standing|.<br>1.45|p<br>Sitting-light offce work|1.5|
|229<br>311|TRAVEL FOR THE WORK<br>BUYING AND SELLING GOODS SU|0.0006<br>00043|0.0014<br>00136|depends on type<br>di|.<br>145|depends on type<br>Siilih ff k|.<br>15|
|||.|.<br>Continue|stanng<br>d on nextpage|.|ttng-gt oce wor|.|
5

|TUS code|TUS desc.|**T**<br>% Rur. tim|**able S. 2 – contin**<br>e<br>% Urb. tim|**ued from previous page**<br>e<br>FAO desc.|Factor|Compendium desc.|Factor|
|---|---|---|---|---|---|---|---|
|312<br>313|PETTY TRADING, STREET AND DOO<br>TRANSPORTING GOODS IN TRUCKS,|0.0018<br>0.0005|0.0041<br>0.0015|walking/strolling around<br>driving a car/truck|2.1<br>2|walking slowly and carrying li<br>automobile or light truck driv|3<br>2|
|314<br>|<br>TRANSPORTING IN HAND CARTS, A<br>|0.0002<br>|0.0017<br>|<br>pulling a hand cart unloaded<br>|4.82<br>|<br>pushing a wheel chair<br>|4<br>|
|315|TRANSPORT OF PASSENGER BY MOT|0.0001|0.0011|driving a car/truck|2|automobile or light truck driv|2|
|317|ANY OTHER ACTIVITY|0.0005|0.0024||0||0|
|319<br>|TRAVEL TO WORK<br>|0.0005<br>|0.0022<br>|depends on type<br>|.<br>|depends on type<br>|.<br>|
|321<br>322|SERVICE IN GOVERNMENT AND SEM<br>SERVICE IN PRIVATE ORGANISATI|0.0033<br>0.0018|0.0155<br>0.0106|writing<br>writing|1.4<br>1.4|Sitting light offce work<br>Sitting light offce work|1.5<br>1.5|
|323|<br>PETTY SERVICE : DOMESTIC SERV|0.0028|0.0058|housework (unspecifed)|2.8|<br>shoe repair general|2.5|
|324|PROFESSIONAL SERVICES : MEDIC|0.0016|0.0037|standing|1.45|standing light (bartending, st|1.8|
|325|PROFESSIONAL SERVICES : COMPU|0.0003|0.0008|standing|1.45|standing light (bartending st|1.8|
|326|<br>TECHNICAL SERVICES: PLUMBING,|0.0004|0.0019|writing|1.4|,<br>electrical work, plumbing|3.5|
|327|OTHERS|0.0018|0.0034||0||0|
|329|TRAVEL TO WORK|00015|00058|deends on te||deends on te||
|411|<br>COOKING FOOD ITEMS, BEVERAGES|.<br>0.0411|.<br>0.0424|p yp<br>peeling vegetables(1.9), makin|.<br>2.4|p yp<br>cooking indian bread on an out|.<br>3|
|421<br>422|CLEANING AND UPKEEP OF DWELLI<br>CLEANING OF UTENSILS|0.0132<br>00090|0.0122<br>00097|Housework unspecifed 2.8, Swe<br>lnin dih|2.8<br>17|cleaning house or cabin, gener<br>whin dih|3<br>23|
||<br>|.<br>|.<br>|ceag ses<br>|.<br>|asg ses<br>|.<br>|
|431|CARE OF TEXTILES: SORTING, ME|0.0076|0.0105|washing clothes|2.8|implied walking putting away c|2.3|
|441|SHOPPING FOR GOODS AND NON-PE|0.0063|0.0061|walking around/strolling|2.1|food shopping standing and wal|2.3|
|451|HOUSEHOLD MANAGEMENT PLANNIN|00006|00008|<br>iti|14|<br>Sittiliht ff k|15|
|461|:<br>DO-IT-YOURSELF HOME IMPROVEME|.<br>0.0039|.<br>0.0036|wrng<br>housework (unspecifed)|.<br>2.8|ng-g oce wor<br>home repair painting fence|.<br>4.5|
|471|PET CARE|0.0003|0.0001|child care(unspecifed)|2.5|feeding animals|2.5|
|481|<br>TRAVEL RELATED TO HOUSEHOLD M|0.0050|0.0034|<br>depends on type|.|<br>depends on type|.|
|491|HOUSEHOLD MAINTENANCE, MANAGE|0.0073|0.0050|housework (unspecifed)|2.8|food shopping standing and wal|2.3|
|511|PHYSICAL CARE OF CHILDREN: WA|00093|00094|bathing child (standing)|35|child care standing|3|
|521|<br>TEACHING, TRAINING AND INSTRU|.<br>0.0008|.<br>0.0021|<br>standing|.<br>1.45|<br>Sitting-light offce work|1.5|
|531|ACCOMPANYING CHILDREN TO PLAC|0.0007|0.0007|depends on type|.|depends on type|.|
|541<br>|PHYSICAL CARE OF THE SICK, DI<br>|0.0007<br>|0.0005<br>|<br>child care unspecifed<br>|2.5|<br>elder care<br>|4|
|551<br>561|ACCOMPANYING ADULTS TO RECEIV<br>SUPERVISING CHILDREN NEEDING|0.0001<br>0.0037|0.0001<br>0.0028|depends on type<br>child care unspecifed|.<br>2.5|depends on type<br>standing light (bartending, st|.<br>1.8|
|562<br>|SUPERVISING ADULTS NEEDING CA<br>|0.0002<br>|0.0002<br>|<br>child care unspecifed<br>|2.5|<br>standing light (bartending, st<br>|1.8|
|571<br>572|TRAVEL RELATED TO CARE OF CHI<br>TRAVEL RELATED TO CARE OF ADU|0.0002<br>0.0001|0.0002<br>0.0001|depends on type<br>depends on type|.<br>.|depends on type<br>depends on type|.<br>.|
|581<br>591|TAKING CARE OF GUESTS/VISITOR<br>ANY OTHER ACTIVITY NOT MENTIO|0.0003<br>0.0004|0.0007<br>0.0005|<br>child care unspecifed<br>.|2.5<br>0|<br>standing light (bartending, st<br>.|1.8<br>0|
|611<br>|COMMUNITY ORGANISED CONSTRUCT<br>|0.0000<br>|0.0000<br>|sawing softwood (intermediate<br>|5<br>|building road<br>|6<br>|
|621|COMMUNITY ORGANISED WORK: COO|0.0000|0.0000|peeling vegetab;es|2.4|cooking indian bread on an out|3|
|631<br>641<br>|VOLUNTEERING WITH FOR AN ORGA<br>VOLUNTEER WORK THROUGH ORGANI<br>|0.0000<br>0.0000<br>|0.0000<br>0.0000<br>|standing<br>standing<br>|1.45<br>1.45<br>|volunteering sitting, moderate<br>volunteering sitting, moderate<br>|2.5<br>2.5<br>|
|651|PARTICIPATION IN MEETINGS OF|0.0001|0.0001|standing|1.45|Volunteer sitting meeting, g|1.5|
|661|INVOLVEMENT IN CIVIC AND RELA|0.0001|0.0000|standing|1.45|volunteer- standing, light wor|2.3|
|671<br>|INFORMAL HELP TO OTHER HOUSEH<br>|0.0004<br>|0.0004<br>|standing<br>|1.45<br>|<br>volunteer- standing, light wor<br>|2.3<br>|
|681|COMMUNITY SERVICES NOT ELSEWH|0.0001|0.0001|standing|1.45|volunteer- standing, light wor|2.3|
|691|TRAVEL RELATED TO COMMUNITY S|0.0001|0.0001|depends on type|.|depends on type|.|
|711<br>|GENERAL EDUCATION: SCHOOL/UNI<br>|0.0239<br>|0.0260<br>|<br>reading<br>|1.4<br>|<br>Miscellaneous sitting, study<br>|1.8<br>|
|721|STUDIES, HOMEWORK AND COURSE|0.0181|0.0230|reading|1.4|Sitting-light offce work|1.5|
|731|ADDITIONAL STUDY NON-FORMAL|0.0002|0.0005|reading|1.4|Sitting-light offce work|1.5|
|741|,<br>NON FORMAL EDUCATION BY CHILD|0.0000|0.0000|reading|1.4|<br>Sitting-light offce work|1.5|
|751|WORK-RELATED TRAINING|0.0001|0.0002|reading|1.4|Sitting-light offce work|1.5|
|761<br>|TRAINING UNDER GOVERNMENT PRO<br>|0.0000<br>|0.0000<br>|reading<br>|1.4<br>|Sitting-light offce work<br>|1.5<br>|
|771|OTHER TRAINING/EDUCATION|0.0002|0.0007|reading|1.4|Sitting-light offce work|1.5|
|781|LEARNING NOT ELSEWHERE CLASSI|0.0003|0.0007|reading|1.4|Sitting-light offce work|1.5|
|791<br>|TRAVEL RELATED TO LEARNING<br>|0.0036<br>|0.0045<br>|depends on type<br>|.<br>|<br>depends on type<br>|.<br>|
|811<br>812|PARTICIPATING IN SOCIAL EVENT<br>PARTICIPATING IN RELIGIOUS AC|0.0012<br>0.0032|0.0010<br>0.0045|standing<br>standing|1.45<br>1.45|religious activities standin<br>religious activities standin|1.5<br>1.5|
|813|PARTICIPATING IN COMMUNITY FU|00001|00001|Dancin|5|<br>reliious activities dancin|5|
|814|<br>SOCIALIZING AT HOME AND OUTSI|.<br>0.0008|.<br>0.0015|g<br>standing|1.45|g g<br>standing, talking outside home|1.8|
|821|ARTS, MAKING MUSIC, HOBBIES A|0.0001|0.0005|playing cards/board games|1.6|miscellaneous sitting arts and|2|
|822<br>|<br>INDOOR AND OUTDOOR SPORTS PAR<br>|0.0020<br>|0.0025<br>|<br>Between running and batting/bo<br>|5.5<br>|<br>cricket<br>|5<br>|
|831|GAMES AND OTHER PAST-TIME ACT|0.0225|0.0176|playing cards/board games|1.6|Miscellaneous sitting board|1.5|
|832<br>|SPECTATOR TO SPORTS, EXHIBITI<br>|0.0015<br>|0.0022<br>|watching tv<br>|1.66<br>|Miscelaneous sitting at spor<br>|1.5<br>|
|841<br>851|OTHER RELATED ACTIVITIES<br>READING, OTHER THAN NEWSPAPER|0.0004<br>0.0031|0.0008<br>0.0051|watching tv<br>reading|1.66<br>1.22|Miscelaneous sitting at spor<br>sitting reading|1.5<br>1.3|
|852|WATCHING TELEVISION AND VIDEO|0.0275|0.0764|watching tv|1.66|inactivity quiet watching tv|1|
|853<br>861|<br>LISTENING TO MUSIC/RADIO<br>ACCESSING INFORMATION BY COMP|0.0040<br>0.0001|0.0022<br>0.0001|<br>listenening to radio/music ave<br>watching tv|1.5<br>1.66|<br>inactivity quiet watching tv<br>sitting reading|1<br>1.3|
|862|VISITING LIBRARY|00001|00003|<br>readin|14|<br>sittin readin|13|
|863<br>871|<br>READING NEWSPAPER, MAGAZINES<br>MASS MEDIA USE AND ENTERTAINM|.<br>0.0015<br>0.0004|.<br>0.0061<br>0.0015|g<br>reading<br>reading|.<br>1.4<br>1.4|g g<br>sitting reading<br>sitting reading|.<br>1.3<br>1.3|
|891|TRAVEL RELATED TO SOCIAL, CUL|0.0033|0.0042|depends on type|.|<br>depends on type|.|
||||Continue|d on nextpage||||
6

|||**Table**|**S. 2 – continue**|**d from previous page**||||
|---|---|---|---|---|---|---|---|
|TUS code|TUS desc.|% Rur. time|% Urb. time|FAO desc.|Factor|Compendium desc.|Factor|
|892|TRAVEL RELATING TO SEARCH OF|0.0001|0.0004|depends on type|.|depends on type|.|
|911|SLEEP AND RELATED ACTIVITIES|0.3766|0.3738|sleeping|1|sleeping|0.9|
|921|EATING AND DRINKING|0.0563|0.0586|eating and drinking|1.5|eating sitting|1.5|
|922|SMOKING, DRINKING ALCOHOL AND|0.0011|0.0005|eating and drinking|1.5|eating sitting|1.5|
|931|PERSONAL HYGIENE AND HEALTH|0.0444|0.0425|Average of washing hands/face|2|grooming|2|
|932|WALKING, EXERCISE MINING, JOG|0.0028|0.0044|Between running and batting/bo|5.5||5|
|941|RECEIVING MEDICAL AND PERSONA|0.0002|0.0003|sitting quietly|1.2|sitting reading|1.3|
|942|RECEIVING MEDICAL AND PERSONA|0.0001|0.0001|sitting quietly|1.2|sitting reading|1.3|
|951|TALKING, GOSSIPING AND QUARRE|0.0486|0.0402|standing average for men and w|1.45|sitting talking on the phone|1.5|
|961|DOING NOTHING, REST AND RELAX|0.0651|0.0540|sitting quietly|1.2|lying quietly doing nothing|1|
|962|FORCED LEISURE|0.0012|0.0009|sitting quietly|1.2|lying quietly doing nothing|1|
|971|INDIVIDUAL RELIGIOUS PRACTICE|0.0037|0.0069|standing|1.2|inactivity light meditation|1|
|981|OTHER ACTIVITIES|0.0038|0.0043|standing|0||0|
|982|RESTING/CONVALESCING DUE TO P|0.0016|0.0015|lying|1.2|lying quietly doing nothing|1|
|991|TRAVEL RELATED TO PERSONAL CA|0.0037|0.0032|depends on type|.|depends on type|.|
|_Source_: Aut|hors’ calculation usingIndia Time-Use Survey19|98-1999, Annex 5|of(FAO/WHO/|UNU Expert Consultation,2001), and|(Ainswort|h et al.,2000)||
7 Table S. 3: Coefficients from Activity Level regressions using 1998-1999 Time-Use Survey

|Sector<br>VARIABLES|Rural<br>Female 15+|Male 15+|Female 6-14|Male 6-14|Urban<br>Female 15+|Male 15+|Female 6-14|Male 6-14|
|---|---|---|---|---|---|---|---|---|
|2.hhsize|-0.024|-0.048**|||-0.021|-0.026|||
||(0.015)|(0.022)|||(0.019)|(0.025)|||
|3.hhsize|-0.032**|-0.030|-0.026|-0.014|-0.043**|-0.012|-0.025|-0.000|
||(0.015)|(0.021)|(0.045)|(0.035)|(0.018)|(0.023)|(0.090)|(0.052)|
|4.hhsize|-0.047***|-0.030|-0.028|-0.006|-0.051***|-0.017|-0.000|0.017|
||(0.016)|(0.022)|(0.044)|(0.034)|(0.019)|(0.024)|(0.090)|(0.052)|
|5.hhsize|-0.049***|-0.032|-0.014|-0.015|-0.059***|-0.024|-0.033|0.025|
||(0.016)|(0.022)|(0.045)|(0.035)|(0.019)|(0.024)|(0.090)|(0.052)|
|6.hhsize|-0.054***|-0.034|-0.018|-0.009|-0.032*|-0.008|-0.037|-0.003|
||(0.016)|(0.023)|(0.045)|(0.035)|(0.019)|(0.025)|(0.091)|(0.053)|
|7.hhsize|-0.036**|-0.021|-0.025|-0.015|-0.061***|0.001|-0.012|0.004|
||(0.017)|(0.024)|(0.045)|(0.036)|(0.021)|(0.028)|(0.092)|(0.055)|
|8.hhsize|-0.040**|-0.020|-0.004|-0.008|-0.064***|-0.019|-0.021|-0.010|
||(0.018)|(0.025)|(0.046)|(0.037)|(0.021)|(0.030)|(0.092)|(0.057)|
|9.hhsize|-0.067***|-0.012|-0.015|-0.019|-0.071***|0.053|-0.089|0.056|
||(0.020)|(0.027)|(0.049)|(0.040)|(0.026)|(0.037)|(0.100)|(0.064)|
|10.hhsize|-0.022|0.022|0.016|0.047|-0.070**|-0.042|-0.085|0.082|
||(0.022)|(0.030)|(0.054)|(0.045)|(0.029)|(0.042)|(0.102)|(0.066)|
|11.hhsize|-0.051*|0.004|-0.099*|-0.107**|-0.069|0.025|0.033|-0.067|
||(0.027)|(0.037)|(0.055)|(0.049)|(0.049)|(0.077)|(0.209)|(0.198)|
|12.hhsize|0.028|-0.020|0.104|0.042|-0.107**|-0.034|-0.189|0.217**|
||(0.037)|(0.048)|(0.074)|(0.061)|(0.044)|(0.074)|(0.154)|(0.085)|
|13.hhsize|-0.184***|-0.020|-0.012|-0.030|-0.051|0.112|-0.165|-0.173|
||(0.044)|(0.061)|(0.086)|(0.105)|(0.042)|(0.069)|(0.135)|(0.106)|
|14.hhsize|-0.094*|-0.191***|-0.094|-0.064|0.060|0.094|-0.146|-0.366***|
||(0.054)|(0.070)|(0.089)|(0.085)|(0.097)|(0.129)|(0.156)|(0.131)|
|headage|-0.014***|-0.007|0.004|-0.014*|-0.007|0.005|0.004|0.003|
||(0.003)|(0.004)|(0.010)|(0.007)|(0.004)|(0.007)|(0.014)|(0.014)|
|headage2|0.000***|0.000*|-0.000|0.000*|0.000|-0.000|-0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|headage3|-0.000***|-0.000|0.000|-0.000|-0.000|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|headfemale|0.002|0.009|-0.000|-0.003|0.021**|-0.038**|0.080***|0.063***|
||(0.008)|(0.013)|(0.017)|(0.017)|(0.009)|(0.018)|(0.025)|(0.023)|
|dmratioa|-0.184***|-0.055*|-0.130**|-0.223***|-0.390***|0.005|-0.403***|-0.388***|
||(0.023)|(0.029)|(0.063)|(0.061)|(0.025)|(0.040)|(0.082)|(0.089)|
|dfratioa|-0.312***|-0.111***|-0.241***|-0.277***|-0.601***|-0.021|-0.575***|-0.530***|
||(0.023)|(0.032)|(0.066)|(0.062)|(0.026)|(0.045)|(0.091)|(0.092)|
|dmratios|-0.172***|-0.142***|-0.034|-0.396***|-0.368***|-0.024|-0.373**|-0.856***|
||(0.031)|(0.038)|(0.107)|(0.099)|(0.038)|(0.057)|(0.151)|(0.158)|
|dfratios|-0.279***|-0.138***|-0.370***|-0.226***|-0.532***|-0.082|-0.591***|-0.069|
||(0.027)|(0.039)|(0.081)|(0.073)|(0.032)|(0.057)|(0.106)|(0.114)|
|dmratioc|-0.041|-0.029|-0.084|-0.185***|-0.243***|0.062|-0.290***|-0.382***|
||(0.031)|(0.042)|(0.072)|(0.071)|(0.036)|(0.062)|(0.104)|(0.106)|
|dfratioc|0.002|0.006|-0.141*|-0.141**|-0.215***|-0.054|-0.429***|-0.324***|
||(0.033)|(0.045)|(0.080)|(0.072)|(0.039)|(0.066)|(0.103)|(0.114)|
|dmratiop|-0.173***<br>|-0.094***<br>|-0.162***<br>|-0.268***<br>|-0.359***<br>|0.009<br>|-0.370***<br>|-0.432***<br>|
||(0.024)|(0.033)|(0.055)|(0.054)|(0.029)|(0.050)|(0.077)|(0.084)|
|Continued on nex|tpage||||||||
8

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-14|Male 6-14|Female 15+|Male 15+|Female 6-14|<br>Male 6-14|
|dfratiop|-0.163***|-0.087**|-0.115**|-0.247***|-0.412***|-0.033|-0.513***|-0.355***|
||(0.026)|(0.036)|(0.057)|(0.058)|(0.031)|(0.052)|(0.081)|(0.087)|
|dmratiom|-0.162***|-0.038|-0.129**|-0.238***|-0.349***|-0.006|-0.217***|-0.380***|
||(0.024)|(0.033)|(0.055)|(0.053)|(0.029)|(0.048)|(0.074)|(0.082)|
|dfratiom|-0.196***|-0.124***|-0.128**|-0.265***|-0.446***|-0.045|-0.524***|-0.317***|
||(0.026)|(0.035)|(0.056)|(0.054)|(0.030)|(0.050)|(0.075)|(0.084)|
|dmratioy|-0.163***|-0.070*|-0.221***|-0.279***|-0.350***|-0.073|-0.298***|-0.230**|
||(0.029)|(0.036)|(0.067)|(0.063)|(0.033)|(0.052)|(0.087)|(0.095)|
|dfratioy|-0.214***|-0.082*|-0.208***|-0.277***|-0.488***|-0.114**|-0.412***|-0.389***|
||(0.029)|(0.043)|(0.070)|(0.065)|(0.031)|(0.056)|(0.088)|(0.091)|
|hhlowcaste|-0.002|-0.003|-0.009|-0.030***|-0.002|0.005|0.023|0.013|
||(0.005)|(0.006)|(0.009)|(0.008)|(0.007)|(0.011)|(0.015)|(0.014)|
|hhtribe|0.028***|0.006|0.006|-0.015*|0.032***|-0.011|0.064***|0.028|
||(0.005)|(0.006)|(0.009)|(0.009)|(0.010)|(0.016)|(0.020)|(0.022)|
|hhmuslim|-0.026***|0.016|0.036*|-0.021|-0.023***|-0.035***|0.015|-0.002|
||(0.009)|(0.012)|(0.019)|(0.015)|(0.006)|(0.011)|(0.014)|(0.014)|
|hhchristian|-0.008|-0.006|0.057**|0.044*|-0.035***|0.030|-0.021|0.020|
||(0.012)|(0.017)|(0.023)|(0.025)|(0.010)|(0.018)|(0.028)|(0.025)|
|hhrelother|-0.001|-0.030*|-0.015|-0.001|0.003|-0.010|0.021|-0.015|
||(0.013)|(0.017)|(0.027)|(0.024)|(0.013)|(0.020)|(0.040)|(0.027)|
|hhedsome|-0.009|0.017*|-0.024**|-0.020**|-0.000|-0.003|-0.012|0.010|
||(0.006)|(0.010)|(0.011)|(0.010)|(0.008)|(0.018)|(0.019)|(0.018)|
|hhedprim|-0.004|-0.003|0.001|0.007|0.025***|0.025|-0.001|0.005|
||(0.006)|(0.010)|(0.012)|(0.012)|(0.009)|(0.018)|(0.020)|(0.018)|
|hhedmid|-0.006|0.022*|-0.028*|-0.018|0.020**|0.044**|0.010|0.001|
||(0.008)|(0.012)|(0.016)|(0.015)|(0.009)|(0.018)|(0.021)|(0.019)|
|hhedsec|-0.015|0.038***|-0.033|-0.019|0.021*|0.022|0.013|0.022|
||(0.010)|(0.014)|(0.021)|(0.019)|(0.011)|(0.020)|(0.025)|(0.023)|
|hhedpostsec|-0.017|0.053**|-0.021|-0.022|0.041***|0.079***|0.038|0.066**|
||(0.017)|(0.024)|(0.037)|(0.032)|(0.014)|(0.025)|(0.033)|(0.030)|
|hhcasual|0.017**|0.022***|-0.016|-0.016|0.007|-0.013|-0.034**|-0.021|
||(0.007)|(0.009)|(0.014)|(0.012)|(0.007)|(0.012)|(0.015)|(0.015)|
|hhother|0.010|-0.056***|-0.009|-0.003|0.001|0.039***|-0.041***|-0.039***|
||(0.009)|(0.012)|(0.018)|(0.017)|(0.006)|(0.009)|(0.012)|(0.012)|
|meana<br>school|-0.001|-0.010***|-0.003|-0.001|-0.003**|-0.006***|-0.003*|-0.002|
||(0.001)|(0.001)|(0.002)|(0.002)|(0.001)|(0.002)|(0.002)|(0.002)|
|lnrmpce|-0.942*|0.524|0.884|0.993|-1.718***|-0.800|0.523|0.418|
||(0.482)|(0.585)|(1.284)|(1.108)|(0.536)|(0.883)|(1.380)|(1.235)|
|lnrmpce2|0.161*|-0.096|-0.159|-0.163|0.287***|0.131|-0.100|-0.092|
||(0.086)|(0.103)|(0.239)|(0.204)|(0.089)|(0.146)|(0.237)|(0.209)|
|lnrmpce3|-0.009*|0.006|0.010|0.009|-0.016***|-0.007|0.006|0.006|
||(0.005)|(0.006)|(0.015)|(0.013)|(0.005)|(0.008)|(0.013)|(0.012)|
|1.hh<br>~~n~~ic2|0.030|0.187***|-0.400***|0.290***|0.060|0.052|-0.133|0.039|
||(0.040)|(0.072)|(0.137)|(0.068)|(0.051)|(0.096)|(0.263)|(0.137)|
|2.hh<br>~~n~~ic2|0.131***|0.071|0.090|0.232***|0.013|0.196|-0.157|0.041|
||(0.035)|(0.049)|(0.081)|(0.073)|(0.047)|(0.145)|(0.232)|(0.129)|
|3.hh<br>~~n~~ic2|-0.037***|0.015|0.003|-0.006|0.091***|0.116***|-0.086*|-0.077|
||(0.009)|(0.016)|(0.013)|(0.012)|(0.024)|(0.044)|(0.048)|(0.058)|
|4.hh<br>~~n~~ic2|0.059<br>|0.102<br>|0.137<br>|0.227<br>|||||
||(0.192)|(0.263)|(0.232)|(0.184)|||||
|Continued on nex|tpage||||||||
9

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|
|5.hh<br>~~n~~ic2|0.063|-0.144|-0.020|0.028|0.150***|-0.005|-0.025|-0.161|
||(0.055)|(0.091)|(0.099)|(0.130)|(0.057)|(0.120)|(0.251)|(0.156)|
|6.hh<br>~~n~~ic2|-0.022|-0.164*|-0.093|0.231|-0.026|0.094|0.007|0.029|
||(0.066)|(0.100)|(0.182)|(0.148)|(0.103)|(0.180)|(0.346)|(0.367)|
|10.hh<br>~~n~~ic2|0.078|0.046|0.040|0.133|0.052*|-0.063|0.035|-0.091|
||(0.062)|(0.125)|(0.120)|(0.194)|(0.029)|(0.067)|(0.072)|(0.063)|
|12.hh<br>~~n~~ic2|0.077|-0.102|0.133|0.154*|||||
||(0.060)|(0.106)|(0.082)|(0.087)|||||
|13.hh<br>~~n~~ic2|0.098|-0.083|0.242**|0.301**|-0.299|-0.022|0.081||
||(0.064)|(0.115)|(0.119)|(0.139)|(0.204)|(0.557)|(0.308)||
|15.hh<br>~~n~~ic2|0.106**|0.006|0.209**|0.214***|-0.038|0.267***|0.030|0.072|
||(0.041)|(0.069)|(0.094)|(0.071)|(0.035)|(0.081)|(0.162)|(0.100)|
|19.hh<br>~~n~~ic2|0.088|0.413*|-0.064|0.146|-0.008|-0.282|-0.084|-0.004|
||(0.105)|(0.235)|(0.143)|(0.193)|(0.050)|(0.196)|(0.097)|(0.092)|
|20.hh<br>~~n~~ic2|0.019|-0.000|0.014|0.221***|0.004|-0.129***|-0.052|-0.135**|
||(0.035)|(0.060)|(0.077)|(0.072)|(0.023)|(0.047)|(0.056)|(0.060)|
|21.hh<br>~~n~~ic2|0.038|-0.042|0.044|0.241**|0.015|0.207***|-0.101|0.082|
||(0.057)|(0.091)|(0.095)|(0.097)|(0.032)|(0.071)|(0.080)|(0.073)|
|22.hh<br>~~n~~ic2|-0.005|0.103|0.109|0.211**|-0.026|-0.061|0.006|-0.289|
||(0.060)|(0.103)|(0.097)|(0.097)|(0.045)|(0.092)|(0.180)|(0.195)|
|23.hh<br>~~n~~ic2|0.050|0.052|0.111|0.112|-0.025|-0.218***|-0.057|-0.083|
||(0.040)|(0.061)|(0.086)|(0.106)|(0.025)|(0.048)|(0.061)|(0.061)|
|24.hh<br>~~n~~ic2|0.067|0.166***|0.169**|0.293***|-0.038|-0.118*|-0.133*|-0.124*|
||(0.041)|(0.064)|(0.085)|(0.097)|(0.029)|(0.063)|(0.070)|(0.064)|
|26.hh<br>~~n~~ic2|0.094**|0.103|0.040|0.170|-0.004|-0.029|-0.055|-0.083|
||(0.048)|(0.078)|(0.103)|(0.107)|(0.030)|(0.068)|(0.079)|(0.074)|
|27.hh<br>~~n~~ic2|0.087**|0.037|0.012|0.090|0.020|-0.148**|0.016|-0.086|
||(0.040)|(0.061)|(0.073)|(0.097)|(0.033)|(0.065)|(0.088)|(0.063)|
|28.hh<br>~~n~~ic2|0.026|-0.279|||-0.085**|0.116*|0.021|-0.097|
||(0.187)|(0.329)|||(0.035)|(0.069)|(0.083)|(0.103)|
|29.hh<br>~~n~~ic2|0.067|0.088|-0.100|0.241|-0.079**|-0.032|0.181**|0.289**|
||(0.071)|(0.183)|(0.301)|(0.177)|(0.038)|(0.078)|(0.092)|(0.117)|
|30.hh<br>~~n~~ic2|0.086*|0.047|-0.028|0.032|0.023|0.001|-0.031|0.000|
||(0.046)|(0.074)|(0.157)|(0.126)|(0.020)|(0.046)|(0.057)|(0.051)|
|31.hh<br>~~n~~ic2|0.104|0.062|0.073||-0.063**|-0.011|-0.075|0.123|
||(0.094)|(0.161)|(0.159)||(0.028)|(0.067)|(0.072)|(0.086)|
|32.hh<br>~~n~~ic2|0.061*|-0.120**|0.122*|0.107|0.014|-0.101|-0.047|-0.116|
||(0.033)|(0.056)|(0.073)|(0.076)|(0.027)|(0.074)|(0.081)|(0.111)|
|33.hh<br>~~n~~ic2|0.025|0.154|-0.129|0.144|-0.032|0.097|-0.095|-0.147**|
||(0.054)|(0.127)|(0.097)|(0.089)|(0.027)|(0.082)|(0.064)|(0.061)|
|34.hh<br>~~n~~ic2|0.068|0.166**|-0.045|0.084|0.025|0.188***|-0.112*|-0.058|
||(0.051)|(0.076)|(0.094)|(0.111)|(0.027)|(0.055)|(0.063)|(0.055)|
|35.hh<br>~~n~~ic2|0.141**|0.199|-0.052|-0.010|-0.013|0.023|0.034|-0.064|
||(0.065)|(0.121)|(0.170)|(0.186)|(0.028)|(0.058)|(0.108)|(0.087)|
|36.hh<br>~~n~~ic2|0.156|0.215|-0.027|0.110|0.017|0.117*|-0.033|-0.088|
||(0.098)|(0.199)|(0.123)|(0.286)|(0.030)|(0.063)|(0.078)|(0.073)|
|37.hh<br>~~n~~ic2|-0.003|-0.137|||-0.032|0.107|-0.003|0.099|
||(0.099)|(0.142)|||(0.041)|(0.077)|(0.083)|(0.106)|
|38.hh<br>~~n~~ic2|0.030<br>|0.158**<br>|0.087<br>|0.210**<br>|-0.023<br>|-0.029<br>|-0.056<br>|0.127*<br>|
||(0.058)|(0.080)|(0.092)|(0.098)|(0.025)|(0.051)|(0.067)|(0.068)|
|Continued on ne|xtpage||||||||
10

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|
|39.hh<br>~~n~~ic2|0.083|0.229***|0.145|0.356***|-0.053|-0.051|-0.129*|-0.046|
||(0.064)|(0.084)|(0.125)|(0.131)|(0.034)|(0.072)|(0.074)|(0.079)|
|40.hh<br>~~n~~ic2|0.161***|0.181***|0.076|0.218***|-0.015|-0.096|-0.084|-0.049|
||(0.045)|(0.065)|(0.086)|(0.079)|(0.031)|(0.060)|(0.079)|(0.070)|
|41.hh<br>~~n~~ic2|-0.131|-0.169|-0.040||-0.047|0.118|-0.116|-0.167|
||(0.151)|(0.201)|(0.207)||(0.060)|(0.190)|(0.140)|(0.133)|
|42.hh<br>~~n~~ic2|0.126*|0.158|0.060|0.144|0.074|-0.187|-0.212|-0.130|
||(0.066)|(0.118)|(0.093)|(0.136)|(0.060)|(0.240)|(0.138)|(0.164)|
|43.hh<br>~~n~~ic2|0.037|0.310|||||||
||(0.238)|(0.318)|||||||
|50.hh<br>~~n~~ic2|0.071***|-0.000|0.015|0.152**|-0.011|-0.040|-0.026|0.015|
||(0.026)|(0.038)|(0.060)|(0.061)|(0.020)|(0.040)|(0.053)|(0.050)|
|51.hh<br>~~n~~ic2|0.054|0.054|-0.011|0.131*|-0.075***|-0.140***|-0.056|-0.038|
||(0.034)|(0.056)|(0.069)|(0.069)|(0.023)|(0.051)|(0.056)|(0.056)|
|60.hh<br>~~n~~ic2|0.033|-0.012|0.106|0.119|0.003|-0.036|-0.099*|-0.032|
||(0.042)|(0.060)|(0.107)|(0.085)|(0.022)|(0.051)|(0.055)|(0.054)|
|61.hh<br>~~n~~ic2|-0.044|-0.027|0.192|-0.076|-0.063|0.116|-0.028|0.198|
||(0.058)|(0.087)|(0.125)|(0.152)|(0.044)|(0.114)|(0.258)|(0.322)|
|62.hh<br>~~n~~ic2|0.051|-0.209|||0.064|-0.051||-0.161*|
||(0.258)|(0.345)|||(0.049)|(0.088)||(0.093)|
|63.hh<br>~~n~~ic2|0.037|0.027|-0.053||-0.019|-0.217***|-0.106|0.042|
||(0.058)|(0.080)|(0.150)||(0.032)|(0.058)|(0.065)|(0.071)|
|64.hh<br>~~n~~ic2|0.013|0.260|0.227|0.166|-0.024|0.152|-0.083|-0.259**|
||(0.121)|(0.240)|(0.443)|(0.185)|(0.044)|(0.115)|(0.109)|(0.119)|
|65.hh<br>~~n~~ic2|0.058**|0.068*|0.143*|0.122*|0.013|0.026|-0.039|-0.007|
||(0.030)|(0.041)|(0.079)|(0.071)|(0.019)|(0.037)|(0.051)|(0.048)|
|66.hh<br>~~n~~ic2|0.054|0.016|0.135|0.156*|-0.006|0.058|-0.122**|-0.036|
||(0.040)|(0.068)|(0.086)|(0.082)|(0.022)|(0.054)|(0.061)|(0.057)|
|67.hh<br>~~n~~ic2|0.096*|0.044|0.057|0.061|-0.012|0.013|-0.018|-0.059|
||(0.050)|(0.093)|(0.109)|(0.118)|(0.021)|(0.053)|(0.056)|(0.050)|
|68.hh<br>~~n~~ic2|0.015|0.098*|0.172**|0.144**|-0.007|0.087**|-0.019|0.023|
||(0.034)|(0.051)|(0.085)|(0.070)|(0.020)|(0.041)|(0.054)|(0.050)|
|69.hh<br>~~n~~ic2|0.028|-0.033|0.196*|0.200**|-0.005|-0.089*|0.011|-0.087|
||(0.049)|(0.071)|(0.117)|(0.096)|(0.024)|(0.053)|(0.061)|(0.053)|
|70.hh<br>~~n~~ic2|0.026|0.112***|0.101*|0.138**|-0.006|-0.004|-0.035|-0.013|
||(0.030)|(0.043)|(0.055)|(0.065)|(0.018)|(0.036)|(0.052)|(0.047)|
|71.hh<br>~~n~~ic2|-0.189|0.028|||0.106*|-0.314**|-0.092|-0.095|
||(0.134)|(0.129)|||(0.064)|(0.134)|(0.108)|(0.084)|
|72.hh<br>~~n~~ic2|0.122|-0.071|-0.438|0.485**|-0.053|-0.108|0.130||
||(0.232)|(0.311)|(0.330)|(0.233)|(0.123)|(0.392)|(0.161)||
|73.hh<br>~~n~~ic2|0.054|0.261*|-0.069|0.474**|-0.016|-0.030|0.081|-0.086|
||(0.086)|(0.149)|(0.131)|(0.222)|(0.037)|(0.083)|(0.074)|(0.074)|
|74.hh<br>~~n~~ic2|-0.017|-0.221||0.099|0.062|-0.104|-0.132|-0.170*|
||(0.323)|(0.224)||(0.214)|(0.061)|(0.116)|(0.191)|(0.099)|
|75.hh<br>~~n~~ic2|0.025|0.094|0.069|0.114|-0.002|0.018|0.038|-0.055|
||(0.054)|(0.079)|(0.101)|(0.100)|(0.027)|(0.057)|(0.070)|(0.106)|
|80.hh<br>~~n~~ic2|0.096**|0.134**|0.025|0.105|-0.046**|-0.009|-0.115**|-0.165***|
||(0.043)|(0.063)|(0.093)|(0.080)|(0.020)|(0.039)|(0.056)|(0.049)|
|81.hh<br>~~n~~ic2|0.075<br>|-0.083<br>|0.053<br>||-0.017<br>|-0.051<br>|-0.107<br>|-0.148**<br>|
||(0.191)|(0.300)|(0.240)||(0.032)|(0.084)|(0.088)|(0.073)|
|Continued on ne|xtpage||||||||
11

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-14|Male 6-14|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|
|82.hh<br>~~n~~ic2|0.007|-0.225|||0.026|-0.012|-0.073|-0.175**|
||(0.210)|(0.278)|||(0.043)|(0.100)|(0.101)|(0.088)|
|83.hh<br>~~n~~ic2|0.207|0.022|||0.069|-0.058|-0.104|-0.141|
||(0.501)|(0.546)|||(0.067)|(0.124)|(0.111)|(0.102)|
|84.hh<br>~~n~~ic2|-0.048|-0.061||-0.038|-0.099|0.269||0.293|
||(0.075)|(0.149)||(0.170)|(0.107)|(0.574)||(0.206)|
|85.hh<br>~~n~~ic2|-0.088|0.189|0.018||-0.012|0.013||-0.103|
||(0.092)|(0.239)|(0.146)||(0.049)|(0.086)||(0.077)|
|89.hh<br>~~n~~ic2|0.042|0.188|||-0.047*|-0.116**|-0.080|-0.185***|
||(0.088)|(0.121)|||(0.027)|(0.057)|(0.100)|(0.064)|
|90.hh<br>~~n~~ic2|0.053**|0.085***|0.046|0.133**|-0.022|-0.005|-0.015|-0.057|
||(0.024)|(0.033)|(0.057)|(0.062)|(0.017)|(0.032)|(0.050)|(0.044)|
|91.hh<br>~~n~~ic2|0.141|-0.064|||-0.004|-0.252***|-0.051|-0.046|
||(0.217)|(0.263)|||(0.039)|(0.090)|(0.097)|(0.104)|
|92.hh<br>~~n~~ic2|0.049|0.139***|0.062|0.102|-0.003|-0.028|0.309***|0.027|
||(0.035)|(0.049)|(0.089)|(0.076)|(0.025)|(0.047)|(0.072)|(0.061)|
|93.hh<br>~~n~~ic2|0.030|0.198***|0.059|0.090|-0.012|-0.007|-0.157**|-0.120*|
||(0.050)|(0.070)|(0.094)|(0.106)|(0.032)|(0.055)|(0.075)|(0.064)|
|94.hh<br>~~n~~ic2|-0.018|0.142*|0.072|0.143|-0.027|0.006|-0.059|-0.018|
||(0.052)|(0.075)|(0.150)|(0.107)|(0.032)|(0.071)|(0.101)|(0.068)|
|95.hh<br>~~n~~ic2|-0.169|-0.357|-0.111|0.113|-0.082**|0.005|-0.101|0.036|
||(0.150)|(0.218)|(0.120)|(0.157)|(0.035)|(0.074)|(0.093)|(0.092)|
|96.hh<br>~~n~~ic2|-0.007|-0.006|0.064|0.145*|-0.012|-0.099**|-0.072|-0.048|
||(0.030)|(0.045)|(0.068)|(0.080)|(0.020)|(0.039)|(0.059)|(0.051)|
|97.hh<br>~~n~~ic2|0.012|-0.044|0.071|0.287***|-0.018|-0.042|-0.016|0.006|
||(0.040)|(0.064)|(0.076)|(0.096)|(0.023)|(0.047)|(0.059)|(0.055)|
|99.hh<br>~~n~~ic2|0.045*|0.176***|-0.087|0.122**|0.014|0.015|-0.064|-0.101*|
||(0.027)|(0.042)|(0.063)|(0.062)|(0.024)|(0.050)|(0.065)|(0.055)|
|100.hh<br>~~n~~ic2|0.116**|0.117|0.143|0.149|0.002|-0.033|0.038|-0.041|
||(0.055)|(0.081)|(0.106)|(0.131)|(0.036)|(0.073)|(0.098)|(0.333)|
|1.hh<br>~~n~~co2|-0.022|-0.117|-0.078|-0.082|0.003|-0.067|-0.143|-0.201|
||(0.062)|(0.101)|(0.210)|(0.211)|(0.057)|(0.105)|(0.252)|(0.209)|
|2.hh<br>~~n~~co2|-0.042|0.043|-0.093|-0.182*|0.001|-0.004|0.091|0.017|
||(0.053)|(0.067)|(0.117)|(0.095)|(0.028)|(0.047)|(0.078)|(0.071)|
|3.hh<br>~~n~~co2|-0.101***|-0.128***|-0.106|-0.091|0.027|0.067|0.029|0.099|
||(0.033)|(0.045)|(0.065)|(0.068)|(0.029)|(0.049)|(0.070)|(0.066)|
|4.hh<br>~~n~~co2|-0.098|-0.248|-0.111|-0.295|||||
||(0.284)|(0.380)|(0.255)|(0.259)|||||
|5.hh<br>~~n~~co2|-0.067|-0.253|-0.010|0.005|0.061|0.106|0.356***|-0.123|
||(0.129)|(0.173)|(0.341)|(0.347)|(0.071)|(0.117)|(0.117)|(0.148)|
|6.hh<br>~~n~~co2|-0.083|-0.061|-0.011|-0.128|0.033|-0.051|-0.208||
||(0.105)|(0.143)|(0.275)|(0.193)|(0.107)|(0.246)|(0.139)||
|7.hh<br>~~n~~co2|0.091|-0.078|-0.058|-0.091|-0.032|-0.017|0.176**|-0.032|
||(0.064)|(0.082)|(0.128)|(0.146)|(0.035)|(0.061)|(0.080)|(0.071)|
|8.hh<br>~~n~~co2|-0.064|-0.225***|-0.033|-0.116|-0.003|0.058|0.220**|-0.002|
||(0.057)|(0.081)|(0.133)|(0.140)|(0.034)|(0.057)|(0.096)|(0.076)|
|9.hh<br>~~n~~co2|-0.059<br>|-0.066<br>|0.243*<br>|-0.185<br>|0.004<br>|0.009<br>|0.111<br>|-0.088<br>|
||(0.048)|(0.082)|(0.130)|(0.124)|(0.047)|(0.096)|(0.228)|(0.275)|
|10.hh<br>~~n~~co2|-0.138<br>|-0.145<br>|-0.012<br>||-0.016<br>|-0.015<br>|0.026<br>|-0.107<br>|
||(0.116)|(0.139)|(0.572)||(0.056)|(0.102)|(0.103)|(0.136)|
|Continued on ne|xtpage||||||||
12

**Table S. 3 – continued from previous page**

|Sector<br>|Rural<br>||||Urban<br>|||<br>|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-14|Male 6-14|Female 15+|Male 15+|Female 6-14|<br>Male 6-14|
|11.hh<br>~~n~~co2|0.214***|-0.087|||||||
||(0.062)|(0.083)|||||||
|12.hh<br>~~n~~co2|-0.094|-0.008|0.103|-0.163|0.040|0.034|0.189***|0.082|
||(0.108)|(0.185)|(0.334)|(0.300)|(0.025)|(0.044)|(0.072)|(0.077)|
|13.hh<br>~~n~~co2|-0.041|-0.171*|-0.124|-0.242**|0.041|0.076|0.065|-0.161|
||(0.076)|(0.096)|(0.178)|(0.101)|(0.050)|(0.090)|(0.153)|(0.123)|
|14.hh<br>~~n~~co2|0.102|0.154|||-0.004|0.063|0.125|-0.043|
||(0.476)|(0.511)|||(0.062)|(0.106)|(0.098)|(0.093)|
|15.hh<br>~~n~~co2|-0.047|-0.118**|-0.015|-0.106|0.039|0.036|-0.066|-0.020|
||(0.037)|(0.049)|(0.095)|(0.080)|(0.027)|(0.048)|(0.080)|(0.069)|
|17.hh<br>~~n~~co2|0.024|-0.089|-0.168|-0.331**|-0.000|-0.036|0.188|-0.104|
||(0.133)|(0.153)|(0.140)|(0.162)|(0.037)|(0.063)|(0.119)|(0.115)|
|18.hh<br>~~n~~co2|0.291|0.232|0.057|0.181|0.135**|-0.059|0.092|-0.040|
||(0.199)|(0.237)|(0.689)|(0.203)|(0.055)|(0.104)|(0.131)|(0.219)|
|19.hh<br>~~n~~co2|-0.017|-0.114|-0.058|-0.204*|0.084**|-0.039|0.053|0.013|
||(0.057)|(0.074)|(0.141)|(0.112)|(0.040)|(0.060)|(0.097)|(0.086)|
|20.hh<br>~~n~~co2|-0.308**|-0.213|-0.107|-0.148|||||
||(0.150)|(0.342)|(0.233)|(0.149)|||||
|21.hh<br>~~n~~co2|-0.102*|-0.031|-0.023|-0.136|0.054|0.101**|0.099|-0.082|
||(0.054)|(0.074)|(0.102)|(0.096)|(0.033)|(0.051)|(0.071)|(0.078)|
|22.hh<br>~~n~~co2|-0.049|-0.153*||0.392**|-0.046|-0.074|0.054|-0.064|
||(0.062)|(0.079)||(0.168)|(0.031)|(0.058)|(0.275)|(0.074)|
|23.hh<br>~~n~~co2|-0.047|-0.000|||0.076**|0.142***|0.185**|-0.008|
||(0.109)|(0.133)|||(0.031)|(0.051)|(0.093)|(0.072)|
|24.hh<br>~~n~~co2|-0.070|0.184***|-0.011|0.402**|0.044*|0.118***|0.067|-0.118*|
||(0.046)|(0.064)|(0.096)|(0.199)|(0.023)|(0.040)|(0.060)|(0.067)|
|25.hh<br>~~n~~co2|-0.070|-0.154**|0.033|-0.154|-0.018|0.195***|0.383***|0.099|
||(0.060)|(0.075)|(0.160)|(0.100)|(0.029)|(0.053)|(0.096)|(0.081)|
|26.hh<br>~~n~~co2|-0.030|-0.106|-0.294*|-0.279**|-0.028|0.009|0.074|0.021|
||(0.070)|(0.094)|(0.172)|(0.140)|(0.030)|(0.051)|(0.100)|(0.068)|
|30.hh<br>~~n~~co2|-0.099**|-0.127**|-0.078|-0.220**|-0.013|0.049|0.049|-0.049|
||(0.040)|(0.058)|(0.081)|(0.089)|(0.021)|(0.038)|(0.062)|(0.055)|
|31.hh<br>~~n~~co2|-0.037|-0.064|-0.049|-0.083|-0.042|0.033|0.095|-0.088|
||(0.040)|(0.053)|(0.079)|(0.084)|(0.049)|(0.072)|(0.107)|(0.100)|
|32.hh<br>~~n~~co2|-0.180|-0.079|-0.132|-0.017|0.050|0.036|0.104|-0.001|
||(0.126)|(0.124)|(0.739)|(0.387)|(0.040)|(0.069)|(0.076)|(0.073)|
|33.hh<br>~~n~~co2|-0.059|-0.080|-0.061|-0.105|0.038|0.041|0.131*|0.089|
||(0.058)|(0.076)|(0.176)|(0.113)|(0.026)|(0.042)|(0.067)|(0.061)|
|34.hh<br>~~n~~co2|-0.056|-0.183|-0.113|-0.179|0.070*|0.104|0.085|0.050|
||(0.096)|(0.114)|(0.100)|(0.135)|(0.041)|(0.074)|(0.134)|(0.129)|
|35.hh<br>~~n~~co2|-0.068**|-0.089**|-0.037|-0.154**|-0.018|0.009|0.136**|-0.019|
||(0.030)|(0.041)|(0.070)|(0.073)|(0.019)|(0.034)|(0.056)|(0.052)|
|36.hh<br>~~n~~co2|-0.059|-0.248***|0.011|-0.172|0.012|-0.013|0.119|-0.032|
||(0.059)|(0.070)|(0.117)|(0.109)|(0.032)|(0.059)|(0.115)|(0.171)|
|37.hh<br>~~n~~co2|0.062|-0.204***|-0.127|-0.139|-0.018|0.075|0.123|-0.158**|
||(0.051)|(0.069)|(0.081)|(0.097)|(0.030)|(0.050)|(0.080)|(0.074)|
|38.hh<br>~~n~~co2|-0.083<br>|-0.114<br>|-0.081<br>|0.014<br>|-0.010<br>|0.012<br>|-0.013<br>|-0.092<br>|
||(0.057)|(0.075)|(0.546)|(0.091)|(0.045)|(0.069)|(0.151)|(0.123)|
|39.hh<br>~~n~~co2|-0.008<br>|-0.364***<br>|0.023<br>|0.053<br>|-0.008<br>|0.086<br>||-0.117<br>|
||(0.091)|(0.139)|(0.139)|(0.217)|(0.051)|(0.080)||(0.132)|
|Continued on ne|xtpage||||||||
13

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|
|40.hh<br>~~n~~co2|-0.036|-0.125***|-0.102|-0.108|-0.001|-0.054|0.096*|-0.054|
||(0.031)|(0.042)|(0.085)|(0.075)|(0.019)|(0.035)|(0.056)|(0.054)|
|41.hh<br>~~n~~co2|-0.053|0.010|0.024|-0.192*|-0.032|-0.060|0.088|-0.075|
||(0.053)|(0.073)|(0.114)|(0.117)|(0.030)|(0.046)|(0.077)|(0.061)|
|42.hh<br>~~n~~co2|-0.212|-0.263||0.155|-0.086|0.042||-0.237|
||(0.277)|(0.371)||(0.262)|(0.084)|(0.094)||(0.488)|
|43.hh<br>~~n~~co2|-0.044|-0.074|-0.092|-0.094|0.030|-0.012|0.147**|-0.041|
||(0.034)|(0.045)|(0.088)|(0.078)|(0.020)|(0.036)|(0.059)|(0.056)|
|44.hh<br>~~n~~co2|-0.151*|-0.094|0.118|0.033|0.018|0.005|0.059|0.127|
||(0.090)|(0.113)|(0.174)|(0.125)|(0.038)|(0.066)|(0.094)|(0.105)|
|45.hh<br>~~n~~co2|0.015|-0.171||0.071|0.116**|0.120|0.115|0.110|
||(0.139)|(0.187)||(0.189)|(0.055)|(0.094)|(0.128)|(0.268)|
|49.hh<br>~~n~~co2|-0.098|0.010|-0.075|-0.178|0.160***|-0.136*|0.028|-0.156|
||(0.063)|(0.084)|(0.122)|(0.121)|(0.045)|(0.076)|(0.093)|(0.196)|
|50.hh<br>~~n~~co2|0.023|-0.060|-0.101|-0.089|0.040|0.271***|-0.008|-0.056|
||(0.059)|(0.078)|(0.143)|(0.110)|(0.031)|(0.048)|(0.078)|(0.063)|
|51.hh<br>~~n~~co2|-0.004|-0.072|||0.071|-0.005||0.045|
||(0.107)|(0.169)|||(0.088)|(0.141)||(0.225)|
|52.hh<br>~~n~~co2|-0.076|-0.058|-0.064|-0.183*|0.029|-0.004|0.091|0.058|
||(0.050)|(0.065)|(0.089)|(0.103)|(0.029)|(0.047)|(0.093)|(0.084)|
|53.hh<br>~~n~~co2|0.015|-0.074|0.069|-0.341**|0.056*|0.092|0.047|-0.066|
||(0.049)|(0.086)|(0.112)|(0.139)|(0.032)|(0.063)|(0.079)|(0.076)|
|54.hh<br>~~n~~co2|-0.066|-0.063|-0.071|-0.179**|0.022|0.070|0.026|-0.041|
||(0.047)|(0.059)|(0.081)|(0.081)|(0.026)|(0.047)|(0.063)|(0.067)|
|55.hh<br>~~n~~co2|0.028|0.029|-0.103|-0.169|0.117***|0.047|0.152|0.064|
||(0.046)|(0.067)|(0.155)|(0.132)|(0.033)|(0.055)|(0.105)|(0.093)|
|56.hh<br>~~n~~co2|-0.023|-0.166***|-0.128|-0.212**|0.012|-0.011|0.410***|-0.059|
||(0.040)|(0.054)|(0.085)|(0.095)|(0.034)|(0.056)|(0.091)|(0.068)|
|57.hh<br>~~n~~co2|-0.086**|-0.127***|-0.046|-0.147*|-0.006|0.055|0.099*|-0.032|
||(0.035)|(0.047)|(0.085)|(0.079)|(0.020)|(0.036)|(0.058)|(0.053)|
|59.hh<br>~~n~~co2|0.005|-0.072|-0.129|-0.223*|0.024|0.009|0.167|0.030|
||(0.045)|(0.055)|(0.097)|(0.115)|(0.034)|(0.060)|(0.133)|(0.078)|
|60.hh<br>~~n~~co2|-0.036|-0.211***|-0.095|-0.067|-0.011|0.102|-0.026|-0.075|
||(0.045)|(0.065)|(0.177)|(0.096)|(0.047)|(0.091)|(0.147)|(0.106)|
|61.hh<br>~~n~~co2|0.012|-0.017|0.023|-0.006|0.048***|0.132***|0.098**|-0.059|
||(0.016)|(0.025)|(0.036)|(0.031)|(0.015)|(0.028)|(0.041)|(0.042)|
|62.hh<br>~~n~~co2|-0.066*|-0.005|0.305***|-0.202**|0.075*|0.066|0.223|-0.060|
||(0.038)|(0.051)|(0.101)|(0.084)|(0.045)|(0.082)|(0.230)|(0.149)|
|63.hh<br>~~n~~co2|0.012|-0.006|0.023|0.023|0.152***|0.259***|0.082*|-0.052|
||(0.016)|(0.026)|(0.037)|(0.032)|(0.019)|(0.033)|(0.045)|(0.049)|
|64.hh<br>~~n~~co2|-0.016|-0.096|-0.048|-0.338*||-0.005|||
||(0.096)|(0.113)|(0.164)|(0.173)||(0.244)|||
|65.hh<br>~~n~~co2|-0.038|-0.178***|-0.101|-0.172***|-0.000|0.175***|0.056|0.016|
||(0.033)|(0.047)|(0.078)|(0.061)|(0.038)|(0.061)|(0.283)|(0.102)|
|66.hh<br>~~n~~co2|-0.076|0.089|-0.047|-0.118|-0.003|0.052|0.272|0.235|
||(0.056)|(0.076)|(0.110)|(0.129)|(0.079)|(0.127)|(0.303)|(0.304)|
|67.hh<br>~~n~~co2|-0.031|-0.020||-0.113*|0.000|0.364|||
||(0.062)|(0.078)||(0.068)|(0.544)|(0.699)|||
|68.hh<br>~~n~~co2|-0.039|0.066|0.070|-0.298**|0.018|-0.133|-0.003|-0.117|
||(0.067)|(0.092)|(0.187)|(0.149)|(0.099)|(0.149)|(0.341)|(0.356)|
|Continued on nex|tpage||||||||
14

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|
|71.hh<br>~~n~~co2|-0.093**|-0.041|-0.228**|-0.193**|0.003|0.186***|0.022|0.035|
||(0.043)|(0.058)|(0.098)|(0.078)|(0.033)|(0.052)|(0.078)|(0.071)|
|72.hh<br>~~n~~co2|0.010|-0.138|-0.005|-0.269**|0.001|0.061|0.129|0.145*|
||(0.066)|(0.087)|(0.169)|(0.114)|(0.034)|(0.056)|(0.080)|(0.085)|
|73.hh<br>~~n~~co2|-0.060|0.126|0.075|0.138|-0.075**|0.078|0.035|-0.020|
||(0.056)|(0.078)|(0.121)|(0.140)|(0.031)|(0.055)|(0.078)|(0.068)|
|74.hh<br>~~n~~co2|-0.035|-0.098|0.031|-0.123|0.073**|0.073|0.185**|0.051|
||(0.053)|(0.070)|(0.176)|(0.128)|(0.033)|(0.054)|(0.076)|(0.081)|
|75.hh<br>~~n~~co2|-0.048|-0.122**|-0.090|-0.250***|0.049*|0.241***|0.172**|0.066|
||(0.037)|(0.053)|(0.081)|(0.084)|(0.026)|(0.044)|(0.068)|(0.067)|
|76.hh<br>~~n~~co2|-0.135|-0.040|0.196||-0.052|-0.028|0.059|-0.094|
||(0.156)|(0.171)|(0.334)||(0.045)|(0.084)|(0.166)|(0.107)|
|77.hh<br>~~n~~co2|0.013|0.078|0.016|-0.285***|0.047**|0.158***|0.112*|-0.088|
||(0.041)|(0.055)|(0.088)|(0.083)|(0.023)|(0.040)|(0.061)|(0.061)|
|78.hh<br>~~n~~co2|-0.031|-0.085|-0.133|-0.208|0.121*|0.180*|0.117|0.325|
||(0.071)|(0.094)|(0.143)|(0.149)|(0.064)|(0.095)|(0.205)|(0.221)|
|79.hh<br>~~n~~co2|0.050|0.087*|-0.087|-0.145|0.055**|0.221***|0.157**|-0.007|
||(0.038)|(0.052)|(0.086)|(0.093)|(0.024)|(0.042)|(0.070)|(0.063)|
|80.hh<br>~~n~~co2|-0.024|0.065|0.099|-0.257|-0.005|0.006|-0.019|-0.229**|
||(0.064)|(0.091)|(0.214)|(0.160)|(0.028)|(0.051)|(0.068)|(0.091)|
|81.hh<br>~~n~~co2|-0.104**|-0.153***|-0.015|-0.096|-0.019|0.051|-0.011|0.080|
||(0.042)|(0.056)|(0.081)|(0.096)|(0.029)|(0.049)|(0.094)|(0.071)|
|82.hh<br>~~n~~co2|-0.045|0.007|-0.060|-0.221**|0.032|0.305**|-0.049|-0.190|
||(0.056)|(0.066)|(0.121)|(0.094)|(0.066)|(0.123)|(0.149)|(0.308)|
|83.hh<br>~~n~~co2|-0.056|-0.121*|0.143|-0.084|0.009|0.090*|0.143**|-0.017|
||(0.048)|(0.066)|(0.091)|(0.115)|(0.031)|(0.050)|(0.071)|(0.083)|
|84.hh<br>~~n~~co2|-0.032|-0.022|-0.053|-0.240**|0.008|0.109***|0.057|-0.105*|
||(0.047)|(0.062)|(0.096)|(0.105)|(0.023)|(0.040)|(0.065)|(0.063)|
|85.hh<br>~~n~~co2|-0.040|-0.123**|-0.062|-0.225***|0.038*|0.003|0.092|0.000|
||(0.037)|(0.048)|(0.074)|(0.083)|(0.022)|(0.039)|(0.063)|(0.061)|
|86.hh<br>~~n~~co2|0.044|-0.049|0.153|-0.088|-0.051|-0.079|0.233**|-0.226**|
||(0.223)|(0.385)|(0.311)|(0.825)|(0.056)|(0.096)|(0.108)|(0.110)|
|87.hh<br>~~n~~co2|-0.048|-0.110*|0.085|-0.206|-0.052|-0.035|0.090|0.017|
||(0.050)|(0.061)|(0.106)|(0.191)|(0.032)|(0.050)|(0.075)|(0.068)|
|88.hh<br>~~n~~co2|-0.019|-0.177**|-0.080|-0.237**|-0.004|0.117**|0.069|-0.101|
||(0.060)|(0.082)|(0.104)|(0.104)|(0.027)|(0.046)|(0.071)|(0.073)|
|89.hh<br>~~n~~co2|-0.023|-0.046|-0.097|-0.144|-0.137***|0.186**|0.144||
||(0.051)|(0.068)|(0.113)|(0.098)|(0.050)|(0.084)|(0.109)||
|90.hh<br>~~n~~co2|-0.092|-0.136|0.231*|0.056|0.055*|0.023|0.055|-0.195**|
||(0.077)|(0.103)|(0.136)|(0.113)|(0.033)|(0.057)|(0.070)|(0.085)|
|91.hh<br>~~n~~co2|-0.071|-0.209||-0.039|0.035|0.214**|0.146|0.038|
||(0.120)|(0.128)||(0.197)|(0.052)|(0.089)|(0.098)|(0.100)|
|93.hh<br>~~n~~co2|0.126*|0.031|0.151|0.055|0.122***|-0.010|0.071|-0.034|
||(0.071)|(0.095)|(0.193)|(0.140)|(0.034)|(0.052)|(0.084)|(0.077)|
|94.hh<br>~~n~~co2|0.036|-0.098*|-0.021|-0.161*|0.047|0.090|0.044|0.149|
||(0.043)|(0.056)|(0.083)|(0.095)|(0.044)|(0.074)|(0.213)|(0.113)|
|95.hh<br>~~n~~co2|-0.052*|0.140***|-0.013|-0.211***|0.037*|0.299***|0.089|-0.040|
||(0.030)|(0.041)|(0.069)|(0.069)|(0.022)|(0.039)|(0.060)|(0.059)|
|96.hh<br>~~n~~co2|-0.064<br>|-0.108<br>|0.632**<br>|-0.226*<br>|0.070<br>|0.264***<br>|0.161<br>|-0.193<br>|
||(0.060)|(0.085)|(0.264)|(0.121)|(0.052)|(0.085)|(0.146)|(0.179)|
|Continued on nex|tpage||||||||
15

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-14|<br>Male 6-14|
|97.hh<br>~~n~~co2|-0.065|-0.284***|-0.083|-0.160*|0.055**|0.141***|0.091|0.011|
||(0.062)|(0.082)|(0.173)|(0.097)|(0.025)|(0.043)|(0.071)|(0.063)|
|98.hh<br>~~n~~co2|-0.012|-0.074*|-0.092|-0.184**|0.041**|0.017|0.089|-0.054|
||(0.033)|(0.044)|(0.062)|(0.075)|(0.020)|(0.036)|(0.058)|(0.055)|
|99.hh<br>~~n~~co2|-0.042|0.006|0.058|-0.134**|0.012|0.192***|0.091|-0.019|
||(0.026)|(0.036)|(0.065)|(0.065)|(0.019)|(0.035)|(0.056)|(0.053)|
|100.hh<br>~~n~~co2|-0.111**|-0.035|-0.121|-0.122|0.014|0.024|-0.026|-0.086|
||(0.054)|(0.077)|(0.102)|(0.128)|(0.034)|(0.067)|(0.097)|(0.333)|
|age|0.025***|0.022***|-0.020|0.271***|0.033***|-0.003|-0.356***|0.214**|
||(0.002)|(0.003)|(0.085)|(0.079)|(0.002)|(0.005)|(0.112)|(0.106)|
|age2|-0.001***|-0.000***|0.002|-0.027***|-0.001***|0.000|0.037***|-0.021*|
||(0.000)|(0.000)|(0.009)|(0.008)|(0.000)|(0.000)|(0.011)|(0.011)|
|age3|0.000***|0.000***|-0.000|0.001***|0.000***|-0.000|-0.001***|0.001*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|edsome|-0.012*|0.000|0.009|-0.022*|0.002|-0.028|-0.052**|0.012|
||(0.006)|(0.011)|(0.012)|(0.012)|(0.008)|(0.020)|(0.020)|(0.021)|
|edprim|-0.013*|0.001|0.005|0.007|-0.000|-0.068***|-0.024|0.049**|
||(0.007)|(0.010)|(0.012)|(0.013)|(0.008)|(0.019)|(0.020)|(0.021)|
|edmid|-0.026***|-0.041***|-0.021|-0.023|0.008|-0.087***|0.005|0.062***|
||(0.008)|(0.011)|(0.015)|(0.015)|(0.008)|(0.019)|(0.022)|(0.024)|
|edsec|-0.029***|-0.057***|-0.045|-0.018|0.001|-0.078***|-0.011|0.047*|
||(0.010)|(0.012)|(0.028)|(0.025)|(0.009)|(0.019)|(0.029)|(0.028)|
|edpostsec|-0.070***|-0.075***|0.073||-0.011|-0.124***||-0.076|
||(0.021)|(0.020)|(0.175)||(0.012)|(0.023)||(0.224)|
|21.pr<br>~~s~~tatus|0.030***|0.028***|0.039|-0.063*|0.087***|0.043**|-0.495**|-0.940***|
||(0.009)|(0.009)|(0.060)|(0.036)|(0.022)|(0.017)|(0.197)|(0.310)|
|31.pr<br>~~s~~tatus|0.069***|-0.026**|0.253***|-0.039|-0.062***|-0.112***|0.367|-0.935***|
||(0.014)|(0.011)|(0.092)|(0.055)|(0.020)|(0.012)|(0.305)|(0.246)|
|41.pr<br>~~s~~tatus|0.095***|0.081***|0.049|-0.238**|0.194***|0.149***||-0.665**|
||(0.019)|(0.016)|(0.122)|(0.104)|(0.029)|(0.021)||(0.279)|
|51.pr<br>~~s~~tatus|0.122***|0.045***|0.206***|0.091**|0.031|0.039***|-0.235|-1.016***|
||(0.010)|(0.009)|(0.066)|(0.040)|(0.020)|(0.012)|(0.217)|(0.223)|
|81.pr<br>~~s~~tatus|-0.305***|-0.303***|-0.228|-0.451***|-0.404***|-0.442***|-1.117***|-1.558***|
||(0.058)|(0.021)|(0.146)|(0.055)|(0.033)|(0.027)|(0.249)|(0.348)|
|91.pr<br>~~s~~tatus|-0.296***|-0.326***|-0.248***|-0.361***|-0.468***|-0.447***|-1.031***|-1.463***|
||(0.014)|(0.013)|(0.058)|(0.035)|(0.025)|(0.023)|(0.227)|(0.345)|
|92.pr<br>~~s~~tatus|-0.147***|-0.196***|-0.115*|-0.176***|-0.354***|-0.379***|-0.992***|-1.483***|
||(0.009)|(0.024)|(0.059)|(0.041)|(0.023)|(0.034)|(0.227)|(0.347)|
|93.pr<br>~~s~~tatus|-0.131***|-0.205***|-0.110*|-0.167***|-0.347***|-0.331***|-0.984***|-1.331***|
||(0.010)|(0.024)|(0.059)|(0.039)|(0.026)|(0.061)|(0.237)|(0.357)|
|94.pr<br>~~s~~tatus|-0.131***|-0.282***|-0.319||-0.381***|-0.345***|-0.891***|-1.427***|
||(0.020)|(0.029)|(0.237)||(0.029)|(0.029)|(0.266)|(0.403)|
|95.pr<br>~~s~~tatus|-0.364***|-0.433***|-0.288***|-0.436***|-0.545***|-0.522***|-0.513*|-1.524***|
||(0.019)|(0.021)|(0.093)|(0.073)|(0.029)|(0.038)|(0.263)|(0.356)|
|97.pr<br>~~s~~tatus|-0.261***|-0.340***|-0.216***|-0.335***|-0.478***|-0.458***|-1.066***|-1.447***|
||(0.014)|(0.015)|(0.059)|(0.037)|(0.026)|(0.026)|(0.228)|(0.345)|
|1.pr<br>~~n~~ic2|0.142**|-0.121*||-0.339**|0.159|-0.036|||
||(0.060)|(0.067)||(0.144)|(0.120)|(0.176)|||
|2.pr<br>~~n~~ic2|-0.016<br>|-0.189***<br>|0.109**<br>|-0.103***<br>|-0.049<br>|-0.248*<br>|||
||(0.020)|(0.027)|(0.053)|(0.032)|(0.053)|(0.135)|||
|Continued on nex|tpage||||||||
16

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-1|4<br>Male 6-14|Female 15+|Male 15+|Female 6-14|<br>Male 6-14|
|3.pr<br>~~n~~ic2|0.026**|0.056***|-0.059|-0.010|-0.129***|-0.131***||0.273|
||(0.012)|(0.016)|(0.055)|(0.041)|(0.033)|(0.039)||(0.316)|
|4.pr<br>~~n~~ic2||-0.561*<br>|||||||
|||(0.313)|||||||
|5.pr<br>~~n~~ic2|0.082|0.177**|-0.035|0.461***|0.123|-0.335**|||
||(0.055)|(0.076)|(0.172)|(0.125)|(0.231)|(0.154)|||
|6.pr<br>~~n~~ic2|0.006|-0.152***|-0.313|-0.242*|-0.548***|-0.321***|||
||(0.103)|(0.056)|(0.221)|(0.125)|(0.096)|(0.116)|||
|10.pr<br>~~n~~ic2|0.214|0.019|||-0.036|-0.257***|||
||(0.152)|(0.140)|||(0.058)|(0.062)|||
|11.pr<br>~~n~~ic2|-0.117<br>|-0.193<br>|||||||
||(0.186)|(0.203)|||||||
|12.pr<br>~~n~~ic2|-0.475***<br>(0.158)|0.444***<br>(0.105)|||||||
|13.pr<br>~~n~~ic2|0.066|0.139|||-0.264|-0.298|||
||(0.093)|(0.096)|||(0.383)|(0.441)|||
|14.pr<br>~~n~~ic2||-0.332<br>(0.832)|||||||
|15.pr<br>~~n~~ic2|0.218***|0.208***|0.418*|0.509**|-0.251***|-0.150||-0.364|
||(0.045)|(0.055)|(0.247)|(0.244)|(0.054)|(0.092)||(0.362)|
|19.pr<br>~~n~~ic2|0.076|-0.465**|||-0.279***|0.256|||
||(0.143)|(0.226)|||(0.078)|(0.204)|||
|20.pr<br>~~n~~ic2|0.059|0.004|-0.056|-0.477***|-0.061|-0.004||-0.125|
||(0.068)|(0.053)|(0.167)|(0.137)|(0.051)|(0.045)||(0.218)|
|21.pr<br>~~n~~ic2|-0.086|-0.081|||-0.349***|-0.337***|-0.635**|-0.319|
||(0.069)|(0.092)|||(0.057)|(0.067)|(0.281)|(0.329)|
|22.pr<br>~~n~~ic2|0.002|-0.126|-0.262|-0.362*|-0.370***|-0.196***|-0.245||
||(0.040)|(0.082)|(0.250)|(0.214)|(0.039)|(0.066)|(0.188)||
|23.pr<br>~~n~~ic2|0.014|-0.019|-0.075|0.023|-0.088***|-0.031|-0.803***|-0.045|
||(0.040)|(0.042)|(0.145)|(0.089)|(0.030)|(0.036)|(0.226)|(0.274)|
|24.pr<br>~~n~~ic2|0.156***|0.029|0.120||-0.258***|-0.058|-0.720*|-1.148**|
||(0.044)|(0.045)|(0.126)||(0.060)|(0.054)|(0.383)|(0.472)|
|25.pr<br>~~n~~ic2||-0.504<br>|||-0.154<br>|-0.048<br>|||
|||(0.370)|||(0.152)|(0.146)|||
|26.pr<br>~~n~~ic2|0.027|-0.008||0.125|-0.298***|-0.222***|-0.184|0.035|
||(0.075)|(0.069)||(0.175)|(0.045)|(0.067)|(0.235)|(0.362)|
|27.pr<br>~~n~~ic2|-0.078*|-0.054||0.202|-0.148***|-0.024||-0.281|
||(0.043)|(0.042)||(0.137)|(0.056)|(0.057)||(0.267)|
|28.pr<br>~~n~~ic2|0.042|-0.293|||-0.300***|-0.420***|||
||(0.157)|(0.213)|||(0.095)|(0.057)|||
|29.pr<br>~~n~~ic2|-0.075<br>|-0.307<br>|||-0.344***<br>|-0.201***<br>|||
||(0.141)|(0.200)|||(0.040)|(0.064)|||
|30.pr<br>~~n~~ic2|-0.009|-0.172***|-0.013|0.250*|-0.388***|-0.167***|||
||(0.032)|(0.058)|(0.098)|(0.130)|(0.028)|(0.042)|||
|31.pr<br>~~n~~ic2|-0.511|-0.271***|||-0.428***|-0.270***|||
||(0.661)|(0.101)|||(0.062)|(0.061)|||
|32.pr<br>~~n~~ic2|-0.083*|-0.006|-0.163||-0.181**|-0.254***|-0.185|-0.002|
||(0.049)|(0.051)|(0.103)||(0.080)|(0.069)|(0.286)|(0.298)|
|33.pr<br>~~n~~ic2|-0.150|-0.218|-0.058|0.141|-0.327|-0.269***|||
||(0.205)|(0.146)|(0.230)|(0.252)|(0.441)|(0.083)|||
|Continued on nex|tpage||||||||
17

**Table S. 3 – continued from previous page**

|Sector|Rural||||Urban||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-14|Male 6-14|Female 15+|Male 15+|Female 6-14|Male 6-14|
|34.pr<br>~~n~~ic2|0.390**|-0.158**|-0.309*|-0.023|-0.276**|-0.238***||0.024|
||(0.168)|(0.062)|(0.183)|(0.181)|(0.135)|(0.047)||(0.277)|
|35.pr<br>~~n~~ic2|-0.221***<br>|-0.131<br>||0.224<br>|-0.400***<br>|-0.307***<br>|||
||(0.084)|(0.111)||(0.201)|(0.068)|(0.055)|||
|36.pr<br>~~n~~ic2||-0.220<br>|||-0.357***<br>|-0.339***<br>|||
|||(0.150)|||(0.079)|(0.063)|||
|37.pr<br>~~n~~ic2||-0.046||||-0.308***|||
|||(0.133)||||(0.091)|||
|38.pr<br>~~n~~ic2|0.023|-0.152***||-0.156|-0.350***|-0.062||-0.005|
||(0.069)|(0.034)||(0.130)|(0.085)|(0.042)||(0.346)|
|39.pr<br>~~n~~ic2|-0.119|-0.090||-0.215||-0.262***||-0.595|
||(0.127)|(0.089)||(0.388)||(0.060)||(0.468)|
|40.pr<br>~~n~~ic2|0.361|-0.315***|||-0.525***|-0.223***|||
||(0.234)|(0.072)|||(0.099)|(0.071)|||
|42.pr<br>~~n~~ic2||-0.271**||||0.054|||
|||(0.118)||||(0.258)|||
|43.pr<br>~~n~~ic2||-0.427*||||0.029|||
|||(0.252)||||(0.235)|||
|50.pr<br>~~n~~ic2|0.388***|0.281***|0.623***|0.382***|0.331***|0.181***|0.533**|0.659**|
||(0.029)|(0.024)|(0.145)|(0.097)|(0.027)|(0.030)|(0.261)|(0.308)|
|51.pr<br>~~n~~ic2|0.143*|0.053|||-0.147***|0.304***|||
||(0.079)|(0.053)|||(0.050)|(0.049)|||
|60<br>i2|0123|0283***|||0429***|0206***|||
|.pr<br>~~n~~c|-.|-.|||-.|-.|||
||(0.178)|(0.063)|||(0.094)|(0.050)|||
|61.pr<br>~~n~~ic2|0.080|-0.035|-0.104|-0.243||-0.443***|||
||(0.064)<br>|(0.033)<br>|(0.242)<br>|(0.260)||(0.107)<br>|||
|63.pr<br>~~n~~ic2|-0.021|-0.046|-0.090||-0.267***|-0.165***|||
||(0.039)|(0.032)|(0.181)||(0.060)|(0.058)|||
|64.pr<br>~~n~~ic2|-0.179|-0.144|||-0.609***|-0.445***|||
||(0.515)|(0.165)|||(0.163)|(0.116)|||
|65.pr<br>~~n~~ic2|-0.148***|-0.338***|-0.446***|-0.343*|-0.470***|-0.371***|-1.024***|-0.586***|
||(0.029)|(0.024)|(0.163)|(0.182)|(0.032)|(0.027)|(0.255)|(0.170)|
|66.pr<br>~~n~~ic2|-0.289***|-0.265***|||-0.547***|-0.361***||0.124|
||(0.090)|(0.068)|||(0.061)|(0.051)||(0.335)|
|67.pr<br>~~n~~ic2|-0.219**|-0.295***|||-0.293**|-0.341***|||
||(0.094)|(0.082)|||(0.130)|(0.049)|||
|68.pr<br>~~n~~ic2|-0.208***|-0.311***||-0.395**|-0.476***|-0.398***||-0.502*|
||(0.052)|(0.040)||(0.200)|(0.042)|(0.033)||(0.277)|
|69.pr<br>~~n~~ic2|-0.098*|-0.106**|-0.153|-0.238|-0.422***|-0.225***||-0.075|
||(0.056)|(0.048)|(0.196)|(0.156)|(0.046)|(0.044)||(0.277)|
|70.pr<br>~~n~~ic2|0.095<br>|-0.216***<br>|||-0.210***<br>|-0.187***<br>|||
||(0.083)|(0.028)|||(0.046)|(0.025)|||
|73.pr<br>~~n~~ic2|-0.534**|-0.304***|||-0.573***|-0.100|||
||(0.272)|(0.110)|||(0.122)|(0.078)|||
|74.pr<br>~~n~~ic2|0.004|0.268**|||-0.000|-0.508**|||
||(0.099)|(0.128)|||(0.352)|(0.251)|||
|75.pr<br>~~n~~ic2||-0.230***|||-0.472***|-0.293***|||
|||(0.063)|||(0.076)|(0.053)|||
|80.pr<br>~~n~~ic2|-0.196|-0.369***|||-0.387***|-0.270***|||
||(0.138)|(0.070)|||(0.042)|(0.034)|||
|Continued on nex|tpage||||||||
18

**Table S. 3 – continued from previous page**

|Sector<br>|Rural<br>||||Urban<br>||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Female 15+|Male 15+|Female 6-14|Male 6-14|Female 15+|Male 15+|Female 6-14|Male 6-14|
|81.pr<br>~~n~~ic2||-0.053|||-0.283***|-0.250***|||
|||(0.161)|||(0.108)|(0.090)|||
|83.pr<br>~~n~~ic2||-0.407***<br>|||-0.168<br>|-0.288***<br>||-0.363<br>|
|||(0.153)|||(0.142)|(0.079)||(0.669)|
|84.pr<br>~~n~~ic2||-0.053||0.362||-0.390|||
|||(0.200)||(0.228)||(0.552)|||
|85.pr<br>~~n~~ic2||-0.071|||-0.644***|-0.282***|||
|||(0.239)|||(0.204)|(0.084)|||
|89.pr<br>~~n~~ic2|0.013|-0.333***|||-0.412***|-0.220***|||
||(0.258)|(0.116)|||(0.092)|(0.052)|||
|90.pr<br>~~n~~ic2|-0.264***<br>|-0.254***<br>|||-0.381***<br>|-0.271***<br>|||
||(0.030)|(0.025)|||(0.027)|(0.025)|||
|92.pr<br>~~n~~ic2|-0.307***|-0.266***|||-0.376***|-0.254***||-1.515***|
||(0.030)|(0.033)|||(0.027)|(0.040)||(0.444)|
|93.pr<br>~~n~~ic2|-0.295***|-0.389***|||-0.389***|-0.279***|||
||(0.057)|(0.058)|||(0.040)|(0.042)|||
|94.pr<br>~~n~~ic2|-0.179**|-0.274***|||-0.504***|-0.202***||-0.094|
||(0.073)|(0.063)|||(0.075)|(0.067)||(0.296)|
|95.pr<br>~~n~~ic2|-0.231|-0.004|||-0.452***|-0.295***|||
||(0.341)|(0.090)|||(0.093)|(0.063)|||
|96.pr<br>~~n~~ic2|-0.091***|-0.113***|-0.132|-0.308***|-0.208***|-0.068**|-0.546***|-0.062|
||(0.024)|(0.027)|(0.089)|(0.104)|(0.024)|(0.029)|(0.157)|(0.273)|
|97.pr<br>~~n~~ic2|-0.062|-0.144***|-0.047|-0.477***|-0.367***|-0.309***||0.060|
||(0.086)|(0.046)|(0.280)|(0.128)|(0.058)|(0.037)||(0.251)|
|99.pr<br>~~n~~ic2|0.021|0.008|0.731***|-0.112|-0.277***|-0.192***|-1.332***|-0.421|
||(0.057)|(0.035)|(0.155)|(0.178)|(0.033)|(0.041)|(0.311)|(0.269)|
|dumplough|0.161***|0.131***|0.119***|0.163***|||||
||(0.009)|(0.006)|(0.037)|(0.027)|||||
|dumsow|0.173***|0.076***|0.145***|0.067|||||
||(0.009)|(0.009)|(0.053)|(0.041)|||||
|dumweeding|0.206***|0.132***|0.205***|0.052*|||||
||(0.006)|(0.008)|(0.027)|(0.028)|||||
|dumharvest|0.432***|0.495***|0.302***|0.401***|||||
||(0.005)|(0.006)|(0.021)|(0.020)|||||
|dumanimal|0.064***|0.059***|0.106***|0.055***|||||
||(0.006)|(0.005)|(0.011)|(0.009)|||||
|dumforest|0.047***|0.148***|0.044|0.037|||||
||(0.011)|(0.013)|(0.031)|(0.029)|||||
|land|0.000|-0.000|-0.003|-0.004***|||||
||(0.001)|(0.001)|(0.002)|(0.001)|||||
|land2|-0.000|-0.000|0.000|0.000***|||||
||(0.000)|(0.000)|(0.000)|(0.000)|||||
|62.pr<br>~~n~~ic2|0.383||||-0.448|-0.277**|||
||(0.384)||||(0.306)|(0.124)|||
|91.pr<br>~~n~~ic2|-0.154*||||-0.340***|-0.106|||
||(0.086)||||(0.071)|(0.105)|||
|dumfrewood|0.100***||0.162***||0.129***||0.354||
||(0.008)||(0.032)||(0.025)||(0.341)||
|dumanimals|0.083***||-0.010||0.184***||-0.108||
||(0.007)||(0.021)||(0.013)||(0.224)||
|Continued on nex|tpage||||||||
19

**Table S. 3 – continued from previous page**

|Sector<br>VARIABLES|Rural<br>Female 15+|Male 15+|Female 6-14|Male 6-14|Urban<br>Female 15+|Male 15+|Female 6-14|Male 6-14|
|---|---|---|---|---|---|---|---|---|
|dumcollectfood|0.058***||0.038*||0.057***||0.087*||
||(0.005)||(0.020)||(0.008)||(0.050)||
|dumhuskpaddy|0.182***||0.569***||0.070*||||
||(0.017)||(0.096)||(0.037)||||
|dumgrindgrain|0.006||0.197||0.077**||||
||(0.019)||(0.124)||(0.033)||||
|dumdung|0.021***||0.076**||0.036||0.212||
||(0.007)||(0.032)||(0.025)||(0.254)||
|dumcwater|0.048***||0.075*||0.030***||0.218*||
||(0.007)||(0.040)||(0.008)||(0.111)||
|dumkitchengarden|-0.034**||0.034||0.051||||
||(0.016)||(0.085)||(0.046)||||
|25.hh<br>~~n~~ic2|||||-0.047|0.158|0.006|0.248*|
||||||(0.070)|(0.186)|(0.178)|(0.135)|
|98.hh<br>~~n~~ic2|||||-0.120|0.013|||
||||||(0.223)|(0.219)|||
|16.hh<br>~~n~~co2|||||0.090|0.236|-0.136||
||||||(0.094)|(0.158)|(0.147)||
|29.hh<br>~~n~~co2|||||0.024|0.101||0.049|
||||||(0.057)|(0.110)||(0.143)|
|92.hh<br>~~n~~co2|||||0.032|0.011|0.087|0.044|
||||||(0.037)|(0.062)|(0.101)|(0.095)|
|82.pr<br>~~n~~ic2|||||-0.401|-0.302***|||
||||||(0.300)|(0.113)|||
|9.hh<br>~~n~~ic2||||||-0.342|||
|||||||(0.506)|||
|41.pr<br>~~n~~ic2||||||-0.189|||
|||||||(0.192)|||
|71.pr<br>~~n~~ic2||||||0.171|||
|||||||(0.114)|||
|72.pr<br>~~n~~ic2||||||-0.161<br>(0.333)|||
|98.pr<br>~~n~~ic2|||||||-1.099***<br>(0.337)||
|Constant|3.846***|0.923|0.195|-0.558|5.609***|3.620**|3.098|2.069|
||(0.898)|(1.101)|(2.313)|(2.011)|(1.081)|(1.781)|(2.694)|(2.500)|
|Observations|17,346|17,930|3,666|4,242|8,014|8,482|1,507|1,763|
|R-squared|0.630|0.619|0.481|0.433|0.567|0.529|0.370|0.589|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999. _Note_ : Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All regressions use sample weights. National Industry Classification (NIC) uses 1987 codes while National Classification of Occupations (NCO) uses 1968 codes, with 100 where missing <u>(e.g.</u> students or women not in the labor force).
20 Table S. 4: Coefficients from Resting Energy Expenditure regressions using National Family Health Survey

|Variable|Female 15-49||Male 15-54||
|---|---|---|---|---|
|Sector|Rural|Urban|Rural|Urban|
|2.hhsize|-2.975|1.622|-1.874|-1.814|
||(3.581)|(6.250)|(5.758)|(5.122)|
|3.hhsize|-3.051|1.629|4.882|10.699**|
||(3.578)|(6.216)|(5.568)|(4.934)|
|4.hhsize|-4.996<br>|-0.531<br>|6.446<br>|18.200***<br>|
||(3.608)|(6.246)|(5.560)|(4.920)|
|5.hhsize|-6.430*|-5.651|7.740|12.574**|
||(3.633)|(6.279)|(5.623)|(5.034)|
|6.hhsize|-8.063**|-8.505|3.115|13.274***|
||(3.658)|(6.321)|(5.687)|(5.150)|
|7.hhsize|-9.787***|-7.321|4.360|4.147|
||(3.689)|(6.358)|(5.765)|(5.333)|
|8.hhsize|-9.172**|-10.054|8.171|7.427|
||(3.722)|(6.424)|(5.862)|(5.636)|
|9.hhsize|-9.519**|-5.764|7.318|10.086*|
||(3.760)|(6.509)|(5.996)|(5.867)|
|10.hhsize|-5.493|-4.173|11.669*|17.364***|
||(3.822)|(6.605)|(6.100)|(6.153)|
|11.hhsize|-5.205<br>|-13.599**<br>|8.325<br>|10.347<br>|
||(3.902)|(6.829)|(6.408)|(6.834)|
|12.hhsize|-5.052|-7.220|17.104**|11.828|
||(4.030)|(6.953)|(6.821)|(7.723)|
|13.hhsize|-6.870*|-12.097|21.261***|26.561***|
||(4.124)|(7.357)|(6.972)|(8.925)|
|14.hhsize|-7.201*|-3.506|26.730***|41.754***|
||(4.302)|(7.542)|(7.807)|(8.895)|
|headage|1.666***|2.903***|-5.446***|0.037|
||(0.405)|(0.723)|(0.842)|(1.133)|
|headage2|-0.032***|-0.045***|0.110***|0.005|
||(0.008)|(0.014)|(0.017)|(0.023)|
|headage3|0.000***|0.000**|-0.001***|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|headfemale|-0.824|-1.748|-5.015***|-9.696***|
||(0.798)|(1.192)|(1.722)|(2.173)|
|hhedsome|-3.352***|3.528**|-9.481***|0.020|
||(0.710)|(1.387)|(1.693)|(2.744)|
|hhedprim|-0.120|5.249***|-5.066***|0.803|
||(0.691)|(1.231)|(1.553)|(2.353)|
|hhedmid|-0.760|2.841**|-6.494***|-1.535|
||(0.827)|(1.374)|(1.742)|(2.553)|
|hhedsec|3.885***|11.068***|-8.540***|-2.501|
||(0.929)|(1.449)|(1.873)|(2.612)|
|hhedpostsec|3.642***|14.817***|-7.975***|-0.736|
||(1.391)|(1.820)|(2.709)|(3.213)|
|meana<br>school|5.599***|4.036***|8.241***|5.369***|
||(0.167)|(0.235)|(0.260)|(0.320)|
|dmratioa|6.431**|-10.692**|-21.263***|-34.436***|
||(3.137)|(5.176)|(5.952)|(8.037)|
|dfratioa|15.820***|-17.454***|54.187***|13.408|
|Continued on n|extpage||||
21

**Table S. 4 – continued from previous page**

|Variable|Female 15-49||Male 15-54||
|---|---|---|---|---|
|Sector|Rural|Urban|Rural|Urban|
||(3.202)|(5.200)|(6.542)|(9.054)|
|dmratios|18.629***|-6.182|-3.525|-60.536***|
||(4.544)|(7.651)|(8.555)|(12.214)|
|dfratios|36.333***<br>|21.981***<br>|53.300***<br>|17.645*<br>|
||(3.823)|(6.359)|(7.306)|(10.559)|
|dmratioc|20.276***|23.940***|1.467|-39.484***|
||(4.252)|(7.029)|(8.994)|(12.669)|
|dfratioc|22.277***|6.602|-4.752|-48.987***|
||(4.482)|(7.359)|(9.468)|(13.238)|
|dmratiop|19.094***<br>|14.714***<br>|-35.040***<br>|-40.522***<br>|
||(3.038)|(5.081)|(6.371)|(9.090)|
|dfratiop|22.097***|9.266*|-40.274***|-42.267***|
||(3.230)|(5.378)|(6.739)|(9.797)|
|dmratiom|28.801***|7.640|4.306|-17.231**|
||(2.950)|(4.850)|(6.061)|(8.653)|
|dfratiom|34.486***|19.348***|9.249|10.335|
||(3.121)|(5.157)|(6.379)|(9.167)|
|dmratioy|39.090***|14.481**|30.785***|1.074|
||(3.693)|(5.756)|(7.101)|(9.691)|
|dfratioy|47.790***|40.141***|66.589***|28.570***|
||(3.622)|(5.746)|(7.441)|(10.538)|
|year1998|-10.536***|-16.580***|||
||(0.547)|(0.910)|||
|year2015|16.299***|18.119***|22.140***|19.993***|
||(0.526)|(0.784)|(0.905)|(1.234)|
|age|12.911***<br>|25.370***<br>|118.001***<br>|115.854***<br>|
||(1.042)|(1.742)|(1.644)|(2.278)|
|age2|-0.283***|-0.558***|-3.798***|-3.681***|
||(0.034)|(0.056)|(0.050)|(0.069)|
|age3|0.002***|0.004***|0.036***|0.035***|
||(0.000)|(0.001)|(0.000)|(0.001)|
|school|-23.718***|-5.171***|-22.962***|-23.177***|
||(1.291)|(1.939)|(2.411)|(3.158)|
|edsome|3.096***|3.710**|5.503***|4.095|
||(0.819)|(1.582)|(2.073)|(3.597)|
|edprim|14.189***|19.432***|11.616***|12.271***|
||(0.719)|(1.219)|(1.735)|(2.898)|
|edmid|15.629***|23.597***|15.369***|20.723***|
||(0.847)|(1.327)|(1.768)|(2.883)|
|edsec|27.228***<br>|34.509***<br>|41.543***<br>|46.982***<br>|
||(0.981)|(1.393)|(1.882)|(2.928)|
|edpostsec|28.709***|38.032***|45.857***|52.373***|
||(1.515)|(1.787)|(2.481)|(3.411)|
|2.pr<br>~~n~~co2|-52.738*|70.267***|-100.326|-125.648**|
||(31.762)|(20.008)|(91.989)|(58.645)|
|3.pr<br>~~n~~co2|39.857|65.778***|-55.920|-102.587*|
||(26.863)|(16.119)|(89.420)|(58.028)|
|4.pr<br>~~n~~co2|-149.005***|70.309***|-29.744|-118.325**|
||(30.807)<br>|(21.144)<br>|(89.244)<br>|(58.067)<br>|
|5.pr<br>~~n~~co2|-61.177|92.300***|-48.603|-58.825|
|Continued on|nextpage||||
22

**Table S. 4 – continued from previous page**

|Variable|Female 15-49||Male 15-54||
|---|---|---|---|---|
|Sector|Rural|Urban|Rural|Urban|
||(37.439)|(28.336)|(92.758)|(62.090)|
|6.pr<br>~~n~~co2|-101.447**|74.756**|-78.567|-169.052**|
||(46.530)|(35.096)|(98.421)|(65.723)|
|7.pr<br>~~n~~co2|-11.414|83.944***|-73.532|-85.223|
||(27.009)|(16.830)|(89.920)|(59.151)|
|8.pr<br>~~n~~co2|-54.344***|57.661***|-64.364|-124.489**|
||(19.584)|(13.799)|(89.464)|(58.606)|
|9.pr<br>~~n~~co2|-26.151|96.116***|-65.986|-88.232|
||(19.741)|(14.232)|(89.317)|(58.445)|
|10.pr<br>~~n~~co2|-34.483|83.105***|-74.823|-105.629*|
||(25.912)|(24.137)|(90.484)|(59.294)|
|11.pr<br>~~n~~co2|-32.508|109.179***|-14.152|-65.104|
||(77.836)|(39.896)|(91.439)|(60.733)|
|12.pr<br>~~n~~co2|-22.144|68.398***|-55.392|-113.036*|
||(22.863)|(15.437)|(90.220)|(58.276)|
|13.pr<br>~~n~~co2|-50.002**|60.222***|-49.854|-95.857*|
||(20.343)|(14.671)|(89.220)|(58.089)|
|14.pr<br>~~n~~co2|-23.384|82.597***|-49.406|-87.802|
||(22.194)|(20.295)|(89.955)|(58.760)|
|15.pr<br>~~n~~co2|-43.438**|62.102***|-64.558|-122.953**|
||(18.789)|(12.812)|(88.943)|(58.009)|
|16.pr<br>~~n~~co2|-40.979**|48.024***|-63.978|-133.417**|
||(18.959)|(13.000)|(88.910)|(57.964)|
|17.pr<br>~~n~~co2|-22.096|40.792**|-58.801|-118.334**|
||(28.628)|(19.726)|(90.268)|(58.718)|
|18.pr<br>~~n~~co2|15.815<br>|81.776***<br>|-55.949<br>|-111.234*<br>|
||(28.986)|(17.056)|(89.515)|(58.670)|
|19.pr<br>~~n~~co2|-58.615**|75.729***|-59.034|-72.318|
||(22.802)|(18.461)|(89.424)|(58.584)|
|20.pr<br>~~n~~co2|-8.373|74.215***|-48.675|-119.518**|
||(22.188)|(17.742)|(89.386)|(58.358)|
|21.pr<br>~~n~~co2|-5.072|63.587***|-43.901|-90.484|
||(23.647)|(18.550)|(89.644)|(58.332)|
|22.pr<br>~~n~~co2|-25.821|84.726***|-35.007|-32.659|
||(24.881)|(21.345)|(90.038)|(58.682)|
|23.pr<br>~~n~~co2|-45.458|82.711***|-82.695|-61.039|
||(36.645)|(25.526)|(91.119)|(58.734)|
|24.pr<br>~~n~~co2|7.637|112.093***|-69.209|-87.427|
||(36.986)|(25.209)|(91.012)|(59.025)|
|25.pr<br>~~n~~co2|-111.385|93.703***|-35.337|-56.433|
||(88.661)|(35.321)|(91.893)|(59.865)|
|26.pr<br>~~n~~co2|-78.736|80.216***|-42.515|-64.718|
||(48.310)|(25.890)|(90.649)|(59.653)|
|27.pr<br>~~n~~co2|-88.242**|35.315|-21.648|-70.214|
||(38.124)|(55.458)|(94.564)|(60.062)|
|29.pr<br>~~n~~co2|-24.379|94.570***|-73.307|-83.235|
||(27.400)|(18.183)|(90.427)|(58.418)|
|30.pr<br>~~n~~co2|-28.005|72.530***|-53.416|-104.433*|
||(21.509)|(14.318)|(89.104)|(58.013)|
|31.pr<br>~~n~~co2|-40.503**|60.758***|-84.915|-134.896**|
|Continued on|nextpage||||
23

**Table S. 4 – continued from previous page**

|Variable|Female 15-49||Male 15-54||
|---|---|---|---|---|
|Sector|Rural|Urban|Rural|Urban|
||(20.116)|(19.640)|(89.486)|(60.820)|
|32.pr<br>~~n~~co2|-26.550|73.614***|12.517|-111.615*|
||(28.569)|(17.083)|(91.546)|(59.392)|
|33.pr<br>~~n~~co2|-64.310**|52.966***|-47.011|-76.619|
||(25.225)|(18.210)|(89.881)|(58.722)|
|34.pr<br>~~n~~co2|-33.088|28.785*|-90.581|-119.037**|
||(24.104)|(14.842)|(89.490)|(58.268)|
|35.pr<br>~~n~~co2|-27.709|60.021***|-59.009|-123.427**|
||(19.624)|(13.401)|(88.978)|(57.922)|
|36.pr<br>~~n~~co2|-91.198**|56.495**|-52.124|-118.436**|
||(38.674)|(26.816)|(89.570)|(58.922)|
|37.pr<br>~~n~~co2|-58.762|160.107***|-43.344|-83.373|
||(49.075)|(40.191)|(89.241)|(58.549)|
|38.pr<br>~~n~~co2|-58.160**|22.338|-96.753|-151.227**|
||(29.664)|(25.710)|(89.878)|(59.521)|
|39.pr<br>~~n~~co2|-11.845|58.264***|-28.851|-114.350*|
||(28.532)|(18.038)|(90.779)|(58.966)|
|40.pr<br>~~n~~co2|-26.821|68.382***|-57.390|-97.834*|
||(18.818)|(13.078)|(88.842)|(57.807)|
|41.pr<br>~~n~~co2|-50.217**|42.914***|-33.501|-85.233|
||(22.479)|(16.052)|(89.345)|(58.135)|
|42.pr<br>~~n~~co2|-17.312|129.657***|-38.533|-73.539|
||(26.367)|(21.509)|(89.734)|(58.236)|
|43.pr<br>~~n~~co2|-31.117*|63.491***|-55.626|-115.575**|
||(18.853)|(13.009)|(88.838)|(57.805)|
|44.pr<br>~~n~~co2|-7.043|63.797***|-52.044|-89.136|
||(21.137)|(14.970)|(89.067)|(57.943)|
|45.pr<br>~~n~~co2|-24.487|106.266***|6.868|-78.042|
||(33.100)|(27.359)|(90.717)|(59.528)|
|49.pr<br>~~n~~co2|-30.580|77.163***|-69.403|-106.116*|
||(19.772)|(14.630)|(88.976)|(57.966)|
|50.pr<br>~~n~~co2|-14.902|77.483***|-40.849|-105.059*|
||(20.121)|(15.489)|(89.062)|(58.076)|
|51.pr<br>~~n~~co2|-36.746*|62.714***|-90.156|-142.504**|
||(19.326)|(13.520)|(89.244)|(58.401)|
|52.pr<br>~~n~~co2|-41.759**|62.633***|-52.573|-100.467*|
||(18.880)|(13.131)|(89.036)|(58.070)|
|53.pr<br>~~n~~co2|-52.063***|33.451***|-83.119|-144.399**|
||(18.882)|(12.836)|(89.601)|(59.518)|
|54.pr<br>~~n~~co2|-36.535*|46.459***|-62.074|-128.858**|
||(19.775)|(13.301)|(88.941)|(57.980)|
|55.pr<br>~~n~~co2|-52.534***|60.026***|-92.140|-157.312***|
||(19.332)|(14.317)|(89.293)|(58.577)|
|56.pr<br>~~n~~co2|-33.124|60.576***|-79.837|-141.793**|
||(20.364)|(13.888)|(89.025)|(58.392)|
|57.pr<br>~~n~~co2|-47.941**|62.903***|-37.518|-96.681*|
||(20.408)|(14.459)|(88.942)|(57.896)|
|59.pr<br>~~n~~co2|-38.225**|55.777***|-57.731|-121.099**|
||(18.952)|(13.375)|(88.879)|(57.849)|
|60.pr<br>~~n~~co2|-34.227*|57.330***|-52.691|-103.535*|
|Continued on|nextpage||||
24

**Table S. 4 – continued from previous page**

|Variable|Female 15-49||Male 15-54||
|---|---|---|---|---|
|Sector|Rural|Urban|Rural|Urban|
||(19.906)|(16.866)|(88.972)|(58.649)|
|61.pr<br>~~n~~co2|-46.872**|32.447**|-81.233|-115.060**|
||(18.581)|(13.407)|(88.817)|(58.105)|
|62.pr<br>~~n~~co2|-43.036**<br>|59.334***<br>|-70.911<br>|-117.767**<br>|
||(18.594)|(13.575)|(88.818)|(58.016)|
|63.pr<br>~~n~~co2|-55.604***|29.557**|-90.136|-148.264**|
||(18.571)|(12.816)|(88.813)|(57.878)|
|64.pr<br>~~n~~co2|-65.167***|49.497***|-89.111|-102.346*|
||(18.769)|(17.895)|(88.988)|(59.281)|
|65.pr<br>~~n~~co2|-51.375***|28.578*|-84.930|-127.441**|
||(18.919)|(15.385)|(88.911)|(58.801)|
|66.pr<br>~~n~~co2|-68.052***|74.344***|-90.855|-86.514|
||(19.212)|(23.613)|(89.140)|(59.929)|
|67.pr<br>~~n~~co2|-40.647|15.210|-163.373*|-158.828**|
||(29.854)|(59.259)|(91.223)|(72.364)|
|68.pr<br>~~n~~co2|-44.153**|48.941***|-46.715|-118.150**|
||(20.438)|(16.642)|(89.037)|(58.301)|
|71.pr<br>~~n~~co2|-40.615*|26.956|-44.177|-119.356**|
||(20.728)|(22.205)|(89.057)|(58.539)|
|72.pr<br>~~n~~co2|-26.806|25.158|-72.734|-121.699**|
||(25.551)|(17.741)|(89.215)|(58.250)|
|73.pr<br>~~n~~co2|-36.963*|54.473***|-81.295|-114.113*|
||(20.147)|(15.096)|(89.107)|(58.270)|
|74.pr<br>~~n~~co2|-20.388|-15.424|-60.990|-141.101**|
||(34.613)|(18.960)|(89.728)|(59.160)|
|75.pr<br>~~n~~co2|-41.251**<br>|57.706***<br>|-82.527<br>|-132.474**<br>|
||(18.796)|(13.093)|(88.957)|(58.028)|
|76.pr<br>~~n~~co2|-51.506**|32.134|-28.443|-116.860*|
||(25.864)|(21.148)|(91.130)|(60.104)|
|77.pr<br>~~n~~co2|-38.764**|45.792***|-55.130|-105.719*|
||(19.431)|(14.149)|(89.050)|(58.001)|
|78.pr<br>~~n~~co2|-51.840***|42.916***|-113.806|-123.609**|
||(18.700)|(13.113)|(89.568)|(59.663)|
|79.pr<br>~~n~~co2|-28.512|66.289***|-85.363|-136.790**|
||(18.651)|(12.722)|(88.871)|(57.868)|
|80.pr<br>~~n~~co2|-10.913|74.132***|-68.742|-134.512**|
||(23.819)|(16.537)|(89.582)|(58.304)|
|81.pr<br>~~n~~co2|-55.488**|53.888***|-80.761|-123.361**|
||(22.403)|(19.535)|(88.895)|(57.975)|
|82.pr<br>~~n~~co2|-24.179<br>|100.303***<br>|-68.076<br>|-120.873**<br>|
||(19.638)|(15.045)|(88.962)|(58.289)|
|83.pr<br>~~n~~co2|-75.973***|98.051***|-61.985|-131.087**|
||(27.220)|(22.821)|(89.109)|(58.139)|
|84.pr<br>~~n~~co2|-35.437|28.669|-62.565|-126.034**|
||(30.314)|(19.811)|(88.933)|(57.909)|
|85.pr<br>~~n~~co2|-24.735|72.645***|-61.042|-122.001**|
||(32.266)|(20.053)|(88.910)|(57.881)|
|86.pr<br>~~n~~co2|-30.035|73.839**|-71.774|-76.157|
||(70.412)|(32.210)|(90.243)|(59.238)|
|87.pr<br>~~n~~co2|-30.342|92.185***|-53.776|-131.366**|
|Continued on|nextpage||||
25

**Table S. 4 – continued from previous page**

|Variable<br>Sector|Female 15-49<br>Rural|Urban|Male 15-54<br>Rural|Urban|
|---|---|---|---|---|
||(30.283)|(23.322)|(88.961)|(57.968)|
|88.pr<br>~~n~~co2|-26.098|86.408***|-34.399|-115.926**|
||(20.850)|(15.093)|(89.084)|(58.013)|
|89.pr<br>~~n~~co2|-60.671***|28.643|-78.134|-108.434*|
||(20.595)|(19.289)|(89.551)|(59.149)|
|90.pr<br>~~n~~co2|-20.635|19.209|-63.367|-112.576*|
||(26.392)|(17.745)|(89.924)|(58.769)|
|91.pr<br>~~n~~co2|-44.442**|41.323**|-49.942|-129.327**|
||(21.331)|(16.201)|(89.769)|(58.200)|
|93.pr<br>~~n~~co2|-56.940***|40.803***|-76.659|-137.442**|
||(18.920)|(13.861)|(88.856)|(57.855)|
|95.pr<br>~~n~~co2|-53.757***|38.149***|-86.577|-145.830**|
||(18.598)|(12.773)|(88.818)|(57.801)|
|96.pr<br>~~n~~co2|-14.113|47.322***|-45.125|-131.301**|
||(21.736)|(16.051)|(89.218)|(58.378)|
|98.pr<br>~~n~~co2|-2.751|100.383***|-39.608|-92.754|
||(32.704)|(37.701)|(88.866)|(57.872)|
|100.pr<br>~~n~~co2|-40.732**|54.888***|-72.404|-125.669**|
||(18.715)|(13.497)|(88.876)|(58.096)|
|active|-3.271|-15.972***|12.291***|-7.061|
||(2.355)|(4.860)|(3.402)|(5.951)|
|land|3.641***|1.291***|4.160***|4.341***|
||(0.129)|(0.296)|(0.260)|(0.536)|
|land2|-0.041***|-0.011**|-0.046***|-0.054***|
||(0.002)|(0.004)|(0.003)|(0.007)|
|1.pr<br>~~n~~co2|||-76.017|-86.825|
||||(92.276)|(61.018)|
|Constant|927.127***|673.685***|499.366***|525.518***|
||(22.777)|(26.364)|(92.027)|(66.540)|
|Observations|202,189|113,497|106,724|65,853|
|R-squared|0.135|0.169|0.351|0.274|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All regressions use the provided sample weights. Reported National Classification of Occupation <u>(NCO) codes use 1968 classification, with 100 for individuals missing the code (e.g.</u> women not in the labor force and students).
26 Table S. 5: National Sample Survey Schedule 10 population means

|Age (In Year)|1983<br>24.65|1988<br>24.87|1994<br>25.67|2000<br>26.05|2005<br>26.86|2010<br>28.05|2012<br>28.46|
|---|---|---|---|---|---|---|---|
|Sex|1.49|1.48|1.48|1.49|1.49|1.48|1.49|
|Household Size|6.29|6.13|5.82|5.92|5.74|5.42|5.32|
|dmratioa|0.24|0.25|0.26|0.26|0.27|0.28|0.28|
|dfratioa|0.24|0.24|0.25|0.25|0.26|0.27|0.28|
|dmratios|0.03|0.03|0.03|0.03|0.04|0.04|0.04|
|dfratios|0.03|0.03|0.03|0.03|0.04|0.04|0.04|
|dmratiob|0.04|0.04|0.03|0.03|0.03|0.02|0.02|
|dfratiob|0.04|0.03|0.03|0.03|0.03|0.02|0.02|
|dmratioc|0.03|0.03|0.03|0.03|0.02|0.02|0.02|
|dfratioc|0.03|0.03|0.03|0.02|0.02|0.02|0.02|
|dmratiop|0.07|0.07|0.07|0.07|0.06|0.06|0.05|
|dfratiop|0.07|0.06|0.06|0.06|0.06|0.05|0.05|
|dmratiom|0.07|0.07|0.06|0.06|0.06|0.06|0.06|
|dfratiom|0.06|0.06|0.05|0.06|0.05|0.05|0.05|
|dmratioy|0.03|0.03|0.03|0.03|0.03|0.03|0.03|
|dfratioy|0.03|0.03|0.03|0.03|0.03|0.03|0.03|
|meana<br>~~s~~chool|3.04|3.31|4.02|4.56|5.11|6.11|6.39|
|rmpce|309.13|306.65|322.98|321.34|339.69|383.27|464.21|
|land|1.23|1.16|0.95|0.75|0.72|0.60|0.57|
|headage|44.55|44.46|44.58|45.13|45.67|46.24|46.30|
|headfemale|0.07|0.07|0.07|0.07|0.08|0.08|0.09|
|hhedsome|0.12|0.12|0.14|0.12|0.10|0.11|0.12|
|hhedprim|0.14|0.14|0.13|0.12|0.14|0.14|0.13|
|hhedmid|0.10|0.09|0.11|0.13|0.14|0.15|0.15|
|hhedsec|0.08|0.09|0.11|0.13|0.13|0.17|0.17|
|hhedpostsec|0.03|0.03|0.05|0.06|0.07|0.08|0.09|
|hhself|0.53|0.51|0.51|0.47|0.53|0.49|0.52|
|hhcasual|0.26|0.31|0.30|0.32|0.29|0.32|0.22|
|hhother|0.21|0.17|0.19|0.20|0.19|0.19|0.16|
|hhagric|0.60|0.58|0.56|0.51|0.49|0.46|0.43|
|Continued on ne|xtpage|||||||
27

**Table S. 5 – continued from previous page**

||1983|1988|1994|2000|2005|2010|2012|
|---|---|---|---|---|---|---|---|
|hhmining|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|hhmannat|0.07|0.07|0.06|0.06|0.07|0.07|0.07|
|hhmanoth|0.04|0.04|0.04|0.04|0.04|0.04|0.05|
|hhutil|0.00|0.01|0.01|0.00|0.01|0.00|0.01|
|hhconstru|0.03|0.04|0.04|0.05|0.07|0.10|0.12|
|hhtrade|0.08|0.08|0.09|0.09|0.11|0.11|0.12|
|hhtransp|0.04|0.04|0.04|0.05|0.06|0.06|0.06|
|hhfre|0.01|0.01|0.01|0.01|0.02|0.02|0.03|
|hhservice|0.10|0.10|0.11|0.10|0.10|0.09|0.09|
|hhunknown|0.03|0.03|0.03|0.09|0.03|0.04|0.04|
|pragric|0.25|0.24|0.23|0.22|0.21|0.19|0.17|
|prmining|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|prmannat|0.03|0.03|0.03|0.03|0.03|0.03|0.03|
|prmanoth|0.01|0.01|0.01|0.01|0.02|0.01|0.02|
|prutil|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|prconstru|0.01|0.01|0.01|0.02|0.02|0.04|0.04|
|prtrade|0.03|0.03|0.03|0.03|0.04|0.04|0.04|
|prtransp|0.01|0.01|0.01|0.01|0.02|0.02|0.02|
|prfre|0.00|0.00|0.00|0.00|0.01|0.01|0.01|
|prservice|0.03|0.03|0.04|0.04|0.04|0.03|0.03|
|prunknown|0.63|0.63|0.62|0.63|0.62|0.63|0.64|
|edsome|0.13|0.13|0.17|0.17|0.16|0.17|0.17|
|edprim|0.11|0.12|0.12|0.12|0.14|0.15|0.14|
|edmid|0.07|0.08|0.10|0.11|0.12|0.14|0.14|
|edsec|0.05|0.06|0.08|0.10|0.11|0.15|0.16|
|edpostsec|0.01|0.02|0.03|0.03|0.05|0.06|0.07|
|dumplough|0.02|0.02|0.02|0.02|0.01|0.01|0.01|
|dumsow|0.02|0.02|0.01|0.02|0.02|0.01|0.01|
|dumweeding|0.02|0.02|0.03|0.02|0.03|0.02|0.02|
|dumharvest|0.12|0.12|0.13|0.12|0.12|0.10|0.09|
|dumanimal|0.02|0.02|0.02|0.02|0.00|0.00|0.00|
|dumforest|0.01|0.00|0.01|0.01|0.01|0.00|0.00|
|Continued on n|extpage|||||||
28

**Table S. 5 – continued from previous page**

||1983|1988|1994|2000|2005|2010|2012|
|---|---|---|---|---|---|---|---|
|dumfrewood|0.07|0.06|0.06|0.06|0.05|0.07|0.07|
|dumanimals|0.06|0.05|0.05|0.04|0.04|0.04|0.03|
|dumcollectfood|0.04|0.03|0.03|0.02|0.02|0.03|0.03|
|dumhuskpaddy|0.04|0.04|0.02|0.03|0.02|0.02|0.01|
|dumgrindgrain|0.06|0.04|0.03|0.03|0.02|0.02|0.01|
|dumdung|0.07|0.07|0.08|0.07|0.06|0.06|0.06|
|dumcwater|0.11|0.10|0.10|0.08|0.07|0.06|0.05|
|dumkitchen|0.02|0.02|0.03|0.02|0.02|0.04|0.04|

_Source_ : Authors’ calculations using National Sample Survey Schedule 10 for rounds 38, 43, 50, 55, 61, 66 and 68. _Note_ : Reported population means use sampling weights.
29 Table S. 6: State-level regressions of caloric intake on Total Energy Expenditure (TEE)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES|State cal.|State cal.|∆cal. 1983-2005|∆cal. 2005-2012|
|State TEE||0.689***|||
|||(0.251)|||
|∆TEE|||0.440|0.666*|
||||(0.630)|(0.332)|
|round43|-8.394|-7.189|||
||(24.181)|(23.798)|||
|round50|-45.295|-60.667|||
||(36.081)|(38.507)|||
|round55|-15.561|-29.320|||
||(35.601)|(37.377)|||
|round61|-101.569**|-128.567***|||
||(42.070)|(43.343)|||
|round66|-167.331***|-184.037***|||
||(42.201)|(37.649)|||
|round68|-139.499***|-152.540***|||
||(41.439)|(41.625)|||
|Constant|2,185.355***|714.280|-122.089***|-24.294*|
||(31.172)|(536.167)|(40.467)|(13.028)|
|Observations|222|222|31|32|
|R-squared|0.757|0.768|0.013|0.086|
|State FE|Yes|Yes|No|No|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Standard errors clustered by state in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Columns 1 and 2 use the full panel of state caloric intake and TEE, with state fixed effects. Columns 3 and 4 are the regression equivalents of Figure 2 in the paper, using only the within-state long-difference for two periods.
30 Table S. 7: Alternative estimates of Total Energy Expenditure (TEE) and components over time

||M|ale 15+||Fe|male 15|+|Child|14 and|under|
|---|---|---|---|---|---|---|---|---|---|
|Year|TEE|REE|AL|TEE|REE|AL|TEE|REE|AL|
|||||Pane|l A: All|India||||
|1983|2767|1424|1.95|2166|1128|1.93|1444|937|1.51|
|1988|2782|1437|1.94|2175|1139|1.92|1431|943|1.49|
|1994|2804|1457|1.93|2198|1154|1.91|1432|953|1.48|
|2000|2808|1475|1.91|2205|1170|1.89|1462|976|1.47|
|2005|2820|1490|1.90|2213|1184|1.88|1467|987|1.47|
|2009|2758|1510|1.84|2199|1202|1.84|1474|1011|1.44|
|2012|2729|1517|1.81|2183|1210|1.81|1481|1020|1.43|
||||Pa|nel B:|Rural s|ector o|nly|||
|1983|2858|1404|2.04|2216|1121|1.98|1451|933|1.52|
|1988|2867|1418|2.02|2219|1131|1.97|1433|939|1.50|
|1994|2895|1436|2.02|2248|1144|1.97|1429|946|1.48|
|2000|2901|1454|2.00|2255|1158|1.95|1458|969|1.48|
|2005|2920|1469|1.99|2261|1170|1.94|1462|981|1.47|
|2009|2842|1490|1.91|2243|1188|1.89|1471|1008|1.44|
|2012|2807|1496|1.88|2223|1194|1.87|1475|1016|1.43|

_Source_ : Authors’ calculations using India Time-Use Survey 1998-1999, National Family Health Survey rounds 2, 3, and 4 and National Sample Survey rounds 38, 43, 50, 55, 61, 66 and 68.

_Note_ : Alternative estimates of Total Energy Expenditure (TEE), Rest Energy Expenditure (REE) and Activity Level (AL). For REE we include a linear time-trend in the regression equation and predictions. For AL we consider a specification pooling all men or all women (regardless of age or sector) and including three way interactions of dummies for primary status, 1-digit primary industry, and education level.
31 Table S. 8: Within-cohort weight gain regressions using National Family Health Survey data

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|Men|Women|Ev. m. wom.|Wom. ex.1998|Men|Women|Ev. m. wom.|Wom. ex.1998|
|year|0.613***|0.549***|0.538***|0.607***|0.682***|0.536***|0.521***|0.608***|
||(0.009)|(0.003)|(0.003)|(0.004)|(0.008)|(0.003)|(0.003)|(0.004)|
|Cohort FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Height control|No|No|No|No|Yes|Yes|Yes|Yes|
|Observations|180,794|892,509|694,387|811,964|180,794|892,509|694,387|811,964|
|R-squared|0.154|0.159|0.140|0.158|0.331|0.257|0.237|0.264|

_Source_ : Authors’ calculations using National Family Health Survey rounds 2, 3, and 4. _Note_ : Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Dependent variable is weight in kilograms. Results are the year coefficient from a regression of sample weight on state dummies and cohort (birth-year) fixed effects, so the reported coefficient on (survey) year captures the linear (annual) time-trend in weight gain within cohort. Columns 5-8 include height as a control to (partially) capture attrition related to nutritional status. Columns 3 and 7 restrict the female sample to only ever married women, making the 2005 and 2015 survey rounds comparable to the 1998 survey round. Columns 4 and 8 exclude the 1998 survey round so the estimated trend is more directly comparable to the one for men, who only have anthropometric data starting in 2005.