metadata: authors: 0: author: World Bank
count: World
docna: 0: docna: Is Natural Capital a Complement to Human
            Capital ? Evidence from 46 Countries
docty: Policy Research Working Paper
theme: Adaptation,Mitigation,Economic Policy,Economic Growth and Planning,Environment and Natural Resource Management,Climate change
abstracts: cdata!: The environment has long been the
            foundation of human flourishing, but its continued
            degradation is threatening to reverse recent development
            gains, especially in human health. This paper analyzes the
            possible complementarity between natural and human capital
            by linking high-resolution deforestation data with health
            outcomes for 0.7 million children across 46 countries.
            Forest loss is often a consequence of economic activities
            that may confer market and other benefits. At the same time,
            it can adversely affect the provision of forest ecosystem
            services and reduce the associated socioeconomic and
            environmental benefits for rural communities. The net effect
            is thus ambiguous. The paper focuses on the hydrological
            services provided by forests and exploits quasi-random
            variation in deforestation upstream to assess the impacts on
            waterborne disease outcomes for rural households downstream.
            The results not only indicate increases in diarrheal disease
            incidence among children under 5 years old, but also offer
            new evidence of early-life exposure to deforestation on
            childhood stunting, a well-known indicator of later-life
            productivity. A case study for Peru shows similar results
            for diarrheal disease, but a weaker effect of forest loss on
            stunting. The paper concludes that maintaining natural
            capital has the potential to generate meaningful
            improvements in long-run human capital.
display_title: Is Natural Capital a Complement to
            Human Capital ? Evidence from 46 Countries
listing_relative_url: /research/2023/11/34203226/natural-capital-complement-human-capital-evidence-46-countries
topicv3: Sustainable Economic Growth m1326464 1100,Stunting m1327504 1445,Deforestation m1503714 3386,Health m1504661 3480
publishtoextweb_dt: 2023-11-27T00:00:00Z
dois: 10.1596/1813-9450-10617
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
model_extractions: Policy Research Working Paper 10617 Is Natural Capital a Complement to Human Capital?

Evidence from 46 Countries _Richard Damania Luis Diego Herrera Garcia Hyungju Kim Leonardo Viotti Esha Zaveri Stefanie Onder Chrissie Pantoja_ Environment, Natural Resources and Blue Economy Global Practice & Sustainable Development Practice Group November 2023
Policy Research Working Paper 10617

# **Abstract**

The environment has long been the foundation of human flourishing, but its continued degradation is threatening to reverse recent development gains, especially in human health. This paper analyzes the possible complementarity between natural and human capital by linking high-resolution deforestation data with health outcomes for 0.7 million children across 46 countries. Forest loss is often a consequence of economic activities that may confer market and other benefits. At the same time, it can adversely affect the provision of forest ecosystem services and reduce the associated socioeconomic and environmental benefits for rural communities. The net effect is thus ambiguous.

The paper focuses on the hydrological services provided by forests and exploits quasi-random variation in deforestation upstream to assess the impacts on waterborne disease outcomes for rural households downstream. The results not only indicate increases in diarrheal disease incidence among children under 5 years old, but also offer new evidence of early-life exposure to deforestation on childhood stunting, a well-known indicator of later-life productivity. A case study for Peru shows similar results for diarrheal disease, but a weaker effect of forest loss on stunting. The paper concludes that maintaining natural capital has the potential to generate meaningful improvements in long-run human capital.

This paper is a product of the Environment, Natural Resources and Blue Economy Global Practice and the Office of the Chief Economist, Sustainable Development Practice Group. 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 rdamania@worldbank.org, lherreragarcia@worldbank.org, hkim32@worldbank.org, leonardoviotti@gmail.com, ezaveri@worldbank.org, sonder@worldbank.org, and chrissiepantoja@gmail.com.

_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
# **Is Natural Capital a Complement to Human Capital? Evidence from 46 Countries**

Damania, Richard<sup>*</sup> r Luis Diego Herrera Garcia<sup>*</sup> r Hyungju Kim<sup>*</sup> r Leonardo Viotti<sup>**</sup> r Esha Zaveri*<sup>†</sup> r Stefanie Onder<sup>*</sup> r Chrissie Pantoja<sup>***</sup> JEL classification: I115, Q51, Q53, Q56, Q57 Keywords: Health and Economic Development, Valuation of Environmental Effects, Water Pollution, Sustainability, Ecosystem Services r The author order was randomized using the author randomization tool of the American Economic Association and has been archived at this link.

Affiliations:

*The World Bank, Washington, DC

** University of Pittsburgh, Pittsburgh, PA

*** Duke University, Durham, NC

† Corresponding author
# **1. Introduction**

The relationship between the state of the environment and human health has become a key topic in global policy discussions due to growing concerns about the rapid depletion of natural resources, and its implications for human health and eventually sustainable development. The environment has long been the foundation of human wellbeing and its degradation is threatening to reverse recent improvements in human health (Whitmee et al., 2015), with possible long-term effects on labor productivity and income (Pattanayak and Pfaff 2009; Pattanayak et al. 2017). The COVID-19 pandemic has demonstrated the critical need to improve our understanding of the multiple interactions between the environment and humans (FAO, UNEP WHO, and WOAH, 2022; World Bank, 2022), which is especially critical for developing countries. Not only are low- and middle-income economies heavily dependent on natural resources (World Bank 2021), but, on average, they also experience more rapid environmental degradation (FAO, 2020; World Bank, 2021), and have worse health outcomes than higher-income countries (Deaton 2013). Nevertheless, the underlying mechanisms of how natural resource degradation affects human health have yet to be fully explored.

In this paper, we focus on one specific type of natural capital – forest ecosystems – which provide a range of socioeconomic and environmental benefits to local and global communities (Chaplin-Kremer et al., 2023; Grammatikopoulou and Vačkářová, 2021; Jenkins and Schaap, 2018; Marta-Pedroso et al. 2014), but have experienced extensive loss and degradation over the last few decades (Hansen et al., 2013, FAO 2020). Forest loss is often a consequence of economic activities that confer commercial and financial benefits. At the same time, it adversely affects the provision of forest ecosystem services and the associated benefits to humans. The net effect is thus ambiguous, requiring an empirical investigation of the underlying causal mechanisms. In this paper, we focus on a forest ecosystem service that has not yet been studied extensively in the literature exploring the relationship between forests and health – namely, hydrological services – and exploit quasirandom variation in deforestation upstream to isolate the impacts on waterborne disease outcomes for rural households downstream.

A better understanding of these causal links can shed new light on the possible complementarity between natural and human capital, which can inform the design of environmental, health, and development strategies (Pattanayak et al., 2017), as investments in both nature and health are necessary to sustain economic growth (Johnson et al., 2021; Ogundary & Awokuse, 2018; Weil, 2007; Narayan et al., 2010; Weil, 2014). In addition, if natural and human capital are indeed complementary factors of production, a degradation of natural resources will not only have environmental and health impacts but will also have significant implications for economic development. Recent work suggests that introducing limited substitutability into production functions can limit economic growth (Smulders and van Soest, 2023) as well as a country’s fragility and resilience to external shocks (Karayalcin and Onder, 2023).

Several recent studies have explored the causal relationships between forest loss and human health, especially with regard to malaria. Malaria is a pervasive disease that affects populations in tropical and subtropical regions and is transmitted by mosquitos, causing fever and flu-like illness and in some case nausea and diarrhea. While deforestation can increase malaria risk factors in some settings, increased malaria risk does not necessarily translate to higher malaria rates in humans, suggesting that context matters. Bauhoff and Busch (2020) find that the deforestation-malaria relationship does not necessarily generalize across Sub-Saharan Africa. Nevertheless, others find positive relationships at the country level for Brazil (Olson et al., 2010; Terrazas et al., 2015), Paraguay (Wayant et al., 2010), Malaysia (Fornace et al., 2016), Nigeria (Berazneva and Byker, 2017) and Indonesia (Garg 2019; Chakraborty, 2021).

2
However, forest loss can also impact human health by interacting with other forms of natural capital, such as air and water. Air and water quality are critical determinants of human health. According to the World Health Organization (WHO), 94 percent of the world's population suffer direct exposure to unsafe concentrations of particulate matter (PM), a standard indicator of air pollution. Air pollution can harm health in both the shortand long-term ( <mark>Juginović et al. 2021;</mark> Giani et al. 2020; Manisalidis et al., 2020; World Bank, 2016), while air purification can be extremely costly (Cohen et al., 2017; Domingo et al., 2021; Huangfu & Atkinson, 2020; Lelieveld et al., 2015; Ritchie & Roser, 2013). Polluted water can cause acute and chronic diseases, including diarrheal disease (DD) and stunting (Zaveri et al., 2020). Today, DD is among the leading causes of death among children under five (Liu et al., 2012; Prüss-Ustün et al., 2014). Moreover, an estimated 13.5 percent of global stunting prevalence is attributable to DD (Danaei et al., 2016), which is widely recognized as a major impediment to development (Galasso & Wagstaff, 2018).

Deforestation and its links to air pollution are often due to forest fires. For instance, prenatal exposure to the 1997 Indonesian forest fires and the resulting exposure to dangerous levels of air pollution have been found to adversely impact child height-for-age scores as well as later life wages for the approximately 1 million Indonesian workers born during this period (Tan-Soo & Pattanayak, 2019). Air pollution generated from Indonesian forest fires can also significantly impact neighboring countries through severe episodes of “seasonal haze”, resulting in increased air-related diseases, including acute respiratory infections (ARI) in Singapore (Sheldon & Sankaran, 2017). Forest fires can also have global impacts. For example, reductions in deforestation rates and associated deforestation fires in the Amazon rainforests, known as “the lungs of the planet”, can significantly reduce PM, preventing thousands of excess deaths (Cobelo et al., 2023; Reddington et al., 2015).

However, fewer studies have investigated the causal links between the loss of forest cover and the hydrological services they provide and human health. Empirical studies in forest science and hydrology show that deforestation can impact access to clean water through changes in water yield and quality. Although forests— especially fast-growing young plantations—may use more water than older, mixed forests and therefore reduce freshwater availability, there is growing evidence that, with proper management, forests can help enhance the resilience of water supplies (Miller et al., 2020). Forest loss upstream can also decrease soil infiltration of water and the removal of pathogen, and increase soil erosion (Filoso et al., 2017; El Kateb et al., 2013; Zheng, 2006), which can result in lower stream water loads of Escherichia coli, higher pH of water and lower stream water turbidity (Cunha et al., 2016). Such water conditions can also render point-of-use chlorination less effective especially for households in low-income countries who cannot afford expensive water treatment (Amrose et al 2015).

Herrera et al. (2017) use a multi-country database to explore the connections between deforestation, watershed conditions, and childhood DD and find that higher upstream tree cover is associated with a lower probability of DD downstream. This effect is significant for rural households but not urban ones, suggesting differing dependence on watershed conditions. In rural areas, the effect of a 30 percent increase in upstream tree cover is similar in magnitude to the effect of improved sanitation but smaller than for improvement of water sources, wealth, or education. Country-level studies further provide evidence that deforestation reduces access to clean drinking water (Mapulanga and Naito, 2019) and undermines the effectiveness of point-of-use chlorination (Rasolofoson et al., 2021), thus further increasing the risk of DD.

Given the importance of upstream forest cover loss to DD, it is possible that upstream forest cover loss could also influence stunting rates through the water channel. Past work has shown that repeated bouts of DD increase the probability of nutritional deficiencies in children, thus stunting child development (Zaveri et al., 2020). Exposure to water pollution can also adversely affect birth outcomes by lowering infant birthweight (Jones, 3
2019), an essential predictor of stunting later in childhood (Christian et al., 2013). However, to the best of our knowledge no evidence yet exists that links the impacts of deforestation to stunting rates.

In this paper, we contribute to this emerging literature by taking a closer look at the water-borne channels and the causal effects of forest loss on human health. Such an endeavor is challenging due to the multiple factors determining health outcomes and the degree of complementarity that must be accounted for by including socioeconomic, demographic, infrastructure, environmental, behavioral, and institutional factors (Pattanayak & Pfaff, 2009). We exploit the wealth of data available in the Demographic and Household Surveys (DHS), which includes not only contemporaneous health and socioeconomic outcomes, but also full birth histories for all live births that occur in the years before the survey. We restrict our sample to 0.7 million children from rural areas<sup>1</sup> in 46 countries from 2000-2020 for which we have spatial coordinates. This allows us to link the health outcomes with high-resolution deforestation data and study the effects of forest loss on the incidence of DD (by linking lags of forest loss to the survey year) as well as stunting (by linking lags of forest loss to the birth year).

Identifying health impacts of forest loss is challenging given a trade-off between health costs and economic benefits that may be associated with agricultural expansion or urbanization following forest loss. To address the potential bias from non-random forest loss and confounding trends, we examine a possible water pathway from deforestation to health outcomes by exploiting quasi-random variation in deforestation upstream to assess the impacts on waterborne disease outcomes for rural households downstream. This allows us to isolate a waterspecific channel through which forest loss impacts health. We also carry out a series of falsification and robustness checks to rule out spurious temporal or spatial correlation and that the observed health outcomes are caused by water- and not air- or vector-borne diseases.

We then replicate the global analysis for Peru, which is one of the 10 most forest-rich countries in the world and has experienced rapid land use change over the last two decades, with 3.4 million hectares of forest lost between 2001 and 2020. One key advantage of this case study is that we are able to complement the DHS data with additional health surveys implemented by Peru’s National Institute of Statistics (a.k.a., INEI) using the DHS methodology. This provides us with a longer repeated cross section and the ability to control more comprehensively for time and location specific trends.

Our analysis indicates that deforestation increases DD incidence among children under five in downstream communities, but also offers new evidence of early-life exposure to deforestation on childhood stunting, a wellknown indicator of later-life productivity. Our results suggest that a 1 percent decline in upstream forest cover increases the probability of DD by 5.8 percent. These results are consistent with previous evidence (Herrera et al., 2017) and highlight the vital role that upstream forests play in regulating water flow and quality downstream. We also present new evidence on the long-lasting and significant health impacts of deforestation, with a 1 percent decline in upstream forest cover around the time of birth increasing the probability of stunting by 3.9 percent. This effect is particularly large in early childhood, measured by the first thousand days of life. These global results also hold for Peru for DD but less so for stunting, which could be due to significant governmentand donor-supported health and sanitation campaigns during our sample period aimed to reduce stunting.

Our analysis has both analytical and practical implications. From an analytical point of view, this study contributes to a growing literature on the complementarities between human and natural capital. It provides further evidence of the importance of the water channel for short- and long-term health outcomes. On the practical side, our results suggest that maintaining (and potentially restoring) natural capital has the potential

> 1 We restrict our sample to non-migrant children, i.e., children whose households have either always lived in their current location, or that have migrated to their current location before the child was conceived.

4
to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The rest of the paper is organized as follows: section 2 explains the data sources and the process of combining the health outcomes with the geospatial information to create a rich micro database, section 3 describes our empirical strategy. Section 4 analyzes the results, and conducts falsification and robustness checks to demonstrate that our results are driven by the water channel. Section 5 concludes.

# **2. Data**

Analyzing the impact of upstream forest cover loss on downstream health outcomes requires combining household surveys with high-resolution spatial data. We use data from DHS and link the spatial coordinates of the household clusters with geospatial data on forest cover (Hansen et al., 2013), upstream and downstream watersheds (Lehner et al., 2008) and climate (Hersbach et al., 2018).

## **2.1. Demographic and Household Surveys (DHS) Data**

The DHS Program, sponsored by the United States Agency for International Development (USAID), provides technical assistance for the implementation of nationally representative, stratified, two-stage cluster sample household surveys that collect data on population, health, and nutrition for over 90 developing countries around the world. We focus our analysis on 91 surveys representing 46 developing countries<sup>2</sup> for the 2000-2020 period. To be included in our sample, each DHS survey must georeference clusters (shown in Figure 1), so that we can match the health outcomes and household characteristics to the environmental and climate data. Following Herrera et al. (2017), we restrict the sample to rural communities, as they are directly dependent on watershed conditions, and focus only on households that have either always lived in their current location or have migrated to their current location before the child was conceived (the rural non-migrant sample). This ensures that health outcomes are measured in the same location of conception and birth where exposure occurred. Given that the health impacts of low water quality are most significant for young children, we restrict the sample further to ages 0 to 5 years, yielding a final sample of 0.7 million children.

> 2 The 46 countries that are used in our analysis were selected based on the following criteria: countries that provide GPS coordinates for each cluster (this rules out most DHS surveys, as only 60 countries provide geo-referenced data), countries that surveyed individuals between 2000-2020, and countries that provide migration data. For the list of countries included in our DHS sample, please refer to Table A1.

5
<!-- Start of picture text --> es yiae<br>ay oe » < ; ’ 4<br>te a ae g * Rural cluster<br>an oe * Urban cluster<br><!-- End of picture text -->
to identify which households have access to safe drinking water through “improved water sources”, such as piped water, boreholes, and packaged water.<sup>3</sup> Households with unsafe drinking water typically access “unimproved water sources”, including unprotected well and surface water.

Contaminated water sources can lead to many chronic diseases, particularly in rural areas of developing countries, where a significant percentage of the poor population – more than a quarter in our sample – continues to use surface water as their main source of drinking water (WHO and UNICEF, 2021). Table 2 reports access to improved water sources in our DHS sample, distinguishing between rich and poor non-migrant households residing in rural and urban areas. While 93 percent of households in urban areas have access to improved water sources, that number decreases to around 73 percent in rural areas. Wealth differences further aggravate access to improved water, with only 56 percent of poor rural households having access to improved water compared to 92 percent of their rich counterparts. These statistics imply that those who are most vulnerable to water pollution are poor people residing in rural areas.

**Table 2: Percent share of household’s source of drinking water by urban/rural and wealth**

|**Water source**|**Urban**|**Rural**|**Urban Rich**|**Urban Poor**|**Rural Rich**|**Rural Poor**|
|---|---|---|---|---|---|---|
|Improved drinking water|92.60|73.26|96.66|71.69|92.26|55.65|
|Unimproved drinking water|7.40|26.74|3.34|28.31|7.74|44.35|
|**Total**|**100**|**100**|**100**|**100**|**100**|**100**|

_Notes:_ The household wealth index from DHS<sup>4</sup> was used to divide households into 5 quintiles based on their level of wealth. Rich in the table refers to the richest quintile (quintile = 5) and poor to the poorest quintile (quintile = 1).

The average incidence of DD and stunting among under five-year-old in our sample is 12.6 and 15.5 percent, respectively (Table A1), but there is considerable variation across countries (Table A2). Figure 2a shows the average incidence of DD is particularly high in Latin American, Caribbean and Sub-Saharan African countries, where every fifth child surveyed experiencing DD on average among the top 10 countries.<sup>5</sup> Stunting rates also very greatly across countries, ranging from 29 and 26 percent in Timor-Leste and Madagascar, respectively, to a mere 3 percent in Colombia, The Gambia, and the Dominican Republic (Figure 2b). Stunting rates are generally high in Sub-Saharan Africa and in all Asian countries in our sample.

> 3 The following water sources are included in the JMP definition of improved drinking water sources: piped water, borehole or tube wells, protected dug wells, protected springs, rainwater, and packaged or delivered water. Any other sources are considered as an unimproved water source.

> 4 The Wealth index from DHS is defined after accounting for water, sanitation, and hygiene (WASH) variables. We eliminate the WASH variables to create our own wealth index using the Principal Component Analysis.

> 5 The highest incidence of DD (in percent) was experienced by Bolivia (25.3), Haiti (23.0), Burundi 2.0), Uganda (22.0), Senegal (21.5), Burkina Faso (20.0), The Gambia (20.0), Cambodia (19.8), and Malawi (19.6).

7
<!-- Start of picture text --> ‘. ft F a,<br>Percent a 4 v<br>with Diarrhea: ofChildren t A<br>Not in DHS .<br><5<br>5-10<br>lm 10-15 4<br>Mm 15-20<br>mm >20<br>AUGUSTIBRD 474222023|<br><!-- End of picture text -->

<!-- Start of picture text --> Percent ofChildren =7 A : v<br>Severely Stunted: f<br>Not in DHS .<br>a<br>5-10<br>Mm 10-15<br>Mm 15-20<br>mm >20<br>IBRD 47423 |<br>AUGUST 2023<br><!-- End of picture text -->
<!-- Start of picture text --> So<br>N<br>oO<br>o<br>€<br>S<br>no}<br>r=}<br>20<br>c=<br>2<br>3<br>=oO<br>6<br>ro<br>82<br>o<br>a<br>o<br>D<br>£<br><o=<br>wo<br>0 1 2 3 4<br>Child Age (Years)<br><!-- End of picture text --> <!-- Start of picture text --> BR<br>c<br>2a<br>><br>2<br>o>ae<br>c—<<br>2<br>32<br>=oO<br>Eoor2<br>oa<br>o<br>2D Cf<br>£<br><oo=><br>0 1 2 3 4<br>Child Age (Years)<br><!-- End of picture text -->
**Figure 4: Cluster coordinates and 5km buffer**

# **2.2. Hydrological Data**

To identify areas upstream and downstream of DHS clusters we used the HydroBASINS product of HydroSHEDS (Lehner et al., 2008), which represents a series of vectorized polygon layers that depict sub-basin boundaries at a global scale. HydroBASINS divides a basin into two sub-basins at every location where two river branches meet which each having their respective upstream area of at least 100 square kilometers (km2). The data is further divided into 12 nested levels using a modification of the Pfafstetter level system (Verdin and Verdin, 1999), where level 1 corresponds to continental scale, and level 12 has an average area of 130km2. HydroSHEDS’ coding scheme also allows for the analysis of watershed topologies, by providing identifiers for the polygon immediately downstream of each sub-basin.

We overlay household cluster-buffer polygons with level 12 HydroSHEDS sub-basins and determine in which sub-basin(s) each cluster-buffer is contained. The index system is then used to identify sub-basins upstream. We define our upstream area as the union of all sub-basins immediately upstream of the sub-basin(s) that contain the cluster-buffer, removing the area of the cluster-buffer in case it intersects the upstream sub-basins. The average size of the upstream watersheds used in our analysis is 456km<sup>2</sup> . However, this size varies considerably depending on the number and size of the upstream watersheds, ranging from merely 2km<sup>2</sup> to nearly 2,300km<sup>2</sup> . We follow this procedure in the inverted direction to identify downstream polygons for placebo tests (Figure 5).

10
**Figure 5: Cluster buffer with upstream and downstream sub-basins**

# **2.3. Environmental and Climate Data**

For forest cover loss, we use the Global Forest Change dataset by Hansen et al. (2013). They quantify the global forest change from a supervised pixel-based image classification of the Landsat satellite images at a spatial resolution of 30 meters,<sup>7</sup> using 2000 forest cover estimates as well as annual indicators from 2001 to 2020 for forest loss and gain. To estimate forest cover changes, we compute standing forests in 2000 and track forest cover loss until 2020. We count the number of forest pixels lost each year within each cluster (converting total counts to percentages), thus creating a panel of annual deforestation at the cluster-level from 2001-2020 (Figure 6). For our identification strategy, it is critical to account for annual deforestation in the watershed upstream from the cluster. We also need annual deforestation downstream for our falsification test. We thus follow the same approach to compute annual deforestation in the watersheds that are located upstream and downstream from each cluster.

> 7 Trees are defined as all vegetation taller than 5 meters in height.

11
**Figure 6: Cluster buffer, sub-basins, and deforestation since 2000**

As precipitation and temperature have been known to be significant predictors of DD (Halpenny et al., 2012; Escamilla et al., 2011), we also construct climate controls for each cluster within its buffer area. In particular, we construct average annual temperature in Celsius and total precipitation in meters for each cluster, using the ERA5-Land product (Muñoz-Sabater et al. 2021). This data is provided as monthly aggregates at 11,132m resolution. To match the temporal resolution of the deforestation data, we further aggregate this data to create yearly means for temperature and total monthly precipitation.

# **2.4. Data for Peru Case Study**

Peru was chosen as a case study for our analysis, given that it is one of the top ten forest-rich countries in the world, which has experienced both rapid changes in land use and health outcomes over the last two decades. Compared to other countries in Latin America, only Brazil has larger forest cover, with Peruvian forests representing 54 percent of the country’s territory (FAO and SERFOR, 2017). However, the country has experienced rapid tree cover loss of about 3.15 million hectares between 2004 and 2020, equivalent to a 4 percent decrease in tree cover since 2000, resulting in 2.03 Gt of uncompensated CO2e emissions.<sup>8</sup> Five Amazon regions - Loreto, Ucayali, San Martin, Madre de Dios, and Amazonas - were responsible for 78 percent of all tree cover loss,<sup>9</sup> which was driven by the growing demand for fuel wood, exports of hardwoods, and expansion into the Peruvian Amazon for cultivation, livestock, and the extraction of minerals (Swenson et al., 2011; Piu and Menton, 2014).

Despite tree cover loss, child health indicators have improved in the last two decades. Particularly, the incidence of diarrhea and stunted growth decreased significantly more in children in rural areas than in urban areas due to various government- and donor-supported interventions and social programs. Specifically, the Peruvian government began a handwashing initiative in 2003 with technical assistance from the World Bank through public education and media messaging to increase handwashing among mothers and young children,<sup>10</sup> which

> 8 Global Forest Watch. “Tree cover loss in [Peru/Madre de Dios]” from www.globalforestwatch.org.

> 9 Global Forest Watch. “Location of tree cover loss in Peru”. From www.globalforestwatch.org.

> 10 The Global Public-Private Partnership for Handwashing (2003-2010) led by the World Bank was implemented by Peru, Senegal, Tanzania, and Vietnam.

12
<!-- Start of picture text --> Quito N<br>°<br>G uayaqui l ° Q<br>%O Po<br>oS %<br>cs YS, ey0 [or©<br>Sm LBerue og 7<br>FERS e 6 3<br>Pie Raman>, (Op S.<br>eee”eerie aegeSr }<br>NBRbene ES B20? SsBo 3<br>Oeoe ed SoD<br>ou Bere Ott < )<br>Cre tae 1 on' By 6co<br>oe Fes: p\ ° cy<br>Nie eeoe<br>OSes, Sg en eo<br>Re GS, 0° o }<br>eee, cto Hy ape<br>QS eS<br>Legend = cee Foo aPaes Bog of<br>Type of resident BOES DO fo estntic. SA<br>@ rural cluster Fe EERO SO . sey<br>© urban cluster Q.9¢ *)bo, Dr ee223] o<br>0 Qe tg ee ree La Paz<br>75150 300 Kilometers ee<br>2,<br><!-- End of picture text -->
<!-- Start of picture text --> 30<br>3<br>E><br>>3 2<br>3<br>§ 15<br>FS<br>oO<br>3A 10<br>5 5<br>a ° Se2005Say2006ge2007a2008See Se2009ee2010 2011ee2012 ee Se2013eee2014See2015 Ce2016Beems2017Revert2018 See2019Peewee!2020<br>Year<br><!-- End of picture text --> <!-- Start of picture text --> 30><br>§<br>E 255<br>3 20-<br>§5154<br>SsS<br>28 105<br>a 55<br>__ 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>ol]een<br>[== Year<br><!-- End of picture text -->
become more contaminated, or more turbid (Cunha et al., 2016), which render less effective the point-of-use chlorination that is commonly used by households in low-income countries (Amrose _et al_ 2015).

To estimate the health impacts of forest cover loss via the water channel, we therefore begin by investigating impacts on diarrhea. Our focus on DD is deliberate since different diseases have different disease ecologies and thus different mechanisms of transmission (Pattanayak and Pfaff, 2009; Berazneva and Byker, 2017; Garg, 2019). For our main specification we estimate equation (1) on the pooled sample using Ordinary Least Squares (OLS), where i is a dummy for whether child _i_ experienced DD at the time of survey in year t. For each child we also record its location in cluster _c_ in province _p_ of country _g_ : YY U U i 1∆F F F 2∆F F F + 1 + 2 + γ i<sup>(1)</sup> YY∆F = αα+ ββF F U and ∆Fccii,ii−1F F+ ββU measure annual changes in the share of forest loss in the upstream ccii−1,ii−2 λλ XXccii λλ XXccii−1 ii<sup>+ ρρ</sup> ggii<sup>+ μμ</sup> UU<sup>+ εε</sup> catchment area relative to survey year _t_ and _t-1_ , respectively. These values are measured for upstream ccii,ii−1 ccii−1,ii−2 catchments, exploiting the natural flow of rivers and the fact that water flows downstream. Forest loss upstream is, thus, orthogonal to downstream health. In this way, we exploit quasi-random variation in forest cover loss that originates upstream to estimate the impacts on water quality and health outcomes downstream. Additionally, since DD may vary over time, we also control for country-by-survey year fixed effects ( ) to account for the year when information on the child’s DD condition was collected as well as unobserved national ggii ρρ macroeconomic conditions which might otherwise confound the relationship between forest exposure and DD. Province fixed effects ( ) are included to control for any time-invariant unobservable differences between provinces that could affect health.<sup>13</sup> For example, access to local nutrition programs is one such factor that may UU μμ be constant across individuals residing in the same location. Several other cluster- and household-specific variables are included in the analysis. and are timevarying variables at the cluster level, measuring annual mean temperature and total precipitation within the ccii ccii−1 5km buffer area for survey year _t_ and _t-1_ , respectively. These climate variables are potentially confounding XX XX factors, as they are known predictors of DD (Halpenny et al., 2012; Escamilla et al., 2011). are controls for household characteristics such as age, sex, and birth order of the child, as well as the mother’s age at the time ii γγ of birth. Since access to clean drinking water reduces the incidence of water-borne diseases like diarrhea, in some specifications we also control for whether a household has access to an improved water source at the time of the survey using the WHO/JMP definition.<sup>14</sup> Lastly, we cluster robust standard errors at the province level to account for arbitrary correlation of observations across time and within provinces.

To estimate stunting impacts of childhood exposure to forest cover loss via the water channel, we use a similar research design but modify the timing of exposure. Specifically, we exploit quasi-random variation in upstream forest cover loss experienced by different birth cohorts and compare height outcomes between exposed and non-exposed cohorts, controlling for average differences in these outcomes across birth years and across clusters. For our main specification we estimate equation (2) on the pooled sample where i is a dummy for whether child _i_ was severely stunted at the time of survey in year _t_ and was born in year _b_ : YY U i F F c + 1 c + + 2 c −1 γ i<sup>(2)</sup> ,cc−3 ii<sup>+ ρρ</sup> ggcc<sup>+ μμ</sup> UU<sup>+ εε</sup> YY = αα+ ββ∆F λλ XX λλ XX

> 13 When estimating equation (1) for the Peru sample, we control for district fixed effects, as access to public health facilities and local nutrition programs is constant across individuals residing in the same district.

> 14 The WHO/JMP definition of improved drinking water sources includes piped water, boreholes or tube wells, protected dug wells, protected springs, rainwater, and packaged or delivered water.

15
U ∆F F F c denotes the cumulative change in upstream forest cover loss over the three years prior to birth year _b_ . We assign the calendar year in which the child spends in utero to be “one year before birth” (in utero ,cc−3 year),<sup>15</sup> so that tree loss in the next calendar year is tree loss in the “year of birth.” We also control for province fixed effects ( ) country-by-birth year ( ) . The country-by-birth-year fixed effects are included to account for age effects in health outcomes as well as unobserved national or seasonal shocks, such as macroeconomic UU ggcc μμ ρρ conditions or weather patterns, which might otherwise confound the relationship between forest exposure and height. The analysis, thus, exploits within-province variation in birth timing relative to forest loss exposure to identify . Equation (2), therefore, compares two types of children from the same province who are subjected to different levels of forest loss exposure based on their year of birth, over and above any unobserved shocks to ββ height that vary by the year of birth, and any long-run trends (or annual patterns) in height in the province of birth.

The reduced-form relationship provides a causal estimate of the downstream health damages caused by upstream environmental degradation, adding to related work on pollution spillovers by Garg et al. (2018), Keiser and Shapiro (2018), Lipscomb and Mobarak (2017) and Sigman (2002, 2005). Further, since the identification strategy uses multiple exposure events over time and space, it alleviates concerns that the results are being driven by confounding factors to health that may be correlated with single events.

# **4. Results**

## **4.1. Global Results**

Table 3 presents estimates of the impact of upstream forest loss on downstream DD using a pooled OLS regression for the global sample. Columns 1, 2 and 3 report parameter estimate of equation (1), sequentially including controls related to climate, individual and household demographic characteristics, and access to improved water sources, respectively. Across all three specifications, we find a positive and statistically significant effect at the five percent level of upstream forest loss on DD incidence in the year preceding the survey. Our preferred specification in column 3 indicates that when the share of forest loss increases from 0 to 1 (a 100-percentage point change), DD incidence increases by 0.69 percentage points. The final row of the table interprets the magnitude of the estimated impact for a one percent forest loss in terms of mean levels of DD incidence. Using the sample average of the probability of DD of 0.12, a one percent decline in upstream forest cover increases the probability of DD by 5.77 percent. These results are consistent with previous evidence (Herrera et al., 2017), and highlight the important role that upstream forests play in regulating water flow and quality.

**Table 3: Impact of upstream forest loss on DD incidence, 2004-2020 (global rural non-migrant sample)**

||**(1)**|**(2)**|**(3)**<br>**(4)**<br>**(5)**|
|---|---|---|---|
||**Depen**|**dent variabl**|**e: Incidence of Diarrheal Disease**|
|Upstream forest loss||||
|Year t-1 to t|0.748**|0.693**|0.693**|
||(0.338)|(0.342)|(0.341)|
|Year t-2 to t-1|-0.450|-0.389|-0.399|
||(0.351)|(0.357)|(0.357)|

15 In utero years are defined as 9 months before the birth date. For example, if a child is born in January of 2003, the birth year will be 2003 and the in-utero year will be 2002. If, on the other hand, a child is born in December of 2003 both the birth and in utero year will be 2003.

16
|Downstream forest loss||||||
|---|---|---|---|---|---|
|Year t-1 to t||||0.342<br>(0.298)||
|Year t-2 to t-1||||0.0904||
|||||(0.306)||
|Upstream forest loss||||||
|Year t to t+1|||||-0.223|
||||||(0.441)|
|Year t+1 to t+2|||||0.595|
||||||(0.426)|
|N|496,484|496,484|496,484|496,484|381,251|
|Fixed Effects|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|
|Demographic Controls|N|Y|Y|Y|Y|
|Improved Water Source|N|N|Y|Y|Y|
|R-sq|0.0343|0.0519|0.0519|0.0519|0.0554|
|Mean diarrheal incidence|12.02%|12.02%|12.02%|||
|Impact of a 1 percent loss in upstream forest cover 1<br>year ago as apercent of mean diarrhea incidence|6.23%|5.77%|5.77%|||

_Notes_ : This table presents pooled OLS estimates of equation (1) where the dependent variable is the DD incidence for nonmigrant children under the age of 5 between 2004-2020 using the global sample. Forest loss is measured in terms of the share of forest loss over the upstream catchment area relative to survey year _t_ of a child. Demographic controls include the age, sex, and birth order of the child, as well as the mother's age at the time of birth and child’s age in months. Climate controls are annual mean temperature, and total precipitation for survey year _t_ and _t-1_ . Improved water source controls for whether the household of child _i_ has access to safe drinking water. Fixed Effects include country-by-survey-year and province fixed effects. Robust standard errors clustered at the province level are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

To validate the research design, we also perform several falsification tests to examine the possibility that these results are driven by spurious spatial or temporal patterns. Following Zaveri et al. (2020), we replace our measure of upstream forest loss with a falsified variable capturing forest loss in the downstream watershed instead. Since water cannot flow upstream, it should not impact health outcomes of households in the cluster. If we were to find a significant health impact, then it would be likely that our baseline results are capturing spurious spatial correlation and other mechanisms of disease transmission. Results in Column 4 show that there is no statistically significant effect of the falsified forest loss variable on the incidence of DD. In addition to the spatial falsification test, we examine the possibility of spurious temporal correlation by controlling for leads of upstream forest loss. Column 5 of Table 3 reports that there is no statistical significance of future upstream forest loss on the incidence of DD. This is consistent with the preceding periods’ upstream forest cover loss increasing the incidence of DD through a water mechanism. These results are also robust to estimating the main specification using logit or probit models (see Table A6).

We also test for the impacts of upstream forest loss on malaria and acute respiratory infections, which are vector-borne diseases transmitted by mosquitos. The channels through which upstream deforestation shapes diarrhea are unlikely to impact these diseases and, therefore, they can act as a placebo check to detect spurious association between upstream forest loss and health outcomes. Note that although a body of research has investigated the relationship between forest loss and malaria with mixed findings (e.g., Bauhoff and Busch, 2020; 17
Chakraborty, 2021; Garg 2019; Berazneva and Byker, 2017), these studies analyze the impact of local forest loss as opposed to the upstream forest loss, which is the focus of this paper. As reported in Table 4, we find no evidence for the vector-borne disease channel, which suggests that our results are driven by the water channel.

**Table 4: Impact of upstream forest loss on vector-borne disease incidence, 2004-2020 (global rural nonmigrant sample)**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**Dependent variable:**|**Incidence of Malaria**|**Dependent variable:**<br>**Respirator**|**Incidence of Acute**<br>**y Infection**|
|Upstream Forest Loss|||||
|Year t-1 to t|0.240||0.191||
||(0.571)||(0.198)||
|Year t-2 to t-1|-0.727||-0.239||
||(0.655)||(0.241)||
|Downstream Forest Loss|||||
|Year t-1 to t||0.198||0.149|
|||(0.532)||(0.112)|
|Year t-2 to t-1||-0.535||-0.173|
|||(0.529)||(0.154)|
|||0.198||0.149|
|N|515,452|515,452|529,623|529,623|
|Fixed Effects|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|
|Demographic Controls|Y|Y|Y|Y|
|R-sq|0.071|0.071|0.026|0. 026|

_Notes_ : This table presents pooled OLS estimates of equation (1) where the dependent variables are the airborne disease incidences of malaria and Acute respiratory infection (ARI) for non-migrant children under the age of 5 between 2004-2020. Malaria is a dummy variable for whether the child had fever in the two weeks prior to the survey. ARI is a dummy variable that returns 1 if a child was coughing in last two weeks with chest related short/rapid breathing and 0 otherwise. See notes to Table 3 for details on specification and variable construction for the other controls. Robust standard errors clustered at the province level are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

In addition to impacting short-term health, can upstream forest cover loss also have long-term health impacts? The well-established fetal origins literature suggests that intrauterine health impacts can lead to lasting health damages (Barker, 1990; Currie and Vogl, 2013; Christian et al., 2013; Almond et al., 2018). The best overall measure of early life child well-being is stunting, which measures height/length by age against WHO standards. Lower height-for-age scores can lead to severe consequences for cognitive development, overall health, and even socio-economic conditions that carry into adulthood (Hoddinott et al., 2013). Childhood stunting has been linked to a wide range of later-life health outcomes including lower economic productivity (Kraay, 2018).

Several potential pathways might be possible between forest cover loss and stunting through water pollution. Past work has shown that repeated bouts of DD increase the probability of nutritional deficiencies in children and, thus, stunted child development (Zaveri et al., 2020). Exposure to water pollution can adversely affect birth outcomes by lowering infant birthweight (Jones, 2019), an important predictor of stunting later in childhood (Christian et al., 2013). Exposure to water pollution can also disrupt the gut microbiome. The first months after birth are particularly critical for establishing the composition of the gut microbiome that persists for the rest of a person’s life (Robertson et al., 2019). There is evidence in the medical literature that this microbiome is difficult to permanently change later in life, although this matter is under debate (REF). If true, it could reduce an individual’s capacity to absorb nutrients throughout their lives, impacting development.

18
Given the importance of upstream forest cover loss to DD, it is possible that upstream forest cover loss could also influence stunting rates. However, thus far the discussion around targeting drivers of stunting has largely ignored the role of forests. To investigate whether upstream forest cover loss can also have long-term health impacts via stunting, we modify our empirical strategy to account for quasi-random variation in upstream forest cover loss around the time of each child’s birth in the DHS sample. For our main specification we estimate equation (2) on the pooled sample. Since children are born at different times, each child has their own exposure based on their birth year. This contrasts with the contemporaneous analysis presented in Table 3 where exposure to forest loss was the same for all children of the same cluster and was solely based on the year they were surveyed. This specification also includes country-by-birth-year fixed effects to control flexibly for changes over time in stunting and forest cover loss.

The results in column 2 of Table 5 indicate that when the share of forest loss in the year preceding birth, i.e., when the child is in-utero, up to the year of birth increases from 0 to 1 (a 100 percentage point change), the likelihood of severe stunting increases by 0.71 percentage points, a result that is statistically significant at the 5 percent level. In other words, a 1 percent decline in upstream forest cover significantly increases the likelihood of severe stunting by 0.0071 percentage points. Using the sample average of the probability of severe stunting of 0.172, a 1 percent decline in upstream forest cover around the time of birth increases the probability of severe stunting by 4.14 percent. The final row of the table interprets the magnitude of the estimated impact of a 1 percent forest loss in terms of mean levels of severe stunting in the sample. Other lags of upstream forest cover loss are positive, but insignificant, suggesting that the transmission channel is indeed through low water quality when the child is in-utero.

**Table 5: Impact of upstream forest loss on childhood stunting, 2004-2020 (global rural non-migrant sample)**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||**D**|**ependent varia**|**ble: Incidence fo**|**r Severe Stuntin**|**g**|
|Upstream Forest Loss||||||
|Birth Year b-1 to b|0.626**|0.713**|0.608*|||
||(0.312)|(0.322)|(0.335)|||
|Birth Year b-2 to b-1||-0.158|-0.279|||
|||(0.281)|(0.264)|||
|Birth Year b-3 to b-2|||0.379<br>(0.279)|||
|Birth Year b-3 to b||||0.236*<br>(0.133)||
|Downstream Forest Loss||||||
|Birth Year b-3 to b|||||0.209<br>(0.181)|
|N|372,040|372,040|372,040|372,040|372,040|
|Fixed Effects|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|
|Demographic Controls|Y|Y|Y|Y|Y|
|R-sq|0.042|0.042|0.042|0.042|0.042|
|Mean stunting incidence|17.22%|||||
|Impact of a 1 percent loss in upstream|3.64%|4.14%|3.53%|||
|forest cover 1 year ago as a percent of<br>mean stuntingincidence||||||

_Notes_ : Table presents estimates of equation (2) where the dependent variable is the stunting incidence for non-migrant children under the age of 5 between years 2004-2020. Forest loss is measured in terms of the share of forest loss over the upstream catchment area relative to birth year 'b' of a child. See notes to Table 3 for details on the variable construction 19
<!-- Start of picture text --> Effects by child age<br>3<br>2<br>D<br>£<br><<br>2<br>7)<br>—o1<br>no]<br>fo}<br>fe)<br>=@<br>=<br>a<br>-1<br>0-12 months 13-24months 25-36 months 37-48 months 49-60 months<br><!-- End of picture text -->
severe stunting in urban clusters. This observation suggests varying degrees of dependence on watershed conditions between rural and urban clusters, highlighting the crucial role of upstream forests specifically for rural areas.

**Table 6: Impact of upstream forest loss on children health, 2004-2020 (global urban non-migrant sample)**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||**Dependen**|**t variable: Diarrhe**|**al Disease**|**Dependent va**|**riable: Severe**|
|||||**Stun**|**ting**|
|Upstream Forest Loss||||||
|Survey Year t-1 to t|0.396|0.332|0.324|||
||(0.281)|(0.279)|(0.278)|||
|Survey Year t-2 to t-1|-0.214|-0.161|-0.163|||
||(0.275)|(0.266)|(0.265)|||
|Birth Year b-1 to b||||0.228<br>(0.301)||
|Birth Year b-3 to b|||||-0.0642<br>(0.144)|
|N|184,151|184,151|184,151|129,217|129,217|
|Fixed Effects|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|
|Demographic Controls|N|Y|Y|Y|Y|
|Improved Water|N|N|Y|N|N|
|R-sq|0. 031|0. 048|0.048|0. 044|0.044|

_Notes_ : Table presents estimates where the dependent variable is the health (diarrheal disease and severe stunting) incidence for non-migrant children residing in urban areas that are under the age of 5 between years 2004-2020. See notes to Table 3 and 5 for details on the variable construction and specifications of equations (1) and (2), respectively. Robust standard errors clustered at the province level and are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

# **4.2. Peru Case Study**

Having established global results, next we test our model for Peru, a country of particular interest for our study given the considerable threats to forests and the ecosystem services they provide. The model for this subset shows qualitatively similar results for DD in Table 7 as our global sample analysis. The results in columns 1, 2, and 3 in Table 7 indicate that when the share of forest loss in the year prior to the survey increases from 0 to 1 (a 100-percentage point change), DD incidence increases by 2.06 percentage points on average. In other words, a 1 percent decline in upstream forest cover significantly increases the likelihood of DD by 0.0206 percentage points. The magnitude of this effect is more than double the effect in the global sample, with a 1 percent decline in upstream forest cover increasing the probability of DD by approximately 16.29 percent. The same falsification tests are also carried out for the Peru sample, including using the falsified forest loss variable (column 4) and urban sample (column 5). The coefficient estimates are insignificant, providing further evidence that we are able to isolate the water channel.

**Table 7: Impact of upstream forest loss on DD incidence, 2004-2020 (Peru sample)**

||**(1)**|**(2)**|**(3)**<br>**(4)**<br>**(5)**|
|---|---|---|---|
||**Depe**|**ndent variable**|**: Incidence for Diarrheal Disease**|
|Upstream forest loss||||
|Year t-1 to t|2.056**|2.047**|2.059***<br>-0.155|
||(0.826)|(0.798)|(0.796)<br>(0.558)|

21
|Year t-2 to t-1|-0.835|-0.756|-0.782||-0.066|
|---|---|---|---|---|---|
||(0.799)|(0.790)|(0.790)||(0.631)|
|Downstream forest loss||||||
|Year t-1 to t||||1.057||
|||||(0.670)||
|Year t-2 to t-1||||-0.161<br>(0.651)||
|N|38,652|38,652|38,652|38,652|83,322|
|Location of cluster|Rural|Rural|Rural|Rural|Urban|
|Fixed Effects|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|
|Demographic Controls|N|Y|Y|Y|Y|
|Improved Water|N|N|Y|Y|Y|
|R-sq|0.057|0.075|0.075|0.075|0.037|
|Mean diarrheal incidence|12.62%|||||
|Impact of a 1 percent loss in upstream forest<br>cover 1 year ago as a percent of mean<br>diarrhea incidence|16.29%|16.24%|16.34%|||

_Notes_ : This table presents pooled OLS estimates of equation (1) where the dependent variable is the DD incidence for nonmigrant children under the age of 5 between 2004-2020 in the Peru sample. See notes to Table 3 for details on the variable construction. Fixed Effects include survey-year and district fixed effects. Robust standard errors clustered at the district level are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

However, the results for the impact of upstream forest loss on the incidence of severe stunting reported in Table 8 show a much weaker impact. We only find a positive coefficient for upstream forest loss in the in-utero year, which is statistically significant at the 10 percent level, when the lags are excluded. This weaker effect relative to the global analysis is likely not surprising given that severe stunting rates in Peru saw large declines of nearly 15 percent between 2004 and 2020 due to numerous government and donor-supported sanitation and health programs. We also do not find statistically significant effects for the falsification test using downstream forest loss (column 5) or the urban sample (column 6).

**Table 8: Impact of upstream forest loss on childhood stunting, 2004-2020 (Peru rural non-migrant sample)**

||**(1)**|**(2)**|**(3)**|**(4)**<br>**(5)**|**(6)**|
|---|---|---|---|---|---|
|||**Dependent**|**variable: Incide**|**nce for Severe Stunting**||
|Upstream Forest Loss||||||
|Birth Year b-1 to b|0.867*|0.845|0.739|||

22
|Birth Year b-2 to b-1|(0.481)|(0.514)<br>0.041<br>(0.473)|(0.500)<br>-0.053<br>(0.509)||||
|---|---|---|---|---|---|---|
|Birth Year b-3 to b-2|||0.0293<br>(0.500)||||
|Birth Year b-3 to b||||0.326<br>(0.212)||-0.035<br>(0.065)|
|Downstream Forest Loss|||||||
|Birth Year b-3 to b|||||0.194<br>(0.226)||
|N|33,683|33,683|33,683|33,683|33,683|75,930|
|Location of cluster|Rural|Rural|Rural|Rural|Rural|Urban|
|Fixed Effects|Y|Y|Y|Y|Y|Y|
|Climate Controls|Y|Y|Y|Y|Y|Y|
|Demographic Controls|Y|Y|Y|Y|Y|Y|
|R-sq|0.096|0.096|0.096|0.096|0.096|0.030|
|Mean stunting incidence|6.95%||||||
|Impact of a 1 percent loss in<br>upstream forest cover 1 year<br>ago as a percent of mean<br>stuntingincidence|12.47%||||||

_Notes_ : Table presents estimates of equation (2) where the dependent variable is the stunting incidence for non-migrant children under the age of 5 between years 2004-2020. See notes to Table 5 for details on the variable construction. Fixed Effects include child’s-birth-year and month, and district fixed effects. Robust standard errors clustered at the district level and are reported in parentheses. * p<0.10, ** p<0.05, *** p<0.01.

# **5. Conclusion**

In this paper, we analyzed the complementarities between natural and human capital using a global analysis of 0.7 million rural non-migrant children across 46 countries. By linking high-resolution forest loss data with individual level health outcomes, our analysis shows that deforestation upstream has significant implications for DD incidence and stunting among children under 5 years old in downstream communities. Moreover, we provide novel evidence that these effects can have long lasting and significant impacts on human capital through stunting. This effect is particularly large if children are exposed to poor water quality in the first thousand days of life. Results for Peru confirm the sign and statistical significance of the result for DD, which is twice as large as the global results. However, the impact on stunting is less pronounced likely due to targeted government and donor health and sanitation campaigns.

Our analysis has both analytical and practical implications. From an analytical point of view, our analysis complements a growing literature on the complementarities between human and natural capital and provides further evidence of the importance of the water channel for short- and long-term health outcomes. On the practical side, our results suggest that maintaining natural capital has the potential to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The study of the complementarities or substitutability between different forms of capital is a critical yet data demanding task to understand the sustainability of economic development (Cohen et al., 2019). Any future research that analyzes the substitutability and/or complementarity between different types of capital or across subcategories of capital in production or consumption process will help improve our understanding significantly.

23